None:\\n mock = decoy.mock(func=deck_conflict.check)\\n monkeypatch.setattr(deck_conflict, \\\"check\\\", mock)\",\n \"def wrap(cls, orig):\\n # hack to give the timestamp this class' specialized methods\\n orig.__class__ = cls\\n return orig\",\n \"def patchit(ofs, aname):\\n def _getter(self, index):\\n \\\"\\\"\\\"herp\\\"\\\"\\\"\\n return self._vec[index]\\n\\n def _setter(self, index, value):\\n \\\"\\\"\\\"derp\\\"\\\"\\\"\\n self._vec[index] = value\\n\\n setattr(Vector3, aname, property(\\n lambda s: _getter(s, ofs),\\n lambda s,v: _setter(s, ofs, v)))\",\n \"def add_setup(setup=None, teardown=None):\\n def decorate_function(test):\\n def wrapper(self):\\n if setup:\\n setup(self)\\n test(self)\\n if teardown:\\n teardown(self)\\n return wrapper\\n return decorate_function\",\n \"def test_function(self):\\n data = (\\n os.path.join(\\n _CURRENT_DIRECTORY,\\n \\\"fake_project\\\",\\n \\\"_modules\\\",\\n \\\"fake_project\\\",\\n \\\"basic.html\\\",\\n ),\\n \\\"set_function_thing\\\",\\n )\\n content = self._get_fake_project_function()\\n\\n expected = textwrap.dedent(\\n '''\\\\\\n def set_function_thing(value, another):\\n \\\"\\\"\\\"Do something here.\\\"\\\"\\\"\\n # Do something with these values\\n # and more comment text, here.\\n #\\n if value:\\n return 2\\n\\n # Another comment\\n return 1'''\\n )\\n\\n self._test(data, content, expected) # pylint: disable=no-value-for-parameter\",\n \"def override(class_to_extend, prefix):\\n def decorator(func):\\n if not hasattr(class_to_extend, func.func_name):\\n raise except_osv(_(\\\"Developper Error\\\"),\\n _(\\\"You can replace the method %s of the class %s. \\\"\\n \\\"Indeed this method doesn't exist\\\")%(func.func_name, class_to_extend))\\n original_function_name = prefix + func.func_name\\n if hasattr(class_to_extend, original_function_name):\\n raise except_osv(_(\\\"Developper Error\\\"),\\n _(\\\"The method %s already exist. \\\"\\n \\\"Please change the prefix name\\\")%original_function_name)\\n setattr(class_to_extend, original_function_name, getattr(class_to_extend, func.func_name))\\n setattr(class_to_extend, func.func_name, func)\\n return class_to_extend\\n return decorator\",\n \"def not_random(func):\\n func.random = False\\n return func\",\n \"def hook(self, name):\\r\\n def wrapper(func):\\r\\n self.hooks.add(name, func)\\r\\n return func\\r\\n return wrapper\",\n \"def patch_mongo(monkeypatch):\\n mock_db = mongomock.MongoClient().todo_database\\n\\n def fake_get_db():\\n return mock_db\\n\\n monkeypatch.setattr(main.data_access, \\\"get_db\\\", fake_get_db)\",\n \"def permanent_function(*old_func):\\n\\n def decorator(func):\\n \\\"\\\"\\\"\\n decorates the given function and makes it a lazy one.\\n\\n :param function func: decorated function.\\n\\n :returns: function result.\\n \\\"\\\"\\\"\\n\\n def wrapper():\\n \\\"\\\"\\\"\\n decorates the given function and makes it a lazy one.\\n\\n :returns: function result.\\n \\\"\\\"\\\"\\n\\n result = caching_services.try_get('permanent', func, None)\\n if result is not None:\\n return result\\n\\n result = func()\\n caching_services.try_set('permanent', result, func, None)\\n return result\\n\\n return update_wrapper(wrapper, func)\\n\\n if len(old_func) > 0:\\n return decorator(old_func[0])\\n\\n return decorator\",\n \"def stubify_method(func):\\n stub_func = mock.create_autospec(func,\\n return_value=ReturnValueStub())\\n\\n stub_func.__doc__ = func.__doc__\\n return stub_func\",\n \"def patch_mock_load_labware_params(\\n decoy: Decoy, monkeypatch: pytest.MonkeyPatch\\n) -> None:\\n for name, func in inspect.getmembers(load_labware_params, inspect.isfunction):\\n monkeypatch.setattr(load_labware_params, name, decoy.mock(func=func))\",\n \"def local_override(self, func):\\n @functools.wraps(func)\\n def inner(*args, **kwargs):\\n local_options = {}\\n option_overrides = kwargs.get('options', kwargs)\\n for k, v in option_overrides.items():\\n if k in self.option_fields:\\n local_options[k] = kwargs.pop(k)\\n\\n # Store current options before the local override in memory, so they\\n # can be used after the function returns to reset the `obj:Options`\\n # instance.\\n current_options = deepcopy(self.__dict__)\\n\\n # Apply overrides and allow the method to run with the overrides\\n # applied.\\n self.override(local_options)\\n result = func(*args, **kwargs)\\n\\n # Wipe out the locally scoped overrides and reset the state back to\\n # what it was before the method was called.\\n self.populate(current_options)\\n return result\\n\\n return inner\",\n \"def test_old_callback_forgotten(self):\\n\\n def callback(connection): # pragma: no cover\\n pass\\n\\n def replacement(connection): # pragma: no cover\\n pass\\n\\n context = Context(SSLv23_METHOD)\\n context.set_tlsext_servername_callback(callback)\\n\\n tracker = ref(callback)\\n del callback\\n\\n context.set_tlsext_servername_callback(replacement)\\n\\n # One run of the garbage collector happens to work on CPython. PyPy\\n # doesn't collect the underlying object until a second run for whatever\\n # reason. That's fine, it still demonstrates our code has properly\\n # dropped the reference.\\n collect()\\n collect()\\n\\n callback = tracker()\\n if callback is not None:\\n referrers = get_referrers(callback)\\n if len(referrers) > 1: # pragma: nocover\\n pytest.fail(\\\"Some references remain: %r\\\" % (referrers,))\",\n \"def _imp_proxy_method(self, name, func):\\n # We just return the original function\\n return func\",\n \"def _generate_test_method(self, method_name, function):\\n suite(*getattr(self, '_suites', set()))(function)\\n setattr(self, method_name, instancemethod(function, self, self.__class__))\",\n \"def data_provider(data_set_source):\\n\\n def test_decorator(fn):\\n # next if statement added 'cause MagicMock in python2 raises\\n # AttributeError.\\n # See https://code.google.com/p/mock/issues/detail?id=67\\n if not hasattr(fn, '__name__'):\\n setattr(fn, '__name__', str(fn))\\n\\n def repl(self, *args):\\n # The setUp method has been called already.\\n # And the tearDown cannot be called after last iteration\\n # The next code solves this contradiction\\n def _set_up():\\n \\\"\\\"\\\"\\n Replace local setUp function to the original TestCase's\\n instance\\n \\\"\\\"\\\"\\n repl._setUp = self.setUp\\n\\n def _tear_down():\\n \\\"\\\"\\\"\\n Replace local tearDown function to the original TestCase's\\n instance\\n \\\"\\\"\\\"\\n repl._tearDown = self.tearDown\\n\\n repl._setUp = _set_up\\n repl._tearDown = _tear_down\\n\\n data_source = _get_data_source(data_set_source)\\n step = 0\\n for i in data_source:\\n repl._tearDown()\\n repl._setUp()\\n\\n try:\\n fn(self, *i)\\n step += 1\\n except AssertionError:\\n msg_tpl = \\\"Step #{step}. Assertion error caught with \\\" \\\\\\n \\\"data set {data_set}\\\"\\n print(msg_tpl.format(step=step, data_set=i))\\n raise\\n\\n return update_wrapper(repl, fn)\\n return test_decorator\",\n \"def function_timer(orig_func):\\n import time\\n\\n @wraps(orig_func)\\n def wrapper(*args, **kwargs):\\n t1 = time.time()\\n result = orig_func(*args, **kwargs)\\n t2 = time.time()\\n print('{} ran in: {} sec'.format(orig_func.__name__, t2))\\n return result\\n\\n return wrapper\",\n \"def before_test(self, func, *args, **kwargs):\\n pass\",\n \"async def test_throttle_on_two_methods(self):\\n\\n class Tester:\\n \\\"\\\"\\\"A tester class for throttling.\\\"\\\"\\\"\\n\\n @Throttle(seconds=3)\\n async def test1(self):\\n \\\"\\\"\\\"Test function for throttle.\\\"\\\"\\\"\\n return True\\n\\n @Throttle(seconds=5)\\n async def test2(self):\\n \\\"\\\"\\\"Test function for throttle.\\\"\\\"\\\"\\n return True\\n\\n tester = Tester()\\n now = time.time()\\n now_plus_4 = now + 4\\n now_plus_6 = now + 6\\n\\n self.assertEqual(await tester.test1(), True)\\n self.assertEqual(await tester.test2(), True)\\n self.assertEqual(await tester.test1(), None)\\n self.assertEqual(await tester.test2(), None)\\n\\n with mock.patch(\\\"time.time\\\", return_value=now_plus_4):\\n self.assertEqual(await tester.test1(), True)\\n self.assertEqual(await tester.test2(), None)\\n\\n with mock.patch(\\\"time.time\\\", return_value=now_plus_6):\\n self.assertEqual(await tester.test1(), None)\\n self.assertEqual(await tester.test2(), True)\",\n \"def custom_cached(name, *old_method, **options):\\n\\n def decorator(method):\\n \\\"\\\"\\\"\\n decorates the given method or function and makes it a lazy one.\\n\\n :param function | property method: decorated method or function.\\n\\n :returns: method or function result.\\n \\\"\\\"\\\"\\n\\n def wrapper(*args, **kwargs):\\n \\\"\\\"\\\"\\n decorates the given method or function and makes it a lazy one.\\n\\n :param object args: function positional arguments.\\n :param object kwargs: function keyword arguments.\\n\\n :returns: method or function result.\\n \\\"\\\"\\\"\\n\\n result = caching_services.try_get(name, method, args,\\n kwargs, **options)\\n if result is not None:\\n return result\\n\\n result = method(*args, **kwargs)\\n caching_services.try_set(name, result, method,\\n args, kwargs, **options)\\n return result\\n\\n return update_wrapper(wrapper, method)\\n\\n if len(old_method) > 0:\\n return decorator(old_method[0])\\n\\n return decorator\",\n \"def minimal_wrap(new_fun):\\n fun_name = new_fun.__name__\\n assert fun_name in dir(torch)\\n torch_fun = getattr(torch, fun_name)\\n new_fun.__doc__ = torch_fun.__doc__\\n STABLE_FUNCTIONS[torch_fun] = new_fun\\n return new_fun\",\n \"def add_fixtures(ctest):\\n\\n def test_setup(funct):\\n \\\"\\\"\\\"Test setUp decorator to add fixture reloading.\\\"\\\"\\\"\\n\\n def decorated_setup():\\n \\\"\\\"\\\"Decorated test setup.\\\"\\\"\\\"\\n testdb.reload_db()\\n funct()\\n return decorated_setup\\n\\n for test in ctest._tests:\\n test.setUp = test_setup(test.setUp)\",\n \"def apply(cls, fs: Any) -> None:\\n logger.debug(\\\"Patching %s with %s.\\\", fs.__class__.__name__, cls.__name__)\\n fs._patch = cls\\n for method_name in cls.patch_methods:\\n # if fs hasn't method, raise AttributeError.\\n origin = getattr(fs, method_name)\\n method = getattr(cls, method_name)\\n bound_method = method.__get__(fs, fs.__class__)\\n setattr(fs, method_name, bound_method)\\n setattr(fs, \\\"_origin_\\\" + method_name, origin)\",\n \"def patch_mock_validation(decoy: Decoy, monkeypatch: pytest.MonkeyPatch) -> None:\\n for name, func in inspect.getmembers(validation, inspect.isfunction):\\n monkeypatch.setattr(validation, name, decoy.mock(func=func))\",\n \"def inject_fixture(request, arg, value):\\n fd_kwargs = {\\n \\\"fixturemanager\\\": request._fixturemanager,\\n \\\"baseid\\\": None,\\n \\\"argname\\\": arg,\\n \\\"func\\\": lambda: value,\\n \\\"scope\\\": \\\"function\\\",\\n \\\"params\\\": None,\\n }\\n\\n if \\\"yieldctx\\\" in get_args(pytest_fixtures.FixtureDef.__init__):\\n fd_kwargs[\\\"yieldctx\\\"] = False\\n\\n fd = pytest_fixtures.FixtureDef(**fd_kwargs)\\n fd.cached_result = (value, 0, None)\\n\\n old_fd = request._fixture_defs.get(arg)\\n add_fixturename = arg not in request.fixturenames\\n\\n def fin():\\n request._fixturemanager._arg2fixturedefs[arg].remove(fd)\\n request._fixture_defs[arg] = old_fd\\n\\n if add_fixturename:\\n request._pyfuncitem._fixtureinfo.names_closure.remove(arg)\\n\\n request.addfinalizer(fin)\\n\\n # inject fixture definition\\n request._fixturemanager._arg2fixturedefs.setdefault(arg, []).insert(0, fd)\\n # inject fixture value in request cache\\n request._fixture_defs[arg] = fd\\n if add_fixturename:\\n request._pyfuncitem._fixtureinfo.names_closure.append(arg)\",\n \"def mock_upgrade_package(\\n mock_get_installed_version, mock_get_latest_version, monkeypatch\\n):\\n\\n def mock_upgrade(self, version=None):\\n if version:\\n sane_version = version.replace(\\\"*\\\", \\\"1.1\\\")\\n self.get_installed_version.return_value = sane_version\\n else:\\n self.get_installed_version.return_value = self.get_latest_version()\\n\\n monkeypatch.setattr(\\\"libgitlab.GitlabHelper.upgrade_package\\\", mock_upgrade)\",\n \"def add_function (self, module, name) :\\n setattr (module, name, self._wrapped (module, name))\",\n \"def __fixture_decorator_factory(fixture_type):\\n def fixture_method(func):\\n MetaTestCase._fixture_accumulator[fixture_type].append(func)\\n func._fixture_type = fixture_type\\n return func\\n return fixture_method\",\n \"def teardown_function(function):\\n bundle.local.reset_mock()\\n bundle.cd.reset_mock()\",\n \"def decorator(func):\\n\\n def wrapper(*args, **kwargs):\\n \\\"\\\"\\\"\\n decorates the given method or function and makes it a lazy one.\\n\\n :param object args: function positional arguments.\\n :param object kwargs: function keyword arguments.\\n\\n :returns: function result.\\n \\\"\\\"\\\"\\n\\n result = caching_services.try_get('extended.permanent', func,\\n args, kwargs, **options)\\n if result is not None:\\n return result\\n\\n result = func(*args, **kwargs)\\n caching_services.try_set('extended.permanent', result, func,\\n args, kwargs, **options)\\n return result\\n\\n return update_wrapper(wrapper, func)\",\n \"def replace_update_function(self, update_function):\\n # remove old function\\n self.remove_update_function()\\n\\n # add new function\\n self.add_update_function(update_function)\",\n \"def replace_update_function(self, update_function):\\n # remove old function\\n self.remove_update_function()\\n\\n # add new function\\n self.add_update_function(update_function)\",\n \"def test_decorator(f):\\n return f\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66126996","0.6246913","0.59958524","0.59808385","0.5970251","0.5970251","0.59676784","0.5964168","0.5732706","0.5707334","0.568837","0.56609994","0.5626188","0.56159544","0.5610572","0.55929077","0.555881","0.55573034","0.55572486","0.5480746","0.54785806","0.5477111","0.5455235","0.5449383","0.5439323","0.54297245","0.5426932","0.54045784","0.53939265","0.53691787","0.5367923","0.53561157","0.53288966","0.532375","0.5299457","0.528944","0.5281429","0.5269444","0.5252311","0.52497953","0.5237709","0.5226505","0.5219306","0.5210733","0.52080923","0.5205092","0.5199599","0.5197942","0.51891625","0.51740175","0.5173131","0.51718587","0.51698023","0.5158365","0.51579404","0.51363105","0.5132726","0.5119035","0.51131886","0.51128954","0.5112554","0.51033807","0.50750893","0.50661564","0.5062744","0.50563586","0.50471944","0.50390404","0.5039034","0.5035457","0.50276464","0.50267553","0.50232273","0.5020917","0.49949545","0.49775672","0.4977304","0.49753088","0.49728027","0.49703166","0.49667287","0.49635512","0.4957784","0.4957573","0.49573243","0.49531645","0.4950204","0.4932033","0.49291918","0.49254984","0.4922695","0.49124408","0.49122512","0.49117184","0.4909502","0.49015293","0.489387","0.489387","0.48912993"],"string":"[\n \"0.66126996\",\n \"0.6246913\",\n \"0.59958524\",\n \"0.59808385\",\n \"0.5970251\",\n \"0.5970251\",\n \"0.59676784\",\n \"0.5964168\",\n \"0.5732706\",\n \"0.5707334\",\n \"0.568837\",\n \"0.56609994\",\n \"0.5626188\",\n \"0.56159544\",\n \"0.5610572\",\n \"0.55929077\",\n \"0.555881\",\n \"0.55573034\",\n \"0.55572486\",\n \"0.5480746\",\n \"0.54785806\",\n \"0.5477111\",\n \"0.5455235\",\n \"0.5449383\",\n \"0.5439323\",\n \"0.54297245\",\n \"0.5426932\",\n \"0.54045784\",\n \"0.53939265\",\n \"0.53691787\",\n \"0.5367923\",\n \"0.53561157\",\n \"0.53288966\",\n \"0.532375\",\n \"0.5299457\",\n \"0.528944\",\n \"0.5281429\",\n \"0.5269444\",\n \"0.5252311\",\n \"0.52497953\",\n \"0.5237709\",\n \"0.5226505\",\n \"0.5219306\",\n \"0.5210733\",\n \"0.52080923\",\n \"0.5205092\",\n \"0.5199599\",\n \"0.5197942\",\n \"0.51891625\",\n \"0.51740175\",\n \"0.5173131\",\n \"0.51718587\",\n \"0.51698023\",\n \"0.5158365\",\n \"0.51579404\",\n \"0.51363105\",\n \"0.5132726\",\n \"0.5119035\",\n \"0.51131886\",\n \"0.51128954\",\n \"0.5112554\",\n \"0.51033807\",\n \"0.50750893\",\n \"0.50661564\",\n \"0.5062744\",\n \"0.50563586\",\n \"0.50471944\",\n \"0.50390404\",\n \"0.5039034\",\n \"0.5035457\",\n \"0.50276464\",\n \"0.50267553\",\n \"0.50232273\",\n \"0.5020917\",\n \"0.49949545\",\n \"0.49775672\",\n \"0.4977304\",\n \"0.49753088\",\n \"0.49728027\",\n \"0.49703166\",\n \"0.49667287\",\n \"0.49635512\",\n \"0.4957784\",\n \"0.4957573\",\n \"0.49573243\",\n \"0.49531645\",\n \"0.4950204\",\n \"0.4932033\",\n \"0.49291918\",\n \"0.49254984\",\n \"0.4922695\",\n \"0.49124408\",\n \"0.49122512\",\n \"0.49117184\",\n \"0.4909502\",\n \"0.49015293\",\n \"0.489387\",\n \"0.489387\",\n \"0.48912993\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.698745"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":85582,"cells":{"query":{"kind":"string","value":"Use python mock to mock an object attribute Mocks the specified objects attribute with the given value. Automatically performs 'addCleanup' for the mock."},"document":{"kind":"string","value":"def mock_object(self, obj, attr_name, new_attr=None, **kwargs):\n if not new_attr:\n new_attr = mock.Mock()\n patcher = mock.patch.object(obj, attr_name, new_attr, **kwargs)\n patcher.start()\n self.addCleanup(patcher.stop)\n return new_attr"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def patch_object(mocker):\n\n def _object(obj_to_patch, attribute, return_value):\n mock_func = mocker.patch.object(obj_to_patch, attribute)\n if return_value is not None:\n mock_func.return_value = return_value\n\n return _object","def mock_side_effect(self, original_object, attribute_name, side_effect):\n original_function = getattr(original_object, attribute_name)\n def original_with_side_effect(*args, **kwargs):\n side_effect()\n return original_function(*args, **kwargs)\n self.mock(original_object, attribute_name, original_with_side_effect)","def decorator(func):\n\n setattr(func, mock_method.attr_name, name)\n return func","def patch(*args, **kwargs):\n kwargs.setdefault('autospec', True)\n return mock.patch.object(*args, **kwargs)","def __getattribute__(self, name):\n attr_value = super(CLITestCase, self).__getattribute__(name)\n\n if callable(attr_value) and name.startswith('test_'):\n @functools.wraps(attr_value)\n def wrapper():\n with mock.patch('pdc_client.PDCClientWithPage') as cls:\n api = MockAPI()\n cls.return_value = api\n return attr_value(api)\n return wrapper\n\n return attr_value","def mock_object(**params):\n return type('Mock', (), params)()","def mock_amazon():\n amazon = Amazon()\n amazon.carrot1 = 'cenoura normal'\n amazon.carrot2 = 'cenoura radioativa'\n amazon.carrot_number = 575\n return amazon","def test_object_awareness(self):\n obj = self.obj\n\n # attribute properties receive on obj ref in the getter/setter that can customize return\n self.assertEqual(obj.cusattr, 10)\n self.assertEqual(obj.settest, 5)\n obj.awaretest = 10\n self.assertEqual(obj.cusattr, 15)\n obj.cusattr = 10\n self.assertEqual(obj.cusattr, 20)\n self.assertEqual(obj.settest, 10)\n\n # attribute value mutates if you do += or similar (combined get-set)\n obj.cusattr += 10\n self.assertEqual(obj.attributes.get(\"cusattr\"), 30)\n self.assertEqual(obj.settest, 30)\n self.assertEqual(obj.cusattr, 40)\n obj.awaretest = 0\n obj.cusattr += 20\n self.assertEqual(obj.attributes.get(\"cusattr\"), 50)\n self.assertEqual(obj.settest, 50)\n self.assertEqual(obj.cusattr, 50)\n del obj.cusattr\n self.assertEqual(obj.cusattr, 5)\n self.assertEqual(obj.settest, 5)","def monkey_path_api_requests(self, method, mock_object=None):\n attr = getattr(api.requests, method)\n mock_method = mock.MagicMock()\n setattr(api.requests, method, mock_method)\n try:\n yield mock_method\n finally:\n setattr(api.requests, method, attr)","def mock_path_mkdir(mocker):\n\n def _path(obj_to_patch, attribute, side_effect):\n mock_func = mocker.patch.object(obj_to_patch, attribute)\n mock_func.side_effect = side_effect\n\n return _path","def testRecordReturnsMockObject(self):\n\t\taction = GetAttributeAction('y', ('key',) , 'z')\n\t\tself.failUnless(action.record() == 'z')","def mock_moira(mocker):\n return mocker.patch(\"moira_lists.moira_api.Moira\")","def test_assert_mock_called_once_with(self):\n mock_obj = mock.MagicMock(return_value='Pos vale')\n\n mock_obj(1, 2, 3, kwarg='kwarg_value')\n\n mock_obj.assert_called_once_with(1, 2, 3, kwarg='kwarg_value')","def testAttributeAssignmentIsIntercepted(self):\n\t \n\t c = Controller()\n\t x = c.mock(KlassBeingMocked)\n\t x.w = 5\n\t c.replay()\n\t try:\n\t x.w\n\t self.fail()\n\t except PlaybackFailure, e:\n\t pass","def _monkeypatch(mod, name, value):\n with unittest.mock.patch.object(mod, name, value):\n yield","def __getattribute__(self, name):\n\n def setup():\n \"\"\"\n Create `zorp_mock`. Register RPC methods, and activate the mock\n object.\n \"\"\"\n\n self.zorp_mock = Mock()\n\n for name, func in six.iteritems(self._get_zorp_mock_methods()):\n self.zorp_mock.server.registry.put(name, func)\n\n self.zorp_mock.start()\n\n def teardown():\n \"\"\"\n Deactivate the mock object.\n \"\"\"\n\n self.zorp_mock.stop()\n\n def func_caller(*funcs):\n \"\"\"\n Return a function that calls each of `funcs` in order.\n \"\"\"\n\n def caller():\n \"\"\"\n Call each of `funcs`.\n \"\"\"\n\n for func in funcs:\n func()\n\n return caller\n\n value = super(ZorpTestCaseMixin, self).__getattribute__(name)\n\n if name == \"setUp\":\n value = func_caller(setup, value)\n elif name == \"tearDown\":\n value = func_caller(value, teardown)\n\n return value","def test_mock_a_class_func():\n print()\n\n myclass = mymodule.MyClass()\n myclass.class_mock_one = Mock()\n myclass.class_mock_one.return_value = 2\n xx = myclass.class_mock_two()\n print(xx)\n myclass.class_mock_one.assert_called_with()","def test_reddit_object_persist(mocker):\n\n def mock_indexer1(reddit_obj): # pylint: disable=missing-docstring\n reddit_obj.changed1 = True\n\n def mock_indexer2(reddit_obj): # pylint: disable=missing-docstring\n reddit_obj.changed2 = True\n\n @reddit_object_persist(mock_indexer1, mock_indexer2)\n def decorated_api_func(mock_reddit_obj): # pylint: disable=missing-docstring\n return mock_reddit_obj\n\n mock_reddit_obj = mocker.Mock()\n reddit_obj = decorated_api_func(mock_reddit_obj)\n assert reddit_obj.changed1 is True\n assert reddit_obj.changed2 is True\n assert reddit_obj == mock_reddit_obj","def _mock_function(self, obj, func):\n setattr(obj, func.__name__, MethodType(func, self.breaker))","def _mock_function(self, obj, func):\n setattr(obj, func.__name__, MethodType(func, self.breaker))","def test_assert_mock_called_once(self):\n mock_obj = mock.MagicMock(return_value='Pos vale')\n\n mock_obj(1, 2, 3, kwarg='kwarg_value')\n\n mock_obj.assert_called_once()","def testUsedAttributeAssignmentCanBeVerified(self): \n\t c = Controller()\n\t x = c.mock(KlassBeingMocked)\n\t x.w = 5\n\t x.h = 3\n\t c.replay()\n\t x.h = 3\n\t x.w = 5\n\t c.verify()","def mock_obj(self, name, methods = [], **kwargs):\n return MockObject(self._context, name, methods, **kwargs)","def test__attach_objects_post_save_hook(\n self, monkeypatch, fake_user, fake_customer, metadata\n ):\n user = fake_user\n customer = fake_customer\n\n # because create_for_user method adds subscriber\n customer.subcriber = None\n customer.save()\n\n # update metadata\n if metadata.get(self.key, \"\"):\n metadata[self.key] = user.id\n\n fake_stripe_session = deepcopy(FAKE_SESSION_I)\n fake_stripe_session[\"metadata\"] = metadata\n\n def mock_checkout_session_get(*args, **kwargs):\n \"\"\"Monkeypatched stripe.Session.retrieve\"\"\"\n return deepcopy(fake_stripe_session)\n\n def mock_customer_get(*args, **kwargs):\n \"\"\"Monkeypatched stripe.Customer.retrieve\"\"\"\n return deepcopy(FAKE_CUSTOMER)\n\n def mock_payment_intent_get(*args, **kwargs):\n \"\"\"Monkeypatched stripe.PaymentIntent.retrieve\"\"\"\n return deepcopy(FAKE_PAYMENT_INTENT_I)\n\n def mock_setup_intent_get(*args, **kwargs):\n \"\"\"Monkeypatched stripe.SetupIntent.retrieve\"\"\"\n return deepcopy(FAKE_SETUP_INTENT_II)\n\n def mock_invoice_get(*args, **kwargs):\n \"\"\"Monkeypatched stripe.Invoice.retrieve\"\"\"\n return deepcopy(FAKE_INVOICE)\n\n def mock_invoice_item_get(*args, **kwargs):\n return deepcopy(FAKE_INVOICEITEM)\n\n def mock_payment_method_get(*args, **kwargs):\n \"\"\"Monkeypatched stripe.PaymentMethod.retrieve\"\"\"\n return deepcopy(FAKE_PAYMENT_METHOD_I)\n\n def mock_subscription_get(*args, **kwargs):\n \"\"\"Monkeypatched stripe.Subscription.retrieve\"\"\"\n return deepcopy(FAKE_SUBSCRIPTION)\n\n def mock_subscriptionitem_get(*args, **kwargs):\n return deepcopy(FAKE_SUBSCRIPTION_ITEM)\n\n def mock_balance_transaction_get(*args, **kwargs):\n \"\"\"Monkeypatched stripe.BalanceTransaction.retrieve\"\"\"\n return deepcopy(FAKE_BALANCE_TRANSACTION)\n\n def mock_product_get(*args, **kwargs):\n \"\"\"Monkeypatched stripe.Product.retrieve\"\"\"\n return deepcopy(FAKE_PRODUCT)\n\n def mock_charge_get(*args, **kwargs):\n \"\"\"Monkeypatched stripe.Charge.retrieve\"\"\"\n return deepcopy(FAKE_CHARGE)\n\n # monkeypatch stripe.checkout.Session.retrieve, stripe.Customer.retrieve, stripe.PaymentIntent.retrieve\n monkeypatch.setattr(\n stripe.checkout.Session, \"retrieve\", mock_checkout_session_get\n )\n monkeypatch.setattr(stripe.Customer, \"retrieve\", mock_customer_get)\n monkeypatch.setattr(stripe.Customer, \"modify\", mock_customer_get)\n monkeypatch.setattr(stripe.PaymentIntent, \"retrieve\", mock_payment_intent_get)\n monkeypatch.setattr(stripe.SetupIntent, \"retrieve\", mock_setup_intent_get)\n\n # because of Reverse o2o field sync due to PaymentIntent.sync_from_stripe_data..\n monkeypatch.setattr(stripe.Invoice, \"retrieve\", mock_invoice_get)\n monkeypatch.setattr(stripe.InvoiceItem, \"retrieve\", mock_invoice_item_get)\n monkeypatch.setattr(stripe.PaymentMethod, \"retrieve\", mock_payment_method_get)\n monkeypatch.setattr(stripe.Subscription, \"retrieve\", mock_subscription_get)\n monkeypatch.setattr(\n stripe.SubscriptionItem, \"retrieve\", mock_subscriptionitem_get\n )\n monkeypatch.setattr(\n stripe.BalanceTransaction, \"retrieve\", mock_balance_transaction_get\n )\n monkeypatch.setattr(stripe.Product, \"retrieve\", mock_product_get)\n monkeypatch.setattr(stripe.Charge, \"retrieve\", mock_charge_get)\n\n # Invoke the sync to invoke _attach_objects_post_save_hook()\n session = Session.sync_from_stripe_data(fake_stripe_session)\n\n # refresh self.customer from db\n customer.refresh_from_db()\n\n assert session\n assert session.customer.id == customer.id\n assert customer.subscriber == user\n if metadata.get(self.key, \"\"):\n assert customer.metadata == {self.key: metadata.get(self.key)}\n else:\n assert customer.metadata == {}","def mock_method(name):\n\n def decorator(func):\n \"\"\"\n Return `func`, with the `mock_method.attr_name` attribute set.\n \"\"\"\n\n setattr(func, mock_method.attr_name, name)\n return func\n\n # Set an attribute on `decorator`. This can be used to discover situations\n # where `mock_method` has accidentally been called without any parameters.\n setattr(decorator, mock_method.decorator_attr_name, True)\n\n return decorator","def test_remove_donation(monkeypatch):\n #We still need an assert, don't know how to mock two user input on one call\n mr.initialize_donors()\n monkeypatch.setattr('mailroom.get_value', lambda x, y: 'Koko')\n mr.remove_donation()\n monkeypatch.setattr('mailroom.get_value', lambda x, y: 'q')\n mr.remove_donation()\n # assert False","def test_get_setting(monkeypatch):\n resp = str(uuid.uuid4())\n arg = str(uuid.uuid4())\n kwarg = str(uuid.uuid4())\n get_secret = Mock(return_value=resp)\n monkeypatch.setattr(\"lambdautils.state.get_secret\", get_secret)\n resp2 = lambdautils.state.get_setting(arg, kwarg=kwarg)\n assert resp2 == resp\n get_secret.assert_called_with(arg, kwarg=kwarg)","def test_one_att(self):\n self.test_attribute.is_down = mock.Mock(return_value=False)\n self.run_mock_analyzer([self.test_attribute, ])\n self.assert_mock_analyzer(self.test_attribute)","def testGetattrWithReturnValueSpecified(self):\n\t\tc = Controller()\n\t\tx = c.mock(KlassBeingMocked)\n\t\tx.g\n\t\tc.setReturn(8)\n\t\tc.replay()\n\t\tself.failUnless(x.g == 8)","def mock_remote_unit(monkeypatch):\n monkeypatch.setattr(\"libgitlab.hookenv.remote_unit\", lambda: \"unit-mock/0\")","def mock_moira_client(mocker):\n return mocker.patch(\"moira_lists.moira_api.get_moira_client\", autospec=True)","def mock_gitlab_get_flag_value(monkeypatch):\n mock_flag_value = mock.Mock()\n mock_flag_value.return_value = None\n monkeypatch.setattr(\"libgitlab._get_flag_value\", mock_flag_value)\n return mock_flag_value","def test_magic_mock():\n\n class Empty:\n pass\n\n empty = Empty()\n empty.abc = MagicMock(return_value=\"abc\") # type: ignore\n\n assert empty.abc(\"hi\") == \"abc\" # type: ignore\n empty.abc.assert_called_with(\"hi\") # type: ignore","def temporary_attr(obj, name, value):\n setattr(obj, name, value)\n try:\n yield obj\n finally:\n delattr(obj, name)","def temporary_attr(obj, name, value):\n setattr(obj, name, value)\n try:\n yield obj\n finally:\n delattr(obj, name)","def add_mock(monkeypatch):\n _mock = Mock()\n monkeypatch.setattr('django.core.cache.cache.add', _mock)\n return _mock","def testAttributeAssignmentCanBePlayedBack(self):\n\t \n\t c = Controller()\n\t x = c.mock(KlassBeingMocked)\n\t x.w = 5\n\t x.w = 6\n\t c.replay()\n\t x.w = 5","def mocked_fogbugz(monkeypatch):\n mocked_fogbugz = mock.Mock()\n mocked_fogbugz_instance = mock.Mock()\n mocked_fogbugz.return_value = mocked_fogbugz_instance\n monkeypatch.setattr(fogbugz, 'FogBugz', mocked_fogbugz)\n return mocked_fogbugz","def mock_unit_db(monkeypatch):\n mock_kv = mock.Mock()\n mock_kv.return_value = unitdata.Storage(path=\":memory:\")\n monkeypatch.setattr(\"libgitlab.unitdata.kv\", mock_kv)","def test__init__(self):\n mocked_reconstructor = Mock()\n mocked_reconstructor.reconstruct.return_value = 'mocked'\n db_response = {'Item': {'test': True}}\n resp = GetResponse(db_response, mocked_reconstructor)\n assert resp.item == 'mocked'\n mocked_reconstructor.reconstruct.assert_called_with(db_response['Item'])","def testUnusedAttributeAssignmentCanBeVerified(self):\n\t \n\t c = Controller()\n\t x = c.mock(KlassBeingMocked)\n\t x.w = 5\n\t c.replay()\n\t self.failUnlessRaises(RecordedCallsWereNotReplayedCorrectly, c.verify)","def mocked_item(mocked_session):\n test_item = mock.Mock()\n test_item.session = mocked_session\n return test_item","def mock_config(monkeypatch: Any) -> None:\n monkeypatch.setattr(\"catapi.config.ENABLE_FOO\", \"true\")\n monkeypatch.setattr(\"catapi.config.ENABLE_BAR\", \"false\")","def test_s3_cleanup_storage(self, mock_class):\n current_s3_fake_objects = {\n \"bucket1\": {\n \"object1\": ObjectInfo(size=10,\n last_modified=datetime.datetime.utcnow()),\n \"object2\": ObjectInfo(size=20,\n last_modified=datetime.datetime.utcnow()),\n \"object3\": ObjectInfo(size=30,\n last_modified=datetime.datetime.utcnow()),\n },\n \"bucket2\": {\n \"object4\": ObjectInfo(size=100,\n last_modified=datetime.datetime(year=2020, month=10, day=1, hour=10, minute=10,\n second=10)),\n \"object5\": ObjectInfo(size=200,\n last_modified=datetime.datetime(year=2020, month=10, day=1, hour=10, minute=11,\n second=10)),\n \"object6\": ObjectInfo(size=300,\n last_modified=datetime.datetime(year=2020, month=10, day=1, hour=10, minute=12,\n second=10)),\n }\n }\n\n mock_class().list_buckets.return_value = list(current_s3_fake_objects.keys())\n mock_class().list_objects.side_effect = [list(current_s3_fake_objects[\"bucket1\"].keys()),\n list(current_s3_fake_objects[\"bucket2\"].keys()),\n list(current_s3_fake_objects[\"bucket2\"].keys())]\n mock_class().stat_object.side_effect = list(current_s3_fake_objects[\"bucket1\"].values()) + \\\n list(current_s3_fake_objects[\"bucket2\"].values())\n\n test_obj = servicex_storage.s3_storage_manager.S3Store(s3_endpoint=\"abc\",\n access_key=\"abc\",\n secret_key=\"abc\")\n\n final_size = test_obj.cleanup_storage(70, 60, 365)[0]\n self.assertEqual(final_size, 60)\n mock_class().remove_objects.assert_called_with(\"bucket2\", [\"object4\", \"object5\", \"object6\"])","def setUp(self):\n self.image = mock.MagicMock(spec=models.Image)\n self.image.name = 'gallery/foo.jpg'","def test_add_donation(monkeypatch):\n #We still need an assert, don't know how to mock two user input on one call\n mr.initialize_donors()\n monkeypatch.setattr('mailroom.get_value', lambda x, y: 'Koko')\n mr.add_donation()\n monkeypatch.setattr('mailroom.get_value', lambda x, y: 'q')\n mr.add_donation()\n # assert False","def test_get_object(self, detail_view, employee_model):\n\n employee = Mock()\n employee_model.objects.get.return_value = Mock()\n detail_view.get_object.return_value = employee\n\n emp = detail_view.get_object(1)\n self.assertEqual(employee, emp)","def test_api_object_update_property(self, api_object):\n attrs_dict = {'uuid_': 'CREATING'}\n api_object.update_public_attrs(attrs_dict)\n assert api_object.uuid_ != 'CREATING'","def mock_time(desired_time):\n # type: (float) -> Any\n return mock.patch.object(time, 'time', return_value=desired_time)","def _setup_mock(self, mock):\n\n mock.Subscription.create.return_value.is_success = True\n mock.Subscription.create.return_value.subscription.id = \"t-sub-id\"","def requestsmock():\n with requests_mock.mock() as m:\n yield m","def __getattr__(self, name):\n raise MockException(\n \"Mock object %s has no attribute '%s'\" %\n (mock_object_names[id(self)], name)\n )","def configure_mock_action_instance(mock_action: Mock) -> Mock:\n mock_instance = Mock(return_value=None)\n mock_action.return_value = mock_instance\n mock_instance.execute = Mock()\n return mock_instance","def testGetattrWithNoReturnValueSpecified(self):\n\t\tc = Controller()\n\t\tx = c.mock(KlassBeingMocked)\n\t\tx.g\n\t\tc.replay()\n\t\tself.failIf(x.g == None)\n\t\ttry:\n\t\t\tx.g\n\t\t\tself.fail()\n\t\texcept PlaybackFailure, e:\n\t\t\tpass","def __init__(self, context, name, methods, **kwargs):\n \n mock_object_names[id(self)] = name\n for method in methods:\n self.__dict__[method] = MockFunction(context, method)\n for (key, value) in kwargs.items():\n self.__dict__[key] = value","def mock_bot(mocker):\n mock_bot = SlackBot()\n mock_bot.slack_user_id = \"UB0T\"\n mocker.patch.object(mock_bot, \"web_client\")\n mocker.patch.object(mock_bot, \"update_person\", side_effect=lambda person: person)\n return mock_bot","def mock_command(mocker):\n def _mock_command(command_class):\n mocker.patch.object(command_class, 'add_arguments', return_value=None)\n mocker.patch.object(command_class, 'pre_add_arguments', return_value=None)\n instance = command_class()\n\n if hasattr(instance, 'host'):\n instance.host.name = '==MOCKED_HOST=='\n\n return instance\n\n return _mock_command","def test_telnetrmq_after_execute(self, mocked):\n agentconf={}\n telnetconf={\"host\":\"telnet.lan\"}\n rmqconf={\"host\":\"rmq.lan\"}\n agent=TelnetRmqAgent(agentconf, telnetconf, rmqconf)\n\n #Setup generic mock for others methods wich are not tested here\n ignoredmocks=Mock()\n agent.telnetclient=ignoredmocks\n agent.rmqclient=ignoredmocks\n \n\n instance = mocked.return_value \n agent.after_execute()\n mocked.assert_called_with(agent)\n mocked.assert_called_with(agent)","def mock_bucket(\n mocker,\n):\n mocker.patch(\"pcluster.models.s3_bucket.get_partition\", return_value=\"fake_partition\")\n mocker.patch(\"pcluster.models.s3_bucket.get_region\", return_value=\"fake-region\")\n mocker.patch(\"pcluster.aws.sts.StsClient.get_account_id\", return_value=\"fake-id\")","def test_rename_donor(monkeypatch):\n #Not finished\n mr.initialize_donors()\n monkeypatch.setattr('mailroom.get_value', lambda x, y: 'q')\n mr.rename_donor()\n # assert False","def test_card_value(mock_card):\n assert mock_card.value == 1","def create_mock_client(self, fake_request_method):\n class FakeHttpLib2(object):\n pass\n\n FakeHttpLib2.request = fake_request_method\n mock_client = self.mox.CreateMock(DNSaasClient)\n mock_client.http_pool = pools.Pool()\n mock_client.http_pool.create = FakeHttpLib2\n mock_client.auth_token = 'token'\n return mock_client","def patch_mock_deck_conflict_check(\n decoy: Decoy, monkeypatch: pytest.MonkeyPatch\n) -> None:\n mock = decoy.mock(func=deck_conflict.check)\n monkeypatch.setattr(deck_conflict, \"check\", mock)","def testSetAttributeRecordsField(self):\n\t\n\t c = Controller()\n\t x = c.mock(KlassBeingMocked)\n\t x.w = 5\n\t self.failUnless(len(c.actions) == 1)\n\t action = c.actions.get(BaseAction((x,'w')))\n\t self.failUnless(action.field == 'w')\n\t self.failUnless(action.value == 5)","def testRecord(self):\n\t\taction = SetAttributeAction('mock', 'y', ('key', ), 5)\n\t\tself.failUnless(action == action.record())","def provider_and_mock_one(monkeypatch, provider_one, mock_inner_provider):\n mock_make_provider = mock.Mock(return_value=mock_inner_provider)\n monkeypatch.setattr(provider_one, 'make_provider', mock_make_provider)\n return provider_one, mock_inner_provider","async def test_attribute(hass, service, attribute):\n calls_1 = async_mock_service(hass, DOMAIN, service)\n\n value = \"dummy\"\n\n await async_reproduce_states(hass, [\n State(ENTITY_1, None,\n {attribute: value})\n ])\n\n await hass.async_block_till_done()\n\n assert len(calls_1) == 1\n assert calls_1[0].data == {'entity_id': ENTITY_1,\n attribute: value}","def set_mock_return_value(magic_mock: MagicMock, return_value: t.Any):\n if magic_mock.__class__.__name__ == \"AsyncMock\":\n # Python 3.8 and above\n magic_mock.return_value = return_value\n else:\n\n async def coroutine(*args, **kwargs):\n return return_value\n\n magic_mock.return_value = coroutine()","def mock_apt_update(monkeypatch):\n mocked_apt_update = mock.Mock(returnvalue=True)\n monkeypatch.setattr(\"libgitlab.apt_update\", mocked_apt_update)\n return mocked_apt_update","def test_s3_get_bucket_info(self, mock_class):\n\n mock_class().list_objects.return_value = list(s3_fake_objects[\"bucket1\"].keys())\n mock_class().stat_object.side_effect = list(s3_fake_objects[\"bucket1\"].values())\n return_value = servicex_storage.s3_storage_manager.BucketInfo(name=\"bucket1\",\n size=60,\n last_modified=datetime.datetime(\n year=2021, month=10,\n day=1, hour=10,\n minute=10, second=10))\n test_obj = servicex_storage.s3_storage_manager.S3Store(s3_endpoint=\"abc\",\n access_key=\"abc\",\n secret_key=\"abc\")\n bucket_info = test_obj.get_bucket_info(\"bucket1\")\n self.assertEqual(bucket_info, return_value)","def test_tango_device_builder():\n with mock.patch(\"tango_simlib.utilities.tango_device_parser.tango\") as mocked_tango:\n mocked_tango.CmdArgType = tango.CmdArgType\n mocked_tango.AttrWriteType = tango.AttrWriteType\n mocked_device_proxy = mock.Mock()\n mocked_tango.DeviceProxy.return_value = mocked_device_proxy\n\n mocked_info = mock.Mock()\n mocked_info.dev_class = \"DevClass\"\n mocked_device_proxy.info.return_value = mocked_info\n\n mocked_command = mock.Mock()\n mocked_command.cmd_name = \"CommandName\"\n mocked_command.in_type = tango.CmdArgType.DevVoid\n mocked_command.out_type = tango.CmdArgType.DevVoid\n mocked_command.disp_level = tango.DispLevel.OPERATOR\n mocked_command.out_type_desc = \"out_type_desc\"\n mocked_command.in_type_desc = \"in_type_desc\"\n\n attr = tango.AttributeInfoEx()\n attr.name = \"AttrName\"\n attr.data_format = tango.AttrDataFormat.SCALAR\n attr.disp_level = tango.DispLevel.OPERATOR\n attr.data_type = 8\n attr.writable = tango.AttrWriteType.READ\n attr.description = \"description\"\n attr.display_unit = \"display_unit\"\n attr.format = \"format\"\n attr.label = \"label\"\n attr.max_alarm = \"max_alarm\"\n attr.max_dim_x = 0\n attr.max_dim_y = 0\n attr.max_value = \"max_value\"\n attr.min_alarm = \"min_alarm\"\n attr.min_value = \"min_value\"\n attr.standard_unit = \"standard_unit\"\n attr.unit = \"unit\"\n attr.writable_attr_name = \"writable_attr_name\"\n\n mocked_device_proxy.get_command_config.return_value = [mocked_command]\n mocked_device_proxy.attribute_list_query_ex.return_value = [attr]\n mocked_device_proxy.get_property_list.return_value = [\"PropA\", \"PropB\"]\n\n tango_args = Namespace(tango_device_name=\"a/b/c\")\n tango_yaml = _build_yaml(tango_args)\n\n parsed_yaml = yaml.load(tango_yaml, Loader=yaml.FullLoader)\n\n mocked_tango.DeviceProxy.assert_called_once_with(\"a/b/c\")\n mocked_device_proxy.get_command_config.assert_called()\n mocked_device_proxy.attribute_list_query_ex.assert_called()\n mocked_device_proxy.get_property_list.assert_called()\n\n attributes = parsed_yaml[0][\"meta\"][\"attributes\"]\n assert attributes == [\n {\n \"disp_level\": \"OPERATOR\",\n \"description\": \"description\",\n \"data_type\": \"DevString\",\n \"format\": \"format\",\n \"max_alarm\": \"max_alarm\",\n \"min_value\": \"min_value\",\n \"display_unit\": \"display_unit\",\n \"writable\": \"READ\",\n \"standard_unit\": \"standard_unit\",\n \"unit\": \"unit\",\n \"name\": \"AttrName\",\n \"data_format\": \"SCALAR\",\n \"label\": \"label\",\n \"max_value\": \"max_value\",\n \"min_alarm\": \"min_alarm\",\n \"writable_attr_name\": \"writable_attr_name\",\n },\n ], \"Attribute config mismatch. attr: {}\".format(attributes)\n\n commands = parsed_yaml[0][\"meta\"][\"commands\"]\n assert commands == [\n {\n \"doc_out\": \"out_type_desc\",\n \"disp_level\": \"OPERATOR\",\n \"name\": \"CommandName\",\n \"doc_in\": \"in_type_desc\",\n \"dtype_out\": \"DevVoid\",\n \"dtype_in\": \"DevVoid\",\n }\n ], \"Command config mismatch. commands: {}\".format(commands)\n\n properties = parsed_yaml[0][\"meta\"][\"properties\"]\n assert properties == [\n {\"name\": \"PropA\"},\n {\"name\": \"PropB\"},\n ], \"Properties config mismatch. properties: {}.\".format(properties)","def mock_schedule(monkeypatch):\n\n def mock_schedule(self):\n return\n\n monkeypatch.setattr(segment.SegmentProducer, \"schedule\", mock_schedule)","def test_timetracking_resource_methods(self, mock_url, resource_name, single_name):\n business_id = 1234\n resource_id = 2345\n resource_ = getattr(self.freshBooksClient, resource_name)\n\n list_response = {resource_name: [], \"meta\": {\"page\": 1, \"pages\": 0, \"per_page\": 15, \"total\": 0}}\n single_response = {single_name: {}}\n\n with patch.object(TimetrackingResource, \"_request\", return_value=list_response) as mock_request:\n resource_.list(business_id)\n mock_request.assert_called_with(\"some_url\", HttpVerbs.GET)\n\n with patch.object(TimetrackingResource, \"_request\", return_value=single_response) as mock_request:\n resource_.get(business_id, resource_id)\n mock_request.assert_called_with(\"some_url\", HttpVerbs.GET)\n\n resource_.create(business_id, {})\n mock_request.assert_called_with(\"some_url\", HttpVerbs.POST, data={single_name: {}})\n\n resource_.update(business_id, resource_id, {})\n mock_request.assert_called_with(\"some_url\", HttpVerbs.PUT, data={single_name: {}})\n\n resource_.delete(business_id, resource_id)\n mock_request.assert_called_with(\"some_url\", HttpVerbs.DELETE)","def test_update_metadata_by_attribute(self):\n pass","def mock_decorator():\n\n def test_method(func):\n @wraps(func)\n def inner_method(*args, **kwargs):\n with patch('builtins.open', new_callable=mock_open) as _, \\\n patch('rekcurd_dashboard.apis.api_project.update_kubernetes_deployment_info',\n new=Mock(return_value=True)) as _:\n return func(*args, **kwargs)\n return inner_method\n return test_method","def test_cleanup_on_success(self, mocker):\n remove_spy = mocker.spy(os, 'remove')\n\n payload = dict(id=\"B\", data={\"some\": \"data\"}, ai_service='A')\n headers = {'x-rh-identity': 'ABC'}\n self.client.post(self.url, json=payload, headers=headers)\n\n remove_spy.assert_called_once()","def test_short_circuit(mock_tools):\n mock_tools.docker = \"tool\"\n\n tool = Docker.verify(mock_tools)\n\n assert tool == \"tool\"\n assert tool == mock_tools.docker","def test_accounting_resource_methods(self, mock_url, resource_name, single_name, delete_via_update):\n account_id = 1234\n resource_id = 2345\n resource_ = getattr(self.freshBooksClient, resource_name)\n\n list_response = {resource_name: [], \"page\": 1, \"pages\": 0, \"per_page\": 15, \"total\": 0}\n single_response = {single_name: {}}\n\n with patch.object(AccountingResource, \"_request\", return_value=list_response) as mock_request:\n resource_.list(account_id)\n mock_request.assert_called_with(\"some_url\", HttpVerbs.GET)\n\n with patch.object(AccountingResource, \"_request\", return_value=single_response) as mock_request:\n resource_.get(account_id, resource_id)\n mock_request.assert_called_with(\"some_url\", HttpVerbs.GET)\n\n resource_.create(account_id, {})\n mock_request.assert_called_with(\"some_url\", HttpVerbs.POST, data={single_name: {}})\n\n resource_.update(account_id, resource_id, {})\n mock_request.assert_called_with(\"some_url\", HttpVerbs.PUT, data={single_name: {}})\n\n resource_.delete(account_id, resource_id)\n if delete_via_update:\n mock_request.assert_called_with(\"some_url\", HttpVerbs.PUT, data={single_name: {\"vis_state\": 1}})\n else:\n mock_request.assert_called_with(\"some_url\", HttpVerbs.DELETE)","def test__call__(self):\n mock = Mock()\n factory = Factory(mock)\n factory()\n mock.assert_called_once_with()","def test_make_api_call_full_url_cached(urllib2):\n my_accessor = RallyAccessor('uname', 'pword', 'base_url')\n my_accessor.get_from_cache = Mock()\n my_accessor.make_url_safe = Mock()\n my_accessor.make_url_safe.return_value = 'safe-url'\n my_accessor.get_from_cache.return_value = 'Data'\n\n response = my_accessor.make_api_call('some-url', full_url=True)\n\n assert_equal(response, 'Data')\n assert_equal(my_accessor.make_url_safe.call_args[0], ('some-url',))\n assert_equal(my_accessor.get_from_cache.call_args[0], ('safe-url',))\n assert_false(urllib2.urlopen.called)","def mock_sync_hardware_api(decoy: Decoy) -> SyncHardwareAPI:\n return decoy.mock(cls=SyncHardwareAPI)","def make_boto_hit(hit_id=None, batch_id=None):\n hit = mock.MagicMock()\n hit.HITId = (hit_id if hit_id else str(uuid.uuid4()))\n hit.RequesterAnnotation = (batch_id if batch_id else str(uuid.uuid4()))\n return hit","def create_mock_api(cls):\n mock_api = Mock(spec=HookedAuroraClientAPI)\n mock_scheduler = Mock()\n mock_scheduler.url = \"http://something_or_other\"\n mock_scheduler_client = Mock()\n mock_scheduler_client.scheduler.return_value = mock_scheduler\n mock_scheduler_client.url = \"http://something_or_other\"\n mock_api = Mock(spec=HookedAuroraClientAPI)\n mock_api.scheduler = mock_scheduler_client\n return (mock_api, mock_scheduler_client)","def mock_msm(temp_dir):\n msm_mock = Mock(spec=MycroftSkillsManager)\n msm_mock.skills_dir = str(temp_dir)\n msm_mock.platform = 'test_platform'\n msm_mock.lock = Mock()\n msm_mock.repo = Mock(spec=SkillRepo)\n msm_mock.repo.get_default_skill_names = Mock(return_value=[\n ('default', ['time', 'weather']),\n ('test_platform', ['test_skill'])\n ])\n msm_mock.device_skill_state = dict(\n skills=[\n dict(name='test_skill', beta=False)\n ]\n )\n skill = Mock()\n skill.is_local = True\n skill.path = str(temp_dir)\n skill.skill_gid = 'test_skill|99.99'\n skill.meta_info = dict(display_name='Test Skill')\n msm_mock.list_all_defaults.return_value = [skill]\n msm_mock.default_skills = dict(test_skill=skill)\n msm_mock.all_skills = [skill]\n msm_mock.local_skills = dict(test_skill=skill)\n\n return msm_mock","def setUp(self):\n self.r_patcher = patch('redis_metrics.models.R')\n self.mock_r = self.r_patcher.start()","def __setattr__(self, name, value):\n if name in ALLOWED_ON_PROXY:\n self.__dict__[name] = value\n else:\n raise AttributeError(\"unsettable: \"+repr(name))","def mock_object(cls, profile=None):\n mo = ManagedObject()\n if profile:\n mo.profile = Profile.get_by_name(profile)\n mo.is_mock = True\n return mo","def test_telnetrmq_before_execute(self, mocked):\n agentconf={}\n telnetconf={\"host\":\"telnet.lan\"}\n rmqconf={\"host\":\"rmq.lan\"}\n agent=TelnetRmqAgent(agentconf, telnetconf, rmqconf)\n\n #Setup generic mock for others methos wich are not tested here\n ignoredmocks=Mock()\n agent.telnetclient=ignoredmocks\n agent.rmqclient=ignoredmocks\n \n\n instance = mocked.return_value \n agent.before_execute()\n mocked.assert_called_with(agent)","def __get__(self, obj, objtype=None):\n try:\n return getattr(obj, self.cache_attr)\n except AttributeError:\n # Call the wrapped function with the obj instance as argument\n setattr(obj, self.cache_attr, self.fget(obj))\n return getattr(obj, self.cache_attr)","def mock_template(monkeypatch):\n monkeypatch.setattr(\"libgitlab.templating.host.os.fchown\", mock.Mock())\n monkeypatch.setattr(\"libgitlab.templating.host.os.chown\", mock.Mock())\n monkeypatch.setattr(\"libgitlab.templating.host.os.fchmod\", mock.Mock())","def test_thingname_nostr(self, mock):\n mock.configure_mock(**(self.config_shadowget(ParamValidationError(\n report='UnitTest'))))\n self.assertRaises(\n ParamValidationError,\n lf.lambda_handler, event=self.lambdaevent, context=None)\n mock.client.return_value.update_thing_shadow.assert_not_called()","def mock_path(mocker):\n\n def _path(module, side_effect):\n mock_func = mocker.patch(f'{module}.Path.mkdir')\n mock_func.side_effect = side_effect\n\n return _path","def test_project_resource_methods(self, mock_url, resource_name, single_name):\n business_id = 1234\n resource_id = 2345\n resource_ = getattr(self.freshBooksClient, resource_name)\n\n list_response = {resource_name: [], \"meta\": {\"page\": 1, \"pages\": 0, \"per_page\": 15, \"total\": 0}}\n single_response = {single_name: {}}\n\n with patch.object(ProjectsResource, \"_request\", return_value=list_response) as mock_request:\n resource_.list(business_id)\n mock_request.assert_called_with(\"some_url\", HttpVerbs.GET)\n\n with patch.object(ProjectsResource, \"_request\", return_value=single_response) as mock_request:\n resource_.get(business_id, resource_id)\n mock_request.assert_called_with(\"some_url\", HttpVerbs.GET)\n\n resource_.create(business_id, {})\n mock_request.assert_called_with(\"some_url\", HttpVerbs.POST, data={single_name: {}})\n\n resource_.update(business_id, resource_id, {})\n mock_request.assert_called_with(\"some_url\", HttpVerbs.PUT, data={single_name: {}})\n\n resource_.delete(business_id, resource_id)\n mock_request.assert_called_with(\"some_url\", HttpVerbs.DELETE)","def mock_return(self, kall):\n name, parent, from_kall = call_parts(kall)\n\n if from_kall:\n attr = \"return_value\"\n else:\n attr = name.split(\".\")[-1]\n if parent is None:\n mock_obj = self\n else:\n mock_obj = self.mock_return(parent)\n return getattr(mock_obj, attr)","def temporary_attribute_value(obj, attr, new_value):\n replaced = False\n old_value = None\n if hasattr(obj, attr):\n try:\n if attr in obj.__dict__:\n replaced = True\n except AttributeError:\n if attr in obj.__slots__:\n replaced = True\n if replaced:\n old_value = getattr(obj, attr)\n setattr(obj, attr, new_value)\n yield replaced, old_value\n if not replaced:\n delattr(obj, attr)\n else:\n setattr(obj, attr, old_value)","def mock_apt_install(monkeypatch):\n mocked_apt_install = mock.Mock(returnvalue=True)\n monkeypatch.setattr(\"libgitlab.apt_install\", mocked_apt_install)\n return mocked_apt_install","def test_alchemy_magic_mock() -> None:\n c = column(\"column\")\n s = AlchemyMagicMock()\n _ = s.filter(or_(c == 5, c == 10))\n _ = s.filter.assert_called_once_with(or_(c == 5, c == 10))\n _ = s.filter.assert_any_call(or_(c == 5, c == 10))\n _ = s.filter.assert_has_calls([mock.call(or_(c == 5, c == 10))])\n s.reset_mock()\n _ = s.filter(c == 5)\n with pytest.raises(AssertionError):\n _ = s.filter.assert_called_once_with(c == 10)","def mock_smile_adam():\n chosen_env = \"adam_multiple_devices_per_zone\"\n with patch(\"homeassistant.components.plugwise.gateway.Smile\") as smile_mock:\n smile_mock.InvalidAuthentication = InvalidAuthentication\n smile_mock.ConnectionFailedError = ConnectionFailedError\n smile_mock.XMLDataMissingError = XMLDataMissingError\n\n smile_mock.return_value.gateway_id = \"fe799307f1624099878210aa0b9f1475\"\n smile_mock.return_value.heater_id = \"90986d591dcd426cae3ec3e8111ff730\"\n smile_mock.return_value.smile_version = \"3.0.15\"\n smile_mock.return_value.smile_type = \"thermostat\"\n smile_mock.return_value.smile_hostname = \"smile98765\"\n\n smile_mock.return_value.notifications = _read_json(chosen_env, \"notifications\")\n\n smile_mock.return_value.connect.side_effect = AsyncMock(return_value=True)\n smile_mock.return_value.full_update_device.side_effect = AsyncMock(\n return_value=True\n )\n smile_mock.return_value.single_master_thermostat.side_effect = Mock(\n return_value=False\n )\n smile_mock.return_value.set_schedule_state.side_effect = AsyncMock(\n return_value=True\n )\n smile_mock.return_value.set_preset.side_effect = AsyncMock(return_value=True)\n smile_mock.return_value.set_temperature.side_effect = AsyncMock(\n return_value=True\n )\n smile_mock.return_value.set_relay_state.side_effect = AsyncMock(\n return_value=True\n )\n\n smile_mock.return_value.get_all_devices.return_value = _read_json(\n chosen_env, \"get_all_devices\"\n )\n smile_mock.return_value.get_device_data.side_effect = partial(\n _get_device_data, chosen_env\n )\n\n yield smile_mock.return_value","def setUp(self):\n self.mock_model = Mock()"],"string":"[\n \"def patch_object(mocker):\\n\\n def _object(obj_to_patch, attribute, return_value):\\n mock_func = mocker.patch.object(obj_to_patch, attribute)\\n if return_value is not None:\\n mock_func.return_value = return_value\\n\\n return _object\",\n \"def mock_side_effect(self, original_object, attribute_name, side_effect):\\n original_function = getattr(original_object, attribute_name)\\n def original_with_side_effect(*args, **kwargs):\\n side_effect()\\n return original_function(*args, **kwargs)\\n self.mock(original_object, attribute_name, original_with_side_effect)\",\n \"def decorator(func):\\n\\n setattr(func, mock_method.attr_name, name)\\n return func\",\n \"def patch(*args, **kwargs):\\n kwargs.setdefault('autospec', True)\\n return mock.patch.object(*args, **kwargs)\",\n \"def __getattribute__(self, name):\\n attr_value = super(CLITestCase, self).__getattribute__(name)\\n\\n if callable(attr_value) and name.startswith('test_'):\\n @functools.wraps(attr_value)\\n def wrapper():\\n with mock.patch('pdc_client.PDCClientWithPage') as cls:\\n api = MockAPI()\\n cls.return_value = api\\n return attr_value(api)\\n return wrapper\\n\\n return attr_value\",\n \"def mock_object(**params):\\n return type('Mock', (), params)()\",\n \"def mock_amazon():\\n amazon = Amazon()\\n amazon.carrot1 = 'cenoura normal'\\n amazon.carrot2 = 'cenoura radioativa'\\n amazon.carrot_number = 575\\n return amazon\",\n \"def test_object_awareness(self):\\n obj = self.obj\\n\\n # attribute properties receive on obj ref in the getter/setter that can customize return\\n self.assertEqual(obj.cusattr, 10)\\n self.assertEqual(obj.settest, 5)\\n obj.awaretest = 10\\n self.assertEqual(obj.cusattr, 15)\\n obj.cusattr = 10\\n self.assertEqual(obj.cusattr, 20)\\n self.assertEqual(obj.settest, 10)\\n\\n # attribute value mutates if you do += or similar (combined get-set)\\n obj.cusattr += 10\\n self.assertEqual(obj.attributes.get(\\\"cusattr\\\"), 30)\\n self.assertEqual(obj.settest, 30)\\n self.assertEqual(obj.cusattr, 40)\\n obj.awaretest = 0\\n obj.cusattr += 20\\n self.assertEqual(obj.attributes.get(\\\"cusattr\\\"), 50)\\n self.assertEqual(obj.settest, 50)\\n self.assertEqual(obj.cusattr, 50)\\n del obj.cusattr\\n self.assertEqual(obj.cusattr, 5)\\n self.assertEqual(obj.settest, 5)\",\n \"def monkey_path_api_requests(self, method, mock_object=None):\\n attr = getattr(api.requests, method)\\n mock_method = mock.MagicMock()\\n setattr(api.requests, method, mock_method)\\n try:\\n yield mock_method\\n finally:\\n setattr(api.requests, method, attr)\",\n \"def mock_path_mkdir(mocker):\\n\\n def _path(obj_to_patch, attribute, side_effect):\\n mock_func = mocker.patch.object(obj_to_patch, attribute)\\n mock_func.side_effect = side_effect\\n\\n return _path\",\n \"def testRecordReturnsMockObject(self):\\n\\t\\taction = GetAttributeAction('y', ('key',) , 'z')\\n\\t\\tself.failUnless(action.record() == 'z')\",\n \"def mock_moira(mocker):\\n return mocker.patch(\\\"moira_lists.moira_api.Moira\\\")\",\n \"def test_assert_mock_called_once_with(self):\\n mock_obj = mock.MagicMock(return_value='Pos vale')\\n\\n mock_obj(1, 2, 3, kwarg='kwarg_value')\\n\\n mock_obj.assert_called_once_with(1, 2, 3, kwarg='kwarg_value')\",\n \"def testAttributeAssignmentIsIntercepted(self):\\n\\t \\n\\t c = Controller()\\n\\t x = c.mock(KlassBeingMocked)\\n\\t x.w = 5\\n\\t c.replay()\\n\\t try:\\n\\t x.w\\n\\t self.fail()\\n\\t except PlaybackFailure, e:\\n\\t pass\",\n \"def _monkeypatch(mod, name, value):\\n with unittest.mock.patch.object(mod, name, value):\\n yield\",\n \"def __getattribute__(self, name):\\n\\n def setup():\\n \\\"\\\"\\\"\\n Create `zorp_mock`. Register RPC methods, and activate the mock\\n object.\\n \\\"\\\"\\\"\\n\\n self.zorp_mock = Mock()\\n\\n for name, func in six.iteritems(self._get_zorp_mock_methods()):\\n self.zorp_mock.server.registry.put(name, func)\\n\\n self.zorp_mock.start()\\n\\n def teardown():\\n \\\"\\\"\\\"\\n Deactivate the mock object.\\n \\\"\\\"\\\"\\n\\n self.zorp_mock.stop()\\n\\n def func_caller(*funcs):\\n \\\"\\\"\\\"\\n Return a function that calls each of `funcs` in order.\\n \\\"\\\"\\\"\\n\\n def caller():\\n \\\"\\\"\\\"\\n Call each of `funcs`.\\n \\\"\\\"\\\"\\n\\n for func in funcs:\\n func()\\n\\n return caller\\n\\n value = super(ZorpTestCaseMixin, self).__getattribute__(name)\\n\\n if name == \\\"setUp\\\":\\n value = func_caller(setup, value)\\n elif name == \\\"tearDown\\\":\\n value = func_caller(value, teardown)\\n\\n return value\",\n \"def test_mock_a_class_func():\\n print()\\n\\n myclass = mymodule.MyClass()\\n myclass.class_mock_one = Mock()\\n myclass.class_mock_one.return_value = 2\\n xx = myclass.class_mock_two()\\n print(xx)\\n myclass.class_mock_one.assert_called_with()\",\n \"def test_reddit_object_persist(mocker):\\n\\n def mock_indexer1(reddit_obj): # pylint: disable=missing-docstring\\n reddit_obj.changed1 = True\\n\\n def mock_indexer2(reddit_obj): # pylint: disable=missing-docstring\\n reddit_obj.changed2 = True\\n\\n @reddit_object_persist(mock_indexer1, mock_indexer2)\\n def decorated_api_func(mock_reddit_obj): # pylint: disable=missing-docstring\\n return mock_reddit_obj\\n\\n mock_reddit_obj = mocker.Mock()\\n reddit_obj = decorated_api_func(mock_reddit_obj)\\n assert reddit_obj.changed1 is True\\n assert reddit_obj.changed2 is True\\n assert reddit_obj == mock_reddit_obj\",\n \"def _mock_function(self, obj, func):\\n setattr(obj, func.__name__, MethodType(func, self.breaker))\",\n \"def _mock_function(self, obj, func):\\n setattr(obj, func.__name__, MethodType(func, self.breaker))\",\n \"def test_assert_mock_called_once(self):\\n mock_obj = mock.MagicMock(return_value='Pos vale')\\n\\n mock_obj(1, 2, 3, kwarg='kwarg_value')\\n\\n mock_obj.assert_called_once()\",\n \"def testUsedAttributeAssignmentCanBeVerified(self): \\n\\t c = Controller()\\n\\t x = c.mock(KlassBeingMocked)\\n\\t x.w = 5\\n\\t x.h = 3\\n\\t c.replay()\\n\\t x.h = 3\\n\\t x.w = 5\\n\\t c.verify()\",\n \"def mock_obj(self, name, methods = [], **kwargs):\\n return MockObject(self._context, name, methods, **kwargs)\",\n \"def test__attach_objects_post_save_hook(\\n self, monkeypatch, fake_user, fake_customer, metadata\\n ):\\n user = fake_user\\n customer = fake_customer\\n\\n # because create_for_user method adds subscriber\\n customer.subcriber = None\\n customer.save()\\n\\n # update metadata\\n if metadata.get(self.key, \\\"\\\"):\\n metadata[self.key] = user.id\\n\\n fake_stripe_session = deepcopy(FAKE_SESSION_I)\\n fake_stripe_session[\\\"metadata\\\"] = metadata\\n\\n def mock_checkout_session_get(*args, **kwargs):\\n \\\"\\\"\\\"Monkeypatched stripe.Session.retrieve\\\"\\\"\\\"\\n return deepcopy(fake_stripe_session)\\n\\n def mock_customer_get(*args, **kwargs):\\n \\\"\\\"\\\"Monkeypatched stripe.Customer.retrieve\\\"\\\"\\\"\\n return deepcopy(FAKE_CUSTOMER)\\n\\n def mock_payment_intent_get(*args, **kwargs):\\n \\\"\\\"\\\"Monkeypatched stripe.PaymentIntent.retrieve\\\"\\\"\\\"\\n return deepcopy(FAKE_PAYMENT_INTENT_I)\\n\\n def mock_setup_intent_get(*args, **kwargs):\\n \\\"\\\"\\\"Monkeypatched stripe.SetupIntent.retrieve\\\"\\\"\\\"\\n return deepcopy(FAKE_SETUP_INTENT_II)\\n\\n def mock_invoice_get(*args, **kwargs):\\n \\\"\\\"\\\"Monkeypatched stripe.Invoice.retrieve\\\"\\\"\\\"\\n return deepcopy(FAKE_INVOICE)\\n\\n def mock_invoice_item_get(*args, **kwargs):\\n return deepcopy(FAKE_INVOICEITEM)\\n\\n def mock_payment_method_get(*args, **kwargs):\\n \\\"\\\"\\\"Monkeypatched stripe.PaymentMethod.retrieve\\\"\\\"\\\"\\n return deepcopy(FAKE_PAYMENT_METHOD_I)\\n\\n def mock_subscription_get(*args, **kwargs):\\n \\\"\\\"\\\"Monkeypatched stripe.Subscription.retrieve\\\"\\\"\\\"\\n return deepcopy(FAKE_SUBSCRIPTION)\\n\\n def mock_subscriptionitem_get(*args, **kwargs):\\n return deepcopy(FAKE_SUBSCRIPTION_ITEM)\\n\\n def mock_balance_transaction_get(*args, **kwargs):\\n \\\"\\\"\\\"Monkeypatched stripe.BalanceTransaction.retrieve\\\"\\\"\\\"\\n return deepcopy(FAKE_BALANCE_TRANSACTION)\\n\\n def mock_product_get(*args, **kwargs):\\n \\\"\\\"\\\"Monkeypatched stripe.Product.retrieve\\\"\\\"\\\"\\n return deepcopy(FAKE_PRODUCT)\\n\\n def mock_charge_get(*args, **kwargs):\\n \\\"\\\"\\\"Monkeypatched stripe.Charge.retrieve\\\"\\\"\\\"\\n return deepcopy(FAKE_CHARGE)\\n\\n # monkeypatch stripe.checkout.Session.retrieve, stripe.Customer.retrieve, stripe.PaymentIntent.retrieve\\n monkeypatch.setattr(\\n stripe.checkout.Session, \\\"retrieve\\\", mock_checkout_session_get\\n )\\n monkeypatch.setattr(stripe.Customer, \\\"retrieve\\\", mock_customer_get)\\n monkeypatch.setattr(stripe.Customer, \\\"modify\\\", mock_customer_get)\\n monkeypatch.setattr(stripe.PaymentIntent, \\\"retrieve\\\", mock_payment_intent_get)\\n monkeypatch.setattr(stripe.SetupIntent, \\\"retrieve\\\", mock_setup_intent_get)\\n\\n # because of Reverse o2o field sync due to PaymentIntent.sync_from_stripe_data..\\n monkeypatch.setattr(stripe.Invoice, \\\"retrieve\\\", mock_invoice_get)\\n monkeypatch.setattr(stripe.InvoiceItem, \\\"retrieve\\\", mock_invoice_item_get)\\n monkeypatch.setattr(stripe.PaymentMethod, \\\"retrieve\\\", mock_payment_method_get)\\n monkeypatch.setattr(stripe.Subscription, \\\"retrieve\\\", mock_subscription_get)\\n monkeypatch.setattr(\\n stripe.SubscriptionItem, \\\"retrieve\\\", mock_subscriptionitem_get\\n )\\n monkeypatch.setattr(\\n stripe.BalanceTransaction, \\\"retrieve\\\", mock_balance_transaction_get\\n )\\n monkeypatch.setattr(stripe.Product, \\\"retrieve\\\", mock_product_get)\\n monkeypatch.setattr(stripe.Charge, \\\"retrieve\\\", mock_charge_get)\\n\\n # Invoke the sync to invoke _attach_objects_post_save_hook()\\n session = Session.sync_from_stripe_data(fake_stripe_session)\\n\\n # refresh self.customer from db\\n customer.refresh_from_db()\\n\\n assert session\\n assert session.customer.id == customer.id\\n assert customer.subscriber == user\\n if metadata.get(self.key, \\\"\\\"):\\n assert customer.metadata == {self.key: metadata.get(self.key)}\\n else:\\n assert customer.metadata == {}\",\n \"def mock_method(name):\\n\\n def decorator(func):\\n \\\"\\\"\\\"\\n Return `func`, with the `mock_method.attr_name` attribute set.\\n \\\"\\\"\\\"\\n\\n setattr(func, mock_method.attr_name, name)\\n return func\\n\\n # Set an attribute on `decorator`. This can be used to discover situations\\n # where `mock_method` has accidentally been called without any parameters.\\n setattr(decorator, mock_method.decorator_attr_name, True)\\n\\n return decorator\",\n \"def test_remove_donation(monkeypatch):\\n #We still need an assert, don't know how to mock two user input on one call\\n mr.initialize_donors()\\n monkeypatch.setattr('mailroom.get_value', lambda x, y: 'Koko')\\n mr.remove_donation()\\n monkeypatch.setattr('mailroom.get_value', lambda x, y: 'q')\\n mr.remove_donation()\\n # assert False\",\n \"def test_get_setting(monkeypatch):\\n resp = str(uuid.uuid4())\\n arg = str(uuid.uuid4())\\n kwarg = str(uuid.uuid4())\\n get_secret = Mock(return_value=resp)\\n monkeypatch.setattr(\\\"lambdautils.state.get_secret\\\", get_secret)\\n resp2 = lambdautils.state.get_setting(arg, kwarg=kwarg)\\n assert resp2 == resp\\n get_secret.assert_called_with(arg, kwarg=kwarg)\",\n \"def test_one_att(self):\\n self.test_attribute.is_down = mock.Mock(return_value=False)\\n self.run_mock_analyzer([self.test_attribute, ])\\n self.assert_mock_analyzer(self.test_attribute)\",\n \"def testGetattrWithReturnValueSpecified(self):\\n\\t\\tc = Controller()\\n\\t\\tx = c.mock(KlassBeingMocked)\\n\\t\\tx.g\\n\\t\\tc.setReturn(8)\\n\\t\\tc.replay()\\n\\t\\tself.failUnless(x.g == 8)\",\n \"def mock_remote_unit(monkeypatch):\\n monkeypatch.setattr(\\\"libgitlab.hookenv.remote_unit\\\", lambda: \\\"unit-mock/0\\\")\",\n \"def mock_moira_client(mocker):\\n return mocker.patch(\\\"moira_lists.moira_api.get_moira_client\\\", autospec=True)\",\n \"def mock_gitlab_get_flag_value(monkeypatch):\\n mock_flag_value = mock.Mock()\\n mock_flag_value.return_value = None\\n monkeypatch.setattr(\\\"libgitlab._get_flag_value\\\", mock_flag_value)\\n return mock_flag_value\",\n \"def test_magic_mock():\\n\\n class Empty:\\n pass\\n\\n empty = Empty()\\n empty.abc = MagicMock(return_value=\\\"abc\\\") # type: ignore\\n\\n assert empty.abc(\\\"hi\\\") == \\\"abc\\\" # type: ignore\\n empty.abc.assert_called_with(\\\"hi\\\") # type: ignore\",\n \"def temporary_attr(obj, name, value):\\n setattr(obj, name, value)\\n try:\\n yield obj\\n finally:\\n delattr(obj, name)\",\n \"def temporary_attr(obj, name, value):\\n setattr(obj, name, value)\\n try:\\n yield obj\\n finally:\\n delattr(obj, name)\",\n \"def add_mock(monkeypatch):\\n _mock = Mock()\\n monkeypatch.setattr('django.core.cache.cache.add', _mock)\\n return _mock\",\n \"def testAttributeAssignmentCanBePlayedBack(self):\\n\\t \\n\\t c = Controller()\\n\\t x = c.mock(KlassBeingMocked)\\n\\t x.w = 5\\n\\t x.w = 6\\n\\t c.replay()\\n\\t x.w = 5\",\n \"def mocked_fogbugz(monkeypatch):\\n mocked_fogbugz = mock.Mock()\\n mocked_fogbugz_instance = mock.Mock()\\n mocked_fogbugz.return_value = mocked_fogbugz_instance\\n monkeypatch.setattr(fogbugz, 'FogBugz', mocked_fogbugz)\\n return mocked_fogbugz\",\n \"def mock_unit_db(monkeypatch):\\n mock_kv = mock.Mock()\\n mock_kv.return_value = unitdata.Storage(path=\\\":memory:\\\")\\n monkeypatch.setattr(\\\"libgitlab.unitdata.kv\\\", mock_kv)\",\n \"def test__init__(self):\\n mocked_reconstructor = Mock()\\n mocked_reconstructor.reconstruct.return_value = 'mocked'\\n db_response = {'Item': {'test': True}}\\n resp = GetResponse(db_response, mocked_reconstructor)\\n assert resp.item == 'mocked'\\n mocked_reconstructor.reconstruct.assert_called_with(db_response['Item'])\",\n \"def testUnusedAttributeAssignmentCanBeVerified(self):\\n\\t \\n\\t c = Controller()\\n\\t x = c.mock(KlassBeingMocked)\\n\\t x.w = 5\\n\\t c.replay()\\n\\t self.failUnlessRaises(RecordedCallsWereNotReplayedCorrectly, c.verify)\",\n \"def mocked_item(mocked_session):\\n test_item = mock.Mock()\\n test_item.session = mocked_session\\n return test_item\",\n \"def mock_config(monkeypatch: Any) -> None:\\n monkeypatch.setattr(\\\"catapi.config.ENABLE_FOO\\\", \\\"true\\\")\\n monkeypatch.setattr(\\\"catapi.config.ENABLE_BAR\\\", \\\"false\\\")\",\n \"def test_s3_cleanup_storage(self, mock_class):\\n current_s3_fake_objects = {\\n \\\"bucket1\\\": {\\n \\\"object1\\\": ObjectInfo(size=10,\\n last_modified=datetime.datetime.utcnow()),\\n \\\"object2\\\": ObjectInfo(size=20,\\n last_modified=datetime.datetime.utcnow()),\\n \\\"object3\\\": ObjectInfo(size=30,\\n last_modified=datetime.datetime.utcnow()),\\n },\\n \\\"bucket2\\\": {\\n \\\"object4\\\": ObjectInfo(size=100,\\n last_modified=datetime.datetime(year=2020, month=10, day=1, hour=10, minute=10,\\n second=10)),\\n \\\"object5\\\": ObjectInfo(size=200,\\n last_modified=datetime.datetime(year=2020, month=10, day=1, hour=10, minute=11,\\n second=10)),\\n \\\"object6\\\": ObjectInfo(size=300,\\n last_modified=datetime.datetime(year=2020, month=10, day=1, hour=10, minute=12,\\n second=10)),\\n }\\n }\\n\\n mock_class().list_buckets.return_value = list(current_s3_fake_objects.keys())\\n mock_class().list_objects.side_effect = [list(current_s3_fake_objects[\\\"bucket1\\\"].keys()),\\n list(current_s3_fake_objects[\\\"bucket2\\\"].keys()),\\n list(current_s3_fake_objects[\\\"bucket2\\\"].keys())]\\n mock_class().stat_object.side_effect = list(current_s3_fake_objects[\\\"bucket1\\\"].values()) + \\\\\\n list(current_s3_fake_objects[\\\"bucket2\\\"].values())\\n\\n test_obj = servicex_storage.s3_storage_manager.S3Store(s3_endpoint=\\\"abc\\\",\\n access_key=\\\"abc\\\",\\n secret_key=\\\"abc\\\")\\n\\n final_size = test_obj.cleanup_storage(70, 60, 365)[0]\\n self.assertEqual(final_size, 60)\\n mock_class().remove_objects.assert_called_with(\\\"bucket2\\\", [\\\"object4\\\", \\\"object5\\\", \\\"object6\\\"])\",\n \"def setUp(self):\\n self.image = mock.MagicMock(spec=models.Image)\\n self.image.name = 'gallery/foo.jpg'\",\n \"def test_add_donation(monkeypatch):\\n #We still need an assert, don't know how to mock two user input on one call\\n mr.initialize_donors()\\n monkeypatch.setattr('mailroom.get_value', lambda x, y: 'Koko')\\n mr.add_donation()\\n monkeypatch.setattr('mailroom.get_value', lambda x, y: 'q')\\n mr.add_donation()\\n # assert False\",\n \"def test_get_object(self, detail_view, employee_model):\\n\\n employee = Mock()\\n employee_model.objects.get.return_value = Mock()\\n detail_view.get_object.return_value = employee\\n\\n emp = detail_view.get_object(1)\\n self.assertEqual(employee, emp)\",\n \"def test_api_object_update_property(self, api_object):\\n attrs_dict = {'uuid_': 'CREATING'}\\n api_object.update_public_attrs(attrs_dict)\\n assert api_object.uuid_ != 'CREATING'\",\n \"def mock_time(desired_time):\\n # type: (float) -> Any\\n return mock.patch.object(time, 'time', return_value=desired_time)\",\n \"def _setup_mock(self, mock):\\n\\n mock.Subscription.create.return_value.is_success = True\\n mock.Subscription.create.return_value.subscription.id = \\\"t-sub-id\\\"\",\n \"def requestsmock():\\n with requests_mock.mock() as m:\\n yield m\",\n \"def __getattr__(self, name):\\n raise MockException(\\n \\\"Mock object %s has no attribute '%s'\\\" %\\n (mock_object_names[id(self)], name)\\n )\",\n \"def configure_mock_action_instance(mock_action: Mock) -> Mock:\\n mock_instance = Mock(return_value=None)\\n mock_action.return_value = mock_instance\\n mock_instance.execute = Mock()\\n return mock_instance\",\n \"def testGetattrWithNoReturnValueSpecified(self):\\n\\t\\tc = Controller()\\n\\t\\tx = c.mock(KlassBeingMocked)\\n\\t\\tx.g\\n\\t\\tc.replay()\\n\\t\\tself.failIf(x.g == None)\\n\\t\\ttry:\\n\\t\\t\\tx.g\\n\\t\\t\\tself.fail()\\n\\t\\texcept PlaybackFailure, e:\\n\\t\\t\\tpass\",\n \"def __init__(self, context, name, methods, **kwargs):\\n \\n mock_object_names[id(self)] = name\\n for method in methods:\\n self.__dict__[method] = MockFunction(context, method)\\n for (key, value) in kwargs.items():\\n self.__dict__[key] = value\",\n \"def mock_bot(mocker):\\n mock_bot = SlackBot()\\n mock_bot.slack_user_id = \\\"UB0T\\\"\\n mocker.patch.object(mock_bot, \\\"web_client\\\")\\n mocker.patch.object(mock_bot, \\\"update_person\\\", side_effect=lambda person: person)\\n return mock_bot\",\n \"def mock_command(mocker):\\n def _mock_command(command_class):\\n mocker.patch.object(command_class, 'add_arguments', return_value=None)\\n mocker.patch.object(command_class, 'pre_add_arguments', return_value=None)\\n instance = command_class()\\n\\n if hasattr(instance, 'host'):\\n instance.host.name = '==MOCKED_HOST=='\\n\\n return instance\\n\\n return _mock_command\",\n \"def test_telnetrmq_after_execute(self, mocked):\\n agentconf={}\\n telnetconf={\\\"host\\\":\\\"telnet.lan\\\"}\\n rmqconf={\\\"host\\\":\\\"rmq.lan\\\"}\\n agent=TelnetRmqAgent(agentconf, telnetconf, rmqconf)\\n\\n #Setup generic mock for others methods wich are not tested here\\n ignoredmocks=Mock()\\n agent.telnetclient=ignoredmocks\\n agent.rmqclient=ignoredmocks\\n \\n\\n instance = mocked.return_value \\n agent.after_execute()\\n mocked.assert_called_with(agent)\\n mocked.assert_called_with(agent)\",\n \"def mock_bucket(\\n mocker,\\n):\\n mocker.patch(\\\"pcluster.models.s3_bucket.get_partition\\\", return_value=\\\"fake_partition\\\")\\n mocker.patch(\\\"pcluster.models.s3_bucket.get_region\\\", return_value=\\\"fake-region\\\")\\n mocker.patch(\\\"pcluster.aws.sts.StsClient.get_account_id\\\", return_value=\\\"fake-id\\\")\",\n \"def test_rename_donor(monkeypatch):\\n #Not finished\\n mr.initialize_donors()\\n monkeypatch.setattr('mailroom.get_value', lambda x, y: 'q')\\n mr.rename_donor()\\n # assert False\",\n \"def test_card_value(mock_card):\\n assert mock_card.value == 1\",\n \"def create_mock_client(self, fake_request_method):\\n class FakeHttpLib2(object):\\n pass\\n\\n FakeHttpLib2.request = fake_request_method\\n mock_client = self.mox.CreateMock(DNSaasClient)\\n mock_client.http_pool = pools.Pool()\\n mock_client.http_pool.create = FakeHttpLib2\\n mock_client.auth_token = 'token'\\n return mock_client\",\n \"def patch_mock_deck_conflict_check(\\n decoy: Decoy, monkeypatch: pytest.MonkeyPatch\\n) -> None:\\n mock = decoy.mock(func=deck_conflict.check)\\n monkeypatch.setattr(deck_conflict, \\\"check\\\", mock)\",\n \"def testSetAttributeRecordsField(self):\\n\\t\\n\\t c = Controller()\\n\\t x = c.mock(KlassBeingMocked)\\n\\t x.w = 5\\n\\t self.failUnless(len(c.actions) == 1)\\n\\t action = c.actions.get(BaseAction((x,'w')))\\n\\t self.failUnless(action.field == 'w')\\n\\t self.failUnless(action.value == 5)\",\n \"def testRecord(self):\\n\\t\\taction = SetAttributeAction('mock', 'y', ('key', ), 5)\\n\\t\\tself.failUnless(action == action.record())\",\n \"def provider_and_mock_one(monkeypatch, provider_one, mock_inner_provider):\\n mock_make_provider = mock.Mock(return_value=mock_inner_provider)\\n monkeypatch.setattr(provider_one, 'make_provider', mock_make_provider)\\n return provider_one, mock_inner_provider\",\n \"async def test_attribute(hass, service, attribute):\\n calls_1 = async_mock_service(hass, DOMAIN, service)\\n\\n value = \\\"dummy\\\"\\n\\n await async_reproduce_states(hass, [\\n State(ENTITY_1, None,\\n {attribute: value})\\n ])\\n\\n await hass.async_block_till_done()\\n\\n assert len(calls_1) == 1\\n assert calls_1[0].data == {'entity_id': ENTITY_1,\\n attribute: value}\",\n \"def set_mock_return_value(magic_mock: MagicMock, return_value: t.Any):\\n if magic_mock.__class__.__name__ == \\\"AsyncMock\\\":\\n # Python 3.8 and above\\n magic_mock.return_value = return_value\\n else:\\n\\n async def coroutine(*args, **kwargs):\\n return return_value\\n\\n magic_mock.return_value = coroutine()\",\n \"def mock_apt_update(monkeypatch):\\n mocked_apt_update = mock.Mock(returnvalue=True)\\n monkeypatch.setattr(\\\"libgitlab.apt_update\\\", mocked_apt_update)\\n return mocked_apt_update\",\n \"def test_s3_get_bucket_info(self, mock_class):\\n\\n mock_class().list_objects.return_value = list(s3_fake_objects[\\\"bucket1\\\"].keys())\\n mock_class().stat_object.side_effect = list(s3_fake_objects[\\\"bucket1\\\"].values())\\n return_value = servicex_storage.s3_storage_manager.BucketInfo(name=\\\"bucket1\\\",\\n size=60,\\n last_modified=datetime.datetime(\\n year=2021, month=10,\\n day=1, hour=10,\\n minute=10, second=10))\\n test_obj = servicex_storage.s3_storage_manager.S3Store(s3_endpoint=\\\"abc\\\",\\n access_key=\\\"abc\\\",\\n secret_key=\\\"abc\\\")\\n bucket_info = test_obj.get_bucket_info(\\\"bucket1\\\")\\n self.assertEqual(bucket_info, return_value)\",\n \"def test_tango_device_builder():\\n with mock.patch(\\\"tango_simlib.utilities.tango_device_parser.tango\\\") as mocked_tango:\\n mocked_tango.CmdArgType = tango.CmdArgType\\n mocked_tango.AttrWriteType = tango.AttrWriteType\\n mocked_device_proxy = mock.Mock()\\n mocked_tango.DeviceProxy.return_value = mocked_device_proxy\\n\\n mocked_info = mock.Mock()\\n mocked_info.dev_class = \\\"DevClass\\\"\\n mocked_device_proxy.info.return_value = mocked_info\\n\\n mocked_command = mock.Mock()\\n mocked_command.cmd_name = \\\"CommandName\\\"\\n mocked_command.in_type = tango.CmdArgType.DevVoid\\n mocked_command.out_type = tango.CmdArgType.DevVoid\\n mocked_command.disp_level = tango.DispLevel.OPERATOR\\n mocked_command.out_type_desc = \\\"out_type_desc\\\"\\n mocked_command.in_type_desc = \\\"in_type_desc\\\"\\n\\n attr = tango.AttributeInfoEx()\\n attr.name = \\\"AttrName\\\"\\n attr.data_format = tango.AttrDataFormat.SCALAR\\n attr.disp_level = tango.DispLevel.OPERATOR\\n attr.data_type = 8\\n attr.writable = tango.AttrWriteType.READ\\n attr.description = \\\"description\\\"\\n attr.display_unit = \\\"display_unit\\\"\\n attr.format = \\\"format\\\"\\n attr.label = \\\"label\\\"\\n attr.max_alarm = \\\"max_alarm\\\"\\n attr.max_dim_x = 0\\n attr.max_dim_y = 0\\n attr.max_value = \\\"max_value\\\"\\n attr.min_alarm = \\\"min_alarm\\\"\\n attr.min_value = \\\"min_value\\\"\\n attr.standard_unit = \\\"standard_unit\\\"\\n attr.unit = \\\"unit\\\"\\n attr.writable_attr_name = \\\"writable_attr_name\\\"\\n\\n mocked_device_proxy.get_command_config.return_value = [mocked_command]\\n mocked_device_proxy.attribute_list_query_ex.return_value = [attr]\\n mocked_device_proxy.get_property_list.return_value = [\\\"PropA\\\", \\\"PropB\\\"]\\n\\n tango_args = Namespace(tango_device_name=\\\"a/b/c\\\")\\n tango_yaml = _build_yaml(tango_args)\\n\\n parsed_yaml = yaml.load(tango_yaml, Loader=yaml.FullLoader)\\n\\n mocked_tango.DeviceProxy.assert_called_once_with(\\\"a/b/c\\\")\\n mocked_device_proxy.get_command_config.assert_called()\\n mocked_device_proxy.attribute_list_query_ex.assert_called()\\n mocked_device_proxy.get_property_list.assert_called()\\n\\n attributes = parsed_yaml[0][\\\"meta\\\"][\\\"attributes\\\"]\\n assert attributes == [\\n {\\n \\\"disp_level\\\": \\\"OPERATOR\\\",\\n \\\"description\\\": \\\"description\\\",\\n \\\"data_type\\\": \\\"DevString\\\",\\n \\\"format\\\": \\\"format\\\",\\n \\\"max_alarm\\\": \\\"max_alarm\\\",\\n \\\"min_value\\\": \\\"min_value\\\",\\n \\\"display_unit\\\": \\\"display_unit\\\",\\n \\\"writable\\\": \\\"READ\\\",\\n \\\"standard_unit\\\": \\\"standard_unit\\\",\\n \\\"unit\\\": \\\"unit\\\",\\n \\\"name\\\": \\\"AttrName\\\",\\n \\\"data_format\\\": \\\"SCALAR\\\",\\n \\\"label\\\": \\\"label\\\",\\n \\\"max_value\\\": \\\"max_value\\\",\\n \\\"min_alarm\\\": \\\"min_alarm\\\",\\n \\\"writable_attr_name\\\": \\\"writable_attr_name\\\",\\n },\\n ], \\\"Attribute config mismatch. attr: {}\\\".format(attributes)\\n\\n commands = parsed_yaml[0][\\\"meta\\\"][\\\"commands\\\"]\\n assert commands == [\\n {\\n \\\"doc_out\\\": \\\"out_type_desc\\\",\\n \\\"disp_level\\\": \\\"OPERATOR\\\",\\n \\\"name\\\": \\\"CommandName\\\",\\n \\\"doc_in\\\": \\\"in_type_desc\\\",\\n \\\"dtype_out\\\": \\\"DevVoid\\\",\\n \\\"dtype_in\\\": \\\"DevVoid\\\",\\n }\\n ], \\\"Command config mismatch. commands: {}\\\".format(commands)\\n\\n properties = parsed_yaml[0][\\\"meta\\\"][\\\"properties\\\"]\\n assert properties == [\\n {\\\"name\\\": \\\"PropA\\\"},\\n {\\\"name\\\": \\\"PropB\\\"},\\n ], \\\"Properties config mismatch. properties: {}.\\\".format(properties)\",\n \"def mock_schedule(monkeypatch):\\n\\n def mock_schedule(self):\\n return\\n\\n monkeypatch.setattr(segment.SegmentProducer, \\\"schedule\\\", mock_schedule)\",\n \"def test_timetracking_resource_methods(self, mock_url, resource_name, single_name):\\n business_id = 1234\\n resource_id = 2345\\n resource_ = getattr(self.freshBooksClient, resource_name)\\n\\n list_response = {resource_name: [], \\\"meta\\\": {\\\"page\\\": 1, \\\"pages\\\": 0, \\\"per_page\\\": 15, \\\"total\\\": 0}}\\n single_response = {single_name: {}}\\n\\n with patch.object(TimetrackingResource, \\\"_request\\\", return_value=list_response) as mock_request:\\n resource_.list(business_id)\\n mock_request.assert_called_with(\\\"some_url\\\", HttpVerbs.GET)\\n\\n with patch.object(TimetrackingResource, \\\"_request\\\", return_value=single_response) as mock_request:\\n resource_.get(business_id, resource_id)\\n mock_request.assert_called_with(\\\"some_url\\\", HttpVerbs.GET)\\n\\n resource_.create(business_id, {})\\n mock_request.assert_called_with(\\\"some_url\\\", HttpVerbs.POST, data={single_name: {}})\\n\\n resource_.update(business_id, resource_id, {})\\n mock_request.assert_called_with(\\\"some_url\\\", HttpVerbs.PUT, data={single_name: {}})\\n\\n resource_.delete(business_id, resource_id)\\n mock_request.assert_called_with(\\\"some_url\\\", HttpVerbs.DELETE)\",\n \"def test_update_metadata_by_attribute(self):\\n pass\",\n \"def mock_decorator():\\n\\n def test_method(func):\\n @wraps(func)\\n def inner_method(*args, **kwargs):\\n with patch('builtins.open', new_callable=mock_open) as _, \\\\\\n patch('rekcurd_dashboard.apis.api_project.update_kubernetes_deployment_info',\\n new=Mock(return_value=True)) as _:\\n return func(*args, **kwargs)\\n return inner_method\\n return test_method\",\n \"def test_cleanup_on_success(self, mocker):\\n remove_spy = mocker.spy(os, 'remove')\\n\\n payload = dict(id=\\\"B\\\", data={\\\"some\\\": \\\"data\\\"}, ai_service='A')\\n headers = {'x-rh-identity': 'ABC'}\\n self.client.post(self.url, json=payload, headers=headers)\\n\\n remove_spy.assert_called_once()\",\n \"def test_short_circuit(mock_tools):\\n mock_tools.docker = \\\"tool\\\"\\n\\n tool = Docker.verify(mock_tools)\\n\\n assert tool == \\\"tool\\\"\\n assert tool == mock_tools.docker\",\n \"def test_accounting_resource_methods(self, mock_url, resource_name, single_name, delete_via_update):\\n account_id = 1234\\n resource_id = 2345\\n resource_ = getattr(self.freshBooksClient, resource_name)\\n\\n list_response = {resource_name: [], \\\"page\\\": 1, \\\"pages\\\": 0, \\\"per_page\\\": 15, \\\"total\\\": 0}\\n single_response = {single_name: {}}\\n\\n with patch.object(AccountingResource, \\\"_request\\\", return_value=list_response) as mock_request:\\n resource_.list(account_id)\\n mock_request.assert_called_with(\\\"some_url\\\", HttpVerbs.GET)\\n\\n with patch.object(AccountingResource, \\\"_request\\\", return_value=single_response) as mock_request:\\n resource_.get(account_id, resource_id)\\n mock_request.assert_called_with(\\\"some_url\\\", HttpVerbs.GET)\\n\\n resource_.create(account_id, {})\\n mock_request.assert_called_with(\\\"some_url\\\", HttpVerbs.POST, data={single_name: {}})\\n\\n resource_.update(account_id, resource_id, {})\\n mock_request.assert_called_with(\\\"some_url\\\", HttpVerbs.PUT, data={single_name: {}})\\n\\n resource_.delete(account_id, resource_id)\\n if delete_via_update:\\n mock_request.assert_called_with(\\\"some_url\\\", HttpVerbs.PUT, data={single_name: {\\\"vis_state\\\": 1}})\\n else:\\n mock_request.assert_called_with(\\\"some_url\\\", HttpVerbs.DELETE)\",\n \"def test__call__(self):\\n mock = Mock()\\n factory = Factory(mock)\\n factory()\\n mock.assert_called_once_with()\",\n \"def test_make_api_call_full_url_cached(urllib2):\\n my_accessor = RallyAccessor('uname', 'pword', 'base_url')\\n my_accessor.get_from_cache = Mock()\\n my_accessor.make_url_safe = Mock()\\n my_accessor.make_url_safe.return_value = 'safe-url'\\n my_accessor.get_from_cache.return_value = 'Data'\\n\\n response = my_accessor.make_api_call('some-url', full_url=True)\\n\\n assert_equal(response, 'Data')\\n assert_equal(my_accessor.make_url_safe.call_args[0], ('some-url',))\\n assert_equal(my_accessor.get_from_cache.call_args[0], ('safe-url',))\\n assert_false(urllib2.urlopen.called)\",\n \"def mock_sync_hardware_api(decoy: Decoy) -> SyncHardwareAPI:\\n return decoy.mock(cls=SyncHardwareAPI)\",\n \"def make_boto_hit(hit_id=None, batch_id=None):\\n hit = mock.MagicMock()\\n hit.HITId = (hit_id if hit_id else str(uuid.uuid4()))\\n hit.RequesterAnnotation = (batch_id if batch_id else str(uuid.uuid4()))\\n return hit\",\n \"def create_mock_api(cls):\\n mock_api = Mock(spec=HookedAuroraClientAPI)\\n mock_scheduler = Mock()\\n mock_scheduler.url = \\\"http://something_or_other\\\"\\n mock_scheduler_client = Mock()\\n mock_scheduler_client.scheduler.return_value = mock_scheduler\\n mock_scheduler_client.url = \\\"http://something_or_other\\\"\\n mock_api = Mock(spec=HookedAuroraClientAPI)\\n mock_api.scheduler = mock_scheduler_client\\n return (mock_api, mock_scheduler_client)\",\n \"def mock_msm(temp_dir):\\n msm_mock = Mock(spec=MycroftSkillsManager)\\n msm_mock.skills_dir = str(temp_dir)\\n msm_mock.platform = 'test_platform'\\n msm_mock.lock = Mock()\\n msm_mock.repo = Mock(spec=SkillRepo)\\n msm_mock.repo.get_default_skill_names = Mock(return_value=[\\n ('default', ['time', 'weather']),\\n ('test_platform', ['test_skill'])\\n ])\\n msm_mock.device_skill_state = dict(\\n skills=[\\n dict(name='test_skill', beta=False)\\n ]\\n )\\n skill = Mock()\\n skill.is_local = True\\n skill.path = str(temp_dir)\\n skill.skill_gid = 'test_skill|99.99'\\n skill.meta_info = dict(display_name='Test Skill')\\n msm_mock.list_all_defaults.return_value = [skill]\\n msm_mock.default_skills = dict(test_skill=skill)\\n msm_mock.all_skills = [skill]\\n msm_mock.local_skills = dict(test_skill=skill)\\n\\n return msm_mock\",\n \"def setUp(self):\\n self.r_patcher = patch('redis_metrics.models.R')\\n self.mock_r = self.r_patcher.start()\",\n \"def __setattr__(self, name, value):\\n if name in ALLOWED_ON_PROXY:\\n self.__dict__[name] = value\\n else:\\n raise AttributeError(\\\"unsettable: \\\"+repr(name))\",\n \"def mock_object(cls, profile=None):\\n mo = ManagedObject()\\n if profile:\\n mo.profile = Profile.get_by_name(profile)\\n mo.is_mock = True\\n return mo\",\n \"def test_telnetrmq_before_execute(self, mocked):\\n agentconf={}\\n telnetconf={\\\"host\\\":\\\"telnet.lan\\\"}\\n rmqconf={\\\"host\\\":\\\"rmq.lan\\\"}\\n agent=TelnetRmqAgent(agentconf, telnetconf, rmqconf)\\n\\n #Setup generic mock for others methos wich are not tested here\\n ignoredmocks=Mock()\\n agent.telnetclient=ignoredmocks\\n agent.rmqclient=ignoredmocks\\n \\n\\n instance = mocked.return_value \\n agent.before_execute()\\n mocked.assert_called_with(agent)\",\n \"def __get__(self, obj, objtype=None):\\n try:\\n return getattr(obj, self.cache_attr)\\n except AttributeError:\\n # Call the wrapped function with the obj instance as argument\\n setattr(obj, self.cache_attr, self.fget(obj))\\n return getattr(obj, self.cache_attr)\",\n \"def mock_template(monkeypatch):\\n monkeypatch.setattr(\\\"libgitlab.templating.host.os.fchown\\\", mock.Mock())\\n monkeypatch.setattr(\\\"libgitlab.templating.host.os.chown\\\", mock.Mock())\\n monkeypatch.setattr(\\\"libgitlab.templating.host.os.fchmod\\\", mock.Mock())\",\n \"def test_thingname_nostr(self, mock):\\n mock.configure_mock(**(self.config_shadowget(ParamValidationError(\\n report='UnitTest'))))\\n self.assertRaises(\\n ParamValidationError,\\n lf.lambda_handler, event=self.lambdaevent, context=None)\\n mock.client.return_value.update_thing_shadow.assert_not_called()\",\n \"def mock_path(mocker):\\n\\n def _path(module, side_effect):\\n mock_func = mocker.patch(f'{module}.Path.mkdir')\\n mock_func.side_effect = side_effect\\n\\n return _path\",\n \"def test_project_resource_methods(self, mock_url, resource_name, single_name):\\n business_id = 1234\\n resource_id = 2345\\n resource_ = getattr(self.freshBooksClient, resource_name)\\n\\n list_response = {resource_name: [], \\\"meta\\\": {\\\"page\\\": 1, \\\"pages\\\": 0, \\\"per_page\\\": 15, \\\"total\\\": 0}}\\n single_response = {single_name: {}}\\n\\n with patch.object(ProjectsResource, \\\"_request\\\", return_value=list_response) as mock_request:\\n resource_.list(business_id)\\n mock_request.assert_called_with(\\\"some_url\\\", HttpVerbs.GET)\\n\\n with patch.object(ProjectsResource, \\\"_request\\\", return_value=single_response) as mock_request:\\n resource_.get(business_id, resource_id)\\n mock_request.assert_called_with(\\\"some_url\\\", HttpVerbs.GET)\\n\\n resource_.create(business_id, {})\\n mock_request.assert_called_with(\\\"some_url\\\", HttpVerbs.POST, data={single_name: {}})\\n\\n resource_.update(business_id, resource_id, {})\\n mock_request.assert_called_with(\\\"some_url\\\", HttpVerbs.PUT, data={single_name: {}})\\n\\n resource_.delete(business_id, resource_id)\\n mock_request.assert_called_with(\\\"some_url\\\", HttpVerbs.DELETE)\",\n \"def mock_return(self, kall):\\n name, parent, from_kall = call_parts(kall)\\n\\n if from_kall:\\n attr = \\\"return_value\\\"\\n else:\\n attr = name.split(\\\".\\\")[-1]\\n if parent is None:\\n mock_obj = self\\n else:\\n mock_obj = self.mock_return(parent)\\n return getattr(mock_obj, attr)\",\n \"def temporary_attribute_value(obj, attr, new_value):\\n replaced = False\\n old_value = None\\n if hasattr(obj, attr):\\n try:\\n if attr in obj.__dict__:\\n replaced = True\\n except AttributeError:\\n if attr in obj.__slots__:\\n replaced = True\\n if replaced:\\n old_value = getattr(obj, attr)\\n setattr(obj, attr, new_value)\\n yield replaced, old_value\\n if not replaced:\\n delattr(obj, attr)\\n else:\\n setattr(obj, attr, old_value)\",\n \"def mock_apt_install(monkeypatch):\\n mocked_apt_install = mock.Mock(returnvalue=True)\\n monkeypatch.setattr(\\\"libgitlab.apt_install\\\", mocked_apt_install)\\n return mocked_apt_install\",\n \"def test_alchemy_magic_mock() -> None:\\n c = column(\\\"column\\\")\\n s = AlchemyMagicMock()\\n _ = s.filter(or_(c == 5, c == 10))\\n _ = s.filter.assert_called_once_with(or_(c == 5, c == 10))\\n _ = s.filter.assert_any_call(or_(c == 5, c == 10))\\n _ = s.filter.assert_has_calls([mock.call(or_(c == 5, c == 10))])\\n s.reset_mock()\\n _ = s.filter(c == 5)\\n with pytest.raises(AssertionError):\\n _ = s.filter.assert_called_once_with(c == 10)\",\n \"def mock_smile_adam():\\n chosen_env = \\\"adam_multiple_devices_per_zone\\\"\\n with patch(\\\"homeassistant.components.plugwise.gateway.Smile\\\") as smile_mock:\\n smile_mock.InvalidAuthentication = InvalidAuthentication\\n smile_mock.ConnectionFailedError = ConnectionFailedError\\n smile_mock.XMLDataMissingError = XMLDataMissingError\\n\\n smile_mock.return_value.gateway_id = \\\"fe799307f1624099878210aa0b9f1475\\\"\\n smile_mock.return_value.heater_id = \\\"90986d591dcd426cae3ec3e8111ff730\\\"\\n smile_mock.return_value.smile_version = \\\"3.0.15\\\"\\n smile_mock.return_value.smile_type = \\\"thermostat\\\"\\n smile_mock.return_value.smile_hostname = \\\"smile98765\\\"\\n\\n smile_mock.return_value.notifications = _read_json(chosen_env, \\\"notifications\\\")\\n\\n smile_mock.return_value.connect.side_effect = AsyncMock(return_value=True)\\n smile_mock.return_value.full_update_device.side_effect = AsyncMock(\\n return_value=True\\n )\\n smile_mock.return_value.single_master_thermostat.side_effect = Mock(\\n return_value=False\\n )\\n smile_mock.return_value.set_schedule_state.side_effect = AsyncMock(\\n return_value=True\\n )\\n smile_mock.return_value.set_preset.side_effect = AsyncMock(return_value=True)\\n smile_mock.return_value.set_temperature.side_effect = AsyncMock(\\n return_value=True\\n )\\n smile_mock.return_value.set_relay_state.side_effect = AsyncMock(\\n return_value=True\\n )\\n\\n smile_mock.return_value.get_all_devices.return_value = _read_json(\\n chosen_env, \\\"get_all_devices\\\"\\n )\\n smile_mock.return_value.get_device_data.side_effect = partial(\\n _get_device_data, chosen_env\\n )\\n\\n yield smile_mock.return_value\",\n \"def setUp(self):\\n self.mock_model = Mock()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6999446","0.64985067","0.6480005","0.61804056","0.6107706","0.6030694","0.59762293","0.58769643","0.58677036","0.58640003","0.5860096","0.585139","0.5840659","0.58258325","0.5818307","0.58063465","0.58023256","0.57815766","0.5776021","0.5776021","0.574817","0.5718762","0.5712571","0.5709135","0.57059336","0.57005954","0.5694464","0.5636033","0.56328994","0.5616554","0.55520016","0.5550459","0.55500853","0.5547539","0.5547539","0.55298436","0.5489901","0.54521924","0.54375565","0.54312325","0.542751","0.53849024","0.53847355","0.53540576","0.53433365","0.53381956","0.5335504","0.53345215","0.53111315","0.53108215","0.5305875","0.52871025","0.5263325","0.5256047","0.525201","0.52457374","0.5245274","0.5230198","0.5227455","0.52244925","0.52213424","0.5214208","0.5209314","0.5206453","0.5186989","0.51559037","0.5152228","0.5149924","0.5140157","0.5137149","0.512975","0.51271117","0.5117625","0.509289","0.509168","0.50863785","0.5084804","0.50805116","0.50796896","0.507242","0.5064345","0.5053597","0.5052521","0.5050167","0.5049356","0.504919","0.5046883","0.504436","0.50398904","0.5034892","0.5033105","0.50298536","0.50117904","0.50109655","0.5005593","0.5004462","0.49988472","0.49977186","0.49924147"],"string":"[\n \"0.6999446\",\n \"0.64985067\",\n \"0.6480005\",\n \"0.61804056\",\n \"0.6107706\",\n \"0.6030694\",\n \"0.59762293\",\n \"0.58769643\",\n \"0.58677036\",\n \"0.58640003\",\n \"0.5860096\",\n \"0.585139\",\n \"0.5840659\",\n \"0.58258325\",\n \"0.5818307\",\n \"0.58063465\",\n \"0.58023256\",\n \"0.57815766\",\n \"0.5776021\",\n \"0.5776021\",\n \"0.574817\",\n \"0.5718762\",\n \"0.5712571\",\n \"0.5709135\",\n \"0.57059336\",\n \"0.57005954\",\n \"0.5694464\",\n \"0.5636033\",\n \"0.56328994\",\n \"0.5616554\",\n \"0.55520016\",\n \"0.5550459\",\n \"0.55500853\",\n \"0.5547539\",\n \"0.5547539\",\n \"0.55298436\",\n \"0.5489901\",\n \"0.54521924\",\n \"0.54375565\",\n \"0.54312325\",\n \"0.542751\",\n \"0.53849024\",\n \"0.53847355\",\n \"0.53540576\",\n \"0.53433365\",\n \"0.53381956\",\n \"0.5335504\",\n \"0.53345215\",\n \"0.53111315\",\n \"0.53108215\",\n \"0.5305875\",\n \"0.52871025\",\n \"0.5263325\",\n \"0.5256047\",\n \"0.525201\",\n \"0.52457374\",\n \"0.5245274\",\n \"0.5230198\",\n \"0.5227455\",\n \"0.52244925\",\n \"0.52213424\",\n \"0.5214208\",\n \"0.5209314\",\n \"0.5206453\",\n \"0.5186989\",\n \"0.51559037\",\n \"0.5152228\",\n \"0.5149924\",\n \"0.5140157\",\n \"0.5137149\",\n \"0.512975\",\n \"0.51271117\",\n \"0.5117625\",\n \"0.509289\",\n \"0.509168\",\n \"0.50863785\",\n \"0.5084804\",\n \"0.50805116\",\n \"0.50796896\",\n \"0.507242\",\n \"0.5064345\",\n \"0.5053597\",\n \"0.5052521\",\n \"0.5050167\",\n \"0.5049356\",\n \"0.504919\",\n \"0.5046883\",\n \"0.504436\",\n \"0.50398904\",\n \"0.5034892\",\n \"0.5033105\",\n \"0.50298536\",\n \"0.50117904\",\n \"0.50109655\",\n \"0.5005593\",\n \"0.5004462\",\n \"0.49988472\",\n \"0.49977186\",\n \"0.49924147\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7388397"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":85583,"cells":{"query":{"kind":"string","value":"ROI on the surface or volume with a given centre and radius"},"document":{"kind":"string","value":"def py_SurfStatROI(centre, radius, surf):\n sys.exit(\"Function py_SurfStatROI is under development\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def objects_radius(self, centre, radius):","def extract_roi_from_volume(volume, in_center, output_shape, fill = 'random'):\n input_shape = volume.shape \n if(fill == 'random'):\n output = np.random.normal(0, 1, size = output_shape)\n else:\n output = np.zeros(output_shape)\n r0max = [int(x/2) for x in output_shape]\n r1max = [output_shape[i] - r0max[i] for i in range(len(r0max))]\n r0 = [min(r0max[i], in_center[i]) for i in range(len(r0max))]\n r1 = [min(r1max[i], input_shape[i] - in_center[i]) for i in range(len(r0max))]\n out_center = r0max\n\n output[np.ix_(range(out_center[0] - r0[0], out_center[0] + r1[0]),\n range(out_center[1] - r0[1], out_center[1] + r1[1]),\n range(out_center[2] - r0[2], out_center[2] + r1[2]))] = \\\n volume[np.ix_(range(in_center[0] - r0[0], in_center[0] + r1[0]),\n range(in_center[1] - r0[1], in_center[1] + r1[1]),\n range(in_center[2] - r0[2], in_center[2] + r1[2]))]\n return output","def extract_roi_from_volume(volume, in_center, output_shape, fill = 'random'):\n input_shape = volume.shape \n if(fill == 'random'):\n output = np.random.normal(0, 1, size = output_shape)\n else:\n output = np.zeros(output_shape)\n r0max = [int(x/2) for x in output_shape]\n r1max = [output_shape[i] - r0max[i] for i in range(len(r0max))]\n r0 = [min(r0max[i], in_center[i]) for i in range(len(r0max))]\n r1 = [min(r1max[i], input_shape[i] - in_center[i]) for i in range(len(r0max))]\n out_center = r0max\n\n output[np.ix_(range(out_center[0] - r0[0], out_center[0] + r1[0]),\n range(out_center[1] - r0[1], out_center[1] + r1[1]),\n range(out_center[2] - r0[2], out_center[2] + r1[2]))] = \\\n volume[np.ix_(range(in_center[0] - r0[0], in_center[0] + r1[0]),\n range(in_center[1] - r0[1], in_center[1] + r1[1]),\n range(in_center[2] - r0[2], in_center[2] + r1[2]))]\n return output","def get_roi(self):\n roi_opts = opts.Curve(frame_width=275, frame_height=125, framewise=True,\n xlabel='Wavelength (nm)', ylabel='Intensity',\n show_grid=True, gridstyle = {'minor_xgrid_line_color': 'lightgray'}, \n toolbar='above')\n roi = hv.DynamicMap(self.roi_curves, streams=[self.box_stream]).opts(roi_opts)\n roi.opts(xlim=(self.param.band.bounds)) \n\n # TO-DO: make line dissapear when no box selected\n line_opts = opts.VLine(color='black', line_dash='dashed', line_width=1)\n center = np.mean(self.band)\n line = hv.VLine(center).opts(line_opts)\n \n return roi*line","def set_roi_to_volume(volume, center, sub_volume):\n volume_shape = volume.shape \n patch_shape = sub_volume.shape\n output_volume = volume\n for i in range(len(center)):\n if(center[i] >= volume_shape[i]):\n return output_volume\n r0max = [int(x/2) for x in patch_shape]\n r1max = [patch_shape[i] - r0max[i] for i in range(len(r0max))]\n r0 = [min(r0max[i], center[i]) for i in range(len(r0max))]\n r1 = [min(r1max[i], volume_shape[i] - center[i]) for i in range(len(r0max))]\n patch_center = r0max\n\n if(len(center) == 3):\n output_volume[np.ix_(range(center[0] - r0[0], center[0] + r1[0]),\n range(center[1] - r0[1], center[1] + r1[1]),\n range(center[2] - r0[2], center[2] + r1[2]))] = \\\n sub_volume[np.ix_(range(patch_center[0] - r0[0], patch_center[0] + r1[0]),\n range(patch_center[1] - r0[1], patch_center[1] + r1[1]),\n range(patch_center[2] - r0[2], patch_center[2] + r1[2]))]\n elif(len(center) == 4):\n output_volume[np.ix_(range(center[0] - r0[0], center[0] + r1[0]),\n range(center[1] - r0[1], center[1] + r1[1]),\n range(center[2] - r0[2], center[2] + r1[2]),\n range(center[3] - r0[3], center[3] + r1[3]))] = \\\n sub_volume[np.ix_(range(patch_center[0] - r0[0], patch_center[0] + r1[0]),\n range(patch_center[1] - r0[1], patch_center[1] + r1[1]),\n range(patch_center[2] - r0[2], patch_center[2] + r1[2]),\n range(patch_center[3] - r0[3], patch_center[3] + r1[3]))]\n else:\n raise ValueError(\"array dimension should be 3 or 4\") \n return output_volume","def set_roi_to_volume(volume, center, sub_volume):\n volume_shape = volume.shape \n patch_shape = sub_volume.shape\n output_volume = volume\n for i in range(len(center)):\n if(center[i] >= volume_shape[i]):\n return output_volume\n r0max = [int(x/2) for x in patch_shape]\n r1max = [patch_shape[i] - r0max[i] for i in range(len(r0max))]\n r0 = [min(r0max[i], center[i]) for i in range(len(r0max))]\n r1 = [min(r1max[i], volume_shape[i] - center[i]) for i in range(len(r0max))]\n patch_center = r0max\n\n if(len(center) == 3):\n output_volume[np.ix_(range(center[0] - r0[0], center[0] + r1[0]),\n range(center[1] - r0[1], center[1] + r1[1]),\n range(center[2] - r0[2], center[2] + r1[2]))] = \\\n sub_volume[np.ix_(range(patch_center[0] - r0[0], patch_center[0] + r1[0]),\n range(patch_center[1] - r0[1], patch_center[1] + r1[1]),\n range(patch_center[2] - r0[2], patch_center[2] + r1[2]))]\n elif(len(center) == 4):\n output_volume[np.ix_(range(center[0] - r0[0], center[0] + r1[0]),\n range(center[1] - r0[1], center[1] + r1[1]),\n range(center[2] - r0[2], center[2] + r1[2]),\n range(center[3] - r0[3], center[3] + r1[3]))] = \\\n sub_volume[np.ix_(range(patch_center[0] - r0[0], patch_center[0] + r1[0]),\n range(patch_center[1] - r0[1], patch_center[1] + r1[1]),\n range(patch_center[2] - r0[2], patch_center[2] + r1[2]),\n range(patch_center[3] - r0[3], patch_center[3] + r1[3]))]\n else:\n raise ValueError(\"array dimension should be 3 or 4\") \n return output_volume","def get_radius(center, rad, speed_limit):\n i = Intersection(center, rad, speed_limit)\n return i.get_radius()","def getROI(image_np, boxes_roi, margin_ratio):\n (left, right, top, bottom) = boxes_roi[0]\n width = right - left\n height = bottom - top\n margin_x = margin_ratio * width\n margin_y = margin_ratio * height\n img_roi = image_np[\n int(top - margin_y): int(bottom + margin_y),\n int(left - margin_x): int(right + margin_x)]\n return img_roi","def get_roi(self):\n rect=lib.is_GetAOI(self.hcam)\n hbin,vbin=self._get_roi_binning()\n return (rect.s32X*hbin,(rect.s32X+rect.s32Width)*hbin,rect.s32Y*vbin,(rect.s32Y+rect.s32Height)*vbin,hbin,vbin)","def __init__(self,r):\n self.radius = r\n self.uc_centered_a = r\n self.uc_centered_b = r*np.sqrt(3.0)","def get_roi_circle(self):\n return self.circle_list","async def get_roi(self):","def calc_R(center):\r\n xc = center[0]\r\n yc = center[1]\r\n return np.sqrt((x-xc)**2 + (y-yc)**2)","def test_update_radius():\n center = Coordinates(1, 1)\n rad1 = 20.3\n speed = 30\n\n i = Intersection(center, rad1, speed)\n\n assert i.get_radius() == 20.3\n\n i.update_radius(56.5)\n\n assert i.get_radius() == 56.5","def __init__(self, center, radius, rotation=0.0, feature_center=None, feature_boarder=None):\n # rotation=math.pi/2 - align the read dot of the camera with the screw\n self._center = center\n self._radius = radius\n self._rotation = rotation\n self._feature_center = feature_center\n self._feature_boarder = feature_boarder\n\n self._slot_bounds = []\n self.set_rotation(rotation)","def extractROI(base_obj, roc, cen, roi_sz,\n filter_sobel=False, filter_smooth=True, filter_window=0): #{\n vrbMsg(1, 'extractROI() base_obj = ' + str(base_obj) +\n ', roc = ' + str(roc) +\n ', cen = ' + str(cen) +\n ', roi_sz = ' + str(roi_sz) +\n ', filter_sobel = ' + str(filter_sobel) +\n ', filter_smooth = ' + str(filter_smooth) +\n ', filter_window = ' + str(filter_window))\n status = 0\n regions_to_sep = ['ankle_l', 'ankle_r', 'elbow_l', 'elbow_r',\n 'hip_l', 'hip_r', 'knee_l', 'knee_r',\n 'wrist_l', 'wrist_r']\n cutBox = w.WlzIBox2()\n cutBox.xMin = int(cen[0] - (roi_sz / 2))\n cutBox.yMin = int(cen[1] - (roi_sz / 2))\n cutBox.xMax = roi_sz + cutBox.xMin - 1\n cutBox.yMax = roi_sz + cutBox.yMin - 1\n err_num = c.c_int(w.WLZ_ERR_NONE)\n roi_obj = w.WlzAssignObject(\n w.WlzCutObjToBox2D(base_obj, cutBox,\n c.c_int(w.WLZ_GREY_UBYTE), c.c_int(0),\n c.c_double(0.0), c.c_double(0.0), c.byref(err_num)), None)\n vrbMsg(5, 'extractROI() roi_obj = ' + str(roi_obj) +\n ', err_num = ' + str(err_num))\n # Window the region to reduce edge effects\n if(not bool(err_num)): #{\n if(roc in regions_to_sep): #{\n # If this ROI is appropriate select just that part of the object\n # which contains the ROI centre\n # Smooth ROI object and normalise it\n vrbMsg(5, 'extractROI() roc = ' + roc + ' - separating regions')\n obj0 = None\n obj1 = None\n obj2 = None\n lbl_ary = None\n max_rgn = 1000\n n_rgn = c.c_int(0)\n sigma = w.WlzDVertex3()\n sigma.vtX = c.c_double(prm_peak_sigma / 2)\n sigma.vtY = c.c_double(prm_peak_sigma / 2)\n sigma.vtZ = c.c_double(1.0)\n order = w.WlzIVertex3()\n order.vtX = c.c_int(0)\n order.vtY = c.c_int(0)\n order.vtZ = c.c_int(0)\n direc = w.WlzIVertex3()\n direc.vtX = c.c_int(1)\n direc.vtY = c.c_int(1)\n direc.vtX = c.c_int(0)\n obj0 = w.WlzAssignObject(\n w.WlzGaussFilter(roi_obj, sigma, order, direc,\n c.c_int(w.WLZ_GREY_INT),\n c.c_int(w.ALG_PAD_ZERO), c.c_double(0.0), c.c_int(0),\n c.byref(err_num)), None)\n vrbMsg(5, 'extractROI() obj0 = ' + str(obj1) +\n ', err_num = ' + str(err_num))\n if(not bool(err_num)): #{\n err_num = w.WlzGreyNormalise(obj0, 0)\n vrbMsg(5, 'extractROI() err_num = ' + str(err_num))\n #}\n if((not bool(err_num)) and bool(filter_window & 1)): #{\n org = w.WlzIVertex2()\n org.vtX = int(cen[0])\n org.vtY = int(cen[1])\n rad = w.WlzIVertex2()\n rad.vtX = int((roi_sz / 2))\n rad.vtY = int((roi_sz / 2))\n err_num = c.c_int(w.WLZ_ERR_NONE)\n obj1 = w.WlzAssignObject(\n w.WlzWindow(obj0, c.c_int(w.WLZ_WINDOWFN_PARZEN),\n org, rad, c.byref(err_num)), None)\n vrbMsg(5, 'extractROI() WlzWindow() err_num = ' + str(err_num))\n if(not bool(err_num)): #{\n dummy = w.WlzFreeObj(obj0)\n obj0 = obj1\n #}\n #}\n # Threshold the object to get foreground\n if(not bool(err_num)): #{\n hilo = w.WLZ_THRESH_HIGH\n tv = w.WlzPixelV()\n tv = c.c_int(prm_peak_minval * 2)\n err_num = c.c_int(w.WLZ_ERR_NONE)\n obj1 = w.WlzAssignObject(\n w.WlzThresholdI(obj0, tv, hilo, c.byref(err_num)), None)\n vrbMsg(5, 'extractROI() obj2 = ' + str(obj1) +\n ', err_num = ' + str(err_num))\n #}\n dummy = w.WlzFreeObj(obj0)\n obj0 = None\n # Perform connected component labeling\n if(not bool(err_num)): #{\n lbl_ary = c.cast(w.AlcCalloc(max_rgn, c.sizeof(w.WlzObject)),\n c.POINTER(c.POINTER(w.WlzObject)))\n err_num = w.WlzLabel(obj1, c.byref(n_rgn), c.byref(lbl_ary),\n max_rgn, 1, c.c_int(w.WLZ_8_CONNECTED))\n #}\n dummy = w.WlzFreeObj(obj1)\n obj1 = None\n # Find which component contains the ROI centre\n n_rgn = n_rgn.value\n for i in range(0, n_rgn): #{\n vrbMsg(5, 'extractROI() lbl_ary[' + str(i) + '] = ' +\n str(lbl_ary[i]))\n err_num = c.c_int(w.WLZ_ERR_NONE)\n hit = w.WlzInsideDomain(lbl_ary[i], 0, cen[1], cen[0],\n c.byref(err_num))\n if(bool(hit)): #{\n vrbMsg(5, 'extractROI() hit')\n obj2 = w.WlzAssignObject(\n w.WlzGreyTransfer(lbl_ary[i], roi_obj, 0,\n c.byref(err_num)), None)\n dummy = w.WlzFreeObj(roi_obj)\n roi_obj = w.WlzAssignObject(\n w.WlzCutObjToBox2D(obj2, cutBox,\n c.c_int(w.WLZ_GREY_UBYTE), c.c_int(0),\n c.c_double(0.0), c.c_double(0.0),\n c.byref(err_num)), None)\n dummy = w.WlzFreeObj(obj2)\n obj2 = None\n break\n #}\n #}\n if(not(lbl_ary is None)): #{\n for i in range(0, n_rgn): #{\n if(bool(lbl_ary[i])): #{\n dummy = w.WlzFreeObj(lbl_ary[i])\n #}\n #}\n w.AlcFree(lbl_ary)\n #}\n #}\n #}\n # Apply Sobel filter so that we match to edges\n if((not bool(err_num)) and filter_sobel): #{\n err_num = c.c_int(w.WLZ_ERR_NONE)\n obj1 = w.WlzAssignObject(\n w.WlzSobel(roi_obj, 1, 1, c.byref(err_num)), None)\n vrbMsg(5, 'extractROI() WlzSobel() err_num = ' + str(err_num))\n if(not bool(err_num)): #{\n dummy = w.WlzFreeObj(roi_obj)\n roi_obj = obj1\n #}\n #}\n if((not bool(err_num)) and filter_smooth): #{\n sigma = w.WlzDVertex3()\n sigma.vtX = 1.0\n sigma.vtY = 1.0\n sigma.vtZ = 0.0\n order = w.WlzIVertex3()\n order.vtX = 0\n order.vtY = 0\n order.vtZ = 0\n direc = w.WlzIVertex3()\n direc.vtX = 1\n direc.vtY = 1\n direc.vtZ = 0\n err_num = c.c_int(w.WLZ_ERR_NONE)\n obj1 = w.WlzAssignObject(\n w.WlzGaussFilter(roi_obj, sigma, order, direc,\n c.c_int(w.WLZ_GREY_ERROR), c.c_int(w.ALG_PAD_NONE), 0.0, 0,\n c.byref(err_num)), None)\n vrbMsg(5, 'extractROI() WlzGaussFilter() err_num = ' + str(err_num))\n if(not bool(err_num)): #{\n dummy = w.WlzFreeObj(roi_obj)\n roi_obj = obj1\n #}\n #}\n # Apply window function again if required\n if((not bool(err_num)) and bool(filter_window & 2)): #{\n org = w.WlzIVertex2()\n org.vtX = int(cen[0])\n org.vtY = int(cen[1])\n rad = w.WlzIVertex2()\n rad.vtX = int((roi_sz / 2))\n rad.vtY = int((roi_sz / 2))\n err_num = c.c_int(w.WLZ_ERR_NONE)\n obj0 = w.WlzAssignObject(\n w.WlzWindow(roi_obj, c.c_int(w.WLZ_WINDOWFN_PARZEN),\n org, rad, c.byref(err_num)), None)\n vrbMsg(5, 'extractROI() WlzWindow() err_num = ' + str(err_num))\n if(not bool(err_num)): #{\n dummy = w.WlzFreeObj(roi_obj)\n roi_obj = obj0\n #}\n #}\n # Shift the region to the origin\n if(not bool(err_num)): #{\n x_shift = -(roi_obj.contents.domain.i.contents.kol1)\n y_shift = -(roi_obj.contents.domain.i.contents.line1)\n err_num = c.c_int(w.WLZ_ERR_NONE)\n obj1 = w.WlzAssignObject(\n w.WlzShiftObject(roi_obj,\n x_shift, y_shift, 0, c.byref(err_num)), None)\n vrbMsg(5, 'extractROI() WlzShiftObject() err_num = ' + str(err_num))\n if(not bool(err_num)): #{\n dummy = w.WlzFreeObj(roi_obj)\n roi_obj = obj1\n #}\n #}\n vrbMsg(1, 'extractROI() status = ' + str(status) +\n ', roi_obj = ' + str(roi_obj))\n return status, roi_obj","def get_radius(self):","def calc_rois(study, slice_type, desired_radius=HEART_RADIUS):\n slice_numbers, slices = common.study_to_numpy(study, slice_type=slice_type, with_slice_number=True)\n slice_locations = common.slice_locations(study, slice_type) # will be used as z-values\n z_values = [slice_locations[0]]\n for i in xrange(len(slice_locations) - 1):\n z_values.append(z_values[-1] + slice_locations[i + 1] - slice_locations[i])\n if z_values[-1] - z_values[-2] < 1:\n z_values[-1] = z_values[-2] + 1 # don't want a floats to blow up\n\n metadata = common.metadata(study, slice_type)\n scale_x, scale_y = metadata['scale_x'], metadata['scale_y']\n assert scale_x == scale_y, 'only support equal scaling currently'\n roi_radius = int(desired_radius / scale_x + 1) # radius in pixels\n num_slices, _, shape_x, shape_y = slices.shape\n\n # original mean over time of slices\n dc = np.mean(slices, 1)\n\n # detect movements\n h1s = np.array([get_harmonic(slices[i]) for i in xrange(num_slices)])\n # remove noise\n m = np.max(h1s) * 0.05\n h1s[h1s < m] = 0\n\n # perform ROI-detection on all slices together\n centroid, regressed_h1s = regression_filter(h1s)\n\n # regress 3d line through centroid of each slice\n centroids = np.array([get_centroid(img) for img in regressed_h1s])\n coords = regress_centroids(centroids, z_values=z_values)\n\n # yield ROIs\n rois, circles = get_ROIs(dc, regressed_h1s, coords, z_values,\n radius=roi_radius)\n\n # code uses x, y as in graphics, so put it back to row/col\n circles = dict((slice_number, (circles[i][0][1], circles[i][0][0]))\n for i, slice_number in enumerate(slice_numbers))\n\n return rois, circles","def __init__( self , center , radius ):\r\n self.center = center\r\n self.radius = radius","async def set_roi(self, pos0=None, shape=None):","def _get_output_volume_roi(self):\n\n # Define output ROI based on provided command line arguments, if such\n # arguments are provided.\n if self.options.outputVolumeROI:\n region_origin_roi = self.options.outputVolumeROI[0:2]\n region_size_roi = self.options.outputVolumeROI[2:4]\n else:\n region_origin_roi = None\n region_size_roi = None\n\n return region_origin_roi, region_size_roi","def cutout(self, centre, radius):","def roi(data):\n return data[data.shape[0] // 2 - args.roi : data.shape[0] // 2 + args.roi, data.shape[1] // 2 - args.roi : data.shape[1] // 2 + args.roi]","def get_center(center, rad, speed_limit):\n i = Intersection(center, rad, speed_limit)\n return i.get_center()","def test_get_radius():\n center = Coordinates(7, 3)\n radius = 12\n\n returned_rad = get_radius(center, radius, 30)\n\n assert returned_rad == radius\n assert returned_rad != center.get_x()\n assert returned_rad != center.get_y()","def sub_image(self, center, radius):\n width = self.img.shape[1]\n height = self.img.shape[0]\n xstart = int(max(center.x - radius, 0))\n xend = int(min(center.x + radius, width))\n ystart = int(max(center.y - radius, 0))\n yend = int(min(center.y + radius, height))\n roi_rect = [xstart, ystart, xend, yend]\n\n sub = self.img[ystart:yend, xstart:xend]\n return Image(sub), roi_rect","def __init__(self, minRA, maxRA, minDec, maxDec, radius_RA, radius_Dec):\n\n self.RA = np.mean([minRA, maxRA])\n self.Dec = np.mean([minDec, maxDec])\n self.radius_RA = radius_RA\n self.radius_Dec = radius_Dec\n\n # define the polygon attached to this area\n \"\"\"\n self.area_poly = areap(self.RA-radius_RA/2.,\n self.RA+radius_RA/2.,\n self.Dec-radius_Dec/2.,\n self.Dec+radius_Dec/2.)\n \"\"\"\n self.area_poly = areap(minRA, maxRA, minDec, maxDec)\n all_patches = self.getpatches(minRA, maxRA, minDec, maxDec)\n\n self.patches = self.inside(all_patches)","def _getRoi(\n self,\n obj: str,\n params: dict,\n media_width: int,\n media_height: int) -> Tuple[float, float, float, float]:\n\n # Get the initial image based on the localization type and requested margins\n localization_type = obj.meta.dtype\n margins_pixels = self._getMargins(localization_type=localization_type, params=params)\n\n # The roi input is done with normalized arguments. But the margins provided\n # are in pixels. So we've got to convert.\n margins_rel = SimpleNamespace(x=margins_pixels.x / media_width, y=margins_pixels.y / media_height)\n\n # Take the position information available and apply the margin.\n # The stored position information is normalized, so we will set it to the\n # provided media pixel width/height, apply the appropriate region of interest (ROI)\n # information, then normalize back since the media_utils requires the ROI data\n # in that format.\n #\n # Position information available per localization type:\n # Point/dot: x, y\n # Line: x, y, u, v\n # Box: x, y, width, height\n #\n # Region of interest format: width, height, x, y\n if localization_type == 'dot':\n\n roi_x = obj.x * media_width\n roi_y = obj.y * media_height\n\n roi = [2*margins_pixels.x + 1,\n 2*margins_pixels.y + 1,\n roi_x - margins_pixels.x,\n roi_y - margins_pixels.y]\n\n elif localization_type == 'line':\n\n x = obj.x * media_width\n y = obj.y * media_height\n u = obj.u * media_width\n v = obj.v * media_height\n\n point_a = SimpleNamespace(x=x, y=y)\n point_b = SimpleNamespace(x=x+u, y=y+v)\n\n width = abs(point_b.x - point_a.x)\n height = abs(point_b.y - point_a.y)\n\n roi_x = min(point_a.x, point_b.x)\n roi_y = min(point_a.y, point_b.y)\n\n roi = [width + 2*margins_pixels.x,\n height + 2*margins_pixels.y,\n roi_x - margins_pixels.x,\n roi_y - margins_pixels.y]\n\n elif localization_type == 'box':\n\n roi_x = obj.x * media_width\n roi_y = obj.y * media_height\n roi_width = obj.width * media_width\n roi_height = obj.height * media_height\n\n roi = [roi_width + 2*margins_pixels.x,\n roi_height + 2*margins_pixels.y,\n roi_x - margins_pixels.x,\n roi_y - margins_pixels.y]\n elif localization_type == 'poly':\n minX = 1.0\n minY = 1.0\n maxX = 0.0\n maxY = 0.0\n for point in obj.points:\n if point[0] > maxX:\n maxX = point[0]\n if point[0] < minX:\n minX = point[0]\n if point[1] > maxY:\n maxY = point[1]\n if point[1] < minY:\n minY = point[1]\n roi_x = minX * media_width\n roi_y = minY * media_height\n roi_width = (maxX-minX) * media_width\n roi_height = (maxY-minY) * media_height\n roi = [roi_width + 2*margins_pixels.x,\n roi_height + 2*margins_pixels.y,\n roi_x - margins_pixels.x,\n roi_y - margins_pixels.y]\n\n else:\n raise Exception(f\"Invalid meta.dtype detected {localization_type}\")\n\n # Don't allow any single pixel width/heights\n # Adding the 0.1 to deal with floating point precision\n roi[0] = max(roi[0], 2.1)\n roi[1] = max(roi[1], 2.1)\n\n # Now, normalize the ROI\n roi[0] = roi[0] / media_width\n roi[2] = roi[2] / media_width\n\n roi[1] = roi[1] / media_height\n roi[3] = roi[3] / media_height\n\n # Force the ROI to be within the image\n for idx, roi_entry in enumerate(roi):\n if roi_entry > 1.0:\n roi[idx] = 1.0\n elif roi_entry < 0.0:\n roi[idx] = 0.0\n\n return tuple(roi)","def circleMask(img, cir_x, cir_y, r, mode, filter=0):\n\n if not mode == 'interior' and not mode == 'exterior':\n print(mode, \"is not a supported mode. Please enter interior or exterior\")\n return 1\n\n #get the dimensions of the image\n n,m = img.shape\n\n #create an open grid for our image\n y,x = np.ogrid[0:n, 0:m]\n #operate on a copy of the image\n copyImg = img.copy()\n\n #get the x and y center points of our image\n center_x = cir_x\n center_y = cir_y\n\n #create a circle mask\n if mode == 'interior':\n circle_mask = (x-center_x)**2 + (y-center_y)**2 <= r**2\n elif mode == 'exterior':\n circle_mask = (x-center_x)**2 + (y-center_y)**2 >= r**2\n\n #black out anywhere within the circle mask\n copyImg[circle_mask] = [filter]\n copyImg[copyImg != filter] = [255-filter]\n\n return copyImg","def get_roi(landmarks):\n centers = compute_centers(landmarks)\n corners = compute_corners(centers, 'face')\n return [corners[i][0] for i in range(len(corners))]","def sector_mask(shape,centre,radius1, radius2 = 0, angle_range = (0, np.pi)):\n\n x,y = np.ogrid[:shape[0],:shape[1]]\n cx,cy = centre\n tmin,tmax = angle_range\n\n # ensure stop angle > start angle\n if tmax < tmin:\n tmax += 2*np.pi\n\n # convert cartesian --> polar coordinates\n r2 = (x-cx)*(x-cx) + (y-cy)*(y-cy)\n theta = np.arctan2(x-cx,y-cy) - tmin\n\n # wrap angles between 0 and 2*pi\n theta %= (2*np.pi)\n\n # circular mask\n circmask = (r2 <= radius1*radius1) & (r2 >= radius2*radius2)\n\n # angular mask\n anglemask = theta <= (tmax-tmin)\n\n return circmask*anglemask","def apply_roi(args, what_roi, img):\n if what_roi == \"Fractions\":\n roi = split_roi(args.roi_frac, True)\n img = img[int(img.shape[0] * roi[2]):\n int(img.shape[0] * roi[3]),\n int(img.shape[1] * roi[0]):\n int(img.shape[1] * roi[1])]\n elif what_roi == \"Pixels\":\n roi = split_roi(args.roi_pix, False)\n img = img[roi[1]: roi[1] + roi[3], roi[0]: roi[0] + roi[2]]\n return img","def region_of_interest(self, shape=None, geometry=None, grid=None,\n corners=None, crs=wgs84, roi=None,\n all_touched=False):\n\n # Initial mask\n if roi is not None:\n mask = np.array(roi, dtype=np.int16)\n else:\n mask = np.zeros((self.ny, self.nx), dtype=np.int16)\n\n # Collect keyword arguments, overriding anything the user\n # inadvertently added\n rasterize_kws = dict(out=mask, all_touched=all_touched)\n\n # Several cases\n if shape is not None:\n import pandas as pd\n inplace = False\n if not isinstance(shape, pd.DataFrame):\n from salem.sio import read_shapefile\n shape = read_shapefile(shape)\n inplace = True\n # corner grid is needed for rasterio\n shape = transform_geopandas(shape, to_crs=self.corner_grid,\n inplace=inplace)\n import rasterio\n from rasterio.features import rasterize\n with rasterio.Env():\n mask = rasterize(shape.geometry, **rasterize_kws)\n if geometry is not None:\n import rasterio\n from rasterio.features import rasterize\n # corner grid is needed for rasterio\n geom = transform_geometry(geometry, crs=crs,\n to_crs=self.corner_grid)\n with rasterio.Env():\n mask = rasterize(np.atleast_1d(geom), **rasterize_kws)\n if grid is not None:\n _tmp = np.ones((grid.ny, grid.nx), dtype=np.int16)\n mask = self.map_gridded_data(_tmp, grid, out=mask).filled(0)\n if corners is not None:\n cgrid = self.center_grid\n xy0, xy1 = corners\n x0, y0 = cgrid.transform(*xy0, crs=crs, nearest=True)\n x1, y1 = cgrid.transform(*xy1, crs=crs, nearest=True)\n mask[np.min([y0, y1]):np.max([y0, y1]) + 1,\n np.min([x0, x1]):np.max([x0, x1]) + 1] = 1\n\n return mask","def area_of_circle(radius):\n return radius","def cropCircleROI(image, additionalCut = 5):\n Rmin = np.min(image.shape[:-1])/3\n Rmin = 1250 / 3040 * image.shape[0]\n Rmax = 1400 / 3040 * image.shape[0]\n\n #downscale image for better performance\n reduceFactor = 5 # squared\n hough_radii = np.arange(Rmin/reduceFactor, Rmax/reduceFactor, dtype = int)\n\n downSampledImage = block_reduce(cv2.cvtColor(image, cv2.COLOR_BGR2GRAY), block_size = (reduceFactor, reduceFactor), func = np.max)\n downSampledEdges = canny(downSampledImage, sigma=3, low_threshold=5, high_threshold=10)\n\n hough_res = hough_circle(downSampledEdges, hough_radii)\n downSampledCircle = np.unravel_index(np.argmax(hough_res, axis=None), hough_res.shape)\n circle = np.array([downSampledCircle[1], downSampledCircle[2], hough_radii[downSampledCircle[0]]])*reduceFactor\n\n circleMask_ = cv2.circle(np.ones(image.shape[:-1],dtype = \"uint8\"), (circle[1], circle[0]), circle[2]-additionalCut, 0, thickness = -1)\n\n return [np.ma.array(image[:,:,i], mask = circleMask_) for i in range (image.shape[2])]","def extract_roi(h5series, dir_out, size_m, size_var=.5, max_ecc=.7,\n dist_border=10, pad_border=40, exclude_overlap=30.,\n bg_amp_kw=BG_DEFAULT_KW, bg_amp_bin=np.nan,\n bg_pha_kw=BG_DEFAULT_KW, bg_pha_bin=np.nan,\n search_enabled=True, ret_roimgr=False):\n h5in = pathlib.Path(h5series)\n dout = pathlib.Path(dir_out)\n\n h5out = dout / FILE_ROI_DATA_H5\n imout = dout / FILE_ROI_DATA_TIF\n slout = dout / FILE_SLICES\n\n # Determine ROI location\n with qpimage.QPSeries(h5file=h5in, h5mode=\"r\") as qps:\n rmgr = ROIManager(qps.identifier)\n if search_enabled:\n for ii in range(len(qps)):\n qpi = qps[ii]\n # find objects\n slices = search.search_phase_objects(\n qpi=qpi,\n size_m=size_m,\n size_var=size_var,\n max_ecc=max_ecc,\n dist_border=dist_border,\n pad_border=pad_border,\n exclude_overlap=exclude_overlap)\n for jj, sl in enumerate(slices):\n slident = \"{}.{}\".format(qpi[\"identifier\"], jj)\n rmgr.add(roislice=sl, image_index=ii,\n roi_index=jj, identifier=slident)\n rmgr.save(slout)\n else:\n rmgr.load(slout)\n\n # Extract ROI images\n with qpimage.QPSeries(h5file=h5in, h5mode=\"r\") as qps, \\\n qpimage.QPSeries(h5file=h5out, h5mode=\"w\") as qps_roi, \\\n tifffile.TiffWriter(str(imout), imagej=True) as tf:\n for ii in range(len(qps)):\n # image to analyze\n qpi = qps[ii]\n # available ROIs\n rois = rmgr.get_from_image_index(ii)\n for jj, (rid, sl) in enumerate(rois):\n # Extract the ROI\n qpisl = qpi.__getitem__(sl)\n # amplitude bg correction\n if bg_amp_kw:\n amp_mask = get_binary(\n qpisl.amp, value_or_method=bg_amp_bin)\n qpisl.compute_bg(which_data=\"amplitude\",\n from_binary=amp_mask,\n **bg_amp_kw)\n # phase bg correction\n if bg_pha_kw:\n pha_mask = get_binary(\n qpisl.pha, value_or_method=bg_pha_bin)\n qpisl.compute_bg(which_data=\"phase\",\n from_binary=pha_mask,\n **bg_pha_kw)\n slident = \"{}.{}\".format(qpi[\"identifier\"], jj)\n if rid != slident:\n # This might happen if the user does not know the\n # image identifier and builds his own `FILE_SLICES`.\n msg = \"Mismatch of slice and QPImage identifiers: \" \\\n + \"{} vs {}!\".format(rid, slident)\n warnings.warn(msg)\n # override `slident` with user identifier\n slident = rid\n qps_roi.add_qpimage(qpisl, identifier=slident)\n\n if len(qps_roi):\n # Write TIF\n # determine largest image\n sxmax = np.max([qq.shape[0] for qq in qps_roi])\n symax = np.max([qq.shape[1] for qq in qps_roi])\n dummy = np.zeros((2, sxmax, symax), dtype=np.float32)\n for qpir in qps_roi:\n dummy[0, :, :] = 0\n dummy[1, :, :] = 1\n res = 1 / qpir[\"pixel size\"] * 1e-6 # use µm\n sx, sy = qpir.shape\n dummy[0, :sx, :sy] = qpir.pha\n dummy[1, :sx, :sy] = qpir.amp\n tf.save(data=dummy, resolution=(res, res, None))\n\n ret = h5out\n if ret_roimgr:\n ret = ret, rmgr\n return ret","def update_radius(intersection, new_rad):\n return intersection.update_radius(new_rad)","def get_roi(self, padding=0, random=True):\n if random:\n ymin = np.max([np.min(self.object_mask[0]) -\n np.random.randint(0, padding), 0])\n ymax = np.min([np.max(self.object_mask[0]) +\n np.random.randint(0, padding),\n int(self.im_height - 1)])\n xmin = np.max([np.min(self.object_mask[1]) -\n np.random.randint(0, padding), 0])\n xmax = np.min([np.max(self.object_mask[1]) +\n np.random.randint(0, padding),\n int(self.im_width - 1)])\n else:\n ymin = np.max([np.min(self.object_mask[0]) - padding, 0])\n ymax = np.min([np.max(self.object_mask[0]) +\n padding, int(self.im_height - 1)])\n xmin = np.max([np.min(self.object_mask[1]) - padding, 0])\n xmax = np.min([np.max(self.object_mask[1]) +\n padding, int(self.im_width - 1)])\n\n self.camera_model.roi = [ymin, xmin, ymax, xmax]\n self.roi_image = self.camera_model.draw_roi(\n self.bgr, copy=True) # bgr is full before crop\n return [ymin, ymax, xmin, xmax]","def refine_center(self, search_size=100, step_size=5, radius_additive=INNER_RADIUS_FULL_SIZE_PHOTO):\n iter = 0\n saves = np.zeros(((search_size*2)**2,3))\n for i_add in xrange(-search_size, search_size, step_size):\n for j_add in xrange(-search_size, search_size, step_size):\n perim_intensities = self.scan_perimeter_intensity(\n self.i_img_center+i_add, self.j_img_center+j_add, self.T, radius_additive, step_size)\n saves[iter, 0] = self.i_img_center+i_add\n saves[iter, 1] = self.j_img_center+j_add\n saves[iter, 2] = perim_intensities[:,3].sum()\n iter += 1\n\n idxs = np.argsort(saves[:,2])\n winner_ij = saves[idxs[-1],:2]\n self.i_img_center = winner_ij[0]\n self.j_img_center = winner_ij[1]\n return(winner_ij[0], winner_ij[1])","def overlay_position(frame, centre, radius):\n if (not (radius is None)) and radius > 10:\n cv2.circle(frame, centre, int(radius),(0, 255, 255), 2)\n cv2.circle(frame, centre, 5, (0, 0, 255), -1)\n return frame","def __init__(self, center, initialRadius, finalRadius, initialAngle, finalAngle, cartesianImageSize,\n polarImageSize):\n self.center = center\n self.initialRadius = initialRadius\n self.finalRadius = finalRadius\n self.initialAngle = initialAngle\n self.finalAngle = finalAngle\n self.cartesianImageSize = cartesianImageSize\n self.polarImageSize = polarImageSize","def sector_mask(shape,centre,radius,angle_range):\n\n x,y = np.ogrid[:shape[0],:shape[1]]\n cx,cy = centre\n tmin,tmax = np.deg2rad(angle_range)\n\n # ensure stop angle > start angle\n if tmax < tmin:\n tmax += 2*np.pi\n\n # convert cartesian --> polar coordinates\n r2 = (x-cx)*(x-cx) + (y-cy)*(y-cy)\n theta = np.arctan2(x-cx,y-cy) - tmin\n\n # wrap angles between 0 and 2*pi\n theta %= (2*np.pi)\n\n # circular mask\n circmask = r2 <= radius*radius\n\n # angular mask\n anglemask = theta <= (tmax-tmin)\n\n return circmask*anglemask","def sector_mask(shape,centre,radius,angle_range):\n\n x,y = np.ogrid[:shape[0],:shape[1]]\n cx,cy = centre\n tmin,tmax = np.deg2rad(angle_range)\n\n # ensure stop angle > start angle\n if tmax < tmin:\n tmax += 2*np.pi\n\n # convert cartesian --> polar coordinates\n r2 = (x-cx)*(x-cx) + (y-cy)*(y-cy)\n theta = np.arctan2(x-cx,y-cy) - tmin\n\n # wrap angles between 0 and 2*pi\n theta %= (2*np.pi)\n\n # circular mask\n circmask = r2 <= radius*radius\n\n # angular mask\n anglemask = theta <= (tmax-tmin)\n\n return circmask*anglemask","def sector_mask(shape,centre,radius,angle_range):\n\n x,y = np.ogrid[:shape[0],:shape[1]]\n cx,cy = centre\n tmin,tmax = np.deg2rad(angle_range)\n\n # ensure stop angle > start angle\n if tmax < tmin:\n tmax += 2*np.pi\n\n # convert cartesian --> polar coordinates\n r2 = (x-cx)*(x-cx) + (y-cy)*(y-cy)\n theta = np.arctan2(x-cx,y-cy) - tmin\n\n # wrap angles between 0 and 2*pi\n theta %= (2*np.pi)\n\n # circular mask\n circmask = r2 <= radius*radius\n\n # angular mask\n anglemask = theta <= (tmax-tmin)\n\n return circmask*anglemask","def get_ground_plane_roi(depth_frame, depth_cutoff, y_cutoff, roi_x, roi_y):\n\th, w = depth_frame.shape[:2]\n\n\tdepth_frame[depth_frame > depth_cutoff] = 0\n\tdepth_frame[:y_cutoff, ...] = 0\n\n\tground_plane_roi = depth_frame.copy()\n\tground_plane_roi[:y_cutoff + 100, ...] = 0\n\tmask = create_circular_mask(h, w, center=[roi_x, roi_y], radius=100)\n\tground_plane_roi[mask] = 0\n\treturn ground_plane_roi","def compute_objects_in_roi_mask(\n cuboids_ego: NDArrayFloat, city_SE3_ego: SE3, avm: ArgoverseStaticMap\n) -> NDArrayBool:\n is_within_roi: NDArrayBool\n if len(cuboids_ego) == 0:\n is_within_roi = np.zeros((0,), dtype=bool)\n return is_within_roi\n cuboid_list_ego: CuboidList = CuboidList(\n [Cuboid.from_numpy(params) for params in cuboids_ego]\n )\n cuboid_list_city = cuboid_list_ego.transform(city_SE3_ego)\n cuboid_list_vertices_m_city = cuboid_list_city.vertices_m\n\n is_within_roi = avm.get_raster_layer_points_boolean(\n cuboid_list_vertices_m_city.reshape(-1, 3)[..., :2], RasterLayerType.ROI\n )\n is_within_roi = is_within_roi.reshape(-1, 8)\n is_within_roi = is_within_roi.any(axis=1)\n return is_within_roi","def select_rois(img):\n # mouse callback function\n def draw_rect_roi(event, x, y, flags, param):\n # grab references to the global variables\n global rect_bbox, rect_endpoint_tmp, drawing\n \n # if the left mouse button was clicked, record the starting\n # (x, y) coordinates and indicate that drawing is being\n # performed. set rect_endpoint_tmp empty list.\n if event == cv2.EVENT_LBUTTONDOWN:\n rect_endpoint_tmp = []\n rect_bbox = [(x, y)]\n drawing = True\n \n # check to see if the left mouse button was released\n elif event == cv2.EVENT_LBUTTONUP:\n # record the ending (x, y) coordinates and indicate that\n # drawing operation is finished\n rect_bbox.append((x, y))\n drawing = False\n \n # draw a rectangle around the region of interest\n p_1, p_2 = rect_bbox\n cv2.rectangle(img, p_1, p_2, color=(0, 255, 0),thickness=1)\n cv2.imshow('image', img)\n \n # for bbox find upper left and bottom right points\n p_1x, p_1y = p_1\n p_2x, p_2y = p_2\n \n lx = min(p_1x, p_2x)\n ty = min(p_1y, p_2y)\n rx = max(p_1x, p_2x)\n by = max(p_1y, p_2y)\n \n # add bbox to list if both points are different\n if (lx, ty) != (rx, by):\n cx = 0.5*(lx+rx)/float(nx//2)\n cy = 0.5*(by+ty)/float(ny//2)\n\n wx = (rx -lx)/float(nx//2)\n wy = (by -ty)/float(ny//2)\n \n bbox = [cx, cy, wx, wy]\n bbox_list_rois.append(bbox)\n \n # if mouse is drawing set tmp rectangle endpoint to (x,y)\n elif event == cv2.EVENT_MOUSEMOVE and drawing:\n rect_endpoint_tmp = [(x, y)]\n \n \n # clone image img and setup the mouse callback function\n img_copy = img.copy()\n cv2.namedWindow('image',cv2.WINDOW_NORMAL)\n cv2.resizeWindow('image', 1600,1200)\n cv2.setMouseCallback('image', draw_rect_roi)\n \n # keep looping until the 'c' key is pressed\n while True:\n # display the image and wait for a keypress\n if not drawing:\n cv2.imshow('image', img)\n elif drawing and rect_endpoint_tmp:\n rect_cpy = img.copy()\n start_point = rect_bbox[0]\n end_point_tmp = rect_endpoint_tmp[0]\n cv2.rectangle(rect_cpy, start_point, end_point_tmp,(0,255,0),1)\n cv2.imshow('image', rect_cpy)\n \n key = cv2.waitKey(1) & 0xFF\n # if the 'c' key is pressed, break from the loop\n if key == ord('c'):\n break\n # close all open windows\n cv2.destroyAllWindows()\n #cv2.waitKey(1)\n return bbox_list_rois","def extract_region_curvilinear(cube, lat_bounds):\n\n cube = cube.copy() \n \n region_mask = create_region_mask(cube.coord('latitude').points, cube.shape, lat_bounds)\n land_ocean_mask = cube.data.mask\n complete_mask = region_mask + land_ocean_mask\n\n cube.data = numpy.ma.asarray(cube.data)\n cube.data.mask = complete_mask\n\n return cube","def set_roi(self, hstart=0, hend=None, vstart=0, vend=None, hbin=1, vbin=1):\n roi=hstart,hend,vstart,vend,hbin,vbin\n hstart,hend,vstart,vend,hbin,vbin=self._trunc_roi(*roi)\n self._set_roi_binning(hbin,vbin)\n lib.is_SetAOI(self.hcam,(hstart//hbin,vstart//vbin,(hend-hstart)//hbin,(vend-vstart)//vbin))\n return self.get_roi()","def _rad_center(self):\n return ((self.rad_hi + self.rad_lo) / 2).to(\"deg\")","def rcs(azmap, rmap, elmap, areaeffmap, sigma0map, vismap, rpol, azpol,\n elpol, DEM_res, DEM_xmin, DEM_ymin, rad_x, rad_y, beamwidth,\n pulsewidth, range_weighting=True, az_conv=0,\n raster_oversampling=1, verbose=True):\n\n nrows, ncols = azmap.shape\n area_unweighted = areaeffmap * sigma0map\n\n pulselength = pulsewidth * 3.e8 / 2. # [m]\n if az_conv is not None:\n az_conv_offset = az_conv / 2.\n else:\n az_conv_offset = 0\n\n beamwidth_rad = beamwidth * np.pi / 180.\n\n if not range_weighting:\n range_weight = 1 # unity\n\n if raster_oversampling == 0:\n N = 1\n elif raster_oversampling == 1:\n N = int(np.ceil(2 * DEM_res / pulselength))\n else:\n N = raster_oversampling\n\n if N != 1:\n # New dimensions\n nc = N * ncols\n nr = N * nrows\n\n # repeat the values NxN, equivalent of rebin in IDL\n elvals = np.repeat(np.repeat(elmap, N, axis=0), N, axis=1)\n areavals = np.repeat(np.repeat(area_unweighted / N ** 2,\n N, axis=0), N, axis=1)\n visvals = np.repeat(np.repeat(vismap, N, axis=0), N, axis=1)\n\n # New x- and y-vectors\n xvec = np.arange(nr) * DEM_res / N + DEM_xmin\n yvec = np.arange(nc) * DEM_res / N + DEM_ymin\n\n xdiff = (xvec - rad_x)\n ydiff = (yvec - rad_y)\n\n # New distance from radar map\n X, Y = np.meshgrid(xdiff, ydiff)\n rvals = np.sqrt(X ** 2 + Y ** 2)\n\n # New azimuth map\n azmap_rad = (np.arctan2(X, Y) + 2 * np.pi) % (2 * np.pi)\n azvals = azmap_rad * 180. / np.pi\n else:\n rvals = rmap\n azvals = azmap\n azmap_rad = azvals * np.pi / 180.\n elvals = elmap\n areavals = area_unweighted\n visvals = vismap\n\n elmap_rad = elvals * np.pi / 180.\n elevations_rad = np.array(elpol) * np.pi / 180.\n\n # Define the area around a point P(range, azimuth) where the cells\n # have a contribution to the RCS. This area is defined with the\n # range limits from range-dr_offset to range+dr_offset and the\n # azimuth limits from azimuth-daz_offset to azimuth+daz_offset.\n #\n # For a Gaussian antenna, azimuth offset more than 2*HPBW does not a\n # have remarkable contribution.\n # With a rectangular pulse and a matched filter cells farer away\n # than pulse length does not a have remarkable contribution.\n\n daz_offset = (2. * beamwidth) + az_conv_offset # [deg]\n dr_offset = pulselength # [m]\n\n nazim = len(azpol)\n nrange = len(rpol)\n # pyart storage format: 2D arrays (naz * nel, nranges)\n rcspolarmap = np.zeros((nazim * len(elpol), nrange)) + np.nan\n\n for rind in range(nrange):\n if verbose:\n logging.info(f'Computing range bin {rpol[rind]:2.1f}')\n rr = rpol[rind]\n\n indr = np.logical_and(np.logical_and(rvals >= rr - dr_offset,\n rvals < rr + dr_offset),\n visvals > 0)\n\n if not np.any(indr):\n continue\n\n indr = np.where(indr)\n\n for azind in range(nazim):\n az = azpol[azind]\n # Inside the loops over range (rr) and azimuth (az), the\n # coordinates (rr, az) describe the point P(rr, az) for which\n # the RCS is calculated. If more than one DEM cell is within\n # the area from az-daz/2 to az+daz/2 and from rr-dr/2 to\n # rr+dr/2, the calculated RCS value is set to all of these\n # cells (next neighbor).\n\n # Get area around rr and az\n azmin = az - daz_offset\n azmax = az + daz_offset\n if azmin < 0:\n azmin = 360. + azmin\n indaz = np.logical_or(np.logical_and(azvals[indr] >= 0,\n azvals[indr] < azmax),\n np.logical_and(azvals[indr] >= azmin,\n azvals[indr] <= 360.))\n elif azmax > 360:\n azmax = azmax - 360.\n indaz = np.logical_or(np.logical_and(azvals[indr] >= azmin,\n azvals[indr] <= 360),\n np.logical_and(azvals[indr] >= 0,\n azvals[indr] < azmax))\n else:\n indaz = np.logical_and(azvals[indr] >= azmin,\n azvals[indr] < azmax)\n\n # Cells that contribute to the cells to set indset\n inda = tuple([indr[0][indaz], indr[1][indaz]])\n\n # Calculate offsets in azimuth and elevation to the\n # point P(rr,az) and the elevation angle of the antenna.\n\n daz_area = azmap_rad[inda] - (az * np.pi / 180.)\n\n indaz = daz_area > np.pi\n daz_area[indaz] = daz_area[indaz] - 2. * np.pi\n\n indaz = daz_area < -np.pi\n daz_area[indaz] = daz_area[indaz] + 2. * np.pi\n\n if range_weighting:\n # Get the weighting factor due to the range offset.\n range_weight = range_weights(rvals[inda], rr,\n pulselength)\n\n ind_rzero = rvals[inda] <= 0.0\n if np.any(ind_rzero):\n continue\n\n for iel, el in enumerate(elevations_rad):\n del_area = elmap_rad[inda] - el\n\n # Get the two-way weighting factor due to the azimuth offset\n # to the main antenna direction (assuming a Gaussian antenna\n # pattern).\n ant_weight = antenna_pattern_gauss(\n daz_area,\n del_area,\n beamwidth_rad,\n twoway=True,\n az_conv=az_conv *\n np.pi /\n 180.,\n units='rad')\n\n # RCS = SUM_j sigma_j\n # = SUM_j sigma0_j * A_eff_j * fa(dphi_j,dteta_j)^2 * fr(drange)\n # where\n # sigma_j : Backscattering cross section of each cell [m^2]\n # sigma0_j : Sigma naught of each cell [1]\n # A_eff_j : Effective area of each cell [m^2]\n # fa : One-way weighting function due to the azimuth\n # and elevation offsets.\n # fr : Range weighting function due to the range offset\n\n # RCS contribution of each cell inside the contribution\n # area.\n rcs_area = ant_weight * range_weight * areavals[inda]\n # Sum up all the contributions\n rcs = np.nansum(rcs_area)\n\n if rcs < RCS_MIN:\n rcs = np.nan\n\n # Set rcs to all values inside the set area.\n\n rcspolarmap[azind + iel * nazim, rind] = rcs\n return rcspolarmap","def roi_vecs(layer_coords, vec_coords, region):\n \n if region == 'crown':\n #find threshold for vectors inside roi\n start_x_lst = []\n stop_x_lst = []\n for i in range(1,5):\n start_x_lst.append(layer_coords[i][0][0])\n stop_x_lst.append(layer_coords[i][-1][0])\n\n start_x = max(start_x_lst)\n stop_x = min(stop_x_lst)\n \n roi_vec_coords = [i for i in vec_coords if i[0][0] in list(range(start_x, stop_x+5))]\n \n return roi_vec_coords\n \n elif region == 'fundus':\n #find threshold for vectors inside roi\n start_x_lst = []\n stop_x_lst = []\n for i in range(1,5):\n start_x_lst.append(layer_coords[i][0][0])\n stop_x_lst.append(layer_coords[i][-1][0])\n\n start_x = max(start_x_lst)\n stop_x = min(stop_x_lst)\n\n # roi_vec_coords = [i for i in vec_coords if i[1][0] in list(range(start_x-10, stop_x+3))]\n roi_vec_coords = [i for i in vec_coords if i[0][0] in list(range(stop_x, start_x))]\n \n # print(roi_vec_coords)\n return roi_vec_coords","def sector_mask(shape, centre, radius, angle_range):\n\n x,y = np.ogrid[:shape[0],:shape[1]]\n cx,cy = centre\n tmin,tmax = np.deg2rad(angle_range)\n\n # ensure stop angle > start angle\n if tmax < tmin:\n tmax += 2*np.pi\n\n # convert cartesian --> polar coordinates\n r2 = (x-cx)*(x-cx) + (y-cy)*(y-cy)\n theta = np.arctan2(x-cx, y-cy) - tmin\n\n # wrap angles between 0 and 2*pi\n theta %= (2*np.pi)\n\n # circular mask\n circmask = r2 <= radius*radius\n\n # angular mask\n anglemask = theta <= (tmax-tmin)\n\n return circmask*anglemask","def render_region(self, image, rect, color, rad):\n corners = rect.inflate(-2*rad, -2*rad)\n for attribute in (\"topleft\", \"topright\", \"bottomleft\", \"bottomright\"):\n pygame.draw.circle(image, color, getattr(corners,attribute), rad)\n image.fill(color, rect.inflate(-2*rad,0))\n image.fill(color, rect.inflate(0,-2*rad))","def get_radius(self):\n return self.r","def _view_roi(array, original_area_slice, axis):\n axis += 1\n sl = (slice(None),) * axis + original_area_slice[axis:]\n return array[sl]","def roi_img(array, top, bottom, left, right):\n if left < right and top < bottom:\n try:\n imgarray_roi = array[top:bottom, left:right] \n return imgarray_roi\n \n except:\n print(\"Error calculating roi. Using full view.\")\n return array\n \n else:\n print(\"ROI error: left >= right or top >= bottom. Using full view instead.\")\n return array","def get_radius(self):\n return self.R","def circle_area(r):\n if r < 0:\n raise ValueError(\"Radius cannot be negative\")\n\n return pi*(r**2)","def get_radius(self):\r\n return 1","def roi_center(roi):\n def slice_center(s):\n return (s.start + s.stop)*0.5\n\n if isinstance(roi, slice):\n return slice_center(roi)\n\n return tuple(slice_center(s) for s in roi)","def polar_box(rad_min, rad_max, ang_min, ang_max, **kwargs):\n delta_angle = (ang_max - ang_min) % 360.0\n ang_max_sequential = ang_min + delta_angle\n maxarc_coords = list(approxArc((0.0, 0.0), rad_max, ang_min,\n \t\t\t\t\t\t\t ang_max_sequential).coords)\n minarc_coords = list(approxArc((0.0, 0.0), rad_min, ang_min,\n \t\t\t\t\t\t\t ang_max_sequential).coords)\n minarc_coords.reverse()\n poly = geo.Polygon(maxarc_coords + minarc_coords)\n return desc.PolygonPatch(poly, **kwargs)","def CropROI(coords,full_img,target_size = None):\n\n\t#Get region of interest crop from the full array using coorinates\n\ttmp_ROI = full_img[int(coords[2]):int(coords[3]),int(coords[0]):int(coords[1])]\n\t#Get the size of the imc image\n #multiple = int(imc_im.shape[1]/tmp_ROI.shape[0])\n #Use the rounded multiple to resize our ROI for image registration\n #HiRes_size = (tmp_ROI.shape[1]*multiple,tmp_ROI.shape[0]*multiple)\n\t#HiRes_size = (imc_im.shape[0],imc_im.shape[1])\n\n\tif target_size is not None:\n\t\t#Remember that cv2 does the axis in the opposite order as numpy\n\t\t# note here that we are no longer using cv2 -- this is skimage resizing...\n\t\ttmp_ROI = resize(tmp_ROI,target_size)\n\n\t#Create a nifti object\n\tnifti_ROI = nib.Nifti1Image(np.rot90(tmp_ROI.T,1), affine=np.eye(4))\n\t#Return image\n\treturn nifti_ROI","def ellipsoid(center, radii, rotation, scales=None, shape=None, minarea=0):\n center = np.array(center)\n radii = np.array(radii)\n rotation = np.array(rotation)\n assert center.shape == (3,)\n assert radii.shape == (3,)\n assert 0 < radii.max(), \"radii should contain at least one positive value\"\n assert rotation.shape == (3, 3)\n if scales is None:\n scales = (1.,) * 3\n scales = np.array(scales)\n assert scales.shape == (3,)\n\n scaled_center = center / scales\n\n # The upper_left_bottom and lower_right_top corners of the smallest cuboid\n # containing the ellipsoid.\n factor = np.array([\n [i, j, k] for k in (-1, 1) for j in (-1, 1) for i in (-1, 1)]).T\n while True:\n radii_rot = np.abs(\n np.diag(1. / scales).dot(rotation.dot(np.diag(radii).dot(factor)))\n ).max(axis=1)\n # In the original scikit-image code, ceil and floor were replaced.\n # https://github.com/scikit-image/scikit-image/blob/master/skimage/draw/draw.py#L127\n upper_left_bottom = np.floor(scaled_center - radii_rot).astype(int)\n lower_right_top = np.ceil(scaled_center + radii_rot).astype(int)\n\n if shape is not None:\n # Constrain upper_left and lower_ight by shape boundary.\n upper_left_bottom = np.maximum(\n upper_left_bottom, np.array([0, 0, 0]))\n lower_right_top = np.minimum(\n lower_right_top, np.array(shape[:3]) - 1)\n\n bounding_shape = lower_right_top - upper_left_bottom + 1\n\n d_lim, r_lim, c_lim = np.ogrid[0:float(bounding_shape[0]),\n 0:float(bounding_shape[1]),\n 0:float(bounding_shape[2])]\n d_org, r_org, c_org = scaled_center - upper_left_bottom\n d_rad, r_rad, c_rad = radii\n rotation_inv = np.linalg.inv(rotation)\n conversion_matrix = rotation_inv.dot(np.diag(scales))\n d, r, c = (d_lim - d_org), (r_lim - r_org), (c_lim - c_org)\n distances = (\n ((d * conversion_matrix[0, 0] +\n r * conversion_matrix[0, 1] +\n c * conversion_matrix[0, 2]) / d_rad) ** 2 +\n ((d * conversion_matrix[1, 0] +\n r * conversion_matrix[1, 1] +\n c * conversion_matrix[1, 2]) / r_rad) ** 2 +\n ((d * conversion_matrix[2, 0] +\n r * conversion_matrix[2, 1] +\n c * conversion_matrix[2, 2]) / c_rad) ** 2\n )\n if distances.size < minarea:\n old_radii = radii.copy()\n radii *= 1.1\n print('Increase radii from ({}) to ({})'.format(old_radii, radii))\n else:\n break\n distance_thresh = 1\n while True:\n dd, rr, cc = np.nonzero(distances < distance_thresh)\n if len(dd) < minarea:\n distance_thresh *= 1.1\n else:\n break\n dd.flags.writeable = True\n rr.flags.writeable = True\n cc.flags.writeable = True\n dd += upper_left_bottom[0]\n rr += upper_left_bottom[1]\n cc += upper_left_bottom[2]\n return dd, rr, cc","def Radius(self, *args):\n return _Bnd.Bnd_Sphere_Radius(self, *args)","def region_of_interest(img):\n # defining a blank mask to start with\n y, x = img.shape[:2]\n # print(\"roi_width : {}, roi_height : {}\".format(x, y))\n\n # 왼쪽과 오른쪽 roi를 동시 설정; 영역의 범위에 따라 보이는 영역이 바뀜\n left1 = [int(0.1*x), int(y)]\n left2 = [int(0.1*x), int(0.1*y)]\n left3 = [int(0.4*x), int(0.1*y)]\n left4 = [int(0.4*x), int(y)]\n\n right1 = [int(0.7*x), int(y)]\n right2 = [int(0.7*x), int(0.1*y)]\n right3 = [int(0.9*x), int(0.1*y)]\n right4 = [int(0.9*x), int(y)]\n\n middle = [int(0.2*x), int(y)]\n\n shaper = np.array([left1, left2, left3, left4, right1, right2, right3, right4, middle])\n\n mask = np.zeros_like(img)\n\n # 흑백, 컬러 구분 없이 흑백으로 채움\n if len(img.shape) > 2:\n ignore_mask_color = (255, 255, 255)\n else:\n ignore_mask_color = 255\n\n # 식별된 구간 제외 전부 검은색칠\n cv2.fillPoly(mask, np.int32([shaper]), ignore_mask_color)\n\n # bitwise_and 연산\n masked_image = cv2.bitwise_and(img, mask)\n # cv2.imshow('ROI', masked_image)\n # cv2.waitKey()\n\n return masked_image","def set_radius(self, rmirr):\n self.F_EXT = 1\n self.RMIRR = rmirr","def select_region_of_interest():\r\n image = np.array(ImageGrab.grab(bbox=None))\r\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\r\n r = cv2.selectROI(windowName='grab roi', img=image, showCrosshair=True, fromCenter=False)\r\n cv2.destroyAllWindows()\r\n return r[0], r[1], r[0] + r[2], r[1] + r[3]","def circumradius(T,binary_mask):\n (x1, y1), (x2, y2), (x3, y3) = T # extracting the points. \n \n D=2*(x1*(y2-y3)+x2*(y3-y1)+x3*(y1-y2)) # Diameter\n if D!=0:\n #Centroid of the cicumcircle\n Ux=(((x1**2+y1**2)*(y2-y3))+((x2**2+y2**2)*(y3-y1))+((x3**2+y3**2)*(y1-y2)))/D\n Uy=(((x1**2+y1**2)*(x3-x2))+((x2**2+y2**2)*(x1-x3))+((x3**2+y3**2)*(x2-x1)))/D\n \n #radius\n r = sqrt((Ux-x2)**2+(Uy-y2)**2)\n r=r+1\n \n #Determining the sign: it is positive if the centroid of the circumcricle is in the foreground\n x=np.floor(Ux).astype(int)\n y=np.floor(Uy).astype(int)\n\n if (x >=binary_mask.shape[0] or y >=binary_mask.shape[1]):\n r=-r\n elif (x<0 or y<0):\n r=-r\n elif binary_mask[x,y]:\n r=r\n else:\n r=-r\n return r\n else:\n return False","def image_iterator(processing_function, image_volume, radius=2, roi=None):\n # This is an ugly way of type-checking but cant get isinstance to see both as the same\n if (MaskableVolume.__name__ in str(type(image_volume))):\n (c, r, d) = image_volume.frameofreference.size\n def get_val(image_volume, z, y, x):\n # image boundary handling is built into BaseVolume.get_val\n return image_volume.get_val(z, y, x)\n def set_val(feature_volume, z, y, x, val):\n feature_volume.set_val(z, y, x, val)\n\n #instantiate a blank BaseVolume of the proper size\n feature_volume = MaskableVolume().fromArray(np.zeros((d, r, c)), image_volume.frameofreference)\n # feature_volume.modality = image_volume.modality\n # feature_volume.feature_label = 'feature'\n elif isinstance(image_volume, np.ndarray):\n if image_volume.ndim == 3:\n d, r, c = image_volume.shape\n elif image_volume.ndim == 2:\n d, r, c = (1, *image_volume.shape)\n image_volume = image_volume.reshape((d, r, c))\n\n # instantiate a blank np.ndarray of the proper size\n feature_volume = np.zeros((d, r, c))\n\n def get_val(image, z, y, x):\n if (z<0 or y<0 or x<0) or (z>=d or y>=r or x>=c):\n return 0\n else:\n return image[z, y, x]\n def set_val(image, z, y, x, val):\n image[z, y, x] = val\n else:\n logger.info('invalid image type supplied ({:s}). Please specify an image of type BaseVolume \\\n or type np.ndarray'.format(str(type(image_volume))))\n return None\n\n # z_radius_range controls 2d neighborhood vs 3d neighborhood for 2d vs 3d images\n if d == 1: # 2D image\n logger.debug(indent('Computing 2D feature with radius: {:d}'.format(radius), l3))\n z_radius_range = [0]\n elif d > 1: # 3D image\n logger.debug(indent('Computing 3D feature with radius: {:d}'.format(radius), l3))\n z_radius_range = range(-radius, radius+1)\n\n # in plane patch range\n radius_range = range(-radius, radius+1)\n\n # timing\n start_feature_calc = time.time()\n\n\n # absolute max indices for imagevolume - for handling request of voxel out of bounds\n cbound = c\n rbound = r\n dbound = d\n\n # set calculation index bounds -- will be redefined if roi is specified\n cstart, cstop = 0, cbound\n rstart, rstop = 0, rbound\n dstart, dstop = 0, dbound\n\n # defines dimensionality\n d_subset = dstop - dstart\n r_subset = rstop - rstart\n c_subset = cstop - cstart\n\n # restrict calculation bounds to roi\n if (roi is not None):\n # get max extents of the mask/ROI to speed up calculation only within ROI cubic volume\n extents = roi.getROIExtents()\n cstart, rstart, dstart = image_volume.frameofreference.getIndices(extents.start)\n cstop, rstop, dstop = np.subtract(image_volume.frameofreference.getIndices(extents.end()), 1)\n logger.info(indent('calculation subset volume x=({xstart:d}->{xstop:d}), '\n 'y=({ystart:d}->{ystop:d}), '\n 'z=({zstart:d}->{zstop:d})'.format(zstart=dstart,\n zstop=dstop,\n ystart=rstart,\n ystop=rstop,\n xstart=cstart,\n xstop=cstop ), l4))\n # redefine feature_volume\n d_subset = dstop - dstart\n r_subset = rstop - rstart\n c_subset = cstop - cstart\n feature_frameofreference = FrameOfReference((extents.start),\n (image_volume.frameofreference.spacing),\n (c_subset, r_subset, d_subset))\n feature_volume = feature_volume.fromArray(np.zeros((d_subset, r_subset, c_subset)), feature_frameofreference)\n\n # # setup an output volume for each feature in processing_function list\n # if (not isinstance(processing_function, list)):\n # tmp = []\n # tmp.append(processing_function)\n # processing_function = tmp\n # feature_volumes = [feature_volume]\n # for funct in processing_function[1:]:\n # feature_volumes.append(np.zeros_like(feature_volume))\n\n # nested loop approach -> slowest, try GPU next\n total_voxels = d * r * c\n subset_total_voxels = d_subset * r_subset * c_subset\n #onepercent = int(subset_total_voxels / 100)\n fivepercent = int(subset_total_voxels / 100 * 5)\n\n idx = -1\n subset_idx = -1\n z_idx = -1\n for z in range(dstart, dstop):\n z_idx += 1\n y_idx = -1\n x_idx = -1\n for y in range(rstart, rstop):\n y_idx += 1\n x_idx = -1\n for x in range(cstart, cstop):\n x_idx += 1\n idx += 1\n if (z<dstart or z>dstop or y<rstart or y>rstop or x<cstart or x>cstop):\n # we shouldnt ever be here\n logger.info('why are we here?!')\n #fill 0 instead\n set_val(feature_volume, z_idx, y_idx, x_idx, 0)\n else:\n subset_idx += 1\n patch_vals = np.zeros((len(z_radius_range), len(radius_range), len(radius_range)))\n for p_z, k_z in enumerate(z_radius_range):\n for p_x, k_x in enumerate(radius_range):\n for p_y, k_y in enumerate(radius_range):\n #logger.info('k_z:{z:d}, k_y:{y:d}, k_x:{x:d}'.format(z=k_z,y=k_y,x=k_x))\n # handle out of bounds requests - replace with 0\n request_z = z+k_z\n request_y = y+k_y\n request_x = x+k_x\n if (request_z < 0 or request_z >= dbound or\n request_y < 0 or request_y >= rbound or\n request_x < 0 or request_x >= cbound):\n val = 0\n else:\n val = get_val(image_volume, request_z, request_y, request_x)\n # store to local image patch\n patch_vals[p_z, p_y, p_x] = val\n\n # for i, funct in enumerate(processing_function):\n proc_value = processing_function(patch_vals)\n set_val(feature_volume, z_idx, y_idx, x_idx, proc_value)\n\n if (False and (subset_idx % fivepercent == 0 or subset_idx == subset_total_voxels-1)):\n logger.debug('feature value at ({x:d}, {y:d}, {z:d})= {e:f}'.format(\n x=z*y*x + y*x + x,\n y=z*y*x + y,\n z=z*y*x,\n e=proc_value))\n\n if ((subset_idx % fivepercent == 0 or subset_idx == subset_total_voxels-1)):\n logger.debug(indent('{p:0.2%} - voxel: {i:d} of {tot:d} (of total: {abstot:d})'.format(\n p=subset_idx/subset_total_voxels,\n i=subset_idx,\n tot=subset_total_voxels,\n abstot=total_voxels), l4))\n\n if isinstance(image_volume, np.ndarray) and d == 1:\n # need to reshape ndarray if input was 2d\n feature_volume = feature_volume.reshape((r_subset, c_subset))\n # for i, feature_volume in enumerate(feature_volumes):\n # feature_volumes[i] = feature_volume.reshape((r_subset, c_subset))\n\n\n end_feature_calc = time.time()\n logger.debug(timer('feature calculation time:', end_feature_calc-start_feature_calc, l3))\n # if len(features_volumes > 1):\n # return feature_volumes\n # else:\n return feature_volume","def _fcn_link_roi(self):\n kwargs = self.cbqt.cbobjs._objs['roi'].to_kwargs(True)\n self.roi.update_from_dict(kwargs)\n self.roi._update_cbar()","def get_roi(self, video=None, window_name=None):\r\n if video is None:\r\n video = self.video_buffer\r\n self.play_video(video=video, window_name=window_name, mouse_callback_func=self._set_rect_roi)\r\n self.roi = dict(x1=int(min(self.x, self.ix)), x2=int(max(self.x, self.ix)),\r\n y1=int(min(self.y, self.iy)), y2=int(max(self.y, self.iy)))\r\n self.roi_set = True\r\n return self.roi","def circular_levelset(shape, center, sqradius, scalerow=1.0):\n grid = np.mgrid[list(map(slice, shape))].T - center\n phi = sqradius - np.sqrt(np.sum((grid.T)**2, 0))\n u = np.float_(phi > 0)\n return u","def query_wise_astroquery(\n ra_deg: float, dec_deg: float, searchradius_arcsec: float = 3.0\n):\n c = SkyCoord(ra_deg, dec_deg, unit=u.deg, frame=\"icrs\")\n\n r = searchradius_arcsec * u.arcsecond\n\n with warnings.catch_warnings():\n warnings.simplefilter(\"ignore\", AstropyWarning)\n allwise = Irsa.query_region(c, catalog=\"allwise_p3as_psd\", radius=r)\n return allwise","def get_surround_volume_v3(volume, distance=5, wall_level=0, prob=False, return_origin_instead_of_bbox=True, padding=5):\n from scipy.ndimage.morphology import distance_transform_edt\n distance = int(np.round(distance))\n\n # Identify the bounding box for the surrouding area.\n\n vol, origin = volume\n\n # if bbox is None:\n bbox = volume_origin_to_bbox(vol > wall_level, origin)\n\n xmin, xmax, ymin, ymax, zmin, zmax = bbox\n roi_xmin = xmin - distance - padding\n roi_ymin = ymin - distance - padding\n roi_zmin = zmin - distance - padding\n roi_xmax = xmax + distance + padding\n roi_ymax = ymax + distance + padding\n roi_zmax = zmax + distance + padding\n roi_bbox = np.array((roi_xmin,roi_xmax,roi_ymin,roi_ymax,roi_zmin,roi_zmax))\n vol_roi = crop_and_pad_volume(vol, in_bbox=bbox, out_bbox=roi_bbox)\n\n dist_vol = distance_transform_edt(vol_roi < wall_level)\n roi_surround_vol = (dist_vol > 0) & (dist_vol < distance) # surround part is True, otherwise False.\n\n if prob:\n surround_vol = np.zeros_like(vol_roi)\n surround_vol[roi_surround_vol] = 1. - vol_roi[roi_surround_vol]\n if return_origin_instead_of_bbox:\n return surround_vol, roi_bbox[[0,2,4]]\n else:\n return surround_vol, roi_bbox\n else:\n if return_origin_instead_of_bbox:\n return roi_surround_vol, roi_bbox[[0,2,4]]\n else:\n return roi_surround_vol, roi_bbox","def radius(self,xc=None,yc=None):\n if xc == None:\n xc = self.x1\n if yc == None:\n yc = self.y1\n self.r = sqrt((self.x-xc)**2+(self.y-yc)**2)","def area_circle(r):\n return (r ** 2) * math.pi","def get_roi(self, index):\n return self.db.get_node('/images/img{:04d}'.format(index)).attrs.roi","def create_test_object(height=128, radius=10):\n center_line_img = np.zeros((height,height,height), dtype=np.uint32)\n for i in range(height):\n center_line_img[i, i, i] = 1\n center_line_img[height-1-i, i, i] = 1\n \n # Scipy distance_transform_edt conventions are opposite of vigra:\n # it calculates distances of non-zero pixels to the zero pixels.\n center_line_img = 1 - center_line_img\n distance_to_line = distance_transform_edt(center_line_img)\n binary_vol = (distance_to_line <= radius).astype(np.uint8)\n return binary_vol","def roi_cli():","def __init__(self, radius):\n self.radius = radius","def create_ring(self):\n\t\tself.north_coords = numpy.add(self.center, self.north)\n\t\tself.northeast_coords = numpy.add(self.center, self.northeast)\n\t\tself.east_coords = numpy.add(self.center, self.east)\n\t\tself.southeast_coords = numpy.add(self.center, self.southeast)\n\t\tself.south_coords = numpy.add(self.center, self.south)\n\t\tself.southwest_coords = numpy.add(self.center, self.southwest)\n\t\tself.west_coords = numpy.add(self.center, self.west)\n\t\tself.northwest_coords = numpy.add(self.center, self.northwest)","def initialize_radius(src):\n\n x_length = (np.amax(src[:, 0]) - np.amin(src[:, 0]))\n y_length = (np.amax(src[:, 1]) - np.amin(src[:, 1]))\n z_length = (np.amax(src[:, 2]) - np.amin(src[:, 2]))\n\n max_length = max(x_length, y_length, z_length)\n\n if max_length > 50:\n radius = 10\n elif max_length > 1:\n radius = 1\n else:\n radius = 0.01\n\n return radius","def get_roi_circle_by_index(self, index):\n return int(self.circle_list[index][\"x\"]), int(self.circle_list[index][\"y\"]), int(self.circle_list[index][\"radius\"])","def create_circle_mask(img, center, radius):\n h, w = img.shape\n # Flipping center ordering here to account for differences in how opencv returns\n # coordinates (standard x,y) and how numpy wants them (r,c)\n act_center = np.array(center[::-1])\n r, c = np.ogrid[0:h, 0:w] - np.array(center[::-1])\n return (r ** 2 + c ** 2) < radius ** 2","def Icosphere(radius=1.0, center=(0.0, 0.0, 0.0), nsub=3):\n mesh = Icosahedron()\n mesh.clear_data()\n mesh = mesh.subdivide(nsub=nsub)\n\n # scale to desired radius and translate origin\n dist = np.linalg.norm(mesh.points, axis=1, keepdims=True) # distance from origin\n mesh.points = mesh.points * (radius / dist) + center\n return mesh","def fit_galaxy(self, ypos, xpos, r_in, r_out = 0):\r\n count_out = []\r\n count_in = []\r\n for j, i in product(np.arange(ypos - (r_out + r_in), ypos + r_out + r_in + 1),np.arange(xpos - (r_out + r_in), xpos + 1 + r_out + r_in)): # Create square\r\n if (j - ypos) ** 2 + (i - xpos) ** 2 <= r_in ** 2 and 0<= j <= self.shapes[0] - 1 and 0<= i <= self.shapes[1] - 1: # make sure points are in a circle\r\n j = int(j)\r\n i = int(i)\r\n if self.raw_image_data[j,i] * self.masked[j,i] == self.raw_image_data[j,i]:\r\n count_in.append(self.raw_image_data[j,i])\r\n self.masked[j,i] = 0 # self.mask_region runs the for loop again\r\n if r_in ** 2 < (j - ypos) ** 2 + (i - xpos) ** 2 <= (r_in + r_out)**2 and 0<= j <= (self.shapes[0] - 1) and 0<= i <= self.shapes[1] - 1: # in the outer ring\r\n j = int(j)\r\n i = int(i)\r\n if self.raw_image_data[j,i] * self.masked[j,i] == self.raw_image_data[j,i]: \r\n count_out.append(self.raw_image_data[j][i]) \r\n self.masked[j,i]\r\n return count_in, count_out","def circle_area(radius):\n return math.pi * radius ** 2","def azizen(self):\n # x0,y0 array pixel coordinates relative to cx,cy\n# ndy0,ndx0=img.shape\n ndy0=self.ndy0\n ndx0=self.ndx0\n x0,y0=np.meshgrid(np.linspace(0,ndx0-1,ndx0)-self.cx,np.linspace(0,ndy0-1,ndy0)-self.cy)\n r0=np.sqrt(x0**2+y0**2)/self.pr0 # fractional radial distance from 0,0\n# self.roi=np.s_[ystart:ystart+self.ny0,xstart:xstart+self.nx0]\n # why not model the zenith angle dependence with polynomial directly\n # rather than linear interpolation between roots.\n roots=np.zeros(51)\n rr=np.arange(51)/100.0\n for i,ref in enumerate(rr):\n roots[i]=np.real(np.roots([self.c3,0,self.c2,0,self.c1,-ref])[-1])\n theta0 = np.interp(r0/2,rr,roots)\n \n phi0 = np.arctan2(x0,y0) - self.rot ####phi (i.e., azimuth) is reckoned with -pi corresponding to north, increasing clockwise, NOTE: pysolar use sub-standard definition\n phi0 = phi0%(2*np.pi)\n\n #####correction for the tilt of the camera\n k=np.array((np.sin(self.azm),np.cos(self.azm),0))\n a=np.array([np.sin(theta0)*np.cos(phi0),np.sin(theta0)*np.sin(phi0),np.cos(theta0)]); \n a = np.transpose(a,[1,2,0])\n b=np.cos(self.beta)*a + np.sin(self.beta)*np.cross(k,a,axisb=2) \\\n + np.reshape(np.outer(np.dot(a,k),k),(self.ndy0,self.ndx0,3))*(1-np.cos(self.beta))\n theta0=np.arctan(np.sqrt(b[:,:,0]**2+b[:,:,1]**2)/b[:,:,2])\n phi0=np.arctan2(b[:,:,1],b[:,:,0])%(2*np.pi)\n# max_theta *= deg2rad \n# valid0 = (theta0<max_theta) & (theta0>0); \n# theta0[valid0]=np.nan;\n self.theta0,self.phi0=theta0,phi0","def f_2(center):\r\n Ri = calc_R(center)\r\n return Ri - Ri.mean()","def polar_radius(self):\n return self.r * (1 - self.f)","def center_on_box(img, radius, min_ref, xmin, xmax, ymin, ymax, na_val=-9999):\n x, y = num.meshgrid(num.arange(-radius, radius), num.arange(-radius, radius))\n coords = [(i, j) for i, j in zip(x.flatten(), y.flatten()) if (i ** 2 + j ** 2) ** 0.5 <= radius]\n fit = [num.mean(img[(xmin + i):(xmax + i), (ymin + j):(ymax + j)]) for i, j in coords]\n if num.nanmin(fit) <= min_ref:\n return num.array(coords[num.nanargmin(fit)])\n else:\n return num.array([na_val, na_val])","def roiChanged(self):\n state = self.roi.getState()\n w, h = state['size']\n # Remember: ROI origin is in bottom-left because camera module has +y pointing upward.\n self.params.system.p0 = pg.Point(self.roi.mapToView(pg.Point(0,h))) # top-left\n self.params.system.p1 = pg.Point(self.roi.mapToView(pg.Point(w,h))) # rop-right\n self.params.system.p2 = pg.Point(self.roi.mapToView(pg.Point(0,0))) # bottom-left\n self.params.updateSystem()","def circle_area(self):\n return np.pi * self.ring_radius ** 2","def circleArea(radius):\n return math.pi * radius * radius","def get_center_radius(ra, dec):\n center_ra = (np.max(ra) + np.min(ra))/2\n center_dec = (np.max(dec) + np.min(dec))/2\n radius = np.max([np.max(ra) - np.min(ra),\n np.max(dec) - np.min(dec)])\n return center_ra, center_dec, radius","def add_circle(self, r_center, c_center, radius, color=BLUE, image=np.full((640, 480, 3), BLACK)):\n circle = np.fromfunction(lambda r, c, _: (r - r_center) ** 2 + (c - c_center) ** 2 <= radius ** 2, image.shape)\n return np.where(circle, color, image)","def Intarea( xc, yc, r, x0, x1, y0, y1):\n\n#\n# Shift the objects so that the circle is at the origin.\n#\n x0 = x0 - xc\n y0 = y0 - yc\n x1 = x1 - xc\n y1 = y1 - yc\n\n return Oneside( x1, y0, y1, r ) + Oneside( y1, -x1, -x0, r ) +\\\n Oneside( -x0, -y1, -y0, r ) + Oneside( -y0, x0, x1, r )","def rad_field_initial_condition(self):\n\n # revert in viewing direct\n angle, _ = f.convert_direction(self.receiver_elevation, self.receiver_azimuth)\n # Looking at the sky\n if angle < 90:\n I_init = (\n self.sun_intensity\n * f.delta_func(self.sun_elevation - self.receiver_elevation)\n * f.delta_func(self.sun_azimuth - self.receiver_azimuth)\n )\n\n # Looking at the ground\n elif angle > 90:\n I_ground = RT_model_1D.calc_direct_beam_intensity(self, 0)\n\n I_lambert = (\n I_ground\n * self.ground_albedo\n * np.cos(np.deg2rad((self.sun_elevation + 180) % 360))\n )\n\n I_specular = (\n I_ground\n * self.ground_albedo\n * f.delta_func(self.sun_elevation + self.receiver_elevation - 180)\n * f.delta_func(self.sun_azimuth - self.receiver_azimuth)\n )\n\n I_init = (\n 1 - self.reflection_type\n ) * I_lambert + self.reflection_type * I_specular\n\n else:\n I_init = np.empty(self.stokes_dim)\n I_init.fill(np.nan)\n\n return I_init","def visibility_angle(minviselmap, azmap, rmap,\n rpol, azpol, elpol, DEM_res, DEM_xmin, DEM_ymin,\n rad_x, rad_y, beamwidth, pulsewidth, az_conv=0,\n raster_oversampling=1, verbose=True):\n ncols, nrows = minviselmap.shape\n\n pulselength = pulsewidth * 3.e8 / 2. # [m]\n az_conv_offset = az_conv / 2.\n\n if raster_oversampling == 0:\n N = 1\n elif raster_oversampling == 1:\n N = int(np.ceil(2 * DEM_res / pulselength))\n else:\n N = raster_oversampling\n\n if N != 1:\n # New dimensions\n nc = N * ncols\n nr = N * nrows\n\n # repeat the values NxN, equivalent of rebin in IDL\n minvisvals = np.repeat(np.repeat(minviselmap, N, axis=0), N, axis=1)\n\n # New x- and y-vectors\n xvec = np.arange(nr) * DEM_res / N + DEM_xmin\n yvec = np.arange(nc) * DEM_res / N + DEM_ymin\n\n xdiff = (xvec - rad_x)\n ydiff = (yvec - rad_y)\n\n # New distance from radar map\n X, Y = np.meshgrid(xdiff, ydiff)\n rvals = np.sqrt(X ** 2 + Y ** 2)\n\n # New azimuth map\n azmap_rad = (np.arctan2(X, Y) + 2 * np.pi) % (2 * np.pi)\n azvals = azmap_rad * 180. / np.pi\n else:\n rvals = rmap\n azvals = azmap\n minvisvals = minviselmap\n\n \"\"\"\n Define the area around a point P(range, azimuth) where the cells\n have a contribution to the visibility. This area is defined with the\n range limits from range-dr/2 to range+dr/2 and the\n azimuth limits from azimuth-daz_offset to azimuth+daz_offset.\n\n For a Gaussian antenna, azimuth offset more than 2*HPBW does not a\n have remarkable contribution.\n With a rectangular pulse and a matched filter cells farer away\n than pulse length does not a have remarkable contribution.\n \"\"\"\n\n daz_offset = (2. * beamwidth) + az_conv_offset # [deg]\n del_offset = (2. * beamwidth) # [deg]\n\n delta_deg = 0.1 # [deg]\n ndaz = int(2 * daz_offset / delta_deg)\n ndel = int(2 * del_offset / delta_deg)\n\n daz_vec = np.arange(ndaz + 1) * delta_deg - daz_offset # [deg]\n del_vec = np.arange(ndel + 1) * delta_deg - del_offset # [deg]\n\n daz_area_antenna, del_area_antenna = np.meshgrid(daz_vec, del_vec)\n\n # Get the two-way weighting factor due to the azimuth and elevation offsets\n # to the main antenna direction (assuming a Gaussian antenna pattern).\n ant_weight = antenna_pattern_gauss(daz_area_antenna, del_area_antenna,\n beamwidth, az_conv=az_conv,\n units='deg')\n\n ant_weight_total = np.nansum(ant_weight)\n\n nazim = len(azpol)\n nrange = len(rpol)\n range_resolution = rpol[1] - rpol[0]\n\n # pyart storage format: 2D arrays (naz * nel, nranges)\n vispol = np.zeros((nazim * len(elpol), nrange))\n\n # Inside the loops over range (rr) and azimuth (az), the\n # coordinates (rr, az) describe the point P(rr, az) for which\n # the visibility is calculated. If more than one DEM cell is within\n # the area from az-daz/2 to az+daz/2 and from rr-dr/2 to\n # rr+dr/2, the calculated visibility value is set to all of these\n # cells (next neighbor).\n\n for iaz in range(nazim):\n logging.info(f'Computing azimuth {azpol[iaz]:2.1f}')\n # Get azimuth values to explore\n azmin = azpol[iaz] - daz_offset\n azmax = azpol[iaz] + daz_offset\n if azmin < 0:\n azmin = 360. + azmin\n indaz = np.logical_or(\n np.logical_and(\n azvals >= 0, azvals < azmax), np.logical_and(\n azvals >= azmin, azvals <= 360.))\n\n elif azmax > 360:\n azmax = azmax - 360.\n indaz = np.logical_or(np.logical_and(azvals >= azmin,\n azvals <= 360),\n np.logical_and(azvals >= 0,\n azvals < azmax))\n else:\n indaz = np.logical_and(azvals >= azmin, azvals < azmax)\n\n if not np.any(indaz):\n logging.warning(\n f'Visibility for azim {azpol[iaz]:f} not known')\n continue\n\n indaz = np.where(indaz)\n\n # These declaration avoids reindexing at every loop turn\n # and save a lot of time\n rvals_indaz = rvals[indaz]\n\n for iel, el in enumerate(elpol):\n # range bins (center pulse) [m]rvec[ir]-dr/2\n rmin_ground = rpol * np.cos(el * np.pi / 180.)\n # range bins (center pulse) [m]rvec[ir]+dr/2\n rmax_ground = (rpol + range_resolution) * np.cos(el * np.pi / 180.)\n\n for ir in range(0, nrange):\n # Get range values to explore\n indr = np.logical_and(rvals_indaz >= rmin_ground[ir],\n rvals_indaz < rmax_ground[ir])\n\n if not np.any(indr):\n logging.warning(\n 'Visibility for az {:f} deg and range {:f} not known' .format(\n azpol[iaz], rpol[ir]))\n vispol[iaz + iel * nazim, ir] = 100.\n continue\n\n # DEM cells that contribute to the cells to set indset\n indcells = tuple([indaz[0][indr], indaz[1][indr]])\n\n ind = minvisvals[indcells] < el - del_offset\n if np.all(ind): # radar beam completely above min vis ang\n if ir > 0:\n if vispol[iaz + iel * nazim, ir - 1] == 100:\n vispol[iaz + iel * nazim, ir] = 100\n else:\n vispol[iaz + iel * nazim,\n ir] = vispol[iaz + iel * nazim, ir - 1]\n else:\n vispol[iaz + iel * nazim, ir] = 100.\n continue\n\n ind = minvisvals[indcells] > el + del_offset\n if np.all(ind): # radar beam completely below min vis ang\n continue\n\n # Calculate offsets in azimuth to the point P(rr,az)\n daz_area = azvals[indcells] - azpol[iaz]\n\n ind = daz_area > 180.\n daz_area[ind] = daz_area[ind] - 360.\n ind = daz_area < -180.\n daz_area[ind] = daz_area[ind] + 360.\n\n vis = 0.\n ind_rzero = rvals[indcells] <= 0.0\n\n if not np.any(ind_rzero):\n vis = vis_weighting(daz_vec, del_vec, daz_area, ant_weight,\n minvisvals[indcells], el)\n\n vis = (vis / ant_weight_total) * 100.\n\n # Set vis to all values inside the set area.\n if ir > 0:\n if vispol[iaz + iel * nazim, ir - 1] < vis:\n vispol[iaz + iel * nazim,\n ir] = vispol.data[iaz + iel * nazim,\n ir - 1]\n else:\n vispol[iaz + iel * nazim, ir] = vis\n else:\n vispol[iaz + iel * nazim, ir] = vis\n\n return vispol"],"string":"[\n \"def objects_radius(self, centre, radius):\",\n \"def extract_roi_from_volume(volume, in_center, output_shape, fill = 'random'):\\n input_shape = volume.shape \\n if(fill == 'random'):\\n output = np.random.normal(0, 1, size = output_shape)\\n else:\\n output = np.zeros(output_shape)\\n r0max = [int(x/2) for x in output_shape]\\n r1max = [output_shape[i] - r0max[i] for i in range(len(r0max))]\\n r0 = [min(r0max[i], in_center[i]) for i in range(len(r0max))]\\n r1 = [min(r1max[i], input_shape[i] - in_center[i]) for i in range(len(r0max))]\\n out_center = r0max\\n\\n output[np.ix_(range(out_center[0] - r0[0], out_center[0] + r1[0]),\\n range(out_center[1] - r0[1], out_center[1] + r1[1]),\\n range(out_center[2] - r0[2], out_center[2] + r1[2]))] = \\\\\\n volume[np.ix_(range(in_center[0] - r0[0], in_center[0] + r1[0]),\\n range(in_center[1] - r0[1], in_center[1] + r1[1]),\\n range(in_center[2] - r0[2], in_center[2] + r1[2]))]\\n return output\",\n \"def extract_roi_from_volume(volume, in_center, output_shape, fill = 'random'):\\n input_shape = volume.shape \\n if(fill == 'random'):\\n output = np.random.normal(0, 1, size = output_shape)\\n else:\\n output = np.zeros(output_shape)\\n r0max = [int(x/2) for x in output_shape]\\n r1max = [output_shape[i] - r0max[i] for i in range(len(r0max))]\\n r0 = [min(r0max[i], in_center[i]) for i in range(len(r0max))]\\n r1 = [min(r1max[i], input_shape[i] - in_center[i]) for i in range(len(r0max))]\\n out_center = r0max\\n\\n output[np.ix_(range(out_center[0] - r0[0], out_center[0] + r1[0]),\\n range(out_center[1] - r0[1], out_center[1] + r1[1]),\\n range(out_center[2] - r0[2], out_center[2] + r1[2]))] = \\\\\\n volume[np.ix_(range(in_center[0] - r0[0], in_center[0] + r1[0]),\\n range(in_center[1] - r0[1], in_center[1] + r1[1]),\\n range(in_center[2] - r0[2], in_center[2] + r1[2]))]\\n return output\",\n \"def get_roi(self):\\n roi_opts = opts.Curve(frame_width=275, frame_height=125, framewise=True,\\n xlabel='Wavelength (nm)', ylabel='Intensity',\\n show_grid=True, gridstyle = {'minor_xgrid_line_color': 'lightgray'}, \\n toolbar='above')\\n roi = hv.DynamicMap(self.roi_curves, streams=[self.box_stream]).opts(roi_opts)\\n roi.opts(xlim=(self.param.band.bounds)) \\n\\n # TO-DO: make line dissapear when no box selected\\n line_opts = opts.VLine(color='black', line_dash='dashed', line_width=1)\\n center = np.mean(self.band)\\n line = hv.VLine(center).opts(line_opts)\\n \\n return roi*line\",\n \"def set_roi_to_volume(volume, center, sub_volume):\\n volume_shape = volume.shape \\n patch_shape = sub_volume.shape\\n output_volume = volume\\n for i in range(len(center)):\\n if(center[i] >= volume_shape[i]):\\n return output_volume\\n r0max = [int(x/2) for x in patch_shape]\\n r1max = [patch_shape[i] - r0max[i] for i in range(len(r0max))]\\n r0 = [min(r0max[i], center[i]) for i in range(len(r0max))]\\n r1 = [min(r1max[i], volume_shape[i] - center[i]) for i in range(len(r0max))]\\n patch_center = r0max\\n\\n if(len(center) == 3):\\n output_volume[np.ix_(range(center[0] - r0[0], center[0] + r1[0]),\\n range(center[1] - r0[1], center[1] + r1[1]),\\n range(center[2] - r0[2], center[2] + r1[2]))] = \\\\\\n sub_volume[np.ix_(range(patch_center[0] - r0[0], patch_center[0] + r1[0]),\\n range(patch_center[1] - r0[1], patch_center[1] + r1[1]),\\n range(patch_center[2] - r0[2], patch_center[2] + r1[2]))]\\n elif(len(center) == 4):\\n output_volume[np.ix_(range(center[0] - r0[0], center[0] + r1[0]),\\n range(center[1] - r0[1], center[1] + r1[1]),\\n range(center[2] - r0[2], center[2] + r1[2]),\\n range(center[3] - r0[3], center[3] + r1[3]))] = \\\\\\n sub_volume[np.ix_(range(patch_center[0] - r0[0], patch_center[0] + r1[0]),\\n range(patch_center[1] - r0[1], patch_center[1] + r1[1]),\\n range(patch_center[2] - r0[2], patch_center[2] + r1[2]),\\n range(patch_center[3] - r0[3], patch_center[3] + r1[3]))]\\n else:\\n raise ValueError(\\\"array dimension should be 3 or 4\\\") \\n return output_volume\",\n \"def set_roi_to_volume(volume, center, sub_volume):\\n volume_shape = volume.shape \\n patch_shape = sub_volume.shape\\n output_volume = volume\\n for i in range(len(center)):\\n if(center[i] >= volume_shape[i]):\\n return output_volume\\n r0max = [int(x/2) for x in patch_shape]\\n r1max = [patch_shape[i] - r0max[i] for i in range(len(r0max))]\\n r0 = [min(r0max[i], center[i]) for i in range(len(r0max))]\\n r1 = [min(r1max[i], volume_shape[i] - center[i]) for i in range(len(r0max))]\\n patch_center = r0max\\n\\n if(len(center) == 3):\\n output_volume[np.ix_(range(center[0] - r0[0], center[0] + r1[0]),\\n range(center[1] - r0[1], center[1] + r1[1]),\\n range(center[2] - r0[2], center[2] + r1[2]))] = \\\\\\n sub_volume[np.ix_(range(patch_center[0] - r0[0], patch_center[0] + r1[0]),\\n range(patch_center[1] - r0[1], patch_center[1] + r1[1]),\\n range(patch_center[2] - r0[2], patch_center[2] + r1[2]))]\\n elif(len(center) == 4):\\n output_volume[np.ix_(range(center[0] - r0[0], center[0] + r1[0]),\\n range(center[1] - r0[1], center[1] + r1[1]),\\n range(center[2] - r0[2], center[2] + r1[2]),\\n range(center[3] - r0[3], center[3] + r1[3]))] = \\\\\\n sub_volume[np.ix_(range(patch_center[0] - r0[0], patch_center[0] + r1[0]),\\n range(patch_center[1] - r0[1], patch_center[1] + r1[1]),\\n range(patch_center[2] - r0[2], patch_center[2] + r1[2]),\\n range(patch_center[3] - r0[3], patch_center[3] + r1[3]))]\\n else:\\n raise ValueError(\\\"array dimension should be 3 or 4\\\") \\n return output_volume\",\n \"def get_radius(center, rad, speed_limit):\\n i = Intersection(center, rad, speed_limit)\\n return i.get_radius()\",\n \"def getROI(image_np, boxes_roi, margin_ratio):\\n (left, right, top, bottom) = boxes_roi[0]\\n width = right - left\\n height = bottom - top\\n margin_x = margin_ratio * width\\n margin_y = margin_ratio * height\\n img_roi = image_np[\\n int(top - margin_y): int(bottom + margin_y),\\n int(left - margin_x): int(right + margin_x)]\\n return img_roi\",\n \"def get_roi(self):\\n rect=lib.is_GetAOI(self.hcam)\\n hbin,vbin=self._get_roi_binning()\\n return (rect.s32X*hbin,(rect.s32X+rect.s32Width)*hbin,rect.s32Y*vbin,(rect.s32Y+rect.s32Height)*vbin,hbin,vbin)\",\n \"def __init__(self,r):\\n self.radius = r\\n self.uc_centered_a = r\\n self.uc_centered_b = r*np.sqrt(3.0)\",\n \"def get_roi_circle(self):\\n return self.circle_list\",\n \"async def get_roi(self):\",\n \"def calc_R(center):\\r\\n xc = center[0]\\r\\n yc = center[1]\\r\\n return np.sqrt((x-xc)**2 + (y-yc)**2)\",\n \"def test_update_radius():\\n center = Coordinates(1, 1)\\n rad1 = 20.3\\n speed = 30\\n\\n i = Intersection(center, rad1, speed)\\n\\n assert i.get_radius() == 20.3\\n\\n i.update_radius(56.5)\\n\\n assert i.get_radius() == 56.5\",\n \"def __init__(self, center, radius, rotation=0.0, feature_center=None, feature_boarder=None):\\n # rotation=math.pi/2 - align the read dot of the camera with the screw\\n self._center = center\\n self._radius = radius\\n self._rotation = rotation\\n self._feature_center = feature_center\\n self._feature_boarder = feature_boarder\\n\\n self._slot_bounds = []\\n self.set_rotation(rotation)\",\n \"def extractROI(base_obj, roc, cen, roi_sz,\\n filter_sobel=False, filter_smooth=True, filter_window=0): #{\\n vrbMsg(1, 'extractROI() base_obj = ' + str(base_obj) +\\n ', roc = ' + str(roc) +\\n ', cen = ' + str(cen) +\\n ', roi_sz = ' + str(roi_sz) +\\n ', filter_sobel = ' + str(filter_sobel) +\\n ', filter_smooth = ' + str(filter_smooth) +\\n ', filter_window = ' + str(filter_window))\\n status = 0\\n regions_to_sep = ['ankle_l', 'ankle_r', 'elbow_l', 'elbow_r',\\n 'hip_l', 'hip_r', 'knee_l', 'knee_r',\\n 'wrist_l', 'wrist_r']\\n cutBox = w.WlzIBox2()\\n cutBox.xMin = int(cen[0] - (roi_sz / 2))\\n cutBox.yMin = int(cen[1] - (roi_sz / 2))\\n cutBox.xMax = roi_sz + cutBox.xMin - 1\\n cutBox.yMax = roi_sz + cutBox.yMin - 1\\n err_num = c.c_int(w.WLZ_ERR_NONE)\\n roi_obj = w.WlzAssignObject(\\n w.WlzCutObjToBox2D(base_obj, cutBox,\\n c.c_int(w.WLZ_GREY_UBYTE), c.c_int(0),\\n c.c_double(0.0), c.c_double(0.0), c.byref(err_num)), None)\\n vrbMsg(5, 'extractROI() roi_obj = ' + str(roi_obj) +\\n ', err_num = ' + str(err_num))\\n # Window the region to reduce edge effects\\n if(not bool(err_num)): #{\\n if(roc in regions_to_sep): #{\\n # If this ROI is appropriate select just that part of the object\\n # which contains the ROI centre\\n # Smooth ROI object and normalise it\\n vrbMsg(5, 'extractROI() roc = ' + roc + ' - separating regions')\\n obj0 = None\\n obj1 = None\\n obj2 = None\\n lbl_ary = None\\n max_rgn = 1000\\n n_rgn = c.c_int(0)\\n sigma = w.WlzDVertex3()\\n sigma.vtX = c.c_double(prm_peak_sigma / 2)\\n sigma.vtY = c.c_double(prm_peak_sigma / 2)\\n sigma.vtZ = c.c_double(1.0)\\n order = w.WlzIVertex3()\\n order.vtX = c.c_int(0)\\n order.vtY = c.c_int(0)\\n order.vtZ = c.c_int(0)\\n direc = w.WlzIVertex3()\\n direc.vtX = c.c_int(1)\\n direc.vtY = c.c_int(1)\\n direc.vtX = c.c_int(0)\\n obj0 = w.WlzAssignObject(\\n w.WlzGaussFilter(roi_obj, sigma, order, direc,\\n c.c_int(w.WLZ_GREY_INT),\\n c.c_int(w.ALG_PAD_ZERO), c.c_double(0.0), c.c_int(0),\\n c.byref(err_num)), None)\\n vrbMsg(5, 'extractROI() obj0 = ' + str(obj1) +\\n ', err_num = ' + str(err_num))\\n if(not bool(err_num)): #{\\n err_num = w.WlzGreyNormalise(obj0, 0)\\n vrbMsg(5, 'extractROI() err_num = ' + str(err_num))\\n #}\\n if((not bool(err_num)) and bool(filter_window & 1)): #{\\n org = w.WlzIVertex2()\\n org.vtX = int(cen[0])\\n org.vtY = int(cen[1])\\n rad = w.WlzIVertex2()\\n rad.vtX = int((roi_sz / 2))\\n rad.vtY = int((roi_sz / 2))\\n err_num = c.c_int(w.WLZ_ERR_NONE)\\n obj1 = w.WlzAssignObject(\\n w.WlzWindow(obj0, c.c_int(w.WLZ_WINDOWFN_PARZEN),\\n org, rad, c.byref(err_num)), None)\\n vrbMsg(5, 'extractROI() WlzWindow() err_num = ' + str(err_num))\\n if(not bool(err_num)): #{\\n dummy = w.WlzFreeObj(obj0)\\n obj0 = obj1\\n #}\\n #}\\n # Threshold the object to get foreground\\n if(not bool(err_num)): #{\\n hilo = w.WLZ_THRESH_HIGH\\n tv = w.WlzPixelV()\\n tv = c.c_int(prm_peak_minval * 2)\\n err_num = c.c_int(w.WLZ_ERR_NONE)\\n obj1 = w.WlzAssignObject(\\n w.WlzThresholdI(obj0, tv, hilo, c.byref(err_num)), None)\\n vrbMsg(5, 'extractROI() obj2 = ' + str(obj1) +\\n ', err_num = ' + str(err_num))\\n #}\\n dummy = w.WlzFreeObj(obj0)\\n obj0 = None\\n # Perform connected component labeling\\n if(not bool(err_num)): #{\\n lbl_ary = c.cast(w.AlcCalloc(max_rgn, c.sizeof(w.WlzObject)),\\n c.POINTER(c.POINTER(w.WlzObject)))\\n err_num = w.WlzLabel(obj1, c.byref(n_rgn), c.byref(lbl_ary),\\n max_rgn, 1, c.c_int(w.WLZ_8_CONNECTED))\\n #}\\n dummy = w.WlzFreeObj(obj1)\\n obj1 = None\\n # Find which component contains the ROI centre\\n n_rgn = n_rgn.value\\n for i in range(0, n_rgn): #{\\n vrbMsg(5, 'extractROI() lbl_ary[' + str(i) + '] = ' +\\n str(lbl_ary[i]))\\n err_num = c.c_int(w.WLZ_ERR_NONE)\\n hit = w.WlzInsideDomain(lbl_ary[i], 0, cen[1], cen[0],\\n c.byref(err_num))\\n if(bool(hit)): #{\\n vrbMsg(5, 'extractROI() hit')\\n obj2 = w.WlzAssignObject(\\n w.WlzGreyTransfer(lbl_ary[i], roi_obj, 0,\\n c.byref(err_num)), None)\\n dummy = w.WlzFreeObj(roi_obj)\\n roi_obj = w.WlzAssignObject(\\n w.WlzCutObjToBox2D(obj2, cutBox,\\n c.c_int(w.WLZ_GREY_UBYTE), c.c_int(0),\\n c.c_double(0.0), c.c_double(0.0),\\n c.byref(err_num)), None)\\n dummy = w.WlzFreeObj(obj2)\\n obj2 = None\\n break\\n #}\\n #}\\n if(not(lbl_ary is None)): #{\\n for i in range(0, n_rgn): #{\\n if(bool(lbl_ary[i])): #{\\n dummy = w.WlzFreeObj(lbl_ary[i])\\n #}\\n #}\\n w.AlcFree(lbl_ary)\\n #}\\n #}\\n #}\\n # Apply Sobel filter so that we match to edges\\n if((not bool(err_num)) and filter_sobel): #{\\n err_num = c.c_int(w.WLZ_ERR_NONE)\\n obj1 = w.WlzAssignObject(\\n w.WlzSobel(roi_obj, 1, 1, c.byref(err_num)), None)\\n vrbMsg(5, 'extractROI() WlzSobel() err_num = ' + str(err_num))\\n if(not bool(err_num)): #{\\n dummy = w.WlzFreeObj(roi_obj)\\n roi_obj = obj1\\n #}\\n #}\\n if((not bool(err_num)) and filter_smooth): #{\\n sigma = w.WlzDVertex3()\\n sigma.vtX = 1.0\\n sigma.vtY = 1.0\\n sigma.vtZ = 0.0\\n order = w.WlzIVertex3()\\n order.vtX = 0\\n order.vtY = 0\\n order.vtZ = 0\\n direc = w.WlzIVertex3()\\n direc.vtX = 1\\n direc.vtY = 1\\n direc.vtZ = 0\\n err_num = c.c_int(w.WLZ_ERR_NONE)\\n obj1 = w.WlzAssignObject(\\n w.WlzGaussFilter(roi_obj, sigma, order, direc,\\n c.c_int(w.WLZ_GREY_ERROR), c.c_int(w.ALG_PAD_NONE), 0.0, 0,\\n c.byref(err_num)), None)\\n vrbMsg(5, 'extractROI() WlzGaussFilter() err_num = ' + str(err_num))\\n if(not bool(err_num)): #{\\n dummy = w.WlzFreeObj(roi_obj)\\n roi_obj = obj1\\n #}\\n #}\\n # Apply window function again if required\\n if((not bool(err_num)) and bool(filter_window & 2)): #{\\n org = w.WlzIVertex2()\\n org.vtX = int(cen[0])\\n org.vtY = int(cen[1])\\n rad = w.WlzIVertex2()\\n rad.vtX = int((roi_sz / 2))\\n rad.vtY = int((roi_sz / 2))\\n err_num = c.c_int(w.WLZ_ERR_NONE)\\n obj0 = w.WlzAssignObject(\\n w.WlzWindow(roi_obj, c.c_int(w.WLZ_WINDOWFN_PARZEN),\\n org, rad, c.byref(err_num)), None)\\n vrbMsg(5, 'extractROI() WlzWindow() err_num = ' + str(err_num))\\n if(not bool(err_num)): #{\\n dummy = w.WlzFreeObj(roi_obj)\\n roi_obj = obj0\\n #}\\n #}\\n # Shift the region to the origin\\n if(not bool(err_num)): #{\\n x_shift = -(roi_obj.contents.domain.i.contents.kol1)\\n y_shift = -(roi_obj.contents.domain.i.contents.line1)\\n err_num = c.c_int(w.WLZ_ERR_NONE)\\n obj1 = w.WlzAssignObject(\\n w.WlzShiftObject(roi_obj,\\n x_shift, y_shift, 0, c.byref(err_num)), None)\\n vrbMsg(5, 'extractROI() WlzShiftObject() err_num = ' + str(err_num))\\n if(not bool(err_num)): #{\\n dummy = w.WlzFreeObj(roi_obj)\\n roi_obj = obj1\\n #}\\n #}\\n vrbMsg(1, 'extractROI() status = ' + str(status) +\\n ', roi_obj = ' + str(roi_obj))\\n return status, roi_obj\",\n \"def get_radius(self):\",\n \"def calc_rois(study, slice_type, desired_radius=HEART_RADIUS):\\n slice_numbers, slices = common.study_to_numpy(study, slice_type=slice_type, with_slice_number=True)\\n slice_locations = common.slice_locations(study, slice_type) # will be used as z-values\\n z_values = [slice_locations[0]]\\n for i in xrange(len(slice_locations) - 1):\\n z_values.append(z_values[-1] + slice_locations[i + 1] - slice_locations[i])\\n if z_values[-1] - z_values[-2] < 1:\\n z_values[-1] = z_values[-2] + 1 # don't want a floats to blow up\\n\\n metadata = common.metadata(study, slice_type)\\n scale_x, scale_y = metadata['scale_x'], metadata['scale_y']\\n assert scale_x == scale_y, 'only support equal scaling currently'\\n roi_radius = int(desired_radius / scale_x + 1) # radius in pixels\\n num_slices, _, shape_x, shape_y = slices.shape\\n\\n # original mean over time of slices\\n dc = np.mean(slices, 1)\\n\\n # detect movements\\n h1s = np.array([get_harmonic(slices[i]) for i in xrange(num_slices)])\\n # remove noise\\n m = np.max(h1s) * 0.05\\n h1s[h1s < m] = 0\\n\\n # perform ROI-detection on all slices together\\n centroid, regressed_h1s = regression_filter(h1s)\\n\\n # regress 3d line through centroid of each slice\\n centroids = np.array([get_centroid(img) for img in regressed_h1s])\\n coords = regress_centroids(centroids, z_values=z_values)\\n\\n # yield ROIs\\n rois, circles = get_ROIs(dc, regressed_h1s, coords, z_values,\\n radius=roi_radius)\\n\\n # code uses x, y as in graphics, so put it back to row/col\\n circles = dict((slice_number, (circles[i][0][1], circles[i][0][0]))\\n for i, slice_number in enumerate(slice_numbers))\\n\\n return rois, circles\",\n \"def __init__( self , center , radius ):\\r\\n self.center = center\\r\\n self.radius = radius\",\n \"async def set_roi(self, pos0=None, shape=None):\",\n \"def _get_output_volume_roi(self):\\n\\n # Define output ROI based on provided command line arguments, if such\\n # arguments are provided.\\n if self.options.outputVolumeROI:\\n region_origin_roi = self.options.outputVolumeROI[0:2]\\n region_size_roi = self.options.outputVolumeROI[2:4]\\n else:\\n region_origin_roi = None\\n region_size_roi = None\\n\\n return region_origin_roi, region_size_roi\",\n \"def cutout(self, centre, radius):\",\n \"def roi(data):\\n return data[data.shape[0] // 2 - args.roi : data.shape[0] // 2 + args.roi, data.shape[1] // 2 - args.roi : data.shape[1] // 2 + args.roi]\",\n \"def get_center(center, rad, speed_limit):\\n i = Intersection(center, rad, speed_limit)\\n return i.get_center()\",\n \"def test_get_radius():\\n center = Coordinates(7, 3)\\n radius = 12\\n\\n returned_rad = get_radius(center, radius, 30)\\n\\n assert returned_rad == radius\\n assert returned_rad != center.get_x()\\n assert returned_rad != center.get_y()\",\n \"def sub_image(self, center, radius):\\n width = self.img.shape[1]\\n height = self.img.shape[0]\\n xstart = int(max(center.x - radius, 0))\\n xend = int(min(center.x + radius, width))\\n ystart = int(max(center.y - radius, 0))\\n yend = int(min(center.y + radius, height))\\n roi_rect = [xstart, ystart, xend, yend]\\n\\n sub = self.img[ystart:yend, xstart:xend]\\n return Image(sub), roi_rect\",\n \"def __init__(self, minRA, maxRA, minDec, maxDec, radius_RA, radius_Dec):\\n\\n self.RA = np.mean([minRA, maxRA])\\n self.Dec = np.mean([minDec, maxDec])\\n self.radius_RA = radius_RA\\n self.radius_Dec = radius_Dec\\n\\n # define the polygon attached to this area\\n \\\"\\\"\\\"\\n self.area_poly = areap(self.RA-radius_RA/2.,\\n self.RA+radius_RA/2.,\\n self.Dec-radius_Dec/2.,\\n self.Dec+radius_Dec/2.)\\n \\\"\\\"\\\"\\n self.area_poly = areap(minRA, maxRA, minDec, maxDec)\\n all_patches = self.getpatches(minRA, maxRA, minDec, maxDec)\\n\\n self.patches = self.inside(all_patches)\",\n \"def _getRoi(\\n self,\\n obj: str,\\n params: dict,\\n media_width: int,\\n media_height: int) -> Tuple[float, float, float, float]:\\n\\n # Get the initial image based on the localization type and requested margins\\n localization_type = obj.meta.dtype\\n margins_pixels = self._getMargins(localization_type=localization_type, params=params)\\n\\n # The roi input is done with normalized arguments. But the margins provided\\n # are in pixels. So we've got to convert.\\n margins_rel = SimpleNamespace(x=margins_pixels.x / media_width, y=margins_pixels.y / media_height)\\n\\n # Take the position information available and apply the margin.\\n # The stored position information is normalized, so we will set it to the\\n # provided media pixel width/height, apply the appropriate region of interest (ROI)\\n # information, then normalize back since the media_utils requires the ROI data\\n # in that format.\\n #\\n # Position information available per localization type:\\n # Point/dot: x, y\\n # Line: x, y, u, v\\n # Box: x, y, width, height\\n #\\n # Region of interest format: width, height, x, y\\n if localization_type == 'dot':\\n\\n roi_x = obj.x * media_width\\n roi_y = obj.y * media_height\\n\\n roi = [2*margins_pixels.x + 1,\\n 2*margins_pixels.y + 1,\\n roi_x - margins_pixels.x,\\n roi_y - margins_pixels.y]\\n\\n elif localization_type == 'line':\\n\\n x = obj.x * media_width\\n y = obj.y * media_height\\n u = obj.u * media_width\\n v = obj.v * media_height\\n\\n point_a = SimpleNamespace(x=x, y=y)\\n point_b = SimpleNamespace(x=x+u, y=y+v)\\n\\n width = abs(point_b.x - point_a.x)\\n height = abs(point_b.y - point_a.y)\\n\\n roi_x = min(point_a.x, point_b.x)\\n roi_y = min(point_a.y, point_b.y)\\n\\n roi = [width + 2*margins_pixels.x,\\n height + 2*margins_pixels.y,\\n roi_x - margins_pixels.x,\\n roi_y - margins_pixels.y]\\n\\n elif localization_type == 'box':\\n\\n roi_x = obj.x * media_width\\n roi_y = obj.y * media_height\\n roi_width = obj.width * media_width\\n roi_height = obj.height * media_height\\n\\n roi = [roi_width + 2*margins_pixels.x,\\n roi_height + 2*margins_pixels.y,\\n roi_x - margins_pixels.x,\\n roi_y - margins_pixels.y]\\n elif localization_type == 'poly':\\n minX = 1.0\\n minY = 1.0\\n maxX = 0.0\\n maxY = 0.0\\n for point in obj.points:\\n if point[0] > maxX:\\n maxX = point[0]\\n if point[0] < minX:\\n minX = point[0]\\n if point[1] > maxY:\\n maxY = point[1]\\n if point[1] < minY:\\n minY = point[1]\\n roi_x = minX * media_width\\n roi_y = minY * media_height\\n roi_width = (maxX-minX) * media_width\\n roi_height = (maxY-minY) * media_height\\n roi = [roi_width + 2*margins_pixels.x,\\n roi_height + 2*margins_pixels.y,\\n roi_x - margins_pixels.x,\\n roi_y - margins_pixels.y]\\n\\n else:\\n raise Exception(f\\\"Invalid meta.dtype detected {localization_type}\\\")\\n\\n # Don't allow any single pixel width/heights\\n # Adding the 0.1 to deal with floating point precision\\n roi[0] = max(roi[0], 2.1)\\n roi[1] = max(roi[1], 2.1)\\n\\n # Now, normalize the ROI\\n roi[0] = roi[0] / media_width\\n roi[2] = roi[2] / media_width\\n\\n roi[1] = roi[1] / media_height\\n roi[3] = roi[3] / media_height\\n\\n # Force the ROI to be within the image\\n for idx, roi_entry in enumerate(roi):\\n if roi_entry > 1.0:\\n roi[idx] = 1.0\\n elif roi_entry < 0.0:\\n roi[idx] = 0.0\\n\\n return tuple(roi)\",\n \"def circleMask(img, cir_x, cir_y, r, mode, filter=0):\\n\\n if not mode == 'interior' and not mode == 'exterior':\\n print(mode, \\\"is not a supported mode. Please enter interior or exterior\\\")\\n return 1\\n\\n #get the dimensions of the image\\n n,m = img.shape\\n\\n #create an open grid for our image\\n y,x = np.ogrid[0:n, 0:m]\\n #operate on a copy of the image\\n copyImg = img.copy()\\n\\n #get the x and y center points of our image\\n center_x = cir_x\\n center_y = cir_y\\n\\n #create a circle mask\\n if mode == 'interior':\\n circle_mask = (x-center_x)**2 + (y-center_y)**2 <= r**2\\n elif mode == 'exterior':\\n circle_mask = (x-center_x)**2 + (y-center_y)**2 >= r**2\\n\\n #black out anywhere within the circle mask\\n copyImg[circle_mask] = [filter]\\n copyImg[copyImg != filter] = [255-filter]\\n\\n return copyImg\",\n \"def get_roi(landmarks):\\n centers = compute_centers(landmarks)\\n corners = compute_corners(centers, 'face')\\n return [corners[i][0] for i in range(len(corners))]\",\n \"def sector_mask(shape,centre,radius1, radius2 = 0, angle_range = (0, np.pi)):\\n\\n x,y = np.ogrid[:shape[0],:shape[1]]\\n cx,cy = centre\\n tmin,tmax = angle_range\\n\\n # ensure stop angle > start angle\\n if tmax < tmin:\\n tmax += 2*np.pi\\n\\n # convert cartesian --> polar coordinates\\n r2 = (x-cx)*(x-cx) + (y-cy)*(y-cy)\\n theta = np.arctan2(x-cx,y-cy) - tmin\\n\\n # wrap angles between 0 and 2*pi\\n theta %= (2*np.pi)\\n\\n # circular mask\\n circmask = (r2 <= radius1*radius1) & (r2 >= radius2*radius2)\\n\\n # angular mask\\n anglemask = theta <= (tmax-tmin)\\n\\n return circmask*anglemask\",\n \"def apply_roi(args, what_roi, img):\\n if what_roi == \\\"Fractions\\\":\\n roi = split_roi(args.roi_frac, True)\\n img = img[int(img.shape[0] * roi[2]):\\n int(img.shape[0] * roi[3]),\\n int(img.shape[1] * roi[0]):\\n int(img.shape[1] * roi[1])]\\n elif what_roi == \\\"Pixels\\\":\\n roi = split_roi(args.roi_pix, False)\\n img = img[roi[1]: roi[1] + roi[3], roi[0]: roi[0] + roi[2]]\\n return img\",\n \"def region_of_interest(self, shape=None, geometry=None, grid=None,\\n corners=None, crs=wgs84, roi=None,\\n all_touched=False):\\n\\n # Initial mask\\n if roi is not None:\\n mask = np.array(roi, dtype=np.int16)\\n else:\\n mask = np.zeros((self.ny, self.nx), dtype=np.int16)\\n\\n # Collect keyword arguments, overriding anything the user\\n # inadvertently added\\n rasterize_kws = dict(out=mask, all_touched=all_touched)\\n\\n # Several cases\\n if shape is not None:\\n import pandas as pd\\n inplace = False\\n if not isinstance(shape, pd.DataFrame):\\n from salem.sio import read_shapefile\\n shape = read_shapefile(shape)\\n inplace = True\\n # corner grid is needed for rasterio\\n shape = transform_geopandas(shape, to_crs=self.corner_grid,\\n inplace=inplace)\\n import rasterio\\n from rasterio.features import rasterize\\n with rasterio.Env():\\n mask = rasterize(shape.geometry, **rasterize_kws)\\n if geometry is not None:\\n import rasterio\\n from rasterio.features import rasterize\\n # corner grid is needed for rasterio\\n geom = transform_geometry(geometry, crs=crs,\\n to_crs=self.corner_grid)\\n with rasterio.Env():\\n mask = rasterize(np.atleast_1d(geom), **rasterize_kws)\\n if grid is not None:\\n _tmp = np.ones((grid.ny, grid.nx), dtype=np.int16)\\n mask = self.map_gridded_data(_tmp, grid, out=mask).filled(0)\\n if corners is not None:\\n cgrid = self.center_grid\\n xy0, xy1 = corners\\n x0, y0 = cgrid.transform(*xy0, crs=crs, nearest=True)\\n x1, y1 = cgrid.transform(*xy1, crs=crs, nearest=True)\\n mask[np.min([y0, y1]):np.max([y0, y1]) + 1,\\n np.min([x0, x1]):np.max([x0, x1]) + 1] = 1\\n\\n return mask\",\n \"def area_of_circle(radius):\\n return radius\",\n \"def cropCircleROI(image, additionalCut = 5):\\n Rmin = np.min(image.shape[:-1])/3\\n Rmin = 1250 / 3040 * image.shape[0]\\n Rmax = 1400 / 3040 * image.shape[0]\\n\\n #downscale image for better performance\\n reduceFactor = 5 # squared\\n hough_radii = np.arange(Rmin/reduceFactor, Rmax/reduceFactor, dtype = int)\\n\\n downSampledImage = block_reduce(cv2.cvtColor(image, cv2.COLOR_BGR2GRAY), block_size = (reduceFactor, reduceFactor), func = np.max)\\n downSampledEdges = canny(downSampledImage, sigma=3, low_threshold=5, high_threshold=10)\\n\\n hough_res = hough_circle(downSampledEdges, hough_radii)\\n downSampledCircle = np.unravel_index(np.argmax(hough_res, axis=None), hough_res.shape)\\n circle = np.array([downSampledCircle[1], downSampledCircle[2], hough_radii[downSampledCircle[0]]])*reduceFactor\\n\\n circleMask_ = cv2.circle(np.ones(image.shape[:-1],dtype = \\\"uint8\\\"), (circle[1], circle[0]), circle[2]-additionalCut, 0, thickness = -1)\\n\\n return [np.ma.array(image[:,:,i], mask = circleMask_) for i in range (image.shape[2])]\",\n \"def extract_roi(h5series, dir_out, size_m, size_var=.5, max_ecc=.7,\\n dist_border=10, pad_border=40, exclude_overlap=30.,\\n bg_amp_kw=BG_DEFAULT_KW, bg_amp_bin=np.nan,\\n bg_pha_kw=BG_DEFAULT_KW, bg_pha_bin=np.nan,\\n search_enabled=True, ret_roimgr=False):\\n h5in = pathlib.Path(h5series)\\n dout = pathlib.Path(dir_out)\\n\\n h5out = dout / FILE_ROI_DATA_H5\\n imout = dout / FILE_ROI_DATA_TIF\\n slout = dout / FILE_SLICES\\n\\n # Determine ROI location\\n with qpimage.QPSeries(h5file=h5in, h5mode=\\\"r\\\") as qps:\\n rmgr = ROIManager(qps.identifier)\\n if search_enabled:\\n for ii in range(len(qps)):\\n qpi = qps[ii]\\n # find objects\\n slices = search.search_phase_objects(\\n qpi=qpi,\\n size_m=size_m,\\n size_var=size_var,\\n max_ecc=max_ecc,\\n dist_border=dist_border,\\n pad_border=pad_border,\\n exclude_overlap=exclude_overlap)\\n for jj, sl in enumerate(slices):\\n slident = \\\"{}.{}\\\".format(qpi[\\\"identifier\\\"], jj)\\n rmgr.add(roislice=sl, image_index=ii,\\n roi_index=jj, identifier=slident)\\n rmgr.save(slout)\\n else:\\n rmgr.load(slout)\\n\\n # Extract ROI images\\n with qpimage.QPSeries(h5file=h5in, h5mode=\\\"r\\\") as qps, \\\\\\n qpimage.QPSeries(h5file=h5out, h5mode=\\\"w\\\") as qps_roi, \\\\\\n tifffile.TiffWriter(str(imout), imagej=True) as tf:\\n for ii in range(len(qps)):\\n # image to analyze\\n qpi = qps[ii]\\n # available ROIs\\n rois = rmgr.get_from_image_index(ii)\\n for jj, (rid, sl) in enumerate(rois):\\n # Extract the ROI\\n qpisl = qpi.__getitem__(sl)\\n # amplitude bg correction\\n if bg_amp_kw:\\n amp_mask = get_binary(\\n qpisl.amp, value_or_method=bg_amp_bin)\\n qpisl.compute_bg(which_data=\\\"amplitude\\\",\\n from_binary=amp_mask,\\n **bg_amp_kw)\\n # phase bg correction\\n if bg_pha_kw:\\n pha_mask = get_binary(\\n qpisl.pha, value_or_method=bg_pha_bin)\\n qpisl.compute_bg(which_data=\\\"phase\\\",\\n from_binary=pha_mask,\\n **bg_pha_kw)\\n slident = \\\"{}.{}\\\".format(qpi[\\\"identifier\\\"], jj)\\n if rid != slident:\\n # This might happen if the user does not know the\\n # image identifier and builds his own `FILE_SLICES`.\\n msg = \\\"Mismatch of slice and QPImage identifiers: \\\" \\\\\\n + \\\"{} vs {}!\\\".format(rid, slident)\\n warnings.warn(msg)\\n # override `slident` with user identifier\\n slident = rid\\n qps_roi.add_qpimage(qpisl, identifier=slident)\\n\\n if len(qps_roi):\\n # Write TIF\\n # determine largest image\\n sxmax = np.max([qq.shape[0] for qq in qps_roi])\\n symax = np.max([qq.shape[1] for qq in qps_roi])\\n dummy = np.zeros((2, sxmax, symax), dtype=np.float32)\\n for qpir in qps_roi:\\n dummy[0, :, :] = 0\\n dummy[1, :, :] = 1\\n res = 1 / qpir[\\\"pixel size\\\"] * 1e-6 # use µm\\n sx, sy = qpir.shape\\n dummy[0, :sx, :sy] = qpir.pha\\n dummy[1, :sx, :sy] = qpir.amp\\n tf.save(data=dummy, resolution=(res, res, None))\\n\\n ret = h5out\\n if ret_roimgr:\\n ret = ret, rmgr\\n return ret\",\n \"def update_radius(intersection, new_rad):\\n return intersection.update_radius(new_rad)\",\n \"def get_roi(self, padding=0, random=True):\\n if random:\\n ymin = np.max([np.min(self.object_mask[0]) -\\n np.random.randint(0, padding), 0])\\n ymax = np.min([np.max(self.object_mask[0]) +\\n np.random.randint(0, padding),\\n int(self.im_height - 1)])\\n xmin = np.max([np.min(self.object_mask[1]) -\\n np.random.randint(0, padding), 0])\\n xmax = np.min([np.max(self.object_mask[1]) +\\n np.random.randint(0, padding),\\n int(self.im_width - 1)])\\n else:\\n ymin = np.max([np.min(self.object_mask[0]) - padding, 0])\\n ymax = np.min([np.max(self.object_mask[0]) +\\n padding, int(self.im_height - 1)])\\n xmin = np.max([np.min(self.object_mask[1]) - padding, 0])\\n xmax = np.min([np.max(self.object_mask[1]) +\\n padding, int(self.im_width - 1)])\\n\\n self.camera_model.roi = [ymin, xmin, ymax, xmax]\\n self.roi_image = self.camera_model.draw_roi(\\n self.bgr, copy=True) # bgr is full before crop\\n return [ymin, ymax, xmin, xmax]\",\n \"def refine_center(self, search_size=100, step_size=5, radius_additive=INNER_RADIUS_FULL_SIZE_PHOTO):\\n iter = 0\\n saves = np.zeros(((search_size*2)**2,3))\\n for i_add in xrange(-search_size, search_size, step_size):\\n for j_add in xrange(-search_size, search_size, step_size):\\n perim_intensities = self.scan_perimeter_intensity(\\n self.i_img_center+i_add, self.j_img_center+j_add, self.T, radius_additive, step_size)\\n saves[iter, 0] = self.i_img_center+i_add\\n saves[iter, 1] = self.j_img_center+j_add\\n saves[iter, 2] = perim_intensities[:,3].sum()\\n iter += 1\\n\\n idxs = np.argsort(saves[:,2])\\n winner_ij = saves[idxs[-1],:2]\\n self.i_img_center = winner_ij[0]\\n self.j_img_center = winner_ij[1]\\n return(winner_ij[0], winner_ij[1])\",\n \"def overlay_position(frame, centre, radius):\\n if (not (radius is None)) and radius > 10:\\n cv2.circle(frame, centre, int(radius),(0, 255, 255), 2)\\n cv2.circle(frame, centre, 5, (0, 0, 255), -1)\\n return frame\",\n \"def __init__(self, center, initialRadius, finalRadius, initialAngle, finalAngle, cartesianImageSize,\\n polarImageSize):\\n self.center = center\\n self.initialRadius = initialRadius\\n self.finalRadius = finalRadius\\n self.initialAngle = initialAngle\\n self.finalAngle = finalAngle\\n self.cartesianImageSize = cartesianImageSize\\n self.polarImageSize = polarImageSize\",\n \"def sector_mask(shape,centre,radius,angle_range):\\n\\n x,y = np.ogrid[:shape[0],:shape[1]]\\n cx,cy = centre\\n tmin,tmax = np.deg2rad(angle_range)\\n\\n # ensure stop angle > start angle\\n if tmax < tmin:\\n tmax += 2*np.pi\\n\\n # convert cartesian --> polar coordinates\\n r2 = (x-cx)*(x-cx) + (y-cy)*(y-cy)\\n theta = np.arctan2(x-cx,y-cy) - tmin\\n\\n # wrap angles between 0 and 2*pi\\n theta %= (2*np.pi)\\n\\n # circular mask\\n circmask = r2 <= radius*radius\\n\\n # angular mask\\n anglemask = theta <= (tmax-tmin)\\n\\n return circmask*anglemask\",\n \"def sector_mask(shape,centre,radius,angle_range):\\n\\n x,y = np.ogrid[:shape[0],:shape[1]]\\n cx,cy = centre\\n tmin,tmax = np.deg2rad(angle_range)\\n\\n # ensure stop angle > start angle\\n if tmax < tmin:\\n tmax += 2*np.pi\\n\\n # convert cartesian --> polar coordinates\\n r2 = (x-cx)*(x-cx) + (y-cy)*(y-cy)\\n theta = np.arctan2(x-cx,y-cy) - tmin\\n\\n # wrap angles between 0 and 2*pi\\n theta %= (2*np.pi)\\n\\n # circular mask\\n circmask = r2 <= radius*radius\\n\\n # angular mask\\n anglemask = theta <= (tmax-tmin)\\n\\n return circmask*anglemask\",\n \"def sector_mask(shape,centre,radius,angle_range):\\n\\n x,y = np.ogrid[:shape[0],:shape[1]]\\n cx,cy = centre\\n tmin,tmax = np.deg2rad(angle_range)\\n\\n # ensure stop angle > start angle\\n if tmax < tmin:\\n tmax += 2*np.pi\\n\\n # convert cartesian --> polar coordinates\\n r2 = (x-cx)*(x-cx) + (y-cy)*(y-cy)\\n theta = np.arctan2(x-cx,y-cy) - tmin\\n\\n # wrap angles between 0 and 2*pi\\n theta %= (2*np.pi)\\n\\n # circular mask\\n circmask = r2 <= radius*radius\\n\\n # angular mask\\n anglemask = theta <= (tmax-tmin)\\n\\n return circmask*anglemask\",\n \"def get_ground_plane_roi(depth_frame, depth_cutoff, y_cutoff, roi_x, roi_y):\\n\\th, w = depth_frame.shape[:2]\\n\\n\\tdepth_frame[depth_frame > depth_cutoff] = 0\\n\\tdepth_frame[:y_cutoff, ...] = 0\\n\\n\\tground_plane_roi = depth_frame.copy()\\n\\tground_plane_roi[:y_cutoff + 100, ...] = 0\\n\\tmask = create_circular_mask(h, w, center=[roi_x, roi_y], radius=100)\\n\\tground_plane_roi[mask] = 0\\n\\treturn ground_plane_roi\",\n \"def compute_objects_in_roi_mask(\\n cuboids_ego: NDArrayFloat, city_SE3_ego: SE3, avm: ArgoverseStaticMap\\n) -> NDArrayBool:\\n is_within_roi: NDArrayBool\\n if len(cuboids_ego) == 0:\\n is_within_roi = np.zeros((0,), dtype=bool)\\n return is_within_roi\\n cuboid_list_ego: CuboidList = CuboidList(\\n [Cuboid.from_numpy(params) for params in cuboids_ego]\\n )\\n cuboid_list_city = cuboid_list_ego.transform(city_SE3_ego)\\n cuboid_list_vertices_m_city = cuboid_list_city.vertices_m\\n\\n is_within_roi = avm.get_raster_layer_points_boolean(\\n cuboid_list_vertices_m_city.reshape(-1, 3)[..., :2], RasterLayerType.ROI\\n )\\n is_within_roi = is_within_roi.reshape(-1, 8)\\n is_within_roi = is_within_roi.any(axis=1)\\n return is_within_roi\",\n \"def select_rois(img):\\n # mouse callback function\\n def draw_rect_roi(event, x, y, flags, param):\\n # grab references to the global variables\\n global rect_bbox, rect_endpoint_tmp, drawing\\n \\n # if the left mouse button was clicked, record the starting\\n # (x, y) coordinates and indicate that drawing is being\\n # performed. set rect_endpoint_tmp empty list.\\n if event == cv2.EVENT_LBUTTONDOWN:\\n rect_endpoint_tmp = []\\n rect_bbox = [(x, y)]\\n drawing = True\\n \\n # check to see if the left mouse button was released\\n elif event == cv2.EVENT_LBUTTONUP:\\n # record the ending (x, y) coordinates and indicate that\\n # drawing operation is finished\\n rect_bbox.append((x, y))\\n drawing = False\\n \\n # draw a rectangle around the region of interest\\n p_1, p_2 = rect_bbox\\n cv2.rectangle(img, p_1, p_2, color=(0, 255, 0),thickness=1)\\n cv2.imshow('image', img)\\n \\n # for bbox find upper left and bottom right points\\n p_1x, p_1y = p_1\\n p_2x, p_2y = p_2\\n \\n lx = min(p_1x, p_2x)\\n ty = min(p_1y, p_2y)\\n rx = max(p_1x, p_2x)\\n by = max(p_1y, p_2y)\\n \\n # add bbox to list if both points are different\\n if (lx, ty) != (rx, by):\\n cx = 0.5*(lx+rx)/float(nx//2)\\n cy = 0.5*(by+ty)/float(ny//2)\\n\\n wx = (rx -lx)/float(nx//2)\\n wy = (by -ty)/float(ny//2)\\n \\n bbox = [cx, cy, wx, wy]\\n bbox_list_rois.append(bbox)\\n \\n # if mouse is drawing set tmp rectangle endpoint to (x,y)\\n elif event == cv2.EVENT_MOUSEMOVE and drawing:\\n rect_endpoint_tmp = [(x, y)]\\n \\n \\n # clone image img and setup the mouse callback function\\n img_copy = img.copy()\\n cv2.namedWindow('image',cv2.WINDOW_NORMAL)\\n cv2.resizeWindow('image', 1600,1200)\\n cv2.setMouseCallback('image', draw_rect_roi)\\n \\n # keep looping until the 'c' key is pressed\\n while True:\\n # display the image and wait for a keypress\\n if not drawing:\\n cv2.imshow('image', img)\\n elif drawing and rect_endpoint_tmp:\\n rect_cpy = img.copy()\\n start_point = rect_bbox[0]\\n end_point_tmp = rect_endpoint_tmp[0]\\n cv2.rectangle(rect_cpy, start_point, end_point_tmp,(0,255,0),1)\\n cv2.imshow('image', rect_cpy)\\n \\n key = cv2.waitKey(1) & 0xFF\\n # if the 'c' key is pressed, break from the loop\\n if key == ord('c'):\\n break\\n # close all open windows\\n cv2.destroyAllWindows()\\n #cv2.waitKey(1)\\n return bbox_list_rois\",\n \"def extract_region_curvilinear(cube, lat_bounds):\\n\\n cube = cube.copy() \\n \\n region_mask = create_region_mask(cube.coord('latitude').points, cube.shape, lat_bounds)\\n land_ocean_mask = cube.data.mask\\n complete_mask = region_mask + land_ocean_mask\\n\\n cube.data = numpy.ma.asarray(cube.data)\\n cube.data.mask = complete_mask\\n\\n return cube\",\n \"def set_roi(self, hstart=0, hend=None, vstart=0, vend=None, hbin=1, vbin=1):\\n roi=hstart,hend,vstart,vend,hbin,vbin\\n hstart,hend,vstart,vend,hbin,vbin=self._trunc_roi(*roi)\\n self._set_roi_binning(hbin,vbin)\\n lib.is_SetAOI(self.hcam,(hstart//hbin,vstart//vbin,(hend-hstart)//hbin,(vend-vstart)//vbin))\\n return self.get_roi()\",\n \"def _rad_center(self):\\n return ((self.rad_hi + self.rad_lo) / 2).to(\\\"deg\\\")\",\n \"def rcs(azmap, rmap, elmap, areaeffmap, sigma0map, vismap, rpol, azpol,\\n elpol, DEM_res, DEM_xmin, DEM_ymin, rad_x, rad_y, beamwidth,\\n pulsewidth, range_weighting=True, az_conv=0,\\n raster_oversampling=1, verbose=True):\\n\\n nrows, ncols = azmap.shape\\n area_unweighted = areaeffmap * sigma0map\\n\\n pulselength = pulsewidth * 3.e8 / 2. # [m]\\n if az_conv is not None:\\n az_conv_offset = az_conv / 2.\\n else:\\n az_conv_offset = 0\\n\\n beamwidth_rad = beamwidth * np.pi / 180.\\n\\n if not range_weighting:\\n range_weight = 1 # unity\\n\\n if raster_oversampling == 0:\\n N = 1\\n elif raster_oversampling == 1:\\n N = int(np.ceil(2 * DEM_res / pulselength))\\n else:\\n N = raster_oversampling\\n\\n if N != 1:\\n # New dimensions\\n nc = N * ncols\\n nr = N * nrows\\n\\n # repeat the values NxN, equivalent of rebin in IDL\\n elvals = np.repeat(np.repeat(elmap, N, axis=0), N, axis=1)\\n areavals = np.repeat(np.repeat(area_unweighted / N ** 2,\\n N, axis=0), N, axis=1)\\n visvals = np.repeat(np.repeat(vismap, N, axis=0), N, axis=1)\\n\\n # New x- and y-vectors\\n xvec = np.arange(nr) * DEM_res / N + DEM_xmin\\n yvec = np.arange(nc) * DEM_res / N + DEM_ymin\\n\\n xdiff = (xvec - rad_x)\\n ydiff = (yvec - rad_y)\\n\\n # New distance from radar map\\n X, Y = np.meshgrid(xdiff, ydiff)\\n rvals = np.sqrt(X ** 2 + Y ** 2)\\n\\n # New azimuth map\\n azmap_rad = (np.arctan2(X, Y) + 2 * np.pi) % (2 * np.pi)\\n azvals = azmap_rad * 180. / np.pi\\n else:\\n rvals = rmap\\n azvals = azmap\\n azmap_rad = azvals * np.pi / 180.\\n elvals = elmap\\n areavals = area_unweighted\\n visvals = vismap\\n\\n elmap_rad = elvals * np.pi / 180.\\n elevations_rad = np.array(elpol) * np.pi / 180.\\n\\n # Define the area around a point P(range, azimuth) where the cells\\n # have a contribution to the RCS. This area is defined with the\\n # range limits from range-dr_offset to range+dr_offset and the\\n # azimuth limits from azimuth-daz_offset to azimuth+daz_offset.\\n #\\n # For a Gaussian antenna, azimuth offset more than 2*HPBW does not a\\n # have remarkable contribution.\\n # With a rectangular pulse and a matched filter cells farer away\\n # than pulse length does not a have remarkable contribution.\\n\\n daz_offset = (2. * beamwidth) + az_conv_offset # [deg]\\n dr_offset = pulselength # [m]\\n\\n nazim = len(azpol)\\n nrange = len(rpol)\\n # pyart storage format: 2D arrays (naz * nel, nranges)\\n rcspolarmap = np.zeros((nazim * len(elpol), nrange)) + np.nan\\n\\n for rind in range(nrange):\\n if verbose:\\n logging.info(f'Computing range bin {rpol[rind]:2.1f}')\\n rr = rpol[rind]\\n\\n indr = np.logical_and(np.logical_and(rvals >= rr - dr_offset,\\n rvals < rr + dr_offset),\\n visvals > 0)\\n\\n if not np.any(indr):\\n continue\\n\\n indr = np.where(indr)\\n\\n for azind in range(nazim):\\n az = azpol[azind]\\n # Inside the loops over range (rr) and azimuth (az), the\\n # coordinates (rr, az) describe the point P(rr, az) for which\\n # the RCS is calculated. If more than one DEM cell is within\\n # the area from az-daz/2 to az+daz/2 and from rr-dr/2 to\\n # rr+dr/2, the calculated RCS value is set to all of these\\n # cells (next neighbor).\\n\\n # Get area around rr and az\\n azmin = az - daz_offset\\n azmax = az + daz_offset\\n if azmin < 0:\\n azmin = 360. + azmin\\n indaz = np.logical_or(np.logical_and(azvals[indr] >= 0,\\n azvals[indr] < azmax),\\n np.logical_and(azvals[indr] >= azmin,\\n azvals[indr] <= 360.))\\n elif azmax > 360:\\n azmax = azmax - 360.\\n indaz = np.logical_or(np.logical_and(azvals[indr] >= azmin,\\n azvals[indr] <= 360),\\n np.logical_and(azvals[indr] >= 0,\\n azvals[indr] < azmax))\\n else:\\n indaz = np.logical_and(azvals[indr] >= azmin,\\n azvals[indr] < azmax)\\n\\n # Cells that contribute to the cells to set indset\\n inda = tuple([indr[0][indaz], indr[1][indaz]])\\n\\n # Calculate offsets in azimuth and elevation to the\\n # point P(rr,az) and the elevation angle of the antenna.\\n\\n daz_area = azmap_rad[inda] - (az * np.pi / 180.)\\n\\n indaz = daz_area > np.pi\\n daz_area[indaz] = daz_area[indaz] - 2. * np.pi\\n\\n indaz = daz_area < -np.pi\\n daz_area[indaz] = daz_area[indaz] + 2. * np.pi\\n\\n if range_weighting:\\n # Get the weighting factor due to the range offset.\\n range_weight = range_weights(rvals[inda], rr,\\n pulselength)\\n\\n ind_rzero = rvals[inda] <= 0.0\\n if np.any(ind_rzero):\\n continue\\n\\n for iel, el in enumerate(elevations_rad):\\n del_area = elmap_rad[inda] - el\\n\\n # Get the two-way weighting factor due to the azimuth offset\\n # to the main antenna direction (assuming a Gaussian antenna\\n # pattern).\\n ant_weight = antenna_pattern_gauss(\\n daz_area,\\n del_area,\\n beamwidth_rad,\\n twoway=True,\\n az_conv=az_conv *\\n np.pi /\\n 180.,\\n units='rad')\\n\\n # RCS = SUM_j sigma_j\\n # = SUM_j sigma0_j * A_eff_j * fa(dphi_j,dteta_j)^2 * fr(drange)\\n # where\\n # sigma_j : Backscattering cross section of each cell [m^2]\\n # sigma0_j : Sigma naught of each cell [1]\\n # A_eff_j : Effective area of each cell [m^2]\\n # fa : One-way weighting function due to the azimuth\\n # and elevation offsets.\\n # fr : Range weighting function due to the range offset\\n\\n # RCS contribution of each cell inside the contribution\\n # area.\\n rcs_area = ant_weight * range_weight * areavals[inda]\\n # Sum up all the contributions\\n rcs = np.nansum(rcs_area)\\n\\n if rcs < RCS_MIN:\\n rcs = np.nan\\n\\n # Set rcs to all values inside the set area.\\n\\n rcspolarmap[azind + iel * nazim, rind] = rcs\\n return rcspolarmap\",\n \"def roi_vecs(layer_coords, vec_coords, region):\\n \\n if region == 'crown':\\n #find threshold for vectors inside roi\\n start_x_lst = []\\n stop_x_lst = []\\n for i in range(1,5):\\n start_x_lst.append(layer_coords[i][0][0])\\n stop_x_lst.append(layer_coords[i][-1][0])\\n\\n start_x = max(start_x_lst)\\n stop_x = min(stop_x_lst)\\n \\n roi_vec_coords = [i for i in vec_coords if i[0][0] in list(range(start_x, stop_x+5))]\\n \\n return roi_vec_coords\\n \\n elif region == 'fundus':\\n #find threshold for vectors inside roi\\n start_x_lst = []\\n stop_x_lst = []\\n for i in range(1,5):\\n start_x_lst.append(layer_coords[i][0][0])\\n stop_x_lst.append(layer_coords[i][-1][0])\\n\\n start_x = max(start_x_lst)\\n stop_x = min(stop_x_lst)\\n\\n # roi_vec_coords = [i for i in vec_coords if i[1][0] in list(range(start_x-10, stop_x+3))]\\n roi_vec_coords = [i for i in vec_coords if i[0][0] in list(range(stop_x, start_x))]\\n \\n # print(roi_vec_coords)\\n return roi_vec_coords\",\n \"def sector_mask(shape, centre, radius, angle_range):\\n\\n x,y = np.ogrid[:shape[0],:shape[1]]\\n cx,cy = centre\\n tmin,tmax = np.deg2rad(angle_range)\\n\\n # ensure stop angle > start angle\\n if tmax < tmin:\\n tmax += 2*np.pi\\n\\n # convert cartesian --> polar coordinates\\n r2 = (x-cx)*(x-cx) + (y-cy)*(y-cy)\\n theta = np.arctan2(x-cx, y-cy) - tmin\\n\\n # wrap angles between 0 and 2*pi\\n theta %= (2*np.pi)\\n\\n # circular mask\\n circmask = r2 <= radius*radius\\n\\n # angular mask\\n anglemask = theta <= (tmax-tmin)\\n\\n return circmask*anglemask\",\n \"def render_region(self, image, rect, color, rad):\\n corners = rect.inflate(-2*rad, -2*rad)\\n for attribute in (\\\"topleft\\\", \\\"topright\\\", \\\"bottomleft\\\", \\\"bottomright\\\"):\\n pygame.draw.circle(image, color, getattr(corners,attribute), rad)\\n image.fill(color, rect.inflate(-2*rad,0))\\n image.fill(color, rect.inflate(0,-2*rad))\",\n \"def get_radius(self):\\n return self.r\",\n \"def _view_roi(array, original_area_slice, axis):\\n axis += 1\\n sl = (slice(None),) * axis + original_area_slice[axis:]\\n return array[sl]\",\n \"def roi_img(array, top, bottom, left, right):\\n if left < right and top < bottom:\\n try:\\n imgarray_roi = array[top:bottom, left:right] \\n return imgarray_roi\\n \\n except:\\n print(\\\"Error calculating roi. Using full view.\\\")\\n return array\\n \\n else:\\n print(\\\"ROI error: left >= right or top >= bottom. Using full view instead.\\\")\\n return array\",\n \"def get_radius(self):\\n return self.R\",\n \"def circle_area(r):\\n if r < 0:\\n raise ValueError(\\\"Radius cannot be negative\\\")\\n\\n return pi*(r**2)\",\n \"def get_radius(self):\\r\\n return 1\",\n \"def roi_center(roi):\\n def slice_center(s):\\n return (s.start + s.stop)*0.5\\n\\n if isinstance(roi, slice):\\n return slice_center(roi)\\n\\n return tuple(slice_center(s) for s in roi)\",\n \"def polar_box(rad_min, rad_max, ang_min, ang_max, **kwargs):\\n delta_angle = (ang_max - ang_min) % 360.0\\n ang_max_sequential = ang_min + delta_angle\\n maxarc_coords = list(approxArc((0.0, 0.0), rad_max, ang_min,\\n \\t\\t\\t\\t\\t\\t\\t ang_max_sequential).coords)\\n minarc_coords = list(approxArc((0.0, 0.0), rad_min, ang_min,\\n \\t\\t\\t\\t\\t\\t\\t ang_max_sequential).coords)\\n minarc_coords.reverse()\\n poly = geo.Polygon(maxarc_coords + minarc_coords)\\n return desc.PolygonPatch(poly, **kwargs)\",\n \"def CropROI(coords,full_img,target_size = None):\\n\\n\\t#Get region of interest crop from the full array using coorinates\\n\\ttmp_ROI = full_img[int(coords[2]):int(coords[3]),int(coords[0]):int(coords[1])]\\n\\t#Get the size of the imc image\\n #multiple = int(imc_im.shape[1]/tmp_ROI.shape[0])\\n #Use the rounded multiple to resize our ROI for image registration\\n #HiRes_size = (tmp_ROI.shape[1]*multiple,tmp_ROI.shape[0]*multiple)\\n\\t#HiRes_size = (imc_im.shape[0],imc_im.shape[1])\\n\\n\\tif target_size is not None:\\n\\t\\t#Remember that cv2 does the axis in the opposite order as numpy\\n\\t\\t# note here that we are no longer using cv2 -- this is skimage resizing...\\n\\t\\ttmp_ROI = resize(tmp_ROI,target_size)\\n\\n\\t#Create a nifti object\\n\\tnifti_ROI = nib.Nifti1Image(np.rot90(tmp_ROI.T,1), affine=np.eye(4))\\n\\t#Return image\\n\\treturn nifti_ROI\",\n \"def ellipsoid(center, radii, rotation, scales=None, shape=None, minarea=0):\\n center = np.array(center)\\n radii = np.array(radii)\\n rotation = np.array(rotation)\\n assert center.shape == (3,)\\n assert radii.shape == (3,)\\n assert 0 < radii.max(), \\\"radii should contain at least one positive value\\\"\\n assert rotation.shape == (3, 3)\\n if scales is None:\\n scales = (1.,) * 3\\n scales = np.array(scales)\\n assert scales.shape == (3,)\\n\\n scaled_center = center / scales\\n\\n # The upper_left_bottom and lower_right_top corners of the smallest cuboid\\n # containing the ellipsoid.\\n factor = np.array([\\n [i, j, k] for k in (-1, 1) for j in (-1, 1) for i in (-1, 1)]).T\\n while True:\\n radii_rot = np.abs(\\n np.diag(1. / scales).dot(rotation.dot(np.diag(radii).dot(factor)))\\n ).max(axis=1)\\n # In the original scikit-image code, ceil and floor were replaced.\\n # https://github.com/scikit-image/scikit-image/blob/master/skimage/draw/draw.py#L127\\n upper_left_bottom = np.floor(scaled_center - radii_rot).astype(int)\\n lower_right_top = np.ceil(scaled_center + radii_rot).astype(int)\\n\\n if shape is not None:\\n # Constrain upper_left and lower_ight by shape boundary.\\n upper_left_bottom = np.maximum(\\n upper_left_bottom, np.array([0, 0, 0]))\\n lower_right_top = np.minimum(\\n lower_right_top, np.array(shape[:3]) - 1)\\n\\n bounding_shape = lower_right_top - upper_left_bottom + 1\\n\\n d_lim, r_lim, c_lim = np.ogrid[0:float(bounding_shape[0]),\\n 0:float(bounding_shape[1]),\\n 0:float(bounding_shape[2])]\\n d_org, r_org, c_org = scaled_center - upper_left_bottom\\n d_rad, r_rad, c_rad = radii\\n rotation_inv = np.linalg.inv(rotation)\\n conversion_matrix = rotation_inv.dot(np.diag(scales))\\n d, r, c = (d_lim - d_org), (r_lim - r_org), (c_lim - c_org)\\n distances = (\\n ((d * conversion_matrix[0, 0] +\\n r * conversion_matrix[0, 1] +\\n c * conversion_matrix[0, 2]) / d_rad) ** 2 +\\n ((d * conversion_matrix[1, 0] +\\n r * conversion_matrix[1, 1] +\\n c * conversion_matrix[1, 2]) / r_rad) ** 2 +\\n ((d * conversion_matrix[2, 0] +\\n r * conversion_matrix[2, 1] +\\n c * conversion_matrix[2, 2]) / c_rad) ** 2\\n )\\n if distances.size < minarea:\\n old_radii = radii.copy()\\n radii *= 1.1\\n print('Increase radii from ({}) to ({})'.format(old_radii, radii))\\n else:\\n break\\n distance_thresh = 1\\n while True:\\n dd, rr, cc = np.nonzero(distances < distance_thresh)\\n if len(dd) < minarea:\\n distance_thresh *= 1.1\\n else:\\n break\\n dd.flags.writeable = True\\n rr.flags.writeable = True\\n cc.flags.writeable = True\\n dd += upper_left_bottom[0]\\n rr += upper_left_bottom[1]\\n cc += upper_left_bottom[2]\\n return dd, rr, cc\",\n \"def Radius(self, *args):\\n return _Bnd.Bnd_Sphere_Radius(self, *args)\",\n \"def region_of_interest(img):\\n # defining a blank mask to start with\\n y, x = img.shape[:2]\\n # print(\\\"roi_width : {}, roi_height : {}\\\".format(x, y))\\n\\n # 왼쪽과 오른쪽 roi를 동시 설정; 영역의 범위에 따라 보이는 영역이 바뀜\\n left1 = [int(0.1*x), int(y)]\\n left2 = [int(0.1*x), int(0.1*y)]\\n left3 = [int(0.4*x), int(0.1*y)]\\n left4 = [int(0.4*x), int(y)]\\n\\n right1 = [int(0.7*x), int(y)]\\n right2 = [int(0.7*x), int(0.1*y)]\\n right3 = [int(0.9*x), int(0.1*y)]\\n right4 = [int(0.9*x), int(y)]\\n\\n middle = [int(0.2*x), int(y)]\\n\\n shaper = np.array([left1, left2, left3, left4, right1, right2, right3, right4, middle])\\n\\n mask = np.zeros_like(img)\\n\\n # 흑백, 컬러 구분 없이 흑백으로 채움\\n if len(img.shape) > 2:\\n ignore_mask_color = (255, 255, 255)\\n else:\\n ignore_mask_color = 255\\n\\n # 식별된 구간 제외 전부 검은색칠\\n cv2.fillPoly(mask, np.int32([shaper]), ignore_mask_color)\\n\\n # bitwise_and 연산\\n masked_image = cv2.bitwise_and(img, mask)\\n # cv2.imshow('ROI', masked_image)\\n # cv2.waitKey()\\n\\n return masked_image\",\n \"def set_radius(self, rmirr):\\n self.F_EXT = 1\\n self.RMIRR = rmirr\",\n \"def select_region_of_interest():\\r\\n image = np.array(ImageGrab.grab(bbox=None))\\r\\n image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\\r\\n r = cv2.selectROI(windowName='grab roi', img=image, showCrosshair=True, fromCenter=False)\\r\\n cv2.destroyAllWindows()\\r\\n return r[0], r[1], r[0] + r[2], r[1] + r[3]\",\n \"def circumradius(T,binary_mask):\\n (x1, y1), (x2, y2), (x3, y3) = T # extracting the points. \\n \\n D=2*(x1*(y2-y3)+x2*(y3-y1)+x3*(y1-y2)) # Diameter\\n if D!=0:\\n #Centroid of the cicumcircle\\n Ux=(((x1**2+y1**2)*(y2-y3))+((x2**2+y2**2)*(y3-y1))+((x3**2+y3**2)*(y1-y2)))/D\\n Uy=(((x1**2+y1**2)*(x3-x2))+((x2**2+y2**2)*(x1-x3))+((x3**2+y3**2)*(x2-x1)))/D\\n \\n #radius\\n r = sqrt((Ux-x2)**2+(Uy-y2)**2)\\n r=r+1\\n \\n #Determining the sign: it is positive if the centroid of the circumcricle is in the foreground\\n x=np.floor(Ux).astype(int)\\n y=np.floor(Uy).astype(int)\\n\\n if (x >=binary_mask.shape[0] or y >=binary_mask.shape[1]):\\n r=-r\\n elif (x<0 or y<0):\\n r=-r\\n elif binary_mask[x,y]:\\n r=r\\n else:\\n r=-r\\n return r\\n else:\\n return False\",\n \"def image_iterator(processing_function, image_volume, radius=2, roi=None):\\n # This is an ugly way of type-checking but cant get isinstance to see both as the same\\n if (MaskableVolume.__name__ in str(type(image_volume))):\\n (c, r, d) = image_volume.frameofreference.size\\n def get_val(image_volume, z, y, x):\\n # image boundary handling is built into BaseVolume.get_val\\n return image_volume.get_val(z, y, x)\\n def set_val(feature_volume, z, y, x, val):\\n feature_volume.set_val(z, y, x, val)\\n\\n #instantiate a blank BaseVolume of the proper size\\n feature_volume = MaskableVolume().fromArray(np.zeros((d, r, c)), image_volume.frameofreference)\\n # feature_volume.modality = image_volume.modality\\n # feature_volume.feature_label = 'feature'\\n elif isinstance(image_volume, np.ndarray):\\n if image_volume.ndim == 3:\\n d, r, c = image_volume.shape\\n elif image_volume.ndim == 2:\\n d, r, c = (1, *image_volume.shape)\\n image_volume = image_volume.reshape((d, r, c))\\n\\n # instantiate a blank np.ndarray of the proper size\\n feature_volume = np.zeros((d, r, c))\\n\\n def get_val(image, z, y, x):\\n if (z<0 or y<0 or x<0) or (z>=d or y>=r or x>=c):\\n return 0\\n else:\\n return image[z, y, x]\\n def set_val(image, z, y, x, val):\\n image[z, y, x] = val\\n else:\\n logger.info('invalid image type supplied ({:s}). Please specify an image of type BaseVolume \\\\\\n or type np.ndarray'.format(str(type(image_volume))))\\n return None\\n\\n # z_radius_range controls 2d neighborhood vs 3d neighborhood for 2d vs 3d images\\n if d == 1: # 2D image\\n logger.debug(indent('Computing 2D feature with radius: {:d}'.format(radius), l3))\\n z_radius_range = [0]\\n elif d > 1: # 3D image\\n logger.debug(indent('Computing 3D feature with radius: {:d}'.format(radius), l3))\\n z_radius_range = range(-radius, radius+1)\\n\\n # in plane patch range\\n radius_range = range(-radius, radius+1)\\n\\n # timing\\n start_feature_calc = time.time()\\n\\n\\n # absolute max indices for imagevolume - for handling request of voxel out of bounds\\n cbound = c\\n rbound = r\\n dbound = d\\n\\n # set calculation index bounds -- will be redefined if roi is specified\\n cstart, cstop = 0, cbound\\n rstart, rstop = 0, rbound\\n dstart, dstop = 0, dbound\\n\\n # defines dimensionality\\n d_subset = dstop - dstart\\n r_subset = rstop - rstart\\n c_subset = cstop - cstart\\n\\n # restrict calculation bounds to roi\\n if (roi is not None):\\n # get max extents of the mask/ROI to speed up calculation only within ROI cubic volume\\n extents = roi.getROIExtents()\\n cstart, rstart, dstart = image_volume.frameofreference.getIndices(extents.start)\\n cstop, rstop, dstop = np.subtract(image_volume.frameofreference.getIndices(extents.end()), 1)\\n logger.info(indent('calculation subset volume x=({xstart:d}->{xstop:d}), '\\n 'y=({ystart:d}->{ystop:d}), '\\n 'z=({zstart:d}->{zstop:d})'.format(zstart=dstart,\\n zstop=dstop,\\n ystart=rstart,\\n ystop=rstop,\\n xstart=cstart,\\n xstop=cstop ), l4))\\n # redefine feature_volume\\n d_subset = dstop - dstart\\n r_subset = rstop - rstart\\n c_subset = cstop - cstart\\n feature_frameofreference = FrameOfReference((extents.start),\\n (image_volume.frameofreference.spacing),\\n (c_subset, r_subset, d_subset))\\n feature_volume = feature_volume.fromArray(np.zeros((d_subset, r_subset, c_subset)), feature_frameofreference)\\n\\n # # setup an output volume for each feature in processing_function list\\n # if (not isinstance(processing_function, list)):\\n # tmp = []\\n # tmp.append(processing_function)\\n # processing_function = tmp\\n # feature_volumes = [feature_volume]\\n # for funct in processing_function[1:]:\\n # feature_volumes.append(np.zeros_like(feature_volume))\\n\\n # nested loop approach -> slowest, try GPU next\\n total_voxels = d * r * c\\n subset_total_voxels = d_subset * r_subset * c_subset\\n #onepercent = int(subset_total_voxels / 100)\\n fivepercent = int(subset_total_voxels / 100 * 5)\\n\\n idx = -1\\n subset_idx = -1\\n z_idx = -1\\n for z in range(dstart, dstop):\\n z_idx += 1\\n y_idx = -1\\n x_idx = -1\\n for y in range(rstart, rstop):\\n y_idx += 1\\n x_idx = -1\\n for x in range(cstart, cstop):\\n x_idx += 1\\n idx += 1\\n if (z<dstart or z>dstop or y<rstart or y>rstop or x<cstart or x>cstop):\\n # we shouldnt ever be here\\n logger.info('why are we here?!')\\n #fill 0 instead\\n set_val(feature_volume, z_idx, y_idx, x_idx, 0)\\n else:\\n subset_idx += 1\\n patch_vals = np.zeros((len(z_radius_range), len(radius_range), len(radius_range)))\\n for p_z, k_z in enumerate(z_radius_range):\\n for p_x, k_x in enumerate(radius_range):\\n for p_y, k_y in enumerate(radius_range):\\n #logger.info('k_z:{z:d}, k_y:{y:d}, k_x:{x:d}'.format(z=k_z,y=k_y,x=k_x))\\n # handle out of bounds requests - replace with 0\\n request_z = z+k_z\\n request_y = y+k_y\\n request_x = x+k_x\\n if (request_z < 0 or request_z >= dbound or\\n request_y < 0 or request_y >= rbound or\\n request_x < 0 or request_x >= cbound):\\n val = 0\\n else:\\n val = get_val(image_volume, request_z, request_y, request_x)\\n # store to local image patch\\n patch_vals[p_z, p_y, p_x] = val\\n\\n # for i, funct in enumerate(processing_function):\\n proc_value = processing_function(patch_vals)\\n set_val(feature_volume, z_idx, y_idx, x_idx, proc_value)\\n\\n if (False and (subset_idx % fivepercent == 0 or subset_idx == subset_total_voxels-1)):\\n logger.debug('feature value at ({x:d}, {y:d}, {z:d})= {e:f}'.format(\\n x=z*y*x + y*x + x,\\n y=z*y*x + y,\\n z=z*y*x,\\n e=proc_value))\\n\\n if ((subset_idx % fivepercent == 0 or subset_idx == subset_total_voxels-1)):\\n logger.debug(indent('{p:0.2%} - voxel: {i:d} of {tot:d} (of total: {abstot:d})'.format(\\n p=subset_idx/subset_total_voxels,\\n i=subset_idx,\\n tot=subset_total_voxels,\\n abstot=total_voxels), l4))\\n\\n if isinstance(image_volume, np.ndarray) and d == 1:\\n # need to reshape ndarray if input was 2d\\n feature_volume = feature_volume.reshape((r_subset, c_subset))\\n # for i, feature_volume in enumerate(feature_volumes):\\n # feature_volumes[i] = feature_volume.reshape((r_subset, c_subset))\\n\\n\\n end_feature_calc = time.time()\\n logger.debug(timer('feature calculation time:', end_feature_calc-start_feature_calc, l3))\\n # if len(features_volumes > 1):\\n # return feature_volumes\\n # else:\\n return feature_volume\",\n \"def _fcn_link_roi(self):\\n kwargs = self.cbqt.cbobjs._objs['roi'].to_kwargs(True)\\n self.roi.update_from_dict(kwargs)\\n self.roi._update_cbar()\",\n \"def get_roi(self, video=None, window_name=None):\\r\\n if video is None:\\r\\n video = self.video_buffer\\r\\n self.play_video(video=video, window_name=window_name, mouse_callback_func=self._set_rect_roi)\\r\\n self.roi = dict(x1=int(min(self.x, self.ix)), x2=int(max(self.x, self.ix)),\\r\\n y1=int(min(self.y, self.iy)), y2=int(max(self.y, self.iy)))\\r\\n self.roi_set = True\\r\\n return self.roi\",\n \"def circular_levelset(shape, center, sqradius, scalerow=1.0):\\n grid = np.mgrid[list(map(slice, shape))].T - center\\n phi = sqradius - np.sqrt(np.sum((grid.T)**2, 0))\\n u = np.float_(phi > 0)\\n return u\",\n \"def query_wise_astroquery(\\n ra_deg: float, dec_deg: float, searchradius_arcsec: float = 3.0\\n):\\n c = SkyCoord(ra_deg, dec_deg, unit=u.deg, frame=\\\"icrs\\\")\\n\\n r = searchradius_arcsec * u.arcsecond\\n\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"ignore\\\", AstropyWarning)\\n allwise = Irsa.query_region(c, catalog=\\\"allwise_p3as_psd\\\", radius=r)\\n return allwise\",\n \"def get_surround_volume_v3(volume, distance=5, wall_level=0, prob=False, return_origin_instead_of_bbox=True, padding=5):\\n from scipy.ndimage.morphology import distance_transform_edt\\n distance = int(np.round(distance))\\n\\n # Identify the bounding box for the surrouding area.\\n\\n vol, origin = volume\\n\\n # if bbox is None:\\n bbox = volume_origin_to_bbox(vol > wall_level, origin)\\n\\n xmin, xmax, ymin, ymax, zmin, zmax = bbox\\n roi_xmin = xmin - distance - padding\\n roi_ymin = ymin - distance - padding\\n roi_zmin = zmin - distance - padding\\n roi_xmax = xmax + distance + padding\\n roi_ymax = ymax + distance + padding\\n roi_zmax = zmax + distance + padding\\n roi_bbox = np.array((roi_xmin,roi_xmax,roi_ymin,roi_ymax,roi_zmin,roi_zmax))\\n vol_roi = crop_and_pad_volume(vol, in_bbox=bbox, out_bbox=roi_bbox)\\n\\n dist_vol = distance_transform_edt(vol_roi < wall_level)\\n roi_surround_vol = (dist_vol > 0) & (dist_vol < distance) # surround part is True, otherwise False.\\n\\n if prob:\\n surround_vol = np.zeros_like(vol_roi)\\n surround_vol[roi_surround_vol] = 1. - vol_roi[roi_surround_vol]\\n if return_origin_instead_of_bbox:\\n return surround_vol, roi_bbox[[0,2,4]]\\n else:\\n return surround_vol, roi_bbox\\n else:\\n if return_origin_instead_of_bbox:\\n return roi_surround_vol, roi_bbox[[0,2,4]]\\n else:\\n return roi_surround_vol, roi_bbox\",\n \"def radius(self,xc=None,yc=None):\\n if xc == None:\\n xc = self.x1\\n if yc == None:\\n yc = self.y1\\n self.r = sqrt((self.x-xc)**2+(self.y-yc)**2)\",\n \"def area_circle(r):\\n return (r ** 2) * math.pi\",\n \"def get_roi(self, index):\\n return self.db.get_node('/images/img{:04d}'.format(index)).attrs.roi\",\n \"def create_test_object(height=128, radius=10):\\n center_line_img = np.zeros((height,height,height), dtype=np.uint32)\\n for i in range(height):\\n center_line_img[i, i, i] = 1\\n center_line_img[height-1-i, i, i] = 1\\n \\n # Scipy distance_transform_edt conventions are opposite of vigra:\\n # it calculates distances of non-zero pixels to the zero pixels.\\n center_line_img = 1 - center_line_img\\n distance_to_line = distance_transform_edt(center_line_img)\\n binary_vol = (distance_to_line <= radius).astype(np.uint8)\\n return binary_vol\",\n \"def roi_cli():\",\n \"def __init__(self, radius):\\n self.radius = radius\",\n \"def create_ring(self):\\n\\t\\tself.north_coords = numpy.add(self.center, self.north)\\n\\t\\tself.northeast_coords = numpy.add(self.center, self.northeast)\\n\\t\\tself.east_coords = numpy.add(self.center, self.east)\\n\\t\\tself.southeast_coords = numpy.add(self.center, self.southeast)\\n\\t\\tself.south_coords = numpy.add(self.center, self.south)\\n\\t\\tself.southwest_coords = numpy.add(self.center, self.southwest)\\n\\t\\tself.west_coords = numpy.add(self.center, self.west)\\n\\t\\tself.northwest_coords = numpy.add(self.center, self.northwest)\",\n \"def initialize_radius(src):\\n\\n x_length = (np.amax(src[:, 0]) - np.amin(src[:, 0]))\\n y_length = (np.amax(src[:, 1]) - np.amin(src[:, 1]))\\n z_length = (np.amax(src[:, 2]) - np.amin(src[:, 2]))\\n\\n max_length = max(x_length, y_length, z_length)\\n\\n if max_length > 50:\\n radius = 10\\n elif max_length > 1:\\n radius = 1\\n else:\\n radius = 0.01\\n\\n return radius\",\n \"def get_roi_circle_by_index(self, index):\\n return int(self.circle_list[index][\\\"x\\\"]), int(self.circle_list[index][\\\"y\\\"]), int(self.circle_list[index][\\\"radius\\\"])\",\n \"def create_circle_mask(img, center, radius):\\n h, w = img.shape\\n # Flipping center ordering here to account for differences in how opencv returns\\n # coordinates (standard x,y) and how numpy wants them (r,c)\\n act_center = np.array(center[::-1])\\n r, c = np.ogrid[0:h, 0:w] - np.array(center[::-1])\\n return (r ** 2 + c ** 2) < radius ** 2\",\n \"def Icosphere(radius=1.0, center=(0.0, 0.0, 0.0), nsub=3):\\n mesh = Icosahedron()\\n mesh.clear_data()\\n mesh = mesh.subdivide(nsub=nsub)\\n\\n # scale to desired radius and translate origin\\n dist = np.linalg.norm(mesh.points, axis=1, keepdims=True) # distance from origin\\n mesh.points = mesh.points * (radius / dist) + center\\n return mesh\",\n \"def fit_galaxy(self, ypos, xpos, r_in, r_out = 0):\\r\\n count_out = []\\r\\n count_in = []\\r\\n for j, i in product(np.arange(ypos - (r_out + r_in), ypos + r_out + r_in + 1),np.arange(xpos - (r_out + r_in), xpos + 1 + r_out + r_in)): # Create square\\r\\n if (j - ypos) ** 2 + (i - xpos) ** 2 <= r_in ** 2 and 0<= j <= self.shapes[0] - 1 and 0<= i <= self.shapes[1] - 1: # make sure points are in a circle\\r\\n j = int(j)\\r\\n i = int(i)\\r\\n if self.raw_image_data[j,i] * self.masked[j,i] == self.raw_image_data[j,i]:\\r\\n count_in.append(self.raw_image_data[j,i])\\r\\n self.masked[j,i] = 0 # self.mask_region runs the for loop again\\r\\n if r_in ** 2 < (j - ypos) ** 2 + (i - xpos) ** 2 <= (r_in + r_out)**2 and 0<= j <= (self.shapes[0] - 1) and 0<= i <= self.shapes[1] - 1: # in the outer ring\\r\\n j = int(j)\\r\\n i = int(i)\\r\\n if self.raw_image_data[j,i] * self.masked[j,i] == self.raw_image_data[j,i]: \\r\\n count_out.append(self.raw_image_data[j][i]) \\r\\n self.masked[j,i]\\r\\n return count_in, count_out\",\n \"def circle_area(radius):\\n return math.pi * radius ** 2\",\n \"def azizen(self):\\n # x0,y0 array pixel coordinates relative to cx,cy\\n# ndy0,ndx0=img.shape\\n ndy0=self.ndy0\\n ndx0=self.ndx0\\n x0,y0=np.meshgrid(np.linspace(0,ndx0-1,ndx0)-self.cx,np.linspace(0,ndy0-1,ndy0)-self.cy)\\n r0=np.sqrt(x0**2+y0**2)/self.pr0 # fractional radial distance from 0,0\\n# self.roi=np.s_[ystart:ystart+self.ny0,xstart:xstart+self.nx0]\\n # why not model the zenith angle dependence with polynomial directly\\n # rather than linear interpolation between roots.\\n roots=np.zeros(51)\\n rr=np.arange(51)/100.0\\n for i,ref in enumerate(rr):\\n roots[i]=np.real(np.roots([self.c3,0,self.c2,0,self.c1,-ref])[-1])\\n theta0 = np.interp(r0/2,rr,roots)\\n \\n phi0 = np.arctan2(x0,y0) - self.rot ####phi (i.e., azimuth) is reckoned with -pi corresponding to north, increasing clockwise, NOTE: pysolar use sub-standard definition\\n phi0 = phi0%(2*np.pi)\\n\\n #####correction for the tilt of the camera\\n k=np.array((np.sin(self.azm),np.cos(self.azm),0))\\n a=np.array([np.sin(theta0)*np.cos(phi0),np.sin(theta0)*np.sin(phi0),np.cos(theta0)]); \\n a = np.transpose(a,[1,2,0])\\n b=np.cos(self.beta)*a + np.sin(self.beta)*np.cross(k,a,axisb=2) \\\\\\n + np.reshape(np.outer(np.dot(a,k),k),(self.ndy0,self.ndx0,3))*(1-np.cos(self.beta))\\n theta0=np.arctan(np.sqrt(b[:,:,0]**2+b[:,:,1]**2)/b[:,:,2])\\n phi0=np.arctan2(b[:,:,1],b[:,:,0])%(2*np.pi)\\n# max_theta *= deg2rad \\n# valid0 = (theta0<max_theta) & (theta0>0); \\n# theta0[valid0]=np.nan;\\n self.theta0,self.phi0=theta0,phi0\",\n \"def f_2(center):\\r\\n Ri = calc_R(center)\\r\\n return Ri - Ri.mean()\",\n \"def polar_radius(self):\\n return self.r * (1 - self.f)\",\n \"def center_on_box(img, radius, min_ref, xmin, xmax, ymin, ymax, na_val=-9999):\\n x, y = num.meshgrid(num.arange(-radius, radius), num.arange(-radius, radius))\\n coords = [(i, j) for i, j in zip(x.flatten(), y.flatten()) if (i ** 2 + j ** 2) ** 0.5 <= radius]\\n fit = [num.mean(img[(xmin + i):(xmax + i), (ymin + j):(ymax + j)]) for i, j in coords]\\n if num.nanmin(fit) <= min_ref:\\n return num.array(coords[num.nanargmin(fit)])\\n else:\\n return num.array([na_val, na_val])\",\n \"def roiChanged(self):\\n state = self.roi.getState()\\n w, h = state['size']\\n # Remember: ROI origin is in bottom-left because camera module has +y pointing upward.\\n self.params.system.p0 = pg.Point(self.roi.mapToView(pg.Point(0,h))) # top-left\\n self.params.system.p1 = pg.Point(self.roi.mapToView(pg.Point(w,h))) # rop-right\\n self.params.system.p2 = pg.Point(self.roi.mapToView(pg.Point(0,0))) # bottom-left\\n self.params.updateSystem()\",\n \"def circle_area(self):\\n return np.pi * self.ring_radius ** 2\",\n \"def circleArea(radius):\\n return math.pi * radius * radius\",\n \"def get_center_radius(ra, dec):\\n center_ra = (np.max(ra) + np.min(ra))/2\\n center_dec = (np.max(dec) + np.min(dec))/2\\n radius = np.max([np.max(ra) - np.min(ra),\\n np.max(dec) - np.min(dec)])\\n return center_ra, center_dec, radius\",\n \"def add_circle(self, r_center, c_center, radius, color=BLUE, image=np.full((640, 480, 3), BLACK)):\\n circle = np.fromfunction(lambda r, c, _: (r - r_center) ** 2 + (c - c_center) ** 2 <= radius ** 2, image.shape)\\n return np.where(circle, color, image)\",\n \"def Intarea( xc, yc, r, x0, x1, y0, y1):\\n\\n#\\n# Shift the objects so that the circle is at the origin.\\n#\\n x0 = x0 - xc\\n y0 = y0 - yc\\n x1 = x1 - xc\\n y1 = y1 - yc\\n\\n return Oneside( x1, y0, y1, r ) + Oneside( y1, -x1, -x0, r ) +\\\\\\n Oneside( -x0, -y1, -y0, r ) + Oneside( -y0, x0, x1, r )\",\n \"def rad_field_initial_condition(self):\\n\\n # revert in viewing direct\\n angle, _ = f.convert_direction(self.receiver_elevation, self.receiver_azimuth)\\n # Looking at the sky\\n if angle < 90:\\n I_init = (\\n self.sun_intensity\\n * f.delta_func(self.sun_elevation - self.receiver_elevation)\\n * f.delta_func(self.sun_azimuth - self.receiver_azimuth)\\n )\\n\\n # Looking at the ground\\n elif angle > 90:\\n I_ground = RT_model_1D.calc_direct_beam_intensity(self, 0)\\n\\n I_lambert = (\\n I_ground\\n * self.ground_albedo\\n * np.cos(np.deg2rad((self.sun_elevation + 180) % 360))\\n )\\n\\n I_specular = (\\n I_ground\\n * self.ground_albedo\\n * f.delta_func(self.sun_elevation + self.receiver_elevation - 180)\\n * f.delta_func(self.sun_azimuth - self.receiver_azimuth)\\n )\\n\\n I_init = (\\n 1 - self.reflection_type\\n ) * I_lambert + self.reflection_type * I_specular\\n\\n else:\\n I_init = np.empty(self.stokes_dim)\\n I_init.fill(np.nan)\\n\\n return I_init\",\n \"def visibility_angle(minviselmap, azmap, rmap,\\n rpol, azpol, elpol, DEM_res, DEM_xmin, DEM_ymin,\\n rad_x, rad_y, beamwidth, pulsewidth, az_conv=0,\\n raster_oversampling=1, verbose=True):\\n ncols, nrows = minviselmap.shape\\n\\n pulselength = pulsewidth * 3.e8 / 2. # [m]\\n az_conv_offset = az_conv / 2.\\n\\n if raster_oversampling == 0:\\n N = 1\\n elif raster_oversampling == 1:\\n N = int(np.ceil(2 * DEM_res / pulselength))\\n else:\\n N = raster_oversampling\\n\\n if N != 1:\\n # New dimensions\\n nc = N * ncols\\n nr = N * nrows\\n\\n # repeat the values NxN, equivalent of rebin in IDL\\n minvisvals = np.repeat(np.repeat(minviselmap, N, axis=0), N, axis=1)\\n\\n # New x- and y-vectors\\n xvec = np.arange(nr) * DEM_res / N + DEM_xmin\\n yvec = np.arange(nc) * DEM_res / N + DEM_ymin\\n\\n xdiff = (xvec - rad_x)\\n ydiff = (yvec - rad_y)\\n\\n # New distance from radar map\\n X, Y = np.meshgrid(xdiff, ydiff)\\n rvals = np.sqrt(X ** 2 + Y ** 2)\\n\\n # New azimuth map\\n azmap_rad = (np.arctan2(X, Y) + 2 * np.pi) % (2 * np.pi)\\n azvals = azmap_rad * 180. / np.pi\\n else:\\n rvals = rmap\\n azvals = azmap\\n minvisvals = minviselmap\\n\\n \\\"\\\"\\\"\\n Define the area around a point P(range, azimuth) where the cells\\n have a contribution to the visibility. This area is defined with the\\n range limits from range-dr/2 to range+dr/2 and the\\n azimuth limits from azimuth-daz_offset to azimuth+daz_offset.\\n\\n For a Gaussian antenna, azimuth offset more than 2*HPBW does not a\\n have remarkable contribution.\\n With a rectangular pulse and a matched filter cells farer away\\n than pulse length does not a have remarkable contribution.\\n \\\"\\\"\\\"\\n\\n daz_offset = (2. * beamwidth) + az_conv_offset # [deg]\\n del_offset = (2. * beamwidth) # [deg]\\n\\n delta_deg = 0.1 # [deg]\\n ndaz = int(2 * daz_offset / delta_deg)\\n ndel = int(2 * del_offset / delta_deg)\\n\\n daz_vec = np.arange(ndaz + 1) * delta_deg - daz_offset # [deg]\\n del_vec = np.arange(ndel + 1) * delta_deg - del_offset # [deg]\\n\\n daz_area_antenna, del_area_antenna = np.meshgrid(daz_vec, del_vec)\\n\\n # Get the two-way weighting factor due to the azimuth and elevation offsets\\n # to the main antenna direction (assuming a Gaussian antenna pattern).\\n ant_weight = antenna_pattern_gauss(daz_area_antenna, del_area_antenna,\\n beamwidth, az_conv=az_conv,\\n units='deg')\\n\\n ant_weight_total = np.nansum(ant_weight)\\n\\n nazim = len(azpol)\\n nrange = len(rpol)\\n range_resolution = rpol[1] - rpol[0]\\n\\n # pyart storage format: 2D arrays (naz * nel, nranges)\\n vispol = np.zeros((nazim * len(elpol), nrange))\\n\\n # Inside the loops over range (rr) and azimuth (az), the\\n # coordinates (rr, az) describe the point P(rr, az) for which\\n # the visibility is calculated. If more than one DEM cell is within\\n # the area from az-daz/2 to az+daz/2 and from rr-dr/2 to\\n # rr+dr/2, the calculated visibility value is set to all of these\\n # cells (next neighbor).\\n\\n for iaz in range(nazim):\\n logging.info(f'Computing azimuth {azpol[iaz]:2.1f}')\\n # Get azimuth values to explore\\n azmin = azpol[iaz] - daz_offset\\n azmax = azpol[iaz] + daz_offset\\n if azmin < 0:\\n azmin = 360. + azmin\\n indaz = np.logical_or(\\n np.logical_and(\\n azvals >= 0, azvals < azmax), np.logical_and(\\n azvals >= azmin, azvals <= 360.))\\n\\n elif azmax > 360:\\n azmax = azmax - 360.\\n indaz = np.logical_or(np.logical_and(azvals >= azmin,\\n azvals <= 360),\\n np.logical_and(azvals >= 0,\\n azvals < azmax))\\n else:\\n indaz = np.logical_and(azvals >= azmin, azvals < azmax)\\n\\n if not np.any(indaz):\\n logging.warning(\\n f'Visibility for azim {azpol[iaz]:f} not known')\\n continue\\n\\n indaz = np.where(indaz)\\n\\n # These declaration avoids reindexing at every loop turn\\n # and save a lot of time\\n rvals_indaz = rvals[indaz]\\n\\n for iel, el in enumerate(elpol):\\n # range bins (center pulse) [m]rvec[ir]-dr/2\\n rmin_ground = rpol * np.cos(el * np.pi / 180.)\\n # range bins (center pulse) [m]rvec[ir]+dr/2\\n rmax_ground = (rpol + range_resolution) * np.cos(el * np.pi / 180.)\\n\\n for ir in range(0, nrange):\\n # Get range values to explore\\n indr = np.logical_and(rvals_indaz >= rmin_ground[ir],\\n rvals_indaz < rmax_ground[ir])\\n\\n if not np.any(indr):\\n logging.warning(\\n 'Visibility for az {:f} deg and range {:f} not known' .format(\\n azpol[iaz], rpol[ir]))\\n vispol[iaz + iel * nazim, ir] = 100.\\n continue\\n\\n # DEM cells that contribute to the cells to set indset\\n indcells = tuple([indaz[0][indr], indaz[1][indr]])\\n\\n ind = minvisvals[indcells] < el - del_offset\\n if np.all(ind): # radar beam completely above min vis ang\\n if ir > 0:\\n if vispol[iaz + iel * nazim, ir - 1] == 100:\\n vispol[iaz + iel * nazim, ir] = 100\\n else:\\n vispol[iaz + iel * nazim,\\n ir] = vispol[iaz + iel * nazim, ir - 1]\\n else:\\n vispol[iaz + iel * nazim, ir] = 100.\\n continue\\n\\n ind = minvisvals[indcells] > el + del_offset\\n if np.all(ind): # radar beam completely below min vis ang\\n continue\\n\\n # Calculate offsets in azimuth to the point P(rr,az)\\n daz_area = azvals[indcells] - azpol[iaz]\\n\\n ind = daz_area > 180.\\n daz_area[ind] = daz_area[ind] - 360.\\n ind = daz_area < -180.\\n daz_area[ind] = daz_area[ind] + 360.\\n\\n vis = 0.\\n ind_rzero = rvals[indcells] <= 0.0\\n\\n if not np.any(ind_rzero):\\n vis = vis_weighting(daz_vec, del_vec, daz_area, ant_weight,\\n minvisvals[indcells], el)\\n\\n vis = (vis / ant_weight_total) * 100.\\n\\n # Set vis to all values inside the set area.\\n if ir > 0:\\n if vispol[iaz + iel * nazim, ir - 1] < vis:\\n vispol[iaz + iel * nazim,\\n ir] = vispol.data[iaz + iel * nazim,\\n ir - 1]\\n else:\\n vispol[iaz + iel * nazim, ir] = vis\\n else:\\n vispol[iaz + iel * nazim, ir] = vis\\n\\n return vispol\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6983759","0.6608037","0.6608037","0.6565604","0.6497947","0.6497947","0.6379288","0.6208821","0.61896527","0.6177499","0.61722773","0.6139702","0.607727","0.6039256","0.59929496","0.5988569","0.5976138","0.597191","0.5934677","0.5894657","0.5884144","0.5852463","0.5846988","0.58342415","0.583034","0.582756","0.5796881","0.57464653","0.57388157","0.57142276","0.57132804","0.5707367","0.569345","0.5683748","0.56508464","0.56460714","0.563249","0.562886","0.561039","0.5603992","0.5603973","0.56029624","0.56029624","0.56029624","0.5602814","0.5602165","0.558654","0.55789137","0.55675906","0.5565383","0.55619043","0.5559182","0.553811","0.55319756","0.552584","0.5519951","0.5518977","0.5518096","0.5504199","0.550095","0.549815","0.54979914","0.5497098","0.54894614","0.5487539","0.5485512","0.5484085","0.5463404","0.54610854","0.5458283","0.54530334","0.54502976","0.5447169","0.5442916","0.544263","0.5431832","0.5426489","0.542398","0.54170996","0.54151905","0.54039836","0.5403147","0.54001594","0.5386808","0.5382415","0.5375386","0.5374009","0.5371357","0.53698146","0.5365539","0.5356506","0.5344429","0.53432673","0.53418237","0.5340511","0.53393936","0.53380996","0.53371376","0.53349817","0.53344387"],"string":"[\n \"0.6983759\",\n \"0.6608037\",\n \"0.6608037\",\n \"0.6565604\",\n \"0.6497947\",\n \"0.6497947\",\n \"0.6379288\",\n \"0.6208821\",\n \"0.61896527\",\n \"0.6177499\",\n \"0.61722773\",\n \"0.6139702\",\n \"0.607727\",\n \"0.6039256\",\n \"0.59929496\",\n \"0.5988569\",\n \"0.5976138\",\n \"0.597191\",\n \"0.5934677\",\n \"0.5894657\",\n \"0.5884144\",\n \"0.5852463\",\n \"0.5846988\",\n \"0.58342415\",\n \"0.583034\",\n \"0.582756\",\n \"0.5796881\",\n \"0.57464653\",\n \"0.57388157\",\n \"0.57142276\",\n \"0.57132804\",\n \"0.5707367\",\n \"0.569345\",\n \"0.5683748\",\n \"0.56508464\",\n \"0.56460714\",\n \"0.563249\",\n \"0.562886\",\n \"0.561039\",\n \"0.5603992\",\n \"0.5603973\",\n \"0.56029624\",\n \"0.56029624\",\n \"0.56029624\",\n \"0.5602814\",\n \"0.5602165\",\n \"0.558654\",\n \"0.55789137\",\n \"0.55675906\",\n \"0.5565383\",\n \"0.55619043\",\n \"0.5559182\",\n \"0.553811\",\n \"0.55319756\",\n \"0.552584\",\n \"0.5519951\",\n \"0.5518977\",\n \"0.5518096\",\n \"0.5504199\",\n \"0.550095\",\n \"0.549815\",\n \"0.54979914\",\n \"0.5497098\",\n \"0.54894614\",\n \"0.5487539\",\n \"0.5485512\",\n \"0.5484085\",\n \"0.5463404\",\n \"0.54610854\",\n \"0.5458283\",\n \"0.54530334\",\n \"0.54502976\",\n \"0.5447169\",\n \"0.5442916\",\n \"0.544263\",\n \"0.5431832\",\n \"0.5426489\",\n \"0.542398\",\n \"0.54170996\",\n \"0.54151905\",\n \"0.54039836\",\n \"0.5403147\",\n \"0.54001594\",\n \"0.5386808\",\n \"0.5382415\",\n \"0.5375386\",\n \"0.5374009\",\n \"0.5371357\",\n \"0.53698146\",\n \"0.5365539\",\n \"0.5356506\",\n \"0.5344429\",\n \"0.53432673\",\n \"0.53418237\",\n \"0.5340511\",\n \"0.53393936\",\n \"0.53380996\",\n \"0.53371376\",\n \"0.53349817\",\n \"0.53344387\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7116994"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":85584,"cells":{"query":{"kind":"string","value":"Check that the games data is correct."},"document":{"kind":"string","value":"def _data_integrity_check(df):\n if df is None or len(df) == 0:\n logging.info(\"Dataframe is empty! No games data recorded.\")\n return\n\n if not isinstance(df, pd.DataFrame):\n raise ValueError(f\"Type of df is {type(df)}, it should be pandas.DataFrame\")\n\n logging.info(f\"Checking integrity of games dataframe with shape {df.shape}...\")\n\n seasons = df['season'].unique()\n max_season = int(max(seasons))\n\n expected_seasons = set(range(config.START_SEASON, max_season + 1))\n\n if set(seasons) != expected_seasons:\n missing_season = expected_seasons - set(seasons)\n raise DataIntegrityError(f\"Most recent season with data is {max_season}\"\n f\" but there's no data for {missing_season}!\")\n\n game_ids = list(df['game_id'])\n if len(game_ids) != len(set(game_ids)):\n raise DataIntegrityError(\"There are duplicate game ids!\")\n\n for season in list(seasons):\n season_types = set(df[df['season'] == season]['type'].unique())\n\n if season != max_season and len(season_types) != 3:\n raise DataIntegrityError(f\"{season} is not the max season of {max_season}\"\n f\"and only has {len(season_types)} season types\")\n\n elif len(season_types) == 1 and season_types != {'pre'}:\n raise DataIntegrityError(f\"There is only 1 season type for {seasons}: {season_types}\"\n f\"and its not 'pre'!\")\n\n elif len(season_types) == 2 and season_types != {'pre', 'reg'}:\n raise DataIntegrityError(f\"There are 2 season types for {season} and they're not ['pre', 'reg']!\")\n\n elif 'reg' in df[df['season'] == season]['type'].unique():\n reg_season_df = df[(df['season'] == season) & (df['type'] == 'reg')]\n weeks = reg_season_df['week'].unique()\n target_weeks = set(range(1, 18))\n missing_weeks = target_weeks.difference(set(weeks))\n if missing_weeks:\n raise DataIntegrityError(f\"{season} reg season is missing weeks {missing_weeks}!\")\n\n logging.info(\"Integrity check passed.\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["async def check_games(self, ctx):\n print(self.data)\n print(self.games_info)","def isValid(self, game):\n return True","def check_game_sanity(game_val, log):\n\n global __problem_deck_index__\n\n supply = game_val.get_supply()\n if game_val.any_resigned():\n return True # Resign leaves deck in arbitrary state, don't catch that\n\n # ignore known bugs.\n #if set(supply).intersection([get_card('Masquerade'), get_card('Black Market'), get_card('Trader')]):\n #return True\n\n # TODO: add score sanity checking here\n last_state = None\n game_state_iterator = game_val.game_state_iterator()\n for game_state in game_state_iterator:\n last_state = game_state\n for player_deck in game_val.get_player_decks():\n parsed_deck_comp = player_deck.Deck()\n computed_deck_comp = last_state.get_deck_composition(\n player_deck.name())\n\n parse_common.delete_keys_with_empty_vals(parsed_deck_comp)\n computed_dict_comp = dict(computed_deck_comp)\n parse_common.delete_keys_with_empty_vals(computed_dict_comp)\n\n if parsed_deck_comp != computed_deck_comp:\n found_something_wrong = False\n for card in set(parsed_deck_comp.keys() +\n computed_deck_comp.keys()):\n if parsed_deck_comp.get(card, 0) != \\\n computed_deck_comp.get(card, 0):\n if not found_something_wrong:\n __problem_deck_index__ += 1\n log.debug('[%d] %18s %9s %9s', __problem_deck_index__, 'card', 'from-data', 'from-sim')\n log.debug('[%d] %-18s %9d %9d', __problem_deck_index__, card, parsed_deck_comp.get(card, 0),\n computed_deck_comp.get(card, 0))\n found_something_wrong = True\n if found_something_wrong:\n try:\n log.debug('[%d] insane game for %s %s: %s', __problem_deck_index__, player_deck.name(), game_val.get_id(),\n ' '.join(map(str, game_val.get_supply())))\n except UnicodeEncodeError, e:\n None\n return False\n return True","def test_game_creation(self):\n self.assertTrue(len(models.Game.objects.all()) == 1)\n game = models.Game.objects.all()[0]\n\n # Hydrocarbon piles initialization test\n number_of_piles = len(settings.HYDROCARBON_STOCKS_PER_PLAYER)\n for i_pile in range(number_of_piles):\n self.assertEqual(0, game.hydrocarbon_piles.get(index=i_pile).stock_amount)\n\n # Events initialization test [TO DO]\n\n # Source buildings loading test\n version_source_buildings = models.SourceBuilding.objects.filter(version=game.version)\n self.assertEqual(version_source_buildings.count(), game.source_buildings.count())\n for version_source_building in version_source_buildings:\n self.assertEqual(version_source_building, game.source_buildings.get(slug=version_source_building.slug))\n\n # Source events loading test [TO DO]\n\n # Source technologies loading test\n version_source_technologies = models.SourceTechnology.objects.filter(version=game.version)\n self.assertEqual(version_source_technologies.count(), game.source_technologies.count())\n for version_source_technology in version_source_technologies:\n self.assertEqual(version_source_technology,\n game.source_technologies.get(slug=version_source_technology.slug))","def test_import(self, game=\"SuperMarioKart-Snes\"):\n self.assertTrue(game in retro.data.list_games())","def test_get_game(self):\n pass","def _check_integrity(self):\n\n count = 0\n for (x, y) in self.__players[ChessGame.BLACK].union(\n self.__players[ChessGame.WHITE]):\n assert (x, y) in self.__board\n count += 1\n\n assert count == len(self.__board)","def test_valid_new_game(self):\n self._game.new_game()\n self.assertIsRUNNING(self._game)\n self.assertIsNotNone(self._game.table)\n self.assertEqual(self._game.table.player_turn, 1,\n \"Game.table unsuccessful init.\")","def test_api_new_game(self):\n\n with self.client as client:\n ...\n response = client.post(\"/api/new-game\")\n game_data = response.get_json() #, data={\"gameId\": game_id, \"board\": game.board}\n self.assertEqual(response.status_code, 200)\n\n self.assertIn(game_data[\"gameId\"], games)\n self.assertIs(type(game_data[\"gameId\"]), str)\n self.assertIs(type(game_data[\"board\"]), list)\n self.assertIs(len(game_data[\"board\"]), 5)","def verify_valid_game(self, lotto_game:List[int]):\n is_valid = True;\n lotto_game_temp = []\n for number in lotto_game:\n if number not in lotto_game_temp:\n lotto_game_temp.append(number)\n else:\n print(number, lotto_game, lotto_game_temp)\n is_valid = False\n raise InvalidLottoGame\n \n lotto_game_temp_length = len(lotto_game_temp)\n if lotto_game_temp_length != LOTTO_GAME_LENGTH and lotto_game_temp_length != LOTTO_GAME_WITH_BONUS_LENGTH:\n print(lotto_game_temp)\n is_valid = False\n raise InvalidLottoGame\n\n return is_valid","def test_no_games(self):\n sgf = \"\"\n try:\n coll = parseSgf(sgf)\n except SgfParseError, e:\n if str(e) == \"Empty collection.\":\n return\n self.assertFalse(1)","def check_game(self):\n gameOver = None\n if self.turn > 4:\n gameOver = self.check_x_won()\n if gameOver is True:\n self.game_x_won()\n return\n\n gameOver = None\n if self.turn > 5:\n gameOver = self.check_o_won()\n if gameOver is True:\n self.game_o_won()\n return\n\n if self.turn >= 9:\n self.game_tie()\n return","def check_valid(self, store):\n\n # Name (of the game) is always required\n if self._name is None or self._name == '':\n raise InvalidTransaction('name not set')\n\n # Action is always required\n if self._action is None or self._action == '':\n raise InvalidTransaction('action not set')\n\n # The remaining validity rules depend upon which action has\n # been specified.\n\n if self._action == 'CREATE':\n if self._name in store:\n raise InvalidTransaction('game already exists')\n\n # Restrict game name letters and numbers.\n if not re.match(\"^[a-zA-Z0-9]*$\", self._name):\n raise InvalidTransaction(\n \"Only letters a-z A-Z and numbers 0-9 are allowed in \"\n \"the game name!\")\n\n elif self._action == 'JOIN':\n\n # Check that self._name is in the store (to verify\n # that the game exists (see FIRE below).\n if self._name not in store:\n raise InvalidTransaction(\n 'Trying to join a game that does not exist')\n\n state = store[self._name]['State']\n\n if state != \"NEW\":\n # Check that the game can be joined (the state is 'NEW')\n raise InvalidTransaction(\n 'The game cannot accept any new participant')\n\n # Check that the game board is the right size (10x10)\n if len(self._board) != self._size:\n raise InvalidTransaction('Game board is not valid size')\n for row in range(0, self._size):\n if len(self._board[row]) != self._size:\n raise InvalidTransaction(\n 'Game board is not valid size')\n\n # Validate that self._board contains hash-like strings\n for row in range(0, self._size):\n for col in range(0, self._size):\n # length of sha512 hexdigest is 32 characters\n if len(self._board[row][col]) != 128:\n raise InvalidTransaction(\"invalid board hash\")\n\n elif self._action == 'FIRE':\n\n if self._name not in store:\n raise InvalidTransaction('no such game')\n\n if self._column is None:\n raise InvalidTransaction(\"Column is required\")\n\n if self._row is None:\n raise InvalidTransaction(\"Row is required\")\n\n # Check that self._column is valid (letter from A-J)\n if not any((c in self._acceptable_columns) for c in self._column):\n raise InvalidTransaction(\n 'Acceptable columns letters are A to J')\n\n # Check that self._row is valid (number from 1-10)\n try:\n row = int(self._row)\n if (row < 1) or (row > 10):\n raise InvalidTransaction(\n 'Acceptable rows numbers are 1 to 10')\n except ValueError:\n raise InvalidTransaction(\n 'Acceptable rows numbers are 1 to 10')\n\n state = store[self._name]['State']\n\n if state in ['P1-WIN', 'P2-WIN']:\n raise InvalidTransaction('game complete')\n\n if state == 'NEW':\n raise InvalidTransaction(\n \"Game doesn't have enough players.\")\n\n player = None\n if state == 'P1-NEXT':\n player1_firing = True\n player = store[self._name]['Player1']\n if player != self._signer_public_key:\n raise InvalidTransaction('invalid player 1')\n elif state == 'P2-NEXT':\n player1_firing = False\n player = store[self._name]['Player2']\n if player != self._signer_public_key:\n raise InvalidTransaction('invalid player 2')\n else:\n raise InvalidTransaction(\n \"invalid state: {}\".format(state))\n\n # Check whether the board's column and row have already been\n # fired upon.\n if player1_firing:\n target_board = store[self._name]['TargetBoard1']\n else:\n target_board = store[self._name]['TargetBoard2']\n\n firing_row = int(self._row) - 1\n firing_column = ord(self._column) - ord('A')\n if target_board[firing_row][firing_column] != '?':\n raise InvalidTransaction(\n \"{} {} has already been fired upon\".format(\n self._column, self._row)\n )\n\n # Make sure a space is revealed if it isn't the first turn.\n if 'LastFireColumn' in store[self._name]:\n if self._reveal_space is None or self._reveal_nonce is None:\n raise InvalidTransaction(\n \"Attempted to fire without revealing target\")\n\n if state == 'P1-NEXT':\n hashed_board = 'HashedBoard1'\n else:\n hashed_board = 'HashedBoard2'\n\n col = ord(store[self._name]['LastFireColumn']) - ord('A')\n row = int(store[self._name]['LastFireRow']) - 1\n hashed_space = hash_space(self._reveal_space,\n self._reveal_nonce)\n if store[self._name][hashed_board][row][col] != hashed_space:\n raise InvalidTransaction(\n \"Hash mismatch on reveal: {} != {}\".format(\n store[self._name][hashed_board][row][col],\n hashed_space))\n\n else:\n raise InvalidTransaction(\n 'invalid action: {}'.format(self._action))","def test_get_player_stats_from_game():\n team = \"Titans\"\n year = \"2018\"\n week = \"1\"\n expected_team_stats = {}\n\n assert ff_team.get_player_stats_from_game(\n team, year, week) == expected_team_stats","def test_valid_game_setup(self):\n self.assertEqual(self._game.active_players(), 1)\n for x in xrange(1, 4):\n self._game.add_player(self._users[x], x)\n self.assertEqual(self._game.active_players(), 4)\n self.assertIsREADY(self._game)","def test_game_info_creation(self):\n\n guess = Guess.objects.all()[0]\n self.assertEqual(guess.game_id, self.game_id )\n self.assertEqual(guess.peg_1, \"R\")\n self.assertEqual(guess.peg_2, \"Y\")\n self.assertEqual(guess.peg_3, \"R\")\n self.assertEqual(guess.peg_4, \"R\")\n self.assertEqual(guess.black_pegs, 3)\n self.assertEqual(guess.white_pegs, 0)","def test_play_game(self):\r\n\r\n \r\n a_players = [RandomPlayer(1), RandomPlayer(2)]\r\n a_x_dist = 3\r\n a_y_dist = 3\r\n a_num_to_win = 1\r\n a_game = Game(a_players, a_x_dist, a_y_dist, a_num_to_win)\r\n\r\n #Game is played to competion\r\n a_game.play_game()\r\n\r\n a_history = a_game.get_history()\r\n\r\n #Go through each move and check to be sure it's valid\r\n for i in range(1,len(a_history)):\r\n #Get copy of the board\r\n prev_board = a_history[i-1]\r\n cur_board = a_history[i]\r\n\r\n #Check if the board chosen is in valid states\r\n self.assertTrue(cur_board in prev_board.get_states(a_players[0].get_id()) or cur_board in prev_board.get_states(a_players[1].get_id()),\\\r\n \"An invalid board state was added to the history\")\r\n\r\n if i == len(a_history) - 1:\r\n self.assertTrue(cur_board.check_win(a_num_to_win, a_players[0].get_id()) or cur_board.check_win(a_num_to_win, a_players[1].get_id()) or cur_board.check_tie())\r\n else: \r\n self.assertFalse(cur_board.check_win(a_num_to_win, a_players[0].get_id()) or cur_board.check_win(a_num_to_win, a_players[1].get_id()) or cur_board.check_tie())","def test_init_with_existing_game(self):\n pass\n # Ensure judge is the same","def check_test_case_validity(test_case_dataset):\n for i, test_case in enumerate(test_case_dataset):\n assert \"NAME\" in test_case, f\"Test case #{i} Invalid NAME\"\n\n assert (\n \"N_STATES\" in test_case\n and isinstance(test_case[\"N_STATES\"], int)\n and 0 < test_case[\"N_STATES\"] <= 64\n ), f\"Test case #{i} Invalid N_STATES\"\n\n assert (\n \"N_SYMBOLS\" in test_case\n and isinstance(test_case[\"N_SYMBOLS\"], int)\n and 0 < test_case[\"N_SYMBOLS\"] <= 64\n ), f\"Test case #{i} Invalid N_SYMBOLS\"\n\n assert (\n \"PLAYER_INPUT_SIZES\" in test_case\n and isinstance(test_case[\"PLAYER_INPUT_SIZES\"], list)\n and len(test_case[\"PLAYER_INPUT_SIZES\"]) > 1\n and all(\n (isinstance(x, int) and x > 0) for x in test_case[\"PLAYER_INPUT_SIZES\"]\n )\n ), f\"Test case #{i} Invalid PLAYER_INPUT_SIZES\"\n\n assert \"REPETITIONS\" not in test_case or (\n isinstance(test_case[\"REPETITIONS\"], int) and 0 < test_case[\"REPETITIONS\"]\n ), f\"Test case #{i} Invalid REPETITIONS\"\n\n assert \"DEBUG\" not in test_case or isinstance(\n test_case[\"DEBUG\"], bool\n ), f\"Test case #{i} Invalid DEBUG\"\n\n assert \"VIRTUAL_MACHINE\" not in test_case or (\n isinstance(test_case[\"VIRTUAL_MACHINE\"], str)\n and test_case[\"VIRTUAL_MACHINE\"] in [\"./spdz2k-party.x\", \"./semi2k-party.x\"]\n ), f\"Test case #{i} Invalid VIRTUAL_MACHINE\"\n\n if \"PLAYER_DATA\" in test_case:\n assert isinstance(\n test_case[\"PLAYER_DATA\"], list\n ), f\"Test case #{i} Invalid PLAYER_DATA - Not a list\"\n for j, size in enumerate(test_case[\"PLAYER_INPUT_SIZES\"]):\n player_data = test_case[\"PLAYER_DATA\"][j]\n max_value = test_case[\"N_SYMBOLS\"]\n assert (\n isinstance(player_data, list)\n and len(player_data) == size\n and all(\n (isinstance(x, int) and 0 <= x <= max_value)\n for x in player_data\n )\n ), f\"Test case #{i} Invalid PLAYER_DATA - User {j} inputs are invalid\"","def test_check_inputs(game):\n\n with warnings.catch_warnings():\n warnings.simplefilter(\"error\")\n game.check_inputs()\n\n assert game.residents == game._all_residents\n assert game.hospitals == game._all_hospitals","def is_not_game_ended(data_map):\n\n continue_game = True\n loser = None\n winner = None\n\n # If a player has not any units, the other player win.\n for i in range(2):\n if not len(data_map['player' + str(i + 1)]) and continue_game:\n loser = data_map['player' + str(i + 1)]\n winner = data_map['player' + str(3 - (i + 1))]\n continue_game = False\n\n # If there's 20 turn without any attack, player1 loose and player2 win.\n if float(data_map['attack_turn']) / 2 > 19:\n loser = data_map['player1']\n winner = data_map['player2']\n continue_game = False\n\n return continue_game, loser, winner","def test_get_game_diff(self):\n pass","def _check_input_data(self):\n\n n0, n1, corr, pval = np.nan, np.nan, np.nan, np.nan\n\n error_code_test = 0\n error_text_test = 'No error occurred'\n try:\n error_code_test, error_msg = self._check_notnull()\n if error_code_test == 0:\n error_code_test, error_msg, n0, n1 = self._check_group_obs(self.test_min_data)\n if error_code_test == 0:\n error_code_test, error_msg, corr, pval = \\\n self._check_spearman_corr(self.min_corr, self.max_p)\n if error_code_test != 0:\n error_text_test = str(error_msg)\n except:\n error_code_test = 9\n error_text_test = 'Unknown Error'\n\n self.checkstats = {'n0': n0, 'n1': n1, 'frame_spearmanR': corr, 'frame_corrPval': pval}\n\n self.error_code_test = error_code_test\n self.error_text_test = error_text_test\n\n return self.error_code_test, self.error_text_test","def ensure_valid_data():\n cursor = connection.cursor()\n cursor.execute(\"SELECT id, name, st_area(geom) FROM firestation_firedepartment where st_area(geom)>6.99\")\n messages = []\n\n for id, name, area in cursor.fetchall():\n messages.append('{0} ({1}) has an area of {2}.'.format(name, id, area))\n\n if messages:\n mail_admins('Invalid Geometries Detected', message='\\n'.join(messages))\n\n cursor.execute(\"SELECT COUNT(*) FROM genericm2m_relatedobject;\")\n generic_count = cursor.fetchone()\n\n if generic_count[0] < 2940:\n generic_count_message = \"Related government units has dropped below 2,940.\"\n mail_admins('Low number of government units alert.', message=generic_count_message)","def do_the_things():\n returned_no_game = False\n returned_game_time = False\n returned_game_final = False\n returned_rival_final = False\n returned_game_soon = False\n returned_rival_soon = False\n returned_game_start = False\n compare_scores = ['0', '0']\n timeout = time.time() + 60 * 60 * 23\n\n while not (returned_no_game or returned_game_final):\n\n try:\n message = False\n rival_message = False\n\n \"\"\" Infinite loops are cool \"\"\"\n if time.time() > timeout:\n break\n\n game_data = get_fresh_data(team)\n\n if game_data:\n \"\"\" The default TZ for mlb is US/Eastern, we'll do some things and make it local TZ \"\"\"\n tz = pytz.timezone('US/Eastern')\n eastern_time = datetime.datetime.strptime(\"%s %s\" % (game_data['time_date'], game_data['ampm']), '%Y/%m/%d %I:%M %p')\n eastern_time = tz.localize(eastern_time)\n pacific_time = eastern_time.astimezone(pytz.timezone(timezone))\n\n opponent, our_score, their_score, venue = set_vars(game_data)\n scores = sorted([our_score, their_score], reverse=True)\n\n if not game_data and not returned_no_game:\n returned_no_game = True\n message = (\"The %s don't have a game scheduled today. Rest well guys!\" % team_hashtag)\n\n if game_data and not returned_game_time:\n returned_game_time = True\n message = ((\"The %s are playing against the %s today, first pitch is at \" + pacific_time.strftime(\"%-I:%M%p %Z\") + \" at %s. %s\") % (team_hashtag, opponent, venue, team_hash2))\n\n if \"Warmup\" in game_data[\"status\"] and not returned_game_soon:\n returned_game_soon = True\n message = ((\"The %s are playing against the %s in a moment, first pitch is at \" + pacific_time.strftime(\"%-I:%M%p %Z\") + \" at %s. %s\") % (team_hashtag, opponent, venue, team_hash2))\n\n if \"In Progress\" in game_data[\"status\"] and compare_scores == ['0', '0'] and not returned_game_start:\n returned_game_start = True\n message = (\"It's gametime! Go %s!!! %s\" % (team_hashtag, team_hash2))\n\n if \"In Progress\" in game_data[\"status\"] and not compare_scores == [our_score, their_score]:\n compare_scores = [our_score, their_score]\n if int(our_score) > int(their_score):\n message = (\"The %s are leading the %s, the score is currently %s-%s. %s\" % (team_hashtag, opponent, scores[0], scores[1], team_hash2))\n elif int(our_score) < int(their_score):\n message = (\"The %s are trailing the %s, the score is currently %s-%s. %s\" % (team_hashtag, opponent, scores[0], scores[1], team_hash2))\n elif int(our_score) == int(their_score):\n message = (\"The %s are tied with the %s, the score is currently %s-%s. %s\" % (team_hashtag, opponent, scores[0], scores[1], team_hash2))\n\n if (\"Game Over\" or \"Final\") in game_data[\"status\"]:\n returned_game_final = True\n if our_score > their_score:\n message = (\"The %s beat the %s today at %s with a score of %s-%s. %s\" % (team_hashtag, opponent, venue, scores[0], scores[1], team_hash2))\n else:\n message = (\"The %s lost against the %s today at %s with a score of %s-%s. Get 'em tomorrow! %s\" % (team_hashtag, opponent, venue, scores[0], scores[1], team_hash2))\n\n if not returned_rival_final:\n rival_game_data = get_fresh_data(rival_team)\n rival_opponent, rival_our_score, rival_their_score, rival_venue = set_vars(rival_game_data)\n rival_scores = sorted([rival_our_score, rival_their_score], reverse=True)\n\n if \"Warmup\" in rival_game_data[\"status\"] and not returned_rival_soon:\n returned_rival_soon = True\n rival_message = (\"The %s game is about to start, go %s!!! %s\") % (rival_team_hashtag, rival_opponent, rival_team_hash2)\n\n if (\"Game Over\" or \"Final\") in rival_game_data[\"status\"]:\n returned_rival_final = True\n if rival_our_score > rival_their_score:\n rival_message = (\"The %s beat the %s today at %s with a score of %s-%s, boooo!\" % (rival_team_hashtag, rival_opponent, rival_venue, rival_scores[0], rival_scores[1]))\n else:\n rival_message = (\"The %s lost against the %s today at %s with a score of %s-%s. Hell yeah!\" % (rival_team_hashtag, rival_opponent, rival_venue, rival_scores[0], rival_scores[1]))\n\n if message:\n if testmode:\n print(message)\n else:\n twitter.tweet(message)\n\n if rival_message:\n if testmode:\n print(rival_message)\n else:\n twitter.tweet(rival_message)\n\n except tweepy.TweepError as e:\n print e.message[0]['code']\n print e.args[0][0]['code']\n\n time.sleep(5)","def getAllValidGames(self):\n\n gamelist = pd.read_csv('Resources/allgames.csv.gz', compression='gzip')\n games = []\n appids = gamelist['appid'].unique()\n i = 0\n pbar = tqdm(total=appids.size)\n pbar.set_description('Processing ')\n appsize = appids.shape[0] - 1\n while (i <= appsize):\n try:\n if self.checkapp(appids[i]):\n games.append(appids[i])\n i += 1\n pbar.update(1)\n except:\n pbar.set_description('{(-_-)}Zzz..')\n sleep(5)\n pbar.set_description('Processing ')\n continue\n pbar.close()\n validgames = pd.DataFrame(games)\n validgames.to_csv('Resources/validgames.csv')","def check_game_status(self):\n for player in (\"1\", \"2\"):\n row_win = np.apply_along_axis(\n lambda x: set(x) == {player}, 1, self.board\n ).any()\n col_win = np.apply_along_axis(\n lambda x: set(x) == {player}, 0, self.board\n ).any()\n d1_win = set(self.data[[0, 4, 8]]) == {player}\n d2_win = set(self.data[[2, 4, 6]]) == {player}\n if any([row_win, col_win, d1_win, d2_win]):\n return (\"win\", player)\n\n if self.counter[\"_\"] == 0:\n return (\"tie\", None)\n else:\n return (\"turn\", \"1\" if self.counter[\"1\"] == self.counter[\"2\"] else \"2\")","def _verify(self, data):\n sections = {\n \"schema\": dict,\n \"defaults\": dict,\n \"projects\": list\n }\n\n for key, type_ in sections.items():\n if key not in data:\n self._report_error(\"Missing section: {}\".format(key))\n return False\n if not isinstance(data[key], type_):\n err = \"Wrong data type for section {}, should be {}\"\n self._report_error(err.format(key, type_))\n return False\n\n for setting in data[\"defaults\"]:\n if setting not in data[\"schema\"]:\n err = \"Invalid setting {0} in default configuration\"\n self._report_error(err.format(setting))\n return False\n\n for i, project in enumerate(data[\"projects\"]):\n if not isinstance(project, dict):\n err = \"Wrong data type for project at index {}\"\n self._report_error(err.format(i))\n return False\n if \"name\" not in project:\n err = \"Missing name for project at index {}\"\n self._report_error(err.format(i))\n return False\n for setting in project:\n if setting not in data[\"schema\"]:\n err = \"Invalid setting {} for project {}\"\n self._report_error(err.format(setting, project[\"name\"]))\n return False\n\n return True","def is_game_complete(game):\n game_round = min(len(game.creator_scores), len(game.invitee_scores))\n creator_score = sum(game.creator_scores[:game_round])\n invitee_score = sum(game.invitee_scores[:game_round])\n return creator_score >= GAME_SCORE_TO_WIN or invitee_score >= GAME_SCORE_TO_WIN","def check_pp_goals(game, key, opp_key, gsl):\n # loading events data\n game_type = get_game_type_from_season_type(game)\n game_events_src_path = os.path.join(\n CONFIG['base_data_dir'], 'game_events', str(game['season']), str(game_type), \"%d.json\" % game['game_id'])\n events_data = json.loads(open(game_events_src_path).read())\n\n pp_goals_from_events = {'home': 0, 'visitor': 0}\n\n for period in events_data:\n for event in events_data[period]:\n if event['type'] != 'goal':\n continue\n # fixing penalty shots erroneously identified as power play goals\n if event['data']['balance'] == 'PP0':\n event['data']['balance'] = 'PS'\n if event['data']['balance'].startswith('PP'):\n pp_goals_from_events[event['data']['team']] += 1\n\n pp_goals_discrepancy = False\n\n pp_goals_from_events['road'] = pp_goals_from_events['visitor']\n\n if pp_goals_from_events[key] != gsl['pp_goals']:\n pp_goals_discrepancy = True\n gsl['pp_goals'] = pp_goals_from_events[key]\n if pp_goals_from_events[opp_key] != gsl['opp_pp_goals']:\n pp_goals_discrepancy = True\n gsl['opp_pp_goals'] = pp_goals_from_events[opp_key]\n\n if pp_goals_discrepancy:\n print(\"\\t+ Found and corrected different number of pp goals retrieved from team stats and game event data\")\n\n return gsl","def check_win(self):\n return UNEXPOSED not in self.get_game() and self.get_game().count(FLAG) == len(self.get_pokemon_location)","def test_get_game_play_by_play(self):\n msg = \"Response status is not 200\"\n response = self.api.get_game_play_by_play(self.game_id)\n self.assertEqual(response.status_code, 200, msg)","def check_state(self, username, descriptor, expected_score, expected_max_score, expected_attempts):\r\n module = self.get_student_module(username, descriptor)\r\n self.assertEqual(module.grade, expected_score)\r\n self.assertEqual(module.max_grade, expected_max_score)\r\n state = json.loads(module.state)\r\n attempts = state['attempts']\r\n self.assertEqual(attempts, expected_attempts)\r\n if attempts > 0:\r\n self.assertTrue('correct_map' in state)\r\n self.assertTrue('student_answers' in state)\r\n self.assertGreater(len(state['correct_map']), 0)\r\n self.assertGreater(len(state['student_answers']), 0)","def test_complete_game1(self):\n m = Game()\n\n plays = [\n ['a:-', 'a:-', 'a:b', 'b:b', 'b:-', 'b:b'],\n ['c:3i', 'c:f', 'c:f', 'c:-', 'd:f', 'd:-', 'd:-'],\n ['a:3i', 'a:2f', 'a:f', 'a:-', 'b:-', 'b:-', 'b:-'],\n ['c:3i', 'c:2f', 'c:-', 'c:f', 'd:-', 'd:b', 'd:b'],\n ['a:5i2rp', 'a:4f', 'a:f', 'a:b-', 'b:-', 'b:b', 'b:k']\n ]\n m.run(plays)\n m.print_stats()\n\n # Did the right team win?\n assert m.teams['a'].win == True\n\n # Did we get 5 turns?\n assert m.turn_count == 5\n\n # There should have been 30 throws in the game\n assert m.get_throw_count() == 30","def check_inputs(self, item_data):\n if not item_data[0] in self.data['pizza']:\n print('Error: ' + item_data[0] + ' pizza does not exist.')\n return False\n\n if not item_data[1] in self.data['pizza'][item_data[0]]:\n print('Error: ' + item_data[1] + ' size does not exist for '\n + item_data[0] + ' pizza.')\n return False\n\n for topping in item_data[2]:\n if not topping in self.data['topping']:\n print('Error: Pizza topping ' + topping + ' does not exist.')\n return False\n return True","def test_sad_purchasePlaces_wrong_compet(self):\n\n rv = self.app.post(\n \"/purchasePlaces\",\n data={\n \"places\": 1,\n \"club\": self.clubs[0][\"name\"],\n \"competition\": \"fake_competition_name\",\n },\n )\n\n assert rv.status_code in [404]\n assert b\"The provided competition is invalid\" in rv.data","async def check_game_over(self, game_id):\n game = await self.get_game(game_id)\n player1_stand = await self.check_player_standing(game[1])\n player2_stand = await self.check_player_standing(game[2])\n if player1_stand and player2_stand:\n return True\n else:\n return False","def testDraw():\n deleteMatches()\n deletePlayers()\n registerPlayer(\"Pikachu\")\n registerPlayer(\"Charmander\")\n registerPlayer(\"Bulbasaur\")\n registerPlayer(\"Squirtle\")\n standings = playerStandings()\n [id1, id2, id3, id4] = [row[0] for row in standings]\n reportMatch(id1, id2, True)\n reportMatch(id3, id4, True)\n standings = playerStandings()\n if not (standings[0][2]==standings[1][2]==standings[2][2]==standings[3][2]):\n raise ValueError(\n \"Players should have the same number of points after drawing\"\n )\n\n print \"3. Draw is recorded properly.\"","def test_invalid_game_setup(self):\n with self.assertRaises(ValueError):\n self._game.add_player(self._creator, 1)\n with self.assertRaises(ValueError):\n self._game.add_player(self._users[1], 0)\n for x in xrange(1, 4):\n self._game.add_player(self._users[x], x)\n with self.assertRaises(ValueError):\n self._game.add_player(self._users[4], 1)","def check_if_game_over():\n check_for_winner()\n check_for_tie()","def test_check_validity(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n game.solve()\n assert game.check_validity()","def check_boards(self):\n succesful = True\n marker = self.game.player\n print(f\"-----Starting check_winning_boards-----\")\n winning_boards= [\n [\n [marker]*3,\n [\" \"]*3,\n [\" \"]*3\n ],\n [\n [\" \"]*3,\n [marker]*3,\n [\" \"]*3\n ],\n [\n [\" \"]*3,\n [\" \"]*3,\n [marker]*3\n ],\n [\n [marker, \" \", \" \"],\n [marker, \" \", \" \"],\n [marker, \" \", \" \"]\n ],\n [\n [\" \",marker, \" \"],\n [\" \",marker, \" \"],\n [\" \",marker, \" \"]\n ],\n [\n [\" \", \" \",marker],\n [\" \", \" \",marker],\n [\" \", \" \",marker]\n ],\n [\n [marker, \" \", \" \"]\n ,[\" \", marker,\" \"],\n [\" \", \" \",marker]\n ],\n [\n [\" \", \" \", marker],\n [\" \",marker, \" \"],\n [marker, \" \", \" \"]\n ]\n ]\n for board in winning_boards:\n if self.game.check_win_conditions(board) != -10:\n succesful = False\n print(f\"board failed checkWins \\n{board}\")\n marker = self.game.ai_player\n print(f\"-----Starting check_winning_boards-----\")\n winning_boards= [\n [\n [marker]*3,\n [\" \"]*3,\n [\" \"]*3\n ],\n [\n [\" \"]*3,\n [marker]*3,\n [\" \"]*3\n ],\n [\n [\" \"]*3,\n [\" \"]*3,\n [marker]*3\n ],\n [\n [marker, \" \", \" \"],\n [marker, \" \", \" \"],\n [marker, \" \", \" \"]\n ],\n [\n [\" \",marker, \" \"],\n [\" \",marker, \" \"],\n [\" \",marker, \" \"]\n ],\n [\n [\" \", \" \",marker],\n [\" \", \" \",marker],\n [\" \", \" \",marker]\n ],\n [\n [marker, \" \", \" \"]\n ,[\" \", marker,\" \"],\n [\" \", \" \",marker]\n ],\n [\n [\" \", \" \", marker],\n [\" \",marker, \" \"],\n [marker, \" \", \" \"]\n ]\n ]\n for board in winning_boards:\n if self.game.check_win_conditions(board) != 10:\n succesful = False\n print(f\"board failed checkWins \\n{board}\")\n \n tie_boards = [\n [ \n [\"O\",\"O\",\"X\"],\n [\"X\",\"O\",\"O\"],\n [\"X\",\"X\",\" \"]\n ],\n [\n [\"O\",\"X\",\" \"],\n [\" \",\"X\",\" \"],\n [\" \",\"O\",\" \"]\n ],\n [\n ['O', 'O', 'X'],\n ['X', 'X', 'O'],\n ['O', 'O', 'X']\n ]\n ]\n for board in tie_boards:\n if self.game.check_win_conditions(board) != 0:\n succesful = False\n print(f\"board failed checkWins \\n{board}\")\n\n print(f\"-----Ending check_winning_boards-----\")","def check_game_end(self):\r\n\r\n if np.all(self.remaining == -1): # end of game\r\n self.show_results() # show the final results\r\n sys.exit() # exit the program\r","def gameOver():\n if len(p1)==0 and len(p1winnings)==0:\n return True\n elif len(p2)==0 and len(p2winnings)==0:\n return True\n return False","def test_empty_game(self):\n sgf = \"()\"\n try:\n coll = parseSgf(sgf)\n except SgfParseError, e:\n if str(e) == \"Empty game.\":\n return\n self.assertFalse(1)","def test_sad_booking_wrong_club(self):\n\n for competition in self.competitions:\n rv = self.app.get(f\"/book/{competition['name']}/wrong_club_name\")\n\n assert rv.status_code in [404]\n assert b\"The provided club is invalid\" in rv.data","def _create_games(self):\n\n ''''''","def validate_data(self, data):\n try:\n event_id = data[\"event\"]\n guests = data[\"guests\"]\n section = data.get(\"section\", None)\n except KeyError:\n return False, \"\", \"\", \"\"\n try:\n event = Event.objects.get(pk=event_id)\n except Event.DoesNotExist:\n return False, \"\", \"\", \"\"\n if not isinstance(guests, list):\n return False, \"\", \"\", \"\"\n if section is not None:\n try:\n event.venue.sections.get(pk=section)\n except Section.DoesNotExist:\n return False, \"\", \"\", \"\"\n return True, event, guests, section","def test_get_game(self):\n #create the api \n api_call = '/_ah/spi/GameApi.get_game'\n app = endpoints.api_server([GameApi], restricted=False)\n testapp = webtest.TestApp(app)\n \n #create two players\n game, first_user, second_user = self._get_new_game() \n \n #the expected request object as a dictionary, to be serialised to JSON by webtest\n request = {\"urlsafe_game_key\":game.key.urlsafe()} \n resp = testapp.post_json(api_call, request)\n \n #check correct default values have been created\n self.assertEqual(resp.json['next_move'], first_user.name)\n self.assertEqual(resp.json['game_over'], False)\n self.assertEqual(resp.json['unmatched_pairs'], \"8\")\n self.assertEqual(resp.json['first_user_score'], \"0\")\n self.assertEqual(resp.json['second_user_score'], \"0\")\n self.assertEqual(resp.json['history'], \"[]\")","async def validateGame(self, ctx):\n if self.progression == \"Création d'une partie\": # Game created\n\n # =-=-=-= CHECKING TOO FAST START =-=-=-= #\n for msgID in self.msgChoiceRole:\n msg = await ctx.channel.fetch_message(msgID)\n if len(msg.embeds[0].fields) != len(msg.reactions):\n await self.wait(ctx)\n return\n\n # =-=-=-= CALCULATING ROLES =-=-=-= #\n self.progression = \"Préparation de la partie\"\n await self.wait(ctx)\n for msgId in self.msgChoiceRole:\n msg = await ctx.channel.fetch_message(msgId)\n for reactions in msg.reactions:\n await self.getRoleFromReaction(ctx, reactions)\n self.players = [member for member in ctx.author.voice.channel.members if not member.bot]\n\n # =-=-=-= CHECKING INVALID PARTY =-=-=-= #\n if len(self.players) < 3:\n self.msgToDelete.append(await ctx.channel.send(\n \"Nombre de joueurs insuffisant : 3 joueurs minimum. (\" + str(\n len(self.players)) + \" actuellement.)\"))\n await asyncio.sleep(5)\n await self.delete()\n return\n elif len(self.roles) < len(self.players) + 3:\n self.msgToDelete.append(\n await ctx.channel.send(\n str(len(self.roles)) + \"rôles choisis : insuffisants pour le nombre de joueurs. (\" +\n str(len(self.players) + 3) + \" rôles minimum,\" + str(len(self.players)) +\n \" joueurs + 3 dans le tas central.)\"))\n await asyncio.sleep(5)\n await self.delete()\n return\n\n elif len(self.roles) > len(self.players) + 3:\n self.msgToDelete.append(await ctx.channel.send(\n \"Nombre de rôles supérieur au nombre de joueurs : \" + str(len(self.roles)) + \"rôles pour \" + str(\n len(self.players)) +\n \" joueurs + 3 dans le tas central. Certains rôles ne seront pas pris en compte.\"))\n\n await self.delAllMsg(waitingTime=5)\n print(\"Start with roles : \" + \", \".join(self.roles))\n self.msgToDelete.append(await ctx.channel.send(\n \"Début de la partie avec les rôles suivants : \" + \", \".join(self.roles)))\n print(\"Start with players : \" + \", \".join(getAuthor(self.players)))\n self.msgToDelete.append(await ctx.channel.send(\n \"Les joueurs sont les suivants : \" + \", \".join(getAuthor(self.players))))\n\n # =-=-=-= START GAME =-=-=-= #\n await self.startingGame(ctx=ctx)\n\n else:\n await self.wait(ctx=ctx)","def _check_season_gaps_in_teams_matches(self, df: pd.DataFrame):\n if self._verbose > 0:\n print(\"Teams:\")\n print(self.teams)\n print(\"Teams playing last season:\")\n print(self.last_season_teams)\n\n omitted_teams_test = check_season_gaps(df, self.test_fixtures_ids)\n omitted_teams_predict = check_season_gaps(df, self.predict_fixtures_ids)\n\n if omitted_teams_predict or omitted_teams_test:\n print(\"Warning: There are some teams with season gaps in data.\")\n print(\"Teams omitted based on predict dataset due to season gap:\")\n print(omitted_teams_predict)\n print(\"Teams omitted based on test dataset due to season gap:\")\n print(omitted_teams_test)\n\n # Delete these teams from datasets\n for team in set(omitted_teams_test + omitted_teams_predict):\n del self.test_fixtures_ids[team]\n del self.predict_fixtures_ids[team]\n\n print(\"You should consider lowering required number of test or predict data samples.\")","def test_check_inputs(\n student_names, project_names, supervisor_names, capacities, seed, clean\n):\n\n *_, game = make_game(\n student_names, project_names, supervisor_names, capacities, seed, clean\n )\n\n with warnings.catch_warnings(record=True):\n warnings.simplefilter(\"error\")\n game.check_inputs()\n\n assert game.students == game._all_students\n assert game.projects == game._all_projects\n assert game.supervisors == game._all_supervisors","def test_get_game_summary(self):\n msg = \"Response status is not 200\"\n response = self.api.get_game_summary(self.game_id)\n self.assertEqual(response.status_code, 200, msg)","def check_loss(self):\n return POKEMON in self.get_game()","def assert_data_correct(self) -> bool:\n if not self.training_folder.exists():\n return False\n # 27: number of characters\n # 27*2: 27 original font characters and 27 folders with morphed version\n if len(list(self.training_folder.iterdir())) not in [27, 27 * 2]:\n return False\n # assert that each character folder has the expected number of images inside\n # expected number is repetitions + original, or just original if no morphing\n # took place\n for directory in self.training_folder.iterdir():\n img_count = len(list(directory.iterdir()))\n if img_count != self.repetitions + 1 and img_count != 1:\n return False\n return True","def __check_board_is_valid(self, board, players):\n total_tiles = board.get_rows() * board.get_cols()\n # check the board is big enough\n if total_tiles < (6 - len(players)) * len(players):\n raise ValueError(\"Board specified by board_size is too small.\")\n # check that the board has enough active tiles\n if len(players) == 3 and total_tiles - board.get_holes_in_board() < 9:\n raise ValueError(\"Too many holes to place all penguins\")\n elif total_tiles - board.get_holes_in_board() < 8:\n raise ValueError(\"Too many holes to place all penguins\")","def test_sad_booking_wrong_compet(self):\n\n for competition in self.competitions:\n rv = self.app.get(f\"/book/wrong_compet_name/{self.clubs[0]['name']}\")\n\n assert rv.status_code in [404]\n assert b\"The provided competition is invalid\" in rv.data","def __len__(self):\n return len(self._games)","def is_valid(data):\n check = [0 for i in range(4)]\n # calculate how many ships are with different lengths\n for i in range(10):\n for j in range(10):\n if type(data[i][j]) == Ship:\n check[data[i][j]._Ship__length - 1] += 1\n # check ships\n for i in range(4):\n if check[i] != (i + 1) * (4 - i):\n return False\n # check corners\n for i in range(1, 10):\n for j in range(10):\n try:\n if type(data[i - 1][j + 1]) == Ship and \\\n type(data[i][j]) == Ship:\n return False\n except:\n pass\n try:\n if type(data[i - 1][j - 1]) == Ship and \\\n type(data[i][j]) == Ship:\n return False\n except:\n pass\n return True","def validate(gear_info, errors):\n\n checklist = [(\"set\", \"NA\"), (\"rarity\", \"NA\"), (\"slot\", \"NA\"),\n (\"level\", \"999\"), (\"ability\", \"999\"), (\"mainStat\", [\"NA\", 999])]\n\n for key, val in checklist:\n # Add fake data if not found\n if key not in gear_info:\n gear_info[key] = val\n\n if \"mainStat\" in key:\n errors['stats'] = 1\n else:\n errors[key] = 1\n print(f\"{key} is not in info.\")\n\n for key, val in gear_info.items():\n # Check stats values if values are too high then change to 999\n if \"Stat\" in key:\n if val[0][-1] == \"P\" and len(val[0]) > 2 and val[1] > 100:\n val[1] = 0\n gear_info[key] = val\n errors['stats'] = 1\n print(\"changed stat\")\n\n # Check other values in gear info\n if key == 'mainStat' and val[0] == \"NA\":\n if gear_info['slot'] == \"Weapon\":\n gear_info = \"Atk\"\n elif gear_info['slot'] == \"Helmet\":\n gear_info = \"HP\"\n elif gear_info['slot'] == \"Armor\":\n gear_info = \"Def\"\n\n elif key == \"level\" and val == \"999\" and len(gear_info['name']) > 1:\n matched = match_gear_name(gear_info['name'])\n if matched > 0:\n print(matched)\n gear_info[key] = str(int(matched))\n errors[key] = 0\n print(\"Fixed level\")\n else:\n gear_info[key] = \"999\"\n errors[key] = 1\n print(\"Found error in level. Change to 999\")\n\n elif key == 'ability' and int(val) > 15:\n gear_info[key] = \"999\"\n errors[key] = 1\n print(\"Found error in ability. Change to 999\")\n\n return gear_info, errors","def test_get_game_boxscore(self):\n msg = \"Response status is not 200\"\n response = self.api.get_game_boxscore(self.game_id)\n self.assertEqual(response.status_code, 200, msg)","def validate_onboarding(data: Dict):\n try:\n saved_data = data['outputs']['messages'][-1]['data']['WORLD_DATA']\n role = (\n 'Wizard' if saved_data[constants.SAVED_DATA_IS_WIZARD_KEY] else 'Apprentice'\n )\n logging.info(f'Validating {role} onboarding.')\n except (IndexError, KeyError) as e:\n logging.warning(\n 'Incomplete data to validate agent onboarding.'\n f'Onboarding saved_data error: {e}'\n )\n return False\n\n rejection_reason = saved_data[constants.WORKER_REJECT_REASON]\n if rejection_reason:\n logging.warning(f'Rejected: {rejection_reason}')\n return False\n\n # Role qualification\n worker = get_worker_by_name(saved_data[constants.SAVED_DATA_WORKER_KEY])\n qual_name, qual_val = saved_data[constants.SAVED_DATA_ROLE_QUALIFICATION_DATA_KEY]\n if not assign_role_training_qualification(worker, qual_name, qual_val):\n return False\n\n logging.info('Onboarding work accepted.')\n return True","def clean(self):\n if self.hours_played < 0:\n raise ValidationError(\"Hours played cannot be negative.\")\n\n zelda_oot = Game.objects.filter(game_name='The Legend of Zelda: Ocarina of Time')\n if zelda_oot:\n zelda_oot = zelda_oot[0]\n if self != zelda_oot and self.hours_played > zelda_oot.hours_played:\n raise ValidationError(\"Liar.\")\n\n super(Game, self).clean()","def _check_game_over(self):\n return self.game_board.check_game_over()","def _check_data_correct(self, expected_data, _):\n data_len = len(expected_data)\n for retry_count in range(self.RETRY_COUNT):\n if retry_count > 0:\n test_info.info('Previous attempts %s' % retry_count)\n try:\n data_loaded = self.board.read_target_memory(self._start, data_len)\n except:\n time.sleep(self.DELAY_BEFORE_RETRY_S)\n continue\n break\n else:\n raise Exception(\"read_target_memory() failed after %i retries\" % self.RETRY_COUNT)\n\n return _same(expected_data, data_loaded)","def _validate(self) -> None:\n for box in self.boxes:\n if any(box[0] == s[0] and box[1] == s[1] for s in self.wall_squares):\n raise RuntimeError('In illegal state. Box should not be inside wall.')\n if box[0] == self.current_location[0] and box[1] == self.current_location[1]:\n raise RuntimeError('In illegal state. Box should not be inside player.')\n if any(self.current_location[0] == s[0] and self.current_location[1] == s[1] for s in self.wall_squares):\n raise RuntimeError('In illegal state. Player should not be inside wall.')","def test_play_quiz_fails(self):\n\n # send post request without json data\n response = self.client().post('/quizzes', json={})\n\n # load response data\n data = json.loads(response.data)\n\n # check response status code and message\n self.assertEqual(response.status_code, 400)\n self.assertEqual(data['success'], False)\n self.assertEqual(data['message'], 'bad request')","def __check_winner(self):\n for i in range(0, 3):\n col = self.__get_col(i)\n if col.get(self.player_char) == 3:\n print('\\nYou win!')\n self.game_ended = True\n return\n if col.get(self.opponent_char) == 3:\n print('\\nYou lose.')\n self.game_ended = True\n return\n row = self.__get_row(i)\n if row.get(self.player_char) == 3:\n print('\\nYou win!')\n self.game_ended = True\n return\n if row.get(self.opponent_char) == 3:\n print('\\nYou lose.')\n self.game_ended = True\n return\n for i in range(0, 2):\n diag = self.__get_diag(i)\n if diag.get(self.player_char) == 3:\n print('\\nYou win!')\n self.game_ended = True\n return\n if diag.get(self.opponent_char) == 3:\n print('\\nYou lose.')\n self.game_ended = True\n return\n if self.state.count(' ') == 0:\n print('\\nDraw!')\n self.game_ended = True","def check_if_game_over():\n # Calling check for winners.\n check_for_winner()\n # Calling check it's tie or not.\n check_if_tie()","def is_data_valid(self):\n errors = []\n for state, filename in self.symbols.items():\n if state is None or state == \"\":\n errors.append(\"Image {} has no state\".format(filename))\n error, file_type = self.check_image(filename)\n if error:\n errors.append(error)\n\n if len(errors) > 0:\n error_msg = os.linesep.join(errors)\n return False, error_msg\n\n return True, \"\"","def test_newsmall6dgame(self):\n with open(\"test_outputs/test_newsmall6dgame.json\") as f:\n expected = json.load(f)\n with open(\"test_inputs/test_newsmall6dgame.json\") as f:\n inputs = json.load(f)\n result = lab.HyperMinesGame(inputs[\"dimensions\"], inputs[\"bombs\"])\n for i in ('dimensions', 'board', 'mask', 'state'):\n self.assertEqual(getattr(result, i), expected[i])","def _checkRoundOver(self):\n\n if not any(player.isAlive() for player in self.teams[0].players):\n self.endGame()","def test_check_win(self):\n # Horizontal wins\n # First row\n self.game.move(0, 0, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(0, 1, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(0, 2, PLAYERX)\n self.assertEqual(self.game.check_win(0, 0, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(0, 1, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(0, 2, PLAYERX), PLAYERX)\n\n # Second row\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.game._board[row][col] = EMPTY\n\n self.game.move(1, 0, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(1, 1, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(1, 2, PLAYERX)\n self.assertEqual(self.game.check_win(1, 0, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(1, 1, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(1, 2, PLAYERX), PLAYERX)\n\n # Third row\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.game._board[row][col] = EMPTY\n\n self.game.move(2, 0, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(2, 1, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(2, 2, PLAYERX)\n self.assertEqual(self.game.check_win(2, 0, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(2, 1, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(2, 2, PLAYERX), PLAYERX)\n\n # Vertical wins\n # First column\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.game._board[row][col] = EMPTY\n\n self.game.move(0, 0, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(1, 0, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(2, 0, PLAYERX)\n self.assertEqual(self.game.check_win(0, 0, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(1, 0, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(2, 0, PLAYERX), PLAYERX)\n\n # Second column\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.game._board[row][col] = EMPTY\n\n self.game.move(0, 1, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(1, 1, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(2, 1, PLAYERX)\n self.assertEqual(self.game.check_win(0, 1, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(1, 1, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(2, 1, PLAYERX), PLAYERX)\n\n # Third column\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.game._board[row][col] = EMPTY\n\n self.game.move(0, 2, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(1, 2, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(2, 2, PLAYERX)\n self.assertEqual(self.game.check_win(0, 2, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(1, 2, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(2, 2, PLAYERX), PLAYERX)\n\n # Diagonal wins\n # Upper left to bottom right\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.game._board[row][col] = EMPTY\n\n self.game.move(0, 0, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(1, 1, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(2, 2, PLAYERX)\n self.assertEqual(self.game.check_win(0, 0, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(1, 1, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(2, 2, PLAYERX), PLAYERX)\n\n # Bottom left to upper right\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.game._board[row][col] = EMPTY\n\n self.game.move(2, 0, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(1, 1, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(0, 2, PLAYERX)\n self.assertEqual(self.game.check_win(2, 0, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(1, 1, PLAYERX), PLAYERX)\n self.assertEqual(self.game.check_win(0, 2, PLAYERX), PLAYERX)\n\n # Draw\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.game._board[row][col] = EMPTY\n\n self.game.move(0, 0, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(0, 1, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(0, 2, PLAYERO)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(1, 0, PLAYERO)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(1, 1, PLAYERO)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(1, 2, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(2, 0, PLAYERX)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(2, 1, PLAYERO)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\n self.game.move(2, 2, PLAYERO)\n for row in range(self.game._dim):\n for col in range(self.game._dim):\n self.assertEqual(self.game.check_win(row, col, PLAYERX), DRAW)","def test_newlarge4dgame(self):\n with open(\"test_outputs/test_newlarge4dgame.json\") as f:\n expected = json.load(f)\n with open(\"test_inputs/test_newlarge4dgame.json\") as f:\n inputs = json.load(f)\n result = lab.HyperMinesGame(inputs[\"dimensions\"], inputs[\"bombs\"])\n for i in ('dimensions', 'board', 'mask', 'state'):\n self.assertEqual(getattr(result, i), expected[i])","def check_winner(self):\n pass","def uber_check_win(self):\n if self.player1.score == self.player2.score:\n print(\"It's a draw!\")\n elif self.player1.score > self.player2.score:\n print(\"Player 1 is a proper bad ass mother fucker\")\n else:\n print(\"Player numma 2 is a proper bad ass mother fucker\")","def checkOpsimData(self, otherdata=None):\n data2check = self.opsim_data\n if otherdata:\n data2check = otherdata\n if type(data2check) != list:\n if type(data2check) == dict:\n if set(self._opsim_keys).issubset(set(data2check.keys())):\n return True, 'good'\n else:\n return False, 'Data is not complete'\n else:\n return False, 'Data is not a list nor a dictionary'\n else:\n nodict = 0\n baddict = 0\n for data in data2check:\n if type(data) != dict:\n nodict += 1\n else:\n if not set(self._opsim_keys).issubset(set(data.keys())):\n baddict += 1\n if (nodict == 0 and baddict == 0):\n return True, 'good'\n else:\n statement = '{0} non dictionary element(s) and {1} uncomplete\\\n dictionary(ies) in data list'.format(nodict, baddict)\n return (False, statement)","def check_validity(self):","def test_set_players_number(self):\n\n # Setup new games and attempt to set thier number of players\n valid_players = [\n 1,\n 2,\n 10,\n 999,\n ]\n for players in valid_players:\n game = Game()\n game.setup_new_game()\n game.set_pack_number(1)\n game.set_starting_chips(100)\n game.set_players_number(players)\n self.assertEqual(game.players_number, players, msg=\"The number of players was not correctly set with \" + str(players) + \" players.\")\n\n # Make sure that the new game state is correctly set\n self.assertEqual(game.state.name, \"get_player_names\", msg=\"The game state was not correctly set after setting the number of players in the game.\")\n\n # Try to set invalid player numbers\n invalid_players = [\n 0,\n -1,\n -100,\n 1.5,\n ]\n for players in invalid_players:\n game = Game()\n game.setup_new_game()\n game.set_pack_number(1)\n game.set_starting_chips(100)\n success = False\n try:\n game.set_players_number(players)\n except InvalidGamePlayersNumber:\n success = True\n self.assertTrue(success, msg=\"An invalid number of players \" + str(players) + \" was able to be set.\")\n\n # Try to reset the number of players to throw an error\n game = Game()\n game.setup_new_game()\n game.set_pack_number(1)\n game.set_starting_chips(100)\n game.set_players_number(3)\n success = False\n try:\n game.set_players_number(2)\n except InvalidGameMethodOrder:\n success = True\n self.assertTrue(success, msg=\"The number of players was incorrectly able to be reset.\")","def _is_valid(self):\n if len(self.slots) == 0:\n print(\"Parking Lot not created\")\n return False\n return True","def test_get_game_faceoffs(self):\n msg = \"Response status is not 200\"\n response = self.api.get_game_faceoffs(self.game_id)\n self.assertEqual(response.status_code, 200, msg)","def start_game_check(self):\n if len(self.pending_players) > 0:\n return False\n else:\n return True","def test(self):\n\t\tx = Team.objects.get(short_name='SF')\n\t\tself.assertEqual(x.nick_name, '49ers')\n\n\t\tx = Game.objects.get(week_number=1,game_number=1)\n\t\tx.fav_score = 21\n\t\tx.udog_score = 14\n\t\tx.spread = 5\n\t\tself.assertEqual(x.favWins(), True)\n\t\tx.spread = 10\n\t\tself.assertEqual(x.favWins(), False)\n\n\t\t# Sunday game at 4pm should be open if time before 1 else closed\n\t\tx.game_date = datetime.datetime(2020,3,1,16,0,0)\n\t\tcurrent_time = datetime.datetime(2020,3,1,11,0,0)\n\t\tself.assertEqual(x.isClosed(current_time=current_time), False)\n\t\tcurrent_time = datetime.datetime(2020,3,1,14,0,0)\n\t\tself.assertEqual(x.isClosed(current_time=current_time), True)\n\n\t\t# Saturday game. Should be closed only if after kick off\n\t\tx.game_date = datetime.datetime(2020,2,29,16,0,0)\n\t\tcurrent_time = datetime.datetime(2020,2,29,15,59,59)\n\t\tself.assertEqual(x.isClosed(current_time=current_time), False)\n\t\tcurrent_time = datetime.datetime(2020,2,29,16,0,1)\n\t\tself.assertEqual(x.isClosed(current_time=current_time), True)\n\n\t\t# Monday game, should be closed as long as it's after 1pm sunday\n\t\tx.game_date = datetime.datetime(2020,3,2,21,0,0)\n\t\tcurrent_time = datetime.datetime(2020,3,1,15,59,59)\n\t\tself.assertEqual(x.isClosed(current_time=current_time), True)\n\t\tcurrent_time = datetime.datetime(2020,3,1,11,59,59)\n\t\tself.assertEqual(x.isClosed(current_time=current_time), False)\n\t\tself.assertEqual(x.isOpen(current_time=current_time), True)\n\t\tcurrent_time = datetime.datetime(2020,3,2,11,59,59)\n\t\tself.assertEqual(x.isClosed(current_time=current_time), True)\n\t\tcurrent_time = datetime.datetime(2020,3,3,11,59,59)\n\t\tself.assertEqual(x.isClosed(current_time=current_time), True)\n\n\t\t# Sunday 9a game should be open until kick off\n\t\tx.game_date = datetime.datetime(2020,3,1,9,0,0)\n\t\tcurrent_time = datetime.datetime(2020,3,1,8,0,0)\n\t\tself.assertEqual(x.isClosed(current_time=current_time), False)\n\t\tcurrent_time = datetime.datetime(2020,3,1,10,0,0)\n\t\tself.assertEqual(x.isClosed(current_time=current_time), True)\n\t\tself.assertEqual(x.isOpen(current_time=current_time), False)\n\n\t\t# Now one check with not setting current_time\n\t\tx.game_date = datetime.datetime(1971,3,1,9,0,0)\n\t\tself.assertEqual(x.isClosed(), True)\n\n\n\t\tuser = User.objects.get(username='Tammer')\n\t\tx = Pick.objects.get(player=user,week_number=1,game_number=1)\n\t\tg = Game.objects.get(week_number=1,game_number=1)\n\t\tself.assertEqual(x.picked_fav, True)\n\t\tself.assertEqual(x.isCorrect(), g.fav_score - g.udog_score > g.spread)","def check_win(self, player):\n def check_row_win(player):\n for row in self.game_state:\n if player == row[0] == row[1] == row[2]:\n return True\n return False\n\n def check_column_win(player):\n # For doing a column check, transpose the grid and do a row check\n trans_game_state = numpy.transpose(self.game_state)\n for row in trans_game_state:\n if player == row[0] == row[1] == row[2]:\n return True\n return False\n\n def check_diag_win(player):\n # Left to right diagonal\n if player == self.game_state[0][0] == self.game_state[1][1] == self.game_state[2][2]:\n return True\n # Right to left diagonal\n if player == self.game_state[0][2] == self.game_state[1][1] == self.game_state[2][0]:\n return True\n return False\n\n if check_column_win(player) or check_diag_win(player) or check_row_win(player):\n return True\n return False","def testing_create_game():\n black_user = request.form['black_email']\n white_user = request.form['white_email']\n stones = json.loads(request.form['stones'])\n create_game_internal(black_user, white_user, stones)\n return ''","def assert_data_correct(self) -> bool:\n corr_char = self.assert_data_characters_correct()\n print(\"Character data correct?\", corr_char)\n corr_font = FontImages().assert_data_correct()\n print(\"Font data correct?\", corr_font)\n corr_frag = self.assert_data_fragments_correct()\n print(\"Fragment data correct?\", corr_frag)\n corr_train_aug = self.assert_train_augmented()\n print(\"Train data augmented?\", corr_train_aug)\n truth_agree = corr_char and corr_font and corr_frag\n return True if truth_agree else False","def test_happy_booking(self):\n\n now = datetime.datetime.now()\n\n for club in self.clubs:\n for competition in self.competitions:\n rv = self.app.get(f\"/book/{competition['name']}/{club['name']}\")\n\n print(rv.data, rv.status_code, \"\\n\")\n\n if server.formatDate(competition[\"date\"]) <= now:\n continue\n\n assert rv.status_code in [200]\n assert (\n str.encode(f\"Places available: {competition['numberOfPlaces']}\")\n in rv.data\n )","def won_game(self):\n for player in self.players:\n if len(player.cards) == 0:\n\n return True\n return False","def check_extracted_data(data):\n for item in data:\n document_name = item[\"document\"]\n data_frame = item[\"data_frame\"]\n entity_position = item[\"entity_position\"]\n relations = item[\"relations\"]\n\n # Check if entities correctly embedded\n entity_embedding = data_frame[\"entity_embedding\"].tolist()\n cnt = 0\n for entity_key in entity_position:\n low, high = entity_position[entity_key]\n cnt += high - low\n if high == low:\n print(CHECK_FAILED_AT_DOCUMENT, document_name, \"in 'entity_embedding', key\", entity_key,\n \"is empty (from\", low, \"to\", high, \")\")\n else:\n try:\n assert abs(min(entity_embedding[low:high]) - max(entity_embedding[low:high])) <= 1\n except AssertionError:\n print(CHECK_FAILED_AT_DOCUMENT, document_name, \"in 'entity_embedding', key\", entity_key,\n \", values from\", low, \"to\", high, \":\", entity_embedding[low:high], \"are inconsistent\")\n try:\n assert cnt == (np.array(entity_embedding) != 0).astype(int).sum()\n except AssertionError:\n print(CHECK_FAILED_AT_DOCUMENT, document_name, \"in total entity embedded tokens\",\n (np.array(entity_embedding) != 0).astype(int).sum(), \"does not match the record\", cnt)\n\n # Check if all relations are valid\n for value in relations.values():\n first = value[\"source\"]\n second = value[\"target\"]\n try:\n assert first in entity_position\n except AssertionError:\n print(CHECK_FAILED_AT_DOCUMENT, document_name, \"in 'relations',\", first,\n \"is not found in record\")\n try:\n assert second in entity_position\n except AssertionError:\n print(CHECK_FAILED_AT_DOCUMENT, document_name, \"in 'relations',\", second,\n \"is not found in record\")","def test_setup_new_game(self):\n\n # Create a new game and make sure it has the correct settings\n game = Game()\n game.setup_new_game()\n self.assertTrue(game.dealer is not None, msg=\"The dealer of the game was not created.\")\n self.assertEqual(game.dealer.cards, [])\n self.assertEqual(game.state.name, \"get_number_of_packs\", msg=\"The initial game state was not correctly set.\")","def checkWin(self):\n winstates = [(0, 1, 2),\n (3, 4, 5),\n (6, 7, 8),\n (0, 3, 6),\n (1, 4, 7),\n (2, 5, 8),\n (0, 4, 8),\n (2, 4, 6)]\n win = False\n for state in winstates:\n if (self.gameState[state[0]] + self.gameState[state[1]] + self.gameState[state[2]]) == 3:\n self.handleWin(1)\n win = True\n elif (self.gameState[state[0]] + self.gameState[state[1]] + self.gameState[state[2]]) == -3:\n self.handleWin(-1)\n win = True\n\n if len([i for i in range(9) if self.gameState[i] == 0]) == 0 and not win:\n print(\"Draw yo\")\n self.handleDraw()\n return None","def checkGameComplete(self):\n for rowKey in self.table:\n for ele in self.table[rowKey]:\n if type(ele) == int:\n return False # means not complete\n return True","def _check_data_valid(self):\n\n is_valid = (sum(~np.isnan(self.data).flatten()) > 0 and self.data.flatten().sum() != 0)\n if not is_valid:\n raise FITSException(f\"No data in {self.survey}\")","def _check_validity(self):\n pass","def is_game_win(self):\n return not self.deck and not self.hand","def check_data(self):\n\n missing_params = {}\n flag = False\n\n missing_params['general'] = {}\n for name, param in self.params.items():\n if not param.check():\n missing_params['general'][name] = param.get_description()\n flag = True\n\n for component, comp_obj in self.components.items():\n missing_params[component], flag_comp = comp_obj.check_data()\n\n # Assign empty component parameters that have a general version:\n empty_general_params = set(missing_params[component]).intersection(\n set(self.params))\n for param in empty_general_params:\n comp_obj.change_param_object(param, self.params[param])\n del missing_params[component][param]\n\n if missing_params[component]:\n flag = True\n\n if flag:\n raise Exception('Following parameters are missing:\\n{}'\n .format(\n self._print_params(missing_params, disp=False)))\n\n return True","def isGameOver(self):\n pass","def test_game_has_ended_match(self):\n game = Game.objects.create(\n allowed_attempts=10,\n end_date=date.today() + timedelta(1),\n min_value=1,\n max_value=10\n )\n self.assertTrue(game.is_active)\n self.assertIsNone(game.compleation_date)\n\n game.attempt_set.create(\n our_number=10,\n user_number=10,\n ip_addr='127.0.0.1'\n )\n self.assertEqual(len(self.recieved), 1)\n self.assertListEqual(self.recieved[0], ['match'])\n self.assertFalse(game.is_active)\n self.assertIsNotNone(game.compleation_date)","def isGameOver(self):\n for i in range(self.rows):\n for j in range(self.columns):\n if self.grid[i][j].face == 'down':\n return False\n #if here then all cards must be face up\n return True","def checkapp(self, app):\n\n data = requests.get('http://store.steampowered.com/api/appdetails?appids={0}&format=json'.format(app)).json()\n\n if data[str(app)][\"success\"]:\n type = data[str(app)][\"data\"]['type']\n if (type != 'game'):\n return False\n return data[str(app)][\"success\"]"],"string":"[\n \"async def check_games(self, ctx):\\n print(self.data)\\n print(self.games_info)\",\n \"def isValid(self, game):\\n return True\",\n \"def check_game_sanity(game_val, log):\\n\\n global __problem_deck_index__\\n\\n supply = game_val.get_supply()\\n if game_val.any_resigned():\\n return True # Resign leaves deck in arbitrary state, don't catch that\\n\\n # ignore known bugs.\\n #if set(supply).intersection([get_card('Masquerade'), get_card('Black Market'), get_card('Trader')]):\\n #return True\\n\\n # TODO: add score sanity checking here\\n last_state = None\\n game_state_iterator = game_val.game_state_iterator()\\n for game_state in game_state_iterator:\\n last_state = game_state\\n for player_deck in game_val.get_player_decks():\\n parsed_deck_comp = player_deck.Deck()\\n computed_deck_comp = last_state.get_deck_composition(\\n player_deck.name())\\n\\n parse_common.delete_keys_with_empty_vals(parsed_deck_comp)\\n computed_dict_comp = dict(computed_deck_comp)\\n parse_common.delete_keys_with_empty_vals(computed_dict_comp)\\n\\n if parsed_deck_comp != computed_deck_comp:\\n found_something_wrong = False\\n for card in set(parsed_deck_comp.keys() +\\n computed_deck_comp.keys()):\\n if parsed_deck_comp.get(card, 0) != \\\\\\n computed_deck_comp.get(card, 0):\\n if not found_something_wrong:\\n __problem_deck_index__ += 1\\n log.debug('[%d] %18s %9s %9s', __problem_deck_index__, 'card', 'from-data', 'from-sim')\\n log.debug('[%d] %-18s %9d %9d', __problem_deck_index__, card, parsed_deck_comp.get(card, 0),\\n computed_deck_comp.get(card, 0))\\n found_something_wrong = True\\n if found_something_wrong:\\n try:\\n log.debug('[%d] insane game for %s %s: %s', __problem_deck_index__, player_deck.name(), game_val.get_id(),\\n ' '.join(map(str, game_val.get_supply())))\\n except UnicodeEncodeError, e:\\n None\\n return False\\n return True\",\n \"def test_game_creation(self):\\n self.assertTrue(len(models.Game.objects.all()) == 1)\\n game = models.Game.objects.all()[0]\\n\\n # Hydrocarbon piles initialization test\\n number_of_piles = len(settings.HYDROCARBON_STOCKS_PER_PLAYER)\\n for i_pile in range(number_of_piles):\\n self.assertEqual(0, game.hydrocarbon_piles.get(index=i_pile).stock_amount)\\n\\n # Events initialization test [TO DO]\\n\\n # Source buildings loading test\\n version_source_buildings = models.SourceBuilding.objects.filter(version=game.version)\\n self.assertEqual(version_source_buildings.count(), game.source_buildings.count())\\n for version_source_building in version_source_buildings:\\n self.assertEqual(version_source_building, game.source_buildings.get(slug=version_source_building.slug))\\n\\n # Source events loading test [TO DO]\\n\\n # Source technologies loading test\\n version_source_technologies = models.SourceTechnology.objects.filter(version=game.version)\\n self.assertEqual(version_source_technologies.count(), game.source_technologies.count())\\n for version_source_technology in version_source_technologies:\\n self.assertEqual(version_source_technology,\\n game.source_technologies.get(slug=version_source_technology.slug))\",\n \"def test_import(self, game=\\\"SuperMarioKart-Snes\\\"):\\n self.assertTrue(game in retro.data.list_games())\",\n \"def test_get_game(self):\\n pass\",\n \"def _check_integrity(self):\\n\\n count = 0\\n for (x, y) in self.__players[ChessGame.BLACK].union(\\n self.__players[ChessGame.WHITE]):\\n assert (x, y) in self.__board\\n count += 1\\n\\n assert count == len(self.__board)\",\n \"def test_valid_new_game(self):\\n self._game.new_game()\\n self.assertIsRUNNING(self._game)\\n self.assertIsNotNone(self._game.table)\\n self.assertEqual(self._game.table.player_turn, 1,\\n \\\"Game.table unsuccessful init.\\\")\",\n \"def test_api_new_game(self):\\n\\n with self.client as client:\\n ...\\n response = client.post(\\\"/api/new-game\\\")\\n game_data = response.get_json() #, data={\\\"gameId\\\": game_id, \\\"board\\\": game.board}\\n self.assertEqual(response.status_code, 200)\\n\\n self.assertIn(game_data[\\\"gameId\\\"], games)\\n self.assertIs(type(game_data[\\\"gameId\\\"]), str)\\n self.assertIs(type(game_data[\\\"board\\\"]), list)\\n self.assertIs(len(game_data[\\\"board\\\"]), 5)\",\n \"def verify_valid_game(self, lotto_game:List[int]):\\n is_valid = True;\\n lotto_game_temp = []\\n for number in lotto_game:\\n if number not in lotto_game_temp:\\n lotto_game_temp.append(number)\\n else:\\n print(number, lotto_game, lotto_game_temp)\\n is_valid = False\\n raise InvalidLottoGame\\n \\n lotto_game_temp_length = len(lotto_game_temp)\\n if lotto_game_temp_length != LOTTO_GAME_LENGTH and lotto_game_temp_length != LOTTO_GAME_WITH_BONUS_LENGTH:\\n print(lotto_game_temp)\\n is_valid = False\\n raise InvalidLottoGame\\n\\n return is_valid\",\n \"def test_no_games(self):\\n sgf = \\\"\\\"\\n try:\\n coll = parseSgf(sgf)\\n except SgfParseError, e:\\n if str(e) == \\\"Empty collection.\\\":\\n return\\n self.assertFalse(1)\",\n \"def check_game(self):\\n gameOver = None\\n if self.turn > 4:\\n gameOver = self.check_x_won()\\n if gameOver is True:\\n self.game_x_won()\\n return\\n\\n gameOver = None\\n if self.turn > 5:\\n gameOver = self.check_o_won()\\n if gameOver is True:\\n self.game_o_won()\\n return\\n\\n if self.turn >= 9:\\n self.game_tie()\\n return\",\n \"def check_valid(self, store):\\n\\n # Name (of the game) is always required\\n if self._name is None or self._name == '':\\n raise InvalidTransaction('name not set')\\n\\n # Action is always required\\n if self._action is None or self._action == '':\\n raise InvalidTransaction('action not set')\\n\\n # The remaining validity rules depend upon which action has\\n # been specified.\\n\\n if self._action == 'CREATE':\\n if self._name in store:\\n raise InvalidTransaction('game already exists')\\n\\n # Restrict game name letters and numbers.\\n if not re.match(\\\"^[a-zA-Z0-9]*$\\\", self._name):\\n raise InvalidTransaction(\\n \\\"Only letters a-z A-Z and numbers 0-9 are allowed in \\\"\\n \\\"the game name!\\\")\\n\\n elif self._action == 'JOIN':\\n\\n # Check that self._name is in the store (to verify\\n # that the game exists (see FIRE below).\\n if self._name not in store:\\n raise InvalidTransaction(\\n 'Trying to join a game that does not exist')\\n\\n state = store[self._name]['State']\\n\\n if state != \\\"NEW\\\":\\n # Check that the game can be joined (the state is 'NEW')\\n raise InvalidTransaction(\\n 'The game cannot accept any new participant')\\n\\n # Check that the game board is the right size (10x10)\\n if len(self._board) != self._size:\\n raise InvalidTransaction('Game board is not valid size')\\n for row in range(0, self._size):\\n if len(self._board[row]) != self._size:\\n raise InvalidTransaction(\\n 'Game board is not valid size')\\n\\n # Validate that self._board contains hash-like strings\\n for row in range(0, self._size):\\n for col in range(0, self._size):\\n # length of sha512 hexdigest is 32 characters\\n if len(self._board[row][col]) != 128:\\n raise InvalidTransaction(\\\"invalid board hash\\\")\\n\\n elif self._action == 'FIRE':\\n\\n if self._name not in store:\\n raise InvalidTransaction('no such game')\\n\\n if self._column is None:\\n raise InvalidTransaction(\\\"Column is required\\\")\\n\\n if self._row is None:\\n raise InvalidTransaction(\\\"Row is required\\\")\\n\\n # Check that self._column is valid (letter from A-J)\\n if not any((c in self._acceptable_columns) for c in self._column):\\n raise InvalidTransaction(\\n 'Acceptable columns letters are A to J')\\n\\n # Check that self._row is valid (number from 1-10)\\n try:\\n row = int(self._row)\\n if (row < 1) or (row > 10):\\n raise InvalidTransaction(\\n 'Acceptable rows numbers are 1 to 10')\\n except ValueError:\\n raise InvalidTransaction(\\n 'Acceptable rows numbers are 1 to 10')\\n\\n state = store[self._name]['State']\\n\\n if state in ['P1-WIN', 'P2-WIN']:\\n raise InvalidTransaction('game complete')\\n\\n if state == 'NEW':\\n raise InvalidTransaction(\\n \\\"Game doesn't have enough players.\\\")\\n\\n player = None\\n if state == 'P1-NEXT':\\n player1_firing = True\\n player = store[self._name]['Player1']\\n if player != self._signer_public_key:\\n raise InvalidTransaction('invalid player 1')\\n elif state == 'P2-NEXT':\\n player1_firing = False\\n player = store[self._name]['Player2']\\n if player != self._signer_public_key:\\n raise InvalidTransaction('invalid player 2')\\n else:\\n raise InvalidTransaction(\\n \\\"invalid state: {}\\\".format(state))\\n\\n # Check whether the board's column and row have already been\\n # fired upon.\\n if player1_firing:\\n target_board = store[self._name]['TargetBoard1']\\n else:\\n target_board = store[self._name]['TargetBoard2']\\n\\n firing_row = int(self._row) - 1\\n firing_column = ord(self._column) - ord('A')\\n if target_board[firing_row][firing_column] != '?':\\n raise InvalidTransaction(\\n \\\"{} {} has already been fired upon\\\".format(\\n self._column, self._row)\\n )\\n\\n # Make sure a space is revealed if it isn't the first turn.\\n if 'LastFireColumn' in store[self._name]:\\n if self._reveal_space is None or self._reveal_nonce is None:\\n raise InvalidTransaction(\\n \\\"Attempted to fire without revealing target\\\")\\n\\n if state == 'P1-NEXT':\\n hashed_board = 'HashedBoard1'\\n else:\\n hashed_board = 'HashedBoard2'\\n\\n col = ord(store[self._name]['LastFireColumn']) - ord('A')\\n row = int(store[self._name]['LastFireRow']) - 1\\n hashed_space = hash_space(self._reveal_space,\\n self._reveal_nonce)\\n if store[self._name][hashed_board][row][col] != hashed_space:\\n raise InvalidTransaction(\\n \\\"Hash mismatch on reveal: {} != {}\\\".format(\\n store[self._name][hashed_board][row][col],\\n hashed_space))\\n\\n else:\\n raise InvalidTransaction(\\n 'invalid action: {}'.format(self._action))\",\n \"def test_get_player_stats_from_game():\\n team = \\\"Titans\\\"\\n year = \\\"2018\\\"\\n week = \\\"1\\\"\\n expected_team_stats = {}\\n\\n assert ff_team.get_player_stats_from_game(\\n team, year, week) == expected_team_stats\",\n \"def test_valid_game_setup(self):\\n self.assertEqual(self._game.active_players(), 1)\\n for x in xrange(1, 4):\\n self._game.add_player(self._users[x], x)\\n self.assertEqual(self._game.active_players(), 4)\\n self.assertIsREADY(self._game)\",\n \"def test_game_info_creation(self):\\n\\n guess = Guess.objects.all()[0]\\n self.assertEqual(guess.game_id, self.game_id )\\n self.assertEqual(guess.peg_1, \\\"R\\\")\\n self.assertEqual(guess.peg_2, \\\"Y\\\")\\n self.assertEqual(guess.peg_3, \\\"R\\\")\\n self.assertEqual(guess.peg_4, \\\"R\\\")\\n self.assertEqual(guess.black_pegs, 3)\\n self.assertEqual(guess.white_pegs, 0)\",\n \"def test_play_game(self):\\r\\n\\r\\n \\r\\n a_players = [RandomPlayer(1), RandomPlayer(2)]\\r\\n a_x_dist = 3\\r\\n a_y_dist = 3\\r\\n a_num_to_win = 1\\r\\n a_game = Game(a_players, a_x_dist, a_y_dist, a_num_to_win)\\r\\n\\r\\n #Game is played to competion\\r\\n a_game.play_game()\\r\\n\\r\\n a_history = a_game.get_history()\\r\\n\\r\\n #Go through each move and check to be sure it's valid\\r\\n for i in range(1,len(a_history)):\\r\\n #Get copy of the board\\r\\n prev_board = a_history[i-1]\\r\\n cur_board = a_history[i]\\r\\n\\r\\n #Check if the board chosen is in valid states\\r\\n self.assertTrue(cur_board in prev_board.get_states(a_players[0].get_id()) or cur_board in prev_board.get_states(a_players[1].get_id()),\\\\\\r\\n \\\"An invalid board state was added to the history\\\")\\r\\n\\r\\n if i == len(a_history) - 1:\\r\\n self.assertTrue(cur_board.check_win(a_num_to_win, a_players[0].get_id()) or cur_board.check_win(a_num_to_win, a_players[1].get_id()) or cur_board.check_tie())\\r\\n else: \\r\\n self.assertFalse(cur_board.check_win(a_num_to_win, a_players[0].get_id()) or cur_board.check_win(a_num_to_win, a_players[1].get_id()) or cur_board.check_tie())\",\n \"def test_init_with_existing_game(self):\\n pass\\n # Ensure judge is the same\",\n \"def check_test_case_validity(test_case_dataset):\\n for i, test_case in enumerate(test_case_dataset):\\n assert \\\"NAME\\\" in test_case, f\\\"Test case #{i} Invalid NAME\\\"\\n\\n assert (\\n \\\"N_STATES\\\" in test_case\\n and isinstance(test_case[\\\"N_STATES\\\"], int)\\n and 0 < test_case[\\\"N_STATES\\\"] <= 64\\n ), f\\\"Test case #{i} Invalid N_STATES\\\"\\n\\n assert (\\n \\\"N_SYMBOLS\\\" in test_case\\n and isinstance(test_case[\\\"N_SYMBOLS\\\"], int)\\n and 0 < test_case[\\\"N_SYMBOLS\\\"] <= 64\\n ), f\\\"Test case #{i} Invalid N_SYMBOLS\\\"\\n\\n assert (\\n \\\"PLAYER_INPUT_SIZES\\\" in test_case\\n and isinstance(test_case[\\\"PLAYER_INPUT_SIZES\\\"], list)\\n and len(test_case[\\\"PLAYER_INPUT_SIZES\\\"]) > 1\\n and all(\\n (isinstance(x, int) and x > 0) for x in test_case[\\\"PLAYER_INPUT_SIZES\\\"]\\n )\\n ), f\\\"Test case #{i} Invalid PLAYER_INPUT_SIZES\\\"\\n\\n assert \\\"REPETITIONS\\\" not in test_case or (\\n isinstance(test_case[\\\"REPETITIONS\\\"], int) and 0 < test_case[\\\"REPETITIONS\\\"]\\n ), f\\\"Test case #{i} Invalid REPETITIONS\\\"\\n\\n assert \\\"DEBUG\\\" not in test_case or isinstance(\\n test_case[\\\"DEBUG\\\"], bool\\n ), f\\\"Test case #{i} Invalid DEBUG\\\"\\n\\n assert \\\"VIRTUAL_MACHINE\\\" not in test_case or (\\n isinstance(test_case[\\\"VIRTUAL_MACHINE\\\"], str)\\n and test_case[\\\"VIRTUAL_MACHINE\\\"] in [\\\"./spdz2k-party.x\\\", \\\"./semi2k-party.x\\\"]\\n ), f\\\"Test case #{i} Invalid VIRTUAL_MACHINE\\\"\\n\\n if \\\"PLAYER_DATA\\\" in test_case:\\n assert isinstance(\\n test_case[\\\"PLAYER_DATA\\\"], list\\n ), f\\\"Test case #{i} Invalid PLAYER_DATA - Not a list\\\"\\n for j, size in enumerate(test_case[\\\"PLAYER_INPUT_SIZES\\\"]):\\n player_data = test_case[\\\"PLAYER_DATA\\\"][j]\\n max_value = test_case[\\\"N_SYMBOLS\\\"]\\n assert (\\n isinstance(player_data, list)\\n and len(player_data) == size\\n and all(\\n (isinstance(x, int) and 0 <= x <= max_value)\\n for x in player_data\\n )\\n ), f\\\"Test case #{i} Invalid PLAYER_DATA - User {j} inputs are invalid\\\"\",\n \"def test_check_inputs(game):\\n\\n with warnings.catch_warnings():\\n warnings.simplefilter(\\\"error\\\")\\n game.check_inputs()\\n\\n assert game.residents == game._all_residents\\n assert game.hospitals == game._all_hospitals\",\n \"def is_not_game_ended(data_map):\\n\\n continue_game = True\\n loser = None\\n winner = None\\n\\n # If a player has not any units, the other player win.\\n for i in range(2):\\n if not len(data_map['player' + str(i + 1)]) and continue_game:\\n loser = data_map['player' + str(i + 1)]\\n winner = data_map['player' + str(3 - (i + 1))]\\n continue_game = False\\n\\n # If there's 20 turn without any attack, player1 loose and player2 win.\\n if float(data_map['attack_turn']) / 2 > 19:\\n loser = data_map['player1']\\n winner = data_map['player2']\\n continue_game = False\\n\\n return continue_game, loser, winner\",\n \"def test_get_game_diff(self):\\n pass\",\n \"def _check_input_data(self):\\n\\n n0, n1, corr, pval = np.nan, np.nan, np.nan, np.nan\\n\\n error_code_test = 0\\n error_text_test = 'No error occurred'\\n try:\\n error_code_test, error_msg = self._check_notnull()\\n if error_code_test == 0:\\n error_code_test, error_msg, n0, n1 = self._check_group_obs(self.test_min_data)\\n if error_code_test == 0:\\n error_code_test, error_msg, corr, pval = \\\\\\n self._check_spearman_corr(self.min_corr, self.max_p)\\n if error_code_test != 0:\\n error_text_test = str(error_msg)\\n except:\\n error_code_test = 9\\n error_text_test = 'Unknown Error'\\n\\n self.checkstats = {'n0': n0, 'n1': n1, 'frame_spearmanR': corr, 'frame_corrPval': pval}\\n\\n self.error_code_test = error_code_test\\n self.error_text_test = error_text_test\\n\\n return self.error_code_test, self.error_text_test\",\n \"def ensure_valid_data():\\n cursor = connection.cursor()\\n cursor.execute(\\\"SELECT id, name, st_area(geom) FROM firestation_firedepartment where st_area(geom)>6.99\\\")\\n messages = []\\n\\n for id, name, area in cursor.fetchall():\\n messages.append('{0} ({1}) has an area of {2}.'.format(name, id, area))\\n\\n if messages:\\n mail_admins('Invalid Geometries Detected', message='\\\\n'.join(messages))\\n\\n cursor.execute(\\\"SELECT COUNT(*) FROM genericm2m_relatedobject;\\\")\\n generic_count = cursor.fetchone()\\n\\n if generic_count[0] < 2940:\\n generic_count_message = \\\"Related government units has dropped below 2,940.\\\"\\n mail_admins('Low number of government units alert.', message=generic_count_message)\",\n \"def do_the_things():\\n returned_no_game = False\\n returned_game_time = False\\n returned_game_final = False\\n returned_rival_final = False\\n returned_game_soon = False\\n returned_rival_soon = False\\n returned_game_start = False\\n compare_scores = ['0', '0']\\n timeout = time.time() + 60 * 60 * 23\\n\\n while not (returned_no_game or returned_game_final):\\n\\n try:\\n message = False\\n rival_message = False\\n\\n \\\"\\\"\\\" Infinite loops are cool \\\"\\\"\\\"\\n if time.time() > timeout:\\n break\\n\\n game_data = get_fresh_data(team)\\n\\n if game_data:\\n \\\"\\\"\\\" The default TZ for mlb is US/Eastern, we'll do some things and make it local TZ \\\"\\\"\\\"\\n tz = pytz.timezone('US/Eastern')\\n eastern_time = datetime.datetime.strptime(\\\"%s %s\\\" % (game_data['time_date'], game_data['ampm']), '%Y/%m/%d %I:%M %p')\\n eastern_time = tz.localize(eastern_time)\\n pacific_time = eastern_time.astimezone(pytz.timezone(timezone))\\n\\n opponent, our_score, their_score, venue = set_vars(game_data)\\n scores = sorted([our_score, their_score], reverse=True)\\n\\n if not game_data and not returned_no_game:\\n returned_no_game = True\\n message = (\\\"The %s don't have a game scheduled today. Rest well guys!\\\" % team_hashtag)\\n\\n if game_data and not returned_game_time:\\n returned_game_time = True\\n message = ((\\\"The %s are playing against the %s today, first pitch is at \\\" + pacific_time.strftime(\\\"%-I:%M%p %Z\\\") + \\\" at %s. %s\\\") % (team_hashtag, opponent, venue, team_hash2))\\n\\n if \\\"Warmup\\\" in game_data[\\\"status\\\"] and not returned_game_soon:\\n returned_game_soon = True\\n message = ((\\\"The %s are playing against the %s in a moment, first pitch is at \\\" + pacific_time.strftime(\\\"%-I:%M%p %Z\\\") + \\\" at %s. %s\\\") % (team_hashtag, opponent, venue, team_hash2))\\n\\n if \\\"In Progress\\\" in game_data[\\\"status\\\"] and compare_scores == ['0', '0'] and not returned_game_start:\\n returned_game_start = True\\n message = (\\\"It's gametime! Go %s!!! %s\\\" % (team_hashtag, team_hash2))\\n\\n if \\\"In Progress\\\" in game_data[\\\"status\\\"] and not compare_scores == [our_score, their_score]:\\n compare_scores = [our_score, their_score]\\n if int(our_score) > int(their_score):\\n message = (\\\"The %s are leading the %s, the score is currently %s-%s. %s\\\" % (team_hashtag, opponent, scores[0], scores[1], team_hash2))\\n elif int(our_score) < int(their_score):\\n message = (\\\"The %s are trailing the %s, the score is currently %s-%s. %s\\\" % (team_hashtag, opponent, scores[0], scores[1], team_hash2))\\n elif int(our_score) == int(their_score):\\n message = (\\\"The %s are tied with the %s, the score is currently %s-%s. %s\\\" % (team_hashtag, opponent, scores[0], scores[1], team_hash2))\\n\\n if (\\\"Game Over\\\" or \\\"Final\\\") in game_data[\\\"status\\\"]:\\n returned_game_final = True\\n if our_score > their_score:\\n message = (\\\"The %s beat the %s today at %s with a score of %s-%s. %s\\\" % (team_hashtag, opponent, venue, scores[0], scores[1], team_hash2))\\n else:\\n message = (\\\"The %s lost against the %s today at %s with a score of %s-%s. Get 'em tomorrow! %s\\\" % (team_hashtag, opponent, venue, scores[0], scores[1], team_hash2))\\n\\n if not returned_rival_final:\\n rival_game_data = get_fresh_data(rival_team)\\n rival_opponent, rival_our_score, rival_their_score, rival_venue = set_vars(rival_game_data)\\n rival_scores = sorted([rival_our_score, rival_their_score], reverse=True)\\n\\n if \\\"Warmup\\\" in rival_game_data[\\\"status\\\"] and not returned_rival_soon:\\n returned_rival_soon = True\\n rival_message = (\\\"The %s game is about to start, go %s!!! %s\\\") % (rival_team_hashtag, rival_opponent, rival_team_hash2)\\n\\n if (\\\"Game Over\\\" or \\\"Final\\\") in rival_game_data[\\\"status\\\"]:\\n returned_rival_final = True\\n if rival_our_score > rival_their_score:\\n rival_message = (\\\"The %s beat the %s today at %s with a score of %s-%s, boooo!\\\" % (rival_team_hashtag, rival_opponent, rival_venue, rival_scores[0], rival_scores[1]))\\n else:\\n rival_message = (\\\"The %s lost against the %s today at %s with a score of %s-%s. Hell yeah!\\\" % (rival_team_hashtag, rival_opponent, rival_venue, rival_scores[0], rival_scores[1]))\\n\\n if message:\\n if testmode:\\n print(message)\\n else:\\n twitter.tweet(message)\\n\\n if rival_message:\\n if testmode:\\n print(rival_message)\\n else:\\n twitter.tweet(rival_message)\\n\\n except tweepy.TweepError as e:\\n print e.message[0]['code']\\n print e.args[0][0]['code']\\n\\n time.sleep(5)\",\n \"def getAllValidGames(self):\\n\\n gamelist = pd.read_csv('Resources/allgames.csv.gz', compression='gzip')\\n games = []\\n appids = gamelist['appid'].unique()\\n i = 0\\n pbar = tqdm(total=appids.size)\\n pbar.set_description('Processing ')\\n appsize = appids.shape[0] - 1\\n while (i <= appsize):\\n try:\\n if self.checkapp(appids[i]):\\n games.append(appids[i])\\n i += 1\\n pbar.update(1)\\n except:\\n pbar.set_description('{(-_-)}Zzz..')\\n sleep(5)\\n pbar.set_description('Processing ')\\n continue\\n pbar.close()\\n validgames = pd.DataFrame(games)\\n validgames.to_csv('Resources/validgames.csv')\",\n \"def check_game_status(self):\\n for player in (\\\"1\\\", \\\"2\\\"):\\n row_win = np.apply_along_axis(\\n lambda x: set(x) == {player}, 1, self.board\\n ).any()\\n col_win = np.apply_along_axis(\\n lambda x: set(x) == {player}, 0, self.board\\n ).any()\\n d1_win = set(self.data[[0, 4, 8]]) == {player}\\n d2_win = set(self.data[[2, 4, 6]]) == {player}\\n if any([row_win, col_win, d1_win, d2_win]):\\n return (\\\"win\\\", player)\\n\\n if self.counter[\\\"_\\\"] == 0:\\n return (\\\"tie\\\", None)\\n else:\\n return (\\\"turn\\\", \\\"1\\\" if self.counter[\\\"1\\\"] == self.counter[\\\"2\\\"] else \\\"2\\\")\",\n \"def _verify(self, data):\\n sections = {\\n \\\"schema\\\": dict,\\n \\\"defaults\\\": dict,\\n \\\"projects\\\": list\\n }\\n\\n for key, type_ in sections.items():\\n if key not in data:\\n self._report_error(\\\"Missing section: {}\\\".format(key))\\n return False\\n if not isinstance(data[key], type_):\\n err = \\\"Wrong data type for section {}, should be {}\\\"\\n self._report_error(err.format(key, type_))\\n return False\\n\\n for setting in data[\\\"defaults\\\"]:\\n if setting not in data[\\\"schema\\\"]:\\n err = \\\"Invalid setting {0} in default configuration\\\"\\n self._report_error(err.format(setting))\\n return False\\n\\n for i, project in enumerate(data[\\\"projects\\\"]):\\n if not isinstance(project, dict):\\n err = \\\"Wrong data type for project at index {}\\\"\\n self._report_error(err.format(i))\\n return False\\n if \\\"name\\\" not in project:\\n err = \\\"Missing name for project at index {}\\\"\\n self._report_error(err.format(i))\\n return False\\n for setting in project:\\n if setting not in data[\\\"schema\\\"]:\\n err = \\\"Invalid setting {} for project {}\\\"\\n self._report_error(err.format(setting, project[\\\"name\\\"]))\\n return False\\n\\n return True\",\n \"def is_game_complete(game):\\n game_round = min(len(game.creator_scores), len(game.invitee_scores))\\n creator_score = sum(game.creator_scores[:game_round])\\n invitee_score = sum(game.invitee_scores[:game_round])\\n return creator_score >= GAME_SCORE_TO_WIN or invitee_score >= GAME_SCORE_TO_WIN\",\n \"def check_pp_goals(game, key, opp_key, gsl):\\n # loading events data\\n game_type = get_game_type_from_season_type(game)\\n game_events_src_path = os.path.join(\\n CONFIG['base_data_dir'], 'game_events', str(game['season']), str(game_type), \\\"%d.json\\\" % game['game_id'])\\n events_data = json.loads(open(game_events_src_path).read())\\n\\n pp_goals_from_events = {'home': 0, 'visitor': 0}\\n\\n for period in events_data:\\n for event in events_data[period]:\\n if event['type'] != 'goal':\\n continue\\n # fixing penalty shots erroneously identified as power play goals\\n if event['data']['balance'] == 'PP0':\\n event['data']['balance'] = 'PS'\\n if event['data']['balance'].startswith('PP'):\\n pp_goals_from_events[event['data']['team']] += 1\\n\\n pp_goals_discrepancy = False\\n\\n pp_goals_from_events['road'] = pp_goals_from_events['visitor']\\n\\n if pp_goals_from_events[key] != gsl['pp_goals']:\\n pp_goals_discrepancy = True\\n gsl['pp_goals'] = pp_goals_from_events[key]\\n if pp_goals_from_events[opp_key] != gsl['opp_pp_goals']:\\n pp_goals_discrepancy = True\\n gsl['opp_pp_goals'] = pp_goals_from_events[opp_key]\\n\\n if pp_goals_discrepancy:\\n print(\\\"\\\\t+ Found and corrected different number of pp goals retrieved from team stats and game event data\\\")\\n\\n return gsl\",\n \"def check_win(self):\\n return UNEXPOSED not in self.get_game() and self.get_game().count(FLAG) == len(self.get_pokemon_location)\",\n \"def test_get_game_play_by_play(self):\\n msg = \\\"Response status is not 200\\\"\\n response = self.api.get_game_play_by_play(self.game_id)\\n self.assertEqual(response.status_code, 200, msg)\",\n \"def check_state(self, username, descriptor, expected_score, expected_max_score, expected_attempts):\\r\\n module = self.get_student_module(username, descriptor)\\r\\n self.assertEqual(module.grade, expected_score)\\r\\n self.assertEqual(module.max_grade, expected_max_score)\\r\\n state = json.loads(module.state)\\r\\n attempts = state['attempts']\\r\\n self.assertEqual(attempts, expected_attempts)\\r\\n if attempts > 0:\\r\\n self.assertTrue('correct_map' in state)\\r\\n self.assertTrue('student_answers' in state)\\r\\n self.assertGreater(len(state['correct_map']), 0)\\r\\n self.assertGreater(len(state['student_answers']), 0)\",\n \"def test_complete_game1(self):\\n m = Game()\\n\\n plays = [\\n ['a:-', 'a:-', 'a:b', 'b:b', 'b:-', 'b:b'],\\n ['c:3i', 'c:f', 'c:f', 'c:-', 'd:f', 'd:-', 'd:-'],\\n ['a:3i', 'a:2f', 'a:f', 'a:-', 'b:-', 'b:-', 'b:-'],\\n ['c:3i', 'c:2f', 'c:-', 'c:f', 'd:-', 'd:b', 'd:b'],\\n ['a:5i2rp', 'a:4f', 'a:f', 'a:b-', 'b:-', 'b:b', 'b:k']\\n ]\\n m.run(plays)\\n m.print_stats()\\n\\n # Did the right team win?\\n assert m.teams['a'].win == True\\n\\n # Did we get 5 turns?\\n assert m.turn_count == 5\\n\\n # There should have been 30 throws in the game\\n assert m.get_throw_count() == 30\",\n \"def check_inputs(self, item_data):\\n if not item_data[0] in self.data['pizza']:\\n print('Error: ' + item_data[0] + ' pizza does not exist.')\\n return False\\n\\n if not item_data[1] in self.data['pizza'][item_data[0]]:\\n print('Error: ' + item_data[1] + ' size does not exist for '\\n + item_data[0] + ' pizza.')\\n return False\\n\\n for topping in item_data[2]:\\n if not topping in self.data['topping']:\\n print('Error: Pizza topping ' + topping + ' does not exist.')\\n return False\\n return True\",\n \"def test_sad_purchasePlaces_wrong_compet(self):\\n\\n rv = self.app.post(\\n \\\"/purchasePlaces\\\",\\n data={\\n \\\"places\\\": 1,\\n \\\"club\\\": self.clubs[0][\\\"name\\\"],\\n \\\"competition\\\": \\\"fake_competition_name\\\",\\n },\\n )\\n\\n assert rv.status_code in [404]\\n assert b\\\"The provided competition is invalid\\\" in rv.data\",\n \"async def check_game_over(self, game_id):\\n game = await self.get_game(game_id)\\n player1_stand = await self.check_player_standing(game[1])\\n player2_stand = await self.check_player_standing(game[2])\\n if player1_stand and player2_stand:\\n return True\\n else:\\n return False\",\n \"def testDraw():\\n deleteMatches()\\n deletePlayers()\\n registerPlayer(\\\"Pikachu\\\")\\n registerPlayer(\\\"Charmander\\\")\\n registerPlayer(\\\"Bulbasaur\\\")\\n registerPlayer(\\\"Squirtle\\\")\\n standings = playerStandings()\\n [id1, id2, id3, id4] = [row[0] for row in standings]\\n reportMatch(id1, id2, True)\\n reportMatch(id3, id4, True)\\n standings = playerStandings()\\n if not (standings[0][2]==standings[1][2]==standings[2][2]==standings[3][2]):\\n raise ValueError(\\n \\\"Players should have the same number of points after drawing\\\"\\n )\\n\\n print \\\"3. Draw is recorded properly.\\\"\",\n \"def test_invalid_game_setup(self):\\n with self.assertRaises(ValueError):\\n self._game.add_player(self._creator, 1)\\n with self.assertRaises(ValueError):\\n self._game.add_player(self._users[1], 0)\\n for x in xrange(1, 4):\\n self._game.add_player(self._users[x], x)\\n with self.assertRaises(ValueError):\\n self._game.add_player(self._users[4], 1)\",\n \"def check_if_game_over():\\n check_for_winner()\\n check_for_tie()\",\n \"def test_check_validity(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n game.solve()\\n assert game.check_validity()\",\n \"def check_boards(self):\\n succesful = True\\n marker = self.game.player\\n print(f\\\"-----Starting check_winning_boards-----\\\")\\n winning_boards= [\\n [\\n [marker]*3,\\n [\\\" \\\"]*3,\\n [\\\" \\\"]*3\\n ],\\n [\\n [\\\" \\\"]*3,\\n [marker]*3,\\n [\\\" \\\"]*3\\n ],\\n [\\n [\\\" \\\"]*3,\\n [\\\" \\\"]*3,\\n [marker]*3\\n ],\\n [\\n [marker, \\\" \\\", \\\" \\\"],\\n [marker, \\\" \\\", \\\" \\\"],\\n [marker, \\\" \\\", \\\" \\\"]\\n ],\\n [\\n [\\\" \\\",marker, \\\" \\\"],\\n [\\\" \\\",marker, \\\" \\\"],\\n [\\\" \\\",marker, \\\" \\\"]\\n ],\\n [\\n [\\\" \\\", \\\" \\\",marker],\\n [\\\" \\\", \\\" \\\",marker],\\n [\\\" \\\", \\\" \\\",marker]\\n ],\\n [\\n [marker, \\\" \\\", \\\" \\\"]\\n ,[\\\" \\\", marker,\\\" \\\"],\\n [\\\" \\\", \\\" \\\",marker]\\n ],\\n [\\n [\\\" \\\", \\\" \\\", marker],\\n [\\\" \\\",marker, \\\" \\\"],\\n [marker, \\\" \\\", \\\" \\\"]\\n ]\\n ]\\n for board in winning_boards:\\n if self.game.check_win_conditions(board) != -10:\\n succesful = False\\n print(f\\\"board failed checkWins \\\\n{board}\\\")\\n marker = self.game.ai_player\\n print(f\\\"-----Starting check_winning_boards-----\\\")\\n winning_boards= [\\n [\\n [marker]*3,\\n [\\\" \\\"]*3,\\n [\\\" \\\"]*3\\n ],\\n [\\n [\\\" \\\"]*3,\\n [marker]*3,\\n [\\\" \\\"]*3\\n ],\\n [\\n [\\\" \\\"]*3,\\n [\\\" \\\"]*3,\\n [marker]*3\\n ],\\n [\\n [marker, \\\" \\\", \\\" \\\"],\\n [marker, \\\" \\\", \\\" \\\"],\\n [marker, \\\" \\\", \\\" \\\"]\\n ],\\n [\\n [\\\" \\\",marker, \\\" \\\"],\\n [\\\" \\\",marker, \\\" \\\"],\\n [\\\" \\\",marker, \\\" \\\"]\\n ],\\n [\\n [\\\" \\\", \\\" \\\",marker],\\n [\\\" \\\", \\\" \\\",marker],\\n [\\\" \\\", \\\" \\\",marker]\\n ],\\n [\\n [marker, \\\" \\\", \\\" \\\"]\\n ,[\\\" \\\", marker,\\\" \\\"],\\n [\\\" \\\", \\\" \\\",marker]\\n ],\\n [\\n [\\\" \\\", \\\" \\\", marker],\\n [\\\" \\\",marker, \\\" \\\"],\\n [marker, \\\" \\\", \\\" \\\"]\\n ]\\n ]\\n for board in winning_boards:\\n if self.game.check_win_conditions(board) != 10:\\n succesful = False\\n print(f\\\"board failed checkWins \\\\n{board}\\\")\\n \\n tie_boards = [\\n [ \\n [\\\"O\\\",\\\"O\\\",\\\"X\\\"],\\n [\\\"X\\\",\\\"O\\\",\\\"O\\\"],\\n [\\\"X\\\",\\\"X\\\",\\\" \\\"]\\n ],\\n [\\n [\\\"O\\\",\\\"X\\\",\\\" \\\"],\\n [\\\" \\\",\\\"X\\\",\\\" \\\"],\\n [\\\" \\\",\\\"O\\\",\\\" \\\"]\\n ],\\n [\\n ['O', 'O', 'X'],\\n ['X', 'X', 'O'],\\n ['O', 'O', 'X']\\n ]\\n ]\\n for board in tie_boards:\\n if self.game.check_win_conditions(board) != 0:\\n succesful = False\\n print(f\\\"board failed checkWins \\\\n{board}\\\")\\n\\n print(f\\\"-----Ending check_winning_boards-----\\\")\",\n \"def check_game_end(self):\\r\\n\\r\\n if np.all(self.remaining == -1): # end of game\\r\\n self.show_results() # show the final results\\r\\n sys.exit() # exit the program\\r\",\n \"def gameOver():\\n if len(p1)==0 and len(p1winnings)==0:\\n return True\\n elif len(p2)==0 and len(p2winnings)==0:\\n return True\\n return False\",\n \"def test_empty_game(self):\\n sgf = \\\"()\\\"\\n try:\\n coll = parseSgf(sgf)\\n except SgfParseError, e:\\n if str(e) == \\\"Empty game.\\\":\\n return\\n self.assertFalse(1)\",\n \"def test_sad_booking_wrong_club(self):\\n\\n for competition in self.competitions:\\n rv = self.app.get(f\\\"/book/{competition['name']}/wrong_club_name\\\")\\n\\n assert rv.status_code in [404]\\n assert b\\\"The provided club is invalid\\\" in rv.data\",\n \"def _create_games(self):\\n\\n ''''''\",\n \"def validate_data(self, data):\\n try:\\n event_id = data[\\\"event\\\"]\\n guests = data[\\\"guests\\\"]\\n section = data.get(\\\"section\\\", None)\\n except KeyError:\\n return False, \\\"\\\", \\\"\\\", \\\"\\\"\\n try:\\n event = Event.objects.get(pk=event_id)\\n except Event.DoesNotExist:\\n return False, \\\"\\\", \\\"\\\", \\\"\\\"\\n if not isinstance(guests, list):\\n return False, \\\"\\\", \\\"\\\", \\\"\\\"\\n if section is not None:\\n try:\\n event.venue.sections.get(pk=section)\\n except Section.DoesNotExist:\\n return False, \\\"\\\", \\\"\\\", \\\"\\\"\\n return True, event, guests, section\",\n \"def test_get_game(self):\\n #create the api \\n api_call = '/_ah/spi/GameApi.get_game'\\n app = endpoints.api_server([GameApi], restricted=False)\\n testapp = webtest.TestApp(app)\\n \\n #create two players\\n game, first_user, second_user = self._get_new_game() \\n \\n #the expected request object as a dictionary, to be serialised to JSON by webtest\\n request = {\\\"urlsafe_game_key\\\":game.key.urlsafe()} \\n resp = testapp.post_json(api_call, request)\\n \\n #check correct default values have been created\\n self.assertEqual(resp.json['next_move'], first_user.name)\\n self.assertEqual(resp.json['game_over'], False)\\n self.assertEqual(resp.json['unmatched_pairs'], \\\"8\\\")\\n self.assertEqual(resp.json['first_user_score'], \\\"0\\\")\\n self.assertEqual(resp.json['second_user_score'], \\\"0\\\")\\n self.assertEqual(resp.json['history'], \\\"[]\\\")\",\n \"async def validateGame(self, ctx):\\n if self.progression == \\\"Création d'une partie\\\": # Game created\\n\\n # =-=-=-= CHECKING TOO FAST START =-=-=-= #\\n for msgID in self.msgChoiceRole:\\n msg = await ctx.channel.fetch_message(msgID)\\n if len(msg.embeds[0].fields) != len(msg.reactions):\\n await self.wait(ctx)\\n return\\n\\n # =-=-=-= CALCULATING ROLES =-=-=-= #\\n self.progression = \\\"Préparation de la partie\\\"\\n await self.wait(ctx)\\n for msgId in self.msgChoiceRole:\\n msg = await ctx.channel.fetch_message(msgId)\\n for reactions in msg.reactions:\\n await self.getRoleFromReaction(ctx, reactions)\\n self.players = [member for member in ctx.author.voice.channel.members if not member.bot]\\n\\n # =-=-=-= CHECKING INVALID PARTY =-=-=-= #\\n if len(self.players) < 3:\\n self.msgToDelete.append(await ctx.channel.send(\\n \\\"Nombre de joueurs insuffisant : 3 joueurs minimum. (\\\" + str(\\n len(self.players)) + \\\" actuellement.)\\\"))\\n await asyncio.sleep(5)\\n await self.delete()\\n return\\n elif len(self.roles) < len(self.players) + 3:\\n self.msgToDelete.append(\\n await ctx.channel.send(\\n str(len(self.roles)) + \\\"rôles choisis : insuffisants pour le nombre de joueurs. (\\\" +\\n str(len(self.players) + 3) + \\\" rôles minimum,\\\" + str(len(self.players)) +\\n \\\" joueurs + 3 dans le tas central.)\\\"))\\n await asyncio.sleep(5)\\n await self.delete()\\n return\\n\\n elif len(self.roles) > len(self.players) + 3:\\n self.msgToDelete.append(await ctx.channel.send(\\n \\\"Nombre de rôles supérieur au nombre de joueurs : \\\" + str(len(self.roles)) + \\\"rôles pour \\\" + str(\\n len(self.players)) +\\n \\\" joueurs + 3 dans le tas central. Certains rôles ne seront pas pris en compte.\\\"))\\n\\n await self.delAllMsg(waitingTime=5)\\n print(\\\"Start with roles : \\\" + \\\", \\\".join(self.roles))\\n self.msgToDelete.append(await ctx.channel.send(\\n \\\"Début de la partie avec les rôles suivants : \\\" + \\\", \\\".join(self.roles)))\\n print(\\\"Start with players : \\\" + \\\", \\\".join(getAuthor(self.players)))\\n self.msgToDelete.append(await ctx.channel.send(\\n \\\"Les joueurs sont les suivants : \\\" + \\\", \\\".join(getAuthor(self.players))))\\n\\n # =-=-=-= START GAME =-=-=-= #\\n await self.startingGame(ctx=ctx)\\n\\n else:\\n await self.wait(ctx=ctx)\",\n \"def _check_season_gaps_in_teams_matches(self, df: pd.DataFrame):\\n if self._verbose > 0:\\n print(\\\"Teams:\\\")\\n print(self.teams)\\n print(\\\"Teams playing last season:\\\")\\n print(self.last_season_teams)\\n\\n omitted_teams_test = check_season_gaps(df, self.test_fixtures_ids)\\n omitted_teams_predict = check_season_gaps(df, self.predict_fixtures_ids)\\n\\n if omitted_teams_predict or omitted_teams_test:\\n print(\\\"Warning: There are some teams with season gaps in data.\\\")\\n print(\\\"Teams omitted based on predict dataset due to season gap:\\\")\\n print(omitted_teams_predict)\\n print(\\\"Teams omitted based on test dataset due to season gap:\\\")\\n print(omitted_teams_test)\\n\\n # Delete these teams from datasets\\n for team in set(omitted_teams_test + omitted_teams_predict):\\n del self.test_fixtures_ids[team]\\n del self.predict_fixtures_ids[team]\\n\\n print(\\\"You should consider lowering required number of test or predict data samples.\\\")\",\n \"def test_check_inputs(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n):\\n\\n *_, game = make_game(\\n student_names, project_names, supervisor_names, capacities, seed, clean\\n )\\n\\n with warnings.catch_warnings(record=True):\\n warnings.simplefilter(\\\"error\\\")\\n game.check_inputs()\\n\\n assert game.students == game._all_students\\n assert game.projects == game._all_projects\\n assert game.supervisors == game._all_supervisors\",\n \"def test_get_game_summary(self):\\n msg = \\\"Response status is not 200\\\"\\n response = self.api.get_game_summary(self.game_id)\\n self.assertEqual(response.status_code, 200, msg)\",\n \"def check_loss(self):\\n return POKEMON in self.get_game()\",\n \"def assert_data_correct(self) -> bool:\\n if not self.training_folder.exists():\\n return False\\n # 27: number of characters\\n # 27*2: 27 original font characters and 27 folders with morphed version\\n if len(list(self.training_folder.iterdir())) not in [27, 27 * 2]:\\n return False\\n # assert that each character folder has the expected number of images inside\\n # expected number is repetitions + original, or just original if no morphing\\n # took place\\n for directory in self.training_folder.iterdir():\\n img_count = len(list(directory.iterdir()))\\n if img_count != self.repetitions + 1 and img_count != 1:\\n return False\\n return True\",\n \"def __check_board_is_valid(self, board, players):\\n total_tiles = board.get_rows() * board.get_cols()\\n # check the board is big enough\\n if total_tiles < (6 - len(players)) * len(players):\\n raise ValueError(\\\"Board specified by board_size is too small.\\\")\\n # check that the board has enough active tiles\\n if len(players) == 3 and total_tiles - board.get_holes_in_board() < 9:\\n raise ValueError(\\\"Too many holes to place all penguins\\\")\\n elif total_tiles - board.get_holes_in_board() < 8:\\n raise ValueError(\\\"Too many holes to place all penguins\\\")\",\n \"def test_sad_booking_wrong_compet(self):\\n\\n for competition in self.competitions:\\n rv = self.app.get(f\\\"/book/wrong_compet_name/{self.clubs[0]['name']}\\\")\\n\\n assert rv.status_code in [404]\\n assert b\\\"The provided competition is invalid\\\" in rv.data\",\n \"def __len__(self):\\n return len(self._games)\",\n \"def is_valid(data):\\n check = [0 for i in range(4)]\\n # calculate how many ships are with different lengths\\n for i in range(10):\\n for j in range(10):\\n if type(data[i][j]) == Ship:\\n check[data[i][j]._Ship__length - 1] += 1\\n # check ships\\n for i in range(4):\\n if check[i] != (i + 1) * (4 - i):\\n return False\\n # check corners\\n for i in range(1, 10):\\n for j in range(10):\\n try:\\n if type(data[i - 1][j + 1]) == Ship and \\\\\\n type(data[i][j]) == Ship:\\n return False\\n except:\\n pass\\n try:\\n if type(data[i - 1][j - 1]) == Ship and \\\\\\n type(data[i][j]) == Ship:\\n return False\\n except:\\n pass\\n return True\",\n \"def validate(gear_info, errors):\\n\\n checklist = [(\\\"set\\\", \\\"NA\\\"), (\\\"rarity\\\", \\\"NA\\\"), (\\\"slot\\\", \\\"NA\\\"),\\n (\\\"level\\\", \\\"999\\\"), (\\\"ability\\\", \\\"999\\\"), (\\\"mainStat\\\", [\\\"NA\\\", 999])]\\n\\n for key, val in checklist:\\n # Add fake data if not found\\n if key not in gear_info:\\n gear_info[key] = val\\n\\n if \\\"mainStat\\\" in key:\\n errors['stats'] = 1\\n else:\\n errors[key] = 1\\n print(f\\\"{key} is not in info.\\\")\\n\\n for key, val in gear_info.items():\\n # Check stats values if values are too high then change to 999\\n if \\\"Stat\\\" in key:\\n if val[0][-1] == \\\"P\\\" and len(val[0]) > 2 and val[1] > 100:\\n val[1] = 0\\n gear_info[key] = val\\n errors['stats'] = 1\\n print(\\\"changed stat\\\")\\n\\n # Check other values in gear info\\n if key == 'mainStat' and val[0] == \\\"NA\\\":\\n if gear_info['slot'] == \\\"Weapon\\\":\\n gear_info = \\\"Atk\\\"\\n elif gear_info['slot'] == \\\"Helmet\\\":\\n gear_info = \\\"HP\\\"\\n elif gear_info['slot'] == \\\"Armor\\\":\\n gear_info = \\\"Def\\\"\\n\\n elif key == \\\"level\\\" and val == \\\"999\\\" and len(gear_info['name']) > 1:\\n matched = match_gear_name(gear_info['name'])\\n if matched > 0:\\n print(matched)\\n gear_info[key] = str(int(matched))\\n errors[key] = 0\\n print(\\\"Fixed level\\\")\\n else:\\n gear_info[key] = \\\"999\\\"\\n errors[key] = 1\\n print(\\\"Found error in level. Change to 999\\\")\\n\\n elif key == 'ability' and int(val) > 15:\\n gear_info[key] = \\\"999\\\"\\n errors[key] = 1\\n print(\\\"Found error in ability. Change to 999\\\")\\n\\n return gear_info, errors\",\n \"def test_get_game_boxscore(self):\\n msg = \\\"Response status is not 200\\\"\\n response = self.api.get_game_boxscore(self.game_id)\\n self.assertEqual(response.status_code, 200, msg)\",\n \"def validate_onboarding(data: Dict):\\n try:\\n saved_data = data['outputs']['messages'][-1]['data']['WORLD_DATA']\\n role = (\\n 'Wizard' if saved_data[constants.SAVED_DATA_IS_WIZARD_KEY] else 'Apprentice'\\n )\\n logging.info(f'Validating {role} onboarding.')\\n except (IndexError, KeyError) as e:\\n logging.warning(\\n 'Incomplete data to validate agent onboarding.'\\n f'Onboarding saved_data error: {e}'\\n )\\n return False\\n\\n rejection_reason = saved_data[constants.WORKER_REJECT_REASON]\\n if rejection_reason:\\n logging.warning(f'Rejected: {rejection_reason}')\\n return False\\n\\n # Role qualification\\n worker = get_worker_by_name(saved_data[constants.SAVED_DATA_WORKER_KEY])\\n qual_name, qual_val = saved_data[constants.SAVED_DATA_ROLE_QUALIFICATION_DATA_KEY]\\n if not assign_role_training_qualification(worker, qual_name, qual_val):\\n return False\\n\\n logging.info('Onboarding work accepted.')\\n return True\",\n \"def clean(self):\\n if self.hours_played < 0:\\n raise ValidationError(\\\"Hours played cannot be negative.\\\")\\n\\n zelda_oot = Game.objects.filter(game_name='The Legend of Zelda: Ocarina of Time')\\n if zelda_oot:\\n zelda_oot = zelda_oot[0]\\n if self != zelda_oot and self.hours_played > zelda_oot.hours_played:\\n raise ValidationError(\\\"Liar.\\\")\\n\\n super(Game, self).clean()\",\n \"def _check_game_over(self):\\n return self.game_board.check_game_over()\",\n \"def _check_data_correct(self, expected_data, _):\\n data_len = len(expected_data)\\n for retry_count in range(self.RETRY_COUNT):\\n if retry_count > 0:\\n test_info.info('Previous attempts %s' % retry_count)\\n try:\\n data_loaded = self.board.read_target_memory(self._start, data_len)\\n except:\\n time.sleep(self.DELAY_BEFORE_RETRY_S)\\n continue\\n break\\n else:\\n raise Exception(\\\"read_target_memory() failed after %i retries\\\" % self.RETRY_COUNT)\\n\\n return _same(expected_data, data_loaded)\",\n \"def _validate(self) -> None:\\n for box in self.boxes:\\n if any(box[0] == s[0] and box[1] == s[1] for s in self.wall_squares):\\n raise RuntimeError('In illegal state. Box should not be inside wall.')\\n if box[0] == self.current_location[0] and box[1] == self.current_location[1]:\\n raise RuntimeError('In illegal state. Box should not be inside player.')\\n if any(self.current_location[0] == s[0] and self.current_location[1] == s[1] for s in self.wall_squares):\\n raise RuntimeError('In illegal state. Player should not be inside wall.')\",\n \"def test_play_quiz_fails(self):\\n\\n # send post request without json data\\n response = self.client().post('/quizzes', json={})\\n\\n # load response data\\n data = json.loads(response.data)\\n\\n # check response status code and message\\n self.assertEqual(response.status_code, 400)\\n self.assertEqual(data['success'], False)\\n self.assertEqual(data['message'], 'bad request')\",\n \"def __check_winner(self):\\n for i in range(0, 3):\\n col = self.__get_col(i)\\n if col.get(self.player_char) == 3:\\n print('\\\\nYou win!')\\n self.game_ended = True\\n return\\n if col.get(self.opponent_char) == 3:\\n print('\\\\nYou lose.')\\n self.game_ended = True\\n return\\n row = self.__get_row(i)\\n if row.get(self.player_char) == 3:\\n print('\\\\nYou win!')\\n self.game_ended = True\\n return\\n if row.get(self.opponent_char) == 3:\\n print('\\\\nYou lose.')\\n self.game_ended = True\\n return\\n for i in range(0, 2):\\n diag = self.__get_diag(i)\\n if diag.get(self.player_char) == 3:\\n print('\\\\nYou win!')\\n self.game_ended = True\\n return\\n if diag.get(self.opponent_char) == 3:\\n print('\\\\nYou lose.')\\n self.game_ended = True\\n return\\n if self.state.count(' ') == 0:\\n print('\\\\nDraw!')\\n self.game_ended = True\",\n \"def check_if_game_over():\\n # Calling check for winners.\\n check_for_winner()\\n # Calling check it's tie or not.\\n check_if_tie()\",\n \"def is_data_valid(self):\\n errors = []\\n for state, filename in self.symbols.items():\\n if state is None or state == \\\"\\\":\\n errors.append(\\\"Image {} has no state\\\".format(filename))\\n error, file_type = self.check_image(filename)\\n if error:\\n errors.append(error)\\n\\n if len(errors) > 0:\\n error_msg = os.linesep.join(errors)\\n return False, error_msg\\n\\n return True, \\\"\\\"\",\n \"def test_newsmall6dgame(self):\\n with open(\\\"test_outputs/test_newsmall6dgame.json\\\") as f:\\n expected = json.load(f)\\n with open(\\\"test_inputs/test_newsmall6dgame.json\\\") as f:\\n inputs = json.load(f)\\n result = lab.HyperMinesGame(inputs[\\\"dimensions\\\"], inputs[\\\"bombs\\\"])\\n for i in ('dimensions', 'board', 'mask', 'state'):\\n self.assertEqual(getattr(result, i), expected[i])\",\n \"def _checkRoundOver(self):\\n\\n if not any(player.isAlive() for player in self.teams[0].players):\\n self.endGame()\",\n \"def test_check_win(self):\\n # Horizontal wins\\n # First row\\n self.game.move(0, 0, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(0, 1, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(0, 2, PLAYERX)\\n self.assertEqual(self.game.check_win(0, 0, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(0, 1, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(0, 2, PLAYERX), PLAYERX)\\n\\n # Second row\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.game._board[row][col] = EMPTY\\n\\n self.game.move(1, 0, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(1, 1, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(1, 2, PLAYERX)\\n self.assertEqual(self.game.check_win(1, 0, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(1, 1, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(1, 2, PLAYERX), PLAYERX)\\n\\n # Third row\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.game._board[row][col] = EMPTY\\n\\n self.game.move(2, 0, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(2, 1, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(2, 2, PLAYERX)\\n self.assertEqual(self.game.check_win(2, 0, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(2, 1, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(2, 2, PLAYERX), PLAYERX)\\n\\n # Vertical wins\\n # First column\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.game._board[row][col] = EMPTY\\n\\n self.game.move(0, 0, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(1, 0, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(2, 0, PLAYERX)\\n self.assertEqual(self.game.check_win(0, 0, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(1, 0, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(2, 0, PLAYERX), PLAYERX)\\n\\n # Second column\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.game._board[row][col] = EMPTY\\n\\n self.game.move(0, 1, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(1, 1, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(2, 1, PLAYERX)\\n self.assertEqual(self.game.check_win(0, 1, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(1, 1, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(2, 1, PLAYERX), PLAYERX)\\n\\n # Third column\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.game._board[row][col] = EMPTY\\n\\n self.game.move(0, 2, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(1, 2, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(2, 2, PLAYERX)\\n self.assertEqual(self.game.check_win(0, 2, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(1, 2, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(2, 2, PLAYERX), PLAYERX)\\n\\n # Diagonal wins\\n # Upper left to bottom right\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.game._board[row][col] = EMPTY\\n\\n self.game.move(0, 0, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(1, 1, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(2, 2, PLAYERX)\\n self.assertEqual(self.game.check_win(0, 0, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(1, 1, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(2, 2, PLAYERX), PLAYERX)\\n\\n # Bottom left to upper right\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.game._board[row][col] = EMPTY\\n\\n self.game.move(2, 0, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(1, 1, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(0, 2, PLAYERX)\\n self.assertEqual(self.game.check_win(2, 0, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(1, 1, PLAYERX), PLAYERX)\\n self.assertEqual(self.game.check_win(0, 2, PLAYERX), PLAYERX)\\n\\n # Draw\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.game._board[row][col] = EMPTY\\n\\n self.game.move(0, 0, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(0, 1, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(0, 2, PLAYERO)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(1, 0, PLAYERO)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(1, 1, PLAYERO)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(1, 2, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(2, 0, PLAYERX)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(2, 1, PLAYERO)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), None)\\n self.game.move(2, 2, PLAYERO)\\n for row in range(self.game._dim):\\n for col in range(self.game._dim):\\n self.assertEqual(self.game.check_win(row, col, PLAYERX), DRAW)\",\n \"def test_newlarge4dgame(self):\\n with open(\\\"test_outputs/test_newlarge4dgame.json\\\") as f:\\n expected = json.load(f)\\n with open(\\\"test_inputs/test_newlarge4dgame.json\\\") as f:\\n inputs = json.load(f)\\n result = lab.HyperMinesGame(inputs[\\\"dimensions\\\"], inputs[\\\"bombs\\\"])\\n for i in ('dimensions', 'board', 'mask', 'state'):\\n self.assertEqual(getattr(result, i), expected[i])\",\n \"def check_winner(self):\\n pass\",\n \"def uber_check_win(self):\\n if self.player1.score == self.player2.score:\\n print(\\\"It's a draw!\\\")\\n elif self.player1.score > self.player2.score:\\n print(\\\"Player 1 is a proper bad ass mother fucker\\\")\\n else:\\n print(\\\"Player numma 2 is a proper bad ass mother fucker\\\")\",\n \"def checkOpsimData(self, otherdata=None):\\n data2check = self.opsim_data\\n if otherdata:\\n data2check = otherdata\\n if type(data2check) != list:\\n if type(data2check) == dict:\\n if set(self._opsim_keys).issubset(set(data2check.keys())):\\n return True, 'good'\\n else:\\n return False, 'Data is not complete'\\n else:\\n return False, 'Data is not a list nor a dictionary'\\n else:\\n nodict = 0\\n baddict = 0\\n for data in data2check:\\n if type(data) != dict:\\n nodict += 1\\n else:\\n if not set(self._opsim_keys).issubset(set(data.keys())):\\n baddict += 1\\n if (nodict == 0 and baddict == 0):\\n return True, 'good'\\n else:\\n statement = '{0} non dictionary element(s) and {1} uncomplete\\\\\\n dictionary(ies) in data list'.format(nodict, baddict)\\n return (False, statement)\",\n \"def check_validity(self):\",\n \"def test_set_players_number(self):\\n\\n # Setup new games and attempt to set thier number of players\\n valid_players = [\\n 1,\\n 2,\\n 10,\\n 999,\\n ]\\n for players in valid_players:\\n game = Game()\\n game.setup_new_game()\\n game.set_pack_number(1)\\n game.set_starting_chips(100)\\n game.set_players_number(players)\\n self.assertEqual(game.players_number, players, msg=\\\"The number of players was not correctly set with \\\" + str(players) + \\\" players.\\\")\\n\\n # Make sure that the new game state is correctly set\\n self.assertEqual(game.state.name, \\\"get_player_names\\\", msg=\\\"The game state was not correctly set after setting the number of players in the game.\\\")\\n\\n # Try to set invalid player numbers\\n invalid_players = [\\n 0,\\n -1,\\n -100,\\n 1.5,\\n ]\\n for players in invalid_players:\\n game = Game()\\n game.setup_new_game()\\n game.set_pack_number(1)\\n game.set_starting_chips(100)\\n success = False\\n try:\\n game.set_players_number(players)\\n except InvalidGamePlayersNumber:\\n success = True\\n self.assertTrue(success, msg=\\\"An invalid number of players \\\" + str(players) + \\\" was able to be set.\\\")\\n\\n # Try to reset the number of players to throw an error\\n game = Game()\\n game.setup_new_game()\\n game.set_pack_number(1)\\n game.set_starting_chips(100)\\n game.set_players_number(3)\\n success = False\\n try:\\n game.set_players_number(2)\\n except InvalidGameMethodOrder:\\n success = True\\n self.assertTrue(success, msg=\\\"The number of players was incorrectly able to be reset.\\\")\",\n \"def _is_valid(self):\\n if len(self.slots) == 0:\\n print(\\\"Parking Lot not created\\\")\\n return False\\n return True\",\n \"def test_get_game_faceoffs(self):\\n msg = \\\"Response status is not 200\\\"\\n response = self.api.get_game_faceoffs(self.game_id)\\n self.assertEqual(response.status_code, 200, msg)\",\n \"def start_game_check(self):\\n if len(self.pending_players) > 0:\\n return False\\n else:\\n return True\",\n \"def test(self):\\n\\t\\tx = Team.objects.get(short_name='SF')\\n\\t\\tself.assertEqual(x.nick_name, '49ers')\\n\\n\\t\\tx = Game.objects.get(week_number=1,game_number=1)\\n\\t\\tx.fav_score = 21\\n\\t\\tx.udog_score = 14\\n\\t\\tx.spread = 5\\n\\t\\tself.assertEqual(x.favWins(), True)\\n\\t\\tx.spread = 10\\n\\t\\tself.assertEqual(x.favWins(), False)\\n\\n\\t\\t# Sunday game at 4pm should be open if time before 1 else closed\\n\\t\\tx.game_date = datetime.datetime(2020,3,1,16,0,0)\\n\\t\\tcurrent_time = datetime.datetime(2020,3,1,11,0,0)\\n\\t\\tself.assertEqual(x.isClosed(current_time=current_time), False)\\n\\t\\tcurrent_time = datetime.datetime(2020,3,1,14,0,0)\\n\\t\\tself.assertEqual(x.isClosed(current_time=current_time), True)\\n\\n\\t\\t# Saturday game. Should be closed only if after kick off\\n\\t\\tx.game_date = datetime.datetime(2020,2,29,16,0,0)\\n\\t\\tcurrent_time = datetime.datetime(2020,2,29,15,59,59)\\n\\t\\tself.assertEqual(x.isClosed(current_time=current_time), False)\\n\\t\\tcurrent_time = datetime.datetime(2020,2,29,16,0,1)\\n\\t\\tself.assertEqual(x.isClosed(current_time=current_time), True)\\n\\n\\t\\t# Monday game, should be closed as long as it's after 1pm sunday\\n\\t\\tx.game_date = datetime.datetime(2020,3,2,21,0,0)\\n\\t\\tcurrent_time = datetime.datetime(2020,3,1,15,59,59)\\n\\t\\tself.assertEqual(x.isClosed(current_time=current_time), True)\\n\\t\\tcurrent_time = datetime.datetime(2020,3,1,11,59,59)\\n\\t\\tself.assertEqual(x.isClosed(current_time=current_time), False)\\n\\t\\tself.assertEqual(x.isOpen(current_time=current_time), True)\\n\\t\\tcurrent_time = datetime.datetime(2020,3,2,11,59,59)\\n\\t\\tself.assertEqual(x.isClosed(current_time=current_time), True)\\n\\t\\tcurrent_time = datetime.datetime(2020,3,3,11,59,59)\\n\\t\\tself.assertEqual(x.isClosed(current_time=current_time), True)\\n\\n\\t\\t# Sunday 9a game should be open until kick off\\n\\t\\tx.game_date = datetime.datetime(2020,3,1,9,0,0)\\n\\t\\tcurrent_time = datetime.datetime(2020,3,1,8,0,0)\\n\\t\\tself.assertEqual(x.isClosed(current_time=current_time), False)\\n\\t\\tcurrent_time = datetime.datetime(2020,3,1,10,0,0)\\n\\t\\tself.assertEqual(x.isClosed(current_time=current_time), True)\\n\\t\\tself.assertEqual(x.isOpen(current_time=current_time), False)\\n\\n\\t\\t# Now one check with not setting current_time\\n\\t\\tx.game_date = datetime.datetime(1971,3,1,9,0,0)\\n\\t\\tself.assertEqual(x.isClosed(), True)\\n\\n\\n\\t\\tuser = User.objects.get(username='Tammer')\\n\\t\\tx = Pick.objects.get(player=user,week_number=1,game_number=1)\\n\\t\\tg = Game.objects.get(week_number=1,game_number=1)\\n\\t\\tself.assertEqual(x.picked_fav, True)\\n\\t\\tself.assertEqual(x.isCorrect(), g.fav_score - g.udog_score > g.spread)\",\n \"def check_win(self, player):\\n def check_row_win(player):\\n for row in self.game_state:\\n if player == row[0] == row[1] == row[2]:\\n return True\\n return False\\n\\n def check_column_win(player):\\n # For doing a column check, transpose the grid and do a row check\\n trans_game_state = numpy.transpose(self.game_state)\\n for row in trans_game_state:\\n if player == row[0] == row[1] == row[2]:\\n return True\\n return False\\n\\n def check_diag_win(player):\\n # Left to right diagonal\\n if player == self.game_state[0][0] == self.game_state[1][1] == self.game_state[2][2]:\\n return True\\n # Right to left diagonal\\n if player == self.game_state[0][2] == self.game_state[1][1] == self.game_state[2][0]:\\n return True\\n return False\\n\\n if check_column_win(player) or check_diag_win(player) or check_row_win(player):\\n return True\\n return False\",\n \"def testing_create_game():\\n black_user = request.form['black_email']\\n white_user = request.form['white_email']\\n stones = json.loads(request.form['stones'])\\n create_game_internal(black_user, white_user, stones)\\n return ''\",\n \"def assert_data_correct(self) -> bool:\\n corr_char = self.assert_data_characters_correct()\\n print(\\\"Character data correct?\\\", corr_char)\\n corr_font = FontImages().assert_data_correct()\\n print(\\\"Font data correct?\\\", corr_font)\\n corr_frag = self.assert_data_fragments_correct()\\n print(\\\"Fragment data correct?\\\", corr_frag)\\n corr_train_aug = self.assert_train_augmented()\\n print(\\\"Train data augmented?\\\", corr_train_aug)\\n truth_agree = corr_char and corr_font and corr_frag\\n return True if truth_agree else False\",\n \"def test_happy_booking(self):\\n\\n now = datetime.datetime.now()\\n\\n for club in self.clubs:\\n for competition in self.competitions:\\n rv = self.app.get(f\\\"/book/{competition['name']}/{club['name']}\\\")\\n\\n print(rv.data, rv.status_code, \\\"\\\\n\\\")\\n\\n if server.formatDate(competition[\\\"date\\\"]) <= now:\\n continue\\n\\n assert rv.status_code in [200]\\n assert (\\n str.encode(f\\\"Places available: {competition['numberOfPlaces']}\\\")\\n in rv.data\\n )\",\n \"def won_game(self):\\n for player in self.players:\\n if len(player.cards) == 0:\\n\\n return True\\n return False\",\n \"def check_extracted_data(data):\\n for item in data:\\n document_name = item[\\\"document\\\"]\\n data_frame = item[\\\"data_frame\\\"]\\n entity_position = item[\\\"entity_position\\\"]\\n relations = item[\\\"relations\\\"]\\n\\n # Check if entities correctly embedded\\n entity_embedding = data_frame[\\\"entity_embedding\\\"].tolist()\\n cnt = 0\\n for entity_key in entity_position:\\n low, high = entity_position[entity_key]\\n cnt += high - low\\n if high == low:\\n print(CHECK_FAILED_AT_DOCUMENT, document_name, \\\"in 'entity_embedding', key\\\", entity_key,\\n \\\"is empty (from\\\", low, \\\"to\\\", high, \\\")\\\")\\n else:\\n try:\\n assert abs(min(entity_embedding[low:high]) - max(entity_embedding[low:high])) <= 1\\n except AssertionError:\\n print(CHECK_FAILED_AT_DOCUMENT, document_name, \\\"in 'entity_embedding', key\\\", entity_key,\\n \\\", values from\\\", low, \\\"to\\\", high, \\\":\\\", entity_embedding[low:high], \\\"are inconsistent\\\")\\n try:\\n assert cnt == (np.array(entity_embedding) != 0).astype(int).sum()\\n except AssertionError:\\n print(CHECK_FAILED_AT_DOCUMENT, document_name, \\\"in total entity embedded tokens\\\",\\n (np.array(entity_embedding) != 0).astype(int).sum(), \\\"does not match the record\\\", cnt)\\n\\n # Check if all relations are valid\\n for value in relations.values():\\n first = value[\\\"source\\\"]\\n second = value[\\\"target\\\"]\\n try:\\n assert first in entity_position\\n except AssertionError:\\n print(CHECK_FAILED_AT_DOCUMENT, document_name, \\\"in 'relations',\\\", first,\\n \\\"is not found in record\\\")\\n try:\\n assert second in entity_position\\n except AssertionError:\\n print(CHECK_FAILED_AT_DOCUMENT, document_name, \\\"in 'relations',\\\", second,\\n \\\"is not found in record\\\")\",\n \"def test_setup_new_game(self):\\n\\n # Create a new game and make sure it has the correct settings\\n game = Game()\\n game.setup_new_game()\\n self.assertTrue(game.dealer is not None, msg=\\\"The dealer of the game was not created.\\\")\\n self.assertEqual(game.dealer.cards, [])\\n self.assertEqual(game.state.name, \\\"get_number_of_packs\\\", msg=\\\"The initial game state was not correctly set.\\\")\",\n \"def checkWin(self):\\n winstates = [(0, 1, 2),\\n (3, 4, 5),\\n (6, 7, 8),\\n (0, 3, 6),\\n (1, 4, 7),\\n (2, 5, 8),\\n (0, 4, 8),\\n (2, 4, 6)]\\n win = False\\n for state in winstates:\\n if (self.gameState[state[0]] + self.gameState[state[1]] + self.gameState[state[2]]) == 3:\\n self.handleWin(1)\\n win = True\\n elif (self.gameState[state[0]] + self.gameState[state[1]] + self.gameState[state[2]]) == -3:\\n self.handleWin(-1)\\n win = True\\n\\n if len([i for i in range(9) if self.gameState[i] == 0]) == 0 and not win:\\n print(\\\"Draw yo\\\")\\n self.handleDraw()\\n return None\",\n \"def checkGameComplete(self):\\n for rowKey in self.table:\\n for ele in self.table[rowKey]:\\n if type(ele) == int:\\n return False # means not complete\\n return True\",\n \"def _check_data_valid(self):\\n\\n is_valid = (sum(~np.isnan(self.data).flatten()) > 0 and self.data.flatten().sum() != 0)\\n if not is_valid:\\n raise FITSException(f\\\"No data in {self.survey}\\\")\",\n \"def _check_validity(self):\\n pass\",\n \"def is_game_win(self):\\n return not self.deck and not self.hand\",\n \"def check_data(self):\\n\\n missing_params = {}\\n flag = False\\n\\n missing_params['general'] = {}\\n for name, param in self.params.items():\\n if not param.check():\\n missing_params['general'][name] = param.get_description()\\n flag = True\\n\\n for component, comp_obj in self.components.items():\\n missing_params[component], flag_comp = comp_obj.check_data()\\n\\n # Assign empty component parameters that have a general version:\\n empty_general_params = set(missing_params[component]).intersection(\\n set(self.params))\\n for param in empty_general_params:\\n comp_obj.change_param_object(param, self.params[param])\\n del missing_params[component][param]\\n\\n if missing_params[component]:\\n flag = True\\n\\n if flag:\\n raise Exception('Following parameters are missing:\\\\n{}'\\n .format(\\n self._print_params(missing_params, disp=False)))\\n\\n return True\",\n \"def isGameOver(self):\\n pass\",\n \"def test_game_has_ended_match(self):\\n game = Game.objects.create(\\n allowed_attempts=10,\\n end_date=date.today() + timedelta(1),\\n min_value=1,\\n max_value=10\\n )\\n self.assertTrue(game.is_active)\\n self.assertIsNone(game.compleation_date)\\n\\n game.attempt_set.create(\\n our_number=10,\\n user_number=10,\\n ip_addr='127.0.0.1'\\n )\\n self.assertEqual(len(self.recieved), 1)\\n self.assertListEqual(self.recieved[0], ['match'])\\n self.assertFalse(game.is_active)\\n self.assertIsNotNone(game.compleation_date)\",\n \"def isGameOver(self):\\n for i in range(self.rows):\\n for j in range(self.columns):\\n if self.grid[i][j].face == 'down':\\n return False\\n #if here then all cards must be face up\\n return True\",\n \"def checkapp(self, app):\\n\\n data = requests.get('http://store.steampowered.com/api/appdetails?appids={0}&format=json'.format(app)).json()\\n\\n if data[str(app)][\\\"success\\\"]:\\n type = data[str(app)][\\\"data\\\"]['type']\\n if (type != 'game'):\\n return False\\n return data[str(app)][\\\"success\\\"]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.75368327","0.70760256","0.69226396","0.6563712","0.65609324","0.6393713","0.63804436","0.637144","0.6327626","0.6323198","0.6308842","0.6299205","0.6270144","0.62564266","0.622065","0.6184035","0.61409825","0.61319643","0.6127695","0.6106813","0.6062647","0.6056403","0.6051926","0.6038774","0.6034623","0.6003426","0.5993964","0.5931996","0.5927241","0.59223765","0.5914955","0.5910266","0.59087074","0.5901123","0.58774734","0.5876486","0.5873004","0.5867025","0.5866865","0.58642715","0.58557117","0.58509034","0.58500165","0.58434707","0.5841117","0.5829003","0.58274496","0.5811256","0.58104104","0.5809454","0.5797546","0.5797495","0.57881135","0.57814527","0.57763094","0.5775175","0.5772122","0.575859","0.5756674","0.5755338","0.57546926","0.57537633","0.57528216","0.5752778","0.57510495","0.5749999","0.5745949","0.5744752","0.57412386","0.5735335","0.57323414","0.57320225","0.57290184","0.57260454","0.5722408","0.57190365","0.5717891","0.5711295","0.57078445","0.5705993","0.56972724","0.5696182","0.56958175","0.56941265","0.56843156","0.5684246","0.56770796","0.56763893","0.56745976","0.5668876","0.5666933","0.5658786","0.56455237","0.56435174","0.5642293","0.5641697","0.5638876","0.56378824","0.5636391","0.5635764"],"string":"[\n \"0.75368327\",\n \"0.70760256\",\n \"0.69226396\",\n \"0.6563712\",\n \"0.65609324\",\n \"0.6393713\",\n \"0.63804436\",\n \"0.637144\",\n \"0.6327626\",\n \"0.6323198\",\n \"0.6308842\",\n \"0.6299205\",\n \"0.6270144\",\n \"0.62564266\",\n \"0.622065\",\n \"0.6184035\",\n \"0.61409825\",\n \"0.61319643\",\n \"0.6127695\",\n \"0.6106813\",\n \"0.6062647\",\n \"0.6056403\",\n \"0.6051926\",\n \"0.6038774\",\n \"0.6034623\",\n \"0.6003426\",\n \"0.5993964\",\n \"0.5931996\",\n \"0.5927241\",\n \"0.59223765\",\n \"0.5914955\",\n \"0.5910266\",\n \"0.59087074\",\n \"0.5901123\",\n \"0.58774734\",\n \"0.5876486\",\n \"0.5873004\",\n \"0.5867025\",\n \"0.5866865\",\n \"0.58642715\",\n \"0.58557117\",\n \"0.58509034\",\n \"0.58500165\",\n \"0.58434707\",\n \"0.5841117\",\n \"0.5829003\",\n \"0.58274496\",\n \"0.5811256\",\n \"0.58104104\",\n \"0.5809454\",\n \"0.5797546\",\n \"0.5797495\",\n \"0.57881135\",\n \"0.57814527\",\n \"0.57763094\",\n \"0.5775175\",\n \"0.5772122\",\n \"0.575859\",\n \"0.5756674\",\n \"0.5755338\",\n \"0.57546926\",\n \"0.57537633\",\n \"0.57528216\",\n \"0.5752778\",\n \"0.57510495\",\n \"0.5749999\",\n \"0.5745949\",\n \"0.5744752\",\n \"0.57412386\",\n \"0.5735335\",\n \"0.57323414\",\n \"0.57320225\",\n \"0.57290184\",\n \"0.57260454\",\n \"0.5722408\",\n \"0.57190365\",\n \"0.5717891\",\n \"0.5711295\",\n \"0.57078445\",\n \"0.5705993\",\n \"0.56972724\",\n \"0.5696182\",\n \"0.56958175\",\n \"0.56941265\",\n \"0.56843156\",\n \"0.5684246\",\n \"0.56770796\",\n \"0.56763893\",\n \"0.56745976\",\n \"0.5668876\",\n \"0.5666933\",\n \"0.5658786\",\n \"0.56455237\",\n \"0.56435174\",\n \"0.5642293\",\n \"0.5641697\",\n \"0.5638876\",\n \"0.56378824\",\n \"0.5636391\",\n \"0.5635764\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.66139734"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":85585,"cells":{"query":{"kind":"string","value":"Gets the latest season and season type in the games data"},"document":{"kind":"string","value":"def _get_latest_season_and_type(df):\n if df is None or len(df) == 0:\n return (None, None)\n\n if not isinstance(df, pd.DataFrame):\n raise ValueError(f\"Type of df is {type(df)}, it should be pandas.DataFrame\")\n\n df = df[df['state_of_game'] == 'POST']\n latest_season = df['season'].max()\n season_types = df[df['season'] == latest_season]['type'].unique()\n latest_season_type = _get_latest_season_type(list(season_types))\n return latest_season, latest_season_type"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def latestGamePack(team):\n lgr= get('schedule', {'ver':'v1', 'sportId':1, 'date':today, 'teamId':team, 'fields':['dates','games','gamePk'] })\n return lgr['dates'][0]['games'][0]['gamePk']","def getseason(data):\n ## Season key is the most reliable\n season = data.get(\"season\")\n if season:\n ## Season key is an integer formatted \"YYS\" and is 2000-based (i.e.- 171 == 2017-Winter)\n season = str(season)\n year = int(f\"20{season[:2]}\")\n ## Anichart Season key is 1-indexed\n season = int(season[2]) - 1\n ## This should normally pass; if it consistently does not, we'll have to investigate why\n try: return SeasonCharts.buildseason(season,year)\n ## If something goes wrong, we'll try another method\n except: print(f\"Failed to parse season: {data['season']}\")\n ## Next, we'll iterate over rankings to try to determine the season/year\n ## There are multiple types of rankings based on season, year, and both combined,\n ## so we'll piece it together based on whatever we come across first\n season,year = None,None\n for ranking in data.get(\"rankings\",list()):\n ## Quicker exit (without just making this loop its own function)\n if season and year: continue\n ## We'll ignore stuff we've already gotten and assume that nothing in\n ## rankings contradicts eachother\n if not season:\n ## Defaults to None one way or another if it's not supplied\n season = ranking.get(\"season\")\n if not year: year = ranking.get(\"year\")\n ## Check if we made it\n if season and year:\n ## As above, this should always work out-of-the-box\n try: return SeasonCharts.buildseason(season,year)\n except: print(season,year)\n ## Welp, we're stumped...\n return None","def _extract_games_data(season, season_type):\n nflscrapr.run(\n 'games',\n season=season,\n season_type=season_type\n )\n nflscrapr_output = etl_tools.extract_from_csv(config.GAMES_DUMP_CSV_PATH)\n return nflscrapr_output","def get_season_no(token, url):\n headers = {'Accept': 'application/json', 'Authorization': token}\n r = requests.get(url, headers=headers)\n json_data = json.loads(r.text).get('data')\n high_season = 1\n for episode in json_data:\n if episode.get('airedSeason') > high_season:\n high_season = episode.get('airedSeason')\n return high_season","def get_games(season, date):\n url = \"http://live.nhl.com/GameData/SeasonSchedule-\" + season + \".json\"\n response = urllib.urlopen(url)\n data = json.loads(response.read())\n games = []\n for game in data:\n if game[\"est\"][:8] == date:\n games.append(game)\n return games","def season_games(year):\n\tLOG.debug('Getting season %d', year)\n\tdata = read_html(io=season_games_url(year),\n\t\t\t\t\t attrs={'id': 'games'},\n\t\t\t\t\t infer_types=False,\n\t\t\t\t\t header=0)\n\tif len(data) != 1:\n\t\traise CantFindTheRightTable\n\tdata = data.pop()\n\n\t# Cleaning.\n\tdel data[\"Unnamed: 3\"]\n\t# The code below issues \"UserWarning: \" So we catch UserWarnings.\n\twith warnings.catch_warnings():\n\t\twarnings.filterwarnings(action='ignore', category=UserWarning,\n\t\t\t\t\t\t\t\tmodule=r'pandas\\.core\\.frame',\n\t\t\t\t\t\t\t\tmessage=(r\"Boolean Series key will be reindexed\"\n\t\t\t\t\t\t\t\t\t\t r\" to match DataFrame index\\.\"))\n\t\t# These rows are mid-table header rows.\n\t\tdata = data[data.Week != \"Week\"][data.Week != \"nan\"]\n\n\tdata['week'] = (data.Week\n\t\t\t\t\t.replace(\"WildCard\", \"wild-card\")\n\t\t\t\t\t.replace(\"Division\", \"divisional\")\n\t\t\t\t\t.replace(\"ConfChamp\", \"conference\")\n\t\t\t\t\t.replace(\"SuperBowl\", \"super-bowl\")\n\t\t\t\t\t.apply(\n\t\t\t\t\t\tlambda s: (int(s)\n\t\t\t\t\t\t\t\t if all(c in '1234567890' for c in s)\n\t\t\t\t\t\t\t\t else s)))\n\tdel data['Week']\n\n\tdata['season'] = year\n\tdata['game_date'] = pd.to_datetime(\n\t\tdata.Date\n\t\t.replace(r\"$\", r\", %d\" % year, regex=True)\n\t\t.replace(r\"^(January|February) (\\d+), \\d+$\", r\"\\1 \\2, %d\" % (year + 1),\n\t\t\t\t regex=True))\n\tdel data['Date']\n\n\tfor column in \"PtsW\", \"PtsL\", \"YdsW\", \"TOW\", \"YdsL\", \"TOL\":\n\t data[column] = data[column].apply(int)\n\n\tdata['WatL'] = data['Unnamed: 5'].apply(lambda x: x == '@')\n\tdel data['Unnamed: 5']\n\tdata['hometeam'] = (~data.WatL * data['Winner/tie'] +\n\t\t\t\t\t\tdata.WatL * data['Loser/tie'])\n\tdata['awayteam'] = (data.WatL * data['Winner/tie'] +\n\t\t\t\t\t\t~data.WatL * data['Loser/tie'])\n\tdata['winner'] = data['Winner/tie']\n\tfor column in 'Winner/tie', 'Loser/tie', \"WatL\":\n\t\tdel data[column]\n\tfor column in 'hometeam', 'awayteam', 'winner':\n\t\tdata[column] = data[column].apply(lambda s: s.split()[-1].lower())\n\n\treturn data","def season_series(game_id, pref_team, other_team, last_season=False):\n\n # Init empty dictionaries and lists\n games_against = list()\n pref_toi = dict()\n pref_goals = dict()\n pref_assists = dict()\n pref_points = dict()\n pref_record = {\"wins\": 0, \"losses\": 0, \"ot\": 0}\n roster_player = True\n\n # If this is the first game of the season, we can set the 'last_season' flag to enable the\n # season series function to check last year's season series between the two teams.\n if not last_season:\n season_start = str(game_id)[0:4]\n season_end = str(int(season_start) + 1)\n yesterday = datetime.now() - timedelta(days=1)\n # yesterday = datetime.now() + timedelta(days=50)\n # schedule_url = (\n # f\"/schedule?teamId={pref_team.team_id}\"\n # f\"&expand=schedule.broadcasts,schedule.teams&startDate=\"\n # f\"{season_start}-08-01&endDate={yesterday:%Y-%m-%d}\"\n # )\n schedule_url = (\n f\"/schedule?teamId={pref_team.team_id}\"\n f\"&expand=schedule.broadcasts,schedule.teams\"\n f\"&season={season_start}{season_end}\"\n )\n else:\n season_start = int(str(game_id)[0:4]) - 1\n season_end = str(int(season_start) + 1)\n yesterday = datetime.now() - timedelta(days=1)\n # yesterday = datetime.now() + timedelta(days=50)\n # schedule_url = (\n # f\"/schedule?teamId={pref_team.team_id}\"\n # f\"&expand=schedule.broadcasts,schedule.teams&startDate=\"\n # f\"{season_start}-08-01&endDate={season_end}-06-01\"\n # )\n schedule_url = (\n f\"/schedule?teamId={pref_team.team_id}\"\n f\"&expand=schedule.broadcasts,schedule.teams\"\n f\"&season={season_start}{season_end}\"\n )\n\n schedule = api.nhl_api(schedule_url).json()\n dates = schedule[\"dates\"]\n\n # Loop through scheduled to get previously played games against\n for date in dates:\n game = date[\"games\"][0]\n game_type = game[\"gameType\"]\n game_id = game[\"gamePk\"]\n game_team_home = game[\"teams\"][\"home\"][\"team\"][\"name\"]\n game_team_away = game[\"teams\"][\"away\"][\"team\"][\"name\"]\n teams = [game_team_away, game_team_home]\n game_status = game[\"status\"][\"abstractGameState\"]\n if game_type == \"R\" and game_status == \"Final\" and other_team.team_name in teams:\n game_feed = f\"/game/{game_id}/feed/live\"\n games_against.append(game_feed)\n\n # If the two teams haven't played yet, just exit this function\n if not games_against:\n return None, None, None\n\n # Loop through newly created games_against list to get each stats\n for feed in games_against:\n game = api.nhl_api(feed).json()\n game_data = game[\"gameData\"]\n home_team_name = game_data[\"teams\"][\"home\"][\"name\"]\n pref_homeaway = \"home\" if home_team_name == pref_team.team_name else \"away\"\n other_homeaway = \"away\" if home_team_name == pref_team.team_name else \"home\"\n\n # Get season series\n end_period = game[\"liveData\"][\"linescore\"][\"currentPeriod\"]\n extra_time = True if end_period > 3 else False\n pref_score = game[\"liveData\"][\"linescore\"][\"teams\"][pref_homeaway][\"goals\"]\n other_score = game[\"liveData\"][\"linescore\"][\"teams\"][other_homeaway][\"goals\"]\n if pref_score > other_score:\n pref_record[\"wins\"] += 1\n elif other_score > pref_score and extra_time:\n pref_record[\"ot\"] += 1\n else:\n pref_record[\"losses\"] += 1\n\n season_series_str = f\"Series: {pref_record['wins']}-\" f\"{pref_record['losses']}-{pref_record['ot']}\"\n\n # Get stats leaders\n # pref_teamstats = game[\"liveData\"][\"boxscore\"][\"teams\"][pref_homeaway][\"teamStats\"]\n pref_playerstats = game[\"liveData\"][\"boxscore\"][\"teams\"][pref_homeaway][\"players\"]\n for id, player in pref_playerstats.items():\n try:\n # Calculate TOI\n player_toi_str = player[\"stats\"][\"skaterStats\"][\"timeOnIce\"]\n player_toi_minutes = int(player_toi_str.split(\":\")[0])\n player_toi_seconds = int(player_toi_str.split(\":\")[1])\n player_toi = (player_toi_minutes * 60) + player_toi_seconds\n pref_toi[id] = pref_toi.get(id, 0) + player_toi\n\n # Point Totals\n player_goal_str = player[\"stats\"][\"skaterStats\"][\"goals\"]\n pref_goals[id] = pref_goals.get(id, 0) + int(player_goal_str)\n player_assist_str = player[\"stats\"][\"skaterStats\"][\"assists\"]\n pref_assists[id] = pref_assists.get(id, 0) + int(player_assist_str)\n player_points = int(player_goal_str) + int(player_assist_str)\n pref_points[id] = pref_points.get(id, 0) + int(player_points)\n\n except KeyError:\n pass\n\n # Calculate Stats Leaders\n sorted_toi = sorted(pref_toi.values(), reverse=True)\n leader_toi = sorted_toi[0]\n\n sorted_points = sorted(pref_points.values(), reverse=True)\n leader_points = sorted_points[0]\n\n # Get TOI leader\n for id in pref_toi.keys():\n if pref_toi[id] == leader_toi:\n player_name = roster.player_attr_by_id(pref_team.roster, id, \"fullName\")\n if player_name is None:\n roster_player = False\n player_id_only = id.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n leader_toi_avg = leader_toi / len(games_against)\n m, s = divmod(leader_toi_avg, 60)\n toi_m = int(m)\n toi_s = int(s)\n toi_s = \"0{}\".format(toi_s) if toi_s < 10 else toi_s\n toi_avg = \"{}:{}\".format(toi_m, toi_s)\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n toi_leader_str = \"TOI Leader: {} with {} / game.\".format(player_short_name, toi_avg)\n\n # Handle tied points leaders\n point_leaders = list()\n for id in pref_points.keys():\n if pref_points[id] == leader_points:\n point_leaders.append(id)\n\n if leader_points == 0:\n points_leader_str = \"Points Leader: None (all players have 0 points).\"\n\n elif len(point_leaders) == 1:\n leader = point_leaders[0]\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n # If the player is no longer on the team, get their information (change string here?)\n if player_name is None:\n roster_player = False\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n if not roster_player:\n points_leader_str = (\n f\"Points Leader: {player_name} with {leader_points} points \"\n f\"({player_goals}G {player_assists}A) \"\n )\n else:\n points_leader_str = \"Points Leader: {} with {} ({}G {}A).\".format(\n player_name, leader_points, player_goals, player_assists\n )\n\n elif len(point_leaders) > 3:\n point_leaders_with_attrs = list()\n for leader in point_leaders:\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n if player_name is None:\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n point_leaders_with_attrs.append(player_short_name)\n\n point_leaders_joined = \", \".join(point_leaders_with_attrs[0:3])\n leftover_leaders = len(point_leaders) - 3\n points_leader_str = (\n f\"Points Leaders: {point_leaders_joined} & {leftover_leaders} others ({leader_points} each).\"\n )\n\n else:\n point_leaders_with_attrs = list()\n for leader in point_leaders:\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n if player_name is None:\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n player_str = f\"{player_short_name} ({player_goals}G {player_assists}A)\"\n point_leaders_with_attrs.append(player_str)\n\n point_leaders_joined = (\n f\", \".join(point_leaders_with_attrs[:-1]) + f\" & {point_leaders_with_attrs[-1]}\"\n )\n points_leader_str = \"Points Leaders: {} with {} each.\".format(point_leaders_joined, leader_points)\n\n return season_series_str, points_leader_str, toi_leader_str","def games(self, competition_id: int, season_id: int) -> DataFrame[Any]:","def get_episode_details(token, url, season):\n u = url + str(season)\n headers = {'Accept': 'application/json', 'Authorization': token}\n r = requests.get(u, headers=headers)\n json_data = json.loads(r.text).get('data')\n season_details = {}\n season_details['current_season'] = season\n if len(json_data) > 1:\n for episode in json_data:\n d = episode.get('firstAired')\n date = datetime.datetime.strptime(d, \"%Y-%m-%d\")\n today = datetime.datetime.today()\n if date.date() >= today.date():\n season_details['next_ep_no'] = episode.get('airedEpisodeNumber')\n season_details['next_air_date'] = episode.get('firstAired')\n season_details['ep_title'] = episode.get('episodeName')\n season_details['ep_overview'] = episode.get('overview')\n break\n else:\n season_details['next_ep_no'] = (json_data[len(json_data) - 1].get('airedEpisodeNumber'))\n season_details['next_air_date'] = (json_data[len(json_data) - 1].get('firstAired'))\n season_details['ep_title'] = (json_data[len(json_data) - 1].get('episodeName'))\n season_details['ep_overview'] = (json_data[len(json_data) - 1].get('overview'))\n else:\n season_details['next_ep_no'] = 1\n season_details['next_air_date'] = (json_data[0].get('firstAired'))\n season_details['ep_title'] = (json_data[0].get('episodeName'))\n season_details['ep_overview'] = (json_data[0].get('overview'))\n if season_details['next_air_date'] == \"\":\n season_details['next_air_date'] = 'TBD'\n if season_details['ep_title'] == \"\" or season_details['ep_title'] is None:\n season_details['ep_title'] = 'TBD'\n if season_details['ep_overview'] == \"\" or season_details['ep_overview'] is None:\n season_details['ep_overview'] = 'TBD'\n return season_details","def get_all_games(season):\n url = BASE_URL.format(season)\n json_data = requests.get(url, headers=HEADERS).json()\n all_games = json_data[\"resultSets\"][0][\"rowSet\"]\n return all_games","def season_game_logs(team, year):\n\n # Check year value\n if year > 2019 or year < 1950:\n raise ValueError('Year Value Incorrect')\n\n # Rename teams that moved\n team = scrape_utils.rename_team(team, year)\n\n # Get HTML content\n url = 'http://www.basketball-reference.com/teams/%s/%s/gamelog' % (team, year)\n r = requests.get(url)\n soup = BeautifulSoup(r.content, \"html.parser\")\n season_stats = soup.find(id='tgl_basic')\n games = season_stats.find('tbody')\n\n # MongoDB Collection\n m = mongo.Mongo()\n\n # To find opponent statistics\n opponent = re.compile('^opp_.*$')\n\n # Loop through every game in a team's season\n for game in games.find_all('tr', {'class': None}):\n\n curr_team = {'team': team}\n opp_team = {}\n\n # Loop through each stat\n for stat in game.find_all('td'):\n\n stat_name = stat['data-stat']\n\n # These are opponent stats\n if re.match(opponent, stat_name):\n opp_team[stat_name[4:]] = scrape_utils.stat_parse(stat_name, stat.string)\n else:\n curr_team[stat_name] = scrape_utils.stat_parse(stat_name, stat.string)\n\n # Remove unnecessary information\n del curr_team['game_season']\n del curr_team['x']\n\n # Rename relocated teams\n curr_team['team'] = scrape_utils.rename_team(team)\n opp_team['team'] = scrape_utils.rename_team(opp_team.pop('id'))\n\n # Use the same ID as basketball reference\n result = {'date': datetime.strptime(curr_team.pop('date_game'), \"%Y-%m-%d\"),\n 'season': year,\n 'result': scrape_utils.determine_home_win(curr_team['game_location'], curr_team.pop('game_result')),\n '_id': game.find('a')['href'][-17:-5]}\n\n # Place the teams in the correct spot depending on who is the home team\n if curr_team.pop('game_location') == 0:\n result['home'] = curr_team\n result['away'] = opp_team\n else:\n result['home'] = opp_team\n result['away'] = curr_team\n\n # Insert into database\n m.insert('game_log', result)","def seasonStats(personId,type = 'gameLog',group = 'hitting'):\n\n #playerInfo = get('people', {'personIds':personId})\n\n\n teamStats = get('person',{ 'ver':'v1' , 'personId':personId,'hydrate':['stats(group={},type={})'.format(group,type),'currentTeam']})\n return teamStats\n #iterate of stats and find the right player id\n #career stats broken\n #fix the season :2019\n #make function to get team id","def get_games(date):\n scoreboard = nba_py.Scoreboard(month=date.month,\n day=date.day,\n year=date.year)\n line_score = scoreboard.line_score()\n game_header = scoreboard.game_header()\n\n games = []\n current_game = {}\n game_sequence = 0\n game_sequence_counter = 0\n\n # Get HOME TEAM and AWAY TEAM data for each boxscore game in line_score.\n for i, value in enumerate(line_score):\n if (value[\"GAME_SEQUENCE\"] != game_sequence):\n game_sequence += 1\n\n current_game[\"GAME_ID\"] = value[\"GAME_ID\"]\n home_team_id = game_header[game_sequence - 1][\"HOME_TEAM_ID\"]\n\n if (home_team_id == value[\"TEAM_ID\"]):\n current_game[\"HOME_TEAM\"] = value[\"TEAM_ABBREVIATION\"]\n current_game[\"HOME_TEAM_WINS_LOSSES\"] = value[\"TEAM_WINS_LOSSES\"]\n current_game[\"HOME_TEAM_PTS\"] = value[\"PTS\"]\n current_game[\"HOME_TEAM_ID\"] = value[\"TEAM_ID\"]\n if (current_game[\"HOME_TEAM\"] in TEAM_ID_DATA):\n current_game[\"HOME_TEAM_IMG\"] = TEAM_ID_DATA[current_game[\"HOME_TEAM\"]][\"img\"]\n else:\n current_game[\"AWAY_TEAM\"] = value[\"TEAM_ABBREVIATION\"]\n current_game[\"AWAY_TEAM_WINS_LOSSES\"] = value[\"TEAM_WINS_LOSSES\"]\n current_game[\"AWAY_TEAM_PTS\"] = value[\"PTS\"]\n current_game[\"AWAY_TEAM_ID\"] = value[\"TEAM_ID\"]\n if (current_game[\"AWAY_TEAM\"] in TEAM_ID_DATA):\n current_game[\"AWAY_TEAM_IMG\"] = TEAM_ID_DATA[current_game[\"AWAY_TEAM\"]][\"img\"]\n\n if (value[\"TEAM_ABBREVIATION\"] in TEAMS):\n if (home_team_id == value[\"TEAM_ID\"]):\n current_game[\"HOME_TEAM_FULL_NAME\"] = TEAMS[value[\"TEAM_ABBREVIATION\"]][\"city\"] + \\\n \" \" + TEAMS[value[\"TEAM_ABBREVIATION\"]][\"name\"]\n else:\n current_game[\"AWAY_TEAM_FULL_NAME\"] = TEAMS[value[\"TEAM_ABBREVIATION\"]][\"city\"] + \\\n \" \" + TEAMS[value[\"TEAM_ABBREVIATION\"]][\"name\"]\n \n game_sequence = value[\"GAME_SEQUENCE\"]\n game_sequence_counter += 1\n elif game_sequence_counter == 1:\n if (\"AWAY_TEAM\" in current_game):\n current_game[\"HOME_TEAM\"] = value[\"TEAM_ABBREVIATION\"]\n current_game[\"HOME_TEAM_WINS_LOSSES\"] = value[\"TEAM_WINS_LOSSES\"]\n current_game[\"HOME_TEAM_PTS\"] = value[\"PTS\"]\n current_game[\"HOME_TEAM_ID\"] = value[\"TEAM_ID\"]\n if (current_game[\"HOME_TEAM\"] in TEAM_ID_DATA):\n current_game[\"HOME_TEAM_IMG\"] = TEAM_ID_DATA[current_game[\"HOME_TEAM\"]][\"img\"]\n else:\n current_game[\"AWAY_TEAM\"] = value[\"TEAM_ABBREVIATION\"]\n current_game[\"AWAY_TEAM_WINS_LOSSES\"] = value[\"TEAM_WINS_LOSSES\"]\n current_game[\"AWAY_TEAM_PTS\"] = value[\"PTS\"]\n current_game[\"AWAY_TEAM_ID\"] = value[\"TEAM_ID\"]\n if (current_game[\"AWAY_TEAM\"] in TEAM_ID_DATA):\n current_game[\"AWAY_TEAM_IMG\"] = TEAM_ID_DATA[current_game[\"AWAY_TEAM\"]][\"img\"]\n\n if (value[\"TEAM_ABBREVIATION\"] in TEAMS):\n if (\"AWAY_TEAM\" in current_game):\n current_game[\"HOME_TEAM_FULL_NAME\"] = TEAMS[value[\"TEAM_ABBREVIATION\"]][\"city\"] + \\\n \" \" + TEAMS[value[\"TEAM_ABBREVIATION\"]][\"name\"]\n else:\n current_game[\"AWAY_TEAM_FULL_NAME\"] = TEAMS[value[\"TEAM_ABBREVIATION\"]][\"city\"] + \\\n \" \" + TEAMS[value[\"TEAM_ABBREVIATION\"]][\"name\"]\n\n current_game[\"GAME_STATUS_TEXT\"] = game_header[game_sequence - 1][\"GAME_STATUS_TEXT\"]\n if not game_header[game_sequence - 1][\"NATL_TV_BROADCASTER_ABBREVIATION\"]:\n current_game[\"BROADCASTER\"] = \"\"\n else:\n current_game[\"BROADCASTER\"] = game_header[game_sequence - 1][\"NATL_TV_BROADCASTER_ABBREVIATION\"]\n\n games.append(current_game)\n\n current_game = {}\n\n game_sequence = value[\"GAME_SEQUENCE\"]\n game_sequence_counter -= 1\n\n east_standings = scoreboard.east_conf_standings_by_day()\n west_standings = scoreboard.west_conf_standings_by_day()\n\n return (games, east_standings, west_standings)","def _get_latest_season_type(season_types_list):\n if not isinstance(season_types_list, (list, tuple)):\n raise ValueError(f\"type of {type(season_types_list)} for season_type_list not list or tuple\")\n\n latest_season_type = season_types_list[0]\n if len(season_types_list) > 1:\n for season_type in season_types_list[1:]:\n if config.SEASON_TYPES_ORDER[season_type] > config.SEASON_TYPES_ORDER[latest_season_type]:\n latest_season_type = season_type\n\n return latest_season_type","def get_player_stats_from_game(team, year, week):","def getSeasonStats(self):\n df_season_agg = self.toSeasonAggFormat()\n\n # Calculate Possessions for each game\n df_season_agg['possessions'] = 0.5 * (df_season_agg['FGA'] + 0.475 * df_season_agg['FTA'] - df_season_agg['OR'] + df_season_agg['TO']) \\\n + 0.5 * (df_season_agg['OppFGA'] + 0.475 * df_season_agg['OppFTA'] - df_season_agg['OppOR'] + df_season_agg['OppTO'])\n\n # Aggregate to Season Summary Level\n season_stats = df_season_agg.groupby(['TeamID', 'Season']).sum()\n\n season_stats = season_stats.rename(columns={'Win':'wins'})\n\n # Season Advanced Stats\n season_stats['o_eff'] = season_stats['Score'] / season_stats['possessions'] * 100\n season_stats['d_eff'] = season_stats['OppScore'] / season_stats['possessions'] * 100\n season_stats['net_eff'] = season_stats['o_eff'] - season_stats['d_eff']\n\n season_stats.drop('DayNum', axis=1, inplace=True)\n season_stats.drop('OppTeamID', axis=1, inplace=True)\n season_stats.drop('rand', axis=1, inplace=True)\n\n return season_stats","async def get_league_players(season): \n if season == '2021-22':\n get_epl = 2021\n if season == '2020-21':\n get_epl = 2020\n if season == '2019-20':\n get_epl = 2019\n async with aiohttp.ClientSession() as session:\n understat = Understat(session)\n player = await understat.get_league_players(\"epl\", get_epl)\n # print(json.dumps(player))\n return player","def season_info(cls, season):\r\n\r\n\t\traw_info = cls.season_rounds(season)\r\n\r\n\t\tinfo = [\r\n\t\t\tseason,\t\t\t\t\t\t\t\t\t\t# 0 -> Season name\r\n\t\t\tlen(raw_info),\t\t\t\t\t\t\t\t# 1 -> Amount of rounds\r\n\t\t\tmin([round[1] for round in raw_info]),\t\t# 2 -> Starting date\r\n\t\t\tmax([round[1] for round in raw_info]),\t\t# 3 -> Latest round date\r\n\t\t\tnp.mean([round[3] for round in raw_info])\t# 4 -> Average round strength\r\n\t\t]\r\n\r\n\t\treturn info","def getshowsbyseason(season,year, session = None):\n url = APIURL.format(season=season, year = year)\n headers = {\"X-CSRF-TOKEN\":session.cookies['X-CSRF-TOKEN']}\n data = alrequests.GET_json(url,session = session, headers=headers)\n return {cat:[Show(**show) for show in shows] for cat,shows in data.items()}","def get_competition_season_type(season):\n default_type = games.models.CompetitionSeason.winter\n try:\n season_name = season.name\n if season_name.find(\"/\") == -1:\n return games.models.CompetitionSeason.summer\n return default_type\n except Exception as e:\n return default_type","def get_latest_games_reviewed(platform, limit):\n platform_num = PLATFORMS.get(platform.lower(), 0)\n\n if platform_num == 0:\n url = _3DJUEGOS_REVIEWS_URL + \"juegos/0f0f0f0/fecha/\"\n else:\n url = _3DJUEGOS_REVIEWS_URL + \"juegos-\" + \\\n platform + \"/0f{}f0f0/fecha/\".format(platform_num)\n\n soup = get_soup_obj(url)\n if not soup:\n return None\n divs = soup.select(\"div.nov_int_txt.wi100\")\n results = {}\n results[\"latestGames\"] = [\n {\"name\": re.sub(\" - .*$\", \"\", div.h2.a.text)} for div in divs[:limit]]\n\n return results","def get_seasons_information():\n\n #getting the guidebox_id variable from show_page.html\n guidebox_id = request.args.get(\"guidebox_id\")\n\n #make API to get season information, gets back list of season information\n seasons_results = guidebox_season_info(guidebox_id)\n\n for season in seasons_results:\n date = season[\"first_airdate\"]\n year = str(date)[0:4]\n season[\"first_airdate\"] = year\n\n return jsonify(seasons_results)","def get_single_game_team_data(game, grouped_shot_data, pp_sit_data):\n game_stat_lines = list()\n game_id = game['game_id']\n home_id = game['home_id']\n road_id = game['road_id']\n game_type = get_game_type_from_season_type(game)\n\n home_stats_src_path = os.path.join(\n CONFIG['base_data_dir'], 'game_team_stats',\n str(game['season']), str(game_type), \"%d_%d.json\" % (game_id, home_id))\n road_stats_src_path = os.path.join(\n CONFIG['base_data_dir'], 'game_team_stats',\n str(game['season']), str(game_type), \"%d_%d.json\" % (game_id, road_id))\n\n # loading raw team game stats (if available)\n raw_stats = dict()\n if os.path.isfile(home_stats_src_path):\n raw_stats['home'] = json.loads(open(home_stats_src_path).read())\n else:\n raw_stats['home'] = dict()\n if os.path.isfile(road_stats_src_path):\n raw_stats['road'] = json.loads(open(road_stats_src_path).read())\n else:\n raw_stats['road'] = dict()\n\n # counting penalties per team\n penalty_counts = get_penalty_counts(game)\n\n for key in ['home', 'road']:\n opp_key = 'road' if key == 'home' else 'home'\n game_stat_line = dict()\n # basic game information\n game_stat_line['game_date'] = game['date']\n game_stat_line['weekday'] = game['weekday']\n game_stat_line['season'] = game['season']\n game_stat_line['season_type'] = game['season_type']\n game_stat_line['round'] = game['round']\n game_stat_line['game_id'] = game_id\n game_stat_line['team_id'] = game[\"%s_id\" % key]\n game_stat_line['team'] = game[\"%s_abbr\" % key]\n game_stat_line['opp_team_id'] = game[\"%s_id\" % opp_key]\n game_stat_line['opp_team'] = game[\"%s_abbr\" % opp_key]\n # identifying team's and opposing team's division (if\n # applicable for current season and season type)\n if (game['season'], game['season_type']) in divisions:\n current_divisions = divisions[game['season'], game['season_type']]\n game_stat_line['division'] = current_divisions[game_stat_line['team']]\n game_stat_line['opp_division'] = current_divisions[game_stat_line['opp_team']]\n # TODO: reactivate when schedule game id is available again\n # game_stat_line['schedule_game_id'] = game['schedule_game_id']\n game_stat_line['arena'] = correct_name(game['arena'])\n game_stat_line['attendance'] = game['attendance']\n if game_stat_line['arena'] in capacities:\n game_stat_line['capacity'] = capacities[game_stat_line['arena']]\n else:\n print(\n \"\\t+ Unable to retrieve capacity \" +\n \"for '%s'\" % game_stat_line['arena'])\n game_stat_line['capacity'] = 0\n # coaches and referees\n if \"%s_coach\" % key in game:\n game_stat_line['coach'] = correct_name(\n game[\"%s_coach\" % key], game['date'])\n if game_stat_line['coach'] not in coaches:\n print(\"+ Unknown coach '%s'\" % game_stat_line['coach'])\n else:\n print(\"\\t+ No coach information found for %s in game %d\" % (\n game_stat_line['team'], game_id))\n game_stat_line['coach'] = correct_name(\n \"%d_%s\" % (game_id, game_stat_line['team']))\n print(\"\\t+ Adjusted to '%s'\" % game_stat_line['coach'])\n if \"%s_coach\" % opp_key in game:\n game_stat_line['opp_coach'] = correct_name(\n game[\"%s_coach\" % opp_key], game['date'])\n if game_stat_line['opp_coach'] not in coaches:\n print(\"+ Unknown coach '%s'\" % game_stat_line['opp_coach'])\n else:\n print(\n \"\\t+ No opposition coach information found \" +\n \"for %s in game %d\" % (game_stat_line['opp_team'], game_id))\n game_stat_line['opp_coach'] = correct_name(\n \"%d_%s\" % (game_id, game_stat_line['opp_team']))\n print(\"\\t+ Adjusted to '%s'\" % game_stat_line['opp_coach'])\n game_stat_line['ref_1'] = correct_name(game['referee_1'])\n game_stat_line['ref_2'] = correct_name(game['referee_2'])\n game_stat_line['lma_1'] = correct_name(game['linesman_1'])\n game_stat_line['lma_2'] = correct_name(game['linesman_2'])\n # outcomes\n game_stat_line['games_played'] = 1\n game_stat_line['home_road'] = key\n game_stat_line['score'] = game[\"%s_score\" % key]\n game_stat_line['goals'] = game[\"%s_score\" % key]\n game_stat_line['opp_score'] = game[\"%s_score\" % opp_key]\n game_stat_line['opp_goals'] = game[\"%s_score\" % opp_key]\n # optionally correcting game scores\n if game_id in game_score_corrections:\n for team_abbr in game_score_corrections[game_id]:\n if game_stat_line['team'] == team_abbr:\n game_stat_line['score'] = game_score_corrections[game_id][team_abbr]\n if game_stat_line['opp_team'] == team_abbr:\n game_stat_line['opp_score'] = game_score_corrections[game_id][team_abbr]\n if game['shootout_game']:\n game_stat_line['game_type'] = 'SO'\n elif game['overtime_game']:\n game_stat_line['game_type'] = 'OT'\n else:\n game_stat_line['game_type'] = ''\n for gsl_key in ['w', 'rw', 'ow', 'sw', 'l', 'rl', 'ol', 'sl']:\n game_stat_line[gsl_key] = 0\n if game_stat_line['score'] > game_stat_line['opp_score']:\n game_stat_line['w'] += 1\n if game['shootout_game']:\n game_stat_line['sw'] += 1\n game_stat_line['goals'] -= 1\n elif game['overtime_game']:\n game_stat_line['ow'] += 1\n else:\n game_stat_line['rw'] += 1\n else:\n game_stat_line['l'] += 1\n if game['shootout_game']:\n game_stat_line['sl'] += 1\n game_stat_line['opp_goals'] -= 1\n elif game['overtime_game']:\n game_stat_line['ol'] += 1\n else:\n game_stat_line['rl'] += 1\n game_stat_line['points'] = (\n game_stat_line['rw'] * 3 + game_stat_line['ow'] * 2 +\n game_stat_line['sw'] * 2 + game_stat_line['sl'] * 1 +\n game_stat_line['ol'] * 1)\n # per-period goals\n for period in [1, 2, 3]:\n game_stat_line[\"goals_%d\" % period] = game[\n \"%s_goals_%d\" % (key, period)]\n game_stat_line[\"opp_goals_%d\" % period] = game[\n \"%s_goals_%d\" % (opp_key, period)]\n # empty-net and extra-attacker goals\n game_stat_line['en_goals'] = game[\"%s_en_goals\" % key]\n game_stat_line['ea_goals'] = game[\"%s_ea_goals\" % key]\n game_stat_line['opp_en_goals'] = game[\"%s_en_goals\" % opp_key]\n game_stat_line['opp_ea_goals'] = game[\"%s_ea_goals\" % opp_key]\n # situation after 20 and 40 minutes respectively\n for situation in [\n 'tied20', 'lead20', 'trail20', 'tied40', 'lead40', 'trail40'\n ]:\n game_stat_line[situation] = False\n if game_stat_line['goals_1'] == game_stat_line['opp_goals_1']:\n game_stat_line['tied20'] = True\n elif game_stat_line['goals_1'] > game_stat_line['opp_goals_1']:\n game_stat_line['lead20'] = True\n else:\n game_stat_line['trail20'] = True\n goals40 = game_stat_line['goals_1'] + game_stat_line['goals_2']\n opp_goals40 = (\n game_stat_line['opp_goals_1'] + game_stat_line['opp_goals_2'])\n if goals40 == opp_goals40:\n game_stat_line['tied40'] = True\n elif goals40 > opp_goals40:\n game_stat_line['lead40'] = True\n else:\n game_stat_line['trail40'] = True\n # scored first?\n if game['first_goal'] == game_stat_line['team']:\n game_stat_line['scored_first'] = True\n game_stat_line['trailed_first'] = False\n elif game['first_goal'] == game_stat_line['opp_team']:\n game_stat_line['scored_first'] = False\n game_stat_line['trailed_first'] = True\n # one-goal, two-goal, three-goal, four-goal-game?\n for goal_game in ['one_goal', 'two_goal', 'three_goal', 'four_goal']:\n game_stat_line[goal_game] = False\n score_diff = abs(\n (game_stat_line['score'] - game_stat_line['en_goals']) -\n (game_stat_line['opp_score'] - game_stat_line['opp_en_goals']))\n # in case the right amount of empty-net goals have been scored, we\n # may end up with a score differential of zero, see game between STR\n # and ING on Mar 3, 2019\n if not score_diff:\n game_stat_line['zero_goal'] = True\n if score_diff == 1:\n game_stat_line['one_goal'] = True\n elif score_diff == 2:\n game_stat_line['two_goal'] = True\n elif score_diff == 3:\n game_stat_line['three_goal'] = True\n elif score_diff > 3:\n game_stat_line['four_goal'] = True\n\n # retrieving score state time spans for current team\n game_stat_line['time_played'] = game['time_played']\n game_stat_line['tied'] = game['tied']\n game_stat_line['tied_pctg'] = round(\n game['tied'] / game['time_played'] * 100, 2)\n if key == 'home':\n game_stat_line['leading'] = game['home_leading']\n game_stat_line['trailing'] = game['road_leading']\n else:\n game_stat_line['leading'] = game['road_leading']\n game_stat_line['trailing'] = game['home_leading']\n game_stat_line['leading_pctg'] = round(\n game_stat_line['leading'] / game['time_played'] * 100, 2)\n game_stat_line['trailing_pctg'] = round(\n game_stat_line['trailing'] / game['time_played'] * 100, 2)\n\n # retrieving raw stats for team and opposing team\n for category, raw_category in RAW_STATS_MAPPING:\n game_stat_line[category] = raw_stats[key].get(raw_category, None)\n game_stat_line[\"opp_%s\" % category] = raw_stats[opp_key].get(raw_category, None)\n\n # checking number of power play goals retrieved from team stats with those registered in event data\n game_stat_line = check_pp_goals(game, key, opp_key, game_stat_line)\n\n # calculating shooting percentages\n if game_stat_line['shots_on_goal']:\n game_stat_line['shot_pctg'] = round(\n game_stat_line['goals'] /\n game_stat_line['shots_on_goal'] * 100., 2)\n else:\n game_stat_line['shot_pctg'] = None\n if game_stat_line['opp_shots_on_goal']:\n game_stat_line['opp_shot_pctg'] = round(\n game_stat_line['opp_goals'] /\n game_stat_line['opp_shots_on_goal'] * 100., 2)\n else:\n game_stat_line['opp_shot_pctg'] = None\n # calculating save percentages\n if game_stat_line['opp_shots_on_goal']:\n game_stat_line['save_pctg'] = round(\n 100 - game_stat_line['opp_goals'] /\n game_stat_line['opp_shots_on_goal'] * 100., 2)\n else:\n game_stat_line['save_pctg'] = None\n if game_stat_line['shots_on_goal']:\n game_stat_line['opp_save_pctg'] = round(\n 100 - game_stat_line['goals'] /\n game_stat_line['shots_on_goal'] * 100., 2)\n else:\n game_stat_line['opp_save_pctg'] = None\n # calculating pdo values\n if (\n game_stat_line['shot_pctg'] is not None and\n game_stat_line['save_pctg'] is not None\n ):\n game_stat_line['pdo'] = round((\n game_stat_line['shot_pctg'] +\n game_stat_line['save_pctg']), 1)\n game_stat_line['opp_pdo'] = round((\n game_stat_line['opp_shot_pctg'] +\n game_stat_line['opp_save_pctg']), 1)\n # calculating power play percentages\n if game_stat_line['pp_opps']:\n game_stat_line['pp_pctg'] = round((\n game_stat_line['pp_goals'] /\n game_stat_line['pp_opps']) * 100., 1)\n else:\n game_stat_line['pp_pctg'] = 0\n if game_stat_line['opp_pp_opps']:\n game_stat_line['opp_pp_pctg'] = round((\n game_stat_line['opp_pp_goals'] /\n game_stat_line['opp_pp_opps']) * 100., 1)\n else:\n game_stat_line['opp_pp_pctg'] = 0\n # calculating penalty killing percentages\n if game_stat_line['sh_opps']:\n game_stat_line['pk_pctg'] = round(\n 100 - game_stat_line['opp_pp_goals'] /\n game_stat_line['sh_opps'] * 100., 1)\n else:\n game_stat_line['pk_pctg'] = 0\n if game_stat_line['opp_sh_opps']:\n game_stat_line['opp_pk_pctg'] = round(\n 100 - game_stat_line['pp_goals'] /\n game_stat_line['opp_sh_opps'] * 100., 1)\n else:\n game_stat_line['opp_pk_pctg'] = 0\n game_stat_line['ev_goals'] = (\n game_stat_line['goals'] -\n game_stat_line['pp_goals'] -\n game_stat_line['sh_goals'])\n game_stat_line['opp_ev_goals'] = (\n game_stat_line['opp_goals'] -\n game_stat_line['opp_pp_goals'] -\n game_stat_line['opp_sh_goals'])\n # faceoffs are treated separately since each of the team game stats\n # datasets only contains the number of won faceoffs and sometimes this\n # one is stored as a string (wtf?)\n game_stat_line['faceoffs_won'] = int(\n raw_stats[key].get('faceOffsWon', 0))\n game_stat_line['faceoffs_lost'] = int(\n raw_stats[opp_key].get('faceOffsWon', 0))\n # calculating overall number of faceoffs and faceoff percentage\n game_stat_line['faceoffs'] = (\n game_stat_line['faceoffs_won'] + game_stat_line['faceoffs_lost'])\n if game_stat_line['faceoffs']:\n game_stat_line['faceoff_pctg'] = round(\n game_stat_line['faceoffs_won'] /\n game_stat_line['faceoffs'] * 100., 1)\n else:\n game_stat_line['faceoff_pctg'] = 0.\n # best players\n game_stat_line['best_plr_id'] = game.get(\n \"%s_best_player_id\" % key, None)\n game_stat_line['best_plr'] = game.get(\"%s_best_player\" % key, None)\n game_stat_line['opp_best_plr_id'] = game.get(\n \"%s_best_player_id\" % opp_key, None)\n game_stat_line['opp_best_plr'] = game.get(\n \"%s_best_player\" % opp_key, None)\n # game-winning-goal\n game_stat_line['gw_goal_team'] = game['gw_goal']\n game_stat_line['gw_goal_player_id'] = game['gw_goal_player_id']\n game_stat_line['gw_goal_first_name'] = game['gw_goal_first_name']\n game_stat_line['gw_goal_last_name'] = game['gw_goal_last_name']\n\n shot_zones_to_retain = ['slot', 'left', 'right', 'blue_line']\n shot_situations_to_retain = [\n 'shots_ev', 'shots_5v5', 'shots_pp', 'shots_sh', 'shots_unblocked',\n 'shots_unblocked_ev', 'shots_unblocked_5v5', 'shots_unblocked_pp',\n 'shots_unblocked_sh', 'shots_on_goal_ev', 'shots_on_goal_5v5',\n 'shots_on_goal_pp', 'shots_on_goal_sh', 'goals_5v5', 'hit_post']\n\n # retrieving shot data for current game and team\n shot_data = grouped_shot_data.get(\n (game_id, game_stat_line['team']), list())\n for item in shot_data:\n if item.startswith(tuple(shot_zones_to_retain)):\n abbr_item = item\n for zone_key, replacement in SHOT_ZONE_ABBREVIATIONS.items():\n abbr_item = abbr_item.replace(zone_key, replacement)\n game_stat_line[abbr_item] = shot_data[item]\n elif item in shot_situations_to_retain:\n game_stat_line[item] = shot_data[item]\n\n # retrieving shots against data for current game and team\n shot_against_data = grouped_shot_data.get(\n (game_id, game_stat_line['opp_team']), list())\n for item in shot_against_data:\n if item.startswith(tuple(shot_zones_to_retain)):\n abbr_item = item\n for zone_key, replacement in SHOT_ZONE_ABBREVIATIONS.items():\n abbr_item = abbr_item.replace(zone_key, replacement)\n game_stat_line[\"%s_a\" % abbr_item] = shot_against_data[item]\n elif item in shot_situations_to_retain:\n game_stat_line[\"opp_%s\" % item] = shot_against_data[item]\n\n try:\n game_stat_line['ev_cf_pctg'] = round(\n game_stat_line['shots_ev'] / (game_stat_line['shots_ev'] + game_stat_line['opp_shots_ev']) * 100, 2)\n except KeyError:\n print(\"\\t+Unable to calculate even strength shots for percentage\")\n game_stat_line['ev_cf_pctg'] = None\n\n for penalty_duration in [2, 5, 10, 20]:\n if penalty_counts[key] and penalty_duration in penalty_counts[key]:\n game_stat_line[\"penalty_%d\" % penalty_duration] = (\n penalty_counts[key][penalty_duration])\n else:\n game_stat_line[\"penalty_%d\" % penalty_duration] = 0\n\n game_stat_line['pp_5v4'] = pp_sit_data[key]['pp_sits']['5v4']\n game_stat_line['pp_5v3'] = pp_sit_data[key]['pp_sits']['5v3']\n game_stat_line['pp_4v3'] = pp_sit_data[key]['pp_sits']['4v3']\n game_stat_line['ppg_5v4'] = pp_sit_data[key]['pp_goals']['5v4']\n game_stat_line['ppg_5v3'] = pp_sit_data[key]['pp_goals']['5v3']\n game_stat_line['ppg_4v3'] = pp_sit_data[key]['pp_goals']['4v3']\n game_stat_line['opp_pp_5v4'] = pp_sit_data[opp_key]['pp_sits']['5v4']\n game_stat_line['opp_pp_5v3'] = pp_sit_data[opp_key]['pp_sits']['5v3']\n game_stat_line['opp_pp_4v3'] = pp_sit_data[opp_key]['pp_sits']['4v3']\n game_stat_line['opp_ppg_5v4'] = pp_sit_data[opp_key]['pp_goals']['5v4']\n game_stat_line['opp_ppg_5v3'] = pp_sit_data[opp_key]['pp_goals']['5v3']\n game_stat_line['opp_ppg_4v3'] = pp_sit_data[opp_key]['pp_goals']['4v3']\n\n # opp_diff = game_stat_line['pp_opps'] - (\n # game_stat_line['pp_5v4'] +\n # game_stat_line['pp_5v3'] +\n # game_stat_line['pp_4v3']\n # )\n # if opp_diff:\n # print(\"\\tpp opp discrepancy of %d for %s\" % (opp_diff, key))\n\n # registering shootout stats (if applicable)\n shootout_stats = get_shootout_stats(game, key, opp_key)\n if shootout_stats:\n game_stat_line = {**game_stat_line, **shootout_stats}\n\n game_stat_lines.append(game_stat_line)\n\n return game_stat_lines","def get_games(self, split: dataset_split.DatasetSplit = None) -> Dict[str, cereal_bar_game.CerealBarGame]:\n if not split:\n all_games: Dict[str, cereal_bar_game.CerealBarGame] = dict()\n for game_id, game in self._train_games.items():\n all_games[game_id] = game\n for game_id, game in self._dev_games.items():\n all_games[game_id] = game\n for game_id, game in self._test_games.items():\n all_games[game_id] = game\n return all_games\n\n if split in {dataset_split.DatasetSplit.TRAIN, dataset_split.DatasetSplit.UPDATE,\n dataset_split.DatasetSplit.VALIDATION}:\n games: Dict[str, cereal_bar_game.CerealBarGame] = dict()\n for game_id, game in self._train_games.items():\n if split == dataset_split.DatasetSplit.TRAIN:\n games[game_id] = game\n elif split == dataset_split.DatasetSplit.UPDATE and game_id in self._update_ids:\n games[game_id] = game\n elif split == dataset_split.DatasetSplit.VALIDATION and game_id in self._val_ids:\n games[game_id] = game\n return games\n elif split == dataset_split.DatasetSplit.DEV:\n return self._dev_games\n elif split == dataset_split.DatasetSplit.TEST:\n return self._test_games\n else:\n raise ValueError('Getting games with specified filename is not supported')","def highest_snowfall_for_year(resort_name, year):\n resort_table = resort_table_dict[resort_name]\n year = int(year)\n query = \"SELECT snowfall FROM %s WHERE CAST(EXTRACT(YEAR FROM status_date) AS INTEGER) = %d\" %(resort_table, year)\n connection = get_connection()\n\n snowfall_list = []\n\n if connection is not None:\n try:\n for row in get_select_query_results(connection, query):\n snowfall_list.append(row)\n except Exception as e:\n print(e, file=sys.stderr)\n connection.close()\n snowfall_list.sort(reverse=True)\n \"\"\"\n need to think about making our own sorter so we can break ties effectively\n \"\"\"\n highest_snowfall = snowfall_list[0]\n return json.dumps(highest_snowfall)","def current(db):\n with Tx(db, factory=tuple_cursor) as cursor:\n cursor.execute('SELECT season_type, season_year, week FROM meta')\n return cursor.fetchone()\n return tuple([None] * 3)","def get_seasons(games: List[BaseGame], reverse=False):\n seasons = list(set([g.Season for g in games]))\n seasons.sort(reverse=reverse)\n return seasons","def getTeamStat(self, year = 2014):\r\n \r\n year_next = (year % 100) + 1\r\n season = str(year) + '-' + str(year_next)\r\n \r\n stat_url = 'http://stats.nba.com/stats/leaguedashteamstats?Conference=&'\\\r\n 'DateFrom=&DateTo=&Division=&GameScope=&GameSegment=&'\\\r\n 'LastNGames=0&LeagueID=00&Location=&MeasureType=Base&'\\\r\n 'Month=0&OpponentTeamID=0&Outcome=&PORound=0&PaceAdjust=N&'\\\r\n 'PerMode=PerGame&Period=0&PlayerExperience=&PlayerPosition=&'\\\r\n 'PlusMinus=N&Rank=N&Season=' + season + '&SeasonSegment=&'\\\r\n 'SeasonType=Regular+Season&ShotClockRange=&StarterBench=&'\\\r\n 'TeamID=0&VsConference=&VsDivision='\r\n \r\n response = requests.get(stat_url)\r\n data = json.loads(response.text)\r\n \r\n headers = data['resultSets'][0]['headers']\r\n stat_data = data['resultSets'][0]['rowSet']\r\n df = pd.DataFrame(stat_data,columns=headers) \r\n \r\n team_df = df[[\"TEAM_ID\",\"TEAM_NAME\",\"GP\",\"W\",\"L\",\"W_PCT\",\"MIN\",\"FGM\",\r\n \"FGA\",\"FG_PCT\",\"FG3M\",\"FG3A\",\"FG3_PCT\",\"FTM\",\"FTA\",\"FT_PCT\",\r\n \"OREB\",\"DREB\",\"REB\",\"AST\",\"TOV\",\"STL\",\"BLK\",\"BLKA\",\"PF\",\r\n \"PFD\",\"PTS\",\"PLUS_MINUS\"]]\r\n \r\n return team_df","def gamesmeta(sched=None):\n if sched is None or sched.empty:\n sched = schedule()\n h = sched.copy()\n a = sched.copy()\n h = h.rename(columns={'home_team': 'team', 'away_team': 'opp'})\n a = a.rename(columns={'away_team': 'team', 'home_team': 'opp'})\n return pd.concat([a, h]).sort_values('game_id')","def get_wr_game():\n wr_games_reg_2015 = NFL_WR_Game_2015.query.filter_by(is_season_totals=False).all()\n clean_games = cleanup_queries(wr_games_reg_2015)\n return json.dumps(clean_games)","def get_video_games(self, **kwargs):\n return self.get('video_games.json', **kwargs)","def retrieveGames():\n result = cs411_game.getGames()\n return prepJSON(result)","async def _get_full_leagues_data(self, server_id: str, season: str=None):\n if season is None:\n season = ''\n\n params = {'season': season}\n url = self.api_url + 'competitions/'\n\n return await self._make_request(url, params, server_id)","def get_player_games(self, year, use_local=True):","def seasons(self):\n if self._season_cache_ver == self._db._version:\n return self._season_cache\n\n # Find out how many seasons in this series by fetching the highest season.\n seasons = self._db.query(type='episode', parent=self._dbrow, attrs=['season'], distinct=True)\n self._season_cache = [Season(self._db, self, row['season']) for row in seasons]\n self._season_cache_ver = self._db._version\n return self._season_cache","def get_dates(season, info):\n url = 'http://www.basketball-reference.com/leagues/NBA_{0}_games.html'.format(season.split('-')[-1])\n rv = requests.get(url)\n soup = BeautifulSoup(rv.text)\n seasons = soup.find_all('table', {'class': 'sortable stats_table'})\n if len(seasons) == 2:\n reg_season, post_season = seasons\n else:\n reg_season, post_season = seasons[0], None\n dates = set()\n for table in [reg_season, post_season]:\n if table:\n rows = table.tbody.find_all('tr')\n for row in rows:\n match = row.find('a', href=True, text='Box Score')\n if match:\n match_code = match['href'].split('/')[2].split('.')[0]\n date = match_code[:-4]\n if info == 'money_lines':\n date = \"-\".join([date[:4], date[4:6], date[6:]])\n dates.add(date)\n return sorted(list(dates))","def latest_season_before(date):\n\tif date.month < 9:\n\t\treturn date.year - 1\n\treturn date.year","def own_games(self):\r\n return sorted(self.games + self.finals, key=lambda g: (g.datetime, g.pitch.rank))","def get(self, season):\n logger.info(f\"Retrieving stats for entire season={season}\")\n\n season_stats = Stats.query.filter_by(season=season).all()\n if not season_stats:\n logger.warning(f\"No stats found for season, season={season}.\")\n abort(404, message=\"Season not found.\")\n\n return _dump_stats(season_stats), 200","def get_te_game():\n te_games_reg_2015 = NFL_TE_Game_2015.query.filter_by(is_season_totals=False).all()\n clean_games = cleanup_queries(te_games_reg_2015)\n return json.dumps(clean_games)","def scrape():\n league_year = Config.get_property(\"league_year\")\n\n # Create table\n season_data = client.season_schedule(league_year)\n season_data = br_enum_to_string(season_data)\n return season_data","def _parse_season_entry (self, season, video, sorting):\n return {\n season['summary']['id']: {\n 'idx': sorting[season['summary']['id']],\n 'id': season['summary']['id'],\n 'text': season['summary']['name'],\n 'shortName': season['summary']['shortName'],\n 'boxarts': {\n 'small': video['boxarts']['_342x192']['jpg']['url'],\n 'big': video['boxarts']['_1280x720']['jpg']['url']\n },\n 'interesting_moment': video['interestingMoment']['_665x375']['jpg']['url'],\n }\n }","def _pull_schedule(self, abbreviation, year):\n if not year:\n year = utils._find_year_for_season('nfl')\n # If stats for the requested season do not exist yet (as is the\n # case right before a new season begins), attempt to pull the\n # previous year's stats. If it exists, use the previous year\n # instead.\n if not utils._url_exists(SCHEDULE_URL % (abbreviation.lower(),\n year)) and \\\n utils._url_exists(SCHEDULE_URL % (abbreviation.lower(),\n str(int(year) - 1))):\n year = str(int(year) - 1)\n doc = pq(SCHEDULE_URL % (abbreviation.lower(), year))\n schedule = utils._get_stats_table(doc, 'table#gamelog%s' % year)\n if not schedule:\n utils._no_data_found()\n return\n self._add_games_to_schedule(schedule, REGULAR_SEASON, year)\n if 'playoff_gamelog%s' % year in str(doc):\n playoffs = utils._get_stats_table(doc,\n 'table#playoff_gamelog%s' % year)\n self._add_games_to_schedule(playoffs, POST_SEASON, year)","def season(self, seasonnum, order='aired'):\n if order=='aired':\n seasons = self.seasons\n elif order == 'dvd':\n seasons = self.dvd_seasons\n try:\n return seasons[seasonnum]\n except KeyError:\n raise SeasonNotFoundError(\n 'Season no %s does not exists' % seasonnum\n ), None, sys.exc_info()[2]","def season(year, week=None, timeout=None, concurrency=_DEFAULT_CONCURRENCY):\n\tLOG.debug('Concurrency = %d', concurrency)\n\tgames = season_games(year)\n\tif week is not None:\n\t\tgames = games[games.week == week]\n\texpected_n = len(games)\n\ttables, futures_to_args, failures = [], {}, []\n\t# See https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor-example.\n\twith futures.ThreadPoolExecutor(concurrency) as pool:\n\t\tfor arg in zip(games.hometeam, games.awayteam, games.week):\n\t\t\targ = arg + (year,)\n\t\t\tfutures_to_args[pool.submit(game_unknown_homeaway, *arg)] = arg\n\t\tfor future in futures.as_completed(futures_to_args, timeout=timeout):\n\t\t\targs = futures_to_args[future]\n\t\t\ttry:\n\t\t\t\ttable = future.result()\n\t\t\texcept Exception as exc:\n\t\t\t\tLOG.exception('Error from %s: %s', args, exc)\n\t\t\telse:\n\t\t\t\tif table is None:\n\t\t\t\t\tLOG.error('Failure: %s', args)\n\t\t\t\t\tfailures.append(args)\n\t\t\t\telse:\n\t\t\t\t\tLOG.info('Success: %s', args)\n\t\t\t\t\ttables.append(table)\n\ttables = games.merge(pd.concat(tables), on=('hometeam', 'awayteam', 'week'))\n\tif __debug__:\n\t\tn = len(tables.groupby(['hometeam', 'awayteam', 'week']))\n\t\tassert n == expected_n, \"Expected %d games, got %d\" % (expected_n, n)\n\treturn tables, failures","def _get_live_games(self):\n response = requests.get(self._get_score_url())\n if response.status_code == 200:\n return [g for g in response.json()['games'] if g['status']['state'] == self.desired_game_state]","def _get_projected_data(self, season):\n try:\n match_data = []\n player_id = self.profile_data[\"id\"]\n matches = season.get_season_data()[\"stats\"][\"projectedPlayerStats\"]\n for match in matches:\n if player_id == match[0]:\n match_data.append(match)\n return match_data\n except Exception as e:\n error_msg = (\"Failed to retrieve player match data: {}\"\n \"\".format(str(e)))\n raise PlayerDataException(error_msg)","def get_fresh_data(get_team):\n\n \"\"\" Create the URL for today \"\"\"\n now = datetime.datetime.now()\n url = \"http://gd2.mlb.com/gdcross/components/game/mlb/year_\" + '{}'.format(now.year) + \"/month_\" + '{:02d}'.format(now.month) + \"/day_\" + '{:02d}'.format(now.day) + \"/miniscoreboard.json\"\n\n \"\"\" Grab the first response and write it to a file, we'll update it once the game starts \"\"\"\n data_write_file = status_dir + '{}'.format(now.year) + '{:02d}'.format(now.month) + '{:02d}'.format(now.day) + \".json\"\n\n \"\"\" Get the json data if the file doesn't exist, or if it's over three minutes old \"\"\"\n if not os.path.isfile(data_write_file) or time.time() - os.path.getmtime(data_write_file) > 60:\n response = urllib.urlopen(url)\n full_data = json.loads(response.read())\n with open(data_write_file, 'w') as outfile:\n json.dump(full_data, outfile, sort_keys=True, indent=2, ensure_ascii=False)\n\n \"\"\" Use the data from the status file \"\"\"\n with open(data_write_file, 'r') as json_data:\n full_data = json.load(json_data)\n\n \"\"\" This will return false if there is no game today, else will return json data for just our team \"\"\"\n my_game = False\n for game in full_data['data']['games']['game']:\n if get_team in game['home_file_code'] or get_team in game['away_file_code']:\n my_game = game\n \n return my_game","def getNumSeasons(self):\n searchURL = \"http://api.tvmaze.com/shows/\" + str(self.__showID) \\\n + \"/seasons\"\n\n response = requests.get(searchURL)\n data = response.json()\n\n return data[-1][\"number\"]","def get_games_data(games):\n\n games_response = requests.get(\n url=f'{settings.GAME_SETUP_URL}/games-data/',\n params={'game_id': games},\n timeout=5 # in sec\n )\n if games_response.status_code == 200:\n return games_response.json().get('games')\n return {}","def current_season():\n td = datetime.datetime.today()\n if td.month > 8:\n return td.year\n return td.year - 1","def get_winner(game):\n return game['winner']","def get_game(self, game_id):\n \n session = requests.session()\n response = session.get(self.baseURL + str(game_id), headers=self.headers)\n soup = BeautifulSoup(response.text)\n \n #get teams\n defeated_by = False \n game_header = soup.find_all(text=re.compile('defeats'))\n \n if len(game_header) == 0:\n game_header = soup.find_all(text=re.compile('defeated by'))\n \n if (len(game_header)) == 0:\n game_header = soup.find_all(text=re.compile('defeat'))\n \n if (len(game_header)) == 0:\n game_header = soup.find_all(text=re.compile('drew'))\n defeated_by = True \n else:\n defeated_by = True \n\n if defeated_by: \n teams = self.remove_long_names(game_header[1]).replace('\\n', '')\n home_team = teams.split(' ')[0]\n away_team = teams.split(' ')[3]\n else:\n teams = self.remove_long_names(game_header[1]).replace('\\n', '')\n home_team = teams.split(' ')[0]\n away_team = teams.split(' ')[2]\n \n date_string = game_header[0].split(' ')\n date_string_find = [date.lower() for date in date_string]\n \n venue = date_string[date_string_find.index('at') + 1]\n \n #get round\n round_num = None\n \n try:\n date_string_find.remove('')\n except:\n pass\n \n try:\n round_num = int(date_string[date_string_find.index('round') + 1])\n except:\n try:\n round_num = date_string_find[date_string_find.index('final') - 1] + ' final'\n except:\n round_num = date_string_find[date_string_find.index('semi-final')]\n \n date = date_string[-3:]\n date = ' '.join(date) \n date = parser.parse(date)\n \n #get attendance\n attend = soup.find_all(text=re.compile('Attendance'))\n attendance = 0\n \n if (len(attend) > 3):\n attendance = int(attend[1].split(' ')[-1])\n \n #get stats \n away_stats = {}\n home_stats = {}\n \n for stat in stats:\n stat_row = soup.find_all('td', text=stat)[0].find_parent('tr')\n elements = stat_row.find_all('td')\n \n if elements[0].text == '-':\n home_stats[stat] = None\n else:\n home_stats[stat] = elements[0].text\n \n if elements[0].text == '-':\n away_stats[stat] = None\n else:\n away_stats[stat] = elements[2].text\n \n return Game(game_id, home_team, away_team, venue, round_num, date, attendance, home_stats, away_stats)","def season(self, title=None, season=None):\n key = f'{self.key}/children?excludeAllLeaves=1'\n if title is not None and not isinstance(title, int):\n return self.fetchItem(key, Season, title__iexact=title)\n elif season is not None or isinstance(title, int):\n if isinstance(title, int):\n index = title\n else:\n index = season\n return self.fetchItem(key, Season, index=index)\n raise BadRequest('Missing argument: title or season is required')","def get_rb_game():\n rb_games_reg_2015 = NFL_RB_Game_2015.query.filter_by(is_season_totals=False).all()\n clean_games = cleanup_queries(rb_games_reg_2015)\n return json.dumps(clean_games)","def find_games(days_ahead=0):\n headers = {\n 'Host': 'stats.nba.com',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:61.0) Gecko/20100101 Firefox/61.0',\n 'Accept': 'application/json, text/plain, */*',\n 'Accept-Language': 'en-US,en;q=0.5',\n 'Referer': 'https://stats.nba.com/',\n 'Accept-Encoding': 'gzip, deflate, br',\n 'Connection': 'keep-alive',\n 'x-nba-stats-origin': 'stats',\n 'x-nba-stats-token': 'true'\n }\n board = scoreboardv2.ScoreboardV2(day_offset=days_ahead, headers=headers).get_data_frames()[0]\n board.replace(id_to_abrv, inplace=True)\n return board[['GAME_DATE_EST', 'GAME_ID', 'HOME_TEAM_ID', 'VISITOR_TEAM_ID']]","def extract_games(self) -> Dict[int, Dict[str, Any]]:\n optadocument = self._get_doc()\n attr = assertget(optadocument, '@attributes')\n matchdata = assertget(optadocument, 'MatchData')\n matches = {}\n for match in matchdata:\n matchattr = assertget(match, '@attributes')\n matchinfo = assertget(match, 'MatchInfo')\n matchinfoattr = assertget(matchinfo, '@attributes')\n game_id = int(assertget(matchattr, 'uID')[1:])\n matches[game_id] = dict(\n # Fields required by the base schema\n game_id=game_id,\n competition_id=int(assertget(attr, 'competition_id')),\n season_id=int(assertget(attr, 'season_id')),\n game_day=int(assertget(matchinfoattr, 'MatchDay')),\n game_date=datetime.strptime(assertget(matchinfo, 'Date'), '%Y-%m-%d %H:%M:%S'),\n # home_team_id=see below,\n # away_team_id=see below,\n # Optional fields\n # home_score=see below,\n # away_score=see below,\n # duration=?\n # referee=?\n # venue=?,\n # attendance=?\n # home_manager=?\n # away_manager=?\n )\n teamdata = assertget(match, 'TeamData')\n for team in teamdata:\n teamattr = assertget(team, '@attributes')\n side = assertget(teamattr, 'Side')\n teamid = assertget(teamattr, 'TeamRef')\n score = assertget(teamattr, 'Score')\n if side == 'Home':\n matches[game_id]['home_team_id'] = int(teamid[1:])\n matches[game_id]['home_score'] = int(score)\n else:\n matches[game_id]['away_team_id'] = int(teamid[1:])\n matches[game_id]['away_score'] = int(score)\n return matches","def get_pbp_json(season, game_id):\n url = \"http://live.nhl.com/GameData/\" + str(season) + \"/\" + str(game_id) + \"/PlayByPlay.json\"\n response = urllib.urlopen(url)\n data = json.loads(response.read())\n return data","def get_win_rate_post_season_for_each_coach(self):\n # get winning games for coaches\n self.post_games_won_for_coaches = (\n self.raw_data_postseason\n [['Season','DayNum','WTeamID']]\n # merge for winning team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_win\",\"LastDayNum\":\"LastDayNum_win\",\"CoachName\":\"CoachName_win\",\"TeamID\":\"TeamID_win\"})\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\n .query(\"which_coach_for_win != 0\")\n .groupby(['Season','CoachName_win','WTeamID'])\n .agg({\"which_coach_for_win\":\"sum\"})\n .reset_index()\n )\n\n # get losing games for coaches\n self.post_games_lose_for_coaches = (\n self.raw_data_postseason\n [['Season','DayNum','LTeamID']]\n # merge for losing team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_lose\",\"LastDayNum\":\"LastDayNum_lose\",\"CoachName\":\"CoachName_lose\",\"TeamID\":\"TeamID_lose\"})\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\n .query(\"which_coach_for_lose != 0\")\n .groupby(['Season','CoachName_lose','LTeamID'])\n .agg({\"which_coach_for_lose\":\"sum\"})\n .reset_index()\n )\n\n # combine games won and lost df for post season\n self.combine_post_games_won_lose = (\n self.post_games_lose_for_coaches\n .merge(self.post_games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\n .pipe(lambda x:x.assign(win_rate_post = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\n .drop(['CoachName_win','WTeamID'],1)\n .rename(columns={\"CoachName_lose\":\"CoachName\",\"LTeamID\":\"TeamID\",\"which_coach_for_lose\":\"post_games_lost\",\"which_coach_for_win\":\"post_games_won\"})\n .fillna(0)\n )","def get(request, season):\n try:\n secret_token = settings.DATA_SECRET_TOKEN\n except AttributeError:\n secret_token = None\n\n if secret_token is not None:\n\n if 'Http-Auth-Token' not in request.headers:\n print('no token header')\n print(request.headers)\n return HttpResponseForbidden()\n\n auth = request.headers['Http-Auth-Token'].split()\n\n if len(auth) != 2:\n print('header wrong length')\n return HttpResponseForbidden()\n\n if auth[0].lower() != \"pool-token\":\n print('no pool-token')\n return HttpResponseForbidden()\n\n token = auth[1]\n\n if secret_token is None or token != secret_token:\n print('mismatch token')\n return HttpResponseForbidden()\n\n if season == 'all' or season == 0:\n matches = Match.objects.all()\n else:\n season_obj = get_object_or_404(Season, number=season)\n matches = Match.objects.filter(season=season_obj)\n\n data = [match.serialize() for match in matches]\n return JsonResponse(data, safe=False)","def get_season(\n current_date: date, hemisphere: str, season_tracking_type: str\n) -> str | None:\n\n if hemisphere == \"equator\":\n return None\n\n if season_tracking_type == TYPE_ASTRONOMICAL:\n spring_start = ephem.next_equinox(str(current_date.year)).datetime()\n summer_start = ephem.next_solstice(str(current_date.year)).datetime()\n autumn_start = ephem.next_equinox(spring_start).datetime()\n winter_start = ephem.next_solstice(summer_start).datetime()\n else:\n spring_start = datetime(2017, 3, 1).replace(year=current_date.year)\n summer_start = spring_start.replace(month=6)\n autumn_start = spring_start.replace(month=9)\n winter_start = spring_start.replace(month=12)\n\n if spring_start <= current_date < summer_start:\n season = STATE_SPRING\n elif summer_start <= current_date < autumn_start:\n season = STATE_SUMMER\n elif autumn_start <= current_date < winter_start:\n season = STATE_AUTUMN\n elif winter_start <= current_date or spring_start > current_date:\n season = STATE_WINTER\n\n # If user is located in the southern hemisphere swap the season\n if hemisphere == NORTHERN:\n return season\n return HEMISPHERE_SEASON_SWAP.get(season)","def get_win_rate_regular_season_for_each_coach(self):\n self.games_won_for_coaches = (\n self.raw_data_regularseason\n [['Season','DayNum','WTeamID']]\n # merge for winning team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_win\",\"LastDayNum\":\"LastDayNum_win\",\"CoachName\":\"CoachName_win\",\"TeamID\":\"TeamID_win\"})\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\n .query(\"which_coach_for_win != 0\")\n .groupby(['Season','CoachName_win','WTeamID'])\n .agg({\"which_coach_for_win\":\"sum\"})\n .reset_index()\n )\n\n self.games_lose_for_coaches = (\n self.raw_data_regularseason\n [['Season','DayNum','LTeamID']]\n # merge for losing team\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\n .rename(columns={\"FirstDayNum\":\"FirstDayNum_lose\",\"LastDayNum\":\"LastDayNum_lose\",\"CoachName\":\"CoachName_lose\",\"TeamID\":\"TeamID_lose\"})\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\n .query(\"which_coach_for_lose != 0\")\n .groupby(['Season','CoachName_lose','LTeamID'])\n .agg({\"which_coach_for_lose\":\"sum\"})\n .reset_index()\n )\n\n # combine games won and lost df\n self.combine_regular_games_won_lose = (\n self.games_lose_for_coaches\n .merge(self.games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\n .pipe(lambda x:x.assign(win_rate_regular = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\n .drop(['CoachName_win','WTeamID'],1)\n .rename(columns={\"CoachName_lose\":\"CoachName\",\"LTeamID\":\"TeamID\",\"which_coach_for_lose\":\"games_lost\",\"which_coach_for_win\":\"games_won\"})\n )","def get_player_data(self, player, season, mtgs=None, past=None, future=None, single=False):\n\n avail = []\n scheduled = []\n\n # Should be empty arrays if None\n if past is None:\n past = []\n if future is None:\n future = []\n\n nplayed = Schedule.objects.filter(meeting__in=past, player=player).count()\n nscheduled = Schedule.objects.filter(meeting__in=future, player=player).count()\n\n av = PlayerAvailability.objects.get_for_season_player(player, season)\n\n p = {\n 'name': player.first + ' ' + player.last,\n 'id': player.id,\n 'isavail': av.available,\n 'scheduled': av.scheduled,\n 'played': av.played,\n 'nplayed': nplayed,\n 'nscheduled': nscheduled + nplayed,\n 'single': single\n }\n\n return p","def get_game_info(game_id):\n url = 'http://stats.nba.com/stats/boxscoresummaryv2?GameID=%s' % (game_id)\n response = requests.get(url, headers=HEADERS)\n while response.status_code != 200:\n response = requests.get(url)\n headers = response.json()['resultSets'][0]['headers']\n data = response.json()['resultSets'][0]['rowSet']\n data = pd.DataFrame(data, columns=headers)\n data = data[['GAME_ID', 'GAMECODE', 'HOME_TEAM_ID', 'VISITOR_TEAM_ID', 'SEASON']].T.squeeze()\n data['GAMECODE'] = data['GAMECODE'].replace('/', '')\n return data","def get_games():\r\n feed = feedparser.parse(FEED_URL)\r\n games = []\r\n for entry in feed.entries:\r\n games.append(Game(title = entry['title']\r\n , link = entry['link']\r\n ))\r\n return games","def get_games():\n feed = feedparser.parse(FEED_URL)\n\n games = []\n for entry in feed['entries']:\n game = Game(entry.title, entry.link)\n games.append(game)\n\n return games","def getPlayerBaseStat(self, year = 2014):\r\n \r\n year_next = (year % 100) + 1\r\n season = str(year) + '-' + str(year_next)\r\n \r\n stat_url = 'http://stats.nba.com/stats/leaguedashplayerstats?College=&'\\\r\n 'Conference=&Country=&DateFrom=&DateTo=&Division=&'\\\r\n 'DraftPick=&DraftYear=&GameScope=&GameSegment=&Height=&'\\\r\n 'LastNGames=0&LeagueID=00&Location=&MeasureType=Base&Month=0&'\\\r\n 'OpponentTeamID=0&Outcome=&PORound=0&PaceAdjust=N&'\\\r\n 'PerMode=PerGame&Period=0&PlayerExperience=&PlayerPosition=&'\\\r\n 'PlusMinus=N&Rank=N&Season='+ season + '&SeasonSegment=&'\\\r\n 'SeasonType=Regular+Season&ShotClockRange=&StarterBench=&'\\\r\n 'TeamID=0&VsConference=&VsDivision=&Weight='\r\n \r\n response = requests.get(stat_url)\r\n data = json.loads(response.text)\r\n \r\n headers = data['resultSets'][0]['headers']\r\n stat_data = data['resultSets'][0]['rowSet']\r\n baseStat_df = pd.DataFrame(stat_data,columns=headers) \r\n \r\n return baseStat_df","def get_current_player_season(self):\n return self.get_player_season(\"current\")","def _find_latest():\n try:\n db = get_master_collection()\n service_details = db.find({\"master.key\": \"release\"}).sort([(\"master.value\", pymongo.DESCENDING)]).limit(1)\n for service in service_details:\n for r in sorted(service[\"master\"][\"value\"], reverse=True):\n latest_release = r\n build_list = service[\"master\"][\"value\"][r]\n break\n break\n\n latest_rel_num = str(latest_release).replace(\"_\", \".\")\n build_list = _natural_sort(build_list)\n for build in build_list:\n latest_build = build\n break\n\n latest_build_num = latest_build\n second_latest_build_num = int(latest_build_num) - 1\n latest = {\"latest_val\": latest_rel_num + \"_\" + latest_build_num,\n \"second_latest_val\": latest_rel_num + \"_\" + str(second_latest_build_num)}\n except Exception as e:\n logger.error(\"Exception in _find_latest : \" + str(e))\n return latest","async def get_season(self, server: model.Server):\n api_url = ('https://eu.api.blizzard.com/sc2/'\n f'ladder/season/{server.id()}')\n payload = {'locale': 'en_US',\n 'access_token': await self.get_access_token()}\n data, status = await self._perform_api_request(api_url, params=payload)\n if status != 200:\n raise InvalidApiResponse(f'{status}: {api_url}')\n\n return model.Season(\n season_id=data.get('seasonId'),\n number=data.get('number'),\n year=data.get('year'),\n server=server,\n start=datetime.fromtimestamp(int(data.get('startDate'))),\n end=datetime.fromtimestamp(int(data.get('endDate')))\n )","def get_upcoming_season(self):\n result = self._method_call(\"UpcomingSeason\")\n return int(result)","def losing_games_stats(self):\n self.losing_games_up_to_2013 = (\n self.df\n .pipe(lambda x:x.assign(losing_num_counts=1))\n .query(\"Season <= 2013\")\n .groupby(['Season','LTeamID'])\n .agg({\"WScore\":\"sum\",\"LScore\":\"sum\",\"LFGM\":\"sum\",\"LFGA\":\"sum\",\"LFGM3\":\"sum\",\"LFGA3\":\"sum\",\"LFTM\":\"sum\",\"LFTA\":\"sum\",\"losing_num_counts\":\"sum\",\n \"LOR\":\"sum\",\"LDR\":\"sum\",\"WFGA\":\"sum\",\"WFGM\":\"sum\",\n \"LAst\":\"sum\",\"LTO\":\"sum\",\"LStl\":\"sum\",\"LBlk\":\"sum\",\"LPF\":\"sum\"})\n .reset_index()\n .rename(columns={\"WScore\":\"winning_opponent_score\"})\n # rebounds\n .pipe(lambda x:x.assign(total_losing_rebounds = x.LOR + x.LDR))\n .pipe(lambda x:x.assign(losing_off_rebounds_percent = x.LOR/x.total_losing_rebounds))\n .pipe(lambda x:x.assign(losing_def_rebounds_percent = x.LDR/x.total_losing_rebounds))\n .pipe(lambda x:x.assign(losing_team_missed_attempts = x.LFGA - x.LFGM))\n .pipe(lambda x:x.assign(winning_opp_team_missed_attempts = x.WFGA - x.WFGM))\n .pipe(lambda x:x.assign(losing_rebound_possession_percent = x.LOR/x.losing_team_missed_attempts))\n .pipe(lambda x:x.assign(losing_rebound_possessiongain_percent = x.LDR/x.winning_opp_team_missed_attempts))\n # blocks, steals, assists and turnovers\n .pipe(lambda x:x.assign(losing_block_opp_FGA_percent = x.LBlk/x.WFGA))\n .pipe(lambda x:x.assign(losing_assist_per_fgm = x.LAst/x.LFGM))\n .pipe(lambda x:x.assign(losing_assist_turnover_ratio = x.LAst/x.LTO))\n # rename columns to prevent duplication when joining with losing stats. example: WFGM_x\n .rename(columns={\"WFGA\":\"WFGA_opp\",\"WFGM\":\"WFGM_opp\"})\n )","def get_number_games(season: str, team_id: int, game_type_code: str = \"R\") -> dict:\n args = arguments.get_arguments()\n\n endpoint = f\"/schedule?teamId={team_id}&season={season}&gameType={game_type_code}\"\n response = api.nhl_api(endpoint)\n\n if response:\n schedule = response.json()\n games_total = schedule[\"totalItems\"]\n return games_total\n\n # If no valid response, just return default number of games (82)\n return 82","def all_games(self):\r\n return sorted(self.games + list(g for sp in self.sub_pools for g in sp.games) + self.finals,\r\n key=lambda g: (g.datetime, g.pitch.rank))","def _get_match_data(self, season):\n try:\n match_data = []\n player_id = self.profile_data[\"id\"]\n matches = season.get_season_data()[\"stats\"][\"actualPlayerStats\"]\n for match in matches:\n if player_id == match[0]:\n match_data.append(match)\n return match_data\n except Exception as e:\n error_msg = (\"Failed to retrieve player match data: {}\"\n \"\".format(str(e)))\n raise PlayerDataException(error_msg)","def run():\n games_db_conn = db.get_db_eng()\n games_query = \"SELECT * FROM GAMES\"\n games_data = etl_tools.extract_from_db(games_db_conn, games_query)\n _data_integrity_check(games_data)\n\n latest_season, latest_season_type = _get_latest_season_and_type(games_data)\n logging.info(f\"Latest season and type in current data: {(latest_season, latest_season_type)}\")\n\n if latest_season is None:\n batch_start_season, batch_start_type = config.START_SEASON, config.SEASON_TYPES[0]\n\n else:\n _truncate_games_table(games_db_conn, latest_season, latest_season_type)\n batch_start_season, batch_start_type = latest_season, latest_season_type\n\n logging.info(f\"Starting batch at {(batch_start_season, batch_start_type)}...\")\n\n batches = _get_seasons_grid(batch_start_season, batch_start_type)\n for batch in batches:\n season, season_type = batch\n logging.info(f\"Starting new batch: extracting data for {season}-{season_type}...\")\n batch_data = _extract_games_data(season, season_type)\n logging.info(f\"Data extracted. Loading {batch_data.shape[0]} rows...\")\n etl_tools.load_to_db(\n games_db_conn,\n 'games',\n batch_data,\n )\n\n logging.info(\"Pipeline completed.\")","def parse_episodes_by_season (self, response_data):\n episodes = {}\n raw_episodes = response_data['value']['videos']\n for episode_id in raw_episodes:\n if self._is_size_key(key=episode_id) == False:\n if (raw_episodes[episode_id]['summary']['type'] == 'episode'):\n episodes.update(self.parse_episode(episode=raw_episodes[episode_id], genres=response_data['value']['genres']))\n return episodes","def __get_season_data(season_num: int, max_episode: int, training_data: bool) -> List[TrainSample]:\n season = EXAM_DATA[season_num]\n drop_players = season.get_drop_mapping(DropType.EXECUTION_DROP, max_episode)\n all_answers = season.get_all_answers(set(drop_players.keys()), max_episode)\n season_data = []\n for answer in all_answers:\n exam_episode = answer.episode\n drop_episodes = drop_players[answer.player]\n drop_episodes = [episode for episode in drop_episodes if exam_episode <= episode]\n if drop_episodes:\n if training_data:\n for player in exam_episode.players:\n season_data.append(TrainSample(answer.player, season_num, min(drop_episodes), answer.episode,\n answer.question, answer.answer, player))\n else:\n for answer_on in [False, True]:\n season_data.append(TrainSample(answer.player, season_num, min(drop_episodes), answer.episode,\n answer.question, answer.answer, answer_on))\n return season_data","def get_player_season(self, season_id):\n return self._pubg.player_season(self.player_id, season_id, self.shard)","def __latest_available_episode(self, predict_season: int, latest_episode: int) -> int:\n max_episode = 0\n for i in range(1, latest_episode + 1):\n if VideoParser.has_parsed_video(predict_season, i):\n max_episode = i\n else:\n return max_episode\n return max_episode","def reduce_games(game):#game=player_games[0]\n res = []\n res.append(('id', game['id']) )\n res.append(('annulled', game['annulled']))\n res.append(('black', int(game['black'])))\n res.append(('white', int(game['white'])))\n res.append(('white_lost', int(game['white_lost']) ))\n res.append(('black_lost', int(game['black_lost']) ))\n res.append(('outcome', game['outcome']))\n res.append(('handicap', game['handicap']))\n res.append(('komi', game['komi']))\n res.append(('ranked', game['ranked']))\n res.append(('width', game['width']))\n res.append(('height', game['height']))\n res.append(('rules', game['rules']))\n res.append(('started', game['started']))\n res.append(('ended', game['ended']))\n players = game['historical_ratings'].keys()\n res.append(('black_rating', game['historical_ratings']['black']['ratings']['overall']['rating'] if 'black' in players else None))\n res.append(('white_rating', game['historical_ratings']['white']['ratings']['overall']['rating'] if 'white' in players else None))\n res.append(('black_deviation', game['historical_ratings']['black']['ratings']['overall']['deviation'] if 'black' in players else None))\n res.append(('white_deviation', game['historical_ratings']['white']['ratings']['overall']['deviation'] if 'white' in players else None))\n res.append(('black_volatility', game['historical_ratings']['black']['ratings']['overall']['volatility'] if 'black' in players else None))\n res.append(('white_volatility', game['historical_ratings']['white']['ratings']['overall']['volatility'] if 'white' in players else None))\n res.append(('black_ranking', game['historical_ratings']['black']['ranking'] if 'black' in players else None))\n res.append(('white_ranking', game['historical_ratings']['white']['ranking'] if 'white' in players else None))\n res.append(('tournament', not game['tournament'] is None))\n return res","def data(self):\n return self.source_data[\"game\"]","def get_wr_season_totals():\n wr_totals = redis_cache('wr_games_key', NFL_WR_Game_2015.query.filter_by(is_season_totals=True).all)\n return wr_totals","def scoreboard(year, month, day, home=None, away=None):\n # get data\n data = mlbgame.data.get_scoreboard(year, month, day)\n # parse data\n parsed = etree.parse(data)\n root = parsed.getroot()\n games = {}\n output = {}\n # loop through games\n for game in root:\n if game.tag == 'data':\n return []\n # get team names\n teams = game.findall('team')\n home_name = teams[0].attrib['name']\n away_name = teams[1].attrib['name']\n # check if teams match parameters\n if (home_name == home and home is not None) \\\n or (away_name == away and away is not None) \\\n or (away is None and home is None):\n # throw all the data into a complicated dictionary\n game_tag = game.tag\n game_data = game.find('game')\n game_id = game_data.attrib['id']\n game_league = game_data.attrib['league']\n game_status = game_data.attrib['status']\n game_start_time = game_data.attrib['start_time']\n home_team_data = teams[0].find('gameteam')\n home_team = home_name\n home_team_runs = int(home_team_data.attrib['R'])\n home_team_hits = int(home_team_data.attrib['H'])\n home_team_errors = int(home_team_data.attrib['E'])\n away_team_data = teams[1].find('gameteam')\n away_team = away_name\n away_team_runs = int(away_team_data.attrib['R'])\n away_team_hits = int(away_team_data.attrib['H'])\n away_team_errors = int(away_team_data.attrib['E'])\n # check type of game\n if game_tag == 'go_game' or game_tag == 'ig_game':\n try:\n w_pitcher_data = game.find('w_pitcher')\n w_pitcher = w_pitcher_data.find('pitcher').attrib['name']\n w_pitcher_wins = int(w_pitcher_data.attrib['wins'])\n w_pitcher_losses = int(w_pitcher_data.attrib['losses'])\n except Exception:\n w_pitcher = \"\"\n w_pitcher_wins = 0\n w_pitcher_losses = 0\n try:\n l_pitcher_data = game.find('l_pitcher')\n l_pitcher = l_pitcher_data.find('pitcher').attrib['name']\n l_pitcher_wins = int(l_pitcher_data.attrib['wins'])\n l_pitcher_losses = int(l_pitcher_data.attrib['losses'])\n except Exception:\n l_pitcher = \"\"\n l_pitcher_wins = 0\n l_pitcher_losses = 0\n try:\n sv_pitcher_data = game.find('sv_pitcher')\n sv_pitcher = sv_pitcher_data.find('pitcher').attrib['name']\n sv_pitcher_saves = int(sv_pitcher_data.attrib['saves'])\n except Exception:\n sv_pitcher = \"\"\n sv_pitcher_saves = 0\n output = {\n 'game_id': game_id,\n 'game_tag': game_tag,\n 'game_league': game_league,\n 'game_status': game_status,\n 'game_start_time': game_start_time,\n 'home_team': home_team,\n 'home_team_runs': home_team_runs,\n 'home_team_hits': home_team_hits,\n 'home_team_errors': home_team_errors,\n 'away_team': away_team,\n 'away_team_runs': away_team_runs,\n 'away_team_hits': away_team_hits,\n 'away_team_errors': away_team_errors,\n 'w_pitcher': w_pitcher,\n 'w_pitcher_wins': w_pitcher_wins,\n 'w_pitcher_losses': w_pitcher_losses,\n 'l_pitcher': l_pitcher,\n 'l_pitcher_wins': l_pitcher_wins,\n 'l_pitcher_losses': l_pitcher_losses,\n 'sv_pitcher': sv_pitcher,\n 'sv_pitcher_saves': sv_pitcher_saves\n }\n # games that were not played\n elif game_tag == 'sg_game':\n try:\n p_pitcher_data = game.findall('p_pitcher')\n p_pitcher_home_data = p_pitcher_data[0]\n p_pitcher_home = p_pitcher_home_data.find(\n 'pitcher').attrib['name']\n p_pitcher_home_wins = int(p_pitcher_home_data.\n attrib['wins'])\n p_pitcher_home_losses = int(p_pitcher_home_data.\n attrib['losses'])\n p_pitcher_away_data = p_pitcher_data[1]\n p_pitcher_away = p_pitcher_away_data.find(\n 'pitcher').attrib['name']\n p_pitcher_away_wins = int(p_pitcher_away_data.\n attrib['wins'])\n p_pitcher_away_losses = int(p_pitcher_away_data.\n attrib['losses'])\n except Exception:\n p_pitcher_home = ''\n p_pitcher_home_wins = 0\n p_pitcher_home_losses = 0\n p_pitcher_away = ''\n p_pitcher_away_wins = 0\n p_pitcher_away_losses = 0\n output = {\n 'game_id': game_id,\n 'game_tag': game_tag,\n 'game_league': game_league,\n 'game_status': game_status,\n 'game_start_time': game_start_time,\n 'home_team': home_team,\n 'home_team_runs': home_team_runs,\n 'home_team_hits': home_team_hits,\n 'home_team_errors': home_team_errors,\n 'away_team': away_team,\n 'away_team_runs': away_team_runs,\n 'away_team_hits': away_team_hits,\n 'away_team_errors': away_team_errors,\n 'p_pitcher_home': p_pitcher_home,\n 'p_pitcher_home_wins': p_pitcher_home_wins,\n 'p_pitcher_home_losses': p_pitcher_home_losses,\n 'p_pitcher_away': p_pitcher_away,\n 'p_pitcher_away_wins': p_pitcher_away_wins,\n 'p_pitcher_away_losses': p_pitcher_away_losses\n }\n # put this dictionary into the larger dictionary\n games[game_id] = output\n return games","def team_season_stats(team):\n\n # Get HTML Content\n url = 'http://www.basketball-reference.com/teams/%s/stats_per_game_totals.html' % team\n r = requests.get(url)\n soup = BeautifulSoup(r.content, \"html.parser\")\n\n # MongoDB Collection\n m = mongo.Mongo()\n\n # Team's yearly stats are displayed in a table\n season_stats = soup.find(id='stats').find('tbody')\n\n # Iterate through each year\n for year in season_stats.find_all('tr', {'class': None}):\n\n season_year = year.find('th').text[0:4]\n season_year = int(season_year) + 1\n season = {'year': season_year}\n\n # Loop through each stat\n for stat in year.find_all('td'):\n season[stat['data-stat']] = stat.string\n\n # Rename relocated teams\n season['team_id'] = scrape_utils.rename_team(season['team_id'])\n season['_id'] = season['team_id'] + '_' + str(season_year)\n\n # Remove unwanted stats\n to_remove = ['rank_team', 'foo', 'g', 'mp_per_g']\n for k in to_remove:\n season.pop(k, None)\n\n # Add to MongoDB\n m.insert('team_season', season)","def get_smmry_data(soup, game_dict):\n\n # Get date and time data.\n try:\n date_soup = soup.find(\"div\", {\"class\": \"spielbericht_tipp_status\"})\n league = date_soup.div.span.text\n date_string = date_soup.div.text\n date = re.search(r'\\d{2}.\\d{2}.\\d{2}', date_string).group(0)\n time = re.search(r'\\d{2}:\\d{2}', date_string).group(0)\n matchday = re.search(r'[|]\\d+', date_string).group(0)[1:]\n\n game_dict[\"league\"] = league\n game_dict[\"fb_date\"] = date\n game_dict[\"fb_time\"] = time\n game_dict[\"matchday\"] = matchday\n except AttributeError:\n pass\n\n # Get game result.\n try:\n result = soup.find(\"div\", {\"class\": \"stand\"}).text\n game_dict[\"result\"] = result\n except AttributeError:\n pass\n\n # Try to get the referee name.\n try:\n referee = soup.find(\"span\", {\"class\": \"schiri_link\"}).text\n game_dict[\"referee\"] = referee\n except AttributeError:\n pass\n\n # Get team, club name and repective url by team.\n try:\n smmry_soup = soup.find(\n \"div\", {\"class\": \"spielbericht_ergebnis_wrapper\"})\n club_title = smmry_soup.find_all(\"img\")\n team_title = smmry_soup.findAll(\"div\", {\"class\": \"teaminfo\"})\n\n # Loop through teams.\n for j, team in enumerate([\"home_\", \"away_\"]):\n game_dict[team + \"team\"] = team_title[j].a[\"title\"]\n game_dict[team + \"team_url\"] = team_title[j].a[\"href\"]\n game_dict[team + \"club\"] = club_title[j][\"title\"]\n except (AttributeError, TypeError):\n pass\n\n return game_dict","def __call__(self, date):\n for game in self._games:\n if game.datetime.year == date.year and \\\n game.datetime.month == date.month and \\\n game.datetime.day == date.day:\n return game\n raise ValueError('No games found for requested date')","def get_games_from_database (self):\n r = requests.get (self.url_endpoint)\n if (r.status_code != 200):\n print (\"Failed to get games:\\n\", r.text)\n return r\n \n games = json.loads (r.text)['games']\n return_list = []\n for game in games:\n return_list.append (game['game_state'])\n return return_list","def test_seasons(self):\n response = Tmdb.season(tmdb_show_id = 69740, season_number = 1)\n self.assertTrue(int(response.status_code) == 200)\n data = response.json()\n self.assertTrue(isinstance(data['episodes'], list))\n # TODO check if all the shows are in the good format (can be from_dict/to_dict)","def get_games_overall(self, uuid):\n\n return self.template(uuid, \"games\")","def test_get_seasonal_statistics___season_to_date(self):\n msg = \"Response status is not 200\"\n response = self.api.get_seasonal_statistics___season_to_date(self.season, self.nhl_season, self.team_id)\n self.assertEqual(response.status_code, 200, msg)","def getPlayerAdvStat(self, stat, year = 2014):\r\n \r\n year_next = (year % 100) + 1\r\n season = str(year) + '-' + str(year_next)\r\n \r\n stat_call = stat.lower()\r\n stat_dict = {'touch':'Possessions', 'possession':'Possessions',\r\n 'speed':'SpeedDistance', 'distance':'SpeedDistance'}\r\n \r\n stat_url = 'http://stats.nba.com/stats/leaguedashptstats?College=&'\\\r\n 'Conference=&Country=&DateFrom=&DateTo=&Division=&'\\\r\n 'DraftPick=&DraftYear=&GameScope=&Height=&LastNGames=0&'\\\r\n 'LeagueID=00&Location=&Month=0&OpponentTeamID=0&Outcome=&'\\\r\n 'PORound=0&PerMode=PerGame&PlayerExperience=&PlayerOr'\\\r\n 'Team=Player&PlayerPosition=&PtMeasureType=' + \\\r\n stat_dict[stat_call] + '&Season=' + season + \\\r\n '&SeasonSegment=&SeasonType=Regular+Season&StarterBench=&'\\\r\n 'TeamID=0&VsConference=&VsDivision=&Weight='\r\n \r\n response = requests.get(stat_url)\r\n data = json.loads(response.text)\r\n \r\n headers = data['resultSets'][0]['headers']\r\n stat_data = data['resultSets'][0]['rowSet']\r\n advStat_df = pd.DataFrame(stat_data,columns=headers) \r\n \r\n return advStat_df","def status_games(game=None):\n if game is None:\n aux = {}\n for i in range(len(games)):\n aux[\"Game \" + str(i)] = games.games_status[i]['Status']\n return aux\n else:\n game = int(game)\n return {\"Game \" + str(game): games.games_status[game]['Status']}","def parse_seasons (self, id, response_data):\n raw_seasons = response_data['value']\n videos = raw_seasons['videos']\n\n # get art video key\n video = {}\n for key, video_candidate in videos.iteritems():\n if not self._is_size_key(key):\n video = video_candidate\n\n # get season index\n sorting = {}\n for idx, season_list_entry in video['seasonList'].iteritems():\n if self._is_size_key(key=idx) == False and idx != 'summary':\n sorting[int(season_list_entry[1])] = int(idx)\n\n seasons = {}\n\n for season in raw_seasons['seasons']:\n if self._is_size_key(key=season) == False:\n seasons.update(self._parse_season_entry(season=raw_seasons['seasons'][season], video=video, sorting=sorting))\n return seasons","def get_previous_game(team_id: int) -> dict:\n\n logging.info(\"Checking the schedule API endpoint for the previous game.\")\n url = f\"teams/{team_id}?expand=team.schedule.previous\"\n\n response = api.nhl_api(url)\n if not response:\n return None\n\n prev_game_json = response.json()\n prev_game_sched = prev_game_json.get(\"teams\")[0].get(\"previousGameSchedule\")\n prev_game_date = prev_game_sched.get(\"dates\")[0].get(\"date\")\n prev_game = prev_game_sched.get(\"dates\")[0].get(\"games\")[0]\n\n return prev_game_date, prev_game","def get_latest_data(self, symbol, n=1):\n raise NotImplementedError","def by_season(self, season):\n return self.get_queryset().filter(season=season)","def standings_by_season(season):\n season = int(season) + 1\n scoreboard = nba_py.Scoreboard(month=7,\n day=1,\n year=season)\n east_standings = scoreboard.east_conf_standings_by_day()\n west_standings = scoreboard.west_conf_standings_by_day()\n\n return render_template(\"standings.html\",\n title=\"standings\",\n east_standings=enumerate(east_standings, 1),\n west_standings=enumerate(west_standings, 1),\n team=CITY_TO_TEAM)","def loadGamePlays(gamelink):\n \n #Open a game link\n game = urllib.request.urlopen(base+gamelink)\n gamedata = json.loads(game.read().decode(game.info().get_param('charset') or 'utf-8'))\n\n #Grab the data about every play that happened\n plays = gamedata['liveData']['plays']['allPlays']\n \n #Also grab the tricodes for the involved teams for future use\n homeTeam = gamedata['gameData']['teams']['home']['triCode']\n awayTeam = gamedata['gameData']['teams']['away']['triCode']\n \n return (plays, homeTeam, awayTeam)","def get_player_war_fg(self, player, kind, year):\n if kind == 'batter':\n war = '$fg.bat.{}.bat_WAR'.format(year)\n off = '$fg.bat.{}.Off'.format(year)\n def_ = '$fg.bat.{}.Def'.format(year)\n res = self._db.Players.aggregate([{'$match': {'Name' : player}},\n {'$project': {'_id' : 0,\n 'war' : war,\n 'off' : off,\n 'def' : def_}}])\n elif kind == 'pitcher':\n war = '$fg.pit.{}.pit_WAR'.format(year)\n res = self._db.Players.aggregate([{'$match': {'Name' : player}},\n {'$project': {'_id' : 0,\n 'war' : war}}])\n return list(res)[0]"],"string":"[\n \"def latestGamePack(team):\\n lgr= get('schedule', {'ver':'v1', 'sportId':1, 'date':today, 'teamId':team, 'fields':['dates','games','gamePk'] })\\n return lgr['dates'][0]['games'][0]['gamePk']\",\n \"def getseason(data):\\n ## Season key is the most reliable\\n season = data.get(\\\"season\\\")\\n if season:\\n ## Season key is an integer formatted \\\"YYS\\\" and is 2000-based (i.e.- 171 == 2017-Winter)\\n season = str(season)\\n year = int(f\\\"20{season[:2]}\\\")\\n ## Anichart Season key is 1-indexed\\n season = int(season[2]) - 1\\n ## This should normally pass; if it consistently does not, we'll have to investigate why\\n try: return SeasonCharts.buildseason(season,year)\\n ## If something goes wrong, we'll try another method\\n except: print(f\\\"Failed to parse season: {data['season']}\\\")\\n ## Next, we'll iterate over rankings to try to determine the season/year\\n ## There are multiple types of rankings based on season, year, and both combined,\\n ## so we'll piece it together based on whatever we come across first\\n season,year = None,None\\n for ranking in data.get(\\\"rankings\\\",list()):\\n ## Quicker exit (without just making this loop its own function)\\n if season and year: continue\\n ## We'll ignore stuff we've already gotten and assume that nothing in\\n ## rankings contradicts eachother\\n if not season:\\n ## Defaults to None one way or another if it's not supplied\\n season = ranking.get(\\\"season\\\")\\n if not year: year = ranking.get(\\\"year\\\")\\n ## Check if we made it\\n if season and year:\\n ## As above, this should always work out-of-the-box\\n try: return SeasonCharts.buildseason(season,year)\\n except: print(season,year)\\n ## Welp, we're stumped...\\n return None\",\n \"def _extract_games_data(season, season_type):\\n nflscrapr.run(\\n 'games',\\n season=season,\\n season_type=season_type\\n )\\n nflscrapr_output = etl_tools.extract_from_csv(config.GAMES_DUMP_CSV_PATH)\\n return nflscrapr_output\",\n \"def get_season_no(token, url):\\n headers = {'Accept': 'application/json', 'Authorization': token}\\n r = requests.get(url, headers=headers)\\n json_data = json.loads(r.text).get('data')\\n high_season = 1\\n for episode in json_data:\\n if episode.get('airedSeason') > high_season:\\n high_season = episode.get('airedSeason')\\n return high_season\",\n \"def get_games(season, date):\\n url = \\\"http://live.nhl.com/GameData/SeasonSchedule-\\\" + season + \\\".json\\\"\\n response = urllib.urlopen(url)\\n data = json.loads(response.read())\\n games = []\\n for game in data:\\n if game[\\\"est\\\"][:8] == date:\\n games.append(game)\\n return games\",\n \"def season_games(year):\\n\\tLOG.debug('Getting season %d', year)\\n\\tdata = read_html(io=season_games_url(year),\\n\\t\\t\\t\\t\\t attrs={'id': 'games'},\\n\\t\\t\\t\\t\\t infer_types=False,\\n\\t\\t\\t\\t\\t header=0)\\n\\tif len(data) != 1:\\n\\t\\traise CantFindTheRightTable\\n\\tdata = data.pop()\\n\\n\\t# Cleaning.\\n\\tdel data[\\\"Unnamed: 3\\\"]\\n\\t# The code below issues \\\"UserWarning: \\\" So we catch UserWarnings.\\n\\twith warnings.catch_warnings():\\n\\t\\twarnings.filterwarnings(action='ignore', category=UserWarning,\\n\\t\\t\\t\\t\\t\\t\\t\\tmodule=r'pandas\\\\.core\\\\.frame',\\n\\t\\t\\t\\t\\t\\t\\t\\tmessage=(r\\\"Boolean Series key will be reindexed\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t r\\\" to match DataFrame index\\\\.\\\"))\\n\\t\\t# These rows are mid-table header rows.\\n\\t\\tdata = data[data.Week != \\\"Week\\\"][data.Week != \\\"nan\\\"]\\n\\n\\tdata['week'] = (data.Week\\n\\t\\t\\t\\t\\t.replace(\\\"WildCard\\\", \\\"wild-card\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"Division\\\", \\\"divisional\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"ConfChamp\\\", \\\"conference\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"SuperBowl\\\", \\\"super-bowl\\\")\\n\\t\\t\\t\\t\\t.apply(\\n\\t\\t\\t\\t\\t\\tlambda s: (int(s)\\n\\t\\t\\t\\t\\t\\t\\t\\t if all(c in '1234567890' for c in s)\\n\\t\\t\\t\\t\\t\\t\\t\\t else s)))\\n\\tdel data['Week']\\n\\n\\tdata['season'] = year\\n\\tdata['game_date'] = pd.to_datetime(\\n\\t\\tdata.Date\\n\\t\\t.replace(r\\\"$\\\", r\\\", %d\\\" % year, regex=True)\\n\\t\\t.replace(r\\\"^(January|February) (\\\\d+), \\\\d+$\\\", r\\\"\\\\1 \\\\2, %d\\\" % (year + 1),\\n\\t\\t\\t\\t regex=True))\\n\\tdel data['Date']\\n\\n\\tfor column in \\\"PtsW\\\", \\\"PtsL\\\", \\\"YdsW\\\", \\\"TOW\\\", \\\"YdsL\\\", \\\"TOL\\\":\\n\\t data[column] = data[column].apply(int)\\n\\n\\tdata['WatL'] = data['Unnamed: 5'].apply(lambda x: x == '@')\\n\\tdel data['Unnamed: 5']\\n\\tdata['hometeam'] = (~data.WatL * data['Winner/tie'] +\\n\\t\\t\\t\\t\\t\\tdata.WatL * data['Loser/tie'])\\n\\tdata['awayteam'] = (data.WatL * data['Winner/tie'] +\\n\\t\\t\\t\\t\\t\\t~data.WatL * data['Loser/tie'])\\n\\tdata['winner'] = data['Winner/tie']\\n\\tfor column in 'Winner/tie', 'Loser/tie', \\\"WatL\\\":\\n\\t\\tdel data[column]\\n\\tfor column in 'hometeam', 'awayteam', 'winner':\\n\\t\\tdata[column] = data[column].apply(lambda s: s.split()[-1].lower())\\n\\n\\treturn data\",\n \"def season_series(game_id, pref_team, other_team, last_season=False):\\n\\n # Init empty dictionaries and lists\\n games_against = list()\\n pref_toi = dict()\\n pref_goals = dict()\\n pref_assists = dict()\\n pref_points = dict()\\n pref_record = {\\\"wins\\\": 0, \\\"losses\\\": 0, \\\"ot\\\": 0}\\n roster_player = True\\n\\n # If this is the first game of the season, we can set the 'last_season' flag to enable the\\n # season series function to check last year's season series between the two teams.\\n if not last_season:\\n season_start = str(game_id)[0:4]\\n season_end = str(int(season_start) + 1)\\n yesterday = datetime.now() - timedelta(days=1)\\n # yesterday = datetime.now() + timedelta(days=50)\\n # schedule_url = (\\n # f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n # f\\\"&expand=schedule.broadcasts,schedule.teams&startDate=\\\"\\n # f\\\"{season_start}-08-01&endDate={yesterday:%Y-%m-%d}\\\"\\n # )\\n schedule_url = (\\n f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n f\\\"&expand=schedule.broadcasts,schedule.teams\\\"\\n f\\\"&season={season_start}{season_end}\\\"\\n )\\n else:\\n season_start = int(str(game_id)[0:4]) - 1\\n season_end = str(int(season_start) + 1)\\n yesterday = datetime.now() - timedelta(days=1)\\n # yesterday = datetime.now() + timedelta(days=50)\\n # schedule_url = (\\n # f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n # f\\\"&expand=schedule.broadcasts,schedule.teams&startDate=\\\"\\n # f\\\"{season_start}-08-01&endDate={season_end}-06-01\\\"\\n # )\\n schedule_url = (\\n f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n f\\\"&expand=schedule.broadcasts,schedule.teams\\\"\\n f\\\"&season={season_start}{season_end}\\\"\\n )\\n\\n schedule = api.nhl_api(schedule_url).json()\\n dates = schedule[\\\"dates\\\"]\\n\\n # Loop through scheduled to get previously played games against\\n for date in dates:\\n game = date[\\\"games\\\"][0]\\n game_type = game[\\\"gameType\\\"]\\n game_id = game[\\\"gamePk\\\"]\\n game_team_home = game[\\\"teams\\\"][\\\"home\\\"][\\\"team\\\"][\\\"name\\\"]\\n game_team_away = game[\\\"teams\\\"][\\\"away\\\"][\\\"team\\\"][\\\"name\\\"]\\n teams = [game_team_away, game_team_home]\\n game_status = game[\\\"status\\\"][\\\"abstractGameState\\\"]\\n if game_type == \\\"R\\\" and game_status == \\\"Final\\\" and other_team.team_name in teams:\\n game_feed = f\\\"/game/{game_id}/feed/live\\\"\\n games_against.append(game_feed)\\n\\n # If the two teams haven't played yet, just exit this function\\n if not games_against:\\n return None, None, None\\n\\n # Loop through newly created games_against list to get each stats\\n for feed in games_against:\\n game = api.nhl_api(feed).json()\\n game_data = game[\\\"gameData\\\"]\\n home_team_name = game_data[\\\"teams\\\"][\\\"home\\\"][\\\"name\\\"]\\n pref_homeaway = \\\"home\\\" if home_team_name == pref_team.team_name else \\\"away\\\"\\n other_homeaway = \\\"away\\\" if home_team_name == pref_team.team_name else \\\"home\\\"\\n\\n # Get season series\\n end_period = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"currentPeriod\\\"]\\n extra_time = True if end_period > 3 else False\\n pref_score = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"teams\\\"][pref_homeaway][\\\"goals\\\"]\\n other_score = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"teams\\\"][other_homeaway][\\\"goals\\\"]\\n if pref_score > other_score:\\n pref_record[\\\"wins\\\"] += 1\\n elif other_score > pref_score and extra_time:\\n pref_record[\\\"ot\\\"] += 1\\n else:\\n pref_record[\\\"losses\\\"] += 1\\n\\n season_series_str = f\\\"Series: {pref_record['wins']}-\\\" f\\\"{pref_record['losses']}-{pref_record['ot']}\\\"\\n\\n # Get stats leaders\\n # pref_teamstats = game[\\\"liveData\\\"][\\\"boxscore\\\"][\\\"teams\\\"][pref_homeaway][\\\"teamStats\\\"]\\n pref_playerstats = game[\\\"liveData\\\"][\\\"boxscore\\\"][\\\"teams\\\"][pref_homeaway][\\\"players\\\"]\\n for id, player in pref_playerstats.items():\\n try:\\n # Calculate TOI\\n player_toi_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"timeOnIce\\\"]\\n player_toi_minutes = int(player_toi_str.split(\\\":\\\")[0])\\n player_toi_seconds = int(player_toi_str.split(\\\":\\\")[1])\\n player_toi = (player_toi_minutes * 60) + player_toi_seconds\\n pref_toi[id] = pref_toi.get(id, 0) + player_toi\\n\\n # Point Totals\\n player_goal_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"goals\\\"]\\n pref_goals[id] = pref_goals.get(id, 0) + int(player_goal_str)\\n player_assist_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"assists\\\"]\\n pref_assists[id] = pref_assists.get(id, 0) + int(player_assist_str)\\n player_points = int(player_goal_str) + int(player_assist_str)\\n pref_points[id] = pref_points.get(id, 0) + int(player_points)\\n\\n except KeyError:\\n pass\\n\\n # Calculate Stats Leaders\\n sorted_toi = sorted(pref_toi.values(), reverse=True)\\n leader_toi = sorted_toi[0]\\n\\n sorted_points = sorted(pref_points.values(), reverse=True)\\n leader_points = sorted_points[0]\\n\\n # Get TOI leader\\n for id in pref_toi.keys():\\n if pref_toi[id] == leader_toi:\\n player_name = roster.player_attr_by_id(pref_team.roster, id, \\\"fullName\\\")\\n if player_name is None:\\n roster_player = False\\n player_id_only = id.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n leader_toi_avg = leader_toi / len(games_against)\\n m, s = divmod(leader_toi_avg, 60)\\n toi_m = int(m)\\n toi_s = int(s)\\n toi_s = \\\"0{}\\\".format(toi_s) if toi_s < 10 else toi_s\\n toi_avg = \\\"{}:{}\\\".format(toi_m, toi_s)\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n toi_leader_str = \\\"TOI Leader: {} with {} / game.\\\".format(player_short_name, toi_avg)\\n\\n # Handle tied points leaders\\n point_leaders = list()\\n for id in pref_points.keys():\\n if pref_points[id] == leader_points:\\n point_leaders.append(id)\\n\\n if leader_points == 0:\\n points_leader_str = \\\"Points Leader: None (all players have 0 points).\\\"\\n\\n elif len(point_leaders) == 1:\\n leader = point_leaders[0]\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n # If the player is no longer on the team, get their information (change string here?)\\n if player_name is None:\\n roster_player = False\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n if not roster_player:\\n points_leader_str = (\\n f\\\"Points Leader: {player_name} with {leader_points} points \\\"\\n f\\\"({player_goals}G {player_assists}A) \\\"\\n )\\n else:\\n points_leader_str = \\\"Points Leader: {} with {} ({}G {}A).\\\".format(\\n player_name, leader_points, player_goals, player_assists\\n )\\n\\n elif len(point_leaders) > 3:\\n point_leaders_with_attrs = list()\\n for leader in point_leaders:\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n if player_name is None:\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n point_leaders_with_attrs.append(player_short_name)\\n\\n point_leaders_joined = \\\", \\\".join(point_leaders_with_attrs[0:3])\\n leftover_leaders = len(point_leaders) - 3\\n points_leader_str = (\\n f\\\"Points Leaders: {point_leaders_joined} & {leftover_leaders} others ({leader_points} each).\\\"\\n )\\n\\n else:\\n point_leaders_with_attrs = list()\\n for leader in point_leaders:\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n if player_name is None:\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n player_str = f\\\"{player_short_name} ({player_goals}G {player_assists}A)\\\"\\n point_leaders_with_attrs.append(player_str)\\n\\n point_leaders_joined = (\\n f\\\", \\\".join(point_leaders_with_attrs[:-1]) + f\\\" & {point_leaders_with_attrs[-1]}\\\"\\n )\\n points_leader_str = \\\"Points Leaders: {} with {} each.\\\".format(point_leaders_joined, leader_points)\\n\\n return season_series_str, points_leader_str, toi_leader_str\",\n \"def games(self, competition_id: int, season_id: int) -> DataFrame[Any]:\",\n \"def get_episode_details(token, url, season):\\n u = url + str(season)\\n headers = {'Accept': 'application/json', 'Authorization': token}\\n r = requests.get(u, headers=headers)\\n json_data = json.loads(r.text).get('data')\\n season_details = {}\\n season_details['current_season'] = season\\n if len(json_data) > 1:\\n for episode in json_data:\\n d = episode.get('firstAired')\\n date = datetime.datetime.strptime(d, \\\"%Y-%m-%d\\\")\\n today = datetime.datetime.today()\\n if date.date() >= today.date():\\n season_details['next_ep_no'] = episode.get('airedEpisodeNumber')\\n season_details['next_air_date'] = episode.get('firstAired')\\n season_details['ep_title'] = episode.get('episodeName')\\n season_details['ep_overview'] = episode.get('overview')\\n break\\n else:\\n season_details['next_ep_no'] = (json_data[len(json_data) - 1].get('airedEpisodeNumber'))\\n season_details['next_air_date'] = (json_data[len(json_data) - 1].get('firstAired'))\\n season_details['ep_title'] = (json_data[len(json_data) - 1].get('episodeName'))\\n season_details['ep_overview'] = (json_data[len(json_data) - 1].get('overview'))\\n else:\\n season_details['next_ep_no'] = 1\\n season_details['next_air_date'] = (json_data[0].get('firstAired'))\\n season_details['ep_title'] = (json_data[0].get('episodeName'))\\n season_details['ep_overview'] = (json_data[0].get('overview'))\\n if season_details['next_air_date'] == \\\"\\\":\\n season_details['next_air_date'] = 'TBD'\\n if season_details['ep_title'] == \\\"\\\" or season_details['ep_title'] is None:\\n season_details['ep_title'] = 'TBD'\\n if season_details['ep_overview'] == \\\"\\\" or season_details['ep_overview'] is None:\\n season_details['ep_overview'] = 'TBD'\\n return season_details\",\n \"def get_all_games(season):\\n url = BASE_URL.format(season)\\n json_data = requests.get(url, headers=HEADERS).json()\\n all_games = json_data[\\\"resultSets\\\"][0][\\\"rowSet\\\"]\\n return all_games\",\n \"def season_game_logs(team, year):\\n\\n # Check year value\\n if year > 2019 or year < 1950:\\n raise ValueError('Year Value Incorrect')\\n\\n # Rename teams that moved\\n team = scrape_utils.rename_team(team, year)\\n\\n # Get HTML content\\n url = 'http://www.basketball-reference.com/teams/%s/%s/gamelog' % (team, year)\\n r = requests.get(url)\\n soup = BeautifulSoup(r.content, \\\"html.parser\\\")\\n season_stats = soup.find(id='tgl_basic')\\n games = season_stats.find('tbody')\\n\\n # MongoDB Collection\\n m = mongo.Mongo()\\n\\n # To find opponent statistics\\n opponent = re.compile('^opp_.*$')\\n\\n # Loop through every game in a team's season\\n for game in games.find_all('tr', {'class': None}):\\n\\n curr_team = {'team': team}\\n opp_team = {}\\n\\n # Loop through each stat\\n for stat in game.find_all('td'):\\n\\n stat_name = stat['data-stat']\\n\\n # These are opponent stats\\n if re.match(opponent, stat_name):\\n opp_team[stat_name[4:]] = scrape_utils.stat_parse(stat_name, stat.string)\\n else:\\n curr_team[stat_name] = scrape_utils.stat_parse(stat_name, stat.string)\\n\\n # Remove unnecessary information\\n del curr_team['game_season']\\n del curr_team['x']\\n\\n # Rename relocated teams\\n curr_team['team'] = scrape_utils.rename_team(team)\\n opp_team['team'] = scrape_utils.rename_team(opp_team.pop('id'))\\n\\n # Use the same ID as basketball reference\\n result = {'date': datetime.strptime(curr_team.pop('date_game'), \\\"%Y-%m-%d\\\"),\\n 'season': year,\\n 'result': scrape_utils.determine_home_win(curr_team['game_location'], curr_team.pop('game_result')),\\n '_id': game.find('a')['href'][-17:-5]}\\n\\n # Place the teams in the correct spot depending on who is the home team\\n if curr_team.pop('game_location') == 0:\\n result['home'] = curr_team\\n result['away'] = opp_team\\n else:\\n result['home'] = opp_team\\n result['away'] = curr_team\\n\\n # Insert into database\\n m.insert('game_log', result)\",\n \"def seasonStats(personId,type = 'gameLog',group = 'hitting'):\\n\\n #playerInfo = get('people', {'personIds':personId})\\n\\n\\n teamStats = get('person',{ 'ver':'v1' , 'personId':personId,'hydrate':['stats(group={},type={})'.format(group,type),'currentTeam']})\\n return teamStats\\n #iterate of stats and find the right player id\\n #career stats broken\\n #fix the season :2019\\n #make function to get team id\",\n \"def get_games(date):\\n scoreboard = nba_py.Scoreboard(month=date.month,\\n day=date.day,\\n year=date.year)\\n line_score = scoreboard.line_score()\\n game_header = scoreboard.game_header()\\n\\n games = []\\n current_game = {}\\n game_sequence = 0\\n game_sequence_counter = 0\\n\\n # Get HOME TEAM and AWAY TEAM data for each boxscore game in line_score.\\n for i, value in enumerate(line_score):\\n if (value[\\\"GAME_SEQUENCE\\\"] != game_sequence):\\n game_sequence += 1\\n\\n current_game[\\\"GAME_ID\\\"] = value[\\\"GAME_ID\\\"]\\n home_team_id = game_header[game_sequence - 1][\\\"HOME_TEAM_ID\\\"]\\n\\n if (home_team_id == value[\\\"TEAM_ID\\\"]):\\n current_game[\\\"HOME_TEAM\\\"] = value[\\\"TEAM_ABBREVIATION\\\"]\\n current_game[\\\"HOME_TEAM_WINS_LOSSES\\\"] = value[\\\"TEAM_WINS_LOSSES\\\"]\\n current_game[\\\"HOME_TEAM_PTS\\\"] = value[\\\"PTS\\\"]\\n current_game[\\\"HOME_TEAM_ID\\\"] = value[\\\"TEAM_ID\\\"]\\n if (current_game[\\\"HOME_TEAM\\\"] in TEAM_ID_DATA):\\n current_game[\\\"HOME_TEAM_IMG\\\"] = TEAM_ID_DATA[current_game[\\\"HOME_TEAM\\\"]][\\\"img\\\"]\\n else:\\n current_game[\\\"AWAY_TEAM\\\"] = value[\\\"TEAM_ABBREVIATION\\\"]\\n current_game[\\\"AWAY_TEAM_WINS_LOSSES\\\"] = value[\\\"TEAM_WINS_LOSSES\\\"]\\n current_game[\\\"AWAY_TEAM_PTS\\\"] = value[\\\"PTS\\\"]\\n current_game[\\\"AWAY_TEAM_ID\\\"] = value[\\\"TEAM_ID\\\"]\\n if (current_game[\\\"AWAY_TEAM\\\"] in TEAM_ID_DATA):\\n current_game[\\\"AWAY_TEAM_IMG\\\"] = TEAM_ID_DATA[current_game[\\\"AWAY_TEAM\\\"]][\\\"img\\\"]\\n\\n if (value[\\\"TEAM_ABBREVIATION\\\"] in TEAMS):\\n if (home_team_id == value[\\\"TEAM_ID\\\"]):\\n current_game[\\\"HOME_TEAM_FULL_NAME\\\"] = TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"city\\\"] + \\\\\\n \\\" \\\" + TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"name\\\"]\\n else:\\n current_game[\\\"AWAY_TEAM_FULL_NAME\\\"] = TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"city\\\"] + \\\\\\n \\\" \\\" + TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"name\\\"]\\n \\n game_sequence = value[\\\"GAME_SEQUENCE\\\"]\\n game_sequence_counter += 1\\n elif game_sequence_counter == 1:\\n if (\\\"AWAY_TEAM\\\" in current_game):\\n current_game[\\\"HOME_TEAM\\\"] = value[\\\"TEAM_ABBREVIATION\\\"]\\n current_game[\\\"HOME_TEAM_WINS_LOSSES\\\"] = value[\\\"TEAM_WINS_LOSSES\\\"]\\n current_game[\\\"HOME_TEAM_PTS\\\"] = value[\\\"PTS\\\"]\\n current_game[\\\"HOME_TEAM_ID\\\"] = value[\\\"TEAM_ID\\\"]\\n if (current_game[\\\"HOME_TEAM\\\"] in TEAM_ID_DATA):\\n current_game[\\\"HOME_TEAM_IMG\\\"] = TEAM_ID_DATA[current_game[\\\"HOME_TEAM\\\"]][\\\"img\\\"]\\n else:\\n current_game[\\\"AWAY_TEAM\\\"] = value[\\\"TEAM_ABBREVIATION\\\"]\\n current_game[\\\"AWAY_TEAM_WINS_LOSSES\\\"] = value[\\\"TEAM_WINS_LOSSES\\\"]\\n current_game[\\\"AWAY_TEAM_PTS\\\"] = value[\\\"PTS\\\"]\\n current_game[\\\"AWAY_TEAM_ID\\\"] = value[\\\"TEAM_ID\\\"]\\n if (current_game[\\\"AWAY_TEAM\\\"] in TEAM_ID_DATA):\\n current_game[\\\"AWAY_TEAM_IMG\\\"] = TEAM_ID_DATA[current_game[\\\"AWAY_TEAM\\\"]][\\\"img\\\"]\\n\\n if (value[\\\"TEAM_ABBREVIATION\\\"] in TEAMS):\\n if (\\\"AWAY_TEAM\\\" in current_game):\\n current_game[\\\"HOME_TEAM_FULL_NAME\\\"] = TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"city\\\"] + \\\\\\n \\\" \\\" + TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"name\\\"]\\n else:\\n current_game[\\\"AWAY_TEAM_FULL_NAME\\\"] = TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"city\\\"] + \\\\\\n \\\" \\\" + TEAMS[value[\\\"TEAM_ABBREVIATION\\\"]][\\\"name\\\"]\\n\\n current_game[\\\"GAME_STATUS_TEXT\\\"] = game_header[game_sequence - 1][\\\"GAME_STATUS_TEXT\\\"]\\n if not game_header[game_sequence - 1][\\\"NATL_TV_BROADCASTER_ABBREVIATION\\\"]:\\n current_game[\\\"BROADCASTER\\\"] = \\\"\\\"\\n else:\\n current_game[\\\"BROADCASTER\\\"] = game_header[game_sequence - 1][\\\"NATL_TV_BROADCASTER_ABBREVIATION\\\"]\\n\\n games.append(current_game)\\n\\n current_game = {}\\n\\n game_sequence = value[\\\"GAME_SEQUENCE\\\"]\\n game_sequence_counter -= 1\\n\\n east_standings = scoreboard.east_conf_standings_by_day()\\n west_standings = scoreboard.west_conf_standings_by_day()\\n\\n return (games, east_standings, west_standings)\",\n \"def _get_latest_season_type(season_types_list):\\n if not isinstance(season_types_list, (list, tuple)):\\n raise ValueError(f\\\"type of {type(season_types_list)} for season_type_list not list or tuple\\\")\\n\\n latest_season_type = season_types_list[0]\\n if len(season_types_list) > 1:\\n for season_type in season_types_list[1:]:\\n if config.SEASON_TYPES_ORDER[season_type] > config.SEASON_TYPES_ORDER[latest_season_type]:\\n latest_season_type = season_type\\n\\n return latest_season_type\",\n \"def get_player_stats_from_game(team, year, week):\",\n \"def getSeasonStats(self):\\n df_season_agg = self.toSeasonAggFormat()\\n\\n # Calculate Possessions for each game\\n df_season_agg['possessions'] = 0.5 * (df_season_agg['FGA'] + 0.475 * df_season_agg['FTA'] - df_season_agg['OR'] + df_season_agg['TO']) \\\\\\n + 0.5 * (df_season_agg['OppFGA'] + 0.475 * df_season_agg['OppFTA'] - df_season_agg['OppOR'] + df_season_agg['OppTO'])\\n\\n # Aggregate to Season Summary Level\\n season_stats = df_season_agg.groupby(['TeamID', 'Season']).sum()\\n\\n season_stats = season_stats.rename(columns={'Win':'wins'})\\n\\n # Season Advanced Stats\\n season_stats['o_eff'] = season_stats['Score'] / season_stats['possessions'] * 100\\n season_stats['d_eff'] = season_stats['OppScore'] / season_stats['possessions'] * 100\\n season_stats['net_eff'] = season_stats['o_eff'] - season_stats['d_eff']\\n\\n season_stats.drop('DayNum', axis=1, inplace=True)\\n season_stats.drop('OppTeamID', axis=1, inplace=True)\\n season_stats.drop('rand', axis=1, inplace=True)\\n\\n return season_stats\",\n \"async def get_league_players(season): \\n if season == '2021-22':\\n get_epl = 2021\\n if season == '2020-21':\\n get_epl = 2020\\n if season == '2019-20':\\n get_epl = 2019\\n async with aiohttp.ClientSession() as session:\\n understat = Understat(session)\\n player = await understat.get_league_players(\\\"epl\\\", get_epl)\\n # print(json.dumps(player))\\n return player\",\n \"def season_info(cls, season):\\r\\n\\r\\n\\t\\traw_info = cls.season_rounds(season)\\r\\n\\r\\n\\t\\tinfo = [\\r\\n\\t\\t\\tseason,\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t# 0 -> Season name\\r\\n\\t\\t\\tlen(raw_info),\\t\\t\\t\\t\\t\\t\\t\\t# 1 -> Amount of rounds\\r\\n\\t\\t\\tmin([round[1] for round in raw_info]),\\t\\t# 2 -> Starting date\\r\\n\\t\\t\\tmax([round[1] for round in raw_info]),\\t\\t# 3 -> Latest round date\\r\\n\\t\\t\\tnp.mean([round[3] for round in raw_info])\\t# 4 -> Average round strength\\r\\n\\t\\t]\\r\\n\\r\\n\\t\\treturn info\",\n \"def getshowsbyseason(season,year, session = None):\\n url = APIURL.format(season=season, year = year)\\n headers = {\\\"X-CSRF-TOKEN\\\":session.cookies['X-CSRF-TOKEN']}\\n data = alrequests.GET_json(url,session = session, headers=headers)\\n return {cat:[Show(**show) for show in shows] for cat,shows in data.items()}\",\n \"def get_competition_season_type(season):\\n default_type = games.models.CompetitionSeason.winter\\n try:\\n season_name = season.name\\n if season_name.find(\\\"/\\\") == -1:\\n return games.models.CompetitionSeason.summer\\n return default_type\\n except Exception as e:\\n return default_type\",\n \"def get_latest_games_reviewed(platform, limit):\\n platform_num = PLATFORMS.get(platform.lower(), 0)\\n\\n if platform_num == 0:\\n url = _3DJUEGOS_REVIEWS_URL + \\\"juegos/0f0f0f0/fecha/\\\"\\n else:\\n url = _3DJUEGOS_REVIEWS_URL + \\\"juegos-\\\" + \\\\\\n platform + \\\"/0f{}f0f0/fecha/\\\".format(platform_num)\\n\\n soup = get_soup_obj(url)\\n if not soup:\\n return None\\n divs = soup.select(\\\"div.nov_int_txt.wi100\\\")\\n results = {}\\n results[\\\"latestGames\\\"] = [\\n {\\\"name\\\": re.sub(\\\" - .*$\\\", \\\"\\\", div.h2.a.text)} for div in divs[:limit]]\\n\\n return results\",\n \"def get_seasons_information():\\n\\n #getting the guidebox_id variable from show_page.html\\n guidebox_id = request.args.get(\\\"guidebox_id\\\")\\n\\n #make API to get season information, gets back list of season information\\n seasons_results = guidebox_season_info(guidebox_id)\\n\\n for season in seasons_results:\\n date = season[\\\"first_airdate\\\"]\\n year = str(date)[0:4]\\n season[\\\"first_airdate\\\"] = year\\n\\n return jsonify(seasons_results)\",\n \"def get_single_game_team_data(game, grouped_shot_data, pp_sit_data):\\n game_stat_lines = list()\\n game_id = game['game_id']\\n home_id = game['home_id']\\n road_id = game['road_id']\\n game_type = get_game_type_from_season_type(game)\\n\\n home_stats_src_path = os.path.join(\\n CONFIG['base_data_dir'], 'game_team_stats',\\n str(game['season']), str(game_type), \\\"%d_%d.json\\\" % (game_id, home_id))\\n road_stats_src_path = os.path.join(\\n CONFIG['base_data_dir'], 'game_team_stats',\\n str(game['season']), str(game_type), \\\"%d_%d.json\\\" % (game_id, road_id))\\n\\n # loading raw team game stats (if available)\\n raw_stats = dict()\\n if os.path.isfile(home_stats_src_path):\\n raw_stats['home'] = json.loads(open(home_stats_src_path).read())\\n else:\\n raw_stats['home'] = dict()\\n if os.path.isfile(road_stats_src_path):\\n raw_stats['road'] = json.loads(open(road_stats_src_path).read())\\n else:\\n raw_stats['road'] = dict()\\n\\n # counting penalties per team\\n penalty_counts = get_penalty_counts(game)\\n\\n for key in ['home', 'road']:\\n opp_key = 'road' if key == 'home' else 'home'\\n game_stat_line = dict()\\n # basic game information\\n game_stat_line['game_date'] = game['date']\\n game_stat_line['weekday'] = game['weekday']\\n game_stat_line['season'] = game['season']\\n game_stat_line['season_type'] = game['season_type']\\n game_stat_line['round'] = game['round']\\n game_stat_line['game_id'] = game_id\\n game_stat_line['team_id'] = game[\\\"%s_id\\\" % key]\\n game_stat_line['team'] = game[\\\"%s_abbr\\\" % key]\\n game_stat_line['opp_team_id'] = game[\\\"%s_id\\\" % opp_key]\\n game_stat_line['opp_team'] = game[\\\"%s_abbr\\\" % opp_key]\\n # identifying team's and opposing team's division (if\\n # applicable for current season and season type)\\n if (game['season'], game['season_type']) in divisions:\\n current_divisions = divisions[game['season'], game['season_type']]\\n game_stat_line['division'] = current_divisions[game_stat_line['team']]\\n game_stat_line['opp_division'] = current_divisions[game_stat_line['opp_team']]\\n # TODO: reactivate when schedule game id is available again\\n # game_stat_line['schedule_game_id'] = game['schedule_game_id']\\n game_stat_line['arena'] = correct_name(game['arena'])\\n game_stat_line['attendance'] = game['attendance']\\n if game_stat_line['arena'] in capacities:\\n game_stat_line['capacity'] = capacities[game_stat_line['arena']]\\n else:\\n print(\\n \\\"\\\\t+ Unable to retrieve capacity \\\" +\\n \\\"for '%s'\\\" % game_stat_line['arena'])\\n game_stat_line['capacity'] = 0\\n # coaches and referees\\n if \\\"%s_coach\\\" % key in game:\\n game_stat_line['coach'] = correct_name(\\n game[\\\"%s_coach\\\" % key], game['date'])\\n if game_stat_line['coach'] not in coaches:\\n print(\\\"+ Unknown coach '%s'\\\" % game_stat_line['coach'])\\n else:\\n print(\\\"\\\\t+ No coach information found for %s in game %d\\\" % (\\n game_stat_line['team'], game_id))\\n game_stat_line['coach'] = correct_name(\\n \\\"%d_%s\\\" % (game_id, game_stat_line['team']))\\n print(\\\"\\\\t+ Adjusted to '%s'\\\" % game_stat_line['coach'])\\n if \\\"%s_coach\\\" % opp_key in game:\\n game_stat_line['opp_coach'] = correct_name(\\n game[\\\"%s_coach\\\" % opp_key], game['date'])\\n if game_stat_line['opp_coach'] not in coaches:\\n print(\\\"+ Unknown coach '%s'\\\" % game_stat_line['opp_coach'])\\n else:\\n print(\\n \\\"\\\\t+ No opposition coach information found \\\" +\\n \\\"for %s in game %d\\\" % (game_stat_line['opp_team'], game_id))\\n game_stat_line['opp_coach'] = correct_name(\\n \\\"%d_%s\\\" % (game_id, game_stat_line['opp_team']))\\n print(\\\"\\\\t+ Adjusted to '%s'\\\" % game_stat_line['opp_coach'])\\n game_stat_line['ref_1'] = correct_name(game['referee_1'])\\n game_stat_line['ref_2'] = correct_name(game['referee_2'])\\n game_stat_line['lma_1'] = correct_name(game['linesman_1'])\\n game_stat_line['lma_2'] = correct_name(game['linesman_2'])\\n # outcomes\\n game_stat_line['games_played'] = 1\\n game_stat_line['home_road'] = key\\n game_stat_line['score'] = game[\\\"%s_score\\\" % key]\\n game_stat_line['goals'] = game[\\\"%s_score\\\" % key]\\n game_stat_line['opp_score'] = game[\\\"%s_score\\\" % opp_key]\\n game_stat_line['opp_goals'] = game[\\\"%s_score\\\" % opp_key]\\n # optionally correcting game scores\\n if game_id in game_score_corrections:\\n for team_abbr in game_score_corrections[game_id]:\\n if game_stat_line['team'] == team_abbr:\\n game_stat_line['score'] = game_score_corrections[game_id][team_abbr]\\n if game_stat_line['opp_team'] == team_abbr:\\n game_stat_line['opp_score'] = game_score_corrections[game_id][team_abbr]\\n if game['shootout_game']:\\n game_stat_line['game_type'] = 'SO'\\n elif game['overtime_game']:\\n game_stat_line['game_type'] = 'OT'\\n else:\\n game_stat_line['game_type'] = ''\\n for gsl_key in ['w', 'rw', 'ow', 'sw', 'l', 'rl', 'ol', 'sl']:\\n game_stat_line[gsl_key] = 0\\n if game_stat_line['score'] > game_stat_line['opp_score']:\\n game_stat_line['w'] += 1\\n if game['shootout_game']:\\n game_stat_line['sw'] += 1\\n game_stat_line['goals'] -= 1\\n elif game['overtime_game']:\\n game_stat_line['ow'] += 1\\n else:\\n game_stat_line['rw'] += 1\\n else:\\n game_stat_line['l'] += 1\\n if game['shootout_game']:\\n game_stat_line['sl'] += 1\\n game_stat_line['opp_goals'] -= 1\\n elif game['overtime_game']:\\n game_stat_line['ol'] += 1\\n else:\\n game_stat_line['rl'] += 1\\n game_stat_line['points'] = (\\n game_stat_line['rw'] * 3 + game_stat_line['ow'] * 2 +\\n game_stat_line['sw'] * 2 + game_stat_line['sl'] * 1 +\\n game_stat_line['ol'] * 1)\\n # per-period goals\\n for period in [1, 2, 3]:\\n game_stat_line[\\\"goals_%d\\\" % period] = game[\\n \\\"%s_goals_%d\\\" % (key, period)]\\n game_stat_line[\\\"opp_goals_%d\\\" % period] = game[\\n \\\"%s_goals_%d\\\" % (opp_key, period)]\\n # empty-net and extra-attacker goals\\n game_stat_line['en_goals'] = game[\\\"%s_en_goals\\\" % key]\\n game_stat_line['ea_goals'] = game[\\\"%s_ea_goals\\\" % key]\\n game_stat_line['opp_en_goals'] = game[\\\"%s_en_goals\\\" % opp_key]\\n game_stat_line['opp_ea_goals'] = game[\\\"%s_ea_goals\\\" % opp_key]\\n # situation after 20 and 40 minutes respectively\\n for situation in [\\n 'tied20', 'lead20', 'trail20', 'tied40', 'lead40', 'trail40'\\n ]:\\n game_stat_line[situation] = False\\n if game_stat_line['goals_1'] == game_stat_line['opp_goals_1']:\\n game_stat_line['tied20'] = True\\n elif game_stat_line['goals_1'] > game_stat_line['opp_goals_1']:\\n game_stat_line['lead20'] = True\\n else:\\n game_stat_line['trail20'] = True\\n goals40 = game_stat_line['goals_1'] + game_stat_line['goals_2']\\n opp_goals40 = (\\n game_stat_line['opp_goals_1'] + game_stat_line['opp_goals_2'])\\n if goals40 == opp_goals40:\\n game_stat_line['tied40'] = True\\n elif goals40 > opp_goals40:\\n game_stat_line['lead40'] = True\\n else:\\n game_stat_line['trail40'] = True\\n # scored first?\\n if game['first_goal'] == game_stat_line['team']:\\n game_stat_line['scored_first'] = True\\n game_stat_line['trailed_first'] = False\\n elif game['first_goal'] == game_stat_line['opp_team']:\\n game_stat_line['scored_first'] = False\\n game_stat_line['trailed_first'] = True\\n # one-goal, two-goal, three-goal, four-goal-game?\\n for goal_game in ['one_goal', 'two_goal', 'three_goal', 'four_goal']:\\n game_stat_line[goal_game] = False\\n score_diff = abs(\\n (game_stat_line['score'] - game_stat_line['en_goals']) -\\n (game_stat_line['opp_score'] - game_stat_line['opp_en_goals']))\\n # in case the right amount of empty-net goals have been scored, we\\n # may end up with a score differential of zero, see game between STR\\n # and ING on Mar 3, 2019\\n if not score_diff:\\n game_stat_line['zero_goal'] = True\\n if score_diff == 1:\\n game_stat_line['one_goal'] = True\\n elif score_diff == 2:\\n game_stat_line['two_goal'] = True\\n elif score_diff == 3:\\n game_stat_line['three_goal'] = True\\n elif score_diff > 3:\\n game_stat_line['four_goal'] = True\\n\\n # retrieving score state time spans for current team\\n game_stat_line['time_played'] = game['time_played']\\n game_stat_line['tied'] = game['tied']\\n game_stat_line['tied_pctg'] = round(\\n game['tied'] / game['time_played'] * 100, 2)\\n if key == 'home':\\n game_stat_line['leading'] = game['home_leading']\\n game_stat_line['trailing'] = game['road_leading']\\n else:\\n game_stat_line['leading'] = game['road_leading']\\n game_stat_line['trailing'] = game['home_leading']\\n game_stat_line['leading_pctg'] = round(\\n game_stat_line['leading'] / game['time_played'] * 100, 2)\\n game_stat_line['trailing_pctg'] = round(\\n game_stat_line['trailing'] / game['time_played'] * 100, 2)\\n\\n # retrieving raw stats for team and opposing team\\n for category, raw_category in RAW_STATS_MAPPING:\\n game_stat_line[category] = raw_stats[key].get(raw_category, None)\\n game_stat_line[\\\"opp_%s\\\" % category] = raw_stats[opp_key].get(raw_category, None)\\n\\n # checking number of power play goals retrieved from team stats with those registered in event data\\n game_stat_line = check_pp_goals(game, key, opp_key, game_stat_line)\\n\\n # calculating shooting percentages\\n if game_stat_line['shots_on_goal']:\\n game_stat_line['shot_pctg'] = round(\\n game_stat_line['goals'] /\\n game_stat_line['shots_on_goal'] * 100., 2)\\n else:\\n game_stat_line['shot_pctg'] = None\\n if game_stat_line['opp_shots_on_goal']:\\n game_stat_line['opp_shot_pctg'] = round(\\n game_stat_line['opp_goals'] /\\n game_stat_line['opp_shots_on_goal'] * 100., 2)\\n else:\\n game_stat_line['opp_shot_pctg'] = None\\n # calculating save percentages\\n if game_stat_line['opp_shots_on_goal']:\\n game_stat_line['save_pctg'] = round(\\n 100 - game_stat_line['opp_goals'] /\\n game_stat_line['opp_shots_on_goal'] * 100., 2)\\n else:\\n game_stat_line['save_pctg'] = None\\n if game_stat_line['shots_on_goal']:\\n game_stat_line['opp_save_pctg'] = round(\\n 100 - game_stat_line['goals'] /\\n game_stat_line['shots_on_goal'] * 100., 2)\\n else:\\n game_stat_line['opp_save_pctg'] = None\\n # calculating pdo values\\n if (\\n game_stat_line['shot_pctg'] is not None and\\n game_stat_line['save_pctg'] is not None\\n ):\\n game_stat_line['pdo'] = round((\\n game_stat_line['shot_pctg'] +\\n game_stat_line['save_pctg']), 1)\\n game_stat_line['opp_pdo'] = round((\\n game_stat_line['opp_shot_pctg'] +\\n game_stat_line['opp_save_pctg']), 1)\\n # calculating power play percentages\\n if game_stat_line['pp_opps']:\\n game_stat_line['pp_pctg'] = round((\\n game_stat_line['pp_goals'] /\\n game_stat_line['pp_opps']) * 100., 1)\\n else:\\n game_stat_line['pp_pctg'] = 0\\n if game_stat_line['opp_pp_opps']:\\n game_stat_line['opp_pp_pctg'] = round((\\n game_stat_line['opp_pp_goals'] /\\n game_stat_line['opp_pp_opps']) * 100., 1)\\n else:\\n game_stat_line['opp_pp_pctg'] = 0\\n # calculating penalty killing percentages\\n if game_stat_line['sh_opps']:\\n game_stat_line['pk_pctg'] = round(\\n 100 - game_stat_line['opp_pp_goals'] /\\n game_stat_line['sh_opps'] * 100., 1)\\n else:\\n game_stat_line['pk_pctg'] = 0\\n if game_stat_line['opp_sh_opps']:\\n game_stat_line['opp_pk_pctg'] = round(\\n 100 - game_stat_line['pp_goals'] /\\n game_stat_line['opp_sh_opps'] * 100., 1)\\n else:\\n game_stat_line['opp_pk_pctg'] = 0\\n game_stat_line['ev_goals'] = (\\n game_stat_line['goals'] -\\n game_stat_line['pp_goals'] -\\n game_stat_line['sh_goals'])\\n game_stat_line['opp_ev_goals'] = (\\n game_stat_line['opp_goals'] -\\n game_stat_line['opp_pp_goals'] -\\n game_stat_line['opp_sh_goals'])\\n # faceoffs are treated separately since each of the team game stats\\n # datasets only contains the number of won faceoffs and sometimes this\\n # one is stored as a string (wtf?)\\n game_stat_line['faceoffs_won'] = int(\\n raw_stats[key].get('faceOffsWon', 0))\\n game_stat_line['faceoffs_lost'] = int(\\n raw_stats[opp_key].get('faceOffsWon', 0))\\n # calculating overall number of faceoffs and faceoff percentage\\n game_stat_line['faceoffs'] = (\\n game_stat_line['faceoffs_won'] + game_stat_line['faceoffs_lost'])\\n if game_stat_line['faceoffs']:\\n game_stat_line['faceoff_pctg'] = round(\\n game_stat_line['faceoffs_won'] /\\n game_stat_line['faceoffs'] * 100., 1)\\n else:\\n game_stat_line['faceoff_pctg'] = 0.\\n # best players\\n game_stat_line['best_plr_id'] = game.get(\\n \\\"%s_best_player_id\\\" % key, None)\\n game_stat_line['best_plr'] = game.get(\\\"%s_best_player\\\" % key, None)\\n game_stat_line['opp_best_plr_id'] = game.get(\\n \\\"%s_best_player_id\\\" % opp_key, None)\\n game_stat_line['opp_best_plr'] = game.get(\\n \\\"%s_best_player\\\" % opp_key, None)\\n # game-winning-goal\\n game_stat_line['gw_goal_team'] = game['gw_goal']\\n game_stat_line['gw_goal_player_id'] = game['gw_goal_player_id']\\n game_stat_line['gw_goal_first_name'] = game['gw_goal_first_name']\\n game_stat_line['gw_goal_last_name'] = game['gw_goal_last_name']\\n\\n shot_zones_to_retain = ['slot', 'left', 'right', 'blue_line']\\n shot_situations_to_retain = [\\n 'shots_ev', 'shots_5v5', 'shots_pp', 'shots_sh', 'shots_unblocked',\\n 'shots_unblocked_ev', 'shots_unblocked_5v5', 'shots_unblocked_pp',\\n 'shots_unblocked_sh', 'shots_on_goal_ev', 'shots_on_goal_5v5',\\n 'shots_on_goal_pp', 'shots_on_goal_sh', 'goals_5v5', 'hit_post']\\n\\n # retrieving shot data for current game and team\\n shot_data = grouped_shot_data.get(\\n (game_id, game_stat_line['team']), list())\\n for item in shot_data:\\n if item.startswith(tuple(shot_zones_to_retain)):\\n abbr_item = item\\n for zone_key, replacement in SHOT_ZONE_ABBREVIATIONS.items():\\n abbr_item = abbr_item.replace(zone_key, replacement)\\n game_stat_line[abbr_item] = shot_data[item]\\n elif item in shot_situations_to_retain:\\n game_stat_line[item] = shot_data[item]\\n\\n # retrieving shots against data for current game and team\\n shot_against_data = grouped_shot_data.get(\\n (game_id, game_stat_line['opp_team']), list())\\n for item in shot_against_data:\\n if item.startswith(tuple(shot_zones_to_retain)):\\n abbr_item = item\\n for zone_key, replacement in SHOT_ZONE_ABBREVIATIONS.items():\\n abbr_item = abbr_item.replace(zone_key, replacement)\\n game_stat_line[\\\"%s_a\\\" % abbr_item] = shot_against_data[item]\\n elif item in shot_situations_to_retain:\\n game_stat_line[\\\"opp_%s\\\" % item] = shot_against_data[item]\\n\\n try:\\n game_stat_line['ev_cf_pctg'] = round(\\n game_stat_line['shots_ev'] / (game_stat_line['shots_ev'] + game_stat_line['opp_shots_ev']) * 100, 2)\\n except KeyError:\\n print(\\\"\\\\t+Unable to calculate even strength shots for percentage\\\")\\n game_stat_line['ev_cf_pctg'] = None\\n\\n for penalty_duration in [2, 5, 10, 20]:\\n if penalty_counts[key] and penalty_duration in penalty_counts[key]:\\n game_stat_line[\\\"penalty_%d\\\" % penalty_duration] = (\\n penalty_counts[key][penalty_duration])\\n else:\\n game_stat_line[\\\"penalty_%d\\\" % penalty_duration] = 0\\n\\n game_stat_line['pp_5v4'] = pp_sit_data[key]['pp_sits']['5v4']\\n game_stat_line['pp_5v3'] = pp_sit_data[key]['pp_sits']['5v3']\\n game_stat_line['pp_4v3'] = pp_sit_data[key]['pp_sits']['4v3']\\n game_stat_line['ppg_5v4'] = pp_sit_data[key]['pp_goals']['5v4']\\n game_stat_line['ppg_5v3'] = pp_sit_data[key]['pp_goals']['5v3']\\n game_stat_line['ppg_4v3'] = pp_sit_data[key]['pp_goals']['4v3']\\n game_stat_line['opp_pp_5v4'] = pp_sit_data[opp_key]['pp_sits']['5v4']\\n game_stat_line['opp_pp_5v3'] = pp_sit_data[opp_key]['pp_sits']['5v3']\\n game_stat_line['opp_pp_4v3'] = pp_sit_data[opp_key]['pp_sits']['4v3']\\n game_stat_line['opp_ppg_5v4'] = pp_sit_data[opp_key]['pp_goals']['5v4']\\n game_stat_line['opp_ppg_5v3'] = pp_sit_data[opp_key]['pp_goals']['5v3']\\n game_stat_line['opp_ppg_4v3'] = pp_sit_data[opp_key]['pp_goals']['4v3']\\n\\n # opp_diff = game_stat_line['pp_opps'] - (\\n # game_stat_line['pp_5v4'] +\\n # game_stat_line['pp_5v3'] +\\n # game_stat_line['pp_4v3']\\n # )\\n # if opp_diff:\\n # print(\\\"\\\\tpp opp discrepancy of %d for %s\\\" % (opp_diff, key))\\n\\n # registering shootout stats (if applicable)\\n shootout_stats = get_shootout_stats(game, key, opp_key)\\n if shootout_stats:\\n game_stat_line = {**game_stat_line, **shootout_stats}\\n\\n game_stat_lines.append(game_stat_line)\\n\\n return game_stat_lines\",\n \"def get_games(self, split: dataset_split.DatasetSplit = None) -> Dict[str, cereal_bar_game.CerealBarGame]:\\n if not split:\\n all_games: Dict[str, cereal_bar_game.CerealBarGame] = dict()\\n for game_id, game in self._train_games.items():\\n all_games[game_id] = game\\n for game_id, game in self._dev_games.items():\\n all_games[game_id] = game\\n for game_id, game in self._test_games.items():\\n all_games[game_id] = game\\n return all_games\\n\\n if split in {dataset_split.DatasetSplit.TRAIN, dataset_split.DatasetSplit.UPDATE,\\n dataset_split.DatasetSplit.VALIDATION}:\\n games: Dict[str, cereal_bar_game.CerealBarGame] = dict()\\n for game_id, game in self._train_games.items():\\n if split == dataset_split.DatasetSplit.TRAIN:\\n games[game_id] = game\\n elif split == dataset_split.DatasetSplit.UPDATE and game_id in self._update_ids:\\n games[game_id] = game\\n elif split == dataset_split.DatasetSplit.VALIDATION and game_id in self._val_ids:\\n games[game_id] = game\\n return games\\n elif split == dataset_split.DatasetSplit.DEV:\\n return self._dev_games\\n elif split == dataset_split.DatasetSplit.TEST:\\n return self._test_games\\n else:\\n raise ValueError('Getting games with specified filename is not supported')\",\n \"def highest_snowfall_for_year(resort_name, year):\\n resort_table = resort_table_dict[resort_name]\\n year = int(year)\\n query = \\\"SELECT snowfall FROM %s WHERE CAST(EXTRACT(YEAR FROM status_date) AS INTEGER) = %d\\\" %(resort_table, year)\\n connection = get_connection()\\n\\n snowfall_list = []\\n\\n if connection is not None:\\n try:\\n for row in get_select_query_results(connection, query):\\n snowfall_list.append(row)\\n except Exception as e:\\n print(e, file=sys.stderr)\\n connection.close()\\n snowfall_list.sort(reverse=True)\\n \\\"\\\"\\\"\\n need to think about making our own sorter so we can break ties effectively\\n \\\"\\\"\\\"\\n highest_snowfall = snowfall_list[0]\\n return json.dumps(highest_snowfall)\",\n \"def current(db):\\n with Tx(db, factory=tuple_cursor) as cursor:\\n cursor.execute('SELECT season_type, season_year, week FROM meta')\\n return cursor.fetchone()\\n return tuple([None] * 3)\",\n \"def get_seasons(games: List[BaseGame], reverse=False):\\n seasons = list(set([g.Season for g in games]))\\n seasons.sort(reverse=reverse)\\n return seasons\",\n \"def getTeamStat(self, year = 2014):\\r\\n \\r\\n year_next = (year % 100) + 1\\r\\n season = str(year) + '-' + str(year_next)\\r\\n \\r\\n stat_url = 'http://stats.nba.com/stats/leaguedashteamstats?Conference=&'\\\\\\r\\n 'DateFrom=&DateTo=&Division=&GameScope=&GameSegment=&'\\\\\\r\\n 'LastNGames=0&LeagueID=00&Location=&MeasureType=Base&'\\\\\\r\\n 'Month=0&OpponentTeamID=0&Outcome=&PORound=0&PaceAdjust=N&'\\\\\\r\\n 'PerMode=PerGame&Period=0&PlayerExperience=&PlayerPosition=&'\\\\\\r\\n 'PlusMinus=N&Rank=N&Season=' + season + '&SeasonSegment=&'\\\\\\r\\n 'SeasonType=Regular+Season&ShotClockRange=&StarterBench=&'\\\\\\r\\n 'TeamID=0&VsConference=&VsDivision='\\r\\n \\r\\n response = requests.get(stat_url)\\r\\n data = json.loads(response.text)\\r\\n \\r\\n headers = data['resultSets'][0]['headers']\\r\\n stat_data = data['resultSets'][0]['rowSet']\\r\\n df = pd.DataFrame(stat_data,columns=headers) \\r\\n \\r\\n team_df = df[[\\\"TEAM_ID\\\",\\\"TEAM_NAME\\\",\\\"GP\\\",\\\"W\\\",\\\"L\\\",\\\"W_PCT\\\",\\\"MIN\\\",\\\"FGM\\\",\\r\\n \\\"FGA\\\",\\\"FG_PCT\\\",\\\"FG3M\\\",\\\"FG3A\\\",\\\"FG3_PCT\\\",\\\"FTM\\\",\\\"FTA\\\",\\\"FT_PCT\\\",\\r\\n \\\"OREB\\\",\\\"DREB\\\",\\\"REB\\\",\\\"AST\\\",\\\"TOV\\\",\\\"STL\\\",\\\"BLK\\\",\\\"BLKA\\\",\\\"PF\\\",\\r\\n \\\"PFD\\\",\\\"PTS\\\",\\\"PLUS_MINUS\\\"]]\\r\\n \\r\\n return team_df\",\n \"def gamesmeta(sched=None):\\n if sched is None or sched.empty:\\n sched = schedule()\\n h = sched.copy()\\n a = sched.copy()\\n h = h.rename(columns={'home_team': 'team', 'away_team': 'opp'})\\n a = a.rename(columns={'away_team': 'team', 'home_team': 'opp'})\\n return pd.concat([a, h]).sort_values('game_id')\",\n \"def get_wr_game():\\n wr_games_reg_2015 = NFL_WR_Game_2015.query.filter_by(is_season_totals=False).all()\\n clean_games = cleanup_queries(wr_games_reg_2015)\\n return json.dumps(clean_games)\",\n \"def get_video_games(self, **kwargs):\\n return self.get('video_games.json', **kwargs)\",\n \"def retrieveGames():\\n result = cs411_game.getGames()\\n return prepJSON(result)\",\n \"async def _get_full_leagues_data(self, server_id: str, season: str=None):\\n if season is None:\\n season = ''\\n\\n params = {'season': season}\\n url = self.api_url + 'competitions/'\\n\\n return await self._make_request(url, params, server_id)\",\n \"def get_player_games(self, year, use_local=True):\",\n \"def seasons(self):\\n if self._season_cache_ver == self._db._version:\\n return self._season_cache\\n\\n # Find out how many seasons in this series by fetching the highest season.\\n seasons = self._db.query(type='episode', parent=self._dbrow, attrs=['season'], distinct=True)\\n self._season_cache = [Season(self._db, self, row['season']) for row in seasons]\\n self._season_cache_ver = self._db._version\\n return self._season_cache\",\n \"def get_dates(season, info):\\n url = 'http://www.basketball-reference.com/leagues/NBA_{0}_games.html'.format(season.split('-')[-1])\\n rv = requests.get(url)\\n soup = BeautifulSoup(rv.text)\\n seasons = soup.find_all('table', {'class': 'sortable stats_table'})\\n if len(seasons) == 2:\\n reg_season, post_season = seasons\\n else:\\n reg_season, post_season = seasons[0], None\\n dates = set()\\n for table in [reg_season, post_season]:\\n if table:\\n rows = table.tbody.find_all('tr')\\n for row in rows:\\n match = row.find('a', href=True, text='Box Score')\\n if match:\\n match_code = match['href'].split('/')[2].split('.')[0]\\n date = match_code[:-4]\\n if info == 'money_lines':\\n date = \\\"-\\\".join([date[:4], date[4:6], date[6:]])\\n dates.add(date)\\n return sorted(list(dates))\",\n \"def latest_season_before(date):\\n\\tif date.month < 9:\\n\\t\\treturn date.year - 1\\n\\treturn date.year\",\n \"def own_games(self):\\r\\n return sorted(self.games + self.finals, key=lambda g: (g.datetime, g.pitch.rank))\",\n \"def get(self, season):\\n logger.info(f\\\"Retrieving stats for entire season={season}\\\")\\n\\n season_stats = Stats.query.filter_by(season=season).all()\\n if not season_stats:\\n logger.warning(f\\\"No stats found for season, season={season}.\\\")\\n abort(404, message=\\\"Season not found.\\\")\\n\\n return _dump_stats(season_stats), 200\",\n \"def get_te_game():\\n te_games_reg_2015 = NFL_TE_Game_2015.query.filter_by(is_season_totals=False).all()\\n clean_games = cleanup_queries(te_games_reg_2015)\\n return json.dumps(clean_games)\",\n \"def scrape():\\n league_year = Config.get_property(\\\"league_year\\\")\\n\\n # Create table\\n season_data = client.season_schedule(league_year)\\n season_data = br_enum_to_string(season_data)\\n return season_data\",\n \"def _parse_season_entry (self, season, video, sorting):\\n return {\\n season['summary']['id']: {\\n 'idx': sorting[season['summary']['id']],\\n 'id': season['summary']['id'],\\n 'text': season['summary']['name'],\\n 'shortName': season['summary']['shortName'],\\n 'boxarts': {\\n 'small': video['boxarts']['_342x192']['jpg']['url'],\\n 'big': video['boxarts']['_1280x720']['jpg']['url']\\n },\\n 'interesting_moment': video['interestingMoment']['_665x375']['jpg']['url'],\\n }\\n }\",\n \"def _pull_schedule(self, abbreviation, year):\\n if not year:\\n year = utils._find_year_for_season('nfl')\\n # If stats for the requested season do not exist yet (as is the\\n # case right before a new season begins), attempt to pull the\\n # previous year's stats. If it exists, use the previous year\\n # instead.\\n if not utils._url_exists(SCHEDULE_URL % (abbreviation.lower(),\\n year)) and \\\\\\n utils._url_exists(SCHEDULE_URL % (abbreviation.lower(),\\n str(int(year) - 1))):\\n year = str(int(year) - 1)\\n doc = pq(SCHEDULE_URL % (abbreviation.lower(), year))\\n schedule = utils._get_stats_table(doc, 'table#gamelog%s' % year)\\n if not schedule:\\n utils._no_data_found()\\n return\\n self._add_games_to_schedule(schedule, REGULAR_SEASON, year)\\n if 'playoff_gamelog%s' % year in str(doc):\\n playoffs = utils._get_stats_table(doc,\\n 'table#playoff_gamelog%s' % year)\\n self._add_games_to_schedule(playoffs, POST_SEASON, year)\",\n \"def season(self, seasonnum, order='aired'):\\n if order=='aired':\\n seasons = self.seasons\\n elif order == 'dvd':\\n seasons = self.dvd_seasons\\n try:\\n return seasons[seasonnum]\\n except KeyError:\\n raise SeasonNotFoundError(\\n 'Season no %s does not exists' % seasonnum\\n ), None, sys.exc_info()[2]\",\n \"def season(year, week=None, timeout=None, concurrency=_DEFAULT_CONCURRENCY):\\n\\tLOG.debug('Concurrency = %d', concurrency)\\n\\tgames = season_games(year)\\n\\tif week is not None:\\n\\t\\tgames = games[games.week == week]\\n\\texpected_n = len(games)\\n\\ttables, futures_to_args, failures = [], {}, []\\n\\t# See https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor-example.\\n\\twith futures.ThreadPoolExecutor(concurrency) as pool:\\n\\t\\tfor arg in zip(games.hometeam, games.awayteam, games.week):\\n\\t\\t\\targ = arg + (year,)\\n\\t\\t\\tfutures_to_args[pool.submit(game_unknown_homeaway, *arg)] = arg\\n\\t\\tfor future in futures.as_completed(futures_to_args, timeout=timeout):\\n\\t\\t\\targs = futures_to_args[future]\\n\\t\\t\\ttry:\\n\\t\\t\\t\\ttable = future.result()\\n\\t\\t\\texcept Exception as exc:\\n\\t\\t\\t\\tLOG.exception('Error from %s: %s', args, exc)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif table is None:\\n\\t\\t\\t\\t\\tLOG.error('Failure: %s', args)\\n\\t\\t\\t\\t\\tfailures.append(args)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tLOG.info('Success: %s', args)\\n\\t\\t\\t\\t\\ttables.append(table)\\n\\ttables = games.merge(pd.concat(tables), on=('hometeam', 'awayteam', 'week'))\\n\\tif __debug__:\\n\\t\\tn = len(tables.groupby(['hometeam', 'awayteam', 'week']))\\n\\t\\tassert n == expected_n, \\\"Expected %d games, got %d\\\" % (expected_n, n)\\n\\treturn tables, failures\",\n \"def _get_live_games(self):\\n response = requests.get(self._get_score_url())\\n if response.status_code == 200:\\n return [g for g in response.json()['games'] if g['status']['state'] == self.desired_game_state]\",\n \"def _get_projected_data(self, season):\\n try:\\n match_data = []\\n player_id = self.profile_data[\\\"id\\\"]\\n matches = season.get_season_data()[\\\"stats\\\"][\\\"projectedPlayerStats\\\"]\\n for match in matches:\\n if player_id == match[0]:\\n match_data.append(match)\\n return match_data\\n except Exception as e:\\n error_msg = (\\\"Failed to retrieve player match data: {}\\\"\\n \\\"\\\".format(str(e)))\\n raise PlayerDataException(error_msg)\",\n \"def get_fresh_data(get_team):\\n\\n \\\"\\\"\\\" Create the URL for today \\\"\\\"\\\"\\n now = datetime.datetime.now()\\n url = \\\"http://gd2.mlb.com/gdcross/components/game/mlb/year_\\\" + '{}'.format(now.year) + \\\"/month_\\\" + '{:02d}'.format(now.month) + \\\"/day_\\\" + '{:02d}'.format(now.day) + \\\"/miniscoreboard.json\\\"\\n\\n \\\"\\\"\\\" Grab the first response and write it to a file, we'll update it once the game starts \\\"\\\"\\\"\\n data_write_file = status_dir + '{}'.format(now.year) + '{:02d}'.format(now.month) + '{:02d}'.format(now.day) + \\\".json\\\"\\n\\n \\\"\\\"\\\" Get the json data if the file doesn't exist, or if it's over three minutes old \\\"\\\"\\\"\\n if not os.path.isfile(data_write_file) or time.time() - os.path.getmtime(data_write_file) > 60:\\n response = urllib.urlopen(url)\\n full_data = json.loads(response.read())\\n with open(data_write_file, 'w') as outfile:\\n json.dump(full_data, outfile, sort_keys=True, indent=2, ensure_ascii=False)\\n\\n \\\"\\\"\\\" Use the data from the status file \\\"\\\"\\\"\\n with open(data_write_file, 'r') as json_data:\\n full_data = json.load(json_data)\\n\\n \\\"\\\"\\\" This will return false if there is no game today, else will return json data for just our team \\\"\\\"\\\"\\n my_game = False\\n for game in full_data['data']['games']['game']:\\n if get_team in game['home_file_code'] or get_team in game['away_file_code']:\\n my_game = game\\n \\n return my_game\",\n \"def getNumSeasons(self):\\n searchURL = \\\"http://api.tvmaze.com/shows/\\\" + str(self.__showID) \\\\\\n + \\\"/seasons\\\"\\n\\n response = requests.get(searchURL)\\n data = response.json()\\n\\n return data[-1][\\\"number\\\"]\",\n \"def get_games_data(games):\\n\\n games_response = requests.get(\\n url=f'{settings.GAME_SETUP_URL}/games-data/',\\n params={'game_id': games},\\n timeout=5 # in sec\\n )\\n if games_response.status_code == 200:\\n return games_response.json().get('games')\\n return {}\",\n \"def current_season():\\n td = datetime.datetime.today()\\n if td.month > 8:\\n return td.year\\n return td.year - 1\",\n \"def get_winner(game):\\n return game['winner']\",\n \"def get_game(self, game_id):\\n \\n session = requests.session()\\n response = session.get(self.baseURL + str(game_id), headers=self.headers)\\n soup = BeautifulSoup(response.text)\\n \\n #get teams\\n defeated_by = False \\n game_header = soup.find_all(text=re.compile('defeats'))\\n \\n if len(game_header) == 0:\\n game_header = soup.find_all(text=re.compile('defeated by'))\\n \\n if (len(game_header)) == 0:\\n game_header = soup.find_all(text=re.compile('defeat'))\\n \\n if (len(game_header)) == 0:\\n game_header = soup.find_all(text=re.compile('drew'))\\n defeated_by = True \\n else:\\n defeated_by = True \\n\\n if defeated_by: \\n teams = self.remove_long_names(game_header[1]).replace('\\\\n', '')\\n home_team = teams.split(' ')[0]\\n away_team = teams.split(' ')[3]\\n else:\\n teams = self.remove_long_names(game_header[1]).replace('\\\\n', '')\\n home_team = teams.split(' ')[0]\\n away_team = teams.split(' ')[2]\\n \\n date_string = game_header[0].split(' ')\\n date_string_find = [date.lower() for date in date_string]\\n \\n venue = date_string[date_string_find.index('at') + 1]\\n \\n #get round\\n round_num = None\\n \\n try:\\n date_string_find.remove('')\\n except:\\n pass\\n \\n try:\\n round_num = int(date_string[date_string_find.index('round') + 1])\\n except:\\n try:\\n round_num = date_string_find[date_string_find.index('final') - 1] + ' final'\\n except:\\n round_num = date_string_find[date_string_find.index('semi-final')]\\n \\n date = date_string[-3:]\\n date = ' '.join(date) \\n date = parser.parse(date)\\n \\n #get attendance\\n attend = soup.find_all(text=re.compile('Attendance'))\\n attendance = 0\\n \\n if (len(attend) > 3):\\n attendance = int(attend[1].split(' ')[-1])\\n \\n #get stats \\n away_stats = {}\\n home_stats = {}\\n \\n for stat in stats:\\n stat_row = soup.find_all('td', text=stat)[0].find_parent('tr')\\n elements = stat_row.find_all('td')\\n \\n if elements[0].text == '-':\\n home_stats[stat] = None\\n else:\\n home_stats[stat] = elements[0].text\\n \\n if elements[0].text == '-':\\n away_stats[stat] = None\\n else:\\n away_stats[stat] = elements[2].text\\n \\n return Game(game_id, home_team, away_team, venue, round_num, date, attendance, home_stats, away_stats)\",\n \"def season(self, title=None, season=None):\\n key = f'{self.key}/children?excludeAllLeaves=1'\\n if title is not None and not isinstance(title, int):\\n return self.fetchItem(key, Season, title__iexact=title)\\n elif season is not None or isinstance(title, int):\\n if isinstance(title, int):\\n index = title\\n else:\\n index = season\\n return self.fetchItem(key, Season, index=index)\\n raise BadRequest('Missing argument: title or season is required')\",\n \"def get_rb_game():\\n rb_games_reg_2015 = NFL_RB_Game_2015.query.filter_by(is_season_totals=False).all()\\n clean_games = cleanup_queries(rb_games_reg_2015)\\n return json.dumps(clean_games)\",\n \"def find_games(days_ahead=0):\\n headers = {\\n 'Host': 'stats.nba.com',\\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:61.0) Gecko/20100101 Firefox/61.0',\\n 'Accept': 'application/json, text/plain, */*',\\n 'Accept-Language': 'en-US,en;q=0.5',\\n 'Referer': 'https://stats.nba.com/',\\n 'Accept-Encoding': 'gzip, deflate, br',\\n 'Connection': 'keep-alive',\\n 'x-nba-stats-origin': 'stats',\\n 'x-nba-stats-token': 'true'\\n }\\n board = scoreboardv2.ScoreboardV2(day_offset=days_ahead, headers=headers).get_data_frames()[0]\\n board.replace(id_to_abrv, inplace=True)\\n return board[['GAME_DATE_EST', 'GAME_ID', 'HOME_TEAM_ID', 'VISITOR_TEAM_ID']]\",\n \"def extract_games(self) -> Dict[int, Dict[str, Any]]:\\n optadocument = self._get_doc()\\n attr = assertget(optadocument, '@attributes')\\n matchdata = assertget(optadocument, 'MatchData')\\n matches = {}\\n for match in matchdata:\\n matchattr = assertget(match, '@attributes')\\n matchinfo = assertget(match, 'MatchInfo')\\n matchinfoattr = assertget(matchinfo, '@attributes')\\n game_id = int(assertget(matchattr, 'uID')[1:])\\n matches[game_id] = dict(\\n # Fields required by the base schema\\n game_id=game_id,\\n competition_id=int(assertget(attr, 'competition_id')),\\n season_id=int(assertget(attr, 'season_id')),\\n game_day=int(assertget(matchinfoattr, 'MatchDay')),\\n game_date=datetime.strptime(assertget(matchinfo, 'Date'), '%Y-%m-%d %H:%M:%S'),\\n # home_team_id=see below,\\n # away_team_id=see below,\\n # Optional fields\\n # home_score=see below,\\n # away_score=see below,\\n # duration=?\\n # referee=?\\n # venue=?,\\n # attendance=?\\n # home_manager=?\\n # away_manager=?\\n )\\n teamdata = assertget(match, 'TeamData')\\n for team in teamdata:\\n teamattr = assertget(team, '@attributes')\\n side = assertget(teamattr, 'Side')\\n teamid = assertget(teamattr, 'TeamRef')\\n score = assertget(teamattr, 'Score')\\n if side == 'Home':\\n matches[game_id]['home_team_id'] = int(teamid[1:])\\n matches[game_id]['home_score'] = int(score)\\n else:\\n matches[game_id]['away_team_id'] = int(teamid[1:])\\n matches[game_id]['away_score'] = int(score)\\n return matches\",\n \"def get_pbp_json(season, game_id):\\n url = \\\"http://live.nhl.com/GameData/\\\" + str(season) + \\\"/\\\" + str(game_id) + \\\"/PlayByPlay.json\\\"\\n response = urllib.urlopen(url)\\n data = json.loads(response.read())\\n return data\",\n \"def get_win_rate_post_season_for_each_coach(self):\\n # get winning games for coaches\\n self.post_games_won_for_coaches = (\\n self.raw_data_postseason\\n [['Season','DayNum','WTeamID']]\\n # merge for winning team\\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\\n .rename(columns={\\\"FirstDayNum\\\":\\\"FirstDayNum_win\\\",\\\"LastDayNum\\\":\\\"LastDayNum_win\\\",\\\"CoachName\\\":\\\"CoachName_win\\\",\\\"TeamID\\\":\\\"TeamID_win\\\"})\\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\\n .query(\\\"which_coach_for_win != 0\\\")\\n .groupby(['Season','CoachName_win','WTeamID'])\\n .agg({\\\"which_coach_for_win\\\":\\\"sum\\\"})\\n .reset_index()\\n )\\n\\n # get losing games for coaches\\n self.post_games_lose_for_coaches = (\\n self.raw_data_postseason\\n [['Season','DayNum','LTeamID']]\\n # merge for losing team\\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\\n .rename(columns={\\\"FirstDayNum\\\":\\\"FirstDayNum_lose\\\",\\\"LastDayNum\\\":\\\"LastDayNum_lose\\\",\\\"CoachName\\\":\\\"CoachName_lose\\\",\\\"TeamID\\\":\\\"TeamID_lose\\\"})\\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\\n .query(\\\"which_coach_for_lose != 0\\\")\\n .groupby(['Season','CoachName_lose','LTeamID'])\\n .agg({\\\"which_coach_for_lose\\\":\\\"sum\\\"})\\n .reset_index()\\n )\\n\\n # combine games won and lost df for post season\\n self.combine_post_games_won_lose = (\\n self.post_games_lose_for_coaches\\n .merge(self.post_games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\\n .pipe(lambda x:x.assign(win_rate_post = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\\n .drop(['CoachName_win','WTeamID'],1)\\n .rename(columns={\\\"CoachName_lose\\\":\\\"CoachName\\\",\\\"LTeamID\\\":\\\"TeamID\\\",\\\"which_coach_for_lose\\\":\\\"post_games_lost\\\",\\\"which_coach_for_win\\\":\\\"post_games_won\\\"})\\n .fillna(0)\\n )\",\n \"def get(request, season):\\n try:\\n secret_token = settings.DATA_SECRET_TOKEN\\n except AttributeError:\\n secret_token = None\\n\\n if secret_token is not None:\\n\\n if 'Http-Auth-Token' not in request.headers:\\n print('no token header')\\n print(request.headers)\\n return HttpResponseForbidden()\\n\\n auth = request.headers['Http-Auth-Token'].split()\\n\\n if len(auth) != 2:\\n print('header wrong length')\\n return HttpResponseForbidden()\\n\\n if auth[0].lower() != \\\"pool-token\\\":\\n print('no pool-token')\\n return HttpResponseForbidden()\\n\\n token = auth[1]\\n\\n if secret_token is None or token != secret_token:\\n print('mismatch token')\\n return HttpResponseForbidden()\\n\\n if season == 'all' or season == 0:\\n matches = Match.objects.all()\\n else:\\n season_obj = get_object_or_404(Season, number=season)\\n matches = Match.objects.filter(season=season_obj)\\n\\n data = [match.serialize() for match in matches]\\n return JsonResponse(data, safe=False)\",\n \"def get_season(\\n current_date: date, hemisphere: str, season_tracking_type: str\\n) -> str | None:\\n\\n if hemisphere == \\\"equator\\\":\\n return None\\n\\n if season_tracking_type == TYPE_ASTRONOMICAL:\\n spring_start = ephem.next_equinox(str(current_date.year)).datetime()\\n summer_start = ephem.next_solstice(str(current_date.year)).datetime()\\n autumn_start = ephem.next_equinox(spring_start).datetime()\\n winter_start = ephem.next_solstice(summer_start).datetime()\\n else:\\n spring_start = datetime(2017, 3, 1).replace(year=current_date.year)\\n summer_start = spring_start.replace(month=6)\\n autumn_start = spring_start.replace(month=9)\\n winter_start = spring_start.replace(month=12)\\n\\n if spring_start <= current_date < summer_start:\\n season = STATE_SPRING\\n elif summer_start <= current_date < autumn_start:\\n season = STATE_SUMMER\\n elif autumn_start <= current_date < winter_start:\\n season = STATE_AUTUMN\\n elif winter_start <= current_date or spring_start > current_date:\\n season = STATE_WINTER\\n\\n # If user is located in the southern hemisphere swap the season\\n if hemisphere == NORTHERN:\\n return season\\n return HEMISPHERE_SEASON_SWAP.get(season)\",\n \"def get_win_rate_regular_season_for_each_coach(self):\\n self.games_won_for_coaches = (\\n self.raw_data_regularseason\\n [['Season','DayNum','WTeamID']]\\n # merge for winning team\\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\\n how='left',left_on=['Season','WTeamID'],right_on=['Season','TeamID'])\\n .rename(columns={\\\"FirstDayNum\\\":\\\"FirstDayNum_win\\\",\\\"LastDayNum\\\":\\\"LastDayNum_win\\\",\\\"CoachName\\\":\\\"CoachName_win\\\",\\\"TeamID\\\":\\\"TeamID_win\\\"})\\n .pipe(lambda x:x.assign(which_coach_for_win = np.where((x.FirstDayNum_win <= x.DayNum) & (x.LastDayNum_win >= x.DayNum),1,0)))\\n .query(\\\"which_coach_for_win != 0\\\")\\n .groupby(['Season','CoachName_win','WTeamID'])\\n .agg({\\\"which_coach_for_win\\\":\\\"sum\\\"})\\n .reset_index()\\n )\\n\\n self.games_lose_for_coaches = (\\n self.raw_data_regularseason\\n [['Season','DayNum','LTeamID']]\\n # merge for losing team\\n .merge(self.num_days_coach_for_season[['Season','TeamID','FirstDayNum','LastDayNum','CoachName']],\\n how='left',left_on=['Season','LTeamID'],right_on=['Season','TeamID'])\\n .rename(columns={\\\"FirstDayNum\\\":\\\"FirstDayNum_lose\\\",\\\"LastDayNum\\\":\\\"LastDayNum_lose\\\",\\\"CoachName\\\":\\\"CoachName_lose\\\",\\\"TeamID\\\":\\\"TeamID_lose\\\"})\\n .pipe(lambda x:x.assign(which_coach_for_lose = np.where((x.FirstDayNum_lose <= x.DayNum) & (x.LastDayNum_lose >= x.DayNum),1,0)))\\n .query(\\\"which_coach_for_lose != 0\\\")\\n .groupby(['Season','CoachName_lose','LTeamID'])\\n .agg({\\\"which_coach_for_lose\\\":\\\"sum\\\"})\\n .reset_index()\\n )\\n\\n # combine games won and lost df\\n self.combine_regular_games_won_lose = (\\n self.games_lose_for_coaches\\n .merge(self.games_won_for_coaches,how='left',left_on=['Season','LTeamID','CoachName_lose'],right_on=['Season','WTeamID','CoachName_win'])\\n .pipe(lambda x:x.assign(win_rate_regular = x.which_coach_for_win/(x.which_coach_for_win + x.which_coach_for_lose)))\\n .drop(['CoachName_win','WTeamID'],1)\\n .rename(columns={\\\"CoachName_lose\\\":\\\"CoachName\\\",\\\"LTeamID\\\":\\\"TeamID\\\",\\\"which_coach_for_lose\\\":\\\"games_lost\\\",\\\"which_coach_for_win\\\":\\\"games_won\\\"})\\n )\",\n \"def get_player_data(self, player, season, mtgs=None, past=None, future=None, single=False):\\n\\n avail = []\\n scheduled = []\\n\\n # Should be empty arrays if None\\n if past is None:\\n past = []\\n if future is None:\\n future = []\\n\\n nplayed = Schedule.objects.filter(meeting__in=past, player=player).count()\\n nscheduled = Schedule.objects.filter(meeting__in=future, player=player).count()\\n\\n av = PlayerAvailability.objects.get_for_season_player(player, season)\\n\\n p = {\\n 'name': player.first + ' ' + player.last,\\n 'id': player.id,\\n 'isavail': av.available,\\n 'scheduled': av.scheduled,\\n 'played': av.played,\\n 'nplayed': nplayed,\\n 'nscheduled': nscheduled + nplayed,\\n 'single': single\\n }\\n\\n return p\",\n \"def get_game_info(game_id):\\n url = 'http://stats.nba.com/stats/boxscoresummaryv2?GameID=%s' % (game_id)\\n response = requests.get(url, headers=HEADERS)\\n while response.status_code != 200:\\n response = requests.get(url)\\n headers = response.json()['resultSets'][0]['headers']\\n data = response.json()['resultSets'][0]['rowSet']\\n data = pd.DataFrame(data, columns=headers)\\n data = data[['GAME_ID', 'GAMECODE', 'HOME_TEAM_ID', 'VISITOR_TEAM_ID', 'SEASON']].T.squeeze()\\n data['GAMECODE'] = data['GAMECODE'].replace('/', '')\\n return data\",\n \"def get_games():\\r\\n feed = feedparser.parse(FEED_URL)\\r\\n games = []\\r\\n for entry in feed.entries:\\r\\n games.append(Game(title = entry['title']\\r\\n , link = entry['link']\\r\\n ))\\r\\n return games\",\n \"def get_games():\\n feed = feedparser.parse(FEED_URL)\\n\\n games = []\\n for entry in feed['entries']:\\n game = Game(entry.title, entry.link)\\n games.append(game)\\n\\n return games\",\n \"def getPlayerBaseStat(self, year = 2014):\\r\\n \\r\\n year_next = (year % 100) + 1\\r\\n season = str(year) + '-' + str(year_next)\\r\\n \\r\\n stat_url = 'http://stats.nba.com/stats/leaguedashplayerstats?College=&'\\\\\\r\\n 'Conference=&Country=&DateFrom=&DateTo=&Division=&'\\\\\\r\\n 'DraftPick=&DraftYear=&GameScope=&GameSegment=&Height=&'\\\\\\r\\n 'LastNGames=0&LeagueID=00&Location=&MeasureType=Base&Month=0&'\\\\\\r\\n 'OpponentTeamID=0&Outcome=&PORound=0&PaceAdjust=N&'\\\\\\r\\n 'PerMode=PerGame&Period=0&PlayerExperience=&PlayerPosition=&'\\\\\\r\\n 'PlusMinus=N&Rank=N&Season='+ season + '&SeasonSegment=&'\\\\\\r\\n 'SeasonType=Regular+Season&ShotClockRange=&StarterBench=&'\\\\\\r\\n 'TeamID=0&VsConference=&VsDivision=&Weight='\\r\\n \\r\\n response = requests.get(stat_url)\\r\\n data = json.loads(response.text)\\r\\n \\r\\n headers = data['resultSets'][0]['headers']\\r\\n stat_data = data['resultSets'][0]['rowSet']\\r\\n baseStat_df = pd.DataFrame(stat_data,columns=headers) \\r\\n \\r\\n return baseStat_df\",\n \"def get_current_player_season(self):\\n return self.get_player_season(\\\"current\\\")\",\n \"def _find_latest():\\n try:\\n db = get_master_collection()\\n service_details = db.find({\\\"master.key\\\": \\\"release\\\"}).sort([(\\\"master.value\\\", pymongo.DESCENDING)]).limit(1)\\n for service in service_details:\\n for r in sorted(service[\\\"master\\\"][\\\"value\\\"], reverse=True):\\n latest_release = r\\n build_list = service[\\\"master\\\"][\\\"value\\\"][r]\\n break\\n break\\n\\n latest_rel_num = str(latest_release).replace(\\\"_\\\", \\\".\\\")\\n build_list = _natural_sort(build_list)\\n for build in build_list:\\n latest_build = build\\n break\\n\\n latest_build_num = latest_build\\n second_latest_build_num = int(latest_build_num) - 1\\n latest = {\\\"latest_val\\\": latest_rel_num + \\\"_\\\" + latest_build_num,\\n \\\"second_latest_val\\\": latest_rel_num + \\\"_\\\" + str(second_latest_build_num)}\\n except Exception as e:\\n logger.error(\\\"Exception in _find_latest : \\\" + str(e))\\n return latest\",\n \"async def get_season(self, server: model.Server):\\n api_url = ('https://eu.api.blizzard.com/sc2/'\\n f'ladder/season/{server.id()}')\\n payload = {'locale': 'en_US',\\n 'access_token': await self.get_access_token()}\\n data, status = await self._perform_api_request(api_url, params=payload)\\n if status != 200:\\n raise InvalidApiResponse(f'{status}: {api_url}')\\n\\n return model.Season(\\n season_id=data.get('seasonId'),\\n number=data.get('number'),\\n year=data.get('year'),\\n server=server,\\n start=datetime.fromtimestamp(int(data.get('startDate'))),\\n end=datetime.fromtimestamp(int(data.get('endDate')))\\n )\",\n \"def get_upcoming_season(self):\\n result = self._method_call(\\\"UpcomingSeason\\\")\\n return int(result)\",\n \"def losing_games_stats(self):\\n self.losing_games_up_to_2013 = (\\n self.df\\n .pipe(lambda x:x.assign(losing_num_counts=1))\\n .query(\\\"Season <= 2013\\\")\\n .groupby(['Season','LTeamID'])\\n .agg({\\\"WScore\\\":\\\"sum\\\",\\\"LScore\\\":\\\"sum\\\",\\\"LFGM\\\":\\\"sum\\\",\\\"LFGA\\\":\\\"sum\\\",\\\"LFGM3\\\":\\\"sum\\\",\\\"LFGA3\\\":\\\"sum\\\",\\\"LFTM\\\":\\\"sum\\\",\\\"LFTA\\\":\\\"sum\\\",\\\"losing_num_counts\\\":\\\"sum\\\",\\n \\\"LOR\\\":\\\"sum\\\",\\\"LDR\\\":\\\"sum\\\",\\\"WFGA\\\":\\\"sum\\\",\\\"WFGM\\\":\\\"sum\\\",\\n \\\"LAst\\\":\\\"sum\\\",\\\"LTO\\\":\\\"sum\\\",\\\"LStl\\\":\\\"sum\\\",\\\"LBlk\\\":\\\"sum\\\",\\\"LPF\\\":\\\"sum\\\"})\\n .reset_index()\\n .rename(columns={\\\"WScore\\\":\\\"winning_opponent_score\\\"})\\n # rebounds\\n .pipe(lambda x:x.assign(total_losing_rebounds = x.LOR + x.LDR))\\n .pipe(lambda x:x.assign(losing_off_rebounds_percent = x.LOR/x.total_losing_rebounds))\\n .pipe(lambda x:x.assign(losing_def_rebounds_percent = x.LDR/x.total_losing_rebounds))\\n .pipe(lambda x:x.assign(losing_team_missed_attempts = x.LFGA - x.LFGM))\\n .pipe(lambda x:x.assign(winning_opp_team_missed_attempts = x.WFGA - x.WFGM))\\n .pipe(lambda x:x.assign(losing_rebound_possession_percent = x.LOR/x.losing_team_missed_attempts))\\n .pipe(lambda x:x.assign(losing_rebound_possessiongain_percent = x.LDR/x.winning_opp_team_missed_attempts))\\n # blocks, steals, assists and turnovers\\n .pipe(lambda x:x.assign(losing_block_opp_FGA_percent = x.LBlk/x.WFGA))\\n .pipe(lambda x:x.assign(losing_assist_per_fgm = x.LAst/x.LFGM))\\n .pipe(lambda x:x.assign(losing_assist_turnover_ratio = x.LAst/x.LTO))\\n # rename columns to prevent duplication when joining with losing stats. example: WFGM_x\\n .rename(columns={\\\"WFGA\\\":\\\"WFGA_opp\\\",\\\"WFGM\\\":\\\"WFGM_opp\\\"})\\n )\",\n \"def get_number_games(season: str, team_id: int, game_type_code: str = \\\"R\\\") -> dict:\\n args = arguments.get_arguments()\\n\\n endpoint = f\\\"/schedule?teamId={team_id}&season={season}&gameType={game_type_code}\\\"\\n response = api.nhl_api(endpoint)\\n\\n if response:\\n schedule = response.json()\\n games_total = schedule[\\\"totalItems\\\"]\\n return games_total\\n\\n # If no valid response, just return default number of games (82)\\n return 82\",\n \"def all_games(self):\\r\\n return sorted(self.games + list(g for sp in self.sub_pools for g in sp.games) + self.finals,\\r\\n key=lambda g: (g.datetime, g.pitch.rank))\",\n \"def _get_match_data(self, season):\\n try:\\n match_data = []\\n player_id = self.profile_data[\\\"id\\\"]\\n matches = season.get_season_data()[\\\"stats\\\"][\\\"actualPlayerStats\\\"]\\n for match in matches:\\n if player_id == match[0]:\\n match_data.append(match)\\n return match_data\\n except Exception as e:\\n error_msg = (\\\"Failed to retrieve player match data: {}\\\"\\n \\\"\\\".format(str(e)))\\n raise PlayerDataException(error_msg)\",\n \"def run():\\n games_db_conn = db.get_db_eng()\\n games_query = \\\"SELECT * FROM GAMES\\\"\\n games_data = etl_tools.extract_from_db(games_db_conn, games_query)\\n _data_integrity_check(games_data)\\n\\n latest_season, latest_season_type = _get_latest_season_and_type(games_data)\\n logging.info(f\\\"Latest season and type in current data: {(latest_season, latest_season_type)}\\\")\\n\\n if latest_season is None:\\n batch_start_season, batch_start_type = config.START_SEASON, config.SEASON_TYPES[0]\\n\\n else:\\n _truncate_games_table(games_db_conn, latest_season, latest_season_type)\\n batch_start_season, batch_start_type = latest_season, latest_season_type\\n\\n logging.info(f\\\"Starting batch at {(batch_start_season, batch_start_type)}...\\\")\\n\\n batches = _get_seasons_grid(batch_start_season, batch_start_type)\\n for batch in batches:\\n season, season_type = batch\\n logging.info(f\\\"Starting new batch: extracting data for {season}-{season_type}...\\\")\\n batch_data = _extract_games_data(season, season_type)\\n logging.info(f\\\"Data extracted. Loading {batch_data.shape[0]} rows...\\\")\\n etl_tools.load_to_db(\\n games_db_conn,\\n 'games',\\n batch_data,\\n )\\n\\n logging.info(\\\"Pipeline completed.\\\")\",\n \"def parse_episodes_by_season (self, response_data):\\n episodes = {}\\n raw_episodes = response_data['value']['videos']\\n for episode_id in raw_episodes:\\n if self._is_size_key(key=episode_id) == False:\\n if (raw_episodes[episode_id]['summary']['type'] == 'episode'):\\n episodes.update(self.parse_episode(episode=raw_episodes[episode_id], genres=response_data['value']['genres']))\\n return episodes\",\n \"def __get_season_data(season_num: int, max_episode: int, training_data: bool) -> List[TrainSample]:\\n season = EXAM_DATA[season_num]\\n drop_players = season.get_drop_mapping(DropType.EXECUTION_DROP, max_episode)\\n all_answers = season.get_all_answers(set(drop_players.keys()), max_episode)\\n season_data = []\\n for answer in all_answers:\\n exam_episode = answer.episode\\n drop_episodes = drop_players[answer.player]\\n drop_episodes = [episode for episode in drop_episodes if exam_episode <= episode]\\n if drop_episodes:\\n if training_data:\\n for player in exam_episode.players:\\n season_data.append(TrainSample(answer.player, season_num, min(drop_episodes), answer.episode,\\n answer.question, answer.answer, player))\\n else:\\n for answer_on in [False, True]:\\n season_data.append(TrainSample(answer.player, season_num, min(drop_episodes), answer.episode,\\n answer.question, answer.answer, answer_on))\\n return season_data\",\n \"def get_player_season(self, season_id):\\n return self._pubg.player_season(self.player_id, season_id, self.shard)\",\n \"def __latest_available_episode(self, predict_season: int, latest_episode: int) -> int:\\n max_episode = 0\\n for i in range(1, latest_episode + 1):\\n if VideoParser.has_parsed_video(predict_season, i):\\n max_episode = i\\n else:\\n return max_episode\\n return max_episode\",\n \"def reduce_games(game):#game=player_games[0]\\n res = []\\n res.append(('id', game['id']) )\\n res.append(('annulled', game['annulled']))\\n res.append(('black', int(game['black'])))\\n res.append(('white', int(game['white'])))\\n res.append(('white_lost', int(game['white_lost']) ))\\n res.append(('black_lost', int(game['black_lost']) ))\\n res.append(('outcome', game['outcome']))\\n res.append(('handicap', game['handicap']))\\n res.append(('komi', game['komi']))\\n res.append(('ranked', game['ranked']))\\n res.append(('width', game['width']))\\n res.append(('height', game['height']))\\n res.append(('rules', game['rules']))\\n res.append(('started', game['started']))\\n res.append(('ended', game['ended']))\\n players = game['historical_ratings'].keys()\\n res.append(('black_rating', game['historical_ratings']['black']['ratings']['overall']['rating'] if 'black' in players else None))\\n res.append(('white_rating', game['historical_ratings']['white']['ratings']['overall']['rating'] if 'white' in players else None))\\n res.append(('black_deviation', game['historical_ratings']['black']['ratings']['overall']['deviation'] if 'black' in players else None))\\n res.append(('white_deviation', game['historical_ratings']['white']['ratings']['overall']['deviation'] if 'white' in players else None))\\n res.append(('black_volatility', game['historical_ratings']['black']['ratings']['overall']['volatility'] if 'black' in players else None))\\n res.append(('white_volatility', game['historical_ratings']['white']['ratings']['overall']['volatility'] if 'white' in players else None))\\n res.append(('black_ranking', game['historical_ratings']['black']['ranking'] if 'black' in players else None))\\n res.append(('white_ranking', game['historical_ratings']['white']['ranking'] if 'white' in players else None))\\n res.append(('tournament', not game['tournament'] is None))\\n return res\",\n \"def data(self):\\n return self.source_data[\\\"game\\\"]\",\n \"def get_wr_season_totals():\\n wr_totals = redis_cache('wr_games_key', NFL_WR_Game_2015.query.filter_by(is_season_totals=True).all)\\n return wr_totals\",\n \"def scoreboard(year, month, day, home=None, away=None):\\n # get data\\n data = mlbgame.data.get_scoreboard(year, month, day)\\n # parse data\\n parsed = etree.parse(data)\\n root = parsed.getroot()\\n games = {}\\n output = {}\\n # loop through games\\n for game in root:\\n if game.tag == 'data':\\n return []\\n # get team names\\n teams = game.findall('team')\\n home_name = teams[0].attrib['name']\\n away_name = teams[1].attrib['name']\\n # check if teams match parameters\\n if (home_name == home and home is not None) \\\\\\n or (away_name == away and away is not None) \\\\\\n or (away is None and home is None):\\n # throw all the data into a complicated dictionary\\n game_tag = game.tag\\n game_data = game.find('game')\\n game_id = game_data.attrib['id']\\n game_league = game_data.attrib['league']\\n game_status = game_data.attrib['status']\\n game_start_time = game_data.attrib['start_time']\\n home_team_data = teams[0].find('gameteam')\\n home_team = home_name\\n home_team_runs = int(home_team_data.attrib['R'])\\n home_team_hits = int(home_team_data.attrib['H'])\\n home_team_errors = int(home_team_data.attrib['E'])\\n away_team_data = teams[1].find('gameteam')\\n away_team = away_name\\n away_team_runs = int(away_team_data.attrib['R'])\\n away_team_hits = int(away_team_data.attrib['H'])\\n away_team_errors = int(away_team_data.attrib['E'])\\n # check type of game\\n if game_tag == 'go_game' or game_tag == 'ig_game':\\n try:\\n w_pitcher_data = game.find('w_pitcher')\\n w_pitcher = w_pitcher_data.find('pitcher').attrib['name']\\n w_pitcher_wins = int(w_pitcher_data.attrib['wins'])\\n w_pitcher_losses = int(w_pitcher_data.attrib['losses'])\\n except Exception:\\n w_pitcher = \\\"\\\"\\n w_pitcher_wins = 0\\n w_pitcher_losses = 0\\n try:\\n l_pitcher_data = game.find('l_pitcher')\\n l_pitcher = l_pitcher_data.find('pitcher').attrib['name']\\n l_pitcher_wins = int(l_pitcher_data.attrib['wins'])\\n l_pitcher_losses = int(l_pitcher_data.attrib['losses'])\\n except Exception:\\n l_pitcher = \\\"\\\"\\n l_pitcher_wins = 0\\n l_pitcher_losses = 0\\n try:\\n sv_pitcher_data = game.find('sv_pitcher')\\n sv_pitcher = sv_pitcher_data.find('pitcher').attrib['name']\\n sv_pitcher_saves = int(sv_pitcher_data.attrib['saves'])\\n except Exception:\\n sv_pitcher = \\\"\\\"\\n sv_pitcher_saves = 0\\n output = {\\n 'game_id': game_id,\\n 'game_tag': game_tag,\\n 'game_league': game_league,\\n 'game_status': game_status,\\n 'game_start_time': game_start_time,\\n 'home_team': home_team,\\n 'home_team_runs': home_team_runs,\\n 'home_team_hits': home_team_hits,\\n 'home_team_errors': home_team_errors,\\n 'away_team': away_team,\\n 'away_team_runs': away_team_runs,\\n 'away_team_hits': away_team_hits,\\n 'away_team_errors': away_team_errors,\\n 'w_pitcher': w_pitcher,\\n 'w_pitcher_wins': w_pitcher_wins,\\n 'w_pitcher_losses': w_pitcher_losses,\\n 'l_pitcher': l_pitcher,\\n 'l_pitcher_wins': l_pitcher_wins,\\n 'l_pitcher_losses': l_pitcher_losses,\\n 'sv_pitcher': sv_pitcher,\\n 'sv_pitcher_saves': sv_pitcher_saves\\n }\\n # games that were not played\\n elif game_tag == 'sg_game':\\n try:\\n p_pitcher_data = game.findall('p_pitcher')\\n p_pitcher_home_data = p_pitcher_data[0]\\n p_pitcher_home = p_pitcher_home_data.find(\\n 'pitcher').attrib['name']\\n p_pitcher_home_wins = int(p_pitcher_home_data.\\n attrib['wins'])\\n p_pitcher_home_losses = int(p_pitcher_home_data.\\n attrib['losses'])\\n p_pitcher_away_data = p_pitcher_data[1]\\n p_pitcher_away = p_pitcher_away_data.find(\\n 'pitcher').attrib['name']\\n p_pitcher_away_wins = int(p_pitcher_away_data.\\n attrib['wins'])\\n p_pitcher_away_losses = int(p_pitcher_away_data.\\n attrib['losses'])\\n except Exception:\\n p_pitcher_home = ''\\n p_pitcher_home_wins = 0\\n p_pitcher_home_losses = 0\\n p_pitcher_away = ''\\n p_pitcher_away_wins = 0\\n p_pitcher_away_losses = 0\\n output = {\\n 'game_id': game_id,\\n 'game_tag': game_tag,\\n 'game_league': game_league,\\n 'game_status': game_status,\\n 'game_start_time': game_start_time,\\n 'home_team': home_team,\\n 'home_team_runs': home_team_runs,\\n 'home_team_hits': home_team_hits,\\n 'home_team_errors': home_team_errors,\\n 'away_team': away_team,\\n 'away_team_runs': away_team_runs,\\n 'away_team_hits': away_team_hits,\\n 'away_team_errors': away_team_errors,\\n 'p_pitcher_home': p_pitcher_home,\\n 'p_pitcher_home_wins': p_pitcher_home_wins,\\n 'p_pitcher_home_losses': p_pitcher_home_losses,\\n 'p_pitcher_away': p_pitcher_away,\\n 'p_pitcher_away_wins': p_pitcher_away_wins,\\n 'p_pitcher_away_losses': p_pitcher_away_losses\\n }\\n # put this dictionary into the larger dictionary\\n games[game_id] = output\\n return games\",\n \"def team_season_stats(team):\\n\\n # Get HTML Content\\n url = 'http://www.basketball-reference.com/teams/%s/stats_per_game_totals.html' % team\\n r = requests.get(url)\\n soup = BeautifulSoup(r.content, \\\"html.parser\\\")\\n\\n # MongoDB Collection\\n m = mongo.Mongo()\\n\\n # Team's yearly stats are displayed in a table\\n season_stats = soup.find(id='stats').find('tbody')\\n\\n # Iterate through each year\\n for year in season_stats.find_all('tr', {'class': None}):\\n\\n season_year = year.find('th').text[0:4]\\n season_year = int(season_year) + 1\\n season = {'year': season_year}\\n\\n # Loop through each stat\\n for stat in year.find_all('td'):\\n season[stat['data-stat']] = stat.string\\n\\n # Rename relocated teams\\n season['team_id'] = scrape_utils.rename_team(season['team_id'])\\n season['_id'] = season['team_id'] + '_' + str(season_year)\\n\\n # Remove unwanted stats\\n to_remove = ['rank_team', 'foo', 'g', 'mp_per_g']\\n for k in to_remove:\\n season.pop(k, None)\\n\\n # Add to MongoDB\\n m.insert('team_season', season)\",\n \"def get_smmry_data(soup, game_dict):\\n\\n # Get date and time data.\\n try:\\n date_soup = soup.find(\\\"div\\\", {\\\"class\\\": \\\"spielbericht_tipp_status\\\"})\\n league = date_soup.div.span.text\\n date_string = date_soup.div.text\\n date = re.search(r'\\\\d{2}.\\\\d{2}.\\\\d{2}', date_string).group(0)\\n time = re.search(r'\\\\d{2}:\\\\d{2}', date_string).group(0)\\n matchday = re.search(r'[|]\\\\d+', date_string).group(0)[1:]\\n\\n game_dict[\\\"league\\\"] = league\\n game_dict[\\\"fb_date\\\"] = date\\n game_dict[\\\"fb_time\\\"] = time\\n game_dict[\\\"matchday\\\"] = matchday\\n except AttributeError:\\n pass\\n\\n # Get game result.\\n try:\\n result = soup.find(\\\"div\\\", {\\\"class\\\": \\\"stand\\\"}).text\\n game_dict[\\\"result\\\"] = result\\n except AttributeError:\\n pass\\n\\n # Try to get the referee name.\\n try:\\n referee = soup.find(\\\"span\\\", {\\\"class\\\": \\\"schiri_link\\\"}).text\\n game_dict[\\\"referee\\\"] = referee\\n except AttributeError:\\n pass\\n\\n # Get team, club name and repective url by team.\\n try:\\n smmry_soup = soup.find(\\n \\\"div\\\", {\\\"class\\\": \\\"spielbericht_ergebnis_wrapper\\\"})\\n club_title = smmry_soup.find_all(\\\"img\\\")\\n team_title = smmry_soup.findAll(\\\"div\\\", {\\\"class\\\": \\\"teaminfo\\\"})\\n\\n # Loop through teams.\\n for j, team in enumerate([\\\"home_\\\", \\\"away_\\\"]):\\n game_dict[team + \\\"team\\\"] = team_title[j].a[\\\"title\\\"]\\n game_dict[team + \\\"team_url\\\"] = team_title[j].a[\\\"href\\\"]\\n game_dict[team + \\\"club\\\"] = club_title[j][\\\"title\\\"]\\n except (AttributeError, TypeError):\\n pass\\n\\n return game_dict\",\n \"def __call__(self, date):\\n for game in self._games:\\n if game.datetime.year == date.year and \\\\\\n game.datetime.month == date.month and \\\\\\n game.datetime.day == date.day:\\n return game\\n raise ValueError('No games found for requested date')\",\n \"def get_games_from_database (self):\\n r = requests.get (self.url_endpoint)\\n if (r.status_code != 200):\\n print (\\\"Failed to get games:\\\\n\\\", r.text)\\n return r\\n \\n games = json.loads (r.text)['games']\\n return_list = []\\n for game in games:\\n return_list.append (game['game_state'])\\n return return_list\",\n \"def test_seasons(self):\\n response = Tmdb.season(tmdb_show_id = 69740, season_number = 1)\\n self.assertTrue(int(response.status_code) == 200)\\n data = response.json()\\n self.assertTrue(isinstance(data['episodes'], list))\\n # TODO check if all the shows are in the good format (can be from_dict/to_dict)\",\n \"def get_games_overall(self, uuid):\\n\\n return self.template(uuid, \\\"games\\\")\",\n \"def test_get_seasonal_statistics___season_to_date(self):\\n msg = \\\"Response status is not 200\\\"\\n response = self.api.get_seasonal_statistics___season_to_date(self.season, self.nhl_season, self.team_id)\\n self.assertEqual(response.status_code, 200, msg)\",\n \"def getPlayerAdvStat(self, stat, year = 2014):\\r\\n \\r\\n year_next = (year % 100) + 1\\r\\n season = str(year) + '-' + str(year_next)\\r\\n \\r\\n stat_call = stat.lower()\\r\\n stat_dict = {'touch':'Possessions', 'possession':'Possessions',\\r\\n 'speed':'SpeedDistance', 'distance':'SpeedDistance'}\\r\\n \\r\\n stat_url = 'http://stats.nba.com/stats/leaguedashptstats?College=&'\\\\\\r\\n 'Conference=&Country=&DateFrom=&DateTo=&Division=&'\\\\\\r\\n 'DraftPick=&DraftYear=&GameScope=&Height=&LastNGames=0&'\\\\\\r\\n 'LeagueID=00&Location=&Month=0&OpponentTeamID=0&Outcome=&'\\\\\\r\\n 'PORound=0&PerMode=PerGame&PlayerExperience=&PlayerOr'\\\\\\r\\n 'Team=Player&PlayerPosition=&PtMeasureType=' + \\\\\\r\\n stat_dict[stat_call] + '&Season=' + season + \\\\\\r\\n '&SeasonSegment=&SeasonType=Regular+Season&StarterBench=&'\\\\\\r\\n 'TeamID=0&VsConference=&VsDivision=&Weight='\\r\\n \\r\\n response = requests.get(stat_url)\\r\\n data = json.loads(response.text)\\r\\n \\r\\n headers = data['resultSets'][0]['headers']\\r\\n stat_data = data['resultSets'][0]['rowSet']\\r\\n advStat_df = pd.DataFrame(stat_data,columns=headers) \\r\\n \\r\\n return advStat_df\",\n \"def status_games(game=None):\\n if game is None:\\n aux = {}\\n for i in range(len(games)):\\n aux[\\\"Game \\\" + str(i)] = games.games_status[i]['Status']\\n return aux\\n else:\\n game = int(game)\\n return {\\\"Game \\\" + str(game): games.games_status[game]['Status']}\",\n \"def parse_seasons (self, id, response_data):\\n raw_seasons = response_data['value']\\n videos = raw_seasons['videos']\\n\\n # get art video key\\n video = {}\\n for key, video_candidate in videos.iteritems():\\n if not self._is_size_key(key):\\n video = video_candidate\\n\\n # get season index\\n sorting = {}\\n for idx, season_list_entry in video['seasonList'].iteritems():\\n if self._is_size_key(key=idx) == False and idx != 'summary':\\n sorting[int(season_list_entry[1])] = int(idx)\\n\\n seasons = {}\\n\\n for season in raw_seasons['seasons']:\\n if self._is_size_key(key=season) == False:\\n seasons.update(self._parse_season_entry(season=raw_seasons['seasons'][season], video=video, sorting=sorting))\\n return seasons\",\n \"def get_previous_game(team_id: int) -> dict:\\n\\n logging.info(\\\"Checking the schedule API endpoint for the previous game.\\\")\\n url = f\\\"teams/{team_id}?expand=team.schedule.previous\\\"\\n\\n response = api.nhl_api(url)\\n if not response:\\n return None\\n\\n prev_game_json = response.json()\\n prev_game_sched = prev_game_json.get(\\\"teams\\\")[0].get(\\\"previousGameSchedule\\\")\\n prev_game_date = prev_game_sched.get(\\\"dates\\\")[0].get(\\\"date\\\")\\n prev_game = prev_game_sched.get(\\\"dates\\\")[0].get(\\\"games\\\")[0]\\n\\n return prev_game_date, prev_game\",\n \"def get_latest_data(self, symbol, n=1):\\n raise NotImplementedError\",\n \"def by_season(self, season):\\n return self.get_queryset().filter(season=season)\",\n \"def standings_by_season(season):\\n season = int(season) + 1\\n scoreboard = nba_py.Scoreboard(month=7,\\n day=1,\\n year=season)\\n east_standings = scoreboard.east_conf_standings_by_day()\\n west_standings = scoreboard.west_conf_standings_by_day()\\n\\n return render_template(\\\"standings.html\\\",\\n title=\\\"standings\\\",\\n east_standings=enumerate(east_standings, 1),\\n west_standings=enumerate(west_standings, 1),\\n team=CITY_TO_TEAM)\",\n \"def loadGamePlays(gamelink):\\n \\n #Open a game link\\n game = urllib.request.urlopen(base+gamelink)\\n gamedata = json.loads(game.read().decode(game.info().get_param('charset') or 'utf-8'))\\n\\n #Grab the data about every play that happened\\n plays = gamedata['liveData']['plays']['allPlays']\\n \\n #Also grab the tricodes for the involved teams for future use\\n homeTeam = gamedata['gameData']['teams']['home']['triCode']\\n awayTeam = gamedata['gameData']['teams']['away']['triCode']\\n \\n return (plays, homeTeam, awayTeam)\",\n \"def get_player_war_fg(self, player, kind, year):\\n if kind == 'batter':\\n war = '$fg.bat.{}.bat_WAR'.format(year)\\n off = '$fg.bat.{}.Off'.format(year)\\n def_ = '$fg.bat.{}.Def'.format(year)\\n res = self._db.Players.aggregate([{'$match': {'Name' : player}},\\n {'$project': {'_id' : 0,\\n 'war' : war,\\n 'off' : off,\\n 'def' : def_}}])\\n elif kind == 'pitcher':\\n war = '$fg.pit.{}.pit_WAR'.format(year)\\n res = self._db.Players.aggregate([{'$match': {'Name' : player}},\\n {'$project': {'_id' : 0,\\n 'war' : war}}])\\n return list(res)[0]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6553931","0.6546608","0.6442716","0.64363","0.6434008","0.6398952","0.62399364","0.6105547","0.6082319","0.6062433","0.59988546","0.59488773","0.59089303","0.5888597","0.5820921","0.5803751","0.57766825","0.5746373","0.57297796","0.5726727","0.5723362","0.57071954","0.56984305","0.5662392","0.5662087","0.5657601","0.56546164","0.5605076","0.55818594","0.5563413","0.55527246","0.5550755","0.54965997","0.54807055","0.54745364","0.5459503","0.54433095","0.544033","0.54391146","0.54311454","0.5426997","0.5424016","0.5420145","0.5411043","0.541087","0.54019535","0.5398166","0.5368815","0.53680736","0.53672904","0.5356845","0.53396136","0.5339037","0.5338079","0.5333531","0.5328087","0.5322883","0.5320404","0.53130186","0.53064126","0.5304074","0.53028107","0.5287375","0.5283375","0.5268819","0.5256505","0.5255939","0.524926","0.5244421","0.5240337","0.5238321","0.5232771","0.52322525","0.5230946","0.5223658","0.5213757","0.5209616","0.51900494","0.5187869","0.51856595","0.5149265","0.513525","0.5134665","0.5134332","0.5133946","0.51310056","0.51286656","0.51271945","0.5123284","0.5122261","0.5114167","0.51020163","0.50987655","0.5095367","0.5095254","0.5093711","0.5090952","0.50909483","0.5085892","0.50828797"],"string":"[\n \"0.6553931\",\n \"0.6546608\",\n \"0.6442716\",\n \"0.64363\",\n \"0.6434008\",\n \"0.6398952\",\n \"0.62399364\",\n \"0.6105547\",\n \"0.6082319\",\n \"0.6062433\",\n \"0.59988546\",\n \"0.59488773\",\n \"0.59089303\",\n \"0.5888597\",\n \"0.5820921\",\n \"0.5803751\",\n \"0.57766825\",\n \"0.5746373\",\n \"0.57297796\",\n \"0.5726727\",\n \"0.5723362\",\n \"0.57071954\",\n \"0.56984305\",\n \"0.5662392\",\n \"0.5662087\",\n \"0.5657601\",\n \"0.56546164\",\n \"0.5605076\",\n \"0.55818594\",\n \"0.5563413\",\n \"0.55527246\",\n \"0.5550755\",\n \"0.54965997\",\n \"0.54807055\",\n \"0.54745364\",\n \"0.5459503\",\n \"0.54433095\",\n \"0.544033\",\n \"0.54391146\",\n \"0.54311454\",\n \"0.5426997\",\n \"0.5424016\",\n \"0.5420145\",\n \"0.5411043\",\n \"0.541087\",\n \"0.54019535\",\n \"0.5398166\",\n \"0.5368815\",\n \"0.53680736\",\n \"0.53672904\",\n \"0.5356845\",\n \"0.53396136\",\n \"0.5339037\",\n \"0.5338079\",\n \"0.5333531\",\n \"0.5328087\",\n \"0.5322883\",\n \"0.5320404\",\n \"0.53130186\",\n \"0.53064126\",\n \"0.5304074\",\n \"0.53028107\",\n \"0.5287375\",\n \"0.5283375\",\n \"0.5268819\",\n \"0.5256505\",\n \"0.5255939\",\n \"0.524926\",\n \"0.5244421\",\n \"0.5240337\",\n \"0.5238321\",\n \"0.5232771\",\n \"0.52322525\",\n \"0.5230946\",\n \"0.5223658\",\n \"0.5213757\",\n \"0.5209616\",\n \"0.51900494\",\n \"0.5187869\",\n \"0.51856595\",\n \"0.5149265\",\n \"0.513525\",\n \"0.5134665\",\n \"0.5134332\",\n \"0.5133946\",\n \"0.51310056\",\n \"0.51286656\",\n \"0.51271945\",\n \"0.5123284\",\n \"0.5122261\",\n \"0.5114167\",\n \"0.51020163\",\n \"0.50987655\",\n \"0.5095367\",\n \"0.5095254\",\n \"0.5093711\",\n \"0.5090952\",\n \"0.50909483\",\n \"0.5085892\",\n \"0.50828797\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7470444"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":85586,"cells":{"query":{"kind":"string","value":"Uses the semantic ordering of season types to get the latest/max"},"document":{"kind":"string","value":"def _get_latest_season_type(season_types_list):\n if not isinstance(season_types_list, (list, tuple)):\n raise ValueError(f\"type of {type(season_types_list)} for season_type_list not list or tuple\")\n\n latest_season_type = season_types_list[0]\n if len(season_types_list) > 1:\n for season_type in season_types_list[1:]:\n if config.SEASON_TYPES_ORDER[season_type] > config.SEASON_TYPES_ORDER[latest_season_type]:\n latest_season_type = season_type\n\n return latest_season_type"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _get_latest_season_and_type(df):\n if df is None or len(df) == 0:\n return (None, None)\n\n if not isinstance(df, pd.DataFrame):\n raise ValueError(f\"Type of df is {type(df)}, it should be pandas.DataFrame\")\n\n df = df[df['state_of_game'] == 'POST']\n latest_season = df['season'].max()\n season_types = df[df['season'] == latest_season]['type'].unique()\n latest_season_type = _get_latest_season_type(list(season_types))\n return latest_season, latest_season_type","def latest_season_before(date):\n\tif date.month < 9:\n\t\treturn date.year - 1\n\treturn date.year","def get_season_no(token, url):\n headers = {'Accept': 'application/json', 'Authorization': token}\n r = requests.get(url, headers=headers)\n json_data = json.loads(r.text).get('data')\n high_season = 1\n for episode in json_data:\n if episode.get('airedSeason') > high_season:\n high_season = episode.get('airedSeason')\n return high_season","def getseason(data):\n ## Season key is the most reliable\n season = data.get(\"season\")\n if season:\n ## Season key is an integer formatted \"YYS\" and is 2000-based (i.e.- 171 == 2017-Winter)\n season = str(season)\n year = int(f\"20{season[:2]}\")\n ## Anichart Season key is 1-indexed\n season = int(season[2]) - 1\n ## This should normally pass; if it consistently does not, we'll have to investigate why\n try: return SeasonCharts.buildseason(season,year)\n ## If something goes wrong, we'll try another method\n except: print(f\"Failed to parse season: {data['season']}\")\n ## Next, we'll iterate over rankings to try to determine the season/year\n ## There are multiple types of rankings based on season, year, and both combined,\n ## so we'll piece it together based on whatever we come across first\n season,year = None,None\n for ranking in data.get(\"rankings\",list()):\n ## Quicker exit (without just making this loop its own function)\n if season and year: continue\n ## We'll ignore stuff we've already gotten and assume that nothing in\n ## rankings contradicts eachother\n if not season:\n ## Defaults to None one way or another if it's not supplied\n season = ranking.get(\"season\")\n if not year: year = ranking.get(\"year\")\n ## Check if we made it\n if season and year:\n ## As above, this should always work out-of-the-box\n try: return SeasonCharts.buildseason(season,year)\n except: print(season,year)\n ## Welp, we're stumped...\n return None","def get_competition_season_type(season):\n default_type = games.models.CompetitionSeason.winter\n try:\n season_name = season.name\n if season_name.find(\"/\") == -1:\n return games.models.CompetitionSeason.summer\n return default_type\n except Exception as e:\n return default_type","def highest_snowfall_for_year(resort_name, year):\n resort_table = resort_table_dict[resort_name]\n year = int(year)\n query = \"SELECT snowfall FROM %s WHERE CAST(EXTRACT(YEAR FROM status_date) AS INTEGER) = %d\" %(resort_table, year)\n connection = get_connection()\n\n snowfall_list = []\n\n if connection is not None:\n try:\n for row in get_select_query_results(connection, query):\n snowfall_list.append(row)\n except Exception as e:\n print(e, file=sys.stderr)\n connection.close()\n snowfall_list.sort(reverse=True)\n \"\"\"\n need to think about making our own sorter so we can break ties effectively\n \"\"\"\n highest_snowfall = snowfall_list[0]\n return json.dumps(highest_snowfall)","def get_seasons(games: List[BaseGame], reverse=False):\n seasons = list(set([g.Season for g in games]))\n seasons.sort(reverse=reverse)\n return seasons","def __latest_available_episode(self, predict_season: int, latest_episode: int) -> int:\n max_episode = 0\n for i in range(1, latest_episode + 1):\n if VideoParser.has_parsed_video(predict_season, i):\n max_episode = i\n else:\n return max_episode\n return max_episode","def get_season(\n current_date: date, hemisphere: str, season_tracking_type: str\n) -> str | None:\n\n if hemisphere == \"equator\":\n return None\n\n if season_tracking_type == TYPE_ASTRONOMICAL:\n spring_start = ephem.next_equinox(str(current_date.year)).datetime()\n summer_start = ephem.next_solstice(str(current_date.year)).datetime()\n autumn_start = ephem.next_equinox(spring_start).datetime()\n winter_start = ephem.next_solstice(summer_start).datetime()\n else:\n spring_start = datetime(2017, 3, 1).replace(year=current_date.year)\n summer_start = spring_start.replace(month=6)\n autumn_start = spring_start.replace(month=9)\n winter_start = spring_start.replace(month=12)\n\n if spring_start <= current_date < summer_start:\n season = STATE_SPRING\n elif summer_start <= current_date < autumn_start:\n season = STATE_SUMMER\n elif autumn_start <= current_date < winter_start:\n season = STATE_AUTUMN\n elif winter_start <= current_date or spring_start > current_date:\n season = STATE_WINTER\n\n # If user is located in the southern hemisphere swap the season\n if hemisphere == NORTHERN:\n return season\n return HEMISPHERE_SEASON_SWAP.get(season)","def season_choices():\n return [(s, s) for s in range(0, 3)]","def find_highest_projected_stocks(self):\n\n sorted_stocks = sorted(self.stock_list,\n key=lambda stock: stock.ryan_rank,\n reverse=True)\n\n return sorted_stocks","def highest_per_movie():\n copy = movies.copy()\n copy['Gross Per Movie'] = copy['Total Gross'] / copy['Number of Movies'] #calculate gross per movie\n year = copy.sort_values(['Gross Per Movie'], ascending = False).reset_index(drop = True).Year.loc[0]\n return ('highest_per_movie', year)","def sort_max_h2h_odds(self):\n\n\t\tfor i,event in enumerate(self.teams):\n\t\t\t# for each event\n\n\t\t\t# init list of 2 dictonarys (1 for each team) that contains the max odds and the site for those odds\n\t\t\tself.max_h2h_odds.append({self.teams[i][0] : [0, \"no site\"], self.teams[i][1] : [0, \"no site\"]})\n\n\t\t\tfor j,team in enumerate(self.teams[i]):\n\t\t\t\t# for each team\n\t\t\t\tfor k,site_odds in enumerate(self.h2h_odds[i]):\n\t\t\t\t\t# for all odds find largest and save\n\t\t\t\t\tif site_odds[j] > self.max_h2h_odds[i][team][0]:\n\t\t\t\t\t\tself.max_h2h_odds[i][team] = [site_odds[j] , self.betting_sites[i][k]]\n\t\t\n\t\tprint('Finished finding max odds')","def update_highest(csevo):\n tmax = [t[np.argmax(N)] for (t, N) in figure_to_data(csevo)]\n\n data = [{\n \"x\": list(range(len(tmax))), \"y\":tmax, \"type\":\"bar\"\n }]\n\n layout = {\n \"title\":'Time of largest abundance',\n \"template\":\"plotly_dark\",\n \"xaxis\":{\"title\":\"Charge state\", \"range\":[0, len(tmax)]},\n \"yaxis\":{\"title\":\"Time (s)\", \"type\":\"log\"}\n }\n\n return {\"data\":data, \"layout\":layout}","def get_movie_longest_runtime():\n tree = get_tree()\n movie_runtimes = [(movie.get(\"title\"), movie.get(\"runtime\")) for movie in tree.getroot().findall(\"movie\")]\n\n # one way\n result = sorted(movie_runtimes, key=lambda movie_runtimes: movie_runtimes[1], reverse = True)[0][0]\n return result\n \n # another way\n # result = max(movie_runtimes, key=lambda movie_runtimes: movie_runtimes[1])[0]\n # return result","def season(self, seasonnum, order='aired'):\n if order=='aired':\n seasons = self.seasons\n elif order == 'dvd':\n seasons = self.dvd_seasons\n try:\n return seasons[seasonnum]\n except KeyError:\n raise SeasonNotFoundError(\n 'Season no %s does not exists' % seasonnum\n ), None, sys.exc_info()[2]","def _find_latest():\n try:\n db = get_master_collection()\n service_details = db.find({\"master.key\": \"release\"}).sort([(\"master.value\", pymongo.DESCENDING)]).limit(1)\n for service in service_details:\n for r in sorted(service[\"master\"][\"value\"], reverse=True):\n latest_release = r\n build_list = service[\"master\"][\"value\"][r]\n break\n break\n\n latest_rel_num = str(latest_release).replace(\"_\", \".\")\n build_list = _natural_sort(build_list)\n for build in build_list:\n latest_build = build\n break\n\n latest_build_num = latest_build\n second_latest_build_num = int(latest_build_num) - 1\n latest = {\"latest_val\": latest_rel_num + \"_\" + latest_build_num,\n \"second_latest_val\": latest_rel_num + \"_\" + str(second_latest_build_num)}\n except Exception as e:\n logger.error(\"Exception in _find_latest : \" + str(e))\n return latest","def getEvolutionMax(self):\n \n return [self.getMaximumAtGivenTime(timeIndex) for timeIndex in range(self.numberOfTimes - 1)]","def seasons(self):\n if self._season_cache_ver == self._db._version:\n return self._season_cache\n\n # Find out how many seasons in this series by fetching the highest season.\n seasons = self._db.query(type='episode', parent=self._dbrow, attrs=['season'], distinct=True)\n self._season_cache = [Season(self._db, self, row['season']) for row in seasons]\n self._season_cache_ver = self._db._version\n return self._season_cache","def latest(scores):\n return scores[-1]","def latest(scores):\n return scores[-1]","def distributeSeason(self):\n i = 1\n for day in self.daylist:\n if i >= monthbeg[5] and i < monthbeg[9]: #june through SEpt as per SCE\n day.season = 'summer' #https://www.sce.com/residential/rates/Time-Of-Use-Residential-Rate-Plans\n i = i + 1\n else:\n day.season = 'winter'\n i = i+1","def _parse_season_entry (self, season, video, sorting):\n return {\n season['summary']['id']: {\n 'idx': sorting[season['summary']['id']],\n 'id': season['summary']['id'],\n 'text': season['summary']['name'],\n 'shortName': season['summary']['shortName'],\n 'boxarts': {\n 'small': video['boxarts']['_342x192']['jpg']['url'],\n 'big': video['boxarts']['_1280x720']['jpg']['url']\n },\n 'interesting_moment': video['interestingMoment']['_665x375']['jpg']['url'],\n }\n }","def current_season():\n td = datetime.datetime.today()\n if td.month > 8:\n return td.year\n return td.year - 1","def media_season(self):\n media_status = self._media_status()[0]\n return media_status.season if media_status else None","def season_series(game_id, pref_team, other_team, last_season=False):\n\n # Init empty dictionaries and lists\n games_against = list()\n pref_toi = dict()\n pref_goals = dict()\n pref_assists = dict()\n pref_points = dict()\n pref_record = {\"wins\": 0, \"losses\": 0, \"ot\": 0}\n roster_player = True\n\n # If this is the first game of the season, we can set the 'last_season' flag to enable the\n # season series function to check last year's season series between the two teams.\n if not last_season:\n season_start = str(game_id)[0:4]\n season_end = str(int(season_start) + 1)\n yesterday = datetime.now() - timedelta(days=1)\n # yesterday = datetime.now() + timedelta(days=50)\n # schedule_url = (\n # f\"/schedule?teamId={pref_team.team_id}\"\n # f\"&expand=schedule.broadcasts,schedule.teams&startDate=\"\n # f\"{season_start}-08-01&endDate={yesterday:%Y-%m-%d}\"\n # )\n schedule_url = (\n f\"/schedule?teamId={pref_team.team_id}\"\n f\"&expand=schedule.broadcasts,schedule.teams\"\n f\"&season={season_start}{season_end}\"\n )\n else:\n season_start = int(str(game_id)[0:4]) - 1\n season_end = str(int(season_start) + 1)\n yesterday = datetime.now() - timedelta(days=1)\n # yesterday = datetime.now() + timedelta(days=50)\n # schedule_url = (\n # f\"/schedule?teamId={pref_team.team_id}\"\n # f\"&expand=schedule.broadcasts,schedule.teams&startDate=\"\n # f\"{season_start}-08-01&endDate={season_end}-06-01\"\n # )\n schedule_url = (\n f\"/schedule?teamId={pref_team.team_id}\"\n f\"&expand=schedule.broadcasts,schedule.teams\"\n f\"&season={season_start}{season_end}\"\n )\n\n schedule = api.nhl_api(schedule_url).json()\n dates = schedule[\"dates\"]\n\n # Loop through scheduled to get previously played games against\n for date in dates:\n game = date[\"games\"][0]\n game_type = game[\"gameType\"]\n game_id = game[\"gamePk\"]\n game_team_home = game[\"teams\"][\"home\"][\"team\"][\"name\"]\n game_team_away = game[\"teams\"][\"away\"][\"team\"][\"name\"]\n teams = [game_team_away, game_team_home]\n game_status = game[\"status\"][\"abstractGameState\"]\n if game_type == \"R\" and game_status == \"Final\" and other_team.team_name in teams:\n game_feed = f\"/game/{game_id}/feed/live\"\n games_against.append(game_feed)\n\n # If the two teams haven't played yet, just exit this function\n if not games_against:\n return None, None, None\n\n # Loop through newly created games_against list to get each stats\n for feed in games_against:\n game = api.nhl_api(feed).json()\n game_data = game[\"gameData\"]\n home_team_name = game_data[\"teams\"][\"home\"][\"name\"]\n pref_homeaway = \"home\" if home_team_name == pref_team.team_name else \"away\"\n other_homeaway = \"away\" if home_team_name == pref_team.team_name else \"home\"\n\n # Get season series\n end_period = game[\"liveData\"][\"linescore\"][\"currentPeriod\"]\n extra_time = True if end_period > 3 else False\n pref_score = game[\"liveData\"][\"linescore\"][\"teams\"][pref_homeaway][\"goals\"]\n other_score = game[\"liveData\"][\"linescore\"][\"teams\"][other_homeaway][\"goals\"]\n if pref_score > other_score:\n pref_record[\"wins\"] += 1\n elif other_score > pref_score and extra_time:\n pref_record[\"ot\"] += 1\n else:\n pref_record[\"losses\"] += 1\n\n season_series_str = f\"Series: {pref_record['wins']}-\" f\"{pref_record['losses']}-{pref_record['ot']}\"\n\n # Get stats leaders\n # pref_teamstats = game[\"liveData\"][\"boxscore\"][\"teams\"][pref_homeaway][\"teamStats\"]\n pref_playerstats = game[\"liveData\"][\"boxscore\"][\"teams\"][pref_homeaway][\"players\"]\n for id, player in pref_playerstats.items():\n try:\n # Calculate TOI\n player_toi_str = player[\"stats\"][\"skaterStats\"][\"timeOnIce\"]\n player_toi_minutes = int(player_toi_str.split(\":\")[0])\n player_toi_seconds = int(player_toi_str.split(\":\")[1])\n player_toi = (player_toi_minutes * 60) + player_toi_seconds\n pref_toi[id] = pref_toi.get(id, 0) + player_toi\n\n # Point Totals\n player_goal_str = player[\"stats\"][\"skaterStats\"][\"goals\"]\n pref_goals[id] = pref_goals.get(id, 0) + int(player_goal_str)\n player_assist_str = player[\"stats\"][\"skaterStats\"][\"assists\"]\n pref_assists[id] = pref_assists.get(id, 0) + int(player_assist_str)\n player_points = int(player_goal_str) + int(player_assist_str)\n pref_points[id] = pref_points.get(id, 0) + int(player_points)\n\n except KeyError:\n pass\n\n # Calculate Stats Leaders\n sorted_toi = sorted(pref_toi.values(), reverse=True)\n leader_toi = sorted_toi[0]\n\n sorted_points = sorted(pref_points.values(), reverse=True)\n leader_points = sorted_points[0]\n\n # Get TOI leader\n for id in pref_toi.keys():\n if pref_toi[id] == leader_toi:\n player_name = roster.player_attr_by_id(pref_team.roster, id, \"fullName\")\n if player_name is None:\n roster_player = False\n player_id_only = id.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n leader_toi_avg = leader_toi / len(games_against)\n m, s = divmod(leader_toi_avg, 60)\n toi_m = int(m)\n toi_s = int(s)\n toi_s = \"0{}\".format(toi_s) if toi_s < 10 else toi_s\n toi_avg = \"{}:{}\".format(toi_m, toi_s)\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n toi_leader_str = \"TOI Leader: {} with {} / game.\".format(player_short_name, toi_avg)\n\n # Handle tied points leaders\n point_leaders = list()\n for id in pref_points.keys():\n if pref_points[id] == leader_points:\n point_leaders.append(id)\n\n if leader_points == 0:\n points_leader_str = \"Points Leader: None (all players have 0 points).\"\n\n elif len(point_leaders) == 1:\n leader = point_leaders[0]\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n # If the player is no longer on the team, get their information (change string here?)\n if player_name is None:\n roster_player = False\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n if not roster_player:\n points_leader_str = (\n f\"Points Leader: {player_name} with {leader_points} points \"\n f\"({player_goals}G {player_assists}A) \"\n )\n else:\n points_leader_str = \"Points Leader: {} with {} ({}G {}A).\".format(\n player_name, leader_points, player_goals, player_assists\n )\n\n elif len(point_leaders) > 3:\n point_leaders_with_attrs = list()\n for leader in point_leaders:\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n if player_name is None:\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n point_leaders_with_attrs.append(player_short_name)\n\n point_leaders_joined = \", \".join(point_leaders_with_attrs[0:3])\n leftover_leaders = len(point_leaders) - 3\n points_leader_str = (\n f\"Points Leaders: {point_leaders_joined} & {leftover_leaders} others ({leader_points} each).\"\n )\n\n else:\n point_leaders_with_attrs = list()\n for leader in point_leaders:\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n if player_name is None:\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n player_str = f\"{player_short_name} ({player_goals}G {player_assists}A)\"\n point_leaders_with_attrs.append(player_str)\n\n point_leaders_joined = (\n f\", \".join(point_leaders_with_attrs[:-1]) + f\" & {point_leaders_with_attrs[-1]}\"\n )\n points_leader_str = \"Points Leaders: {} with {} each.\".format(point_leaders_joined, leader_points)\n\n return season_series_str, points_leader_str, toi_leader_str","def extract_seasonal_component(original_ts, ppy):\n \"\"\"\n # === get in-sample data\n original_ts = original_ts[:-out_of_sample]\n \"\"\"\n if seasonality_test(original_ts, ppy):\n # print(\"seasonal\")\n # ==== get moving averages\n ma_ts = moving_averages(original_ts, ppy)\n\n # ==== get seasonality indices\n le_ts = original_ts * 100 / ma_ts\n le_ts = np.hstack((le_ts, np.full((ppy - (len(le_ts) % ppy)), np.nan)))\n le_ts = np.reshape(le_ts, (-1, ppy))\n si = np.nanmean(le_ts, 0)\n norm = np.sum(si) / (ppy * 100)\n si = si / norm\n else:\n # print(\"NOT seasonal\")\n si = np.full(ppy, 100)\n return si","def calcSeasonModified( monthNum ):\r\n\r\n if monthNum == 12 or monthNum == 1 or monthNum == 2:\r\n return 0\r\n\r\n elif monthNum == 6 or monthNum == 7 or monthNum == 7:\r\n return 1\r\n\r\n else:\r\n return 3","def season_top(bot):\n if len(bot.message.words) < 3:\n return irc.Response('Number to cut off is missing. The proper command is !season top <number> [condensed?]', pm_user=True)\n\n try:\n top_cut = int(bot.message.words[2])\n condensed = len(bot.message.words) >= 4 and bot.message.words[3].lower() in ('yes', 'true', '1')\n ranking = sorted(seasonal.get_rankings(\"ssbwiiu.json\").items(), key=lambda x: x[1], reverse=True)\n\n if len(ranking) == 0:\n return irc.Response('No one is ranked right now', pm_user=True)\n\n separator = ', ' if condensed else '\\n'\n ranking = ['{0[0]} ({0[1]} points)'.format(player) for player in ranking]\n return irc.Response(separator.join(ranking[:top_cut]), pm_user=True)\n except ValueError as e:\n return irc.Response('Number to cut off must be a number.', pm_user=True)","def convert_season(row): \n if row[\"month\"] >= 8:\n return int(row[\"season\"][:4])\n else:\n return int(row[\"season\"][-4:])","def calculate_greatest(self):\n greatest = 0\n for resourceList in self.loading.values():\n for time, use in resourceList:\n if use > greatest:\n greatest = use\n self.emit(\"greatest_calculated\",greatest)\n return greatest","def calc_lf_season(seasons, fuel_region_yh, average_fuel_yd):\n seasons_lfs = {}\n for season, yeardays_modelled in seasons.items():\n\n average_fuel_yd_full_year = np.average(\n average_fuel_yd[:, ],\n axis=1)\n\n # Calculate maximum hour in year\n max_load_h_days_season = np.max(\n fuel_region_yh[:, yeardays_modelled],\n axis=2)\n\n max_load_h_season = np.max(max_load_h_days_season, axis=1)\n\n # Unable local RuntimeWarning: divide by zero encountered\n with np.errstate(divide='ignore', invalid='ignore'):\n\n #convert to percentage\n season_lf = (average_fuel_yd_full_year / max_load_h_season) * 100\n\n # Replace\n season_lf[np.isinf(season_lf)] = 0\n season_lf[np.isnan(season_lf)] = 0\n\n seasons_lfs[season] = season_lf\n\n return seasons_lfs","def get_day_highest_rainfall(raindata_list_raw_values_from_urlstream):\n\tlist_with_nonempty_values = remove_emptystring_values(raindata_list_raw_values_from_urlstream) # casting the comparison key to another type to compare is undesirable, first cast the type to the correct data type, then store it in the list for comparison.\n\thighest_day_rain = max(list_with_nonempty_values, key=lambda row: int(row[1])) # lambda expression works IFF there is NO empty string bad data before cast to int! '' Empty string data in second element column must first be removed from source.\n\treturn highest_day_rain","def get_most_relevant_run(runs):\n\n # if there is a current run in the set pick it\n most_relevant_run = next(\n (run for run in runs if run.availability == AvailabilityType.current.value),\n None,\n )\n\n if not most_relevant_run:\n # if there a future runs in the set, pick the one with earliest start date\n runs = runs.order_by(\"best_start_date\")\n most_relevant_run = next(\n (\n run\n for run in runs\n if run.availability\n in [\n AvailabilityType.upcoming.value,\n AvailabilityType.starting_soon.value,\n ]\n ),\n None,\n )\n\n if not most_relevant_run:\n # get latest past run by start date\n most_relevant_run = next((run for run in runs.reverse()))\n\n return most_relevant_run","def latest(self):\n return self.series.tail(1)[0]","def _drop_last_season_championship_matches(self, df: pd.DataFrame) -> pd.DataFrame:\n max_season = df[\"season\"].max()\n\n if self._resume:\n # Check if max season changed\n if max_season != self._model_settings[\"max_season\"]:\n raise ValueError(\"Current max season is different than previous one. Model must be retrained.\")\n else:\n self.max_season = self._model_settings[\"max_season\"]\n else:\n self.max_season = max_season\n\n return df.loc[~((df[\"season\"] == max_season) & (df[\"league\"] == \"CH\"))]","def give_greatest_spart(cls, spart_list):\n if len(spart_list) == 1:\n return spart_list[0]\n sorted_list = cls.sort_by_dominance(spart_list)\n if not(sorted_list[0] > sorted_list[1]):\n print(\"The two largest elements are non-comparable\")\n return []\n else:\n return sorted_list[0]","def most_major_mergers_since(ts, Mvir_min=0.8e12, Mvir_max=2e12, z_merger_max=4.0, no_merger_value=None):\n dbid, Mvir = ts.calculate_all(\"dbid()\",\"Mvir\")\n mask = (Mvir>Mvir_min)*(Mvir<Mvir_max)\n\n most_major = []\n for current_id in dbid[mask]:\n halo = db.get_halo(current_id)\n redshift, ratio, halos = get_mergers_of_major_progenitor(halo)\n m_mask = np.array(redshift)<z_merger_max\n if m_mask.sum()>0:\n most_major.append(min(ratio[m_mask]))\n else:\n most_major.append(no_merger_value)\n return np.array(most_major)","def get_highest_rank(category_url, scrape_date = \"ALLTIME\"):\n\tif scrape_date == \"ALLTIME\":\n\t\treturn execute_sql(\"SELECT rank from rankings where category_url='%s' \\\n\t\t\torder by rank desc limit 1\" % category_url).fetchall()\t\t\n\telse:\n\t\treturn execute_sql(\"SELECT rank FROM rankings WHERE category_url='%s' \\\n\t\t\tAND scrape_date='%s' ORDER BY rank DESC LIMIT 1\" % (category_url, \\\n\t\t\tscrape_date)).fetchall()","def seasonal_series(t, y, edges=None, hard=False):\n t = ensure_tarray(t)\n if len(t) == 1:\n return [t], [y]\n season_ixs = season_indices(t, edges=edges, hard=hard)\n ts = []\n ys = []\n for season in season_ixs:\n ts.append(astropy.time.Time(t.jyear[season], format='jyear', scale=t.scale))\n ys.append(y[season])\n return ts, ys","def seasonNumber(self):\n return self.index","def get_latest_bar_value(self, symbol, val_type):\n try:\n bars_list = self.latest_symbol_data[symbol]\n except KeyError:\n print(\"That Symbol is not available in the historical data\")\n raise\n else:\n return getattr(bars_list[-1][1], val_type)","def six_best_months():\n if not monthly_averages:\n return [('', 0.0), ('', 0.0), ('', 0.0), ('', 0.0), ('', 0.0), ('', 0.0)]\n monthly_averages.sort(key=operator.itemgetter(1),reverse=True)\n return monthly_averages[:6]","def compute_levels_seasons(self, Y: t.Tensor, idxs: t.Tensor):\n y_transformed, _ = Y.median(1)\n y_transformed = y_transformed.reshape(-1, 1)\n levels = y_transformed.repeat(1, Y.shape[1])\n seasonalities = None\n\n return levels, None","def getNumSeasons(self):\n searchURL = \"http://api.tvmaze.com/shows/\" + str(self.__showID) \\\n + \"/seasons\"\n\n response = requests.get(searchURL)\n data = response.json()\n\n return data[-1][\"number\"]","def get_sectors_with_max_and_min_stocks():\n stock_by_sector = Counter(counter['sector'] for counter in data if counter['sector'] != 'n/a')\n return stock_by_sector.most_common()[0][0], stock_by_sector.most_common()[-1][0]","def parse_episodes_by_season (self, response_data):\n episodes = {}\n raw_episodes = response_data['value']['videos']\n for episode_id in raw_episodes:\n if self._is_size_key(key=episode_id) == False:\n if (raw_episodes[episode_id]['summary']['type'] == 'episode'):\n episodes.update(self.parse_episode(episode=raw_episodes[episode_id], genres=response_data['value']['genres']))\n return episodes","def get_year_with_most_rain(totals_list_per_day_from_datasource):\n\train_by_year_collection = {}\n\tfor row in totals_list_per_day_from_datasource:\n\t\tyear_component_of_parsed_date = row[0].split('-')[2]\n\t\tif None == rain_by_year_collection.get(year_component_of_parsed_date):\n\t\t\train_by_year_collection[year_component_of_parsed_date] = 0\n\t\tif '' == row[1]:\n\t\t\tcontinue\n\t\train_by_year_collection[year_component_of_parsed_date] += int(row[1])\n\tmax_rainy_year = max(rain_by_year_collection.items(), key=max_rain_compare) # use .items() and always searches based on the structure given, here .items() returns the dictionary as a tuple only.\n\tyear_with_most_rain = { max_rainy_year[0] : max_rainy_year[1] }\n\t#year_most_rain = { key : value for key, value in rain_by_year.items() if value == max_value[1] } # find key/year in the dictionary by value/rain reverse thinking.\n\treturn year_with_most_rain","def max_rain_compare(rain_by_year):\n\treturn rain_by_year[1]","def parse_seasons (self, id, response_data):\n raw_seasons = response_data['value']\n videos = raw_seasons['videos']\n\n # get art video key\n video = {}\n for key, video_candidate in videos.iteritems():\n if not self._is_size_key(key):\n video = video_candidate\n\n # get season index\n sorting = {}\n for idx, season_list_entry in video['seasonList'].iteritems():\n if self._is_size_key(key=idx) == False and idx != 'summary':\n sorting[int(season_list_entry[1])] = int(idx)\n\n seasons = {}\n\n for season in raw_seasons['seasons']:\n if self._is_size_key(key=season) == False:\n seasons.update(self._parse_season_entry(season=raw_seasons['seasons'][season], video=video, sorting=sorting))\n return seasons","def brightest_source(frq=151, sliced_list=catalog.srcs):\n max_obj = sliced_list[0]\n for gleam_obj in sliced_list:\n if gleam_obj.flux_by_frq[frq] > max_obj.flux_by_frq[frq]:\n max_obj = gleam_obj\n print(\"Largest flux value encountered:\", max_obj.flux_by_frq[frq])\n print(\"Name of associated object:\", max_obj.name)\n print(\"Index of associated object:\", lookup(max_obj.name))\n return max_obj","def find_max(weather_data):\n if len(weather_data) == 0:\n return()\n\n value = float(weather_data[0])\n position = 0\n\n for index, weather in enumerate(weather_data):\n if float(weather) >= value:\n value= float(weather)\n position = index\n\n return(value, position)","def getBestConcepts(self):\n maxScore = -1\n bestConcepts = []\n chunkTokens = self.getTokens()\n for token in chunkTokens:\n for concept in token.umlsConcepts:\n if concept.score > maxScore:\n maxScore = concept.score\n\n if maxScore > 0:\n for tokens in chunkTokens:\n for concept in token.umlsConcepts:\n if concept.score == maxScore:\n bestConcepts.append(concept)\n return bestConcepts","def __get_season_data(season_num: int, max_episode: int, training_data: bool) -> List[TrainSample]:\n season = EXAM_DATA[season_num]\n drop_players = season.get_drop_mapping(DropType.EXECUTION_DROP, max_episode)\n all_answers = season.get_all_answers(set(drop_players.keys()), max_episode)\n season_data = []\n for answer in all_answers:\n exam_episode = answer.episode\n drop_episodes = drop_players[answer.player]\n drop_episodes = [episode for episode in drop_episodes if exam_episode <= episode]\n if drop_episodes:\n if training_data:\n for player in exam_episode.players:\n season_data.append(TrainSample(answer.player, season_num, min(drop_episodes), answer.episode,\n answer.question, answer.answer, player))\n else:\n for answer_on in [False, True]:\n season_data.append(TrainSample(answer.player, season_num, min(drop_episodes), answer.episode,\n answer.question, answer.answer, answer_on))\n return season_data","def getSeason(date):\n\n date = validate.timestamp(date)\n day = date.dayofyear\n leap_year = int(date.is_leap_year)\n\n spring = numpy.arange(80, 172) + leap_year\n summer = numpy.arange(172, 264) + leap_year\n autumn = numpy.arange(264, 355) + leap_year\n\n if day in spring:\n season = \"spring\"\n elif day in summer:\n season = \"summer\"\n elif day in autumn:\n season = \"autumn\"\n else:\n season = \"winter\"\n\n return season","def get_max_min_time(series: object, aggregation: str) -> dict:\n\n dates = pd.to_datetime(series)\n\n if aggregation == 'year':\n\n return {'max': dates.max().year,\n 'min': dates.min().year}\n\n elif aggregation is None:\n\n return {'max': dates.max(),\n 'min': dates.min()}","def latestGamePack(team):\n lgr= get('schedule', {'ver':'v1', 'sportId':1, 'date':today, 'teamId':team, 'fields':['dates','games','gamePk'] })\n return lgr['dates'][0]['games'][0]['gamePk']","def season_limits(self):\n if self.season == \"summer\":\n return\n neighbors = [(0, 1), (1, 0), (-1, 0), (0, -1)]\n points_list = []\n season_pixels = []\n season_edges = []\n width = self.grid.width\n height = self.grid.height\n x = 0\n while x < width:\n y = 0\n while y < height:\n points_list.append((x, y))\n if self.season == \"spring\" or self.season == \"winter\":\n if self.grid.pix_copy[x, y] == (0, 0, 255):\n season_pixels.append((x, y))\n else:\n if self.grid.pix_copy[x, y] == (255, 255, 255):\n season_pixels.append((x, y))\n y += 1\n x += 1\n for points in points_list:\n k = 0\n while k < (len(neighbors)):\n xp = points[0] + neighbors[k][0]\n yp = points[1] + neighbors[k][1]\n condition = self.filter_edges_one(points[0], points[1], xp, yp, season_edges)\n if condition:\n season_edges.append((xp, yp))\n k += 1\n return season_edges","def get_latest_bars_values(self, symbol, val_type, N=1):\n raise NotImplementedError(\"Should implement get_latest_bars_values()\")","def get_latest_bars_values(self, symbol, val_type, N=1):\n try:\n bars_list = self.get_latest_bars(symbol, N)\n except KeyError:\n print(\"That Symbol is not available in the historical data\")\n raise\n else:\n return np.array([getattr(b[1], val_type) for b in bars_list])","def reduce2lat_seasonal( mv, seasons=seasonsyr, vid=None ):\n if vid==None:\n vid = 'reduced_'+mv.id\n # Note that the averager function returns a variable with meaningless id.\n # The climatology function returns the same id as mv, which we also don't want.\n\n # The slicers in time.py require getBounds() to work.\n # If it doesn't, we'll have to give it one.\n # Setting the _bounds_ attribute will do it.\n for ax in mv.getAxisList():\n if ax.getBounds() is None:\n ax._bounds_ = ax.genGenericBounds()\n timeax = timeAxis(mv)\n if timeax.units=='months':\n # Special check necessary for LEGATES obs data, because\n # climatology() won't accept this incomplete specification\n timeax.units = 'months since 0001-01-01'\n mvseas = seasons.climatology(mv)\n if mvseas is None:\n # Among other cases, this can happen if mv has all missing values.\n return None\n \n axes = allAxes( mv )\n axis_names = [ a.id for a in axes if a.id!='lat' and a.id!='time']\n axes_string = '('+')('.join(axis_names)+')'\n\n if len(axes_string)>2:\n avmv = averager( mvseas, axis=axes_string )\n else:\n avmv = mvseas\n avmv.id = vid\n\n avmv = delete_singleton_axis( avmv, vid='time' )\n avmv.units = mv.units\n return avmv","def reduce2latlon_seasonal( mv, seasons=seasonsyr, vid=None ):\n # This differs from reduce2lat_seasonal only in the line \"axis_names =\"....\n # I need to think about how to structure the code so there's less cut-and-paste!\n if vid==None:\n vid = 'reduced_'+mv.id\n # Note that the averager function returns a variable with meaningless id.\n # The climatology function returns the same id as mv, which we also don't want.\n\n # The slicers in time.py require getBounds() to work.\n # If it doesn't, we'll have to give it one.\n # Setting the _bounds_ attribute will do it.\n timeax = timeAxis(mv)\n if timeax.getBounds()==None:\n timeax._bounds_ = timeax.genGenericBounds()\n mvseas = seasons.climatology(mv)\n \n axes = allAxes( mv )\n axis_names = [ a.id for a in axes if a.id!='lat' and a.id!='lon' and a.id!='time']\n axes_string = '('+')('.join(axis_names)+')'\n\n if len(axes_string)>2:\n for axis in mvseas.getAxisList():\n if axis.getBounds() is None:\n axis._bounds_ = axis.genGenericBounds()\n avmv = averager( mvseas, axis=axes_string )\n else:\n avmv = mvseas\n if avmv is None: return avmv\n avmv.id = vid\n if hasattr(mv,'units'): avmv.units = mv.units\n avmv = delete_singleton_axis( avmv, vid='time' )\n avmv.units = mv.units\n return avmv","def get_parser_best(self):\n if len(self.parses):\n return min(self, key=lambda parse: parse.parser_rank)\n else:\n return None","def timeave_seasonal( mv, seasons=seasonsyr ):\n return seasons.climatology(mv)","def most_stable():\n \n \n \n \n return Z","def seasonality(df):\n df_datetime = pd.DatetimeIndex(df.date_time)\n df[\"month\"] = df_datetime.month\n df = drop_cols(df, [\"date_time\"])\n\n return df","def get_max_pop_year(group):\n max_year_val = group.loc[group[\"mean\"].idxmax()][[\"year_id\", \"mean\"]]\n\n return max_year_val","def _select_most_recent_symbols_chunk(self, sid_group):\n cols = self.equity_symbol_mappings.c\n\n # These are the columns we actually want.\n data_cols = (cols.sid,) + tuple(cols[name] for name in symbol_columns)\n\n # Also select the max of end_date so that all non-grouped fields take\n # on the value associated with the max end_date. The SQLite docs say\n # this:\n #\n # When the min() or max() aggregate functions are used in an aggregate\n # query, all bare columns in the result set take values from the input\n # row which also contains the minimum or maximum. Only the built-in\n # min() and max() functions work this way.\n #\n # See https://www.sqlite.org/lang_select.html#resultset, for more info.\n to_select = data_cols + (sa.func.max(cols.end_date),)\n\n return sa.select(\n to_select,\n ).where(\n cols.sid.in_(map(int, sid_group))\n ).group_by(\n cols.sid,\n )","def getSeasonalARIMAModel(self, ts):\n if not isinstance(ts, pd.Series):\n raise TypeError('Pandas Time Series required.')\n\n if not isinstance(self.df, pd.DataFrame):\n raise TypeError('Pandas DataFrame required.')\n\n if not 'close' in self.df.columns:\n raise AttributeError(\"Pandas DataFrame 'close' column required.\")\n\n \"\"\"Parameters for SARIMAX\"\"\"\n model = SARIMAX(ts, trend='n', order=(0,1,0), seasonal_order=(1,1,1,12))\n return model.fit(disp=-1)","def get_dryspells_perseason(dryspells, seasons=((12, 1, 2), (3, 4, 5),\n (6, 7, 8), (9, 10, 11))):\n dryspells_seasons = []\n for season in seasons:\n eveSeas = []\n for eveLand in dryspells:\n eves = [e for e in eveLand if e.start_date().month in season]\n eveSeas.append(eves)\n dryspells_seasons.append(eveSeas)\n\n return dryspells_seasons","def _sort_by_satellite(self) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\n time = []\n satellite = []\n system = []\n for sat in sorted(self.dset.unique(\"satellite\"), reverse=True):\n idx = self.dset.filter(satellite=sat)\n time.extend(self.dset.time.gps.datetime[idx])\n satellite.extend(self.dset.satellite[idx])\n system.extend(self.dset.system[idx])\n \n return np.array([time]), np.array([satellite]), np.array([system])","def give_season_suggestion():\n current_month = datetime.datetime.now().month\n all_month = (\n None, 'январь', 'февраль', 'март', 'апрель', 'май', 'июнь',\n 'июль', 'август', 'сентябрь', 'октябрь', 'ноябрь', 'декабрь')\n\n # Autumn.\n if current_month in [9, 10, 11]:\n month_list = [\n 'осень', 'осенний', 'осеннее', 'осенние', 'осенняя', 'сентябрь',\n 'октябрь', 'ноябрь', 'сентября', 'октября', 'ноября',\n 'сентябрьский', 'октябрьский', 'ноябрьский', 'свитер',\n 'листопад']\n suggestion_list = give_item_by_tag(month_list)\n if suggestion_list == '':\n suggestion_list = \"добавить в ваши рецепты что-то осенее;)\"\n suggest = f\"\"\"\n_________________¶\n________________¶¶\n___________¶___¶¶¶___¶\n___________¶¶¶¶¶¶¶¶/¶¶\n____¶_______¶¶¶¶|¶¶/¶¶________¶\n_¶¶¶¶¶¶______¶¶¶|¶/¶¶_______¶¶¶¶¶\n__¶¶¶¶¶¶____¶¶¶¶|/¶¶¶____¶¶¶¶¶¶\n_¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶|¶¶¶/¶¶¶¶¶/¶¶¶¶¶¶\n____¶¶¶¶¶¶¶¶¶¶¶¶|¶¶/¶¶¶¶¶/¶¶¶¶\n¶____¶¶¶¶¶¶¶¶¶¶¶|¶/¶¶¶¶¶/¶¶¶_____¶\n¶¶¶_¶¶¶¶¶¶¶¶¶¶¶¶|/¶¶¶¶¶/¶¶¶¶__¶¶¶¶\n¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶|¶¶¶¶¶/¶¶¶¶¶¶¶¶¶¶¶\n__¶¶¶¶¶¶¶¶¶¶¶¶¶¶|¶¶¶¶/¶¶¶¶¶/¶¶¶¶\n___¶¶¶¶¶¶¶¶¶¶¶¶¶|¶¶¶/¶¶¶¶/¶¶¶¶¶\n_¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶|¶¶/¶¶¶/¶¶¶¶¶¶¶¶¶¶\n________¶¶¶¶¶¶¶¶|¶/¶/¶¶¶¶¶¶\n_______________¶¶\n______________¶¶ На дворе {all_month[current_month]}, осень.\n______________¶¶ самое время приготовить что-то уютное,\n______________¶¶ бодрящее из ваших рецептов.\n_______________¶¶ Рекомендуем:\n ________________¶¶¶ {suggestion_list}\n \"\"\"\n\n # Winter.\n elif current_month in [1, 2, 12]:\n month_list = [\n 'зима', 'зимний', 'зимнее', 'зимние', 'зимняя', 'январь',\n 'декабрь', 'февраль', 'декабря', 'января', 'февраля',\n 'декабрьский', 'январский', 'февральский', 'свитер', 'снег',\n 'снегопад', 'иней', 'зимушка', 'Зимушка', 'новый год',\n 'новым годом', 'рождество', 'рождественский']\n suggestion_list = give_item_by_tag(month_list)\n if suggestion_list == '':\n suggestion_list = \"добавить в ваши рецепты что-то зимнее;)\"\n suggest = f\"\"\"\n_____________________$$\n_____________________$$\n_____________$$$$____$$____$$$$\n_____ ____$$____$$$$$$$$$$$$$$____$$\n_________$$________$$$$$$________$$\n_$$______$$__________$$__________$$______$$\n_$$$$$$__$ $__________$$__________$$__$$$$$$\n_____$$$$$$__________$$__________$$$$$$\n_______$$$$$$________$$________$$$$$$\n___$$$$______$$ $$____$$____$$$$______$$$$\n_$$______________$$$$$$$$$$______________$$\n___________________$$$$$$\n_$$______________$$$ $$$$$$$______________$$\n___$$$$______$$$$____$$____$$$$______$$$$\n_______$$$$$$________$$________$$$$$$\n_____$$$$$$__________$$__ ________$$$$$$\n_$$$$$$__$$__________$$__________$$__$$$$$$\n_$$______$$__________$$__________$$______$$\n_________$$________$$$$$$_____ ___$$\n_________$$____$$$$$$$$$$$$$$____$$ На дворе {all_month[current_month]}, зима.\n_____________$$$$____$$____$$$$ самое время приготовить что-то приятное,\n_____________________$$ согревающее из ваших рецептов.\n___________\n Рекомендуем:\n {suggestion_list}\n \"\"\"\n\n # Spring.\n elif current_month in [3, 4, 5]:\n month_list = [\n 'весна', 'весенний', 'весеннее', 'весенние', 'весенняя', 'март',\n 'апрель', 'май', 'марта', 'апреля', 'мая', 'мартовский',\n 'апрельский', 'майский', 'свежий']\n suggestion_list = give_item_by_tag(month_list)\n if suggestion_list == '':\n suggestion_list = \"добавить в ваши рецепты что-то весеннее;)\"\n suggest = f\"\"\"\n______d$$$$b_d$$$$$b\n_____$$$$$$$$$$$$$$$$$\n____$$$$$$P_$$$$$$$$$\n_____V$P_______Y$$P\n___________________d$$b\n___$$$$_________$$$$$$\n___$$$ $$$______$$$$$$$\n____Y$$$$$$$$$$$$$$$$P\n______Y$P_$$$$$$$\n__________Y$$$P_$\n__________________$\n_________________$\n_________________ $\n__________________$$$$$_\n_________________$$$$$$$$ $\n__$$$$$$$_______$$$$$$\n_$$$$$$$$$__$$$$$ На дворе {all_month[current_month]}, весна.\n$_ _____$$$$$ Самое время бодрящих, легкий ароматов!\n________$$_$ Рекомендуем:\n _________$$$ {suggestion_list}\n__________$$\n___________$\n \"\"\"\n\n # Summer.\n elif current_month in [6, 7, 8]:\n month_list = [\n 'лето', 'летний', 'летнее', 'летние', 'летняя', 'июнь', 'июль',\n 'август', 'июля', 'июня', 'августа', 'июньский', 'июльский',\n 'августовский', 'тропический', 'тропики', 'экзотика',\n 'экзотический', 'отпуск']\n suggestion_list = give_item_by_tag(month_list)\n if suggestion_list == '':\n suggestion_list = \"добавить в ваши рецепты что-то летнее;)\"\n suggest = f\"\"\"\n________________ ¶¶¶¶¶¶¶¶\n_______________ ¶¶¶¶\n___¶¶¶¶¶¶¶¶¶__ ¶¶¶____¶¶¶\n_ ¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶\n¶¶¶¶¶ ______¶¶¶¶¶¶¶¶____¶¶¶¶¶\n_______ ¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶_____¶¶\n______ ¶¶¶¶__¶¶¶_¶¶¶¶¶¶¶¶¶\n____ ¶¶¶¶___¶¶¶___¶¶¶_¶¶¶¶¶\n___ ¶¶¶¶¶__¶¶¶¶_¶¶_¶¶¶_¶¶¶¶¶\n___ ¶¶¶___¶¶¶¶¶_¶¶__¶¶¶__¶¶¶\n__ ¶¶____¶¶¶¶¶___¶¶_¶¶¶¶_¶¶¶\n________ ¶¶¶_____¶¶___¶¶__¶\n_________ ¶¶¶____¶¶¶__¶¶__¶\n_________ ¶¶¶_____¶¶___¶\n_________ ¶¶______¶¶\n_________________ ¶¶¶\n_________________ ¶¶¶ На дворе {all_month[current_month]}, лето.\n_________________ ¶¶¶ Самое время экзотических коктейлей и\n________________ ¶¶¶¶ сладости фруктов!\n________________ ¶¶¶ Рекомендуем:\n ____________ ¶¶¶_¶¶¶ {suggestion_list}\n______________ ¶¶¶¶¶¶¶¶\n\n \"\"\"\n return suggest","def find_latest_version(versions):\n\n highest_version = 0\n for version in versions:\n version = parse_version(version)\n\n if version > highest_version:\n highest_version = version\n\n return highest_version","def get_latest_scene(s3_scene_jsons):\n # Fetch all 'scene*.json' files and load Scenes\n scenes = [open_remote_pb_object(scene_json, Scene) for scene_json in s3_scene_jsons]\n\n # Find Scene with latest creation timestamp\n creation_ts = [_s.creation_date.ToMicroseconds() for _s in scenes]\n index = creation_ts.index(max(creation_ts))\n return scenes[index], s3_scene_jsons[index]","def get_top_public_momends(max_count):\n _obj = Momend.objects.filter(privacy=Momend.PRIVACY_CHOICES['Public']).order_by('create_date').reverse()\n\n return _obj if (_obj.count() < max_count) else _obj[_obj.count() - max_count:]","def reduce_time_seasonal( mv, seasons=seasonsyr, vid=None ):\n if vid==None:\n vid = 'reduced_'+mv.id\n # Note that the averager function returns a variable with meaningless id.\n # The climatology function returns the same id as mv, which we also don't want.\n\n # The slicers in time.py require getBounds() to work.\n # If it doesn't, we'll have to give it one.\n # Setting the _bounds_ attribute will do it.\n timeax = timeAxis(mv)\n if timeax is None:\n print \"WARNING- no time axis in\",mv.id\n return None\n if timeax.getBounds()==None:\n timeax._bounds_ = timeax.genGenericBounds()\n mvseas = seasons.climatology(mv)\n if mvseas is None:\n print \"WARNING- cannot compute climatology for\",mv.id,seasons.seasons\n print \"...probably there is no data for times in the requested season.\"\n return None\n avmv = mvseas\n avmv.id = vid\n if hasattr(mv,'units'): avmv.units = mv.units\n avmv = delete_singleton_axis( avmv, vid='time' )\n avmv.units = mv.units\n return avmv","def seasonStats(personId,type = 'gameLog',group = 'hitting'):\n\n #playerInfo = get('people', {'personIds':personId})\n\n\n teamStats = get('person',{ 'ver':'v1' , 'personId':personId,'hydrate':['stats(group={},type={})'.format(group,type),'currentTeam']})\n return teamStats\n #iterate of stats and find the right player id\n #career stats broken\n #fix the season :2019\n #make function to get team id","def current_season() -> int:\n now = datetime.now()\n month, year = now.month, now.year\n if month < 4:\n year -= 1\n return year","def standardComposition_Max(self):\n temp = np.fmax(self.rulesList[0], self.rulesList[1])\n for r in self.rulesList[2:]:\n temp = np.fmax(temp, r)\n\n self.fuzzy_output = temp","def season_edges(t):\n offset = season_offset(t)\n yr_min = t.datetime.min().year\n left_frac = t.jyear.min() % yr_min\n if left_frac < offset:\n ex_left = 1\n else:\n ex_left = 0\n edges = np.arange(yr_min - ex_left + offset, t.jyear.max() + 1, 1.0)\n return astropy.time.Time(edges, format='jyear')","def youngest(self):\n # Your implementation here","def get_last_seg_date(self, story):\n\n # retrieve the LAST story segment (ordered by published date)\n # if this entry doesn't exist, there are no story segments. Return the\n # oldest possible date.\n\n # ...various errors due to unicode problems....due to the data coming from either\n # the database, or the website. Not quite sure what is going on here, whether it's my\n # system, or not. But this code makes it work for the lowest common denominator.\n try:\n story_segs = StorySegment.objects.filter(story=story).order_by(\"-published\")[0]\n \"\"\"\n try:\n print (\"lastdate for {0}: {1}\".format(story.title, story_segs.published))\n except UnicodeEncodeError:\n print (\"lastdate for {0}: {1}\".format(story.title.encode('utf-8'), story_segs.published))\n \"\"\"\n return story_segs.published\n except IndexError:\n \"\"\"\n try:\n print(\" no lastdate found for story [{0}]\".format(story.title))\n except UnicodeEncodeError:\n print(\" no lastdate found for story [{0}]\".format(story.title.encode('utf-8')))\n \"\"\"\n return datetime.min.replace(tzinfo=utc)","def get_top_news_and_the_rest(self):\n queryset = self.news.order_by('-marked', '-publication_date')\n return queryset.first(), queryset[1:]","def get_year_desc():\n return Year.objects.all().order_by('-name')","def get_stock_symbol_with_highest_cap():\n #data2 = _cap_str_to_mln_float('cap')\n symbol_max = dict()\n for items in data:\n if items['symbol'] in symbol_max.keys():\n symbol_max[items['symbol']] = max(symbol_max[items['symbol']], _cap_str_to_mln_float(items['cap']))\n else:\n symbol_max[items['symbol']] = _cap_str_to_mln_float(items['cap'])\n\n value = sorted(symbol_max.items(), key = lambda x:x[1], reverse=True)[0][0]\n #sorted(symbol_max.items(), key = lambda x:x[1])\n return value","def set_season_time(season): \n if season == '2021-22':\n startdate = time.strptime('13-08-2021', '%d-%m-%Y')\n startdate = datetime.fromtimestamp(mktime(startdate))\n enddate = time.strptime('08-10-2021', '%d-%m-%Y')\n enddate = datetime.fromtimestamp(mktime(enddate))\n if season == '2020-21':\n startdate = time.strptime('12-08-2020', '%d-%m-%Y')\n startdate = datetime.fromtimestamp(mktime(startdate))\n enddate = time.strptime('26-07-2021', '%d-%m-%Y')\n enddate = datetime.fromtimestamp(mktime(enddate))\n if season == '2019-20':\n startdate = time.strptime('09-08-2019', '%d-%m-%Y')\n startdate = datetime.fromtimestamp(mktime(startdate))\n enddate = time.strptime('26-07-2020', '%d-%m-%Y')\n enddate = datetime.fromtimestamp(mktime(enddate))\n return startdate, enddate","def hottest_summer(df) -> int:\n df['summer_max'] = df[['JUN', 'JUL', 'AUG']].max(axis=1)\n hottest_year = df['summer_max'].idxmax()\n return hottest_year","def search_season_episode(self,strz):\t\n\t\tpattern = compile(\"(S(\\d\\d)E(\\d\\d))\") #S01E03\n\t\tsep = pattern.search(strz)\t\t\n\t\tif sep is not None:\n\t\t\tse= sep.group(1)\n\t\t\tseason = sep.group(2)\n\t\t\tepisode = sep.group(3)\n\t\t\treturn strz.replace(se,\"\")\n\t\t\t\n\t\tpattern = compile(\"((\\d\\d)x(\\d\\d))\") #01x03\n\t\tsep = pattern.search(strz)\t\t\n\t\tif sep is not None:\n\t\t\tse= sep.group(1)\n\t\t\tseason = sep.group(2)\n\t\t\tepisode = sep.group(3)\n\t\t\treturn strz.replace(se,\"\")\n\t\t\t\n\t\tpattern = compile(\"(Ep(\\d\\d))\") #Ep03\n\t\tsep = pattern.search(strz)\t\t\n\t\tif sep is not None:\n\t\t\tse= sep.group(1)\n\t\t\tepisode = sep.group(2)\n\t\t\treturn strz.replace(se,\"\")","def extract_season_episode_from_str(s):\n m = check_for_season_episode_code(s)\n\n if not m:\n return 1, 1\n\n return int(m.group(1)), int(m.group(2))","def get_latest_unwatched(conn, show_stub):\n\t\tsql_query = f'SELECT \"show_name\", \"season\", \"episode_number\", \"episode_title\" FROM shows WHERE show_stub = \"{show_stub}\" AND \"watched_status\" = 0 ORDER BY \"season\", \"episode\"'\n\t\t\n\t\tepisode_list = execute_sql(conn, sql_query).fetchall()\n\t\t\n\t\treturn episode_list","def latest(scores: list) -> int:\n return scores[-1]","def get_upcoming_season(self):\n result = self._method_call(\"UpcomingSeason\")\n return int(result)","def local_maxima_to_storm_tracks(local_max_dict_by_time, first_numeric_id):\n\n prev_primary_id_numeric = first_numeric_id - 1\n prev_secondary_id_numeric = first_numeric_id - 1\n num_times = len(local_max_dict_by_time)\n\n for i in range(num_times):\n this_num_storm_objects = len(local_max_dict_by_time[i][LATITUDES_KEY])\n this_empty_2d_list = [\n ['' for _ in range(0)] for _ in range(this_num_storm_objects)\n ]\n\n local_max_dict_by_time[i].update({\n PRIMARY_IDS_KEY: [''] * this_num_storm_objects,\n SECONDARY_IDS_KEY: [''] * this_num_storm_objects,\n PREV_SECONDARY_IDS_KEY: this_empty_2d_list,\n NEXT_SECONDARY_IDS_KEY: copy.deepcopy(this_empty_2d_list)\n })\n\n # If first time step, just create storm IDs and don't worry about\n # linking.\n if i == 0:\n for j in range(this_num_storm_objects):\n (local_max_dict_by_time[i][PRIMARY_IDS_KEY][j],\n prev_primary_id_numeric\n ) = _create_partial_storm_id(prev_primary_id_numeric)\n\n (local_max_dict_by_time[i][SECONDARY_IDS_KEY][j],\n prev_secondary_id_numeric\n ) = _create_partial_storm_id(prev_secondary_id_numeric)\n\n continue\n\n # Handle mergers between [i]th and [i - 1]th time steps. This\n # occurs when two storms at [i - 1]th time merge into one storm at\n # [i]th time.\n this_current_to_prev_matrix = copy.deepcopy(\n local_max_dict_by_time[i][CURRENT_TO_PREV_MATRIX_KEY]\n )\n\n this_dict = _local_maxima_to_tracks_mergers(\n current_local_max_dict=local_max_dict_by_time[i],\n previous_local_max_dict=local_max_dict_by_time[i - 1],\n current_to_previous_matrix=this_current_to_prev_matrix,\n prev_primary_id_numeric=prev_primary_id_numeric,\n prev_secondary_id_numeric=prev_secondary_id_numeric)\n\n local_max_dict_by_time[i] = this_dict[CURRENT_LOCAL_MAXIMA_KEY]\n local_max_dict_by_time[i - 1] = this_dict[PREVIOUS_LOCAL_MAXIMA_KEY]\n this_current_to_prev_matrix = this_dict[CURRENT_TO_PREV_MATRIX_KEY]\n prev_primary_id_numeric = this_dict[PREVIOUS_PRIMARY_ID_KEY]\n prev_secondary_id_numeric = this_dict[PREVIOUS_SECONDARY_ID_KEY]\n\n this_old_to_new_dict = this_dict[OLD_TO_NEW_PRIMARY_IDS_KEY]\n\n for j in range(i + 1):\n local_max_dict_by_time[j][PRIMARY_IDS_KEY] = [\n p if p not in this_old_to_new_dict else\n this_old_to_new_dict[p]\n for p in local_max_dict_by_time[j][PRIMARY_IDS_KEY]\n ]\n\n # Handle splits between [i]th and [i - 1]th time steps. This\n # occurs when one storm at [i - 1]th time splits into two storms at\n # [i]th time.\n this_dict = _local_maxima_to_tracks_splits(\n current_local_max_dict=local_max_dict_by_time[i],\n previous_local_max_dict=local_max_dict_by_time[i - 1],\n current_to_previous_matrix=this_current_to_prev_matrix,\n prev_secondary_id_numeric=prev_secondary_id_numeric)\n\n local_max_dict_by_time[i] = this_dict[CURRENT_LOCAL_MAXIMA_KEY]\n local_max_dict_by_time[i - 1] = this_dict[PREVIOUS_LOCAL_MAXIMA_KEY]\n this_current_to_prev_matrix = this_dict[CURRENT_TO_PREV_MATRIX_KEY]\n prev_secondary_id_numeric = this_dict[PREVIOUS_SECONDARY_ID_KEY]\n\n # Handle simple connections between [i]th and [i - 1]th time steps.\n # This occurs when one storm at [i - 1]th time has either zero or\n # one successors at [i]th time. In other words, there is either a\n # one-to-one connection or no connection.\n this_dict = _local_maxima_to_tracks_simple(\n current_local_max_dict=local_max_dict_by_time[i],\n previous_local_max_dict=local_max_dict_by_time[i - 1],\n current_to_previous_matrix=this_current_to_prev_matrix,\n prev_primary_id_numeric=prev_primary_id_numeric,\n prev_secondary_id_numeric=prev_secondary_id_numeric)\n\n local_max_dict_by_time[i] = this_dict[CURRENT_LOCAL_MAXIMA_KEY]\n local_max_dict_by_time[i - 1] = this_dict[PREVIOUS_LOCAL_MAXIMA_KEY]\n prev_primary_id_numeric = this_dict[PREVIOUS_PRIMARY_ID_KEY]\n prev_secondary_id_numeric = this_dict[PREVIOUS_SECONDARY_ID_KEY]\n\n all_primary_id_strings = []\n all_secondary_id_strings = []\n all_first_prev_sec_id_strings = []\n all_second_prev_sec_id_strings = []\n all_first_next_sec_id_strings = []\n all_second_next_sec_id_strings = []\n\n all_times_unix_sec = numpy.array([], dtype=int)\n all_spc_date_strings = []\n all_centroid_latitudes_deg = numpy.array([])\n all_centroid_longitudes_deg = numpy.array([])\n all_centroid_x_metres = numpy.array([])\n all_centroid_y_metres = numpy.array([])\n\n include_polygons = POLYGON_OBJECTS_LATLNG_KEY in local_max_dict_by_time[0]\n\n all_polygon_rows_arraylist = []\n all_polygon_columns_arraylist = []\n all_polygon_lat_arraylist_deg = []\n all_polygon_lng_arraylist_deg = []\n all_polygon_objects_latlng = numpy.array([], dtype=object)\n all_polygon_objects_rowcol = numpy.array([], dtype=object)\n\n for i in range(num_times):\n this_num_storm_objects = len(\n local_max_dict_by_time[i][LATITUDES_KEY]\n )\n\n all_primary_id_strings += local_max_dict_by_time[i][PRIMARY_IDS_KEY]\n all_secondary_id_strings += local_max_dict_by_time[i][SECONDARY_IDS_KEY]\n\n all_first_prev_sec_id_strings += [\n x[0] if len(x) > 0 else ''\n for x in local_max_dict_by_time[i][PREV_SECONDARY_IDS_KEY]\n ]\n\n all_second_prev_sec_id_strings += [\n x[1] if len(x) > 1 else ''\n for x in local_max_dict_by_time[i][PREV_SECONDARY_IDS_KEY]\n ]\n\n all_first_next_sec_id_strings += [\n x[0] if len(x) > 0 else ''\n for x in local_max_dict_by_time[i][NEXT_SECONDARY_IDS_KEY]\n ]\n\n all_second_next_sec_id_strings += [\n x[1] if len(x) > 1 else ''\n for x in local_max_dict_by_time[i][NEXT_SECONDARY_IDS_KEY]\n ]\n\n these_times_unix_sec = numpy.full(\n this_num_storm_objects,\n local_max_dict_by_time[i][VALID_TIME_KEY], dtype=int\n )\n\n if len(these_times_unix_sec) > 0:\n these_spc_date_strings = this_num_storm_objects * [\n time_conversion.time_to_spc_date_string(these_times_unix_sec[0])\n ]\n else:\n these_spc_date_strings = []\n\n all_times_unix_sec = numpy.concatenate((\n all_times_unix_sec, these_times_unix_sec\n ))\n all_spc_date_strings += these_spc_date_strings\n\n all_centroid_latitudes_deg = numpy.concatenate((\n all_centroid_latitudes_deg,\n local_max_dict_by_time[i][LATITUDES_KEY]\n ))\n all_centroid_longitudes_deg = numpy.concatenate((\n all_centroid_longitudes_deg,\n local_max_dict_by_time[i][LONGITUDES_KEY]\n ))\n all_centroid_x_metres = numpy.concatenate((\n all_centroid_x_metres, local_max_dict_by_time[i][X_COORDS_KEY]\n ))\n all_centroid_y_metres = numpy.concatenate((\n all_centroid_y_metres, local_max_dict_by_time[i][Y_COORDS_KEY]\n ))\n\n if not include_polygons:\n continue\n\n all_polygon_rows_arraylist += local_max_dict_by_time[i][\n GRID_POINT_ROWS_KEY]\n all_polygon_columns_arraylist += local_max_dict_by_time[i][\n GRID_POINT_COLUMNS_KEY]\n all_polygon_lat_arraylist_deg += local_max_dict_by_time[i][\n GRID_POINT_LATITUDES_KEY]\n all_polygon_lng_arraylist_deg += local_max_dict_by_time[i][\n GRID_POINT_LONGITUDES_KEY]\n\n all_polygon_objects_latlng = numpy.concatenate((\n all_polygon_objects_latlng,\n local_max_dict_by_time[i][POLYGON_OBJECTS_LATLNG_KEY]\n ))\n all_polygon_objects_rowcol = numpy.concatenate((\n all_polygon_objects_rowcol,\n local_max_dict_by_time[i][POLYGON_OBJECTS_ROWCOL_KEY]\n ))\n\n storm_object_dict = {\n tracking_utils.PRIMARY_ID_COLUMN: all_primary_id_strings,\n tracking_utils.SECONDARY_ID_COLUMN: all_secondary_id_strings,\n tracking_utils.FIRST_PREV_SECONDARY_ID_COLUMN:\n all_first_prev_sec_id_strings,\n tracking_utils.SECOND_PREV_SECONDARY_ID_COLUMN:\n all_second_prev_sec_id_strings,\n tracking_utils.FIRST_NEXT_SECONDARY_ID_COLUMN:\n all_first_next_sec_id_strings,\n tracking_utils.SECOND_NEXT_SECONDARY_ID_COLUMN:\n all_second_next_sec_id_strings,\n tracking_utils.VALID_TIME_COLUMN: all_times_unix_sec,\n tracking_utils.SPC_DATE_COLUMN: all_spc_date_strings,\n tracking_utils.CENTROID_LATITUDE_COLUMN: all_centroid_latitudes_deg,\n tracking_utils.CENTROID_LONGITUDE_COLUMN: all_centroid_longitudes_deg,\n CENTROID_X_COLUMN: all_centroid_x_metres,\n CENTROID_Y_COLUMN: all_centroid_y_metres\n }\n\n if include_polygons:\n storm_object_dict.update({\n tracking_utils.ROWS_IN_STORM_COLUMN: all_polygon_rows_arraylist,\n tracking_utils.COLUMNS_IN_STORM_COLUMN:\n all_polygon_columns_arraylist,\n tracking_utils.LATITUDES_IN_STORM_COLUMN:\n all_polygon_lat_arraylist_deg,\n tracking_utils.LONGITUDES_IN_STORM_COLUMN:\n all_polygon_lng_arraylist_deg,\n tracking_utils.LATLNG_POLYGON_COLUMN: all_polygon_objects_latlng,\n tracking_utils.ROWCOL_POLYGON_COLUMN: all_polygon_objects_rowcol\n })\n\n storm_object_table = pandas.DataFrame.from_dict(storm_object_dict)\n\n # Create full IDs (primary + secondary).\n full_id_strings = partial_to_full_ids(\n primary_id_strings=storm_object_table[\n tracking_utils.PRIMARY_ID_COLUMN].values.tolist(),\n secondary_id_strings=storm_object_table[\n tracking_utils.SECONDARY_ID_COLUMN].values.tolist()\n )\n\n argument_dict = {tracking_utils.FULL_ID_COLUMN: full_id_strings}\n return storm_object_table.assign(**argument_dict)","def get_latest_bar_value(self, symbol, val_type):\n raise NotImplementedError(\"Should implement get_latest_bar_value()\")","def max(self):\n\n return time_stat(self, stat=\"max\")","def parse_season_information_for_video (self, video):\n season_info = {\n 'episode_count': None,\n 'seasons_label': None,\n 'seasons_count': None\n }\n if video['summary']['type'] == 'show':\n season_info = {\n 'episode_count': video['episodeCount'],\n 'seasons_label': video['numSeasonsLabel'],\n 'seasons_count': video['seasonCount']\n }\n return season_info","def season_indices(t, edges=None, hard=False):\n if edges is None:\n edges = season_edges(t)\n season_ixs = np.digitize(t.jyear, edges.jyear)\n N = season_ixs.size\n seasons = []\n for ix in np.unique(season_ixs):\n season_members = np.arange(N)[season_ixs == ix] # bool array to index array\n seasons.append(season_members)\n if hard:\n if t[seasons[0]].jyear.max() < edges[0].jyear:\n seasons = seasons[1:]\n if t[seasons[-1]].jyear.min() > edges[-1].jyear:\n seasons = seasons[:-1]\n return seasons","def seasonal_calc(t, y, func, edges=None):\n ts, ys = seasonal_series(t, y, edges=edges)\n t_means = [t.jyear.mean() for t in ts]\n t_means = astropy.time.Time(t_means, format='jyear', scale=t.scale)\n f_y = np.array([func(y) for y in ys])\n return t_means, f_y","def get_top_n_leaders(self, kind, stat, year, n):\n if stat in ['WAR', 'rank', 'G']:\n sort_key = 'fg.{0}.{1}.{0}_{2}'.format(kind, year, stat)\n else:\n sort_key = 'fg.{}.{}.{}'.format(kind, year, stat)\n\n lb = self._db.Players.find({}).sort(sort_key, -1).limit(n)\n\n return [x['fg'][kind][str(year)] for x in lb]","def get_latest(self, n = 1):\n \n if not ElasticConnection._es.indices.exists(index=self.index):\n print('Index ' + self.index + ' not exists')\n return []\n \n print('%s Getting the latest behavior model in %r...' % (datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S'), self.index))\n \n response = ElasticConnection._es.search(\n index = self.index,\n body = {\n \"size\": n,\n \"query\": {\n \"range\": {\n \"timestamp\": {\n \"to\": self.before_micros / 1000,\n \"include_upper\": False\n }\n }\n },\n \"sort\": [\n {\n \"timestamp\": { \"order\": \"desc\" }\n }\n ]\n }\n )\n \n hits = response['hits']['hits']\n \n if len(hits) == 0:\n print('%s Could not find any behavior models.' % datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S'))\n return []\n else:\n print('%s Returning the %d latest behavior model.' % (datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S'), len(hits)))\n return [ d['_source'] for d in hits]"],"string":"[\n \"def _get_latest_season_and_type(df):\\n if df is None or len(df) == 0:\\n return (None, None)\\n\\n if not isinstance(df, pd.DataFrame):\\n raise ValueError(f\\\"Type of df is {type(df)}, it should be pandas.DataFrame\\\")\\n\\n df = df[df['state_of_game'] == 'POST']\\n latest_season = df['season'].max()\\n season_types = df[df['season'] == latest_season]['type'].unique()\\n latest_season_type = _get_latest_season_type(list(season_types))\\n return latest_season, latest_season_type\",\n \"def latest_season_before(date):\\n\\tif date.month < 9:\\n\\t\\treturn date.year - 1\\n\\treturn date.year\",\n \"def get_season_no(token, url):\\n headers = {'Accept': 'application/json', 'Authorization': token}\\n r = requests.get(url, headers=headers)\\n json_data = json.loads(r.text).get('data')\\n high_season = 1\\n for episode in json_data:\\n if episode.get('airedSeason') > high_season:\\n high_season = episode.get('airedSeason')\\n return high_season\",\n \"def getseason(data):\\n ## Season key is the most reliable\\n season = data.get(\\\"season\\\")\\n if season:\\n ## Season key is an integer formatted \\\"YYS\\\" and is 2000-based (i.e.- 171 == 2017-Winter)\\n season = str(season)\\n year = int(f\\\"20{season[:2]}\\\")\\n ## Anichart Season key is 1-indexed\\n season = int(season[2]) - 1\\n ## This should normally pass; if it consistently does not, we'll have to investigate why\\n try: return SeasonCharts.buildseason(season,year)\\n ## If something goes wrong, we'll try another method\\n except: print(f\\\"Failed to parse season: {data['season']}\\\")\\n ## Next, we'll iterate over rankings to try to determine the season/year\\n ## There are multiple types of rankings based on season, year, and both combined,\\n ## so we'll piece it together based on whatever we come across first\\n season,year = None,None\\n for ranking in data.get(\\\"rankings\\\",list()):\\n ## Quicker exit (without just making this loop its own function)\\n if season and year: continue\\n ## We'll ignore stuff we've already gotten and assume that nothing in\\n ## rankings contradicts eachother\\n if not season:\\n ## Defaults to None one way or another if it's not supplied\\n season = ranking.get(\\\"season\\\")\\n if not year: year = ranking.get(\\\"year\\\")\\n ## Check if we made it\\n if season and year:\\n ## As above, this should always work out-of-the-box\\n try: return SeasonCharts.buildseason(season,year)\\n except: print(season,year)\\n ## Welp, we're stumped...\\n return None\",\n \"def get_competition_season_type(season):\\n default_type = games.models.CompetitionSeason.winter\\n try:\\n season_name = season.name\\n if season_name.find(\\\"/\\\") == -1:\\n return games.models.CompetitionSeason.summer\\n return default_type\\n except Exception as e:\\n return default_type\",\n \"def highest_snowfall_for_year(resort_name, year):\\n resort_table = resort_table_dict[resort_name]\\n year = int(year)\\n query = \\\"SELECT snowfall FROM %s WHERE CAST(EXTRACT(YEAR FROM status_date) AS INTEGER) = %d\\\" %(resort_table, year)\\n connection = get_connection()\\n\\n snowfall_list = []\\n\\n if connection is not None:\\n try:\\n for row in get_select_query_results(connection, query):\\n snowfall_list.append(row)\\n except Exception as e:\\n print(e, file=sys.stderr)\\n connection.close()\\n snowfall_list.sort(reverse=True)\\n \\\"\\\"\\\"\\n need to think about making our own sorter so we can break ties effectively\\n \\\"\\\"\\\"\\n highest_snowfall = snowfall_list[0]\\n return json.dumps(highest_snowfall)\",\n \"def get_seasons(games: List[BaseGame], reverse=False):\\n seasons = list(set([g.Season for g in games]))\\n seasons.sort(reverse=reverse)\\n return seasons\",\n \"def __latest_available_episode(self, predict_season: int, latest_episode: int) -> int:\\n max_episode = 0\\n for i in range(1, latest_episode + 1):\\n if VideoParser.has_parsed_video(predict_season, i):\\n max_episode = i\\n else:\\n return max_episode\\n return max_episode\",\n \"def get_season(\\n current_date: date, hemisphere: str, season_tracking_type: str\\n) -> str | None:\\n\\n if hemisphere == \\\"equator\\\":\\n return None\\n\\n if season_tracking_type == TYPE_ASTRONOMICAL:\\n spring_start = ephem.next_equinox(str(current_date.year)).datetime()\\n summer_start = ephem.next_solstice(str(current_date.year)).datetime()\\n autumn_start = ephem.next_equinox(spring_start).datetime()\\n winter_start = ephem.next_solstice(summer_start).datetime()\\n else:\\n spring_start = datetime(2017, 3, 1).replace(year=current_date.year)\\n summer_start = spring_start.replace(month=6)\\n autumn_start = spring_start.replace(month=9)\\n winter_start = spring_start.replace(month=12)\\n\\n if spring_start <= current_date < summer_start:\\n season = STATE_SPRING\\n elif summer_start <= current_date < autumn_start:\\n season = STATE_SUMMER\\n elif autumn_start <= current_date < winter_start:\\n season = STATE_AUTUMN\\n elif winter_start <= current_date or spring_start > current_date:\\n season = STATE_WINTER\\n\\n # If user is located in the southern hemisphere swap the season\\n if hemisphere == NORTHERN:\\n return season\\n return HEMISPHERE_SEASON_SWAP.get(season)\",\n \"def season_choices():\\n return [(s, s) for s in range(0, 3)]\",\n \"def find_highest_projected_stocks(self):\\n\\n sorted_stocks = sorted(self.stock_list,\\n key=lambda stock: stock.ryan_rank,\\n reverse=True)\\n\\n return sorted_stocks\",\n \"def highest_per_movie():\\n copy = movies.copy()\\n copy['Gross Per Movie'] = copy['Total Gross'] / copy['Number of Movies'] #calculate gross per movie\\n year = copy.sort_values(['Gross Per Movie'], ascending = False).reset_index(drop = True).Year.loc[0]\\n return ('highest_per_movie', year)\",\n \"def sort_max_h2h_odds(self):\\n\\n\\t\\tfor i,event in enumerate(self.teams):\\n\\t\\t\\t# for each event\\n\\n\\t\\t\\t# init list of 2 dictonarys (1 for each team) that contains the max odds and the site for those odds\\n\\t\\t\\tself.max_h2h_odds.append({self.teams[i][0] : [0, \\\"no site\\\"], self.teams[i][1] : [0, \\\"no site\\\"]})\\n\\n\\t\\t\\tfor j,team in enumerate(self.teams[i]):\\n\\t\\t\\t\\t# for each team\\n\\t\\t\\t\\tfor k,site_odds in enumerate(self.h2h_odds[i]):\\n\\t\\t\\t\\t\\t# for all odds find largest and save\\n\\t\\t\\t\\t\\tif site_odds[j] > self.max_h2h_odds[i][team][0]:\\n\\t\\t\\t\\t\\t\\tself.max_h2h_odds[i][team] = [site_odds[j] , self.betting_sites[i][k]]\\n\\t\\t\\n\\t\\tprint('Finished finding max odds')\",\n \"def update_highest(csevo):\\n tmax = [t[np.argmax(N)] for (t, N) in figure_to_data(csevo)]\\n\\n data = [{\\n \\\"x\\\": list(range(len(tmax))), \\\"y\\\":tmax, \\\"type\\\":\\\"bar\\\"\\n }]\\n\\n layout = {\\n \\\"title\\\":'Time of largest abundance',\\n \\\"template\\\":\\\"plotly_dark\\\",\\n \\\"xaxis\\\":{\\\"title\\\":\\\"Charge state\\\", \\\"range\\\":[0, len(tmax)]},\\n \\\"yaxis\\\":{\\\"title\\\":\\\"Time (s)\\\", \\\"type\\\":\\\"log\\\"}\\n }\\n\\n return {\\\"data\\\":data, \\\"layout\\\":layout}\",\n \"def get_movie_longest_runtime():\\n tree = get_tree()\\n movie_runtimes = [(movie.get(\\\"title\\\"), movie.get(\\\"runtime\\\")) for movie in tree.getroot().findall(\\\"movie\\\")]\\n\\n # one way\\n result = sorted(movie_runtimes, key=lambda movie_runtimes: movie_runtimes[1], reverse = True)[0][0]\\n return result\\n \\n # another way\\n # result = max(movie_runtimes, key=lambda movie_runtimes: movie_runtimes[1])[0]\\n # return result\",\n \"def season(self, seasonnum, order='aired'):\\n if order=='aired':\\n seasons = self.seasons\\n elif order == 'dvd':\\n seasons = self.dvd_seasons\\n try:\\n return seasons[seasonnum]\\n except KeyError:\\n raise SeasonNotFoundError(\\n 'Season no %s does not exists' % seasonnum\\n ), None, sys.exc_info()[2]\",\n \"def _find_latest():\\n try:\\n db = get_master_collection()\\n service_details = db.find({\\\"master.key\\\": \\\"release\\\"}).sort([(\\\"master.value\\\", pymongo.DESCENDING)]).limit(1)\\n for service in service_details:\\n for r in sorted(service[\\\"master\\\"][\\\"value\\\"], reverse=True):\\n latest_release = r\\n build_list = service[\\\"master\\\"][\\\"value\\\"][r]\\n break\\n break\\n\\n latest_rel_num = str(latest_release).replace(\\\"_\\\", \\\".\\\")\\n build_list = _natural_sort(build_list)\\n for build in build_list:\\n latest_build = build\\n break\\n\\n latest_build_num = latest_build\\n second_latest_build_num = int(latest_build_num) - 1\\n latest = {\\\"latest_val\\\": latest_rel_num + \\\"_\\\" + latest_build_num,\\n \\\"second_latest_val\\\": latest_rel_num + \\\"_\\\" + str(second_latest_build_num)}\\n except Exception as e:\\n logger.error(\\\"Exception in _find_latest : \\\" + str(e))\\n return latest\",\n \"def getEvolutionMax(self):\\n \\n return [self.getMaximumAtGivenTime(timeIndex) for timeIndex in range(self.numberOfTimes - 1)]\",\n \"def seasons(self):\\n if self._season_cache_ver == self._db._version:\\n return self._season_cache\\n\\n # Find out how many seasons in this series by fetching the highest season.\\n seasons = self._db.query(type='episode', parent=self._dbrow, attrs=['season'], distinct=True)\\n self._season_cache = [Season(self._db, self, row['season']) for row in seasons]\\n self._season_cache_ver = self._db._version\\n return self._season_cache\",\n \"def latest(scores):\\n return scores[-1]\",\n \"def latest(scores):\\n return scores[-1]\",\n \"def distributeSeason(self):\\n i = 1\\n for day in self.daylist:\\n if i >= monthbeg[5] and i < monthbeg[9]: #june through SEpt as per SCE\\n day.season = 'summer' #https://www.sce.com/residential/rates/Time-Of-Use-Residential-Rate-Plans\\n i = i + 1\\n else:\\n day.season = 'winter'\\n i = i+1\",\n \"def _parse_season_entry (self, season, video, sorting):\\n return {\\n season['summary']['id']: {\\n 'idx': sorting[season['summary']['id']],\\n 'id': season['summary']['id'],\\n 'text': season['summary']['name'],\\n 'shortName': season['summary']['shortName'],\\n 'boxarts': {\\n 'small': video['boxarts']['_342x192']['jpg']['url'],\\n 'big': video['boxarts']['_1280x720']['jpg']['url']\\n },\\n 'interesting_moment': video['interestingMoment']['_665x375']['jpg']['url'],\\n }\\n }\",\n \"def current_season():\\n td = datetime.datetime.today()\\n if td.month > 8:\\n return td.year\\n return td.year - 1\",\n \"def media_season(self):\\n media_status = self._media_status()[0]\\n return media_status.season if media_status else None\",\n \"def season_series(game_id, pref_team, other_team, last_season=False):\\n\\n # Init empty dictionaries and lists\\n games_against = list()\\n pref_toi = dict()\\n pref_goals = dict()\\n pref_assists = dict()\\n pref_points = dict()\\n pref_record = {\\\"wins\\\": 0, \\\"losses\\\": 0, \\\"ot\\\": 0}\\n roster_player = True\\n\\n # If this is the first game of the season, we can set the 'last_season' flag to enable the\\n # season series function to check last year's season series between the two teams.\\n if not last_season:\\n season_start = str(game_id)[0:4]\\n season_end = str(int(season_start) + 1)\\n yesterday = datetime.now() - timedelta(days=1)\\n # yesterday = datetime.now() + timedelta(days=50)\\n # schedule_url = (\\n # f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n # f\\\"&expand=schedule.broadcasts,schedule.teams&startDate=\\\"\\n # f\\\"{season_start}-08-01&endDate={yesterday:%Y-%m-%d}\\\"\\n # )\\n schedule_url = (\\n f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n f\\\"&expand=schedule.broadcasts,schedule.teams\\\"\\n f\\\"&season={season_start}{season_end}\\\"\\n )\\n else:\\n season_start = int(str(game_id)[0:4]) - 1\\n season_end = str(int(season_start) + 1)\\n yesterday = datetime.now() - timedelta(days=1)\\n # yesterday = datetime.now() + timedelta(days=50)\\n # schedule_url = (\\n # f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n # f\\\"&expand=schedule.broadcasts,schedule.teams&startDate=\\\"\\n # f\\\"{season_start}-08-01&endDate={season_end}-06-01\\\"\\n # )\\n schedule_url = (\\n f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n f\\\"&expand=schedule.broadcasts,schedule.teams\\\"\\n f\\\"&season={season_start}{season_end}\\\"\\n )\\n\\n schedule = api.nhl_api(schedule_url).json()\\n dates = schedule[\\\"dates\\\"]\\n\\n # Loop through scheduled to get previously played games against\\n for date in dates:\\n game = date[\\\"games\\\"][0]\\n game_type = game[\\\"gameType\\\"]\\n game_id = game[\\\"gamePk\\\"]\\n game_team_home = game[\\\"teams\\\"][\\\"home\\\"][\\\"team\\\"][\\\"name\\\"]\\n game_team_away = game[\\\"teams\\\"][\\\"away\\\"][\\\"team\\\"][\\\"name\\\"]\\n teams = [game_team_away, game_team_home]\\n game_status = game[\\\"status\\\"][\\\"abstractGameState\\\"]\\n if game_type == \\\"R\\\" and game_status == \\\"Final\\\" and other_team.team_name in teams:\\n game_feed = f\\\"/game/{game_id}/feed/live\\\"\\n games_against.append(game_feed)\\n\\n # If the two teams haven't played yet, just exit this function\\n if not games_against:\\n return None, None, None\\n\\n # Loop through newly created games_against list to get each stats\\n for feed in games_against:\\n game = api.nhl_api(feed).json()\\n game_data = game[\\\"gameData\\\"]\\n home_team_name = game_data[\\\"teams\\\"][\\\"home\\\"][\\\"name\\\"]\\n pref_homeaway = \\\"home\\\" if home_team_name == pref_team.team_name else \\\"away\\\"\\n other_homeaway = \\\"away\\\" if home_team_name == pref_team.team_name else \\\"home\\\"\\n\\n # Get season series\\n end_period = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"currentPeriod\\\"]\\n extra_time = True if end_period > 3 else False\\n pref_score = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"teams\\\"][pref_homeaway][\\\"goals\\\"]\\n other_score = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"teams\\\"][other_homeaway][\\\"goals\\\"]\\n if pref_score > other_score:\\n pref_record[\\\"wins\\\"] += 1\\n elif other_score > pref_score and extra_time:\\n pref_record[\\\"ot\\\"] += 1\\n else:\\n pref_record[\\\"losses\\\"] += 1\\n\\n season_series_str = f\\\"Series: {pref_record['wins']}-\\\" f\\\"{pref_record['losses']}-{pref_record['ot']}\\\"\\n\\n # Get stats leaders\\n # pref_teamstats = game[\\\"liveData\\\"][\\\"boxscore\\\"][\\\"teams\\\"][pref_homeaway][\\\"teamStats\\\"]\\n pref_playerstats = game[\\\"liveData\\\"][\\\"boxscore\\\"][\\\"teams\\\"][pref_homeaway][\\\"players\\\"]\\n for id, player in pref_playerstats.items():\\n try:\\n # Calculate TOI\\n player_toi_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"timeOnIce\\\"]\\n player_toi_minutes = int(player_toi_str.split(\\\":\\\")[0])\\n player_toi_seconds = int(player_toi_str.split(\\\":\\\")[1])\\n player_toi = (player_toi_minutes * 60) + player_toi_seconds\\n pref_toi[id] = pref_toi.get(id, 0) + player_toi\\n\\n # Point Totals\\n player_goal_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"goals\\\"]\\n pref_goals[id] = pref_goals.get(id, 0) + int(player_goal_str)\\n player_assist_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"assists\\\"]\\n pref_assists[id] = pref_assists.get(id, 0) + int(player_assist_str)\\n player_points = int(player_goal_str) + int(player_assist_str)\\n pref_points[id] = pref_points.get(id, 0) + int(player_points)\\n\\n except KeyError:\\n pass\\n\\n # Calculate Stats Leaders\\n sorted_toi = sorted(pref_toi.values(), reverse=True)\\n leader_toi = sorted_toi[0]\\n\\n sorted_points = sorted(pref_points.values(), reverse=True)\\n leader_points = sorted_points[0]\\n\\n # Get TOI leader\\n for id in pref_toi.keys():\\n if pref_toi[id] == leader_toi:\\n player_name = roster.player_attr_by_id(pref_team.roster, id, \\\"fullName\\\")\\n if player_name is None:\\n roster_player = False\\n player_id_only = id.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n leader_toi_avg = leader_toi / len(games_against)\\n m, s = divmod(leader_toi_avg, 60)\\n toi_m = int(m)\\n toi_s = int(s)\\n toi_s = \\\"0{}\\\".format(toi_s) if toi_s < 10 else toi_s\\n toi_avg = \\\"{}:{}\\\".format(toi_m, toi_s)\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n toi_leader_str = \\\"TOI Leader: {} with {} / game.\\\".format(player_short_name, toi_avg)\\n\\n # Handle tied points leaders\\n point_leaders = list()\\n for id in pref_points.keys():\\n if pref_points[id] == leader_points:\\n point_leaders.append(id)\\n\\n if leader_points == 0:\\n points_leader_str = \\\"Points Leader: None (all players have 0 points).\\\"\\n\\n elif len(point_leaders) == 1:\\n leader = point_leaders[0]\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n # If the player is no longer on the team, get their information (change string here?)\\n if player_name is None:\\n roster_player = False\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n if not roster_player:\\n points_leader_str = (\\n f\\\"Points Leader: {player_name} with {leader_points} points \\\"\\n f\\\"({player_goals}G {player_assists}A) \\\"\\n )\\n else:\\n points_leader_str = \\\"Points Leader: {} with {} ({}G {}A).\\\".format(\\n player_name, leader_points, player_goals, player_assists\\n )\\n\\n elif len(point_leaders) > 3:\\n point_leaders_with_attrs = list()\\n for leader in point_leaders:\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n if player_name is None:\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n point_leaders_with_attrs.append(player_short_name)\\n\\n point_leaders_joined = \\\", \\\".join(point_leaders_with_attrs[0:3])\\n leftover_leaders = len(point_leaders) - 3\\n points_leader_str = (\\n f\\\"Points Leaders: {point_leaders_joined} & {leftover_leaders} others ({leader_points} each).\\\"\\n )\\n\\n else:\\n point_leaders_with_attrs = list()\\n for leader in point_leaders:\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n if player_name is None:\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n player_str = f\\\"{player_short_name} ({player_goals}G {player_assists}A)\\\"\\n point_leaders_with_attrs.append(player_str)\\n\\n point_leaders_joined = (\\n f\\\", \\\".join(point_leaders_with_attrs[:-1]) + f\\\" & {point_leaders_with_attrs[-1]}\\\"\\n )\\n points_leader_str = \\\"Points Leaders: {} with {} each.\\\".format(point_leaders_joined, leader_points)\\n\\n return season_series_str, points_leader_str, toi_leader_str\",\n \"def extract_seasonal_component(original_ts, ppy):\\n \\\"\\\"\\\"\\n # === get in-sample data\\n original_ts = original_ts[:-out_of_sample]\\n \\\"\\\"\\\"\\n if seasonality_test(original_ts, ppy):\\n # print(\\\"seasonal\\\")\\n # ==== get moving averages\\n ma_ts = moving_averages(original_ts, ppy)\\n\\n # ==== get seasonality indices\\n le_ts = original_ts * 100 / ma_ts\\n le_ts = np.hstack((le_ts, np.full((ppy - (len(le_ts) % ppy)), np.nan)))\\n le_ts = np.reshape(le_ts, (-1, ppy))\\n si = np.nanmean(le_ts, 0)\\n norm = np.sum(si) / (ppy * 100)\\n si = si / norm\\n else:\\n # print(\\\"NOT seasonal\\\")\\n si = np.full(ppy, 100)\\n return si\",\n \"def calcSeasonModified( monthNum ):\\r\\n\\r\\n if monthNum == 12 or monthNum == 1 or monthNum == 2:\\r\\n return 0\\r\\n\\r\\n elif monthNum == 6 or monthNum == 7 or monthNum == 7:\\r\\n return 1\\r\\n\\r\\n else:\\r\\n return 3\",\n \"def season_top(bot):\\n if len(bot.message.words) < 3:\\n return irc.Response('Number to cut off is missing. The proper command is !season top <number> [condensed?]', pm_user=True)\\n\\n try:\\n top_cut = int(bot.message.words[2])\\n condensed = len(bot.message.words) >= 4 and bot.message.words[3].lower() in ('yes', 'true', '1')\\n ranking = sorted(seasonal.get_rankings(\\\"ssbwiiu.json\\\").items(), key=lambda x: x[1], reverse=True)\\n\\n if len(ranking) == 0:\\n return irc.Response('No one is ranked right now', pm_user=True)\\n\\n separator = ', ' if condensed else '\\\\n'\\n ranking = ['{0[0]} ({0[1]} points)'.format(player) for player in ranking]\\n return irc.Response(separator.join(ranking[:top_cut]), pm_user=True)\\n except ValueError as e:\\n return irc.Response('Number to cut off must be a number.', pm_user=True)\",\n \"def convert_season(row): \\n if row[\\\"month\\\"] >= 8:\\n return int(row[\\\"season\\\"][:4])\\n else:\\n return int(row[\\\"season\\\"][-4:])\",\n \"def calculate_greatest(self):\\n greatest = 0\\n for resourceList in self.loading.values():\\n for time, use in resourceList:\\n if use > greatest:\\n greatest = use\\n self.emit(\\\"greatest_calculated\\\",greatest)\\n return greatest\",\n \"def calc_lf_season(seasons, fuel_region_yh, average_fuel_yd):\\n seasons_lfs = {}\\n for season, yeardays_modelled in seasons.items():\\n\\n average_fuel_yd_full_year = np.average(\\n average_fuel_yd[:, ],\\n axis=1)\\n\\n # Calculate maximum hour in year\\n max_load_h_days_season = np.max(\\n fuel_region_yh[:, yeardays_modelled],\\n axis=2)\\n\\n max_load_h_season = np.max(max_load_h_days_season, axis=1)\\n\\n # Unable local RuntimeWarning: divide by zero encountered\\n with np.errstate(divide='ignore', invalid='ignore'):\\n\\n #convert to percentage\\n season_lf = (average_fuel_yd_full_year / max_load_h_season) * 100\\n\\n # Replace\\n season_lf[np.isinf(season_lf)] = 0\\n season_lf[np.isnan(season_lf)] = 0\\n\\n seasons_lfs[season] = season_lf\\n\\n return seasons_lfs\",\n \"def get_day_highest_rainfall(raindata_list_raw_values_from_urlstream):\\n\\tlist_with_nonempty_values = remove_emptystring_values(raindata_list_raw_values_from_urlstream) # casting the comparison key to another type to compare is undesirable, first cast the type to the correct data type, then store it in the list for comparison.\\n\\thighest_day_rain = max(list_with_nonempty_values, key=lambda row: int(row[1])) # lambda expression works IFF there is NO empty string bad data before cast to int! '' Empty string data in second element column must first be removed from source.\\n\\treturn highest_day_rain\",\n \"def get_most_relevant_run(runs):\\n\\n # if there is a current run in the set pick it\\n most_relevant_run = next(\\n (run for run in runs if run.availability == AvailabilityType.current.value),\\n None,\\n )\\n\\n if not most_relevant_run:\\n # if there a future runs in the set, pick the one with earliest start date\\n runs = runs.order_by(\\\"best_start_date\\\")\\n most_relevant_run = next(\\n (\\n run\\n for run in runs\\n if run.availability\\n in [\\n AvailabilityType.upcoming.value,\\n AvailabilityType.starting_soon.value,\\n ]\\n ),\\n None,\\n )\\n\\n if not most_relevant_run:\\n # get latest past run by start date\\n most_relevant_run = next((run for run in runs.reverse()))\\n\\n return most_relevant_run\",\n \"def latest(self):\\n return self.series.tail(1)[0]\",\n \"def _drop_last_season_championship_matches(self, df: pd.DataFrame) -> pd.DataFrame:\\n max_season = df[\\\"season\\\"].max()\\n\\n if self._resume:\\n # Check if max season changed\\n if max_season != self._model_settings[\\\"max_season\\\"]:\\n raise ValueError(\\\"Current max season is different than previous one. Model must be retrained.\\\")\\n else:\\n self.max_season = self._model_settings[\\\"max_season\\\"]\\n else:\\n self.max_season = max_season\\n\\n return df.loc[~((df[\\\"season\\\"] == max_season) & (df[\\\"league\\\"] == \\\"CH\\\"))]\",\n \"def give_greatest_spart(cls, spart_list):\\n if len(spart_list) == 1:\\n return spart_list[0]\\n sorted_list = cls.sort_by_dominance(spart_list)\\n if not(sorted_list[0] > sorted_list[1]):\\n print(\\\"The two largest elements are non-comparable\\\")\\n return []\\n else:\\n return sorted_list[0]\",\n \"def most_major_mergers_since(ts, Mvir_min=0.8e12, Mvir_max=2e12, z_merger_max=4.0, no_merger_value=None):\\n dbid, Mvir = ts.calculate_all(\\\"dbid()\\\",\\\"Mvir\\\")\\n mask = (Mvir>Mvir_min)*(Mvir<Mvir_max)\\n\\n most_major = []\\n for current_id in dbid[mask]:\\n halo = db.get_halo(current_id)\\n redshift, ratio, halos = get_mergers_of_major_progenitor(halo)\\n m_mask = np.array(redshift)<z_merger_max\\n if m_mask.sum()>0:\\n most_major.append(min(ratio[m_mask]))\\n else:\\n most_major.append(no_merger_value)\\n return np.array(most_major)\",\n \"def get_highest_rank(category_url, scrape_date = \\\"ALLTIME\\\"):\\n\\tif scrape_date == \\\"ALLTIME\\\":\\n\\t\\treturn execute_sql(\\\"SELECT rank from rankings where category_url='%s' \\\\\\n\\t\\t\\torder by rank desc limit 1\\\" % category_url).fetchall()\\t\\t\\n\\telse:\\n\\t\\treturn execute_sql(\\\"SELECT rank FROM rankings WHERE category_url='%s' \\\\\\n\\t\\t\\tAND scrape_date='%s' ORDER BY rank DESC LIMIT 1\\\" % (category_url, \\\\\\n\\t\\t\\tscrape_date)).fetchall()\",\n \"def seasonal_series(t, y, edges=None, hard=False):\\n t = ensure_tarray(t)\\n if len(t) == 1:\\n return [t], [y]\\n season_ixs = season_indices(t, edges=edges, hard=hard)\\n ts = []\\n ys = []\\n for season in season_ixs:\\n ts.append(astropy.time.Time(t.jyear[season], format='jyear', scale=t.scale))\\n ys.append(y[season])\\n return ts, ys\",\n \"def seasonNumber(self):\\n return self.index\",\n \"def get_latest_bar_value(self, symbol, val_type):\\n try:\\n bars_list = self.latest_symbol_data[symbol]\\n except KeyError:\\n print(\\\"That Symbol is not available in the historical data\\\")\\n raise\\n else:\\n return getattr(bars_list[-1][1], val_type)\",\n \"def six_best_months():\\n if not monthly_averages:\\n return [('', 0.0), ('', 0.0), ('', 0.0), ('', 0.0), ('', 0.0), ('', 0.0)]\\n monthly_averages.sort(key=operator.itemgetter(1),reverse=True)\\n return monthly_averages[:6]\",\n \"def compute_levels_seasons(self, Y: t.Tensor, idxs: t.Tensor):\\n y_transformed, _ = Y.median(1)\\n y_transformed = y_transformed.reshape(-1, 1)\\n levels = y_transformed.repeat(1, Y.shape[1])\\n seasonalities = None\\n\\n return levels, None\",\n \"def getNumSeasons(self):\\n searchURL = \\\"http://api.tvmaze.com/shows/\\\" + str(self.__showID) \\\\\\n + \\\"/seasons\\\"\\n\\n response = requests.get(searchURL)\\n data = response.json()\\n\\n return data[-1][\\\"number\\\"]\",\n \"def get_sectors_with_max_and_min_stocks():\\n stock_by_sector = Counter(counter['sector'] for counter in data if counter['sector'] != 'n/a')\\n return stock_by_sector.most_common()[0][0], stock_by_sector.most_common()[-1][0]\",\n \"def parse_episodes_by_season (self, response_data):\\n episodes = {}\\n raw_episodes = response_data['value']['videos']\\n for episode_id in raw_episodes:\\n if self._is_size_key(key=episode_id) == False:\\n if (raw_episodes[episode_id]['summary']['type'] == 'episode'):\\n episodes.update(self.parse_episode(episode=raw_episodes[episode_id], genres=response_data['value']['genres']))\\n return episodes\",\n \"def get_year_with_most_rain(totals_list_per_day_from_datasource):\\n\\train_by_year_collection = {}\\n\\tfor row in totals_list_per_day_from_datasource:\\n\\t\\tyear_component_of_parsed_date = row[0].split('-')[2]\\n\\t\\tif None == rain_by_year_collection.get(year_component_of_parsed_date):\\n\\t\\t\\train_by_year_collection[year_component_of_parsed_date] = 0\\n\\t\\tif '' == row[1]:\\n\\t\\t\\tcontinue\\n\\t\\train_by_year_collection[year_component_of_parsed_date] += int(row[1])\\n\\tmax_rainy_year = max(rain_by_year_collection.items(), key=max_rain_compare) # use .items() and always searches based on the structure given, here .items() returns the dictionary as a tuple only.\\n\\tyear_with_most_rain = { max_rainy_year[0] : max_rainy_year[1] }\\n\\t#year_most_rain = { key : value for key, value in rain_by_year.items() if value == max_value[1] } # find key/year in the dictionary by value/rain reverse thinking.\\n\\treturn year_with_most_rain\",\n \"def max_rain_compare(rain_by_year):\\n\\treturn rain_by_year[1]\",\n \"def parse_seasons (self, id, response_data):\\n raw_seasons = response_data['value']\\n videos = raw_seasons['videos']\\n\\n # get art video key\\n video = {}\\n for key, video_candidate in videos.iteritems():\\n if not self._is_size_key(key):\\n video = video_candidate\\n\\n # get season index\\n sorting = {}\\n for idx, season_list_entry in video['seasonList'].iteritems():\\n if self._is_size_key(key=idx) == False and idx != 'summary':\\n sorting[int(season_list_entry[1])] = int(idx)\\n\\n seasons = {}\\n\\n for season in raw_seasons['seasons']:\\n if self._is_size_key(key=season) == False:\\n seasons.update(self._parse_season_entry(season=raw_seasons['seasons'][season], video=video, sorting=sorting))\\n return seasons\",\n \"def brightest_source(frq=151, sliced_list=catalog.srcs):\\n max_obj = sliced_list[0]\\n for gleam_obj in sliced_list:\\n if gleam_obj.flux_by_frq[frq] > max_obj.flux_by_frq[frq]:\\n max_obj = gleam_obj\\n print(\\\"Largest flux value encountered:\\\", max_obj.flux_by_frq[frq])\\n print(\\\"Name of associated object:\\\", max_obj.name)\\n print(\\\"Index of associated object:\\\", lookup(max_obj.name))\\n return max_obj\",\n \"def find_max(weather_data):\\n if len(weather_data) == 0:\\n return()\\n\\n value = float(weather_data[0])\\n position = 0\\n\\n for index, weather in enumerate(weather_data):\\n if float(weather) >= value:\\n value= float(weather)\\n position = index\\n\\n return(value, position)\",\n \"def getBestConcepts(self):\\n maxScore = -1\\n bestConcepts = []\\n chunkTokens = self.getTokens()\\n for token in chunkTokens:\\n for concept in token.umlsConcepts:\\n if concept.score > maxScore:\\n maxScore = concept.score\\n\\n if maxScore > 0:\\n for tokens in chunkTokens:\\n for concept in token.umlsConcepts:\\n if concept.score == maxScore:\\n bestConcepts.append(concept)\\n return bestConcepts\",\n \"def __get_season_data(season_num: int, max_episode: int, training_data: bool) -> List[TrainSample]:\\n season = EXAM_DATA[season_num]\\n drop_players = season.get_drop_mapping(DropType.EXECUTION_DROP, max_episode)\\n all_answers = season.get_all_answers(set(drop_players.keys()), max_episode)\\n season_data = []\\n for answer in all_answers:\\n exam_episode = answer.episode\\n drop_episodes = drop_players[answer.player]\\n drop_episodes = [episode for episode in drop_episodes if exam_episode <= episode]\\n if drop_episodes:\\n if training_data:\\n for player in exam_episode.players:\\n season_data.append(TrainSample(answer.player, season_num, min(drop_episodes), answer.episode,\\n answer.question, answer.answer, player))\\n else:\\n for answer_on in [False, True]:\\n season_data.append(TrainSample(answer.player, season_num, min(drop_episodes), answer.episode,\\n answer.question, answer.answer, answer_on))\\n return season_data\",\n \"def getSeason(date):\\n\\n date = validate.timestamp(date)\\n day = date.dayofyear\\n leap_year = int(date.is_leap_year)\\n\\n spring = numpy.arange(80, 172) + leap_year\\n summer = numpy.arange(172, 264) + leap_year\\n autumn = numpy.arange(264, 355) + leap_year\\n\\n if day in spring:\\n season = \\\"spring\\\"\\n elif day in summer:\\n season = \\\"summer\\\"\\n elif day in autumn:\\n season = \\\"autumn\\\"\\n else:\\n season = \\\"winter\\\"\\n\\n return season\",\n \"def get_max_min_time(series: object, aggregation: str) -> dict:\\n\\n dates = pd.to_datetime(series)\\n\\n if aggregation == 'year':\\n\\n return {'max': dates.max().year,\\n 'min': dates.min().year}\\n\\n elif aggregation is None:\\n\\n return {'max': dates.max(),\\n 'min': dates.min()}\",\n \"def latestGamePack(team):\\n lgr= get('schedule', {'ver':'v1', 'sportId':1, 'date':today, 'teamId':team, 'fields':['dates','games','gamePk'] })\\n return lgr['dates'][0]['games'][0]['gamePk']\",\n \"def season_limits(self):\\n if self.season == \\\"summer\\\":\\n return\\n neighbors = [(0, 1), (1, 0), (-1, 0), (0, -1)]\\n points_list = []\\n season_pixels = []\\n season_edges = []\\n width = self.grid.width\\n height = self.grid.height\\n x = 0\\n while x < width:\\n y = 0\\n while y < height:\\n points_list.append((x, y))\\n if self.season == \\\"spring\\\" or self.season == \\\"winter\\\":\\n if self.grid.pix_copy[x, y] == (0, 0, 255):\\n season_pixels.append((x, y))\\n else:\\n if self.grid.pix_copy[x, y] == (255, 255, 255):\\n season_pixels.append((x, y))\\n y += 1\\n x += 1\\n for points in points_list:\\n k = 0\\n while k < (len(neighbors)):\\n xp = points[0] + neighbors[k][0]\\n yp = points[1] + neighbors[k][1]\\n condition = self.filter_edges_one(points[0], points[1], xp, yp, season_edges)\\n if condition:\\n season_edges.append((xp, yp))\\n k += 1\\n return season_edges\",\n \"def get_latest_bars_values(self, symbol, val_type, N=1):\\n raise NotImplementedError(\\\"Should implement get_latest_bars_values()\\\")\",\n \"def get_latest_bars_values(self, symbol, val_type, N=1):\\n try:\\n bars_list = self.get_latest_bars(symbol, N)\\n except KeyError:\\n print(\\\"That Symbol is not available in the historical data\\\")\\n raise\\n else:\\n return np.array([getattr(b[1], val_type) for b in bars_list])\",\n \"def reduce2lat_seasonal( mv, seasons=seasonsyr, vid=None ):\\n if vid==None:\\n vid = 'reduced_'+mv.id\\n # Note that the averager function returns a variable with meaningless id.\\n # The climatology function returns the same id as mv, which we also don't want.\\n\\n # The slicers in time.py require getBounds() to work.\\n # If it doesn't, we'll have to give it one.\\n # Setting the _bounds_ attribute will do it.\\n for ax in mv.getAxisList():\\n if ax.getBounds() is None:\\n ax._bounds_ = ax.genGenericBounds()\\n timeax = timeAxis(mv)\\n if timeax.units=='months':\\n # Special check necessary for LEGATES obs data, because\\n # climatology() won't accept this incomplete specification\\n timeax.units = 'months since 0001-01-01'\\n mvseas = seasons.climatology(mv)\\n if mvseas is None:\\n # Among other cases, this can happen if mv has all missing values.\\n return None\\n \\n axes = allAxes( mv )\\n axis_names = [ a.id for a in axes if a.id!='lat' and a.id!='time']\\n axes_string = '('+')('.join(axis_names)+')'\\n\\n if len(axes_string)>2:\\n avmv = averager( mvseas, axis=axes_string )\\n else:\\n avmv = mvseas\\n avmv.id = vid\\n\\n avmv = delete_singleton_axis( avmv, vid='time' )\\n avmv.units = mv.units\\n return avmv\",\n \"def reduce2latlon_seasonal( mv, seasons=seasonsyr, vid=None ):\\n # This differs from reduce2lat_seasonal only in the line \\\"axis_names =\\\"....\\n # I need to think about how to structure the code so there's less cut-and-paste!\\n if vid==None:\\n vid = 'reduced_'+mv.id\\n # Note that the averager function returns a variable with meaningless id.\\n # The climatology function returns the same id as mv, which we also don't want.\\n\\n # The slicers in time.py require getBounds() to work.\\n # If it doesn't, we'll have to give it one.\\n # Setting the _bounds_ attribute will do it.\\n timeax = timeAxis(mv)\\n if timeax.getBounds()==None:\\n timeax._bounds_ = timeax.genGenericBounds()\\n mvseas = seasons.climatology(mv)\\n \\n axes = allAxes( mv )\\n axis_names = [ a.id for a in axes if a.id!='lat' and a.id!='lon' and a.id!='time']\\n axes_string = '('+')('.join(axis_names)+')'\\n\\n if len(axes_string)>2:\\n for axis in mvseas.getAxisList():\\n if axis.getBounds() is None:\\n axis._bounds_ = axis.genGenericBounds()\\n avmv = averager( mvseas, axis=axes_string )\\n else:\\n avmv = mvseas\\n if avmv is None: return avmv\\n avmv.id = vid\\n if hasattr(mv,'units'): avmv.units = mv.units\\n avmv = delete_singleton_axis( avmv, vid='time' )\\n avmv.units = mv.units\\n return avmv\",\n \"def get_parser_best(self):\\n if len(self.parses):\\n return min(self, key=lambda parse: parse.parser_rank)\\n else:\\n return None\",\n \"def timeave_seasonal( mv, seasons=seasonsyr ):\\n return seasons.climatology(mv)\",\n \"def most_stable():\\n \\n \\n \\n \\n return Z\",\n \"def seasonality(df):\\n df_datetime = pd.DatetimeIndex(df.date_time)\\n df[\\\"month\\\"] = df_datetime.month\\n df = drop_cols(df, [\\\"date_time\\\"])\\n\\n return df\",\n \"def get_max_pop_year(group):\\n max_year_val = group.loc[group[\\\"mean\\\"].idxmax()][[\\\"year_id\\\", \\\"mean\\\"]]\\n\\n return max_year_val\",\n \"def _select_most_recent_symbols_chunk(self, sid_group):\\n cols = self.equity_symbol_mappings.c\\n\\n # These are the columns we actually want.\\n data_cols = (cols.sid,) + tuple(cols[name] for name in symbol_columns)\\n\\n # Also select the max of end_date so that all non-grouped fields take\\n # on the value associated with the max end_date. The SQLite docs say\\n # this:\\n #\\n # When the min() or max() aggregate functions are used in an aggregate\\n # query, all bare columns in the result set take values from the input\\n # row which also contains the minimum or maximum. Only the built-in\\n # min() and max() functions work this way.\\n #\\n # See https://www.sqlite.org/lang_select.html#resultset, for more info.\\n to_select = data_cols + (sa.func.max(cols.end_date),)\\n\\n return sa.select(\\n to_select,\\n ).where(\\n cols.sid.in_(map(int, sid_group))\\n ).group_by(\\n cols.sid,\\n )\",\n \"def getSeasonalARIMAModel(self, ts):\\n if not isinstance(ts, pd.Series):\\n raise TypeError('Pandas Time Series required.')\\n\\n if not isinstance(self.df, pd.DataFrame):\\n raise TypeError('Pandas DataFrame required.')\\n\\n if not 'close' in self.df.columns:\\n raise AttributeError(\\\"Pandas DataFrame 'close' column required.\\\")\\n\\n \\\"\\\"\\\"Parameters for SARIMAX\\\"\\\"\\\"\\n model = SARIMAX(ts, trend='n', order=(0,1,0), seasonal_order=(1,1,1,12))\\n return model.fit(disp=-1)\",\n \"def get_dryspells_perseason(dryspells, seasons=((12, 1, 2), (3, 4, 5),\\n (6, 7, 8), (9, 10, 11))):\\n dryspells_seasons = []\\n for season in seasons:\\n eveSeas = []\\n for eveLand in dryspells:\\n eves = [e for e in eveLand if e.start_date().month in season]\\n eveSeas.append(eves)\\n dryspells_seasons.append(eveSeas)\\n\\n return dryspells_seasons\",\n \"def _sort_by_satellite(self) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:\\n time = []\\n satellite = []\\n system = []\\n for sat in sorted(self.dset.unique(\\\"satellite\\\"), reverse=True):\\n idx = self.dset.filter(satellite=sat)\\n time.extend(self.dset.time.gps.datetime[idx])\\n satellite.extend(self.dset.satellite[idx])\\n system.extend(self.dset.system[idx])\\n \\n return np.array([time]), np.array([satellite]), np.array([system])\",\n \"def give_season_suggestion():\\n current_month = datetime.datetime.now().month\\n all_month = (\\n None, 'январь', 'февраль', 'март', 'апрель', 'май', 'июнь',\\n 'июль', 'август', 'сентябрь', 'октябрь', 'ноябрь', 'декабрь')\\n\\n # Autumn.\\n if current_month in [9, 10, 11]:\\n month_list = [\\n 'осень', 'осенний', 'осеннее', 'осенние', 'осенняя', 'сентябрь',\\n 'октябрь', 'ноябрь', 'сентября', 'октября', 'ноября',\\n 'сентябрьский', 'октябрьский', 'ноябрьский', 'свитер',\\n 'листопад']\\n suggestion_list = give_item_by_tag(month_list)\\n if suggestion_list == '':\\n suggestion_list = \\\"добавить в ваши рецепты что-то осенее;)\\\"\\n suggest = f\\\"\\\"\\\"\\n_________________¶\\n________________¶¶\\n___________¶___¶¶¶___¶\\n___________¶¶¶¶¶¶¶¶/¶¶\\n____¶_______¶¶¶¶|¶¶/¶¶________¶\\n_¶¶¶¶¶¶______¶¶¶|¶/¶¶_______¶¶¶¶¶\\n__¶¶¶¶¶¶____¶¶¶¶|/¶¶¶____¶¶¶¶¶¶\\n_¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶|¶¶¶/¶¶¶¶¶/¶¶¶¶¶¶\\n____¶¶¶¶¶¶¶¶¶¶¶¶|¶¶/¶¶¶¶¶/¶¶¶¶\\n¶____¶¶¶¶¶¶¶¶¶¶¶|¶/¶¶¶¶¶/¶¶¶_____¶\\n¶¶¶_¶¶¶¶¶¶¶¶¶¶¶¶|/¶¶¶¶¶/¶¶¶¶__¶¶¶¶\\n¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶|¶¶¶¶¶/¶¶¶¶¶¶¶¶¶¶¶\\n__¶¶¶¶¶¶¶¶¶¶¶¶¶¶|¶¶¶¶/¶¶¶¶¶/¶¶¶¶\\n___¶¶¶¶¶¶¶¶¶¶¶¶¶|¶¶¶/¶¶¶¶/¶¶¶¶¶\\n_¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶|¶¶/¶¶¶/¶¶¶¶¶¶¶¶¶¶\\n________¶¶¶¶¶¶¶¶|¶/¶/¶¶¶¶¶¶\\n_______________¶¶\\n______________¶¶ На дворе {all_month[current_month]}, осень.\\n______________¶¶ самое время приготовить что-то уютное,\\n______________¶¶ бодрящее из ваших рецептов.\\n_______________¶¶ Рекомендуем:\\n ________________¶¶¶ {suggestion_list}\\n \\\"\\\"\\\"\\n\\n # Winter.\\n elif current_month in [1, 2, 12]:\\n month_list = [\\n 'зима', 'зимний', 'зимнее', 'зимние', 'зимняя', 'январь',\\n 'декабрь', 'февраль', 'декабря', 'января', 'февраля',\\n 'декабрьский', 'январский', 'февральский', 'свитер', 'снег',\\n 'снегопад', 'иней', 'зимушка', 'Зимушка', 'новый год',\\n 'новым годом', 'рождество', 'рождественский']\\n suggestion_list = give_item_by_tag(month_list)\\n if suggestion_list == '':\\n suggestion_list = \\\"добавить в ваши рецепты что-то зимнее;)\\\"\\n suggest = f\\\"\\\"\\\"\\n_____________________$$\\n_____________________$$\\n_____________$$$$____$$____$$$$\\n_____ ____$$____$$$$$$$$$$$$$$____$$\\n_________$$________$$$$$$________$$\\n_$$______$$__________$$__________$$______$$\\n_$$$$$$__$ $__________$$__________$$__$$$$$$\\n_____$$$$$$__________$$__________$$$$$$\\n_______$$$$$$________$$________$$$$$$\\n___$$$$______$$ $$____$$____$$$$______$$$$\\n_$$______________$$$$$$$$$$______________$$\\n___________________$$$$$$\\n_$$______________$$$ $$$$$$$______________$$\\n___$$$$______$$$$____$$____$$$$______$$$$\\n_______$$$$$$________$$________$$$$$$\\n_____$$$$$$__________$$__ ________$$$$$$\\n_$$$$$$__$$__________$$__________$$__$$$$$$\\n_$$______$$__________$$__________$$______$$\\n_________$$________$$$$$$_____ ___$$\\n_________$$____$$$$$$$$$$$$$$____$$ На дворе {all_month[current_month]}, зима.\\n_____________$$$$____$$____$$$$ самое время приготовить что-то приятное,\\n_____________________$$ согревающее из ваших рецептов.\\n___________\\n Рекомендуем:\\n {suggestion_list}\\n \\\"\\\"\\\"\\n\\n # Spring.\\n elif current_month in [3, 4, 5]:\\n month_list = [\\n 'весна', 'весенний', 'весеннее', 'весенние', 'весенняя', 'март',\\n 'апрель', 'май', 'марта', 'апреля', 'мая', 'мартовский',\\n 'апрельский', 'майский', 'свежий']\\n suggestion_list = give_item_by_tag(month_list)\\n if suggestion_list == '':\\n suggestion_list = \\\"добавить в ваши рецепты что-то весеннее;)\\\"\\n suggest = f\\\"\\\"\\\"\\n______d$$$$b_d$$$$$b\\n_____$$$$$$$$$$$$$$$$$\\n____$$$$$$P_$$$$$$$$$\\n_____V$P_______Y$$P\\n___________________d$$b\\n___$$$$_________$$$$$$\\n___$$$ $$$______$$$$$$$\\n____Y$$$$$$$$$$$$$$$$P\\n______Y$P_$$$$$$$\\n__________Y$$$P_$\\n__________________$\\n_________________$\\n_________________ $\\n__________________$$$$$_\\n_________________$$$$$$$$ $\\n__$$$$$$$_______$$$$$$\\n_$$$$$$$$$__$$$$$ На дворе {all_month[current_month]}, весна.\\n$_ _____$$$$$ Самое время бодрящих, легкий ароматов!\\n________$$_$ Рекомендуем:\\n _________$$$ {suggestion_list}\\n__________$$\\n___________$\\n \\\"\\\"\\\"\\n\\n # Summer.\\n elif current_month in [6, 7, 8]:\\n month_list = [\\n 'лето', 'летний', 'летнее', 'летние', 'летняя', 'июнь', 'июль',\\n 'август', 'июля', 'июня', 'августа', 'июньский', 'июльский',\\n 'августовский', 'тропический', 'тропики', 'экзотика',\\n 'экзотический', 'отпуск']\\n suggestion_list = give_item_by_tag(month_list)\\n if suggestion_list == '':\\n suggestion_list = \\\"добавить в ваши рецепты что-то летнее;)\\\"\\n suggest = f\\\"\\\"\\\"\\n________________ ¶¶¶¶¶¶¶¶\\n_______________ ¶¶¶¶\\n___¶¶¶¶¶¶¶¶¶__ ¶¶¶____¶¶¶\\n_ ¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶\\n¶¶¶¶¶ ______¶¶¶¶¶¶¶¶____¶¶¶¶¶\\n_______ ¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶_____¶¶\\n______ ¶¶¶¶__¶¶¶_¶¶¶¶¶¶¶¶¶\\n____ ¶¶¶¶___¶¶¶___¶¶¶_¶¶¶¶¶\\n___ ¶¶¶¶¶__¶¶¶¶_¶¶_¶¶¶_¶¶¶¶¶\\n___ ¶¶¶___¶¶¶¶¶_¶¶__¶¶¶__¶¶¶\\n__ ¶¶____¶¶¶¶¶___¶¶_¶¶¶¶_¶¶¶\\n________ ¶¶¶_____¶¶___¶¶__¶\\n_________ ¶¶¶____¶¶¶__¶¶__¶\\n_________ ¶¶¶_____¶¶___¶\\n_________ ¶¶______¶¶\\n_________________ ¶¶¶\\n_________________ ¶¶¶ На дворе {all_month[current_month]}, лето.\\n_________________ ¶¶¶ Самое время экзотических коктейлей и\\n________________ ¶¶¶¶ сладости фруктов!\\n________________ ¶¶¶ Рекомендуем:\\n ____________ ¶¶¶_¶¶¶ {suggestion_list}\\n______________ ¶¶¶¶¶¶¶¶\\n\\n \\\"\\\"\\\"\\n return suggest\",\n \"def find_latest_version(versions):\\n\\n highest_version = 0\\n for version in versions:\\n version = parse_version(version)\\n\\n if version > highest_version:\\n highest_version = version\\n\\n return highest_version\",\n \"def get_latest_scene(s3_scene_jsons):\\n # Fetch all 'scene*.json' files and load Scenes\\n scenes = [open_remote_pb_object(scene_json, Scene) for scene_json in s3_scene_jsons]\\n\\n # Find Scene with latest creation timestamp\\n creation_ts = [_s.creation_date.ToMicroseconds() for _s in scenes]\\n index = creation_ts.index(max(creation_ts))\\n return scenes[index], s3_scene_jsons[index]\",\n \"def get_top_public_momends(max_count):\\n _obj = Momend.objects.filter(privacy=Momend.PRIVACY_CHOICES['Public']).order_by('create_date').reverse()\\n\\n return _obj if (_obj.count() < max_count) else _obj[_obj.count() - max_count:]\",\n \"def reduce_time_seasonal( mv, seasons=seasonsyr, vid=None ):\\n if vid==None:\\n vid = 'reduced_'+mv.id\\n # Note that the averager function returns a variable with meaningless id.\\n # The climatology function returns the same id as mv, which we also don't want.\\n\\n # The slicers in time.py require getBounds() to work.\\n # If it doesn't, we'll have to give it one.\\n # Setting the _bounds_ attribute will do it.\\n timeax = timeAxis(mv)\\n if timeax is None:\\n print \\\"WARNING- no time axis in\\\",mv.id\\n return None\\n if timeax.getBounds()==None:\\n timeax._bounds_ = timeax.genGenericBounds()\\n mvseas = seasons.climatology(mv)\\n if mvseas is None:\\n print \\\"WARNING- cannot compute climatology for\\\",mv.id,seasons.seasons\\n print \\\"...probably there is no data for times in the requested season.\\\"\\n return None\\n avmv = mvseas\\n avmv.id = vid\\n if hasattr(mv,'units'): avmv.units = mv.units\\n avmv = delete_singleton_axis( avmv, vid='time' )\\n avmv.units = mv.units\\n return avmv\",\n \"def seasonStats(personId,type = 'gameLog',group = 'hitting'):\\n\\n #playerInfo = get('people', {'personIds':personId})\\n\\n\\n teamStats = get('person',{ 'ver':'v1' , 'personId':personId,'hydrate':['stats(group={},type={})'.format(group,type),'currentTeam']})\\n return teamStats\\n #iterate of stats and find the right player id\\n #career stats broken\\n #fix the season :2019\\n #make function to get team id\",\n \"def current_season() -> int:\\n now = datetime.now()\\n month, year = now.month, now.year\\n if month < 4:\\n year -= 1\\n return year\",\n \"def standardComposition_Max(self):\\n temp = np.fmax(self.rulesList[0], self.rulesList[1])\\n for r in self.rulesList[2:]:\\n temp = np.fmax(temp, r)\\n\\n self.fuzzy_output = temp\",\n \"def season_edges(t):\\n offset = season_offset(t)\\n yr_min = t.datetime.min().year\\n left_frac = t.jyear.min() % yr_min\\n if left_frac < offset:\\n ex_left = 1\\n else:\\n ex_left = 0\\n edges = np.arange(yr_min - ex_left + offset, t.jyear.max() + 1, 1.0)\\n return astropy.time.Time(edges, format='jyear')\",\n \"def youngest(self):\\n # Your implementation here\",\n \"def get_last_seg_date(self, story):\\n\\n # retrieve the LAST story segment (ordered by published date)\\n # if this entry doesn't exist, there are no story segments. Return the\\n # oldest possible date.\\n\\n # ...various errors due to unicode problems....due to the data coming from either\\n # the database, or the website. Not quite sure what is going on here, whether it's my\\n # system, or not. But this code makes it work for the lowest common denominator.\\n try:\\n story_segs = StorySegment.objects.filter(story=story).order_by(\\\"-published\\\")[0]\\n \\\"\\\"\\\"\\n try:\\n print (\\\"lastdate for {0}: {1}\\\".format(story.title, story_segs.published))\\n except UnicodeEncodeError:\\n print (\\\"lastdate for {0}: {1}\\\".format(story.title.encode('utf-8'), story_segs.published))\\n \\\"\\\"\\\"\\n return story_segs.published\\n except IndexError:\\n \\\"\\\"\\\"\\n try:\\n print(\\\" no lastdate found for story [{0}]\\\".format(story.title))\\n except UnicodeEncodeError:\\n print(\\\" no lastdate found for story [{0}]\\\".format(story.title.encode('utf-8')))\\n \\\"\\\"\\\"\\n return datetime.min.replace(tzinfo=utc)\",\n \"def get_top_news_and_the_rest(self):\\n queryset = self.news.order_by('-marked', '-publication_date')\\n return queryset.first(), queryset[1:]\",\n \"def get_year_desc():\\n return Year.objects.all().order_by('-name')\",\n \"def get_stock_symbol_with_highest_cap():\\n #data2 = _cap_str_to_mln_float('cap')\\n symbol_max = dict()\\n for items in data:\\n if items['symbol'] in symbol_max.keys():\\n symbol_max[items['symbol']] = max(symbol_max[items['symbol']], _cap_str_to_mln_float(items['cap']))\\n else:\\n symbol_max[items['symbol']] = _cap_str_to_mln_float(items['cap'])\\n\\n value = sorted(symbol_max.items(), key = lambda x:x[1], reverse=True)[0][0]\\n #sorted(symbol_max.items(), key = lambda x:x[1])\\n return value\",\n \"def set_season_time(season): \\n if season == '2021-22':\\n startdate = time.strptime('13-08-2021', '%d-%m-%Y')\\n startdate = datetime.fromtimestamp(mktime(startdate))\\n enddate = time.strptime('08-10-2021', '%d-%m-%Y')\\n enddate = datetime.fromtimestamp(mktime(enddate))\\n if season == '2020-21':\\n startdate = time.strptime('12-08-2020', '%d-%m-%Y')\\n startdate = datetime.fromtimestamp(mktime(startdate))\\n enddate = time.strptime('26-07-2021', '%d-%m-%Y')\\n enddate = datetime.fromtimestamp(mktime(enddate))\\n if season == '2019-20':\\n startdate = time.strptime('09-08-2019', '%d-%m-%Y')\\n startdate = datetime.fromtimestamp(mktime(startdate))\\n enddate = time.strptime('26-07-2020', '%d-%m-%Y')\\n enddate = datetime.fromtimestamp(mktime(enddate))\\n return startdate, enddate\",\n \"def hottest_summer(df) -> int:\\n df['summer_max'] = df[['JUN', 'JUL', 'AUG']].max(axis=1)\\n hottest_year = df['summer_max'].idxmax()\\n return hottest_year\",\n \"def search_season_episode(self,strz):\\t\\n\\t\\tpattern = compile(\\\"(S(\\\\d\\\\d)E(\\\\d\\\\d))\\\") #S01E03\\n\\t\\tsep = pattern.search(strz)\\t\\t\\n\\t\\tif sep is not None:\\n\\t\\t\\tse= sep.group(1)\\n\\t\\t\\tseason = sep.group(2)\\n\\t\\t\\tepisode = sep.group(3)\\n\\t\\t\\treturn strz.replace(se,\\\"\\\")\\n\\t\\t\\t\\n\\t\\tpattern = compile(\\\"((\\\\d\\\\d)x(\\\\d\\\\d))\\\") #01x03\\n\\t\\tsep = pattern.search(strz)\\t\\t\\n\\t\\tif sep is not None:\\n\\t\\t\\tse= sep.group(1)\\n\\t\\t\\tseason = sep.group(2)\\n\\t\\t\\tepisode = sep.group(3)\\n\\t\\t\\treturn strz.replace(se,\\\"\\\")\\n\\t\\t\\t\\n\\t\\tpattern = compile(\\\"(Ep(\\\\d\\\\d))\\\") #Ep03\\n\\t\\tsep = pattern.search(strz)\\t\\t\\n\\t\\tif sep is not None:\\n\\t\\t\\tse= sep.group(1)\\n\\t\\t\\tepisode = sep.group(2)\\n\\t\\t\\treturn strz.replace(se,\\\"\\\")\",\n \"def extract_season_episode_from_str(s):\\n m = check_for_season_episode_code(s)\\n\\n if not m:\\n return 1, 1\\n\\n return int(m.group(1)), int(m.group(2))\",\n \"def get_latest_unwatched(conn, show_stub):\\n\\t\\tsql_query = f'SELECT \\\"show_name\\\", \\\"season\\\", \\\"episode_number\\\", \\\"episode_title\\\" FROM shows WHERE show_stub = \\\"{show_stub}\\\" AND \\\"watched_status\\\" = 0 ORDER BY \\\"season\\\", \\\"episode\\\"'\\n\\t\\t\\n\\t\\tepisode_list = execute_sql(conn, sql_query).fetchall()\\n\\t\\t\\n\\t\\treturn episode_list\",\n \"def latest(scores: list) -> int:\\n return scores[-1]\",\n \"def get_upcoming_season(self):\\n result = self._method_call(\\\"UpcomingSeason\\\")\\n return int(result)\",\n \"def local_maxima_to_storm_tracks(local_max_dict_by_time, first_numeric_id):\\n\\n prev_primary_id_numeric = first_numeric_id - 1\\n prev_secondary_id_numeric = first_numeric_id - 1\\n num_times = len(local_max_dict_by_time)\\n\\n for i in range(num_times):\\n this_num_storm_objects = len(local_max_dict_by_time[i][LATITUDES_KEY])\\n this_empty_2d_list = [\\n ['' for _ in range(0)] for _ in range(this_num_storm_objects)\\n ]\\n\\n local_max_dict_by_time[i].update({\\n PRIMARY_IDS_KEY: [''] * this_num_storm_objects,\\n SECONDARY_IDS_KEY: [''] * this_num_storm_objects,\\n PREV_SECONDARY_IDS_KEY: this_empty_2d_list,\\n NEXT_SECONDARY_IDS_KEY: copy.deepcopy(this_empty_2d_list)\\n })\\n\\n # If first time step, just create storm IDs and don't worry about\\n # linking.\\n if i == 0:\\n for j in range(this_num_storm_objects):\\n (local_max_dict_by_time[i][PRIMARY_IDS_KEY][j],\\n prev_primary_id_numeric\\n ) = _create_partial_storm_id(prev_primary_id_numeric)\\n\\n (local_max_dict_by_time[i][SECONDARY_IDS_KEY][j],\\n prev_secondary_id_numeric\\n ) = _create_partial_storm_id(prev_secondary_id_numeric)\\n\\n continue\\n\\n # Handle mergers between [i]th and [i - 1]th time steps. This\\n # occurs when two storms at [i - 1]th time merge into one storm at\\n # [i]th time.\\n this_current_to_prev_matrix = copy.deepcopy(\\n local_max_dict_by_time[i][CURRENT_TO_PREV_MATRIX_KEY]\\n )\\n\\n this_dict = _local_maxima_to_tracks_mergers(\\n current_local_max_dict=local_max_dict_by_time[i],\\n previous_local_max_dict=local_max_dict_by_time[i - 1],\\n current_to_previous_matrix=this_current_to_prev_matrix,\\n prev_primary_id_numeric=prev_primary_id_numeric,\\n prev_secondary_id_numeric=prev_secondary_id_numeric)\\n\\n local_max_dict_by_time[i] = this_dict[CURRENT_LOCAL_MAXIMA_KEY]\\n local_max_dict_by_time[i - 1] = this_dict[PREVIOUS_LOCAL_MAXIMA_KEY]\\n this_current_to_prev_matrix = this_dict[CURRENT_TO_PREV_MATRIX_KEY]\\n prev_primary_id_numeric = this_dict[PREVIOUS_PRIMARY_ID_KEY]\\n prev_secondary_id_numeric = this_dict[PREVIOUS_SECONDARY_ID_KEY]\\n\\n this_old_to_new_dict = this_dict[OLD_TO_NEW_PRIMARY_IDS_KEY]\\n\\n for j in range(i + 1):\\n local_max_dict_by_time[j][PRIMARY_IDS_KEY] = [\\n p if p not in this_old_to_new_dict else\\n this_old_to_new_dict[p]\\n for p in local_max_dict_by_time[j][PRIMARY_IDS_KEY]\\n ]\\n\\n # Handle splits between [i]th and [i - 1]th time steps. This\\n # occurs when one storm at [i - 1]th time splits into two storms at\\n # [i]th time.\\n this_dict = _local_maxima_to_tracks_splits(\\n current_local_max_dict=local_max_dict_by_time[i],\\n previous_local_max_dict=local_max_dict_by_time[i - 1],\\n current_to_previous_matrix=this_current_to_prev_matrix,\\n prev_secondary_id_numeric=prev_secondary_id_numeric)\\n\\n local_max_dict_by_time[i] = this_dict[CURRENT_LOCAL_MAXIMA_KEY]\\n local_max_dict_by_time[i - 1] = this_dict[PREVIOUS_LOCAL_MAXIMA_KEY]\\n this_current_to_prev_matrix = this_dict[CURRENT_TO_PREV_MATRIX_KEY]\\n prev_secondary_id_numeric = this_dict[PREVIOUS_SECONDARY_ID_KEY]\\n\\n # Handle simple connections between [i]th and [i - 1]th time steps.\\n # This occurs when one storm at [i - 1]th time has either zero or\\n # one successors at [i]th time. In other words, there is either a\\n # one-to-one connection or no connection.\\n this_dict = _local_maxima_to_tracks_simple(\\n current_local_max_dict=local_max_dict_by_time[i],\\n previous_local_max_dict=local_max_dict_by_time[i - 1],\\n current_to_previous_matrix=this_current_to_prev_matrix,\\n prev_primary_id_numeric=prev_primary_id_numeric,\\n prev_secondary_id_numeric=prev_secondary_id_numeric)\\n\\n local_max_dict_by_time[i] = this_dict[CURRENT_LOCAL_MAXIMA_KEY]\\n local_max_dict_by_time[i - 1] = this_dict[PREVIOUS_LOCAL_MAXIMA_KEY]\\n prev_primary_id_numeric = this_dict[PREVIOUS_PRIMARY_ID_KEY]\\n prev_secondary_id_numeric = this_dict[PREVIOUS_SECONDARY_ID_KEY]\\n\\n all_primary_id_strings = []\\n all_secondary_id_strings = []\\n all_first_prev_sec_id_strings = []\\n all_second_prev_sec_id_strings = []\\n all_first_next_sec_id_strings = []\\n all_second_next_sec_id_strings = []\\n\\n all_times_unix_sec = numpy.array([], dtype=int)\\n all_spc_date_strings = []\\n all_centroid_latitudes_deg = numpy.array([])\\n all_centroid_longitudes_deg = numpy.array([])\\n all_centroid_x_metres = numpy.array([])\\n all_centroid_y_metres = numpy.array([])\\n\\n include_polygons = POLYGON_OBJECTS_LATLNG_KEY in local_max_dict_by_time[0]\\n\\n all_polygon_rows_arraylist = []\\n all_polygon_columns_arraylist = []\\n all_polygon_lat_arraylist_deg = []\\n all_polygon_lng_arraylist_deg = []\\n all_polygon_objects_latlng = numpy.array([], dtype=object)\\n all_polygon_objects_rowcol = numpy.array([], dtype=object)\\n\\n for i in range(num_times):\\n this_num_storm_objects = len(\\n local_max_dict_by_time[i][LATITUDES_KEY]\\n )\\n\\n all_primary_id_strings += local_max_dict_by_time[i][PRIMARY_IDS_KEY]\\n all_secondary_id_strings += local_max_dict_by_time[i][SECONDARY_IDS_KEY]\\n\\n all_first_prev_sec_id_strings += [\\n x[0] if len(x) > 0 else ''\\n for x in local_max_dict_by_time[i][PREV_SECONDARY_IDS_KEY]\\n ]\\n\\n all_second_prev_sec_id_strings += [\\n x[1] if len(x) > 1 else ''\\n for x in local_max_dict_by_time[i][PREV_SECONDARY_IDS_KEY]\\n ]\\n\\n all_first_next_sec_id_strings += [\\n x[0] if len(x) > 0 else ''\\n for x in local_max_dict_by_time[i][NEXT_SECONDARY_IDS_KEY]\\n ]\\n\\n all_second_next_sec_id_strings += [\\n x[1] if len(x) > 1 else ''\\n for x in local_max_dict_by_time[i][NEXT_SECONDARY_IDS_KEY]\\n ]\\n\\n these_times_unix_sec = numpy.full(\\n this_num_storm_objects,\\n local_max_dict_by_time[i][VALID_TIME_KEY], dtype=int\\n )\\n\\n if len(these_times_unix_sec) > 0:\\n these_spc_date_strings = this_num_storm_objects * [\\n time_conversion.time_to_spc_date_string(these_times_unix_sec[0])\\n ]\\n else:\\n these_spc_date_strings = []\\n\\n all_times_unix_sec = numpy.concatenate((\\n all_times_unix_sec, these_times_unix_sec\\n ))\\n all_spc_date_strings += these_spc_date_strings\\n\\n all_centroid_latitudes_deg = numpy.concatenate((\\n all_centroid_latitudes_deg,\\n local_max_dict_by_time[i][LATITUDES_KEY]\\n ))\\n all_centroid_longitudes_deg = numpy.concatenate((\\n all_centroid_longitudes_deg,\\n local_max_dict_by_time[i][LONGITUDES_KEY]\\n ))\\n all_centroid_x_metres = numpy.concatenate((\\n all_centroid_x_metres, local_max_dict_by_time[i][X_COORDS_KEY]\\n ))\\n all_centroid_y_metres = numpy.concatenate((\\n all_centroid_y_metres, local_max_dict_by_time[i][Y_COORDS_KEY]\\n ))\\n\\n if not include_polygons:\\n continue\\n\\n all_polygon_rows_arraylist += local_max_dict_by_time[i][\\n GRID_POINT_ROWS_KEY]\\n all_polygon_columns_arraylist += local_max_dict_by_time[i][\\n GRID_POINT_COLUMNS_KEY]\\n all_polygon_lat_arraylist_deg += local_max_dict_by_time[i][\\n GRID_POINT_LATITUDES_KEY]\\n all_polygon_lng_arraylist_deg += local_max_dict_by_time[i][\\n GRID_POINT_LONGITUDES_KEY]\\n\\n all_polygon_objects_latlng = numpy.concatenate((\\n all_polygon_objects_latlng,\\n local_max_dict_by_time[i][POLYGON_OBJECTS_LATLNG_KEY]\\n ))\\n all_polygon_objects_rowcol = numpy.concatenate((\\n all_polygon_objects_rowcol,\\n local_max_dict_by_time[i][POLYGON_OBJECTS_ROWCOL_KEY]\\n ))\\n\\n storm_object_dict = {\\n tracking_utils.PRIMARY_ID_COLUMN: all_primary_id_strings,\\n tracking_utils.SECONDARY_ID_COLUMN: all_secondary_id_strings,\\n tracking_utils.FIRST_PREV_SECONDARY_ID_COLUMN:\\n all_first_prev_sec_id_strings,\\n tracking_utils.SECOND_PREV_SECONDARY_ID_COLUMN:\\n all_second_prev_sec_id_strings,\\n tracking_utils.FIRST_NEXT_SECONDARY_ID_COLUMN:\\n all_first_next_sec_id_strings,\\n tracking_utils.SECOND_NEXT_SECONDARY_ID_COLUMN:\\n all_second_next_sec_id_strings,\\n tracking_utils.VALID_TIME_COLUMN: all_times_unix_sec,\\n tracking_utils.SPC_DATE_COLUMN: all_spc_date_strings,\\n tracking_utils.CENTROID_LATITUDE_COLUMN: all_centroid_latitudes_deg,\\n tracking_utils.CENTROID_LONGITUDE_COLUMN: all_centroid_longitudes_deg,\\n CENTROID_X_COLUMN: all_centroid_x_metres,\\n CENTROID_Y_COLUMN: all_centroid_y_metres\\n }\\n\\n if include_polygons:\\n storm_object_dict.update({\\n tracking_utils.ROWS_IN_STORM_COLUMN: all_polygon_rows_arraylist,\\n tracking_utils.COLUMNS_IN_STORM_COLUMN:\\n all_polygon_columns_arraylist,\\n tracking_utils.LATITUDES_IN_STORM_COLUMN:\\n all_polygon_lat_arraylist_deg,\\n tracking_utils.LONGITUDES_IN_STORM_COLUMN:\\n all_polygon_lng_arraylist_deg,\\n tracking_utils.LATLNG_POLYGON_COLUMN: all_polygon_objects_latlng,\\n tracking_utils.ROWCOL_POLYGON_COLUMN: all_polygon_objects_rowcol\\n })\\n\\n storm_object_table = pandas.DataFrame.from_dict(storm_object_dict)\\n\\n # Create full IDs (primary + secondary).\\n full_id_strings = partial_to_full_ids(\\n primary_id_strings=storm_object_table[\\n tracking_utils.PRIMARY_ID_COLUMN].values.tolist(),\\n secondary_id_strings=storm_object_table[\\n tracking_utils.SECONDARY_ID_COLUMN].values.tolist()\\n )\\n\\n argument_dict = {tracking_utils.FULL_ID_COLUMN: full_id_strings}\\n return storm_object_table.assign(**argument_dict)\",\n \"def get_latest_bar_value(self, symbol, val_type):\\n raise NotImplementedError(\\\"Should implement get_latest_bar_value()\\\")\",\n \"def max(self):\\n\\n return time_stat(self, stat=\\\"max\\\")\",\n \"def parse_season_information_for_video (self, video):\\n season_info = {\\n 'episode_count': None,\\n 'seasons_label': None,\\n 'seasons_count': None\\n }\\n if video['summary']['type'] == 'show':\\n season_info = {\\n 'episode_count': video['episodeCount'],\\n 'seasons_label': video['numSeasonsLabel'],\\n 'seasons_count': video['seasonCount']\\n }\\n return season_info\",\n \"def season_indices(t, edges=None, hard=False):\\n if edges is None:\\n edges = season_edges(t)\\n season_ixs = np.digitize(t.jyear, edges.jyear)\\n N = season_ixs.size\\n seasons = []\\n for ix in np.unique(season_ixs):\\n season_members = np.arange(N)[season_ixs == ix] # bool array to index array\\n seasons.append(season_members)\\n if hard:\\n if t[seasons[0]].jyear.max() < edges[0].jyear:\\n seasons = seasons[1:]\\n if t[seasons[-1]].jyear.min() > edges[-1].jyear:\\n seasons = seasons[:-1]\\n return seasons\",\n \"def seasonal_calc(t, y, func, edges=None):\\n ts, ys = seasonal_series(t, y, edges=edges)\\n t_means = [t.jyear.mean() for t in ts]\\n t_means = astropy.time.Time(t_means, format='jyear', scale=t.scale)\\n f_y = np.array([func(y) for y in ys])\\n return t_means, f_y\",\n \"def get_top_n_leaders(self, kind, stat, year, n):\\n if stat in ['WAR', 'rank', 'G']:\\n sort_key = 'fg.{0}.{1}.{0}_{2}'.format(kind, year, stat)\\n else:\\n sort_key = 'fg.{}.{}.{}'.format(kind, year, stat)\\n\\n lb = self._db.Players.find({}).sort(sort_key, -1).limit(n)\\n\\n return [x['fg'][kind][str(year)] for x in lb]\",\n \"def get_latest(self, n = 1):\\n \\n if not ElasticConnection._es.indices.exists(index=self.index):\\n print('Index ' + self.index + ' not exists')\\n return []\\n \\n print('%s Getting the latest behavior model in %r...' % (datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S'), self.index))\\n \\n response = ElasticConnection._es.search(\\n index = self.index,\\n body = {\\n \\\"size\\\": n,\\n \\\"query\\\": {\\n \\\"range\\\": {\\n \\\"timestamp\\\": {\\n \\\"to\\\": self.before_micros / 1000,\\n \\\"include_upper\\\": False\\n }\\n }\\n },\\n \\\"sort\\\": [\\n {\\n \\\"timestamp\\\": { \\\"order\\\": \\\"desc\\\" }\\n }\\n ]\\n }\\n )\\n \\n hits = response['hits']['hits']\\n \\n if len(hits) == 0:\\n print('%s Could not find any behavior models.' % datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S'))\\n return []\\n else:\\n print('%s Returning the %d latest behavior model.' % (datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S'), len(hits)))\\n return [ d['_source'] for d in hits]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.672219","0.5860014","0.57655185","0.57589823","0.55447674","0.5494647","0.5436334","0.5394017","0.53333473","0.5325742","0.52458227","0.52447414","0.52128184","0.5152866","0.51410383","0.5102868","0.5095066","0.5065986","0.5064631","0.50637007","0.50637007","0.505879","0.5054823","0.5027999","0.50237775","0.4966669","0.49591097","0.49479103","0.4902462","0.48784855","0.48662037","0.48637614","0.4858753","0.48529607","0.48310274","0.4829563","0.48253545","0.48187312","0.48137745","0.4813567","0.481249","0.48124215","0.47936854","0.47915968","0.47868705","0.47834596","0.47714663","0.47653738","0.4759362","0.47543472","0.4753121","0.47498587","0.47430664","0.47381866","0.4733098","0.47278664","0.47263157","0.47248417","0.4720157","0.47147474","0.47095513","0.47077778","0.47053203","0.4695375","0.46942627","0.4687732","0.46809548","0.46768212","0.4670086","0.46693224","0.4665064","0.46561578","0.46524116","0.46492645","0.46461233","0.464537","0.4642442","0.46267655","0.4625476","0.46234742","0.46209034","0.46197143","0.46187755","0.46149984","0.46130988","0.46070844","0.46061295","0.46019804","0.4598149","0.45903283","0.45880204","0.45860246","0.45786467","0.45740685","0.45620874","0.45619938","0.45587403","0.4558282","0.45568904","0.4555217"],"string":"[\n \"0.672219\",\n \"0.5860014\",\n \"0.57655185\",\n \"0.57589823\",\n \"0.55447674\",\n \"0.5494647\",\n \"0.5436334\",\n \"0.5394017\",\n \"0.53333473\",\n \"0.5325742\",\n \"0.52458227\",\n \"0.52447414\",\n \"0.52128184\",\n \"0.5152866\",\n \"0.51410383\",\n \"0.5102868\",\n \"0.5095066\",\n \"0.5065986\",\n \"0.5064631\",\n \"0.50637007\",\n \"0.50637007\",\n \"0.505879\",\n \"0.5054823\",\n \"0.5027999\",\n \"0.50237775\",\n \"0.4966669\",\n \"0.49591097\",\n \"0.49479103\",\n \"0.4902462\",\n \"0.48784855\",\n \"0.48662037\",\n \"0.48637614\",\n \"0.4858753\",\n \"0.48529607\",\n \"0.48310274\",\n \"0.4829563\",\n \"0.48253545\",\n \"0.48187312\",\n \"0.48137745\",\n \"0.4813567\",\n \"0.481249\",\n \"0.48124215\",\n \"0.47936854\",\n \"0.47915968\",\n \"0.47868705\",\n \"0.47834596\",\n \"0.47714663\",\n \"0.47653738\",\n \"0.4759362\",\n \"0.47543472\",\n \"0.4753121\",\n \"0.47498587\",\n \"0.47430664\",\n \"0.47381866\",\n \"0.4733098\",\n \"0.47278664\",\n \"0.47263157\",\n \"0.47248417\",\n \"0.4720157\",\n \"0.47147474\",\n \"0.47095513\",\n \"0.47077778\",\n \"0.47053203\",\n \"0.4695375\",\n \"0.46942627\",\n \"0.4687732\",\n \"0.46809548\",\n \"0.46768212\",\n \"0.4670086\",\n \"0.46693224\",\n \"0.4665064\",\n \"0.46561578\",\n \"0.46524116\",\n \"0.46492645\",\n \"0.46461233\",\n \"0.464537\",\n \"0.4642442\",\n \"0.46267655\",\n \"0.4625476\",\n \"0.46234742\",\n \"0.46209034\",\n \"0.46197143\",\n \"0.46187755\",\n \"0.46149984\",\n \"0.46130988\",\n \"0.46070844\",\n \"0.46061295\",\n \"0.46019804\",\n \"0.4598149\",\n \"0.45903283\",\n \"0.45880204\",\n \"0.45860246\",\n \"0.45786467\",\n \"0.45740685\",\n \"0.45620874\",\n \"0.45619938\",\n \"0.45587403\",\n \"0.4558282\",\n \"0.45568904\",\n \"0.4555217\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71224105"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":85587,"cells":{"query":{"kind":"string","value":"Removes the latest season and season type from df."},"document":{"kind":"string","value":"def _truncate_games_df(df, season, season_type):\n return df[(df['season'] != season) | (df['type'] != season_type)]"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _drop_last_season_championship_matches(self, df: pd.DataFrame) -> pd.DataFrame:\n max_season = df[\"season\"].max()\n\n if self._resume:\n # Check if max season changed\n if max_season != self._model_settings[\"max_season\"]:\n raise ValueError(\"Current max season is different than previous one. Model must be retrained.\")\n else:\n self.max_season = self._model_settings[\"max_season\"]\n else:\n self.max_season = max_season\n\n return df.loc[~((df[\"season\"] == max_season) & (df[\"league\"] == \"CH\"))]","def seasonality(df):\n df_datetime = pd.DatetimeIndex(df.date_time)\n df[\"month\"] = df_datetime.month\n df = drop_cols(df, [\"date_time\"])\n\n return df","def _get_latest_season_and_type(df):\n if df is None or len(df) == 0:\n return (None, None)\n\n if not isinstance(df, pd.DataFrame):\n raise ValueError(f\"Type of df is {type(df)}, it should be pandas.DataFrame\")\n\n df = df[df['state_of_game'] == 'POST']\n latest_season = df['season'].max()\n season_types = df[df['season'] == latest_season]['type'].unique()\n latest_season_type = _get_latest_season_type(list(season_types))\n return latest_season, latest_season_type","def remove_old_songs(df, too_old=1970):\n drop_indices = df.index[df['year'] < too_old].tolist()\n df = df.drop(drop_indices)\n return df","def clean_up(df: pd.DataFrame) -> pd.DataFrame:\n\n # Hereafter df is sorted by date, which is helpful as it allows using .iloc[-1]\n # to get current (or most recent known) situation per location\n # (Otherwise we'd have to groupby agg -> min date, and then filter)\n df = df.sort_values(\n [Columns.LOCATION_NAME, Columns.DATE, Columns.CASE_TYPE], ascending=True\n )\n\n return df","def update_season_table(session, sched_tbl, season_df):\n date = datetime.date(datetime.now())\n update_query = session.query(sched_tbl).filter(sched_tbl.start_time < date,\n sched_tbl.home_team_score == 0).order_by(sched_tbl.start_time)\n if update_query.count() == 0:\n # print(\"Season is up to date; Returning without performing an update.\") Test/logging statement\n return\n\n all_update_rows = update_query.all()\n first_game_time = all_update_rows[0].start_time\n last_game_time = all_update_rows[len(all_update_rows) - 1].start_time\n\n # Reduce season to games between first and last game time\n season_df[\"start_time\"] = season_df[\"start_time\"].dt.tz_localize(None)\n update_df = season_df.loc[(season_df.start_time >= first_game_time) & (season_df.start_time <= last_game_time)]\n\n for row in all_update_rows:\n game = update_df.loc[(update_df.home_team == row.home_team) & (update_df.away_team == row.away_team) &\n (update_df.start_time.dt.date == datetime.date(row.start_time))]\n if len(game) == 0:\n # This catches playoff games which do not end up happening (i.e. a game 7 in a series a team sweeps), and\n # removes it from the database\n session.delete(row)\n else:\n row.home_team_score = int(game.home_team_score)\n row.away_team_score = int(game.away_team_score)\n row.start_time = game.start_time.dt.to_pydatetime()[0] # Convert Pandas TimeStamp to datetime\n session.add(row)","def remove_DateIndex(dataset):\n dataset.to_csv('normalized_Dataset.csv', mode='w', header=True)\n dataset = pd.read_csv(\"normalized_Dataset.csv\")\n dataset = dataset.drop('date', axis=1)\n return dataset","def clean(df):","def normalize_dataframe(df=None):\n #Drops all the columns that are not 'close'\n col_to_drop = df.columns.levels[1].values\n col_to_drop = np.delete(col_to_drop,np.where(col_to_drop==\"close\"))\n df = df.drop(columns=col_to_drop, level=1)\n #eliminates the double level in columns by deleting the 'close' label. \n #Only ticker label is necessary.\n df.columns = df.columns.droplevel(1)\n return df","def remove_not_available(df):\n drop_indices = df.index[df['genre'] == 'Not Available'].tolist()\n df = df.drop(drop_indices)\n return df","def toSeasonAggFormat(self):\n df_a = self.toHomeAwayFormat().copy()\n df_b = df_a.copy()\n\n for col in list(df_a.columns).copy():\n if col[0] == \"H\":\n df_a = df_a.rename(columns={col:(col[1:])})\n\n if col[0] == \"A\":\n df_a = df_a.rename(columns={col:('Opp'+col[1:])})\n\n #df_a['Win'] = 1\n\n for col in list(df_b.columns).copy():\n if col[0] == \"H\":\n df_b = df_b.rename(columns={col:('Opp'+col[1:])})\n\n if col[0] == \"A\":\n df_b = df_b.rename(columns={col:(col[1:])})\n\n df_c = df_a.append(df_b, sort=True)\n\n return df_c","def replace_season(data,season):\n if not SeasonCharts.matchseason(season):\n raise SeasonCharts.SeasonError\n ## Check data format\n if test_rawdata(data):\n for cat,shows in data.items():\n for show in shows: show['season'] = season\n else:\n for show in data: show['season'] = season","def clean(dataframe):\n # replace 'unknown' in Pop. density with np.nan\n dataframe = dataframe.replace('unknown', np.nan)\n\n # remove spaces from column names and content\n dataframe.columns = dataframe.columns.str.strip()\n\n # change YYYYMMDD to days of the year\n date_debug = []\n for i in range(1, 366):\n date_debug.append(i)\n\n dataframe2 = {'YYYYMMDD': date_debug}\n dataframe['YYYYMMDD'] = dataframe2['YYYYMMDD']\n\n return dataframe","def preprocess(df):\n drop_cols = ['duration_ms', 'key', 'mode', 'time_signature', 'popularity', 'tempo']\n drop_cols += ['track_id', 'track_name', 'artist_name']\n for col in drop_cols:\n if col in list(df.columns):\n df = df.drop(columns=col)\n return df","def clean_station_data(station_df):\n # TODO implement data preparation here\n # Fix the datetime field\n\n # Cast to numeric fields where necessary\n\n # Interpolate missing data","def fix_date(df):\n df.insert(2, \"timestamp\", df[\"TestDate\"])\n\n mask = df[\"TestDate\"] <= df[\"StorageDate\"]\n print(\"Removing %.2f%% of unusual data\" % ((len(df) - np.sum(mask)) * 100 / len(df)))\n df = df[mask]\n\n mask = df[\"StorageDate\"] - df[\"TestDate\"] > pd.Timedelta(days=90)\n print(\"Fixing %.2f%% of outdated data\" % (np.sum(mask) * 100 / len(df)))\n df[\"timestamp\"].values[mask] = df[\"StorageDate\"].values[mask]\n return df","def date_cleaner(dataset):\n dataset['document_last_edition'] = dataset['meta_lastEdition']\n dataset = dataset.drop(['meta_lastEdition'], axis=1)\n \n \n \"\"\"\n Get column to correct date format\n \"\"\"\n dataset['document_last_edition'] = dataset['document_last_edition'].apply(lambda x: str(unify_date_format(x))[:10]) \n \n \n \"\"\"\n meta_lastPublication renaming\n \"\"\"\n dataset['document_last_publication'] = dataset['meta_lastPublication']\n dataset = dataset.drop(['meta_lastPublication'], axis=1)\n\n # DROP HOURS/M/S\n dataset['document_last_publication'] = dataset['document_last_publication'].apply(lambda x: str(unify_date_format(x))[:10]) \n \n \n # META CREATED DATE\n dataset['meta_created_date'] = dataset['meta_created_date'].str.replace('_', '-')\n dataset['meta_created_date'] = dataset['meta_created_date'].apply(lambda x: str(unify_date_format(x))[:10])\n dataset['document_created_at'] = dataset['meta_created_date']\n dataset = dataset.drop(['meta_created_date'], axis=1)\n\n # META_REVISED_MODIFIED\n dataset['document_revised_modified'] = dataset['meta_revised_modified']\n dataset = dataset.drop(['meta_revised_modified'], axis=1) \n \n \n date_column_list = ['document_created_at','document_last_edition', 'document_last_publication', 'document_revised_modified']\n \n \"\"\"\n \n THE PLAN IS TO FIRST REPLACE EMPTY SPOTS IN META_CREATED_DATE WITH CREATED_AT\n THEN WE DROP CREATED_AT\n THEN WE REPLACE EMPTY SPOTS IN OTHER COLUMNS WITH document_created_at\n \"\"\" \n \n dataset[date_column_list] = dataset[date_column_list].replace('Not Specified', np.nan)\n dataset[date_column_list] = dataset[date_column_list].replace('Not Specif', np.nan)\n dataset[date_column_list] = dataset[date_column_list].replace('nan', np.nan) \n dataset['document_created_at'].fillna(dataset['created_at'], inplace=True) \n dataset = dataset.drop(['created_at'], axis=1)\n \n dataset['document_last_edition'].fillna(dataset['document_created_at'], inplace=True)\n dataset['document_last_publication'].fillna(dataset['document_created_at'], inplace=True)\n dataset['document_revised_modified'].fillna(dataset['document_created_at'], inplace=True)\n \n \n\n \n \"\"\"\n FIXING NON-EXISTING DATES IN DATASET\n \"\"\"\n \n dataset = dataset.replace(['2020-1-29'], ['2020-01-29'])\n \n \n \n created_at_unique = list(dataset['document_created_at'].unique())\n last_edition_unique = list(dataset['document_last_edition'].unique())\n last_publication_unique = list(dataset['document_last_publication'].unique())\n revised_modified_unique = list(dataset['document_revised_modified'].unique())\n \n \n # IF LIST NEED TO GET UPDATED\n invalid_created_at = is_valid_date(created_at_unique)\n invalid_last_edition_unique = is_valid_date(last_edition_unique)\n invalid_last_publication_unique = is_valid_date(last_publication_unique)\n invalid_revised_modified_unique = is_valid_date(revised_modified_unique) \n invalid_dates = list(set(itertools.chain(invalid_created_at, invalid_last_edition_unique, invalid_last_publication_unique, invalid_revised_modified_unique)))\n \n \n \n \n # Non-existing dates from the list\n dataset = dataset.replace(['2019-04-31', '2016-11-31', '2019-09-31', '2015-02-31', '2017-04-31', '2015-11-31', '2015-09-31', '2017-02-29', '2018-09-31', '2017-06-31', '2018-04-31', '2015-04-31', '2018-11-31', '2017-09-31', '2015-02-29', '2019-02-29', '2019-06-31', '2018-02-29', '2016-02-30', '2016-06-31', '2016-09-31', '2018-06-31', '2019-18-03', '2020-02-31', '9999-12-31'], \n ['2019-04-30', '2016-11-30', '2019-09-30', '2015-02-28', '2017-04-30', '2015-11-30', '2015-09-30', '2017-02-28', '2018-09-30', '2017-06-30', '2018-04-30', '2015-04-30', '2018-11-30', '2017-09-30', '2015-02-28', '2019-02-28', '2019-06-30', '2018-02-28', '2016-02-28', '2016-06-30', '2016-09-30', '2018-06-30', '2019-03-18', '2020-02-28', '1999-12-31'])\n\n\n \n \n\n\n return dataset","def latest_season_before(date):\n\tif date.month < 9:\n\t\treturn date.year - 1\n\treturn date.year","def _truncate_games_table(db_conn, season, season_type):\n delete_statement = f\"DELETE FROM games WHERE season = {season} and type = '{season_type}'\"\n logging.info(f\"Truncating games table with statement: {delete_statement}\")\n db_conn.execute(delete_statement)","def season_games(year):\n\tLOG.debug('Getting season %d', year)\n\tdata = read_html(io=season_games_url(year),\n\t\t\t\t\t attrs={'id': 'games'},\n\t\t\t\t\t infer_types=False,\n\t\t\t\t\t header=0)\n\tif len(data) != 1:\n\t\traise CantFindTheRightTable\n\tdata = data.pop()\n\n\t# Cleaning.\n\tdel data[\"Unnamed: 3\"]\n\t# The code below issues \"UserWarning: \" So we catch UserWarnings.\n\twith warnings.catch_warnings():\n\t\twarnings.filterwarnings(action='ignore', category=UserWarning,\n\t\t\t\t\t\t\t\tmodule=r'pandas\\.core\\.frame',\n\t\t\t\t\t\t\t\tmessage=(r\"Boolean Series key will be reindexed\"\n\t\t\t\t\t\t\t\t\t\t r\" to match DataFrame index\\.\"))\n\t\t# These rows are mid-table header rows.\n\t\tdata = data[data.Week != \"Week\"][data.Week != \"nan\"]\n\n\tdata['week'] = (data.Week\n\t\t\t\t\t.replace(\"WildCard\", \"wild-card\")\n\t\t\t\t\t.replace(\"Division\", \"divisional\")\n\t\t\t\t\t.replace(\"ConfChamp\", \"conference\")\n\t\t\t\t\t.replace(\"SuperBowl\", \"super-bowl\")\n\t\t\t\t\t.apply(\n\t\t\t\t\t\tlambda s: (int(s)\n\t\t\t\t\t\t\t\t if all(c in '1234567890' for c in s)\n\t\t\t\t\t\t\t\t else s)))\n\tdel data['Week']\n\n\tdata['season'] = year\n\tdata['game_date'] = pd.to_datetime(\n\t\tdata.Date\n\t\t.replace(r\"$\", r\", %d\" % year, regex=True)\n\t\t.replace(r\"^(January|February) (\\d+), \\d+$\", r\"\\1 \\2, %d\" % (year + 1),\n\t\t\t\t regex=True))\n\tdel data['Date']\n\n\tfor column in \"PtsW\", \"PtsL\", \"YdsW\", \"TOW\", \"YdsL\", \"TOL\":\n\t data[column] = data[column].apply(int)\n\n\tdata['WatL'] = data['Unnamed: 5'].apply(lambda x: x == '@')\n\tdel data['Unnamed: 5']\n\tdata['hometeam'] = (~data.WatL * data['Winner/tie'] +\n\t\t\t\t\t\tdata.WatL * data['Loser/tie'])\n\tdata['awayteam'] = (data.WatL * data['Winner/tie'] +\n\t\t\t\t\t\t~data.WatL * data['Loser/tie'])\n\tdata['winner'] = data['Winner/tie']\n\tfor column in 'Winner/tie', 'Loser/tie', \"WatL\":\n\t\tdel data[column]\n\tfor column in 'hometeam', 'awayteam', 'winner':\n\t\tdata[column] = data[column].apply(lambda s: s.split()[-1].lower())\n\n\treturn data","def clean_up_raw(df_raw):\n # exclude_subset = ['well', 'tile', 'cell', 'intensity', 'blob'] # causes issues with later joins, maybe a pandas bug\n import lasagna.utils\n df_raw[CYCLE] = df_raw[CYCLE].astype(int)\n df_raw = df_raw.sort_values([WELL, TILE, CELL, BLOB, CYCLE, CHANNEL])\n return df_raw","def removeLegacy(self, path=None):\n\n df = pd.read_csv(path, compression='gzip')\n print(df.shape)\n gamelist = pd.read_csv('Resources/Genres.csv.gz', usecols=['appid'])\n gamelist = pd.DataFrame(gamelist.appid.unique(), columns=['appid'])\n print(gamelist)\n filter_df = pd.merge(df, gamelist, on='appid', how='inner')\n filter_df = filter_df.dropna()\n filter_df = filter_df.sort_values(['steamid', 'appid'], ascending=[True, True])\n print('done')\n print(filter_df.shape)\n print(filter_df)\n print(np.setdiff1d(df['appid'].unique(), filter_df['appid'].unique()))\n filter_df.to_csv(path, compression='gzip', columns=['steamid', 'appid', 'rating'], index=None)","def terminate(self):\n self.league.season = None\n for team in self.teams:\n team.team.season = None","def clean_catalogue(\n source,\n dest=f\"{pd.Timestamp.now().strftime('%y%m%d%H%M%S')}.csv\",\n window=None\n ):\n catalogue = pd.read_csv(source, parse_dates=[3,4,5])\n catalogue.drop_duplicates(ignore_index=True, inplace=True)\n catalogue.sort_values(by=[\"dt_on\"], ignore_index=True, inplace=True)\n if window is not None: # 430367\n count = 0\n rm_idx = []\n for station in catalogue.station.unique():\n subset = catalogue.loc[catalogue[\"station\"] == station]\n remove = []\n dt_on_ = subset[\"dt_on\"].iloc[0]\n for i, dt_on in enumerate(subset[\"dt_on\"]):\n if i == 0:\n continue\n elif dt_on < dt_on_ + pd.Timedelta(window, unit=\"sec\"):\n remove.append(i)\n else:\n dt_on_ = dt_on\n rm_idx.append(subset.index[remove])\n count += len(remove)\n print(f\"Removing {count} entries...\")\n rm_idx = [item for sub_idx in rm_idx for item in sub_idx]\n catalogue.drop(catalogue.index[rm_idx], inplace=True)\n catalogue.reset_index(drop=True, inplace=True)\n catalogue.to_csv(dest)\n print(f\"Catalogue saved to {dest}.\")","def drop_duplicates(df):\n w = Window().partitionBy(col(\"card_id\")).orderBy(col(\"dt\") )\n df2 = df.select(\"*\", lag(df.nombreestacion).over(w).alias(\"lag_station\"))\n df2 = df2.select(\"*\", lag(df.dt).over(w).alias(\"lag_dt\"))\n df2 = df2.withColumn(\"diff_seconds\", col(\"dt\").cast(\"long\")- col(\"lag_dt\").cast(\"long\"))\n df2 = df2.withColumn(\"same_station\", col('nombreestacion') == col('lag_station'))\n\n mask = (df2.same_station) & (df2.diff_seconds < 1800)#1800 seconds = 30 mins\n df2 = df2.withColumn(\"drop_value\", mask).na.fill(False)\n return df2.filter(~df2.drop_value)","def clean_data(df):\n\n global cato\n # YearRemodAdd: Remodel date (same as construction date if no remodeling or additions)\n df.YearRemodAdd = np.where(df.YearRemodAdd < df.YearBuilt, df.YearBuilt, df.YearRemodAdd)\n assert len(df.loc[df.YearRemodAdd < df.YearBuilt]) == 0, 'Check YearRemodAdd - should be greater or equal then YearBuilt'\n \n # Check range of years\n yr_max = 2022\n # Some values of GarageYrBlt are corrupt. Fix them by replacing them with the YearBuilt\n df.GarageYrBlt = np.where(df.GarageYrBlt > yr_max, df.YearBuilt, df.GarageYrBlt)\n assert df.YearBuilt.max() < yr_max and df.YearBuilt.min() > 1800, 'Check YearBuilt min() and max()'\n assert df.YearRemodAdd.max() < yr_max and df.YearRemodAdd.min() > 1900, 'Check YearRemodAdd min() and max()'\n assert df.YrSold.max() < yr_max and df.YrSold.min() > 2000, 'Check YrSold min() and max()'\n assert df.GarageYrBlt.max() < yr_max and df.GarageYrBlt.min() >= 0, 'Check GarageYrBlt min() and max()'\n \n # Check values of ordinal catagorical variables\n for k in cato.keys():\n assert set(df[k].unique()).difference(df[k].cat.categories) == set(), f'Check values of {k}'\n \n # Check typos in nominal categorical variables\n df['Exterior2nd'] = df['Exterior2nd'].replace({'Brk Cmn':'BrkComm', 'CmentBd':'CemntBd', 'Wd Shng':'WdShing'})\n # Renew a data type after replacement\n df['Exterior2nd'] = df['Exterior2nd'].astype(\"category\")\n if \"None\" not in df['Exterior2nd'].cat.categories:\n df['Exterior2nd'].cat.add_categories(\"None\", inplace=True)\n\n return df","def remove_rows_with_non_english_movies(df):\n df = df[df['original_language'] == 'en']\n return df","def _drop_duplicates(df: DataFrame, cols: Sequence[str]) -> DataFrame:\n df = df.drop_duplicates(subset=cols, keep=\"last\")\n return df.sort_values(by=cols, ignore_index=True)","def cut_frame_tail(df):\n # TODO\n return df","def getLatest(df):\n df_info = df.iloc[:,0:5]\n df_last = df.iloc[:,-1]\n df_info['latest'] = df_last\n \n return df_info","def games(self, competition_id: int, season_id: int) -> DataFrame[Any]:","def _get_latest_season_type(season_types_list):\n if not isinstance(season_types_list, (list, tuple)):\n raise ValueError(f\"type of {type(season_types_list)} for season_type_list not list or tuple\")\n\n latest_season_type = season_types_list[0]\n if len(season_types_list) > 1:\n for season_type in season_types_list[1:]:\n if config.SEASON_TYPES_ORDER[season_type] > config.SEASON_TYPES_ORDER[latest_season_type]:\n latest_season_type = season_type\n\n return latest_season_type","def clean_and_save_timeseries(df):\n drop_columns = ['Lat', \n 'Long', \n 'Province/State']\n\n df.drop(columns=drop_columns, inplace = True)\n \n df_grouped = df.groupby(['Country/Region'], as_index=False).sum()\n df_grouped = df_grouped.set_index('Country/Region').transpose()\n df_grouped.reset_index(level=0, inplace=True)\n df_grouped.rename(columns={'index': 'Date'}, inplace=True)\n df_grouped['Date'] = pd.to_datetime(df_grouped['Date'])\n\n df_grouped.to_csv('../data/worldwide_timeseries.csv', index=False)","def __clean_df(self):\n self.__convert_min()","def remove_different_category_time(self):\n if self._isCollection:\n self.time_series_data.remove_different_time_index()\n else:\n warnings.warn('Setup mainCategoryCol is necessary for this function')\n return self","def fetch_training_df(df):\n\n gen_df = df.copy()\n gen_df.drop(['artist_name', 'title', 'release'], axis=1, inplace=True)\n return gen_df","def remove_players_wo_positions(df):\n df = df[pd.notnull(df['FantPos'])]\n return df","def clean_data(df, to_pm=TO_PM, drop_cols=DROP_COLS, min_seasons=5):\n cleaning_pipe = Pipeline(\n [\n (\"to_per_minute\", cp.ToPerMinute(to_pm)),\n (\"get_month\", cp.GetDebutMonth()),\n (\"fill_na\", cp.FillNA()),\n (\"drop_columns\", cp.DropFeatures(drop_cols)),\n (\"create_labels\", cp.CreateLabels(min_seasons)),\n ]\n )\n return cleaning_pipe.fit_transform(df)","def fix_negatives(data_path: str, plot: bool = False) -> None:\n csvs = [x for x in data_path.iterdir() if 'covidtimeseries' in str(x)]\n for csv in tqdm(csvs, desc=\"Regions\"):\n roi = str(csv).split('.')[0].split('_')[-1]\n df = pd.read_csv(csv)\n # Exclude final day because it is often a partial count.\n df = df.iloc[:-1]\n df = fix_neg(df, roi, plot=plot)\n df.to_csv(data_path / (csv.name.split('.')[0]+'.csv'))","def clean_dataframe(df, column_list, length):\n list_of_actual_columns = list(dataframe.columns.values)\n for each_column in column_list:\n if each_column not in list_of_actual_columns:\n return False\n if number_of_rows < 1000:\n return False\n return True\n\n category_value_counts = df['Type'].value_counts()\n category_value_counts.to_csv('category_value_counts')\n cdf = pd.read_csv('category_value_counts')\n df = df.merge(cdf, left_on='Type', right_on='OldCategory')\n df['Datetime'] = pd.to_datetime(df['Datetime'])\n df[(df['Datetime'] > '2019-12-31')]\n df.sort_values(by='Datetime', ascending=False)","def get_competition_season_type(season):\n default_type = games.models.CompetitionSeason.winter\n try:\n season_name = season.name\n if season_name.find(\"/\") == -1:\n return games.models.CompetitionSeason.summer\n return default_type\n except Exception as e:\n return default_type","def get_latest_league_data(self, df):\n max_date = pd.to_datetime(df[\"Date\"]).max()\n df = df[df[\"Date\"] == max_date]\n [latest_league_file_dir] = df[\"File\"].values\n df = self.extract_df(latest_league_file_dir)\n return df","def convert_season(row): \n if row[\"month\"] >= 8:\n return int(row[\"season\"][:4])\n else:\n return int(row[\"season\"][-4:])","def remove_closed_store(data: pyspark.sql.dataframe.DataFrame) -> pyspark.sql.dataframe.DataFrame:\n closed_store = pd.read_csv('../data/closedStore/Closed stores list.csv')\n closed_store_list = closed_store['Store'].unique()\n \n for store in closed_store_list:\n data = data[data.Store !=store] # remove the colsed store\n return data","def featurize_date_col(df, col, remove_when_done=False):\n df['{}_isholiday'.format(col)] = df[col].apply(is_russian_2017_holiday)\n df['{}_wday'.format(col)] = df[col].apply(lambda x: x.dayofweek)\n df['{}_yday'.format(col)] = df[col].apply(lambda x: x.dayofyear)\n if remove_when_done:\n df = df.drop(col, axis=1)\n return df","def clean_meteo_data(self, df):\n for col in df.columns:\n df[col] = df[col].str.replace(',', '.').astype(\"float\")\n# df_nan = df[df.isna().any(axis=1)]\n# print(\"Check Nans:\",df_nan.shape[0])\n df=df.fillna(method='ffill')\n# df_nan = df[df.isna().any(axis=1)]\n# print(\"Check Nans:\",df_nan.shape[0])\n# print(\"shape selected sensor data:\",df.shape)\n df=df.dropna()\n df=df.resample(\"10T\").mean()\n df=df.reset_index()\n df['dag']=df['datetime'].dt.day\n return df","def clean_df(df):\n # TODO: get birthdays for people missing birthdays\n df = df[~df.birthday.isnull()]\n df[\"birthday\"] = pd.to_datetime(df[\"birthday\"])\n return df","def strip_static_cols(df):\n for col in df.columns:\n if len((df[col]).unique()) == 1:\n df.drop(columns=[col], inplace=True)\n return df","def clean_data(df_turnstiles):\n\n # sort values in a such a way that the duplicate values sit directly below the originals, so they will be removed.\n df_turnstiles.sort_values(\n [\"C/A\", \"UNIT\", \"SCP\", \"STATION\", \"DATE_TIME\"],\n inplace=True,\n ascending=False,\n )\n # keeps top row, deletes others\n df_turnstiles.drop_duplicates(\n subset=[\"C/A\", \"UNIT\", \"SCP\", \"STATION\", \"DATE_TIME\"], inplace=True\n )\n\n # remove DESC column\n df_turnstiles = df_turnstiles.drop([\"DESC\"], axis=1, errors=\"ignore\")\n\n # remove the many spaces in the EXITS column name\n df_turnstiles.rename(\n columns={\n \"EXITS \": \"EXITS\"\n },\n inplace=True,\n )\n\n return df_turnstiles","def clean_file(df):\n df_clean = df.drop_duplicates()\n df_no_zeros = df_clean[df_clean[2] != 0]\n df_sorted = df_no_zeros.sort()\n\n return df_sorted","def cleanFeatures(merged, featureType):\n minimumInningsPitched = 10\n minimumAtBats = 10\n merged = merged[(merged['ip'] > minimumInningsPitched) | (merged['ab_y'] > minimumAtBats)]\n\n if featureType == 'historical':\n merged['is_winner'] = merged['Winner'].apply(lambda x: 0 if isinstance(x, float) else 1)\n modelData = merged[['h_x', 'bb_x', 'so_x', 'er', 'sv', 'w', 'ip', 'h_y', 'hr_y',\n 'rbi', 'bb_y', 'so_y', 'ab_y', 'is_winner']]\n elif featureType == 'projected':\n modelData = merged[['player_id', 'league_y', 'h_x', 'bb_x', 'so_x', 'er', 'sv', 'w', 'ip', 'h_y', 'hr_y',\n 'rbi', 'bb_y', 'so_y', 'ab_y']]\n modelData.rename(columns={\n 'league_y': 'league'\n }, inplace=True)\n\n modelData.is_copy = False\n modelData = modelData.replace([np.inf, -np.inf], np.nan).fillna(0)\n\n return modelData.drop_duplicates()","def get_datetime_seasons(\n self, train: NumpyOrPandas, outp_role: Optional[ColumnRole] = None\n ) -> Optional[LAMLTransformer]:\n _, datetimes = self.get_cols_for_datetime(train)\n for col in copy(datetimes):\n if len(train.roles[col].seasonality) == 0 and train.roles[col].country is None:\n datetimes.remove(col)\n\n if len(datetimes) == 0:\n return\n\n if outp_role is None:\n outp_role = NumericRole(np.float32)\n\n date_as_cat = SequentialTransformer(\n [\n ColumnsSelector(keys=datetimes),\n DateSeasons(outp_role),\n ]\n )\n return date_as_cat","def clean_weather_csv(df: pd.DataFrame) -> pd.DataFrame:\n if 'Min_VisibilitykM' in df.columns:\n df.rename(columns={'Min_VisibilitykM': 'Min_VisibilityKm'},\n inplace=True)\n if 'Min_DewpointC' in df.columns:\n df.rename(columns={'Min_DewpointC': 'MinDew_pointC'}, inplace=True)\n\n cols = map(convert_to_snake_case, df.columns)\n df.columns = cols\n\n for col in ['max_visibility_km', 'min_visibility_km', 'mean_visibility_km',\n 'max_gust_speed_km_h', 'cloud_cover']:\n df[col] = df[col].fillna(df[col].mean())\n\n df['events'] = df.events.fillna('No Events')\n return df","def remove_staxml_end_date(inv):\n for net in inv:\n for sta in net:\n if len(sta.channels) > 3:\n # Find chan with latest end date, and remove it\n for chan in set([chan.code for chan in sta.channels]):\n max(sta.select(channel=chan).channels,\n lambda c: c.end_time)[0].end_date = None\n return","def clean_stockdata(filename):\n pct_cols = ['Change',\n 'Dividend Yield',\n 'EPS growth next 5 years',\n 'EPS growth past 5 years',\n 'Float Short',\n 'Performance (Half Year)',\n 'Performance (Month)',\n 'Performance (Quarter)',\n 'Performance (Week)',\n 'Performance (YTD)',\n 'Performance (Year)',\n 'Sales growth past 5 years',\n 'Payout Ratio',\n 'EPS (ttm)',\n 'EPS growth this year',\n 'EPS growth next year',\n 'EPS growth past 5 years',\n 'EPS growth next 5 years',\n 'Sales growth past 5 years',\n 'EPS growth quarter over quarter',\n 'Sales growth quarter over quarter',\n 'Insider Ownership',\n 'Insider Transactions',\n 'Institutional Ownership',\n 'Institutional Transactions',\n 'Float Short',\n 'Return on Assets',\n 'Return on Equity',\n 'Return on Investment',\n 'Gross Margin',\n 'Operating Margin',\n 'Profit Margin',\n 'Volatility (Week)',\n 'Volatility (Month)',\n '20-Day Simple Moving Average',\n '50-Day Simple Moving Average',\n '200-Day Simple Moving Average',\n '50-Day High',\n '50-Day Low',\n '52-Week High',\n '52-Week Low',\n 'Change from Open',\n 'Gap',\n 'Change']\n df = pd.read_csv(filename)\n df[pct_cols] = df[pct_cols].applymap(clean_pcts)\n df.drop('No.', axis=1, inplace=True)\n df.to_csv(filename, index=False)","def _remove_redundant_columns(self):\n self.dataframe.drop(['letter', 'sentiment'], axis=1, inplace=True)","def _prune_empty_time_series(\n request: tensorboard_service.WriteTensorboardRunDataRequest,\n):\n for (time_series_idx, time_series_data) in reversed(\n list(enumerate(request.time_series_data))\n ):\n if not time_series_data.values:\n del request.time_series_data[time_series_idx]","def dropDuplicateStars(df):\n df = df.drop_duplicates(subset =\"host_name\", keep = 'first')\n return df","def _clean_up_spectrometer_df_header(unformatted_spectrometer_df):\n\n timestamp_column, epoch_column = unformatted_spectrometer_df.columns[[0, 1]]\n spectrometer_df_with_clean_header = unformatted_spectrometer_df.rename(\n # Timestamp column is in local time, epoch_time column is in UTC\n columns={timestamp_column: \"timestamp\", epoch_column: \"epoch_time\"}\n )\n # 'epoch_time' column is redundant to 'timestamp' column. Remove it\n spectrometer_df_with_clean_header.drop([\"epoch_time\"], axis=1, inplace=True)\n\n return spectrometer_df_with_clean_header","def clean_store_csv(df: pd.DataFrame) -> pd.DataFrame:\n cols = map(convert_to_snake_case, df.columns)\n df.columns = cols\n\n for col in ['promo2_since_week', 'promo2_since_year',\n 'competition_distance', 'competition_open_since_month',\n 'competition_open_since_year']:\n df[col] = df[col].fillna(df[col].mean())\n\n df['promo_interval'] = df.promo_interval.fillna('None')\n return df","def split_by_standard(df, compl_data):\n \n #Find non-standard simulations\n standard_simulations = { dyn for dyn in compl_data if is_gpcrmd_community} \n df_standard = df[df.columns[df.columns.isin(standard_simulations)]]\n \n return(df_standard)","def team_games(df, team):\n print(df[\n ( df['team'] == team ) & \n ( df['player'] == 'Team' )\n ].drop_duplicates(subset=['gameid']).shape)\n return df[\n ( df['team'] == team ) & \n ( df['player'] == 'Team' )\n ].drop_duplicates(subset=['gameid'])","def clean_data(gross):\n\n # modify data type\n gross['week_ending'] = pd.to_datetime(gross['week_ending']).dt.strftime('%Y-%m-%d')\n gross['label'] = [1 if x >= 0 else 0 for x in gross['diff_in_dollars']]\n # ain't too proud has weird special characters\n gross['show'] = [x.split('¡')[0] for x in gross['show']]\n # beautiful carol king has different names\n gross['show'] = [re.sub(r'\\s+', ' ', x.translate(\n str.maketrans(string.punctuation, ' ' * len(string.punctuation))).lower().strip()) for x in gross['show']]\n gross.loc[gross['show'] == 'beautiful the carole king musical', 'show'] = 'beautiful'\n\n return gross","def mapback(df):\n df.set_index(df['Datetime'], drop=False, inplace=True) #keep for later use\n df.sort_index(inplace=True)","def select_feats(df):\n cols = list(df)\n for col in cols:\n if col not in config[\"feats\"] and col != \"label\":\n df = df.drop(columns=col)\n return df","def clean_data(df):\n \n any_location_id_missing = (df.PULocationID > 263) | (df.DOLocationID > 263)\n df = df.drop(df.index[any_location_id_missing])\n \n df = df[df.tpep_dropoff_datetime > df.tpep_pickup_datetime]\n\n df.PULocationID.replace([104, 105], 103)\n \n return df","def remove_columns(df):\n avg = np.mean(df[df['sentiment'] != 'None']['sentiment'].astype('float'))\n df['sentiment'] = df['sentiment'].replace('None', avg).astype('float')\n\n to_remove = []\n print('column(s) removed: ')\n for column in df.columns:\n print(column)\n if(np.unique(df[column][df[column].notnull()]).shape[0] < 2):\n print(column)\n to_remove.append(column)\n \n return df.drop(columns = to_remove)","def cleanData(data):\r\n \r\n indexes = data.index[pd.isnull(data[\"release date\"])]\r\n # data = data.drop(data.index[[2172, 3781, 7245, 12475, 14756, 15292, 49295, 93523, 99723]],axis=0,inplace=False)\r\n data = data.drop(indexes,axis=0,inplace=False)\r\n data = data.drop([\"video release date\", 'IMDb URL'],axis=1)\r\n \r\n \r\n data.gender = data.gender.astype(\"category\")\r\n data.occupation = data.occupation.astype(\"category\")\r\n \r\n categorical_variables = [\"gender\", \"occupation\"]\r\n for variable in categorical_variables:\r\n dummies = pd.get_dummies(data[variable], prefix=variable)\r\n data = pd.concat([data, dummies], axis=1)\r\n data.drop([variable], axis=1, inplace=True)\r\n data['movieID'] = data['movieID'].astype('int')\r\n data['userID'] = data['userID'].astype('int')\r\n \r\n\t#data = dateTime(data)\r\n \r\n return data","def latest_close(df):\n return df['close'].iloc[-1]","def test_structural_remove_columns_all_1_0(self):\n cp = Plotter.from_smiles(['CCCC', 'CCCC'], sim_type=\"structural\")\n self.assertTrue(cp._Plotter__df_descriptors.empty)","def reorder_bgc_data(df):\n st_col_list = ['time','lat','lon','depth','year','month','week','dayofyear','float_id','cycle']\n st_cols =df[st_col_list]\n non_st_cols = df.drop(st_col_list, axis=1)\n reorder_df = pd.concat([st_cols, non_st_cols], axis=1, sort=False)\n \n sci_col_list = [\"SCIENTIFIC_CALIB_COEFFICIENT\",\"SCIENTIFIC_CALIB_COMMENT\",\"SCIENTIFIC_CALIB_DATE\",\"SCIENTIFIC_CALIB_EQUATION\"]\n sci_cols =reorder_df[sci_col_list]\n non_sci_cols = reorder_df.drop(sci_col_list, axis=1)\n neworder_df = pd.concat([non_sci_cols,sci_cols], axis=1, sort=False)\n return neworder_df","def drop_unnecessary_columns(df):\n df = df.drop([\n 'id',\n 'imdb_id',\n 'poster_path',\n 'video',\n 'status',\n 'weighted_rating', # Only average_rating was used for this project\n 'original_title',\n 'crew', # Used in production_score\n 'producers', # Used in production_score\n 'executive_producers', # Used in production_score\n 'cast', # Used in production_score\n 'director', # Used in production_score\n 'production_companies', # Used in production_score\n 'production_countries', # Binarized\n 'genres', # Binarized\n 'original_language', # Binarized\n 'adult', # No adult movies in the dataset, so no variance between movies\n 'release_date', # Not being considered for this project\n 'overview',\n 'title',\n 'tagline',\n 'vote_average', # Ratings have been binned\n 'popularity', # Only considering average_rating\n 'vote_count', # We are making a predictor, so it makes no sense to use vote counts as input\n 'revenue', # We are making a predictor, so it makes no sense to use revenue as input\n 'keywords', # Not considering keywords for this project\n 'revenue_divide_budget', # We are making a predictor, so it makes no sense to use revenue/budget as input\n ], 1)\n return df","def reduce_time_seasonal( mv, seasons=seasonsyr, vid=None ):\n if vid==None:\n vid = 'reduced_'+mv.id\n # Note that the averager function returns a variable with meaningless id.\n # The climatology function returns the same id as mv, which we also don't want.\n\n # The slicers in time.py require getBounds() to work.\n # If it doesn't, we'll have to give it one.\n # Setting the _bounds_ attribute will do it.\n timeax = timeAxis(mv)\n if timeax is None:\n print \"WARNING- no time axis in\",mv.id\n return None\n if timeax.getBounds()==None:\n timeax._bounds_ = timeax.genGenericBounds()\n mvseas = seasons.climatology(mv)\n if mvseas is None:\n print \"WARNING- cannot compute climatology for\",mv.id,seasons.seasons\n print \"...probably there is no data for times in the requested season.\"\n return None\n avmv = mvseas\n avmv.id = vid\n if hasattr(mv,'units'): avmv.units = mv.units\n avmv = delete_singleton_axis( avmv, vid='time' )\n avmv.units = mv.units\n return avmv","def current_season():\n td = datetime.datetime.today()\n if td.month > 8:\n return td.year\n return td.year - 1","def prepare_output_df(df: DataFrame, kind: str) -> DataFrame:\r\n columns = get_export_columns(kind)\r\n to_drop = list(filter(lambda x: x not in columns.keys(), df.columns.to_list())) # For any columns not in the get_export_columns()\r\n df = df.drop(columns=to_drop) # mapping, drop them from the DataFrame.\r\n df = df.rename(columns=columns)\r\n return df","def clean_googletrend_csv(df: pd.DataFrame) -> pd.DataFrame:\n cols = map(convert_to_snake_case, df.columns)\n df.columns = cols\n\n # Create a 'state' column, which is just the last 2 of the 'file' column\n # except where the 'file' column ends in 'HB,NI'\n df['state'] = df.file.str[-2:]\n df.loc[df.state == 'NI', 'state'] = 'HB,NI'\n\n # The googletrend.csv file contains a column 'week', formatted like\n # \"2013-01-06 - 2013-01-12\", where the first date is the Sunday and the\n # second date is the Saturday of the given week. Every other csv file with\n # a date-like column simply contains a date. So we want to convert the\n # 'week' column to a 'date' column.\n # In order to do that, we'll create a whole new dataframe, 'week_lookup',\n # containing dates and the start of the week that corresponds to each date.\n # Then we'll merge them together and clean up any extra columns.\n\n # Get the earliest and latest dates in the dataframe\n date_min = pd.to_datetime(df.week.min()[:10])\n # Note below it's -10: to get the last day of the max week\n date_max = pd.to_datetime(df.week.max()[-10:])\n\n # Create a new dataframe, week_lookup, listing all days in the\n # period and their corresponding week\n days = pd.date_range(date_min, date_max, freq='D')\n week_lookup = pd.DataFrame({'date': days})\n\n # To get the start of the week, find the number of the day in the week\n week_lookup['num'] = week_lookup['date'].dt.dayofweek\n # Monday is day 0 in Pandas, so add 1 to get Sundays (and mod 7 to be sure)\n week_lookup['offset'] = (week_lookup['num'] + 1) % 7\n # Sunday of any given week = date - the number of the day in the week\n week_lookup['Week_Start'] = \\\n week_lookup['date'] - pd.to_timedelta(week_lookup['offset'], unit='D')\n week_lookup.drop(['num', 'offset'], axis='columns', inplace=True)\n\n # Create column week_start in df to match up to week_lookup\n df['week_start'] = pd.to_datetime(df.week.str[:10])\n\n # Re-merge week_lookup back into google so we end up with the\n # appropriate 7 days for each week, and drop auxiliary columns\n df = week_lookup.merge(df, left_on='Week_Start', right_on='week_start')\n df.drop(['file', 'Week_Start', 'week'], axis='columns', inplace=True)\n # Remove extraneous dates from df (these are limited by train.csv)\n df = df[(df.date >= pd.to_datetime('2013-01-01')) &\n (df.date <= pd.to_datetime('2015-07-31'))]\n\n return df","def removePlayer(df, name):\n if name in getPlayerList(df):\n df = df[df.name != name]\n return df","def get_obj_df():\n obj_df = pd.read_csv('data/object.csv')\n obj_df = obj_df.drop_duplicates()[['course_id', 'module_id', 'category', 'start']] \n obj_df['start'] = pd.to_datetime(obj_df[obj_df['start'] != 'null']['start'])\n return obj_df","def _check_season_gaps_in_teams_matches(self, df: pd.DataFrame):\n if self._verbose > 0:\n print(\"Teams:\")\n print(self.teams)\n print(\"Teams playing last season:\")\n print(self.last_season_teams)\n\n omitted_teams_test = check_season_gaps(df, self.test_fixtures_ids)\n omitted_teams_predict = check_season_gaps(df, self.predict_fixtures_ids)\n\n if omitted_teams_predict or omitted_teams_test:\n print(\"Warning: There are some teams with season gaps in data.\")\n print(\"Teams omitted based on predict dataset due to season gap:\")\n print(omitted_teams_predict)\n print(\"Teams omitted based on test dataset due to season gap:\")\n print(omitted_teams_test)\n\n # Delete these teams from datasets\n for team in set(omitted_teams_test + omitted_teams_predict):\n del self.test_fixtures_ids[team]\n del self.predict_fixtures_ids[team]\n\n print(\"You should consider lowering required number of test or predict data samples.\")","def setup_df(ticker):\n stock = yf.Ticker(ticker)\n df = stock.history(period=\"max\")\n del df['Dividends']\n del df['Stock Splits']\n return df","def _clean_dataset(df: pd.DataFrame) -> pd.DataFrame:\n df = df.loc[:, ~df.columns.str.contains(\"^Unnamed\")]\n df = df.dropna()\n return df","def remove_duplicates_on_rt(self, peak_df):\n duplicate_df =peak_df[peak_df['sec_id'].duplicated(keep=False)]\n print (\"the duplicates at this stage are: \")\n display(duplicate_df)\n dup_ids = set(duplicate_df['sec_id'].values)\n name_rt_dict = {}\n\n for dupid in dup_ids:\n\n dup_indexes = []\n dup_peaks = peak_df[peak_df.sec_id == dupid]\n\n for index, row in dup_peaks.iterrows():\n dup_indexes.append(index)\n name_rt_dict[index] = [row['compound'], row['rt']]\n\n\n keep_index = self.get_closest_rt_match(name_rt_dict)\n peak_df = self.drop_duplicates(dup_indexes, keep_index, peak_df)\n\n return peak_df","def __clean_repeated_columns(self, df, column_type):\n for column in df.columns:\n if column_type in column.lower():\n # Fill main column with data from \"prefix + _\" type column names.\n df[column_type[:-1]].fillna(df[column], inplace=True)\n # Drop the \"prefix + _\" type column names.\n df.drop(column, axis=1, inplace=True)","def test_drop_zero_variance_on_subset_columns_with_zv_removals(data):\n step = DropZVColumnsStep(['released', 'episodes'], naomit=True)\n bdf = step.prepare(data).bake(data)\n\n assert 'name' in bdf.columns\n assert 'released' in bdf.columns\n assert 'episodes' not in bdf.columns","def data_cleaning(df):\n df = df.dropna(how='all')\n \n return df","def remove_last_period(mm):\n \n m = mm.model\n variables = [\"harv\", \"age\"]\n constraints = [\"harv\", \"age\", \"env\"]\n\n harv_vars = pg.get_variables(m, \"harv\")\n max_period = max([float(i.varName.split(\",\")[-1][:-1]) for i in harv_vars])\n\n for v in variables:\n all_vars = pg.get_variables(m, v, filter_values={-1: max_period})\n for v in all_vars:\n m.remove(v)\n for c in constraints:\n all_cons = pg.get_constraints(m, c, filter_values={-1: max_period})\n for a in all_cons:\n m.remove(a)\n m.update()","def fyber_video_dataframe_cleaner(dataframe):\r\n print(\"Cleaning Fyber Video...\")\r\n df = dataframe\r\n df = df.fillna(0)\r\n \r\n delete_list = ['application_id', 'completions', 'ecpm_eur', 'ecpm_usd', \r\n 'fills', 'revenue_eur', 'unique_impressions']\r\n \r\n for entry in delete_list:\r\n del df[entry]\r\n \r\n df['Ad Type'] = 'video'\r\n df['Partner'] = 'Fyber_Video'\r\n df['Total_Code_Served'] = 0\r\n df['Clicks'] = 0\r\n \r\n df = df.rename(columns={'date':'Day', 'application_name':'App',\r\n \"ad_format\":\"AdUnit\", \"Ad Type\":\"UnitType\", \r\n \"country\":\"Country\", \"requests\":\"Requests\", \r\n \"impressions\":\"Impressions\", \"clicks\":\"Clicks\", \r\n \"revenue_usd\":\"Revenue\"})\r\n \r\n df['App'] = df['App'].replace({\"IMVU iOS Primary Wall\":\"IMVU iOS\", \r\n \"IMVU iOS External Offer Wall\":\"IMVU iOS\", \r\n \"IMVU Google Play\":\"IMVU Android\"}) \r\n\r\n df['Impressions'] = df['Impressions'].apply(lambda x:int(x))\r\n df['Requests'] = df['Requests'].apply(lambda x:int(x))\r\n\r\n df = df[[\"Day\", \"App\", \"AdUnit\", \"UnitType\", \"Country\", \r\n \"Total_Code_Served\", \"Requests\", \"Impressions\", \r\n \"Clicks\", \"Revenue\", \"Partner\"]]\r\n\r\n drop_index_list = []\r\n for num in list(df.index):\r\n if df.loc[num, 'App'] == 'Blue Bar Bundle ' or df.loc[num, 'App'] == 'NEXT Featured Offers':\r\n drop_index_list.append(num)\r\n\r\n df = df.drop(drop_index_list, axis=0)\r\n\r\n return df","def get_pre_df(temp_pre_df):\n \n event_time_max = temp_pre_df['event_time'].max()\n cat_dfs = []\n for num in np.arange(0,(1080/2)+1,30)[1:]:\n # making <= null i.e keeping >\n temp_pre_df.loc[temp_pre_df['event_time'] <= int(num), 'event_time'] = np.nan\n for col in ['event_name', 'specialty', 'plan_type']:\n cat_df = temp_pre_df.groupby([\"id\", col]).agg({\"event_time\": 'count'}).unstack(level=col)\n cat_df = cat_df/(event_time_max-num)\n cat_df.columns = ['__'.join(['normChange', col, name, str(int(num))]) for name in cat_df.columns.droplevel()]\n cat_dfs.append(cat_df)\n pre_df = pd.concat(cat_dfs, axis = 1) \n return pre_df.fillna(0)","def overwrite_season(function):\n @functools.wraps(function)\n def inner(*args, season = None, **kw):\n data = function(*args,**kw)\n if season:\n replace_season(data,season)\n return data\n return inner","def clean_ts(self, df_in, date_col, ts_col):\n df_dates=[parser.parse(t) for t in df_in[date_col]]\n df_in[date_col]=df_dates\n df_in['timestamp'] = [(t - datetime(1970, 1, 1)) / \\\n timedelta(seconds=1) for t in df_dates]\n df_in[ts_col]=pd.to_numeric(df_in[ts_col], errors='coerce')\n df_in=df_in[df_in['timestamp']>=self.begin_ts]\n df_in=df_in[df_in['timestamp']<=self.end_ts]\n df_in=df_in[['timestamp', ts_col]].dropna().\\\n sort_values(by='timestamp').\\\n copy()\n df_in['date']=[datetime.utcfromtimestamp(t)\\\n for t in df_in['timestamp']]\n return df_in","def season_type(self, season_type):\n\n self._season_type = season_type","def get_full_data(raw_data_path, return_missing_column_list=False):\n\n # Read raw_data into individual dataframes\n df21 = pd.read_csv(raw_data_path + '/2020-21/gws/merged_gw.csv',\n encoding='utf_8')\n df20 = pd.read_csv(raw_data_path + '/2019-20/gws/merged_gw.csv',\n encoding='utf_8')\n df19 = pd.read_csv(raw_data_path + '/2018-19/gws/merged_gw.csv',\n encoding='ISO-8859-1')\n df18 = pd.read_csv(raw_data_path + '/2017-18/gws/merged_gw.csv',\n encoding='ISO-8859-1')\n df17 = pd.read_csv(raw_data_path + '/2016-17/gws/merged_gw.csv',\n encoding='ISO-8859-1')\n\n # Add a column identifying the season to each dataframe\n df21['season'] = 21\n df20['season'] = 20\n df19['season'] = 19\n df18['season'] = 18\n df17['season'] = 17\n\n # Create a list of all dataframes\n dflist = [df17, df18, df19, df20, df21]\n\n # Create a list of all columns existing in each season\n consistent_columns = []\n for column in list(df21.columns):\n if column in list(df20.columns) and \\\n column in list(df19.columns) and \\\n column in list(df18.columns) and \\\n column in list(df17.columns):\n consistent_columns.append(column)\n\n # Create a dictionary with columns that are only in some years.\n # Key = column name, value = years for which the column exists.\n missing_columns = {}\n for df in dflist:\n for column in list(df.columns):\n if not column in consistent_columns:\n if column in missing_columns.keys():\n missing_columns[column].append(df.loc[0, ['season']][0])\n else:\n missing_columns[column] = [df.loc[0, ['season']][0]]\n\n # Concatenate seasons based on consistent columns\n frames_to_concat = [\n df21[consistent_columns], df20[consistent_columns],\n df19[consistent_columns], df18[consistent_columns],\n df17[consistent_columns]\n ]\n complete_data = pd.concat(frames_to_concat)\n\n # Remove trailing underscores and numbers from names\n complete_data.name = complete_data.name.str.rstrip('_1234567890')\n\n # Swap out underscores for spaces within names\n complete_data.name = complete_data.name.str.replace('_', ' ')\n\n # Drop column 'round' as it is the same as 'GW'\n complete_data.drop(columns=['round'], inplace=True)\n\n # Set names to lowercase\n complete_data.name = complete_data.name.apply(lambda n: n.lower())\n\n # Correct game weeks for 2020\n complete_data['GW'] = complete_data['GW'].apply(correct_2020)\n\n # Match player position\n complete_data, players_raw_seasons = match_position(complete_data, raw_data_path)\n\n # Add dreamteam count for each player for the last season\n complete_data = add_dreamteam_count(complete_data, players_raw_seasons)\n\n # Add team name for last two years to dataframe\n complete_data = add_team(complete_data, players_raw_seasons, raw_data_path)\n\n # Add opponent strength\n complete_data = add_opponent_strength(complete_data)\n\n # Add own team strength\n complete_data = add_team_strength(complete_data)\n\n # Sort ascending by player name and kickoff-date and separate kickoff-date and -time\n complete_data = sort_kickoff(complete_data)\n\n if return_missing_column_list:\n return complete_data, missing_columns\n return complete_data","def prune_data(self, ts):\n sql = \"delete from %s where dateTime < %d\" % (self.dbm.table_name, ts)\n self.dbm.getSql(sql)\n try:\n # sqlite databases need some help to stay small\n self.dbm.getSql('vacuum')\n except Exception as e:\n pass","def clean_data(df_name):\n\n wines = df_name\n wines = wines.rename(columns={'Vintage': 'Year'})\n wines['Location'] = wines['Appellation'].apply(lambda x: x['Region']['Name'])\n wines['Region'] = wines['Appellation'].apply(lambda x: x['Name'])\n wines['Type'] = wines['Varietal'].apply(lambda x: x['WineType']['Name'])\n wines['Grape'] = wines['Varietal'].apply(lambda x: x['Name'])\n wines['Reviews'] = wines['Community'].apply(lambda x: x['Reviews']['Url'])\n drop_columns = ['Appellation', 'Community', 'Description', 'GeoLocation', 'Labels', 'ProductAttributes','Ratings','Retail', 'Url', 'Varietal', 'Vineyard', 'Vintages']\n wines.drop(drop_columns, axis=1, inplace=True)\n wines = wines[['Id', 'Name', 'Year', 'Type', 'Grape', 'Location', 'Region', 'PriceRetail', 'PriceMin', 'PriceMax', 'Reviews']]\n wines['CurrentReviews'] = '' #wines['CurrentReviews'].apply(lambda x: [\"\"])\n wines['PriorReviews'] = '' #wines['PriorReviews'].apply(lambda x: [''])\n\n return wines","def station_stats(df):","def dataendclean(df, x, inplace=False):\r\n # Examine Mean Values\r\n if inplace:\r\n df = df\r\n else:\r\n df = df.copy()\r\n\r\n jump = df[abs(df.loc[:, x].diff()) > 1.0]\r\n try:\r\n for i in range(len(jump)):\r\n if jump.index[i] < df.index[50]:\r\n df = df[df.index > jump.index[i]]\r\n printmes(\"Dropped from beginning to \" + str(jump.index[i]))\r\n if jump.index[i] > df.index[-50]:\r\n df = df[df.index < jump.index[i]]\r\n printmes(\"Dropped from end to \" + str(jump.index[i]))\r\n except IndexError:\r\n printmes('No Jumps')\r\n return df","def glicko2_summarize(df, min_weeks=4, use_prior=False):\n\n # Loop through seasons and weeks to create full history of ratings by team\n results = pd.DataFrame()\n for season in df['Season'].sort_values().unique():\n for week in df[df['Season']==season]['Week'].sort_values().unique():\n if week > min_weeks:\n if week == min_weeks + 1:\n season_df = df[df['Season']==season].copy()\n uniqueteamids = pd.concat([season_df['VisID'],\n season_df['HomeID']]).unique()\n if use_prior == True and season > df['Season'].min():\n ratings = np.repeat(1500, len(uniqueteamids))\n ratingsdeviance = np.repeat(350, len(uniqueteamids))\n sigma = np.repeat(0.06, len(uniqueteamids))\n glicko_stats = pd.DataFrame({'ratings': ratings,\n 'ratingsdeviance': ratingsdeviance,\n 'sigma': sigma}, index=uniqueteamids)\n prior = results[results['Season']==season-1]\n prior_id_mask = [True if id in uniqueteamids else False for id in prior['TeamID']]\n prior = prior[prior_id_mask]\n prior = prior.sort_values('Week').groupby('TeamID').tail(1)\n prior = prior.drop('Week',1)\n prior = prior.set_index('TeamID')\n glicko_stats.loc[prior.index, 'ratings'] = prior['Glicko_Rating'] - (prior['Glicko_Rating'] - 1500)/2\n glicko_stats.loc[prior.index, 'ratingsdeviance'] = prior['Glicko_Rating_Deviance'] - (prior['Glicko_Rating_Deviance'] - 350)/2\n glicko_stats.loc[prior.index, 'sigma'] = prior['Glicko_Sigma'] - (prior['Glicko_Sigma'] - 0.06)/2\n else:\n ratings = np.repeat(1500, len(uniqueteamids))\n ratingsdeviance = np.repeat(350, len(uniqueteamids))\n sigma = np.repeat(0.06, len(uniqueteamids))\n glicko_stats = pd.DataFrame({'ratings': ratings,\n 'ratingsdeviance': ratingsdeviance,\n 'sigma': sigma}, index=uniqueteamids)\n\n week_df = df[(df['Season']==season) & (df['Week']<week)].copy()\n glicko_stats = glicko2(week_df, uniqueteamids, glicko_stats)\n\n\n glicko_results = glicko_stats.reset_index()\n print(glicko_results.head(), season)\n glicko_results.columns = ['TeamID','Glicko_Rating',\n 'Glicko_Rating_Deviance',\n 'Glicko_Sigma']\n glicko_results['Season'] = season\n glicko_results['Week'] = week\n results = pd.concat([results, glicko_results], axis=0,\n ignore_index=True)\n\n # Join the ratings to the original schedule of games\n df = df.merge(results, left_on=['Season','Week','HomeID'],\n right_on=['Season','Week','TeamID'],\n suffixes=('','_Home'))\n df.drop('TeamID', 1, inplace=True)\n\n df = df.merge(results, left_on=['Season','Week','VisID'],\n right_on=['Season','Week','TeamID'],\n suffixes=('','_Away'))\n df.drop('TeamID', 1, inplace=True)\n\n # Create key and set index to join with n_game summaries dataset.\n df.set_index(['HomeID', 'VisID', 'Season', 'Week'], inplace=True)\n df = df[['Glicko_Rating', 'Glicko_Rating_Deviance', 'Glicko_Sigma',\n 'Glicko_Rating_Away', 'Glicko_Rating_Deviance_Away',\n 'Glicko_Sigma_Away']]\n df.columns = ['Glicko_Rating_Home', 'Glicko_Rating_Deviance_Home',\n 'Glicko_Sigma_Home', 'Glicko_Rating_Away',\n 'Glicko_Rating_Deviance_Away', 'Glicko_Sigma_Away']\n\n return df","def clean_data(df, new_name=None):\n\n if new_name:\n original_name = df[\"from\"].unique().tolist()\n df.replace(original_name, new_name, inplace=True)\n df.dropna(subset=['text'], inplace=True)\n df.set_index('time', inplace=True, drop=False)\n return df","def getseason(data):\n ## Season key is the most reliable\n season = data.get(\"season\")\n if season:\n ## Season key is an integer formatted \"YYS\" and is 2000-based (i.e.- 171 == 2017-Winter)\n season = str(season)\n year = int(f\"20{season[:2]}\")\n ## Anichart Season key is 1-indexed\n season = int(season[2]) - 1\n ## This should normally pass; if it consistently does not, we'll have to investigate why\n try: return SeasonCharts.buildseason(season,year)\n ## If something goes wrong, we'll try another method\n except: print(f\"Failed to parse season: {data['season']}\")\n ## Next, we'll iterate over rankings to try to determine the season/year\n ## There are multiple types of rankings based on season, year, and both combined,\n ## so we'll piece it together based on whatever we come across first\n season,year = None,None\n for ranking in data.get(\"rankings\",list()):\n ## Quicker exit (without just making this loop its own function)\n if season and year: continue\n ## We'll ignore stuff we've already gotten and assume that nothing in\n ## rankings contradicts eachother\n if not season:\n ## Defaults to None one way or another if it's not supplied\n season = ranking.get(\"season\")\n if not year: year = ranking.get(\"year\")\n ## Check if we made it\n if season and year:\n ## As above, this should always work out-of-the-box\n try: return SeasonCharts.buildseason(season,year)\n except: print(season,year)\n ## Welp, we're stumped...\n return None","def clean_data(df, start = 1995, stop = 2018):\n country_iso3_code = pd.read_html('https://unstats.un.org/unsd/methodology/m49/')\n country_iso3_code = country_iso3_code[0]['ISO-alpha3 code']\n df = df.loc[df.country_iso3_code.isin(country_iso3_code)]\n df = df.set_index(['indicator', 'country_iso3_code', 'country', 'year']).unstack(level = 0)\n df.columns = df.columns.get_level_values(1)\n df = df.rename(columns = {'NY.GDP.PCAP.KD.ZG': 'pc_GDP_growth',\n 'NY.GDP.PCAP.PP.CD': 'pc_GDP_PPP'})\n df = df.reset_index()\n df = df.loc[(df.year >= (start - 1)) & (df.year <= stop)]\n df = df.dropna()\n return df"],"string":"[\n \"def _drop_last_season_championship_matches(self, df: pd.DataFrame) -> pd.DataFrame:\\n max_season = df[\\\"season\\\"].max()\\n\\n if self._resume:\\n # Check if max season changed\\n if max_season != self._model_settings[\\\"max_season\\\"]:\\n raise ValueError(\\\"Current max season is different than previous one. Model must be retrained.\\\")\\n else:\\n self.max_season = self._model_settings[\\\"max_season\\\"]\\n else:\\n self.max_season = max_season\\n\\n return df.loc[~((df[\\\"season\\\"] == max_season) & (df[\\\"league\\\"] == \\\"CH\\\"))]\",\n \"def seasonality(df):\\n df_datetime = pd.DatetimeIndex(df.date_time)\\n df[\\\"month\\\"] = df_datetime.month\\n df = drop_cols(df, [\\\"date_time\\\"])\\n\\n return df\",\n \"def _get_latest_season_and_type(df):\\n if df is None or len(df) == 0:\\n return (None, None)\\n\\n if not isinstance(df, pd.DataFrame):\\n raise ValueError(f\\\"Type of df is {type(df)}, it should be pandas.DataFrame\\\")\\n\\n df = df[df['state_of_game'] == 'POST']\\n latest_season = df['season'].max()\\n season_types = df[df['season'] == latest_season]['type'].unique()\\n latest_season_type = _get_latest_season_type(list(season_types))\\n return latest_season, latest_season_type\",\n \"def remove_old_songs(df, too_old=1970):\\n drop_indices = df.index[df['year'] < too_old].tolist()\\n df = df.drop(drop_indices)\\n return df\",\n \"def clean_up(df: pd.DataFrame) -> pd.DataFrame:\\n\\n # Hereafter df is sorted by date, which is helpful as it allows using .iloc[-1]\\n # to get current (or most recent known) situation per location\\n # (Otherwise we'd have to groupby agg -> min date, and then filter)\\n df = df.sort_values(\\n [Columns.LOCATION_NAME, Columns.DATE, Columns.CASE_TYPE], ascending=True\\n )\\n\\n return df\",\n \"def update_season_table(session, sched_tbl, season_df):\\n date = datetime.date(datetime.now())\\n update_query = session.query(sched_tbl).filter(sched_tbl.start_time < date,\\n sched_tbl.home_team_score == 0).order_by(sched_tbl.start_time)\\n if update_query.count() == 0:\\n # print(\\\"Season is up to date; Returning without performing an update.\\\") Test/logging statement\\n return\\n\\n all_update_rows = update_query.all()\\n first_game_time = all_update_rows[0].start_time\\n last_game_time = all_update_rows[len(all_update_rows) - 1].start_time\\n\\n # Reduce season to games between first and last game time\\n season_df[\\\"start_time\\\"] = season_df[\\\"start_time\\\"].dt.tz_localize(None)\\n update_df = season_df.loc[(season_df.start_time >= first_game_time) & (season_df.start_time <= last_game_time)]\\n\\n for row in all_update_rows:\\n game = update_df.loc[(update_df.home_team == row.home_team) & (update_df.away_team == row.away_team) &\\n (update_df.start_time.dt.date == datetime.date(row.start_time))]\\n if len(game) == 0:\\n # This catches playoff games which do not end up happening (i.e. a game 7 in a series a team sweeps), and\\n # removes it from the database\\n session.delete(row)\\n else:\\n row.home_team_score = int(game.home_team_score)\\n row.away_team_score = int(game.away_team_score)\\n row.start_time = game.start_time.dt.to_pydatetime()[0] # Convert Pandas TimeStamp to datetime\\n session.add(row)\",\n \"def remove_DateIndex(dataset):\\n dataset.to_csv('normalized_Dataset.csv', mode='w', header=True)\\n dataset = pd.read_csv(\\\"normalized_Dataset.csv\\\")\\n dataset = dataset.drop('date', axis=1)\\n return dataset\",\n \"def clean(df):\",\n \"def normalize_dataframe(df=None):\\n #Drops all the columns that are not 'close'\\n col_to_drop = df.columns.levels[1].values\\n col_to_drop = np.delete(col_to_drop,np.where(col_to_drop==\\\"close\\\"))\\n df = df.drop(columns=col_to_drop, level=1)\\n #eliminates the double level in columns by deleting the 'close' label. \\n #Only ticker label is necessary.\\n df.columns = df.columns.droplevel(1)\\n return df\",\n \"def remove_not_available(df):\\n drop_indices = df.index[df['genre'] == 'Not Available'].tolist()\\n df = df.drop(drop_indices)\\n return df\",\n \"def toSeasonAggFormat(self):\\n df_a = self.toHomeAwayFormat().copy()\\n df_b = df_a.copy()\\n\\n for col in list(df_a.columns).copy():\\n if col[0] == \\\"H\\\":\\n df_a = df_a.rename(columns={col:(col[1:])})\\n\\n if col[0] == \\\"A\\\":\\n df_a = df_a.rename(columns={col:('Opp'+col[1:])})\\n\\n #df_a['Win'] = 1\\n\\n for col in list(df_b.columns).copy():\\n if col[0] == \\\"H\\\":\\n df_b = df_b.rename(columns={col:('Opp'+col[1:])})\\n\\n if col[0] == \\\"A\\\":\\n df_b = df_b.rename(columns={col:(col[1:])})\\n\\n df_c = df_a.append(df_b, sort=True)\\n\\n return df_c\",\n \"def replace_season(data,season):\\n if not SeasonCharts.matchseason(season):\\n raise SeasonCharts.SeasonError\\n ## Check data format\\n if test_rawdata(data):\\n for cat,shows in data.items():\\n for show in shows: show['season'] = season\\n else:\\n for show in data: show['season'] = season\",\n \"def clean(dataframe):\\n # replace 'unknown' in Pop. density with np.nan\\n dataframe = dataframe.replace('unknown', np.nan)\\n\\n # remove spaces from column names and content\\n dataframe.columns = dataframe.columns.str.strip()\\n\\n # change YYYYMMDD to days of the year\\n date_debug = []\\n for i in range(1, 366):\\n date_debug.append(i)\\n\\n dataframe2 = {'YYYYMMDD': date_debug}\\n dataframe['YYYYMMDD'] = dataframe2['YYYYMMDD']\\n\\n return dataframe\",\n \"def preprocess(df):\\n drop_cols = ['duration_ms', 'key', 'mode', 'time_signature', 'popularity', 'tempo']\\n drop_cols += ['track_id', 'track_name', 'artist_name']\\n for col in drop_cols:\\n if col in list(df.columns):\\n df = df.drop(columns=col)\\n return df\",\n \"def clean_station_data(station_df):\\n # TODO implement data preparation here\\n # Fix the datetime field\\n\\n # Cast to numeric fields where necessary\\n\\n # Interpolate missing data\",\n \"def fix_date(df):\\n df.insert(2, \\\"timestamp\\\", df[\\\"TestDate\\\"])\\n\\n mask = df[\\\"TestDate\\\"] <= df[\\\"StorageDate\\\"]\\n print(\\\"Removing %.2f%% of unusual data\\\" % ((len(df) - np.sum(mask)) * 100 / len(df)))\\n df = df[mask]\\n\\n mask = df[\\\"StorageDate\\\"] - df[\\\"TestDate\\\"] > pd.Timedelta(days=90)\\n print(\\\"Fixing %.2f%% of outdated data\\\" % (np.sum(mask) * 100 / len(df)))\\n df[\\\"timestamp\\\"].values[mask] = df[\\\"StorageDate\\\"].values[mask]\\n return df\",\n \"def date_cleaner(dataset):\\n dataset['document_last_edition'] = dataset['meta_lastEdition']\\n dataset = dataset.drop(['meta_lastEdition'], axis=1)\\n \\n \\n \\\"\\\"\\\"\\n Get column to correct date format\\n \\\"\\\"\\\"\\n dataset['document_last_edition'] = dataset['document_last_edition'].apply(lambda x: str(unify_date_format(x))[:10]) \\n \\n \\n \\\"\\\"\\\"\\n meta_lastPublication renaming\\n \\\"\\\"\\\"\\n dataset['document_last_publication'] = dataset['meta_lastPublication']\\n dataset = dataset.drop(['meta_lastPublication'], axis=1)\\n\\n # DROP HOURS/M/S\\n dataset['document_last_publication'] = dataset['document_last_publication'].apply(lambda x: str(unify_date_format(x))[:10]) \\n \\n \\n # META CREATED DATE\\n dataset['meta_created_date'] = dataset['meta_created_date'].str.replace('_', '-')\\n dataset['meta_created_date'] = dataset['meta_created_date'].apply(lambda x: str(unify_date_format(x))[:10])\\n dataset['document_created_at'] = dataset['meta_created_date']\\n dataset = dataset.drop(['meta_created_date'], axis=1)\\n\\n # META_REVISED_MODIFIED\\n dataset['document_revised_modified'] = dataset['meta_revised_modified']\\n dataset = dataset.drop(['meta_revised_modified'], axis=1) \\n \\n \\n date_column_list = ['document_created_at','document_last_edition', 'document_last_publication', 'document_revised_modified']\\n \\n \\\"\\\"\\\"\\n \\n THE PLAN IS TO FIRST REPLACE EMPTY SPOTS IN META_CREATED_DATE WITH CREATED_AT\\n THEN WE DROP CREATED_AT\\n THEN WE REPLACE EMPTY SPOTS IN OTHER COLUMNS WITH document_created_at\\n \\\"\\\"\\\" \\n \\n dataset[date_column_list] = dataset[date_column_list].replace('Not Specified', np.nan)\\n dataset[date_column_list] = dataset[date_column_list].replace('Not Specif', np.nan)\\n dataset[date_column_list] = dataset[date_column_list].replace('nan', np.nan) \\n dataset['document_created_at'].fillna(dataset['created_at'], inplace=True) \\n dataset = dataset.drop(['created_at'], axis=1)\\n \\n dataset['document_last_edition'].fillna(dataset['document_created_at'], inplace=True)\\n dataset['document_last_publication'].fillna(dataset['document_created_at'], inplace=True)\\n dataset['document_revised_modified'].fillna(dataset['document_created_at'], inplace=True)\\n \\n \\n\\n \\n \\\"\\\"\\\"\\n FIXING NON-EXISTING DATES IN DATASET\\n \\\"\\\"\\\"\\n \\n dataset = dataset.replace(['2020-1-29'], ['2020-01-29'])\\n \\n \\n \\n created_at_unique = list(dataset['document_created_at'].unique())\\n last_edition_unique = list(dataset['document_last_edition'].unique())\\n last_publication_unique = list(dataset['document_last_publication'].unique())\\n revised_modified_unique = list(dataset['document_revised_modified'].unique())\\n \\n \\n # IF LIST NEED TO GET UPDATED\\n invalid_created_at = is_valid_date(created_at_unique)\\n invalid_last_edition_unique = is_valid_date(last_edition_unique)\\n invalid_last_publication_unique = is_valid_date(last_publication_unique)\\n invalid_revised_modified_unique = is_valid_date(revised_modified_unique) \\n invalid_dates = list(set(itertools.chain(invalid_created_at, invalid_last_edition_unique, invalid_last_publication_unique, invalid_revised_modified_unique)))\\n \\n \\n \\n \\n # Non-existing dates from the list\\n dataset = dataset.replace(['2019-04-31', '2016-11-31', '2019-09-31', '2015-02-31', '2017-04-31', '2015-11-31', '2015-09-31', '2017-02-29', '2018-09-31', '2017-06-31', '2018-04-31', '2015-04-31', '2018-11-31', '2017-09-31', '2015-02-29', '2019-02-29', '2019-06-31', '2018-02-29', '2016-02-30', '2016-06-31', '2016-09-31', '2018-06-31', '2019-18-03', '2020-02-31', '9999-12-31'], \\n ['2019-04-30', '2016-11-30', '2019-09-30', '2015-02-28', '2017-04-30', '2015-11-30', '2015-09-30', '2017-02-28', '2018-09-30', '2017-06-30', '2018-04-30', '2015-04-30', '2018-11-30', '2017-09-30', '2015-02-28', '2019-02-28', '2019-06-30', '2018-02-28', '2016-02-28', '2016-06-30', '2016-09-30', '2018-06-30', '2019-03-18', '2020-02-28', '1999-12-31'])\\n\\n\\n \\n \\n\\n\\n return dataset\",\n \"def latest_season_before(date):\\n\\tif date.month < 9:\\n\\t\\treturn date.year - 1\\n\\treturn date.year\",\n \"def _truncate_games_table(db_conn, season, season_type):\\n delete_statement = f\\\"DELETE FROM games WHERE season = {season} and type = '{season_type}'\\\"\\n logging.info(f\\\"Truncating games table with statement: {delete_statement}\\\")\\n db_conn.execute(delete_statement)\",\n \"def season_games(year):\\n\\tLOG.debug('Getting season %d', year)\\n\\tdata = read_html(io=season_games_url(year),\\n\\t\\t\\t\\t\\t attrs={'id': 'games'},\\n\\t\\t\\t\\t\\t infer_types=False,\\n\\t\\t\\t\\t\\t header=0)\\n\\tif len(data) != 1:\\n\\t\\traise CantFindTheRightTable\\n\\tdata = data.pop()\\n\\n\\t# Cleaning.\\n\\tdel data[\\\"Unnamed: 3\\\"]\\n\\t# The code below issues \\\"UserWarning: \\\" So we catch UserWarnings.\\n\\twith warnings.catch_warnings():\\n\\t\\twarnings.filterwarnings(action='ignore', category=UserWarning,\\n\\t\\t\\t\\t\\t\\t\\t\\tmodule=r'pandas\\\\.core\\\\.frame',\\n\\t\\t\\t\\t\\t\\t\\t\\tmessage=(r\\\"Boolean Series key will be reindexed\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t r\\\" to match DataFrame index\\\\.\\\"))\\n\\t\\t# These rows are mid-table header rows.\\n\\t\\tdata = data[data.Week != \\\"Week\\\"][data.Week != \\\"nan\\\"]\\n\\n\\tdata['week'] = (data.Week\\n\\t\\t\\t\\t\\t.replace(\\\"WildCard\\\", \\\"wild-card\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"Division\\\", \\\"divisional\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"ConfChamp\\\", \\\"conference\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"SuperBowl\\\", \\\"super-bowl\\\")\\n\\t\\t\\t\\t\\t.apply(\\n\\t\\t\\t\\t\\t\\tlambda s: (int(s)\\n\\t\\t\\t\\t\\t\\t\\t\\t if all(c in '1234567890' for c in s)\\n\\t\\t\\t\\t\\t\\t\\t\\t else s)))\\n\\tdel data['Week']\\n\\n\\tdata['season'] = year\\n\\tdata['game_date'] = pd.to_datetime(\\n\\t\\tdata.Date\\n\\t\\t.replace(r\\\"$\\\", r\\\", %d\\\" % year, regex=True)\\n\\t\\t.replace(r\\\"^(January|February) (\\\\d+), \\\\d+$\\\", r\\\"\\\\1 \\\\2, %d\\\" % (year + 1),\\n\\t\\t\\t\\t regex=True))\\n\\tdel data['Date']\\n\\n\\tfor column in \\\"PtsW\\\", \\\"PtsL\\\", \\\"YdsW\\\", \\\"TOW\\\", \\\"YdsL\\\", \\\"TOL\\\":\\n\\t data[column] = data[column].apply(int)\\n\\n\\tdata['WatL'] = data['Unnamed: 5'].apply(lambda x: x == '@')\\n\\tdel data['Unnamed: 5']\\n\\tdata['hometeam'] = (~data.WatL * data['Winner/tie'] +\\n\\t\\t\\t\\t\\t\\tdata.WatL * data['Loser/tie'])\\n\\tdata['awayteam'] = (data.WatL * data['Winner/tie'] +\\n\\t\\t\\t\\t\\t\\t~data.WatL * data['Loser/tie'])\\n\\tdata['winner'] = data['Winner/tie']\\n\\tfor column in 'Winner/tie', 'Loser/tie', \\\"WatL\\\":\\n\\t\\tdel data[column]\\n\\tfor column in 'hometeam', 'awayteam', 'winner':\\n\\t\\tdata[column] = data[column].apply(lambda s: s.split()[-1].lower())\\n\\n\\treturn data\",\n \"def clean_up_raw(df_raw):\\n # exclude_subset = ['well', 'tile', 'cell', 'intensity', 'blob'] # causes issues with later joins, maybe a pandas bug\\n import lasagna.utils\\n df_raw[CYCLE] = df_raw[CYCLE].astype(int)\\n df_raw = df_raw.sort_values([WELL, TILE, CELL, BLOB, CYCLE, CHANNEL])\\n return df_raw\",\n \"def removeLegacy(self, path=None):\\n\\n df = pd.read_csv(path, compression='gzip')\\n print(df.shape)\\n gamelist = pd.read_csv('Resources/Genres.csv.gz', usecols=['appid'])\\n gamelist = pd.DataFrame(gamelist.appid.unique(), columns=['appid'])\\n print(gamelist)\\n filter_df = pd.merge(df, gamelist, on='appid', how='inner')\\n filter_df = filter_df.dropna()\\n filter_df = filter_df.sort_values(['steamid', 'appid'], ascending=[True, True])\\n print('done')\\n print(filter_df.shape)\\n print(filter_df)\\n print(np.setdiff1d(df['appid'].unique(), filter_df['appid'].unique()))\\n filter_df.to_csv(path, compression='gzip', columns=['steamid', 'appid', 'rating'], index=None)\",\n \"def terminate(self):\\n self.league.season = None\\n for team in self.teams:\\n team.team.season = None\",\n \"def clean_catalogue(\\n source,\\n dest=f\\\"{pd.Timestamp.now().strftime('%y%m%d%H%M%S')}.csv\\\",\\n window=None\\n ):\\n catalogue = pd.read_csv(source, parse_dates=[3,4,5])\\n catalogue.drop_duplicates(ignore_index=True, inplace=True)\\n catalogue.sort_values(by=[\\\"dt_on\\\"], ignore_index=True, inplace=True)\\n if window is not None: # 430367\\n count = 0\\n rm_idx = []\\n for station in catalogue.station.unique():\\n subset = catalogue.loc[catalogue[\\\"station\\\"] == station]\\n remove = []\\n dt_on_ = subset[\\\"dt_on\\\"].iloc[0]\\n for i, dt_on in enumerate(subset[\\\"dt_on\\\"]):\\n if i == 0:\\n continue\\n elif dt_on < dt_on_ + pd.Timedelta(window, unit=\\\"sec\\\"):\\n remove.append(i)\\n else:\\n dt_on_ = dt_on\\n rm_idx.append(subset.index[remove])\\n count += len(remove)\\n print(f\\\"Removing {count} entries...\\\")\\n rm_idx = [item for sub_idx in rm_idx for item in sub_idx]\\n catalogue.drop(catalogue.index[rm_idx], inplace=True)\\n catalogue.reset_index(drop=True, inplace=True)\\n catalogue.to_csv(dest)\\n print(f\\\"Catalogue saved to {dest}.\\\")\",\n \"def drop_duplicates(df):\\n w = Window().partitionBy(col(\\\"card_id\\\")).orderBy(col(\\\"dt\\\") )\\n df2 = df.select(\\\"*\\\", lag(df.nombreestacion).over(w).alias(\\\"lag_station\\\"))\\n df2 = df2.select(\\\"*\\\", lag(df.dt).over(w).alias(\\\"lag_dt\\\"))\\n df2 = df2.withColumn(\\\"diff_seconds\\\", col(\\\"dt\\\").cast(\\\"long\\\")- col(\\\"lag_dt\\\").cast(\\\"long\\\"))\\n df2 = df2.withColumn(\\\"same_station\\\", col('nombreestacion') == col('lag_station'))\\n\\n mask = (df2.same_station) & (df2.diff_seconds < 1800)#1800 seconds = 30 mins\\n df2 = df2.withColumn(\\\"drop_value\\\", mask).na.fill(False)\\n return df2.filter(~df2.drop_value)\",\n \"def clean_data(df):\\n\\n global cato\\n # YearRemodAdd: Remodel date (same as construction date if no remodeling or additions)\\n df.YearRemodAdd = np.where(df.YearRemodAdd < df.YearBuilt, df.YearBuilt, df.YearRemodAdd)\\n assert len(df.loc[df.YearRemodAdd < df.YearBuilt]) == 0, 'Check YearRemodAdd - should be greater or equal then YearBuilt'\\n \\n # Check range of years\\n yr_max = 2022\\n # Some values of GarageYrBlt are corrupt. Fix them by replacing them with the YearBuilt\\n df.GarageYrBlt = np.where(df.GarageYrBlt > yr_max, df.YearBuilt, df.GarageYrBlt)\\n assert df.YearBuilt.max() < yr_max and df.YearBuilt.min() > 1800, 'Check YearBuilt min() and max()'\\n assert df.YearRemodAdd.max() < yr_max and df.YearRemodAdd.min() > 1900, 'Check YearRemodAdd min() and max()'\\n assert df.YrSold.max() < yr_max and df.YrSold.min() > 2000, 'Check YrSold min() and max()'\\n assert df.GarageYrBlt.max() < yr_max and df.GarageYrBlt.min() >= 0, 'Check GarageYrBlt min() and max()'\\n \\n # Check values of ordinal catagorical variables\\n for k in cato.keys():\\n assert set(df[k].unique()).difference(df[k].cat.categories) == set(), f'Check values of {k}'\\n \\n # Check typos in nominal categorical variables\\n df['Exterior2nd'] = df['Exterior2nd'].replace({'Brk Cmn':'BrkComm', 'CmentBd':'CemntBd', 'Wd Shng':'WdShing'})\\n # Renew a data type after replacement\\n df['Exterior2nd'] = df['Exterior2nd'].astype(\\\"category\\\")\\n if \\\"None\\\" not in df['Exterior2nd'].cat.categories:\\n df['Exterior2nd'].cat.add_categories(\\\"None\\\", inplace=True)\\n\\n return df\",\n \"def remove_rows_with_non_english_movies(df):\\n df = df[df['original_language'] == 'en']\\n return df\",\n \"def _drop_duplicates(df: DataFrame, cols: Sequence[str]) -> DataFrame:\\n df = df.drop_duplicates(subset=cols, keep=\\\"last\\\")\\n return df.sort_values(by=cols, ignore_index=True)\",\n \"def cut_frame_tail(df):\\n # TODO\\n return df\",\n \"def getLatest(df):\\n df_info = df.iloc[:,0:5]\\n df_last = df.iloc[:,-1]\\n df_info['latest'] = df_last\\n \\n return df_info\",\n \"def games(self, competition_id: int, season_id: int) -> DataFrame[Any]:\",\n \"def _get_latest_season_type(season_types_list):\\n if not isinstance(season_types_list, (list, tuple)):\\n raise ValueError(f\\\"type of {type(season_types_list)} for season_type_list not list or tuple\\\")\\n\\n latest_season_type = season_types_list[0]\\n if len(season_types_list) > 1:\\n for season_type in season_types_list[1:]:\\n if config.SEASON_TYPES_ORDER[season_type] > config.SEASON_TYPES_ORDER[latest_season_type]:\\n latest_season_type = season_type\\n\\n return latest_season_type\",\n \"def clean_and_save_timeseries(df):\\n drop_columns = ['Lat', \\n 'Long', \\n 'Province/State']\\n\\n df.drop(columns=drop_columns, inplace = True)\\n \\n df_grouped = df.groupby(['Country/Region'], as_index=False).sum()\\n df_grouped = df_grouped.set_index('Country/Region').transpose()\\n df_grouped.reset_index(level=0, inplace=True)\\n df_grouped.rename(columns={'index': 'Date'}, inplace=True)\\n df_grouped['Date'] = pd.to_datetime(df_grouped['Date'])\\n\\n df_grouped.to_csv('../data/worldwide_timeseries.csv', index=False)\",\n \"def __clean_df(self):\\n self.__convert_min()\",\n \"def remove_different_category_time(self):\\n if self._isCollection:\\n self.time_series_data.remove_different_time_index()\\n else:\\n warnings.warn('Setup mainCategoryCol is necessary for this function')\\n return self\",\n \"def fetch_training_df(df):\\n\\n gen_df = df.copy()\\n gen_df.drop(['artist_name', 'title', 'release'], axis=1, inplace=True)\\n return gen_df\",\n \"def remove_players_wo_positions(df):\\n df = df[pd.notnull(df['FantPos'])]\\n return df\",\n \"def clean_data(df, to_pm=TO_PM, drop_cols=DROP_COLS, min_seasons=5):\\n cleaning_pipe = Pipeline(\\n [\\n (\\\"to_per_minute\\\", cp.ToPerMinute(to_pm)),\\n (\\\"get_month\\\", cp.GetDebutMonth()),\\n (\\\"fill_na\\\", cp.FillNA()),\\n (\\\"drop_columns\\\", cp.DropFeatures(drop_cols)),\\n (\\\"create_labels\\\", cp.CreateLabels(min_seasons)),\\n ]\\n )\\n return cleaning_pipe.fit_transform(df)\",\n \"def fix_negatives(data_path: str, plot: bool = False) -> None:\\n csvs = [x for x in data_path.iterdir() if 'covidtimeseries' in str(x)]\\n for csv in tqdm(csvs, desc=\\\"Regions\\\"):\\n roi = str(csv).split('.')[0].split('_')[-1]\\n df = pd.read_csv(csv)\\n # Exclude final day because it is often a partial count.\\n df = df.iloc[:-1]\\n df = fix_neg(df, roi, plot=plot)\\n df.to_csv(data_path / (csv.name.split('.')[0]+'.csv'))\",\n \"def clean_dataframe(df, column_list, length):\\n list_of_actual_columns = list(dataframe.columns.values)\\n for each_column in column_list:\\n if each_column not in list_of_actual_columns:\\n return False\\n if number_of_rows < 1000:\\n return False\\n return True\\n\\n category_value_counts = df['Type'].value_counts()\\n category_value_counts.to_csv('category_value_counts')\\n cdf = pd.read_csv('category_value_counts')\\n df = df.merge(cdf, left_on='Type', right_on='OldCategory')\\n df['Datetime'] = pd.to_datetime(df['Datetime'])\\n df[(df['Datetime'] > '2019-12-31')]\\n df.sort_values(by='Datetime', ascending=False)\",\n \"def get_competition_season_type(season):\\n default_type = games.models.CompetitionSeason.winter\\n try:\\n season_name = season.name\\n if season_name.find(\\\"/\\\") == -1:\\n return games.models.CompetitionSeason.summer\\n return default_type\\n except Exception as e:\\n return default_type\",\n \"def get_latest_league_data(self, df):\\n max_date = pd.to_datetime(df[\\\"Date\\\"]).max()\\n df = df[df[\\\"Date\\\"] == max_date]\\n [latest_league_file_dir] = df[\\\"File\\\"].values\\n df = self.extract_df(latest_league_file_dir)\\n return df\",\n \"def convert_season(row): \\n if row[\\\"month\\\"] >= 8:\\n return int(row[\\\"season\\\"][:4])\\n else:\\n return int(row[\\\"season\\\"][-4:])\",\n \"def remove_closed_store(data: pyspark.sql.dataframe.DataFrame) -> pyspark.sql.dataframe.DataFrame:\\n closed_store = pd.read_csv('../data/closedStore/Closed stores list.csv')\\n closed_store_list = closed_store['Store'].unique()\\n \\n for store in closed_store_list:\\n data = data[data.Store !=store] # remove the colsed store\\n return data\",\n \"def featurize_date_col(df, col, remove_when_done=False):\\n df['{}_isholiday'.format(col)] = df[col].apply(is_russian_2017_holiday)\\n df['{}_wday'.format(col)] = df[col].apply(lambda x: x.dayofweek)\\n df['{}_yday'.format(col)] = df[col].apply(lambda x: x.dayofyear)\\n if remove_when_done:\\n df = df.drop(col, axis=1)\\n return df\",\n \"def clean_meteo_data(self, df):\\n for col in df.columns:\\n df[col] = df[col].str.replace(',', '.').astype(\\\"float\\\")\\n# df_nan = df[df.isna().any(axis=1)]\\n# print(\\\"Check Nans:\\\",df_nan.shape[0])\\n df=df.fillna(method='ffill')\\n# df_nan = df[df.isna().any(axis=1)]\\n# print(\\\"Check Nans:\\\",df_nan.shape[0])\\n# print(\\\"shape selected sensor data:\\\",df.shape)\\n df=df.dropna()\\n df=df.resample(\\\"10T\\\").mean()\\n df=df.reset_index()\\n df['dag']=df['datetime'].dt.day\\n return df\",\n \"def clean_df(df):\\n # TODO: get birthdays for people missing birthdays\\n df = df[~df.birthday.isnull()]\\n df[\\\"birthday\\\"] = pd.to_datetime(df[\\\"birthday\\\"])\\n return df\",\n \"def strip_static_cols(df):\\n for col in df.columns:\\n if len((df[col]).unique()) == 1:\\n df.drop(columns=[col], inplace=True)\\n return df\",\n \"def clean_data(df_turnstiles):\\n\\n # sort values in a such a way that the duplicate values sit directly below the originals, so they will be removed.\\n df_turnstiles.sort_values(\\n [\\\"C/A\\\", \\\"UNIT\\\", \\\"SCP\\\", \\\"STATION\\\", \\\"DATE_TIME\\\"],\\n inplace=True,\\n ascending=False,\\n )\\n # keeps top row, deletes others\\n df_turnstiles.drop_duplicates(\\n subset=[\\\"C/A\\\", \\\"UNIT\\\", \\\"SCP\\\", \\\"STATION\\\", \\\"DATE_TIME\\\"], inplace=True\\n )\\n\\n # remove DESC column\\n df_turnstiles = df_turnstiles.drop([\\\"DESC\\\"], axis=1, errors=\\\"ignore\\\")\\n\\n # remove the many spaces in the EXITS column name\\n df_turnstiles.rename(\\n columns={\\n \\\"EXITS \\\": \\\"EXITS\\\"\\n },\\n inplace=True,\\n )\\n\\n return df_turnstiles\",\n \"def clean_file(df):\\n df_clean = df.drop_duplicates()\\n df_no_zeros = df_clean[df_clean[2] != 0]\\n df_sorted = df_no_zeros.sort()\\n\\n return df_sorted\",\n \"def cleanFeatures(merged, featureType):\\n minimumInningsPitched = 10\\n minimumAtBats = 10\\n merged = merged[(merged['ip'] > minimumInningsPitched) | (merged['ab_y'] > minimumAtBats)]\\n\\n if featureType == 'historical':\\n merged['is_winner'] = merged['Winner'].apply(lambda x: 0 if isinstance(x, float) else 1)\\n modelData = merged[['h_x', 'bb_x', 'so_x', 'er', 'sv', 'w', 'ip', 'h_y', 'hr_y',\\n 'rbi', 'bb_y', 'so_y', 'ab_y', 'is_winner']]\\n elif featureType == 'projected':\\n modelData = merged[['player_id', 'league_y', 'h_x', 'bb_x', 'so_x', 'er', 'sv', 'w', 'ip', 'h_y', 'hr_y',\\n 'rbi', 'bb_y', 'so_y', 'ab_y']]\\n modelData.rename(columns={\\n 'league_y': 'league'\\n }, inplace=True)\\n\\n modelData.is_copy = False\\n modelData = modelData.replace([np.inf, -np.inf], np.nan).fillna(0)\\n\\n return modelData.drop_duplicates()\",\n \"def get_datetime_seasons(\\n self, train: NumpyOrPandas, outp_role: Optional[ColumnRole] = None\\n ) -> Optional[LAMLTransformer]:\\n _, datetimes = self.get_cols_for_datetime(train)\\n for col in copy(datetimes):\\n if len(train.roles[col].seasonality) == 0 and train.roles[col].country is None:\\n datetimes.remove(col)\\n\\n if len(datetimes) == 0:\\n return\\n\\n if outp_role is None:\\n outp_role = NumericRole(np.float32)\\n\\n date_as_cat = SequentialTransformer(\\n [\\n ColumnsSelector(keys=datetimes),\\n DateSeasons(outp_role),\\n ]\\n )\\n return date_as_cat\",\n \"def clean_weather_csv(df: pd.DataFrame) -> pd.DataFrame:\\n if 'Min_VisibilitykM' in df.columns:\\n df.rename(columns={'Min_VisibilitykM': 'Min_VisibilityKm'},\\n inplace=True)\\n if 'Min_DewpointC' in df.columns:\\n df.rename(columns={'Min_DewpointC': 'MinDew_pointC'}, inplace=True)\\n\\n cols = map(convert_to_snake_case, df.columns)\\n df.columns = cols\\n\\n for col in ['max_visibility_km', 'min_visibility_km', 'mean_visibility_km',\\n 'max_gust_speed_km_h', 'cloud_cover']:\\n df[col] = df[col].fillna(df[col].mean())\\n\\n df['events'] = df.events.fillna('No Events')\\n return df\",\n \"def remove_staxml_end_date(inv):\\n for net in inv:\\n for sta in net:\\n if len(sta.channels) > 3:\\n # Find chan with latest end date, and remove it\\n for chan in set([chan.code for chan in sta.channels]):\\n max(sta.select(channel=chan).channels,\\n lambda c: c.end_time)[0].end_date = None\\n return\",\n \"def clean_stockdata(filename):\\n pct_cols = ['Change',\\n 'Dividend Yield',\\n 'EPS growth next 5 years',\\n 'EPS growth past 5 years',\\n 'Float Short',\\n 'Performance (Half Year)',\\n 'Performance (Month)',\\n 'Performance (Quarter)',\\n 'Performance (Week)',\\n 'Performance (YTD)',\\n 'Performance (Year)',\\n 'Sales growth past 5 years',\\n 'Payout Ratio',\\n 'EPS (ttm)',\\n 'EPS growth this year',\\n 'EPS growth next year',\\n 'EPS growth past 5 years',\\n 'EPS growth next 5 years',\\n 'Sales growth past 5 years',\\n 'EPS growth quarter over quarter',\\n 'Sales growth quarter over quarter',\\n 'Insider Ownership',\\n 'Insider Transactions',\\n 'Institutional Ownership',\\n 'Institutional Transactions',\\n 'Float Short',\\n 'Return on Assets',\\n 'Return on Equity',\\n 'Return on Investment',\\n 'Gross Margin',\\n 'Operating Margin',\\n 'Profit Margin',\\n 'Volatility (Week)',\\n 'Volatility (Month)',\\n '20-Day Simple Moving Average',\\n '50-Day Simple Moving Average',\\n '200-Day Simple Moving Average',\\n '50-Day High',\\n '50-Day Low',\\n '52-Week High',\\n '52-Week Low',\\n 'Change from Open',\\n 'Gap',\\n 'Change']\\n df = pd.read_csv(filename)\\n df[pct_cols] = df[pct_cols].applymap(clean_pcts)\\n df.drop('No.', axis=1, inplace=True)\\n df.to_csv(filename, index=False)\",\n \"def _remove_redundant_columns(self):\\n self.dataframe.drop(['letter', 'sentiment'], axis=1, inplace=True)\",\n \"def _prune_empty_time_series(\\n request: tensorboard_service.WriteTensorboardRunDataRequest,\\n):\\n for (time_series_idx, time_series_data) in reversed(\\n list(enumerate(request.time_series_data))\\n ):\\n if not time_series_data.values:\\n del request.time_series_data[time_series_idx]\",\n \"def dropDuplicateStars(df):\\n df = df.drop_duplicates(subset =\\\"host_name\\\", keep = 'first')\\n return df\",\n \"def _clean_up_spectrometer_df_header(unformatted_spectrometer_df):\\n\\n timestamp_column, epoch_column = unformatted_spectrometer_df.columns[[0, 1]]\\n spectrometer_df_with_clean_header = unformatted_spectrometer_df.rename(\\n # Timestamp column is in local time, epoch_time column is in UTC\\n columns={timestamp_column: \\\"timestamp\\\", epoch_column: \\\"epoch_time\\\"}\\n )\\n # 'epoch_time' column is redundant to 'timestamp' column. Remove it\\n spectrometer_df_with_clean_header.drop([\\\"epoch_time\\\"], axis=1, inplace=True)\\n\\n return spectrometer_df_with_clean_header\",\n \"def clean_store_csv(df: pd.DataFrame) -> pd.DataFrame:\\n cols = map(convert_to_snake_case, df.columns)\\n df.columns = cols\\n\\n for col in ['promo2_since_week', 'promo2_since_year',\\n 'competition_distance', 'competition_open_since_month',\\n 'competition_open_since_year']:\\n df[col] = df[col].fillna(df[col].mean())\\n\\n df['promo_interval'] = df.promo_interval.fillna('None')\\n return df\",\n \"def split_by_standard(df, compl_data):\\n \\n #Find non-standard simulations\\n standard_simulations = { dyn for dyn in compl_data if is_gpcrmd_community} \\n df_standard = df[df.columns[df.columns.isin(standard_simulations)]]\\n \\n return(df_standard)\",\n \"def team_games(df, team):\\n print(df[\\n ( df['team'] == team ) & \\n ( df['player'] == 'Team' )\\n ].drop_duplicates(subset=['gameid']).shape)\\n return df[\\n ( df['team'] == team ) & \\n ( df['player'] == 'Team' )\\n ].drop_duplicates(subset=['gameid'])\",\n \"def clean_data(gross):\\n\\n # modify data type\\n gross['week_ending'] = pd.to_datetime(gross['week_ending']).dt.strftime('%Y-%m-%d')\\n gross['label'] = [1 if x >= 0 else 0 for x in gross['diff_in_dollars']]\\n # ain't too proud has weird special characters\\n gross['show'] = [x.split('¡')[0] for x in gross['show']]\\n # beautiful carol king has different names\\n gross['show'] = [re.sub(r'\\\\s+', ' ', x.translate(\\n str.maketrans(string.punctuation, ' ' * len(string.punctuation))).lower().strip()) for x in gross['show']]\\n gross.loc[gross['show'] == 'beautiful the carole king musical', 'show'] = 'beautiful'\\n\\n return gross\",\n \"def mapback(df):\\n df.set_index(df['Datetime'], drop=False, inplace=True) #keep for later use\\n df.sort_index(inplace=True)\",\n \"def select_feats(df):\\n cols = list(df)\\n for col in cols:\\n if col not in config[\\\"feats\\\"] and col != \\\"label\\\":\\n df = df.drop(columns=col)\\n return df\",\n \"def clean_data(df):\\n \\n any_location_id_missing = (df.PULocationID > 263) | (df.DOLocationID > 263)\\n df = df.drop(df.index[any_location_id_missing])\\n \\n df = df[df.tpep_dropoff_datetime > df.tpep_pickup_datetime]\\n\\n df.PULocationID.replace([104, 105], 103)\\n \\n return df\",\n \"def remove_columns(df):\\n avg = np.mean(df[df['sentiment'] != 'None']['sentiment'].astype('float'))\\n df['sentiment'] = df['sentiment'].replace('None', avg).astype('float')\\n\\n to_remove = []\\n print('column(s) removed: ')\\n for column in df.columns:\\n print(column)\\n if(np.unique(df[column][df[column].notnull()]).shape[0] < 2):\\n print(column)\\n to_remove.append(column)\\n \\n return df.drop(columns = to_remove)\",\n \"def cleanData(data):\\r\\n \\r\\n indexes = data.index[pd.isnull(data[\\\"release date\\\"])]\\r\\n # data = data.drop(data.index[[2172, 3781, 7245, 12475, 14756, 15292, 49295, 93523, 99723]],axis=0,inplace=False)\\r\\n data = data.drop(indexes,axis=0,inplace=False)\\r\\n data = data.drop([\\\"video release date\\\", 'IMDb URL'],axis=1)\\r\\n \\r\\n \\r\\n data.gender = data.gender.astype(\\\"category\\\")\\r\\n data.occupation = data.occupation.astype(\\\"category\\\")\\r\\n \\r\\n categorical_variables = [\\\"gender\\\", \\\"occupation\\\"]\\r\\n for variable in categorical_variables:\\r\\n dummies = pd.get_dummies(data[variable], prefix=variable)\\r\\n data = pd.concat([data, dummies], axis=1)\\r\\n data.drop([variable], axis=1, inplace=True)\\r\\n data['movieID'] = data['movieID'].astype('int')\\r\\n data['userID'] = data['userID'].astype('int')\\r\\n \\r\\n\\t#data = dateTime(data)\\r\\n \\r\\n return data\",\n \"def latest_close(df):\\n return df['close'].iloc[-1]\",\n \"def test_structural_remove_columns_all_1_0(self):\\n cp = Plotter.from_smiles(['CCCC', 'CCCC'], sim_type=\\\"structural\\\")\\n self.assertTrue(cp._Plotter__df_descriptors.empty)\",\n \"def reorder_bgc_data(df):\\n st_col_list = ['time','lat','lon','depth','year','month','week','dayofyear','float_id','cycle']\\n st_cols =df[st_col_list]\\n non_st_cols = df.drop(st_col_list, axis=1)\\n reorder_df = pd.concat([st_cols, non_st_cols], axis=1, sort=False)\\n \\n sci_col_list = [\\\"SCIENTIFIC_CALIB_COEFFICIENT\\\",\\\"SCIENTIFIC_CALIB_COMMENT\\\",\\\"SCIENTIFIC_CALIB_DATE\\\",\\\"SCIENTIFIC_CALIB_EQUATION\\\"]\\n sci_cols =reorder_df[sci_col_list]\\n non_sci_cols = reorder_df.drop(sci_col_list, axis=1)\\n neworder_df = pd.concat([non_sci_cols,sci_cols], axis=1, sort=False)\\n return neworder_df\",\n \"def drop_unnecessary_columns(df):\\n df = df.drop([\\n 'id',\\n 'imdb_id',\\n 'poster_path',\\n 'video',\\n 'status',\\n 'weighted_rating', # Only average_rating was used for this project\\n 'original_title',\\n 'crew', # Used in production_score\\n 'producers', # Used in production_score\\n 'executive_producers', # Used in production_score\\n 'cast', # Used in production_score\\n 'director', # Used in production_score\\n 'production_companies', # Used in production_score\\n 'production_countries', # Binarized\\n 'genres', # Binarized\\n 'original_language', # Binarized\\n 'adult', # No adult movies in the dataset, so no variance between movies\\n 'release_date', # Not being considered for this project\\n 'overview',\\n 'title',\\n 'tagline',\\n 'vote_average', # Ratings have been binned\\n 'popularity', # Only considering average_rating\\n 'vote_count', # We are making a predictor, so it makes no sense to use vote counts as input\\n 'revenue', # We are making a predictor, so it makes no sense to use revenue as input\\n 'keywords', # Not considering keywords for this project\\n 'revenue_divide_budget', # We are making a predictor, so it makes no sense to use revenue/budget as input\\n ], 1)\\n return df\",\n \"def reduce_time_seasonal( mv, seasons=seasonsyr, vid=None ):\\n if vid==None:\\n vid = 'reduced_'+mv.id\\n # Note that the averager function returns a variable with meaningless id.\\n # The climatology function returns the same id as mv, which we also don't want.\\n\\n # The slicers in time.py require getBounds() to work.\\n # If it doesn't, we'll have to give it one.\\n # Setting the _bounds_ attribute will do it.\\n timeax = timeAxis(mv)\\n if timeax is None:\\n print \\\"WARNING- no time axis in\\\",mv.id\\n return None\\n if timeax.getBounds()==None:\\n timeax._bounds_ = timeax.genGenericBounds()\\n mvseas = seasons.climatology(mv)\\n if mvseas is None:\\n print \\\"WARNING- cannot compute climatology for\\\",mv.id,seasons.seasons\\n print \\\"...probably there is no data for times in the requested season.\\\"\\n return None\\n avmv = mvseas\\n avmv.id = vid\\n if hasattr(mv,'units'): avmv.units = mv.units\\n avmv = delete_singleton_axis( avmv, vid='time' )\\n avmv.units = mv.units\\n return avmv\",\n \"def current_season():\\n td = datetime.datetime.today()\\n if td.month > 8:\\n return td.year\\n return td.year - 1\",\n \"def prepare_output_df(df: DataFrame, kind: str) -> DataFrame:\\r\\n columns = get_export_columns(kind)\\r\\n to_drop = list(filter(lambda x: x not in columns.keys(), df.columns.to_list())) # For any columns not in the get_export_columns()\\r\\n df = df.drop(columns=to_drop) # mapping, drop them from the DataFrame.\\r\\n df = df.rename(columns=columns)\\r\\n return df\",\n \"def clean_googletrend_csv(df: pd.DataFrame) -> pd.DataFrame:\\n cols = map(convert_to_snake_case, df.columns)\\n df.columns = cols\\n\\n # Create a 'state' column, which is just the last 2 of the 'file' column\\n # except where the 'file' column ends in 'HB,NI'\\n df['state'] = df.file.str[-2:]\\n df.loc[df.state == 'NI', 'state'] = 'HB,NI'\\n\\n # The googletrend.csv file contains a column 'week', formatted like\\n # \\\"2013-01-06 - 2013-01-12\\\", where the first date is the Sunday and the\\n # second date is the Saturday of the given week. Every other csv file with\\n # a date-like column simply contains a date. So we want to convert the\\n # 'week' column to a 'date' column.\\n # In order to do that, we'll create a whole new dataframe, 'week_lookup',\\n # containing dates and the start of the week that corresponds to each date.\\n # Then we'll merge them together and clean up any extra columns.\\n\\n # Get the earliest and latest dates in the dataframe\\n date_min = pd.to_datetime(df.week.min()[:10])\\n # Note below it's -10: to get the last day of the max week\\n date_max = pd.to_datetime(df.week.max()[-10:])\\n\\n # Create a new dataframe, week_lookup, listing all days in the\\n # period and their corresponding week\\n days = pd.date_range(date_min, date_max, freq='D')\\n week_lookup = pd.DataFrame({'date': days})\\n\\n # To get the start of the week, find the number of the day in the week\\n week_lookup['num'] = week_lookup['date'].dt.dayofweek\\n # Monday is day 0 in Pandas, so add 1 to get Sundays (and mod 7 to be sure)\\n week_lookup['offset'] = (week_lookup['num'] + 1) % 7\\n # Sunday of any given week = date - the number of the day in the week\\n week_lookup['Week_Start'] = \\\\\\n week_lookup['date'] - pd.to_timedelta(week_lookup['offset'], unit='D')\\n week_lookup.drop(['num', 'offset'], axis='columns', inplace=True)\\n\\n # Create column week_start in df to match up to week_lookup\\n df['week_start'] = pd.to_datetime(df.week.str[:10])\\n\\n # Re-merge week_lookup back into google so we end up with the\\n # appropriate 7 days for each week, and drop auxiliary columns\\n df = week_lookup.merge(df, left_on='Week_Start', right_on='week_start')\\n df.drop(['file', 'Week_Start', 'week'], axis='columns', inplace=True)\\n # Remove extraneous dates from df (these are limited by train.csv)\\n df = df[(df.date >= pd.to_datetime('2013-01-01')) &\\n (df.date <= pd.to_datetime('2015-07-31'))]\\n\\n return df\",\n \"def removePlayer(df, name):\\n if name in getPlayerList(df):\\n df = df[df.name != name]\\n return df\",\n \"def get_obj_df():\\n obj_df = pd.read_csv('data/object.csv')\\n obj_df = obj_df.drop_duplicates()[['course_id', 'module_id', 'category', 'start']] \\n obj_df['start'] = pd.to_datetime(obj_df[obj_df['start'] != 'null']['start'])\\n return obj_df\",\n \"def _check_season_gaps_in_teams_matches(self, df: pd.DataFrame):\\n if self._verbose > 0:\\n print(\\\"Teams:\\\")\\n print(self.teams)\\n print(\\\"Teams playing last season:\\\")\\n print(self.last_season_teams)\\n\\n omitted_teams_test = check_season_gaps(df, self.test_fixtures_ids)\\n omitted_teams_predict = check_season_gaps(df, self.predict_fixtures_ids)\\n\\n if omitted_teams_predict or omitted_teams_test:\\n print(\\\"Warning: There are some teams with season gaps in data.\\\")\\n print(\\\"Teams omitted based on predict dataset due to season gap:\\\")\\n print(omitted_teams_predict)\\n print(\\\"Teams omitted based on test dataset due to season gap:\\\")\\n print(omitted_teams_test)\\n\\n # Delete these teams from datasets\\n for team in set(omitted_teams_test + omitted_teams_predict):\\n del self.test_fixtures_ids[team]\\n del self.predict_fixtures_ids[team]\\n\\n print(\\\"You should consider lowering required number of test or predict data samples.\\\")\",\n \"def setup_df(ticker):\\n stock = yf.Ticker(ticker)\\n df = stock.history(period=\\\"max\\\")\\n del df['Dividends']\\n del df['Stock Splits']\\n return df\",\n \"def _clean_dataset(df: pd.DataFrame) -> pd.DataFrame:\\n df = df.loc[:, ~df.columns.str.contains(\\\"^Unnamed\\\")]\\n df = df.dropna()\\n return df\",\n \"def remove_duplicates_on_rt(self, peak_df):\\n duplicate_df =peak_df[peak_df['sec_id'].duplicated(keep=False)]\\n print (\\\"the duplicates at this stage are: \\\")\\n display(duplicate_df)\\n dup_ids = set(duplicate_df['sec_id'].values)\\n name_rt_dict = {}\\n\\n for dupid in dup_ids:\\n\\n dup_indexes = []\\n dup_peaks = peak_df[peak_df.sec_id == dupid]\\n\\n for index, row in dup_peaks.iterrows():\\n dup_indexes.append(index)\\n name_rt_dict[index] = [row['compound'], row['rt']]\\n\\n\\n keep_index = self.get_closest_rt_match(name_rt_dict)\\n peak_df = self.drop_duplicates(dup_indexes, keep_index, peak_df)\\n\\n return peak_df\",\n \"def __clean_repeated_columns(self, df, column_type):\\n for column in df.columns:\\n if column_type in column.lower():\\n # Fill main column with data from \\\"prefix + _\\\" type column names.\\n df[column_type[:-1]].fillna(df[column], inplace=True)\\n # Drop the \\\"prefix + _\\\" type column names.\\n df.drop(column, axis=1, inplace=True)\",\n \"def test_drop_zero_variance_on_subset_columns_with_zv_removals(data):\\n step = DropZVColumnsStep(['released', 'episodes'], naomit=True)\\n bdf = step.prepare(data).bake(data)\\n\\n assert 'name' in bdf.columns\\n assert 'released' in bdf.columns\\n assert 'episodes' not in bdf.columns\",\n \"def data_cleaning(df):\\n df = df.dropna(how='all')\\n \\n return df\",\n \"def remove_last_period(mm):\\n \\n m = mm.model\\n variables = [\\\"harv\\\", \\\"age\\\"]\\n constraints = [\\\"harv\\\", \\\"age\\\", \\\"env\\\"]\\n\\n harv_vars = pg.get_variables(m, \\\"harv\\\")\\n max_period = max([float(i.varName.split(\\\",\\\")[-1][:-1]) for i in harv_vars])\\n\\n for v in variables:\\n all_vars = pg.get_variables(m, v, filter_values={-1: max_period})\\n for v in all_vars:\\n m.remove(v)\\n for c in constraints:\\n all_cons = pg.get_constraints(m, c, filter_values={-1: max_period})\\n for a in all_cons:\\n m.remove(a)\\n m.update()\",\n \"def fyber_video_dataframe_cleaner(dataframe):\\r\\n print(\\\"Cleaning Fyber Video...\\\")\\r\\n df = dataframe\\r\\n df = df.fillna(0)\\r\\n \\r\\n delete_list = ['application_id', 'completions', 'ecpm_eur', 'ecpm_usd', \\r\\n 'fills', 'revenue_eur', 'unique_impressions']\\r\\n \\r\\n for entry in delete_list:\\r\\n del df[entry]\\r\\n \\r\\n df['Ad Type'] = 'video'\\r\\n df['Partner'] = 'Fyber_Video'\\r\\n df['Total_Code_Served'] = 0\\r\\n df['Clicks'] = 0\\r\\n \\r\\n df = df.rename(columns={'date':'Day', 'application_name':'App',\\r\\n \\\"ad_format\\\":\\\"AdUnit\\\", \\\"Ad Type\\\":\\\"UnitType\\\", \\r\\n \\\"country\\\":\\\"Country\\\", \\\"requests\\\":\\\"Requests\\\", \\r\\n \\\"impressions\\\":\\\"Impressions\\\", \\\"clicks\\\":\\\"Clicks\\\", \\r\\n \\\"revenue_usd\\\":\\\"Revenue\\\"})\\r\\n \\r\\n df['App'] = df['App'].replace({\\\"IMVU iOS Primary Wall\\\":\\\"IMVU iOS\\\", \\r\\n \\\"IMVU iOS External Offer Wall\\\":\\\"IMVU iOS\\\", \\r\\n \\\"IMVU Google Play\\\":\\\"IMVU Android\\\"}) \\r\\n\\r\\n df['Impressions'] = df['Impressions'].apply(lambda x:int(x))\\r\\n df['Requests'] = df['Requests'].apply(lambda x:int(x))\\r\\n\\r\\n df = df[[\\\"Day\\\", \\\"App\\\", \\\"AdUnit\\\", \\\"UnitType\\\", \\\"Country\\\", \\r\\n \\\"Total_Code_Served\\\", \\\"Requests\\\", \\\"Impressions\\\", \\r\\n \\\"Clicks\\\", \\\"Revenue\\\", \\\"Partner\\\"]]\\r\\n\\r\\n drop_index_list = []\\r\\n for num in list(df.index):\\r\\n if df.loc[num, 'App'] == 'Blue Bar Bundle ' or df.loc[num, 'App'] == 'NEXT Featured Offers':\\r\\n drop_index_list.append(num)\\r\\n\\r\\n df = df.drop(drop_index_list, axis=0)\\r\\n\\r\\n return df\",\n \"def get_pre_df(temp_pre_df):\\n \\n event_time_max = temp_pre_df['event_time'].max()\\n cat_dfs = []\\n for num in np.arange(0,(1080/2)+1,30)[1:]:\\n # making <= null i.e keeping >\\n temp_pre_df.loc[temp_pre_df['event_time'] <= int(num), 'event_time'] = np.nan\\n for col in ['event_name', 'specialty', 'plan_type']:\\n cat_df = temp_pre_df.groupby([\\\"id\\\", col]).agg({\\\"event_time\\\": 'count'}).unstack(level=col)\\n cat_df = cat_df/(event_time_max-num)\\n cat_df.columns = ['__'.join(['normChange', col, name, str(int(num))]) for name in cat_df.columns.droplevel()]\\n cat_dfs.append(cat_df)\\n pre_df = pd.concat(cat_dfs, axis = 1) \\n return pre_df.fillna(0)\",\n \"def overwrite_season(function):\\n @functools.wraps(function)\\n def inner(*args, season = None, **kw):\\n data = function(*args,**kw)\\n if season:\\n replace_season(data,season)\\n return data\\n return inner\",\n \"def clean_ts(self, df_in, date_col, ts_col):\\n df_dates=[parser.parse(t) for t in df_in[date_col]]\\n df_in[date_col]=df_dates\\n df_in['timestamp'] = [(t - datetime(1970, 1, 1)) / \\\\\\n timedelta(seconds=1) for t in df_dates]\\n df_in[ts_col]=pd.to_numeric(df_in[ts_col], errors='coerce')\\n df_in=df_in[df_in['timestamp']>=self.begin_ts]\\n df_in=df_in[df_in['timestamp']<=self.end_ts]\\n df_in=df_in[['timestamp', ts_col]].dropna().\\\\\\n sort_values(by='timestamp').\\\\\\n copy()\\n df_in['date']=[datetime.utcfromtimestamp(t)\\\\\\n for t in df_in['timestamp']]\\n return df_in\",\n \"def season_type(self, season_type):\\n\\n self._season_type = season_type\",\n \"def get_full_data(raw_data_path, return_missing_column_list=False):\\n\\n # Read raw_data into individual dataframes\\n df21 = pd.read_csv(raw_data_path + '/2020-21/gws/merged_gw.csv',\\n encoding='utf_8')\\n df20 = pd.read_csv(raw_data_path + '/2019-20/gws/merged_gw.csv',\\n encoding='utf_8')\\n df19 = pd.read_csv(raw_data_path + '/2018-19/gws/merged_gw.csv',\\n encoding='ISO-8859-1')\\n df18 = pd.read_csv(raw_data_path + '/2017-18/gws/merged_gw.csv',\\n encoding='ISO-8859-1')\\n df17 = pd.read_csv(raw_data_path + '/2016-17/gws/merged_gw.csv',\\n encoding='ISO-8859-1')\\n\\n # Add a column identifying the season to each dataframe\\n df21['season'] = 21\\n df20['season'] = 20\\n df19['season'] = 19\\n df18['season'] = 18\\n df17['season'] = 17\\n\\n # Create a list of all dataframes\\n dflist = [df17, df18, df19, df20, df21]\\n\\n # Create a list of all columns existing in each season\\n consistent_columns = []\\n for column in list(df21.columns):\\n if column in list(df20.columns) and \\\\\\n column in list(df19.columns) and \\\\\\n column in list(df18.columns) and \\\\\\n column in list(df17.columns):\\n consistent_columns.append(column)\\n\\n # Create a dictionary with columns that are only in some years.\\n # Key = column name, value = years for which the column exists.\\n missing_columns = {}\\n for df in dflist:\\n for column in list(df.columns):\\n if not column in consistent_columns:\\n if column in missing_columns.keys():\\n missing_columns[column].append(df.loc[0, ['season']][0])\\n else:\\n missing_columns[column] = [df.loc[0, ['season']][0]]\\n\\n # Concatenate seasons based on consistent columns\\n frames_to_concat = [\\n df21[consistent_columns], df20[consistent_columns],\\n df19[consistent_columns], df18[consistent_columns],\\n df17[consistent_columns]\\n ]\\n complete_data = pd.concat(frames_to_concat)\\n\\n # Remove trailing underscores and numbers from names\\n complete_data.name = complete_data.name.str.rstrip('_1234567890')\\n\\n # Swap out underscores for spaces within names\\n complete_data.name = complete_data.name.str.replace('_', ' ')\\n\\n # Drop column 'round' as it is the same as 'GW'\\n complete_data.drop(columns=['round'], inplace=True)\\n\\n # Set names to lowercase\\n complete_data.name = complete_data.name.apply(lambda n: n.lower())\\n\\n # Correct game weeks for 2020\\n complete_data['GW'] = complete_data['GW'].apply(correct_2020)\\n\\n # Match player position\\n complete_data, players_raw_seasons = match_position(complete_data, raw_data_path)\\n\\n # Add dreamteam count for each player for the last season\\n complete_data = add_dreamteam_count(complete_data, players_raw_seasons)\\n\\n # Add team name for last two years to dataframe\\n complete_data = add_team(complete_data, players_raw_seasons, raw_data_path)\\n\\n # Add opponent strength\\n complete_data = add_opponent_strength(complete_data)\\n\\n # Add own team strength\\n complete_data = add_team_strength(complete_data)\\n\\n # Sort ascending by player name and kickoff-date and separate kickoff-date and -time\\n complete_data = sort_kickoff(complete_data)\\n\\n if return_missing_column_list:\\n return complete_data, missing_columns\\n return complete_data\",\n \"def prune_data(self, ts):\\n sql = \\\"delete from %s where dateTime < %d\\\" % (self.dbm.table_name, ts)\\n self.dbm.getSql(sql)\\n try:\\n # sqlite databases need some help to stay small\\n self.dbm.getSql('vacuum')\\n except Exception as e:\\n pass\",\n \"def clean_data(df_name):\\n\\n wines = df_name\\n wines = wines.rename(columns={'Vintage': 'Year'})\\n wines['Location'] = wines['Appellation'].apply(lambda x: x['Region']['Name'])\\n wines['Region'] = wines['Appellation'].apply(lambda x: x['Name'])\\n wines['Type'] = wines['Varietal'].apply(lambda x: x['WineType']['Name'])\\n wines['Grape'] = wines['Varietal'].apply(lambda x: x['Name'])\\n wines['Reviews'] = wines['Community'].apply(lambda x: x['Reviews']['Url'])\\n drop_columns = ['Appellation', 'Community', 'Description', 'GeoLocation', 'Labels', 'ProductAttributes','Ratings','Retail', 'Url', 'Varietal', 'Vineyard', 'Vintages']\\n wines.drop(drop_columns, axis=1, inplace=True)\\n wines = wines[['Id', 'Name', 'Year', 'Type', 'Grape', 'Location', 'Region', 'PriceRetail', 'PriceMin', 'PriceMax', 'Reviews']]\\n wines['CurrentReviews'] = '' #wines['CurrentReviews'].apply(lambda x: [\\\"\\\"])\\n wines['PriorReviews'] = '' #wines['PriorReviews'].apply(lambda x: [''])\\n\\n return wines\",\n \"def station_stats(df):\",\n \"def dataendclean(df, x, inplace=False):\\r\\n # Examine Mean Values\\r\\n if inplace:\\r\\n df = df\\r\\n else:\\r\\n df = df.copy()\\r\\n\\r\\n jump = df[abs(df.loc[:, x].diff()) > 1.0]\\r\\n try:\\r\\n for i in range(len(jump)):\\r\\n if jump.index[i] < df.index[50]:\\r\\n df = df[df.index > jump.index[i]]\\r\\n printmes(\\\"Dropped from beginning to \\\" + str(jump.index[i]))\\r\\n if jump.index[i] > df.index[-50]:\\r\\n df = df[df.index < jump.index[i]]\\r\\n printmes(\\\"Dropped from end to \\\" + str(jump.index[i]))\\r\\n except IndexError:\\r\\n printmes('No Jumps')\\r\\n return df\",\n \"def glicko2_summarize(df, min_weeks=4, use_prior=False):\\n\\n # Loop through seasons and weeks to create full history of ratings by team\\n results = pd.DataFrame()\\n for season in df['Season'].sort_values().unique():\\n for week in df[df['Season']==season]['Week'].sort_values().unique():\\n if week > min_weeks:\\n if week == min_weeks + 1:\\n season_df = df[df['Season']==season].copy()\\n uniqueteamids = pd.concat([season_df['VisID'],\\n season_df['HomeID']]).unique()\\n if use_prior == True and season > df['Season'].min():\\n ratings = np.repeat(1500, len(uniqueteamids))\\n ratingsdeviance = np.repeat(350, len(uniqueteamids))\\n sigma = np.repeat(0.06, len(uniqueteamids))\\n glicko_stats = pd.DataFrame({'ratings': ratings,\\n 'ratingsdeviance': ratingsdeviance,\\n 'sigma': sigma}, index=uniqueteamids)\\n prior = results[results['Season']==season-1]\\n prior_id_mask = [True if id in uniqueteamids else False for id in prior['TeamID']]\\n prior = prior[prior_id_mask]\\n prior = prior.sort_values('Week').groupby('TeamID').tail(1)\\n prior = prior.drop('Week',1)\\n prior = prior.set_index('TeamID')\\n glicko_stats.loc[prior.index, 'ratings'] = prior['Glicko_Rating'] - (prior['Glicko_Rating'] - 1500)/2\\n glicko_stats.loc[prior.index, 'ratingsdeviance'] = prior['Glicko_Rating_Deviance'] - (prior['Glicko_Rating_Deviance'] - 350)/2\\n glicko_stats.loc[prior.index, 'sigma'] = prior['Glicko_Sigma'] - (prior['Glicko_Sigma'] - 0.06)/2\\n else:\\n ratings = np.repeat(1500, len(uniqueteamids))\\n ratingsdeviance = np.repeat(350, len(uniqueteamids))\\n sigma = np.repeat(0.06, len(uniqueteamids))\\n glicko_stats = pd.DataFrame({'ratings': ratings,\\n 'ratingsdeviance': ratingsdeviance,\\n 'sigma': sigma}, index=uniqueteamids)\\n\\n week_df = df[(df['Season']==season) & (df['Week']<week)].copy()\\n glicko_stats = glicko2(week_df, uniqueteamids, glicko_stats)\\n\\n\\n glicko_results = glicko_stats.reset_index()\\n print(glicko_results.head(), season)\\n glicko_results.columns = ['TeamID','Glicko_Rating',\\n 'Glicko_Rating_Deviance',\\n 'Glicko_Sigma']\\n glicko_results['Season'] = season\\n glicko_results['Week'] = week\\n results = pd.concat([results, glicko_results], axis=0,\\n ignore_index=True)\\n\\n # Join the ratings to the original schedule of games\\n df = df.merge(results, left_on=['Season','Week','HomeID'],\\n right_on=['Season','Week','TeamID'],\\n suffixes=('','_Home'))\\n df.drop('TeamID', 1, inplace=True)\\n\\n df = df.merge(results, left_on=['Season','Week','VisID'],\\n right_on=['Season','Week','TeamID'],\\n suffixes=('','_Away'))\\n df.drop('TeamID', 1, inplace=True)\\n\\n # Create key and set index to join with n_game summaries dataset.\\n df.set_index(['HomeID', 'VisID', 'Season', 'Week'], inplace=True)\\n df = df[['Glicko_Rating', 'Glicko_Rating_Deviance', 'Glicko_Sigma',\\n 'Glicko_Rating_Away', 'Glicko_Rating_Deviance_Away',\\n 'Glicko_Sigma_Away']]\\n df.columns = ['Glicko_Rating_Home', 'Glicko_Rating_Deviance_Home',\\n 'Glicko_Sigma_Home', 'Glicko_Rating_Away',\\n 'Glicko_Rating_Deviance_Away', 'Glicko_Sigma_Away']\\n\\n return df\",\n \"def clean_data(df, new_name=None):\\n\\n if new_name:\\n original_name = df[\\\"from\\\"].unique().tolist()\\n df.replace(original_name, new_name, inplace=True)\\n df.dropna(subset=['text'], inplace=True)\\n df.set_index('time', inplace=True, drop=False)\\n return df\",\n \"def getseason(data):\\n ## Season key is the most reliable\\n season = data.get(\\\"season\\\")\\n if season:\\n ## Season key is an integer formatted \\\"YYS\\\" and is 2000-based (i.e.- 171 == 2017-Winter)\\n season = str(season)\\n year = int(f\\\"20{season[:2]}\\\")\\n ## Anichart Season key is 1-indexed\\n season = int(season[2]) - 1\\n ## This should normally pass; if it consistently does not, we'll have to investigate why\\n try: return SeasonCharts.buildseason(season,year)\\n ## If something goes wrong, we'll try another method\\n except: print(f\\\"Failed to parse season: {data['season']}\\\")\\n ## Next, we'll iterate over rankings to try to determine the season/year\\n ## There are multiple types of rankings based on season, year, and both combined,\\n ## so we'll piece it together based on whatever we come across first\\n season,year = None,None\\n for ranking in data.get(\\\"rankings\\\",list()):\\n ## Quicker exit (without just making this loop its own function)\\n if season and year: continue\\n ## We'll ignore stuff we've already gotten and assume that nothing in\\n ## rankings contradicts eachother\\n if not season:\\n ## Defaults to None one way or another if it's not supplied\\n season = ranking.get(\\\"season\\\")\\n if not year: year = ranking.get(\\\"year\\\")\\n ## Check if we made it\\n if season and year:\\n ## As above, this should always work out-of-the-box\\n try: return SeasonCharts.buildseason(season,year)\\n except: print(season,year)\\n ## Welp, we're stumped...\\n return None\",\n \"def clean_data(df, start = 1995, stop = 2018):\\n country_iso3_code = pd.read_html('https://unstats.un.org/unsd/methodology/m49/')\\n country_iso3_code = country_iso3_code[0]['ISO-alpha3 code']\\n df = df.loc[df.country_iso3_code.isin(country_iso3_code)]\\n df = df.set_index(['indicator', 'country_iso3_code', 'country', 'year']).unstack(level = 0)\\n df.columns = df.columns.get_level_values(1)\\n df = df.rename(columns = {'NY.GDP.PCAP.KD.ZG': 'pc_GDP_growth',\\n 'NY.GDP.PCAP.PP.CD': 'pc_GDP_PPP'})\\n df = df.reset_index()\\n df = df.loc[(df.year >= (start - 1)) & (df.year <= stop)]\\n df = df.dropna()\\n return df\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.44943875\",\n \"0.44898292\",\n \"0.44824472\",\n \"0.44771147\",\n \"0.44751763\",\n \"0.44725433\",\n \"0.44693547\",\n \"0.44631696\",\n \"0.44617534\",\n \"0.44554505\",\n \"0.44489124\",\n \"0.44485292\",\n \"0.444696\",\n \"0.4440589\",\n \"0.44385678\",\n \"0.4422582\",\n \"0.44138104\",\n \"0.44112977\",\n \"0.44037762\",\n \"0.44015962\",\n \"0.4401124\",\n \"0.4397019\",\n \"0.43953663\",\n \"0.4395235\",\n \"0.4394315\",\n \"0.43929726\",\n \"0.43899062\",\n \"0.4389483\",\n \"0.43873298\",\n \"0.4379682\",\n \"0.4376021\",\n \"0.43667805\",\n \"0.4362684\",\n \"0.43594992\",\n \"0.43592742\",\n \"0.43557155\",\n \"0.43434483\",\n \"0.4342155\",\n \"0.43415394\",\n \"0.43339688\",\n \"0.4330888\",\n \"0.4323711\",\n \"0.43094063\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7332864"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":85588,"cells":{"query":{"kind":"string","value":"Drops rows from the games table that are of season and season_type."},"document":{"kind":"string","value":"def _truncate_games_table(db_conn, season, season_type):\n delete_statement = f\"DELETE FROM games WHERE season = {season} and type = '{season_type}'\"\n logging.info(f\"Truncating games table with statement: {delete_statement}\")\n db_conn.execute(delete_statement)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _truncate_games_df(df, season, season_type):\n return df[(df['season'] != season) | (df['type'] != season_type)]","def update_season_table(session, sched_tbl, season_df):\n date = datetime.date(datetime.now())\n update_query = session.query(sched_tbl).filter(sched_tbl.start_time < date,\n sched_tbl.home_team_score == 0).order_by(sched_tbl.start_time)\n if update_query.count() == 0:\n # print(\"Season is up to date; Returning without performing an update.\") Test/logging statement\n return\n\n all_update_rows = update_query.all()\n first_game_time = all_update_rows[0].start_time\n last_game_time = all_update_rows[len(all_update_rows) - 1].start_time\n\n # Reduce season to games between first and last game time\n season_df[\"start_time\"] = season_df[\"start_time\"].dt.tz_localize(None)\n update_df = season_df.loc[(season_df.start_time >= first_game_time) & (season_df.start_time <= last_game_time)]\n\n for row in all_update_rows:\n game = update_df.loc[(update_df.home_team == row.home_team) & (update_df.away_team == row.away_team) &\n (update_df.start_time.dt.date == datetime.date(row.start_time))]\n if len(game) == 0:\n # This catches playoff games which do not end up happening (i.e. a game 7 in a series a team sweeps), and\n # removes it from the database\n session.delete(row)\n else:\n row.home_team_score = int(game.home_team_score)\n row.away_team_score = int(game.away_team_score)\n row.start_time = game.start_time.dt.to_pydatetime()[0] # Convert Pandas TimeStamp to datetime\n session.add(row)","def _drop_last_season_championship_matches(self, df: pd.DataFrame) -> pd.DataFrame:\n max_season = df[\"season\"].max()\n\n if self._resume:\n # Check if max season changed\n if max_season != self._model_settings[\"max_season\"]:\n raise ValueError(\"Current max season is different than previous one. Model must be retrained.\")\n else:\n self.max_season = self._model_settings[\"max_season\"]\n else:\n self.max_season = max_season\n\n return df.loc[~((df[\"season\"] == max_season) & (df[\"league\"] == \"CH\"))]","def season_games(year):\n\tLOG.debug('Getting season %d', year)\n\tdata = read_html(io=season_games_url(year),\n\t\t\t\t\t attrs={'id': 'games'},\n\t\t\t\t\t infer_types=False,\n\t\t\t\t\t header=0)\n\tif len(data) != 1:\n\t\traise CantFindTheRightTable\n\tdata = data.pop()\n\n\t# Cleaning.\n\tdel data[\"Unnamed: 3\"]\n\t# The code below issues \"UserWarning: \" So we catch UserWarnings.\n\twith warnings.catch_warnings():\n\t\twarnings.filterwarnings(action='ignore', category=UserWarning,\n\t\t\t\t\t\t\t\tmodule=r'pandas\\.core\\.frame',\n\t\t\t\t\t\t\t\tmessage=(r\"Boolean Series key will be reindexed\"\n\t\t\t\t\t\t\t\t\t\t r\" to match DataFrame index\\.\"))\n\t\t# These rows are mid-table header rows.\n\t\tdata = data[data.Week != \"Week\"][data.Week != \"nan\"]\n\n\tdata['week'] = (data.Week\n\t\t\t\t\t.replace(\"WildCard\", \"wild-card\")\n\t\t\t\t\t.replace(\"Division\", \"divisional\")\n\t\t\t\t\t.replace(\"ConfChamp\", \"conference\")\n\t\t\t\t\t.replace(\"SuperBowl\", \"super-bowl\")\n\t\t\t\t\t.apply(\n\t\t\t\t\t\tlambda s: (int(s)\n\t\t\t\t\t\t\t\t if all(c in '1234567890' for c in s)\n\t\t\t\t\t\t\t\t else s)))\n\tdel data['Week']\n\n\tdata['season'] = year\n\tdata['game_date'] = pd.to_datetime(\n\t\tdata.Date\n\t\t.replace(r\"$\", r\", %d\" % year, regex=True)\n\t\t.replace(r\"^(January|February) (\\d+), \\d+$\", r\"\\1 \\2, %d\" % (year + 1),\n\t\t\t\t regex=True))\n\tdel data['Date']\n\n\tfor column in \"PtsW\", \"PtsL\", \"YdsW\", \"TOW\", \"YdsL\", \"TOL\":\n\t data[column] = data[column].apply(int)\n\n\tdata['WatL'] = data['Unnamed: 5'].apply(lambda x: x == '@')\n\tdel data['Unnamed: 5']\n\tdata['hometeam'] = (~data.WatL * data['Winner/tie'] +\n\t\t\t\t\t\tdata.WatL * data['Loser/tie'])\n\tdata['awayteam'] = (data.WatL * data['Winner/tie'] +\n\t\t\t\t\t\t~data.WatL * data['Loser/tie'])\n\tdata['winner'] = data['Winner/tie']\n\tfor column in 'Winner/tie', 'Loser/tie', \"WatL\":\n\t\tdel data[column]\n\tfor column in 'hometeam', 'awayteam', 'winner':\n\t\tdata[column] = data[column].apply(lambda s: s.split()[-1].lower())\n\n\treturn data","def games(self, competition_id: int, season_id: int) -> DataFrame[Any]:","def remove_games(self, games):\n for game in games:\n response = self._session.get(self._remove_game_url,\n params={\n 'type': 'game',\n 'id': game.game_id\n })\n response.raise_for_status()","def delete_duplicate_game_docs(self):\n gids = self.find_duplicate_game_docs()\n for gid in gids:\n games = list(self._db.Games.aggregate([{'$match':\n {'gid' : gid}},\n {'$project':\n {'date': 1}}]))\n delete = find_earlier_date(games)\n self._db.Games.remove(delete)","def data_cleaning():\n conn = get_connect()\n conn.execute(\"DELETE FROM championMatchData WHERE kills < 2 AND deaths < 2 AND assists < 2\")\n conn.commit()\n conn.close()\n return","def _check_season_gaps_in_teams_matches(self, df: pd.DataFrame):\n if self._verbose > 0:\n print(\"Teams:\")\n print(self.teams)\n print(\"Teams playing last season:\")\n print(self.last_season_teams)\n\n omitted_teams_test = check_season_gaps(df, self.test_fixtures_ids)\n omitted_teams_predict = check_season_gaps(df, self.predict_fixtures_ids)\n\n if omitted_teams_predict or omitted_teams_test:\n print(\"Warning: There are some teams with season gaps in data.\")\n print(\"Teams omitted based on predict dataset due to season gap:\")\n print(omitted_teams_predict)\n print(\"Teams omitted based on test dataset due to season gap:\")\n print(omitted_teams_test)\n\n # Delete these teams from datasets\n for team in set(omitted_teams_test + omitted_teams_predict):\n del self.test_fixtures_ids[team]\n del self.predict_fixtures_ids[team]\n\n print(\"You should consider lowering required number of test or predict data samples.\")","def removeLegacy(self, path=None):\n\n df = pd.read_csv(path, compression='gzip')\n print(df.shape)\n gamelist = pd.read_csv('Resources/Genres.csv.gz', usecols=['appid'])\n gamelist = pd.DataFrame(gamelist.appid.unique(), columns=['appid'])\n print(gamelist)\n filter_df = pd.merge(df, gamelist, on='appid', how='inner')\n filter_df = filter_df.dropna()\n filter_df = filter_df.sort_values(['steamid', 'appid'], ascending=[True, True])\n print('done')\n print(filter_df.shape)\n print(filter_df)\n print(np.setdiff1d(df['appid'].unique(), filter_df['appid'].unique()))\n filter_df.to_csv(path, compression='gzip', columns=['steamid', 'appid', 'rating'], index=None)","def remove_not_available(df):\n drop_indices = df.index[df['genre'] == 'Not Available'].tolist()\n df = df.drop(drop_indices)\n return df","def remove_old_songs(df, too_old=1970):\n drop_indices = df.index[df['year'] < too_old].tolist()\n df = df.drop(drop_indices)\n return df","async def delete_all_games(self):\n all_games = await ex.conn.fetch(\"SELECT gameid FROM blackjack.games\")\n for games in all_games:\n game_id = games[0]\n await self.delete_game(game_id)","def terminate(self):\n self.league.season = None\n for team in self.teams:\n team.team.season = None","def _extract_games_data(season, season_type):\n nflscrapr.run(\n 'games',\n season=season,\n season_type=season_type\n )\n nflscrapr_output = etl_tools.extract_from_csv(config.GAMES_DUMP_CSV_PATH)\n return nflscrapr_output","def by_season(self, season):\n return self.get_queryset().filter(season=season)","def season_game_logs(team, year):\n\n # Check year value\n if year > 2019 or year < 1950:\n raise ValueError('Year Value Incorrect')\n\n # Rename teams that moved\n team = scrape_utils.rename_team(team, year)\n\n # Get HTML content\n url = 'http://www.basketball-reference.com/teams/%s/%s/gamelog' % (team, year)\n r = requests.get(url)\n soup = BeautifulSoup(r.content, \"html.parser\")\n season_stats = soup.find(id='tgl_basic')\n games = season_stats.find('tbody')\n\n # MongoDB Collection\n m = mongo.Mongo()\n\n # To find opponent statistics\n opponent = re.compile('^opp_.*$')\n\n # Loop through every game in a team's season\n for game in games.find_all('tr', {'class': None}):\n\n curr_team = {'team': team}\n opp_team = {}\n\n # Loop through each stat\n for stat in game.find_all('td'):\n\n stat_name = stat['data-stat']\n\n # These are opponent stats\n if re.match(opponent, stat_name):\n opp_team[stat_name[4:]] = scrape_utils.stat_parse(stat_name, stat.string)\n else:\n curr_team[stat_name] = scrape_utils.stat_parse(stat_name, stat.string)\n\n # Remove unnecessary information\n del curr_team['game_season']\n del curr_team['x']\n\n # Rename relocated teams\n curr_team['team'] = scrape_utils.rename_team(team)\n opp_team['team'] = scrape_utils.rename_team(opp_team.pop('id'))\n\n # Use the same ID as basketball reference\n result = {'date': datetime.strptime(curr_team.pop('date_game'), \"%Y-%m-%d\"),\n 'season': year,\n 'result': scrape_utils.determine_home_win(curr_team['game_location'], curr_team.pop('game_result')),\n '_id': game.find('a')['href'][-17:-5]}\n\n # Place the teams in the correct spot depending on who is the home team\n if curr_team.pop('game_location') == 0:\n result['home'] = curr_team\n result['away'] = opp_team\n else:\n result['home'] = opp_team\n result['away'] = curr_team\n\n # Insert into database\n m.insert('game_log', result)","def _data_integrity_check(df):\n if df is None or len(df) == 0:\n logging.info(\"Dataframe is empty! No games data recorded.\")\n return\n\n if not isinstance(df, pd.DataFrame):\n raise ValueError(f\"Type of df is {type(df)}, it should be pandas.DataFrame\")\n\n logging.info(f\"Checking integrity of games dataframe with shape {df.shape}...\")\n\n seasons = df['season'].unique()\n max_season = int(max(seasons))\n\n expected_seasons = set(range(config.START_SEASON, max_season + 1))\n\n if set(seasons) != expected_seasons:\n missing_season = expected_seasons - set(seasons)\n raise DataIntegrityError(f\"Most recent season with data is {max_season}\"\n f\" but there's no data for {missing_season}!\")\n\n game_ids = list(df['game_id'])\n if len(game_ids) != len(set(game_ids)):\n raise DataIntegrityError(\"There are duplicate game ids!\")\n\n for season in list(seasons):\n season_types = set(df[df['season'] == season]['type'].unique())\n\n if season != max_season and len(season_types) != 3:\n raise DataIntegrityError(f\"{season} is not the max season of {max_season}\"\n f\"and only has {len(season_types)} season types\")\n\n elif len(season_types) == 1 and season_types != {'pre'}:\n raise DataIntegrityError(f\"There is only 1 season type for {seasons}: {season_types}\"\n f\"and its not 'pre'!\")\n\n elif len(season_types) == 2 and season_types != {'pre', 'reg'}:\n raise DataIntegrityError(f\"There are 2 season types for {season} and they're not ['pre', 'reg']!\")\n\n elif 'reg' in df[df['season'] == season]['type'].unique():\n reg_season_df = df[(df['season'] == season) & (df['type'] == 'reg')]\n weeks = reg_season_df['week'].unique()\n target_weeks = set(range(1, 18))\n missing_weeks = target_weeks.difference(set(weeks))\n if missing_weeks:\n raise DataIntegrityError(f\"{season} reg season is missing weeks {missing_weeks}!\")\n\n logging.info(\"Integrity check passed.\")","def delete_all_games(self):\r\n\r\n # Deletes all rows from the database table\r\n conn_2 = sqlite3.connect('scribble_database.db')\r\n sql_2 = 'DELETE FROM paintings'\r\n cur_2 = conn_2.cursor()\r\n cur_2.execute(sql_2)\r\n conn_2.commit()\r\n\r\n # clears screen\r\n self.action_label.destroy()\r\n self.painting_name.destroy()\r\n self.w['bg'] = 'White'\r\n self.w.delete(ALL)\r\n\r\n # clears all guesses/available game ids from their menus\r\n self.menu_bar.destroy()\r\n self.action_label = Message(self.master, text='Database emptied', bg='Black', fg='orange', font=20,\r\n width=330)\r\n self.action_label.pack()\r\n self.action_label.place(x=500, y=440)\r\n self.menu_bar = Menu(self.master)\r\n self.guess_menu = Menu(self.menu_bar)\r\n self.file_menu = Menu(self.menu_bar, tearoff=1)\r\n self.menu_bar.add_cascade(label=\"Available game IDs: \", menu=self.file_menu)\r\n self.menu_bar.add_cascade(label=\"Guesses of displayed games: \", menu=self.guess_menu)\r\n self.master.config(menu=self.menu_bar)","def season_series(game_id, pref_team, other_team, last_season=False):\n\n # Init empty dictionaries and lists\n games_against = list()\n pref_toi = dict()\n pref_goals = dict()\n pref_assists = dict()\n pref_points = dict()\n pref_record = {\"wins\": 0, \"losses\": 0, \"ot\": 0}\n roster_player = True\n\n # If this is the first game of the season, we can set the 'last_season' flag to enable the\n # season series function to check last year's season series between the two teams.\n if not last_season:\n season_start = str(game_id)[0:4]\n season_end = str(int(season_start) + 1)\n yesterday = datetime.now() - timedelta(days=1)\n # yesterday = datetime.now() + timedelta(days=50)\n # schedule_url = (\n # f\"/schedule?teamId={pref_team.team_id}\"\n # f\"&expand=schedule.broadcasts,schedule.teams&startDate=\"\n # f\"{season_start}-08-01&endDate={yesterday:%Y-%m-%d}\"\n # )\n schedule_url = (\n f\"/schedule?teamId={pref_team.team_id}\"\n f\"&expand=schedule.broadcasts,schedule.teams\"\n f\"&season={season_start}{season_end}\"\n )\n else:\n season_start = int(str(game_id)[0:4]) - 1\n season_end = str(int(season_start) + 1)\n yesterday = datetime.now() - timedelta(days=1)\n # yesterday = datetime.now() + timedelta(days=50)\n # schedule_url = (\n # f\"/schedule?teamId={pref_team.team_id}\"\n # f\"&expand=schedule.broadcasts,schedule.teams&startDate=\"\n # f\"{season_start}-08-01&endDate={season_end}-06-01\"\n # )\n schedule_url = (\n f\"/schedule?teamId={pref_team.team_id}\"\n f\"&expand=schedule.broadcasts,schedule.teams\"\n f\"&season={season_start}{season_end}\"\n )\n\n schedule = api.nhl_api(schedule_url).json()\n dates = schedule[\"dates\"]\n\n # Loop through scheduled to get previously played games against\n for date in dates:\n game = date[\"games\"][0]\n game_type = game[\"gameType\"]\n game_id = game[\"gamePk\"]\n game_team_home = game[\"teams\"][\"home\"][\"team\"][\"name\"]\n game_team_away = game[\"teams\"][\"away\"][\"team\"][\"name\"]\n teams = [game_team_away, game_team_home]\n game_status = game[\"status\"][\"abstractGameState\"]\n if game_type == \"R\" and game_status == \"Final\" and other_team.team_name in teams:\n game_feed = f\"/game/{game_id}/feed/live\"\n games_against.append(game_feed)\n\n # If the two teams haven't played yet, just exit this function\n if not games_against:\n return None, None, None\n\n # Loop through newly created games_against list to get each stats\n for feed in games_against:\n game = api.nhl_api(feed).json()\n game_data = game[\"gameData\"]\n home_team_name = game_data[\"teams\"][\"home\"][\"name\"]\n pref_homeaway = \"home\" if home_team_name == pref_team.team_name else \"away\"\n other_homeaway = \"away\" if home_team_name == pref_team.team_name else \"home\"\n\n # Get season series\n end_period = game[\"liveData\"][\"linescore\"][\"currentPeriod\"]\n extra_time = True if end_period > 3 else False\n pref_score = game[\"liveData\"][\"linescore\"][\"teams\"][pref_homeaway][\"goals\"]\n other_score = game[\"liveData\"][\"linescore\"][\"teams\"][other_homeaway][\"goals\"]\n if pref_score > other_score:\n pref_record[\"wins\"] += 1\n elif other_score > pref_score and extra_time:\n pref_record[\"ot\"] += 1\n else:\n pref_record[\"losses\"] += 1\n\n season_series_str = f\"Series: {pref_record['wins']}-\" f\"{pref_record['losses']}-{pref_record['ot']}\"\n\n # Get stats leaders\n # pref_teamstats = game[\"liveData\"][\"boxscore\"][\"teams\"][pref_homeaway][\"teamStats\"]\n pref_playerstats = game[\"liveData\"][\"boxscore\"][\"teams\"][pref_homeaway][\"players\"]\n for id, player in pref_playerstats.items():\n try:\n # Calculate TOI\n player_toi_str = player[\"stats\"][\"skaterStats\"][\"timeOnIce\"]\n player_toi_minutes = int(player_toi_str.split(\":\")[0])\n player_toi_seconds = int(player_toi_str.split(\":\")[1])\n player_toi = (player_toi_minutes * 60) + player_toi_seconds\n pref_toi[id] = pref_toi.get(id, 0) + player_toi\n\n # Point Totals\n player_goal_str = player[\"stats\"][\"skaterStats\"][\"goals\"]\n pref_goals[id] = pref_goals.get(id, 0) + int(player_goal_str)\n player_assist_str = player[\"stats\"][\"skaterStats\"][\"assists\"]\n pref_assists[id] = pref_assists.get(id, 0) + int(player_assist_str)\n player_points = int(player_goal_str) + int(player_assist_str)\n pref_points[id] = pref_points.get(id, 0) + int(player_points)\n\n except KeyError:\n pass\n\n # Calculate Stats Leaders\n sorted_toi = sorted(pref_toi.values(), reverse=True)\n leader_toi = sorted_toi[0]\n\n sorted_points = sorted(pref_points.values(), reverse=True)\n leader_points = sorted_points[0]\n\n # Get TOI leader\n for id in pref_toi.keys():\n if pref_toi[id] == leader_toi:\n player_name = roster.player_attr_by_id(pref_team.roster, id, \"fullName\")\n if player_name is None:\n roster_player = False\n player_id_only = id.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n leader_toi_avg = leader_toi / len(games_against)\n m, s = divmod(leader_toi_avg, 60)\n toi_m = int(m)\n toi_s = int(s)\n toi_s = \"0{}\".format(toi_s) if toi_s < 10 else toi_s\n toi_avg = \"{}:{}\".format(toi_m, toi_s)\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n toi_leader_str = \"TOI Leader: {} with {} / game.\".format(player_short_name, toi_avg)\n\n # Handle tied points leaders\n point_leaders = list()\n for id in pref_points.keys():\n if pref_points[id] == leader_points:\n point_leaders.append(id)\n\n if leader_points == 0:\n points_leader_str = \"Points Leader: None (all players have 0 points).\"\n\n elif len(point_leaders) == 1:\n leader = point_leaders[0]\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n # If the player is no longer on the team, get their information (change string here?)\n if player_name is None:\n roster_player = False\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n if not roster_player:\n points_leader_str = (\n f\"Points Leader: {player_name} with {leader_points} points \"\n f\"({player_goals}G {player_assists}A) \"\n )\n else:\n points_leader_str = \"Points Leader: {} with {} ({}G {}A).\".format(\n player_name, leader_points, player_goals, player_assists\n )\n\n elif len(point_leaders) > 3:\n point_leaders_with_attrs = list()\n for leader in point_leaders:\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n if player_name is None:\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n point_leaders_with_attrs.append(player_short_name)\n\n point_leaders_joined = \", \".join(point_leaders_with_attrs[0:3])\n leftover_leaders = len(point_leaders) - 3\n points_leader_str = (\n f\"Points Leaders: {point_leaders_joined} & {leftover_leaders} others ({leader_points} each).\"\n )\n\n else:\n point_leaders_with_attrs = list()\n for leader in point_leaders:\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n if player_name is None:\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n player_str = f\"{player_short_name} ({player_goals}G {player_assists}A)\"\n point_leaders_with_attrs.append(player_str)\n\n point_leaders_joined = (\n f\", \".join(point_leaders_with_attrs[:-1]) + f\" & {point_leaders_with_attrs[-1]}\"\n )\n points_leader_str = \"Points Leaders: {} with {} each.\".format(point_leaders_joined, leader_points)\n\n return season_series_str, points_leader_str, toi_leader_str","def _discard_matches(self,\n df: pd.DataFrame,\n teams_fixtures_ids: Dict[str, List[int]]) -> Dict[str, List[int]]:\n discarded_fixtures_ids = {}\n\n if self._ndiscard:\n for t in self.last_season_teams:\n discarded_fixtures_ids[t] = teams_fixtures_ids[t][-self._ndiscard:]\n teams_fixtures_ids[t] = teams_fixtures_ids[t][:-self._ndiscard]\n\n # Check if all discarded matches are within a single season\n unique_discarded_fixtures_ids = set(chain(*discarded_fixtures_ids.values()))\n mask_discarded = df[\"id\"].isin(unique_discarded_fixtures_ids)\n seasons_in_discarded_df = df[mask_discarded].loc[:, \"season\"].unique()\n\n if any(s != self.max_season for s in seasons_in_discarded_df):\n raise ValueError(\"Discarded matches must be within the last season. \\n\"\n \"Please lower the number of matches to discard.\")\n\n # Some teams may have played more than once so we need to be sure that we discarded\n # all matches correctly.\n for t in self.last_season_teams:\n teams_fixtures_ids[t] = [j for j in teams_fixtures_ids[t] if j not in unique_discarded_fixtures_ids]\n\n return teams_fixtures_ids","def deletePlayers():\n DB = connect()\n c = DB.cursor()\n c.execute(\"DELETE FROM tournament\")\n DB.commit()\n DB.close()","def get_dryspells_perseason(dryspells, seasons=((12, 1, 2), (3, 4, 5),\n (6, 7, 8), (9, 10, 11))):\n dryspells_seasons = []\n for season in seasons:\n eveSeas = []\n for eveLand in dryspells:\n eves = [e for e in eveLand if e.start_date().month in season]\n eveSeas.append(eves)\n dryspells_seasons.append(eveSeas)\n\n return dryspells_seasons","def leave_game(players_cursor, states_cursor, user, room_id):\n leave_query = '''DELETE FROM players_table WHERE user = ? AND room_id = ?'''\n players_cursor.execute(leave_query, (user, room_id))\n FRAMES.append(display_game(players_cursor, states_cursor, user, room_id))","def delete_all(sid):\n Game.objects.all().delete()","def game_to_SQL(self, cursor, gid, games):\n\t\t\n\t\tvalues = []\n\t\tquery_with = \"WITH games AS (\"\n\t\tfor game in games.findall('.'):\n\t\t\tquery_with += \"SELECT ? AS gid, ? AS time_date, ? AS game_pk, ? AS venue, ? AS venue_id, ? AS away_team_id, ? AS home_team_id, ? AS home_time, ? AS home_time_zone, ? AS game_nbr, ? AS home_team_runs, ? AS away_team_runs, ? AS is_perfect_game, ? AS is_no_hitter, ? AS [des] UNION \"\n\t\t\tvalues.append(gid)\n\t\t\tif 'time_date' in game.attrib:\n\t\t\t\tvalues.append(game.attrib['time_date'])\n\t\t\telse:\n\t\t\t\ttry:\n\t\t\t\t\tgame.attrib['time'].strptime(input, '%H:%M')\n\t\t\t\t\tvalues.append(game.attrib['id'][:10] + \" \" + game.attrib['time'])\n\t\t\t\texcept:\n\t\t\t\t\tvalues.append(game.attrib['id'][:10])\n\t\t\t\t\tpass\n\t\t\tvalues.append(game.attrib['game_pk'])\n\t\t\tvalues.append(game.attrib['venue'])\n\t\t\tvalues.append(game.attrib['venue_id'])\n\t\t\tvalues.append(game.attrib['away_team_id'])\n\t\t\tvalues.append(game.attrib['home_team_id'])\n\t\t\tvalues.append(game.attrib['home_time'])\n\t\t\tvalues.append(game.attrib['home_time_zone'])\n\t\t\tif 'game_nbr' in game.attrib:\n\t\t\t\tvalues.append(game.attrib['game_nbr'])\n\t\t\telse: \n\t\t\t\tvalues.append(gid[-1])\n\t\t\tif 'home_team_runs' in game.attrib:\n\t\t\t\tvalues.append(game.attrib['home_team_runs'])\n\t\t\telse:\n\t\t\t\tvalues.append(None)\n\t\t\tif 'away_team_runs' in game.attrib:\n\t\t\t\tvalues.append(game.attrib['away_team_runs'])\n\t\t\telse:\n\t\t\t\tvalues.append(None)\n\t\t\tif 'is_perfect_game' in game.attrib:\n\t\t\t\tvalues.append(game.attrib['is_perfect_game'])\n\t\t\telse:\n\t\t\t\tvalues.append(None)\n\t\t\tif 'is_no_hitter' in game.attrib:\n\t\t\t\tvalues.append(game.attrib['is_no_hitter'])\n\t\t\telse:\n\t\t\t\tvalues.append(None)\n\t\t\tif 'description' not in game.attrib: \n\t\t\t\tvalues.append(None)\n\t\t\telse:\n\t\t\t\tvalues.append(game.attrib['description'])\n\t\tquery_with = query_with[:-7] + \")\"\n\n\t\tinsert = query_with + \"INSERT INTO game (gid, game_date, game_pk, venue, venue_id, away_team_id, home_team_id, home_time, home_time_zone, game_nbr, home_team_runs, away_team_runs, is_perfect_game, is_no_hitter, [description]) \"\n\t\tinsert += \"SELECT g.gid, g.time_date, g.game_pk, g.venue, g.venue_id, g.away_team_id, g.home_team_id, g.home_time, g.home_time_zone, g.game_nbr, g.home_team_runs, g.away_team_runs, g.is_perfect_game, g.is_no_hitter, g.des FROM games g;\"\n\t\t\n\t\tcursor.execute(insert, values)","def team_games(df, team):\n print(df[\n ( df['team'] == team ) & \n ( df['player'] == 'Team' )\n ].drop_duplicates(subset=['gameid']).shape)\n return df[\n ( df['team'] == team ) & \n ( df['player'] == 'Team' )\n ].drop_duplicates(subset=['gameid'])","def _valid_games(self, alias):\n return Query(alias.gid).filter(\n alias.species_id == self.species.id,\n alias.background_id == self.background.id,\n alias.start >= self.start,\n alias.start <= self.end\n )","def deletePlayers():\n conn = connect()\n c = conn.cursor()\n # Clears the \"players\" table, but does not get rid of the table.\n c.execute(\"delete from players;\")\n conn.commit()\n conn.close()","def delete(self, show_id, season_id, session):\n try:\n show = db.show_by_id(show_id, session=session)\n except NoResultFound:\n raise NotFoundError('show with ID %s not found' % show_id)\n try:\n season = db.season_by_id(season_id, session)\n except NoResultFound:\n raise NotFoundError('season with ID %s not found' % season_id)\n if not db.season_in_show(show_id, season_id):\n raise BadRequest(f'season with id {season_id} does not belong to show {show_id}')\n\n args = delete_parser.parse_args()\n db.remove_series_entity(show.name, season.identifier, args.get('forget'))\n\n return success_response(f'successfully removed season {season_id} from show {show_id}')","def prune_data(self, ts):\n sql = \"delete from %s where dateTime < %d\" % (self.dbm.table_name, ts)\n self.dbm.getSql(sql)\n try:\n # sqlite databases need some help to stay small\n self.dbm.getSql('vacuum')\n except Exception as e:\n pass","async def delete_game(self, game_id):\n game = await self.get_game(game_id)\n await ex.conn.execute(\"DELETE FROM blackjack.games WHERE gameid = $1\", game_id)\n await ex.conn.execute(\"DELETE FROM blackjack.currentstatus WHERE userid = $1\", game[1])\n await ex.conn.execute(\"DELETE FROM blackjack.currentstatus WHERE userid = $1\", game[2])\n log.console(f\"Game {game_id} deleted.\")","def get_games(season, date):\n url = \"http://live.nhl.com/GameData/SeasonSchedule-\" + season + \".json\"\n response = urllib.urlopen(url)\n data = json.loads(response.read())\n games = []\n for game in data:\n if game[\"est\"][:8] == date:\n games.append(game)\n return games","def delete(self, show_id, session):\n try:\n show = db.show_by_id(show_id, session=session)\n except NoResultFound:\n raise NotFoundError('show with ID %s not found' % show_id)\n args = delete_parser.parse_args()\n forget = args.get('forget')\n for season in show.seasons:\n db.remove_series_entity(show.name, season.identifier, forget)\n return success_response('successfully removed all series %s seasons from DB' % show_id)","def season(year, week=None, timeout=None, concurrency=_DEFAULT_CONCURRENCY):\n\tLOG.debug('Concurrency = %d', concurrency)\n\tgames = season_games(year)\n\tif week is not None:\n\t\tgames = games[games.week == week]\n\texpected_n = len(games)\n\ttables, futures_to_args, failures = [], {}, []\n\t# See https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor-example.\n\twith futures.ThreadPoolExecutor(concurrency) as pool:\n\t\tfor arg in zip(games.hometeam, games.awayteam, games.week):\n\t\t\targ = arg + (year,)\n\t\t\tfutures_to_args[pool.submit(game_unknown_homeaway, *arg)] = arg\n\t\tfor future in futures.as_completed(futures_to_args, timeout=timeout):\n\t\t\targs = futures_to_args[future]\n\t\t\ttry:\n\t\t\t\ttable = future.result()\n\t\t\texcept Exception as exc:\n\t\t\t\tLOG.exception('Error from %s: %s', args, exc)\n\t\t\telse:\n\t\t\t\tif table is None:\n\t\t\t\t\tLOG.error('Failure: %s', args)\n\t\t\t\t\tfailures.append(args)\n\t\t\t\telse:\n\t\t\t\t\tLOG.info('Success: %s', args)\n\t\t\t\t\ttables.append(table)\n\ttables = games.merge(pd.concat(tables), on=('hometeam', 'awayteam', 'week'))\n\tif __debug__:\n\t\tn = len(tables.groupby(['hometeam', 'awayteam', 'week']))\n\t\tassert n == expected_n, \"Expected %d games, got %d\" % (expected_n, n)\n\treturn tables, failures","def losing_games_stats(self):\n self.losing_games_up_to_2013 = (\n self.df\n .pipe(lambda x:x.assign(losing_num_counts=1))\n .query(\"Season <= 2013\")\n .groupby(['Season','LTeamID'])\n .agg({\"WScore\":\"sum\",\"LScore\":\"sum\",\"LFGM\":\"sum\",\"LFGA\":\"sum\",\"LFGM3\":\"sum\",\"LFGA3\":\"sum\",\"LFTM\":\"sum\",\"LFTA\":\"sum\",\"losing_num_counts\":\"sum\",\n \"LOR\":\"sum\",\"LDR\":\"sum\",\"WFGA\":\"sum\",\"WFGM\":\"sum\",\n \"LAst\":\"sum\",\"LTO\":\"sum\",\"LStl\":\"sum\",\"LBlk\":\"sum\",\"LPF\":\"sum\"})\n .reset_index()\n .rename(columns={\"WScore\":\"winning_opponent_score\"})\n # rebounds\n .pipe(lambda x:x.assign(total_losing_rebounds = x.LOR + x.LDR))\n .pipe(lambda x:x.assign(losing_off_rebounds_percent = x.LOR/x.total_losing_rebounds))\n .pipe(lambda x:x.assign(losing_def_rebounds_percent = x.LDR/x.total_losing_rebounds))\n .pipe(lambda x:x.assign(losing_team_missed_attempts = x.LFGA - x.LFGM))\n .pipe(lambda x:x.assign(winning_opp_team_missed_attempts = x.WFGA - x.WFGM))\n .pipe(lambda x:x.assign(losing_rebound_possession_percent = x.LOR/x.losing_team_missed_attempts))\n .pipe(lambda x:x.assign(losing_rebound_possessiongain_percent = x.LDR/x.winning_opp_team_missed_attempts))\n # blocks, steals, assists and turnovers\n .pipe(lambda x:x.assign(losing_block_opp_FGA_percent = x.LBlk/x.WFGA))\n .pipe(lambda x:x.assign(losing_assist_per_fgm = x.LAst/x.LFGM))\n .pipe(lambda x:x.assign(losing_assist_turnover_ratio = x.LAst/x.LTO))\n # rename columns to prevent duplication when joining with losing stats. example: WFGM_x\n .rename(columns={\"WFGA\":\"WFGA_opp\",\"WFGM\":\"WFGM_opp\"})\n )","def deletePlayers():\n # gets connection to tournament database in conn object\n conn = connect()\n # gets the cursor to execute queries\n c = conn.cursor()\n # executes delete query to delete all records in PLAYER table\n c.execute(\"DELETE FROM PLAYER;\")\n # commits the changes perform on PLAYER table after delete statement executes\n conn.commit()\n # closes the connection to tournament database\n conn.close()","def deletePlayers():\n cursor.execute(\"\"\"delete from players\"\"\")","def deletePlayers():\n\n query = (\"DELETE FROM players;\")\n results = executeQuery({'dbname': 'tournament', 'query' : query, 'type' : 'delete'})","def replace_season(data,season):\n if not SeasonCharts.matchseason(season):\n raise SeasonCharts.SeasonError\n ## Check data format\n if test_rawdata(data):\n for cat,shows in data.items():\n for show in shows: show['season'] = season\n else:\n for show in data: show['season'] = season","def get_seasons(games: List[BaseGame], reverse=False):\n seasons = list(set([g.Season for g in games]))\n seasons.sort(reverse=reverse)\n return seasons","def as_games(self):\n self._assert_no_aggregate()\n\n self._sort_tables = [types.Game]\n ids = self._ids('game', self._sorter)\n results = []\n q = 'SELECT %s FROM game %s %s'\n with Tx(self._db) as cursor:\n q = q % (\n types.select_columns(types.Game),\n _prefix_and(_sql_pkey_in(cursor, ['gsis_id'], ids['game'])),\n self._sorter.sql(tabtype=types.Game),\n )\n cursor.execute(q)\n\n for row in cursor.fetchall():\n results.append(types.Game.from_row(self._db, row))\n return results","def season_game_log_save(year, collection):\n try:\n df = PlayerGameLogs(season_nullable=year).get_data_frames()[0]\n df_final = df.filter(\n ['PLAYER_ID', 'PLAYER_NAME', 'TEAM_ID', 'TEAM_ABBREVIATION', 'TEAM_NAME', 'GAME_ID', 'GAME_DATE', 'MATCHUP',\n 'WL', 'MIN', 'FGM', 'FGA', 'FG_PCT', 'FG3M', 'FG3A', 'FG3_PCT', 'FTM', 'FTA', 'FT_PCT', 'OREB', 'DREB', 'REB',\n 'AST', 'TOV', 'STL', 'BLK', 'BLKA', 'PF', 'PFD', 'PTS', 'PLUS_MINUS', 'NBA_FANTASY_PTS'], axis=1)\n collection.insert_many(df_final.to_dict('records'))\n except OperationFailure as e:\n print(e)","def __delete(self, table, conditions, values):\n tables = [\n table,\n ]\n if table == \"Varieties\":\n start_table = 0\n end_table = -1\n for i in range(0, len(tables)):\n if tables[i] == \"Varieties\":\n tables.pop(i)\n for j in range(0, len(conditions)):\n if conditions[j].startswith(\"resource_id\"):\n index = self._match_variety_table(values[j])\n if conditions[j].endswith(\" =\"):\n start_table = index\n end_table = index + 1\n elif conditions[j].endswith(\" <\") or conditions[j].endswith(\n \" <=\"\n ):\n end_table = index + 1\n elif conditions[j].endswith(\" >\") or conditions[j].endswith(\n \" >=\"\n ):\n start_table = index\n\n tables = self.variety_tables[start_table:end_table]\n\n # Slightly improved version\n all_ok = True\n cursor = self.conn.cursor(dictionary=True)\n\n for talbe in tables:\n request = \"DELETE FROM {table} WHERE {conditions}\"\n request = request.format(\n table=table, conditions=\" %s AND\".join(conditions + [\"0\"])[0:-5]\n )\n try:\n cursor.execute(request, tuple(values))\n except Exception as error:\n _logger.exception(str(error))\n all_ok = False\n else:\n self.conn.commit()\n _logger.debug(\"DELETE REQUEST ON {0} OK.\".format(table))\n cursor.close()\n\n return all_ok","def drop_team(self, vid):\n return self \\\n .team(vid) \\\n .sideEffect(__.drop()) \\\n .count()","def deletePlayers():\n DB = dbc()\n DB.cursor().execute('DELETE FROM players')\n DB.commit()\n DB.close()","def deletePlayers():\n with _connect_db() as (conn, cur):\n cur.execute(\"\"\"DELETE FROM players;\"\"\")\n conn.commit()","def drop(self):\n c = self.cursor()\n for table in ['experiment','fact']:\n c.execute(\"drop table if exists {}\".format(table))\n self.commit()","def opponent_games(df, team, gameids):\n games = df[df.gameid.isin(gameids)]\n opponent_games = games[games.team != team].drop_duplicates(subset=['gameid'])[['gameid', 'team']]\n opponent_games.columns = ['gameid', 'opponent']\n\n return opponent_games","def deletePlayers():\n conn, cur = connect()\n cur.execute(\"DELETE FROM PLAYERS;\")\n conn.commit()\n conn.close()","def remove(table, id_):\n\n if common.confirm_option():\n ID = 0\n\n for game in table:\n if game[ID] == id_:\n table.remove(game)\n\n return table","def removePlayer(df, name):\n if name in getPlayerList(df):\n df = df[df.name != name]\n return df","def event11022120():\n header(11022120)\n\n item_type, item_id = define_args('Bi')\n end_if_this_event_slot_on()\n if_new_game_count_greater_than_or_equal(1, 1)\n end_if_condition_false(1)\n item.remove_items_from_player(item_type, item_id, 0)","def delete(self, show_id, season_id, session):\n try:\n db.show_by_id(show_id, session=session)\n except NoResultFound:\n raise NotFoundError('show with ID %s not found' % show_id)\n try:\n season = db.season_by_id(season_id, session)\n except NoResultFound:\n raise NotFoundError('seasons with ID %s not found' % season_id)\n if not db.season_in_show(show_id, season_id):\n raise BadRequest(f'season with id {season_id} does not belong to show {show_id}')\n\n args = release_delete_parser.parse_args()\n downloaded = args.get('downloaded') is True if args.get('downloaded') is not None else None\n release_items = []\n for release in season.releases:\n if (\n downloaded\n and release.downloaded\n or downloaded is False\n and not release.downloaded\n or not downloaded\n ):\n release_items.append(release)\n\n for release in release_items:\n if args.get('forget'):\n fire_event('forget', release.title)\n db.delete_season_release_by_id(release.id)\n return success_response(\n f'successfully deleted all releases for season {season_id} from show {show_id}'\n )","def erase_scores(self):\n self.database.erase_scores(self.difficulty)","def seasonality(df):\n df_datetime = pd.DatetimeIndex(df.date_time)\n df[\"month\"] = df_datetime.month\n df = drop_cols(df, [\"date_time\"])\n\n return df","def seasons(self):\n if self._season_cache_ver == self._db._version:\n return self._season_cache\n\n # Find out how many seasons in this series by fetching the highest season.\n seasons = self._db.query(type='episode', parent=self._dbrow, attrs=['season'], distinct=True)\n self._season_cache = [Season(self._db, self, row['season']) for row in seasons]\n self._season_cache_ver = self._db._version\n return self._season_cache","def remove(table, id_):\n\n removed = False\n\n for item in table:\n if item[0] == id_[0]:\n table.remove(item)\n removed = True\n\n if not removed:\n ui.print_error_message(\"There isn't a game with such ID!\")\n return table","def get_schedules_for_season(self, season, season_type=\"REG\"):\n try:\n season = int(season)\n if season_type not in [\"REG\", \"PRE\", \"POST\"]:\n raise ValueError\n except (ValueError, TypeError):\n raise FantasyDataError('Error: Invalid method parameters')\n\n season_param = \"{0}{1}\".format(season, season_type)\n result = self._method_call(\"Schedules/{season}\", season=season_param)\n return result","def cleanup(pgconn):\n cursor = pgconn.cursor()\n config = util.get_config()\n for table, prop in zip(\n [\"ldm_rtstats\", \"ldm_rtstats_hourly\", \"ldm_rtstats_daily\"],\n [\n \"retain_rtstats_raw[hours]\",\n \"retain_rtstats_hourly[days]\",\n \"retain_rtstats_daily[days]\",\n ],\n ):\n interval = config.get(prop)\n if interval is None or interval <= 0:\n continue\n timecol = \"entry_added\" if table == \"ldm_rtstats\" else \"valid\"\n multiplier = 24 if table != \"ldm_rtstats\" else 1\n hours = interval * multiplier\n cursor.execute(\n \"\"\"\n DELETE from \"\"\"\n + table\n + \"\"\"\n WHERE \"\"\"\n + timecol\n + \"\"\" < (now() - '%s hours'::interval)\n \"\"\"\n % (hours,)\n )\n # print(\"Removed %s rows from table: %s\" % (cursor.rowcount, table))\n cursor.close()\n pgconn.commit()","def remove_from_winning(game: List[int]) -> None:\n while True:\n game_copy = game.copy()\n row = randint(1, len(game_copy))\n if game_copy[row-1] < 1:\n continue\n matches = randint(1, game_copy[row-1])\n remove_matches(game_copy, row - 1, matches)\n if not is_winning(game_copy):\n remove_matches(game, row - 1, matches)\n break\n print(\"{} matches on row {} have been removed.\".format(matches, row))","def delete_game(game_id):\n\n game = filter(lambda t: t[\"id\"] == game_id, games)\n if len(game) == 0:\n abort(404)\n games.remove(game[0])\n return jsonify({\"result\": True})","def delete_game(game_id):\n\n game = Game.query.get(game_id)\n db.session.delete(game)\n db.session.commit()","def droptables(db, cursor):\n cursor.execute('''DROP TABLE IF EXISTS worlds;''')\n cursor.execute('''DROP TABLE IF EXISTS characters''')\n cursor.execute('''DROP TABLE IF EXISTS chardata''')\n db.commit()","def remove_training_reserves():\n reserves = TrainingReserve.objects.all()\n now = timezone.now()\n for reserve in reserves:\n if reserve.date < now:\n reserve.delete()","def losses(self):\n return [g for g in self.games if g.winner is not self.team]","def deletePlayers():\n db_conn = connect()\n db_cursor = db_conn.cursor()\n db_cursor.execute(\"delete from players;\")\n db_conn.commit()\n db_conn.close()","def on_death(self):\n self.bark('I was killed.')\n self.where = self.category.spawn\n self.life = self.life_max\n for this in self.battle_set.get().npcs.all():\n this.delete()\n # self.cool -= 9001\n self.save()\n return True","def deletePlayers():\n #deletes the contents of table players\n DB().execute(\"DELETE FROM players\", True)","def remove_rows(self, rows, regroup=False):\n self.table.remove_rows(np.atleast_1d(rows))\n if regroup:\n for col in ['setup', 'calib', 'calibbit', 'comb_id', 'bkg_id']:\n if col in self.keys():\n del self.table[col]\n self.set_configurations()\n self.set_calibration_groups()\n self.set_combination_groups()","def clean_database(self, option):\n\n if (option == 'electoral_roll'):\n self.db[self.distelect_table_or_collection].delete_many({})\n self.db[self.elector_table_or_collection].delete_many({})\n\n elif (option == 'stats'):\n self.db[self.district_stats_table_or_collection].delete_many({})\n self.db[self.canton_stats_table_or_collection].delete_many({})\n self.db[self.province_stats_table_or_collection].delete_many({})","def delete_teams_by_date(self, datetime):\n self.execute(TABELLE['teams']['delete']['by_date'], (datetime,))","def test_no_previous_study_version(self):\n self.study_version_1.delete()\n self.study_version_2.delete()\n response = self.client.get(self.get_url(self.study.pk))\n context = response.context\n table = context['source_trait_table']\n self.assertEqual(len(table.data), 0)\n for trait in self.source_traits_v3:\n self.assertNotIn(trait, table.data)","def remove_columns(lst):\n cols_rem = ['yearID','Team','lgID','Name','X','playerID','pops']\n\n for item in cols_rem:\n if item in lst:\n lst.remove(item)\n\n return(lst)","def prepare(self):\n # Remove rows where the temporal scope could not be parsed correctly.\n self.triples = self.triples[(self.triples.time_begin != -99999) & (self.triples.time_end != -99999)]\n\n # Remove rows with start time after the end time.\n time_diffs = self.triples[\"time_end\"] - self.triples[\"time_begin\"]\n invalid_times = time_diffs[time_diffs < 0].index\n self.triples.drop([x for x in invalid_times], inplace=True, axis=\"index\")\n\n # Remove all entities that no longer have any facts associated with them from the dictionary.\n entity_counts = Utils.get_entity_counts(self.triples, self.entities)\n for idx, count in entity_counts.items():\n if count == 0:\n del self.entities[idx]\n\n # Remove all relations that no longer have any facts associated with them from the dictionary.\n relation_counts = Utils.get_group_counts(self.triples.groupby(\"relation_id\"), self.relations)\n for idx, count in relation_counts.items():\n if count == 0:\n del self.relations[idx]","def get_all_games(season):\n url = BASE_URL.format(season)\n json_data = requests.get(url, headers=HEADERS).json()\n all_games = json_data[\"resultSets\"][0][\"rowSet\"]\n return all_games","def drop_non_wind(ds):\n return ds.drop(['fdir', 't2m', 'ssrd'])","def deletePlayers():\n db = connect()\n c = db.cursor()\n query = (\"DELETE FROM players;\")\n c.execute(query)\n db.commit()\n db.close()","def season_type(self, season_type):\n\n self._season_type = season_type","def test_delete_pokemon_slot_6(self):\n response = self.client.post(\"/teams/create/\",\n {\n \"trainer\": str(self.trainer_id),\n })\n team_pk = response.json()[\"id\"]\n slot = 6\n\n pokemon_name = \"Gastrodon\"\n\n response = self.client.post(\"/teams/add/\", {\n \"id\":str(team_pk),\n \"slot\": str(slot),\n \"pokemon_name\": pokemon_name,\n })\n\n response = self.client.delete(\"/teams/deletepokemon/{}/{}/\".format(team_pk, str(slot)))\n self.assertEqual(response.status_code, 204)\n poke = Team.objects.get(pk=team_pk)\n self.assertFalse(poke.slot_6_pokemon)","def getSeasonStats(self):\n df_season_agg = self.toSeasonAggFormat()\n\n # Calculate Possessions for each game\n df_season_agg['possessions'] = 0.5 * (df_season_agg['FGA'] + 0.475 * df_season_agg['FTA'] - df_season_agg['OR'] + df_season_agg['TO']) \\\n + 0.5 * (df_season_agg['OppFGA'] + 0.475 * df_season_agg['OppFTA'] - df_season_agg['OppOR'] + df_season_agg['OppTO'])\n\n # Aggregate to Season Summary Level\n season_stats = df_season_agg.groupby(['TeamID', 'Season']).sum()\n\n season_stats = season_stats.rename(columns={'Win':'wins'})\n\n # Season Advanced Stats\n season_stats['o_eff'] = season_stats['Score'] / season_stats['possessions'] * 100\n season_stats['d_eff'] = season_stats['OppScore'] / season_stats['possessions'] * 100\n season_stats['net_eff'] = season_stats['o_eff'] - season_stats['d_eff']\n\n season_stats.drop('DayNum', axis=1, inplace=True)\n season_stats.drop('OppTeamID', axis=1, inplace=True)\n season_stats.drop('rand', axis=1, inplace=True)\n\n return season_stats","def all_seasons_save():\n year_arr = [str(f'{i}-{str((i + 1))[-2:]}') for i in reversed(range(1950, 2021))]\n for year in year_arr:\n c_name = f'season_player_game_logs_{year.replace(\"-\", \"_\")}'\n try:\n coll = db[c_name]\n season_game_log_save(year, coll)\n print(f'{c_name} successfully saved')\n time.sleep(1)\n except OperationFailure as e:\n print(f'{c_name} not saved!!')\n print(e)","def purge_old() -> None:\n conn = sqlite3.connect('rss.db')\n c = conn.cursor()\n c.execute(\n \"\"\"delete from entries where title not in\n (select title from entries order by year desc, month desc, day desc\n limit 20)\n \"\"\"\n )\n conn.commit()\n conn.close()","def deletePlayers():\n db = connect()\n db_cursor = db.cursor()\n query = \"DELETE FROM players\"\n db_cursor.execute(query)\n db.commit()\n db.close()","def deletePlayers():\n conn = psycopg2.connect(\"dbname=tournament\")\n c = conn.cursor()\n c.execute(\"TRUNCATE player CASCADE\")\n conn.commit()\n conn.close()","def cull(self) -> None:\n for player in self.players:\n to_remove = [creature for creature in player.battle_line if creature.damage_taken >= creature.power()]\n for creature in to_remove:\n player.battle_line.remove(creature)\n to_remove.destroyed(self, creature)","def deleteBolts(self):\n a = self.get_bolts()\n for i in self.get_bolts():\n if i.y>GAME_HEIGHT:\n a.remove(i)\n self.set_plyrbolts(0)\n elif i.y<=-BOLT_HEIGHT:\n a.remove(i)","def remove_player_velocities(team):\n \n # Either end with _vx or _vy or _speed\n velocity_columns=[x for x in team.columns if re.match(r\".*_vx$|.*_vy$|.*_speed$\",x)]\n team.drop(velocity_columns,axis='columns',inplace=True)\n return team","def delete_salary_rows(self, debug: bool = False) -> None:\n try:\n sql = \"DELETE FROM SALARIES where id < 0 and yearid = {};\".format(self._yearid)\n if debug:\n print(sql)\n\n self._cursor.execute(sql)\n self._connection.commit()\n\n except Error as error:\n self._connection.rollback()\n raise RuntimeError(\"Database error deleting old rows\", error)","def clean_pokemon_database(\n df: pd.DataFrame,\n selected_generations: list,\n remove_legendary: bool = True\n):\n\n # Filter only selected generations\n df = df[df.generation.isin(selected_generations)]\n\n # Remove legendary\n if remove_legendary:\n df = df[df.is_legendary == 0]\n\n # Rename type columns\n df = df.rename(columns={\"type1\": \"primary_type\", \"type2\": \"secondary_type\"})\n\n # Drop NA values\n df = df.dropna()\n\n # When returning many variables, it is a good practice to give them names:\n return df","def deletePlayers():\n dbConn = connect()\n c = dbConn.cursor()\n c.execute(\"DELETE FROM player\")\n dbConn.commit()\n dbConn.close()","def clean_catalogue(\n source,\n dest=f\"{pd.Timestamp.now().strftime('%y%m%d%H%M%S')}.csv\",\n window=None\n ):\n catalogue = pd.read_csv(source, parse_dates=[3,4,5])\n catalogue.drop_duplicates(ignore_index=True, inplace=True)\n catalogue.sort_values(by=[\"dt_on\"], ignore_index=True, inplace=True)\n if window is not None: # 430367\n count = 0\n rm_idx = []\n for station in catalogue.station.unique():\n subset = catalogue.loc[catalogue[\"station\"] == station]\n remove = []\n dt_on_ = subset[\"dt_on\"].iloc[0]\n for i, dt_on in enumerate(subset[\"dt_on\"]):\n if i == 0:\n continue\n elif dt_on < dt_on_ + pd.Timedelta(window, unit=\"sec\"):\n remove.append(i)\n else:\n dt_on_ = dt_on\n rm_idx.append(subset.index[remove])\n count += len(remove)\n print(f\"Removing {count} entries...\")\n rm_idx = [item for sub_idx in rm_idx for item in sub_idx]\n catalogue.drop(catalogue.index[rm_idx], inplace=True)\n catalogue.reset_index(drop=True, inplace=True)\n catalogue.to_csv(dest)\n print(f\"Catalogue saved to {dest}.\")","def deletePlayers():\n conn, c = connect()\n\n q = \"DELETE FROM PLAYERS;\"\n c.execute(q)\n c.close()\n conn.commit()\n conn.close()","def deletePlayers():\n conn = connect()\n cursor = conn.cursor()\n cursor.execute(\"DELETE FROM players\")\n conn.commit()\n conn.close()","def clean(self):\n self.df = _data.prune(self.df, [REGEX_PATTERN_GCI, REGEX_PATTERN_DB_ID])\n self.df, _ = _data.remove_totally_failed_tests(self.df)\n self.is_cleaned = True","def __get_season_data(season_num: int, max_episode: int, training_data: bool) -> List[TrainSample]:\n season = EXAM_DATA[season_num]\n drop_players = season.get_drop_mapping(DropType.EXECUTION_DROP, max_episode)\n all_answers = season.get_all_answers(set(drop_players.keys()), max_episode)\n season_data = []\n for answer in all_answers:\n exam_episode = answer.episode\n drop_episodes = drop_players[answer.player]\n drop_episodes = [episode for episode in drop_episodes if exam_episode <= episode]\n if drop_episodes:\n if training_data:\n for player in exam_episode.players:\n season_data.append(TrainSample(answer.player, season_num, min(drop_episodes), answer.episode,\n answer.question, answer.answer, player))\n else:\n for answer_on in [False, True]:\n season_data.append(TrainSample(answer.player, season_num, min(drop_episodes), answer.episode,\n answer.question, answer.answer, answer_on))\n return season_data","def strip_ds(ds):\n if 'brain' in np.unique(ds.sa.all_ROIs):\n ds = ds[(ds.sa.all_ROIs != 'brain'), :]\n print('excluded the rest of the brain from the dataset')\n if 'overlap' in np.unique(ds.sa.all_ROIs):\n ds = ds[(ds.sa.all_ROIs != 'overlap'), :]\n print('excluded overlap from the dataset')\n return ds","def clean():\n filter_phase_data()\n combine_phase_data()\n remove_duplicates_phase_data()","def cleanup():\n cat = CSVCatalog.CSVCatalog()\n cat.drop_table(\"people\", force_drop=True)\n cat.drop_table(\"batting\", force_drop=True)\n cat.drop_table(\"teams\", force_drop=True)","def get_wr_game():\n wr_games_reg_2015 = NFL_WR_Game_2015.query.filter_by(is_season_totals=False).all()\n clean_games = cleanup_queries(wr_games_reg_2015)\n return json.dumps(clean_games)"],"string":"[\n \"def _truncate_games_df(df, season, season_type):\\n return df[(df['season'] != season) | (df['type'] != season_type)]\",\n \"def update_season_table(session, sched_tbl, season_df):\\n date = datetime.date(datetime.now())\\n update_query = session.query(sched_tbl).filter(sched_tbl.start_time < date,\\n sched_tbl.home_team_score == 0).order_by(sched_tbl.start_time)\\n if update_query.count() == 0:\\n # print(\\\"Season is up to date; Returning without performing an update.\\\") Test/logging statement\\n return\\n\\n all_update_rows = update_query.all()\\n first_game_time = all_update_rows[0].start_time\\n last_game_time = all_update_rows[len(all_update_rows) - 1].start_time\\n\\n # Reduce season to games between first and last game time\\n season_df[\\\"start_time\\\"] = season_df[\\\"start_time\\\"].dt.tz_localize(None)\\n update_df = season_df.loc[(season_df.start_time >= first_game_time) & (season_df.start_time <= last_game_time)]\\n\\n for row in all_update_rows:\\n game = update_df.loc[(update_df.home_team == row.home_team) & (update_df.away_team == row.away_team) &\\n (update_df.start_time.dt.date == datetime.date(row.start_time))]\\n if len(game) == 0:\\n # This catches playoff games which do not end up happening (i.e. a game 7 in a series a team sweeps), and\\n # removes it from the database\\n session.delete(row)\\n else:\\n row.home_team_score = int(game.home_team_score)\\n row.away_team_score = int(game.away_team_score)\\n row.start_time = game.start_time.dt.to_pydatetime()[0] # Convert Pandas TimeStamp to datetime\\n session.add(row)\",\n \"def _drop_last_season_championship_matches(self, df: pd.DataFrame) -> pd.DataFrame:\\n max_season = df[\\\"season\\\"].max()\\n\\n if self._resume:\\n # Check if max season changed\\n if max_season != self._model_settings[\\\"max_season\\\"]:\\n raise ValueError(\\\"Current max season is different than previous one. Model must be retrained.\\\")\\n else:\\n self.max_season = self._model_settings[\\\"max_season\\\"]\\n else:\\n self.max_season = max_season\\n\\n return df.loc[~((df[\\\"season\\\"] == max_season) & (df[\\\"league\\\"] == \\\"CH\\\"))]\",\n \"def season_games(year):\\n\\tLOG.debug('Getting season %d', year)\\n\\tdata = read_html(io=season_games_url(year),\\n\\t\\t\\t\\t\\t attrs={'id': 'games'},\\n\\t\\t\\t\\t\\t infer_types=False,\\n\\t\\t\\t\\t\\t header=0)\\n\\tif len(data) != 1:\\n\\t\\traise CantFindTheRightTable\\n\\tdata = data.pop()\\n\\n\\t# Cleaning.\\n\\tdel data[\\\"Unnamed: 3\\\"]\\n\\t# The code below issues \\\"UserWarning: \\\" So we catch UserWarnings.\\n\\twith warnings.catch_warnings():\\n\\t\\twarnings.filterwarnings(action='ignore', category=UserWarning,\\n\\t\\t\\t\\t\\t\\t\\t\\tmodule=r'pandas\\\\.core\\\\.frame',\\n\\t\\t\\t\\t\\t\\t\\t\\tmessage=(r\\\"Boolean Series key will be reindexed\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t r\\\" to match DataFrame index\\\\.\\\"))\\n\\t\\t# These rows are mid-table header rows.\\n\\t\\tdata = data[data.Week != \\\"Week\\\"][data.Week != \\\"nan\\\"]\\n\\n\\tdata['week'] = (data.Week\\n\\t\\t\\t\\t\\t.replace(\\\"WildCard\\\", \\\"wild-card\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"Division\\\", \\\"divisional\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"ConfChamp\\\", \\\"conference\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"SuperBowl\\\", \\\"super-bowl\\\")\\n\\t\\t\\t\\t\\t.apply(\\n\\t\\t\\t\\t\\t\\tlambda s: (int(s)\\n\\t\\t\\t\\t\\t\\t\\t\\t if all(c in '1234567890' for c in s)\\n\\t\\t\\t\\t\\t\\t\\t\\t else s)))\\n\\tdel data['Week']\\n\\n\\tdata['season'] = year\\n\\tdata['game_date'] = pd.to_datetime(\\n\\t\\tdata.Date\\n\\t\\t.replace(r\\\"$\\\", r\\\", %d\\\" % year, regex=True)\\n\\t\\t.replace(r\\\"^(January|February) (\\\\d+), \\\\d+$\\\", r\\\"\\\\1 \\\\2, %d\\\" % (year + 1),\\n\\t\\t\\t\\t regex=True))\\n\\tdel data['Date']\\n\\n\\tfor column in \\\"PtsW\\\", \\\"PtsL\\\", \\\"YdsW\\\", \\\"TOW\\\", \\\"YdsL\\\", \\\"TOL\\\":\\n\\t data[column] = data[column].apply(int)\\n\\n\\tdata['WatL'] = data['Unnamed: 5'].apply(lambda x: x == '@')\\n\\tdel data['Unnamed: 5']\\n\\tdata['hometeam'] = (~data.WatL * data['Winner/tie'] +\\n\\t\\t\\t\\t\\t\\tdata.WatL * data['Loser/tie'])\\n\\tdata['awayteam'] = (data.WatL * data['Winner/tie'] +\\n\\t\\t\\t\\t\\t\\t~data.WatL * data['Loser/tie'])\\n\\tdata['winner'] = data['Winner/tie']\\n\\tfor column in 'Winner/tie', 'Loser/tie', \\\"WatL\\\":\\n\\t\\tdel data[column]\\n\\tfor column in 'hometeam', 'awayteam', 'winner':\\n\\t\\tdata[column] = data[column].apply(lambda s: s.split()[-1].lower())\\n\\n\\treturn data\",\n \"def games(self, competition_id: int, season_id: int) -> DataFrame[Any]:\",\n \"def remove_games(self, games):\\n for game in games:\\n response = self._session.get(self._remove_game_url,\\n params={\\n 'type': 'game',\\n 'id': game.game_id\\n })\\n response.raise_for_status()\",\n \"def delete_duplicate_game_docs(self):\\n gids = self.find_duplicate_game_docs()\\n for gid in gids:\\n games = list(self._db.Games.aggregate([{'$match':\\n {'gid' : gid}},\\n {'$project':\\n {'date': 1}}]))\\n delete = find_earlier_date(games)\\n self._db.Games.remove(delete)\",\n \"def data_cleaning():\\n conn = get_connect()\\n conn.execute(\\\"DELETE FROM championMatchData WHERE kills < 2 AND deaths < 2 AND assists < 2\\\")\\n conn.commit()\\n conn.close()\\n return\",\n \"def _check_season_gaps_in_teams_matches(self, df: pd.DataFrame):\\n if self._verbose > 0:\\n print(\\\"Teams:\\\")\\n print(self.teams)\\n print(\\\"Teams playing last season:\\\")\\n print(self.last_season_teams)\\n\\n omitted_teams_test = check_season_gaps(df, self.test_fixtures_ids)\\n omitted_teams_predict = check_season_gaps(df, self.predict_fixtures_ids)\\n\\n if omitted_teams_predict or omitted_teams_test:\\n print(\\\"Warning: There are some teams with season gaps in data.\\\")\\n print(\\\"Teams omitted based on predict dataset due to season gap:\\\")\\n print(omitted_teams_predict)\\n print(\\\"Teams omitted based on test dataset due to season gap:\\\")\\n print(omitted_teams_test)\\n\\n # Delete these teams from datasets\\n for team in set(omitted_teams_test + omitted_teams_predict):\\n del self.test_fixtures_ids[team]\\n del self.predict_fixtures_ids[team]\\n\\n print(\\\"You should consider lowering required number of test or predict data samples.\\\")\",\n \"def removeLegacy(self, path=None):\\n\\n df = pd.read_csv(path, compression='gzip')\\n print(df.shape)\\n gamelist = pd.read_csv('Resources/Genres.csv.gz', usecols=['appid'])\\n gamelist = pd.DataFrame(gamelist.appid.unique(), columns=['appid'])\\n print(gamelist)\\n filter_df = pd.merge(df, gamelist, on='appid', how='inner')\\n filter_df = filter_df.dropna()\\n filter_df = filter_df.sort_values(['steamid', 'appid'], ascending=[True, True])\\n print('done')\\n print(filter_df.shape)\\n print(filter_df)\\n print(np.setdiff1d(df['appid'].unique(), filter_df['appid'].unique()))\\n filter_df.to_csv(path, compression='gzip', columns=['steamid', 'appid', 'rating'], index=None)\",\n \"def remove_not_available(df):\\n drop_indices = df.index[df['genre'] == 'Not Available'].tolist()\\n df = df.drop(drop_indices)\\n return df\",\n \"def remove_old_songs(df, too_old=1970):\\n drop_indices = df.index[df['year'] < too_old].tolist()\\n df = df.drop(drop_indices)\\n return df\",\n \"async def delete_all_games(self):\\n all_games = await ex.conn.fetch(\\\"SELECT gameid FROM blackjack.games\\\")\\n for games in all_games:\\n game_id = games[0]\\n await self.delete_game(game_id)\",\n \"def terminate(self):\\n self.league.season = None\\n for team in self.teams:\\n team.team.season = None\",\n \"def _extract_games_data(season, season_type):\\n nflscrapr.run(\\n 'games',\\n season=season,\\n season_type=season_type\\n )\\n nflscrapr_output = etl_tools.extract_from_csv(config.GAMES_DUMP_CSV_PATH)\\n return nflscrapr_output\",\n \"def by_season(self, season):\\n return self.get_queryset().filter(season=season)\",\n \"def season_game_logs(team, year):\\n\\n # Check year value\\n if year > 2019 or year < 1950:\\n raise ValueError('Year Value Incorrect')\\n\\n # Rename teams that moved\\n team = scrape_utils.rename_team(team, year)\\n\\n # Get HTML content\\n url = 'http://www.basketball-reference.com/teams/%s/%s/gamelog' % (team, year)\\n r = requests.get(url)\\n soup = BeautifulSoup(r.content, \\\"html.parser\\\")\\n season_stats = soup.find(id='tgl_basic')\\n games = season_stats.find('tbody')\\n\\n # MongoDB Collection\\n m = mongo.Mongo()\\n\\n # To find opponent statistics\\n opponent = re.compile('^opp_.*$')\\n\\n # Loop through every game in a team's season\\n for game in games.find_all('tr', {'class': None}):\\n\\n curr_team = {'team': team}\\n opp_team = {}\\n\\n # Loop through each stat\\n for stat in game.find_all('td'):\\n\\n stat_name = stat['data-stat']\\n\\n # These are opponent stats\\n if re.match(opponent, stat_name):\\n opp_team[stat_name[4:]] = scrape_utils.stat_parse(stat_name, stat.string)\\n else:\\n curr_team[stat_name] = scrape_utils.stat_parse(stat_name, stat.string)\\n\\n # Remove unnecessary information\\n del curr_team['game_season']\\n del curr_team['x']\\n\\n # Rename relocated teams\\n curr_team['team'] = scrape_utils.rename_team(team)\\n opp_team['team'] = scrape_utils.rename_team(opp_team.pop('id'))\\n\\n # Use the same ID as basketball reference\\n result = {'date': datetime.strptime(curr_team.pop('date_game'), \\\"%Y-%m-%d\\\"),\\n 'season': year,\\n 'result': scrape_utils.determine_home_win(curr_team['game_location'], curr_team.pop('game_result')),\\n '_id': game.find('a')['href'][-17:-5]}\\n\\n # Place the teams in the correct spot depending on who is the home team\\n if curr_team.pop('game_location') == 0:\\n result['home'] = curr_team\\n result['away'] = opp_team\\n else:\\n result['home'] = opp_team\\n result['away'] = curr_team\\n\\n # Insert into database\\n m.insert('game_log', result)\",\n \"def _data_integrity_check(df):\\n if df is None or len(df) == 0:\\n logging.info(\\\"Dataframe is empty! No games data recorded.\\\")\\n return\\n\\n if not isinstance(df, pd.DataFrame):\\n raise ValueError(f\\\"Type of df is {type(df)}, it should be pandas.DataFrame\\\")\\n\\n logging.info(f\\\"Checking integrity of games dataframe with shape {df.shape}...\\\")\\n\\n seasons = df['season'].unique()\\n max_season = int(max(seasons))\\n\\n expected_seasons = set(range(config.START_SEASON, max_season + 1))\\n\\n if set(seasons) != expected_seasons:\\n missing_season = expected_seasons - set(seasons)\\n raise DataIntegrityError(f\\\"Most recent season with data is {max_season}\\\"\\n f\\\" but there's no data for {missing_season}!\\\")\\n\\n game_ids = list(df['game_id'])\\n if len(game_ids) != len(set(game_ids)):\\n raise DataIntegrityError(\\\"There are duplicate game ids!\\\")\\n\\n for season in list(seasons):\\n season_types = set(df[df['season'] == season]['type'].unique())\\n\\n if season != max_season and len(season_types) != 3:\\n raise DataIntegrityError(f\\\"{season} is not the max season of {max_season}\\\"\\n f\\\"and only has {len(season_types)} season types\\\")\\n\\n elif len(season_types) == 1 and season_types != {'pre'}:\\n raise DataIntegrityError(f\\\"There is only 1 season type for {seasons}: {season_types}\\\"\\n f\\\"and its not 'pre'!\\\")\\n\\n elif len(season_types) == 2 and season_types != {'pre', 'reg'}:\\n raise DataIntegrityError(f\\\"There are 2 season types for {season} and they're not ['pre', 'reg']!\\\")\\n\\n elif 'reg' in df[df['season'] == season]['type'].unique():\\n reg_season_df = df[(df['season'] == season) & (df['type'] == 'reg')]\\n weeks = reg_season_df['week'].unique()\\n target_weeks = set(range(1, 18))\\n missing_weeks = target_weeks.difference(set(weeks))\\n if missing_weeks:\\n raise DataIntegrityError(f\\\"{season} reg season is missing weeks {missing_weeks}!\\\")\\n\\n logging.info(\\\"Integrity check passed.\\\")\",\n \"def delete_all_games(self):\\r\\n\\r\\n # Deletes all rows from the database table\\r\\n conn_2 = sqlite3.connect('scribble_database.db')\\r\\n sql_2 = 'DELETE FROM paintings'\\r\\n cur_2 = conn_2.cursor()\\r\\n cur_2.execute(sql_2)\\r\\n conn_2.commit()\\r\\n\\r\\n # clears screen\\r\\n self.action_label.destroy()\\r\\n self.painting_name.destroy()\\r\\n self.w['bg'] = 'White'\\r\\n self.w.delete(ALL)\\r\\n\\r\\n # clears all guesses/available game ids from their menus\\r\\n self.menu_bar.destroy()\\r\\n self.action_label = Message(self.master, text='Database emptied', bg='Black', fg='orange', font=20,\\r\\n width=330)\\r\\n self.action_label.pack()\\r\\n self.action_label.place(x=500, y=440)\\r\\n self.menu_bar = Menu(self.master)\\r\\n self.guess_menu = Menu(self.menu_bar)\\r\\n self.file_menu = Menu(self.menu_bar, tearoff=1)\\r\\n self.menu_bar.add_cascade(label=\\\"Available game IDs: \\\", menu=self.file_menu)\\r\\n self.menu_bar.add_cascade(label=\\\"Guesses of displayed games: \\\", menu=self.guess_menu)\\r\\n self.master.config(menu=self.menu_bar)\",\n \"def season_series(game_id, pref_team, other_team, last_season=False):\\n\\n # Init empty dictionaries and lists\\n games_against = list()\\n pref_toi = dict()\\n pref_goals = dict()\\n pref_assists = dict()\\n pref_points = dict()\\n pref_record = {\\\"wins\\\": 0, \\\"losses\\\": 0, \\\"ot\\\": 0}\\n roster_player = True\\n\\n # If this is the first game of the season, we can set the 'last_season' flag to enable the\\n # season series function to check last year's season series between the two teams.\\n if not last_season:\\n season_start = str(game_id)[0:4]\\n season_end = str(int(season_start) + 1)\\n yesterday = datetime.now() - timedelta(days=1)\\n # yesterday = datetime.now() + timedelta(days=50)\\n # schedule_url = (\\n # f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n # f\\\"&expand=schedule.broadcasts,schedule.teams&startDate=\\\"\\n # f\\\"{season_start}-08-01&endDate={yesterday:%Y-%m-%d}\\\"\\n # )\\n schedule_url = (\\n f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n f\\\"&expand=schedule.broadcasts,schedule.teams\\\"\\n f\\\"&season={season_start}{season_end}\\\"\\n )\\n else:\\n season_start = int(str(game_id)[0:4]) - 1\\n season_end = str(int(season_start) + 1)\\n yesterday = datetime.now() - timedelta(days=1)\\n # yesterday = datetime.now() + timedelta(days=50)\\n # schedule_url = (\\n # f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n # f\\\"&expand=schedule.broadcasts,schedule.teams&startDate=\\\"\\n # f\\\"{season_start}-08-01&endDate={season_end}-06-01\\\"\\n # )\\n schedule_url = (\\n f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n f\\\"&expand=schedule.broadcasts,schedule.teams\\\"\\n f\\\"&season={season_start}{season_end}\\\"\\n )\\n\\n schedule = api.nhl_api(schedule_url).json()\\n dates = schedule[\\\"dates\\\"]\\n\\n # Loop through scheduled to get previously played games against\\n for date in dates:\\n game = date[\\\"games\\\"][0]\\n game_type = game[\\\"gameType\\\"]\\n game_id = game[\\\"gamePk\\\"]\\n game_team_home = game[\\\"teams\\\"][\\\"home\\\"][\\\"team\\\"][\\\"name\\\"]\\n game_team_away = game[\\\"teams\\\"][\\\"away\\\"][\\\"team\\\"][\\\"name\\\"]\\n teams = [game_team_away, game_team_home]\\n game_status = game[\\\"status\\\"][\\\"abstractGameState\\\"]\\n if game_type == \\\"R\\\" and game_status == \\\"Final\\\" and other_team.team_name in teams:\\n game_feed = f\\\"/game/{game_id}/feed/live\\\"\\n games_against.append(game_feed)\\n\\n # If the two teams haven't played yet, just exit this function\\n if not games_against:\\n return None, None, None\\n\\n # Loop through newly created games_against list to get each stats\\n for feed in games_against:\\n game = api.nhl_api(feed).json()\\n game_data = game[\\\"gameData\\\"]\\n home_team_name = game_data[\\\"teams\\\"][\\\"home\\\"][\\\"name\\\"]\\n pref_homeaway = \\\"home\\\" if home_team_name == pref_team.team_name else \\\"away\\\"\\n other_homeaway = \\\"away\\\" if home_team_name == pref_team.team_name else \\\"home\\\"\\n\\n # Get season series\\n end_period = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"currentPeriod\\\"]\\n extra_time = True if end_period > 3 else False\\n pref_score = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"teams\\\"][pref_homeaway][\\\"goals\\\"]\\n other_score = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"teams\\\"][other_homeaway][\\\"goals\\\"]\\n if pref_score > other_score:\\n pref_record[\\\"wins\\\"] += 1\\n elif other_score > pref_score and extra_time:\\n pref_record[\\\"ot\\\"] += 1\\n else:\\n pref_record[\\\"losses\\\"] += 1\\n\\n season_series_str = f\\\"Series: {pref_record['wins']}-\\\" f\\\"{pref_record['losses']}-{pref_record['ot']}\\\"\\n\\n # Get stats leaders\\n # pref_teamstats = game[\\\"liveData\\\"][\\\"boxscore\\\"][\\\"teams\\\"][pref_homeaway][\\\"teamStats\\\"]\\n pref_playerstats = game[\\\"liveData\\\"][\\\"boxscore\\\"][\\\"teams\\\"][pref_homeaway][\\\"players\\\"]\\n for id, player in pref_playerstats.items():\\n try:\\n # Calculate TOI\\n player_toi_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"timeOnIce\\\"]\\n player_toi_minutes = int(player_toi_str.split(\\\":\\\")[0])\\n player_toi_seconds = int(player_toi_str.split(\\\":\\\")[1])\\n player_toi = (player_toi_minutes * 60) + player_toi_seconds\\n pref_toi[id] = pref_toi.get(id, 0) + player_toi\\n\\n # Point Totals\\n player_goal_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"goals\\\"]\\n pref_goals[id] = pref_goals.get(id, 0) + int(player_goal_str)\\n player_assist_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"assists\\\"]\\n pref_assists[id] = pref_assists.get(id, 0) + int(player_assist_str)\\n player_points = int(player_goal_str) + int(player_assist_str)\\n pref_points[id] = pref_points.get(id, 0) + int(player_points)\\n\\n except KeyError:\\n pass\\n\\n # Calculate Stats Leaders\\n sorted_toi = sorted(pref_toi.values(), reverse=True)\\n leader_toi = sorted_toi[0]\\n\\n sorted_points = sorted(pref_points.values(), reverse=True)\\n leader_points = sorted_points[0]\\n\\n # Get TOI leader\\n for id in pref_toi.keys():\\n if pref_toi[id] == leader_toi:\\n player_name = roster.player_attr_by_id(pref_team.roster, id, \\\"fullName\\\")\\n if player_name is None:\\n roster_player = False\\n player_id_only = id.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n leader_toi_avg = leader_toi / len(games_against)\\n m, s = divmod(leader_toi_avg, 60)\\n toi_m = int(m)\\n toi_s = int(s)\\n toi_s = \\\"0{}\\\".format(toi_s) if toi_s < 10 else toi_s\\n toi_avg = \\\"{}:{}\\\".format(toi_m, toi_s)\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n toi_leader_str = \\\"TOI Leader: {} with {} / game.\\\".format(player_short_name, toi_avg)\\n\\n # Handle tied points leaders\\n point_leaders = list()\\n for id in pref_points.keys():\\n if pref_points[id] == leader_points:\\n point_leaders.append(id)\\n\\n if leader_points == 0:\\n points_leader_str = \\\"Points Leader: None (all players have 0 points).\\\"\\n\\n elif len(point_leaders) == 1:\\n leader = point_leaders[0]\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n # If the player is no longer on the team, get their information (change string here?)\\n if player_name is None:\\n roster_player = False\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n if not roster_player:\\n points_leader_str = (\\n f\\\"Points Leader: {player_name} with {leader_points} points \\\"\\n f\\\"({player_goals}G {player_assists}A) \\\"\\n )\\n else:\\n points_leader_str = \\\"Points Leader: {} with {} ({}G {}A).\\\".format(\\n player_name, leader_points, player_goals, player_assists\\n )\\n\\n elif len(point_leaders) > 3:\\n point_leaders_with_attrs = list()\\n for leader in point_leaders:\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n if player_name is None:\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n point_leaders_with_attrs.append(player_short_name)\\n\\n point_leaders_joined = \\\", \\\".join(point_leaders_with_attrs[0:3])\\n leftover_leaders = len(point_leaders) - 3\\n points_leader_str = (\\n f\\\"Points Leaders: {point_leaders_joined} & {leftover_leaders} others ({leader_points} each).\\\"\\n )\\n\\n else:\\n point_leaders_with_attrs = list()\\n for leader in point_leaders:\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n if player_name is None:\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n player_str = f\\\"{player_short_name} ({player_goals}G {player_assists}A)\\\"\\n point_leaders_with_attrs.append(player_str)\\n\\n point_leaders_joined = (\\n f\\\", \\\".join(point_leaders_with_attrs[:-1]) + f\\\" & {point_leaders_with_attrs[-1]}\\\"\\n )\\n points_leader_str = \\\"Points Leaders: {} with {} each.\\\".format(point_leaders_joined, leader_points)\\n\\n return season_series_str, points_leader_str, toi_leader_str\",\n \"def _discard_matches(self,\\n df: pd.DataFrame,\\n teams_fixtures_ids: Dict[str, List[int]]) -> Dict[str, List[int]]:\\n discarded_fixtures_ids = {}\\n\\n if self._ndiscard:\\n for t in self.last_season_teams:\\n discarded_fixtures_ids[t] = teams_fixtures_ids[t][-self._ndiscard:]\\n teams_fixtures_ids[t] = teams_fixtures_ids[t][:-self._ndiscard]\\n\\n # Check if all discarded matches are within a single season\\n unique_discarded_fixtures_ids = set(chain(*discarded_fixtures_ids.values()))\\n mask_discarded = df[\\\"id\\\"].isin(unique_discarded_fixtures_ids)\\n seasons_in_discarded_df = df[mask_discarded].loc[:, \\\"season\\\"].unique()\\n\\n if any(s != self.max_season for s in seasons_in_discarded_df):\\n raise ValueError(\\\"Discarded matches must be within the last season. \\\\n\\\"\\n \\\"Please lower the number of matches to discard.\\\")\\n\\n # Some teams may have played more than once so we need to be sure that we discarded\\n # all matches correctly.\\n for t in self.last_season_teams:\\n teams_fixtures_ids[t] = [j for j in teams_fixtures_ids[t] if j not in unique_discarded_fixtures_ids]\\n\\n return teams_fixtures_ids\",\n \"def deletePlayers():\\n DB = connect()\\n c = DB.cursor()\\n c.execute(\\\"DELETE FROM tournament\\\")\\n DB.commit()\\n DB.close()\",\n \"def get_dryspells_perseason(dryspells, seasons=((12, 1, 2), (3, 4, 5),\\n (6, 7, 8), (9, 10, 11))):\\n dryspells_seasons = []\\n for season in seasons:\\n eveSeas = []\\n for eveLand in dryspells:\\n eves = [e for e in eveLand if e.start_date().month in season]\\n eveSeas.append(eves)\\n dryspells_seasons.append(eveSeas)\\n\\n return dryspells_seasons\",\n \"def leave_game(players_cursor, states_cursor, user, room_id):\\n leave_query = '''DELETE FROM players_table WHERE user = ? AND room_id = ?'''\\n players_cursor.execute(leave_query, (user, room_id))\\n FRAMES.append(display_game(players_cursor, states_cursor, user, room_id))\",\n \"def delete_all(sid):\\n Game.objects.all().delete()\",\n \"def game_to_SQL(self, cursor, gid, games):\\n\\t\\t\\n\\t\\tvalues = []\\n\\t\\tquery_with = \\\"WITH games AS (\\\"\\n\\t\\tfor game in games.findall('.'):\\n\\t\\t\\tquery_with += \\\"SELECT ? AS gid, ? AS time_date, ? AS game_pk, ? AS venue, ? AS venue_id, ? AS away_team_id, ? AS home_team_id, ? AS home_time, ? AS home_time_zone, ? AS game_nbr, ? AS home_team_runs, ? AS away_team_runs, ? AS is_perfect_game, ? AS is_no_hitter, ? AS [des] UNION \\\"\\n\\t\\t\\tvalues.append(gid)\\n\\t\\t\\tif 'time_date' in game.attrib:\\n\\t\\t\\t\\tvalues.append(game.attrib['time_date'])\\n\\t\\t\\telse:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tgame.attrib['time'].strptime(input, '%H:%M')\\n\\t\\t\\t\\t\\tvalues.append(game.attrib['id'][:10] + \\\" \\\" + game.attrib['time'])\\n\\t\\t\\t\\texcept:\\n\\t\\t\\t\\t\\tvalues.append(game.attrib['id'][:10])\\n\\t\\t\\t\\t\\tpass\\n\\t\\t\\tvalues.append(game.attrib['game_pk'])\\n\\t\\t\\tvalues.append(game.attrib['venue'])\\n\\t\\t\\tvalues.append(game.attrib['venue_id'])\\n\\t\\t\\tvalues.append(game.attrib['away_team_id'])\\n\\t\\t\\tvalues.append(game.attrib['home_team_id'])\\n\\t\\t\\tvalues.append(game.attrib['home_time'])\\n\\t\\t\\tvalues.append(game.attrib['home_time_zone'])\\n\\t\\t\\tif 'game_nbr' in game.attrib:\\n\\t\\t\\t\\tvalues.append(game.attrib['game_nbr'])\\n\\t\\t\\telse: \\n\\t\\t\\t\\tvalues.append(gid[-1])\\n\\t\\t\\tif 'home_team_runs' in game.attrib:\\n\\t\\t\\t\\tvalues.append(game.attrib['home_team_runs'])\\n\\t\\t\\telse:\\n\\t\\t\\t\\tvalues.append(None)\\n\\t\\t\\tif 'away_team_runs' in game.attrib:\\n\\t\\t\\t\\tvalues.append(game.attrib['away_team_runs'])\\n\\t\\t\\telse:\\n\\t\\t\\t\\tvalues.append(None)\\n\\t\\t\\tif 'is_perfect_game' in game.attrib:\\n\\t\\t\\t\\tvalues.append(game.attrib['is_perfect_game'])\\n\\t\\t\\telse:\\n\\t\\t\\t\\tvalues.append(None)\\n\\t\\t\\tif 'is_no_hitter' in game.attrib:\\n\\t\\t\\t\\tvalues.append(game.attrib['is_no_hitter'])\\n\\t\\t\\telse:\\n\\t\\t\\t\\tvalues.append(None)\\n\\t\\t\\tif 'description' not in game.attrib: \\n\\t\\t\\t\\tvalues.append(None)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tvalues.append(game.attrib['description'])\\n\\t\\tquery_with = query_with[:-7] + \\\")\\\"\\n\\n\\t\\tinsert = query_with + \\\"INSERT INTO game (gid, game_date, game_pk, venue, venue_id, away_team_id, home_team_id, home_time, home_time_zone, game_nbr, home_team_runs, away_team_runs, is_perfect_game, is_no_hitter, [description]) \\\"\\n\\t\\tinsert += \\\"SELECT g.gid, g.time_date, g.game_pk, g.venue, g.venue_id, g.away_team_id, g.home_team_id, g.home_time, g.home_time_zone, g.game_nbr, g.home_team_runs, g.away_team_runs, g.is_perfect_game, g.is_no_hitter, g.des FROM games g;\\\"\\n\\t\\t\\n\\t\\tcursor.execute(insert, values)\",\n \"def team_games(df, team):\\n print(df[\\n ( df['team'] == team ) & \\n ( df['player'] == 'Team' )\\n ].drop_duplicates(subset=['gameid']).shape)\\n return df[\\n ( df['team'] == team ) & \\n ( df['player'] == 'Team' )\\n ].drop_duplicates(subset=['gameid'])\",\n \"def _valid_games(self, alias):\\n return Query(alias.gid).filter(\\n alias.species_id == self.species.id,\\n alias.background_id == self.background.id,\\n alias.start >= self.start,\\n alias.start <= self.end\\n )\",\n \"def deletePlayers():\\n conn = connect()\\n c = conn.cursor()\\n # Clears the \\\"players\\\" table, but does not get rid of the table.\\n c.execute(\\\"delete from players;\\\")\\n conn.commit()\\n conn.close()\",\n \"def delete(self, show_id, season_id, session):\\n try:\\n show = db.show_by_id(show_id, session=session)\\n except NoResultFound:\\n raise NotFoundError('show with ID %s not found' % show_id)\\n try:\\n season = db.season_by_id(season_id, session)\\n except NoResultFound:\\n raise NotFoundError('season with ID %s not found' % season_id)\\n if not db.season_in_show(show_id, season_id):\\n raise BadRequest(f'season with id {season_id} does not belong to show {show_id}')\\n\\n args = delete_parser.parse_args()\\n db.remove_series_entity(show.name, season.identifier, args.get('forget'))\\n\\n return success_response(f'successfully removed season {season_id} from show {show_id}')\",\n \"def prune_data(self, ts):\\n sql = \\\"delete from %s where dateTime < %d\\\" % (self.dbm.table_name, ts)\\n self.dbm.getSql(sql)\\n try:\\n # sqlite databases need some help to stay small\\n self.dbm.getSql('vacuum')\\n except Exception as e:\\n pass\",\n \"async def delete_game(self, game_id):\\n game = await self.get_game(game_id)\\n await ex.conn.execute(\\\"DELETE FROM blackjack.games WHERE gameid = $1\\\", game_id)\\n await ex.conn.execute(\\\"DELETE FROM blackjack.currentstatus WHERE userid = $1\\\", game[1])\\n await ex.conn.execute(\\\"DELETE FROM blackjack.currentstatus WHERE userid = $1\\\", game[2])\\n log.console(f\\\"Game {game_id} deleted.\\\")\",\n \"def get_games(season, date):\\n url = \\\"http://live.nhl.com/GameData/SeasonSchedule-\\\" + season + \\\".json\\\"\\n response = urllib.urlopen(url)\\n data = json.loads(response.read())\\n games = []\\n for game in data:\\n if game[\\\"est\\\"][:8] == date:\\n games.append(game)\\n return games\",\n \"def delete(self, show_id, session):\\n try:\\n show = db.show_by_id(show_id, session=session)\\n except NoResultFound:\\n raise NotFoundError('show with ID %s not found' % show_id)\\n args = delete_parser.parse_args()\\n forget = args.get('forget')\\n for season in show.seasons:\\n db.remove_series_entity(show.name, season.identifier, forget)\\n return success_response('successfully removed all series %s seasons from DB' % show_id)\",\n \"def season(year, week=None, timeout=None, concurrency=_DEFAULT_CONCURRENCY):\\n\\tLOG.debug('Concurrency = %d', concurrency)\\n\\tgames = season_games(year)\\n\\tif week is not None:\\n\\t\\tgames = games[games.week == week]\\n\\texpected_n = len(games)\\n\\ttables, futures_to_args, failures = [], {}, []\\n\\t# See https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor-example.\\n\\twith futures.ThreadPoolExecutor(concurrency) as pool:\\n\\t\\tfor arg in zip(games.hometeam, games.awayteam, games.week):\\n\\t\\t\\targ = arg + (year,)\\n\\t\\t\\tfutures_to_args[pool.submit(game_unknown_homeaway, *arg)] = arg\\n\\t\\tfor future in futures.as_completed(futures_to_args, timeout=timeout):\\n\\t\\t\\targs = futures_to_args[future]\\n\\t\\t\\ttry:\\n\\t\\t\\t\\ttable = future.result()\\n\\t\\t\\texcept Exception as exc:\\n\\t\\t\\t\\tLOG.exception('Error from %s: %s', args, exc)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif table is None:\\n\\t\\t\\t\\t\\tLOG.error('Failure: %s', args)\\n\\t\\t\\t\\t\\tfailures.append(args)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tLOG.info('Success: %s', args)\\n\\t\\t\\t\\t\\ttables.append(table)\\n\\ttables = games.merge(pd.concat(tables), on=('hometeam', 'awayteam', 'week'))\\n\\tif __debug__:\\n\\t\\tn = len(tables.groupby(['hometeam', 'awayteam', 'week']))\\n\\t\\tassert n == expected_n, \\\"Expected %d games, got %d\\\" % (expected_n, n)\\n\\treturn tables, failures\",\n \"def losing_games_stats(self):\\n self.losing_games_up_to_2013 = (\\n self.df\\n .pipe(lambda x:x.assign(losing_num_counts=1))\\n .query(\\\"Season <= 2013\\\")\\n .groupby(['Season','LTeamID'])\\n .agg({\\\"WScore\\\":\\\"sum\\\",\\\"LScore\\\":\\\"sum\\\",\\\"LFGM\\\":\\\"sum\\\",\\\"LFGA\\\":\\\"sum\\\",\\\"LFGM3\\\":\\\"sum\\\",\\\"LFGA3\\\":\\\"sum\\\",\\\"LFTM\\\":\\\"sum\\\",\\\"LFTA\\\":\\\"sum\\\",\\\"losing_num_counts\\\":\\\"sum\\\",\\n \\\"LOR\\\":\\\"sum\\\",\\\"LDR\\\":\\\"sum\\\",\\\"WFGA\\\":\\\"sum\\\",\\\"WFGM\\\":\\\"sum\\\",\\n \\\"LAst\\\":\\\"sum\\\",\\\"LTO\\\":\\\"sum\\\",\\\"LStl\\\":\\\"sum\\\",\\\"LBlk\\\":\\\"sum\\\",\\\"LPF\\\":\\\"sum\\\"})\\n .reset_index()\\n .rename(columns={\\\"WScore\\\":\\\"winning_opponent_score\\\"})\\n # rebounds\\n .pipe(lambda x:x.assign(total_losing_rebounds = x.LOR + x.LDR))\\n .pipe(lambda x:x.assign(losing_off_rebounds_percent = x.LOR/x.total_losing_rebounds))\\n .pipe(lambda x:x.assign(losing_def_rebounds_percent = x.LDR/x.total_losing_rebounds))\\n .pipe(lambda x:x.assign(losing_team_missed_attempts = x.LFGA - x.LFGM))\\n .pipe(lambda x:x.assign(winning_opp_team_missed_attempts = x.WFGA - x.WFGM))\\n .pipe(lambda x:x.assign(losing_rebound_possession_percent = x.LOR/x.losing_team_missed_attempts))\\n .pipe(lambda x:x.assign(losing_rebound_possessiongain_percent = x.LDR/x.winning_opp_team_missed_attempts))\\n # blocks, steals, assists and turnovers\\n .pipe(lambda x:x.assign(losing_block_opp_FGA_percent = x.LBlk/x.WFGA))\\n .pipe(lambda x:x.assign(losing_assist_per_fgm = x.LAst/x.LFGM))\\n .pipe(lambda x:x.assign(losing_assist_turnover_ratio = x.LAst/x.LTO))\\n # rename columns to prevent duplication when joining with losing stats. example: WFGM_x\\n .rename(columns={\\\"WFGA\\\":\\\"WFGA_opp\\\",\\\"WFGM\\\":\\\"WFGM_opp\\\"})\\n )\",\n \"def deletePlayers():\\n # gets connection to tournament database in conn object\\n conn = connect()\\n # gets the cursor to execute queries\\n c = conn.cursor()\\n # executes delete query to delete all records in PLAYER table\\n c.execute(\\\"DELETE FROM PLAYER;\\\")\\n # commits the changes perform on PLAYER table after delete statement executes\\n conn.commit()\\n # closes the connection to tournament database\\n conn.close()\",\n \"def deletePlayers():\\n cursor.execute(\\\"\\\"\\\"delete from players\\\"\\\"\\\")\",\n \"def deletePlayers():\\n\\n query = (\\\"DELETE FROM players;\\\")\\n results = executeQuery({'dbname': 'tournament', 'query' : query, 'type' : 'delete'})\",\n \"def replace_season(data,season):\\n if not SeasonCharts.matchseason(season):\\n raise SeasonCharts.SeasonError\\n ## Check data format\\n if test_rawdata(data):\\n for cat,shows in data.items():\\n for show in shows: show['season'] = season\\n else:\\n for show in data: show['season'] = season\",\n \"def get_seasons(games: List[BaseGame], reverse=False):\\n seasons = list(set([g.Season for g in games]))\\n seasons.sort(reverse=reverse)\\n return seasons\",\n \"def as_games(self):\\n self._assert_no_aggregate()\\n\\n self._sort_tables = [types.Game]\\n ids = self._ids('game', self._sorter)\\n results = []\\n q = 'SELECT %s FROM game %s %s'\\n with Tx(self._db) as cursor:\\n q = q % (\\n types.select_columns(types.Game),\\n _prefix_and(_sql_pkey_in(cursor, ['gsis_id'], ids['game'])),\\n self._sorter.sql(tabtype=types.Game),\\n )\\n cursor.execute(q)\\n\\n for row in cursor.fetchall():\\n results.append(types.Game.from_row(self._db, row))\\n return results\",\n \"def season_game_log_save(year, collection):\\n try:\\n df = PlayerGameLogs(season_nullable=year).get_data_frames()[0]\\n df_final = df.filter(\\n ['PLAYER_ID', 'PLAYER_NAME', 'TEAM_ID', 'TEAM_ABBREVIATION', 'TEAM_NAME', 'GAME_ID', 'GAME_DATE', 'MATCHUP',\\n 'WL', 'MIN', 'FGM', 'FGA', 'FG_PCT', 'FG3M', 'FG3A', 'FG3_PCT', 'FTM', 'FTA', 'FT_PCT', 'OREB', 'DREB', 'REB',\\n 'AST', 'TOV', 'STL', 'BLK', 'BLKA', 'PF', 'PFD', 'PTS', 'PLUS_MINUS', 'NBA_FANTASY_PTS'], axis=1)\\n collection.insert_many(df_final.to_dict('records'))\\n except OperationFailure as e:\\n print(e)\",\n \"def __delete(self, table, conditions, values):\\n tables = [\\n table,\\n ]\\n if table == \\\"Varieties\\\":\\n start_table = 0\\n end_table = -1\\n for i in range(0, len(tables)):\\n if tables[i] == \\\"Varieties\\\":\\n tables.pop(i)\\n for j in range(0, len(conditions)):\\n if conditions[j].startswith(\\\"resource_id\\\"):\\n index = self._match_variety_table(values[j])\\n if conditions[j].endswith(\\\" =\\\"):\\n start_table = index\\n end_table = index + 1\\n elif conditions[j].endswith(\\\" <\\\") or conditions[j].endswith(\\n \\\" <=\\\"\\n ):\\n end_table = index + 1\\n elif conditions[j].endswith(\\\" >\\\") or conditions[j].endswith(\\n \\\" >=\\\"\\n ):\\n start_table = index\\n\\n tables = self.variety_tables[start_table:end_table]\\n\\n # Slightly improved version\\n all_ok = True\\n cursor = self.conn.cursor(dictionary=True)\\n\\n for talbe in tables:\\n request = \\\"DELETE FROM {table} WHERE {conditions}\\\"\\n request = request.format(\\n table=table, conditions=\\\" %s AND\\\".join(conditions + [\\\"0\\\"])[0:-5]\\n )\\n try:\\n cursor.execute(request, tuple(values))\\n except Exception as error:\\n _logger.exception(str(error))\\n all_ok = False\\n else:\\n self.conn.commit()\\n _logger.debug(\\\"DELETE REQUEST ON {0} OK.\\\".format(table))\\n cursor.close()\\n\\n return all_ok\",\n \"def drop_team(self, vid):\\n return self \\\\\\n .team(vid) \\\\\\n .sideEffect(__.drop()) \\\\\\n .count()\",\n \"def deletePlayers():\\n DB = dbc()\\n DB.cursor().execute('DELETE FROM players')\\n DB.commit()\\n DB.close()\",\n \"def deletePlayers():\\n with _connect_db() as (conn, cur):\\n cur.execute(\\\"\\\"\\\"DELETE FROM players;\\\"\\\"\\\")\\n conn.commit()\",\n \"def drop(self):\\n c = self.cursor()\\n for table in ['experiment','fact']:\\n c.execute(\\\"drop table if exists {}\\\".format(table))\\n self.commit()\",\n \"def opponent_games(df, team, gameids):\\n games = df[df.gameid.isin(gameids)]\\n opponent_games = games[games.team != team].drop_duplicates(subset=['gameid'])[['gameid', 'team']]\\n opponent_games.columns = ['gameid', 'opponent']\\n\\n return opponent_games\",\n \"def deletePlayers():\\n conn, cur = connect()\\n cur.execute(\\\"DELETE FROM PLAYERS;\\\")\\n conn.commit()\\n conn.close()\",\n \"def remove(table, id_):\\n\\n if common.confirm_option():\\n ID = 0\\n\\n for game in table:\\n if game[ID] == id_:\\n table.remove(game)\\n\\n return table\",\n \"def removePlayer(df, name):\\n if name in getPlayerList(df):\\n df = df[df.name != name]\\n return df\",\n \"def event11022120():\\n header(11022120)\\n\\n item_type, item_id = define_args('Bi')\\n end_if_this_event_slot_on()\\n if_new_game_count_greater_than_or_equal(1, 1)\\n end_if_condition_false(1)\\n item.remove_items_from_player(item_type, item_id, 0)\",\n \"def delete(self, show_id, season_id, session):\\n try:\\n db.show_by_id(show_id, session=session)\\n except NoResultFound:\\n raise NotFoundError('show with ID %s not found' % show_id)\\n try:\\n season = db.season_by_id(season_id, session)\\n except NoResultFound:\\n raise NotFoundError('seasons with ID %s not found' % season_id)\\n if not db.season_in_show(show_id, season_id):\\n raise BadRequest(f'season with id {season_id} does not belong to show {show_id}')\\n\\n args = release_delete_parser.parse_args()\\n downloaded = args.get('downloaded') is True if args.get('downloaded') is not None else None\\n release_items = []\\n for release in season.releases:\\n if (\\n downloaded\\n and release.downloaded\\n or downloaded is False\\n and not release.downloaded\\n or not downloaded\\n ):\\n release_items.append(release)\\n\\n for release in release_items:\\n if args.get('forget'):\\n fire_event('forget', release.title)\\n db.delete_season_release_by_id(release.id)\\n return success_response(\\n f'successfully deleted all releases for season {season_id} from show {show_id}'\\n )\",\n \"def erase_scores(self):\\n self.database.erase_scores(self.difficulty)\",\n \"def seasonality(df):\\n df_datetime = pd.DatetimeIndex(df.date_time)\\n df[\\\"month\\\"] = df_datetime.month\\n df = drop_cols(df, [\\\"date_time\\\"])\\n\\n return df\",\n \"def seasons(self):\\n if self._season_cache_ver == self._db._version:\\n return self._season_cache\\n\\n # Find out how many seasons in this series by fetching the highest season.\\n seasons = self._db.query(type='episode', parent=self._dbrow, attrs=['season'], distinct=True)\\n self._season_cache = [Season(self._db, self, row['season']) for row in seasons]\\n self._season_cache_ver = self._db._version\\n return self._season_cache\",\n \"def remove(table, id_):\\n\\n removed = False\\n\\n for item in table:\\n if item[0] == id_[0]:\\n table.remove(item)\\n removed = True\\n\\n if not removed:\\n ui.print_error_message(\\\"There isn't a game with such ID!\\\")\\n return table\",\n \"def get_schedules_for_season(self, season, season_type=\\\"REG\\\"):\\n try:\\n season = int(season)\\n if season_type not in [\\\"REG\\\", \\\"PRE\\\", \\\"POST\\\"]:\\n raise ValueError\\n except (ValueError, TypeError):\\n raise FantasyDataError('Error: Invalid method parameters')\\n\\n season_param = \\\"{0}{1}\\\".format(season, season_type)\\n result = self._method_call(\\\"Schedules/{season}\\\", season=season_param)\\n return result\",\n \"def cleanup(pgconn):\\n cursor = pgconn.cursor()\\n config = util.get_config()\\n for table, prop in zip(\\n [\\\"ldm_rtstats\\\", \\\"ldm_rtstats_hourly\\\", \\\"ldm_rtstats_daily\\\"],\\n [\\n \\\"retain_rtstats_raw[hours]\\\",\\n \\\"retain_rtstats_hourly[days]\\\",\\n \\\"retain_rtstats_daily[days]\\\",\\n ],\\n ):\\n interval = config.get(prop)\\n if interval is None or interval <= 0:\\n continue\\n timecol = \\\"entry_added\\\" if table == \\\"ldm_rtstats\\\" else \\\"valid\\\"\\n multiplier = 24 if table != \\\"ldm_rtstats\\\" else 1\\n hours = interval * multiplier\\n cursor.execute(\\n \\\"\\\"\\\"\\n DELETE from \\\"\\\"\\\"\\n + table\\n + \\\"\\\"\\\"\\n WHERE \\\"\\\"\\\"\\n + timecol\\n + \\\"\\\"\\\" < (now() - '%s hours'::interval)\\n \\\"\\\"\\\"\\n % (hours,)\\n )\\n # print(\\\"Removed %s rows from table: %s\\\" % (cursor.rowcount, table))\\n cursor.close()\\n pgconn.commit()\",\n \"def remove_from_winning(game: List[int]) -> None:\\n while True:\\n game_copy = game.copy()\\n row = randint(1, len(game_copy))\\n if game_copy[row-1] < 1:\\n continue\\n matches = randint(1, game_copy[row-1])\\n remove_matches(game_copy, row - 1, matches)\\n if not is_winning(game_copy):\\n remove_matches(game, row - 1, matches)\\n break\\n print(\\\"{} matches on row {} have been removed.\\\".format(matches, row))\",\n \"def delete_game(game_id):\\n\\n game = filter(lambda t: t[\\\"id\\\"] == game_id, games)\\n if len(game) == 0:\\n abort(404)\\n games.remove(game[0])\\n return jsonify({\\\"result\\\": True})\",\n \"def delete_game(game_id):\\n\\n game = Game.query.get(game_id)\\n db.session.delete(game)\\n db.session.commit()\",\n \"def droptables(db, cursor):\\n cursor.execute('''DROP TABLE IF EXISTS worlds;''')\\n cursor.execute('''DROP TABLE IF EXISTS characters''')\\n cursor.execute('''DROP TABLE IF EXISTS chardata''')\\n db.commit()\",\n \"def remove_training_reserves():\\n reserves = TrainingReserve.objects.all()\\n now = timezone.now()\\n for reserve in reserves:\\n if reserve.date < now:\\n reserve.delete()\",\n \"def losses(self):\\n return [g for g in self.games if g.winner is not self.team]\",\n \"def deletePlayers():\\n db_conn = connect()\\n db_cursor = db_conn.cursor()\\n db_cursor.execute(\\\"delete from players;\\\")\\n db_conn.commit()\\n db_conn.close()\",\n \"def on_death(self):\\n self.bark('I was killed.')\\n self.where = self.category.spawn\\n self.life = self.life_max\\n for this in self.battle_set.get().npcs.all():\\n this.delete()\\n # self.cool -= 9001\\n self.save()\\n return True\",\n \"def deletePlayers():\\n #deletes the contents of table players\\n DB().execute(\\\"DELETE FROM players\\\", True)\",\n \"def remove_rows(self, rows, regroup=False):\\n self.table.remove_rows(np.atleast_1d(rows))\\n if regroup:\\n for col in ['setup', 'calib', 'calibbit', 'comb_id', 'bkg_id']:\\n if col in self.keys():\\n del self.table[col]\\n self.set_configurations()\\n self.set_calibration_groups()\\n self.set_combination_groups()\",\n \"def clean_database(self, option):\\n\\n if (option == 'electoral_roll'):\\n self.db[self.distelect_table_or_collection].delete_many({})\\n self.db[self.elector_table_or_collection].delete_many({})\\n\\n elif (option == 'stats'):\\n self.db[self.district_stats_table_or_collection].delete_many({})\\n self.db[self.canton_stats_table_or_collection].delete_many({})\\n self.db[self.province_stats_table_or_collection].delete_many({})\",\n \"def delete_teams_by_date(self, datetime):\\n self.execute(TABELLE['teams']['delete']['by_date'], (datetime,))\",\n \"def test_no_previous_study_version(self):\\n self.study_version_1.delete()\\n self.study_version_2.delete()\\n response = self.client.get(self.get_url(self.study.pk))\\n context = response.context\\n table = context['source_trait_table']\\n self.assertEqual(len(table.data), 0)\\n for trait in self.source_traits_v3:\\n self.assertNotIn(trait, table.data)\",\n \"def remove_columns(lst):\\n cols_rem = ['yearID','Team','lgID','Name','X','playerID','pops']\\n\\n for item in cols_rem:\\n if item in lst:\\n lst.remove(item)\\n\\n return(lst)\",\n \"def prepare(self):\\n # Remove rows where the temporal scope could not be parsed correctly.\\n self.triples = self.triples[(self.triples.time_begin != -99999) & (self.triples.time_end != -99999)]\\n\\n # Remove rows with start time after the end time.\\n time_diffs = self.triples[\\\"time_end\\\"] - self.triples[\\\"time_begin\\\"]\\n invalid_times = time_diffs[time_diffs < 0].index\\n self.triples.drop([x for x in invalid_times], inplace=True, axis=\\\"index\\\")\\n\\n # Remove all entities that no longer have any facts associated with them from the dictionary.\\n entity_counts = Utils.get_entity_counts(self.triples, self.entities)\\n for idx, count in entity_counts.items():\\n if count == 0:\\n del self.entities[idx]\\n\\n # Remove all relations that no longer have any facts associated with them from the dictionary.\\n relation_counts = Utils.get_group_counts(self.triples.groupby(\\\"relation_id\\\"), self.relations)\\n for idx, count in relation_counts.items():\\n if count == 0:\\n del self.relations[idx]\",\n \"def get_all_games(season):\\n url = BASE_URL.format(season)\\n json_data = requests.get(url, headers=HEADERS).json()\\n all_games = json_data[\\\"resultSets\\\"][0][\\\"rowSet\\\"]\\n return all_games\",\n \"def drop_non_wind(ds):\\n return ds.drop(['fdir', 't2m', 'ssrd'])\",\n \"def deletePlayers():\\n db = connect()\\n c = db.cursor()\\n query = (\\\"DELETE FROM players;\\\")\\n c.execute(query)\\n db.commit()\\n db.close()\",\n \"def season_type(self, season_type):\\n\\n self._season_type = season_type\",\n \"def test_delete_pokemon_slot_6(self):\\n response = self.client.post(\\\"/teams/create/\\\",\\n {\\n \\\"trainer\\\": str(self.trainer_id),\\n })\\n team_pk = response.json()[\\\"id\\\"]\\n slot = 6\\n\\n pokemon_name = \\\"Gastrodon\\\"\\n\\n response = self.client.post(\\\"/teams/add/\\\", {\\n \\\"id\\\":str(team_pk),\\n \\\"slot\\\": str(slot),\\n \\\"pokemon_name\\\": pokemon_name,\\n })\\n\\n response = self.client.delete(\\\"/teams/deletepokemon/{}/{}/\\\".format(team_pk, str(slot)))\\n self.assertEqual(response.status_code, 204)\\n poke = Team.objects.get(pk=team_pk)\\n self.assertFalse(poke.slot_6_pokemon)\",\n \"def getSeasonStats(self):\\n df_season_agg = self.toSeasonAggFormat()\\n\\n # Calculate Possessions for each game\\n df_season_agg['possessions'] = 0.5 * (df_season_agg['FGA'] + 0.475 * df_season_agg['FTA'] - df_season_agg['OR'] + df_season_agg['TO']) \\\\\\n + 0.5 * (df_season_agg['OppFGA'] + 0.475 * df_season_agg['OppFTA'] - df_season_agg['OppOR'] + df_season_agg['OppTO'])\\n\\n # Aggregate to Season Summary Level\\n season_stats = df_season_agg.groupby(['TeamID', 'Season']).sum()\\n\\n season_stats = season_stats.rename(columns={'Win':'wins'})\\n\\n # Season Advanced Stats\\n season_stats['o_eff'] = season_stats['Score'] / season_stats['possessions'] * 100\\n season_stats['d_eff'] = season_stats['OppScore'] / season_stats['possessions'] * 100\\n season_stats['net_eff'] = season_stats['o_eff'] - season_stats['d_eff']\\n\\n season_stats.drop('DayNum', axis=1, inplace=True)\\n season_stats.drop('OppTeamID', axis=1, inplace=True)\\n season_stats.drop('rand', axis=1, inplace=True)\\n\\n return season_stats\",\n \"def all_seasons_save():\\n year_arr = [str(f'{i}-{str((i + 1))[-2:]}') for i in reversed(range(1950, 2021))]\\n for year in year_arr:\\n c_name = f'season_player_game_logs_{year.replace(\\\"-\\\", \\\"_\\\")}'\\n try:\\n coll = db[c_name]\\n season_game_log_save(year, coll)\\n print(f'{c_name} successfully saved')\\n time.sleep(1)\\n except OperationFailure as e:\\n print(f'{c_name} not saved!!')\\n print(e)\",\n \"def purge_old() -> None:\\n conn = sqlite3.connect('rss.db')\\n c = conn.cursor()\\n c.execute(\\n \\\"\\\"\\\"delete from entries where title not in\\n (select title from entries order by year desc, month desc, day desc\\n limit 20)\\n \\\"\\\"\\\"\\n )\\n conn.commit()\\n conn.close()\",\n \"def deletePlayers():\\n db = connect()\\n db_cursor = db.cursor()\\n query = \\\"DELETE FROM players\\\"\\n db_cursor.execute(query)\\n db.commit()\\n db.close()\",\n \"def deletePlayers():\\n conn = psycopg2.connect(\\\"dbname=tournament\\\")\\n c = conn.cursor()\\n c.execute(\\\"TRUNCATE player CASCADE\\\")\\n conn.commit()\\n conn.close()\",\n \"def cull(self) -> None:\\n for player in self.players:\\n to_remove = [creature for creature in player.battle_line if creature.damage_taken >= creature.power()]\\n for creature in to_remove:\\n player.battle_line.remove(creature)\\n to_remove.destroyed(self, creature)\",\n \"def deleteBolts(self):\\n a = self.get_bolts()\\n for i in self.get_bolts():\\n if i.y>GAME_HEIGHT:\\n a.remove(i)\\n self.set_plyrbolts(0)\\n elif i.y<=-BOLT_HEIGHT:\\n a.remove(i)\",\n \"def remove_player_velocities(team):\\n \\n # Either end with _vx or _vy or _speed\\n velocity_columns=[x for x in team.columns if re.match(r\\\".*_vx$|.*_vy$|.*_speed$\\\",x)]\\n team.drop(velocity_columns,axis='columns',inplace=True)\\n return team\",\n \"def delete_salary_rows(self, debug: bool = False) -> None:\\n try:\\n sql = \\\"DELETE FROM SALARIES where id < 0 and yearid = {};\\\".format(self._yearid)\\n if debug:\\n print(sql)\\n\\n self._cursor.execute(sql)\\n self._connection.commit()\\n\\n except Error as error:\\n self._connection.rollback()\\n raise RuntimeError(\\\"Database error deleting old rows\\\", error)\",\n \"def clean_pokemon_database(\\n df: pd.DataFrame,\\n selected_generations: list,\\n remove_legendary: bool = True\\n):\\n\\n # Filter only selected generations\\n df = df[df.generation.isin(selected_generations)]\\n\\n # Remove legendary\\n if remove_legendary:\\n df = df[df.is_legendary == 0]\\n\\n # Rename type columns\\n df = df.rename(columns={\\\"type1\\\": \\\"primary_type\\\", \\\"type2\\\": \\\"secondary_type\\\"})\\n\\n # Drop NA values\\n df = df.dropna()\\n\\n # When returning many variables, it is a good practice to give them names:\\n return df\",\n \"def deletePlayers():\\n dbConn = connect()\\n c = dbConn.cursor()\\n c.execute(\\\"DELETE FROM player\\\")\\n dbConn.commit()\\n dbConn.close()\",\n \"def clean_catalogue(\\n source,\\n dest=f\\\"{pd.Timestamp.now().strftime('%y%m%d%H%M%S')}.csv\\\",\\n window=None\\n ):\\n catalogue = pd.read_csv(source, parse_dates=[3,4,5])\\n catalogue.drop_duplicates(ignore_index=True, inplace=True)\\n catalogue.sort_values(by=[\\\"dt_on\\\"], ignore_index=True, inplace=True)\\n if window is not None: # 430367\\n count = 0\\n rm_idx = []\\n for station in catalogue.station.unique():\\n subset = catalogue.loc[catalogue[\\\"station\\\"] == station]\\n remove = []\\n dt_on_ = subset[\\\"dt_on\\\"].iloc[0]\\n for i, dt_on in enumerate(subset[\\\"dt_on\\\"]):\\n if i == 0:\\n continue\\n elif dt_on < dt_on_ + pd.Timedelta(window, unit=\\\"sec\\\"):\\n remove.append(i)\\n else:\\n dt_on_ = dt_on\\n rm_idx.append(subset.index[remove])\\n count += len(remove)\\n print(f\\\"Removing {count} entries...\\\")\\n rm_idx = [item for sub_idx in rm_idx for item in sub_idx]\\n catalogue.drop(catalogue.index[rm_idx], inplace=True)\\n catalogue.reset_index(drop=True, inplace=True)\\n catalogue.to_csv(dest)\\n print(f\\\"Catalogue saved to {dest}.\\\")\",\n \"def deletePlayers():\\n conn, c = connect()\\n\\n q = \\\"DELETE FROM PLAYERS;\\\"\\n c.execute(q)\\n c.close()\\n conn.commit()\\n conn.close()\",\n \"def deletePlayers():\\n conn = connect()\\n cursor = conn.cursor()\\n cursor.execute(\\\"DELETE FROM players\\\")\\n conn.commit()\\n conn.close()\",\n \"def clean(self):\\n self.df = _data.prune(self.df, [REGEX_PATTERN_GCI, REGEX_PATTERN_DB_ID])\\n self.df, _ = _data.remove_totally_failed_tests(self.df)\\n self.is_cleaned = True\",\n \"def __get_season_data(season_num: int, max_episode: int, training_data: bool) -> List[TrainSample]:\\n season = EXAM_DATA[season_num]\\n drop_players = season.get_drop_mapping(DropType.EXECUTION_DROP, max_episode)\\n all_answers = season.get_all_answers(set(drop_players.keys()), max_episode)\\n season_data = []\\n for answer in all_answers:\\n exam_episode = answer.episode\\n drop_episodes = drop_players[answer.player]\\n drop_episodes = [episode for episode in drop_episodes if exam_episode <= episode]\\n if drop_episodes:\\n if training_data:\\n for player in exam_episode.players:\\n season_data.append(TrainSample(answer.player, season_num, min(drop_episodes), answer.episode,\\n answer.question, answer.answer, player))\\n else:\\n for answer_on in [False, True]:\\n season_data.append(TrainSample(answer.player, season_num, min(drop_episodes), answer.episode,\\n answer.question, answer.answer, answer_on))\\n return season_data\",\n \"def strip_ds(ds):\\n if 'brain' in np.unique(ds.sa.all_ROIs):\\n ds = ds[(ds.sa.all_ROIs != 'brain'), :]\\n print('excluded the rest of the brain from the dataset')\\n if 'overlap' in np.unique(ds.sa.all_ROIs):\\n ds = ds[(ds.sa.all_ROIs != 'overlap'), :]\\n print('excluded overlap from the dataset')\\n return ds\",\n \"def clean():\\n filter_phase_data()\\n combine_phase_data()\\n remove_duplicates_phase_data()\",\n \"def cleanup():\\n cat = CSVCatalog.CSVCatalog()\\n cat.drop_table(\\\"people\\\", force_drop=True)\\n cat.drop_table(\\\"batting\\\", force_drop=True)\\n cat.drop_table(\\\"teams\\\", force_drop=True)\",\n \"def get_wr_game():\\n wr_games_reg_2015 = NFL_WR_Game_2015.query.filter_by(is_season_totals=False).all()\\n clean_games = cleanup_queries(wr_games_reg_2015)\\n return json.dumps(clean_games)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.762441","0.6251675","0.61136353","0.5862718","0.56220615","0.54519236","0.5399716","0.537986","0.53599685","0.5343498","0.5125028","0.51231134","0.50927806","0.5061084","0.50600594","0.5034044","0.502096","0.49832642","0.4961268","0.4951709","0.49427244","0.49339727","0.4915383","0.489233","0.4882568","0.4868787","0.48657307","0.4857415","0.483404","0.4828217","0.4820796","0.48045295","0.48013866","0.48013514","0.480055","0.4799403","0.4793115","0.47627348","0.47578377","0.47576684","0.47533968","0.47429374","0.4741924","0.47358924","0.46846515","0.46680084","0.46678892","0.46527982","0.46448418","0.46421722","0.46417505","0.46401814","0.46379983","0.46201286","0.46196547","0.4618656","0.46045995","0.46031356","0.45975947","0.45896092","0.45844254","0.45676538","0.4563102","0.45608416","0.45586184","0.45503184","0.4536086","0.4534392","0.45325828","0.4524065","0.45220768","0.45209906","0.45118842","0.45054695","0.44997475","0.4498222","0.44929343","0.44925588","0.44916922","0.44900808","0.44880843","0.4476223","0.44663087","0.4463931","0.44591334","0.44561017","0.44529542","0.44505125","0.4448643","0.44465408","0.444476","0.4444209","0.4444016","0.44346896","0.44292387","0.44207975","0.4405866","0.43994373","0.43986103","0.43954962"],"string":"[\n \"0.762441\",\n \"0.6251675\",\n \"0.61136353\",\n \"0.5862718\",\n \"0.56220615\",\n \"0.54519236\",\n \"0.5399716\",\n \"0.537986\",\n \"0.53599685\",\n \"0.5343498\",\n \"0.5125028\",\n \"0.51231134\",\n \"0.50927806\",\n \"0.5061084\",\n \"0.50600594\",\n \"0.5034044\",\n \"0.502096\",\n \"0.49832642\",\n \"0.4961268\",\n \"0.4951709\",\n \"0.49427244\",\n \"0.49339727\",\n \"0.4915383\",\n \"0.489233\",\n \"0.4882568\",\n \"0.4868787\",\n \"0.48657307\",\n \"0.4857415\",\n \"0.483404\",\n \"0.4828217\",\n \"0.4820796\",\n \"0.48045295\",\n \"0.48013866\",\n \"0.48013514\",\n \"0.480055\",\n \"0.4799403\",\n \"0.4793115\",\n \"0.47627348\",\n \"0.47578377\",\n \"0.47576684\",\n \"0.47533968\",\n \"0.47429374\",\n \"0.4741924\",\n \"0.47358924\",\n \"0.46846515\",\n \"0.46680084\",\n \"0.46678892\",\n \"0.46527982\",\n \"0.46448418\",\n \"0.46421722\",\n \"0.46417505\",\n \"0.46401814\",\n \"0.46379983\",\n \"0.46201286\",\n \"0.46196547\",\n \"0.4618656\",\n \"0.46045995\",\n \"0.46031356\",\n \"0.45975947\",\n \"0.45896092\",\n \"0.45844254\",\n \"0.45676538\",\n \"0.4563102\",\n \"0.45608416\",\n \"0.45586184\",\n \"0.45503184\",\n \"0.4536086\",\n \"0.4534392\",\n \"0.45325828\",\n \"0.4524065\",\n \"0.45220768\",\n \"0.45209906\",\n \"0.45118842\",\n \"0.45054695\",\n \"0.44997475\",\n \"0.4498222\",\n \"0.44929343\",\n \"0.44925588\",\n \"0.44916922\",\n \"0.44900808\",\n \"0.44880843\",\n \"0.4476223\",\n \"0.44663087\",\n \"0.4463931\",\n \"0.44591334\",\n \"0.44561017\",\n \"0.44529542\",\n \"0.44505125\",\n \"0.4448643\",\n \"0.44465408\",\n \"0.444476\",\n \"0.4444209\",\n \"0.4444016\",\n \"0.44346896\",\n \"0.44292387\",\n \"0.44207975\",\n \"0.4405866\",\n \"0.43994373\",\n \"0.43986103\",\n \"0.43954962\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7035129"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":85589,"cells":{"query":{"kind":"string","value":"Enumerates all combos of season and season types"},"document":{"kind":"string","value":"def _get_seasons_grid(start_season, start_season_type):\n if start_season not in list(range(config.START_SEASON, config.CURRENT_SEASON + 1)):\n raise ValueError(f\"Start season of {start_season} not valid.\")\n\n if start_season_type not in config.SEASON_TYPES:\n raise ValueError(f\"Start season type {start_season_type} not valid.\")\n\n initial_season_types = [\n s for s in config.SEASON_TYPES\n if config.SEASON_TYPES_ORDER[s] >= config.SEASON_TYPES_ORDER[start_season_type]\n ]\n grid = [(start_season, season_type) for season_type in initial_season_types]\n\n if start_season < config.CURRENT_SEASON:\n for season in range(start_season + 1, config.CURRENT_SEASON + 1):\n grid += [(season, season_type) for season_type in config.SEASON_TYPES]\n\n return grid"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def season_choices():\n return [(s, s) for s in range(0, 3)]","def distributeSeason(self):\n i = 1\n for day in self.daylist:\n if i >= monthbeg[5] and i < monthbeg[9]: #june through SEpt as per SCE\n day.season = 'summer' #https://www.sce.com/residential/rates/Time-Of-Use-Residential-Rate-Plans\n i = i + 1\n else:\n day.season = 'winter'\n i = i+1","def get_dryspells_perseason(dryspells, seasons=((12, 1, 2), (3, 4, 5),\n (6, 7, 8), (9, 10, 11))):\n dryspells_seasons = []\n for season in seasons:\n eveSeas = []\n for eveLand in dryspells:\n eves = [e for e in eveLand if e.start_date().month in season]\n eveSeas.append(eves)\n dryspells_seasons.append(eveSeas)\n\n return dryspells_seasons","def Seasons(year):\n mar_equinox = _FindSeasonChange(0, year, 3, 19)\n jun_solstice = _FindSeasonChange(90, year, 6, 19)\n sep_equinox = _FindSeasonChange(180, year, 9, 21)\n dec_solstice = _FindSeasonChange(270, year, 12, 20)\n return SeasonInfo(mar_equinox, jun_solstice, sep_equinox, dec_solstice)","def test_season_iter(self):\n self.assertEquals(\n len(\n [episode for episode in self.t['Life on Mars'][1]]\n ),\n 8\n )","def get_schedules_for_season(self, season, season_type=\"REG\"):\n try:\n season = int(season)\n if season_type not in [\"REG\", \"PRE\", \"POST\"]:\n raise ValueError\n except (ValueError, TypeError):\n raise FantasyDataError('Error: Invalid method parameters')\n\n season_param = \"{0}{1}\".format(season, season_type)\n result = self._method_call(\"Schedules/{season}\", season=season_param)\n return result","def br_enum_to_string(season):\n new_season = []\n for game in season:\n game_dict = dict()\n keys = game.keys()\n for key in keys:\n if type(game[key]) not in [str, int, float, datetime]:\n game_dict[key] = game[key].value # Extract value from enum here\n else:\n game_dict[key] = game[key]\n new_season.append(game_dict)\n return new_season","def get_competition_season_type(season):\n default_type = games.models.CompetitionSeason.winter\n try:\n season_name = season.name\n if season_name.find(\"/\") == -1:\n return games.models.CompetitionSeason.summer\n return default_type\n except Exception as e:\n return default_type","def seasons(years, timeout=None, concurrency=_DEFAULT_CONCURRENCY):\n\ttables, failures = [], []\n\tfor year in years:\n\t\tLOG.info('=' * 10 + ' %d ' + '=' * 10, year)\n\t\ttable, failure = season(year, timeout=timeout, concurrency=concurrency)\n\t\ttables.append(table)\n\t\tfailures.extend(failure)\n\treturn pd.concat(tables), failures","def season_indices(t, edges=None, hard=False):\n if edges is None:\n edges = season_edges(t)\n season_ixs = np.digitize(t.jyear, edges.jyear)\n N = season_ixs.size\n seasons = []\n for ix in np.unique(season_ixs):\n season_members = np.arange(N)[season_ixs == ix] # bool array to index array\n seasons.append(season_members)\n if hard:\n if t[seasons[0]].jyear.max() < edges[0].jyear:\n seasons = seasons[1:]\n if t[seasons[-1]].jyear.min() > edges[-1].jyear:\n seasons = seasons[:-1]\n return seasons","def get_seasons(seasons):\n if '-to-' in seasons[0]:\n from_, to = map(int, seasons[0].split('-to-'))\n years = list(range(from_, to+1))\n seasons = []\n for i in range(0, len(years)-1):\n season = \"-\".join(map(str, [years[i], years[i+1]]))\n seasons.append(season)\n return seasons","def all_seasons(cls, verbose=False):\r\n\r\n\t\twith open('Data/index.txt', 'r', encoding='utf-8') as f:\r\n\t\t\tseason_list = f.read().splitlines()\r\n\t\t\r\n\t\tfolder_list = os.listdir('Data')\r\n\r\n\t\t# Seasons that are both in the season index and have a folder\r\n\t\tvalid_seasons = [\r\n\t\t\tseason_name\r\n\t\t\tfor season_name in season_list\r\n\t\t\tif cls.season_folder(season_name) in folder_list\r\n\t\t]\r\n\r\n\t\tif verbose:\r\n\t\t\tfor ind, season_name in enumerate(valid_seasons):\r\n\t\t\t\tvalid_seasons[ind] = cls.season_info(season_name)\r\n\r\n\t\treturn valid_seasons","def get_seasons(seasons):\n if '-to-' in seasons:\n from_, to = map(int, seasons.split('-to-'))\n years = list(range(from_, to+1))\n seasons = []\n for i in range(0, len(years)-1):\n season = \"-\".join(map(str, [years[i], years[i+1]]))\n seasons.append(season)\n return seasons","def test_seasons(self):\n response = Tmdb.season(tmdb_show_id = 69740, season_number = 1)\n self.assertTrue(int(response.status_code) == 200)\n data = response.json()\n self.assertTrue(isinstance(data['episodes'], list))\n # TODO check if all the shows are in the good format (can be from_dict/to_dict)","def seasons(self):\n if self._season_cache_ver == self._db._version:\n return self._season_cache\n\n # Find out how many seasons in this series by fetching the highest season.\n seasons = self._db.query(type='episode', parent=self._dbrow, attrs=['season'], distinct=True)\n self._season_cache = [Season(self._db, self, row['season']) for row in seasons]\n self._season_cache_ver = self._db._version\n return self._season_cache","def episode_choices():\n return [(e, e) for e in range(0, 2)]","def set_season_time(season): \n if season == '2021-22':\n startdate = time.strptime('13-08-2021', '%d-%m-%Y')\n startdate = datetime.fromtimestamp(mktime(startdate))\n enddate = time.strptime('08-10-2021', '%d-%m-%Y')\n enddate = datetime.fromtimestamp(mktime(enddate))\n if season == '2020-21':\n startdate = time.strptime('12-08-2020', '%d-%m-%Y')\n startdate = datetime.fromtimestamp(mktime(startdate))\n enddate = time.strptime('26-07-2021', '%d-%m-%Y')\n enddate = datetime.fromtimestamp(mktime(enddate))\n if season == '2019-20':\n startdate = time.strptime('09-08-2019', '%d-%m-%Y')\n startdate = datetime.fromtimestamp(mktime(startdate))\n enddate = time.strptime('26-07-2020', '%d-%m-%Y')\n enddate = datetime.fromtimestamp(mktime(enddate))\n return startdate, enddate","def get_season_list_BDEW(weather_data):\n season_list = []\n\n for j, date_obj in enumerate(weather_data.index):\n YEAR = date_obj.year\n\n winter_end = dt.datetime(YEAR, 3, 21, 00, 00, 00)\n winter_start = dt.datetime(YEAR, 10, 31, 00, 00, 00)\n summer_start = dt.datetime(YEAR, 5, 15, 00, 00, 00)\n summer_end = dt.datetime(YEAR, 9, 15, 00, 00, 00)\n\n if date_obj <= winter_end or date_obj > winter_start:\n season_list.append('Winter') # Winter\n\n elif date_obj > summer_start and date_obj <= summer_end:\n season_list.append('Sommer') # Summer\n\n else:\n season_list.append('Übergangszeit') # Transition\n\n return season_list","def seasonal_series(t, y, edges=None, hard=False):\n t = ensure_tarray(t)\n if len(t) == 1:\n return [t], [y]\n season_ixs = season_indices(t, edges=edges, hard=hard)\n ts = []\n ys = []\n for season in season_ixs:\n ts.append(astropy.time.Time(t.jyear[season], format='jyear', scale=t.scale))\n ys.append(y[season])\n return ts, ys","def get_seasons(games: List[BaseGame], reverse=False):\n seasons = list(set([g.Season for g in games]))\n seasons.sort(reverse=reverse)\n return seasons","def compute_levels_seasons(self, Y: t.Tensor, idxs: t.Tensor):\n # Lookup parameters per serie\n #seasonality = self.seasonality\n embeds = self.embeds(idxs)\n lev_sms = t.sigmoid(embeds[:, 0])\n\n # Initialize seasonalities\n seas_prod = t.ones(len(Y[:,0]), device=Y.device)\n seasonalities1 = []\n seasonalities2 = []\n seas_sms1 = t.ones(1, device=Y.device)\n seas_sms2 = t.ones(1, device=Y.device)\n\n if len(self.seasonality)>0:\n seas_sms1 = t.sigmoid(embeds[:, 1])\n init_seas1 = t.exp(embeds[:, 2:(2+self.seasonality[0])]).unbind(1)\n assert len(init_seas1) == self.seasonality[0]\n\n for i in range(len(init_seas1)):\n seasonalities1 += [init_seas1[i]]\n seasonalities1 += [init_seas1[0]]\n seas_prod = seas_prod * init_seas1[0]\n\n if len(self.seasonality)==2:\n seas_sms2 = t.sigmoid(embeds[:, 2+self.seasonality[0]])\n init_seas2 = t.exp(embeds[:, 3+self.seasonality[0]:]).unbind(1)\n assert len(init_seas2) == self.seasonality[1]\n\n for i in range(len(init_seas2)):\n seasonalities2 += [init_seas2[i]]\n seasonalities2 += [init_seas2[0]]\n seas_prod = seas_prod * init_seas2[0]\n\n # Initialize levels\n levels = []\n levels += [Y[:,0]/seas_prod]\n\n # Recursive seasonalities and levels\n ys = Y.unbind(1)\n n_time = len(ys)\n for t_idx in range(1, n_time):\n seas_prod_t = t.ones(len(Y[:,t_idx]), device=Y.device)\n if len(self.seasonality)>0:\n seas_prod_t = seas_prod_t * seasonalities1[t_idx]\n if len(self.seasonality)==2:\n seas_prod_t = seas_prod_t * seasonalities2[t_idx]\n\n newlev = lev_sms * (ys[t_idx] / seas_prod_t) + (1-lev_sms) * levels[t_idx-1]\n levels += [newlev]\n\n if len(self.seasonality)==1:\n newseason1 = seas_sms1 * (ys[t_idx] / newlev) + (1-seas_sms1) * seasonalities1[t_idx]\n seasonalities1 += [newseason1]\n\n if len(self.seasonality)==2:\n newseason1 = seas_sms1 * (ys[t_idx] / (newlev * seasonalities2[t_idx])) + \\\n (1-seas_sms1) * seasonalities1[t_idx]\n seasonalities1 += [newseason1]\n newseason2 = seas_sms2 * (ys[t_idx] / (newlev * seasonalities1[t_idx])) + \\\n (1-seas_sms2) * seasonalities2[t_idx]\n seasonalities2 += [newseason2]\n\n levels = t.stack(levels).transpose(1,0)\n\n seasonalities = []\n\n if len(self.seasonality)>0:\n seasonalities += [t.stack(seasonalities1).transpose(1,0)]\n\n if len(self.seasonality)==2:\n seasonalities += [t.stack(seasonalities2).transpose(1,0)]\n\n return levels, seasonalities","def season_type(self, season_type):\n\n self._season_type = season_type","def getSeason(date):\n\n date = validate.timestamp(date)\n day = date.dayofyear\n leap_year = int(date.is_leap_year)\n\n spring = numpy.arange(80, 172) + leap_year\n summer = numpy.arange(172, 264) + leap_year\n autumn = numpy.arange(264, 355) + leap_year\n\n if day in spring:\n season = \"spring\"\n elif day in summer:\n season = \"summer\"\n elif day in autumn:\n season = \"autumn\"\n else:\n season = \"winter\"\n\n return season","def test_valid_for(self):\n july = datetime.date(1983, 7, 3)\n result = {(s.name, s.valid_for(july))\n for s in seasons.southern_meteo.seasons}\n expected = {('spring', False),\n ('summer', False),\n ('autumn', False),\n ('winter', True)\n }\n assert result == expected","def getseason(data):\n ## Season key is the most reliable\n season = data.get(\"season\")\n if season:\n ## Season key is an integer formatted \"YYS\" and is 2000-based (i.e.- 171 == 2017-Winter)\n season = str(season)\n year = int(f\"20{season[:2]}\")\n ## Anichart Season key is 1-indexed\n season = int(season[2]) - 1\n ## This should normally pass; if it consistently does not, we'll have to investigate why\n try: return SeasonCharts.buildseason(season,year)\n ## If something goes wrong, we'll try another method\n except: print(f\"Failed to parse season: {data['season']}\")\n ## Next, we'll iterate over rankings to try to determine the season/year\n ## There are multiple types of rankings based on season, year, and both combined,\n ## so we'll piece it together based on whatever we come across first\n season,year = None,None\n for ranking in data.get(\"rankings\",list()):\n ## Quicker exit (without just making this loop its own function)\n if season and year: continue\n ## We'll ignore stuff we've already gotten and assume that nothing in\n ## rankings contradicts eachother\n if not season:\n ## Defaults to None one way or another if it's not supplied\n season = ranking.get(\"season\")\n if not year: year = ranking.get(\"year\")\n ## Check if we made it\n if season and year:\n ## As above, this should always work out-of-the-box\n try: return SeasonCharts.buildseason(season,year)\n except: print(season,year)\n ## Welp, we're stumped...\n return None","def set_season(date_obj):\n date_year = date_obj.year\n\n for key, val in SEASONS.items():\n start = datetime(year=date_year, month=val['start']['month'], day=val['start']['day'])\n end = datetime(year=date_year, month=val['end']['month'], day=val['end']['day'])\n if key == 'Winter':\n start_year = date_year - 1 if date_obj.month in [1, 2, 3] else date_year\n end_year = date_year + 1 if date_obj.month == 12 else date_year\n start = datetime(year=start_year, month=val['start']['month'], day=val['start']['day'])\n end = datetime(year=end_year, month=val['end']['month'], day=val['end']['day'])\n\n if start <= date_obj <= end:\n return key","def sarima_configs(seasonal=[1, 24, 168]):\n\n models = list()\n # define config lists\n p_params = [0, 1, 2]\n d_params = [0, 1]\n q_params = [0, 1, 2]\n t_params = ['n', 'c', 't', 'ct']\n P_params = [0, 1, 2]\n D_params = [0, 1]\n Q_params = [0, 1, 2]\n m_params = seasonal\n # create config instances\n for p in p_params:\n for d in d_params:\n for q in q_params:\n for t in t_params:\n for P in P_params:\n for D in D_params:\n for Q in Q_params:\n for m in m_params:\n cfg = [(p, d, q), (P, D, Q, m), t]\n models.append(cfg)\n return models","def list_chassis_per_season(start_year=1950, end_year=2020, file_changed=False):\n\n def _get_chassis_names(years):\n req = requests.get(WIKIPEDIA_F1_URL)\n soup = BeautifulSoup(req.content, 'html.parser')\n links = soup.find_all('a')\n\n for link in links:\n link_text = ['Formula', 'One', 'season', 'cars']\n if str(link.get('href')).split('_')[-4:] == link_text:\n link_season = str(WIKIPEDIA + link.get('href')).replace(' ', '')\n season = str(link.get('href')).replace(':', '_').split('_')[-5:-4][0]\n\n if int(season) in years:\n\n req = requests.get(link_season)\n soup = BeautifulSoup(req.content, 'html.parser')\n team_divs = soup.findAll('div', attrs={'class': 'mw-category-group'})\n teams_season_list = []\n\n for team_div in team_divs:\n team_links = team_div.findAll('a')\n for team_link in team_links:\n teams_season_list.append(team_link.get('title'))\n\n\n # Checking missing teams\n missing_teams = MISSING_CHASSIS.get(season)\n for missing_team in missing_teams:\n if missing_team not in teams_season_list:\n teams_season_list.append(missing_team)\n\n seasons_chassis[season] = teams_season_list\n\n save_json(seasons_chassis)\n\n return seasons_chassis\n\n seasons_chassis = load_json(F1_CHASSIS)\n\n # Check if we got the chassis names for all seasons listed\n update_list = []\n\n for year in range(start_year, end_year + 1):\n if str(year) not in seasons_chassis.keys():\n update_list.append(year)\n\n chassis = _get_chassis_names(update_list) if update_list else seasons_chassis\n\n return chassis","def season_limits(self):\n if self.season == \"summer\":\n return\n neighbors = [(0, 1), (1, 0), (-1, 0), (0, -1)]\n points_list = []\n season_pixels = []\n season_edges = []\n width = self.grid.width\n height = self.grid.height\n x = 0\n while x < width:\n y = 0\n while y < height:\n points_list.append((x, y))\n if self.season == \"spring\" or self.season == \"winter\":\n if self.grid.pix_copy[x, y] == (0, 0, 255):\n season_pixels.append((x, y))\n else:\n if self.grid.pix_copy[x, y] == (255, 255, 255):\n season_pixels.append((x, y))\n y += 1\n x += 1\n for points in points_list:\n k = 0\n while k < (len(neighbors)):\n xp = points[0] + neighbors[k][0]\n yp = points[1] + neighbors[k][1]\n condition = self.filter_edges_one(points[0], points[1], xp, yp, season_edges)\n if condition:\n season_edges.append((xp, yp))\n k += 1\n return season_edges","def timeave_seasonal( mv, seasons=seasonsyr ):\n return seasons.climatology(mv)","def season_info(cls, season):\r\n\r\n\t\traw_info = cls.season_rounds(season)\r\n\r\n\t\tinfo = [\r\n\t\t\tseason,\t\t\t\t\t\t\t\t\t\t# 0 -> Season name\r\n\t\t\tlen(raw_info),\t\t\t\t\t\t\t\t# 1 -> Amount of rounds\r\n\t\t\tmin([round[1] for round in raw_info]),\t\t# 2 -> Starting date\r\n\t\t\tmax([round[1] for round in raw_info]),\t\t# 3 -> Latest round date\r\n\t\t\tnp.mean([round[3] for round in raw_info])\t# 4 -> Average round strength\r\n\t\t]\r\n\r\n\t\treturn info","def get_season_dates(date, season):\n start_date_start = date\n start_date_end = date\n if season == \"Spring\":\n start_date_start = date.replace(month=4)\n start_date_end = date.replace(month=6, day=30)\n elif season == \"Summer\":\n start_date_start = date.replace(month=7)\n start_date_end = date.replace(month=9, day=30)\n elif season == \"Fall\":\n start_date_start = date.replace(month=10)\n start_date_end = date.replace(month=12, day=31)\n elif season == \"Winter\":\n start_date_start = date.replace(month=1)\n start_date_end = date.replace(month=3, day=31)\n return start_date_start, start_date_end","def season_edges(t):\n offset = season_offset(t)\n yr_min = t.datetime.min().year\n left_frac = t.jyear.min() % yr_min\n if left_frac < offset:\n ex_left = 1\n else:\n ex_left = 0\n edges = np.arange(yr_min - ex_left + offset, t.jyear.max() + 1, 1.0)\n return astropy.time.Time(edges, format='jyear')","def test_show_iter(self):\n self.assertEquals(\n len(\n [season for season in self.t['Life on Mars']]\n ),\n 2\n )","def __iter__(self):\n return iter(self._enums)","def get_seasons(link, debug=False):\n\n if debug:\n print(\"begin get_seasons()\")\n\n # get the BeautifulSoup data\n show_url = \"https://www.imdb.com/\" + link + \"episodes/\"\n tv_soup = bs4.BeautifulSoup(requests.get(show_url).text, features=\"html.parser\")\n\n # We are acquiring this data from a drop down, which the below line selects\n select_elem = tv_soup.select('#bySeason')\n seasons = []\n # account for the possibility of a one season show\n if len(select_elem) == 0:\n seasons.append(1)\n else:\n # get contents of drop down\n options = select_elem[0].select('option')\n\n # add each season\n for season in options:\n seasons.append(season.get('value'))\n if debug:\n print(f\"Seasons {seasons}\")\n\n return seasons","def get_possible_timeframes(level: str, section_type: str,\n student_obj, section_table) -> Set[int]:\n set_choices = dict()\n for st in SECTION_NAME_MAPPING.keys():\n section_id = student_obj.__getattribute__(f'{st}_info_id')\n if section_id: # section has been started\n set_choices[st] = int(section_table.row_dict[section_id].activity_timescale) // 30\n else:\n set_choices[st] = None\n\n if level == 'bronze':\n if 6 in set_choices.values(): # one section already on 6 months\n return {3} # only choice left is 3 months\n else:\n return {3, 6}\n\n elif level == 'silver':\n if section_type == 'vol':\n return {6}\n elif section_type == 'skill':\n if set_choices['phys'] == 3:\n return {6}\n elif set_choices['phys'] == 6:\n return {3}\n else:\n return {3, 6}\n elif section_type == 'phys':\n if set_choices['skill'] == 3:\n return {6}\n elif set_choices['skill'] == 6:\n return {3}\n else:\n return {3, 6}\n\n elif level == 'gold':\n if section_type == 'vol':\n return {12}\n elif section_type == 'skill':\n if set_choices['phys'] == 6:\n return {12}\n elif set_choices['phys'] == 12:\n return {6}\n else:\n return {6, 12}\n elif section_type == 'phys':\n if set_choices['skill'] == 6:\n return {12}\n elif set_choices['skill'] == 12:\n return {6}\n else:\n return {6, 12}","def speed_setting_season(self):\n if self.season == \"spring\":\n self.grid.speed_values[self.path_color] = 4\n self.grid.speed_values[(0, 0, 255)] = 0.1\n elif self.season == \"winter\":\n self.grid.speed_values[self.path_color] = 3\n self.grid.speed_values[(0, 0, 255)] = 0.1\n elif self.season == \"fall\":\n self.grid.speed_values[self.path_color] = 6\n elif self.season == \"summer\":\n pass","def gather_archtypes(meta_url):\r\n soup = hot_soup(meta_url)\r\n archtypes = { strat: [] for strat in ['aggro', 'control', 'combo'] }\r\n for strat in soup.find_all(class_='Stable')[0].find_all(rowspan=True): # In this table, only the style type headers use 'rowspan'\r\n strat_str = strat.contents[0].lower() # Get corrosponding key for archtypes dict\r\n item = strat.parent\r\n\r\n # Gather each archtype under each strategy type\r\n while len(archtypes[strat_str]) < int(strat['rowspan']) - 1: # Rowspan == number of archtypes under this style\r\n item = item.next_sibling\r\n if isinstance(item, NavigableString):\r\n continue\r\n if item.a: # If this sibling has a link, we know it's what we're looking for\r\n text = item.a.text\r\n num_decks = int(item.contents[3].text)\r\n archtypes[strat_str].append((text, num_decks))\r\n \r\n return archtypes","def season(self, seasonnum, order='aired'):\n if order=='aired':\n seasons = self.seasons\n elif order == 'dvd':\n seasons = self.dvd_seasons\n try:\n return seasons[seasonnum]\n except KeyError:\n raise SeasonNotFoundError(\n 'Season no %s does not exists' % seasonnum\n ), None, sys.exc_info()[2]","def getshowsbyseason(season,year, session = None):\n url = APIURL.format(season=season, year = year)\n headers = {\"X-CSRF-TOKEN\":session.cookies['X-CSRF-TOKEN']}\n data = alrequests.GET_json(url,session = session, headers=headers)\n return {cat:[Show(**show) for show in shows] for cat,shows in data.items()}","def season_rounds(cls, season):\r\n\t\t\r\n\t\tfolder_name = cls.season_folder(season)\r\n\t\tround_list = os.listdir(f'Data/{folder_name}')\r\n\r\n\t\tall_rounds = []\r\n\r\n\t\tfor round_file in round_list:\r\n\t\t\twith open(f'Data/{folder_name}/{round_file}', 'r', encoding='utf-8') as f:\r\n\t\t\t\tround_info = f.read().splitlines()\r\n\r\n\t\t\tround_number = round_file[:-4]\r\n\t\t\tfull_round_name = f\"{season} R{round_number}\"\r\n\r\n\t\t\tround_date = int(round_info[0])\r\n\t\t\tlookup_ind = DATES.month_diff(round_date, DATES.MIN_DATE)\r\n\r\n\t\t\t# If the round isn't actually counted for TWOW Glicko\r\n\t\t\tif full_round_name not in cls.ROUNDS[lookup_ind].keys():\r\n\t\t\t\tcontinue\r\n\r\n\t\t\tcontestant_count = len(round_info) - 1\r\n\r\n\t\t\tstrength = cls.ROUNDS[lookup_ind][full_round_name][0]\r\n\r\n\t\t\tall_rounds.append([\r\n\t\t\t\tround_number,\r\n\t\t\t\tround_date,\r\n\t\t\t\tcontestant_count,\r\n\t\t\t\tstrength\r\n\t\t\t])\r\n\t\t\r\n\t\treturn all_rounds","def season_months(season):\n\n ssn=['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug',\n 'sep', 'oct', 'nov', 'dec', 'djf', 'mam', 'jja', 'son',\n 'mayjun', 'julaug', 'marapr', 'jjas', 'ond', 'ann']\n\n imon = [1, 2, 3, 4, 5, 6, 7, 8,\n 9, 10, 11, 12, [1,2,12], [3,4,5], [6,7,8], [9,10,11],\n [5,6], [7,8], [3,4], [6,7,8,9], [10,11,12], list(range(1,13))]\n\n try:\n ifind = ssn.index(season.lower())\n except ValueError:\n raise ValueError('Season not found! Valid seasons: ' + ', '.join(ssn))\n\n months = imon[ifind]\n\n # Make sure the output is a list\n if isinstance(months, int):\n months =[months]\n\n return months","def parse_episodes_by_season (self, response_data):\n episodes = {}\n raw_episodes = response_data['value']['videos']\n for episode_id in raw_episodes:\n if self._is_size_key(key=episode_id) == False:\n if (raw_episodes[episode_id]['summary']['type'] == 'episode'):\n episodes.update(self.parse_episode(episode=raw_episodes[episode_id], genres=response_data['value']['genres']))\n return episodes","def parse_season(self, response):\n # gather competition numbers and titles from the page\n competition_numbers = response.css('select[name=\"evt\"] optgroup option::attr(value)').getall()\n competition_titles = response.css('select[name=\"evt\"] optgroup option::text').getall()\n url_components = urlparse(response.url)\n\n for competition_number, competition_title in zip(competition_numbers, competition_titles):\n # assemble direct URL for this competition\n competition_url = f'{url_components.scheme}://{url_components.netloc}/Results.aspx?evt={competition_number}'\n\n # pass along metadata for use in next steps\n response.meta.update(dict(competition_title=competition_title,\n competition_id=competition_number))\n\n # scrape the competition page\n yield scrapy.Request(url=competition_url,\n callback=self.parse_competition,\n meta=response.meta)","def chn_season_type_recognize(str):\n season_type = None\n if '第一季度' in str:\n season_type = 'Q1'\n elif '第二季度' in str:\n season_type = 'Q2'\n elif '半年度' in str:\n season_type = 'Q2'\n elif '第三季度' in str:\n season_type = 'Q3'\n elif '年度报告' in str:\n season_type = 'FY'\n\n return season_type","def seasonality(df):\n df_datetime = pd.DatetimeIndex(df.date_time)\n df[\"month\"] = df_datetime.month\n df = drop_cols(df, [\"date_time\"])\n\n return df","def season(year, week=None, timeout=None, concurrency=_DEFAULT_CONCURRENCY):\n\tLOG.debug('Concurrency = %d', concurrency)\n\tgames = season_games(year)\n\tif week is not None:\n\t\tgames = games[games.week == week]\n\texpected_n = len(games)\n\ttables, futures_to_args, failures = [], {}, []\n\t# See https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor-example.\n\twith futures.ThreadPoolExecutor(concurrency) as pool:\n\t\tfor arg in zip(games.hometeam, games.awayteam, games.week):\n\t\t\targ = arg + (year,)\n\t\t\tfutures_to_args[pool.submit(game_unknown_homeaway, *arg)] = arg\n\t\tfor future in futures.as_completed(futures_to_args, timeout=timeout):\n\t\t\targs = futures_to_args[future]\n\t\t\ttry:\n\t\t\t\ttable = future.result()\n\t\t\texcept Exception as exc:\n\t\t\t\tLOG.exception('Error from %s: %s', args, exc)\n\t\t\telse:\n\t\t\t\tif table is None:\n\t\t\t\t\tLOG.error('Failure: %s', args)\n\t\t\t\t\tfailures.append(args)\n\t\t\t\telse:\n\t\t\t\t\tLOG.info('Success: %s', args)\n\t\t\t\t\ttables.append(table)\n\ttables = games.merge(pd.concat(tables), on=('hometeam', 'awayteam', 'week'))\n\tif __debug__:\n\t\tn = len(tables.groupby(['hometeam', 'awayteam', 'week']))\n\t\tassert n == expected_n, \"Expected %d games, got %d\" % (expected_n, n)\n\treturn tables, failures","def by_season(self, season):\n return self.get_queryset().filter(season=season)","def seasonStats(personId,type = 'gameLog',group = 'hitting'):\n\n #playerInfo = get('people', {'personIds':personId})\n\n\n teamStats = get('person',{ 'ver':'v1' , 'personId':personId,'hydrate':['stats(group={},type={})'.format(group,type),'currentTeam']})\n return teamStats\n #iterate of stats and find the right player id\n #career stats broken\n #fix the season :2019\n #make function to get team id","def standings_by_season(season):\n season = int(season) + 1\n scoreboard = nba_py.Scoreboard(month=7,\n day=1,\n year=season)\n east_standings = scoreboard.east_conf_standings_by_day()\n west_standings = scoreboard.west_conf_standings_by_day()\n\n return render_template(\"standings.html\",\n title=\"standings\",\n east_standings=enumerate(east_standings, 1),\n west_standings=enumerate(west_standings, 1),\n team=CITY_TO_TEAM)","def season_series(game_id, pref_team, other_team, last_season=False):\n\n # Init empty dictionaries and lists\n games_against = list()\n pref_toi = dict()\n pref_goals = dict()\n pref_assists = dict()\n pref_points = dict()\n pref_record = {\"wins\": 0, \"losses\": 0, \"ot\": 0}\n roster_player = True\n\n # If this is the first game of the season, we can set the 'last_season' flag to enable the\n # season series function to check last year's season series between the two teams.\n if not last_season:\n season_start = str(game_id)[0:4]\n season_end = str(int(season_start) + 1)\n yesterday = datetime.now() - timedelta(days=1)\n # yesterday = datetime.now() + timedelta(days=50)\n # schedule_url = (\n # f\"/schedule?teamId={pref_team.team_id}\"\n # f\"&expand=schedule.broadcasts,schedule.teams&startDate=\"\n # f\"{season_start}-08-01&endDate={yesterday:%Y-%m-%d}\"\n # )\n schedule_url = (\n f\"/schedule?teamId={pref_team.team_id}\"\n f\"&expand=schedule.broadcasts,schedule.teams\"\n f\"&season={season_start}{season_end}\"\n )\n else:\n season_start = int(str(game_id)[0:4]) - 1\n season_end = str(int(season_start) + 1)\n yesterday = datetime.now() - timedelta(days=1)\n # yesterday = datetime.now() + timedelta(days=50)\n # schedule_url = (\n # f\"/schedule?teamId={pref_team.team_id}\"\n # f\"&expand=schedule.broadcasts,schedule.teams&startDate=\"\n # f\"{season_start}-08-01&endDate={season_end}-06-01\"\n # )\n schedule_url = (\n f\"/schedule?teamId={pref_team.team_id}\"\n f\"&expand=schedule.broadcasts,schedule.teams\"\n f\"&season={season_start}{season_end}\"\n )\n\n schedule = api.nhl_api(schedule_url).json()\n dates = schedule[\"dates\"]\n\n # Loop through scheduled to get previously played games against\n for date in dates:\n game = date[\"games\"][0]\n game_type = game[\"gameType\"]\n game_id = game[\"gamePk\"]\n game_team_home = game[\"teams\"][\"home\"][\"team\"][\"name\"]\n game_team_away = game[\"teams\"][\"away\"][\"team\"][\"name\"]\n teams = [game_team_away, game_team_home]\n game_status = game[\"status\"][\"abstractGameState\"]\n if game_type == \"R\" and game_status == \"Final\" and other_team.team_name in teams:\n game_feed = f\"/game/{game_id}/feed/live\"\n games_against.append(game_feed)\n\n # If the two teams haven't played yet, just exit this function\n if not games_against:\n return None, None, None\n\n # Loop through newly created games_against list to get each stats\n for feed in games_against:\n game = api.nhl_api(feed).json()\n game_data = game[\"gameData\"]\n home_team_name = game_data[\"teams\"][\"home\"][\"name\"]\n pref_homeaway = \"home\" if home_team_name == pref_team.team_name else \"away\"\n other_homeaway = \"away\" if home_team_name == pref_team.team_name else \"home\"\n\n # Get season series\n end_period = game[\"liveData\"][\"linescore\"][\"currentPeriod\"]\n extra_time = True if end_period > 3 else False\n pref_score = game[\"liveData\"][\"linescore\"][\"teams\"][pref_homeaway][\"goals\"]\n other_score = game[\"liveData\"][\"linescore\"][\"teams\"][other_homeaway][\"goals\"]\n if pref_score > other_score:\n pref_record[\"wins\"] += 1\n elif other_score > pref_score and extra_time:\n pref_record[\"ot\"] += 1\n else:\n pref_record[\"losses\"] += 1\n\n season_series_str = f\"Series: {pref_record['wins']}-\" f\"{pref_record['losses']}-{pref_record['ot']}\"\n\n # Get stats leaders\n # pref_teamstats = game[\"liveData\"][\"boxscore\"][\"teams\"][pref_homeaway][\"teamStats\"]\n pref_playerstats = game[\"liveData\"][\"boxscore\"][\"teams\"][pref_homeaway][\"players\"]\n for id, player in pref_playerstats.items():\n try:\n # Calculate TOI\n player_toi_str = player[\"stats\"][\"skaterStats\"][\"timeOnIce\"]\n player_toi_minutes = int(player_toi_str.split(\":\")[0])\n player_toi_seconds = int(player_toi_str.split(\":\")[1])\n player_toi = (player_toi_minutes * 60) + player_toi_seconds\n pref_toi[id] = pref_toi.get(id, 0) + player_toi\n\n # Point Totals\n player_goal_str = player[\"stats\"][\"skaterStats\"][\"goals\"]\n pref_goals[id] = pref_goals.get(id, 0) + int(player_goal_str)\n player_assist_str = player[\"stats\"][\"skaterStats\"][\"assists\"]\n pref_assists[id] = pref_assists.get(id, 0) + int(player_assist_str)\n player_points = int(player_goal_str) + int(player_assist_str)\n pref_points[id] = pref_points.get(id, 0) + int(player_points)\n\n except KeyError:\n pass\n\n # Calculate Stats Leaders\n sorted_toi = sorted(pref_toi.values(), reverse=True)\n leader_toi = sorted_toi[0]\n\n sorted_points = sorted(pref_points.values(), reverse=True)\n leader_points = sorted_points[0]\n\n # Get TOI leader\n for id in pref_toi.keys():\n if pref_toi[id] == leader_toi:\n player_name = roster.player_attr_by_id(pref_team.roster, id, \"fullName\")\n if player_name is None:\n roster_player = False\n player_id_only = id.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n leader_toi_avg = leader_toi / len(games_against)\n m, s = divmod(leader_toi_avg, 60)\n toi_m = int(m)\n toi_s = int(s)\n toi_s = \"0{}\".format(toi_s) if toi_s < 10 else toi_s\n toi_avg = \"{}:{}\".format(toi_m, toi_s)\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n toi_leader_str = \"TOI Leader: {} with {} / game.\".format(player_short_name, toi_avg)\n\n # Handle tied points leaders\n point_leaders = list()\n for id in pref_points.keys():\n if pref_points[id] == leader_points:\n point_leaders.append(id)\n\n if leader_points == 0:\n points_leader_str = \"Points Leader: None (all players have 0 points).\"\n\n elif len(point_leaders) == 1:\n leader = point_leaders[0]\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n # If the player is no longer on the team, get their information (change string here?)\n if player_name is None:\n roster_player = False\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n if not roster_player:\n points_leader_str = (\n f\"Points Leader: {player_name} with {leader_points} points \"\n f\"({player_goals}G {player_assists}A) \"\n )\n else:\n points_leader_str = \"Points Leader: {} with {} ({}G {}A).\".format(\n player_name, leader_points, player_goals, player_assists\n )\n\n elif len(point_leaders) > 3:\n point_leaders_with_attrs = list()\n for leader in point_leaders:\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n if player_name is None:\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n point_leaders_with_attrs.append(player_short_name)\n\n point_leaders_joined = \", \".join(point_leaders_with_attrs[0:3])\n leftover_leaders = len(point_leaders) - 3\n points_leader_str = (\n f\"Points Leaders: {point_leaders_joined} & {leftover_leaders} others ({leader_points} each).\"\n )\n\n else:\n point_leaders_with_attrs = list()\n for leader in point_leaders:\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n if player_name is None:\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n player_str = f\"{player_short_name} ({player_goals}G {player_assists}A)\"\n point_leaders_with_attrs.append(player_str)\n\n point_leaders_joined = (\n f\", \".join(point_leaders_with_attrs[:-1]) + f\" & {point_leaders_with_attrs[-1]}\"\n )\n points_leader_str = \"Points Leaders: {} with {} each.\".format(point_leaders_joined, leader_points)\n\n return season_series_str, points_leader_str, toi_leader_str","def test_domain_and_target_type(self):\n t = Enumerate([2, \"asfa\", \"ipsi\"])\n assert t.domain_type == \"categorical\"\n assert t.target_type == \"integer\"","def season_days(season, leap=False):\n\n # Index of first day of each month\n ndays = days_per_month(leap=leap)\n ndays.insert(0,1)\n days = np.cumsum(ndays)\n\n # Index of months for this season\n imon = season_months(season)\n\n # Days of the year for this season\n if isinstance(imon, list):\n # Iterate over months in this season\n idays=[]\n for m in imon:\n idays.extend(list(range(days[m-1], days[m])))\n else:\n # Single month\n idays = list(range(days[imon-1], days[imon]))\n\n return idays","def get_season(\n current_date: date, hemisphere: str, season_tracking_type: str\n) -> str | None:\n\n if hemisphere == \"equator\":\n return None\n\n if season_tracking_type == TYPE_ASTRONOMICAL:\n spring_start = ephem.next_equinox(str(current_date.year)).datetime()\n summer_start = ephem.next_solstice(str(current_date.year)).datetime()\n autumn_start = ephem.next_equinox(spring_start).datetime()\n winter_start = ephem.next_solstice(summer_start).datetime()\n else:\n spring_start = datetime(2017, 3, 1).replace(year=current_date.year)\n summer_start = spring_start.replace(month=6)\n autumn_start = spring_start.replace(month=9)\n winter_start = spring_start.replace(month=12)\n\n if spring_start <= current_date < summer_start:\n season = STATE_SPRING\n elif summer_start <= current_date < autumn_start:\n season = STATE_SUMMER\n elif autumn_start <= current_date < winter_start:\n season = STATE_AUTUMN\n elif winter_start <= current_date or spring_start > current_date:\n season = STATE_WINTER\n\n # If user is located in the southern hemisphere swap the season\n if hemisphere == NORTHERN:\n return season\n return HEMISPHERE_SEASON_SWAP.get(season)","def get_all_games(season):\n url = BASE_URL.format(season)\n json_data = requests.get(url, headers=HEADERS).json()\n all_games = json_data[\"resultSets\"][0][\"rowSet\"]\n return all_games","def compute_levels_seasons(self, Y: t.Tensor, idxs: t.Tensor):\n y_transformed, _ = Y.median(1)\n y_transformed = y_transformed.reshape(-1, 1)\n levels = y_transformed.repeat(1, Y.shape[1])\n seasonalities = None\n\n return levels, None","def parse_seasons (self, id, response_data):\n raw_seasons = response_data['value']\n videos = raw_seasons['videos']\n\n # get art video key\n video = {}\n for key, video_candidate in videos.iteritems():\n if not self._is_size_key(key):\n video = video_candidate\n\n # get season index\n sorting = {}\n for idx, season_list_entry in video['seasonList'].iteritems():\n if self._is_size_key(key=idx) == False and idx != 'summary':\n sorting[int(season_list_entry[1])] = int(idx)\n\n seasons = {}\n\n for season in raw_seasons['seasons']:\n if self._is_size_key(key=season) == False:\n seasons.update(self._parse_season_entry(season=raw_seasons['seasons'][season], video=video, sorting=sorting))\n return seasons","def get_seasons_information():\n\n #getting the guidebox_id variable from show_page.html\n guidebox_id = request.args.get(\"guidebox_id\")\n\n #make API to get season information, gets back list of season information\n seasons_results = guidebox_season_info(guidebox_id)\n\n for season in seasons_results:\n date = season[\"first_airdate\"]\n year = str(date)[0:4]\n season[\"first_airdate\"] = year\n\n return jsonify(seasons_results)","def test_enumerating_stereo_options(self, toolkit_class):\n\n if toolkit_class.is_available():\n toolkit = toolkit_class()\n\n # test undefined only\n mol = Molecule.from_smiles(\n \"ClC=CCl\", toolkit_registry=toolkit, allow_undefined_stereo=True\n )\n isomers = mol.enumerate_stereoisomers(\n undefined_only=True, rationalise=False\n )\n\n assert len(isomers) == 2\n for isomer in isomers:\n assert isomer.n_conformers == 0\n\n mol = Molecule.from_smiles(\n r\"Cl/C=C\\Cl\", toolkit_registry=toolkit, allow_undefined_stereo=True\n )\n isomers = mol.enumerate_stereoisomers(\n undefined_only=True, rationalise=False\n )\n\n assert isomers == []\n\n mol = Molecule.from_smiles(\n r\"Cl/C=C\\Cl\", toolkit_registry=toolkit, allow_undefined_stereo=True\n )\n isomers = mol.enumerate_stereoisomers(\n undefined_only=False, rationalise=False\n )\n\n assert len(isomers) == 1\n\n # test max isomers\n mol = Molecule.from_smiles(\n \"BrC=C[C@H]1OC(C2)(F)C2(Cl)C1\",\n toolkit_registry=toolkit,\n allow_undefined_stereo=True,\n )\n isomers = mol.enumerate_stereoisomers(\n max_isomers=5,\n undefined_only=True,\n toolkit_registry=toolkit,\n rationalise=True,\n )\n\n assert len(isomers) <= 5\n for isomer in isomers:\n assert isomer.n_conformers == 1\n\n else:\n pytest.skip(\"Required toolkit is unavailable\")","def report_type_choices():\n\n rts = report_types()\n rcs = report_categories()\n return [(c, [(rt.report_type, rt.name) for rt in rts if rt.category == c]) for c in rcs]","def test_enumerating_stereo_options(self, toolkit_class):\n\n if toolkit_class.is_available():\n toolkit = toolkit_class()\n\n # test undefined only\n mol = Molecule.from_smiles(\n \"ClC=CCl\", toolkit_registry=toolkit, allow_undefined_stereo=True\n )\n isomers = mol.enumerate_stereoisomers(\n undefined_only=True, rationalise=False\n )\n\n assert len(isomers) == 2\n for isomer in isomers:\n assert isomer.n_conformers == 0\n\n mol = Molecule.from_smiles(\n \"Cl/C=C\\Cl\", toolkit_registry=toolkit, allow_undefined_stereo=True\n )\n isomers = mol.enumerate_stereoisomers(\n undefined_only=True, rationalise=False\n )\n\n assert isomers == []\n\n mol = Molecule.from_smiles(\n \"Cl/C=C\\Cl\", toolkit_registry=toolkit, allow_undefined_stereo=True\n )\n isomers = mol.enumerate_stereoisomers(\n undefined_only=False, rationalise=False\n )\n\n assert len(isomers) == 1\n\n # test max isomers\n mol = Molecule.from_smiles(\n \"BrC=C[C@H]1OC(C2)(F)C2(Cl)C1\",\n toolkit_registry=toolkit,\n allow_undefined_stereo=True,\n )\n isomers = mol.enumerate_stereoisomers(\n max_isomers=5,\n undefined_only=True,\n toolkit_registry=toolkit,\n rationalise=True,\n )\n\n assert len(isomers) <= 5\n for isomer in isomers:\n assert isomer.n_conformers == 1\n\n else:\n pytest.skip(\"Required toolkit is unavailable\")","def give_season_suggestion():\n current_month = datetime.datetime.now().month\n all_month = (\n None, 'январь', 'февраль', 'март', 'апрель', 'май', 'июнь',\n 'июль', 'август', 'сентябрь', 'октябрь', 'ноябрь', 'декабрь')\n\n # Autumn.\n if current_month in [9, 10, 11]:\n month_list = [\n 'осень', 'осенний', 'осеннее', 'осенние', 'осенняя', 'сентябрь',\n 'октябрь', 'ноябрь', 'сентября', 'октября', 'ноября',\n 'сентябрьский', 'октябрьский', 'ноябрьский', 'свитер',\n 'листопад']\n suggestion_list = give_item_by_tag(month_list)\n if suggestion_list == '':\n suggestion_list = \"добавить в ваши рецепты что-то осенее;)\"\n suggest = f\"\"\"\n_________________¶\n________________¶¶\n___________¶___¶¶¶___¶\n___________¶¶¶¶¶¶¶¶/¶¶\n____¶_______¶¶¶¶|¶¶/¶¶________¶\n_¶¶¶¶¶¶______¶¶¶|¶/¶¶_______¶¶¶¶¶\n__¶¶¶¶¶¶____¶¶¶¶|/¶¶¶____¶¶¶¶¶¶\n_¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶|¶¶¶/¶¶¶¶¶/¶¶¶¶¶¶\n____¶¶¶¶¶¶¶¶¶¶¶¶|¶¶/¶¶¶¶¶/¶¶¶¶\n¶____¶¶¶¶¶¶¶¶¶¶¶|¶/¶¶¶¶¶/¶¶¶_____¶\n¶¶¶_¶¶¶¶¶¶¶¶¶¶¶¶|/¶¶¶¶¶/¶¶¶¶__¶¶¶¶\n¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶|¶¶¶¶¶/¶¶¶¶¶¶¶¶¶¶¶\n__¶¶¶¶¶¶¶¶¶¶¶¶¶¶|¶¶¶¶/¶¶¶¶¶/¶¶¶¶\n___¶¶¶¶¶¶¶¶¶¶¶¶¶|¶¶¶/¶¶¶¶/¶¶¶¶¶\n_¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶|¶¶/¶¶¶/¶¶¶¶¶¶¶¶¶¶\n________¶¶¶¶¶¶¶¶|¶/¶/¶¶¶¶¶¶\n_______________¶¶\n______________¶¶ На дворе {all_month[current_month]}, осень.\n______________¶¶ самое время приготовить что-то уютное,\n______________¶¶ бодрящее из ваших рецептов.\n_______________¶¶ Рекомендуем:\n ________________¶¶¶ {suggestion_list}\n \"\"\"\n\n # Winter.\n elif current_month in [1, 2, 12]:\n month_list = [\n 'зима', 'зимний', 'зимнее', 'зимние', 'зимняя', 'январь',\n 'декабрь', 'февраль', 'декабря', 'января', 'февраля',\n 'декабрьский', 'январский', 'февральский', 'свитер', 'снег',\n 'снегопад', 'иней', 'зимушка', 'Зимушка', 'новый год',\n 'новым годом', 'рождество', 'рождественский']\n suggestion_list = give_item_by_tag(month_list)\n if suggestion_list == '':\n suggestion_list = \"добавить в ваши рецепты что-то зимнее;)\"\n suggest = f\"\"\"\n_____________________$$\n_____________________$$\n_____________$$$$____$$____$$$$\n_____ ____$$____$$$$$$$$$$$$$$____$$\n_________$$________$$$$$$________$$\n_$$______$$__________$$__________$$______$$\n_$$$$$$__$ $__________$$__________$$__$$$$$$\n_____$$$$$$__________$$__________$$$$$$\n_______$$$$$$________$$________$$$$$$\n___$$$$______$$ $$____$$____$$$$______$$$$\n_$$______________$$$$$$$$$$______________$$\n___________________$$$$$$\n_$$______________$$$ $$$$$$$______________$$\n___$$$$______$$$$____$$____$$$$______$$$$\n_______$$$$$$________$$________$$$$$$\n_____$$$$$$__________$$__ ________$$$$$$\n_$$$$$$__$$__________$$__________$$__$$$$$$\n_$$______$$__________$$__________$$______$$\n_________$$________$$$$$$_____ ___$$\n_________$$____$$$$$$$$$$$$$$____$$ На дворе {all_month[current_month]}, зима.\n_____________$$$$____$$____$$$$ самое время приготовить что-то приятное,\n_____________________$$ согревающее из ваших рецептов.\n___________\n Рекомендуем:\n {suggestion_list}\n \"\"\"\n\n # Spring.\n elif current_month in [3, 4, 5]:\n month_list = [\n 'весна', 'весенний', 'весеннее', 'весенние', 'весенняя', 'март',\n 'апрель', 'май', 'марта', 'апреля', 'мая', 'мартовский',\n 'апрельский', 'майский', 'свежий']\n suggestion_list = give_item_by_tag(month_list)\n if suggestion_list == '':\n suggestion_list = \"добавить в ваши рецепты что-то весеннее;)\"\n suggest = f\"\"\"\n______d$$$$b_d$$$$$b\n_____$$$$$$$$$$$$$$$$$\n____$$$$$$P_$$$$$$$$$\n_____V$P_______Y$$P\n___________________d$$b\n___$$$$_________$$$$$$\n___$$$ $$$______$$$$$$$\n____Y$$$$$$$$$$$$$$$$P\n______Y$P_$$$$$$$\n__________Y$$$P_$\n__________________$\n_________________$\n_________________ $\n__________________$$$$$_\n_________________$$$$$$$$ $\n__$$$$$$$_______$$$$$$\n_$$$$$$$$$__$$$$$ На дворе {all_month[current_month]}, весна.\n$_ _____$$$$$ Самое время бодрящих, легкий ароматов!\n________$$_$ Рекомендуем:\n _________$$$ {suggestion_list}\n__________$$\n___________$\n \"\"\"\n\n # Summer.\n elif current_month in [6, 7, 8]:\n month_list = [\n 'лето', 'летний', 'летнее', 'летние', 'летняя', 'июнь', 'июль',\n 'август', 'июля', 'июня', 'августа', 'июньский', 'июльский',\n 'августовский', 'тропический', 'тропики', 'экзотика',\n 'экзотический', 'отпуск']\n suggestion_list = give_item_by_tag(month_list)\n if suggestion_list == '':\n suggestion_list = \"добавить в ваши рецепты что-то летнее;)\"\n suggest = f\"\"\"\n________________ ¶¶¶¶¶¶¶¶\n_______________ ¶¶¶¶\n___¶¶¶¶¶¶¶¶¶__ ¶¶¶____¶¶¶\n_ ¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶\n¶¶¶¶¶ ______¶¶¶¶¶¶¶¶____¶¶¶¶¶\n_______ ¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶_____¶¶\n______ ¶¶¶¶__¶¶¶_¶¶¶¶¶¶¶¶¶\n____ ¶¶¶¶___¶¶¶___¶¶¶_¶¶¶¶¶\n___ ¶¶¶¶¶__¶¶¶¶_¶¶_¶¶¶_¶¶¶¶¶\n___ ¶¶¶___¶¶¶¶¶_¶¶__¶¶¶__¶¶¶\n__ ¶¶____¶¶¶¶¶___¶¶_¶¶¶¶_¶¶¶\n________ ¶¶¶_____¶¶___¶¶__¶\n_________ ¶¶¶____¶¶¶__¶¶__¶\n_________ ¶¶¶_____¶¶___¶\n_________ ¶¶______¶¶\n_________________ ¶¶¶\n_________________ ¶¶¶ На дворе {all_month[current_month]}, лето.\n_________________ ¶¶¶ Самое время экзотических коктейлей и\n________________ ¶¶¶¶ сладости фруктов!\n________________ ¶¶¶ Рекомендуем:\n ____________ ¶¶¶_¶¶¶ {suggestion_list}\n______________ ¶¶¶¶¶¶¶¶\n\n \"\"\"\n return suggest","def season_games(year):\n\tLOG.debug('Getting season %d', year)\n\tdata = read_html(io=season_games_url(year),\n\t\t\t\t\t attrs={'id': 'games'},\n\t\t\t\t\t infer_types=False,\n\t\t\t\t\t header=0)\n\tif len(data) != 1:\n\t\traise CantFindTheRightTable\n\tdata = data.pop()\n\n\t# Cleaning.\n\tdel data[\"Unnamed: 3\"]\n\t# The code below issues \"UserWarning: \" So we catch UserWarnings.\n\twith warnings.catch_warnings():\n\t\twarnings.filterwarnings(action='ignore', category=UserWarning,\n\t\t\t\t\t\t\t\tmodule=r'pandas\\.core\\.frame',\n\t\t\t\t\t\t\t\tmessage=(r\"Boolean Series key will be reindexed\"\n\t\t\t\t\t\t\t\t\t\t r\" to match DataFrame index\\.\"))\n\t\t# These rows are mid-table header rows.\n\t\tdata = data[data.Week != \"Week\"][data.Week != \"nan\"]\n\n\tdata['week'] = (data.Week\n\t\t\t\t\t.replace(\"WildCard\", \"wild-card\")\n\t\t\t\t\t.replace(\"Division\", \"divisional\")\n\t\t\t\t\t.replace(\"ConfChamp\", \"conference\")\n\t\t\t\t\t.replace(\"SuperBowl\", \"super-bowl\")\n\t\t\t\t\t.apply(\n\t\t\t\t\t\tlambda s: (int(s)\n\t\t\t\t\t\t\t\t if all(c in '1234567890' for c in s)\n\t\t\t\t\t\t\t\t else s)))\n\tdel data['Week']\n\n\tdata['season'] = year\n\tdata['game_date'] = pd.to_datetime(\n\t\tdata.Date\n\t\t.replace(r\"$\", r\", %d\" % year, regex=True)\n\t\t.replace(r\"^(January|February) (\\d+), \\d+$\", r\"\\1 \\2, %d\" % (year + 1),\n\t\t\t\t regex=True))\n\tdel data['Date']\n\n\tfor column in \"PtsW\", \"PtsL\", \"YdsW\", \"TOW\", \"YdsL\", \"TOL\":\n\t data[column] = data[column].apply(int)\n\n\tdata['WatL'] = data['Unnamed: 5'].apply(lambda x: x == '@')\n\tdel data['Unnamed: 5']\n\tdata['hometeam'] = (~data.WatL * data['Winner/tie'] +\n\t\t\t\t\t\tdata.WatL * data['Loser/tie'])\n\tdata['awayteam'] = (data.WatL * data['Winner/tie'] +\n\t\t\t\t\t\t~data.WatL * data['Loser/tie'])\n\tdata['winner'] = data['Winner/tie']\n\tfor column in 'Winner/tie', 'Loser/tie', \"WatL\":\n\t\tdel data[column]\n\tfor column in 'hometeam', 'awayteam', 'winner':\n\t\tdata[column] = data[column].apply(lambda s: s.split()[-1].lower())\n\n\treturn data","def next(self):\n season = self.value\n season = (season + 1) % len(Season.__members__.items())\n return Season(season)","def set_season(self, season):\n self.set_date_range(dt.date(season, 1, 1),\n dt.date(season, 12, 31))","def test_get_season_19_march(self, calendar, expected):\n date = datetime.date(2017, 3, 19)\n assert calendar.get_season(date) == expected","def replace_season(data,season):\n if not SeasonCharts.matchseason(season):\n raise SeasonCharts.SeasonError\n ## Check data format\n if test_rawdata(data):\n for cat,shows in data.items():\n for show in shows: show['season'] = season\n else:\n for show in data: show['season'] = season","def map_season(x, figsize=(8, 6), **kwargs):\n\n from pycmbs.mapping import map_plot\n\n nvals = len(x.data)\n if nvals == 12:\n year = True\n elif nvals == 4:\n year = False\n else:\n raise ValueError('Only data for 4-seasons or monthly data is supported!')\n\n #/// checks ///\n if x.data.ndim != 3:\n print x.data.ndim\n raise ValueError('only 3D data supported')\n\n if 'vmin' not in kwargs.keys():\n raise ValueError('vmin argument is obligatory for map_seasons()')\n if 'vmax' not in kwargs.keys():\n raise ValueError('vmax argument is obligatory for map_seasons()')\n\n if kwargs['vmin'] is None:\n raise ValueError('vmin MUST NOT be None!')\n if kwargs['vmax'] is None:\n raise ValueError('vmax MUST NOT be None!')\n\n #/// figure and axes\n if 'figure' in kwargs:\n f = kwargs['figure']\n else:\n f = plt.figure(figsize=figsize)\n\n if 'title' in kwargs:\n tit = kwargs.pop('title')\n else:\n tit = x.label\n\n if 'drawparallels' in kwargs:\n drawparallels = kwargs.pop('drawparallels')\n else:\n drawparallels = False\n\n if 'savefile' in kwargs:\n savefile = kwargs.pop('savefile')\n if '.nc' in savefile:\n savefile = savefile[:-3]\n else:\n savefile = None\n\n # plot\n if year:\n labels = ['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL', 'AUG', 'SEP', 'OCT', 'NOV', 'DEC']\n else:\n labels = ['DJF', 'MAM', 'JJA', 'SON']\n\n # check dates\n if year:\n mo = 1\n for t in x.time:\n if x.num2date(t).month != mo:\n print x.num2date(t), mo\n raise ValueError('Invalid monthly sequence! Can not plot results!')\n mo += 1\n\n #/// in case that an overlay is provided, this needs to be processed for each timestep individually\n if 'overlay' in kwargs.keys():\n overlays = kwargs.pop('overlay')\n else:\n overlays = None\n\n for i in range(nvals):\n if year:\n ax = f.add_subplot(4, 3, i + 1)\n #if i % 3 == 2:\n if i > 8:\n show_colorbar = True\n else:\n show_colorbar = False\n else:\n ax = f.add_subplot(2, 2, i + 1)\n if 'show_colorbar' in kwargs:\n show_colorbar = kwargs.pop('show_colorbar')\n else:\n show_colorbar = True\n\n d = x.copy()\n d.data = x.data[i, :, :]\n d.label = labels[i]\n\n if overlays is None:\n overlay = None\n else:\n overlay = overlays[i, :, :]\n\n if savefile is not None:\n tmpoutname = savefile + '_' + labels[i]\n else:\n tmpoutname = None\n\n map_plot(d, ax=ax, show_colorbar=show_colorbar, overlay=overlay,\n savefile=tmpoutname, colorbar_orientation='horizontal',\n drawparallels=drawparallels, **kwargs)\n del d\n f.suptitle(tit, size=16)\n return f","def calcSeason(ra, time):\n # Reference RA and equinox to anchor ra/season reference - RA = 0 is overhead at autumnal equinox\n # autumn equinox 2014 happened on september 23 --> equinox MJD\n Equinox = 2456923.5 - 2400000.5\n # convert ra into 'days'\n dayRA = ra / 360 * 365.25\n firstSeasonBegan = Equinox + dayRA - 0.5 * 365.25\n seasons = (time - firstSeasonBegan) / 365.25\n # Set first season to 0\n seasons = seasons - np.floor(np.min(seasons))\n return seasons","def iter_combos(include_unknown=False):\n if include_unknown:\n return _combos\n else:\n return _combos[:-7]","def opinion_type_list():\n for type_ in orm.DataFlagOpinionType.select():\n click.echo(type_.name)","def get_tracking_stats(\n measure_type: TrackingMeasureType,\n seasons: List[str],\n season_types: List[SeasonType],\n player_or_team: PlayerOrTeam,\n **kwargs,\n) -> List[Any]:\n all_season_stats = []\n for season in seasons:\n for season_type in season_types:\n # Making too many requests in a short period can result in timeouts. Add a delay between requests\n time.sleep(2)\n results = get_tracking_results_for_stat_measure(\n measure_type, season, season_type, player_or_team, **kwargs\n )\n stats = RESPONSE_MODEL_MAP[measure_type](**results)\n for stat in stats.results:\n stat.season = f\"{season} {season_type}\"\n all_season_stats += stats.results\n return all_season_stats","def closed_statuses_for_all_types(self):\n\n controller = LogicaOrderController(self.env)\n closed_statuses = controller.type_and_statuses_for_closed_statusgroups()\n types_and_statuses = []\n for type_, statuses in closed_statuses.iteritems():\n types_and_statuses.append(type_)\n types_and_statuses.extend(statuses)\n\n return closed_statuses, types_and_statuses","def _build_enum_groups(self):\n for group in self.used_enum_groups:\n if group not in self.group_to_enum_list:\n print(f'Warning: Enum group {group} has no values defined.')\n continue\n\n group_type = 'basic'\n if group in self.bitmask_groups:\n group_type = 'bitmask'\n\n values_used = set()\n enum_name_list = self.group_to_enum_list[group]\n enum_tuple_list = []\n for enum_name in enum_name_list:\n if enum_name not in self.enum_list:\n continue\n\n if enum_name not in self.enum_name_to_value:\n print(f'Warning: enum {enum_name} has no value')\n continue\n\n if not self._enum_should_collect(enum_name):\n continue\n\n enum_value = self.enum_name_to_value[enum_name]\n if enum_value not in values_used:\n enum_value_str = self._case_value(enum_name)\n enum_tuple_list.append((enum_value, enum_value_str, enum_name))\n values_used.add(enum_value)\n\n if enum_name in self.bitmask_enums:\n group_type = 'bitmask'\n\n enum_tuple_list.sort()\n\n group_max_len = 0\n for enum_info in enum_tuple_list:\n wrapper_enum_value_name = enum_info[2][3:].lower()\n if wrapper_enum_value_name in RESERVED_NAMES:\n wrapper_enum_value_name = wrapper_enum_value_name + '_'\n if len(wrapper_enum_value_name) > group_max_len:\n group_max_len = len(wrapper_enum_value_name)\n\n group_enum_string_entries = []\n group_enum_to_base_zero_entries = []\n base_zero_to_group_enum_entries = []\n group_wrapper_enum_value_definitions = []\n base_zero_value = 0\n\n for enum_info in enum_tuple_list:\n wrapper_enum_value_name = enum_info[2][3:].lower()\n\n if wrapper_enum_value_name in RESERVED_NAMES:\n wrapper_enum_value_name = wrapper_enum_value_name + '_'\n\n formatting = {\n 'ENUM_VALUE': enum_info[1],\n 'ENUM_STRING': enum_info[2],\n 'ENUM_BASE_ZERO_VALUE': base_zero_value,\n 'ENUM_VERSION': self._enum_version(enum_info[2]),\n 'WRAPPER_ENUM_TYPE_NAME': self.to_type_name(group),\n 'WRAPPER_ENUM_VALUE_NAME': wrapper_enum_value_name,\n }\n\n string_entry = templates.ENUM_STRING_MAKE_ENTRY[group_type].format(**formatting)\n group_enum_string_entries.append(string_entry)\n group_wrapper_enum_value_definitions.append(\n ' {:{}} = {:>6}u /* {} */'.format(\n wrapper_enum_value_name,\n group_max_len,\n enum_info[1],\n self._enum_version(enum_info[2])))\n\n if group_type == 'basic':\n group_enum_to_base_zero_make_entry = templates.ENUM_TO_BASE_ZERO_MAKE_ENTRY.format(**formatting)\n base_zero_to_group_enum_make_entry = templates.BASE_ZERO_TO_ENUM_MAKE_ENTRY.format(**formatting)\n group_enum_to_base_zero_entries.append(group_enum_to_base_zero_make_entry)\n base_zero_to_group_enum_entries.append(base_zero_to_group_enum_make_entry)\n base_zero_value = base_zero_value + 1\n\n if enum_tuple_list:\n wrapper_enum_name = self.to_type_name(group)\n definitions = ',\\n'.join(sorted(group_wrapper_enum_value_definitions))\n string_entries = '\\n'.join(sorted(group_enum_string_entries))\n formatting = {\n 'WRAPPER_ENUM_TYPE_NAME': wrapper_enum_name,\n 'WRAPPER_ENUM_STRING_FN_NAME': self.split_to_body_and_ext(group)[0],\n 'GROUP_NAME': group,\n 'WRAPPER_ENUM_VALUE_DEFINITIONS': definitions,\n 'GROUP_ENUM_STRING_ENTRIES': string_entries,\n 'GROUP_ENUM_TO_BASE_ZERO_ENTRIES': '\\n'.join(group_enum_to_base_zero_entries),\n 'BASE_ZERO_TO_GROUP_ENUM_ENTRIES': '\\n'.join(base_zero_to_group_enum_entries),\n }\n\n if group_type == 'basic':\n self.enum_to_base_zero_function_declarations.append(\n templates.ENUM_TO_BASE_ZERO_FUNCTION_DECLARATION.format(**formatting)\n )\n self.base_zero_to_enum_function_declarations.append(\n templates.BASE_ZERO_TO_ENUM_FUNCTION_DECLARATION.format(**formatting)\n )\n self.enum_base_zero_function_definitions.append(\n templates.ENUM_BASE_ZERO_FUNCTION_DEFINITION.format(**formatting)\n )\n self.untyped_enum_string_function_declarations.append(\n templates.UNTYPED_ENUM_STRING_FUNCTION_DECLARATION.format(**formatting)\n )\n self.untyped_enum_string_function_definitions.append(\n templates.UNTYPED_ENUM_STRING_FUNCTION_DEFINITION.format(**formatting)\n )\n\n self.enum_string_function_declarations.append(\n templates.ENUM_STRING_FUNCTION_DECLARATION[group_type].format(**formatting)\n )\n self.enum_string_function_definitions.append(\n templates.ENUM_STRING_FUNCTION_DEFINITION[group_type].format(**formatting)\n )\n self.wrapper_enum_declarations.append(\n templates.WRAPPER_ENUM_DECLARATION.format(**formatting)\n )\n self.enum_helper_definitions.append(\n templates.ENUM_HELPER_DEFINITION[group_type].format(**formatting)\n )","def enum(self):\n for l, u in self:\n for i in range(l, u):\n yield i","def _get_latest_season_and_type(df):\n if df is None or len(df) == 0:\n return (None, None)\n\n if not isinstance(df, pd.DataFrame):\n raise ValueError(f\"Type of df is {type(df)}, it should be pandas.DataFrame\")\n\n df = df[df['state_of_game'] == 'POST']\n latest_season = df['season'].max()\n season_types = df[df['season'] == latest_season]['type'].unique()\n latest_season_type = _get_latest_season_type(list(season_types))\n return latest_season, latest_season_type","def choices(cls):\n # return list(map(tuple, cls.__members__.items()))\n return [(int(code), name) for name, code in cls.__members__.items()]","def return_football_season(date=datetime.datetime.today()):\n date_aux = subtract_months(date, 6)\n beginning_year = str(date_aux.year)\n ending_year = date_aux.year + 1\n ending_year = str(ending_year)[-2:]\n season = ''.join([beginning_year, '-', ending_year])\n return season","def test_18_dataverse_types_all(self):\n print (self.test_18_dataverse_types_all.__doc__)\n\n kwargs = dict(select_year=2016)\n stats_maker = StatsMakerDataverses(**kwargs)\n r = stats_maker.get_dataverse_counts_by_type()\n\n # check number of entries\n self.assertEqual(len(r.result_data['records']), 7)\n\n # check first listing\n first_listing = {'dataversetype': u'RESEARCHERS',\n 'dataversetype_label': u'RESEARCHERS',\n 'percent_string': '38.8%',\n 'total_count': 291,\n 'type_count': 113}\n self.assertEqual(dict(r.result_data['records'][0]), first_listing)\n\n # check last listing\n last_listing = {'dataversetype': u'LABORATORY',\n 'dataversetype_label': u'LABORATORY',\n 'percent_string': '0.3%',\n 'total_count': 291,\n 'type_count': 1}\n\n self.assertEqual(dict(r.result_data['records'][-1]), last_listing)\n\n # -------------------------\n # Include UNCATEGORIZED Dataverses\n # -------------------------\n kwargs = dict(select_year=2016)\n stats_maker = StatsMakerDataverses(**kwargs)\n r = stats_maker.get_dataverse_counts_by_type(exclude_uncategorized=False)\n\n # check number of entries\n self.assertEqual(len(r.result_data['records']), 8)\n\n # check UNCATEGORIZED listing\n uncat_listing = {'dataversetype': u'UNCATEGORIZED',\n 'dataversetype_label': u'UNCATEGORIZED',\n 'percent_string': '18.3%',\n 'total_count': 356,\n 'type_count': 65}\n self.assertEqual(dict(r.result_data['records'][2]), uncat_listing)","def scrape():\n league_year = Config.get_property(\"league_year\")\n\n # Create table\n season_data = client.season_schedule(league_year)\n season_data = br_enum_to_string(season_data)\n return season_data","def _generate_hourly_seasonality(\n self,\n time_range: np.ndarray,\n hourly_seasonality_params: List[Tuple[float, float]],\n ) -> np.ndarray:\n\n return sum(\n [\n alpha * np.cos(2 * np.pi * (i + 1) * time_range / 24)\n + beta * np.sin(2 * np.pi * (i + 1) * time_range / 24)\n for i, (alpha, beta) in enumerate(hourly_seasonality_params)\n ]\n )","def fetch_seasons_for_show(self, params):\n show_id = params.get('show_id', [''])[0]\n raw_season_list = self.netflix_session.fetch_seasons_for_show(\n id=show_id)\n if 'error' in raw_season_list:\n return raw_season_list\n # check if we have sesons,\n # announced shows that are not available yet have none\n if 'seasons' not in raw_season_list.get('value', {}):\n return []\n seasons = self.netflix_session.parse_seasons(\n id=show_id,\n response_data=raw_season_list)\n return seasons","def episodes(self):\n episodes = []\n for season in self.seasons:\n episodes.extend(season.episodes)\n return episodes","def get_chapters_from(self, season, episode):\n\n show = self.tvShow\n if season not in show:\n return []\n # Gets remaning episodes for current season\n episodes = [show[season][iepisode]\n for iepisode in range(episode + 1, len(show[season]) + 1)]\n # Get episodes for new seasons\n episodes += [show[iseason][iepisode]\n for iseason in range(season + 1, len(show))\n for iepisode in range(1, len(show[iseason]))]\n return [Episode(self, x) for x in episodes]","def test_enumerating_stereobonds(self, toolkit_class):\n\n if toolkit_class.is_available():\n toolkit = toolkit_class()\n mol = Molecule.from_smiles(\n \"ClC=CCl\", allow_undefined_stereo=True, toolkit_registry=toolkit\n )\n\n # use the default options\n isomers = mol.enumerate_stereoisomers()\n assert len(isomers) == 2\n\n assert mol not in isomers\n # make sure the input molecule is only different by bond stereo\n for ismol in isomers:\n assert (\n Molecule.are_isomorphic(\n mol,\n ismol,\n return_atom_map=False,\n bond_stereochemistry_matching=False,\n )[0]\n is True\n )\n assert mol.is_isomorphic_with(ismol) is False\n\n # make sure the isomers are different\n assert isomers[0].is_isomorphic_with(isomers[1]) is False\n\n else:\n pytest.skip(\"Required toolkit is unavailable\")","def test_enumerating_stereobonds(self, toolkit_class):\n\n if toolkit_class.is_available():\n toolkit = toolkit_class()\n mol = Molecule.from_smiles(\n \"ClC=CCl\", allow_undefined_stereo=True, toolkit_registry=toolkit\n )\n\n # use the default options\n isomers = mol.enumerate_stereoisomers()\n assert len(isomers) == 2\n\n assert mol not in isomers\n # make sure the input molecule is only different by bond stereo\n for ismol in isomers:\n assert (\n Molecule.are_isomorphic(\n mol,\n ismol,\n return_atom_map=False,\n bond_stereochemistry_matching=False,\n )[0]\n is True\n )\n assert mol.is_isomorphic_with(ismol) is False\n\n # make sure the isomers are different\n assert isomers[0].is_isomorphic_with(isomers[1]) is False\n\n else:\n pytest.skip(\"Required toolkit is unavailable\")","def test_durations_per_type_num_players(self):\n type_of_player = [ss.Player, ss.LazyPlayer, ss.ResilientPlayer]\n sim = ss.Simulation(player_field=type_of_player)\n run = sim.durations_per_type()\n assert list(run.keys()) == ['Player', 'LazyPlayer', 'ResilientPlayer']","def sameSeason( pMonth, cMonth ):\r\n if pMonth == 12 or pMonth == 1 or pMonth == 2:\r\n if cMonth == 12 or cMonth == 1 or cMonth == 2:\r\n return True\r\n else:\r\n return False\r\n\r\n if pMonth == 3 or pMonth == 4 or pMonth == 5:\r\n if cMonth == 3 or cMonth == 4 or cMonth == 5:\r\n return True\r\n else:\r\n return False\r\n\r\n if pMonth == 6 or pMonth == 7 or pMonth == 8:\r\n if cMonth == 6 or cMonth == 7 or cMonth == 8:\r\n return True\r\n else:\r\n return False\r\n\r\n if pMonth == 9 or pMonth == 10 or pMonth == 11:\r\n if cMonth == 9 or cMonth == 10 or cMonth == 11:\r\n return True\r\n else:\r\n return False","def determine_available_voice_types(sim_info: SimInfo) -> Tuple[CommonVoiceActorType]:\n from sims4communitylib.utils.sims.common_age_species_utils import CommonAgeSpeciesUtils\n from sims4communitylib.utils.sims.common_species_utils import CommonSpeciesUtils\n from sims4communitylib.utils.sims.common_age_utils import CommonAgeUtils\n if CommonAgeSpeciesUtils.is_teen_adult_or_elder_human(sim_info):\n result: Tuple[CommonVoiceActorType] = (\n CommonVoiceActorType.MUTE,\n CommonVoiceActorType.ADULT_HUMAN_AMBIGUOUS_1,\n CommonVoiceActorType.ADULT_HUMAN_FEMININE_1,\n CommonVoiceActorType.ADULT_HUMAN_FEMININE_2,\n CommonVoiceActorType.ADULT_HUMAN_MASCULINE_1,\n CommonVoiceActorType.ADULT_HUMAN_MASCULINE_2,\n CommonVoiceActorType.ADULT_HUMAN_MASCULINE_3,\n CommonVoiceActorType.KYLO_REN_1,\n CommonVoiceActorType.REY_1,\n CommonVoiceActorType.HONDO_OHNAKA_1,\n )\n elif CommonAgeSpeciesUtils.is_child_human(sim_info):\n result: Tuple[CommonVoiceActorType] = (\n CommonVoiceActorType.MUTE,\n CommonVoiceActorType.CHILD_HUMAN_AMBIGUOUS_1,\n CommonVoiceActorType.CHILD_HUMAN_AMBIGUOUS_2,\n CommonVoiceActorType.KYLO_REN_1,\n CommonVoiceActorType.REY_1,\n CommonVoiceActorType.HONDO_OHNAKA_1,\n )\n elif CommonAgeSpeciesUtils.is_toddler_human(sim_info):\n result: Tuple[CommonVoiceActorType] = (\n CommonVoiceActorType.MUTE,\n CommonVoiceActorType.TODDLER_HUMAN_AMBIGUOUS_1,\n )\n elif CommonSpeciesUtils.is_large_dog(sim_info) and CommonAgeUtils.is_teen_adult_or_elder(sim_info):\n result: Tuple[CommonVoiceActorType] = (\n CommonVoiceActorType.MUTE,\n CommonVoiceActorType.ADULT_DOG_AMBIGUOUS_1,\n CommonVoiceActorType.ADULT_DOG_AMBIGUOUS_2,\n CommonVoiceActorType.ADULT_DOG_AMBIGUOUS_3,\n CommonVoiceActorType.ADULT_DOG_AMBIGUOUS_4\n )\n elif CommonSpeciesUtils.is_large_dog(sim_info) and CommonAgeUtils.is_child(sim_info):\n result: Tuple[CommonVoiceActorType] = (\n CommonVoiceActorType.MUTE,\n CommonVoiceActorType.CHILD_DOG_AMBIGUOUS_1,\n )\n elif CommonSpeciesUtils.is_small_dog(sim_info) and CommonAgeUtils.is_teen_adult_or_elder(sim_info):\n result: Tuple[CommonVoiceActorType] = (\n CommonVoiceActorType.MUTE,\n CommonVoiceActorType.ADULT_DOG_AMBIGUOUS_1,\n CommonVoiceActorType.ADULT_DOG_AMBIGUOUS_2,\n CommonVoiceActorType.ADULT_DOG_AMBIGUOUS_3,\n CommonVoiceActorType.ADULT_DOG_AMBIGUOUS_4\n )\n elif CommonSpeciesUtils.is_small_dog(sim_info) and CommonAgeUtils.is_child(sim_info):\n result: Tuple[CommonVoiceActorType] = (\n CommonVoiceActorType.MUTE,\n CommonVoiceActorType.CHILD_DOG_AMBIGUOUS_1,\n )\n elif CommonSpeciesUtils.is_cat(sim_info) and CommonAgeUtils.is_teen_adult_or_elder(sim_info):\n result: Tuple[CommonVoiceActorType] = (\n CommonVoiceActorType.MUTE,\n CommonVoiceActorType.ADULT_CAT_AMBIGUOUS_1,\n CommonVoiceActorType.ADULT_CAT_AMBIGUOUS_2,\n )\n elif CommonSpeciesUtils.is_cat(sim_info) and CommonAgeUtils.is_child(sim_info):\n result: Tuple[CommonVoiceActorType] = (\n CommonVoiceActorType.MUTE,\n CommonVoiceActorType.CHILD_CAT_AMBIGUOUS_1,\n )\n elif CommonSpeciesUtils.is_fox(sim_info) and CommonAgeUtils.is_teen_adult_or_elder(sim_info):\n result: Tuple[CommonVoiceActorType] = (\n CommonVoiceActorType.MUTE,\n CommonVoiceActorType.ADULT_FOX_AMBIGUOUS_1,\n )\n else:\n result: Tuple[CommonVoiceActorType] = (\n CommonVoiceActorType.MUTE,\n )\n return result","def test_time_type_state_types(day):\n\n assert day_time_info(day.hours_0).types == {TimeType.NIGHT}\n assert day_time_info(day.hours_1).types == {TimeType.NIGHT}\n assert day_time_info(day.hours_2).types == {TimeType.NIGHT}\n assert day_time_info(day.hours_3).types == {TimeType.NIGHT}\n assert day_time_info(day.hours_4).types == {TimeType.NIGHT}\n assert day_time_info(day.hours_5).types == {TimeType.MORNING}\n assert day_time_info(day.hours_6).types == {TimeType.MORNING}\n assert day_time_info(day.hours_7).types == {TimeType.MORNING}\n assert day_time_info(day.hours_8).types == {TimeType.MORNING}\n assert day_time_info(day.hours_9).types == {TimeType.MORNING}\n assert day_time_info(day.hours_10).types == {TimeType.MIDMORNING}\n assert day_time_info(day.hours_11).types == {TimeType.MIDMORNING}\n assert day_time_info(day.hours_12).types == {TimeType.NOON}\n assert day_time_info(day.hours_13).types == {TimeType.AFTERNOON}\n assert day_time_info(day.hours_14).types == {TimeType.AFTERNOON}\n assert day_time_info(day.hours_15).types == {TimeType.AFTERNOON}\n assert day_time_info(day.hours_16).types == {TimeType.AFTERNOON}\n assert day_time_info(day.hours_17).types == {TimeType.AFTERNOON}\n assert day_time_info(day.hours_18).types == {TimeType.EVENING}\n assert day_time_info(day.hours_19).types == {TimeType.EVENING}\n assert day_time_info(day.hours_20).types == {TimeType.EVENING}\n assert day_time_info(day.hours_21).types == {TimeType.EVENING}\n assert day_time_info(day.hours_22).types == {TimeType.EVENING}\n assert day_time_info(day.hours_23).types == {TimeType.NIGHT}","def spreads(lines, sport):\n value = []\n for game in lines:\n combos = it.product(game['away_odds'].items(), game['home_odds'].items())\n try:\n value.extend([f'{sport} {game[\"game\"]} {combo[0][0]}: {combo[0][1][0]} {combo[0][1][1]} and '\n f'{combo[1][0]}: {combo[1][1][0]} {combo[1][1][1]}\\n\\n' for combo in combos\n if combo[0][1][0] + combo[1][1][0] >= 0 and combo[0][1][1] + combo[1][1][1] >= 0])\n except TypeError:\n print(combos)\n\n return value","def python_type(self) -> type:\n return SeasonEntity","def read_seasons(): # type: () -> Dict[int, str]\n csv_seasons = {}\n with open(\"rgl_seasons.csv\", encoding=\"utf-8\") as f:\n for line in f:\n if not line:\n continue\n season_id, season_name = line.split(\",\")\n csv_seasons[int(season_id)] = season_name\n return csv_seasons","def bfs_season(self, season_edges):\n if self.season == \"summer\":\n return\n pixel_neighbors = [(0, 1), (1, 0), (-1, 0), (0, -1)]\n season_boundaries = []\n depth_parameters = None\n if self.season == \"spring\":\n depth_parameters = 15\n elif self.season == \"winter\":\n depth_parameters = 7\n elif self.season == \"fall\":\n depth_parameters = 1\n elif self.season == \"summer\":\n depth_parameters = 0\n for points in season_edges:\n parent_elevation = float(self.grid.ele_copy[points[0]][points[1]])\n if self.season == \"winter\":\n if self.grid.pix_copy[points[0], points[1]] == (0, 0, 255):\n season_boundaries.append((points[0], points[1]))\n queue = []\n explored_set = {}\n queue.append((points[0], points[1]))\n\n # BFS Implementation\n while len(queue) != 0:\n current_node = queue.pop(0)\n if current_node[0] == points[0] + depth_parameters or \\\n current_node[1] == points[1] + depth_parameters or \\\n current_node[0] == points[0] - depth_parameters or \\\n current_node[1] == points[1] - depth_parameters:\n break\n if current_node not in explored_set:\n explored_set[(current_node[0], current_node[1])] = 0\n else:\n continue\n for k in range(len(pixel_neighbors)):\n x_value = current_node[0] + pixel_neighbors[k][0]\n y_value = current_node[1] + pixel_neighbors[k][1]\n if self.season == \"spring\":\n if x_value >= 0 and x_value < self.grid.width and y_value >= 0 and y_value < self.grid.height:\n if (x_value, y_value) not in queue and (x_value, y_value) not in explored_set:\n if (not (float(self.grid.ele_copy[x_value][y_value]) - parent_elevation > 1)):\n if self.grid.pix_copy[x_value, y_value] != (205, 0, 101):\n queue.append((x_value, y_value))\n if self.grid.pix_copy[x_value, y_value] != (0, 0, 255):\n season_boundaries.append((x_value, y_value))\n elif self.season == \"winter\":\n if x_value >= 0 and x_value < self.grid.width and y_value >= 0 and y_value < self.grid.height:\n if (x_value, y_value) not in queue and (x_value, y_value) not in explored_set:\n queue.append((x_value, y_value))\n if self.grid.pix_copy[x_value, y_value] == (0, 0, 255):\n season_boundaries.append((x_value, y_value))\n elif self.season == \"fall\":\n if x_value >= 0 and x_value < self.grid.width and y_value >= 0 and y_value < self.grid.height:\n if (x_value, y_value) not in queue and (x_value, y_value) not in explored_set:\n queue.append((x_value, y_value))\n if self.grid.pix_copy[x_value, y_value] == (248, 148, 18) \\\n or self.grid.pix_copy[x_value, y_value] == (0, 0, 0):\n season_boundaries.append((x_value, y_value))\n while len(queue) != 0:\n current_node = queue.pop(0)\n xc = current_node[0]\n yc = current_node[1]\n if self.season == \"spring\":\n if xc >= 0 and xc < self.grid.width and yc >= 0 and yc < self.grid.height:\n if self.grid.pix_copy[xc, yc] != (0, 0, 255):\n if (not (float(self.grid.ele_copy[xc][yc]) - parent_elevation >= 1)):\n season_boundaries.append((xc, yc))\n elif self.season == \"winter\":\n if xc >= 0 and xc < self.grid.width and yc >= 0 and yc < self.grid.height:\n if self.grid.pix_copy[xc, yc] == (0, 0, 255):\n season_boundaries.append((xc, yc))\n elif self.season == \"fall\":\n if xc >= 0 and xc < self.grid.width and yc >= 0 and yc < self.grid.height:\n if self.grid.pix_copy[xc, yc] == (248, 148, 18) \\\n or self.grid.pix_copy[xc, yc] == (0, 0, 0):\n season_boundaries.append((xc, yc))\n self.update_map(season_boundaries)\n print(\"map updated for season\")\n print(\"Entering Astar\")","def seasonality(time, period, amplitude=1 , phase=0):\n season_time = ((time + phase) % period) / period\n return amplitude * seasonal_pattern(season_time)","def all_seasons_save():\n year_arr = [str(f'{i}-{str((i + 1))[-2:]}') for i in reversed(range(1950, 2021))]\n for year in year_arr:\n c_name = f'season_player_game_logs_{year.replace(\"-\", \"_\")}'\n try:\n coll = db[c_name]\n season_game_log_save(year, coll)\n print(f'{c_name} successfully saved')\n time.sleep(1)\n except OperationFailure as e:\n print(f'{c_name} not saved!!')\n print(e)","def determine_axes_to_group_by(is_volume: bool) -> Set[Axes]:\n if is_volume:\n return {Axes.ROUND, Axes.CH}\n else:\n return {Axes.ROUND, Axes.CH, Axes.ZPLANE}","def seasonality(time, period, amplitude=1, phase=0):\n\tseason_time = ((time + phase) % period) / period\n\treturn amplitude * seasonal_pattern(season_time)","def test_get_ordinal():\n for (index, status) in enumerate(['draft', 'published', 'archived']):\n yield (tools.eq_, BlogPostStatus.get_ordinal(status), index)"],"string":"[\n \"def season_choices():\\n return [(s, s) for s in range(0, 3)]\",\n \"def distributeSeason(self):\\n i = 1\\n for day in self.daylist:\\n if i >= monthbeg[5] and i < monthbeg[9]: #june through SEpt as per SCE\\n day.season = 'summer' #https://www.sce.com/residential/rates/Time-Of-Use-Residential-Rate-Plans\\n i = i + 1\\n else:\\n day.season = 'winter'\\n i = i+1\",\n \"def get_dryspells_perseason(dryspells, seasons=((12, 1, 2), (3, 4, 5),\\n (6, 7, 8), (9, 10, 11))):\\n dryspells_seasons = []\\n for season in seasons:\\n eveSeas = []\\n for eveLand in dryspells:\\n eves = [e for e in eveLand if e.start_date().month in season]\\n eveSeas.append(eves)\\n dryspells_seasons.append(eveSeas)\\n\\n return dryspells_seasons\",\n \"def Seasons(year):\\n mar_equinox = _FindSeasonChange(0, year, 3, 19)\\n jun_solstice = _FindSeasonChange(90, year, 6, 19)\\n sep_equinox = _FindSeasonChange(180, year, 9, 21)\\n dec_solstice = _FindSeasonChange(270, year, 12, 20)\\n return SeasonInfo(mar_equinox, jun_solstice, sep_equinox, dec_solstice)\",\n \"def test_season_iter(self):\\n self.assertEquals(\\n len(\\n [episode for episode in self.t['Life on Mars'][1]]\\n ),\\n 8\\n )\",\n \"def get_schedules_for_season(self, season, season_type=\\\"REG\\\"):\\n try:\\n season = int(season)\\n if season_type not in [\\\"REG\\\", \\\"PRE\\\", \\\"POST\\\"]:\\n raise ValueError\\n except (ValueError, TypeError):\\n raise FantasyDataError('Error: Invalid method parameters')\\n\\n season_param = \\\"{0}{1}\\\".format(season, season_type)\\n result = self._method_call(\\\"Schedules/{season}\\\", season=season_param)\\n return result\",\n \"def br_enum_to_string(season):\\n new_season = []\\n for game in season:\\n game_dict = dict()\\n keys = game.keys()\\n for key in keys:\\n if type(game[key]) not in [str, int, float, datetime]:\\n game_dict[key] = game[key].value # Extract value from enum here\\n else:\\n game_dict[key] = game[key]\\n new_season.append(game_dict)\\n return new_season\",\n \"def get_competition_season_type(season):\\n default_type = games.models.CompetitionSeason.winter\\n try:\\n season_name = season.name\\n if season_name.find(\\\"/\\\") == -1:\\n return games.models.CompetitionSeason.summer\\n return default_type\\n except Exception as e:\\n return default_type\",\n \"def seasons(years, timeout=None, concurrency=_DEFAULT_CONCURRENCY):\\n\\ttables, failures = [], []\\n\\tfor year in years:\\n\\t\\tLOG.info('=' * 10 + ' %d ' + '=' * 10, year)\\n\\t\\ttable, failure = season(year, timeout=timeout, concurrency=concurrency)\\n\\t\\ttables.append(table)\\n\\t\\tfailures.extend(failure)\\n\\treturn pd.concat(tables), failures\",\n \"def season_indices(t, edges=None, hard=False):\\n if edges is None:\\n edges = season_edges(t)\\n season_ixs = np.digitize(t.jyear, edges.jyear)\\n N = season_ixs.size\\n seasons = []\\n for ix in np.unique(season_ixs):\\n season_members = np.arange(N)[season_ixs == ix] # bool array to index array\\n seasons.append(season_members)\\n if hard:\\n if t[seasons[0]].jyear.max() < edges[0].jyear:\\n seasons = seasons[1:]\\n if t[seasons[-1]].jyear.min() > edges[-1].jyear:\\n seasons = seasons[:-1]\\n return seasons\",\n \"def get_seasons(seasons):\\n if '-to-' in seasons[0]:\\n from_, to = map(int, seasons[0].split('-to-'))\\n years = list(range(from_, to+1))\\n seasons = []\\n for i in range(0, len(years)-1):\\n season = \\\"-\\\".join(map(str, [years[i], years[i+1]]))\\n seasons.append(season)\\n return seasons\",\n \"def all_seasons(cls, verbose=False):\\r\\n\\r\\n\\t\\twith open('Data/index.txt', 'r', encoding='utf-8') as f:\\r\\n\\t\\t\\tseason_list = f.read().splitlines()\\r\\n\\t\\t\\r\\n\\t\\tfolder_list = os.listdir('Data')\\r\\n\\r\\n\\t\\t# Seasons that are both in the season index and have a folder\\r\\n\\t\\tvalid_seasons = [\\r\\n\\t\\t\\tseason_name\\r\\n\\t\\t\\tfor season_name in season_list\\r\\n\\t\\t\\tif cls.season_folder(season_name) in folder_list\\r\\n\\t\\t]\\r\\n\\r\\n\\t\\tif verbose:\\r\\n\\t\\t\\tfor ind, season_name in enumerate(valid_seasons):\\r\\n\\t\\t\\t\\tvalid_seasons[ind] = cls.season_info(season_name)\\r\\n\\r\\n\\t\\treturn valid_seasons\",\n \"def get_seasons(seasons):\\n if '-to-' in seasons:\\n from_, to = map(int, seasons.split('-to-'))\\n years = list(range(from_, to+1))\\n seasons = []\\n for i in range(0, len(years)-1):\\n season = \\\"-\\\".join(map(str, [years[i], years[i+1]]))\\n seasons.append(season)\\n return seasons\",\n \"def test_seasons(self):\\n response = Tmdb.season(tmdb_show_id = 69740, season_number = 1)\\n self.assertTrue(int(response.status_code) == 200)\\n data = response.json()\\n self.assertTrue(isinstance(data['episodes'], list))\\n # TODO check if all the shows are in the good format (can be from_dict/to_dict)\",\n \"def seasons(self):\\n if self._season_cache_ver == self._db._version:\\n return self._season_cache\\n\\n # Find out how many seasons in this series by fetching the highest season.\\n seasons = self._db.query(type='episode', parent=self._dbrow, attrs=['season'], distinct=True)\\n self._season_cache = [Season(self._db, self, row['season']) for row in seasons]\\n self._season_cache_ver = self._db._version\\n return self._season_cache\",\n \"def episode_choices():\\n return [(e, e) for e in range(0, 2)]\",\n \"def set_season_time(season): \\n if season == '2021-22':\\n startdate = time.strptime('13-08-2021', '%d-%m-%Y')\\n startdate = datetime.fromtimestamp(mktime(startdate))\\n enddate = time.strptime('08-10-2021', '%d-%m-%Y')\\n enddate = datetime.fromtimestamp(mktime(enddate))\\n if season == '2020-21':\\n startdate = time.strptime('12-08-2020', '%d-%m-%Y')\\n startdate = datetime.fromtimestamp(mktime(startdate))\\n enddate = time.strptime('26-07-2021', '%d-%m-%Y')\\n enddate = datetime.fromtimestamp(mktime(enddate))\\n if season == '2019-20':\\n startdate = time.strptime('09-08-2019', '%d-%m-%Y')\\n startdate = datetime.fromtimestamp(mktime(startdate))\\n enddate = time.strptime('26-07-2020', '%d-%m-%Y')\\n enddate = datetime.fromtimestamp(mktime(enddate))\\n return startdate, enddate\",\n \"def get_season_list_BDEW(weather_data):\\n season_list = []\\n\\n for j, date_obj in enumerate(weather_data.index):\\n YEAR = date_obj.year\\n\\n winter_end = dt.datetime(YEAR, 3, 21, 00, 00, 00)\\n winter_start = dt.datetime(YEAR, 10, 31, 00, 00, 00)\\n summer_start = dt.datetime(YEAR, 5, 15, 00, 00, 00)\\n summer_end = dt.datetime(YEAR, 9, 15, 00, 00, 00)\\n\\n if date_obj <= winter_end or date_obj > winter_start:\\n season_list.append('Winter') # Winter\\n\\n elif date_obj > summer_start and date_obj <= summer_end:\\n season_list.append('Sommer') # Summer\\n\\n else:\\n season_list.append('Übergangszeit') # Transition\\n\\n return season_list\",\n \"def seasonal_series(t, y, edges=None, hard=False):\\n t = ensure_tarray(t)\\n if len(t) == 1:\\n return [t], [y]\\n season_ixs = season_indices(t, edges=edges, hard=hard)\\n ts = []\\n ys = []\\n for season in season_ixs:\\n ts.append(astropy.time.Time(t.jyear[season], format='jyear', scale=t.scale))\\n ys.append(y[season])\\n return ts, ys\",\n \"def get_seasons(games: List[BaseGame], reverse=False):\\n seasons = list(set([g.Season for g in games]))\\n seasons.sort(reverse=reverse)\\n return seasons\",\n \"def compute_levels_seasons(self, Y: t.Tensor, idxs: t.Tensor):\\n # Lookup parameters per serie\\n #seasonality = self.seasonality\\n embeds = self.embeds(idxs)\\n lev_sms = t.sigmoid(embeds[:, 0])\\n\\n # Initialize seasonalities\\n seas_prod = t.ones(len(Y[:,0]), device=Y.device)\\n seasonalities1 = []\\n seasonalities2 = []\\n seas_sms1 = t.ones(1, device=Y.device)\\n seas_sms2 = t.ones(1, device=Y.device)\\n\\n if len(self.seasonality)>0:\\n seas_sms1 = t.sigmoid(embeds[:, 1])\\n init_seas1 = t.exp(embeds[:, 2:(2+self.seasonality[0])]).unbind(1)\\n assert len(init_seas1) == self.seasonality[0]\\n\\n for i in range(len(init_seas1)):\\n seasonalities1 += [init_seas1[i]]\\n seasonalities1 += [init_seas1[0]]\\n seas_prod = seas_prod * init_seas1[0]\\n\\n if len(self.seasonality)==2:\\n seas_sms2 = t.sigmoid(embeds[:, 2+self.seasonality[0]])\\n init_seas2 = t.exp(embeds[:, 3+self.seasonality[0]:]).unbind(1)\\n assert len(init_seas2) == self.seasonality[1]\\n\\n for i in range(len(init_seas2)):\\n seasonalities2 += [init_seas2[i]]\\n seasonalities2 += [init_seas2[0]]\\n seas_prod = seas_prod * init_seas2[0]\\n\\n # Initialize levels\\n levels = []\\n levels += [Y[:,0]/seas_prod]\\n\\n # Recursive seasonalities and levels\\n ys = Y.unbind(1)\\n n_time = len(ys)\\n for t_idx in range(1, n_time):\\n seas_prod_t = t.ones(len(Y[:,t_idx]), device=Y.device)\\n if len(self.seasonality)>0:\\n seas_prod_t = seas_prod_t * seasonalities1[t_idx]\\n if len(self.seasonality)==2:\\n seas_prod_t = seas_prod_t * seasonalities2[t_idx]\\n\\n newlev = lev_sms * (ys[t_idx] / seas_prod_t) + (1-lev_sms) * levels[t_idx-1]\\n levels += [newlev]\\n\\n if len(self.seasonality)==1:\\n newseason1 = seas_sms1 * (ys[t_idx] / newlev) + (1-seas_sms1) * seasonalities1[t_idx]\\n seasonalities1 += [newseason1]\\n\\n if len(self.seasonality)==2:\\n newseason1 = seas_sms1 * (ys[t_idx] / (newlev * seasonalities2[t_idx])) + \\\\\\n (1-seas_sms1) * seasonalities1[t_idx]\\n seasonalities1 += [newseason1]\\n newseason2 = seas_sms2 * (ys[t_idx] / (newlev * seasonalities1[t_idx])) + \\\\\\n (1-seas_sms2) * seasonalities2[t_idx]\\n seasonalities2 += [newseason2]\\n\\n levels = t.stack(levels).transpose(1,0)\\n\\n seasonalities = []\\n\\n if len(self.seasonality)>0:\\n seasonalities += [t.stack(seasonalities1).transpose(1,0)]\\n\\n if len(self.seasonality)==2:\\n seasonalities += [t.stack(seasonalities2).transpose(1,0)]\\n\\n return levels, seasonalities\",\n \"def season_type(self, season_type):\\n\\n self._season_type = season_type\",\n \"def getSeason(date):\\n\\n date = validate.timestamp(date)\\n day = date.dayofyear\\n leap_year = int(date.is_leap_year)\\n\\n spring = numpy.arange(80, 172) + leap_year\\n summer = numpy.arange(172, 264) + leap_year\\n autumn = numpy.arange(264, 355) + leap_year\\n\\n if day in spring:\\n season = \\\"spring\\\"\\n elif day in summer:\\n season = \\\"summer\\\"\\n elif day in autumn:\\n season = \\\"autumn\\\"\\n else:\\n season = \\\"winter\\\"\\n\\n return season\",\n \"def test_valid_for(self):\\n july = datetime.date(1983, 7, 3)\\n result = {(s.name, s.valid_for(july))\\n for s in seasons.southern_meteo.seasons}\\n expected = {('spring', False),\\n ('summer', False),\\n ('autumn', False),\\n ('winter', True)\\n }\\n assert result == expected\",\n \"def getseason(data):\\n ## Season key is the most reliable\\n season = data.get(\\\"season\\\")\\n if season:\\n ## Season key is an integer formatted \\\"YYS\\\" and is 2000-based (i.e.- 171 == 2017-Winter)\\n season = str(season)\\n year = int(f\\\"20{season[:2]}\\\")\\n ## Anichart Season key is 1-indexed\\n season = int(season[2]) - 1\\n ## This should normally pass; if it consistently does not, we'll have to investigate why\\n try: return SeasonCharts.buildseason(season,year)\\n ## If something goes wrong, we'll try another method\\n except: print(f\\\"Failed to parse season: {data['season']}\\\")\\n ## Next, we'll iterate over rankings to try to determine the season/year\\n ## There are multiple types of rankings based on season, year, and both combined,\\n ## so we'll piece it together based on whatever we come across first\\n season,year = None,None\\n for ranking in data.get(\\\"rankings\\\",list()):\\n ## Quicker exit (without just making this loop its own function)\\n if season and year: continue\\n ## We'll ignore stuff we've already gotten and assume that nothing in\\n ## rankings contradicts eachother\\n if not season:\\n ## Defaults to None one way or another if it's not supplied\\n season = ranking.get(\\\"season\\\")\\n if not year: year = ranking.get(\\\"year\\\")\\n ## Check if we made it\\n if season and year:\\n ## As above, this should always work out-of-the-box\\n try: return SeasonCharts.buildseason(season,year)\\n except: print(season,year)\\n ## Welp, we're stumped...\\n return None\",\n \"def set_season(date_obj):\\n date_year = date_obj.year\\n\\n for key, val in SEASONS.items():\\n start = datetime(year=date_year, month=val['start']['month'], day=val['start']['day'])\\n end = datetime(year=date_year, month=val['end']['month'], day=val['end']['day'])\\n if key == 'Winter':\\n start_year = date_year - 1 if date_obj.month in [1, 2, 3] else date_year\\n end_year = date_year + 1 if date_obj.month == 12 else date_year\\n start = datetime(year=start_year, month=val['start']['month'], day=val['start']['day'])\\n end = datetime(year=end_year, month=val['end']['month'], day=val['end']['day'])\\n\\n if start <= date_obj <= end:\\n return key\",\n \"def sarima_configs(seasonal=[1, 24, 168]):\\n\\n models = list()\\n # define config lists\\n p_params = [0, 1, 2]\\n d_params = [0, 1]\\n q_params = [0, 1, 2]\\n t_params = ['n', 'c', 't', 'ct']\\n P_params = [0, 1, 2]\\n D_params = [0, 1]\\n Q_params = [0, 1, 2]\\n m_params = seasonal\\n # create config instances\\n for p in p_params:\\n for d in d_params:\\n for q in q_params:\\n for t in t_params:\\n for P in P_params:\\n for D in D_params:\\n for Q in Q_params:\\n for m in m_params:\\n cfg = [(p, d, q), (P, D, Q, m), t]\\n models.append(cfg)\\n return models\",\n \"def list_chassis_per_season(start_year=1950, end_year=2020, file_changed=False):\\n\\n def _get_chassis_names(years):\\n req = requests.get(WIKIPEDIA_F1_URL)\\n soup = BeautifulSoup(req.content, 'html.parser')\\n links = soup.find_all('a')\\n\\n for link in links:\\n link_text = ['Formula', 'One', 'season', 'cars']\\n if str(link.get('href')).split('_')[-4:] == link_text:\\n link_season = str(WIKIPEDIA + link.get('href')).replace(' ', '')\\n season = str(link.get('href')).replace(':', '_').split('_')[-5:-4][0]\\n\\n if int(season) in years:\\n\\n req = requests.get(link_season)\\n soup = BeautifulSoup(req.content, 'html.parser')\\n team_divs = soup.findAll('div', attrs={'class': 'mw-category-group'})\\n teams_season_list = []\\n\\n for team_div in team_divs:\\n team_links = team_div.findAll('a')\\n for team_link in team_links:\\n teams_season_list.append(team_link.get('title'))\\n\\n\\n # Checking missing teams\\n missing_teams = MISSING_CHASSIS.get(season)\\n for missing_team in missing_teams:\\n if missing_team not in teams_season_list:\\n teams_season_list.append(missing_team)\\n\\n seasons_chassis[season] = teams_season_list\\n\\n save_json(seasons_chassis)\\n\\n return seasons_chassis\\n\\n seasons_chassis = load_json(F1_CHASSIS)\\n\\n # Check if we got the chassis names for all seasons listed\\n update_list = []\\n\\n for year in range(start_year, end_year + 1):\\n if str(year) not in seasons_chassis.keys():\\n update_list.append(year)\\n\\n chassis = _get_chassis_names(update_list) if update_list else seasons_chassis\\n\\n return chassis\",\n \"def season_limits(self):\\n if self.season == \\\"summer\\\":\\n return\\n neighbors = [(0, 1), (1, 0), (-1, 0), (0, -1)]\\n points_list = []\\n season_pixels = []\\n season_edges = []\\n width = self.grid.width\\n height = self.grid.height\\n x = 0\\n while x < width:\\n y = 0\\n while y < height:\\n points_list.append((x, y))\\n if self.season == \\\"spring\\\" or self.season == \\\"winter\\\":\\n if self.grid.pix_copy[x, y] == (0, 0, 255):\\n season_pixels.append((x, y))\\n else:\\n if self.grid.pix_copy[x, y] == (255, 255, 255):\\n season_pixels.append((x, y))\\n y += 1\\n x += 1\\n for points in points_list:\\n k = 0\\n while k < (len(neighbors)):\\n xp = points[0] + neighbors[k][0]\\n yp = points[1] + neighbors[k][1]\\n condition = self.filter_edges_one(points[0], points[1], xp, yp, season_edges)\\n if condition:\\n season_edges.append((xp, yp))\\n k += 1\\n return season_edges\",\n \"def timeave_seasonal( mv, seasons=seasonsyr ):\\n return seasons.climatology(mv)\",\n \"def season_info(cls, season):\\r\\n\\r\\n\\t\\traw_info = cls.season_rounds(season)\\r\\n\\r\\n\\t\\tinfo = [\\r\\n\\t\\t\\tseason,\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t# 0 -> Season name\\r\\n\\t\\t\\tlen(raw_info),\\t\\t\\t\\t\\t\\t\\t\\t# 1 -> Amount of rounds\\r\\n\\t\\t\\tmin([round[1] for round in raw_info]),\\t\\t# 2 -> Starting date\\r\\n\\t\\t\\tmax([round[1] for round in raw_info]),\\t\\t# 3 -> Latest round date\\r\\n\\t\\t\\tnp.mean([round[3] for round in raw_info])\\t# 4 -> Average round strength\\r\\n\\t\\t]\\r\\n\\r\\n\\t\\treturn info\",\n \"def get_season_dates(date, season):\\n start_date_start = date\\n start_date_end = date\\n if season == \\\"Spring\\\":\\n start_date_start = date.replace(month=4)\\n start_date_end = date.replace(month=6, day=30)\\n elif season == \\\"Summer\\\":\\n start_date_start = date.replace(month=7)\\n start_date_end = date.replace(month=9, day=30)\\n elif season == \\\"Fall\\\":\\n start_date_start = date.replace(month=10)\\n start_date_end = date.replace(month=12, day=31)\\n elif season == \\\"Winter\\\":\\n start_date_start = date.replace(month=1)\\n start_date_end = date.replace(month=3, day=31)\\n return start_date_start, start_date_end\",\n \"def season_edges(t):\\n offset = season_offset(t)\\n yr_min = t.datetime.min().year\\n left_frac = t.jyear.min() % yr_min\\n if left_frac < offset:\\n ex_left = 1\\n else:\\n ex_left = 0\\n edges = np.arange(yr_min - ex_left + offset, t.jyear.max() + 1, 1.0)\\n return astropy.time.Time(edges, format='jyear')\",\n \"def test_show_iter(self):\\n self.assertEquals(\\n len(\\n [season for season in self.t['Life on Mars']]\\n ),\\n 2\\n )\",\n \"def __iter__(self):\\n return iter(self._enums)\",\n \"def get_seasons(link, debug=False):\\n\\n if debug:\\n print(\\\"begin get_seasons()\\\")\\n\\n # get the BeautifulSoup data\\n show_url = \\\"https://www.imdb.com/\\\" + link + \\\"episodes/\\\"\\n tv_soup = bs4.BeautifulSoup(requests.get(show_url).text, features=\\\"html.parser\\\")\\n\\n # We are acquiring this data from a drop down, which the below line selects\\n select_elem = tv_soup.select('#bySeason')\\n seasons = []\\n # account for the possibility of a one season show\\n if len(select_elem) == 0:\\n seasons.append(1)\\n else:\\n # get contents of drop down\\n options = select_elem[0].select('option')\\n\\n # add each season\\n for season in options:\\n seasons.append(season.get('value'))\\n if debug:\\n print(f\\\"Seasons {seasons}\\\")\\n\\n return seasons\",\n \"def get_possible_timeframes(level: str, section_type: str,\\n student_obj, section_table) -> Set[int]:\\n set_choices = dict()\\n for st in SECTION_NAME_MAPPING.keys():\\n section_id = student_obj.__getattribute__(f'{st}_info_id')\\n if section_id: # section has been started\\n set_choices[st] = int(section_table.row_dict[section_id].activity_timescale) // 30\\n else:\\n set_choices[st] = None\\n\\n if level == 'bronze':\\n if 6 in set_choices.values(): # one section already on 6 months\\n return {3} # only choice left is 3 months\\n else:\\n return {3, 6}\\n\\n elif level == 'silver':\\n if section_type == 'vol':\\n return {6}\\n elif section_type == 'skill':\\n if set_choices['phys'] == 3:\\n return {6}\\n elif set_choices['phys'] == 6:\\n return {3}\\n else:\\n return {3, 6}\\n elif section_type == 'phys':\\n if set_choices['skill'] == 3:\\n return {6}\\n elif set_choices['skill'] == 6:\\n return {3}\\n else:\\n return {3, 6}\\n\\n elif level == 'gold':\\n if section_type == 'vol':\\n return {12}\\n elif section_type == 'skill':\\n if set_choices['phys'] == 6:\\n return {12}\\n elif set_choices['phys'] == 12:\\n return {6}\\n else:\\n return {6, 12}\\n elif section_type == 'phys':\\n if set_choices['skill'] == 6:\\n return {12}\\n elif set_choices['skill'] == 12:\\n return {6}\\n else:\\n return {6, 12}\",\n \"def speed_setting_season(self):\\n if self.season == \\\"spring\\\":\\n self.grid.speed_values[self.path_color] = 4\\n self.grid.speed_values[(0, 0, 255)] = 0.1\\n elif self.season == \\\"winter\\\":\\n self.grid.speed_values[self.path_color] = 3\\n self.grid.speed_values[(0, 0, 255)] = 0.1\\n elif self.season == \\\"fall\\\":\\n self.grid.speed_values[self.path_color] = 6\\n elif self.season == \\\"summer\\\":\\n pass\",\n \"def gather_archtypes(meta_url):\\r\\n soup = hot_soup(meta_url)\\r\\n archtypes = { strat: [] for strat in ['aggro', 'control', 'combo'] }\\r\\n for strat in soup.find_all(class_='Stable')[0].find_all(rowspan=True): # In this table, only the style type headers use 'rowspan'\\r\\n strat_str = strat.contents[0].lower() # Get corrosponding key for archtypes dict\\r\\n item = strat.parent\\r\\n\\r\\n # Gather each archtype under each strategy type\\r\\n while len(archtypes[strat_str]) < int(strat['rowspan']) - 1: # Rowspan == number of archtypes under this style\\r\\n item = item.next_sibling\\r\\n if isinstance(item, NavigableString):\\r\\n continue\\r\\n if item.a: # If this sibling has a link, we know it's what we're looking for\\r\\n text = item.a.text\\r\\n num_decks = int(item.contents[3].text)\\r\\n archtypes[strat_str].append((text, num_decks))\\r\\n \\r\\n return archtypes\",\n \"def season(self, seasonnum, order='aired'):\\n if order=='aired':\\n seasons = self.seasons\\n elif order == 'dvd':\\n seasons = self.dvd_seasons\\n try:\\n return seasons[seasonnum]\\n except KeyError:\\n raise SeasonNotFoundError(\\n 'Season no %s does not exists' % seasonnum\\n ), None, sys.exc_info()[2]\",\n \"def getshowsbyseason(season,year, session = None):\\n url = APIURL.format(season=season, year = year)\\n headers = {\\\"X-CSRF-TOKEN\\\":session.cookies['X-CSRF-TOKEN']}\\n data = alrequests.GET_json(url,session = session, headers=headers)\\n return {cat:[Show(**show) for show in shows] for cat,shows in data.items()}\",\n \"def season_rounds(cls, season):\\r\\n\\t\\t\\r\\n\\t\\tfolder_name = cls.season_folder(season)\\r\\n\\t\\tround_list = os.listdir(f'Data/{folder_name}')\\r\\n\\r\\n\\t\\tall_rounds = []\\r\\n\\r\\n\\t\\tfor round_file in round_list:\\r\\n\\t\\t\\twith open(f'Data/{folder_name}/{round_file}', 'r', encoding='utf-8') as f:\\r\\n\\t\\t\\t\\tround_info = f.read().splitlines()\\r\\n\\r\\n\\t\\t\\tround_number = round_file[:-4]\\r\\n\\t\\t\\tfull_round_name = f\\\"{season} R{round_number}\\\"\\r\\n\\r\\n\\t\\t\\tround_date = int(round_info[0])\\r\\n\\t\\t\\tlookup_ind = DATES.month_diff(round_date, DATES.MIN_DATE)\\r\\n\\r\\n\\t\\t\\t# If the round isn't actually counted for TWOW Glicko\\r\\n\\t\\t\\tif full_round_name not in cls.ROUNDS[lookup_ind].keys():\\r\\n\\t\\t\\t\\tcontinue\\r\\n\\r\\n\\t\\t\\tcontestant_count = len(round_info) - 1\\r\\n\\r\\n\\t\\t\\tstrength = cls.ROUNDS[lookup_ind][full_round_name][0]\\r\\n\\r\\n\\t\\t\\tall_rounds.append([\\r\\n\\t\\t\\t\\tround_number,\\r\\n\\t\\t\\t\\tround_date,\\r\\n\\t\\t\\t\\tcontestant_count,\\r\\n\\t\\t\\t\\tstrength\\r\\n\\t\\t\\t])\\r\\n\\t\\t\\r\\n\\t\\treturn all_rounds\",\n \"def season_months(season):\\n\\n ssn=['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug',\\n 'sep', 'oct', 'nov', 'dec', 'djf', 'mam', 'jja', 'son',\\n 'mayjun', 'julaug', 'marapr', 'jjas', 'ond', 'ann']\\n\\n imon = [1, 2, 3, 4, 5, 6, 7, 8,\\n 9, 10, 11, 12, [1,2,12], [3,4,5], [6,7,8], [9,10,11],\\n [5,6], [7,8], [3,4], [6,7,8,9], [10,11,12], list(range(1,13))]\\n\\n try:\\n ifind = ssn.index(season.lower())\\n except ValueError:\\n raise ValueError('Season not found! Valid seasons: ' + ', '.join(ssn))\\n\\n months = imon[ifind]\\n\\n # Make sure the output is a list\\n if isinstance(months, int):\\n months =[months]\\n\\n return months\",\n \"def parse_episodes_by_season (self, response_data):\\n episodes = {}\\n raw_episodes = response_data['value']['videos']\\n for episode_id in raw_episodes:\\n if self._is_size_key(key=episode_id) == False:\\n if (raw_episodes[episode_id]['summary']['type'] == 'episode'):\\n episodes.update(self.parse_episode(episode=raw_episodes[episode_id], genres=response_data['value']['genres']))\\n return episodes\",\n \"def parse_season(self, response):\\n # gather competition numbers and titles from the page\\n competition_numbers = response.css('select[name=\\\"evt\\\"] optgroup option::attr(value)').getall()\\n competition_titles = response.css('select[name=\\\"evt\\\"] optgroup option::text').getall()\\n url_components = urlparse(response.url)\\n\\n for competition_number, competition_title in zip(competition_numbers, competition_titles):\\n # assemble direct URL for this competition\\n competition_url = f'{url_components.scheme}://{url_components.netloc}/Results.aspx?evt={competition_number}'\\n\\n # pass along metadata for use in next steps\\n response.meta.update(dict(competition_title=competition_title,\\n competition_id=competition_number))\\n\\n # scrape the competition page\\n yield scrapy.Request(url=competition_url,\\n callback=self.parse_competition,\\n meta=response.meta)\",\n \"def chn_season_type_recognize(str):\\n season_type = None\\n if '第一季度' in str:\\n season_type = 'Q1'\\n elif '第二季度' in str:\\n season_type = 'Q2'\\n elif '半年度' in str:\\n season_type = 'Q2'\\n elif '第三季度' in str:\\n season_type = 'Q3'\\n elif '年度报告' in str:\\n season_type = 'FY'\\n\\n return season_type\",\n \"def seasonality(df):\\n df_datetime = pd.DatetimeIndex(df.date_time)\\n df[\\\"month\\\"] = df_datetime.month\\n df = drop_cols(df, [\\\"date_time\\\"])\\n\\n return df\",\n \"def season(year, week=None, timeout=None, concurrency=_DEFAULT_CONCURRENCY):\\n\\tLOG.debug('Concurrency = %d', concurrency)\\n\\tgames = season_games(year)\\n\\tif week is not None:\\n\\t\\tgames = games[games.week == week]\\n\\texpected_n = len(games)\\n\\ttables, futures_to_args, failures = [], {}, []\\n\\t# See https://docs.python.org/3/library/concurrent.futures.html#threadpoolexecutor-example.\\n\\twith futures.ThreadPoolExecutor(concurrency) as pool:\\n\\t\\tfor arg in zip(games.hometeam, games.awayteam, games.week):\\n\\t\\t\\targ = arg + (year,)\\n\\t\\t\\tfutures_to_args[pool.submit(game_unknown_homeaway, *arg)] = arg\\n\\t\\tfor future in futures.as_completed(futures_to_args, timeout=timeout):\\n\\t\\t\\targs = futures_to_args[future]\\n\\t\\t\\ttry:\\n\\t\\t\\t\\ttable = future.result()\\n\\t\\t\\texcept Exception as exc:\\n\\t\\t\\t\\tLOG.exception('Error from %s: %s', args, exc)\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif table is None:\\n\\t\\t\\t\\t\\tLOG.error('Failure: %s', args)\\n\\t\\t\\t\\t\\tfailures.append(args)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tLOG.info('Success: %s', args)\\n\\t\\t\\t\\t\\ttables.append(table)\\n\\ttables = games.merge(pd.concat(tables), on=('hometeam', 'awayteam', 'week'))\\n\\tif __debug__:\\n\\t\\tn = len(tables.groupby(['hometeam', 'awayteam', 'week']))\\n\\t\\tassert n == expected_n, \\\"Expected %d games, got %d\\\" % (expected_n, n)\\n\\treturn tables, failures\",\n \"def by_season(self, season):\\n return self.get_queryset().filter(season=season)\",\n \"def seasonStats(personId,type = 'gameLog',group = 'hitting'):\\n\\n #playerInfo = get('people', {'personIds':personId})\\n\\n\\n teamStats = get('person',{ 'ver':'v1' , 'personId':personId,'hydrate':['stats(group={},type={})'.format(group,type),'currentTeam']})\\n return teamStats\\n #iterate of stats and find the right player id\\n #career stats broken\\n #fix the season :2019\\n #make function to get team id\",\n \"def standings_by_season(season):\\n season = int(season) + 1\\n scoreboard = nba_py.Scoreboard(month=7,\\n day=1,\\n year=season)\\n east_standings = scoreboard.east_conf_standings_by_day()\\n west_standings = scoreboard.west_conf_standings_by_day()\\n\\n return render_template(\\\"standings.html\\\",\\n title=\\\"standings\\\",\\n east_standings=enumerate(east_standings, 1),\\n west_standings=enumerate(west_standings, 1),\\n team=CITY_TO_TEAM)\",\n \"def season_series(game_id, pref_team, other_team, last_season=False):\\n\\n # Init empty dictionaries and lists\\n games_against = list()\\n pref_toi = dict()\\n pref_goals = dict()\\n pref_assists = dict()\\n pref_points = dict()\\n pref_record = {\\\"wins\\\": 0, \\\"losses\\\": 0, \\\"ot\\\": 0}\\n roster_player = True\\n\\n # If this is the first game of the season, we can set the 'last_season' flag to enable the\\n # season series function to check last year's season series between the two teams.\\n if not last_season:\\n season_start = str(game_id)[0:4]\\n season_end = str(int(season_start) + 1)\\n yesterday = datetime.now() - timedelta(days=1)\\n # yesterday = datetime.now() + timedelta(days=50)\\n # schedule_url = (\\n # f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n # f\\\"&expand=schedule.broadcasts,schedule.teams&startDate=\\\"\\n # f\\\"{season_start}-08-01&endDate={yesterday:%Y-%m-%d}\\\"\\n # )\\n schedule_url = (\\n f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n f\\\"&expand=schedule.broadcasts,schedule.teams\\\"\\n f\\\"&season={season_start}{season_end}\\\"\\n )\\n else:\\n season_start = int(str(game_id)[0:4]) - 1\\n season_end = str(int(season_start) + 1)\\n yesterday = datetime.now() - timedelta(days=1)\\n # yesterday = datetime.now() + timedelta(days=50)\\n # schedule_url = (\\n # f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n # f\\\"&expand=schedule.broadcasts,schedule.teams&startDate=\\\"\\n # f\\\"{season_start}-08-01&endDate={season_end}-06-01\\\"\\n # )\\n schedule_url = (\\n f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n f\\\"&expand=schedule.broadcasts,schedule.teams\\\"\\n f\\\"&season={season_start}{season_end}\\\"\\n )\\n\\n schedule = api.nhl_api(schedule_url).json()\\n dates = schedule[\\\"dates\\\"]\\n\\n # Loop through scheduled to get previously played games against\\n for date in dates:\\n game = date[\\\"games\\\"][0]\\n game_type = game[\\\"gameType\\\"]\\n game_id = game[\\\"gamePk\\\"]\\n game_team_home = game[\\\"teams\\\"][\\\"home\\\"][\\\"team\\\"][\\\"name\\\"]\\n game_team_away = game[\\\"teams\\\"][\\\"away\\\"][\\\"team\\\"][\\\"name\\\"]\\n teams = [game_team_away, game_team_home]\\n game_status = game[\\\"status\\\"][\\\"abstractGameState\\\"]\\n if game_type == \\\"R\\\" and game_status == \\\"Final\\\" and other_team.team_name in teams:\\n game_feed = f\\\"/game/{game_id}/feed/live\\\"\\n games_against.append(game_feed)\\n\\n # If the two teams haven't played yet, just exit this function\\n if not games_against:\\n return None, None, None\\n\\n # Loop through newly created games_against list to get each stats\\n for feed in games_against:\\n game = api.nhl_api(feed).json()\\n game_data = game[\\\"gameData\\\"]\\n home_team_name = game_data[\\\"teams\\\"][\\\"home\\\"][\\\"name\\\"]\\n pref_homeaway = \\\"home\\\" if home_team_name == pref_team.team_name else \\\"away\\\"\\n other_homeaway = \\\"away\\\" if home_team_name == pref_team.team_name else \\\"home\\\"\\n\\n # Get season series\\n end_period = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"currentPeriod\\\"]\\n extra_time = True if end_period > 3 else False\\n pref_score = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"teams\\\"][pref_homeaway][\\\"goals\\\"]\\n other_score = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"teams\\\"][other_homeaway][\\\"goals\\\"]\\n if pref_score > other_score:\\n pref_record[\\\"wins\\\"] += 1\\n elif other_score > pref_score and extra_time:\\n pref_record[\\\"ot\\\"] += 1\\n else:\\n pref_record[\\\"losses\\\"] += 1\\n\\n season_series_str = f\\\"Series: {pref_record['wins']}-\\\" f\\\"{pref_record['losses']}-{pref_record['ot']}\\\"\\n\\n # Get stats leaders\\n # pref_teamstats = game[\\\"liveData\\\"][\\\"boxscore\\\"][\\\"teams\\\"][pref_homeaway][\\\"teamStats\\\"]\\n pref_playerstats = game[\\\"liveData\\\"][\\\"boxscore\\\"][\\\"teams\\\"][pref_homeaway][\\\"players\\\"]\\n for id, player in pref_playerstats.items():\\n try:\\n # Calculate TOI\\n player_toi_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"timeOnIce\\\"]\\n player_toi_minutes = int(player_toi_str.split(\\\":\\\")[0])\\n player_toi_seconds = int(player_toi_str.split(\\\":\\\")[1])\\n player_toi = (player_toi_minutes * 60) + player_toi_seconds\\n pref_toi[id] = pref_toi.get(id, 0) + player_toi\\n\\n # Point Totals\\n player_goal_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"goals\\\"]\\n pref_goals[id] = pref_goals.get(id, 0) + int(player_goal_str)\\n player_assist_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"assists\\\"]\\n pref_assists[id] = pref_assists.get(id, 0) + int(player_assist_str)\\n player_points = int(player_goal_str) + int(player_assist_str)\\n pref_points[id] = pref_points.get(id, 0) + int(player_points)\\n\\n except KeyError:\\n pass\\n\\n # Calculate Stats Leaders\\n sorted_toi = sorted(pref_toi.values(), reverse=True)\\n leader_toi = sorted_toi[0]\\n\\n sorted_points = sorted(pref_points.values(), reverse=True)\\n leader_points = sorted_points[0]\\n\\n # Get TOI leader\\n for id in pref_toi.keys():\\n if pref_toi[id] == leader_toi:\\n player_name = roster.player_attr_by_id(pref_team.roster, id, \\\"fullName\\\")\\n if player_name is None:\\n roster_player = False\\n player_id_only = id.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n leader_toi_avg = leader_toi / len(games_against)\\n m, s = divmod(leader_toi_avg, 60)\\n toi_m = int(m)\\n toi_s = int(s)\\n toi_s = \\\"0{}\\\".format(toi_s) if toi_s < 10 else toi_s\\n toi_avg = \\\"{}:{}\\\".format(toi_m, toi_s)\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n toi_leader_str = \\\"TOI Leader: {} with {} / game.\\\".format(player_short_name, toi_avg)\\n\\n # Handle tied points leaders\\n point_leaders = list()\\n for id in pref_points.keys():\\n if pref_points[id] == leader_points:\\n point_leaders.append(id)\\n\\n if leader_points == 0:\\n points_leader_str = \\\"Points Leader: None (all players have 0 points).\\\"\\n\\n elif len(point_leaders) == 1:\\n leader = point_leaders[0]\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n # If the player is no longer on the team, get their information (change string here?)\\n if player_name is None:\\n roster_player = False\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n if not roster_player:\\n points_leader_str = (\\n f\\\"Points Leader: {player_name} with {leader_points} points \\\"\\n f\\\"({player_goals}G {player_assists}A) \\\"\\n )\\n else:\\n points_leader_str = \\\"Points Leader: {} with {} ({}G {}A).\\\".format(\\n player_name, leader_points, player_goals, player_assists\\n )\\n\\n elif len(point_leaders) > 3:\\n point_leaders_with_attrs = list()\\n for leader in point_leaders:\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n if player_name is None:\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n point_leaders_with_attrs.append(player_short_name)\\n\\n point_leaders_joined = \\\", \\\".join(point_leaders_with_attrs[0:3])\\n leftover_leaders = len(point_leaders) - 3\\n points_leader_str = (\\n f\\\"Points Leaders: {point_leaders_joined} & {leftover_leaders} others ({leader_points} each).\\\"\\n )\\n\\n else:\\n point_leaders_with_attrs = list()\\n for leader in point_leaders:\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n if player_name is None:\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n player_str = f\\\"{player_short_name} ({player_goals}G {player_assists}A)\\\"\\n point_leaders_with_attrs.append(player_str)\\n\\n point_leaders_joined = (\\n f\\\", \\\".join(point_leaders_with_attrs[:-1]) + f\\\" & {point_leaders_with_attrs[-1]}\\\"\\n )\\n points_leader_str = \\\"Points Leaders: {} with {} each.\\\".format(point_leaders_joined, leader_points)\\n\\n return season_series_str, points_leader_str, toi_leader_str\",\n \"def test_domain_and_target_type(self):\\n t = Enumerate([2, \\\"asfa\\\", \\\"ipsi\\\"])\\n assert t.domain_type == \\\"categorical\\\"\\n assert t.target_type == \\\"integer\\\"\",\n \"def season_days(season, leap=False):\\n\\n # Index of first day of each month\\n ndays = days_per_month(leap=leap)\\n ndays.insert(0,1)\\n days = np.cumsum(ndays)\\n\\n # Index of months for this season\\n imon = season_months(season)\\n\\n # Days of the year for this season\\n if isinstance(imon, list):\\n # Iterate over months in this season\\n idays=[]\\n for m in imon:\\n idays.extend(list(range(days[m-1], days[m])))\\n else:\\n # Single month\\n idays = list(range(days[imon-1], days[imon]))\\n\\n return idays\",\n \"def get_season(\\n current_date: date, hemisphere: str, season_tracking_type: str\\n) -> str | None:\\n\\n if hemisphere == \\\"equator\\\":\\n return None\\n\\n if season_tracking_type == TYPE_ASTRONOMICAL:\\n spring_start = ephem.next_equinox(str(current_date.year)).datetime()\\n summer_start = ephem.next_solstice(str(current_date.year)).datetime()\\n autumn_start = ephem.next_equinox(spring_start).datetime()\\n winter_start = ephem.next_solstice(summer_start).datetime()\\n else:\\n spring_start = datetime(2017, 3, 1).replace(year=current_date.year)\\n summer_start = spring_start.replace(month=6)\\n autumn_start = spring_start.replace(month=9)\\n winter_start = spring_start.replace(month=12)\\n\\n if spring_start <= current_date < summer_start:\\n season = STATE_SPRING\\n elif summer_start <= current_date < autumn_start:\\n season = STATE_SUMMER\\n elif autumn_start <= current_date < winter_start:\\n season = STATE_AUTUMN\\n elif winter_start <= current_date or spring_start > current_date:\\n season = STATE_WINTER\\n\\n # If user is located in the southern hemisphere swap the season\\n if hemisphere == NORTHERN:\\n return season\\n return HEMISPHERE_SEASON_SWAP.get(season)\",\n \"def get_all_games(season):\\n url = BASE_URL.format(season)\\n json_data = requests.get(url, headers=HEADERS).json()\\n all_games = json_data[\\\"resultSets\\\"][0][\\\"rowSet\\\"]\\n return all_games\",\n \"def compute_levels_seasons(self, Y: t.Tensor, idxs: t.Tensor):\\n y_transformed, _ = Y.median(1)\\n y_transformed = y_transformed.reshape(-1, 1)\\n levels = y_transformed.repeat(1, Y.shape[1])\\n seasonalities = None\\n\\n return levels, None\",\n \"def parse_seasons (self, id, response_data):\\n raw_seasons = response_data['value']\\n videos = raw_seasons['videos']\\n\\n # get art video key\\n video = {}\\n for key, video_candidate in videos.iteritems():\\n if not self._is_size_key(key):\\n video = video_candidate\\n\\n # get season index\\n sorting = {}\\n for idx, season_list_entry in video['seasonList'].iteritems():\\n if self._is_size_key(key=idx) == False and idx != 'summary':\\n sorting[int(season_list_entry[1])] = int(idx)\\n\\n seasons = {}\\n\\n for season in raw_seasons['seasons']:\\n if self._is_size_key(key=season) == False:\\n seasons.update(self._parse_season_entry(season=raw_seasons['seasons'][season], video=video, sorting=sorting))\\n return seasons\",\n \"def get_seasons_information():\\n\\n #getting the guidebox_id variable from show_page.html\\n guidebox_id = request.args.get(\\\"guidebox_id\\\")\\n\\n #make API to get season information, gets back list of season information\\n seasons_results = guidebox_season_info(guidebox_id)\\n\\n for season in seasons_results:\\n date = season[\\\"first_airdate\\\"]\\n year = str(date)[0:4]\\n season[\\\"first_airdate\\\"] = year\\n\\n return jsonify(seasons_results)\",\n \"def test_enumerating_stereo_options(self, toolkit_class):\\n\\n if toolkit_class.is_available():\\n toolkit = toolkit_class()\\n\\n # test undefined only\\n mol = Molecule.from_smiles(\\n \\\"ClC=CCl\\\", toolkit_registry=toolkit, allow_undefined_stereo=True\\n )\\n isomers = mol.enumerate_stereoisomers(\\n undefined_only=True, rationalise=False\\n )\\n\\n assert len(isomers) == 2\\n for isomer in isomers:\\n assert isomer.n_conformers == 0\\n\\n mol = Molecule.from_smiles(\\n r\\\"Cl/C=C\\\\Cl\\\", toolkit_registry=toolkit, allow_undefined_stereo=True\\n )\\n isomers = mol.enumerate_stereoisomers(\\n undefined_only=True, rationalise=False\\n )\\n\\n assert isomers == []\\n\\n mol = Molecule.from_smiles(\\n r\\\"Cl/C=C\\\\Cl\\\", toolkit_registry=toolkit, allow_undefined_stereo=True\\n )\\n isomers = mol.enumerate_stereoisomers(\\n undefined_only=False, rationalise=False\\n )\\n\\n assert len(isomers) == 1\\n\\n # test max isomers\\n mol = Molecule.from_smiles(\\n \\\"BrC=C[C@H]1OC(C2)(F)C2(Cl)C1\\\",\\n toolkit_registry=toolkit,\\n allow_undefined_stereo=True,\\n )\\n isomers = mol.enumerate_stereoisomers(\\n max_isomers=5,\\n undefined_only=True,\\n toolkit_registry=toolkit,\\n rationalise=True,\\n )\\n\\n assert len(isomers) <= 5\\n for isomer in isomers:\\n assert isomer.n_conformers == 1\\n\\n else:\\n pytest.skip(\\\"Required toolkit is unavailable\\\")\",\n \"def report_type_choices():\\n\\n rts = report_types()\\n rcs = report_categories()\\n return [(c, [(rt.report_type, rt.name) for rt in rts if rt.category == c]) for c in rcs]\",\n \"def test_enumerating_stereo_options(self, toolkit_class):\\n\\n if toolkit_class.is_available():\\n toolkit = toolkit_class()\\n\\n # test undefined only\\n mol = Molecule.from_smiles(\\n \\\"ClC=CCl\\\", toolkit_registry=toolkit, allow_undefined_stereo=True\\n )\\n isomers = mol.enumerate_stereoisomers(\\n undefined_only=True, rationalise=False\\n )\\n\\n assert len(isomers) == 2\\n for isomer in isomers:\\n assert isomer.n_conformers == 0\\n\\n mol = Molecule.from_smiles(\\n \\\"Cl/C=C\\\\Cl\\\", toolkit_registry=toolkit, allow_undefined_stereo=True\\n )\\n isomers = mol.enumerate_stereoisomers(\\n undefined_only=True, rationalise=False\\n )\\n\\n assert isomers == []\\n\\n mol = Molecule.from_smiles(\\n \\\"Cl/C=C\\\\Cl\\\", toolkit_registry=toolkit, allow_undefined_stereo=True\\n )\\n isomers = mol.enumerate_stereoisomers(\\n undefined_only=False, rationalise=False\\n )\\n\\n assert len(isomers) == 1\\n\\n # test max isomers\\n mol = Molecule.from_smiles(\\n \\\"BrC=C[C@H]1OC(C2)(F)C2(Cl)C1\\\",\\n toolkit_registry=toolkit,\\n allow_undefined_stereo=True,\\n )\\n isomers = mol.enumerate_stereoisomers(\\n max_isomers=5,\\n undefined_only=True,\\n toolkit_registry=toolkit,\\n rationalise=True,\\n )\\n\\n assert len(isomers) <= 5\\n for isomer in isomers:\\n assert isomer.n_conformers == 1\\n\\n else:\\n pytest.skip(\\\"Required toolkit is unavailable\\\")\",\n \"def give_season_suggestion():\\n current_month = datetime.datetime.now().month\\n all_month = (\\n None, 'январь', 'февраль', 'март', 'апрель', 'май', 'июнь',\\n 'июль', 'август', 'сентябрь', 'октябрь', 'ноябрь', 'декабрь')\\n\\n # Autumn.\\n if current_month in [9, 10, 11]:\\n month_list = [\\n 'осень', 'осенний', 'осеннее', 'осенние', 'осенняя', 'сентябрь',\\n 'октябрь', 'ноябрь', 'сентября', 'октября', 'ноября',\\n 'сентябрьский', 'октябрьский', 'ноябрьский', 'свитер',\\n 'листопад']\\n suggestion_list = give_item_by_tag(month_list)\\n if suggestion_list == '':\\n suggestion_list = \\\"добавить в ваши рецепты что-то осенее;)\\\"\\n suggest = f\\\"\\\"\\\"\\n_________________¶\\n________________¶¶\\n___________¶___¶¶¶___¶\\n___________¶¶¶¶¶¶¶¶/¶¶\\n____¶_______¶¶¶¶|¶¶/¶¶________¶\\n_¶¶¶¶¶¶______¶¶¶|¶/¶¶_______¶¶¶¶¶\\n__¶¶¶¶¶¶____¶¶¶¶|/¶¶¶____¶¶¶¶¶¶\\n_¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶|¶¶¶/¶¶¶¶¶/¶¶¶¶¶¶\\n____¶¶¶¶¶¶¶¶¶¶¶¶|¶¶/¶¶¶¶¶/¶¶¶¶\\n¶____¶¶¶¶¶¶¶¶¶¶¶|¶/¶¶¶¶¶/¶¶¶_____¶\\n¶¶¶_¶¶¶¶¶¶¶¶¶¶¶¶|/¶¶¶¶¶/¶¶¶¶__¶¶¶¶\\n¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶|¶¶¶¶¶/¶¶¶¶¶¶¶¶¶¶¶\\n__¶¶¶¶¶¶¶¶¶¶¶¶¶¶|¶¶¶¶/¶¶¶¶¶/¶¶¶¶\\n___¶¶¶¶¶¶¶¶¶¶¶¶¶|¶¶¶/¶¶¶¶/¶¶¶¶¶\\n_¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶|¶¶/¶¶¶/¶¶¶¶¶¶¶¶¶¶\\n________¶¶¶¶¶¶¶¶|¶/¶/¶¶¶¶¶¶\\n_______________¶¶\\n______________¶¶ На дворе {all_month[current_month]}, осень.\\n______________¶¶ самое время приготовить что-то уютное,\\n______________¶¶ бодрящее из ваших рецептов.\\n_______________¶¶ Рекомендуем:\\n ________________¶¶¶ {suggestion_list}\\n \\\"\\\"\\\"\\n\\n # Winter.\\n elif current_month in [1, 2, 12]:\\n month_list = [\\n 'зима', 'зимний', 'зимнее', 'зимние', 'зимняя', 'январь',\\n 'декабрь', 'февраль', 'декабря', 'января', 'февраля',\\n 'декабрьский', 'январский', 'февральский', 'свитер', 'снег',\\n 'снегопад', 'иней', 'зимушка', 'Зимушка', 'новый год',\\n 'новым годом', 'рождество', 'рождественский']\\n suggestion_list = give_item_by_tag(month_list)\\n if suggestion_list == '':\\n suggestion_list = \\\"добавить в ваши рецепты что-то зимнее;)\\\"\\n suggest = f\\\"\\\"\\\"\\n_____________________$$\\n_____________________$$\\n_____________$$$$____$$____$$$$\\n_____ ____$$____$$$$$$$$$$$$$$____$$\\n_________$$________$$$$$$________$$\\n_$$______$$__________$$__________$$______$$\\n_$$$$$$__$ $__________$$__________$$__$$$$$$\\n_____$$$$$$__________$$__________$$$$$$\\n_______$$$$$$________$$________$$$$$$\\n___$$$$______$$ $$____$$____$$$$______$$$$\\n_$$______________$$$$$$$$$$______________$$\\n___________________$$$$$$\\n_$$______________$$$ $$$$$$$______________$$\\n___$$$$______$$$$____$$____$$$$______$$$$\\n_______$$$$$$________$$________$$$$$$\\n_____$$$$$$__________$$__ ________$$$$$$\\n_$$$$$$__$$__________$$__________$$__$$$$$$\\n_$$______$$__________$$__________$$______$$\\n_________$$________$$$$$$_____ ___$$\\n_________$$____$$$$$$$$$$$$$$____$$ На дворе {all_month[current_month]}, зима.\\n_____________$$$$____$$____$$$$ самое время приготовить что-то приятное,\\n_____________________$$ согревающее из ваших рецептов.\\n___________\\n Рекомендуем:\\n {suggestion_list}\\n \\\"\\\"\\\"\\n\\n # Spring.\\n elif current_month in [3, 4, 5]:\\n month_list = [\\n 'весна', 'весенний', 'весеннее', 'весенние', 'весенняя', 'март',\\n 'апрель', 'май', 'марта', 'апреля', 'мая', 'мартовский',\\n 'апрельский', 'майский', 'свежий']\\n suggestion_list = give_item_by_tag(month_list)\\n if suggestion_list == '':\\n suggestion_list = \\\"добавить в ваши рецепты что-то весеннее;)\\\"\\n suggest = f\\\"\\\"\\\"\\n______d$$$$b_d$$$$$b\\n_____$$$$$$$$$$$$$$$$$\\n____$$$$$$P_$$$$$$$$$\\n_____V$P_______Y$$P\\n___________________d$$b\\n___$$$$_________$$$$$$\\n___$$$ $$$______$$$$$$$\\n____Y$$$$$$$$$$$$$$$$P\\n______Y$P_$$$$$$$\\n__________Y$$$P_$\\n__________________$\\n_________________$\\n_________________ $\\n__________________$$$$$_\\n_________________$$$$$$$$ $\\n__$$$$$$$_______$$$$$$\\n_$$$$$$$$$__$$$$$ На дворе {all_month[current_month]}, весна.\\n$_ _____$$$$$ Самое время бодрящих, легкий ароматов!\\n________$$_$ Рекомендуем:\\n _________$$$ {suggestion_list}\\n__________$$\\n___________$\\n \\\"\\\"\\\"\\n\\n # Summer.\\n elif current_month in [6, 7, 8]:\\n month_list = [\\n 'лето', 'летний', 'летнее', 'летние', 'летняя', 'июнь', 'июль',\\n 'август', 'июля', 'июня', 'августа', 'июньский', 'июльский',\\n 'августовский', 'тропический', 'тропики', 'экзотика',\\n 'экзотический', 'отпуск']\\n suggestion_list = give_item_by_tag(month_list)\\n if suggestion_list == '':\\n suggestion_list = \\\"добавить в ваши рецепты что-то летнее;)\\\"\\n suggest = f\\\"\\\"\\\"\\n________________ ¶¶¶¶¶¶¶¶\\n_______________ ¶¶¶¶\\n___¶¶¶¶¶¶¶¶¶__ ¶¶¶____¶¶¶\\n_ ¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶\\n¶¶¶¶¶ ______¶¶¶¶¶¶¶¶____¶¶¶¶¶\\n_______ ¶¶¶¶¶¶¶¶¶¶¶¶¶¶¶_____¶¶\\n______ ¶¶¶¶__¶¶¶_¶¶¶¶¶¶¶¶¶\\n____ ¶¶¶¶___¶¶¶___¶¶¶_¶¶¶¶¶\\n___ ¶¶¶¶¶__¶¶¶¶_¶¶_¶¶¶_¶¶¶¶¶\\n___ ¶¶¶___¶¶¶¶¶_¶¶__¶¶¶__¶¶¶\\n__ ¶¶____¶¶¶¶¶___¶¶_¶¶¶¶_¶¶¶\\n________ ¶¶¶_____¶¶___¶¶__¶\\n_________ ¶¶¶____¶¶¶__¶¶__¶\\n_________ ¶¶¶_____¶¶___¶\\n_________ ¶¶______¶¶\\n_________________ ¶¶¶\\n_________________ ¶¶¶ На дворе {all_month[current_month]}, лето.\\n_________________ ¶¶¶ Самое время экзотических коктейлей и\\n________________ ¶¶¶¶ сладости фруктов!\\n________________ ¶¶¶ Рекомендуем:\\n ____________ ¶¶¶_¶¶¶ {suggestion_list}\\n______________ ¶¶¶¶¶¶¶¶\\n\\n \\\"\\\"\\\"\\n return suggest\",\n \"def season_games(year):\\n\\tLOG.debug('Getting season %d', year)\\n\\tdata = read_html(io=season_games_url(year),\\n\\t\\t\\t\\t\\t attrs={'id': 'games'},\\n\\t\\t\\t\\t\\t infer_types=False,\\n\\t\\t\\t\\t\\t header=0)\\n\\tif len(data) != 1:\\n\\t\\traise CantFindTheRightTable\\n\\tdata = data.pop()\\n\\n\\t# Cleaning.\\n\\tdel data[\\\"Unnamed: 3\\\"]\\n\\t# The code below issues \\\"UserWarning: \\\" So we catch UserWarnings.\\n\\twith warnings.catch_warnings():\\n\\t\\twarnings.filterwarnings(action='ignore', category=UserWarning,\\n\\t\\t\\t\\t\\t\\t\\t\\tmodule=r'pandas\\\\.core\\\\.frame',\\n\\t\\t\\t\\t\\t\\t\\t\\tmessage=(r\\\"Boolean Series key will be reindexed\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t r\\\" to match DataFrame index\\\\.\\\"))\\n\\t\\t# These rows are mid-table header rows.\\n\\t\\tdata = data[data.Week != \\\"Week\\\"][data.Week != \\\"nan\\\"]\\n\\n\\tdata['week'] = (data.Week\\n\\t\\t\\t\\t\\t.replace(\\\"WildCard\\\", \\\"wild-card\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"Division\\\", \\\"divisional\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"ConfChamp\\\", \\\"conference\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"SuperBowl\\\", \\\"super-bowl\\\")\\n\\t\\t\\t\\t\\t.apply(\\n\\t\\t\\t\\t\\t\\tlambda s: (int(s)\\n\\t\\t\\t\\t\\t\\t\\t\\t if all(c in '1234567890' for c in s)\\n\\t\\t\\t\\t\\t\\t\\t\\t else s)))\\n\\tdel data['Week']\\n\\n\\tdata['season'] = year\\n\\tdata['game_date'] = pd.to_datetime(\\n\\t\\tdata.Date\\n\\t\\t.replace(r\\\"$\\\", r\\\", %d\\\" % year, regex=True)\\n\\t\\t.replace(r\\\"^(January|February) (\\\\d+), \\\\d+$\\\", r\\\"\\\\1 \\\\2, %d\\\" % (year + 1),\\n\\t\\t\\t\\t regex=True))\\n\\tdel data['Date']\\n\\n\\tfor column in \\\"PtsW\\\", \\\"PtsL\\\", \\\"YdsW\\\", \\\"TOW\\\", \\\"YdsL\\\", \\\"TOL\\\":\\n\\t data[column] = data[column].apply(int)\\n\\n\\tdata['WatL'] = data['Unnamed: 5'].apply(lambda x: x == '@')\\n\\tdel data['Unnamed: 5']\\n\\tdata['hometeam'] = (~data.WatL * data['Winner/tie'] +\\n\\t\\t\\t\\t\\t\\tdata.WatL * data['Loser/tie'])\\n\\tdata['awayteam'] = (data.WatL * data['Winner/tie'] +\\n\\t\\t\\t\\t\\t\\t~data.WatL * data['Loser/tie'])\\n\\tdata['winner'] = data['Winner/tie']\\n\\tfor column in 'Winner/tie', 'Loser/tie', \\\"WatL\\\":\\n\\t\\tdel data[column]\\n\\tfor column in 'hometeam', 'awayteam', 'winner':\\n\\t\\tdata[column] = data[column].apply(lambda s: s.split()[-1].lower())\\n\\n\\treturn data\",\n \"def next(self):\\n season = self.value\\n season = (season + 1) % len(Season.__members__.items())\\n return Season(season)\",\n \"def set_season(self, season):\\n self.set_date_range(dt.date(season, 1, 1),\\n dt.date(season, 12, 31))\",\n \"def test_get_season_19_march(self, calendar, expected):\\n date = datetime.date(2017, 3, 19)\\n assert calendar.get_season(date) == expected\",\n \"def replace_season(data,season):\\n if not SeasonCharts.matchseason(season):\\n raise SeasonCharts.SeasonError\\n ## Check data format\\n if test_rawdata(data):\\n for cat,shows in data.items():\\n for show in shows: show['season'] = season\\n else:\\n for show in data: show['season'] = season\",\n \"def map_season(x, figsize=(8, 6), **kwargs):\\n\\n from pycmbs.mapping import map_plot\\n\\n nvals = len(x.data)\\n if nvals == 12:\\n year = True\\n elif nvals == 4:\\n year = False\\n else:\\n raise ValueError('Only data for 4-seasons or monthly data is supported!')\\n\\n #/// checks ///\\n if x.data.ndim != 3:\\n print x.data.ndim\\n raise ValueError('only 3D data supported')\\n\\n if 'vmin' not in kwargs.keys():\\n raise ValueError('vmin argument is obligatory for map_seasons()')\\n if 'vmax' not in kwargs.keys():\\n raise ValueError('vmax argument is obligatory for map_seasons()')\\n\\n if kwargs['vmin'] is None:\\n raise ValueError('vmin MUST NOT be None!')\\n if kwargs['vmax'] is None:\\n raise ValueError('vmax MUST NOT be None!')\\n\\n #/// figure and axes\\n if 'figure' in kwargs:\\n f = kwargs['figure']\\n else:\\n f = plt.figure(figsize=figsize)\\n\\n if 'title' in kwargs:\\n tit = kwargs.pop('title')\\n else:\\n tit = x.label\\n\\n if 'drawparallels' in kwargs:\\n drawparallels = kwargs.pop('drawparallels')\\n else:\\n drawparallels = False\\n\\n if 'savefile' in kwargs:\\n savefile = kwargs.pop('savefile')\\n if '.nc' in savefile:\\n savefile = savefile[:-3]\\n else:\\n savefile = None\\n\\n # plot\\n if year:\\n labels = ['JAN', 'FEB', 'MAR', 'APR', 'MAY', 'JUN', 'JUL', 'AUG', 'SEP', 'OCT', 'NOV', 'DEC']\\n else:\\n labels = ['DJF', 'MAM', 'JJA', 'SON']\\n\\n # check dates\\n if year:\\n mo = 1\\n for t in x.time:\\n if x.num2date(t).month != mo:\\n print x.num2date(t), mo\\n raise ValueError('Invalid monthly sequence! Can not plot results!')\\n mo += 1\\n\\n #/// in case that an overlay is provided, this needs to be processed for each timestep individually\\n if 'overlay' in kwargs.keys():\\n overlays = kwargs.pop('overlay')\\n else:\\n overlays = None\\n\\n for i in range(nvals):\\n if year:\\n ax = f.add_subplot(4, 3, i + 1)\\n #if i % 3 == 2:\\n if i > 8:\\n show_colorbar = True\\n else:\\n show_colorbar = False\\n else:\\n ax = f.add_subplot(2, 2, i + 1)\\n if 'show_colorbar' in kwargs:\\n show_colorbar = kwargs.pop('show_colorbar')\\n else:\\n show_colorbar = True\\n\\n d = x.copy()\\n d.data = x.data[i, :, :]\\n d.label = labels[i]\\n\\n if overlays is None:\\n overlay = None\\n else:\\n overlay = overlays[i, :, :]\\n\\n if savefile is not None:\\n tmpoutname = savefile + '_' + labels[i]\\n else:\\n tmpoutname = None\\n\\n map_plot(d, ax=ax, show_colorbar=show_colorbar, overlay=overlay,\\n savefile=tmpoutname, colorbar_orientation='horizontal',\\n drawparallels=drawparallels, **kwargs)\\n del d\\n f.suptitle(tit, size=16)\\n return f\",\n \"def calcSeason(ra, time):\\n # Reference RA and equinox to anchor ra/season reference - RA = 0 is overhead at autumnal equinox\\n # autumn equinox 2014 happened on september 23 --> equinox MJD\\n Equinox = 2456923.5 - 2400000.5\\n # convert ra into 'days'\\n dayRA = ra / 360 * 365.25\\n firstSeasonBegan = Equinox + dayRA - 0.5 * 365.25\\n seasons = (time - firstSeasonBegan) / 365.25\\n # Set first season to 0\\n seasons = seasons - np.floor(np.min(seasons))\\n return seasons\",\n \"def iter_combos(include_unknown=False):\\n if include_unknown:\\n return _combos\\n else:\\n return _combos[:-7]\",\n \"def opinion_type_list():\\n for type_ in orm.DataFlagOpinionType.select():\\n click.echo(type_.name)\",\n \"def get_tracking_stats(\\n measure_type: TrackingMeasureType,\\n seasons: List[str],\\n season_types: List[SeasonType],\\n player_or_team: PlayerOrTeam,\\n **kwargs,\\n) -> List[Any]:\\n all_season_stats = []\\n for season in seasons:\\n for season_type in season_types:\\n # Making too many requests in a short period can result in timeouts. Add a delay between requests\\n time.sleep(2)\\n results = get_tracking_results_for_stat_measure(\\n measure_type, season, season_type, player_or_team, **kwargs\\n )\\n stats = RESPONSE_MODEL_MAP[measure_type](**results)\\n for stat in stats.results:\\n stat.season = f\\\"{season} {season_type}\\\"\\n all_season_stats += stats.results\\n return all_season_stats\",\n \"def closed_statuses_for_all_types(self):\\n\\n controller = LogicaOrderController(self.env)\\n closed_statuses = controller.type_and_statuses_for_closed_statusgroups()\\n types_and_statuses = []\\n for type_, statuses in closed_statuses.iteritems():\\n types_and_statuses.append(type_)\\n types_and_statuses.extend(statuses)\\n\\n return closed_statuses, types_and_statuses\",\n \"def _build_enum_groups(self):\\n for group in self.used_enum_groups:\\n if group not in self.group_to_enum_list:\\n print(f'Warning: Enum group {group} has no values defined.')\\n continue\\n\\n group_type = 'basic'\\n if group in self.bitmask_groups:\\n group_type = 'bitmask'\\n\\n values_used = set()\\n enum_name_list = self.group_to_enum_list[group]\\n enum_tuple_list = []\\n for enum_name in enum_name_list:\\n if enum_name not in self.enum_list:\\n continue\\n\\n if enum_name not in self.enum_name_to_value:\\n print(f'Warning: enum {enum_name} has no value')\\n continue\\n\\n if not self._enum_should_collect(enum_name):\\n continue\\n\\n enum_value = self.enum_name_to_value[enum_name]\\n if enum_value not in values_used:\\n enum_value_str = self._case_value(enum_name)\\n enum_tuple_list.append((enum_value, enum_value_str, enum_name))\\n values_used.add(enum_value)\\n\\n if enum_name in self.bitmask_enums:\\n group_type = 'bitmask'\\n\\n enum_tuple_list.sort()\\n\\n group_max_len = 0\\n for enum_info in enum_tuple_list:\\n wrapper_enum_value_name = enum_info[2][3:].lower()\\n if wrapper_enum_value_name in RESERVED_NAMES:\\n wrapper_enum_value_name = wrapper_enum_value_name + '_'\\n if len(wrapper_enum_value_name) > group_max_len:\\n group_max_len = len(wrapper_enum_value_name)\\n\\n group_enum_string_entries = []\\n group_enum_to_base_zero_entries = []\\n base_zero_to_group_enum_entries = []\\n group_wrapper_enum_value_definitions = []\\n base_zero_value = 0\\n\\n for enum_info in enum_tuple_list:\\n wrapper_enum_value_name = enum_info[2][3:].lower()\\n\\n if wrapper_enum_value_name in RESERVED_NAMES:\\n wrapper_enum_value_name = wrapper_enum_value_name + '_'\\n\\n formatting = {\\n 'ENUM_VALUE': enum_info[1],\\n 'ENUM_STRING': enum_info[2],\\n 'ENUM_BASE_ZERO_VALUE': base_zero_value,\\n 'ENUM_VERSION': self._enum_version(enum_info[2]),\\n 'WRAPPER_ENUM_TYPE_NAME': self.to_type_name(group),\\n 'WRAPPER_ENUM_VALUE_NAME': wrapper_enum_value_name,\\n }\\n\\n string_entry = templates.ENUM_STRING_MAKE_ENTRY[group_type].format(**formatting)\\n group_enum_string_entries.append(string_entry)\\n group_wrapper_enum_value_definitions.append(\\n ' {:{}} = {:>6}u /* {} */'.format(\\n wrapper_enum_value_name,\\n group_max_len,\\n enum_info[1],\\n self._enum_version(enum_info[2])))\\n\\n if group_type == 'basic':\\n group_enum_to_base_zero_make_entry = templates.ENUM_TO_BASE_ZERO_MAKE_ENTRY.format(**formatting)\\n base_zero_to_group_enum_make_entry = templates.BASE_ZERO_TO_ENUM_MAKE_ENTRY.format(**formatting)\\n group_enum_to_base_zero_entries.append(group_enum_to_base_zero_make_entry)\\n base_zero_to_group_enum_entries.append(base_zero_to_group_enum_make_entry)\\n base_zero_value = base_zero_value + 1\\n\\n if enum_tuple_list:\\n wrapper_enum_name = self.to_type_name(group)\\n definitions = ',\\\\n'.join(sorted(group_wrapper_enum_value_definitions))\\n string_entries = '\\\\n'.join(sorted(group_enum_string_entries))\\n formatting = {\\n 'WRAPPER_ENUM_TYPE_NAME': wrapper_enum_name,\\n 'WRAPPER_ENUM_STRING_FN_NAME': self.split_to_body_and_ext(group)[0],\\n 'GROUP_NAME': group,\\n 'WRAPPER_ENUM_VALUE_DEFINITIONS': definitions,\\n 'GROUP_ENUM_STRING_ENTRIES': string_entries,\\n 'GROUP_ENUM_TO_BASE_ZERO_ENTRIES': '\\\\n'.join(group_enum_to_base_zero_entries),\\n 'BASE_ZERO_TO_GROUP_ENUM_ENTRIES': '\\\\n'.join(base_zero_to_group_enum_entries),\\n }\\n\\n if group_type == 'basic':\\n self.enum_to_base_zero_function_declarations.append(\\n templates.ENUM_TO_BASE_ZERO_FUNCTION_DECLARATION.format(**formatting)\\n )\\n self.base_zero_to_enum_function_declarations.append(\\n templates.BASE_ZERO_TO_ENUM_FUNCTION_DECLARATION.format(**formatting)\\n )\\n self.enum_base_zero_function_definitions.append(\\n templates.ENUM_BASE_ZERO_FUNCTION_DEFINITION.format(**formatting)\\n )\\n self.untyped_enum_string_function_declarations.append(\\n templates.UNTYPED_ENUM_STRING_FUNCTION_DECLARATION.format(**formatting)\\n )\\n self.untyped_enum_string_function_definitions.append(\\n templates.UNTYPED_ENUM_STRING_FUNCTION_DEFINITION.format(**formatting)\\n )\\n\\n self.enum_string_function_declarations.append(\\n templates.ENUM_STRING_FUNCTION_DECLARATION[group_type].format(**formatting)\\n )\\n self.enum_string_function_definitions.append(\\n templates.ENUM_STRING_FUNCTION_DEFINITION[group_type].format(**formatting)\\n )\\n self.wrapper_enum_declarations.append(\\n templates.WRAPPER_ENUM_DECLARATION.format(**formatting)\\n )\\n self.enum_helper_definitions.append(\\n templates.ENUM_HELPER_DEFINITION[group_type].format(**formatting)\\n )\",\n \"def enum(self):\\n for l, u in self:\\n for i in range(l, u):\\n yield i\",\n \"def _get_latest_season_and_type(df):\\n if df is None or len(df) == 0:\\n return (None, None)\\n\\n if not isinstance(df, pd.DataFrame):\\n raise ValueError(f\\\"Type of df is {type(df)}, it should be pandas.DataFrame\\\")\\n\\n df = df[df['state_of_game'] == 'POST']\\n latest_season = df['season'].max()\\n season_types = df[df['season'] == latest_season]['type'].unique()\\n latest_season_type = _get_latest_season_type(list(season_types))\\n return latest_season, latest_season_type\",\n \"def choices(cls):\\n # return list(map(tuple, cls.__members__.items()))\\n return [(int(code), name) for name, code in cls.__members__.items()]\",\n \"def return_football_season(date=datetime.datetime.today()):\\n date_aux = subtract_months(date, 6)\\n beginning_year = str(date_aux.year)\\n ending_year = date_aux.year + 1\\n ending_year = str(ending_year)[-2:]\\n season = ''.join([beginning_year, '-', ending_year])\\n return season\",\n \"def test_18_dataverse_types_all(self):\\n print (self.test_18_dataverse_types_all.__doc__)\\n\\n kwargs = dict(select_year=2016)\\n stats_maker = StatsMakerDataverses(**kwargs)\\n r = stats_maker.get_dataverse_counts_by_type()\\n\\n # check number of entries\\n self.assertEqual(len(r.result_data['records']), 7)\\n\\n # check first listing\\n first_listing = {'dataversetype': u'RESEARCHERS',\\n 'dataversetype_label': u'RESEARCHERS',\\n 'percent_string': '38.8%',\\n 'total_count': 291,\\n 'type_count': 113}\\n self.assertEqual(dict(r.result_data['records'][0]), first_listing)\\n\\n # check last listing\\n last_listing = {'dataversetype': u'LABORATORY',\\n 'dataversetype_label': u'LABORATORY',\\n 'percent_string': '0.3%',\\n 'total_count': 291,\\n 'type_count': 1}\\n\\n self.assertEqual(dict(r.result_data['records'][-1]), last_listing)\\n\\n # -------------------------\\n # Include UNCATEGORIZED Dataverses\\n # -------------------------\\n kwargs = dict(select_year=2016)\\n stats_maker = StatsMakerDataverses(**kwargs)\\n r = stats_maker.get_dataverse_counts_by_type(exclude_uncategorized=False)\\n\\n # check number of entries\\n self.assertEqual(len(r.result_data['records']), 8)\\n\\n # check UNCATEGORIZED listing\\n uncat_listing = {'dataversetype': u'UNCATEGORIZED',\\n 'dataversetype_label': u'UNCATEGORIZED',\\n 'percent_string': '18.3%',\\n 'total_count': 356,\\n 'type_count': 65}\\n self.assertEqual(dict(r.result_data['records'][2]), uncat_listing)\",\n \"def scrape():\\n league_year = Config.get_property(\\\"league_year\\\")\\n\\n # Create table\\n season_data = client.season_schedule(league_year)\\n season_data = br_enum_to_string(season_data)\\n return season_data\",\n \"def _generate_hourly_seasonality(\\n self,\\n time_range: np.ndarray,\\n hourly_seasonality_params: List[Tuple[float, float]],\\n ) -> np.ndarray:\\n\\n return sum(\\n [\\n alpha * np.cos(2 * np.pi * (i + 1) * time_range / 24)\\n + beta * np.sin(2 * np.pi * (i + 1) * time_range / 24)\\n for i, (alpha, beta) in enumerate(hourly_seasonality_params)\\n ]\\n )\",\n \"def fetch_seasons_for_show(self, params):\\n show_id = params.get('show_id', [''])[0]\\n raw_season_list = self.netflix_session.fetch_seasons_for_show(\\n id=show_id)\\n if 'error' in raw_season_list:\\n return raw_season_list\\n # check if we have sesons,\\n # announced shows that are not available yet have none\\n if 'seasons' not in raw_season_list.get('value', {}):\\n return []\\n seasons = self.netflix_session.parse_seasons(\\n id=show_id,\\n response_data=raw_season_list)\\n return seasons\",\n \"def episodes(self):\\n episodes = []\\n for season in self.seasons:\\n episodes.extend(season.episodes)\\n return episodes\",\n \"def get_chapters_from(self, season, episode):\\n\\n show = self.tvShow\\n if season not in show:\\n return []\\n # Gets remaning episodes for current season\\n episodes = [show[season][iepisode]\\n for iepisode in range(episode + 1, len(show[season]) + 1)]\\n # Get episodes for new seasons\\n episodes += [show[iseason][iepisode]\\n for iseason in range(season + 1, len(show))\\n for iepisode in range(1, len(show[iseason]))]\\n return [Episode(self, x) for x in episodes]\",\n \"def test_enumerating_stereobonds(self, toolkit_class):\\n\\n if toolkit_class.is_available():\\n toolkit = toolkit_class()\\n mol = Molecule.from_smiles(\\n \\\"ClC=CCl\\\", allow_undefined_stereo=True, toolkit_registry=toolkit\\n )\\n\\n # use the default options\\n isomers = mol.enumerate_stereoisomers()\\n assert len(isomers) == 2\\n\\n assert mol not in isomers\\n # make sure the input molecule is only different by bond stereo\\n for ismol in isomers:\\n assert (\\n Molecule.are_isomorphic(\\n mol,\\n ismol,\\n return_atom_map=False,\\n bond_stereochemistry_matching=False,\\n )[0]\\n is True\\n )\\n assert mol.is_isomorphic_with(ismol) is False\\n\\n # make sure the isomers are different\\n assert isomers[0].is_isomorphic_with(isomers[1]) is False\\n\\n else:\\n pytest.skip(\\\"Required toolkit is unavailable\\\")\",\n \"def test_enumerating_stereobonds(self, toolkit_class):\\n\\n if toolkit_class.is_available():\\n toolkit = toolkit_class()\\n mol = Molecule.from_smiles(\\n \\\"ClC=CCl\\\", allow_undefined_stereo=True, toolkit_registry=toolkit\\n )\\n\\n # use the default options\\n isomers = mol.enumerate_stereoisomers()\\n assert len(isomers) == 2\\n\\n assert mol not in isomers\\n # make sure the input molecule is only different by bond stereo\\n for ismol in isomers:\\n assert (\\n Molecule.are_isomorphic(\\n mol,\\n ismol,\\n return_atom_map=False,\\n bond_stereochemistry_matching=False,\\n )[0]\\n is True\\n )\\n assert mol.is_isomorphic_with(ismol) is False\\n\\n # make sure the isomers are different\\n assert isomers[0].is_isomorphic_with(isomers[1]) is False\\n\\n else:\\n pytest.skip(\\\"Required toolkit is unavailable\\\")\",\n \"def test_durations_per_type_num_players(self):\\n type_of_player = [ss.Player, ss.LazyPlayer, ss.ResilientPlayer]\\n sim = ss.Simulation(player_field=type_of_player)\\n run = sim.durations_per_type()\\n assert list(run.keys()) == ['Player', 'LazyPlayer', 'ResilientPlayer']\",\n \"def sameSeason( pMonth, cMonth ):\\r\\n if pMonth == 12 or pMonth == 1 or pMonth == 2:\\r\\n if cMonth == 12 or cMonth == 1 or cMonth == 2:\\r\\n return True\\r\\n else:\\r\\n return False\\r\\n\\r\\n if pMonth == 3 or pMonth == 4 or pMonth == 5:\\r\\n if cMonth == 3 or cMonth == 4 or cMonth == 5:\\r\\n return True\\r\\n else:\\r\\n return False\\r\\n\\r\\n if pMonth == 6 or pMonth == 7 or pMonth == 8:\\r\\n if cMonth == 6 or cMonth == 7 or cMonth == 8:\\r\\n return True\\r\\n else:\\r\\n return False\\r\\n\\r\\n if pMonth == 9 or pMonth == 10 or pMonth == 11:\\r\\n if cMonth == 9 or cMonth == 10 or cMonth == 11:\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"def determine_available_voice_types(sim_info: SimInfo) -> Tuple[CommonVoiceActorType]:\\n from sims4communitylib.utils.sims.common_age_species_utils import CommonAgeSpeciesUtils\\n from sims4communitylib.utils.sims.common_species_utils import CommonSpeciesUtils\\n from sims4communitylib.utils.sims.common_age_utils import CommonAgeUtils\\n if CommonAgeSpeciesUtils.is_teen_adult_or_elder_human(sim_info):\\n result: Tuple[CommonVoiceActorType] = (\\n CommonVoiceActorType.MUTE,\\n CommonVoiceActorType.ADULT_HUMAN_AMBIGUOUS_1,\\n CommonVoiceActorType.ADULT_HUMAN_FEMININE_1,\\n CommonVoiceActorType.ADULT_HUMAN_FEMININE_2,\\n CommonVoiceActorType.ADULT_HUMAN_MASCULINE_1,\\n CommonVoiceActorType.ADULT_HUMAN_MASCULINE_2,\\n CommonVoiceActorType.ADULT_HUMAN_MASCULINE_3,\\n CommonVoiceActorType.KYLO_REN_1,\\n CommonVoiceActorType.REY_1,\\n CommonVoiceActorType.HONDO_OHNAKA_1,\\n )\\n elif CommonAgeSpeciesUtils.is_child_human(sim_info):\\n result: Tuple[CommonVoiceActorType] = (\\n CommonVoiceActorType.MUTE,\\n CommonVoiceActorType.CHILD_HUMAN_AMBIGUOUS_1,\\n CommonVoiceActorType.CHILD_HUMAN_AMBIGUOUS_2,\\n CommonVoiceActorType.KYLO_REN_1,\\n CommonVoiceActorType.REY_1,\\n CommonVoiceActorType.HONDO_OHNAKA_1,\\n )\\n elif CommonAgeSpeciesUtils.is_toddler_human(sim_info):\\n result: Tuple[CommonVoiceActorType] = (\\n CommonVoiceActorType.MUTE,\\n CommonVoiceActorType.TODDLER_HUMAN_AMBIGUOUS_1,\\n )\\n elif CommonSpeciesUtils.is_large_dog(sim_info) and CommonAgeUtils.is_teen_adult_or_elder(sim_info):\\n result: Tuple[CommonVoiceActorType] = (\\n CommonVoiceActorType.MUTE,\\n CommonVoiceActorType.ADULT_DOG_AMBIGUOUS_1,\\n CommonVoiceActorType.ADULT_DOG_AMBIGUOUS_2,\\n CommonVoiceActorType.ADULT_DOG_AMBIGUOUS_3,\\n CommonVoiceActorType.ADULT_DOG_AMBIGUOUS_4\\n )\\n elif CommonSpeciesUtils.is_large_dog(sim_info) and CommonAgeUtils.is_child(sim_info):\\n result: Tuple[CommonVoiceActorType] = (\\n CommonVoiceActorType.MUTE,\\n CommonVoiceActorType.CHILD_DOG_AMBIGUOUS_1,\\n )\\n elif CommonSpeciesUtils.is_small_dog(sim_info) and CommonAgeUtils.is_teen_adult_or_elder(sim_info):\\n result: Tuple[CommonVoiceActorType] = (\\n CommonVoiceActorType.MUTE,\\n CommonVoiceActorType.ADULT_DOG_AMBIGUOUS_1,\\n CommonVoiceActorType.ADULT_DOG_AMBIGUOUS_2,\\n CommonVoiceActorType.ADULT_DOG_AMBIGUOUS_3,\\n CommonVoiceActorType.ADULT_DOG_AMBIGUOUS_4\\n )\\n elif CommonSpeciesUtils.is_small_dog(sim_info) and CommonAgeUtils.is_child(sim_info):\\n result: Tuple[CommonVoiceActorType] = (\\n CommonVoiceActorType.MUTE,\\n CommonVoiceActorType.CHILD_DOG_AMBIGUOUS_1,\\n )\\n elif CommonSpeciesUtils.is_cat(sim_info) and CommonAgeUtils.is_teen_adult_or_elder(sim_info):\\n result: Tuple[CommonVoiceActorType] = (\\n CommonVoiceActorType.MUTE,\\n CommonVoiceActorType.ADULT_CAT_AMBIGUOUS_1,\\n CommonVoiceActorType.ADULT_CAT_AMBIGUOUS_2,\\n )\\n elif CommonSpeciesUtils.is_cat(sim_info) and CommonAgeUtils.is_child(sim_info):\\n result: Tuple[CommonVoiceActorType] = (\\n CommonVoiceActorType.MUTE,\\n CommonVoiceActorType.CHILD_CAT_AMBIGUOUS_1,\\n )\\n elif CommonSpeciesUtils.is_fox(sim_info) and CommonAgeUtils.is_teen_adult_or_elder(sim_info):\\n result: Tuple[CommonVoiceActorType] = (\\n CommonVoiceActorType.MUTE,\\n CommonVoiceActorType.ADULT_FOX_AMBIGUOUS_1,\\n )\\n else:\\n result: Tuple[CommonVoiceActorType] = (\\n CommonVoiceActorType.MUTE,\\n )\\n return result\",\n \"def test_time_type_state_types(day):\\n\\n assert day_time_info(day.hours_0).types == {TimeType.NIGHT}\\n assert day_time_info(day.hours_1).types == {TimeType.NIGHT}\\n assert day_time_info(day.hours_2).types == {TimeType.NIGHT}\\n assert day_time_info(day.hours_3).types == {TimeType.NIGHT}\\n assert day_time_info(day.hours_4).types == {TimeType.NIGHT}\\n assert day_time_info(day.hours_5).types == {TimeType.MORNING}\\n assert day_time_info(day.hours_6).types == {TimeType.MORNING}\\n assert day_time_info(day.hours_7).types == {TimeType.MORNING}\\n assert day_time_info(day.hours_8).types == {TimeType.MORNING}\\n assert day_time_info(day.hours_9).types == {TimeType.MORNING}\\n assert day_time_info(day.hours_10).types == {TimeType.MIDMORNING}\\n assert day_time_info(day.hours_11).types == {TimeType.MIDMORNING}\\n assert day_time_info(day.hours_12).types == {TimeType.NOON}\\n assert day_time_info(day.hours_13).types == {TimeType.AFTERNOON}\\n assert day_time_info(day.hours_14).types == {TimeType.AFTERNOON}\\n assert day_time_info(day.hours_15).types == {TimeType.AFTERNOON}\\n assert day_time_info(day.hours_16).types == {TimeType.AFTERNOON}\\n assert day_time_info(day.hours_17).types == {TimeType.AFTERNOON}\\n assert day_time_info(day.hours_18).types == {TimeType.EVENING}\\n assert day_time_info(day.hours_19).types == {TimeType.EVENING}\\n assert day_time_info(day.hours_20).types == {TimeType.EVENING}\\n assert day_time_info(day.hours_21).types == {TimeType.EVENING}\\n assert day_time_info(day.hours_22).types == {TimeType.EVENING}\\n assert day_time_info(day.hours_23).types == {TimeType.NIGHT}\",\n \"def spreads(lines, sport):\\n value = []\\n for game in lines:\\n combos = it.product(game['away_odds'].items(), game['home_odds'].items())\\n try:\\n value.extend([f'{sport} {game[\\\"game\\\"]} {combo[0][0]}: {combo[0][1][0]} {combo[0][1][1]} and '\\n f'{combo[1][0]}: {combo[1][1][0]} {combo[1][1][1]}\\\\n\\\\n' for combo in combos\\n if combo[0][1][0] + combo[1][1][0] >= 0 and combo[0][1][1] + combo[1][1][1] >= 0])\\n except TypeError:\\n print(combos)\\n\\n return value\",\n \"def python_type(self) -> type:\\n return SeasonEntity\",\n \"def read_seasons(): # type: () -> Dict[int, str]\\n csv_seasons = {}\\n with open(\\\"rgl_seasons.csv\\\", encoding=\\\"utf-8\\\") as f:\\n for line in f:\\n if not line:\\n continue\\n season_id, season_name = line.split(\\\",\\\")\\n csv_seasons[int(season_id)] = season_name\\n return csv_seasons\",\n \"def bfs_season(self, season_edges):\\n if self.season == \\\"summer\\\":\\n return\\n pixel_neighbors = [(0, 1), (1, 0), (-1, 0), (0, -1)]\\n season_boundaries = []\\n depth_parameters = None\\n if self.season == \\\"spring\\\":\\n depth_parameters = 15\\n elif self.season == \\\"winter\\\":\\n depth_parameters = 7\\n elif self.season == \\\"fall\\\":\\n depth_parameters = 1\\n elif self.season == \\\"summer\\\":\\n depth_parameters = 0\\n for points in season_edges:\\n parent_elevation = float(self.grid.ele_copy[points[0]][points[1]])\\n if self.season == \\\"winter\\\":\\n if self.grid.pix_copy[points[0], points[1]] == (0, 0, 255):\\n season_boundaries.append((points[0], points[1]))\\n queue = []\\n explored_set = {}\\n queue.append((points[0], points[1]))\\n\\n # BFS Implementation\\n while len(queue) != 0:\\n current_node = queue.pop(0)\\n if current_node[0] == points[0] + depth_parameters or \\\\\\n current_node[1] == points[1] + depth_parameters or \\\\\\n current_node[0] == points[0] - depth_parameters or \\\\\\n current_node[1] == points[1] - depth_parameters:\\n break\\n if current_node not in explored_set:\\n explored_set[(current_node[0], current_node[1])] = 0\\n else:\\n continue\\n for k in range(len(pixel_neighbors)):\\n x_value = current_node[0] + pixel_neighbors[k][0]\\n y_value = current_node[1] + pixel_neighbors[k][1]\\n if self.season == \\\"spring\\\":\\n if x_value >= 0 and x_value < self.grid.width and y_value >= 0 and y_value < self.grid.height:\\n if (x_value, y_value) not in queue and (x_value, y_value) not in explored_set:\\n if (not (float(self.grid.ele_copy[x_value][y_value]) - parent_elevation > 1)):\\n if self.grid.pix_copy[x_value, y_value] != (205, 0, 101):\\n queue.append((x_value, y_value))\\n if self.grid.pix_copy[x_value, y_value] != (0, 0, 255):\\n season_boundaries.append((x_value, y_value))\\n elif self.season == \\\"winter\\\":\\n if x_value >= 0 and x_value < self.grid.width and y_value >= 0 and y_value < self.grid.height:\\n if (x_value, y_value) not in queue and (x_value, y_value) not in explored_set:\\n queue.append((x_value, y_value))\\n if self.grid.pix_copy[x_value, y_value] == (0, 0, 255):\\n season_boundaries.append((x_value, y_value))\\n elif self.season == \\\"fall\\\":\\n if x_value >= 0 and x_value < self.grid.width and y_value >= 0 and y_value < self.grid.height:\\n if (x_value, y_value) not in queue and (x_value, y_value) not in explored_set:\\n queue.append((x_value, y_value))\\n if self.grid.pix_copy[x_value, y_value] == (248, 148, 18) \\\\\\n or self.grid.pix_copy[x_value, y_value] == (0, 0, 0):\\n season_boundaries.append((x_value, y_value))\\n while len(queue) != 0:\\n current_node = queue.pop(0)\\n xc = current_node[0]\\n yc = current_node[1]\\n if self.season == \\\"spring\\\":\\n if xc >= 0 and xc < self.grid.width and yc >= 0 and yc < self.grid.height:\\n if self.grid.pix_copy[xc, yc] != (0, 0, 255):\\n if (not (float(self.grid.ele_copy[xc][yc]) - parent_elevation >= 1)):\\n season_boundaries.append((xc, yc))\\n elif self.season == \\\"winter\\\":\\n if xc >= 0 and xc < self.grid.width and yc >= 0 and yc < self.grid.height:\\n if self.grid.pix_copy[xc, yc] == (0, 0, 255):\\n season_boundaries.append((xc, yc))\\n elif self.season == \\\"fall\\\":\\n if xc >= 0 and xc < self.grid.width and yc >= 0 and yc < self.grid.height:\\n if self.grid.pix_copy[xc, yc] == (248, 148, 18) \\\\\\n or self.grid.pix_copy[xc, yc] == (0, 0, 0):\\n season_boundaries.append((xc, yc))\\n self.update_map(season_boundaries)\\n print(\\\"map updated for season\\\")\\n print(\\\"Entering Astar\\\")\",\n \"def seasonality(time, period, amplitude=1 , phase=0):\\n season_time = ((time + phase) % period) / period\\n return amplitude * seasonal_pattern(season_time)\",\n \"def all_seasons_save():\\n year_arr = [str(f'{i}-{str((i + 1))[-2:]}') for i in reversed(range(1950, 2021))]\\n for year in year_arr:\\n c_name = f'season_player_game_logs_{year.replace(\\\"-\\\", \\\"_\\\")}'\\n try:\\n coll = db[c_name]\\n season_game_log_save(year, coll)\\n print(f'{c_name} successfully saved')\\n time.sleep(1)\\n except OperationFailure as e:\\n print(f'{c_name} not saved!!')\\n print(e)\",\n \"def determine_axes_to_group_by(is_volume: bool) -> Set[Axes]:\\n if is_volume:\\n return {Axes.ROUND, Axes.CH}\\n else:\\n return {Axes.ROUND, Axes.CH, Axes.ZPLANE}\",\n \"def seasonality(time, period, amplitude=1, phase=0):\\n\\tseason_time = ((time + phase) % period) / period\\n\\treturn amplitude * seasonal_pattern(season_time)\",\n \"def test_get_ordinal():\\n for (index, status) in enumerate(['draft', 'published', 'archived']):\\n yield (tools.eq_, BlogPostStatus.get_ordinal(status), index)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7313621","0.60445046","0.59210426","0.5904497","0.5747831","0.57103455","0.5611361","0.55891776","0.5507902","0.5506329","0.5493918","0.5421824","0.5413071","0.5396443","0.5385352","0.53806645","0.5351801","0.5346506","0.5319504","0.52745056","0.5232151","0.5217945","0.51696265","0.5108867","0.5093014","0.5062395","0.50366366","0.5012362","0.49992827","0.49968952","0.4995336","0.49872118","0.49690032","0.49596828","0.4954443","0.49174118","0.48831686","0.48581704","0.48465845","0.4822981","0.4798874","0.47975302","0.47707775","0.4767338","0.47556856","0.4750801","0.47495925","0.47358322","0.47239476","0.47232592","0.47077718","0.46944538","0.46819812","0.46814665","0.4671693","0.46548313","0.46531633","0.46482056","0.4642415","0.4639882","0.46398208","0.46395633","0.46375105","0.46312308","0.46269238","0.46163744","0.46045104","0.45750976","0.4560406","0.45506382","0.4548716","0.45462847","0.45380068","0.4537949","0.4522896","0.45142764","0.45077676","0.45070565","0.44988254","0.44937095","0.4493189","0.4488331","0.4486332","0.44822553","0.4472451","0.44691914","0.44691914","0.4460171","0.4445482","0.4444282","0.44436836","0.44421503","0.44411826","0.4432235","0.44282886","0.442001","0.44120115","0.44100714","0.44068176","0.4402248"],"string":"[\n \"0.7313621\",\n \"0.60445046\",\n \"0.59210426\",\n \"0.5904497\",\n \"0.5747831\",\n \"0.57103455\",\n \"0.5611361\",\n \"0.55891776\",\n \"0.5507902\",\n \"0.5506329\",\n \"0.5493918\",\n \"0.5421824\",\n \"0.5413071\",\n \"0.5396443\",\n \"0.5385352\",\n \"0.53806645\",\n \"0.5351801\",\n \"0.5346506\",\n \"0.5319504\",\n \"0.52745056\",\n \"0.5232151\",\n \"0.5217945\",\n \"0.51696265\",\n \"0.5108867\",\n \"0.5093014\",\n \"0.5062395\",\n \"0.50366366\",\n \"0.5012362\",\n \"0.49992827\",\n \"0.49968952\",\n \"0.4995336\",\n \"0.49872118\",\n \"0.49690032\",\n \"0.49596828\",\n \"0.4954443\",\n \"0.49174118\",\n \"0.48831686\",\n \"0.48581704\",\n \"0.48465845\",\n \"0.4822981\",\n \"0.4798874\",\n \"0.47975302\",\n \"0.47707775\",\n \"0.4767338\",\n \"0.47556856\",\n \"0.4750801\",\n \"0.47495925\",\n \"0.47358322\",\n \"0.47239476\",\n \"0.47232592\",\n \"0.47077718\",\n \"0.46944538\",\n \"0.46819812\",\n \"0.46814665\",\n \"0.4671693\",\n \"0.46548313\",\n \"0.46531633\",\n \"0.46482056\",\n \"0.4642415\",\n \"0.4639882\",\n \"0.46398208\",\n \"0.46395633\",\n \"0.46375105\",\n \"0.46312308\",\n \"0.46269238\",\n \"0.46163744\",\n \"0.46045104\",\n \"0.45750976\",\n \"0.4560406\",\n \"0.45506382\",\n \"0.4548716\",\n \"0.45462847\",\n \"0.45380068\",\n \"0.4537949\",\n \"0.4522896\",\n \"0.45142764\",\n \"0.45077676\",\n \"0.45070565\",\n \"0.44988254\",\n \"0.44937095\",\n \"0.4493189\",\n \"0.4488331\",\n \"0.4486332\",\n \"0.44822553\",\n \"0.4472451\",\n \"0.44691914\",\n \"0.44691914\",\n \"0.4460171\",\n \"0.4445482\",\n \"0.4444282\",\n \"0.44436836\",\n \"0.44421503\",\n \"0.44411826\",\n \"0.4432235\",\n \"0.44282886\",\n \"0.442001\",\n \"0.44120115\",\n \"0.44100714\",\n \"0.44068176\",\n \"0.4402248\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6033037"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":85590,"cells":{"query":{"kind":"string","value":"Runs the nflscrapr R script for the given season and type"},"document":{"kind":"string","value":"def _extract_games_data(season, season_type):\n nflscrapr.run(\n 'games',\n season=season,\n season_type=season_type\n )\n nflscrapr_output = etl_tools.extract_from_csv(config.GAMES_DUMP_CSV_PATH)\n return nflscrapr_output"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def scrape():\n league_year = Config.get_property(\"league_year\")\n\n # Create table\n season_data = client.season_schedule(league_year)\n season_data = br_enum_to_string(season_data)\n return season_data","def get_schedules_for_season(self, season, season_type=\"REG\"):\n try:\n season = int(season)\n if season_type not in [\"REG\", \"PRE\", \"POST\"]:\n raise ValueError\n except (ValueError, TypeError):\n raise FantasyDataError('Error: Invalid method parameters')\n\n season_param = \"{0}{1}\".format(season, season_type)\n result = self._method_call(\"Schedules/{season}\", season=season_param)\n return result","def schedule_url(year, stype, week):\n xmlurl = 'http://www.nfl.com/ajax/scorestrip?'\n if stype == 'POST':\n week += 17\n if week == 21: # NFL.com you so silly\n week += 1\n return '%sseason=%s&seasonType=%s&week=%s' % (xmlurl, year, stype, week)","def run_scrape(url: ParseResult, seasons_regex: str, episodes_regex: str) -> Optional[list[str]]:\n\n supported_sites: dict[str, Callable[[ParseResult, str], Optional[list[str]]]]\n supported_sites = {\n 'www.thenetnaija.com': scrapeSites.netnaija,\n 'www.lightdl.xyz': scrapeSites.lightdl,\n 'o2tvseries.com': scrapeSites.o2tvseries,\n }\n if url.netloc not in supported_sites:\n logger.error(\"Website from url not implemented\")\n raise exceptions.UnimplementedSite('Getting download links from that site is not yet implimented')\n\n return supported_sites[url.netloc](url, seasons_regex, episodes_regex)","def scrape(selection_type, launch_date, flat_type=None, town=None):\n click.secho(\"\\nStarting to scrape for...\", fg=\"yellow\")\n click.secho(\n f\"Type : {selection_type}, Launch Date : {launch_date}, \"\n f\"Flat Type : {flat_type if flat_type else 'All'} Room, \"\n f\"Town : {town if town else 'All'}\",\n )\n links = get_links_to_scrape(selection_type, launch_date, flat_type, town)\n data = scrape_links(links)\n return data","def main():\n if len(sys.argv) != 3:\n print(\"Usage: python season-get [year] [season, lowercase]\")\n return None\n\n year = sys.argv[1]\n season = sys.argv[2]\n print(\"Pulling from API\")\n\n API_url = \"http://api.jikan.moe/season/{}/{}\".format(year, season)\n\n # Error checking\n try:\n data = urllib.request.urlopen(API_url)\n except urllib.error.URLError as e:\n print(\"Error - {}\".format(e))\n return None\n print(\"Complete!\\n\")\n\n result = json.loads(data.read())['season']\n\n print(\"Loading IDs\")\n target_name = '{}-{}.txt'.format(year, season)\n target = open('./input/{}'.format(target_name), 'w')\n\n for anime in result:\n target.write(str(anime['mal_id']) + '\\n')\n print(\"Complete!\\n\")","def run():\n parser = argparse.ArgumentParser(description='Retrieve the latest prices for a specified video game')\n parser.add_argument('title', help='Title of the game')\n parser.add_argument('-p', '--platform', help='Platform the game is on')\n args = parser.parse_args()\n\n if (not args.platform):\n platform = \"any\"\n else:\n platform = args.platform\n\n scraper = Scraper()\n results = scraper.searchAll(game=args.title, platform=platform)\n for result in results:\n print('${0:6}| {1:7}| {2:8}| {3}'.format(result[2], result[1], result[3], result[0]))","def main(self, model=None):\n result = pd.DataFrame()\n if model is None:\n model = Model.default()\n for season in [2011, 2012, 2013]:\n print(season)\n r = Runner(Season(season), model)\n current = r.run()\n result = result.append(current)\n return result","def season_rank(bot):\n if len(bot.message.words) < 3:\n return irc.Response('URL parameter is missing. The proper command is !season rank <url>', pm_user=True)\n try:\n with open('season.txt', 'a') as f:\n f.write(bot.message.words[2])\n f.write('\\n')\n seasonal.update_rankings(bot.message.words[2], conf['challonge'])\n return irc.Response('Successfully updated the seasonal rankings', pm_user=True)\n except Exception as e:\n return irc.Response('An error occurred: ' + str(e), pm_user=True)","def run(self,circos_location, seasons=True):\n self.taxonomy_plot(seasons)\n print(seasons)\n filename=self.place+'_'+self.year+'.png'\n self.run_circos(filename, circos_location)\n print('In folder ', self.folder_name)","def run_scrapping():\n date = datetime.now().strftime(\"%Y-%m-%d\")\n size = 100\n r = list(range(size))\n random.shuffle(r)\n for i in r:\n scrap_page(url_page.format(i), date)\n print(str(i) + \" / \" + str(size))","def getseason(data):\n ## Season key is the most reliable\n season = data.get(\"season\")\n if season:\n ## Season key is an integer formatted \"YYS\" and is 2000-based (i.e.- 171 == 2017-Winter)\n season = str(season)\n year = int(f\"20{season[:2]}\")\n ## Anichart Season key is 1-indexed\n season = int(season[2]) - 1\n ## This should normally pass; if it consistently does not, we'll have to investigate why\n try: return SeasonCharts.buildseason(season,year)\n ## If something goes wrong, we'll try another method\n except: print(f\"Failed to parse season: {data['season']}\")\n ## Next, we'll iterate over rankings to try to determine the season/year\n ## There are multiple types of rankings based on season, year, and both combined,\n ## so we'll piece it together based on whatever we come across first\n season,year = None,None\n for ranking in data.get(\"rankings\",list()):\n ## Quicker exit (without just making this loop its own function)\n if season and year: continue\n ## We'll ignore stuff we've already gotten and assume that nothing in\n ## rankings contradicts eachother\n if not season:\n ## Defaults to None one way or another if it's not supplied\n season = ranking.get(\"season\")\n if not year: year = ranking.get(\"year\")\n ## Check if we made it\n if season and year:\n ## As above, this should always work out-of-the-box\n try: return SeasonCharts.buildseason(season,year)\n except: print(season,year)\n ## Welp, we're stumped...\n return None","def scrape_main() -> None:\n\n logger.info(\"Starting scrape\")\n search_info = construct_scrape_regex_patterns(grab_scrape_info())\n links = run_scrape(\n url=search_info['url'],\n seasons_regex=search_info['seasons'],\n episodes_regex=search_info['episodes']\n )\n if links:\n logger.debug(\"Writing urls to file\")\n with open('urls.txt', 'w') as f:\n for link in links:\n f.write(link + '\\n')\n else:\n logger.warning(\"No links available\")","def parse(self, response):\n # gather season numbers and titles from the page\n season_numbers = response.css('select[name=\"sea\"] option::attr(value)').getall()\n season_titles = response.css('select[name=\"sea\"] option::text').getall()\n\n for season_number, season_title in zip(season_numbers, season_titles):\n # assemble URL for this season\n season_url = response.url + \"/?sea=\" + season_number\n\n # scrape the season page\n yield scrapy.Request(url=season_url,\n callback=self.parse_season,\n meta=dict(season_id=season_number,\n season_title=season_title.split(\" \")[0]))","def pageScrape(season_url, wr):\r\n\r\n\thtml = urlopen(season_url)\r\n\tsoup = BeautifulSoup(html, \"lxml\")\r\n\r\n\t# our desired table has id = 'results'\r\n\t# we take the rows in the table's children\r\n\t# But we make sure to ignore the first two, since they're unrelated\r\n\ttable = soup.find(id = 'results').findChildren(\"tr\")[2:]\r\n\tsoup.decompose()\r\n\tfor elem in table:\r\n\t\tdesired_row = []\r\n\t\tif not elem.has_attr('class'): # There are some rows we don't want\r\n\t\t# these rows have 'classes' while the ones we do want don't\r\n\t\t\trank = int(elem.find('th').string)\r\n\t\t\tvals = elem.findAll('td')\r\n\t\t\tplayer = vals[0].a.string\r\n\t\t\turl = vals[0].a.get('href') \r\n\t\t\t# url to the player's page on PFR\r\n\t\t\t# Used as a unique identifier for each player\r\n\t\t\tyear = int(vals[1].string)\r\n\t\t\tage = 0 # some players don't have an age input\r\n\t\t\tif vals[2].string: \r\n\t\t\t\tage = int(vals[2].string)\r\n\t\t\tdraft = str(vals[3].string)\r\n\t\t\ttm = '2TM' # players who've played on two teams\r\n\t\t\tif vals[4].a:\r\n\t\t\t\ttm = vals[4].a.string\r\n\t\t\tlg = \"\"\r\n\t\t\tif vals[5].a:# some players don't have a league?\r\n\t\t\t\tlg = vals[5].a.string \r\n\t\t\tg = 0\r\n\t\t\tif vals[6].string:\r\n\t\t\t\tg = int(vals[6].string)\r\n\t\t\tgs = 0\r\n\t\t\tif vals[7].string:\r\n\t\t\t\tgs = int(vals[7].string)\r\n\t\t\tyrs = int(vals[8].string)\r\n\t\t\tpb = int(vals[9].string)\r\n\t\t\tAP1 = int(vals[10].string)\r\n\t\t\tAV = int(vals[11].string)\r\n\t\t\tPosition = getPosition(url, year)\t\t\r\n\r\n\t\t\t# writing to file\r\n\t\t\tdesired_row.extend((rank, player, url, year, age,\r\n\t\t\t\tdraft, tm, lg, g, gs, yrs, pb, AP1, AV, Position))\r\n\t\t\twr.writerow(desired_row)","def get_number_games(season: str, team_id: int, game_type_code: str = \"R\") -> dict:\n args = arguments.get_arguments()\n\n endpoint = f\"/schedule?teamId={team_id}&season={season}&gameType={game_type_code}\"\n response = api.nhl_api(endpoint)\n\n if response:\n schedule = response.json()\n games_total = schedule[\"totalItems\"]\n return games_total\n\n # If no valid response, just return default number of games (82)\n return 82","def get_competition_season_type(season):\n default_type = games.models.CompetitionSeason.winter\n try:\n season_name = season.name\n if season_name.find(\"/\") == -1:\n return games.models.CompetitionSeason.summer\n return default_type\n except Exception as e:\n return default_type","def change_season(self, season: str) -> dict:\n\n # Check if the season exists\n if season not in [\"plant_off\", \"summer\", \"winter\"]:\n raise RequestError\n\n # Create the requests' parameters\n param = ('command={'\n '\"sl_appl_msg\":{'\n '\"client\":\"' + self.id + '\",'\n '\"cmd_name\":\"thermo_season_req\",'\n '\"cseq\":' + str(self._cseq) + ','\n '\"season\":\"' + season + '\"'\n '},'\n '\"sl_appl_msg_type\":\"domo\",'\n '\"sl_client_id\":\"' + self.id + '\",'\n '\"sl_cmd\":\"sl_data_req\"'\n '}')\n \n # Send the post request\n response = requests.post(self._host, params=param, headers=self.header)\n\n # Increment the cseq counter\n self._cseq += 1\n\n # Check if the response is valid\n if not response.json()['sl_data_ack_reason'] == 0:\n raise RequestError\n\n # After every action performed we update the list of items\n self.update_lists()\n\n # Return the json of the response\n return response.json()","def parse_season(self, response):\n # gather competition numbers and titles from the page\n competition_numbers = response.css('select[name=\"evt\"] optgroup option::attr(value)').getall()\n competition_titles = response.css('select[name=\"evt\"] optgroup option::text').getall()\n url_components = urlparse(response.url)\n\n for competition_number, competition_title in zip(competition_numbers, competition_titles):\n # assemble direct URL for this competition\n competition_url = f'{url_components.scheme}://{url_components.netloc}/Results.aspx?evt={competition_number}'\n\n # pass along metadata for use in next steps\n response.meta.update(dict(competition_title=competition_title,\n competition_id=competition_number))\n\n # scrape the competition page\n yield scrapy.Request(url=competition_url,\n callback=self.parse_competition,\n meta=response.meta)","def run():\n games_db_conn = db.get_db_eng()\n games_query = \"SELECT * FROM GAMES\"\n games_data = etl_tools.extract_from_db(games_db_conn, games_query)\n _data_integrity_check(games_data)\n\n latest_season, latest_season_type = _get_latest_season_and_type(games_data)\n logging.info(f\"Latest season and type in current data: {(latest_season, latest_season_type)}\")\n\n if latest_season is None:\n batch_start_season, batch_start_type = config.START_SEASON, config.SEASON_TYPES[0]\n\n else:\n _truncate_games_table(games_db_conn, latest_season, latest_season_type)\n batch_start_season, batch_start_type = latest_season, latest_season_type\n\n logging.info(f\"Starting batch at {(batch_start_season, batch_start_type)}...\")\n\n batches = _get_seasons_grid(batch_start_season, batch_start_type)\n for batch in batches:\n season, season_type = batch\n logging.info(f\"Starting new batch: extracting data for {season}-{season_type}...\")\n batch_data = _extract_games_data(season, season_type)\n logging.info(f\"Data extracted. Loading {batch_data.shape[0]} rows...\")\n etl_tools.load_to_db(\n games_db_conn,\n 'games',\n batch_data,\n )\n\n logging.info(\"Pipeline completed.\")","def seasonStats(personId,type = 'gameLog',group = 'hitting'):\n\n #playerInfo = get('people', {'personIds':personId})\n\n\n teamStats = get('person',{ 'ver':'v1' , 'personId':personId,'hydrate':['stats(group={},type={})'.format(group,type),'currentTeam']})\n return teamStats\n #iterate of stats and find the right player id\n #career stats broken\n #fix the season :2019\n #make function to get team id","async def roster(\n self, ctx: commands.Context, season: Optional[YearFinder] = None, *, search: HockeyTeams\n ) -> None:\n season_str = None\n season_url = \"\"\n if season:\n if season.group(3):\n if (int(season.group(3)) - int(season.group(1))) > 1:\n return await ctx.send(_(\"Dates must be only 1 year apart.\"))\n if (int(season.group(3)) - int(season.group(1))) <= 0:\n return await ctx.send(_(\"Dates must be only 1 year apart.\"))\n if int(season.group(1)) > datetime.now().year:\n return await ctx.send(_(\"Please select a year prior to now.\"))\n season_str = f\"{season.group(1)}{season.group(3)}\"\n else:\n if int(season.group(1)) > datetime.now().year:\n return await ctx.send(_(\"Please select a year prior to now.\"))\n year = int(season.group(1)) + 1\n season_str = f\"{season.group(1)}{year}\"\n if season:\n season_url = f\"?season={season_str}\"\n if search is None:\n return await ctx.send(_(\"You must provide a valid current team.\"))\n rosters = {}\n players = []\n teams = [team for team in TEAMS if search.lower() in team.lower()]\n if teams != []:\n for team in teams:\n url = f\"{BASE_URL}/api/v1/teams/{TEAMS[team]['id']}/roster{season_url}\"\n async with self.session.get(url) as resp:\n data = await resp.json()\n if \"roster\" in data:\n for player in data[\"roster\"]:\n players.append(player[\"person\"][\"id\"])\n else:\n return await ctx.send(_(\"No team name was provided.\"))\n\n if players:\n await BaseMenu(\n source=PlayerPages(pages=players, season=season_str),\n cog=self,\n delete_message_after=False,\n clear_reactions_after=True,\n timeout=60,\n ).start(ctx=ctx)\n else:\n if season:\n year = _(\" in the {season} season\").format(\n season=f\"{season.group(1)}-{season.group(3)}\"\n )\n else:\n year = \"\"\n await ctx.send(\n _(\"I could not find a roster for the {team}{year}.\").format(team=team, year=year)\n )","def nflweek(self, irc, msg, args, optlist, optweek):\n \n url = self._b64decode('aHR0cDovL3MzLmFtYXpvbmF3cy5jb20vbmZsZ2MvYWxsU2NoZWR1bGUuanM=')\n \n usePre, useNext, outputWeek = False, False, False\n for (option, arg) in optlist:\n if option == 'pre':\n usePre = True\n \n if optweek:\n if optweek == \"next\":\n useNext = True\n elif optweek.isdigit():\n if usePre: \n if 1 <= int(optweek) <= 4:\n outputWeek = \"Preseason Week %s\" % optweek\n else:\n irc.reply(\"ERROR: Preseason week number must be between 1 and 4.\")\n return\n else:\n if 1 <= int(optweek) <= 17:\n outputWeek = \"Week %s\" % optweek\n else:\n irc.reply(\"ERROR: Week must be between 1-17\")\n return \n\n try:\n req = urllib2.Request(url)\n html = (urllib2.urlopen(req)).read()\n except:\n irc.reply(\"Failed to open: %s\" % url)\n return\n \n jsondata = json.loads(html)\n\n week = jsondata.get('week', None) # work with the week data so we know where we are.\n\n if week is None:\n irc.reply(\"Failed to load schedule.\")\n return\n\n currentWeekName = week.get('current', {'current': None}).get('weekName', None) \n nextWeekName = week.get('next', {'next': None}).get('weekName', None) \n\n if currentWeekName is None:\n irc.reply(\"Cannot figure out the current week.\")\n return\n\n games = jsondata.get('content', None) # data in games.\n \n if games is None:\n irc.reply(\"Failed to load the games data.\")\n return\n \n if outputWeek:\n games = [item['games'] for item in games if item['weekName'] == outputWeek]\n weekOutput = outputWeek\n elif useNext:\n games = [item['games'] for item in games if item['weekName'] == nextWeekName]\n weekOutput = nextWeekName\n else:\n games = [item['games'] for item in games if item['weekName'] == currentWeekName]\n weekOutput = currentWeekName\n \n append_list = []\n\n for games in games:\n for t in games:\n awayTeam = self._translateTeam('team', 'nid', t['awayTeamId'])\n homeTeam = self._translateTeam('team', 'nid', t['homeTeamId'])\n append_list.append(\"[\" + t['date']['num'] + \"] \" + awayTeam + \"@\" + homeTeam + \" \" + t['date']['time'])\n \n descstring = string.join([item for item in append_list], \" | \")\n output = \"{0} :: {1}\".format(ircutils.bold(weekOutput), descstring)\n \n irc.reply(output)","def main(num, rank):\n\n file_existed = os.path.isfile(FILE_NAME)\n\n if rank:\n rank = int(rank)\n \n # if a rank is passed, we assume the user wants to open the product at the\n # rank. But if the local file doesn't exist, we query the API, and retry.\n if rank and file_existed:\n browse_rank(load_posts(), rank)\n else:\n posts = get_todays_posts()\n end_index = num if num <= len(posts) else len(posts)\n posts = posts[:end_index]\n\n if not rank:\n print_posts(posts)\n\n save_posts(posts)\n \n if rank:\n # rank was passed, and url could not be opened in first try\n browse_rank(load_posts(), rank)","def nflschedule(self, irc, msg, args, optlist, optteam):\n \n fullSchedule = False\n for (option, arg) in optlist:\n if option == 'full':\n fullSchedule = True\n \n optteam = optteam.upper()\n \n if optteam not in self._validteams():\n irc.reply(\"Team not found. Must be one of: %s\" % self._validteams())\n return\n \n lookupteam = self._translateTeam('yahoo', 'team', optteam) # don't need a check for 0 here because we validate prior.\n \n if fullSchedule: # diff url/method.\n url = self._b64decode('aHR0cDovL3Nwb3J0cy55YWhvby5jb20vbmZsL3RlYW1z') + '/%s/schedule' % lookupteam\n\n try:\n request = urllib2.Request(url)\n html = (urllib2.urlopen(request)).read()\n except:\n irc.reply(\"Failed to open: %s\" % url)\n return\n \n soup = BeautifulSoup(html)\n table = soup.find('table', attrs={'summary':'Regular Season Games'})\n \n if not table:\n irc.reply(\"ERROR: Failed to find schedule for: %s\") % optteam\n return\n \n tbody = table.find('tbody')\n rows = tbody.findAll('tr')\n\n append_list = []\n\n for row in rows:\n tds = row.findAll('td')\n week = tds[0]\n \n if row.find('td', attrs={'class':'title bye'}):\n date = \"BYE\"\n opp = \"\"\n score = \"\"\n appendString = \"W{0}-{1}\".format(ircutils.bold(week.getText()), ircutils.underline(\"BYE\"))\n else:\n date = tds[1].getText()\n dateSplit = date.split(',', 1) # take the date, dump the rest.\n date = dateSplit[1]\n opp = tds[2] # with how the Tag/string comes in, we need to extract one part and format the other.\n oppName = opp.find('span')\n if oppName:\n oppName.extract()\n oppTeam = opp.find('a').getText() \n #opp = tds[2].find('span').getText()\n #opp = self._translateTeam('team','full', opp) # use the db to make a full team small.\n score = tds[3].getText().replace('EDT','').replace('EST','').replace('pm','').replace('am','') # strip the garbage\n #score = score.replace('W', ircutils.mircColor('W', 'green')).replace('L', ircutils.mircColor('L', 'red'))\n appendString = \"W{0}-{1} {2} {3}\".format(ircutils.bold(week.getText()), date.strip(), oppTeam.strip(), score.strip())\n \n append_list.append(appendString)\n\n descstring = string.join([item for item in append_list], \" | \")\n output = \"{0} SCHED :: {1}\".format(ircutils.mircColor(optteam, 'red'), descstring)\n irc.reply(output)\n else:\n url = self._b64decode('aHR0cDovL3Nwb3J0cy55YWhvby5jb20vbmZsL3RlYW1z') + '/%s/calendar/rss.xml' % lookupteam\n \n try:\n req = urllib2.Request(url)\n response = urllib2.urlopen(req)\n html = response.read()\n except:\n irc.reply(\"Cannot open: %s\" % url)\n return\n\n # clean this stuff up\n html = html.replace('<![CDATA[','').replace(']]>','').replace('EDT','').replace('\\xc2\\xa0',' ')\n\n soup = BeautifulSoup(html)\n items = soup.find('channel').findAll('item')\n \n append_list = []\n\n for item in items:\n title = item.find('title').renderContents().strip() # title is good.\n day, date = title.split(',')\n desc = item.find('description') # everything in desc but its messy.\n desctext = desc.findAll(text=True) # get all text, first, but its in a list.\n descappend = (''.join(desctext).strip()) # list transform into a string.\n if not descappend.startswith('@'): # if something is @, it's before, but vs. otherwise.\n descappend = 'vs. ' + descappend\n descappend += \" [\" + date.strip() + \"]\"\n append_list.append(descappend) # put all into a list.\n\n \n descstring = string.join([item for item in append_list], \" | \")\n output = \"{0} {1}\".format(ircutils.bold(optteam), descstring)\n irc.reply(output)","def run_search(search_info: dict[str, str]) -> dict[str, Optional[set[str]]]:\n results = dict()\n\n func: Callable[[str], Optional[set[str]]]\n for func in searchSites.netnaija, searchSites.lightdl:\n if not search_info['season']:\n results[func.__name__] = func(\n search_info['series']\n )\n elif search_info['season'] and search_info['episode']:\n results[func.__name__] = func(\n ' '.join([search_info['series'], search_info['season'], search_info['episode']])\n )\n else:\n results[func.__name__] = func(\n ' '.join([search_info['series'], search_info['season']])\n )\n\n return results","def scrape_all():\n\n # Scrape team information by season\n for team in scrape_utils.team_names():\n team_season_stats(team)\n # Each season\n print(team)\n for year in range(2019, 2020):\n # Game Logs\n season_game_logs(team, year)\n\n # Starting Lineups\n #player_scraper.get_starting_lineups(team, year)\n\n # Init mongo to get game IDS for box score scraping\n m = mongo.Mongo()\n\n # Game Information (Box Score and Play by Play)\n for year in range(2015, 2020):\n player_scraper.get_starting_lineups(year)\n for game in m.find('game_log', {'season': year}, {'_id': 1}):\n #team_scraper.play_by_play(game['_id'])\n player_scraper.player_box_score(game['_id'])\n\n print(game['_id'])\n\n\n\n # Get player information\n for player in scrape_utils.get_active_players():\n print(player)\n player_scraper.player_per_game(player)\n\n # Get betting lines (By Year) need from 2014\n for year in range(2015, 2020):\n team_scraper.betting_lines(2019)","def scrape(self):\n self._validate_date_range(self.start_date, self.end_date)\n self._validate_team()\n self._cache_source()\n soup = self.season_raw_cache[self.start_date.year]\n df = self._parse_raw(soup)\n return self._apply_filters(df)","def season(self, title=None, season=None):\n key = f'{self.key}/children?excludeAllLeaves=1'\n if title is not None and not isinstance(title, int):\n return self.fetchItem(key, Season, title__iexact=title)\n elif season is not None or isinstance(title, int):\n if isinstance(title, int):\n index = title\n else:\n index = season\n return self.fetchItem(key, Season, index=index)\n raise BadRequest('Missing argument: title or season is required')","def _parse_fname_season(cmd_args):\n f_name_base = 'sfav2_CONUS_{}_to_{}'\n season_start = '093012' # Start month, day, & hour for seasonal accum\n\n date_in = adjust_date(cmd_args)\n\n if (not check_ftype(cmd_args)):\n print('{} not valid for seasonal accumulation period. Downloading as NetCDF'.format(cmd_args.f_type))\n f_type = 'nc'\n else:\n f_type = cmd_args.f_type\n\n # If we are in the new year of the winter season (i.e., Jan 2020 of the\n # 2019-2020 winter season), adjust the start year defining the winter season\n if (date_in.month < 9):\n start_yr = date_in.year - 1\n else:\n start_yr = date_in.year\n\n date_start = '{}{}'.format(start_yr, season_start)\n date_end = datetime.strftime(date_in, '%Y%m%d%H')\n\n f_name = f_name_base.format(date_start, date_end)\n\n f_name = '{}.{}'.format(f_name, f_type)\n\n return f_name","def parse_season(filename):\n print_info('Attempting to parse {0}'.format(filename))\n print_info('Extracting season from {0}'.format(filename))\n for regex in SEASON_REGEX:\n m = re.search(regex, filename)\n\n if m is None:\n continue\n\n extracted_season = m.group('Season').lower()\n print_info('Extracted season: {0}'.format(extracted_season))\n\n season_num = int(extracted_season)\n if season_num is not None and season_num > 0:\n print_info('Season might be: {0}'.format(season_num))\n return 'S' + format_num(season_num)\n return 'S01'","def environmental_science_news():\n\n return general_scraper(['http://mesva.univaq.it/?q=avvisi/cl-clm/52671'])","def season_series(game_id, pref_team, other_team, last_season=False):\n\n # Init empty dictionaries and lists\n games_against = list()\n pref_toi = dict()\n pref_goals = dict()\n pref_assists = dict()\n pref_points = dict()\n pref_record = {\"wins\": 0, \"losses\": 0, \"ot\": 0}\n roster_player = True\n\n # If this is the first game of the season, we can set the 'last_season' flag to enable the\n # season series function to check last year's season series between the two teams.\n if not last_season:\n season_start = str(game_id)[0:4]\n season_end = str(int(season_start) + 1)\n yesterday = datetime.now() - timedelta(days=1)\n # yesterday = datetime.now() + timedelta(days=50)\n # schedule_url = (\n # f\"/schedule?teamId={pref_team.team_id}\"\n # f\"&expand=schedule.broadcasts,schedule.teams&startDate=\"\n # f\"{season_start}-08-01&endDate={yesterday:%Y-%m-%d}\"\n # )\n schedule_url = (\n f\"/schedule?teamId={pref_team.team_id}\"\n f\"&expand=schedule.broadcasts,schedule.teams\"\n f\"&season={season_start}{season_end}\"\n )\n else:\n season_start = int(str(game_id)[0:4]) - 1\n season_end = str(int(season_start) + 1)\n yesterday = datetime.now() - timedelta(days=1)\n # yesterday = datetime.now() + timedelta(days=50)\n # schedule_url = (\n # f\"/schedule?teamId={pref_team.team_id}\"\n # f\"&expand=schedule.broadcasts,schedule.teams&startDate=\"\n # f\"{season_start}-08-01&endDate={season_end}-06-01\"\n # )\n schedule_url = (\n f\"/schedule?teamId={pref_team.team_id}\"\n f\"&expand=schedule.broadcasts,schedule.teams\"\n f\"&season={season_start}{season_end}\"\n )\n\n schedule = api.nhl_api(schedule_url).json()\n dates = schedule[\"dates\"]\n\n # Loop through scheduled to get previously played games against\n for date in dates:\n game = date[\"games\"][0]\n game_type = game[\"gameType\"]\n game_id = game[\"gamePk\"]\n game_team_home = game[\"teams\"][\"home\"][\"team\"][\"name\"]\n game_team_away = game[\"teams\"][\"away\"][\"team\"][\"name\"]\n teams = [game_team_away, game_team_home]\n game_status = game[\"status\"][\"abstractGameState\"]\n if game_type == \"R\" and game_status == \"Final\" and other_team.team_name in teams:\n game_feed = f\"/game/{game_id}/feed/live\"\n games_against.append(game_feed)\n\n # If the two teams haven't played yet, just exit this function\n if not games_against:\n return None, None, None\n\n # Loop through newly created games_against list to get each stats\n for feed in games_against:\n game = api.nhl_api(feed).json()\n game_data = game[\"gameData\"]\n home_team_name = game_data[\"teams\"][\"home\"][\"name\"]\n pref_homeaway = \"home\" if home_team_name == pref_team.team_name else \"away\"\n other_homeaway = \"away\" if home_team_name == pref_team.team_name else \"home\"\n\n # Get season series\n end_period = game[\"liveData\"][\"linescore\"][\"currentPeriod\"]\n extra_time = True if end_period > 3 else False\n pref_score = game[\"liveData\"][\"linescore\"][\"teams\"][pref_homeaway][\"goals\"]\n other_score = game[\"liveData\"][\"linescore\"][\"teams\"][other_homeaway][\"goals\"]\n if pref_score > other_score:\n pref_record[\"wins\"] += 1\n elif other_score > pref_score and extra_time:\n pref_record[\"ot\"] += 1\n else:\n pref_record[\"losses\"] += 1\n\n season_series_str = f\"Series: {pref_record['wins']}-\" f\"{pref_record['losses']}-{pref_record['ot']}\"\n\n # Get stats leaders\n # pref_teamstats = game[\"liveData\"][\"boxscore\"][\"teams\"][pref_homeaway][\"teamStats\"]\n pref_playerstats = game[\"liveData\"][\"boxscore\"][\"teams\"][pref_homeaway][\"players\"]\n for id, player in pref_playerstats.items():\n try:\n # Calculate TOI\n player_toi_str = player[\"stats\"][\"skaterStats\"][\"timeOnIce\"]\n player_toi_minutes = int(player_toi_str.split(\":\")[0])\n player_toi_seconds = int(player_toi_str.split(\":\")[1])\n player_toi = (player_toi_minutes * 60) + player_toi_seconds\n pref_toi[id] = pref_toi.get(id, 0) + player_toi\n\n # Point Totals\n player_goal_str = player[\"stats\"][\"skaterStats\"][\"goals\"]\n pref_goals[id] = pref_goals.get(id, 0) + int(player_goal_str)\n player_assist_str = player[\"stats\"][\"skaterStats\"][\"assists\"]\n pref_assists[id] = pref_assists.get(id, 0) + int(player_assist_str)\n player_points = int(player_goal_str) + int(player_assist_str)\n pref_points[id] = pref_points.get(id, 0) + int(player_points)\n\n except KeyError:\n pass\n\n # Calculate Stats Leaders\n sorted_toi = sorted(pref_toi.values(), reverse=True)\n leader_toi = sorted_toi[0]\n\n sorted_points = sorted(pref_points.values(), reverse=True)\n leader_points = sorted_points[0]\n\n # Get TOI leader\n for id in pref_toi.keys():\n if pref_toi[id] == leader_toi:\n player_name = roster.player_attr_by_id(pref_team.roster, id, \"fullName\")\n if player_name is None:\n roster_player = False\n player_id_only = id.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n leader_toi_avg = leader_toi / len(games_against)\n m, s = divmod(leader_toi_avg, 60)\n toi_m = int(m)\n toi_s = int(s)\n toi_s = \"0{}\".format(toi_s) if toi_s < 10 else toi_s\n toi_avg = \"{}:{}\".format(toi_m, toi_s)\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n toi_leader_str = \"TOI Leader: {} with {} / game.\".format(player_short_name, toi_avg)\n\n # Handle tied points leaders\n point_leaders = list()\n for id in pref_points.keys():\n if pref_points[id] == leader_points:\n point_leaders.append(id)\n\n if leader_points == 0:\n points_leader_str = \"Points Leader: None (all players have 0 points).\"\n\n elif len(point_leaders) == 1:\n leader = point_leaders[0]\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n # If the player is no longer on the team, get their information (change string here?)\n if player_name is None:\n roster_player = False\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n if not roster_player:\n points_leader_str = (\n f\"Points Leader: {player_name} with {leader_points} points \"\n f\"({player_goals}G {player_assists}A) \"\n )\n else:\n points_leader_str = \"Points Leader: {} with {} ({}G {}A).\".format(\n player_name, leader_points, player_goals, player_assists\n )\n\n elif len(point_leaders) > 3:\n point_leaders_with_attrs = list()\n for leader in point_leaders:\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n if player_name is None:\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n point_leaders_with_attrs.append(player_short_name)\n\n point_leaders_joined = \", \".join(point_leaders_with_attrs[0:3])\n leftover_leaders = len(point_leaders) - 3\n points_leader_str = (\n f\"Points Leaders: {point_leaders_joined} & {leftover_leaders} others ({leader_points} each).\"\n )\n\n else:\n point_leaders_with_attrs = list()\n for leader in point_leaders:\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \"fullName\")\n if player_name is None:\n player_id_only = leader.replace(\"ID\", \"\")\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \"fullName\")\n player_goals = pref_goals[leader]\n player_assists = pref_assists[leader]\n player_short_name = f\"{player_name[0]}. {' '.join(player_name.split()[1:])}\"\n player_str = f\"{player_short_name} ({player_goals}G {player_assists}A)\"\n point_leaders_with_attrs.append(player_str)\n\n point_leaders_joined = (\n f\", \".join(point_leaders_with_attrs[:-1]) + f\" & {point_leaders_with_attrs[-1]}\"\n )\n points_leader_str = \"Points Leaders: {} with {} each.\".format(point_leaders_joined, leader_points)\n\n return season_series_str, points_leader_str, toi_leader_str","def test_seasons(self):\n response = Tmdb.season(tmdb_show_id = 69740, season_number = 1)\n self.assertTrue(int(response.status_code) == 200)\n data = response.json()\n self.assertTrue(isinstance(data['episodes'], list))\n # TODO check if all the shows are in the good format (can be from_dict/to_dict)","def season_type(self, season_type):\n\n self._season_type = season_type","def nflffprojections(self, irc, msg, args, opttype):\n \n validtypes = { 'QB':'0','RB':'2','WR':'4','TE':'6','D/ST':'16','K':'17','FLEX':'23'}\n \n if opttype and opttype not in validtypes:\n irc.reply(\"Type must be one of: %s\" % validtypes.keys())\n return\n \n url = self._b64decode('aHR0cDovL2dhbWVzLmVzcG4uZ28uY29tL2ZmbC90b29scy9wcm9qZWN0aW9ucz8=')\n\n if opttype:\n url += '?&slotCategoryId=%s' % validtypes[opttype]\n \n try:\n req = urllib2.Request(url)\n html = (urllib2.urlopen(req)).read()\n except:\n irc.reply(\"Failed to open: %s\" % opttype)\n return\n \n html = html.replace(' ',' ')\n \n soup = BeautifulSoup(html)\n table = soup.find('table', attrs={'id':'playertable_0'})\n rows = table.findAll('tr')[2:12]\n\n append_list = []\n\n for row in rows:\n rank = row.find('td')\n player = rank.findNext('td')\n projections = row.find('td', attrs={'class':'playertableStat appliedPoints'})\n append_list.append(rank.getText() + \". \" + ircutils.bold(player.getText()) + \" (\" + projections.getText() + \")\")\n\n descstring = string.join([item for item in append_list], \" | \") # put the list together.\n\n if opttype:\n title = \"Top 10 FF projections at: %s\" % opttype\n else:\n title = \"Top 10 FF projections\"\n \n output = \"{0} :: {1}\".format(ircutils.mircColor(title, 'red'), descstring)\n irc.reply(output)","def main():\n\n creds = None\n # The file token.json stores the user's access and refresh tokens, and is\n # created automatically when the authorization flow completes for the first\n # time.\n\n # There was no need to do this with Google Sheets... I just felt like being extra.\n if os.path.exists('token.json'):\n creds = Credentials.from_authorized_user_file('token.json', SCOPES)\n # If there are no (valid) credentials available, let the user log in.\n if not creds or not creds.valid:\n if creds and creds.expired and creds.refresh_token:\n creds.refresh(Request())\n else:\n flow = InstalledAppFlow.from_client_secrets_file(\n 'credentials.json', SCOPES)\n creds = flow.run_local_server(port=0)\n # Save the credentials for the next run\n with open('token.json', 'w') as token:\n token.write(creds.to_json())\n\n service = build('sheets', 'v4', credentials=creds)\n\n # Call the Sheets API\n sheet = service.spreadsheets()\n map_df = pd.read_csv('data/rel_map.csv')\n \n for i, rows in map_df.iterrows():\n print(rows.show_season, rows.lead)\n first_url = rows.show_season\n write_from_URL(wiki_head + first_url, sheet, rows.lead, rows.show_season)","def main():\n var_type = sys.argv[1]\n start_yr = int(sys.argv[2])\n end_yr = int(sys.argv[3])\n print(var_type,start_yr,end_yr,dataFol)\n \n data = imd.get_data(var_type, start_yr, end_yr, fn_format='yearwise',file_dir=str(dataFol)) # Path must be str because imdlib needs str","async def news(self, ctx, typex):\n if typex in options['news']['sport_type']:\n output = news_output(get_sport_data())\n elif typex in options['news']['world_type']:\n output = news_output(get_news_data(\"https://www.novinky.cz/zahranicni\"))\n elif typex in options['news']['home_type']:\n output = news_output(get_news_data(\"https://www.novinky.cz/domaci\"))\n else:\n await ctx.send(\"Zadán nesprávný typ novinek\")\n return\n\n for embed in output:\n await ctx.send(embed=embed)","def season(self, seasonnum, order='aired'):\n if order=='aired':\n seasons = self.seasons\n elif order == 'dvd':\n seasons = self.dvd_seasons\n try:\n return seasons[seasonnum]\n except KeyError:\n raise SeasonNotFoundError(\n 'Season no %s does not exists' % seasonnum\n ), None, sys.exc_info()[2]","def main():\n\n # generate a token, we will be sending several queries off\n token = gen_token()\n # build the query string\n s_d, e_d = prev_quarter_boundaries(datetime.datetime.utcnow())\n s_str = s_d.strftime(\"%Y-%m-%d\")\n e_str = e_d.strftime(\"%Y-%m-%d\")\n query_str = (\n 'filingsource:\"Taiwan TWSE\" AND ' +\n 'enddate:[' + s_str + ' TO ' +\n e_str + ']'\n )\n # pull docs\n docs_res = documents_stringquery(query_str, False, token=token)\n # read out number of hits\n num_filings = docs_res['totalHits']\n # print it out\n print('Filing count from last quarter: ' + str(num_filings))","def with_season(title, season):\n videos = list(mythVideo.searchVideos(title = title, season = season))\n\n for video in videos:\n video.label = video.title + \" - \" + video.subtitle\n video.url = \"/videos/\" + video.title + \"/\" + video.hash\n\n videos = sorted(videos, key = lambda video: video.episode)\n return render_template('list.html', items = videos, page_title = title + \" Season \" + str(season))","def exec(nb,tickers,pa,typ,First):\n dl = Downloader() # the SEC_edgar_downloader package\n if First: # if First == True\n # loading the data\n for t in tickers: # looping over all tickers\n dl.get(typ, t,amount=nb, download_details=True)\n\n assert os.path.isdir(pa),'Wrong File Directory Check Again' # assert the SEC edgar folder is created \n\n # proccesing it\n for t in tickers:\n writingtxt(findhtml(pa,t,typ),t,nb,typ)","def get_html_document(self, team, src_type, season=None):\n if src_type == 'roster':\n # preparing url to team's roster page\n team_url_component = team.team_name.lower().replace(\" \", \"\")\n # creating url like 'https://www.nhl.com/ducks/roster'\n if season is not None:\n team_url = \"/\".join((\n self.NHL_SITE_PREFIX,\n team_url_component,\n self.NHL_SITE_ROSTER_SUFFIX,\n str(season)))\n else:\n team_url = \"/\".join((\n self.NHL_SITE_PREFIX,\n team_url_component,\n self.NHL_SITE_ROSTER_SUFFIX))\n elif src_type == 'system':\n # preparing url to team's prospects page\n team_url_component = team.team_name.lower().replace(\" \", \"\")\n team_site_prefix = self.TEAM_SITE_PREFIX.replace(\n \"%s\", team_url_component)\n # creating url like\n # 'http://ducks.ice.nhl.com/club/roster.htm?type=prospect'\n team_url = \"\".join((\n team_site_prefix,\n self.TEAM_SITE_ROSTER_SUFFIX))\n elif src_type == 'contracts':\n # preparing url to team's prospects page\n team_url_component = team.team_name.lower().replace(\" \", \"\")\n team_url = \"\".join((\n self.CAPFRIENDLY_SITE_PREFIX, team_url_component))\n\n try:\n req = requests.get(team_url)\n except requests.exceptions.ConnectionError:\n # TODO: returning empty document tree\n return None\n return html.fromstring(req.text)","def main():\n download_insert_title_basics()\n download_insert_title_principals()\n download_insert_name_basics()\n download_insert_title_ratings()\n scrap_keywords()\n create_and_insert_soup()\n return","def main() -> None:\n\n download_data_tools.initial_message()\n\n # S&P 500 companies, initial year and time step\n stocks: List[str] = download_data_tools.get_stocks(['all'])\n dates: List[str] = ['1992-01', '2012-12']\n time_step: str = '1d'\n\n # Basic folders\n download_data_tools.start_folders()\n\n # Run analysis\n # Download data\n portfolio_download_data(stocks, dates, time_step)\n\n print('Ay vamos!!!')","def scrape_year(year, upgrade_discipline):\n for discipline in DISCIPLINE_MAP[upgrade_discipline]:\n logger.info('Getting {} events for {}'.format(discipline, year))\n response = session.get('{}/results/{}/{}'.format(baseurl, year, discipline))\n response.raise_for_status()\n tree = html.fromstring(response.text)\n parent_id = ''\n parent_name = ''\n\n for element in tree.xpath('//table[contains(@class,\"results_home\")]//tr'):\n # No results early in the year\n if not element.xpath('td/a'):\n continue\n\n # Extract event names, dates, and IDs from link text\n event_anchor = element.xpath('td/a')[0]\n event_date = element.xpath('td[@class=\"date\"]')[0].text\n event_id = event_anchor.get('href').split('/')[2]\n\n # Series results are linked by date; single events by name\n if event_date:\n event_date = event_date.strip()\n event_name = event_anchor.text\n else:\n event_date = event_anchor.text.strip()\n event_name = parent_name\n\n event_discipline = get_discipline(event_name, discipline)\n if not event_discipline:\n logger.warn('Found Event id={} name={} date={} with blacklisted discipline'.format(event_id, event_name, event_date))\n continue\n\n if element.get('class') == 'multi-day-event-child':\n # multi-day-event-child class used for series events\n logger.info('Found Event id={} name={} date={} discipline={} with series {}'.format(\n event_id, event_name, event_date, event_discipline, parent_id))\n if parent_id:\n (Event.insert(id=event_id,\n name=event_name,\n discipline=event_discipline,\n year=year,\n date=event_date,\n series_id=parent_id,\n parent_id=None)\n .on_conflict(conflict_target=[Event.id],\n preserve=[Event.name, Event.discipline, Event.year, Event.date, Event.series, Event.parent])\n .execute())\n else:\n logger.warn('Found multi-day-event-child without a series!')\n else:\n if element.get('class') == 'multi-day-event' or '-' in event_date:\n # Assume anything with a date range is a series\n logger.info('Found Series id={} name={} dates={}'.format(event_id, event_name, event_date))\n (Series.insert(id=event_id,\n name=event_name,\n year=year,\n dates=event_date)\n .on_conflict(conflict_target=[Series.id],\n preserve=[Series.name, Series.year, Series.dates])\n .execute())\n parent_id = event_id\n parent_name = event_name\n else:\n # Single date with no parent, must be a standalone event\n logger.info('Found Event id={} name={} date={} discipline={}'.format(event_id, event_name, event_date, event_discipline))\n (Event.insert(id=event_id,\n name=event_name,\n discipline=event_discipline,\n year=year,\n date=event_date,\n series_id=None,\n parent_id=None)\n .on_conflict(conflict_target=[Event.id],\n preserve=[Event.name, Event.discipline, Event.year, Event.date, Event.series, Event.parent])\n .execute())","def run(self):\n while self.i < len(self.series):\n # Grab line + RSS\n s = self.series[self.i]\n rss = self.request_rss(s.feedUrl)\n\n # Compose Episodes\n ep_dicts = []\n for entry in rss['entries']:\n ep_dicts.append(Episode(s, entry).__dict__)\n\n # Build result dict\n result_dict = dict()\n result_dict['series'] = deepcopy(s.__dict__)\n result_dict['series']['genres'] = \\\n result_dict['series']['genres'].split(';')\n result_dict['series']['type'] = 'series'\n result_dict['episodes'] = ep_dicts\n\n # Store podcast\n self.storer.store(result_dict)\n\n # Move onto the next one\n self.i += 20\n print(\"Retrieved \" + str(s.id))","def _loop_daily(config, configfile, topdatadir, startdate, model_forcing):\n\n delta = datetime.timedelta(days=1)\n scriptdir = config['SETUP']['LISFDIR'] + '/lis/utils/usaf/s2s/s2s_modules/s2spost/'\n\n # The very first day may be missing. Gracefully handle this\n firstdate = startdate\n if _is_lis_output_missing(firstdate, model_forcing):\n firstdate += delta\n\n if startdate.month == 12:\n enddate = datetime.datetime(year=(startdate.year + 1),\n month=1,\n day=1)\n else:\n enddate = datetime.datetime(year=startdate.year,\n month=(startdate.month + 1),\n day=1)\n\n curdate = firstdate\n while curdate <= enddate:\n cmd = f\"python {scriptdir}/daily_s2spost_nc.py {configfile}\"\n for model in [\"SURFACEMODEL\", \"ROUTING\"]:\n cmd += f\" lis_fcst/{model_forcing}/{model}/\"\n cmd += f\"{curdate.year:04d}{curdate.month:02d}\"\n cmd += \"/LIS_HIST_\"\n cmd += f\"{curdate.year:04d}{curdate.month:02d}{curdate.day:02d}\"\n cmd += \"0000.d01.nc\"\n\n cmd += f\" {topdatadir}/cf_{model_forcing}_\"\n cmd += f\"{startdate.year:04d}{startdate.month:02d}\"\n\n cmd += f\" {curdate.year:04d}{curdate.month:02d}{curdate.day:02d}00\"\n\n cmd += f\" {model_forcing}\"\n\n print(cmd)\n returncode = subprocess.call(cmd, shell=True)\n if returncode != 0:\n print(\"[ERR] Problem running CF conversion!\")\n sys.exit(1)\n\n curdate += delta","def test_scraper(self):\n scrap = ScraperModule(url=self.anime_link, host=\"mixdrop\")\n pages = scrap.pages\n print(f\"pages: {pages}\")\n get_pages = scrap.get_pages()\n first_link = scrap.get_links(pages[0])\n print(first_link)\n episodes = scrap.get_episodes()\n print(episodes)","def search_season_episode(self,strz):\t\n\t\tpattern = compile(\"(S(\\d\\d)E(\\d\\d))\") #S01E03\n\t\tsep = pattern.search(strz)\t\t\n\t\tif sep is not None:\n\t\t\tse= sep.group(1)\n\t\t\tseason = sep.group(2)\n\t\t\tepisode = sep.group(3)\n\t\t\treturn strz.replace(se,\"\")\n\t\t\t\n\t\tpattern = compile(\"((\\d\\d)x(\\d\\d))\") #01x03\n\t\tsep = pattern.search(strz)\t\t\n\t\tif sep is not None:\n\t\t\tse= sep.group(1)\n\t\t\tseason = sep.group(2)\n\t\t\tepisode = sep.group(3)\n\t\t\treturn strz.replace(se,\"\")\n\t\t\t\n\t\tpattern = compile(\"(Ep(\\d\\d))\") #Ep03\n\t\tsep = pattern.search(strz)\t\t\n\t\tif sep is not None:\n\t\t\tse= sep.group(1)\n\t\t\tepisode = sep.group(2)\n\t\t\treturn strz.replace(se,\"\")","def distributeSeason(self):\n i = 1\n for day in self.daylist:\n if i >= monthbeg[5] and i < monthbeg[9]: #june through SEpt as per SCE\n day.season = 'summer' #https://www.sce.com/residential/rates/Time-Of-Use-Residential-Rate-Plans\n i = i + 1\n else:\n day.season = 'winter'\n i = i+1","def main(args):\n parser = create_parser()\n\n if not args:\n parser.print_usage()\n sys.exit(1)\n\n parsed_args = parser.parse_args(args)\n scrape_url(parsed_args.url)","def main():\n\n from scrapy.crawler import CrawlerProcess\n from scrapy.utils.project import get_project_settings\n\n process = CrawlerProcess(get_project_settings())\n process.crawl(NCBIGeoSpider)\n process.start()","def set_season_time(season): \n if season == '2021-22':\n startdate = time.strptime('13-08-2021', '%d-%m-%Y')\n startdate = datetime.fromtimestamp(mktime(startdate))\n enddate = time.strptime('08-10-2021', '%d-%m-%Y')\n enddate = datetime.fromtimestamp(mktime(enddate))\n if season == '2020-21':\n startdate = time.strptime('12-08-2020', '%d-%m-%Y')\n startdate = datetime.fromtimestamp(mktime(startdate))\n enddate = time.strptime('26-07-2021', '%d-%m-%Y')\n enddate = datetime.fromtimestamp(mktime(enddate))\n if season == '2019-20':\n startdate = time.strptime('09-08-2019', '%d-%m-%Y')\n startdate = datetime.fromtimestamp(mktime(startdate))\n enddate = time.strptime('26-07-2020', '%d-%m-%Y')\n enddate = datetime.fromtimestamp(mktime(enddate))\n return startdate, enddate","def run_twittercrawler(type, *params):\n tweets = None\n if type == 'timeline': # Call specific crawl function based on type\n tweets = by_timeline(params[0])\n elif type == 'search':\n tweets = by_search(params[0])\n elif type == 'streaming':\n print('Streaming functionality not yet implemented')\n return None\n\n return [tweet._json for tweet in tweets]","def run_rss(self):\n\n pass","def run_script(self, params, config_no):\n raise NotImplementedError()","def crawl_start(crawl_obj):\n \n res = None\n\n if crawl_obj.type in ['user', 'song'] :\n res = eval('crawl_' + crawl_obj.type)(crawl_obj)\n elif crawl_obj.type in ['artist', 'album'] :\n web_data = requests.get(crawl_obj.url, headers = cheat_headers)\n soup = bs4.BeautifulSoup(web_data.text, 'lxml')\n\n res = eval('crawl_' + crawl_obj.type)(crawl_obj.type, soup)\n else:\n print(\"Object type UNKNOWN!\")\n \n return res","def run(pars, #parameter files\n #directory of scenario files\n scen_dir = r'C:\\LS\\03_TOOLS\\_git\\COVID_01\\scenarios',\n \n #map to scenario files\n scen_d = {\n 'NoNPI':'NPI_Scenario1_None.R',\n 'BI1918':'NPI_Scenario2_Bootsma_1918Influenza.R',\n 'SouthKorea':'NPI_Scenario3_SouthKorea.R',\n 'Reduced':'NPI_Scenario4_ReducedGamma.R', \n }\n ):\n \n \n \n #===========================================================================\n # precheck \n #===========================================================================\n assert len(pars)==4, 'unexpected inputs count'\n print('pars: \\n%s'%pars)\n \n #check the R Environment variables\n assert 'R_USER' in os.environ\n assert 'R_HOME' in os.environ\n \n #print('R_USER=%s \\nR_HOME=%s'%(os.getenv('R_USER'), os.getenv('R_HOME')))\n\n \n \n \n \n #===========================================================================\n # setup\n #===========================================================================\n s = setup.Setup(setup_name = 'mid_utah_'+pars[2],\n spatial_setup = WestCoastSpatialSetup(),\n nsim = int(pars[1]),\n ti = datetime.date(2020, 3, 6),\n tf = datetime.date(2020, 10, 1),\n interactive = False,\n write_csv = True,\n dt = 1/4)\n \n #===========================================================================\n # set the scenario parmaters\n #===========================================================================\n\n \n \n assert pars[2] in scen_d, 'unrecognized scenario: %s'%pars[2]\n \n rfp = os.path.join(scen_dir, scen_d[pars[2]])\n assert os.path.exists(rfp)\n \n s.script_npi = rfp\n \n print('set script_npi=%s'%s.script_npi)\n\n #===========================================================================\n # execute\n #===========================================================================\n\n print()\n print()\n print(f\">>> Starting {s.nsim} model runs on {pars[3]} processes\")\n print(f\">>> Setup *** {s.setup_name} *** from {s.ti} to {s.tf} !\")\n print(f\">>> writing to folder : {s.datadir}{s.setup_name}\")\n print()\n print()\n \n tic = time.time()\n \n res_l = seir.run_parallel(s, int(pars[3]))\n print(f\">>> Runs done in {time.time()-tic} seconds...\")","def main(train_stock, val_stock, window_size, batch_size, ep_count,\n strategy=\"t-dqn\", model_name=\"model_debug\", pretrained=False,\n debug=False):\n agent = Agent(window_size, strategy=strategy, pretrained=pretrained, model_name=model_name)\n \n train_data = get_stock_data(train_stock)\n val_data = get_stock_data(val_stock)\n\n initial_offset = val_data[1] - val_data[0]\n\n for episode in range(1, ep_count + 1):\n train_result = train_model(agent, episode, train_data, ep_count=ep_count,\n batch_size=batch_size, window_size=window_size)\n val_result, _ = evaluate_model(agent, val_data, window_size, debug)\n show_train_result(train_result, val_result, initial_offset)","def betting_lines(year):\n\n # MongoDB Collection\n m = mongo.Mongo()\n\n # Webapges are by dates\n all_dates = m.find('game_log', {'season': year}, {'_id': 0, 'date': 1}).distinct('date')\n\n browser = webdriver.Chrome('chromedriver')\n\n # Iterate through each date in a season\n for game_date in all_dates:\n\n # Get URL\n url = 'https://classic.sportsbookreview.com/betting-odds/nba-basketball/money-line/?date=' + datetime.strftime(game_date, '%Y%m%d')\n\n scrape_betting_page(url, browser, m, game_date)\n\n browser.close()","async def get_league_players(season): \n if season == '2021-22':\n get_epl = 2021\n if season == '2020-21':\n get_epl = 2020\n if season == '2019-20':\n get_epl = 2019\n async with aiohttp.ClientSession() as session:\n understat = Understat(session)\n player = await understat.get_league_players(\"epl\", get_epl)\n # print(json.dumps(player))\n return player","def runSpider(spider, searchterm = None, fullink = None, spiderbotid = -1):\n sclogic.runSpider(spider, searchterm, fullink, spiderbotid)","def chn_season_type_recognize(str):\n season_type = None\n if '第一季度' in str:\n season_type = 'Q1'\n elif '第二季度' in str:\n season_type = 'Q2'\n elif '半年度' in str:\n season_type = 'Q2'\n elif '第三季度' in str:\n season_type = 'Q3'\n elif '年度报告' in str:\n season_type = 'FY'\n\n return season_type","def get_games(season, date):\n url = \"http://live.nhl.com/GameData/SeasonSchedule-\" + season + \".json\"\n response = urllib.urlopen(url)\n data = json.loads(response.read())\n games = []\n for game in data:\n if game[\"est\"][:8] == date:\n games.append(game)\n return games","def extract_season_episode(string):\n series, episode = (None, None)\n\n # The series page url contains \"<name>_sX_eY\"\n m = re.match('.*[sS](\\d+)_?[eE](\\d+).*', string)\n if m:\n series, episode = m.groups()\n series = int(series)\n episode = int(episode)\n\n else:\n # Matches \"XxY\" OR unicode x (\\xd7 / ×)\n m = re.search(\"(\\d+)[x|\\xd7](\\d+)\", string)\n if m:\n series, episode = m.groups()\n series = int(series)\n episode = int(episode)\n else:\n m = re.search(\"S(\\d+)E(\\d+)\", string)\n if m:\n series, episode = m.groups()\n series = int(series)\n episode = int(episode)\n\n else:\n # Broke Girls – Season 4 Episode 22 – And the In and Out\n f = re.findall('(.+?)season\\s(\\d+)\\sepisode\\s(\\d+)', string + \" \", re.I)\n if f:\n _, series, episode = f[0]\n series = int(series)\n episode = int(episode)\n\n else:\n # Broke Girls – saison 5 épisode 16\n f = re.findall(\n '(.+?)\\ssaison\\s(\\d+)\\s\\xe9pisode\\s(\\d+)\\s',\n string + \" \", re.I)\n if f:\n _, series, episode = f[0]\n series = int(series)\n episode = int(episode)\n else:\n # 'Dragon Ball Super: Temporada 1 - Episodio 11 (2015)' TODO can be optimized\n f = re.findall('(.+?)\\stemporada\\s(\\d+)(.*)\\sepisodio\\s(\\d+)\\s', string + \" \", re.I)\n if f:\n _, series, __, episode = f[0]\n series = int(series)\n episode = int(episode)\n else:\n # Broke Girls – saison 5 episode 16\n f = re.findall(\n '(.+?)\\ssaison\\s(\\d+)\\s\\episode\\s(\\d+)\\s',\n string + \" \", re.I)\n if f:\n _, series, episode = f[0]\n series = int(series)\n episode = int(episode)\n\n return series, episode","def get_season(\n current_date: date, hemisphere: str, season_tracking_type: str\n) -> str | None:\n\n if hemisphere == \"equator\":\n return None\n\n if season_tracking_type == TYPE_ASTRONOMICAL:\n spring_start = ephem.next_equinox(str(current_date.year)).datetime()\n summer_start = ephem.next_solstice(str(current_date.year)).datetime()\n autumn_start = ephem.next_equinox(spring_start).datetime()\n winter_start = ephem.next_solstice(summer_start).datetime()\n else:\n spring_start = datetime(2017, 3, 1).replace(year=current_date.year)\n summer_start = spring_start.replace(month=6)\n autumn_start = spring_start.replace(month=9)\n winter_start = spring_start.replace(month=12)\n\n if spring_start <= current_date < summer_start:\n season = STATE_SPRING\n elif summer_start <= current_date < autumn_start:\n season = STATE_SUMMER\n elif autumn_start <= current_date < winter_start:\n season = STATE_AUTUMN\n elif winter_start <= current_date or spring_start > current_date:\n season = STATE_WINTER\n\n # If user is located in the southern hemisphere swap the season\n if hemisphere == NORTHERN:\n return season\n return HEMISPHERE_SEASON_SWAP.get(season)","def get_season_url(\n base_url: str, year: Optional[int] = None, season: Optional[str] = None\n) -> str:\n if year is None or season is None:\n return f\"{base_url}/season\"\n return f\"{base_url}/season/{year}/{season.lower()}\"","def test_get_seasonal_statistics___season_to_date(self):\n msg = \"Response status is not 200\"\n response = self.api.get_seasonal_statistics___season_to_date(self.season, self.nhl_season, self.team_id)\n self.assertEqual(response.status_code, 200, msg)","def main(options, (gender, races)):\n results = aggregate_races(gender, races)\n results.score()\n if options.exclude_scoreless:\n results.purge_scoreless_runners()\n if options.show_races:\n for row in RaceDumper(results.races):\n print row\n print\n for row in SeasonsBestDumper(results.results, options.distance):\n print row\n else:\n for row in ResultsDumper(results.results, options.distance):\n print row\n print\n for row in ScoreDumper(results.scores):\n print row","def get_seasons_information():\n\n #getting the guidebox_id variable from show_page.html\n guidebox_id = request.args.get(\"guidebox_id\")\n\n #make API to get season information, gets back list of season information\n seasons_results = guidebox_season_info(guidebox_id)\n\n for season in seasons_results:\n date = season[\"first_airdate\"]\n year = str(date)[0:4]\n season[\"first_airdate\"] = year\n\n return jsonify(seasons_results)","def do(sts):\n sts = sts.replace(tzinfo=pytz.timezone(\"UTC\"))\n\n ets = sts + datetime.timedelta(days=1)\n\n interval = datetime.timedelta(hours=3)\n\n now = sts\n while now < ets:\n archivefn = now.strftime((\"/mesonet/ARCHIVE/data/%Y/%m/%d/model/\"\n \"NARR/rad_%Y%m%d%H%M.nc\"))\n if os.path.isfile(archivefn):\n now += interval\n continue\n uri = now.strftime((\"http://nomads.ncdc.noaa.gov/thredds/ncss/\"\n \"grid/narr-a/%Y%m/%Y%m%d/\"\n \"narr-a_221_%Y%m%d_%H00_000.grb?\"\n \"var=Downward_shortwave_radiation_flux&spatial=all\"\n \"&temporal=all\"))\n\n try:\n req = urllib2.Request(uri)\n data = urllib2.urlopen(req).read()\n except:\n print 'NARR Download failed for: %s' % (uri,)\n sys.exit()\n\n tmpfn = tempfile.mktemp()\n o = open(tmpfn, 'w')\n o.write(data)\n o.close()\n\n cmd = (\"/home/ldm/bin/pqinsert -p 'data a %s bogus \"\n \"model/NARR/rad_%s.nc nc' %s\"\n ) % (now.strftime(\"%Y%m%d%H%M\"),\n now.strftime(\"%Y%m%d%H%M\"), tmpfn)\n subprocess.call(cmd, shell=True)\n\n os.remove(tmpfn)\n now += interval","def main():\n exit_if_already_started()\n while True:\n for timeframe in ['all', 'month', 'week']:\n subreddits = load_list('subs.txt')\n while subreddits:\n # Grab all images/comments from sub, remove from list\n parse_subreddit(subreddits.pop(0), timeframe)","def getCycleScript(self,town,cycle):\n #--Schedules\n reWanderCell = re.compile('wander[, ]+(\\d+)',re.I)\n rePosCell = re.compile('positionCell +(\\-?\\d+) +(\\-?\\d+) +(\\-?\\d+).+\"(.+)\"')\n townCode = self.code.get(town,0)\n townSchedule = self.schedule[town]\n npcs = sorted(townSchedule.keys())\n townCode\n out = cStringIO.StringIO()\n cycleCode = ''\n if townCode:\n for line in townCode[0]+townCode[cycle]:\n cycleCode += '\\t'+line+'\\n'\n out.write(self.tsCycle1.substitute(town=town,cycle=`cycle`,cycleCode=cycleCode))\n for npc in npcs:\n out.write('if ( \"%s\"->getDisabled )\\n' % (npc,))\n out.write('elseif ( \"%s\"->getItemCount SC_offSchedule != 0 )\\n' % (npc,))\n for (condition,npcSchedule) in townSchedule[npc]:\n if condition == False:\n out.write('else\\n')\n else:\n out.write('elseif ( %s )\\n' % (condition,))\n (pos,ai) = npcSchedule[cycle-1]\n out.write('\\tif ( action < 20 )\\n')\n out.write('\\t\\t\"%s\"->%s\\n' % (npc,pos))\n if ai != 'NOAI':\n #--Wandering in exterior cell?\n maWanderCell = reWanderCell.match(ai)\n maPosCell = rePosCell.match(pos)\n if maWanderCell and (int(maWanderCell.group(1)) > 0) and (maPosCell.group(4).find(',') == -1):\n xx,yy,zz,cell = maPosCell.groups()\n out.write('\\t\\t\"%s\"->aiTravel %s %s %s\\n' % (npc,xx,yy,zz))\n out.write('\\t\\tset action to 10\\n\\telse\\n')\n out.write('\\t\\t\"%s\"->ai%s\\n' % (npc,ai))\n out.write('\\tendif\\n')\n out.write('endif\\n')\n out.write(\"if ( action != 10 )\\n\\tset action to 20\\nendif\\n\")\n out.write('end\\n')\n return out.getvalue()","def standings_by_season(season):\n season = int(season) + 1\n scoreboard = nba_py.Scoreboard(month=7,\n day=1,\n year=season)\n east_standings = scoreboard.east_conf_standings_by_day()\n west_standings = scoreboard.west_conf_standings_by_day()\n\n return render_template(\"standings.html\",\n title=\"standings\",\n east_standings=enumerate(east_standings, 1),\n west_standings=enumerate(west_standings, 1),\n team=CITY_TO_TEAM)","def get_seasons(link, debug=False):\n\n if debug:\n print(\"begin get_seasons()\")\n\n # get the BeautifulSoup data\n show_url = \"https://www.imdb.com/\" + link + \"episodes/\"\n tv_soup = bs4.BeautifulSoup(requests.get(show_url).text, features=\"html.parser\")\n\n # We are acquiring this data from a drop down, which the below line selects\n select_elem = tv_soup.select('#bySeason')\n seasons = []\n # account for the possibility of a one season show\n if len(select_elem) == 0:\n seasons.append(1)\n else:\n # get contents of drop down\n options = select_elem[0].select('option')\n\n # add each season\n for season in options:\n seasons.append(season.get('value'))\n if debug:\n print(f\"Seasons {seasons}\")\n\n return seasons","def main():\n # Return needed Data Frames to analyze\n data_frame, seasons, col, labels, stats, kaggle = load_frames()\n\n # Create the maps now\n create_shot_maps(data_frame,seasons)\n create_scenario_map()\n \n # Create the Plots\n plot_season_graphs(stats)\n plot_pie_charts(kaggle)\n plot_shot_timings(kaggle)\n plot_radar(stats, col, labels)","def run_it():\n initialize()\n parser = get_parser()\n args = None\n first_parse = True\n while(True):\n if first_parse is True:\n first_parse = False\n args = parser.parse_args()\n \n else:\n # print(textwrap.dedent(\n # '''\\\n # Search again like in the beginning.\n # -- You can either choose best rated or list mode.\n # -- This time, you can insert the search string without double quotes.\n # Remember the list mode options!\n # 0: torrent project.\n # 1: the pirate bay.\n # 2: 1337x.\n # 3: eztv.\n # 4: limetorrents.\n # 5: isohunt.\n # '''))\n sys.exit(0)\n print('Or.. if you want to exit just write \"' +\n Colors.LRED + 'Q' + Colors.ENDC + '\" or \"' +\n Colors.LRED + 'q' + Colors.ENDC + '\".')\n input_parse = input('>> ').replace(\"'\", \"\").replace('\"', '')\n if input_parse in ['Q', 'q']:\n sys.exit(1)\n\n args = parser.parse_args(input_parse.split(' ', 2))\n \n if args.str_search.strip() == \"\":\n print('Please insert an appropiate non-empty string.')\n else:\n args.str_search = args.str_search.replace('_',' ').replace(\"'\",'')\n\n movieName = args.str_search\n #print(args.str_search)\n auto = AutoPy(*insert(args))\n auto.movieName = movieName\n auto.get_content()\n auto.select_torrent()\n auto.download_torrent()","def run_downloader(selects, url, dlpath, **kwargs):\n print \"Downloading\", url, \"to\", dlpath\n try:\n isos = ThreddsCollector(url, selects=selects, **kwargs).run()\n XmlDownloader.run(isos, dlpath)\n except Exception as e:\n print >>sys.stderr, \"Problem with\", url, \"ex:\", e","def main():\n args = parser.parse_args()\n scraper = brf.BrefScraper(args.teams,\n str(args.year),\n args.playoffs,\n args.csv)\n\n if args.plot != '':\n plotter = Plot.Plot(scraper, not args.not_histogram)\n plotter.set_default_axes(1, args.x_hint, args.y_axis[0], args.y_axis[1])\n plotter.plot(args.plot, args.average)\n elif args.plot == '' and args.csv == 'w':\n scraper.save_to_file()","def runCrawler(crawlerid):\n sclogic.runCrawler(crawlerid)","def init_script() -> str:\n symbol = \"NET\"\n raw_data = fetch_stock_data(symbol)\n parsed_data = parse_data(raw_data)\n plot_url = create_chart(parsed_data, symbol)\n logger.info(plot_url)\n return plot_url","def import_data(nb, tickers, types, First = True):\n pa = os.getcwd().replace(\"\\\\\",\"/\") + \"/sec-edgar-filings\"\n for i in types: # looping over different types\n exec(nb,tickers,pa,i,First)","def general_news():\n\n return general_scraper(['http://mesva.univaq.it/'])","def getskaters(league, year):\n \n url = 'https://www.eliteprospects.com/league/' + league + '/stats/' + year + '?page='\n # print('Collects data from ' + 'https://www.eliteprospects.com/league/' + league + '/stats/' + year)\n \n print(\"Beginning scrape of \" + league + \" skater data from \" + year + \".\")\n \n # Return list with all plyers for season in link \n players = []\n \n page = (requests.get(url+str(1), timeout = 500))\n first_page_string = str(page)\n \n while first_page_string == '<Response [403]>':\n print(\"Just got a 403 Error before entering the page. Time to Sleep, then re-obtain the link.\")\n time.sleep(100)\n page = (requests.get(url+str(1), timeout = 500))\n first_page_string = str(page)\n print(\"Changed the string before entering the page. Let's try again\")\n \n if (str(first_page_string) == '<Response [404]>'):\n print(\"ERROR: \" + str(first_page_string) + \" on league: \" + league + \" in year: \" + year + \". Data doesn't exist for this league in this year.\")\n \n else:\n \n for i in range(1,99):\n page = requests.get(url+str(i), timeout = 500) \n page_string = str(page)\n \n while page_string == '<Response [403]>':\n print(\"Just got a 403 Error within the page. Time to Sleep, then re-obtain the link.\")\n time.sleep(100)\n page = requests.get(url+str(i), timeout = 500) \n page_string = str(page)\n print(\"Changed the string within the page. Let's try again\")\n \n soup = BeautifulSoup(page.content, \"html.parser\")\n\n # Get data for players table\n player_table = soup.find( \"table\", {\"class\":\"table table-striped table-sortable player-stats highlight-stats season\"})\n \n try:\n df_players = tableDataText(player_table)\n \n except AttributeError:\n print(\"BREAK: TABLE NONE ERROR: \" + str(requests.get(url+str(i), timeout = 500)) + \" On League: \" + league + \" In Year: \" + year)\n break\n \n if len(df_players)>0:\n\n if df_players['#'].count()>0:\n # Remove empty rows\n df_players = df_players[df_players['#']!=''].reset_index(drop=True)\n\n # Extract href links in table\n href_row = []\n for link in player_table.find_all('a'):\n href_row.append(link.attrs['href'])\n\n # Create data frame, rename and only keep links to players\n df_links = pd.DataFrame(href_row) \n df_links.rename(columns={ df_links.columns[0]:\"link\"}, inplace=True)\n df_links= df_links[df_links['link'].str.contains(\"/player/\")].reset_index(drop=True) \n\n # Add links to players\n df_players['link']=df_links['link'] \n\n players.append(df_players)\n\n # Wait 3 seconds before going to next\n #time.sleep(1)\n #print(\"Scraped page \" + str(i))\n \n else:\n #print(\"Scraped final page of: \" + league + \" In Year: \" + year)\n break\n\n \n if len(players)!=0:\n df_players = pd.concat(players).reset_index()\n\n df_players.columns = map(str.lower, df_players.columns)\n\n # Clean up dataset\n df_players['season'] = year\n df_players['league'] = league\n\n df_players = df_players.drop(['index','#'], axis=1).reset_index(drop=True)\n\n df_players['playername'] = df_players['player'].str.replace(r\"\\(.*\\)\",\"\")\n df_players['position'] = df_players['player'].str.extract('.*\\((.*)\\).*')\n df_players['position'] = np.where(pd.isna(df_players['position']), \"F\", df_players['position'])\n\n df_players['fw_def'] = df_players['position'].str.contains('LW|RW|C|F')\n df_players.loc[df_players['position'].str.contains('LW|RW|C'), 'fw_def'] = 'FW'\n df_players.loc[df_players['position'].str.contains('D'), 'fw_def'] = 'DEF'\n\n # Adjust columns; transform data\n team = df_players['team'].str.split(\"“\", n=1, expand=True)\n df_players['team'] = team[0]\n\n # drop player-column\n df_players = df_players.drop(columns = ['fw_def'], axis=1)\n print(\"Successfully scraped all \" + league + \" skater data from \" + year + \".\")\n\n return df_players\n \n else: print(\"LENGTH 0 ERROR: \" + str(requests.get(url+str(1), timeout = 500)) + \" On League: \" + league + \" In Year: \" + year)","def run_spider_on_zyte(spider_name):\n print(f'Running spider {spider_name}...')\n data = dict(project=project_id, spider=spider_name)\n response = requests.post('https://app.scrapinghub.com/api/run.json', data=data, auth=(api_key, ''))\n return response.json()['jobid']","def main(rundir, outputfile):\n # Read avg_count for all runs in the ranking\n results = list()\n for run in Runs(rundir):\n filename = run.get_file(name='results/analytics.json')\n doc = util.read_object(filename=filename)\n results.append(doc)\n # Delay execution to allow for testing running post-processing\n # workflows\n time.sleep(1)\n # Write analytics results. Ensure that output directory exists:\n # influenced by http://stackoverflow.com/a/12517490\n if not os.path.exists(os.path.dirname(outputfile)):\n try:\n os.makedirs(os.path.dirname(outputfile))\n except OSError as exc: # guard against race condition\n if exc.errno != errno.EEXIST:\n raise\n with open(outputfile, \"at\") as f:\n json.dump(results, f)","def scrap(self):\n scrapper = RaceDayScrapper()\n return scrapper.get()","def main():\n # the url for african daily and global daily\n african_dialy_url = \"https://data.chc.ucsb.edu/products/CHIRPS-2.0/africa_daily/tifs/p25/\"\n global_daily_url = \"https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/tifs/p25/\"\n\n\n each_year_list = GetRasterYears(url=african_dialy_url)\n new_path = makenewdir(each_year_list)\n years_new_list = fecthrasterurl(url=african_dialy_url)\n downloadwithwget(each_year_list, years_new_list, new_path)","def init_bot():\n\n # We create the Reddit instance.\n reddit = praw.Reddit(client_id=config.APP_ID, client_secret=config.APP_SECRET,\n user_agent=config.USER_AGENT, username=config.REDDIT_USERNAME,\n password=config.REDDIT_PASSWORD)\n\n # Check if we have the 3 required arguments.\n if len(sys.argv) == 3:\n\n method = sys.argv[1]\n day = sys.argv[2]\n\n if method == \"sticky\":\n if day == \"monday\":\n post_monday(reddit)\n elif day == \"wednesday\":\n post_wednesday(reddit)\n elif day == \"friday\":\n post_friday(reddit)\n\n elif method == \"unsticky\":\n if day == \"monday\":\n unsticky_post(reddit, MONDAY_FILE)\n elif day == \"wednesday\":\n unsticky_post(reddit, WEDNESDAY_FILE)\n elif day == \"friday\":\n unsticky_post(reddit, FRIDAY_FILE)","def run(self, script, **kwargs):\r\n # don't return a value from a script\r\n kwargs['nout'] = 0\r\n return self.call(script, **kwargs)","def set_season(self, season):\n self.set_date_range(dt.date(season, 1, 1),\n dt.date(season, 12, 31))","def run(self, **kwargs):\n try:\n super().run(**kwargs)\n self.extract_nfvbench_results()\n self.__logger.info(\"NFVbench results were successfully parsed\")\n except Exception: # pylint: disable=broad-except\n self.__logger.exception(\"Cannot parse NFVbench results\")\n return self.EX_RUN_ERROR\n return self.EX_OK","def season_games(year):\n\tLOG.debug('Getting season %d', year)\n\tdata = read_html(io=season_games_url(year),\n\t\t\t\t\t attrs={'id': 'games'},\n\t\t\t\t\t infer_types=False,\n\t\t\t\t\t header=0)\n\tif len(data) != 1:\n\t\traise CantFindTheRightTable\n\tdata = data.pop()\n\n\t# Cleaning.\n\tdel data[\"Unnamed: 3\"]\n\t# The code below issues \"UserWarning: \" So we catch UserWarnings.\n\twith warnings.catch_warnings():\n\t\twarnings.filterwarnings(action='ignore', category=UserWarning,\n\t\t\t\t\t\t\t\tmodule=r'pandas\\.core\\.frame',\n\t\t\t\t\t\t\t\tmessage=(r\"Boolean Series key will be reindexed\"\n\t\t\t\t\t\t\t\t\t\t r\" to match DataFrame index\\.\"))\n\t\t# These rows are mid-table header rows.\n\t\tdata = data[data.Week != \"Week\"][data.Week != \"nan\"]\n\n\tdata['week'] = (data.Week\n\t\t\t\t\t.replace(\"WildCard\", \"wild-card\")\n\t\t\t\t\t.replace(\"Division\", \"divisional\")\n\t\t\t\t\t.replace(\"ConfChamp\", \"conference\")\n\t\t\t\t\t.replace(\"SuperBowl\", \"super-bowl\")\n\t\t\t\t\t.apply(\n\t\t\t\t\t\tlambda s: (int(s)\n\t\t\t\t\t\t\t\t if all(c in '1234567890' for c in s)\n\t\t\t\t\t\t\t\t else s)))\n\tdel data['Week']\n\n\tdata['season'] = year\n\tdata['game_date'] = pd.to_datetime(\n\t\tdata.Date\n\t\t.replace(r\"$\", r\", %d\" % year, regex=True)\n\t\t.replace(r\"^(January|February) (\\d+), \\d+$\", r\"\\1 \\2, %d\" % (year + 1),\n\t\t\t\t regex=True))\n\tdel data['Date']\n\n\tfor column in \"PtsW\", \"PtsL\", \"YdsW\", \"TOW\", \"YdsL\", \"TOL\":\n\t data[column] = data[column].apply(int)\n\n\tdata['WatL'] = data['Unnamed: 5'].apply(lambda x: x == '@')\n\tdel data['Unnamed: 5']\n\tdata['hometeam'] = (~data.WatL * data['Winner/tie'] +\n\t\t\t\t\t\tdata.WatL * data['Loser/tie'])\n\tdata['awayteam'] = (data.WatL * data['Winner/tie'] +\n\t\t\t\t\t\t~data.WatL * data['Loser/tie'])\n\tdata['winner'] = data['Winner/tie']\n\tfor column in 'Winner/tie', 'Loser/tie', \"WatL\":\n\t\tdel data[column]\n\tfor column in 'hometeam', 'awayteam', 'winner':\n\t\tdata[column] = data[column].apply(lambda s: s.split()[-1].lower())\n\n\treturn data","def main():\n with open(\"page_data.yaml\", 'r') as inputstr:\n config_data = yaml.safe_load(inputstr)\n ointf = OutputInterface('template.txt')\n table_data = get_song_artist_matches()\n ofilen = config_data['directory'] + os.sep + 'common_songs.html'\n title = 'Song Titles and Band Name Overlap'\n header = ['No.', 'Artist', 'Peak', 'Date', 'Song/Artist', 'Peak',\n 'Date', 'Song']\n ointf.build_page(ofilen, title, header, fmt_table(table_data))\n ointf.inject(XTRAEDIT)\n ointf.output()","def getNumSeasons(self):\n searchURL = \"http://api.tvmaze.com/shows/\" + str(self.__showID) \\\n + \"/seasons\"\n\n response = requests.get(searchURL)\n data = response.json()\n\n return data[-1][\"number\"]","def _handle_runs(self, relpath, params):\r\n runs_by_day = self._partition_runs_by_day()\r\n args = self._default_template_args('run_list')\r\n args['runs_by_day'] = runs_by_day\r\n self._send_content(self._renderer.render_name('base', args), 'text/html')","def run_script(self):\n pass","def get_league_skater_boxcars(league, seasons):\n\n if len(set(seasons))==1:\n scraped_season_list = str(seasons)\n elif len(set(seasons))>2:\n scraped_season_list = str(((str(tuple(sorted(tuple(set(seasons))))).replace(\"'\", \"\").replace(\"(\", \"\").replace(\")\", \"\").replace(\"[\", \"\").replace(\"]\", \"\"))).split(\", \")[:-1]).replace(\"'\", \"\").replace(\"[\", \"\").replace(\"]\", \"\") + \", and \" + str(((str(tuple(sorted(tuple(set(seasons))))).replace(\"'\", \"\").replace(\"(\", \"\").replace(\")\", \"\").replace(\"[\", \"\").replace(\"]\", \"\"))).split(\", \")[-1])\n else:\n scraped_season_list = str(((str(tuple(sorted(tuple(set(seasons))))).replace(\"'\", \"\").replace(\"(\", \"\").replace(\")\", \"\").replace(\"[\", \"\").replace(\"]\", \"\"))).split(\", \")[:-1]).replace(\"'\", \"\").replace(\"[\", \"\").replace(\"]\", \"\") + \" and \" + str(((str(tuple(sorted(tuple(set(seasons))))).replace(\"'\", \"\").replace(\"(\", \"\").replace(\")\", \"\").replace(\"[\", \"\").replace(\"]\", \"\"))).split(\", \")[-1])\n \n \n global hidden_patrick\n hidden_patrick = 0\n global error\n error = 0\n \n output = pd.DataFrame()\n \n if type(seasons) == str:\n single = getskaters(league, seasons)\n output = output.append(single)\n print(\"Scraping \" + league + \" data is complete. You scraped skater data from \" + seasons + \".\")\n return(output)\n \n elif ((type(seasons) == tuple) or (type(seasons) == list)):\n \n for i in range(0, len(seasons)):\n try:\n single = getskaters(league, seasons[i])\n output = output.append(single)\n except KeyboardInterrupt as e:\n hidden_patrick = 4\n error = e\n return output\n except (ConnectionError,\n HTTPError,\n ReadTimeout,\n ConnectTimeout) as e:\n hidden_patrick = 5\n error = e\n return output\n \n print(\"Scraping \" + league + \" data is complete. You scraped skater data from \" + scraped_season_list + \".\") \n return(output)"],"string":"[\n \"def scrape():\\n league_year = Config.get_property(\\\"league_year\\\")\\n\\n # Create table\\n season_data = client.season_schedule(league_year)\\n season_data = br_enum_to_string(season_data)\\n return season_data\",\n \"def get_schedules_for_season(self, season, season_type=\\\"REG\\\"):\\n try:\\n season = int(season)\\n if season_type not in [\\\"REG\\\", \\\"PRE\\\", \\\"POST\\\"]:\\n raise ValueError\\n except (ValueError, TypeError):\\n raise FantasyDataError('Error: Invalid method parameters')\\n\\n season_param = \\\"{0}{1}\\\".format(season, season_type)\\n result = self._method_call(\\\"Schedules/{season}\\\", season=season_param)\\n return result\",\n \"def schedule_url(year, stype, week):\\n xmlurl = 'http://www.nfl.com/ajax/scorestrip?'\\n if stype == 'POST':\\n week += 17\\n if week == 21: # NFL.com you so silly\\n week += 1\\n return '%sseason=%s&seasonType=%s&week=%s' % (xmlurl, year, stype, week)\",\n \"def run_scrape(url: ParseResult, seasons_regex: str, episodes_regex: str) -> Optional[list[str]]:\\n\\n supported_sites: dict[str, Callable[[ParseResult, str], Optional[list[str]]]]\\n supported_sites = {\\n 'www.thenetnaija.com': scrapeSites.netnaija,\\n 'www.lightdl.xyz': scrapeSites.lightdl,\\n 'o2tvseries.com': scrapeSites.o2tvseries,\\n }\\n if url.netloc not in supported_sites:\\n logger.error(\\\"Website from url not implemented\\\")\\n raise exceptions.UnimplementedSite('Getting download links from that site is not yet implimented')\\n\\n return supported_sites[url.netloc](url, seasons_regex, episodes_regex)\",\n \"def scrape(selection_type, launch_date, flat_type=None, town=None):\\n click.secho(\\\"\\\\nStarting to scrape for...\\\", fg=\\\"yellow\\\")\\n click.secho(\\n f\\\"Type : {selection_type}, Launch Date : {launch_date}, \\\"\\n f\\\"Flat Type : {flat_type if flat_type else 'All'} Room, \\\"\\n f\\\"Town : {town if town else 'All'}\\\",\\n )\\n links = get_links_to_scrape(selection_type, launch_date, flat_type, town)\\n data = scrape_links(links)\\n return data\",\n \"def main():\\n if len(sys.argv) != 3:\\n print(\\\"Usage: python season-get [year] [season, lowercase]\\\")\\n return None\\n\\n year = sys.argv[1]\\n season = sys.argv[2]\\n print(\\\"Pulling from API\\\")\\n\\n API_url = \\\"http://api.jikan.moe/season/{}/{}\\\".format(year, season)\\n\\n # Error checking\\n try:\\n data = urllib.request.urlopen(API_url)\\n except urllib.error.URLError as e:\\n print(\\\"Error - {}\\\".format(e))\\n return None\\n print(\\\"Complete!\\\\n\\\")\\n\\n result = json.loads(data.read())['season']\\n\\n print(\\\"Loading IDs\\\")\\n target_name = '{}-{}.txt'.format(year, season)\\n target = open('./input/{}'.format(target_name), 'w')\\n\\n for anime in result:\\n target.write(str(anime['mal_id']) + '\\\\n')\\n print(\\\"Complete!\\\\n\\\")\",\n \"def run():\\n parser = argparse.ArgumentParser(description='Retrieve the latest prices for a specified video game')\\n parser.add_argument('title', help='Title of the game')\\n parser.add_argument('-p', '--platform', help='Platform the game is on')\\n args = parser.parse_args()\\n\\n if (not args.platform):\\n platform = \\\"any\\\"\\n else:\\n platform = args.platform\\n\\n scraper = Scraper()\\n results = scraper.searchAll(game=args.title, platform=platform)\\n for result in results:\\n print('${0:6}| {1:7}| {2:8}| {3}'.format(result[2], result[1], result[3], result[0]))\",\n \"def main(self, model=None):\\n result = pd.DataFrame()\\n if model is None:\\n model = Model.default()\\n for season in [2011, 2012, 2013]:\\n print(season)\\n r = Runner(Season(season), model)\\n current = r.run()\\n result = result.append(current)\\n return result\",\n \"def season_rank(bot):\\n if len(bot.message.words) < 3:\\n return irc.Response('URL parameter is missing. The proper command is !season rank <url>', pm_user=True)\\n try:\\n with open('season.txt', 'a') as f:\\n f.write(bot.message.words[2])\\n f.write('\\\\n')\\n seasonal.update_rankings(bot.message.words[2], conf['challonge'])\\n return irc.Response('Successfully updated the seasonal rankings', pm_user=True)\\n except Exception as e:\\n return irc.Response('An error occurred: ' + str(e), pm_user=True)\",\n \"def run(self,circos_location, seasons=True):\\n self.taxonomy_plot(seasons)\\n print(seasons)\\n filename=self.place+'_'+self.year+'.png'\\n self.run_circos(filename, circos_location)\\n print('In folder ', self.folder_name)\",\n \"def run_scrapping():\\n date = datetime.now().strftime(\\\"%Y-%m-%d\\\")\\n size = 100\\n r = list(range(size))\\n random.shuffle(r)\\n for i in r:\\n scrap_page(url_page.format(i), date)\\n print(str(i) + \\\" / \\\" + str(size))\",\n \"def getseason(data):\\n ## Season key is the most reliable\\n season = data.get(\\\"season\\\")\\n if season:\\n ## Season key is an integer formatted \\\"YYS\\\" and is 2000-based (i.e.- 171 == 2017-Winter)\\n season = str(season)\\n year = int(f\\\"20{season[:2]}\\\")\\n ## Anichart Season key is 1-indexed\\n season = int(season[2]) - 1\\n ## This should normally pass; if it consistently does not, we'll have to investigate why\\n try: return SeasonCharts.buildseason(season,year)\\n ## If something goes wrong, we'll try another method\\n except: print(f\\\"Failed to parse season: {data['season']}\\\")\\n ## Next, we'll iterate over rankings to try to determine the season/year\\n ## There are multiple types of rankings based on season, year, and both combined,\\n ## so we'll piece it together based on whatever we come across first\\n season,year = None,None\\n for ranking in data.get(\\\"rankings\\\",list()):\\n ## Quicker exit (without just making this loop its own function)\\n if season and year: continue\\n ## We'll ignore stuff we've already gotten and assume that nothing in\\n ## rankings contradicts eachother\\n if not season:\\n ## Defaults to None one way or another if it's not supplied\\n season = ranking.get(\\\"season\\\")\\n if not year: year = ranking.get(\\\"year\\\")\\n ## Check if we made it\\n if season and year:\\n ## As above, this should always work out-of-the-box\\n try: return SeasonCharts.buildseason(season,year)\\n except: print(season,year)\\n ## Welp, we're stumped...\\n return None\",\n \"def scrape_main() -> None:\\n\\n logger.info(\\\"Starting scrape\\\")\\n search_info = construct_scrape_regex_patterns(grab_scrape_info())\\n links = run_scrape(\\n url=search_info['url'],\\n seasons_regex=search_info['seasons'],\\n episodes_regex=search_info['episodes']\\n )\\n if links:\\n logger.debug(\\\"Writing urls to file\\\")\\n with open('urls.txt', 'w') as f:\\n for link in links:\\n f.write(link + '\\\\n')\\n else:\\n logger.warning(\\\"No links available\\\")\",\n \"def parse(self, response):\\n # gather season numbers and titles from the page\\n season_numbers = response.css('select[name=\\\"sea\\\"] option::attr(value)').getall()\\n season_titles = response.css('select[name=\\\"sea\\\"] option::text').getall()\\n\\n for season_number, season_title in zip(season_numbers, season_titles):\\n # assemble URL for this season\\n season_url = response.url + \\\"/?sea=\\\" + season_number\\n\\n # scrape the season page\\n yield scrapy.Request(url=season_url,\\n callback=self.parse_season,\\n meta=dict(season_id=season_number,\\n season_title=season_title.split(\\\" \\\")[0]))\",\n \"def pageScrape(season_url, wr):\\r\\n\\r\\n\\thtml = urlopen(season_url)\\r\\n\\tsoup = BeautifulSoup(html, \\\"lxml\\\")\\r\\n\\r\\n\\t# our desired table has id = 'results'\\r\\n\\t# we take the rows in the table's children\\r\\n\\t# But we make sure to ignore the first two, since they're unrelated\\r\\n\\ttable = soup.find(id = 'results').findChildren(\\\"tr\\\")[2:]\\r\\n\\tsoup.decompose()\\r\\n\\tfor elem in table:\\r\\n\\t\\tdesired_row = []\\r\\n\\t\\tif not elem.has_attr('class'): # There are some rows we don't want\\r\\n\\t\\t# these rows have 'classes' while the ones we do want don't\\r\\n\\t\\t\\trank = int(elem.find('th').string)\\r\\n\\t\\t\\tvals = elem.findAll('td')\\r\\n\\t\\t\\tplayer = vals[0].a.string\\r\\n\\t\\t\\turl = vals[0].a.get('href') \\r\\n\\t\\t\\t# url to the player's page on PFR\\r\\n\\t\\t\\t# Used as a unique identifier for each player\\r\\n\\t\\t\\tyear = int(vals[1].string)\\r\\n\\t\\t\\tage = 0 # some players don't have an age input\\r\\n\\t\\t\\tif vals[2].string: \\r\\n\\t\\t\\t\\tage = int(vals[2].string)\\r\\n\\t\\t\\tdraft = str(vals[3].string)\\r\\n\\t\\t\\ttm = '2TM' # players who've played on two teams\\r\\n\\t\\t\\tif vals[4].a:\\r\\n\\t\\t\\t\\ttm = vals[4].a.string\\r\\n\\t\\t\\tlg = \\\"\\\"\\r\\n\\t\\t\\tif vals[5].a:# some players don't have a league?\\r\\n\\t\\t\\t\\tlg = vals[5].a.string \\r\\n\\t\\t\\tg = 0\\r\\n\\t\\t\\tif vals[6].string:\\r\\n\\t\\t\\t\\tg = int(vals[6].string)\\r\\n\\t\\t\\tgs = 0\\r\\n\\t\\t\\tif vals[7].string:\\r\\n\\t\\t\\t\\tgs = int(vals[7].string)\\r\\n\\t\\t\\tyrs = int(vals[8].string)\\r\\n\\t\\t\\tpb = int(vals[9].string)\\r\\n\\t\\t\\tAP1 = int(vals[10].string)\\r\\n\\t\\t\\tAV = int(vals[11].string)\\r\\n\\t\\t\\tPosition = getPosition(url, year)\\t\\t\\r\\n\\r\\n\\t\\t\\t# writing to file\\r\\n\\t\\t\\tdesired_row.extend((rank, player, url, year, age,\\r\\n\\t\\t\\t\\tdraft, tm, lg, g, gs, yrs, pb, AP1, AV, Position))\\r\\n\\t\\t\\twr.writerow(desired_row)\",\n \"def get_number_games(season: str, team_id: int, game_type_code: str = \\\"R\\\") -> dict:\\n args = arguments.get_arguments()\\n\\n endpoint = f\\\"/schedule?teamId={team_id}&season={season}&gameType={game_type_code}\\\"\\n response = api.nhl_api(endpoint)\\n\\n if response:\\n schedule = response.json()\\n games_total = schedule[\\\"totalItems\\\"]\\n return games_total\\n\\n # If no valid response, just return default number of games (82)\\n return 82\",\n \"def get_competition_season_type(season):\\n default_type = games.models.CompetitionSeason.winter\\n try:\\n season_name = season.name\\n if season_name.find(\\\"/\\\") == -1:\\n return games.models.CompetitionSeason.summer\\n return default_type\\n except Exception as e:\\n return default_type\",\n \"def change_season(self, season: str) -> dict:\\n\\n # Check if the season exists\\n if season not in [\\\"plant_off\\\", \\\"summer\\\", \\\"winter\\\"]:\\n raise RequestError\\n\\n # Create the requests' parameters\\n param = ('command={'\\n '\\\"sl_appl_msg\\\":{'\\n '\\\"client\\\":\\\"' + self.id + '\\\",'\\n '\\\"cmd_name\\\":\\\"thermo_season_req\\\",'\\n '\\\"cseq\\\":' + str(self._cseq) + ','\\n '\\\"season\\\":\\\"' + season + '\\\"'\\n '},'\\n '\\\"sl_appl_msg_type\\\":\\\"domo\\\",'\\n '\\\"sl_client_id\\\":\\\"' + self.id + '\\\",'\\n '\\\"sl_cmd\\\":\\\"sl_data_req\\\"'\\n '}')\\n \\n # Send the post request\\n response = requests.post(self._host, params=param, headers=self.header)\\n\\n # Increment the cseq counter\\n self._cseq += 1\\n\\n # Check if the response is valid\\n if not response.json()['sl_data_ack_reason'] == 0:\\n raise RequestError\\n\\n # After every action performed we update the list of items\\n self.update_lists()\\n\\n # Return the json of the response\\n return response.json()\",\n \"def parse_season(self, response):\\n # gather competition numbers and titles from the page\\n competition_numbers = response.css('select[name=\\\"evt\\\"] optgroup option::attr(value)').getall()\\n competition_titles = response.css('select[name=\\\"evt\\\"] optgroup option::text').getall()\\n url_components = urlparse(response.url)\\n\\n for competition_number, competition_title in zip(competition_numbers, competition_titles):\\n # assemble direct URL for this competition\\n competition_url = f'{url_components.scheme}://{url_components.netloc}/Results.aspx?evt={competition_number}'\\n\\n # pass along metadata for use in next steps\\n response.meta.update(dict(competition_title=competition_title,\\n competition_id=competition_number))\\n\\n # scrape the competition page\\n yield scrapy.Request(url=competition_url,\\n callback=self.parse_competition,\\n meta=response.meta)\",\n \"def run():\\n games_db_conn = db.get_db_eng()\\n games_query = \\\"SELECT * FROM GAMES\\\"\\n games_data = etl_tools.extract_from_db(games_db_conn, games_query)\\n _data_integrity_check(games_data)\\n\\n latest_season, latest_season_type = _get_latest_season_and_type(games_data)\\n logging.info(f\\\"Latest season and type in current data: {(latest_season, latest_season_type)}\\\")\\n\\n if latest_season is None:\\n batch_start_season, batch_start_type = config.START_SEASON, config.SEASON_TYPES[0]\\n\\n else:\\n _truncate_games_table(games_db_conn, latest_season, latest_season_type)\\n batch_start_season, batch_start_type = latest_season, latest_season_type\\n\\n logging.info(f\\\"Starting batch at {(batch_start_season, batch_start_type)}...\\\")\\n\\n batches = _get_seasons_grid(batch_start_season, batch_start_type)\\n for batch in batches:\\n season, season_type = batch\\n logging.info(f\\\"Starting new batch: extracting data for {season}-{season_type}...\\\")\\n batch_data = _extract_games_data(season, season_type)\\n logging.info(f\\\"Data extracted. Loading {batch_data.shape[0]} rows...\\\")\\n etl_tools.load_to_db(\\n games_db_conn,\\n 'games',\\n batch_data,\\n )\\n\\n logging.info(\\\"Pipeline completed.\\\")\",\n \"def seasonStats(personId,type = 'gameLog',group = 'hitting'):\\n\\n #playerInfo = get('people', {'personIds':personId})\\n\\n\\n teamStats = get('person',{ 'ver':'v1' , 'personId':personId,'hydrate':['stats(group={},type={})'.format(group,type),'currentTeam']})\\n return teamStats\\n #iterate of stats and find the right player id\\n #career stats broken\\n #fix the season :2019\\n #make function to get team id\",\n \"async def roster(\\n self, ctx: commands.Context, season: Optional[YearFinder] = None, *, search: HockeyTeams\\n ) -> None:\\n season_str = None\\n season_url = \\\"\\\"\\n if season:\\n if season.group(3):\\n if (int(season.group(3)) - int(season.group(1))) > 1:\\n return await ctx.send(_(\\\"Dates must be only 1 year apart.\\\"))\\n if (int(season.group(3)) - int(season.group(1))) <= 0:\\n return await ctx.send(_(\\\"Dates must be only 1 year apart.\\\"))\\n if int(season.group(1)) > datetime.now().year:\\n return await ctx.send(_(\\\"Please select a year prior to now.\\\"))\\n season_str = f\\\"{season.group(1)}{season.group(3)}\\\"\\n else:\\n if int(season.group(1)) > datetime.now().year:\\n return await ctx.send(_(\\\"Please select a year prior to now.\\\"))\\n year = int(season.group(1)) + 1\\n season_str = f\\\"{season.group(1)}{year}\\\"\\n if season:\\n season_url = f\\\"?season={season_str}\\\"\\n if search is None:\\n return await ctx.send(_(\\\"You must provide a valid current team.\\\"))\\n rosters = {}\\n players = []\\n teams = [team for team in TEAMS if search.lower() in team.lower()]\\n if teams != []:\\n for team in teams:\\n url = f\\\"{BASE_URL}/api/v1/teams/{TEAMS[team]['id']}/roster{season_url}\\\"\\n async with self.session.get(url) as resp:\\n data = await resp.json()\\n if \\\"roster\\\" in data:\\n for player in data[\\\"roster\\\"]:\\n players.append(player[\\\"person\\\"][\\\"id\\\"])\\n else:\\n return await ctx.send(_(\\\"No team name was provided.\\\"))\\n\\n if players:\\n await BaseMenu(\\n source=PlayerPages(pages=players, season=season_str),\\n cog=self,\\n delete_message_after=False,\\n clear_reactions_after=True,\\n timeout=60,\\n ).start(ctx=ctx)\\n else:\\n if season:\\n year = _(\\\" in the {season} season\\\").format(\\n season=f\\\"{season.group(1)}-{season.group(3)}\\\"\\n )\\n else:\\n year = \\\"\\\"\\n await ctx.send(\\n _(\\\"I could not find a roster for the {team}{year}.\\\").format(team=team, year=year)\\n )\",\n \"def nflweek(self, irc, msg, args, optlist, optweek):\\n \\n url = self._b64decode('aHR0cDovL3MzLmFtYXpvbmF3cy5jb20vbmZsZ2MvYWxsU2NoZWR1bGUuanM=')\\n \\n usePre, useNext, outputWeek = False, False, False\\n for (option, arg) in optlist:\\n if option == 'pre':\\n usePre = True\\n \\n if optweek:\\n if optweek == \\\"next\\\":\\n useNext = True\\n elif optweek.isdigit():\\n if usePre: \\n if 1 <= int(optweek) <= 4:\\n outputWeek = \\\"Preseason Week %s\\\" % optweek\\n else:\\n irc.reply(\\\"ERROR: Preseason week number must be between 1 and 4.\\\")\\n return\\n else:\\n if 1 <= int(optweek) <= 17:\\n outputWeek = \\\"Week %s\\\" % optweek\\n else:\\n irc.reply(\\\"ERROR: Week must be between 1-17\\\")\\n return \\n\\n try:\\n req = urllib2.Request(url)\\n html = (urllib2.urlopen(req)).read()\\n except:\\n irc.reply(\\\"Failed to open: %s\\\" % url)\\n return\\n \\n jsondata = json.loads(html)\\n\\n week = jsondata.get('week', None) # work with the week data so we know where we are.\\n\\n if week is None:\\n irc.reply(\\\"Failed to load schedule.\\\")\\n return\\n\\n currentWeekName = week.get('current', {'current': None}).get('weekName', None) \\n nextWeekName = week.get('next', {'next': None}).get('weekName', None) \\n\\n if currentWeekName is None:\\n irc.reply(\\\"Cannot figure out the current week.\\\")\\n return\\n\\n games = jsondata.get('content', None) # data in games.\\n \\n if games is None:\\n irc.reply(\\\"Failed to load the games data.\\\")\\n return\\n \\n if outputWeek:\\n games = [item['games'] for item in games if item['weekName'] == outputWeek]\\n weekOutput = outputWeek\\n elif useNext:\\n games = [item['games'] for item in games if item['weekName'] == nextWeekName]\\n weekOutput = nextWeekName\\n else:\\n games = [item['games'] for item in games if item['weekName'] == currentWeekName]\\n weekOutput = currentWeekName\\n \\n append_list = []\\n\\n for games in games:\\n for t in games:\\n awayTeam = self._translateTeam('team', 'nid', t['awayTeamId'])\\n homeTeam = self._translateTeam('team', 'nid', t['homeTeamId'])\\n append_list.append(\\\"[\\\" + t['date']['num'] + \\\"] \\\" + awayTeam + \\\"@\\\" + homeTeam + \\\" \\\" + t['date']['time'])\\n \\n descstring = string.join([item for item in append_list], \\\" | \\\")\\n output = \\\"{0} :: {1}\\\".format(ircutils.bold(weekOutput), descstring)\\n \\n irc.reply(output)\",\n \"def main(num, rank):\\n\\n file_existed = os.path.isfile(FILE_NAME)\\n\\n if rank:\\n rank = int(rank)\\n \\n # if a rank is passed, we assume the user wants to open the product at the\\n # rank. But if the local file doesn't exist, we query the API, and retry.\\n if rank and file_existed:\\n browse_rank(load_posts(), rank)\\n else:\\n posts = get_todays_posts()\\n end_index = num if num <= len(posts) else len(posts)\\n posts = posts[:end_index]\\n\\n if not rank:\\n print_posts(posts)\\n\\n save_posts(posts)\\n \\n if rank:\\n # rank was passed, and url could not be opened in first try\\n browse_rank(load_posts(), rank)\",\n \"def nflschedule(self, irc, msg, args, optlist, optteam):\\n \\n fullSchedule = False\\n for (option, arg) in optlist:\\n if option == 'full':\\n fullSchedule = True\\n \\n optteam = optteam.upper()\\n \\n if optteam not in self._validteams():\\n irc.reply(\\\"Team not found. Must be one of: %s\\\" % self._validteams())\\n return\\n \\n lookupteam = self._translateTeam('yahoo', 'team', optteam) # don't need a check for 0 here because we validate prior.\\n \\n if fullSchedule: # diff url/method.\\n url = self._b64decode('aHR0cDovL3Nwb3J0cy55YWhvby5jb20vbmZsL3RlYW1z') + '/%s/schedule' % lookupteam\\n\\n try:\\n request = urllib2.Request(url)\\n html = (urllib2.urlopen(request)).read()\\n except:\\n irc.reply(\\\"Failed to open: %s\\\" % url)\\n return\\n \\n soup = BeautifulSoup(html)\\n table = soup.find('table', attrs={'summary':'Regular Season Games'})\\n \\n if not table:\\n irc.reply(\\\"ERROR: Failed to find schedule for: %s\\\") % optteam\\n return\\n \\n tbody = table.find('tbody')\\n rows = tbody.findAll('tr')\\n\\n append_list = []\\n\\n for row in rows:\\n tds = row.findAll('td')\\n week = tds[0]\\n \\n if row.find('td', attrs={'class':'title bye'}):\\n date = \\\"BYE\\\"\\n opp = \\\"\\\"\\n score = \\\"\\\"\\n appendString = \\\"W{0}-{1}\\\".format(ircutils.bold(week.getText()), ircutils.underline(\\\"BYE\\\"))\\n else:\\n date = tds[1].getText()\\n dateSplit = date.split(',', 1) # take the date, dump the rest.\\n date = dateSplit[1]\\n opp = tds[2] # with how the Tag/string comes in, we need to extract one part and format the other.\\n oppName = opp.find('span')\\n if oppName:\\n oppName.extract()\\n oppTeam = opp.find('a').getText() \\n #opp = tds[2].find('span').getText()\\n #opp = self._translateTeam('team','full', opp) # use the db to make a full team small.\\n score = tds[3].getText().replace('EDT','').replace('EST','').replace('pm','').replace('am','') # strip the garbage\\n #score = score.replace('W', ircutils.mircColor('W', 'green')).replace('L', ircutils.mircColor('L', 'red'))\\n appendString = \\\"W{0}-{1} {2} {3}\\\".format(ircutils.bold(week.getText()), date.strip(), oppTeam.strip(), score.strip())\\n \\n append_list.append(appendString)\\n\\n descstring = string.join([item for item in append_list], \\\" | \\\")\\n output = \\\"{0} SCHED :: {1}\\\".format(ircutils.mircColor(optteam, 'red'), descstring)\\n irc.reply(output)\\n else:\\n url = self._b64decode('aHR0cDovL3Nwb3J0cy55YWhvby5jb20vbmZsL3RlYW1z') + '/%s/calendar/rss.xml' % lookupteam\\n \\n try:\\n req = urllib2.Request(url)\\n response = urllib2.urlopen(req)\\n html = response.read()\\n except:\\n irc.reply(\\\"Cannot open: %s\\\" % url)\\n return\\n\\n # clean this stuff up\\n html = html.replace('<![CDATA[','').replace(']]>','').replace('EDT','').replace('\\\\xc2\\\\xa0',' ')\\n\\n soup = BeautifulSoup(html)\\n items = soup.find('channel').findAll('item')\\n \\n append_list = []\\n\\n for item in items:\\n title = item.find('title').renderContents().strip() # title is good.\\n day, date = title.split(',')\\n desc = item.find('description') # everything in desc but its messy.\\n desctext = desc.findAll(text=True) # get all text, first, but its in a list.\\n descappend = (''.join(desctext).strip()) # list transform into a string.\\n if not descappend.startswith('@'): # if something is @, it's before, but vs. otherwise.\\n descappend = 'vs. ' + descappend\\n descappend += \\\" [\\\" + date.strip() + \\\"]\\\"\\n append_list.append(descappend) # put all into a list.\\n\\n \\n descstring = string.join([item for item in append_list], \\\" | \\\")\\n output = \\\"{0} {1}\\\".format(ircutils.bold(optteam), descstring)\\n irc.reply(output)\",\n \"def run_search(search_info: dict[str, str]) -> dict[str, Optional[set[str]]]:\\n results = dict()\\n\\n func: Callable[[str], Optional[set[str]]]\\n for func in searchSites.netnaija, searchSites.lightdl:\\n if not search_info['season']:\\n results[func.__name__] = func(\\n search_info['series']\\n )\\n elif search_info['season'] and search_info['episode']:\\n results[func.__name__] = func(\\n ' '.join([search_info['series'], search_info['season'], search_info['episode']])\\n )\\n else:\\n results[func.__name__] = func(\\n ' '.join([search_info['series'], search_info['season']])\\n )\\n\\n return results\",\n \"def scrape_all():\\n\\n # Scrape team information by season\\n for team in scrape_utils.team_names():\\n team_season_stats(team)\\n # Each season\\n print(team)\\n for year in range(2019, 2020):\\n # Game Logs\\n season_game_logs(team, year)\\n\\n # Starting Lineups\\n #player_scraper.get_starting_lineups(team, year)\\n\\n # Init mongo to get game IDS for box score scraping\\n m = mongo.Mongo()\\n\\n # Game Information (Box Score and Play by Play)\\n for year in range(2015, 2020):\\n player_scraper.get_starting_lineups(year)\\n for game in m.find('game_log', {'season': year}, {'_id': 1}):\\n #team_scraper.play_by_play(game['_id'])\\n player_scraper.player_box_score(game['_id'])\\n\\n print(game['_id'])\\n\\n\\n\\n # Get player information\\n for player in scrape_utils.get_active_players():\\n print(player)\\n player_scraper.player_per_game(player)\\n\\n # Get betting lines (By Year) need from 2014\\n for year in range(2015, 2020):\\n team_scraper.betting_lines(2019)\",\n \"def scrape(self):\\n self._validate_date_range(self.start_date, self.end_date)\\n self._validate_team()\\n self._cache_source()\\n soup = self.season_raw_cache[self.start_date.year]\\n df = self._parse_raw(soup)\\n return self._apply_filters(df)\",\n \"def season(self, title=None, season=None):\\n key = f'{self.key}/children?excludeAllLeaves=1'\\n if title is not None and not isinstance(title, int):\\n return self.fetchItem(key, Season, title__iexact=title)\\n elif season is not None or isinstance(title, int):\\n if isinstance(title, int):\\n index = title\\n else:\\n index = season\\n return self.fetchItem(key, Season, index=index)\\n raise BadRequest('Missing argument: title or season is required')\",\n \"def _parse_fname_season(cmd_args):\\n f_name_base = 'sfav2_CONUS_{}_to_{}'\\n season_start = '093012' # Start month, day, & hour for seasonal accum\\n\\n date_in = adjust_date(cmd_args)\\n\\n if (not check_ftype(cmd_args)):\\n print('{} not valid for seasonal accumulation period. Downloading as NetCDF'.format(cmd_args.f_type))\\n f_type = 'nc'\\n else:\\n f_type = cmd_args.f_type\\n\\n # If we are in the new year of the winter season (i.e., Jan 2020 of the\\n # 2019-2020 winter season), adjust the start year defining the winter season\\n if (date_in.month < 9):\\n start_yr = date_in.year - 1\\n else:\\n start_yr = date_in.year\\n\\n date_start = '{}{}'.format(start_yr, season_start)\\n date_end = datetime.strftime(date_in, '%Y%m%d%H')\\n\\n f_name = f_name_base.format(date_start, date_end)\\n\\n f_name = '{}.{}'.format(f_name, f_type)\\n\\n return f_name\",\n \"def parse_season(filename):\\n print_info('Attempting to parse {0}'.format(filename))\\n print_info('Extracting season from {0}'.format(filename))\\n for regex in SEASON_REGEX:\\n m = re.search(regex, filename)\\n\\n if m is None:\\n continue\\n\\n extracted_season = m.group('Season').lower()\\n print_info('Extracted season: {0}'.format(extracted_season))\\n\\n season_num = int(extracted_season)\\n if season_num is not None and season_num > 0:\\n print_info('Season might be: {0}'.format(season_num))\\n return 'S' + format_num(season_num)\\n return 'S01'\",\n \"def environmental_science_news():\\n\\n return general_scraper(['http://mesva.univaq.it/?q=avvisi/cl-clm/52671'])\",\n \"def season_series(game_id, pref_team, other_team, last_season=False):\\n\\n # Init empty dictionaries and lists\\n games_against = list()\\n pref_toi = dict()\\n pref_goals = dict()\\n pref_assists = dict()\\n pref_points = dict()\\n pref_record = {\\\"wins\\\": 0, \\\"losses\\\": 0, \\\"ot\\\": 0}\\n roster_player = True\\n\\n # If this is the first game of the season, we can set the 'last_season' flag to enable the\\n # season series function to check last year's season series between the two teams.\\n if not last_season:\\n season_start = str(game_id)[0:4]\\n season_end = str(int(season_start) + 1)\\n yesterday = datetime.now() - timedelta(days=1)\\n # yesterday = datetime.now() + timedelta(days=50)\\n # schedule_url = (\\n # f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n # f\\\"&expand=schedule.broadcasts,schedule.teams&startDate=\\\"\\n # f\\\"{season_start}-08-01&endDate={yesterday:%Y-%m-%d}\\\"\\n # )\\n schedule_url = (\\n f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n f\\\"&expand=schedule.broadcasts,schedule.teams\\\"\\n f\\\"&season={season_start}{season_end}\\\"\\n )\\n else:\\n season_start = int(str(game_id)[0:4]) - 1\\n season_end = str(int(season_start) + 1)\\n yesterday = datetime.now() - timedelta(days=1)\\n # yesterday = datetime.now() + timedelta(days=50)\\n # schedule_url = (\\n # f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n # f\\\"&expand=schedule.broadcasts,schedule.teams&startDate=\\\"\\n # f\\\"{season_start}-08-01&endDate={season_end}-06-01\\\"\\n # )\\n schedule_url = (\\n f\\\"/schedule?teamId={pref_team.team_id}\\\"\\n f\\\"&expand=schedule.broadcasts,schedule.teams\\\"\\n f\\\"&season={season_start}{season_end}\\\"\\n )\\n\\n schedule = api.nhl_api(schedule_url).json()\\n dates = schedule[\\\"dates\\\"]\\n\\n # Loop through scheduled to get previously played games against\\n for date in dates:\\n game = date[\\\"games\\\"][0]\\n game_type = game[\\\"gameType\\\"]\\n game_id = game[\\\"gamePk\\\"]\\n game_team_home = game[\\\"teams\\\"][\\\"home\\\"][\\\"team\\\"][\\\"name\\\"]\\n game_team_away = game[\\\"teams\\\"][\\\"away\\\"][\\\"team\\\"][\\\"name\\\"]\\n teams = [game_team_away, game_team_home]\\n game_status = game[\\\"status\\\"][\\\"abstractGameState\\\"]\\n if game_type == \\\"R\\\" and game_status == \\\"Final\\\" and other_team.team_name in teams:\\n game_feed = f\\\"/game/{game_id}/feed/live\\\"\\n games_against.append(game_feed)\\n\\n # If the two teams haven't played yet, just exit this function\\n if not games_against:\\n return None, None, None\\n\\n # Loop through newly created games_against list to get each stats\\n for feed in games_against:\\n game = api.nhl_api(feed).json()\\n game_data = game[\\\"gameData\\\"]\\n home_team_name = game_data[\\\"teams\\\"][\\\"home\\\"][\\\"name\\\"]\\n pref_homeaway = \\\"home\\\" if home_team_name == pref_team.team_name else \\\"away\\\"\\n other_homeaway = \\\"away\\\" if home_team_name == pref_team.team_name else \\\"home\\\"\\n\\n # Get season series\\n end_period = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"currentPeriod\\\"]\\n extra_time = True if end_period > 3 else False\\n pref_score = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"teams\\\"][pref_homeaway][\\\"goals\\\"]\\n other_score = game[\\\"liveData\\\"][\\\"linescore\\\"][\\\"teams\\\"][other_homeaway][\\\"goals\\\"]\\n if pref_score > other_score:\\n pref_record[\\\"wins\\\"] += 1\\n elif other_score > pref_score and extra_time:\\n pref_record[\\\"ot\\\"] += 1\\n else:\\n pref_record[\\\"losses\\\"] += 1\\n\\n season_series_str = f\\\"Series: {pref_record['wins']}-\\\" f\\\"{pref_record['losses']}-{pref_record['ot']}\\\"\\n\\n # Get stats leaders\\n # pref_teamstats = game[\\\"liveData\\\"][\\\"boxscore\\\"][\\\"teams\\\"][pref_homeaway][\\\"teamStats\\\"]\\n pref_playerstats = game[\\\"liveData\\\"][\\\"boxscore\\\"][\\\"teams\\\"][pref_homeaway][\\\"players\\\"]\\n for id, player in pref_playerstats.items():\\n try:\\n # Calculate TOI\\n player_toi_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"timeOnIce\\\"]\\n player_toi_minutes = int(player_toi_str.split(\\\":\\\")[0])\\n player_toi_seconds = int(player_toi_str.split(\\\":\\\")[1])\\n player_toi = (player_toi_minutes * 60) + player_toi_seconds\\n pref_toi[id] = pref_toi.get(id, 0) + player_toi\\n\\n # Point Totals\\n player_goal_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"goals\\\"]\\n pref_goals[id] = pref_goals.get(id, 0) + int(player_goal_str)\\n player_assist_str = player[\\\"stats\\\"][\\\"skaterStats\\\"][\\\"assists\\\"]\\n pref_assists[id] = pref_assists.get(id, 0) + int(player_assist_str)\\n player_points = int(player_goal_str) + int(player_assist_str)\\n pref_points[id] = pref_points.get(id, 0) + int(player_points)\\n\\n except KeyError:\\n pass\\n\\n # Calculate Stats Leaders\\n sorted_toi = sorted(pref_toi.values(), reverse=True)\\n leader_toi = sorted_toi[0]\\n\\n sorted_points = sorted(pref_points.values(), reverse=True)\\n leader_points = sorted_points[0]\\n\\n # Get TOI leader\\n for id in pref_toi.keys():\\n if pref_toi[id] == leader_toi:\\n player_name = roster.player_attr_by_id(pref_team.roster, id, \\\"fullName\\\")\\n if player_name is None:\\n roster_player = False\\n player_id_only = id.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n leader_toi_avg = leader_toi / len(games_against)\\n m, s = divmod(leader_toi_avg, 60)\\n toi_m = int(m)\\n toi_s = int(s)\\n toi_s = \\\"0{}\\\".format(toi_s) if toi_s < 10 else toi_s\\n toi_avg = \\\"{}:{}\\\".format(toi_m, toi_s)\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n toi_leader_str = \\\"TOI Leader: {} with {} / game.\\\".format(player_short_name, toi_avg)\\n\\n # Handle tied points leaders\\n point_leaders = list()\\n for id in pref_points.keys():\\n if pref_points[id] == leader_points:\\n point_leaders.append(id)\\n\\n if leader_points == 0:\\n points_leader_str = \\\"Points Leader: None (all players have 0 points).\\\"\\n\\n elif len(point_leaders) == 1:\\n leader = point_leaders[0]\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n # If the player is no longer on the team, get their information (change string here?)\\n if player_name is None:\\n roster_player = False\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n if not roster_player:\\n points_leader_str = (\\n f\\\"Points Leader: {player_name} with {leader_points} points \\\"\\n f\\\"({player_goals}G {player_assists}A) \\\"\\n )\\n else:\\n points_leader_str = \\\"Points Leader: {} with {} ({}G {}A).\\\".format(\\n player_name, leader_points, player_goals, player_assists\\n )\\n\\n elif len(point_leaders) > 3:\\n point_leaders_with_attrs = list()\\n for leader in point_leaders:\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n if player_name is None:\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n point_leaders_with_attrs.append(player_short_name)\\n\\n point_leaders_joined = \\\", \\\".join(point_leaders_with_attrs[0:3])\\n leftover_leaders = len(point_leaders) - 3\\n points_leader_str = (\\n f\\\"Points Leaders: {point_leaders_joined} & {leftover_leaders} others ({leader_points} each).\\\"\\n )\\n\\n else:\\n point_leaders_with_attrs = list()\\n for leader in point_leaders:\\n player_name = roster.player_attr_by_id(pref_team.roster, leader, \\\"fullName\\\")\\n if player_name is None:\\n player_id_only = leader.replace(\\\"ID\\\", \\\"\\\")\\n player_name = roster.nonroster_player_attr_by_id(player_id_only, \\\"fullName\\\")\\n player_goals = pref_goals[leader]\\n player_assists = pref_assists[leader]\\n player_short_name = f\\\"{player_name[0]}. {' '.join(player_name.split()[1:])}\\\"\\n player_str = f\\\"{player_short_name} ({player_goals}G {player_assists}A)\\\"\\n point_leaders_with_attrs.append(player_str)\\n\\n point_leaders_joined = (\\n f\\\", \\\".join(point_leaders_with_attrs[:-1]) + f\\\" & {point_leaders_with_attrs[-1]}\\\"\\n )\\n points_leader_str = \\\"Points Leaders: {} with {} each.\\\".format(point_leaders_joined, leader_points)\\n\\n return season_series_str, points_leader_str, toi_leader_str\",\n \"def test_seasons(self):\\n response = Tmdb.season(tmdb_show_id = 69740, season_number = 1)\\n self.assertTrue(int(response.status_code) == 200)\\n data = response.json()\\n self.assertTrue(isinstance(data['episodes'], list))\\n # TODO check if all the shows are in the good format (can be from_dict/to_dict)\",\n \"def season_type(self, season_type):\\n\\n self._season_type = season_type\",\n \"def nflffprojections(self, irc, msg, args, opttype):\\n \\n validtypes = { 'QB':'0','RB':'2','WR':'4','TE':'6','D/ST':'16','K':'17','FLEX':'23'}\\n \\n if opttype and opttype not in validtypes:\\n irc.reply(\\\"Type must be one of: %s\\\" % validtypes.keys())\\n return\\n \\n url = self._b64decode('aHR0cDovL2dhbWVzLmVzcG4uZ28uY29tL2ZmbC90b29scy9wcm9qZWN0aW9ucz8=')\\n\\n if opttype:\\n url += '?&slotCategoryId=%s' % validtypes[opttype]\\n \\n try:\\n req = urllib2.Request(url)\\n html = (urllib2.urlopen(req)).read()\\n except:\\n irc.reply(\\\"Failed to open: %s\\\" % opttype)\\n return\\n \\n html = html.replace(' ',' ')\\n \\n soup = BeautifulSoup(html)\\n table = soup.find('table', attrs={'id':'playertable_0'})\\n rows = table.findAll('tr')[2:12]\\n\\n append_list = []\\n\\n for row in rows:\\n rank = row.find('td')\\n player = rank.findNext('td')\\n projections = row.find('td', attrs={'class':'playertableStat appliedPoints'})\\n append_list.append(rank.getText() + \\\". \\\" + ircutils.bold(player.getText()) + \\\" (\\\" + projections.getText() + \\\")\\\")\\n\\n descstring = string.join([item for item in append_list], \\\" | \\\") # put the list together.\\n\\n if opttype:\\n title = \\\"Top 10 FF projections at: %s\\\" % opttype\\n else:\\n title = \\\"Top 10 FF projections\\\"\\n \\n output = \\\"{0} :: {1}\\\".format(ircutils.mircColor(title, 'red'), descstring)\\n irc.reply(output)\",\n \"def main():\\n\\n creds = None\\n # The file token.json stores the user's access and refresh tokens, and is\\n # created automatically when the authorization flow completes for the first\\n # time.\\n\\n # There was no need to do this with Google Sheets... I just felt like being extra.\\n if os.path.exists('token.json'):\\n creds = Credentials.from_authorized_user_file('token.json', SCOPES)\\n # If there are no (valid) credentials available, let the user log in.\\n if not creds or not creds.valid:\\n if creds and creds.expired and creds.refresh_token:\\n creds.refresh(Request())\\n else:\\n flow = InstalledAppFlow.from_client_secrets_file(\\n 'credentials.json', SCOPES)\\n creds = flow.run_local_server(port=0)\\n # Save the credentials for the next run\\n with open('token.json', 'w') as token:\\n token.write(creds.to_json())\\n\\n service = build('sheets', 'v4', credentials=creds)\\n\\n # Call the Sheets API\\n sheet = service.spreadsheets()\\n map_df = pd.read_csv('data/rel_map.csv')\\n \\n for i, rows in map_df.iterrows():\\n print(rows.show_season, rows.lead)\\n first_url = rows.show_season\\n write_from_URL(wiki_head + first_url, sheet, rows.lead, rows.show_season)\",\n \"def main():\\n var_type = sys.argv[1]\\n start_yr = int(sys.argv[2])\\n end_yr = int(sys.argv[3])\\n print(var_type,start_yr,end_yr,dataFol)\\n \\n data = imd.get_data(var_type, start_yr, end_yr, fn_format='yearwise',file_dir=str(dataFol)) # Path must be str because imdlib needs str\",\n \"async def news(self, ctx, typex):\\n if typex in options['news']['sport_type']:\\n output = news_output(get_sport_data())\\n elif typex in options['news']['world_type']:\\n output = news_output(get_news_data(\\\"https://www.novinky.cz/zahranicni\\\"))\\n elif typex in options['news']['home_type']:\\n output = news_output(get_news_data(\\\"https://www.novinky.cz/domaci\\\"))\\n else:\\n await ctx.send(\\\"Zadán nesprávný typ novinek\\\")\\n return\\n\\n for embed in output:\\n await ctx.send(embed=embed)\",\n \"def season(self, seasonnum, order='aired'):\\n if order=='aired':\\n seasons = self.seasons\\n elif order == 'dvd':\\n seasons = self.dvd_seasons\\n try:\\n return seasons[seasonnum]\\n except KeyError:\\n raise SeasonNotFoundError(\\n 'Season no %s does not exists' % seasonnum\\n ), None, sys.exc_info()[2]\",\n \"def main():\\n\\n # generate a token, we will be sending several queries off\\n token = gen_token()\\n # build the query string\\n s_d, e_d = prev_quarter_boundaries(datetime.datetime.utcnow())\\n s_str = s_d.strftime(\\\"%Y-%m-%d\\\")\\n e_str = e_d.strftime(\\\"%Y-%m-%d\\\")\\n query_str = (\\n 'filingsource:\\\"Taiwan TWSE\\\" AND ' +\\n 'enddate:[' + s_str + ' TO ' +\\n e_str + ']'\\n )\\n # pull docs\\n docs_res = documents_stringquery(query_str, False, token=token)\\n # read out number of hits\\n num_filings = docs_res['totalHits']\\n # print it out\\n print('Filing count from last quarter: ' + str(num_filings))\",\n \"def with_season(title, season):\\n videos = list(mythVideo.searchVideos(title = title, season = season))\\n\\n for video in videos:\\n video.label = video.title + \\\" - \\\" + video.subtitle\\n video.url = \\\"/videos/\\\" + video.title + \\\"/\\\" + video.hash\\n\\n videos = sorted(videos, key = lambda video: video.episode)\\n return render_template('list.html', items = videos, page_title = title + \\\" Season \\\" + str(season))\",\n \"def exec(nb,tickers,pa,typ,First):\\n dl = Downloader() # the SEC_edgar_downloader package\\n if First: # if First == True\\n # loading the data\\n for t in tickers: # looping over all tickers\\n dl.get(typ, t,amount=nb, download_details=True)\\n\\n assert os.path.isdir(pa),'Wrong File Directory Check Again' # assert the SEC edgar folder is created \\n\\n # proccesing it\\n for t in tickers:\\n writingtxt(findhtml(pa,t,typ),t,nb,typ)\",\n \"def get_html_document(self, team, src_type, season=None):\\n if src_type == 'roster':\\n # preparing url to team's roster page\\n team_url_component = team.team_name.lower().replace(\\\" \\\", \\\"\\\")\\n # creating url like 'https://www.nhl.com/ducks/roster'\\n if season is not None:\\n team_url = \\\"/\\\".join((\\n self.NHL_SITE_PREFIX,\\n team_url_component,\\n self.NHL_SITE_ROSTER_SUFFIX,\\n str(season)))\\n else:\\n team_url = \\\"/\\\".join((\\n self.NHL_SITE_PREFIX,\\n team_url_component,\\n self.NHL_SITE_ROSTER_SUFFIX))\\n elif src_type == 'system':\\n # preparing url to team's prospects page\\n team_url_component = team.team_name.lower().replace(\\\" \\\", \\\"\\\")\\n team_site_prefix = self.TEAM_SITE_PREFIX.replace(\\n \\\"%s\\\", team_url_component)\\n # creating url like\\n # 'http://ducks.ice.nhl.com/club/roster.htm?type=prospect'\\n team_url = \\\"\\\".join((\\n team_site_prefix,\\n self.TEAM_SITE_ROSTER_SUFFIX))\\n elif src_type == 'contracts':\\n # preparing url to team's prospects page\\n team_url_component = team.team_name.lower().replace(\\\" \\\", \\\"\\\")\\n team_url = \\\"\\\".join((\\n self.CAPFRIENDLY_SITE_PREFIX, team_url_component))\\n\\n try:\\n req = requests.get(team_url)\\n except requests.exceptions.ConnectionError:\\n # TODO: returning empty document tree\\n return None\\n return html.fromstring(req.text)\",\n \"def main():\\n download_insert_title_basics()\\n download_insert_title_principals()\\n download_insert_name_basics()\\n download_insert_title_ratings()\\n scrap_keywords()\\n create_and_insert_soup()\\n return\",\n \"def main() -> None:\\n\\n download_data_tools.initial_message()\\n\\n # S&P 500 companies, initial year and time step\\n stocks: List[str] = download_data_tools.get_stocks(['all'])\\n dates: List[str] = ['1992-01', '2012-12']\\n time_step: str = '1d'\\n\\n # Basic folders\\n download_data_tools.start_folders()\\n\\n # Run analysis\\n # Download data\\n portfolio_download_data(stocks, dates, time_step)\\n\\n print('Ay vamos!!!')\",\n \"def scrape_year(year, upgrade_discipline):\\n for discipline in DISCIPLINE_MAP[upgrade_discipline]:\\n logger.info('Getting {} events for {}'.format(discipline, year))\\n response = session.get('{}/results/{}/{}'.format(baseurl, year, discipline))\\n response.raise_for_status()\\n tree = html.fromstring(response.text)\\n parent_id = ''\\n parent_name = ''\\n\\n for element in tree.xpath('//table[contains(@class,\\\"results_home\\\")]//tr'):\\n # No results early in the year\\n if not element.xpath('td/a'):\\n continue\\n\\n # Extract event names, dates, and IDs from link text\\n event_anchor = element.xpath('td/a')[0]\\n event_date = element.xpath('td[@class=\\\"date\\\"]')[0].text\\n event_id = event_anchor.get('href').split('/')[2]\\n\\n # Series results are linked by date; single events by name\\n if event_date:\\n event_date = event_date.strip()\\n event_name = event_anchor.text\\n else:\\n event_date = event_anchor.text.strip()\\n event_name = parent_name\\n\\n event_discipline = get_discipline(event_name, discipline)\\n if not event_discipline:\\n logger.warn('Found Event id={} name={} date={} with blacklisted discipline'.format(event_id, event_name, event_date))\\n continue\\n\\n if element.get('class') == 'multi-day-event-child':\\n # multi-day-event-child class used for series events\\n logger.info('Found Event id={} name={} date={} discipline={} with series {}'.format(\\n event_id, event_name, event_date, event_discipline, parent_id))\\n if parent_id:\\n (Event.insert(id=event_id,\\n name=event_name,\\n discipline=event_discipline,\\n year=year,\\n date=event_date,\\n series_id=parent_id,\\n parent_id=None)\\n .on_conflict(conflict_target=[Event.id],\\n preserve=[Event.name, Event.discipline, Event.year, Event.date, Event.series, Event.parent])\\n .execute())\\n else:\\n logger.warn('Found multi-day-event-child without a series!')\\n else:\\n if element.get('class') == 'multi-day-event' or '-' in event_date:\\n # Assume anything with a date range is a series\\n logger.info('Found Series id={} name={} dates={}'.format(event_id, event_name, event_date))\\n (Series.insert(id=event_id,\\n name=event_name,\\n year=year,\\n dates=event_date)\\n .on_conflict(conflict_target=[Series.id],\\n preserve=[Series.name, Series.year, Series.dates])\\n .execute())\\n parent_id = event_id\\n parent_name = event_name\\n else:\\n # Single date with no parent, must be a standalone event\\n logger.info('Found Event id={} name={} date={} discipline={}'.format(event_id, event_name, event_date, event_discipline))\\n (Event.insert(id=event_id,\\n name=event_name,\\n discipline=event_discipline,\\n year=year,\\n date=event_date,\\n series_id=None,\\n parent_id=None)\\n .on_conflict(conflict_target=[Event.id],\\n preserve=[Event.name, Event.discipline, Event.year, Event.date, Event.series, Event.parent])\\n .execute())\",\n \"def run(self):\\n while self.i < len(self.series):\\n # Grab line + RSS\\n s = self.series[self.i]\\n rss = self.request_rss(s.feedUrl)\\n\\n # Compose Episodes\\n ep_dicts = []\\n for entry in rss['entries']:\\n ep_dicts.append(Episode(s, entry).__dict__)\\n\\n # Build result dict\\n result_dict = dict()\\n result_dict['series'] = deepcopy(s.__dict__)\\n result_dict['series']['genres'] = \\\\\\n result_dict['series']['genres'].split(';')\\n result_dict['series']['type'] = 'series'\\n result_dict['episodes'] = ep_dicts\\n\\n # Store podcast\\n self.storer.store(result_dict)\\n\\n # Move onto the next one\\n self.i += 20\\n print(\\\"Retrieved \\\" + str(s.id))\",\n \"def _loop_daily(config, configfile, topdatadir, startdate, model_forcing):\\n\\n delta = datetime.timedelta(days=1)\\n scriptdir = config['SETUP']['LISFDIR'] + '/lis/utils/usaf/s2s/s2s_modules/s2spost/'\\n\\n # The very first day may be missing. Gracefully handle this\\n firstdate = startdate\\n if _is_lis_output_missing(firstdate, model_forcing):\\n firstdate += delta\\n\\n if startdate.month == 12:\\n enddate = datetime.datetime(year=(startdate.year + 1),\\n month=1,\\n day=1)\\n else:\\n enddate = datetime.datetime(year=startdate.year,\\n month=(startdate.month + 1),\\n day=1)\\n\\n curdate = firstdate\\n while curdate <= enddate:\\n cmd = f\\\"python {scriptdir}/daily_s2spost_nc.py {configfile}\\\"\\n for model in [\\\"SURFACEMODEL\\\", \\\"ROUTING\\\"]:\\n cmd += f\\\" lis_fcst/{model_forcing}/{model}/\\\"\\n cmd += f\\\"{curdate.year:04d}{curdate.month:02d}\\\"\\n cmd += \\\"/LIS_HIST_\\\"\\n cmd += f\\\"{curdate.year:04d}{curdate.month:02d}{curdate.day:02d}\\\"\\n cmd += \\\"0000.d01.nc\\\"\\n\\n cmd += f\\\" {topdatadir}/cf_{model_forcing}_\\\"\\n cmd += f\\\"{startdate.year:04d}{startdate.month:02d}\\\"\\n\\n cmd += f\\\" {curdate.year:04d}{curdate.month:02d}{curdate.day:02d}00\\\"\\n\\n cmd += f\\\" {model_forcing}\\\"\\n\\n print(cmd)\\n returncode = subprocess.call(cmd, shell=True)\\n if returncode != 0:\\n print(\\\"[ERR] Problem running CF conversion!\\\")\\n sys.exit(1)\\n\\n curdate += delta\",\n \"def test_scraper(self):\\n scrap = ScraperModule(url=self.anime_link, host=\\\"mixdrop\\\")\\n pages = scrap.pages\\n print(f\\\"pages: {pages}\\\")\\n get_pages = scrap.get_pages()\\n first_link = scrap.get_links(pages[0])\\n print(first_link)\\n episodes = scrap.get_episodes()\\n print(episodes)\",\n \"def search_season_episode(self,strz):\\t\\n\\t\\tpattern = compile(\\\"(S(\\\\d\\\\d)E(\\\\d\\\\d))\\\") #S01E03\\n\\t\\tsep = pattern.search(strz)\\t\\t\\n\\t\\tif sep is not None:\\n\\t\\t\\tse= sep.group(1)\\n\\t\\t\\tseason = sep.group(2)\\n\\t\\t\\tepisode = sep.group(3)\\n\\t\\t\\treturn strz.replace(se,\\\"\\\")\\n\\t\\t\\t\\n\\t\\tpattern = compile(\\\"((\\\\d\\\\d)x(\\\\d\\\\d))\\\") #01x03\\n\\t\\tsep = pattern.search(strz)\\t\\t\\n\\t\\tif sep is not None:\\n\\t\\t\\tse= sep.group(1)\\n\\t\\t\\tseason = sep.group(2)\\n\\t\\t\\tepisode = sep.group(3)\\n\\t\\t\\treturn strz.replace(se,\\\"\\\")\\n\\t\\t\\t\\n\\t\\tpattern = compile(\\\"(Ep(\\\\d\\\\d))\\\") #Ep03\\n\\t\\tsep = pattern.search(strz)\\t\\t\\n\\t\\tif sep is not None:\\n\\t\\t\\tse= sep.group(1)\\n\\t\\t\\tepisode = sep.group(2)\\n\\t\\t\\treturn strz.replace(se,\\\"\\\")\",\n \"def distributeSeason(self):\\n i = 1\\n for day in self.daylist:\\n if i >= monthbeg[5] and i < monthbeg[9]: #june through SEpt as per SCE\\n day.season = 'summer' #https://www.sce.com/residential/rates/Time-Of-Use-Residential-Rate-Plans\\n i = i + 1\\n else:\\n day.season = 'winter'\\n i = i+1\",\n \"def main(args):\\n parser = create_parser()\\n\\n if not args:\\n parser.print_usage()\\n sys.exit(1)\\n\\n parsed_args = parser.parse_args(args)\\n scrape_url(parsed_args.url)\",\n \"def main():\\n\\n from scrapy.crawler import CrawlerProcess\\n from scrapy.utils.project import get_project_settings\\n\\n process = CrawlerProcess(get_project_settings())\\n process.crawl(NCBIGeoSpider)\\n process.start()\",\n \"def set_season_time(season): \\n if season == '2021-22':\\n startdate = time.strptime('13-08-2021', '%d-%m-%Y')\\n startdate = datetime.fromtimestamp(mktime(startdate))\\n enddate = time.strptime('08-10-2021', '%d-%m-%Y')\\n enddate = datetime.fromtimestamp(mktime(enddate))\\n if season == '2020-21':\\n startdate = time.strptime('12-08-2020', '%d-%m-%Y')\\n startdate = datetime.fromtimestamp(mktime(startdate))\\n enddate = time.strptime('26-07-2021', '%d-%m-%Y')\\n enddate = datetime.fromtimestamp(mktime(enddate))\\n if season == '2019-20':\\n startdate = time.strptime('09-08-2019', '%d-%m-%Y')\\n startdate = datetime.fromtimestamp(mktime(startdate))\\n enddate = time.strptime('26-07-2020', '%d-%m-%Y')\\n enddate = datetime.fromtimestamp(mktime(enddate))\\n return startdate, enddate\",\n \"def run_twittercrawler(type, *params):\\n tweets = None\\n if type == 'timeline': # Call specific crawl function based on type\\n tweets = by_timeline(params[0])\\n elif type == 'search':\\n tweets = by_search(params[0])\\n elif type == 'streaming':\\n print('Streaming functionality not yet implemented')\\n return None\\n\\n return [tweet._json for tweet in tweets]\",\n \"def run_rss(self):\\n\\n pass\",\n \"def run_script(self, params, config_no):\\n raise NotImplementedError()\",\n \"def crawl_start(crawl_obj):\\n \\n res = None\\n\\n if crawl_obj.type in ['user', 'song'] :\\n res = eval('crawl_' + crawl_obj.type)(crawl_obj)\\n elif crawl_obj.type in ['artist', 'album'] :\\n web_data = requests.get(crawl_obj.url, headers = cheat_headers)\\n soup = bs4.BeautifulSoup(web_data.text, 'lxml')\\n\\n res = eval('crawl_' + crawl_obj.type)(crawl_obj.type, soup)\\n else:\\n print(\\\"Object type UNKNOWN!\\\")\\n \\n return res\",\n \"def run(pars, #parameter files\\n #directory of scenario files\\n scen_dir = r'C:\\\\LS\\\\03_TOOLS\\\\_git\\\\COVID_01\\\\scenarios',\\n \\n #map to scenario files\\n scen_d = {\\n 'NoNPI':'NPI_Scenario1_None.R',\\n 'BI1918':'NPI_Scenario2_Bootsma_1918Influenza.R',\\n 'SouthKorea':'NPI_Scenario3_SouthKorea.R',\\n 'Reduced':'NPI_Scenario4_ReducedGamma.R', \\n }\\n ):\\n \\n \\n \\n #===========================================================================\\n # precheck \\n #===========================================================================\\n assert len(pars)==4, 'unexpected inputs count'\\n print('pars: \\\\n%s'%pars)\\n \\n #check the R Environment variables\\n assert 'R_USER' in os.environ\\n assert 'R_HOME' in os.environ\\n \\n #print('R_USER=%s \\\\nR_HOME=%s'%(os.getenv('R_USER'), os.getenv('R_HOME')))\\n\\n \\n \\n \\n \\n #===========================================================================\\n # setup\\n #===========================================================================\\n s = setup.Setup(setup_name = 'mid_utah_'+pars[2],\\n spatial_setup = WestCoastSpatialSetup(),\\n nsim = int(pars[1]),\\n ti = datetime.date(2020, 3, 6),\\n tf = datetime.date(2020, 10, 1),\\n interactive = False,\\n write_csv = True,\\n dt = 1/4)\\n \\n #===========================================================================\\n # set the scenario parmaters\\n #===========================================================================\\n\\n \\n \\n assert pars[2] in scen_d, 'unrecognized scenario: %s'%pars[2]\\n \\n rfp = os.path.join(scen_dir, scen_d[pars[2]])\\n assert os.path.exists(rfp)\\n \\n s.script_npi = rfp\\n \\n print('set script_npi=%s'%s.script_npi)\\n\\n #===========================================================================\\n # execute\\n #===========================================================================\\n\\n print()\\n print()\\n print(f\\\">>> Starting {s.nsim} model runs on {pars[3]} processes\\\")\\n print(f\\\">>> Setup *** {s.setup_name} *** from {s.ti} to {s.tf} !\\\")\\n print(f\\\">>> writing to folder : {s.datadir}{s.setup_name}\\\")\\n print()\\n print()\\n \\n tic = time.time()\\n \\n res_l = seir.run_parallel(s, int(pars[3]))\\n print(f\\\">>> Runs done in {time.time()-tic} seconds...\\\")\",\n \"def main(train_stock, val_stock, window_size, batch_size, ep_count,\\n strategy=\\\"t-dqn\\\", model_name=\\\"model_debug\\\", pretrained=False,\\n debug=False):\\n agent = Agent(window_size, strategy=strategy, pretrained=pretrained, model_name=model_name)\\n \\n train_data = get_stock_data(train_stock)\\n val_data = get_stock_data(val_stock)\\n\\n initial_offset = val_data[1] - val_data[0]\\n\\n for episode in range(1, ep_count + 1):\\n train_result = train_model(agent, episode, train_data, ep_count=ep_count,\\n batch_size=batch_size, window_size=window_size)\\n val_result, _ = evaluate_model(agent, val_data, window_size, debug)\\n show_train_result(train_result, val_result, initial_offset)\",\n \"def betting_lines(year):\\n\\n # MongoDB Collection\\n m = mongo.Mongo()\\n\\n # Webapges are by dates\\n all_dates = m.find('game_log', {'season': year}, {'_id': 0, 'date': 1}).distinct('date')\\n\\n browser = webdriver.Chrome('chromedriver')\\n\\n # Iterate through each date in a season\\n for game_date in all_dates:\\n\\n # Get URL\\n url = 'https://classic.sportsbookreview.com/betting-odds/nba-basketball/money-line/?date=' + datetime.strftime(game_date, '%Y%m%d')\\n\\n scrape_betting_page(url, browser, m, game_date)\\n\\n browser.close()\",\n \"async def get_league_players(season): \\n if season == '2021-22':\\n get_epl = 2021\\n if season == '2020-21':\\n get_epl = 2020\\n if season == '2019-20':\\n get_epl = 2019\\n async with aiohttp.ClientSession() as session:\\n understat = Understat(session)\\n player = await understat.get_league_players(\\\"epl\\\", get_epl)\\n # print(json.dumps(player))\\n return player\",\n \"def runSpider(spider, searchterm = None, fullink = None, spiderbotid = -1):\\n sclogic.runSpider(spider, searchterm, fullink, spiderbotid)\",\n \"def chn_season_type_recognize(str):\\n season_type = None\\n if '第一季度' in str:\\n season_type = 'Q1'\\n elif '第二季度' in str:\\n season_type = 'Q2'\\n elif '半年度' in str:\\n season_type = 'Q2'\\n elif '第三季度' in str:\\n season_type = 'Q3'\\n elif '年度报告' in str:\\n season_type = 'FY'\\n\\n return season_type\",\n \"def get_games(season, date):\\n url = \\\"http://live.nhl.com/GameData/SeasonSchedule-\\\" + season + \\\".json\\\"\\n response = urllib.urlopen(url)\\n data = json.loads(response.read())\\n games = []\\n for game in data:\\n if game[\\\"est\\\"][:8] == date:\\n games.append(game)\\n return games\",\n \"def extract_season_episode(string):\\n series, episode = (None, None)\\n\\n # The series page url contains \\\"<name>_sX_eY\\\"\\n m = re.match('.*[sS](\\\\d+)_?[eE](\\\\d+).*', string)\\n if m:\\n series, episode = m.groups()\\n series = int(series)\\n episode = int(episode)\\n\\n else:\\n # Matches \\\"XxY\\\" OR unicode x (\\\\xd7 / ×)\\n m = re.search(\\\"(\\\\d+)[x|\\\\xd7](\\\\d+)\\\", string)\\n if m:\\n series, episode = m.groups()\\n series = int(series)\\n episode = int(episode)\\n else:\\n m = re.search(\\\"S(\\\\d+)E(\\\\d+)\\\", string)\\n if m:\\n series, episode = m.groups()\\n series = int(series)\\n episode = int(episode)\\n\\n else:\\n # Broke Girls – Season 4 Episode 22 – And the In and Out\\n f = re.findall('(.+?)season\\\\s(\\\\d+)\\\\sepisode\\\\s(\\\\d+)', string + \\\" \\\", re.I)\\n if f:\\n _, series, episode = f[0]\\n series = int(series)\\n episode = int(episode)\\n\\n else:\\n # Broke Girls – saison 5 épisode 16\\n f = re.findall(\\n '(.+?)\\\\ssaison\\\\s(\\\\d+)\\\\s\\\\xe9pisode\\\\s(\\\\d+)\\\\s',\\n string + \\\" \\\", re.I)\\n if f:\\n _, series, episode = f[0]\\n series = int(series)\\n episode = int(episode)\\n else:\\n # 'Dragon Ball Super: Temporada 1 - Episodio 11 (2015)' TODO can be optimized\\n f = re.findall('(.+?)\\\\stemporada\\\\s(\\\\d+)(.*)\\\\sepisodio\\\\s(\\\\d+)\\\\s', string + \\\" \\\", re.I)\\n if f:\\n _, series, __, episode = f[0]\\n series = int(series)\\n episode = int(episode)\\n else:\\n # Broke Girls – saison 5 episode 16\\n f = re.findall(\\n '(.+?)\\\\ssaison\\\\s(\\\\d+)\\\\s\\\\episode\\\\s(\\\\d+)\\\\s',\\n string + \\\" \\\", re.I)\\n if f:\\n _, series, episode = f[0]\\n series = int(series)\\n episode = int(episode)\\n\\n return series, episode\",\n \"def get_season(\\n current_date: date, hemisphere: str, season_tracking_type: str\\n) -> str | None:\\n\\n if hemisphere == \\\"equator\\\":\\n return None\\n\\n if season_tracking_type == TYPE_ASTRONOMICAL:\\n spring_start = ephem.next_equinox(str(current_date.year)).datetime()\\n summer_start = ephem.next_solstice(str(current_date.year)).datetime()\\n autumn_start = ephem.next_equinox(spring_start).datetime()\\n winter_start = ephem.next_solstice(summer_start).datetime()\\n else:\\n spring_start = datetime(2017, 3, 1).replace(year=current_date.year)\\n summer_start = spring_start.replace(month=6)\\n autumn_start = spring_start.replace(month=9)\\n winter_start = spring_start.replace(month=12)\\n\\n if spring_start <= current_date < summer_start:\\n season = STATE_SPRING\\n elif summer_start <= current_date < autumn_start:\\n season = STATE_SUMMER\\n elif autumn_start <= current_date < winter_start:\\n season = STATE_AUTUMN\\n elif winter_start <= current_date or spring_start > current_date:\\n season = STATE_WINTER\\n\\n # If user is located in the southern hemisphere swap the season\\n if hemisphere == NORTHERN:\\n return season\\n return HEMISPHERE_SEASON_SWAP.get(season)\",\n \"def get_season_url(\\n base_url: str, year: Optional[int] = None, season: Optional[str] = None\\n) -> str:\\n if year is None or season is None:\\n return f\\\"{base_url}/season\\\"\\n return f\\\"{base_url}/season/{year}/{season.lower()}\\\"\",\n \"def test_get_seasonal_statistics___season_to_date(self):\\n msg = \\\"Response status is not 200\\\"\\n response = self.api.get_seasonal_statistics___season_to_date(self.season, self.nhl_season, self.team_id)\\n self.assertEqual(response.status_code, 200, msg)\",\n \"def main(options, (gender, races)):\\n results = aggregate_races(gender, races)\\n results.score()\\n if options.exclude_scoreless:\\n results.purge_scoreless_runners()\\n if options.show_races:\\n for row in RaceDumper(results.races):\\n print row\\n print\\n for row in SeasonsBestDumper(results.results, options.distance):\\n print row\\n else:\\n for row in ResultsDumper(results.results, options.distance):\\n print row\\n print\\n for row in ScoreDumper(results.scores):\\n print row\",\n \"def get_seasons_information():\\n\\n #getting the guidebox_id variable from show_page.html\\n guidebox_id = request.args.get(\\\"guidebox_id\\\")\\n\\n #make API to get season information, gets back list of season information\\n seasons_results = guidebox_season_info(guidebox_id)\\n\\n for season in seasons_results:\\n date = season[\\\"first_airdate\\\"]\\n year = str(date)[0:4]\\n season[\\\"first_airdate\\\"] = year\\n\\n return jsonify(seasons_results)\",\n \"def do(sts):\\n sts = sts.replace(tzinfo=pytz.timezone(\\\"UTC\\\"))\\n\\n ets = sts + datetime.timedelta(days=1)\\n\\n interval = datetime.timedelta(hours=3)\\n\\n now = sts\\n while now < ets:\\n archivefn = now.strftime((\\\"/mesonet/ARCHIVE/data/%Y/%m/%d/model/\\\"\\n \\\"NARR/rad_%Y%m%d%H%M.nc\\\"))\\n if os.path.isfile(archivefn):\\n now += interval\\n continue\\n uri = now.strftime((\\\"http://nomads.ncdc.noaa.gov/thredds/ncss/\\\"\\n \\\"grid/narr-a/%Y%m/%Y%m%d/\\\"\\n \\\"narr-a_221_%Y%m%d_%H00_000.grb?\\\"\\n \\\"var=Downward_shortwave_radiation_flux&spatial=all\\\"\\n \\\"&temporal=all\\\"))\\n\\n try:\\n req = urllib2.Request(uri)\\n data = urllib2.urlopen(req).read()\\n except:\\n print 'NARR Download failed for: %s' % (uri,)\\n sys.exit()\\n\\n tmpfn = tempfile.mktemp()\\n o = open(tmpfn, 'w')\\n o.write(data)\\n o.close()\\n\\n cmd = (\\\"/home/ldm/bin/pqinsert -p 'data a %s bogus \\\"\\n \\\"model/NARR/rad_%s.nc nc' %s\\\"\\n ) % (now.strftime(\\\"%Y%m%d%H%M\\\"),\\n now.strftime(\\\"%Y%m%d%H%M\\\"), tmpfn)\\n subprocess.call(cmd, shell=True)\\n\\n os.remove(tmpfn)\\n now += interval\",\n \"def main():\\n exit_if_already_started()\\n while True:\\n for timeframe in ['all', 'month', 'week']:\\n subreddits = load_list('subs.txt')\\n while subreddits:\\n # Grab all images/comments from sub, remove from list\\n parse_subreddit(subreddits.pop(0), timeframe)\",\n \"def getCycleScript(self,town,cycle):\\n #--Schedules\\n reWanderCell = re.compile('wander[, ]+(\\\\d+)',re.I)\\n rePosCell = re.compile('positionCell +(\\\\-?\\\\d+) +(\\\\-?\\\\d+) +(\\\\-?\\\\d+).+\\\"(.+)\\\"')\\n townCode = self.code.get(town,0)\\n townSchedule = self.schedule[town]\\n npcs = sorted(townSchedule.keys())\\n townCode\\n out = cStringIO.StringIO()\\n cycleCode = ''\\n if townCode:\\n for line in townCode[0]+townCode[cycle]:\\n cycleCode += '\\\\t'+line+'\\\\n'\\n out.write(self.tsCycle1.substitute(town=town,cycle=`cycle`,cycleCode=cycleCode))\\n for npc in npcs:\\n out.write('if ( \\\"%s\\\"->getDisabled )\\\\n' % (npc,))\\n out.write('elseif ( \\\"%s\\\"->getItemCount SC_offSchedule != 0 )\\\\n' % (npc,))\\n for (condition,npcSchedule) in townSchedule[npc]:\\n if condition == False:\\n out.write('else\\\\n')\\n else:\\n out.write('elseif ( %s )\\\\n' % (condition,))\\n (pos,ai) = npcSchedule[cycle-1]\\n out.write('\\\\tif ( action < 20 )\\\\n')\\n out.write('\\\\t\\\\t\\\"%s\\\"->%s\\\\n' % (npc,pos))\\n if ai != 'NOAI':\\n #--Wandering in exterior cell?\\n maWanderCell = reWanderCell.match(ai)\\n maPosCell = rePosCell.match(pos)\\n if maWanderCell and (int(maWanderCell.group(1)) > 0) and (maPosCell.group(4).find(',') == -1):\\n xx,yy,zz,cell = maPosCell.groups()\\n out.write('\\\\t\\\\t\\\"%s\\\"->aiTravel %s %s %s\\\\n' % (npc,xx,yy,zz))\\n out.write('\\\\t\\\\tset action to 10\\\\n\\\\telse\\\\n')\\n out.write('\\\\t\\\\t\\\"%s\\\"->ai%s\\\\n' % (npc,ai))\\n out.write('\\\\tendif\\\\n')\\n out.write('endif\\\\n')\\n out.write(\\\"if ( action != 10 )\\\\n\\\\tset action to 20\\\\nendif\\\\n\\\")\\n out.write('end\\\\n')\\n return out.getvalue()\",\n \"def standings_by_season(season):\\n season = int(season) + 1\\n scoreboard = nba_py.Scoreboard(month=7,\\n day=1,\\n year=season)\\n east_standings = scoreboard.east_conf_standings_by_day()\\n west_standings = scoreboard.west_conf_standings_by_day()\\n\\n return render_template(\\\"standings.html\\\",\\n title=\\\"standings\\\",\\n east_standings=enumerate(east_standings, 1),\\n west_standings=enumerate(west_standings, 1),\\n team=CITY_TO_TEAM)\",\n \"def get_seasons(link, debug=False):\\n\\n if debug:\\n print(\\\"begin get_seasons()\\\")\\n\\n # get the BeautifulSoup data\\n show_url = \\\"https://www.imdb.com/\\\" + link + \\\"episodes/\\\"\\n tv_soup = bs4.BeautifulSoup(requests.get(show_url).text, features=\\\"html.parser\\\")\\n\\n # We are acquiring this data from a drop down, which the below line selects\\n select_elem = tv_soup.select('#bySeason')\\n seasons = []\\n # account for the possibility of a one season show\\n if len(select_elem) == 0:\\n seasons.append(1)\\n else:\\n # get contents of drop down\\n options = select_elem[0].select('option')\\n\\n # add each season\\n for season in options:\\n seasons.append(season.get('value'))\\n if debug:\\n print(f\\\"Seasons {seasons}\\\")\\n\\n return seasons\",\n \"def main():\\n # Return needed Data Frames to analyze\\n data_frame, seasons, col, labels, stats, kaggle = load_frames()\\n\\n # Create the maps now\\n create_shot_maps(data_frame,seasons)\\n create_scenario_map()\\n \\n # Create the Plots\\n plot_season_graphs(stats)\\n plot_pie_charts(kaggle)\\n plot_shot_timings(kaggle)\\n plot_radar(stats, col, labels)\",\n \"def run_it():\\n initialize()\\n parser = get_parser()\\n args = None\\n first_parse = True\\n while(True):\\n if first_parse is True:\\n first_parse = False\\n args = parser.parse_args()\\n \\n else:\\n # print(textwrap.dedent(\\n # '''\\\\\\n # Search again like in the beginning.\\n # -- You can either choose best rated or list mode.\\n # -- This time, you can insert the search string without double quotes.\\n # Remember the list mode options!\\n # 0: torrent project.\\n # 1: the pirate bay.\\n # 2: 1337x.\\n # 3: eztv.\\n # 4: limetorrents.\\n # 5: isohunt.\\n # '''))\\n sys.exit(0)\\n print('Or.. if you want to exit just write \\\"' +\\n Colors.LRED + 'Q' + Colors.ENDC + '\\\" or \\\"' +\\n Colors.LRED + 'q' + Colors.ENDC + '\\\".')\\n input_parse = input('>> ').replace(\\\"'\\\", \\\"\\\").replace('\\\"', '')\\n if input_parse in ['Q', 'q']:\\n sys.exit(1)\\n\\n args = parser.parse_args(input_parse.split(' ', 2))\\n \\n if args.str_search.strip() == \\\"\\\":\\n print('Please insert an appropiate non-empty string.')\\n else:\\n args.str_search = args.str_search.replace('_',' ').replace(\\\"'\\\",'')\\n\\n movieName = args.str_search\\n #print(args.str_search)\\n auto = AutoPy(*insert(args))\\n auto.movieName = movieName\\n auto.get_content()\\n auto.select_torrent()\\n auto.download_torrent()\",\n \"def run_downloader(selects, url, dlpath, **kwargs):\\n print \\\"Downloading\\\", url, \\\"to\\\", dlpath\\n try:\\n isos = ThreddsCollector(url, selects=selects, **kwargs).run()\\n XmlDownloader.run(isos, dlpath)\\n except Exception as e:\\n print >>sys.stderr, \\\"Problem with\\\", url, \\\"ex:\\\", e\",\n \"def main():\\n args = parser.parse_args()\\n scraper = brf.BrefScraper(args.teams,\\n str(args.year),\\n args.playoffs,\\n args.csv)\\n\\n if args.plot != '':\\n plotter = Plot.Plot(scraper, not args.not_histogram)\\n plotter.set_default_axes(1, args.x_hint, args.y_axis[0], args.y_axis[1])\\n plotter.plot(args.plot, args.average)\\n elif args.plot == '' and args.csv == 'w':\\n scraper.save_to_file()\",\n \"def runCrawler(crawlerid):\\n sclogic.runCrawler(crawlerid)\",\n \"def init_script() -> str:\\n symbol = \\\"NET\\\"\\n raw_data = fetch_stock_data(symbol)\\n parsed_data = parse_data(raw_data)\\n plot_url = create_chart(parsed_data, symbol)\\n logger.info(plot_url)\\n return plot_url\",\n \"def import_data(nb, tickers, types, First = True):\\n pa = os.getcwd().replace(\\\"\\\\\\\\\\\",\\\"/\\\") + \\\"/sec-edgar-filings\\\"\\n for i in types: # looping over different types\\n exec(nb,tickers,pa,i,First)\",\n \"def general_news():\\n\\n return general_scraper(['http://mesva.univaq.it/'])\",\n \"def getskaters(league, year):\\n \\n url = 'https://www.eliteprospects.com/league/' + league + '/stats/' + year + '?page='\\n # print('Collects data from ' + 'https://www.eliteprospects.com/league/' + league + '/stats/' + year)\\n \\n print(\\\"Beginning scrape of \\\" + league + \\\" skater data from \\\" + year + \\\".\\\")\\n \\n # Return list with all plyers for season in link \\n players = []\\n \\n page = (requests.get(url+str(1), timeout = 500))\\n first_page_string = str(page)\\n \\n while first_page_string == '<Response [403]>':\\n print(\\\"Just got a 403 Error before entering the page. Time to Sleep, then re-obtain the link.\\\")\\n time.sleep(100)\\n page = (requests.get(url+str(1), timeout = 500))\\n first_page_string = str(page)\\n print(\\\"Changed the string before entering the page. Let's try again\\\")\\n \\n if (str(first_page_string) == '<Response [404]>'):\\n print(\\\"ERROR: \\\" + str(first_page_string) + \\\" on league: \\\" + league + \\\" in year: \\\" + year + \\\". Data doesn't exist for this league in this year.\\\")\\n \\n else:\\n \\n for i in range(1,99):\\n page = requests.get(url+str(i), timeout = 500) \\n page_string = str(page)\\n \\n while page_string == '<Response [403]>':\\n print(\\\"Just got a 403 Error within the page. Time to Sleep, then re-obtain the link.\\\")\\n time.sleep(100)\\n page = requests.get(url+str(i), timeout = 500) \\n page_string = str(page)\\n print(\\\"Changed the string within the page. Let's try again\\\")\\n \\n soup = BeautifulSoup(page.content, \\\"html.parser\\\")\\n\\n # Get data for players table\\n player_table = soup.find( \\\"table\\\", {\\\"class\\\":\\\"table table-striped table-sortable player-stats highlight-stats season\\\"})\\n \\n try:\\n df_players = tableDataText(player_table)\\n \\n except AttributeError:\\n print(\\\"BREAK: TABLE NONE ERROR: \\\" + str(requests.get(url+str(i), timeout = 500)) + \\\" On League: \\\" + league + \\\" In Year: \\\" + year)\\n break\\n \\n if len(df_players)>0:\\n\\n if df_players['#'].count()>0:\\n # Remove empty rows\\n df_players = df_players[df_players['#']!=''].reset_index(drop=True)\\n\\n # Extract href links in table\\n href_row = []\\n for link in player_table.find_all('a'):\\n href_row.append(link.attrs['href'])\\n\\n # Create data frame, rename and only keep links to players\\n df_links = pd.DataFrame(href_row) \\n df_links.rename(columns={ df_links.columns[0]:\\\"link\\\"}, inplace=True)\\n df_links= df_links[df_links['link'].str.contains(\\\"/player/\\\")].reset_index(drop=True) \\n\\n # Add links to players\\n df_players['link']=df_links['link'] \\n\\n players.append(df_players)\\n\\n # Wait 3 seconds before going to next\\n #time.sleep(1)\\n #print(\\\"Scraped page \\\" + str(i))\\n \\n else:\\n #print(\\\"Scraped final page of: \\\" + league + \\\" In Year: \\\" + year)\\n break\\n\\n \\n if len(players)!=0:\\n df_players = pd.concat(players).reset_index()\\n\\n df_players.columns = map(str.lower, df_players.columns)\\n\\n # Clean up dataset\\n df_players['season'] = year\\n df_players['league'] = league\\n\\n df_players = df_players.drop(['index','#'], axis=1).reset_index(drop=True)\\n\\n df_players['playername'] = df_players['player'].str.replace(r\\\"\\\\(.*\\\\)\\\",\\\"\\\")\\n df_players['position'] = df_players['player'].str.extract('.*\\\\((.*)\\\\).*')\\n df_players['position'] = np.where(pd.isna(df_players['position']), \\\"F\\\", df_players['position'])\\n\\n df_players['fw_def'] = df_players['position'].str.contains('LW|RW|C|F')\\n df_players.loc[df_players['position'].str.contains('LW|RW|C'), 'fw_def'] = 'FW'\\n df_players.loc[df_players['position'].str.contains('D'), 'fw_def'] = 'DEF'\\n\\n # Adjust columns; transform data\\n team = df_players['team'].str.split(\\\"“\\\", n=1, expand=True)\\n df_players['team'] = team[0]\\n\\n # drop player-column\\n df_players = df_players.drop(columns = ['fw_def'], axis=1)\\n print(\\\"Successfully scraped all \\\" + league + \\\" skater data from \\\" + year + \\\".\\\")\\n\\n return df_players\\n \\n else: print(\\\"LENGTH 0 ERROR: \\\" + str(requests.get(url+str(1), timeout = 500)) + \\\" On League: \\\" + league + \\\" In Year: \\\" + year)\",\n \"def run_spider_on_zyte(spider_name):\\n print(f'Running spider {spider_name}...')\\n data = dict(project=project_id, spider=spider_name)\\n response = requests.post('https://app.scrapinghub.com/api/run.json', data=data, auth=(api_key, ''))\\n return response.json()['jobid']\",\n \"def main(rundir, outputfile):\\n # Read avg_count for all runs in the ranking\\n results = list()\\n for run in Runs(rundir):\\n filename = run.get_file(name='results/analytics.json')\\n doc = util.read_object(filename=filename)\\n results.append(doc)\\n # Delay execution to allow for testing running post-processing\\n # workflows\\n time.sleep(1)\\n # Write analytics results. Ensure that output directory exists:\\n # influenced by http://stackoverflow.com/a/12517490\\n if not os.path.exists(os.path.dirname(outputfile)):\\n try:\\n os.makedirs(os.path.dirname(outputfile))\\n except OSError as exc: # guard against race condition\\n if exc.errno != errno.EEXIST:\\n raise\\n with open(outputfile, \\\"at\\\") as f:\\n json.dump(results, f)\",\n \"def scrap(self):\\n scrapper = RaceDayScrapper()\\n return scrapper.get()\",\n \"def main():\\n # the url for african daily and global daily\\n african_dialy_url = \\\"https://data.chc.ucsb.edu/products/CHIRPS-2.0/africa_daily/tifs/p25/\\\"\\n global_daily_url = \\\"https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_daily/tifs/p25/\\\"\\n\\n\\n each_year_list = GetRasterYears(url=african_dialy_url)\\n new_path = makenewdir(each_year_list)\\n years_new_list = fecthrasterurl(url=african_dialy_url)\\n downloadwithwget(each_year_list, years_new_list, new_path)\",\n \"def init_bot():\\n\\n # We create the Reddit instance.\\n reddit = praw.Reddit(client_id=config.APP_ID, client_secret=config.APP_SECRET,\\n user_agent=config.USER_AGENT, username=config.REDDIT_USERNAME,\\n password=config.REDDIT_PASSWORD)\\n\\n # Check if we have the 3 required arguments.\\n if len(sys.argv) == 3:\\n\\n method = sys.argv[1]\\n day = sys.argv[2]\\n\\n if method == \\\"sticky\\\":\\n if day == \\\"monday\\\":\\n post_monday(reddit)\\n elif day == \\\"wednesday\\\":\\n post_wednesday(reddit)\\n elif day == \\\"friday\\\":\\n post_friday(reddit)\\n\\n elif method == \\\"unsticky\\\":\\n if day == \\\"monday\\\":\\n unsticky_post(reddit, MONDAY_FILE)\\n elif day == \\\"wednesday\\\":\\n unsticky_post(reddit, WEDNESDAY_FILE)\\n elif day == \\\"friday\\\":\\n unsticky_post(reddit, FRIDAY_FILE)\",\n \"def run(self, script, **kwargs):\\r\\n # don't return a value from a script\\r\\n kwargs['nout'] = 0\\r\\n return self.call(script, **kwargs)\",\n \"def set_season(self, season):\\n self.set_date_range(dt.date(season, 1, 1),\\n dt.date(season, 12, 31))\",\n \"def run(self, **kwargs):\\n try:\\n super().run(**kwargs)\\n self.extract_nfvbench_results()\\n self.__logger.info(\\\"NFVbench results were successfully parsed\\\")\\n except Exception: # pylint: disable=broad-except\\n self.__logger.exception(\\\"Cannot parse NFVbench results\\\")\\n return self.EX_RUN_ERROR\\n return self.EX_OK\",\n \"def season_games(year):\\n\\tLOG.debug('Getting season %d', year)\\n\\tdata = read_html(io=season_games_url(year),\\n\\t\\t\\t\\t\\t attrs={'id': 'games'},\\n\\t\\t\\t\\t\\t infer_types=False,\\n\\t\\t\\t\\t\\t header=0)\\n\\tif len(data) != 1:\\n\\t\\traise CantFindTheRightTable\\n\\tdata = data.pop()\\n\\n\\t# Cleaning.\\n\\tdel data[\\\"Unnamed: 3\\\"]\\n\\t# The code below issues \\\"UserWarning: \\\" So we catch UserWarnings.\\n\\twith warnings.catch_warnings():\\n\\t\\twarnings.filterwarnings(action='ignore', category=UserWarning,\\n\\t\\t\\t\\t\\t\\t\\t\\tmodule=r'pandas\\\\.core\\\\.frame',\\n\\t\\t\\t\\t\\t\\t\\t\\tmessage=(r\\\"Boolean Series key will be reindexed\\\"\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t r\\\" to match DataFrame index\\\\.\\\"))\\n\\t\\t# These rows are mid-table header rows.\\n\\t\\tdata = data[data.Week != \\\"Week\\\"][data.Week != \\\"nan\\\"]\\n\\n\\tdata['week'] = (data.Week\\n\\t\\t\\t\\t\\t.replace(\\\"WildCard\\\", \\\"wild-card\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"Division\\\", \\\"divisional\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"ConfChamp\\\", \\\"conference\\\")\\n\\t\\t\\t\\t\\t.replace(\\\"SuperBowl\\\", \\\"super-bowl\\\")\\n\\t\\t\\t\\t\\t.apply(\\n\\t\\t\\t\\t\\t\\tlambda s: (int(s)\\n\\t\\t\\t\\t\\t\\t\\t\\t if all(c in '1234567890' for c in s)\\n\\t\\t\\t\\t\\t\\t\\t\\t else s)))\\n\\tdel data['Week']\\n\\n\\tdata['season'] = year\\n\\tdata['game_date'] = pd.to_datetime(\\n\\t\\tdata.Date\\n\\t\\t.replace(r\\\"$\\\", r\\\", %d\\\" % year, regex=True)\\n\\t\\t.replace(r\\\"^(January|February) (\\\\d+), \\\\d+$\\\", r\\\"\\\\1 \\\\2, %d\\\" % (year + 1),\\n\\t\\t\\t\\t regex=True))\\n\\tdel data['Date']\\n\\n\\tfor column in \\\"PtsW\\\", \\\"PtsL\\\", \\\"YdsW\\\", \\\"TOW\\\", \\\"YdsL\\\", \\\"TOL\\\":\\n\\t data[column] = data[column].apply(int)\\n\\n\\tdata['WatL'] = data['Unnamed: 5'].apply(lambda x: x == '@')\\n\\tdel data['Unnamed: 5']\\n\\tdata['hometeam'] = (~data.WatL * data['Winner/tie'] +\\n\\t\\t\\t\\t\\t\\tdata.WatL * data['Loser/tie'])\\n\\tdata['awayteam'] = (data.WatL * data['Winner/tie'] +\\n\\t\\t\\t\\t\\t\\t~data.WatL * data['Loser/tie'])\\n\\tdata['winner'] = data['Winner/tie']\\n\\tfor column in 'Winner/tie', 'Loser/tie', \\\"WatL\\\":\\n\\t\\tdel data[column]\\n\\tfor column in 'hometeam', 'awayteam', 'winner':\\n\\t\\tdata[column] = data[column].apply(lambda s: s.split()[-1].lower())\\n\\n\\treturn data\",\n \"def main():\\n with open(\\\"page_data.yaml\\\", 'r') as inputstr:\\n config_data = yaml.safe_load(inputstr)\\n ointf = OutputInterface('template.txt')\\n table_data = get_song_artist_matches()\\n ofilen = config_data['directory'] + os.sep + 'common_songs.html'\\n title = 'Song Titles and Band Name Overlap'\\n header = ['No.', 'Artist', 'Peak', 'Date', 'Song/Artist', 'Peak',\\n 'Date', 'Song']\\n ointf.build_page(ofilen, title, header, fmt_table(table_data))\\n ointf.inject(XTRAEDIT)\\n ointf.output()\",\n \"def getNumSeasons(self):\\n searchURL = \\\"http://api.tvmaze.com/shows/\\\" + str(self.__showID) \\\\\\n + \\\"/seasons\\\"\\n\\n response = requests.get(searchURL)\\n data = response.json()\\n\\n return data[-1][\\\"number\\\"]\",\n \"def _handle_runs(self, relpath, params):\\r\\n runs_by_day = self._partition_runs_by_day()\\r\\n args = self._default_template_args('run_list')\\r\\n args['runs_by_day'] = runs_by_day\\r\\n self._send_content(self._renderer.render_name('base', args), 'text/html')\",\n \"def run_script(self):\\n pass\",\n \"def get_league_skater_boxcars(league, seasons):\\n\\n if len(set(seasons))==1:\\n scraped_season_list = str(seasons)\\n elif len(set(seasons))>2:\\n scraped_season_list = str(((str(tuple(sorted(tuple(set(seasons))))).replace(\\\"'\\\", \\\"\\\").replace(\\\"(\\\", \\\"\\\").replace(\\\")\\\", \\\"\\\").replace(\\\"[\\\", \\\"\\\").replace(\\\"]\\\", \\\"\\\"))).split(\\\", \\\")[:-1]).replace(\\\"'\\\", \\\"\\\").replace(\\\"[\\\", \\\"\\\").replace(\\\"]\\\", \\\"\\\") + \\\", and \\\" + str(((str(tuple(sorted(tuple(set(seasons))))).replace(\\\"'\\\", \\\"\\\").replace(\\\"(\\\", \\\"\\\").replace(\\\")\\\", \\\"\\\").replace(\\\"[\\\", \\\"\\\").replace(\\\"]\\\", \\\"\\\"))).split(\\\", \\\")[-1])\\n else:\\n scraped_season_list = str(((str(tuple(sorted(tuple(set(seasons))))).replace(\\\"'\\\", \\\"\\\").replace(\\\"(\\\", \\\"\\\").replace(\\\")\\\", \\\"\\\").replace(\\\"[\\\", \\\"\\\").replace(\\\"]\\\", \\\"\\\"))).split(\\\", \\\")[:-1]).replace(\\\"'\\\", \\\"\\\").replace(\\\"[\\\", \\\"\\\").replace(\\\"]\\\", \\\"\\\") + \\\" and \\\" + str(((str(tuple(sorted(tuple(set(seasons))))).replace(\\\"'\\\", \\\"\\\").replace(\\\"(\\\", \\\"\\\").replace(\\\")\\\", \\\"\\\").replace(\\\"[\\\", \\\"\\\").replace(\\\"]\\\", \\\"\\\"))).split(\\\", \\\")[-1])\\n \\n \\n global hidden_patrick\\n hidden_patrick = 0\\n global error\\n error = 0\\n \\n output = pd.DataFrame()\\n \\n if type(seasons) == str:\\n single = getskaters(league, seasons)\\n output = output.append(single)\\n print(\\\"Scraping \\\" + league + \\\" data is complete. You scraped skater data from \\\" + seasons + \\\".\\\")\\n return(output)\\n \\n elif ((type(seasons) == tuple) or (type(seasons) == list)):\\n \\n for i in range(0, len(seasons)):\\n try:\\n single = getskaters(league, seasons[i])\\n output = output.append(single)\\n except KeyboardInterrupt as e:\\n hidden_patrick = 4\\n error = e\\n return output\\n except (ConnectionError,\\n HTTPError,\\n ReadTimeout,\\n ConnectTimeout) as e:\\n hidden_patrick = 5\\n error = e\\n return output\\n \\n print(\\\"Scraping \\\" + league + \\\" data is complete. You scraped skater data from \\\" + scraped_season_list + \\\".\\\") \\n return(output)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.58400744","0.5825069","0.5700488","0.56795895","0.5633705","0.550612","0.5492933","0.54907745","0.5442901","0.5320366","0.52981234","0.5222515","0.51775146","0.5153715","0.5135699","0.5109447","0.50783205","0.5008098","0.50059414","0.5002093","0.4990885","0.498329","0.4982615","0.49470055","0.4946725","0.49246705","0.49063054","0.48841184","0.48786658","0.48676956","0.48567146","0.4850559","0.4848778","0.4834647","0.48231465","0.48102278","0.4793431","0.47902244","0.47814056","0.47773147","0.47687218","0.47670037","0.4765678","0.4751691","0.47514525","0.47494775","0.47440743","0.47382346","0.47339398","0.4733608","0.47147053","0.47118372","0.47051007","0.47018722","0.4677203","0.46717992","0.46648043","0.4649823","0.4628307","0.4625115","0.46243328","0.4623839","0.46217725","0.461092","0.46078342","0.4601212","0.45951602","0.45876125","0.45814326","0.45765418","0.45754403","0.45733452","0.45704257","0.45698413","0.45692915","0.45627856","0.4553951","0.45469862","0.4543888","0.4543738","0.45426998","0.45285755","0.4519902","0.4516814","0.4513669","0.4503769","0.45026708","0.44920436","0.44842544","0.44841242","0.4482882","0.44813943","0.4479721","0.44785246","0.44725218","0.44666478","0.44520986","0.4448323","0.44473904","0.44443074"],"string":"[\n \"0.58400744\",\n \"0.5825069\",\n \"0.5700488\",\n \"0.56795895\",\n \"0.5633705\",\n \"0.550612\",\n \"0.5492933\",\n \"0.54907745\",\n \"0.5442901\",\n \"0.5320366\",\n \"0.52981234\",\n \"0.5222515\",\n \"0.51775146\",\n \"0.5153715\",\n \"0.5135699\",\n \"0.5109447\",\n \"0.50783205\",\n \"0.5008098\",\n \"0.50059414\",\n \"0.5002093\",\n \"0.4990885\",\n \"0.498329\",\n \"0.4982615\",\n \"0.49470055\",\n \"0.4946725\",\n \"0.49246705\",\n \"0.49063054\",\n \"0.48841184\",\n \"0.48786658\",\n \"0.48676956\",\n \"0.48567146\",\n \"0.4850559\",\n \"0.4848778\",\n \"0.4834647\",\n \"0.48231465\",\n \"0.48102278\",\n \"0.4793431\",\n \"0.47902244\",\n \"0.47814056\",\n \"0.47773147\",\n \"0.47687218\",\n \"0.47670037\",\n \"0.4765678\",\n \"0.4751691\",\n \"0.47514525\",\n \"0.47494775\",\n \"0.47440743\",\n \"0.47382346\",\n \"0.47339398\",\n \"0.4733608\",\n \"0.47147053\",\n \"0.47118372\",\n \"0.47051007\",\n \"0.47018722\",\n \"0.4677203\",\n \"0.46717992\",\n \"0.46648043\",\n \"0.4649823\",\n \"0.4628307\",\n \"0.4625115\",\n \"0.46243328\",\n \"0.4623839\",\n \"0.46217725\",\n \"0.461092\",\n \"0.46078342\",\n \"0.4601212\",\n \"0.45951602\",\n \"0.45876125\",\n \"0.45814326\",\n \"0.45765418\",\n \"0.45754403\",\n \"0.45733452\",\n \"0.45704257\",\n \"0.45698413\",\n \"0.45692915\",\n \"0.45627856\",\n \"0.4553951\",\n \"0.45469862\",\n \"0.4543888\",\n \"0.4543738\",\n \"0.45426998\",\n \"0.45285755\",\n \"0.4519902\",\n \"0.4516814\",\n \"0.4513669\",\n \"0.4503769\",\n \"0.45026708\",\n \"0.44920436\",\n \"0.44842544\",\n \"0.44841242\",\n \"0.4482882\",\n \"0.44813943\",\n \"0.4479721\",\n \"0.44785246\",\n \"0.44725218\",\n \"0.44666478\",\n \"0.44520986\",\n \"0.4448323\",\n \"0.44473904\",\n \"0.44443074\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.61678904"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":85591,"cells":{"query":{"kind":"string","value":"Runs the workflow for extracting and loading games data. Finds the starting point for extracting new data Extracts new data using the nflscrapr module Loads to database"},"document":{"kind":"string","value":"def run():\n games_db_conn = db.get_db_eng()\n games_query = \"SELECT * FROM GAMES\"\n games_data = etl_tools.extract_from_db(games_db_conn, games_query)\n _data_integrity_check(games_data)\n\n latest_season, latest_season_type = _get_latest_season_and_type(games_data)\n logging.info(f\"Latest season and type in current data: {(latest_season, latest_season_type)}\")\n\n if latest_season is None:\n batch_start_season, batch_start_type = config.START_SEASON, config.SEASON_TYPES[0]\n\n else:\n _truncate_games_table(games_db_conn, latest_season, latest_season_type)\n batch_start_season, batch_start_type = latest_season, latest_season_type\n\n logging.info(f\"Starting batch at {(batch_start_season, batch_start_type)}...\")\n\n batches = _get_seasons_grid(batch_start_season, batch_start_type)\n for batch in batches:\n season, season_type = batch\n logging.info(f\"Starting new batch: extracting data for {season}-{season_type}...\")\n batch_data = _extract_games_data(season, season_type)\n logging.info(f\"Data extracted. Loading {batch_data.shape[0]} rows...\")\n etl_tools.load_to_db(\n games_db_conn,\n 'games',\n batch_data,\n )\n\n logging.info(\"Pipeline completed.\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def download_and_preprocess(self):\n print('Preparing steering angle database.')\n print('Downloading...')\n self.download()\n print('Preprocessing...')\n self.preprocess()","def scrape_all():\n\n # Scrape team information by season\n for team in scrape_utils.team_names():\n team_season_stats(team)\n # Each season\n print(team)\n for year in range(2019, 2020):\n # Game Logs\n season_game_logs(team, year)\n\n # Starting Lineups\n #player_scraper.get_starting_lineups(team, year)\n\n # Init mongo to get game IDS for box score scraping\n m = mongo.Mongo()\n\n # Game Information (Box Score and Play by Play)\n for year in range(2015, 2020):\n player_scraper.get_starting_lineups(year)\n for game in m.find('game_log', {'season': year}, {'_id': 1}):\n #team_scraper.play_by_play(game['_id'])\n player_scraper.player_box_score(game['_id'])\n\n print(game['_id'])\n\n\n\n # Get player information\n for player in scrape_utils.get_active_players():\n print(player)\n player_scraper.player_per_game(player)\n\n # Get betting lines (By Year) need from 2014\n for year in range(2015, 2020):\n team_scraper.betting_lines(2019)","def load_raw_data(apps, schema_editor):\n from season.import_raw_data import InitialDataProcessor\n matches_path = str(BASE_DIR) + '/season/migrations/matches.csv'\n deliveries_path = str(BASE_DIR) + '/season/migrations/deliveries.csv'\n # Initialization path to read data\n load_data = InitialDataProcessor(matches_path=matches_path, deliveries_path=deliveries_path)\n # transform data frame and save the data step by step\n # only support new season import for the first tym when data structure is ready to use\n load_data.transform_input_save()","def main():\n download_insert_title_basics()\n download_insert_title_principals()\n download_insert_name_basics()\n download_insert_title_ratings()\n scrap_keywords()\n create_and_insert_soup()\n return","def run_all():\n db = DBInterface()\n year = Config.get_property(\"league_year\")\n session = Session(bind=db.engine)\n\n scraper.scrape_all(db, session, year)\n session.commit()\n\n bets.predict_all(db, session)\n session.commit()\n session.close()","def run(self):\n data_provider = DataProvider(self.config)\n hex_attr_df = data_provider.read_hex_bin_attributes()\n hex_distance_df = data_provider.read_hex_bin_distances()\n city_states = data_provider.read_city_states(self.city_states_filename)\n neighborhood = data_provider.read_neighborhood_data()\n popular_bins = data_provider.read_popular_hex_bins()\n num_episodes = self.config['RL_parameters']['num_episodes']\n ind_episodes = self.config['RL_parameters']['ind_episodes']\n exp_decay_multiplier = self.config['RL_parameters']['exp_decay_multiplier']\n\n q_ind = None\n r_table = None\n xi_matrix = None\n\n best_episode = None\n best_model = {}\n\n progress_bar = tqdm(xrange(num_episodes))\n for episode_id in progress_bar:\n progress_bar.set_description(\"Episode: {}\".format(episode_id))\n current_best = -1000000\n\n # Create episode\n ind_exploration_factor = np.e ** (-1 * episode_id * exp_decay_multiplier / ind_episodes)\n\n episode = Episode(self.config,\n episode_id,\n ind_exploration_factor,\n hex_attr_df,\n hex_distance_df,\n city_states,\n neighborhood,\n popular_bins,\n q_ind,\n r_table,\n xi_matrix)\n\n # Run episode\n tables = episode.run()\n q_ind = tables['q_ind']\n r_table = tables['r_table']\n xi_matrix = tables['xi_matrix']\n episode_tracker = tables['episode_tracker']\n\n # Uncomment for logging if running a job, comment during experiments\n # otherwise it leads to insanely huge logging output which is useless\n\n # self.logger.info(\"\"\"\n # Expt: {} Episode: {} Earnings: {}\n # Pax rides: {} Relocation rides: {} Unmet demand: {}\n # \"\"\".format(self.expt_name, episode_id,\n # episode_tracker.gross_earnings,\n # episode_tracker.successful_waits,\n # episode_tracker.relocation_rides,\n # episode_tracker.unmet_demand))\n # self.logger.info(\"----------------------------------\")\n\n self.training_tracker.update_RL_tracker(\n episode_id, episode_tracker.gross_earnings,\n episode_tracker.successful_waits, episode_tracker.unsuccessful_waits,\n episode_tracker.unmet_demand, episode_tracker.relocation_rides,\n episode_tracker.DET, episode_tracker.DPRT, episode_tracker.DWT,\n episode_tracker.DRT, episode_tracker.DCT)\n\n # Keep track of the best episode\n if self.objective == 'revenue':\n if episode_tracker.gross_earnings >= current_best:\n best_episode = episode_tracker\n current_best = best_episode.gross_earnings\n else: # self.objective == 'pickups':\n if episode_tracker.successful_waits >= current_best:\n best_episode = episode_tracker\n current_best = episode_tracker.successful_waits\n\n # Keep track of the best model\n best_model['ind_exploration_factor'] = ind_exploration_factor\n best_model['config'] = self.config\n best_model['q_ind'] = q_ind\n best_model['r_table'] = r_table\n best_model['xi_matrix'] = xi_matrix\n best_model['training_tracker'] = self.training_tracker\n\n # After finishing training\n self.logger.info(\"Expt: {} Earnings: {} Met Demand: {} Unmet Demand: {}\".format(self.expt_name,\n best_episode.gross_earnings,\n best_episode.successful_waits,\n best_episode.unmet_demand))\n return best_episode, best_model, self.training_tracker","def start(self):\n start_time = time()\n\n def callback(res):\n env, data, game_id = res.result()\n if env is None:\n logger.info('invalid data: {}'.format(game_id))\n return\n\n self.save_data(data, game_id)\n logger.debug(f\"game {game_id}\"\n f\"halfmoves={env.num_halfmoves:3} {env.winner:12}\"\n f\"{' by resign ' if env.resigned else ' '}\"\n f\"{env.observation.split(' ')[0]}\")\n\n with ProcessPoolExecutor(max_workers=3) as executor:\n games = self.get_games_from_all_files()\n\n # poisoned reference (memleak)\n for i, game in enumerate(games):\n job = executor.submit(get_buffer, self.config, game, len(games), i)\n job.add_done_callback(callback)\n # for res in as_completed([executor.submit(get_buffer, self.config, game, len(games), i) for i, game in enumerate(games)]):","def start_crawling(self):\n if util.is_None(self.team) or (not util.is_None(self.team) and util.is_None(self.team.team_fifa_api_id)):\n # If one of the follow:\n # 1) team not stored in the DB\n # 2) Team fifa api id not stored in the DB\n # --> looking for name and fifa_api_id\n team_long_name, team_fifa_api_id = self.look_for_base_data()\n\n if util.is_None(self.team):\n # team not present in the DB\n self.team = Team.write_new_team(team_long_name, team_fifa_api_id)\n else:\n # team present in the DB, but without set the team_fifa_api_id\n self.team.team_fifa_api_id = team_fifa_api_id\n self.team = Team.update(self.team)\n\n # looking for players belonging this team\n link_players_found = self.look_for_players()\n for player_link, player_name in link_players_found.items():\n player_fifa_api_id = player_link[25:]\n player = Player.read_by_fifa_api_id(player_fifa_api_id)\n\n # crawl the player if and only if on of the following happens:\n # 1) PLAYER DOES NOT EXIST IN THE DB\n # 2) PLAYER ATTRIBUTES DO NOT EXIST IN THE DB\n # 3) PLAYER ATTRIBUTES IN THE DB ARE OLD\n # 4) FORCE PARSING OF PLAYER ATTRIBUTES\n if \\\n not player \\\n or not player.get_last_player_attributes() \\\n or util.compare_time_to_now(player.get_last_player_attributes().date, self.day_passed) \\\n or self.force_parsing:\n log.debug(\"Player to crawl [\"+player_link+\", \"+player_name+\"]\")\n cp = CrawlerPlayer(player, player_link)\n cp.start_crawling()\n\n # looking for build up play\n attributes_found = self.look_for_team_attributes()\n if len(attributes_found) > 0:\n self.team.save_team_attributes(attributes_found)","def run():\n logger.info(f\"Process started:\")\n logger.info(f\"Converting Glove file to Word2Vec format\")\n convert_to_word2vec.convert(\n \"./data/source/glove.6B.50d.txt\", \"./data/source/glove.6B.50d.w2vformat.txt\"\n )\n\n logger.info(f\"Extracting Click Stream data\")\n extract_click_stream_data()\n\n logger.info(\"Extracting Wiki articles\")\n extract_wiki_articles()\n\n logger.info(f\"Generating Clickstream dataset\")\n generate_datasets()\n\n logger.info(\"Tokenizing articles\")\n WikiArticlesTokenizer().process()\n\n logger.info(\"Creating dataset with Wiki Articles\")\n create_wiki_articles_dataset()","def crawl_database():\n\n LOGGING.push(\"Attempting to request featured games.\")\n participants = get_featured()\n LOGGING.push(\"Got @\" + str(len(participants)) + \"@ participants.\")\n\n # NOTE: Only 40 summoners can be requested at a time\n participants = random.sample(participants, min(40, len(participants)))\n\n ids = SESSION.get_ids(participants)\n search_players = [ids[player]['id'] for player in ids.keys()]\n\n LOGGING.push(\n \"Now attempting to crawl players with a breadth of @\" +\n str(BREADTH) + \"@ and depth of ^\" + str(DEPTH) + \"^.\"\n )\n\n # NOTE: Creates the original call stack to crawl players\n for player in search_players:\n crawl_player(player, DEPTH, BREADTH)\n\n LOGGING.push(\"Finished crawling database.\")","def launch(self, force_prep=False):\n #TODO process upploaded csv\n assert self.ready()\n self.launch_progress = 0\n self.set_status(\"Gathering data\")\n if 'O' not in set(self.labels.dict.values()):\n self.add_labels({max(list(self.labels.dict.keys()))+1:'O'})\n\n processed_file_path = os.path.join(DATASETS_PATH, self.dataset_uuid, 'processed.csv')\n bert_file_path = os.path.join(DATASETS_PATH, self.dataset_uuid, 'processed.bert')\n elmo_file_path = os.path.join(DATASETS_PATH, self.dataset_uuid, 'processed.elmo')\n nlp_file_path = os.path.join(DATASETS_PATH, self.dataset_uuid, 'processed.nlp')\n sbert_file_path = os.path.join(DATASETS_PATH, self.dataset_uuid, 'processed.sbert')\n if os.path.exists(processed_file_path) and not force_prep:\n df = pd.read_csv(processed_file_path)\n if 'span_label' in df.columns:\n df['span_label']=df['span_label'].apply(eval)\n # Let's say loading the file is ~half the launch time\n # (if the file already exists)\n self.total = 2\n self.update(1)\n else:\n datafiles = [os.path.join(DATASETS_PATH, self.dataset_uuid, d) \\\n for d in os.listdir(os.path.join(DATASETS_PATH, self.dataset_uuid))]\n df = concat_dataset(datafiles)\n # expand total to account for time it takes to initialize the model\n self.total = len(df)*(1.1) \n self.set_status(\"Preprocessing data\")\n df = self.process_data(df, processed_file_path)\n\n # load list of 'allennlp.data.instance's. allennlp.data.instance can store true labels and tag info internally.\n if os.path.exists(nlp_file_path) and not force_prep:\n with open(nlp_file_path, 'rb') as f:\n sentences = pickle.load(f)\n else:\n #TODO define a universal reader for certain format\n # reader = RestaurantsDatasetReader()\n # data = reader.read(processed_file_path)\n #TODO handle when aux files do not exist\n pass\n bert_emb = np.load(bert_file_path, allow_pickle=True)\n elmo_emb = np.load(elmo_file_path, allow_pickle=True)\n sbert_emb = np.load(sbert_file_path, allow_pickle=True)\n for s, b, e, sb in zip(sentences, bert_emb, elmo_emb, sbert_emb):\n s.fields['bert'] = b\n s.fields['sbert'] = sb\n s.fields['elmo'] = e\n\n df['bert'] = bert_emb\n df['sbert'] = [sb for sb in sbert_emb]\n df['elmo'] = elmo_emb\n df['text_nlp'] = sentences\n\n columns_to_drop = list(\n set(df.columns).intersection(set(['span_label','file','label'])))\n df = df.drop(columns=columns_to_drop).reset_index()\n # since df['text_nlp'] contains true label info, drop 'labels' column.\n columns_to_drop = list(set(df.columns).difference(set(['index', 'Unnamed: 0', 'text', 'labels', 'split', 'bert', 'sbert',\n 'elmo', 'text_nlp'])))\n if len(columns_to_drop) > 0:\n df = df.drop(columns=columns_to_drop)\n df_train = df[df['split']=='train']\n df_dev = df[df['split'] == 'dev']\n df_valid = df[df['split'] == 'valid']\n df_test = df[df['split'] == 'test']\n\n self.text_inv_dict = dict(\n zip(list(df['text']),list(df.index))\n )\n\n # TODO split heldout set if necessary\n # for now, passing empty df as heldout set\n df_heldout = df_test\n\n self.emb_dict = Embeddings(df)\n\n self.set_status(\"Initializing modeler\")\n self.modeler = Modeler(df_train, df_dev, df_valid, df_test, df_heldout, self.labels, emb_dict=self.emb_dict)\n\n self.launch_progress = 1.0\n self.set_status(\"Finished\")\n return self.modeler","def Start(self):\n self.state.hist_count = 0\n self.state.history_paths = []\n\n if self.args.history_path:\n self.state.history_paths.append(self.args.history_path)\n else:\n self.state.history_paths = self.GuessHistoryPaths(self.args.username)\n\n if not self.state.history_paths:\n raise flow_base.FlowError(\"Could not find valid History paths.\")\n\n filename = \"places.sqlite\"\n for path in self.state.history_paths:\n self.CallFlow(\n file_finder.FileFinder.__name__,\n paths=[os.path.join(path, \"**2\", filename)],\n pathtype=self.args.pathtype,\n action=rdf_file_finder.FileFinderAction.Download(),\n next_state=self.ParseFiles.__name__)","def importData():\n #importChallengeDataToDB()\n importTrendingDataToDB()","def run(self):\n\n #Load old cache\n if '-r' not in sys.argv:\n old_db = SiteDB([], 'upwork')\n old_db.load()\n\n #Set it to current DBs old cache\n self.db.pred = old_db.cache\n\n #Generate crawler object\n \n crawler = self.generate_crawler()\n\n #Generate DB for latest scan\n if '-full' not in sys.argv:\n self.db = crawler.crawl()\n else:\n _ = crawler.crawl()\n\n if '-r' not in sys.argv:\n #Get deleted info\n _ = self.db.compare(old_db)\n\n targets = ['https://community.upwork.com/t5/Announcements/bd-p/news', \\\n 'https://community.upwork.com/t5/Freelancers/bd-p/freelancers', \\\n 'https://community.upwork.com/t5/Clients/bd-p/clients', \\\n 'https://community.upwork.com/t5/Agencies/bd-p/Agencies']\n target = None\n for tar in targets:\n if tar in sys.argv:\n target = tar\n\n #Write result\n #self.db.write(target_name=target)","def scrape_new_game_urls(self):\n dfs = []\n downloaded_files = os.listdir(self.pgn_persisting_base_path)\n downloaded_pgn = [int(re.findall('pgn_(\\d+).txt', x)[0]) for x in downloaded_files if x.endswith('txt')]\n\n all_files = pd.read_csv(self.urls_persisting_base_path + self.reference_url_file, sep=';')\n\n print(f\"Downloaded: {len(downloaded_pgn)}\")\n print(f\"Total: {all_files['game_link'].nunique()}\")\n self.scrapped_games = all_files[~all_files['game_id'].isin(downloaded_pgn)]","def mover_get_data(lfns,\n path,\n sitename,\n queuename,\n stageinTries,\n inputpoolfcstring=\"xmlcatalog_file:PoolFileCatalog.xml\",\n ub=\"outdated\", # to be removed\n dsname=\"\",\n dsdict={},\n rucio_dataset_dictionary={},\n guids=[],\n analysisJob=False,\n usect=True,\n pinitdir=\"\",\n proxycheck=True,\n spsetup=\"\",\n tokens=[],\n userid=\"\",\n inputDir=\"\",\n jobId=None,\n jobDefId=\"\",\n access_dict=None,\n scope_dict=None,\n workDir=\"\",\n DN=None,\n dbh=None,\n jobPars=\"\",\n cmtconfig=\"\",\n filesizeIn=[],\n checksumIn=[],\n transferType=None,\n experiment=\"\",\n eventService=False,\n sourceSite=\"\"):\n\n tolog(\"Mover get data started\")\n\n statusPFCTurl = None\n pilotErrorDiag = \"\"\n\n # FAX counters (will be reported in jobMetrics; only relevant when FAX has been activated after a stage-in failure)\n N_filesWithoutFAX = 0\n N_filesWithFAX = 0\n bytesWithoutFAX = 0L\n bytesWithFAX = 0L\n\n # FAX control variable, if FAX is used as primary site mover in combination with direct I/O\n usedFAXandDirectIO = False\n\n # The FAX variables above will be stored in a dictionary, to be returned by this function\n FAX_dictionary = {}\n\n # Is the DBRelease file available locally?\n DBReleaseIsAvailable = handleDBRelease(dbh, lfns, jobPars, path)\n\n # Should stage-in be aborted? (if there are only locally available DBRelease files in the stage-in list)\n if abortStageIn(dbh, lfns, DBReleaseIsAvailable):\n return 0, pilotErrorDiag, statusPFCTurl, FAX_dictionary\n\n # Setup the dictionary necessary for all instrumentation\n report = getInitialTracingReport(userid, sitename, dsname, \"get_sm\", analysisJob, jobId, jobDefId, DN)\n\n if stageinTries != 0:\n get_RETRY = min(stageinTries, MAX_NUMBER_OF_RETRIES)\n else:\n get_RETRY = MAX_RETRY\n get_TIMEOUT = 5*3600/get_RETRY\n\n fail = 0\n guidfname = {}\n error = PilotErrors()\n\n region = readpar('region')\n\n # Space tokens currently not used for input files\n # # check if there is are any space tokens\n # _token = getProperSpaceTokenList(token, listSEs, len(lfns))\n\n # Select the correct mover\n copycmd, setup = getCopytool(mode=\"get\")\n\n # Get the sitemover object corresponding to the default copy command\n sitemover = getSiteMover(copycmd, setup)\n\n # Get the experiment object\n thisExperiment = getExperiment(experiment)\n\n # Get the name for the PFC file\n _path = path\n if eventService:\n # Update the path (create the PFC in one level above the payload workdir)\n path = os.path.abspath(os.path.join(path, '..'))\n pfc_name = getPFCName(path, inputpoolfcstring)\n # done with the event server modification (related to the PFC generation), reset the path again\n path = _path\n\n # Build the file info dictionary (use the filesize and checksum from the dispatcher if possible) and create the PFC\n # Format: fileInfoDic[file_nr] = (guid, gpfn, fsize, fchecksum, filetype, copytool)\n # replicas_dic[guid1] = [ replica1, .. ] where replicaN is an object of class replica\n ec, pilotErrorDiag, fileInfoDic, totalFileSize, replicas_dic = \\\n getFileInfo(region, ub, queuename, guids, dsname, dsdict, lfns, pinitdir, analysisJob, tokens, DN, sitemover, error, path, dbh, DBReleaseIsAvailable,\\\n scope_dict, pfc_name=pfc_name, filesizeIn=filesizeIn, checksumIn=checksumIn, thisExperiment=thisExperiment)\n if ec != 0:\n return ec, pilotErrorDiag, statusPFCTurl, FAX_dictionary\n\n # Until the Mover PFC file is no longer needed, call the TURL based PFC \"PoolFileCatalogTURL.xml\"\n pfc_name_turl = pfc_name.replace(\".xml\", \"TURL.xml\")\n\n # Create a SURL to space token dictionary\n tokens_dictionary = getSurlTokenDictionary(lfns, tokens)\n\n # Create a TURL based PFC if necessary/requested (i.e. if copy tool should not be used [useCT=False] and\n # if oldPrefix and newPrefix are not already set in copysetup [useSetPrefixes=False])\n ec, pilotErrorDiag, createdPFCTURL, usect = PFC4TURLs(analysisJob, transferType, fileInfoDic, pfc_name_turl, sitemover, sitename, usect, dsdict, eventService, tokens_dictionary, sitename, sourceSite, lfns)\n if ec != 0:\n return ec, pilotErrorDiag, statusPFCTurl, FAX_dictionary\n\n # Correct the total file size for the DBRelease file if necessary\n totalFileSize = correctTotalFileSize(totalFileSize, fileInfoDic, lfns, dbh, DBReleaseIsAvailable)\n\n # Only bother with the size checks if the copy tool is to be used (non-direct access mode)\n if usect:\n # Get a proper maxinputsize from schedconfig/default \n _maxinputsize = getMaxInputSize()\n\n # Check the total input file size\n ec, pilotErrorDiag = verifyInputFileSize(totalFileSize, _maxinputsize, error)\n if ec != 0:\n return ec, pilotErrorDiag, statusPFCTurl, FAX_dictionary\n\n # Do we have enough local space to stage in all data and run the job?\n ec, pilotErrorDiag = verifyAvailableSpace(sitemover, totalFileSize, path, error)\n if ec != 0:\n return ec, pilotErrorDiag, statusPFCTurl, FAX_dictionary\n\n # Get the replica dictionary from file (used when the primary replica can not be staged due to some temporary error)\n replica_dictionary = getReplicaDictionaryFile(path)\n\n # file counters\n N_files_on_tape = 0\n N_root_files = 0\n N_non_root_files = 0\n\n # If FAX is used as a primary site mover then set the default FAX mode to true, otherwise to false (normal mode)\n if copycmd == \"fax\":\n usedFAXMode = True\n else:\n usedFAXMode = False\n\n # Use isOneByOneFileTransfer() to determine if files should be transferred one by one or all at once\n if not sitemover.isOneByOneFileTransfer():\n\n # Note: this mode is used by the aria2c site mover only\n # Normal stage-in is below\n\n tolog(\"All files will be transferred at once\")\n\n # Extract the file info for the first file in the dictionary\n guid, gpfn, lfn, fsize, fchecksum, filetype, copytool = extractInputFileInfo(fileInfoDic[0], lfns)\n file_access = getFileAccess(access_dict, lfn)\n dsname = getDataset(lfn, dsdict)\n\n # Perform stage-in using the sitemover wrapper method\n s, pErrorText = sitemover_get_all_data(sitemover, error, gpfn, lfn, path, fsize=fsize, spsetup=spsetup, fchecksum=fchecksum,\\\n guid=guid, analysisJob=analysisJob, usect=usect, pinitdir=pinitdir, proxycheck=proxycheck,\\\n sitename=sitename, token=None, timeout=get_TIMEOUT, dsname=dsname, userid=userid, report=report,\\\n access=file_access, inputDir=inputDir, jobId=jobId, workDir=workDir, cmtconfig=cmtconfig, lfns=lfns,\\\n experiment=experiment, replicas_dic=replicas_dic, dsdict=dsdict, scope_dict=scope_dict)\n if s != 0:\n tolog('!!WARNING!!2999!! Failed during stage-in of multiple files: %s' % (error.getErrorStr(s)))\n tolog(\"Exit code: %s\" % (s))\n fail = s\n\n # Normal stage-in (one by one file transfers)\n if sitemover.isOneByOneFileTransfer() or fail != 0:\n \n tolog(\"Files will be transferred one by one\")\n\n # Reset any previous failure\n fail = 0\n\n # Loop over all files in the file info dictionary\n number_of_files = len(fileInfoDic.keys())\n tolog(\"Will process %d file(s)\" % (number_of_files))\n for nr in range(number_of_files):\n # Extract the file info from the dictionary\n guid, gpfn, lfn, fsize, fchecksum, filetype, copytool = extractInputFileInfo(fileInfoDic[nr], lfns)\n\n # Has the copycmd/copytool changed? (E.g. due to FAX) If so, update the sitemover object\n if copytool != copycmd:\n copycmd = copytool\n # Get the sitemover object corresponding to the new copy command\n sitemover = getSiteMover(copycmd, setup)\n tolog(\"Site mover object updated since copytool has changed\")\n\n # Update the dataset name\n dsname = getDataset(lfn, dsdict)\n proper_dsname = getDataset(lfn, rucio_dataset_dictionary)\n scope = getFileScope(scope_dict, lfn)\n\n # Update the tracing report with the proper container/dataset name\n report = updateReport(report, gpfn, proper_dsname, fsize, sitemover)\n report['scope'] = scope\n\n # The DBRelease file might already have been handled, go to next file\n if isDBReleaseFile(dbh, lfn) and DBReleaseIsAvailable:\n updateFileState(lfn, workDir, jobId, mode=\"transfer_mode\", state=\"no_transfer\", type=\"input\")\n guidfname[guid] = lfn # needed for verification below\n continue\n else:\n tolog(\"(Not a DBRelease file)\")\n\n tolog(\"Mover is preparing to copy file %d/%d (lfn: %s guid: %s dsname: %s)\" % (nr+1, number_of_files, lfn, guid, dsname))\n tolog('Copying %s to %s (file catalog checksum: \\\"%s\\\", fsize: %s) using %s (%s)' %\\\n (gpfn, path, fchecksum, fsize, sitemover.getID(), sitemover.getSetup()))\n\n # Get the number of replica retries\n get_RETRY_replicas = getNumberOfReplicaRetries(createdPFCTURL, replica_dictionary, guid)\n\n file_access = getFileAccess(access_dict, lfn)\n\n # Loop over get function to allow for multiple get attempts for a file\n will_use_direct_io = False\n get_attempt = 0\n\n #get_RETRY = 1 #2 #PN\n while get_attempt < get_RETRY:\n if get_attempt > 0:\n _rest = 5*60\n tolog(\"(Waiting %d seconds before next stage-in attempt)\" % (_rest))\n sleep(_rest)\n tolog(\"Get attempt %d/%d\" % (get_attempt + 1, get_RETRY))\n replica_number = 0\n replica_transferred = False\n s = 1\n\n # Loop over replicas\n while s != 0 and replica_number < get_RETRY_replicas:\n # Grab the gpfn from the replicas dictionary in case alternative replica stage-in is allowed\n gpfn = getAlternativeReplica(gpfn, guid, replica_number, createdPFCTURL, replica_dictionary)\n\n # Perform stage-in using the sitemover wrapper method\n s, pErrorText, N_files_on_tape, N_root_files, N_non_root_files, replica_transferred, will_use_direct_io = sitemover_get_data(sitemover, error,\\\n get_RETRY, get_RETRY_replicas, get_attempt,\\\n replica_number, N_files_on_tape, N_root_files,\\\n N_non_root_files, gpfn, lfn, path,\\\n fsize=fsize, spsetup=spsetup, fchecksum=fchecksum,\\\n guid=guid, analysisJob=analysisJob, usect=usect,\\\n pinitdir=pinitdir, proxycheck=proxycheck,\\\n sitename=sitename, token=None, timeout=get_TIMEOUT,\\\n dsname=dsname, userid=userid, report=report,\\\n access=file_access, inputDir=inputDir, jobId=jobId,\\\n workDir=workDir, cmtconfig=cmtconfig,\\\n experiment=experiment, scope_dict=scope_dict,\\\n sourceSite=sourceSite)\n # Get out of the multiple replica loop\n if replica_transferred:\n break\n\n # Increase the replica attempt counter in case the previous replica could not be transferred\n replica_number += 1\n\n # Get out of the multiple get attempt loop\n if replica_transferred:\n break\n\n # Increase the get attempt counter in case of failure to transfer the file\n get_attempt += 1\n\n # Increase the successful file transfer counter (used only when reporting FAX transfers)\n if s == 0:\n # note the special case if FAX is the primary site mover (normally FAX is the fallback)\n if sitemover.copyCommand == \"fax\":\n N_filesWithFAX += 1\n bytesWithFAX += long(fsize)\n else:\n # Normal case\n N_filesWithoutFAX += 1\n bytesWithoutFAX += long(fsize)\n\n if s != 0:\n # Normal stage-in failed, now try with FAX if possible\n if error.isPilotFAXErrorCode(s):\n if isFAXAllowed(filetype, gpfn) and transferType != \"fax\" and sitemover.copyCommand != \"fax\": # no point in trying to fallback to fax if the fax transfer above failed\n tolog(\"Normal stage-in failed, will attempt to use FAX\")\n usedFAXMode = True\n\n # Get the FAX site mover\n old_sitemover = sitemover\n sitemover = getSiteMover(\"fax\", \"\")\n\n # Perform stage-in using the sitemover wrapper method\n s, pErrorText, N_files_on_tape, N_root_files, N_non_root_files, replica_transferred, will_use_direct_io = sitemover_get_data(sitemover, error,\\\n get_RETRY, get_RETRY_replicas, get_attempt, replica_number,\\\n N_files_on_tape, N_root_files, N_non_root_files,\\\n gpfn, lfn, path,\\\n fsize=fsize, spsetup=spsetup, fchecksum=fchecksum,\\\n guid=guid, analysisJob=analysisJob, usect=usect,\\\n pinitdir=pinitdir, proxycheck=proxycheck,\\\n sitename=sitename, token=None, timeout=get_TIMEOUT,\\\n dsname=dsname, userid=userid, report=report,\\\n access=file_access, inputDir=inputDir, jobId=jobId,\\\n workDir=workDir, cmtconfig=cmtconfig, experiment=experiment)\n if replica_transferred:\n tolog(\"FAX site mover managed to transfer file from remote site (resetting error code to zero)\")\n pilotErrorDiag = \"\"\n s = 0\n\n # Increase the successful FAX transfer counter\n N_filesWithFAX += 1\n bytesWithFAX += long(fsize)\n else:\n tolog(\"FAX site mover also failed to transfer file from remote site, giving up\")\n\n # restore the old sitemover\n del sitemover\n sitemover = old_sitemover\n else:\n tolog(\"(Not an error code eligible for FAX fail-over)\")\n\n if s != 0:\n tolog('!!FAILED!!2999!! Failed to transfer %s: %s (%s)' % (os.path.basename(gpfn), s, error.getErrorStr(s)))\n tolog(\"Exit code: %s\" % (s))\n\n # report corrupt file to consistency server if needed\n if s == error.ERR_GETADMISMATCH or s == error.ERR_GETMD5MISMATCH or s == error.ERR_GETWRONGSIZE or s == error.ERR_NOSUCHFILE:\n reportFileCorruption(gpfn, sitemover)\n\n # exception for object stores\n if (gpfn.startswith(\"s3:\") or 'objectstore' in gpfn) and '.log.tgz' in gpfn:\n tolog(\"!!FAILED!!2999!! Failed to transfer a log file from S3 objectstore. Will skip it and continue the job.\")\n else:\n fail = s\n break\n\n # Build the dictionary used to create the PFC for the TRF\n # In the case of FAX, use the global paths if direct access is to be used for the particlar file\n if usedFAXMode and will_use_direct_io:\n # The site mover needed here is the FAX site mover since the global file path methods are defined there only\n old_sitemover = sitemover\n sitemover = getSiteMover(\"fax\", \"\")\n guidfname[guid] = sitemover.findGlobalFilePath(lfn, dsname, sitename, sourceSite)\n\n # Restore the old sitemover\n del sitemover\n sitemover = old_sitemover\n\n # If FAX is used as a primary site mover, in combination with direct access, set the usedFAXandDirectIO flag\n # this will later be used to update the run command (e.g. --lfcHost is not needed etc)\n if copycmd == \"fax\":\n usedFAXandDirectIO = True\n else:\n guidfname[guid] = lfn # local_file_name\n\n if fail == 0:\n # Make sure the PFC has the correct number of files\n fail, pilotErrorDiag = verifyPFCIntegrity(guidfname, lfns, dbh, DBReleaseIsAvailable, error)\n\n # Now that the Mover PFC file is no longer needed, back it up and rename the TURL based PFC if it exists\n # (the original PFC is no longer needed. Move it away, and then create the PFC for the trf/runAthena)\n # backupPFC4Mover(pfc_name)\n\n # Create a standard PFC with SURLs if needed (basically this is default)\n # note: if FAX was used as a primary site mover in combination with direct I/O, then the SURLs will actually be TURLs\n # but there is no need to use the special TURL creation method PFC4TURL used above (FAX will have returned the TURLs instead)\n createStandardPFC4TRF(createdPFCTURL, pfc_name_turl, pfc_name, guidfname)\n\n tolog(\"Number of identified root files : %d\" % (N_root_files))\n tolog(\"Number of transferred non-root files: %d\" % (N_non_root_files))\n\n if usedFAXMode:\n tolog(\"Number of files without FAX : %d (normal transfers)\" % (N_filesWithoutFAX))\n tolog(\"Number of files with FAX : %d (successful FAX transfers)\" % (N_filesWithFAX))\n tolog(\"Bytes without FAX : %d (normal transfers)\" % (bytesWithoutFAX))\n tolog(\"Bytes with FAX : %d (successful FAX transfers)\" % (bytesWithFAX))\n\n if N_files_on_tape > 0:\n tolog(\"!!WARNING!!2999!! Number of skipped files: %d (not staged)\" % (N_files_on_tape))\n if N_root_files == 0:\n # This should only happen for user jobs\n tolog(\"Mover get_data failed since no root files could be transferred\")\n fail = error.ERR_NOSTAGEDFILES\n else:\n tolog(\"Mover get_data finished (partial)\")\n else:\n if fail == 0:\n tolog(\"Get successful\")\n tolog(\"Mover get_data finished\")\n else:\n tolog(\"Mover get_data finished (failed)\")\n tolog(\"Will return exit code = %d, pilotErrorDiag = %s\" % (fail, pilotErrorDiag)) \n\n # Now populate the FAX dictionary before finishing\n FAX_dictionary = getFAXDictionary(N_filesWithoutFAX, N_filesWithFAX, bytesWithoutFAX, bytesWithFAX, usedFAXandDirectIO)\n\n return fail, pilotErrorDiag, statusPFCTurl, FAX_dictionary","def start() -> None:\n\n # PREPARE\n clone_game_files()\n\n # SIMULATE\n turns = run.simulation()\n\n # LOG\n logs = read.combine_logs(turns)\n\n # CALCULATE\n results = calculate.results(logs)\n\n # DISPLAY\n visualize.charts(results)\n\n # CLEAN\n remove_cloned_files()","def _extract_games_data(season, season_type):\n nflscrapr.run(\n 'games',\n season=season,\n season_type=season_type\n )\n nflscrapr_output = etl_tools.extract_from_csv(config.GAMES_DUMP_CSV_PATH)\n return nflscrapr_output","def scrape(self):\n\n self.jobs_load()\n self.new_jobs = []\n\n for bot in self.bot_squad:\n self.new_jobs += bot.scrape_all_pages()\n\n self.statistics(self.new_jobs)\n print('SCRAPE COMPLETE. NOTE: Resulting job list still in RAM')\n print('We observed %d new jobs' % len(self.new_jobs))","def process_dataset(self):\n\n logger.info('\\n')\n logger.info('=' * 40)\n logger.info('=\\t DeepRank Data Set')\n logger.info('=')\n logger.info('=\\t Training data')\n for f in self.train_database:\n logger.info(f'=\\t -> {f}')\n logger.info('=')\n if self.valid_database:\n logger.info('=\\t Validation data')\n for f in self.valid_database:\n logger.info(f'=\\t -> {f}')\n logger.info('=')\n if self.test_database:\n logger.info('=\\t Test data')\n for f in self.test_database:\n logger.info(f'=\\t -> {f}')\n logger.info('=')\n logger.info('=' * 40 + '\\n')\n sys.stdout.flush()\n\n # check if the files are ok\n self.check_hdf5_files(self.train_database)\n\n if self.valid_database:\n self.valid_database = self.check_hdf5_files(\n self.valid_database)\n\n if self.test_database:\n self.test_database = self.check_hdf5_files(\n self.test_database)\n\n # create the indexing system\n # alows to associate each mol to an index\n # and get fname and mol name from the index\n self.create_index_molecules()\n\n # get the actual feature name\n if self.mapfly:\n self.get_raw_feature_name()\n else:\n self.get_mapped_feature_name()\n\n # get the pairing\n self.get_pairing_feature()\n\n # get grid shape\n self.get_grid_shape()\n\n # get the input shape\n self.get_input_shape()\n\n # get renormalization factor\n if self.normalize_features or self.normalize_targets or self.clip_features:\n if self.mapfly:\n self.compute_norm()\n else:\n self.get_norm()\n\n logger.info('\\n')\n logger.info(\" Data Set Info:\")\n logger.info(\n f' Augmentation : {self.use_rotation} rotations')\n logger.info(\n f' Training set : {self.ntrain} conformations')\n logger.info(\n f' Validation set : {self.nvalid} conformations')\n logger.info(\n f' Test set : {self.ntest} conformations')\n logger.info(f' Number of channels : {self.input_shape[0]}')\n logger.info(f' Grid Size : {self.data_shape[1]}, '\n f'{self.data_shape[2]}, {self.data_shape[3]}')\n sys.stdout.flush()","def _load_data(self):\n self.mapper = Mapper()\n self.mapper.generate_vocabulary(self.review_summary_file)\n self.X_fwd, self.X_bwd, self.Y = self.mapper.get_tensor(reverseflag=True)\n # Store all the mapper values in a dict for later recovery\n self.mapper_dict = dict()\n self.mapper_dict['seq_length'] = self.mapper.get_seq_length()\n self.mapper_dict['vocab_size'] = self.mapper.get_vocabulary_size()\n self.mapper_dict['rev_map'] = self.mapper.get_reverse_map()\n # Split into test and train data\n self._split_train_tst()","def fetch_data(args):\n logger.debug(\"Running the fetch_data function\")\n\n #Loading the config\n with open(os.path.join(\"Config\",\"config.yml\"), \"r\") as f:\n config = yaml.safe_load(f)\n\n #Starting the scraping process\n tstart = datetime.datetime.now()\n err_count = 0\n\n logger.info(\"Starting web scraping now.\")\n for i in range(config[\"fetch_data\"][\"indices\"][\"start\"], config[\"fetch_data\"][\"indices\"][\"end\"]+1):\n try:\n time.sleep(1)\n req_link1 = \"http://www.gutenberg.org/cache/epub/\" + str(i) + \"/pg\" + str(i) + \".txt\"\n response1 = requests.get(req_link1)\n \n req_link2 = \"http://www.gutenberg.org/files/\" + str(i) + \"/\" + str(i) + \"-0.txt\"\n response2 = requests.get(req_link2)\n \n response1.encoding = \"UTF-8\"\n response2.encoding = \"UTF-8\"\n \n if response1.status_code == 200:\n with open(config[\"fetch_data\"][\"save_location\"] + str(i) + \".txt\", \"w\", encoding=\"UTF-8\") as text_file:\n text_file.write(response1.text)\n \n elif response2.status_code == 200:\n with open(config[\"fetch_data\"][\"save_location\"] + str(i) + \".txt\", \"w\", encoding=\"UTF-8\") as text_file:\n text_file.write(response2.text)\n \n else:\n err_count = err_count + 1 \n logger.error(\"Status Code {} returned for index {}\".format(response.status_code, i))\n \n if i % 500 == 0:\n time.sleep(30)\n logger.info(\"At Index {}. Time Elapsed: {}\".format(i, datetime.datetime.now()-tstart)) \n\n except Exception as e:\n logger.error(e)\n \n logger.info(\"Total Errorred documents: {}\".format(err_count))\n logger.info(\"Total Successful documents: {}\".format(config[\"fetch_data\"][\"indices\"][\"end\"] - config[\"fetch_data\"][\"indices\"][\"start\"] + 1 -err_count))\n logger.info(\"Total Time taken: {}\".format(datetime.datetime.now()-tstart))\n\n return","def scrape_fifa_game(url, competition):\n # Need to add referee data.\n\n data = scrape_url(url)\n data = data.split(\"<h2>Advertisement</h2>\")[0]\n soup = BeautifulSoup(data)\n \n contents = soup.find(\"div\", {\"id\": \"mainContent\" })\n \n # Really, none of these games have a home team.\n\n #teams = get_contents(contents.find(\"div\", \"bold large teams\"))\n team1 = get_contents(contents.find(\"div\", \"lnupTeam\").find(\"div\", \"bold medium\"))\n team2 = get_contents(contents.find(\"div\", \"lnupTeam away\").find(\"div\", \"bold medium\"))\n\n #import pdb; pdb.set_trace()\n\n \n\n #try:\n # team1, team2 = [e.strip() for e in teams.split(\"-\")]\n #except:\n # import pdb; pdb.set_trace()\n\n score_string = get_contents(contents.find(\"div\", \"bold large result\"))\n\n if 'a.e.t.' in score_string:\n score_string = score_string.split('a.e.t')[0]\n\n team1_score, team2_score = [int(e) for e in score_string.split(\"(\")[0].split(\":\")]\n\n # Implement this if header order is more unpredictable.\n #game_head = contents.findAll(\"thead\")\n #head_teas = game_head.findAll(\"td\", text=True\n\n game_header = contents.find(\"thead\")\n game_info = contents.find(\"tbody\")\n \n game_ths = [get_contents(e) for e in game_header.findAll(\"td\")]\n game_tds = [get_contents(e) for e in game_info.findAll(\"td\")]\n\n game_dict = dict(zip(game_ths, game_tds))\n\n\n match = date_string = time = location = attendance = None\n\n #import pdb; pdb.set_trace()\n\n if 'Match' in game_dict:\n match = game_dict['Match']\n\n if 'Date' in game_dict:\n date_string = game_dict['Date']\n\n #'Time' \n\n if 'Attendance' in game_dict:\n if game_dict['Attendance']:\n attendance = int(game_dict['Attendance'])\n\n if 'Venue / Stadium' in game_dict:\n location = game_dict['Venue / Stadium']\n\n for e in 'Match', 'Date', 'Attendance', 'Venue / Stadium', 'Time': \n if e in game_dict:\n game_dict.pop(e)\n #print(game_dict.keys())\n\n # Standardize city and stadium fields\n try:\n city, stadium = [e.strip() for e in location.rsplit(\"/\", 1)]\n except:\n import pdb; pdb.set_trace()\n\n # Avoid duplication of city name?\n if stadium.endswith(city):\n nlocation = stadium\n else:\n nlocation = \"%s, %s\" % (stadium, city)\n\n date = datetime.datetime.strptime(date_string.strip(), \"%d %B %Y\")\n\n return {\n \"team1\": team1,\n \"team2\": team2,\n 'team1_score': team1_score,\n 'team2_score': team2_score,\n 'competition': competition,\n 'season': str(date.year),\n \"date\": date,\n \"location\": nlocation,\n \"attendance\": attendance,\n \"sources\": [url],\n }","def run(self):\n\t\tfor source in self.sources:\n\t\t\tstringutil.print_color(Fore.GREEN, 'Downloading from Source: %s' % source.get_alias())\n\t\t\tfor r in source.get_elements():\n\t\t\t\tr.set_source(source)\n\t\t\t\tself._queue.put(r)\n\n\t\t\t\t# Extra tracking stuff below:\n\t\t\t\twith self._c_lock:\n\t\t\t\t\tself._total_count+= 1\n\t\t\t\tif self._testing_cache is not None:\n\t\t\t\t\tself._testing_cache.append(r)\n\t\t#print(\"Element loading complete.\\n\")\n\t\tself._running = False","def load_and_process_fixtures_data(self) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\n df = self._dbmanager.query_fixtures_data(self._seasons)\n if df.empty:\n raise ValueError(\"Empty fixtures dataframe.\")\n\n df = self._drop_last_season_championship_matches(df)\n\n self.teams = get_unique_teams(df)\n self.last_season_teams = get_last_season_unique_teams(df)\n # Get fixtures ids for each team\n teams_fixtures_ids = {t: df[(df[\"home\"] == t) | (df[\"away\"] == t)].loc[:, \"id\"].tolist() for t in self.teams}\n\n self._check_missing_columns(df)\n df = self._check_nan_values(df, teams_fixtures_ids)\n teams_fixtures_ids = self._discard_matches(df, teams_fixtures_ids)\n\n if not self._resume:\n # Use last n ids for predictions and last m ids for testing\n for t in self.last_season_teams:\n self.predict_fixtures_ids[t] = teams_fixtures_ids[t][-NPREDICT:]\n teams_fixtures_ids[t] = teams_fixtures_ids[t][:-NPREDICT]\n\n self.test_fixtures_ids[t] = teams_fixtures_ids[t][-self._ntest:]\n teams_fixtures_ids[t] = teams_fixtures_ids[t][:-self._ntest]\n\n # Rest of ids is counted as train set\n self.train_fixtures_ids = teams_fixtures_ids\n else:\n if self.teams_names_bitlen != self._model_settings[\"teams_names_bitlen\"]:\n raise ValueError(\"Current bitlength required to encode all teams names is higher than previous one.\")\n\n # Check whether teams has not changed\n if self.teams != self._model_settings[\"teams\"]:\n raise ValueError(\"Teams differ from previous run. \\n\"\n f\"New: {self.teams} \\n\"\n f\"Old: {self._model_settings['teams']}\")\n\n if self.last_season_teams != self._model_settings[\"last_season_teams\"]:\n raise ValueError(\"Last season teams differ from previous run. \\n\"\n f\"New: {self.last_season_teams} \\n\"\n f\"Old: {self._model_settings['last_season_teams']}\")\n\n # Check whether fixtures ids match from previous run\n for t in self.last_season_teams:\n predict_fixtures_ids = teams_fixtures_ids[t][-NPREDICT:]\n teams_fixtures_ids[t] = teams_fixtures_ids[t][:-NPREDICT]\n\n test_fixtures_ids = teams_fixtures_ids[t][-self._ntest:]\n teams_fixtures_ids[t] = teams_fixtures_ids[t][:-self._ntest]\n\n if predict_fixtures_ids != self._model_settings[\"predict_fixtures_ids\"][t]:\n raise ValueError(f\"{t} predict fixtures ids differ from previous run. \\n\"\n f\"New: {predict_fixtures_ids} \\n\"\n f\"Old: {self._model_settings['predict_fixtures_ids'][t]}\")\n if test_fixtures_ids != self._model_settings['test_fixtures_ids'][t]:\n raise ValueError(f\"{t} test fixtures ids differ from previous run. \\n\"\n f\"New: {test_fixtures_ids} \\n\"\n f\"Old: {self._model_settings['test_fixtures_ids'][t]}\")\n if teams_fixtures_ids[t] != self._model_settings['train_fixtures_ids'][t]:\n raise ValueError(f\"{t} train fixtures ids differ from previous run. \\n\"\n f\"New: {teams_fixtures_ids[t]} \\n\"\n f\"Old: {self._model_settings['train_fixtures_ids'][t]}\")\n\n # Checks passed, load previously saved data\n self.teams = self._model_settings[\"teams\"]\n self.last_season_teams = self._model_settings[\"last_season_teams\"]\n self.train_fixtures_ids = self._model_settings[\"train_fixtures_ids\"]\n self.test_fixtures_ids = self._model_settings[\"test_fixtures_ids\"]\n self.predict_fixtures_ids = self._model_settings[\"predict_fixtures_ids\"]\n\n self._check_season_gaps_in_teams_matches(df)\n\n # Split original dataset into train, test, and predict datasets\n df_train, df_test, df_predict = self._mask_out_dataset(df)\n self._get_unique_teams_from_datasets(df_train, df_test, df_predict)\n\n self._check_changes_in_teams()\n self._count_samples(df_train, df_test, df_predict)\n\n return df_train, df_test, df_predict","def games_recommendation_trainer(directory: str):\n try:\n # load training dataset\n sub_directory = os.path.join(directory, 'Users_data', 'games_daily_train_selected_played.csv')\n user_game_pairs, user_index_dict, app_index_dict = load_games_training_set(sub_directory)\n \n # load games description\n sub_directory = os.path.join(directory, 'Games_data', 'games_description_stem.json')\n corpus = load_games_description_feature(sub_directory, app_index_dict)\n \n # load games genres\n sub_directory = os.path.join(directory, 'Games_data', 'games_genres.csv')\n games_genres_matrix = get_games_genres(sub_directory, app_index_dict)\n \n # load games price\n sub_directory = os.path.join(directory, 'Games_data', 'games_price_modify.csv')\n price_matrix = games_price_feature(sub_directory, app_index_dict)\n \n except OSError:\n logging.getLogger(__name__).info(sub_directory + \n \" does not exist. Please run 'osna collect' and 'osna preprocess'.\")\n return\n \n # make directory for results\n result_directory = os.path.join(directory, 'Results')\n if not os.path.isdir(result_directory): os.makedirs(result_directory)\n \n params_range = {'min_df': [3, 5], 'max_df': [0.8, 0.5], 'isOtherFeatures': [True, False]}\n params_list = []\n for min_df in params_range['min_df']:\n for max_df in params_range['max_df']:\n for isOtherFeatures in params_range['isOtherFeatures']:\n params = defaultdict(int)\n params['min_df'] = min_df\n params['max_df'] = max_df\n params['isOtherFeatures'] = isOtherFeatures\n params_list.append(params)\n\n \n # k-fold cross validation\n k = 5\n k_ = 1 # for faster training speed, supposed to be 5\n data_train, data_test = user_games_split(len(user_game_pairs), k)\n \n # Finding best parameters for content-based model\n# best_params = {'min_df': 3, 'max_df': 0.5, 'isOtherFeatures': True}\n best_params = {'recall_content': 0}\n for params in params_list:\n \n tfidf_matrix, tf = games_description_tokenize(corpus, params)\n \n for i in range(k_):\n \n train_data_matrix, test_data_matrix = users_games_matrix_trans(user_game_pairs, data_train[i], data_test[i], len(user_index_dict), len(app_index_dict)) \n games_features_matrix = get_games_features_matrix(tfidf_matrix, games_genres_matrix, price_matrix, params) \n users_games_rating = train_content_based(train_data_matrix, games_features_matrix)\n recall_content = osna.evaluate.recall_rate(users_games_rating, user_game_pairs, data_test[i], [30])\n params['recall_content'] += recall_content[30] / k_\n \n if params['recall_content'] > best_params['recall_content']: \n best_params = params\n logging.getLogger(__name__).info(params)\n \n logging.getLogger(__name__).info('best_params: '+str(best_params))\n save_recall_content(result_directory, params_list)\n \n # Evaluating all four models\n topN_range = [5, 10, 15, 20, 25, 30]\n params = best_params.copy()\n params.pop('recall_content', None)\n tfidf_matrix, tf = games_description_tokenize(corpus, params)\n \n for i in range(k_):\n \n train_data_matrix, test_data_matrix = users_games_matrix_trans(user_game_pairs, data_train[i], data_test[i], len(user_index_dict), len(app_index_dict)) \n \n # Baseline - Normal distribution\n users_games_rating = train_random(train_data_matrix)\n recall_base = osna.evaluate.recall_rate(users_games_rating, user_game_pairs, data_test[i], topN_range)\n update_recall_rate(params, 'recall_base', recall_base, k_)\n rmse_base = osna.evaluate.RMSE(users_games_rating, test_data_matrix)\n params['rmse_base'] += rmse_base / k_\n \n # Content-based model\n games_features_matrix = get_games_features_matrix(tfidf_matrix, games_genres_matrix, price_matrix, params) \n users_games_rating = train_content_based(train_data_matrix, games_features_matrix)\n recall_content = osna.evaluate.recall_rate(users_games_rating, user_game_pairs, data_test[i], topN_range)\n update_recall_rate(params, 'recall_content', recall_content, k_)\n rmse_content = osna.evaluate.RMSE(users_games_rating, test_data_matrix)\n params['rmse_content'] += rmse_content / k_\n save_training_model(result_directory, {'games_features_matrix': games_features_matrix, 'app_index_dict': app_index_dict})\n \n # CF Item-Item\n CF_item_prediction = CF_item_predict(train_data_matrix)\n CF_recall_item = osna.evaluate.recall_rate(CF_item_prediction, user_game_pairs, data_test[i], topN_range)\n update_recall_rate(params, 'recall_CF_item', CF_recall_item, k_)\n CF_RMSE_item = osna.evaluate.RMSE(CF_item_prediction, test_data_matrix)\n params['rmse_CF_item'] += CF_RMSE_item / k_\n \n # CF User-User\n CF_user_prediction = CF_user_predict(train_data_matrix)\n CF_recall_user = osna.evaluate.recall_rate(CF_user_prediction, user_game_pairs, data_test[i], topN_range)\n update_recall_rate(params, 'recall_CF_user', CF_recall_user, k_)\n CF_RMSE_user = osna.evaluate.RMSE(CF_user_prediction, test_data_matrix)\n params['rmse_CF_user'] += CF_RMSE_user / k_\n \n logging.getLogger(__name__).info(params)\n \n save_result_all(result_directory, params)","def scrape_game_urls(self):\n scrapped_games = pd.DataFrame(columns=['game_link', 'game_quality', 'game_comments_qtd'])\n for i in range(self.initial_page, self.final_page+1):\n print(\"getting links of page %d ...\" % i)\n url = self.base_urls_page + str(i)\n self.driver.get(url)\n\n evn_game_links, evn_game_qualities, evn_game_comments_number = self.scrape_page_games('evn_list')\n odd_game_links, odd_game_qualities, odd_game_comments_number = self.scrape_page_games('odd_list')\n\n scrapped_games = pd.concat([\n scrapped_games,\n pd.DataFrame({\n 'game_link': evn_game_links + odd_game_links,\n 'game_quality': evn_game_qualities + odd_game_qualities,\n 'game_comments_qtd': evn_game_comments_number + odd_game_comments_number\n })\n ])\n\n scrapped_games = self.format_links_table(scrapped_games)\n self.scrapped_games = scrapped_games","def explore(self):\n\n i = 0\n while True:\n i += 1\n \n state_counts = {game.__class__.__name__: Counter() for game in self.games} \n\n policies_prime = []\n pi_sum = 0\n v_sum = 0\n counter = 0\n \n # bookkeeping\n log.info(f'Starting Exploration Iteration #{i} ...')\n\n # for task in tasks...\n for _ in range(self.args['taskBatchSize']):\n\n # create deepcopy for training a theta'\n policy_prime = copy.deepcopy(self.nnet)\n \n # sample a game (task)\n game = np.random.choice(self.games, p=self.probs)\n log.info(f'Sampled game {type(game).__name__} ...')\n\n # multiprocess to get our training examples\n iterationTrainExamples = deque([], maxlen=self.args['maxlenOfQueue'])\n iterationTrainExamples = run_apply_async_multiprocessing(self.executeEpisode, [(MCTS(game, self.nnet, self.args), type(game)(), self.args.copy())] * self.args['numEps'], self.args['numWorkers'], desc='Self Play')\n iterationTrainExamples, iter_counters = zip(*iterationTrainExamples)\n\n iterationTrainExamples = list(itertools.chain.from_iterable(iterationTrainExamples))\n state_counts[game.__class__.__name__] += sum(iter_counters, Counter())\n\n # shuffle examples before training\n shuffle(iterationTrainExamples)\n\n # train our network\n pi_v_losses = policy_prime.train(iterationTrainExamples)\n\n policies_prime.append(policy_prime.state_dict())\n\n for pi,v in pi_v_losses:\n pi_sum += pi\n v_sum += v\n counter += 1\n \n # compute average parameters and load into self.nnet\n self.nnet.load_average_params(policies_prime)\n\n # training new network, keeping a copy of the old one\n self.nnet.save_checkpoint(folder=self.args['checkpoint'] + '/exploring', filename='temp.pth.tar')\n self.pnet.load_checkpoint(folder=self.args['checkpoint'] + '/exploring', filename='temp.pth.tar')\n pmcts = MCTS(self.games[0], self.pnet, self.args)\n\n\n # Arena if we choose to run it\n if self.args['arenaComparePerGame'] > 0:\n # ARENA\n nmcts = MCTS(self.games[0], self.nnet, self.args)\n\n log.info('PITTING AGAINST PREVIOUS VERSION')\n arena = Arena()\n pwins, nwins, draws = arena.playGames(self.pnet, self.nnet, self.args, self.games)\n\n log.info('NEW/PREV WINS : %d / %d ; DRAWS : %d' % (nwins, pwins, draws))\n if pwins + nwins == 0 or float(nwins) / (pwins + nwins) < self.args['updateThreshold']:\n log.info('REJECTING NEW MODEL')\n self.nnet.load_checkpoint(folder=self.args['checkpoint'] + '/exploring', filename='temp.pth.tar')\n else:\n log.info('ACCEPTING NEW MODEL')\n self.nnet.save_checkpoint(folder=self.args['checkpoint'] + '/exploring', filename=self.getCheckpointFile(i))\n self.nnet.save_checkpoint(folder=self.args['checkpoint'] + '/exploring', filename='best.pth.tar')\n\n log.info('Iteration Complete. Writing counts to \"%s/%s\"...', *self.args['json_folder_file'])\n # create the json file\n path = os.path.join(self.args['json_folder_file'][0], self.args['json_folder_file'][1])\n with open(path, 'a+') as f:\n if os.stat(path).st_size == 0: ## file just created/empty\n log.info('No counts found. Writing to empty file.')\n old_counts = {game.__class__.__name__: Counter() for game in self.games}\n else: ## load the counts from the file\n log.info('Loading counts...')\n f.seek(0)\n str_counts = f.read()\n # print('STRING OF JSON:', type(str_counts), str_counts)\n old_counts = json.loads(str_counts)\n old_counts = {game: Counter(v) for game, v in old_counts.items()}\n master_counts = {game.__class__.__name__: state_counts[game.__class__.__name__]+old_counts[game.__class__.__name__] for game in self.games}\n # countiung logic: turn {gametype -> Counter} into {gametype -> {state -> count}}\n master_counts = {game: dict(counter) for game, counter in master_counts.items()}\n log.info('Writing...')\n f.truncate(0) #clear file\n json.dump(master_counts, f)\n log.info('Counts written to json file \"%s/%s\"...', *self.args['json_folder_file'])","def extract_data():\n books = WebScraper().get_top_100_data()\n time.sleep(2)\n BookDetailsWebScrapper().save_book_details(books)\n _save_extract_state(books)","def startGame():\n #roundnumber\n eel.updateRoundNumber()\n # start page\n eel.updateStartPage([startPage.getTitle(), startPage.getUrl()])\n eel.updateStartPageDescription(startPage.getFirstSentence())\n # goal page\n eel.updateGoalPage([goalPage.getTitle(), goalPage.getUrl()])\n eel.updateGoalPageDescription(goalPage.getFirstSentence())\n # ui updates\n eel.updateCurrentPage(\n [wikiPageStackTrace[-1].getTitle(), wikiPageStackTrace[-1].getUrl()])\n eel.updateCurrentPageDescription(wikiPageStackTrace[-1].getFirstSentence())\n eel.printInPageList(wikiPageStackTrace[-1].getOnlyLinksListJS())\n # loader\n time.sleep(0.5)\n eel.hideLoader()","def reInitAndRun(self):\n self.playlists = self.readPlaylistData()\n self.audioDF = self.readAudioData(shouldProcess=True)\n self.clusterLabels = []\n self.models = Clusterers(k=len(self.playlists))\n self.processAndCluster()\n self.analyzeResults()","def perform_scraping(current_session):\r\n\r\n # List Array storing all relevant decision information\r\n final_data_fetch = []\r\n pagination_index = global_constants['NUMBER_PAGE_TO_SCRAPE_FIRST']\r\n while pagination_index < global_constants['NUMBER_PAGE_TO_SCRAPE_LAST']:\r\n print(\"Page:\", pagination_index, \" Collected records:\", len(final_data_fetch))\r\n\r\n # Get relevant admit-reject page based on pagination value\r\n result = current_session.get(global_constants['ALL_RESULTS_URL'] + str(pagination_index),\r\n headers=dict(referer=global_constants['ALL_RESULTS_URL']))\r\n tree = lxml_html.fromstring(result.content)\r\n\r\n # Get Nodes containing individual decisions for each page(approx 20 per page)\r\n decision_buckets = tree.xpath('//*[@class=\"row\"]/div[@class=\"col-sm-6\"]/div[@class=\"panel panel-warning\"]/div[@class=\"panel-body\"]')\r\n\r\n # If decision buckets are empty, captcha page has been encountered\r\n if len(decision_buckets) == 0:\r\n print(\"Captcha Time\")\r\n time.sleep(120)\r\n continue\r\n\r\n for individual_decision_bucket in decision_buckets:\r\n\r\n current_admit_status = ((individual_decision_bucket.xpath('./div[1]/div[2]/label'))[0]).text.strip()\r\n\r\n # Fetch results only if ADMIT or REJECT\r\n if current_admit_status.lower() == 'admit' or current_admit_status.lower() == 'reject':\r\n\r\n # Get relevant information from html page returned in response\r\n current_bucket_university_course = ((individual_decision_bucket.xpath('./div[1]/div[1]/h4/small'))[0]).text.replace(\"\\n\",\"\").strip()\r\n current_gre = get_gre_or_toefl(((((individual_decision_bucket.xpath('./div[2]/div[1]'))[0]).getchildren())[1]).tail)\r\n current_toefl = get_gre_or_toefl(((((individual_decision_bucket.xpath('./div[2]/div[2]'))[0]).getchildren())[1]).tail)\r\n current_gpa = get_gpa(((((individual_decision_bucket.xpath('./div[2]/div[3]'))[0]).getchildren())[1]).tail)\r\n current_workex = get_workex_months(((((individual_decision_bucket.xpath('./div[2]/div[4]'))[0]).getchildren())[1]).tail)\r\n\r\n current_university, current_course = split_bucket_university_course(current_bucket_university_course.lower())\r\n # Append decision information to final bucket only if minimum criteria met\r\n if current_university is not None and filter_criteria_met(current_gre, current_gpa, current_toefl):\r\n\r\n # Get UG College from profile of user\r\n profile_page_path = ((individual_decision_bucket.xpath('./div[1]/div[1]/h4/a'))[0]).attrib['href']\r\n profile_result = current_session.get(global_constants['HOME_PAGE'] + profile_page_path,\r\n headers=dict(referer=global_constants['PAST_RESULTS_URL']))\r\n profile_tree = lxml_html.fromstring(profile_result.content)\r\n ug_details_bucket = (profile_tree.xpath('//div[@class=\"col-sm-12 card\"][1]'))\r\n if len(ug_details_bucket) >= 1:\r\n ug_details_bucket = ug_details_bucket[0]\r\n current_ug_course = ((ug_details_bucket.xpath('./div[1]/div[7]/p[1]/b[1]'))[0]).text.replace(\"\\n\", \"\").strip()\r\n current_ug_college = ((ug_details_bucket.xpath('./div[1]/div[7]/p[2]'))[0]).text.replace(\"\\n\", \"\").strip()\r\n\r\n final_data_fetch.append([current_course, current_university, current_gpa, current_gre, current_toefl,\r\n current_workex, current_ug_course, current_ug_college, current_admit_status])\r\n\r\n # Add sleep time to allow for web scraping in undetected manner\r\n sleep_delay = random.choice([0, 1, 2, 3])\r\n time.sleep(sleep_delay)\r\n pagination_index += 1\r\n\r\n # Export final_data to excel sheet\r\n export_to_file(final_data_fetch)","def step(self, actions):\n\n representative_data = []\n original_data = []\n\n actions = np.array(actions).reshape(3, -1)\n\n for np_data, df_data, calculator, som, action in zip(self.np_data_list, self.df_data_list, self.calculators, self.som_objects, actions):\n\n representative_days, cluster_numbers = calculator.get_representative_days(\n som, np_data, action)\n\n representative_days = pd.DataFrame(representative_days)\n\n representative_days = self.wide_to_long(representative_days)\n approximation_calc = ApproximateData(df_data, 4)\n representative_days = ApproximateData(df_data, 4).get_load_duration_curve(\n representative_days, cluster_numbers)\n\n representative_data.append(representative_days)\n\n # original_days = approximation_calc.get_load_duration_curve(\n # year=\"2013\")\n\n\n\n # original_data.append(original_days)\n\n # metrics_calculator = Metrics(original_data[0], representative_data[0], original_data[1],\n # representative_data[1], original_data[2], representative_data[2], \"dc\")\n\n pv_original = pd.read_csv(\n '{}data/processed/resources/pv_processed.csv'.format(project_dir))\n wind_original = pd.read_csv(\n '{}data/processed/resources/onshore_processed.csv'.format(project_dir))\n load_original = pd.read_csv(\n '{}data/processed/demand/load_NG/load_processed_normalised.csv'.format(project_dir))\n\n pv_original_ldcs, wind_original_ldcs, load_original_ldcs = get_each_ldc(pv_original, wind_original, load_original)\n\n multi_year_metrics_calculator = MultiYearMetrics(pv_original_ldcs, representative_data[0], wind_original_ldcs, representative_data[1], load_original_ldcs, representative_data[2], self.year_start)\n multi_year_metrics = multi_year_metrics_calculator.get_multi_year_average_metrics(\"dc\")\n multi_year_metrics = multi_year_metrics.reset_index()\n # logger.debug(\"multi_year_metrics: \\n{}\".format(multi_year_metrics))\n\n nrmse = multi_year_metrics[multi_year_metrics['metric'] == 'nrmse dc'].iloc[0].value\n rae = multi_year_metrics[multi_year_metrics['metric'] == 'rae dc'].iloc[0].value\n correlation = multi_year_metrics[multi_year_metrics['metric'] == 'correlation'].iloc[0].value\n\n # error_metrics = metrics_calculator.get_mean_error_metrics()\n # nrmse = error_metrics.iloc[1].value\n # rae = error_metrics.iloc[2].value\n # correlation = error_metrics.iloc[0].value\n # reward = -error_metrics.value.sum()\n # logger.info(\"error_metrics: {}\".format(error_metrics))\n # logger.info(\"error_metrics: {}\".format(error_metrics.iloc[0]))\n\n # return reward\n return nrmse, rae, correlation","def read_data(self):\n print 'Getting team stats...'\n self.team_stats = get_team_stats(self.recent_years)\n\n print 'Getting matches...'\n self.matches = get_matches(\n with_team_stats=True,\n duplicate_with_reversed=self.duplicate_with_reversed,\n exclude_ties=self.exclude_ties,\n recent_years=self.recent_years,\n use_these_team_stats=self.team_stats,\n )","def get_data(args, load_extracted=True):\n path = args.data_path1\n tokenizer_en = tokener()\n table = str.maketrans(\"\", \"\", '\"#$%&\\'()*+-/:;<=>@[\\\\]^_`{|}~')\n if load_extracted:\n df = load_pickle(\"df_unencoded.pkl\")\n else:\n logger.info(\"Extracting CNN stories...\")\n df = pd.DataFrame(index=[i for i in range(len(os.listdir(path)))], columns=[\"body\", \"highlights\"])\n for idx, file in tqdm(enumerate(os.listdir(path)), total=len(os.listdir(path))):\n with open(os.path.join(path, file), encoding=\"utf8\") as csv_file:\n csv_reader = csv.reader(csv_file)\n text = \"\"\n for row in csv_reader:\n text += \"\".join(t for t in row)\n highlights = re.search(\"@highlight(.*)\", text).group(1)\n highlights = highlights.replace(\"@highlight\", \". \")\n body = text[:re.search(\"@highlight\", text).span(0)[0]]\n df.iloc[idx][\"body\"] = body\n df.iloc[idx][\"highlights\"] = highlights\n \n if len(args.data_path2) > 2:\n path = args.data_path2\n logger.info(\"Extracting dailymail stories...\")\n df1 = pd.DataFrame(index=[i for i in range(len(os.listdir(path)))], columns=[\"body\", \"highlights\"])\n for idx, file in tqdm(enumerate(os.listdir(path)), total=len(os.listdir(path))):\n with open(os.path.join(path, file), encoding=\"utf8\") as csv_file:\n csv_reader = csv.reader(csv_file)\n text = \"\"\n for row in csv_reader:\n text += \"\".join(t for t in row)\n highlights = re.search(\"@highlight(.*)\", text).group(1)\n highlights = highlights.replace(\"@highlight\", \". \")\n body = text[:re.search(\"@highlight\", text).span(0)[0]]\n df1.iloc[idx][\"body\"] = body\n df1.iloc[idx][\"highlights\"] = highlights\n df = pd.concat([df, df1], ignore_index=True)\n del df1\n \n save_as_pickle(\"df_unencoded.pkl\", df)\n logger.info(\"Dataset length: %d\" % len(df)) \n \n if (args.level == \"word\") or (args.level == \"char\"):\n logger.info(\"Tokenizing and cleaning extracted text...\")\n df.loc[:, \"body\"] = df.apply(lambda x: clean_and_tokenize_text(x[\"body\"], table, tokenizer_en), axis=1)\n df.loc[:, \"highlights\"] = df.apply(lambda x: clean_and_tokenize_text(x[\"highlights\"], table, tokenizer_en), \\\n axis=1)\n df.loc[:, \"body_length\"] = df.apply(lambda x: len(x['body']), axis=1)\n df.loc[:, \"highlights_length\"] = df.apply(lambda x: len(x['highlights']), axis=1)\n df = df[(df[\"body_length\"] > 0) & (df[\"highlights_length\"] > 0)]\n \n logger.info(\"Limiting to max features length, building vocab and converting to id tokens...\")\n df = df[df[\"body_length\"] <= args.max_features_length]\n v = vocab(level=args.level)\n v.build_vocab(df[\"body\"])\n v.build_vocab(df[\"highlights\"])\n df.loc[:, \"body\"] = df.apply(lambda x: v.convert_w2idx(x[\"body\"]), axis=1)\n df.loc[:, \"highlights\"] = df.apply(lambda x: v.convert_w2idx(x[\"highlights\"]), axis=1)\n df.loc[:, \"highlights\"] = df.apply(lambda x: pad_sos_eos(x[\"highlights\"], 0, 2), axis=1)\n save_as_pickle(\"df_encoded.pkl\", df)\n save_as_pickle(\"vocab.pkl\", v)\n \n elif args.level == \"bpe\":\n encoder = Encoder(vocab_size=args.bpe_vocab_size, pct_bpe=args.bpe_word_ratio, word_tokenizer=tokenizer_en.tokenize)\n df.loc[:, \"body\"] = df.apply(lambda x: clean_and_tokenize_text(x[\"body\"], table, tokenizer_en, clean_only=True), axis=1)\n df.loc[:, \"highlights\"] = df.apply(lambda x: clean_and_tokenize_text(x[\"highlights\"], table, tokenizer_en, clean_only=True), \\\n axis=1)\n logger.info(\"Training bpe, this might take a while...\")\n text_list = list(df[\"body\"])\n text_list.extend(list(df[\"highlights\"]))\n encoder.fit(text_list); del text_list\n \n logger.info(\"Tokenizing to ids and limiting to max features length...\")\n df.loc[:, \"body\"] = df.apply(lambda x: next(encoder.transform([x[\"body\"]])), axis=1)\n df.loc[:, \"highlights\"] = df.apply(lambda x: next(encoder.transform([x[\"highlights\"]])), axis=1)\n df.loc[:, \"body_length\"] = df.apply(lambda x: len(x['body']), axis=1)\n df.loc[:, \"highlights_length\"] = df.apply(lambda x: len(x['highlights']), axis=1)\n df = df[(df[\"body_length\"] > 0) & (df[\"highlights_length\"] > 0)]\n df = df[df[\"body_length\"] <= args.max_features_length]\n \n '''\n logger.info(\"Converting tokens to ids...\")\n df.loc[:, \"body\"] = df.apply(lambda x: next(encoder.transform(list(\" \".join(t for t in x[\"body\"])))),\\\n axis=1)\n df.loc[:, \"highlights\"] = df.apply(lambda x: next(encoder.transform(list(\" \".join(t for t in x[\"highlights\"])))),\\\n axis=1)\n '''\n df.loc[:, \"highlights\"] = df.apply(lambda x: pad_sos_eos(x[\"highlights\"], encoder.word_vocab[\"__sos\"], encoder.word_vocab[\"__eos\"]),\\\n axis=1)\n \n save_as_pickle(\"df_encoded.pkl\", df)\n encoder.save(\"./data/vocab.pkl\")\n return df","def scrape_pipeline(args):\n kickoff = args.kickoff\n fname = args.fname\n d = DbHelper()\n s = Scraper()\n c = Crawler(20)\n if fname is not None:\n app_names = pd.read_csv(fname)['packageName'].tolist()\n apps = [list(a) for a in zip(app_names, d.app_names_to_uuids(app_names))]\n else:\n apps = None\n\n # start by updating top apps\n if not args.skip_top:\n logger.info(\"getting top apps...\")\n new_top_list = c.get_top_apps_list()\n logger.info(\"scraping top apps not in DB...\")\n s.scrape_missing(new_top_list, compare_top=True)\n logger.info(\"updating top apps...\")\n d.update_top_apps(new_top_list)\n\n if kickoff == True:\n s = None\n if fname is None:\n # use crawler to get list of package names\n logger.error(\"Crawler for package names not implemented yet\")\n return\n else:\n # use specified file of package names\n s = Scraper(input_file=fname)\n\n # use scraper\n logger.info(\"Starting efficient scrape...\")\n s.efficient_scrape()\n logger.info(\"...efficient scrape done\")\n else:\n # use updater\n logger.info(\"Starting updater...\")\n if fname is None:\n u = Updater()\n else:\n u = Updater(input_file=fname)\n u.update_apps()\n logger.info(\"...update done\")\n\n # crawl privacy policies\n c.crawl_app_privacy_policies(app_list=apps)\n\n if args.no_decompile:\n # download only\n logger.info(\"Starting download...\")\n downloader = Downloader()\n if apps is None:\n downloader.download_all_from_db(top=True)\n else:\n downloader.download(apps)\n logger.info(\"...done\")\n else:\n # download/decompile\n logger.info(\"Starting download and decompile...\")\n download_decompile_all()\n logger.info(\"...download and decompile done\")\n logger.info(\"run analysis pipeline now\")","def run(self):\n\n self.preprocess()\n self.restore_ratings()\n self.prepare_UI()\n self.loop_through_units()\n self.cleanup()\n\n print('\\nAll Done - results are available in:\\n\\t{}'.format(self.out_dir))","def scrape_teams():\n url_list = []\n name_list = []\n pos_list = []\n csv_path = \"../CSV_data/ActivePlayerList.csv\"\n with open(csv_path) as csv_file:\n reader = csv.reader(csv_file,skipinitialspace=True)\n for row in reader:\n pos_list.append(row[0])\n name_list.append(row[1])\n profile_url = (row[2]+\"profile\").replace(\"gamelogs?season=\", \"\")\n url_list.append(profile_url)\n\n for pos,name,url in zip(pos_list,name_list,url_list):\n file_path = \"../CSV_data/Test_Team_Scraping\"\n try:\n soup = BeautifulSoup(ul.urlopen(url).read(), \"html.parser\")\n # assign field names\n player_name = soup.find(\"span\", {\"class\" : \"player-name\"}).string\n print player_name, url\n fieldNames = soup.find(\"tr\", {\"class\" : \"player-table-key\"}).findAll(\"td\")\n numColumns = len(fieldNames)\n # pull the statistics\n #table = soup.findAll(\"table\", {\"class\":\"data-table1\"})\n tables = soup.find_all(\"table\", { \"summary\" : \"Career Stats For %s\" % (name)})\n seasons = tables[0]\n #print seasons\n body = tables.find( \"tbody\" )\n #body1 = body[0]\n print body\n print \"test1\"\n rows = body.findAll(\"tr\")\n print \"Test2\"\n rowsList = []\n for i in range(len(rows)):\n if len(rows[i]) > 2:\n print \"test3\"\n rowsList.append(rows[i])\n print rows[i]\n # remove row[0] which contains field names\n del rowsList[len(rowsList)-1]\n except IOError, e:\n print 'Failed to open url'\n print '-------------------------------------'\n if hasattr(e, 'code'):\n print 'We failed with error code - %s.' % e.code\n elif hasattr(e, 'reason'):\n print \"The error object has the following 'reason' attribute :\"\n print e.reason\n return False\n\n except IndexError:\n print 'No regular season data: Index error'\n print '-------------------------------------'\n #return False\n\n except AttributeError:\n print 'No regular season data: Attribute error'\n print '-------------------------------------'\n #return False","def run(self):\r\n self.collect_data()","def setup_training(self):\n print('setup training called')\n self.steps_done = 0\n self.current_episode_num = 1\n self.total_reward = 0\n\n # self.optimizer = optim.RMSprop(policy_net.parameters())\n self.memory = ReplayMemory(300000)\n self.total_reward_history = []\n # self.loss_history = []\n self.positions = []\n self.n_destroyed_crates = 0\n self.is_in_bomb_range = False","def run(self):\n if not os.path.exists(self.output_folder):\n os.makedirs(self.output_folder)\n for entry in glob.glob(os.path.join(self.data_folder, self.data_expression)):\n f = open(entry)\n text = json.loads(f.read())\n f.close()\n self.create_page_objects(text)","def fetch_data():\n log = logging.getLogger(__name__)\n log.info('Checking data files...')\n if not os.path.isfile('CGN.txt'):\n params_cgn = {\n 'institute.code': ['NLD037'],\n # 'crops': ['tomato'],\n 'taxonomy.genus': ['Solanum', 'Lycopersicon'],\n 'taxonomy.species': species\n }\n cgn = GenesysParser(params_cgn)\n cgn.fetch2json('CGN.txt')\n log.info('CGN data has been saved.')\n else:\n log.info('CGN data file already exists.')\n\n if not os.path.isfile('USDA.txt'):\n params_usda = {\n 'institute.code': usda_all,\n # 'crops': ['tomato'],\n 'taxonomy.genus': ['Solanum', 'Lycopersicon'],\n 'taxonomy.species': species\n }\n usda = GenesysParser(params_usda)\n usda.fetch2json('USDA.txt')\n log.info('USDA data has been saved.')\n else:\n log.info('USDA data file already exists.')","def _load(self):\n\t\tpool = []\n\t\tview = []\n\t\tlibrary = []\n\n\t\tif is_file(\"~/comiccrawler/pool.json\"):\n\t\t\tpool = json.loads(content_read(\"~/comiccrawler/pool.json\"))\n\n\t\tif is_file(\"~/comiccrawler/view.json\"):\n\t\t\tview = json.loads(content_read(\"~/comiccrawler/view.json\"))\n\n\t\tif is_file(\"~/comiccrawler/library.json\"):\n\t\t\tlibrary = json.loads(content_read(\"~/comiccrawler/library.json\"))\n\n\t\tfor m_data in pool:\n\t\t\t# reset state\n\t\t\tif m_data[\"state\"] in (\"DOWNLOADING\", \"ANALYZING\"):\n\t\t\t\tm_data[\"state\"] = \"ERROR\"\n\t\t\t# build episodes\n\t\t\tepisodes = []\n\t\t\tfor ep_data in m_data[\"episodes\"]:\n\t\t\t\tepisodes.append(Episode(**ep_data))\n\t\t\tm_data[\"episodes\"] = episodes\n\t\t\tmission = Mission(**m_data)\n\t\t\tself._add(mission)\n\n\t\tfor url in view:\n\t\t\tself.view[url] = self.pool[url]\n\n\t\tfor url in library:\n\t\t\tself.library[url] = self.pool[url]\n\n\t\tself.bubble(\"MISSION_LIST_REARRANGED\", self.view)\n\t\tself.bubble(\"MISSION_LIST_REARRANGED\", self.library)","def _run(self):\n data = None\n\n now = datetime.now().timestamp()\n\n if not os.path.exists(self.cache_file) or now - os.path.getmtime(self.cache_file) > MAX_FILE_AGE:\n data = self._download()\n self._write_cache_file(data)\n elif not self._dma_facilities_map:\n self.log.info(f\"Using cached file: {self.cache_file}\")\n data = self._read_cache_file()\n\n if data:\n self._process(self._unzip(data))\n self.log.info(\"Done loading..\")\n else:\n self.log.debug(\"Facilities are still fresh..\")\n\n threading.Timer(CHECK_INTERVAL, self._run).start()","def get_player_team_data(self, start_date, end_date = None, \n get_player_data_ind = True, get_team_data_ind = True, \n pre_player_data_dir = None, pre_team_data_dir = None):\n #Converts start and end date from string to datetime\n start_date = datetime.strptime(start_date, '%Y-%m-%d').date()\n \n if end_date:\n end_date = datetime.strptime(end_date, '%Y-%m-%d').date()\n else: \n end_date = start_date\n \n if pre_player_data_dir:\n try: \n #Reads in the existing player dataset to append the scraped data to \n exist_player_data = pd.read_csv(pre_player_data_dir)\n except:\n raise Exception('Cannot read in existing player dataset please ensure the directory is correct')\n \n if pre_team_data_dir:\n try: \n #Reads in the existing player dataset to append the scraped data to \n exist_team_data = pd.read_csv(pre_team_data_dir)\n except:\n raise Exception('Cannot read in existing team dataset please ensure the directory is correct')\n \n delta = end_date - start_date \n #Appends list of date between start and end date to strings\n date_list = []\n for i in range(delta.days + 1):\n day = start_date + timedelta(days=i)\n date_list.append(str(day))\n \n for date in date_list:\n \n print(f'Now scraping data from NBA games on {date}')\n home_team_list = get_list_of_hometeams(self.driver, date)\n\n if len(home_team_list) > 0:\n\n counter = 1 \n\n for home_team in home_team_list:\n \n if counter == 1: \n if get_player_data_ind: \n player_df_full = get_player_data(home_team = team_full_abrv_config[home_team]['Full Name'], \n date_played = date, \n driver = self.driver)\n if get_team_data_ind:\n team_df_full = get_team_data(home_team = team_full_abrv_config[home_team]['Full Name'], \n date_played = date, \n driver = self.driver)\n else:\n if get_player_data_ind: \n player_df_full = player_df_full.append(get_player_data(home_team = team_full_abrv_config[home_team]['Full Name'], \n date_played = date, \n driver = self.driver), ignore_index=True)\n if get_team_data_ind:\n team_df_full = team_df_full.append(get_team_data(home_team = team_full_abrv_config[home_team]['Full Name'], \n date_played = date, \n driver = self.driver), ignore_index=True)\n counter+=1\n \n if pre_player_data_dir:\n exist_player_data = exist_player_data.append(player_df_full)\n exist_player_data.to_csv(pre_player_data_dir, index = False)\n print(f'Updated player dataset will be overwritten in {pre_player_data_dir}')\n \n if pre_team_data_dir:\n exist_team_data = exist_team_data.append(team_df_full)\n exist_team_data.to_csv(pre_team_data_dir, index = False)\n print(f'Updated team dataset will be overwritten in {pre_team_data_dir}')\n \n if pre_player_data_dir and pre_team_data_dir:\n return exist_player_data, exist_team_data\n elif pre_player_data_dir:\n return exist_player_data\n elif pre_team_data_dir:\n return exist_team_data\n elif get_player_data_ind and get_team_data_ind:\n return player_df_full, team_df_full \n elif get_player_data_ind:\n return player_df_full\n elif get_team_data_ind:\n return team_df_full","def insert_entities_staging(self):\n\n for year in range(1900, CURRENT_YEAR, YEARS_RANGE):\n self.load_wikidata(\"movies\", MOVIES_BY_YEAR_SPARQL_QUERY, INSERT_MOVIE_SQL_QUERY, INSERT_MOVIE_MAP_COLUMNS, year, YEARS_RANGE)\n\n self.load_wikidata(\"songs\", SONGS_BY_YEAR_SPARQL_QUERY, INSERT_SONG_SQL_QUERY, INSERT_SONG_MAP_COLUMNS, year, YEARS_RANGE, True)\n self.load_wikidata(\"tvshows\", TVSHOWS_SPARQL_QUERY, INSERT_TVSHOW_SQL_QUERY, INSERT_TVSHOW_MAP_COLUMNS)\n self.load_wikidata(\"animatedmovies\", ANIMATEDMOVIES_SPARQL_QUERY, INSERT_ANIMATEDMOVIE_SQL_QUERY,\n INSERT_ANIMATEDMOVIE_MAP_COLUMNS)\n self.load_wikidata(\"videogames\", VIDEOGAMES_SPARQL_QUERY, INSERT_VIDEOGAME_SQL_QUERY, INSERT_VIDEOGAME_MAP_COLUMNS)\n self.load_wikidata(\"books\", BOOKS_SPARQL_QUERY, INSERT_BOOK_SQL_QUERY, INSERT_BOOK_MAP_COLUMNS)","def run(self):\n\t\tsettings.from_json(self._settings)\n\t\tsql.init_from_settings()\n\t\tself._session = sql.session()\n\t\tself.progress.clear(status=\"Starting up...\", running=True)\n\t\tfailed = False\n\n\t\tfor nxt_id in self._reader:\n\t\t\ttry:\n\t\t\t\turl = self._session.query(sql.URL).filter(sql.URL.id == nxt_id).first()\n\t\t\t\tif not url:\n\t\t\t\t\traise Exception(\"Unknown URL ID provided: (%s}\" % nxt_id)\n\n\t\t\t\tfile = url.file\n\t\t\t\tpath = SanitizedRelFile(base=settings.get(\"output.base_dir\"), file_path=str(file.path))\n\n\t\t\t\tself.progress.set_file(path.relative())\n\t\t\t\tself.progress.set_status(\"Attempting to Handle URL...\")\n\t\t\t\tself.progress.set_running(True)\n\n\t\t\t\ttask = handlers.HandlerTask(url=url.address, file_obj=path)\n\t\t\t\tresp = handlers.handle(task, self.progress)\n\n\t\t\t\tis_album_parent = False\n\n\t\t\t\twith self._db_lock:\n\t\t\t\t\tif resp.album_urls:\n\t\t\t\t\t\tif url.album_id:\n\t\t\t\t\t\t\tresp.album_urls = [] # Ignore nested Albums to avoid recursion.\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\turl.album_id = str(uuid.uuid4())\n\t\t\t\t\t\t\tis_album_parent = True\n\t\t\t\t\telse:\n\t\t\t\t\t\tresp.album_urls = []\n\n\t\t\t\t\turl.failed = not resp.success\n\t\t\t\t\turl.failure_reason = resp.failure_reason\n\t\t\t\t\turl.last_handler = resp.handler\n\t\t\t\t\turl.album_is_parent = is_album_parent\n\n\t\t\t\t\tif resp.rel_file:\n\t\t\t\t\t\tfile.downloaded = True\n\t\t\t\t\t\tfile.path = resp.rel_file.relative()\n\t\t\t\t\t\tfile.hash = None\n\t\t\t\t\t\tutime(resp.rel_file.absolute(), times=(time(), time()))\n\n\t\t\t\t\tself._session.commit()\n\n\t\t\t\t# Once *all* processing is completed on this URL, the Downloader needs to ACK it.\n\t\t\t\t# If any additional Album URLS were located, they should be sent before the ACK.\n\t\t\t\tself._ack_queue.put(AckPacket(\n\t\t\t\t\turl_id=nxt_id,\n\t\t\t\t\textra_urls=resp.album_urls\n\t\t\t\t))\n\t\t\t\tself.progress.clear(status=\"Waiting for URL...\")\n\t\t\texcept Exception as ex:\n\t\t\t\tfailed = str(ex)\n\t\t\t\tself._ack_queue.put(AckPacket(\n\t\t\t\t\turl_id=nxt_id,\n\t\t\t\t\textra_urls=[]\n\t\t\t\t))\n\t\t\t\tprint(ex)\n\t\t\t\ttraceback.print_exc()\n\t\t\t\tself.progress.set_error(\"Exited with error: {%s}\" % failed)\n\t\t\t\tbreak\n\n\t\tsql.close()\n\t\tself.progress.clear(\"Finished.\" if not failed else \"Exited with error: %s\" % failed, running=False)","def run_job(self):\n try:\n info(\"fetching testing invoice data from get_data_after_rescoring_flag_check function\")\n testing_data = self.get_data_after_rescoring_flag_check()\n self.common_util.dataframe_empty_check(testing_data)\n info(\"geting and processing data from customer history\")\n customer_history_data_query = self.query.get_customer_history_data_query()\n customer_history_data = self.database.get_data_from_database(customer_history_data_query)\n self.common_util.dataframe_empty_check(testing_data)\n self.common_util.dataframe_empty_check(customer_history_data)\n model_name = self.common_util.load_last_model_name('SC_LR')\n info(\"inferencing started\")\n predicted_data = self.common_util.get_selfcure_lr_prediction(testing_data, customer_history_data, 'SC_LR')\n info(\"storing prediction to the table\")\n self.database.save_prediction_to_table(predicted_data, model_name)\n info(\"Appling P flag for all predcitited invoice\")\n self.database.update_processed_flag_for_open_invoice(predicted_data)\n except Exception as err:\n error('Exception Occured while running inferencingJob job :' + str(err))\n raise Exception('Exception Occured while running inferencingJob job : {}'.format(str(err)))","def main(self):\n base_url = self.env.get(\"base_url\", BASE_URL)\n self.env[\"url\"] = self.get_g2m_url(base_url)\n self.output(\"Found URL %s\" % self.env[\"url\"])\n self.env[\"build\"] = self.get_g2m_build(base_url)\n self.output(\"Build number %s\" % self.env[\"build\"])","def runner(self):\n\n print('[ INFO ]: Initializing the forest fires program runner...')\n\n df, features, predictor = self.preprocess()","def main_function_start(movies_data_path):\n nltk.download('stopwords')\n logistic_regression_pkl_file = \"./saved_models/logistic_regression_model.pkl\"\n trained_model_present = check_model_file_exists(logistic_regression_pkl_file)\n if not trained_model_present:\n logging.info(\"Calling Data Processor for data preprocessing.\")\n preprocessed_data_file_path = data_processor(movies_data_path).preprocess_data()\n logging.info(\"Data preprocessing is done.\")\n prediction = model(args.title, args.description, preprocessed_data_file_path, trained_model_present).classification_model()\n\n else:\n prediction = model(args.title, args.description, \"\", trained_model_present).classification_model()\n\n final_answer = {}\n final_answer[\"title\"] = args.title\n final_answer[\"description\"] = args.description\n final_answer[\"genre\"] = prediction\n warnings.simplefilter(action='ignore', category=FutureWarning)\n return final_answer","def perform_extraction(self) -> None:\n\n self._process_datasets_all_frames()","def main():\n\n parser = argparse.ArgumentParser('Download all data into a folder')\n parser.add_argument('--homepage_url', help='url for adap-kdb homepage', required=True)\n parser.add_argument('--msp_path', help='path for msv file', required=True)\n parser.add_argument('--user_name', help='username for login', required=True)\n parser.add_argument('--user_password', help='user password fpr login', required=True)\n args = parser.parse_args()\n\n homepage_url = args.homepage_url\n msp_path = args.msp_path\n user_name = args.user_name\n user_password = args.user_password\n\n individual_search_test(homepage_url, msp_path, user_name, user_password)","def _collect_and_train(self) -> None:\n self.info_process('\\n\\n')\n self.info_process('Performing daily data collection and model training...')\n\n for symbol in Settings.get_symbols(self):\n # Interrupt collection if the collection loop was stopped\n if not self._running:\n break\n\n # Revert data to last stable day.\n date_last_collected_for = self.time().now().date()\n # If it's past midnight, move back a day.\n if self.time().get_secs_to_open() < timedelta(hours=9, minutes=30).total_seconds():\n date_last_collected_for -= timedelta(days=1)\n # Move back two market days from the most recent market day.\n date_last_collected_for = self.time().get_prev_mkt_day(date_last_collected_for)\n date_last_collected_for = self.time().get_prev_mkt_day(date_last_collected_for)\n # Remove mongo price data after the stable day.\n self.mongo().remove_price_data_after(symbol, date_last_collected_for, today=self.time().now().today())\n date_rest_available_for = self.time().get_next_mkt_day(date_last_collected_for)\n\n # Collect yesterday's polygon-rest data and train on it.\n if self._train_on_rest_data(symbol, date_rest_available_for):\n self.info_process(f'Trained {symbol} on yesterday\\'s polygon rest data')\n else:\n self.warn_process(f'Invalid {symbol} rest data collected for {date_rest_available_for}. '\n f'Discarding them and attempting to use cached stream data instead')\n if self._train_on_stream_data(symbol, date_rest_available_for):\n self.info_process(f'Trained {symbol} on yesterday\\'s polygon stream data')\n else:\n self.warn_process(f'Invalid {symbol} candles cached for {date_rest_available_for}. '\n f'Could not find valid data to train on yesterday!')\n\n # Load today's polygon-stream data and train on it.\n date_cache_available_for = self.time().get_next_mkt_day(date_rest_available_for)\n if self._train_on_stream_data(symbol, date_cache_available_for):\n self.info_process(f'Trained {symbol} on today\\'s polygon stream data')\n else:\n self.warn_process(f'Invalid {symbol} candles cached for {date_rest_available_for}. '\n f'Could not find valid data to train on today!')","def train_epoch(self) -> None:\n ct = self.config.training\n total_games = self._get_total_games()\n print(f\"Total Games: {total_games}\")\n train_size = int(0.9 * total_games)\n dataset_wrapper = DatasetWrapper(self.config)\n self.agent.model.fit(\n dataset_wrapper.get_dataset(train_size),\n epochs=ct.epoch_to_checkpoint,\n validation_data=dataset_wrapper.get_dataset(train_size, is_training=False),\n )","def run(self):\n self.load_template()\n self.load_data()\n self.load_files()\n self.render_content()\n self.process()\n # pprint(self.data)","def run(self):\n for i in range(self.exploration_steps):\n self.single_step(i)\n if self.save_checkpoints:\n self.save_results()\n self.save_results()\n if self.verbose:\n print(\"\\nExploration completed\")\n return","def main():\n\n # Create a directory to store copies of all the relevant HTML files (those\n # will be used in testing).\n print('Setting up backup dir if needed ...')\n create_dir(BACKUP_DIR)\n\n # Make backup of the IMDB top 250 movies page\n print('Access top 250 page, making backup ...')\n top_250_html = simple_get(TOP_250_URL)\n top_250_dom = BeautifulSoup(top_250_html, \"lxml\")\n\n make_backup(os.path.join(BACKUP_DIR, 'index.html'), top_250_html)\n\n # extract the top 250 movies\n print('Scraping top 250 page ...')\n url_strings = scrape_top_250(top_250_dom)\n\n # grab all relevant information from the 250 movie web pages\n rows = []\n for i, url in enumerate(url_strings): # Enumerate, a great Python trick!\n print('Scraping movie %d ...' % i)\n\n # Grab web page\n movie_html = simple_get(url)\n\n # Extract relevant information for each movie\n movie_dom = BeautifulSoup(movie_html, \"lxml\")\n rows.append(scrape_movie_page(movie_dom))\n\n # Save one of the IMDB's movie pages (for testing)\n if i == 83:\n html_file = os.path.join(BACKUP_DIR, 'movie-%03d.html' % i)\n make_backup(html_file, movie_html)\n\n # Save a CSV file with the relevant information for the top 250 movies.\n print('Saving CSV ...')\n save_csv(os.path.join(SCRIPT_DIR, 'top250movies.csv'), rows)","def run(self):\n try:\n if myGameVersion != None:\n myVersionedRepoURL = myGameVersion.getRepositoryURL()\n if not versionedRepoURL == myVersionedRepoURL:\n # Cache miss: we don't have the current version for\r\n # this game, and so we need to load it from the web.\r\n saveGameToCache(self.theKey, theGame)\n except Exception as e:\n e.printStackTrace()","def run(self) -> None:\n for episode in range(1, self.episodes + 1):\n print('Episode:', episode)\n steps, state_action_history = self.run_one_episode()\n self.steps_per_episode.append(steps)\n if episode % parameters.CACHING_INTERVAL == 0 or steps < 1000:\n visualize.animate_track(state_action_history, f'agent-{episode}')\n\n print('Training completed.')\n visualize.plot_steps_per_episode(self.steps_per_episode)\n visualize.plot_epsilon(self.agent.epsilon_history)\n\n if parameters.VISUALIZE_FINAL_GAME:\n print('Showing one episode with the greedy strategy.')\n self.agent.epsilon = 0\n steps, state_action_history = self.run_one_episode()\n print(f'Episode completed in {steps} steps.')\n visualize.animate_track(state_action_history)","def prepare_data(self) -> None:\n if (self.root).is_dir():\n logger.info(\"Found the dataset.\")\n else:\n download_and_extract(self.root, DOWNLOAD_INFO)","def main_flow():\n user_click, user_click_time = reader.get_user_click(\"../data/rating.txt\") # dict: key:user_id value:[item0,item1...]\n item_info = reader.get_item_info(\"../data/movies.txt\")\n\n sim_info = cal_sim_info(user_click, user_click_time)\n debug_itemsim(item_info, sim_info)\n recom_result = cal_recom_result(sim_info, user_click)\n print(recom_result[\"1\"])\n debug_recomresult(recom_result, item_info)","def fetch(self):\n \n self.genre=\"Review\"\n \n try:\n self.parent_uri = self.currenturi\n \n self.total_threads_count = 0\n self.last_timestamp = datetime( 1980,1,1 )\n if tg.config.get(path='Connector',key='collegenet_max_threads_to_process'):\n self.max_threads_count = int(tg.config.get(path='Connector',key='collegenet_max_threads_to_process'))\n else:\n self.max_threads_count = None\n \n self.collegenetTimelag_max = tg.config.get(path='Connector', key='collegenet_search_timeLag_max')\n self.collegenetTimelag_min = tg.config.get(path='Connector', key='collegenet_search_timeLag_min')\n log.info(self.log_msg('Time Logs :::::::'))\n log.info(self.collegenetTimelag_max)\n log.info(self.collegenetTimelag_min)\n \n## if self.collegenetTimelag_min and self.collegenetTimelag_max:\n## randomTimeLag = random.randint(self.collegenetTimelag_min,self.collegenetTimelag_max) / 1000.0000\n## log.info('sleeping for %s seconds between requests'%randomTimeLag)\n## time.sleep(randomTimeLag)\n \n if not self.__setSoup():\n log.exception(self.log_msg('Soup not set ..... '))\n return False\n \n #post_no = 0 #Remove\n while True:\n #if post_no==3: #Remove\n # break #Remove\n #post_no = post_no + 1 #Remove\n \n currenturi = self.currenturi\n if not self.__addPosts():\n break \n #Get \"post\" information\n #break #Remove\n self.currenturi = currenturi\n if self.collegenetTimelag_min and self.collegenetTimelag_max:\n randomTimeLag = random.randint(self.collegenetTimelag_min,self.collegenetTimelag_max) / 1000.0000\n log.info('sleeping for %s seconds between requests'%randomTimeLag)\n time.sleep(randomTimeLag)\n #We need to set soup again as addPosts() method is changing self.currenturi and self.soup\n if not self.__setSoup():\n log.info(self.log_msg('Soup not set.... break while loop.'))\n break\n \n try:\n next_uri = baseuri + self.soup.find('div',id='search_results_controls').find('a',text=re.compile('>')) \\\n .parent['href']\n self.currenturi = next_uri\n log.info(self.log_msg('Next URI :::::::;'))\n log.info(next_uri)\n except:\n log.exception(self.log_msg('Next Post link not found'))\n break\n \n## if self.collegenetTimelag_min and self.collegenetTimelag_max:\n## randomTimeLag = random.randint(self.collegenetTimelag_min,self.collegenetTimelag_max) / 1000.0000\n## log.info('sleeping for %s seconds between requests'%randomTimeLag)\n## time.sleep(randomTimeLag)\n \n if not self.__setSoup():\n log.info(self.log_msg('Soup not set.... break while loop.'))\n break\n \n return True\n \n except:\n log.exception(self.log_msg('Exception in fetch'))\n return False","def run_main():\n\n #Check for sold bikes\n checkSold(auto=True) #Change Auto to True to prevent user input\n\n #Find all available URLs split by Make & Model - Find Make\n print(\"Getting Makes...\")\n makes = getMakes()\n \n #Find all Models for each Make\n print(\"Getting Models...\")\n models = []\n for make in tqdm(makes, desc=\"Makes\"):\n models += getModels(make)\n\n\n #Find all URLs for each Model - Scrape bikes on each model\n errlog = \"\"\n print(\"Scraping Bikes...\")\n for model in tqdm(models, desc=\"Models\"):\n #Get urls for each model\n urlsTemp = getURLs(model)\n\n #Remove duplicates\n urlsTemp = removeDups(urlsTemp)\n\n #Remove listings already found\n urlsTemp = newURLs(\"motorcycles\", urlsTemp)\n\n #Get model description\n try:\n modelDesc = model.split(\"/\")[2].replace(\"model-\", \"\")\n except:\n modelDesc = \"Listings\"\n\n #Find motorbike details on all urls for this model\n #Split by model to prevent large datasets changing during code runtime\n for url in tqdm(urlsTemp, desc=modelDesc, leave=False):\n temp = Motorcycle(url)\n if not temp.na:\n temp.dbInsert()\n else:\n errlog += url + \"|\"\n \n #Finish\n if not errlog:\n print(\"Errors Found: \", errlog)\n if not printlog:\n print(printlog)\n \n print(\"Done!\")","def do(self):\n super().do()\n logger.info(\"TrainPipeStep started...\")\n records = self._get_current_step_records()\n logger.debug(\"load pipestep records: {}\".format(records))\n self.num_models = len(records)\n self.num_epochs = self.num_models * TrainerConfig.epochs\n self.update_status(Status.running)\n self.master = create_master()\n self._train_multi_models(records)\n self.master.join()\n ReportServer().output_step_all_records(step_name=self.task.step_name)\n self.master.close()\n ReportServer().backup_output_path()\n self.update_status(Status.finished)","def collect_data(self, data_folder):\n print(\"Start collecting\")\n input_file_path = os.path.join(data_folder, \"dblp.xml\")\n with codecs.open(input_file_path, \"r\", encoding=\"iso-8859-1\") as file:\n start = time.time()\n tmp = start\n\n title = \"\"\n key_prefix = \"\"\n authors = []\n personal_information = False\n publications_type = [\"article\", \"inproceedings\", \"proceedings\", \"book\",\n \"incollection\", \"phdthesis\", \"mastersthesis\"]\n start_publication_regex = r\"<([^\\/]*?) (.*)>\" # </article><article mdate=\"2017-05-28\" key=\"journals/acta/Simon83\">\n end_publication_regex = r\"<\\/(.*?)>\" # </article><article mdate=\"2017-05-28\" key=\"journals/acta/Simon83\">\n author_regex = r\"<author>(.*)</author>\" # <author>Katsuyuki Tateishi</author>\n title_regex = r\"<title>(.*)</title>\" # <title>A quadratic speedup theorem ...</title>\n start_person_regex = r'<www.*key=\"(.*)\">' #<www mdate=\"2009-06-10\" key=\"homepages/32/3977\">\n end_person_regex = r\"<\\/www>\"\n note_url_regex = r\"<note.*?>|<url.*?>\"\n inside_publication = False\n inside_person = False\n for i, line in tqdm(enumerate(file)):\n # Test end of publication, if true add publication\n result = regex.search(end_publication_regex, line)\n if result:\n if result.group(1) in publications_type:\n if inside_publication and authors and title:\n # This a a publication, lets add it to the dictionnay author -> title\n self.__add_publication(authors, title)\n title = \"\"\n authors = []\n inside_publication = False\n personal_information = False\n key_prefix = \"\"\n\n #Test beginning of a publication\n result = regex.search(start_publication_regex, line)\n if result and result.group(1) in publications_type:\n inside_publication = True\n title = \"\"\n authors = []\n personal_information = False\n key_prefix = \"\"\n\n #Check if author\n result = regex.search(author_regex, line)\n if result and (inside_publication or inside_person):\n authors.append(result.group(1))\n #Check if title\n result = regex.search(title_regex, line)\n if result and (inside_publication or inside_person):\n title = result.group(1)\n #Check if containt url or note\n result = regex.search(note_url_regex, line)\n if result:\n #if inside_person and (\"<note\" in line or \"<url\" in line):\n personal_information = True\n # Check if end of person, if true add author\n result = regex.search(end_person_regex, line)\n if result and inside_person:\n if authors and \"homepages/\" in key_prefix and title == \"Home Page\":\n self.__add_author(key_prefix, authors, personal_information)\n key_prefix = \"\"\n authors = []\n inside_person = False\n personal_information = False\n title = \"\"\n #Check start person\n result = regex.search(start_person_regex, line)\n if result:\n inside_person = True\n key_prefix = result.group(1)\n\n\n self.__save_everything(data_folder)\n self.__merge_data()\n self.__save_everything(data_folder)","def startworking():\r\n #In the future have the manager program or from the website implement this arguments to a route\r\n #the program will download the file from the website\r\n global exe_name\r\n global Task_Conditional\r\n task_data = None\r\n while task_data is None:\r\n task_data = recieve_data_from_server(\"get_task\")\r\n if task_data is None:\r\n time.sleep(5)\r\n else:\r\n exe_name = task_data[\"exe_name\"]\r\n print('Working on the task \"{}\"'.format(exe_name))\r\n get_file(exe_name)\r\n Task_Conditional = task_data[\"Task_conditional\"]\r\n print(\"loading\")\r\n t1 = time.time()\r\n task_divider(task_data[\"first_num\"], task_data[\"last_num\"])\r\n t2 = time.time()\r\n print(\"ready {}\".format(t2-t1))","def phase_two_data():\n from pathlib import Path\n try:\n import cPickle as pickle\n except ImportError:\n import pickle\n \n from annotation import parse_fulltext\n from features import ALL_FEATURES\n \n from feature_template import apply_templates\n from feature_selection import filter_by_frequency\n from feature_encoding import encode\n\n # Feature templates considered if heading by 1:\n # ----------------------------\n # Position + Voice\n # Path length + Clause layer\n # 1 Predicate + Path\n # Path + Position + Voice\n # Path + Position + Voice + Predicate\n # 1 Head word stem + Predicate\n # 1 Head word stem + Predicate + Path\n # 1 Head word stem + Phrase\n # Clause layer + Position + Predicate\n templates = [tuple([f.name]) for f in ALL_FEATURES] + \\\n [('path_to_frame', 'frame'), ('head_stem', 'frame'), ('head_stem', 'frame', 'path_to_frame'), ('head_stem', 'phrase_type')]\n \n size = 40\n instances = []\n for i, p in enumerate(Path(\"/cs/fs2/home/hxiao/Downloads/fndata-1.5/fulltext/\").glob(\"*.xml\")):\n if i == size:\n break\n sys.stderr.write(\"Processing file: '%s'\\n\" %p.absolute())\n annotations = parse_fulltext(str(p.absolute()))\n instances += make_training_data(ALL_FEATURES, annotations)\n\n sys.stderr.write(\"Feature selection...\\n\")\n x, y = zip(*instances)\n x = apply_templates(x, templates)\n features = filter_by_frequency(x, 5)\n sys.stderr.write(\"Feature encoding...\\n\")\n x, feature_map = encode(x, features)\n \n sys.stderr.write(\"Dumping data...\\n\") \n pickle.dump((x, y, ALL_FEATURES, templates, feature_map), open('dump/test_data.pkl', 'w'))\n import pdb\n pdb.set_trace()\n print len(instances)","def run(self):\n\n for url in self.urls:\n try:\n # Use requests to retrieve web page data\n print(url)\n response = session.get(url, ) # allow_redirects=True)\n\n if response.status_code != 200:\n print('Failed to retrieve page, URL: {0}, error: {1}\\n'.format(url, response.status_code))\n return\n\n # Get web page data from HTML response\n content = get_json_data(response.text)\n\n # Compile data into dictionary to be used for reporting\n summary_data = generate_report(content)\n\n # Generate/print report\n print_report(summary_data)\n\n except Exception as error:\n print('Scraper failed to run for URL {0}, error: {1}, {2}\\n'.format(\n url, type(error).__name__, error\n ))\n\n # time.sleep(1) # for load concerns","def start_process(self):\n print 50 * '*' + '\\n' + 10 * '*' + ' STARTING SCANNING PROCESS ' + 10 * '*' + '\\n' + 50 * '*'\n\n while True:\n print str(self.stream_list) + str(self.end_times_list)\n\n self.check_if_stream_should_end()\n\n if self.is_time_to_get_game_data_for_day:\n self.write_days_games_data()\n\n # Read in file to see if it is time to analyze twitter\n read_path = self.get_write_path_for_days_games()\n\n try:\n with open(read_path) as f:\n data = json.load(f)\n current_time = datetime.datetime.now().strftime('%H:%M')\n for idx, game in enumerate(data):\n game_time = dateutil.parser.parse(game['start_time']).strftime('%H:%M')\n if game_time == current_time and not game['being_streamed']:\n # TODO - Figure out how to call a fork or child process for a certain amount of time\n # TODO - Refactor this\n self.update_is_streamed_json(index=idx)\n print 'Time to get twitter data.'\n\n search_terms_home = self.keyword_generator.generate_search_terms(game['home_team_id'])\n search_terms_away = self.keyword_generator.generate_search_terms(game['away_team_id'])\n keyword_string_home = ','.join(search_terms_home)\n keyword_string_away = ','.join(search_terms_away)\n\n keyword_string = keyword_string_home + ',' + keyword_string_away\n game_name = datetime.datetime.now().strftime('%Y-%m-%d') + '-' + game['title'].replace(' ', '-')\n\n data_gatherer = DataGatherer()\n stream = data_gatherer.get_tweet_stream(keyword_string, game['uuid'], game_name)\n self.stream_list.append(stream)\n self.end_times_list.append(self.get_time_to_end_stream(1))\n\n except IOError:\n print 'File not found'\n\n # restart loop after sleep, given by our tick_time\n self.sleep_for(self.tick_time_in_seconds)","def get_fresh_data(get_team):\n\n \"\"\" Create the URL for today \"\"\"\n now = datetime.datetime.now()\n url = \"http://gd2.mlb.com/gdcross/components/game/mlb/year_\" + '{}'.format(now.year) + \"/month_\" + '{:02d}'.format(now.month) + \"/day_\" + '{:02d}'.format(now.day) + \"/miniscoreboard.json\"\n\n \"\"\" Grab the first response and write it to a file, we'll update it once the game starts \"\"\"\n data_write_file = status_dir + '{}'.format(now.year) + '{:02d}'.format(now.month) + '{:02d}'.format(now.day) + \".json\"\n\n \"\"\" Get the json data if the file doesn't exist, or if it's over three minutes old \"\"\"\n if not os.path.isfile(data_write_file) or time.time() - os.path.getmtime(data_write_file) > 60:\n response = urllib.urlopen(url)\n full_data = json.loads(response.read())\n with open(data_write_file, 'w') as outfile:\n json.dump(full_data, outfile, sort_keys=True, indent=2, ensure_ascii=False)\n\n \"\"\" Use the data from the status file \"\"\"\n with open(data_write_file, 'r') as json_data:\n full_data = json.load(json_data)\n\n \"\"\" This will return false if there is no game today, else will return json data for just our team \"\"\"\n my_game = False\n for game in full_data['data']['games']['game']:\n if get_team in game['home_file_code'] or get_team in game['away_file_code']:\n my_game = game\n \n return my_game","def parse_rism_folder(self):\n self._set_job_name()\n self._find_named_files()\n self._load_files()\n self._parse_input()\n self._check_results()\n self._parse_results()\n self._load_molecule()\n self._calculate_runtime()","def main():\n # Initialize key variables\n alldata = [[\n 'Meet', 'City', 'Country', 'Course', 'Event ID', 'Distance', 'Stroke',\n 'Round', 'Gender', 'Firstname', 'Lastname', 'Birthyear', 'Height cm',\n 'Weight Kg', 'BMI', 'Speed / Kg', 'Speed m/s', 'Time']]\n finadata = []\n olympicdata = []\n ts_start = int(time.time())\n\n # Get filename\n parser = argparse.ArgumentParser()\n parser.add_argument(\n '-l', '--lenex_directory',\n help='Name of directory with LENEX XML files.',\n type=str, required=True)\n parser.add_argument(\n '-o', '--olympic_directory',\n help='Name of directory with Olympic XLSX files.',\n type=str, required=True)\n parser.add_argument(\n '-p', '--profile_directory',\n help='Name of directory with athlete profiles.',\n type=str, required=True)\n parser.add_argument(\n '-d', '--database_file',\n help='Name of database file.',\n type=str, required=True)\n args = parser.parse_args()\n lenex_directory = args.lenex_directory\n profile_directory = args.profile_directory\n database_file = args.database_file\n olympic_directory = args.olympic_directory\n\n # Get the profiles\n profiles = _read_profiles(profile_directory)\n\n # Process Fina data\n finadata = _lenex(lenex_directory, profiles)\n\n # Process Olympic data\n olympicdata = _olympic(olympic_directory, profiles)\n\n # Get all data\n alldata.extend(finadata)\n alldata.extend(olympicdata)\n\n # Create output file\n with open(database_file, 'w') as f_handle:\n writer = csv.writer(f_handle, delimiter='|')\n writer.writerows(alldata)\n\n # Print status\n print('Swimmer event results created: {}'.format(len(alldata) - 1))\n print('Duration: {}'.format(int(time.time() - ts_start)))","def _generate_and_load_batch(self, tempdir, org_config, options) -> dict:\n options = {\n **options,\n \"working_directory\": tempdir,\n \"set_recently_viewed\": False,\n \"ignore_row_errors\": self.ignore_row_errors,\n \"drop_missing_schema\": self.drop_missing_schema,\n }\n subtask_config = TaskConfig({\"options\": options})\n subtask = GenerateAndLoadDataFromYaml(\n project_config=self.project_config,\n task_config=subtask_config,\n org_config=org_config,\n flow=self.flow,\n name=self.name,\n stepnum=self.stepnum,\n )\n subtask()\n return subtask.return_values[\"load_results\"][0]","def __init__(self, workflow, **kwds):\n self.kwds = kwds\n self.url = self.get('url', 'cmsweb.cern.ch')\n WorkflowManager.__init__(self, workflow, self.url)\n self.workload = getWorkload(self.url, workflow)\n self.cacheID = self.winfo.get('StepOneConfigCacheID', '')\n self.config = getConfig(self.url, self.cacheID)\n self.pileup_dataset = self._pileup_dataset()\n self.priority = self._priority()\n self.era = self.get('era', 'Summer12')\n self.lfn = self.get('lfn', '/store/mc')\n self.special_name = self.get('specialName', '')\n self.max_rss = self.get('maxRSS', 2300000)\n self.max_vsize = self.get('maxVSize', 4100000000)\n self.input_dataset = ''\n self.pileup_scenario = ''\n self.global_tag = self.get('globalTag', '')\n self.campaign = self.get('campaign', '')\n self.max_merge_events = self.get('maxMergeEvents', 50000)\n self.activity = self.get('activity', 'reprocessing')\n self.restrict = self.get('restrict', 'None')\n self.site_use = self.get('site', None)\n self.site_cust = self.get('site_cust', None)\n self.xrootd = self.get('xrootd', 0)\n self.ext_tag = self.get('ext', '')\n self.team = self.get('team', '')\n\n # perform various initialization\n self._init()\n\n # custom settings\n # Construct processed dataset version\n if self.pileup_scenario:\n self.pileup_scenario = self.pileup_scenario+'_' \n\n specialprocstring = kwds.get('specialName', '')\n if specialprocstring:\n self.special_name = specialprocstring + '_'\n\n # ProcessingString\n inprocstring = kwds.get('procstring', '')\n if inprocstring:\n self.procstring = inprocstring\n else:\n self.procstring = self.special_name + self.pileup_scenario +\\\n self.global_tag + self.ext_tag\n\n # ProcessingVersion\n inprocversion = kwds.get('procversion', '')\n if inprocversion:\n self.procversion = inprocversion\n else:\n self.procversion = self.dataset_version(self.era, self.procstring)","def dataframe():\n\t#allows function to access station, gmt, and miss_station functions\n global stations\n\tglobal gmt\n\tglobal miss_station\n\t\n\t#read predictor file\n\tcontrol = cfg.read_yaml('../registry/graphs.yaml')\n\tpred_ctrl = cfg.read_yaml(cfg.get_config_path(control.pred_file))\n\tpredd_ctrl = cfg.read_yaml(cfg.get_config_path(control.predd_file))\n\n\t#get file paths and update database\n\tpredictor_file_path = control.predictor_file_path\n\tpredictand_file_path = control.predictand_file_path\n\tpred_file_id = update(predictor_file_path)\n\tpredd_file_id = update(predictand_file_path)\n\t\n\t#store lead time and date range\n\tlead_time = control.lead_time\n\tdate_range = control.date_range\n\n\t#get info for fetch many dates\n\tstart,end,stride = read_pred.parse_range(date_range)\n\tfcst_ref_time = control.date_range[0].split('-')[0][-2:]\n\t\n\t#initialize list of predictors\n\tpred_list = pred_ctrl.predictors\n\tpredictor = []\n\n\t#loops through predictors to build camps data objects\n\tfor entry_dict in pred_list:\n\t\t#formats metadata\n\t\tpred = create.preprocess_entries(entry_dict, fcst_ref_time)\n\t\t\n\t\t#adds info to metadata that's not currently being stored\n\t\tpred.search_metadata['reserved2'] = lead_time*3600\n pred.search_metadata['file_id'] = pred_file_id\n\t\tpred.search_metadata['reserved1'] = 'vector'\n\n\t\t#build camps data objects for each day\n\t\tvariable = fetch_many_dates(predictor_file_path,start,end,stride,pred.search_metadata)\n\t\t\n\t\t#appends all data to single camps object\n\t\tif variable[0] is not None:\n\t\t\tvar = variable[0]\n\t\t\tarrs = []\n\t\t\tfor i in range(len(variable)):\n\t\t\t\tarrs.append(variable[i].data)\n\t\t\tvar.data = np.stack(arrs)\n\t\t\tpredictor.append(var)\n\n\t#initializes list of predictands\n\tpredd_list = predd_ctrl.predictands\n predictand = []\n\t\n\t#loops through predictands to build camps data objects\n for entry_dict in predd_list:\n\t\t#formats metadata\n \tvertical_coordinate = entry_dict.pop('Vertical_Coordinate')\n\t\tentry_dict['file_id'] = predd_file_id\n\n\t\t#build camps objects for each day\n variable = fetch_many_dates(predictand_file_path,start, end, stride, entry_dict)\n\n\t\t#append all data to single camps object\n var = variable[0]\n arrs = []\n for i in range(len(variable)):\n arrs.append(variable[i].data)\n try:\n\t\t\tvar.data = np.stack(arrs)\n\t\t\tpredictand.append(var)\n\t\texcept:\n\t\t\tprint(\"Can't read \" + variable.name)\n\n\t#getting predictor station and time data\n\tpredr = Dataset(predictor_file_path[0])\n\tpredr_stat = predr.variables['station'][:]\n\tif lead_time == 3:\n\t\tpredr_time = predr.variables['OM__phenomenonTimeInstant'][:]\n\telif lead_time == 6:\n\t\tpredr_time = predr.variables['OM__phenomenonTimeInstant1'][:]\n\telif lead_time == 12:\n\t\tpredr_time = predr.variables['OM__phenomenonTimeInstant2'][:]\n\tpredr.close()\n\n\t#reformatting predictor station and time data\n\tpredr_stations = stations(predr_stat)\n\tpredr_gmt = gmt(predr_time)\n\t\n\t#getting predictand station and time data\n\tpredd = Dataset(predictand_file_path[0])\n\tpredd_stat = predd.variables['station'][:]\n\tpredd_time = predd.variables['OM__resultTime'][:]\n\tpredd.close()\n\t\n\t#reformatting predictand station and time data\n\tpredd_stations = stations(predd_stat)\n\tpredd_gmt = gmt(predd_time)\n\n\t#choosing predictand observations that line up with predictor time\n\thour = (predictor[0].metadata['FcstTime_hour']/3600) + lead_time\n\tdays = len(predd_gmt)/24\n\tpredd_hours = [0]*days\n k=0\n for i in range(len(predd_gmt)):\n if i%24 == hour:\n\t\t\tpredd_hours[k]=predd_gmt[i]\n\t\t\tk+=1\n\t\n\t#catches when GFS data doesn't cover the last day of the month\n\tif len(predr_gmt) < len(predd_hours):\n\t\tpredd_hours = predd_hours[:-1]\t\n\t\n\t#find missing stations\n\tmiss_stations = miss_station(predr_stations,predd_stations)\n\tstations = predd_stations\n\t\n\t#station and time array\n\tinfo = [['',''] for k in range(len(predr_gmt)*len(stations))]\n\tfor i in range(len(predr_gmt)):\n\t\tfor j in range(len(stations)):\n\t\t\tk = i*len(stations)+j\n\t\t\tinfo[k][0]=predr_gmt[i]\n\t\t\tinfo[k][1]=stations[j]\n\n\t#create column names\n\tnames = ['']*(len(predictor)+len(predictand)+2)\n\tnames[0]='Time'\n\tnames[1]='Station'\n\n\t#creating array\n\tarr = np.zeros((len(stations)*len(predr_gmt),len(predictor)+len(predictand)))\n\t\n\t#adding predictor data\n\tfor i in range(len(predictor)):\n\t\t#remove lead time and forecast reference time from variable name\n\t\t#and add variable name to column list of final dataframe\n\t\tif lead_time == 12:\n\t\t\tnames[i+2]='GFS_'+predictor[i].get_variable_name()[:-11]\n\t\telse:\n\t\t\t names[i+2]='GFS_'+predictor[i].get_variable_name()[:-10]\n\n\t\t#create pandas dataframe of data and sort alphabetically by station name\n\t\tpredictor[i].data = np.squeeze(predictor[i].data,axis=2)\n\t\tpredictor[i].data = pd.DataFrame(predictor[i].data,columns=predr_stations,index=predr_gmt)\n\t\tpredictor[i].data = predictor[i].data.reindex(sorted(predictor[i].data.columns),axis=1)\n\t\t\n\t\t#remove stations with no predictand data\n\t\tk=0\n\t\ta=miss_stations[:]\n\t\tfor j in predictor[i].data.columns:\n\t\t\tif not a:\n\t\t\t\tbreak\n\t\t\tif j==a[k]:\n\t\t\t\tpredictor[i].data=predictor[i].data.drop(j,axis=1)\n\t\t\t\tdel a[k]\n\t\t\n\t\t#add data to final dataframe\n\t\tfor b in range(len(predr_gmt)):\n\t\t\tfor c in range(len(stations)):\n\t\t\t\tk = b*len(stations)+c\n\t\t\t\tarr[k][i] = predictor[i].data.iloc[b][c]\n\n\t#add predictand data\n\tfor i in range(len(predictand)):\n\t\t#removing extra underscore, adding variable name to column names\n\t\tnames[len(predictor)+2+i]='METAR_'+predictand[i].get_variable_name()[:-1]\n\t\n\t\t#resize array and create pandas dataframe\n\t\tpredictand[i].data = np.squeeze(predictand[i].data,axis=2)\n\t\tpredictand[i].data = pd.DataFrame(predictand[i].data,columns=predd_stations,index=predd_hours)\n\t\tpredictand[i].data = predictand[i].data.reindex(sorted(predictand[i].data.columns),axis=1)\n\t\t\n\t\t#remove extra days of predictand data\n\t\tpredictand[i].data = predictand[i].data.iloc[0:len(predr_time),:]\n\t\t\t\n\t\t#add predictand data to array\n\t\tfor b in range(len(predr_gmt)):\n\t\t\tfor c in range(len(stations)):\n\t\t\t\tk = b*len(stations)+c\n\t\t\t\tval = predictand[i].data.iloc[b][c]\n\t\t\t\t\n\t\t\t\t#catch metar fill data\n\t\t\t\tif val == 9999: \n\t\t\t\t\tval = np.nan\n\t\t\t\tarr[k][len(predictor)+i]=val\n\t\n\t#add station and time data to array and save as csv\n\tdata = np.concatenate([info,arr],axis = 1)\n\tto_save = pd.DataFrame(data,columns=names)\n\tto_save.to_csv(str(start)+'_'+str(end)+'_'+str(lead_time)+'hrs.csv')","def main():\n\n # parses arguments\n parser = argparse.ArgumentParser()\n\n parser.add_argument('-s', action='store', dest='start_index', type=int,\n help='The starting index for events. Default is 0')\n\n parser.add_argument('-e', action='store', dest='end_index', type=int,\n help='The starting index for events. Default is 5,000')\n\n results = parser.parse_args()\n\n start_index = results.start_index or 0\n\n end_index = results.end_index or 5000\n\n scraper = Scraper()\n\n # these are the event column titles from the sample import csv given by localist\n event_column_titles = [\n 'Title','Description','Date From','Date To','Recurrence','Start Time','End Time',\n 'Location','Address','City','State','Event Website','Room','Keywords','Tags',\n 'Photo URL','Ticket URL','Cost','Hashtag','Facebook URL','Group','Department',\n 'Allow User Activity','Allow User Attendance','Visibility','Featured Tabs',\n 'Sponsored','Venue Page Only','Exclude From Trending','Event Types','Invited Audience', 'Original URL',\n 'Location Details'\n ]\n\n out_stream = open('event_import.csv', 'w')\n\n writer = Writer(event_column_titles, out_stream)\n\n writer.write_headers()\n\n # iterates through the specified event numbers and scrapes each one and writes\n # it to the output file\n for i in range(start_index, end_index + 1):\n current_url = 'http://test-ucscevents.pantheonsite.io/event/' + str(i)\n print(\"processing url: \" + current_url)\n r = requests.get(current_url)\n if r.status_code != requests.codes.ok:\n print(' 404')\n else:\n soup = get_soup_from_url(current_url)\n events = scraper.scrape_event(soup)\n for event in events:\n event['Original URL'] = current_url\n\n writer.write_object(event) # event written to output file here\n\n out_stream.close()","def main(args):\n\tif not os.path.isdir(args.dir):\n\t\tprint \"The specified folder is not a directory.\"\n\t\tsys.exit(1)\n\tNUMBER_OF_FILES = len(os.listdir(args.dir))\n\tif args.num_of_files:\n\t\tNUMBER_OF_FILES = args.num_of_files\n\tprint \"Parsing\", NUMBER_OF_FILES, \"files\"\n\tsql = None\n\tif not args.stdout:\n\t\tsql = sqlite3.connect(args.database)\n\t\tsql.execute(\"\"\"PRAGMA foreign_keys = ON;\"\"\")\n\t\tsql.execute(\"\"\"CREATE TABLE airdates(\n\t\t\tgame INTEGER PRIMARY KEY,\n\t\t\tairdate TEXT\n\t\t);\"\"\")\n\t\tsql.execute(\"\"\"CREATE TABLE documents(\n\t\t\tid INTEGER PRIMARY KEY AUTOINCREMENT,\n\t\t\tclue TEXT,\n\t\t\tanswer TEXT\n\t\t);\"\"\")\n\t\tsql.execute(\"\"\"CREATE TABLE categories(\n\t\t\tid INTEGER PRIMARY KEY AUTOINCREMENT,\n\t\t\tcategory TEXT UNIQUE\n\t\t);\"\"\")\n\t\tsql.execute(\"\"\"CREATE TABLE clues(\n\t\t\tid INTEGER PRIMARY KEY AUTOINCREMENT,\n\t\t\tgame INTEGER,\n\t\t\tround INTEGER,\n\t\t\tvalue INTEGER,\n\t\t\tFOREIGN KEY(id) REFERENCES documents(id),\n\t\t\tFOREIGN KEY(game) REFERENCES airdates(game)\n\t\t);\"\"\")\n\t\tsql.execute(\"\"\"CREATE TABLE classifications(\n\t\t\tclue_id INTEGER,\n\t\t\tcategory_id INTEGER,\n\t\t\tFOREIGN KEY(clue_id) REFERENCES clues(id),\n\t\t\tFOREIGN KEY(category_id) REFERENCES categories(id)\n\t\t);\"\"\")\n\tfor i, file_name in enumerate(glob(os.path.join(args.dir, \"*.html\")), 1):\n\t\twith open(os.path.abspath(file_name)) as f:\n\t\t\tparse_game(f, sql, i)\n\tif not args.stdout:\n\t\tsql.commit()\n\tprint \"All done\"","def run(self):\n self._build_docker_images()\n self._build_docker_image_test()\n self._run_workflow_in_matrix_of_envs()","def fetch(self):\n self.genre = \"Review\"\n try:\n if not self.__setSoup():\n log.info(self.log_msg(\"Soup not set,returning false\"))\n return False\n #if not self._getParentPage():\n # log.info(self.log_msg(\"Parent page not found\"))\n while True:\n parent_page_soup = copy.copy(self.soup)\n # log.info(self.log_msg('current uri%s'%parent_page_soup))\n if not self.__addReviews():\n log.info(self.log_msg('fetched all reviews for the url %s'\\\n %self.task.instance_data['uri']))\n \n log.info(self.log_msg('Next page%s'%self.currenturi))\n try:\n \n # self.currenturi = self.task.instance_data['uri'].rsplit\\\n # ('/', 1)[0] + '/' + self.soup.find('a', \\\n # title='Go to the next page')['href']\n self.currenturi = 'http://www.phonedog.com' + parent_page_soup.find('a',title='Go to the next page')['href']\n \n if not self.__setSoup():\n log.info(self.log_msg('soup not set for the uri %s'%\\\n self.currenturi))\n break\n except:\n log.info(self.log_msg('Next page not found for the uri %s'%\\\n self.currenturi))\n break\n return True\n except:\n log.exception(self.log_msg(\"Exception in fetch\"))\n return False","def _generate_and_load_initial_batch(self, working_directory: Path):\n\n template_dir = Path(working_directory) / \"template_1\"\n template_dir.mkdir()\n # changes here should often be reflected in\n # data_generator_opts and data_loader_opts\n\n channel_decl = self.channel_configs[0]\n\n plugin_options = {\n \"pid\": \"0\",\n \"big_ids\": \"True\",\n }\n # if it's efficient to do the whole load in one go, let's just do that.\n if self.run_until.gap < MIN_PORTION_SIZE:\n num_records = self.run_until.gap\n else:\n num_records = 1 # smallest possible batch to get to parallelizing fast\n results = self._generate_and_load_batch(\n template_dir,\n channel_decl.org_config,\n {\n \"generator_yaml\": self.options.get(\"recipe\"),\n \"num_records\": num_records,\n \"num_records_tablename\": self.run_until.sobject_name or COUNT_REPS,\n \"loading_rules\": self.loading_rules,\n \"vars\": channel_decl.merge_recipe_options(self.recipe_options),\n \"plugin_options\": plugin_options,\n \"bulk_mode\": self.bulk_mode,\n },\n )\n self.update_running_totals_from_load_step_results(results)\n\n # rename directory to reflect real number of sets created.\n wd = SnowfakeryWorkingDirectory(template_dir)\n if self.run_until.sobject_name:\n self.sets_finished_while_generating_template = wd.get_record_counts()[\n self.run_until.sobject_name\n ]\n else:\n self.sets_finished_while_generating_template = num_records\n\n new_template_dir = data_loader_new_directory_name(template_dir, self.run_until)\n shutil.move(template_dir, new_template_dir)\n template_dir = new_template_dir\n\n # don't send data tables to child processes. All they\n # care about are ID->OID mappings\n wd = SnowfakeryWorkingDirectory(template_dir)\n self._cleanup_object_tables(*wd.setup_engine())\n\n return template_dir, wd.relevant_sobjects()","def Start(self):\n self.state.hist_count = 0\n # List of paths where history files are located\n self.state.history_paths = []\n if self.args.history_path:\n self.state.history_paths.append(self.args.history_path)\n\n if not self.state.history_paths:\n self.state.history_paths = self.GuessHistoryPaths(self.args.username)\n\n if not self.state.history_paths:\n raise flow_base.FlowError(\"Could not find valid History paths.\")\n\n filenames = [\"History\"]\n if self.args.get_archive:\n filenames.append(\"Archived History\")\n\n for path in self.state.history_paths:\n for fname in filenames:\n self.CallFlow(\n file_finder.FileFinder.__name__,\n paths=[os.path.join(path, fname)],\n pathtype=self.args.pathtype,\n action=rdf_file_finder.FileFinderAction.Download(),\n next_state=self.ParseFiles.__name__)","def load_data():\n\n # Load data from categories\n comp = fetch_20newsgroups(subset='all', categories=['comp.graphics', 'comp.sys.mac.hardware', 'comp.windows.x'], \\\n shuffle=True, random_state=1, remove=('headers', 'footers', 'quotes'))\n science = fetch_20newsgroups(subset='all', categories=['sci.crypt', 'sci.electronics', 'sci.space'], \\\n shuffle=True, random_state=1, remove=('headers', 'footers', 'quotes'))\n politics = fetch_20newsgroups(subset='all', categories=['talk.politics.guns', 'talk.politics.mideast'], \\\n shuffle=True, random_state=1, remove=('headers', 'footers', 'quotes'))\n religion = fetch_20newsgroups(subset='all', categories=['alt.atheism', 'soc.religion.christian'], \\\n shuffle=True, random_state=1, remove=('headers', 'footers', 'quotes'))\n recreation = fetch_20newsgroups(subset='all', categories=['rec.autos', 'rec.sport.baseball', 'rec.sport.hockey'], \\\n shuffle=True, random_state=1, remove=('headers', 'footers', 'quotes'))\n\n # Print total number of documents\n data_len = [len(comp.data), len(science.data), len(politics.data), len(recreation.data), len(religion.data)]\n\n # Subsample classes to create a balanced dataset\n sub_k = min(data_len)\n comp.data, comp.target = [list(t) for t in zip(*random.sample(list(zip(comp.data, comp.target)), sub_k))]\n science.data, science.target = [list(t) for t in zip(*random.sample(list(zip(science.data, science.target)), sub_k))]\n politics.data, politics.target = [list(t) for t in zip(*random.sample(list(zip(politics.data, politics.target)), sub_k))]\n religion.data, religion.target = [list(t) for t in zip(*random.sample(list(zip(religion.data, religion.target)), sub_k))]\n recreation.data, recreation.target = [list(t) for t in zip(*random.sample(list(zip(recreation.data, recreation.target)), sub_k))]\n\n # Subcategories labels\n subcat_comp = np.array(comp.target)\n subcat_scien = np.array(science.target) + len(comp.target_names)\n subcat_polit = np.array(politics.target) + len(comp.target_names) + len(science.target_names)\n subcat_rel = np.array(religion.target) + len(comp.target_names) + len(science.target_names) + len(politics.target_names)\n subcat_rec = np.array(recreation.target) + len(comp.target_names) + len(science.target_names) + len(politics.target_names) + len(religion.target_names)\n\n # Assign labels to train data based on categories\n y_comp = np.ones(len(comp.data))\n y_scien = 2*np.ones(len(science.data))\n y_polit = 3*np.ones(len(politics.data))\n y_rel = 4*np.ones(len(religion.data))\n y_rec = 5*np.ones(len(recreation.data))\n labels = np.concatenate((y_comp,y_scien,y_polit,y_rel,y_rec), axis=None)\n\n # Computers\n train_comp, test_comp, y_train_comp, y_test_comp, subcat_comp_train, subcat_comp_test = train_test_split(comp.data, y_comp, subcat_comp, test_size=0.2, random_state=42)\n train_comp, val_comp, y_train_comp, y_val_comp, subcat_comp_train, subcat_comp_val = train_test_split(train_comp, y_train_comp, subcat_comp_train, test_size=0.25, random_state=42)\n\n # Sciences\n train_scien, test_scien, y_train_scien, y_test_scien, subcat_scien_train, subcat_scien_test = train_test_split(science.data, y_scien, subcat_scien, test_size=0.2, random_state=42)\n train_scien, val_scien, y_train_scien, y_val_scien, subcat_scien_train, subcat_scien_val = train_test_split(train_scien, y_train_scien, subcat_scien_train, test_size=0.25, random_state=42)\n\n # Politics\n train_polit, test_polit, y_train_polit, y_test_polit, subcat_polit_train, subcat_polit_test = train_test_split(politics.data, y_polit, subcat_polit, test_size=0.2, random_state=42)\n train_polit, val_polit, y_train_polit, y_val_polit, subcat_polit_train, subcat_polit_val = train_test_split(train_polit, y_train_polit, subcat_polit_train, test_size=0.25, random_state=42)\n\n # Religion\n train_rel, test_rel, y_train_rel, y_test_rel, subcat_rel_train, subcat_rel_test = train_test_split(religion.data, y_rel, subcat_rel, test_size=0.2, random_state=42)\n train_rel, val_rel, y_train_rel, y_val_rel, subcat_rel_train, subcat_rel_val = train_test_split(train_rel, y_train_rel, subcat_rel_train, test_size=0.25, random_state=42)\n\n # Recreation\n train_rec, test_rec, y_train_rec, y_test_rec, subcat_rec_train, subcat_rec_test = train_test_split(recreation.data, y_rec, subcat_rec, test_size=0.2, random_state=42)\n train_rec, val_rec, y_train_rec, y_val_rec, subcat_rec_train, subcat_rec_val = train_test_split(train_rec, y_train_rec, subcat_rec_train, test_size=0.25, random_state=42)\n\n # Corpus from all categories in train set\n newsgroups_train = train_comp + train_scien + train_polit + train_rel + train_rec\n #print(f\"Total number of documents in all categories in the train set is {len(newsgroups_train)}.\")\n train_labels = np.concatenate((y_train_comp,y_train_scien,y_train_polit,y_train_rel,y_train_rec), axis=None)\n #print(train_labels.shape)\n train_subcat = np.concatenate((subcat_comp_train,subcat_scien_train,subcat_polit_train,subcat_rel_train,subcat_rec_train), axis=None)\n #print(train_subcat.shape)\n\n # Corpus from all categories in test set\n newsgroups_test = test_comp + test_scien + test_polit + test_rel + test_rec\n test_labels = np.concatenate((y_test_comp,y_test_scien,y_test_polit,y_test_rel,y_test_rec), axis=None)\n test_subcat = np.concatenate((subcat_comp_test,subcat_scien_test,subcat_polit_test,subcat_rel_test,subcat_rec_test), axis=None)\n\n # Corpus from all categories in validation set\n newsgroups_val = val_comp + val_scien + val_polit + val_rel + val_rec\n val_labels = np.concatenate((y_val_comp,y_val_scien,y_val_polit,y_val_rel,y_val_rec), axis=None)\n val_subcat = np.concatenate((subcat_comp_val,subcat_scien_val,subcat_polit_val,subcat_rel_val,subcat_rec_val), axis=None)\n\n # Data Split\n total = len(test_labels) + len(val_labels) + len(train_labels)\n\n return newsgroups_train, train_labels, newsgroups_test, test_labels, newsgroups_val, val_labels, train_subcat, test_subcat, val_subcat","def ingest():\n db.delete_dataset_records(DATASET_ID)\n\n db.insert_dataset({\n 'dataset_id': DATASET_ID,\n 'title': 'North American Breeding Bird Survey (BBS)',\n 'version': '2016.0',\n 'url': 'https://www.pwrc.usgs.gov/bbs/'})\n\n to_taxon_id = insert_taxa()\n to_place_id = insert_places()\n to_event_id = insert_events(to_place_id)\n insert_counts(to_event_id, to_taxon_id)","def main():\n try:\n jobs = {}\n scraped_jobs = []\n\n scraped_jobs = read_from_text_file(\"\", scraped_jobs_filename)\n\n for URL in URLs:\n if \"www.linkedin.com\" in URL:\n (jobs, scraped_jobs) = linkedin_meta_search(URL, jobs, scraped_jobs)\n if \"www.python.org\" in URL:\n (jobs, scraped_jobs) = pythondotorg_meta_search(URL, jobs, scraped_jobs)\n if \"www.ziprecruiter.com\" in URL:\n (jobs, scraped_jobs) = ziprecruiter_meta_search(URL, jobs, scraped_jobs)\n\n write_to_text_file(\"\", \"scraped_jobs.txt\", scraped_jobs)\n\n for id in jobs.keys():\n score, link, title, company, date_posted, location, full_text = jobs[id]\n\n print(f\"\\nEvaluating {title} at {company} ({id}).\")\n\n try:\n if \"yesterday\" in date_posted:\n age_in_days = 1\n elif \"just now\" in date_posted:\n age_in_days = 0\n elif \"hours ago\" in date_posted or \"hour ago\" in date_posted:\n age_in_days = float(date_posted.split()[0]) / 24\n else:\n datetimePosted = parser.parse(date_posted)\n age = datetime.now().timestamp() - datetimePosted.timestamp()\n age_in_days = int(age / 24 / 60 / 60)\n except parser.ParserError as e:\n print(f\"{e}: {date_posted} couldn't be parsed properly from {link}\")\n age_in_days = 0\n age_reduction = int(age_in_days ** 2)\n score -= age_reduction\n if age_reduction > 0:\n print(\n f\"... docking {age_reduction} from the score ({score}) \\\n because the listing is {age_in_days} days old.\"\n )\n\n for search_phrase in search_phrases.keys():\n if search_phrase.lower() in title.lower():\n\n score_adjustment = 2 * search_phrases[search_phrase]\n score += score_adjustment\n if score_adjustment > 0:\n score_adjustment = \"+\" + str(score_adjustment)\n print(\n f\"... {score_adjustment} points ({score}) for \\\n {search_phrase} being in {title}.\"\n )\n if search_phrase.lower() in full_text.lower():\n score_adjustment = search_phrases[search_phrase]\n score += score_adjustment\n if score_adjustment > 0:\n score_adjustment = \"+\" + str(score_adjustment)\n print(\n f\"... {score_adjustment} points ({score}) for {search_phrase} \\\n being in the text of the listing.\"\n )\n jobs[id][0] = score\n\n print(\"\\n\")\n\n send_email(jobs)\n\n except Exception as e:\n send_email(f\"Not working: {e}\") # \".message}, {e.args}\")","def run(self, data):\n\n if data and self.application:\n # Build tuples for embedding index\n if self.application.embeddings:\n data = [(x, element, None) for x, element in enumerate(data)]\n\n # Process workflow\n with st.spinner(\"Running workflow....\"):\n results = []\n for result in self.application.workflow(self.name, data):\n # Store result\n results.append(result)\n\n # Write result if this isn't an indexing workflow\n if not self.application.embeddings:\n st.write(result)\n\n # Store workflow results\n self.data = results","def run(self):\n\t\tself._run_neural_network()\n\t\tself._run_webots()\n\n\t\t# We first wait for webots inputs\n\t\twebotsTurn = True\n\t\tneuralNetworkTurn = False\n\t\twhile True:\n\t\t\tif webotsTurn:\n\t\t\t\tprint \"reading data from webots:\"\n\t\t\t\twbtData = self._wbt_read_data()\n\t\t\t\tprint \"sending data to nn...\"\n\t\t\t\tself._send_data_to_nn(wbtData)\n\t\t\t\twebotsTurn = False\n\t\t\t\tneuralNetworkTurn = True\n\n\t\t\telif neuralNetworkTurn:\n\n\t\t\t\tprint \"reading data from nn:\"\n\t\t\t\tnnData = self._nn_read_data()\n\t\t\t\tif self._neuralNetwork.poll() != None: break\n\t\t\t\tprint \"sending data to webots...\"\n\t\t\t\tself._send_data_to_wbt(nnData)\n\t\t\t\tneuralNetworkTurn = False\n\t\t\t\twebotsTurn = True","def download_all_data(self) -> None:\n print(\"Download in progress.\")\n self.download_data(os.environ[\"NC_TOKEN_TRAIN_CHARACTERS\"], \"nextcloud\")\n self.download_data(os.environ[\"NC_TOKEN_TRAIN_FRAGMENTS\"], \"nextcloud\")\n self.download_data(os.environ[\"HABBAKUK_URL\"], \"generic_url\")\n print(\"Download complete!\")","def __run_game(game):\n\n while not game['lost']:\n\n game['move'], game['moveScores'] = my_algorithm(game)\n\n check_game_lost(game)\n\n # I changed the order of this loop to record information\n # about the algorithm before the board is moved so\n # in the move log csv the initial board will show scores and\n # a planned move instead of having the scores off by 1\n record_move(game)\n\n move(game)\n\n return game","def extract(self):\n from table_to_redshift import main as rds2rs\n\n self.mkstats()\n self.mkcampaignrollups()\n self.mkrollups()\n\n for table, table_id in [\n ('visits', 'visit_id'),\n ('visitors', 'visitor_id'),\n ('campaigns', 'campaign_id'),\n ('events', 'event_id'),\n ('clients', 'client_id'),\n ('campaign_properties', 'campaign_property_id'),\n ('user_clients', 'user_client_id'),\n ]:\n debug('Uploading {} ..'.format(table))\n rds2rs(table)\n debug('Done.') # poor man's timer\n\n self.mkstats()\n self.mkcampaignrollups()\n self.mkrollups()\n info('RDS extraction complete, waiting for next run')","def crawl(self):\n if os.path.exists(self.__work_path):\n shutil.rmtree(self.__work_path)\n print '\\nOld Data Was Found And Removed.\\n'\n\n initial_first_run = True\n initial_recursion_depth = 0\n initial_prev_link_size = 0\n for url in self.__urls:\n self.__start_recursion(url, initial_first_run,\n initial_recursion_depth, initial_prev_link_size)\n\n Crawler.mission_report(self.__work_path)","def run_workflow(EMBEDDING_BASE_PATH):\n train_tweets_path, val_tweets_path, test_tweets_path, image_dataset = run_pre_workflow()\n\n input_images, train_tweets, val_tweets, test_tweets, glove_embeddings = replica_catalog(train_tweets_path, val_tweets_path, test_tweets_path, image_dataset, EMBEDDING_BASE_PATH)\n\n preprocess_tweets, preprocess_images, train_resnet, hpo_train_resnet, train_bilstm, hpo_train_bilstm, resnet_inference, bilstm_inference, late_fusion = transformation_catalog()\n \n sites_catalog()\n\n pegasus_properties()\n \n wf = Workflow('Crisis_Computing_Workflow')\n\n # --------------------------------------------------- TEXT PIPELINE ------------------------------------------------------ \n\n # Job 1: Preprocess tweets\n preprocessed_train_tweets = File('preprocessed_train_tweets.csv')\n preprocessed_val_tweets = File('preprocessed_val_tweets.csv')\n preprocessed_test_tweets = File('preprocessed_test_tweets.csv')\n \n job_preprocess_tweets = [Job(preprocess_tweets) for i in range(3)]\n job_preprocess_tweets[0].add_inputs(train_tweets)\n job_preprocess_tweets[0].add_outputs(preprocessed_train_tweets)\n job_preprocess_tweets[0].add_args('--filename', 'train_tweets.csv')\n \n job_preprocess_tweets[1].add_inputs(val_tweets)\n job_preprocess_tweets[1].add_outputs(preprocessed_val_tweets)\n job_preprocess_tweets[1].add_args('--filename', 'val_tweets.csv')\n \n job_preprocess_tweets[2].add_inputs(test_tweets)\n job_preprocess_tweets[2].add_outputs(preprocessed_test_tweets)\n job_preprocess_tweets[2].add_args('--filename', 'test_tweets.csv')\n\n\n # Job 2: HPO Bi-LSTM\n bilstm_best_params = File('best_bilstm_hpo_params.txt')\n\n job_hpo_train_bilstm = Job(hpo_train_bilstm)\\\n .add_inputs(glove_embeddings, preprocessed_train_tweets, preprocessed_val_tweets, preprocessed_test_tweets)\\\n .add_outputs(bilstm_best_params)\\\n .add_args('--trials', BILSTM_NUM_TRIALS)\n\n\n # Job 3: Train Bi-LSTM using best parameters from HPO study and output loss and accuracy curves\n trained_bilstm_model = File('bilstm_final_model.h5') \n bilstm_loss_curve = File('Loss_curve_bilstm.png')\n bilstm_accuracy_curve = File('Accuracy_curve_bilstm.png')\n\n\n job_train_bilstm = Job(train_bilstm)\\\n .add_inputs(glove_embeddings, preprocessed_train_tweets, preprocessed_val_tweets, preprocessed_test_tweets, bilstm_best_params)\\\n .add_outputs(bilstm_loss_curve, bilstm_accuracy_curve, trained_bilstm_model)\\\n\n\n # Job 4: Run inference on best Bi-LSTM model to produce output on test dataset along with confusion matrix\n bilstm_train_output_prob = File('bilstm_train_output.csv')\n bilstm_test_output_prob = File('bilstm_test_output.csv')\n bilstm_confusion_matrix = File('bilstm_confusion_matrix.png')\n\n job_bilstm_inference = Job(bilstm_inference)\\\n .add_inputs(preprocessed_train_tweets, preprocessed_val_tweets, preprocessed_test_tweets, trained_bilstm_model)\\\n .add_outputs(bilstm_train_output_prob, bilstm_test_output_prob, bilstm_confusion_matrix)\n\n\n # --------------------------------------------------- IMAGE PIPELINE ------------------------------------------------------ \n\n \n # Job 1: Preprocess images\n prefix = \"resized_\"\n job_preprocess_images = [Job(preprocess_images) for i in range(NUM_WORKERS)]\n resized_images = split_preprocess_jobs(job_preprocess_images, input_images, prefix)\n\n # Job 2: HPO ResNet-50\n resnet_best_params = File('best_resnet_hpo_params.txt')\n\n job_hpo_train_resnet = Job(hpo_train_resnet)\\\n .add_inputs(*resized_images)\\\n .add_args('--trials', RESNET_NUM_TRIALS)\\\n .add_outputs(resnet_best_params)\\\n .add_profiles(Namespace.PEGASUS, key=\"maxwalltime\", value=MAXTIMEWALL)\n\n\n # Job 3: Train ResNet-50 using best parameters from HPO study and output loss and accuracy curves\n trained_resnet_model = File('resnet_final_model.pth')\n resnet_loss_curve = File('Loss_curve_resnet.png')\n resnet_accuracy_curve = File('Accuracy_curve_resnet.png')\n\n job_train_resnet = Job(train_resnet)\\\n .add_inputs(*resized_images, resnet_best_params)\\\n .add_outputs(resnet_loss_curve, resnet_accuracy_curve, trained_resnet_model)\\\n .add_profiles(Namespace.PEGASUS, key=\"maxwalltime\", value=MAXTIMEWALL)\n\n\n # Job 4: Run inference on best ResNet-50 model to produce output on test dataset along with confusion matrix\n resnet_train_output_prob = File('resnet_train_output.csv')\n resnet_confusion_matrix = File('resnet_confusion_matrix.png')\n resnet_test_output_prob = File('resnet_test_output.csv') \n\n job_resnet_inference = Job(resnet_inference)\\\n .add_inputs(*resized_images, trained_resnet_model)\\\n .add_outputs(resnet_train_output_prob, resnet_test_output_prob, resnet_confusion_matrix)\n\n \n \n # --------------------------------------------------- LATE FUSION ------------------------------------------------------ \n\n # Job 1: Late Fusion\n confusion_matrix_MPC = File('late_fusion_MPC.png')\n confusion_matrix_LR = File('late_fusion_LR.png')\n confusion_matrix_MLP = File('late_fusion_MLP.png')\n report_MLP = File('late_fusion_MLP.csv')\n report_MPC = File('late_fusion_MPC.csv')\n report_LR = File('late_fusion_LR.csv')\n\n job_late_fusion = Job(late_fusion)\\\n .add_inputs(resnet_train_output_prob, resnet_test_output_prob, bilstm_train_output_prob, bilstm_test_output_prob)\\\n .add_outputs(confusion_matrix_MPC, confusion_matrix_LR, confusion_matrix_MLP, report_MLP, report_MPC, report_LR)\n\n wf.add_jobs(*job_preprocess_tweets, *job_preprocess_images, job_bilstm_inference, job_hpo_train_bilstm, job_train_bilstm, job_hpo_train_resnet, job_train_resnet, job_resnet_inference, job_late_fusion)\n\n try:\n wf.plan(submit=False, sites=[\"donut\"], output_sites=[\"donut\"], dir=\"submit\")\n #wf.wait()\n #wf.statistics()\n except PegasusClientError as e:\n print(e.output)\n \n #plot_workflow_graph(wf)\n \n return","def main(): \n while True:\n city, month, day = get_filters()\n\n df = generate_stats(city, month, day)\n\n # step through data\n step_through = input('\\nWould you like to step through raw data? Type \"yes\" to step through: \\n')\n if step_through.lower() == 'yes':\n step_through_data(df)\n\n # restart\n restart = input('\\nWould you like to restart? Type \"yes\" to restart.\\n')\n if restart.lower() != 'yes':\n break","def main():\n\n creds = None\n # The file token.json stores the user's access and refresh tokens, and is\n # created automatically when the authorization flow completes for the first\n # time.\n\n # There was no need to do this with Google Sheets... I just felt like being extra.\n if os.path.exists('token.json'):\n creds = Credentials.from_authorized_user_file('token.json', SCOPES)\n # If there are no (valid) credentials available, let the user log in.\n if not creds or not creds.valid:\n if creds and creds.expired and creds.refresh_token:\n creds.refresh(Request())\n else:\n flow = InstalledAppFlow.from_client_secrets_file(\n 'credentials.json', SCOPES)\n creds = flow.run_local_server(port=0)\n # Save the credentials for the next run\n with open('token.json', 'w') as token:\n token.write(creds.to_json())\n\n service = build('sheets', 'v4', credentials=creds)\n\n # Call the Sheets API\n sheet = service.spreadsheets()\n map_df = pd.read_csv('data/rel_map.csv')\n \n for i, rows in map_df.iterrows():\n print(rows.show_season, rows.lead)\n first_url = rows.show_season\n write_from_URL(wiki_head + first_url, sheet, rows.lead, rows.show_season)","def run(self):\n if self.mode == 'remote':\n raise NotImplementedError('No auto run for remote jobs.')\n finished = False\n while not finished:\n if self.current_epoch == self.nepochs:\n logger.info('Reached {} epochs. Finishing.'.format(self.current_epoch))\n finished = True\n elif self.app.available_gpus >= self.nmin:\n n_spawns = self.app.available_gpus\n if self.current_epoch == 1:\n self.app.initialize_folders()\n gen_folders = '{}/*'.format(self.app.generator_folder)\n spawn_folders = self.app.move_generators_to_input(gen_folders)\n else:\n # First move possibly finished productions to their data folder\n try:\n self.app.move_trajs_to_folder(spawn_folders)\n except:\n pass\n self.app.update_metadata()\n self.fit_model()\n self.spawns = self.respawn_from_MSM(search_type='counts', n_spawns=n_spawns)\n spawn_folders = self.app.prepare_spawns(self.spawns, self.current_epoch)\n # Plot where chosen spawns are in the tICA landscape\n f, ax = plot_tica_landscape(self.ttrajs)\n plot_spawns(self.spawns, self.ttrajs, ax=ax)\n fig_fname = '{today}_e{epoch}_spawns.pdf'.format(today=datetime.date.today().isoformat(), epoch=self.current_epoch)\n f.savefig(fig_fname)\n\n self.app.run_GPUs_bash(\n folders=spawn_folders\n )\n self.current_epoch += 1\n else:\n logger.info('{} available GPUs. Minimum per epoch is {}'.format(self.app.available_gpus, self.nmin))\n logger.info('Going to sleep for {} seconds'.format(self.sleeptime))\n sleep(self.sleeptime)","def run(self):\n page = self.fetch_data(self.url)\n stock_list = self.pop_stock_list(page)\n self.write_csv(stock_list)","def run_load(rootpath):\n global CSV_PATH\n CSV_PATH = rootpath+'/csv_files/'\n load_movies_details()\n load_movies_cast()\n load_movies_reviews()","def analyze(self):\n self.logger.info('Analysing game footage: %s', self.path)\n while not self.game_in_progress:\n self.start_game()\n while self.game_in_progress:\n self.analyze_game()","def data_preprocessing():\n lineid_content = get_lineid_content()\n print('Read movie_lines.txt file complete...')\n convos = get_convos()\n print('Read movie_conversations.txt file complete...')\n print('Building dataset')\n get_data(lineid_content, convos)","def run_downloader(self):\n \"\"\"calls to the file downloader\"\"\"\n try:\n html = self.get_page(self.url)\n soup = self.get_soup(html)\n if soup is not None: # If we have soup -\n self.get_links(soup)\n self.get_files()\n else:\n self.producer(\"THESS_ENV_CITYOFTHESS_DAILY_YEARLY_DATA_ERROR\", 'data source format is not as expected',\n e)\n return False\n except Exception as e:\n self.producer(\"THESS_ENV_CITYOFTHESS_DAILY_YEARLY_DATA_ERROR\", 'data source format is not as expected', e)\n\n return False\n return True"],"string":"[\n \"def download_and_preprocess(self):\\n print('Preparing steering angle database.')\\n print('Downloading...')\\n self.download()\\n print('Preprocessing...')\\n self.preprocess()\",\n \"def scrape_all():\\n\\n # Scrape team information by season\\n for team in scrape_utils.team_names():\\n team_season_stats(team)\\n # Each season\\n print(team)\\n for year in range(2019, 2020):\\n # Game Logs\\n season_game_logs(team, year)\\n\\n # Starting Lineups\\n #player_scraper.get_starting_lineups(team, year)\\n\\n # Init mongo to get game IDS for box score scraping\\n m = mongo.Mongo()\\n\\n # Game Information (Box Score and Play by Play)\\n for year in range(2015, 2020):\\n player_scraper.get_starting_lineups(year)\\n for game in m.find('game_log', {'season': year}, {'_id': 1}):\\n #team_scraper.play_by_play(game['_id'])\\n player_scraper.player_box_score(game['_id'])\\n\\n print(game['_id'])\\n\\n\\n\\n # Get player information\\n for player in scrape_utils.get_active_players():\\n print(player)\\n player_scraper.player_per_game(player)\\n\\n # Get betting lines (By Year) need from 2014\\n for year in range(2015, 2020):\\n team_scraper.betting_lines(2019)\",\n \"def load_raw_data(apps, schema_editor):\\n from season.import_raw_data import InitialDataProcessor\\n matches_path = str(BASE_DIR) + '/season/migrations/matches.csv'\\n deliveries_path = str(BASE_DIR) + '/season/migrations/deliveries.csv'\\n # Initialization path to read data\\n load_data = InitialDataProcessor(matches_path=matches_path, deliveries_path=deliveries_path)\\n # transform data frame and save the data step by step\\n # only support new season import for the first tym when data structure is ready to use\\n load_data.transform_input_save()\",\n \"def main():\\n download_insert_title_basics()\\n download_insert_title_principals()\\n download_insert_name_basics()\\n download_insert_title_ratings()\\n scrap_keywords()\\n create_and_insert_soup()\\n return\",\n \"def run_all():\\n db = DBInterface()\\n year = Config.get_property(\\\"league_year\\\")\\n session = Session(bind=db.engine)\\n\\n scraper.scrape_all(db, session, year)\\n session.commit()\\n\\n bets.predict_all(db, session)\\n session.commit()\\n session.close()\",\n \"def run(self):\\n data_provider = DataProvider(self.config)\\n hex_attr_df = data_provider.read_hex_bin_attributes()\\n hex_distance_df = data_provider.read_hex_bin_distances()\\n city_states = data_provider.read_city_states(self.city_states_filename)\\n neighborhood = data_provider.read_neighborhood_data()\\n popular_bins = data_provider.read_popular_hex_bins()\\n num_episodes = self.config['RL_parameters']['num_episodes']\\n ind_episodes = self.config['RL_parameters']['ind_episodes']\\n exp_decay_multiplier = self.config['RL_parameters']['exp_decay_multiplier']\\n\\n q_ind = None\\n r_table = None\\n xi_matrix = None\\n\\n best_episode = None\\n best_model = {}\\n\\n progress_bar = tqdm(xrange(num_episodes))\\n for episode_id in progress_bar:\\n progress_bar.set_description(\\\"Episode: {}\\\".format(episode_id))\\n current_best = -1000000\\n\\n # Create episode\\n ind_exploration_factor = np.e ** (-1 * episode_id * exp_decay_multiplier / ind_episodes)\\n\\n episode = Episode(self.config,\\n episode_id,\\n ind_exploration_factor,\\n hex_attr_df,\\n hex_distance_df,\\n city_states,\\n neighborhood,\\n popular_bins,\\n q_ind,\\n r_table,\\n xi_matrix)\\n\\n # Run episode\\n tables = episode.run()\\n q_ind = tables['q_ind']\\n r_table = tables['r_table']\\n xi_matrix = tables['xi_matrix']\\n episode_tracker = tables['episode_tracker']\\n\\n # Uncomment for logging if running a job, comment during experiments\\n # otherwise it leads to insanely huge logging output which is useless\\n\\n # self.logger.info(\\\"\\\"\\\"\\n # Expt: {} Episode: {} Earnings: {}\\n # Pax rides: {} Relocation rides: {} Unmet demand: {}\\n # \\\"\\\"\\\".format(self.expt_name, episode_id,\\n # episode_tracker.gross_earnings,\\n # episode_tracker.successful_waits,\\n # episode_tracker.relocation_rides,\\n # episode_tracker.unmet_demand))\\n # self.logger.info(\\\"----------------------------------\\\")\\n\\n self.training_tracker.update_RL_tracker(\\n episode_id, episode_tracker.gross_earnings,\\n episode_tracker.successful_waits, episode_tracker.unsuccessful_waits,\\n episode_tracker.unmet_demand, episode_tracker.relocation_rides,\\n episode_tracker.DET, episode_tracker.DPRT, episode_tracker.DWT,\\n episode_tracker.DRT, episode_tracker.DCT)\\n\\n # Keep track of the best episode\\n if self.objective == 'revenue':\\n if episode_tracker.gross_earnings >= current_best:\\n best_episode = episode_tracker\\n current_best = best_episode.gross_earnings\\n else: # self.objective == 'pickups':\\n if episode_tracker.successful_waits >= current_best:\\n best_episode = episode_tracker\\n current_best = episode_tracker.successful_waits\\n\\n # Keep track of the best model\\n best_model['ind_exploration_factor'] = ind_exploration_factor\\n best_model['config'] = self.config\\n best_model['q_ind'] = q_ind\\n best_model['r_table'] = r_table\\n best_model['xi_matrix'] = xi_matrix\\n best_model['training_tracker'] = self.training_tracker\\n\\n # After finishing training\\n self.logger.info(\\\"Expt: {} Earnings: {} Met Demand: {} Unmet Demand: {}\\\".format(self.expt_name,\\n best_episode.gross_earnings,\\n best_episode.successful_waits,\\n best_episode.unmet_demand))\\n return best_episode, best_model, self.training_tracker\",\n \"def start(self):\\n start_time = time()\\n\\n def callback(res):\\n env, data, game_id = res.result()\\n if env is None:\\n logger.info('invalid data: {}'.format(game_id))\\n return\\n\\n self.save_data(data, game_id)\\n logger.debug(f\\\"game {game_id}\\\"\\n f\\\"halfmoves={env.num_halfmoves:3} {env.winner:12}\\\"\\n f\\\"{' by resign ' if env.resigned else ' '}\\\"\\n f\\\"{env.observation.split(' ')[0]}\\\")\\n\\n with ProcessPoolExecutor(max_workers=3) as executor:\\n games = self.get_games_from_all_files()\\n\\n # poisoned reference (memleak)\\n for i, game in enumerate(games):\\n job = executor.submit(get_buffer, self.config, game, len(games), i)\\n job.add_done_callback(callback)\\n # for res in as_completed([executor.submit(get_buffer, self.config, game, len(games), i) for i, game in enumerate(games)]):\",\n \"def start_crawling(self):\\n if util.is_None(self.team) or (not util.is_None(self.team) and util.is_None(self.team.team_fifa_api_id)):\\n # If one of the follow:\\n # 1) team not stored in the DB\\n # 2) Team fifa api id not stored in the DB\\n # --> looking for name and fifa_api_id\\n team_long_name, team_fifa_api_id = self.look_for_base_data()\\n\\n if util.is_None(self.team):\\n # team not present in the DB\\n self.team = Team.write_new_team(team_long_name, team_fifa_api_id)\\n else:\\n # team present in the DB, but without set the team_fifa_api_id\\n self.team.team_fifa_api_id = team_fifa_api_id\\n self.team = Team.update(self.team)\\n\\n # looking for players belonging this team\\n link_players_found = self.look_for_players()\\n for player_link, player_name in link_players_found.items():\\n player_fifa_api_id = player_link[25:]\\n player = Player.read_by_fifa_api_id(player_fifa_api_id)\\n\\n # crawl the player if and only if on of the following happens:\\n # 1) PLAYER DOES NOT EXIST IN THE DB\\n # 2) PLAYER ATTRIBUTES DO NOT EXIST IN THE DB\\n # 3) PLAYER ATTRIBUTES IN THE DB ARE OLD\\n # 4) FORCE PARSING OF PLAYER ATTRIBUTES\\n if \\\\\\n not player \\\\\\n or not player.get_last_player_attributes() \\\\\\n or util.compare_time_to_now(player.get_last_player_attributes().date, self.day_passed) \\\\\\n or self.force_parsing:\\n log.debug(\\\"Player to crawl [\\\"+player_link+\\\", \\\"+player_name+\\\"]\\\")\\n cp = CrawlerPlayer(player, player_link)\\n cp.start_crawling()\\n\\n # looking for build up play\\n attributes_found = self.look_for_team_attributes()\\n if len(attributes_found) > 0:\\n self.team.save_team_attributes(attributes_found)\",\n \"def run():\\n logger.info(f\\\"Process started:\\\")\\n logger.info(f\\\"Converting Glove file to Word2Vec format\\\")\\n convert_to_word2vec.convert(\\n \\\"./data/source/glove.6B.50d.txt\\\", \\\"./data/source/glove.6B.50d.w2vformat.txt\\\"\\n )\\n\\n logger.info(f\\\"Extracting Click Stream data\\\")\\n extract_click_stream_data()\\n\\n logger.info(\\\"Extracting Wiki articles\\\")\\n extract_wiki_articles()\\n\\n logger.info(f\\\"Generating Clickstream dataset\\\")\\n generate_datasets()\\n\\n logger.info(\\\"Tokenizing articles\\\")\\n WikiArticlesTokenizer().process()\\n\\n logger.info(\\\"Creating dataset with Wiki Articles\\\")\\n create_wiki_articles_dataset()\",\n \"def crawl_database():\\n\\n LOGGING.push(\\\"Attempting to request featured games.\\\")\\n participants = get_featured()\\n LOGGING.push(\\\"Got @\\\" + str(len(participants)) + \\\"@ participants.\\\")\\n\\n # NOTE: Only 40 summoners can be requested at a time\\n participants = random.sample(participants, min(40, len(participants)))\\n\\n ids = SESSION.get_ids(participants)\\n search_players = [ids[player]['id'] for player in ids.keys()]\\n\\n LOGGING.push(\\n \\\"Now attempting to crawl players with a breadth of @\\\" +\\n str(BREADTH) + \\\"@ and depth of ^\\\" + str(DEPTH) + \\\"^.\\\"\\n )\\n\\n # NOTE: Creates the original call stack to crawl players\\n for player in search_players:\\n crawl_player(player, DEPTH, BREADTH)\\n\\n LOGGING.push(\\\"Finished crawling database.\\\")\",\n \"def launch(self, force_prep=False):\\n #TODO process upploaded csv\\n assert self.ready()\\n self.launch_progress = 0\\n self.set_status(\\\"Gathering data\\\")\\n if 'O' not in set(self.labels.dict.values()):\\n self.add_labels({max(list(self.labels.dict.keys()))+1:'O'})\\n\\n processed_file_path = os.path.join(DATASETS_PATH, self.dataset_uuid, 'processed.csv')\\n bert_file_path = os.path.join(DATASETS_PATH, self.dataset_uuid, 'processed.bert')\\n elmo_file_path = os.path.join(DATASETS_PATH, self.dataset_uuid, 'processed.elmo')\\n nlp_file_path = os.path.join(DATASETS_PATH, self.dataset_uuid, 'processed.nlp')\\n sbert_file_path = os.path.join(DATASETS_PATH, self.dataset_uuid, 'processed.sbert')\\n if os.path.exists(processed_file_path) and not force_prep:\\n df = pd.read_csv(processed_file_path)\\n if 'span_label' in df.columns:\\n df['span_label']=df['span_label'].apply(eval)\\n # Let's say loading the file is ~half the launch time\\n # (if the file already exists)\\n self.total = 2\\n self.update(1)\\n else:\\n datafiles = [os.path.join(DATASETS_PATH, self.dataset_uuid, d) \\\\\\n for d in os.listdir(os.path.join(DATASETS_PATH, self.dataset_uuid))]\\n df = concat_dataset(datafiles)\\n # expand total to account for time it takes to initialize the model\\n self.total = len(df)*(1.1) \\n self.set_status(\\\"Preprocessing data\\\")\\n df = self.process_data(df, processed_file_path)\\n\\n # load list of 'allennlp.data.instance's. allennlp.data.instance can store true labels and tag info internally.\\n if os.path.exists(nlp_file_path) and not force_prep:\\n with open(nlp_file_path, 'rb') as f:\\n sentences = pickle.load(f)\\n else:\\n #TODO define a universal reader for certain format\\n # reader = RestaurantsDatasetReader()\\n # data = reader.read(processed_file_path)\\n #TODO handle when aux files do not exist\\n pass\\n bert_emb = np.load(bert_file_path, allow_pickle=True)\\n elmo_emb = np.load(elmo_file_path, allow_pickle=True)\\n sbert_emb = np.load(sbert_file_path, allow_pickle=True)\\n for s, b, e, sb in zip(sentences, bert_emb, elmo_emb, sbert_emb):\\n s.fields['bert'] = b\\n s.fields['sbert'] = sb\\n s.fields['elmo'] = e\\n\\n df['bert'] = bert_emb\\n df['sbert'] = [sb for sb in sbert_emb]\\n df['elmo'] = elmo_emb\\n df['text_nlp'] = sentences\\n\\n columns_to_drop = list(\\n set(df.columns).intersection(set(['span_label','file','label'])))\\n df = df.drop(columns=columns_to_drop).reset_index()\\n # since df['text_nlp'] contains true label info, drop 'labels' column.\\n columns_to_drop = list(set(df.columns).difference(set(['index', 'Unnamed: 0', 'text', 'labels', 'split', 'bert', 'sbert',\\n 'elmo', 'text_nlp'])))\\n if len(columns_to_drop) > 0:\\n df = df.drop(columns=columns_to_drop)\\n df_train = df[df['split']=='train']\\n df_dev = df[df['split'] == 'dev']\\n df_valid = df[df['split'] == 'valid']\\n df_test = df[df['split'] == 'test']\\n\\n self.text_inv_dict = dict(\\n zip(list(df['text']),list(df.index))\\n )\\n\\n # TODO split heldout set if necessary\\n # for now, passing empty df as heldout set\\n df_heldout = df_test\\n\\n self.emb_dict = Embeddings(df)\\n\\n self.set_status(\\\"Initializing modeler\\\")\\n self.modeler = Modeler(df_train, df_dev, df_valid, df_test, df_heldout, self.labels, emb_dict=self.emb_dict)\\n\\n self.launch_progress = 1.0\\n self.set_status(\\\"Finished\\\")\\n return self.modeler\",\n \"def Start(self):\\n self.state.hist_count = 0\\n self.state.history_paths = []\\n\\n if self.args.history_path:\\n self.state.history_paths.append(self.args.history_path)\\n else:\\n self.state.history_paths = self.GuessHistoryPaths(self.args.username)\\n\\n if not self.state.history_paths:\\n raise flow_base.FlowError(\\\"Could not find valid History paths.\\\")\\n\\n filename = \\\"places.sqlite\\\"\\n for path in self.state.history_paths:\\n self.CallFlow(\\n file_finder.FileFinder.__name__,\\n paths=[os.path.join(path, \\\"**2\\\", filename)],\\n pathtype=self.args.pathtype,\\n action=rdf_file_finder.FileFinderAction.Download(),\\n next_state=self.ParseFiles.__name__)\",\n \"def importData():\\n #importChallengeDataToDB()\\n importTrendingDataToDB()\",\n \"def run(self):\\n\\n #Load old cache\\n if '-r' not in sys.argv:\\n old_db = SiteDB([], 'upwork')\\n old_db.load()\\n\\n #Set it to current DBs old cache\\n self.db.pred = old_db.cache\\n\\n #Generate crawler object\\n \\n crawler = self.generate_crawler()\\n\\n #Generate DB for latest scan\\n if '-full' not in sys.argv:\\n self.db = crawler.crawl()\\n else:\\n _ = crawler.crawl()\\n\\n if '-r' not in sys.argv:\\n #Get deleted info\\n _ = self.db.compare(old_db)\\n\\n targets = ['https://community.upwork.com/t5/Announcements/bd-p/news', \\\\\\n 'https://community.upwork.com/t5/Freelancers/bd-p/freelancers', \\\\\\n 'https://community.upwork.com/t5/Clients/bd-p/clients', \\\\\\n 'https://community.upwork.com/t5/Agencies/bd-p/Agencies']\\n target = None\\n for tar in targets:\\n if tar in sys.argv:\\n target = tar\\n\\n #Write result\\n #self.db.write(target_name=target)\",\n \"def scrape_new_game_urls(self):\\n dfs = []\\n downloaded_files = os.listdir(self.pgn_persisting_base_path)\\n downloaded_pgn = [int(re.findall('pgn_(\\\\d+).txt', x)[0]) for x in downloaded_files if x.endswith('txt')]\\n\\n all_files = pd.read_csv(self.urls_persisting_base_path + self.reference_url_file, sep=';')\\n\\n print(f\\\"Downloaded: {len(downloaded_pgn)}\\\")\\n print(f\\\"Total: {all_files['game_link'].nunique()}\\\")\\n self.scrapped_games = all_files[~all_files['game_id'].isin(downloaded_pgn)]\",\n \"def mover_get_data(lfns,\\n path,\\n sitename,\\n queuename,\\n stageinTries,\\n inputpoolfcstring=\\\"xmlcatalog_file:PoolFileCatalog.xml\\\",\\n ub=\\\"outdated\\\", # to be removed\\n dsname=\\\"\\\",\\n dsdict={},\\n rucio_dataset_dictionary={},\\n guids=[],\\n analysisJob=False,\\n usect=True,\\n pinitdir=\\\"\\\",\\n proxycheck=True,\\n spsetup=\\\"\\\",\\n tokens=[],\\n userid=\\\"\\\",\\n inputDir=\\\"\\\",\\n jobId=None,\\n jobDefId=\\\"\\\",\\n access_dict=None,\\n scope_dict=None,\\n workDir=\\\"\\\",\\n DN=None,\\n dbh=None,\\n jobPars=\\\"\\\",\\n cmtconfig=\\\"\\\",\\n filesizeIn=[],\\n checksumIn=[],\\n transferType=None,\\n experiment=\\\"\\\",\\n eventService=False,\\n sourceSite=\\\"\\\"):\\n\\n tolog(\\\"Mover get data started\\\")\\n\\n statusPFCTurl = None\\n pilotErrorDiag = \\\"\\\"\\n\\n # FAX counters (will be reported in jobMetrics; only relevant when FAX has been activated after a stage-in failure)\\n N_filesWithoutFAX = 0\\n N_filesWithFAX = 0\\n bytesWithoutFAX = 0L\\n bytesWithFAX = 0L\\n\\n # FAX control variable, if FAX is used as primary site mover in combination with direct I/O\\n usedFAXandDirectIO = False\\n\\n # The FAX variables above will be stored in a dictionary, to be returned by this function\\n FAX_dictionary = {}\\n\\n # Is the DBRelease file available locally?\\n DBReleaseIsAvailable = handleDBRelease(dbh, lfns, jobPars, path)\\n\\n # Should stage-in be aborted? (if there are only locally available DBRelease files in the stage-in list)\\n if abortStageIn(dbh, lfns, DBReleaseIsAvailable):\\n return 0, pilotErrorDiag, statusPFCTurl, FAX_dictionary\\n\\n # Setup the dictionary necessary for all instrumentation\\n report = getInitialTracingReport(userid, sitename, dsname, \\\"get_sm\\\", analysisJob, jobId, jobDefId, DN)\\n\\n if stageinTries != 0:\\n get_RETRY = min(stageinTries, MAX_NUMBER_OF_RETRIES)\\n else:\\n get_RETRY = MAX_RETRY\\n get_TIMEOUT = 5*3600/get_RETRY\\n\\n fail = 0\\n guidfname = {}\\n error = PilotErrors()\\n\\n region = readpar('region')\\n\\n # Space tokens currently not used for input files\\n # # check if there is are any space tokens\\n # _token = getProperSpaceTokenList(token, listSEs, len(lfns))\\n\\n # Select the correct mover\\n copycmd, setup = getCopytool(mode=\\\"get\\\")\\n\\n # Get the sitemover object corresponding to the default copy command\\n sitemover = getSiteMover(copycmd, setup)\\n\\n # Get the experiment object\\n thisExperiment = getExperiment(experiment)\\n\\n # Get the name for the PFC file\\n _path = path\\n if eventService:\\n # Update the path (create the PFC in one level above the payload workdir)\\n path = os.path.abspath(os.path.join(path, '..'))\\n pfc_name = getPFCName(path, inputpoolfcstring)\\n # done with the event server modification (related to the PFC generation), reset the path again\\n path = _path\\n\\n # Build the file info dictionary (use the filesize and checksum from the dispatcher if possible) and create the PFC\\n # Format: fileInfoDic[file_nr] = (guid, gpfn, fsize, fchecksum, filetype, copytool)\\n # replicas_dic[guid1] = [ replica1, .. ] where replicaN is an object of class replica\\n ec, pilotErrorDiag, fileInfoDic, totalFileSize, replicas_dic = \\\\\\n getFileInfo(region, ub, queuename, guids, dsname, dsdict, lfns, pinitdir, analysisJob, tokens, DN, sitemover, error, path, dbh, DBReleaseIsAvailable,\\\\\\n scope_dict, pfc_name=pfc_name, filesizeIn=filesizeIn, checksumIn=checksumIn, thisExperiment=thisExperiment)\\n if ec != 0:\\n return ec, pilotErrorDiag, statusPFCTurl, FAX_dictionary\\n\\n # Until the Mover PFC file is no longer needed, call the TURL based PFC \\\"PoolFileCatalogTURL.xml\\\"\\n pfc_name_turl = pfc_name.replace(\\\".xml\\\", \\\"TURL.xml\\\")\\n\\n # Create a SURL to space token dictionary\\n tokens_dictionary = getSurlTokenDictionary(lfns, tokens)\\n\\n # Create a TURL based PFC if necessary/requested (i.e. if copy tool should not be used [useCT=False] and\\n # if oldPrefix and newPrefix are not already set in copysetup [useSetPrefixes=False])\\n ec, pilotErrorDiag, createdPFCTURL, usect = PFC4TURLs(analysisJob, transferType, fileInfoDic, pfc_name_turl, sitemover, sitename, usect, dsdict, eventService, tokens_dictionary, sitename, sourceSite, lfns)\\n if ec != 0:\\n return ec, pilotErrorDiag, statusPFCTurl, FAX_dictionary\\n\\n # Correct the total file size for the DBRelease file if necessary\\n totalFileSize = correctTotalFileSize(totalFileSize, fileInfoDic, lfns, dbh, DBReleaseIsAvailable)\\n\\n # Only bother with the size checks if the copy tool is to be used (non-direct access mode)\\n if usect:\\n # Get a proper maxinputsize from schedconfig/default \\n _maxinputsize = getMaxInputSize()\\n\\n # Check the total input file size\\n ec, pilotErrorDiag = verifyInputFileSize(totalFileSize, _maxinputsize, error)\\n if ec != 0:\\n return ec, pilotErrorDiag, statusPFCTurl, FAX_dictionary\\n\\n # Do we have enough local space to stage in all data and run the job?\\n ec, pilotErrorDiag = verifyAvailableSpace(sitemover, totalFileSize, path, error)\\n if ec != 0:\\n return ec, pilotErrorDiag, statusPFCTurl, FAX_dictionary\\n\\n # Get the replica dictionary from file (used when the primary replica can not be staged due to some temporary error)\\n replica_dictionary = getReplicaDictionaryFile(path)\\n\\n # file counters\\n N_files_on_tape = 0\\n N_root_files = 0\\n N_non_root_files = 0\\n\\n # If FAX is used as a primary site mover then set the default FAX mode to true, otherwise to false (normal mode)\\n if copycmd == \\\"fax\\\":\\n usedFAXMode = True\\n else:\\n usedFAXMode = False\\n\\n # Use isOneByOneFileTransfer() to determine if files should be transferred one by one or all at once\\n if not sitemover.isOneByOneFileTransfer():\\n\\n # Note: this mode is used by the aria2c site mover only\\n # Normal stage-in is below\\n\\n tolog(\\\"All files will be transferred at once\\\")\\n\\n # Extract the file info for the first file in the dictionary\\n guid, gpfn, lfn, fsize, fchecksum, filetype, copytool = extractInputFileInfo(fileInfoDic[0], lfns)\\n file_access = getFileAccess(access_dict, lfn)\\n dsname = getDataset(lfn, dsdict)\\n\\n # Perform stage-in using the sitemover wrapper method\\n s, pErrorText = sitemover_get_all_data(sitemover, error, gpfn, lfn, path, fsize=fsize, spsetup=spsetup, fchecksum=fchecksum,\\\\\\n guid=guid, analysisJob=analysisJob, usect=usect, pinitdir=pinitdir, proxycheck=proxycheck,\\\\\\n sitename=sitename, token=None, timeout=get_TIMEOUT, dsname=dsname, userid=userid, report=report,\\\\\\n access=file_access, inputDir=inputDir, jobId=jobId, workDir=workDir, cmtconfig=cmtconfig, lfns=lfns,\\\\\\n experiment=experiment, replicas_dic=replicas_dic, dsdict=dsdict, scope_dict=scope_dict)\\n if s != 0:\\n tolog('!!WARNING!!2999!! Failed during stage-in of multiple files: %s' % (error.getErrorStr(s)))\\n tolog(\\\"Exit code: %s\\\" % (s))\\n fail = s\\n\\n # Normal stage-in (one by one file transfers)\\n if sitemover.isOneByOneFileTransfer() or fail != 0:\\n \\n tolog(\\\"Files will be transferred one by one\\\")\\n\\n # Reset any previous failure\\n fail = 0\\n\\n # Loop over all files in the file info dictionary\\n number_of_files = len(fileInfoDic.keys())\\n tolog(\\\"Will process %d file(s)\\\" % (number_of_files))\\n for nr in range(number_of_files):\\n # Extract the file info from the dictionary\\n guid, gpfn, lfn, fsize, fchecksum, filetype, copytool = extractInputFileInfo(fileInfoDic[nr], lfns)\\n\\n # Has the copycmd/copytool changed? (E.g. due to FAX) If so, update the sitemover object\\n if copytool != copycmd:\\n copycmd = copytool\\n # Get the sitemover object corresponding to the new copy command\\n sitemover = getSiteMover(copycmd, setup)\\n tolog(\\\"Site mover object updated since copytool has changed\\\")\\n\\n # Update the dataset name\\n dsname = getDataset(lfn, dsdict)\\n proper_dsname = getDataset(lfn, rucio_dataset_dictionary)\\n scope = getFileScope(scope_dict, lfn)\\n\\n # Update the tracing report with the proper container/dataset name\\n report = updateReport(report, gpfn, proper_dsname, fsize, sitemover)\\n report['scope'] = scope\\n\\n # The DBRelease file might already have been handled, go to next file\\n if isDBReleaseFile(dbh, lfn) and DBReleaseIsAvailable:\\n updateFileState(lfn, workDir, jobId, mode=\\\"transfer_mode\\\", state=\\\"no_transfer\\\", type=\\\"input\\\")\\n guidfname[guid] = lfn # needed for verification below\\n continue\\n else:\\n tolog(\\\"(Not a DBRelease file)\\\")\\n\\n tolog(\\\"Mover is preparing to copy file %d/%d (lfn: %s guid: %s dsname: %s)\\\" % (nr+1, number_of_files, lfn, guid, dsname))\\n tolog('Copying %s to %s (file catalog checksum: \\\\\\\"%s\\\\\\\", fsize: %s) using %s (%s)' %\\\\\\n (gpfn, path, fchecksum, fsize, sitemover.getID(), sitemover.getSetup()))\\n\\n # Get the number of replica retries\\n get_RETRY_replicas = getNumberOfReplicaRetries(createdPFCTURL, replica_dictionary, guid)\\n\\n file_access = getFileAccess(access_dict, lfn)\\n\\n # Loop over get function to allow for multiple get attempts for a file\\n will_use_direct_io = False\\n get_attempt = 0\\n\\n #get_RETRY = 1 #2 #PN\\n while get_attempt < get_RETRY:\\n if get_attempt > 0:\\n _rest = 5*60\\n tolog(\\\"(Waiting %d seconds before next stage-in attempt)\\\" % (_rest))\\n sleep(_rest)\\n tolog(\\\"Get attempt %d/%d\\\" % (get_attempt + 1, get_RETRY))\\n replica_number = 0\\n replica_transferred = False\\n s = 1\\n\\n # Loop over replicas\\n while s != 0 and replica_number < get_RETRY_replicas:\\n # Grab the gpfn from the replicas dictionary in case alternative replica stage-in is allowed\\n gpfn = getAlternativeReplica(gpfn, guid, replica_number, createdPFCTURL, replica_dictionary)\\n\\n # Perform stage-in using the sitemover wrapper method\\n s, pErrorText, N_files_on_tape, N_root_files, N_non_root_files, replica_transferred, will_use_direct_io = sitemover_get_data(sitemover, error,\\\\\\n get_RETRY, get_RETRY_replicas, get_attempt,\\\\\\n replica_number, N_files_on_tape, N_root_files,\\\\\\n N_non_root_files, gpfn, lfn, path,\\\\\\n fsize=fsize, spsetup=spsetup, fchecksum=fchecksum,\\\\\\n guid=guid, analysisJob=analysisJob, usect=usect,\\\\\\n pinitdir=pinitdir, proxycheck=proxycheck,\\\\\\n sitename=sitename, token=None, timeout=get_TIMEOUT,\\\\\\n dsname=dsname, userid=userid, report=report,\\\\\\n access=file_access, inputDir=inputDir, jobId=jobId,\\\\\\n workDir=workDir, cmtconfig=cmtconfig,\\\\\\n experiment=experiment, scope_dict=scope_dict,\\\\\\n sourceSite=sourceSite)\\n # Get out of the multiple replica loop\\n if replica_transferred:\\n break\\n\\n # Increase the replica attempt counter in case the previous replica could not be transferred\\n replica_number += 1\\n\\n # Get out of the multiple get attempt loop\\n if replica_transferred:\\n break\\n\\n # Increase the get attempt counter in case of failure to transfer the file\\n get_attempt += 1\\n\\n # Increase the successful file transfer counter (used only when reporting FAX transfers)\\n if s == 0:\\n # note the special case if FAX is the primary site mover (normally FAX is the fallback)\\n if sitemover.copyCommand == \\\"fax\\\":\\n N_filesWithFAX += 1\\n bytesWithFAX += long(fsize)\\n else:\\n # Normal case\\n N_filesWithoutFAX += 1\\n bytesWithoutFAX += long(fsize)\\n\\n if s != 0:\\n # Normal stage-in failed, now try with FAX if possible\\n if error.isPilotFAXErrorCode(s):\\n if isFAXAllowed(filetype, gpfn) and transferType != \\\"fax\\\" and sitemover.copyCommand != \\\"fax\\\": # no point in trying to fallback to fax if the fax transfer above failed\\n tolog(\\\"Normal stage-in failed, will attempt to use FAX\\\")\\n usedFAXMode = True\\n\\n # Get the FAX site mover\\n old_sitemover = sitemover\\n sitemover = getSiteMover(\\\"fax\\\", \\\"\\\")\\n\\n # Perform stage-in using the sitemover wrapper method\\n s, pErrorText, N_files_on_tape, N_root_files, N_non_root_files, replica_transferred, will_use_direct_io = sitemover_get_data(sitemover, error,\\\\\\n get_RETRY, get_RETRY_replicas, get_attempt, replica_number,\\\\\\n N_files_on_tape, N_root_files, N_non_root_files,\\\\\\n gpfn, lfn, path,\\\\\\n fsize=fsize, spsetup=spsetup, fchecksum=fchecksum,\\\\\\n guid=guid, analysisJob=analysisJob, usect=usect,\\\\\\n pinitdir=pinitdir, proxycheck=proxycheck,\\\\\\n sitename=sitename, token=None, timeout=get_TIMEOUT,\\\\\\n dsname=dsname, userid=userid, report=report,\\\\\\n access=file_access, inputDir=inputDir, jobId=jobId,\\\\\\n workDir=workDir, cmtconfig=cmtconfig, experiment=experiment)\\n if replica_transferred:\\n tolog(\\\"FAX site mover managed to transfer file from remote site (resetting error code to zero)\\\")\\n pilotErrorDiag = \\\"\\\"\\n s = 0\\n\\n # Increase the successful FAX transfer counter\\n N_filesWithFAX += 1\\n bytesWithFAX += long(fsize)\\n else:\\n tolog(\\\"FAX site mover also failed to transfer file from remote site, giving up\\\")\\n\\n # restore the old sitemover\\n del sitemover\\n sitemover = old_sitemover\\n else:\\n tolog(\\\"(Not an error code eligible for FAX fail-over)\\\")\\n\\n if s != 0:\\n tolog('!!FAILED!!2999!! Failed to transfer %s: %s (%s)' % (os.path.basename(gpfn), s, error.getErrorStr(s)))\\n tolog(\\\"Exit code: %s\\\" % (s))\\n\\n # report corrupt file to consistency server if needed\\n if s == error.ERR_GETADMISMATCH or s == error.ERR_GETMD5MISMATCH or s == error.ERR_GETWRONGSIZE or s == error.ERR_NOSUCHFILE:\\n reportFileCorruption(gpfn, sitemover)\\n\\n # exception for object stores\\n if (gpfn.startswith(\\\"s3:\\\") or 'objectstore' in gpfn) and '.log.tgz' in gpfn:\\n tolog(\\\"!!FAILED!!2999!! Failed to transfer a log file from S3 objectstore. Will skip it and continue the job.\\\")\\n else:\\n fail = s\\n break\\n\\n # Build the dictionary used to create the PFC for the TRF\\n # In the case of FAX, use the global paths if direct access is to be used for the particlar file\\n if usedFAXMode and will_use_direct_io:\\n # The site mover needed here is the FAX site mover since the global file path methods are defined there only\\n old_sitemover = sitemover\\n sitemover = getSiteMover(\\\"fax\\\", \\\"\\\")\\n guidfname[guid] = sitemover.findGlobalFilePath(lfn, dsname, sitename, sourceSite)\\n\\n # Restore the old sitemover\\n del sitemover\\n sitemover = old_sitemover\\n\\n # If FAX is used as a primary site mover, in combination with direct access, set the usedFAXandDirectIO flag\\n # this will later be used to update the run command (e.g. --lfcHost is not needed etc)\\n if copycmd == \\\"fax\\\":\\n usedFAXandDirectIO = True\\n else:\\n guidfname[guid] = lfn # local_file_name\\n\\n if fail == 0:\\n # Make sure the PFC has the correct number of files\\n fail, pilotErrorDiag = verifyPFCIntegrity(guidfname, lfns, dbh, DBReleaseIsAvailable, error)\\n\\n # Now that the Mover PFC file is no longer needed, back it up and rename the TURL based PFC if it exists\\n # (the original PFC is no longer needed. Move it away, and then create the PFC for the trf/runAthena)\\n # backupPFC4Mover(pfc_name)\\n\\n # Create a standard PFC with SURLs if needed (basically this is default)\\n # note: if FAX was used as a primary site mover in combination with direct I/O, then the SURLs will actually be TURLs\\n # but there is no need to use the special TURL creation method PFC4TURL used above (FAX will have returned the TURLs instead)\\n createStandardPFC4TRF(createdPFCTURL, pfc_name_turl, pfc_name, guidfname)\\n\\n tolog(\\\"Number of identified root files : %d\\\" % (N_root_files))\\n tolog(\\\"Number of transferred non-root files: %d\\\" % (N_non_root_files))\\n\\n if usedFAXMode:\\n tolog(\\\"Number of files without FAX : %d (normal transfers)\\\" % (N_filesWithoutFAX))\\n tolog(\\\"Number of files with FAX : %d (successful FAX transfers)\\\" % (N_filesWithFAX))\\n tolog(\\\"Bytes without FAX : %d (normal transfers)\\\" % (bytesWithoutFAX))\\n tolog(\\\"Bytes with FAX : %d (successful FAX transfers)\\\" % (bytesWithFAX))\\n\\n if N_files_on_tape > 0:\\n tolog(\\\"!!WARNING!!2999!! Number of skipped files: %d (not staged)\\\" % (N_files_on_tape))\\n if N_root_files == 0:\\n # This should only happen for user jobs\\n tolog(\\\"Mover get_data failed since no root files could be transferred\\\")\\n fail = error.ERR_NOSTAGEDFILES\\n else:\\n tolog(\\\"Mover get_data finished (partial)\\\")\\n else:\\n if fail == 0:\\n tolog(\\\"Get successful\\\")\\n tolog(\\\"Mover get_data finished\\\")\\n else:\\n tolog(\\\"Mover get_data finished (failed)\\\")\\n tolog(\\\"Will return exit code = %d, pilotErrorDiag = %s\\\" % (fail, pilotErrorDiag)) \\n\\n # Now populate the FAX dictionary before finishing\\n FAX_dictionary = getFAXDictionary(N_filesWithoutFAX, N_filesWithFAX, bytesWithoutFAX, bytesWithFAX, usedFAXandDirectIO)\\n\\n return fail, pilotErrorDiag, statusPFCTurl, FAX_dictionary\",\n \"def start() -> None:\\n\\n # PREPARE\\n clone_game_files()\\n\\n # SIMULATE\\n turns = run.simulation()\\n\\n # LOG\\n logs = read.combine_logs(turns)\\n\\n # CALCULATE\\n results = calculate.results(logs)\\n\\n # DISPLAY\\n visualize.charts(results)\\n\\n # CLEAN\\n remove_cloned_files()\",\n \"def _extract_games_data(season, season_type):\\n nflscrapr.run(\\n 'games',\\n season=season,\\n season_type=season_type\\n )\\n nflscrapr_output = etl_tools.extract_from_csv(config.GAMES_DUMP_CSV_PATH)\\n return nflscrapr_output\",\n \"def scrape(self):\\n\\n self.jobs_load()\\n self.new_jobs = []\\n\\n for bot in self.bot_squad:\\n self.new_jobs += bot.scrape_all_pages()\\n\\n self.statistics(self.new_jobs)\\n print('SCRAPE COMPLETE. NOTE: Resulting job list still in RAM')\\n print('We observed %d new jobs' % len(self.new_jobs))\",\n \"def process_dataset(self):\\n\\n logger.info('\\\\n')\\n logger.info('=' * 40)\\n logger.info('=\\\\t DeepRank Data Set')\\n logger.info('=')\\n logger.info('=\\\\t Training data')\\n for f in self.train_database:\\n logger.info(f'=\\\\t -> {f}')\\n logger.info('=')\\n if self.valid_database:\\n logger.info('=\\\\t Validation data')\\n for f in self.valid_database:\\n logger.info(f'=\\\\t -> {f}')\\n logger.info('=')\\n if self.test_database:\\n logger.info('=\\\\t Test data')\\n for f in self.test_database:\\n logger.info(f'=\\\\t -> {f}')\\n logger.info('=')\\n logger.info('=' * 40 + '\\\\n')\\n sys.stdout.flush()\\n\\n # check if the files are ok\\n self.check_hdf5_files(self.train_database)\\n\\n if self.valid_database:\\n self.valid_database = self.check_hdf5_files(\\n self.valid_database)\\n\\n if self.test_database:\\n self.test_database = self.check_hdf5_files(\\n self.test_database)\\n\\n # create the indexing system\\n # alows to associate each mol to an index\\n # and get fname and mol name from the index\\n self.create_index_molecules()\\n\\n # get the actual feature name\\n if self.mapfly:\\n self.get_raw_feature_name()\\n else:\\n self.get_mapped_feature_name()\\n\\n # get the pairing\\n self.get_pairing_feature()\\n\\n # get grid shape\\n self.get_grid_shape()\\n\\n # get the input shape\\n self.get_input_shape()\\n\\n # get renormalization factor\\n if self.normalize_features or self.normalize_targets or self.clip_features:\\n if self.mapfly:\\n self.compute_norm()\\n else:\\n self.get_norm()\\n\\n logger.info('\\\\n')\\n logger.info(\\\" Data Set Info:\\\")\\n logger.info(\\n f' Augmentation : {self.use_rotation} rotations')\\n logger.info(\\n f' Training set : {self.ntrain} conformations')\\n logger.info(\\n f' Validation set : {self.nvalid} conformations')\\n logger.info(\\n f' Test set : {self.ntest} conformations')\\n logger.info(f' Number of channels : {self.input_shape[0]}')\\n logger.info(f' Grid Size : {self.data_shape[1]}, '\\n f'{self.data_shape[2]}, {self.data_shape[3]}')\\n sys.stdout.flush()\",\n \"def _load_data(self):\\n self.mapper = Mapper()\\n self.mapper.generate_vocabulary(self.review_summary_file)\\n self.X_fwd, self.X_bwd, self.Y = self.mapper.get_tensor(reverseflag=True)\\n # Store all the mapper values in a dict for later recovery\\n self.mapper_dict = dict()\\n self.mapper_dict['seq_length'] = self.mapper.get_seq_length()\\n self.mapper_dict['vocab_size'] = self.mapper.get_vocabulary_size()\\n self.mapper_dict['rev_map'] = self.mapper.get_reverse_map()\\n # Split into test and train data\\n self._split_train_tst()\",\n \"def fetch_data(args):\\n logger.debug(\\\"Running the fetch_data function\\\")\\n\\n #Loading the config\\n with open(os.path.join(\\\"Config\\\",\\\"config.yml\\\"), \\\"r\\\") as f:\\n config = yaml.safe_load(f)\\n\\n #Starting the scraping process\\n tstart = datetime.datetime.now()\\n err_count = 0\\n\\n logger.info(\\\"Starting web scraping now.\\\")\\n for i in range(config[\\\"fetch_data\\\"][\\\"indices\\\"][\\\"start\\\"], config[\\\"fetch_data\\\"][\\\"indices\\\"][\\\"end\\\"]+1):\\n try:\\n time.sleep(1)\\n req_link1 = \\\"http://www.gutenberg.org/cache/epub/\\\" + str(i) + \\\"/pg\\\" + str(i) + \\\".txt\\\"\\n response1 = requests.get(req_link1)\\n \\n req_link2 = \\\"http://www.gutenberg.org/files/\\\" + str(i) + \\\"/\\\" + str(i) + \\\"-0.txt\\\"\\n response2 = requests.get(req_link2)\\n \\n response1.encoding = \\\"UTF-8\\\"\\n response2.encoding = \\\"UTF-8\\\"\\n \\n if response1.status_code == 200:\\n with open(config[\\\"fetch_data\\\"][\\\"save_location\\\"] + str(i) + \\\".txt\\\", \\\"w\\\", encoding=\\\"UTF-8\\\") as text_file:\\n text_file.write(response1.text)\\n \\n elif response2.status_code == 200:\\n with open(config[\\\"fetch_data\\\"][\\\"save_location\\\"] + str(i) + \\\".txt\\\", \\\"w\\\", encoding=\\\"UTF-8\\\") as text_file:\\n text_file.write(response2.text)\\n \\n else:\\n err_count = err_count + 1 \\n logger.error(\\\"Status Code {} returned for index {}\\\".format(response.status_code, i))\\n \\n if i % 500 == 0:\\n time.sleep(30)\\n logger.info(\\\"At Index {}. Time Elapsed: {}\\\".format(i, datetime.datetime.now()-tstart)) \\n\\n except Exception as e:\\n logger.error(e)\\n \\n logger.info(\\\"Total Errorred documents: {}\\\".format(err_count))\\n logger.info(\\\"Total Successful documents: {}\\\".format(config[\\\"fetch_data\\\"][\\\"indices\\\"][\\\"end\\\"] - config[\\\"fetch_data\\\"][\\\"indices\\\"][\\\"start\\\"] + 1 -err_count))\\n logger.info(\\\"Total Time taken: {}\\\".format(datetime.datetime.now()-tstart))\\n\\n return\",\n \"def scrape_fifa_game(url, competition):\\n # Need to add referee data.\\n\\n data = scrape_url(url)\\n data = data.split(\\\"<h2>Advertisement</h2>\\\")[0]\\n soup = BeautifulSoup(data)\\n \\n contents = soup.find(\\\"div\\\", {\\\"id\\\": \\\"mainContent\\\" })\\n \\n # Really, none of these games have a home team.\\n\\n #teams = get_contents(contents.find(\\\"div\\\", \\\"bold large teams\\\"))\\n team1 = get_contents(contents.find(\\\"div\\\", \\\"lnupTeam\\\").find(\\\"div\\\", \\\"bold medium\\\"))\\n team2 = get_contents(contents.find(\\\"div\\\", \\\"lnupTeam away\\\").find(\\\"div\\\", \\\"bold medium\\\"))\\n\\n #import pdb; pdb.set_trace()\\n\\n \\n\\n #try:\\n # team1, team2 = [e.strip() for e in teams.split(\\\"-\\\")]\\n #except:\\n # import pdb; pdb.set_trace()\\n\\n score_string = get_contents(contents.find(\\\"div\\\", \\\"bold large result\\\"))\\n\\n if 'a.e.t.' in score_string:\\n score_string = score_string.split('a.e.t')[0]\\n\\n team1_score, team2_score = [int(e) for e in score_string.split(\\\"(\\\")[0].split(\\\":\\\")]\\n\\n # Implement this if header order is more unpredictable.\\n #game_head = contents.findAll(\\\"thead\\\")\\n #head_teas = game_head.findAll(\\\"td\\\", text=True\\n\\n game_header = contents.find(\\\"thead\\\")\\n game_info = contents.find(\\\"tbody\\\")\\n \\n game_ths = [get_contents(e) for e in game_header.findAll(\\\"td\\\")]\\n game_tds = [get_contents(e) for e in game_info.findAll(\\\"td\\\")]\\n\\n game_dict = dict(zip(game_ths, game_tds))\\n\\n\\n match = date_string = time = location = attendance = None\\n\\n #import pdb; pdb.set_trace()\\n\\n if 'Match' in game_dict:\\n match = game_dict['Match']\\n\\n if 'Date' in game_dict:\\n date_string = game_dict['Date']\\n\\n #'Time' \\n\\n if 'Attendance' in game_dict:\\n if game_dict['Attendance']:\\n attendance = int(game_dict['Attendance'])\\n\\n if 'Venue / Stadium' in game_dict:\\n location = game_dict['Venue / Stadium']\\n\\n for e in 'Match', 'Date', 'Attendance', 'Venue / Stadium', 'Time': \\n if e in game_dict:\\n game_dict.pop(e)\\n #print(game_dict.keys())\\n\\n # Standardize city and stadium fields\\n try:\\n city, stadium = [e.strip() for e in location.rsplit(\\\"/\\\", 1)]\\n except:\\n import pdb; pdb.set_trace()\\n\\n # Avoid duplication of city name?\\n if stadium.endswith(city):\\n nlocation = stadium\\n else:\\n nlocation = \\\"%s, %s\\\" % (stadium, city)\\n\\n date = datetime.datetime.strptime(date_string.strip(), \\\"%d %B %Y\\\")\\n\\n return {\\n \\\"team1\\\": team1,\\n \\\"team2\\\": team2,\\n 'team1_score': team1_score,\\n 'team2_score': team2_score,\\n 'competition': competition,\\n 'season': str(date.year),\\n \\\"date\\\": date,\\n \\\"location\\\": nlocation,\\n \\\"attendance\\\": attendance,\\n \\\"sources\\\": [url],\\n }\",\n \"def run(self):\\n\\t\\tfor source in self.sources:\\n\\t\\t\\tstringutil.print_color(Fore.GREEN, 'Downloading from Source: %s' % source.get_alias())\\n\\t\\t\\tfor r in source.get_elements():\\n\\t\\t\\t\\tr.set_source(source)\\n\\t\\t\\t\\tself._queue.put(r)\\n\\n\\t\\t\\t\\t# Extra tracking stuff below:\\n\\t\\t\\t\\twith self._c_lock:\\n\\t\\t\\t\\t\\tself._total_count+= 1\\n\\t\\t\\t\\tif self._testing_cache is not None:\\n\\t\\t\\t\\t\\tself._testing_cache.append(r)\\n\\t\\t#print(\\\"Element loading complete.\\\\n\\\")\\n\\t\\tself._running = False\",\n \"def load_and_process_fixtures_data(self) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:\\n df = self._dbmanager.query_fixtures_data(self._seasons)\\n if df.empty:\\n raise ValueError(\\\"Empty fixtures dataframe.\\\")\\n\\n df = self._drop_last_season_championship_matches(df)\\n\\n self.teams = get_unique_teams(df)\\n self.last_season_teams = get_last_season_unique_teams(df)\\n # Get fixtures ids for each team\\n teams_fixtures_ids = {t: df[(df[\\\"home\\\"] == t) | (df[\\\"away\\\"] == t)].loc[:, \\\"id\\\"].tolist() for t in self.teams}\\n\\n self._check_missing_columns(df)\\n df = self._check_nan_values(df, teams_fixtures_ids)\\n teams_fixtures_ids = self._discard_matches(df, teams_fixtures_ids)\\n\\n if not self._resume:\\n # Use last n ids for predictions and last m ids for testing\\n for t in self.last_season_teams:\\n self.predict_fixtures_ids[t] = teams_fixtures_ids[t][-NPREDICT:]\\n teams_fixtures_ids[t] = teams_fixtures_ids[t][:-NPREDICT]\\n\\n self.test_fixtures_ids[t] = teams_fixtures_ids[t][-self._ntest:]\\n teams_fixtures_ids[t] = teams_fixtures_ids[t][:-self._ntest]\\n\\n # Rest of ids is counted as train set\\n self.train_fixtures_ids = teams_fixtures_ids\\n else:\\n if self.teams_names_bitlen != self._model_settings[\\\"teams_names_bitlen\\\"]:\\n raise ValueError(\\\"Current bitlength required to encode all teams names is higher than previous one.\\\")\\n\\n # Check whether teams has not changed\\n if self.teams != self._model_settings[\\\"teams\\\"]:\\n raise ValueError(\\\"Teams differ from previous run. \\\\n\\\"\\n f\\\"New: {self.teams} \\\\n\\\"\\n f\\\"Old: {self._model_settings['teams']}\\\")\\n\\n if self.last_season_teams != self._model_settings[\\\"last_season_teams\\\"]:\\n raise ValueError(\\\"Last season teams differ from previous run. \\\\n\\\"\\n f\\\"New: {self.last_season_teams} \\\\n\\\"\\n f\\\"Old: {self._model_settings['last_season_teams']}\\\")\\n\\n # Check whether fixtures ids match from previous run\\n for t in self.last_season_teams:\\n predict_fixtures_ids = teams_fixtures_ids[t][-NPREDICT:]\\n teams_fixtures_ids[t] = teams_fixtures_ids[t][:-NPREDICT]\\n\\n test_fixtures_ids = teams_fixtures_ids[t][-self._ntest:]\\n teams_fixtures_ids[t] = teams_fixtures_ids[t][:-self._ntest]\\n\\n if predict_fixtures_ids != self._model_settings[\\\"predict_fixtures_ids\\\"][t]:\\n raise ValueError(f\\\"{t} predict fixtures ids differ from previous run. \\\\n\\\"\\n f\\\"New: {predict_fixtures_ids} \\\\n\\\"\\n f\\\"Old: {self._model_settings['predict_fixtures_ids'][t]}\\\")\\n if test_fixtures_ids != self._model_settings['test_fixtures_ids'][t]:\\n raise ValueError(f\\\"{t} test fixtures ids differ from previous run. \\\\n\\\"\\n f\\\"New: {test_fixtures_ids} \\\\n\\\"\\n f\\\"Old: {self._model_settings['test_fixtures_ids'][t]}\\\")\\n if teams_fixtures_ids[t] != self._model_settings['train_fixtures_ids'][t]:\\n raise ValueError(f\\\"{t} train fixtures ids differ from previous run. \\\\n\\\"\\n f\\\"New: {teams_fixtures_ids[t]} \\\\n\\\"\\n f\\\"Old: {self._model_settings['train_fixtures_ids'][t]}\\\")\\n\\n # Checks passed, load previously saved data\\n self.teams = self._model_settings[\\\"teams\\\"]\\n self.last_season_teams = self._model_settings[\\\"last_season_teams\\\"]\\n self.train_fixtures_ids = self._model_settings[\\\"train_fixtures_ids\\\"]\\n self.test_fixtures_ids = self._model_settings[\\\"test_fixtures_ids\\\"]\\n self.predict_fixtures_ids = self._model_settings[\\\"predict_fixtures_ids\\\"]\\n\\n self._check_season_gaps_in_teams_matches(df)\\n\\n # Split original dataset into train, test, and predict datasets\\n df_train, df_test, df_predict = self._mask_out_dataset(df)\\n self._get_unique_teams_from_datasets(df_train, df_test, df_predict)\\n\\n self._check_changes_in_teams()\\n self._count_samples(df_train, df_test, df_predict)\\n\\n return df_train, df_test, df_predict\",\n \"def games_recommendation_trainer(directory: str):\\n try:\\n # load training dataset\\n sub_directory = os.path.join(directory, 'Users_data', 'games_daily_train_selected_played.csv')\\n user_game_pairs, user_index_dict, app_index_dict = load_games_training_set(sub_directory)\\n \\n # load games description\\n sub_directory = os.path.join(directory, 'Games_data', 'games_description_stem.json')\\n corpus = load_games_description_feature(sub_directory, app_index_dict)\\n \\n # load games genres\\n sub_directory = os.path.join(directory, 'Games_data', 'games_genres.csv')\\n games_genres_matrix = get_games_genres(sub_directory, app_index_dict)\\n \\n # load games price\\n sub_directory = os.path.join(directory, 'Games_data', 'games_price_modify.csv')\\n price_matrix = games_price_feature(sub_directory, app_index_dict)\\n \\n except OSError:\\n logging.getLogger(__name__).info(sub_directory + \\n \\\" does not exist. Please run 'osna collect' and 'osna preprocess'.\\\")\\n return\\n \\n # make directory for results\\n result_directory = os.path.join(directory, 'Results')\\n if not os.path.isdir(result_directory): os.makedirs(result_directory)\\n \\n params_range = {'min_df': [3, 5], 'max_df': [0.8, 0.5], 'isOtherFeatures': [True, False]}\\n params_list = []\\n for min_df in params_range['min_df']:\\n for max_df in params_range['max_df']:\\n for isOtherFeatures in params_range['isOtherFeatures']:\\n params = defaultdict(int)\\n params['min_df'] = min_df\\n params['max_df'] = max_df\\n params['isOtherFeatures'] = isOtherFeatures\\n params_list.append(params)\\n\\n \\n # k-fold cross validation\\n k = 5\\n k_ = 1 # for faster training speed, supposed to be 5\\n data_train, data_test = user_games_split(len(user_game_pairs), k)\\n \\n # Finding best parameters for content-based model\\n# best_params = {'min_df': 3, 'max_df': 0.5, 'isOtherFeatures': True}\\n best_params = {'recall_content': 0}\\n for params in params_list:\\n \\n tfidf_matrix, tf = games_description_tokenize(corpus, params)\\n \\n for i in range(k_):\\n \\n train_data_matrix, test_data_matrix = users_games_matrix_trans(user_game_pairs, data_train[i], data_test[i], len(user_index_dict), len(app_index_dict)) \\n games_features_matrix = get_games_features_matrix(tfidf_matrix, games_genres_matrix, price_matrix, params) \\n users_games_rating = train_content_based(train_data_matrix, games_features_matrix)\\n recall_content = osna.evaluate.recall_rate(users_games_rating, user_game_pairs, data_test[i], [30])\\n params['recall_content'] += recall_content[30] / k_\\n \\n if params['recall_content'] > best_params['recall_content']: \\n best_params = params\\n logging.getLogger(__name__).info(params)\\n \\n logging.getLogger(__name__).info('best_params: '+str(best_params))\\n save_recall_content(result_directory, params_list)\\n \\n # Evaluating all four models\\n topN_range = [5, 10, 15, 20, 25, 30]\\n params = best_params.copy()\\n params.pop('recall_content', None)\\n tfidf_matrix, tf = games_description_tokenize(corpus, params)\\n \\n for i in range(k_):\\n \\n train_data_matrix, test_data_matrix = users_games_matrix_trans(user_game_pairs, data_train[i], data_test[i], len(user_index_dict), len(app_index_dict)) \\n \\n # Baseline - Normal distribution\\n users_games_rating = train_random(train_data_matrix)\\n recall_base = osna.evaluate.recall_rate(users_games_rating, user_game_pairs, data_test[i], topN_range)\\n update_recall_rate(params, 'recall_base', recall_base, k_)\\n rmse_base = osna.evaluate.RMSE(users_games_rating, test_data_matrix)\\n params['rmse_base'] += rmse_base / k_\\n \\n # Content-based model\\n games_features_matrix = get_games_features_matrix(tfidf_matrix, games_genres_matrix, price_matrix, params) \\n users_games_rating = train_content_based(train_data_matrix, games_features_matrix)\\n recall_content = osna.evaluate.recall_rate(users_games_rating, user_game_pairs, data_test[i], topN_range)\\n update_recall_rate(params, 'recall_content', recall_content, k_)\\n rmse_content = osna.evaluate.RMSE(users_games_rating, test_data_matrix)\\n params['rmse_content'] += rmse_content / k_\\n save_training_model(result_directory, {'games_features_matrix': games_features_matrix, 'app_index_dict': app_index_dict})\\n \\n # CF Item-Item\\n CF_item_prediction = CF_item_predict(train_data_matrix)\\n CF_recall_item = osna.evaluate.recall_rate(CF_item_prediction, user_game_pairs, data_test[i], topN_range)\\n update_recall_rate(params, 'recall_CF_item', CF_recall_item, k_)\\n CF_RMSE_item = osna.evaluate.RMSE(CF_item_prediction, test_data_matrix)\\n params['rmse_CF_item'] += CF_RMSE_item / k_\\n \\n # CF User-User\\n CF_user_prediction = CF_user_predict(train_data_matrix)\\n CF_recall_user = osna.evaluate.recall_rate(CF_user_prediction, user_game_pairs, data_test[i], topN_range)\\n update_recall_rate(params, 'recall_CF_user', CF_recall_user, k_)\\n CF_RMSE_user = osna.evaluate.RMSE(CF_user_prediction, test_data_matrix)\\n params['rmse_CF_user'] += CF_RMSE_user / k_\\n \\n logging.getLogger(__name__).info(params)\\n \\n save_result_all(result_directory, params)\",\n \"def scrape_game_urls(self):\\n scrapped_games = pd.DataFrame(columns=['game_link', 'game_quality', 'game_comments_qtd'])\\n for i in range(self.initial_page, self.final_page+1):\\n print(\\\"getting links of page %d ...\\\" % i)\\n url = self.base_urls_page + str(i)\\n self.driver.get(url)\\n\\n evn_game_links, evn_game_qualities, evn_game_comments_number = self.scrape_page_games('evn_list')\\n odd_game_links, odd_game_qualities, odd_game_comments_number = self.scrape_page_games('odd_list')\\n\\n scrapped_games = pd.concat([\\n scrapped_games,\\n pd.DataFrame({\\n 'game_link': evn_game_links + odd_game_links,\\n 'game_quality': evn_game_qualities + odd_game_qualities,\\n 'game_comments_qtd': evn_game_comments_number + odd_game_comments_number\\n })\\n ])\\n\\n scrapped_games = self.format_links_table(scrapped_games)\\n self.scrapped_games = scrapped_games\",\n \"def explore(self):\\n\\n i = 0\\n while True:\\n i += 1\\n \\n state_counts = {game.__class__.__name__: Counter() for game in self.games} \\n\\n policies_prime = []\\n pi_sum = 0\\n v_sum = 0\\n counter = 0\\n \\n # bookkeeping\\n log.info(f'Starting Exploration Iteration #{i} ...')\\n\\n # for task in tasks...\\n for _ in range(self.args['taskBatchSize']):\\n\\n # create deepcopy for training a theta'\\n policy_prime = copy.deepcopy(self.nnet)\\n \\n # sample a game (task)\\n game = np.random.choice(self.games, p=self.probs)\\n log.info(f'Sampled game {type(game).__name__} ...')\\n\\n # multiprocess to get our training examples\\n iterationTrainExamples = deque([], maxlen=self.args['maxlenOfQueue'])\\n iterationTrainExamples = run_apply_async_multiprocessing(self.executeEpisode, [(MCTS(game, self.nnet, self.args), type(game)(), self.args.copy())] * self.args['numEps'], self.args['numWorkers'], desc='Self Play')\\n iterationTrainExamples, iter_counters = zip(*iterationTrainExamples)\\n\\n iterationTrainExamples = list(itertools.chain.from_iterable(iterationTrainExamples))\\n state_counts[game.__class__.__name__] += sum(iter_counters, Counter())\\n\\n # shuffle examples before training\\n shuffle(iterationTrainExamples)\\n\\n # train our network\\n pi_v_losses = policy_prime.train(iterationTrainExamples)\\n\\n policies_prime.append(policy_prime.state_dict())\\n\\n for pi,v in pi_v_losses:\\n pi_sum += pi\\n v_sum += v\\n counter += 1\\n \\n # compute average parameters and load into self.nnet\\n self.nnet.load_average_params(policies_prime)\\n\\n # training new network, keeping a copy of the old one\\n self.nnet.save_checkpoint(folder=self.args['checkpoint'] + '/exploring', filename='temp.pth.tar')\\n self.pnet.load_checkpoint(folder=self.args['checkpoint'] + '/exploring', filename='temp.pth.tar')\\n pmcts = MCTS(self.games[0], self.pnet, self.args)\\n\\n\\n # Arena if we choose to run it\\n if self.args['arenaComparePerGame'] > 0:\\n # ARENA\\n nmcts = MCTS(self.games[0], self.nnet, self.args)\\n\\n log.info('PITTING AGAINST PREVIOUS VERSION')\\n arena = Arena()\\n pwins, nwins, draws = arena.playGames(self.pnet, self.nnet, self.args, self.games)\\n\\n log.info('NEW/PREV WINS : %d / %d ; DRAWS : %d' % (nwins, pwins, draws))\\n if pwins + nwins == 0 or float(nwins) / (pwins + nwins) < self.args['updateThreshold']:\\n log.info('REJECTING NEW MODEL')\\n self.nnet.load_checkpoint(folder=self.args['checkpoint'] + '/exploring', filename='temp.pth.tar')\\n else:\\n log.info('ACCEPTING NEW MODEL')\\n self.nnet.save_checkpoint(folder=self.args['checkpoint'] + '/exploring', filename=self.getCheckpointFile(i))\\n self.nnet.save_checkpoint(folder=self.args['checkpoint'] + '/exploring', filename='best.pth.tar')\\n\\n log.info('Iteration Complete. Writing counts to \\\"%s/%s\\\"...', *self.args['json_folder_file'])\\n # create the json file\\n path = os.path.join(self.args['json_folder_file'][0], self.args['json_folder_file'][1])\\n with open(path, 'a+') as f:\\n if os.stat(path).st_size == 0: ## file just created/empty\\n log.info('No counts found. Writing to empty file.')\\n old_counts = {game.__class__.__name__: Counter() for game in self.games}\\n else: ## load the counts from the file\\n log.info('Loading counts...')\\n f.seek(0)\\n str_counts = f.read()\\n # print('STRING OF JSON:', type(str_counts), str_counts)\\n old_counts = json.loads(str_counts)\\n old_counts = {game: Counter(v) for game, v in old_counts.items()}\\n master_counts = {game.__class__.__name__: state_counts[game.__class__.__name__]+old_counts[game.__class__.__name__] for game in self.games}\\n # countiung logic: turn {gametype -> Counter} into {gametype -> {state -> count}}\\n master_counts = {game: dict(counter) for game, counter in master_counts.items()}\\n log.info('Writing...')\\n f.truncate(0) #clear file\\n json.dump(master_counts, f)\\n log.info('Counts written to json file \\\"%s/%s\\\"...', *self.args['json_folder_file'])\",\n \"def extract_data():\\n books = WebScraper().get_top_100_data()\\n time.sleep(2)\\n BookDetailsWebScrapper().save_book_details(books)\\n _save_extract_state(books)\",\n \"def startGame():\\n #roundnumber\\n eel.updateRoundNumber()\\n # start page\\n eel.updateStartPage([startPage.getTitle(), startPage.getUrl()])\\n eel.updateStartPageDescription(startPage.getFirstSentence())\\n # goal page\\n eel.updateGoalPage([goalPage.getTitle(), goalPage.getUrl()])\\n eel.updateGoalPageDescription(goalPage.getFirstSentence())\\n # ui updates\\n eel.updateCurrentPage(\\n [wikiPageStackTrace[-1].getTitle(), wikiPageStackTrace[-1].getUrl()])\\n eel.updateCurrentPageDescription(wikiPageStackTrace[-1].getFirstSentence())\\n eel.printInPageList(wikiPageStackTrace[-1].getOnlyLinksListJS())\\n # loader\\n time.sleep(0.5)\\n eel.hideLoader()\",\n \"def reInitAndRun(self):\\n self.playlists = self.readPlaylistData()\\n self.audioDF = self.readAudioData(shouldProcess=True)\\n self.clusterLabels = []\\n self.models = Clusterers(k=len(self.playlists))\\n self.processAndCluster()\\n self.analyzeResults()\",\n \"def perform_scraping(current_session):\\r\\n\\r\\n # List Array storing all relevant decision information\\r\\n final_data_fetch = []\\r\\n pagination_index = global_constants['NUMBER_PAGE_TO_SCRAPE_FIRST']\\r\\n while pagination_index < global_constants['NUMBER_PAGE_TO_SCRAPE_LAST']:\\r\\n print(\\\"Page:\\\", pagination_index, \\\" Collected records:\\\", len(final_data_fetch))\\r\\n\\r\\n # Get relevant admit-reject page based on pagination value\\r\\n result = current_session.get(global_constants['ALL_RESULTS_URL'] + str(pagination_index),\\r\\n headers=dict(referer=global_constants['ALL_RESULTS_URL']))\\r\\n tree = lxml_html.fromstring(result.content)\\r\\n\\r\\n # Get Nodes containing individual decisions for each page(approx 20 per page)\\r\\n decision_buckets = tree.xpath('//*[@class=\\\"row\\\"]/div[@class=\\\"col-sm-6\\\"]/div[@class=\\\"panel panel-warning\\\"]/div[@class=\\\"panel-body\\\"]')\\r\\n\\r\\n # If decision buckets are empty, captcha page has been encountered\\r\\n if len(decision_buckets) == 0:\\r\\n print(\\\"Captcha Time\\\")\\r\\n time.sleep(120)\\r\\n continue\\r\\n\\r\\n for individual_decision_bucket in decision_buckets:\\r\\n\\r\\n current_admit_status = ((individual_decision_bucket.xpath('./div[1]/div[2]/label'))[0]).text.strip()\\r\\n\\r\\n # Fetch results only if ADMIT or REJECT\\r\\n if current_admit_status.lower() == 'admit' or current_admit_status.lower() == 'reject':\\r\\n\\r\\n # Get relevant information from html page returned in response\\r\\n current_bucket_university_course = ((individual_decision_bucket.xpath('./div[1]/div[1]/h4/small'))[0]).text.replace(\\\"\\\\n\\\",\\\"\\\").strip()\\r\\n current_gre = get_gre_or_toefl(((((individual_decision_bucket.xpath('./div[2]/div[1]'))[0]).getchildren())[1]).tail)\\r\\n current_toefl = get_gre_or_toefl(((((individual_decision_bucket.xpath('./div[2]/div[2]'))[0]).getchildren())[1]).tail)\\r\\n current_gpa = get_gpa(((((individual_decision_bucket.xpath('./div[2]/div[3]'))[0]).getchildren())[1]).tail)\\r\\n current_workex = get_workex_months(((((individual_decision_bucket.xpath('./div[2]/div[4]'))[0]).getchildren())[1]).tail)\\r\\n\\r\\n current_university, current_course = split_bucket_university_course(current_bucket_university_course.lower())\\r\\n # Append decision information to final bucket only if minimum criteria met\\r\\n if current_university is not None and filter_criteria_met(current_gre, current_gpa, current_toefl):\\r\\n\\r\\n # Get UG College from profile of user\\r\\n profile_page_path = ((individual_decision_bucket.xpath('./div[1]/div[1]/h4/a'))[0]).attrib['href']\\r\\n profile_result = current_session.get(global_constants['HOME_PAGE'] + profile_page_path,\\r\\n headers=dict(referer=global_constants['PAST_RESULTS_URL']))\\r\\n profile_tree = lxml_html.fromstring(profile_result.content)\\r\\n ug_details_bucket = (profile_tree.xpath('//div[@class=\\\"col-sm-12 card\\\"][1]'))\\r\\n if len(ug_details_bucket) >= 1:\\r\\n ug_details_bucket = ug_details_bucket[0]\\r\\n current_ug_course = ((ug_details_bucket.xpath('./div[1]/div[7]/p[1]/b[1]'))[0]).text.replace(\\\"\\\\n\\\", \\\"\\\").strip()\\r\\n current_ug_college = ((ug_details_bucket.xpath('./div[1]/div[7]/p[2]'))[0]).text.replace(\\\"\\\\n\\\", \\\"\\\").strip()\\r\\n\\r\\n final_data_fetch.append([current_course, current_university, current_gpa, current_gre, current_toefl,\\r\\n current_workex, current_ug_course, current_ug_college, current_admit_status])\\r\\n\\r\\n # Add sleep time to allow for web scraping in undetected manner\\r\\n sleep_delay = random.choice([0, 1, 2, 3])\\r\\n time.sleep(sleep_delay)\\r\\n pagination_index += 1\\r\\n\\r\\n # Export final_data to excel sheet\\r\\n export_to_file(final_data_fetch)\",\n \"def step(self, actions):\\n\\n representative_data = []\\n original_data = []\\n\\n actions = np.array(actions).reshape(3, -1)\\n\\n for np_data, df_data, calculator, som, action in zip(self.np_data_list, self.df_data_list, self.calculators, self.som_objects, actions):\\n\\n representative_days, cluster_numbers = calculator.get_representative_days(\\n som, np_data, action)\\n\\n representative_days = pd.DataFrame(representative_days)\\n\\n representative_days = self.wide_to_long(representative_days)\\n approximation_calc = ApproximateData(df_data, 4)\\n representative_days = ApproximateData(df_data, 4).get_load_duration_curve(\\n representative_days, cluster_numbers)\\n\\n representative_data.append(representative_days)\\n\\n # original_days = approximation_calc.get_load_duration_curve(\\n # year=\\\"2013\\\")\\n\\n\\n\\n # original_data.append(original_days)\\n\\n # metrics_calculator = Metrics(original_data[0], representative_data[0], original_data[1],\\n # representative_data[1], original_data[2], representative_data[2], \\\"dc\\\")\\n\\n pv_original = pd.read_csv(\\n '{}data/processed/resources/pv_processed.csv'.format(project_dir))\\n wind_original = pd.read_csv(\\n '{}data/processed/resources/onshore_processed.csv'.format(project_dir))\\n load_original = pd.read_csv(\\n '{}data/processed/demand/load_NG/load_processed_normalised.csv'.format(project_dir))\\n\\n pv_original_ldcs, wind_original_ldcs, load_original_ldcs = get_each_ldc(pv_original, wind_original, load_original)\\n\\n multi_year_metrics_calculator = MultiYearMetrics(pv_original_ldcs, representative_data[0], wind_original_ldcs, representative_data[1], load_original_ldcs, representative_data[2], self.year_start)\\n multi_year_metrics = multi_year_metrics_calculator.get_multi_year_average_metrics(\\\"dc\\\")\\n multi_year_metrics = multi_year_metrics.reset_index()\\n # logger.debug(\\\"multi_year_metrics: \\\\n{}\\\".format(multi_year_metrics))\\n\\n nrmse = multi_year_metrics[multi_year_metrics['metric'] == 'nrmse dc'].iloc[0].value\\n rae = multi_year_metrics[multi_year_metrics['metric'] == 'rae dc'].iloc[0].value\\n correlation = multi_year_metrics[multi_year_metrics['metric'] == 'correlation'].iloc[0].value\\n\\n # error_metrics = metrics_calculator.get_mean_error_metrics()\\n # nrmse = error_metrics.iloc[1].value\\n # rae = error_metrics.iloc[2].value\\n # correlation = error_metrics.iloc[0].value\\n # reward = -error_metrics.value.sum()\\n # logger.info(\\\"error_metrics: {}\\\".format(error_metrics))\\n # logger.info(\\\"error_metrics: {}\\\".format(error_metrics.iloc[0]))\\n\\n # return reward\\n return nrmse, rae, correlation\",\n \"def read_data(self):\\n print 'Getting team stats...'\\n self.team_stats = get_team_stats(self.recent_years)\\n\\n print 'Getting matches...'\\n self.matches = get_matches(\\n with_team_stats=True,\\n duplicate_with_reversed=self.duplicate_with_reversed,\\n exclude_ties=self.exclude_ties,\\n recent_years=self.recent_years,\\n use_these_team_stats=self.team_stats,\\n )\",\n \"def get_data(args, load_extracted=True):\\n path = args.data_path1\\n tokenizer_en = tokener()\\n table = str.maketrans(\\\"\\\", \\\"\\\", '\\\"#$%&\\\\'()*+-/:;<=>@[\\\\\\\\]^_`{|}~')\\n if load_extracted:\\n df = load_pickle(\\\"df_unencoded.pkl\\\")\\n else:\\n logger.info(\\\"Extracting CNN stories...\\\")\\n df = pd.DataFrame(index=[i for i in range(len(os.listdir(path)))], columns=[\\\"body\\\", \\\"highlights\\\"])\\n for idx, file in tqdm(enumerate(os.listdir(path)), total=len(os.listdir(path))):\\n with open(os.path.join(path, file), encoding=\\\"utf8\\\") as csv_file:\\n csv_reader = csv.reader(csv_file)\\n text = \\\"\\\"\\n for row in csv_reader:\\n text += \\\"\\\".join(t for t in row)\\n highlights = re.search(\\\"@highlight(.*)\\\", text).group(1)\\n highlights = highlights.replace(\\\"@highlight\\\", \\\". \\\")\\n body = text[:re.search(\\\"@highlight\\\", text).span(0)[0]]\\n df.iloc[idx][\\\"body\\\"] = body\\n df.iloc[idx][\\\"highlights\\\"] = highlights\\n \\n if len(args.data_path2) > 2:\\n path = args.data_path2\\n logger.info(\\\"Extracting dailymail stories...\\\")\\n df1 = pd.DataFrame(index=[i for i in range(len(os.listdir(path)))], columns=[\\\"body\\\", \\\"highlights\\\"])\\n for idx, file in tqdm(enumerate(os.listdir(path)), total=len(os.listdir(path))):\\n with open(os.path.join(path, file), encoding=\\\"utf8\\\") as csv_file:\\n csv_reader = csv.reader(csv_file)\\n text = \\\"\\\"\\n for row in csv_reader:\\n text += \\\"\\\".join(t for t in row)\\n highlights = re.search(\\\"@highlight(.*)\\\", text).group(1)\\n highlights = highlights.replace(\\\"@highlight\\\", \\\". \\\")\\n body = text[:re.search(\\\"@highlight\\\", text).span(0)[0]]\\n df1.iloc[idx][\\\"body\\\"] = body\\n df1.iloc[idx][\\\"highlights\\\"] = highlights\\n df = pd.concat([df, df1], ignore_index=True)\\n del df1\\n \\n save_as_pickle(\\\"df_unencoded.pkl\\\", df)\\n logger.info(\\\"Dataset length: %d\\\" % len(df)) \\n \\n if (args.level == \\\"word\\\") or (args.level == \\\"char\\\"):\\n logger.info(\\\"Tokenizing and cleaning extracted text...\\\")\\n df.loc[:, \\\"body\\\"] = df.apply(lambda x: clean_and_tokenize_text(x[\\\"body\\\"], table, tokenizer_en), axis=1)\\n df.loc[:, \\\"highlights\\\"] = df.apply(lambda x: clean_and_tokenize_text(x[\\\"highlights\\\"], table, tokenizer_en), \\\\\\n axis=1)\\n df.loc[:, \\\"body_length\\\"] = df.apply(lambda x: len(x['body']), axis=1)\\n df.loc[:, \\\"highlights_length\\\"] = df.apply(lambda x: len(x['highlights']), axis=1)\\n df = df[(df[\\\"body_length\\\"] > 0) & (df[\\\"highlights_length\\\"] > 0)]\\n \\n logger.info(\\\"Limiting to max features length, building vocab and converting to id tokens...\\\")\\n df = df[df[\\\"body_length\\\"] <= args.max_features_length]\\n v = vocab(level=args.level)\\n v.build_vocab(df[\\\"body\\\"])\\n v.build_vocab(df[\\\"highlights\\\"])\\n df.loc[:, \\\"body\\\"] = df.apply(lambda x: v.convert_w2idx(x[\\\"body\\\"]), axis=1)\\n df.loc[:, \\\"highlights\\\"] = df.apply(lambda x: v.convert_w2idx(x[\\\"highlights\\\"]), axis=1)\\n df.loc[:, \\\"highlights\\\"] = df.apply(lambda x: pad_sos_eos(x[\\\"highlights\\\"], 0, 2), axis=1)\\n save_as_pickle(\\\"df_encoded.pkl\\\", df)\\n save_as_pickle(\\\"vocab.pkl\\\", v)\\n \\n elif args.level == \\\"bpe\\\":\\n encoder = Encoder(vocab_size=args.bpe_vocab_size, pct_bpe=args.bpe_word_ratio, word_tokenizer=tokenizer_en.tokenize)\\n df.loc[:, \\\"body\\\"] = df.apply(lambda x: clean_and_tokenize_text(x[\\\"body\\\"], table, tokenizer_en, clean_only=True), axis=1)\\n df.loc[:, \\\"highlights\\\"] = df.apply(lambda x: clean_and_tokenize_text(x[\\\"highlights\\\"], table, tokenizer_en, clean_only=True), \\\\\\n axis=1)\\n logger.info(\\\"Training bpe, this might take a while...\\\")\\n text_list = list(df[\\\"body\\\"])\\n text_list.extend(list(df[\\\"highlights\\\"]))\\n encoder.fit(text_list); del text_list\\n \\n logger.info(\\\"Tokenizing to ids and limiting to max features length...\\\")\\n df.loc[:, \\\"body\\\"] = df.apply(lambda x: next(encoder.transform([x[\\\"body\\\"]])), axis=1)\\n df.loc[:, \\\"highlights\\\"] = df.apply(lambda x: next(encoder.transform([x[\\\"highlights\\\"]])), axis=1)\\n df.loc[:, \\\"body_length\\\"] = df.apply(lambda x: len(x['body']), axis=1)\\n df.loc[:, \\\"highlights_length\\\"] = df.apply(lambda x: len(x['highlights']), axis=1)\\n df = df[(df[\\\"body_length\\\"] > 0) & (df[\\\"highlights_length\\\"] > 0)]\\n df = df[df[\\\"body_length\\\"] <= args.max_features_length]\\n \\n '''\\n logger.info(\\\"Converting tokens to ids...\\\")\\n df.loc[:, \\\"body\\\"] = df.apply(lambda x: next(encoder.transform(list(\\\" \\\".join(t for t in x[\\\"body\\\"])))),\\\\\\n axis=1)\\n df.loc[:, \\\"highlights\\\"] = df.apply(lambda x: next(encoder.transform(list(\\\" \\\".join(t for t in x[\\\"highlights\\\"])))),\\\\\\n axis=1)\\n '''\\n df.loc[:, \\\"highlights\\\"] = df.apply(lambda x: pad_sos_eos(x[\\\"highlights\\\"], encoder.word_vocab[\\\"__sos\\\"], encoder.word_vocab[\\\"__eos\\\"]),\\\\\\n axis=1)\\n \\n save_as_pickle(\\\"df_encoded.pkl\\\", df)\\n encoder.save(\\\"./data/vocab.pkl\\\")\\n return df\",\n \"def scrape_pipeline(args):\\n kickoff = args.kickoff\\n fname = args.fname\\n d = DbHelper()\\n s = Scraper()\\n c = Crawler(20)\\n if fname is not None:\\n app_names = pd.read_csv(fname)['packageName'].tolist()\\n apps = [list(a) for a in zip(app_names, d.app_names_to_uuids(app_names))]\\n else:\\n apps = None\\n\\n # start by updating top apps\\n if not args.skip_top:\\n logger.info(\\\"getting top apps...\\\")\\n new_top_list = c.get_top_apps_list()\\n logger.info(\\\"scraping top apps not in DB...\\\")\\n s.scrape_missing(new_top_list, compare_top=True)\\n logger.info(\\\"updating top apps...\\\")\\n d.update_top_apps(new_top_list)\\n\\n if kickoff == True:\\n s = None\\n if fname is None:\\n # use crawler to get list of package names\\n logger.error(\\\"Crawler for package names not implemented yet\\\")\\n return\\n else:\\n # use specified file of package names\\n s = Scraper(input_file=fname)\\n\\n # use scraper\\n logger.info(\\\"Starting efficient scrape...\\\")\\n s.efficient_scrape()\\n logger.info(\\\"...efficient scrape done\\\")\\n else:\\n # use updater\\n logger.info(\\\"Starting updater...\\\")\\n if fname is None:\\n u = Updater()\\n else:\\n u = Updater(input_file=fname)\\n u.update_apps()\\n logger.info(\\\"...update done\\\")\\n\\n # crawl privacy policies\\n c.crawl_app_privacy_policies(app_list=apps)\\n\\n if args.no_decompile:\\n # download only\\n logger.info(\\\"Starting download...\\\")\\n downloader = Downloader()\\n if apps is None:\\n downloader.download_all_from_db(top=True)\\n else:\\n downloader.download(apps)\\n logger.info(\\\"...done\\\")\\n else:\\n # download/decompile\\n logger.info(\\\"Starting download and decompile...\\\")\\n download_decompile_all()\\n logger.info(\\\"...download and decompile done\\\")\\n logger.info(\\\"run analysis pipeline now\\\")\",\n \"def run(self):\\n\\n self.preprocess()\\n self.restore_ratings()\\n self.prepare_UI()\\n self.loop_through_units()\\n self.cleanup()\\n\\n print('\\\\nAll Done - results are available in:\\\\n\\\\t{}'.format(self.out_dir))\",\n \"def scrape_teams():\\n url_list = []\\n name_list = []\\n pos_list = []\\n csv_path = \\\"../CSV_data/ActivePlayerList.csv\\\"\\n with open(csv_path) as csv_file:\\n reader = csv.reader(csv_file,skipinitialspace=True)\\n for row in reader:\\n pos_list.append(row[0])\\n name_list.append(row[1])\\n profile_url = (row[2]+\\\"profile\\\").replace(\\\"gamelogs?season=\\\", \\\"\\\")\\n url_list.append(profile_url)\\n\\n for pos,name,url in zip(pos_list,name_list,url_list):\\n file_path = \\\"../CSV_data/Test_Team_Scraping\\\"\\n try:\\n soup = BeautifulSoup(ul.urlopen(url).read(), \\\"html.parser\\\")\\n # assign field names\\n player_name = soup.find(\\\"span\\\", {\\\"class\\\" : \\\"player-name\\\"}).string\\n print player_name, url\\n fieldNames = soup.find(\\\"tr\\\", {\\\"class\\\" : \\\"player-table-key\\\"}).findAll(\\\"td\\\")\\n numColumns = len(fieldNames)\\n # pull the statistics\\n #table = soup.findAll(\\\"table\\\", {\\\"class\\\":\\\"data-table1\\\"})\\n tables = soup.find_all(\\\"table\\\", { \\\"summary\\\" : \\\"Career Stats For %s\\\" % (name)})\\n seasons = tables[0]\\n #print seasons\\n body = tables.find( \\\"tbody\\\" )\\n #body1 = body[0]\\n print body\\n print \\\"test1\\\"\\n rows = body.findAll(\\\"tr\\\")\\n print \\\"Test2\\\"\\n rowsList = []\\n for i in range(len(rows)):\\n if len(rows[i]) > 2:\\n print \\\"test3\\\"\\n rowsList.append(rows[i])\\n print rows[i]\\n # remove row[0] which contains field names\\n del rowsList[len(rowsList)-1]\\n except IOError, e:\\n print 'Failed to open url'\\n print '-------------------------------------'\\n if hasattr(e, 'code'):\\n print 'We failed with error code - %s.' % e.code\\n elif hasattr(e, 'reason'):\\n print \\\"The error object has the following 'reason' attribute :\\\"\\n print e.reason\\n return False\\n\\n except IndexError:\\n print 'No regular season data: Index error'\\n print '-------------------------------------'\\n #return False\\n\\n except AttributeError:\\n print 'No regular season data: Attribute error'\\n print '-------------------------------------'\\n #return False\",\n \"def run(self):\\r\\n self.collect_data()\",\n \"def setup_training(self):\\n print('setup training called')\\n self.steps_done = 0\\n self.current_episode_num = 1\\n self.total_reward = 0\\n\\n # self.optimizer = optim.RMSprop(policy_net.parameters())\\n self.memory = ReplayMemory(300000)\\n self.total_reward_history = []\\n # self.loss_history = []\\n self.positions = []\\n self.n_destroyed_crates = 0\\n self.is_in_bomb_range = False\",\n \"def run(self):\\n if not os.path.exists(self.output_folder):\\n os.makedirs(self.output_folder)\\n for entry in glob.glob(os.path.join(self.data_folder, self.data_expression)):\\n f = open(entry)\\n text = json.loads(f.read())\\n f.close()\\n self.create_page_objects(text)\",\n \"def fetch_data():\\n log = logging.getLogger(__name__)\\n log.info('Checking data files...')\\n if not os.path.isfile('CGN.txt'):\\n params_cgn = {\\n 'institute.code': ['NLD037'],\\n # 'crops': ['tomato'],\\n 'taxonomy.genus': ['Solanum', 'Lycopersicon'],\\n 'taxonomy.species': species\\n }\\n cgn = GenesysParser(params_cgn)\\n cgn.fetch2json('CGN.txt')\\n log.info('CGN data has been saved.')\\n else:\\n log.info('CGN data file already exists.')\\n\\n if not os.path.isfile('USDA.txt'):\\n params_usda = {\\n 'institute.code': usda_all,\\n # 'crops': ['tomato'],\\n 'taxonomy.genus': ['Solanum', 'Lycopersicon'],\\n 'taxonomy.species': species\\n }\\n usda = GenesysParser(params_usda)\\n usda.fetch2json('USDA.txt')\\n log.info('USDA data has been saved.')\\n else:\\n log.info('USDA data file already exists.')\",\n \"def _load(self):\\n\\t\\tpool = []\\n\\t\\tview = []\\n\\t\\tlibrary = []\\n\\n\\t\\tif is_file(\\\"~/comiccrawler/pool.json\\\"):\\n\\t\\t\\tpool = json.loads(content_read(\\\"~/comiccrawler/pool.json\\\"))\\n\\n\\t\\tif is_file(\\\"~/comiccrawler/view.json\\\"):\\n\\t\\t\\tview = json.loads(content_read(\\\"~/comiccrawler/view.json\\\"))\\n\\n\\t\\tif is_file(\\\"~/comiccrawler/library.json\\\"):\\n\\t\\t\\tlibrary = json.loads(content_read(\\\"~/comiccrawler/library.json\\\"))\\n\\n\\t\\tfor m_data in pool:\\n\\t\\t\\t# reset state\\n\\t\\t\\tif m_data[\\\"state\\\"] in (\\\"DOWNLOADING\\\", \\\"ANALYZING\\\"):\\n\\t\\t\\t\\tm_data[\\\"state\\\"] = \\\"ERROR\\\"\\n\\t\\t\\t# build episodes\\n\\t\\t\\tepisodes = []\\n\\t\\t\\tfor ep_data in m_data[\\\"episodes\\\"]:\\n\\t\\t\\t\\tepisodes.append(Episode(**ep_data))\\n\\t\\t\\tm_data[\\\"episodes\\\"] = episodes\\n\\t\\t\\tmission = Mission(**m_data)\\n\\t\\t\\tself._add(mission)\\n\\n\\t\\tfor url in view:\\n\\t\\t\\tself.view[url] = self.pool[url]\\n\\n\\t\\tfor url in library:\\n\\t\\t\\tself.library[url] = self.pool[url]\\n\\n\\t\\tself.bubble(\\\"MISSION_LIST_REARRANGED\\\", self.view)\\n\\t\\tself.bubble(\\\"MISSION_LIST_REARRANGED\\\", self.library)\",\n \"def _run(self):\\n data = None\\n\\n now = datetime.now().timestamp()\\n\\n if not os.path.exists(self.cache_file) or now - os.path.getmtime(self.cache_file) > MAX_FILE_AGE:\\n data = self._download()\\n self._write_cache_file(data)\\n elif not self._dma_facilities_map:\\n self.log.info(f\\\"Using cached file: {self.cache_file}\\\")\\n data = self._read_cache_file()\\n\\n if data:\\n self._process(self._unzip(data))\\n self.log.info(\\\"Done loading..\\\")\\n else:\\n self.log.debug(\\\"Facilities are still fresh..\\\")\\n\\n threading.Timer(CHECK_INTERVAL, self._run).start()\",\n \"def get_player_team_data(self, start_date, end_date = None, \\n get_player_data_ind = True, get_team_data_ind = True, \\n pre_player_data_dir = None, pre_team_data_dir = None):\\n #Converts start and end date from string to datetime\\n start_date = datetime.strptime(start_date, '%Y-%m-%d').date()\\n \\n if end_date:\\n end_date = datetime.strptime(end_date, '%Y-%m-%d').date()\\n else: \\n end_date = start_date\\n \\n if pre_player_data_dir:\\n try: \\n #Reads in the existing player dataset to append the scraped data to \\n exist_player_data = pd.read_csv(pre_player_data_dir)\\n except:\\n raise Exception('Cannot read in existing player dataset please ensure the directory is correct')\\n \\n if pre_team_data_dir:\\n try: \\n #Reads in the existing player dataset to append the scraped data to \\n exist_team_data = pd.read_csv(pre_team_data_dir)\\n except:\\n raise Exception('Cannot read in existing team dataset please ensure the directory is correct')\\n \\n delta = end_date - start_date \\n #Appends list of date between start and end date to strings\\n date_list = []\\n for i in range(delta.days + 1):\\n day = start_date + timedelta(days=i)\\n date_list.append(str(day))\\n \\n for date in date_list:\\n \\n print(f'Now scraping data from NBA games on {date}')\\n home_team_list = get_list_of_hometeams(self.driver, date)\\n\\n if len(home_team_list) > 0:\\n\\n counter = 1 \\n\\n for home_team in home_team_list:\\n \\n if counter == 1: \\n if get_player_data_ind: \\n player_df_full = get_player_data(home_team = team_full_abrv_config[home_team]['Full Name'], \\n date_played = date, \\n driver = self.driver)\\n if get_team_data_ind:\\n team_df_full = get_team_data(home_team = team_full_abrv_config[home_team]['Full Name'], \\n date_played = date, \\n driver = self.driver)\\n else:\\n if get_player_data_ind: \\n player_df_full = player_df_full.append(get_player_data(home_team = team_full_abrv_config[home_team]['Full Name'], \\n date_played = date, \\n driver = self.driver), ignore_index=True)\\n if get_team_data_ind:\\n team_df_full = team_df_full.append(get_team_data(home_team = team_full_abrv_config[home_team]['Full Name'], \\n date_played = date, \\n driver = self.driver), ignore_index=True)\\n counter+=1\\n \\n if pre_player_data_dir:\\n exist_player_data = exist_player_data.append(player_df_full)\\n exist_player_data.to_csv(pre_player_data_dir, index = False)\\n print(f'Updated player dataset will be overwritten in {pre_player_data_dir}')\\n \\n if pre_team_data_dir:\\n exist_team_data = exist_team_data.append(team_df_full)\\n exist_team_data.to_csv(pre_team_data_dir, index = False)\\n print(f'Updated team dataset will be overwritten in {pre_team_data_dir}')\\n \\n if pre_player_data_dir and pre_team_data_dir:\\n return exist_player_data, exist_team_data\\n elif pre_player_data_dir:\\n return exist_player_data\\n elif pre_team_data_dir:\\n return exist_team_data\\n elif get_player_data_ind and get_team_data_ind:\\n return player_df_full, team_df_full \\n elif get_player_data_ind:\\n return player_df_full\\n elif get_team_data_ind:\\n return team_df_full\",\n \"def insert_entities_staging(self):\\n\\n for year in range(1900, CURRENT_YEAR, YEARS_RANGE):\\n self.load_wikidata(\\\"movies\\\", MOVIES_BY_YEAR_SPARQL_QUERY, INSERT_MOVIE_SQL_QUERY, INSERT_MOVIE_MAP_COLUMNS, year, YEARS_RANGE)\\n\\n self.load_wikidata(\\\"songs\\\", SONGS_BY_YEAR_SPARQL_QUERY, INSERT_SONG_SQL_QUERY, INSERT_SONG_MAP_COLUMNS, year, YEARS_RANGE, True)\\n self.load_wikidata(\\\"tvshows\\\", TVSHOWS_SPARQL_QUERY, INSERT_TVSHOW_SQL_QUERY, INSERT_TVSHOW_MAP_COLUMNS)\\n self.load_wikidata(\\\"animatedmovies\\\", ANIMATEDMOVIES_SPARQL_QUERY, INSERT_ANIMATEDMOVIE_SQL_QUERY,\\n INSERT_ANIMATEDMOVIE_MAP_COLUMNS)\\n self.load_wikidata(\\\"videogames\\\", VIDEOGAMES_SPARQL_QUERY, INSERT_VIDEOGAME_SQL_QUERY, INSERT_VIDEOGAME_MAP_COLUMNS)\\n self.load_wikidata(\\\"books\\\", BOOKS_SPARQL_QUERY, INSERT_BOOK_SQL_QUERY, INSERT_BOOK_MAP_COLUMNS)\",\n \"def run(self):\\n\\t\\tsettings.from_json(self._settings)\\n\\t\\tsql.init_from_settings()\\n\\t\\tself._session = sql.session()\\n\\t\\tself.progress.clear(status=\\\"Starting up...\\\", running=True)\\n\\t\\tfailed = False\\n\\n\\t\\tfor nxt_id in self._reader:\\n\\t\\t\\ttry:\\n\\t\\t\\t\\turl = self._session.query(sql.URL).filter(sql.URL.id == nxt_id).first()\\n\\t\\t\\t\\tif not url:\\n\\t\\t\\t\\t\\traise Exception(\\\"Unknown URL ID provided: (%s}\\\" % nxt_id)\\n\\n\\t\\t\\t\\tfile = url.file\\n\\t\\t\\t\\tpath = SanitizedRelFile(base=settings.get(\\\"output.base_dir\\\"), file_path=str(file.path))\\n\\n\\t\\t\\t\\tself.progress.set_file(path.relative())\\n\\t\\t\\t\\tself.progress.set_status(\\\"Attempting to Handle URL...\\\")\\n\\t\\t\\t\\tself.progress.set_running(True)\\n\\n\\t\\t\\t\\ttask = handlers.HandlerTask(url=url.address, file_obj=path)\\n\\t\\t\\t\\tresp = handlers.handle(task, self.progress)\\n\\n\\t\\t\\t\\tis_album_parent = False\\n\\n\\t\\t\\t\\twith self._db_lock:\\n\\t\\t\\t\\t\\tif resp.album_urls:\\n\\t\\t\\t\\t\\t\\tif url.album_id:\\n\\t\\t\\t\\t\\t\\t\\tresp.album_urls = [] # Ignore nested Albums to avoid recursion.\\n\\t\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\t\\turl.album_id = str(uuid.uuid4())\\n\\t\\t\\t\\t\\t\\t\\tis_album_parent = True\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tresp.album_urls = []\\n\\n\\t\\t\\t\\t\\turl.failed = not resp.success\\n\\t\\t\\t\\t\\turl.failure_reason = resp.failure_reason\\n\\t\\t\\t\\t\\turl.last_handler = resp.handler\\n\\t\\t\\t\\t\\turl.album_is_parent = is_album_parent\\n\\n\\t\\t\\t\\t\\tif resp.rel_file:\\n\\t\\t\\t\\t\\t\\tfile.downloaded = True\\n\\t\\t\\t\\t\\t\\tfile.path = resp.rel_file.relative()\\n\\t\\t\\t\\t\\t\\tfile.hash = None\\n\\t\\t\\t\\t\\t\\tutime(resp.rel_file.absolute(), times=(time(), time()))\\n\\n\\t\\t\\t\\t\\tself._session.commit()\\n\\n\\t\\t\\t\\t# Once *all* processing is completed on this URL, the Downloader needs to ACK it.\\n\\t\\t\\t\\t# If any additional Album URLS were located, they should be sent before the ACK.\\n\\t\\t\\t\\tself._ack_queue.put(AckPacket(\\n\\t\\t\\t\\t\\turl_id=nxt_id,\\n\\t\\t\\t\\t\\textra_urls=resp.album_urls\\n\\t\\t\\t\\t))\\n\\t\\t\\t\\tself.progress.clear(status=\\\"Waiting for URL...\\\")\\n\\t\\t\\texcept Exception as ex:\\n\\t\\t\\t\\tfailed = str(ex)\\n\\t\\t\\t\\tself._ack_queue.put(AckPacket(\\n\\t\\t\\t\\t\\turl_id=nxt_id,\\n\\t\\t\\t\\t\\textra_urls=[]\\n\\t\\t\\t\\t))\\n\\t\\t\\t\\tprint(ex)\\n\\t\\t\\t\\ttraceback.print_exc()\\n\\t\\t\\t\\tself.progress.set_error(\\\"Exited with error: {%s}\\\" % failed)\\n\\t\\t\\t\\tbreak\\n\\n\\t\\tsql.close()\\n\\t\\tself.progress.clear(\\\"Finished.\\\" if not failed else \\\"Exited with error: %s\\\" % failed, running=False)\",\n \"def run_job(self):\\n try:\\n info(\\\"fetching testing invoice data from get_data_after_rescoring_flag_check function\\\")\\n testing_data = self.get_data_after_rescoring_flag_check()\\n self.common_util.dataframe_empty_check(testing_data)\\n info(\\\"geting and processing data from customer history\\\")\\n customer_history_data_query = self.query.get_customer_history_data_query()\\n customer_history_data = self.database.get_data_from_database(customer_history_data_query)\\n self.common_util.dataframe_empty_check(testing_data)\\n self.common_util.dataframe_empty_check(customer_history_data)\\n model_name = self.common_util.load_last_model_name('SC_LR')\\n info(\\\"inferencing started\\\")\\n predicted_data = self.common_util.get_selfcure_lr_prediction(testing_data, customer_history_data, 'SC_LR')\\n info(\\\"storing prediction to the table\\\")\\n self.database.save_prediction_to_table(predicted_data, model_name)\\n info(\\\"Appling P flag for all predcitited invoice\\\")\\n self.database.update_processed_flag_for_open_invoice(predicted_data)\\n except Exception as err:\\n error('Exception Occured while running inferencingJob job :' + str(err))\\n raise Exception('Exception Occured while running inferencingJob job : {}'.format(str(err)))\",\n \"def main(self):\\n base_url = self.env.get(\\\"base_url\\\", BASE_URL)\\n self.env[\\\"url\\\"] = self.get_g2m_url(base_url)\\n self.output(\\\"Found URL %s\\\" % self.env[\\\"url\\\"])\\n self.env[\\\"build\\\"] = self.get_g2m_build(base_url)\\n self.output(\\\"Build number %s\\\" % self.env[\\\"build\\\"])\",\n \"def runner(self):\\n\\n print('[ INFO ]: Initializing the forest fires program runner...')\\n\\n df, features, predictor = self.preprocess()\",\n \"def main_function_start(movies_data_path):\\n nltk.download('stopwords')\\n logistic_regression_pkl_file = \\\"./saved_models/logistic_regression_model.pkl\\\"\\n trained_model_present = check_model_file_exists(logistic_regression_pkl_file)\\n if not trained_model_present:\\n logging.info(\\\"Calling Data Processor for data preprocessing.\\\")\\n preprocessed_data_file_path = data_processor(movies_data_path).preprocess_data()\\n logging.info(\\\"Data preprocessing is done.\\\")\\n prediction = model(args.title, args.description, preprocessed_data_file_path, trained_model_present).classification_model()\\n\\n else:\\n prediction = model(args.title, args.description, \\\"\\\", trained_model_present).classification_model()\\n\\n final_answer = {}\\n final_answer[\\\"title\\\"] = args.title\\n final_answer[\\\"description\\\"] = args.description\\n final_answer[\\\"genre\\\"] = prediction\\n warnings.simplefilter(action='ignore', category=FutureWarning)\\n return final_answer\",\n \"def perform_extraction(self) -> None:\\n\\n self._process_datasets_all_frames()\",\n \"def main():\\n\\n parser = argparse.ArgumentParser('Download all data into a folder')\\n parser.add_argument('--homepage_url', help='url for adap-kdb homepage', required=True)\\n parser.add_argument('--msp_path', help='path for msv file', required=True)\\n parser.add_argument('--user_name', help='username for login', required=True)\\n parser.add_argument('--user_password', help='user password fpr login', required=True)\\n args = parser.parse_args()\\n\\n homepage_url = args.homepage_url\\n msp_path = args.msp_path\\n user_name = args.user_name\\n user_password = args.user_password\\n\\n individual_search_test(homepage_url, msp_path, user_name, user_password)\",\n \"def _collect_and_train(self) -> None:\\n self.info_process('\\\\n\\\\n')\\n self.info_process('Performing daily data collection and model training...')\\n\\n for symbol in Settings.get_symbols(self):\\n # Interrupt collection if the collection loop was stopped\\n if not self._running:\\n break\\n\\n # Revert data to last stable day.\\n date_last_collected_for = self.time().now().date()\\n # If it's past midnight, move back a day.\\n if self.time().get_secs_to_open() < timedelta(hours=9, minutes=30).total_seconds():\\n date_last_collected_for -= timedelta(days=1)\\n # Move back two market days from the most recent market day.\\n date_last_collected_for = self.time().get_prev_mkt_day(date_last_collected_for)\\n date_last_collected_for = self.time().get_prev_mkt_day(date_last_collected_for)\\n # Remove mongo price data after the stable day.\\n self.mongo().remove_price_data_after(symbol, date_last_collected_for, today=self.time().now().today())\\n date_rest_available_for = self.time().get_next_mkt_day(date_last_collected_for)\\n\\n # Collect yesterday's polygon-rest data and train on it.\\n if self._train_on_rest_data(symbol, date_rest_available_for):\\n self.info_process(f'Trained {symbol} on yesterday\\\\'s polygon rest data')\\n else:\\n self.warn_process(f'Invalid {symbol} rest data collected for {date_rest_available_for}. '\\n f'Discarding them and attempting to use cached stream data instead')\\n if self._train_on_stream_data(symbol, date_rest_available_for):\\n self.info_process(f'Trained {symbol} on yesterday\\\\'s polygon stream data')\\n else:\\n self.warn_process(f'Invalid {symbol} candles cached for {date_rest_available_for}. '\\n f'Could not find valid data to train on yesterday!')\\n\\n # Load today's polygon-stream data and train on it.\\n date_cache_available_for = self.time().get_next_mkt_day(date_rest_available_for)\\n if self._train_on_stream_data(symbol, date_cache_available_for):\\n self.info_process(f'Trained {symbol} on today\\\\'s polygon stream data')\\n else:\\n self.warn_process(f'Invalid {symbol} candles cached for {date_rest_available_for}. '\\n f'Could not find valid data to train on today!')\",\n \"def train_epoch(self) -> None:\\n ct = self.config.training\\n total_games = self._get_total_games()\\n print(f\\\"Total Games: {total_games}\\\")\\n train_size = int(0.9 * total_games)\\n dataset_wrapper = DatasetWrapper(self.config)\\n self.agent.model.fit(\\n dataset_wrapper.get_dataset(train_size),\\n epochs=ct.epoch_to_checkpoint,\\n validation_data=dataset_wrapper.get_dataset(train_size, is_training=False),\\n )\",\n \"def run(self):\\n self.load_template()\\n self.load_data()\\n self.load_files()\\n self.render_content()\\n self.process()\\n # pprint(self.data)\",\n \"def run(self):\\n for i in range(self.exploration_steps):\\n self.single_step(i)\\n if self.save_checkpoints:\\n self.save_results()\\n self.save_results()\\n if self.verbose:\\n print(\\\"\\\\nExploration completed\\\")\\n return\",\n \"def main():\\n\\n # Create a directory to store copies of all the relevant HTML files (those\\n # will be used in testing).\\n print('Setting up backup dir if needed ...')\\n create_dir(BACKUP_DIR)\\n\\n # Make backup of the IMDB top 250 movies page\\n print('Access top 250 page, making backup ...')\\n top_250_html = simple_get(TOP_250_URL)\\n top_250_dom = BeautifulSoup(top_250_html, \\\"lxml\\\")\\n\\n make_backup(os.path.join(BACKUP_DIR, 'index.html'), top_250_html)\\n\\n # extract the top 250 movies\\n print('Scraping top 250 page ...')\\n url_strings = scrape_top_250(top_250_dom)\\n\\n # grab all relevant information from the 250 movie web pages\\n rows = []\\n for i, url in enumerate(url_strings): # Enumerate, a great Python trick!\\n print('Scraping movie %d ...' % i)\\n\\n # Grab web page\\n movie_html = simple_get(url)\\n\\n # Extract relevant information for each movie\\n movie_dom = BeautifulSoup(movie_html, \\\"lxml\\\")\\n rows.append(scrape_movie_page(movie_dom))\\n\\n # Save one of the IMDB's movie pages (for testing)\\n if i == 83:\\n html_file = os.path.join(BACKUP_DIR, 'movie-%03d.html' % i)\\n make_backup(html_file, movie_html)\\n\\n # Save a CSV file with the relevant information for the top 250 movies.\\n print('Saving CSV ...')\\n save_csv(os.path.join(SCRIPT_DIR, 'top250movies.csv'), rows)\",\n \"def run(self):\\n try:\\n if myGameVersion != None:\\n myVersionedRepoURL = myGameVersion.getRepositoryURL()\\n if not versionedRepoURL == myVersionedRepoURL:\\n # Cache miss: we don't have the current version for\\r\\n # this game, and so we need to load it from the web.\\r\\n saveGameToCache(self.theKey, theGame)\\n except Exception as e:\\n e.printStackTrace()\",\n \"def run(self) -> None:\\n for episode in range(1, self.episodes + 1):\\n print('Episode:', episode)\\n steps, state_action_history = self.run_one_episode()\\n self.steps_per_episode.append(steps)\\n if episode % parameters.CACHING_INTERVAL == 0 or steps < 1000:\\n visualize.animate_track(state_action_history, f'agent-{episode}')\\n\\n print('Training completed.')\\n visualize.plot_steps_per_episode(self.steps_per_episode)\\n visualize.plot_epsilon(self.agent.epsilon_history)\\n\\n if parameters.VISUALIZE_FINAL_GAME:\\n print('Showing one episode with the greedy strategy.')\\n self.agent.epsilon = 0\\n steps, state_action_history = self.run_one_episode()\\n print(f'Episode completed in {steps} steps.')\\n visualize.animate_track(state_action_history)\",\n \"def prepare_data(self) -> None:\\n if (self.root).is_dir():\\n logger.info(\\\"Found the dataset.\\\")\\n else:\\n download_and_extract(self.root, DOWNLOAD_INFO)\",\n \"def main_flow():\\n user_click, user_click_time = reader.get_user_click(\\\"../data/rating.txt\\\") # dict: key:user_id value:[item0,item1...]\\n item_info = reader.get_item_info(\\\"../data/movies.txt\\\")\\n\\n sim_info = cal_sim_info(user_click, user_click_time)\\n debug_itemsim(item_info, sim_info)\\n recom_result = cal_recom_result(sim_info, user_click)\\n print(recom_result[\\\"1\\\"])\\n debug_recomresult(recom_result, item_info)\",\n \"def fetch(self):\\n \\n self.genre=\\\"Review\\\"\\n \\n try:\\n self.parent_uri = self.currenturi\\n \\n self.total_threads_count = 0\\n self.last_timestamp = datetime( 1980,1,1 )\\n if tg.config.get(path='Connector',key='collegenet_max_threads_to_process'):\\n self.max_threads_count = int(tg.config.get(path='Connector',key='collegenet_max_threads_to_process'))\\n else:\\n self.max_threads_count = None\\n \\n self.collegenetTimelag_max = tg.config.get(path='Connector', key='collegenet_search_timeLag_max')\\n self.collegenetTimelag_min = tg.config.get(path='Connector', key='collegenet_search_timeLag_min')\\n log.info(self.log_msg('Time Logs :::::::'))\\n log.info(self.collegenetTimelag_max)\\n log.info(self.collegenetTimelag_min)\\n \\n## if self.collegenetTimelag_min and self.collegenetTimelag_max:\\n## randomTimeLag = random.randint(self.collegenetTimelag_min,self.collegenetTimelag_max) / 1000.0000\\n## log.info('sleeping for %s seconds between requests'%randomTimeLag)\\n## time.sleep(randomTimeLag)\\n \\n if not self.__setSoup():\\n log.exception(self.log_msg('Soup not set ..... '))\\n return False\\n \\n #post_no = 0 #Remove\\n while True:\\n #if post_no==3: #Remove\\n # break #Remove\\n #post_no = post_no + 1 #Remove\\n \\n currenturi = self.currenturi\\n if not self.__addPosts():\\n break \\n #Get \\\"post\\\" information\\n #break #Remove\\n self.currenturi = currenturi\\n if self.collegenetTimelag_min and self.collegenetTimelag_max:\\n randomTimeLag = random.randint(self.collegenetTimelag_min,self.collegenetTimelag_max) / 1000.0000\\n log.info('sleeping for %s seconds between requests'%randomTimeLag)\\n time.sleep(randomTimeLag)\\n #We need to set soup again as addPosts() method is changing self.currenturi and self.soup\\n if not self.__setSoup():\\n log.info(self.log_msg('Soup not set.... break while loop.'))\\n break\\n \\n try:\\n next_uri = baseuri + self.soup.find('div',id='search_results_controls').find('a',text=re.compile('>')) \\\\\\n .parent['href']\\n self.currenturi = next_uri\\n log.info(self.log_msg('Next URI :::::::;'))\\n log.info(next_uri)\\n except:\\n log.exception(self.log_msg('Next Post link not found'))\\n break\\n \\n## if self.collegenetTimelag_min and self.collegenetTimelag_max:\\n## randomTimeLag = random.randint(self.collegenetTimelag_min,self.collegenetTimelag_max) / 1000.0000\\n## log.info('sleeping for %s seconds between requests'%randomTimeLag)\\n## time.sleep(randomTimeLag)\\n \\n if not self.__setSoup():\\n log.info(self.log_msg('Soup not set.... break while loop.'))\\n break\\n \\n return True\\n \\n except:\\n log.exception(self.log_msg('Exception in fetch'))\\n return False\",\n \"def run_main():\\n\\n #Check for sold bikes\\n checkSold(auto=True) #Change Auto to True to prevent user input\\n\\n #Find all available URLs split by Make & Model - Find Make\\n print(\\\"Getting Makes...\\\")\\n makes = getMakes()\\n \\n #Find all Models for each Make\\n print(\\\"Getting Models...\\\")\\n models = []\\n for make in tqdm(makes, desc=\\\"Makes\\\"):\\n models += getModels(make)\\n\\n\\n #Find all URLs for each Model - Scrape bikes on each model\\n errlog = \\\"\\\"\\n print(\\\"Scraping Bikes...\\\")\\n for model in tqdm(models, desc=\\\"Models\\\"):\\n #Get urls for each model\\n urlsTemp = getURLs(model)\\n\\n #Remove duplicates\\n urlsTemp = removeDups(urlsTemp)\\n\\n #Remove listings already found\\n urlsTemp = newURLs(\\\"motorcycles\\\", urlsTemp)\\n\\n #Get model description\\n try:\\n modelDesc = model.split(\\\"/\\\")[2].replace(\\\"model-\\\", \\\"\\\")\\n except:\\n modelDesc = \\\"Listings\\\"\\n\\n #Find motorbike details on all urls for this model\\n #Split by model to prevent large datasets changing during code runtime\\n for url in tqdm(urlsTemp, desc=modelDesc, leave=False):\\n temp = Motorcycle(url)\\n if not temp.na:\\n temp.dbInsert()\\n else:\\n errlog += url + \\\"|\\\"\\n \\n #Finish\\n if not errlog:\\n print(\\\"Errors Found: \\\", errlog)\\n if not printlog:\\n print(printlog)\\n \\n print(\\\"Done!\\\")\",\n \"def do(self):\\n super().do()\\n logger.info(\\\"TrainPipeStep started...\\\")\\n records = self._get_current_step_records()\\n logger.debug(\\\"load pipestep records: {}\\\".format(records))\\n self.num_models = len(records)\\n self.num_epochs = self.num_models * TrainerConfig.epochs\\n self.update_status(Status.running)\\n self.master = create_master()\\n self._train_multi_models(records)\\n self.master.join()\\n ReportServer().output_step_all_records(step_name=self.task.step_name)\\n self.master.close()\\n ReportServer().backup_output_path()\\n self.update_status(Status.finished)\",\n \"def collect_data(self, data_folder):\\n print(\\\"Start collecting\\\")\\n input_file_path = os.path.join(data_folder, \\\"dblp.xml\\\")\\n with codecs.open(input_file_path, \\\"r\\\", encoding=\\\"iso-8859-1\\\") as file:\\n start = time.time()\\n tmp = start\\n\\n title = \\\"\\\"\\n key_prefix = \\\"\\\"\\n authors = []\\n personal_information = False\\n publications_type = [\\\"article\\\", \\\"inproceedings\\\", \\\"proceedings\\\", \\\"book\\\",\\n \\\"incollection\\\", \\\"phdthesis\\\", \\\"mastersthesis\\\"]\\n start_publication_regex = r\\\"<([^\\\\/]*?) (.*)>\\\" # </article><article mdate=\\\"2017-05-28\\\" key=\\\"journals/acta/Simon83\\\">\\n end_publication_regex = r\\\"<\\\\/(.*?)>\\\" # </article><article mdate=\\\"2017-05-28\\\" key=\\\"journals/acta/Simon83\\\">\\n author_regex = r\\\"<author>(.*)</author>\\\" # <author>Katsuyuki Tateishi</author>\\n title_regex = r\\\"<title>(.*)</title>\\\" # <title>A quadratic speedup theorem ...</title>\\n start_person_regex = r'<www.*key=\\\"(.*)\\\">' #<www mdate=\\\"2009-06-10\\\" key=\\\"homepages/32/3977\\\">\\n end_person_regex = r\\\"<\\\\/www>\\\"\\n note_url_regex = r\\\"<note.*?>|<url.*?>\\\"\\n inside_publication = False\\n inside_person = False\\n for i, line in tqdm(enumerate(file)):\\n # Test end of publication, if true add publication\\n result = regex.search(end_publication_regex, line)\\n if result:\\n if result.group(1) in publications_type:\\n if inside_publication and authors and title:\\n # This a a publication, lets add it to the dictionnay author -> title\\n self.__add_publication(authors, title)\\n title = \\\"\\\"\\n authors = []\\n inside_publication = False\\n personal_information = False\\n key_prefix = \\\"\\\"\\n\\n #Test beginning of a publication\\n result = regex.search(start_publication_regex, line)\\n if result and result.group(1) in publications_type:\\n inside_publication = True\\n title = \\\"\\\"\\n authors = []\\n personal_information = False\\n key_prefix = \\\"\\\"\\n\\n #Check if author\\n result = regex.search(author_regex, line)\\n if result and (inside_publication or inside_person):\\n authors.append(result.group(1))\\n #Check if title\\n result = regex.search(title_regex, line)\\n if result and (inside_publication or inside_person):\\n title = result.group(1)\\n #Check if containt url or note\\n result = regex.search(note_url_regex, line)\\n if result:\\n #if inside_person and (\\\"<note\\\" in line or \\\"<url\\\" in line):\\n personal_information = True\\n # Check if end of person, if true add author\\n result = regex.search(end_person_regex, line)\\n if result and inside_person:\\n if authors and \\\"homepages/\\\" in key_prefix and title == \\\"Home Page\\\":\\n self.__add_author(key_prefix, authors, personal_information)\\n key_prefix = \\\"\\\"\\n authors = []\\n inside_person = False\\n personal_information = False\\n title = \\\"\\\"\\n #Check start person\\n result = regex.search(start_person_regex, line)\\n if result:\\n inside_person = True\\n key_prefix = result.group(1)\\n\\n\\n self.__save_everything(data_folder)\\n self.__merge_data()\\n self.__save_everything(data_folder)\",\n \"def startworking():\\r\\n #In the future have the manager program or from the website implement this arguments to a route\\r\\n #the program will download the file from the website\\r\\n global exe_name\\r\\n global Task_Conditional\\r\\n task_data = None\\r\\n while task_data is None:\\r\\n task_data = recieve_data_from_server(\\\"get_task\\\")\\r\\n if task_data is None:\\r\\n time.sleep(5)\\r\\n else:\\r\\n exe_name = task_data[\\\"exe_name\\\"]\\r\\n print('Working on the task \\\"{}\\\"'.format(exe_name))\\r\\n get_file(exe_name)\\r\\n Task_Conditional = task_data[\\\"Task_conditional\\\"]\\r\\n print(\\\"loading\\\")\\r\\n t1 = time.time()\\r\\n task_divider(task_data[\\\"first_num\\\"], task_data[\\\"last_num\\\"])\\r\\n t2 = time.time()\\r\\n print(\\\"ready {}\\\".format(t2-t1))\",\n \"def phase_two_data():\\n from pathlib import Path\\n try:\\n import cPickle as pickle\\n except ImportError:\\n import pickle\\n \\n from annotation import parse_fulltext\\n from features import ALL_FEATURES\\n \\n from feature_template import apply_templates\\n from feature_selection import filter_by_frequency\\n from feature_encoding import encode\\n\\n # Feature templates considered if heading by 1:\\n # ----------------------------\\n # Position + Voice\\n # Path length + Clause layer\\n # 1 Predicate + Path\\n # Path + Position + Voice\\n # Path + Position + Voice + Predicate\\n # 1 Head word stem + Predicate\\n # 1 Head word stem + Predicate + Path\\n # 1 Head word stem + Phrase\\n # Clause layer + Position + Predicate\\n templates = [tuple([f.name]) for f in ALL_FEATURES] + \\\\\\n [('path_to_frame', 'frame'), ('head_stem', 'frame'), ('head_stem', 'frame', 'path_to_frame'), ('head_stem', 'phrase_type')]\\n \\n size = 40\\n instances = []\\n for i, p in enumerate(Path(\\\"/cs/fs2/home/hxiao/Downloads/fndata-1.5/fulltext/\\\").glob(\\\"*.xml\\\")):\\n if i == size:\\n break\\n sys.stderr.write(\\\"Processing file: '%s'\\\\n\\\" %p.absolute())\\n annotations = parse_fulltext(str(p.absolute()))\\n instances += make_training_data(ALL_FEATURES, annotations)\\n\\n sys.stderr.write(\\\"Feature selection...\\\\n\\\")\\n x, y = zip(*instances)\\n x = apply_templates(x, templates)\\n features = filter_by_frequency(x, 5)\\n sys.stderr.write(\\\"Feature encoding...\\\\n\\\")\\n x, feature_map = encode(x, features)\\n \\n sys.stderr.write(\\\"Dumping data...\\\\n\\\") \\n pickle.dump((x, y, ALL_FEATURES, templates, feature_map), open('dump/test_data.pkl', 'w'))\\n import pdb\\n pdb.set_trace()\\n print len(instances)\",\n \"def run(self):\\n\\n for url in self.urls:\\n try:\\n # Use requests to retrieve web page data\\n print(url)\\n response = session.get(url, ) # allow_redirects=True)\\n\\n if response.status_code != 200:\\n print('Failed to retrieve page, URL: {0}, error: {1}\\\\n'.format(url, response.status_code))\\n return\\n\\n # Get web page data from HTML response\\n content = get_json_data(response.text)\\n\\n # Compile data into dictionary to be used for reporting\\n summary_data = generate_report(content)\\n\\n # Generate/print report\\n print_report(summary_data)\\n\\n except Exception as error:\\n print('Scraper failed to run for URL {0}, error: {1}, {2}\\\\n'.format(\\n url, type(error).__name__, error\\n ))\\n\\n # time.sleep(1) # for load concerns\",\n \"def start_process(self):\\n print 50 * '*' + '\\\\n' + 10 * '*' + ' STARTING SCANNING PROCESS ' + 10 * '*' + '\\\\n' + 50 * '*'\\n\\n while True:\\n print str(self.stream_list) + str(self.end_times_list)\\n\\n self.check_if_stream_should_end()\\n\\n if self.is_time_to_get_game_data_for_day:\\n self.write_days_games_data()\\n\\n # Read in file to see if it is time to analyze twitter\\n read_path = self.get_write_path_for_days_games()\\n\\n try:\\n with open(read_path) as f:\\n data = json.load(f)\\n current_time = datetime.datetime.now().strftime('%H:%M')\\n for idx, game in enumerate(data):\\n game_time = dateutil.parser.parse(game['start_time']).strftime('%H:%M')\\n if game_time == current_time and not game['being_streamed']:\\n # TODO - Figure out how to call a fork or child process for a certain amount of time\\n # TODO - Refactor this\\n self.update_is_streamed_json(index=idx)\\n print 'Time to get twitter data.'\\n\\n search_terms_home = self.keyword_generator.generate_search_terms(game['home_team_id'])\\n search_terms_away = self.keyword_generator.generate_search_terms(game['away_team_id'])\\n keyword_string_home = ','.join(search_terms_home)\\n keyword_string_away = ','.join(search_terms_away)\\n\\n keyword_string = keyword_string_home + ',' + keyword_string_away\\n game_name = datetime.datetime.now().strftime('%Y-%m-%d') + '-' + game['title'].replace(' ', '-')\\n\\n data_gatherer = DataGatherer()\\n stream = data_gatherer.get_tweet_stream(keyword_string, game['uuid'], game_name)\\n self.stream_list.append(stream)\\n self.end_times_list.append(self.get_time_to_end_stream(1))\\n\\n except IOError:\\n print 'File not found'\\n\\n # restart loop after sleep, given by our tick_time\\n self.sleep_for(self.tick_time_in_seconds)\",\n \"def get_fresh_data(get_team):\\n\\n \\\"\\\"\\\" Create the URL for today \\\"\\\"\\\"\\n now = datetime.datetime.now()\\n url = \\\"http://gd2.mlb.com/gdcross/components/game/mlb/year_\\\" + '{}'.format(now.year) + \\\"/month_\\\" + '{:02d}'.format(now.month) + \\\"/day_\\\" + '{:02d}'.format(now.day) + \\\"/miniscoreboard.json\\\"\\n\\n \\\"\\\"\\\" Grab the first response and write it to a file, we'll update it once the game starts \\\"\\\"\\\"\\n data_write_file = status_dir + '{}'.format(now.year) + '{:02d}'.format(now.month) + '{:02d}'.format(now.day) + \\\".json\\\"\\n\\n \\\"\\\"\\\" Get the json data if the file doesn't exist, or if it's over three minutes old \\\"\\\"\\\"\\n if not os.path.isfile(data_write_file) or time.time() - os.path.getmtime(data_write_file) > 60:\\n response = urllib.urlopen(url)\\n full_data = json.loads(response.read())\\n with open(data_write_file, 'w') as outfile:\\n json.dump(full_data, outfile, sort_keys=True, indent=2, ensure_ascii=False)\\n\\n \\\"\\\"\\\" Use the data from the status file \\\"\\\"\\\"\\n with open(data_write_file, 'r') as json_data:\\n full_data = json.load(json_data)\\n\\n \\\"\\\"\\\" This will return false if there is no game today, else will return json data for just our team \\\"\\\"\\\"\\n my_game = False\\n for game in full_data['data']['games']['game']:\\n if get_team in game['home_file_code'] or get_team in game['away_file_code']:\\n my_game = game\\n \\n return my_game\",\n \"def parse_rism_folder(self):\\n self._set_job_name()\\n self._find_named_files()\\n self._load_files()\\n self._parse_input()\\n self._check_results()\\n self._parse_results()\\n self._load_molecule()\\n self._calculate_runtime()\",\n \"def main():\\n # Initialize key variables\\n alldata = [[\\n 'Meet', 'City', 'Country', 'Course', 'Event ID', 'Distance', 'Stroke',\\n 'Round', 'Gender', 'Firstname', 'Lastname', 'Birthyear', 'Height cm',\\n 'Weight Kg', 'BMI', 'Speed / Kg', 'Speed m/s', 'Time']]\\n finadata = []\\n olympicdata = []\\n ts_start = int(time.time())\\n\\n # Get filename\\n parser = argparse.ArgumentParser()\\n parser.add_argument(\\n '-l', '--lenex_directory',\\n help='Name of directory with LENEX XML files.',\\n type=str, required=True)\\n parser.add_argument(\\n '-o', '--olympic_directory',\\n help='Name of directory with Olympic XLSX files.',\\n type=str, required=True)\\n parser.add_argument(\\n '-p', '--profile_directory',\\n help='Name of directory with athlete profiles.',\\n type=str, required=True)\\n parser.add_argument(\\n '-d', '--database_file',\\n help='Name of database file.',\\n type=str, required=True)\\n args = parser.parse_args()\\n lenex_directory = args.lenex_directory\\n profile_directory = args.profile_directory\\n database_file = args.database_file\\n olympic_directory = args.olympic_directory\\n\\n # Get the profiles\\n profiles = _read_profiles(profile_directory)\\n\\n # Process Fina data\\n finadata = _lenex(lenex_directory, profiles)\\n\\n # Process Olympic data\\n olympicdata = _olympic(olympic_directory, profiles)\\n\\n # Get all data\\n alldata.extend(finadata)\\n alldata.extend(olympicdata)\\n\\n # Create output file\\n with open(database_file, 'w') as f_handle:\\n writer = csv.writer(f_handle, delimiter='|')\\n writer.writerows(alldata)\\n\\n # Print status\\n print('Swimmer event results created: {}'.format(len(alldata) - 1))\\n print('Duration: {}'.format(int(time.time() - ts_start)))\",\n \"def _generate_and_load_batch(self, tempdir, org_config, options) -> dict:\\n options = {\\n **options,\\n \\\"working_directory\\\": tempdir,\\n \\\"set_recently_viewed\\\": False,\\n \\\"ignore_row_errors\\\": self.ignore_row_errors,\\n \\\"drop_missing_schema\\\": self.drop_missing_schema,\\n }\\n subtask_config = TaskConfig({\\\"options\\\": options})\\n subtask = GenerateAndLoadDataFromYaml(\\n project_config=self.project_config,\\n task_config=subtask_config,\\n org_config=org_config,\\n flow=self.flow,\\n name=self.name,\\n stepnum=self.stepnum,\\n )\\n subtask()\\n return subtask.return_values[\\\"load_results\\\"][0]\",\n \"def __init__(self, workflow, **kwds):\\n self.kwds = kwds\\n self.url = self.get('url', 'cmsweb.cern.ch')\\n WorkflowManager.__init__(self, workflow, self.url)\\n self.workload = getWorkload(self.url, workflow)\\n self.cacheID = self.winfo.get('StepOneConfigCacheID', '')\\n self.config = getConfig(self.url, self.cacheID)\\n self.pileup_dataset = self._pileup_dataset()\\n self.priority = self._priority()\\n self.era = self.get('era', 'Summer12')\\n self.lfn = self.get('lfn', '/store/mc')\\n self.special_name = self.get('specialName', '')\\n self.max_rss = self.get('maxRSS', 2300000)\\n self.max_vsize = self.get('maxVSize', 4100000000)\\n self.input_dataset = ''\\n self.pileup_scenario = ''\\n self.global_tag = self.get('globalTag', '')\\n self.campaign = self.get('campaign', '')\\n self.max_merge_events = self.get('maxMergeEvents', 50000)\\n self.activity = self.get('activity', 'reprocessing')\\n self.restrict = self.get('restrict', 'None')\\n self.site_use = self.get('site', None)\\n self.site_cust = self.get('site_cust', None)\\n self.xrootd = self.get('xrootd', 0)\\n self.ext_tag = self.get('ext', '')\\n self.team = self.get('team', '')\\n\\n # perform various initialization\\n self._init()\\n\\n # custom settings\\n # Construct processed dataset version\\n if self.pileup_scenario:\\n self.pileup_scenario = self.pileup_scenario+'_' \\n\\n specialprocstring = kwds.get('specialName', '')\\n if specialprocstring:\\n self.special_name = specialprocstring + '_'\\n\\n # ProcessingString\\n inprocstring = kwds.get('procstring', '')\\n if inprocstring:\\n self.procstring = inprocstring\\n else:\\n self.procstring = self.special_name + self.pileup_scenario +\\\\\\n self.global_tag + self.ext_tag\\n\\n # ProcessingVersion\\n inprocversion = kwds.get('procversion', '')\\n if inprocversion:\\n self.procversion = inprocversion\\n else:\\n self.procversion = self.dataset_version(self.era, self.procstring)\",\n \"def dataframe():\\n\\t#allows function to access station, gmt, and miss_station functions\\n global stations\\n\\tglobal gmt\\n\\tglobal miss_station\\n\\t\\n\\t#read predictor file\\n\\tcontrol = cfg.read_yaml('../registry/graphs.yaml')\\n\\tpred_ctrl = cfg.read_yaml(cfg.get_config_path(control.pred_file))\\n\\tpredd_ctrl = cfg.read_yaml(cfg.get_config_path(control.predd_file))\\n\\n\\t#get file paths and update database\\n\\tpredictor_file_path = control.predictor_file_path\\n\\tpredictand_file_path = control.predictand_file_path\\n\\tpred_file_id = update(predictor_file_path)\\n\\tpredd_file_id = update(predictand_file_path)\\n\\t\\n\\t#store lead time and date range\\n\\tlead_time = control.lead_time\\n\\tdate_range = control.date_range\\n\\n\\t#get info for fetch many dates\\n\\tstart,end,stride = read_pred.parse_range(date_range)\\n\\tfcst_ref_time = control.date_range[0].split('-')[0][-2:]\\n\\t\\n\\t#initialize list of predictors\\n\\tpred_list = pred_ctrl.predictors\\n\\tpredictor = []\\n\\n\\t#loops through predictors to build camps data objects\\n\\tfor entry_dict in pred_list:\\n\\t\\t#formats metadata\\n\\t\\tpred = create.preprocess_entries(entry_dict, fcst_ref_time)\\n\\t\\t\\n\\t\\t#adds info to metadata that's not currently being stored\\n\\t\\tpred.search_metadata['reserved2'] = lead_time*3600\\n pred.search_metadata['file_id'] = pred_file_id\\n\\t\\tpred.search_metadata['reserved1'] = 'vector'\\n\\n\\t\\t#build camps data objects for each day\\n\\t\\tvariable = fetch_many_dates(predictor_file_path,start,end,stride,pred.search_metadata)\\n\\t\\t\\n\\t\\t#appends all data to single camps object\\n\\t\\tif variable[0] is not None:\\n\\t\\t\\tvar = variable[0]\\n\\t\\t\\tarrs = []\\n\\t\\t\\tfor i in range(len(variable)):\\n\\t\\t\\t\\tarrs.append(variable[i].data)\\n\\t\\t\\tvar.data = np.stack(arrs)\\n\\t\\t\\tpredictor.append(var)\\n\\n\\t#initializes list of predictands\\n\\tpredd_list = predd_ctrl.predictands\\n predictand = []\\n\\t\\n\\t#loops through predictands to build camps data objects\\n for entry_dict in predd_list:\\n\\t\\t#formats metadata\\n \\tvertical_coordinate = entry_dict.pop('Vertical_Coordinate')\\n\\t\\tentry_dict['file_id'] = predd_file_id\\n\\n\\t\\t#build camps objects for each day\\n variable = fetch_many_dates(predictand_file_path,start, end, stride, entry_dict)\\n\\n\\t\\t#append all data to single camps object\\n var = variable[0]\\n arrs = []\\n for i in range(len(variable)):\\n arrs.append(variable[i].data)\\n try:\\n\\t\\t\\tvar.data = np.stack(arrs)\\n\\t\\t\\tpredictand.append(var)\\n\\t\\texcept:\\n\\t\\t\\tprint(\\\"Can't read \\\" + variable.name)\\n\\n\\t#getting predictor station and time data\\n\\tpredr = Dataset(predictor_file_path[0])\\n\\tpredr_stat = predr.variables['station'][:]\\n\\tif lead_time == 3:\\n\\t\\tpredr_time = predr.variables['OM__phenomenonTimeInstant'][:]\\n\\telif lead_time == 6:\\n\\t\\tpredr_time = predr.variables['OM__phenomenonTimeInstant1'][:]\\n\\telif lead_time == 12:\\n\\t\\tpredr_time = predr.variables['OM__phenomenonTimeInstant2'][:]\\n\\tpredr.close()\\n\\n\\t#reformatting predictor station and time data\\n\\tpredr_stations = stations(predr_stat)\\n\\tpredr_gmt = gmt(predr_time)\\n\\t\\n\\t#getting predictand station and time data\\n\\tpredd = Dataset(predictand_file_path[0])\\n\\tpredd_stat = predd.variables['station'][:]\\n\\tpredd_time = predd.variables['OM__resultTime'][:]\\n\\tpredd.close()\\n\\t\\n\\t#reformatting predictand station and time data\\n\\tpredd_stations = stations(predd_stat)\\n\\tpredd_gmt = gmt(predd_time)\\n\\n\\t#choosing predictand observations that line up with predictor time\\n\\thour = (predictor[0].metadata['FcstTime_hour']/3600) + lead_time\\n\\tdays = len(predd_gmt)/24\\n\\tpredd_hours = [0]*days\\n k=0\\n for i in range(len(predd_gmt)):\\n if i%24 == hour:\\n\\t\\t\\tpredd_hours[k]=predd_gmt[i]\\n\\t\\t\\tk+=1\\n\\t\\n\\t#catches when GFS data doesn't cover the last day of the month\\n\\tif len(predr_gmt) < len(predd_hours):\\n\\t\\tpredd_hours = predd_hours[:-1]\\t\\n\\t\\n\\t#find missing stations\\n\\tmiss_stations = miss_station(predr_stations,predd_stations)\\n\\tstations = predd_stations\\n\\t\\n\\t#station and time array\\n\\tinfo = [['',''] for k in range(len(predr_gmt)*len(stations))]\\n\\tfor i in range(len(predr_gmt)):\\n\\t\\tfor j in range(len(stations)):\\n\\t\\t\\tk = i*len(stations)+j\\n\\t\\t\\tinfo[k][0]=predr_gmt[i]\\n\\t\\t\\tinfo[k][1]=stations[j]\\n\\n\\t#create column names\\n\\tnames = ['']*(len(predictor)+len(predictand)+2)\\n\\tnames[0]='Time'\\n\\tnames[1]='Station'\\n\\n\\t#creating array\\n\\tarr = np.zeros((len(stations)*len(predr_gmt),len(predictor)+len(predictand)))\\n\\t\\n\\t#adding predictor data\\n\\tfor i in range(len(predictor)):\\n\\t\\t#remove lead time and forecast reference time from variable name\\n\\t\\t#and add variable name to column list of final dataframe\\n\\t\\tif lead_time == 12:\\n\\t\\t\\tnames[i+2]='GFS_'+predictor[i].get_variable_name()[:-11]\\n\\t\\telse:\\n\\t\\t\\t names[i+2]='GFS_'+predictor[i].get_variable_name()[:-10]\\n\\n\\t\\t#create pandas dataframe of data and sort alphabetically by station name\\n\\t\\tpredictor[i].data = np.squeeze(predictor[i].data,axis=2)\\n\\t\\tpredictor[i].data = pd.DataFrame(predictor[i].data,columns=predr_stations,index=predr_gmt)\\n\\t\\tpredictor[i].data = predictor[i].data.reindex(sorted(predictor[i].data.columns),axis=1)\\n\\t\\t\\n\\t\\t#remove stations with no predictand data\\n\\t\\tk=0\\n\\t\\ta=miss_stations[:]\\n\\t\\tfor j in predictor[i].data.columns:\\n\\t\\t\\tif not a:\\n\\t\\t\\t\\tbreak\\n\\t\\t\\tif j==a[k]:\\n\\t\\t\\t\\tpredictor[i].data=predictor[i].data.drop(j,axis=1)\\n\\t\\t\\t\\tdel a[k]\\n\\t\\t\\n\\t\\t#add data to final dataframe\\n\\t\\tfor b in range(len(predr_gmt)):\\n\\t\\t\\tfor c in range(len(stations)):\\n\\t\\t\\t\\tk = b*len(stations)+c\\n\\t\\t\\t\\tarr[k][i] = predictor[i].data.iloc[b][c]\\n\\n\\t#add predictand data\\n\\tfor i in range(len(predictand)):\\n\\t\\t#removing extra underscore, adding variable name to column names\\n\\t\\tnames[len(predictor)+2+i]='METAR_'+predictand[i].get_variable_name()[:-1]\\n\\t\\n\\t\\t#resize array and create pandas dataframe\\n\\t\\tpredictand[i].data = np.squeeze(predictand[i].data,axis=2)\\n\\t\\tpredictand[i].data = pd.DataFrame(predictand[i].data,columns=predd_stations,index=predd_hours)\\n\\t\\tpredictand[i].data = predictand[i].data.reindex(sorted(predictand[i].data.columns),axis=1)\\n\\t\\t\\n\\t\\t#remove extra days of predictand data\\n\\t\\tpredictand[i].data = predictand[i].data.iloc[0:len(predr_time),:]\\n\\t\\t\\t\\n\\t\\t#add predictand data to array\\n\\t\\tfor b in range(len(predr_gmt)):\\n\\t\\t\\tfor c in range(len(stations)):\\n\\t\\t\\t\\tk = b*len(stations)+c\\n\\t\\t\\t\\tval = predictand[i].data.iloc[b][c]\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t#catch metar fill data\\n\\t\\t\\t\\tif val == 9999: \\n\\t\\t\\t\\t\\tval = np.nan\\n\\t\\t\\t\\tarr[k][len(predictor)+i]=val\\n\\t\\n\\t#add station and time data to array and save as csv\\n\\tdata = np.concatenate([info,arr],axis = 1)\\n\\tto_save = pd.DataFrame(data,columns=names)\\n\\tto_save.to_csv(str(start)+'_'+str(end)+'_'+str(lead_time)+'hrs.csv')\",\n \"def main():\\n\\n # parses arguments\\n parser = argparse.ArgumentParser()\\n\\n parser.add_argument('-s', action='store', dest='start_index', type=int,\\n help='The starting index for events. Default is 0')\\n\\n parser.add_argument('-e', action='store', dest='end_index', type=int,\\n help='The starting index for events. Default is 5,000')\\n\\n results = parser.parse_args()\\n\\n start_index = results.start_index or 0\\n\\n end_index = results.end_index or 5000\\n\\n scraper = Scraper()\\n\\n # these are the event column titles from the sample import csv given by localist\\n event_column_titles = [\\n 'Title','Description','Date From','Date To','Recurrence','Start Time','End Time',\\n 'Location','Address','City','State','Event Website','Room','Keywords','Tags',\\n 'Photo URL','Ticket URL','Cost','Hashtag','Facebook URL','Group','Department',\\n 'Allow User Activity','Allow User Attendance','Visibility','Featured Tabs',\\n 'Sponsored','Venue Page Only','Exclude From Trending','Event Types','Invited Audience', 'Original URL',\\n 'Location Details'\\n ]\\n\\n out_stream = open('event_import.csv', 'w')\\n\\n writer = Writer(event_column_titles, out_stream)\\n\\n writer.write_headers()\\n\\n # iterates through the specified event numbers and scrapes each one and writes\\n # it to the output file\\n for i in range(start_index, end_index + 1):\\n current_url = 'http://test-ucscevents.pantheonsite.io/event/' + str(i)\\n print(\\\"processing url: \\\" + current_url)\\n r = requests.get(current_url)\\n if r.status_code != requests.codes.ok:\\n print(' 404')\\n else:\\n soup = get_soup_from_url(current_url)\\n events = scraper.scrape_event(soup)\\n for event in events:\\n event['Original URL'] = current_url\\n\\n writer.write_object(event) # event written to output file here\\n\\n out_stream.close()\",\n \"def main(args):\\n\\tif not os.path.isdir(args.dir):\\n\\t\\tprint \\\"The specified folder is not a directory.\\\"\\n\\t\\tsys.exit(1)\\n\\tNUMBER_OF_FILES = len(os.listdir(args.dir))\\n\\tif args.num_of_files:\\n\\t\\tNUMBER_OF_FILES = args.num_of_files\\n\\tprint \\\"Parsing\\\", NUMBER_OF_FILES, \\\"files\\\"\\n\\tsql = None\\n\\tif not args.stdout:\\n\\t\\tsql = sqlite3.connect(args.database)\\n\\t\\tsql.execute(\\\"\\\"\\\"PRAGMA foreign_keys = ON;\\\"\\\"\\\")\\n\\t\\tsql.execute(\\\"\\\"\\\"CREATE TABLE airdates(\\n\\t\\t\\tgame INTEGER PRIMARY KEY,\\n\\t\\t\\tairdate TEXT\\n\\t\\t);\\\"\\\"\\\")\\n\\t\\tsql.execute(\\\"\\\"\\\"CREATE TABLE documents(\\n\\t\\t\\tid INTEGER PRIMARY KEY AUTOINCREMENT,\\n\\t\\t\\tclue TEXT,\\n\\t\\t\\tanswer TEXT\\n\\t\\t);\\\"\\\"\\\")\\n\\t\\tsql.execute(\\\"\\\"\\\"CREATE TABLE categories(\\n\\t\\t\\tid INTEGER PRIMARY KEY AUTOINCREMENT,\\n\\t\\t\\tcategory TEXT UNIQUE\\n\\t\\t);\\\"\\\"\\\")\\n\\t\\tsql.execute(\\\"\\\"\\\"CREATE TABLE clues(\\n\\t\\t\\tid INTEGER PRIMARY KEY AUTOINCREMENT,\\n\\t\\t\\tgame INTEGER,\\n\\t\\t\\tround INTEGER,\\n\\t\\t\\tvalue INTEGER,\\n\\t\\t\\tFOREIGN KEY(id) REFERENCES documents(id),\\n\\t\\t\\tFOREIGN KEY(game) REFERENCES airdates(game)\\n\\t\\t);\\\"\\\"\\\")\\n\\t\\tsql.execute(\\\"\\\"\\\"CREATE TABLE classifications(\\n\\t\\t\\tclue_id INTEGER,\\n\\t\\t\\tcategory_id INTEGER,\\n\\t\\t\\tFOREIGN KEY(clue_id) REFERENCES clues(id),\\n\\t\\t\\tFOREIGN KEY(category_id) REFERENCES categories(id)\\n\\t\\t);\\\"\\\"\\\")\\n\\tfor i, file_name in enumerate(glob(os.path.join(args.dir, \\\"*.html\\\")), 1):\\n\\t\\twith open(os.path.abspath(file_name)) as f:\\n\\t\\t\\tparse_game(f, sql, i)\\n\\tif not args.stdout:\\n\\t\\tsql.commit()\\n\\tprint \\\"All done\\\"\",\n \"def run(self):\\n self._build_docker_images()\\n self._build_docker_image_test()\\n self._run_workflow_in_matrix_of_envs()\",\n \"def fetch(self):\\n self.genre = \\\"Review\\\"\\n try:\\n if not self.__setSoup():\\n log.info(self.log_msg(\\\"Soup not set,returning false\\\"))\\n return False\\n #if not self._getParentPage():\\n # log.info(self.log_msg(\\\"Parent page not found\\\"))\\n while True:\\n parent_page_soup = copy.copy(self.soup)\\n # log.info(self.log_msg('current uri%s'%parent_page_soup))\\n if not self.__addReviews():\\n log.info(self.log_msg('fetched all reviews for the url %s'\\\\\\n %self.task.instance_data['uri']))\\n \\n log.info(self.log_msg('Next page%s'%self.currenturi))\\n try:\\n \\n # self.currenturi = self.task.instance_data['uri'].rsplit\\\\\\n # ('/', 1)[0] + '/' + self.soup.find('a', \\\\\\n # title='Go to the next page')['href']\\n self.currenturi = 'http://www.phonedog.com' + parent_page_soup.find('a',title='Go to the next page')['href']\\n \\n if not self.__setSoup():\\n log.info(self.log_msg('soup not set for the uri %s'%\\\\\\n self.currenturi))\\n break\\n except:\\n log.info(self.log_msg('Next page not found for the uri %s'%\\\\\\n self.currenturi))\\n break\\n return True\\n except:\\n log.exception(self.log_msg(\\\"Exception in fetch\\\"))\\n return False\",\n \"def _generate_and_load_initial_batch(self, working_directory: Path):\\n\\n template_dir = Path(working_directory) / \\\"template_1\\\"\\n template_dir.mkdir()\\n # changes here should often be reflected in\\n # data_generator_opts and data_loader_opts\\n\\n channel_decl = self.channel_configs[0]\\n\\n plugin_options = {\\n \\\"pid\\\": \\\"0\\\",\\n \\\"big_ids\\\": \\\"True\\\",\\n }\\n # if it's efficient to do the whole load in one go, let's just do that.\\n if self.run_until.gap < MIN_PORTION_SIZE:\\n num_records = self.run_until.gap\\n else:\\n num_records = 1 # smallest possible batch to get to parallelizing fast\\n results = self._generate_and_load_batch(\\n template_dir,\\n channel_decl.org_config,\\n {\\n \\\"generator_yaml\\\": self.options.get(\\\"recipe\\\"),\\n \\\"num_records\\\": num_records,\\n \\\"num_records_tablename\\\": self.run_until.sobject_name or COUNT_REPS,\\n \\\"loading_rules\\\": self.loading_rules,\\n \\\"vars\\\": channel_decl.merge_recipe_options(self.recipe_options),\\n \\\"plugin_options\\\": plugin_options,\\n \\\"bulk_mode\\\": self.bulk_mode,\\n },\\n )\\n self.update_running_totals_from_load_step_results(results)\\n\\n # rename directory to reflect real number of sets created.\\n wd = SnowfakeryWorkingDirectory(template_dir)\\n if self.run_until.sobject_name:\\n self.sets_finished_while_generating_template = wd.get_record_counts()[\\n self.run_until.sobject_name\\n ]\\n else:\\n self.sets_finished_while_generating_template = num_records\\n\\n new_template_dir = data_loader_new_directory_name(template_dir, self.run_until)\\n shutil.move(template_dir, new_template_dir)\\n template_dir = new_template_dir\\n\\n # don't send data tables to child processes. All they\\n # care about are ID->OID mappings\\n wd = SnowfakeryWorkingDirectory(template_dir)\\n self._cleanup_object_tables(*wd.setup_engine())\\n\\n return template_dir, wd.relevant_sobjects()\",\n \"def Start(self):\\n self.state.hist_count = 0\\n # List of paths where history files are located\\n self.state.history_paths = []\\n if self.args.history_path:\\n self.state.history_paths.append(self.args.history_path)\\n\\n if not self.state.history_paths:\\n self.state.history_paths = self.GuessHistoryPaths(self.args.username)\\n\\n if not self.state.history_paths:\\n raise flow_base.FlowError(\\\"Could not find valid History paths.\\\")\\n\\n filenames = [\\\"History\\\"]\\n if self.args.get_archive:\\n filenames.append(\\\"Archived History\\\")\\n\\n for path in self.state.history_paths:\\n for fname in filenames:\\n self.CallFlow(\\n file_finder.FileFinder.__name__,\\n paths=[os.path.join(path, fname)],\\n pathtype=self.args.pathtype,\\n action=rdf_file_finder.FileFinderAction.Download(),\\n next_state=self.ParseFiles.__name__)\",\n \"def load_data():\\n\\n # Load data from categories\\n comp = fetch_20newsgroups(subset='all', categories=['comp.graphics', 'comp.sys.mac.hardware', 'comp.windows.x'], \\\\\\n shuffle=True, random_state=1, remove=('headers', 'footers', 'quotes'))\\n science = fetch_20newsgroups(subset='all', categories=['sci.crypt', 'sci.electronics', 'sci.space'], \\\\\\n shuffle=True, random_state=1, remove=('headers', 'footers', 'quotes'))\\n politics = fetch_20newsgroups(subset='all', categories=['talk.politics.guns', 'talk.politics.mideast'], \\\\\\n shuffle=True, random_state=1, remove=('headers', 'footers', 'quotes'))\\n religion = fetch_20newsgroups(subset='all', categories=['alt.atheism', 'soc.religion.christian'], \\\\\\n shuffle=True, random_state=1, remove=('headers', 'footers', 'quotes'))\\n recreation = fetch_20newsgroups(subset='all', categories=['rec.autos', 'rec.sport.baseball', 'rec.sport.hockey'], \\\\\\n shuffle=True, random_state=1, remove=('headers', 'footers', 'quotes'))\\n\\n # Print total number of documents\\n data_len = [len(comp.data), len(science.data), len(politics.data), len(recreation.data), len(religion.data)]\\n\\n # Subsample classes to create a balanced dataset\\n sub_k = min(data_len)\\n comp.data, comp.target = [list(t) for t in zip(*random.sample(list(zip(comp.data, comp.target)), sub_k))]\\n science.data, science.target = [list(t) for t in zip(*random.sample(list(zip(science.data, science.target)), sub_k))]\\n politics.data, politics.target = [list(t) for t in zip(*random.sample(list(zip(politics.data, politics.target)), sub_k))]\\n religion.data, religion.target = [list(t) for t in zip(*random.sample(list(zip(religion.data, religion.target)), sub_k))]\\n recreation.data, recreation.target = [list(t) for t in zip(*random.sample(list(zip(recreation.data, recreation.target)), sub_k))]\\n\\n # Subcategories labels\\n subcat_comp = np.array(comp.target)\\n subcat_scien = np.array(science.target) + len(comp.target_names)\\n subcat_polit = np.array(politics.target) + len(comp.target_names) + len(science.target_names)\\n subcat_rel = np.array(religion.target) + len(comp.target_names) + len(science.target_names) + len(politics.target_names)\\n subcat_rec = np.array(recreation.target) + len(comp.target_names) + len(science.target_names) + len(politics.target_names) + len(religion.target_names)\\n\\n # Assign labels to train data based on categories\\n y_comp = np.ones(len(comp.data))\\n y_scien = 2*np.ones(len(science.data))\\n y_polit = 3*np.ones(len(politics.data))\\n y_rel = 4*np.ones(len(religion.data))\\n y_rec = 5*np.ones(len(recreation.data))\\n labels = np.concatenate((y_comp,y_scien,y_polit,y_rel,y_rec), axis=None)\\n\\n # Computers\\n train_comp, test_comp, y_train_comp, y_test_comp, subcat_comp_train, subcat_comp_test = train_test_split(comp.data, y_comp, subcat_comp, test_size=0.2, random_state=42)\\n train_comp, val_comp, y_train_comp, y_val_comp, subcat_comp_train, subcat_comp_val = train_test_split(train_comp, y_train_comp, subcat_comp_train, test_size=0.25, random_state=42)\\n\\n # Sciences\\n train_scien, test_scien, y_train_scien, y_test_scien, subcat_scien_train, subcat_scien_test = train_test_split(science.data, y_scien, subcat_scien, test_size=0.2, random_state=42)\\n train_scien, val_scien, y_train_scien, y_val_scien, subcat_scien_train, subcat_scien_val = train_test_split(train_scien, y_train_scien, subcat_scien_train, test_size=0.25, random_state=42)\\n\\n # Politics\\n train_polit, test_polit, y_train_polit, y_test_polit, subcat_polit_train, subcat_polit_test = train_test_split(politics.data, y_polit, subcat_polit, test_size=0.2, random_state=42)\\n train_polit, val_polit, y_train_polit, y_val_polit, subcat_polit_train, subcat_polit_val = train_test_split(train_polit, y_train_polit, subcat_polit_train, test_size=0.25, random_state=42)\\n\\n # Religion\\n train_rel, test_rel, y_train_rel, y_test_rel, subcat_rel_train, subcat_rel_test = train_test_split(religion.data, y_rel, subcat_rel, test_size=0.2, random_state=42)\\n train_rel, val_rel, y_train_rel, y_val_rel, subcat_rel_train, subcat_rel_val = train_test_split(train_rel, y_train_rel, subcat_rel_train, test_size=0.25, random_state=42)\\n\\n # Recreation\\n train_rec, test_rec, y_train_rec, y_test_rec, subcat_rec_train, subcat_rec_test = train_test_split(recreation.data, y_rec, subcat_rec, test_size=0.2, random_state=42)\\n train_rec, val_rec, y_train_rec, y_val_rec, subcat_rec_train, subcat_rec_val = train_test_split(train_rec, y_train_rec, subcat_rec_train, test_size=0.25, random_state=42)\\n\\n # Corpus from all categories in train set\\n newsgroups_train = train_comp + train_scien + train_polit + train_rel + train_rec\\n #print(f\\\"Total number of documents in all categories in the train set is {len(newsgroups_train)}.\\\")\\n train_labels = np.concatenate((y_train_comp,y_train_scien,y_train_polit,y_train_rel,y_train_rec), axis=None)\\n #print(train_labels.shape)\\n train_subcat = np.concatenate((subcat_comp_train,subcat_scien_train,subcat_polit_train,subcat_rel_train,subcat_rec_train), axis=None)\\n #print(train_subcat.shape)\\n\\n # Corpus from all categories in test set\\n newsgroups_test = test_comp + test_scien + test_polit + test_rel + test_rec\\n test_labels = np.concatenate((y_test_comp,y_test_scien,y_test_polit,y_test_rel,y_test_rec), axis=None)\\n test_subcat = np.concatenate((subcat_comp_test,subcat_scien_test,subcat_polit_test,subcat_rel_test,subcat_rec_test), axis=None)\\n\\n # Corpus from all categories in validation set\\n newsgroups_val = val_comp + val_scien + val_polit + val_rel + val_rec\\n val_labels = np.concatenate((y_val_comp,y_val_scien,y_val_polit,y_val_rel,y_val_rec), axis=None)\\n val_subcat = np.concatenate((subcat_comp_val,subcat_scien_val,subcat_polit_val,subcat_rel_val,subcat_rec_val), axis=None)\\n\\n # Data Split\\n total = len(test_labels) + len(val_labels) + len(train_labels)\\n\\n return newsgroups_train, train_labels, newsgroups_test, test_labels, newsgroups_val, val_labels, train_subcat, test_subcat, val_subcat\",\n \"def ingest():\\n db.delete_dataset_records(DATASET_ID)\\n\\n db.insert_dataset({\\n 'dataset_id': DATASET_ID,\\n 'title': 'North American Breeding Bird Survey (BBS)',\\n 'version': '2016.0',\\n 'url': 'https://www.pwrc.usgs.gov/bbs/'})\\n\\n to_taxon_id = insert_taxa()\\n to_place_id = insert_places()\\n to_event_id = insert_events(to_place_id)\\n insert_counts(to_event_id, to_taxon_id)\",\n \"def main():\\n try:\\n jobs = {}\\n scraped_jobs = []\\n\\n scraped_jobs = read_from_text_file(\\\"\\\", scraped_jobs_filename)\\n\\n for URL in URLs:\\n if \\\"www.linkedin.com\\\" in URL:\\n (jobs, scraped_jobs) = linkedin_meta_search(URL, jobs, scraped_jobs)\\n if \\\"www.python.org\\\" in URL:\\n (jobs, scraped_jobs) = pythondotorg_meta_search(URL, jobs, scraped_jobs)\\n if \\\"www.ziprecruiter.com\\\" in URL:\\n (jobs, scraped_jobs) = ziprecruiter_meta_search(URL, jobs, scraped_jobs)\\n\\n write_to_text_file(\\\"\\\", \\\"scraped_jobs.txt\\\", scraped_jobs)\\n\\n for id in jobs.keys():\\n score, link, title, company, date_posted, location, full_text = jobs[id]\\n\\n print(f\\\"\\\\nEvaluating {title} at {company} ({id}).\\\")\\n\\n try:\\n if \\\"yesterday\\\" in date_posted:\\n age_in_days = 1\\n elif \\\"just now\\\" in date_posted:\\n age_in_days = 0\\n elif \\\"hours ago\\\" in date_posted or \\\"hour ago\\\" in date_posted:\\n age_in_days = float(date_posted.split()[0]) / 24\\n else:\\n datetimePosted = parser.parse(date_posted)\\n age = datetime.now().timestamp() - datetimePosted.timestamp()\\n age_in_days = int(age / 24 / 60 / 60)\\n except parser.ParserError as e:\\n print(f\\\"{e}: {date_posted} couldn't be parsed properly from {link}\\\")\\n age_in_days = 0\\n age_reduction = int(age_in_days ** 2)\\n score -= age_reduction\\n if age_reduction > 0:\\n print(\\n f\\\"... docking {age_reduction} from the score ({score}) \\\\\\n because the listing is {age_in_days} days old.\\\"\\n )\\n\\n for search_phrase in search_phrases.keys():\\n if search_phrase.lower() in title.lower():\\n\\n score_adjustment = 2 * search_phrases[search_phrase]\\n score += score_adjustment\\n if score_adjustment > 0:\\n score_adjustment = \\\"+\\\" + str(score_adjustment)\\n print(\\n f\\\"... {score_adjustment} points ({score}) for \\\\\\n {search_phrase} being in {title}.\\\"\\n )\\n if search_phrase.lower() in full_text.lower():\\n score_adjustment = search_phrases[search_phrase]\\n score += score_adjustment\\n if score_adjustment > 0:\\n score_adjustment = \\\"+\\\" + str(score_adjustment)\\n print(\\n f\\\"... {score_adjustment} points ({score}) for {search_phrase} \\\\\\n being in the text of the listing.\\\"\\n )\\n jobs[id][0] = score\\n\\n print(\\\"\\\\n\\\")\\n\\n send_email(jobs)\\n\\n except Exception as e:\\n send_email(f\\\"Not working: {e}\\\") # \\\".message}, {e.args}\\\")\",\n \"def run(self, data):\\n\\n if data and self.application:\\n # Build tuples for embedding index\\n if self.application.embeddings:\\n data = [(x, element, None) for x, element in enumerate(data)]\\n\\n # Process workflow\\n with st.spinner(\\\"Running workflow....\\\"):\\n results = []\\n for result in self.application.workflow(self.name, data):\\n # Store result\\n results.append(result)\\n\\n # Write result if this isn't an indexing workflow\\n if not self.application.embeddings:\\n st.write(result)\\n\\n # Store workflow results\\n self.data = results\",\n \"def run(self):\\n\\t\\tself._run_neural_network()\\n\\t\\tself._run_webots()\\n\\n\\t\\t# We first wait for webots inputs\\n\\t\\twebotsTurn = True\\n\\t\\tneuralNetworkTurn = False\\n\\t\\twhile True:\\n\\t\\t\\tif webotsTurn:\\n\\t\\t\\t\\tprint \\\"reading data from webots:\\\"\\n\\t\\t\\t\\twbtData = self._wbt_read_data()\\n\\t\\t\\t\\tprint \\\"sending data to nn...\\\"\\n\\t\\t\\t\\tself._send_data_to_nn(wbtData)\\n\\t\\t\\t\\twebotsTurn = False\\n\\t\\t\\t\\tneuralNetworkTurn = True\\n\\n\\t\\t\\telif neuralNetworkTurn:\\n\\n\\t\\t\\t\\tprint \\\"reading data from nn:\\\"\\n\\t\\t\\t\\tnnData = self._nn_read_data()\\n\\t\\t\\t\\tif self._neuralNetwork.poll() != None: break\\n\\t\\t\\t\\tprint \\\"sending data to webots...\\\"\\n\\t\\t\\t\\tself._send_data_to_wbt(nnData)\\n\\t\\t\\t\\tneuralNetworkTurn = False\\n\\t\\t\\t\\twebotsTurn = True\",\n \"def download_all_data(self) -> None:\\n print(\\\"Download in progress.\\\")\\n self.download_data(os.environ[\\\"NC_TOKEN_TRAIN_CHARACTERS\\\"], \\\"nextcloud\\\")\\n self.download_data(os.environ[\\\"NC_TOKEN_TRAIN_FRAGMENTS\\\"], \\\"nextcloud\\\")\\n self.download_data(os.environ[\\\"HABBAKUK_URL\\\"], \\\"generic_url\\\")\\n print(\\\"Download complete!\\\")\",\n \"def __run_game(game):\\n\\n while not game['lost']:\\n\\n game['move'], game['moveScores'] = my_algorithm(game)\\n\\n check_game_lost(game)\\n\\n # I changed the order of this loop to record information\\n # about the algorithm before the board is moved so\\n # in the move log csv the initial board will show scores and\\n # a planned move instead of having the scores off by 1\\n record_move(game)\\n\\n move(game)\\n\\n return game\",\n \"def extract(self):\\n from table_to_redshift import main as rds2rs\\n\\n self.mkstats()\\n self.mkcampaignrollups()\\n self.mkrollups()\\n\\n for table, table_id in [\\n ('visits', 'visit_id'),\\n ('visitors', 'visitor_id'),\\n ('campaigns', 'campaign_id'),\\n ('events', 'event_id'),\\n ('clients', 'client_id'),\\n ('campaign_properties', 'campaign_property_id'),\\n ('user_clients', 'user_client_id'),\\n ]:\\n debug('Uploading {} ..'.format(table))\\n rds2rs(table)\\n debug('Done.') # poor man's timer\\n\\n self.mkstats()\\n self.mkcampaignrollups()\\n self.mkrollups()\\n info('RDS extraction complete, waiting for next run')\",\n \"def crawl(self):\\n if os.path.exists(self.__work_path):\\n shutil.rmtree(self.__work_path)\\n print '\\\\nOld Data Was Found And Removed.\\\\n'\\n\\n initial_first_run = True\\n initial_recursion_depth = 0\\n initial_prev_link_size = 0\\n for url in self.__urls:\\n self.__start_recursion(url, initial_first_run,\\n initial_recursion_depth, initial_prev_link_size)\\n\\n Crawler.mission_report(self.__work_path)\",\n \"def run_workflow(EMBEDDING_BASE_PATH):\\n train_tweets_path, val_tweets_path, test_tweets_path, image_dataset = run_pre_workflow()\\n\\n input_images, train_tweets, val_tweets, test_tweets, glove_embeddings = replica_catalog(train_tweets_path, val_tweets_path, test_tweets_path, image_dataset, EMBEDDING_BASE_PATH)\\n\\n preprocess_tweets, preprocess_images, train_resnet, hpo_train_resnet, train_bilstm, hpo_train_bilstm, resnet_inference, bilstm_inference, late_fusion = transformation_catalog()\\n \\n sites_catalog()\\n\\n pegasus_properties()\\n \\n wf = Workflow('Crisis_Computing_Workflow')\\n\\n # --------------------------------------------------- TEXT PIPELINE ------------------------------------------------------ \\n\\n # Job 1: Preprocess tweets\\n preprocessed_train_tweets = File('preprocessed_train_tweets.csv')\\n preprocessed_val_tweets = File('preprocessed_val_tweets.csv')\\n preprocessed_test_tweets = File('preprocessed_test_tweets.csv')\\n \\n job_preprocess_tweets = [Job(preprocess_tweets) for i in range(3)]\\n job_preprocess_tweets[0].add_inputs(train_tweets)\\n job_preprocess_tweets[0].add_outputs(preprocessed_train_tweets)\\n job_preprocess_tweets[0].add_args('--filename', 'train_tweets.csv')\\n \\n job_preprocess_tweets[1].add_inputs(val_tweets)\\n job_preprocess_tweets[1].add_outputs(preprocessed_val_tweets)\\n job_preprocess_tweets[1].add_args('--filename', 'val_tweets.csv')\\n \\n job_preprocess_tweets[2].add_inputs(test_tweets)\\n job_preprocess_tweets[2].add_outputs(preprocessed_test_tweets)\\n job_preprocess_tweets[2].add_args('--filename', 'test_tweets.csv')\\n\\n\\n # Job 2: HPO Bi-LSTM\\n bilstm_best_params = File('best_bilstm_hpo_params.txt')\\n\\n job_hpo_train_bilstm = Job(hpo_train_bilstm)\\\\\\n .add_inputs(glove_embeddings, preprocessed_train_tweets, preprocessed_val_tweets, preprocessed_test_tweets)\\\\\\n .add_outputs(bilstm_best_params)\\\\\\n .add_args('--trials', BILSTM_NUM_TRIALS)\\n\\n\\n # Job 3: Train Bi-LSTM using best parameters from HPO study and output loss and accuracy curves\\n trained_bilstm_model = File('bilstm_final_model.h5') \\n bilstm_loss_curve = File('Loss_curve_bilstm.png')\\n bilstm_accuracy_curve = File('Accuracy_curve_bilstm.png')\\n\\n\\n job_train_bilstm = Job(train_bilstm)\\\\\\n .add_inputs(glove_embeddings, preprocessed_train_tweets, preprocessed_val_tweets, preprocessed_test_tweets, bilstm_best_params)\\\\\\n .add_outputs(bilstm_loss_curve, bilstm_accuracy_curve, trained_bilstm_model)\\\\\\n\\n\\n # Job 4: Run inference on best Bi-LSTM model to produce output on test dataset along with confusion matrix\\n bilstm_train_output_prob = File('bilstm_train_output.csv')\\n bilstm_test_output_prob = File('bilstm_test_output.csv')\\n bilstm_confusion_matrix = File('bilstm_confusion_matrix.png')\\n\\n job_bilstm_inference = Job(bilstm_inference)\\\\\\n .add_inputs(preprocessed_train_tweets, preprocessed_val_tweets, preprocessed_test_tweets, trained_bilstm_model)\\\\\\n .add_outputs(bilstm_train_output_prob, bilstm_test_output_prob, bilstm_confusion_matrix)\\n\\n\\n # --------------------------------------------------- IMAGE PIPELINE ------------------------------------------------------ \\n\\n \\n # Job 1: Preprocess images\\n prefix = \\\"resized_\\\"\\n job_preprocess_images = [Job(preprocess_images) for i in range(NUM_WORKERS)]\\n resized_images = split_preprocess_jobs(job_preprocess_images, input_images, prefix)\\n\\n # Job 2: HPO ResNet-50\\n resnet_best_params = File('best_resnet_hpo_params.txt')\\n\\n job_hpo_train_resnet = Job(hpo_train_resnet)\\\\\\n .add_inputs(*resized_images)\\\\\\n .add_args('--trials', RESNET_NUM_TRIALS)\\\\\\n .add_outputs(resnet_best_params)\\\\\\n .add_profiles(Namespace.PEGASUS, key=\\\"maxwalltime\\\", value=MAXTIMEWALL)\\n\\n\\n # Job 3: Train ResNet-50 using best parameters from HPO study and output loss and accuracy curves\\n trained_resnet_model = File('resnet_final_model.pth')\\n resnet_loss_curve = File('Loss_curve_resnet.png')\\n resnet_accuracy_curve = File('Accuracy_curve_resnet.png')\\n\\n job_train_resnet = Job(train_resnet)\\\\\\n .add_inputs(*resized_images, resnet_best_params)\\\\\\n .add_outputs(resnet_loss_curve, resnet_accuracy_curve, trained_resnet_model)\\\\\\n .add_profiles(Namespace.PEGASUS, key=\\\"maxwalltime\\\", value=MAXTIMEWALL)\\n\\n\\n # Job 4: Run inference on best ResNet-50 model to produce output on test dataset along with confusion matrix\\n resnet_train_output_prob = File('resnet_train_output.csv')\\n resnet_confusion_matrix = File('resnet_confusion_matrix.png')\\n resnet_test_output_prob = File('resnet_test_output.csv') \\n\\n job_resnet_inference = Job(resnet_inference)\\\\\\n .add_inputs(*resized_images, trained_resnet_model)\\\\\\n .add_outputs(resnet_train_output_prob, resnet_test_output_prob, resnet_confusion_matrix)\\n\\n \\n \\n # --------------------------------------------------- LATE FUSION ------------------------------------------------------ \\n\\n # Job 1: Late Fusion\\n confusion_matrix_MPC = File('late_fusion_MPC.png')\\n confusion_matrix_LR = File('late_fusion_LR.png')\\n confusion_matrix_MLP = File('late_fusion_MLP.png')\\n report_MLP = File('late_fusion_MLP.csv')\\n report_MPC = File('late_fusion_MPC.csv')\\n report_LR = File('late_fusion_LR.csv')\\n\\n job_late_fusion = Job(late_fusion)\\\\\\n .add_inputs(resnet_train_output_prob, resnet_test_output_prob, bilstm_train_output_prob, bilstm_test_output_prob)\\\\\\n .add_outputs(confusion_matrix_MPC, confusion_matrix_LR, confusion_matrix_MLP, report_MLP, report_MPC, report_LR)\\n\\n wf.add_jobs(*job_preprocess_tweets, *job_preprocess_images, job_bilstm_inference, job_hpo_train_bilstm, job_train_bilstm, job_hpo_train_resnet, job_train_resnet, job_resnet_inference, job_late_fusion)\\n\\n try:\\n wf.plan(submit=False, sites=[\\\"donut\\\"], output_sites=[\\\"donut\\\"], dir=\\\"submit\\\")\\n #wf.wait()\\n #wf.statistics()\\n except PegasusClientError as e:\\n print(e.output)\\n \\n #plot_workflow_graph(wf)\\n \\n return\",\n \"def main(): \\n while True:\\n city, month, day = get_filters()\\n\\n df = generate_stats(city, month, day)\\n\\n # step through data\\n step_through = input('\\\\nWould you like to step through raw data? Type \\\"yes\\\" to step through: \\\\n')\\n if step_through.lower() == 'yes':\\n step_through_data(df)\\n\\n # restart\\n restart = input('\\\\nWould you like to restart? Type \\\"yes\\\" to restart.\\\\n')\\n if restart.lower() != 'yes':\\n break\",\n \"def main():\\n\\n creds = None\\n # The file token.json stores the user's access and refresh tokens, and is\\n # created automatically when the authorization flow completes for the first\\n # time.\\n\\n # There was no need to do this with Google Sheets... I just felt like being extra.\\n if os.path.exists('token.json'):\\n creds = Credentials.from_authorized_user_file('token.json', SCOPES)\\n # If there are no (valid) credentials available, let the user log in.\\n if not creds or not creds.valid:\\n if creds and creds.expired and creds.refresh_token:\\n creds.refresh(Request())\\n else:\\n flow = InstalledAppFlow.from_client_secrets_file(\\n 'credentials.json', SCOPES)\\n creds = flow.run_local_server(port=0)\\n # Save the credentials for the next run\\n with open('token.json', 'w') as token:\\n token.write(creds.to_json())\\n\\n service = build('sheets', 'v4', credentials=creds)\\n\\n # Call the Sheets API\\n sheet = service.spreadsheets()\\n map_df = pd.read_csv('data/rel_map.csv')\\n \\n for i, rows in map_df.iterrows():\\n print(rows.show_season, rows.lead)\\n first_url = rows.show_season\\n write_from_URL(wiki_head + first_url, sheet, rows.lead, rows.show_season)\",\n \"def run(self):\\n if self.mode == 'remote':\\n raise NotImplementedError('No auto run for remote jobs.')\\n finished = False\\n while not finished:\\n if self.current_epoch == self.nepochs:\\n logger.info('Reached {} epochs. Finishing.'.format(self.current_epoch))\\n finished = True\\n elif self.app.available_gpus >= self.nmin:\\n n_spawns = self.app.available_gpus\\n if self.current_epoch == 1:\\n self.app.initialize_folders()\\n gen_folders = '{}/*'.format(self.app.generator_folder)\\n spawn_folders = self.app.move_generators_to_input(gen_folders)\\n else:\\n # First move possibly finished productions to their data folder\\n try:\\n self.app.move_trajs_to_folder(spawn_folders)\\n except:\\n pass\\n self.app.update_metadata()\\n self.fit_model()\\n self.spawns = self.respawn_from_MSM(search_type='counts', n_spawns=n_spawns)\\n spawn_folders = self.app.prepare_spawns(self.spawns, self.current_epoch)\\n # Plot where chosen spawns are in the tICA landscape\\n f, ax = plot_tica_landscape(self.ttrajs)\\n plot_spawns(self.spawns, self.ttrajs, ax=ax)\\n fig_fname = '{today}_e{epoch}_spawns.pdf'.format(today=datetime.date.today().isoformat(), epoch=self.current_epoch)\\n f.savefig(fig_fname)\\n\\n self.app.run_GPUs_bash(\\n folders=spawn_folders\\n )\\n self.current_epoch += 1\\n else:\\n logger.info('{} available GPUs. Minimum per epoch is {}'.format(self.app.available_gpus, self.nmin))\\n logger.info('Going to sleep for {} seconds'.format(self.sleeptime))\\n sleep(self.sleeptime)\",\n \"def run(self):\\n page = self.fetch_data(self.url)\\n stock_list = self.pop_stock_list(page)\\n self.write_csv(stock_list)\",\n \"def run_load(rootpath):\\n global CSV_PATH\\n CSV_PATH = rootpath+'/csv_files/'\\n load_movies_details()\\n load_movies_cast()\\n load_movies_reviews()\",\n \"def analyze(self):\\n self.logger.info('Analysing game footage: %s', self.path)\\n while not self.game_in_progress:\\n self.start_game()\\n while self.game_in_progress:\\n self.analyze_game()\",\n \"def data_preprocessing():\\n lineid_content = get_lineid_content()\\n print('Read movie_lines.txt file complete...')\\n convos = get_convos()\\n print('Read movie_conversations.txt file complete...')\\n print('Building dataset')\\n get_data(lineid_content, convos)\",\n \"def run_downloader(self):\\n \\\"\\\"\\\"calls to the file downloader\\\"\\\"\\\"\\n try:\\n html = self.get_page(self.url)\\n soup = self.get_soup(html)\\n if soup is not None: # If we have soup -\\n self.get_links(soup)\\n self.get_files()\\n else:\\n self.producer(\\\"THESS_ENV_CITYOFTHESS_DAILY_YEARLY_DATA_ERROR\\\", 'data source format is not as expected',\\n e)\\n return False\\n except Exception as e:\\n self.producer(\\\"THESS_ENV_CITYOFTHESS_DAILY_YEARLY_DATA_ERROR\\\", 'data source format is not as expected', e)\\n\\n return False\\n return True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6121077","0.6028184","0.59892666","0.59821534","0.5883398","0.585514","0.58488256","0.583428","0.5776301","0.577098","0.57684034","0.5755793","0.57253456","0.5703388","0.56687576","0.56505823","0.56160414","0.560584","0.56016344","0.5601598","0.55712277","0.5545588","0.5535417","0.5534017","0.55240715","0.5520249","0.5516911","0.55086297","0.54976434","0.5488685","0.54461527","0.5444634","0.544356","0.5439761","0.5423717","0.54161286","0.54143953","0.5408362","0.53858954","0.5384257","0.538084","0.5377573","0.53768927","0.5376478","0.53536505","0.53511345","0.5347199","0.5325931","0.5316099","0.53146535","0.5309323","0.5309118","0.5306936","0.53031164","0.5298958","0.5297225","0.5296774","0.5290483","0.52902806","0.52893084","0.5284722","0.527293","0.52727175","0.5247464","0.52454346","0.52447397","0.52444637","0.52406305","0.5237631","0.52324086","0.5230423","0.52303714","0.5229858","0.52277744","0.5225865","0.52252346","0.52174","0.5214346","0.5213953","0.5213333","0.5211364","0.52095187","0.52074945","0.52056205","0.52040505","0.52022195","0.51990867","0.51972073","0.51915205","0.5190978","0.518593","0.51848423","0.51820004","0.5178723","0.51733017","0.51721865","0.5169207","0.5167114","0.51663107","0.51648736"],"string":"[\n \"0.6121077\",\n \"0.6028184\",\n \"0.59892666\",\n \"0.59821534\",\n \"0.5883398\",\n \"0.585514\",\n \"0.58488256\",\n \"0.583428\",\n \"0.5776301\",\n \"0.577098\",\n \"0.57684034\",\n \"0.5755793\",\n \"0.57253456\",\n \"0.5703388\",\n \"0.56687576\",\n \"0.56505823\",\n \"0.56160414\",\n \"0.560584\",\n \"0.56016344\",\n \"0.5601598\",\n \"0.55712277\",\n \"0.5545588\",\n \"0.5535417\",\n \"0.5534017\",\n \"0.55240715\",\n \"0.5520249\",\n \"0.5516911\",\n \"0.55086297\",\n \"0.54976434\",\n \"0.5488685\",\n \"0.54461527\",\n \"0.5444634\",\n \"0.544356\",\n \"0.5439761\",\n \"0.5423717\",\n \"0.54161286\",\n \"0.54143953\",\n \"0.5408362\",\n \"0.53858954\",\n \"0.5384257\",\n \"0.538084\",\n \"0.5377573\",\n \"0.53768927\",\n \"0.5376478\",\n \"0.53536505\",\n \"0.53511345\",\n \"0.5347199\",\n \"0.5325931\",\n \"0.5316099\",\n \"0.53146535\",\n \"0.5309323\",\n \"0.5309118\",\n \"0.5306936\",\n \"0.53031164\",\n \"0.5298958\",\n \"0.5297225\",\n \"0.5296774\",\n \"0.5290483\",\n \"0.52902806\",\n \"0.52893084\",\n \"0.5284722\",\n \"0.527293\",\n \"0.52727175\",\n \"0.5247464\",\n \"0.52454346\",\n \"0.52447397\",\n \"0.52444637\",\n \"0.52406305\",\n \"0.5237631\",\n \"0.52324086\",\n \"0.5230423\",\n \"0.52303714\",\n \"0.5229858\",\n \"0.52277744\",\n \"0.5225865\",\n \"0.52252346\",\n \"0.52174\",\n \"0.5214346\",\n \"0.5213953\",\n \"0.5213333\",\n \"0.5211364\",\n \"0.52095187\",\n \"0.52074945\",\n \"0.52056205\",\n \"0.52040505\",\n \"0.52022195\",\n \"0.51990867\",\n \"0.51972073\",\n \"0.51915205\",\n \"0.5190978\",\n \"0.518593\",\n \"0.51848423\",\n \"0.51820004\",\n \"0.5178723\",\n \"0.51733017\",\n \"0.51721865\",\n \"0.5169207\",\n \"0.5167114\",\n \"0.51663107\",\n \"0.51648736\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72591263"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":85592,"cells":{"query":{"kind":"string","value":"Convert the labelme annotations to coco format and validate the annotations with randomly picked 5 images. If the annotation is incorrect, the program terminates."},"document":{"kind":"string","value":"def convert_labelme_to_coco(path_to_data):\r\n # convert labelme annotations to coco\r\n labelme2coco.convert(path_to_data, path_to_data + r'\\coco_annotation.json')\r\n\r\n # Open the coco format data\r\n with open(path_to_data + r'\\coco_annotation.json') as f:\r\n coco_d = json.load(f)\r\n\r\n # Get the category IDs for each category and create a new \"categories\" section.\r\n categories = []\r\n # for category in coco_d['categories']:\r\n # if category['name'] == 'Bad':\r\n # categories.append({\"id\": category['id'],\r\n # \"name\": category['id'],\r\n # \"supercategory\": category['id'],\r\n # \"isthing\": 1,\r\n # \"color\": [222, 23, 1]\r\n # })\r\n # elif category['name'] == 'Good':\r\n # categories.append({\"id\": category['id'],\r\n # \"name\": \"Good\",\r\n # \"supercategory\": \"Good\",\r\n # \"isthing\": 1,\r\n # \"color\": [133, 23, 1]\r\n # })\r\n\r\n # Update the \"catogories\" section of the coco format data with the correct category IDs.\r\n # coco_d['categories'] = categories\r\n\r\n categories = []\r\n for cat in coco_d['categories']:\r\n cat['isthing'] = 1\r\n categories.append(cat['name'])\r\n\r\n # Fix the segmentation and bbox.\r\n for annot in coco_d['annotations']:\r\n annot['bbox_mode'] = 0\r\n seg = annot['segmentation'][0]\r\n annot['bbox'] = seg\r\n annot['segmentation'] = [[seg[0], seg[1], seg[0], seg[3], seg[2], seg[3], seg[2], seg[1]]]\r\n\r\n # Save the modified coco format data.\r\n with open(path_to_data + r'\\coco_annotation.json', 'w') as j:\r\n json.dump(coco_d, j, sort_keys=True, indent=4)\r\n\r\n # Show the images to the user to validate the annotations.\r\n # Register the image information.\r\n register_coco_instances(\"coco_visualise\", {}, path_to_data + r\"/coco_annotation.json\",\r\n path_to_data)\r\n MetadataCatalog.get(\"meta_visualise\").set(thing_classes=categories)\r\n # MetadataCatalog.get(\"meta_train\").set(thing_classes=[\"Bad\", \"Good\"], thing_colors=[(172, 0, 0), (229, 0, 0)])\r\n train_metadata = MetadataCatalog.get(\"meta_visualise\")\r\n coco_train_dataset = DatasetCatalog.get(\"coco_visualise\")\r\n\r\n st.write('Showing the randomly picked 5 images. Check if the annotation is correctly embedded.')\r\n # Randomly pick 5 images to show to the user to validate the annotations.\r\n for d in random.sample(coco_train_dataset, 5):\r\n im = Image.open(d['file_name'])\r\n im_array = np.asarray(im)\r\n v = Visualizer(im_array, metadata=train_metadata, instance_mode=ColorMode.SEGMENTATION, scale=0.5)\r\n v = v.draw_dataset_dict(d)\r\n pil_image = Image.fromarray(v.get_image())\r\n st.image(pil_image)\r\n # window = tk.Toplevel()\r\n # window.tkimage = ImageTk.PhotoImage(pil_image)\r\n # window.attributes('-topmost', True)\r\n # label = tk.Label(window, image=window.tkimage)\r\n # label.pack()\r\n # button_close = tk.Button(window, text=\"Close\", command=window.destroy)\r\n # button_close.pack(fill='x')\r\n\r\n # Confirm the annotations with user. If the annotations are correct, it will proceed further.\r\n # If not, it terminates the program.\r\n # if messagebox.askyesno(title=\"Validate Annotations\", message=\"Were all annotations correct?\"):\r\n # pass\r\n DatasetCatalog.clear()\r\n MetadataCatalog.clear()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def generate_annotation(target, images, bbox_path):\n good_image_paths = []\n bad_image_paths = []\n bb = read_annotation_yolov5(bbox_path)\n for path in images:\n basename = os.path.basename(path) # extract file name only (e.g., bear_013.jpg)\n basename_no_ext = os.path.splitext(basename)[0] # extract file name (e.g., bear_013)\n\n label_filepath = os.path.join(target, f'{basename_no_ext}.txt')\n item = bb[int(basename_no_ext.split('_')[-1])] # e.g., 0.556, 0.6145, 0.3718, 0.5958\n # validation that annotation is between 0 and 1.\n if item[0] <= 0 or item[1] <= 0 or item[2] <= 0 or item[3] <= 0 \\\n or item[0] >= 1 or item[1] >= 1 or item[2] >= 1 or item[3] >= 1:\n print(f\"{basename_no_ext} has out of range value: {item[0]} {item[1]} {item[2]} {item[3]}\")\n bad_image_paths.append(path)\n continue\n\n with open(label_filepath, 'w') as out_file: # a label file is same as corresponding image file name\n cls_id = classes.index(cls)\n out_file.write(f\"{cls_id} {item[0]} {item[1]} {item[2]} {item[3]}\")\n good_image_paths.append(path)\n # print(f\"{basename_no_ext:} {cls_id} {item[0]} {item[1]} {item[2]} {item[3]}\")\n\n return good_image_paths, bad_image_paths","def coco_format(type_, id_list, annotation_url_list, file_list, result_list, label_list, coco_flag=0):\n annotations = []\n for i, result in enumerate(result_list):\n temp = {}\n annotation_url = annotation_url_list[i]\n file_path = file_list[i]\n temp['id'] = id_list[i]\n temp['annotation'] = []\n im = cv2.imread(file_path)\n height, width, _ = im.shape\n if result.shape[0] == 0:\n temp['annotation'] = json.dumps(temp['annotation'])\n annotations.append(temp)\n with open(annotation_url, 'w') as w:\n w.write(temp['annotation'])\n continue\n else:\n for j in range(result.shape[0]):\n cls_id = int(result[j][0]) + 1 + coco_flag\n x1 = result[j][1]\n x2 = result[j][3]\n y1 = result[j][2]\n y2 = result[j][4]\n score = result[j][5]\n width = max(0, x2 - x1)\n height = max(0, y2 - y1)\n if cls_id in label_list:\n temp['annotation'].append({\n 'area': width * height,\n 'bbox': [x1, y1, width, height],\n 'category_id': cls_id,\n 'iscrowd': 0,\n 'segmentation': [[x1, y1, x2, y1, x2, y2, x1, y2]],\n 'score': score\n })\n if type_ == 2 and len(temp['annotation']) > 0:\n temp['annotation'] = [temp['annotation'][0]]\n temp['annotation'][0].pop('area')\n temp['annotation'][0].pop('bbox')\n temp['annotation'][0].pop('iscrowd')\n temp['annotation'][0].pop('segmentation')\n temp['annotation'] = json.dumps(temp['annotation'])\n annotations.append(temp)\n with open(annotation_url, 'w') as wr:\n wr.write(temp['annotation'])\n return annotations","def test_annot():\n annots = ['aparc', 'aparc.a2005s']\n for a in annots:\n annot_path = pjoin(data_path, \"label\", \"%s.%s.annot\" % (\"lh\", a))\n labels, ctab, names = read_annot(annot_path)\n assert_true(labels.shape == (163842, ))\n assert_true(ctab.shape == (len(names), 5))","def convert_labels() -> None:\n data_folder = 'images'\n validation_split = 0.10\n\n # Convert annotations and split into validation and train set\n number_images = int(len(os.listdir(data_folder)) / 2)\n train_size = int(number_images * (1 - validation_split))\n val_size = number_images - train_size\n\n print(f'Training dataset size: {train_size}')\n print(f'Validation dataset size: {val_size}')\n\n with open('train.txt', 'w') as train_file, open('val.txt', 'w') as val_file:\n files = os.listdir(data_folder)\n print(len(files))\n # shuffle otherwise validation is from the same session\n random.shuffle(files)\n processed = 0\n for file_name in files:\n if file_name.split('.')[1] == 'jpg':\n # if image has no labels\n write = False\n if processed < train_size:\n file_to_write = train_file\n else:\n file_to_write = val_file\n\n with open(f'{data_folder}/{file_name}'.split('.')[0] + '.txt') as label_file:\n labels = []\n for line in label_file:\n line = line.split(' ')\n line[-1] = line[-1].rstrip()\n\n img = cv2.imread(f'{data_folder}/{file_name}')\n img_height = img.shape[0]\n img_width = img.shape[1]\n \n x = float(line[1]) * img_width\n y = float(line[2]) * img_height\n w = float(line[3]) * img_width\n h = float(line[4]) * img_height\n\n xmin = int(x - w/2)\n ymin = int(y - h/2)\n xmax = int(x + w/2)\n ymax = int(y + h/2)\n\n labels.append(f' {xmin},{ymin},{xmax},{ymax},{line[0]}')\n if len(labels) > 0:\n write = True\n file_to_write.write(f'{data_folder}/{file_name}')\n for label in labels:\n file_to_write.write(label)\n if write:\n file_to_write.write('\\n') \n processed += 1\n print(f'[{processed}/{number_images}] Processed {file_name}')","def create_coco_label(is_training):\n from pycocotools.coco import COCO\n\n coco_root = config.coco_root\n data_type = config.val_data_type\n if is_training:\n data_type = config.train_data_type\n\n # Classes need to train or test.\n train_cls = config.coco_classes\n train_cls_dict = {}\n for i, cls in enumerate(train_cls):\n train_cls_dict[cls] = i\n\n anno_json = os.path.join(coco_root, config.instances_set.format(data_type))\n\n coco = COCO(anno_json)\n classs_dict = {}\n cat_ids = coco.loadCats(coco.getCatIds())\n for cat in cat_ids:\n classs_dict[cat[\"id\"]] = cat[\"name\"]\n\n image_ids = coco.getImgIds()\n images = []\n image_path_dict = {}\n image_anno_dict = {}\n\n for img_id in image_ids:\n image_info = coco.loadImgs(img_id)\n file_name = image_info[0][\"file_name\"]\n anno_ids = coco.getAnnIds(imgIds=img_id, iscrowd=None)\n anno = coco.loadAnns(anno_ids)\n image_path = os.path.join(coco_root, data_type, file_name)\n annos = []\n iscrowd = False\n for label in anno:\n bbox = label[\"bbox\"]\n class_name = classs_dict[label[\"category_id\"]]\n iscrowd = iscrowd or label[\"iscrowd\"]\n if class_name in train_cls:\n x_min, x_max = bbox[0], bbox[0] + bbox[2]\n y_min, y_max = bbox[1], bbox[1] + bbox[3]\n annos.append(list(map(round, [y_min, x_min, y_max, x_max])) + [train_cls_dict[class_name]])\n\n if not is_training and iscrowd:\n continue\n if len(annos) >= 1:\n images.append(img_id)\n image_path_dict[img_id] = image_path\n image_anno_dict[img_id] = np.array(annos)\n\n return images, image_path_dict, image_anno_dict","def convert_celeba_annots(root_dir, out_dir):\n\n img_id = 0\n ann_id = 0\n cat_id = 1\n\n\n categories = [{\"id\": 1, \"name\": 'face'}]\n images = []\n annotations = []\n\n print('Parsing annotation file')\n ann_file = os.path.join(root_dir, 'Anno', 'list_bbox_celeba.txt')\n celeba_annot_dict = parse_celeba_gt(ann_file) # [im-file] = [[x,y,w,h], ...]\n\n # Get partition\n print('Parsing partition file')\n partition_file = os.path.join(root_dir, 'Eval', 'list_eval_partition.txt')\n train_imgs, val_imgs, test_imgs = parse_celeba_partition(partition_file)\n\n for subset, img_ids in zip(['train', 'val'], [train_imgs, val_imgs]):\n print('Starting %s' % subset)\n ann_dict = {}\n for filename in img_ids:\n if len(images) % 500 == 0:\n print(\"Processed %s images, %s annotations\" % (\n len(images), len(annotations)))\n\n image = {}\n image['id'] = img_id\n img_id += 1\n im = Image.open(os.path.join(root_dir, 'img_celeba', filename))\n image['width'] = im.width\n image['height'] = im.height\n image['file_name'] = filename\n images.append(image)\n\n for gt_bbox in celeba_annot_dict[filename]: # CelebA only has one box per image, though\n ann = {}\n ann['id'] = ann_id\n ann_id += 1\n ann['image_id'] = image['id']\n ann['segmentation'] = []\n ann['category_id'] = cat_id # 1:\"face\" for CelebA\n ann['iscrowd'] = 0\n ann['area'] = gt_bbox[2] * gt_bbox[3]\n ann['bbox'] = gt_bbox\n annotations.append(ann)\n\n ann_dict['images'] = images\n ann_dict['categories'] = categories\n ann_dict['annotations'] = annotations\n print(\"Num categories: %s\" % len(categories))\n print(\"Num images: %s\" % len(images))\n print(\"Num annotations: %s\" % len(annotations))\n\n json_name = 'instances_CelebA_{}.json'.format(subset)\n with open(os.path.join(out_dir, json_name), 'w', encoding='utf8') as outfile:\n outfile.write(json.dumps(ann_dict, cls=MyEncoder, indent=4))\n outfile.close()","def _load_all(self, anno_file, shuffle):\n image_set_index = []\n labels = []\n coco = COCO(anno_file)\n img_ids = coco.getImgIds()\n #print(img_ids)\n cars=[3,6,8]\n pedestrians=[1]\n cyclists=[2,4]\n lights=[10]\n signs=[13]\n\n apex_categories=cars+pedestrians+cyclists+lights+signs\n cnt=0\n humanonly=0\n human_count=0\n\n for img_id in img_ids:\n relevant=False\n # filename\n image_info = coco.loadImgs(img_id)[0]\n filename = image_info[\"file_name\"]\n #print(filename)\n #subdir = filename.split('_')[1]\n height = image_info[\"height\"]\n width = image_info[\"width\"]\n # label\n anno_ids = coco.getAnnIds(imgIds=img_id)\n annos = coco.loadAnns(anno_ids)\n label = []\n\n #print(\"listing categories for filename: \"+filename)\n\n hashumans=False\n for anno in annos:\n cat_id = int(anno[\"category_id\"])\n if(cat_id in apex_categories):\n cat_reduced= 0 if (cat_id in cars) else 1 if(cat_id in pedestrians) else 2 if(cat_id in cyclists) else 3 if(cat_id in lights) else 4\n bbox = anno[\"bbox\"]\n assert len(bbox) == 4\n xmin = float(bbox[0]) / width\n ymin = float(bbox[1]) / height\n xmax = xmin + float(bbox[2]) / width\n ymax = ymin + float(bbox[3]) / height\n label.append([cat_reduced, xmin, ymin, xmax, ymax, 0])\n #print(\"category: %d\"%cat_reduced)\n if (cat_id in pedestrians):\n hashumans=True\n if(cat_id not in pedestrians): #at least one non-person object is necessary\n relevant=True\n\n if(label and not relevant):\n humanonly+=1\n if label and relevant:\n if(hashumans):\n human_count+=1\n #print(\"adding \"+filename)\n labels.append(np.array(label))\n image_set_index.append(os.path.join(self.set, filename))\n cnt+=1\n print(\"added %d images\"%cnt)\n print(\"%d images has only humans\"%humanonly)\n print(\"%d registered images has humans\"%human_count)\n\n if shuffle:\n import random\n indices = range(len(image_set_index))\n random.shuffle(indices)\n image_set_index = [image_set_index[i] for i in indices]\n labels = [labels[i] for i in indices]\n # store the results\n self.image_set_index = image_set_index\n self.labels = labels","def test_nominal_case(self):\n\n image_filename, boxes = list(annotation.read(self.filename))\n self.assertEqual(image_filename, 'image.jpg')\n self.assertEqual(len(boxes), 2)\n width = 400\n height = 300\n b = boxes[0]\n self.assertEqual(b.xmin, 10 / width)\n self.assertEqual(b.ymin, 20 / height)\n self.assertEqual(b.xmax, 30 / width)\n self.assertEqual(b.ymax, 40 / height)","def setup_annotations(self):\n sbd_path = get_data_path('sbd')\n target_path = pjoin(self.root, 'SegmentationClass/pre_encoded')\n if not os.path.exists(target_path): os.makedirs(target_path)\n path = pjoin(sbd_path, 'dataset/train.txt')\n sbd_train_list = tuple(open(path, 'r'))\n sbd_train_list = [id_.rstrip() for id_ in sbd_train_list]\n train_aug = self.files['train'] + sbd_train_list\n\n # keep unique elements (stable)\n train_aug = [train_aug[i] for i in \\\n sorted(np.unique(train_aug, return_index=True)[1])]\n self.files['train_aug'] = train_aug\n set_diff = set(self.files['val']) - set(train_aug) # remove overlap\n self.files['train_aug_val'] = list(set_diff)\n\n pre_encoded = glob.glob(pjoin(target_path, '*.png'))\n expected = np.unique(self.files['train_aug'] + self.files['val']).size\n\n if len(pre_encoded) != expected:\n print(\"Pre-encoding segmentation masks...\")\n for ii in tqdm(sbd_train_list):\n lbl_path = pjoin(sbd_path, 'dataset/cls', ii + '.mat')\n data = io.loadmat(lbl_path)\n lbl = data['GTcls'][0]['Segmentation'][0].astype(np.int32)\n lbl = m.toimage(lbl, high=lbl.max(), low=lbl.min())\n m.imsave(pjoin(target_path, ii + '.png'), lbl)\n\n for ii in tqdm(self.files['trainval']):\n fname = ii + '.png'\n lbl_path = pjoin(self.root, 'SegmentationClass', fname)\n lbl = self.encode_segmap(m.imread(lbl_path))\n lbl = m.toimage(lbl, high=lbl.max(), low=lbl.min())\n m.imsave(pjoin(target_path, fname), lbl)\n\n assert expected == 9733, 'unexpected dataset sizes'","def coco_metrics(pred_data, anno_json, config):\n from pycocotools.coco import COCO\n from pycocotools.cocoeval import COCOeval\n num_classes = config.num_classes\n\n #Classes need to train or test.\n val_cls = config.classes\n val_cls_dict = {}\n for i, cls in enumerate(val_cls):\n val_cls_dict[i] = cls\n coco_gt = COCO(anno_json)\n classs_dict = {}\n cat_ids = coco_gt.loadCats(coco_gt.getCatIds())\n for cat in cat_ids:\n classs_dict[cat[\"name\"]] = cat[\"id\"]\n\n predictions = []\n img_ids = []\n\n for sample in pred_data:\n pred_boxes = sample['boxes']\n box_scores = sample['box_scores']\n img_id = sample['img_id']\n h, w = sample['image_shape']\n\n final_boxes = []\n final_label = []\n final_score = []\n img_ids.append(img_id)\n\n for c in range(1, num_classes):\n class_box_scores = box_scores[:, c]\n score_mask = class_box_scores > config.min_score\n class_box_scores = class_box_scores[score_mask]\n class_boxes = pred_boxes[score_mask] * [h, w, h, w]\n\n if score_mask.any():\n nms_index = apply_nms(class_boxes, class_box_scores, config.nms_threshold, config.max_boxes)\n class_boxes = class_boxes[nms_index]\n class_box_scores = class_box_scores[nms_index]\n\n final_boxes += class_boxes.tolist()\n final_score += class_box_scores.tolist()\n final_label += [classs_dict[val_cls_dict[c]]] * len(class_box_scores)\n\n for loc, label, score in zip(final_boxes, final_label, final_score):\n res = {}\n res['image_id'] = img_id\n res['bbox'] = [loc[1], loc[0], loc[3] - loc[1], loc[2] - loc[0]]\n res['score'] = score\n res['category_id'] = label\n predictions.append(res)\n if not os.path.exists('./eval_out'):\n os.makedirs('./eval_out')\n with open('./eval_out/predictions.json', 'w') as f:\n json.dump(predictions, f)\n\n coco_dt = coco_gt.loadRes('./eval_out/predictions.json')\n E = COCOeval(coco_gt, coco_dt, iouType='bbox')\n E.params.imgIds = img_ids\n E.evaluate()\n E.accumulate()\n E.summarize()\n return E.stats[0]","def main(root_dir):\n # load annotations\n print('Loading instances and annotations...')\n captions_file = json.load(open('{}/annotations/captions_train2017.json'.format(root_dir), 'r'))\n categories_file = json.load(open('{}/annotations/instances_train2017.json'.format(root_dir), 'r'))\n print('Done.')\n\n # group categories by image\n image_categories = group_categories(categories_file)\n\n # group captions by image\n image_captions = group_captions(captions_file['annotations'])\n\n # get filename of each image\n image_file = get_filename(captions_file['images'])\n\n # assign each category an id.\n # we are not using the default ids given in the dataset because\n # the id ranges are not continuous.\n category_id, id_category = map_category_id(categories_file['categories'])\n \n # save parsed coco dataset\n save_dataset(image_categories, image_captions, image_file, category_id, id_category, root_dir)","def get_data(input_path):\n all_imgs = []\n classes_count = {}\n class_mapping = {}\n\n # parsing Flag\n visualise = False\n\n # MSCOCO directory\n data_path = input_path\n\n print('Parsing annotation files')\n annot_path = os.path.join(data_path, 'annotations_bbox')\n imgs_path = os.path.join(data_path, 'images')\n\n # images directory (train, val, trainval, test)\n imgsets_path_trainval = os.path.join(data_path, 'images', 'trainval.txt')\n imgsets_path_train = os.path.join(data_path, 'images', 'train.txt')\n imgsets_path_val = os.path.join(data_path, 'images', 'val.txt')\n imgsets_path_test = os.path.join(data_path, 'images', 'test.txt')\n\n trainval_files = []\n train_files = []\n val_files = []\n test_files = []\n\n with open(imgsets_path_trainval) as f:\n for line in f:\n trainval_files.append(line.strip())\n\n with open(imgsets_path_train) as f:\n for line in f:\n train_files.append(line.strip())\n\n with open(imgsets_path_val) as f:\n for line in f:\n val_files.append(line.strip())\n\n # test-set (default) not included in MSCOCO\n if os.path.isfile(imgsets_path_test):\n with open(imgsets_path_test) as f:\n for line in f:\n test_files.append(line.strip())\n\n # annotation read\n annots_train = json.load(open(os.path.join(annot_path, 'bbox_train2017.json'), 'r'))\n annots_val = json.load(open(os.path.join(annot_path, 'bbox_val2017.json'), 'r'))\n annots = dict()\n annots['train'] = annots_train\n annots['val'] = annots_val\n\n for part in ['train', 'val']:\n annots_keys = tqdm(annots[part].keys())\n for img_name in annots_keys:\n annots_keys.set_description(\"Processing %s\" % img_name)\n for bbox in annots[part][img_name]:\n class_name = bbox['label'].replace(' ', '')\n all_imgs.append({\n \"filepath\": os.path.join(data_path, 'images', '%s2017' % part, \"%s.jpg\" % img_name),\n \"width\": None,\n \"height\": None,\n \"bboxes\": [{\n \"class\": class_name,\n \"x1\": bbox['bbox']['x1'],\n \"y1\": bbox['bbox']['x2'],\n \"x2\": bbox['bbox']['y1'],\n \"y2\": bbox['bbox']['y2'],\n \"difficult\": False\n }],\n \"image_id\": img_name,\n \"imageset\": part\n })\n if class_name not in classes_count:\n classes_count[class_name] = 1\n else:\n classes_count[class_name] += 1\n if class_name not in class_mapping:\n class_mapping[class_name] = len(class_mapping)\n\n # visualise bounding boxes\n if visualise:\n img = cv2.imread(annotation_data['filepath'])\n for bbox in annotation_data['bboxes']:\n cv2.rectangle(img, (bbox['x1'], bbox['y1']), (bbox['x2'], bbox['y2']), (0, 0, 255))\n cv2.imshow('img', img)\n print(annotation_data['imageset'])\n cv2.waitKey(0)\n\n return all_imgs, classes_count, class_mapping","def main(gt_dir='./data/Oxford_Robot_ICCV19/anno', devkit_dir = './dataset/robot_list/'):\n with open(join(devkit_dir, 'info.json'), 'r') as fp:\n info = json.load(fp)\n image_path_list = join(devkit_dir, 'val.txt')\n label_path_list = join(devkit_dir, 'label.txt')\n mapping = np.array(info['label2train'], dtype=np.int)\n gt_imgs = open(label_path_list, 'r').read().splitlines()\n gt_imgs = [join(gt_dir, x) for x in gt_imgs]\n\n for ind in range(len(gt_imgs)):\n label = np.array(Image.open(gt_imgs[ind]))\n label = label_mapping(label, mapping)\n label = label[:,:,0].astype(np.uint8)\n name_tmp = gt_imgs[ind].replace('anno','anno_color')\n save([label, name_tmp])\n \n return","def load_annotations(self, image_index):\n\t\t\t# Get ground truth annotations.\n\t\t\tannotations_ids = self.coco.getAnnIds(imgIds=self.image_ids[image_index], iscrowd=False)\n\t\t\tannotations = {'labels': np.empty((0,)), 'bboxes': np.empty((0, 4))}\n\n\t\t\t# If needed get info for masks.\n\t\t\tif self.mask:\n\t\t\t\timport cv2\n\n\t\t\t\t# Get image info.\n\t\t\t\timage_info = self.coco.loadImgs(self.image_ids[image_index])[0]\n\t\t\t\tannotations['masks'] = []\n\n\t\t\t# Some images appear to miss annotations (like image with id 257034).\n\t\t\tif len(annotations_ids) == 0:\n\t\t\t\treturn annotations\n\n\n\t\t\t# Parse annotations\n\t\t\tcoco_annotations = self.coco.loadAnns(annotations_ids)\n\t\t\tfor idx, a in enumerate(coco_annotations):\n\t\t\t\t# Some annotations have basically no width / height, skip them.\n\t\t\t\tif a['bbox'][2] < 1 or a['bbox'][3] < 1:\n\t\t\t\t\tcontinue\n\n\t\t\t\tannotations['labels'] = np.concatenate([annotations['labels'], [self.coco_label_to_label(a['category_id'])]], axis=0)\n\t\t\t\tannotations['bboxes'] = np.concatenate([annotations['bboxes'], [[\n\t\t\t\t\ta['bbox'][0],\n\t\t\t\t\ta['bbox'][1],\n\t\t\t\t\ta['bbox'][0] + a['bbox'][2],\n\t\t\t\t\ta['bbox'][1] + a['bbox'][3],\n\t\t\t\t]]], axis=0)\n\n\t\t\t\t# If needed get annotations for masks.\n\t\t\t\tif self.mask:\n\t\t\t\t\tif 'segmentation' not in a:\n\t\t\t\t\t\traise ValueError('Expected \\'segmentation\\' key in annotation, got: {}'.format(a))\n\n\t\t\t\t\tmask = np.zeros((image_info['height'], image_info['width'], 1), dtype=np.uint8)\n\t\t\t\t\tfor seg in a['segmentation']:\n\t\t\t\t\t\tpoints = np.array(seg).reshape((len(seg) // 2, 2)).astype(int)\n\n\t\t\t\t\t\t# Draw mask.\n\t\t\t\t\t\tcv2.fillPoly(mask, [points.astype(int)], (1,))\n\n\t\t\t\t\tannotations['masks'].append(mask.astype(float))\n\n\n\t\t\treturn annotations","def merge_coco_annotations(anno_file_list, save_dir, pre_fixs, keep_cls=13):\n annos = mmcv.load(anno_file_list[0])\n categories =[info for info in annos['categories'] if info['id'] == keep_cls]\n info = annos['info']\n licenses = annos['licenses']\n del annos\n\n new_coco_annotations = {}\n new_annotations = []\n new_images = []\n image_idx_offset = 0\n anno_idx_offset = 0\n for file_i, file_path in tqdm(enumerate(anno_file_list), total=len(anno_file_list)):\n annos = mmcv.load(file_path)\n cur_images = annos['images']\n cur_annotations = annos['annotations']\n for img_info in cur_images: # loop through image_infos\n img_info['id'] += image_idx_offset\n img_info['file_name'] = os.path.join(pre_fixs[file_i], img_info['file_name'])\n new_images.append(img_info)\n for anno_info in cur_annotations: # loop through boxes\n anno_info['id'] += anno_idx_offset\n anno_info['image_id'] += image_idx_offset\n cat_id = keep_cls if anno_info['category_id'] == keep_cls else 15\n anno_info['category_id'] = cat_id\n new_annotations.append(anno_info)\n\n image_idx_offset += len(cur_images)\n anno_idx_offset += len(cur_annotations)\n\n new_coco_annotations['info'] = info\n new_coco_annotations['licenses'] = licenses\n new_coco_annotations['categories'] = categories\n new_coco_annotations['annotations'] = new_annotations\n new_coco_annotations['images'] = new_images\n\n print('convert done\\nsaving coco annotations')\n mmcv.dump(new_coco_annotations, save_dir)\n print('all done!')","def draw_coco_bbox(img,labels):\n \n # w,h,_=img.shape\n for bbox in labels:\n\n x1 = int(bbox['points']['x1'])\n y1 = int(bbox['points']['y1'])\n x2 = int(bbox['points']['x2'])\n y2 = int(bbox['points']['y2'])\n c = str(bbox['probability'])\n # font\n font = cv2.FONT_HERSHEY_SIMPLEX\n \n # org\n org = (int(x1), int(y1))\n \n # fontScale\n fontScale = 1\n LABELS = ['ConcreteCrack','Spalling','Efflorescene','Exposure']\n \n # Blue color in BGR\n # color_efflorescene = \n\n color = (56,255,225)\n\n \n # Line thickness of 2 px\n thickness = 2\n \n # Using cv2.putText() method\n img = cv2.putText(img, str(c), org, font, \n fontScale, color, thickness, cv2.LINE_AA)\n\n\n img = cv2.rectangle(img,(int(x1),int(y1)),(int(x2),int(y2)),color,2)\n return img","def prepare_train_coco_data(args):\n image_dir, annotation_file, data_dir = args.train_coco_image_dir, args.train_coco_annotation_file, args.train_coco_data_dir\n batch_size = args.batch_size\n basic_model = args.basic_model\n num_roi = args.num_roi\n\n coco = COCO(annotation_file)\n\n img_ids = list(coco.imgToAnns.keys())\n img_files = []\n img_heights = []\n img_widths = []\n anchor_files = []\n gt_classes = []\n gt_bboxes = []\n\n for img_id in img_ids:\n img_files.append(os.path.join(image_dir, coco.imgs[img_id]['file_name'])) \n img_heights.append(coco.imgs[img_id]['height']) \n img_widths.append(coco.imgs[img_id]['width']) \n anchor_files.append(os.path.join(data_dir, os.path.splitext(coco.imgs[img_id]['file_name'])[0]+'_'+basic_model+'_anchor.npz')) \n\n classes = [] \n bboxes = [] \n for ann in coco.imgToAnns[img_id]: \n classes.append(coco_category_to_class[ann['category_id']]) \n bboxes.append([ann['bbox'][1], ann['bbox'][0], ann['bbox'][3]+1, ann['bbox'][2]+1]) \n\n gt_classes.append(classes) \n gt_bboxes.append(bboxes) \n \n print(\"Building the training dataset...\")\n dataset = DataSet(img_ids, img_files, img_heights, img_widths, batch_size, anchor_files, gt_classes, gt_bboxes, True, True)\n print(\"Dataset built.\")\n return coco, dataset","def prepare_val_coco_data(args):\n image_dir, annotation_file = args.val_coco_image_dir, args.val_coco_annotation_file\n\n coco = COCO(annotation_file)\n\n img_ids = list(coco.imgToAnns.keys())\n img_files = []\n img_heights = []\n img_widths = []\n\n for img_id in img_ids:\n img_files.append(os.path.join(image_dir, coco.imgs[img_id]['file_name']))\n img_heights.append(coco.imgs[img_id]['height']) \n img_widths.append(coco.imgs[img_id]['width']) \n\n print(\"Building the validation dataset...\")\n dataset = DataSet(img_ids, img_files, img_heights, img_widths)\n print(\"Dataset built.\")\n return coco, dataset","def mapping_image_to_label (self, labels_df, polygons, fpath_tiff): \n \n unread_tiff = rasterio.open(fpath_tiff)\n\n #Projecting the coordinates to that CRS \n proj = Proj(init='epsg:32618')\n data = []\n labels = []\n failed = []\n \n src = rasterio.open(fpath_tiff, 'r')\n outfolder = '/train/batch'\n \n print (\"Hold on tight! Mapping each image to its respective label...\")\n \n \n for num, row in labels_df.iterrows():\n try:\n \n \n roof_material_num = 0\n polygon0 = polygons [num]\n polygon0['coordinates'] = self.transforming_coordinates(polygon0['coordinates'], proj)\n masked_image, out_transform = rasterio.mask.mask(src,[polygon0], filled = True, crop=True, nodata = 0)\n img_image = reshape_as_image (masked_image)\n \n #Defining the name of the image file as \"buildingID+roofMaterial+png\" and its path \n img_path = os.path.join (outfolder, str (row['id'])+'-'+ str (row['roof_material'])+'.png')\n \n #swapping the color channels from RGB2BGR\n img_image = cv2.cvtColor (img_image, cv2.COLOR_RGB2BGR) #img_image is a numpy array\n \n #resizing the image dimensions to 128x128 to match ImageNet dimensions\n img_image = cv2.resize(img_image, (128, 128))\n \n #writing the image in the file\n #cv2.imwrite (img_path, img_image)\n # update the data and labels lists, respectively\n data.append(img_image) #data is a list\n labels.append(row['roof_material'])\n \n except Exception as e:\n print (e)\n failed.append (num)\n \n \n #print number of images we failed to crop and write \n print (\"Bad News First: Failed to write\", len(failed), \"Images.\")\n print (\"Good News: Successfully mapped\", len (data), \"Images.\")\n data = np.array(data)\n labels = np.array(labels)\n #batch = data.sample(frac=0.5, replace=False, random_state=1)\n #print(\"Size and shape of validY: {}\\n\".format(batch.shape))\n return data, labels","def create_masks(image_folder: str, annotation_path: str, outpath: str):\n\n train_reader = ReaderAnnotation(annotation_path)\n\n all_images = os.listdir(image_folder)\n annotated_images = train_reader.annotation.keys()\n\n creator = MaskCreator()\n\n for key in annotated_images:\n file_extension = \".JPG\"\n if not os.path.isfile(\n os.path.join(\n image_folder,\n key.split(\".\")[0] + file_extension,\n )\n ):\n file_extension = file_extension.lower()\n\n image_name = os.path.join(\n image_folder,\n key.split(\".\")[0] + file_extension,\n )\n print(image_name)\n\n out_image_path = os.path.join(outpath, os.path.split(image_name)[-1])\n assert os.path.exists(out_image_path), \"Out image path doesn't exist\"\n\n image = plt.imread(image_name)\n h, w, c = image.shape\n\n regions = train_reader.get(key)[\"regions\"]\n # less than minimal distance\n radius = int(train_reader.get_radius_min(regions=regions) * 0.9)\n\n masks = []\n for _, center in regions.items():\n masks.append(\n creator.create_circular_mask(\n h=h,\n w=w,\n center=(\n int(center[\"shape_attributes\"][\"cx\"]),\n int(center[\"shape_attributes\"][\"cy\"]),\n ),\n radius=radius,\n )\n )\n\n if len(masks) > 50:\n masks = [creator._unite_masks(masks)]\n\n if masks:\n creator.visualize(\n image=image,\n masks=masks,\n filename=out_image_path,\n use_image=False,\n )\n else:\n creator._create_empty_mask(image=image, filename=out_image_path)\n\n print(\"Empty images:\")\n for empty_image in list(set(all_images) - set(annotated_images)):\n if os.path.exists(out_image_path):\n continue\n empty_image = os.path.join(image_folder, empty_image)\n print(empty_image)\n image = plt.imread(empty_image)\n creator._create_empty_mask(\n image=image,\n filename=os.path.join(\n outpath,\n os.path.split(empty_image)[-1],\n ),\n )","def test_labels_warning(self):\n with pytest.warns(\n YellowbrickWarning, match=\"both classes and encoder specified\"\n ):\n oz = ClassificationScoreVisualizer(\n GaussianNB(),\n classes=[\"a\", \"b\", \"c\"],\n encoder={0: \"foo\", 1: \"bar\", 2: \"zap\"},\n )\n labels = oz._labels()\n npt.assert_array_equal(labels, [\"foo\", \"bar\", \"zap\"])","def _load_action_annotation(self, anno_filepath):\n # Check\n assert os.path.exists(anno_filepath), \\\n 'Path does not exist: {}'.format(anno_filepath)\n # Open and read\n lines = None\n with open(anno_filepath) as f:\n # format: imgidx x1 y1 x2 y2 label_list\n # whre label list look like this: 0 0 0 0 1 0 0 (assume here has six action classes)\n lines = f.readlines()\n \n if not lines:\n print\n print \"missing anno_filepath:\", anno_filepath\n sys.exit(1)\n\n # init\n image_index, gt_roidb = [], []\n\n # Process\n for line in lines:\n # Initialize\n boxes = np.zeros((1, 4), dtype=np.uint16)\n gt_classes = np.zeros(1, dtype=np.int32)\n overlaps = np.zeros((1, self.num_classes), dtype=np.float32)\n\n line = line.strip().split()\n args = 0\n imgidx = line[args]\n image_index.append(imgidx)\n\n args += 1\n x1, y1, x2, y2 = line[args: args + 4]\n x1 = float(x1) - 1\n y1 = float(y1) - 1\n x2 = float(x2) - 1\n y2 = float(y2) - 1\n\n args += 4\n classname = line[args]\n cls = self._class_to_ind[classname.lower().strip()]\n\n gt_classes[0] = cls\n boxes[0, :] = [x1, y1, x2, y2]\n overlaps[0, cls] = 1.0\n overlaps = scipy.sparse.csr_matrix(overlaps)\n\n # \n img_anno_dict = {\n 'boxes' : boxes, \n 'gt_classes': gt_classes, \n 'gt_overlaps' : overlaps, \n 'flipped' : False}\n gt_roidb.append(img_anno_dict)\n\n return image_index, gt_roidb","def convert_csv_to_cocoV2(src_path, out_path, rad_ids=[], pre_coco_anno_path=None, debug=False):\n assert isinstance(rad_ids, list)\n csv_ann_file = os.path.join(src_path, 'train.csv')\n data_path = os.path.join(src_path, 'train')\n os.makedirs(out_path, exist_ok=True)\n\n print('1. reading csv annotations...')\n csv_annotations, clsid_to_clsname = Read_csv_annotation(csv_ann_file, include_radId=True)\n\n pre_coco_ann_infos = mmcv.load(pre_coco_anno_path)\n img_shape_dict = {img_info['file_name']: (img_info['height'], img_info['width'])\n for img_info in pre_coco_ann_infos['images']}\n\n for rad_id in rad_ids:\n print('2. Getting the annotations of Radiologist {}.'.format(rad_id))\n rad_annotations = split_image_bboxes(csv_annotations, rad_id)\n\n # if debug:\n # print('visualize the annotations of rad_id: {}'.format(rad_id))\n # vis_idxes = np.random.randint(0, len(rad_annotations), size=20).tolist()\n # visualize_one_image(\n # data_path,\n # rad_annotations,\n # idxes=vis_idxes,\n # clsid_to_clsname=clsid_to_clsname,\n # save_dir='/mnt/group-ai-medical-2/private/zehuigong/torch_code/mmdetection/demo/rad_id%d' % rad_id)\n\n print('3. Converting annotations into coco format.')\n coco_annotations = {}\n image = {\"id\": 0,\n 'width': 0,\n 'height': 0,\n 'file_name': '',\n 'license': 1,\n 'flickr_url': '',\n 'coco_url': '',\n 'date_captured': ''}\n annotation = {'id': 0,\n 'image_id': 0,\n 'category_id': 0,\n 'segmentation': [],\n 'area': 0,\n 'bbox': [],\n 'iscrowd': 0}\n images = []\n annotations = []\n\n num_anno = 0\n for img_i, (img_name, img_boxes) in enumerate(rad_annotations.items()):\n h, w = img_shape_dict[img_name + '.png']\n\n image['id'] = img_i + 1\n image['width'] = w\n image['height'] = h\n image['file_name'] = img_name + '.png'\n time_now = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n image['date_captured'] = time_now\n images.append(copy.deepcopy(image))\n\n for anno_idx in range(img_boxes.shape[0]): # loop through objs in one image\n num_anno += 1\n annotation['id'] = num_anno\n annotation['image_id'] = img_i + 1\n annotation['category_id'] = int(img_boxes[anno_idx, -1]) + 1\n annotation['iscrowd'] = 0\n annotation['segmentation'], annotation['area'] = \\\n bbox2mask(img_boxes[anno_idx, :4])\n annotation['bbox'] = xyxy2xywh(img_boxes[anno_idx, :4])\n annotations.append(copy.deepcopy(annotation))\n annotation['segmentation'].clear()\n\n coco_annotations['info'] = info\n coco_annotations['licenses'] = licenses\n coco_annotations['categories'] = categories\n coco_annotations['annotations'] = annotations\n coco_annotations['images'] = images\n\n print('saving coco annotations of rad_id {}'.format(rad_id))\n mmcv.dump(coco_annotations, os.path.join(out_path, 'vinbig_trainR{}.json'.format(rad_id)))\n print('all done!')","def prepare_example(image_path, annotations, label_map_dict):\n print(\"encoding %s\" % image_path)\n with tf.gfile.GFile(image_path, 'rb') as fid:\n encoded_png = fid.read()\n encoded_png_io = io.BytesIO(encoded_png)\n image = pil.open(encoded_png_io)\n\n if image.format != 'PNG':\n raise ValueError('Image format error')\n\n key = hashlib.sha256(encoded_png).hexdigest()\n # obtain attributes\n width, height = image.size\n img_filename = image_path.split('/')[-1]\n\n xmin = []\n ymin = []\n xmax = []\n ymax = []\n classes = []\n classes_text = []\n truncated = []\n occlud = []\n\n xmin.append(int(annotations[2]) / width)\n ymin.append(int(annotations[3]) / height)\n xmax.append(int(annotations[4]) / width)\n ymax.append(int(annotations[5]) / height)\n class_name = annotations[1]\n classes_text.append(class_name)\n classes.append(label_map_dict[class_name])\n classes_text = [class_text.encode('utf-8') for class_text in classes_text]\n trun, occ = annotations[6].split(',')\n truncated.append(int(trun))\n occlud.append(int(occ))\n\n example = tf.train.Example(features=tf.train.Features(feature={\n 'image/height': dataset_util.int64_feature(height),\n 'image/width': dataset_util.int64_feature(width),\n 'image/filename': dataset_util.bytes_feature(img_filename.encode('utf8')),\n 'image/source_id': dataset_util.bytes_feature(img_filename.encode('utf8')),\n 'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')),\n 'image/encoded': dataset_util.bytes_feature(encoded_png),\n 'image/format': dataset_util.bytes_feature('png'.encode('utf8')),\n 'image/object/bbox/xmin': dataset_util.float_list_feature(xmin),\n 'image/object/bbox/xmax': dataset_util.float_list_feature(xmax),\n 'image/object/bbox/ymin': dataset_util.float_list_feature(ymin),\n 'image/object/bbox/ymax': dataset_util.float_list_feature(ymax),\n 'image/object/class/text': dataset_util.bytes_list_feature(classes_text),\n 'image/object/class/label': dataset_util.int64_list_feature(classes),\n 'image/object/truncated': dataset_util.int64_list_feature(truncated),\n 'image/object/view': dataset_util.int64_list_feature(occlud),\n }))\n return example","def load_data(self, annotation_json, images_dir):\r\n # Load json from file\r\n json_file = open(annotation_json)\r\n coco_json = json.load(json_file)\r\n json_file.close()\r\n \r\n # Add the class names using the base method from utils.Dataset\r\n source_name = \"coco_like\"\r\n ids={}\r\n i=0\r\n for category in coco_json['categories']:\r\n i+=1\r\n class_id = category['id']\r\n ids[class_id]=i\r\n class_name = category['name']\r\n if class_id < 1:\r\n print('Error: Class id for \"{}\" cannot be less than one. (0 is reserved for the background)'.format(class_name))\r\n return\r\n \r\n self.add_class(source_name, class_id, class_name)\r\n for annotation in coco_json['annotations']:\r\n annotation[\"category_id\"]=ids[annotation[\"category_id\"]]\r\n \r\n # Get all annotations\r\n \r\n annotations = {}\r\n for annotation in coco_json['annotations']:\r\n image_id = annotation['image_id']\r\n if image_id not in annotations:\r\n annotations[image_id] = []\r\n annotations[image_id].append(annotation)\r\n \r\n # Get all images and add them to the dataset\r\n seen_images = {}\r\n for image in coco_json['images']:\r\n image_id = image['id']\r\n if image_id in seen_images:\r\n print(\"Warning: Skipping duplicate image id: {}\".format(image))\r\n else:\r\n seen_images[image_id] = image\r\n try:\r\n image_file_name = image['file_name']\r\n image_width = image['width']\r\n image_height = image['height']\r\n except KeyError as key:\r\n print(\"Warning: Skipping image (id: {}) with missing key: {}\".format(image_id, key))\r\n \r\n image_path = os.path.abspath(os.path.join(images_dir, image_file_name))\r\n image_annotations = annotations[image_id]\r\n \r\n # Add the image using the base method from utils.Dataset\r\n self.add_image(\r\n source=source_name,\r\n image_id=image_id,\r\n path=image_path,\r\n width=image_width,\r\n height=image_height,\r\n annotations=image_annotations\r\n )","def _annotate_images(self, frameList):\n\t\timage_array = frameList\n\t\tfor i, image in enumerate(image_array):\n\t\t\tif i in list(self.detection_frames.keys()):\n\t\t\t\tfor obj in list(self.detection_frames[i].keys()):\n\t\t\t\t\tx, y, w, h, frame, text, track_id = self.detection_index[obj]\n\t\t\t\t\tCOLOR = COLORS[text]\n\t\t\t\t\t# if the detection is human\n\t\t\t\t\tif text == 'face':\n\t\t\t\t\t\ttext = text + \" id:{}\".format(track_id)\n\t\t\t\t\t\t# predict \n\t\t\t\t\t\tgender = self.genderDetect.classify(image[y:y+h, x:x+w, :])\n\t\t\t\t\t\tgender = 'female' if gender[0] < 0.5 else 'male'\n\t\t\t\t\t\tcv2.putText(image, gender, (x + w // 2 - 10, y + h + 15),\n\t\t\t\t\t\t\tcv2.FONT_HERSHEY_SIMPLEX, 0.6, COLOR, 2, cv2.LINE_AA)\n\n\t\t\t\t\timage_array[i] = cv2.rectangle(image_array[i], (x, y), (x + w, y + h), COLOR, 2)\n\t\t\t\t\tcv2.putText(image_array[i], text, (x, y - 5),\n\t\t\t\t\t\tcv2.FONT_HERSHEY_SIMPLEX, 0.6, COLOR, 2, cv2.LINE_AA)\n\n\n\t\t\tcv2.imwrite(f'{SAVE_PATH}{self.clip}{i}.jpg', image_array[i])","def __call__(self, *args, **kwargs):\n\n dataset = TextOnlyCocoAnnotation()\n\n for image_name in tqdm(sorted(os.listdir(self.folder))):\n if image_name.endswith('JPG'):\n image_path = os.path.join(self.folder, image_name)\n annotation_path = os.path.join(self.folder, image_name.replace('.JPG', '.gt'))\n\n with open(annotation_path, encoding='utf-8-sig') as read_file:\n content = [line.strip() for line in read_file.readlines()]\n for line in content:\n dataset.add_bbox(image_path, imagesize.get(image_path),\n self.parse_line(line))\n\n return dataset","def test_decode_labels_warning(self):\n with pytest.warns(\n YellowbrickWarning, match=\"both classes and encoder specified\"\n ):\n oz = ClassificationScoreVisualizer(\n GaussianNB(),\n classes=[\"a\", \"b\", \"c\"],\n encoder={0: \"foo\", 1: \"bar\", 2: \"zap\"},\n )\n encoded = oz._decode_labels([0, 1, 2])\n npt.assert_array_equal(encoded, [\"foo\", \"bar\", \"zap\"])","def __init__(self,\n ann_file,\n img_prefix,\n img_means,\n img_stds,\n img_expected_sizes,\n size_divisor=None,\n flip_ratio=0,\n be_cell_size=32,\n be_random_ratio=0.5,\n proposal_file=None,\n num_max_proposals=1000,\n with_mask=False,\n with_crowd=False,\n with_label=True,\n test_mode=False,\n with_background_erasing=False,\n debug=False):\n # load annotations from annotation file\n self.img_infos = self.load_annotations(ann_file)\n # prefix of image path\n self.img_prefix = img_prefix\n # image normalization parameters\n self.img_means, self.img_stds = img_means, img_stds\n # img_expected_sizes list\n self.img_expected_sizes = img_expected_sizes \\\n if isinstance(img_expected_sizes, list) else [img_expected_sizes]\n assert is_list_of(self.img_expected_sizes, tuple)\n # size divisor\n self.size_divisor = size_divisor\n # flip ratio\n self.flip_ratio = flip_ratio\n # background erasing settings\n self.be_cell_size = be_cell_size\n self.be_random_ratio = be_random_ratio\n\n # load the pkl proposal file\n if proposal_file is not None:\n self.proposals = self.load_proposals(proposal_file)\n else:\n self.proposals = None\n self.num_max_proposals = num_max_proposals\n\n # filter images with no annotation during training\n if not test_mode:\n valid_inds = self._filter_imgs()\n self.img_infos = [self.img_infos[i] for i in valid_inds]\n if self.proposals is not None:\n self.proposals = [self.proposals[i] for i in valid_inds]\n\n # with mask or not\n self.with_mask = with_mask\n # with crowd object or not\n self.with_crowd = with_crowd\n # training with label or not\n self.with_label = with_label\n # test mode\n self.test_mode = test_mode\n # with background erasing or not\n self.with_background_erasing = with_background_erasing\n # debug mode\n self.debug = debug\n if self.debug:\n self.img_infos = self.img_infos[:50]\n\n # set aspect ratio flag for images\n if not self.test_mode:\n self._set_group_flag()\n\n # transforms\n self.img_transforms = ImageTransforms(img_means=self.img_means,\n img_stds=self.img_stds,\n size_divisor=self.size_divisor)\n self.bbox_transforms = BboxTransforms()\n self.mask_transforms = MaskTransforms()\n self.background_erasing = BackgroundErasing()","def test_annotate_image(self):\n im_path = test_im_dir / \"square-im-1.png\"\n image = li.Image(im_path)\n\n label = li.Label(\"Test Label\", (0.50, 0.50))\n image.annotate(label)\n annotated_im = image.data\n\n # Ensure the shape is retained\n shape_expected = image.data_original.shape\n shape_test = annotated_im.shape\n self.assertEqual(\n shape_test, shape_expected, msg=\"shape not retained after annotation\"\n )\n\n if PLOT:\n imsave(\"/tmp/annotate_image.png\", annotated_im, check_contrast=False)","def random_image():\n # Pick random background color\n bg_color = np.array([random.randint(0, 255) for _ in range(3)])\n height = random.randint(300, 350)\n width = random.randint(350, 400)\n print(\"bg_color: \" + str(bg_color))\n print(\"height: \" + str(height))\n print(\"width: \" + str(width))\n\n image = np.ones([height, width, 3], dtype=np.uint8) * bg_color\n mask = np.zeros([height, width, 3], dtype=np.uint8)\n labels = []\n assert image.shape == mask.shape\n\n # Generate a few random shapes\n shapes = []\n boxes = []\n N = random.randint(1, 4)\n for _ in range(N):\n shape, color, dims = random_shape(height, width)\n shapes.append((shape, color, dims))\n x, y, s = dims\n boxes.append([y - s, x - s, y + s, x + s])\n # Apply non-max suppression wit 0.3 threshold to avoid\n # shapes covering each other\n keep_ixs = utils.non_max_suppression(\n np.array(boxes), np.arange(N), 0.1)\n shapes = [s for i, s in enumerate(shapes) if i in keep_ixs]\n\n for shape, color, dims in shapes:\n x, y, s = dims\n if shape == 'square':\n image = cv2.rectangle(image, (x - s, y - s),\n (x + s, y + s), color, -1)\n mask = cv2.rectangle(mask, (x - s, y - s),\n (x + s, y + s), 1, -1)\n labels.append([1, x, y, s])\n elif shape == \"circle\":\n image = cv2.circle(image, (x, y), s, color, -1)\n mask = cv2.circle(mask, (x, y), s, 2, -1)\n labels.append([2, x, y, s])\n elif shape == \"triangle\":\n points = np.array([[(x, y - s),\n (x - s / math.sin(math.radians(60)), y + s),\n (x + s / math.sin(math.radians(60)), y + s),\n ]], dtype=np.int32)\n image = cv2.fillPoly(image, points, color)\n mask = cv2.fillPoly(mask, points, 3)\n labels.append([3, x, y, s])\n\n return image, mask, np.array(labels)","def generate_tamil_images(label_file, fonts_dir, output_dir):\r\n with io.open(label_file, 'r', encoding='utf-8') as f:\r\n labels = f.read().splitlines()\r\n\r\n image_dir = os.path.join(output_dir, 'images')\r\n if not os.path.exists(image_dir):\r\n os.makedirs(os.path.join(image_dir))\r\n\r\n # Get a list of the fonts.\r\n fonts = glob.glob(os.path.join(fonts_dir, '*.ttf'))\r\n\r\n labels_csv = io.open(os.path.join(output_dir, 'labels-map.csv'), 'w',\r\n encoding='utf-8')\r\n\r\n total_count = 0\r\n prev_count = 0\r\n folder_count = 0\r\n for character in labels:\r\n folder_count += 1\r\n #print('%s: %d',character,len(character))\r\n # Print image count roughly every 5000 images.\r\n if total_count - prev_count > 5000:\r\n prev_count = total_count\r\n print('{} images generated...'.format(total_count))\r\n mychar = character\r\n for font in fonts:\r\n total_count += 1\r\n image = Image.new('L', (IMAGE_WIDTH, IMAGE_HEIGHT), color='white')\r\n font = ImageFont.truetype(font, int(80/len(mychar)))\r\n drawing = ImageDraw.Draw(image)\r\n w, h = drawing.textsize(mychar, font=font)\r\n drawing.text(\r\n ((IMAGE_WIDTH-w)/2, (IMAGE_HEIGHT-h)/2),\r\n mychar,\r\n fill=(0),\r\n font=font\r\n )\r\n file_string = '{}_{}.jpg'.format(mychar, total_count)\r\n #file_path = os.path.join(image_dir, 'f'+str(folder_count))\r\n file_path = os.path.join(image_dir, file_string)\r\n orgFile = file_path\r\n image.save(file_path)\r\n \r\n\r\n #directory creation\r\n mvDir = os.path.join(image_dir, mychar + '_' + str(folder_count))\r\n if not os.path.exists(mvDir):\r\n os.makedirs(mvDir)\r\n \r\n for i in range(DISTORTION_COUNT):\r\n total_count += 1\r\n dist_string = '{}_{}.jpg'.format(mychar, total_count)\r\n #file_path = os.path.join(image_dir, 'f'+str(folder_count))\r\n file_path = os.path.join(image_dir, dist_string)\r\n arr = numpy.array(image)\r\n\r\n distorted_array = elastic_distort(\r\n arr, alpha=random.randint(30, 36),\r\n sigma=random.randint(5, 6)\r\n )\r\n distorted_image = Image.fromarray(distorted_array)\r\n distorted_image.save(file_path)\r\n img = Image.open(file_path)\r\n dr = ImageDraw.Draw(img)\r\n cor = (0,0, IMAGE_WIDTH,IMAGE_HEIGHT)\r\n dr.rectangle(cor, outline=\"white\", width=5)\r\n img.save(file_path)\r\n distLoc = os.path.join(mvDir, dist_string)\r\n shutil.move(file_path, distLoc)\r\n labels_csv.write(u'{},{}\\n'.format(mychar, distLoc))\r\n origLoc = os.path.join(mvDir, file_string)\r\n newPath = os.path.join(mvDir, origLoc)\r\n shutil.move(orgFile, newPath)\r\n labels_csv.write(u'{},{}\\n'.format(mychar, newPath))\r\n \r\n print('Finished generating {} images.'.format(total_count))\r\n labels_csv.close()","def detect_labels(path):\n from google.cloud import vision\n client = vision.ImageAnnotatorClient()\n\n # [START vision_python_migration_label_detection]\n with io.open(path, 'rb') as image_file:\n content = image_file.read()\n\n image = vision.types.Image(content=content)\n\n response = client.label_detection(image=image)\n labels = response.label_annotations\n ss=labels[0].description \n ss.split('/')[0]\n os.system(\"./ILOVEAPPLE/sort {} {}\".format(ss, path))\n # [END vision_python_migration_label_detection]","def preprocess_human_annotations_p8(problem_id, source_pickle, annotation_csv):\n label_dim, ix_to_label, label_to_ix, _, _ = get_label_params(problem_id)\n\n annotation_df = pd.read_csv(annotation_csv)\n annotation_df = annotation_df.fillna(0)\n n_annotations = len(annotation_df)\n\n with open(source_pickle) as fp:\n ast_programs = cPickle.load(fp)\n\n programs = []\n program2label = {}\n\n for i in tqdm(range(n_annotations)):\n annotation_frame = annotation_df.iloc[i]\n program_id = int(annotation_frame['ID'])\n targets = [int(annotation_frame[ix_to_label[ix]])\n for ix in xrange(label_dim)]\n\n ast = ast_programs[program_id]\n removeColors(ast)\n ast = flatten_ast(ast)\n program = ' '.join(ast)\n\n programs.append(program)\n program2label[program] = targets\n\n programs = list(set(programs))\n labels = [program2label[prog] for prog in programs]\n \n return programs, labels","def _load_kitti_annotation(self, index):\n\n if self._image_set == 'test':\n lines = []\n else:\n filename = os.path.join(self._data_path, 'training', 'label_2', index + '.txt')\n lines = []\n with open(filename) as f:\n for line in f:\n words = line.split()\n cls = words[0]\n truncation = float(words[1])\n occlusion = int(words[2])\n height = float(words[7]) - float(words[5])\n if cls in self._class_to_ind and truncation < 0.5 and occlusion < 3 and height > 25:\n #if cls in self._class_to_ind:\n lines.append(line)\n\n num_objs = len(lines)\n \n boxes = np.zeros((num_objs, 4), dtype=np.float32)\n gt_classes = np.zeros((num_objs), dtype=np.int32)\n overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)\n\n for idx, line in enumerate(lines):\n words = line.split()\n cls = self._class_to_ind[words[0]]\n boxes[idx, :] = [float(num) for num in words[4:8]]\n gt_classes[idx] = cls\n overlaps[idx, cls] = 1.0\n\n overlaps = scipy.sparse.csr_matrix(overlaps)\n\n return {'boxes' : boxes,\n 'gt_classes' : gt_classes,\n 'gt_overlaps' : overlaps,\n 'flipped' : False}","def create_facemask_label(is_training):\n facemask_dir = config.voc_dir\n cls_map = {name: i for i, name in enumerate(config.coco_classes)}\n sub_dir = 'train' if is_training else 'val'\n facemask_dir = os.path.join(facemask_dir, sub_dir)\n if not os.path.isdir(facemask_dir):\n raise ValueError(f'Cannot find {sub_dir} dataset path.')\n\n image_dir = anno_dir = facemask_dir\n if os.path.isdir(os.path.join(facemask_dir, 'images')):\n image_dir = os.path.join(facemask_dir, 'images')\n if os.path.isdir(os.path.join(facemask_dir, 'annotations')):\n anno_dir = os.path.join(facemask_dir, 'annotations')\n\n if not is_training:\n data_dir = config.facemask_root\n json_file = os.path.join(data_dir, config.instances_set.format(sub_dir))\n file_dir = os.path.split(json_file)[0]\n if not os.path.isdir(file_dir):\n os.makedirs(file_dir)\n json_dict = {\"images\": [], \"type\": \"instances\", \"annotations\": [],\n \"categories\": []}\n bnd_id = 1\n\n image_files_dict = {}\n image_anno_dict = {}\n images = []\n for anno_file in os.listdir(anno_dir):\n print(anno_file)\n if not anno_file.endswith('xml'):\n continue\n tree = et.parse(os.path.join(anno_dir, anno_file))\n root_node = tree.getroot()\n file_name = root_node.find('filename').text\n file_name = file_name.split('.')[0] + '.jpg'\n img_id = get_imageId_from_fackmask(file_name)\n image_path = os.path.join(image_dir, file_name)\n print(image_path)\n if not os.path.isfile(image_path):\n print(f'Cannot find image {file_name} according to annotations.')\n continue\n\n labels = []\n for obj in root_node.iter('object'):\n cls_name = obj.find('name').text\n if cls_name not in cls_map:\n print(f'Label \"{cls_name}\" not in \"{config.coco_classes}\"')\n continue\n bnd_box = obj.find('bndbox')\n x_min = int(float(bnd_box.find('xmin').text)) - 1\n y_min = int(float(bnd_box.find('ymin').text)) - 1\n x_max = int(float(bnd_box.find('xmax').text)) - 1\n y_max = int(float(bnd_box.find('ymax').text)) - 1\n labels.append([y_min, x_min, y_max, x_max, cls_map[cls_name]])\n\n if not is_training:\n o_width = abs(x_max - x_min)\n o_height = abs(y_max - y_min)\n ann = {'area': o_width * o_height, 'iscrowd': 0, 'image_id': \\\n img_id, 'bbox': [x_min, y_min, o_width, o_height], \\\n 'category_id': cls_map[cls_name], 'id': bnd_id, \\\n 'ignore': 0, \\\n 'segmentation': []}\n json_dict['annotations'].append(ann)\n bnd_id = bnd_id + 1\n\n if labels:\n images.append(img_id)\n image_files_dict[img_id] = image_path\n image_anno_dict[img_id] = np.array(labels)\n\n if not is_training:\n size = root_node.find(\"size\")\n width = int(size.find('width').text)\n height = int(size.find('height').text)\n image = {'file_name': file_name, 'height': height, 'width': width,\n 'id': img_id}\n json_dict['images'].append(image)\n\n if not is_training:\n for cls_name, cid in cls_map.items():\n cat = {'supercategory': 'none', 'id': cid, 'name': cls_name}\n json_dict['categories'].append(cat)\n json_fp = open(json_file, 'w')\n json_str = json.dumps(json_dict)\n json_fp.write(json_str)\n json_fp.close()\n\n return images, image_files_dict, image_anno_dict","def __call__(self, *args, **kwargs):\n\n dataset = TextOnlyCocoAnnotation()\n\n n_images = 1000 if self.is_train else 500\n for i in tqdm(range(1, n_images + 1)):\n image_path = os.path.join(self.images_folder, 'img_{}.jpg'.format(i))\n annotation_path = os.path.join(self.annotations_folder, 'gt_img_{}.txt'.format(i))\n\n with open(annotation_path, encoding='utf-8-sig') as read_file:\n content = [line.strip() for line in read_file.readlines()]\n for line in content:\n dataset.add_bbox(image_path, imagesize.get(image_path), self.parse_line(line))\n\n return dataset","def detection(test_img):\n # TODO: Step 2 : Your Detection code should go here.\n no_row = len(test_img)\n no_col = len(test_img[0])\n thresh = 100\n label_img = np.zeros((no_row, no_col))\n uf = []\n \n #first pass\n l = 1\n for i in range(0, no_row):\n for j in range(0, no_col):\n if test_img[i,j] < 255 - thresh:\n if i == 0 and j == 0:\n label_img[i,j] = l\n l = l+1\n elif i == 0 and j != 0:\n if label_img[i,j-1] == 0:\n label_img[i,j] = l\n l = l+1\n else:\n label_img[i,j] = label_img[i,j-1]\n elif i != 0 and j == 0:\n if label_img[i-1,j] == 0:\n label_img[i,j] = l\n l = l+1\n else:\n label_img[i,j] = label_img[i-1,j]\n else:\n if label_img[i-1,j] == 0 and label_img[i,j-1] == 0:\n label_img[i,j] = l\n l = l+1\n elif label_img[i-1,j] == 0 and label_img[i,j-1] != 0:\n label_img[i,j] = label_img[i,j-1]\n elif label_img[i-1,j] != 0 and label_img[i,j-1] == 0:\n label_img[i,j] = label_img[i-1,j]\n else:\n if label_img[i,j-1] == label_img[i-1,j]:\n label_img[i,j] = label_img[i,j-1]\n else:\n label_img[i,j] = min(label_img[i-1,j],label_img[i,j-1])\n uf.append([min(label_img[i,j-1],label_img[i-1,j]),max(label_img[i,j-1],label_img[i-1,j])])\n l = l - 1\n \n #2nd pass\n def ufds(x,l):\n b = []\n for i in range(1,l+1):\n b.append([i])\n for j in x:\n i1 = 0\n i2 = 0\n for k in range(0,len(b)):\n if j[0] in b[k]:\n i1 = k\n if j[1] in b[k]:\n i2 = k\n if i1 != i2:\n b[i1] = b[i1] + b[i2]\n del b[i2]\n return b\n \n bman = ufds(uf,l)\n \n #3rd pass\n uf_arr = np.zeros(l)\n for i in range(0,len(uf_arr)):\n for j in bman:\n if i+1 in j:\n uf_arr[i] = min(j) \n \n fin_img = np.zeros((no_row, no_col))\n for i in range(0, no_row):\n for j in range(0, no_col):\n if label_img[i,j] != 0:\n fin_img[i,j] = uf_arr[int(label_img[i,j] - 1)]\n \n all_label = []\n '''\n for i in bman:\n all_label.append(min(i))\n ''' \n for i in range(0, no_row):\n for j in range(0, no_col):\n if fin_img[i,j] !=0 and fin_img[i,j] not in all_label:\n all_label.append(fin_img[i,j])\n \n # main image\n k_img = np.zeros((no_row, no_col))\n for i in range(0, no_row):\n for j in range(0, no_col):\n if fin_img[i,j] != 0:\n k_img[i,j] = (all_label.index(fin_img[i,j]) + 1)\n \n k_list = []\n for i in range(1,len(all_label)+1):\n x = None\n y = None\n hx = None\n hy = None\n for j in range(0,no_row):\n for k in range(0,no_col):\n if k_img[j,k] == i:\n if y == None and x == None:\n y = j\n hy = j\n x = k\n hx = k\n else:\n if y > j:\n y = j\n if hy < j:\n hy = j\n if x > k:\n x = k\n if hx < k:\n hx = k\n k_list.append({\"bbox\": [x, y, hx-x, hy-y]})\n \n return k_list\n \n #raise NotImplementedError","def classify_face(im):\r\n faces = get_encoded_faces()\r\n faces_encoded = list(faces.values())\r\n known_face_names = list(faces.keys())\r\n\r\n img = cv2.imread(im, 1)\r\n \"\"\"\r\n Resize optinal \r\n \"\"\"\r\n #img = cv2.resize(img, (0, 0), fx=0.5, fy=0.5)\r\n #img = img[:,:,::-1]\r\n face_locations = face_recognition.face_locations(img)\r\n unknown_face_encodings = face_recognition.face_encodings(img, face_locations)\r\n\r\n face_names = []\r\n for face_encoding in unknown_face_encodings:\r\n # See if the face is a match for the known face(s)\r\n matches = face_recognition.compare_faces(faces_encoded, face_encoding)\r\n name = \"Unknown\"\r\n\r\n # use the known face with the smallest distance to the new face\r\n face_distances = face_recognition.face_distance(faces_encoded, face_encoding)\r\n best_match_index = np.argmin(face_distances)\r\n if matches[best_match_index]:\r\n name = known_face_names[best_match_index]\r\n\r\n face_names.append(name)\r\n\r\n \"\"\"\r\n All the photo lables in the faces foler end with (number) so a simiple .find(\"(\") command takes the () away from\r\n the label leaving us with the full name of the person\r\n\r\n \"\"\"\r\n\r\n result = name.find('(') \r\n fullname = (name[:result])\r\n \"\"\"\r\n If face_recogntion module recognizes a face but that face is not in the faces module then \r\n it will print unknown and we print 12345678 to use it on the start attednace program \r\n\r\n \"\"\"\r\n if (name == \"Unknown\"):\r\n print(\"12345678\")\r\n else:\r\n \"\"\"\r\n f'{len(face_locayion)}-people - will return the number of people in photo taken by Nao'\r\n \"\"\"\r\n print (f'{len(face_locations)}-people')\r\n print (fullname)\r\n print(courseid)\r\n print (lateornot)\r\n c34 = fullname.find(' ')\r\n firstname = (fullname[:c34])\r\n lastname = (fullname[c34:])\r\n \"\"\"\r\n We get all the data courseid , fristname , lastname, datetime1,and late or not and submited on the website \r\n \r\n\r\n \"\"\"\r\n login_data = {\r\n\t 'Course': courseid,\r\n\t 'FirstName': firstname,\r\n\t 'LastName': lastname,\r\n\t 'Date': datetime2,\r\n\t 'Attendance': 'on',\r\n\t 'Late': latev,\r\n\t 'submitbutton': 'Submit'\r\n }\r\n if(fullname == \"Unknow\"):\r\n \tprint(\"I-dont-know-you\")\r\n else:\r\n \r\n with requests.Session() as s:\r\n \turl = \"https://rbattendance.000webhostapp.com/update.php\"\r\n \tr = s.get(url)\r\n \tsoup = BeautifulSoup(r.content, 'html5lib')\r\n \tr = s.post(url, data = login_data)\r\n \t#print(r.content)\r\n \r\n \r\n\r\n\r\n\r\n\r\n \"\"\"\r\n This for loop is reponsible for drawing on the image \r\n \"\"\"\r\n\r\n for (top, right, bottom, left), name in zip(face_locations, face_names):\r\n # Draw a box around the face\r\n cv2.rectangle(img, (left-20, top-20), (right+20, bottom+20), (255, 0, 0), 2)\r\n\r\n # Draw a label with a name below the face\r\n cv2.rectangle(img, (left-20, bottom -15), (right+20, bottom+20), (255, 0, 0), cv2.FILLED)\r\n font = cv2.FONT_HERSHEY_DUPLEX\r\n cv2.putText(img, name, (left -20, bottom + 15), font, 1.0, (255, 255, 255), 2)\r\n\r\n\r\n # Display the resulting image\r\n \r\n \r\n while True:\r\n #cv2.imshow('Video', img)\r\n #if cv2.waitKey(1) & 0xFF == ord('q'):\r\n return face_names","def __get_coco_masks(anns: list, img):\n width = img.shape[1]\n height = img.shape[0]\n\n if len(anns) == 0:\n raise AnnError('Empty annotation detected.')\n \n classes = []\n mask = np.zeros((height, width), dtype=np.int32)\n \n for inst_idx, ann in enumerate(anns):\n cat_id = ann['category_id']\n classes.append(cat_id)\n\n m = cocoseg_to_binary(ann['segmentation'], height, width) # zero one mask\n m = m.astype(np.int32) * (inst_idx + 1)\n mask[m > 0] = 255\n\n classes = np.asarray(classes)\n return mask, classes","def load_datasets(filepath, sample_list, label_list, mark, a4c_or_a2c, m):\n # here can adjust n to apply your datasets\n if mark:\n n = 4000\n else:\n n = 4000\n dst_pair1 = np.zeros(shape=(n, m, m, 1), dtype=np.float32)\n dst_pair2 = np.zeros(shape=(n, m, m, 1), dtype=np.float32)\n dst_label = np.zeros(shape=(n,), dtype=np.int32)\n k = 0\n label_list_copy = copy.deepcopy(label_list)\n for number in range(len(sample_list)):\n label = label_list_copy[sample_list[number]-1] # o--->up 1--->down\n start_mark = label.pop()\n for i in (label):\n position = label.index(i)\n if position == len(label)-1:\n break\n j = label[position+1]\n for t in range(i,j):\n # load imgs: from number i to number j-1-->pair1\n # i+1 j-->pair2\n img_p1 = cv2.imread(filepath+\"Patient\"+(\"000\"+str(sample_list[number]))[-4:] +\n \"\\\\a\"+str(a4c_or_a2c)+\"c\\\\\"+str(t)+'.png', 0)\n img_p2 = cv2.imread(filepath+\"Patient\"+(\"000\"+str(sample_list[number]))[-4:] +\n \"\\\\a\"+str(a4c_or_a2c)+\"c\\\\\"+str(t+1)+'.png', 0)\n # cut and unsamping use cv2.resize\n # original 600*800--cut-->512*512--->resize by cv2 ---> m*m\n dst_pair1[k, :, :, 0] = cv2.resize(img_p1[80:592, 176:688].reshape(512, -1, 1), (m, m))/255.0\n dst_pair2[k, :, :, 0] = cv2.resize(img_p2[80:592, 176:688].reshape(512, -1, 1), (m, m))/255.0\n if start_mark == 0: # up\n dst_label[k] = 0 \n else:\n dst_label[k] = 1 \n k += 1\n if start_mark == 0:\n start_mark = 1\n else:\n start_mark = 0\n if mark == 1:\n pathname = 'train'\n elif mark == 0:\n pathname = 'test'\n else:\n pathname = \"val\"\n # save the imgs for augmentation before training.\n os.mkdir('../'+pathname+'p1/') \n os.mkdir('../'+pathname+'p2/')\n K = 0\n for i in (dst_pair1[:k]):\n preprocessing.image.save_img('../'+pathname+'p1/'+str(K)+'.png', i)\n K += 1\n K = 0\n for i in (dst_pair2[:k]):\n preprocessing.image.save_img('../'+pathname+'p2/'+str(K)+'.png', i)\n K += 1\n return dst_pair1[:k], dst_pair2[:k], dst_label[:k]","def load_coco_ann_files(self):\n if self.type == 'train':\n datasets = [\n (os.path.join(self.dataset_root, 'coco', 'train2014'),\n COCO(os.path.join(self.dataset_root, 'coco',\n 'annotations_trainval2014', 'person_keypoints_train2014.json'))),\n (os.path.join(self.dataset_root, 'coco', 'train2017'),\n COCO(os.path.join(self.dataset_root, 'coco',\n 'annotations_trainval2017', 'person_keypoints_train2017.json'))),\n # (os.path.join(self.dataset_root, 'mpii', 'images'),\n # COCO(os.path.join(self.dataset_root, 'mpii',\n # 'annotations', 'train.json')))\n ]\n else:\n datasets = [\n (os.path.join(self.dataset_root, 'coco', 'val2014'),\n COCO(os.path.join(self.dataset_root, 'coco',\n 'annotations_trainval2014', 'person_keypoints_val2014.json'))),\n (os.path.join(self.dataset_root, 'coco', 'val2017'),\n COCO(os.path.join(self.dataset_root, 'coco',\n 'annotations_trainval2017', 'person_keypoints_val2017.json')))\n ]\n\n dict_list = []\n for dataset_path, dataset in datasets:\n img_ids = dataset.getImgIds()\n\n for idx in img_ids:\n try:\n img = dataset.loadImgs([idx])[0]\n ann_ids = dataset.getAnnIds([idx])\n anns = dataset.loadAnns(ann_ids)\n\n if [ann['keypoints'] for ann in anns] and not all([ann['keypoints'] == [0]*51 for ann in anns]):\n keypoints = [ann['keypoints'] for ann in anns if ann['keypoints'] != [0]*51]\n for i in range(len(keypoints)):\n if 'coco' in dataset_path:\n keypoints[i] = keypoints[i] + ([0, 0, 0] if not (keypoints[i][17] and keypoints[i][20])\n else [(keypoints[i][15] + keypoints[i][18]) // 2, (keypoints[i][16] + keypoints[i][19]) // 2, 1])\n else:\n keypoints[i] = keypoints[i] + ([0, 0, 0] if not (keypoints[i][41] and keypoints[i][38])\n else [(keypoints[i][39] + keypoints[i][36]) // 2, (keypoints[i][40] + keypoints[i][37]) // 2, 1])\n\n if len([kp for kp in keypoints if kp != [0]*54]) <= 4:\n dict_list.append({'path': os.path.join(dataset_path, img[\"file_name\"]),\n 'keypoints': [kp for kp in keypoints if kp != [0]*54]})\n except:\n print(f'Skipped: {idx}')\n\n final_dataset = pd.DataFrame.from_dict(dict_list)\n\n return final_dataset","def prepare_cityscapes_data(seed=1, percent=30.0, version=2017):\n def _save_anno(name, images, annotations):\n \"\"\"Save annotation\n \"\"\"\n print('>> Processing data {}.json saved ({} images {} annotations)'.format(\n name, len(images), len(annotations)))\n new_anno = {}\n new_anno['images'] = images\n new_anno['annotations'] = annotations\n new_anno['categories'] = anno['categories']\n\n with open(\n '{root}/{save_name}.json'.format(\n save_name=name, root=DATA_DIR),\n 'w') as f:\n json.dump(new_anno, f)\n print('>> Data {}.json saved ({} images {} annotations)'.format(\n name, len(images), len(annotations)))\n\n np.random.seed(seed)\n \n anno = json.load(open(os.path.join(DATA_DIR, 'instancesonly_filtered_gtFine_train.json')))\n\n image_list = anno['images']\n labeled_tot = int(percent / 100. * len(image_list))\n #labeled_ind = np.random.choice(range(len(image_list)), size=labeled_tot)\n labeled_ind = np.arange(len(image_list))\n np.random.shuffle(labeled_ind)\n labeled_ind = labeled_ind[0:labeled_tot]\n\n labeled_id = []\n labeled_images = []\n unlabeled_images = []\n labeled_ind = set(labeled_ind)\n for i in range(len(image_list)):\n if i in labeled_ind:\n labeled_images.append(image_list[i])\n labeled_id.append(image_list[i]['id'])\n else:\n unlabeled_images.append(image_list[i])\n\n # get all annotations of labeled images\n labeled_id = set(labeled_id)\n labeled_annotations = []\n unlabeled_annotations = []\n for an in anno['annotations']:\n if an['image_id'] in labeled_id:\n labeled_annotations.append(an)\n else:\n unlabeled_annotations.append(an)\n\n # save labeled and unlabeled\n save_name = 'instancesonly_filtered_gtFine_train.{seed}@{tot}'.format(\n version=version, seed=seed, tot=int(percent))\n _save_anno(save_name, labeled_images, labeled_annotations)\n save_name = 'instancesonly_filtered_gtFine_train.{seed}@{tot}-unlabeled'.format(\n version=version, seed=seed, tot=int(percent))\n _save_anno(save_name, unlabeled_images, unlabeled_annotations)","def test_annotate_blank(self):\n x_size = 500\n y_size = 500\n\n label = li.Label(\"Test Label\", (0.20, 0.20))\n test_im = li.ImageBlank(None, x_size, y_size)\n test_im.annotate(label)\n annotated_im = test_im.data\n\n # Ensure the shape is retained\n shape_expected = (y_size, x_size, 4)\n shape_test = annotated_im.shape\n self.assertEqual(\n shape_test, shape_expected, msg=\"shape not retained after annotation\"\n )\n\n # Test that there are darker text areas\n flattened = annotated_im[..., :3].mean(axis=-1)\n n_black_pixels = (flattened < 10).sum()\n\n self.assertGreater(n_black_pixels, 50, msg=\"Not enough black/text pixels\")\n self.assertLess(n_black_pixels, 5000, msg=\"Too many black/text pixels\")","def test_multicamgrablabeler():\n import infer_sequence\n base_dir = 'data/batch2/test_data'\n labeler = MultiCamGrabLabeler('data/batch3')\n for i in range(0, 13):\n filename = str(10000000 + i + 1)[1:] + '.jpg'\n cam_data = {'cam1_color_file': os.path.join(base_dir, '4', filename),\n 'cam1_dept_file': os.path.join(base_dir, '6', filename),\n 'cam2_color_file': os.path.join(base_dir, '5', filename),\n 'cam2_dept_file': os.path.join(base_dir, '7', filename),\n 'xeye_image': [os.path.join(base_dir, '0', filename),\n os.path.join(base_dir, '1', filename),\n os.path.join(base_dir, '2', filename),\n os.path.join(base_dir, '3', filename),\n ]\n }\n labeler.add_files(cam_data)\n results = labeler.gen_label()\n if not results:\n continue\n images = []\n for r in results:\n images.extend(r['images'])\n infer_sequence.show_multi_images(images, resize_ratio=0.5, cols=4)","def get_coco_gt(self, coco, img_id, height, width, img_name):\n annIds = coco.getAnnIds(imgIds=[img_id], iscrowd=None)\n # assert annIds is not None and annIds > 0, 'No annotation for %s' % str(img_id)\n anns = coco.loadAnns(annIds)\n # assert len(anns) > 0, 'No annotation for %s' % str(img_id)\n masks = []\n classes = []\n bboxes = []\n\n for ann in anns:\n id = cat_id_to_real_id(ann['category_id'])\n # id = coco_cat_id_to_voc_id(ann['category_id'])\n if id != 0:\n classes.append(id)\n\n m = coco.annToMask(ann) # {0, 1} mask\n assert m.shape[0] == height and m.shape[1] == width, \\\n 'image %s and ann %s don''t match' % (img_id, ann)\n masks.append(m)\n\n bboxes.append(ann['bbox'])\n\n masks = np.asarray(masks)\n classes = np.asarray(classes)\n bboxes = np.asarray(bboxes)\n\n # to x1, y1, x2, y2\n num_classes = bboxes.shape[0]\n if num_classes <= 0:\n bboxes = np.zeros([0, 4], dtype=np.float32)\n classes = np.zeros([0], dtype=np.float32)\n num_classes = 0\n print('None Annotations %s' % img_name)\n bboxes[:, 2] = bboxes[:, 0] + bboxes[:, 2]\n bboxes[:, 3] = bboxes[:, 0] + bboxes[:, 3]\n\n bboxes = bboxes.astype(np.float32)\n classes = classes.astype(np.float32)\n masks = masks.astype(np.uint8)\n assert masks.shape[0] == bboxes.shape[0], 'Shape Error'\n\n return num_classes, masks, bboxes, classes","def create_voc_label(is_training):\n voc_dir = config.voc_dir\n cls_map = {name: i for i, name in enumerate(config.coco_classes)}\n sub_dir = 'train' if is_training else 'eval'\n voc_dir = os.path.join(voc_dir, sub_dir)\n if not os.path.isdir(voc_dir):\n raise ValueError(f'Cannot find {sub_dir} dataset path.')\n\n image_dir = anno_dir = voc_dir\n if os.path.isdir(os.path.join(voc_dir, 'Images')):\n image_dir = os.path.join(voc_dir, 'Images')\n if os.path.isdir(os.path.join(voc_dir, 'Annotations')):\n anno_dir = os.path.join(voc_dir, 'Annotations')\n\n if not is_training:\n data_dir = config.voc_root\n json_file = os.path.join(data_dir, config.instances_set.format(sub_dir))\n file_dir = os.path.split(json_file)[0]\n if not os.path.isdir(file_dir):\n os.makedirs(file_dir)\n json_dict = {\"images\": [], \"type\": \"instances\", \"annotations\": [],\n \"categories\": []}\n bnd_id = 1\n\n image_files_dict = {}\n image_anno_dict = {}\n images = []\n for anno_file in os.listdir(anno_dir):\n print(anno_file)\n if not anno_file.endswith('xml'):\n continue\n tree = et.parse(os.path.join(anno_dir, anno_file))\n root_node = tree.getroot()\n file_name = root_node.find('filename').text\n img_id = get_imageId_from_fileName(file_name)\n image_path = os.path.join(image_dir, file_name)\n print(image_path)\n if not os.path.isfile(image_path):\n print(f'Cannot find image {file_name} according to annotations.')\n continue\n\n labels = []\n for obj in root_node.iter('object'):\n cls_name = obj.find('name').text\n if cls_name not in cls_map:\n print(f'Label \"{cls_name}\" not in \"{config.coco_classes}\"')\n continue\n bnd_box = obj.find('bndbox')\n x_min = int(float(bnd_box.find('xmin').text)) - 1\n y_min = int(float(bnd_box.find('ymin').text)) - 1\n x_max = int(float(bnd_box.find('xmax').text)) - 1\n y_max = int(float(bnd_box.find('ymax').text)) - 1\n labels.append([y_min, x_min, y_max, x_max, cls_map[cls_name]])\n\n if not is_training:\n o_width = abs(x_max - x_min)\n o_height = abs(y_max - y_min)\n ann = {'area': o_width * o_height, 'iscrowd': 0, 'image_id': \\\n img_id, 'bbox': [x_min, y_min, o_width, o_height], \\\n 'category_id': cls_map[cls_name], 'id': bnd_id, \\\n 'ignore': 0, \\\n 'segmentation': []}\n json_dict['annotations'].append(ann)\n bnd_id = bnd_id + 1\n\n if labels:\n images.append(img_id)\n image_files_dict[img_id] = image_path\n image_anno_dict[img_id] = np.array(labels)\n\n if not is_training:\n size = root_node.find(\"size\")\n width = int(size.find('width').text)\n height = int(size.find('height').text)\n image = {'file_name': file_name, 'height': height, 'width': width,\n 'id': img_id}\n json_dict['images'].append(image)\n\n if not is_training:\n for cls_name, cid in cls_map.items():\n cat = {'supercategory': 'none', 'id': cid, 'name': cls_name}\n json_dict['categories'].append(cat)\n json_fp = open(json_file, 'w')\n json_str = json.dumps(json_dict)\n json_fp.write(json_str)\n json_fp.close()\n\n return images, image_files_dict, image_anno_dict","def predict_labels(model):\n test_datagen = ImageDataGenerator(featurewise_center=True,\n featurewise_std_normalization=True\n #rescale=1. / 255,\n #samplewise_center=True,\n #samplewise_std_normalization=True\n )\n test_datagen.fit(test_data)\n # datagen.fit(val_data)\n # create generator for train data\n test_generator = test_datagen.flow(\n test_data,\n batch_size=batch_size,\n shuffle=False)\n pred_prob=model.predict_generator(test_generator,test_data.shape[0])\n pred_prob=pred_prob[:,0]\n def pre_class(x):\n \tif x<0.5:\n return 0\n else:\n return 1\n #def true_label(id):\n #\tif 'f0' in id:\n #\t return 0\n # elif 'f1' in id: \n # return 1\n #\telse:\n #\t pass\n #pred_true=map(true_label,test_id)\n #pred_true=np.array(pred_true)\n #print roc_auc_score(val_target, pred_prob)\n #prediction=map(pre_class,pred_prob)\n #print confusion_matrix(val_target,prediction)\n with open(\"prediction.csv\", \"w\") as f: \n\tp_writer = csv.writer(f, delimiter=',', lineterminator='\\n')\n for id,label in zip(test_id,pred_prob):\n\t p_writer.writerow([id, label])\n\t\n #base_path = \"PZ/test/test/\"\n\n #with open(\"prediction.csv\", \"w\") as f:\n # p_writer = csv.writer(f, delimiter=',', lineterminator='\\n')\n # for _, _, imgs in os.walk(base_path):\n # for im in imgs:\n # pic_id = im.split(\".\")[0]\n #img = cv2.imread(base_path+im)\n #img = cv2.resize(img, (img_width, img_height), cv2.INTER_LINEAR)\n #img = img.transpose((2,0,1))\n #img = np.expand_dims(img,axis=0)\n #img = load_img(base_path + im)\n #img = imresize(img, size=(img_height, img_width))\n #test_x = img_to_array(img).reshape(3, img_height, img_width)\n #test_x = test_x.reshape((1,) + test_x.shape)\n #test_datagen.fit(img)\n #test_generator = test_datagen.flow(img,\n # batch_size=1,\n # shuffle=False)\n #prediction = model.predict_generator(test_generator, 1)\n #p_writer.writerow([pic_id, prediction])","def _add_roidb_from_annotations(self, entry):\n ann_ids = self._COCO.getAnnIds(imgIds=entry['id'], iscrowd=None)\n objs = self._COCO.loadAnns(ann_ids)\n width = entry['width']\n height = entry['height']\n # valid objs\n # change the annotation boxes from 'xywh' to 'xyxy'\n valid_objs = []\n for obj in objs:\n x1 = np.max((0, obj['bbox'][0]))\n y1 = np.max((0, obj['bbox'][1]))\n x2 = np.min((width, x1 + np.max((0, obj['bbox'][2]))))\n y2 = np.min((height, y1 + np.max((0, obj['bbox'][3]))))\n if obj['area'] > 0 and x2 >= x1 and y2 >= y1:\n obj['clean_box'] = [x1, y1, x2, y2]\n valid_objs.append(obj)\n objs = valid_objs\n num_objs = len(objs)\n\n bboxes = np.zeros((num_objs, 4), dtype=entry['bboxes'].dtype)\n gt_classes = np.zeros((num_objs), dtype=entry['gt_classes'].dtype)\n\n coco_cat_id_to_class_ind = dict(\n [(self._class_to_coco_cat_id[cls], self._class_to_ind[cls]) for cls in self._classes[1:]])\n for ix, obj in enumerate(objs):\n bboxes[ix, :] = obj['clean_box']\n gt_classes[ix] = coco_cat_id_to_class_ind[obj['category_id']]\n entry['bboxes'] = np.append(entry['bboxes'], bboxes, axis=0)\n entry['gt_classes'] = np.append(entry['gt_classes'], gt_classes)","def create_yolo_trainval(\n annotations: Annotations,\n labels: \"list[str]\" = None,\n save_dir: PathLike = \"yolo_trainval/\", \n prefix: PathLike = \"data/\", \n train_ratio: float = 80/100,\n shuffle: bool = True,\n random_seed: int = 149_843_046_101,\n exist_ok: bool = False,\n):\n assert 0.0 <= train_ratio <= 1.0, \"train_ratio must be in 0...1\"\n\n save_dir = Path(save_dir).expanduser().resolve()\n prefix = Path(prefix)\n train_dir = save_dir / \"train/\"\n valid_dir = save_dir / \"val/\"\n\n save_dir.mkdir(exist_ok=exist_ok)\n train_dir.mkdir(exist_ok=exist_ok)\n valid_dir.mkdir(exist_ok=exist_ok)\n\n labels = labels or sorted(annotations.labels())\n labels_to_numbers = {l: str(n) for n, l in enumerate(labels)}\n\n annotations.map_labels(labels_to_numbers)\n\n if shuffle:\n # FIXME: Ugly `.annotations`, should change this.\n # `Annotations` should be an iterable or collection.\n annotations.annotations = sorted(annotations, key=lambda a: a.image_path)\n random_gen = Random(random_seed)\n random_gen.shuffle(annotations)\n\n len_train = int(train_ratio * len(annotations))\n\n def create_annotation(indexed_annotation: \"tuple[int, Annotation]\") -> str:\n i, annotation = indexed_annotation\n\n dir = train_dir if i < len_train else valid_dir\n img_filename = dir / f\"im_{i:06}{annotation.image_path.suffix}\"\n ann_filename = img_filename.with_suffix(\".txt\")\n ann_content = annotation.yolo_repr()\n\n try:\n shutil.copyfile(annotation.image_path, img_filename)\n ann_filename.write_text(ann_content)\n except KeyboardInterrupt:\n shutil.copyfile(annotation.image_path, img_filename)\n ann_filename.write_text(ann_content)\n exit()\n\n return img_filename.name\n\n image_names = thread_map(create_annotation, enumerate(annotations), \n total=len(annotations), unit=\"imgs\")\n\n train_file = save_dir / \"train.txt\"\n valid_file = save_dir / \"val.txt\"\n names_file = save_dir / \"obj.names\"\n\n train_file.write_text(\n \"\\n\".join(str(prefix / f\"train/{n}\") for n in image_names[:len_train]))\n valid_file.write_text(\n \"\\n\".join(str(prefix / f\"val/{n}\") for n in image_names[len_train:]))\n\n names_file.write_text(\"\\n\".join(labels))","def load_png_data():\n m=1 #训练文件个数\n n=1 #测试文件个数\n train_set_x=[]#训练数据集\n train_set_y=[]#训练标签集\n\n test_set_x=[]#测试数据集\n test_set_y=[]#测试标签集\n\n train_data={}\n\n train_path=r\".\\dataset\\train_label\\\\\"\n dirs=os.listdir(train_path)\n\n for file in dirs:\n srcImg=cv2.imread(train_path+file)\n #将label数据集保存为numpy格式并保存\n npImg=np.array(srcImg)\n np.save(train_path+str(m)+'.npy',npImg)\n train_set_x.append(npImg)\n\n\n NoiseImg = GaussianNoise(srcImg, 25, 4, 0.8)\n npNoiseImg = np.array(NoiseImg)\n cv2.imwrite(r\".\\dataset\\trainset\\\\\"+str(m)+'.png', NoiseImg, [int(cv2.IMWRITE_PNG_STRATEGY_DEFAULT)])\n np.save(r\".\\dataset\\trainset\\\\\" + str(m) + '.npy', npNoiseImg)\n train_set_y.append(npNoiseImg)\n m=m+1\n train_data['train_set_x']=train_set_x\n train_data['train_set_y']=train_set_y\n\n test_path = r\".\\dataset\\test_label\\\\\"\n dirs_test = os.listdir(test_path)\n for file in dirs_test:\n srcImg=cv2.imread(test_path+file)\n #将label数据集保存为numpy格式并保存\n npImg=np.array(srcImg)\n np.save(test_path+str(n)+'.npy',npImg)\n test_set_x.append(npImg)\n\n\n NoiseImg = GaussianNoise(srcImg, 25, 4, 0.8)\n npNoiseImg = np.array(NoiseImg)\n cv2.imwrite(r\".\\dataset\\testset\\\\\"+str(n)+'.png', NoiseImg, [int(cv2.IMWRITE_PNG_STRATEGY_DEFAULT)])\n np.save(r\".\\dataset\\testset\\\\\" + str(n) + '.npy', npNoiseImg)\n test_set_y.append(npNoiseImg)\n n=n+1\n train_data['test_set_x']=test_set_x\n train_data['test_set_y']=test_set_y\n\n np.savez(r\"E:\\DeepLearning\\CNNDenoiser\\dataset\\train_data.npz\",**train_data)","def _annotation_from_index(self, index, _COCO):\n im_ann = _COCO.loadImgs(index)[0]\n width = im_ann['width']\n height = im_ann['height']\n\n annIds = _COCO.getAnnIds(imgIds=index, iscrowd=None)\n objs = _COCO.loadAnns(annIds)\n # Sanitize bboxes -- some are invalid\n valid_objs = []\n for obj in objs:\n x1 = np.max((0, obj['bbox'][0]))\n y1 = np.max((0, obj['bbox'][1]))\n x2 = np.min((width - 1, x1 + np.max((0, obj['bbox'][2] - 1))))\n y2 = np.min((height - 1, y1 + np.max((0, obj['bbox'][3] - 1))))\n if obj['area'] > 0 and x2 >= x1 and y2 >= y1:\n obj['clean_bbox'] = [x1, y1, x2, y2]\n valid_objs.append(obj)\n objs = valid_objs\n num_objs = len(objs)\n\n res = np.zeros((num_objs, 5))\n\n # Lookup table to map from COCO category ids to our internal class\n # indices\n for ix, obj in enumerate(objs):\n cls = self._thing_dataset_id_to_contiguous_id[obj['category_id']]\n res[ix, 0:4] = obj['clean_bbox']\n res[ix, 4] = cls\n\n return res","def do_parse_annotations(xmls_path_in, xml_path_out):\n label = \"puzzle piece\"\n new_xml = \"<?xml version='1.0' encoding='ISO-8859-1'?>\"\n new_xml += \"<dataset>\\n<name>Puzzle dataset</name>\\n<comment>Created by imglab tool.</comment>\\n\"\n new_xml += \"<images>\\n\"\n for i, annot in enumerate(glob.glob(xmls_path_in + \"/*.xml\")):\n tree = ET.parse(annot)\n root = tree.getroot()\n left = root[6][4][0].text\n top = root[6][4][1].text\n width = 70#str(int(root[6][4][2].text) - int(left))\n height = 70#str(int(root[6][4][3].text) - int(top))\n new_xml += \"{}<image file='{}jpg'>\\n\".format(1 * ' ', annot[:-3])\n new_xml += \"{}<box top='{}' left='{}' width='{}' height='{}'>\\n\".format(2 * ' ', top, left, width, height)\n new_xml += \"{}<label>{}</label>\\n{}</box>\\n'{}</image>\\n\".format(3 * ' ', label, 2 * ' ', 1 * ' ')\n\n new_xml += \"</images>\\n</dataset>\"\n out_file = open(xml_path_out, \"w\")\n out_file.write(new_xml)\n out_file.close()","def main():\n args = get_args()\n FILE = args.FILE\n annotations = args.annotations\n outfile = args.outfile\n \n \n if not os.path.isfile(FILE):\n die('\"{}\" is not a file'.format(FILE))\n if not os.path.isfile(annotations):\n die('\"{}\" is not a file'.format(annotations))\n if os.path.isfile(FILE) and os.path.isfile(annotations):\n reader = csv.DictReader(open(FILE), delimiter = '\\t', fieldnames = (\"qseqid\", \"sseqid\", \"pident\", \"length\", \"mismatch\", \"gapopen\", \"qstart\", \"qend\", \"sstart\", \"send\", \"evalue\", \"bitscore\"))\n reader_a = csv.DictReader(open(annotations), fieldnames = (\"centroid\", \"domain\", \"kingdom\", \"phylum\", \"class\", \"order\", \"genus\", \"species\"))\n reader_b = csv.reader(open(annotations, 'r'))\n anno_dict = {}\n for row in reader_b:\n key1 = row[0]\n anno_dict[key1] = row[1:]\n\n #print(anno_dict)\n \n \"\"\"for dct in map(dict, reader_a):\n genus = (f\"{dct['genus']}\")\n species = (f\"{dct['species']}\")\n if genus == \"\": \n print(\"NA\")\n else:\n print(genus)\n if species == \"\":\n print(\"NA\")\n else:\n print(species)\"\"\"\n for dct in map(dict, reader):\n seq_id = (f\"{dct['sseqid']}\") \n pident = (f\"{dct['pident']}\")\n #print(seq_id)\n for dct_a in map(dict, reader_a):\n genus = (f\"{dct_a['genus']}\")\n species = (f\"{dct_a['species']}\")\n if any(seq_id == key for key in anno_dict): \n \"\"\"print(seq_id)\n print(pident)\n print(genus)\n print(species)\n #find a way to print genus and species of seq_id\n \"\"\"\n \n else:\n warn('Cannot find seq \"{}\" in lookup'.format(seq_id))\n \"\"\"for line_a in reader_a:\n an_id = (line_a['centroid']) \n print('\"{}\" is an_id'.format(an_id)) \n for line in reader:\n seq_id = (line['sseqid'])\n print('\"{}\" is seq_id'.format(seq_id))\n if seq_id == an_id:\n print(\"hi\")\n else:\n warn('Cannot find seq \"{}\" in lookup'.format(seq_id))\n \"\"\"\n #pprint.pprint(dict_list)\n #pprint.pprint(dict_list_a)\n #for key, value in d1.items():\n #if key is 'sseqid':\n #print(value)\n #print(dict_list_a['centroid']) ","def _load_colabeled_img(self) -> np.ndarray:\n return tifffile.imread(str(self.colabel_img))","def validate_labels(labels, path):\n for labels_ in labels.values():\n for label in labels_:\n for ann in label['annotations']:\n assert len(ann['segmentation']) == 1\n assert len(ann['segmentation'][0]) % 2 == 0\n\n label['annotations'] = [\n ann\n for ann in label['annotations']\n if len(ann['segmentation'][0]) >= 6\n ]\n assert len(label['annotations']) > 0\n label['file_name'] = path + '/' + label['file_name']\n\n for k in labels:\n labels[k] = [\n label for label in labels[k]\n if os.path.exists(label['file_name'])\n ]\n return labels","def initiate_yolo_detect(images_path, save_to_path, detections_file='pickles/bounding_boxes.pickle'):\n for filename in os.listdir(images_path):\n bound_boxes = detect_objects_on_image(\n os.path.join(images_path, filename), detections_file)\n predictions_path = os.path.join(\n save_to_path, 'predictions_' + filename)\n print('predictions path', predictions_path)\n copy2('predictions_' + os.path.basename(image_directory) +\n '.png', predictions_path)","def isOk(annots):\n if annots == []:\n return True\n for a in annots:\n for label in a.labels:\n if (label != 'hasOkCopies' and\n label != 'hasBadCopies' and\n not label.startswith('count_')):\n return False\n return True","def load_data5():\n# dirname = 'cifar-10-batches-py'\n# origin = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'\n# path = get_file(dirname, origin=origin, untar=True)\n# path= './cifar-10-batches-py'\n (x_train, y_train), (x_test, y_test) = cifar10.load_data()\n# Below shows a test class has 999 examples instead of the claimed 1000\n# tclasscount=np.zeros((10,), dtype=int)\n# for i in range(0, len(y_test)-1):\n# tclasscount[y_test[i][0]]= tclasscount[y_test[i][0]] + 1\n# print('Test class count',tclasscount)\n num_train_samples = 50000\n num_5_class = 25000\n num_5_test = 4999 # should be 5000 if all the categories had 1000 in them but they do not. One is missing.\n print('x_train shape orig:', x_train.shape)\n print('More:', x_train.shape[1:])\n print('y_test shape',y_test.shape)\n\n x5_train = np.empty((num_5_class, 32, 32, 3), dtype='uint8')\n y5_train = np.empty((num_5_class,), dtype='uint8')\n\n count=0\n\n for i in range(0, len(y_train)-1):\n if (y_train[i][0] == 2) or (y_train[i][0] == 3) or (y_train[i][0] == 4) or (y_train[i][0] == 5) or (y_train[i][0] == 7):\n x5_train[count]=x_train[i]\n y5_train[count]=y_train[i]\n count=count+1\n \n # find test data of interest\n count=0\n x5_test=np.empty((num_5_test, 32, 32, 3), dtype='uint8')\n y5_test= np.empty((num_5_test,), dtype='uint8')\n\n for i in range(0, len(y_test)-1):\n if (y_test[i][0] == 2) or (y_test[i][0] == 3) or (y_test[i][0] == 4) or (y_test[i][0] == 5) or (y_test[i][0] == 7):\n x5_test[count]=x_test[i]\n y5_test[count]=y_test[i]\n count=count+1\n# Below shows class 7 is only 999 and not 1000 examples!!! One horse got away it seems.\n# if(y_test[i][0] == 2):\n# c2=c2+1\n# if(y_test[i][0] == 3):\n# c3=c3+1\n# if(y_test[i][0] == 4):\n# c4=c4+1\n# if(y_test[i][0] == 5):\n# c5=c5+1\n# if(y_test[i][0] == 7):\n# c7=c7+1\n# print('c2count, c3count, c4count, c5count, c7count',c2,c3,c3,c5,c7)\n# print('y5tstshape',y5_test.shape, count)\n# print('y5tst',y5_test)\n# return (x_train, y_train), (x_test, y_test)\n return (x5_train, y5_train), (x5_test, y5_test)","def __call__(self, src, label):\r\n \"\"\"color distort\"\"\"\r\n # img = random_color_distort(src)\r\n\r\n # print(\"previous label shape = \", label.shape)\r\n target = np.zeros(shape=(label.shape[0],))\r\n\r\n \"\"\"Pyramid Anchor sampling\"\"\"\r\n img, boxes, label = self.random_baiducrop(src, label[:, :4], target)\r\n # print(\"label shape = \", label.shape)\r\n # print('boxes shape =', boxes.shape)\r\n bbox = boxes\r\n # img = mx.nd.array(img)\r\n\r\n \"\"\"color distort\"\"\"\r\n img = mx.nd.array(img)\r\n img = random_color_distort(img)\r\n\r\n # \"\"\"random crop, keep aspect ration=1\"\"\"\r\n # h, w, _ = img.shape\r\n # bbox, crop_size = random_crop_with_constraints(label, (w, h))\r\n # x_offset, y_offset, new_width, new_height = crop_size\r\n # img = mx.image.fixed_crop(img, x_offset, y_offset, new_width, new_height)\r\n\r\n \"\"\"resize with random interpolation\"\"\"\r\n h, w, _ = img.shape\r\n interp = np.random.randint(0, 5)\r\n img = gimage.imresize(img, self._width, self._height, interp=interp)\r\n bbox = gbbox.resize(bbox, (w, h), (self._width, self._height))\r\n\r\n \"\"\"random horizontal flip\"\"\"\r\n h, w, _ = img.shape\r\n img, flips = gimage.random_flip(img, px=0.5)\r\n bbox = gbbox.flip(bbox, (w, h), flip_x=flips[0])\r\n\r\n \"\"\"To Tensor & Normalization\"\"\"\r\n img = mx.nd.image.to_tensor(img)\r\n img = mx.nd.image.normalize(img, mean=self._mean, std=self._std)\r\n\r\n if self._anchors is None:\r\n return img, bbox\r\n\r\n # @TODO: generating training target so cpu workers can help reduce the workload on gpu\r\n face_anchors, head_anchors, body_anchors = self._anchors\r\n gt_bboxes = mx.nd.array(bbox[:, :4]).expand_dims(0)\r\n gt_ids = mx.nd.zeros((1, gt_bboxes.shape[1], 1), dtype=gt_bboxes.dtype)\r\n\r\n face_cls_targets, face_box_targets, _ = self._target_generator(\r\n face_anchors, None, gt_bboxes, gt_ids)\r\n\r\n head_cls_targets, head_box_targets, _ = self._target_generator(\r\n head_anchors, None, gt_bboxes, gt_ids)\r\n\r\n body_cls_targets, body_box_targets, _ = self._target_generator(\r\n body_anchors, None, gt_bboxes, gt_ids)\r\n\r\n return img, \\\r\n face_cls_targets[0], head_cls_targets[0], body_cls_targets[0], \\\r\n face_box_targets[0], head_box_targets[0], body_box_targets[0]","def load_annotations(self, ann_file, N, kind):\n\n self.coco = COCOPoint(ann_file, N=N, kind=kind)\n # The order of returned `cat_ids` will not\n # change with the order of the CLASSES\n self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES)\n\n self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}\n self.img_ids = self.coco.get_img_ids()\n data_infos = []\n total_ann_ids = []\n for i in self.img_ids:\n info = self.coco.load_imgs([i])[0]\n info[\"filename\"] = info[\"file_name\"]\n data_infos.append(info)\n ann_ids = self.coco.get_ann_ids(img_ids=[i])\n total_ann_ids.extend(ann_ids)\n assert len(set(total_ann_ids)) == len(\n total_ann_ids\n ), f\"Annotation ids in '{ann_file}' are not unique!\"\n return data_infos","def load_tiny_imagenet(directory):\n path_train, path_val, path_test = directory + '/train', directory + '/val', directory + '/test'\n labels = os.listdir(path_train)\n train_data = []\n train_labels = []\n for label in labels:\n imgs_path = os.path.join(path_train, label, 'images')\n imgs = os.listdir(imgs_path)\n for img_name in imgs:\n img_path = os.path.join(imgs_path, img_name)\n img = cv2.imread(img_path)\n b, g, r = cv2.split(img)\n img = cv2.merge([r,g,b]).reshape(-1, 64, 64, 3)\n train_data.append(img)\n train_labels.append(label)\n train_data = np.concatenate(train_data)\n train_labels = np.array(train_labels, dtype='str')\n \n test_data = []\n test_labels = []\n with open(path_val+'/val_annotations.txt', 'r') as f:\n val_annotations = [line.strip().split('\\t') for line in f]\n val_annotations = np.array(val_annotations)\n imgs_path = os.path.join(path_val, 'images')\n imgs = os.listdir(imgs_path)\n for img_name in imgs:\n img_path = os.path.join(imgs_path, img_name)\n img = cv2.imread(img_path)\n b, g, r = cv2.split(img)\n img = cv2.merge([r,g,b]).reshape(-1, 64, 64, 3)\n test_data.append(img)\n label = val_annotations[val_annotations[:, 0] == img_name, 1].astype('U9')\n test_labels.append(label)\n test_data = np.concatenate(test_data)\n test_labels = np.concatenate(test_labels)\n test_labels = np.array(test_labels, dtype='str')\n \n _, train_labels = np.unique(train_labels, return_inverse=True)\n _, test_labels = np.unique(test_labels, return_inverse=True)\n \n del r, g, b, label, labels, imgs_path, img_name, img, imgs, val_annotations\n \n return train_data, train_labels, test_data, test_labels","def evaluate(\n args,\n generator,\n model,\n iou_threshold=0.5,\n score_threshold=0.05,\n max_detections=100,\n save_path=None\n):\n # gather all detections and annotations\n all_detections = _get_detections(args, generator, model, score_threshold=score_threshold, max_detections=max_detections, save_path=save_path)\n all_annotations = _get_annotations(generator)\n # print('all detections:', all_detections)\n # print('all all_annotations:', all_annotations)\n average_precisions = {}\n\n # all_detections = pickle.load(open('all_detections.pkl', 'rb'))\n # all_annotations = pickle.load(open('all_annotations.pkl', 'rb'))\n # pickle.dump(all_detections, open('all_detections.pkl', 'wb'))\n # pickle.dump(all_annotations, open('all_annotations.pkl', 'wb'))\n\n # process detections and annotations\n # print('###########')\n # print(generator.num_classes())\n\n for label in range(generator.num_classes()):\n if not generator.has_label(label):\n print('generator has not label')\n continue\n\n false_positives = np.zeros((0,))\n true_positives = np.zeros((0,))\n false_negatives = np.zeros((0,))\n scores = np.zeros((0,))\n num_annotations = 0.0\n\n for i in range(generator.size()):\n detections = all_detections[i][label]\n annotations = all_annotations[i][label]\n num_annotations += annotations.shape[0]\n detected_annotations = []\n\n for d in detections:\n scores = np.append(scores, d[4])\n\n if annotations.shape[0] == 0:\n # print('NO bbox annos')\n # print(d)\n false_positives = np.append(false_positives, 0)\n true_positives = np.append(true_positives, 0)\n false_negatives = np.append(false_negatives, 1)\n continue\n\n overlaps = compute_overlap(np.expand_dims(d, axis=0), annotations)\n assigned_annotation = np.argmax(overlaps, axis=1)\n max_overlap = overlaps[0, assigned_annotation]\n\n if max_overlap >= iou_threshold and assigned_annotation not in detected_annotations:\n false_positives = np.append(false_positives, 0)\n true_positives = np.append(true_positives, 1)\n detected_annotations.append(assigned_annotation)\n else:\n false_positives = np.append(false_positives, 1)\n true_positives = np.append(true_positives, 0)\n\n # no annotations -> AP for this class is 0 (is this correct?)\n if num_annotations == 0:\n average_precisions[label] = 0, 0\n continue\n\n # print(num_annotations)\n # print(all_detections[0][label].shape)\n # print(all_detections[0][label])\n TP = sum(true_positives)\n FP = sum(false_positives)\n FN = sum(false_negatives)\n # sort by score\n indices = np.argsort(-scores)\n false_positives = false_positives[indices]\n true_positives = true_positives[indices]\n\n # compute false positives and true positives\n false_positives = np.cumsum(false_positives)\n true_positives = np.cumsum(true_positives)\n\n # compute recall and precision\n recall = true_positives / num_annotations\n precision = true_positives / np.maximum(true_positives + false_positives, np.finfo(np.float64).eps)\n\n\n print('TP:', TP)\n print('FP:', FP)\n print('FN:', FN)\n print('Recall:', TP / (TP + FN))\n print('Precision:', TP / (TP + FP))\n\n # compute average precision\n average_precision = _compute_ap(recall, precision)\n average_precisions[label] = average_precision, num_annotations\n\n return average_precisions","def main():\n feature_extraction_model = \"HOG\"\n # feature_extraction_models = [\"CM\", \"HOG\"]\n feature_extraction_model_1 = \"CM\"\n dimension_reduction_model = \"PCA\"\n k_value = 10\n dim_k_value = 40\n # K_value = 20\n # lab_folder = \"Dataset3/Labelled/Set1\"\n # unlab_folder = \"Dataset3/Unlabelled/Set 2\"\n lab_folder = get_input_folder(\"Labelled Folder\")\n unlab_folder = get_input_folder(\"Classify\")\n start = time.time()\n # ================================================================================================================\n # labelled Images\n dim_red = DimensionReduction(feature_extraction_model, dimension_reduction_model, dim_k_value,\n folder_metadata=lab_folder,\n metadata_collection=\"labelled\")\n obj_feat_lab = dim_red.get_object_feature_matrix()\n features_list_lab = np.array(obj_feat_lab['featureVector'].tolist())\n images_list_lab = np.array(obj_feat_lab['imageId'])\n # filtering the labelled set\n dorsal_list, palmar_list = filter_images_by_label(images_list_lab)\n\n # unlabelled images\n dim_red = DimensionReduction(feature_extraction_model, dimension_reduction_model, dim_k_value,\n folder_metadata=unlab_folder,\n metadata_collection=\"unlabelled\")\n obj_feat_unlab = dim_red.get_object_feature_matrix()\n features_list_unlab = np.array(obj_feat_unlab['featureVector'].tolist())\n images_list_unlab = np.array(obj_feat_unlab['imageId'])\n\n # ================================================================================================================\n # labelled Images\n dim_red = DimensionReduction(feature_extraction_model_1, dimension_reduction_model, dim_k_value,\n folder_metadata=lab_folder,\n metadata_collection=\"labelled\")\n obj_feat_lab_1 = dim_red.get_object_feature_matrix()\n features_list_lab_1 = np.array(obj_feat_lab_1['featureVector'].tolist())\n # images_list_lab = np.array(obj_feat_lab_1['imageId'])\n # filtering the labelled set\n\n\n # unlabelled images\n dim_red = DimensionReduction(feature_extraction_model_1, dimension_reduction_model, dim_k_value,\n folder_metadata=unlab_folder,\n metadata_collection=\"unlabelled\")\n obj_feat_unlab_1 = dim_red.get_object_feature_matrix()\n features_list_unlab_1 = np.array(obj_feat_unlab_1['featureVector'].tolist())\n # images_list_unlab = np.array(obj_feat_unlab['imageId'])\n features_list_lab = np.concatenate((features_list_lab, features_list_lab_1), axis=1)\n features_list_unlab = np.concatenate((features_list_unlab, features_list_unlab_1), axis=1)\n\n # ================================================================================================================\n\n dorsal_list, palmar_list = filter_images_by_label(images_list_lab)\n features_list = np.concatenate((features_list_lab, features_list_unlab))\n images_list = np.concatenate((images_list_lab, images_list_unlab))\n images_list = list(images_list)\n # Finding Similarity Matrix\n cos_sim = cosine_similarity(features_list)\n sim_graph = np.empty((0, len(cos_sim)))\n for row in cos_sim:\n k_largest = np.argsort(-np.array(row))[1:k_value + 1]\n sim_graph_row = [d if i in k_largest else 0 for i, d in enumerate(row)]\n sim_graph = np.append(sim_graph, np.array([sim_graph_row]), axis=0)\n\n row_sums = sim_graph.sum(axis=1)\n sim_graph = sim_graph / row_sums[:, np.newaxis]\n idx = 0\n results_dorsal = ppr(sim_graph, images_list, dorsal_list)\n results_palmar = ppr(sim_graph, images_list, palmar_list)\n final_results = {}\n\n for img in images_list_unlab:\n if results_dorsal[img] < results_palmar[img]:\n final_results[img] = \"dorsal\"\n else:\n final_results[img] = \"palmar\"\n\n actual_labels = fetch_actual_labels(images_list_unlab)\n print(\"Classification\")\n no_correct = 0\n correctly_classified = []\n incorrectly_classified = []\n print(\"| ImageId | Prediction | Actual |\")\n for r in final_results:\n print(\"| {} | {} | {} |\".format(r, final_results[r], actual_labels[r]))\n if final_results[r] == actual_labels[r]:\n correctly_classified.append(r)\n no_correct += 1\n else:\n incorrectly_classified.append(r)\n\n print(\"Correctly classified: {}\\n\".format(correctly_classified))\n print(\"InCorrectly classified: {}\\n\".format(incorrectly_classified))\n\n print(\"Classification Accuracy: {}%\".format(no_correct / len(images_list_unlab) * 100))\n print(\"Execution time: {} seconds\".format(time.time() - start))","def _classification(text_path_list, id_list, label_list):\n textnum = len(text_path_list)\n batched_num = ((textnum - 1) // classify.BATCH_SIZE + 1) * classify.BATCH_SIZE\n for i in range(batched_num - textnum):\n text_path_list.append(text_path_list[0])\n id_list.append(id_list[0])\n annotations = classify_obj.inference(text_path_list, id_list, label_list) #\n return annotations[0:textnum]","def gen_for_label(l, extra_args, out_file):\n print('Generating pic for cif label {} ...'.format(l))\n\n LIMIT = 10\n links = google_images_download.get_image_links(\n search_keywords=[l], keywords=['high resolution'], requests_delay=0, limit=LIMIT)\n\n at = 0\n while at < LIMIT:\n target = links[at]\n try:\n print(\"Generating {} from {} ... \".format(out_file, target))\n gen_args = [\n str(target), '--use_cifar', '--cifar_dir', cifar_dir, '--cifar_label', l,\n out_file\n ] + extra_args\n subprocess.check_call(['python3', 'main.py'] + gen_args)\n print(\"done generating {}\".format(out_file))\n return target\n except subprocess.CalledProcessError as e:\n print(\"encountered error in {} for {}, trying next url\".format(target, out_file))\n at += 1\n raise Error(\"Could not generate pic for cifar label {}, increase LIMIT\".format(l))","def _load_pascal_annotation(self, index):\n imagefile = os.path.join(self._data_path, 'Images', index + self._image_ext)\n labelfile = os.path.join(self._data_path, 'GroundTruth', index + '.txt')\n print 'Loading: image {} and label {}'.format(imagefile, labelfile)\n\n labels = np.loadtxt(labelfile) + 1\n image = cv2.imread(imagefile)\n\n gt_classes = []\n gt_boxes = []\n\n boxes, classes = self._get_boxes(image, labels)\n\n boxes = np.array(boxes)\n classes = np.array(classes)\n\n scores = np.ones(len(boxes), dtype=np.float32)\n dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32)\n\n # keep = nms(dets, 0.2)\n _, keep = non_max_suppression_fast(dets, 0.2)\n boxes = boxes[keep, :]\n classes = classes[keep]\n\n for n, (x, y, w, h) in enumerate(boxes):\n # gt_boxes.append([min(x, w), min(y, h), min(abs(w - x), w), min(abs(h - y), h)])\n gt_boxes.append([x, y, w - x, h - y])\n gt_classes.append(classes[n])\n\n overlaps = np.zeros((len(gt_boxes), len(self._classes)), dtype=np.float32)\n for n, obj in enumerate(gt_boxes):\n overlaps[n, gt_classes[n]] = 1.0\n\n overlaps = scipy.sparse.csr_matrix(overlaps)\n\n return {'boxes': np.array(gt_boxes),\n 'gt_classes': np.array(gt_classes, dtype=np.int32),\n 'gt_overlaps': overlaps,\n 'flipped': False}\n\n # def get_data_from_tag(node, tag):\n # return node.getElementsByTagName(tag)[0].childNodes[0].data\n\n # with open(filename) as f:\n # data = minidom.parseString(f.read())\n\n # objs = data.getElementsByTagName('object')\n # num_objs = len(objs)\n\n # boxes = np.zeros((num_objs, 4), dtype=np.uint16)\n # gt_classes = np.zeros((num_objs), dtype=np.int32)\n # overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)\n\n # # Load object bounding boxes into a data frame.\n # for ix, obj in enumerate(objs):\n # # Make pixel indexes 0-based\n # x1 = float(get_data_from_tag(obj, 'xmin')) - 1\n # y1 = float(get_data_from_tag(obj, 'ymin')) - 1\n # x2 = float(get_data_from_tag(obj, 'xmax')) - 1\n # y2 = float(get_data_from_tag(obj, 'ymax')) - 1\n # cls = self._class_to_ind[str(get_data_from_tag(obj, \"name\")).lower().strip()]\n # boxes[ix, :] = [x1, y1, x2, y2]\n # gt_classes[ix] = cls\n # overlaps[ix, cls] = 1.0\n\n # overlaps = scipy.sparse.csr_matrix(overlaps)\n\n # return {'boxes': boxes,\n # 'gt_classes': gt_classes,\n # 'gt_overlaps': overlaps,\n # 'flipped': False}","def draw_label_on_image(root_folder_path,root_folder_name,img_name,img_type,class_name,bb_color,bb_list):\n img_path=os.path.join(root_folder_path,root_folder_name,img_type,img_name+\".png\")\n img=cv2.imread(img_path)\n for each_bb in bb_list:\n cv2.rectangle(img,(each_bb[0],each_bb[2]),(each_bb[1],each_bb[3]),bb_color,3)\n cv2.putText(img,class_name,(each_bb[0],each_bb[3]),cv2.FONT_HERSHEY_SIMPLEX,1,(255,0,0),2,cv2.LINE_AA)\n cv2.imwrite(img_path,img)","def test_classifier(self):\n \n files = 0\n tp = 0\n fp = 0\n tn = 0\n fn = 0\n \n for testFile in os.listdir(self.testing_dir):\n if os.path.splitext(testFile)[1] in self.valid:\n\n files += 1\n fileName = self.testing_dir + \"/\" + testFile\n\n img = cv2.imread(fileName).astype(np.float32)\n self.Helpers.logger.info(\"Loaded test image \" + fileName)\n \n img = cv2.resize(img, (self.Helpers.confs[\"cnn\"][\"data\"][\"dim\"], \n self.Helpers.confs[\"cnn\"][\"data\"][\"dim\"]))\n img = self.reshape(img)\n \n prediction = self.get_predictions(img)\n \n msg = \"\"\n if prediction == 1 and \"_1.\" in testFile:\n tp += 1\n msg = \"Acute Lymphoblastic Leukemia correctly detected (True Positive)\"\n elif prediction == 1 and \"_0.\" in testFile:\n fp += 1\n msg = \"Acute Lymphoblastic Leukemia incorrectly detected (False Positive)\"\n elif prediction == 0 and \"_0.\" in testFile:\n tn += 1\n msg = \"Acute Lymphoblastic Leukemia correctly not detected (True Negative)\"\n elif prediction == 0 and \"_1.\" in testFile:\n fn += 1\n msg = \"Acute Lymphoblastic Leukemia incorrectly not detected (False Negative)\"\n self.Helpers.logger.info(msg)\n \n self.Helpers.logger.info(\"Images Classifier: \" + str(files))\n self.Helpers.logger.info(\"True Positives: \" + str(tp))\n self.Helpers.logger.info(\"False Positives: \" + str(fp))\n self.Helpers.logger.info(\"True Negatives: \" + str(tn))\n self.Helpers.logger.info(\"False Negatives: \" + str(fn))","def correctionToAnnotation(listOfAnnotations):\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\n indexAnnotation = input(u'Give the index of the annotation : ')\n # make sure to have the right index\n while True:\n try:\n indexAnnotation = int(indexAnnotation)\n if indexAnnotation < len(listOfAnnotations):\n break\n else:\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\n indexAnnotation = input(u'Index out of bounds. Repeat : ')\n except ValueError:\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\n indexAnnotation = input(u'Index given is not integral. Repeat : ')\n # get the new annotation element\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\n annotatorGeneralInput = input(u\"Old annotation is '{0}'. Give new annotation : \".format(listOfAnnotations[indexAnnotation]))\n # make sure to have the right general annotation\n while True:\n if annotatorGeneralInput in [u'0', u'1', u'0.0', u'0.1', u'0.2', u'1.0', u'1.1', u'1.2', u'1.3', u'1.4']:\n break\n else:\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\n annotatorGeneralInput = input(u'Repeat annotation: ')\n # if we still need to specify what type of alignment or misalignment\n if annotatorGeneralInput in [u'0', u'1']:\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\n # get the second part of the annotation (aligned or not)\n annotatorSpecificInput = input(u'Specific type annotation: ')\n typeAnswers = [u'0', u'1', u'2'] if annotatorGeneralInput == 0 else [u'0', u'1', u'2', u'3', u'4']\n # make sure to have the right specific annotation\n while True:\n if annotatorSpecificInput in typeAnswers:\n break\n else:\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\n annotatorSpecificInput = input(u'Repeat type annotation: ')\n # make the replacement\n listOfAnnotations[indexAnnotation] = float(u'{0}.{1}'.format(annotatorGeneralInput, annotatorSpecificInput))\n # if the right answer was given in the right format right away\n else:\n # make the replacement\n listOfAnnotations[indexAnnotation] = float(annotatorGeneralInput)\n # get back to the standard annotation\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\n annotatorGeneralInput = input(u'Correctly replaced. Back to current annotation : ')\n if annotatorGeneralInput in [u'c', u'correct']:\n annotatorGeneralInput, listOfAnnotations = correctionToAnnotation(listOfAnnotations)\n return annotatorGeneralInput, listOfAnnotations","def preprocessing_labels1(y,c = 1.,m = 0.6, f = 0.2 ,dataset = 'mnist'):\n perm_mnist = [3,5,8,6,0,4,7,9,2,1]\n perm_fmnist = [0,2,6,3,4,5,7,9,1,8]\n perm = [0,1,2,3,4,5,6,7,8,9]\n perm_cifar10 = [0,8,1,9,2,6,3,5,4,7]\n n = y.shape[0]\n y_res1 = np.zeros((int(c*n),2))\n print(int(c*n))\n y_res3 = np.zeros((int(f*n),10))\n if dataset == 'cifar10':\n perm = perm_cifar10\n elif dataset == 'mnist':\n perm = perm_mnist\n elif dataset == 'fashion_mnist':\n perm = perm_fmnist\n if dataset == 'cifar10':\n y_res2= np.zeros((int(m*n),5))\n for i in range(n):\n if i< int(c*n):\n if np.argmax(y[i]) in [0,1,8,9]:\n y_res1[i,0] = 1\n else :\n y_res1[i,1] = 1\n if i<int(m*n):\n if np.argmax(y[i]) in [0,8]:\n y_res2[i,0] = 1\n elif np.argmax(y[i]) in [1,9]:\n y_res2[i,1] = 1\n elif np.argmax(y[i]) in [2,6]:\n y_res2[i,2] = 1\n elif np.argmax(y[i]) in [3,5]:\n y_res2[i,3] = 1\n elif np.argmax(y[i]) in [4,7]:\n y_res2[i,4] = 1\n if i<int(f*n):\n y_res3[i,np.argmax(y[i])] = 1\n return(y_res1,y_res2,y_res3)\n else :\n y_res2= np.zeros((int(m*n),4))\n for i in range(n):\n if i< int(c*n):\n if np.argmax(y[i]) in perm[0:5]:\n y_res1[i,0] = 1\n else :\n y_res1[i,1] = 1\n if i<int(m*n):\n if np.argmax(y[i]) in perm[0:3]:\n y_res2[i,0] = 1\n elif np.argmax(y[i]) in perm[3:5]:\n y_res2[i,1] = 1\n elif np.argmax(y[i]) in perm[5:8]:\n y_res2[i,2] = 1\n elif np.argmax(y[i]) in perm[8:]:\n y_res2[i,3] = 1\n if i<int(f*n):\n y_res3[i,np.argmax(y[i])] = 1\n return(y_res1,y_res2,y_res3)","def get_random_data(annotation_line, input_shape, random=True, max_boxes=20, jitter=.3, hue=.1, sat=1.5, val=1.5,\n proc_img=True):\n line = annotation_line.split()\n image = Image.open(line[0])\n iw, ih = image.size\n h, w = input_shape\n box = np.array([np.array(list(map(int, box.split(',')))) for box in line[1:]])\n\n if not random:\n # resize image\n scale = min(w / iw, h / ih)\n nw = int(iw * scale)\n nh = int(ih * scale)\n dx = (w - nw) // 2\n dy = (h - nh) // 2\n image_data = 0\n if proc_img:\n image = image.resize((nw, nh), Image.BICUBIC)\n new_image = Image.new('RGB', (w, h), (0, 0, 0))\n new_image.paste(image, (dx, dy))\n image_data = np.array(new_image) / 255.\n\n # correct boxes\n box_data = np.zeros((max_boxes, 5))\n if len(box) > 0:\n np.random.shuffle(box)\n if len(box) > max_boxes:\n box = box[:max_boxes]\n box[:, [0, 2]] = box[:, [0, 2]] * scale + dx\n box[:, [1, 3]] = box[:, [1, 3]] * scale + dy\n box_data[:len(box)] = box\n\n return image_data, box_data\n\n # resize image\n new_ar = w / h * rand(1 - jitter, 1 + jitter) / rand(1 - jitter, 1 + jitter)\n scale = rand(.25, 2)\n if new_ar < 1:\n nh = int(scale * h)\n nw = int(nh * new_ar)\n else:\n nw = int(scale * w)\n nh = int(nw / new_ar)\n image = image.resize((nw, nh), Image.BICUBIC)\n\n # place image\n dx = int(rand(0, w - nw))\n dy = int(rand(0, h - nh))\n new_image = Image.new('RGB', (w, h), (0, 0, 0))\n new_image.paste(image, (dx, dy))\n image = new_image\n\n # flip image or not\n flip = rand() < .5\n if flip:\n image = image.transpose(Image.FLIP_LEFT_RIGHT)\n\n # distort image\n hue = rand(-hue, hue)\n sat = rand(1, sat) if rand() < .5 else 1 / rand(1, sat)\n val = rand(1, val) if rand() < .5 else 1 / rand(1, val)\n x = rgb_to_hsv(np.array(image) / 255.)\n x[..., 0] += hue\n x[..., 0][x[..., 0] > 1] -= 1\n x[..., 0][x[..., 0] < 0] += 1\n x[..., 1] *= sat\n x[..., 2] *= val\n x[x > 1] = 1\n x[x < 0] = 0\n image_data = hsv_to_rgb(x) # numpy array, 0 to 1\n\n # correct boxes\n box_data = np.zeros((max_boxes, 5))\n if len(box) > 0:\n np.random.shuffle(box)\n box[:, [0, 2]] = box[:, [0, 2]] * nw / iw + dx\n box[:, [1, 3]] = box[:, [1, 3]] * nh / ih + dy\n if flip:\n box[:, [0, 2]] = w - box[:, [2, 0]]\n box[:, 0:2][box[:, 0:2] < 0] = 0\n box[:, 2][box[:, 2] > w] = w\n box[:, 3][box[:, 3] > h] = h\n box_w = box[:, 2] - box[:, 0]\n box_h = box[:, 3] - box[:, 1]\n box = box[np.logical_and(box_w > 1, box_h > 1)] # discard invalid box\n if len(box) > max_boxes:\n box = box[:max_boxes]\n box_data[:len(box)] = box\n\n return image_data, box_data","def generate_cand_val(\n cand_path,\n img_path_root,\n save_path_root,\n annotation_path,\n sequence=1,\n plot_size=9,\n sigma_t=1,\n):\n\n cands = io.loadmat(cand_path)[\"candi\"]\n try:\n annotations = io.loadmat(str(annotation_path))[\"result\"]\n annotations[:, 1] = annotations[:, 1] + 30\n annotations[:, 2] = annotations[:, 2] + 30\n except:\n annotations = None\n\n lim_size = np.array([1040, 1392])\n\n lim_size = lim_size + 60\n\n for cand_id, cand in enumerate(cands):\n length = int(cand[0])\n frame = cand[6] + 1\n center_min = (cand[1:3] + cand[3:5]) / 2 - 64\n center_min = center_min[::-1]\n center_min[0] = center_min[0].clip(0, lim_size[0] - 128)\n center_min[1] = center_min[1].clip(0, lim_size[1] - 128)\n center_min = center_min.astype(np.int)\n center_max = center_min + 128\n\n if frame == 1:\n length += 2\n elif frame == 2:\n length += 3\n frame -= 1\n else:\n length += 4\n frame -= 2\n\n for_calcurate_max_frame = length / 16 + 1\n if (frame + for_calcurate_max_frame * 16) > 1013:\n if (frame + length) > 1013:\n print(length)\n length = length - ((frame + length) - 1013)\n\n depth = int(length / 16) + 1\n\n depth = depth * 16\n\n if length < 256:\n imgs_targets_tra(\n length,\n img_path_root,\n frame,\n sequence,\n center_min,\n center_max,\n annotations,\n depth,\n plot_size,\n sigma_t,\n save_path_root,\n cand_id,\n )","def detect_image(yolo_v3_model, image_paths, batch_frames, output_path, train_input_size, classes_file_path, \n score_threshold, iou_threshold, num_of_anchor_bbox, strides, anchors, show = False, \n rectangle_colors = ''):\n \n # obtain number of classes\n num_of_classes = len(read_class_names(classes_file_path))\n \n # create list to store images\n original_images = []\n \n # iterate over images in chronological order (last image is image of interest to put bbox)\n for x in range(batch_frames):\n \n # obtain original image\n original_image = cv2.imread(image_paths[x])\n \n # append original image to original_images list\n original_images.append(original_image[:])\n \n # convert original image to grayscale \n image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)\n \n # preprocess image\n image = transform_images(image[:], train_input_size)\n\n # obtain concat frame if none exist\n if x == 0: \n\n concat_image = image[:]\n\n # concatenate subsequent frames to concat_image\n else:\n\n concat_image = np.concatenate((concat_image, image), axis = -1)\n \n # add batch dimensions to concatenated image \n concat_image = concat_image[np.newaxis, ...].astype(np.float32)\n \n # create constant tensor from concatenated image and feed it to yolo_v3_model\n batched_input = tf.constant(concat_image)\n yolo_output = yolo_v3_model(batched_input)\n \n # list to store bboxes from respective scales\n pred_bbox = []\n \n # iterate over 3 scales\n for i in range(3):\n\n # decode resepctive yolo_output from each scale\n pred_result = decode(yolo_output = yolo_output[i], num_of_anchor_bbox = num_of_anchor_bbox, \n classes = num_of_classes, strides = strides, anchors = anchors, index = i)\n \n # obtain results of shape (:, 5 + num_classes), i.e all bboxes\n pred_result_reshaped = tf.reshape(pred_result, (-1, tf.shape(pred_result)[-1]))\n \n # append to pred_bbox\n pred_bbox.append(pred_result_reshaped)\n \n # concatenate all bboxes from all scales\n pred_bbox = tf.concat(pred_bbox, axis = 0)\n \n # post process all bboxes using latest image in orignal_images\n bboxes = postprocess_boxes(pred_bbox, original_images[-1], train_input_size, score_threshold)\n \n # non maximal supression for bboxes\n bboxes = nms(bboxes, iou_threshold, method = 'nms')\n \n # draw bbox on latest image in orignal_images\n image = draw_bbox(original_images[-1], bboxes, classes_file_path, rectangle_colors = rectangle_colors)\n \n # save image if path to save is given\n if output_path != '': cv2.imwrite(output_path, image)\n \n # display image if show is true \n if show:\n \n # show the image\n cv2.imshow(\"predicted image\", image)\n \n # load and hold the image\n cv2.waitKey(0)\n \n # to close the window after the required kill value was provided\n cv2.destroyAllWindows()\n \n return image","def classify_images():\n\n # Load the desired image\n img_path = 'dataset/colorize_images/n02085782_919.jpg'\n img = image.load_img(img_path, target_size=(299, 299))\n x = image.img_to_array(img)\n x = np.expand_dims(x, axis=0)\n x = preprocess_input(x)\n\n model = InceptionV3(weights=\"imagenet\")\n preds = model.predict(x)\n # decode the results into a list of tuples (class, description, probability)\n # (one such list for each sample in the batch)\n print('Predicted:', decode_predictions(preds, top=3)[0])","def __call__(self, *args, **kwargs):\n\n dataset = TextOnlyCocoAnnotation()\n\n if self.subset == 'train':\n image_paths, annotation_paths = self.collect_train_paths()\n elif self.subset == 'val':\n image_paths, annotation_paths = self.collect_val_paths()\n\n for image_path, annotation_path in tqdm(zip(image_paths, annotation_paths)):\n word_annotations = []\n with open(annotation_path, encoding='utf-8-sig') as read_file:\n content = [line.strip() for line in read_file.readlines()]\n for line in content:\n word_annotations.append(self.parse_line(line))\n should_add = not self.is_latin_required\n if self.is_latin_required:\n for word_annotation in word_annotations:\n if word_annotation['attributes']['language'].lower() == 'latin':\n should_add = True\n break\n if should_add:\n for word_annotation in word_annotations:\n dataset.add_bbox(image_path, imagesize.get(image_path), word_annotation)\n\n return dataset","def __call__(self, *args, **kwargs):\n\n dataset = TextOnlyCocoAnnotation()\n\n image_paths, annotation_paths = self.collect_train_paths()\n\n for image_path, annotation_path in tqdm(zip(image_paths, annotation_paths)):\n word_annotations = []\n with open(annotation_path, encoding='utf-8-sig') as read_file:\n content = [line.strip() for line in read_file.readlines()]\n for line in content:\n word_annotations.append(self.parse_line(line))\n should_add = not self.is_latin_required\n if self.is_latin_required:\n for word_annotation in word_annotations:\n if word_annotation['attributes']['language'].lower() == 'latin':\n should_add = True\n break\n if should_add:\n for word_annotation in word_annotations:\n dataset.add_bbox(image_path, imagesize.get(image_path), word_annotation)\n\n return dataset","def detect(model, dataset_dir, subset):\n print(\"Running on {}\".format(dataset_dir))\n\n # Create directory\n if not os.path.exists(RESULTS_DIR):\n os.makedirs(RESULTS_DIR)\n submit_dir = \"submit_{:%Y%m%dT%H%M%S}\".format(datetime.datetime.now())\n submit_dir = os.path.join(RESULTS_DIR, submit_dir)\n os.makedirs(submit_dir)\n\n # Read dataset\n dataset = TamperDataset()\n dataset.load_tamper(dataset_dir, subset)\n dataset.prepare()\n # Load over images\n submission = []\n f1 = 0\n print(len(dataset.image_ids))\n # for image_id in dataset.image_ids:\n # # Load image and run detection\n # image = dataset.load_image(image_id)\n # # Detect objects\n # r = model.detect([image], verbose=0)[0]\n\n # # Encode image to RLE. Returns a string of multiple lines\n # source_id = dataset.image_info[image_id][\"id\"]\n # rle = mask_to_rle(source_id, r[\"masks\"], r[\"scores\"])\n # submission.append(rle)\n # # Save image with masks\n\n # N = r[\"scores\"].shape[0]\n # if not N:\n # \tH, W, C = image.shape\n # \tmask = np.zeros((H,W))\n\n \t\n # else:\n\n # H, W, C = image.shape\n\n # idx = np.argsort(-r[\"scores\"])\n # mask = r[\"masks\"][:,:,idx[0]].astype(np.float32)\n\n # bbox = r[\"rois\"][idx[0], :4]\n\n # y1, x1, y2, x2 = bbox\n\n\n\n # mask = dense_crf(image, mask)\n\n # mask = np.where(mask >= 0.5, 255, 0)\n\n # H, W, C = image.shape\n\n # full_mask = np.zeros((H, W))\n # full_mask[y1:y2, x1:x2] = mask\n\n for image_id in dataset.image_ids:\n # Load image and run detection\n image = dataset.load_image(image_id)\n # ela=dataset.load_ela(image_id)\n # Detect objects\n # r = model.detect([image],[ela], verbose=0)[0]\n r = model.detect([image],verbose=0)[0]\n\n # Encode image to RLE. Returns a string of multiple lines\n source_id = dataset.image_info[image_id][\"id\"]\n rle = mask_to_rle(source_id, r[\"masks\"], r[\"scores\"])\n submission.append(rle)\n # Save image with masks\n\n N = r[\"scores\"].shape[0]\n if not N:\n H, W, C = image.shape\n mask = np.zeros((H,W))\n\n \n else:\n idx = np.argsort(-r[\"scores\"])\n mask = r[\"masks\"][:,:,idx[0]].astype(np.uint8)\n\n # save_image(mask, submit_dir, name=dataset.image_info[image_id][\"id\"]) \n\n\n annotation = dataset.load_annaation(image_id)\n annotation = np.where(annotation >= 0.5, 1, 0) \n f = get_FM(mask, annotation)\n f1 += f\n\n print(f1/len(dataset.image_ids))\n\n\n\n\n # save_image(mask, submit_dir, name=dataset.image_info[image_id][\"id\"]) \n\n # visualize.display_instances(\n # image, r['rois'], r['masks'], r['class_ids'],\n # dataset.class_names, r['scores'],\n # show_bbox=False, show_mask=False,\n # title=\"Predictions\")\n # plt.savefig(\"{}/{}.png\".format(submit_dir, dataset.image_info[image_id][\"id\"]))\n\n # Save to csv file\n # submission = \"ImageId,EncodedPixels\\n\" + \"\\n\".join(submission)\n # file_path = os.path.join(submit_dir, \"submit.csv\")\n # with open(file_path, \"w\") as f:\n # f.write(submission)\n print(\"Saved to \", submit_dir)","def decode_coco_detection_example(example, input_range=None):\n image = tf.image.convert_image_dtype(example['image'], dtype=tf.float32)\n\n ### normalize\n if input_range:\n image = image * (input_range[1] - input_range[0]) + input_range[0]\n else:\n mean_rgb = tf.constant(MEAN_RGB, shape=[1, 1, 3], dtype=tf.float32)\n std_rgb = tf.constant(STDDEV_RGB, shape=[1, 1, 3], dtype=tf.float32)\n image = (image - mean_rgb) / std_rgb\n\n boxes = decode_boxes(example['objects']['bbox'], tf.shape(image)[0:2])\n\n target = {\n 'area': example['objects']['area'],\n 'boxes': boxes,\n 'objects/id': example['objects']['id'],\n 'is_crowd': example['objects']['is_crowd'],\n 'labels': example['objects']['label'] + 1, # 0'th class is padding.\n }\n\n # Filters objects to exclude degenerate boxes.\n keep = tf.where(tf.logical_and(boxes[:, 2] > boxes[:, 0],\n boxes[:, 3] > boxes[:, 1]))[:, 0]\n target_kept = {k: tf.gather(v, keep) for k, v in target.items()}\n target_kept = target\n\n target_kept['orig_size'] = tf.cast(tf.shape(image)[0:2], dtype=tf.int32)\n target_kept['size'] = tf.identity(target_kept['orig_size'])\n target_kept['image/id'] = example['image/id']\n\n return {\n 'inputs': image,\n 'label': target_kept,\n }","def _check_load_bbox(self, coco, entry):\n ann_ids = coco.getAnnIds(imgIds=entry['id'], iscrowd=None)\n objs = coco.loadAnns(ann_ids)\n # check valid bboxes\n valid_objs = []\n valid_segs = []\n width = entry['width']\n height = entry['height']\n for obj in objs:\n if obj.get('ignore', 0) == 1:\n continue\n # crowd objs cannot be used for segmentation\n if obj.get('iscrowd', 0) == 1:\n continue\n # need accurate floating point box representation\n x1, y1, w, h = obj['bbox']\n x2, y2 = x1 + np.maximum(0, w), y1 + np.maximum(0, h)\n # clip to image boundary\n x1 = np.minimum(width, np.maximum(0, x1))\n y1 = np.minimum(height, np.maximum(0, y1))\n x2 = np.minimum(width, np.maximum(0, x2))\n y2 = np.minimum(height, np.maximum(0, y2))\n # require non-zero seg area and more than 1x1 box size\n if obj['area'] > self._min_object_area and x2 > x1 and y2 > y1 \\\n and (x2 - x1) * (y2 - y1) >= 4:\n contiguous_cid = self.json_id_to_contiguous[obj['category_id']]\n valid_objs.append([x1, y1, x2, y2, contiguous_cid])\n\n segs = obj['segmentation'] # polygon or RLE\n assert isinstance(segs, list) or isinstance(segs, dict), '{}'.format(obj.get('iscrowd', 0))\n if isinstance(segs, list):\n valid_segs.append([np.asarray(p).reshape(-1, 2).astype('float32')\n for p in segs if len(p) >= 6])\n else:\n valid_segs.append(segs)\n # there is no easy way to return a polygon placeholder: None is returned\n # in validation, None cannot be used for batchify -> drop label in transform\n # in training: empty images should be be skipped\n if not valid_objs:\n valid_objs = None\n valid_segs = None\n else:\n valid_objs = np.asarray(valid_objs).astype('float32')\n return valid_objs, valid_segs","def visualize_detections(detection_result,\n label_map,\n font_path=None,\n font_size=10,\n line_width=1,\n score_thresh=.5,\n max_num_viz=None,\n color_per_instance_mask=True):\n image = np.copy(detection_result['image'])\n height, width = image.shape[:2]\n\n scores = detection_result['scores']\n boxes = detection_result['boxes']\n classes = detection_result['classes']\n\n detection_indices = scores >= score_thresh\n scores = scores[detection_indices]\n boxes = boxes[detection_indices]\n classes = classes[detection_indices]\n\n masks = None\n if 'masks' in detection_result:\n masks = detection_result['masks']\n masks = masks[detection_indices]\n\n num_detections = scores.shape[0]\n if max_num_viz is not None:\n num_detections = np.minimum(num_detections, max_num_viz)\n \n color_map = get_color_map(len(label_map) + 1)\n\n for i in range(num_detections):\n ymin, xmin, ymax, xmax = boxes[i].astype(np.int32)\n ymin = np.maximum(0, ymin)\n xmin = np.maximum(0, xmin)\n ymax = np.minimum(height - 1, ymax)\n xmax = np.minimum(width - 1, xmax)\n color = color_map[classes[i]]\n if color_per_instance_mask:\n mask_color = (color_map[i] + 10) % color_map.shape[0]\n if (mask_color[0] < 50).all(): \n mask_color = (color_map[i] + 15) % color_map.shape[0]\n else:\n mask_color = color\n\n image[ymin : ymax, \n np.maximum(xmin - line_width // 2, 0) : \n np.minimum(xmin + line_width - line_width // 2, width)] = color\n image[ymin : ymax, \n np.maximum(xmax - line_width // 2, 0) : \n np.minimum(xmax + line_width - line_width // 2, width)] = color\n image[np.maximum(ymin - line_width // 2, 0) : \n np.minimum(ymin + line_width - line_width // 2, height), \n xmin : xmax] = color\n image[np.maximum(ymax - line_width // 2, 0) : \n np.minimum(ymax + line_width - line_width // 2, height), \n xmin : xmax] = color\n\n detection_label_text = '%s: %.2f' % (\n label_map[classes[i]], int(scores[i] * 100) / 100)\n\n if font_path is not None: \n font = ImageFont.truetype(font_path, size=font_size)\n else:\n font = ImageFont.load_default()\n text_width, text_height = font.getsize(detection_label_text)\n\n x = xmin\n y = np.maximum(ymin - text_height, 0)\n \n image[y : np.minimum(y + text_height, height), \n x : np.minimum(x + text_width, width)] = color \n\n img_obj = Image.fromarray(image)\n draw = ImageDraw.Draw(img_obj)\n draw.text((x, y), detection_label_text, TEXT_COLOR, font=font)\n image = np.array(img_obj)\n\n if masks is not None:\n draw_mask(image, masks[i], color=mask_color)\n\n return image","def training_data_generation(DATA_DIR, img_height_size, img_width_size, label_list):\r\n \r\n img_ms_files = glob.glob(DATA_DIR + '\\\\Train_MS' + '\\\\Train_*.tif')\r\n img_pan_files = glob.glob(DATA_DIR + '\\\\Train_Pan' + '\\\\Train_*.tif')\r\n polygon_files = glob.glob(DATA_DIR + '\\\\Train_Polygons' + '\\\\Train_*.geojson')\r\n \r\n img_ms_array_list = []\r\n img_pan_array_list = []\r\n mask_array_list = []\r\n \r\n for file in range(len(img_ms_files)):\r\n with rasterio.open(img_ms_files[file]) as f:\r\n metadata = f.profile\r\n img_ms = np.transpose(f.read(tuple(np.arange(metadata['count']) + 1)), [1, 2, 0])\r\n \r\n with rasterio.open(img_pan_files[file]) as g:\r\n metadata_pan = g.profile\r\n img_pan = np.expand_dims(g.read(1), axis = 2)\r\n \r\n ms_to_pan_ratio = metadata['transform'][0] / metadata_pan['transform'][0]\r\n \r\n if (img_height_size % ms_to_pan_ratio) != 0 or (img_width_size % ms_to_pan_ratio) != 0:\r\n raise ValueError('Please make sure that both img_height_size and img_width_size can be divided by {}'.format(int(ms_to_pan_ratio)))\r\n \r\n mask = training_mask_generation(img_pan_files[file], polygon_files[file], labels = label_list)\r\n \r\n img_ms_array, img_pan_array, mask_array = image_clip_to_segment_and_convert(img_ms, img_pan, mask, ms_to_pan_ratio, \r\n img_height_size, img_width_size)\r\n \r\n img_ms_array_list.append(img_ms_array)\r\n img_pan_array_list.append(img_pan_array)\r\n mask_array_list.append(mask_array)\r\n \r\n img_ms_full_array = np.concatenate(img_ms_array_list, axis = 0)\r\n img_pan_full_array = np.concatenate(img_pan_array_list, axis = 0)\r\n mask_full_array = to_categorical(np.concatenate(mask_array_list, axis = 0), num_classes = len(label_list))\r\n \r\n return img_ms_full_array, img_pan_full_array, mask_full_array","def my_evaluate(original_image, img_id, annotations, min_score=0.4, max_overlap=0.3, top_k=200):\n\n # Transform\n image = normalize(to_tensor(resize(original_image)))\n\n # Move to default device\n image = image.to(device)\n\n # Forward prop.\n predicted_locs, predicted_scores = model(image.unsqueeze(0))\n\n # Detect objects in SSD output\n det_boxes, det_labels, det_scores = model.detect_objects(predicted_locs, predicted_scores, min_score=min_score,\n max_overlap=max_overlap, top_k=top_k)\n\n # Move detections to the CPU\n det_boxes = det_boxes[0].to('cpu')\n\n # Transform to original image dimensions\n original_dims = torch.FloatTensor(\n [original_image.width, original_image.height, original_image.width, original_image.height]).unsqueeze(0)\n det_boxes = det_boxes * original_dims\n\n # Decode class integer labels\n det_labels = [rev_label_map[l] for l in det_labels[0].to('cpu').tolist()]\n det_scores = det_scores[0].to('cpu').tolist()\n\n if det_labels != ['background']:\n for i in range(det_boxes.size(0)):\n \n # For every detection, see whether it matches a ground truth\n match = False\n for j in range(len(annotations['boxes'])):\n if annotations['labels'][j] == -1: # this is being set when a ground truth already matched\n continue\n match = box_match(det_boxes[i], \n det_labels[i], \n torch.Tensor(annotations['boxes'][j]), \n rev_label_map[annotations['labels'][j]])\n if match:\n annotations['labels'][j] = -1\n break\n \n if match: # true positive if the detection is correct and matched a ground truth\n all_results['true_positive'] += 1\n else: # false positive if the detection did not match a ground truth\n all_results['false_positive'] += 1\n \n # After all detections were checked, let us see whether the detector missed something\n for label in annotations['labels']: \n if label == -1: # This is set after a detection matched this ground truth\n continue\n \n # false negative if we reach this line, since the ground truth object was not found\n all_results['false_negative'] += 1","def generate_labels(pics):\r\n return []","def preprocess_human_annotations_p1(problem_id, source_pickle, annotation_pickle):\n label_dim, _, label_to_ix, _, _ = get_label_params(problem_id)\n\n with open(annotation_pickle) as fp:\n annotations = cPickle.load(fp)\n n_annotations = len(annotations)\n\n with open(source_pickle) as fp:\n ast_programs = cPickle.load(fp)\n\n programs = []\n program2label = {}\n\n for i in tqdm(range(n_annotations)):\n annotation = annotations[i]\n program_id = int(annotation['answerId'])\n targets = json.loads(annotation['gradeData'])\n targets = convert_V2_to_V3(targets)\n targets = labels_to_numpy(targets, label_dim, label_to_ix).tolist()\n\n ast = ast_programs[program_id]\n removeColors(ast)\n ast = flatten_ast(ast)\n program = ' '.join(ast)\n\n programs.append(program)\n program2label[program] = targets\n\n # important, make sure there are no duplicates here\n programs = list(set(programs))\n labels = [program2label[prog] for prog in programs]\n\n return programs, labels","def prep_image_data(arg_dict):\n cat_df = pd.read_csv(arg_dict['category_file'],\n skiprows=1,\n sep='\\s+')\n bbox_df = pd.read_csv(arg_dict['bbox_file'],\n skiprows=1,\n sep='\\s+')\n img_dir = arg_dict['image_dir']\n\n combo_df = pd.merge(cat_df, bbox_df, how='outer', on='image_name')\n combo_df['image_name'] = combo_df['image_name'].apply(\n lambda x: x[len('img'):-len('.jpg')])\n labels = Labels(combo_df, img_dir, n_images_loaded=-1)\n labels.set_data_target('raw_image', chunksize=3000)\n return labels","def __call__(self):\n\n dataset = TextOnlyCocoAnnotation()\n\n with open(self.path) as read_file:\n\n json_loaded = json.load(read_file)\n\n for i, value in tqdm(json_loaded['imgs'].items()):\n image_path = os.path.join(os.path.dirname(self.path), 'train2014',\n value['file_name'])\n dataset_type = value['set']\n\n if dataset_type not in self.sets:\n print(dataset_type)\n continue\n\n for annotation_id in json_loaded['imgToAnns'][i]:\n annotation_value = json_loaded['anns'][str(annotation_id)]\n word_annotation = self.parse_annotation_instance(annotation_value)\n dataset.add_bbox(image_path, imagesize.get(image_path), word_annotation)\n\n return dataset","def process(file_name):\n img=Image.open(str(file_name))\n cim_resized = img.resize((40,40), resample=Image.LANCZOS)\n n = cim_resized.convert('L')\n cropped = np.array(n).astype(np.float64)\n im=Image.fromarray(cropped)\n im.show()\n normalized_cropped_image = cropped - np.mean(cropped)\n normalized_cropped_image = normalized_cropped_image.reshape((-1, image_size, image_size, num_channels)).astype(np.float32)\n predicted_arr = predict(normalized_cropped_image)\n label = ''.join(['' if int(x[0]) == 10 else str(x[0]) for x in list(predicted_arr)])\n print 'LABEL: ' + label","def extract_images(f):\r\n if f == \"train\":\r\n D_train = numpy_array_stick(\"/home/luzihao/xiaoluo/xiyuan/CNN/standard2/\",10)\r\n #D_train = numpy_array_stick(\"D:\\\\FDU\\\\Template\\\\FDUROP\\\\face_detection_and_recognition\\\\standard2\\\\\",10)\r\n #\"X\" means data,\"Y\" means label\r\n Y_train = D_train[1]\r\n X_train = D_train[0].reshape(people*10,80,80,1)\r\n return X_train,Y_train\r\n elif f == \"test\":\r\n D_test = numpy_array_stick(\"/home/luzihao/xiaoluo/xiyuan/CNN/standard2/\",1)\r\n #D_test = numpy_array_stick(\"D:\\\\FDU\\\\Template\\\\FDUROP\\\\face_detection_and_recognition\\\\standard2\\\\\",1)\r\n #\"X\" means data,\"Y\" means label\r\n Y_test = D_test[1]\r\n X_test = D_test[0].reshape(people*1,80,80,1)\r\n return X_test,Y_test","def demo(net, image_name,num_class,save_ff):\r\n\r\n # Load the demo image\r\n #im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)\r\n im_file=image_name\r\n im = cv2.imread(im_file)\r\n\r\n # Detect all object classes and regress object bounds\r\n timer = Timer()\r\n timer.tic()\r\n #for zzz in range(100):\r\n scores, boxes = im_detect(net, im)\r\n timer.toc()\r\n print ('Detection took {:.3f}s for '\r\n '{:d} object proposals').format(timer.total_time, boxes.shape[0])\r\n\r\n # Visualize detections for each class\r\n CONF_THRESH = 0.35\r\n NMS_THRESH = 0.3\r\n thresh=CONF_THRESH\r\n for cls_ind, cls in enumerate(range(num_class)):#CLASSES[1:]\r\n cls_ind += 1 # because we skipped background\r\n # cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]\r\n # cls_scores = scores[:, cls_ind]\r\n # dets = np.hstack((cls_boxes,\r\n # cls_scores[:, np.newaxis])).astype(np.float32)\r\n inds = np.where(scores[:, cls_ind] > thresh)[0]\r\n cls_scores = scores[inds, cls_ind]\r\n if cfg.TEST.AGNOSTIC:\r\n cls_boxes = boxes[inds, 4:8]\r\n else:\r\n cls_boxes = boxes[inds, cls_ind*4:(cls_ind+1)*4]\r\n dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \\\r\n .astype(np.float32, copy=False)\r\n keep = nms(dets, NMS_THRESH)\r\n dets = dets[keep, :]\r\n #vis_detections(im, cls, dets, thresh=CONF_THRESH)\r\n inds = np.where(dets[:, -1] >= thresh)[0]\r\n if len(inds) == 0:\r\n continue\r\n\r\n im_tmp = im#im[:, :, (2, 1, 0)]\r\n for i in inds:\r\n bbox = dets[i, :4]\r\n score = dets[i, -1]\r\n print bbox,score,cls\r\n cv2.rectangle(im_tmp, (bbox[0],bbox[1]), (bbox[2],bbox[3]), (0,0,255),2)\r\n #save_ff=\"/storage2/liushuai/faster_rcnn/FasterRCNN-Encapsulation-Cplusplus/faster_cxx_lib_ev2641/test_result.jpg\"\r\n im_tmp = im#im[:, :, (2, 1, 0)]\r\n cv2.imwrite(save_ff,im_tmp)\r\n #save_pic(im, cls, dets, thresh=CONF_THRESH,save_ff)\r","def test():\n import os\n import ClearMap.ImageProcessing.SpotDetection as self\n reload(self)\n import ClearMap.IO as io \n import ClearMap.Settings as settings\n \n basedir = settings.ClearMapPath;\n #fn = '/home/ckirst/Science/Projects/BrainActivityMap/Data/iDISCO_2015_06/Adult cfos C row 20HF 150524.ims';\n fn = os.path.join(basedir, 'Test/Data/Synthetic/label_iDISCO_\\d{3}.tif');\n fn = os.path.join(basedir, 'Test/Data/OME/16-17-27_0_8X-s3-20HF_UltraII_C00_xyz-Table Z\\d{4}.ome.tif');\n #fn = '/run/media/ckirst/ChristophsBackuk4TB/iDISCO_2015_06/Adult cfos C row 20HF 150524.ims';\n #fn = '/home/nicolas/Windows/Nico/cfosRegistrations/Adult cfos C row 20HF 150524 - Copy.ims';\n #fn = '/home/ckirst/Science/Projects/BrainActivityMap/iDISCO_2015_04/test for spots added spot.ims'\n\n img = io.readData(fn);\n #img = dataset[0:500,0:500,1000:1008];\n #img = dataset[600:1000,1600:1800,800:830];\n #img = dataset[500:1500,500:1500,800:809]; \n img = img.astype('int16');\n \n #m = sys.modules['iDISCO.ImageProcessing.SpotDetection']\n #c = self.detectCells(img);\n \n c = self.detectCells(img, dogSize = None, cellShapeThreshold = 1, cellShapeFile = '/home/ckirst/Science/Projects/BrainActivityMap/Analysis/iDISCO/Test/Data/CellShape/cellshape_\\d{3}.tif');\n \n print ('done, found %d cells !' % c[0].shape[0])\n\n\n #test intensities:\n import numpy;\n x = numpy.random.rand(30,30,10);\n centers = numpy.array([[0,0,0], [29,29,9]]);\n i = self.findIntensity(x, centers, boxSize = (1,1,1));\n print (i)","def simulate_user_annotations(self):\r\n\r\n # Check existence of clustering results\r\n\r\n if len(self.recognized_faces) == 0:\r\n\r\n # Try to load YAML files\r\n if os.path.exists(self.cluster_files_path):\r\n\r\n print 'Loading YAML files with clustering results'\r\n logger.debug('Loading YAML file with clustering results')\r\n\r\n self.recognized_faces = []\r\n for yaml_file in os.listdir(self.cluster_files_path):\r\n yaml_file_path = os.path.join(\r\n self.cluster_files_path, yaml_file)\r\n with open(yaml_file_path) as f:\r\n self.recognized_faces.append(yaml.load(f))\r\n\r\n print 'YAML file with clustering results loaded'\r\n logger.debug('YAML file with clustering results loaded')\r\n\r\n else:\r\n\r\n print 'Warning! No clustering results found!'\r\n logger.warning('No clustering results found!')\r\n\r\n return\r\n\r\n auto_p_counter = 0\r\n\r\n user_rec_faces = []\r\n\r\n # Iterate through automatic recognized faces\r\n for auto_p_dict in self.recognized_faces:\r\n\r\n segment_list = auto_p_dict[c.SEGMENTS_KEY]\r\n\r\n # Get first segment\r\n if len(segment_list) >= 1:\r\n\r\n first_segment = segment_list[0]\r\n\r\n segment_ann_tag = first_segment[c.ANN_TAG_KEY]\r\n\r\n auto_p_dict[c.ASSIGNED_TAG_KEY] = segment_ann_tag\r\n\r\n user_rec_faces.append(auto_p_dict)\r\n\r\n auto_p_counter += 1\r\n\r\n self.recognized_faces = user_rec_faces\r\n\r\n # Save clustering result in YAML files\r\n\r\n # Remove previous files\r\n if os.path.exists(self.cluster_files_path):\r\n shutil.rmtree(self.cluster_files_path)\r\n # Create directory for people clustering results\r\n os.makedirs(self.cluster_files_path)\r\n\r\n counter = 0\r\n for person_dict in self.recognized_faces:\r\n yaml_file_name = str(counter) + '.YAML'\r\n yaml_file_path = os.path.join(self.cluster_files_path, yaml_file_name)\r\n utils.save_YAML_file(yaml_file_path, person_dict)\r\n counter += 1","def detect_labels(path):\n client = vision.ImageAnnotatorClient()\n\n with io.open(path, 'rb') as image_file:\n content = image_file.read()\n\n image = vision.types.Image(content=content)\n\n response = client.label_detection(image=image)\n labels = response.label_annotations\n #print('Labels:')\n\n #for label in labels:\n # print(label.description)\n return labels","def draw_all_labels(df,root_folder_path,root_folder_name,logger):\n # df=df[df.type==\"Color\"]\n len_images=df['file_id'].nunique()\n perc_list=[i*0.05 for i in range(0,20,1)]\n grouped_df=df.groupby(['file_id','class'])\n coordinate_names=['x_max','x_min','y_max','y_min']\n group_len=len(grouped_df)\n\n class_label_dict={}\n label_info_list=[]\n for ind,(name, group) in enumerate(grouped_df):\n img_name,class_name=name\n img_type=group['type'].values[0]\n bb_list=group[coordinate_names].values.astype(int)\n if class_name not in class_label_dict.keys():\n class_label_dict[class_name]=get_random_color()\n bb_color=class_label_dict[class_name]\n label_info_list.append([img_name,img_type,class_name,bb_color,bb_list])\n draw_label_on_image(root_folder_path,root_folder_name,img_name,img_type,class_name,bb_color,bb_list)\n perc=float(\"{:.2f}\".format((ind+1)/group_len))\n if perc in perc_list:\n perc_list.remove(perc)\n logger.write(\"Classes annotated: \"+str(ind+1)+\"/\"+str(group_len))\n # print(\"Label list generated.\")\n # pool = Pool(1)\n # pool.starmap(draw_label_on_one_image, zip(\n # label_info_list, itertools.repeat(root_folder_path), itertools.repeat(root_folder_name)))\n # pool.close()\n # pool.join()\n # print(\"Drawing labels is finished.\")","def main():\n labels, data = load_image_data()\n print(labels.shape, data.shape)","def annotate_image(self, image, results):\n predictedObjects = []\n for i in range(len(results)):\n objectParameters = []\n x = int(results[i][1])\n y = int(results[i][2])\n w = int(results[i][3])\n h = int(results[i][4])\n # print(x, y, w, h, results[i][0])\n imageHeight, imageWidth, _ = image.shape\n w = w // 2\n h = h // 2\n # change to truncate boxes which go outside the image\n xmin, xmax, ymin, ymax = 0, 0, 0, 0\n xmin = 3 if not max(x - w, 0) else (x - w)\n xmax = imageWidth - 3 if not min(x + w - imageWidth, 0) \\\n else (x + w)\n ymin = 1 if not max(y - h, 0) else (y - h)\n ymax = imageHeight - 3 if not min(y + h - imageHeight, 0) \\\n else (y + h)\n if self.verbose:\n print('Class : ' + results[i][0] + ', [x, y, w, h] [' +\n str(x) + ', ' + str(y) + ', ' + str(w) + ', ' + str(h) +\n '] Confidence : ' + str(results[i][5]))\n \n # Each class must have a unique color\n color = tuple([(j * (1+self.classes.index(results[i][0])) % 255) \\\n for j in self.seed])\n cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color, 2)\n if ymin <= 20:\n cv2.rectangle(\n image, (xmin, ymin), (xmax, ymin + 20), color, -1\n )\n cv2.putText(\n image, results[i][0] + ': %.2f' % results[i][5],\n (xmin+5, ymin+15), cv2.FONT_HERSHEY_SIMPLEX, 0.5,\n (255, 255, 255), 2\n )\n else:\n cv2.rectangle(image, (xmin, ymin), (xmax, ymin-20), color, -1)\n cv2.putText(\n image, results[i][0] + ': %.2f' % results[i][5],\n (xmin+5, ymin-8), cv2.FONT_HERSHEY_SIMPLEX, 0.5,\n (255, 255, 255), 2\n )\n objectParameters = [\n results[i][0], xmin, ymin, xmax, ymax, results[i][5]\n ]\n predictedObjects.append(objectParameters)\n return image, predictedObjects\n # if self.outputFile:\n # cv2.imwrite(self.outputFile,image)","def draw_image(res, train, label, output_dir, fig, write_file=False):\n size = len(label)\n infer_res, infer_conf, gt_conf = compare_result(res, label)\n if not os.path.exists(output_dir+\"/correct\"):\n os.makedirs(output_dir+\"/correct\")\n if not os.path.exists(output_dir + \"/incorrect\"):\n os.makedirs(output_dir + \"/incorrect\")\n\n if write_file:\n cor = open(output_dir+\"/res_correct.txt\", 'a')\n err = open(output_dir+\"/res_incorrect.txt\", 'a')\n # right inferences\n r = 0\n # error inferences\n e = 0\n # error details\n count = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n for i in range(size):\n img = Image.fromarray(train[i].reshape(fig, fig).astype(np.uint8))\n if infer_res[i] == label[i]:\n r += 1\n img.save(output_dir+\"/correct/\"+str(i)+\"_\"+str(infer_res[i])+\"_\"+str(infer_conf[i])+\".png\")\n if write_file:\n cor.write(str(i)+\"_\"+str(infer_res[i])+\"_\"+str(infer_conf[i])+str(res[i][0][0][0])+'\\n')\n\n else:\n\n e += 1\n img.save(output_dir + \"/incorrect/\" + str(i) + \"_gt:\" + str(label[i]) + \"_\" + str(\n gt_conf[i]) + \"_inf:\" + str(infer_res[i]) + \"_\" + str(infer_conf[i]) + \".png\")\n count[label[i]] += 1\n if write_file:\n err.write(str(i) + \"_gt:\"+str(label[i]) + \"_\"+str(gt_conf[i]) + \"_inf:\"+str(infer_res[i])+\"_\" +\n str(infer_conf[i]) + str(res[i][0][0][0]) + '\\n')\n if write_file:\n cor.close()\n err.close()\n print(\"correct inference results:\"+str(r))\n print(\"incorrect inference results:\" + str(e))\n print(\"incorrect inference results details:\" + str(count))","def make_sub_data_train(data, config):\n sub_input_sequence = []\n sub_label_sequence = []\n\n\tfor scale in range(2,5):\t \n\n\t for i in range(len(data)):\n\n\t\t#input_, label_, = preprocess(data[i], config.scale) # do bicbuic only one scale\n\t\tinput_, label_, = preprocess(data[i], scale) # do bicbuic turn around all scale\n\t\n\t\tif len(input_.shape) == 3: # is color\n\t\t h, w, c = input_.shape\n\t\telse:\n\t\t h, w = input_.shape # is grayscale\n\t\n\t\t#checkimage(input_)\t\t\n\n\t\tnx, ny = 0, 0\n\t\tfor x in range(0, h - config.image_size + 1, config.stride):\n\t\t nx += 1; ny = 0\n\t\t for y in range(0, w - config.image_size + 1, config.stride):\n\t\t\tny += 1\n\n\t\t\tsub_input = input_[x: x + config.image_size, y: y + config.image_size] # 41 * 41\n\t\t\tsub_label = label_[x: x + config.label_size, y: y + config.label_size] # 41 * 41\n\n\n\t\t\t# Reshape the subinput and sublabel\n\t\t\tsub_input = sub_input.reshape([config.image_size, config.image_size, config.c_dim])\n\t\t\tsub_label = sub_label.reshape([config.label_size, config.label_size, config.c_dim])\n\n\t\t\t# Normialize\n\t\t\tsub_input = sub_input / 255.0\n\t\t\tsub_label = sub_label / 255.0\n\t\t\t\n\t\t\t#cv2.imshow(\"im1\",sub_input)\n\t\t\t#cv2.imshow(\"im2\",sub_label)\n\t\t\t#cv2.imshow(\"residual\",sub_input - sub_label)\n\t\t\t#cv2.waitKey(0)\n\n\t\t\t# Rotate 90,180,270\n\t\t\tfor angle in range(0,360,90):\t\n\t\t\t\tsub_input = rotate(sub_input,angle)\t\n\t\t\t\tsub_label = rotate(sub_label,angle)\t\n\t\t\n\t\t\t\t# Add to sequence\n\t\t\t\tsub_input_sequence.append(sub_input)\n\t\t\t\tsub_label_sequence.append(sub_label)\n\n\t\t\t\tcv2.imshow(\"im1\",sub_input)\n\t\t\t\tcv2.imshow(\"im2\",sub_label)\n\t\t\t\tcv2.imshow(\"residual\",sub_input - sub_label)\n\t\t\t\tcv2.waitKey(1)\n\t\t\t\t\n\n \n # NOTE: The nx, ny can be ignore in train\n return sub_input_sequence, sub_label_sequence, nx, ny","def Valid_Image_data_generator(folderlist,resize = (920,1200),Transformation = True, scaling = True):\n\n while True:\n total_classes = len(folderlist.keys())\n keys = folderlist.keys()\n Images = []\n Image_label = []\n for key in tqdm(folderlist.keys()):\n for j in range(len(folderlist[key])):\n img_label = folderlist[key][j]\n img = Image.open(img_label,'r')\n h = resize[1]\n l = int(img.size[1]*h/img.size[0])\n img = img.resize((h,l), Image.ANTIALIAS)\n background = Image.new('RGB', (resize[1], resize[0]), (255, 255, 255))\n img_w, img_h = img.size\n bg_w, bg_h = background.size\n offset = (int((bg_w - img_w) / 2), int((bg_h - img_h) / 2))\n background.paste(img, offset)\n background = np.asarray(background)\n if Transformation == True:\n rotation = rotate(background,random.choice(range(360)))\n translate = translate_xy(background,random.choice(range(resize[0]/4)),random.choice(range(resize[1]/4)))\n flip = cv2.flip(rotation,1)\n Y = np.concatenate((rotation[np.newaxis,:,:,:],flip[np.newaxis,:,:,:],translate[np.newaxis,:,:,:]))\n Images.append(Y)\n Images.append(background[np.newaxis,:,:,:])\n Image_label.append([key for i in range(4)]) # Four because we are doing rot,trans,flip and one original Image\n else:\n Images.append(background[np.newaxis,:,:,:])\n Image_label.append([key])\n Image_label = np.concatenate(Image_label)\n Images = np.concatenate(Images)\n Image_label = np.array(pd.get_dummies(Image_label))\n X_Image , Y_Image = shuffle(Images,Image_label,random_state=0)\n if scaling == True:\n X_Image = X_Image/255\n else:\n X_Image = X_Image\n return (X_Image,Y_Image)","def main() -> None:\n\n # Define file name\n file_name = define_file()\n\n # Open chosen image\n img = image.load_img(IMAGES + file_name, color_mode='grayscale')\n\n # Show user image\n plt.imshow(img)\n plt.show()\n\n # Convert image to array\n img_arr = image.img_to_array(img)\n img_arr = np.array([img_arr])\n img_arr = img_arr.astype(\"float32\") / 255.0\n\n # Classify image\n img_class = classification(img_arr)\n\n # Suggest user add noise to original image\n if img_class == ORIGINAL:\n while True:\n command = input('Seems like your image is original. Do you want to add noise? y/n: ')\n if command.strip().lower() in ('y', 'yes'):\n noisy_array = noise(img_arr)\n display(img_arr, noisy_array)\n img = image.array_to_img(noisy_array[0])\n img.save(IMAGES + file_name[:-4] + '_noise' + file_name[-4:])\n main()\n elif command.strip().lower() in ('n', 'no'):\n main()\n else:\n continue\n\n # Suggest user remove noise from noised image\n elif img_class == NOISED:\n while True:\n command = input('Seems like your image has noise. Do you want to remove noise? y/n: ')\n if command.strip().lower() in ('y', 'yes'):\n denoise_array = denoise_image(img_arr)\n display(img_arr, denoise_array)\n img = image.array_to_img(denoise_array[0])\n img.save(IMAGES + file_name[:-4] + '_denoised' + file_name[-4:])\n main()\n elif command.strip().lower() in ('n', 'no'):\n main()\n else:\n continue\n\n # Image denoised. Nothing to do\n else:\n print('Seems like your image denoised.')\n main()"],"string":"[\n \"def generate_annotation(target, images, bbox_path):\\n good_image_paths = []\\n bad_image_paths = []\\n bb = read_annotation_yolov5(bbox_path)\\n for path in images:\\n basename = os.path.basename(path) # extract file name only (e.g., bear_013.jpg)\\n basename_no_ext = os.path.splitext(basename)[0] # extract file name (e.g., bear_013)\\n\\n label_filepath = os.path.join(target, f'{basename_no_ext}.txt')\\n item = bb[int(basename_no_ext.split('_')[-1])] # e.g., 0.556, 0.6145, 0.3718, 0.5958\\n # validation that annotation is between 0 and 1.\\n if item[0] <= 0 or item[1] <= 0 or item[2] <= 0 or item[3] <= 0 \\\\\\n or item[0] >= 1 or item[1] >= 1 or item[2] >= 1 or item[3] >= 1:\\n print(f\\\"{basename_no_ext} has out of range value: {item[0]} {item[1]} {item[2]} {item[3]}\\\")\\n bad_image_paths.append(path)\\n continue\\n\\n with open(label_filepath, 'w') as out_file: # a label file is same as corresponding image file name\\n cls_id = classes.index(cls)\\n out_file.write(f\\\"{cls_id} {item[0]} {item[1]} {item[2]} {item[3]}\\\")\\n good_image_paths.append(path)\\n # print(f\\\"{basename_no_ext:} {cls_id} {item[0]} {item[1]} {item[2]} {item[3]}\\\")\\n\\n return good_image_paths, bad_image_paths\",\n \"def coco_format(type_, id_list, annotation_url_list, file_list, result_list, label_list, coco_flag=0):\\n annotations = []\\n for i, result in enumerate(result_list):\\n temp = {}\\n annotation_url = annotation_url_list[i]\\n file_path = file_list[i]\\n temp['id'] = id_list[i]\\n temp['annotation'] = []\\n im = cv2.imread(file_path)\\n height, width, _ = im.shape\\n if result.shape[0] == 0:\\n temp['annotation'] = json.dumps(temp['annotation'])\\n annotations.append(temp)\\n with open(annotation_url, 'w') as w:\\n w.write(temp['annotation'])\\n continue\\n else:\\n for j in range(result.shape[0]):\\n cls_id = int(result[j][0]) + 1 + coco_flag\\n x1 = result[j][1]\\n x2 = result[j][3]\\n y1 = result[j][2]\\n y2 = result[j][4]\\n score = result[j][5]\\n width = max(0, x2 - x1)\\n height = max(0, y2 - y1)\\n if cls_id in label_list:\\n temp['annotation'].append({\\n 'area': width * height,\\n 'bbox': [x1, y1, width, height],\\n 'category_id': cls_id,\\n 'iscrowd': 0,\\n 'segmentation': [[x1, y1, x2, y1, x2, y2, x1, y2]],\\n 'score': score\\n })\\n if type_ == 2 and len(temp['annotation']) > 0:\\n temp['annotation'] = [temp['annotation'][0]]\\n temp['annotation'][0].pop('area')\\n temp['annotation'][0].pop('bbox')\\n temp['annotation'][0].pop('iscrowd')\\n temp['annotation'][0].pop('segmentation')\\n temp['annotation'] = json.dumps(temp['annotation'])\\n annotations.append(temp)\\n with open(annotation_url, 'w') as wr:\\n wr.write(temp['annotation'])\\n return annotations\",\n \"def test_annot():\\n annots = ['aparc', 'aparc.a2005s']\\n for a in annots:\\n annot_path = pjoin(data_path, \\\"label\\\", \\\"%s.%s.annot\\\" % (\\\"lh\\\", a))\\n labels, ctab, names = read_annot(annot_path)\\n assert_true(labels.shape == (163842, ))\\n assert_true(ctab.shape == (len(names), 5))\",\n \"def convert_labels() -> None:\\n data_folder = 'images'\\n validation_split = 0.10\\n\\n # Convert annotations and split into validation and train set\\n number_images = int(len(os.listdir(data_folder)) / 2)\\n train_size = int(number_images * (1 - validation_split))\\n val_size = number_images - train_size\\n\\n print(f'Training dataset size: {train_size}')\\n print(f'Validation dataset size: {val_size}')\\n\\n with open('train.txt', 'w') as train_file, open('val.txt', 'w') as val_file:\\n files = os.listdir(data_folder)\\n print(len(files))\\n # shuffle otherwise validation is from the same session\\n random.shuffle(files)\\n processed = 0\\n for file_name in files:\\n if file_name.split('.')[1] == 'jpg':\\n # if image has no labels\\n write = False\\n if processed < train_size:\\n file_to_write = train_file\\n else:\\n file_to_write = val_file\\n\\n with open(f'{data_folder}/{file_name}'.split('.')[0] + '.txt') as label_file:\\n labels = []\\n for line in label_file:\\n line = line.split(' ')\\n line[-1] = line[-1].rstrip()\\n\\n img = cv2.imread(f'{data_folder}/{file_name}')\\n img_height = img.shape[0]\\n img_width = img.shape[1]\\n \\n x = float(line[1]) * img_width\\n y = float(line[2]) * img_height\\n w = float(line[3]) * img_width\\n h = float(line[4]) * img_height\\n\\n xmin = int(x - w/2)\\n ymin = int(y - h/2)\\n xmax = int(x + w/2)\\n ymax = int(y + h/2)\\n\\n labels.append(f' {xmin},{ymin},{xmax},{ymax},{line[0]}')\\n if len(labels) > 0:\\n write = True\\n file_to_write.write(f'{data_folder}/{file_name}')\\n for label in labels:\\n file_to_write.write(label)\\n if write:\\n file_to_write.write('\\\\n') \\n processed += 1\\n print(f'[{processed}/{number_images}] Processed {file_name}')\",\n \"def create_coco_label(is_training):\\n from pycocotools.coco import COCO\\n\\n coco_root = config.coco_root\\n data_type = config.val_data_type\\n if is_training:\\n data_type = config.train_data_type\\n\\n # Classes need to train or test.\\n train_cls = config.coco_classes\\n train_cls_dict = {}\\n for i, cls in enumerate(train_cls):\\n train_cls_dict[cls] = i\\n\\n anno_json = os.path.join(coco_root, config.instances_set.format(data_type))\\n\\n coco = COCO(anno_json)\\n classs_dict = {}\\n cat_ids = coco.loadCats(coco.getCatIds())\\n for cat in cat_ids:\\n classs_dict[cat[\\\"id\\\"]] = cat[\\\"name\\\"]\\n\\n image_ids = coco.getImgIds()\\n images = []\\n image_path_dict = {}\\n image_anno_dict = {}\\n\\n for img_id in image_ids:\\n image_info = coco.loadImgs(img_id)\\n file_name = image_info[0][\\\"file_name\\\"]\\n anno_ids = coco.getAnnIds(imgIds=img_id, iscrowd=None)\\n anno = coco.loadAnns(anno_ids)\\n image_path = os.path.join(coco_root, data_type, file_name)\\n annos = []\\n iscrowd = False\\n for label in anno:\\n bbox = label[\\\"bbox\\\"]\\n class_name = classs_dict[label[\\\"category_id\\\"]]\\n iscrowd = iscrowd or label[\\\"iscrowd\\\"]\\n if class_name in train_cls:\\n x_min, x_max = bbox[0], bbox[0] + bbox[2]\\n y_min, y_max = bbox[1], bbox[1] + bbox[3]\\n annos.append(list(map(round, [y_min, x_min, y_max, x_max])) + [train_cls_dict[class_name]])\\n\\n if not is_training and iscrowd:\\n continue\\n if len(annos) >= 1:\\n images.append(img_id)\\n image_path_dict[img_id] = image_path\\n image_anno_dict[img_id] = np.array(annos)\\n\\n return images, image_path_dict, image_anno_dict\",\n \"def convert_celeba_annots(root_dir, out_dir):\\n\\n img_id = 0\\n ann_id = 0\\n cat_id = 1\\n\\n\\n categories = [{\\\"id\\\": 1, \\\"name\\\": 'face'}]\\n images = []\\n annotations = []\\n\\n print('Parsing annotation file')\\n ann_file = os.path.join(root_dir, 'Anno', 'list_bbox_celeba.txt')\\n celeba_annot_dict = parse_celeba_gt(ann_file) # [im-file] = [[x,y,w,h], ...]\\n\\n # Get partition\\n print('Parsing partition file')\\n partition_file = os.path.join(root_dir, 'Eval', 'list_eval_partition.txt')\\n train_imgs, val_imgs, test_imgs = parse_celeba_partition(partition_file)\\n\\n for subset, img_ids in zip(['train', 'val'], [train_imgs, val_imgs]):\\n print('Starting %s' % subset)\\n ann_dict = {}\\n for filename in img_ids:\\n if len(images) % 500 == 0:\\n print(\\\"Processed %s images, %s annotations\\\" % (\\n len(images), len(annotations)))\\n\\n image = {}\\n image['id'] = img_id\\n img_id += 1\\n im = Image.open(os.path.join(root_dir, 'img_celeba', filename))\\n image['width'] = im.width\\n image['height'] = im.height\\n image['file_name'] = filename\\n images.append(image)\\n\\n for gt_bbox in celeba_annot_dict[filename]: # CelebA only has one box per image, though\\n ann = {}\\n ann['id'] = ann_id\\n ann_id += 1\\n ann['image_id'] = image['id']\\n ann['segmentation'] = []\\n ann['category_id'] = cat_id # 1:\\\"face\\\" for CelebA\\n ann['iscrowd'] = 0\\n ann['area'] = gt_bbox[2] * gt_bbox[3]\\n ann['bbox'] = gt_bbox\\n annotations.append(ann)\\n\\n ann_dict['images'] = images\\n ann_dict['categories'] = categories\\n ann_dict['annotations'] = annotations\\n print(\\\"Num categories: %s\\\" % len(categories))\\n print(\\\"Num images: %s\\\" % len(images))\\n print(\\\"Num annotations: %s\\\" % len(annotations))\\n\\n json_name = 'instances_CelebA_{}.json'.format(subset)\\n with open(os.path.join(out_dir, json_name), 'w', encoding='utf8') as outfile:\\n outfile.write(json.dumps(ann_dict, cls=MyEncoder, indent=4))\\n outfile.close()\",\n \"def _load_all(self, anno_file, shuffle):\\n image_set_index = []\\n labels = []\\n coco = COCO(anno_file)\\n img_ids = coco.getImgIds()\\n #print(img_ids)\\n cars=[3,6,8]\\n pedestrians=[1]\\n cyclists=[2,4]\\n lights=[10]\\n signs=[13]\\n\\n apex_categories=cars+pedestrians+cyclists+lights+signs\\n cnt=0\\n humanonly=0\\n human_count=0\\n\\n for img_id in img_ids:\\n relevant=False\\n # filename\\n image_info = coco.loadImgs(img_id)[0]\\n filename = image_info[\\\"file_name\\\"]\\n #print(filename)\\n #subdir = filename.split('_')[1]\\n height = image_info[\\\"height\\\"]\\n width = image_info[\\\"width\\\"]\\n # label\\n anno_ids = coco.getAnnIds(imgIds=img_id)\\n annos = coco.loadAnns(anno_ids)\\n label = []\\n\\n #print(\\\"listing categories for filename: \\\"+filename)\\n\\n hashumans=False\\n for anno in annos:\\n cat_id = int(anno[\\\"category_id\\\"])\\n if(cat_id in apex_categories):\\n cat_reduced= 0 if (cat_id in cars) else 1 if(cat_id in pedestrians) else 2 if(cat_id in cyclists) else 3 if(cat_id in lights) else 4\\n bbox = anno[\\\"bbox\\\"]\\n assert len(bbox) == 4\\n xmin = float(bbox[0]) / width\\n ymin = float(bbox[1]) / height\\n xmax = xmin + float(bbox[2]) / width\\n ymax = ymin + float(bbox[3]) / height\\n label.append([cat_reduced, xmin, ymin, xmax, ymax, 0])\\n #print(\\\"category: %d\\\"%cat_reduced)\\n if (cat_id in pedestrians):\\n hashumans=True\\n if(cat_id not in pedestrians): #at least one non-person object is necessary\\n relevant=True\\n\\n if(label and not relevant):\\n humanonly+=1\\n if label and relevant:\\n if(hashumans):\\n human_count+=1\\n #print(\\\"adding \\\"+filename)\\n labels.append(np.array(label))\\n image_set_index.append(os.path.join(self.set, filename))\\n cnt+=1\\n print(\\\"added %d images\\\"%cnt)\\n print(\\\"%d images has only humans\\\"%humanonly)\\n print(\\\"%d registered images has humans\\\"%human_count)\\n\\n if shuffle:\\n import random\\n indices = range(len(image_set_index))\\n random.shuffle(indices)\\n image_set_index = [image_set_index[i] for i in indices]\\n labels = [labels[i] for i in indices]\\n # store the results\\n self.image_set_index = image_set_index\\n self.labels = labels\",\n \"def test_nominal_case(self):\\n\\n image_filename, boxes = list(annotation.read(self.filename))\\n self.assertEqual(image_filename, 'image.jpg')\\n self.assertEqual(len(boxes), 2)\\n width = 400\\n height = 300\\n b = boxes[0]\\n self.assertEqual(b.xmin, 10 / width)\\n self.assertEqual(b.ymin, 20 / height)\\n self.assertEqual(b.xmax, 30 / width)\\n self.assertEqual(b.ymax, 40 / height)\",\n \"def setup_annotations(self):\\n sbd_path = get_data_path('sbd')\\n target_path = pjoin(self.root, 'SegmentationClass/pre_encoded')\\n if not os.path.exists(target_path): os.makedirs(target_path)\\n path = pjoin(sbd_path, 'dataset/train.txt')\\n sbd_train_list = tuple(open(path, 'r'))\\n sbd_train_list = [id_.rstrip() for id_ in sbd_train_list]\\n train_aug = self.files['train'] + sbd_train_list\\n\\n # keep unique elements (stable)\\n train_aug = [train_aug[i] for i in \\\\\\n sorted(np.unique(train_aug, return_index=True)[1])]\\n self.files['train_aug'] = train_aug\\n set_diff = set(self.files['val']) - set(train_aug) # remove overlap\\n self.files['train_aug_val'] = list(set_diff)\\n\\n pre_encoded = glob.glob(pjoin(target_path, '*.png'))\\n expected = np.unique(self.files['train_aug'] + self.files['val']).size\\n\\n if len(pre_encoded) != expected:\\n print(\\\"Pre-encoding segmentation masks...\\\")\\n for ii in tqdm(sbd_train_list):\\n lbl_path = pjoin(sbd_path, 'dataset/cls', ii + '.mat')\\n data = io.loadmat(lbl_path)\\n lbl = data['GTcls'][0]['Segmentation'][0].astype(np.int32)\\n lbl = m.toimage(lbl, high=lbl.max(), low=lbl.min())\\n m.imsave(pjoin(target_path, ii + '.png'), lbl)\\n\\n for ii in tqdm(self.files['trainval']):\\n fname = ii + '.png'\\n lbl_path = pjoin(self.root, 'SegmentationClass', fname)\\n lbl = self.encode_segmap(m.imread(lbl_path))\\n lbl = m.toimage(lbl, high=lbl.max(), low=lbl.min())\\n m.imsave(pjoin(target_path, fname), lbl)\\n\\n assert expected == 9733, 'unexpected dataset sizes'\",\n \"def coco_metrics(pred_data, anno_json, config):\\n from pycocotools.coco import COCO\\n from pycocotools.cocoeval import COCOeval\\n num_classes = config.num_classes\\n\\n #Classes need to train or test.\\n val_cls = config.classes\\n val_cls_dict = {}\\n for i, cls in enumerate(val_cls):\\n val_cls_dict[i] = cls\\n coco_gt = COCO(anno_json)\\n classs_dict = {}\\n cat_ids = coco_gt.loadCats(coco_gt.getCatIds())\\n for cat in cat_ids:\\n classs_dict[cat[\\\"name\\\"]] = cat[\\\"id\\\"]\\n\\n predictions = []\\n img_ids = []\\n\\n for sample in pred_data:\\n pred_boxes = sample['boxes']\\n box_scores = sample['box_scores']\\n img_id = sample['img_id']\\n h, w = sample['image_shape']\\n\\n final_boxes = []\\n final_label = []\\n final_score = []\\n img_ids.append(img_id)\\n\\n for c in range(1, num_classes):\\n class_box_scores = box_scores[:, c]\\n score_mask = class_box_scores > config.min_score\\n class_box_scores = class_box_scores[score_mask]\\n class_boxes = pred_boxes[score_mask] * [h, w, h, w]\\n\\n if score_mask.any():\\n nms_index = apply_nms(class_boxes, class_box_scores, config.nms_threshold, config.max_boxes)\\n class_boxes = class_boxes[nms_index]\\n class_box_scores = class_box_scores[nms_index]\\n\\n final_boxes += class_boxes.tolist()\\n final_score += class_box_scores.tolist()\\n final_label += [classs_dict[val_cls_dict[c]]] * len(class_box_scores)\\n\\n for loc, label, score in zip(final_boxes, final_label, final_score):\\n res = {}\\n res['image_id'] = img_id\\n res['bbox'] = [loc[1], loc[0], loc[3] - loc[1], loc[2] - loc[0]]\\n res['score'] = score\\n res['category_id'] = label\\n predictions.append(res)\\n if not os.path.exists('./eval_out'):\\n os.makedirs('./eval_out')\\n with open('./eval_out/predictions.json', 'w') as f:\\n json.dump(predictions, f)\\n\\n coco_dt = coco_gt.loadRes('./eval_out/predictions.json')\\n E = COCOeval(coco_gt, coco_dt, iouType='bbox')\\n E.params.imgIds = img_ids\\n E.evaluate()\\n E.accumulate()\\n E.summarize()\\n return E.stats[0]\",\n \"def main(root_dir):\\n # load annotations\\n print('Loading instances and annotations...')\\n captions_file = json.load(open('{}/annotations/captions_train2017.json'.format(root_dir), 'r'))\\n categories_file = json.load(open('{}/annotations/instances_train2017.json'.format(root_dir), 'r'))\\n print('Done.')\\n\\n # group categories by image\\n image_categories = group_categories(categories_file)\\n\\n # group captions by image\\n image_captions = group_captions(captions_file['annotations'])\\n\\n # get filename of each image\\n image_file = get_filename(captions_file['images'])\\n\\n # assign each category an id.\\n # we are not using the default ids given in the dataset because\\n # the id ranges are not continuous.\\n category_id, id_category = map_category_id(categories_file['categories'])\\n \\n # save parsed coco dataset\\n save_dataset(image_categories, image_captions, image_file, category_id, id_category, root_dir)\",\n \"def get_data(input_path):\\n all_imgs = []\\n classes_count = {}\\n class_mapping = {}\\n\\n # parsing Flag\\n visualise = False\\n\\n # MSCOCO directory\\n data_path = input_path\\n\\n print('Parsing annotation files')\\n annot_path = os.path.join(data_path, 'annotations_bbox')\\n imgs_path = os.path.join(data_path, 'images')\\n\\n # images directory (train, val, trainval, test)\\n imgsets_path_trainval = os.path.join(data_path, 'images', 'trainval.txt')\\n imgsets_path_train = os.path.join(data_path, 'images', 'train.txt')\\n imgsets_path_val = os.path.join(data_path, 'images', 'val.txt')\\n imgsets_path_test = os.path.join(data_path, 'images', 'test.txt')\\n\\n trainval_files = []\\n train_files = []\\n val_files = []\\n test_files = []\\n\\n with open(imgsets_path_trainval) as f:\\n for line in f:\\n trainval_files.append(line.strip())\\n\\n with open(imgsets_path_train) as f:\\n for line in f:\\n train_files.append(line.strip())\\n\\n with open(imgsets_path_val) as f:\\n for line in f:\\n val_files.append(line.strip())\\n\\n # test-set (default) not included in MSCOCO\\n if os.path.isfile(imgsets_path_test):\\n with open(imgsets_path_test) as f:\\n for line in f:\\n test_files.append(line.strip())\\n\\n # annotation read\\n annots_train = json.load(open(os.path.join(annot_path, 'bbox_train2017.json'), 'r'))\\n annots_val = json.load(open(os.path.join(annot_path, 'bbox_val2017.json'), 'r'))\\n annots = dict()\\n annots['train'] = annots_train\\n annots['val'] = annots_val\\n\\n for part in ['train', 'val']:\\n annots_keys = tqdm(annots[part].keys())\\n for img_name in annots_keys:\\n annots_keys.set_description(\\\"Processing %s\\\" % img_name)\\n for bbox in annots[part][img_name]:\\n class_name = bbox['label'].replace(' ', '')\\n all_imgs.append({\\n \\\"filepath\\\": os.path.join(data_path, 'images', '%s2017' % part, \\\"%s.jpg\\\" % img_name),\\n \\\"width\\\": None,\\n \\\"height\\\": None,\\n \\\"bboxes\\\": [{\\n \\\"class\\\": class_name,\\n \\\"x1\\\": bbox['bbox']['x1'],\\n \\\"y1\\\": bbox['bbox']['x2'],\\n \\\"x2\\\": bbox['bbox']['y1'],\\n \\\"y2\\\": bbox['bbox']['y2'],\\n \\\"difficult\\\": False\\n }],\\n \\\"image_id\\\": img_name,\\n \\\"imageset\\\": part\\n })\\n if class_name not in classes_count:\\n classes_count[class_name] = 1\\n else:\\n classes_count[class_name] += 1\\n if class_name not in class_mapping:\\n class_mapping[class_name] = len(class_mapping)\\n\\n # visualise bounding boxes\\n if visualise:\\n img = cv2.imread(annotation_data['filepath'])\\n for bbox in annotation_data['bboxes']:\\n cv2.rectangle(img, (bbox['x1'], bbox['y1']), (bbox['x2'], bbox['y2']), (0, 0, 255))\\n cv2.imshow('img', img)\\n print(annotation_data['imageset'])\\n cv2.waitKey(0)\\n\\n return all_imgs, classes_count, class_mapping\",\n \"def main(gt_dir='./data/Oxford_Robot_ICCV19/anno', devkit_dir = './dataset/robot_list/'):\\n with open(join(devkit_dir, 'info.json'), 'r') as fp:\\n info = json.load(fp)\\n image_path_list = join(devkit_dir, 'val.txt')\\n label_path_list = join(devkit_dir, 'label.txt')\\n mapping = np.array(info['label2train'], dtype=np.int)\\n gt_imgs = open(label_path_list, 'r').read().splitlines()\\n gt_imgs = [join(gt_dir, x) for x in gt_imgs]\\n\\n for ind in range(len(gt_imgs)):\\n label = np.array(Image.open(gt_imgs[ind]))\\n label = label_mapping(label, mapping)\\n label = label[:,:,0].astype(np.uint8)\\n name_tmp = gt_imgs[ind].replace('anno','anno_color')\\n save([label, name_tmp])\\n \\n return\",\n \"def load_annotations(self, image_index):\\n\\t\\t\\t# Get ground truth annotations.\\n\\t\\t\\tannotations_ids = self.coco.getAnnIds(imgIds=self.image_ids[image_index], iscrowd=False)\\n\\t\\t\\tannotations = {'labels': np.empty((0,)), 'bboxes': np.empty((0, 4))}\\n\\n\\t\\t\\t# If needed get info for masks.\\n\\t\\t\\tif self.mask:\\n\\t\\t\\t\\timport cv2\\n\\n\\t\\t\\t\\t# Get image info.\\n\\t\\t\\t\\timage_info = self.coco.loadImgs(self.image_ids[image_index])[0]\\n\\t\\t\\t\\tannotations['masks'] = []\\n\\n\\t\\t\\t# Some images appear to miss annotations (like image with id 257034).\\n\\t\\t\\tif len(annotations_ids) == 0:\\n\\t\\t\\t\\treturn annotations\\n\\n\\n\\t\\t\\t# Parse annotations\\n\\t\\t\\tcoco_annotations = self.coco.loadAnns(annotations_ids)\\n\\t\\t\\tfor idx, a in enumerate(coco_annotations):\\n\\t\\t\\t\\t# Some annotations have basically no width / height, skip them.\\n\\t\\t\\t\\tif a['bbox'][2] < 1 or a['bbox'][3] < 1:\\n\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t\\tannotations['labels'] = np.concatenate([annotations['labels'], [self.coco_label_to_label(a['category_id'])]], axis=0)\\n\\t\\t\\t\\tannotations['bboxes'] = np.concatenate([annotations['bboxes'], [[\\n\\t\\t\\t\\t\\ta['bbox'][0],\\n\\t\\t\\t\\t\\ta['bbox'][1],\\n\\t\\t\\t\\t\\ta['bbox'][0] + a['bbox'][2],\\n\\t\\t\\t\\t\\ta['bbox'][1] + a['bbox'][3],\\n\\t\\t\\t\\t]]], axis=0)\\n\\n\\t\\t\\t\\t# If needed get annotations for masks.\\n\\t\\t\\t\\tif self.mask:\\n\\t\\t\\t\\t\\tif 'segmentation' not in a:\\n\\t\\t\\t\\t\\t\\traise ValueError('Expected \\\\'segmentation\\\\' key in annotation, got: {}'.format(a))\\n\\n\\t\\t\\t\\t\\tmask = np.zeros((image_info['height'], image_info['width'], 1), dtype=np.uint8)\\n\\t\\t\\t\\t\\tfor seg in a['segmentation']:\\n\\t\\t\\t\\t\\t\\tpoints = np.array(seg).reshape((len(seg) // 2, 2)).astype(int)\\n\\n\\t\\t\\t\\t\\t\\t# Draw mask.\\n\\t\\t\\t\\t\\t\\tcv2.fillPoly(mask, [points.astype(int)], (1,))\\n\\n\\t\\t\\t\\t\\tannotations['masks'].append(mask.astype(float))\\n\\n\\n\\t\\t\\treturn annotations\",\n \"def merge_coco_annotations(anno_file_list, save_dir, pre_fixs, keep_cls=13):\\n annos = mmcv.load(anno_file_list[0])\\n categories =[info for info in annos['categories'] if info['id'] == keep_cls]\\n info = annos['info']\\n licenses = annos['licenses']\\n del annos\\n\\n new_coco_annotations = {}\\n new_annotations = []\\n new_images = []\\n image_idx_offset = 0\\n anno_idx_offset = 0\\n for file_i, file_path in tqdm(enumerate(anno_file_list), total=len(anno_file_list)):\\n annos = mmcv.load(file_path)\\n cur_images = annos['images']\\n cur_annotations = annos['annotations']\\n for img_info in cur_images: # loop through image_infos\\n img_info['id'] += image_idx_offset\\n img_info['file_name'] = os.path.join(pre_fixs[file_i], img_info['file_name'])\\n new_images.append(img_info)\\n for anno_info in cur_annotations: # loop through boxes\\n anno_info['id'] += anno_idx_offset\\n anno_info['image_id'] += image_idx_offset\\n cat_id = keep_cls if anno_info['category_id'] == keep_cls else 15\\n anno_info['category_id'] = cat_id\\n new_annotations.append(anno_info)\\n\\n image_idx_offset += len(cur_images)\\n anno_idx_offset += len(cur_annotations)\\n\\n new_coco_annotations['info'] = info\\n new_coco_annotations['licenses'] = licenses\\n new_coco_annotations['categories'] = categories\\n new_coco_annotations['annotations'] = new_annotations\\n new_coco_annotations['images'] = new_images\\n\\n print('convert done\\\\nsaving coco annotations')\\n mmcv.dump(new_coco_annotations, save_dir)\\n print('all done!')\",\n \"def draw_coco_bbox(img,labels):\\n \\n # w,h,_=img.shape\\n for bbox in labels:\\n\\n x1 = int(bbox['points']['x1'])\\n y1 = int(bbox['points']['y1'])\\n x2 = int(bbox['points']['x2'])\\n y2 = int(bbox['points']['y2'])\\n c = str(bbox['probability'])\\n # font\\n font = cv2.FONT_HERSHEY_SIMPLEX\\n \\n # org\\n org = (int(x1), int(y1))\\n \\n # fontScale\\n fontScale = 1\\n LABELS = ['ConcreteCrack','Spalling','Efflorescene','Exposure']\\n \\n # Blue color in BGR\\n # color_efflorescene = \\n\\n color = (56,255,225)\\n\\n \\n # Line thickness of 2 px\\n thickness = 2\\n \\n # Using cv2.putText() method\\n img = cv2.putText(img, str(c), org, font, \\n fontScale, color, thickness, cv2.LINE_AA)\\n\\n\\n img = cv2.rectangle(img,(int(x1),int(y1)),(int(x2),int(y2)),color,2)\\n return img\",\n \"def prepare_train_coco_data(args):\\n image_dir, annotation_file, data_dir = args.train_coco_image_dir, args.train_coco_annotation_file, args.train_coco_data_dir\\n batch_size = args.batch_size\\n basic_model = args.basic_model\\n num_roi = args.num_roi\\n\\n coco = COCO(annotation_file)\\n\\n img_ids = list(coco.imgToAnns.keys())\\n img_files = []\\n img_heights = []\\n img_widths = []\\n anchor_files = []\\n gt_classes = []\\n gt_bboxes = []\\n\\n for img_id in img_ids:\\n img_files.append(os.path.join(image_dir, coco.imgs[img_id]['file_name'])) \\n img_heights.append(coco.imgs[img_id]['height']) \\n img_widths.append(coco.imgs[img_id]['width']) \\n anchor_files.append(os.path.join(data_dir, os.path.splitext(coco.imgs[img_id]['file_name'])[0]+'_'+basic_model+'_anchor.npz')) \\n\\n classes = [] \\n bboxes = [] \\n for ann in coco.imgToAnns[img_id]: \\n classes.append(coco_category_to_class[ann['category_id']]) \\n bboxes.append([ann['bbox'][1], ann['bbox'][0], ann['bbox'][3]+1, ann['bbox'][2]+1]) \\n\\n gt_classes.append(classes) \\n gt_bboxes.append(bboxes) \\n \\n print(\\\"Building the training dataset...\\\")\\n dataset = DataSet(img_ids, img_files, img_heights, img_widths, batch_size, anchor_files, gt_classes, gt_bboxes, True, True)\\n print(\\\"Dataset built.\\\")\\n return coco, dataset\",\n \"def prepare_val_coco_data(args):\\n image_dir, annotation_file = args.val_coco_image_dir, args.val_coco_annotation_file\\n\\n coco = COCO(annotation_file)\\n\\n img_ids = list(coco.imgToAnns.keys())\\n img_files = []\\n img_heights = []\\n img_widths = []\\n\\n for img_id in img_ids:\\n img_files.append(os.path.join(image_dir, coco.imgs[img_id]['file_name']))\\n img_heights.append(coco.imgs[img_id]['height']) \\n img_widths.append(coco.imgs[img_id]['width']) \\n\\n print(\\\"Building the validation dataset...\\\")\\n dataset = DataSet(img_ids, img_files, img_heights, img_widths)\\n print(\\\"Dataset built.\\\")\\n return coco, dataset\",\n \"def mapping_image_to_label (self, labels_df, polygons, fpath_tiff): \\n \\n unread_tiff = rasterio.open(fpath_tiff)\\n\\n #Projecting the coordinates to that CRS \\n proj = Proj(init='epsg:32618')\\n data = []\\n labels = []\\n failed = []\\n \\n src = rasterio.open(fpath_tiff, 'r')\\n outfolder = '/train/batch'\\n \\n print (\\\"Hold on tight! Mapping each image to its respective label...\\\")\\n \\n \\n for num, row in labels_df.iterrows():\\n try:\\n \\n \\n roof_material_num = 0\\n polygon0 = polygons [num]\\n polygon0['coordinates'] = self.transforming_coordinates(polygon0['coordinates'], proj)\\n masked_image, out_transform = rasterio.mask.mask(src,[polygon0], filled = True, crop=True, nodata = 0)\\n img_image = reshape_as_image (masked_image)\\n \\n #Defining the name of the image file as \\\"buildingID+roofMaterial+png\\\" and its path \\n img_path = os.path.join (outfolder, str (row['id'])+'-'+ str (row['roof_material'])+'.png')\\n \\n #swapping the color channels from RGB2BGR\\n img_image = cv2.cvtColor (img_image, cv2.COLOR_RGB2BGR) #img_image is a numpy array\\n \\n #resizing the image dimensions to 128x128 to match ImageNet dimensions\\n img_image = cv2.resize(img_image, (128, 128))\\n \\n #writing the image in the file\\n #cv2.imwrite (img_path, img_image)\\n # update the data and labels lists, respectively\\n data.append(img_image) #data is a list\\n labels.append(row['roof_material'])\\n \\n except Exception as e:\\n print (e)\\n failed.append (num)\\n \\n \\n #print number of images we failed to crop and write \\n print (\\\"Bad News First: Failed to write\\\", len(failed), \\\"Images.\\\")\\n print (\\\"Good News: Successfully mapped\\\", len (data), \\\"Images.\\\")\\n data = np.array(data)\\n labels = np.array(labels)\\n #batch = data.sample(frac=0.5, replace=False, random_state=1)\\n #print(\\\"Size and shape of validY: {}\\\\n\\\".format(batch.shape))\\n return data, labels\",\n \"def create_masks(image_folder: str, annotation_path: str, outpath: str):\\n\\n train_reader = ReaderAnnotation(annotation_path)\\n\\n all_images = os.listdir(image_folder)\\n annotated_images = train_reader.annotation.keys()\\n\\n creator = MaskCreator()\\n\\n for key in annotated_images:\\n file_extension = \\\".JPG\\\"\\n if not os.path.isfile(\\n os.path.join(\\n image_folder,\\n key.split(\\\".\\\")[0] + file_extension,\\n )\\n ):\\n file_extension = file_extension.lower()\\n\\n image_name = os.path.join(\\n image_folder,\\n key.split(\\\".\\\")[0] + file_extension,\\n )\\n print(image_name)\\n\\n out_image_path = os.path.join(outpath, os.path.split(image_name)[-1])\\n assert os.path.exists(out_image_path), \\\"Out image path doesn't exist\\\"\\n\\n image = plt.imread(image_name)\\n h, w, c = image.shape\\n\\n regions = train_reader.get(key)[\\\"regions\\\"]\\n # less than minimal distance\\n radius = int(train_reader.get_radius_min(regions=regions) * 0.9)\\n\\n masks = []\\n for _, center in regions.items():\\n masks.append(\\n creator.create_circular_mask(\\n h=h,\\n w=w,\\n center=(\\n int(center[\\\"shape_attributes\\\"][\\\"cx\\\"]),\\n int(center[\\\"shape_attributes\\\"][\\\"cy\\\"]),\\n ),\\n radius=radius,\\n )\\n )\\n\\n if len(masks) > 50:\\n masks = [creator._unite_masks(masks)]\\n\\n if masks:\\n creator.visualize(\\n image=image,\\n masks=masks,\\n filename=out_image_path,\\n use_image=False,\\n )\\n else:\\n creator._create_empty_mask(image=image, filename=out_image_path)\\n\\n print(\\\"Empty images:\\\")\\n for empty_image in list(set(all_images) - set(annotated_images)):\\n if os.path.exists(out_image_path):\\n continue\\n empty_image = os.path.join(image_folder, empty_image)\\n print(empty_image)\\n image = plt.imread(empty_image)\\n creator._create_empty_mask(\\n image=image,\\n filename=os.path.join(\\n outpath,\\n os.path.split(empty_image)[-1],\\n ),\\n )\",\n \"def test_labels_warning(self):\\n with pytest.warns(\\n YellowbrickWarning, match=\\\"both classes and encoder specified\\\"\\n ):\\n oz = ClassificationScoreVisualizer(\\n GaussianNB(),\\n classes=[\\\"a\\\", \\\"b\\\", \\\"c\\\"],\\n encoder={0: \\\"foo\\\", 1: \\\"bar\\\", 2: \\\"zap\\\"},\\n )\\n labels = oz._labels()\\n npt.assert_array_equal(labels, [\\\"foo\\\", \\\"bar\\\", \\\"zap\\\"])\",\n \"def _load_action_annotation(self, anno_filepath):\\n # Check\\n assert os.path.exists(anno_filepath), \\\\\\n 'Path does not exist: {}'.format(anno_filepath)\\n # Open and read\\n lines = None\\n with open(anno_filepath) as f:\\n # format: imgidx x1 y1 x2 y2 label_list\\n # whre label list look like this: 0 0 0 0 1 0 0 (assume here has six action classes)\\n lines = f.readlines()\\n \\n if not lines:\\n print\\n print \\\"missing anno_filepath:\\\", anno_filepath\\n sys.exit(1)\\n\\n # init\\n image_index, gt_roidb = [], []\\n\\n # Process\\n for line in lines:\\n # Initialize\\n boxes = np.zeros((1, 4), dtype=np.uint16)\\n gt_classes = np.zeros(1, dtype=np.int32)\\n overlaps = np.zeros((1, self.num_classes), dtype=np.float32)\\n\\n line = line.strip().split()\\n args = 0\\n imgidx = line[args]\\n image_index.append(imgidx)\\n\\n args += 1\\n x1, y1, x2, y2 = line[args: args + 4]\\n x1 = float(x1) - 1\\n y1 = float(y1) - 1\\n x2 = float(x2) - 1\\n y2 = float(y2) - 1\\n\\n args += 4\\n classname = line[args]\\n cls = self._class_to_ind[classname.lower().strip()]\\n\\n gt_classes[0] = cls\\n boxes[0, :] = [x1, y1, x2, y2]\\n overlaps[0, cls] = 1.0\\n overlaps = scipy.sparse.csr_matrix(overlaps)\\n\\n # \\n img_anno_dict = {\\n 'boxes' : boxes, \\n 'gt_classes': gt_classes, \\n 'gt_overlaps' : overlaps, \\n 'flipped' : False}\\n gt_roidb.append(img_anno_dict)\\n\\n return image_index, gt_roidb\",\n \"def convert_csv_to_cocoV2(src_path, out_path, rad_ids=[], pre_coco_anno_path=None, debug=False):\\n assert isinstance(rad_ids, list)\\n csv_ann_file = os.path.join(src_path, 'train.csv')\\n data_path = os.path.join(src_path, 'train')\\n os.makedirs(out_path, exist_ok=True)\\n\\n print('1. reading csv annotations...')\\n csv_annotations, clsid_to_clsname = Read_csv_annotation(csv_ann_file, include_radId=True)\\n\\n pre_coco_ann_infos = mmcv.load(pre_coco_anno_path)\\n img_shape_dict = {img_info['file_name']: (img_info['height'], img_info['width'])\\n for img_info in pre_coco_ann_infos['images']}\\n\\n for rad_id in rad_ids:\\n print('2. Getting the annotations of Radiologist {}.'.format(rad_id))\\n rad_annotations = split_image_bboxes(csv_annotations, rad_id)\\n\\n # if debug:\\n # print('visualize the annotations of rad_id: {}'.format(rad_id))\\n # vis_idxes = np.random.randint(0, len(rad_annotations), size=20).tolist()\\n # visualize_one_image(\\n # data_path,\\n # rad_annotations,\\n # idxes=vis_idxes,\\n # clsid_to_clsname=clsid_to_clsname,\\n # save_dir='/mnt/group-ai-medical-2/private/zehuigong/torch_code/mmdetection/demo/rad_id%d' % rad_id)\\n\\n print('3. Converting annotations into coco format.')\\n coco_annotations = {}\\n image = {\\\"id\\\": 0,\\n 'width': 0,\\n 'height': 0,\\n 'file_name': '',\\n 'license': 1,\\n 'flickr_url': '',\\n 'coco_url': '',\\n 'date_captured': ''}\\n annotation = {'id': 0,\\n 'image_id': 0,\\n 'category_id': 0,\\n 'segmentation': [],\\n 'area': 0,\\n 'bbox': [],\\n 'iscrowd': 0}\\n images = []\\n annotations = []\\n\\n num_anno = 0\\n for img_i, (img_name, img_boxes) in enumerate(rad_annotations.items()):\\n h, w = img_shape_dict[img_name + '.png']\\n\\n image['id'] = img_i + 1\\n image['width'] = w\\n image['height'] = h\\n image['file_name'] = img_name + '.png'\\n time_now = time.strftime(\\\"%Y-%m-%d %H:%M:%S\\\", time.localtime())\\n image['date_captured'] = time_now\\n images.append(copy.deepcopy(image))\\n\\n for anno_idx in range(img_boxes.shape[0]): # loop through objs in one image\\n num_anno += 1\\n annotation['id'] = num_anno\\n annotation['image_id'] = img_i + 1\\n annotation['category_id'] = int(img_boxes[anno_idx, -1]) + 1\\n annotation['iscrowd'] = 0\\n annotation['segmentation'], annotation['area'] = \\\\\\n bbox2mask(img_boxes[anno_idx, :4])\\n annotation['bbox'] = xyxy2xywh(img_boxes[anno_idx, :4])\\n annotations.append(copy.deepcopy(annotation))\\n annotation['segmentation'].clear()\\n\\n coco_annotations['info'] = info\\n coco_annotations['licenses'] = licenses\\n coco_annotations['categories'] = categories\\n coco_annotations['annotations'] = annotations\\n coco_annotations['images'] = images\\n\\n print('saving coco annotations of rad_id {}'.format(rad_id))\\n mmcv.dump(coco_annotations, os.path.join(out_path, 'vinbig_trainR{}.json'.format(rad_id)))\\n print('all done!')\",\n \"def prepare_example(image_path, annotations, label_map_dict):\\n print(\\\"encoding %s\\\" % image_path)\\n with tf.gfile.GFile(image_path, 'rb') as fid:\\n encoded_png = fid.read()\\n encoded_png_io = io.BytesIO(encoded_png)\\n image = pil.open(encoded_png_io)\\n\\n if image.format != 'PNG':\\n raise ValueError('Image format error')\\n\\n key = hashlib.sha256(encoded_png).hexdigest()\\n # obtain attributes\\n width, height = image.size\\n img_filename = image_path.split('/')[-1]\\n\\n xmin = []\\n ymin = []\\n xmax = []\\n ymax = []\\n classes = []\\n classes_text = []\\n truncated = []\\n occlud = []\\n\\n xmin.append(int(annotations[2]) / width)\\n ymin.append(int(annotations[3]) / height)\\n xmax.append(int(annotations[4]) / width)\\n ymax.append(int(annotations[5]) / height)\\n class_name = annotations[1]\\n classes_text.append(class_name)\\n classes.append(label_map_dict[class_name])\\n classes_text = [class_text.encode('utf-8') for class_text in classes_text]\\n trun, occ = annotations[6].split(',')\\n truncated.append(int(trun))\\n occlud.append(int(occ))\\n\\n example = tf.train.Example(features=tf.train.Features(feature={\\n 'image/height': dataset_util.int64_feature(height),\\n 'image/width': dataset_util.int64_feature(width),\\n 'image/filename': dataset_util.bytes_feature(img_filename.encode('utf8')),\\n 'image/source_id': dataset_util.bytes_feature(img_filename.encode('utf8')),\\n 'image/key/sha256': dataset_util.bytes_feature(key.encode('utf8')),\\n 'image/encoded': dataset_util.bytes_feature(encoded_png),\\n 'image/format': dataset_util.bytes_feature('png'.encode('utf8')),\\n 'image/object/bbox/xmin': dataset_util.float_list_feature(xmin),\\n 'image/object/bbox/xmax': dataset_util.float_list_feature(xmax),\\n 'image/object/bbox/ymin': dataset_util.float_list_feature(ymin),\\n 'image/object/bbox/ymax': dataset_util.float_list_feature(ymax),\\n 'image/object/class/text': dataset_util.bytes_list_feature(classes_text),\\n 'image/object/class/label': dataset_util.int64_list_feature(classes),\\n 'image/object/truncated': dataset_util.int64_list_feature(truncated),\\n 'image/object/view': dataset_util.int64_list_feature(occlud),\\n }))\\n return example\",\n \"def load_data(self, annotation_json, images_dir):\\r\\n # Load json from file\\r\\n json_file = open(annotation_json)\\r\\n coco_json = json.load(json_file)\\r\\n json_file.close()\\r\\n \\r\\n # Add the class names using the base method from utils.Dataset\\r\\n source_name = \\\"coco_like\\\"\\r\\n ids={}\\r\\n i=0\\r\\n for category in coco_json['categories']:\\r\\n i+=1\\r\\n class_id = category['id']\\r\\n ids[class_id]=i\\r\\n class_name = category['name']\\r\\n if class_id < 1:\\r\\n print('Error: Class id for \\\"{}\\\" cannot be less than one. (0 is reserved for the background)'.format(class_name))\\r\\n return\\r\\n \\r\\n self.add_class(source_name, class_id, class_name)\\r\\n for annotation in coco_json['annotations']:\\r\\n annotation[\\\"category_id\\\"]=ids[annotation[\\\"category_id\\\"]]\\r\\n \\r\\n # Get all annotations\\r\\n \\r\\n annotations = {}\\r\\n for annotation in coco_json['annotations']:\\r\\n image_id = annotation['image_id']\\r\\n if image_id not in annotations:\\r\\n annotations[image_id] = []\\r\\n annotations[image_id].append(annotation)\\r\\n \\r\\n # Get all images and add them to the dataset\\r\\n seen_images = {}\\r\\n for image in coco_json['images']:\\r\\n image_id = image['id']\\r\\n if image_id in seen_images:\\r\\n print(\\\"Warning: Skipping duplicate image id: {}\\\".format(image))\\r\\n else:\\r\\n seen_images[image_id] = image\\r\\n try:\\r\\n image_file_name = image['file_name']\\r\\n image_width = image['width']\\r\\n image_height = image['height']\\r\\n except KeyError as key:\\r\\n print(\\\"Warning: Skipping image (id: {}) with missing key: {}\\\".format(image_id, key))\\r\\n \\r\\n image_path = os.path.abspath(os.path.join(images_dir, image_file_name))\\r\\n image_annotations = annotations[image_id]\\r\\n \\r\\n # Add the image using the base method from utils.Dataset\\r\\n self.add_image(\\r\\n source=source_name,\\r\\n image_id=image_id,\\r\\n path=image_path,\\r\\n width=image_width,\\r\\n height=image_height,\\r\\n annotations=image_annotations\\r\\n )\",\n \"def _annotate_images(self, frameList):\\n\\t\\timage_array = frameList\\n\\t\\tfor i, image in enumerate(image_array):\\n\\t\\t\\tif i in list(self.detection_frames.keys()):\\n\\t\\t\\t\\tfor obj in list(self.detection_frames[i].keys()):\\n\\t\\t\\t\\t\\tx, y, w, h, frame, text, track_id = self.detection_index[obj]\\n\\t\\t\\t\\t\\tCOLOR = COLORS[text]\\n\\t\\t\\t\\t\\t# if the detection is human\\n\\t\\t\\t\\t\\tif text == 'face':\\n\\t\\t\\t\\t\\t\\ttext = text + \\\" id:{}\\\".format(track_id)\\n\\t\\t\\t\\t\\t\\t# predict \\n\\t\\t\\t\\t\\t\\tgender = self.genderDetect.classify(image[y:y+h, x:x+w, :])\\n\\t\\t\\t\\t\\t\\tgender = 'female' if gender[0] < 0.5 else 'male'\\n\\t\\t\\t\\t\\t\\tcv2.putText(image, gender, (x + w // 2 - 10, y + h + 15),\\n\\t\\t\\t\\t\\t\\t\\tcv2.FONT_HERSHEY_SIMPLEX, 0.6, COLOR, 2, cv2.LINE_AA)\\n\\n\\t\\t\\t\\t\\timage_array[i] = cv2.rectangle(image_array[i], (x, y), (x + w, y + h), COLOR, 2)\\n\\t\\t\\t\\t\\tcv2.putText(image_array[i], text, (x, y - 5),\\n\\t\\t\\t\\t\\t\\tcv2.FONT_HERSHEY_SIMPLEX, 0.6, COLOR, 2, cv2.LINE_AA)\\n\\n\\n\\t\\t\\tcv2.imwrite(f'{SAVE_PATH}{self.clip}{i}.jpg', image_array[i])\",\n \"def __call__(self, *args, **kwargs):\\n\\n dataset = TextOnlyCocoAnnotation()\\n\\n for image_name in tqdm(sorted(os.listdir(self.folder))):\\n if image_name.endswith('JPG'):\\n image_path = os.path.join(self.folder, image_name)\\n annotation_path = os.path.join(self.folder, image_name.replace('.JPG', '.gt'))\\n\\n with open(annotation_path, encoding='utf-8-sig') as read_file:\\n content = [line.strip() for line in read_file.readlines()]\\n for line in content:\\n dataset.add_bbox(image_path, imagesize.get(image_path),\\n self.parse_line(line))\\n\\n return dataset\",\n \"def test_decode_labels_warning(self):\\n with pytest.warns(\\n YellowbrickWarning, match=\\\"both classes and encoder specified\\\"\\n ):\\n oz = ClassificationScoreVisualizer(\\n GaussianNB(),\\n classes=[\\\"a\\\", \\\"b\\\", \\\"c\\\"],\\n encoder={0: \\\"foo\\\", 1: \\\"bar\\\", 2: \\\"zap\\\"},\\n )\\n encoded = oz._decode_labels([0, 1, 2])\\n npt.assert_array_equal(encoded, [\\\"foo\\\", \\\"bar\\\", \\\"zap\\\"])\",\n \"def __init__(self,\\n ann_file,\\n img_prefix,\\n img_means,\\n img_stds,\\n img_expected_sizes,\\n size_divisor=None,\\n flip_ratio=0,\\n be_cell_size=32,\\n be_random_ratio=0.5,\\n proposal_file=None,\\n num_max_proposals=1000,\\n with_mask=False,\\n with_crowd=False,\\n with_label=True,\\n test_mode=False,\\n with_background_erasing=False,\\n debug=False):\\n # load annotations from annotation file\\n self.img_infos = self.load_annotations(ann_file)\\n # prefix of image path\\n self.img_prefix = img_prefix\\n # image normalization parameters\\n self.img_means, self.img_stds = img_means, img_stds\\n # img_expected_sizes list\\n self.img_expected_sizes = img_expected_sizes \\\\\\n if isinstance(img_expected_sizes, list) else [img_expected_sizes]\\n assert is_list_of(self.img_expected_sizes, tuple)\\n # size divisor\\n self.size_divisor = size_divisor\\n # flip ratio\\n self.flip_ratio = flip_ratio\\n # background erasing settings\\n self.be_cell_size = be_cell_size\\n self.be_random_ratio = be_random_ratio\\n\\n # load the pkl proposal file\\n if proposal_file is not None:\\n self.proposals = self.load_proposals(proposal_file)\\n else:\\n self.proposals = None\\n self.num_max_proposals = num_max_proposals\\n\\n # filter images with no annotation during training\\n if not test_mode:\\n valid_inds = self._filter_imgs()\\n self.img_infos = [self.img_infos[i] for i in valid_inds]\\n if self.proposals is not None:\\n self.proposals = [self.proposals[i] for i in valid_inds]\\n\\n # with mask or not\\n self.with_mask = with_mask\\n # with crowd object or not\\n self.with_crowd = with_crowd\\n # training with label or not\\n self.with_label = with_label\\n # test mode\\n self.test_mode = test_mode\\n # with background erasing or not\\n self.with_background_erasing = with_background_erasing\\n # debug mode\\n self.debug = debug\\n if self.debug:\\n self.img_infos = self.img_infos[:50]\\n\\n # set aspect ratio flag for images\\n if not self.test_mode:\\n self._set_group_flag()\\n\\n # transforms\\n self.img_transforms = ImageTransforms(img_means=self.img_means,\\n img_stds=self.img_stds,\\n size_divisor=self.size_divisor)\\n self.bbox_transforms = BboxTransforms()\\n self.mask_transforms = MaskTransforms()\\n self.background_erasing = BackgroundErasing()\",\n \"def test_annotate_image(self):\\n im_path = test_im_dir / \\\"square-im-1.png\\\"\\n image = li.Image(im_path)\\n\\n label = li.Label(\\\"Test Label\\\", (0.50, 0.50))\\n image.annotate(label)\\n annotated_im = image.data\\n\\n # Ensure the shape is retained\\n shape_expected = image.data_original.shape\\n shape_test = annotated_im.shape\\n self.assertEqual(\\n shape_test, shape_expected, msg=\\\"shape not retained after annotation\\\"\\n )\\n\\n if PLOT:\\n imsave(\\\"/tmp/annotate_image.png\\\", annotated_im, check_contrast=False)\",\n \"def random_image():\\n # Pick random background color\\n bg_color = np.array([random.randint(0, 255) for _ in range(3)])\\n height = random.randint(300, 350)\\n width = random.randint(350, 400)\\n print(\\\"bg_color: \\\" + str(bg_color))\\n print(\\\"height: \\\" + str(height))\\n print(\\\"width: \\\" + str(width))\\n\\n image = np.ones([height, width, 3], dtype=np.uint8) * bg_color\\n mask = np.zeros([height, width, 3], dtype=np.uint8)\\n labels = []\\n assert image.shape == mask.shape\\n\\n # Generate a few random shapes\\n shapes = []\\n boxes = []\\n N = random.randint(1, 4)\\n for _ in range(N):\\n shape, color, dims = random_shape(height, width)\\n shapes.append((shape, color, dims))\\n x, y, s = dims\\n boxes.append([y - s, x - s, y + s, x + s])\\n # Apply non-max suppression wit 0.3 threshold to avoid\\n # shapes covering each other\\n keep_ixs = utils.non_max_suppression(\\n np.array(boxes), np.arange(N), 0.1)\\n shapes = [s for i, s in enumerate(shapes) if i in keep_ixs]\\n\\n for shape, color, dims in shapes:\\n x, y, s = dims\\n if shape == 'square':\\n image = cv2.rectangle(image, (x - s, y - s),\\n (x + s, y + s), color, -1)\\n mask = cv2.rectangle(mask, (x - s, y - s),\\n (x + s, y + s), 1, -1)\\n labels.append([1, x, y, s])\\n elif shape == \\\"circle\\\":\\n image = cv2.circle(image, (x, y), s, color, -1)\\n mask = cv2.circle(mask, (x, y), s, 2, -1)\\n labels.append([2, x, y, s])\\n elif shape == \\\"triangle\\\":\\n points = np.array([[(x, y - s),\\n (x - s / math.sin(math.radians(60)), y + s),\\n (x + s / math.sin(math.radians(60)), y + s),\\n ]], dtype=np.int32)\\n image = cv2.fillPoly(image, points, color)\\n mask = cv2.fillPoly(mask, points, 3)\\n labels.append([3, x, y, s])\\n\\n return image, mask, np.array(labels)\",\n \"def generate_tamil_images(label_file, fonts_dir, output_dir):\\r\\n with io.open(label_file, 'r', encoding='utf-8') as f:\\r\\n labels = f.read().splitlines()\\r\\n\\r\\n image_dir = os.path.join(output_dir, 'images')\\r\\n if not os.path.exists(image_dir):\\r\\n os.makedirs(os.path.join(image_dir))\\r\\n\\r\\n # Get a list of the fonts.\\r\\n fonts = glob.glob(os.path.join(fonts_dir, '*.ttf'))\\r\\n\\r\\n labels_csv = io.open(os.path.join(output_dir, 'labels-map.csv'), 'w',\\r\\n encoding='utf-8')\\r\\n\\r\\n total_count = 0\\r\\n prev_count = 0\\r\\n folder_count = 0\\r\\n for character in labels:\\r\\n folder_count += 1\\r\\n #print('%s: %d',character,len(character))\\r\\n # Print image count roughly every 5000 images.\\r\\n if total_count - prev_count > 5000:\\r\\n prev_count = total_count\\r\\n print('{} images generated...'.format(total_count))\\r\\n mychar = character\\r\\n for font in fonts:\\r\\n total_count += 1\\r\\n image = Image.new('L', (IMAGE_WIDTH, IMAGE_HEIGHT), color='white')\\r\\n font = ImageFont.truetype(font, int(80/len(mychar)))\\r\\n drawing = ImageDraw.Draw(image)\\r\\n w, h = drawing.textsize(mychar, font=font)\\r\\n drawing.text(\\r\\n ((IMAGE_WIDTH-w)/2, (IMAGE_HEIGHT-h)/2),\\r\\n mychar,\\r\\n fill=(0),\\r\\n font=font\\r\\n )\\r\\n file_string = '{}_{}.jpg'.format(mychar, total_count)\\r\\n #file_path = os.path.join(image_dir, 'f'+str(folder_count))\\r\\n file_path = os.path.join(image_dir, file_string)\\r\\n orgFile = file_path\\r\\n image.save(file_path)\\r\\n \\r\\n\\r\\n #directory creation\\r\\n mvDir = os.path.join(image_dir, mychar + '_' + str(folder_count))\\r\\n if not os.path.exists(mvDir):\\r\\n os.makedirs(mvDir)\\r\\n \\r\\n for i in range(DISTORTION_COUNT):\\r\\n total_count += 1\\r\\n dist_string = '{}_{}.jpg'.format(mychar, total_count)\\r\\n #file_path = os.path.join(image_dir, 'f'+str(folder_count))\\r\\n file_path = os.path.join(image_dir, dist_string)\\r\\n arr = numpy.array(image)\\r\\n\\r\\n distorted_array = elastic_distort(\\r\\n arr, alpha=random.randint(30, 36),\\r\\n sigma=random.randint(5, 6)\\r\\n )\\r\\n distorted_image = Image.fromarray(distorted_array)\\r\\n distorted_image.save(file_path)\\r\\n img = Image.open(file_path)\\r\\n dr = ImageDraw.Draw(img)\\r\\n cor = (0,0, IMAGE_WIDTH,IMAGE_HEIGHT)\\r\\n dr.rectangle(cor, outline=\\\"white\\\", width=5)\\r\\n img.save(file_path)\\r\\n distLoc = os.path.join(mvDir, dist_string)\\r\\n shutil.move(file_path, distLoc)\\r\\n labels_csv.write(u'{},{}\\\\n'.format(mychar, distLoc))\\r\\n origLoc = os.path.join(mvDir, file_string)\\r\\n newPath = os.path.join(mvDir, origLoc)\\r\\n shutil.move(orgFile, newPath)\\r\\n labels_csv.write(u'{},{}\\\\n'.format(mychar, newPath))\\r\\n \\r\\n print('Finished generating {} images.'.format(total_count))\\r\\n labels_csv.close()\",\n \"def detect_labels(path):\\n from google.cloud import vision\\n client = vision.ImageAnnotatorClient()\\n\\n # [START vision_python_migration_label_detection]\\n with io.open(path, 'rb') as image_file:\\n content = image_file.read()\\n\\n image = vision.types.Image(content=content)\\n\\n response = client.label_detection(image=image)\\n labels = response.label_annotations\\n ss=labels[0].description \\n ss.split('/')[0]\\n os.system(\\\"./ILOVEAPPLE/sort {} {}\\\".format(ss, path))\\n # [END vision_python_migration_label_detection]\",\n \"def preprocess_human_annotations_p8(problem_id, source_pickle, annotation_csv):\\n label_dim, ix_to_label, label_to_ix, _, _ = get_label_params(problem_id)\\n\\n annotation_df = pd.read_csv(annotation_csv)\\n annotation_df = annotation_df.fillna(0)\\n n_annotations = len(annotation_df)\\n\\n with open(source_pickle) as fp:\\n ast_programs = cPickle.load(fp)\\n\\n programs = []\\n program2label = {}\\n\\n for i in tqdm(range(n_annotations)):\\n annotation_frame = annotation_df.iloc[i]\\n program_id = int(annotation_frame['ID'])\\n targets = [int(annotation_frame[ix_to_label[ix]])\\n for ix in xrange(label_dim)]\\n\\n ast = ast_programs[program_id]\\n removeColors(ast)\\n ast = flatten_ast(ast)\\n program = ' '.join(ast)\\n\\n programs.append(program)\\n program2label[program] = targets\\n\\n programs = list(set(programs))\\n labels = [program2label[prog] for prog in programs]\\n \\n return programs, labels\",\n \"def _load_kitti_annotation(self, index):\\n\\n if self._image_set == 'test':\\n lines = []\\n else:\\n filename = os.path.join(self._data_path, 'training', 'label_2', index + '.txt')\\n lines = []\\n with open(filename) as f:\\n for line in f:\\n words = line.split()\\n cls = words[0]\\n truncation = float(words[1])\\n occlusion = int(words[2])\\n height = float(words[7]) - float(words[5])\\n if cls in self._class_to_ind and truncation < 0.5 and occlusion < 3 and height > 25:\\n #if cls in self._class_to_ind:\\n lines.append(line)\\n\\n num_objs = len(lines)\\n \\n boxes = np.zeros((num_objs, 4), dtype=np.float32)\\n gt_classes = np.zeros((num_objs), dtype=np.int32)\\n overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)\\n\\n for idx, line in enumerate(lines):\\n words = line.split()\\n cls = self._class_to_ind[words[0]]\\n boxes[idx, :] = [float(num) for num in words[4:8]]\\n gt_classes[idx] = cls\\n overlaps[idx, cls] = 1.0\\n\\n overlaps = scipy.sparse.csr_matrix(overlaps)\\n\\n return {'boxes' : boxes,\\n 'gt_classes' : gt_classes,\\n 'gt_overlaps' : overlaps,\\n 'flipped' : False}\",\n \"def create_facemask_label(is_training):\\n facemask_dir = config.voc_dir\\n cls_map = {name: i for i, name in enumerate(config.coco_classes)}\\n sub_dir = 'train' if is_training else 'val'\\n facemask_dir = os.path.join(facemask_dir, sub_dir)\\n if not os.path.isdir(facemask_dir):\\n raise ValueError(f'Cannot find {sub_dir} dataset path.')\\n\\n image_dir = anno_dir = facemask_dir\\n if os.path.isdir(os.path.join(facemask_dir, 'images')):\\n image_dir = os.path.join(facemask_dir, 'images')\\n if os.path.isdir(os.path.join(facemask_dir, 'annotations')):\\n anno_dir = os.path.join(facemask_dir, 'annotations')\\n\\n if not is_training:\\n data_dir = config.facemask_root\\n json_file = os.path.join(data_dir, config.instances_set.format(sub_dir))\\n file_dir = os.path.split(json_file)[0]\\n if not os.path.isdir(file_dir):\\n os.makedirs(file_dir)\\n json_dict = {\\\"images\\\": [], \\\"type\\\": \\\"instances\\\", \\\"annotations\\\": [],\\n \\\"categories\\\": []}\\n bnd_id = 1\\n\\n image_files_dict = {}\\n image_anno_dict = {}\\n images = []\\n for anno_file in os.listdir(anno_dir):\\n print(anno_file)\\n if not anno_file.endswith('xml'):\\n continue\\n tree = et.parse(os.path.join(anno_dir, anno_file))\\n root_node = tree.getroot()\\n file_name = root_node.find('filename').text\\n file_name = file_name.split('.')[0] + '.jpg'\\n img_id = get_imageId_from_fackmask(file_name)\\n image_path = os.path.join(image_dir, file_name)\\n print(image_path)\\n if not os.path.isfile(image_path):\\n print(f'Cannot find image {file_name} according to annotations.')\\n continue\\n\\n labels = []\\n for obj in root_node.iter('object'):\\n cls_name = obj.find('name').text\\n if cls_name not in cls_map:\\n print(f'Label \\\"{cls_name}\\\" not in \\\"{config.coco_classes}\\\"')\\n continue\\n bnd_box = obj.find('bndbox')\\n x_min = int(float(bnd_box.find('xmin').text)) - 1\\n y_min = int(float(bnd_box.find('ymin').text)) - 1\\n x_max = int(float(bnd_box.find('xmax').text)) - 1\\n y_max = int(float(bnd_box.find('ymax').text)) - 1\\n labels.append([y_min, x_min, y_max, x_max, cls_map[cls_name]])\\n\\n if not is_training:\\n o_width = abs(x_max - x_min)\\n o_height = abs(y_max - y_min)\\n ann = {'area': o_width * o_height, 'iscrowd': 0, 'image_id': \\\\\\n img_id, 'bbox': [x_min, y_min, o_width, o_height], \\\\\\n 'category_id': cls_map[cls_name], 'id': bnd_id, \\\\\\n 'ignore': 0, \\\\\\n 'segmentation': []}\\n json_dict['annotations'].append(ann)\\n bnd_id = bnd_id + 1\\n\\n if labels:\\n images.append(img_id)\\n image_files_dict[img_id] = image_path\\n image_anno_dict[img_id] = np.array(labels)\\n\\n if not is_training:\\n size = root_node.find(\\\"size\\\")\\n width = int(size.find('width').text)\\n height = int(size.find('height').text)\\n image = {'file_name': file_name, 'height': height, 'width': width,\\n 'id': img_id}\\n json_dict['images'].append(image)\\n\\n if not is_training:\\n for cls_name, cid in cls_map.items():\\n cat = {'supercategory': 'none', 'id': cid, 'name': cls_name}\\n json_dict['categories'].append(cat)\\n json_fp = open(json_file, 'w')\\n json_str = json.dumps(json_dict)\\n json_fp.write(json_str)\\n json_fp.close()\\n\\n return images, image_files_dict, image_anno_dict\",\n \"def __call__(self, *args, **kwargs):\\n\\n dataset = TextOnlyCocoAnnotation()\\n\\n n_images = 1000 if self.is_train else 500\\n for i in tqdm(range(1, n_images + 1)):\\n image_path = os.path.join(self.images_folder, 'img_{}.jpg'.format(i))\\n annotation_path = os.path.join(self.annotations_folder, 'gt_img_{}.txt'.format(i))\\n\\n with open(annotation_path, encoding='utf-8-sig') as read_file:\\n content = [line.strip() for line in read_file.readlines()]\\n for line in content:\\n dataset.add_bbox(image_path, imagesize.get(image_path), self.parse_line(line))\\n\\n return dataset\",\n \"def detection(test_img):\\n # TODO: Step 2 : Your Detection code should go here.\\n no_row = len(test_img)\\n no_col = len(test_img[0])\\n thresh = 100\\n label_img = np.zeros((no_row, no_col))\\n uf = []\\n \\n #first pass\\n l = 1\\n for i in range(0, no_row):\\n for j in range(0, no_col):\\n if test_img[i,j] < 255 - thresh:\\n if i == 0 and j == 0:\\n label_img[i,j] = l\\n l = l+1\\n elif i == 0 and j != 0:\\n if label_img[i,j-1] == 0:\\n label_img[i,j] = l\\n l = l+1\\n else:\\n label_img[i,j] = label_img[i,j-1]\\n elif i != 0 and j == 0:\\n if label_img[i-1,j] == 0:\\n label_img[i,j] = l\\n l = l+1\\n else:\\n label_img[i,j] = label_img[i-1,j]\\n else:\\n if label_img[i-1,j] == 0 and label_img[i,j-1] == 0:\\n label_img[i,j] = l\\n l = l+1\\n elif label_img[i-1,j] == 0 and label_img[i,j-1] != 0:\\n label_img[i,j] = label_img[i,j-1]\\n elif label_img[i-1,j] != 0 and label_img[i,j-1] == 0:\\n label_img[i,j] = label_img[i-1,j]\\n else:\\n if label_img[i,j-1] == label_img[i-1,j]:\\n label_img[i,j] = label_img[i,j-1]\\n else:\\n label_img[i,j] = min(label_img[i-1,j],label_img[i,j-1])\\n uf.append([min(label_img[i,j-1],label_img[i-1,j]),max(label_img[i,j-1],label_img[i-1,j])])\\n l = l - 1\\n \\n #2nd pass\\n def ufds(x,l):\\n b = []\\n for i in range(1,l+1):\\n b.append([i])\\n for j in x:\\n i1 = 0\\n i2 = 0\\n for k in range(0,len(b)):\\n if j[0] in b[k]:\\n i1 = k\\n if j[1] in b[k]:\\n i2 = k\\n if i1 != i2:\\n b[i1] = b[i1] + b[i2]\\n del b[i2]\\n return b\\n \\n bman = ufds(uf,l)\\n \\n #3rd pass\\n uf_arr = np.zeros(l)\\n for i in range(0,len(uf_arr)):\\n for j in bman:\\n if i+1 in j:\\n uf_arr[i] = min(j) \\n \\n fin_img = np.zeros((no_row, no_col))\\n for i in range(0, no_row):\\n for j in range(0, no_col):\\n if label_img[i,j] != 0:\\n fin_img[i,j] = uf_arr[int(label_img[i,j] - 1)]\\n \\n all_label = []\\n '''\\n for i in bman:\\n all_label.append(min(i))\\n ''' \\n for i in range(0, no_row):\\n for j in range(0, no_col):\\n if fin_img[i,j] !=0 and fin_img[i,j] not in all_label:\\n all_label.append(fin_img[i,j])\\n \\n # main image\\n k_img = np.zeros((no_row, no_col))\\n for i in range(0, no_row):\\n for j in range(0, no_col):\\n if fin_img[i,j] != 0:\\n k_img[i,j] = (all_label.index(fin_img[i,j]) + 1)\\n \\n k_list = []\\n for i in range(1,len(all_label)+1):\\n x = None\\n y = None\\n hx = None\\n hy = None\\n for j in range(0,no_row):\\n for k in range(0,no_col):\\n if k_img[j,k] == i:\\n if y == None and x == None:\\n y = j\\n hy = j\\n x = k\\n hx = k\\n else:\\n if y > j:\\n y = j\\n if hy < j:\\n hy = j\\n if x > k:\\n x = k\\n if hx < k:\\n hx = k\\n k_list.append({\\\"bbox\\\": [x, y, hx-x, hy-y]})\\n \\n return k_list\\n \\n #raise NotImplementedError\",\n \"def classify_face(im):\\r\\n faces = get_encoded_faces()\\r\\n faces_encoded = list(faces.values())\\r\\n known_face_names = list(faces.keys())\\r\\n\\r\\n img = cv2.imread(im, 1)\\r\\n \\\"\\\"\\\"\\r\\n Resize optinal \\r\\n \\\"\\\"\\\"\\r\\n #img = cv2.resize(img, (0, 0), fx=0.5, fy=0.5)\\r\\n #img = img[:,:,::-1]\\r\\n face_locations = face_recognition.face_locations(img)\\r\\n unknown_face_encodings = face_recognition.face_encodings(img, face_locations)\\r\\n\\r\\n face_names = []\\r\\n for face_encoding in unknown_face_encodings:\\r\\n # See if the face is a match for the known face(s)\\r\\n matches = face_recognition.compare_faces(faces_encoded, face_encoding)\\r\\n name = \\\"Unknown\\\"\\r\\n\\r\\n # use the known face with the smallest distance to the new face\\r\\n face_distances = face_recognition.face_distance(faces_encoded, face_encoding)\\r\\n best_match_index = np.argmin(face_distances)\\r\\n if matches[best_match_index]:\\r\\n name = known_face_names[best_match_index]\\r\\n\\r\\n face_names.append(name)\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n All the photo lables in the faces foler end with (number) so a simiple .find(\\\"(\\\") command takes the () away from\\r\\n the label leaving us with the full name of the person\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n result = name.find('(') \\r\\n fullname = (name[:result])\\r\\n \\\"\\\"\\\"\\r\\n If face_recogntion module recognizes a face but that face is not in the faces module then \\r\\n it will print unknown and we print 12345678 to use it on the start attednace program \\r\\n\\r\\n \\\"\\\"\\\"\\r\\n if (name == \\\"Unknown\\\"):\\r\\n print(\\\"12345678\\\")\\r\\n else:\\r\\n \\\"\\\"\\\"\\r\\n f'{len(face_locayion)}-people - will return the number of people in photo taken by Nao'\\r\\n \\\"\\\"\\\"\\r\\n print (f'{len(face_locations)}-people')\\r\\n print (fullname)\\r\\n print(courseid)\\r\\n print (lateornot)\\r\\n c34 = fullname.find(' ')\\r\\n firstname = (fullname[:c34])\\r\\n lastname = (fullname[c34:])\\r\\n \\\"\\\"\\\"\\r\\n We get all the data courseid , fristname , lastname, datetime1,and late or not and submited on the website \\r\\n \\r\\n\\r\\n \\\"\\\"\\\"\\r\\n login_data = {\\r\\n\\t 'Course': courseid,\\r\\n\\t 'FirstName': firstname,\\r\\n\\t 'LastName': lastname,\\r\\n\\t 'Date': datetime2,\\r\\n\\t 'Attendance': 'on',\\r\\n\\t 'Late': latev,\\r\\n\\t 'submitbutton': 'Submit'\\r\\n }\\r\\n if(fullname == \\\"Unknow\\\"):\\r\\n \\tprint(\\\"I-dont-know-you\\\")\\r\\n else:\\r\\n \\r\\n with requests.Session() as s:\\r\\n \\turl = \\\"https://rbattendance.000webhostapp.com/update.php\\\"\\r\\n \\tr = s.get(url)\\r\\n \\tsoup = BeautifulSoup(r.content, 'html5lib')\\r\\n \\tr = s.post(url, data = login_data)\\r\\n \\t#print(r.content)\\r\\n \\r\\n \\r\\n\\r\\n\\r\\n\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n This for loop is reponsible for drawing on the image \\r\\n \\\"\\\"\\\"\\r\\n\\r\\n for (top, right, bottom, left), name in zip(face_locations, face_names):\\r\\n # Draw a box around the face\\r\\n cv2.rectangle(img, (left-20, top-20), (right+20, bottom+20), (255, 0, 0), 2)\\r\\n\\r\\n # Draw a label with a name below the face\\r\\n cv2.rectangle(img, (left-20, bottom -15), (right+20, bottom+20), (255, 0, 0), cv2.FILLED)\\r\\n font = cv2.FONT_HERSHEY_DUPLEX\\r\\n cv2.putText(img, name, (left -20, bottom + 15), font, 1.0, (255, 255, 255), 2)\\r\\n\\r\\n\\r\\n # Display the resulting image\\r\\n \\r\\n \\r\\n while True:\\r\\n #cv2.imshow('Video', img)\\r\\n #if cv2.waitKey(1) & 0xFF == ord('q'):\\r\\n return face_names\",\n \"def __get_coco_masks(anns: list, img):\\n width = img.shape[1]\\n height = img.shape[0]\\n\\n if len(anns) == 0:\\n raise AnnError('Empty annotation detected.')\\n \\n classes = []\\n mask = np.zeros((height, width), dtype=np.int32)\\n \\n for inst_idx, ann in enumerate(anns):\\n cat_id = ann['category_id']\\n classes.append(cat_id)\\n\\n m = cocoseg_to_binary(ann['segmentation'], height, width) # zero one mask\\n m = m.astype(np.int32) * (inst_idx + 1)\\n mask[m > 0] = 255\\n\\n classes = np.asarray(classes)\\n return mask, classes\",\n \"def load_datasets(filepath, sample_list, label_list, mark, a4c_or_a2c, m):\\n # here can adjust n to apply your datasets\\n if mark:\\n n = 4000\\n else:\\n n = 4000\\n dst_pair1 = np.zeros(shape=(n, m, m, 1), dtype=np.float32)\\n dst_pair2 = np.zeros(shape=(n, m, m, 1), dtype=np.float32)\\n dst_label = np.zeros(shape=(n,), dtype=np.int32)\\n k = 0\\n label_list_copy = copy.deepcopy(label_list)\\n for number in range(len(sample_list)):\\n label = label_list_copy[sample_list[number]-1] # o--->up 1--->down\\n start_mark = label.pop()\\n for i in (label):\\n position = label.index(i)\\n if position == len(label)-1:\\n break\\n j = label[position+1]\\n for t in range(i,j):\\n # load imgs: from number i to number j-1-->pair1\\n # i+1 j-->pair2\\n img_p1 = cv2.imread(filepath+\\\"Patient\\\"+(\\\"000\\\"+str(sample_list[number]))[-4:] +\\n \\\"\\\\\\\\a\\\"+str(a4c_or_a2c)+\\\"c\\\\\\\\\\\"+str(t)+'.png', 0)\\n img_p2 = cv2.imread(filepath+\\\"Patient\\\"+(\\\"000\\\"+str(sample_list[number]))[-4:] +\\n \\\"\\\\\\\\a\\\"+str(a4c_or_a2c)+\\\"c\\\\\\\\\\\"+str(t+1)+'.png', 0)\\n # cut and unsamping use cv2.resize\\n # original 600*800--cut-->512*512--->resize by cv2 ---> m*m\\n dst_pair1[k, :, :, 0] = cv2.resize(img_p1[80:592, 176:688].reshape(512, -1, 1), (m, m))/255.0\\n dst_pair2[k, :, :, 0] = cv2.resize(img_p2[80:592, 176:688].reshape(512, -1, 1), (m, m))/255.0\\n if start_mark == 0: # up\\n dst_label[k] = 0 \\n else:\\n dst_label[k] = 1 \\n k += 1\\n if start_mark == 0:\\n start_mark = 1\\n else:\\n start_mark = 0\\n if mark == 1:\\n pathname = 'train'\\n elif mark == 0:\\n pathname = 'test'\\n else:\\n pathname = \\\"val\\\"\\n # save the imgs for augmentation before training.\\n os.mkdir('../'+pathname+'p1/') \\n os.mkdir('../'+pathname+'p2/')\\n K = 0\\n for i in (dst_pair1[:k]):\\n preprocessing.image.save_img('../'+pathname+'p1/'+str(K)+'.png', i)\\n K += 1\\n K = 0\\n for i in (dst_pair2[:k]):\\n preprocessing.image.save_img('../'+pathname+'p2/'+str(K)+'.png', i)\\n K += 1\\n return dst_pair1[:k], dst_pair2[:k], dst_label[:k]\",\n \"def load_coco_ann_files(self):\\n if self.type == 'train':\\n datasets = [\\n (os.path.join(self.dataset_root, 'coco', 'train2014'),\\n COCO(os.path.join(self.dataset_root, 'coco',\\n 'annotations_trainval2014', 'person_keypoints_train2014.json'))),\\n (os.path.join(self.dataset_root, 'coco', 'train2017'),\\n COCO(os.path.join(self.dataset_root, 'coco',\\n 'annotations_trainval2017', 'person_keypoints_train2017.json'))),\\n # (os.path.join(self.dataset_root, 'mpii', 'images'),\\n # COCO(os.path.join(self.dataset_root, 'mpii',\\n # 'annotations', 'train.json')))\\n ]\\n else:\\n datasets = [\\n (os.path.join(self.dataset_root, 'coco', 'val2014'),\\n COCO(os.path.join(self.dataset_root, 'coco',\\n 'annotations_trainval2014', 'person_keypoints_val2014.json'))),\\n (os.path.join(self.dataset_root, 'coco', 'val2017'),\\n COCO(os.path.join(self.dataset_root, 'coco',\\n 'annotations_trainval2017', 'person_keypoints_val2017.json')))\\n ]\\n\\n dict_list = []\\n for dataset_path, dataset in datasets:\\n img_ids = dataset.getImgIds()\\n\\n for idx in img_ids:\\n try:\\n img = dataset.loadImgs([idx])[0]\\n ann_ids = dataset.getAnnIds([idx])\\n anns = dataset.loadAnns(ann_ids)\\n\\n if [ann['keypoints'] for ann in anns] and not all([ann['keypoints'] == [0]*51 for ann in anns]):\\n keypoints = [ann['keypoints'] for ann in anns if ann['keypoints'] != [0]*51]\\n for i in range(len(keypoints)):\\n if 'coco' in dataset_path:\\n keypoints[i] = keypoints[i] + ([0, 0, 0] if not (keypoints[i][17] and keypoints[i][20])\\n else [(keypoints[i][15] + keypoints[i][18]) // 2, (keypoints[i][16] + keypoints[i][19]) // 2, 1])\\n else:\\n keypoints[i] = keypoints[i] + ([0, 0, 0] if not (keypoints[i][41] and keypoints[i][38])\\n else [(keypoints[i][39] + keypoints[i][36]) // 2, (keypoints[i][40] + keypoints[i][37]) // 2, 1])\\n\\n if len([kp for kp in keypoints if kp != [0]*54]) <= 4:\\n dict_list.append({'path': os.path.join(dataset_path, img[\\\"file_name\\\"]),\\n 'keypoints': [kp for kp in keypoints if kp != [0]*54]})\\n except:\\n print(f'Skipped: {idx}')\\n\\n final_dataset = pd.DataFrame.from_dict(dict_list)\\n\\n return final_dataset\",\n \"def prepare_cityscapes_data(seed=1, percent=30.0, version=2017):\\n def _save_anno(name, images, annotations):\\n \\\"\\\"\\\"Save annotation\\n \\\"\\\"\\\"\\n print('>> Processing data {}.json saved ({} images {} annotations)'.format(\\n name, len(images), len(annotations)))\\n new_anno = {}\\n new_anno['images'] = images\\n new_anno['annotations'] = annotations\\n new_anno['categories'] = anno['categories']\\n\\n with open(\\n '{root}/{save_name}.json'.format(\\n save_name=name, root=DATA_DIR),\\n 'w') as f:\\n json.dump(new_anno, f)\\n print('>> Data {}.json saved ({} images {} annotations)'.format(\\n name, len(images), len(annotations)))\\n\\n np.random.seed(seed)\\n \\n anno = json.load(open(os.path.join(DATA_DIR, 'instancesonly_filtered_gtFine_train.json')))\\n\\n image_list = anno['images']\\n labeled_tot = int(percent / 100. * len(image_list))\\n #labeled_ind = np.random.choice(range(len(image_list)), size=labeled_tot)\\n labeled_ind = np.arange(len(image_list))\\n np.random.shuffle(labeled_ind)\\n labeled_ind = labeled_ind[0:labeled_tot]\\n\\n labeled_id = []\\n labeled_images = []\\n unlabeled_images = []\\n labeled_ind = set(labeled_ind)\\n for i in range(len(image_list)):\\n if i in labeled_ind:\\n labeled_images.append(image_list[i])\\n labeled_id.append(image_list[i]['id'])\\n else:\\n unlabeled_images.append(image_list[i])\\n\\n # get all annotations of labeled images\\n labeled_id = set(labeled_id)\\n labeled_annotations = []\\n unlabeled_annotations = []\\n for an in anno['annotations']:\\n if an['image_id'] in labeled_id:\\n labeled_annotations.append(an)\\n else:\\n unlabeled_annotations.append(an)\\n\\n # save labeled and unlabeled\\n save_name = 'instancesonly_filtered_gtFine_train.{seed}@{tot}'.format(\\n version=version, seed=seed, tot=int(percent))\\n _save_anno(save_name, labeled_images, labeled_annotations)\\n save_name = 'instancesonly_filtered_gtFine_train.{seed}@{tot}-unlabeled'.format(\\n version=version, seed=seed, tot=int(percent))\\n _save_anno(save_name, unlabeled_images, unlabeled_annotations)\",\n \"def test_annotate_blank(self):\\n x_size = 500\\n y_size = 500\\n\\n label = li.Label(\\\"Test Label\\\", (0.20, 0.20))\\n test_im = li.ImageBlank(None, x_size, y_size)\\n test_im.annotate(label)\\n annotated_im = test_im.data\\n\\n # Ensure the shape is retained\\n shape_expected = (y_size, x_size, 4)\\n shape_test = annotated_im.shape\\n self.assertEqual(\\n shape_test, shape_expected, msg=\\\"shape not retained after annotation\\\"\\n )\\n\\n # Test that there are darker text areas\\n flattened = annotated_im[..., :3].mean(axis=-1)\\n n_black_pixels = (flattened < 10).sum()\\n\\n self.assertGreater(n_black_pixels, 50, msg=\\\"Not enough black/text pixels\\\")\\n self.assertLess(n_black_pixels, 5000, msg=\\\"Too many black/text pixels\\\")\",\n \"def test_multicamgrablabeler():\\n import infer_sequence\\n base_dir = 'data/batch2/test_data'\\n labeler = MultiCamGrabLabeler('data/batch3')\\n for i in range(0, 13):\\n filename = str(10000000 + i + 1)[1:] + '.jpg'\\n cam_data = {'cam1_color_file': os.path.join(base_dir, '4', filename),\\n 'cam1_dept_file': os.path.join(base_dir, '6', filename),\\n 'cam2_color_file': os.path.join(base_dir, '5', filename),\\n 'cam2_dept_file': os.path.join(base_dir, '7', filename),\\n 'xeye_image': [os.path.join(base_dir, '0', filename),\\n os.path.join(base_dir, '1', filename),\\n os.path.join(base_dir, '2', filename),\\n os.path.join(base_dir, '3', filename),\\n ]\\n }\\n labeler.add_files(cam_data)\\n results = labeler.gen_label()\\n if not results:\\n continue\\n images = []\\n for r in results:\\n images.extend(r['images'])\\n infer_sequence.show_multi_images(images, resize_ratio=0.5, cols=4)\",\n \"def get_coco_gt(self, coco, img_id, height, width, img_name):\\n annIds = coco.getAnnIds(imgIds=[img_id], iscrowd=None)\\n # assert annIds is not None and annIds > 0, 'No annotation for %s' % str(img_id)\\n anns = coco.loadAnns(annIds)\\n # assert len(anns) > 0, 'No annotation for %s' % str(img_id)\\n masks = []\\n classes = []\\n bboxes = []\\n\\n for ann in anns:\\n id = cat_id_to_real_id(ann['category_id'])\\n # id = coco_cat_id_to_voc_id(ann['category_id'])\\n if id != 0:\\n classes.append(id)\\n\\n m = coco.annToMask(ann) # {0, 1} mask\\n assert m.shape[0] == height and m.shape[1] == width, \\\\\\n 'image %s and ann %s don''t match' % (img_id, ann)\\n masks.append(m)\\n\\n bboxes.append(ann['bbox'])\\n\\n masks = np.asarray(masks)\\n classes = np.asarray(classes)\\n bboxes = np.asarray(bboxes)\\n\\n # to x1, y1, x2, y2\\n num_classes = bboxes.shape[0]\\n if num_classes <= 0:\\n bboxes = np.zeros([0, 4], dtype=np.float32)\\n classes = np.zeros([0], dtype=np.float32)\\n num_classes = 0\\n print('None Annotations %s' % img_name)\\n bboxes[:, 2] = bboxes[:, 0] + bboxes[:, 2]\\n bboxes[:, 3] = bboxes[:, 0] + bboxes[:, 3]\\n\\n bboxes = bboxes.astype(np.float32)\\n classes = classes.astype(np.float32)\\n masks = masks.astype(np.uint8)\\n assert masks.shape[0] == bboxes.shape[0], 'Shape Error'\\n\\n return num_classes, masks, bboxes, classes\",\n \"def create_voc_label(is_training):\\n voc_dir = config.voc_dir\\n cls_map = {name: i for i, name in enumerate(config.coco_classes)}\\n sub_dir = 'train' if is_training else 'eval'\\n voc_dir = os.path.join(voc_dir, sub_dir)\\n if not os.path.isdir(voc_dir):\\n raise ValueError(f'Cannot find {sub_dir} dataset path.')\\n\\n image_dir = anno_dir = voc_dir\\n if os.path.isdir(os.path.join(voc_dir, 'Images')):\\n image_dir = os.path.join(voc_dir, 'Images')\\n if os.path.isdir(os.path.join(voc_dir, 'Annotations')):\\n anno_dir = os.path.join(voc_dir, 'Annotations')\\n\\n if not is_training:\\n data_dir = config.voc_root\\n json_file = os.path.join(data_dir, config.instances_set.format(sub_dir))\\n file_dir = os.path.split(json_file)[0]\\n if not os.path.isdir(file_dir):\\n os.makedirs(file_dir)\\n json_dict = {\\\"images\\\": [], \\\"type\\\": \\\"instances\\\", \\\"annotations\\\": [],\\n \\\"categories\\\": []}\\n bnd_id = 1\\n\\n image_files_dict = {}\\n image_anno_dict = {}\\n images = []\\n for anno_file in os.listdir(anno_dir):\\n print(anno_file)\\n if not anno_file.endswith('xml'):\\n continue\\n tree = et.parse(os.path.join(anno_dir, anno_file))\\n root_node = tree.getroot()\\n file_name = root_node.find('filename').text\\n img_id = get_imageId_from_fileName(file_name)\\n image_path = os.path.join(image_dir, file_name)\\n print(image_path)\\n if not os.path.isfile(image_path):\\n print(f'Cannot find image {file_name} according to annotations.')\\n continue\\n\\n labels = []\\n for obj in root_node.iter('object'):\\n cls_name = obj.find('name').text\\n if cls_name not in cls_map:\\n print(f'Label \\\"{cls_name}\\\" not in \\\"{config.coco_classes}\\\"')\\n continue\\n bnd_box = obj.find('bndbox')\\n x_min = int(float(bnd_box.find('xmin').text)) - 1\\n y_min = int(float(bnd_box.find('ymin').text)) - 1\\n x_max = int(float(bnd_box.find('xmax').text)) - 1\\n y_max = int(float(bnd_box.find('ymax').text)) - 1\\n labels.append([y_min, x_min, y_max, x_max, cls_map[cls_name]])\\n\\n if not is_training:\\n o_width = abs(x_max - x_min)\\n o_height = abs(y_max - y_min)\\n ann = {'area': o_width * o_height, 'iscrowd': 0, 'image_id': \\\\\\n img_id, 'bbox': [x_min, y_min, o_width, o_height], \\\\\\n 'category_id': cls_map[cls_name], 'id': bnd_id, \\\\\\n 'ignore': 0, \\\\\\n 'segmentation': []}\\n json_dict['annotations'].append(ann)\\n bnd_id = bnd_id + 1\\n\\n if labels:\\n images.append(img_id)\\n image_files_dict[img_id] = image_path\\n image_anno_dict[img_id] = np.array(labels)\\n\\n if not is_training:\\n size = root_node.find(\\\"size\\\")\\n width = int(size.find('width').text)\\n height = int(size.find('height').text)\\n image = {'file_name': file_name, 'height': height, 'width': width,\\n 'id': img_id}\\n json_dict['images'].append(image)\\n\\n if not is_training:\\n for cls_name, cid in cls_map.items():\\n cat = {'supercategory': 'none', 'id': cid, 'name': cls_name}\\n json_dict['categories'].append(cat)\\n json_fp = open(json_file, 'w')\\n json_str = json.dumps(json_dict)\\n json_fp.write(json_str)\\n json_fp.close()\\n\\n return images, image_files_dict, image_anno_dict\",\n \"def predict_labels(model):\\n test_datagen = ImageDataGenerator(featurewise_center=True,\\n featurewise_std_normalization=True\\n #rescale=1. / 255,\\n #samplewise_center=True,\\n #samplewise_std_normalization=True\\n )\\n test_datagen.fit(test_data)\\n # datagen.fit(val_data)\\n # create generator for train data\\n test_generator = test_datagen.flow(\\n test_data,\\n batch_size=batch_size,\\n shuffle=False)\\n pred_prob=model.predict_generator(test_generator,test_data.shape[0])\\n pred_prob=pred_prob[:,0]\\n def pre_class(x):\\n \\tif x<0.5:\\n return 0\\n else:\\n return 1\\n #def true_label(id):\\n #\\tif 'f0' in id:\\n #\\t return 0\\n # elif 'f1' in id: \\n # return 1\\n #\\telse:\\n #\\t pass\\n #pred_true=map(true_label,test_id)\\n #pred_true=np.array(pred_true)\\n #print roc_auc_score(val_target, pred_prob)\\n #prediction=map(pre_class,pred_prob)\\n #print confusion_matrix(val_target,prediction)\\n with open(\\\"prediction.csv\\\", \\\"w\\\") as f: \\n\\tp_writer = csv.writer(f, delimiter=',', lineterminator='\\\\n')\\n for id,label in zip(test_id,pred_prob):\\n\\t p_writer.writerow([id, label])\\n\\t\\n #base_path = \\\"PZ/test/test/\\\"\\n\\n #with open(\\\"prediction.csv\\\", \\\"w\\\") as f:\\n # p_writer = csv.writer(f, delimiter=',', lineterminator='\\\\n')\\n # for _, _, imgs in os.walk(base_path):\\n # for im in imgs:\\n # pic_id = im.split(\\\".\\\")[0]\\n #img = cv2.imread(base_path+im)\\n #img = cv2.resize(img, (img_width, img_height), cv2.INTER_LINEAR)\\n #img = img.transpose((2,0,1))\\n #img = np.expand_dims(img,axis=0)\\n #img = load_img(base_path + im)\\n #img = imresize(img, size=(img_height, img_width))\\n #test_x = img_to_array(img).reshape(3, img_height, img_width)\\n #test_x = test_x.reshape((1,) + test_x.shape)\\n #test_datagen.fit(img)\\n #test_generator = test_datagen.flow(img,\\n # batch_size=1,\\n # shuffle=False)\\n #prediction = model.predict_generator(test_generator, 1)\\n #p_writer.writerow([pic_id, prediction])\",\n \"def _add_roidb_from_annotations(self, entry):\\n ann_ids = self._COCO.getAnnIds(imgIds=entry['id'], iscrowd=None)\\n objs = self._COCO.loadAnns(ann_ids)\\n width = entry['width']\\n height = entry['height']\\n # valid objs\\n # change the annotation boxes from 'xywh' to 'xyxy'\\n valid_objs = []\\n for obj in objs:\\n x1 = np.max((0, obj['bbox'][0]))\\n y1 = np.max((0, obj['bbox'][1]))\\n x2 = np.min((width, x1 + np.max((0, obj['bbox'][2]))))\\n y2 = np.min((height, y1 + np.max((0, obj['bbox'][3]))))\\n if obj['area'] > 0 and x2 >= x1 and y2 >= y1:\\n obj['clean_box'] = [x1, y1, x2, y2]\\n valid_objs.append(obj)\\n objs = valid_objs\\n num_objs = len(objs)\\n\\n bboxes = np.zeros((num_objs, 4), dtype=entry['bboxes'].dtype)\\n gt_classes = np.zeros((num_objs), dtype=entry['gt_classes'].dtype)\\n\\n coco_cat_id_to_class_ind = dict(\\n [(self._class_to_coco_cat_id[cls], self._class_to_ind[cls]) for cls in self._classes[1:]])\\n for ix, obj in enumerate(objs):\\n bboxes[ix, :] = obj['clean_box']\\n gt_classes[ix] = coco_cat_id_to_class_ind[obj['category_id']]\\n entry['bboxes'] = np.append(entry['bboxes'], bboxes, axis=0)\\n entry['gt_classes'] = np.append(entry['gt_classes'], gt_classes)\",\n \"def create_yolo_trainval(\\n annotations: Annotations,\\n labels: \\\"list[str]\\\" = None,\\n save_dir: PathLike = \\\"yolo_trainval/\\\", \\n prefix: PathLike = \\\"data/\\\", \\n train_ratio: float = 80/100,\\n shuffle: bool = True,\\n random_seed: int = 149_843_046_101,\\n exist_ok: bool = False,\\n):\\n assert 0.0 <= train_ratio <= 1.0, \\\"train_ratio must be in 0...1\\\"\\n\\n save_dir = Path(save_dir).expanduser().resolve()\\n prefix = Path(prefix)\\n train_dir = save_dir / \\\"train/\\\"\\n valid_dir = save_dir / \\\"val/\\\"\\n\\n save_dir.mkdir(exist_ok=exist_ok)\\n train_dir.mkdir(exist_ok=exist_ok)\\n valid_dir.mkdir(exist_ok=exist_ok)\\n\\n labels = labels or sorted(annotations.labels())\\n labels_to_numbers = {l: str(n) for n, l in enumerate(labels)}\\n\\n annotations.map_labels(labels_to_numbers)\\n\\n if shuffle:\\n # FIXME: Ugly `.annotations`, should change this.\\n # `Annotations` should be an iterable or collection.\\n annotations.annotations = sorted(annotations, key=lambda a: a.image_path)\\n random_gen = Random(random_seed)\\n random_gen.shuffle(annotations)\\n\\n len_train = int(train_ratio * len(annotations))\\n\\n def create_annotation(indexed_annotation: \\\"tuple[int, Annotation]\\\") -> str:\\n i, annotation = indexed_annotation\\n\\n dir = train_dir if i < len_train else valid_dir\\n img_filename = dir / f\\\"im_{i:06}{annotation.image_path.suffix}\\\"\\n ann_filename = img_filename.with_suffix(\\\".txt\\\")\\n ann_content = annotation.yolo_repr()\\n\\n try:\\n shutil.copyfile(annotation.image_path, img_filename)\\n ann_filename.write_text(ann_content)\\n except KeyboardInterrupt:\\n shutil.copyfile(annotation.image_path, img_filename)\\n ann_filename.write_text(ann_content)\\n exit()\\n\\n return img_filename.name\\n\\n image_names = thread_map(create_annotation, enumerate(annotations), \\n total=len(annotations), unit=\\\"imgs\\\")\\n\\n train_file = save_dir / \\\"train.txt\\\"\\n valid_file = save_dir / \\\"val.txt\\\"\\n names_file = save_dir / \\\"obj.names\\\"\\n\\n train_file.write_text(\\n \\\"\\\\n\\\".join(str(prefix / f\\\"train/{n}\\\") for n in image_names[:len_train]))\\n valid_file.write_text(\\n \\\"\\\\n\\\".join(str(prefix / f\\\"val/{n}\\\") for n in image_names[len_train:]))\\n\\n names_file.write_text(\\\"\\\\n\\\".join(labels))\",\n \"def load_png_data():\\n m=1 #训练文件个数\\n n=1 #测试文件个数\\n train_set_x=[]#训练数据集\\n train_set_y=[]#训练标签集\\n\\n test_set_x=[]#测试数据集\\n test_set_y=[]#测试标签集\\n\\n train_data={}\\n\\n train_path=r\\\".\\\\dataset\\\\train_label\\\\\\\\\\\"\\n dirs=os.listdir(train_path)\\n\\n for file in dirs:\\n srcImg=cv2.imread(train_path+file)\\n #将label数据集保存为numpy格式并保存\\n npImg=np.array(srcImg)\\n np.save(train_path+str(m)+'.npy',npImg)\\n train_set_x.append(npImg)\\n\\n\\n NoiseImg = GaussianNoise(srcImg, 25, 4, 0.8)\\n npNoiseImg = np.array(NoiseImg)\\n cv2.imwrite(r\\\".\\\\dataset\\\\trainset\\\\\\\\\\\"+str(m)+'.png', NoiseImg, [int(cv2.IMWRITE_PNG_STRATEGY_DEFAULT)])\\n np.save(r\\\".\\\\dataset\\\\trainset\\\\\\\\\\\" + str(m) + '.npy', npNoiseImg)\\n train_set_y.append(npNoiseImg)\\n m=m+1\\n train_data['train_set_x']=train_set_x\\n train_data['train_set_y']=train_set_y\\n\\n test_path = r\\\".\\\\dataset\\\\test_label\\\\\\\\\\\"\\n dirs_test = os.listdir(test_path)\\n for file in dirs_test:\\n srcImg=cv2.imread(test_path+file)\\n #将label数据集保存为numpy格式并保存\\n npImg=np.array(srcImg)\\n np.save(test_path+str(n)+'.npy',npImg)\\n test_set_x.append(npImg)\\n\\n\\n NoiseImg = GaussianNoise(srcImg, 25, 4, 0.8)\\n npNoiseImg = np.array(NoiseImg)\\n cv2.imwrite(r\\\".\\\\dataset\\\\testset\\\\\\\\\\\"+str(n)+'.png', NoiseImg, [int(cv2.IMWRITE_PNG_STRATEGY_DEFAULT)])\\n np.save(r\\\".\\\\dataset\\\\testset\\\\\\\\\\\" + str(n) + '.npy', npNoiseImg)\\n test_set_y.append(npNoiseImg)\\n n=n+1\\n train_data['test_set_x']=test_set_x\\n train_data['test_set_y']=test_set_y\\n\\n np.savez(r\\\"E:\\\\DeepLearning\\\\CNNDenoiser\\\\dataset\\\\train_data.npz\\\",**train_data)\",\n \"def _annotation_from_index(self, index, _COCO):\\n im_ann = _COCO.loadImgs(index)[0]\\n width = im_ann['width']\\n height = im_ann['height']\\n\\n annIds = _COCO.getAnnIds(imgIds=index, iscrowd=None)\\n objs = _COCO.loadAnns(annIds)\\n # Sanitize bboxes -- some are invalid\\n valid_objs = []\\n for obj in objs:\\n x1 = np.max((0, obj['bbox'][0]))\\n y1 = np.max((0, obj['bbox'][1]))\\n x2 = np.min((width - 1, x1 + np.max((0, obj['bbox'][2] - 1))))\\n y2 = np.min((height - 1, y1 + np.max((0, obj['bbox'][3] - 1))))\\n if obj['area'] > 0 and x2 >= x1 and y2 >= y1:\\n obj['clean_bbox'] = [x1, y1, x2, y2]\\n valid_objs.append(obj)\\n objs = valid_objs\\n num_objs = len(objs)\\n\\n res = np.zeros((num_objs, 5))\\n\\n # Lookup table to map from COCO category ids to our internal class\\n # indices\\n for ix, obj in enumerate(objs):\\n cls = self._thing_dataset_id_to_contiguous_id[obj['category_id']]\\n res[ix, 0:4] = obj['clean_bbox']\\n res[ix, 4] = cls\\n\\n return res\",\n \"def do_parse_annotations(xmls_path_in, xml_path_out):\\n label = \\\"puzzle piece\\\"\\n new_xml = \\\"<?xml version='1.0' encoding='ISO-8859-1'?>\\\"\\n new_xml += \\\"<dataset>\\\\n<name>Puzzle dataset</name>\\\\n<comment>Created by imglab tool.</comment>\\\\n\\\"\\n new_xml += \\\"<images>\\\\n\\\"\\n for i, annot in enumerate(glob.glob(xmls_path_in + \\\"/*.xml\\\")):\\n tree = ET.parse(annot)\\n root = tree.getroot()\\n left = root[6][4][0].text\\n top = root[6][4][1].text\\n width = 70#str(int(root[6][4][2].text) - int(left))\\n height = 70#str(int(root[6][4][3].text) - int(top))\\n new_xml += \\\"{}<image file='{}jpg'>\\\\n\\\".format(1 * ' ', annot[:-3])\\n new_xml += \\\"{}<box top='{}' left='{}' width='{}' height='{}'>\\\\n\\\".format(2 * ' ', top, left, width, height)\\n new_xml += \\\"{}<label>{}</label>\\\\n{}</box>\\\\n'{}</image>\\\\n\\\".format(3 * ' ', label, 2 * ' ', 1 * ' ')\\n\\n new_xml += \\\"</images>\\\\n</dataset>\\\"\\n out_file = open(xml_path_out, \\\"w\\\")\\n out_file.write(new_xml)\\n out_file.close()\",\n \"def main():\\n args = get_args()\\n FILE = args.FILE\\n annotations = args.annotations\\n outfile = args.outfile\\n \\n \\n if not os.path.isfile(FILE):\\n die('\\\"{}\\\" is not a file'.format(FILE))\\n if not os.path.isfile(annotations):\\n die('\\\"{}\\\" is not a file'.format(annotations))\\n if os.path.isfile(FILE) and os.path.isfile(annotations):\\n reader = csv.DictReader(open(FILE), delimiter = '\\\\t', fieldnames = (\\\"qseqid\\\", \\\"sseqid\\\", \\\"pident\\\", \\\"length\\\", \\\"mismatch\\\", \\\"gapopen\\\", \\\"qstart\\\", \\\"qend\\\", \\\"sstart\\\", \\\"send\\\", \\\"evalue\\\", \\\"bitscore\\\"))\\n reader_a = csv.DictReader(open(annotations), fieldnames = (\\\"centroid\\\", \\\"domain\\\", \\\"kingdom\\\", \\\"phylum\\\", \\\"class\\\", \\\"order\\\", \\\"genus\\\", \\\"species\\\"))\\n reader_b = csv.reader(open(annotations, 'r'))\\n anno_dict = {}\\n for row in reader_b:\\n key1 = row[0]\\n anno_dict[key1] = row[1:]\\n\\n #print(anno_dict)\\n \\n \\\"\\\"\\\"for dct in map(dict, reader_a):\\n genus = (f\\\"{dct['genus']}\\\")\\n species = (f\\\"{dct['species']}\\\")\\n if genus == \\\"\\\": \\n print(\\\"NA\\\")\\n else:\\n print(genus)\\n if species == \\\"\\\":\\n print(\\\"NA\\\")\\n else:\\n print(species)\\\"\\\"\\\"\\n for dct in map(dict, reader):\\n seq_id = (f\\\"{dct['sseqid']}\\\") \\n pident = (f\\\"{dct['pident']}\\\")\\n #print(seq_id)\\n for dct_a in map(dict, reader_a):\\n genus = (f\\\"{dct_a['genus']}\\\")\\n species = (f\\\"{dct_a['species']}\\\")\\n if any(seq_id == key for key in anno_dict): \\n \\\"\\\"\\\"print(seq_id)\\n print(pident)\\n print(genus)\\n print(species)\\n #find a way to print genus and species of seq_id\\n \\\"\\\"\\\"\\n \\n else:\\n warn('Cannot find seq \\\"{}\\\" in lookup'.format(seq_id))\\n \\\"\\\"\\\"for line_a in reader_a:\\n an_id = (line_a['centroid']) \\n print('\\\"{}\\\" is an_id'.format(an_id)) \\n for line in reader:\\n seq_id = (line['sseqid'])\\n print('\\\"{}\\\" is seq_id'.format(seq_id))\\n if seq_id == an_id:\\n print(\\\"hi\\\")\\n else:\\n warn('Cannot find seq \\\"{}\\\" in lookup'.format(seq_id))\\n \\\"\\\"\\\"\\n #pprint.pprint(dict_list)\\n #pprint.pprint(dict_list_a)\\n #for key, value in d1.items():\\n #if key is 'sseqid':\\n #print(value)\\n #print(dict_list_a['centroid']) \",\n \"def _load_colabeled_img(self) -> np.ndarray:\\n return tifffile.imread(str(self.colabel_img))\",\n \"def validate_labels(labels, path):\\n for labels_ in labels.values():\\n for label in labels_:\\n for ann in label['annotations']:\\n assert len(ann['segmentation']) == 1\\n assert len(ann['segmentation'][0]) % 2 == 0\\n\\n label['annotations'] = [\\n ann\\n for ann in label['annotations']\\n if len(ann['segmentation'][0]) >= 6\\n ]\\n assert len(label['annotations']) > 0\\n label['file_name'] = path + '/' + label['file_name']\\n\\n for k in labels:\\n labels[k] = [\\n label for label in labels[k]\\n if os.path.exists(label['file_name'])\\n ]\\n return labels\",\n \"def initiate_yolo_detect(images_path, save_to_path, detections_file='pickles/bounding_boxes.pickle'):\\n for filename in os.listdir(images_path):\\n bound_boxes = detect_objects_on_image(\\n os.path.join(images_path, filename), detections_file)\\n predictions_path = os.path.join(\\n save_to_path, 'predictions_' + filename)\\n print('predictions path', predictions_path)\\n copy2('predictions_' + os.path.basename(image_directory) +\\n '.png', predictions_path)\",\n \"def isOk(annots):\\n if annots == []:\\n return True\\n for a in annots:\\n for label in a.labels:\\n if (label != 'hasOkCopies' and\\n label != 'hasBadCopies' and\\n not label.startswith('count_')):\\n return False\\n return True\",\n \"def load_data5():\\n# dirname = 'cifar-10-batches-py'\\n# origin = 'https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'\\n# path = get_file(dirname, origin=origin, untar=True)\\n# path= './cifar-10-batches-py'\\n (x_train, y_train), (x_test, y_test) = cifar10.load_data()\\n# Below shows a test class has 999 examples instead of the claimed 1000\\n# tclasscount=np.zeros((10,), dtype=int)\\n# for i in range(0, len(y_test)-1):\\n# tclasscount[y_test[i][0]]= tclasscount[y_test[i][0]] + 1\\n# print('Test class count',tclasscount)\\n num_train_samples = 50000\\n num_5_class = 25000\\n num_5_test = 4999 # should be 5000 if all the categories had 1000 in them but they do not. One is missing.\\n print('x_train shape orig:', x_train.shape)\\n print('More:', x_train.shape[1:])\\n print('y_test shape',y_test.shape)\\n\\n x5_train = np.empty((num_5_class, 32, 32, 3), dtype='uint8')\\n y5_train = np.empty((num_5_class,), dtype='uint8')\\n\\n count=0\\n\\n for i in range(0, len(y_train)-1):\\n if (y_train[i][0] == 2) or (y_train[i][0] == 3) or (y_train[i][0] == 4) or (y_train[i][0] == 5) or (y_train[i][0] == 7):\\n x5_train[count]=x_train[i]\\n y5_train[count]=y_train[i]\\n count=count+1\\n \\n # find test data of interest\\n count=0\\n x5_test=np.empty((num_5_test, 32, 32, 3), dtype='uint8')\\n y5_test= np.empty((num_5_test,), dtype='uint8')\\n\\n for i in range(0, len(y_test)-1):\\n if (y_test[i][0] == 2) or (y_test[i][0] == 3) or (y_test[i][0] == 4) or (y_test[i][0] == 5) or (y_test[i][0] == 7):\\n x5_test[count]=x_test[i]\\n y5_test[count]=y_test[i]\\n count=count+1\\n# Below shows class 7 is only 999 and not 1000 examples!!! One horse got away it seems.\\n# if(y_test[i][0] == 2):\\n# c2=c2+1\\n# if(y_test[i][0] == 3):\\n# c3=c3+1\\n# if(y_test[i][0] == 4):\\n# c4=c4+1\\n# if(y_test[i][0] == 5):\\n# c5=c5+1\\n# if(y_test[i][0] == 7):\\n# c7=c7+1\\n# print('c2count, c3count, c4count, c5count, c7count',c2,c3,c3,c5,c7)\\n# print('y5tstshape',y5_test.shape, count)\\n# print('y5tst',y5_test)\\n# return (x_train, y_train), (x_test, y_test)\\n return (x5_train, y5_train), (x5_test, y5_test)\",\n \"def __call__(self, src, label):\\r\\n \\\"\\\"\\\"color distort\\\"\\\"\\\"\\r\\n # img = random_color_distort(src)\\r\\n\\r\\n # print(\\\"previous label shape = \\\", label.shape)\\r\\n target = np.zeros(shape=(label.shape[0],))\\r\\n\\r\\n \\\"\\\"\\\"Pyramid Anchor sampling\\\"\\\"\\\"\\r\\n img, boxes, label = self.random_baiducrop(src, label[:, :4], target)\\r\\n # print(\\\"label shape = \\\", label.shape)\\r\\n # print('boxes shape =', boxes.shape)\\r\\n bbox = boxes\\r\\n # img = mx.nd.array(img)\\r\\n\\r\\n \\\"\\\"\\\"color distort\\\"\\\"\\\"\\r\\n img = mx.nd.array(img)\\r\\n img = random_color_distort(img)\\r\\n\\r\\n # \\\"\\\"\\\"random crop, keep aspect ration=1\\\"\\\"\\\"\\r\\n # h, w, _ = img.shape\\r\\n # bbox, crop_size = random_crop_with_constraints(label, (w, h))\\r\\n # x_offset, y_offset, new_width, new_height = crop_size\\r\\n # img = mx.image.fixed_crop(img, x_offset, y_offset, new_width, new_height)\\r\\n\\r\\n \\\"\\\"\\\"resize with random interpolation\\\"\\\"\\\"\\r\\n h, w, _ = img.shape\\r\\n interp = np.random.randint(0, 5)\\r\\n img = gimage.imresize(img, self._width, self._height, interp=interp)\\r\\n bbox = gbbox.resize(bbox, (w, h), (self._width, self._height))\\r\\n\\r\\n \\\"\\\"\\\"random horizontal flip\\\"\\\"\\\"\\r\\n h, w, _ = img.shape\\r\\n img, flips = gimage.random_flip(img, px=0.5)\\r\\n bbox = gbbox.flip(bbox, (w, h), flip_x=flips[0])\\r\\n\\r\\n \\\"\\\"\\\"To Tensor & Normalization\\\"\\\"\\\"\\r\\n img = mx.nd.image.to_tensor(img)\\r\\n img = mx.nd.image.normalize(img, mean=self._mean, std=self._std)\\r\\n\\r\\n if self._anchors is None:\\r\\n return img, bbox\\r\\n\\r\\n # @TODO: generating training target so cpu workers can help reduce the workload on gpu\\r\\n face_anchors, head_anchors, body_anchors = self._anchors\\r\\n gt_bboxes = mx.nd.array(bbox[:, :4]).expand_dims(0)\\r\\n gt_ids = mx.nd.zeros((1, gt_bboxes.shape[1], 1), dtype=gt_bboxes.dtype)\\r\\n\\r\\n face_cls_targets, face_box_targets, _ = self._target_generator(\\r\\n face_anchors, None, gt_bboxes, gt_ids)\\r\\n\\r\\n head_cls_targets, head_box_targets, _ = self._target_generator(\\r\\n head_anchors, None, gt_bboxes, gt_ids)\\r\\n\\r\\n body_cls_targets, body_box_targets, _ = self._target_generator(\\r\\n body_anchors, None, gt_bboxes, gt_ids)\\r\\n\\r\\n return img, \\\\\\r\\n face_cls_targets[0], head_cls_targets[0], body_cls_targets[0], \\\\\\r\\n face_box_targets[0], head_box_targets[0], body_box_targets[0]\",\n \"def load_annotations(self, ann_file, N, kind):\\n\\n self.coco = COCOPoint(ann_file, N=N, kind=kind)\\n # The order of returned `cat_ids` will not\\n # change with the order of the CLASSES\\n self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES)\\n\\n self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}\\n self.img_ids = self.coco.get_img_ids()\\n data_infos = []\\n total_ann_ids = []\\n for i in self.img_ids:\\n info = self.coco.load_imgs([i])[0]\\n info[\\\"filename\\\"] = info[\\\"file_name\\\"]\\n data_infos.append(info)\\n ann_ids = self.coco.get_ann_ids(img_ids=[i])\\n total_ann_ids.extend(ann_ids)\\n assert len(set(total_ann_ids)) == len(\\n total_ann_ids\\n ), f\\\"Annotation ids in '{ann_file}' are not unique!\\\"\\n return data_infos\",\n \"def load_tiny_imagenet(directory):\\n path_train, path_val, path_test = directory + '/train', directory + '/val', directory + '/test'\\n labels = os.listdir(path_train)\\n train_data = []\\n train_labels = []\\n for label in labels:\\n imgs_path = os.path.join(path_train, label, 'images')\\n imgs = os.listdir(imgs_path)\\n for img_name in imgs:\\n img_path = os.path.join(imgs_path, img_name)\\n img = cv2.imread(img_path)\\n b, g, r = cv2.split(img)\\n img = cv2.merge([r,g,b]).reshape(-1, 64, 64, 3)\\n train_data.append(img)\\n train_labels.append(label)\\n train_data = np.concatenate(train_data)\\n train_labels = np.array(train_labels, dtype='str')\\n \\n test_data = []\\n test_labels = []\\n with open(path_val+'/val_annotations.txt', 'r') as f:\\n val_annotations = [line.strip().split('\\\\t') for line in f]\\n val_annotations = np.array(val_annotations)\\n imgs_path = os.path.join(path_val, 'images')\\n imgs = os.listdir(imgs_path)\\n for img_name in imgs:\\n img_path = os.path.join(imgs_path, img_name)\\n img = cv2.imread(img_path)\\n b, g, r = cv2.split(img)\\n img = cv2.merge([r,g,b]).reshape(-1, 64, 64, 3)\\n test_data.append(img)\\n label = val_annotations[val_annotations[:, 0] == img_name, 1].astype('U9')\\n test_labels.append(label)\\n test_data = np.concatenate(test_data)\\n test_labels = np.concatenate(test_labels)\\n test_labels = np.array(test_labels, dtype='str')\\n \\n _, train_labels = np.unique(train_labels, return_inverse=True)\\n _, test_labels = np.unique(test_labels, return_inverse=True)\\n \\n del r, g, b, label, labels, imgs_path, img_name, img, imgs, val_annotations\\n \\n return train_data, train_labels, test_data, test_labels\",\n \"def evaluate(\\n args,\\n generator,\\n model,\\n iou_threshold=0.5,\\n score_threshold=0.05,\\n max_detections=100,\\n save_path=None\\n):\\n # gather all detections and annotations\\n all_detections = _get_detections(args, generator, model, score_threshold=score_threshold, max_detections=max_detections, save_path=save_path)\\n all_annotations = _get_annotations(generator)\\n # print('all detections:', all_detections)\\n # print('all all_annotations:', all_annotations)\\n average_precisions = {}\\n\\n # all_detections = pickle.load(open('all_detections.pkl', 'rb'))\\n # all_annotations = pickle.load(open('all_annotations.pkl', 'rb'))\\n # pickle.dump(all_detections, open('all_detections.pkl', 'wb'))\\n # pickle.dump(all_annotations, open('all_annotations.pkl', 'wb'))\\n\\n # process detections and annotations\\n # print('###########')\\n # print(generator.num_classes())\\n\\n for label in range(generator.num_classes()):\\n if not generator.has_label(label):\\n print('generator has not label')\\n continue\\n\\n false_positives = np.zeros((0,))\\n true_positives = np.zeros((0,))\\n false_negatives = np.zeros((0,))\\n scores = np.zeros((0,))\\n num_annotations = 0.0\\n\\n for i in range(generator.size()):\\n detections = all_detections[i][label]\\n annotations = all_annotations[i][label]\\n num_annotations += annotations.shape[0]\\n detected_annotations = []\\n\\n for d in detections:\\n scores = np.append(scores, d[4])\\n\\n if annotations.shape[0] == 0:\\n # print('NO bbox annos')\\n # print(d)\\n false_positives = np.append(false_positives, 0)\\n true_positives = np.append(true_positives, 0)\\n false_negatives = np.append(false_negatives, 1)\\n continue\\n\\n overlaps = compute_overlap(np.expand_dims(d, axis=0), annotations)\\n assigned_annotation = np.argmax(overlaps, axis=1)\\n max_overlap = overlaps[0, assigned_annotation]\\n\\n if max_overlap >= iou_threshold and assigned_annotation not in detected_annotations:\\n false_positives = np.append(false_positives, 0)\\n true_positives = np.append(true_positives, 1)\\n detected_annotations.append(assigned_annotation)\\n else:\\n false_positives = np.append(false_positives, 1)\\n true_positives = np.append(true_positives, 0)\\n\\n # no annotations -> AP for this class is 0 (is this correct?)\\n if num_annotations == 0:\\n average_precisions[label] = 0, 0\\n continue\\n\\n # print(num_annotations)\\n # print(all_detections[0][label].shape)\\n # print(all_detections[0][label])\\n TP = sum(true_positives)\\n FP = sum(false_positives)\\n FN = sum(false_negatives)\\n # sort by score\\n indices = np.argsort(-scores)\\n false_positives = false_positives[indices]\\n true_positives = true_positives[indices]\\n\\n # compute false positives and true positives\\n false_positives = np.cumsum(false_positives)\\n true_positives = np.cumsum(true_positives)\\n\\n # compute recall and precision\\n recall = true_positives / num_annotations\\n precision = true_positives / np.maximum(true_positives + false_positives, np.finfo(np.float64).eps)\\n\\n\\n print('TP:', TP)\\n print('FP:', FP)\\n print('FN:', FN)\\n print('Recall:', TP / (TP + FN))\\n print('Precision:', TP / (TP + FP))\\n\\n # compute average precision\\n average_precision = _compute_ap(recall, precision)\\n average_precisions[label] = average_precision, num_annotations\\n\\n return average_precisions\",\n \"def main():\\n feature_extraction_model = \\\"HOG\\\"\\n # feature_extraction_models = [\\\"CM\\\", \\\"HOG\\\"]\\n feature_extraction_model_1 = \\\"CM\\\"\\n dimension_reduction_model = \\\"PCA\\\"\\n k_value = 10\\n dim_k_value = 40\\n # K_value = 20\\n # lab_folder = \\\"Dataset3/Labelled/Set1\\\"\\n # unlab_folder = \\\"Dataset3/Unlabelled/Set 2\\\"\\n lab_folder = get_input_folder(\\\"Labelled Folder\\\")\\n unlab_folder = get_input_folder(\\\"Classify\\\")\\n start = time.time()\\n # ================================================================================================================\\n # labelled Images\\n dim_red = DimensionReduction(feature_extraction_model, dimension_reduction_model, dim_k_value,\\n folder_metadata=lab_folder,\\n metadata_collection=\\\"labelled\\\")\\n obj_feat_lab = dim_red.get_object_feature_matrix()\\n features_list_lab = np.array(obj_feat_lab['featureVector'].tolist())\\n images_list_lab = np.array(obj_feat_lab['imageId'])\\n # filtering the labelled set\\n dorsal_list, palmar_list = filter_images_by_label(images_list_lab)\\n\\n # unlabelled images\\n dim_red = DimensionReduction(feature_extraction_model, dimension_reduction_model, dim_k_value,\\n folder_metadata=unlab_folder,\\n metadata_collection=\\\"unlabelled\\\")\\n obj_feat_unlab = dim_red.get_object_feature_matrix()\\n features_list_unlab = np.array(obj_feat_unlab['featureVector'].tolist())\\n images_list_unlab = np.array(obj_feat_unlab['imageId'])\\n\\n # ================================================================================================================\\n # labelled Images\\n dim_red = DimensionReduction(feature_extraction_model_1, dimension_reduction_model, dim_k_value,\\n folder_metadata=lab_folder,\\n metadata_collection=\\\"labelled\\\")\\n obj_feat_lab_1 = dim_red.get_object_feature_matrix()\\n features_list_lab_1 = np.array(obj_feat_lab_1['featureVector'].tolist())\\n # images_list_lab = np.array(obj_feat_lab_1['imageId'])\\n # filtering the labelled set\\n\\n\\n # unlabelled images\\n dim_red = DimensionReduction(feature_extraction_model_1, dimension_reduction_model, dim_k_value,\\n folder_metadata=unlab_folder,\\n metadata_collection=\\\"unlabelled\\\")\\n obj_feat_unlab_1 = dim_red.get_object_feature_matrix()\\n features_list_unlab_1 = np.array(obj_feat_unlab_1['featureVector'].tolist())\\n # images_list_unlab = np.array(obj_feat_unlab['imageId'])\\n features_list_lab = np.concatenate((features_list_lab, features_list_lab_1), axis=1)\\n features_list_unlab = np.concatenate((features_list_unlab, features_list_unlab_1), axis=1)\\n\\n # ================================================================================================================\\n\\n dorsal_list, palmar_list = filter_images_by_label(images_list_lab)\\n features_list = np.concatenate((features_list_lab, features_list_unlab))\\n images_list = np.concatenate((images_list_lab, images_list_unlab))\\n images_list = list(images_list)\\n # Finding Similarity Matrix\\n cos_sim = cosine_similarity(features_list)\\n sim_graph = np.empty((0, len(cos_sim)))\\n for row in cos_sim:\\n k_largest = np.argsort(-np.array(row))[1:k_value + 1]\\n sim_graph_row = [d if i in k_largest else 0 for i, d in enumerate(row)]\\n sim_graph = np.append(sim_graph, np.array([sim_graph_row]), axis=0)\\n\\n row_sums = sim_graph.sum(axis=1)\\n sim_graph = sim_graph / row_sums[:, np.newaxis]\\n idx = 0\\n results_dorsal = ppr(sim_graph, images_list, dorsal_list)\\n results_palmar = ppr(sim_graph, images_list, palmar_list)\\n final_results = {}\\n\\n for img in images_list_unlab:\\n if results_dorsal[img] < results_palmar[img]:\\n final_results[img] = \\\"dorsal\\\"\\n else:\\n final_results[img] = \\\"palmar\\\"\\n\\n actual_labels = fetch_actual_labels(images_list_unlab)\\n print(\\\"Classification\\\")\\n no_correct = 0\\n correctly_classified = []\\n incorrectly_classified = []\\n print(\\\"| ImageId | Prediction | Actual |\\\")\\n for r in final_results:\\n print(\\\"| {} | {} | {} |\\\".format(r, final_results[r], actual_labels[r]))\\n if final_results[r] == actual_labels[r]:\\n correctly_classified.append(r)\\n no_correct += 1\\n else:\\n incorrectly_classified.append(r)\\n\\n print(\\\"Correctly classified: {}\\\\n\\\".format(correctly_classified))\\n print(\\\"InCorrectly classified: {}\\\\n\\\".format(incorrectly_classified))\\n\\n print(\\\"Classification Accuracy: {}%\\\".format(no_correct / len(images_list_unlab) * 100))\\n print(\\\"Execution time: {} seconds\\\".format(time.time() - start))\",\n \"def _classification(text_path_list, id_list, label_list):\\n textnum = len(text_path_list)\\n batched_num = ((textnum - 1) // classify.BATCH_SIZE + 1) * classify.BATCH_SIZE\\n for i in range(batched_num - textnum):\\n text_path_list.append(text_path_list[0])\\n id_list.append(id_list[0])\\n annotations = classify_obj.inference(text_path_list, id_list, label_list) #\\n return annotations[0:textnum]\",\n \"def gen_for_label(l, extra_args, out_file):\\n print('Generating pic for cif label {} ...'.format(l))\\n\\n LIMIT = 10\\n links = google_images_download.get_image_links(\\n search_keywords=[l], keywords=['high resolution'], requests_delay=0, limit=LIMIT)\\n\\n at = 0\\n while at < LIMIT:\\n target = links[at]\\n try:\\n print(\\\"Generating {} from {} ... \\\".format(out_file, target))\\n gen_args = [\\n str(target), '--use_cifar', '--cifar_dir', cifar_dir, '--cifar_label', l,\\n out_file\\n ] + extra_args\\n subprocess.check_call(['python3', 'main.py'] + gen_args)\\n print(\\\"done generating {}\\\".format(out_file))\\n return target\\n except subprocess.CalledProcessError as e:\\n print(\\\"encountered error in {} for {}, trying next url\\\".format(target, out_file))\\n at += 1\\n raise Error(\\\"Could not generate pic for cifar label {}, increase LIMIT\\\".format(l))\",\n \"def _load_pascal_annotation(self, index):\\n imagefile = os.path.join(self._data_path, 'Images', index + self._image_ext)\\n labelfile = os.path.join(self._data_path, 'GroundTruth', index + '.txt')\\n print 'Loading: image {} and label {}'.format(imagefile, labelfile)\\n\\n labels = np.loadtxt(labelfile) + 1\\n image = cv2.imread(imagefile)\\n\\n gt_classes = []\\n gt_boxes = []\\n\\n boxes, classes = self._get_boxes(image, labels)\\n\\n boxes = np.array(boxes)\\n classes = np.array(classes)\\n\\n scores = np.ones(len(boxes), dtype=np.float32)\\n dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32)\\n\\n # keep = nms(dets, 0.2)\\n _, keep = non_max_suppression_fast(dets, 0.2)\\n boxes = boxes[keep, :]\\n classes = classes[keep]\\n\\n for n, (x, y, w, h) in enumerate(boxes):\\n # gt_boxes.append([min(x, w), min(y, h), min(abs(w - x), w), min(abs(h - y), h)])\\n gt_boxes.append([x, y, w - x, h - y])\\n gt_classes.append(classes[n])\\n\\n overlaps = np.zeros((len(gt_boxes), len(self._classes)), dtype=np.float32)\\n for n, obj in enumerate(gt_boxes):\\n overlaps[n, gt_classes[n]] = 1.0\\n\\n overlaps = scipy.sparse.csr_matrix(overlaps)\\n\\n return {'boxes': np.array(gt_boxes),\\n 'gt_classes': np.array(gt_classes, dtype=np.int32),\\n 'gt_overlaps': overlaps,\\n 'flipped': False}\\n\\n # def get_data_from_tag(node, tag):\\n # return node.getElementsByTagName(tag)[0].childNodes[0].data\\n\\n # with open(filename) as f:\\n # data = minidom.parseString(f.read())\\n\\n # objs = data.getElementsByTagName('object')\\n # num_objs = len(objs)\\n\\n # boxes = np.zeros((num_objs, 4), dtype=np.uint16)\\n # gt_classes = np.zeros((num_objs), dtype=np.int32)\\n # overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)\\n\\n # # Load object bounding boxes into a data frame.\\n # for ix, obj in enumerate(objs):\\n # # Make pixel indexes 0-based\\n # x1 = float(get_data_from_tag(obj, 'xmin')) - 1\\n # y1 = float(get_data_from_tag(obj, 'ymin')) - 1\\n # x2 = float(get_data_from_tag(obj, 'xmax')) - 1\\n # y2 = float(get_data_from_tag(obj, 'ymax')) - 1\\n # cls = self._class_to_ind[str(get_data_from_tag(obj, \\\"name\\\")).lower().strip()]\\n # boxes[ix, :] = [x1, y1, x2, y2]\\n # gt_classes[ix] = cls\\n # overlaps[ix, cls] = 1.0\\n\\n # overlaps = scipy.sparse.csr_matrix(overlaps)\\n\\n # return {'boxes': boxes,\\n # 'gt_classes': gt_classes,\\n # 'gt_overlaps': overlaps,\\n # 'flipped': False}\",\n \"def draw_label_on_image(root_folder_path,root_folder_name,img_name,img_type,class_name,bb_color,bb_list):\\n img_path=os.path.join(root_folder_path,root_folder_name,img_type,img_name+\\\".png\\\")\\n img=cv2.imread(img_path)\\n for each_bb in bb_list:\\n cv2.rectangle(img,(each_bb[0],each_bb[2]),(each_bb[1],each_bb[3]),bb_color,3)\\n cv2.putText(img,class_name,(each_bb[0],each_bb[3]),cv2.FONT_HERSHEY_SIMPLEX,1,(255,0,0),2,cv2.LINE_AA)\\n cv2.imwrite(img_path,img)\",\n \"def test_classifier(self):\\n \\n files = 0\\n tp = 0\\n fp = 0\\n tn = 0\\n fn = 0\\n \\n for testFile in os.listdir(self.testing_dir):\\n if os.path.splitext(testFile)[1] in self.valid:\\n\\n files += 1\\n fileName = self.testing_dir + \\\"/\\\" + testFile\\n\\n img = cv2.imread(fileName).astype(np.float32)\\n self.Helpers.logger.info(\\\"Loaded test image \\\" + fileName)\\n \\n img = cv2.resize(img, (self.Helpers.confs[\\\"cnn\\\"][\\\"data\\\"][\\\"dim\\\"], \\n self.Helpers.confs[\\\"cnn\\\"][\\\"data\\\"][\\\"dim\\\"]))\\n img = self.reshape(img)\\n \\n prediction = self.get_predictions(img)\\n \\n msg = \\\"\\\"\\n if prediction == 1 and \\\"_1.\\\" in testFile:\\n tp += 1\\n msg = \\\"Acute Lymphoblastic Leukemia correctly detected (True Positive)\\\"\\n elif prediction == 1 and \\\"_0.\\\" in testFile:\\n fp += 1\\n msg = \\\"Acute Lymphoblastic Leukemia incorrectly detected (False Positive)\\\"\\n elif prediction == 0 and \\\"_0.\\\" in testFile:\\n tn += 1\\n msg = \\\"Acute Lymphoblastic Leukemia correctly not detected (True Negative)\\\"\\n elif prediction == 0 and \\\"_1.\\\" in testFile:\\n fn += 1\\n msg = \\\"Acute Lymphoblastic Leukemia incorrectly not detected (False Negative)\\\"\\n self.Helpers.logger.info(msg)\\n \\n self.Helpers.logger.info(\\\"Images Classifier: \\\" + str(files))\\n self.Helpers.logger.info(\\\"True Positives: \\\" + str(tp))\\n self.Helpers.logger.info(\\\"False Positives: \\\" + str(fp))\\n self.Helpers.logger.info(\\\"True Negatives: \\\" + str(tn))\\n self.Helpers.logger.info(\\\"False Negatives: \\\" + str(fn))\",\n \"def correctionToAnnotation(listOfAnnotations):\\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\\n indexAnnotation = input(u'Give the index of the annotation : ')\\n # make sure to have the right index\\n while True:\\n try:\\n indexAnnotation = int(indexAnnotation)\\n if indexAnnotation < len(listOfAnnotations):\\n break\\n else:\\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\\n indexAnnotation = input(u'Index out of bounds. Repeat : ')\\n except ValueError:\\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\\n indexAnnotation = input(u'Index given is not integral. Repeat : ')\\n # get the new annotation element\\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\\n annotatorGeneralInput = input(u\\\"Old annotation is '{0}'. Give new annotation : \\\".format(listOfAnnotations[indexAnnotation]))\\n # make sure to have the right general annotation\\n while True:\\n if annotatorGeneralInput in [u'0', u'1', u'0.0', u'0.1', u'0.2', u'1.0', u'1.1', u'1.2', u'1.3', u'1.4']:\\n break\\n else:\\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\\n annotatorGeneralInput = input(u'Repeat annotation: ')\\n # if we still need to specify what type of alignment or misalignment\\n if annotatorGeneralInput in [u'0', u'1']:\\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\\n # get the second part of the annotation (aligned or not)\\n annotatorSpecificInput = input(u'Specific type annotation: ')\\n typeAnswers = [u'0', u'1', u'2'] if annotatorGeneralInput == 0 else [u'0', u'1', u'2', u'3', u'4']\\n # make sure to have the right specific annotation\\n while True:\\n if annotatorSpecificInput in typeAnswers:\\n break\\n else:\\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\\n annotatorSpecificInput = input(u'Repeat type annotation: ')\\n # make the replacement\\n listOfAnnotations[indexAnnotation] = float(u'{0}.{1}'.format(annotatorGeneralInput, annotatorSpecificInput))\\n # if the right answer was given in the right format right away\\n else:\\n # make the replacement\\n listOfAnnotations[indexAnnotation] = float(annotatorGeneralInput)\\n # get back to the standard annotation\\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\\n annotatorGeneralInput = input(u'Correctly replaced. Back to current annotation : ')\\n if annotatorGeneralInput in [u'c', u'correct']:\\n annotatorGeneralInput, listOfAnnotations = correctionToAnnotation(listOfAnnotations)\\n return annotatorGeneralInput, listOfAnnotations\",\n \"def preprocessing_labels1(y,c = 1.,m = 0.6, f = 0.2 ,dataset = 'mnist'):\\n perm_mnist = [3,5,8,6,0,4,7,9,2,1]\\n perm_fmnist = [0,2,6,3,4,5,7,9,1,8]\\n perm = [0,1,2,3,4,5,6,7,8,9]\\n perm_cifar10 = [0,8,1,9,2,6,3,5,4,7]\\n n = y.shape[0]\\n y_res1 = np.zeros((int(c*n),2))\\n print(int(c*n))\\n y_res3 = np.zeros((int(f*n),10))\\n if dataset == 'cifar10':\\n perm = perm_cifar10\\n elif dataset == 'mnist':\\n perm = perm_mnist\\n elif dataset == 'fashion_mnist':\\n perm = perm_fmnist\\n if dataset == 'cifar10':\\n y_res2= np.zeros((int(m*n),5))\\n for i in range(n):\\n if i< int(c*n):\\n if np.argmax(y[i]) in [0,1,8,9]:\\n y_res1[i,0] = 1\\n else :\\n y_res1[i,1] = 1\\n if i<int(m*n):\\n if np.argmax(y[i]) in [0,8]:\\n y_res2[i,0] = 1\\n elif np.argmax(y[i]) in [1,9]:\\n y_res2[i,1] = 1\\n elif np.argmax(y[i]) in [2,6]:\\n y_res2[i,2] = 1\\n elif np.argmax(y[i]) in [3,5]:\\n y_res2[i,3] = 1\\n elif np.argmax(y[i]) in [4,7]:\\n y_res2[i,4] = 1\\n if i<int(f*n):\\n y_res3[i,np.argmax(y[i])] = 1\\n return(y_res1,y_res2,y_res3)\\n else :\\n y_res2= np.zeros((int(m*n),4))\\n for i in range(n):\\n if i< int(c*n):\\n if np.argmax(y[i]) in perm[0:5]:\\n y_res1[i,0] = 1\\n else :\\n y_res1[i,1] = 1\\n if i<int(m*n):\\n if np.argmax(y[i]) in perm[0:3]:\\n y_res2[i,0] = 1\\n elif np.argmax(y[i]) in perm[3:5]:\\n y_res2[i,1] = 1\\n elif np.argmax(y[i]) in perm[5:8]:\\n y_res2[i,2] = 1\\n elif np.argmax(y[i]) in perm[8:]:\\n y_res2[i,3] = 1\\n if i<int(f*n):\\n y_res3[i,np.argmax(y[i])] = 1\\n return(y_res1,y_res2,y_res3)\",\n \"def get_random_data(annotation_line, input_shape, random=True, max_boxes=20, jitter=.3, hue=.1, sat=1.5, val=1.5,\\n proc_img=True):\\n line = annotation_line.split()\\n image = Image.open(line[0])\\n iw, ih = image.size\\n h, w = input_shape\\n box = np.array([np.array(list(map(int, box.split(',')))) for box in line[1:]])\\n\\n if not random:\\n # resize image\\n scale = min(w / iw, h / ih)\\n nw = int(iw * scale)\\n nh = int(ih * scale)\\n dx = (w - nw) // 2\\n dy = (h - nh) // 2\\n image_data = 0\\n if proc_img:\\n image = image.resize((nw, nh), Image.BICUBIC)\\n new_image = Image.new('RGB', (w, h), (0, 0, 0))\\n new_image.paste(image, (dx, dy))\\n image_data = np.array(new_image) / 255.\\n\\n # correct boxes\\n box_data = np.zeros((max_boxes, 5))\\n if len(box) > 0:\\n np.random.shuffle(box)\\n if len(box) > max_boxes:\\n box = box[:max_boxes]\\n box[:, [0, 2]] = box[:, [0, 2]] * scale + dx\\n box[:, [1, 3]] = box[:, [1, 3]] * scale + dy\\n box_data[:len(box)] = box\\n\\n return image_data, box_data\\n\\n # resize image\\n new_ar = w / h * rand(1 - jitter, 1 + jitter) / rand(1 - jitter, 1 + jitter)\\n scale = rand(.25, 2)\\n if new_ar < 1:\\n nh = int(scale * h)\\n nw = int(nh * new_ar)\\n else:\\n nw = int(scale * w)\\n nh = int(nw / new_ar)\\n image = image.resize((nw, nh), Image.BICUBIC)\\n\\n # place image\\n dx = int(rand(0, w - nw))\\n dy = int(rand(0, h - nh))\\n new_image = Image.new('RGB', (w, h), (0, 0, 0))\\n new_image.paste(image, (dx, dy))\\n image = new_image\\n\\n # flip image or not\\n flip = rand() < .5\\n if flip:\\n image = image.transpose(Image.FLIP_LEFT_RIGHT)\\n\\n # distort image\\n hue = rand(-hue, hue)\\n sat = rand(1, sat) if rand() < .5 else 1 / rand(1, sat)\\n val = rand(1, val) if rand() < .5 else 1 / rand(1, val)\\n x = rgb_to_hsv(np.array(image) / 255.)\\n x[..., 0] += hue\\n x[..., 0][x[..., 0] > 1] -= 1\\n x[..., 0][x[..., 0] < 0] += 1\\n x[..., 1] *= sat\\n x[..., 2] *= val\\n x[x > 1] = 1\\n x[x < 0] = 0\\n image_data = hsv_to_rgb(x) # numpy array, 0 to 1\\n\\n # correct boxes\\n box_data = np.zeros((max_boxes, 5))\\n if len(box) > 0:\\n np.random.shuffle(box)\\n box[:, [0, 2]] = box[:, [0, 2]] * nw / iw + dx\\n box[:, [1, 3]] = box[:, [1, 3]] * nh / ih + dy\\n if flip:\\n box[:, [0, 2]] = w - box[:, [2, 0]]\\n box[:, 0:2][box[:, 0:2] < 0] = 0\\n box[:, 2][box[:, 2] > w] = w\\n box[:, 3][box[:, 3] > h] = h\\n box_w = box[:, 2] - box[:, 0]\\n box_h = box[:, 3] - box[:, 1]\\n box = box[np.logical_and(box_w > 1, box_h > 1)] # discard invalid box\\n if len(box) > max_boxes:\\n box = box[:max_boxes]\\n box_data[:len(box)] = box\\n\\n return image_data, box_data\",\n \"def generate_cand_val(\\n cand_path,\\n img_path_root,\\n save_path_root,\\n annotation_path,\\n sequence=1,\\n plot_size=9,\\n sigma_t=1,\\n):\\n\\n cands = io.loadmat(cand_path)[\\\"candi\\\"]\\n try:\\n annotations = io.loadmat(str(annotation_path))[\\\"result\\\"]\\n annotations[:, 1] = annotations[:, 1] + 30\\n annotations[:, 2] = annotations[:, 2] + 30\\n except:\\n annotations = None\\n\\n lim_size = np.array([1040, 1392])\\n\\n lim_size = lim_size + 60\\n\\n for cand_id, cand in enumerate(cands):\\n length = int(cand[0])\\n frame = cand[6] + 1\\n center_min = (cand[1:3] + cand[3:5]) / 2 - 64\\n center_min = center_min[::-1]\\n center_min[0] = center_min[0].clip(0, lim_size[0] - 128)\\n center_min[1] = center_min[1].clip(0, lim_size[1] - 128)\\n center_min = center_min.astype(np.int)\\n center_max = center_min + 128\\n\\n if frame == 1:\\n length += 2\\n elif frame == 2:\\n length += 3\\n frame -= 1\\n else:\\n length += 4\\n frame -= 2\\n\\n for_calcurate_max_frame = length / 16 + 1\\n if (frame + for_calcurate_max_frame * 16) > 1013:\\n if (frame + length) > 1013:\\n print(length)\\n length = length - ((frame + length) - 1013)\\n\\n depth = int(length / 16) + 1\\n\\n depth = depth * 16\\n\\n if length < 256:\\n imgs_targets_tra(\\n length,\\n img_path_root,\\n frame,\\n sequence,\\n center_min,\\n center_max,\\n annotations,\\n depth,\\n plot_size,\\n sigma_t,\\n save_path_root,\\n cand_id,\\n )\",\n \"def detect_image(yolo_v3_model, image_paths, batch_frames, output_path, train_input_size, classes_file_path, \\n score_threshold, iou_threshold, num_of_anchor_bbox, strides, anchors, show = False, \\n rectangle_colors = ''):\\n \\n # obtain number of classes\\n num_of_classes = len(read_class_names(classes_file_path))\\n \\n # create list to store images\\n original_images = []\\n \\n # iterate over images in chronological order (last image is image of interest to put bbox)\\n for x in range(batch_frames):\\n \\n # obtain original image\\n original_image = cv2.imread(image_paths[x])\\n \\n # append original image to original_images list\\n original_images.append(original_image[:])\\n \\n # convert original image to grayscale \\n image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)\\n \\n # preprocess image\\n image = transform_images(image[:], train_input_size)\\n\\n # obtain concat frame if none exist\\n if x == 0: \\n\\n concat_image = image[:]\\n\\n # concatenate subsequent frames to concat_image\\n else:\\n\\n concat_image = np.concatenate((concat_image, image), axis = -1)\\n \\n # add batch dimensions to concatenated image \\n concat_image = concat_image[np.newaxis, ...].astype(np.float32)\\n \\n # create constant tensor from concatenated image and feed it to yolo_v3_model\\n batched_input = tf.constant(concat_image)\\n yolo_output = yolo_v3_model(batched_input)\\n \\n # list to store bboxes from respective scales\\n pred_bbox = []\\n \\n # iterate over 3 scales\\n for i in range(3):\\n\\n # decode resepctive yolo_output from each scale\\n pred_result = decode(yolo_output = yolo_output[i], num_of_anchor_bbox = num_of_anchor_bbox, \\n classes = num_of_classes, strides = strides, anchors = anchors, index = i)\\n \\n # obtain results of shape (:, 5 + num_classes), i.e all bboxes\\n pred_result_reshaped = tf.reshape(pred_result, (-1, tf.shape(pred_result)[-1]))\\n \\n # append to pred_bbox\\n pred_bbox.append(pred_result_reshaped)\\n \\n # concatenate all bboxes from all scales\\n pred_bbox = tf.concat(pred_bbox, axis = 0)\\n \\n # post process all bboxes using latest image in orignal_images\\n bboxes = postprocess_boxes(pred_bbox, original_images[-1], train_input_size, score_threshold)\\n \\n # non maximal supression for bboxes\\n bboxes = nms(bboxes, iou_threshold, method = 'nms')\\n \\n # draw bbox on latest image in orignal_images\\n image = draw_bbox(original_images[-1], bboxes, classes_file_path, rectangle_colors = rectangle_colors)\\n \\n # save image if path to save is given\\n if output_path != '': cv2.imwrite(output_path, image)\\n \\n # display image if show is true \\n if show:\\n \\n # show the image\\n cv2.imshow(\\\"predicted image\\\", image)\\n \\n # load and hold the image\\n cv2.waitKey(0)\\n \\n # to close the window after the required kill value was provided\\n cv2.destroyAllWindows()\\n \\n return image\",\n \"def classify_images():\\n\\n # Load the desired image\\n img_path = 'dataset/colorize_images/n02085782_919.jpg'\\n img = image.load_img(img_path, target_size=(299, 299))\\n x = image.img_to_array(img)\\n x = np.expand_dims(x, axis=0)\\n x = preprocess_input(x)\\n\\n model = InceptionV3(weights=\\\"imagenet\\\")\\n preds = model.predict(x)\\n # decode the results into a list of tuples (class, description, probability)\\n # (one such list for each sample in the batch)\\n print('Predicted:', decode_predictions(preds, top=3)[0])\",\n \"def __call__(self, *args, **kwargs):\\n\\n dataset = TextOnlyCocoAnnotation()\\n\\n if self.subset == 'train':\\n image_paths, annotation_paths = self.collect_train_paths()\\n elif self.subset == 'val':\\n image_paths, annotation_paths = self.collect_val_paths()\\n\\n for image_path, annotation_path in tqdm(zip(image_paths, annotation_paths)):\\n word_annotations = []\\n with open(annotation_path, encoding='utf-8-sig') as read_file:\\n content = [line.strip() for line in read_file.readlines()]\\n for line in content:\\n word_annotations.append(self.parse_line(line))\\n should_add = not self.is_latin_required\\n if self.is_latin_required:\\n for word_annotation in word_annotations:\\n if word_annotation['attributes']['language'].lower() == 'latin':\\n should_add = True\\n break\\n if should_add:\\n for word_annotation in word_annotations:\\n dataset.add_bbox(image_path, imagesize.get(image_path), word_annotation)\\n\\n return dataset\",\n \"def __call__(self, *args, **kwargs):\\n\\n dataset = TextOnlyCocoAnnotation()\\n\\n image_paths, annotation_paths = self.collect_train_paths()\\n\\n for image_path, annotation_path in tqdm(zip(image_paths, annotation_paths)):\\n word_annotations = []\\n with open(annotation_path, encoding='utf-8-sig') as read_file:\\n content = [line.strip() for line in read_file.readlines()]\\n for line in content:\\n word_annotations.append(self.parse_line(line))\\n should_add = not self.is_latin_required\\n if self.is_latin_required:\\n for word_annotation in word_annotations:\\n if word_annotation['attributes']['language'].lower() == 'latin':\\n should_add = True\\n break\\n if should_add:\\n for word_annotation in word_annotations:\\n dataset.add_bbox(image_path, imagesize.get(image_path), word_annotation)\\n\\n return dataset\",\n \"def detect(model, dataset_dir, subset):\\n print(\\\"Running on {}\\\".format(dataset_dir))\\n\\n # Create directory\\n if not os.path.exists(RESULTS_DIR):\\n os.makedirs(RESULTS_DIR)\\n submit_dir = \\\"submit_{:%Y%m%dT%H%M%S}\\\".format(datetime.datetime.now())\\n submit_dir = os.path.join(RESULTS_DIR, submit_dir)\\n os.makedirs(submit_dir)\\n\\n # Read dataset\\n dataset = TamperDataset()\\n dataset.load_tamper(dataset_dir, subset)\\n dataset.prepare()\\n # Load over images\\n submission = []\\n f1 = 0\\n print(len(dataset.image_ids))\\n # for image_id in dataset.image_ids:\\n # # Load image and run detection\\n # image = dataset.load_image(image_id)\\n # # Detect objects\\n # r = model.detect([image], verbose=0)[0]\\n\\n # # Encode image to RLE. Returns a string of multiple lines\\n # source_id = dataset.image_info[image_id][\\\"id\\\"]\\n # rle = mask_to_rle(source_id, r[\\\"masks\\\"], r[\\\"scores\\\"])\\n # submission.append(rle)\\n # # Save image with masks\\n\\n # N = r[\\\"scores\\\"].shape[0]\\n # if not N:\\n # \\tH, W, C = image.shape\\n # \\tmask = np.zeros((H,W))\\n\\n \\t\\n # else:\\n\\n # H, W, C = image.shape\\n\\n # idx = np.argsort(-r[\\\"scores\\\"])\\n # mask = r[\\\"masks\\\"][:,:,idx[0]].astype(np.float32)\\n\\n # bbox = r[\\\"rois\\\"][idx[0], :4]\\n\\n # y1, x1, y2, x2 = bbox\\n\\n\\n\\n # mask = dense_crf(image, mask)\\n\\n # mask = np.where(mask >= 0.5, 255, 0)\\n\\n # H, W, C = image.shape\\n\\n # full_mask = np.zeros((H, W))\\n # full_mask[y1:y2, x1:x2] = mask\\n\\n for image_id in dataset.image_ids:\\n # Load image and run detection\\n image = dataset.load_image(image_id)\\n # ela=dataset.load_ela(image_id)\\n # Detect objects\\n # r = model.detect([image],[ela], verbose=0)[0]\\n r = model.detect([image],verbose=0)[0]\\n\\n # Encode image to RLE. Returns a string of multiple lines\\n source_id = dataset.image_info[image_id][\\\"id\\\"]\\n rle = mask_to_rle(source_id, r[\\\"masks\\\"], r[\\\"scores\\\"])\\n submission.append(rle)\\n # Save image with masks\\n\\n N = r[\\\"scores\\\"].shape[0]\\n if not N:\\n H, W, C = image.shape\\n mask = np.zeros((H,W))\\n\\n \\n else:\\n idx = np.argsort(-r[\\\"scores\\\"])\\n mask = r[\\\"masks\\\"][:,:,idx[0]].astype(np.uint8)\\n\\n # save_image(mask, submit_dir, name=dataset.image_info[image_id][\\\"id\\\"]) \\n\\n\\n annotation = dataset.load_annaation(image_id)\\n annotation = np.where(annotation >= 0.5, 1, 0) \\n f = get_FM(mask, annotation)\\n f1 += f\\n\\n print(f1/len(dataset.image_ids))\\n\\n\\n\\n\\n # save_image(mask, submit_dir, name=dataset.image_info[image_id][\\\"id\\\"]) \\n\\n # visualize.display_instances(\\n # image, r['rois'], r['masks'], r['class_ids'],\\n # dataset.class_names, r['scores'],\\n # show_bbox=False, show_mask=False,\\n # title=\\\"Predictions\\\")\\n # plt.savefig(\\\"{}/{}.png\\\".format(submit_dir, dataset.image_info[image_id][\\\"id\\\"]))\\n\\n # Save to csv file\\n # submission = \\\"ImageId,EncodedPixels\\\\n\\\" + \\\"\\\\n\\\".join(submission)\\n # file_path = os.path.join(submit_dir, \\\"submit.csv\\\")\\n # with open(file_path, \\\"w\\\") as f:\\n # f.write(submission)\\n print(\\\"Saved to \\\", submit_dir)\",\n \"def decode_coco_detection_example(example, input_range=None):\\n image = tf.image.convert_image_dtype(example['image'], dtype=tf.float32)\\n\\n ### normalize\\n if input_range:\\n image = image * (input_range[1] - input_range[0]) + input_range[0]\\n else:\\n mean_rgb = tf.constant(MEAN_RGB, shape=[1, 1, 3], dtype=tf.float32)\\n std_rgb = tf.constant(STDDEV_RGB, shape=[1, 1, 3], dtype=tf.float32)\\n image = (image - mean_rgb) / std_rgb\\n\\n boxes = decode_boxes(example['objects']['bbox'], tf.shape(image)[0:2])\\n\\n target = {\\n 'area': example['objects']['area'],\\n 'boxes': boxes,\\n 'objects/id': example['objects']['id'],\\n 'is_crowd': example['objects']['is_crowd'],\\n 'labels': example['objects']['label'] + 1, # 0'th class is padding.\\n }\\n\\n # Filters objects to exclude degenerate boxes.\\n keep = tf.where(tf.logical_and(boxes[:, 2] > boxes[:, 0],\\n boxes[:, 3] > boxes[:, 1]))[:, 0]\\n target_kept = {k: tf.gather(v, keep) for k, v in target.items()}\\n target_kept = target\\n\\n target_kept['orig_size'] = tf.cast(tf.shape(image)[0:2], dtype=tf.int32)\\n target_kept['size'] = tf.identity(target_kept['orig_size'])\\n target_kept['image/id'] = example['image/id']\\n\\n return {\\n 'inputs': image,\\n 'label': target_kept,\\n }\",\n \"def _check_load_bbox(self, coco, entry):\\n ann_ids = coco.getAnnIds(imgIds=entry['id'], iscrowd=None)\\n objs = coco.loadAnns(ann_ids)\\n # check valid bboxes\\n valid_objs = []\\n valid_segs = []\\n width = entry['width']\\n height = entry['height']\\n for obj in objs:\\n if obj.get('ignore', 0) == 1:\\n continue\\n # crowd objs cannot be used for segmentation\\n if obj.get('iscrowd', 0) == 1:\\n continue\\n # need accurate floating point box representation\\n x1, y1, w, h = obj['bbox']\\n x2, y2 = x1 + np.maximum(0, w), y1 + np.maximum(0, h)\\n # clip to image boundary\\n x1 = np.minimum(width, np.maximum(0, x1))\\n y1 = np.minimum(height, np.maximum(0, y1))\\n x2 = np.minimum(width, np.maximum(0, x2))\\n y2 = np.minimum(height, np.maximum(0, y2))\\n # require non-zero seg area and more than 1x1 box size\\n if obj['area'] > self._min_object_area and x2 > x1 and y2 > y1 \\\\\\n and (x2 - x1) * (y2 - y1) >= 4:\\n contiguous_cid = self.json_id_to_contiguous[obj['category_id']]\\n valid_objs.append([x1, y1, x2, y2, contiguous_cid])\\n\\n segs = obj['segmentation'] # polygon or RLE\\n assert isinstance(segs, list) or isinstance(segs, dict), '{}'.format(obj.get('iscrowd', 0))\\n if isinstance(segs, list):\\n valid_segs.append([np.asarray(p).reshape(-1, 2).astype('float32')\\n for p in segs if len(p) >= 6])\\n else:\\n valid_segs.append(segs)\\n # there is no easy way to return a polygon placeholder: None is returned\\n # in validation, None cannot be used for batchify -> drop label in transform\\n # in training: empty images should be be skipped\\n if not valid_objs:\\n valid_objs = None\\n valid_segs = None\\n else:\\n valid_objs = np.asarray(valid_objs).astype('float32')\\n return valid_objs, valid_segs\",\n \"def visualize_detections(detection_result,\\n label_map,\\n font_path=None,\\n font_size=10,\\n line_width=1,\\n score_thresh=.5,\\n max_num_viz=None,\\n color_per_instance_mask=True):\\n image = np.copy(detection_result['image'])\\n height, width = image.shape[:2]\\n\\n scores = detection_result['scores']\\n boxes = detection_result['boxes']\\n classes = detection_result['classes']\\n\\n detection_indices = scores >= score_thresh\\n scores = scores[detection_indices]\\n boxes = boxes[detection_indices]\\n classes = classes[detection_indices]\\n\\n masks = None\\n if 'masks' in detection_result:\\n masks = detection_result['masks']\\n masks = masks[detection_indices]\\n\\n num_detections = scores.shape[0]\\n if max_num_viz is not None:\\n num_detections = np.minimum(num_detections, max_num_viz)\\n \\n color_map = get_color_map(len(label_map) + 1)\\n\\n for i in range(num_detections):\\n ymin, xmin, ymax, xmax = boxes[i].astype(np.int32)\\n ymin = np.maximum(0, ymin)\\n xmin = np.maximum(0, xmin)\\n ymax = np.minimum(height - 1, ymax)\\n xmax = np.minimum(width - 1, xmax)\\n color = color_map[classes[i]]\\n if color_per_instance_mask:\\n mask_color = (color_map[i] + 10) % color_map.shape[0]\\n if (mask_color[0] < 50).all(): \\n mask_color = (color_map[i] + 15) % color_map.shape[0]\\n else:\\n mask_color = color\\n\\n image[ymin : ymax, \\n np.maximum(xmin - line_width // 2, 0) : \\n np.minimum(xmin + line_width - line_width // 2, width)] = color\\n image[ymin : ymax, \\n np.maximum(xmax - line_width // 2, 0) : \\n np.minimum(xmax + line_width - line_width // 2, width)] = color\\n image[np.maximum(ymin - line_width // 2, 0) : \\n np.minimum(ymin + line_width - line_width // 2, height), \\n xmin : xmax] = color\\n image[np.maximum(ymax - line_width // 2, 0) : \\n np.minimum(ymax + line_width - line_width // 2, height), \\n xmin : xmax] = color\\n\\n detection_label_text = '%s: %.2f' % (\\n label_map[classes[i]], int(scores[i] * 100) / 100)\\n\\n if font_path is not None: \\n font = ImageFont.truetype(font_path, size=font_size)\\n else:\\n font = ImageFont.load_default()\\n text_width, text_height = font.getsize(detection_label_text)\\n\\n x = xmin\\n y = np.maximum(ymin - text_height, 0)\\n \\n image[y : np.minimum(y + text_height, height), \\n x : np.minimum(x + text_width, width)] = color \\n\\n img_obj = Image.fromarray(image)\\n draw = ImageDraw.Draw(img_obj)\\n draw.text((x, y), detection_label_text, TEXT_COLOR, font=font)\\n image = np.array(img_obj)\\n\\n if masks is not None:\\n draw_mask(image, masks[i], color=mask_color)\\n\\n return image\",\n \"def training_data_generation(DATA_DIR, img_height_size, img_width_size, label_list):\\r\\n \\r\\n img_ms_files = glob.glob(DATA_DIR + '\\\\\\\\Train_MS' + '\\\\\\\\Train_*.tif')\\r\\n img_pan_files = glob.glob(DATA_DIR + '\\\\\\\\Train_Pan' + '\\\\\\\\Train_*.tif')\\r\\n polygon_files = glob.glob(DATA_DIR + '\\\\\\\\Train_Polygons' + '\\\\\\\\Train_*.geojson')\\r\\n \\r\\n img_ms_array_list = []\\r\\n img_pan_array_list = []\\r\\n mask_array_list = []\\r\\n \\r\\n for file in range(len(img_ms_files)):\\r\\n with rasterio.open(img_ms_files[file]) as f:\\r\\n metadata = f.profile\\r\\n img_ms = np.transpose(f.read(tuple(np.arange(metadata['count']) + 1)), [1, 2, 0])\\r\\n \\r\\n with rasterio.open(img_pan_files[file]) as g:\\r\\n metadata_pan = g.profile\\r\\n img_pan = np.expand_dims(g.read(1), axis = 2)\\r\\n \\r\\n ms_to_pan_ratio = metadata['transform'][0] / metadata_pan['transform'][0]\\r\\n \\r\\n if (img_height_size % ms_to_pan_ratio) != 0 or (img_width_size % ms_to_pan_ratio) != 0:\\r\\n raise ValueError('Please make sure that both img_height_size and img_width_size can be divided by {}'.format(int(ms_to_pan_ratio)))\\r\\n \\r\\n mask = training_mask_generation(img_pan_files[file], polygon_files[file], labels = label_list)\\r\\n \\r\\n img_ms_array, img_pan_array, mask_array = image_clip_to_segment_and_convert(img_ms, img_pan, mask, ms_to_pan_ratio, \\r\\n img_height_size, img_width_size)\\r\\n \\r\\n img_ms_array_list.append(img_ms_array)\\r\\n img_pan_array_list.append(img_pan_array)\\r\\n mask_array_list.append(mask_array)\\r\\n \\r\\n img_ms_full_array = np.concatenate(img_ms_array_list, axis = 0)\\r\\n img_pan_full_array = np.concatenate(img_pan_array_list, axis = 0)\\r\\n mask_full_array = to_categorical(np.concatenate(mask_array_list, axis = 0), num_classes = len(label_list))\\r\\n \\r\\n return img_ms_full_array, img_pan_full_array, mask_full_array\",\n \"def my_evaluate(original_image, img_id, annotations, min_score=0.4, max_overlap=0.3, top_k=200):\\n\\n # Transform\\n image = normalize(to_tensor(resize(original_image)))\\n\\n # Move to default device\\n image = image.to(device)\\n\\n # Forward prop.\\n predicted_locs, predicted_scores = model(image.unsqueeze(0))\\n\\n # Detect objects in SSD output\\n det_boxes, det_labels, det_scores = model.detect_objects(predicted_locs, predicted_scores, min_score=min_score,\\n max_overlap=max_overlap, top_k=top_k)\\n\\n # Move detections to the CPU\\n det_boxes = det_boxes[0].to('cpu')\\n\\n # Transform to original image dimensions\\n original_dims = torch.FloatTensor(\\n [original_image.width, original_image.height, original_image.width, original_image.height]).unsqueeze(0)\\n det_boxes = det_boxes * original_dims\\n\\n # Decode class integer labels\\n det_labels = [rev_label_map[l] for l in det_labels[0].to('cpu').tolist()]\\n det_scores = det_scores[0].to('cpu').tolist()\\n\\n if det_labels != ['background']:\\n for i in range(det_boxes.size(0)):\\n \\n # For every detection, see whether it matches a ground truth\\n match = False\\n for j in range(len(annotations['boxes'])):\\n if annotations['labels'][j] == -1: # this is being set when a ground truth already matched\\n continue\\n match = box_match(det_boxes[i], \\n det_labels[i], \\n torch.Tensor(annotations['boxes'][j]), \\n rev_label_map[annotations['labels'][j]])\\n if match:\\n annotations['labels'][j] = -1\\n break\\n \\n if match: # true positive if the detection is correct and matched a ground truth\\n all_results['true_positive'] += 1\\n else: # false positive if the detection did not match a ground truth\\n all_results['false_positive'] += 1\\n \\n # After all detections were checked, let us see whether the detector missed something\\n for label in annotations['labels']: \\n if label == -1: # This is set after a detection matched this ground truth\\n continue\\n \\n # false negative if we reach this line, since the ground truth object was not found\\n all_results['false_negative'] += 1\",\n \"def generate_labels(pics):\\r\\n return []\",\n \"def preprocess_human_annotations_p1(problem_id, source_pickle, annotation_pickle):\\n label_dim, _, label_to_ix, _, _ = get_label_params(problem_id)\\n\\n with open(annotation_pickle) as fp:\\n annotations = cPickle.load(fp)\\n n_annotations = len(annotations)\\n\\n with open(source_pickle) as fp:\\n ast_programs = cPickle.load(fp)\\n\\n programs = []\\n program2label = {}\\n\\n for i in tqdm(range(n_annotations)):\\n annotation = annotations[i]\\n program_id = int(annotation['answerId'])\\n targets = json.loads(annotation['gradeData'])\\n targets = convert_V2_to_V3(targets)\\n targets = labels_to_numpy(targets, label_dim, label_to_ix).tolist()\\n\\n ast = ast_programs[program_id]\\n removeColors(ast)\\n ast = flatten_ast(ast)\\n program = ' '.join(ast)\\n\\n programs.append(program)\\n program2label[program] = targets\\n\\n # important, make sure there are no duplicates here\\n programs = list(set(programs))\\n labels = [program2label[prog] for prog in programs]\\n\\n return programs, labels\",\n \"def prep_image_data(arg_dict):\\n cat_df = pd.read_csv(arg_dict['category_file'],\\n skiprows=1,\\n sep='\\\\s+')\\n bbox_df = pd.read_csv(arg_dict['bbox_file'],\\n skiprows=1,\\n sep='\\\\s+')\\n img_dir = arg_dict['image_dir']\\n\\n combo_df = pd.merge(cat_df, bbox_df, how='outer', on='image_name')\\n combo_df['image_name'] = combo_df['image_name'].apply(\\n lambda x: x[len('img'):-len('.jpg')])\\n labels = Labels(combo_df, img_dir, n_images_loaded=-1)\\n labels.set_data_target('raw_image', chunksize=3000)\\n return labels\",\n \"def __call__(self):\\n\\n dataset = TextOnlyCocoAnnotation()\\n\\n with open(self.path) as read_file:\\n\\n json_loaded = json.load(read_file)\\n\\n for i, value in tqdm(json_loaded['imgs'].items()):\\n image_path = os.path.join(os.path.dirname(self.path), 'train2014',\\n value['file_name'])\\n dataset_type = value['set']\\n\\n if dataset_type not in self.sets:\\n print(dataset_type)\\n continue\\n\\n for annotation_id in json_loaded['imgToAnns'][i]:\\n annotation_value = json_loaded['anns'][str(annotation_id)]\\n word_annotation = self.parse_annotation_instance(annotation_value)\\n dataset.add_bbox(image_path, imagesize.get(image_path), word_annotation)\\n\\n return dataset\",\n \"def process(file_name):\\n img=Image.open(str(file_name))\\n cim_resized = img.resize((40,40), resample=Image.LANCZOS)\\n n = cim_resized.convert('L')\\n cropped = np.array(n).astype(np.float64)\\n im=Image.fromarray(cropped)\\n im.show()\\n normalized_cropped_image = cropped - np.mean(cropped)\\n normalized_cropped_image = normalized_cropped_image.reshape((-1, image_size, image_size, num_channels)).astype(np.float32)\\n predicted_arr = predict(normalized_cropped_image)\\n label = ''.join(['' if int(x[0]) == 10 else str(x[0]) for x in list(predicted_arr)])\\n print 'LABEL: ' + label\",\n \"def extract_images(f):\\r\\n if f == \\\"train\\\":\\r\\n D_train = numpy_array_stick(\\\"/home/luzihao/xiaoluo/xiyuan/CNN/standard2/\\\",10)\\r\\n #D_train = numpy_array_stick(\\\"D:\\\\\\\\FDU\\\\\\\\Template\\\\\\\\FDUROP\\\\\\\\face_detection_and_recognition\\\\\\\\standard2\\\\\\\\\\\",10)\\r\\n #\\\"X\\\" means data,\\\"Y\\\" means label\\r\\n Y_train = D_train[1]\\r\\n X_train = D_train[0].reshape(people*10,80,80,1)\\r\\n return X_train,Y_train\\r\\n elif f == \\\"test\\\":\\r\\n D_test = numpy_array_stick(\\\"/home/luzihao/xiaoluo/xiyuan/CNN/standard2/\\\",1)\\r\\n #D_test = numpy_array_stick(\\\"D:\\\\\\\\FDU\\\\\\\\Template\\\\\\\\FDUROP\\\\\\\\face_detection_and_recognition\\\\\\\\standard2\\\\\\\\\\\",1)\\r\\n #\\\"X\\\" means data,\\\"Y\\\" means label\\r\\n Y_test = D_test[1]\\r\\n X_test = D_test[0].reshape(people*1,80,80,1)\\r\\n return X_test,Y_test\",\n \"def demo(net, image_name,num_class,save_ff):\\r\\n\\r\\n # Load the demo image\\r\\n #im_file = os.path.join(cfg.DATA_DIR, 'demo', image_name)\\r\\n im_file=image_name\\r\\n im = cv2.imread(im_file)\\r\\n\\r\\n # Detect all object classes and regress object bounds\\r\\n timer = Timer()\\r\\n timer.tic()\\r\\n #for zzz in range(100):\\r\\n scores, boxes = im_detect(net, im)\\r\\n timer.toc()\\r\\n print ('Detection took {:.3f}s for '\\r\\n '{:d} object proposals').format(timer.total_time, boxes.shape[0])\\r\\n\\r\\n # Visualize detections for each class\\r\\n CONF_THRESH = 0.35\\r\\n NMS_THRESH = 0.3\\r\\n thresh=CONF_THRESH\\r\\n for cls_ind, cls in enumerate(range(num_class)):#CLASSES[1:]\\r\\n cls_ind += 1 # because we skipped background\\r\\n # cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]\\r\\n # cls_scores = scores[:, cls_ind]\\r\\n # dets = np.hstack((cls_boxes,\\r\\n # cls_scores[:, np.newaxis])).astype(np.float32)\\r\\n inds = np.where(scores[:, cls_ind] > thresh)[0]\\r\\n cls_scores = scores[inds, cls_ind]\\r\\n if cfg.TEST.AGNOSTIC:\\r\\n cls_boxes = boxes[inds, 4:8]\\r\\n else:\\r\\n cls_boxes = boxes[inds, cls_ind*4:(cls_ind+1)*4]\\r\\n dets = np.hstack((cls_boxes, cls_scores[:, np.newaxis])) \\\\\\r\\n .astype(np.float32, copy=False)\\r\\n keep = nms(dets, NMS_THRESH)\\r\\n dets = dets[keep, :]\\r\\n #vis_detections(im, cls, dets, thresh=CONF_THRESH)\\r\\n inds = np.where(dets[:, -1] >= thresh)[0]\\r\\n if len(inds) == 0:\\r\\n continue\\r\\n\\r\\n im_tmp = im#im[:, :, (2, 1, 0)]\\r\\n for i in inds:\\r\\n bbox = dets[i, :4]\\r\\n score = dets[i, -1]\\r\\n print bbox,score,cls\\r\\n cv2.rectangle(im_tmp, (bbox[0],bbox[1]), (bbox[2],bbox[3]), (0,0,255),2)\\r\\n #save_ff=\\\"/storage2/liushuai/faster_rcnn/FasterRCNN-Encapsulation-Cplusplus/faster_cxx_lib_ev2641/test_result.jpg\\\"\\r\\n im_tmp = im#im[:, :, (2, 1, 0)]\\r\\n cv2.imwrite(save_ff,im_tmp)\\r\\n #save_pic(im, cls, dets, thresh=CONF_THRESH,save_ff)\\r\",\n \"def test():\\n import os\\n import ClearMap.ImageProcessing.SpotDetection as self\\n reload(self)\\n import ClearMap.IO as io \\n import ClearMap.Settings as settings\\n \\n basedir = settings.ClearMapPath;\\n #fn = '/home/ckirst/Science/Projects/BrainActivityMap/Data/iDISCO_2015_06/Adult cfos C row 20HF 150524.ims';\\n fn = os.path.join(basedir, 'Test/Data/Synthetic/label_iDISCO_\\\\d{3}.tif');\\n fn = os.path.join(basedir, 'Test/Data/OME/16-17-27_0_8X-s3-20HF_UltraII_C00_xyz-Table Z\\\\d{4}.ome.tif');\\n #fn = '/run/media/ckirst/ChristophsBackuk4TB/iDISCO_2015_06/Adult cfos C row 20HF 150524.ims';\\n #fn = '/home/nicolas/Windows/Nico/cfosRegistrations/Adult cfos C row 20HF 150524 - Copy.ims';\\n #fn = '/home/ckirst/Science/Projects/BrainActivityMap/iDISCO_2015_04/test for spots added spot.ims'\\n\\n img = io.readData(fn);\\n #img = dataset[0:500,0:500,1000:1008];\\n #img = dataset[600:1000,1600:1800,800:830];\\n #img = dataset[500:1500,500:1500,800:809]; \\n img = img.astype('int16');\\n \\n #m = sys.modules['iDISCO.ImageProcessing.SpotDetection']\\n #c = self.detectCells(img);\\n \\n c = self.detectCells(img, dogSize = None, cellShapeThreshold = 1, cellShapeFile = '/home/ckirst/Science/Projects/BrainActivityMap/Analysis/iDISCO/Test/Data/CellShape/cellshape_\\\\d{3}.tif');\\n \\n print ('done, found %d cells !' % c[0].shape[0])\\n\\n\\n #test intensities:\\n import numpy;\\n x = numpy.random.rand(30,30,10);\\n centers = numpy.array([[0,0,0], [29,29,9]]);\\n i = self.findIntensity(x, centers, boxSize = (1,1,1));\\n print (i)\",\n \"def simulate_user_annotations(self):\\r\\n\\r\\n # Check existence of clustering results\\r\\n\\r\\n if len(self.recognized_faces) == 0:\\r\\n\\r\\n # Try to load YAML files\\r\\n if os.path.exists(self.cluster_files_path):\\r\\n\\r\\n print 'Loading YAML files with clustering results'\\r\\n logger.debug('Loading YAML file with clustering results')\\r\\n\\r\\n self.recognized_faces = []\\r\\n for yaml_file in os.listdir(self.cluster_files_path):\\r\\n yaml_file_path = os.path.join(\\r\\n self.cluster_files_path, yaml_file)\\r\\n with open(yaml_file_path) as f:\\r\\n self.recognized_faces.append(yaml.load(f))\\r\\n\\r\\n print 'YAML file with clustering results loaded'\\r\\n logger.debug('YAML file with clustering results loaded')\\r\\n\\r\\n else:\\r\\n\\r\\n print 'Warning! No clustering results found!'\\r\\n logger.warning('No clustering results found!')\\r\\n\\r\\n return\\r\\n\\r\\n auto_p_counter = 0\\r\\n\\r\\n user_rec_faces = []\\r\\n\\r\\n # Iterate through automatic recognized faces\\r\\n for auto_p_dict in self.recognized_faces:\\r\\n\\r\\n segment_list = auto_p_dict[c.SEGMENTS_KEY]\\r\\n\\r\\n # Get first segment\\r\\n if len(segment_list) >= 1:\\r\\n\\r\\n first_segment = segment_list[0]\\r\\n\\r\\n segment_ann_tag = first_segment[c.ANN_TAG_KEY]\\r\\n\\r\\n auto_p_dict[c.ASSIGNED_TAG_KEY] = segment_ann_tag\\r\\n\\r\\n user_rec_faces.append(auto_p_dict)\\r\\n\\r\\n auto_p_counter += 1\\r\\n\\r\\n self.recognized_faces = user_rec_faces\\r\\n\\r\\n # Save clustering result in YAML files\\r\\n\\r\\n # Remove previous files\\r\\n if os.path.exists(self.cluster_files_path):\\r\\n shutil.rmtree(self.cluster_files_path)\\r\\n # Create directory for people clustering results\\r\\n os.makedirs(self.cluster_files_path)\\r\\n\\r\\n counter = 0\\r\\n for person_dict in self.recognized_faces:\\r\\n yaml_file_name = str(counter) + '.YAML'\\r\\n yaml_file_path = os.path.join(self.cluster_files_path, yaml_file_name)\\r\\n utils.save_YAML_file(yaml_file_path, person_dict)\\r\\n counter += 1\",\n \"def detect_labels(path):\\n client = vision.ImageAnnotatorClient()\\n\\n with io.open(path, 'rb') as image_file:\\n content = image_file.read()\\n\\n image = vision.types.Image(content=content)\\n\\n response = client.label_detection(image=image)\\n labels = response.label_annotations\\n #print('Labels:')\\n\\n #for label in labels:\\n # print(label.description)\\n return labels\",\n \"def draw_all_labels(df,root_folder_path,root_folder_name,logger):\\n # df=df[df.type==\\\"Color\\\"]\\n len_images=df['file_id'].nunique()\\n perc_list=[i*0.05 for i in range(0,20,1)]\\n grouped_df=df.groupby(['file_id','class'])\\n coordinate_names=['x_max','x_min','y_max','y_min']\\n group_len=len(grouped_df)\\n\\n class_label_dict={}\\n label_info_list=[]\\n for ind,(name, group) in enumerate(grouped_df):\\n img_name,class_name=name\\n img_type=group['type'].values[0]\\n bb_list=group[coordinate_names].values.astype(int)\\n if class_name not in class_label_dict.keys():\\n class_label_dict[class_name]=get_random_color()\\n bb_color=class_label_dict[class_name]\\n label_info_list.append([img_name,img_type,class_name,bb_color,bb_list])\\n draw_label_on_image(root_folder_path,root_folder_name,img_name,img_type,class_name,bb_color,bb_list)\\n perc=float(\\\"{:.2f}\\\".format((ind+1)/group_len))\\n if perc in perc_list:\\n perc_list.remove(perc)\\n logger.write(\\\"Classes annotated: \\\"+str(ind+1)+\\\"/\\\"+str(group_len))\\n # print(\\\"Label list generated.\\\")\\n # pool = Pool(1)\\n # pool.starmap(draw_label_on_one_image, zip(\\n # label_info_list, itertools.repeat(root_folder_path), itertools.repeat(root_folder_name)))\\n # pool.close()\\n # pool.join()\\n # print(\\\"Drawing labels is finished.\\\")\",\n \"def main():\\n labels, data = load_image_data()\\n print(labels.shape, data.shape)\",\n \"def annotate_image(self, image, results):\\n predictedObjects = []\\n for i in range(len(results)):\\n objectParameters = []\\n x = int(results[i][1])\\n y = int(results[i][2])\\n w = int(results[i][3])\\n h = int(results[i][4])\\n # print(x, y, w, h, results[i][0])\\n imageHeight, imageWidth, _ = image.shape\\n w = w // 2\\n h = h // 2\\n # change to truncate boxes which go outside the image\\n xmin, xmax, ymin, ymax = 0, 0, 0, 0\\n xmin = 3 if not max(x - w, 0) else (x - w)\\n xmax = imageWidth - 3 if not min(x + w - imageWidth, 0) \\\\\\n else (x + w)\\n ymin = 1 if not max(y - h, 0) else (y - h)\\n ymax = imageHeight - 3 if not min(y + h - imageHeight, 0) \\\\\\n else (y + h)\\n if self.verbose:\\n print('Class : ' + results[i][0] + ', [x, y, w, h] [' +\\n str(x) + ', ' + str(y) + ', ' + str(w) + ', ' + str(h) +\\n '] Confidence : ' + str(results[i][5]))\\n \\n # Each class must have a unique color\\n color = tuple([(j * (1+self.classes.index(results[i][0])) % 255) \\\\\\n for j in self.seed])\\n cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color, 2)\\n if ymin <= 20:\\n cv2.rectangle(\\n image, (xmin, ymin), (xmax, ymin + 20), color, -1\\n )\\n cv2.putText(\\n image, results[i][0] + ': %.2f' % results[i][5],\\n (xmin+5, ymin+15), cv2.FONT_HERSHEY_SIMPLEX, 0.5,\\n (255, 255, 255), 2\\n )\\n else:\\n cv2.rectangle(image, (xmin, ymin), (xmax, ymin-20), color, -1)\\n cv2.putText(\\n image, results[i][0] + ': %.2f' % results[i][5],\\n (xmin+5, ymin-8), cv2.FONT_HERSHEY_SIMPLEX, 0.5,\\n (255, 255, 255), 2\\n )\\n objectParameters = [\\n results[i][0], xmin, ymin, xmax, ymax, results[i][5]\\n ]\\n predictedObjects.append(objectParameters)\\n return image, predictedObjects\\n # if self.outputFile:\\n # cv2.imwrite(self.outputFile,image)\",\n \"def draw_image(res, train, label, output_dir, fig, write_file=False):\\n size = len(label)\\n infer_res, infer_conf, gt_conf = compare_result(res, label)\\n if not os.path.exists(output_dir+\\\"/correct\\\"):\\n os.makedirs(output_dir+\\\"/correct\\\")\\n if not os.path.exists(output_dir + \\\"/incorrect\\\"):\\n os.makedirs(output_dir + \\\"/incorrect\\\")\\n\\n if write_file:\\n cor = open(output_dir+\\\"/res_correct.txt\\\", 'a')\\n err = open(output_dir+\\\"/res_incorrect.txt\\\", 'a')\\n # right inferences\\n r = 0\\n # error inferences\\n e = 0\\n # error details\\n count = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\\n for i in range(size):\\n img = Image.fromarray(train[i].reshape(fig, fig).astype(np.uint8))\\n if infer_res[i] == label[i]:\\n r += 1\\n img.save(output_dir+\\\"/correct/\\\"+str(i)+\\\"_\\\"+str(infer_res[i])+\\\"_\\\"+str(infer_conf[i])+\\\".png\\\")\\n if write_file:\\n cor.write(str(i)+\\\"_\\\"+str(infer_res[i])+\\\"_\\\"+str(infer_conf[i])+str(res[i][0][0][0])+'\\\\n')\\n\\n else:\\n\\n e += 1\\n img.save(output_dir + \\\"/incorrect/\\\" + str(i) + \\\"_gt:\\\" + str(label[i]) + \\\"_\\\" + str(\\n gt_conf[i]) + \\\"_inf:\\\" + str(infer_res[i]) + \\\"_\\\" + str(infer_conf[i]) + \\\".png\\\")\\n count[label[i]] += 1\\n if write_file:\\n err.write(str(i) + \\\"_gt:\\\"+str(label[i]) + \\\"_\\\"+str(gt_conf[i]) + \\\"_inf:\\\"+str(infer_res[i])+\\\"_\\\" +\\n str(infer_conf[i]) + str(res[i][0][0][0]) + '\\\\n')\\n if write_file:\\n cor.close()\\n err.close()\\n print(\\\"correct inference results:\\\"+str(r))\\n print(\\\"incorrect inference results:\\\" + str(e))\\n print(\\\"incorrect inference results details:\\\" + str(count))\",\n \"def make_sub_data_train(data, config):\\n sub_input_sequence = []\\n sub_label_sequence = []\\n\\n\\tfor scale in range(2,5):\\t \\n\\n\\t for i in range(len(data)):\\n\\n\\t\\t#input_, label_, = preprocess(data[i], config.scale) # do bicbuic only one scale\\n\\t\\tinput_, label_, = preprocess(data[i], scale) # do bicbuic turn around all scale\\n\\t\\n\\t\\tif len(input_.shape) == 3: # is color\\n\\t\\t h, w, c = input_.shape\\n\\t\\telse:\\n\\t\\t h, w = input_.shape # is grayscale\\n\\t\\n\\t\\t#checkimage(input_)\\t\\t\\n\\n\\t\\tnx, ny = 0, 0\\n\\t\\tfor x in range(0, h - config.image_size + 1, config.stride):\\n\\t\\t nx += 1; ny = 0\\n\\t\\t for y in range(0, w - config.image_size + 1, config.stride):\\n\\t\\t\\tny += 1\\n\\n\\t\\t\\tsub_input = input_[x: x + config.image_size, y: y + config.image_size] # 41 * 41\\n\\t\\t\\tsub_label = label_[x: x + config.label_size, y: y + config.label_size] # 41 * 41\\n\\n\\n\\t\\t\\t# Reshape the subinput and sublabel\\n\\t\\t\\tsub_input = sub_input.reshape([config.image_size, config.image_size, config.c_dim])\\n\\t\\t\\tsub_label = sub_label.reshape([config.label_size, config.label_size, config.c_dim])\\n\\n\\t\\t\\t# Normialize\\n\\t\\t\\tsub_input = sub_input / 255.0\\n\\t\\t\\tsub_label = sub_label / 255.0\\n\\t\\t\\t\\n\\t\\t\\t#cv2.imshow(\\\"im1\\\",sub_input)\\n\\t\\t\\t#cv2.imshow(\\\"im2\\\",sub_label)\\n\\t\\t\\t#cv2.imshow(\\\"residual\\\",sub_input - sub_label)\\n\\t\\t\\t#cv2.waitKey(0)\\n\\n\\t\\t\\t# Rotate 90,180,270\\n\\t\\t\\tfor angle in range(0,360,90):\\t\\n\\t\\t\\t\\tsub_input = rotate(sub_input,angle)\\t\\n\\t\\t\\t\\tsub_label = rotate(sub_label,angle)\\t\\n\\t\\t\\n\\t\\t\\t\\t# Add to sequence\\n\\t\\t\\t\\tsub_input_sequence.append(sub_input)\\n\\t\\t\\t\\tsub_label_sequence.append(sub_label)\\n\\n\\t\\t\\t\\tcv2.imshow(\\\"im1\\\",sub_input)\\n\\t\\t\\t\\tcv2.imshow(\\\"im2\\\",sub_label)\\n\\t\\t\\t\\tcv2.imshow(\\\"residual\\\",sub_input - sub_label)\\n\\t\\t\\t\\tcv2.waitKey(1)\\n\\t\\t\\t\\t\\n\\n \\n # NOTE: The nx, ny can be ignore in train\\n return sub_input_sequence, sub_label_sequence, nx, ny\",\n \"def Valid_Image_data_generator(folderlist,resize = (920,1200),Transformation = True, scaling = True):\\n\\n while True:\\n total_classes = len(folderlist.keys())\\n keys = folderlist.keys()\\n Images = []\\n Image_label = []\\n for key in tqdm(folderlist.keys()):\\n for j in range(len(folderlist[key])):\\n img_label = folderlist[key][j]\\n img = Image.open(img_label,'r')\\n h = resize[1]\\n l = int(img.size[1]*h/img.size[0])\\n img = img.resize((h,l), Image.ANTIALIAS)\\n background = Image.new('RGB', (resize[1], resize[0]), (255, 255, 255))\\n img_w, img_h = img.size\\n bg_w, bg_h = background.size\\n offset = (int((bg_w - img_w) / 2), int((bg_h - img_h) / 2))\\n background.paste(img, offset)\\n background = np.asarray(background)\\n if Transformation == True:\\n rotation = rotate(background,random.choice(range(360)))\\n translate = translate_xy(background,random.choice(range(resize[0]/4)),random.choice(range(resize[1]/4)))\\n flip = cv2.flip(rotation,1)\\n Y = np.concatenate((rotation[np.newaxis,:,:,:],flip[np.newaxis,:,:,:],translate[np.newaxis,:,:,:]))\\n Images.append(Y)\\n Images.append(background[np.newaxis,:,:,:])\\n Image_label.append([key for i in range(4)]) # Four because we are doing rot,trans,flip and one original Image\\n else:\\n Images.append(background[np.newaxis,:,:,:])\\n Image_label.append([key])\\n Image_label = np.concatenate(Image_label)\\n Images = np.concatenate(Images)\\n Image_label = np.array(pd.get_dummies(Image_label))\\n X_Image , Y_Image = shuffle(Images,Image_label,random_state=0)\\n if scaling == True:\\n X_Image = X_Image/255\\n else:\\n X_Image = X_Image\\n return (X_Image,Y_Image)\",\n \"def main() -> None:\\n\\n # Define file name\\n file_name = define_file()\\n\\n # Open chosen image\\n img = image.load_img(IMAGES + file_name, color_mode='grayscale')\\n\\n # Show user image\\n plt.imshow(img)\\n plt.show()\\n\\n # Convert image to array\\n img_arr = image.img_to_array(img)\\n img_arr = np.array([img_arr])\\n img_arr = img_arr.astype(\\\"float32\\\") / 255.0\\n\\n # Classify image\\n img_class = classification(img_arr)\\n\\n # Suggest user add noise to original image\\n if img_class == ORIGINAL:\\n while True:\\n command = input('Seems like your image is original. Do you want to add noise? y/n: ')\\n if command.strip().lower() in ('y', 'yes'):\\n noisy_array = noise(img_arr)\\n display(img_arr, noisy_array)\\n img = image.array_to_img(noisy_array[0])\\n img.save(IMAGES + file_name[:-4] + '_noise' + file_name[-4:])\\n main()\\n elif command.strip().lower() in ('n', 'no'):\\n main()\\n else:\\n continue\\n\\n # Suggest user remove noise from noised image\\n elif img_class == NOISED:\\n while True:\\n command = input('Seems like your image has noise. Do you want to remove noise? y/n: ')\\n if command.strip().lower() in ('y', 'yes'):\\n denoise_array = denoise_image(img_arr)\\n display(img_arr, denoise_array)\\n img = image.array_to_img(denoise_array[0])\\n img.save(IMAGES + file_name[:-4] + '_denoised' + file_name[-4:])\\n main()\\n elif command.strip().lower() in ('n', 'no'):\\n main()\\n else:\\n continue\\n\\n # Image denoised. Nothing to do\\n else:\\n print('Seems like your image denoised.')\\n main()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6922452","0.6743097","0.67311317","0.6724994","0.64523804","0.6366201","0.6221984","0.6215864","0.6204359","0.6179897","0.61457837","0.613554","0.6132579","0.60457397","0.602493","0.5998027","0.5937625","0.5865713","0.5820261","0.5809766","0.5806412","0.58004904","0.5782317","0.5748367","0.5728812","0.57170683","0.56858355","0.56764394","0.5667582","0.5658231","0.56541336","0.5652474","0.56519854","0.56444335","0.5642556","0.5635829","0.5635651","0.5632784","0.5629942","0.5625505","0.5623428","0.5619276","0.5612531","0.5612021","0.5599636","0.5587596","0.55846095","0.55798995","0.5566923","0.5561608","0.55496466","0.5536834","0.5526115","0.55189025","0.55088496","0.5507781","0.55049694","0.5480479","0.54725945","0.546423","0.545747","0.5450337","0.5449614","0.5447988","0.5442426","0.5441775","0.54388785","0.5435393","0.54277396","0.5423661","0.54175556","0.5414705","0.5412971","0.5409523","0.54080564","0.54040235","0.54039973","0.5393225","0.5393074","0.53772676","0.5359118","0.5353412","0.5351753","0.534464","0.5333766","0.5329557","0.53294057","0.5324973","0.53239655","0.5318509","0.53100574","0.53088856","0.53087044","0.5307377","0.5301847","0.5300712","0.5298183","0.52971905","0.52964944","0.5291461"],"string":"[\n \"0.6922452\",\n \"0.6743097\",\n \"0.67311317\",\n \"0.6724994\",\n \"0.64523804\",\n \"0.6366201\",\n \"0.6221984\",\n \"0.6215864\",\n \"0.6204359\",\n \"0.6179897\",\n \"0.61457837\",\n \"0.613554\",\n \"0.6132579\",\n \"0.60457397\",\n \"0.602493\",\n \"0.5998027\",\n \"0.5937625\",\n \"0.5865713\",\n \"0.5820261\",\n \"0.5809766\",\n \"0.5806412\",\n \"0.58004904\",\n \"0.5782317\",\n \"0.5748367\",\n \"0.5728812\",\n \"0.57170683\",\n \"0.56858355\",\n \"0.56764394\",\n \"0.5667582\",\n \"0.5658231\",\n \"0.56541336\",\n \"0.5652474\",\n \"0.56519854\",\n \"0.56444335\",\n \"0.5642556\",\n \"0.5635829\",\n \"0.5635651\",\n \"0.5632784\",\n \"0.5629942\",\n \"0.5625505\",\n \"0.5623428\",\n \"0.5619276\",\n \"0.5612531\",\n \"0.5612021\",\n \"0.5599636\",\n \"0.5587596\",\n \"0.55846095\",\n \"0.55798995\",\n \"0.5566923\",\n \"0.5561608\",\n \"0.55496466\",\n \"0.5536834\",\n \"0.5526115\",\n \"0.55189025\",\n \"0.55088496\",\n \"0.5507781\",\n \"0.55049694\",\n \"0.5480479\",\n \"0.54725945\",\n \"0.546423\",\n \"0.545747\",\n \"0.5450337\",\n \"0.5449614\",\n \"0.5447988\",\n \"0.5442426\",\n \"0.5441775\",\n \"0.54388785\",\n \"0.5435393\",\n \"0.54277396\",\n \"0.5423661\",\n \"0.54175556\",\n \"0.5414705\",\n \"0.5412971\",\n \"0.5409523\",\n \"0.54080564\",\n \"0.54040235\",\n \"0.54039973\",\n \"0.5393225\",\n \"0.5393074\",\n \"0.53772676\",\n \"0.5359118\",\n \"0.5353412\",\n \"0.5351753\",\n \"0.534464\",\n \"0.5333766\",\n \"0.5329557\",\n \"0.53294057\",\n \"0.5324973\",\n \"0.53239655\",\n \"0.5318509\",\n \"0.53100574\",\n \"0.53088856\",\n \"0.53087044\",\n \"0.5307377\",\n \"0.5301847\",\n \"0.5300712\",\n \"0.5298183\",\n \"0.52971905\",\n \"0.52964944\",\n \"0.5291461\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6996364"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":85593,"cells":{"query":{"kind":"string","value":"Update the prediction model with the new data."},"document":{"kind":"string","value":"def update_model(path_to_data, path_to_model):\r\n # Open the annotation files.\r\n with open(path_to_data + r'\\coco_annotation.json') as f:\r\n coco_d = json.load(f)\r\n # Get the categories.\r\n categories = []\r\n for cat in coco_d['categories']:\r\n categories.append(cat['name'])\r\n\r\n # Register the new data.\r\n register_coco_instances(\"coco_update\", {}, path_to_data + r\"\\coco_annotation.json\", path_to_data)\r\n MetadataCatalog.get(\"meta_update\").set(thing_classes=categories)\r\n # MetadataCatalog.get(\"meta_update\").set(thing_classes=[\"Bad\", \"Good\"], thing_colors=[(172, 0, 0), (229, 0, 0)])\r\n\r\n # Set the parameters.\r\n cfg = get_cfg()\r\n cfg.merge_from_file(model_zoo.get_config_file(\"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml\"))\r\n cfg.DATASETS.TRAIN = (\"coco_update\",)\r\n cfg.OUTPUT_DIR = path_to_model\r\n cfg.DATASETS.TEST = ()\r\n cfg.DATALOADER.NUM_WORKERS = 2\r\n cfg.MODEL.DEVICE = 'cpu'\r\n cfg.MODEL.WEIGHTS = os.path.join(cfg.OUTPUT_DIR, \"model_final.pth\")\r\n cfg.SOLVER.IMS_PER_BATCH = 1\r\n cfg.SOLVER.BASE_LR = 0.00025\r\n cfg.SOLVER.MAX_ITER = 400\r\n cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 10\r\n cfg.MODEL.ROI_HEADS.NUM_CLASSES = len(categories)\r\n\r\n # Update the model.\r\n trainer = DefaultTrainer(cfg)\r\n trainer.resume_or_load(resume=False)\r\n trainer.train()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def update_predictions(data):\n # TODO: Priority 1 - update predictions with inference results\n # TODO: Understand from a research team exactly what the data is going to look like\n trackID = data[0]\n prediction = data[1]\n confidence = data[2]\n to_Insert_Array = [trackID, prediction, confidence]\n OutPut_Data[trackID] = to_Insert_Array","def predict_and_update(self, z):","def update_predictor(self):\n estimator = tf.estimator.Estimator(self.model_fn,\n self.model_dir,\n params={},\n )\n self.saved_path = estimator.export_saved_model('saved_model', \n self._serv_input_fn(),\n )\n self._build_predictor()","def update_predictions(self, context):\n x, y, o = context.get_predictions()\n self.x_eval += x\n self.y_eval += y\n self.o_eval += o\n self.write_predictions(o)","def predictions(self, predictions):\n\n self._predictions = predictions","def predict(self):\n for column in self.data_to_predict.columns:\n if column not in list(self.selected_features_):\n self.data_to_predict.drop(column, axis=1, inplace=True)\n for column in list(self.selected_features_):\n if column not in self.data_to_predict.columns:\n self.data_to_predict.loc[:, column] = 0\n self.predictions = self.model.predict(\n self.data_to_predict[self.selected_features_])","def update_data(self):\n self._model.update()\n self.__refresh()","def setPrediction(self,pred):\n self.prediction = pred","def fit_predict(self):\n self.classifier = self.model\n self.classifier.fit(self.X_sample, self.y_sample)\n self.y_pred = self.classifier.predict(self.X_test)","def update(self, x_train_single, updated_h):\n x_row = x_train_single.toarray()\n for i in range(self.num_models):\n self.models[i].partial_fit(x_row, [updated_h[i]])","def predict(self, model, context, data):\n pass","def _update_model(self, X_all, Y_all):\n if self.model is None:\n self._create_model(X_all, Y_all)\n else:\n self.model.set_XY(X_all, Y_all)\n\n # WARNING: Even if self.max_iters=0, the hyperparameters are bit modified...\n if self.max_iters > 0:\n # --- update the model maximizing the marginal likelihood.\n if self.optimize_restarts==1:\n self.model.optimize(optimizer=self.optimizer, max_iters = self.max_iters, messages=False, ipython_notebook=False)\n else:\n self.model.optimize_restarts(num_restarts=self.optimize_restarts, optimizer=self.optimizer, max_iters = self.max_iters, verbose=self.verbose)","def update_predict_network(self):\n states, actions, rewards, new_states, is_terminals = self.memory.sample(self.batch_size)\n\n preprocessed_states, preprocessed_new_states = self.preprocessor.process_batch(states, new_states)\n\n actions = self.preprocessor.process_action(actions)\n # update network\n q_values = self.cal_target_q_values(preprocessed_new_states)\n max_q_values = np.max(q_values, axis=1)\n max_q_values[is_terminals] = 0.0\n targets = rewards + self.gamma * max_q_values\n targets = np.expand_dims(targets, axis=1)\n\n self.q_network.train_on_batch([preprocessed_states, actions], targets)\n if self.num_steps % self.target_update_freq ==0:\n print(\"Update target network at %d steps\" % self.num_steps)\n self.update_target_network()","def update(self, labels, preds):\n raise NotImplementedError()","def _update(self):\n print(\"Saving prediction json files...\")\n self._dump_json()\n print(\"Saving prediction json files done...\")\n print(\"Saving prediction images...\")\n self._dump_image()\n print(\"Saving prediction images done...\")","def postprocess(self, prediction_dict, **params):\r\n pass","def run_prediction(self):\r\n self.get_prediction_indices()\r\n self.walk_forward_prediction()","def doPredict(self, data: StockData) -> float:\r\n pass","def update(self,\n validation_labels: np.array,\n prediction_probability: np.array,\n history: History,\n class_labels: list,\n count: np.array,\n current_class: int) -> None:\n self.training[1] = history.history['accuracy'][-1]\n self.validation[1] = history.history['val_accuracy'][-1]\n self.create_chart(history)","def predict(self, data):\n\t\traise NotImplementedError","def update(self, y_true: list[Number], y_pred: list[Number]) -> ForecastingMetric:","def predict(self, data, version='default'):\n if self.transform_service:\n data = self.transform_service.predict(data, version)\n return self.model_service.predict(data, version)","def predict(self):\n\n\t\t# Gets odometry for current move (velocity of robot and rotational velocity or robot)\n\t\tself.robot.get_odometry()\n\n\t\t# Updates matricies A, G, P\n\t\tself.robot.update_prediction_matrices()\n\t\t\n\t\t# Predicts position based on old position and odometry readings that are input into the system model\n\t\tself.robot.position = self.robot.position + self.robot.process_model\n\n\t\t# Creates Prediction Propogation matrix\n\t\tself.robot.P = np.dot(self.robot.A, np.dot(self.robot.P, self.robot.A.T)) + np.dot(self.robot.G, np.dot(self.robot.Q, self.robot.G.T))","def update_target_model(self):\n self.target_model.set_weights(self.model.get_weights())","def update_target_model(self):\n self.target_model.set_weights(self.model.get_weights())","def update_target_model(self):\n self.target_model.set_weights(self.model.get_weights())","def predict(self):\n self.kf.predict()\n self.nb_kf_pred += 1\n if self.time_since_update > 0:\n self.hit_streak = 0\n self.time_since_update += 1\n self.history.append(self.kf.x[:2].reshape(-1))\n return self.history[-1]","def _update(self):\n num_new_evals = (self.metamodel.model_evaluations - self._last_rebuild)\n if num_new_evals >= self.rebuild_interval:\n self._built = True\n self._last_rebuild = self.metamodel.model_evaluations\n\n # Rebuild relevance function and make it usable on arrays.\n self._relevance_function = self._construct_relevance_function()\n rel_fun = np.vectorize(self._relevance_function)\n\n # Learn relevance prediction model\n data = self.metamodel.history.get_model_evaluations()\n relevance_values = rel_fun(data[:, -1])\n self._predictor.fit(data[:, :-1], relevance_values)\n return","def update(self, y_pred, y_true):\n self.y_pred.append(y_pred.detach().cpu())\n self.y_true.append(y_true.detach().cpu())","def update(self, y_pred, y_true):\n self.y_pred.append(y_pred.detach().cpu())\n self.y_true.append(y_true.detach().cpu())","def updateModel(self):\n pass","def update(self, y_true, y_pred):\n self.y_true.append(y_true)\n self.y_pred.append(y_pred)","def update_model(self):\n pass # TODO: Implement this.","def update_model(self):\n pass","def predict(self):\n batch = get_predict_batch(1, num_rec_out=self.num_test_rec)\n self.g_model.test_batch(\n batch, self.global_step, num_rec_out=self.num_test_rec)","def fit_predict(self):\n raise AttributeError","def update_dead_reckoning(self):\n now = time.time()\n time_diff_s = now - self._last_observation_s\n self._last_observation_s = now\n\n self._prediction_step(time_diff_s)","def _update_model(self, normalization_type='stats'):\n if self.num_acquisitions % self.model_update_interval == 0:\n\n # input that goes into the model (is unziped in case there are categorical variables)\n X_inmodel = self.space.unzip_inputs(self.X)\n\n # Y_inmodel is the output that goes into the model\n if self.normalize_Y:\n Y_inmodel = normalize(self.Y, normalization_type)\n else:\n Y_inmodel = self.Y\n\n self.model.updateModel(X_inmodel, Y_inmodel, None, None)","def predict(self, data):\r\n return self.sess.run([self.predict_op, self.Mu], feed_dict={self.X: data})","def _keras_refresh(self):\n kerasbackend.clear_session()\n\n # Load the keras model architecture and weights from disk\n keras_model = keras.models.load_model(self.path + self.model.name + '.h5')\n keras_model._make_predict_function()\n # Point the model's estimator in the sklearn pipeline to the keras model architecture and weights \n self.model.pipe.named_steps['estimator'].model = keras_model","def update(self):\r\n\r\n self.target.load_state_dict(self.model.state_dict())\r\n self.target.eval()","def update(self):\n # GPS data\n self.model.GPS_latitude.set(self._kernel.data.lat)\n self.model.GPS_longitude.set(self._kernel.data.lon)\n \n self.model.GPS_heading.set(self._kernel.data.gps_heading)\n self.model.GPS_speed.set(self._kernel.data.speed)\n self.model.GPS_altitude.set(self._kernel.data.altitude)\n \n self.model.GPS_fix.set(self._kernel.data.fix)\n self.model.GPS_satellite_count.set(self._kernel.data.num_sat)\n \n # compass data\n self.model.compass_heading.set(self._kernel.data.compass_heading)\n \n # time data\n self.model.time.set(self._kernel.data.timestamp.isoformat())\n self.model.date.set(self._kernel.data.datestamp.isoformat())\n \n # other data\n self.model.temperature.set(self._kernel.data.temperature)","def update(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> None:\n self.accuracy.update(y_pred, y_true)\n self.roc_auc.update(y_pred, y_true)\n self.auc_score.update(y_pred, y_true)\n self.precision.update(y_pred, y_true)\n self.recall.update(y_pred, y_true)\n self.f1.update(y_pred, y_true)\n self.hamming_distance.update(y_pred, y_true)\n self.stat_scores.update(y_pred, y_true)","def predict(self, model, x_test):\n pass","def update_or_create_from_raw_data(\n cls, prediction_data: pd.Series, future_only=False\n ) -> Optional[\"Prediction\"]:\n predicted_margin, predicted_margin_winner = cls._calculate_predictions(\n prediction_data, \"margin\"\n )\n (\n predicted_win_probability,\n predicted_proba_winner,\n ) = cls._calculate_predictions(prediction_data, \"win_probability\")\n\n # For now, each estimator predicts margins or win probabilities, but not both.\n # If we eventually have an estimator that predicts both, we're defaulting\n # to the predicted winner by margin, but we may want to revisit this\n predicted_winner = predicted_margin_winner or predicted_proba_winner\n\n assert predicted_winner is not None, (\n \"Each prediction should have a predicted_winner:\\n\" f\"{prediction_data}\"\n )\n\n match, ml_model = cls._matching_relations_for_update(\n prediction_data, future_only\n )\n\n if (match is None) or (ml_model is None):\n return None\n\n match_ml_model_name = (\n lambda prediction: prediction.ml_model.name == ml_model.name\n )\n\n matching_predictions = filter(\n match_ml_model_name,\n match.predictions,\n )\n\n try:\n prediction = next(matching_predictions)\n prediction.update(\n predicted_margin=predicted_margin,\n predicted_win_probability=predicted_win_probability,\n predicted_winner=predicted_winner,\n )\n except StopIteration:\n prediction = Prediction(\n match=match,\n ml_model=ml_model,\n predicted_margin=predicted_margin,\n predicted_win_probability=predicted_win_probability,\n predicted_winner=predicted_winner,\n )\n prediction.create()\n\n prediction.update_correctness()\n\n return prediction","def after_pred(self):\n # If training then skip\n if self.training:\n return\n\n # Get ground truths in epoch 0 i.e. start of training\n if self.epoch == 0:\n self.y_true.extend(self.y.cpu().flatten().numpy())\n\n # Get predictions from each batch and add them to prediction container\n y_pred = self.pred.detach().cpu()\n \n self.y_pred.extend(y_pred.flatten().numpy())","def predict(self, data, verbose=0):\n\n if self.model is None:\n raise StandardError('Model is not built. Run build method or load model before fitting')\n return self.model.predict(data, 1, verbose) # ,steps)","def predict(self, data, verbose=0):\n\n if self.model is None:\n raise StandardError('Model is not built. Run build method or load model before fitting')\n return self.model.predict(data, 1, verbose) # ,steps)","def update_prediction_run(session: Session, prediction_run: PredictionModelRunTimestamp):\n session.add(prediction_run)\n session.commit()","def predict(self, trained_model, prediction_datetime):\n return trained_model.predict()","def predict(self, data):\n\n prediction = None\n if self.model is not None:\n prediction = self.model.predict(data)\n return prediction","def predict(self, delta):\n self.position = self.sensor_model.sample_prediction(self.position+delta)","def predict(self, new_table):\r\n \r\n if self.is_leaf:\r\n if len(self.path) == 1:\r\n self.cur.execute(\"SELECT ROUND(AVG(\" + self.y_name + \")) FROM \" + self.table_name + \" WHERE \" + self.path[0] + \";\")\r\n else:\r\n self.cur.execute(\"SELECT ROUND(AVG(\" + self.y_name + \")) FROM \" + self.table_name + \" WHERE \" + \" AND \".join(self.path) + \";\")\r\n y = self.cur.fetchone()[0]\r\n self.cur.execute(\"UPDATE \" + new_table + \" SET preds = \" + str(y) + \" WHERE \" + \" AND \".join(self.path) + \";\")\r\n \r\n else:\r\n self.lhs.predict(new_table)\r\n self.rhs.predict(new_table)","def predict(self, to_predict):\n\t\treturn self.model.predict(to_predict)","def predict(self, data_in):\n pass","def predict_batch(self, model, context, data=None):\n pass","def update_model(self, masked_data, masked_binary_data, can_query):\n # Update the model after new data has been revealed, if required.\n self.model.update_model(masked_binary_data, self.previous_selections, self.question_encodings)","def predict(self, data):\n return self.result.predict(data)","def update_fit(self, X, y):\n pass","def update(self, data):\n if self.mode == 'image':\n data = self.preprocess(data)\n self.main_object.set_data(data)\n\n vmin, vmax = self._parse_vrange(data)\n self.main_object.set_clim([vmin, vmax])\n\n if self.mode == 'histogram':\n raise NotImplementedError(\"Updating layer data is not in supported in 'histogram' mode. \")\n\n if self.mode == 'curve':\n x_data, y_data = self.preprocess(data)\n self.main_object.set_data(x_data, y_data)\n self.update_lims()\n\n if self.mode == 'loss':\n raise NotImplementedError(\"Updating layer data is not in supported in 'loss' mode. \")","def make_predictions(self):\n \n self.Y = self.X.dot(self.w)","def update_model(model, data, encoders, epochs, batch_size):\n model.fit(\n dict(data.drop(\"total_return\", axis=1)),\n data.total_return,\n epochs=epochs,\n batch_size=batch_size,\n verbose=False,\n )\n\n factors = pd.DataFrame(model.get_layer(\"date_embedding\").get_weights()[0]).reindex(\n data.date.unique()\n )\n\n loadings = pd.DataFrame(\n model.get_layer(\"ticker_embedding\").get_weights()[0]\n ).reindex(data.ticker.unique())\n\n loadings.index = encoders[\"ticker\"].inverse_transform(loadings.index)\n\n # rotating loadings so the factors are uncorrelated and unit variance\n rotated_loadings = loadings.dot(np.linalg.cholesky(factors.cov()))\n rotated_loadings.index.name = \"ticker\"\n rotated_loadings.columns.name = \"factor\"\n return rotated_loadings","def predict(self):\n for track in self.tracks:\n track.predict(self.kf)","def predict(self):\n for track in self.tracks:\n track.predict(self.kf)","def predict(self):\n for track in self.tracks:\n track.predict(self.kf)","def predict(self):\n path = self._artifact_repo.artifact_path(self._ARTIFACT_MODEL)\n model = tf.keras.models.load_model(path)\n\n _, _, x_test, y_test = self._load_data()\n x_test = tf.keras.utils.normalize(x_test, axis=1)\n\n preds = model.predict(x_test)\n self._show_cf_matrix(np.array([np.argmax(probas) for probas in preds]), y_test)","def predict(self, kf):\n self.mean, self.covariance = kf.predict(self.mean, self.covariance)\n self.age += 1\n self.time_since_update += 1","def predict(self, kf):\n self.mean, self.covariance = kf.predict(self.mean, self.covariance)\n self.age += 1\n self.time_since_update += 1","def predict(self):\n if ((self.kf.x[6] + self.kf.x[2]) <= 0):\n self.kf.x[6] *= 0.0\n self.kf.predict()\n self.age += 1\n if (self.time_since_update > 0):\n self.hit_streak = 0\n self.time_since_update += 1\n self.history.append(convert_x_to_bbox(self.kf.x))\n return self.history[-1]","def predict(self):\n raise NotImplementedError","def update(self, s, a, y):\r\n features = self.featurize_state(s)\r\n self.models[a].partial_fit([features], [y])","def _update_model(self, new_model):\n super()._update_model(new_model)\n\n if 'e' in self.tr_params:\n if self.state_no_train_de is None:\n for i in range(self.n_emissions - self.nr_no_train_de):\n self.B[i] = (1 - self.learning_rate) * new_model['B'][\n i\n ] + self.learning_rate * self.B[i]\n else:\n for i in range(self.n_d_emissions):\n if i < self.n_d_emissions - self.nr_no_train_de:\n self.B[i] = (1 - self.learning_rate) * new_model['B'][\n i\n ] + self.learning_rate * self.B[i]\n else:\n self.B[i][: -self.state_no_train_de, :] = (\n (1 - self.learning_rate)\n * new_model['B'][i][: -self.state_no_train_de, :]\n + self.learning_rate *\n self.B[i][: -self.state_no_train_de, :]\n )\n\n for i in range(self.n_emissions):\n normalise(new_model['B'][i], axis=1)","def update_weights(self, example):\n pred = self.predict(example)\n if pred != example.label:\n self.weights[example.label] = self.weights[example.label] + example.fvector\n self.weights[pred] = self.weights[pred] - example.fvector","def predict(self, data: List):","def predict(self, instances):\r\n raise NotImplementedError","def process(self, data):\n return self.estimator.predict(data)","def makePredictions(self, data, batchSize=1):\n ds = tf.data.Dataset.from_tensor_slices(data)\n ds = ds.window(self.lookBack, shift=self.forecast, drop_remainder=True)\n ds = ds.flat_map(lambda w: w.batch(self.lookBack))\n ds = ds.batch(batchSize).prefetch(1)\n prediction = self.model.predict(ds)\n return prediction","def _posttrain(self, dataset):\n Classifier._posttrain(self, dataset)\n if self.params.retrainable:\n self.__changedData_isset = False","def update_model(self, states, actions):\n self.learner.add_to_data(states, actions)\n self.learner.train_learner()\n self.iterations += 1","def predict(self, data):\n return self.model.predict(data, batch_size=data.shape[1])","def predict(self, data):\n return self.forward_propagate(data)","def on_predict_begin(self, logs=None):","def on_predict_begin(self, logs=None):","def predict(self, data):\n data['predicted'] = self.sentiment_classifier.predict_estimator(data)\n return data","def predict(self):\n for track in self.tracks:\n track.predict(self.kf)\n #track.du_doan(self.kf_test)","def update(self, **attribute_kwargs) -> Prediction:\n if self.id is None:\n raise ValidationError(\"must have an ID\")\n\n for key, value in attribute_kwargs.items():\n setattr(self, key, value)\n\n self.validate()\n\n query = \"\"\"\n mutation(\n $id: ID!,\n $matchId: ID!,\n $mlModelId: ID!,\n $predictedWinnerId: ID!,\n $predictedMargin: Float,\n $predictedWinProbability: Float,\n $wasCorrect: Boolean\n ) {\n updatePrediction(\n id: $id,\n data: {\n match: { connect: $matchId },\n mlModel: { connect: $mlModelId },\n predictedWinner: { connect: $predictedWinnerId },\n predictedMargin: $predictedMargin,\n predictedWinProbability: $predictedWinProbability,\n wasCorrect: $wasCorrect\n }\n ) {\n _id\n }\n }\n \"\"\"\n variables = {\n \"id\": self.id,\n \"matchId\": self.match and self.match.id,\n \"mlModelId\": self.ml_model and self.ml_model.id,\n \"predictedWinnerId\": self.predicted_winner and self.predicted_winner.id,\n \"predictedMargin\": self.predicted_margin,\n \"predictedWinProbability\": self.predicted_win_probability,\n \"wasCorrect\": self.was_correct,\n }\n\n self.db_client().graphql(query, variables)\n\n return self","def predict(self, data: np.array) -> np.array:\n return self.model.predict(squeeze_keep_batch(data))","def predict(self, data, verbose=0):\n\n if self.model is None:\n raise StandardError('Model is not built. Run build method or load model before fitting')\n # print len(data[0])\n # print type(data[0])\n # print data.shape\n return self.model.predict(data, 1, verbose) # ,steps)","def update(self, data: Mapping[str, np.ndarray]) -> Self:\n\n raise NotImplementedError","def predict_single(self, data, version='default'):\n if self.transform_service:\n data = self.transform_service.predict_single(data, version)\n return self.model_service.predict_single(data, version)","def predict(self, obs):\n pass","def update(self, x_stack, y_stack):\n if self.model_name == \"MLPv1\":\n x_stack = np.reshape(x_stack, [-1, self.input_size])\n else:\n x_stack = np.reshape(x_stack, [-1, self.seq_size, self.input_size, 1])\n return self.session.run([self.loss, self.train], feed_dict={self.x: x_stack, self.y: y_stack})","def predict(self, X):\n ...","def predict(self, X):\n ...","def predict(self, X):\n ...","def save_prediction(self):\n if DataLoader.data is None:\n messagebox.showerror(\"Information\", \"Data file is empty, please load the data first.\")\n return\n if Trainer.y_pred is None:\n messagebox.showerror(\"Information\", \"Preciction has not been made, please train a new model and predict or \"\n \"load a model and predict.\")\n return\n\n path = filedialog.asksaveasfile(mode='w', defaultextension=\".csv\", filetypes=[(\"csv files\", '*.csv'),\n (\"xlsx files\", '*.xlsx'),\n (\"dat files\", '*.dat')])\n\n copy_data = DataLoader.data.copy()\n copy_data['prediction'] = Trainer.y_pred\n copy_data.to_csv(path, index=False)\n\n # Clears memory\n copy_data.drop(copy_data.index, inplace=True)\n del copy_data","def update(self, preds: torch.Tensor, target: torch.Tensor):\n true_positives, predicted_positives, actual_positives = _fbeta_update(\n preds, target, self.num_classes, self.threshold, self.multilabel\n )\n\n self.true_positives += true_positives\n self.predicted_positives += predicted_positives\n self.actual_positives += actual_positives","def test_03_predict(self):\n today = date.today()\n log_file = os.path.join(LOG_DIR, \"{}-predict-{}-{}.log\".format(LOG_PREFIX, today.year, today.month))\n if os.path.exists(log_file):\n os.remove(log_file)\n \n ## update the log\n y_pred = [0]\n y_proba = [0.6,0.4]\n runtime = \"00:00:02\"\n model_version = 0.1\n country = \"india\"\n target_date = '2018-01-05'\n\n update_predict_log(country, y_pred,y_proba,target_date,runtime,\n model_version, test=True, prefix=LOG_PREFIX)\n \n self.assertTrue(os.path.exists(log_file))","def after_epoch(self):\n\n self.y_true = np.array(self.y_true) # Ground truths\n self.y_pred = np.array(self.y_pred) # Predictions for this epoch\n self.residuals = self.y_true - self.y_pred # Residuals for this epoch\n self.x_bounds = (np.min(self.y_true), np.max(self.y_true)) # x bounds for the graph (min and max of ground truths)\n self.y_bounds = (-np.max(self.residuals), np.max(self.residuals)) # y bounds for the graph (max residual either side of 0)\n \n # Update the graph with this epoch's \"data\"\n self.update_graph(residuals = self.residuals, \n y_true = self.y_true, \n x_bounds = self.x_bounds, \n y_bounds = self.y_bounds)","def train(self, dataset):\n \"*** YOUR CODE HERE question 1 ***\"\n while True:\n trainingComplete = True\n data = dataset.iterate_once(1)\n\n for feature, label in data:\n\n if nn.as_scalar(label) != self.get_prediction(feature):\n self.w.update(feature, nn.as_scalar(label))\n trainingComplete = False\n\n if trainingComplete:\n break"],"string":"[\n \"def update_predictions(data):\\n # TODO: Priority 1 - update predictions with inference results\\n # TODO: Understand from a research team exactly what the data is going to look like\\n trackID = data[0]\\n prediction = data[1]\\n confidence = data[2]\\n to_Insert_Array = [trackID, prediction, confidence]\\n OutPut_Data[trackID] = to_Insert_Array\",\n \"def predict_and_update(self, z):\",\n \"def update_predictor(self):\\n estimator = tf.estimator.Estimator(self.model_fn,\\n self.model_dir,\\n params={},\\n )\\n self.saved_path = estimator.export_saved_model('saved_model', \\n self._serv_input_fn(),\\n )\\n self._build_predictor()\",\n \"def update_predictions(self, context):\\n x, y, o = context.get_predictions()\\n self.x_eval += x\\n self.y_eval += y\\n self.o_eval += o\\n self.write_predictions(o)\",\n \"def predictions(self, predictions):\\n\\n self._predictions = predictions\",\n \"def predict(self):\\n for column in self.data_to_predict.columns:\\n if column not in list(self.selected_features_):\\n self.data_to_predict.drop(column, axis=1, inplace=True)\\n for column in list(self.selected_features_):\\n if column not in self.data_to_predict.columns:\\n self.data_to_predict.loc[:, column] = 0\\n self.predictions = self.model.predict(\\n self.data_to_predict[self.selected_features_])\",\n \"def update_data(self):\\n self._model.update()\\n self.__refresh()\",\n \"def setPrediction(self,pred):\\n self.prediction = pred\",\n \"def fit_predict(self):\\n self.classifier = self.model\\n self.classifier.fit(self.X_sample, self.y_sample)\\n self.y_pred = self.classifier.predict(self.X_test)\",\n \"def update(self, x_train_single, updated_h):\\n x_row = x_train_single.toarray()\\n for i in range(self.num_models):\\n self.models[i].partial_fit(x_row, [updated_h[i]])\",\n \"def predict(self, model, context, data):\\n pass\",\n \"def _update_model(self, X_all, Y_all):\\n if self.model is None:\\n self._create_model(X_all, Y_all)\\n else:\\n self.model.set_XY(X_all, Y_all)\\n\\n # WARNING: Even if self.max_iters=0, the hyperparameters are bit modified...\\n if self.max_iters > 0:\\n # --- update the model maximizing the marginal likelihood.\\n if self.optimize_restarts==1:\\n self.model.optimize(optimizer=self.optimizer, max_iters = self.max_iters, messages=False, ipython_notebook=False)\\n else:\\n self.model.optimize_restarts(num_restarts=self.optimize_restarts, optimizer=self.optimizer, max_iters = self.max_iters, verbose=self.verbose)\",\n \"def update_predict_network(self):\\n states, actions, rewards, new_states, is_terminals = self.memory.sample(self.batch_size)\\n\\n preprocessed_states, preprocessed_new_states = self.preprocessor.process_batch(states, new_states)\\n\\n actions = self.preprocessor.process_action(actions)\\n # update network\\n q_values = self.cal_target_q_values(preprocessed_new_states)\\n max_q_values = np.max(q_values, axis=1)\\n max_q_values[is_terminals] = 0.0\\n targets = rewards + self.gamma * max_q_values\\n targets = np.expand_dims(targets, axis=1)\\n\\n self.q_network.train_on_batch([preprocessed_states, actions], targets)\\n if self.num_steps % self.target_update_freq ==0:\\n print(\\\"Update target network at %d steps\\\" % self.num_steps)\\n self.update_target_network()\",\n \"def update(self, labels, preds):\\n raise NotImplementedError()\",\n \"def _update(self):\\n print(\\\"Saving prediction json files...\\\")\\n self._dump_json()\\n print(\\\"Saving prediction json files done...\\\")\\n print(\\\"Saving prediction images...\\\")\\n self._dump_image()\\n print(\\\"Saving prediction images done...\\\")\",\n \"def postprocess(self, prediction_dict, **params):\\r\\n pass\",\n \"def run_prediction(self):\\r\\n self.get_prediction_indices()\\r\\n self.walk_forward_prediction()\",\n \"def doPredict(self, data: StockData) -> float:\\r\\n pass\",\n \"def update(self,\\n validation_labels: np.array,\\n prediction_probability: np.array,\\n history: History,\\n class_labels: list,\\n count: np.array,\\n current_class: int) -> None:\\n self.training[1] = history.history['accuracy'][-1]\\n self.validation[1] = history.history['val_accuracy'][-1]\\n self.create_chart(history)\",\n \"def predict(self, data):\\n\\t\\traise NotImplementedError\",\n \"def update(self, y_true: list[Number], y_pred: list[Number]) -> ForecastingMetric:\",\n \"def predict(self, data, version='default'):\\n if self.transform_service:\\n data = self.transform_service.predict(data, version)\\n return self.model_service.predict(data, version)\",\n \"def predict(self):\\n\\n\\t\\t# Gets odometry for current move (velocity of robot and rotational velocity or robot)\\n\\t\\tself.robot.get_odometry()\\n\\n\\t\\t# Updates matricies A, G, P\\n\\t\\tself.robot.update_prediction_matrices()\\n\\t\\t\\n\\t\\t# Predicts position based on old position and odometry readings that are input into the system model\\n\\t\\tself.robot.position = self.robot.position + self.robot.process_model\\n\\n\\t\\t# Creates Prediction Propogation matrix\\n\\t\\tself.robot.P = np.dot(self.robot.A, np.dot(self.robot.P, self.robot.A.T)) + np.dot(self.robot.G, np.dot(self.robot.Q, self.robot.G.T))\",\n \"def update_target_model(self):\\n self.target_model.set_weights(self.model.get_weights())\",\n \"def update_target_model(self):\\n self.target_model.set_weights(self.model.get_weights())\",\n \"def update_target_model(self):\\n self.target_model.set_weights(self.model.get_weights())\",\n \"def predict(self):\\n self.kf.predict()\\n self.nb_kf_pred += 1\\n if self.time_since_update > 0:\\n self.hit_streak = 0\\n self.time_since_update += 1\\n self.history.append(self.kf.x[:2].reshape(-1))\\n return self.history[-1]\",\n \"def _update(self):\\n num_new_evals = (self.metamodel.model_evaluations - self._last_rebuild)\\n if num_new_evals >= self.rebuild_interval:\\n self._built = True\\n self._last_rebuild = self.metamodel.model_evaluations\\n\\n # Rebuild relevance function and make it usable on arrays.\\n self._relevance_function = self._construct_relevance_function()\\n rel_fun = np.vectorize(self._relevance_function)\\n\\n # Learn relevance prediction model\\n data = self.metamodel.history.get_model_evaluations()\\n relevance_values = rel_fun(data[:, -1])\\n self._predictor.fit(data[:, :-1], relevance_values)\\n return\",\n \"def update(self, y_pred, y_true):\\n self.y_pred.append(y_pred.detach().cpu())\\n self.y_true.append(y_true.detach().cpu())\",\n \"def update(self, y_pred, y_true):\\n self.y_pred.append(y_pred.detach().cpu())\\n self.y_true.append(y_true.detach().cpu())\",\n \"def updateModel(self):\\n pass\",\n \"def update(self, y_true, y_pred):\\n self.y_true.append(y_true)\\n self.y_pred.append(y_pred)\",\n \"def update_model(self):\\n pass # TODO: Implement this.\",\n \"def update_model(self):\\n pass\",\n \"def predict(self):\\n batch = get_predict_batch(1, num_rec_out=self.num_test_rec)\\n self.g_model.test_batch(\\n batch, self.global_step, num_rec_out=self.num_test_rec)\",\n \"def fit_predict(self):\\n raise AttributeError\",\n \"def update_dead_reckoning(self):\\n now = time.time()\\n time_diff_s = now - self._last_observation_s\\n self._last_observation_s = now\\n\\n self._prediction_step(time_diff_s)\",\n \"def _update_model(self, normalization_type='stats'):\\n if self.num_acquisitions % self.model_update_interval == 0:\\n\\n # input that goes into the model (is unziped in case there are categorical variables)\\n X_inmodel = self.space.unzip_inputs(self.X)\\n\\n # Y_inmodel is the output that goes into the model\\n if self.normalize_Y:\\n Y_inmodel = normalize(self.Y, normalization_type)\\n else:\\n Y_inmodel = self.Y\\n\\n self.model.updateModel(X_inmodel, Y_inmodel, None, None)\",\n \"def predict(self, data):\\r\\n return self.sess.run([self.predict_op, self.Mu], feed_dict={self.X: data})\",\n \"def _keras_refresh(self):\\n kerasbackend.clear_session()\\n\\n # Load the keras model architecture and weights from disk\\n keras_model = keras.models.load_model(self.path + self.model.name + '.h5')\\n keras_model._make_predict_function()\\n # Point the model's estimator in the sklearn pipeline to the keras model architecture and weights \\n self.model.pipe.named_steps['estimator'].model = keras_model\",\n \"def update(self):\\r\\n\\r\\n self.target.load_state_dict(self.model.state_dict())\\r\\n self.target.eval()\",\n \"def update(self):\\n # GPS data\\n self.model.GPS_latitude.set(self._kernel.data.lat)\\n self.model.GPS_longitude.set(self._kernel.data.lon)\\n \\n self.model.GPS_heading.set(self._kernel.data.gps_heading)\\n self.model.GPS_speed.set(self._kernel.data.speed)\\n self.model.GPS_altitude.set(self._kernel.data.altitude)\\n \\n self.model.GPS_fix.set(self._kernel.data.fix)\\n self.model.GPS_satellite_count.set(self._kernel.data.num_sat)\\n \\n # compass data\\n self.model.compass_heading.set(self._kernel.data.compass_heading)\\n \\n # time data\\n self.model.time.set(self._kernel.data.timestamp.isoformat())\\n self.model.date.set(self._kernel.data.datestamp.isoformat())\\n \\n # other data\\n self.model.temperature.set(self._kernel.data.temperature)\",\n \"def update(self, y_pred: torch.Tensor, y_true: torch.Tensor) -> None:\\n self.accuracy.update(y_pred, y_true)\\n self.roc_auc.update(y_pred, y_true)\\n self.auc_score.update(y_pred, y_true)\\n self.precision.update(y_pred, y_true)\\n self.recall.update(y_pred, y_true)\\n self.f1.update(y_pred, y_true)\\n self.hamming_distance.update(y_pred, y_true)\\n self.stat_scores.update(y_pred, y_true)\",\n \"def predict(self, model, x_test):\\n pass\",\n \"def update_or_create_from_raw_data(\\n cls, prediction_data: pd.Series, future_only=False\\n ) -> Optional[\\\"Prediction\\\"]:\\n predicted_margin, predicted_margin_winner = cls._calculate_predictions(\\n prediction_data, \\\"margin\\\"\\n )\\n (\\n predicted_win_probability,\\n predicted_proba_winner,\\n ) = cls._calculate_predictions(prediction_data, \\\"win_probability\\\")\\n\\n # For now, each estimator predicts margins or win probabilities, but not both.\\n # If we eventually have an estimator that predicts both, we're defaulting\\n # to the predicted winner by margin, but we may want to revisit this\\n predicted_winner = predicted_margin_winner or predicted_proba_winner\\n\\n assert predicted_winner is not None, (\\n \\\"Each prediction should have a predicted_winner:\\\\n\\\" f\\\"{prediction_data}\\\"\\n )\\n\\n match, ml_model = cls._matching_relations_for_update(\\n prediction_data, future_only\\n )\\n\\n if (match is None) or (ml_model is None):\\n return None\\n\\n match_ml_model_name = (\\n lambda prediction: prediction.ml_model.name == ml_model.name\\n )\\n\\n matching_predictions = filter(\\n match_ml_model_name,\\n match.predictions,\\n )\\n\\n try:\\n prediction = next(matching_predictions)\\n prediction.update(\\n predicted_margin=predicted_margin,\\n predicted_win_probability=predicted_win_probability,\\n predicted_winner=predicted_winner,\\n )\\n except StopIteration:\\n prediction = Prediction(\\n match=match,\\n ml_model=ml_model,\\n predicted_margin=predicted_margin,\\n predicted_win_probability=predicted_win_probability,\\n predicted_winner=predicted_winner,\\n )\\n prediction.create()\\n\\n prediction.update_correctness()\\n\\n return prediction\",\n \"def after_pred(self):\\n # If training then skip\\n if self.training:\\n return\\n\\n # Get ground truths in epoch 0 i.e. start of training\\n if self.epoch == 0:\\n self.y_true.extend(self.y.cpu().flatten().numpy())\\n\\n # Get predictions from each batch and add them to prediction container\\n y_pred = self.pred.detach().cpu()\\n \\n self.y_pred.extend(y_pred.flatten().numpy())\",\n \"def predict(self, data, verbose=0):\\n\\n if self.model is None:\\n raise StandardError('Model is not built. Run build method or load model before fitting')\\n return self.model.predict(data, 1, verbose) # ,steps)\",\n \"def predict(self, data, verbose=0):\\n\\n if self.model is None:\\n raise StandardError('Model is not built. Run build method or load model before fitting')\\n return self.model.predict(data, 1, verbose) # ,steps)\",\n \"def update_prediction_run(session: Session, prediction_run: PredictionModelRunTimestamp):\\n session.add(prediction_run)\\n session.commit()\",\n \"def predict(self, trained_model, prediction_datetime):\\n return trained_model.predict()\",\n \"def predict(self, data):\\n\\n prediction = None\\n if self.model is not None:\\n prediction = self.model.predict(data)\\n return prediction\",\n \"def predict(self, delta):\\n self.position = self.sensor_model.sample_prediction(self.position+delta)\",\n \"def predict(self, new_table):\\r\\n \\r\\n if self.is_leaf:\\r\\n if len(self.path) == 1:\\r\\n self.cur.execute(\\\"SELECT ROUND(AVG(\\\" + self.y_name + \\\")) FROM \\\" + self.table_name + \\\" WHERE \\\" + self.path[0] + \\\";\\\")\\r\\n else:\\r\\n self.cur.execute(\\\"SELECT ROUND(AVG(\\\" + self.y_name + \\\")) FROM \\\" + self.table_name + \\\" WHERE \\\" + \\\" AND \\\".join(self.path) + \\\";\\\")\\r\\n y = self.cur.fetchone()[0]\\r\\n self.cur.execute(\\\"UPDATE \\\" + new_table + \\\" SET preds = \\\" + str(y) + \\\" WHERE \\\" + \\\" AND \\\".join(self.path) + \\\";\\\")\\r\\n \\r\\n else:\\r\\n self.lhs.predict(new_table)\\r\\n self.rhs.predict(new_table)\",\n \"def predict(self, to_predict):\\n\\t\\treturn self.model.predict(to_predict)\",\n \"def predict(self, data_in):\\n pass\",\n \"def predict_batch(self, model, context, data=None):\\n pass\",\n \"def update_model(self, masked_data, masked_binary_data, can_query):\\n # Update the model after new data has been revealed, if required.\\n self.model.update_model(masked_binary_data, self.previous_selections, self.question_encodings)\",\n \"def predict(self, data):\\n return self.result.predict(data)\",\n \"def update_fit(self, X, y):\\n pass\",\n \"def update(self, data):\\n if self.mode == 'image':\\n data = self.preprocess(data)\\n self.main_object.set_data(data)\\n\\n vmin, vmax = self._parse_vrange(data)\\n self.main_object.set_clim([vmin, vmax])\\n\\n if self.mode == 'histogram':\\n raise NotImplementedError(\\\"Updating layer data is not in supported in 'histogram' mode. \\\")\\n\\n if self.mode == 'curve':\\n x_data, y_data = self.preprocess(data)\\n self.main_object.set_data(x_data, y_data)\\n self.update_lims()\\n\\n if self.mode == 'loss':\\n raise NotImplementedError(\\\"Updating layer data is not in supported in 'loss' mode. \\\")\",\n \"def make_predictions(self):\\n \\n self.Y = self.X.dot(self.w)\",\n \"def update_model(model, data, encoders, epochs, batch_size):\\n model.fit(\\n dict(data.drop(\\\"total_return\\\", axis=1)),\\n data.total_return,\\n epochs=epochs,\\n batch_size=batch_size,\\n verbose=False,\\n )\\n\\n factors = pd.DataFrame(model.get_layer(\\\"date_embedding\\\").get_weights()[0]).reindex(\\n data.date.unique()\\n )\\n\\n loadings = pd.DataFrame(\\n model.get_layer(\\\"ticker_embedding\\\").get_weights()[0]\\n ).reindex(data.ticker.unique())\\n\\n loadings.index = encoders[\\\"ticker\\\"].inverse_transform(loadings.index)\\n\\n # rotating loadings so the factors are uncorrelated and unit variance\\n rotated_loadings = loadings.dot(np.linalg.cholesky(factors.cov()))\\n rotated_loadings.index.name = \\\"ticker\\\"\\n rotated_loadings.columns.name = \\\"factor\\\"\\n return rotated_loadings\",\n \"def predict(self):\\n for track in self.tracks:\\n track.predict(self.kf)\",\n \"def predict(self):\\n for track in self.tracks:\\n track.predict(self.kf)\",\n \"def predict(self):\\n for track in self.tracks:\\n track.predict(self.kf)\",\n \"def predict(self):\\n path = self._artifact_repo.artifact_path(self._ARTIFACT_MODEL)\\n model = tf.keras.models.load_model(path)\\n\\n _, _, x_test, y_test = self._load_data()\\n x_test = tf.keras.utils.normalize(x_test, axis=1)\\n\\n preds = model.predict(x_test)\\n self._show_cf_matrix(np.array([np.argmax(probas) for probas in preds]), y_test)\",\n \"def predict(self, kf):\\n self.mean, self.covariance = kf.predict(self.mean, self.covariance)\\n self.age += 1\\n self.time_since_update += 1\",\n \"def predict(self, kf):\\n self.mean, self.covariance = kf.predict(self.mean, self.covariance)\\n self.age += 1\\n self.time_since_update += 1\",\n \"def predict(self):\\n if ((self.kf.x[6] + self.kf.x[2]) <= 0):\\n self.kf.x[6] *= 0.0\\n self.kf.predict()\\n self.age += 1\\n if (self.time_since_update > 0):\\n self.hit_streak = 0\\n self.time_since_update += 1\\n self.history.append(convert_x_to_bbox(self.kf.x))\\n return self.history[-1]\",\n \"def predict(self):\\n raise NotImplementedError\",\n \"def update(self, s, a, y):\\r\\n features = self.featurize_state(s)\\r\\n self.models[a].partial_fit([features], [y])\",\n \"def _update_model(self, new_model):\\n super()._update_model(new_model)\\n\\n if 'e' in self.tr_params:\\n if self.state_no_train_de is None:\\n for i in range(self.n_emissions - self.nr_no_train_de):\\n self.B[i] = (1 - self.learning_rate) * new_model['B'][\\n i\\n ] + self.learning_rate * self.B[i]\\n else:\\n for i in range(self.n_d_emissions):\\n if i < self.n_d_emissions - self.nr_no_train_de:\\n self.B[i] = (1 - self.learning_rate) * new_model['B'][\\n i\\n ] + self.learning_rate * self.B[i]\\n else:\\n self.B[i][: -self.state_no_train_de, :] = (\\n (1 - self.learning_rate)\\n * new_model['B'][i][: -self.state_no_train_de, :]\\n + self.learning_rate *\\n self.B[i][: -self.state_no_train_de, :]\\n )\\n\\n for i in range(self.n_emissions):\\n normalise(new_model['B'][i], axis=1)\",\n \"def update_weights(self, example):\\n pred = self.predict(example)\\n if pred != example.label:\\n self.weights[example.label] = self.weights[example.label] + example.fvector\\n self.weights[pred] = self.weights[pred] - example.fvector\",\n \"def predict(self, data: List):\",\n \"def predict(self, instances):\\r\\n raise NotImplementedError\",\n \"def process(self, data):\\n return self.estimator.predict(data)\",\n \"def makePredictions(self, data, batchSize=1):\\n ds = tf.data.Dataset.from_tensor_slices(data)\\n ds = ds.window(self.lookBack, shift=self.forecast, drop_remainder=True)\\n ds = ds.flat_map(lambda w: w.batch(self.lookBack))\\n ds = ds.batch(batchSize).prefetch(1)\\n prediction = self.model.predict(ds)\\n return prediction\",\n \"def _posttrain(self, dataset):\\n Classifier._posttrain(self, dataset)\\n if self.params.retrainable:\\n self.__changedData_isset = False\",\n \"def update_model(self, states, actions):\\n self.learner.add_to_data(states, actions)\\n self.learner.train_learner()\\n self.iterations += 1\",\n \"def predict(self, data):\\n return self.model.predict(data, batch_size=data.shape[1])\",\n \"def predict(self, data):\\n return self.forward_propagate(data)\",\n \"def on_predict_begin(self, logs=None):\",\n \"def on_predict_begin(self, logs=None):\",\n \"def predict(self, data):\\n data['predicted'] = self.sentiment_classifier.predict_estimator(data)\\n return data\",\n \"def predict(self):\\n for track in self.tracks:\\n track.predict(self.kf)\\n #track.du_doan(self.kf_test)\",\n \"def update(self, **attribute_kwargs) -> Prediction:\\n if self.id is None:\\n raise ValidationError(\\\"must have an ID\\\")\\n\\n for key, value in attribute_kwargs.items():\\n setattr(self, key, value)\\n\\n self.validate()\\n\\n query = \\\"\\\"\\\"\\n mutation(\\n $id: ID!,\\n $matchId: ID!,\\n $mlModelId: ID!,\\n $predictedWinnerId: ID!,\\n $predictedMargin: Float,\\n $predictedWinProbability: Float,\\n $wasCorrect: Boolean\\n ) {\\n updatePrediction(\\n id: $id,\\n data: {\\n match: { connect: $matchId },\\n mlModel: { connect: $mlModelId },\\n predictedWinner: { connect: $predictedWinnerId },\\n predictedMargin: $predictedMargin,\\n predictedWinProbability: $predictedWinProbability,\\n wasCorrect: $wasCorrect\\n }\\n ) {\\n _id\\n }\\n }\\n \\\"\\\"\\\"\\n variables = {\\n \\\"id\\\": self.id,\\n \\\"matchId\\\": self.match and self.match.id,\\n \\\"mlModelId\\\": self.ml_model and self.ml_model.id,\\n \\\"predictedWinnerId\\\": self.predicted_winner and self.predicted_winner.id,\\n \\\"predictedMargin\\\": self.predicted_margin,\\n \\\"predictedWinProbability\\\": self.predicted_win_probability,\\n \\\"wasCorrect\\\": self.was_correct,\\n }\\n\\n self.db_client().graphql(query, variables)\\n\\n return self\",\n \"def predict(self, data: np.array) -> np.array:\\n return self.model.predict(squeeze_keep_batch(data))\",\n \"def predict(self, data, verbose=0):\\n\\n if self.model is None:\\n raise StandardError('Model is not built. Run build method or load model before fitting')\\n # print len(data[0])\\n # print type(data[0])\\n # print data.shape\\n return self.model.predict(data, 1, verbose) # ,steps)\",\n \"def update(self, data: Mapping[str, np.ndarray]) -> Self:\\n\\n raise NotImplementedError\",\n \"def predict_single(self, data, version='default'):\\n if self.transform_service:\\n data = self.transform_service.predict_single(data, version)\\n return self.model_service.predict_single(data, version)\",\n \"def predict(self, obs):\\n pass\",\n \"def update(self, x_stack, y_stack):\\n if self.model_name == \\\"MLPv1\\\":\\n x_stack = np.reshape(x_stack, [-1, self.input_size])\\n else:\\n x_stack = np.reshape(x_stack, [-1, self.seq_size, self.input_size, 1])\\n return self.session.run([self.loss, self.train], feed_dict={self.x: x_stack, self.y: y_stack})\",\n \"def predict(self, X):\\n ...\",\n \"def predict(self, X):\\n ...\",\n \"def predict(self, X):\\n ...\",\n \"def save_prediction(self):\\n if DataLoader.data is None:\\n messagebox.showerror(\\\"Information\\\", \\\"Data file is empty, please load the data first.\\\")\\n return\\n if Trainer.y_pred is None:\\n messagebox.showerror(\\\"Information\\\", \\\"Preciction has not been made, please train a new model and predict or \\\"\\n \\\"load a model and predict.\\\")\\n return\\n\\n path = filedialog.asksaveasfile(mode='w', defaultextension=\\\".csv\\\", filetypes=[(\\\"csv files\\\", '*.csv'),\\n (\\\"xlsx files\\\", '*.xlsx'),\\n (\\\"dat files\\\", '*.dat')])\\n\\n copy_data = DataLoader.data.copy()\\n copy_data['prediction'] = Trainer.y_pred\\n copy_data.to_csv(path, index=False)\\n\\n # Clears memory\\n copy_data.drop(copy_data.index, inplace=True)\\n del copy_data\",\n \"def update(self, preds: torch.Tensor, target: torch.Tensor):\\n true_positives, predicted_positives, actual_positives = _fbeta_update(\\n preds, target, self.num_classes, self.threshold, self.multilabel\\n )\\n\\n self.true_positives += true_positives\\n self.predicted_positives += predicted_positives\\n self.actual_positives += actual_positives\",\n \"def test_03_predict(self):\\n today = date.today()\\n log_file = os.path.join(LOG_DIR, \\\"{}-predict-{}-{}.log\\\".format(LOG_PREFIX, today.year, today.month))\\n if os.path.exists(log_file):\\n os.remove(log_file)\\n \\n ## update the log\\n y_pred = [0]\\n y_proba = [0.6,0.4]\\n runtime = \\\"00:00:02\\\"\\n model_version = 0.1\\n country = \\\"india\\\"\\n target_date = '2018-01-05'\\n\\n update_predict_log(country, y_pred,y_proba,target_date,runtime,\\n model_version, test=True, prefix=LOG_PREFIX)\\n \\n self.assertTrue(os.path.exists(log_file))\",\n \"def after_epoch(self):\\n\\n self.y_true = np.array(self.y_true) # Ground truths\\n self.y_pred = np.array(self.y_pred) # Predictions for this epoch\\n self.residuals = self.y_true - self.y_pred # Residuals for this epoch\\n self.x_bounds = (np.min(self.y_true), np.max(self.y_true)) # x bounds for the graph (min and max of ground truths)\\n self.y_bounds = (-np.max(self.residuals), np.max(self.residuals)) # y bounds for the graph (max residual either side of 0)\\n \\n # Update the graph with this epoch's \\\"data\\\"\\n self.update_graph(residuals = self.residuals, \\n y_true = self.y_true, \\n x_bounds = self.x_bounds, \\n y_bounds = self.y_bounds)\",\n \"def train(self, dataset):\\n \\\"*** YOUR CODE HERE question 1 ***\\\"\\n while True:\\n trainingComplete = True\\n data = dataset.iterate_once(1)\\n\\n for feature, label in data:\\n\\n if nn.as_scalar(label) != self.get_prediction(feature):\\n self.w.update(feature, nn.as_scalar(label))\\n trainingComplete = False\\n\\n if trainingComplete:\\n break\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72333366","0.7135157","0.7065093","0.7044976","0.67507654","0.66787064","0.66733956","0.66451544","0.66342115","0.6553452","0.6528085","0.6525013","0.64743006","0.64256346","0.6359152","0.6347298","0.6312711","0.62738705","0.6263622","0.62602943","0.6237629","0.623587","0.62249863","0.62214214","0.62214214","0.62214214","0.6211547","0.62108","0.6210486","0.6210486","0.6206274","0.61916393","0.61901796","0.61847615","0.61779493","0.6145577","0.61401206","0.61351407","0.61313117","0.6120716","0.6103385","0.6098078","0.607596","0.60687333","0.6061576","0.6048232","0.604645","0.604645","0.60401976","0.6033977","0.6033604","0.6012634","0.6002353","0.5997081","0.59864825","0.5983309","0.5977945","0.5964493","0.59574944","0.59505546","0.5949082","0.5948406","0.59417915","0.59417915","0.59417915","0.59396696","0.5933507","0.5933507","0.59216195","0.5915464","0.5914911","0.58980244","0.58975065","0.5894051","0.5886455","0.58825773","0.58798295","0.58725655","0.5871134","0.5867721","0.5866627","0.58509946","0.58509946","0.5840348","0.58385015","0.5837683","0.58372325","0.58263284","0.58227974","0.58203703","0.58166105","0.5816501","0.58138925","0.58138925","0.58138925","0.58105433","0.5805389","0.58043694","0.5802694","0.58003676"],"string":"[\n \"0.72333366\",\n \"0.7135157\",\n \"0.7065093\",\n \"0.7044976\",\n \"0.67507654\",\n \"0.66787064\",\n \"0.66733956\",\n \"0.66451544\",\n \"0.66342115\",\n \"0.6553452\",\n \"0.6528085\",\n \"0.6525013\",\n \"0.64743006\",\n \"0.64256346\",\n \"0.6359152\",\n \"0.6347298\",\n \"0.6312711\",\n \"0.62738705\",\n \"0.6263622\",\n \"0.62602943\",\n \"0.6237629\",\n \"0.623587\",\n \"0.62249863\",\n \"0.62214214\",\n \"0.62214214\",\n \"0.62214214\",\n \"0.6211547\",\n \"0.62108\",\n \"0.6210486\",\n \"0.6210486\",\n \"0.6206274\",\n \"0.61916393\",\n \"0.61901796\",\n \"0.61847615\",\n \"0.61779493\",\n \"0.6145577\",\n \"0.61401206\",\n \"0.61351407\",\n \"0.61313117\",\n \"0.6120716\",\n \"0.6103385\",\n \"0.6098078\",\n \"0.607596\",\n \"0.60687333\",\n \"0.6061576\",\n \"0.6048232\",\n \"0.604645\",\n \"0.604645\",\n \"0.60401976\",\n \"0.6033977\",\n \"0.6033604\",\n \"0.6012634\",\n \"0.6002353\",\n \"0.5997081\",\n \"0.59864825\",\n \"0.5983309\",\n \"0.5977945\",\n \"0.5964493\",\n \"0.59574944\",\n \"0.59505546\",\n \"0.5949082\",\n \"0.5948406\",\n \"0.59417915\",\n \"0.59417915\",\n \"0.59417915\",\n \"0.59396696\",\n \"0.5933507\",\n \"0.5933507\",\n \"0.59216195\",\n \"0.5915464\",\n \"0.5914911\",\n \"0.58980244\",\n \"0.58975065\",\n \"0.5894051\",\n \"0.5886455\",\n \"0.58825773\",\n \"0.58798295\",\n \"0.58725655\",\n \"0.5871134\",\n \"0.5867721\",\n \"0.5866627\",\n \"0.58509946\",\n \"0.58509946\",\n \"0.5840348\",\n \"0.58385015\",\n \"0.5837683\",\n \"0.58372325\",\n \"0.58263284\",\n \"0.58227974\",\n \"0.58203703\",\n \"0.58166105\",\n \"0.5816501\",\n \"0.58138925\",\n \"0.58138925\",\n \"0.58138925\",\n \"0.58105433\",\n \"0.5805389\",\n \"0.58043694\",\n \"0.5802694\",\n \"0.58003676\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5957653"},"document_rank":{"kind":"string","value":"58"}}},{"rowIdx":85594,"cells":{"query":{"kind":"string","value":"Read the poses_bounds.npy file produced by LLFF imgs2poses.py."},"document":{"kind":"string","value":"def read_meta(in_dir, use_ndc):\n poses_bounds = np.load(os.path.join(in_dir, 'poses_bounds.npy')) # (N_images, 17)\n\n c2ws = poses_bounds[:, :15].reshape(-1, 3, 5) # (N_images, 3, 5)\n bounds = poses_bounds[:, -2:] # (N_images, 2)\n H, W, focal = c2ws[0, :, -1]\n\n # correct c2ws: original c2ws has rotation in form \"down right back\", change to \"right up back\".\n # See https://github.com/bmild/nerf/issues/34\n c2ws = np.concatenate([c2ws[..., 1:2], -c2ws[..., :1], c2ws[..., 2:4]], -1)\n\n # (N_images, 3, 4), (4, 4)\n c2ws, pose_avg = center_poses(c2ws) # pose_avg @ c2ws -> centred c2ws\n\n if use_ndc:\n # correct scale so that the nearest depth is at a little more than 1.0\n # See https://github.com/bmild/nerf/issues/34\n near_original = bounds.min()\n scale_factor = near_original * 0.75 # 0.75 is the default parameter\n # the nearest depth is at 1/0.75=1.33\n bounds /= scale_factor\n c2ws[..., 3] /= scale_factor\n \n c2ws = convert3x4_4x4(c2ws) # (N, 4, 4)\n\n results = {\n 'c2ws': c2ws, # (N, 4, 4) np\n 'bounds': bounds, # (N_images, 2) np\n 'H': int(H), # scalar\n 'W': int(W), # scalar\n 'focal': focal, # scalar\n 'pose_avg': pose_avg, # (4, 4) np\n }\n return results"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _get_las_bounds(fpath):\n with laspy.file.File(fpath) as f:\n return Bounds(*(f.header.min + f.header.max))","def load_poses(self):\n print('Loading poses for sequence ' + self.sequence + '...')\n\n pose_file = os.path.join(self.pose_path, self.sequence + '.txt')\n\n # Read and parse the poses\n try:\n self.T_w_cam0 = []\n with open(pose_file, 'r') as f:\n for line in f.readlines():\n T = np.fromstring(line, dtype=float, sep=' ')\n T = T.reshape(3, 4)\n T = np.vstack((T, [0, 0, 0, 1]))\n self.T_w_cam0.append(T)\n print('done.')\n\n except FileNotFoundError:\n print('Ground truth poses are not avaialble for sequence ' +\n self.sequence + '.')","def read_for_validation(p):\n return read_cropped_image_npy(p, False)","def read_for_training(p):\n return read_cropped_image_npy(p, True)","def read_annotation_yolov5(bbox_path):\n\n # image_paths = get_lists_in_dir(rawImage_dir)\n\n dw = 1./(camera_resolution[0]) # 1 / image width\n dh = 1./(camera_resolution[1]) # 1 / image height\n\n # Read in bbox coordinate information from bbox_information.txt\n dimension_list = []\n with open(bbox_path, 'r') as annotation_file:\n content = annotation_file.read().splitlines()\n\n for n in content:\n # x = int(n.split()[0])+int(n.split()[2])/2\n # y = int(n.split()[1])+int(n.split()[3])/2\n # w = int(n.split()[2])\n # h = int(n.split()[3])\n #\n # x = x*dw\n # w = w*dw\n # y = y*dh\n # h = h*dh\n\n bb = n.split()\n w = int(bb[2])\n h = int(bb[3])\n\n start_x = int(bb[0])\n start_y = int(bb[1])\n\n center_x = start_x + w / 2\n center_y = start_y + h / 2\n\n x = center_x * dw\n y = center_y * dh\n w = w * dw\n h = h * dh\n \n dimension_list.append((x, y, w, h))\n\n return dimension_list","def read_pts(filename):\n lines = open(filename).read().splitlines()\n lines = lines[3:71]\n\n landmarks = []\n ibug_index = 1 # count from 1 to 68 for all ibug landmarks\n for l in lines:\n coords = l.split()\n landmarks.append(eos.core.Landmark(str(ibug_index), [float(coords[0]), float(coords[1])]))\n ibug_index = ibug_index + 1\n\n return landmarks","def load_bbox(depth_dir, view):\n base_filename = os.path.join(depth_dir, \"%05d\" % view)\n if os.path.exists(base_filename + \".npz\"):\n npz_dict = np.load(base_filename + \".npz\")\n if 'bbox' in npz_dict:\n crop = npz_dict['bbox']\n else:\n crop = None\n else:\n crop = None\n if crop is None:\n crop_files = glob(base_filename + \"_bbox*\")\n if len(crop_files) == 1:\n crop = np.load(crop_files[0])\n elif len(crop_files) > 1:\n error(\"Crop file base '%s_bbox' matches multiple files\" % base_filename)\n return crop","def read_pts_file(filename):\n lines = open(filename).read().splitlines()\n if int(lines[1:2][0].split('n_points:')[-1]) != 68:\n print ('No 68-landmark format founded')\n return None\n lines = lines[3:71]\n\n landmarks = []\n for l in lines:\n coords = l.split()\n landmarks.append([float(coords[0]), float(coords[1])])\n return landmarks","def read_positions():\n return np.genfromtxt(\"POSITIONS.OUT\").transpose()","def load_annotations(path, img_w, img_h):\n bboxes = []\n with open(path, 'r') as file:\n for row in file:\n _, xc , yc, w, h = row.split()\n xc = float(xc)*img_w\n yc = float(yc)*img_h\n w = float(w)*img_w\n h = float(h)*img_h\n bboxes.append([xc - w/2 , yc - h/2, xc + w/2 , yc + h/2])\n\n return bboxes","def read_all_poses(world):\n dataset_root='./jsonfile'\n path = os.path.join(dataset_root, world, 'image_structs.mat')\n data = sio.loadmat(path)\n\n xyz = data['image_structs']['world_pos']\n image_names = data['image_structs']['image_name'][0]\n\n dire=data['image_structs']['direction']\n #rot = data['image_structs']['R'][0]\n #scale = data['scale'][0][0]\n\n n = xyz.shape[1]\n x = [xyz[0][i][0][0] for i in range(n)]\n y = [0 for i in range(n)]\n z = [xyz[0][i][2][0] for i in range(n)]\n px= [dire[0][i][0][0] for i in range(n)]\n py= [0 for i in range(n)]\n pz= [dire[0][i][2][0] for i in range(n)]\n#============================================================\n names = [name[0][:-4] for name in image_names]\n if len(names) != len(x):\n raise ValueError('number of image names are not equal to the number of '\n 'poses {} != {}'.format(len(names), len(x)))\n output = {}\n for i in range(n):\n output[names[i]] = [x[i], z[i], px[i], pz[i]]\n return output","def read_map_file(path):\r\n with open(path) as f:\r\n dir_name = os.path.dirname(path)\r\n img = cv2.imread(dir_name + '/' + f.readline().strip())\r\n assert img.shape[0] > 0 and img.shape[1] > 0, 'Can not open image file'\r\n meter_per_pixel = float(f.readline().strip())\r\n ori_str = f.readline().strip().split()\r\n origin = np.array([int(ori_str[0]), int(ori_str[1])])\r\n init_heading = float(ori_str[2])\r\n return img, meter_per_pixel, origin, init_heading","def get_bounds():\n bounds = [\n (0.1, 0.5), # Omega_m\n (0.05, 0.15) # beta\n ]\n return np.array(bounds)","def read_binning_file(file_name, lmax):\n\n bin_lo,bin_hi,bin_c = plt.loadtxt(file_name,unpack=True)\n id = np.where(bin_hi <lmax)\n bin_lo,bin_hi,bin_c=bin_lo[id],bin_hi[id],bin_c[id]\n if bin_lo[0]<2:\n bin_lo[0]=2\n bin_hi=bin_hi.astype(np.int)\n bin_lo=bin_lo.astype(np.int)\n bin_size=bin_hi-bin_lo+1\n return (bin_lo,bin_hi,bin_c,bin_size)","def _get_clip_loc_in_array(self):\r\n\r\n # coordinates of upperleft and lowerright points of binding box\r\n box_ulx, box_uly, box_lrx, box_lry = self.clip_box[0][0], self.clip_box[0][1], \\\r\n self.clip_box[1][0], self.clip_box[1][1]\r\n\r\n # Get the offsets that correspond to the bounding box corner coordinates.\r\n offsets_ul = gdal.ApplyGeoTransform(self.inv_gt, box_ulx, box_uly)\r\n offsets_lr = gdal.ApplyGeoTransform(self.inv_gt, box_lrx, box_lry)\r\n\r\n # The offsets are returned as floating point, but we need integers.\r\n self.off_ulx, self.off_uly = map(int, offsets_ul)\r\n self.off_lrx, self.off_lry = map(int, offsets_lr)\r\n\r\n # Compute the numbers of rows and columns to extract, based on the offsets.\r\n self.row = self.off_lry - self.off_uly\r\n self.column = self.off_lrx - self.off_ulx","def bounds(self):\n return self._bboxes[0][0] #TODO: merge all coverages","def get_mask_arr(path):\n with rasterio.open(path) as src:\n img = src.read().transpose((1, 2, 0))\n seg = np.array(img, dtype=int)\n\n return seg[:, :, 0]","def read_depth_map(depth_map_ifp):\n # See:\n # https://github.com/simonfuhrmann/mve/wiki/MVE-File-Format#the-mvei-image-format\n # https://github.com/simonfuhrmann/mve/blob/master/libs/mve/image_io.cc\n\n with open(depth_map_ifp, \"rb\") as fid:\n mvei_file_signature = MVEFileHandler._read_next_bytes(\n fid, 11, \"ccccccccccc\"\n )\n width = MVEFileHandler._read_next_bytes(fid, 4, \"i\")[0]\n height = MVEFileHandler._read_next_bytes(fid, 4, \"i\")[0]\n channels = MVEFileHandler._read_next_bytes(fid, 4, \"i\")[0]\n assert channels == 1\n raw_type = MVEFileHandler._read_next_bytes(fid, 4, \"i\")[0]\n assert raw_type == 9 # IMAGE_TYPE_FLOAT\n num_elements = width * height * channels\n data = np.asarray(\n MVEFileHandler._read_next_bytes(\n fid, num_elements * 4, \"f\" * num_elements\n )\n )\n return data.reshape((height, width))","def get_bbox(fname):\r\n fname = fname.split('_') # fname -> list\r\n i = fname.index('bbox')\r\n return map(float, fname[i+1:i+5]) # m\r","def read_voc_annotation_file(annotation_file_path, labelmap_dict, has_scores=False):\n root = etree.parse(annotation_file_path)\n image_id = root.find('filename').text\n objects = root.findall('object')\n\n boxes = np.array([]).reshape(0, 4)\n classes = np.array([])\n scores = np.array([])\n\n for item in objects:\n name = item.find('name').text\n class_id = labelmap_dict[name]\n class_array = np.array([class_id])\n classes = np.concatenate([classes, class_array], axis=0)\n\n if has_scores:\n score = item.find('score').text\n score_array = np.array([score]).astype(np.float)\n scores = np.concatenate([scores, score_array], axis=0)\n\n bndbox = item.find('bndbox')\n ymin = bndbox.find('ymin').text\n xmin = bndbox.find('xmin').text\n ymax = bndbox.find('ymax').text\n xmax = bndbox.find('xmax').text\n bndbox_array = np.expand_dims(np.array([ymin, xmin, ymax, xmax]).astype(np.float32), axis=0)\n boxes = np.concatenate([boxes, bndbox_array], axis=0)\n\n if boxes.ndim < 2:\n boxes = np.expand_dims(boxes, axis=0)\n\n classes = classes.astype(np.int32)\n\n if has_scores:\n return image_id, boxes, classes, scores\n else:\n return image_id, boxes, classes","def get_bbox(fname):\r\n fname = fname.split('_') # fname -> list\r\n i = fname.index('bbox')\r\n return list(map(float, fname[i+1:i+5])) # m\r","def get_document_bounds(image_file, feature):\n # client = vision.ImageAnnotatorClient()\n\n bounds = []\n\n\n# No need for this .... \n # with io.open(image_file, 'rb') as image_file:\n # content = image_file.read()\n\n # image = types.Image(content=content)\n\n # response = client.document_text_detection(image=image)\n # document = response.full_text_annotation\n\n # with open('8130processed.json', 'w') as outfile:\n # outfile.write(MessageToJson(response))\n\n\n# We already have the document bounds of the image inside of 8130processed.json no\n\n f = open ('processed_8130-1output-1-to-1.json', \"r\")\n data = json.load(f)\n datas=json.dumps(data)\n # print(data)\n\n response = json_format.Parse(datas, vision.types.AnnotateFileResponse())\n # print(response)\n document = response.inputConfig\n # Collect specified feature bounds by enumerating all document features\n for page in document.pages:\n for block in page.blocks:\n for paragraph in block.paragraphs:\n for word in paragraph.words:\n for symbol in word.symbols:\n if (feature == FeatureType.SYMBOL):\n bounds.append(symbol.bounding_box)\n\n if (feature == FeatureType.WORD):\n bounds.append(word.bounding_box)\n\n if (feature == FeatureType.PARA):\n bounds.append(paragraph.bounding_box)\n\n if (feature == FeatureType.BLOCK):\n bounds.append(block.bounding_box)\n\n # The list `bounds` contains the coordinates of the bounding boxes.\n return bounds","def get_bbox(fname):\r\n\r\n fname = fname.split('_') # fname -> list\r\n i = fname.index('bbox')\r\n return list(map(float, fname[i+1:i+5])) # m\r","def check_input_extents(config, shakefile=None, bounds=None):\n if shakefile is None and bounds is None:\n raise Exception('Must define either a shakemap file or bounds')\n modelname = config.keys()[0]\n # Make dummy geodict to use\n if bounds is None:\n evdict = ShakeGrid.getFileGeoDict(shakefile)\n else:\n evdict = GeoDict.createDictFromBox(\n bounds['xmin'], bounds['xmax'],\n bounds['ymin'], bounds['ymax'],\n 0.00001, 0.00001, inside=False)\n\n # Check extents of all input layers\n notcovered = []\n notcovgdicts = []\n newbounds = None\n for item, value in config[modelname]['layers'].items():\n if 'file' in value.keys():\n filelook = value['file']\n if getFileType(filelook) == 'gmt':\n tmpgd, _ = GMTGrid.getFileGeoDict(filelook)\n else:\n tmpgd, _ = GDALGrid.getFileGeoDict(filelook)\n # See if tempgd contains evdict\n contains = tmpgd.contains(evdict)\n if not contains:\n notcovered.append(filelook)\n notcovgdicts.append(tmpgd)\n # print(filelook)\n if len(notcovered) > 0:\n # Figure out what bounds COULD be run\n xmins = [gd.xmin for gd in notcovgdicts]\n xmaxs = [gd.xmax for gd in notcovgdicts]\n ymins = [gd.ymin for gd in notcovgdicts]\n ymaxs = [gd.ymax for gd in notcovgdicts]\n\n # Set in by a buffer of 0.05 degrees because mapio doesn't like \n # when bounds are exactly the same for getboundswithin\n newbounds = dict(xmin=evdict.xmin + 0.05,\n xmax=evdict.xmax - 0.05,\n ymin=evdict.ymin + 0.05,\n ymax=evdict.ymax - 0.05)\n # Which one is the problem?\n if evdict.xmin < np.max(xmins):\n newbounds['xmin'] = np.max(xmins) + 0.05\n if evdict.xmax > np.min(xmaxs):\n newbounds['xmax'] = np.min(xmaxs) - 0.05\n if evdict.ymin < np.max(ymins):\n newbounds['ymin'] = np.max(ymins) + 0.05\n if evdict.ymax > np.min(ymaxs):\n newbounds['ymax'] = np.min(ymaxs) - 0.05\n\n # See if this is a possible extent\n try:\n test = GeoDict.createDictFromBox(\n newbounds['xmin'], newbounds['xmax'],\n newbounds['ymin'], newbounds['ymax'],\n 0.00001, 0.00001, inside=False)\n except BaseException:\n print('Cannot make new bounds that will work')\n newbounds = None\n\n return notcovered, newbounds","def getBoundingBoxes():\n allBoundingBoxes = BoundingBoxes()\n import glob\n import os\n # Read ground truths\n pth = \"../Wildtrack_dataset/annotations_positions/*.json\"\n files = glob.glob(pth)\n files.sort()\n # Class representing bounding boxes (ground truths and detections)\n allBoundingBoxes = BoundingBoxes()\n # Read GT detections from txt files\n # Each value of each line is \"class_id, x, y, width, height\" respectively\n # Class_id represents the class of the bounding box\n # x, y represents the most top-left coordinates of the bounding box\n # x2, y2 represents the most bottom-right coordinates of the bounding box\n for idx, f in enumerate(files):\n with open(f) as j:\n data = json.load(j)\n # print(f)\n for d in data:\n for v in d['views']:\n if v['viewNum'] != 0 or v['xmax'] == -1:\n continue\n\n idClass = 'person' # class\n x = float(v['xmin']) # confidence\n y = float(v['ymin'])\n w = float(v['xmax'])\n h = float(v['ymax'])\n bb = BoundingBox(\n str(idx),\n idClass,\n x,\n y,\n w,\n h,\n CoordinatesType.Absolute, (1920, 1080),\n BBType.GroundTruth,\n format=BBFormat.XYX2Y2)\n allBoundingBoxes.addBoundingBox(bb)\n \n\n\n # Read detections\n with open(\"wildtrack_yolo_tiny.out\", \"rb\") as fin:\n pred = pickle.load(fin)\n for idx, (k, value) in enumerate(pred.items()):\n for d in value:\n if d['tag'] != \"person\":\n continue\n \n box = d['box']\n idClass = 'person' # class\n x = float(box[0]) # confidence\n y = float(box[1])\n w = float(box[2])\n h = float(box[3])\n bb = BoundingBox(\n str(idx),\n idClass,\n x,\n y,\n w,\n h,\n CoordinatesType.Absolute, (1920, 1080),\n BBType.Detected,\n d['score'],\n format=BBFormat.XYX2Y2)\n allBoundingBoxes.addBoundingBox(bb)\n return allBoundingBoxes","def read_kitti_R0_rect(filename):\n\n with open(filename) as f:\n for line in f:\n data = line.split(' ')\n if data[0] == 'R0_rect:':\n R0_rect = np.array([float(x) for x in data[1:13]])\n R0_rect = R0_rect.reshape(3,3)\n rect_4x4 = np.zeros([4, 4], dtype=R0_rect.dtype)\n rect_4x4[3, 3] = 1.\n rect_4x4[:3, :3] = R0_rect\n return rect_4x4\n\n raise Exception(\n 'Could not find entry for P2 in calib file {}'.format(filename))","def read_velocities(filename, return_grid=True, return_proj=False):\n ds = gdal.Open(filename)\n #Get dimensions\n nc = ds.RasterXSize\n nr = ds.RasterYSize\n \n geotransform = ds.GetGeoTransform()\n xOrigin = geotransform[0]\n xPix = geotransform[1] #pixel width in x-direction\n yOrigin = geotransform[3]\n yPix = geotransform[5] #pixel height in y-direction\n \n lons = xOrigin + np.arange(0, nc)*xPix\n lats = yOrigin + np.arange(0, nr)*yPix\n \n x, y = np.meshgrid(lons, lats)\n \n vband = ds.GetRasterBand(1)\n varr = vband.ReadAsArray()\n \n if return_grid and return_proj:\n return x, y, varr, ds.GetProjection()\n elif return_grid:\n return x, y, varr\n else: \n return varr","def read(self):\n stable_poses = []\n f = open(self.filepath_, \"r\")\n data = [line.split() for line in f]\n for i in range(len(data)):\n if len(data[i]) > 0 and data[i][0] == \"p\":\n p = float(data[i][1])\n r = [[data[i+1][1], data[i+1][2], data[i+1][3]], [data[i+2][0], data[i+2][1],\n data[i+2][2]], [data[i+3][0], data[i+3][1], data[i+3][2]]]\n r = np.array(r).astype(np.float64)\n x0 = np.array([data[i+4][1], data[i+4][2], data[i+4][3]]).astype(np.float64)\n stable_poses.append(sp.StablePose(p, r, x0))\n return stable_poses","def extract_frames( fname, bnd ):\n if os.uname()[0] == 'Linux':\n savefunc = numpy.save\n elif os.uname()[0] == 'Darwin':\n # why the !@$# doesn't Mac save readable .npy files??\n savefunc = numpy.savetxt\n fh = open( fname, 'r' )\n bnd_arr = numpy.loadtxt( bnd )\n nx, ny = bnd_arr.shape\n bnd_arr = bnd_arr.ravel()\n # save files in a special folder\n part = fname.rpartition( '/' )\n cell_name = part[-1]\n # find the subfolder name from the beginning of the cell\n # name. subfolder must have been created already.\n subfolder = cell_name.partition( '-' )[0]\n savedir = part[0] + '/' + 'frames/' + subfolder +'/'\n\n make_dir( savedir )\n # print 'savedir', savedir\n \n # loop over lines in <fname> and save each as nx x ny array\n #k = 0. \n fromstring = numpy.fromstring\n for k, line in enumerate( fh.readlines() ):\n arr = fromstring( line, sep='\\t' )\n # remove boundary\n arr = bnd_arr * arr\n arr.resize( (nx,ny) )\n #numpy.save( savedir + cell_name + '_' + str(k), arr )\n savefunc( savedir + cell_name + '_' + str(k), arr )","def get_bounds(shakefile, parameter='pga', threshold=2):\n from mapio.shake import ShakeGrid\n\n shakemap = ShakeGrid.load(shakefile, adjust='res')\n if parameter == 'pga':\n vals = shakemap.getLayer('pga')\n elif parameter == 'pgv':\n vals = shakemap.getLayer('pgv')\n else:\n raise Exception('parameter not valid')\n xmin, xmax, ymin, ymax = vals.getBounds()\n lons = np.linspace(xmin, xmax, vals.getGeoDict().nx)\n lats = np.linspace(ymax, ymin, vals.getGeoDict().ny) # backwards so it plots right\n row, col = np.where(vals.getData() > float(threshold))\n lonmin = lons[col].min()\n lonmax = lons[col].max()\n latmin = lats[row].min()\n latmax = lats[row].max()\n\n boundaries1 = {'dx': 100, 'dy': 100., 'nx': 100., 'ny': 100} # dummy fillers, only really care about bounds\n if xmin < lonmin:\n boundaries1['xmin'] = lonmin\n else:\n boundaries1['xmin'] = xmin\n if xmax > lonmax:\n boundaries1['xmax'] = lonmax\n else:\n boundaries1['xmax'] = xmax\n if ymin < latmin:\n boundaries1['ymin'] = latmin\n else:\n boundaries1['ymin'] = ymin\n if ymax > latmax:\n boundaries1['ymax'] = latmax\n else:\n boundaries1['ymax'] = ymax\n\n return boundaries1","def read_groups_particles(filename):\n \n f = open(filename,'r')\n\n Ntot = fromstring(f.read(4),int32)[0]\n Pos\t = fromstring(f.read(3*4*Ntot),float32)\n Pos.shape = (Ntot,3)\n f.close()\n \n return Pos","def bounds(self):\n return self._bboxes[0][0] #TODO: merge all coverages","def Read_FitsPoints(input_path,fname):\n\twave = []; dwave = []\n\tbreak_ind = 0 \t\n\n\tfilename_fullpath = input_path + fname\n\twith open(filename_fullpath) as f:\n\t\tlines = f.readlines()\n\t\tfor i in xrange(1,len(lines)):\n\t\t\tif re.search('a',lines[i]):\n\t\t\t\tbreak_ind = i -1\n\n\twave = np.loadtxt(filename_fullpath,usecols=[0])\n\tdwave = np.loadtxt(filename_fullpath,usecols=[1])\n\t\n\t# Section b\n\twave_b = wave[:break_ind]; dwave_b = dwave[:break_ind]\n\t\n\t# Section a\n\twave_a = wave[break_ind:]; dwave_a = dwave[break_ind:]\n\t\n\tpl.plot(wave_a,dwave_a,'o',label='Segment a')\n\tpl.plot(wave_b,dwave_b,'o',label='Segment b')\n\tpl.legend(loc='best')\n\tpl.ylim([-0.1,0.1])\n\tpl.xlabel(r'Wavelength $\\AA$')\n\tpl.ylabel(r'$\\Delta \\lambda \\AA$')\n\tpl.savefig(input_path + 'plots/' + fname + '.png')\n\n\tpl.clf()\n\n\treturn [wave_a,dwave_a], [wave_b,dwave_b]","def _ReadExtent(self):\n # Read the mesh file as line strings, remove lines with comment = !\n v = np.array(np.__version__.split('.')[0:2], dtype=int)\n FileName = self.GetMeshFileName()\n try:\n if v[0] >= 1 and v[1] >= 10:\n # max_rows in numpy versions >= 1.10\n msh = np.genfromtxt(FileName, delimiter='\\n', dtype=np.str,comments='!', max_rows=1)\n else:\n # This reads whole file :(\n msh = np.genfromtxt(FileName, delimiter='\\n', dtype=np.str, comments='!')[0]\n except (IOError, OSError) as fe:\n raise _helpers.PVGeoError(str(fe))\n # Fist line is the size of the model\n self.__sizeM = np.array(msh.ravel()[0].split(), dtype=int)\n # Check if the mesh is a UBC 2D mesh\n if self.__sizeM.shape[0] == 1:\n # Read in data from file\n xpts, xdisc, zpts, zdisc = ubcMeshReaderBase._ubcMesh2D_part(FileName)\n nx = np.sum(np.array(xdisc,dtype=int))+1\n nz = np.sum(np.array(zdisc,dtype=int))+1\n return (0,nx, 0,1, 0,nz)\n # Check if the mesh is a UBC 3D mesh or OcTree\n elif self.__sizeM.shape[0] >= 3:\n # Get mesh dimensions\n dim = self.__sizeM[0:3]\n ne,nn,nz = dim[0], dim[1], dim[2]\n return (0,ne, 0,nn, 0,nz)\n else:\n raise _helpers.PVGeoError('File format not recognized')","def load_pts_features(path):\n\n #\n # Your code here\n #\n\n pts = [np.empty((123, 2)), np.empty((123, 2))]\n feats = [np.empty((123, 128)), np.empty((123, 128))]\n\n return pts, feats","def read_mesh_bbox(filename=None, nodes=None):\n if filename != None:\n nodes = read_mesh_nodes(filename)\n return np.array([[np.min(nodes[:,0]), np.min(nodes[:,1]), np.min(nodes[:,2])],\n [np.max(nodes[:,0]), np.max(nodes[:,1]), np.max(nodes[:,2])]])","def bounds(self, pos):","def get_bounds(shakefile, parameter='pga', threshold=2.0):\n shakemap = ShakeGrid.load(shakefile, adjust='res')\n if parameter == 'pga':\n vals = shakemap.getLayer('pga')\n elif parameter == 'pgv':\n vals = shakemap.getLayer('pgv')\n else:\n raise Exception('parameter not valid')\n xmin, xmax, ymin, ymax = vals.getBounds()\n lons = np.linspace(xmin, xmax, vals.getGeoDict().nx)\n lats = np.linspace(ymax, ymin, vals.getGeoDict().ny)\n row, col = np.where(vals.getData() > float(threshold))\n lonmin = lons[col].min()\n lonmax = lons[col].max()\n latmin = lats[row].min()\n latmax = lats[row].max()\n\n # dummy fillers, only really care about bounds\n boundaries1 = {'dx': 100, 'dy': 100., 'nx': 100., 'ny': 100}\n\n if xmin < lonmin:\n boundaries1['xmin'] = lonmin\n else:\n boundaries1['xmin'] = xmin\n if xmax > lonmax:\n boundaries1['xmax'] = lonmax\n else:\n boundaries1['xmax'] = xmax\n if ymin < latmin:\n boundaries1['ymin'] = latmin\n else:\n boundaries1['ymin'] = ymin\n if ymax > latmax:\n boundaries1['ymax'] = latmax\n else:\n boundaries1['ymax'] = ymax\n\n return boundaries1","def bbox_from_openpose(openpose_file, rescale=1.2, detection_thresh=0.2):\n with open(openpose_file, 'r') as f:\n keypoints = json.load(f)['people'][0]['pose_keypoints_2d']\n keypoints = np.reshape(np.array(keypoints), (-1,3))\n valid = keypoints[:,-1] > detection_thresh\n valid_keypoints = keypoints[valid][:,:-1]\n center = valid_keypoints.mean(axis=0)\n bbox_size = (valid_keypoints.max(axis=0) - valid_keypoints.min(axis=0)).max()\n # adjust bounding box tightness\n scale = bbox_size / 200.0\n scale *= rescale\n return center, scale","def bbox_from_openpose(openpose_file, rescale=1.2, detection_thresh=0.2):\n with open(openpose_file, 'r') as f:\n keypoints = json.load(f)['people'][0]['pose_keypoints_2d']\n keypoints = np.reshape(np.array(keypoints), (-1,3))\n valid = keypoints[:,-1] > detection_thresh\n valid_keypoints = keypoints[valid][:,:-1]\n center = valid_keypoints.mean(axis=0)\n bbox_size = (valid_keypoints.max(axis=0) - valid_keypoints.min(axis=0)).max()\n # adjust bounding box tightness\n scale = bbox_size / 200.0\n scale *= rescale\n return center, scale","def read_keypoints(file_path: str) -> np.array:\n # Set filter for body keypoints\n keypoints_filter = list(keypoints_map.values())\n\n # Read json file with keypoints\n keypoints = None\n with open(file_path, 'r') as f:\n for row in f.readlines():\n keypoints = json.loads(row)\n\n # Extract and concatenate selected keypoints\n if keypoints['people']:\n results = np.concatenate([\n np.array(keypoints['people'][0]['pose_keypoints_2d']).reshape(-1, 3)[keypoints_filter, :2],\n np.array(keypoints['people'][0]['hand_left_keypoints_2d']).reshape(-1, 3)[:, :2],\n np.array(keypoints['people'][0]['hand_right_keypoints_2d']).reshape(-1, 3)[:, :2]],\n axis=0\n )\n else:\n results = np.zeros((1, 0))\n\n return results","def extract_2d_poses(self, file_name: str, force: bool = False) -> None:\n folder_dir = os.path.join(self.dataset_dir, file_name + '.zip')\n\n # Extract zip in dataset dir only if is has not been extracted before or if it has to ver overwritten\n if os.path.exists(self.poses_dir) and not force:\n print(\"The poses folder already exists. If you want to unroll them again and overwrite the current poses, \"\n \"please use the option force=True\")\n else:\n print('Extracting 2d poses...')\n with zipfile.ZipFile(folder_dir, 'r') as zip_ref:\n zip_ref.extractall(self.dataset_dir)\n print('Finish extracting 2d poses')","def get_data(self):\n \n coordinates = parsing.parse_contour_file(self._filename)\n return parsing.poly_to_mask(coordinates, *self._shape)","def read_data(path):\n with h5py.File(path, \"r\") as f:\n transformed_poses = np.array(f['transformed_poses'])\n extracted_poses = np.array(f['poses'])\n target = np.array(f['target'])\n \n return extracted_poses, transformed_poses, target","def read_mesh_boundary(sFilename_boundary_in):\n iReturn_code = 1\n if os.path.isfile(sFilename_boundary_in):\n pass\n else:\n print('This mesh file does not exist: ', sFilename_boundary_in )\n iReturn_code = 0\n return iReturn_code\n\n \n pDriver_json = ogr.GetDriverByName('GeoJSON') \n pDataset_mesh = pDriver_json.Open(sFilename_boundary_in, gdal.GA_ReadOnly)\n pLayer_mesh = pDataset_mesh.GetLayer(0)\n pSpatial_reference_out = pLayer_mesh.GetSpatialRef()\n ldefn = pLayer_mesh.GetLayerDefn() \n\n #we also need to spatial reference\n for pFeature_mesh in pLayer_mesh:\n pGeometry_mesh = pFeature_mesh.GetGeometryRef() \n pGeometrytype_boundary = pGeometry_mesh.GetGeometryName()\n if(pGeometrytype_boundary == 'POLYGON'): \n pBoundary_ogr = pGeometry_mesh \n else:\n if(pGeometrytype_boundary == 'MULTIPOLYGON'): \n nLine = pGeometry_mesh.GetGeometryCount()\n for i in range(nLine):\n pBoundary_ogr = pGeometry_mesh.GetGeometryRef(i)\n \n pass\n else:\n pass\n pass \n \n \n pBoundary_wkt = pBoundary_ogr.ExportToWkt()\n aExtent = pBoundary_ogr.GetEnvelope()\n min_x, max_x, min_y, max_y = aExtent\n \n return pBoundary_wkt, aExtent","def read_proj_info(fname, nangles=None, convert_vec=True):\n \n if fname[-4:] == 'toml':\n proj_geom = read_from_toml(fname, nangles=nangles)\n if proj_geom['type'] == 'cone' and convert_vec:\n proj_geom = geom_2vec(proj_geom)\n\n elif fname[-3:] == 'pkl':\n with open(fname, 'rb') as f:\n proj_geom = pickle.load(f)\n proj_geom['nangles'] = proj_geom['Vectors'].shape[0]\n \n # if toml and cone beam, c\n \n return proj_geom","def read_poses_for_camera(record_path, camera_name):\n\n # Resolve pose.txt file path for camera\n poses_path = os.path.join(record_path, camera_name, 'pose.txt')\n if os.path.exists(poses_path):\n poses = read_poses_dict(poses_path)\n else:\n # Sample type dataset (aka zpark-sample)\n poses_path = os.path.join(record_path, camera_name + '.txt')\n poses = read_poses_dict_6(poses_path)\n return poses","def import_phase_map(self, file):\r\n filename, extension = os.path.splitext(file)\r\n flag = False\r\n if extension == '.txt':\r\n p = np.loadtxt(file, dtype=int, delimiter=' ')\r\n if np.shape(p) != self.size:\r\n return\r\n if np.shape(p) == (self.height, self.width):\r\n p = p.T\r\n m = np.zeros((self.width, self.height, 3), dtype=np.uint8)\r\n m[:, :, 0] = p\r\n m[:, :, 1] = p\r\n m[:, :, 2] = p\r\n self.maps[os.path.basename(filename)] = {'data': m}\r\n elif extension == '.fits':\r\n hdu = fits.open(file)\r\n p = fits.getdata(file)\r\n if np.shape(p) == self.size and np.max(p)<3:\r\n self.maps[os.path.basename(filename)] = {'map': p}\r\n flag = True\r\n elif np.shape(p) == self.size and np.max(p)>3:\r\n m = np.zeros((self.width, self.height, 3), dtype=np.uint8)\r\n m[:, :, 0] = p\r\n m[:, :, 1] = p\r\n m[:, :, 2] = p\r\n self.maps[os.path.basename(filename)] = {'data': m}\r\n else:\r\n print(\"Wrong dimensions \")\r\n elif extension == '.bmp':\r\n p = pygame.image.load(file)\r\n p = pygame.surfarray.array3d(p)\r\n self.maps[os.path.basename(filename)] = {'data': p}\r\n else:\r\n raise UserWarning('File extension %s not supported.' % extension)\r\n\r\n # if np.shape(self.maps[filename]) != (1024, 768, 3):\r\n # raise UserWarning('Wrong dimensions for SLM of size %i x %i (x %i)' % (self.width, self.height, 3))\r\n\r\n # self.save_maps()\r\n return filename, flag","def asc_to_ndarray(fname, path='./'):\r\n header, headlines = get_header(fname, path)\r\n f = file(path+fname,'r')\r\n \r\n for i in xrange(headlines):\r\n f.readline() \r\n \r\n ncols = header['ncols']\r\n nrows = header['nrows']\r\n cellsize = header['cellsize']\r\n \r\n long = header['xllcorner'] + np.arange(ncols) * cellsize\r\n lat = header['yllcorner'] + np.arange(nrows) * cellsize\r\n data = np.zeros((nrows, ncols), dtype=float)\r\n for i in xrange(nrows):\r\n line = f.readline()\r\n data[i,:] = np.fromstring(line, dtype=float, sep=' ')\r\n # print data.shape, nrows, ncols\r\n f.close()\r\n \r\n if header.has_key('NODATA_value'):\r\n data = np.ma.masked_array(data, mask=data==header['NODATA_value'])\r\n \r\n return long, lat, data","def anglor(infile, sequence):\n return np.loadtxt(infile, usecols=1).clip(min=-180, max=180).reshape((1, -1, 1))","def parse_prnu_file():\n hdf_name = r'C:\\Users\\nmishra\\Workspace\\TEMPO\\PRNU_map\\\n batch_2017Jun20_TEMPO_PRNU_-20Tccd__46Tfpe_3pixSpectral_3pixSpatial.h5'\n file = h5py.File(hdf_name, 'r')\n prnu = file.get('prnu')\n prnu = np.array(prnu).transpose()\n quad_d = prnu[2:1030, 10:1034]\n quad_c = prnu[2:1030, 1078:2102]\n quad_a = prnu[1062:2090, 10:1034]\n quad_b = prnu[1062:2090, 1078:2102]\n prnu_map_lower = np.concatenate((quad_d, quad_c), axis=1)\n prnu_map_upper = np.concatenate((quad_a, quad_b), axis=1)\n prnu_map = np.concatenate((prnu_map_lower, prnu_map_upper), axis=0)\n return prnu_map","def get_gridinfo(filename, xpoints, ypoints):\n with open(f\"{filename[:-4]}.wld\", encoding=\"ascii\") as fh:\n lines = fh.readlines()\n dx = float(lines[0])\n dy = float(lines[3])\n west = float(lines[4])\n north = float(lines[5])\n south = north + dy * ypoints\n lats = np.arange(0, ypoints) * (0 - dy) + south\n lons = np.arange(0, xpoints) * dx + west\n return lons, lats","def read_im_and_landmarks(fname):\n\tim = cv2.imread(fname, cv2.IMREAD_COLOR)\n\tim = cv2.resize(im, (im.shape[1] * SCALE_FACTOR,\n im.shape[0] * SCALE_FACTOR))\n\ts = get_landmarks(im)\n\treturn im, s","def load_spe(filename):\n def read_at(data, pos, size, ntype):\n raw.seek(pos)\n return np.fromfile(raw, ntype, size)\n raw = open(filename, 'rb')\n xdim = np.int64(read_at(raw, 42, 1, np.int16)[0])\n ydim = np.int64(read_at(raw, 656, 1, np.int16)[0])\n arr = read_at(raw, 4100, xdim*ydim, np.uint16)\n arr = arr.reshape((ydim, xdim))\n print('data shape: {}'.format(np.shape(arr)))\n if np.shape(arr)[0] == 1:\n arr = arr[0]\n print('data shape: {}'.format(np.shape(arr)))\n return arr","def _get_las_npoints(fpath):\n with laspy.file.File(fpath) as f:\n return f.header.count","def read_prnu_files():\n file_path = r'C:\\Users\\nmishra\\Workspace\\TEMPO\\PRNU_map\\PRNU_map_Median'\n prnu_mask_a = np.genfromtxt(file_path +'/' + 'Quad A_Final_PRNU.csv',\n delimiter=',')\n prnu_mask_b = np.genfromtxt(file_path +'/' + 'Quad B_Final_PRNU.csv',\n delimiter=',')\n prnu_mask_c = np.genfromtxt(file_path +'/' + 'Quad C_Final_PRNU.csv',\n delimiter=',')\n prnu_mask_d = np.genfromtxt(file_path +'/' + 'Quad D_Final_PRNU.csv',\n delimiter=',')\n prnu_mask = [prnu_mask_a, prnu_mask_b, prnu_mask_c, prnu_mask_d]\n return prnu_mask","def bounds(sceneid: str) -> Dict:\n scene_params = sentinel2_parser(sceneid)\n preview_file = \"{scheme}://{bucket}/{prefix}/{preview_file}\".format(**scene_params)\n with rasterio.open(preview_file) as src_dst:\n bounds = transform_bounds(\n src_dst.crs, constants.WGS84_CRS, *src_dst.bounds, densify_pts=21\n )\n\n return dict(sceneid=sceneid, bounds=bounds)","def le_binario_mgbp(filebin,nt,nc):\n return np.fromfile(filebin,'<f4').reshape(nt,nc)","def le_binario_mgbp(filebin,nt,nc):\n return np.fromfile(filebin,'<f4').reshape(nt,nc)","def read_for_validation(p):\r\n return read_cropped_image(p, False)","def bounding_var(self, field):\n if field not in self.bounding_fields:\n raise KeyError(\"Invalid field name: %s. Valid fields are: %s\"%\n (field, self.bounding_fields))\n fname = os.path.join(self.logs_dir, \"bounding_\"+field+\".dat\")\n data = np.loadtxt(fname, skiprows=2)\n return data","def get_bounds(self):\r\n left, bottom, front = 10000, 10000, 10000\r\n right, top, back = -10000, -10000, -10000\r\n for b in self.buf:\r\n for v in b.vertices:\r\n if v[0] < left:\r\n left = v[0]\r\n if v[0] > right:\r\n right = v[0]\r\n if v[1] < bottom:\r\n bottom = v[1]\r\n if v[1] > top:\r\n top = v[1]\r\n if v[2] < front:\r\n front = v[2]\r\n if v[2] > back:\r\n back = v[2]\r\n\r\n return (left, bottom, front, right, top, back)","def read_body(filepath, **kwargs):\n with open(filepath, 'r') as infile:\n coords = numpy.loadtxt(infile, unpack=True, **kwargs)\n return coords","def read_exposure(fname, patchralims, patchdeclims, mask=True):\n from astropy.io import fits\n \n hdr = fits.getheader(fname)\n data = fits.getdata(fname)\n unc = fits.getdata(fname.replace(\"sci\", \"unc\"))\n \n s = PostageStamp()\n s.filtername = hdr[\"FILTER\"]\n s.nx, s.ny = hdr[\"NAXIS1\"], hdr[\"NAXIS2\"]\n pixscale = hdr[\"PIXSCALE\"]\n PA = hdr[\"ROT\"]\n npsf = hdr[\"NPSF\"]\n\n # --- WCS ---\n s.scale = 1.0/pixscale * np.eye(2)\n s.dpix_dsky = np.matmul(s.scale, rotation_matrix(np.deg2rad(PA)))\n s.crpix = np.array([hdr[\"CRPIX0\"], hdr[\"CRPIX1\"]])\n s.crval = np.array([hdr[\"CRVAL0\"], hdr[\"CRVAL1\"]])\n\n # --- PSF ---\n s.psf = get_psf(npsf)\n\n # -- PIXEL DATA ---\n # x,y\n # note inversion here\n s.ypix, s.xpix = np.meshgrid(np.arange(s.ny), np.arange(s.nx))\n \n # restrict to pixels in patch, and reshape all images to 1D\n sky = pixelcoords_to_skycoords(s)\n inpatch = ((sky[0] > patchralims[0]) & (sky[0] < patchralims[1]) &\n (sky[1] > patchdeclims[0]) & (sky[1] < patchdeclims[1]))\n assert inpatch.sum() > 0\n\n if not mask:\n s.good_pixel = np.copy(inpatch)\n inpatch = slice(None)\n else:\n s.nx = inpatch.sum()\n s.ny = 1\n\n s.xpix = s.xpix.reshape(-1)[inpatch]\n s.ypix = s.ypix.reshape(-1)[inpatch]\n \n # fluxes and uncertainties within patch\n s.pixel_values = data.reshape(-1)[inpatch]\n s.ierr = 1. / unc.reshape(-1)[inpatch]\n \n return s","def get_bounds(shape, affine):\n adim, bdim, cdim = shape\n adim -= 1\n bdim -= 1\n cdim -= 1\n # form a collection of vectors for each 8 corners of the box\n box = np.array([[0., 0, 0, 1],\n [adim, 0, 0, 1],\n [0, bdim, 0, 1],\n [0, 0, cdim, 1],\n [adim, bdim, 0, 1],\n [adim, 0, cdim, 1],\n [0, bdim, cdim, 1],\n [adim, bdim, cdim, 1]]).T\n box = np.dot(affine, box)[:3]\n return zip(box.min(axis=-1), box.max(axis=-1))","def GetBounds(bbox, delta, N):\n bounds = np.zeros(4, dtype=np.uint8)\n bounds[0] = max(0, math.floor(bbox[0] - delta))\n bounds[1] = min(N-1, math.ceil(bbox[0] + bbox[2] + delta))\n bounds[2] = max(0, math.floor(bbox[1] - delta))\n bounds[3] = min(N-1, math.ceil(bbox[1] + bbox[3] + delta))\n\n return bounds","def read_region_mask(grid='Nh50km'):\n\n mask_path = ('/oldhome/apbarret/data/seaice_indices/'\n 'Arctic_region_mask_Meier_AnnGlaciol2007_Nh50km.dat')\n nrow = 360\n ncol = 360\n \n result = xr.DataArray(np.fromfile(mask_path, dtype=float).reshape(nrow,ncol),\n dims=['x','y'])\n return result","def get_bounds(self, ped_pos):\n top_left_x = ped_pos[:, 0] - self.neighborhood_size / 2\n top_left_y = ped_pos[:, 1] + self.neighborhood_size / 2\n bottom_right_x = ped_pos[:, 0] + self.neighborhood_size / 2\n bottom_right_y = ped_pos[:, 1] - self.neighborhood_size / 2\n\n top_left = torch.stack([top_left_x, top_left_y], dim=1)\n bottom_right = torch.stack([bottom_right_x, bottom_right_y], dim=1)\n\n return top_left, bottom_right","def get_bounding_box_from_xml_path(path: Path) -> np.ndarray:\n with open(path, mode=\"r\") as file:\n bs = BeautifulSoup(file, \"xml\")\n\n x_min = float(bs.bndbox.xmin.string)\n x_max = float(bs.bndbox.xmax.string)\n y_min = float(bs.bndbox.ymin.string)\n y_max = float(bs.bndbox.ymax.string)\n\n return np.array([x_min, y_min, x_max - x_min, y_max - y_min])","def read_parameters_diff_file(coords):\n param_map = hp.read_map(source +\n \"kids_data/\"\n \"COM_CompMap_Compton-SZMap-milca-\"\n \"ymaps_2048_R2.00.fits\")\n params = []\n for point in coords:\n ra, dec = point\n index = declratoindex(dec, ra)\n params.append(param_map[index])\n return params","def bounds(sceneid):\n scene_params = _sentinel_parse_scene_id(sceneid)\n preview_file = os.path.join(\n scene_params[\"aws_bucket\"],\n scene_params[\"aws_prefix\"],\n scene_params[\"preview_file\"],\n )\n with rasterio.open(preview_file) as src:\n wgs_bounds = transform_bounds(\n *[src.crs, \"epsg:4326\"] + list(src.bounds), densify_pts=21\n )\n\n info = {\"sceneid\": sceneid}\n info[\"bounds\"] = list(wgs_bounds)\n\n return info","def read_2d(fpath):\n #check_isfile(fpath)\n objs = []\n ids = []\n with open(fpath, 'r') as f:\n for ff in f:\n id_position, players_x, players_y = token_position(ff)\n objs.append((players_x, players_y))\n ids.append(id_position)\n objs = np.array(objs)\n ids = np.array(ids)\n return ids, objs","def readpil3d(self):\r\n\r\n # Read the data in as an array.\r\n res = np.loadtxt(self.name, delimiter=' ')\r\n\r\n # Split into useful chunks\r\n self.pos = res[:, 0:3] # Grid point locations\r\n self.Pn = res[:, 3:4] # Normal pressure [Pa]\r\n self.flux = res[:, -1] # Flux\r","def _load(self) -> np.ndarray:\n with self._fs.open(self._filepath, mode=\"r\") as f:\n image = Image.open(f).convert(\"RGBA\")\n return np.asarray(image)","def prepare_data(self, file):\n maps = np.load(file)\n pred, gt, not_care = maps[-3:]\n return self.get_polygon(pred), self.get_polygon(gt), self.get_polygon(not_care)","def save_clips(fname, raw, bounds, mask, start_from=0):\n # data prep\n mask = np.asarray(mask)\n # save data\n if os.path.isfile(fname):\n _save_clips_append(fname, raw, bounds, mask, start_from)\n else:\n _save_clips_new(fname, raw, bounds, mask)","def read_numpy_txt(self, infname):\n InArr = np.loadtxt(infname)\n inlon = InArr[:,0]\n inlat = InArr[:,1]\n inZ = InArr[:,2]\n # self.mask = ~self.mask\n self.Zarr=inZ.reshape(self.lonArr.shape)[::-1,:]\n self.mask[np.isnan(self.Zarr)]=1\n # if (np.array_equal(inlon.reshape(self.lonArr.shape), self.lonArr) and np.array_equal(inlat.reshape(self.lonArr.shape)[::-1,:], self.latArr)):\n # self.Zarr=inZ.reshape(self.lonArr.shape)[::-1,:]\n # self.mask[self.Zarr==np.nan]=1\n # else:\n # print 'error!'\n # elif (np.array_equal(inlon.reshape(self.lonArr.shape), self.lonArr) and np.array_equal(inlat.reshape(self.lonArr.shape), self.latArr)):\n \n return","def read_pts_from_grid(jparams):\n print(\"=== Reading points from grid ===\")\n input_data = rasterio.open(jparams[\"input-file\"])\n nodata_value = input_data.nodata\n raw_data = input_data.read()\n ncols = input_data.width\n nrows = input_data.height\n shape = input_data.shape\n PixelSizeX = input_data.transform[0]\n PixelSizeY = -input_data.transform[4]\n #transform_matrix = input_data.transform\n #print(raw_data[0])\n #lower_left_corner = transform_matrix * (0,input_data.height)\n # outlist = []\n # row_cnt = 0\n # for rev_row in reversed(range(0,nrows)):\n # for col in range(0,ncols):\n # z=raw_data[0][row_cnt][col]\n # if z!=nodata_value:\n # outlist.append([(rev_row+0.5)*PixelSizeY, (col+0.5)*PixelSizeX, z])\n # row_cnt+=1\n # print(len(raw_data[0][1]))\n # print(len(np.array(generate_raster_points(nrows, ncols, raw_data, nodata_value, PixelSizeX, PixelSizeY))))\n return np.array(generate_raster_points(nrows, ncols, raw_data, nodata_value, PixelSizeX, PixelSizeY))\n # Tip: the most efficient implementation of this function does not use any loops. Use numpy functions instead.","def GeneralProfile(binsz, filename):\n data_image = fits.open(filename)\n data_image= data_image[1]\n glons = np.arange(LonLow, LonHigh+binsz, binsz) \n glon_bounds = Table()\n glon_bounds['CHANNEL'] = np.arange(len(glons) - 1)\n glon_bounds['GLON_MIN'] = np.float64(glons[:-1])\n glon_bounds['GLON_MAX'] = np.float64(glons[1:])\n data = compute_longitude_profile(glon_bounds = glon_bounds, binsz=binsz, image=data_image, datatype=2, emission=4, tev=0)\n return data","def list_bounds(self) -> None:\n print(\"\")\n print(\n \"Mu = {}\".format(self.experiment_file_dict[\"motors\"][\"mu\"][\"bounds\"])\n )\n print(\n \"Eta = {}\".format(self.experiment_file_dict[\"motors\"][\"eta\"][\"bounds\"])\n )\n print(\n \"Chi = {}\".format(self.experiment_file_dict[\"motors\"][\"chi\"][\"bounds\"])\n )\n print(\n \"Phi = {}\".format(self.experiment_file_dict[\"motors\"][\"phi\"][\"bounds\"])\n )\n print(\n \"Nu = {}\".format(self.experiment_file_dict[\"motors\"][\"nu\"][\"bounds\"])\n )\n print(\n \"Del = {}\".format(self.experiment_file_dict[\"motors\"][\"del\"][\"bounds\"])\n )\n print(\"\")","def read_bounding_boxes(filename):\n f = open(filename)\n objects = []\n weight = 0\n height = 0\n for line in f:\n print(line)\n first_word = line.split(';')[0]\n if first_word == \"Dimensions\":\n weight = line.split(';')[1]\n height = line.split(';')[2]\n if first_word == \"Object\":\n objects.append((line.split(';')[1], line.split(';')[2], line.split(';')[4],\n line.split(';')[5], line.split(';')[6], line.split(';')[7]))\n return weight, height, objects","def readPFM(file):\n file = open(file, 'rb')\n\n color = None\n width = None\n height = None\n scale = None\n endian = None\n\n header = file.readline().rstrip()\n if header == b'PF':\n color = True\n elif header == b'Pf':\n color = False\n else:\n raise Exception('Not a PFM file.')\n\n dims = file.readline()\n try:\n width, height = list(map(int, dims.split()))\n except:\n raise Exception('Malformed PFM header.')\n\n scale = float(file.readline().rstrip())\n if scale < 0: # little-endian\n endian = '<'\n scale = -scale\n else:\n endian = '>' # big-endian\n\n data = np.fromfile(file, endian + 'f')\n shape = (height, width, 3) if color else (height, width, 1)\n\n data = np.reshape(data, shape)\n data = np.flipud(data)\n return data, scale","def read_seaice_mask(file=r'C:\\Users\\apbarret\\Documents\\data\\sea_ice_index\\Arctic_region_mask_Meier_AnnGlaciol2007.msk'):\n cols = 304\n rows = 448\n img = np.fromfile(file, dtype='byte').reshape(rows, cols)\n return img","def read_raw_data(file_name: str, ROWS: int, COLS: int, OFFSET=0) -> list:\r\n FILE = open(file_name, mode=\"r\")\r\n\r\n # Reading the data in the Single Dimensional form\r\n img = np.fromfile(\r\n FILE, dtype=np.uint8, count=ROWS * COLS, offset=((ROWS * COLS) * OFFSET)\r\n )\r\n\r\n # Shaping the data to the two dimensional format\r\n img = np.reshape(img, (ROWS, COLS)).tolist()\r\n\r\n FILE.close()\r\n return img","def file_parser(file_name):\n h = 480\n w = 640\n out = []\n with open(file_name, 'r') as f:\n line_num = 1\n for line in f:\n if line_num < 17:\n # Read to where data starts\n line_num += 1\n continue\n elif line_num > 74:\n break\n # print(list(map(int, line.strip().split(\" \"))))\n vals = line.split()\n # print(list(\"\".join(line)))\n # print(line.split())\n assert(float(vals[2]) < 640)\n assert(float(vals[3]) < 480)\n point = [float(vals[2]) * w, float(vals[3]) * h]\n # print(point)\n out.append(point)\n line_num += 1\n\n out.append([0,0])\n out.append([w-1, 0])\n out.append([0, h-1])\n out.append([w-1, h-2])\n return out","def getPoses(modelname):\n load_file = bUtils.readTextFile(modelname + '::poses')\n if load_file == '':\n return []\n poses = json.loads(load_file)\n return poses.keys()","def _load_pascal_annotation(self, index):\n imagefile = os.path.join(self._data_path, 'Images', index + self._image_ext)\n labelfile = os.path.join(self._data_path, 'GroundTruth', index + '.txt')\n print 'Loading: image {} and label {}'.format(imagefile, labelfile)\n\n labels = np.loadtxt(labelfile) + 1\n image = cv2.imread(imagefile)\n\n gt_classes = []\n gt_boxes = []\n\n boxes, classes = self._get_boxes(image, labels)\n\n boxes = np.array(boxes)\n classes = np.array(classes)\n\n scores = np.ones(len(boxes), dtype=np.float32)\n dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32)\n\n # keep = nms(dets, 0.2)\n _, keep = non_max_suppression_fast(dets, 0.2)\n boxes = boxes[keep, :]\n classes = classes[keep]\n\n for n, (x, y, w, h) in enumerate(boxes):\n # gt_boxes.append([min(x, w), min(y, h), min(abs(w - x), w), min(abs(h - y), h)])\n gt_boxes.append([x, y, w - x, h - y])\n gt_classes.append(classes[n])\n\n overlaps = np.zeros((len(gt_boxes), len(self._classes)), dtype=np.float32)\n for n, obj in enumerate(gt_boxes):\n overlaps[n, gt_classes[n]] = 1.0\n\n overlaps = scipy.sparse.csr_matrix(overlaps)\n\n return {'boxes': np.array(gt_boxes),\n 'gt_classes': np.array(gt_classes, dtype=np.int32),\n 'gt_overlaps': overlaps,\n 'flipped': False}\n\n # def get_data_from_tag(node, tag):\n # return node.getElementsByTagName(tag)[0].childNodes[0].data\n\n # with open(filename) as f:\n # data = minidom.parseString(f.read())\n\n # objs = data.getElementsByTagName('object')\n # num_objs = len(objs)\n\n # boxes = np.zeros((num_objs, 4), dtype=np.uint16)\n # gt_classes = np.zeros((num_objs), dtype=np.int32)\n # overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)\n\n # # Load object bounding boxes into a data frame.\n # for ix, obj in enumerate(objs):\n # # Make pixel indexes 0-based\n # x1 = float(get_data_from_tag(obj, 'xmin')) - 1\n # y1 = float(get_data_from_tag(obj, 'ymin')) - 1\n # x2 = float(get_data_from_tag(obj, 'xmax')) - 1\n # y2 = float(get_data_from_tag(obj, 'ymax')) - 1\n # cls = self._class_to_ind[str(get_data_from_tag(obj, \"name\")).lower().strip()]\n # boxes[ix, :] = [x1, y1, x2, y2]\n # gt_classes[ix] = cls\n # overlaps[ix, cls] = 1.0\n\n # overlaps = scipy.sparse.csr_matrix(overlaps)\n\n # return {'boxes': boxes,\n # 'gt_classes': gt_classes,\n # 'gt_overlaps': overlaps,\n # 'flipped': False}","def get_bounds():\n return [0.00], [1.00]","def read_xyz_file(filename, num_spatial_dimensions):\n print(\"Reading data from XYZ file.\")\n\n particle_positions = []\n frame_number = 0\n line_number = 0\n frame_particles = 0\n with open(filename, 'r') as input_file:\n for line in input_file:\n if line_number == 0:\n # Check for blank line at end of file\n if line != \"\":\n frame_particles = int(line)\n particle_positions.append(np.zeros((frame_particles, num_spatial_dimensions)))\n elif line_number == 1:\n pass\n else:\n for dimension in range(num_spatial_dimensions):\n particle_positions[frame_number][line_number-2][dimension] = line.split()[1:][dimension]\n line_number += 1\n # If we have reached the last particle in the frame, reset counter for next frame\n if line_number == (frame_particles + 2):\n line_number = 0\n frame_number += 1\n\n print(\"XYZ read complete.\")\n\n return particle_positions","def readMAXIPOLdataLuis(filename):\n\n ia=[]; ja=[]\n i=[]; j=[]; beam=[]; sig=[]; cts=[]\n for line in open(filename, \"r\"):\n line = line.strip().split()\n i1, j1, b1, s1, c1 = (int(line[0]), int(line[1]), \n float(line[2]), float(line[3]), int(line[4]))\n ia.append(i1); ja.append(j1)\n if b1 != 0 and s1 !=0:\n ## only keep pixels with data\n i.append(i1); j.append(j1); beam.append(b1)\n sig.append(s1); cts.append(c1)\n\n beam = asarray(beam, float64)\n sig = asarray(sig, float64)\n ## map i and j (before deletion) onto (-1,1)\n x = array([2*(ii-min(ia))/(max(ia)-min(ia))-1 for ii in i], float64)\n y = array([2*(jj-min(ja))/(max(ja)-min(ja))-1 for jj in j], float64)\n\n return BeamData(x, y, beam, sig, cts=cts)","def read_2d_analysis_data(f):\n \n data = np.transpose(np.loadtxt(f, dtype=np.float64))\n x = data[0]\n y = data[1]\n\n return x, y","def test_calc_bbox():\n with xr.open_rasterio(TEST_RASTER_PATH) as src:\n xr_res = ds.utils.calc_res(src)\n xr_bounds = ds.utils.calc_bbox(src.x.values, src.y.values, xr_res)\n with rasterio.open(TEST_RASTER_PATH) as src:\n rio_bounds = src.bounds\n assert np.allclose(xr_bounds, rio_bounds, atol=1.0) # allow for absolute diff of 1.0","def read_zone_file(cls, fname):\n with open(fname) as f:\n lines = f.readlines()\n\n # Initialize layer\n lay = 0\n\n # Initialize data counter\n totlen = 0\n i = 0\n\n # First line contains array dimensions\n dimstring = lines.pop(0).strip().split()\n nlay, nrow, ncol = (int(v) for v in dimstring)\n zones = np.zeros((nlay, nrow, ncol), dtype=np.int32)\n\n # The number of values to read before placing\n # them into the zone array\n datalen = nrow * ncol\n totaldatalen = nlay * nrow * ncol\n\n # List of valid values for LOCAT\n locats = [\"CONSTANT\", \"INTERNAL\", \"EXTERNAL\"]\n\n # ITERATE OVER THE ROWS\n for line in lines:\n if totlen == totaldatalen:\n break\n\n rowitems = line.strip().split()\n\n # Skip blank lines\n if len(rowitems) == 0:\n continue\n\n # HEADER\n if rowitems[0].upper() in locats:\n vals = []\n locat = rowitems[0].upper()\n\n if locat == \"CONSTANT\":\n iconst = int(rowitems[1])\n else:\n fmt = rowitems[1].strip(\"()\")\n fmtin, iprn = (int(v) for v in fmt.split(\"I\"))\n\n # ZONE DATA\n else:\n if locat == \"CONSTANT\":\n vals = np.ones((nrow, ncol), dtype=int) * iconst\n lay += 1\n elif locat == \"INTERNAL\":\n # READ ZONES\n rowvals = [int(v) for v in rowitems]\n s = \"Too many values encountered on this line.\"\n assert len(rowvals) <= fmtin, s\n vals.extend(rowvals)\n\n elif locat == \"EXTERNAL\":\n # READ EXTERNAL FILE\n fname = rowitems[0]\n if not os.path.isfile(fname):\n errmsg = f'Could not find external file \"{fname}\"'\n raise Exception(errmsg)\n with open(fname) as ext_f:\n ext_flines = ext_f.readlines()\n for ext_frow in ext_flines:\n ext_frowitems = ext_frow.strip().split()\n rowvals = [int(v) for v in ext_frowitems]\n vals.extend(rowvals)\n if len(vals) != datalen:\n errmsg = (\n \"The number of values read from external \"\n 'file \"{}\" does not match the expected '\n \"number.\".format(len(vals))\n )\n raise Exception(errmsg)\n else:\n # Should not get here\n raise Exception(f\"Locat not recognized: {locat}\")\n\n # IGNORE COMPOSITE ZONES\n\n if len(vals) == datalen:\n # place values for the previous layer into the zone array\n vals = np.array(vals, dtype=int).reshape((nrow, ncol))\n zones[lay, :, :] = vals[:, :]\n lay += 1\n totlen += len(rowitems)\n i += 1\n s = (\n \"The number of values read ({:,.0f})\"\n \" does not match the number expected\"\n \" ({:,.0f})\".format(totlen, nlay * nrow * ncol)\n )\n assert totlen == nlay * nrow * ncol, s\n return zones","def process_bounding_boxes(ground_truth_file: Path) -> List[Dict]:\n index_shifts = defaultdict(lambda: 0)\n index_shifts[\"cubicle\"] = 1108\n folder_name = ground_truth_file.parent.name\n \n annotations = []\n ground_truth_dataframe = pd.read_csv(str(ground_truth_file),\n header=None,\n sep=\" \",\n names=[\"frameID\", \"0\", \"x\", \"y\", \"width\", \"height\"])\n for index, row in ground_truth_dataframe.iterrows():\n image_id = int(row[\"frameID\"]) + index_shifts[folder_name]\n image_id = f\"{folder_name}_{str(image_id)}\"\n x = int(row[\"x\"])\n y = int(row[\"y\"])\n width = int(row[\"width\"])\n height = int(row[\"height\"])\n\n area = width * height\n bbox = [x, y, width, height]\n\n label_data = {\"id\": index,\n \"image_id\": image_id,\n \"category_id\": 1,\n \"bbox\": bbox,\n \"area\": area,\n \"iscrowd\": False}\n annotations.append(label_data)\n\n return annotations","def load_n3d_coords(file_path): \n \n import core.nuc_io as io\n\n seq_pos_dict = {}\n coords_dict = {} \n \n with io.open_file(file_path) as file_obj:\n chromo = None\n \n for line in file_obj:\n \n data = line.split()\n n_items = len(data)\n \n if not n_items:\n continue\n \n elif data[0] == '#':\n continue\n \n elif n_items == 3:\n chromo, n_coords, n_models = data\n \n #if chromo.lower()[:3] == 'chr':\n # chromo = chromo[3:]\n \n if chromo in coords_dict:\n raise Exception('Duplicate chromosome \"%s\" records in file %s' % (chromo, file_path))\n \n n_coords = int(n_coords)\n n_models = int(n_models)\n \n chromo_seq_pos = np.empty(n_coords, int)\n chromo_coords = np.empty((n_models, n_coords, 3), float)\n \n coords_dict[chromo] = chromo_coords\n seq_pos_dict[chromo] = chromo_seq_pos\n \n check = (n_models * 3) + 1\n i = 0\n \n elif not chromo:\n raise Exception('Missing chromosome record in file %s' % file_path)\n \n elif n_items != check:\n msg = 'Data size in file %s does not match Position + Models * Positions * 3'\n raise Exception(msg % file_path)\n \n else:\n chromo_seq_pos[i] = int(data[0])\n \n coord = [float(x) for x in data[1:]]\n coord = np.array(coord).reshape(n_models, 3)\n chromo_coords[:,i] = coord\n i += 1\n \n return seq_pos_dict, coords_dict","def readGP(loc, fname):\n with open(\"/\".join([loc, fname]), \"r\") as f:\n lines = f.readlines()\n\n data = []\n\n for line in lines[2:]:\n data.append([float(val) for val in line.split()])\n\n data = np.concatenate(data)\n\n line1 = lines[1].split()\n num_pts = float(line1[0])\n dt = float(line1[1])\n shift = float(line1[4])\n\n return data, num_pts, dt, shift","def readXYZBinPos(self,phys,xyzname):\r\n XYZBinReader.XYZBinReader(self.checkPath(xyzname)).read(phys.posvec)","def le_binario_mgb(filebin,nt,nc):\n return np.fromfile(filebin,'<f4').reshape(nt,nc)","def le_binario_mgb(filebin,nt,nc):\n return np.fromfile(filebin,'<f4').reshape(nt,nc)","def le_binario_mgb(filebin,nt,nc):\n return np.fromfile(filebin,'<f4').reshape(nt,nc)"],"string":"[\n \"def _get_las_bounds(fpath):\\n with laspy.file.File(fpath) as f:\\n return Bounds(*(f.header.min + f.header.max))\",\n \"def load_poses(self):\\n print('Loading poses for sequence ' + self.sequence + '...')\\n\\n pose_file = os.path.join(self.pose_path, self.sequence + '.txt')\\n\\n # Read and parse the poses\\n try:\\n self.T_w_cam0 = []\\n with open(pose_file, 'r') as f:\\n for line in f.readlines():\\n T = np.fromstring(line, dtype=float, sep=' ')\\n T = T.reshape(3, 4)\\n T = np.vstack((T, [0, 0, 0, 1]))\\n self.T_w_cam0.append(T)\\n print('done.')\\n\\n except FileNotFoundError:\\n print('Ground truth poses are not avaialble for sequence ' +\\n self.sequence + '.')\",\n \"def read_for_validation(p):\\n return read_cropped_image_npy(p, False)\",\n \"def read_for_training(p):\\n return read_cropped_image_npy(p, True)\",\n \"def read_annotation_yolov5(bbox_path):\\n\\n # image_paths = get_lists_in_dir(rawImage_dir)\\n\\n dw = 1./(camera_resolution[0]) # 1 / image width\\n dh = 1./(camera_resolution[1]) # 1 / image height\\n\\n # Read in bbox coordinate information from bbox_information.txt\\n dimension_list = []\\n with open(bbox_path, 'r') as annotation_file:\\n content = annotation_file.read().splitlines()\\n\\n for n in content:\\n # x = int(n.split()[0])+int(n.split()[2])/2\\n # y = int(n.split()[1])+int(n.split()[3])/2\\n # w = int(n.split()[2])\\n # h = int(n.split()[3])\\n #\\n # x = x*dw\\n # w = w*dw\\n # y = y*dh\\n # h = h*dh\\n\\n bb = n.split()\\n w = int(bb[2])\\n h = int(bb[3])\\n\\n start_x = int(bb[0])\\n start_y = int(bb[1])\\n\\n center_x = start_x + w / 2\\n center_y = start_y + h / 2\\n\\n x = center_x * dw\\n y = center_y * dh\\n w = w * dw\\n h = h * dh\\n \\n dimension_list.append((x, y, w, h))\\n\\n return dimension_list\",\n \"def read_pts(filename):\\n lines = open(filename).read().splitlines()\\n lines = lines[3:71]\\n\\n landmarks = []\\n ibug_index = 1 # count from 1 to 68 for all ibug landmarks\\n for l in lines:\\n coords = l.split()\\n landmarks.append(eos.core.Landmark(str(ibug_index), [float(coords[0]), float(coords[1])]))\\n ibug_index = ibug_index + 1\\n\\n return landmarks\",\n \"def load_bbox(depth_dir, view):\\n base_filename = os.path.join(depth_dir, \\\"%05d\\\" % view)\\n if os.path.exists(base_filename + \\\".npz\\\"):\\n npz_dict = np.load(base_filename + \\\".npz\\\")\\n if 'bbox' in npz_dict:\\n crop = npz_dict['bbox']\\n else:\\n crop = None\\n else:\\n crop = None\\n if crop is None:\\n crop_files = glob(base_filename + \\\"_bbox*\\\")\\n if len(crop_files) == 1:\\n crop = np.load(crop_files[0])\\n elif len(crop_files) > 1:\\n error(\\\"Crop file base '%s_bbox' matches multiple files\\\" % base_filename)\\n return crop\",\n \"def read_pts_file(filename):\\n lines = open(filename).read().splitlines()\\n if int(lines[1:2][0].split('n_points:')[-1]) != 68:\\n print ('No 68-landmark format founded')\\n return None\\n lines = lines[3:71]\\n\\n landmarks = []\\n for l in lines:\\n coords = l.split()\\n landmarks.append([float(coords[0]), float(coords[1])])\\n return landmarks\",\n \"def read_positions():\\n return np.genfromtxt(\\\"POSITIONS.OUT\\\").transpose()\",\n \"def load_annotations(path, img_w, img_h):\\n bboxes = []\\n with open(path, 'r') as file:\\n for row in file:\\n _, xc , yc, w, h = row.split()\\n xc = float(xc)*img_w\\n yc = float(yc)*img_h\\n w = float(w)*img_w\\n h = float(h)*img_h\\n bboxes.append([xc - w/2 , yc - h/2, xc + w/2 , yc + h/2])\\n\\n return bboxes\",\n \"def read_all_poses(world):\\n dataset_root='./jsonfile'\\n path = os.path.join(dataset_root, world, 'image_structs.mat')\\n data = sio.loadmat(path)\\n\\n xyz = data['image_structs']['world_pos']\\n image_names = data['image_structs']['image_name'][0]\\n\\n dire=data['image_structs']['direction']\\n #rot = data['image_structs']['R'][0]\\n #scale = data['scale'][0][0]\\n\\n n = xyz.shape[1]\\n x = [xyz[0][i][0][0] for i in range(n)]\\n y = [0 for i in range(n)]\\n z = [xyz[0][i][2][0] for i in range(n)]\\n px= [dire[0][i][0][0] for i in range(n)]\\n py= [0 for i in range(n)]\\n pz= [dire[0][i][2][0] for i in range(n)]\\n#============================================================\\n names = [name[0][:-4] for name in image_names]\\n if len(names) != len(x):\\n raise ValueError('number of image names are not equal to the number of '\\n 'poses {} != {}'.format(len(names), len(x)))\\n output = {}\\n for i in range(n):\\n output[names[i]] = [x[i], z[i], px[i], pz[i]]\\n return output\",\n \"def read_map_file(path):\\r\\n with open(path) as f:\\r\\n dir_name = os.path.dirname(path)\\r\\n img = cv2.imread(dir_name + '/' + f.readline().strip())\\r\\n assert img.shape[0] > 0 and img.shape[1] > 0, 'Can not open image file'\\r\\n meter_per_pixel = float(f.readline().strip())\\r\\n ori_str = f.readline().strip().split()\\r\\n origin = np.array([int(ori_str[0]), int(ori_str[1])])\\r\\n init_heading = float(ori_str[2])\\r\\n return img, meter_per_pixel, origin, init_heading\",\n \"def get_bounds():\\n bounds = [\\n (0.1, 0.5), # Omega_m\\n (0.05, 0.15) # beta\\n ]\\n return np.array(bounds)\",\n \"def read_binning_file(file_name, lmax):\\n\\n bin_lo,bin_hi,bin_c = plt.loadtxt(file_name,unpack=True)\\n id = np.where(bin_hi <lmax)\\n bin_lo,bin_hi,bin_c=bin_lo[id],bin_hi[id],bin_c[id]\\n if bin_lo[0]<2:\\n bin_lo[0]=2\\n bin_hi=bin_hi.astype(np.int)\\n bin_lo=bin_lo.astype(np.int)\\n bin_size=bin_hi-bin_lo+1\\n return (bin_lo,bin_hi,bin_c,bin_size)\",\n \"def _get_clip_loc_in_array(self):\\r\\n\\r\\n # coordinates of upperleft and lowerright points of binding box\\r\\n box_ulx, box_uly, box_lrx, box_lry = self.clip_box[0][0], self.clip_box[0][1], \\\\\\r\\n self.clip_box[1][0], self.clip_box[1][1]\\r\\n\\r\\n # Get the offsets that correspond to the bounding box corner coordinates.\\r\\n offsets_ul = gdal.ApplyGeoTransform(self.inv_gt, box_ulx, box_uly)\\r\\n offsets_lr = gdal.ApplyGeoTransform(self.inv_gt, box_lrx, box_lry)\\r\\n\\r\\n # The offsets are returned as floating point, but we need integers.\\r\\n self.off_ulx, self.off_uly = map(int, offsets_ul)\\r\\n self.off_lrx, self.off_lry = map(int, offsets_lr)\\r\\n\\r\\n # Compute the numbers of rows and columns to extract, based on the offsets.\\r\\n self.row = self.off_lry - self.off_uly\\r\\n self.column = self.off_lrx - self.off_ulx\",\n \"def bounds(self):\\n return self._bboxes[0][0] #TODO: merge all coverages\",\n \"def get_mask_arr(path):\\n with rasterio.open(path) as src:\\n img = src.read().transpose((1, 2, 0))\\n seg = np.array(img, dtype=int)\\n\\n return seg[:, :, 0]\",\n \"def read_depth_map(depth_map_ifp):\\n # See:\\n # https://github.com/simonfuhrmann/mve/wiki/MVE-File-Format#the-mvei-image-format\\n # https://github.com/simonfuhrmann/mve/blob/master/libs/mve/image_io.cc\\n\\n with open(depth_map_ifp, \\\"rb\\\") as fid:\\n mvei_file_signature = MVEFileHandler._read_next_bytes(\\n fid, 11, \\\"ccccccccccc\\\"\\n )\\n width = MVEFileHandler._read_next_bytes(fid, 4, \\\"i\\\")[0]\\n height = MVEFileHandler._read_next_bytes(fid, 4, \\\"i\\\")[0]\\n channels = MVEFileHandler._read_next_bytes(fid, 4, \\\"i\\\")[0]\\n assert channels == 1\\n raw_type = MVEFileHandler._read_next_bytes(fid, 4, \\\"i\\\")[0]\\n assert raw_type == 9 # IMAGE_TYPE_FLOAT\\n num_elements = width * height * channels\\n data = np.asarray(\\n MVEFileHandler._read_next_bytes(\\n fid, num_elements * 4, \\\"f\\\" * num_elements\\n )\\n )\\n return data.reshape((height, width))\",\n \"def get_bbox(fname):\\r\\n fname = fname.split('_') # fname -> list\\r\\n i = fname.index('bbox')\\r\\n return map(float, fname[i+1:i+5]) # m\\r\",\n \"def read_voc_annotation_file(annotation_file_path, labelmap_dict, has_scores=False):\\n root = etree.parse(annotation_file_path)\\n image_id = root.find('filename').text\\n objects = root.findall('object')\\n\\n boxes = np.array([]).reshape(0, 4)\\n classes = np.array([])\\n scores = np.array([])\\n\\n for item in objects:\\n name = item.find('name').text\\n class_id = labelmap_dict[name]\\n class_array = np.array([class_id])\\n classes = np.concatenate([classes, class_array], axis=0)\\n\\n if has_scores:\\n score = item.find('score').text\\n score_array = np.array([score]).astype(np.float)\\n scores = np.concatenate([scores, score_array], axis=0)\\n\\n bndbox = item.find('bndbox')\\n ymin = bndbox.find('ymin').text\\n xmin = bndbox.find('xmin').text\\n ymax = bndbox.find('ymax').text\\n xmax = bndbox.find('xmax').text\\n bndbox_array = np.expand_dims(np.array([ymin, xmin, ymax, xmax]).astype(np.float32), axis=0)\\n boxes = np.concatenate([boxes, bndbox_array], axis=0)\\n\\n if boxes.ndim < 2:\\n boxes = np.expand_dims(boxes, axis=0)\\n\\n classes = classes.astype(np.int32)\\n\\n if has_scores:\\n return image_id, boxes, classes, scores\\n else:\\n return image_id, boxes, classes\",\n \"def get_bbox(fname):\\r\\n fname = fname.split('_') # fname -> list\\r\\n i = fname.index('bbox')\\r\\n return list(map(float, fname[i+1:i+5])) # m\\r\",\n \"def get_document_bounds(image_file, feature):\\n # client = vision.ImageAnnotatorClient()\\n\\n bounds = []\\n\\n\\n# No need for this .... \\n # with io.open(image_file, 'rb') as image_file:\\n # content = image_file.read()\\n\\n # image = types.Image(content=content)\\n\\n # response = client.document_text_detection(image=image)\\n # document = response.full_text_annotation\\n\\n # with open('8130processed.json', 'w') as outfile:\\n # outfile.write(MessageToJson(response))\\n\\n\\n# We already have the document bounds of the image inside of 8130processed.json no\\n\\n f = open ('processed_8130-1output-1-to-1.json', \\\"r\\\")\\n data = json.load(f)\\n datas=json.dumps(data)\\n # print(data)\\n\\n response = json_format.Parse(datas, vision.types.AnnotateFileResponse())\\n # print(response)\\n document = response.inputConfig\\n # Collect specified feature bounds by enumerating all document features\\n for page in document.pages:\\n for block in page.blocks:\\n for paragraph in block.paragraphs:\\n for word in paragraph.words:\\n for symbol in word.symbols:\\n if (feature == FeatureType.SYMBOL):\\n bounds.append(symbol.bounding_box)\\n\\n if (feature == FeatureType.WORD):\\n bounds.append(word.bounding_box)\\n\\n if (feature == FeatureType.PARA):\\n bounds.append(paragraph.bounding_box)\\n\\n if (feature == FeatureType.BLOCK):\\n bounds.append(block.bounding_box)\\n\\n # The list `bounds` contains the coordinates of the bounding boxes.\\n return bounds\",\n \"def get_bbox(fname):\\r\\n\\r\\n fname = fname.split('_') # fname -> list\\r\\n i = fname.index('bbox')\\r\\n return list(map(float, fname[i+1:i+5])) # m\\r\",\n \"def check_input_extents(config, shakefile=None, bounds=None):\\n if shakefile is None and bounds is None:\\n raise Exception('Must define either a shakemap file or bounds')\\n modelname = config.keys()[0]\\n # Make dummy geodict to use\\n if bounds is None:\\n evdict = ShakeGrid.getFileGeoDict(shakefile)\\n else:\\n evdict = GeoDict.createDictFromBox(\\n bounds['xmin'], bounds['xmax'],\\n bounds['ymin'], bounds['ymax'],\\n 0.00001, 0.00001, inside=False)\\n\\n # Check extents of all input layers\\n notcovered = []\\n notcovgdicts = []\\n newbounds = None\\n for item, value in config[modelname]['layers'].items():\\n if 'file' in value.keys():\\n filelook = value['file']\\n if getFileType(filelook) == 'gmt':\\n tmpgd, _ = GMTGrid.getFileGeoDict(filelook)\\n else:\\n tmpgd, _ = GDALGrid.getFileGeoDict(filelook)\\n # See if tempgd contains evdict\\n contains = tmpgd.contains(evdict)\\n if not contains:\\n notcovered.append(filelook)\\n notcovgdicts.append(tmpgd)\\n # print(filelook)\\n if len(notcovered) > 0:\\n # Figure out what bounds COULD be run\\n xmins = [gd.xmin for gd in notcovgdicts]\\n xmaxs = [gd.xmax for gd in notcovgdicts]\\n ymins = [gd.ymin for gd in notcovgdicts]\\n ymaxs = [gd.ymax for gd in notcovgdicts]\\n\\n # Set in by a buffer of 0.05 degrees because mapio doesn't like \\n # when bounds are exactly the same for getboundswithin\\n newbounds = dict(xmin=evdict.xmin + 0.05,\\n xmax=evdict.xmax - 0.05,\\n ymin=evdict.ymin + 0.05,\\n ymax=evdict.ymax - 0.05)\\n # Which one is the problem?\\n if evdict.xmin < np.max(xmins):\\n newbounds['xmin'] = np.max(xmins) + 0.05\\n if evdict.xmax > np.min(xmaxs):\\n newbounds['xmax'] = np.min(xmaxs) - 0.05\\n if evdict.ymin < np.max(ymins):\\n newbounds['ymin'] = np.max(ymins) + 0.05\\n if evdict.ymax > np.min(ymaxs):\\n newbounds['ymax'] = np.min(ymaxs) - 0.05\\n\\n # See if this is a possible extent\\n try:\\n test = GeoDict.createDictFromBox(\\n newbounds['xmin'], newbounds['xmax'],\\n newbounds['ymin'], newbounds['ymax'],\\n 0.00001, 0.00001, inside=False)\\n except BaseException:\\n print('Cannot make new bounds that will work')\\n newbounds = None\\n\\n return notcovered, newbounds\",\n \"def getBoundingBoxes():\\n allBoundingBoxes = BoundingBoxes()\\n import glob\\n import os\\n # Read ground truths\\n pth = \\\"../Wildtrack_dataset/annotations_positions/*.json\\\"\\n files = glob.glob(pth)\\n files.sort()\\n # Class representing bounding boxes (ground truths and detections)\\n allBoundingBoxes = BoundingBoxes()\\n # Read GT detections from txt files\\n # Each value of each line is \\\"class_id, x, y, width, height\\\" respectively\\n # Class_id represents the class of the bounding box\\n # x, y represents the most top-left coordinates of the bounding box\\n # x2, y2 represents the most bottom-right coordinates of the bounding box\\n for idx, f in enumerate(files):\\n with open(f) as j:\\n data = json.load(j)\\n # print(f)\\n for d in data:\\n for v in d['views']:\\n if v['viewNum'] != 0 or v['xmax'] == -1:\\n continue\\n\\n idClass = 'person' # class\\n x = float(v['xmin']) # confidence\\n y = float(v['ymin'])\\n w = float(v['xmax'])\\n h = float(v['ymax'])\\n bb = BoundingBox(\\n str(idx),\\n idClass,\\n x,\\n y,\\n w,\\n h,\\n CoordinatesType.Absolute, (1920, 1080),\\n BBType.GroundTruth,\\n format=BBFormat.XYX2Y2)\\n allBoundingBoxes.addBoundingBox(bb)\\n \\n\\n\\n # Read detections\\n with open(\\\"wildtrack_yolo_tiny.out\\\", \\\"rb\\\") as fin:\\n pred = pickle.load(fin)\\n for idx, (k, value) in enumerate(pred.items()):\\n for d in value:\\n if d['tag'] != \\\"person\\\":\\n continue\\n \\n box = d['box']\\n idClass = 'person' # class\\n x = float(box[0]) # confidence\\n y = float(box[1])\\n w = float(box[2])\\n h = float(box[3])\\n bb = BoundingBox(\\n str(idx),\\n idClass,\\n x,\\n y,\\n w,\\n h,\\n CoordinatesType.Absolute, (1920, 1080),\\n BBType.Detected,\\n d['score'],\\n format=BBFormat.XYX2Y2)\\n allBoundingBoxes.addBoundingBox(bb)\\n return allBoundingBoxes\",\n \"def read_kitti_R0_rect(filename):\\n\\n with open(filename) as f:\\n for line in f:\\n data = line.split(' ')\\n if data[0] == 'R0_rect:':\\n R0_rect = np.array([float(x) for x in data[1:13]])\\n R0_rect = R0_rect.reshape(3,3)\\n rect_4x4 = np.zeros([4, 4], dtype=R0_rect.dtype)\\n rect_4x4[3, 3] = 1.\\n rect_4x4[:3, :3] = R0_rect\\n return rect_4x4\\n\\n raise Exception(\\n 'Could not find entry for P2 in calib file {}'.format(filename))\",\n \"def read_velocities(filename, return_grid=True, return_proj=False):\\n ds = gdal.Open(filename)\\n #Get dimensions\\n nc = ds.RasterXSize\\n nr = ds.RasterYSize\\n \\n geotransform = ds.GetGeoTransform()\\n xOrigin = geotransform[0]\\n xPix = geotransform[1] #pixel width in x-direction\\n yOrigin = geotransform[3]\\n yPix = geotransform[5] #pixel height in y-direction\\n \\n lons = xOrigin + np.arange(0, nc)*xPix\\n lats = yOrigin + np.arange(0, nr)*yPix\\n \\n x, y = np.meshgrid(lons, lats)\\n \\n vband = ds.GetRasterBand(1)\\n varr = vband.ReadAsArray()\\n \\n if return_grid and return_proj:\\n return x, y, varr, ds.GetProjection()\\n elif return_grid:\\n return x, y, varr\\n else: \\n return varr\",\n \"def read(self):\\n stable_poses = []\\n f = open(self.filepath_, \\\"r\\\")\\n data = [line.split() for line in f]\\n for i in range(len(data)):\\n if len(data[i]) > 0 and data[i][0] == \\\"p\\\":\\n p = float(data[i][1])\\n r = [[data[i+1][1], data[i+1][2], data[i+1][3]], [data[i+2][0], data[i+2][1],\\n data[i+2][2]], [data[i+3][0], data[i+3][1], data[i+3][2]]]\\n r = np.array(r).astype(np.float64)\\n x0 = np.array([data[i+4][1], data[i+4][2], data[i+4][3]]).astype(np.float64)\\n stable_poses.append(sp.StablePose(p, r, x0))\\n return stable_poses\",\n \"def extract_frames( fname, bnd ):\\n if os.uname()[0] == 'Linux':\\n savefunc = numpy.save\\n elif os.uname()[0] == 'Darwin':\\n # why the !@$# doesn't Mac save readable .npy files??\\n savefunc = numpy.savetxt\\n fh = open( fname, 'r' )\\n bnd_arr = numpy.loadtxt( bnd )\\n nx, ny = bnd_arr.shape\\n bnd_arr = bnd_arr.ravel()\\n # save files in a special folder\\n part = fname.rpartition( '/' )\\n cell_name = part[-1]\\n # find the subfolder name from the beginning of the cell\\n # name. subfolder must have been created already.\\n subfolder = cell_name.partition( '-' )[0]\\n savedir = part[0] + '/' + 'frames/' + subfolder +'/'\\n\\n make_dir( savedir )\\n # print 'savedir', savedir\\n \\n # loop over lines in <fname> and save each as nx x ny array\\n #k = 0. \\n fromstring = numpy.fromstring\\n for k, line in enumerate( fh.readlines() ):\\n arr = fromstring( line, sep='\\\\t' )\\n # remove boundary\\n arr = bnd_arr * arr\\n arr.resize( (nx,ny) )\\n #numpy.save( savedir + cell_name + '_' + str(k), arr )\\n savefunc( savedir + cell_name + '_' + str(k), arr )\",\n \"def get_bounds(shakefile, parameter='pga', threshold=2):\\n from mapio.shake import ShakeGrid\\n\\n shakemap = ShakeGrid.load(shakefile, adjust='res')\\n if parameter == 'pga':\\n vals = shakemap.getLayer('pga')\\n elif parameter == 'pgv':\\n vals = shakemap.getLayer('pgv')\\n else:\\n raise Exception('parameter not valid')\\n xmin, xmax, ymin, ymax = vals.getBounds()\\n lons = np.linspace(xmin, xmax, vals.getGeoDict().nx)\\n lats = np.linspace(ymax, ymin, vals.getGeoDict().ny) # backwards so it plots right\\n row, col = np.where(vals.getData() > float(threshold))\\n lonmin = lons[col].min()\\n lonmax = lons[col].max()\\n latmin = lats[row].min()\\n latmax = lats[row].max()\\n\\n boundaries1 = {'dx': 100, 'dy': 100., 'nx': 100., 'ny': 100} # dummy fillers, only really care about bounds\\n if xmin < lonmin:\\n boundaries1['xmin'] = lonmin\\n else:\\n boundaries1['xmin'] = xmin\\n if xmax > lonmax:\\n boundaries1['xmax'] = lonmax\\n else:\\n boundaries1['xmax'] = xmax\\n if ymin < latmin:\\n boundaries1['ymin'] = latmin\\n else:\\n boundaries1['ymin'] = ymin\\n if ymax > latmax:\\n boundaries1['ymax'] = latmax\\n else:\\n boundaries1['ymax'] = ymax\\n\\n return boundaries1\",\n \"def read_groups_particles(filename):\\n \\n f = open(filename,'r')\\n\\n Ntot = fromstring(f.read(4),int32)[0]\\n Pos\\t = fromstring(f.read(3*4*Ntot),float32)\\n Pos.shape = (Ntot,3)\\n f.close()\\n \\n return Pos\",\n \"def bounds(self):\\n return self._bboxes[0][0] #TODO: merge all coverages\",\n \"def Read_FitsPoints(input_path,fname):\\n\\twave = []; dwave = []\\n\\tbreak_ind = 0 \\t\\n\\n\\tfilename_fullpath = input_path + fname\\n\\twith open(filename_fullpath) as f:\\n\\t\\tlines = f.readlines()\\n\\t\\tfor i in xrange(1,len(lines)):\\n\\t\\t\\tif re.search('a',lines[i]):\\n\\t\\t\\t\\tbreak_ind = i -1\\n\\n\\twave = np.loadtxt(filename_fullpath,usecols=[0])\\n\\tdwave = np.loadtxt(filename_fullpath,usecols=[1])\\n\\t\\n\\t# Section b\\n\\twave_b = wave[:break_ind]; dwave_b = dwave[:break_ind]\\n\\t\\n\\t# Section a\\n\\twave_a = wave[break_ind:]; dwave_a = dwave[break_ind:]\\n\\t\\n\\tpl.plot(wave_a,dwave_a,'o',label='Segment a')\\n\\tpl.plot(wave_b,dwave_b,'o',label='Segment b')\\n\\tpl.legend(loc='best')\\n\\tpl.ylim([-0.1,0.1])\\n\\tpl.xlabel(r'Wavelength $\\\\AA$')\\n\\tpl.ylabel(r'$\\\\Delta \\\\lambda \\\\AA$')\\n\\tpl.savefig(input_path + 'plots/' + fname + '.png')\\n\\n\\tpl.clf()\\n\\n\\treturn [wave_a,dwave_a], [wave_b,dwave_b]\",\n \"def _ReadExtent(self):\\n # Read the mesh file as line strings, remove lines with comment = !\\n v = np.array(np.__version__.split('.')[0:2], dtype=int)\\n FileName = self.GetMeshFileName()\\n try:\\n if v[0] >= 1 and v[1] >= 10:\\n # max_rows in numpy versions >= 1.10\\n msh = np.genfromtxt(FileName, delimiter='\\\\n', dtype=np.str,comments='!', max_rows=1)\\n else:\\n # This reads whole file :(\\n msh = np.genfromtxt(FileName, delimiter='\\\\n', dtype=np.str, comments='!')[0]\\n except (IOError, OSError) as fe:\\n raise _helpers.PVGeoError(str(fe))\\n # Fist line is the size of the model\\n self.__sizeM = np.array(msh.ravel()[0].split(), dtype=int)\\n # Check if the mesh is a UBC 2D mesh\\n if self.__sizeM.shape[0] == 1:\\n # Read in data from file\\n xpts, xdisc, zpts, zdisc = ubcMeshReaderBase._ubcMesh2D_part(FileName)\\n nx = np.sum(np.array(xdisc,dtype=int))+1\\n nz = np.sum(np.array(zdisc,dtype=int))+1\\n return (0,nx, 0,1, 0,nz)\\n # Check if the mesh is a UBC 3D mesh or OcTree\\n elif self.__sizeM.shape[0] >= 3:\\n # Get mesh dimensions\\n dim = self.__sizeM[0:3]\\n ne,nn,nz = dim[0], dim[1], dim[2]\\n return (0,ne, 0,nn, 0,nz)\\n else:\\n raise _helpers.PVGeoError('File format not recognized')\",\n \"def load_pts_features(path):\\n\\n #\\n # Your code here\\n #\\n\\n pts = [np.empty((123, 2)), np.empty((123, 2))]\\n feats = [np.empty((123, 128)), np.empty((123, 128))]\\n\\n return pts, feats\",\n \"def read_mesh_bbox(filename=None, nodes=None):\\n if filename != None:\\n nodes = read_mesh_nodes(filename)\\n return np.array([[np.min(nodes[:,0]), np.min(nodes[:,1]), np.min(nodes[:,2])],\\n [np.max(nodes[:,0]), np.max(nodes[:,1]), np.max(nodes[:,2])]])\",\n \"def bounds(self, pos):\",\n \"def get_bounds(shakefile, parameter='pga', threshold=2.0):\\n shakemap = ShakeGrid.load(shakefile, adjust='res')\\n if parameter == 'pga':\\n vals = shakemap.getLayer('pga')\\n elif parameter == 'pgv':\\n vals = shakemap.getLayer('pgv')\\n else:\\n raise Exception('parameter not valid')\\n xmin, xmax, ymin, ymax = vals.getBounds()\\n lons = np.linspace(xmin, xmax, vals.getGeoDict().nx)\\n lats = np.linspace(ymax, ymin, vals.getGeoDict().ny)\\n row, col = np.where(vals.getData() > float(threshold))\\n lonmin = lons[col].min()\\n lonmax = lons[col].max()\\n latmin = lats[row].min()\\n latmax = lats[row].max()\\n\\n # dummy fillers, only really care about bounds\\n boundaries1 = {'dx': 100, 'dy': 100., 'nx': 100., 'ny': 100}\\n\\n if xmin < lonmin:\\n boundaries1['xmin'] = lonmin\\n else:\\n boundaries1['xmin'] = xmin\\n if xmax > lonmax:\\n boundaries1['xmax'] = lonmax\\n else:\\n boundaries1['xmax'] = xmax\\n if ymin < latmin:\\n boundaries1['ymin'] = latmin\\n else:\\n boundaries1['ymin'] = ymin\\n if ymax > latmax:\\n boundaries1['ymax'] = latmax\\n else:\\n boundaries1['ymax'] = ymax\\n\\n return boundaries1\",\n \"def bbox_from_openpose(openpose_file, rescale=1.2, detection_thresh=0.2):\\n with open(openpose_file, 'r') as f:\\n keypoints = json.load(f)['people'][0]['pose_keypoints_2d']\\n keypoints = np.reshape(np.array(keypoints), (-1,3))\\n valid = keypoints[:,-1] > detection_thresh\\n valid_keypoints = keypoints[valid][:,:-1]\\n center = valid_keypoints.mean(axis=0)\\n bbox_size = (valid_keypoints.max(axis=0) - valid_keypoints.min(axis=0)).max()\\n # adjust bounding box tightness\\n scale = bbox_size / 200.0\\n scale *= rescale\\n return center, scale\",\n \"def bbox_from_openpose(openpose_file, rescale=1.2, detection_thresh=0.2):\\n with open(openpose_file, 'r') as f:\\n keypoints = json.load(f)['people'][0]['pose_keypoints_2d']\\n keypoints = np.reshape(np.array(keypoints), (-1,3))\\n valid = keypoints[:,-1] > detection_thresh\\n valid_keypoints = keypoints[valid][:,:-1]\\n center = valid_keypoints.mean(axis=0)\\n bbox_size = (valid_keypoints.max(axis=0) - valid_keypoints.min(axis=0)).max()\\n # adjust bounding box tightness\\n scale = bbox_size / 200.0\\n scale *= rescale\\n return center, scale\",\n \"def read_keypoints(file_path: str) -> np.array:\\n # Set filter for body keypoints\\n keypoints_filter = list(keypoints_map.values())\\n\\n # Read json file with keypoints\\n keypoints = None\\n with open(file_path, 'r') as f:\\n for row in f.readlines():\\n keypoints = json.loads(row)\\n\\n # Extract and concatenate selected keypoints\\n if keypoints['people']:\\n results = np.concatenate([\\n np.array(keypoints['people'][0]['pose_keypoints_2d']).reshape(-1, 3)[keypoints_filter, :2],\\n np.array(keypoints['people'][0]['hand_left_keypoints_2d']).reshape(-1, 3)[:, :2],\\n np.array(keypoints['people'][0]['hand_right_keypoints_2d']).reshape(-1, 3)[:, :2]],\\n axis=0\\n )\\n else:\\n results = np.zeros((1, 0))\\n\\n return results\",\n \"def extract_2d_poses(self, file_name: str, force: bool = False) -> None:\\n folder_dir = os.path.join(self.dataset_dir, file_name + '.zip')\\n\\n # Extract zip in dataset dir only if is has not been extracted before or if it has to ver overwritten\\n if os.path.exists(self.poses_dir) and not force:\\n print(\\\"The poses folder already exists. If you want to unroll them again and overwrite the current poses, \\\"\\n \\\"please use the option force=True\\\")\\n else:\\n print('Extracting 2d poses...')\\n with zipfile.ZipFile(folder_dir, 'r') as zip_ref:\\n zip_ref.extractall(self.dataset_dir)\\n print('Finish extracting 2d poses')\",\n \"def get_data(self):\\n \\n coordinates = parsing.parse_contour_file(self._filename)\\n return parsing.poly_to_mask(coordinates, *self._shape)\",\n \"def read_data(path):\\n with h5py.File(path, \\\"r\\\") as f:\\n transformed_poses = np.array(f['transformed_poses'])\\n extracted_poses = np.array(f['poses'])\\n target = np.array(f['target'])\\n \\n return extracted_poses, transformed_poses, target\",\n \"def read_mesh_boundary(sFilename_boundary_in):\\n iReturn_code = 1\\n if os.path.isfile(sFilename_boundary_in):\\n pass\\n else:\\n print('This mesh file does not exist: ', sFilename_boundary_in )\\n iReturn_code = 0\\n return iReturn_code\\n\\n \\n pDriver_json = ogr.GetDriverByName('GeoJSON') \\n pDataset_mesh = pDriver_json.Open(sFilename_boundary_in, gdal.GA_ReadOnly)\\n pLayer_mesh = pDataset_mesh.GetLayer(0)\\n pSpatial_reference_out = pLayer_mesh.GetSpatialRef()\\n ldefn = pLayer_mesh.GetLayerDefn() \\n\\n #we also need to spatial reference\\n for pFeature_mesh in pLayer_mesh:\\n pGeometry_mesh = pFeature_mesh.GetGeometryRef() \\n pGeometrytype_boundary = pGeometry_mesh.GetGeometryName()\\n if(pGeometrytype_boundary == 'POLYGON'): \\n pBoundary_ogr = pGeometry_mesh \\n else:\\n if(pGeometrytype_boundary == 'MULTIPOLYGON'): \\n nLine = pGeometry_mesh.GetGeometryCount()\\n for i in range(nLine):\\n pBoundary_ogr = pGeometry_mesh.GetGeometryRef(i)\\n \\n pass\\n else:\\n pass\\n pass \\n \\n \\n pBoundary_wkt = pBoundary_ogr.ExportToWkt()\\n aExtent = pBoundary_ogr.GetEnvelope()\\n min_x, max_x, min_y, max_y = aExtent\\n \\n return pBoundary_wkt, aExtent\",\n \"def read_proj_info(fname, nangles=None, convert_vec=True):\\n \\n if fname[-4:] == 'toml':\\n proj_geom = read_from_toml(fname, nangles=nangles)\\n if proj_geom['type'] == 'cone' and convert_vec:\\n proj_geom = geom_2vec(proj_geom)\\n\\n elif fname[-3:] == 'pkl':\\n with open(fname, 'rb') as f:\\n proj_geom = pickle.load(f)\\n proj_geom['nangles'] = proj_geom['Vectors'].shape[0]\\n \\n # if toml and cone beam, c\\n \\n return proj_geom\",\n \"def read_poses_for_camera(record_path, camera_name):\\n\\n # Resolve pose.txt file path for camera\\n poses_path = os.path.join(record_path, camera_name, 'pose.txt')\\n if os.path.exists(poses_path):\\n poses = read_poses_dict(poses_path)\\n else:\\n # Sample type dataset (aka zpark-sample)\\n poses_path = os.path.join(record_path, camera_name + '.txt')\\n poses = read_poses_dict_6(poses_path)\\n return poses\",\n \"def import_phase_map(self, file):\\r\\n filename, extension = os.path.splitext(file)\\r\\n flag = False\\r\\n if extension == '.txt':\\r\\n p = np.loadtxt(file, dtype=int, delimiter=' ')\\r\\n if np.shape(p) != self.size:\\r\\n return\\r\\n if np.shape(p) == (self.height, self.width):\\r\\n p = p.T\\r\\n m = np.zeros((self.width, self.height, 3), dtype=np.uint8)\\r\\n m[:, :, 0] = p\\r\\n m[:, :, 1] = p\\r\\n m[:, :, 2] = p\\r\\n self.maps[os.path.basename(filename)] = {'data': m}\\r\\n elif extension == '.fits':\\r\\n hdu = fits.open(file)\\r\\n p = fits.getdata(file)\\r\\n if np.shape(p) == self.size and np.max(p)<3:\\r\\n self.maps[os.path.basename(filename)] = {'map': p}\\r\\n flag = True\\r\\n elif np.shape(p) == self.size and np.max(p)>3:\\r\\n m = np.zeros((self.width, self.height, 3), dtype=np.uint8)\\r\\n m[:, :, 0] = p\\r\\n m[:, :, 1] = p\\r\\n m[:, :, 2] = p\\r\\n self.maps[os.path.basename(filename)] = {'data': m}\\r\\n else:\\r\\n print(\\\"Wrong dimensions \\\")\\r\\n elif extension == '.bmp':\\r\\n p = pygame.image.load(file)\\r\\n p = pygame.surfarray.array3d(p)\\r\\n self.maps[os.path.basename(filename)] = {'data': p}\\r\\n else:\\r\\n raise UserWarning('File extension %s not supported.' % extension)\\r\\n\\r\\n # if np.shape(self.maps[filename]) != (1024, 768, 3):\\r\\n # raise UserWarning('Wrong dimensions for SLM of size %i x %i (x %i)' % (self.width, self.height, 3))\\r\\n\\r\\n # self.save_maps()\\r\\n return filename, flag\",\n \"def asc_to_ndarray(fname, path='./'):\\r\\n header, headlines = get_header(fname, path)\\r\\n f = file(path+fname,'r')\\r\\n \\r\\n for i in xrange(headlines):\\r\\n f.readline() \\r\\n \\r\\n ncols = header['ncols']\\r\\n nrows = header['nrows']\\r\\n cellsize = header['cellsize']\\r\\n \\r\\n long = header['xllcorner'] + np.arange(ncols) * cellsize\\r\\n lat = header['yllcorner'] + np.arange(nrows) * cellsize\\r\\n data = np.zeros((nrows, ncols), dtype=float)\\r\\n for i in xrange(nrows):\\r\\n line = f.readline()\\r\\n data[i,:] = np.fromstring(line, dtype=float, sep=' ')\\r\\n # print data.shape, nrows, ncols\\r\\n f.close()\\r\\n \\r\\n if header.has_key('NODATA_value'):\\r\\n data = np.ma.masked_array(data, mask=data==header['NODATA_value'])\\r\\n \\r\\n return long, lat, data\",\n \"def anglor(infile, sequence):\\n return np.loadtxt(infile, usecols=1).clip(min=-180, max=180).reshape((1, -1, 1))\",\n \"def parse_prnu_file():\\n hdf_name = r'C:\\\\Users\\\\nmishra\\\\Workspace\\\\TEMPO\\\\PRNU_map\\\\\\n batch_2017Jun20_TEMPO_PRNU_-20Tccd__46Tfpe_3pixSpectral_3pixSpatial.h5'\\n file = h5py.File(hdf_name, 'r')\\n prnu = file.get('prnu')\\n prnu = np.array(prnu).transpose()\\n quad_d = prnu[2:1030, 10:1034]\\n quad_c = prnu[2:1030, 1078:2102]\\n quad_a = prnu[1062:2090, 10:1034]\\n quad_b = prnu[1062:2090, 1078:2102]\\n prnu_map_lower = np.concatenate((quad_d, quad_c), axis=1)\\n prnu_map_upper = np.concatenate((quad_a, quad_b), axis=1)\\n prnu_map = np.concatenate((prnu_map_lower, prnu_map_upper), axis=0)\\n return prnu_map\",\n \"def get_gridinfo(filename, xpoints, ypoints):\\n with open(f\\\"{filename[:-4]}.wld\\\", encoding=\\\"ascii\\\") as fh:\\n lines = fh.readlines()\\n dx = float(lines[0])\\n dy = float(lines[3])\\n west = float(lines[4])\\n north = float(lines[5])\\n south = north + dy * ypoints\\n lats = np.arange(0, ypoints) * (0 - dy) + south\\n lons = np.arange(0, xpoints) * dx + west\\n return lons, lats\",\n \"def read_im_and_landmarks(fname):\\n\\tim = cv2.imread(fname, cv2.IMREAD_COLOR)\\n\\tim = cv2.resize(im, (im.shape[1] * SCALE_FACTOR,\\n im.shape[0] * SCALE_FACTOR))\\n\\ts = get_landmarks(im)\\n\\treturn im, s\",\n \"def load_spe(filename):\\n def read_at(data, pos, size, ntype):\\n raw.seek(pos)\\n return np.fromfile(raw, ntype, size)\\n raw = open(filename, 'rb')\\n xdim = np.int64(read_at(raw, 42, 1, np.int16)[0])\\n ydim = np.int64(read_at(raw, 656, 1, np.int16)[0])\\n arr = read_at(raw, 4100, xdim*ydim, np.uint16)\\n arr = arr.reshape((ydim, xdim))\\n print('data shape: {}'.format(np.shape(arr)))\\n if np.shape(arr)[0] == 1:\\n arr = arr[0]\\n print('data shape: {}'.format(np.shape(arr)))\\n return arr\",\n \"def _get_las_npoints(fpath):\\n with laspy.file.File(fpath) as f:\\n return f.header.count\",\n \"def read_prnu_files():\\n file_path = r'C:\\\\Users\\\\nmishra\\\\Workspace\\\\TEMPO\\\\PRNU_map\\\\PRNU_map_Median'\\n prnu_mask_a = np.genfromtxt(file_path +'/' + 'Quad A_Final_PRNU.csv',\\n delimiter=',')\\n prnu_mask_b = np.genfromtxt(file_path +'/' + 'Quad B_Final_PRNU.csv',\\n delimiter=',')\\n prnu_mask_c = np.genfromtxt(file_path +'/' + 'Quad C_Final_PRNU.csv',\\n delimiter=',')\\n prnu_mask_d = np.genfromtxt(file_path +'/' + 'Quad D_Final_PRNU.csv',\\n delimiter=',')\\n prnu_mask = [prnu_mask_a, prnu_mask_b, prnu_mask_c, prnu_mask_d]\\n return prnu_mask\",\n \"def bounds(sceneid: str) -> Dict:\\n scene_params = sentinel2_parser(sceneid)\\n preview_file = \\\"{scheme}://{bucket}/{prefix}/{preview_file}\\\".format(**scene_params)\\n with rasterio.open(preview_file) as src_dst:\\n bounds = transform_bounds(\\n src_dst.crs, constants.WGS84_CRS, *src_dst.bounds, densify_pts=21\\n )\\n\\n return dict(sceneid=sceneid, bounds=bounds)\",\n \"def le_binario_mgbp(filebin,nt,nc):\\n return np.fromfile(filebin,'<f4').reshape(nt,nc)\",\n \"def le_binario_mgbp(filebin,nt,nc):\\n return np.fromfile(filebin,'<f4').reshape(nt,nc)\",\n \"def read_for_validation(p):\\r\\n return read_cropped_image(p, False)\",\n \"def bounding_var(self, field):\\n if field not in self.bounding_fields:\\n raise KeyError(\\\"Invalid field name: %s. Valid fields are: %s\\\"%\\n (field, self.bounding_fields))\\n fname = os.path.join(self.logs_dir, \\\"bounding_\\\"+field+\\\".dat\\\")\\n data = np.loadtxt(fname, skiprows=2)\\n return data\",\n \"def get_bounds(self):\\r\\n left, bottom, front = 10000, 10000, 10000\\r\\n right, top, back = -10000, -10000, -10000\\r\\n for b in self.buf:\\r\\n for v in b.vertices:\\r\\n if v[0] < left:\\r\\n left = v[0]\\r\\n if v[0] > right:\\r\\n right = v[0]\\r\\n if v[1] < bottom:\\r\\n bottom = v[1]\\r\\n if v[1] > top:\\r\\n top = v[1]\\r\\n if v[2] < front:\\r\\n front = v[2]\\r\\n if v[2] > back:\\r\\n back = v[2]\\r\\n\\r\\n return (left, bottom, front, right, top, back)\",\n \"def read_body(filepath, **kwargs):\\n with open(filepath, 'r') as infile:\\n coords = numpy.loadtxt(infile, unpack=True, **kwargs)\\n return coords\",\n \"def read_exposure(fname, patchralims, patchdeclims, mask=True):\\n from astropy.io import fits\\n \\n hdr = fits.getheader(fname)\\n data = fits.getdata(fname)\\n unc = fits.getdata(fname.replace(\\\"sci\\\", \\\"unc\\\"))\\n \\n s = PostageStamp()\\n s.filtername = hdr[\\\"FILTER\\\"]\\n s.nx, s.ny = hdr[\\\"NAXIS1\\\"], hdr[\\\"NAXIS2\\\"]\\n pixscale = hdr[\\\"PIXSCALE\\\"]\\n PA = hdr[\\\"ROT\\\"]\\n npsf = hdr[\\\"NPSF\\\"]\\n\\n # --- WCS ---\\n s.scale = 1.0/pixscale * np.eye(2)\\n s.dpix_dsky = np.matmul(s.scale, rotation_matrix(np.deg2rad(PA)))\\n s.crpix = np.array([hdr[\\\"CRPIX0\\\"], hdr[\\\"CRPIX1\\\"]])\\n s.crval = np.array([hdr[\\\"CRVAL0\\\"], hdr[\\\"CRVAL1\\\"]])\\n\\n # --- PSF ---\\n s.psf = get_psf(npsf)\\n\\n # -- PIXEL DATA ---\\n # x,y\\n # note inversion here\\n s.ypix, s.xpix = np.meshgrid(np.arange(s.ny), np.arange(s.nx))\\n \\n # restrict to pixels in patch, and reshape all images to 1D\\n sky = pixelcoords_to_skycoords(s)\\n inpatch = ((sky[0] > patchralims[0]) & (sky[0] < patchralims[1]) &\\n (sky[1] > patchdeclims[0]) & (sky[1] < patchdeclims[1]))\\n assert inpatch.sum() > 0\\n\\n if not mask:\\n s.good_pixel = np.copy(inpatch)\\n inpatch = slice(None)\\n else:\\n s.nx = inpatch.sum()\\n s.ny = 1\\n\\n s.xpix = s.xpix.reshape(-1)[inpatch]\\n s.ypix = s.ypix.reshape(-1)[inpatch]\\n \\n # fluxes and uncertainties within patch\\n s.pixel_values = data.reshape(-1)[inpatch]\\n s.ierr = 1. / unc.reshape(-1)[inpatch]\\n \\n return s\",\n \"def get_bounds(shape, affine):\\n adim, bdim, cdim = shape\\n adim -= 1\\n bdim -= 1\\n cdim -= 1\\n # form a collection of vectors for each 8 corners of the box\\n box = np.array([[0., 0, 0, 1],\\n [adim, 0, 0, 1],\\n [0, bdim, 0, 1],\\n [0, 0, cdim, 1],\\n [adim, bdim, 0, 1],\\n [adim, 0, cdim, 1],\\n [0, bdim, cdim, 1],\\n [adim, bdim, cdim, 1]]).T\\n box = np.dot(affine, box)[:3]\\n return zip(box.min(axis=-1), box.max(axis=-1))\",\n \"def GetBounds(bbox, delta, N):\\n bounds = np.zeros(4, dtype=np.uint8)\\n bounds[0] = max(0, math.floor(bbox[0] - delta))\\n bounds[1] = min(N-1, math.ceil(bbox[0] + bbox[2] + delta))\\n bounds[2] = max(0, math.floor(bbox[1] - delta))\\n bounds[3] = min(N-1, math.ceil(bbox[1] + bbox[3] + delta))\\n\\n return bounds\",\n \"def read_region_mask(grid='Nh50km'):\\n\\n mask_path = ('/oldhome/apbarret/data/seaice_indices/'\\n 'Arctic_region_mask_Meier_AnnGlaciol2007_Nh50km.dat')\\n nrow = 360\\n ncol = 360\\n \\n result = xr.DataArray(np.fromfile(mask_path, dtype=float).reshape(nrow,ncol),\\n dims=['x','y'])\\n return result\",\n \"def get_bounds(self, ped_pos):\\n top_left_x = ped_pos[:, 0] - self.neighborhood_size / 2\\n top_left_y = ped_pos[:, 1] + self.neighborhood_size / 2\\n bottom_right_x = ped_pos[:, 0] + self.neighborhood_size / 2\\n bottom_right_y = ped_pos[:, 1] - self.neighborhood_size / 2\\n\\n top_left = torch.stack([top_left_x, top_left_y], dim=1)\\n bottom_right = torch.stack([bottom_right_x, bottom_right_y], dim=1)\\n\\n return top_left, bottom_right\",\n \"def get_bounding_box_from_xml_path(path: Path) -> np.ndarray:\\n with open(path, mode=\\\"r\\\") as file:\\n bs = BeautifulSoup(file, \\\"xml\\\")\\n\\n x_min = float(bs.bndbox.xmin.string)\\n x_max = float(bs.bndbox.xmax.string)\\n y_min = float(bs.bndbox.ymin.string)\\n y_max = float(bs.bndbox.ymax.string)\\n\\n return np.array([x_min, y_min, x_max - x_min, y_max - y_min])\",\n \"def read_parameters_diff_file(coords):\\n param_map = hp.read_map(source +\\n \\\"kids_data/\\\"\\n \\\"COM_CompMap_Compton-SZMap-milca-\\\"\\n \\\"ymaps_2048_R2.00.fits\\\")\\n params = []\\n for point in coords:\\n ra, dec = point\\n index = declratoindex(dec, ra)\\n params.append(param_map[index])\\n return params\",\n \"def bounds(sceneid):\\n scene_params = _sentinel_parse_scene_id(sceneid)\\n preview_file = os.path.join(\\n scene_params[\\\"aws_bucket\\\"],\\n scene_params[\\\"aws_prefix\\\"],\\n scene_params[\\\"preview_file\\\"],\\n )\\n with rasterio.open(preview_file) as src:\\n wgs_bounds = transform_bounds(\\n *[src.crs, \\\"epsg:4326\\\"] + list(src.bounds), densify_pts=21\\n )\\n\\n info = {\\\"sceneid\\\": sceneid}\\n info[\\\"bounds\\\"] = list(wgs_bounds)\\n\\n return info\",\n \"def read_2d(fpath):\\n #check_isfile(fpath)\\n objs = []\\n ids = []\\n with open(fpath, 'r') as f:\\n for ff in f:\\n id_position, players_x, players_y = token_position(ff)\\n objs.append((players_x, players_y))\\n ids.append(id_position)\\n objs = np.array(objs)\\n ids = np.array(ids)\\n return ids, objs\",\n \"def readpil3d(self):\\r\\n\\r\\n # Read the data in as an array.\\r\\n res = np.loadtxt(self.name, delimiter=' ')\\r\\n\\r\\n # Split into useful chunks\\r\\n self.pos = res[:, 0:3] # Grid point locations\\r\\n self.Pn = res[:, 3:4] # Normal pressure [Pa]\\r\\n self.flux = res[:, -1] # Flux\\r\",\n \"def _load(self) -> np.ndarray:\\n with self._fs.open(self._filepath, mode=\\\"r\\\") as f:\\n image = Image.open(f).convert(\\\"RGBA\\\")\\n return np.asarray(image)\",\n \"def prepare_data(self, file):\\n maps = np.load(file)\\n pred, gt, not_care = maps[-3:]\\n return self.get_polygon(pred), self.get_polygon(gt), self.get_polygon(not_care)\",\n \"def save_clips(fname, raw, bounds, mask, start_from=0):\\n # data prep\\n mask = np.asarray(mask)\\n # save data\\n if os.path.isfile(fname):\\n _save_clips_append(fname, raw, bounds, mask, start_from)\\n else:\\n _save_clips_new(fname, raw, bounds, mask)\",\n \"def read_numpy_txt(self, infname):\\n InArr = np.loadtxt(infname)\\n inlon = InArr[:,0]\\n inlat = InArr[:,1]\\n inZ = InArr[:,2]\\n # self.mask = ~self.mask\\n self.Zarr=inZ.reshape(self.lonArr.shape)[::-1,:]\\n self.mask[np.isnan(self.Zarr)]=1\\n # if (np.array_equal(inlon.reshape(self.lonArr.shape), self.lonArr) and np.array_equal(inlat.reshape(self.lonArr.shape)[::-1,:], self.latArr)):\\n # self.Zarr=inZ.reshape(self.lonArr.shape)[::-1,:]\\n # self.mask[self.Zarr==np.nan]=1\\n # else:\\n # print 'error!'\\n # elif (np.array_equal(inlon.reshape(self.lonArr.shape), self.lonArr) and np.array_equal(inlat.reshape(self.lonArr.shape), self.latArr)):\\n \\n return\",\n \"def read_pts_from_grid(jparams):\\n print(\\\"=== Reading points from grid ===\\\")\\n input_data = rasterio.open(jparams[\\\"input-file\\\"])\\n nodata_value = input_data.nodata\\n raw_data = input_data.read()\\n ncols = input_data.width\\n nrows = input_data.height\\n shape = input_data.shape\\n PixelSizeX = input_data.transform[0]\\n PixelSizeY = -input_data.transform[4]\\n #transform_matrix = input_data.transform\\n #print(raw_data[0])\\n #lower_left_corner = transform_matrix * (0,input_data.height)\\n # outlist = []\\n # row_cnt = 0\\n # for rev_row in reversed(range(0,nrows)):\\n # for col in range(0,ncols):\\n # z=raw_data[0][row_cnt][col]\\n # if z!=nodata_value:\\n # outlist.append([(rev_row+0.5)*PixelSizeY, (col+0.5)*PixelSizeX, z])\\n # row_cnt+=1\\n # print(len(raw_data[0][1]))\\n # print(len(np.array(generate_raster_points(nrows, ncols, raw_data, nodata_value, PixelSizeX, PixelSizeY))))\\n return np.array(generate_raster_points(nrows, ncols, raw_data, nodata_value, PixelSizeX, PixelSizeY))\\n # Tip: the most efficient implementation of this function does not use any loops. Use numpy functions instead.\",\n \"def GeneralProfile(binsz, filename):\\n data_image = fits.open(filename)\\n data_image= data_image[1]\\n glons = np.arange(LonLow, LonHigh+binsz, binsz) \\n glon_bounds = Table()\\n glon_bounds['CHANNEL'] = np.arange(len(glons) - 1)\\n glon_bounds['GLON_MIN'] = np.float64(glons[:-1])\\n glon_bounds['GLON_MAX'] = np.float64(glons[1:])\\n data = compute_longitude_profile(glon_bounds = glon_bounds, binsz=binsz, image=data_image, datatype=2, emission=4, tev=0)\\n return data\",\n \"def list_bounds(self) -> None:\\n print(\\\"\\\")\\n print(\\n \\\"Mu = {}\\\".format(self.experiment_file_dict[\\\"motors\\\"][\\\"mu\\\"][\\\"bounds\\\"])\\n )\\n print(\\n \\\"Eta = {}\\\".format(self.experiment_file_dict[\\\"motors\\\"][\\\"eta\\\"][\\\"bounds\\\"])\\n )\\n print(\\n \\\"Chi = {}\\\".format(self.experiment_file_dict[\\\"motors\\\"][\\\"chi\\\"][\\\"bounds\\\"])\\n )\\n print(\\n \\\"Phi = {}\\\".format(self.experiment_file_dict[\\\"motors\\\"][\\\"phi\\\"][\\\"bounds\\\"])\\n )\\n print(\\n \\\"Nu = {}\\\".format(self.experiment_file_dict[\\\"motors\\\"][\\\"nu\\\"][\\\"bounds\\\"])\\n )\\n print(\\n \\\"Del = {}\\\".format(self.experiment_file_dict[\\\"motors\\\"][\\\"del\\\"][\\\"bounds\\\"])\\n )\\n print(\\\"\\\")\",\n \"def read_bounding_boxes(filename):\\n f = open(filename)\\n objects = []\\n weight = 0\\n height = 0\\n for line in f:\\n print(line)\\n first_word = line.split(';')[0]\\n if first_word == \\\"Dimensions\\\":\\n weight = line.split(';')[1]\\n height = line.split(';')[2]\\n if first_word == \\\"Object\\\":\\n objects.append((line.split(';')[1], line.split(';')[2], line.split(';')[4],\\n line.split(';')[5], line.split(';')[6], line.split(';')[7]))\\n return weight, height, objects\",\n \"def readPFM(file):\\n file = open(file, 'rb')\\n\\n color = None\\n width = None\\n height = None\\n scale = None\\n endian = None\\n\\n header = file.readline().rstrip()\\n if header == b'PF':\\n color = True\\n elif header == b'Pf':\\n color = False\\n else:\\n raise Exception('Not a PFM file.')\\n\\n dims = file.readline()\\n try:\\n width, height = list(map(int, dims.split()))\\n except:\\n raise Exception('Malformed PFM header.')\\n\\n scale = float(file.readline().rstrip())\\n if scale < 0: # little-endian\\n endian = '<'\\n scale = -scale\\n else:\\n endian = '>' # big-endian\\n\\n data = np.fromfile(file, endian + 'f')\\n shape = (height, width, 3) if color else (height, width, 1)\\n\\n data = np.reshape(data, shape)\\n data = np.flipud(data)\\n return data, scale\",\n \"def read_seaice_mask(file=r'C:\\\\Users\\\\apbarret\\\\Documents\\\\data\\\\sea_ice_index\\\\Arctic_region_mask_Meier_AnnGlaciol2007.msk'):\\n cols = 304\\n rows = 448\\n img = np.fromfile(file, dtype='byte').reshape(rows, cols)\\n return img\",\n \"def read_raw_data(file_name: str, ROWS: int, COLS: int, OFFSET=0) -> list:\\r\\n FILE = open(file_name, mode=\\\"r\\\")\\r\\n\\r\\n # Reading the data in the Single Dimensional form\\r\\n img = np.fromfile(\\r\\n FILE, dtype=np.uint8, count=ROWS * COLS, offset=((ROWS * COLS) * OFFSET)\\r\\n )\\r\\n\\r\\n # Shaping the data to the two dimensional format\\r\\n img = np.reshape(img, (ROWS, COLS)).tolist()\\r\\n\\r\\n FILE.close()\\r\\n return img\",\n \"def file_parser(file_name):\\n h = 480\\n w = 640\\n out = []\\n with open(file_name, 'r') as f:\\n line_num = 1\\n for line in f:\\n if line_num < 17:\\n # Read to where data starts\\n line_num += 1\\n continue\\n elif line_num > 74:\\n break\\n # print(list(map(int, line.strip().split(\\\" \\\"))))\\n vals = line.split()\\n # print(list(\\\"\\\".join(line)))\\n # print(line.split())\\n assert(float(vals[2]) < 640)\\n assert(float(vals[3]) < 480)\\n point = [float(vals[2]) * w, float(vals[3]) * h]\\n # print(point)\\n out.append(point)\\n line_num += 1\\n\\n out.append([0,0])\\n out.append([w-1, 0])\\n out.append([0, h-1])\\n out.append([w-1, h-2])\\n return out\",\n \"def getPoses(modelname):\\n load_file = bUtils.readTextFile(modelname + '::poses')\\n if load_file == '':\\n return []\\n poses = json.loads(load_file)\\n return poses.keys()\",\n \"def _load_pascal_annotation(self, index):\\n imagefile = os.path.join(self._data_path, 'Images', index + self._image_ext)\\n labelfile = os.path.join(self._data_path, 'GroundTruth', index + '.txt')\\n print 'Loading: image {} and label {}'.format(imagefile, labelfile)\\n\\n labels = np.loadtxt(labelfile) + 1\\n image = cv2.imread(imagefile)\\n\\n gt_classes = []\\n gt_boxes = []\\n\\n boxes, classes = self._get_boxes(image, labels)\\n\\n boxes = np.array(boxes)\\n classes = np.array(classes)\\n\\n scores = np.ones(len(boxes), dtype=np.float32)\\n dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32)\\n\\n # keep = nms(dets, 0.2)\\n _, keep = non_max_suppression_fast(dets, 0.2)\\n boxes = boxes[keep, :]\\n classes = classes[keep]\\n\\n for n, (x, y, w, h) in enumerate(boxes):\\n # gt_boxes.append([min(x, w), min(y, h), min(abs(w - x), w), min(abs(h - y), h)])\\n gt_boxes.append([x, y, w - x, h - y])\\n gt_classes.append(classes[n])\\n\\n overlaps = np.zeros((len(gt_boxes), len(self._classes)), dtype=np.float32)\\n for n, obj in enumerate(gt_boxes):\\n overlaps[n, gt_classes[n]] = 1.0\\n\\n overlaps = scipy.sparse.csr_matrix(overlaps)\\n\\n return {'boxes': np.array(gt_boxes),\\n 'gt_classes': np.array(gt_classes, dtype=np.int32),\\n 'gt_overlaps': overlaps,\\n 'flipped': False}\\n\\n # def get_data_from_tag(node, tag):\\n # return node.getElementsByTagName(tag)[0].childNodes[0].data\\n\\n # with open(filename) as f:\\n # data = minidom.parseString(f.read())\\n\\n # objs = data.getElementsByTagName('object')\\n # num_objs = len(objs)\\n\\n # boxes = np.zeros((num_objs, 4), dtype=np.uint16)\\n # gt_classes = np.zeros((num_objs), dtype=np.int32)\\n # overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)\\n\\n # # Load object bounding boxes into a data frame.\\n # for ix, obj in enumerate(objs):\\n # # Make pixel indexes 0-based\\n # x1 = float(get_data_from_tag(obj, 'xmin')) - 1\\n # y1 = float(get_data_from_tag(obj, 'ymin')) - 1\\n # x2 = float(get_data_from_tag(obj, 'xmax')) - 1\\n # y2 = float(get_data_from_tag(obj, 'ymax')) - 1\\n # cls = self._class_to_ind[str(get_data_from_tag(obj, \\\"name\\\")).lower().strip()]\\n # boxes[ix, :] = [x1, y1, x2, y2]\\n # gt_classes[ix] = cls\\n # overlaps[ix, cls] = 1.0\\n\\n # overlaps = scipy.sparse.csr_matrix(overlaps)\\n\\n # return {'boxes': boxes,\\n # 'gt_classes': gt_classes,\\n # 'gt_overlaps': overlaps,\\n # 'flipped': False}\",\n \"def get_bounds():\\n return [0.00], [1.00]\",\n \"def read_xyz_file(filename, num_spatial_dimensions):\\n print(\\\"Reading data from XYZ file.\\\")\\n\\n particle_positions = []\\n frame_number = 0\\n line_number = 0\\n frame_particles = 0\\n with open(filename, 'r') as input_file:\\n for line in input_file:\\n if line_number == 0:\\n # Check for blank line at end of file\\n if line != \\\"\\\":\\n frame_particles = int(line)\\n particle_positions.append(np.zeros((frame_particles, num_spatial_dimensions)))\\n elif line_number == 1:\\n pass\\n else:\\n for dimension in range(num_spatial_dimensions):\\n particle_positions[frame_number][line_number-2][dimension] = line.split()[1:][dimension]\\n line_number += 1\\n # If we have reached the last particle in the frame, reset counter for next frame\\n if line_number == (frame_particles + 2):\\n line_number = 0\\n frame_number += 1\\n\\n print(\\\"XYZ read complete.\\\")\\n\\n return particle_positions\",\n \"def readMAXIPOLdataLuis(filename):\\n\\n ia=[]; ja=[]\\n i=[]; j=[]; beam=[]; sig=[]; cts=[]\\n for line in open(filename, \\\"r\\\"):\\n line = line.strip().split()\\n i1, j1, b1, s1, c1 = (int(line[0]), int(line[1]), \\n float(line[2]), float(line[3]), int(line[4]))\\n ia.append(i1); ja.append(j1)\\n if b1 != 0 and s1 !=0:\\n ## only keep pixels with data\\n i.append(i1); j.append(j1); beam.append(b1)\\n sig.append(s1); cts.append(c1)\\n\\n beam = asarray(beam, float64)\\n sig = asarray(sig, float64)\\n ## map i and j (before deletion) onto (-1,1)\\n x = array([2*(ii-min(ia))/(max(ia)-min(ia))-1 for ii in i], float64)\\n y = array([2*(jj-min(ja))/(max(ja)-min(ja))-1 for jj in j], float64)\\n\\n return BeamData(x, y, beam, sig, cts=cts)\",\n \"def read_2d_analysis_data(f):\\n \\n data = np.transpose(np.loadtxt(f, dtype=np.float64))\\n x = data[0]\\n y = data[1]\\n\\n return x, y\",\n \"def test_calc_bbox():\\n with xr.open_rasterio(TEST_RASTER_PATH) as src:\\n xr_res = ds.utils.calc_res(src)\\n xr_bounds = ds.utils.calc_bbox(src.x.values, src.y.values, xr_res)\\n with rasterio.open(TEST_RASTER_PATH) as src:\\n rio_bounds = src.bounds\\n assert np.allclose(xr_bounds, rio_bounds, atol=1.0) # allow for absolute diff of 1.0\",\n \"def read_zone_file(cls, fname):\\n with open(fname) as f:\\n lines = f.readlines()\\n\\n # Initialize layer\\n lay = 0\\n\\n # Initialize data counter\\n totlen = 0\\n i = 0\\n\\n # First line contains array dimensions\\n dimstring = lines.pop(0).strip().split()\\n nlay, nrow, ncol = (int(v) for v in dimstring)\\n zones = np.zeros((nlay, nrow, ncol), dtype=np.int32)\\n\\n # The number of values to read before placing\\n # them into the zone array\\n datalen = nrow * ncol\\n totaldatalen = nlay * nrow * ncol\\n\\n # List of valid values for LOCAT\\n locats = [\\\"CONSTANT\\\", \\\"INTERNAL\\\", \\\"EXTERNAL\\\"]\\n\\n # ITERATE OVER THE ROWS\\n for line in lines:\\n if totlen == totaldatalen:\\n break\\n\\n rowitems = line.strip().split()\\n\\n # Skip blank lines\\n if len(rowitems) == 0:\\n continue\\n\\n # HEADER\\n if rowitems[0].upper() in locats:\\n vals = []\\n locat = rowitems[0].upper()\\n\\n if locat == \\\"CONSTANT\\\":\\n iconst = int(rowitems[1])\\n else:\\n fmt = rowitems[1].strip(\\\"()\\\")\\n fmtin, iprn = (int(v) for v in fmt.split(\\\"I\\\"))\\n\\n # ZONE DATA\\n else:\\n if locat == \\\"CONSTANT\\\":\\n vals = np.ones((nrow, ncol), dtype=int) * iconst\\n lay += 1\\n elif locat == \\\"INTERNAL\\\":\\n # READ ZONES\\n rowvals = [int(v) for v in rowitems]\\n s = \\\"Too many values encountered on this line.\\\"\\n assert len(rowvals) <= fmtin, s\\n vals.extend(rowvals)\\n\\n elif locat == \\\"EXTERNAL\\\":\\n # READ EXTERNAL FILE\\n fname = rowitems[0]\\n if not os.path.isfile(fname):\\n errmsg = f'Could not find external file \\\"{fname}\\\"'\\n raise Exception(errmsg)\\n with open(fname) as ext_f:\\n ext_flines = ext_f.readlines()\\n for ext_frow in ext_flines:\\n ext_frowitems = ext_frow.strip().split()\\n rowvals = [int(v) for v in ext_frowitems]\\n vals.extend(rowvals)\\n if len(vals) != datalen:\\n errmsg = (\\n \\\"The number of values read from external \\\"\\n 'file \\\"{}\\\" does not match the expected '\\n \\\"number.\\\".format(len(vals))\\n )\\n raise Exception(errmsg)\\n else:\\n # Should not get here\\n raise Exception(f\\\"Locat not recognized: {locat}\\\")\\n\\n # IGNORE COMPOSITE ZONES\\n\\n if len(vals) == datalen:\\n # place values for the previous layer into the zone array\\n vals = np.array(vals, dtype=int).reshape((nrow, ncol))\\n zones[lay, :, :] = vals[:, :]\\n lay += 1\\n totlen += len(rowitems)\\n i += 1\\n s = (\\n \\\"The number of values read ({:,.0f})\\\"\\n \\\" does not match the number expected\\\"\\n \\\" ({:,.0f})\\\".format(totlen, nlay * nrow * ncol)\\n )\\n assert totlen == nlay * nrow * ncol, s\\n return zones\",\n \"def process_bounding_boxes(ground_truth_file: Path) -> List[Dict]:\\n index_shifts = defaultdict(lambda: 0)\\n index_shifts[\\\"cubicle\\\"] = 1108\\n folder_name = ground_truth_file.parent.name\\n \\n annotations = []\\n ground_truth_dataframe = pd.read_csv(str(ground_truth_file),\\n header=None,\\n sep=\\\" \\\",\\n names=[\\\"frameID\\\", \\\"0\\\", \\\"x\\\", \\\"y\\\", \\\"width\\\", \\\"height\\\"])\\n for index, row in ground_truth_dataframe.iterrows():\\n image_id = int(row[\\\"frameID\\\"]) + index_shifts[folder_name]\\n image_id = f\\\"{folder_name}_{str(image_id)}\\\"\\n x = int(row[\\\"x\\\"])\\n y = int(row[\\\"y\\\"])\\n width = int(row[\\\"width\\\"])\\n height = int(row[\\\"height\\\"])\\n\\n area = width * height\\n bbox = [x, y, width, height]\\n\\n label_data = {\\\"id\\\": index,\\n \\\"image_id\\\": image_id,\\n \\\"category_id\\\": 1,\\n \\\"bbox\\\": bbox,\\n \\\"area\\\": area,\\n \\\"iscrowd\\\": False}\\n annotations.append(label_data)\\n\\n return annotations\",\n \"def load_n3d_coords(file_path): \\n \\n import core.nuc_io as io\\n\\n seq_pos_dict = {}\\n coords_dict = {} \\n \\n with io.open_file(file_path) as file_obj:\\n chromo = None\\n \\n for line in file_obj:\\n \\n data = line.split()\\n n_items = len(data)\\n \\n if not n_items:\\n continue\\n \\n elif data[0] == '#':\\n continue\\n \\n elif n_items == 3:\\n chromo, n_coords, n_models = data\\n \\n #if chromo.lower()[:3] == 'chr':\\n # chromo = chromo[3:]\\n \\n if chromo in coords_dict:\\n raise Exception('Duplicate chromosome \\\"%s\\\" records in file %s' % (chromo, file_path))\\n \\n n_coords = int(n_coords)\\n n_models = int(n_models)\\n \\n chromo_seq_pos = np.empty(n_coords, int)\\n chromo_coords = np.empty((n_models, n_coords, 3), float)\\n \\n coords_dict[chromo] = chromo_coords\\n seq_pos_dict[chromo] = chromo_seq_pos\\n \\n check = (n_models * 3) + 1\\n i = 0\\n \\n elif not chromo:\\n raise Exception('Missing chromosome record in file %s' % file_path)\\n \\n elif n_items != check:\\n msg = 'Data size in file %s does not match Position + Models * Positions * 3'\\n raise Exception(msg % file_path)\\n \\n else:\\n chromo_seq_pos[i] = int(data[0])\\n \\n coord = [float(x) for x in data[1:]]\\n coord = np.array(coord).reshape(n_models, 3)\\n chromo_coords[:,i] = coord\\n i += 1\\n \\n return seq_pos_dict, coords_dict\",\n \"def readGP(loc, fname):\\n with open(\\\"/\\\".join([loc, fname]), \\\"r\\\") as f:\\n lines = f.readlines()\\n\\n data = []\\n\\n for line in lines[2:]:\\n data.append([float(val) for val in line.split()])\\n\\n data = np.concatenate(data)\\n\\n line1 = lines[1].split()\\n num_pts = float(line1[0])\\n dt = float(line1[1])\\n shift = float(line1[4])\\n\\n return data, num_pts, dt, shift\",\n \"def readXYZBinPos(self,phys,xyzname):\\r\\n XYZBinReader.XYZBinReader(self.checkPath(xyzname)).read(phys.posvec)\",\n \"def le_binario_mgb(filebin,nt,nc):\\n return np.fromfile(filebin,'<f4').reshape(nt,nc)\",\n \"def le_binario_mgb(filebin,nt,nc):\\n return np.fromfile(filebin,'<f4').reshape(nt,nc)\",\n \"def le_binario_mgb(filebin,nt,nc):\\n return np.fromfile(filebin,'<f4').reshape(nt,nc)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.66865915","0.61436146","0.60371596","0.5696726","0.5668809","0.5654928","0.56231886","0.5586926","0.5579912","0.55704826","0.5489618","0.5483596","0.54541075","0.54122","0.5406048","0.5370545","0.53350437","0.5314571","0.5290624","0.52806646","0.52789634","0.52783275","0.5248999","0.5232847","0.52191085","0.52171487","0.5205642","0.51793146","0.51725227","0.5159608","0.51587045","0.51534885","0.5149617","0.51460487","0.5135356","0.5132706","0.51274776","0.51157844","0.5109863","0.5109863","0.5106388","0.5085843","0.50783616","0.50667614","0.5056146","0.5053092","0.50397015","0.5027702","0.50235116","0.5003384","0.50031155","0.50002474","0.4999956","0.49883732","0.49799526","0.49776295","0.49764597","0.49722412","0.49722412","0.49469173","0.49416253","0.49369827","0.4929216","0.49277857","0.49234122","0.4922201","0.49165106","0.49060187","0.49050257","0.4900774","0.4900416","0.48997518","0.48997062","0.48976666","0.48927966","0.48916236","0.48901576","0.4886487","0.48856312","0.48786935","0.48759454","0.486576","0.4858837","0.484965","0.48394802","0.48355386","0.48289254","0.4825214","0.48245186","0.4823768","0.48237395","0.48235276","0.48222786","0.48221773","0.4812144","0.4809239","0.48065293","0.4797325","0.4797325","0.4797325"],"string":"[\n \"0.66865915\",\n \"0.61436146\",\n \"0.60371596\",\n \"0.5696726\",\n \"0.5668809\",\n \"0.5654928\",\n \"0.56231886\",\n \"0.5586926\",\n \"0.5579912\",\n \"0.55704826\",\n \"0.5489618\",\n \"0.5483596\",\n \"0.54541075\",\n \"0.54122\",\n \"0.5406048\",\n \"0.5370545\",\n \"0.53350437\",\n \"0.5314571\",\n \"0.5290624\",\n \"0.52806646\",\n \"0.52789634\",\n \"0.52783275\",\n \"0.5248999\",\n \"0.5232847\",\n \"0.52191085\",\n \"0.52171487\",\n \"0.5205642\",\n \"0.51793146\",\n \"0.51725227\",\n \"0.5159608\",\n \"0.51587045\",\n \"0.51534885\",\n \"0.5149617\",\n \"0.51460487\",\n \"0.5135356\",\n \"0.5132706\",\n \"0.51274776\",\n \"0.51157844\",\n \"0.5109863\",\n \"0.5109863\",\n \"0.5106388\",\n \"0.5085843\",\n \"0.50783616\",\n \"0.50667614\",\n \"0.5056146\",\n \"0.5053092\",\n \"0.50397015\",\n \"0.5027702\",\n \"0.50235116\",\n \"0.5003384\",\n \"0.50031155\",\n \"0.50002474\",\n \"0.4999956\",\n \"0.49883732\",\n \"0.49799526\",\n \"0.49776295\",\n \"0.49764597\",\n \"0.49722412\",\n \"0.49722412\",\n \"0.49469173\",\n \"0.49416253\",\n \"0.49369827\",\n \"0.4929216\",\n \"0.49277857\",\n \"0.49234122\",\n \"0.4922201\",\n \"0.49165106\",\n \"0.49060187\",\n \"0.49050257\",\n \"0.4900774\",\n \"0.4900416\",\n \"0.48997518\",\n \"0.48997062\",\n \"0.48976666\",\n \"0.48927966\",\n \"0.48916236\",\n \"0.48901576\",\n \"0.4886487\",\n \"0.48856312\",\n \"0.48786935\",\n \"0.48759454\",\n \"0.486576\",\n \"0.4858837\",\n \"0.484965\",\n \"0.48394802\",\n \"0.48355386\",\n \"0.48289254\",\n \"0.4825214\",\n \"0.48245186\",\n \"0.4823768\",\n \"0.48237395\",\n \"0.48235276\",\n \"0.48222786\",\n \"0.48221773\",\n \"0.4812144\",\n \"0.4809239\",\n \"0.48065293\",\n \"0.4797325\",\n \"0.4797325\",\n \"0.4797325\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.59269446"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":85595,"cells":{"query":{"kind":"string","value":"Overload this method when doing extra feature processes or data augmentation."},"document":{"kind":"string","value":"def __getitem__(self, idx):\n record = self.records[idx]\n return np.array(record['feat']), np.array(record['label'], dtype=np.int64)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def feature():\n pass","def feat():\n pass","def augment(self, *args, **kwargs):\n pass","def _data_augmentation(feature_dict):\n image_features = feature_dict[_transformed_name(constants.IMAGE_KEY)]\n image_features = _image_augmentation(image_features)\n feature_dict[_transformed_name(constants.IMAGE_KEY)] = image_features\n return feature_dict","def _derived_features(self):\n for created_feature, creator in self.feature_creators.items():\n self.parameters[created_feature] = creator(self.parameters)","def augment(self, image):\n pass","def process_sample_train(self):\n raise NotImplementedError","def add_feature(self, feat: Feature) -> None:\n self.data_features.append(feat)","def add_feature(self, feature):\n self.features += [feature]\n for stock in self.stocks:\n feature(self.stock_data[stock])","def __call__(self, *args, **kwargs):\n self.features = dict((k, v()) for k, v in self.features.items())\n return self.features","def do_manipulations(self, *args, **kwargs):\n pass","def extract_features(self):\n self.extract_features_static()\n self.extract_features_dynamic()","def extractFeatures(self, datum):\n abstract","def _preprocess(self, feature_dict):\n return feature_dict","def attach_feature(self, feature):\r\n\r\n # Filter out literally identical features\r\n if feature in self._features:\r\n return # the feature is already present\r\n\r\n # Filter out functionally identical features.\r\n # Features may use their on_attach method to raise\r\n # toolbox.AlreadyThere if they detect that some\r\n # installed feature does the same thing already\r\n attach = getattr(feature, 'on_attach', None)\r\n if attach is not None:\r\n try:\r\n attach(self)\r\n except toolbox.AlreadyThere:\r\n return\r\n self.execute_callbacks_times.setdefault(feature, 0)\r\n #it would be nice if we could require a specific class instead of\r\n #a \"workalike\" so we could do actual error checking\r\n #if not isinstance(feature, toolbox.Feature):\r\n # raise TypeError(\"Expected gof.toolbox.Feature instance, got \"+\\\r\n # str(type(feature)))\r\n\r\n # Add the feature\r\n self._features.append(feature)","def extract_feature(self, article) :\n pass","def precompute(self, features, mode, params):\n return None","def updateAttributesAfterAdding(self):\n layer = self.sender()\n while self.addedFeatures:\n featureId = self.addedFeatures.pop()\n #begining the edit command\n # layer.beginEditCommand(self.tr(\"DSG Tools reclassification tool: adjusting feature's attributes\"))\n #accessing added features\n editBuffer = layer.editBuffer()\n features = editBuffer.addedFeatures()\n for key in features.keys():\n #just checking the newly added feature, the other I don't care\n if key == featureId:\n feature = features[key]\n #setting the attributes using the reclassification dictionary\n self.setFeatureAttributes(feature, editBuffer)\n layer.endEditCommand()","def _add_feature(self, feature):\n\n if feature.name in self.feature_name_index:\n logger.info(\"Feature %s already exists at %i, overwriting\" %\n (feature.name, self.feature_name_index[feature.name]))\n self.features[self.feature_name_index[feature.name]] = feature\n else:\n self.features.append(feature)\n self.feature_name_index[feature.name] = len(self.features) - 1\n logger.info(\"Adding %s to model at location %i\" % (\n feature.name, len(self.features)))\n self._add_domain_fault_above(feature)\n self._add_unconformity_above(feature)\n feature.set_model(self)","def features(self, state, action, next_state):\n raise NotImplementedError","def generateFeatures(self, data):\n pass","def feature_forward(self, x):\n raise NotImplementedError","def finalizeFeatures(self):\n pass","def fDataChanged(self):\n\n self._layerManager.getAimsFeatures()","def add_features(self, obj, annotation):\n if annotation['problem']:\n obj.add(folia.Feature, subset='problem', cls=annotation['problem'])\n if annotation['pos']:\n obj.add(folia.Feature, subset='pos', cls=annotation['pos'])","def processing(self):\n pass","def features_combine():\n\n\n\t# PROCESSING AUDIO","def extract_features_dynamic(self):\n self.feature_dynamic_imports()\n self.feature_dynamic_filesystem()\n self.feature_dynamic_network()\n self.feature_dynamic_registry()\n self.feature_dynamic_windowsapi()","def input(self,feature):\n \n order = feature.getAttribute(ORDER)\n if order == 1:\n # Read the attributes from the CSV features into a dictionary\n original_value = (feature.getAttribute(ORIGINAL_VALUE)).lower()\n value_english=feature.getAttribute(REAL_VALUE_ENGLISH)\n value_french=feature.getAttribute(REAL_VALUE_FRENCH)\n code_value=feature.getAttribute(CODE_VALUE)\n csv_row = CsvMetaDataValueMapper(original_value, value_english, value_french, code_value)\n self.csv_features[original_value] = csv_row # Insert into the CSV dictionnary \n else:\n # Load the metadata features\n self.meta_features.append(feature)","def __process_element(data):\n print('prosessing {}'.format(data))\n x_i = data[0]\n y_i = data[1]\n\n file_name = FeatureExtractor.get_file_name(x_i, feature_name)\n try:\n # try to load if file already exist\n np.load(out_path / file_name, allow_pickle=True)\n print('info: {} loaded from .npy !'.format(file_name))\n new_labels.append([file_name, y_i])\n except FileNotFoundError or OSError or EOFError:\n # OSError and EOFError are raised if file are inconsistent\n voice_activation = np.load(source_path / x_i, allow_pickle=True)\n mean_voice_activation = FeatureExtractor.get_mean_voice_activation(voice_activation[0])\n # this is kind-of standard\n FeatureExtractor.save_feature([voice_activation[0], mean_voice_activation], feature_name, out_path, x_i,\n y_i, new_labels)","def process_feature(self, feature):\n self.num_features += 1\n\n def create_int_feature(values):\n feature = tf.train.Feature(\n int64_list=tf.train.Int64List(value=list(values)))\n return feature\n\n features = collections.OrderedDict()\n features[\"unique_ids\"] = create_int_feature([feature.unique_id])\n features[\"input_ids\"] = create_int_feature(feature.input_ids)\n features[\"input_mask\"] = create_int_feature(feature.input_mask)\n features[\"segment_ids\"] = create_int_feature(feature.segment_ids)\n\n if not self.is_test:\n features[\"label\"] = create_int_feature([feature.label])\n\n tf_example = tf.train.Example(features=tf.train.Features(feature=features))\n self._writer.write(tf_example.SerializeToString())","def __init__(self):\n \n self.csv_features = {} # Create dictionary to load the CSV features\n self.meta_features = [] # Create list to load the metadata features","def make_processing_functions(self):\n return","def _training_before_hook(self):\n pass","def load_features(self, features):\n pass\n # self.features = features","def Submodel_addProcessingCallback(*args):\n return _libsbml.Submodel_addProcessingCallback(*args)","def post_processor(self):","def mutation(self, base_offsprings, model_features_count) :","def add_dummy_feature(X, value=...):\n ...","def _extract_additional_features(\n self, eopatch: EOPatch, images: Iterable[np.ndarray], shape: tuple[int, ...]\n ) -> None:\n additional_data = cast(List[FeatureRenameSpec], self.additional_data) # verified by `if` in _extract_data\n for (ftype, _, new_name), band_info in zip(additional_data, self.requested_additional_bands):\n tiffs = [tar[band_info.name + \".tif\"] for tar in images]\n eopatch[ftype, new_name] = self._extract_array(tiffs, 0, shape, band_info.output_types[0])","def _training_after_hook(self):\n pass","def impress(self):\n raise NotImplementedError","def __init__(self, features=None, **kwargs):\n super(FeatureIO, self).__init__(**kwargs)\n self.features = features","def process(self):\n raise NotImplementedError","def preprocess(self):","def _fusion(self, expert_outputs):\n raise NotImplementedError","def _transform_data(self, *args, **kwargs) -> None:\n raise NotImplementedError","def extract_feat(self, imgs):\n pass","def extract_feat(self, imgs):\n pass","def __feature_change(train_x, train_y, val_x, test_x, processors):\n if processors:\n for processor in processors:\n train_x, val_x, test_x = processor(train_x, train_y, val_x, test_x)\n return train_x, val_x, test_x","def addProcessingCallback(*args):\n return _libsbml.Submodel_addProcessingCallback(*args)","def process(self, xaf):\n raise NotImplementedError(\"process() method must be overriden\")","def pre_process(self):\n pass","def pre_process(self):\n pass","def pre_process(self):\n pass","def pre_process(self):\n pass","def pre_process(self):\n pass","def process(self, data):\n allocating = (self._output is None)\n ind = 0\n for i, (name, feature) in enumerate(self.features):\n if allocating:\n x = feature.compute(data)\n self.feature_indices[name] = (ind, ind+x.size)\n ind += x.size\n\n if self._output is None:\n self._output = x\n else:\n self._output = np.hstack([self._output, x])\n else:\n self._output[self.feature_indices[name][0]:\n self.feature_indices[name][1]] = \\\n feature.compute(data)\n\n return self._output","def feature(self):\n Feature(run=default_frame, flags=TE)\n Feature(run=load(\"window_functions.tests.rows_frame\", \"feature\"), flags=TE)\n Feature(run=load(\"window_functions.tests.range_frame\", \"feature\"), flags=TE)\n Feature(run=load(\"window_functions.tests.range_overflow\", \"feature\"), flags=TE)\n Feature(run=load(\"window_functions.tests.range_datetime\", \"feature\"), flags=TE)\n Feature(run=load(\"window_functions.tests.range_errors\", \"feature\"), flags=TE)","def feed_treat(self):\r\n # TODO\r\n pass","def feature(self, feature):\n\n self._feature = feature","def feature(self, feature):\n\n self._feature = feature","def findFeatures(self):\n\t\tpass","def features(self, features):\n\n self._features = features","def enter_feature(self, feature):\n print feature.what","def extract_features(self, *args, **kwargs):\n return self(*args, **kwargs)","def transform(self, *args, **kwargs):\n raise NotImplementedError","def _concatenate_features(features):\n pass","def pre_process(cls, *args, **kwargs):\n pass","def pre_process(cls, *args, **kwargs):\n pass","def pre_process(cls, *args, **kwargs):\n pass","def pre_process(cls, *args, **kwargs):\n pass","def preprocess_feature(df):","def add_extra_args(self):\n pass","def make_returnn_audio_features_func():\n return _extract","def __init__(self, features, plot_design, feature_description_class):\n super().__init__(features, plot_design, feature_description_class)","def support(self):","def featureprepare(self,df):\n try:\n df = self.featureselection(df)\n emp_len_dict= pickleload(self.dict_employ_len) # Load emp len\n df['emp_length'] = df['emp_length'].map(emp_len_dict)\n df['Long_emp_length'] = df['emp_length'].apply(lambda x: 'Yes' if x == 10 else 'No') # creating new feature\n df[\"emp_title\"].fillna('Missing', inplace=True)\n\n # Handling missing numerical value\n dict_Mean_var = pickleload(self.dict_Mean_var)\n for col, mean_val in dict_Mean_var.items():\n df[col].fillna(mean_val, inplace=True)\n\n # Handling rare values\n Freqlabels = pickleload(self.Freqlabels)\n for variable, frequent_labels in Freqlabels.items():\n df[variable] = np.where(df[variable].isin(frequent_labels), df[variable], 'Rare')\n\n # Encoding Categorical features\n x = pickleload(self.labelEncoder)\n for features, labels in x.items():\n df.loc[:, features] = labels.transform(df.loc[:, features])\n return df\n except Exception as e:\n self._Logger.error(\"Error in feature preparation: {}\".format(e))","def _apply_transform(self):\n pass","def pipeline(self):\n\n transformers = []\n\n custom = self.CustomFeature()\n #transformers.append(('custom', custom))\n n_features = int(self.n_features/2)\n\n #kbest = SelectKBest(score_func=chi2, k=n_features)\n #transformers.append(('kbest', kbest))\n\n # pca = PCA(n_components=n_features, svd_solver='randomized', whiten=True)\n # transformers.append(('pca', pca))\n\n if self.definer.problem_type == 'classification':\n extraTC = SelectFromModel(ExtraTreesClassifier(criterion='entropy'))\n else:\n extraTC = SelectFromModel(ExtraTreesRegressor())\n\n transformers.append(('extraTC', extraTC))\n\n #scaler = StandardScaler()\n #transformers.append(('scaler', scaler))\n #binarizer = Binarizer()\n return FeatureUnion(transformers)","def _hook(self):","def parse_features(self, skip=...):\n ...","def parse_features(self, skip=...):\n ...","def update(self) -> None:\n self.faithful = self.is_faithful()\n if self.faithful:\n old_class_names = self.class_names\n old_training_image_names = self.training_image_names\n self.class_names = self.find_class_names()\n self.training_image_names = self.find_training_image_names()\n self.extracted_features = list()\n if old_class_names != self.class_names or old_training_image_names != self.training_image_names:\n self.generate_csv_dictionary()\n return","def test_extra_column(self, example_dataset, expected_result):\n\n example_dataset = example_dataset.assign(extra=1)\n\n transformer = PreprocessFeatures()\n result = transformer.fit_transform(example_dataset)\n\n assert (result == expected_result).all()","def before_feature(context, feature):\n context.params_dict = {} # The input parameters\n context.types = {} # The datatype of each variable\n context.feature_name = context.feature.name.replace(\" \", \"_\").lower()\n context.dag_path = f\"dags/{context.feature_name}.dot\" # Where to save the causal DAG\n context.observational = False # Are we using observational data? Tag with @observational(\"path_to_csv_file.csv\") to make it so","def train(self):\n\t\traise NotImplementedError","def _transform_fn(features, mode):\n\t\tprint('Before feature transform_fn')\n\t\tfor k in features:\n\t\t\tprint(features[k].shape)\n\t\tcontext_features, example_features = feature_lib.encode_listwise_features(\n\t\t\t\tfeatures,\n\t\t\t\tinput_size=input_size,\n\t\t\t\tcontext_feature_columns=context_feature_columns,\n\t\t\t\texample_feature_columns=example_feature_columns,\n\t\t\t\tmode=mode)\n\t\tprint('After feature transform_fn')\n\t\tfor k in example_features:\n\t\t\tprint(k)\n\t\t\tprint(example_features[k].shape)\n\t\tfor k in context_features:\n\t\t\tprint(k)\n\t\t\tprint(context_features[k].shape)\n\t\treturn context_features, example_features","def _transform(self, dataset):\n raise NotImplementedError()","def __init__(self, **kwargs):\n FeatureDefinition.__init__(self)\n\n nbTypes = self._getTypeNumber(kwargs)\n \n print(\"BETTER FEATURES\")\n \n \n block_transformer = FeatureUnion( [ #CAREFUL IF YOU CHANGE THIS - see cleanTransformers method!!!!\n (\"xywh\", Pipeline([\n ('selector', NodeTransformerXYWH_v2()),\n #v1 ('xywh', StandardScaler(copy=False, with_mean=True, with_std=True)) #use in-place scaling\n ('xywh', QuantileTransformer(n_quantiles=self.n_QUANTILES, copy=False)) #use in-place scaling\n ])\n )\n , (\"neighbors\", Pipeline([\n ('selector', NodeTransformerNeighbors()),\n #v1 ('neighbors', StandardScaler(copy=False, with_mean=True, with_std=True)) #use in-place scaling\n ('neighbors', QuantileTransformer(n_quantiles=self.n_QUANTILES, copy=False)) #use in-place scaling\n ])\n )\n , (\"1hot\", Pipeline([\n ('1hot', Node1HotFeatures()) #does the 1-hot encoding directly\n ])\n )\n ])\n grid_line_transformer = GridLine_NodeTransformer_v2()\n \n self._node_transformer = TransformerListByType([block_transformer, grid_line_transformer]) \n \n edge_BB_transformer = FeatureUnion( [ #CAREFUL IF YOU CHANGE THIS - see cleanTransformers method!!!!\n (\"1hot\", Pipeline([\n ('1hot', Edge1HotFeatures(PageNumberSimpleSequenciality()))\n ])\n )\n , (\"boolean\", Pipeline([\n ('boolean', EdgeBooleanFeatures_v2())\n ])\n )\n , (\"numerical\", Pipeline([\n ('selector', EdgeNumericalSelector()),\n #v1 ('numerical', StandardScaler(copy=False, with_mean=True, with_std=True)) #use in-place scaling\n ('numerical', QuantileTransformer(n_quantiles=self.n_QUANTILES, copy=False)) #use in-place scaling\n ])\n )\n ] )\n edge_BL_transformer = DU_ABPTableRG.Block2GridLine_EdgeTransformer()\n edge_LL_transformer = DU_ABPTableRG.GridLine2GridLine_EdgeTransformer()\n self._edge_transformer = TransformerListByType([edge_BB_transformer,\n edge_BL_transformer,\n edge_BL_transformer, # useless but required\n edge_LL_transformer \n ])\n \n self.tfidfNodeTextVectorizer = None #tdifNodeTextVectorizer","def __init__(self, *args, **kwargs):\n AssembledFeatureReader.__init__(self, *args, **kwargs)\n self.extra_columns = kwargs.get(\"extra_columns\", 0)","def apply(self): # pragma: no cover\n raise NotImplementedError","def my_feature_xxx(df: pd.DataFrame):\n\n # CODE HERE\n\n return df","def run_preprocessing(self, serie):\n pass","def process(self):\n raise NotImplementedError('Method must be implemented by subclass.')","def __init__(self, features=None):\n self.features = features","def __setitem__(self, feature, value):\n setattr(self, feature, value)","def advanced_features(self, advanced_features):\n\n self._advanced_features = advanced_features","def extract(self):\r\n raise NotImplementedError()","def generate_feature(self):\n for cmd in self.get_filtered_cmd_names():\n self.command_info[cmd] = self.feature_cmd_params[cmd]","def preprocess(self):\n pass"],"string":"[\n \"def feature():\\n pass\",\n \"def feat():\\n pass\",\n \"def augment(self, *args, **kwargs):\\n pass\",\n \"def _data_augmentation(feature_dict):\\n image_features = feature_dict[_transformed_name(constants.IMAGE_KEY)]\\n image_features = _image_augmentation(image_features)\\n feature_dict[_transformed_name(constants.IMAGE_KEY)] = image_features\\n return feature_dict\",\n \"def _derived_features(self):\\n for created_feature, creator in self.feature_creators.items():\\n self.parameters[created_feature] = creator(self.parameters)\",\n \"def augment(self, image):\\n pass\",\n \"def process_sample_train(self):\\n raise NotImplementedError\",\n \"def add_feature(self, feat: Feature) -> None:\\n self.data_features.append(feat)\",\n \"def add_feature(self, feature):\\n self.features += [feature]\\n for stock in self.stocks:\\n feature(self.stock_data[stock])\",\n \"def __call__(self, *args, **kwargs):\\n self.features = dict((k, v()) for k, v in self.features.items())\\n return self.features\",\n \"def do_manipulations(self, *args, **kwargs):\\n pass\",\n \"def extract_features(self):\\n self.extract_features_static()\\n self.extract_features_dynamic()\",\n \"def extractFeatures(self, datum):\\n abstract\",\n \"def _preprocess(self, feature_dict):\\n return feature_dict\",\n \"def attach_feature(self, feature):\\r\\n\\r\\n # Filter out literally identical features\\r\\n if feature in self._features:\\r\\n return # the feature is already present\\r\\n\\r\\n # Filter out functionally identical features.\\r\\n # Features may use their on_attach method to raise\\r\\n # toolbox.AlreadyThere if they detect that some\\r\\n # installed feature does the same thing already\\r\\n attach = getattr(feature, 'on_attach', None)\\r\\n if attach is not None:\\r\\n try:\\r\\n attach(self)\\r\\n except toolbox.AlreadyThere:\\r\\n return\\r\\n self.execute_callbacks_times.setdefault(feature, 0)\\r\\n #it would be nice if we could require a specific class instead of\\r\\n #a \\\"workalike\\\" so we could do actual error checking\\r\\n #if not isinstance(feature, toolbox.Feature):\\r\\n # raise TypeError(\\\"Expected gof.toolbox.Feature instance, got \\\"+\\\\\\r\\n # str(type(feature)))\\r\\n\\r\\n # Add the feature\\r\\n self._features.append(feature)\",\n \"def extract_feature(self, article) :\\n pass\",\n \"def precompute(self, features, mode, params):\\n return None\",\n \"def updateAttributesAfterAdding(self):\\n layer = self.sender()\\n while self.addedFeatures:\\n featureId = self.addedFeatures.pop()\\n #begining the edit command\\n # layer.beginEditCommand(self.tr(\\\"DSG Tools reclassification tool: adjusting feature's attributes\\\"))\\n #accessing added features\\n editBuffer = layer.editBuffer()\\n features = editBuffer.addedFeatures()\\n for key in features.keys():\\n #just checking the newly added feature, the other I don't care\\n if key == featureId:\\n feature = features[key]\\n #setting the attributes using the reclassification dictionary\\n self.setFeatureAttributes(feature, editBuffer)\\n layer.endEditCommand()\",\n \"def _add_feature(self, feature):\\n\\n if feature.name in self.feature_name_index:\\n logger.info(\\\"Feature %s already exists at %i, overwriting\\\" %\\n (feature.name, self.feature_name_index[feature.name]))\\n self.features[self.feature_name_index[feature.name]] = feature\\n else:\\n self.features.append(feature)\\n self.feature_name_index[feature.name] = len(self.features) - 1\\n logger.info(\\\"Adding %s to model at location %i\\\" % (\\n feature.name, len(self.features)))\\n self._add_domain_fault_above(feature)\\n self._add_unconformity_above(feature)\\n feature.set_model(self)\",\n \"def features(self, state, action, next_state):\\n raise NotImplementedError\",\n \"def generateFeatures(self, data):\\n pass\",\n \"def feature_forward(self, x):\\n raise NotImplementedError\",\n \"def finalizeFeatures(self):\\n pass\",\n \"def fDataChanged(self):\\n\\n self._layerManager.getAimsFeatures()\",\n \"def add_features(self, obj, annotation):\\n if annotation['problem']:\\n obj.add(folia.Feature, subset='problem', cls=annotation['problem'])\\n if annotation['pos']:\\n obj.add(folia.Feature, subset='pos', cls=annotation['pos'])\",\n \"def processing(self):\\n pass\",\n \"def features_combine():\\n\\n\\n\\t# PROCESSING AUDIO\",\n \"def extract_features_dynamic(self):\\n self.feature_dynamic_imports()\\n self.feature_dynamic_filesystem()\\n self.feature_dynamic_network()\\n self.feature_dynamic_registry()\\n self.feature_dynamic_windowsapi()\",\n \"def input(self,feature):\\n \\n order = feature.getAttribute(ORDER)\\n if order == 1:\\n # Read the attributes from the CSV features into a dictionary\\n original_value = (feature.getAttribute(ORIGINAL_VALUE)).lower()\\n value_english=feature.getAttribute(REAL_VALUE_ENGLISH)\\n value_french=feature.getAttribute(REAL_VALUE_FRENCH)\\n code_value=feature.getAttribute(CODE_VALUE)\\n csv_row = CsvMetaDataValueMapper(original_value, value_english, value_french, code_value)\\n self.csv_features[original_value] = csv_row # Insert into the CSV dictionnary \\n else:\\n # Load the metadata features\\n self.meta_features.append(feature)\",\n \"def __process_element(data):\\n print('prosessing {}'.format(data))\\n x_i = data[0]\\n y_i = data[1]\\n\\n file_name = FeatureExtractor.get_file_name(x_i, feature_name)\\n try:\\n # try to load if file already exist\\n np.load(out_path / file_name, allow_pickle=True)\\n print('info: {} loaded from .npy !'.format(file_name))\\n new_labels.append([file_name, y_i])\\n except FileNotFoundError or OSError or EOFError:\\n # OSError and EOFError are raised if file are inconsistent\\n voice_activation = np.load(source_path / x_i, allow_pickle=True)\\n mean_voice_activation = FeatureExtractor.get_mean_voice_activation(voice_activation[0])\\n # this is kind-of standard\\n FeatureExtractor.save_feature([voice_activation[0], mean_voice_activation], feature_name, out_path, x_i,\\n y_i, new_labels)\",\n \"def process_feature(self, feature):\\n self.num_features += 1\\n\\n def create_int_feature(values):\\n feature = tf.train.Feature(\\n int64_list=tf.train.Int64List(value=list(values)))\\n return feature\\n\\n features = collections.OrderedDict()\\n features[\\\"unique_ids\\\"] = create_int_feature([feature.unique_id])\\n features[\\\"input_ids\\\"] = create_int_feature(feature.input_ids)\\n features[\\\"input_mask\\\"] = create_int_feature(feature.input_mask)\\n features[\\\"segment_ids\\\"] = create_int_feature(feature.segment_ids)\\n\\n if not self.is_test:\\n features[\\\"label\\\"] = create_int_feature([feature.label])\\n\\n tf_example = tf.train.Example(features=tf.train.Features(feature=features))\\n self._writer.write(tf_example.SerializeToString())\",\n \"def __init__(self):\\n \\n self.csv_features = {} # Create dictionary to load the CSV features\\n self.meta_features = [] # Create list to load the metadata features\",\n \"def make_processing_functions(self):\\n return\",\n \"def _training_before_hook(self):\\n pass\",\n \"def load_features(self, features):\\n pass\\n # self.features = features\",\n \"def Submodel_addProcessingCallback(*args):\\n return _libsbml.Submodel_addProcessingCallback(*args)\",\n \"def post_processor(self):\",\n \"def mutation(self, base_offsprings, model_features_count) :\",\n \"def add_dummy_feature(X, value=...):\\n ...\",\n \"def _extract_additional_features(\\n self, eopatch: EOPatch, images: Iterable[np.ndarray], shape: tuple[int, ...]\\n ) -> None:\\n additional_data = cast(List[FeatureRenameSpec], self.additional_data) # verified by `if` in _extract_data\\n for (ftype, _, new_name), band_info in zip(additional_data, self.requested_additional_bands):\\n tiffs = [tar[band_info.name + \\\".tif\\\"] for tar in images]\\n eopatch[ftype, new_name] = self._extract_array(tiffs, 0, shape, band_info.output_types[0])\",\n \"def _training_after_hook(self):\\n pass\",\n \"def impress(self):\\n raise NotImplementedError\",\n \"def __init__(self, features=None, **kwargs):\\n super(FeatureIO, self).__init__(**kwargs)\\n self.features = features\",\n \"def process(self):\\n raise NotImplementedError\",\n \"def preprocess(self):\",\n \"def _fusion(self, expert_outputs):\\n raise NotImplementedError\",\n \"def _transform_data(self, *args, **kwargs) -> None:\\n raise NotImplementedError\",\n \"def extract_feat(self, imgs):\\n pass\",\n \"def extract_feat(self, imgs):\\n pass\",\n \"def __feature_change(train_x, train_y, val_x, test_x, processors):\\n if processors:\\n for processor in processors:\\n train_x, val_x, test_x = processor(train_x, train_y, val_x, test_x)\\n return train_x, val_x, test_x\",\n \"def addProcessingCallback(*args):\\n return _libsbml.Submodel_addProcessingCallback(*args)\",\n \"def process(self, xaf):\\n raise NotImplementedError(\\\"process() method must be overriden\\\")\",\n \"def pre_process(self):\\n pass\",\n \"def pre_process(self):\\n pass\",\n \"def pre_process(self):\\n pass\",\n \"def pre_process(self):\\n pass\",\n \"def pre_process(self):\\n pass\",\n \"def process(self, data):\\n allocating = (self._output is None)\\n ind = 0\\n for i, (name, feature) in enumerate(self.features):\\n if allocating:\\n x = feature.compute(data)\\n self.feature_indices[name] = (ind, ind+x.size)\\n ind += x.size\\n\\n if self._output is None:\\n self._output = x\\n else:\\n self._output = np.hstack([self._output, x])\\n else:\\n self._output[self.feature_indices[name][0]:\\n self.feature_indices[name][1]] = \\\\\\n feature.compute(data)\\n\\n return self._output\",\n \"def feature(self):\\n Feature(run=default_frame, flags=TE)\\n Feature(run=load(\\\"window_functions.tests.rows_frame\\\", \\\"feature\\\"), flags=TE)\\n Feature(run=load(\\\"window_functions.tests.range_frame\\\", \\\"feature\\\"), flags=TE)\\n Feature(run=load(\\\"window_functions.tests.range_overflow\\\", \\\"feature\\\"), flags=TE)\\n Feature(run=load(\\\"window_functions.tests.range_datetime\\\", \\\"feature\\\"), flags=TE)\\n Feature(run=load(\\\"window_functions.tests.range_errors\\\", \\\"feature\\\"), flags=TE)\",\n \"def feed_treat(self):\\r\\n # TODO\\r\\n pass\",\n \"def feature(self, feature):\\n\\n self._feature = feature\",\n \"def feature(self, feature):\\n\\n self._feature = feature\",\n \"def findFeatures(self):\\n\\t\\tpass\",\n \"def features(self, features):\\n\\n self._features = features\",\n \"def enter_feature(self, feature):\\n print feature.what\",\n \"def extract_features(self, *args, **kwargs):\\n return self(*args, **kwargs)\",\n \"def transform(self, *args, **kwargs):\\n raise NotImplementedError\",\n \"def _concatenate_features(features):\\n pass\",\n \"def pre_process(cls, *args, **kwargs):\\n pass\",\n \"def pre_process(cls, *args, **kwargs):\\n pass\",\n \"def pre_process(cls, *args, **kwargs):\\n pass\",\n \"def pre_process(cls, *args, **kwargs):\\n pass\",\n \"def preprocess_feature(df):\",\n \"def add_extra_args(self):\\n pass\",\n \"def make_returnn_audio_features_func():\\n return _extract\",\n \"def __init__(self, features, plot_design, feature_description_class):\\n super().__init__(features, plot_design, feature_description_class)\",\n \"def support(self):\",\n \"def featureprepare(self,df):\\n try:\\n df = self.featureselection(df)\\n emp_len_dict= pickleload(self.dict_employ_len) # Load emp len\\n df['emp_length'] = df['emp_length'].map(emp_len_dict)\\n df['Long_emp_length'] = df['emp_length'].apply(lambda x: 'Yes' if x == 10 else 'No') # creating new feature\\n df[\\\"emp_title\\\"].fillna('Missing', inplace=True)\\n\\n # Handling missing numerical value\\n dict_Mean_var = pickleload(self.dict_Mean_var)\\n for col, mean_val in dict_Mean_var.items():\\n df[col].fillna(mean_val, inplace=True)\\n\\n # Handling rare values\\n Freqlabels = pickleload(self.Freqlabels)\\n for variable, frequent_labels in Freqlabels.items():\\n df[variable] = np.where(df[variable].isin(frequent_labels), df[variable], 'Rare')\\n\\n # Encoding Categorical features\\n x = pickleload(self.labelEncoder)\\n for features, labels in x.items():\\n df.loc[:, features] = labels.transform(df.loc[:, features])\\n return df\\n except Exception as e:\\n self._Logger.error(\\\"Error in feature preparation: {}\\\".format(e))\",\n \"def _apply_transform(self):\\n pass\",\n \"def pipeline(self):\\n\\n transformers = []\\n\\n custom = self.CustomFeature()\\n #transformers.append(('custom', custom))\\n n_features = int(self.n_features/2)\\n\\n #kbest = SelectKBest(score_func=chi2, k=n_features)\\n #transformers.append(('kbest', kbest))\\n\\n # pca = PCA(n_components=n_features, svd_solver='randomized', whiten=True)\\n # transformers.append(('pca', pca))\\n\\n if self.definer.problem_type == 'classification':\\n extraTC = SelectFromModel(ExtraTreesClassifier(criterion='entropy'))\\n else:\\n extraTC = SelectFromModel(ExtraTreesRegressor())\\n\\n transformers.append(('extraTC', extraTC))\\n\\n #scaler = StandardScaler()\\n #transformers.append(('scaler', scaler))\\n #binarizer = Binarizer()\\n return FeatureUnion(transformers)\",\n \"def _hook(self):\",\n \"def parse_features(self, skip=...):\\n ...\",\n \"def parse_features(self, skip=...):\\n ...\",\n \"def update(self) -> None:\\n self.faithful = self.is_faithful()\\n if self.faithful:\\n old_class_names = self.class_names\\n old_training_image_names = self.training_image_names\\n self.class_names = self.find_class_names()\\n self.training_image_names = self.find_training_image_names()\\n self.extracted_features = list()\\n if old_class_names != self.class_names or old_training_image_names != self.training_image_names:\\n self.generate_csv_dictionary()\\n return\",\n \"def test_extra_column(self, example_dataset, expected_result):\\n\\n example_dataset = example_dataset.assign(extra=1)\\n\\n transformer = PreprocessFeatures()\\n result = transformer.fit_transform(example_dataset)\\n\\n assert (result == expected_result).all()\",\n \"def before_feature(context, feature):\\n context.params_dict = {} # The input parameters\\n context.types = {} # The datatype of each variable\\n context.feature_name = context.feature.name.replace(\\\" \\\", \\\"_\\\").lower()\\n context.dag_path = f\\\"dags/{context.feature_name}.dot\\\" # Where to save the causal DAG\\n context.observational = False # Are we using observational data? Tag with @observational(\\\"path_to_csv_file.csv\\\") to make it so\",\n \"def train(self):\\n\\t\\traise NotImplementedError\",\n \"def _transform_fn(features, mode):\\n\\t\\tprint('Before feature transform_fn')\\n\\t\\tfor k in features:\\n\\t\\t\\tprint(features[k].shape)\\n\\t\\tcontext_features, example_features = feature_lib.encode_listwise_features(\\n\\t\\t\\t\\tfeatures,\\n\\t\\t\\t\\tinput_size=input_size,\\n\\t\\t\\t\\tcontext_feature_columns=context_feature_columns,\\n\\t\\t\\t\\texample_feature_columns=example_feature_columns,\\n\\t\\t\\t\\tmode=mode)\\n\\t\\tprint('After feature transform_fn')\\n\\t\\tfor k in example_features:\\n\\t\\t\\tprint(k)\\n\\t\\t\\tprint(example_features[k].shape)\\n\\t\\tfor k in context_features:\\n\\t\\t\\tprint(k)\\n\\t\\t\\tprint(context_features[k].shape)\\n\\t\\treturn context_features, example_features\",\n \"def _transform(self, dataset):\\n raise NotImplementedError()\",\n \"def __init__(self, **kwargs):\\n FeatureDefinition.__init__(self)\\n\\n nbTypes = self._getTypeNumber(kwargs)\\n \\n print(\\\"BETTER FEATURES\\\")\\n \\n \\n block_transformer = FeatureUnion( [ #CAREFUL IF YOU CHANGE THIS - see cleanTransformers method!!!!\\n (\\\"xywh\\\", Pipeline([\\n ('selector', NodeTransformerXYWH_v2()),\\n #v1 ('xywh', StandardScaler(copy=False, with_mean=True, with_std=True)) #use in-place scaling\\n ('xywh', QuantileTransformer(n_quantiles=self.n_QUANTILES, copy=False)) #use in-place scaling\\n ])\\n )\\n , (\\\"neighbors\\\", Pipeline([\\n ('selector', NodeTransformerNeighbors()),\\n #v1 ('neighbors', StandardScaler(copy=False, with_mean=True, with_std=True)) #use in-place scaling\\n ('neighbors', QuantileTransformer(n_quantiles=self.n_QUANTILES, copy=False)) #use in-place scaling\\n ])\\n )\\n , (\\\"1hot\\\", Pipeline([\\n ('1hot', Node1HotFeatures()) #does the 1-hot encoding directly\\n ])\\n )\\n ])\\n grid_line_transformer = GridLine_NodeTransformer_v2()\\n \\n self._node_transformer = TransformerListByType([block_transformer, grid_line_transformer]) \\n \\n edge_BB_transformer = FeatureUnion( [ #CAREFUL IF YOU CHANGE THIS - see cleanTransformers method!!!!\\n (\\\"1hot\\\", Pipeline([\\n ('1hot', Edge1HotFeatures(PageNumberSimpleSequenciality()))\\n ])\\n )\\n , (\\\"boolean\\\", Pipeline([\\n ('boolean', EdgeBooleanFeatures_v2())\\n ])\\n )\\n , (\\\"numerical\\\", Pipeline([\\n ('selector', EdgeNumericalSelector()),\\n #v1 ('numerical', StandardScaler(copy=False, with_mean=True, with_std=True)) #use in-place scaling\\n ('numerical', QuantileTransformer(n_quantiles=self.n_QUANTILES, copy=False)) #use in-place scaling\\n ])\\n )\\n ] )\\n edge_BL_transformer = DU_ABPTableRG.Block2GridLine_EdgeTransformer()\\n edge_LL_transformer = DU_ABPTableRG.GridLine2GridLine_EdgeTransformer()\\n self._edge_transformer = TransformerListByType([edge_BB_transformer,\\n edge_BL_transformer,\\n edge_BL_transformer, # useless but required\\n edge_LL_transformer \\n ])\\n \\n self.tfidfNodeTextVectorizer = None #tdifNodeTextVectorizer\",\n \"def __init__(self, *args, **kwargs):\\n AssembledFeatureReader.__init__(self, *args, **kwargs)\\n self.extra_columns = kwargs.get(\\\"extra_columns\\\", 0)\",\n \"def apply(self): # pragma: no cover\\n raise NotImplementedError\",\n \"def my_feature_xxx(df: pd.DataFrame):\\n\\n # CODE HERE\\n\\n return df\",\n \"def run_preprocessing(self, serie):\\n pass\",\n \"def process(self):\\n raise NotImplementedError('Method must be implemented by subclass.')\",\n \"def __init__(self, features=None):\\n self.features = features\",\n \"def __setitem__(self, feature, value):\\n setattr(self, feature, value)\",\n \"def advanced_features(self, advanced_features):\\n\\n self._advanced_features = advanced_features\",\n \"def extract(self):\\r\\n raise NotImplementedError()\",\n \"def generate_feature(self):\\n for cmd in self.get_filtered_cmd_names():\\n self.command_info[cmd] = self.feature_cmd_params[cmd]\",\n \"def preprocess(self):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67851365","0.6742683","0.6596476","0.64233947","0.6299479","0.62270296","0.61860555","0.61621183","0.6025776","0.6020395","0.5981607","0.59511673","0.5922805","0.5918472","0.59114283","0.5909907","0.59008974","0.5896712","0.5865747","0.58578086","0.5854944","0.58338207","0.5808781","0.5779085","0.5778527","0.57753545","0.57579166","0.57504785","0.575019","0.5749484","0.57453036","0.5729404","0.5725336","0.57232916","0.5718009","0.56938124","0.5681277","0.5679236","0.567635","0.5668795","0.5666721","0.5663377","0.56556916","0.56437826","0.56420803","0.5633447","0.56050533","0.5594198","0.5594198","0.55827373","0.5576853","0.557182","0.55708325","0.55708325","0.55708325","0.55708325","0.55708325","0.5557622","0.5556402","0.553844","0.5536175","0.5536175","0.5534603","0.5531971","0.5528181","0.55273455","0.5521137","0.5511974","0.551197","0.551197","0.551197","0.551197","0.55040044","0.55013937","0.5501333","0.54998887","0.5497743","0.5489866","0.54773134","0.54733187","0.54727244","0.54707026","0.54707026","0.54627705","0.54598063","0.5458083","0.5446779","0.5438408","0.54350406","0.5426382","0.54058975","0.53931975","0.5392985","0.5392325","0.53885597","0.5388469","0.53872854","0.5385837","0.53851","0.53811985","0.53801644"],"string":"[\n \"0.67851365\",\n \"0.6742683\",\n \"0.6596476\",\n \"0.64233947\",\n \"0.6299479\",\n \"0.62270296\",\n \"0.61860555\",\n \"0.61621183\",\n \"0.6025776\",\n \"0.6020395\",\n \"0.5981607\",\n \"0.59511673\",\n \"0.5922805\",\n \"0.5918472\",\n \"0.59114283\",\n \"0.5909907\",\n \"0.59008974\",\n \"0.5896712\",\n \"0.5865747\",\n \"0.58578086\",\n \"0.5854944\",\n \"0.58338207\",\n \"0.5808781\",\n \"0.5779085\",\n \"0.5778527\",\n \"0.57753545\",\n \"0.57579166\",\n \"0.57504785\",\n \"0.575019\",\n \"0.5749484\",\n \"0.57453036\",\n \"0.5729404\",\n \"0.5725336\",\n \"0.57232916\",\n \"0.5718009\",\n \"0.56938124\",\n \"0.5681277\",\n \"0.5679236\",\n \"0.567635\",\n \"0.5668795\",\n \"0.5666721\",\n \"0.5663377\",\n \"0.56556916\",\n \"0.56437826\",\n \"0.56420803\",\n \"0.5633447\",\n \"0.56050533\",\n \"0.5594198\",\n \"0.5594198\",\n \"0.55827373\",\n \"0.5576853\",\n \"0.557182\",\n \"0.55708325\",\n \"0.55708325\",\n \"0.55708325\",\n \"0.55708325\",\n \"0.55708325\",\n \"0.5557622\",\n \"0.5556402\",\n \"0.553844\",\n \"0.5536175\",\n \"0.5536175\",\n \"0.5534603\",\n \"0.5531971\",\n \"0.5528181\",\n \"0.55273455\",\n \"0.5521137\",\n \"0.5511974\",\n \"0.551197\",\n \"0.551197\",\n \"0.551197\",\n \"0.551197\",\n \"0.55040044\",\n \"0.55013937\",\n \"0.5501333\",\n \"0.54998887\",\n \"0.5497743\",\n \"0.5489866\",\n \"0.54773134\",\n \"0.54733187\",\n \"0.54727244\",\n \"0.54707026\",\n \"0.54707026\",\n \"0.54627705\",\n \"0.54598063\",\n \"0.5458083\",\n \"0.5446779\",\n \"0.5438408\",\n \"0.54350406\",\n \"0.5426382\",\n \"0.54058975\",\n \"0.53931975\",\n \"0.5392985\",\n \"0.5392325\",\n \"0.53885597\",\n \"0.5388469\",\n \"0.53872854\",\n \"0.5385837\",\n \"0.53851\",\n \"0.53811985\",\n \"0.53801644\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":85596,"cells":{"query":{"kind":"string","value":"Convert an array of export statements (what we get from envextra in the config) into a dict"},"document":{"kind":"string","value":"def env_lines_to_dict(self, env_lines):\n env_dict = {}\n for env_line in env_lines:\n split_env_line = shlex.split(env_line)\n if split_env_line[0] == \"export\":\n split_env_line = split_env_line[1:]\n for item in split_env_line:\n if \"=\" in item:\n k, v = item.split(\"=\", 1)\n env_dict[k] = v\n return env_dict"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def export_configurations():\n pass","def export(self) -> Dict[str, Any]:\n return {\n \"name\": self.name,\n \"channels\": [channel for channel in self.channels],\n \"packages\": self.packages.export(),\n \"logs\": [log for log in self.logs],\n \"actions\": [action for action in self.actions],\n \"debug\": [debug for debug in self.debug],\n }","def get_command_env(self) -> Sequence[Mapping[str, str]]:\n return [\n {\"name\": \"DAGSTER_COMPRESSED_EXECUTE_STEP_ARGS\", \"value\": self._get_compressed_args()},\n ]","def exports():","def exporters():\n return dict(_exporters)","def env_dict(self): # type: () -> t.Dict[str, str]\n return dict((item[0], item[1]) for item in [e.split('=', 1) for e in self.env])","def _extract_commands():\n module_names = [\n module.name for module in pkgutil.iter_modules(projectstarter.commands.__path__)\n ]\n commands = {}\n for module_name in module_names:\n module = importlib.import_module(f\"projectstarter.commands.{module_name}\")\n if module.__doc__ is not None:\n commands[module_name] = {\n \"description\": module.__doc__.strip().lower(),\n \"func_parse\": module.__getattribute__(\"parse\"),\n \"func_run\": module.__getattribute__(\"run\"),\n }\n return commands","def _get_export_dir_dict(self):\n export_dir = self.gluster_manager.get_gluster_vol_option(\n NFS_EXPORT_DIR)\n edh = {}\n if export_dir:\n # see\n # https://github.com/gluster/glusterfs\n # /blob/aa19909/xlators/nfs/server/src/nfs.c#L1582\n # regarding the format of nfs.export-dir\n edl = export_dir.split(',')\n # parsing export_dir into a dict of {dir: [hostpec,..]..}\n # format\n r = re.compile('\\A/(.*)\\((.*)\\)\\Z')\n for ed in edl:\n d, e = r.match(ed).groups()\n edh[d] = e.split('|')\n return edh","def _all_env_vars_to_dict(self):\n all_env_vars = {}\n for k, v in self.env.items():\n c_vars = {}\n for x, y in v['vars'].items():\n c_vars[f'{k}_{x}'] = y['value']\n all_env_vars = {**all_env_vars, **c_vars}\n return all_env_vars","def export_vars(env_vars_dict):\n env_vars = ['{}={}'.format(k, env_vars_dict[k])\n for k in env_vars_dict.keys()]\n with open(\"vars.env\", \"w\") as file:\n for item in env_vars[:-1]:\n file.write(\"{}\\n\".format(item))\n file.write(\"{}\".format(env_vars[-1]))","def _get_env(self):\n env = {}\n for k, v in os.environ.items():\n k = k.decode() if isinstance(k, bytes) else k\n v = v.decode() if isinstance(v, bytes) else v\n env[k] = v\n return list(env.items())","def export(self) -> dict:\n output = {}\n for source, packages in self.items():\n source_packages = {}\n for name, info in packages.items():\n if info.spec_is_custom():\n source_packages[name] = info.spec\n else:\n version = info.get_version_from_spec()\n if version:\n source_packages[name] = version\n else:\n source_packages[name] = \"*\"\n if source_packages:\n output[source] = source_packages\n return output","def environments(self):\n envs = self.config[\"tox\"][\"envlist\"]\n #result = re.split(\"[^a-zA-Z0-9]\", envs)\n result = re.split(r'\\n| ,|,', envs)\n #print ([string for string in result if string != \"\"])\n result = (([string.strip() for string in result if string != \"\"]))\n print(list(dict.fromkeys(result)))\n return ((list(dict.fromkeys(result))))","def export_str(self):\n return \"\\n\".join(\"export %s=%s\" % (k, v) for k, v in self.items())","def env_dicts(self, package_name):\n ret = {}\n ret_multi = {}\n # First process the global variables\n for package, pairs in self._sorted_data:\n for name, value in pairs.items():\n if package is None:\n if isinstance(value, list):\n ret_multi[name] = value\n else:\n ret[name] = value\n\n # Then the package scoped vars, that will override the globals\n for package, pairs in self._sorted_data:\n for name, value in pairs.items():\n if package == package_name:\n if isinstance(value, list):\n ret_multi[name] = value\n if name in ret: # Already exists a global variable, remove it\n del ret[name]\n else:\n ret[name] = value\n if name in ret_multi: # Already exists a list global variable, remove it\n del ret_multi[name]\n return ret, ret_multi","def _get_output_dictionary(self):\n\n return_dictionary = {}\n\n for output_path in self.provided_outputs:\n return_dictionary[output_path.full_path] = self.get_value(output_path)\n\n return return_dictionary","def envs():\n\n # update and grab the envs from the metadata keys\n metadata = _init()\n return list(metadata.keys())","def get_prop_list(self):\n import_list = []\n if os.path.exists(os.path.join(EXPORT_DIR, \"ENV\")):\n for i in os.listdir(os.path.join(EXPORT_DIR, \"ENV\")):\n import_list.append(os.path.join(EXPORT_DIR, \"ENV\", i))\n return import_list\n else:\n gui.MessageDialog(\n \"Could Not find Exported File from Daz Studio\",\n type=c4d.GEMB_ICONEXCLAMATION,\n )","def _env(env):\n return dict((key.strip(), val)\n for line in env.strip().splitlines()\n for key, _, val in [line.partition('=')])","def send_env(self, keys):\n if keys is None:\n return self.env\n return dict((key, self.env.get(key, '')) for key in keys)","def build_ac_export_infos(self):\n if not self.configure_ac_info:\n return\n export_variables = self.configure_ac_info[\"configure_ac\"][\"export_variables\"]\n variables = self.configure_ac_info[\"configure_ac\"][\"variables\"]\n for export_var in export_variables:\n src = variables.get(export_var, dict())\n dest = export_variables.get(export_var, dict())\n if len(src) == 0:\n continue\n\n if len(dest.get(\"defined\", list())) != 0 or len(dest.get(\"undefined\", list())) != 0:\n continue\n\n export_variables[export_var] = src\n\n for preset_export_var in preset_output_variables:\n if preset_export_var in export_variables:\n continue\n\n src = variables.get(preset_export_var, dict())\n if len(src) == 0:\n continue\n\n export_variables[preset_export_var] = src","def parse_st_cmd_env(st_cmd_filepath):\n re_env = re.compile(\"^epicsEnvSet[ \\t\\\"\\(]*([\\w]+)[, \\t\\\"\\)]*([^\\\"\\n]*)\")\n env = {}\n with open(st_cmd_filepath, \"r\") as f:\n for line in f:\n match = re_env.search(line)\n if match:\n env[match.group(1)] = match.group(2)\n return env","def getSolutionExportVariableNames(cls):\n return {}","def _generate_environment(self):\n envvars = {}\n for key in self.envvars:\n try:\n envvars[key] = os.environ[key]\n except KeyError:\n continue\n\n # Warn the user that we cannot support secrets\n if envvars:\n logger.warning(\"This API does not support environment secrets.\")\n return envvars","def all_envs():\n return all_tasks.keys()","def wrapper_environment(args):\n\n return {\n ENVIRONMENT_KEY: json.dumps({\n 'verbose': args.verbose,\n 'cc': shlex.split(args.cc),\n 'cxx': shlex.split(args.cxx)\n })\n }","def environ_expansions():\r\n global _environ_expansions\r\n\r\n if _environ_expansions:\r\n return _environ_expansions\r\n\r\n _environ_expansions = {}\r\n for key, value in os.environ.items():\r\n _environ_expansions['ENV_%s' % key] = value\r\n\r\n return _environ_expansions","def _create_extra_environment(self):\n return {}","def show_env():\n envs = [\"PATH\", \"ORACLE_HOME\", \"TNS_ADMIN\", \"NLS_LANG\"]\n result = {}\n for env in envs:\n if env in os.environ:\n result[env] = os.environ[env]\n return result","def _get_interpreter_values(token='ZEPPELIN_INTERPRETER') -> dict:\n env_variables = {}\n for env_variable in os.environ:\n if env_variable.startswith(token):\n env_variables[env_variable] = os.environ[env_variable]\n\n LOG.info('%s-based token environment variables: %s', token, env_variables)\n\n return env_variables","def create_export_policy():\n config = {\n \"export\": {\n \"tenant\": \"intersite-testsuite\",\n \"app\": \"app\",\n \"epg\": \"epg\",\n \"remote_epg\": \"intersite-testsuite-app-epg\",\n \"remote_sites\": [\n {\n \"site\": {\n \"name\": \"Site2\",\n \"interfaces\": [\n {\n \"l3out\": {\n \"name\": \"l3out\",\n \"tenant\": \"intersite-testsuite\"\n }\n }\n ]\n }\n }\n ]\n }\n }\n return config","def env_vars(self):\n if self._m.spec and self._m.spec.container:\n return k8s_object.ListAsDictionaryWrapper(\n self._m.spec.container.env, self._messages.EnvVar)","def load_env(env_files):\n env = {}\n for env_file in env_files:\n with open(env_file) as f:\n for line in f:\n if line and line[0] != \"#\":\n try:\n index = line.index(\"=\")\n env[line[:index].strip()] = line[index + 1 :].strip()\n except ValueError:\n # Ignore lines that don't have a '='\n pass\n return env","def as_invocation_environment_dict(self):\n lib_env_var = self._platform.resolve_platform_specific({\n 'darwin': lambda: 'DYLD_LIBRARY_PATH',\n 'linux': lambda: 'LD_LIBRARY_PATH',\n })\n return {\n 'PATH': create_path_env_var(self.path_entries),\n lib_env_var: create_path_env_var(self.library_dirs),\n }","def find_environ_config_vars():\n # only allow secret key and database uri for now\n envvars = [\"SQLALCHEMY_DATABASE_URI\", \"SECRET_KEY\"]\n results = {}\n for key in envvars:\n if key in os.environ:\n results[key] = os.environ[key]\n return results","def export(command):\n if len(command) == 1:\n return 0\n for var in command[1:]:\n try:\n var = var.split('=')\n environ[var[0]] = var[1]\n except IndexError:\n environ[var[0]] = \"\"\n return 0","def dump():\n import pprint\n import json\n\n notice('dumping env...')\n pprint.pprint(env)\n\n if _config:\n notice('dumping config...')\n print _config","def export_inference(modules):\n result = {}\n for module in modules:\n result[module.module_type] = json.loads(module.export())\n return json.dumps(result)","def to_dict(self, exclusions: list = []) -> dict:\n step_details = {name: step.to_dict() for name, step in self.run.items()}\n order = {str(i_step): name for i_step, name in enumerate(self.run.keys())}\n return dict(order=order, details=step_details)","def get_export_options(options):\n export_options = {\n key: value\n for (key, value) in iteritems(options)\n if key in Export.EXPORT_OPTION_FIELDS\n }\n\n return export_options","def parse(env, platform_name=None):\n\n platform_name = platform_name or PLATFORM\n\n result = {}\n for variable, value in env.items():\n\n # Platform specific values\n if isinstance(value, dict):\n value = value.get(platform_name, \"\")\n\n if not value:\n continue\n\n # Allow to have lists as values in the tool data\n if isinstance(value, (list, tuple)):\n value = \";\".join(value)\n\n result[variable] = value\n\n return result","def describe_cost_management_exports(self):\n return [{\"name\": self.export_name, \"container\": self.container, \"directory\": self.directory}]","def get_environ_dict():\n return {\n 'os.environ': _get_os_environ_dict((\n 'AUTH_DOMAIN',\n 'CURRENT_CONFIGURATION_VERSION',\n 'CURRENT_MODULE_ID',\n 'CURRENT_VERSION_ID',\n 'DEFAULT_VERSION_HOSTNAME',\n 'FEDERATED_IDENTITY',\n 'FEDERATED_PROVIDER',\n 'GAE_LOCAL_VM_RUNTIME',\n 'HTTP_HOST',\n 'HTTP_PROXY',\n 'HTTP_X_APPENGINE_HTTPS',\n 'HTTP_X_APPENGINE_QUEUENAME',\n 'HTTP_X_ORIGINAL_HOST',\n 'HTTP_X_ORIGINAL_SCHEME',\n 'SERVER_NAME',\n 'SERVER_PORT',\n 'SERVER_SOFTWARE',\n 'USER_IS_ADMIN',\n )),\n 'app_identity': _get_app_identity_dict((\n 'get_service_account_name',\n 'get_application_id',\n 'get_default_version_hostname',\n )),\n 'modules': _get_modules_dict((\n 'get_current_module_name',\n 'get_current_version_name',\n 'get_current_instance_id',\n 'get_modules',\n 'get_versions',\n 'get_default_version',\n 'get_hostname',\n )),\n 'namespace_manager': _get_namespace_manager_dict((\n 'get_namespace',\n 'google_apps_namespace',\n )),\n }","def serialize(self):\n return dict(env_name=self.name, type=self.type, env_kwargs=deepcopy(self._init_kwargs))","def get_execution_envs_str(self):\n\n return ' '.join(str(x) for x in self.execution_envs)","def export(self, **env):\n with self.lock:\n for key, value in env.items():\n self.environment[key] = value","def _setenv(self):\n tokens = {}\n tokens[\"CT_TIMESTAMP\"] = self._timestamp\n tokens[\"CT_SUBMITTER\"] = self._node.name()\n # tokens[\"CT_HIPBASE\"] = self._file[\"hipbase\"]\n tokens[\"CT_SCENE\"] = self._scene\n tokens[\"CT_PROJECT\"] = self.project_name\n\n for token in tokens:\n hou.putenv(token, tokens[token])\n\n return tokens","def get_commands_dict() -> dict:\n commands_dict = {}\n f = open(f\"data/metadata/commands.dict.txt\", \"r\", encoding=\"utf-8\").read()\n for command in f.split(\"\\n\"):\n commands_dict[command.split(\":\")[0]] = command.split(\":\")[1]\n return commands_dict","def get_environment_map(self):\n return {'oxmeta': self.env}","def get_outputs():\n outputs = {}\n for obj in vars(acsploit.output).values():\n if hasattr(obj, 'OUTPUT_NAME'):\n outputs[obj.OUTPUT_NAME] = obj\n\n return outputs","def get_export_prompts(obj_bp):\n \n prompts = {}\n \n def add_prompt(prompt):\n if prompt.Type == 'NUMBER':\n prompts[prompt.name] = str(prompt.NumberValue)\n if prompt.Type == 'QUANTITY':\n prompts[prompt.name] = str(prompt.QuantityValue)\n if prompt.Type == 'COMBOBOX':\n prompts[prompt.name] = str(prompt.COL_EnumItem[prompt.EnumIndex].name)\n if prompt.Type == 'CHECKBOX':\n prompts[prompt.name] = str(prompt.CheckBoxValue)\n if prompt.Type == 'TEXT':\n prompts[prompt.name] = str(prompt.TextValue)\n if prompt.Type == 'DISTANCE':\n prompts[prompt.name] = str(round(unit.meter_to_active_unit(prompt.DistanceValue),2))\n if prompt.Type == 'ANGLE':\n prompts[prompt.name] = str(prompt.AngleValue)\n if prompt.Type == 'PERCENTAGE':\n prompts[prompt.name] = str(prompt.PercentageValue)\n if prompt.Type == 'PRICE':\n prompts[prompt.name] = str(prompt.PriceValue)\n \n def add_child_prompts(obj):\n for child in obj.children:\n if child.mv.type == 'BPASSEMBLY':\n add_prompts(child)\n if len(child.children) > 0:\n add_child_prompts(child)\n \n def add_prompts(obj):\n for prompt in obj.mv.PromptPage.COL_Prompt:\n if prompt.export:\n add_prompt(prompt)\n \n add_prompts(obj_bp)\n add_child_prompts(obj_bp)\n\n return prompts","def prepare_env_for_all_codes(i):\n\n # Check vars\n if 'code_deps' not in i: return {'cm_return':1, 'cm_error':'\"code_deps\" is not defined in \"code prepare_env_for_all_codes\"'}\n\n include_paths=[]\n lib_paths=[]\n\n # Load OS\n os_uoa=''\n if 'os_uoa' in i and i['os_uoa']!='': os_uoa=i['os_uoa']\n elif 'cm_default_os_uoa' in cm_kernel.ini['dcfg'] and cm_kernel.ini['dcfg']['cm_default_os_uoa']!='':\n os_uoa=cm_kernel.ini['dcfg']['cm_default_os_uoa']\n\n if os_uoa=='' not in i:\n return {'cm_return':1, 'cm_error':'\"os_uoa\" is not defined and not in kernel'}\n\n ii={'cm_run_module_uoa':ini['cfg']['cm_modules']['os'],\n 'cm_action':'load',\n 'cm_data_uoa':os_uoa}\n r=cm_kernel.access(ii)\n if r['cm_return']>0: return r\n\n os_cfg=r['cm_data_obj']['cfg']\n os_path=r['cm_path']\n os_uid=r['cm_uid']\n os_alias=r['cm_alias']\n\n s_code_deps=''\n a_code_deps=[]\n if 'code_deps' in i:\n for xx in i['code_deps']:\n yy=xx.keys()[0]\n x=xx[yy]\n\n if x=='':\n return {'cm_return':1, 'cm_error':'dependency \"'+yy+'\" is empty, please check your input'}\n\n # Check if code was installed\n if i.get('no_strict_check','')!='yes':\n ii={'cm_run_module_uoa':ini['cm_module_uid'],\n 'cm_action':'load',\n 'cm_data_uoa':x}\n r=cm_kernel.access(ii)\n if r['cm_return']==16:\n return {'cm_return':1, 'cm_error':'dependency is not resolved - code '+x+' ('+yy+') is not installed'}\n elif r['cm_return']>0: return r\n code_cfg=r['cm_data_obj']['cfg']\n if code_cfg.get('build_finished_successfully','')!='yes':\n return {'cm_return':1, 'cm_error':'dependency is not resolved - code '+x+' ('+yy+') is not installed'}\n\n code_path=r['cm_path']\n include_paths.append(os.path.join(code_path, 'include'))\n\n if 'state_input' in code_cfg and \\\n 'run_set_env2' in code_cfg['state_input'] and \\\n 'CM_TARGET_FILE' in code_cfg['state_input']['run_set_env2']:\n lib_paths.append(os.path.join(code_path, os_cfg['lib_dir'], \n code_cfg['state_input']['run_set_env2']['CM_TARGET_FILE']))\n\n # Environment script\n r=get_env({'cm_data_uoa':x, 'os_uoa':os_uoa})\n if r['cm_return']>0: return r\n\n# z=os_cfg['env_call']+' '+os.path.join(cm_kernel.ini[cm_kernel.env_cm_bin],r['cm_string'])\n z1=os_cfg['env_set']+' '+yy+'='+os_cfg['env_quotes']+x+os_cfg['env_quotes']\n z=os_cfg['env_call']+' '+r['cm_string']\n\n if s_code_deps!='': s_code_deps+=' '+os_cfg['env_separator']+' '\n s_code_deps+=z1\n if s_code_deps!='': s_code_deps+=' '+os_cfg['env_separator']+' '\n s_code_deps+=z\n # FGG added again setting environment variable since calling other scripts can change it\n # for example, we set CM_CODE_DEP_COMPILER and then call GMP that was compiled with another\n # compiler, then it will change this variable to a wrong value and further tools will \n # not be working correctly ...\n if s_code_deps!='': s_code_deps+=' '+os_cfg['env_separator']+' '\n s_code_deps+=z1\n\n a_code_deps.append(z1)\n a_code_deps.append(z)\n a_code_deps.append(z1)\n\n return {'cm_return':0, 'cm_string':s_code_deps, 'cm_array':a_code_deps, 'env_separator': os_cfg['env_separator'],\n 'include_paths':include_paths, 'lib_paths':lib_paths}","def _iter_commands(self):\n return {entry_point.name: entry_point for entry_point in\n pkg_resources.iter_entry_points('chanjo.subcommands')}","def export(myenv):\n for item in myenv.split(\",\"):\n if item:\n try:\n key, val = item.split(\"=\")\n os.environ[key] = val\n # Variables to be deleted will not split on '=', throwing ValueError\n except ValueError:\n try:\n del os.environ[item]\n # If a NoneType sneaks through, it will throw TypeEror on 'del'\n except TypeError:\n continue","def _get_os_environ_dict(keys):\n return {k: os.environ.get(k, _UNDEFINED) for k in keys}","def gyp_defines():\n return dict(arg.split('=', 1)\n for arg in shlex.split(os.environ.get('GYP_DEFINES', '')))","def to_env(self, d):\n return {k.upper(): str(v) for k, v in d.items()}","def get_environment_vars():\n return {env: os.environ[env] for env in\n params.ENV_DIRS if env in os.environ}","def _prepend_env_paths(content, names):\n export_env_vars = ['export %(k)s=%(v)s:${%(k)s}' %dict(\n k=name, v=os.environ.get(name, '')) for name in names]\n return '\\n'.join(export_env_vars + [content])","def _get_config_dict():\r\n return CONFIGS","def export_to_versionable_dict(exploration):\n return {\n 'param_changes': exploration.param_change_dicts,\n 'param_specs': exploration.param_specs_dict,\n 'states': [export_state_internals_to_dict(\n exploration.id, state_id, human_readable_dests=True)\n for state_id in exploration.state_ids]\n }","def _prepare_publish_environments():\n env = copy.deepcopy(os.environ)\n\n project_name = os.getenv(\"AVALON_PROJECT\")\n asset_name = os.getenv(\"AVALON_ASSET\")\n\n env[\"AVALON_PROJECT\"] = project_name\n env[\"AVALON_ASSET\"] = asset_name\n env[\"AVALON_TASK\"] = os.getenv(\"AVALON_TASK\")\n env[\"AVALON_WORKDIR\"] = os.getenv(\"AVALON_WORKDIR\")\n env[\"AVALON_APP\"] = f\"hosts.{publish_host}\"\n env[\"AVALON_APP_NAME\"] = \"celaction/local\"\n\n env[\"PYBLISH_HOSTS\"] = publish_host\n\n os.environ.update(env)","def _build_env(self, args):\n env = os.environ.copy()\n env['ANSIBLE_CONFIG'] = os.path.join(ANSIBLE_DIR, 'ansible.cfg')\n if hasattr(args, 'stdout_callback'):\n env['ANSIBLE_STDOUT_CALLBACK'] = args.stdout_callback\n return env","def read_environ():\n\n environ = {}\n environ['BASEDIR'] = os.environ['TS_BASEDIR']\n environ['CONFIG_NL'] = os.environ['TS_CONFIG_NL']\n environ['NL_TS_SWITCH'] = os.environ['TS_NL_TS_SWITCH']\n environ['DT_FILE'] = os.environ['TS_DT_FILE']\n environ['REFOUTDIR'] = os.environ['TS_REFOUTDIR']\n environ['VERBOSE'] = os.environ['TS_VERBOSE']\n environ['RUNDIR'] = os.environ['TS_RUNDIR']\n environ['LOGFILE'] = os.environ['TS_LOGFILE']\n environ['NAMELISTDIR'] = os.environ['TS_NAMELISTDIR']\n environ['TOLERANCE'] = os.environ['TS_TOLERANCE']\n environ['FORCEMATCH'] = os.environ['TS_FORCEMATCH']\n environ['TUNING_ITERATIONS'] = os.environ['TS_TUNING_ITERATIONS']\n environ['TUNE_THRESHOLDS'] = os.environ['TS_TUNE_THRESHOLDS']\n environ['RESET_THRESHOLDS'] = os.environ['TS_RESET_THRESHOLDS']\n environ['ICON'] = os.environ['TS_ICON']\n environ['YUFILE'] = os.environ['TS_YUFILE']\n return environ","def env_tokens(self):\r\n\r\n # Find the env JSON file\r\n if self.SERVICE_VARIANT:\r\n env_path = self.REPO_ROOT.parent / \"{service}.env.json\".format(service=self.SERVICE_VARIANT)\r\n else:\r\n env_path = path(\"env.json\").abspath()\r\n\r\n # If the file does not exist, here or one level up,\r\n # issue a warning and return an empty dict\r\n if not env_path.isfile():\r\n env_path = env_path.parent.parent / env_path.basename()\r\n if not env_path.isfile():\r\n print(\r\n \"Warning: could not find environment JSON file \"\r\n \"at '{path}'\".format(path=env_path),\r\n file=sys.stderr,\r\n )\r\n return dict()\r\n\r\n # Otherwise, load the file as JSON and return the resulting dict\r\n try:\r\n with open(env_path) as env_file:\r\n return json.load(env_file)\r\n\r\n except ValueError:\r\n print(\r\n \"Error: Could not parse JSON \"\r\n \"in {path}\".format(path=env_path),\r\n file=sys.stderr,\r\n )\r\n sys.exit(1)","def environ_to_dict(environ):\n return dict((k, v) for k, v in environ.iteritems()\n if k.isupper())","def get_registered_analysis_targets() -> Dict[str, Any]:\n result: Dict[str, Any] = {}\n for groupindex in config.targets:\n result[groupindex] = {}\n result[groupindex][\"path\"] = os.path.dirname(config.targets[groupindex])\n result[groupindex][\"projects\"] = get_analysis_target_index(groupindex)\n return result","def get_procedures(self) -> dict:\n return json.loads(json.dumps(Config.get_procedures(), cls=Message.Encoder))","def modules():\n cmd = \"{} -M\".format(_detect_os())\n ret = {}\n ret[\"static\"] = []\n ret[\"shared\"] = []\n out = __salt__[\"cmd.run\"](cmd).splitlines()\n for line in out:\n comps = line.split()\n if not comps:\n continue\n if \"(static)\" in line:\n ret[\"static\"].append(comps[0])\n if \"(shared)\" in line:\n ret[\"shared\"].append(comps[0])\n return ret","def __init__(self, export_tuples=None):\n self._exports = export_tuples if export_tuples else []","def args_map_custom(cls) -> dict:\n args = {}\n args.update(cls.args_map_export())\n args.update({\"json_flat\": False})\n return args","def get_imports(self) -> str:\n\n imports = [\n \"_ = require('lodash');\",\n \"stream = require('stream');\",\n f\"const hookRegister = require('./{settings.ARTILLERY_HOOK_FILE}').hookRegister;\",\n f\"hook = require('./{settings.ARTILLERY_LIB_FOLDER}/hooks').hook;\",\n f\"utils = require('./{settings.ARTILLERY_LIB_FOLDER}/providers');\",\n f\"settings = require('./{settings.ARTILLERY_LIB_FOLDER}/settings');\",\n f\"StatsCollector = require('./{settings.ARTILLERY_LIB_FOLDER}/statsCollector').StatsCollector;\",\n f\"profiles = require('./{settings.ARTILLERY_LIB_FOLDER}/profiles').profiles;\",\n f\"influx = require('./{settings.ARTILLERY_LIB_FOLDER}/influx').client;\",\n ]\n return \"\\n\".join(imports)","def get_env_info(self):\n self.reset()\n env_info = {\n \"n_agents\": self.n_agents,\n \"api_type\": self.api_type,\n \"action_type\": self.action_type\n }\n # update the agent ids, will used in the weights map.\n # default work well with the sumo multi-agents\n agent_ids = list(self.get_init_state().keys()) if self.n_agents > 1 else [0]\n env_info.update({\"agent_ids\": agent_ids})\n\n return env_info","def get_commands():\n\n commands = {}\n\n if not settings.configured:\n return commands\n\n for app_config in reversed(list(apps.get_app_configs())):\n if app_config.label.startswith(lib_name):\n path = os.path.join(app_config.path, 'management')\n commands.update({name: app_config.name for name in find_commands(path)})\n\n return commands","def write_metrics_env_sh(pnda_env):\n with open('./metrics_env.sh', 'w') as pnda_env_sh_file:\n for section in pnda_env:\n for setting in pnda_env[section]:\n val = '\"%s\"' % pnda_env[section][setting] if isinstance(\n pnda_env[section][setting], (list, tuple)) else pnda_env[section][setting]\n pnda_env_sh_file.write('export %s=%s\\n' % (setting, val))","def GetBashEnvFromFile(this, filename):\n DB_RE = re.compile(\"export (.+)=(.+)\")\n ret = {}\n if filename is not None:\n with open( filename, \"r\" ) as f:\n for line in f:\n m = DB_RE.search(line.strip())\n if m:\n name = m.group(1)\n val = m.group(2)\n # Check for quotes\n if val[0] in \"'\\\"\" and val[0]==val[-1]:\n val = val[1:-1]\n ret[name] = val\n for name in (MYSQL_HOST, MYSQL_USER, MYSQL_PASSWORD, MYSQL_DATABASE):\n if name not in ret:\n try:\n ret[name] = os.environ[name]\n except KeyError as e:\n logging.error(\"%s not in environment not in %s\",name,filename)\n raise\n return ret","def extract_execute_reports(events):\n result = []\n for e in events:\n evt = IpuTraceEvent.FromString(e)\n if evt.type == IpuTraceEvent.EXECUTE:\n try:\n module = evt.execute.module_name.decode('utf-8')\n rep = evt.execute.execution_report.decode('utf-8')\n if len(rep) > 0:\n result += [(module, rep)]\n except UnicodeDecodeError:\n pass\n return result","def default_environment():\n return dict(_VARS)","def setup_global_env(config, env):\n envs = config.get('env', {})\n if isinstance(envs, dict):\n for val in listify(envs.get('global', [])):\n env.append('export {}'.format(val))","def read_env_file(path: str) -> dict: \n with open(path, 'r') as f:\n return dict(tuple(line.replace('\\n', '').split('=',1)) for line\n in f.readlines() if not line.startswith('#'))","def exported(*variables):\n ignored(variables)","def parse_env_file_form_dict(env='base'):\r\n cur_path = os.path.abspath(os.path.dirname(__file__))\r\n file_path = os.path.join(cur_path, r\"./config/{}.env\".format(env))\r\n\r\n with open(file_path, 'r') as fh:\r\n logger.debug(\"Env file fetched is {}.env\".format(env))\r\n vars_dict = dict(\r\n tuple(line.split('='))\r\n for line in fh.readlines() if not line.startswith('#')\r\n )\r\n # print(\"Parsed dict values are {}\".format(vars_dict))\r\n return vars_dict","def concourse_env(self) -> Dict[str, str]:\n concourse_env_dict = {}\n for attr in self.__fields__.values():\n if attr.field_info.extra.get(\"concourse_env_var\"):\n attr_val = self.dict()[attr.name]\n if attr_val is not None:\n val_transform = attr.field_info.extra.get(\"env_transform\") or str\n concourse_env_dict[\n attr.field_info.extra.get(\"concourse_env_var\")\n ] = val_transform(attr_val)\n return concourse_env_dict","def dumpenv(self):\n\n print('-------------------------------')\n pprint.pprint(dict(os.environ))\n print('-------------------------------')","def _load_command_dict(self, path=None):","def generate_manifest_dict(self):\n\n annotations = dict()\n\n for build_project in self.projects.get('build', []):\n for annotation in build_project.get('annotation', []):\n annotations[annotation['name']] = annotation['value']\n\n product = annotations.get('PRODUCT', 'unknown')\n version = annotations.get('VERSION', 'unknown')\n bld_num = annotations.get('BLD_NUM', '9999')\n manifest_name = '{}-{}-{}'.format(product, version, bld_num)\n\n return {\n manifest_name: {\n 'remotes': self.remotes,\n 'defaults': self.defaults,\n 'projects': self.projects\n }\n }","def get_environment() -> dict:\n go_path = subprocess.check_output([\"go\", \"env\", \"GOPATH\"]).decode().strip()\n setup_script_path = f\"{go_path}/src/github.com/harmony-one/harmony/scripts/setup_bls_build_flags.sh\"\n response = subprocess.check_output([\"bash\", setup_script_path, \"-v\"], timeout=5)\n environment = json.loads(response)\n environment[\"HOME\"] = os.environ.get(\"HOME\")\n return environment","def environments(self):\n env_txt = self.config[\"tox\"][\"envlist\"]\n env_lst_raw = env_txt.strip().replace(\"\\n\",\",\").split(\",\")\n env_lst = [x.strip() for x in env_lst_raw if x != \"\"]\n return env_lst","def _convert_profile_to_docker_envs(profile):\n profile = profile._asdict()\n return dict([(key.upper(), value) for key, value in six.iteritems(profile)])","def script_commands(self):\n return self.get_data(\"script_commands\")","def get_console_scripts_info():\n entry_points_map = pkg_resources.get_entry_map('rbco.nautilusscripts', 'console_scripts')\n return [\n {\n 'name': ep.name,\n 'module': ep.module_name,\n 'function': ep.attrs[0],\n }\n for ep in entry_points_map.itervalues()\n if not ep.module_name.endswith('.install')\n ]","def get_compilers(**kwargs) -> T.Mapping[str, str]:\n env = os.environ\n\n for k, v in kwargs.items():\n c = env.get(k, \"\")\n if v not in c:\n c = shutil.which(v)\n if not c:\n raise FileNotFoundError(v)\n env.update({k: c})\n\n return env","def get_addon_consts(self) -> Dict[str, str]:\n from azure.cli.command_modules.acs._consts import (\n ADDONS, CONST_ACC_SGX_QUOTE_HELPER_ENABLED,\n CONST_AZURE_KEYVAULT_SECRETS_PROVIDER_ADDON_NAME,\n CONST_AZURE_POLICY_ADDON_NAME, CONST_CONFCOM_ADDON_NAME,\n CONST_HTTP_APPLICATION_ROUTING_ADDON_NAME,\n CONST_INGRESS_APPGW_ADDON_NAME,\n CONST_INGRESS_APPGW_APPLICATION_GATEWAY_ID,\n CONST_INGRESS_APPGW_APPLICATION_GATEWAY_NAME,\n CONST_INGRESS_APPGW_SUBNET_CIDR, CONST_INGRESS_APPGW_SUBNET_ID,\n CONST_INGRESS_APPGW_WATCH_NAMESPACE,\n CONST_KUBE_DASHBOARD_ADDON_NAME, CONST_MONITORING_ADDON_NAME,\n CONST_MONITORING_LOG_ANALYTICS_WORKSPACE_RESOURCE_ID,\n CONST_MONITORING_USING_AAD_MSI_AUTH,\n CONST_OPEN_SERVICE_MESH_ADDON_NAME, CONST_ROTATION_POLL_INTERVAL,\n CONST_SECRET_ROTATION_ENABLED, CONST_VIRTUAL_NODE_ADDON_NAME,\n CONST_VIRTUAL_NODE_SUBNET_NAME)\n\n addon_consts = {}\n addon_consts[\"ADDONS\"] = ADDONS\n addon_consts[\n \"CONST_ACC_SGX_QUOTE_HELPER_ENABLED\"\n ] = CONST_ACC_SGX_QUOTE_HELPER_ENABLED\n addon_consts[\n \"CONST_AZURE_POLICY_ADDON_NAME\"\n ] = CONST_AZURE_POLICY_ADDON_NAME\n addon_consts[\"CONST_CONFCOM_ADDON_NAME\"] = CONST_CONFCOM_ADDON_NAME\n addon_consts[\n \"CONST_HTTP_APPLICATION_ROUTING_ADDON_NAME\"\n ] = CONST_HTTP_APPLICATION_ROUTING_ADDON_NAME\n addon_consts[\n \"CONST_INGRESS_APPGW_ADDON_NAME\"\n ] = CONST_INGRESS_APPGW_ADDON_NAME\n addon_consts[\n \"CONST_INGRESS_APPGW_APPLICATION_GATEWAY_ID\"\n ] = CONST_INGRESS_APPGW_APPLICATION_GATEWAY_ID\n addon_consts[\n \"CONST_INGRESS_APPGW_APPLICATION_GATEWAY_NAME\"\n ] = CONST_INGRESS_APPGW_APPLICATION_GATEWAY_NAME\n addon_consts[\n \"CONST_INGRESS_APPGW_SUBNET_CIDR\"\n ] = CONST_INGRESS_APPGW_SUBNET_CIDR\n addon_consts[\n \"CONST_INGRESS_APPGW_SUBNET_ID\"\n ] = CONST_INGRESS_APPGW_SUBNET_ID\n addon_consts[\n \"CONST_INGRESS_APPGW_WATCH_NAMESPACE\"\n ] = CONST_INGRESS_APPGW_WATCH_NAMESPACE\n addon_consts[\n \"CONST_KUBE_DASHBOARD_ADDON_NAME\"\n ] = CONST_KUBE_DASHBOARD_ADDON_NAME\n addon_consts[\n \"CONST_MONITORING_ADDON_NAME\"\n ] = CONST_MONITORING_ADDON_NAME\n addon_consts[\n \"CONST_MONITORING_LOG_ANALYTICS_WORKSPACE_RESOURCE_ID\"\n ] = CONST_MONITORING_LOG_ANALYTICS_WORKSPACE_RESOURCE_ID\n addon_consts[\n \"CONST_OPEN_SERVICE_MESH_ADDON_NAME\"\n ] = CONST_OPEN_SERVICE_MESH_ADDON_NAME\n addon_consts[\n \"CONST_VIRTUAL_NODE_ADDON_NAME\"\n ] = CONST_VIRTUAL_NODE_ADDON_NAME\n addon_consts[\n \"CONST_VIRTUAL_NODE_SUBNET_NAME\"\n ] = CONST_VIRTUAL_NODE_SUBNET_NAME\n addon_consts[\n \"CONST_AZURE_KEYVAULT_SECRETS_PROVIDER_ADDON_NAME\"\n ] = CONST_AZURE_KEYVAULT_SECRETS_PROVIDER_ADDON_NAME\n addon_consts[\n \"CONST_SECRET_ROTATION_ENABLED\"\n ] = CONST_SECRET_ROTATION_ENABLED\n addon_consts[\n \"CONST_ROTATION_POLL_INTERVAL\"\n ] = CONST_ROTATION_POLL_INTERVAL\n\n addon_consts[\n \"CONST_MONITORING_USING_AAD_MSI_AUTH\"\n ] = CONST_MONITORING_USING_AAD_MSI_AUTH\n return addon_consts","def load_entry_points(group_name):\n entry_points = {}\n for entry_point in iter_entry_points(group=group_name, name=None):\n entry_points[entry_point.name] = entry_point.load()\n return entry_points","def get_execution_envs(self):\n return self.execution_envs","def setup_commands(self):\n return self.get_data(\"setup_commands\")","def prepare_env(self, project=None, env=None):\n if project == None or env == None:\n return [None, '']\n else:\n memory_file = BytesIO()\n with zipfile.ZipFile(memory_file, 'w') as zf:\n if env.bundle != None and env.bundle.storage != '':\n try:\n bundle_buffer = StringIO()\n if 'http://' in env.bundle.storage or 'https://' in env.bundle.storage:\n bundle_buffer = self.web_get_file(env.bundle.storage)\n else:\n bundle_buffer = self.storage_get_file('bundle', env.bundle.storage)\n\n data = zipfile.ZipInfo(\"bundle.%s\"%(env.bundle.storage.split(\"/\")[-1].split(\".\")[-1]))\n data.date_time = time.localtime(time.time())[:6]\n data.compress_type = zipfile.ZIP_DEFLATED\n data.external_attr |= 0o777 << 16 # -rwx-rwx-rwx\n zf.writestr(data, bundle_buffer.read())\n except:\n print(traceback.print_exc())\n\n try:\n json_buffer = StringIO()\n json_buffer.write(env.to_json())\n json_buffer.seek(0)\n\n data = zipfile.ZipInfo(\"env.json\")\n data.date_time = time.localtime(time.time())[:6]\n data.compress_type = zipfile.ZIP_DEFLATED\n data.external_attr |= 0o777 << 16 # -rwx-rwx-rwx\n zf.writestr(data, json_buffer.read())\n except:\n print(traceback.print_exc())\n memory_file.seek(0)\n\n return [memory_file, \"project-%s-env-%s.zip\"%(str(project.id), str(env.id))]","def to_dict(self, export_style: str = \"matchms\"):\n if export_style == \"matchms\":\n return dict(self._data)\n key_conversions = load_export_key_conversions(export_style=export_style)\n keep_list = [x for x in self._data.keys() if x in key_conversions]\n converted_dict = {}\n for key in keep_list:\n value = self._data[key]\n if value != \"\" and key_conversions[key] != \"\":\n converted_dict[key_conversions[key]] = self._data[key]\n return converted_dict","def parse_sambamba_output(self):\r\n exons = []\r\n with open (self.file_path, \"r\") as sambamba_output:\r\n for line in sambamba_output:\r\n if line.startswith('#'):\r\n fields = line.strip().split()\r\n else:\r\n description = list(line.strip().split())\r\n i = 0\r\n exon_dict = {}\r\n while i<len(fields):\r\n exon_dict[fields[i]] = description[i]\r\n i += 1\r\n exons.append(exon_dict)\r\n return exons","def get_command_arguments(self, format_vars):\n rval = {}\n for setting, value in self.settings.items():\n if setting in self.command_arguments:\n if value:\n rval[setting] = self.command_arguments[setting].format(**format_vars)\n else:\n rval[setting] = \"\"\n else:\n rval[setting] = value\n return rval"],"string":"[\n \"def export_configurations():\\n pass\",\n \"def export(self) -> Dict[str, Any]:\\n return {\\n \\\"name\\\": self.name,\\n \\\"channels\\\": [channel for channel in self.channels],\\n \\\"packages\\\": self.packages.export(),\\n \\\"logs\\\": [log for log in self.logs],\\n \\\"actions\\\": [action for action in self.actions],\\n \\\"debug\\\": [debug for debug in self.debug],\\n }\",\n \"def get_command_env(self) -> Sequence[Mapping[str, str]]:\\n return [\\n {\\\"name\\\": \\\"DAGSTER_COMPRESSED_EXECUTE_STEP_ARGS\\\", \\\"value\\\": self._get_compressed_args()},\\n ]\",\n \"def exports():\",\n \"def exporters():\\n return dict(_exporters)\",\n \"def env_dict(self): # type: () -> t.Dict[str, str]\\n return dict((item[0], item[1]) for item in [e.split('=', 1) for e in self.env])\",\n \"def _extract_commands():\\n module_names = [\\n module.name for module in pkgutil.iter_modules(projectstarter.commands.__path__)\\n ]\\n commands = {}\\n for module_name in module_names:\\n module = importlib.import_module(f\\\"projectstarter.commands.{module_name}\\\")\\n if module.__doc__ is not None:\\n commands[module_name] = {\\n \\\"description\\\": module.__doc__.strip().lower(),\\n \\\"func_parse\\\": module.__getattribute__(\\\"parse\\\"),\\n \\\"func_run\\\": module.__getattribute__(\\\"run\\\"),\\n }\\n return commands\",\n \"def _get_export_dir_dict(self):\\n export_dir = self.gluster_manager.get_gluster_vol_option(\\n NFS_EXPORT_DIR)\\n edh = {}\\n if export_dir:\\n # see\\n # https://github.com/gluster/glusterfs\\n # /blob/aa19909/xlators/nfs/server/src/nfs.c#L1582\\n # regarding the format of nfs.export-dir\\n edl = export_dir.split(',')\\n # parsing export_dir into a dict of {dir: [hostpec,..]..}\\n # format\\n r = re.compile('\\\\A/(.*)\\\\((.*)\\\\)\\\\Z')\\n for ed in edl:\\n d, e = r.match(ed).groups()\\n edh[d] = e.split('|')\\n return edh\",\n \"def _all_env_vars_to_dict(self):\\n all_env_vars = {}\\n for k, v in self.env.items():\\n c_vars = {}\\n for x, y in v['vars'].items():\\n c_vars[f'{k}_{x}'] = y['value']\\n all_env_vars = {**all_env_vars, **c_vars}\\n return all_env_vars\",\n \"def export_vars(env_vars_dict):\\n env_vars = ['{}={}'.format(k, env_vars_dict[k])\\n for k in env_vars_dict.keys()]\\n with open(\\\"vars.env\\\", \\\"w\\\") as file:\\n for item in env_vars[:-1]:\\n file.write(\\\"{}\\\\n\\\".format(item))\\n file.write(\\\"{}\\\".format(env_vars[-1]))\",\n \"def _get_env(self):\\n env = {}\\n for k, v in os.environ.items():\\n k = k.decode() if isinstance(k, bytes) else k\\n v = v.decode() if isinstance(v, bytes) else v\\n env[k] = v\\n return list(env.items())\",\n \"def export(self) -> dict:\\n output = {}\\n for source, packages in self.items():\\n source_packages = {}\\n for name, info in packages.items():\\n if info.spec_is_custom():\\n source_packages[name] = info.spec\\n else:\\n version = info.get_version_from_spec()\\n if version:\\n source_packages[name] = version\\n else:\\n source_packages[name] = \\\"*\\\"\\n if source_packages:\\n output[source] = source_packages\\n return output\",\n \"def environments(self):\\n envs = self.config[\\\"tox\\\"][\\\"envlist\\\"]\\n #result = re.split(\\\"[^a-zA-Z0-9]\\\", envs)\\n result = re.split(r'\\\\n| ,|,', envs)\\n #print ([string for string in result if string != \\\"\\\"])\\n result = (([string.strip() for string in result if string != \\\"\\\"]))\\n print(list(dict.fromkeys(result)))\\n return ((list(dict.fromkeys(result))))\",\n \"def export_str(self):\\n return \\\"\\\\n\\\".join(\\\"export %s=%s\\\" % (k, v) for k, v in self.items())\",\n \"def env_dicts(self, package_name):\\n ret = {}\\n ret_multi = {}\\n # First process the global variables\\n for package, pairs in self._sorted_data:\\n for name, value in pairs.items():\\n if package is None:\\n if isinstance(value, list):\\n ret_multi[name] = value\\n else:\\n ret[name] = value\\n\\n # Then the package scoped vars, that will override the globals\\n for package, pairs in self._sorted_data:\\n for name, value in pairs.items():\\n if package == package_name:\\n if isinstance(value, list):\\n ret_multi[name] = value\\n if name in ret: # Already exists a global variable, remove it\\n del ret[name]\\n else:\\n ret[name] = value\\n if name in ret_multi: # Already exists a list global variable, remove it\\n del ret_multi[name]\\n return ret, ret_multi\",\n \"def _get_output_dictionary(self):\\n\\n return_dictionary = {}\\n\\n for output_path in self.provided_outputs:\\n return_dictionary[output_path.full_path] = self.get_value(output_path)\\n\\n return return_dictionary\",\n \"def envs():\\n\\n # update and grab the envs from the metadata keys\\n metadata = _init()\\n return list(metadata.keys())\",\n \"def get_prop_list(self):\\n import_list = []\\n if os.path.exists(os.path.join(EXPORT_DIR, \\\"ENV\\\")):\\n for i in os.listdir(os.path.join(EXPORT_DIR, \\\"ENV\\\")):\\n import_list.append(os.path.join(EXPORT_DIR, \\\"ENV\\\", i))\\n return import_list\\n else:\\n gui.MessageDialog(\\n \\\"Could Not find Exported File from Daz Studio\\\",\\n type=c4d.GEMB_ICONEXCLAMATION,\\n )\",\n \"def _env(env):\\n return dict((key.strip(), val)\\n for line in env.strip().splitlines()\\n for key, _, val in [line.partition('=')])\",\n \"def send_env(self, keys):\\n if keys is None:\\n return self.env\\n return dict((key, self.env.get(key, '')) for key in keys)\",\n \"def build_ac_export_infos(self):\\n if not self.configure_ac_info:\\n return\\n export_variables = self.configure_ac_info[\\\"configure_ac\\\"][\\\"export_variables\\\"]\\n variables = self.configure_ac_info[\\\"configure_ac\\\"][\\\"variables\\\"]\\n for export_var in export_variables:\\n src = variables.get(export_var, dict())\\n dest = export_variables.get(export_var, dict())\\n if len(src) == 0:\\n continue\\n\\n if len(dest.get(\\\"defined\\\", list())) != 0 or len(dest.get(\\\"undefined\\\", list())) != 0:\\n continue\\n\\n export_variables[export_var] = src\\n\\n for preset_export_var in preset_output_variables:\\n if preset_export_var in export_variables:\\n continue\\n\\n src = variables.get(preset_export_var, dict())\\n if len(src) == 0:\\n continue\\n\\n export_variables[preset_export_var] = src\",\n \"def parse_st_cmd_env(st_cmd_filepath):\\n re_env = re.compile(\\\"^epicsEnvSet[ \\\\t\\\\\\\"\\\\(]*([\\\\w]+)[, \\\\t\\\\\\\"\\\\)]*([^\\\\\\\"\\\\n]*)\\\")\\n env = {}\\n with open(st_cmd_filepath, \\\"r\\\") as f:\\n for line in f:\\n match = re_env.search(line)\\n if match:\\n env[match.group(1)] = match.group(2)\\n return env\",\n \"def getSolutionExportVariableNames(cls):\\n return {}\",\n \"def _generate_environment(self):\\n envvars = {}\\n for key in self.envvars:\\n try:\\n envvars[key] = os.environ[key]\\n except KeyError:\\n continue\\n\\n # Warn the user that we cannot support secrets\\n if envvars:\\n logger.warning(\\\"This API does not support environment secrets.\\\")\\n return envvars\",\n \"def all_envs():\\n return all_tasks.keys()\",\n \"def wrapper_environment(args):\\n\\n return {\\n ENVIRONMENT_KEY: json.dumps({\\n 'verbose': args.verbose,\\n 'cc': shlex.split(args.cc),\\n 'cxx': shlex.split(args.cxx)\\n })\\n }\",\n \"def environ_expansions():\\r\\n global _environ_expansions\\r\\n\\r\\n if _environ_expansions:\\r\\n return _environ_expansions\\r\\n\\r\\n _environ_expansions = {}\\r\\n for key, value in os.environ.items():\\r\\n _environ_expansions['ENV_%s' % key] = value\\r\\n\\r\\n return _environ_expansions\",\n \"def _create_extra_environment(self):\\n return {}\",\n \"def show_env():\\n envs = [\\\"PATH\\\", \\\"ORACLE_HOME\\\", \\\"TNS_ADMIN\\\", \\\"NLS_LANG\\\"]\\n result = {}\\n for env in envs:\\n if env in os.environ:\\n result[env] = os.environ[env]\\n return result\",\n \"def _get_interpreter_values(token='ZEPPELIN_INTERPRETER') -> dict:\\n env_variables = {}\\n for env_variable in os.environ:\\n if env_variable.startswith(token):\\n env_variables[env_variable] = os.environ[env_variable]\\n\\n LOG.info('%s-based token environment variables: %s', token, env_variables)\\n\\n return env_variables\",\n \"def create_export_policy():\\n config = {\\n \\\"export\\\": {\\n \\\"tenant\\\": \\\"intersite-testsuite\\\",\\n \\\"app\\\": \\\"app\\\",\\n \\\"epg\\\": \\\"epg\\\",\\n \\\"remote_epg\\\": \\\"intersite-testsuite-app-epg\\\",\\n \\\"remote_sites\\\": [\\n {\\n \\\"site\\\": {\\n \\\"name\\\": \\\"Site2\\\",\\n \\\"interfaces\\\": [\\n {\\n \\\"l3out\\\": {\\n \\\"name\\\": \\\"l3out\\\",\\n \\\"tenant\\\": \\\"intersite-testsuite\\\"\\n }\\n }\\n ]\\n }\\n }\\n ]\\n }\\n }\\n return config\",\n \"def env_vars(self):\\n if self._m.spec and self._m.spec.container:\\n return k8s_object.ListAsDictionaryWrapper(\\n self._m.spec.container.env, self._messages.EnvVar)\",\n \"def load_env(env_files):\\n env = {}\\n for env_file in env_files:\\n with open(env_file) as f:\\n for line in f:\\n if line and line[0] != \\\"#\\\":\\n try:\\n index = line.index(\\\"=\\\")\\n env[line[:index].strip()] = line[index + 1 :].strip()\\n except ValueError:\\n # Ignore lines that don't have a '='\\n pass\\n return env\",\n \"def as_invocation_environment_dict(self):\\n lib_env_var = self._platform.resolve_platform_specific({\\n 'darwin': lambda: 'DYLD_LIBRARY_PATH',\\n 'linux': lambda: 'LD_LIBRARY_PATH',\\n })\\n return {\\n 'PATH': create_path_env_var(self.path_entries),\\n lib_env_var: create_path_env_var(self.library_dirs),\\n }\",\n \"def find_environ_config_vars():\\n # only allow secret key and database uri for now\\n envvars = [\\\"SQLALCHEMY_DATABASE_URI\\\", \\\"SECRET_KEY\\\"]\\n results = {}\\n for key in envvars:\\n if key in os.environ:\\n results[key] = os.environ[key]\\n return results\",\n \"def export(command):\\n if len(command) == 1:\\n return 0\\n for var in command[1:]:\\n try:\\n var = var.split('=')\\n environ[var[0]] = var[1]\\n except IndexError:\\n environ[var[0]] = \\\"\\\"\\n return 0\",\n \"def dump():\\n import pprint\\n import json\\n\\n notice('dumping env...')\\n pprint.pprint(env)\\n\\n if _config:\\n notice('dumping config...')\\n print _config\",\n \"def export_inference(modules):\\n result = {}\\n for module in modules:\\n result[module.module_type] = json.loads(module.export())\\n return json.dumps(result)\",\n \"def to_dict(self, exclusions: list = []) -> dict:\\n step_details = {name: step.to_dict() for name, step in self.run.items()}\\n order = {str(i_step): name for i_step, name in enumerate(self.run.keys())}\\n return dict(order=order, details=step_details)\",\n \"def get_export_options(options):\\n export_options = {\\n key: value\\n for (key, value) in iteritems(options)\\n if key in Export.EXPORT_OPTION_FIELDS\\n }\\n\\n return export_options\",\n \"def parse(env, platform_name=None):\\n\\n platform_name = platform_name or PLATFORM\\n\\n result = {}\\n for variable, value in env.items():\\n\\n # Platform specific values\\n if isinstance(value, dict):\\n value = value.get(platform_name, \\\"\\\")\\n\\n if not value:\\n continue\\n\\n # Allow to have lists as values in the tool data\\n if isinstance(value, (list, tuple)):\\n value = \\\";\\\".join(value)\\n\\n result[variable] = value\\n\\n return result\",\n \"def describe_cost_management_exports(self):\\n return [{\\\"name\\\": self.export_name, \\\"container\\\": self.container, \\\"directory\\\": self.directory}]\",\n \"def get_environ_dict():\\n return {\\n 'os.environ': _get_os_environ_dict((\\n 'AUTH_DOMAIN',\\n 'CURRENT_CONFIGURATION_VERSION',\\n 'CURRENT_MODULE_ID',\\n 'CURRENT_VERSION_ID',\\n 'DEFAULT_VERSION_HOSTNAME',\\n 'FEDERATED_IDENTITY',\\n 'FEDERATED_PROVIDER',\\n 'GAE_LOCAL_VM_RUNTIME',\\n 'HTTP_HOST',\\n 'HTTP_PROXY',\\n 'HTTP_X_APPENGINE_HTTPS',\\n 'HTTP_X_APPENGINE_QUEUENAME',\\n 'HTTP_X_ORIGINAL_HOST',\\n 'HTTP_X_ORIGINAL_SCHEME',\\n 'SERVER_NAME',\\n 'SERVER_PORT',\\n 'SERVER_SOFTWARE',\\n 'USER_IS_ADMIN',\\n )),\\n 'app_identity': _get_app_identity_dict((\\n 'get_service_account_name',\\n 'get_application_id',\\n 'get_default_version_hostname',\\n )),\\n 'modules': _get_modules_dict((\\n 'get_current_module_name',\\n 'get_current_version_name',\\n 'get_current_instance_id',\\n 'get_modules',\\n 'get_versions',\\n 'get_default_version',\\n 'get_hostname',\\n )),\\n 'namespace_manager': _get_namespace_manager_dict((\\n 'get_namespace',\\n 'google_apps_namespace',\\n )),\\n }\",\n \"def serialize(self):\\n return dict(env_name=self.name, type=self.type, env_kwargs=deepcopy(self._init_kwargs))\",\n \"def get_execution_envs_str(self):\\n\\n return ' '.join(str(x) for x in self.execution_envs)\",\n \"def export(self, **env):\\n with self.lock:\\n for key, value in env.items():\\n self.environment[key] = value\",\n \"def _setenv(self):\\n tokens = {}\\n tokens[\\\"CT_TIMESTAMP\\\"] = self._timestamp\\n tokens[\\\"CT_SUBMITTER\\\"] = self._node.name()\\n # tokens[\\\"CT_HIPBASE\\\"] = self._file[\\\"hipbase\\\"]\\n tokens[\\\"CT_SCENE\\\"] = self._scene\\n tokens[\\\"CT_PROJECT\\\"] = self.project_name\\n\\n for token in tokens:\\n hou.putenv(token, tokens[token])\\n\\n return tokens\",\n \"def get_commands_dict() -> dict:\\n commands_dict = {}\\n f = open(f\\\"data/metadata/commands.dict.txt\\\", \\\"r\\\", encoding=\\\"utf-8\\\").read()\\n for command in f.split(\\\"\\\\n\\\"):\\n commands_dict[command.split(\\\":\\\")[0]] = command.split(\\\":\\\")[1]\\n return commands_dict\",\n \"def get_environment_map(self):\\n return {'oxmeta': self.env}\",\n \"def get_outputs():\\n outputs = {}\\n for obj in vars(acsploit.output).values():\\n if hasattr(obj, 'OUTPUT_NAME'):\\n outputs[obj.OUTPUT_NAME] = obj\\n\\n return outputs\",\n \"def get_export_prompts(obj_bp):\\n \\n prompts = {}\\n \\n def add_prompt(prompt):\\n if prompt.Type == 'NUMBER':\\n prompts[prompt.name] = str(prompt.NumberValue)\\n if prompt.Type == 'QUANTITY':\\n prompts[prompt.name] = str(prompt.QuantityValue)\\n if prompt.Type == 'COMBOBOX':\\n prompts[prompt.name] = str(prompt.COL_EnumItem[prompt.EnumIndex].name)\\n if prompt.Type == 'CHECKBOX':\\n prompts[prompt.name] = str(prompt.CheckBoxValue)\\n if prompt.Type == 'TEXT':\\n prompts[prompt.name] = str(prompt.TextValue)\\n if prompt.Type == 'DISTANCE':\\n prompts[prompt.name] = str(round(unit.meter_to_active_unit(prompt.DistanceValue),2))\\n if prompt.Type == 'ANGLE':\\n prompts[prompt.name] = str(prompt.AngleValue)\\n if prompt.Type == 'PERCENTAGE':\\n prompts[prompt.name] = str(prompt.PercentageValue)\\n if prompt.Type == 'PRICE':\\n prompts[prompt.name] = str(prompt.PriceValue)\\n \\n def add_child_prompts(obj):\\n for child in obj.children:\\n if child.mv.type == 'BPASSEMBLY':\\n add_prompts(child)\\n if len(child.children) > 0:\\n add_child_prompts(child)\\n \\n def add_prompts(obj):\\n for prompt in obj.mv.PromptPage.COL_Prompt:\\n if prompt.export:\\n add_prompt(prompt)\\n \\n add_prompts(obj_bp)\\n add_child_prompts(obj_bp)\\n\\n return prompts\",\n \"def prepare_env_for_all_codes(i):\\n\\n # Check vars\\n if 'code_deps' not in i: return {'cm_return':1, 'cm_error':'\\\"code_deps\\\" is not defined in \\\"code prepare_env_for_all_codes\\\"'}\\n\\n include_paths=[]\\n lib_paths=[]\\n\\n # Load OS\\n os_uoa=''\\n if 'os_uoa' in i and i['os_uoa']!='': os_uoa=i['os_uoa']\\n elif 'cm_default_os_uoa' in cm_kernel.ini['dcfg'] and cm_kernel.ini['dcfg']['cm_default_os_uoa']!='':\\n os_uoa=cm_kernel.ini['dcfg']['cm_default_os_uoa']\\n\\n if os_uoa=='' not in i:\\n return {'cm_return':1, 'cm_error':'\\\"os_uoa\\\" is not defined and not in kernel'}\\n\\n ii={'cm_run_module_uoa':ini['cfg']['cm_modules']['os'],\\n 'cm_action':'load',\\n 'cm_data_uoa':os_uoa}\\n r=cm_kernel.access(ii)\\n if r['cm_return']>0: return r\\n\\n os_cfg=r['cm_data_obj']['cfg']\\n os_path=r['cm_path']\\n os_uid=r['cm_uid']\\n os_alias=r['cm_alias']\\n\\n s_code_deps=''\\n a_code_deps=[]\\n if 'code_deps' in i:\\n for xx in i['code_deps']:\\n yy=xx.keys()[0]\\n x=xx[yy]\\n\\n if x=='':\\n return {'cm_return':1, 'cm_error':'dependency \\\"'+yy+'\\\" is empty, please check your input'}\\n\\n # Check if code was installed\\n if i.get('no_strict_check','')!='yes':\\n ii={'cm_run_module_uoa':ini['cm_module_uid'],\\n 'cm_action':'load',\\n 'cm_data_uoa':x}\\n r=cm_kernel.access(ii)\\n if r['cm_return']==16:\\n return {'cm_return':1, 'cm_error':'dependency is not resolved - code '+x+' ('+yy+') is not installed'}\\n elif r['cm_return']>0: return r\\n code_cfg=r['cm_data_obj']['cfg']\\n if code_cfg.get('build_finished_successfully','')!='yes':\\n return {'cm_return':1, 'cm_error':'dependency is not resolved - code '+x+' ('+yy+') is not installed'}\\n\\n code_path=r['cm_path']\\n include_paths.append(os.path.join(code_path, 'include'))\\n\\n if 'state_input' in code_cfg and \\\\\\n 'run_set_env2' in code_cfg['state_input'] and \\\\\\n 'CM_TARGET_FILE' in code_cfg['state_input']['run_set_env2']:\\n lib_paths.append(os.path.join(code_path, os_cfg['lib_dir'], \\n code_cfg['state_input']['run_set_env2']['CM_TARGET_FILE']))\\n\\n # Environment script\\n r=get_env({'cm_data_uoa':x, 'os_uoa':os_uoa})\\n if r['cm_return']>0: return r\\n\\n# z=os_cfg['env_call']+' '+os.path.join(cm_kernel.ini[cm_kernel.env_cm_bin],r['cm_string'])\\n z1=os_cfg['env_set']+' '+yy+'='+os_cfg['env_quotes']+x+os_cfg['env_quotes']\\n z=os_cfg['env_call']+' '+r['cm_string']\\n\\n if s_code_deps!='': s_code_deps+=' '+os_cfg['env_separator']+' '\\n s_code_deps+=z1\\n if s_code_deps!='': s_code_deps+=' '+os_cfg['env_separator']+' '\\n s_code_deps+=z\\n # FGG added again setting environment variable since calling other scripts can change it\\n # for example, we set CM_CODE_DEP_COMPILER and then call GMP that was compiled with another\\n # compiler, then it will change this variable to a wrong value and further tools will \\n # not be working correctly ...\\n if s_code_deps!='': s_code_deps+=' '+os_cfg['env_separator']+' '\\n s_code_deps+=z1\\n\\n a_code_deps.append(z1)\\n a_code_deps.append(z)\\n a_code_deps.append(z1)\\n\\n return {'cm_return':0, 'cm_string':s_code_deps, 'cm_array':a_code_deps, 'env_separator': os_cfg['env_separator'],\\n 'include_paths':include_paths, 'lib_paths':lib_paths}\",\n \"def _iter_commands(self):\\n return {entry_point.name: entry_point for entry_point in\\n pkg_resources.iter_entry_points('chanjo.subcommands')}\",\n \"def export(myenv):\\n for item in myenv.split(\\\",\\\"):\\n if item:\\n try:\\n key, val = item.split(\\\"=\\\")\\n os.environ[key] = val\\n # Variables to be deleted will not split on '=', throwing ValueError\\n except ValueError:\\n try:\\n del os.environ[item]\\n # If a NoneType sneaks through, it will throw TypeEror on 'del'\\n except TypeError:\\n continue\",\n \"def _get_os_environ_dict(keys):\\n return {k: os.environ.get(k, _UNDEFINED) for k in keys}\",\n \"def gyp_defines():\\n return dict(arg.split('=', 1)\\n for arg in shlex.split(os.environ.get('GYP_DEFINES', '')))\",\n \"def to_env(self, d):\\n return {k.upper(): str(v) for k, v in d.items()}\",\n \"def get_environment_vars():\\n return {env: os.environ[env] for env in\\n params.ENV_DIRS if env in os.environ}\",\n \"def _prepend_env_paths(content, names):\\n export_env_vars = ['export %(k)s=%(v)s:${%(k)s}' %dict(\\n k=name, v=os.environ.get(name, '')) for name in names]\\n return '\\\\n'.join(export_env_vars + [content])\",\n \"def _get_config_dict():\\r\\n return CONFIGS\",\n \"def export_to_versionable_dict(exploration):\\n return {\\n 'param_changes': exploration.param_change_dicts,\\n 'param_specs': exploration.param_specs_dict,\\n 'states': [export_state_internals_to_dict(\\n exploration.id, state_id, human_readable_dests=True)\\n for state_id in exploration.state_ids]\\n }\",\n \"def _prepare_publish_environments():\\n env = copy.deepcopy(os.environ)\\n\\n project_name = os.getenv(\\\"AVALON_PROJECT\\\")\\n asset_name = os.getenv(\\\"AVALON_ASSET\\\")\\n\\n env[\\\"AVALON_PROJECT\\\"] = project_name\\n env[\\\"AVALON_ASSET\\\"] = asset_name\\n env[\\\"AVALON_TASK\\\"] = os.getenv(\\\"AVALON_TASK\\\")\\n env[\\\"AVALON_WORKDIR\\\"] = os.getenv(\\\"AVALON_WORKDIR\\\")\\n env[\\\"AVALON_APP\\\"] = f\\\"hosts.{publish_host}\\\"\\n env[\\\"AVALON_APP_NAME\\\"] = \\\"celaction/local\\\"\\n\\n env[\\\"PYBLISH_HOSTS\\\"] = publish_host\\n\\n os.environ.update(env)\",\n \"def _build_env(self, args):\\n env = os.environ.copy()\\n env['ANSIBLE_CONFIG'] = os.path.join(ANSIBLE_DIR, 'ansible.cfg')\\n if hasattr(args, 'stdout_callback'):\\n env['ANSIBLE_STDOUT_CALLBACK'] = args.stdout_callback\\n return env\",\n \"def read_environ():\\n\\n environ = {}\\n environ['BASEDIR'] = os.environ['TS_BASEDIR']\\n environ['CONFIG_NL'] = os.environ['TS_CONFIG_NL']\\n environ['NL_TS_SWITCH'] = os.environ['TS_NL_TS_SWITCH']\\n environ['DT_FILE'] = os.environ['TS_DT_FILE']\\n environ['REFOUTDIR'] = os.environ['TS_REFOUTDIR']\\n environ['VERBOSE'] = os.environ['TS_VERBOSE']\\n environ['RUNDIR'] = os.environ['TS_RUNDIR']\\n environ['LOGFILE'] = os.environ['TS_LOGFILE']\\n environ['NAMELISTDIR'] = os.environ['TS_NAMELISTDIR']\\n environ['TOLERANCE'] = os.environ['TS_TOLERANCE']\\n environ['FORCEMATCH'] = os.environ['TS_FORCEMATCH']\\n environ['TUNING_ITERATIONS'] = os.environ['TS_TUNING_ITERATIONS']\\n environ['TUNE_THRESHOLDS'] = os.environ['TS_TUNE_THRESHOLDS']\\n environ['RESET_THRESHOLDS'] = os.environ['TS_RESET_THRESHOLDS']\\n environ['ICON'] = os.environ['TS_ICON']\\n environ['YUFILE'] = os.environ['TS_YUFILE']\\n return environ\",\n \"def env_tokens(self):\\r\\n\\r\\n # Find the env JSON file\\r\\n if self.SERVICE_VARIANT:\\r\\n env_path = self.REPO_ROOT.parent / \\\"{service}.env.json\\\".format(service=self.SERVICE_VARIANT)\\r\\n else:\\r\\n env_path = path(\\\"env.json\\\").abspath()\\r\\n\\r\\n # If the file does not exist, here or one level up,\\r\\n # issue a warning and return an empty dict\\r\\n if not env_path.isfile():\\r\\n env_path = env_path.parent.parent / env_path.basename()\\r\\n if not env_path.isfile():\\r\\n print(\\r\\n \\\"Warning: could not find environment JSON file \\\"\\r\\n \\\"at '{path}'\\\".format(path=env_path),\\r\\n file=sys.stderr,\\r\\n )\\r\\n return dict()\\r\\n\\r\\n # Otherwise, load the file as JSON and return the resulting dict\\r\\n try:\\r\\n with open(env_path) as env_file:\\r\\n return json.load(env_file)\\r\\n\\r\\n except ValueError:\\r\\n print(\\r\\n \\\"Error: Could not parse JSON \\\"\\r\\n \\\"in {path}\\\".format(path=env_path),\\r\\n file=sys.stderr,\\r\\n )\\r\\n sys.exit(1)\",\n \"def environ_to_dict(environ):\\n return dict((k, v) for k, v in environ.iteritems()\\n if k.isupper())\",\n \"def get_registered_analysis_targets() -> Dict[str, Any]:\\n result: Dict[str, Any] = {}\\n for groupindex in config.targets:\\n result[groupindex] = {}\\n result[groupindex][\\\"path\\\"] = os.path.dirname(config.targets[groupindex])\\n result[groupindex][\\\"projects\\\"] = get_analysis_target_index(groupindex)\\n return result\",\n \"def get_procedures(self) -> dict:\\n return json.loads(json.dumps(Config.get_procedures(), cls=Message.Encoder))\",\n \"def modules():\\n cmd = \\\"{} -M\\\".format(_detect_os())\\n ret = {}\\n ret[\\\"static\\\"] = []\\n ret[\\\"shared\\\"] = []\\n out = __salt__[\\\"cmd.run\\\"](cmd).splitlines()\\n for line in out:\\n comps = line.split()\\n if not comps:\\n continue\\n if \\\"(static)\\\" in line:\\n ret[\\\"static\\\"].append(comps[0])\\n if \\\"(shared)\\\" in line:\\n ret[\\\"shared\\\"].append(comps[0])\\n return ret\",\n \"def __init__(self, export_tuples=None):\\n self._exports = export_tuples if export_tuples else []\",\n \"def args_map_custom(cls) -> dict:\\n args = {}\\n args.update(cls.args_map_export())\\n args.update({\\\"json_flat\\\": False})\\n return args\",\n \"def get_imports(self) -> str:\\n\\n imports = [\\n \\\"_ = require('lodash');\\\",\\n \\\"stream = require('stream');\\\",\\n f\\\"const hookRegister = require('./{settings.ARTILLERY_HOOK_FILE}').hookRegister;\\\",\\n f\\\"hook = require('./{settings.ARTILLERY_LIB_FOLDER}/hooks').hook;\\\",\\n f\\\"utils = require('./{settings.ARTILLERY_LIB_FOLDER}/providers');\\\",\\n f\\\"settings = require('./{settings.ARTILLERY_LIB_FOLDER}/settings');\\\",\\n f\\\"StatsCollector = require('./{settings.ARTILLERY_LIB_FOLDER}/statsCollector').StatsCollector;\\\",\\n f\\\"profiles = require('./{settings.ARTILLERY_LIB_FOLDER}/profiles').profiles;\\\",\\n f\\\"influx = require('./{settings.ARTILLERY_LIB_FOLDER}/influx').client;\\\",\\n ]\\n return \\\"\\\\n\\\".join(imports)\",\n \"def get_env_info(self):\\n self.reset()\\n env_info = {\\n \\\"n_agents\\\": self.n_agents,\\n \\\"api_type\\\": self.api_type,\\n \\\"action_type\\\": self.action_type\\n }\\n # update the agent ids, will used in the weights map.\\n # default work well with the sumo multi-agents\\n agent_ids = list(self.get_init_state().keys()) if self.n_agents > 1 else [0]\\n env_info.update({\\\"agent_ids\\\": agent_ids})\\n\\n return env_info\",\n \"def get_commands():\\n\\n commands = {}\\n\\n if not settings.configured:\\n return commands\\n\\n for app_config in reversed(list(apps.get_app_configs())):\\n if app_config.label.startswith(lib_name):\\n path = os.path.join(app_config.path, 'management')\\n commands.update({name: app_config.name for name in find_commands(path)})\\n\\n return commands\",\n \"def write_metrics_env_sh(pnda_env):\\n with open('./metrics_env.sh', 'w') as pnda_env_sh_file:\\n for section in pnda_env:\\n for setting in pnda_env[section]:\\n val = '\\\"%s\\\"' % pnda_env[section][setting] if isinstance(\\n pnda_env[section][setting], (list, tuple)) else pnda_env[section][setting]\\n pnda_env_sh_file.write('export %s=%s\\\\n' % (setting, val))\",\n \"def GetBashEnvFromFile(this, filename):\\n DB_RE = re.compile(\\\"export (.+)=(.+)\\\")\\n ret = {}\\n if filename is not None:\\n with open( filename, \\\"r\\\" ) as f:\\n for line in f:\\n m = DB_RE.search(line.strip())\\n if m:\\n name = m.group(1)\\n val = m.group(2)\\n # Check for quotes\\n if val[0] in \\\"'\\\\\\\"\\\" and val[0]==val[-1]:\\n val = val[1:-1]\\n ret[name] = val\\n for name in (MYSQL_HOST, MYSQL_USER, MYSQL_PASSWORD, MYSQL_DATABASE):\\n if name not in ret:\\n try:\\n ret[name] = os.environ[name]\\n except KeyError as e:\\n logging.error(\\\"%s not in environment not in %s\\\",name,filename)\\n raise\\n return ret\",\n \"def extract_execute_reports(events):\\n result = []\\n for e in events:\\n evt = IpuTraceEvent.FromString(e)\\n if evt.type == IpuTraceEvent.EXECUTE:\\n try:\\n module = evt.execute.module_name.decode('utf-8')\\n rep = evt.execute.execution_report.decode('utf-8')\\n if len(rep) > 0:\\n result += [(module, rep)]\\n except UnicodeDecodeError:\\n pass\\n return result\",\n \"def default_environment():\\n return dict(_VARS)\",\n \"def setup_global_env(config, env):\\n envs = config.get('env', {})\\n if isinstance(envs, dict):\\n for val in listify(envs.get('global', [])):\\n env.append('export {}'.format(val))\",\n \"def read_env_file(path: str) -> dict: \\n with open(path, 'r') as f:\\n return dict(tuple(line.replace('\\\\n', '').split('=',1)) for line\\n in f.readlines() if not line.startswith('#'))\",\n \"def exported(*variables):\\n ignored(variables)\",\n \"def parse_env_file_form_dict(env='base'):\\r\\n cur_path = os.path.abspath(os.path.dirname(__file__))\\r\\n file_path = os.path.join(cur_path, r\\\"./config/{}.env\\\".format(env))\\r\\n\\r\\n with open(file_path, 'r') as fh:\\r\\n logger.debug(\\\"Env file fetched is {}.env\\\".format(env))\\r\\n vars_dict = dict(\\r\\n tuple(line.split('='))\\r\\n for line in fh.readlines() if not line.startswith('#')\\r\\n )\\r\\n # print(\\\"Parsed dict values are {}\\\".format(vars_dict))\\r\\n return vars_dict\",\n \"def concourse_env(self) -> Dict[str, str]:\\n concourse_env_dict = {}\\n for attr in self.__fields__.values():\\n if attr.field_info.extra.get(\\\"concourse_env_var\\\"):\\n attr_val = self.dict()[attr.name]\\n if attr_val is not None:\\n val_transform = attr.field_info.extra.get(\\\"env_transform\\\") or str\\n concourse_env_dict[\\n attr.field_info.extra.get(\\\"concourse_env_var\\\")\\n ] = val_transform(attr_val)\\n return concourse_env_dict\",\n \"def dumpenv(self):\\n\\n print('-------------------------------')\\n pprint.pprint(dict(os.environ))\\n print('-------------------------------')\",\n \"def _load_command_dict(self, path=None):\",\n \"def generate_manifest_dict(self):\\n\\n annotations = dict()\\n\\n for build_project in self.projects.get('build', []):\\n for annotation in build_project.get('annotation', []):\\n annotations[annotation['name']] = annotation['value']\\n\\n product = annotations.get('PRODUCT', 'unknown')\\n version = annotations.get('VERSION', 'unknown')\\n bld_num = annotations.get('BLD_NUM', '9999')\\n manifest_name = '{}-{}-{}'.format(product, version, bld_num)\\n\\n return {\\n manifest_name: {\\n 'remotes': self.remotes,\\n 'defaults': self.defaults,\\n 'projects': self.projects\\n }\\n }\",\n \"def get_environment() -> dict:\\n go_path = subprocess.check_output([\\\"go\\\", \\\"env\\\", \\\"GOPATH\\\"]).decode().strip()\\n setup_script_path = f\\\"{go_path}/src/github.com/harmony-one/harmony/scripts/setup_bls_build_flags.sh\\\"\\n response = subprocess.check_output([\\\"bash\\\", setup_script_path, \\\"-v\\\"], timeout=5)\\n environment = json.loads(response)\\n environment[\\\"HOME\\\"] = os.environ.get(\\\"HOME\\\")\\n return environment\",\n \"def environments(self):\\n env_txt = self.config[\\\"tox\\\"][\\\"envlist\\\"]\\n env_lst_raw = env_txt.strip().replace(\\\"\\\\n\\\",\\\",\\\").split(\\\",\\\")\\n env_lst = [x.strip() for x in env_lst_raw if x != \\\"\\\"]\\n return env_lst\",\n \"def _convert_profile_to_docker_envs(profile):\\n profile = profile._asdict()\\n return dict([(key.upper(), value) for key, value in six.iteritems(profile)])\",\n \"def script_commands(self):\\n return self.get_data(\\\"script_commands\\\")\",\n \"def get_console_scripts_info():\\n entry_points_map = pkg_resources.get_entry_map('rbco.nautilusscripts', 'console_scripts')\\n return [\\n {\\n 'name': ep.name,\\n 'module': ep.module_name,\\n 'function': ep.attrs[0],\\n }\\n for ep in entry_points_map.itervalues()\\n if not ep.module_name.endswith('.install')\\n ]\",\n \"def get_compilers(**kwargs) -> T.Mapping[str, str]:\\n env = os.environ\\n\\n for k, v in kwargs.items():\\n c = env.get(k, \\\"\\\")\\n if v not in c:\\n c = shutil.which(v)\\n if not c:\\n raise FileNotFoundError(v)\\n env.update({k: c})\\n\\n return env\",\n \"def get_addon_consts(self) -> Dict[str, str]:\\n from azure.cli.command_modules.acs._consts import (\\n ADDONS, CONST_ACC_SGX_QUOTE_HELPER_ENABLED,\\n CONST_AZURE_KEYVAULT_SECRETS_PROVIDER_ADDON_NAME,\\n CONST_AZURE_POLICY_ADDON_NAME, CONST_CONFCOM_ADDON_NAME,\\n CONST_HTTP_APPLICATION_ROUTING_ADDON_NAME,\\n CONST_INGRESS_APPGW_ADDON_NAME,\\n CONST_INGRESS_APPGW_APPLICATION_GATEWAY_ID,\\n CONST_INGRESS_APPGW_APPLICATION_GATEWAY_NAME,\\n CONST_INGRESS_APPGW_SUBNET_CIDR, CONST_INGRESS_APPGW_SUBNET_ID,\\n CONST_INGRESS_APPGW_WATCH_NAMESPACE,\\n CONST_KUBE_DASHBOARD_ADDON_NAME, CONST_MONITORING_ADDON_NAME,\\n CONST_MONITORING_LOG_ANALYTICS_WORKSPACE_RESOURCE_ID,\\n CONST_MONITORING_USING_AAD_MSI_AUTH,\\n CONST_OPEN_SERVICE_MESH_ADDON_NAME, CONST_ROTATION_POLL_INTERVAL,\\n CONST_SECRET_ROTATION_ENABLED, CONST_VIRTUAL_NODE_ADDON_NAME,\\n CONST_VIRTUAL_NODE_SUBNET_NAME)\\n\\n addon_consts = {}\\n addon_consts[\\\"ADDONS\\\"] = ADDONS\\n addon_consts[\\n \\\"CONST_ACC_SGX_QUOTE_HELPER_ENABLED\\\"\\n ] = CONST_ACC_SGX_QUOTE_HELPER_ENABLED\\n addon_consts[\\n \\\"CONST_AZURE_POLICY_ADDON_NAME\\\"\\n ] = CONST_AZURE_POLICY_ADDON_NAME\\n addon_consts[\\\"CONST_CONFCOM_ADDON_NAME\\\"] = CONST_CONFCOM_ADDON_NAME\\n addon_consts[\\n \\\"CONST_HTTP_APPLICATION_ROUTING_ADDON_NAME\\\"\\n ] = CONST_HTTP_APPLICATION_ROUTING_ADDON_NAME\\n addon_consts[\\n \\\"CONST_INGRESS_APPGW_ADDON_NAME\\\"\\n ] = CONST_INGRESS_APPGW_ADDON_NAME\\n addon_consts[\\n \\\"CONST_INGRESS_APPGW_APPLICATION_GATEWAY_ID\\\"\\n ] = CONST_INGRESS_APPGW_APPLICATION_GATEWAY_ID\\n addon_consts[\\n \\\"CONST_INGRESS_APPGW_APPLICATION_GATEWAY_NAME\\\"\\n ] = CONST_INGRESS_APPGW_APPLICATION_GATEWAY_NAME\\n addon_consts[\\n \\\"CONST_INGRESS_APPGW_SUBNET_CIDR\\\"\\n ] = CONST_INGRESS_APPGW_SUBNET_CIDR\\n addon_consts[\\n \\\"CONST_INGRESS_APPGW_SUBNET_ID\\\"\\n ] = CONST_INGRESS_APPGW_SUBNET_ID\\n addon_consts[\\n \\\"CONST_INGRESS_APPGW_WATCH_NAMESPACE\\\"\\n ] = CONST_INGRESS_APPGW_WATCH_NAMESPACE\\n addon_consts[\\n \\\"CONST_KUBE_DASHBOARD_ADDON_NAME\\\"\\n ] = CONST_KUBE_DASHBOARD_ADDON_NAME\\n addon_consts[\\n \\\"CONST_MONITORING_ADDON_NAME\\\"\\n ] = CONST_MONITORING_ADDON_NAME\\n addon_consts[\\n \\\"CONST_MONITORING_LOG_ANALYTICS_WORKSPACE_RESOURCE_ID\\\"\\n ] = CONST_MONITORING_LOG_ANALYTICS_WORKSPACE_RESOURCE_ID\\n addon_consts[\\n \\\"CONST_OPEN_SERVICE_MESH_ADDON_NAME\\\"\\n ] = CONST_OPEN_SERVICE_MESH_ADDON_NAME\\n addon_consts[\\n \\\"CONST_VIRTUAL_NODE_ADDON_NAME\\\"\\n ] = CONST_VIRTUAL_NODE_ADDON_NAME\\n addon_consts[\\n \\\"CONST_VIRTUAL_NODE_SUBNET_NAME\\\"\\n ] = CONST_VIRTUAL_NODE_SUBNET_NAME\\n addon_consts[\\n \\\"CONST_AZURE_KEYVAULT_SECRETS_PROVIDER_ADDON_NAME\\\"\\n ] = CONST_AZURE_KEYVAULT_SECRETS_PROVIDER_ADDON_NAME\\n addon_consts[\\n \\\"CONST_SECRET_ROTATION_ENABLED\\\"\\n ] = CONST_SECRET_ROTATION_ENABLED\\n addon_consts[\\n \\\"CONST_ROTATION_POLL_INTERVAL\\\"\\n ] = CONST_ROTATION_POLL_INTERVAL\\n\\n addon_consts[\\n \\\"CONST_MONITORING_USING_AAD_MSI_AUTH\\\"\\n ] = CONST_MONITORING_USING_AAD_MSI_AUTH\\n return addon_consts\",\n \"def load_entry_points(group_name):\\n entry_points = {}\\n for entry_point in iter_entry_points(group=group_name, name=None):\\n entry_points[entry_point.name] = entry_point.load()\\n return entry_points\",\n \"def get_execution_envs(self):\\n return self.execution_envs\",\n \"def setup_commands(self):\\n return self.get_data(\\\"setup_commands\\\")\",\n \"def prepare_env(self, project=None, env=None):\\n if project == None or env == None:\\n return [None, '']\\n else:\\n memory_file = BytesIO()\\n with zipfile.ZipFile(memory_file, 'w') as zf:\\n if env.bundle != None and env.bundle.storage != '':\\n try:\\n bundle_buffer = StringIO()\\n if 'http://' in env.bundle.storage or 'https://' in env.bundle.storage:\\n bundle_buffer = self.web_get_file(env.bundle.storage)\\n else:\\n bundle_buffer = self.storage_get_file('bundle', env.bundle.storage)\\n\\n data = zipfile.ZipInfo(\\\"bundle.%s\\\"%(env.bundle.storage.split(\\\"/\\\")[-1].split(\\\".\\\")[-1]))\\n data.date_time = time.localtime(time.time())[:6]\\n data.compress_type = zipfile.ZIP_DEFLATED\\n data.external_attr |= 0o777 << 16 # -rwx-rwx-rwx\\n zf.writestr(data, bundle_buffer.read())\\n except:\\n print(traceback.print_exc())\\n\\n try:\\n json_buffer = StringIO()\\n json_buffer.write(env.to_json())\\n json_buffer.seek(0)\\n\\n data = zipfile.ZipInfo(\\\"env.json\\\")\\n data.date_time = time.localtime(time.time())[:6]\\n data.compress_type = zipfile.ZIP_DEFLATED\\n data.external_attr |= 0o777 << 16 # -rwx-rwx-rwx\\n zf.writestr(data, json_buffer.read())\\n except:\\n print(traceback.print_exc())\\n memory_file.seek(0)\\n\\n return [memory_file, \\\"project-%s-env-%s.zip\\\"%(str(project.id), str(env.id))]\",\n \"def to_dict(self, export_style: str = \\\"matchms\\\"):\\n if export_style == \\\"matchms\\\":\\n return dict(self._data)\\n key_conversions = load_export_key_conversions(export_style=export_style)\\n keep_list = [x for x in self._data.keys() if x in key_conversions]\\n converted_dict = {}\\n for key in keep_list:\\n value = self._data[key]\\n if value != \\\"\\\" and key_conversions[key] != \\\"\\\":\\n converted_dict[key_conversions[key]] = self._data[key]\\n return converted_dict\",\n \"def parse_sambamba_output(self):\\r\\n exons = []\\r\\n with open (self.file_path, \\\"r\\\") as sambamba_output:\\r\\n for line in sambamba_output:\\r\\n if line.startswith('#'):\\r\\n fields = line.strip().split()\\r\\n else:\\r\\n description = list(line.strip().split())\\r\\n i = 0\\r\\n exon_dict = {}\\r\\n while i<len(fields):\\r\\n exon_dict[fields[i]] = description[i]\\r\\n i += 1\\r\\n exons.append(exon_dict)\\r\\n return exons\",\n \"def get_command_arguments(self, format_vars):\\n rval = {}\\n for setting, value in self.settings.items():\\n if setting in self.command_arguments:\\n if value:\\n rval[setting] = self.command_arguments[setting].format(**format_vars)\\n else:\\n rval[setting] = \\\"\\\"\\n else:\\n rval[setting] = value\\n return rval\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.59988654","0.59330225","0.5903955","0.56446517","0.5643221","0.56416047","0.54888237","0.5403373","0.53202504","0.52638626","0.5262855","0.5257865","0.5199736","0.5194403","0.5183345","0.5182272","0.51688004","0.5123766","0.5117179","0.5108427","0.51010203","0.5098488","0.5087582","0.50554955","0.5044979","0.50332624","0.50326777","0.50316286","0.5026873","0.5025713","0.50138193","0.4997946","0.49742183","0.4969861","0.49667817","0.49478883","0.49451727","0.49190018","0.4904018","0.4892469","0.4891799","0.48432988","0.48394164","0.4836197","0.4834373","0.48255616","0.4800712","0.47940046","0.47889465","0.4778259","0.47502387","0.47453007","0.4739579","0.47235084","0.47233087","0.47221026","0.47108358","0.47080564","0.46968374","0.46923476","0.46833718","0.46823812","0.46779194","0.46778187","0.4677781","0.46730492","0.46727496","0.46716896","0.4668594","0.46578768","0.46560937","0.4647743","0.46455315","0.4644302","0.4627829","0.46227694","0.46227613","0.46194664","0.46166134","0.46130362","0.4595372","0.4589915","0.45581967","0.45544916","0.4550982","0.4543574","0.4543306","0.45418644","0.45416993","0.45372772","0.4535617","0.45320076","0.45308906","0.4527054","0.45200798","0.45159647","0.45155972","0.4513969","0.45051575","0.45031923"],"string":"[\n \"0.59988654\",\n \"0.59330225\",\n \"0.5903955\",\n \"0.56446517\",\n \"0.5643221\",\n \"0.56416047\",\n \"0.54888237\",\n \"0.5403373\",\n \"0.53202504\",\n \"0.52638626\",\n \"0.5262855\",\n \"0.5257865\",\n \"0.5199736\",\n \"0.5194403\",\n \"0.5183345\",\n \"0.5182272\",\n \"0.51688004\",\n \"0.5123766\",\n \"0.5117179\",\n \"0.5108427\",\n \"0.51010203\",\n \"0.5098488\",\n \"0.5087582\",\n \"0.50554955\",\n \"0.5044979\",\n \"0.50332624\",\n \"0.50326777\",\n \"0.50316286\",\n \"0.5026873\",\n \"0.5025713\",\n \"0.50138193\",\n \"0.4997946\",\n \"0.49742183\",\n \"0.4969861\",\n \"0.49667817\",\n \"0.49478883\",\n \"0.49451727\",\n \"0.49190018\",\n \"0.4904018\",\n \"0.4892469\",\n \"0.4891799\",\n \"0.48432988\",\n \"0.48394164\",\n \"0.4836197\",\n \"0.4834373\",\n \"0.48255616\",\n \"0.4800712\",\n \"0.47940046\",\n \"0.47889465\",\n \"0.4778259\",\n \"0.47502387\",\n \"0.47453007\",\n \"0.4739579\",\n \"0.47235084\",\n \"0.47233087\",\n \"0.47221026\",\n \"0.47108358\",\n \"0.47080564\",\n \"0.46968374\",\n \"0.46923476\",\n \"0.46833718\",\n \"0.46823812\",\n \"0.46779194\",\n \"0.46778187\",\n \"0.4677781\",\n \"0.46730492\",\n \"0.46727496\",\n \"0.46716896\",\n \"0.4668594\",\n \"0.46578768\",\n \"0.46560937\",\n \"0.4647743\",\n \"0.46455315\",\n \"0.4644302\",\n \"0.4627829\",\n \"0.46227694\",\n \"0.46227613\",\n \"0.46194664\",\n \"0.46166134\",\n \"0.46130362\",\n \"0.4595372\",\n \"0.4589915\",\n \"0.45581967\",\n \"0.45544916\",\n \"0.4550982\",\n \"0.4543574\",\n \"0.4543306\",\n \"0.45418644\",\n \"0.45416993\",\n \"0.45372772\",\n \"0.4535617\",\n \"0.45320076\",\n \"0.45308906\",\n \"0.4527054\",\n \"0.45200798\",\n \"0.45159647\",\n \"0.45155972\",\n \"0.4513969\",\n \"0.45051575\",\n \"0.45031923\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6357385"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":85597,"cells":{"query":{"kind":"string","value":"Construct a job submission script"},"document":{"kind":"string","value":"def job_script(self):\n quoted_arguments = quote_arguments([self._command_template])\n quoted_environment = quote_environment(self.env_dict)\n job_header_lines = \"\\n\".join(\n \"%s = %s\" % (k, v) for k, v in self.job_header_dict.items()\n )\n return self._script_template % {\n \"shebang\": self.shebang,\n \"job_header\": job_header_lines,\n \"quoted_environment\": quoted_environment,\n \"quoted_arguments\": quoted_arguments,\n \"executable\": self.executable,\n }"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def build_submission_script(path,\n script_name,\n save_history=True,\n walltime=10,\n allocation='p30653',\n cores=1,\n memory=4):\n\n # define paths\n path = abspath(path)\n job_script_path = join(path, 'scripts', 'submit.sh')\n\n # copy run script to scripts directory\n run_script = abspath(__file__).rsplit('/', maxsplit=2)[0]\n run_script = join(run_script, 'scripts', script_name)\n shutil.copy(run_script, join(path, 'scripts'))\n\n # determine queue\n if walltime <= 4:\n queue = 'short'\n elif walltime <= 48:\n queue = 'normal'\n else:\n queue = 'long'\n\n # declare outer script that reads PATH from file\n job_script = open(job_script_path, 'w')\n job_script.write('#!/bin/bash\\n')\n\n # move to job directory\n job_script.write('cd {:s} \\n\\n'.format(path))\n\n # begin outer script for processing job\n job_script.write('while IFS=$\\'\\\\t\\' read P\\n')\n job_script.write('do\\n')\n job_script.write('b_id=$(echo $(basename ${P}) | cut -f 1 -d \\'.\\')\\n')\n job_script.write(' JOB=`msub - << EOJ\\n\\n')\n\n # =========== begin submission script for individual batch ============\n job_script.write('#! /bin/bash\\n')\n job_script.write('#MSUB -A {:s} \\n'.format(allocation))\n job_script.write('#MSUB -q {:s} \\n'.format(queue))\n job_script.write('#MSUB -l walltime={0:02d}:00:00 \\n'.format(walltime))\n job_script.write('#MSUB -m abe \\n')\n #job_script.write('#MSUB -M sebastian@u.northwestern.edu \\n')\n job_script.write('#MSUB -o ./log/${b_id}/outlog \\n')\n job_script.write('#MSUB -e ./log/${b_id}/errlog \\n')\n job_script.write('#MSUB -N ${b_id} \\n')\n job_script.write('#MSUB -l nodes=1:ppn={:d} \\n'.format(cores))\n job_script.write('#MSUB -l mem={:d}gb \\n\\n'.format(memory))\n\n # load python module and metabolism virtual environment\n job_script.write('module load python/anaconda3.6\\n')\n job_script.write('source activate ~/pythonenvs/growth_env\\n\\n')\n\n # move to job directory\n job_script.write('cd {:s} \\n\\n'.format(path))\n\n # run script\n job_script.write('python ./scripts/{:s}'.format(script_name)+' ${P} ')\n args = (save_history,)\n job_script.write('-s {:d}\\n'.format(*args))\n job_script.write('EOJ\\n')\n job_script.write('`\\n\\n')\n # ============= end submission script for individual batch ============\n\n # print job id\n #job_script.write('echo \"JobID = ${JOB} submitted on `date`\"\\n')\n job_script.write('done < ./batches/index.txt \\n')\n job_script.write('echo \"All batches submitted as of `date`\"\\n')\n job_script.write('exit\\n')\n\n # close the file\n job_script.close()\n\n # change the permissions\n chmod(job_script_path, 0o755)","def make_job(self, script, factors=None):\n job = {'script': script}\n if factors is not None:\n job['factors'] = factors\n return job","def prepare_parafly_slurm_job_script(sBasename_job, sBasename_parafly, sDirectory_job, sEmail, iWalltime_in = None, nNode_in = None, nThread_in=None, sJob_name_in =None, sPython_env_in =None, sQueue_in=None):\n if iWalltime_in is not None:\n iWalltime = iWalltime_in \n else:\n iWalltime = 2\n if nNode_in is not None:\n iNode = nNode_in \n else:\n iNode = 1\n if nThread_in is not None:\n nThread = nThread_in \n else:\n nThread = 40\n \n if sJob_name_in is not None:\n sJob_name = sJob_name_in \n else:\n sJob_name = 'parafly'\n if sPython_env_in is not None:\n sPython_env = sPython_env_in \n else:\n sPython_env = 'base'\n \n if sQueue_in is not None:\n sQueue = sQueue_in \n else:\n sQueue = 'short'\n \n sWalltime =\"{:0d}\".format(iWalltime )\n sNode = \"{:0d}\".format(iNode )\n sThread = \"{:0d}\".format(nThread )\n \n os.chdir(sDirectory_job)\n \n ofs = open(sBasename_job,\"w\") #write mode \n sLine = '#!/bin/bash' + '\\n'\n ofs.write( sLine ) \n sLine = '#SBATCH --account=esmd' + '\\n'\n ofs.write( sLine ) \n\n #sLine = '#SBATCH --begin=now+1minutes' + '\\n'\n #ofs.write( sLine ) \n\n sLine = '#SBATCH --cpus-per-task=1 ' + '\\n'\n ofs.write( sLine ) \n\n sLine = '#SBATCH --dependency=singleton ' + '\\n'\n ofs.write( sLine )\n sLine = '#SBATCH --error=stderr_%j.err' + '\\n'\n ofs.write( sLine ) \n sLine = '#SBATCH --job-name=' + sJob_name + ' # create a name for your job' + '\\n'\n ofs.write( sLine ) \n sLine = '#SBATCH --mail-type=ALL' + '\\n'\n ofs.write( sLine ) \n sLine = '#SBATCH --mail-user=' + sEmail + '\\n'\n ofs.write( sLine ) \n\n sLine = '#SBATCH --nodes=' + sNode + ' # node count' + '\\n'\n ofs.write( sLine ) \n sLine = '#SBATCH --ntasks=' + sThread + ' # total number of tasks' + '\\n'\n ofs.write( sLine ) \n sLine = '#SBATCH --output=stdout_%j.out' + '\\n'\n ofs.write( sLine ) \n\n sLine = '#SBATCH --partition=' + sQueue + '\\n' #can be improved here\n ofs.write( sLine ) \n sLine = '#SBATCH --time=' + sWalltime +':00:00 # total run time limit (HH:MM:SS)' + '\\n'\n ofs.write( sLine ) \n\n sLine = 'module purge' + '\\n'\n ofs.write( sLine ) \n sLine = 'module load parafly/2013' + '\\n'\n ofs.write( sLine ) \n sLine = 'module load anaconda3/2019.03' + '\\n'\n ofs.write( sLine ) \n sLine = 'source /share/apps/anaconda3/2019.03/etc/profile.d/conda.sh' + '\\n'\n ofs.write( sLine ) \n sLine = 'unset PYTHONHOME' + '\\n'\n ofs.write( sLine ) \n sLine = 'conda activate ' + sPython_env + '\\n'\n ofs.write( sLine ) \n\n sLine = 'ParaFly -c ' + sBasename_parafly + ' -CPU ' + sThread + ' -failed_cmds rerun.txt' + '\\n'\n ofs.write( sLine ) \n \n sLine = 'echo \" Job \" ' + '${SLURM_JOBID}' + ' is launched' + '\\n'\n ofs.write( sLine ) \n\n sLine = 'conda deactivate' + '\\n'\n ofs.write( sLine ) \n \n sLine = 'echo \"Finished\"' + '\\n'\n ofs.write( sLine ) \n ofs.close() \n \n return","def scriptGen(self,tmpd='/tmp/jose',libRev='last',submode='qsub',\n redirect=1,PBSoptions=''):\n jobname=self.name\n outdir=self.outd\n qsubdir=scratchdir+'/qsub/'+todayDate() #subdirectory to deposit the script\n if not os.path.exists(qsubdir): pastry('/bin/mkdir -p '+qsubdir)\n script=qsubdir+'/'+jobname+'.sh' #full script file name\n\n if len(jobname) > 15:\n sys.stderr.write('Error: job name '+jobname+' cannot exceed 15 characters')\n return ''\n if not os.path.exists(outdir): os.system('/bin/mkdir -p '+outdir)\n buf=''\n ulimit=int(float(mem_limit)*1024) #maximum resident memory size (Kb) to prevent swapping\n wd=tmpd+'/${PBS_JOBID}'\n #wd=tmpd+'/'+ re.compile('\\W').sub('',self.name) +'_$$' #working directory\n logname=jobname+'.log'\n local_log=wd+'/'+logname\n remote_log=outdir+'/'+logname\n buf= '#!/bin/bash\\n\\n'\n buf+= PBSoptions+'\\n\\n'\n buf+= '#bash function to update library\\n'\n buf+= self.updateNodeLib(libRev)+'\\n\\n'\n buf+= '#bash function to import temporary libs\\n'\n buf+= self.shared_temporal_libraries()+'\\n\\n'\n buf+= '#bash function to clean exit\\n'\n buf+= self.cleanup_exit(submode=submode)+'\\n\\n'\n buf+= 'echo \"'+script+'\"\\n' #write script name withing script body\n buf+= 'hostname\\n' #node where job will be run\n buf+= 'echo $PBS_JOBID\\n'\n buf+= 'ulimit -m '+`ulimit`+' #maximum memory\\n'\n buf+= 'source ~/.bash_profile >/dev/null #environment variables\\n'\n buf+= 'wd='+wd+' #working directory\\n'\n buf+= '/bin/mkdir -p $wd\\n'\n buf+= 'export LOCAL_LOG=\"'+local_log+'\"\\n'\n buf+= '/bin/touch $LOCAL_LOG\\n'\n if submode=='sub' and redirect:\n buf+='exec &> $LOCAL_LOG #redirect STODOUT, STDERR to LOCAL_LOG\\n' \n buf+= 'export REMOTE_LOG=\"'+remote_log+'\"\\n'\n\n but+= '#clean up old log file\\n'\n buf+= 'if [ -f $REMOTE_LOG ]; then\\n' \n buf+= ' /bin/rm -f $REMOTE_LOG\\n'\n buf+= 'fi\\n\\n'\n\n buf+= 'trap \"cleanup_exit 1\" TERM #in case of killing job\\n\\n'\n\n buf+= '#update node code library && import libraries\\n'\n buf+= 'if !('\n buf+= 'updateNodeLib && ' \n buf+= 'shared_temporal_libraries _PREPARE_'\n buf+= ');then\\n'\n buf+= ' cleanup_exit 1\\n'\n buf+= 'fi\\n\\n'\n \n buf+= '/bin/cp '+' '.join(self.inpl)+' $wd #bring input files\\n' \n buf+= 'cd $wd\\n\\n'\n buf+= '#Test command success\\n'\n buf+= 'exs=0 #variable holding script exit status\\n'\n buf+= 'if !('\n buf+= self.exe\n buf+= ');then\\n'\n buf+= ' exs=1\\n'\n buf+= 'fi\\n\\n'\n buf+= '#move even partial results (exs=1)\\n'\n buf+= '/bin/mv '+' '.join(self.outl)+' '+outdir+'\\n'\n buf+= 'cleanup_exit $exs'\n\n open(script,'w').write(buf)\n pastry('chmod u+x '+script)\n\n return script","def make_jobscript(i_file, atm_file=None, dt_file=None, dp_file=None,\n fp_file=None, pp_file=None, sd_file=None, dsl_file=None,\n om_file=None, extra_vars=extra_vars,\n lapse_rate=6.0, ys=0.0, ye=1000.0, yts=10, yextra=100,\n mpi_exec=mpi_exec, pism_exec=pism_exec, pism_root=pism_root,\n nodes=1, time='24:00:00', out_dir=None, job_name='unnamed',\n ntasks_per_node=36, **boot_kwargs):\n\n # expand path to PISM root\n pism_root = os.path.abspath(pism_root)\n\n # get input and component model arguments\n input_args = get_input_args(i_file, pism_root=pism_root, **boot_kwargs)\n atm_args = get_atm_args(atm_file=atm_file, lapse_rate=lapse_rate,\n dt_file=dt_file, dp_file=dp_file, fp_file=fp_file,\n pp_file=pp_file, pism_root=pism_root)\n surface_args = get_surface_args(sd_file=sd_file, pism_root=pism_root)\n ocean_args = get_ocean_args(dsl_file=dsl_file, om_file=om_file, pism_root=pism_root)\n\n # format script\n script = template.format(**locals())\n\n # write script to file\n script_path = os.path.join(out_dir, 'job.' + job_name + '.sh')\n with open(script_path, 'w') as f:\n f.write(script)\n\n # return path to job script\n return script_path","def create_job(script, args, inputs, outputs, tools):\n job = Job(script)\n for arg in args:\n job.addArguments(arg)\n\n # all jobs will have access to python scripts\n for tool in tools:\n job.uses(tools[tool], link=Link.INPUT)\n\n for input in inputs:\n job.uses(input, link=Link.INPUT)\n\n for output in outputs:\n job.uses(output, link=Link.OUTPUT, transfer=True)\n\n #job.addProfile(Profile(Namespace.PEGASUS, 'clusters.size', '20'))\n return job","def createjob(args):\n ncell = args.ncell\n nmg = args.nmg\n nsi = args.nsi\n nvac = args.nvac\n a0 = args.a0\n temp = args.temp\n nseeds = args.nseeds\n seeds = args.seeds\n nsteps = args.nsteps\n foldername_append = args.foldername_append\n pot = args.pot\n submit = args.submit\n submitdebug = args.submitdebug\n submittime_hours = args.submittime_hours\n test = args.test\n testfiles = args.testfiles\n nodes = args.nodes\n verbose = args.verbose\n\n\n ### check if ase runner/quippy/lammpps-data formats are known\n ase_formats = mu.ase_get_known_formats_class(verbose=True)\n ase_formats.check_if_default_formats_known(copy_and_adapt_formatspy_anyhow=False)\n\n # definex ffsocket inet/unix\n if nodes == 1:\n ffsocket = \"unix\"\n elif nodes > 1:\n ffsocket = \"inet\"\n else:\n sys.exit(\"Number of nodes has to be positive!\")\n\n\n # define ntasks, neval\n lmp_par = 2 # = OMP_NUM_THREADS\n ntasks = cores = nodes * 28\n ipi_inst = 4 # for sure best on fidis\n neval = ipi_inst*2 # was alwasy better, for ompi and impi\n\n ##### get the seed(s).\n if type(seeds) == bool:\n seeds = random.sample(range(1, 999999), nseeds)\n print('seeds',seeds)\n if test == True:\n nseeds = 1\n seeds = [1]\n print('seeds',seeds)\n nseeds = len(seeds)\n\n ##### a few checks\n scripts = mu.scripts()\n mypot = mu.mypot(pot)\n if submit is True or submitdebug is True:\n hostcheck = os.environ[\"myhost\"]\n if hostcheck == \"\":\n sys.exit('host unknown 87')\n\n\n ##### here only chck if the potential can be set up. (in.lmp)\n ##### the same command is then executed for every kmc folder\n ace = mu.ase_calculate_ene(pot=pot,\n potpath=False,\n units='eV',geopt=False,kmc=True,verbose=verbose)\n ace.pot_get_and_ase_lmp_cmd(kmc=True,temp=temp,nsteps=nsteps,ffsocket=ffsocket)\n\n ##### if test\n if test == True:\n nsteps = 50\n\n file_ipi_input_runner = scripts + \"/i-pi-mc_scripts/input-runner.xml\"\n\n\n ####################################\n # get directory\n ####################################\n if verbose:\n print(\"get directory\")\n pcsi = nsi/ncell**3.*100\n pcmg = nmg/ncell**3.*100\n pcvac = nvac/ncell**3.*100\n if args.cubic == True:\n pc = \"cubic\"\n else:\n pc = \"primitive\"\n directory = str(ncell)+\"x\"+str(ncell)+\"x\"+str(ncell)+\"_\"+pc+\"_\"+pot+\"_\"+\\\n str(temp)+\"K_\"+\\\n str(nvac)+\"Vac_\"+str(nmg)+\"Mg_\"+str(nsi)+\"Si__\"+\\\n str(round(pcvac,3))+\"pctVac_\"+str(round(pcmg,3))+\"pctMg_\"+str(round(pcsi,3))+\"pctSi\"\n if foldername_append != \"\":\n directory = directory+\"_\"+foldername_append\n\n ###############################################\n # make the structure\n ###############################################\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell,nsi,nmg,nvac,a0,create_fake_vacancy = True,cubic=args.cubic)\n atomsc = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell,nsi,nmg,nvac,a0,cubic=args.cubic)\n\n # make the atomic structure\n # this was to play ... not necessary now?\n if False:\n nndist = a0/np.sqrt(2.)\n\n from ase.io import read as ase_read\n from ase.io import write as ase_write\n\n ###############################################\n # get the amount of 1NN in a relly large cell\n ###############################################\n atomsc_fakevac_i = ase_read('dataxx.extxyz3',index=\":\",format='extxyz') # works, cell ist not changed\n #atomsc_fakevac_i = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=10,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\"XX_0\")\n #nn = mu.ase_get_neighborlist(atomsc_fakevac_i,atomnr=0,cutoff=3.,skin=0.1)\n #print(\"nn\",nn,'len',len(nn))\n #nn = mu.ase_get_neighborlist(atomsc_fakevac_i,atomnr=0,cutoff=8.5,skin=0.1)\n #print(\"nn\",nn,'len',len(nn))\n #sys.exit()\n\n print(len(atomsc_fakevac_i),type(atomsc_fakevac_i))\n\n for idx,i in enumerate(atomsc_fakevac_i):\n print('aa',atomsc_fakevac_i[idx].positions[0])\n #print('aa',i.positions[0])\n print('ipi')\n atomsc_fakevac_i = ase_read('dataxx.ipi2',index=\":\",format='ipi') # works, cell ist not changed\n print(len(atomsc_fakevac_i),type(atomsc_fakevac_i))\n for idx,i in enumerate(atomsc_fakevac_i):\n print('aa',atomsc_fakevac_i[idx].positions[0])\n #print('aa',i.positions[0])\n print('quippy')\n atomsc_fakevac_i = ase_read('dataxx.quippy.xyz2',index=\":\",format='quippy') # works, cell ist not changed\n\n\n\n filename = '../sim.xyz'\n filename = '../simulation.pos_0.xyz'\n mu.count_amount_1NN_around_vacancies(filename,cutoffa=nndist,cutoffb=a0,skin=0.1,format='ipi')\n sys.exit()\n\n def mysave_quippy_xyz(atomsc_fakevac,text=False):\n if type(text) == bool:\n sys.exit('define text')\n atomsc_fakevac.write('data.quippy.xyz',format='quippy',append=True)\n #atomsc_fakevac.write('data.xyz',format=\"extxyz\",append=True)\n atomsc_fakevac.write('data'+text+'.quippy.xyz',format='quippy',append=True)\n #atomsc_fakevac.write('data'+text+'.xyz',format=\"extxyz\",append=True)\n return\n\n # create Al with single vacancy\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\"XX_0\")\n NN_1_indices, NN_2_indices = mu.ase_get_neighborlist_1NN_2NN(atomsc_fakevac,atomnr=0,cutoffa=nndist,cutoffb=a0,skin=0.1)\n #print('from ....',(atomsc_fakevac.positions)[0])\n #for i in NN_1_indices:\n # print((atomsc_fakevac.positions)[i])\n print('NN_1_indices (orig ):',NN_1_indices)\n print('NN_2_indices (orig ):',NN_2_indices)\n #sys.exit()\n atomsc_fakevac.write('dataxx.quippy.xyz',format='quippy',append=True)\n atomsc_fakevac.write('dataxx.poscar',format='vasp',append=True)\n atomsc_fakevac.write('dataxx.ipi',format='ipi',append=True) # works, currently so implemented that it canges cell\n atomsc_fakevac.write('dataxx.xyz',format='xyz',append=True)\n atomsc_fakevac.write('dataxx.extxyz',format='extxyz',append=True)\n atomsc_fakevac.write('dataxx.lammps-data',format='lammps-data',append=True)\n atomsc_fakevac.write('dataxx.lammps-runner',format='lammps-runner',append=True)\n\n atomsc_fakevac_a = ase_read('dataxx.extxyz',format='extxyz') # works, cell ist not changed\n atomsc_fakevac_a.write('dataxx.extxyz2',format='extxyz',append=True) # works, cell is not changed\n\n atomsc_fakevac_b = ase_read('dataxx.xyz',format='xyz') # not working # but this should work\n atomsc_fakevac_b.write('dataxx.xyz2',format='xyz',append=True) # this is working\n\n atomsc_fakevac_c = ase_read('dataxx.ipi',format='ipi') # works, currently so implemented that it canges cell\n #print('ipi cell',atomsc_fakevac_c.get_cell())\n\n atomsc_fakevac_c.write('dataxx.ipi2',format='ipi',append=True) # works, just writes the cell it gests.\n atomsc_fakevac_c.write('dataxx.ipi2_poscar',format='vasp',append=True) # works, just writes the cell it gests.\n NN_1_indices, NN_2_indices = mu.ase_get_neighborlist_1NN_2NN(atomsc_fakevac_c,atomnr=0,cutoffa=nndist,cutoffb=a0,skin=0.1)\n print('NN_1_indices (ipi ):',NN_1_indices)\n print('NN_2_indices (ipi ):',NN_2_indices)\n #print('from ....',(atomsc_fakevac_c.positions)[0])\n #for i in NN_1_indices:\n # print((atomsc_fakevac_c.positions)[i])\n\n atomsc_fakevac_cc = ase_read('dataxx.ipi2_poscar',format='vasp') # works, currently so implemented that it canges cell\n atomsc_fakevac_cc.write('dataxx.ipi2_poscar2',format='vasp',append=True)\n atomsc_fakevac_cc.write('dataxx.ipi2_poscar2_ipi',format='ipi',append=True) # works, just writes the cell it gests.\n #print('ipi cell2 (ext):',atomsc_fakevac_cc.get_cell())\n #print()\n #print('now quippy')\n atomsc_fakevac_d = ase_read('dataxx.quippy.xyz',format='quippy')\n #print('quippy cell (ext)',atomsc_fakevac_d.get_cell())\n atomsc_fakevac_d.write('dataxx.quippy.xyz2',format='quippy',append=True)\n atomsc_fakevac_d.write('dataxx.quippy.xyz2_extxyz',format='extxyz',append=True)\n NN_1_indices, NN_2_indices = mu.ase_get_neighborlist_1NN_2NN(atomsc_fakevac_d,atomnr=0,cutoffa=nndist,cutoffb=a0,skin=0.1)\n print('NN_1_indices (quippy):',NN_1_indices)\n print('NN_2_indices (quippy):',NN_2_indices)\n #print('from ....',(atomsc_fakevac_d.positions)[0])\n #for i in NN_1_indices:\n # print((atomsc_fakevac_d.positions)[i])\n path = \"/home/glensk/kmc/run_michele/Si6Mg6V1.1_/simulation.pos_libatom_2struct.xyz\"\n atomsc_fakevac_e = ase_read(path,format='quippy')\n\n NN_1_indices, NN_2_indices = mu.ase_get_neighborlist_1NN_2NN(atomsc_fakevac_e,atomnr=0,cutoffa=nndist,cutoffb=a0,skin=0.1)\n print('NN_1_indices (kmc ):',NN_1_indices)\n print('NN_2_indices (kmc ):',NN_2_indices)\n sys.exit()\n\n NN_1_indices = mu.ase_get_neighborlist(atomsc_fakevac,atomnr=0,cutoff=nndist,skin=0.1)\n NN_1_2_indices_tmp = mu.ase_get_neighborlist(atomsc_fakevac,atomnr=0,cutoff=a0,skin=0.1)\n print('NN_1_indices :',NN_1_indices)\n NN_2_indices = np.sort(np.array(mu.diff(NN_1_2_indices_tmp,NN_1_indices)))\n print('NN_2_indices :',NN_2_indices)\n NN_1_2_indices = np.concatenate((NN_1_indices, NN_2_indices ))\n print('NN_1_2_indices:',NN_1_2_indices)\n\n\n # fill only 1NN (with one species)\n for i in [ 'Mg', 'Si' ]:\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\"XX_0\")\n mysave_quippy_xyz(atomsc_fakevac,text=\"1NN\")\n for ii in NN_1_indices:\n atomsc_fakevac[ii].symbol = i\n mysave_quippy_xyz(atomsc_fakevac,text=\"1NN\")\n\n # fill only 2NN (with one species)\n for i in [ 'Mg', 'Si' ]:\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\"XX_0\")\n mysave_quippy_xyz(atomsc_fakevac,text=\"2NN\")\n for ii in NN_2_indices:\n atomsc_fakevac[ii].symbol = i\n mysave_quippy_xyz(atomsc_fakevac,text=\"2NN\")\n\n # fill 1NN and 2NN (with one species)\n for i in [ 'Mg', 'Si' ]:\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\"XX_0\")\n mysave_quippy_xyz(atomsc_fakevac,text=\"1and2NN\")\n for ii in NN_1_2_indices:\n atomsc_fakevac[ii].symbol = i\n mysave_quippy_xyz(atomsc_fakevac,text=\"1and2NN\")\n\n # dif compositions in 1NN shell\n filling = [ 2,4,6,8,10]\n for fi in filling:\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\"XX_0\")\n mysave_quippy_xyz(atomsc_fakevac,text=\"1NN_diffcomp\")\n for idx,ii in enumerate(NN_1_indices):\n if idx < fi: ch = \"Mg\"\n else: ch = \"Si\"\n atomsc_fakevac[ii].symbol = ch\n mysave_quippy_xyz(atomsc_fakevac,text=\"1NN_diffcomp\")\n\n\n sys.exit()\n\n #mu.ase_get_known_formats(show=True, add_missing_formats=False, copy_formats=False, verbose=False,show_formatspy=True)\n for i in [ 'Mg', 'Si' ]:\n for ii in [ 0,1,2,3,4,5]:\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=i+'_'+str(ii))\n\n\n sys.exit()\n\n\n # show the input variables\n print('--------------------------- check the input --------------------------------')\n print('JOBS (nseeds) ',nseeds,'(defined by -nseeds / or -seeds)')\n print('seeds ',seeds)\n print('nsteps ',nsteps)\n print()\n print('ncell ',ncell,\"(\",atomsc.get_number_of_atoms(),\"atoms )\")\n print('nsi ',nsi, \"(\",pcsi,\"at%)\")\n print('nmg ',nmg,\"(\",pcmg,\"at%)\")\n print('nvac ',nvac,\"(\",pcvac,\"at%)\")\n print('a0 ',a0,\"angstrom\")\n print('temp ',temp,\"K\")\n print()\n print('mypot.pot ',mypot.pot)\n print('mypot.potpath ',mypot.potpath)\n print()\n print('directory ',directory)\n print('submit ',submit)\n print('submitdebug ',submitdebug)\n print()\n print('nodes ',nodes)\n print('ffsocket ',ffsocket)\n #print('python ver ',sys.version_info[0])\n #print()\n print('--------------------------- check the input --------------------------------')\n if submit == True or submitdebug == True:\n mu.get_from_prompt_Yy_orexit(\"Are the ine input variables ok? [y]es: \")\n\n # make the directory\n if os.path.isdir(directory):\n mu.get_from_prompt_Yy_orexit(\"This main directory exists already, shall I add jobs? [y]es: \")\n mu.mkdir(directory)\n\n # create README.md\n IPI_COMMAND = os.environ[\"IPI_COMMAND\"]\n LAMMPS_COMMAND = os.environ[\"LAMMPS_COMMAND\"]\n mu.create_READMEtxt(directory,add=[\"# to start manually (1): python \"+IPI_COMMAND+\" input-runner.xml\",\"# to start manually (2):\"+LAMMPS_COMMAND+\" < in.lmp\"])\n\n for seed in seeds:\n\n # make jobdirectory\n jobdir = directory+'/seed'+str(seed)\n print('jobdir',jobdir)\n if os.path.exists(jobdir):\n sys.exit(\"jobdirectory \"+str(jobdir)+\" already exists!\")\n mu.mkdir(jobdir)\n\n # get data.lmp and data.ipi\n atomsc.write(jobdir+'/data.runnerformat.lmp',format='lammps-runner')\n atomsc_fakevac.write(jobdir+'/data.ipi',format='ipi')\n atomsc_fakevac.write(jobdir+'/data.extxyz',format='extxyz')\n #atomsc_fakevac.write(jobdir+'/data_fakevac.ipi',format='ipi')\n\n if testfiles == True:\n atomsc.write(jobdir+'/data.lmp',format='lammps-data')\n atomsc.write(jobdir+'/data.POSCAR',format='vasp')\n atomsc.write(jobdir+'/data.xyz',format='xyz')\n atomsc.write(jobdir+'/data.extxyz',format='extxyz')\n atomsc.write(jobdir+'/data.espresso-in',format='espresso-in')\n\n # create in.lmp\n ace = mu.ase_calculate_ene(pot=pot,potpath=mypot.potpath,\n units='eV',geopt=False,kmc=True,verbose=verbose)\n address = socket.gethostname()+\"_\"+os.path.basename(jobdir)\n print('address',address)\n ace.pot_get_and_ase_lmp_cmd(kmc=True,temp=temp,nsteps=nsteps,ffsocket=ffsocket,address=address)\n mu.lammps_write_inputfile(folder=jobdir,filename='in.lmp',positions='data.runnerformat.lmp',ace=ace)\n\n # create input-runner.xml (should be made without copying)\n mu.create_ipi_kmc_inputfile(jobdir,filename=\"input-runner.xml\",nsteps=nsteps,stride=100,seed=seed,a0=a0,ncell=ncell,nsi=nsi,nmg=nmg,nvac=nvac,neval=neval,temp=temp,nodes=nodes,address=address,testrun=test,cubic=args.cubic)\n\n # create submit-ipi-kmc.sh (should be made without copying)\n mu.create_submitskript_ipi_kmc(jobdir+\"/submit-ipi-kmc.sh\",nodes,ntasks,\n lmp_par=lmp_par,\n ipi_inst=ipi_inst,\n ffsocket=ffsocket,\n submittime_hours=submittime_hours,\n SBATCH=True)\n\n # create osubmit-ipi-kmc.sh (should be made without copying)\n mu.create_submitskript_ipi_kmc(jobdir+\"/osubmit-ipi-kmc.sh\",nodes,ntasks,\n lmp_par=lmp_par,\n ipi_inst=ipi_inst,\n ffsocket=ffsocket,\n submittime_hours=submittime_hours,\n SBATCH=False)\n\n # submit the job (execute either this or submit-ipi-kmc.sh_all3, not both)\n #mu.submitjob(submit=submit,submitdebug=submitdebug,jobdir=jobdir,submitskript=\"submit-ipi-kmc.sh\")\n\n # get submit-ipi-kmc.sh_all3 (should be made without copying)\n if nseeds == 3:\n mu.create_submitskript_ipi_kmc(directory+\"/submit-ipi-kmc.sh_all3\",nodes,ntasks,\n lmp_par=lmp_par,\n ipi_inst=ipi_inst,\n ffsocket=ffsocket,\n submittime_hours=submittime_hours,\n SBATCH=True,\n LOOPFOLDER=True)\n\n # submit the job (execute either this or submit-ipi-kmc.sh_all3, not both)\n #mu.submitjob(submit=submit,submitdebug=submitdebug,jobdir=directory,submitskript=\"submit-ipi-kmc.sh_all3\")\n if submit == True:\n mu.submitjob(submit_to_que=True,submit_to_debug_que=False,jobdir=directory,submitskript=\"submit-ipi-kmc.sh_all3\")\n\n\n print('done')\n return","def submit(self, **kwargs):\n pwd = curdir\n wd = dirname(self.logFile)\n chdir(wd)\n d = OrderedDict()\n #d['universe'] = 'vanilla'\n #d['executable'] = self.command\n\td['job-name'] = self.name\n\td['nodes'] = 1\n\td['partition'] = defaults.get('queue')\n\td['time'] = defaults.get(\"cputime\")\n\td['mem'] = defaults.get(\"memory\")\n d['output'] = op_join(wd,\"output.log\")\n d['error'] = op_join(wd,\"output.err\")\n csi_file = open(\"submit.sh\", \"w\")\n\tcsi_file.write(\"#!/bin/bash\\n\")\n data = [\"#SBATCH --%s=%s\\n\" % (k, v) for k, v in d.iteritems()]\n csi_file.write(\"\".join(data))\n\tcsi_file.write(\"export DAMPE_WORKFLOW_SERVER_URL=%s\\n\"%DAMPE_WORKFLOW_URL)\n csi_file.write(\"bash script\\n\")\n csi_file.close()\n output = self.__run__(\"sbatch submit.sh\")\n chdir(pwd)\n return self.__regexId__(output)","def _build_container_script(self, name, job_settings, logger):\n if not isinstance(job_settings, dict) or \\\n not isinstance(name, basestring):\n logger.error(\"Singularity Script malformed\")\n return None\n\n if 'image' not in job_settings or 'command' not in job_settings or\\\n 'max_time' not in job_settings:\n logger.error(\"Singularity Script malformed\")\n return None\n\n script = '#!/bin/bash -l\\n\\n'\n\n # NOTE an uploaded script could also be interesting to execute\n if 'pre' in job_settings:\n for entry in job_settings['pre']:\n script += entry + '\\n'\n\n# ################### Torque settings ###################\n if 'nodes' in job_settings:\n resources_request = \"nodes={}\".format(job_settings['nodes'])\n\n if 'tasks_per_node' in job_settings:\n resources_request += ':ppn={}'.format(\n job_settings['tasks_per_node'])\n\n script += '#PBS -l walltime={}\\n'.format(resources_request)\n else:\n if 'tasks_per_node' in job_settings:\n logger.error(\n r\"Specify 'tasks_per_node' while 'nodes' is not specified\")\n\n # if 'tasks' in job_settings:\n # script += '#qsub -n ' + str(job_settings['tasks']) + '\\n'\n\n script += '#PBS -l walltime={}\\n\\n'.format(job_settings['max_time'])\n# #######################################################\n\n script += '\\n# DYNAMIC VARIABLES\\n\\n'\n\n # NOTE an uploaded script could also be interesting to execute\n if 'pre' in job_settings:\n for entry in job_settings['pre']:\n script += entry + '\\n'\n\n script += '\\nmpirun singularity exec '\n\n if 'home' in job_settings and job_settings['home'] != '':\n script += '-H ' + job_settings['home'] + ' '\n\n if 'volumes' in job_settings:\n for volume in job_settings['volumes']:\n script += '-B ' + volume + ' '\n\n # add executable and arguments\n script += job_settings['image'] + ' ' + job_settings['command'] + '\\n'\n\n # NOTE an uploaded script could also be interesting to execute\n if 'post' in job_settings:\n for entry in job_settings['post']:\n script += entry + '\\n'\n\n return script","def gen_jobs(fpath, num_runs, netid):\n\n run = \"\"\n run += \"import sys\\n\"\n run += \"import subprocess\\n\"\n run += \"cmd_array = (\"\n for i in range(num_runs):\n run += \"r\\\"python test.py %d\\\"\" % i\n run += \",\\n\"\n\n run += \")\\n\"\n run += \"p = subprocess.Popen(cmd_array[int(sys.argv[1])-1], shell=True, stdout=subprocess.PIPE)\\n\"\n run += \"out = p.stdout.read()\"\n# run += \"print cmd_array[int(sys.argv[1])]\"\n\n script_name = \"test\"\n\n if verbose:\n print \"Writing array script: \" + \"run.\" + script_name + \".py\"\n f = open(os.path.join(fpath, \"run.\" + script_name + \".py\"), 'w')\n f.write(\"%s\\n\" % run)\n\n f = open(os.path.join(fpath, \"submit_run.\" + script_name + \".sh\"), 'w')\n submit_run = \"#!/bin/csh\\n\"\n submit_run += \"#$ -N %s\\n\" % (\"job_%d\" % num_runs)\n submit_run += \"#$ -t 1:%d\\n\" % (num_runs)\n submit_run += \"#$ -M %s@nd.edu\\n\\n\" % (netid)\n# submit_run += \"#$ -q short\"\n# submit_run += \"#$ -r y\"\n submit_run += \"python run.%s.py ${SGE_TASK_ID}\" % (script_name)\n\n if verbose:\n print \"Writing submit shell script: \" + \"submit_run.\" + script_name + \".sh\"\n f.write(\"%s\\n\" % submit_run)","def submit_job(jobscript, qsub_settings):\n flatten = lambda l: [item for sublist in l for item in sublist]\n batch_options = flatten([sge_option_string(k,v).split() for k, v in qsub_settings[\"options\"].items()])\n batch_resources = flatten([sge_resource_string(k, v).split() for k, v in qsub_settings[\"resources\"].items()])\n try:\n # -terse means only the jobid is returned rather than the normal 'Your job...' string\n jobid = subprocess.check_output([\"qsub\", \"-terse\"] + batch_options + batch_resources + [jobscript]).decode().rstrip()\n except subprocess.CalledProcessError as e:\n raise e\n except Exception as e:\n raise e\n return jobid","def createScriptJob(self):\n\n self.scriptJob = cmds.scriptJob(\n event=[\"SelectionChanged\", partial(self.selectScriptJob, self.outlinerControls)], runOnce=False,\n parent=\"pyArtRigCreatorUi\", kws=True, per=False)\n self.rigUiInst.scriptJobs.append(self.scriptJob)","def create_job(jobrun, vcf_filenames):\n if jobrun == \"cluster\":\n \"\"\"\n Supports only PBS clusters for now.\n \"\"\"\n for i in vcf_filenames:\n job_name = os.path.basename(i)\n job_print_string = \"#PBS -N %s\\n#PBS -M apirani@med.umich.edu\\n#PBS -m abe\\n#PBS -V\\n#PBS -l nodes=1:ppn=4,pmem=4000mb,walltime=72:00:00\\n#PBS -q fluxod\\n#PBS -A esnitkin_fluxod\\n#PBS -l qos=flux\\n\\n/home/apirani/anaconda/bin/python /nfs/esnitkin/bin_group/scripts/Scripts_v2.0/variants_position_analysis/reason_job.py -filter2_only_snp_vcf_dir %s -filter2_only_snp_vcf_file %s\\n\" % (job_name, args.filter2_only_snp_vcf_dir, i)\n job_file_name = \"%s.pbs\" % (i)\n f1=open(job_file_name, 'w+')\n f1.write(job_print_string)\n f1.close()\n #os.system(\"mv %s/*.pbs %s/temp\" % (args.filter2_only_snp_vcf_dir, args.filter2_only_snp_vcf_dir))\n pbs_dir = args.filter2_only_snp_vcf_dir + \"/*.pbs\"\n pbs_scripts = glob.glob(pbs_dir)\n for i in pbs_scripts:\n print \"Running: qsub %s\" % i\n #os.system(\"qsub %s\" % i)\n\n elif jobrun == \"parallel-local\":\n \"\"\"\n Generate a Command list of each job and run it in parallel on different cores available on local system\n \"\"\"\n command_array = []\n command_file = \"%s/commands_list.sh\" % args.filter2_only_snp_vcf_dir\n f3 = open(command_file, 'w+')\n\n\n for i in vcf_filenames:\n job_name = os.path.basename(i)\n job_print_string = \"#PBS -N %s\\n#PBS -M apirani@med.umich.edu\\n#PBS -m abe\\n#PBS -V\\n#PBS -l nodes=1:ppn=4,pmem=4000mb,walltime=72:00:00\\n#PBS -q fluxod\\n#PBS -A esnitkin_fluxod\\n#PBS -l qos=flux\\n\\n/home/apirani/anaconda/bin/python /nfs/esnitkin/bin_group/scripts/Scripts_v2.0/variants_position_analysis/reason_job.py -filter2_only_snp_vcf_dir %s -filter2_only_snp_vcf_file %s\\n\" % (job_name, args.filter2_only_snp_vcf_dir, i)\n job_file_name = \"%s.pbs\" % (i)\n f1=open(job_file_name, 'w+')\n f1.write(job_print_string)\n f1.close()\n #os.system(\"mv %s/*.pbs %s/temp\" % (args.filter2_only_snp_vcf_dir, args.filter2_only_snp_vcf_dir))\n pbs_dir = args.filter2_only_snp_vcf_dir + \"/*.pbs\"\n pbs_scripts = glob.glob(pbs_dir)\n\n\n for i in pbs_scripts:\n f3.write(\"bash %s\\n\" % i)\n f3.close()\n with open(command_file, 'r') as fpp:\n for lines in fpp:\n lines = lines.strip()\n command_array.append(lines)\n fpp.close()\n print len(command_array)\n if args.numcores:\n num_cores = int(num_cores)\n else:\n num_cores = multiprocessing.cpu_count()\n results = Parallel(n_jobs=num_cores)(delayed(run_command)(command) for command in command_array)\n\n elif jobrun == \"parallel-single-cluster\":\n print \" \"\n else:\n \"\"\"\n Generate a Command list of each job and run it on local system one at a time\n \"\"\"\n command_array = []\n command_file = \"%s/commands_list.sh\" % args.filter2_only_snp_vcf_dir\n os.system(\"bash %s\" % command_file)","def _submit_to_queue(self, script_file):","def write_and_submit_job(setting, manager, job_name, script_address):\n backup_folder = script_address.rsplit('/', maxsplit=1)[0]\n job_script_folder = backup_folder + '/Jobs/'\n job_output_file = job_script_folder + 'output.txt'\n print(job_output_file)\n if not os.path.exists(job_script_folder):\n os.makedirs(job_script_folder)\n job_script_address = job_script_folder + 'jobscript_'+manager+'.sh'\n with open(job_script_address, \"w\") as string_file:\n if manager == 'OGE':\n string_file.write(sungrid.job_script(setting, job_name=job_name, script_address=script_address, job_output_folder=job_script_folder))\n elif manager == 'Slurm':\n string_file.write(slurm.job_script(setting, job_name=job_name, script_address=script_address, job_output_file=job_output_file))\n else:\n raise ValueError(\"manager should be in ['OGE', 'Slurm']\")\n string_file.close()\n for root, dir_list, file_list in os.walk(backup_folder):\n for f in dir_list+file_list:\n os.chmod(os.path.join(root, f), 0o754)\n if manager == 'OGE':\n submit_cmd = 'qsub ' + job_script_address\n elif manager == 'Slurm':\n submit_cmd = 'sbatch ' + job_script_address\n else:\n raise ValueError(\"manager should be in ['OGE', 'Slurm']\")\n os.system(submit_cmd)","def generic_script(contents,\n job_name=None,\n stdout='/dev/null',\n stderr='/dev/null',\n shebang='#!/bin/bash',\n numnodes=None,\n numcpu=None,\n queue=None,\n walltime=None,\n mem=None,\n pmem=None):\n me = __file__\n current_time = time.strftime('%H:%M %D')\n\n if job_name is None:\n job_name = 'unnamed_job'\n\n if numnodes is None:\n numnodes = str(config.getint('pbs','numnodes'))#str(configuration.numnodes)\n\n if numcpu is None:\n numcpu = str(config.getint('pbs','numprocs'))#str(configuration.numprocs)\n\n if pmem:\n pmem = ',pmem=' + pmem\n else:\n pmem=''\n\n\n if mem:\n mem = ',mem=' + mem\n else:\n mem=''\n\n\n if queue is None:\n queue = config.read('pbs','queue')#configuration.queue\n\n additional_configuration_lines = []\n\n if queue is not None:\n additional_configuration_lines.append(\"#PBS -q %(queue)s\" % locals())\n\n if walltime is None:\n walltime = config.getint('pbs','walltime')#configuration.walltime\n\n if walltime is not None:\n additional_configuration_lines.append(\"#PBS -l walltime=%(walltime)s\" % locals())\n\n additional_configuration = '\\n'.join(additional_configuration_lines)\n\n the_script = \"\"\"#!/bin/bash\n# Created by pbs.py\n#PBS -N unnamed_job\n#PBS -l npcus=1\n#PBS -l walltime=1:00:00\n#PBS -j oe\n\n{ad_confg}\n\n{c}\n\"\"\".format(ad_confg = additional_configuration, c=contents)\n\n return the_script","def create_hourly_jobflow_manual(self, script, queue_name_out, queue_name_in, current_folder_name, log_prefix, pig_args):\n # TODO: standardize message packing/unpacking\n pig_command_args = ['/home/hadoop/bin/pig']\n for varname in pig_args.keys():\n pig_command_args.append('-p')\n pig_command_args.append(varname + '=' + pig_args[varname])\n pig_command_args.append(script)\n pig_command = '|'.join(pig_command_args)\n \n queue_message = '\\t'.join([pig_command, queue_name_out, current_folder_name + '|' + log_prefix])\n self.write_message(queue_name_in, queue_message)","def sendjob(self,bashscript):","def create_template(issue, cpu_count, memory, work_dir, cmd):\n # Prepare SLURM shell script contents\n template = \"#!/bin/bash\\n\" \\\n \"#SBATCH -N 1\\n\" \\\n \"#SBATCH --ntasks={cpu_count}\\n\" \\\n \"#SBATCH --mem={memory}\\n\" \\\n \"#SBATCH --time=1-00:00\\n\" \\\n \"#SBATCH --job-name={jobid}\\n\" \\\n \"#SBATCH -o {work_dir}/job_%j.out\\n\" \\\n \"#SBATCH -e {work_dir}/job_%j.err\\n\" \\\n \"source /mnt/nas2/redmine/applications/.virtualenvs/OLCRedmineAutomator/bin/activate\\n\" \\\n \"{cmd}\".format(cpu_count=cpu_count,\n memory=memory,\n jobid=issue.id,\n work_dir=work_dir,\n cmd=cmd)\n\n # Path to SLURM shell script\n file_path = os.path.join(BIO_REQUESTS_DIR, str(issue.id), str(issue.id) + '_slurm.sh')\n\n # Write SLURM job to shell script\n with open(file_path, 'w+') as file:\n file.write(template)\n\n make_executable(file_path)\n\n return file_path","def build_metric_submit_file(self, metric):\n\n log_dir = self.rsv.get_metric_log_dir()\n environment = \"PATH=/usr/bin:/bin\\n\"\n condor_id = metric.get_unique_name()\n arguments = \"-v 3 -r -u %s %s %s\" % (metric.host, metric.name, metric.get_settings())\n timestamp = strftime(\"%Y-%m-%d %H:%M:%S %Z\")\n\n probe_interval = metric.get_probe_interval()\n if not probe_interval:\n cron = metric.get_cron_entry()\n if not cron:\n self.rsv.log(\"ERROR\", \"Invalid cron time for metric %s on host %s. Will not start.\" %\n (metric.name, metric.host))\n return \"\"\n\n submit = \"\"\n submit += \"######################################################################\\n\"\n submit += \"# Temporary submit file generated by rsv-control\\n\"\n submit += \"# Generated at %s \" % timestamp\n submit += \"######################################################################\\n\"\n submit += \"Environment = %s\\n\" % environment\n\n if probe_interval:\n submit += \"DeferralPrepTime = ifThenElse(%d - ScheddInterval + 31 > 0, %d - ScheddInterval + 31, 180) \\n\" % (probe_interval, probe_interval)\n submit += \"DeferralTime = (CurrentTime + %d + random(30))\\n\" % probe_interval\n submit += \"DeferralWindow = 99999999\\n\"\n submit += \"+OSGRSVProbeInterval = %d\\n\" % probe_interval\n else:\n submit += \"CronPrepTime = 180\\n\"\n submit += \"CronWindow = 99999999\\n\"\n submit += \"CronMonth = %s\\n\" % cron[\"Month\"]\n submit += \"CronDayOfWeek = %s\\n\" % cron[\"DayOfWeek\"]\n submit += \"CronDayOfMonth = %s\\n\" % cron[\"DayOfMonth\"]\n submit += \"CronHour = %s\\n\" % cron[\"Hour\"]\n submit += \"CronMinute = %s\\n\" % cron[\"Minute\"]\n submit += \"Executable = %s\\n\" % self.rsv.get_wrapper()\n submit += \"Error = %s/%s.err\\n\" % (log_dir, condor_id)\n submit += \"Output = %s/%s.out\\n\" % (log_dir, condor_id)\n submit += \"Log = %s/%s.log\\n\" % (log_dir, condor_id)\n submit += \"Arguments = %s\\n\" % arguments\n submit += \"Universe = local\\n\"\n submit += \"Notification = never\\n\"\n submit += \"OnExitRemove = false\\n\"\n submit += \"PeriodicRelease = HoldReasonCode =!= 1\\n\"\n submit += \"+OSGRSV = \\\"metrics\\\"\\n\"\n submit += \"+OSGRSVHost = \\\"%s\\\"\\n\" % metric.host\n submit += \"+OSGRSVMetric = \\\"%s\\\"\\n\" % metric.name\n submit += \"+OSGRSVUniqueName = \\\"%s\\\"\\n\" % condor_id\n submit += \"Queue\\n\"\n \n return submit","def _build_job_submission_call(self,\n name,\n job_settings,\n logger):\n raise NotImplementedError(\n \"'_build_job_submission_call' not implemented.\")","def main():\n if len(sys.argv) < 3:\n print 'USAGE: python robscript.py script params_json'\n return\n\n script_loc = sys.argv[1]\n params_json_loc = sys.argv[2]\n\n send_upload = create_print('[UPLOAD] ')\n on_status_update = create_print('[STATUS] ')\n on_error = create_print('[ERROR] ')\n on_finish = create_print('Finished. ')\n\n params = None\n with open(params_json_loc) as f:\n params = json.load(f)\n\n script = None\n with open(script_loc) as f:\n script = jinja2.Template(f.read()).render(params)\n\n run_job(script, send_upload, on_status_update, on_error, on_finish, '')","def main():\n parser = argparse.ArgumentParser()\n parser.add_argument('--file', '-f',\n type=argparse.FileType('r'),\n required=True)\n args = parser.parse_args()\n\n project_yaml = yaml.safe_load(args.file)\n\n # Get the release name from the file path\n release = os.path.split(os.path.dirname(args.file.name))[1]\n\n create_jobs(release, project_yaml)","def create_job_object(message, environment_image):\n\n PYTHONUNBUFFERED_ENV = client.V1EnvVar(name=\"PYTHONUNBUFFERED\", value=\"1\")\n AUTH_TOKEN_ENV = client.V1EnvVar(name=\"AUTH_TOKEN\", value=AUTH_TOKEN)\n EVALAI_API_SERVER_ENV = client.V1EnvVar(\n name=\"EVALAI_API_SERVER\", value=EVALAI_API_SERVER\n )\n MESSAGE_BODY_ENV = client.V1EnvVar(name=\"BODY\", value=json.dumps(message))\n submission_pk = message[\"submission_pk\"]\n image = message[\"submitted_image_uri\"]\n # Configureate Pod agent container\n agent_container = client.V1Container(\n name=\"agent\", image=image, env=[PYTHONUNBUFFERED_ENV]\n )\n # Configureate Pod environment container\n environment_container = client.V1Container(\n name=\"environment\",\n image=environment_image,\n env=[\n PYTHONUNBUFFERED_ENV,\n AUTH_TOKEN_ENV,\n EVALAI_API_SERVER_ENV,\n MESSAGE_BODY_ENV,\n ],\n resources=client.V1ResourceRequirements(\n limits={\"nvidia.com/gpu\": \"1\"}\n ),\n )\n # Create and configurate a spec section\n template = client.V1PodTemplateSpec(\n metadata=client.V1ObjectMeta(labels={\"app\": \"evaluation\"}),\n spec=client.V1PodSpec(\n containers=[environment_container, agent_container],\n restart_policy=\"Never\",\n ),\n )\n # Create the specification of deployment\n spec = client.V1JobSpec(backoff_limit=1, template=template)\n # Instantiate the job object\n job = client.V1Job(\n api_version=\"batch/v1\",\n kind=\"Job\",\n metadata=client.V1ObjectMeta(\n name=\"submission-{0}\".format(submission_pk)\n ),\n spec=spec,\n )\n return job","def createJob(self, executable ):\n thisfileFolderPath = os.path.dirname(inspect.getfile( inspect.currentframe() ))\n inp = open( os.path.join(thisfileFolderPath,\"job.oar.tpl\"), 'r')\n t = Template(inp.read())\n \n s = t.substitute(executable=executable)\n \n completePath = os.path.join(self.oarRunFolder,\"job.oar.sh\")\n outp = open(completePath, 'w')\n outp.write(s)\n outp.close()\n \n os.system('chmod +x ' + completePath)\n print \"OAR: created job file: \", completePath\n return completePath","def parse_jobscript():\n p = argparse.ArgumentParser(description=\"SGE snakemake submit script\")\n p.add_argument(\"jobscript\", help=\"Snakemake jobscript with job properties.\")\n return p.parse_args().jobscript","def _create_job(self,\n name,\n environment_string,\n description='',\n platform='LINUX'):\n job = data_types.Job()\n job.name = name\n if environment_string.strip():\n job.environment_string = environment_string\n job.platform = platform\n job.descripton = description\n job.put()\n\n return job","def build_job_scripts(model_list, scenario_list, output_dir, cassandra_config_dir, cassandra_log_dir,\n cassandra_main_script, sbatch_account, sbatch_partition='slurm', sbatch_walltime='01:00:00',\n sbatch_ntasks=3, sbatch_nodes=3, sbatch_jobname='cassie', sbatch_logdir='.', template=None):\n\n # use default configuration template file if user does not give one\n if template is None:\n template = pkg_resources.resource_filename('cassie', 'data/sbatch_template.sh')\n\n # existing tags to replace in the template file\n model_tag = '<model>'\n scenario_tag = '<scenario>'\n account_tag = '<account>'\n partition_tag = '<partition>'\n ntasks_tag = '<ntasks>'\n nodes_tag = '<nodes>'\n time_tag = '<walltime>'\n jobname_tag = '<jobname>'\n logdir_tag = '<logdir>'\n cassandra_configdir_tag = '<cassconfigdir>'\n cassandra_logdir_tag = '<casslogdir>'\n cassandra_script_tag = '<cassmainscript>'\n\n for model in model_list:\n for scenario in scenario_list:\n\n output_file = os.path.join(output_dir, f'run_{model.lower()}_{scenario}.sh')\n\n with open(output_file, 'w') as out:\n with open(template) as get:\n\n f = get.read()\n\n # replace tag names with dynamic content\n fx = f.replace(model_tag, model)\n fx = fx.replace(scenario_tag, scenario)\n\n fx = fx.replace(account_tag, sbatch_account)\n fx = fx.replace(partition_tag, sbatch_partition)\n fx = fx.replace(ntasks_tag, str(sbatch_ntasks))\n fx = fx.replace(nodes_tag, str(sbatch_nodes))\n fx = fx.replace(time_tag, sbatch_walltime)\n fx = fx.replace(jobname_tag, sbatch_jobname)\n fx = fx.replace(logdir_tag, sbatch_logdir)\n\n fx = fx.replace(cassandra_configdir_tag, cassandra_config_dir)\n fx = fx.replace(cassandra_logdir_tag, cassandra_log_dir)\n fx = fx.replace(cassandra_script_tag, cassandra_main_script)\n\n out.write(fx)","def submit_job(sample_config, jobname, rundir, cliargs, extramodules=[]):\n slurmfile_path = os.path.join(rundir, \"{}.slurm\".format(jobname))\n with open(slurmfile_path, \"w\") as slurmfile:\n slurmfile.write(\"#! /bin/bash -l\\n\")\n slurmfile.write(\"#SBATCH -A {}\\n\".format(cliargs.project))\n slurmfile.write(\"#SBATCH -o {}.out\\n\".format(jobname))\n slurmfile.write(\"#SBATCH -e {}.err\\n\".format(jobname))\n slurmfile.write(\"#SBATCH -J {}.job\\n\".format(jobname))\n if cliargs.threads<16 :\n slurmfile.write(\"#SBATCH -p core -n {}\\n\".format(cliargs.threads))\n else:\n slurmfile.write(\"#SBATCH -p node -n {}\\n\".format(cliargs.threads))\n slurmfile.write(\"#SBATCH -t {}\\n\".format(cliargs.time))\n if hasattr(cliargs, \"email\"):\n slurmfile.write(\"#SBATCH --mail-user {}\\n\".format(cliargs.email))\n slurmfile.write(\"#SBATCH --mail-type=ALL\\n\")\n if hasattr(cliargs, \"qos\"):\n slurmfile.write(\"#SBATCH --qos={}\".format(cliargs.qos))\n slurmfile.write(\"\\n\\n\")\n slurmfile.write(\"set -e\\n\")\n slurmfile.write(\"source activate {}\\n\".format(cliargs.env))\n slurmfile.write(\"module load bioinfo-tools\\n\")\n for module in extramodules:\n slurmfile.write(module)\n\n slurmfile.write(\"deNovo_pipeline.py --global-config {} \"\n \"--sample-config {}\\n\\n\".format(cliargs.global_config, sample_config))\n\n command=(\"sbatch\", slurmfile_path)\n print(command)\n try:\n if cliargs.dry_run:\n return 0\n except AttributeError as e:\n print(\"Warning! Could not determine if dry-run, running the command anyway: {}\".format(e))\n return subprocess.call(command)","def submit(self,\n script: str,\n out_script_fname: str,\n job_name: str,\n log_fname: str = \"log\",\n n_core: int = 1,\n max_cpu_hour: Optional[int] = None,\n max_mem_gb: Optional[int] = None,\n depend_job_ids: Optional[List[str]] = None,\n wait: bool = False) -> str:\n logger.info(f\"Submit command(s):\\n{script}\")\n header = (self.gen_sge_header if self.scheduler_name == \"sge\"\n else self.gen_slurm_header)(job_name,\n log_fname,\n n_core,\n max_cpu_hour,\n max_mem_gb,\n depend_job_ids,\n wait)\n with open(out_script_fname, \"w\") as f:\n f.write('\\n'.join(filter(None, [\"#!/bin/bash\",\n header + \"\\n\",\n self.prefix_command,\n script + \"\\n\"])))\n return (run_command(f\"{self.submit_command} {out_script_fname}\")\n .split()[2 if self.scheduler_name == \"sge\" else -1])","def make_job_data(url, script_fn):\n wd = os.path.dirname(script_fn)\n job_name = '{0}-{1}-{1}'.format(\n os.path.basename(script_fn),\n url.split(\"/\")[-1],\n str(uuid.uuid4())[:8],\n )\n job_data = {\"job_name\": job_name,\n \"cwd\": wd,\n \"script_fn\": script_fn }\n return job_data","def createJobs(jobsfile, infiles, outdir, name, nchunks, channel, year, **kwargs):\n tag = kwargs.get('tag', \"\") # TODO: add\n tes = kwargs.get('tes', 1.)\n ltf = kwargs.get('ltf', 1.)\n jtf = kwargs.get('jtf', 1.)\n Zmass = kwargs.get('Zmass', False)\n prefetch = kwargs.get('prefetch', False)\n cmd = 'python postprocessors/job.py -i %s -o %s -N %s -n %i -c %s -y %s'%(','.join(infiles),outdir,name,nchunks,channel,year)\n if tes!=1.:\n cmd += \" --tes %.3f\"%(tes)\n if ltf!=1.:\n cmd += \" --ltf %.3f\"%(ltf)\n if jtf!=1.:\n cmd += \" --jtf %.3f\"%(jtf)\n if Zmass and channel=='mumu':\n cmd += \" --Zmass\"\n if prefetch:\n cmd += \" -p\"\n if tag:\n cmd += \" -l %s\"%tag\n if args.verbose:\n print cmd\n jobsfile.write(cmd+'\\n')\n return 1","def create_script_job(self):\n script_job_no = pm.scriptJob(\n e=[\"SelectionChanged\",\n 'if pm.ls(sl=1) and pm.ls(sl=1)[0].name() == \"%s\":\\n'\n ' pm.displayPref(displayAffected=False)\\n'\n 'else:\\n'\n ' pm.displayPref(displayAffected=True)' % self.light.name()\n ]\n )\n self.store_data('%s' % script_job_no)","def submit_job_submission_job(cfg: ElasticBlastConfig):\n dry_run = cfg.cluster.dry_run\n\n subs = {\n 'K8S_JOB_SUBMIT_JOBS' : K8S_JOB_SUBMIT_JOBS,\n 'ELB_DOCKER_IMAGE' : ELB_CJS_DOCKER_IMAGE_GCP,\n 'ELB_GCP_PROJECT' : cfg.gcp.project,\n 'ELB_GCP_ZONE' : cfg.gcp.zone,\n 'ELB_RESULTS' : cfg.cluster.results,\n 'ELB_CLUSTER_NAME' : cfg.cluster.name,\n 'ELB_PD_SIZE' : cfg.cluster.pd_size,\n 'ELB_LABELS' : cfg.cluster.labels,\n # For autoscaling\n 'ELB_NUM_NODES' : str(cfg.cluster.num_nodes),\n 'ELB_USE_LOCAL_SSD': str(cfg.cluster.use_local_ssd).lower()\n }\n logging.debug(f\"Submitting job submission job: {ELB_CJS_DOCKER_IMAGE_GCP}\")\n with TemporaryDirectory() as d:\n set_extraction_path(d)\n job_yaml = os.path.join(d, 'job-submit-jobs.yaml')\n with open(job_yaml, 'wt') as f:\n f.write(substitute_params(resource_string('elastic_blast', 'templates/job-submit-jobs.yaml.template').decode(), subs))\n cmd = f\"kubectl --context={cfg.appstate.k8s_ctx} apply -f {job_yaml}\"\n if dry_run:\n logging.info(cmd)\n else:\n safe_exec(cmd)","def makeJob(kallisto, index, meta, bootstraps, files, single, s=1, l=180): \n cmd = \"%(kallisto)s quant -i %(index)s -o %(meta)s \" % locals()\n for file in files: \n cmd += \" ../%s\" % file \n if single: \n cmd += \" --single -l %(l)i -s %(s)i\" % locals()\n cmd += \" &> %s.log.txt\" % meta\n return cmd","def __init__(self, ccinput, script=None, queue=\"slurm\", mem=500,\n cpus=1, time=\"00:15:00\", partition=None, jobname=\"CCJob\",\n software=None, meta_file=\"meta.json\"):\n self.ccinput = ccinput\n self.script = script\n self.software = software\n if type(queue) == str:\n self.queue = queue_factory(queue)\n elif type(queue) == ccjob.Queue.SLURM:\n self.queue = queue\n\n self.jobid = None\n\n self.meta = {\"status\": None,\n \"wdir\" : self.ccinput.wdir,\n \"infile\" : self.ccinput.filename,\n \"basename\" : self.ccinput.basename\n }\n self.meta_filename = os.path.basename(meta_file)\n self.meta_filepath = os.path.join(self.ccinput.wdir, meta_file)\n\n # submit options\n self.options = {\"memory\": mem, \"cpus\": cpus, \"time\": time,\n \"partition\": partition, \"jobname\": jobname}\n self.custom_options = []\n self.software_options = []","def create_job(project, description):\n randomnames = open(os.path.join(\"Anemone\", \"templates\", \"namegen.html\")).readlines()\n jobname = (\"Quick.\" +\n random.choice(randomnames)[:-1] + # for some reason choice gives extra space\n random.choice(randomnames)[:-1]) # for some reason choice gives extra space\n\n newjob = Job.create(project=project, name=jobname, description=description)\n newjob.name = newjob.name + \".{0:0=3d}\".format(newjob.id)\n newjob.save()\n return newjob","def submit(self, content):\n script = self.shebang\n script += content.encode('utf8', 'replace')\n if not script.endswith(b'\\n'):\n script += b'\\n'\n try:\n out, err = self.p.communicate(script)\n except KeyboardInterrupt:\n self._interrupt()\n return\n out = py3compat.bytes_to_str(out)\n err = py3compat.bytes_to_str(err)\n # Get the jobid\n self._jobid = 0\n if out.find(\"Submitted batch job\") == 0:\n self._jobid = int(out.split(' ')[-1])\n self._is_started = True\n if self._args_jobid:\n self._userns[self._args_jobid] = self._jobid\n else:\n sys.stderr.write(\"Error during job submission\\n\")\n sys.stderr.write(\"Submission arguments : {0}\\n\".format(' '.join(self.cmd)))\n self._is_terminated = True\n return (out, err)","def submit(user, user_file):\n user_job = '{\"localFile\" : \"userfile\", \"destFile\" : \"primes.py\"}'.replace(\n \"userfile\", user_file)\n run(['bash', 'submit.sh', user_file, user_job, user])","def start_job(planner, job_template, domain, problem):\n template_file = open(job_template, 'r')\n job_string = template_file.read()\\\n .replace('$DOMAIN', str(domain))\\\n .replace('$PROBLEM', str(problem))\\\n .replace('$LOWERDOMAIN', str(domain).lower())\\\n .replace('$LOWERPROBLEM', str(problem).lower())\\\n .replace('$PLANNER', planner)\n print(job_string)","def _generate_job_action(self, job):\n # Derive the entrypoint command, the same content that might be written by self.get_jobscript(job)\n use_threads = \"--force-use-threads\" if not job.is_group() else \"\"\n\n exec_job = self.format_job(\n self.exec_job, job, _quote_all=True, use_threads=use_threads\n )\n\n # Now that we've parsed the job resource requirements, add to exec\n exec_job += self.get_default_resources_args()\n\n # The full command to download the archive, extract, and run\n # For snakemake bases, we must activate the conda environment, but\n # for custom images we must allow this to fail (hence || true)\n commands = [\n \"/bin/bash\",\n \"-c\",\n \"mkdir -p /workdir && cd /workdir && wget -O /download.py https://gist.githubusercontent.com/vsoch/84886ef6469bedeeb9a79a4eb7aec0d1/raw/181499f8f17163dcb2f89822079938cbfbd258cc/download.py && chmod +x /download.py && source activate snakemake || true && pip install crc32c && python /download.py download %s %s /tmp/workdir.tar.gz && tar -xzvf /tmp/workdir.tar.gz && %s\"\n % (self.bucket.name, self.pipeline_package, exec_job),\n ]\n\n # We are only generating one action, one job per run\n # https://cloud.google.com/life-sciences/docs/reference/rest/v2beta/projects.locations.pipelines/run#Action\n action = {\n \"containerName\": \"snakejob-{}-{}\".format(job.name, job.jobid),\n \"imageUri\": self.container_image,\n \"commands\": commands,\n \"environment\": self._generate_environment(),\n \"labels\": self._generate_pipeline_labels(job),\n }\n return action","def _create_job_message(self):\n #TODO: Final check of source file, add xml settings, allow for user\n # to set priority, verify all job data is correct format\n\n if not hasattr(self.required_files, '_get_message'):\n self.add_file_collection()\n\n if self.pool and hasattr(self.pool, 'id'):\n pool_options = {'poolId': self.pool.id}\n\n elif self.pool:\n pool_options = {'poolId': str(self.pool)}\n\n else:\n size = max(int(self.instances), 1)\n pool_options = {'autoPoolSpecification': self._auto_pool(size)}\n\n job_message = {\n 'Name': str(self.name),\n 'Type': self._api.jobtype(),\n 'RequiredFiles': self.required_files._get_message(\"submit\"),\n 'Parameters': list(self._filter_params()),\n 'JobFile': str(self.source),\n 'Settings': str(self.settings),\n 'Priority': 'Medium'\n }\n job_message.update(pool_options)\n\n self._log.debug(\"Job message: {0}\".format(job_message))\n return job_message","def prepare_pr_condor_job(self, pool_type, pool_address, number_of_jobs, subtask_index, data_files, rank='0', extraArgs=''):\n ############\n copasi_file = 'auto_copasi_%d.$(Process).cps' % subtask_index\n output_file = 'output_%d.$(Process).txt' % subtask_index\n \n \n \n if pool_type == 'ec2':\n binary_dir = '/usr/local/bin'\n transfer_executable = 'NO'\n else:\n binary_dir, binary = os.path.split(settings.COPASI_LOCAL_BINARY)\n transfer_executable = 'YES'\n \n input_files_string = ', '\n for data_file in data_files:\n input_files_string += (data_file + ', ')\n input_files_string = input_files_string.rstrip(', ')\n\n condor_job_string = Template(condor_spec.raw_condor_job_string).substitute(copasiFile=copasi_file, \n otherFiles=input_files_string,\n rank=rank,\n binary_dir = binary_dir,\n transfer_executable = transfer_executable,\n pool_type = pool_type,\n pool_address = pool_address,\n subtask=str(subtask_index),\n n = number_of_jobs,\n outputFile = output_file,\n extraArgs='',\n )\n \n condor_job_filename = 'auto_condor_%d.job'%subtask_index\n condor_job_full_filename = os.path.join(self.path, condor_job_filename)\n condor_file = open(condor_job_full_filename, 'w')\n condor_file.write(condor_job_string)\n condor_file.close()\n\n return condor_job_filename","def createScript_sbatch(self):\n tools_createScript_sbatch(\n sbatch_script_file_name = self.sbatchFile_addMEM,\n executable = self.executable_addMEM,\n command_line_parameters = self.cfgFiles_addMEM_modified,\n input_file_names = self.inputFiles,\n output_file_names = self.outputFiles,\n script_file_names = self.shFiles_addMEM_modified,\n log_file_names = self.logFiles_addMEM,\n keep_logs = False,\n working_dir = self.workingDir,\n max_num_jobs = 100000000, # it's really silly to limit the number of jobs; use an enormous number as the ,,fix''\n cvmfs_error_log = self.cvmfs_error_log,\n pool_id = self.pool_id,\n use_home = self.use_home,\n validate_outputs = self.check_output_files,\n max_num_submittedJobs = 2000,\n )","def _submit_to_queue(self, script_file):\n if sys.version_info[0] < 3:\n process = Popen(['qsub', script_file], stdout=PIPE, stderr=PIPE)\n else:\n # need string not bytes so must use universal_newlines\n process = Popen(['qsub', script_file], stdout=PIPE, stderr=PIPE, universal_newlines=True)\n\n out, err = process.communicate()\n # grab the returncode. SGE returns 0 if the job was successful\n queue_id = None\n if process.returncode == 0:\n try:\n # output should of the form\n # Your job 1659048 (\"NAME_OF_JOB\") has been submitted\n queue_id = int(out.split(' ')[2])\n except:\n # probably error parsing job code\n logger.critical(\"Could not parse job id following qsub...\")\n return SubmitResults(qid=queue_id, out=out, err=err, process=process)","def send_job(self):\n graph = self.processgraphEdit.toPlainText()\n # info(self.iface, graph)\n response = self.connection.job_create(json.loads(graph))\n if response.status_code == 201:\n info(self.iface, \"Successfully created new job, Response: {}\".format(response.status_code))\n else:\n warning(self.iface, \"Not able to created new job, Response: {}\".format(str(response.json())))","def _submit_to_queue(self, script_file):\n if sys.version_info[0] < 3:\n process = Popen(['llsubmit', script_file], stdout=PIPE, stderr=PIPE)\n else:\n # need string not bytes so must use universal_newlines\n process = Popen(['llsubmit', script_file], stdout=PIPE, stderr=PIPE, universal_newlines=True)\n\n out, err = process.communicate()\n # grab the return code. llsubmit returns 0 if the job was successful\n queue_id = None\n if process.returncode == 0:\n try:\n # on JUQUEEN, output should of the form\n #llsubmit: The job \"juqueen1c1.zam.kfa-juelich.de.281506\" has been submitted.\n token = out.split()[3]\n s = token.split(\".\")[-1].replace('\"', \"\")\n queue_id = int(s)\n except:\n # probably error parsing job code\n logger.critical(\"Could not parse job id following llsubmit...\")\n raise\n\n return SubmitResults(qid=queue_id, out=out, err=err, process=process)","def create_job(self, name: str) -> Slurm:\n LOG.info(\"Create a slurm job with name %s\", name)\n job = Slurm(\n name,\n {\"account\": self.account, \"time\": self.time,},\n scripts_dir=str(self.scripts_dir),\n log_dir=str(self.log_dir),\n )\n return job","def submit_job(script_name,\n\t\t\ttask_name, \n\t\t\timage_url=None,\n\t\t\tscale_tier = None, \n\t\t\textra_args=None, \n\t\t\tmaster_type=None, \n\t\t\taccelerator_type=None):\n\n\tcreds = get_creds()\n\tproject_id = 'projects/{}'.format(project_id)\n\t\n\tjob_name = task_name+\"_\" + datetime.datetime.now().strftime(\"%y%m%d_%H%M%S\")\n\n\targs = ['--task', task_name]\n\n\tif extra_args:\n\t\targs += extra_args\n\n\tif not scale_tier:\n\t\tscale_tier = 'BASIC'\n\n\ttraining_inputs = {\n\t\t'args': args,\n\t\t'pythonVersion': '3.7',\n\t\t'scaleTier': scale_tier,\n\t\t'region': 'us-west1',\n\t\t'jobDir': \"gs://ds-ml-nlp/ai_platform_job_files\",\n\t}\n\n\ttraining_inputs.update({'packageUris': [\"gs://ds-ml-nlp/ai_platform_job_files/lyrical_clustering-0.1.tar.gz\"],\n\t\t\t\t\t\t\t\t'pythonModule': f'scripts.{script_name}',\n\t\t\t\t\t\t\t\t'runtimeVersion': '2.1',})\n\n\tif scale_tier == 'CUSTOM':\n\t\ttraining_inputs.update({'masterType': master_type})\n\n\tif accelerator_type:\n\t\ttraining_inputs.update({'masterConfig': {'acceleratorConfig': {'count': 1, 'type': accelerator_type}}})\n\t\t\n\tjob_spec = {\"jobId\": job_name, \"trainingInput\": training_inputs}\n\tcloudml = discovery.build(\"ml\", \"v1\", cache_discovery=False, credentials=creds)\n\trequest = cloudml.projects().jobs().create(body=job_spec, parent=project_id)\n\ttry:\n\t\tresponse = request.execute()\n\texcept HttpError as err:\n\t\tlogging.error('There was an error creating the training job.'\n\t\t\t\t\t ' Check the details:')\n\t\tlogging.error(err._get_reason())","def submit():\n subtime = datetime.datetime.now()\n args = parse_args()\n \n # get the course's configuration\n with open(args.config) as cfgfile:\n cfg = json.load(cfgfile)\n \n # Confirm the correct directory\n wdir = os.getcwd()\n resp = input(\"The current directory is:\\n\\n\\t\" + wdir \n + \"\\n\\nIs this the directory you want to submit? (y/n): \")\n if resp not in [\"y\", \"Y\", \"yes\", \"Yes\", \"YES\"]:\n return\n \n # Get the assignment the student is submitting\n resp = \"\"\n while resp not in cfg[\"assignments\"]:\n resp = input(\"\\nSelect an assignment from the list:\\n\\n\\t\" \n + \"\\n\\t\".join(textwrap.wrap(\", \".join(cfg[\"assignments\"])))\n + \"\\n\\nAssignment: \")\n assignment = resp\n\n # Get the section to which the user wants to submit\n resp = \"\"\n desc = \"\\n\\t\".join([\"{:3}: {}\".format(key, cfg[\"sections\"][key][\"desc\"]) \n for key in cfg[\"sections\"].keys()])\n\n while resp not in cfg[\"sections\"].keys():\n resp = input(\"\\nSelect a section number from the list:\\n\\n\\t\" \n + desc + \"\\n\\nSection: \")\n section = cfg[\"sections\"][resp]\n\n # Collect the username and list of files to tar\n username = os.environ.get(\"USER\")\n files = glob.glob(\"*\")\n\n # Create the submission directory and copy in all the files\n submitdir = \"{}_{}_{}\".format(assignment, cfg[\"course\"], username)\n os.mkdir(submitdir)\n for f in files:\n shutil.copy(f, os.path.join(submitdir, f))\n\n # Create the archive, then delete the submission directory\n tarname = \"{}.{}.{}.{}.tgz\".format(\n assignment, cfg[\"course\"], username, int(time.time())\n )\n with tarfile.open(tarname, mode=\"w:gz\") as tar:\n tar.add(submitdir)\n shutil.rmtree(submitdir)\n\n # Build the email addresses\n domain = cfg[\"domain\"]\n faddr = \"{}@{}\".format(username, domain)\n taddr = \"{}@{}\".format(section[\"ta\"], domain)\n \n if \"cc\" in section:\n ccaddrs = [\"{}@{}\".format(netid, domain) for netid in section[\"cc\"]]\n else:\n ccaddrs = []\n\n if \"bcc\" in section:\n bccaddrs = [\"{}@{}\".format(netid, domain) for netid in section[\"bcc\"]]\n else:\n bccaddrs = []\n\n # Build the email headers\n msg = email.mime.multipart.MIMEMultipart()\n msg[\"From\"] = faddr\n msg[\"To\"] = taddr\n msg[\"Cc\"] = \", \".join(ccaddrs)\n msg[\"Subject\"] = \"{} submission for student {} in {}\".format(\n assignment, username, cfg[\"course\"]\n )\n\n # Build the attachment and compute a check sum\n attachment = email.mime.base.MIMEBase('application', 'octet-stream')\n with open(tarname, 'rb') as f:\n contents = f.read()\n chksum = hashlib.md5(contents).hexdigest()\n attachment.set_payload(contents)\n \n email.encoders.encode_base64(attachment)\n attachment.add_header(\"Content-Disposition\", \n \"attachment; filename={}\".format(tarname)\n )\n msg.attach(attachment)\n \n # Build and attach the message text\n msg.attach(email.mime.text.MIMEText(\n \"Submission from {}\\n\".format(username)\n + \"Course: {}\\n\".format(cfg[\"course\"])\n + \"Assignment: {}\\n\".format(assignment)\n + \"Time: {}\\n\".format(subtime.strftime(\"%m/%d/%Y %H:%M:%S\"))\n + \"MD5Sum: {}\\n\".format(chksum),\n \"plain\"))\n \n # Send the email\n server = smtplib.SMTP('localhost')\n server.sendmail(faddr, [taddr] + ccaddrs + bccaddrs, msg.as_string())\n server.quit()","def submit_mc_jobs_htcondor(in_args=sys.argv[1:], mg5_dir=MG5_DIR):\n\n # Handle user options. The user can pass all the same options as they\n # would if running the program locally. However, for certain options,\n # we interecpt the argument(s) and modify if necessary.\n parser = argparse.ArgumentParser(description=__doc__)\n\n # Options for the job submitter\n parser.add_argument(\"jobIdRange\",\n help=\"Specify job ID range to run over. The ID is used\"\n \" as the random number generator seed, so manual \"\n \"control is needed to avoid making the same files. \"\n \"Must be of the form: startID, endID. \",\n nargs=2, type=int) # no metavar, bug with positionals\n parser.add_argument(\"--oDir\",\n help=\"Directory for output HepMC files. \"\n \"If no directory is specified, an automatic one will \"\n \"be created at: \"\n \"/hdfs/user/<username>/NMSSMPheno/MG5_aMC/<energy>TeV/<output>/<date>\"\n \", where <output> refers to the output directory as \"\n \"specified in the card.\",\n default=\"\")\n # All other program arguments to pass to program directly.\n parser.add_argument(\"--args\",\n help=\"All other program arguments. \"\n \"You MUST specify this after all other options\",\n nargs=argparse.REMAINDER)\n # Some generic script options\n parser.add_argument(\"--dry\",\n help=\"Dry run, don't submit to queue.\",\n action='store_true')\n parser.add_argument(\"-v\",\n help=\"Display debug messages.\",\n action='store_true')\n args = parser.parse_args(args=in_args)\n\n mg5_parser = MG5ArgParser()\n mg5_args = mg5_parser.parse_args(args.args)\n\n log.info('>>> Creating jobs')\n\n if args.v:\n log.setLevel(logging.DEBUG)\n\n log.debug('program args: %s' % args)\n log.debug('mg5 args: %s' % mg5_args)\n\n # Do some checks\n # -------------------------------------------------------------------------\n if not os.path.isdir(mg5_dir):\n raise RuntimeError('MG5_DIR does not correspond to an actual directory')\n\n if args.jobIdRange[0] < 1:\n raise RuntimeError('The first jobIdRange argument must be >= 1.')\n\n if args.jobIdRange[1] < args.jobIdRange[0]:\n raise RuntimeError('The second jobIdRange argument must be >= the first.')\n\n # Get the input card from user's options & check it exists\n card = mg5_args.card\n if not card:\n raise RuntimeError('You did not specify an input card!')\n if not os.path.isfile(card):\n raise RuntimeError('Input card %s does not exist!' % card)\n if os.path.dirname(card) != 'input_cards':\n raise RuntimeError('Put your card in input_cards directory')\n args.card = card\n args.channel = get_value_from_card(args.card, 'output')\n\n # Get CoM energy\n # -------------------------------------------------------------------------\n args.energy = int(get_value_from_card(args.card, 'ebeam1')) * 2 / 1000\n\n # Auto generate output directory if necessary\n # -------------------------------------------------------------------------\n if args.oDir == \"\":\n args.oDir = generate_dir_soolin(args.channel, args.energy)\n\n check_create_dir(args.oDir)\n\n # Dicts to hold thing to be copied before/after the job runs\n copy_to_local = {}\n copy_from_local = {}\n\n # Make the program zip and put it in hdfs\n # -------------------------------------------------------------------------\n # don't want any trailing \"/\"\n if mg5_dir.endswith(\"/\"):\n mg5_dir = mg5_dir.rstrip('/')\n version = re.findall(r'MG5_aMC_v.*', mg5_dir)[0]\n log.info('Creating tar file of MG5 installation, please wait...')\n call(['tar', 'czf', '%s.tgz' % version, '-C', os.path.dirname(mg5_dir), version])\n # copy to hdfs\n zip_dir = '/hdfs/user/%s/NMSSMPheno/zips/' % (os.environ['LOGNAME'])\n check_create_dir(zip_dir)\n zip_filename = '%s.tgz' % version\n zip_path = os.path.join(zip_dir, zip_filename)\n call(['hadoop', 'fs', '-copyFromLocal', '-f', zip_filename, zip_dir.replace('/hdfs', '')])\n os.remove(zip_filename)\n copy_to_local[zip_path] = 'MG5_aMC.tgz'\n\n # Copy across input cards to hdfs to sandbox them\n # -------------------------------------------------------------------------\n if not args.dry:\n log.debug('Copying across input_cards...')\n call(['hadoop', 'fs', '-copyFromLocal', '-f',\n 'input_cards', args.oDir.replace('/hdfs', '')])\n copy_to_local[os.path.join(args.oDir, 'input_cards')] = 'input_cards'\n\n # Copy run script to outputdir to sandbox it\n # -------------------------------------------------------------------------\n sandbox_script = os.path.join(args.oDir, 'run_mg5.py')\n copy_to_local[sandbox_script] = 'run_mg5.py'\n if not args.dry:\n log.debug('Copying across exe...')\n shutil.copy2('run_mg5.py', sandbox_script)\n\n # Setup log directory\n # -------------------------------------------------------------------------\n log_dir = '%s/logs' % generate_subdir(args.channel, args.energy)\n check_create_dir(log_dir)\n\n # File stem common for all dag and status files\n # -------------------------------------------------------------------------\n file_stem = os.path.join(generate_subdir(args.channel, args.energy),\n strftime(\"%H%M%S\"))\n check_create_dir(os.path.dirname(file_stem))\n\n # Make DAG file\n # -------------------------------------------------------------------------\n dag_name = file_stem + '.dag'\n status_name = file_stem + '.status'\n write_dag_file(dag_filename=dag_name,\n condor_filename='HTCondor/mcJob.condor',\n status_filename=status_name,\n copyToLocal=copy_to_local, copyFromLocal=copy_from_local,\n log_dir=log_dir, args=args)\n\n # Submit it\n # -------------------------------------------------------------------------\n if args.dry:\n log.warning('Dry run - not submitting jobs or copying files.')\n else:\n call(['condor_submit_dag', dag_name])\n log.info('Check status with:')\n log.info('DAGstatus.py %s' % status_name)\n log.info('Condor log files written to: %s' % log_dir)\n print''","def build_submission(config):\n\n soup = bs(HTML_TEMPLATE, 'html5lib')\n\n # Set title\n title = soup.find('title')\n title.clear()\n my_title = 'A{} | {}'.format(config['assignment'], config['firstlast'])\n title.append(my_title)\n\n # Set course title\n course = soup.find(id='course')\n course.clear()\n if config['course'] == 446:\n course_title = 'ECSE 446: Realistic Image Synthesis'\n elif config['course'] == 546:\n course_title = 'ECSE 546: Advanced Image Synthesis'\n elif config['course'] == 598:\n course_title = 'COMP 598: Realistic Image Synthesis'\n else:\n raise ValueError('Error: invalid course number')\n course.append(course_title)\n\n # Set assignment number\n assignment = soup.find(id='assignment')\n assignment.clear()\n assignment.append('Assignment {}'.format(config['assignment']))\n\n # Set student's name\n name = soup.find(id='firstlast')\n name.clear()\n name.append(config['firstlast'])\n\n # Set student's ID\n id = soup.find(id='id')\n id.clear()\n id.append(bs('ID <code>{}</code>'.format(config['id']), 'html5lib'))\n\n # Create directory for convert images\n if not os.path.exists('pngs'):\n os.makedirs('pngs')\n\n # Insert all rendered scenes\n renders = soup.find(id='renders')\n for i, task in enumerate(config['renders']):\n slider = '<h1>{}</h1>\\n'.format(task['scene'])\n slider_title = task['scene'].replace(' ', '_').lower()\n convert_exr(task['render'], 'pngs/{}.png'.format(slider_title))\n slider += build_slider(slider_title)\n renders.append(bs(slider, 'html.parser'))\n\n # Save\n output = 'a{}_{}.html'.format(config['assignment'], config['id'])\n with open(output, 'w') as out_f:\n out_f.write(str(soup))","def _submit_to_queue(self, script_file):\n if sys.version_info[0] < 3:\n process = Popen(['sbatch', script_file], stdout=PIPE, stderr=PIPE)\n else:\n # need string not bytes so must use universal_newlines\n process = Popen(['sbatch', script_file], stdout=PIPE, stderr=PIPE, universal_newlines=True)\n\n out, err = process.communicate()\n\n # grab the returncode. SLURM returns 0 if the job was successful\n queue_id = None\n if process.returncode == 0:\n try:\n # output should of the form '2561553.sdb' or '352353.jessup' - just grab the first part for job id\n queue_id = int(out.split()[3])\n logger.info('Job submission was successful and queue_id is {}'.format(queue_id))\n except:\n # probably error parsing job code\n logger.critical('Could not parse job id following slurm...')\n return SubmitResults(qid=queue_id, out=out, err=err, process=process)","def submit(self, script, **kwargs):\n raise NotImplementedError()","def write_script(self, fnum, beg, end):\n fname = self.get_batch_file(fnum, beg, end, missing=self['missing'])\n\n self['job_name'] = self.get_job_name(fnum, beg, end)\n\n runconf=self.runconf\n vers=runconf['reredux_config']\n self['meds_file'] = files.get_meds_file(vers, fnum,\n type=runconf['sim_type'])\n\n if runconf['sim_type'] == 'egret':\n self['psf_file'] = files.get_psf_file(vers, fnum)\n\n self['config_file'] = files.get_config_file(self['run'])\n self['output_file'] = files.get_output_file(self['run'], fnum, beg, end)\n self['log_file'] = files.get_log_file(self['run'], fnum, beg, end)\n self['beg'] = beg\n self['end'] = end\n\n self['seed'] = numpy.random.randint(0, high=2**32-1)\n\n if self['missing'] and os.path.exists(self['output_file']):\n if os.path.exists(fname):\n os.remove(fname)\n return\n\n self.njobs_written += 1\n print(\"writing:\",fname)\n with open(fname,'w') as fobj:\n text = self.get_text()\n fobj.write(text)","def submit_single_job(self, single_job):\n\t\tjob_name = self.jobname_prefix_edit.get_edit_text()\n\t\tjob_name = os.path.expanduser(job_name)\n\t\tjob_name = '%s_%s'%(job_name, self.jobnumber_edit.get_edit_text())\n\t\t\n\t\tqsub_option = self.other_options_edit.get_edit_text()\n\t\twalltime = self.walltime_edit.get_edit_text()\n\t\tworkdir = os.path.expanduser(self.workdir_edit.get_edit_text())\n\t\truntime_output_stdout= self.runtime_output_checkbox.get_state()\n\t\tno_of_nodes = int(self.nodes_edit.get_edit_text())\n\t\tppn = self.myri_ppn_edit.get_edit_text()\n\t\tsource_bash = self.source_bash_profile_checkbox.get_state()\n\t\t\n\t\treturn_code = self.write_job_to_file(single_job, job_name, no_of_nodes, \\\n\t\t\t\tqsub_option, ppn=ppn, workdir=workdir, walltime=walltime, runtime_output_stdout=runtime_output_stdout,\\\n\t\t\t\tsource_bash=source_bash)\n\t\t\n\t\tqsub_output_stdout = ''\n\t\tqsub_output_stderr = ''\n\t\tif self.just_write_down_checkbox.get_state():\n\t\t\tqsub_output_stdout = '%s written.'%job_name\n\t\telse:\n\t\t\tcommand_handler = subprocess.Popen('qsub %s'%job_name, shell=True, stderr=subprocess.PIPE, stdout=subprocess.PIPE)\t#2008-10-20 shell=True allows command_line to be a string, rather than a list of command and its arguments\n\t\t\tqsub_output_stdout = command_handler.stdout.read().replace('\\n', ' ')\n\t\t\tqsub_output_stderr = command_handler.stderr.read().replace('\\n', ' ')\n\t\t\t\"\"\"\n\t\t\tqsub_output = os.popen3('qsub %s'%job_name)\n\t\t\tqsub_output_stdout = qsub_output[1].read().replace('\\n', ' ')\n\t\t\tqsub_output_stderr = qsub_output[2].read().replace('\\n', ' ')\n\t\t\t\"\"\"\n\t\treturn (qsub_output_stdout, qsub_output_stderr)","def _create_pbs_file(self, jobname, num_jobs, pf_pbs, pf_input_package_template, pf_output_package_template):\n\n # create unique compute directory\n pd_compute = None # run_shell_cmd(\"mktemp --tmpdir={}\".format(self._prl_options[\"pbs-pd-root-compute\"]))\n\n pbs_text = PBS._generate_pbs_header_array(num_jobs, jobname, self._prl_options, pd_compute=pd_compute)\n\n pbs_text += \"\\n{}\\n\".format(\n PBS._generate_call_command(self._env,\n pf_input_package_template,\n pf_output_package_template,\n self._prl_options,\n pd_compute=pd_compute\n )\n )\n\n # write to file\n write_to_file(pbs_text, pf_pbs)","def build_consumer_submit_file(self, consumer):\n log_dir = self.rsv.get_consumer_log_dir()\n\n environment = \"PATH=/usr/bin:/bin;\"\n environment += consumer.get_environment()\n\n condor_id = consumer.get_unique_name()\n arguments = consumer.get_args_string()\n timestamp = strftime(\"%Y-%m-%d %H:%M:%S %Z\")\n\n submit = \"\"\n submit += \"######################################################################\\n\"\n submit += \"# Temporary submit file generated by rsv-control\\n\"\n submit += \"# Generated at %s\\n\" % timestamp\n submit += \"######################################################################\\n\"\n submit += \"Arguments = %s\\n\" % arguments\n submit += \"DeferralPrepTime = 180\\n\"\n submit += \"DeferralTime = (CurrentTime + 300 + random(30))\\n\"\n submit += \"DeferralWindow = 99999999\\n\"\n submit += \"Environment = %s\\n\" % environment\n submit += \"Executable = %s\\n\" % consumer.executable\n submit += \"Error = %s/%s.err\\n\" % (log_dir, condor_id)\n submit += \"Output = %s/%s.out\\n\" % (log_dir, condor_id)\n submit += \"Log = %s/%s.log\\n\" % (log_dir, condor_id)\n submit += \"Universe = local\\n\"\n submit += \"Notification = never\\n\"\n submit += \"OnExitRemove = false\\n\"\n submit += \"PeriodicRelease = (HoldReasonCode =!= 1) \" + \\\n \"&& ((CurrentTime - EnteredCurrentStatus) > 60)\\n\"\n submit += \"+OSGRSV = \\\"consumers\\\"\\n\"\n submit += \"+OSGRSVUniqueName = \\\"%s\\\"\\n\" % condor_id\n submit += \"Queue\\n\"\n\n return submit","def jobs(\n ctx: typer.Context,\n op_id: str = typer.Argument(\n ...,\n autocompletion=completion_op_id,\n callback=check_for_op_id,\n help=\"A valid op-id. e.g. get_markets_prices\",\n ),\n param_string: Optional[str] = typer.Option(\n None,\n \"--param-string\",\n \"-p\",\n help=\"Optional. Full or partial parameters as a json encoded dictionary string. \"\n \"Keys must be valid parameters for selected op_id.\",\n ),\n default_params: bool = typer.Option(\n False,\n \"-d\",\n \"--default-params\",\n help=\"Include all parameters that are required, or have default values. \"\n \"Missing values will be 'NOTSET'.\",\n ),\n callback_path: Optional[Path] = typer.Option(\n None,\n \"-c\",\n \"--callbacks\",\n help=\"Optional. Path to custom callbacks to be used. \",\n ),\n file_name: str = typer.Option(\n \"created-jobs/${esi_job_op_id}-${esi_job_uid}\",\n \"-n\",\n \"--file-name\",\n help=(\n \"File name for the new job, must be unique if multiple jobs. \"\n \"Can include directories, \"\n \"and the file type suffix will be added based on --format-id.\"\n ),\n ),\n data_path: Optional[Path] = typer.Option(\n None,\n \"--data-file\",\n \"-i\",\n help=(\n \"Optional. Path to json, csv, or yaml file with full or partial parameters. \"\n \"Must result in a list of dicts.\"\n ),\n ),\n format_id: FormatChoices = typer.Option(\n FormatChoices.json,\n \"-f\",\n \"--format-id\",\n show_choices=True,\n help=\"Output file format.\",\n ),\n path_out: Path = typer.Argument(\n \"./tmp\",\n help=\"Parent path for saving the new jobs, will be prepended to --file-name.\",\n ),\n):\n operation_manifest: OperationManifest = ctx.obj[\"operation_manifest\"]\n # path_out = optional_object(path_out, Path, \".\")\n if path_out.is_file:\n typer.BadParameter(\"path_out must not be a file.\")\n file_data: Optional[List[Dict]] = get_params_from_file(data_path)\n parameters: Dict = decode_param_string(param_string)\n if callback_path is None:\n callback_collection = default_callback_collection()\n else:\n callback_collection = load_callbacks(callback_path)\n jobs_: List[EsiJob] = []\n try:\n op_info = operation_manifest.op_info(op_id)\n if not file_data:\n job = op_info.create_job(\n parameters,\n callback_collection,\n include_default_params=default_params,\n # only_required_default_params=False,\n # allow_notset=False,\n )\n jobs_.append(job)\n else:\n for params in file_data:\n params.update(parameters)\n job = op_info.create_job(\n params,\n callback_collection,\n include_default_params=default_params,\n # only_required_default_params=False,\n # allow_notset=False,\n )\n jobs_.append(job)\n except Exception as ex:\n raise typer.BadParameter(\n f\"Exception creating job. {ex.__class__.__name__}: {ex}\"\n )\n for job in jobs_:\n file_path = resolve_job_file_path(job, file_name, path_out)\n try:\n save_path = job.serialize_file(file_path, format_id)\n except Exception as ex:\n raise typer.BadParameter(\n f\"Error saving job to {save_path}. {ex.__class__.__name__}, {ex}\"\n )\n logger.info(\"Saved job %s at %s\", job.uid, file_path)\n typer.echo(f\"{len(jobs_)} jobs saved to {path_out}\")\n report_finished_task(ctx)","def write_job_file(job_name, py_file_name='main.py',\n sbatch_path='/Users/omarschall/vanilla-rtrl/job_scripts/',\n scratch_path='/scratch/oem214/vanilla-rtrl/',\n nodes=1, ppn=1, mem=16, n_hours=24):\n\n job_file = os.path.join(sbatch_path, job_name + '.s')\n log_name = os.path.join('log', job_name)\n\n with open(job_file, 'w') as f:\n f.write(\n '#! /bin/bash\\n'\n + '\\n'\n + '#SBATCH --nodes={}\\n'.format(nodes)\n + '#SBATCH --ntasks-per-node=1\\n'\n + '#SBATCH --cpus-per-task={}\\n'.format(ppn)\n + '#SBATCH --mem={}GB\\n'.format(mem)\n + '#SBATCH --time={}:00:00\\n'.format(n_hours)\n + '#SBATCH --job-name={}\\n'.format(job_name[0:16])\n + '#SBATCH --output={}log/{}.o\\n'.format(scratch_path, job_name[0:16])\n + '\\n'\n + 'module purge\\n'\n + 'SAVEPATH={}library/{}\\n'.format(scratch_path, job_name)\n + 'export SAVEPATH\\n'\n + 'module load python3/intel/3.6.3\\n'\n + 'cd /home/oem214/py3.6.3\\n'\n + 'source py3.6.3/bin/activate\\n'\n + 'cd {}\\n'.format(scratch_path)\n + 'pwd > {}.log\\n'.format(log_name)\n + 'date >> {}.log\\n'.format(log_name)\n + 'which python >> {}.log\\n'.format(log_name)\n + 'python {}\\n'.format(py_file_name)\n )\n\n return job_file","def create_job(self, job):\n call = subprocess.Popen(self.cli + [PlatformJenkinsJavaCLI.CREATE_JOB, job.name], stdin=subprocess.PIPE)\n out, err = call.communicate(input=platform_ci.jjb.get_job_as_xml(job, self.template_dir))\n call.wait()\n if call.returncode != 0:\n logging.info(out)\n logging.error(err)\n raise PlatformJenkinsException(\"Creating job failed: \" + job.name)","def SubmitJob(jobid, cntSubmitJobDict, numseq_this_user, g_params): # {{{\n# for each job rstdir, keep three log files,\n# 1.seqs finished, finished_seq log keeps all information, finished_index_log\n# can be very compact to speed up reading, e.g.\n# 1-5 7-9 etc\n# 2.seqs queued remotely , format:\n# index node remote_jobid\n# 3. format of the torun_idx_file\n# origIndex\n gen_logfile = g_params['gen_logfile']\n # gen_errfile = g_params['gen_errfile']\n name_server = g_params['name_server']\n\n webcom.loginfo(\"SubmitJob for %s, numseq_this_user=%d\"%(jobid, numseq_this_user), gen_logfile)\n\n path_static = g_params['path_static']\n path_cache = g_params['path_cache']\n\n path_result = os.path.join(path_static, 'result')\n path_log = os.path.join(path_static, 'log')\n\n rstdir = \"%s/%s\"%(path_result, jobid)\n outpath_result = \"%s/%s\"%(rstdir, jobid)\n if not os.path.exists(outpath_result):\n os.mkdir(outpath_result)\n\n finished_idx_file = \"%s/finished_seqindex.txt\"%(rstdir)\n failed_idx_file = \"%s/failed_seqindex.txt\"%(rstdir)\n remotequeue_idx_file = \"%s/remotequeue_seqindex.txt\"%(rstdir)\n torun_idx_file = \"%s/torun_seqindex.txt\"%(rstdir) # ordered seq index to run\n cnttry_idx_file = \"%s/cntsubmittry_seqindex.txt\"%(rstdir)#index file to keep log of tries\n\n runjob_errfile = \"%s/%s\"%(rstdir, \"runjob.err\")\n runjob_logfile = \"%s/%s\"%(rstdir, \"runjob.log\")\n finished_seq_file = \"%s/finished_seqs.txt\"%(outpath_result)\n query_parafile = \"%s/query.para.txt\"%(rstdir)\n query_para = webcom.LoadJsonFromFile(query_parafile)\n tmpdir = \"%s/tmpdir\"%(rstdir)\n qdinittagfile = \"%s/runjob.qdinit\"%(rstdir)\n failedtagfile = \"%s/%s\"%(rstdir, \"runjob.failed\")\n starttagfile = \"%s/%s\"%(rstdir, \"runjob.start\")\n cache_process_finish_tagfile = \"%s/cache_processed.finish\"%(rstdir)\n fafile = \"%s/query.fa\"%(rstdir)\n split_seq_dir = \"%s/splitaa\"%(tmpdir)\n forceruntagfile = \"%s/forcerun\"%(rstdir)\n lastprocessed_cache_idx_file = \"%s/lastprocessed_cache_idx.txt\"%(rstdir)\n variant_file = \"%s/variants.fa\"%(rstdir)\n\n if os.path.exists(forceruntagfile):\n isForceRun = True\n else:\n isForceRun = False\n\n finished_idx_list = []\n failed_idx_list = [] # [origIndex]\n if os.path.exists(finished_idx_file):\n finished_idx_list = list(set(myfunc.ReadIDList(finished_idx_file)))\n if os.path.exists(failed_idx_file):\n failed_idx_list = list(set(myfunc.ReadIDList(failed_idx_file)))\n\n processed_idx_set = set(finished_idx_list) | set(failed_idx_list)\n\n jobinfofile = \"%s/jobinfo\"%(rstdir)\n jobinfo = \"\"\n if os.path.exists(jobinfofile):\n jobinfo = myfunc.ReadFile(jobinfofile).strip()\n jobinfolist = jobinfo.split(\"\\t\")\n email = \"\"\n if len(jobinfolist) >= 8:\n email = jobinfolist[6]\n method_submission = jobinfolist[7]\n\n # the first time when the this jobid is processed, do the following\n # 1. generate a file with sorted seqindex\n # 2. generate splitted sequence files named by the original seqindex\n if not os.path.exists(qdinittagfile): #initialization#{{{\n if not os.path.exists(tmpdir):\n os.mkdir(tmpdir)\n if isForceRun or os.path.exists(cache_process_finish_tagfile):\n isCacheProcessingFinished = True\n else:\n isCacheProcessingFinished = False\n\n # ==== 1.dealing with cached results \n (seqIDList, seqAnnoList, seqList) = myfunc.ReadFasta(fafile)\n if len(seqIDList) <= 0:\n webcom.WriteDateTimeTagFile(failedtagfile, runjob_logfile, runjob_errfile)\n webcom.loginfo(\"Read query seq file failed. Zero sequence read in\", runjob_errfile)\n return 1\n\n if 'DEBUG' in g_params and g_params['DEBUG']:\n msg = \"jobid = %s, isCacheProcessingFinished=%s, MAX_CACHE_PROCESS=%d\"%(\n jobid, str(isCacheProcessingFinished), g_params['MAX_CACHE_PROCESS'])\n webcom.loginfo(msg, gen_logfile)\n\n if not isCacheProcessingFinished:\n finished_idx_set = set(finished_idx_list)\n\n lastprocessed_idx = -1\n if os.path.exists(lastprocessed_cache_idx_file):\n try:\n lastprocessed_idx = int(myfunc.ReadFile(lastprocessed_cache_idx_file))\n except:\n lastprocessed_idx = -1\n\n cnt_processed_cache = 0\n for i in range(lastprocessed_idx+1, len(seqIDList)):\n if i in finished_idx_set:\n continue\n outpath_this_seq = \"%s/%s\"%(outpath_result, \"seq_%d\"%i)\n subfoldername_this_seq = \"seq_%d\"%(i)\n md5_key = hashlib.md5(seqList[i].encode('utf-8')).hexdigest()\n subfoldername = md5_key[:2]\n cachedir = \"%s/%s/%s\"%(path_cache, subfoldername, md5_key)\n zipfile_cache = cachedir + \".zip\"\n\n if os.path.exists(cachedir) or os.path.exists(zipfile_cache):\n if os.path.exists(cachedir):\n try:\n shutil.copytree(cachedir, outpath_this_seq)\n except Exception as e:\n msg = \"Failed to copytree %s -> %s\"%(cachedir, outpath_this_seq)\n webcom.loginfo(\"%s with errmsg=%s\"%(msg, str(e)), runjob_errfile)\n elif os.path.exists(zipfile_cache):\n if os.path.getsize(zipfile_cache) == 0:\n os.remove(zipfile_cache) # remove empty archived result zip file\n else:\n cmd = [\"unzip\", zipfile_cache, \"-d\", outpath_result]\n webcom.RunCmd(cmd, runjob_logfile, runjob_errfile)\n if os.path.exists(outpath_this_seq):\n shutil.rmtree(outpath_this_seq)\n if os.path.exists(os.path.join(outpath_result, md5_key)):\n shutil.move(os.path.join(outpath_result, md5_key), outpath_this_seq)\n\n fafile_this_seq = '%s/seq.fa'%(outpath_this_seq)\n if os.path.exists(outpath_this_seq) and webcom.IsCheckPredictionPassed(outpath_this_seq, name_server):\n myfunc.WriteFile('>%s\\n%s\\n'%(seqAnnoList[i], seqList[i]), fafile_this_seq, 'w', True)\n if not os.path.exists(starttagfile): #write start tagfile\n webcom.WriteDateTimeTagFile(starttagfile, runjob_logfile, runjob_errfile)\n\n info_finish = webcom.GetInfoFinish(name_server, outpath_this_seq,\n i, len(seqList[i]), seqAnnoList[i], source_result=\"cached\", runtime=0.0)\n myfunc.WriteFile(\"\\t\".join(info_finish)+\"\\n\",\n finished_seq_file, \"a\", isFlush=True)\n myfunc.WriteFile(\"%d\\n\"%(i), finished_idx_file, \"a\", True)\n\n if 'DEBUG' in g_params and g_params['DEBUG']:\n webcom.loginfo(\"Get result from cache for seq_%d\"%(i), gen_logfile)\n if cnt_processed_cache+1 >= g_params['MAX_CACHE_PROCESS']:\n myfunc.WriteFile(str(i), lastprocessed_cache_idx_file, \"w\", True)\n return 0\n cnt_processed_cache += 1\n\n webcom.WriteDateTimeTagFile(cache_process_finish_tagfile, runjob_logfile, runjob_errfile)\n\n # Regenerate toRunDict\n toRunDict = {}\n for i in range(len(seqIDList)):\n if not i in processed_idx_set:\n toRunDict[i] = [seqList[i], 0, seqAnnoList[i].replace('\\t', ' ')]\n\n if name_server == \"topcons2\":\n webcom.ResetToRunDictByScampiSingle(toRunDict, g_params['script_scampi'], tmpdir, runjob_logfile, runjob_errfile)\n sortedlist = sorted(list(toRunDict.items()), key=lambda x:x[1][1], reverse=True)\n\n # Write splitted fasta file and write a torunlist.txt\n if not os.path.exists(split_seq_dir):\n os.mkdir(split_seq_dir)\n\n torun_index_str_list = [str(x[0]) for x in sortedlist]\n if len(torun_index_str_list)>0:\n myfunc.WriteFile(\"\\n\".join(torun_index_str_list)+\"\\n\", torun_idx_file, \"w\", True)\n else:\n myfunc.WriteFile(\"\", torun_idx_file, \"w\", True)\n\n # write cnttry file for each jobs to run\n cntTryDict = {}\n for idx in torun_index_str_list:\n cntTryDict[int(idx)] = 0\n json.dump(cntTryDict, open(cnttry_idx_file, \"w\"))\n\n for item in sortedlist:\n origIndex = item[0]\n seq = item[1][0]\n description = item[1][2]\n seqfile_this_seq = \"%s/%s\"%(split_seq_dir, \"query_%d.fa\"%(origIndex))\n seqcontent = \">%s\\n%s\\n\"%(description, seq)\n myfunc.WriteFile(seqcontent, seqfile_this_seq, \"w\", True)\n # qdinit file is written at the end of initialization, to make sure\n # that initialization is either not started or completed\n webcom.WriteDateTimeTagFile(qdinittagfile, runjob_logfile, runjob_errfile)\n#}}}\n\n\n # 3. try to submit the job \n toRunIndexList = [] # index in str\n processedIndexSet = set([]) #seq index set that are already processed\n submitted_loginfo_list = []\n if os.path.exists(torun_idx_file):\n toRunIndexList = myfunc.ReadIDList(torun_idx_file)\n # unique the list but keep the order\n toRunIndexList = myfunc.uniquelist(toRunIndexList)\n if len(toRunIndexList) > 0:\n iToRun = 0\n numToRun = len(toRunIndexList)\n for node in cntSubmitJobDict:\n if \"DEBUG\" in g_params and g_params['DEBUG']:\n webcom.loginfo(\"Trying to submitjob to the node=%s\\n\"%(str(node)), gen_logfile)\n if iToRun >= numToRun:\n if \"DEBUG\" in g_params and g_params['DEBUG']:\n webcom.loginfo(\"iToRun(%d) >= numToRun(%d). Stop SubmitJob for jobid=%s\\n\"%(iToRun, numToRun, jobid), gen_logfile)\n break\n wsdl_url = \"http://%s/pred/api_submitseq/?wsdl\"%(node)\n try:\n myclient = Client(wsdl_url, cache=None, timeout=30)\n except:\n webcom.loginfo(\"Failed to access %s\"%(wsdl_url), gen_logfile)\n continue\n\n if \"DEBUG\" in g_params and g_params['DEBUG']:\n webcom.loginfo(\"iToRun=%d, numToRun=%d\\n\"%(iToRun, numToRun), gen_logfile)\n [cnt, maxnum, queue_method] = cntSubmitJobDict[node]\n cnttry = 0\n while cnt < maxnum and iToRun < numToRun:\n origIndex = int(toRunIndexList[iToRun])\n seqfile_this_seq = \"%s/%s\"%(split_seq_dir, \"query_%d.fa\"%(origIndex))\n # ignore already existing query seq, this is an ugly solution,\n # the generation of torunindexlist has a bug\n outpath_this_seq = \"%s/%s\"%(outpath_result, \"seq_%d\"%origIndex)\n if os.path.exists(outpath_this_seq):\n iToRun += 1\n continue\n\n if 'DEBUG' in g_params and g_params['DEBUG']:\n webcom.loginfo(\"DEBUG: cnt (%d) < maxnum (%d) \"\\\n \"and iToRun(%d) < numToRun(%d)\"%(cnt, maxnum, iToRun, numToRun), gen_logfile)\n fastaseq = \"\"\n seqid = \"\"\n seqanno = \"\"\n seq = \"\"\n if not os.path.exists(seqfile_this_seq):\n all_seqfile = \"%s/query.fa\"%(rstdir)\n try:\n (allseqidlist, allannolist, allseqlist) = myfunc.ReadFasta(all_seqfile)\n seqid = allseqidlist[origIndex]\n seqanno = allannolist[origIndex]\n seq = allseqlist[origIndex]\n fastaseq = \">%s\\n%s\\n\" % (seqanno, seq)\n except KeyError:\n pass\n else:\n fastaseq = myfunc.ReadFile(seqfile_this_seq)#seq text in fasta format\n (seqid, seqanno, seq) = myfunc.ReadSingleFasta(seqfile_this_seq)\n\n isSubmitSuccess = False\n if len(seq) > 0:\n query_para['name_software'] = webcom.GetNameSoftware(name_server.lower(), queue_method)\n query_para['queue_method'] = queue_method\n if name_server.lower() == \"pathopred\":\n variant_text = myfunc.ReadFile(variant_file)\n query_para['variants'] = variant_text\n # also include the identifier name as a query parameter\n query_para['identifier_name'] = seqid\n\n para_str = json.dumps(query_para, sort_keys=True)\n jobname = \"\"\n if email not in g_params['vip_user_list']:\n useemail = \"\"\n else:\n useemail = email\n try:\n myfunc.WriteFile(\"\\tSubmitting seq %4d \"%(origIndex),\n gen_logfile, \"a\", True)\n rtValue = myclient.service.submitjob_remote(fastaseq, para_str,\n jobname, useemail, str(numseq_this_user), str(isForceRun))\n except Exception as e:\n webcom.loginfo(\"Failed to run myclient.service.submitjob_remote with errmsg=%s\"%(str(e)), gen_logfile)\n rtValue = []\n pass\n\n cnttry += 1\n if len(rtValue) >= 1:\n strs = rtValue[0]\n if len(strs) >=5:\n remote_jobid = strs[0]\n result_url = strs[1]\n numseq_str = strs[2]\n errinfo = strs[3]\n warninfo = strs[4]\n if remote_jobid != \"None\" and remote_jobid != \"\":\n isSubmitSuccess = True\n epochtime = time.time()\n # 6 fields in the file remotequeue_idx_file\n txt = \"%d\\t%s\\t%s\\t%s\\t%s\\t%f\"%( origIndex,\n node, remote_jobid, seqanno.replace('\\t', ' '), seq,\n epochtime)\n submitted_loginfo_list.append(txt)\n cnttry = 0 #reset cnttry to zero\n else:\n webcom.loginfo(\"bad wsdl return value\", gen_logfile)\n\n if isSubmitSuccess:\n cnt += 1\n myfunc.WriteFile(\" succeeded on node %s\\n\"%(node), gen_logfile, \"a\", True)\n else:\n myfunc.WriteFile(\" failed on node %s\\n\"%(node), gen_logfile, \"a\", True)\n\n if isSubmitSuccess or cnttry >= g_params['MAX_SUBMIT_TRY']:\n iToRun += 1\n processedIndexSet.add(str(origIndex))\n if 'DEBUG' in g_params and g_params['DEBUG']:\n webcom.loginfo(\"DEBUG: jobid %s processedIndexSet.add(str(%d))\\n\"%(jobid, origIndex), gen_logfile)\n # update cntSubmitJobDict for this node\n cntSubmitJobDict[node][0] = cnt\n\n # finally, append submitted_loginfo_list to remotequeue_idx_file \n if 'DEBUG' in g_params and g_params['DEBUG']:\n webcom.loginfo(\"DEBUG: len(submitted_loginfo_list)=%d\\n\"%(len(submitted_loginfo_list)), gen_logfile)\n if len(submitted_loginfo_list)>0:\n myfunc.WriteFile(\"\\n\".join(submitted_loginfo_list)+\"\\n\", remotequeue_idx_file, \"a\", True)\n # update torun_idx_file\n newToRunIndexList = []\n for idx in toRunIndexList:\n if not idx in processedIndexSet:\n newToRunIndexList.append(idx)\n if 'DEBUG' in g_params and g_params['DEBUG']:\n webcom.loginfo(\"DEBUG: jobid %s, newToRunIndexList=\"%(jobid) + \" \".join( newToRunIndexList), gen_logfile)\n\n if len(newToRunIndexList)>0:\n myfunc.WriteFile(\"\\n\".join(newToRunIndexList)+\"\\n\", torun_idx_file, \"w\", True)\n else:\n myfunc.WriteFile(\"\", torun_idx_file, \"w\", True)\n\n return 0","def _create_job_spec(\n self,\n job_id: Text,\n training_input: Dict[Text, Any],\n job_labels: Optional[Dict[Text, Text]] = None) -> Dict[Text, Any]:\n pass","def write_dag_script(s):\n assert len(s.jobs) in (1,2),'ERROR: write_dag_script should be called from the final merge JobSet'\n s.dag = os.path.join( s.jobs[0].submitdir, 'global.dag')\n f = open(s.dag,'w')\n # condor submit scripts\n for dep in s.get_deps():\n print >>f,'Job %s %s'%(dep.jobname(),dep.condorfile)\n for job in s.jobs:\n print >>f,'Job %s %s'%(job.jobname(),job.condorfile)\n # retry instructions\n for dep in s.get_deps():\n print >>f,'Retry %s %s'%(dep.jobname(),NRETRY)\n for job in s.jobs:\n print >>f,'Retry %s %s'%(job.jobname(),NRETRY)\n a_parent = ' '.join( [ dep.jobname() for dep in s.get_deps() ] )\n for job in s.jobs:\n a_child = job.jobname()\n print >>f,'PARENT %s CHILD %s'%(a_parent,a_child)\n f.close()","def submit(\n command: str,\n namespace: argparse.Namespace,\n sge_dir: Path = Path(os.path.curdir),\n job_name=\"\",\n) -> int:\n id_file = Path(\"sge.id\")\n job_script = Path(\"sge_job\")\n stop_script = Path(\"sge_stop\")\n epilogue_script = Path(\"sge_epilogue\")\n\n # check if job is already running\n if id_file.exists():\n with open(id_file) as fptr:\n job_id = int(fptr.read())\n if job_id in get_jobs_in_queue():\n LOGGER.error(\n \"job seems to be in the queue already (with id %d)\",\n job_id,\n )\n return -1\n\n # set up args for the job script\n args = {\n \"command\": command,\n \"cpus\": namespace.cpus,\n \"email\": namespace.email,\n \"memory\": namespace.memory,\n \"queues\": namespace.queues,\n \"sge_dir\": sge_dir,\n \"time\": namespace.time,\n }\n if job_name:\n args[\"job_name\"] = job_name\n\n # create job script\n LOGGER.debug(\"create job script\")\n with open(job_script, \"w\") as fptr:\n fptr.write(templates.get_template(\"sge_job.j2\").render(args=args))\n job_script.chmod(0o755)\n LOGGER.debug(\"done\")\n\n # submit job\n LOGGER.debug(\"submit job\")\n output = subprocess.check_output([\"qsub\", str(job_script)]).decode()\n match = REGEX_QSUB.match(output)\n if not match:\n raise RuntimeError(\"Cannot parse job id from qsub command output\")\n job_id = int(match.group(1))\n LOGGER.debug(\"done\")\n\n LOGGER.debug(\"write id to file\")\n with open(id_file, \"w\") as fptr:\n fptr.write(str(job_id) + \"\\n\")\n LOGGER.debug(\"done\")\n\n # write stop script\n LOGGER.debug(\"create stop script\")\n with open(stop_script, \"w\") as fptr:\n fptr.write(templates.get_template(\"sge_stop.j2\").render(job_id=job_id))\n stop_script.chmod(0o755)\n LOGGER.debug(\"done\")\n\n # write epilogue script\n LOGGER.debug(\"create epilogue script\")\n with open(epilogue_script, \"w\") as fptr:\n fptr.write(templates.get_template(\"sge_epilogue.j2\").render(job_id=job_id))\n epilogue_script.chmod(0o755)\n LOGGER.debug(\"done\")\n\n return job_id","def create_script(sh_file, cmds, max_workers, num_nodes=1):\n output = os.path.dirname(sh_file)\n job_name = os.path.splitext(os.path.basename(sh_file))[0]\n err_file = os.path.join(output,\"{0}.error\".format(job_name))\n complete_file = os.path.join(output, \"{0}.complete\".format(job_name))\n with open(sh_file, 'w') as of:\n of.write(\"#!/bin/bash\\n\")\n of.write(\"#PBS -N {0}\\n\".format(job_name))\n of.write(\"#PBS -l nodes={0}:ppn={1}\\n\".format(num_nodes,max_workers))\n of.write(\"#PBS -l walltime=2:30:00\\n\")\n of.write(\"#PBS -l vmem=8g\\n\")\n of.write(\"#PBS -j eo\\n\")\n of.write(\"#PBS Join_Path={0}\\n\".format(os.path.join(output,\"%s.err\"%job_name)))\n of.write(\"module load samtools/1.9\\n\")\n of.write(\"module load bedtools/2.27.1\\n\")\n of.write(\"{0}\\n\".format(cmds[0]))\n of.write(\"if [ $? -ne 0 ]; then \\n\\ttouch {0};exit 1 \\nfi\\n\".format(err_file))\n of.write(\"{0}\\n\".format(cmds[1]))\n of.write(\"if [ $? -ne 0 ]; then \\n\\ttouch {0}\\nelse\\n\\ttouch {1} \\nfi\\n\".format(err_file, complete_file))\n os.system(\"chmod 755 %s\" % sh_file)","def _jobfile(self):\n job = self.job.format(fnum=self.fnum)\n with open(job, 'w') as f:\n f.write('#!/bin/sh\\n' + self.phast_cmmd + self.cleanup_cmmd)","def generateReleaseRunBB(self, job):\n pass","def make_submission(commit: CommitDetails):\n\n submitter, commit_id = commit.submitter, commit.commit_id\n\n subprocess.run(f\"git checkout --force {commit_id}\", shell=True, stdout=subprocess.DEVNULL,\n stderr=subprocess.DEVNULL, cwd=f\"{SUBS_DIR}/{submitter}/{ASSIGNMENT}\", check=True)\n\n submission_path = os.path.realpath(f\"{SUBS_DIR}/{submitter}/{ASSIGNMENT}\")\n subprocess.run(f\"./add_sub_manually.sh {submission_path}\", shell=True, check=False)","def getSubmissionCommandPrefix(options, cleanupFile=None):\n submitData={}\n #priority\n priority=options.priority\n if cleanupFile is not None:\n priority = min(0,priority)\n else:\n priority = min(-1,priority-10)\n submitData['priority']=str(priority)\n submitData['interpreter']=PYTHON\n\n # job name and output files\n if cleanupFile is not None:\n submitData['jobName']=\"%s.cleanup\" % (options.jobName)\n submitData['output']=\"%s.cleanup.out\" % (cleanupFile)\n submitData['error']=\"%s.cleanup.err\" % (cleanupFile)\n submitData['waitfor']=options.jobName if options.queue != SLURM else options.jobid\n else:\n submitData['jobName']=options.jobName\n submitData['output']=\"%s%stasks.out\" %(options.tmpDir, os.sep)\n submitData['error']=\"%s%stasks.err\" % (options.tmpDir, os.sep)\n if isinstance(options.splits,int):\n submitData['tasks'] = \"1-%d\" % options.splits\n else:\n if options.splits[0]==options.splits[1]:\n submitData['tasks']=str(options.splits[0])\n else:\n submitData['tasks']=\"%d-%d\" % tuple(options.splits)\n if options.throttle>0:\n submitData['throttle']=str(options.throttle)\n if options.wait:\n submitData['sync']=True\n if isinstance(options.sgeOptions,str):\n submitData['options']=shlex.split(options.sgeOptions)\n else:\n submitData['options']=options.sgeOptions\n\n if options.queue==SGE:\n return getSGECommandPrefix(submitData)\n else:\n return getSLURMCommandPrefix(submitData)","def submit_scripts(self, out):\n program_folder = os.path.join(out, self.out)\n for config in self.configurations:\n config.submit_script(program_folder)\n return None","def main(self):\n self.jamf_url = self.env.get(\"JSS_URL\")\n self.jamf_user = self.env.get(\"API_USERNAME\")\n self.jamf_password = self.env.get(\"API_PASSWORD\")\n self.script_path = self.env.get(\"script_path\")\n self.script_name = self.env.get(\"script_name\")\n self.script_category = self.env.get(\"script_category\")\n self.script_priority = self.env.get(\"script_priority\")\n self.osrequirements = self.env.get(\"osrequirements\")\n self.script_info = self.env.get(\"script_info\")\n self.script_notes = self.env.get(\"script_notes\")\n self.script_parameter4 = self.env.get(\"script_parameter4\")\n self.script_parameter5 = self.env.get(\"script_parameter5\")\n self.script_parameter6 = self.env.get(\"script_parameter6\")\n self.script_parameter7 = self.env.get(\"script_parameter7\")\n self.script_parameter8 = self.env.get(\"script_parameter8\")\n self.script_parameter9 = self.env.get(\"script_parameter9\")\n self.script_parameter10 = self.env.get(\"script_parameter10\")\n self.script_parameter11 = self.env.get(\"script_parameter11\")\n self.replace = self.env.get(\"replace_script\")\n self.sleep = self.env.get(\"sleep\")\n # handle setting replace in overrides\n if not self.replace or self.replace == \"False\":\n self.replace = False\n\n # clear any pre-existing summary result\n if \"jamfscriptuploader_summary_result\" in self.env:\n del self.env[\"jamfscriptuploader_summary_result\"]\n script_uploaded = False\n\n # obtain the relevant credentials\n token = self.handle_uapi_auth(self.jamf_url, self.jamf_user, self.jamf_password)\n\n # get the id for a category if supplied\n if self.script_category:\n self.output(\"Checking categories for {}\".format(self.script_category))\n\n # check for existing category - requires obj_name\n obj_type = \"category\"\n obj_name = self.script_category\n category_id = self.get_uapi_obj_id_from_name(\n self.jamf_url,\n obj_type,\n obj_name,\n token,\n )\n\n if not category_id:\n self.output(\"WARNING: Category not found!\")\n category_id = \"-1\"\n else:\n self.output(\n \"Category {} found: ID={}\".format(self.script_category, category_id)\n )\n else:\n self.script_category = \"\"\n category_id = \"-1\"\n\n # handle files with a relative path\n if not self.script_path.startswith(\"/\"):\n found_template = self.get_path_to_file(self.script_path)\n if found_template:\n self.script_path = found_template\n else:\n raise ProcessorError(f\"ERROR: Script file {self.script_path} not found\")\n\n # now start the process of uploading the object\n if not self.script_name:\n self.script_name = os.path.basename(self.script_path)\n\n # check for existing script\n self.output(\n \"Checking for existing '{}' on {}\".format(self.script_name, self.jamf_url)\n )\n self.output(\n \"Full path: {}\".format(self.script_path),\n verbose_level=2,\n )\n obj_type = \"script\"\n obj_name = self.script_name\n obj_id = self.get_uapi_obj_id_from_name(\n self.jamf_url,\n obj_type,\n obj_name,\n token,\n )\n\n if obj_id:\n self.output(\n \"Script '{}' already exists: ID {}\".format(self.script_name, obj_id)\n )\n if self.replace:\n self.output(\n \"Replacing existing script as 'replace_script' is set to {}\".format(\n self.replace\n ),\n verbose_level=1,\n )\n else:\n self.output(\n \"Not replacing existing script. Use replace_script='True' to enforce.\",\n verbose_level=1,\n )\n return\n\n # post the script\n self.upload_script(\n self.jamf_url,\n self.script_name,\n self.script_path,\n category_id,\n self.script_category,\n self.script_info,\n self.script_notes,\n self.script_priority,\n self.script_parameter4,\n self.script_parameter5,\n self.script_parameter6,\n self.script_parameter7,\n self.script_parameter8,\n self.script_parameter9,\n self.script_parameter10,\n self.script_parameter11,\n self.osrequirements,\n token,\n obj_id,\n )\n script_uploaded = True\n\n # output the summary\n self.env[\"script_name\"] = self.script_name\n self.env[\"script_uploaded\"] = script_uploaded\n if script_uploaded:\n self.env[\"jamfscriptuploader_summary_result\"] = {\n \"summary_text\": \"The following scripts were created or updated in Jamf Pro:\",\n \"report_fields\": [\n \"script\",\n \"path\",\n \"category\",\n \"priority\",\n \"os_req\",\n \"info\",\n \"notes\",\n \"P4\",\n \"P5\",\n \"P6\",\n \"P7\",\n \"P8\",\n \"P9\",\n \"P10\",\n \"P11\",\n ],\n \"data\": {\n \"script\": self.script_name,\n \"path\": self.script_path,\n \"category\": self.script_category,\n \"priority\": str(self.script_priority),\n \"info\": self.script_info,\n \"os_req\": self.osrequirements,\n \"notes\": self.script_notes,\n \"P4\": self.script_parameter4,\n \"P5\": self.script_parameter5,\n \"P6\": self.script_parameter6,\n \"P7\": self.script_parameter7,\n \"P8\": self.script_parameter8,\n \"P9\": self.script_parameter9,\n \"P10\": self.script_parameter10,\n \"P11\": self.script_parameter11,\n },\n }","def _submit_to_queue(self, script_file):\n if sys.version_info[0] < 3:\n process = Popen(['qsub', script_file], stdout=PIPE, stderr=PIPE)\n else:\n # need string not bytes so must use universal_newlines\n process = Popen(['qsub', script_file], stdout=PIPE, stderr=PIPE, universal_newlines=True)\n\n out, err = process.communicate()\n # grab the return code. PBS returns 0 if the job was successful\n queue_id = None\n if process.returncode == 0:\n try:\n # output should of the form '2561553.sdb' or '352353.jessup' - just grab the first part for job id\n queue_id = int(out.split('.')[0])\n except:\n # probably error parsing job code\n logger.critical(\"Could not parse job id following qsub...\")\n return SubmitResults(qid=queue_id, out=out, err=err, process=process)","def run( **kwargs ):\n\n # combine options using correct preference\n options = dict(PARAMS.items())\n options.update( getCallerLocals().items() )\n options.update( kwargs.items() )\n\n def setupJob( session ):\n\n jt = session.createJobTemplate()\n jt.workingDirectory = os.getcwd()\n jt.jobEnvironment = { 'BASH_ENV' : '~/.bashrc' }\n jt.args = []\n jt.nativeSpecification = \"-V -q %s -p %i -N %s %s\" % \\\n (options.get(\"job_queue\", global_options.cluster_queue ),\n options.get(\"job_priority\", global_options.cluster_priority ),\n \"_\" + re.sub( \"[:]\", \"_\", os.path.basename(options.get(\"outfile\", \"ruffus\" ))),\n options.get(\"job_options\", global_options.cluster_options))\n\n # keep stdout and stderr separate\n jt.joinFiles=False\n\n return jt\n\n shellfile = os.path.join( os.getcwd(), \"shell.log\" )\n \n # run multiple jobs\n if options.get( \"statements\" ):\n\n statement_list = []\n for statement in options.get(\"statements\"): \n options[\"statement\"] = statement\n statement_list.append(buildStatement( **options))\n \n if options.get( \"dryrun\", False ): return\n\n # get session for process - only one is permitted\n pid = os.getpid()\n if pid not in global_sessions: \n\n L.debug( \"creating new drmaa session for pid %i\" % pid )\n global_sessions[pid]=drmaa.Session() \n global_sessions[pid].initialize()\n\n session = global_sessions[pid]\n \n jt = setupJob( session )\n \n jobids, filenames = [], []\n for statement in statement_list:\n # create job script\n tmpfile = tempfile.NamedTemporaryFile( dir = os.getcwd() , delete = False )\n tmpfile.write( \"#!/bin/bash\\n\" ) # -l -O expand_aliases\\n\" )\n tmpfile.write( 'echo \"START--------------------------------\" >> %s \\n' % shellfile )\n tmpfile.write( '''echo 'statement=%s' >> %s\\n''' % (statement, shellfile) )\n tmpfile.write( \"set &>> %s\\n\" % shellfile)\n tmpfile.write( \"module list &>> %s\\n\" % shellfile )\n tmpfile.write( 'echo \"END----------------------------------\" >> %s \\n' % shellfile )\n tmpfile.write( expandStatement(statement) + \"\\n\" )\n tmpfile.close()\n\n # build paths\n job_path = os.path.abspath( tmpfile.name )\n stdout_path = job_path + \".stdout\" \n stderr_path = job_path + \".stderr\" \n\n jt.remoteCommand = job_path\n jt.outputPath=\":\"+ stdout_path\n jt.errorPath=\":\" + stderr_path\n\n os.chmod( job_path, stat.S_IRWXG | stat.S_IRWXU )\n\n jobid = session.runJob(jt)\n jobids.append( jobid )\n filenames.append( (job_path, stdout_path, stderr_path) )\n\n L.debug( \"job has been submitted with jobid %s\" % str(jobid ))\n \n L.debug( \"waiting for %i jobs to finish \" % len(jobids) )\n session.synchronize(jobids, drmaa.Session.TIMEOUT_WAIT_FOREVER, False)\n \n # collect and clean up\n for jobid, statement, paths in zip( jobids, statement_list, filenames) :\n job_path, stdout_path, stderr_path = paths\n retval = session.wait(jobid, drmaa.Session.TIMEOUT_WAIT_FOREVER)\n\n stdout, stderr = getStdoutStderr( stdout_path, stderr_path )\n\n if retval.exitStatus != 0:\n raise PipelineError( \"---------------------------------------\\n\"\n \"Child was terminated by signal %i: \\n\"\n \"The stderr was: \\n%s\\n%s\\n\" \n \"---------------------------------------\\n\" % \\\n (retval.exitStatus, \n \"\".join( stderr),\n statement ) )\n\n os.unlink( job_path )\n \n session.deleteJobTemplate(jt)\n\n # run a single parallel job\n elif (options.get( \"job_queue\" ) or options.get( \"to_cluster\" )) and not global_options.without_cluster:\n\n statement = buildStatement( **options )\n\n if options.get( \"dryrun\", False ): return\n\n tmpfile = tempfile.NamedTemporaryFile( dir = os.getcwd() , delete = False )\n tmpfile.write( \"#!/bin/bash\\n\" ) # -l -O expand_aliases\\n\" )\n\n tmpfile.write( 'echo \"START--------------------------------\" >> %s \\n' % shellfile )\n tmpfile.write( 'echo \"statement=%s\" >> %s\\n' % (statement, shellfile) )\n tmpfile.write( \"set &>> %s\\n\" % shellfile)\n tmpfile.write( \"module list &>> %s\\n\" % shellfile )\n tmpfile.write( 'echo \"END----------------------------------\" >> %s \\n' % shellfile )\n tmpfile.write( expandStatement( statement ) + \"\\n\" )\n tmpfile.close()\n\n job_path = os.path.abspath( tmpfile.name )\n stdout_path = job_path + \".stdout\" \n stderr_path = job_path + \".stderr\" \n\n os.chmod( job_path, stat.S_IRWXG | stat.S_IRWXU )\n\n # get session for process - only one is permitted\n pid = os.getpid()\n if pid not in global_sessions:\n L.debug( \"creating new drmaa session for pid %i\" % pid )\n global_sessions[pid]=drmaa.Session() \n global_sessions[pid].initialize()\n\n session = global_sessions[pid]\n\n jt = setupJob( session )\n\n jt.remoteCommand = job_path\n # later: allow redirection of stdout and stderr to files; can even be across hosts?\n jt.outputPath=\":\"+ stdout_path\n jt.errorPath=\":\" + stderr_path\n\n if \"job_array\" in options and options[\"job_array\"] != None:\n # run an array job\n start, end, increment = options.get(\"job_array\" )\n L.debug(\"starting an array job: %i-%i,%i\" % (start, end, increment ))\n # sge works with 1-based, closed intervals\n jobids = session.runBulkJobs( jt, start+1, end, increment )\n L.debug( \"%i array jobs have been submitted as jobid %s\" % (len(jobids), jobids[0]) )\n retval = session.synchronize(jobids, drmaa.Session.TIMEOUT_WAIT_FOREVER, True)\n else:\n jobid = session.runJob(jt)\n L.debug( \"job has been submitted with jobid %s\" % str(jobid ))\n try:\n retval = session.wait(jobid, drmaa.Session.TIMEOUT_WAIT_FOREVER)\n except Exception, msg:\n # ignore message 24 in PBS\n # code 24: drmaa: Job finished but resource usage information and/or termination status could not be provided.\":\n if not msg.message.startswith(\"code 24\"): raise\n retval = None\n\n stdout, stderr = getStdoutStderr( stdout_path, stderr_path )\n\n if \"job_array\" not in options:\n if retval and retval.exitStatus != 0:\n raise PipelineError( \"---------------------------------------\\n\"\n \"Child was terminated by signal %i: \\n\"\n \"The stderr was: \\n%s\\n%s\\n\"\n \"-----------------------------------------\" % \\\n (retval.exitStatus, \n \"\".join( stderr), statement))\n \n session.deleteJobTemplate(jt)\n os.unlink( job_path )\n\n else:\n statement = buildStatement( **options )\n\n if options.get( \"dryrun\", False ): return\n \n if \"<(\" in statement:\n if \"'\" in statement: raise ValueError( \"advanced bash syntax combined with single quotes\" )\n statement = \"\"\"/bin/bash -c '%s'\"\"\" % statement\n\n process = subprocess.Popen( expandStatement( statement ),\n cwd = os.getcwd(), \n shell = True,\n stdin = subprocess.PIPE,\n stdout = subprocess.PIPE,\n stderr = subprocess.PIPE )\n\n # process.stdin.close()\n stdout, stderr = process.communicate()\n\n if process.returncode != 0:\n raise PipelineError( \"---------------------------------------\\n\"\n \"Child was terminated by signal %i: \\n\"\n \"The stderr was: \\n%s\\n%s\\n\"\n \"-----------------------------------------\" % \\\n (-process.returncode, stderr, statement ))","def submit(fragment,njobs,nevts,outdir=\"\",first=None,indices=None,logdir=\"\",tag=\"\",dry=False,slc6=False,verb=0):\n print(\">>> Submitting...\")\n indir = os.path.dirname(fragment) or '.'\n fullfrag = os.path.abspath(fragment)\n ensuredir(os.path.join(indir,logdir)) # log directory\n ensuredir(outdir) # ensure output directory exists before submitting\n #args = f\"{outdir} {fullfrag} maxevts={nevts} index=$(ProcId) seed=$$([$(ProcId)+1])\" # start from 0\n args = f\"{outdir} {fullfrag} maxevts={nevts} index=$$([$(ProcId)+1]) seed=$$([$(ProcId)+1])\" # start from 1\n if tag:\n args += f\" tag={tag}\"\n if indices:\n indices_ = [ ]\n for index in indices:\n if isinstance(index,str) and index.count(':')==1:\n start, end = index.split(':') # e.g. '1:4' = [1, 2, 3, 4]\n for i in range(int(start),int(end)+1):\n indices_.append(i)\n else:\n indices_.append(int(index))\n args = args.replace('$(ProcId)','$(i)')\n queue = f\"-queue i in {', '.join(str(i) for i in indices_)}\"\n #queue = f\"-a 'queue i from ( {', '.join(str(i) for i in indices_)} )'\"\n elif first:\n args = args.replace('$(ProcId)','$(i)')\n queue = f\"-queue i from seq {first} {first+njobs-1} \\|\"\n #queue = f\"-a 'queue from seq {first} {njobs}|'\"\n else:\n queue = f\"-queue {njobs}\"\n name = f\"{os.path.basename(fragment).replace('.py','')}\"\n log = os.path.join(logdir,f\"submit_fragment{tag}.$(ClusterId).$(ProcId).log\")\n subcmd = f\"condor_submit submit_fragment.sub -a 'initialdir={indir}' -a 'mylogfile={log}'\"\n subcmd += f\" -a 'arguments={args}'\" # -a 'should_transfer_files=no'\n subcmd += f\" -batch-name {name} {queue}\" #-queue '{queue}'\n if slc6:\n subcmd += f\" -a 'requirements = (OpSysAndVer =?= \\\"SLCern6\\\")'\"\n if verb>=4:\n subcmd += \" -verbose\"\n print(\">>> \"+subcmd)\n if not dry:\n os.system(subcmd)","def __create_jobs_bin__(self):\n # | - __create_jobs_bin__\n folder_dir = os.path.join(self.root_dir, self.working_dir, \"jobs_bin\")\n # folder_dir = self.root_dir + \"/jobs_bin\"\n\n if not os.path.exists(folder_dir):\n # print(\"KDJFDI__\")\n # print(folder_dir)\n os.makedirs(folder_dir)\n # __|","def generate_job(self, additional_settings_dict):\n\n self.final_job = additional_settings_dict\n\n header = (\n \"# Measurement file: \\n \"\n \"# Project: \" + self.variables[\"Current_project\"] + \"\\n \"\n \"# Sensor Type: \" + self.variables[\"Current_sensor\"] + \"\\n \"\n \"# ID: \" + self.variables[\"Current_filename\"] + \"\\n \"\n \"# Operator: \" + self.variables[\"Current_operator\"] + \"\\n \"\n \"# Date: \" + str(time.asctime()) + \"\\n\\n\"\n )\n\n IVCV_dict = self.generate_IVCV(\"\") # here additional header can be added\n strip_dict = self.generate_strip(\"\")\n\n if IVCV_dict:\n self.final_job.update({\"IVCV\": IVCV_dict})\n if strip_dict:\n self.final_job.update({\"stripscan\": strip_dict})\n\n # Check if filepath is a valid path\n if self.variables[\"Current_filename\"] and os.path.isdir(\n self.variables[\"Current_directory\"]\n ):\n self.final_job.update({\"Header\": header})\n self.queue_to_measure.put({\"Measurement\": self.final_job})\n self.log.info(\"Sendet job: \" + str({\"Measurement\": self.final_job}))\n else:\n self.log.error(\n \"Please enter a valid path and name for the measurement file.\"\n )","def create_job_ymls(job_template_args, job_template_dir, job_template_name, output_dir, upload=True):\n for i, template_args in enumerate(job_template_args):\n template_loader = jinja2.FileSystemLoader(searchpath=job_template_dir)\n template_env = jinja2.Environment(loader=template_loader)\n template_file = job_template_name\n template = template_env.get_template(template_file)\n output_text = template.render(template_args)\n job_yml = 'vista-job-{}.yml'.format(i)\n job_yml_path = join(output_dir, job_yml)\n \n if not exists(output_dir):\n mkdir(output_dir)\n with open(job_yml_path, 'w') as output_template:\n output_template.write(output_text)\n if upload:\n upload_blob(UPLOAD_BUCKET, job_yml_path, job_yml)\n print(job_yml, 'uploaded')","def submit_step(root_dir, config, step):\r\n\r\n premable = \"\"\"\r\n export PATH=~/project-pasaniuc/software/anaconda3/bin:$PATH\r\n export PYTHONNOUSERSITE=True\r\n \"\"\"\r\n\r\n assert(step in ['mean_std', 'beta', 'phe_g', 'phe', 'zsc'])\r\n script ='python gwas_sim.py --step {step} --root_dir {root_dir}'.format(step=step, root_dir=root_dir)\r\n \r\n if step in ['mean_std', 'phe_g', 'zsc']:\r\n script += ' --part_i $((SGE_TASK_ID - 1))'\r\n job_num = config['num_part']\r\n else:\r\n job_num = 1\r\n print(script)\r\n \r\n if step == 'mean_std':\r\n memory = '4G'\r\n duration='00:20:00'\r\n elif step == 'beta':\r\n memory = '4G'\r\n duration='00:05:00'\r\n elif step == 'phe_g' or step == 'zsc':\r\n memory = '6G'\r\n duration='01:00:00'\r\n elif step == 'phe':\r\n memory = '6G'\r\n duration='00:10:00'\r\n else:\r\n assert(False)\r\n \r\n submit_script(script=script, \r\n premable=premable,\r\n jobnum=job_num,\r\n name=step,\r\n memory=memory,\r\n duration=duration)","async def create_job(response: Response,\n request: Request,\n job: Job = Body(\n ...,\n example={\n \"id_video\": \"bbb_0.mp4\",\n \"bitrate\": 7000,\n \"speed\": \"ultrafast\",\n },\n )\n ): \n \n\n # get an ID and return to client\n id_job = mngr.getID()\n logger.debug(\"got id_job %s\" %id_job)\n resp = [\"http:/\"]\n resp.append(request.headers['host'])\n resp.append(id_job)\n response.headers[\"Location\"] = \"/\".join(resp)\n\n # create the task\n mngr.newJob(id_job, \n job.id_video, \n job.bitrate, \n job.speed)\n\n return id_job","def _create_job_spec(\n self,\n job_id: Text,\n training_input: Dict[Text, Any],\n job_labels: Optional[Dict[Text, Text]] = None) -> Dict[Text, Any]:\n\n job_spec = {\n 'display_name': job_id,\n 'job_spec': training_input,\n 'labels': job_labels,\n }\n return job_spec","def createJob(appName, appUID):\n logger.debug('[FLASKWEB /jobs/<appName>/<appUID>] Job Request for %s' % appName)\n global dispatcher\n applist = [a['name'] for a in db.getAllApps()]\n if appName in applist:\n if request.method == 'POST':\n logger.debug(\"POST Request for a new job\")\n # TODO: Get user\n file = request.files['file']\n text = request.form['text'] if 'text' in request.form else None\n k3logging = True if 'logging' in request.form else False\n jsonlog = True if 'jsonlog' in request.form else False\n jsonfinal = True if 'jsonfinal' in request.form else False\n perf_profile = True if 'perf_profile' in request.form else False\n perf_frequency = request.form['perf_frequency'] if 'perf_frequency' in request.form else ''\n cmd_prefix = request.form['cmd_prefix'] if 'cmd_prefix' in request.form else None\n cmd_infix = request.form['cmd_infix'] if 'cmd_infix' in request.form else None\n cmd_suffix = request.form['cmd_suffix'] if 'cmd_suffix' in request.form else None\n core_dump = True if 'core_dump' in request.form else False\n stdout = request.form['stdout'] if 'stdout' in request.form else False\n user = request.form['user'] if 'user' in request.form else 'anonymous'\n tag = request.form['tag'] if 'tag' in request.form else ''\n\n # User handling: jsonfinal is a qualifier flag for the json logging flag\n if jsonfinal and not jsonlog:\n jsonlog = True\n\n logger.debug(\"K3 LOGGING is : %s\" % (\"ON\" if k3logging else \"OFF\"))\n logger.debug(\"JSON LOGGING is : %s\" % (\"ON\" if jsonlog else \"OFF\"))\n logger.debug(\"JSON FINAL LOGGING is : %s\" % (\"ON\" if jsonfinal else \"OFF\"))\n logger.debug(\"PERF PROFILING is : %s\" % (\"ON\" if perf_profile else \"OFF\"))\n logger.debug(\"CORE DUMPS are : %s\" % (\"ON\" if core_dump else \"OFF\"))\n # trials = int(request.form['trials']) if 'trials' in request.form else 1\n\n # Check for valid submission\n if not file and not text:\n logger.error('Error. Cannot create job: No input file and no YAML for enviornment configuration provided. ')\n return render_template(\"error.html\", code=404, message=\"Invalid job request\")\n\n # Post new job request, get job ID & submit time\n thisjob = dict(appName=appName, appUID=appUID, user=user, tag=tag)\n jobId, time = db.insertJob(thisjob)\n thisjob = dict(jobId=jobId, time=time)\n\n # Save yaml to file (either from file or text input)\n path = os.path.join(webapp.config['UPLOADED_JOBS_DEST'], appName)\n if not os.path.exists(path):\n os.mkdir(path)\n\n path = os.path.join(path, str(jobId))\n filename = 'role.yaml'\n if not os.path.exists(path):\n os.mkdir(path)\n\n if file:\n file.save(os.path.join(path, filename))\n else:\n with open(os.path.join(path, filename), 'w') as file:\n file.write(text)\n\n # Create new Mesos K3 Job\n apploc = webapp.config['ADDR']+os.path.join(webapp.config['UPLOADED_APPS_URL'], appName, appUID, appName)\n\n newJob = Job(binary=apploc, appName=appName, jobId=jobId,\n rolefile=os.path.join(path, filename), logging=k3logging,\n jsonlog=jsonlog, jsonfinal=jsonfinal,\n perf_profile=perf_profile, perf_frequency=perf_frequency, core_dump=core_dump,\n cmd_prefix=cmd_prefix, cmd_infix=cmd_infix, cmd_suffix=cmd_suffix)\n\n # Submit to Mesos\n dispatcher.submit(newJob)\n\n thisjob = dict(thisjob, url=dispatcher.getSandboxURL(jobId), status='SUBMITTED')\n\n if 'application/json' in request.headers['Accept']:\n return jsonify(thisjob), 202\n else:\n return render_template('last.html', appName=appName, lastjob=thisjob)\n\n elif request.method == 'GET':\n jobs = db.getJobs(appName=appName)\n if 'application/json' in request.headers['Accept']:\n return jsonify(dict(jobs=jobs))\n else:\n preload = request.args['preload'] if 'preload' in request.args else 'Sample'\n logger.debug(\"YAML file preload = %s\" % preload)\n if preload == 'Instructions':\n yamlFile = 'role_file_template.yaml'\n elif preload == 'Last':\n lastJobId = max([ d['jobId'] for d in jobs])\n path = os.path.join(webapp.config['UPLOADED_JOBS_DEST'], appName, \"%d\" % lastJobId, 'role.yaml')\n logger.debug(\" YAML PATH= %s\" % path)\n if os.path.exists(path):\n yamlFile = path\n else:\n yamlFile = None\n else:\n yamlFile = 'sample.yaml'\n if yamlFile:\n with open(yamlFile, 'r') as f:\n sample = f.read()\n else:\n sample = \"(No YAML file to display)\"\n return render_template(\"newjob.html\", name=appName, uid=appUID, sample=sample)\n\n else:\n return returnError(\"There is no application, %s\" % appName, 404)","def _submit_to_queue(self, script_file):\n if sys.version_info[0] < 3:\n process = Popen(['msub', script_file], stdout=PIPE, stderr=PIPE)\n else:\n # need string not bytes so must use universal_newlines\n process = Popen(['msub', script_file], stdout=PIPE, stderr=PIPE, universal_newlines=True)\n\n out, err = process.communicate()\n queue_id = None\n if process.returncode == 0:\n # grab the returncode. MOAB returns 0 if the job was successful\n try:\n # output should be the queue_id\n queue_id = int(out.split()[0])\n except:\n # probably error parsing job code\n logger.critical('Could not parse job id following msub...')\n\n return SubmitResults(qid=queue_id, out=out, err=err, process=process)","def setup_file_generation_submission(cls, sess, submission_id=None):\n submission_id = cls.generation_submission_id if not submission_id else submission_id\n submission = sess.query(Submission).filter(Submission.submission_id == submission_id).one()\n\n # Create D1 jobs ready for generation route to be called\n cls.insert_job(\n sess,\n FILE_TYPE_DICT['award_procurement'],\n JOB_STATUS_DICT['ready'],\n JOB_TYPE_DICT['file_upload'],\n submission.submission_id\n )\n award_roc_val_job = cls.insert_job(\n sess,\n FILE_TYPE_DICT['award_procurement'],\n JOB_STATUS_DICT['waiting'],\n JOB_TYPE_DICT['csv_record_validation'],\n submission.submission_id\n )\n # Create E and F jobs ready for check route\n exec_comp_job = cls.insert_job(\n sess,\n FILE_TYPE_DICT['executive_compensation'],\n JOB_STATUS_DICT['finished'],\n JOB_TYPE_DICT['file_upload'],\n submission.submission_id\n )\n sub_award_job = cls.insert_job(\n sess,\n FILE_TYPE_DICT['sub_award'],\n JOB_STATUS_DICT['invalid'],\n JOB_TYPE_DICT['file_upload'],\n submission.submission_id\n )\n sub_award_job.error_message = \"File was invalid\"\n\n # Create D2 jobs\n cls.insert_job(\n sess,\n FILE_TYPE_DICT['award'],\n JOB_STATUS_DICT['finished'],\n JOB_TYPE_DICT['file_upload'],\n submission.submission_id\n )\n cls.insert_job(\n sess,\n FILE_TYPE_DICT['award'],\n JOB_STATUS_DICT['invalid'],\n JOB_TYPE_DICT['csv_record_validation'],\n submission.submission_id\n )\n # Create dependency\n exec_comp_dep = JobDependency(\n job_id=exec_comp_job.job_id,\n prerequisite_id=award_roc_val_job.job_id\n )\n sess.add(exec_comp_dep)\n sess.commit()","def write_submit_script(fp, content, data):\n\n for k, v in data.items():\n placeholder = \"{\" + str(k).upper() + \"}\"\n content = content.replace(placeholder, str(v))\n\n keys_left = re.findall(r\"!$\\{(.+?)\\}\", content)\n if len(keys_left) > 0:\n _LOGGER.warning(\n \"> Warning: %d submission template variables are not \" \"populated: '%s'\",\n len(keys_left),\n str(keys_left),\n )\n\n if not fp:\n print(content)\n return content\n else:\n outdir = os.path.dirname(fp)\n if outdir and not os.path.isdir(outdir):\n os.makedirs(outdir)\n with open(fp, \"w\") as f:\n f.write(content)\n return fp","def build_experiment(\n experiment_name,\n policy,\n estimator,\n dataset,\n policy_params,\n estimator_params,\n dataset_params,\n output_folder=\"./policy_yamls/\",\n slurm_output=\"./outputs/\",\n):\n\n policy_dict = build_obj_spec(\n policy,\n policy_params,\n experiment_name=experiment_name,\n obj_type=\"policy\",\n output=output_folder,\n )\n estimator_dict = build_obj_spec(\n estimator,\n estimator_params,\n experiment_name=experiment_name,\n obj_type=\"estimator\",\n output=output_folder,\n )\n dataset_dict = build_obj_spec(\n dataset,\n dataset_params,\n experiment_name=experiment_name,\n obj_type=\"dataset\",\n output=output_folder,\n )\n\n experiment_dir = os.path.join(output_folder, experiment_name)\n slurm_output = os.path.join(slurm_output, experiment_name + \".out\")\n slurm_script = SHELL_SCRIPT.format(slurm_output, experiment_dir)\n\n with open(os.path.join(experiment_dir, \"script.sbatch\"), \"w\") as file:\n file.write(slurm_script)","def build_run_script(path, script_name, save_history):\n\n # define paths\n path = abspath(path)\n job_script_path = join(path, 'scripts', 'run.sh')\n\n # copy run script to scripts directory\n run_script = abspath(__file__).rsplit('/', maxsplit=2)[0]\n run_script = join(run_script, 'scripts', script_name)\n shutil.copy(run_script, join(path, 'scripts'))\n\n # declare outer script that reads PATH from file\n job_script = open(job_script_path, 'w')\n job_script.write('#!/bin/bash\\n')\n\n # move to job directory\n job_script.write('cd {:s} \\n\\n'.format(path))\n\n # run each batch\n job_script.write('echo \"Starting all batches at `date`\"\\n')\n job_script.write('while read P; do\\n')\n job_script.write('echo \"Processing batch ${P}\"\\n')\n job_script.write('python ./scripts/{:s}'.format(script_name)+' ${P} ')\n args = (save_history,)\n job_script.write('-s {:d}\\n'.format(*args))\n job_script.write('done < ./batches/index.txt \\n')\n job_script.write('echo \"Job completed at `date`\"\\n')\n job_script.write('exit\\n')\n\n # close the file\n job_script.close()\n\n # change the permissions\n chmod(job_script_path, 0o755)","def create(cfg, jobs):\n server = jenkins_utils.server_factory(cfg)\n libjobs.createJobs(server, jobs)","def create_compilation_job(CompilationJobName=None, RoleArn=None, InputConfig=None, OutputConfig=None, StoppingCondition=None):\n pass","def jobserver_job():\n return _MakeJob()","def prepare_ss_process_job(self, pool_type, pool_address, jobs, script_path, rank='0'):\n ############\n #The rest of the processing is moved to condor, by the file ss_results_process.py\n ############\n \n #Prepare the condor job file\n \n input_file_string = ''\n args_string = ''\n for job in jobs:\n input_file_string += job.job_output + ', '\n args_string += job.job_output + ' '\n input_file_string = input_file_string.rstrip(', ')\n args_string = args_string.rstrip(' ')\n output = 'results.txt'\n \n job_template = Template(condor_spec.condor_string_header + condor_spec.results_process_spec_string)\n\n job_string = job_template.substitute(pool_type=pool_type,\n pool_address=pool_address,\n script=script_path,\n args=args_string,\n input_files=input_file_string,\n output='results',\n output_files = output,\n rank=rank)\n job_filename = 'results.job'\n job_file = open(os.path.join(self.path, job_filename), 'w')\n job_file.write(job_string)\n job_file.close()\n \n return job_filename","def launch(logger, base_url, group_id, token):\n # The JWT token contains a user id in its metadata. Lets extract it\n token_helper = common.TokenHelper(token)\n user_id = token_helper.get_user_id()\n logger.info(\"User ID {}\".format(user_id))\n\n # Use the provided helper functions to create the app specific arguments.\n # These are template parameters which will be swapped for things like start frame, end frame, on multi-part jobs.\n args = compute_arguments.get_nuke_arguments(NUKE_SCRIPT_WRITE_NODE)\n\n # Make a random job name\n name = \"example_\" + str(time.time())\n\n region = common.select_region(logger, \"Select a Region to run the compute job in\")\n\n # Next, use the payload helper to build the job request body\n payload = compute.make_job_request(user_id, group_id, NUKE_COMPUTE_APP_ID, \"/data/dropbox/{}\".format(NUKE_SCRIPT_PATH_WITHIN_DROPBOX), \n name, 1, 100, 1, region, args)\n\n # Now its time to actually launch it \n compute_response = compute.create_job(base_url, group_id, token, payload)\n if compute_response.status_code == 200:\n compute_dict = common.convert_response(compute_response) \n logger.error(\"Create job succeeded: {}\".format(compute_dict['id']))\n return compute_dict['id']\n else:\n logger.error(\"Create job failed [{}] {}\".format(compute_response.status_code, compute_response.text))\n return None","def get_script_str(self, job_name, launch_dir, executable, qout_path, qerr_path,\n stdin=None, stdout=None, stderr=None, exec_args=None):\n # PbsPro does not accept job_names longer than 15 chars.\n if len(job_name) > 14 and isinstance(self, PbsProAdapter):\n job_name = job_name[:14]\n\n # Construct the header for the Queue Manager.\n qheader = self._make_qheader(job_name, qout_path, qerr_path)\n\n # Add the bash section.\n se = ScriptEditor()\n\n # Cd to launch_dir immediately.\n se.add_line(\"cd \" + os.path.abspath(launch_dir))\n\n if self.setup:\n se.add_comment(\"Setup section\")\n se.add_lines(self.setup)\n se.add_emptyline()\n\n if self.modules:\n # stderr is redirected to mods.err file.\n # module load 2>> mods.err\n se.add_comment(\"Load Modules\")\n se.add_line(\"module purge\")\n se.load_modules(self.modules)\n se.add_emptyline()\n\n se.add_comment(\"OpenMp Environment\")\n if self.has_omp:\n se.declare_vars(self.omp_env)\n se.add_emptyline()\n else:\n se.declare_vars({\"OMP_NUM_THREADS\": 1})\n\n if self.shell_env:\n se.add_comment(\"Shell Environment\")\n se.declare_vars(self.shell_env)\n se.add_emptyline()\n\n if self.pre_run:\n se.add_comment(\"Commands before execution\")\n se.add_lines(self.pre_run)\n se.add_emptyline()\n\n # Construct the string to run the executable with MPI and mpi_procs.\n if is_string(executable):\n line = self.mpi_runner.string_to_run(self, executable,\n stdin=stdin, stdout=stdout, stderr=stderr, exec_args=exec_args)\n se.add_line(line)\n else:\n assert isinstance(executable, (list, tuple))\n se.add_lines(executable)\n\n if self.post_run:\n se.add_emptyline()\n se.add_comment(\"Commands after execution\")\n se.add_lines(self.post_run)\n\n return qheader + se.get_script_str() + \"\\n\"","def test_cluster_jobs_script(self):\r\n\r\n qiime_config = load_qiime_config()\r\n submit_script = qiime_config['cluster_jobs_fp']\r\n\r\n if (submit_script):\r\n full_path = which(submit_script)\r\n if full_path:\r\n submit_script = full_path\r\n self.assertTrue(exists(submit_script),\r\n \"cluster_jobs_fp is not set to a valid path in qiime config: %s\" % submit_script)\r\n # check if executable\r\n self.assertTrue(access(submit_script, X_OK),\r\n \"cluster_jobs_fp is not executable: %s\" % submit_script)\r\n else:\r\n # Can't run in parallel, but not a critical error\r\n pass","def submit_scripts(self, out):\n program_folder = os.path.join(out, self.out)\n for config in self.configurations:\n config.submit_CaVEMan_scripts(\n program_folder, self.path2exe, self.ref_fai, self.file1, self.file2,\n self.config_file, self.qsub_dir, self.mstep_script, self.merge_script, self.estep_script\n )\n return None","def write_pbs_runjob(name, nnodes, nprocessors, pmem, walltime, binary):\n runjob = open('runjob', 'w')\n runjob.write('#!/bin/sh\\n')\n runjob.write('#PBS -N {}\\n'.format(name))\n runjob.write('#PBS -o test.out\\n')\n runjob.write('#PBS -e test.err\\n')\n runjob.write('#PBS -r n\\n')\n runjob.write('#PBS -l walltime={}\\n'.format(walltime))\n runjob.write('#PBS -l nodes={}:ppn={}\\n'.format(nnodes, nprocessors))\n runjob.write('#PBS -l pmem={}\\n'.format(pmem))\n runjob.write('#PBS -W group_list=hennig\\n\\n')\n runjob.write('cd $PBS_O_WORKDIR\\n\\n')\n runjob.write('mpirun {} > job.log\\n\\n'.format(binary))\n runjob.write('echo \\'Done.\\'\\n')\n runjob.close()","def create_submission(self, model_fn):\n output_fn = self._to_output_fn(model_fn)\n\n self._create_submission(output_fn)\n self._post_processing(output_fn)","def main():\n gh = Github(os.environ['GH_TOKEN'])\n # gh_token = os.environ['GH_TOKEN']\n gc_token_path = os.environ['GC_STORAGE_KEY']\n\n vars_file_path = os.getenv('VARS_FILE', \"\")\n pipeline_id = os.getenv('CI_PIPELINE_ID', 0)\n\n repo = gh.get_repo(\"kubeinit/kubeinit\")\n branches = repo.get_branches()\n\n output = 0\n # Something linke:\n # url = \"https://gitlab.com/kubeinit/kubeinit-ci/pipelines/\"\n url = os.getenv('CI_PIPELINE_URL', \"\")\n print(\"The job results will be published in runtime at: \" + url)\n\n for branch in branches:\n for pr in repo.get_pulls(state='open', sort='created', base=branch.name):\n labels = [item.name for item in pr.labels]\n\n sha = pr.head.sha\n committer_email = repo.get_commit(sha=sha).commit.committer.email\n print(committer_email)\n\n execute = False\n # We assign the executed label to avoid executing this agains the same PR over and over\n # We mark the PR as e2e-executed\n\n for label in labels:\n if re.match(r\".*-.*-.*-.*-.*-.*-.*\", label):\n print('Matching a PR label')\n params = label.split(\"-\")\n distro = params[0]\n driver = params[1]\n master = params[2]\n worker = params[4]\n scenario = params[6]\n execute = True\n remove_label(label, pr, repo)\n break\n\n if execute:\n now = datetime.now()\n now.strftime(\"%m.%d.%Y.%H.%M.%S\")\n job_name = pipeline_id + \"-\" + distro + \"-\" + driver + \"-\" + master + \"-\" + worker + \"-\" + scenario + \"-\" + now.strftime(\"%Y.%m.%d.%H.%M.%S\")\n print(\"Let's run the e2e job, distro %s driver %s \" % (distro, driver))\n print(\"-------------\")\n print(\"-------------\")\n print(\"Running the e2e job for: \" + str(pr.number) + \" \" + pr.title)\n print(\"-------------\")\n print(\"-------------\")\n print(\"-------------\")\n\n # We update the status to show that we are executing the e2e test\n print(\"Current status\")\n print(repo.get_commit(sha=sha).get_statuses())\n repo.get_commit(sha=sha).create_status(state=\"pending\",\n target_url=url + str(pipeline_id),\n description=\"Running...\",\n context=\"%s-%s-%s-controller-%s-compute-%s\" % (distro,\n driver,\n master,\n worker,\n scenario))\n print(\"The pipeline ID is: \" + str(pipeline_id))\n print(\"The clouds.yml path is: \" + str(vars_file_path))\n # We trigger the e2e job\n start_time = time.time()\n try:\n print(\"We call the downstream job configuring its parameters\")\n subprocess.check_call(\"./ci/run_kubeinit.sh %s %s %s %s %s %s %s %s\" % (str(branch.name),\n str(pr.number),\n str(vars_file_path),\n str(distro),\n str(driver),\n str(master),\n str(worker),\n str(scenario)),\n shell=True)\n except Exception as e:\n print('An exception hapened executing Ansible')\n print(e)\n output = 1\n\n try:\n print(\"Render ara data\")\n subprocess.check_call(\"./ci/ara.sh %s\" % (str(job_name) + \"-\" + str(output)), shell=True)\n except Exception as e:\n print('An exception hapened rendering ara data')\n print(e)\n output = 1\n\n print(\"starting the uploader job\")\n # No matter if the job passed or failed we always use go as the suffix\n # upload_error = upload_logs_to_github(str(job_name) + \"-\" + str(output), gh_token)\n upload_error = upload_logs_to_google_cloud(str(job_name) + \"-\" + str(output), gc_token_path)\n render_index(gc_token_path)\n print(\"finishing the uploader job\")\n\n if output == 0:\n state = \"success\"\n else:\n state = \"failure\"\n\n desc = (\"Ended with %s in %s minutes\" % (state, round((time.time() - start_time) / 60, 2)))\n\n print(desc)\n print(state)\n\n if upload_error == 1:\n dest_url = url + str(pipeline_id)\n else:\n dest_url = 'https://storage.googleapis.com/kubeinit-ci/jobs/pr/' + str(job_name) + \"-\" + str(output) + '/index.html'\n\n print(\"The destination URL is: \" + dest_url)\n # We update the status with the job result\n repo.get_commit(sha=sha).create_status(state=state,\n target_url=dest_url,\n description=desc,\n context=\"%s-%s-%s-controller-%s-compute-%s\" % (distro,\n driver,\n master,\n worker,\n scenario))\n else:\n print(\"No need to do anything\")\n if execute:\n exit()","def create_submission_pipeline(submission_id: str):\n from anubis.utils.rpc import enqueue_autograde_pipeline\n from anubis.lms.submissions import init_submission\n\n # Log the creation event\n logger.info(\n \"Creating submission pipeline job {}\".format(submission_id),\n extra={\n \"submission_id\": submission_id,\n },\n )\n\n # Calculate the maximum number of jobs allowed in the cluster\n max_jobs = get_config_int('PIPELINE_MAX_JOBS', default=10)\n\n # Initialize kube client\n config.load_incluster_config()\n\n # Cleanup finished jobs\n active_jobs = reap_pipeline_jobs()\n\n if active_jobs > max_jobs:\n logger.info(\n \"TOO many jobs - re-enqueue {}\".format(submission_id),\n extra={\"submission_id\": submission_id},\n )\n enqueue_autograde_pipeline(submission_id)\n exit(0)\n\n # Get the database entry for the submission\n submission = Submission.query.filter(\n Submission.id == submission_id\n ).first()\n\n # Make sure that the submission exists\n if submission is None:\n logger.error(\n \"Unable to find submission rpc.test_repo\",\n extra={\n \"submission_id\": submission_id,\n },\n )\n return\n\n # If the build field is not present, then\n # we need to initialize the submission.\n if submission.build is None:\n init_submission(submission, commit=True)\n\n submission.processed = False\n submission.state = 'Initializing Pipeline'\n db.session.commit()\n\n # Create k8s job object\n job = create_pipeline_job_obj(submission)\n\n # Log the pipeline job creation\n logger.debug(\"creating pipeline job: \" + job.to_str())\n\n # Send to kube api\n batch_v1 = client.BatchV1Api()\n batch_v1.create_namespaced_job(body=job, namespace=\"anubis\")","def submit(STAGE_ID, *options):\n if any([opt.startswith(\"-t\") for opt in options]):\n wrapper = \"qsubwrapmpi\"\n else:\n wrapper = \"qsubwrap\"\n options = \" \".join(options)\n cmd = \"%s -v STAGE_ID=%s %s %s\" % (wrapper, STAGE_ID, options, jobscript)\n status, output = getstatusoutput(cmd)\n result = output\n alog.info(\"Submitted (status, output, cmd): %s\", (status, output, cmd))\n if status != 0:\n raise QsubException(status, output, cmd)\n return result.split(\".\", 1)[0]"],"string":"[\n \"def build_submission_script(path,\\n script_name,\\n save_history=True,\\n walltime=10,\\n allocation='p30653',\\n cores=1,\\n memory=4):\\n\\n # define paths\\n path = abspath(path)\\n job_script_path = join(path, 'scripts', 'submit.sh')\\n\\n # copy run script to scripts directory\\n run_script = abspath(__file__).rsplit('/', maxsplit=2)[0]\\n run_script = join(run_script, 'scripts', script_name)\\n shutil.copy(run_script, join(path, 'scripts'))\\n\\n # determine queue\\n if walltime <= 4:\\n queue = 'short'\\n elif walltime <= 48:\\n queue = 'normal'\\n else:\\n queue = 'long'\\n\\n # declare outer script that reads PATH from file\\n job_script = open(job_script_path, 'w')\\n job_script.write('#!/bin/bash\\\\n')\\n\\n # move to job directory\\n job_script.write('cd {:s} \\\\n\\\\n'.format(path))\\n\\n # begin outer script for processing job\\n job_script.write('while IFS=$\\\\'\\\\\\\\t\\\\' read P\\\\n')\\n job_script.write('do\\\\n')\\n job_script.write('b_id=$(echo $(basename ${P}) | cut -f 1 -d \\\\'.\\\\')\\\\n')\\n job_script.write(' JOB=`msub - << EOJ\\\\n\\\\n')\\n\\n # =========== begin submission script for individual batch ============\\n job_script.write('#! /bin/bash\\\\n')\\n job_script.write('#MSUB -A {:s} \\\\n'.format(allocation))\\n job_script.write('#MSUB -q {:s} \\\\n'.format(queue))\\n job_script.write('#MSUB -l walltime={0:02d}:00:00 \\\\n'.format(walltime))\\n job_script.write('#MSUB -m abe \\\\n')\\n #job_script.write('#MSUB -M sebastian@u.northwestern.edu \\\\n')\\n job_script.write('#MSUB -o ./log/${b_id}/outlog \\\\n')\\n job_script.write('#MSUB -e ./log/${b_id}/errlog \\\\n')\\n job_script.write('#MSUB -N ${b_id} \\\\n')\\n job_script.write('#MSUB -l nodes=1:ppn={:d} \\\\n'.format(cores))\\n job_script.write('#MSUB -l mem={:d}gb \\\\n\\\\n'.format(memory))\\n\\n # load python module and metabolism virtual environment\\n job_script.write('module load python/anaconda3.6\\\\n')\\n job_script.write('source activate ~/pythonenvs/growth_env\\\\n\\\\n')\\n\\n # move to job directory\\n job_script.write('cd {:s} \\\\n\\\\n'.format(path))\\n\\n # run script\\n job_script.write('python ./scripts/{:s}'.format(script_name)+' ${P} ')\\n args = (save_history,)\\n job_script.write('-s {:d}\\\\n'.format(*args))\\n job_script.write('EOJ\\\\n')\\n job_script.write('`\\\\n\\\\n')\\n # ============= end submission script for individual batch ============\\n\\n # print job id\\n #job_script.write('echo \\\"JobID = ${JOB} submitted on `date`\\\"\\\\n')\\n job_script.write('done < ./batches/index.txt \\\\n')\\n job_script.write('echo \\\"All batches submitted as of `date`\\\"\\\\n')\\n job_script.write('exit\\\\n')\\n\\n # close the file\\n job_script.close()\\n\\n # change the permissions\\n chmod(job_script_path, 0o755)\",\n \"def make_job(self, script, factors=None):\\n job = {'script': script}\\n if factors is not None:\\n job['factors'] = factors\\n return job\",\n \"def prepare_parafly_slurm_job_script(sBasename_job, sBasename_parafly, sDirectory_job, sEmail, iWalltime_in = None, nNode_in = None, nThread_in=None, sJob_name_in =None, sPython_env_in =None, sQueue_in=None):\\n if iWalltime_in is not None:\\n iWalltime = iWalltime_in \\n else:\\n iWalltime = 2\\n if nNode_in is not None:\\n iNode = nNode_in \\n else:\\n iNode = 1\\n if nThread_in is not None:\\n nThread = nThread_in \\n else:\\n nThread = 40\\n \\n if sJob_name_in is not None:\\n sJob_name = sJob_name_in \\n else:\\n sJob_name = 'parafly'\\n if sPython_env_in is not None:\\n sPython_env = sPython_env_in \\n else:\\n sPython_env = 'base'\\n \\n if sQueue_in is not None:\\n sQueue = sQueue_in \\n else:\\n sQueue = 'short'\\n \\n sWalltime =\\\"{:0d}\\\".format(iWalltime )\\n sNode = \\\"{:0d}\\\".format(iNode )\\n sThread = \\\"{:0d}\\\".format(nThread )\\n \\n os.chdir(sDirectory_job)\\n \\n ofs = open(sBasename_job,\\\"w\\\") #write mode \\n sLine = '#!/bin/bash' + '\\\\n'\\n ofs.write( sLine ) \\n sLine = '#SBATCH --account=esmd' + '\\\\n'\\n ofs.write( sLine ) \\n\\n #sLine = '#SBATCH --begin=now+1minutes' + '\\\\n'\\n #ofs.write( sLine ) \\n\\n sLine = '#SBATCH --cpus-per-task=1 ' + '\\\\n'\\n ofs.write( sLine ) \\n\\n sLine = '#SBATCH --dependency=singleton ' + '\\\\n'\\n ofs.write( sLine )\\n sLine = '#SBATCH --error=stderr_%j.err' + '\\\\n'\\n ofs.write( sLine ) \\n sLine = '#SBATCH --job-name=' + sJob_name + ' # create a name for your job' + '\\\\n'\\n ofs.write( sLine ) \\n sLine = '#SBATCH --mail-type=ALL' + '\\\\n'\\n ofs.write( sLine ) \\n sLine = '#SBATCH --mail-user=' + sEmail + '\\\\n'\\n ofs.write( sLine ) \\n\\n sLine = '#SBATCH --nodes=' + sNode + ' # node count' + '\\\\n'\\n ofs.write( sLine ) \\n sLine = '#SBATCH --ntasks=' + sThread + ' # total number of tasks' + '\\\\n'\\n ofs.write( sLine ) \\n sLine = '#SBATCH --output=stdout_%j.out' + '\\\\n'\\n ofs.write( sLine ) \\n\\n sLine = '#SBATCH --partition=' + sQueue + '\\\\n' #can be improved here\\n ofs.write( sLine ) \\n sLine = '#SBATCH --time=' + sWalltime +':00:00 # total run time limit (HH:MM:SS)' + '\\\\n'\\n ofs.write( sLine ) \\n\\n sLine = 'module purge' + '\\\\n'\\n ofs.write( sLine ) \\n sLine = 'module load parafly/2013' + '\\\\n'\\n ofs.write( sLine ) \\n sLine = 'module load anaconda3/2019.03' + '\\\\n'\\n ofs.write( sLine ) \\n sLine = 'source /share/apps/anaconda3/2019.03/etc/profile.d/conda.sh' + '\\\\n'\\n ofs.write( sLine ) \\n sLine = 'unset PYTHONHOME' + '\\\\n'\\n ofs.write( sLine ) \\n sLine = 'conda activate ' + sPython_env + '\\\\n'\\n ofs.write( sLine ) \\n\\n sLine = 'ParaFly -c ' + sBasename_parafly + ' -CPU ' + sThread + ' -failed_cmds rerun.txt' + '\\\\n'\\n ofs.write( sLine ) \\n \\n sLine = 'echo \\\" Job \\\" ' + '${SLURM_JOBID}' + ' is launched' + '\\\\n'\\n ofs.write( sLine ) \\n\\n sLine = 'conda deactivate' + '\\\\n'\\n ofs.write( sLine ) \\n \\n sLine = 'echo \\\"Finished\\\"' + '\\\\n'\\n ofs.write( sLine ) \\n ofs.close() \\n \\n return\",\n \"def scriptGen(self,tmpd='/tmp/jose',libRev='last',submode='qsub',\\n redirect=1,PBSoptions=''):\\n jobname=self.name\\n outdir=self.outd\\n qsubdir=scratchdir+'/qsub/'+todayDate() #subdirectory to deposit the script\\n if not os.path.exists(qsubdir): pastry('/bin/mkdir -p '+qsubdir)\\n script=qsubdir+'/'+jobname+'.sh' #full script file name\\n\\n if len(jobname) > 15:\\n sys.stderr.write('Error: job name '+jobname+' cannot exceed 15 characters')\\n return ''\\n if not os.path.exists(outdir): os.system('/bin/mkdir -p '+outdir)\\n buf=''\\n ulimit=int(float(mem_limit)*1024) #maximum resident memory size (Kb) to prevent swapping\\n wd=tmpd+'/${PBS_JOBID}'\\n #wd=tmpd+'/'+ re.compile('\\\\W').sub('',self.name) +'_$$' #working directory\\n logname=jobname+'.log'\\n local_log=wd+'/'+logname\\n remote_log=outdir+'/'+logname\\n buf= '#!/bin/bash\\\\n\\\\n'\\n buf+= PBSoptions+'\\\\n\\\\n'\\n buf+= '#bash function to update library\\\\n'\\n buf+= self.updateNodeLib(libRev)+'\\\\n\\\\n'\\n buf+= '#bash function to import temporary libs\\\\n'\\n buf+= self.shared_temporal_libraries()+'\\\\n\\\\n'\\n buf+= '#bash function to clean exit\\\\n'\\n buf+= self.cleanup_exit(submode=submode)+'\\\\n\\\\n'\\n buf+= 'echo \\\"'+script+'\\\"\\\\n' #write script name withing script body\\n buf+= 'hostname\\\\n' #node where job will be run\\n buf+= 'echo $PBS_JOBID\\\\n'\\n buf+= 'ulimit -m '+`ulimit`+' #maximum memory\\\\n'\\n buf+= 'source ~/.bash_profile >/dev/null #environment variables\\\\n'\\n buf+= 'wd='+wd+' #working directory\\\\n'\\n buf+= '/bin/mkdir -p $wd\\\\n'\\n buf+= 'export LOCAL_LOG=\\\"'+local_log+'\\\"\\\\n'\\n buf+= '/bin/touch $LOCAL_LOG\\\\n'\\n if submode=='sub' and redirect:\\n buf+='exec &> $LOCAL_LOG #redirect STODOUT, STDERR to LOCAL_LOG\\\\n' \\n buf+= 'export REMOTE_LOG=\\\"'+remote_log+'\\\"\\\\n'\\n\\n but+= '#clean up old log file\\\\n'\\n buf+= 'if [ -f $REMOTE_LOG ]; then\\\\n' \\n buf+= ' /bin/rm -f $REMOTE_LOG\\\\n'\\n buf+= 'fi\\\\n\\\\n'\\n\\n buf+= 'trap \\\"cleanup_exit 1\\\" TERM #in case of killing job\\\\n\\\\n'\\n\\n buf+= '#update node code library && import libraries\\\\n'\\n buf+= 'if !('\\n buf+= 'updateNodeLib && ' \\n buf+= 'shared_temporal_libraries _PREPARE_'\\n buf+= ');then\\\\n'\\n buf+= ' cleanup_exit 1\\\\n'\\n buf+= 'fi\\\\n\\\\n'\\n \\n buf+= '/bin/cp '+' '.join(self.inpl)+' $wd #bring input files\\\\n' \\n buf+= 'cd $wd\\\\n\\\\n'\\n buf+= '#Test command success\\\\n'\\n buf+= 'exs=0 #variable holding script exit status\\\\n'\\n buf+= 'if !('\\n buf+= self.exe\\n buf+= ');then\\\\n'\\n buf+= ' exs=1\\\\n'\\n buf+= 'fi\\\\n\\\\n'\\n buf+= '#move even partial results (exs=1)\\\\n'\\n buf+= '/bin/mv '+' '.join(self.outl)+' '+outdir+'\\\\n'\\n buf+= 'cleanup_exit $exs'\\n\\n open(script,'w').write(buf)\\n pastry('chmod u+x '+script)\\n\\n return script\",\n \"def make_jobscript(i_file, atm_file=None, dt_file=None, dp_file=None,\\n fp_file=None, pp_file=None, sd_file=None, dsl_file=None,\\n om_file=None, extra_vars=extra_vars,\\n lapse_rate=6.0, ys=0.0, ye=1000.0, yts=10, yextra=100,\\n mpi_exec=mpi_exec, pism_exec=pism_exec, pism_root=pism_root,\\n nodes=1, time='24:00:00', out_dir=None, job_name='unnamed',\\n ntasks_per_node=36, **boot_kwargs):\\n\\n # expand path to PISM root\\n pism_root = os.path.abspath(pism_root)\\n\\n # get input and component model arguments\\n input_args = get_input_args(i_file, pism_root=pism_root, **boot_kwargs)\\n atm_args = get_atm_args(atm_file=atm_file, lapse_rate=lapse_rate,\\n dt_file=dt_file, dp_file=dp_file, fp_file=fp_file,\\n pp_file=pp_file, pism_root=pism_root)\\n surface_args = get_surface_args(sd_file=sd_file, pism_root=pism_root)\\n ocean_args = get_ocean_args(dsl_file=dsl_file, om_file=om_file, pism_root=pism_root)\\n\\n # format script\\n script = template.format(**locals())\\n\\n # write script to file\\n script_path = os.path.join(out_dir, 'job.' + job_name + '.sh')\\n with open(script_path, 'w') as f:\\n f.write(script)\\n\\n # return path to job script\\n return script_path\",\n \"def create_job(script, args, inputs, outputs, tools):\\n job = Job(script)\\n for arg in args:\\n job.addArguments(arg)\\n\\n # all jobs will have access to python scripts\\n for tool in tools:\\n job.uses(tools[tool], link=Link.INPUT)\\n\\n for input in inputs:\\n job.uses(input, link=Link.INPUT)\\n\\n for output in outputs:\\n job.uses(output, link=Link.OUTPUT, transfer=True)\\n\\n #job.addProfile(Profile(Namespace.PEGASUS, 'clusters.size', '20'))\\n return job\",\n \"def createjob(args):\\n ncell = args.ncell\\n nmg = args.nmg\\n nsi = args.nsi\\n nvac = args.nvac\\n a0 = args.a0\\n temp = args.temp\\n nseeds = args.nseeds\\n seeds = args.seeds\\n nsteps = args.nsteps\\n foldername_append = args.foldername_append\\n pot = args.pot\\n submit = args.submit\\n submitdebug = args.submitdebug\\n submittime_hours = args.submittime_hours\\n test = args.test\\n testfiles = args.testfiles\\n nodes = args.nodes\\n verbose = args.verbose\\n\\n\\n ### check if ase runner/quippy/lammpps-data formats are known\\n ase_formats = mu.ase_get_known_formats_class(verbose=True)\\n ase_formats.check_if_default_formats_known(copy_and_adapt_formatspy_anyhow=False)\\n\\n # definex ffsocket inet/unix\\n if nodes == 1:\\n ffsocket = \\\"unix\\\"\\n elif nodes > 1:\\n ffsocket = \\\"inet\\\"\\n else:\\n sys.exit(\\\"Number of nodes has to be positive!\\\")\\n\\n\\n # define ntasks, neval\\n lmp_par = 2 # = OMP_NUM_THREADS\\n ntasks = cores = nodes * 28\\n ipi_inst = 4 # for sure best on fidis\\n neval = ipi_inst*2 # was alwasy better, for ompi and impi\\n\\n ##### get the seed(s).\\n if type(seeds) == bool:\\n seeds = random.sample(range(1, 999999), nseeds)\\n print('seeds',seeds)\\n if test == True:\\n nseeds = 1\\n seeds = [1]\\n print('seeds',seeds)\\n nseeds = len(seeds)\\n\\n ##### a few checks\\n scripts = mu.scripts()\\n mypot = mu.mypot(pot)\\n if submit is True or submitdebug is True:\\n hostcheck = os.environ[\\\"myhost\\\"]\\n if hostcheck == \\\"\\\":\\n sys.exit('host unknown 87')\\n\\n\\n ##### here only chck if the potential can be set up. (in.lmp)\\n ##### the same command is then executed for every kmc folder\\n ace = mu.ase_calculate_ene(pot=pot,\\n potpath=False,\\n units='eV',geopt=False,kmc=True,verbose=verbose)\\n ace.pot_get_and_ase_lmp_cmd(kmc=True,temp=temp,nsteps=nsteps,ffsocket=ffsocket)\\n\\n ##### if test\\n if test == True:\\n nsteps = 50\\n\\n file_ipi_input_runner = scripts + \\\"/i-pi-mc_scripts/input-runner.xml\\\"\\n\\n\\n ####################################\\n # get directory\\n ####################################\\n if verbose:\\n print(\\\"get directory\\\")\\n pcsi = nsi/ncell**3.*100\\n pcmg = nmg/ncell**3.*100\\n pcvac = nvac/ncell**3.*100\\n if args.cubic == True:\\n pc = \\\"cubic\\\"\\n else:\\n pc = \\\"primitive\\\"\\n directory = str(ncell)+\\\"x\\\"+str(ncell)+\\\"x\\\"+str(ncell)+\\\"_\\\"+pc+\\\"_\\\"+pot+\\\"_\\\"+\\\\\\n str(temp)+\\\"K_\\\"+\\\\\\n str(nvac)+\\\"Vac_\\\"+str(nmg)+\\\"Mg_\\\"+str(nsi)+\\\"Si__\\\"+\\\\\\n str(round(pcvac,3))+\\\"pctVac_\\\"+str(round(pcmg,3))+\\\"pctMg_\\\"+str(round(pcsi,3))+\\\"pctSi\\\"\\n if foldername_append != \\\"\\\":\\n directory = directory+\\\"_\\\"+foldername_append\\n\\n ###############################################\\n # make the structure\\n ###############################################\\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell,nsi,nmg,nvac,a0,create_fake_vacancy = True,cubic=args.cubic)\\n atomsc = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell,nsi,nmg,nvac,a0,cubic=args.cubic)\\n\\n # make the atomic structure\\n # this was to play ... not necessary now?\\n if False:\\n nndist = a0/np.sqrt(2.)\\n\\n from ase.io import read as ase_read\\n from ase.io import write as ase_write\\n\\n ###############################################\\n # get the amount of 1NN in a relly large cell\\n ###############################################\\n atomsc_fakevac_i = ase_read('dataxx.extxyz3',index=\\\":\\\",format='extxyz') # works, cell ist not changed\\n #atomsc_fakevac_i = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=10,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\\\"XX_0\\\")\\n #nn = mu.ase_get_neighborlist(atomsc_fakevac_i,atomnr=0,cutoff=3.,skin=0.1)\\n #print(\\\"nn\\\",nn,'len',len(nn))\\n #nn = mu.ase_get_neighborlist(atomsc_fakevac_i,atomnr=0,cutoff=8.5,skin=0.1)\\n #print(\\\"nn\\\",nn,'len',len(nn))\\n #sys.exit()\\n\\n print(len(atomsc_fakevac_i),type(atomsc_fakevac_i))\\n\\n for idx,i in enumerate(atomsc_fakevac_i):\\n print('aa',atomsc_fakevac_i[idx].positions[0])\\n #print('aa',i.positions[0])\\n print('ipi')\\n atomsc_fakevac_i = ase_read('dataxx.ipi2',index=\\\":\\\",format='ipi') # works, cell ist not changed\\n print(len(atomsc_fakevac_i),type(atomsc_fakevac_i))\\n for idx,i in enumerate(atomsc_fakevac_i):\\n print('aa',atomsc_fakevac_i[idx].positions[0])\\n #print('aa',i.positions[0])\\n print('quippy')\\n atomsc_fakevac_i = ase_read('dataxx.quippy.xyz2',index=\\\":\\\",format='quippy') # works, cell ist not changed\\n\\n\\n\\n filename = '../sim.xyz'\\n filename = '../simulation.pos_0.xyz'\\n mu.count_amount_1NN_around_vacancies(filename,cutoffa=nndist,cutoffb=a0,skin=0.1,format='ipi')\\n sys.exit()\\n\\n def mysave_quippy_xyz(atomsc_fakevac,text=False):\\n if type(text) == bool:\\n sys.exit('define text')\\n atomsc_fakevac.write('data.quippy.xyz',format='quippy',append=True)\\n #atomsc_fakevac.write('data.xyz',format=\\\"extxyz\\\",append=True)\\n atomsc_fakevac.write('data'+text+'.quippy.xyz',format='quippy',append=True)\\n #atomsc_fakevac.write('data'+text+'.xyz',format=\\\"extxyz\\\",append=True)\\n return\\n\\n # create Al with single vacancy\\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\\\"XX_0\\\")\\n NN_1_indices, NN_2_indices = mu.ase_get_neighborlist_1NN_2NN(atomsc_fakevac,atomnr=0,cutoffa=nndist,cutoffb=a0,skin=0.1)\\n #print('from ....',(atomsc_fakevac.positions)[0])\\n #for i in NN_1_indices:\\n # print((atomsc_fakevac.positions)[i])\\n print('NN_1_indices (orig ):',NN_1_indices)\\n print('NN_2_indices (orig ):',NN_2_indices)\\n #sys.exit()\\n atomsc_fakevac.write('dataxx.quippy.xyz',format='quippy',append=True)\\n atomsc_fakevac.write('dataxx.poscar',format='vasp',append=True)\\n atomsc_fakevac.write('dataxx.ipi',format='ipi',append=True) # works, currently so implemented that it canges cell\\n atomsc_fakevac.write('dataxx.xyz',format='xyz',append=True)\\n atomsc_fakevac.write('dataxx.extxyz',format='extxyz',append=True)\\n atomsc_fakevac.write('dataxx.lammps-data',format='lammps-data',append=True)\\n atomsc_fakevac.write('dataxx.lammps-runner',format='lammps-runner',append=True)\\n\\n atomsc_fakevac_a = ase_read('dataxx.extxyz',format='extxyz') # works, cell ist not changed\\n atomsc_fakevac_a.write('dataxx.extxyz2',format='extxyz',append=True) # works, cell is not changed\\n\\n atomsc_fakevac_b = ase_read('dataxx.xyz',format='xyz') # not working # but this should work\\n atomsc_fakevac_b.write('dataxx.xyz2',format='xyz',append=True) # this is working\\n\\n atomsc_fakevac_c = ase_read('dataxx.ipi',format='ipi') # works, currently so implemented that it canges cell\\n #print('ipi cell',atomsc_fakevac_c.get_cell())\\n\\n atomsc_fakevac_c.write('dataxx.ipi2',format='ipi',append=True) # works, just writes the cell it gests.\\n atomsc_fakevac_c.write('dataxx.ipi2_poscar',format='vasp',append=True) # works, just writes the cell it gests.\\n NN_1_indices, NN_2_indices = mu.ase_get_neighborlist_1NN_2NN(atomsc_fakevac_c,atomnr=0,cutoffa=nndist,cutoffb=a0,skin=0.1)\\n print('NN_1_indices (ipi ):',NN_1_indices)\\n print('NN_2_indices (ipi ):',NN_2_indices)\\n #print('from ....',(atomsc_fakevac_c.positions)[0])\\n #for i in NN_1_indices:\\n # print((atomsc_fakevac_c.positions)[i])\\n\\n atomsc_fakevac_cc = ase_read('dataxx.ipi2_poscar',format='vasp') # works, currently so implemented that it canges cell\\n atomsc_fakevac_cc.write('dataxx.ipi2_poscar2',format='vasp',append=True)\\n atomsc_fakevac_cc.write('dataxx.ipi2_poscar2_ipi',format='ipi',append=True) # works, just writes the cell it gests.\\n #print('ipi cell2 (ext):',atomsc_fakevac_cc.get_cell())\\n #print()\\n #print('now quippy')\\n atomsc_fakevac_d = ase_read('dataxx.quippy.xyz',format='quippy')\\n #print('quippy cell (ext)',atomsc_fakevac_d.get_cell())\\n atomsc_fakevac_d.write('dataxx.quippy.xyz2',format='quippy',append=True)\\n atomsc_fakevac_d.write('dataxx.quippy.xyz2_extxyz',format='extxyz',append=True)\\n NN_1_indices, NN_2_indices = mu.ase_get_neighborlist_1NN_2NN(atomsc_fakevac_d,atomnr=0,cutoffa=nndist,cutoffb=a0,skin=0.1)\\n print('NN_1_indices (quippy):',NN_1_indices)\\n print('NN_2_indices (quippy):',NN_2_indices)\\n #print('from ....',(atomsc_fakevac_d.positions)[0])\\n #for i in NN_1_indices:\\n # print((atomsc_fakevac_d.positions)[i])\\n path = \\\"/home/glensk/kmc/run_michele/Si6Mg6V1.1_/simulation.pos_libatom_2struct.xyz\\\"\\n atomsc_fakevac_e = ase_read(path,format='quippy')\\n\\n NN_1_indices, NN_2_indices = mu.ase_get_neighborlist_1NN_2NN(atomsc_fakevac_e,atomnr=0,cutoffa=nndist,cutoffb=a0,skin=0.1)\\n print('NN_1_indices (kmc ):',NN_1_indices)\\n print('NN_2_indices (kmc ):',NN_2_indices)\\n sys.exit()\\n\\n NN_1_indices = mu.ase_get_neighborlist(atomsc_fakevac,atomnr=0,cutoff=nndist,skin=0.1)\\n NN_1_2_indices_tmp = mu.ase_get_neighborlist(atomsc_fakevac,atomnr=0,cutoff=a0,skin=0.1)\\n print('NN_1_indices :',NN_1_indices)\\n NN_2_indices = np.sort(np.array(mu.diff(NN_1_2_indices_tmp,NN_1_indices)))\\n print('NN_2_indices :',NN_2_indices)\\n NN_1_2_indices = np.concatenate((NN_1_indices, NN_2_indices ))\\n print('NN_1_2_indices:',NN_1_2_indices)\\n\\n\\n # fill only 1NN (with one species)\\n for i in [ 'Mg', 'Si' ]:\\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\\\"XX_0\\\")\\n mysave_quippy_xyz(atomsc_fakevac,text=\\\"1NN\\\")\\n for ii in NN_1_indices:\\n atomsc_fakevac[ii].symbol = i\\n mysave_quippy_xyz(atomsc_fakevac,text=\\\"1NN\\\")\\n\\n # fill only 2NN (with one species)\\n for i in [ 'Mg', 'Si' ]:\\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\\\"XX_0\\\")\\n mysave_quippy_xyz(atomsc_fakevac,text=\\\"2NN\\\")\\n for ii in NN_2_indices:\\n atomsc_fakevac[ii].symbol = i\\n mysave_quippy_xyz(atomsc_fakevac,text=\\\"2NN\\\")\\n\\n # fill 1NN and 2NN (with one species)\\n for i in [ 'Mg', 'Si' ]:\\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\\\"XX_0\\\")\\n mysave_quippy_xyz(atomsc_fakevac,text=\\\"1and2NN\\\")\\n for ii in NN_1_2_indices:\\n atomsc_fakevac[ii].symbol = i\\n mysave_quippy_xyz(atomsc_fakevac,text=\\\"1and2NN\\\")\\n\\n # dif compositions in 1NN shell\\n filling = [ 2,4,6,8,10]\\n for fi in filling:\\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=\\\"XX_0\\\")\\n mysave_quippy_xyz(atomsc_fakevac,text=\\\"1NN_diffcomp\\\")\\n for idx,ii in enumerate(NN_1_indices):\\n if idx < fi: ch = \\\"Mg\\\"\\n else: ch = \\\"Si\\\"\\n atomsc_fakevac[ii].symbol = ch\\n mysave_quippy_xyz(atomsc_fakevac,text=\\\"1NN_diffcomp\\\")\\n\\n\\n sys.exit()\\n\\n #mu.ase_get_known_formats(show=True, add_missing_formats=False, copy_formats=False, verbose=False,show_formatspy=True)\\n for i in [ 'Mg', 'Si' ]:\\n for ii in [ 0,1,2,3,4,5]:\\n atomsc_fakevac = mu.get_ase_atoms_object_kmc_al_si_mg_vac(ncell=5,nsi=0,nmg=0,nvac=1,a0=a0,cubic=False,create_fake_vacancy = True,normal_ordering=i+'_'+str(ii))\\n\\n\\n sys.exit()\\n\\n\\n # show the input variables\\n print('--------------------------- check the input --------------------------------')\\n print('JOBS (nseeds) ',nseeds,'(defined by -nseeds / or -seeds)')\\n print('seeds ',seeds)\\n print('nsteps ',nsteps)\\n print()\\n print('ncell ',ncell,\\\"(\\\",atomsc.get_number_of_atoms(),\\\"atoms )\\\")\\n print('nsi ',nsi, \\\"(\\\",pcsi,\\\"at%)\\\")\\n print('nmg ',nmg,\\\"(\\\",pcmg,\\\"at%)\\\")\\n print('nvac ',nvac,\\\"(\\\",pcvac,\\\"at%)\\\")\\n print('a0 ',a0,\\\"angstrom\\\")\\n print('temp ',temp,\\\"K\\\")\\n print()\\n print('mypot.pot ',mypot.pot)\\n print('mypot.potpath ',mypot.potpath)\\n print()\\n print('directory ',directory)\\n print('submit ',submit)\\n print('submitdebug ',submitdebug)\\n print()\\n print('nodes ',nodes)\\n print('ffsocket ',ffsocket)\\n #print('python ver ',sys.version_info[0])\\n #print()\\n print('--------------------------- check the input --------------------------------')\\n if submit == True or submitdebug == True:\\n mu.get_from_prompt_Yy_orexit(\\\"Are the ine input variables ok? [y]es: \\\")\\n\\n # make the directory\\n if os.path.isdir(directory):\\n mu.get_from_prompt_Yy_orexit(\\\"This main directory exists already, shall I add jobs? [y]es: \\\")\\n mu.mkdir(directory)\\n\\n # create README.md\\n IPI_COMMAND = os.environ[\\\"IPI_COMMAND\\\"]\\n LAMMPS_COMMAND = os.environ[\\\"LAMMPS_COMMAND\\\"]\\n mu.create_READMEtxt(directory,add=[\\\"# to start manually (1): python \\\"+IPI_COMMAND+\\\" input-runner.xml\\\",\\\"# to start manually (2):\\\"+LAMMPS_COMMAND+\\\" < in.lmp\\\"])\\n\\n for seed in seeds:\\n\\n # make jobdirectory\\n jobdir = directory+'/seed'+str(seed)\\n print('jobdir',jobdir)\\n if os.path.exists(jobdir):\\n sys.exit(\\\"jobdirectory \\\"+str(jobdir)+\\\" already exists!\\\")\\n mu.mkdir(jobdir)\\n\\n # get data.lmp and data.ipi\\n atomsc.write(jobdir+'/data.runnerformat.lmp',format='lammps-runner')\\n atomsc_fakevac.write(jobdir+'/data.ipi',format='ipi')\\n atomsc_fakevac.write(jobdir+'/data.extxyz',format='extxyz')\\n #atomsc_fakevac.write(jobdir+'/data_fakevac.ipi',format='ipi')\\n\\n if testfiles == True:\\n atomsc.write(jobdir+'/data.lmp',format='lammps-data')\\n atomsc.write(jobdir+'/data.POSCAR',format='vasp')\\n atomsc.write(jobdir+'/data.xyz',format='xyz')\\n atomsc.write(jobdir+'/data.extxyz',format='extxyz')\\n atomsc.write(jobdir+'/data.espresso-in',format='espresso-in')\\n\\n # create in.lmp\\n ace = mu.ase_calculate_ene(pot=pot,potpath=mypot.potpath,\\n units='eV',geopt=False,kmc=True,verbose=verbose)\\n address = socket.gethostname()+\\\"_\\\"+os.path.basename(jobdir)\\n print('address',address)\\n ace.pot_get_and_ase_lmp_cmd(kmc=True,temp=temp,nsteps=nsteps,ffsocket=ffsocket,address=address)\\n mu.lammps_write_inputfile(folder=jobdir,filename='in.lmp',positions='data.runnerformat.lmp',ace=ace)\\n\\n # create input-runner.xml (should be made without copying)\\n mu.create_ipi_kmc_inputfile(jobdir,filename=\\\"input-runner.xml\\\",nsteps=nsteps,stride=100,seed=seed,a0=a0,ncell=ncell,nsi=nsi,nmg=nmg,nvac=nvac,neval=neval,temp=temp,nodes=nodes,address=address,testrun=test,cubic=args.cubic)\\n\\n # create submit-ipi-kmc.sh (should be made without copying)\\n mu.create_submitskript_ipi_kmc(jobdir+\\\"/submit-ipi-kmc.sh\\\",nodes,ntasks,\\n lmp_par=lmp_par,\\n ipi_inst=ipi_inst,\\n ffsocket=ffsocket,\\n submittime_hours=submittime_hours,\\n SBATCH=True)\\n\\n # create osubmit-ipi-kmc.sh (should be made without copying)\\n mu.create_submitskript_ipi_kmc(jobdir+\\\"/osubmit-ipi-kmc.sh\\\",nodes,ntasks,\\n lmp_par=lmp_par,\\n ipi_inst=ipi_inst,\\n ffsocket=ffsocket,\\n submittime_hours=submittime_hours,\\n SBATCH=False)\\n\\n # submit the job (execute either this or submit-ipi-kmc.sh_all3, not both)\\n #mu.submitjob(submit=submit,submitdebug=submitdebug,jobdir=jobdir,submitskript=\\\"submit-ipi-kmc.sh\\\")\\n\\n # get submit-ipi-kmc.sh_all3 (should be made without copying)\\n if nseeds == 3:\\n mu.create_submitskript_ipi_kmc(directory+\\\"/submit-ipi-kmc.sh_all3\\\",nodes,ntasks,\\n lmp_par=lmp_par,\\n ipi_inst=ipi_inst,\\n ffsocket=ffsocket,\\n submittime_hours=submittime_hours,\\n SBATCH=True,\\n LOOPFOLDER=True)\\n\\n # submit the job (execute either this or submit-ipi-kmc.sh_all3, not both)\\n #mu.submitjob(submit=submit,submitdebug=submitdebug,jobdir=directory,submitskript=\\\"submit-ipi-kmc.sh_all3\\\")\\n if submit == True:\\n mu.submitjob(submit_to_que=True,submit_to_debug_que=False,jobdir=directory,submitskript=\\\"submit-ipi-kmc.sh_all3\\\")\\n\\n\\n print('done')\\n return\",\n \"def submit(self, **kwargs):\\n pwd = curdir\\n wd = dirname(self.logFile)\\n chdir(wd)\\n d = OrderedDict()\\n #d['universe'] = 'vanilla'\\n #d['executable'] = self.command\\n\\td['job-name'] = self.name\\n\\td['nodes'] = 1\\n\\td['partition'] = defaults.get('queue')\\n\\td['time'] = defaults.get(\\\"cputime\\\")\\n\\td['mem'] = defaults.get(\\\"memory\\\")\\n d['output'] = op_join(wd,\\\"output.log\\\")\\n d['error'] = op_join(wd,\\\"output.err\\\")\\n csi_file = open(\\\"submit.sh\\\", \\\"w\\\")\\n\\tcsi_file.write(\\\"#!/bin/bash\\\\n\\\")\\n data = [\\\"#SBATCH --%s=%s\\\\n\\\" % (k, v) for k, v in d.iteritems()]\\n csi_file.write(\\\"\\\".join(data))\\n\\tcsi_file.write(\\\"export DAMPE_WORKFLOW_SERVER_URL=%s\\\\n\\\"%DAMPE_WORKFLOW_URL)\\n csi_file.write(\\\"bash script\\\\n\\\")\\n csi_file.close()\\n output = self.__run__(\\\"sbatch submit.sh\\\")\\n chdir(pwd)\\n return self.__regexId__(output)\",\n \"def _build_container_script(self, name, job_settings, logger):\\n if not isinstance(job_settings, dict) or \\\\\\n not isinstance(name, basestring):\\n logger.error(\\\"Singularity Script malformed\\\")\\n return None\\n\\n if 'image' not in job_settings or 'command' not in job_settings or\\\\\\n 'max_time' not in job_settings:\\n logger.error(\\\"Singularity Script malformed\\\")\\n return None\\n\\n script = '#!/bin/bash -l\\\\n\\\\n'\\n\\n # NOTE an uploaded script could also be interesting to execute\\n if 'pre' in job_settings:\\n for entry in job_settings['pre']:\\n script += entry + '\\\\n'\\n\\n# ################### Torque settings ###################\\n if 'nodes' in job_settings:\\n resources_request = \\\"nodes={}\\\".format(job_settings['nodes'])\\n\\n if 'tasks_per_node' in job_settings:\\n resources_request += ':ppn={}'.format(\\n job_settings['tasks_per_node'])\\n\\n script += '#PBS -l walltime={}\\\\n'.format(resources_request)\\n else:\\n if 'tasks_per_node' in job_settings:\\n logger.error(\\n r\\\"Specify 'tasks_per_node' while 'nodes' is not specified\\\")\\n\\n # if 'tasks' in job_settings:\\n # script += '#qsub -n ' + str(job_settings['tasks']) + '\\\\n'\\n\\n script += '#PBS -l walltime={}\\\\n\\\\n'.format(job_settings['max_time'])\\n# #######################################################\\n\\n script += '\\\\n# DYNAMIC VARIABLES\\\\n\\\\n'\\n\\n # NOTE an uploaded script could also be interesting to execute\\n if 'pre' in job_settings:\\n for entry in job_settings['pre']:\\n script += entry + '\\\\n'\\n\\n script += '\\\\nmpirun singularity exec '\\n\\n if 'home' in job_settings and job_settings['home'] != '':\\n script += '-H ' + job_settings['home'] + ' '\\n\\n if 'volumes' in job_settings:\\n for volume in job_settings['volumes']:\\n script += '-B ' + volume + ' '\\n\\n # add executable and arguments\\n script += job_settings['image'] + ' ' + job_settings['command'] + '\\\\n'\\n\\n # NOTE an uploaded script could also be interesting to execute\\n if 'post' in job_settings:\\n for entry in job_settings['post']:\\n script += entry + '\\\\n'\\n\\n return script\",\n \"def gen_jobs(fpath, num_runs, netid):\\n\\n run = \\\"\\\"\\n run += \\\"import sys\\\\n\\\"\\n run += \\\"import subprocess\\\\n\\\"\\n run += \\\"cmd_array = (\\\"\\n for i in range(num_runs):\\n run += \\\"r\\\\\\\"python test.py %d\\\\\\\"\\\" % i\\n run += \\\",\\\\n\\\"\\n\\n run += \\\")\\\\n\\\"\\n run += \\\"p = subprocess.Popen(cmd_array[int(sys.argv[1])-1], shell=True, stdout=subprocess.PIPE)\\\\n\\\"\\n run += \\\"out = p.stdout.read()\\\"\\n# run += \\\"print cmd_array[int(sys.argv[1])]\\\"\\n\\n script_name = \\\"test\\\"\\n\\n if verbose:\\n print \\\"Writing array script: \\\" + \\\"run.\\\" + script_name + \\\".py\\\"\\n f = open(os.path.join(fpath, \\\"run.\\\" + script_name + \\\".py\\\"), 'w')\\n f.write(\\\"%s\\\\n\\\" % run)\\n\\n f = open(os.path.join(fpath, \\\"submit_run.\\\" + script_name + \\\".sh\\\"), 'w')\\n submit_run = \\\"#!/bin/csh\\\\n\\\"\\n submit_run += \\\"#$ -N %s\\\\n\\\" % (\\\"job_%d\\\" % num_runs)\\n submit_run += \\\"#$ -t 1:%d\\\\n\\\" % (num_runs)\\n submit_run += \\\"#$ -M %s@nd.edu\\\\n\\\\n\\\" % (netid)\\n# submit_run += \\\"#$ -q short\\\"\\n# submit_run += \\\"#$ -r y\\\"\\n submit_run += \\\"python run.%s.py ${SGE_TASK_ID}\\\" % (script_name)\\n\\n if verbose:\\n print \\\"Writing submit shell script: \\\" + \\\"submit_run.\\\" + script_name + \\\".sh\\\"\\n f.write(\\\"%s\\\\n\\\" % submit_run)\",\n \"def submit_job(jobscript, qsub_settings):\\n flatten = lambda l: [item for sublist in l for item in sublist]\\n batch_options = flatten([sge_option_string(k,v).split() for k, v in qsub_settings[\\\"options\\\"].items()])\\n batch_resources = flatten([sge_resource_string(k, v).split() for k, v in qsub_settings[\\\"resources\\\"].items()])\\n try:\\n # -terse means only the jobid is returned rather than the normal 'Your job...' string\\n jobid = subprocess.check_output([\\\"qsub\\\", \\\"-terse\\\"] + batch_options + batch_resources + [jobscript]).decode().rstrip()\\n except subprocess.CalledProcessError as e:\\n raise e\\n except Exception as e:\\n raise e\\n return jobid\",\n \"def createScriptJob(self):\\n\\n self.scriptJob = cmds.scriptJob(\\n event=[\\\"SelectionChanged\\\", partial(self.selectScriptJob, self.outlinerControls)], runOnce=False,\\n parent=\\\"pyArtRigCreatorUi\\\", kws=True, per=False)\\n self.rigUiInst.scriptJobs.append(self.scriptJob)\",\n \"def create_job(jobrun, vcf_filenames):\\n if jobrun == \\\"cluster\\\":\\n \\\"\\\"\\\"\\n Supports only PBS clusters for now.\\n \\\"\\\"\\\"\\n for i in vcf_filenames:\\n job_name = os.path.basename(i)\\n job_print_string = \\\"#PBS -N %s\\\\n#PBS -M apirani@med.umich.edu\\\\n#PBS -m abe\\\\n#PBS -V\\\\n#PBS -l nodes=1:ppn=4,pmem=4000mb,walltime=72:00:00\\\\n#PBS -q fluxod\\\\n#PBS -A esnitkin_fluxod\\\\n#PBS -l qos=flux\\\\n\\\\n/home/apirani/anaconda/bin/python /nfs/esnitkin/bin_group/scripts/Scripts_v2.0/variants_position_analysis/reason_job.py -filter2_only_snp_vcf_dir %s -filter2_only_snp_vcf_file %s\\\\n\\\" % (job_name, args.filter2_only_snp_vcf_dir, i)\\n job_file_name = \\\"%s.pbs\\\" % (i)\\n f1=open(job_file_name, 'w+')\\n f1.write(job_print_string)\\n f1.close()\\n #os.system(\\\"mv %s/*.pbs %s/temp\\\" % (args.filter2_only_snp_vcf_dir, args.filter2_only_snp_vcf_dir))\\n pbs_dir = args.filter2_only_snp_vcf_dir + \\\"/*.pbs\\\"\\n pbs_scripts = glob.glob(pbs_dir)\\n for i in pbs_scripts:\\n print \\\"Running: qsub %s\\\" % i\\n #os.system(\\\"qsub %s\\\" % i)\\n\\n elif jobrun == \\\"parallel-local\\\":\\n \\\"\\\"\\\"\\n Generate a Command list of each job and run it in parallel on different cores available on local system\\n \\\"\\\"\\\"\\n command_array = []\\n command_file = \\\"%s/commands_list.sh\\\" % args.filter2_only_snp_vcf_dir\\n f3 = open(command_file, 'w+')\\n\\n\\n for i in vcf_filenames:\\n job_name = os.path.basename(i)\\n job_print_string = \\\"#PBS -N %s\\\\n#PBS -M apirani@med.umich.edu\\\\n#PBS -m abe\\\\n#PBS -V\\\\n#PBS -l nodes=1:ppn=4,pmem=4000mb,walltime=72:00:00\\\\n#PBS -q fluxod\\\\n#PBS -A esnitkin_fluxod\\\\n#PBS -l qos=flux\\\\n\\\\n/home/apirani/anaconda/bin/python /nfs/esnitkin/bin_group/scripts/Scripts_v2.0/variants_position_analysis/reason_job.py -filter2_only_snp_vcf_dir %s -filter2_only_snp_vcf_file %s\\\\n\\\" % (job_name, args.filter2_only_snp_vcf_dir, i)\\n job_file_name = \\\"%s.pbs\\\" % (i)\\n f1=open(job_file_name, 'w+')\\n f1.write(job_print_string)\\n f1.close()\\n #os.system(\\\"mv %s/*.pbs %s/temp\\\" % (args.filter2_only_snp_vcf_dir, args.filter2_only_snp_vcf_dir))\\n pbs_dir = args.filter2_only_snp_vcf_dir + \\\"/*.pbs\\\"\\n pbs_scripts = glob.glob(pbs_dir)\\n\\n\\n for i in pbs_scripts:\\n f3.write(\\\"bash %s\\\\n\\\" % i)\\n f3.close()\\n with open(command_file, 'r') as fpp:\\n for lines in fpp:\\n lines = lines.strip()\\n command_array.append(lines)\\n fpp.close()\\n print len(command_array)\\n if args.numcores:\\n num_cores = int(num_cores)\\n else:\\n num_cores = multiprocessing.cpu_count()\\n results = Parallel(n_jobs=num_cores)(delayed(run_command)(command) for command in command_array)\\n\\n elif jobrun == \\\"parallel-single-cluster\\\":\\n print \\\" \\\"\\n else:\\n \\\"\\\"\\\"\\n Generate a Command list of each job and run it on local system one at a time\\n \\\"\\\"\\\"\\n command_array = []\\n command_file = \\\"%s/commands_list.sh\\\" % args.filter2_only_snp_vcf_dir\\n os.system(\\\"bash %s\\\" % command_file)\",\n \"def _submit_to_queue(self, script_file):\",\n \"def write_and_submit_job(setting, manager, job_name, script_address):\\n backup_folder = script_address.rsplit('/', maxsplit=1)[0]\\n job_script_folder = backup_folder + '/Jobs/'\\n job_output_file = job_script_folder + 'output.txt'\\n print(job_output_file)\\n if not os.path.exists(job_script_folder):\\n os.makedirs(job_script_folder)\\n job_script_address = job_script_folder + 'jobscript_'+manager+'.sh'\\n with open(job_script_address, \\\"w\\\") as string_file:\\n if manager == 'OGE':\\n string_file.write(sungrid.job_script(setting, job_name=job_name, script_address=script_address, job_output_folder=job_script_folder))\\n elif manager == 'Slurm':\\n string_file.write(slurm.job_script(setting, job_name=job_name, script_address=script_address, job_output_file=job_output_file))\\n else:\\n raise ValueError(\\\"manager should be in ['OGE', 'Slurm']\\\")\\n string_file.close()\\n for root, dir_list, file_list in os.walk(backup_folder):\\n for f in dir_list+file_list:\\n os.chmod(os.path.join(root, f), 0o754)\\n if manager == 'OGE':\\n submit_cmd = 'qsub ' + job_script_address\\n elif manager == 'Slurm':\\n submit_cmd = 'sbatch ' + job_script_address\\n else:\\n raise ValueError(\\\"manager should be in ['OGE', 'Slurm']\\\")\\n os.system(submit_cmd)\",\n \"def generic_script(contents,\\n job_name=None,\\n stdout='/dev/null',\\n stderr='/dev/null',\\n shebang='#!/bin/bash',\\n numnodes=None,\\n numcpu=None,\\n queue=None,\\n walltime=None,\\n mem=None,\\n pmem=None):\\n me = __file__\\n current_time = time.strftime('%H:%M %D')\\n\\n if job_name is None:\\n job_name = 'unnamed_job'\\n\\n if numnodes is None:\\n numnodes = str(config.getint('pbs','numnodes'))#str(configuration.numnodes)\\n\\n if numcpu is None:\\n numcpu = str(config.getint('pbs','numprocs'))#str(configuration.numprocs)\\n\\n if pmem:\\n pmem = ',pmem=' + pmem\\n else:\\n pmem=''\\n\\n\\n if mem:\\n mem = ',mem=' + mem\\n else:\\n mem=''\\n\\n\\n if queue is None:\\n queue = config.read('pbs','queue')#configuration.queue\\n\\n additional_configuration_lines = []\\n\\n if queue is not None:\\n additional_configuration_lines.append(\\\"#PBS -q %(queue)s\\\" % locals())\\n\\n if walltime is None:\\n walltime = config.getint('pbs','walltime')#configuration.walltime\\n\\n if walltime is not None:\\n additional_configuration_lines.append(\\\"#PBS -l walltime=%(walltime)s\\\" % locals())\\n\\n additional_configuration = '\\\\n'.join(additional_configuration_lines)\\n\\n the_script = \\\"\\\"\\\"#!/bin/bash\\n# Created by pbs.py\\n#PBS -N unnamed_job\\n#PBS -l npcus=1\\n#PBS -l walltime=1:00:00\\n#PBS -j oe\\n\\n{ad_confg}\\n\\n{c}\\n\\\"\\\"\\\".format(ad_confg = additional_configuration, c=contents)\\n\\n return the_script\",\n \"def create_hourly_jobflow_manual(self, script, queue_name_out, queue_name_in, current_folder_name, log_prefix, pig_args):\\n # TODO: standardize message packing/unpacking\\n pig_command_args = ['/home/hadoop/bin/pig']\\n for varname in pig_args.keys():\\n pig_command_args.append('-p')\\n pig_command_args.append(varname + '=' + pig_args[varname])\\n pig_command_args.append(script)\\n pig_command = '|'.join(pig_command_args)\\n \\n queue_message = '\\\\t'.join([pig_command, queue_name_out, current_folder_name + '|' + log_prefix])\\n self.write_message(queue_name_in, queue_message)\",\n \"def sendjob(self,bashscript):\",\n \"def create_template(issue, cpu_count, memory, work_dir, cmd):\\n # Prepare SLURM shell script contents\\n template = \\\"#!/bin/bash\\\\n\\\" \\\\\\n \\\"#SBATCH -N 1\\\\n\\\" \\\\\\n \\\"#SBATCH --ntasks={cpu_count}\\\\n\\\" \\\\\\n \\\"#SBATCH --mem={memory}\\\\n\\\" \\\\\\n \\\"#SBATCH --time=1-00:00\\\\n\\\" \\\\\\n \\\"#SBATCH --job-name={jobid}\\\\n\\\" \\\\\\n \\\"#SBATCH -o {work_dir}/job_%j.out\\\\n\\\" \\\\\\n \\\"#SBATCH -e {work_dir}/job_%j.err\\\\n\\\" \\\\\\n \\\"source /mnt/nas2/redmine/applications/.virtualenvs/OLCRedmineAutomator/bin/activate\\\\n\\\" \\\\\\n \\\"{cmd}\\\".format(cpu_count=cpu_count,\\n memory=memory,\\n jobid=issue.id,\\n work_dir=work_dir,\\n cmd=cmd)\\n\\n # Path to SLURM shell script\\n file_path = os.path.join(BIO_REQUESTS_DIR, str(issue.id), str(issue.id) + '_slurm.sh')\\n\\n # Write SLURM job to shell script\\n with open(file_path, 'w+') as file:\\n file.write(template)\\n\\n make_executable(file_path)\\n\\n return file_path\",\n \"def build_metric_submit_file(self, metric):\\n\\n log_dir = self.rsv.get_metric_log_dir()\\n environment = \\\"PATH=/usr/bin:/bin\\\\n\\\"\\n condor_id = metric.get_unique_name()\\n arguments = \\\"-v 3 -r -u %s %s %s\\\" % (metric.host, metric.name, metric.get_settings())\\n timestamp = strftime(\\\"%Y-%m-%d %H:%M:%S %Z\\\")\\n\\n probe_interval = metric.get_probe_interval()\\n if not probe_interval:\\n cron = metric.get_cron_entry()\\n if not cron:\\n self.rsv.log(\\\"ERROR\\\", \\\"Invalid cron time for metric %s on host %s. Will not start.\\\" %\\n (metric.name, metric.host))\\n return \\\"\\\"\\n\\n submit = \\\"\\\"\\n submit += \\\"######################################################################\\\\n\\\"\\n submit += \\\"# Temporary submit file generated by rsv-control\\\\n\\\"\\n submit += \\\"# Generated at %s \\\" % timestamp\\n submit += \\\"######################################################################\\\\n\\\"\\n submit += \\\"Environment = %s\\\\n\\\" % environment\\n\\n if probe_interval:\\n submit += \\\"DeferralPrepTime = ifThenElse(%d - ScheddInterval + 31 > 0, %d - ScheddInterval + 31, 180) \\\\n\\\" % (probe_interval, probe_interval)\\n submit += \\\"DeferralTime = (CurrentTime + %d + random(30))\\\\n\\\" % probe_interval\\n submit += \\\"DeferralWindow = 99999999\\\\n\\\"\\n submit += \\\"+OSGRSVProbeInterval = %d\\\\n\\\" % probe_interval\\n else:\\n submit += \\\"CronPrepTime = 180\\\\n\\\"\\n submit += \\\"CronWindow = 99999999\\\\n\\\"\\n submit += \\\"CronMonth = %s\\\\n\\\" % cron[\\\"Month\\\"]\\n submit += \\\"CronDayOfWeek = %s\\\\n\\\" % cron[\\\"DayOfWeek\\\"]\\n submit += \\\"CronDayOfMonth = %s\\\\n\\\" % cron[\\\"DayOfMonth\\\"]\\n submit += \\\"CronHour = %s\\\\n\\\" % cron[\\\"Hour\\\"]\\n submit += \\\"CronMinute = %s\\\\n\\\" % cron[\\\"Minute\\\"]\\n submit += \\\"Executable = %s\\\\n\\\" % self.rsv.get_wrapper()\\n submit += \\\"Error = %s/%s.err\\\\n\\\" % (log_dir, condor_id)\\n submit += \\\"Output = %s/%s.out\\\\n\\\" % (log_dir, condor_id)\\n submit += \\\"Log = %s/%s.log\\\\n\\\" % (log_dir, condor_id)\\n submit += \\\"Arguments = %s\\\\n\\\" % arguments\\n submit += \\\"Universe = local\\\\n\\\"\\n submit += \\\"Notification = never\\\\n\\\"\\n submit += \\\"OnExitRemove = false\\\\n\\\"\\n submit += \\\"PeriodicRelease = HoldReasonCode =!= 1\\\\n\\\"\\n submit += \\\"+OSGRSV = \\\\\\\"metrics\\\\\\\"\\\\n\\\"\\n submit += \\\"+OSGRSVHost = \\\\\\\"%s\\\\\\\"\\\\n\\\" % metric.host\\n submit += \\\"+OSGRSVMetric = \\\\\\\"%s\\\\\\\"\\\\n\\\" % metric.name\\n submit += \\\"+OSGRSVUniqueName = \\\\\\\"%s\\\\\\\"\\\\n\\\" % condor_id\\n submit += \\\"Queue\\\\n\\\"\\n \\n return submit\",\n \"def _build_job_submission_call(self,\\n name,\\n job_settings,\\n logger):\\n raise NotImplementedError(\\n \\\"'_build_job_submission_call' not implemented.\\\")\",\n \"def main():\\n if len(sys.argv) < 3:\\n print 'USAGE: python robscript.py script params_json'\\n return\\n\\n script_loc = sys.argv[1]\\n params_json_loc = sys.argv[2]\\n\\n send_upload = create_print('[UPLOAD] ')\\n on_status_update = create_print('[STATUS] ')\\n on_error = create_print('[ERROR] ')\\n on_finish = create_print('Finished. ')\\n\\n params = None\\n with open(params_json_loc) as f:\\n params = json.load(f)\\n\\n script = None\\n with open(script_loc) as f:\\n script = jinja2.Template(f.read()).render(params)\\n\\n run_job(script, send_upload, on_status_update, on_error, on_finish, '')\",\n \"def main():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('--file', '-f',\\n type=argparse.FileType('r'),\\n required=True)\\n args = parser.parse_args()\\n\\n project_yaml = yaml.safe_load(args.file)\\n\\n # Get the release name from the file path\\n release = os.path.split(os.path.dirname(args.file.name))[1]\\n\\n create_jobs(release, project_yaml)\",\n \"def create_job_object(message, environment_image):\\n\\n PYTHONUNBUFFERED_ENV = client.V1EnvVar(name=\\\"PYTHONUNBUFFERED\\\", value=\\\"1\\\")\\n AUTH_TOKEN_ENV = client.V1EnvVar(name=\\\"AUTH_TOKEN\\\", value=AUTH_TOKEN)\\n EVALAI_API_SERVER_ENV = client.V1EnvVar(\\n name=\\\"EVALAI_API_SERVER\\\", value=EVALAI_API_SERVER\\n )\\n MESSAGE_BODY_ENV = client.V1EnvVar(name=\\\"BODY\\\", value=json.dumps(message))\\n submission_pk = message[\\\"submission_pk\\\"]\\n image = message[\\\"submitted_image_uri\\\"]\\n # Configureate Pod agent container\\n agent_container = client.V1Container(\\n name=\\\"agent\\\", image=image, env=[PYTHONUNBUFFERED_ENV]\\n )\\n # Configureate Pod environment container\\n environment_container = client.V1Container(\\n name=\\\"environment\\\",\\n image=environment_image,\\n env=[\\n PYTHONUNBUFFERED_ENV,\\n AUTH_TOKEN_ENV,\\n EVALAI_API_SERVER_ENV,\\n MESSAGE_BODY_ENV,\\n ],\\n resources=client.V1ResourceRequirements(\\n limits={\\\"nvidia.com/gpu\\\": \\\"1\\\"}\\n ),\\n )\\n # Create and configurate a spec section\\n template = client.V1PodTemplateSpec(\\n metadata=client.V1ObjectMeta(labels={\\\"app\\\": \\\"evaluation\\\"}),\\n spec=client.V1PodSpec(\\n containers=[environment_container, agent_container],\\n restart_policy=\\\"Never\\\",\\n ),\\n )\\n # Create the specification of deployment\\n spec = client.V1JobSpec(backoff_limit=1, template=template)\\n # Instantiate the job object\\n job = client.V1Job(\\n api_version=\\\"batch/v1\\\",\\n kind=\\\"Job\\\",\\n metadata=client.V1ObjectMeta(\\n name=\\\"submission-{0}\\\".format(submission_pk)\\n ),\\n spec=spec,\\n )\\n return job\",\n \"def createJob(self, executable ):\\n thisfileFolderPath = os.path.dirname(inspect.getfile( inspect.currentframe() ))\\n inp = open( os.path.join(thisfileFolderPath,\\\"job.oar.tpl\\\"), 'r')\\n t = Template(inp.read())\\n \\n s = t.substitute(executable=executable)\\n \\n completePath = os.path.join(self.oarRunFolder,\\\"job.oar.sh\\\")\\n outp = open(completePath, 'w')\\n outp.write(s)\\n outp.close()\\n \\n os.system('chmod +x ' + completePath)\\n print \\\"OAR: created job file: \\\", completePath\\n return completePath\",\n \"def parse_jobscript():\\n p = argparse.ArgumentParser(description=\\\"SGE snakemake submit script\\\")\\n p.add_argument(\\\"jobscript\\\", help=\\\"Snakemake jobscript with job properties.\\\")\\n return p.parse_args().jobscript\",\n \"def _create_job(self,\\n name,\\n environment_string,\\n description='',\\n platform='LINUX'):\\n job = data_types.Job()\\n job.name = name\\n if environment_string.strip():\\n job.environment_string = environment_string\\n job.platform = platform\\n job.descripton = description\\n job.put()\\n\\n return job\",\n \"def build_job_scripts(model_list, scenario_list, output_dir, cassandra_config_dir, cassandra_log_dir,\\n cassandra_main_script, sbatch_account, sbatch_partition='slurm', sbatch_walltime='01:00:00',\\n sbatch_ntasks=3, sbatch_nodes=3, sbatch_jobname='cassie', sbatch_logdir='.', template=None):\\n\\n # use default configuration template file if user does not give one\\n if template is None:\\n template = pkg_resources.resource_filename('cassie', 'data/sbatch_template.sh')\\n\\n # existing tags to replace in the template file\\n model_tag = '<model>'\\n scenario_tag = '<scenario>'\\n account_tag = '<account>'\\n partition_tag = '<partition>'\\n ntasks_tag = '<ntasks>'\\n nodes_tag = '<nodes>'\\n time_tag = '<walltime>'\\n jobname_tag = '<jobname>'\\n logdir_tag = '<logdir>'\\n cassandra_configdir_tag = '<cassconfigdir>'\\n cassandra_logdir_tag = '<casslogdir>'\\n cassandra_script_tag = '<cassmainscript>'\\n\\n for model in model_list:\\n for scenario in scenario_list:\\n\\n output_file = os.path.join(output_dir, f'run_{model.lower()}_{scenario}.sh')\\n\\n with open(output_file, 'w') as out:\\n with open(template) as get:\\n\\n f = get.read()\\n\\n # replace tag names with dynamic content\\n fx = f.replace(model_tag, model)\\n fx = fx.replace(scenario_tag, scenario)\\n\\n fx = fx.replace(account_tag, sbatch_account)\\n fx = fx.replace(partition_tag, sbatch_partition)\\n fx = fx.replace(ntasks_tag, str(sbatch_ntasks))\\n fx = fx.replace(nodes_tag, str(sbatch_nodes))\\n fx = fx.replace(time_tag, sbatch_walltime)\\n fx = fx.replace(jobname_tag, sbatch_jobname)\\n fx = fx.replace(logdir_tag, sbatch_logdir)\\n\\n fx = fx.replace(cassandra_configdir_tag, cassandra_config_dir)\\n fx = fx.replace(cassandra_logdir_tag, cassandra_log_dir)\\n fx = fx.replace(cassandra_script_tag, cassandra_main_script)\\n\\n out.write(fx)\",\n \"def submit_job(sample_config, jobname, rundir, cliargs, extramodules=[]):\\n slurmfile_path = os.path.join(rundir, \\\"{}.slurm\\\".format(jobname))\\n with open(slurmfile_path, \\\"w\\\") as slurmfile:\\n slurmfile.write(\\\"#! /bin/bash -l\\\\n\\\")\\n slurmfile.write(\\\"#SBATCH -A {}\\\\n\\\".format(cliargs.project))\\n slurmfile.write(\\\"#SBATCH -o {}.out\\\\n\\\".format(jobname))\\n slurmfile.write(\\\"#SBATCH -e {}.err\\\\n\\\".format(jobname))\\n slurmfile.write(\\\"#SBATCH -J {}.job\\\\n\\\".format(jobname))\\n if cliargs.threads<16 :\\n slurmfile.write(\\\"#SBATCH -p core -n {}\\\\n\\\".format(cliargs.threads))\\n else:\\n slurmfile.write(\\\"#SBATCH -p node -n {}\\\\n\\\".format(cliargs.threads))\\n slurmfile.write(\\\"#SBATCH -t {}\\\\n\\\".format(cliargs.time))\\n if hasattr(cliargs, \\\"email\\\"):\\n slurmfile.write(\\\"#SBATCH --mail-user {}\\\\n\\\".format(cliargs.email))\\n slurmfile.write(\\\"#SBATCH --mail-type=ALL\\\\n\\\")\\n if hasattr(cliargs, \\\"qos\\\"):\\n slurmfile.write(\\\"#SBATCH --qos={}\\\".format(cliargs.qos))\\n slurmfile.write(\\\"\\\\n\\\\n\\\")\\n slurmfile.write(\\\"set -e\\\\n\\\")\\n slurmfile.write(\\\"source activate {}\\\\n\\\".format(cliargs.env))\\n slurmfile.write(\\\"module load bioinfo-tools\\\\n\\\")\\n for module in extramodules:\\n slurmfile.write(module)\\n\\n slurmfile.write(\\\"deNovo_pipeline.py --global-config {} \\\"\\n \\\"--sample-config {}\\\\n\\\\n\\\".format(cliargs.global_config, sample_config))\\n\\n command=(\\\"sbatch\\\", slurmfile_path)\\n print(command)\\n try:\\n if cliargs.dry_run:\\n return 0\\n except AttributeError as e:\\n print(\\\"Warning! Could not determine if dry-run, running the command anyway: {}\\\".format(e))\\n return subprocess.call(command)\",\n \"def submit(self,\\n script: str,\\n out_script_fname: str,\\n job_name: str,\\n log_fname: str = \\\"log\\\",\\n n_core: int = 1,\\n max_cpu_hour: Optional[int] = None,\\n max_mem_gb: Optional[int] = None,\\n depend_job_ids: Optional[List[str]] = None,\\n wait: bool = False) -> str:\\n logger.info(f\\\"Submit command(s):\\\\n{script}\\\")\\n header = (self.gen_sge_header if self.scheduler_name == \\\"sge\\\"\\n else self.gen_slurm_header)(job_name,\\n log_fname,\\n n_core,\\n max_cpu_hour,\\n max_mem_gb,\\n depend_job_ids,\\n wait)\\n with open(out_script_fname, \\\"w\\\") as f:\\n f.write('\\\\n'.join(filter(None, [\\\"#!/bin/bash\\\",\\n header + \\\"\\\\n\\\",\\n self.prefix_command,\\n script + \\\"\\\\n\\\"])))\\n return (run_command(f\\\"{self.submit_command} {out_script_fname}\\\")\\n .split()[2 if self.scheduler_name == \\\"sge\\\" else -1])\",\n \"def make_job_data(url, script_fn):\\n wd = os.path.dirname(script_fn)\\n job_name = '{0}-{1}-{1}'.format(\\n os.path.basename(script_fn),\\n url.split(\\\"/\\\")[-1],\\n str(uuid.uuid4())[:8],\\n )\\n job_data = {\\\"job_name\\\": job_name,\\n \\\"cwd\\\": wd,\\n \\\"script_fn\\\": script_fn }\\n return job_data\",\n \"def createJobs(jobsfile, infiles, outdir, name, nchunks, channel, year, **kwargs):\\n tag = kwargs.get('tag', \\\"\\\") # TODO: add\\n tes = kwargs.get('tes', 1.)\\n ltf = kwargs.get('ltf', 1.)\\n jtf = kwargs.get('jtf', 1.)\\n Zmass = kwargs.get('Zmass', False)\\n prefetch = kwargs.get('prefetch', False)\\n cmd = 'python postprocessors/job.py -i %s -o %s -N %s -n %i -c %s -y %s'%(','.join(infiles),outdir,name,nchunks,channel,year)\\n if tes!=1.:\\n cmd += \\\" --tes %.3f\\\"%(tes)\\n if ltf!=1.:\\n cmd += \\\" --ltf %.3f\\\"%(ltf)\\n if jtf!=1.:\\n cmd += \\\" --jtf %.3f\\\"%(jtf)\\n if Zmass and channel=='mumu':\\n cmd += \\\" --Zmass\\\"\\n if prefetch:\\n cmd += \\\" -p\\\"\\n if tag:\\n cmd += \\\" -l %s\\\"%tag\\n if args.verbose:\\n print cmd\\n jobsfile.write(cmd+'\\\\n')\\n return 1\",\n \"def create_script_job(self):\\n script_job_no = pm.scriptJob(\\n e=[\\\"SelectionChanged\\\",\\n 'if pm.ls(sl=1) and pm.ls(sl=1)[0].name() == \\\"%s\\\":\\\\n'\\n ' pm.displayPref(displayAffected=False)\\\\n'\\n 'else:\\\\n'\\n ' pm.displayPref(displayAffected=True)' % self.light.name()\\n ]\\n )\\n self.store_data('%s' % script_job_no)\",\n \"def submit_job_submission_job(cfg: ElasticBlastConfig):\\n dry_run = cfg.cluster.dry_run\\n\\n subs = {\\n 'K8S_JOB_SUBMIT_JOBS' : K8S_JOB_SUBMIT_JOBS,\\n 'ELB_DOCKER_IMAGE' : ELB_CJS_DOCKER_IMAGE_GCP,\\n 'ELB_GCP_PROJECT' : cfg.gcp.project,\\n 'ELB_GCP_ZONE' : cfg.gcp.zone,\\n 'ELB_RESULTS' : cfg.cluster.results,\\n 'ELB_CLUSTER_NAME' : cfg.cluster.name,\\n 'ELB_PD_SIZE' : cfg.cluster.pd_size,\\n 'ELB_LABELS' : cfg.cluster.labels,\\n # For autoscaling\\n 'ELB_NUM_NODES' : str(cfg.cluster.num_nodes),\\n 'ELB_USE_LOCAL_SSD': str(cfg.cluster.use_local_ssd).lower()\\n }\\n logging.debug(f\\\"Submitting job submission job: {ELB_CJS_DOCKER_IMAGE_GCP}\\\")\\n with TemporaryDirectory() as d:\\n set_extraction_path(d)\\n job_yaml = os.path.join(d, 'job-submit-jobs.yaml')\\n with open(job_yaml, 'wt') as f:\\n f.write(substitute_params(resource_string('elastic_blast', 'templates/job-submit-jobs.yaml.template').decode(), subs))\\n cmd = f\\\"kubectl --context={cfg.appstate.k8s_ctx} apply -f {job_yaml}\\\"\\n if dry_run:\\n logging.info(cmd)\\n else:\\n safe_exec(cmd)\",\n \"def makeJob(kallisto, index, meta, bootstraps, files, single, s=1, l=180): \\n cmd = \\\"%(kallisto)s quant -i %(index)s -o %(meta)s \\\" % locals()\\n for file in files: \\n cmd += \\\" ../%s\\\" % file \\n if single: \\n cmd += \\\" --single -l %(l)i -s %(s)i\\\" % locals()\\n cmd += \\\" &> %s.log.txt\\\" % meta\\n return cmd\",\n \"def __init__(self, ccinput, script=None, queue=\\\"slurm\\\", mem=500,\\n cpus=1, time=\\\"00:15:00\\\", partition=None, jobname=\\\"CCJob\\\",\\n software=None, meta_file=\\\"meta.json\\\"):\\n self.ccinput = ccinput\\n self.script = script\\n self.software = software\\n if type(queue) == str:\\n self.queue = queue_factory(queue)\\n elif type(queue) == ccjob.Queue.SLURM:\\n self.queue = queue\\n\\n self.jobid = None\\n\\n self.meta = {\\\"status\\\": None,\\n \\\"wdir\\\" : self.ccinput.wdir,\\n \\\"infile\\\" : self.ccinput.filename,\\n \\\"basename\\\" : self.ccinput.basename\\n }\\n self.meta_filename = os.path.basename(meta_file)\\n self.meta_filepath = os.path.join(self.ccinput.wdir, meta_file)\\n\\n # submit options\\n self.options = {\\\"memory\\\": mem, \\\"cpus\\\": cpus, \\\"time\\\": time,\\n \\\"partition\\\": partition, \\\"jobname\\\": jobname}\\n self.custom_options = []\\n self.software_options = []\",\n \"def create_job(project, description):\\n randomnames = open(os.path.join(\\\"Anemone\\\", \\\"templates\\\", \\\"namegen.html\\\")).readlines()\\n jobname = (\\\"Quick.\\\" +\\n random.choice(randomnames)[:-1] + # for some reason choice gives extra space\\n random.choice(randomnames)[:-1]) # for some reason choice gives extra space\\n\\n newjob = Job.create(project=project, name=jobname, description=description)\\n newjob.name = newjob.name + \\\".{0:0=3d}\\\".format(newjob.id)\\n newjob.save()\\n return newjob\",\n \"def submit(self, content):\\n script = self.shebang\\n script += content.encode('utf8', 'replace')\\n if not script.endswith(b'\\\\n'):\\n script += b'\\\\n'\\n try:\\n out, err = self.p.communicate(script)\\n except KeyboardInterrupt:\\n self._interrupt()\\n return\\n out = py3compat.bytes_to_str(out)\\n err = py3compat.bytes_to_str(err)\\n # Get the jobid\\n self._jobid = 0\\n if out.find(\\\"Submitted batch job\\\") == 0:\\n self._jobid = int(out.split(' ')[-1])\\n self._is_started = True\\n if self._args_jobid:\\n self._userns[self._args_jobid] = self._jobid\\n else:\\n sys.stderr.write(\\\"Error during job submission\\\\n\\\")\\n sys.stderr.write(\\\"Submission arguments : {0}\\\\n\\\".format(' '.join(self.cmd)))\\n self._is_terminated = True\\n return (out, err)\",\n \"def submit(user, user_file):\\n user_job = '{\\\"localFile\\\" : \\\"userfile\\\", \\\"destFile\\\" : \\\"primes.py\\\"}'.replace(\\n \\\"userfile\\\", user_file)\\n run(['bash', 'submit.sh', user_file, user_job, user])\",\n \"def start_job(planner, job_template, domain, problem):\\n template_file = open(job_template, 'r')\\n job_string = template_file.read()\\\\\\n .replace('$DOMAIN', str(domain))\\\\\\n .replace('$PROBLEM', str(problem))\\\\\\n .replace('$LOWERDOMAIN', str(domain).lower())\\\\\\n .replace('$LOWERPROBLEM', str(problem).lower())\\\\\\n .replace('$PLANNER', planner)\\n print(job_string)\",\n \"def _generate_job_action(self, job):\\n # Derive the entrypoint command, the same content that might be written by self.get_jobscript(job)\\n use_threads = \\\"--force-use-threads\\\" if not job.is_group() else \\\"\\\"\\n\\n exec_job = self.format_job(\\n self.exec_job, job, _quote_all=True, use_threads=use_threads\\n )\\n\\n # Now that we've parsed the job resource requirements, add to exec\\n exec_job += self.get_default_resources_args()\\n\\n # The full command to download the archive, extract, and run\\n # For snakemake bases, we must activate the conda environment, but\\n # for custom images we must allow this to fail (hence || true)\\n commands = [\\n \\\"/bin/bash\\\",\\n \\\"-c\\\",\\n \\\"mkdir -p /workdir && cd /workdir && wget -O /download.py https://gist.githubusercontent.com/vsoch/84886ef6469bedeeb9a79a4eb7aec0d1/raw/181499f8f17163dcb2f89822079938cbfbd258cc/download.py && chmod +x /download.py && source activate snakemake || true && pip install crc32c && python /download.py download %s %s /tmp/workdir.tar.gz && tar -xzvf /tmp/workdir.tar.gz && %s\\\"\\n % (self.bucket.name, self.pipeline_package, exec_job),\\n ]\\n\\n # We are only generating one action, one job per run\\n # https://cloud.google.com/life-sciences/docs/reference/rest/v2beta/projects.locations.pipelines/run#Action\\n action = {\\n \\\"containerName\\\": \\\"snakejob-{}-{}\\\".format(job.name, job.jobid),\\n \\\"imageUri\\\": self.container_image,\\n \\\"commands\\\": commands,\\n \\\"environment\\\": self._generate_environment(),\\n \\\"labels\\\": self._generate_pipeline_labels(job),\\n }\\n return action\",\n \"def _create_job_message(self):\\n #TODO: Final check of source file, add xml settings, allow for user\\n # to set priority, verify all job data is correct format\\n\\n if not hasattr(self.required_files, '_get_message'):\\n self.add_file_collection()\\n\\n if self.pool and hasattr(self.pool, 'id'):\\n pool_options = {'poolId': self.pool.id}\\n\\n elif self.pool:\\n pool_options = {'poolId': str(self.pool)}\\n\\n else:\\n size = max(int(self.instances), 1)\\n pool_options = {'autoPoolSpecification': self._auto_pool(size)}\\n\\n job_message = {\\n 'Name': str(self.name),\\n 'Type': self._api.jobtype(),\\n 'RequiredFiles': self.required_files._get_message(\\\"submit\\\"),\\n 'Parameters': list(self._filter_params()),\\n 'JobFile': str(self.source),\\n 'Settings': str(self.settings),\\n 'Priority': 'Medium'\\n }\\n job_message.update(pool_options)\\n\\n self._log.debug(\\\"Job message: {0}\\\".format(job_message))\\n return job_message\",\n \"def prepare_pr_condor_job(self, pool_type, pool_address, number_of_jobs, subtask_index, data_files, rank='0', extraArgs=''):\\n ############\\n copasi_file = 'auto_copasi_%d.$(Process).cps' % subtask_index\\n output_file = 'output_%d.$(Process).txt' % subtask_index\\n \\n \\n \\n if pool_type == 'ec2':\\n binary_dir = '/usr/local/bin'\\n transfer_executable = 'NO'\\n else:\\n binary_dir, binary = os.path.split(settings.COPASI_LOCAL_BINARY)\\n transfer_executable = 'YES'\\n \\n input_files_string = ', '\\n for data_file in data_files:\\n input_files_string += (data_file + ', ')\\n input_files_string = input_files_string.rstrip(', ')\\n\\n condor_job_string = Template(condor_spec.raw_condor_job_string).substitute(copasiFile=copasi_file, \\n otherFiles=input_files_string,\\n rank=rank,\\n binary_dir = binary_dir,\\n transfer_executable = transfer_executable,\\n pool_type = pool_type,\\n pool_address = pool_address,\\n subtask=str(subtask_index),\\n n = number_of_jobs,\\n outputFile = output_file,\\n extraArgs='',\\n )\\n \\n condor_job_filename = 'auto_condor_%d.job'%subtask_index\\n condor_job_full_filename = os.path.join(self.path, condor_job_filename)\\n condor_file = open(condor_job_full_filename, 'w')\\n condor_file.write(condor_job_string)\\n condor_file.close()\\n\\n return condor_job_filename\",\n \"def createScript_sbatch(self):\\n tools_createScript_sbatch(\\n sbatch_script_file_name = self.sbatchFile_addMEM,\\n executable = self.executable_addMEM,\\n command_line_parameters = self.cfgFiles_addMEM_modified,\\n input_file_names = self.inputFiles,\\n output_file_names = self.outputFiles,\\n script_file_names = self.shFiles_addMEM_modified,\\n log_file_names = self.logFiles_addMEM,\\n keep_logs = False,\\n working_dir = self.workingDir,\\n max_num_jobs = 100000000, # it's really silly to limit the number of jobs; use an enormous number as the ,,fix''\\n cvmfs_error_log = self.cvmfs_error_log,\\n pool_id = self.pool_id,\\n use_home = self.use_home,\\n validate_outputs = self.check_output_files,\\n max_num_submittedJobs = 2000,\\n )\",\n \"def _submit_to_queue(self, script_file):\\n if sys.version_info[0] < 3:\\n process = Popen(['qsub', script_file], stdout=PIPE, stderr=PIPE)\\n else:\\n # need string not bytes so must use universal_newlines\\n process = Popen(['qsub', script_file], stdout=PIPE, stderr=PIPE, universal_newlines=True)\\n\\n out, err = process.communicate()\\n # grab the returncode. SGE returns 0 if the job was successful\\n queue_id = None\\n if process.returncode == 0:\\n try:\\n # output should of the form\\n # Your job 1659048 (\\\"NAME_OF_JOB\\\") has been submitted\\n queue_id = int(out.split(' ')[2])\\n except:\\n # probably error parsing job code\\n logger.critical(\\\"Could not parse job id following qsub...\\\")\\n return SubmitResults(qid=queue_id, out=out, err=err, process=process)\",\n \"def send_job(self):\\n graph = self.processgraphEdit.toPlainText()\\n # info(self.iface, graph)\\n response = self.connection.job_create(json.loads(graph))\\n if response.status_code == 201:\\n info(self.iface, \\\"Successfully created new job, Response: {}\\\".format(response.status_code))\\n else:\\n warning(self.iface, \\\"Not able to created new job, Response: {}\\\".format(str(response.json())))\",\n \"def _submit_to_queue(self, script_file):\\n if sys.version_info[0] < 3:\\n process = Popen(['llsubmit', script_file], stdout=PIPE, stderr=PIPE)\\n else:\\n # need string not bytes so must use universal_newlines\\n process = Popen(['llsubmit', script_file], stdout=PIPE, stderr=PIPE, universal_newlines=True)\\n\\n out, err = process.communicate()\\n # grab the return code. llsubmit returns 0 if the job was successful\\n queue_id = None\\n if process.returncode == 0:\\n try:\\n # on JUQUEEN, output should of the form\\n #llsubmit: The job \\\"juqueen1c1.zam.kfa-juelich.de.281506\\\" has been submitted.\\n token = out.split()[3]\\n s = token.split(\\\".\\\")[-1].replace('\\\"', \\\"\\\")\\n queue_id = int(s)\\n except:\\n # probably error parsing job code\\n logger.critical(\\\"Could not parse job id following llsubmit...\\\")\\n raise\\n\\n return SubmitResults(qid=queue_id, out=out, err=err, process=process)\",\n \"def create_job(self, name: str) -> Slurm:\\n LOG.info(\\\"Create a slurm job with name %s\\\", name)\\n job = Slurm(\\n name,\\n {\\\"account\\\": self.account, \\\"time\\\": self.time,},\\n scripts_dir=str(self.scripts_dir),\\n log_dir=str(self.log_dir),\\n )\\n return job\",\n \"def submit_job(script_name,\\n\\t\\t\\ttask_name, \\n\\t\\t\\timage_url=None,\\n\\t\\t\\tscale_tier = None, \\n\\t\\t\\textra_args=None, \\n\\t\\t\\tmaster_type=None, \\n\\t\\t\\taccelerator_type=None):\\n\\n\\tcreds = get_creds()\\n\\tproject_id = 'projects/{}'.format(project_id)\\n\\t\\n\\tjob_name = task_name+\\\"_\\\" + datetime.datetime.now().strftime(\\\"%y%m%d_%H%M%S\\\")\\n\\n\\targs = ['--task', task_name]\\n\\n\\tif extra_args:\\n\\t\\targs += extra_args\\n\\n\\tif not scale_tier:\\n\\t\\tscale_tier = 'BASIC'\\n\\n\\ttraining_inputs = {\\n\\t\\t'args': args,\\n\\t\\t'pythonVersion': '3.7',\\n\\t\\t'scaleTier': scale_tier,\\n\\t\\t'region': 'us-west1',\\n\\t\\t'jobDir': \\\"gs://ds-ml-nlp/ai_platform_job_files\\\",\\n\\t}\\n\\n\\ttraining_inputs.update({'packageUris': [\\\"gs://ds-ml-nlp/ai_platform_job_files/lyrical_clustering-0.1.tar.gz\\\"],\\n\\t\\t\\t\\t\\t\\t\\t\\t'pythonModule': f'scripts.{script_name}',\\n\\t\\t\\t\\t\\t\\t\\t\\t'runtimeVersion': '2.1',})\\n\\n\\tif scale_tier == 'CUSTOM':\\n\\t\\ttraining_inputs.update({'masterType': master_type})\\n\\n\\tif accelerator_type:\\n\\t\\ttraining_inputs.update({'masterConfig': {'acceleratorConfig': {'count': 1, 'type': accelerator_type}}})\\n\\t\\t\\n\\tjob_spec = {\\\"jobId\\\": job_name, \\\"trainingInput\\\": training_inputs}\\n\\tcloudml = discovery.build(\\\"ml\\\", \\\"v1\\\", cache_discovery=False, credentials=creds)\\n\\trequest = cloudml.projects().jobs().create(body=job_spec, parent=project_id)\\n\\ttry:\\n\\t\\tresponse = request.execute()\\n\\texcept HttpError as err:\\n\\t\\tlogging.error('There was an error creating the training job.'\\n\\t\\t\\t\\t\\t ' Check the details:')\\n\\t\\tlogging.error(err._get_reason())\",\n \"def submit():\\n subtime = datetime.datetime.now()\\n args = parse_args()\\n \\n # get the course's configuration\\n with open(args.config) as cfgfile:\\n cfg = json.load(cfgfile)\\n \\n # Confirm the correct directory\\n wdir = os.getcwd()\\n resp = input(\\\"The current directory is:\\\\n\\\\n\\\\t\\\" + wdir \\n + \\\"\\\\n\\\\nIs this the directory you want to submit? (y/n): \\\")\\n if resp not in [\\\"y\\\", \\\"Y\\\", \\\"yes\\\", \\\"Yes\\\", \\\"YES\\\"]:\\n return\\n \\n # Get the assignment the student is submitting\\n resp = \\\"\\\"\\n while resp not in cfg[\\\"assignments\\\"]:\\n resp = input(\\\"\\\\nSelect an assignment from the list:\\\\n\\\\n\\\\t\\\" \\n + \\\"\\\\n\\\\t\\\".join(textwrap.wrap(\\\", \\\".join(cfg[\\\"assignments\\\"])))\\n + \\\"\\\\n\\\\nAssignment: \\\")\\n assignment = resp\\n\\n # Get the section to which the user wants to submit\\n resp = \\\"\\\"\\n desc = \\\"\\\\n\\\\t\\\".join([\\\"{:3}: {}\\\".format(key, cfg[\\\"sections\\\"][key][\\\"desc\\\"]) \\n for key in cfg[\\\"sections\\\"].keys()])\\n\\n while resp not in cfg[\\\"sections\\\"].keys():\\n resp = input(\\\"\\\\nSelect a section number from the list:\\\\n\\\\n\\\\t\\\" \\n + desc + \\\"\\\\n\\\\nSection: \\\")\\n section = cfg[\\\"sections\\\"][resp]\\n\\n # Collect the username and list of files to tar\\n username = os.environ.get(\\\"USER\\\")\\n files = glob.glob(\\\"*\\\")\\n\\n # Create the submission directory and copy in all the files\\n submitdir = \\\"{}_{}_{}\\\".format(assignment, cfg[\\\"course\\\"], username)\\n os.mkdir(submitdir)\\n for f in files:\\n shutil.copy(f, os.path.join(submitdir, f))\\n\\n # Create the archive, then delete the submission directory\\n tarname = \\\"{}.{}.{}.{}.tgz\\\".format(\\n assignment, cfg[\\\"course\\\"], username, int(time.time())\\n )\\n with tarfile.open(tarname, mode=\\\"w:gz\\\") as tar:\\n tar.add(submitdir)\\n shutil.rmtree(submitdir)\\n\\n # Build the email addresses\\n domain = cfg[\\\"domain\\\"]\\n faddr = \\\"{}@{}\\\".format(username, domain)\\n taddr = \\\"{}@{}\\\".format(section[\\\"ta\\\"], domain)\\n \\n if \\\"cc\\\" in section:\\n ccaddrs = [\\\"{}@{}\\\".format(netid, domain) for netid in section[\\\"cc\\\"]]\\n else:\\n ccaddrs = []\\n\\n if \\\"bcc\\\" in section:\\n bccaddrs = [\\\"{}@{}\\\".format(netid, domain) for netid in section[\\\"bcc\\\"]]\\n else:\\n bccaddrs = []\\n\\n # Build the email headers\\n msg = email.mime.multipart.MIMEMultipart()\\n msg[\\\"From\\\"] = faddr\\n msg[\\\"To\\\"] = taddr\\n msg[\\\"Cc\\\"] = \\\", \\\".join(ccaddrs)\\n msg[\\\"Subject\\\"] = \\\"{} submission for student {} in {}\\\".format(\\n assignment, username, cfg[\\\"course\\\"]\\n )\\n\\n # Build the attachment and compute a check sum\\n attachment = email.mime.base.MIMEBase('application', 'octet-stream')\\n with open(tarname, 'rb') as f:\\n contents = f.read()\\n chksum = hashlib.md5(contents).hexdigest()\\n attachment.set_payload(contents)\\n \\n email.encoders.encode_base64(attachment)\\n attachment.add_header(\\\"Content-Disposition\\\", \\n \\\"attachment; filename={}\\\".format(tarname)\\n )\\n msg.attach(attachment)\\n \\n # Build and attach the message text\\n msg.attach(email.mime.text.MIMEText(\\n \\\"Submission from {}\\\\n\\\".format(username)\\n + \\\"Course: {}\\\\n\\\".format(cfg[\\\"course\\\"])\\n + \\\"Assignment: {}\\\\n\\\".format(assignment)\\n + \\\"Time: {}\\\\n\\\".format(subtime.strftime(\\\"%m/%d/%Y %H:%M:%S\\\"))\\n + \\\"MD5Sum: {}\\\\n\\\".format(chksum),\\n \\\"plain\\\"))\\n \\n # Send the email\\n server = smtplib.SMTP('localhost')\\n server.sendmail(faddr, [taddr] + ccaddrs + bccaddrs, msg.as_string())\\n server.quit()\",\n \"def submit_mc_jobs_htcondor(in_args=sys.argv[1:], mg5_dir=MG5_DIR):\\n\\n # Handle user options. The user can pass all the same options as they\\n # would if running the program locally. However, for certain options,\\n # we interecpt the argument(s) and modify if necessary.\\n parser = argparse.ArgumentParser(description=__doc__)\\n\\n # Options for the job submitter\\n parser.add_argument(\\\"jobIdRange\\\",\\n help=\\\"Specify job ID range to run over. The ID is used\\\"\\n \\\" as the random number generator seed, so manual \\\"\\n \\\"control is needed to avoid making the same files. \\\"\\n \\\"Must be of the form: startID, endID. \\\",\\n nargs=2, type=int) # no metavar, bug with positionals\\n parser.add_argument(\\\"--oDir\\\",\\n help=\\\"Directory for output HepMC files. \\\"\\n \\\"If no directory is specified, an automatic one will \\\"\\n \\\"be created at: \\\"\\n \\\"/hdfs/user/<username>/NMSSMPheno/MG5_aMC/<energy>TeV/<output>/<date>\\\"\\n \\\", where <output> refers to the output directory as \\\"\\n \\\"specified in the card.\\\",\\n default=\\\"\\\")\\n # All other program arguments to pass to program directly.\\n parser.add_argument(\\\"--args\\\",\\n help=\\\"All other program arguments. \\\"\\n \\\"You MUST specify this after all other options\\\",\\n nargs=argparse.REMAINDER)\\n # Some generic script options\\n parser.add_argument(\\\"--dry\\\",\\n help=\\\"Dry run, don't submit to queue.\\\",\\n action='store_true')\\n parser.add_argument(\\\"-v\\\",\\n help=\\\"Display debug messages.\\\",\\n action='store_true')\\n args = parser.parse_args(args=in_args)\\n\\n mg5_parser = MG5ArgParser()\\n mg5_args = mg5_parser.parse_args(args.args)\\n\\n log.info('>>> Creating jobs')\\n\\n if args.v:\\n log.setLevel(logging.DEBUG)\\n\\n log.debug('program args: %s' % args)\\n log.debug('mg5 args: %s' % mg5_args)\\n\\n # Do some checks\\n # -------------------------------------------------------------------------\\n if not os.path.isdir(mg5_dir):\\n raise RuntimeError('MG5_DIR does not correspond to an actual directory')\\n\\n if args.jobIdRange[0] < 1:\\n raise RuntimeError('The first jobIdRange argument must be >= 1.')\\n\\n if args.jobIdRange[1] < args.jobIdRange[0]:\\n raise RuntimeError('The second jobIdRange argument must be >= the first.')\\n\\n # Get the input card from user's options & check it exists\\n card = mg5_args.card\\n if not card:\\n raise RuntimeError('You did not specify an input card!')\\n if not os.path.isfile(card):\\n raise RuntimeError('Input card %s does not exist!' % card)\\n if os.path.dirname(card) != 'input_cards':\\n raise RuntimeError('Put your card in input_cards directory')\\n args.card = card\\n args.channel = get_value_from_card(args.card, 'output')\\n\\n # Get CoM energy\\n # -------------------------------------------------------------------------\\n args.energy = int(get_value_from_card(args.card, 'ebeam1')) * 2 / 1000\\n\\n # Auto generate output directory if necessary\\n # -------------------------------------------------------------------------\\n if args.oDir == \\\"\\\":\\n args.oDir = generate_dir_soolin(args.channel, args.energy)\\n\\n check_create_dir(args.oDir)\\n\\n # Dicts to hold thing to be copied before/after the job runs\\n copy_to_local = {}\\n copy_from_local = {}\\n\\n # Make the program zip and put it in hdfs\\n # -------------------------------------------------------------------------\\n # don't want any trailing \\\"/\\\"\\n if mg5_dir.endswith(\\\"/\\\"):\\n mg5_dir = mg5_dir.rstrip('/')\\n version = re.findall(r'MG5_aMC_v.*', mg5_dir)[0]\\n log.info('Creating tar file of MG5 installation, please wait...')\\n call(['tar', 'czf', '%s.tgz' % version, '-C', os.path.dirname(mg5_dir), version])\\n # copy to hdfs\\n zip_dir = '/hdfs/user/%s/NMSSMPheno/zips/' % (os.environ['LOGNAME'])\\n check_create_dir(zip_dir)\\n zip_filename = '%s.tgz' % version\\n zip_path = os.path.join(zip_dir, zip_filename)\\n call(['hadoop', 'fs', '-copyFromLocal', '-f', zip_filename, zip_dir.replace('/hdfs', '')])\\n os.remove(zip_filename)\\n copy_to_local[zip_path] = 'MG5_aMC.tgz'\\n\\n # Copy across input cards to hdfs to sandbox them\\n # -------------------------------------------------------------------------\\n if not args.dry:\\n log.debug('Copying across input_cards...')\\n call(['hadoop', 'fs', '-copyFromLocal', '-f',\\n 'input_cards', args.oDir.replace('/hdfs', '')])\\n copy_to_local[os.path.join(args.oDir, 'input_cards')] = 'input_cards'\\n\\n # Copy run script to outputdir to sandbox it\\n # -------------------------------------------------------------------------\\n sandbox_script = os.path.join(args.oDir, 'run_mg5.py')\\n copy_to_local[sandbox_script] = 'run_mg5.py'\\n if not args.dry:\\n log.debug('Copying across exe...')\\n shutil.copy2('run_mg5.py', sandbox_script)\\n\\n # Setup log directory\\n # -------------------------------------------------------------------------\\n log_dir = '%s/logs' % generate_subdir(args.channel, args.energy)\\n check_create_dir(log_dir)\\n\\n # File stem common for all dag and status files\\n # -------------------------------------------------------------------------\\n file_stem = os.path.join(generate_subdir(args.channel, args.energy),\\n strftime(\\\"%H%M%S\\\"))\\n check_create_dir(os.path.dirname(file_stem))\\n\\n # Make DAG file\\n # -------------------------------------------------------------------------\\n dag_name = file_stem + '.dag'\\n status_name = file_stem + '.status'\\n write_dag_file(dag_filename=dag_name,\\n condor_filename='HTCondor/mcJob.condor',\\n status_filename=status_name,\\n copyToLocal=copy_to_local, copyFromLocal=copy_from_local,\\n log_dir=log_dir, args=args)\\n\\n # Submit it\\n # -------------------------------------------------------------------------\\n if args.dry:\\n log.warning('Dry run - not submitting jobs or copying files.')\\n else:\\n call(['condor_submit_dag', dag_name])\\n log.info('Check status with:')\\n log.info('DAGstatus.py %s' % status_name)\\n log.info('Condor log files written to: %s' % log_dir)\\n print''\",\n \"def build_submission(config):\\n\\n soup = bs(HTML_TEMPLATE, 'html5lib')\\n\\n # Set title\\n title = soup.find('title')\\n title.clear()\\n my_title = 'A{} | {}'.format(config['assignment'], config['firstlast'])\\n title.append(my_title)\\n\\n # Set course title\\n course = soup.find(id='course')\\n course.clear()\\n if config['course'] == 446:\\n course_title = 'ECSE 446: Realistic Image Synthesis'\\n elif config['course'] == 546:\\n course_title = 'ECSE 546: Advanced Image Synthesis'\\n elif config['course'] == 598:\\n course_title = 'COMP 598: Realistic Image Synthesis'\\n else:\\n raise ValueError('Error: invalid course number')\\n course.append(course_title)\\n\\n # Set assignment number\\n assignment = soup.find(id='assignment')\\n assignment.clear()\\n assignment.append('Assignment {}'.format(config['assignment']))\\n\\n # Set student's name\\n name = soup.find(id='firstlast')\\n name.clear()\\n name.append(config['firstlast'])\\n\\n # Set student's ID\\n id = soup.find(id='id')\\n id.clear()\\n id.append(bs('ID <code>{}</code>'.format(config['id']), 'html5lib'))\\n\\n # Create directory for convert images\\n if not os.path.exists('pngs'):\\n os.makedirs('pngs')\\n\\n # Insert all rendered scenes\\n renders = soup.find(id='renders')\\n for i, task in enumerate(config['renders']):\\n slider = '<h1>{}</h1>\\\\n'.format(task['scene'])\\n slider_title = task['scene'].replace(' ', '_').lower()\\n convert_exr(task['render'], 'pngs/{}.png'.format(slider_title))\\n slider += build_slider(slider_title)\\n renders.append(bs(slider, 'html.parser'))\\n\\n # Save\\n output = 'a{}_{}.html'.format(config['assignment'], config['id'])\\n with open(output, 'w') as out_f:\\n out_f.write(str(soup))\",\n \"def _submit_to_queue(self, script_file):\\n if sys.version_info[0] < 3:\\n process = Popen(['sbatch', script_file], stdout=PIPE, stderr=PIPE)\\n else:\\n # need string not bytes so must use universal_newlines\\n process = Popen(['sbatch', script_file], stdout=PIPE, stderr=PIPE, universal_newlines=True)\\n\\n out, err = process.communicate()\\n\\n # grab the returncode. SLURM returns 0 if the job was successful\\n queue_id = None\\n if process.returncode == 0:\\n try:\\n # output should of the form '2561553.sdb' or '352353.jessup' - just grab the first part for job id\\n queue_id = int(out.split()[3])\\n logger.info('Job submission was successful and queue_id is {}'.format(queue_id))\\n except:\\n # probably error parsing job code\\n logger.critical('Could not parse job id following slurm...')\\n return SubmitResults(qid=queue_id, out=out, err=err, process=process)\",\n \"def submit(self, script, **kwargs):\\n raise NotImplementedError()\",\n \"def write_script(self, fnum, beg, end):\\n fname = self.get_batch_file(fnum, beg, end, missing=self['missing'])\\n\\n self['job_name'] = self.get_job_name(fnum, beg, end)\\n\\n runconf=self.runconf\\n vers=runconf['reredux_config']\\n self['meds_file'] = files.get_meds_file(vers, fnum,\\n type=runconf['sim_type'])\\n\\n if runconf['sim_type'] == 'egret':\\n self['psf_file'] = files.get_psf_file(vers, fnum)\\n\\n self['config_file'] = files.get_config_file(self['run'])\\n self['output_file'] = files.get_output_file(self['run'], fnum, beg, end)\\n self['log_file'] = files.get_log_file(self['run'], fnum, beg, end)\\n self['beg'] = beg\\n self['end'] = end\\n\\n self['seed'] = numpy.random.randint(0, high=2**32-1)\\n\\n if self['missing'] and os.path.exists(self['output_file']):\\n if os.path.exists(fname):\\n os.remove(fname)\\n return\\n\\n self.njobs_written += 1\\n print(\\\"writing:\\\",fname)\\n with open(fname,'w') as fobj:\\n text = self.get_text()\\n fobj.write(text)\",\n \"def submit_single_job(self, single_job):\\n\\t\\tjob_name = self.jobname_prefix_edit.get_edit_text()\\n\\t\\tjob_name = os.path.expanduser(job_name)\\n\\t\\tjob_name = '%s_%s'%(job_name, self.jobnumber_edit.get_edit_text())\\n\\t\\t\\n\\t\\tqsub_option = self.other_options_edit.get_edit_text()\\n\\t\\twalltime = self.walltime_edit.get_edit_text()\\n\\t\\tworkdir = os.path.expanduser(self.workdir_edit.get_edit_text())\\n\\t\\truntime_output_stdout= self.runtime_output_checkbox.get_state()\\n\\t\\tno_of_nodes = int(self.nodes_edit.get_edit_text())\\n\\t\\tppn = self.myri_ppn_edit.get_edit_text()\\n\\t\\tsource_bash = self.source_bash_profile_checkbox.get_state()\\n\\t\\t\\n\\t\\treturn_code = self.write_job_to_file(single_job, job_name, no_of_nodes, \\\\\\n\\t\\t\\t\\tqsub_option, ppn=ppn, workdir=workdir, walltime=walltime, runtime_output_stdout=runtime_output_stdout,\\\\\\n\\t\\t\\t\\tsource_bash=source_bash)\\n\\t\\t\\n\\t\\tqsub_output_stdout = ''\\n\\t\\tqsub_output_stderr = ''\\n\\t\\tif self.just_write_down_checkbox.get_state():\\n\\t\\t\\tqsub_output_stdout = '%s written.'%job_name\\n\\t\\telse:\\n\\t\\t\\tcommand_handler = subprocess.Popen('qsub %s'%job_name, shell=True, stderr=subprocess.PIPE, stdout=subprocess.PIPE)\\t#2008-10-20 shell=True allows command_line to be a string, rather than a list of command and its arguments\\n\\t\\t\\tqsub_output_stdout = command_handler.stdout.read().replace('\\\\n', ' ')\\n\\t\\t\\tqsub_output_stderr = command_handler.stderr.read().replace('\\\\n', ' ')\\n\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\tqsub_output = os.popen3('qsub %s'%job_name)\\n\\t\\t\\tqsub_output_stdout = qsub_output[1].read().replace('\\\\n', ' ')\\n\\t\\t\\tqsub_output_stderr = qsub_output[2].read().replace('\\\\n', ' ')\\n\\t\\t\\t\\\"\\\"\\\"\\n\\t\\treturn (qsub_output_stdout, qsub_output_stderr)\",\n \"def _create_pbs_file(self, jobname, num_jobs, pf_pbs, pf_input_package_template, pf_output_package_template):\\n\\n # create unique compute directory\\n pd_compute = None # run_shell_cmd(\\\"mktemp --tmpdir={}\\\".format(self._prl_options[\\\"pbs-pd-root-compute\\\"]))\\n\\n pbs_text = PBS._generate_pbs_header_array(num_jobs, jobname, self._prl_options, pd_compute=pd_compute)\\n\\n pbs_text += \\\"\\\\n{}\\\\n\\\".format(\\n PBS._generate_call_command(self._env,\\n pf_input_package_template,\\n pf_output_package_template,\\n self._prl_options,\\n pd_compute=pd_compute\\n )\\n )\\n\\n # write to file\\n write_to_file(pbs_text, pf_pbs)\",\n \"def build_consumer_submit_file(self, consumer):\\n log_dir = self.rsv.get_consumer_log_dir()\\n\\n environment = \\\"PATH=/usr/bin:/bin;\\\"\\n environment += consumer.get_environment()\\n\\n condor_id = consumer.get_unique_name()\\n arguments = consumer.get_args_string()\\n timestamp = strftime(\\\"%Y-%m-%d %H:%M:%S %Z\\\")\\n\\n submit = \\\"\\\"\\n submit += \\\"######################################################################\\\\n\\\"\\n submit += \\\"# Temporary submit file generated by rsv-control\\\\n\\\"\\n submit += \\\"# Generated at %s\\\\n\\\" % timestamp\\n submit += \\\"######################################################################\\\\n\\\"\\n submit += \\\"Arguments = %s\\\\n\\\" % arguments\\n submit += \\\"DeferralPrepTime = 180\\\\n\\\"\\n submit += \\\"DeferralTime = (CurrentTime + 300 + random(30))\\\\n\\\"\\n submit += \\\"DeferralWindow = 99999999\\\\n\\\"\\n submit += \\\"Environment = %s\\\\n\\\" % environment\\n submit += \\\"Executable = %s\\\\n\\\" % consumer.executable\\n submit += \\\"Error = %s/%s.err\\\\n\\\" % (log_dir, condor_id)\\n submit += \\\"Output = %s/%s.out\\\\n\\\" % (log_dir, condor_id)\\n submit += \\\"Log = %s/%s.log\\\\n\\\" % (log_dir, condor_id)\\n submit += \\\"Universe = local\\\\n\\\"\\n submit += \\\"Notification = never\\\\n\\\"\\n submit += \\\"OnExitRemove = false\\\\n\\\"\\n submit += \\\"PeriodicRelease = (HoldReasonCode =!= 1) \\\" + \\\\\\n \\\"&& ((CurrentTime - EnteredCurrentStatus) > 60)\\\\n\\\"\\n submit += \\\"+OSGRSV = \\\\\\\"consumers\\\\\\\"\\\\n\\\"\\n submit += \\\"+OSGRSVUniqueName = \\\\\\\"%s\\\\\\\"\\\\n\\\" % condor_id\\n submit += \\\"Queue\\\\n\\\"\\n\\n return submit\",\n \"def jobs(\\n ctx: typer.Context,\\n op_id: str = typer.Argument(\\n ...,\\n autocompletion=completion_op_id,\\n callback=check_for_op_id,\\n help=\\\"A valid op-id. e.g. get_markets_prices\\\",\\n ),\\n param_string: Optional[str] = typer.Option(\\n None,\\n \\\"--param-string\\\",\\n \\\"-p\\\",\\n help=\\\"Optional. Full or partial parameters as a json encoded dictionary string. \\\"\\n \\\"Keys must be valid parameters for selected op_id.\\\",\\n ),\\n default_params: bool = typer.Option(\\n False,\\n \\\"-d\\\",\\n \\\"--default-params\\\",\\n help=\\\"Include all parameters that are required, or have default values. \\\"\\n \\\"Missing values will be 'NOTSET'.\\\",\\n ),\\n callback_path: Optional[Path] = typer.Option(\\n None,\\n \\\"-c\\\",\\n \\\"--callbacks\\\",\\n help=\\\"Optional. Path to custom callbacks to be used. \\\",\\n ),\\n file_name: str = typer.Option(\\n \\\"created-jobs/${esi_job_op_id}-${esi_job_uid}\\\",\\n \\\"-n\\\",\\n \\\"--file-name\\\",\\n help=(\\n \\\"File name for the new job, must be unique if multiple jobs. \\\"\\n \\\"Can include directories, \\\"\\n \\\"and the file type suffix will be added based on --format-id.\\\"\\n ),\\n ),\\n data_path: Optional[Path] = typer.Option(\\n None,\\n \\\"--data-file\\\",\\n \\\"-i\\\",\\n help=(\\n \\\"Optional. Path to json, csv, or yaml file with full or partial parameters. \\\"\\n \\\"Must result in a list of dicts.\\\"\\n ),\\n ),\\n format_id: FormatChoices = typer.Option(\\n FormatChoices.json,\\n \\\"-f\\\",\\n \\\"--format-id\\\",\\n show_choices=True,\\n help=\\\"Output file format.\\\",\\n ),\\n path_out: Path = typer.Argument(\\n \\\"./tmp\\\",\\n help=\\\"Parent path for saving the new jobs, will be prepended to --file-name.\\\",\\n ),\\n):\\n operation_manifest: OperationManifest = ctx.obj[\\\"operation_manifest\\\"]\\n # path_out = optional_object(path_out, Path, \\\".\\\")\\n if path_out.is_file:\\n typer.BadParameter(\\\"path_out must not be a file.\\\")\\n file_data: Optional[List[Dict]] = get_params_from_file(data_path)\\n parameters: Dict = decode_param_string(param_string)\\n if callback_path is None:\\n callback_collection = default_callback_collection()\\n else:\\n callback_collection = load_callbacks(callback_path)\\n jobs_: List[EsiJob] = []\\n try:\\n op_info = operation_manifest.op_info(op_id)\\n if not file_data:\\n job = op_info.create_job(\\n parameters,\\n callback_collection,\\n include_default_params=default_params,\\n # only_required_default_params=False,\\n # allow_notset=False,\\n )\\n jobs_.append(job)\\n else:\\n for params in file_data:\\n params.update(parameters)\\n job = op_info.create_job(\\n params,\\n callback_collection,\\n include_default_params=default_params,\\n # only_required_default_params=False,\\n # allow_notset=False,\\n )\\n jobs_.append(job)\\n except Exception as ex:\\n raise typer.BadParameter(\\n f\\\"Exception creating job. {ex.__class__.__name__}: {ex}\\\"\\n )\\n for job in jobs_:\\n file_path = resolve_job_file_path(job, file_name, path_out)\\n try:\\n save_path = job.serialize_file(file_path, format_id)\\n except Exception as ex:\\n raise typer.BadParameter(\\n f\\\"Error saving job to {save_path}. {ex.__class__.__name__}, {ex}\\\"\\n )\\n logger.info(\\\"Saved job %s at %s\\\", job.uid, file_path)\\n typer.echo(f\\\"{len(jobs_)} jobs saved to {path_out}\\\")\\n report_finished_task(ctx)\",\n \"def write_job_file(job_name, py_file_name='main.py',\\n sbatch_path='/Users/omarschall/vanilla-rtrl/job_scripts/',\\n scratch_path='/scratch/oem214/vanilla-rtrl/',\\n nodes=1, ppn=1, mem=16, n_hours=24):\\n\\n job_file = os.path.join(sbatch_path, job_name + '.s')\\n log_name = os.path.join('log', job_name)\\n\\n with open(job_file, 'w') as f:\\n f.write(\\n '#! /bin/bash\\\\n'\\n + '\\\\n'\\n + '#SBATCH --nodes={}\\\\n'.format(nodes)\\n + '#SBATCH --ntasks-per-node=1\\\\n'\\n + '#SBATCH --cpus-per-task={}\\\\n'.format(ppn)\\n + '#SBATCH --mem={}GB\\\\n'.format(mem)\\n + '#SBATCH --time={}:00:00\\\\n'.format(n_hours)\\n + '#SBATCH --job-name={}\\\\n'.format(job_name[0:16])\\n + '#SBATCH --output={}log/{}.o\\\\n'.format(scratch_path, job_name[0:16])\\n + '\\\\n'\\n + 'module purge\\\\n'\\n + 'SAVEPATH={}library/{}\\\\n'.format(scratch_path, job_name)\\n + 'export SAVEPATH\\\\n'\\n + 'module load python3/intel/3.6.3\\\\n'\\n + 'cd /home/oem214/py3.6.3\\\\n'\\n + 'source py3.6.3/bin/activate\\\\n'\\n + 'cd {}\\\\n'.format(scratch_path)\\n + 'pwd > {}.log\\\\n'.format(log_name)\\n + 'date >> {}.log\\\\n'.format(log_name)\\n + 'which python >> {}.log\\\\n'.format(log_name)\\n + 'python {}\\\\n'.format(py_file_name)\\n )\\n\\n return job_file\",\n \"def create_job(self, job):\\n call = subprocess.Popen(self.cli + [PlatformJenkinsJavaCLI.CREATE_JOB, job.name], stdin=subprocess.PIPE)\\n out, err = call.communicate(input=platform_ci.jjb.get_job_as_xml(job, self.template_dir))\\n call.wait()\\n if call.returncode != 0:\\n logging.info(out)\\n logging.error(err)\\n raise PlatformJenkinsException(\\\"Creating job failed: \\\" + job.name)\",\n \"def SubmitJob(jobid, cntSubmitJobDict, numseq_this_user, g_params): # {{{\\n# for each job rstdir, keep three log files,\\n# 1.seqs finished, finished_seq log keeps all information, finished_index_log\\n# can be very compact to speed up reading, e.g.\\n# 1-5 7-9 etc\\n# 2.seqs queued remotely , format:\\n# index node remote_jobid\\n# 3. format of the torun_idx_file\\n# origIndex\\n gen_logfile = g_params['gen_logfile']\\n # gen_errfile = g_params['gen_errfile']\\n name_server = g_params['name_server']\\n\\n webcom.loginfo(\\\"SubmitJob for %s, numseq_this_user=%d\\\"%(jobid, numseq_this_user), gen_logfile)\\n\\n path_static = g_params['path_static']\\n path_cache = g_params['path_cache']\\n\\n path_result = os.path.join(path_static, 'result')\\n path_log = os.path.join(path_static, 'log')\\n\\n rstdir = \\\"%s/%s\\\"%(path_result, jobid)\\n outpath_result = \\\"%s/%s\\\"%(rstdir, jobid)\\n if not os.path.exists(outpath_result):\\n os.mkdir(outpath_result)\\n\\n finished_idx_file = \\\"%s/finished_seqindex.txt\\\"%(rstdir)\\n failed_idx_file = \\\"%s/failed_seqindex.txt\\\"%(rstdir)\\n remotequeue_idx_file = \\\"%s/remotequeue_seqindex.txt\\\"%(rstdir)\\n torun_idx_file = \\\"%s/torun_seqindex.txt\\\"%(rstdir) # ordered seq index to run\\n cnttry_idx_file = \\\"%s/cntsubmittry_seqindex.txt\\\"%(rstdir)#index file to keep log of tries\\n\\n runjob_errfile = \\\"%s/%s\\\"%(rstdir, \\\"runjob.err\\\")\\n runjob_logfile = \\\"%s/%s\\\"%(rstdir, \\\"runjob.log\\\")\\n finished_seq_file = \\\"%s/finished_seqs.txt\\\"%(outpath_result)\\n query_parafile = \\\"%s/query.para.txt\\\"%(rstdir)\\n query_para = webcom.LoadJsonFromFile(query_parafile)\\n tmpdir = \\\"%s/tmpdir\\\"%(rstdir)\\n qdinittagfile = \\\"%s/runjob.qdinit\\\"%(rstdir)\\n failedtagfile = \\\"%s/%s\\\"%(rstdir, \\\"runjob.failed\\\")\\n starttagfile = \\\"%s/%s\\\"%(rstdir, \\\"runjob.start\\\")\\n cache_process_finish_tagfile = \\\"%s/cache_processed.finish\\\"%(rstdir)\\n fafile = \\\"%s/query.fa\\\"%(rstdir)\\n split_seq_dir = \\\"%s/splitaa\\\"%(tmpdir)\\n forceruntagfile = \\\"%s/forcerun\\\"%(rstdir)\\n lastprocessed_cache_idx_file = \\\"%s/lastprocessed_cache_idx.txt\\\"%(rstdir)\\n variant_file = \\\"%s/variants.fa\\\"%(rstdir)\\n\\n if os.path.exists(forceruntagfile):\\n isForceRun = True\\n else:\\n isForceRun = False\\n\\n finished_idx_list = []\\n failed_idx_list = [] # [origIndex]\\n if os.path.exists(finished_idx_file):\\n finished_idx_list = list(set(myfunc.ReadIDList(finished_idx_file)))\\n if os.path.exists(failed_idx_file):\\n failed_idx_list = list(set(myfunc.ReadIDList(failed_idx_file)))\\n\\n processed_idx_set = set(finished_idx_list) | set(failed_idx_list)\\n\\n jobinfofile = \\\"%s/jobinfo\\\"%(rstdir)\\n jobinfo = \\\"\\\"\\n if os.path.exists(jobinfofile):\\n jobinfo = myfunc.ReadFile(jobinfofile).strip()\\n jobinfolist = jobinfo.split(\\\"\\\\t\\\")\\n email = \\\"\\\"\\n if len(jobinfolist) >= 8:\\n email = jobinfolist[6]\\n method_submission = jobinfolist[7]\\n\\n # the first time when the this jobid is processed, do the following\\n # 1. generate a file with sorted seqindex\\n # 2. generate splitted sequence files named by the original seqindex\\n if not os.path.exists(qdinittagfile): #initialization#{{{\\n if not os.path.exists(tmpdir):\\n os.mkdir(tmpdir)\\n if isForceRun or os.path.exists(cache_process_finish_tagfile):\\n isCacheProcessingFinished = True\\n else:\\n isCacheProcessingFinished = False\\n\\n # ==== 1.dealing with cached results \\n (seqIDList, seqAnnoList, seqList) = myfunc.ReadFasta(fafile)\\n if len(seqIDList) <= 0:\\n webcom.WriteDateTimeTagFile(failedtagfile, runjob_logfile, runjob_errfile)\\n webcom.loginfo(\\\"Read query seq file failed. Zero sequence read in\\\", runjob_errfile)\\n return 1\\n\\n if 'DEBUG' in g_params and g_params['DEBUG']:\\n msg = \\\"jobid = %s, isCacheProcessingFinished=%s, MAX_CACHE_PROCESS=%d\\\"%(\\n jobid, str(isCacheProcessingFinished), g_params['MAX_CACHE_PROCESS'])\\n webcom.loginfo(msg, gen_logfile)\\n\\n if not isCacheProcessingFinished:\\n finished_idx_set = set(finished_idx_list)\\n\\n lastprocessed_idx = -1\\n if os.path.exists(lastprocessed_cache_idx_file):\\n try:\\n lastprocessed_idx = int(myfunc.ReadFile(lastprocessed_cache_idx_file))\\n except:\\n lastprocessed_idx = -1\\n\\n cnt_processed_cache = 0\\n for i in range(lastprocessed_idx+1, len(seqIDList)):\\n if i in finished_idx_set:\\n continue\\n outpath_this_seq = \\\"%s/%s\\\"%(outpath_result, \\\"seq_%d\\\"%i)\\n subfoldername_this_seq = \\\"seq_%d\\\"%(i)\\n md5_key = hashlib.md5(seqList[i].encode('utf-8')).hexdigest()\\n subfoldername = md5_key[:2]\\n cachedir = \\\"%s/%s/%s\\\"%(path_cache, subfoldername, md5_key)\\n zipfile_cache = cachedir + \\\".zip\\\"\\n\\n if os.path.exists(cachedir) or os.path.exists(zipfile_cache):\\n if os.path.exists(cachedir):\\n try:\\n shutil.copytree(cachedir, outpath_this_seq)\\n except Exception as e:\\n msg = \\\"Failed to copytree %s -> %s\\\"%(cachedir, outpath_this_seq)\\n webcom.loginfo(\\\"%s with errmsg=%s\\\"%(msg, str(e)), runjob_errfile)\\n elif os.path.exists(zipfile_cache):\\n if os.path.getsize(zipfile_cache) == 0:\\n os.remove(zipfile_cache) # remove empty archived result zip file\\n else:\\n cmd = [\\\"unzip\\\", zipfile_cache, \\\"-d\\\", outpath_result]\\n webcom.RunCmd(cmd, runjob_logfile, runjob_errfile)\\n if os.path.exists(outpath_this_seq):\\n shutil.rmtree(outpath_this_seq)\\n if os.path.exists(os.path.join(outpath_result, md5_key)):\\n shutil.move(os.path.join(outpath_result, md5_key), outpath_this_seq)\\n\\n fafile_this_seq = '%s/seq.fa'%(outpath_this_seq)\\n if os.path.exists(outpath_this_seq) and webcom.IsCheckPredictionPassed(outpath_this_seq, name_server):\\n myfunc.WriteFile('>%s\\\\n%s\\\\n'%(seqAnnoList[i], seqList[i]), fafile_this_seq, 'w', True)\\n if not os.path.exists(starttagfile): #write start tagfile\\n webcom.WriteDateTimeTagFile(starttagfile, runjob_logfile, runjob_errfile)\\n\\n info_finish = webcom.GetInfoFinish(name_server, outpath_this_seq,\\n i, len(seqList[i]), seqAnnoList[i], source_result=\\\"cached\\\", runtime=0.0)\\n myfunc.WriteFile(\\\"\\\\t\\\".join(info_finish)+\\\"\\\\n\\\",\\n finished_seq_file, \\\"a\\\", isFlush=True)\\n myfunc.WriteFile(\\\"%d\\\\n\\\"%(i), finished_idx_file, \\\"a\\\", True)\\n\\n if 'DEBUG' in g_params and g_params['DEBUG']:\\n webcom.loginfo(\\\"Get result from cache for seq_%d\\\"%(i), gen_logfile)\\n if cnt_processed_cache+1 >= g_params['MAX_CACHE_PROCESS']:\\n myfunc.WriteFile(str(i), lastprocessed_cache_idx_file, \\\"w\\\", True)\\n return 0\\n cnt_processed_cache += 1\\n\\n webcom.WriteDateTimeTagFile(cache_process_finish_tagfile, runjob_logfile, runjob_errfile)\\n\\n # Regenerate toRunDict\\n toRunDict = {}\\n for i in range(len(seqIDList)):\\n if not i in processed_idx_set:\\n toRunDict[i] = [seqList[i], 0, seqAnnoList[i].replace('\\\\t', ' ')]\\n\\n if name_server == \\\"topcons2\\\":\\n webcom.ResetToRunDictByScampiSingle(toRunDict, g_params['script_scampi'], tmpdir, runjob_logfile, runjob_errfile)\\n sortedlist = sorted(list(toRunDict.items()), key=lambda x:x[1][1], reverse=True)\\n\\n # Write splitted fasta file and write a torunlist.txt\\n if not os.path.exists(split_seq_dir):\\n os.mkdir(split_seq_dir)\\n\\n torun_index_str_list = [str(x[0]) for x in sortedlist]\\n if len(torun_index_str_list)>0:\\n myfunc.WriteFile(\\\"\\\\n\\\".join(torun_index_str_list)+\\\"\\\\n\\\", torun_idx_file, \\\"w\\\", True)\\n else:\\n myfunc.WriteFile(\\\"\\\", torun_idx_file, \\\"w\\\", True)\\n\\n # write cnttry file for each jobs to run\\n cntTryDict = {}\\n for idx in torun_index_str_list:\\n cntTryDict[int(idx)] = 0\\n json.dump(cntTryDict, open(cnttry_idx_file, \\\"w\\\"))\\n\\n for item in sortedlist:\\n origIndex = item[0]\\n seq = item[1][0]\\n description = item[1][2]\\n seqfile_this_seq = \\\"%s/%s\\\"%(split_seq_dir, \\\"query_%d.fa\\\"%(origIndex))\\n seqcontent = \\\">%s\\\\n%s\\\\n\\\"%(description, seq)\\n myfunc.WriteFile(seqcontent, seqfile_this_seq, \\\"w\\\", True)\\n # qdinit file is written at the end of initialization, to make sure\\n # that initialization is either not started or completed\\n webcom.WriteDateTimeTagFile(qdinittagfile, runjob_logfile, runjob_errfile)\\n#}}}\\n\\n\\n # 3. try to submit the job \\n toRunIndexList = [] # index in str\\n processedIndexSet = set([]) #seq index set that are already processed\\n submitted_loginfo_list = []\\n if os.path.exists(torun_idx_file):\\n toRunIndexList = myfunc.ReadIDList(torun_idx_file)\\n # unique the list but keep the order\\n toRunIndexList = myfunc.uniquelist(toRunIndexList)\\n if len(toRunIndexList) > 0:\\n iToRun = 0\\n numToRun = len(toRunIndexList)\\n for node in cntSubmitJobDict:\\n if \\\"DEBUG\\\" in g_params and g_params['DEBUG']:\\n webcom.loginfo(\\\"Trying to submitjob to the node=%s\\\\n\\\"%(str(node)), gen_logfile)\\n if iToRun >= numToRun:\\n if \\\"DEBUG\\\" in g_params and g_params['DEBUG']:\\n webcom.loginfo(\\\"iToRun(%d) >= numToRun(%d). Stop SubmitJob for jobid=%s\\\\n\\\"%(iToRun, numToRun, jobid), gen_logfile)\\n break\\n wsdl_url = \\\"http://%s/pred/api_submitseq/?wsdl\\\"%(node)\\n try:\\n myclient = Client(wsdl_url, cache=None, timeout=30)\\n except:\\n webcom.loginfo(\\\"Failed to access %s\\\"%(wsdl_url), gen_logfile)\\n continue\\n\\n if \\\"DEBUG\\\" in g_params and g_params['DEBUG']:\\n webcom.loginfo(\\\"iToRun=%d, numToRun=%d\\\\n\\\"%(iToRun, numToRun), gen_logfile)\\n [cnt, maxnum, queue_method] = cntSubmitJobDict[node]\\n cnttry = 0\\n while cnt < maxnum and iToRun < numToRun:\\n origIndex = int(toRunIndexList[iToRun])\\n seqfile_this_seq = \\\"%s/%s\\\"%(split_seq_dir, \\\"query_%d.fa\\\"%(origIndex))\\n # ignore already existing query seq, this is an ugly solution,\\n # the generation of torunindexlist has a bug\\n outpath_this_seq = \\\"%s/%s\\\"%(outpath_result, \\\"seq_%d\\\"%origIndex)\\n if os.path.exists(outpath_this_seq):\\n iToRun += 1\\n continue\\n\\n if 'DEBUG' in g_params and g_params['DEBUG']:\\n webcom.loginfo(\\\"DEBUG: cnt (%d) < maxnum (%d) \\\"\\\\\\n \\\"and iToRun(%d) < numToRun(%d)\\\"%(cnt, maxnum, iToRun, numToRun), gen_logfile)\\n fastaseq = \\\"\\\"\\n seqid = \\\"\\\"\\n seqanno = \\\"\\\"\\n seq = \\\"\\\"\\n if not os.path.exists(seqfile_this_seq):\\n all_seqfile = \\\"%s/query.fa\\\"%(rstdir)\\n try:\\n (allseqidlist, allannolist, allseqlist) = myfunc.ReadFasta(all_seqfile)\\n seqid = allseqidlist[origIndex]\\n seqanno = allannolist[origIndex]\\n seq = allseqlist[origIndex]\\n fastaseq = \\\">%s\\\\n%s\\\\n\\\" % (seqanno, seq)\\n except KeyError:\\n pass\\n else:\\n fastaseq = myfunc.ReadFile(seqfile_this_seq)#seq text in fasta format\\n (seqid, seqanno, seq) = myfunc.ReadSingleFasta(seqfile_this_seq)\\n\\n isSubmitSuccess = False\\n if len(seq) > 0:\\n query_para['name_software'] = webcom.GetNameSoftware(name_server.lower(), queue_method)\\n query_para['queue_method'] = queue_method\\n if name_server.lower() == \\\"pathopred\\\":\\n variant_text = myfunc.ReadFile(variant_file)\\n query_para['variants'] = variant_text\\n # also include the identifier name as a query parameter\\n query_para['identifier_name'] = seqid\\n\\n para_str = json.dumps(query_para, sort_keys=True)\\n jobname = \\\"\\\"\\n if email not in g_params['vip_user_list']:\\n useemail = \\\"\\\"\\n else:\\n useemail = email\\n try:\\n myfunc.WriteFile(\\\"\\\\tSubmitting seq %4d \\\"%(origIndex),\\n gen_logfile, \\\"a\\\", True)\\n rtValue = myclient.service.submitjob_remote(fastaseq, para_str,\\n jobname, useemail, str(numseq_this_user), str(isForceRun))\\n except Exception as e:\\n webcom.loginfo(\\\"Failed to run myclient.service.submitjob_remote with errmsg=%s\\\"%(str(e)), gen_logfile)\\n rtValue = []\\n pass\\n\\n cnttry += 1\\n if len(rtValue) >= 1:\\n strs = rtValue[0]\\n if len(strs) >=5:\\n remote_jobid = strs[0]\\n result_url = strs[1]\\n numseq_str = strs[2]\\n errinfo = strs[3]\\n warninfo = strs[4]\\n if remote_jobid != \\\"None\\\" and remote_jobid != \\\"\\\":\\n isSubmitSuccess = True\\n epochtime = time.time()\\n # 6 fields in the file remotequeue_idx_file\\n txt = \\\"%d\\\\t%s\\\\t%s\\\\t%s\\\\t%s\\\\t%f\\\"%( origIndex,\\n node, remote_jobid, seqanno.replace('\\\\t', ' '), seq,\\n epochtime)\\n submitted_loginfo_list.append(txt)\\n cnttry = 0 #reset cnttry to zero\\n else:\\n webcom.loginfo(\\\"bad wsdl return value\\\", gen_logfile)\\n\\n if isSubmitSuccess:\\n cnt += 1\\n myfunc.WriteFile(\\\" succeeded on node %s\\\\n\\\"%(node), gen_logfile, \\\"a\\\", True)\\n else:\\n myfunc.WriteFile(\\\" failed on node %s\\\\n\\\"%(node), gen_logfile, \\\"a\\\", True)\\n\\n if isSubmitSuccess or cnttry >= g_params['MAX_SUBMIT_TRY']:\\n iToRun += 1\\n processedIndexSet.add(str(origIndex))\\n if 'DEBUG' in g_params and g_params['DEBUG']:\\n webcom.loginfo(\\\"DEBUG: jobid %s processedIndexSet.add(str(%d))\\\\n\\\"%(jobid, origIndex), gen_logfile)\\n # update cntSubmitJobDict for this node\\n cntSubmitJobDict[node][0] = cnt\\n\\n # finally, append submitted_loginfo_list to remotequeue_idx_file \\n if 'DEBUG' in g_params and g_params['DEBUG']:\\n webcom.loginfo(\\\"DEBUG: len(submitted_loginfo_list)=%d\\\\n\\\"%(len(submitted_loginfo_list)), gen_logfile)\\n if len(submitted_loginfo_list)>0:\\n myfunc.WriteFile(\\\"\\\\n\\\".join(submitted_loginfo_list)+\\\"\\\\n\\\", remotequeue_idx_file, \\\"a\\\", True)\\n # update torun_idx_file\\n newToRunIndexList = []\\n for idx in toRunIndexList:\\n if not idx in processedIndexSet:\\n newToRunIndexList.append(idx)\\n if 'DEBUG' in g_params and g_params['DEBUG']:\\n webcom.loginfo(\\\"DEBUG: jobid %s, newToRunIndexList=\\\"%(jobid) + \\\" \\\".join( newToRunIndexList), gen_logfile)\\n\\n if len(newToRunIndexList)>0:\\n myfunc.WriteFile(\\\"\\\\n\\\".join(newToRunIndexList)+\\\"\\\\n\\\", torun_idx_file, \\\"w\\\", True)\\n else:\\n myfunc.WriteFile(\\\"\\\", torun_idx_file, \\\"w\\\", True)\\n\\n return 0\",\n \"def _create_job_spec(\\n self,\\n job_id: Text,\\n training_input: Dict[Text, Any],\\n job_labels: Optional[Dict[Text, Text]] = None) -> Dict[Text, Any]:\\n pass\",\n \"def write_dag_script(s):\\n assert len(s.jobs) in (1,2),'ERROR: write_dag_script should be called from the final merge JobSet'\\n s.dag = os.path.join( s.jobs[0].submitdir, 'global.dag')\\n f = open(s.dag,'w')\\n # condor submit scripts\\n for dep in s.get_deps():\\n print >>f,'Job %s %s'%(dep.jobname(),dep.condorfile)\\n for job in s.jobs:\\n print >>f,'Job %s %s'%(job.jobname(),job.condorfile)\\n # retry instructions\\n for dep in s.get_deps():\\n print >>f,'Retry %s %s'%(dep.jobname(),NRETRY)\\n for job in s.jobs:\\n print >>f,'Retry %s %s'%(job.jobname(),NRETRY)\\n a_parent = ' '.join( [ dep.jobname() for dep in s.get_deps() ] )\\n for job in s.jobs:\\n a_child = job.jobname()\\n print >>f,'PARENT %s CHILD %s'%(a_parent,a_child)\\n f.close()\",\n \"def submit(\\n command: str,\\n namespace: argparse.Namespace,\\n sge_dir: Path = Path(os.path.curdir),\\n job_name=\\\"\\\",\\n) -> int:\\n id_file = Path(\\\"sge.id\\\")\\n job_script = Path(\\\"sge_job\\\")\\n stop_script = Path(\\\"sge_stop\\\")\\n epilogue_script = Path(\\\"sge_epilogue\\\")\\n\\n # check if job is already running\\n if id_file.exists():\\n with open(id_file) as fptr:\\n job_id = int(fptr.read())\\n if job_id in get_jobs_in_queue():\\n LOGGER.error(\\n \\\"job seems to be in the queue already (with id %d)\\\",\\n job_id,\\n )\\n return -1\\n\\n # set up args for the job script\\n args = {\\n \\\"command\\\": command,\\n \\\"cpus\\\": namespace.cpus,\\n \\\"email\\\": namespace.email,\\n \\\"memory\\\": namespace.memory,\\n \\\"queues\\\": namespace.queues,\\n \\\"sge_dir\\\": sge_dir,\\n \\\"time\\\": namespace.time,\\n }\\n if job_name:\\n args[\\\"job_name\\\"] = job_name\\n\\n # create job script\\n LOGGER.debug(\\\"create job script\\\")\\n with open(job_script, \\\"w\\\") as fptr:\\n fptr.write(templates.get_template(\\\"sge_job.j2\\\").render(args=args))\\n job_script.chmod(0o755)\\n LOGGER.debug(\\\"done\\\")\\n\\n # submit job\\n LOGGER.debug(\\\"submit job\\\")\\n output = subprocess.check_output([\\\"qsub\\\", str(job_script)]).decode()\\n match = REGEX_QSUB.match(output)\\n if not match:\\n raise RuntimeError(\\\"Cannot parse job id from qsub command output\\\")\\n job_id = int(match.group(1))\\n LOGGER.debug(\\\"done\\\")\\n\\n LOGGER.debug(\\\"write id to file\\\")\\n with open(id_file, \\\"w\\\") as fptr:\\n fptr.write(str(job_id) + \\\"\\\\n\\\")\\n LOGGER.debug(\\\"done\\\")\\n\\n # write stop script\\n LOGGER.debug(\\\"create stop script\\\")\\n with open(stop_script, \\\"w\\\") as fptr:\\n fptr.write(templates.get_template(\\\"sge_stop.j2\\\").render(job_id=job_id))\\n stop_script.chmod(0o755)\\n LOGGER.debug(\\\"done\\\")\\n\\n # write epilogue script\\n LOGGER.debug(\\\"create epilogue script\\\")\\n with open(epilogue_script, \\\"w\\\") as fptr:\\n fptr.write(templates.get_template(\\\"sge_epilogue.j2\\\").render(job_id=job_id))\\n epilogue_script.chmod(0o755)\\n LOGGER.debug(\\\"done\\\")\\n\\n return job_id\",\n \"def create_script(sh_file, cmds, max_workers, num_nodes=1):\\n output = os.path.dirname(sh_file)\\n job_name = os.path.splitext(os.path.basename(sh_file))[0]\\n err_file = os.path.join(output,\\\"{0}.error\\\".format(job_name))\\n complete_file = os.path.join(output, \\\"{0}.complete\\\".format(job_name))\\n with open(sh_file, 'w') as of:\\n of.write(\\\"#!/bin/bash\\\\n\\\")\\n of.write(\\\"#PBS -N {0}\\\\n\\\".format(job_name))\\n of.write(\\\"#PBS -l nodes={0}:ppn={1}\\\\n\\\".format(num_nodes,max_workers))\\n of.write(\\\"#PBS -l walltime=2:30:00\\\\n\\\")\\n of.write(\\\"#PBS -l vmem=8g\\\\n\\\")\\n of.write(\\\"#PBS -j eo\\\\n\\\")\\n of.write(\\\"#PBS Join_Path={0}\\\\n\\\".format(os.path.join(output,\\\"%s.err\\\"%job_name)))\\n of.write(\\\"module load samtools/1.9\\\\n\\\")\\n of.write(\\\"module load bedtools/2.27.1\\\\n\\\")\\n of.write(\\\"{0}\\\\n\\\".format(cmds[0]))\\n of.write(\\\"if [ $? -ne 0 ]; then \\\\n\\\\ttouch {0};exit 1 \\\\nfi\\\\n\\\".format(err_file))\\n of.write(\\\"{0}\\\\n\\\".format(cmds[1]))\\n of.write(\\\"if [ $? -ne 0 ]; then \\\\n\\\\ttouch {0}\\\\nelse\\\\n\\\\ttouch {1} \\\\nfi\\\\n\\\".format(err_file, complete_file))\\n os.system(\\\"chmod 755 %s\\\" % sh_file)\",\n \"def _jobfile(self):\\n job = self.job.format(fnum=self.fnum)\\n with open(job, 'w') as f:\\n f.write('#!/bin/sh\\\\n' + self.phast_cmmd + self.cleanup_cmmd)\",\n \"def generateReleaseRunBB(self, job):\\n pass\",\n \"def make_submission(commit: CommitDetails):\\n\\n submitter, commit_id = commit.submitter, commit.commit_id\\n\\n subprocess.run(f\\\"git checkout --force {commit_id}\\\", shell=True, stdout=subprocess.DEVNULL,\\n stderr=subprocess.DEVNULL, cwd=f\\\"{SUBS_DIR}/{submitter}/{ASSIGNMENT}\\\", check=True)\\n\\n submission_path = os.path.realpath(f\\\"{SUBS_DIR}/{submitter}/{ASSIGNMENT}\\\")\\n subprocess.run(f\\\"./add_sub_manually.sh {submission_path}\\\", shell=True, check=False)\",\n \"def getSubmissionCommandPrefix(options, cleanupFile=None):\\n submitData={}\\n #priority\\n priority=options.priority\\n if cleanupFile is not None:\\n priority = min(0,priority)\\n else:\\n priority = min(-1,priority-10)\\n submitData['priority']=str(priority)\\n submitData['interpreter']=PYTHON\\n\\n # job name and output files\\n if cleanupFile is not None:\\n submitData['jobName']=\\\"%s.cleanup\\\" % (options.jobName)\\n submitData['output']=\\\"%s.cleanup.out\\\" % (cleanupFile)\\n submitData['error']=\\\"%s.cleanup.err\\\" % (cleanupFile)\\n submitData['waitfor']=options.jobName if options.queue != SLURM else options.jobid\\n else:\\n submitData['jobName']=options.jobName\\n submitData['output']=\\\"%s%stasks.out\\\" %(options.tmpDir, os.sep)\\n submitData['error']=\\\"%s%stasks.err\\\" % (options.tmpDir, os.sep)\\n if isinstance(options.splits,int):\\n submitData['tasks'] = \\\"1-%d\\\" % options.splits\\n else:\\n if options.splits[0]==options.splits[1]:\\n submitData['tasks']=str(options.splits[0])\\n else:\\n submitData['tasks']=\\\"%d-%d\\\" % tuple(options.splits)\\n if options.throttle>0:\\n submitData['throttle']=str(options.throttle)\\n if options.wait:\\n submitData['sync']=True\\n if isinstance(options.sgeOptions,str):\\n submitData['options']=shlex.split(options.sgeOptions)\\n else:\\n submitData['options']=options.sgeOptions\\n\\n if options.queue==SGE:\\n return getSGECommandPrefix(submitData)\\n else:\\n return getSLURMCommandPrefix(submitData)\",\n \"def submit_scripts(self, out):\\n program_folder = os.path.join(out, self.out)\\n for config in self.configurations:\\n config.submit_script(program_folder)\\n return None\",\n \"def main(self):\\n self.jamf_url = self.env.get(\\\"JSS_URL\\\")\\n self.jamf_user = self.env.get(\\\"API_USERNAME\\\")\\n self.jamf_password = self.env.get(\\\"API_PASSWORD\\\")\\n self.script_path = self.env.get(\\\"script_path\\\")\\n self.script_name = self.env.get(\\\"script_name\\\")\\n self.script_category = self.env.get(\\\"script_category\\\")\\n self.script_priority = self.env.get(\\\"script_priority\\\")\\n self.osrequirements = self.env.get(\\\"osrequirements\\\")\\n self.script_info = self.env.get(\\\"script_info\\\")\\n self.script_notes = self.env.get(\\\"script_notes\\\")\\n self.script_parameter4 = self.env.get(\\\"script_parameter4\\\")\\n self.script_parameter5 = self.env.get(\\\"script_parameter5\\\")\\n self.script_parameter6 = self.env.get(\\\"script_parameter6\\\")\\n self.script_parameter7 = self.env.get(\\\"script_parameter7\\\")\\n self.script_parameter8 = self.env.get(\\\"script_parameter8\\\")\\n self.script_parameter9 = self.env.get(\\\"script_parameter9\\\")\\n self.script_parameter10 = self.env.get(\\\"script_parameter10\\\")\\n self.script_parameter11 = self.env.get(\\\"script_parameter11\\\")\\n self.replace = self.env.get(\\\"replace_script\\\")\\n self.sleep = self.env.get(\\\"sleep\\\")\\n # handle setting replace in overrides\\n if not self.replace or self.replace == \\\"False\\\":\\n self.replace = False\\n\\n # clear any pre-existing summary result\\n if \\\"jamfscriptuploader_summary_result\\\" in self.env:\\n del self.env[\\\"jamfscriptuploader_summary_result\\\"]\\n script_uploaded = False\\n\\n # obtain the relevant credentials\\n token = self.handle_uapi_auth(self.jamf_url, self.jamf_user, self.jamf_password)\\n\\n # get the id for a category if supplied\\n if self.script_category:\\n self.output(\\\"Checking categories for {}\\\".format(self.script_category))\\n\\n # check for existing category - requires obj_name\\n obj_type = \\\"category\\\"\\n obj_name = self.script_category\\n category_id = self.get_uapi_obj_id_from_name(\\n self.jamf_url,\\n obj_type,\\n obj_name,\\n token,\\n )\\n\\n if not category_id:\\n self.output(\\\"WARNING: Category not found!\\\")\\n category_id = \\\"-1\\\"\\n else:\\n self.output(\\n \\\"Category {} found: ID={}\\\".format(self.script_category, category_id)\\n )\\n else:\\n self.script_category = \\\"\\\"\\n category_id = \\\"-1\\\"\\n\\n # handle files with a relative path\\n if not self.script_path.startswith(\\\"/\\\"):\\n found_template = self.get_path_to_file(self.script_path)\\n if found_template:\\n self.script_path = found_template\\n else:\\n raise ProcessorError(f\\\"ERROR: Script file {self.script_path} not found\\\")\\n\\n # now start the process of uploading the object\\n if not self.script_name:\\n self.script_name = os.path.basename(self.script_path)\\n\\n # check for existing script\\n self.output(\\n \\\"Checking for existing '{}' on {}\\\".format(self.script_name, self.jamf_url)\\n )\\n self.output(\\n \\\"Full path: {}\\\".format(self.script_path),\\n verbose_level=2,\\n )\\n obj_type = \\\"script\\\"\\n obj_name = self.script_name\\n obj_id = self.get_uapi_obj_id_from_name(\\n self.jamf_url,\\n obj_type,\\n obj_name,\\n token,\\n )\\n\\n if obj_id:\\n self.output(\\n \\\"Script '{}' already exists: ID {}\\\".format(self.script_name, obj_id)\\n )\\n if self.replace:\\n self.output(\\n \\\"Replacing existing script as 'replace_script' is set to {}\\\".format(\\n self.replace\\n ),\\n verbose_level=1,\\n )\\n else:\\n self.output(\\n \\\"Not replacing existing script. Use replace_script='True' to enforce.\\\",\\n verbose_level=1,\\n )\\n return\\n\\n # post the script\\n self.upload_script(\\n self.jamf_url,\\n self.script_name,\\n self.script_path,\\n category_id,\\n self.script_category,\\n self.script_info,\\n self.script_notes,\\n self.script_priority,\\n self.script_parameter4,\\n self.script_parameter5,\\n self.script_parameter6,\\n self.script_parameter7,\\n self.script_parameter8,\\n self.script_parameter9,\\n self.script_parameter10,\\n self.script_parameter11,\\n self.osrequirements,\\n token,\\n obj_id,\\n )\\n script_uploaded = True\\n\\n # output the summary\\n self.env[\\\"script_name\\\"] = self.script_name\\n self.env[\\\"script_uploaded\\\"] = script_uploaded\\n if script_uploaded:\\n self.env[\\\"jamfscriptuploader_summary_result\\\"] = {\\n \\\"summary_text\\\": \\\"The following scripts were created or updated in Jamf Pro:\\\",\\n \\\"report_fields\\\": [\\n \\\"script\\\",\\n \\\"path\\\",\\n \\\"category\\\",\\n \\\"priority\\\",\\n \\\"os_req\\\",\\n \\\"info\\\",\\n \\\"notes\\\",\\n \\\"P4\\\",\\n \\\"P5\\\",\\n \\\"P6\\\",\\n \\\"P7\\\",\\n \\\"P8\\\",\\n \\\"P9\\\",\\n \\\"P10\\\",\\n \\\"P11\\\",\\n ],\\n \\\"data\\\": {\\n \\\"script\\\": self.script_name,\\n \\\"path\\\": self.script_path,\\n \\\"category\\\": self.script_category,\\n \\\"priority\\\": str(self.script_priority),\\n \\\"info\\\": self.script_info,\\n \\\"os_req\\\": self.osrequirements,\\n \\\"notes\\\": self.script_notes,\\n \\\"P4\\\": self.script_parameter4,\\n \\\"P5\\\": self.script_parameter5,\\n \\\"P6\\\": self.script_parameter6,\\n \\\"P7\\\": self.script_parameter7,\\n \\\"P8\\\": self.script_parameter8,\\n \\\"P9\\\": self.script_parameter9,\\n \\\"P10\\\": self.script_parameter10,\\n \\\"P11\\\": self.script_parameter11,\\n },\\n }\",\n \"def _submit_to_queue(self, script_file):\\n if sys.version_info[0] < 3:\\n process = Popen(['qsub', script_file], stdout=PIPE, stderr=PIPE)\\n else:\\n # need string not bytes so must use universal_newlines\\n process = Popen(['qsub', script_file], stdout=PIPE, stderr=PIPE, universal_newlines=True)\\n\\n out, err = process.communicate()\\n # grab the return code. PBS returns 0 if the job was successful\\n queue_id = None\\n if process.returncode == 0:\\n try:\\n # output should of the form '2561553.sdb' or '352353.jessup' - just grab the first part for job id\\n queue_id = int(out.split('.')[0])\\n except:\\n # probably error parsing job code\\n logger.critical(\\\"Could not parse job id following qsub...\\\")\\n return SubmitResults(qid=queue_id, out=out, err=err, process=process)\",\n \"def run( **kwargs ):\\n\\n # combine options using correct preference\\n options = dict(PARAMS.items())\\n options.update( getCallerLocals().items() )\\n options.update( kwargs.items() )\\n\\n def setupJob( session ):\\n\\n jt = session.createJobTemplate()\\n jt.workingDirectory = os.getcwd()\\n jt.jobEnvironment = { 'BASH_ENV' : '~/.bashrc' }\\n jt.args = []\\n jt.nativeSpecification = \\\"-V -q %s -p %i -N %s %s\\\" % \\\\\\n (options.get(\\\"job_queue\\\", global_options.cluster_queue ),\\n options.get(\\\"job_priority\\\", global_options.cluster_priority ),\\n \\\"_\\\" + re.sub( \\\"[:]\\\", \\\"_\\\", os.path.basename(options.get(\\\"outfile\\\", \\\"ruffus\\\" ))),\\n options.get(\\\"job_options\\\", global_options.cluster_options))\\n\\n # keep stdout and stderr separate\\n jt.joinFiles=False\\n\\n return jt\\n\\n shellfile = os.path.join( os.getcwd(), \\\"shell.log\\\" )\\n \\n # run multiple jobs\\n if options.get( \\\"statements\\\" ):\\n\\n statement_list = []\\n for statement in options.get(\\\"statements\\\"): \\n options[\\\"statement\\\"] = statement\\n statement_list.append(buildStatement( **options))\\n \\n if options.get( \\\"dryrun\\\", False ): return\\n\\n # get session for process - only one is permitted\\n pid = os.getpid()\\n if pid not in global_sessions: \\n\\n L.debug( \\\"creating new drmaa session for pid %i\\\" % pid )\\n global_sessions[pid]=drmaa.Session() \\n global_sessions[pid].initialize()\\n\\n session = global_sessions[pid]\\n \\n jt = setupJob( session )\\n \\n jobids, filenames = [], []\\n for statement in statement_list:\\n # create job script\\n tmpfile = tempfile.NamedTemporaryFile( dir = os.getcwd() , delete = False )\\n tmpfile.write( \\\"#!/bin/bash\\\\n\\\" ) # -l -O expand_aliases\\\\n\\\" )\\n tmpfile.write( 'echo \\\"START--------------------------------\\\" >> %s \\\\n' % shellfile )\\n tmpfile.write( '''echo 'statement=%s' >> %s\\\\n''' % (statement, shellfile) )\\n tmpfile.write( \\\"set &>> %s\\\\n\\\" % shellfile)\\n tmpfile.write( \\\"module list &>> %s\\\\n\\\" % shellfile )\\n tmpfile.write( 'echo \\\"END----------------------------------\\\" >> %s \\\\n' % shellfile )\\n tmpfile.write( expandStatement(statement) + \\\"\\\\n\\\" )\\n tmpfile.close()\\n\\n # build paths\\n job_path = os.path.abspath( tmpfile.name )\\n stdout_path = job_path + \\\".stdout\\\" \\n stderr_path = job_path + \\\".stderr\\\" \\n\\n jt.remoteCommand = job_path\\n jt.outputPath=\\\":\\\"+ stdout_path\\n jt.errorPath=\\\":\\\" + stderr_path\\n\\n os.chmod( job_path, stat.S_IRWXG | stat.S_IRWXU )\\n\\n jobid = session.runJob(jt)\\n jobids.append( jobid )\\n filenames.append( (job_path, stdout_path, stderr_path) )\\n\\n L.debug( \\\"job has been submitted with jobid %s\\\" % str(jobid ))\\n \\n L.debug( \\\"waiting for %i jobs to finish \\\" % len(jobids) )\\n session.synchronize(jobids, drmaa.Session.TIMEOUT_WAIT_FOREVER, False)\\n \\n # collect and clean up\\n for jobid, statement, paths in zip( jobids, statement_list, filenames) :\\n job_path, stdout_path, stderr_path = paths\\n retval = session.wait(jobid, drmaa.Session.TIMEOUT_WAIT_FOREVER)\\n\\n stdout, stderr = getStdoutStderr( stdout_path, stderr_path )\\n\\n if retval.exitStatus != 0:\\n raise PipelineError( \\\"---------------------------------------\\\\n\\\"\\n \\\"Child was terminated by signal %i: \\\\n\\\"\\n \\\"The stderr was: \\\\n%s\\\\n%s\\\\n\\\" \\n \\\"---------------------------------------\\\\n\\\" % \\\\\\n (retval.exitStatus, \\n \\\"\\\".join( stderr),\\n statement ) )\\n\\n os.unlink( job_path )\\n \\n session.deleteJobTemplate(jt)\\n\\n # run a single parallel job\\n elif (options.get( \\\"job_queue\\\" ) or options.get( \\\"to_cluster\\\" )) and not global_options.without_cluster:\\n\\n statement = buildStatement( **options )\\n\\n if options.get( \\\"dryrun\\\", False ): return\\n\\n tmpfile = tempfile.NamedTemporaryFile( dir = os.getcwd() , delete = False )\\n tmpfile.write( \\\"#!/bin/bash\\\\n\\\" ) # -l -O expand_aliases\\\\n\\\" )\\n\\n tmpfile.write( 'echo \\\"START--------------------------------\\\" >> %s \\\\n' % shellfile )\\n tmpfile.write( 'echo \\\"statement=%s\\\" >> %s\\\\n' % (statement, shellfile) )\\n tmpfile.write( \\\"set &>> %s\\\\n\\\" % shellfile)\\n tmpfile.write( \\\"module list &>> %s\\\\n\\\" % shellfile )\\n tmpfile.write( 'echo \\\"END----------------------------------\\\" >> %s \\\\n' % shellfile )\\n tmpfile.write( expandStatement( statement ) + \\\"\\\\n\\\" )\\n tmpfile.close()\\n\\n job_path = os.path.abspath( tmpfile.name )\\n stdout_path = job_path + \\\".stdout\\\" \\n stderr_path = job_path + \\\".stderr\\\" \\n\\n os.chmod( job_path, stat.S_IRWXG | stat.S_IRWXU )\\n\\n # get session for process - only one is permitted\\n pid = os.getpid()\\n if pid not in global_sessions:\\n L.debug( \\\"creating new drmaa session for pid %i\\\" % pid )\\n global_sessions[pid]=drmaa.Session() \\n global_sessions[pid].initialize()\\n\\n session = global_sessions[pid]\\n\\n jt = setupJob( session )\\n\\n jt.remoteCommand = job_path\\n # later: allow redirection of stdout and stderr to files; can even be across hosts?\\n jt.outputPath=\\\":\\\"+ stdout_path\\n jt.errorPath=\\\":\\\" + stderr_path\\n\\n if \\\"job_array\\\" in options and options[\\\"job_array\\\"] != None:\\n # run an array job\\n start, end, increment = options.get(\\\"job_array\\\" )\\n L.debug(\\\"starting an array job: %i-%i,%i\\\" % (start, end, increment ))\\n # sge works with 1-based, closed intervals\\n jobids = session.runBulkJobs( jt, start+1, end, increment )\\n L.debug( \\\"%i array jobs have been submitted as jobid %s\\\" % (len(jobids), jobids[0]) )\\n retval = session.synchronize(jobids, drmaa.Session.TIMEOUT_WAIT_FOREVER, True)\\n else:\\n jobid = session.runJob(jt)\\n L.debug( \\\"job has been submitted with jobid %s\\\" % str(jobid ))\\n try:\\n retval = session.wait(jobid, drmaa.Session.TIMEOUT_WAIT_FOREVER)\\n except Exception, msg:\\n # ignore message 24 in PBS\\n # code 24: drmaa: Job finished but resource usage information and/or termination status could not be provided.\\\":\\n if not msg.message.startswith(\\\"code 24\\\"): raise\\n retval = None\\n\\n stdout, stderr = getStdoutStderr( stdout_path, stderr_path )\\n\\n if \\\"job_array\\\" not in options:\\n if retval and retval.exitStatus != 0:\\n raise PipelineError( \\\"---------------------------------------\\\\n\\\"\\n \\\"Child was terminated by signal %i: \\\\n\\\"\\n \\\"The stderr was: \\\\n%s\\\\n%s\\\\n\\\"\\n \\\"-----------------------------------------\\\" % \\\\\\n (retval.exitStatus, \\n \\\"\\\".join( stderr), statement))\\n \\n session.deleteJobTemplate(jt)\\n os.unlink( job_path )\\n\\n else:\\n statement = buildStatement( **options )\\n\\n if options.get( \\\"dryrun\\\", False ): return\\n \\n if \\\"<(\\\" in statement:\\n if \\\"'\\\" in statement: raise ValueError( \\\"advanced bash syntax combined with single quotes\\\" )\\n statement = \\\"\\\"\\\"/bin/bash -c '%s'\\\"\\\"\\\" % statement\\n\\n process = subprocess.Popen( expandStatement( statement ),\\n cwd = os.getcwd(), \\n shell = True,\\n stdin = subprocess.PIPE,\\n stdout = subprocess.PIPE,\\n stderr = subprocess.PIPE )\\n\\n # process.stdin.close()\\n stdout, stderr = process.communicate()\\n\\n if process.returncode != 0:\\n raise PipelineError( \\\"---------------------------------------\\\\n\\\"\\n \\\"Child was terminated by signal %i: \\\\n\\\"\\n \\\"The stderr was: \\\\n%s\\\\n%s\\\\n\\\"\\n \\\"-----------------------------------------\\\" % \\\\\\n (-process.returncode, stderr, statement ))\",\n \"def submit(fragment,njobs,nevts,outdir=\\\"\\\",first=None,indices=None,logdir=\\\"\\\",tag=\\\"\\\",dry=False,slc6=False,verb=0):\\n print(\\\">>> Submitting...\\\")\\n indir = os.path.dirname(fragment) or '.'\\n fullfrag = os.path.abspath(fragment)\\n ensuredir(os.path.join(indir,logdir)) # log directory\\n ensuredir(outdir) # ensure output directory exists before submitting\\n #args = f\\\"{outdir} {fullfrag} maxevts={nevts} index=$(ProcId) seed=$$([$(ProcId)+1])\\\" # start from 0\\n args = f\\\"{outdir} {fullfrag} maxevts={nevts} index=$$([$(ProcId)+1]) seed=$$([$(ProcId)+1])\\\" # start from 1\\n if tag:\\n args += f\\\" tag={tag}\\\"\\n if indices:\\n indices_ = [ ]\\n for index in indices:\\n if isinstance(index,str) and index.count(':')==1:\\n start, end = index.split(':') # e.g. '1:4' = [1, 2, 3, 4]\\n for i in range(int(start),int(end)+1):\\n indices_.append(i)\\n else:\\n indices_.append(int(index))\\n args = args.replace('$(ProcId)','$(i)')\\n queue = f\\\"-queue i in {', '.join(str(i) for i in indices_)}\\\"\\n #queue = f\\\"-a 'queue i from ( {', '.join(str(i) for i in indices_)} )'\\\"\\n elif first:\\n args = args.replace('$(ProcId)','$(i)')\\n queue = f\\\"-queue i from seq {first} {first+njobs-1} \\\\|\\\"\\n #queue = f\\\"-a 'queue from seq {first} {njobs}|'\\\"\\n else:\\n queue = f\\\"-queue {njobs}\\\"\\n name = f\\\"{os.path.basename(fragment).replace('.py','')}\\\"\\n log = os.path.join(logdir,f\\\"submit_fragment{tag}.$(ClusterId).$(ProcId).log\\\")\\n subcmd = f\\\"condor_submit submit_fragment.sub -a 'initialdir={indir}' -a 'mylogfile={log}'\\\"\\n subcmd += f\\\" -a 'arguments={args}'\\\" # -a 'should_transfer_files=no'\\n subcmd += f\\\" -batch-name {name} {queue}\\\" #-queue '{queue}'\\n if slc6:\\n subcmd += f\\\" -a 'requirements = (OpSysAndVer =?= \\\\\\\"SLCern6\\\\\\\")'\\\"\\n if verb>=4:\\n subcmd += \\\" -verbose\\\"\\n print(\\\">>> \\\"+subcmd)\\n if not dry:\\n os.system(subcmd)\",\n \"def __create_jobs_bin__(self):\\n # | - __create_jobs_bin__\\n folder_dir = os.path.join(self.root_dir, self.working_dir, \\\"jobs_bin\\\")\\n # folder_dir = self.root_dir + \\\"/jobs_bin\\\"\\n\\n if not os.path.exists(folder_dir):\\n # print(\\\"KDJFDI__\\\")\\n # print(folder_dir)\\n os.makedirs(folder_dir)\\n # __|\",\n \"def generate_job(self, additional_settings_dict):\\n\\n self.final_job = additional_settings_dict\\n\\n header = (\\n \\\"# Measurement file: \\\\n \\\"\\n \\\"# Project: \\\" + self.variables[\\\"Current_project\\\"] + \\\"\\\\n \\\"\\n \\\"# Sensor Type: \\\" + self.variables[\\\"Current_sensor\\\"] + \\\"\\\\n \\\"\\n \\\"# ID: \\\" + self.variables[\\\"Current_filename\\\"] + \\\"\\\\n \\\"\\n \\\"# Operator: \\\" + self.variables[\\\"Current_operator\\\"] + \\\"\\\\n \\\"\\n \\\"# Date: \\\" + str(time.asctime()) + \\\"\\\\n\\\\n\\\"\\n )\\n\\n IVCV_dict = self.generate_IVCV(\\\"\\\") # here additional header can be added\\n strip_dict = self.generate_strip(\\\"\\\")\\n\\n if IVCV_dict:\\n self.final_job.update({\\\"IVCV\\\": IVCV_dict})\\n if strip_dict:\\n self.final_job.update({\\\"stripscan\\\": strip_dict})\\n\\n # Check if filepath is a valid path\\n if self.variables[\\\"Current_filename\\\"] and os.path.isdir(\\n self.variables[\\\"Current_directory\\\"]\\n ):\\n self.final_job.update({\\\"Header\\\": header})\\n self.queue_to_measure.put({\\\"Measurement\\\": self.final_job})\\n self.log.info(\\\"Sendet job: \\\" + str({\\\"Measurement\\\": self.final_job}))\\n else:\\n self.log.error(\\n \\\"Please enter a valid path and name for the measurement file.\\\"\\n )\",\n \"def create_job_ymls(job_template_args, job_template_dir, job_template_name, output_dir, upload=True):\\n for i, template_args in enumerate(job_template_args):\\n template_loader = jinja2.FileSystemLoader(searchpath=job_template_dir)\\n template_env = jinja2.Environment(loader=template_loader)\\n template_file = job_template_name\\n template = template_env.get_template(template_file)\\n output_text = template.render(template_args)\\n job_yml = 'vista-job-{}.yml'.format(i)\\n job_yml_path = join(output_dir, job_yml)\\n \\n if not exists(output_dir):\\n mkdir(output_dir)\\n with open(job_yml_path, 'w') as output_template:\\n output_template.write(output_text)\\n if upload:\\n upload_blob(UPLOAD_BUCKET, job_yml_path, job_yml)\\n print(job_yml, 'uploaded')\",\n \"def submit_step(root_dir, config, step):\\r\\n\\r\\n premable = \\\"\\\"\\\"\\r\\n export PATH=~/project-pasaniuc/software/anaconda3/bin:$PATH\\r\\n export PYTHONNOUSERSITE=True\\r\\n \\\"\\\"\\\"\\r\\n\\r\\n assert(step in ['mean_std', 'beta', 'phe_g', 'phe', 'zsc'])\\r\\n script ='python gwas_sim.py --step {step} --root_dir {root_dir}'.format(step=step, root_dir=root_dir)\\r\\n \\r\\n if step in ['mean_std', 'phe_g', 'zsc']:\\r\\n script += ' --part_i $((SGE_TASK_ID - 1))'\\r\\n job_num = config['num_part']\\r\\n else:\\r\\n job_num = 1\\r\\n print(script)\\r\\n \\r\\n if step == 'mean_std':\\r\\n memory = '4G'\\r\\n duration='00:20:00'\\r\\n elif step == 'beta':\\r\\n memory = '4G'\\r\\n duration='00:05:00'\\r\\n elif step == 'phe_g' or step == 'zsc':\\r\\n memory = '6G'\\r\\n duration='01:00:00'\\r\\n elif step == 'phe':\\r\\n memory = '6G'\\r\\n duration='00:10:00'\\r\\n else:\\r\\n assert(False)\\r\\n \\r\\n submit_script(script=script, \\r\\n premable=premable,\\r\\n jobnum=job_num,\\r\\n name=step,\\r\\n memory=memory,\\r\\n duration=duration)\",\n \"async def create_job(response: Response,\\n request: Request,\\n job: Job = Body(\\n ...,\\n example={\\n \\\"id_video\\\": \\\"bbb_0.mp4\\\",\\n \\\"bitrate\\\": 7000,\\n \\\"speed\\\": \\\"ultrafast\\\",\\n },\\n )\\n ): \\n \\n\\n # get an ID and return to client\\n id_job = mngr.getID()\\n logger.debug(\\\"got id_job %s\\\" %id_job)\\n resp = [\\\"http:/\\\"]\\n resp.append(request.headers['host'])\\n resp.append(id_job)\\n response.headers[\\\"Location\\\"] = \\\"/\\\".join(resp)\\n\\n # create the task\\n mngr.newJob(id_job, \\n job.id_video, \\n job.bitrate, \\n job.speed)\\n\\n return id_job\",\n \"def _create_job_spec(\\n self,\\n job_id: Text,\\n training_input: Dict[Text, Any],\\n job_labels: Optional[Dict[Text, Text]] = None) -> Dict[Text, Any]:\\n\\n job_spec = {\\n 'display_name': job_id,\\n 'job_spec': training_input,\\n 'labels': job_labels,\\n }\\n return job_spec\",\n \"def createJob(appName, appUID):\\n logger.debug('[FLASKWEB /jobs/<appName>/<appUID>] Job Request for %s' % appName)\\n global dispatcher\\n applist = [a['name'] for a in db.getAllApps()]\\n if appName in applist:\\n if request.method == 'POST':\\n logger.debug(\\\"POST Request for a new job\\\")\\n # TODO: Get user\\n file = request.files['file']\\n text = request.form['text'] if 'text' in request.form else None\\n k3logging = True if 'logging' in request.form else False\\n jsonlog = True if 'jsonlog' in request.form else False\\n jsonfinal = True if 'jsonfinal' in request.form else False\\n perf_profile = True if 'perf_profile' in request.form else False\\n perf_frequency = request.form['perf_frequency'] if 'perf_frequency' in request.form else ''\\n cmd_prefix = request.form['cmd_prefix'] if 'cmd_prefix' in request.form else None\\n cmd_infix = request.form['cmd_infix'] if 'cmd_infix' in request.form else None\\n cmd_suffix = request.form['cmd_suffix'] if 'cmd_suffix' in request.form else None\\n core_dump = True if 'core_dump' in request.form else False\\n stdout = request.form['stdout'] if 'stdout' in request.form else False\\n user = request.form['user'] if 'user' in request.form else 'anonymous'\\n tag = request.form['tag'] if 'tag' in request.form else ''\\n\\n # User handling: jsonfinal is a qualifier flag for the json logging flag\\n if jsonfinal and not jsonlog:\\n jsonlog = True\\n\\n logger.debug(\\\"K3 LOGGING is : %s\\\" % (\\\"ON\\\" if k3logging else \\\"OFF\\\"))\\n logger.debug(\\\"JSON LOGGING is : %s\\\" % (\\\"ON\\\" if jsonlog else \\\"OFF\\\"))\\n logger.debug(\\\"JSON FINAL LOGGING is : %s\\\" % (\\\"ON\\\" if jsonfinal else \\\"OFF\\\"))\\n logger.debug(\\\"PERF PROFILING is : %s\\\" % (\\\"ON\\\" if perf_profile else \\\"OFF\\\"))\\n logger.debug(\\\"CORE DUMPS are : %s\\\" % (\\\"ON\\\" if core_dump else \\\"OFF\\\"))\\n # trials = int(request.form['trials']) if 'trials' in request.form else 1\\n\\n # Check for valid submission\\n if not file and not text:\\n logger.error('Error. Cannot create job: No input file and no YAML for enviornment configuration provided. ')\\n return render_template(\\\"error.html\\\", code=404, message=\\\"Invalid job request\\\")\\n\\n # Post new job request, get job ID & submit time\\n thisjob = dict(appName=appName, appUID=appUID, user=user, tag=tag)\\n jobId, time = db.insertJob(thisjob)\\n thisjob = dict(jobId=jobId, time=time)\\n\\n # Save yaml to file (either from file or text input)\\n path = os.path.join(webapp.config['UPLOADED_JOBS_DEST'], appName)\\n if not os.path.exists(path):\\n os.mkdir(path)\\n\\n path = os.path.join(path, str(jobId))\\n filename = 'role.yaml'\\n if not os.path.exists(path):\\n os.mkdir(path)\\n\\n if file:\\n file.save(os.path.join(path, filename))\\n else:\\n with open(os.path.join(path, filename), 'w') as file:\\n file.write(text)\\n\\n # Create new Mesos K3 Job\\n apploc = webapp.config['ADDR']+os.path.join(webapp.config['UPLOADED_APPS_URL'], appName, appUID, appName)\\n\\n newJob = Job(binary=apploc, appName=appName, jobId=jobId,\\n rolefile=os.path.join(path, filename), logging=k3logging,\\n jsonlog=jsonlog, jsonfinal=jsonfinal,\\n perf_profile=perf_profile, perf_frequency=perf_frequency, core_dump=core_dump,\\n cmd_prefix=cmd_prefix, cmd_infix=cmd_infix, cmd_suffix=cmd_suffix)\\n\\n # Submit to Mesos\\n dispatcher.submit(newJob)\\n\\n thisjob = dict(thisjob, url=dispatcher.getSandboxURL(jobId), status='SUBMITTED')\\n\\n if 'application/json' in request.headers['Accept']:\\n return jsonify(thisjob), 202\\n else:\\n return render_template('last.html', appName=appName, lastjob=thisjob)\\n\\n elif request.method == 'GET':\\n jobs = db.getJobs(appName=appName)\\n if 'application/json' in request.headers['Accept']:\\n return jsonify(dict(jobs=jobs))\\n else:\\n preload = request.args['preload'] if 'preload' in request.args else 'Sample'\\n logger.debug(\\\"YAML file preload = %s\\\" % preload)\\n if preload == 'Instructions':\\n yamlFile = 'role_file_template.yaml'\\n elif preload == 'Last':\\n lastJobId = max([ d['jobId'] for d in jobs])\\n path = os.path.join(webapp.config['UPLOADED_JOBS_DEST'], appName, \\\"%d\\\" % lastJobId, 'role.yaml')\\n logger.debug(\\\" YAML PATH= %s\\\" % path)\\n if os.path.exists(path):\\n yamlFile = path\\n else:\\n yamlFile = None\\n else:\\n yamlFile = 'sample.yaml'\\n if yamlFile:\\n with open(yamlFile, 'r') as f:\\n sample = f.read()\\n else:\\n sample = \\\"(No YAML file to display)\\\"\\n return render_template(\\\"newjob.html\\\", name=appName, uid=appUID, sample=sample)\\n\\n else:\\n return returnError(\\\"There is no application, %s\\\" % appName, 404)\",\n \"def _submit_to_queue(self, script_file):\\n if sys.version_info[0] < 3:\\n process = Popen(['msub', script_file], stdout=PIPE, stderr=PIPE)\\n else:\\n # need string not bytes so must use universal_newlines\\n process = Popen(['msub', script_file], stdout=PIPE, stderr=PIPE, universal_newlines=True)\\n\\n out, err = process.communicate()\\n queue_id = None\\n if process.returncode == 0:\\n # grab the returncode. MOAB returns 0 if the job was successful\\n try:\\n # output should be the queue_id\\n queue_id = int(out.split()[0])\\n except:\\n # probably error parsing job code\\n logger.critical('Could not parse job id following msub...')\\n\\n return SubmitResults(qid=queue_id, out=out, err=err, process=process)\",\n \"def setup_file_generation_submission(cls, sess, submission_id=None):\\n submission_id = cls.generation_submission_id if not submission_id else submission_id\\n submission = sess.query(Submission).filter(Submission.submission_id == submission_id).one()\\n\\n # Create D1 jobs ready for generation route to be called\\n cls.insert_job(\\n sess,\\n FILE_TYPE_DICT['award_procurement'],\\n JOB_STATUS_DICT['ready'],\\n JOB_TYPE_DICT['file_upload'],\\n submission.submission_id\\n )\\n award_roc_val_job = cls.insert_job(\\n sess,\\n FILE_TYPE_DICT['award_procurement'],\\n JOB_STATUS_DICT['waiting'],\\n JOB_TYPE_DICT['csv_record_validation'],\\n submission.submission_id\\n )\\n # Create E and F jobs ready for check route\\n exec_comp_job = cls.insert_job(\\n sess,\\n FILE_TYPE_DICT['executive_compensation'],\\n JOB_STATUS_DICT['finished'],\\n JOB_TYPE_DICT['file_upload'],\\n submission.submission_id\\n )\\n sub_award_job = cls.insert_job(\\n sess,\\n FILE_TYPE_DICT['sub_award'],\\n JOB_STATUS_DICT['invalid'],\\n JOB_TYPE_DICT['file_upload'],\\n submission.submission_id\\n )\\n sub_award_job.error_message = \\\"File was invalid\\\"\\n\\n # Create D2 jobs\\n cls.insert_job(\\n sess,\\n FILE_TYPE_DICT['award'],\\n JOB_STATUS_DICT['finished'],\\n JOB_TYPE_DICT['file_upload'],\\n submission.submission_id\\n )\\n cls.insert_job(\\n sess,\\n FILE_TYPE_DICT['award'],\\n JOB_STATUS_DICT['invalid'],\\n JOB_TYPE_DICT['csv_record_validation'],\\n submission.submission_id\\n )\\n # Create dependency\\n exec_comp_dep = JobDependency(\\n job_id=exec_comp_job.job_id,\\n prerequisite_id=award_roc_val_job.job_id\\n )\\n sess.add(exec_comp_dep)\\n sess.commit()\",\n \"def write_submit_script(fp, content, data):\\n\\n for k, v in data.items():\\n placeholder = \\\"{\\\" + str(k).upper() + \\\"}\\\"\\n content = content.replace(placeholder, str(v))\\n\\n keys_left = re.findall(r\\\"!$\\\\{(.+?)\\\\}\\\", content)\\n if len(keys_left) > 0:\\n _LOGGER.warning(\\n \\\"> Warning: %d submission template variables are not \\\" \\\"populated: '%s'\\\",\\n len(keys_left),\\n str(keys_left),\\n )\\n\\n if not fp:\\n print(content)\\n return content\\n else:\\n outdir = os.path.dirname(fp)\\n if outdir and not os.path.isdir(outdir):\\n os.makedirs(outdir)\\n with open(fp, \\\"w\\\") as f:\\n f.write(content)\\n return fp\",\n \"def build_experiment(\\n experiment_name,\\n policy,\\n estimator,\\n dataset,\\n policy_params,\\n estimator_params,\\n dataset_params,\\n output_folder=\\\"./policy_yamls/\\\",\\n slurm_output=\\\"./outputs/\\\",\\n):\\n\\n policy_dict = build_obj_spec(\\n policy,\\n policy_params,\\n experiment_name=experiment_name,\\n obj_type=\\\"policy\\\",\\n output=output_folder,\\n )\\n estimator_dict = build_obj_spec(\\n estimator,\\n estimator_params,\\n experiment_name=experiment_name,\\n obj_type=\\\"estimator\\\",\\n output=output_folder,\\n )\\n dataset_dict = build_obj_spec(\\n dataset,\\n dataset_params,\\n experiment_name=experiment_name,\\n obj_type=\\\"dataset\\\",\\n output=output_folder,\\n )\\n\\n experiment_dir = os.path.join(output_folder, experiment_name)\\n slurm_output = os.path.join(slurm_output, experiment_name + \\\".out\\\")\\n slurm_script = SHELL_SCRIPT.format(slurm_output, experiment_dir)\\n\\n with open(os.path.join(experiment_dir, \\\"script.sbatch\\\"), \\\"w\\\") as file:\\n file.write(slurm_script)\",\n \"def build_run_script(path, script_name, save_history):\\n\\n # define paths\\n path = abspath(path)\\n job_script_path = join(path, 'scripts', 'run.sh')\\n\\n # copy run script to scripts directory\\n run_script = abspath(__file__).rsplit('/', maxsplit=2)[0]\\n run_script = join(run_script, 'scripts', script_name)\\n shutil.copy(run_script, join(path, 'scripts'))\\n\\n # declare outer script that reads PATH from file\\n job_script = open(job_script_path, 'w')\\n job_script.write('#!/bin/bash\\\\n')\\n\\n # move to job directory\\n job_script.write('cd {:s} \\\\n\\\\n'.format(path))\\n\\n # run each batch\\n job_script.write('echo \\\"Starting all batches at `date`\\\"\\\\n')\\n job_script.write('while read P; do\\\\n')\\n job_script.write('echo \\\"Processing batch ${P}\\\"\\\\n')\\n job_script.write('python ./scripts/{:s}'.format(script_name)+' ${P} ')\\n args = (save_history,)\\n job_script.write('-s {:d}\\\\n'.format(*args))\\n job_script.write('done < ./batches/index.txt \\\\n')\\n job_script.write('echo \\\"Job completed at `date`\\\"\\\\n')\\n job_script.write('exit\\\\n')\\n\\n # close the file\\n job_script.close()\\n\\n # change the permissions\\n chmod(job_script_path, 0o755)\",\n \"def create(cfg, jobs):\\n server = jenkins_utils.server_factory(cfg)\\n libjobs.createJobs(server, jobs)\",\n \"def create_compilation_job(CompilationJobName=None, RoleArn=None, InputConfig=None, OutputConfig=None, StoppingCondition=None):\\n pass\",\n \"def jobserver_job():\\n return _MakeJob()\",\n \"def prepare_ss_process_job(self, pool_type, pool_address, jobs, script_path, rank='0'):\\n ############\\n #The rest of the processing is moved to condor, by the file ss_results_process.py\\n ############\\n \\n #Prepare the condor job file\\n \\n input_file_string = ''\\n args_string = ''\\n for job in jobs:\\n input_file_string += job.job_output + ', '\\n args_string += job.job_output + ' '\\n input_file_string = input_file_string.rstrip(', ')\\n args_string = args_string.rstrip(' ')\\n output = 'results.txt'\\n \\n job_template = Template(condor_spec.condor_string_header + condor_spec.results_process_spec_string)\\n\\n job_string = job_template.substitute(pool_type=pool_type,\\n pool_address=pool_address,\\n script=script_path,\\n args=args_string,\\n input_files=input_file_string,\\n output='results',\\n output_files = output,\\n rank=rank)\\n job_filename = 'results.job'\\n job_file = open(os.path.join(self.path, job_filename), 'w')\\n job_file.write(job_string)\\n job_file.close()\\n \\n return job_filename\",\n \"def launch(logger, base_url, group_id, token):\\n # The JWT token contains a user id in its metadata. Lets extract it\\n token_helper = common.TokenHelper(token)\\n user_id = token_helper.get_user_id()\\n logger.info(\\\"User ID {}\\\".format(user_id))\\n\\n # Use the provided helper functions to create the app specific arguments.\\n # These are template parameters which will be swapped for things like start frame, end frame, on multi-part jobs.\\n args = compute_arguments.get_nuke_arguments(NUKE_SCRIPT_WRITE_NODE)\\n\\n # Make a random job name\\n name = \\\"example_\\\" + str(time.time())\\n\\n region = common.select_region(logger, \\\"Select a Region to run the compute job in\\\")\\n\\n # Next, use the payload helper to build the job request body\\n payload = compute.make_job_request(user_id, group_id, NUKE_COMPUTE_APP_ID, \\\"/data/dropbox/{}\\\".format(NUKE_SCRIPT_PATH_WITHIN_DROPBOX), \\n name, 1, 100, 1, region, args)\\n\\n # Now its time to actually launch it \\n compute_response = compute.create_job(base_url, group_id, token, payload)\\n if compute_response.status_code == 200:\\n compute_dict = common.convert_response(compute_response) \\n logger.error(\\\"Create job succeeded: {}\\\".format(compute_dict['id']))\\n return compute_dict['id']\\n else:\\n logger.error(\\\"Create job failed [{}] {}\\\".format(compute_response.status_code, compute_response.text))\\n return None\",\n \"def get_script_str(self, job_name, launch_dir, executable, qout_path, qerr_path,\\n stdin=None, stdout=None, stderr=None, exec_args=None):\\n # PbsPro does not accept job_names longer than 15 chars.\\n if len(job_name) > 14 and isinstance(self, PbsProAdapter):\\n job_name = job_name[:14]\\n\\n # Construct the header for the Queue Manager.\\n qheader = self._make_qheader(job_name, qout_path, qerr_path)\\n\\n # Add the bash section.\\n se = ScriptEditor()\\n\\n # Cd to launch_dir immediately.\\n se.add_line(\\\"cd \\\" + os.path.abspath(launch_dir))\\n\\n if self.setup:\\n se.add_comment(\\\"Setup section\\\")\\n se.add_lines(self.setup)\\n se.add_emptyline()\\n\\n if self.modules:\\n # stderr is redirected to mods.err file.\\n # module load 2>> mods.err\\n se.add_comment(\\\"Load Modules\\\")\\n se.add_line(\\\"module purge\\\")\\n se.load_modules(self.modules)\\n se.add_emptyline()\\n\\n se.add_comment(\\\"OpenMp Environment\\\")\\n if self.has_omp:\\n se.declare_vars(self.omp_env)\\n se.add_emptyline()\\n else:\\n se.declare_vars({\\\"OMP_NUM_THREADS\\\": 1})\\n\\n if self.shell_env:\\n se.add_comment(\\\"Shell Environment\\\")\\n se.declare_vars(self.shell_env)\\n se.add_emptyline()\\n\\n if self.pre_run:\\n se.add_comment(\\\"Commands before execution\\\")\\n se.add_lines(self.pre_run)\\n se.add_emptyline()\\n\\n # Construct the string to run the executable with MPI and mpi_procs.\\n if is_string(executable):\\n line = self.mpi_runner.string_to_run(self, executable,\\n stdin=stdin, stdout=stdout, stderr=stderr, exec_args=exec_args)\\n se.add_line(line)\\n else:\\n assert isinstance(executable, (list, tuple))\\n se.add_lines(executable)\\n\\n if self.post_run:\\n se.add_emptyline()\\n se.add_comment(\\\"Commands after execution\\\")\\n se.add_lines(self.post_run)\\n\\n return qheader + se.get_script_str() + \\\"\\\\n\\\"\",\n \"def test_cluster_jobs_script(self):\\r\\n\\r\\n qiime_config = load_qiime_config()\\r\\n submit_script = qiime_config['cluster_jobs_fp']\\r\\n\\r\\n if (submit_script):\\r\\n full_path = which(submit_script)\\r\\n if full_path:\\r\\n submit_script = full_path\\r\\n self.assertTrue(exists(submit_script),\\r\\n \\\"cluster_jobs_fp is not set to a valid path in qiime config: %s\\\" % submit_script)\\r\\n # check if executable\\r\\n self.assertTrue(access(submit_script, X_OK),\\r\\n \\\"cluster_jobs_fp is not executable: %s\\\" % submit_script)\\r\\n else:\\r\\n # Can't run in parallel, but not a critical error\\r\\n pass\",\n \"def submit_scripts(self, out):\\n program_folder = os.path.join(out, self.out)\\n for config in self.configurations:\\n config.submit_CaVEMan_scripts(\\n program_folder, self.path2exe, self.ref_fai, self.file1, self.file2,\\n self.config_file, self.qsub_dir, self.mstep_script, self.merge_script, self.estep_script\\n )\\n return None\",\n \"def write_pbs_runjob(name, nnodes, nprocessors, pmem, walltime, binary):\\n runjob = open('runjob', 'w')\\n runjob.write('#!/bin/sh\\\\n')\\n runjob.write('#PBS -N {}\\\\n'.format(name))\\n runjob.write('#PBS -o test.out\\\\n')\\n runjob.write('#PBS -e test.err\\\\n')\\n runjob.write('#PBS -r n\\\\n')\\n runjob.write('#PBS -l walltime={}\\\\n'.format(walltime))\\n runjob.write('#PBS -l nodes={}:ppn={}\\\\n'.format(nnodes, nprocessors))\\n runjob.write('#PBS -l pmem={}\\\\n'.format(pmem))\\n runjob.write('#PBS -W group_list=hennig\\\\n\\\\n')\\n runjob.write('cd $PBS_O_WORKDIR\\\\n\\\\n')\\n runjob.write('mpirun {} > job.log\\\\n\\\\n'.format(binary))\\n runjob.write('echo \\\\'Done.\\\\'\\\\n')\\n runjob.close()\",\n \"def create_submission(self, model_fn):\\n output_fn = self._to_output_fn(model_fn)\\n\\n self._create_submission(output_fn)\\n self._post_processing(output_fn)\",\n \"def main():\\n gh = Github(os.environ['GH_TOKEN'])\\n # gh_token = os.environ['GH_TOKEN']\\n gc_token_path = os.environ['GC_STORAGE_KEY']\\n\\n vars_file_path = os.getenv('VARS_FILE', \\\"\\\")\\n pipeline_id = os.getenv('CI_PIPELINE_ID', 0)\\n\\n repo = gh.get_repo(\\\"kubeinit/kubeinit\\\")\\n branches = repo.get_branches()\\n\\n output = 0\\n # Something linke:\\n # url = \\\"https://gitlab.com/kubeinit/kubeinit-ci/pipelines/\\\"\\n url = os.getenv('CI_PIPELINE_URL', \\\"\\\")\\n print(\\\"The job results will be published in runtime at: \\\" + url)\\n\\n for branch in branches:\\n for pr in repo.get_pulls(state='open', sort='created', base=branch.name):\\n labels = [item.name for item in pr.labels]\\n\\n sha = pr.head.sha\\n committer_email = repo.get_commit(sha=sha).commit.committer.email\\n print(committer_email)\\n\\n execute = False\\n # We assign the executed label to avoid executing this agains the same PR over and over\\n # We mark the PR as e2e-executed\\n\\n for label in labels:\\n if re.match(r\\\".*-.*-.*-.*-.*-.*-.*\\\", label):\\n print('Matching a PR label')\\n params = label.split(\\\"-\\\")\\n distro = params[0]\\n driver = params[1]\\n master = params[2]\\n worker = params[4]\\n scenario = params[6]\\n execute = True\\n remove_label(label, pr, repo)\\n break\\n\\n if execute:\\n now = datetime.now()\\n now.strftime(\\\"%m.%d.%Y.%H.%M.%S\\\")\\n job_name = pipeline_id + \\\"-\\\" + distro + \\\"-\\\" + driver + \\\"-\\\" + master + \\\"-\\\" + worker + \\\"-\\\" + scenario + \\\"-\\\" + now.strftime(\\\"%Y.%m.%d.%H.%M.%S\\\")\\n print(\\\"Let's run the e2e job, distro %s driver %s \\\" % (distro, driver))\\n print(\\\"-------------\\\")\\n print(\\\"-------------\\\")\\n print(\\\"Running the e2e job for: \\\" + str(pr.number) + \\\" \\\" + pr.title)\\n print(\\\"-------------\\\")\\n print(\\\"-------------\\\")\\n print(\\\"-------------\\\")\\n\\n # We update the status to show that we are executing the e2e test\\n print(\\\"Current status\\\")\\n print(repo.get_commit(sha=sha).get_statuses())\\n repo.get_commit(sha=sha).create_status(state=\\\"pending\\\",\\n target_url=url + str(pipeline_id),\\n description=\\\"Running...\\\",\\n context=\\\"%s-%s-%s-controller-%s-compute-%s\\\" % (distro,\\n driver,\\n master,\\n worker,\\n scenario))\\n print(\\\"The pipeline ID is: \\\" + str(pipeline_id))\\n print(\\\"The clouds.yml path is: \\\" + str(vars_file_path))\\n # We trigger the e2e job\\n start_time = time.time()\\n try:\\n print(\\\"We call the downstream job configuring its parameters\\\")\\n subprocess.check_call(\\\"./ci/run_kubeinit.sh %s %s %s %s %s %s %s %s\\\" % (str(branch.name),\\n str(pr.number),\\n str(vars_file_path),\\n str(distro),\\n str(driver),\\n str(master),\\n str(worker),\\n str(scenario)),\\n shell=True)\\n except Exception as e:\\n print('An exception hapened executing Ansible')\\n print(e)\\n output = 1\\n\\n try:\\n print(\\\"Render ara data\\\")\\n subprocess.check_call(\\\"./ci/ara.sh %s\\\" % (str(job_name) + \\\"-\\\" + str(output)), shell=True)\\n except Exception as e:\\n print('An exception hapened rendering ara data')\\n print(e)\\n output = 1\\n\\n print(\\\"starting the uploader job\\\")\\n # No matter if the job passed or failed we always use go as the suffix\\n # upload_error = upload_logs_to_github(str(job_name) + \\\"-\\\" + str(output), gh_token)\\n upload_error = upload_logs_to_google_cloud(str(job_name) + \\\"-\\\" + str(output), gc_token_path)\\n render_index(gc_token_path)\\n print(\\\"finishing the uploader job\\\")\\n\\n if output == 0:\\n state = \\\"success\\\"\\n else:\\n state = \\\"failure\\\"\\n\\n desc = (\\\"Ended with %s in %s minutes\\\" % (state, round((time.time() - start_time) / 60, 2)))\\n\\n print(desc)\\n print(state)\\n\\n if upload_error == 1:\\n dest_url = url + str(pipeline_id)\\n else:\\n dest_url = 'https://storage.googleapis.com/kubeinit-ci/jobs/pr/' + str(job_name) + \\\"-\\\" + str(output) + '/index.html'\\n\\n print(\\\"The destination URL is: \\\" + dest_url)\\n # We update the status with the job result\\n repo.get_commit(sha=sha).create_status(state=state,\\n target_url=dest_url,\\n description=desc,\\n context=\\\"%s-%s-%s-controller-%s-compute-%s\\\" % (distro,\\n driver,\\n master,\\n worker,\\n scenario))\\n else:\\n print(\\\"No need to do anything\\\")\\n if execute:\\n exit()\",\n \"def create_submission_pipeline(submission_id: str):\\n from anubis.utils.rpc import enqueue_autograde_pipeline\\n from anubis.lms.submissions import init_submission\\n\\n # Log the creation event\\n logger.info(\\n \\\"Creating submission pipeline job {}\\\".format(submission_id),\\n extra={\\n \\\"submission_id\\\": submission_id,\\n },\\n )\\n\\n # Calculate the maximum number of jobs allowed in the cluster\\n max_jobs = get_config_int('PIPELINE_MAX_JOBS', default=10)\\n\\n # Initialize kube client\\n config.load_incluster_config()\\n\\n # Cleanup finished jobs\\n active_jobs = reap_pipeline_jobs()\\n\\n if active_jobs > max_jobs:\\n logger.info(\\n \\\"TOO many jobs - re-enqueue {}\\\".format(submission_id),\\n extra={\\\"submission_id\\\": submission_id},\\n )\\n enqueue_autograde_pipeline(submission_id)\\n exit(0)\\n\\n # Get the database entry for the submission\\n submission = Submission.query.filter(\\n Submission.id == submission_id\\n ).first()\\n\\n # Make sure that the submission exists\\n if submission is None:\\n logger.error(\\n \\\"Unable to find submission rpc.test_repo\\\",\\n extra={\\n \\\"submission_id\\\": submission_id,\\n },\\n )\\n return\\n\\n # If the build field is not present, then\\n # we need to initialize the submission.\\n if submission.build is None:\\n init_submission(submission, commit=True)\\n\\n submission.processed = False\\n submission.state = 'Initializing Pipeline'\\n db.session.commit()\\n\\n # Create k8s job object\\n job = create_pipeline_job_obj(submission)\\n\\n # Log the pipeline job creation\\n logger.debug(\\\"creating pipeline job: \\\" + job.to_str())\\n\\n # Send to kube api\\n batch_v1 = client.BatchV1Api()\\n batch_v1.create_namespaced_job(body=job, namespace=\\\"anubis\\\")\",\n \"def submit(STAGE_ID, *options):\\n if any([opt.startswith(\\\"-t\\\") for opt in options]):\\n wrapper = \\\"qsubwrapmpi\\\"\\n else:\\n wrapper = \\\"qsubwrap\\\"\\n options = \\\" \\\".join(options)\\n cmd = \\\"%s -v STAGE_ID=%s %s %s\\\" % (wrapper, STAGE_ID, options, jobscript)\\n status, output = getstatusoutput(cmd)\\n result = output\\n alog.info(\\\"Submitted (status, output, cmd): %s\\\", (status, output, cmd))\\n if status != 0:\\n raise QsubException(status, output, cmd)\\n return result.split(\\\".\\\", 1)[0]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7447285","0.70335376","0.6996281","0.68916833","0.68905133","0.6690032","0.65249985","0.6521264","0.6519338","0.6451723","0.64462316","0.64395326","0.63563967","0.63540643","0.6347614","0.63037384","0.6277216","0.6258536","0.6221125","0.6196768","0.61899996","0.61889106","0.6087146","0.6075293","0.6075206","0.60729665","0.60711837","0.6069728","0.6040419","0.6032803","0.60304725","0.6030095","0.6027794","0.6026267","0.6007112","0.60057575","0.60046196","0.59950346","0.5979632","0.59684455","0.59523636","0.5941193","0.592484","0.59234214","0.59017193","0.58972895","0.58871996","0.58760744","0.586931","0.5868029","0.58652395","0.58561003","0.5850181","0.57970196","0.5788946","0.5787812","0.57826465","0.57778","0.5777025","0.57675225","0.57496774","0.57436013","0.5730725","0.572938","0.57239634","0.5719109","0.57152","0.5681321","0.56773597","0.56654716","0.56634134","0.5650389","0.5629354","0.5627956","0.56226486","0.56214696","0.5603783","0.5588954","0.5568472","0.556763","0.55592215","0.55515695","0.5551506","0.5550015","0.55404496","0.5534162","0.55132514","0.5503839","0.5503642","0.55032647","0.5497813","0.5490777","0.54885006","0.54758716","0.5475513","0.5471854","0.54691243","0.5467704","0.5462188","0.54577786"],"string":"[\n \"0.7447285\",\n \"0.70335376\",\n \"0.6996281\",\n \"0.68916833\",\n \"0.68905133\",\n \"0.6690032\",\n \"0.65249985\",\n \"0.6521264\",\n \"0.6519338\",\n \"0.6451723\",\n \"0.64462316\",\n \"0.64395326\",\n \"0.63563967\",\n \"0.63540643\",\n \"0.6347614\",\n \"0.63037384\",\n \"0.6277216\",\n \"0.6258536\",\n \"0.6221125\",\n \"0.6196768\",\n \"0.61899996\",\n \"0.61889106\",\n \"0.6087146\",\n \"0.6075293\",\n \"0.6075206\",\n \"0.60729665\",\n \"0.60711837\",\n \"0.6069728\",\n \"0.6040419\",\n \"0.6032803\",\n \"0.60304725\",\n \"0.6030095\",\n \"0.6027794\",\n \"0.6026267\",\n \"0.6007112\",\n \"0.60057575\",\n \"0.60046196\",\n \"0.59950346\",\n \"0.5979632\",\n \"0.59684455\",\n \"0.59523636\",\n \"0.5941193\",\n \"0.592484\",\n \"0.59234214\",\n \"0.59017193\",\n \"0.58972895\",\n \"0.58871996\",\n \"0.58760744\",\n \"0.586931\",\n \"0.5868029\",\n \"0.58652395\",\n \"0.58561003\",\n \"0.5850181\",\n \"0.57970196\",\n \"0.5788946\",\n \"0.5787812\",\n \"0.57826465\",\n \"0.57778\",\n \"0.5777025\",\n \"0.57675225\",\n \"0.57496774\",\n \"0.57436013\",\n \"0.5730725\",\n \"0.572938\",\n \"0.57239634\",\n \"0.5719109\",\n \"0.57152\",\n \"0.5681321\",\n \"0.56773597\",\n \"0.56654716\",\n \"0.56634134\",\n \"0.5650389\",\n \"0.5629354\",\n \"0.5627956\",\n \"0.56226486\",\n \"0.56214696\",\n \"0.5603783\",\n \"0.5588954\",\n \"0.5568472\",\n \"0.556763\",\n \"0.55592215\",\n \"0.55515695\",\n \"0.5551506\",\n \"0.5550015\",\n \"0.55404496\",\n \"0.5534162\",\n \"0.55132514\",\n \"0.5503839\",\n \"0.5503642\",\n \"0.55032647\",\n \"0.5497813\",\n \"0.5490777\",\n \"0.54885006\",\n \"0.54758716\",\n \"0.5475513\",\n \"0.5471854\",\n \"0.54691243\",\n \"0.5467704\",\n \"0.5462188\",\n \"0.54577786\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.64803964"},"document_rank":{"kind":"string","value":"9"}}},{"rowIdx":85598,"cells":{"query":{"kind":"string","value":"Quote a string or list of strings using the Condor submit file \"new\" argument quoting rules. Returns str The arguments in a quoted form. Warnings You will need to surround the result in doublequotes before using it in the Arguments attribute. Examples >>> quote_arguments([\"3\", \"simple\", \"arguments\"]) '3 simple arguments' >>> quote_arguments([\"one\", \"two with spaces\", \"three\"]) 'one \\'two with spaces\\' three' >>> quote_arguments([\"one\", \"\\\"two\\\"\", \"spacy 'quoted' argument\"]) 'one \"\"two\"\" \\'spacey \\'\\'quoted\\'\\' argument\\''"},"document":{"kind":"string","value":"def quote_arguments(args):\n if isinstance(args, str):\n args_list = [args]\n else:\n args_list = args\n\n quoted_args = []\n for a in args_list:\n qa = _double_up_quotes(a)\n if \" \" in qa or \"'\" in qa:\n qa = \"'\" + qa + \"'\"\n quoted_args.append(qa)\n return \" \".join(quoted_args)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def argument_list_quote(arguments):\n args = []\n for arg in arguments:\n args.append(argument_quote(arg))\n return '\"%s\"' % ' '.join(args)","def _quote_arguments(args):\n return map(lambda x: '\"{}\"'.format(x) if ' ' in x else '{}'.format(x), args)","def argument_quote(argument):\n argument = argument.replace('\"', '\"\"')\n if ' ' in argument:\n argument = argument.replace(\"'\", \"''\")\n argument = \"'\" + argument + \"'\"\n return argument","def _nt_quote_args(args):\n # XXX this doesn't seem very robust to me -- but if the Windows guys\n # say it'll work, I guess I'll have to accept it. (What if an arg\n # contains quotes? What other magic characters, other than spaces,\n # have to be escaped? Is there an escaping mechanism other than\n # quoting?)\n for i, arg in enumerate(args):\n if ' ' in arg:\n args[i] = '\"%s\"' % arg\n return args","def _merge_quote_args(self, args_list):\n\n if len(args_list) <= 1:\n return args_list\n \n index = 0\n while index < len(args_list):\n # if the current argument starts with a quote but does not end with a quote,\n # then the argument must have been wrongly split.\n if args_list[index].startswith(\"\\\"\"):\n while index+1 < len(args_list):\n if _ends_in_unescaped_quote(args_list[index].strip(\".\")):\n break\n args_list[index] += \", \" + args_list[index+1]\n args_list.pop(index+1)\n index += 1\n\n return args_list","def quote(*a, **kw):\n return quote(*a, **kw)","def _quote(self, arg):\n arg = arg.replace('\\\\', '\\\\\\\\')\n arg = arg.replace('\"', '\\\\\"')\n return '\"%s\"' % arg","def AddQuoteArgument(parser):\n parser.add_argument(\n '--quote',\n help=(\n 'Specifies the character that encloses values from columns that have '\n 'string data type. The value of this argument has to be a character '\n 'in Hex ASCII Code. For example, \"22\" represents double quotes. '\n 'This flag is only available for MySQL and Postgres. If this flag is '\n 'not provided, double quotes character will be used as the default '\n 'value.'\n ),\n )","def list2cmdline(seq):\n\n result = []\n needquote = False\n for arg in seq:\n bs_buf = []\n\n # Add a space to separate this argument from the others\n if result:\n result.append(' ')\n\n needquote = (\" \" in arg) or (\"\\t\" in arg) or (not arg) or (\"(\" in arg) or (\")\" in arg)\n if needquote:\n result.append('\"')\n\n for c in arg:\n if c == '\\\\':\n # Don't know if we need to double yet.\n bs_buf.append(c)\n elif c == '\"':\n # Double backslashes.\n result.append('\\\\' * len(bs_buf) * 2)\n bs_buf = []\n result.append('\\\\\"')\n else:\n # Normal char\n if bs_buf:\n result.extend(bs_buf)\n bs_buf = []\n result.append(c)\n\n # Add remaining backslashes, if any.\n if bs_buf:\n result.extend(bs_buf)\n\n if needquote:\n result.extend(bs_buf)\n result.append('\"')\n\n return ''.join(result)","def _escapeArg(arg):\n #XXX There is a *lot* more that we should escape here.\n return arg.replace('\"', r'\\\"')","def get_argument_string(self, arguments):\n fixed_arguments = self._repopulate_required_arguments(arguments)\n return ' '.join(fixed_arguments)","def sh_quote_safe(arg):\n return (\"'\" + str(arg).replace(\"'\", r\"'\\''\") + \"'\")","def split_and_honor_quotation_marks(cmdline):\n\n # See\n # http://msdn.microsoft.com/library/en-us/vccelng/htm/progs_12.asp\n\n # Step 1: Translate all literal quotes into QUOTE. Justify number\n # of backspaces before quotes.\n tokens = []\n bs_buf = \"\"\n QUOTE = 1 # \\\", literal quote\n for c in cmdline:\n if c == '\\\\':\n bs_buf += c\n elif c == '\"' and bs_buf:\n # A quote preceded by some number of backslashes.\n num_bs = len(bs_buf)\n tokens.extend([\"\\\\\"] * (num_bs//2))\n bs_buf = \"\"\n if num_bs % 2:\n # Odd. Quote should be placed literally in array\n tokens.append(QUOTE)\n else:\n # Even. This quote serves as a string delimiter\n tokens.append('\"')\n\n else:\n # Normal character (or quote without any preceding\n # backslashes)\n if bs_buf:\n # We have backspaces in buffer. Output these.\n tokens.extend(list(bs_buf))\n bs_buf = \"\"\n\n tokens.append(c)\n\n # Step 2: split into arguments\n result = [] # Array of strings\n quoted = False\n arg = [] # Current argument\n tokens.append(\" \")\n for c in tokens:\n if c == '\"':\n # Toggle quote status\n quoted = not quoted\n arg.append('\"')\n elif c == QUOTE:\n arg.append('\"')\n elif c in (' ', '\\t'):\n if quoted:\n arg.append(c)\n else:\n # End of argument. Output, if anything.\n if arg:\n result.append(''.join(arg))\n arg = []\n else:\n # Normal character\n arg.append(c)\n \n return result","def coerce_quotes(quotes):\n orig, quotes = quotes, str(quotes) if quotes else None\n if quotes not in [None, '\"', \"'\"]:\n raise ValueError(\"{!r} is not a valid quote type\".format(orig))\n return quotes","def split_args(args):\n words = []\n quoted_words = []\n\n quoted = re.compile('\"([^\"]+)\"')\n for value in quoted.findall(args):\n quoted_words.append(value)\n \n new_str = args\n\n for i in quoted_words:\n new_str = re.sub('\"[^\"]+\"', '', new_str)\n\n for i in new_str.split():\n words.append(i)\n \n words.extend(quoted_words)\n \n return words","def quote(s):\n # Based on shlex.quote. Bun unlike shlex, it quotes every string and\n # not just the ones that contain unsafe characters.\n return \"'\" + s.replace(\"'\", \"'\\\"'\\\"'\") + \"'\"","def sh_quote_unsafe(arg):\n return ('\"' + _DQUOTE_RE.sub(r'\\1\\1\\\"', str(arg)) + '\"' )","def shlex_join(argv):\n def quote(arg):\n if arg.find(\" \") >= 0:\n return '\"%s\"' % arg\n else:\n return arg\n return \" \".join([quote(arg) for arg in argv])","def argumentsParser(args):\n\targuments = []\n\tif args.find('\"') > -1:\n\t\tt_arguments = args.split('\"')\n\t\tfor a in t_arguments:\n\t\t\tif a == '' or a == ' ':\n\t\t\t\tpass\n\t\t\telif a[-1] == ' ':\n\t\t\t\targuments.append(a[:-1])\n\t\t\telse:\n\t\t\t\targuments.append(a)\n\telif args.find(\"'\") > -1:\n\t\tt_arguments = args.split(\"'\")\n\t\tfor a in t_arguments:\n\t\t\tif a == '' or a == ' ':\n\t\t\t\tpass\n\t\t\telif a[-1] == ' ':\n\t\t\t\targuments.append(a[:-1])\n\t\t\telse:\n\t\t\t\targuments.append(a)\n\telif args == ' ':\n\t\tpass\n\telse:\n\t\targuments = args.split(' ')\n\treturn arguments","def safe_args(args,\r\n max_args=None,\r\n config=None,\r\n argfile=None,\r\n delimiter='\\n',\r\n quoter=None,\r\n delete=True):\r\n max_args = max_args or (config or Config.load()).getdefault('max_subprocess_args', int, 10)\r\n if len(args) > max_args:\r\n def create_argfile(fp):\r\n fp.write(delimiter.join(args))\r\n fp.close()\r\n return [quoter(fp.name) if quoter else '@%s' % fp.name]\r\n\r\n if argfile:\r\n try:\r\n with safe_open(argfile, 'w') as fp:\r\n yield create_argfile(fp)\r\n finally:\r\n if delete and os.path.exists(argfile):\r\n os.unlink(argfile)\r\n else:\r\n with temporary_file(cleanup=delete) as fp:\r\n yield create_argfile(fp)\r\n else:\r\n yield args","def arguments(self, args=[]):\n if args is None:\n args = []\n\n if not isinstance(args, (list, tuple)):\n args = (args,)\n\n serialize_me = []\n\n for arg in args:\n if isinstance(arg, str):\n serialize_me.append({'str': arg})\n else:\n serialize_me.append(dict(((unicode(arg._meta), arg.pk),)))\n\n self._arguments = json.dumps(serialize_me)","def cmdify(self):\n return \" \".join(\n itertools.chain(\n [_quote_if_contains(self.command, r\"[\\s^()]\")],\n (_quote_if_contains(arg, r\"[\\s^]\") for arg in self.args),\n )\n )","def quote(s):\n if isinstance(s, str):\n if \" \" in s or len(s.split()) > 1:\n start, end = s[0], s[-1]\n if start != end or start not in ('\"', \"'\"):\n q1s, q1d, q3s, q3d = \"'\", '\"', 3 * \"'\", 3 * '\"'\n if q1d not in s:\n s = q1d + s + q1d\n elif q1s not in s:\n s = q1s + s + q1s\n elif q3d not in s:\n s = q3d + s + q3d\n elif q3s not in s:\n s = q3s + s + q3s\n return s","def posix_command(command, *args, **kwargs):\n # pylint: disable = redefined-outer-name\n return ' '.join([\n \"'%s'\" % (token.replace(\"'\", \"'\\\\''\")) if needq(token) else token\n for token in map(_make_formatter(*args, **kwargs),\n split_command(command))\n ])","def _repopulate_required_arguments(self, arguments):\n fixed_arguments = []\n original_arguments = self.arguments.split()\n\n original_argument_index = 0\n argument_index = 0\n\n while original_argument_index < len(original_arguments):\n original_argument = original_arguments[original_argument_index]\n if (argument_index < len(arguments) and\n original_argument == arguments[argument_index]):\n argument_index += 1\n fixed_arguments.append(original_argument)\n elif (original_argument in self.required_arguments or\n original_argument.split('=')[0] in self.required_arguments or\n '\"' in original_argument or \"'\" in original_argument):\n fixed_arguments.append(original_argument)\n\n original_argument_index += 1\n\n return fixed_arguments","def quote_spaces(arg):\n if ' ' in arg or '\\t' in arg:\n return '\"%s\"' % arg\n else:\n return str(arg)","def quotes(self, quotes):\n\n self._quotes = quotes","def embeded_triple_quotes():\n pass","def shquote(arg):\n for c in '\"', \"'\", \"\\\\\", \"#\":\n if c in arg:\n return repr(arg)\n if arg.split() != [arg]:\n return repr(arg)\n return arg","def shQuote(text):\n\treturn \"'%s'\" % text.replace(\"'\", r\"'\\''\")","def make_args_string(args):\n if not args:\n return \"\"\n\n if not isinstance(args, six.string_types):\n assert isinstance(args, (list, tuple))\n args = \" \".join([str(x) for x in args])\n args_text = \"{0}\".format(args)\n return args_text.strip()","def sh_quote_safe_cmdline(args):\n return str.join(' ', (sh_quote_safe(arg) for arg in args))","def shellquote(s):\n return '\"' + s.replace(\"'\", \"'\\\\''\") + '\"'","def quote(s):\n\n\ts = \"'\" + s.replace(\"'\", \"\"\"'\"'\"'\"\"\") + \"'\"\n\n\t#get rid of gratuitous leading and trailing empty strings\n\tif s.startswith(\"''\"): s = s[2:]\n\tif s.endswith(\"''\"): s = s[:-2]\n\n\treturn s","def quote(value):\n return DoubleQuotedScalarString(value)","def shellquote(arg):\n if re.match('^[-_.:/=a-zA-Z0-9]*$', arg):\n return arg\n else:\n return \"'%s'\" % arg.replace(\"'\", r\"'\\''\")","def format_arguments(*args, **kwargs) -> str:\n return ', '.join(chain([f'{arg!r}' for arg in args],\n [f'{key}={value!r}' for key, value in kwargs.items()]))","def format_args(self, **kwargs: Any) -> str:\n decl = self.declaration\n _, args = decl.split(\"(\", 1)\n\n # To keep consistency between clang versions we force the \"void\"\n # argument. Earlier versions of clang has 'foo()' and newer ones\n # have 'foo(void)'\n if args == \")\": # pragma: no cover\n args = \"void)\"\n return \"(\" + args","def shell_command(self):\n # TODO: fix this naive version by adding quotes where appropriate\n return \" \".join(self.args)","def _joinArgv(argv):\n cmdstr = \"\"\n for arg in argv:\n if ' ' in arg:\n cmdstr += '\"%s\"' % _escapeArg(arg)\n else:\n cmdstr += _escapeArg(arg)\n cmdstr += ' '\n if cmdstr.endswith(' '): cmdstr = cmdstr[:-1] # strip trailing space\n return cmdstr","def parseArgs(argStr, stripQuotes=False):\r\n if (argStr.startswith(\"(\")):\r\n argStr = argStr[1:]\r\n if (argStr.endswith(\")\")):\r\n argStr = argStr[:-1]\r\n else:\r\n raise AssertionError(\"Expected argStr to end with ')'\")\r\n\r\n rtn = []\r\n argsStr = argStr.strip()\r\n while (True):\r\n startIndex = _findArgStart(argStr)\r\n if (startIndex == None):\r\n break\r\n argStr = argStr[startIndex:]\r\n endIndex = _findArgEnd(argStr)\r\n if (endIndex == len(argStr) - 1):\r\n rtn.append(argStr.strip())\r\n break \r\n t = argStr[:endIndex].strip()\r\n if (stripQuotes and t[0] in QUOTES and t[-1] in QUOTES):\r\n t = t[1:-1]\r\n rtn.append(t)\r\n argStr = argStr[endIndex:]\r\n return rtn","def _make_split_command():\n argre = r'[^\"\\s]\\S*|\"[^\\\\\"]*(?:\\\\[\\\\\"][^\\\\\"]*)*\"'\n check = _re.compile(\n r'\\s*(?:%(arg)s)(?:\\s+(?:%(arg)s))*\\s*$' % dict(arg=argre)\n ).match\n split = _re.compile(argre).findall\n strip = _ft.partial(_re.compile(r'\\\\([\\\\\"])').sub, r'\\1')\n\n def split_command(command): # pylint: disable = redefined-outer-name\n \"\"\"\n Split generic commandline into single arguments\n\n The command splitter splits between tokens. Tokens are non-whitespace\n sequences or double quoted strings. Inside those double quotes can be\n escaped with a backslash. So have to be backslashes.\n\n Stolen from <http://opensource.perlig.de/svnmailer/>.\n\n :Return: Parser for generic commandlines\n :Rtype: callable\n \"\"\"\n if not check(command):\n raise ValueError(\"Invalid command string %r\" % (command,))\n\n return [\n strip(arg[1:-1]) if arg.startswith('\"') else arg\n for arg in split(command)\n ]\n\n return split_command","def shlex_quote(s):\n if not s:\n return \"''\"\n # PKGW: builtin not available in Python 2\n ###if _find_unsafe(s) is None:\n ### return s\n\n # use single quotes, and put single quotes into double quotes\n # the string $'b is then quoted as '$'\"'\"'b'\n return \"'\" + s.replace(\"'\", \"'\\\"'\\\"'\") + \"'\"","def expand_args(cmd_args):\n if isinstance(cmd_args, (tuple, list)):\n args_list = list(cmd_args)\n else:\n args_list = shlex.split(cmd_args)\n return args_list","def quot(string):\r\n return string.replace('\"', \"'\")","def quote(value, *args, **kwargs):\n return parse.quote(encode(value, *args, **kwargs))","def sh_quote_unsafe_cmdline(args):\n return str.join(' ', (sh_quote_unsafe(arg) for arg in args))","def _StringQuote(s, quote='\"', escape='\\\\'):\n entity = {'\\f': '\\\\f', '\\n': '\\\\n', '\\r': '\\\\r', '\\t': '\\\\t'}\n chars = []\n if quote:\n chars.append(quote)\n for c in s:\n if c in (escape, quote):\n chars.append(escape)\n elif c in entity:\n c = entity[c]\n chars.append(c)\n if quote:\n chars.append(quote)\n return ''.join(chars)","def quote(value):\n single = value.find(\"'\")\n double = value.find('\"')\n multiline = value.find('\\n') != -1\n if multiline or ((single != -1) and (double != -1)):\n if value.find('\"\"\"') == -1 and value[0] != '\"' and value[-1] != '\"':\n s = '\"\"\"%s\"\"\"' % value\n else:\n s = \"'''%s'''\" % value\n elif (single != -1) and (double == -1):\n s = '\"%s\"' % value\n else:\n s = \"'%s'\" % value\n return s","def _quote(v):\n return '\"' + v + '\"' if ' ' in v else v","def build_arguments(self, *cmd_args, **cmd_kwargs):\n args = []\n args.extend(cmd_args)\n\n for raw_key, value in cmd_kwargs.items():\n if len(raw_key) == 1:\n args.append('-{}'.format(raw_key))\n else:\n key = raw_key.replace('_', '-')\n args.append('--{}'.format(key))\n\n if value is True:\n # If True, it is enough.\n # e.g.: system=True translates to --system\n continue\n\n args.append(str(value))\n\n return args","def quote(s):\n if not s:\n return \"''\"\n if _find_unsafe(s) is None:\n return s\n\n # use single quotes, and put single quotes into double quotes\n # the string $'b is then quoted as '$'\"'\"'b'\n return \"'\" + s.replace(\"'\", \"'\\\"'\\\"'\") + \"'\"","def shlex_quote(s):\n if not s:\n return \"''\"\n if _find_unsafe(s) is None:\n return s\n\n # use single quotes, and put single quotes into double quotes\n # the string $'b is then quoted as '$'\"'\"'b'\n return \"'\" + s.replace(\"'\", \"'\\\"'\\\"'\") + \"'\"","def csv_quoting_examples():\n name_table = read_csv_file(\"name_table.csv\", \",\")\n name_table.append([1, 2, 3])\n write_csv_file(name_table, \"name_table_minimal.csv\", \",\", csv.QUOTE_MINIMAL)\n write_csv_file(name_table, \"name_table_all.csv\", \",\", csv.QUOTE_ALL)\n write_csv_file(name_table, \"name_table_nonnumeric.csv\", \",\", csv.QUOTE_NONNUMERIC)\n #write_csv_file(name_table, \"name_table_none.csv\", \",\", csv.QUOTE_NONE) # no escapechar is set for lots of quotes","def csv_quoting_examples():\n name_table = read_csv_file(\"name_table.csv\", \",\")\n name_table.append([1, 2, 3])\n write_csv_file(name_table, \"name_table_minimal.csv\", \",\", csv.QUOTE_MINIMAL)\n write_csv_file(name_table, \"name_table_all.csv\", \",\", csv.QUOTE_ALL)\n write_csv_file(name_table, \"name_table_nonnumeric.csv\", \",\", csv.QUOTE_NONNUMERIC)\n #write_csv_file(name_table, \"name_table_none.csv\", \",\", csv.QUOTE_NONE) # no escapechar is set for lots of quotes","def escape_quotes(self, val):\n if val.startswith(self.quote) and val.endswith(self.quote):\n # make sure any previously escaped quotes are not re-escaped\n middle = val[1:-1].replace(\"\\\\\" + self.quote, self.quote)\n middle = middle.replace(self.quote, \"\\\\\" + self.quote)\n val = \"%s%s%s\" % (self.quote, middle, self.quote)\n\n return val","def _add_argument(self, args=''):\n\n sys.argv += args.split(' ')","def strip_quotes(arg):\n quote_chars = '\"' + \"'\"\n\n if len(arg) > 1 and arg[0] == arg[-1] and arg[0] in quote_chars:\n arg = arg[1:-1]\n return arg","def strip_quotes(arg):\n quote_chars = '\"' + \"'\"\n\n if len(arg) > 1 and arg[0] == arg[-1] and arg[0] in quote_chars:\n arg = arg[1:-1]\n return arg","def arg_list():\n arg_list = [\n ['-d', '--domain', 'Specify the domain you are using'],\n ['-t', '--template-path', 'Specify template path'],\n ['-s', '--secrets-path', 'Specify template path'],\n ['-p', '--project', 'Specify a project name'],\n ['-c', '--cloud-platform', 'Specify the platform used'],\n ['-so', '--secrets-only', 'Generate secrets only'],\n ['-db', '--database-host', 'Specify the database host'],\n ['-dbc', '--database-connection-name', 'Specify the database connection name (GCP)'],\n ['-sbn', '--storage-bucket-name', 'Specify storage bucket name'],\n ['-sb', '--storage-backend', 'Specify storage backend s3/gcp/filesystem'],\n ['--acm', '--aws-cert-arn', 'Specify AWS ACM'],\n ['--sg-id', '--aws-alg-sg-id', 'Specify AWS SG ID'],\n ['--sentry', '--senty-dsn', 'Specify Sentry DSN'],\n ['-e', '--environment', 'Specify environment'],\n ['-g', '--gather', 'enable Gather yes or no'],\n ['--cm', '--cert-manager', 'Using cert manager?'],\n ['-m', '--modules', 'Aether modules i.e odk,ui,sync'],\n ['-r', '--redis-url', 'Redis endpoint for CouchDB sync'],\n ['-cdb', '--couchdb-url', 'Redis endpoint for CouchDB sync'],\n ['-gc', '--google-client-id', ' Google client ID for CouchDB sync']\n ]\n return arg_list","def _make_posix_command():\n qsearch = _re.compile(r'[^a-zA-Z\\d_./-]').search\n needq = lambda x: not x or qsearch(x)\n\n def posix_command(command, *args, **kwargs):\n \"\"\"\n Return a POSIX shell suitable commandline\n\n Either args or kwargs or neither of them can be set. There cannot be\n set both of them.\n\n :Parameters:\n `command` : ``str``\n Generic commandline, possibly containing substitutions, filled by\n args or kwargs. See `split_command` for generic commandline\n syntax.\n\n `args` : ``tuple``\n Substitution tuple\n\n `kwargs` : ``dict``\n Substitution dict\n\n :Return: Strictly quoted shell commandline for POSIX shells\n :Rtype: ``str``\n \"\"\"\n # pylint: disable = redefined-outer-name\n return ' '.join([\n \"'%s'\" % (token.replace(\"'\", \"'\\\\''\")) if needq(token) else token\n for token in map(_make_formatter(*args, **kwargs),\n split_command(command))\n ])\n return posix_command","def _create_arguments(self, args):\n assert isinstance(args, (list, tuple))\n\n arguments = []\n index = 0\n for arg in args:\n assert isinstance(arg, (list, tuple))\n assert len(arg) == 2 or len(arg) == 3\n\n identifier = arg[0]\n if isinstance(arg[1], str):\n idl_type = self._create_type(\n arg[1], is_optional=(len(arg) == 3))\n else:\n idl_type = arg[1]\n\n default_value = None\n if len(arg) == 3:\n default_value = self._create_literal_constant(arg[2])\n\n arguments.append(\n Argument.IR(\n identifier,\n index=index,\n idl_type=idl_type,\n default_value=default_value))\n\n index += 1\n\n return arguments","def set_magic_quotes_runtime():\n raise NotImplementedError()","def test_quote_str():\n assert pmisc.quote_str(5) == 5\n assert pmisc.quote_str(\"Hello!\") == '\"Hello!\"'\n assert pmisc.quote_str('He said \"hello!\"') == \"'He said \\\"hello!\\\"'\"","def test_parse_simple_quote_with_double_quote(self):\n with self.assertRaisesRegexp(Exception, re.escape(\"the quote included a (\\\") character\")):\n api.parse_quote(\" We accept the love we think we \\\" deserve. - Stephen Chbosky\",\n simple_format=True)","def build_command_line_parameters(params, command_name=\"-param\"):\n if params is None:\n return \"\"\n res = []\n for k, v in sorted(params.items()):\n if '\"' in v:\n v = v.replace('\"', '\\\\\"')\n one = '{2} {0}=\"{1}\"'.format(k, v, command_name)\n res.append(one)\n return \" \".join(res)","def cmdstr(cmd):\n if isinstance(cmd, str):\n return cmd\n\n quoted = []\n for arg in cmd:\n if isinstance(arg, Path):\n arg = str(arg)\n if ' ' in arg:\n arg = '\"%s\"' % (arg,)\n quoted.append(arg)\n return ' '.join(quoted)","def _input_args(self, args: List[str]):\n assert self._call is None, f\"You need to specify all inputs before calling `{self._call}`\"\n assert isinstance(args, list), f\"{args} is a {type(args)}, expected a list of strings!\"\n assert len(args) > 0, f\"Expected a non-empty argument list!\"\n assert all(isinstance(a, str) for a in args), f\"Expected a list of strings, not {[type(a) for a in args]}!\"\n # all arguments could potentially be filenames that we write to, so let's just add them\n self._write_files |= set(args)\n # add dummy argument zero\n args = [\"\"] + args\n # allocate args in memory\n arg_strings = [self._str(a, \"arg\") for a in args]\n # allocate a pointer array for argv\n self.data += [f\"argv: .word \" + \" \".join(\"0\" for _ in range(len(args)))]\n # load argc and argv\n self._args += [\"\", \"# argument count in a0\", f\"li a0, {len(args)}\"]\n self._args += [\"\", \"# load pointers to argument strings into argv\", f\"la a1, argv\"]\n for ii, aa in enumerate(arg_strings):\n self._args += [f\"la t1, {aa}\", f\"sw t1, {ii * 4}(a1)\"]","def get_command_arg_list(\n self, command_name: str, to_parse: Union[Statement, str], preserve_quotes: bool\n ) -> Tuple[Statement, List[str]]:\n # Check if to_parse needs to be converted to a Statement\n if not isinstance(to_parse, Statement):\n to_parse = self.parse(command_name + ' ' + to_parse)\n\n if preserve_quotes:\n return to_parse, to_parse.arg_list\n else:\n return to_parse, to_parse.argv[1:]","def quote_list(the_list):\n return [\"'%s'\" % element for element in the_list]","def quote(m):\n return '\"' + m + '\"'","def escape_args(self, *args):\n\t\treturn tuple((self.escape(arg) for arg in args))","def _escapeString(self, value):\n if '\"' in value and \"'\" in value:\n substrings = value.split(\"\\\"\")\n result = [\"concat(\"]\n for substring in substrings:\n result.append(\"\\\"%s\\\"\" % substring)\n result.append(\", '\\\"', \")\n result = result[0:-1]\n if value.endswith('\"'):\n result.append(\", '\\\"'\")\n return \"\".join(result) + \")\"\n\n if '\"' in value:\n return \"'%s'\" % value\n return \"\\\"%s\\\"\" % value","def rebuild_command(args):\n return \"%s\\n\" % (\" \".join(args)).replace(\"\\\\\", \"\\\\\\\\\")","def command_list_to_str(command):\n return \" \".join(pipes.quote(i) for i in command)","def quoteString(s):\n if s is None:\n return None\n quoted = str(s).replace(\"\\\\\", \"\\\\\\\\\").replace(\"'\", \"\\\\'\")\n return \"'{}'\".format(quoted)","def test_sqpp_escape_double_quotes(self):\n self.assertEqual(self.parser.parse_query('expr1 \\\\\" expr2 +(expr3) -expr4 \\\\\" + (expr5)'),\n ['+', 'expr1', '+', '\\\\\"', '+', 'expr2', '+', 'expr3', '-', 'expr4', '+', '\\\\\"', '+', 'expr5'])","def test_add_quote(self):\n\n # Set up for test\n path = tests.test_util.init_quotefile(self.tempdir, \"quotes5.txt\")\n quote = api.Quote(\" This is an added quote.\", \"Another author\", \"Publication\", [\"tag1, tag2\"])\n\n # Call method being tested\n api.add_quote(path, quote)\n\n # Check results. Read the resulting text file and verify\n with open(path, 'rb') as file:\n data = file.read()\n text_data = data.decode('utf-8')\n expected = u'They that can give up essential liberty to obtain a little temporary safety deserve neither liberty nor safety. | Ben Franklin | | U' + os.linesep + \\\n u'This is an added quote. | Another author | Publication | tag1, tag2' + os.linesep\n self.assertEquals(expected, text_data)","def quote_environment(env):\n if not isinstance(env, dict):\n raise TypeError(\"env must be a dict\")\n\n entries = []\n for k, v in env.items():\n qv = _double_up_quotes(str(v))\n if \" \" in qv or \"'\" in qv:\n qv = \"'\" + qv + \"'\"\n entries.append(\"%s=%s\" % (k, qv))\n\n return \" \".join(entries)","def standardise_quotes(self, val):\n if val.startswith(self.altquote) and val.endswith(self.altquote):\n middle = val[1:-1]\n val = \"%s%s%s\" % (self.quote, middle, self.quote)\n\n val = self.escape_quotes(val)\n\n return val","async def addquote(self, ctx, *, quote : str):\n if not self.addquote_regex.match(quote):\n await ctx.send(\"`Quote must be in this format (including surrounding single quotes):\\n'some quote here - quote author'`\")\n else:\n with open(\"data/quotes.txt\", \"a\") as text_file:\n text_file.write(f\"{quote}\\n\")\n await ctx.send('`Quote added!`')","def arg_str(self):\n\n args = ['self']\n args.extend([x.name for x in self.required_inputs])\n args.extend([\"{}=None\".format(x.name) for x in self.optional_inputs])\n\n return \", \".join(args)","def CreateArgFile( arguments, tmpDir ):\n tmpFile = os.path.join( tmpDir, \"args.txt\" )\n\n with io.open( tmpFile, 'w', encoding=\"utf-8-sig\" ) as fileHandle:\n fileHandle.write( \"\\n\".join( arguments ) )\n\n return tmpFile","def _strip_quotes(file_arg):\n return re.sub(\"^[\\'\\\"]|[\\'\\\"]$\", \"\", file_arg)","def escape_triple_quotes(text):\n return text.replace(u'\"\"\"', u'\\\\\"\\\\\"\\\\\"')","def split_arguments(s, windows=IS_WINDOWS):\n # from http://stackoverflow.com/a/35900070\n if windows:\n RE_CMD_LEX = r'''\"((?:\"\"|\\\\[\"\\\\]|[^\"])*)\"?()|(\\\\\\\\(?=\\\\*\")|\\\\\")'\n r'|(&&?|\\|\\|?|\\d?>|[<])|([^\\s\"&|<>]+)|(\\s+)|(.)'''\n else:\n RE_CMD_LEX = r'''\"((?:\\\\[\"\\\\]|[^\"])*)\"|'([^']*)'|(\\\\.)|'\n r'(&&?|\\|\\|?|\\d?\\>|[<])|([^\\s'\"\\\\&|<>]+)|(\\s+)|(.)'''\n\n args = []\n accu = None # collects pieces of one arg\n for qs, qss, esc, pipe, word, white, fail in re.findall(RE_CMD_LEX, s):\n if word:\n pass # most frequent\n elif esc:\n word = esc[1]\n elif white or pipe:\n if accu is not None:\n args.append(accu)\n if pipe:\n args.append(pipe)\n accu = None\n continue\n elif fail:\n raise ValueError(\"invalid or incomplete shell string\")\n elif qs:\n word = qs.replace('\\\\\"', '\"').replace('\\\\\\\\', '\\\\')\n if windows:\n word = word.replace('\"\"', '\"')\n else:\n word = qss # may be even empty; must be last\n\n accu = (accu or '') + word\n\n if accu is not None:\n args.append(accu)\n\n return args","def test_sqpp_escape_single_quotes(self):\n self.assertEqual(self.parser.parse_query(\"expr1 \\\\' expr2 +(expr3) -expr4 \\\\' + (expr5)\"),\n ['+', 'expr1', '+', \"\\\\'\", '+', 'expr2', '+', 'expr3', '-', 'expr4', '+', \"\\\\'\", '+', 'expr5'])","def csv_quote_escape(self, the_string):\n the_string = the_string.replace('\"', r'\"\"')\n\n the_string = '\"' + the_string + '\"'\n\n return the_string","def esc_quotes(strng):\n\n return strng.replace('\"','\\\\\"').replace(\"'\",\"\\\\'\")","def add_arguments(cls):\n return [\n (('--yes',), dict(action='store_true', help='clean .git repo')),\n (('--variable', '-s'),\n dict(nargs='+', help='set extra variable,format is name:value')),\n (('--skip-builtin',),\n dict(action='store_true', help='skip replace builtin variable')),\n\n (('--dir',), dict(nargs='?', default=os.getcwd(),\n help='set working directory')),\n (('--debug',), dict(action='store_true', help='open debug mode')),\n (('--dry-run',), dict(action='store_true',\n help='print command instead execute it')),\n (('--verbose', '-v'), dict(action='count')),\n ]","def test_parse_quotes_doublequote(self):\n with self.assertRaisesRegexp(Exception, re.escape(\"the quote included a (\\\") character\")):\n api.parse_quote(\" This is a quote\\\". | Author | Publication | tag1, tag2 , tag3 \",\n simple_format=False)","def test_sqpp_single_quotes(self):\n self.assertEqual(self.parser.parse_query(\"(expr1) - expr2 | 'expressions - in + quotes | (are) not - parsed ' - (expr3) | expr4\"),\n ['+', 'expr1', '-', 'expr2', '|', \"'expressions - in + quotes | (are) not - parsed '\", '-', 'expr3', '|', 'expr4'])\n #['+', '+ \\'expressions - in + quotes | (are) not - parsed \\' | expr1 | expr4',\n # '+', '- expr3 | expr1 | expr4',\n # '+', '+ \\'expressions - in + quotes | (are) not - parsed \\' - expr2 | expr4',\n # '+', '- expr3 - expr2 | expr4'])","def quote(s):\n if not s:\n return \"''\"\n if _find_unsafe(s) is None:\n return \"'\" + s + \"'\"\n\n # use single quotes, and put single quotes into double quotes\n # the string $'b is then quoted as '$'\"'\"'b'\n return \"'\" + s.replace(\"'\", \"'\\\"'\\\"'\") + \"'\"","def test_sqpp_double_quotes(self):\n self.assertEqual(self.parser.parse_query(\n '(expr1) - expr2 | \"expressions - in + quotes | (are) not - parsed \" - (expr3) | expr4'),\n ['+', 'expr1', '-', 'expr2', '|', '\"expressions - in + quotes | (are) not - parsed \"', '-', 'expr3', '|', 'expr4'])\n #['+', '+ \"expressions - in + quotes | (are) not - parsed \" | expr1 | expr4',\n # '+', '- expr3 | expr1 | expr4',\n # '+', '+ \"expressions - in + quotes | (are) not - parsed \" - expr2 | expr4',\n # '+', '- expr3 - expr2 | expr4'])","def handle_arguments(self, string, root, opening, closing):\n\n\t\t# The actual argument string (ignore whitespace)\n\t\targs = string[opening + 1 : closing].replace(\" \", \"\")\n\n\t\t# The argument sequence must be at the start of the phrase\n\t\t# and must match the allowed argument regular expression\n\t\tif opening > 0 or not self.arguments.match(args):\n\n\t\t\tif opening == 0:\n\t\t\t\traise errors.ParseError(\"Invalid argument sequence!\")\n\n\t\t\t# If escape_meta does indeed escape a character and removes\n\t\t\t# a backward slash, the positions 'opening' and 'closing' are no\n\t\t\t# longer valid. escape_meta does a search for the next meta\n\t\t\t# character though, which is then the closing parantheses,\n\t\t\t# so we can use its index value (in the now escaped string)\n\t\t\tstring, meta = self.escape_meta(string, opening)\n\t\t\tstring, meta = self.escape_meta(string, meta.start())\n\n\t\t\treturn string, root, meta\n\n\t\tif \"!\" in args:\n\t\t\troot.override = True\n\t\t\targs = args.replace(\"!\", \"\")\n\n\t\tif \"+\" in args:\n\t\t\troot.increment = True\n\t\t\targs = args.replace(\"+\", \"\")\n\n\t\troot.arguments = [int(i) for i in args.split(\",\") if i]\n\n\t\t# Remove the argument string including parantheses\n\t\tstring = string[closing + 1:]\n\n\t\tmeta = self.meta.search(string)\n\n\t\treturn string, root, meta","def wrap_with_in_single_quote(s):\n return \"'{}'\".format(s)","def wrap_with_in_single_quote(s):\n return \"'{}'\".format(s)","def collect_quotes(self):\n quotes = [q for q in self.quotes if not q.invalid]\n childquotes = [q for s in self.children for q in s.collect_quotes()]\n return quotes + childquotes","def test_split_arguments_and_remove_quotes(self, unused_test_name,\n input_string, expected):\n result = shlex.split(input_string)\n self.assertEqual(result, expected)","def arg_to_str_list(self) -> list:\n arg_list = []\n for arg in [*self.args]:\n if hasattr(arg, \"_ref\"):\n arg_list.append(arg.ref)\n else:\n arg_list.append(arg)\n return arg_list"],"string":"[\n \"def argument_list_quote(arguments):\\n args = []\\n for arg in arguments:\\n args.append(argument_quote(arg))\\n return '\\\"%s\\\"' % ' '.join(args)\",\n \"def _quote_arguments(args):\\n return map(lambda x: '\\\"{}\\\"'.format(x) if ' ' in x else '{}'.format(x), args)\",\n \"def argument_quote(argument):\\n argument = argument.replace('\\\"', '\\\"\\\"')\\n if ' ' in argument:\\n argument = argument.replace(\\\"'\\\", \\\"''\\\")\\n argument = \\\"'\\\" + argument + \\\"'\\\"\\n return argument\",\n \"def _nt_quote_args(args):\\n # XXX this doesn't seem very robust to me -- but if the Windows guys\\n # say it'll work, I guess I'll have to accept it. (What if an arg\\n # contains quotes? What other magic characters, other than spaces,\\n # have to be escaped? Is there an escaping mechanism other than\\n # quoting?)\\n for i, arg in enumerate(args):\\n if ' ' in arg:\\n args[i] = '\\\"%s\\\"' % arg\\n return args\",\n \"def _merge_quote_args(self, args_list):\\n\\n if len(args_list) <= 1:\\n return args_list\\n \\n index = 0\\n while index < len(args_list):\\n # if the current argument starts with a quote but does not end with a quote,\\n # then the argument must have been wrongly split.\\n if args_list[index].startswith(\\\"\\\\\\\"\\\"):\\n while index+1 < len(args_list):\\n if _ends_in_unescaped_quote(args_list[index].strip(\\\".\\\")):\\n break\\n args_list[index] += \\\", \\\" + args_list[index+1]\\n args_list.pop(index+1)\\n index += 1\\n\\n return args_list\",\n \"def quote(*a, **kw):\\n return quote(*a, **kw)\",\n \"def _quote(self, arg):\\n arg = arg.replace('\\\\\\\\', '\\\\\\\\\\\\\\\\')\\n arg = arg.replace('\\\"', '\\\\\\\\\\\"')\\n return '\\\"%s\\\"' % arg\",\n \"def AddQuoteArgument(parser):\\n parser.add_argument(\\n '--quote',\\n help=(\\n 'Specifies the character that encloses values from columns that have '\\n 'string data type. The value of this argument has to be a character '\\n 'in Hex ASCII Code. For example, \\\"22\\\" represents double quotes. '\\n 'This flag is only available for MySQL and Postgres. If this flag is '\\n 'not provided, double quotes character will be used as the default '\\n 'value.'\\n ),\\n )\",\n \"def list2cmdline(seq):\\n\\n result = []\\n needquote = False\\n for arg in seq:\\n bs_buf = []\\n\\n # Add a space to separate this argument from the others\\n if result:\\n result.append(' ')\\n\\n needquote = (\\\" \\\" in arg) or (\\\"\\\\t\\\" in arg) or (not arg) or (\\\"(\\\" in arg) or (\\\")\\\" in arg)\\n if needquote:\\n result.append('\\\"')\\n\\n for c in arg:\\n if c == '\\\\\\\\':\\n # Don't know if we need to double yet.\\n bs_buf.append(c)\\n elif c == '\\\"':\\n # Double backslashes.\\n result.append('\\\\\\\\' * len(bs_buf) * 2)\\n bs_buf = []\\n result.append('\\\\\\\\\\\"')\\n else:\\n # Normal char\\n if bs_buf:\\n result.extend(bs_buf)\\n bs_buf = []\\n result.append(c)\\n\\n # Add remaining backslashes, if any.\\n if bs_buf:\\n result.extend(bs_buf)\\n\\n if needquote:\\n result.extend(bs_buf)\\n result.append('\\\"')\\n\\n return ''.join(result)\",\n \"def _escapeArg(arg):\\n #XXX There is a *lot* more that we should escape here.\\n return arg.replace('\\\"', r'\\\\\\\"')\",\n \"def get_argument_string(self, arguments):\\n fixed_arguments = self._repopulate_required_arguments(arguments)\\n return ' '.join(fixed_arguments)\",\n \"def sh_quote_safe(arg):\\n return (\\\"'\\\" + str(arg).replace(\\\"'\\\", r\\\"'\\\\''\\\") + \\\"'\\\")\",\n \"def split_and_honor_quotation_marks(cmdline):\\n\\n # See\\n # http://msdn.microsoft.com/library/en-us/vccelng/htm/progs_12.asp\\n\\n # Step 1: Translate all literal quotes into QUOTE. Justify number\\n # of backspaces before quotes.\\n tokens = []\\n bs_buf = \\\"\\\"\\n QUOTE = 1 # \\\\\\\", literal quote\\n for c in cmdline:\\n if c == '\\\\\\\\':\\n bs_buf += c\\n elif c == '\\\"' and bs_buf:\\n # A quote preceded by some number of backslashes.\\n num_bs = len(bs_buf)\\n tokens.extend([\\\"\\\\\\\\\\\"] * (num_bs//2))\\n bs_buf = \\\"\\\"\\n if num_bs % 2:\\n # Odd. Quote should be placed literally in array\\n tokens.append(QUOTE)\\n else:\\n # Even. This quote serves as a string delimiter\\n tokens.append('\\\"')\\n\\n else:\\n # Normal character (or quote without any preceding\\n # backslashes)\\n if bs_buf:\\n # We have backspaces in buffer. Output these.\\n tokens.extend(list(bs_buf))\\n bs_buf = \\\"\\\"\\n\\n tokens.append(c)\\n\\n # Step 2: split into arguments\\n result = [] # Array of strings\\n quoted = False\\n arg = [] # Current argument\\n tokens.append(\\\" \\\")\\n for c in tokens:\\n if c == '\\\"':\\n # Toggle quote status\\n quoted = not quoted\\n arg.append('\\\"')\\n elif c == QUOTE:\\n arg.append('\\\"')\\n elif c in (' ', '\\\\t'):\\n if quoted:\\n arg.append(c)\\n else:\\n # End of argument. Output, if anything.\\n if arg:\\n result.append(''.join(arg))\\n arg = []\\n else:\\n # Normal character\\n arg.append(c)\\n \\n return result\",\n \"def coerce_quotes(quotes):\\n orig, quotes = quotes, str(quotes) if quotes else None\\n if quotes not in [None, '\\\"', \\\"'\\\"]:\\n raise ValueError(\\\"{!r} is not a valid quote type\\\".format(orig))\\n return quotes\",\n \"def split_args(args):\\n words = []\\n quoted_words = []\\n\\n quoted = re.compile('\\\"([^\\\"]+)\\\"')\\n for value in quoted.findall(args):\\n quoted_words.append(value)\\n \\n new_str = args\\n\\n for i in quoted_words:\\n new_str = re.sub('\\\"[^\\\"]+\\\"', '', new_str)\\n\\n for i in new_str.split():\\n words.append(i)\\n \\n words.extend(quoted_words)\\n \\n return words\",\n \"def quote(s):\\n # Based on shlex.quote. Bun unlike shlex, it quotes every string and\\n # not just the ones that contain unsafe characters.\\n return \\\"'\\\" + s.replace(\\\"'\\\", \\\"'\\\\\\\"'\\\\\\\"'\\\") + \\\"'\\\"\",\n \"def sh_quote_unsafe(arg):\\n return ('\\\"' + _DQUOTE_RE.sub(r'\\\\1\\\\1\\\\\\\"', str(arg)) + '\\\"' )\",\n \"def shlex_join(argv):\\n def quote(arg):\\n if arg.find(\\\" \\\") >= 0:\\n return '\\\"%s\\\"' % arg\\n else:\\n return arg\\n return \\\" \\\".join([quote(arg) for arg in argv])\",\n \"def argumentsParser(args):\\n\\targuments = []\\n\\tif args.find('\\\"') > -1:\\n\\t\\tt_arguments = args.split('\\\"')\\n\\t\\tfor a in t_arguments:\\n\\t\\t\\tif a == '' or a == ' ':\\n\\t\\t\\t\\tpass\\n\\t\\t\\telif a[-1] == ' ':\\n\\t\\t\\t\\targuments.append(a[:-1])\\n\\t\\t\\telse:\\n\\t\\t\\t\\targuments.append(a)\\n\\telif args.find(\\\"'\\\") > -1:\\n\\t\\tt_arguments = args.split(\\\"'\\\")\\n\\t\\tfor a in t_arguments:\\n\\t\\t\\tif a == '' or a == ' ':\\n\\t\\t\\t\\tpass\\n\\t\\t\\telif a[-1] == ' ':\\n\\t\\t\\t\\targuments.append(a[:-1])\\n\\t\\t\\telse:\\n\\t\\t\\t\\targuments.append(a)\\n\\telif args == ' ':\\n\\t\\tpass\\n\\telse:\\n\\t\\targuments = args.split(' ')\\n\\treturn arguments\",\n \"def safe_args(args,\\r\\n max_args=None,\\r\\n config=None,\\r\\n argfile=None,\\r\\n delimiter='\\\\n',\\r\\n quoter=None,\\r\\n delete=True):\\r\\n max_args = max_args or (config or Config.load()).getdefault('max_subprocess_args', int, 10)\\r\\n if len(args) > max_args:\\r\\n def create_argfile(fp):\\r\\n fp.write(delimiter.join(args))\\r\\n fp.close()\\r\\n return [quoter(fp.name) if quoter else '@%s' % fp.name]\\r\\n\\r\\n if argfile:\\r\\n try:\\r\\n with safe_open(argfile, 'w') as fp:\\r\\n yield create_argfile(fp)\\r\\n finally:\\r\\n if delete and os.path.exists(argfile):\\r\\n os.unlink(argfile)\\r\\n else:\\r\\n with temporary_file(cleanup=delete) as fp:\\r\\n yield create_argfile(fp)\\r\\n else:\\r\\n yield args\",\n \"def arguments(self, args=[]):\\n if args is None:\\n args = []\\n\\n if not isinstance(args, (list, tuple)):\\n args = (args,)\\n\\n serialize_me = []\\n\\n for arg in args:\\n if isinstance(arg, str):\\n serialize_me.append({'str': arg})\\n else:\\n serialize_me.append(dict(((unicode(arg._meta), arg.pk),)))\\n\\n self._arguments = json.dumps(serialize_me)\",\n \"def cmdify(self):\\n return \\\" \\\".join(\\n itertools.chain(\\n [_quote_if_contains(self.command, r\\\"[\\\\s^()]\\\")],\\n (_quote_if_contains(arg, r\\\"[\\\\s^]\\\") for arg in self.args),\\n )\\n )\",\n \"def quote(s):\\n if isinstance(s, str):\\n if \\\" \\\" in s or len(s.split()) > 1:\\n start, end = s[0], s[-1]\\n if start != end or start not in ('\\\"', \\\"'\\\"):\\n q1s, q1d, q3s, q3d = \\\"'\\\", '\\\"', 3 * \\\"'\\\", 3 * '\\\"'\\n if q1d not in s:\\n s = q1d + s + q1d\\n elif q1s not in s:\\n s = q1s + s + q1s\\n elif q3d not in s:\\n s = q3d + s + q3d\\n elif q3s not in s:\\n s = q3s + s + q3s\\n return s\",\n \"def posix_command(command, *args, **kwargs):\\n # pylint: disable = redefined-outer-name\\n return ' '.join([\\n \\\"'%s'\\\" % (token.replace(\\\"'\\\", \\\"'\\\\\\\\''\\\")) if needq(token) else token\\n for token in map(_make_formatter(*args, **kwargs),\\n split_command(command))\\n ])\",\n \"def _repopulate_required_arguments(self, arguments):\\n fixed_arguments = []\\n original_arguments = self.arguments.split()\\n\\n original_argument_index = 0\\n argument_index = 0\\n\\n while original_argument_index < len(original_arguments):\\n original_argument = original_arguments[original_argument_index]\\n if (argument_index < len(arguments) and\\n original_argument == arguments[argument_index]):\\n argument_index += 1\\n fixed_arguments.append(original_argument)\\n elif (original_argument in self.required_arguments or\\n original_argument.split('=')[0] in self.required_arguments or\\n '\\\"' in original_argument or \\\"'\\\" in original_argument):\\n fixed_arguments.append(original_argument)\\n\\n original_argument_index += 1\\n\\n return fixed_arguments\",\n \"def quote_spaces(arg):\\n if ' ' in arg or '\\\\t' in arg:\\n return '\\\"%s\\\"' % arg\\n else:\\n return str(arg)\",\n \"def quotes(self, quotes):\\n\\n self._quotes = quotes\",\n \"def embeded_triple_quotes():\\n pass\",\n \"def shquote(arg):\\n for c in '\\\"', \\\"'\\\", \\\"\\\\\\\\\\\", \\\"#\\\":\\n if c in arg:\\n return repr(arg)\\n if arg.split() != [arg]:\\n return repr(arg)\\n return arg\",\n \"def shQuote(text):\\n\\treturn \\\"'%s'\\\" % text.replace(\\\"'\\\", r\\\"'\\\\''\\\")\",\n \"def make_args_string(args):\\n if not args:\\n return \\\"\\\"\\n\\n if not isinstance(args, six.string_types):\\n assert isinstance(args, (list, tuple))\\n args = \\\" \\\".join([str(x) for x in args])\\n args_text = \\\"{0}\\\".format(args)\\n return args_text.strip()\",\n \"def sh_quote_safe_cmdline(args):\\n return str.join(' ', (sh_quote_safe(arg) for arg in args))\",\n \"def shellquote(s):\\n return '\\\"' + s.replace(\\\"'\\\", \\\"'\\\\\\\\''\\\") + '\\\"'\",\n \"def quote(s):\\n\\n\\ts = \\\"'\\\" + s.replace(\\\"'\\\", \\\"\\\"\\\"'\\\"'\\\"'\\\"\\\"\\\") + \\\"'\\\"\\n\\n\\t#get rid of gratuitous leading and trailing empty strings\\n\\tif s.startswith(\\\"''\\\"): s = s[2:]\\n\\tif s.endswith(\\\"''\\\"): s = s[:-2]\\n\\n\\treturn s\",\n \"def quote(value):\\n return DoubleQuotedScalarString(value)\",\n \"def shellquote(arg):\\n if re.match('^[-_.:/=a-zA-Z0-9]*$', arg):\\n return arg\\n else:\\n return \\\"'%s'\\\" % arg.replace(\\\"'\\\", r\\\"'\\\\''\\\")\",\n \"def format_arguments(*args, **kwargs) -> str:\\n return ', '.join(chain([f'{arg!r}' for arg in args],\\n [f'{key}={value!r}' for key, value in kwargs.items()]))\",\n \"def format_args(self, **kwargs: Any) -> str:\\n decl = self.declaration\\n _, args = decl.split(\\\"(\\\", 1)\\n\\n # To keep consistency between clang versions we force the \\\"void\\\"\\n # argument. Earlier versions of clang has 'foo()' and newer ones\\n # have 'foo(void)'\\n if args == \\\")\\\": # pragma: no cover\\n args = \\\"void)\\\"\\n return \\\"(\\\" + args\",\n \"def shell_command(self):\\n # TODO: fix this naive version by adding quotes where appropriate\\n return \\\" \\\".join(self.args)\",\n \"def _joinArgv(argv):\\n cmdstr = \\\"\\\"\\n for arg in argv:\\n if ' ' in arg:\\n cmdstr += '\\\"%s\\\"' % _escapeArg(arg)\\n else:\\n cmdstr += _escapeArg(arg)\\n cmdstr += ' '\\n if cmdstr.endswith(' '): cmdstr = cmdstr[:-1] # strip trailing space\\n return cmdstr\",\n \"def parseArgs(argStr, stripQuotes=False):\\r\\n if (argStr.startswith(\\\"(\\\")):\\r\\n argStr = argStr[1:]\\r\\n if (argStr.endswith(\\\")\\\")):\\r\\n argStr = argStr[:-1]\\r\\n else:\\r\\n raise AssertionError(\\\"Expected argStr to end with ')'\\\")\\r\\n\\r\\n rtn = []\\r\\n argsStr = argStr.strip()\\r\\n while (True):\\r\\n startIndex = _findArgStart(argStr)\\r\\n if (startIndex == None):\\r\\n break\\r\\n argStr = argStr[startIndex:]\\r\\n endIndex = _findArgEnd(argStr)\\r\\n if (endIndex == len(argStr) - 1):\\r\\n rtn.append(argStr.strip())\\r\\n break \\r\\n t = argStr[:endIndex].strip()\\r\\n if (stripQuotes and t[0] in QUOTES and t[-1] in QUOTES):\\r\\n t = t[1:-1]\\r\\n rtn.append(t)\\r\\n argStr = argStr[endIndex:]\\r\\n return rtn\",\n \"def _make_split_command():\\n argre = r'[^\\\"\\\\s]\\\\S*|\\\"[^\\\\\\\\\\\"]*(?:\\\\\\\\[\\\\\\\\\\\"][^\\\\\\\\\\\"]*)*\\\"'\\n check = _re.compile(\\n r'\\\\s*(?:%(arg)s)(?:\\\\s+(?:%(arg)s))*\\\\s*$' % dict(arg=argre)\\n ).match\\n split = _re.compile(argre).findall\\n strip = _ft.partial(_re.compile(r'\\\\\\\\([\\\\\\\\\\\"])').sub, r'\\\\1')\\n\\n def split_command(command): # pylint: disable = redefined-outer-name\\n \\\"\\\"\\\"\\n Split generic commandline into single arguments\\n\\n The command splitter splits between tokens. Tokens are non-whitespace\\n sequences or double quoted strings. Inside those double quotes can be\\n escaped with a backslash. So have to be backslashes.\\n\\n Stolen from <http://opensource.perlig.de/svnmailer/>.\\n\\n :Return: Parser for generic commandlines\\n :Rtype: callable\\n \\\"\\\"\\\"\\n if not check(command):\\n raise ValueError(\\\"Invalid command string %r\\\" % (command,))\\n\\n return [\\n strip(arg[1:-1]) if arg.startswith('\\\"') else arg\\n for arg in split(command)\\n ]\\n\\n return split_command\",\n \"def shlex_quote(s):\\n if not s:\\n return \\\"''\\\"\\n # PKGW: builtin not available in Python 2\\n ###if _find_unsafe(s) is None:\\n ### return s\\n\\n # use single quotes, and put single quotes into double quotes\\n # the string $'b is then quoted as '$'\\\"'\\\"'b'\\n return \\\"'\\\" + s.replace(\\\"'\\\", \\\"'\\\\\\\"'\\\\\\\"'\\\") + \\\"'\\\"\",\n \"def expand_args(cmd_args):\\n if isinstance(cmd_args, (tuple, list)):\\n args_list = list(cmd_args)\\n else:\\n args_list = shlex.split(cmd_args)\\n return args_list\",\n \"def quot(string):\\r\\n return string.replace('\\\"', \\\"'\\\")\",\n \"def quote(value, *args, **kwargs):\\n return parse.quote(encode(value, *args, **kwargs))\",\n \"def sh_quote_unsafe_cmdline(args):\\n return str.join(' ', (sh_quote_unsafe(arg) for arg in args))\",\n \"def _StringQuote(s, quote='\\\"', escape='\\\\\\\\'):\\n entity = {'\\\\f': '\\\\\\\\f', '\\\\n': '\\\\\\\\n', '\\\\r': '\\\\\\\\r', '\\\\t': '\\\\\\\\t'}\\n chars = []\\n if quote:\\n chars.append(quote)\\n for c in s:\\n if c in (escape, quote):\\n chars.append(escape)\\n elif c in entity:\\n c = entity[c]\\n chars.append(c)\\n if quote:\\n chars.append(quote)\\n return ''.join(chars)\",\n \"def quote(value):\\n single = value.find(\\\"'\\\")\\n double = value.find('\\\"')\\n multiline = value.find('\\\\n') != -1\\n if multiline or ((single != -1) and (double != -1)):\\n if value.find('\\\"\\\"\\\"') == -1 and value[0] != '\\\"' and value[-1] != '\\\"':\\n s = '\\\"\\\"\\\"%s\\\"\\\"\\\"' % value\\n else:\\n s = \\\"'''%s'''\\\" % value\\n elif (single != -1) and (double == -1):\\n s = '\\\"%s\\\"' % value\\n else:\\n s = \\\"'%s'\\\" % value\\n return s\",\n \"def _quote(v):\\n return '\\\"' + v + '\\\"' if ' ' in v else v\",\n \"def build_arguments(self, *cmd_args, **cmd_kwargs):\\n args = []\\n args.extend(cmd_args)\\n\\n for raw_key, value in cmd_kwargs.items():\\n if len(raw_key) == 1:\\n args.append('-{}'.format(raw_key))\\n else:\\n key = raw_key.replace('_', '-')\\n args.append('--{}'.format(key))\\n\\n if value is True:\\n # If True, it is enough.\\n # e.g.: system=True translates to --system\\n continue\\n\\n args.append(str(value))\\n\\n return args\",\n \"def quote(s):\\n if not s:\\n return \\\"''\\\"\\n if _find_unsafe(s) is None:\\n return s\\n\\n # use single quotes, and put single quotes into double quotes\\n # the string $'b is then quoted as '$'\\\"'\\\"'b'\\n return \\\"'\\\" + s.replace(\\\"'\\\", \\\"'\\\\\\\"'\\\\\\\"'\\\") + \\\"'\\\"\",\n \"def shlex_quote(s):\\n if not s:\\n return \\\"''\\\"\\n if _find_unsafe(s) is None:\\n return s\\n\\n # use single quotes, and put single quotes into double quotes\\n # the string $'b is then quoted as '$'\\\"'\\\"'b'\\n return \\\"'\\\" + s.replace(\\\"'\\\", \\\"'\\\\\\\"'\\\\\\\"'\\\") + \\\"'\\\"\",\n \"def csv_quoting_examples():\\n name_table = read_csv_file(\\\"name_table.csv\\\", \\\",\\\")\\n name_table.append([1, 2, 3])\\n write_csv_file(name_table, \\\"name_table_minimal.csv\\\", \\\",\\\", csv.QUOTE_MINIMAL)\\n write_csv_file(name_table, \\\"name_table_all.csv\\\", \\\",\\\", csv.QUOTE_ALL)\\n write_csv_file(name_table, \\\"name_table_nonnumeric.csv\\\", \\\",\\\", csv.QUOTE_NONNUMERIC)\\n #write_csv_file(name_table, \\\"name_table_none.csv\\\", \\\",\\\", csv.QUOTE_NONE) # no escapechar is set for lots of quotes\",\n \"def csv_quoting_examples():\\n name_table = read_csv_file(\\\"name_table.csv\\\", \\\",\\\")\\n name_table.append([1, 2, 3])\\n write_csv_file(name_table, \\\"name_table_minimal.csv\\\", \\\",\\\", csv.QUOTE_MINIMAL)\\n write_csv_file(name_table, \\\"name_table_all.csv\\\", \\\",\\\", csv.QUOTE_ALL)\\n write_csv_file(name_table, \\\"name_table_nonnumeric.csv\\\", \\\",\\\", csv.QUOTE_NONNUMERIC)\\n #write_csv_file(name_table, \\\"name_table_none.csv\\\", \\\",\\\", csv.QUOTE_NONE) # no escapechar is set for lots of quotes\",\n \"def escape_quotes(self, val):\\n if val.startswith(self.quote) and val.endswith(self.quote):\\n # make sure any previously escaped quotes are not re-escaped\\n middle = val[1:-1].replace(\\\"\\\\\\\\\\\" + self.quote, self.quote)\\n middle = middle.replace(self.quote, \\\"\\\\\\\\\\\" + self.quote)\\n val = \\\"%s%s%s\\\" % (self.quote, middle, self.quote)\\n\\n return val\",\n \"def _add_argument(self, args=''):\\n\\n sys.argv += args.split(' ')\",\n \"def strip_quotes(arg):\\n quote_chars = '\\\"' + \\\"'\\\"\\n\\n if len(arg) > 1 and arg[0] == arg[-1] and arg[0] in quote_chars:\\n arg = arg[1:-1]\\n return arg\",\n \"def strip_quotes(arg):\\n quote_chars = '\\\"' + \\\"'\\\"\\n\\n if len(arg) > 1 and arg[0] == arg[-1] and arg[0] in quote_chars:\\n arg = arg[1:-1]\\n return arg\",\n \"def arg_list():\\n arg_list = [\\n ['-d', '--domain', 'Specify the domain you are using'],\\n ['-t', '--template-path', 'Specify template path'],\\n ['-s', '--secrets-path', 'Specify template path'],\\n ['-p', '--project', 'Specify a project name'],\\n ['-c', '--cloud-platform', 'Specify the platform used'],\\n ['-so', '--secrets-only', 'Generate secrets only'],\\n ['-db', '--database-host', 'Specify the database host'],\\n ['-dbc', '--database-connection-name', 'Specify the database connection name (GCP)'],\\n ['-sbn', '--storage-bucket-name', 'Specify storage bucket name'],\\n ['-sb', '--storage-backend', 'Specify storage backend s3/gcp/filesystem'],\\n ['--acm', '--aws-cert-arn', 'Specify AWS ACM'],\\n ['--sg-id', '--aws-alg-sg-id', 'Specify AWS SG ID'],\\n ['--sentry', '--senty-dsn', 'Specify Sentry DSN'],\\n ['-e', '--environment', 'Specify environment'],\\n ['-g', '--gather', 'enable Gather yes or no'],\\n ['--cm', '--cert-manager', 'Using cert manager?'],\\n ['-m', '--modules', 'Aether modules i.e odk,ui,sync'],\\n ['-r', '--redis-url', 'Redis endpoint for CouchDB sync'],\\n ['-cdb', '--couchdb-url', 'Redis endpoint for CouchDB sync'],\\n ['-gc', '--google-client-id', ' Google client ID for CouchDB sync']\\n ]\\n return arg_list\",\n \"def _make_posix_command():\\n qsearch = _re.compile(r'[^a-zA-Z\\\\d_./-]').search\\n needq = lambda x: not x or qsearch(x)\\n\\n def posix_command(command, *args, **kwargs):\\n \\\"\\\"\\\"\\n Return a POSIX shell suitable commandline\\n\\n Either args or kwargs or neither of them can be set. There cannot be\\n set both of them.\\n\\n :Parameters:\\n `command` : ``str``\\n Generic commandline, possibly containing substitutions, filled by\\n args or kwargs. See `split_command` for generic commandline\\n syntax.\\n\\n `args` : ``tuple``\\n Substitution tuple\\n\\n `kwargs` : ``dict``\\n Substitution dict\\n\\n :Return: Strictly quoted shell commandline for POSIX shells\\n :Rtype: ``str``\\n \\\"\\\"\\\"\\n # pylint: disable = redefined-outer-name\\n return ' '.join([\\n \\\"'%s'\\\" % (token.replace(\\\"'\\\", \\\"'\\\\\\\\''\\\")) if needq(token) else token\\n for token in map(_make_formatter(*args, **kwargs),\\n split_command(command))\\n ])\\n return posix_command\",\n \"def _create_arguments(self, args):\\n assert isinstance(args, (list, tuple))\\n\\n arguments = []\\n index = 0\\n for arg in args:\\n assert isinstance(arg, (list, tuple))\\n assert len(arg) == 2 or len(arg) == 3\\n\\n identifier = arg[0]\\n if isinstance(arg[1], str):\\n idl_type = self._create_type(\\n arg[1], is_optional=(len(arg) == 3))\\n else:\\n idl_type = arg[1]\\n\\n default_value = None\\n if len(arg) == 3:\\n default_value = self._create_literal_constant(arg[2])\\n\\n arguments.append(\\n Argument.IR(\\n identifier,\\n index=index,\\n idl_type=idl_type,\\n default_value=default_value))\\n\\n index += 1\\n\\n return arguments\",\n \"def set_magic_quotes_runtime():\\n raise NotImplementedError()\",\n \"def test_quote_str():\\n assert pmisc.quote_str(5) == 5\\n assert pmisc.quote_str(\\\"Hello!\\\") == '\\\"Hello!\\\"'\\n assert pmisc.quote_str('He said \\\"hello!\\\"') == \\\"'He said \\\\\\\"hello!\\\\\\\"'\\\"\",\n \"def test_parse_simple_quote_with_double_quote(self):\\n with self.assertRaisesRegexp(Exception, re.escape(\\\"the quote included a (\\\\\\\") character\\\")):\\n api.parse_quote(\\\" We accept the love we think we \\\\\\\" deserve. - Stephen Chbosky\\\",\\n simple_format=True)\",\n \"def build_command_line_parameters(params, command_name=\\\"-param\\\"):\\n if params is None:\\n return \\\"\\\"\\n res = []\\n for k, v in sorted(params.items()):\\n if '\\\"' in v:\\n v = v.replace('\\\"', '\\\\\\\\\\\"')\\n one = '{2} {0}=\\\"{1}\\\"'.format(k, v, command_name)\\n res.append(one)\\n return \\\" \\\".join(res)\",\n \"def cmdstr(cmd):\\n if isinstance(cmd, str):\\n return cmd\\n\\n quoted = []\\n for arg in cmd:\\n if isinstance(arg, Path):\\n arg = str(arg)\\n if ' ' in arg:\\n arg = '\\\"%s\\\"' % (arg,)\\n quoted.append(arg)\\n return ' '.join(quoted)\",\n \"def _input_args(self, args: List[str]):\\n assert self._call is None, f\\\"You need to specify all inputs before calling `{self._call}`\\\"\\n assert isinstance(args, list), f\\\"{args} is a {type(args)}, expected a list of strings!\\\"\\n assert len(args) > 0, f\\\"Expected a non-empty argument list!\\\"\\n assert all(isinstance(a, str) for a in args), f\\\"Expected a list of strings, not {[type(a) for a in args]}!\\\"\\n # all arguments could potentially be filenames that we write to, so let's just add them\\n self._write_files |= set(args)\\n # add dummy argument zero\\n args = [\\\"\\\"] + args\\n # allocate args in memory\\n arg_strings = [self._str(a, \\\"arg\\\") for a in args]\\n # allocate a pointer array for argv\\n self.data += [f\\\"argv: .word \\\" + \\\" \\\".join(\\\"0\\\" for _ in range(len(args)))]\\n # load argc and argv\\n self._args += [\\\"\\\", \\\"# argument count in a0\\\", f\\\"li a0, {len(args)}\\\"]\\n self._args += [\\\"\\\", \\\"# load pointers to argument strings into argv\\\", f\\\"la a1, argv\\\"]\\n for ii, aa in enumerate(arg_strings):\\n self._args += [f\\\"la t1, {aa}\\\", f\\\"sw t1, {ii * 4}(a1)\\\"]\",\n \"def get_command_arg_list(\\n self, command_name: str, to_parse: Union[Statement, str], preserve_quotes: bool\\n ) -> Tuple[Statement, List[str]]:\\n # Check if to_parse needs to be converted to a Statement\\n if not isinstance(to_parse, Statement):\\n to_parse = self.parse(command_name + ' ' + to_parse)\\n\\n if preserve_quotes:\\n return to_parse, to_parse.arg_list\\n else:\\n return to_parse, to_parse.argv[1:]\",\n \"def quote_list(the_list):\\n return [\\\"'%s'\\\" % element for element in the_list]\",\n \"def quote(m):\\n return '\\\"' + m + '\\\"'\",\n \"def escape_args(self, *args):\\n\\t\\treturn tuple((self.escape(arg) for arg in args))\",\n \"def _escapeString(self, value):\\n if '\\\"' in value and \\\"'\\\" in value:\\n substrings = value.split(\\\"\\\\\\\"\\\")\\n result = [\\\"concat(\\\"]\\n for substring in substrings:\\n result.append(\\\"\\\\\\\"%s\\\\\\\"\\\" % substring)\\n result.append(\\\", '\\\\\\\"', \\\")\\n result = result[0:-1]\\n if value.endswith('\\\"'):\\n result.append(\\\", '\\\\\\\"'\\\")\\n return \\\"\\\".join(result) + \\\")\\\"\\n\\n if '\\\"' in value:\\n return \\\"'%s'\\\" % value\\n return \\\"\\\\\\\"%s\\\\\\\"\\\" % value\",\n \"def rebuild_command(args):\\n return \\\"%s\\\\n\\\" % (\\\" \\\".join(args)).replace(\\\"\\\\\\\\\\\", \\\"\\\\\\\\\\\\\\\\\\\")\",\n \"def command_list_to_str(command):\\n return \\\" \\\".join(pipes.quote(i) for i in command)\",\n \"def quoteString(s):\\n if s is None:\\n return None\\n quoted = str(s).replace(\\\"\\\\\\\\\\\", \\\"\\\\\\\\\\\\\\\\\\\").replace(\\\"'\\\", \\\"\\\\\\\\'\\\")\\n return \\\"'{}'\\\".format(quoted)\",\n \"def test_sqpp_escape_double_quotes(self):\\n self.assertEqual(self.parser.parse_query('expr1 \\\\\\\\\\\" expr2 +(expr3) -expr4 \\\\\\\\\\\" + (expr5)'),\\n ['+', 'expr1', '+', '\\\\\\\\\\\"', '+', 'expr2', '+', 'expr3', '-', 'expr4', '+', '\\\\\\\\\\\"', '+', 'expr5'])\",\n \"def test_add_quote(self):\\n\\n # Set up for test\\n path = tests.test_util.init_quotefile(self.tempdir, \\\"quotes5.txt\\\")\\n quote = api.Quote(\\\" This is an added quote.\\\", \\\"Another author\\\", \\\"Publication\\\", [\\\"tag1, tag2\\\"])\\n\\n # Call method being tested\\n api.add_quote(path, quote)\\n\\n # Check results. Read the resulting text file and verify\\n with open(path, 'rb') as file:\\n data = file.read()\\n text_data = data.decode('utf-8')\\n expected = u'They that can give up essential liberty to obtain a little temporary safety deserve neither liberty nor safety. | Ben Franklin | | U' + os.linesep + \\\\\\n u'This is an added quote. | Another author | Publication | tag1, tag2' + os.linesep\\n self.assertEquals(expected, text_data)\",\n \"def quote_environment(env):\\n if not isinstance(env, dict):\\n raise TypeError(\\\"env must be a dict\\\")\\n\\n entries = []\\n for k, v in env.items():\\n qv = _double_up_quotes(str(v))\\n if \\\" \\\" in qv or \\\"'\\\" in qv:\\n qv = \\\"'\\\" + qv + \\\"'\\\"\\n entries.append(\\\"%s=%s\\\" % (k, qv))\\n\\n return \\\" \\\".join(entries)\",\n \"def standardise_quotes(self, val):\\n if val.startswith(self.altquote) and val.endswith(self.altquote):\\n middle = val[1:-1]\\n val = \\\"%s%s%s\\\" % (self.quote, middle, self.quote)\\n\\n val = self.escape_quotes(val)\\n\\n return val\",\n \"async def addquote(self, ctx, *, quote : str):\\n if not self.addquote_regex.match(quote):\\n await ctx.send(\\\"`Quote must be in this format (including surrounding single quotes):\\\\n'some quote here - quote author'`\\\")\\n else:\\n with open(\\\"data/quotes.txt\\\", \\\"a\\\") as text_file:\\n text_file.write(f\\\"{quote}\\\\n\\\")\\n await ctx.send('`Quote added!`')\",\n \"def arg_str(self):\\n\\n args = ['self']\\n args.extend([x.name for x in self.required_inputs])\\n args.extend([\\\"{}=None\\\".format(x.name) for x in self.optional_inputs])\\n\\n return \\\", \\\".join(args)\",\n \"def CreateArgFile( arguments, tmpDir ):\\n tmpFile = os.path.join( tmpDir, \\\"args.txt\\\" )\\n\\n with io.open( tmpFile, 'w', encoding=\\\"utf-8-sig\\\" ) as fileHandle:\\n fileHandle.write( \\\"\\\\n\\\".join( arguments ) )\\n\\n return tmpFile\",\n \"def _strip_quotes(file_arg):\\n return re.sub(\\\"^[\\\\'\\\\\\\"]|[\\\\'\\\\\\\"]$\\\", \\\"\\\", file_arg)\",\n \"def escape_triple_quotes(text):\\n return text.replace(u'\\\"\\\"\\\"', u'\\\\\\\\\\\"\\\\\\\\\\\"\\\\\\\\\\\"')\",\n \"def split_arguments(s, windows=IS_WINDOWS):\\n # from http://stackoverflow.com/a/35900070\\n if windows:\\n RE_CMD_LEX = r'''\\\"((?:\\\"\\\"|\\\\\\\\[\\\"\\\\\\\\]|[^\\\"])*)\\\"?()|(\\\\\\\\\\\\\\\\(?=\\\\\\\\*\\\")|\\\\\\\\\\\")'\\n r'|(&&?|\\\\|\\\\|?|\\\\d?>|[<])|([^\\\\s\\\"&|<>]+)|(\\\\s+)|(.)'''\\n else:\\n RE_CMD_LEX = r'''\\\"((?:\\\\\\\\[\\\"\\\\\\\\]|[^\\\"])*)\\\"|'([^']*)'|(\\\\\\\\.)|'\\n r'(&&?|\\\\|\\\\|?|\\\\d?\\\\>|[<])|([^\\\\s'\\\"\\\\\\\\&|<>]+)|(\\\\s+)|(.)'''\\n\\n args = []\\n accu = None # collects pieces of one arg\\n for qs, qss, esc, pipe, word, white, fail in re.findall(RE_CMD_LEX, s):\\n if word:\\n pass # most frequent\\n elif esc:\\n word = esc[1]\\n elif white or pipe:\\n if accu is not None:\\n args.append(accu)\\n if pipe:\\n args.append(pipe)\\n accu = None\\n continue\\n elif fail:\\n raise ValueError(\\\"invalid or incomplete shell string\\\")\\n elif qs:\\n word = qs.replace('\\\\\\\\\\\"', '\\\"').replace('\\\\\\\\\\\\\\\\', '\\\\\\\\')\\n if windows:\\n word = word.replace('\\\"\\\"', '\\\"')\\n else:\\n word = qss # may be even empty; must be last\\n\\n accu = (accu or '') + word\\n\\n if accu is not None:\\n args.append(accu)\\n\\n return args\",\n \"def test_sqpp_escape_single_quotes(self):\\n self.assertEqual(self.parser.parse_query(\\\"expr1 \\\\\\\\' expr2 +(expr3) -expr4 \\\\\\\\' + (expr5)\\\"),\\n ['+', 'expr1', '+', \\\"\\\\\\\\'\\\", '+', 'expr2', '+', 'expr3', '-', 'expr4', '+', \\\"\\\\\\\\'\\\", '+', 'expr5'])\",\n \"def csv_quote_escape(self, the_string):\\n the_string = the_string.replace('\\\"', r'\\\"\\\"')\\n\\n the_string = '\\\"' + the_string + '\\\"'\\n\\n return the_string\",\n \"def esc_quotes(strng):\\n\\n return strng.replace('\\\"','\\\\\\\\\\\"').replace(\\\"'\\\",\\\"\\\\\\\\'\\\")\",\n \"def add_arguments(cls):\\n return [\\n (('--yes',), dict(action='store_true', help='clean .git repo')),\\n (('--variable', '-s'),\\n dict(nargs='+', help='set extra variable,format is name:value')),\\n (('--skip-builtin',),\\n dict(action='store_true', help='skip replace builtin variable')),\\n\\n (('--dir',), dict(nargs='?', default=os.getcwd(),\\n help='set working directory')),\\n (('--debug',), dict(action='store_true', help='open debug mode')),\\n (('--dry-run',), dict(action='store_true',\\n help='print command instead execute it')),\\n (('--verbose', '-v'), dict(action='count')),\\n ]\",\n \"def test_parse_quotes_doublequote(self):\\n with self.assertRaisesRegexp(Exception, re.escape(\\\"the quote included a (\\\\\\\") character\\\")):\\n api.parse_quote(\\\" This is a quote\\\\\\\". | Author | Publication | tag1, tag2 , tag3 \\\",\\n simple_format=False)\",\n \"def test_sqpp_single_quotes(self):\\n self.assertEqual(self.parser.parse_query(\\\"(expr1) - expr2 | 'expressions - in + quotes | (are) not - parsed ' - (expr3) | expr4\\\"),\\n ['+', 'expr1', '-', 'expr2', '|', \\\"'expressions - in + quotes | (are) not - parsed '\\\", '-', 'expr3', '|', 'expr4'])\\n #['+', '+ \\\\'expressions - in + quotes | (are) not - parsed \\\\' | expr1 | expr4',\\n # '+', '- expr3 | expr1 | expr4',\\n # '+', '+ \\\\'expressions - in + quotes | (are) not - parsed \\\\' - expr2 | expr4',\\n # '+', '- expr3 - expr2 | expr4'])\",\n \"def quote(s):\\n if not s:\\n return \\\"''\\\"\\n if _find_unsafe(s) is None:\\n return \\\"'\\\" + s + \\\"'\\\"\\n\\n # use single quotes, and put single quotes into double quotes\\n # the string $'b is then quoted as '$'\\\"'\\\"'b'\\n return \\\"'\\\" + s.replace(\\\"'\\\", \\\"'\\\\\\\"'\\\\\\\"'\\\") + \\\"'\\\"\",\n \"def test_sqpp_double_quotes(self):\\n self.assertEqual(self.parser.parse_query(\\n '(expr1) - expr2 | \\\"expressions - in + quotes | (are) not - parsed \\\" - (expr3) | expr4'),\\n ['+', 'expr1', '-', 'expr2', '|', '\\\"expressions - in + quotes | (are) not - parsed \\\"', '-', 'expr3', '|', 'expr4'])\\n #['+', '+ \\\"expressions - in + quotes | (are) not - parsed \\\" | expr1 | expr4',\\n # '+', '- expr3 | expr1 | expr4',\\n # '+', '+ \\\"expressions - in + quotes | (are) not - parsed \\\" - expr2 | expr4',\\n # '+', '- expr3 - expr2 | expr4'])\",\n \"def handle_arguments(self, string, root, opening, closing):\\n\\n\\t\\t# The actual argument string (ignore whitespace)\\n\\t\\targs = string[opening + 1 : closing].replace(\\\" \\\", \\\"\\\")\\n\\n\\t\\t# The argument sequence must be at the start of the phrase\\n\\t\\t# and must match the allowed argument regular expression\\n\\t\\tif opening > 0 or not self.arguments.match(args):\\n\\n\\t\\t\\tif opening == 0:\\n\\t\\t\\t\\traise errors.ParseError(\\\"Invalid argument sequence!\\\")\\n\\n\\t\\t\\t# If escape_meta does indeed escape a character and removes\\n\\t\\t\\t# a backward slash, the positions 'opening' and 'closing' are no\\n\\t\\t\\t# longer valid. escape_meta does a search for the next meta\\n\\t\\t\\t# character though, which is then the closing parantheses,\\n\\t\\t\\t# so we can use its index value (in the now escaped string)\\n\\t\\t\\tstring, meta = self.escape_meta(string, opening)\\n\\t\\t\\tstring, meta = self.escape_meta(string, meta.start())\\n\\n\\t\\t\\treturn string, root, meta\\n\\n\\t\\tif \\\"!\\\" in args:\\n\\t\\t\\troot.override = True\\n\\t\\t\\targs = args.replace(\\\"!\\\", \\\"\\\")\\n\\n\\t\\tif \\\"+\\\" in args:\\n\\t\\t\\troot.increment = True\\n\\t\\t\\targs = args.replace(\\\"+\\\", \\\"\\\")\\n\\n\\t\\troot.arguments = [int(i) for i in args.split(\\\",\\\") if i]\\n\\n\\t\\t# Remove the argument string including parantheses\\n\\t\\tstring = string[closing + 1:]\\n\\n\\t\\tmeta = self.meta.search(string)\\n\\n\\t\\treturn string, root, meta\",\n \"def wrap_with_in_single_quote(s):\\n return \\\"'{}'\\\".format(s)\",\n \"def wrap_with_in_single_quote(s):\\n return \\\"'{}'\\\".format(s)\",\n \"def collect_quotes(self):\\n quotes = [q for q in self.quotes if not q.invalid]\\n childquotes = [q for s in self.children for q in s.collect_quotes()]\\n return quotes + childquotes\",\n \"def test_split_arguments_and_remove_quotes(self, unused_test_name,\\n input_string, expected):\\n result = shlex.split(input_string)\\n self.assertEqual(result, expected)\",\n \"def arg_to_str_list(self) -> list:\\n arg_list = []\\n for arg in [*self.args]:\\n if hasattr(arg, \\\"_ref\\\"):\\n arg_list.append(arg.ref)\\n else:\\n arg_list.append(arg)\\n return arg_list\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7215548","0.6580144","0.6086504","0.5937315","0.5752094","0.56268567","0.55315644","0.544126","0.54088515","0.533452","0.5203533","0.5165263","0.51386887","0.51292366","0.5123674","0.50810844","0.5078379","0.5072677","0.50487226","0.50456464","0.50137526","0.5012342","0.49789307","0.49776384","0.49652985","0.49605203","0.49450463","0.49227175","0.49053797","0.48929685","0.48775458","0.48465735","0.48425567","0.48402077","0.48275083","0.4822483","0.4811011","0.47775143","0.47770435","0.47758552","0.476481","0.47060865","0.46915329","0.46902505","0.4682298","0.46787202","0.46729308","0.46672603","0.46648324","0.46573478","0.46356702","0.4627889","0.46251234","0.46249396","0.46249396","0.46130103","0.46126008","0.4591743","0.4591743","0.456812","0.45482042","0.45474353","0.45468777","0.45349622","0.4529316","0.4526354","0.4525817","0.45214224","0.45205864","0.44964597","0.4478879","0.44787255","0.44645244","0.4457844","0.44390586","0.44342318","0.44324377","0.44182742","0.44173643","0.44040787","0.43975058","0.43966123","0.43896133","0.43862846","0.43777207","0.4366334","0.43654752","0.43628347","0.43617377","0.43591306","0.43523097","0.4347738","0.43444544","0.43412954","0.4321566","0.4315471","0.4315471","0.43153143","0.43110982","0.4307662"],"string":"[\n \"0.7215548\",\n \"0.6580144\",\n \"0.6086504\",\n \"0.5937315\",\n \"0.5752094\",\n \"0.56268567\",\n \"0.55315644\",\n \"0.544126\",\n \"0.54088515\",\n \"0.533452\",\n \"0.5203533\",\n \"0.5165263\",\n \"0.51386887\",\n \"0.51292366\",\n \"0.5123674\",\n \"0.50810844\",\n \"0.5078379\",\n \"0.5072677\",\n \"0.50487226\",\n \"0.50456464\",\n \"0.50137526\",\n \"0.5012342\",\n \"0.49789307\",\n \"0.49776384\",\n \"0.49652985\",\n \"0.49605203\",\n \"0.49450463\",\n \"0.49227175\",\n \"0.49053797\",\n \"0.48929685\",\n \"0.48775458\",\n \"0.48465735\",\n \"0.48425567\",\n \"0.48402077\",\n \"0.48275083\",\n \"0.4822483\",\n \"0.4811011\",\n \"0.47775143\",\n \"0.47770435\",\n \"0.47758552\",\n \"0.476481\",\n \"0.47060865\",\n \"0.46915329\",\n \"0.46902505\",\n \"0.4682298\",\n \"0.46787202\",\n \"0.46729308\",\n \"0.46672603\",\n \"0.46648324\",\n \"0.46573478\",\n \"0.46356702\",\n \"0.4627889\",\n \"0.46251234\",\n \"0.46249396\",\n \"0.46249396\",\n \"0.46130103\",\n \"0.46126008\",\n \"0.4591743\",\n \"0.4591743\",\n \"0.456812\",\n \"0.45482042\",\n \"0.45474353\",\n \"0.45468777\",\n \"0.45349622\",\n \"0.4529316\",\n \"0.4526354\",\n \"0.4525817\",\n \"0.45214224\",\n \"0.45205864\",\n \"0.44964597\",\n \"0.4478879\",\n \"0.44787255\",\n \"0.44645244\",\n \"0.4457844\",\n \"0.44390586\",\n \"0.44342318\",\n \"0.44324377\",\n \"0.44182742\",\n \"0.44173643\",\n \"0.44040787\",\n \"0.43975058\",\n \"0.43966123\",\n \"0.43896133\",\n \"0.43862846\",\n \"0.43777207\",\n \"0.4366334\",\n \"0.43654752\",\n \"0.43628347\",\n \"0.43617377\",\n \"0.43591306\",\n \"0.43523097\",\n \"0.4347738\",\n \"0.43444544\",\n \"0.43412954\",\n \"0.4321566\",\n \"0.4315471\",\n \"0.4315471\",\n \"0.43153143\",\n \"0.43110982\",\n \"0.4307662\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.74514025"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":85599,"cells":{"query":{"kind":"string","value":"Quote a dict of strings using the Condor submit file \"new\" environment quoting rules. Returns str The environment in quoted form. Warnings You will need to surround the result in doublequotes before using it in the Environment attribute. Examples >>> from collections import OrderedDict >>> quote_environment(OrderedDict([(\"one\", 1), (\"two\", '\"2\"'), (\"three\", \"spacey 'quoted' value\")])) 'one=1 two=\"\"2\"\" three=\\'spacey \\'\\'quoted\\'\\' value\\''"},"document":{"kind":"string","value":"def quote_environment(env):\n if not isinstance(env, dict):\n raise TypeError(\"env must be a dict\")\n\n entries = []\n for k, v in env.items():\n qv = _double_up_quotes(str(v))\n if \" \" in qv or \"'\" in qv:\n qv = \"'\" + qv + \"'\"\n entries.append(\"%s=%s\" % (k, qv))\n\n return \" \".join(entries)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _build_environment(envdict):\n lines = []\n for k, v in envdict.iteritems():\n if \" \" in v: # NOTE: per the spec, one might want to handle all 'whitespace' chars.\n v = v.replace(\"'\", \"''\")\n v = \"'%s'\" % v\n v = v.replace('\"', '\"\"')\n lines.append('%s=%s' % (k, v))\n return '\"%s\"' % ' '.join(lines)","def add_to_environment(v):\n return \"Environment='{}'\".format(\n \"\\nEnvironment=\".join(\n \"{}={}\".format(k, \"\".join(map(str, v))) for k, v in iteritems(v)\n )\n if isinstance(v, dict)\n else v\n )","def test_single_quoted(self):\n e = yaenv.core.EnvVar(\"key = 'value'\\n\")\n assert e.key == 'key'\n assert e.value == 'value'\n assert not e._interpolate","def test_double_quoted(self):\n e = yaenv.core.EnvVar('key = \"value\"\\n')\n assert e.key == 'key'\n assert e.value == 'value'\n assert e._interpolate","def quote_json(_json: dict) -> str:\n stringified = json.dumps(_json)\n return quote(stringified)","def __MakeEnvironment(self):\n environment= os.environ.copy()\n\n for key, value in self.__context.items():\n if type(value) is str:\n name = \"QMV_\" + key.replace(\".\", \"__\")\n environment[name]= value\n\n return environment","def MakeEnvironment(self, context):\n\n # Start with any environment variables that are already present\n # in the environment.\n environment = os.environ.copy()\n # Copy context variables into the environment.\n for key, value in context.items():\n name = \"QMV_\" + key\n environment[name] = value\n # Extract additional environment variable assignments from the\n # 'Environment' field.\n for assignment in self.environment:\n if \"=\" in assignment:\n # Break the assignment at the first equals sign.\n variable, value = string.split(assignment, \"=\", 1)\n environment[variable] = value\n else:\n raise ValueError, \\\n qm.error(\"invalid environment assignment\",\n assignment=assignment)\n return environment","def MakeEnvironment(self, context):\n\n # Start with any environment variables that are already present\n # in the environment.\n environment = os.environ.copy()\n # Copy context variables into the environment.\n for key, value in context.items():\n name = \"QMV_\" + key\n environment[name] = value\n return environment","def flatten_and_write(dotenv_path, dotenv_as_dict, quote_mode='always'):\n with open(dotenv_path, 'w') as f:\n for k, v in dotenv_as_dict.items():\n str_format = _get_format(v, quote_mode)\n f.write(str_format.format(key=k, value=v))\n return True","def _quote(v):\n return '\"' + v + '\"' if ' ' in v else v","def test_unquoted(self):\n e = yaenv.core.EnvVar('key = value\\n')\n assert e.key == 'key'\n assert e.value == 'value'\n assert e._interpolate","def wrapper_environment(args):\n\n return {\n ENVIRONMENT_KEY: json.dumps({\n 'verbose': args.verbose,\n 'cc': shlex.split(args.cc),\n 'cxx': shlex.split(args.cxx)\n })\n }","def to_env(self, d):\n return {k.upper(): str(v) for k, v in d.items()}","def get_formatted_env_vars() -> str:\n res = \"\"\n for k, v in os.environ.items():\n res += '{0}={1}\\n'.format(k, v)\n return res","def putenv(space, envstr):\n try:\n key, value = envstr.split(\"=\")\n except ValueError:\n return space.w_True\n os.environ[key] = value\n return space.w_True","def quote(value, *args, **kwargs):\n return parse.quote(encode(value, *args, **kwargs))","def test_env_top_dict(self, monkeypatch):\n with open(\".scuba.yml\", \"w\") as f:\n f.write(\n r\"\"\"\n image: na\n environment:\n FOO: This is foo\n FOO_WITH_QUOTES: \"\\\"Quoted foo\\\"\" # Quotes included in value\n BAR: \"This is bar\"\n MAGIC: 42\n SWITCH_1: true # YAML boolean\n SWITCH_2: \"true\" # YAML string\n EMPTY: \"\"\n EXTERNAL: # Comes from os env\n EXTERNAL_NOTSET: # Missing in os env\n \"\"\"\n )\n\n monkeypatch.setenv(\"EXTERNAL\", \"Outside world\")\n monkeypatch.delenv(\"EXTERNAL_NOTSET\", raising=False)\n\n config = scuba.config.load_config(\".scuba.yml\")\n\n expect = dict(\n FOO=\"This is foo\",\n FOO_WITH_QUOTES='\"Quoted foo\"',\n BAR=\"This is bar\",\n MAGIC=\"42\", # N.B. string\n SWITCH_1=\"True\", # Unfortunately this is due to str(bool(1))\n SWITCH_2=\"true\",\n EMPTY=\"\",\n EXTERNAL=\"Outside world\",\n EXTERNAL_NOTSET=\"\",\n )\n assert expect == config.environment","def env_dict(self): # type: () -> t.Dict[str, str]\n return dict((item[0], item[1]) for item in [e.split('=', 1) for e in self.env])","def _quote(self, arg):\n arg = arg.replace('\\\\', '\\\\\\\\')\n arg = arg.replace('\"', '\\\\\"')\n return '\"%s\"' % arg","def _escapeString(self, value):\n if '\"' in value and \"'\" in value:\n substrings = value.split(\"\\\"\")\n result = [\"concat(\"]\n for substring in substrings:\n result.append(\"\\\"%s\\\"\" % substring)\n result.append(\", '\\\"', \")\n result = result[0:-1]\n if value.endswith('\"'):\n result.append(\", '\\\"'\")\n return \"\".join(result) + \")\"\n\n if '\"' in value:\n return \"'%s'\" % value\n return \"\\\"%s\\\"\" % value","def _make_env(environment):\n env = []\n for key, value in environment.items():\n env.append({\"name\": key, \"value\": value})\n\n return env","def esc_quotes(strng):\n\n return strng.replace('\"','\\\\\"').replace(\"'\",\"\\\\'\")","def patch_environment(**kwargs):\n for key, value in kwargs.items():\n os.environ[key.upper()] = str(value)\n\n yield\n\n for key in kwargs:\n if key.upper() in os.environ:\n del os.environ[key.upper()]","def quoted(val: str) -> str:\n return f'\"{val}\"' if ' ' in val else val","def _generate_environment(self):\n envvars = {}\n for key in self.envvars:\n try:\n envvars[key] = os.environ[key]\n except KeyError:\n continue\n\n # Warn the user that we cannot support secrets\n if envvars:\n logger.warning(\"This API does not support environment secrets.\")\n return envvars","def shellquote(s):\n return '\"' + s.replace(\"'\", \"'\\\\''\") + '\"'","def create_dictionary_string(\n dict_,\n open_dict=\"<<\",\n close_dict=\">>\",\n field_join=\"\\n\",\n key_value_join=\" \",\n has_empty_fields=False,\n):\n if has_empty_fields:\n dict_ = clear_empty_fields(dict_)\n\n return \"\".join(\n [\n open_dict,\n field_join.join([key_value_join.join(f) for f in dict_.items()]),\n close_dict,\n ]\n )","def urllib_quote_parameters(inputdictionary):\r\n if type(inputdictionary) is not dict:\r\n raise TypeError(\"urllib_quote_parameters' inputstringdictionary parameter must be a dict, not '\"+str(type(inputstring))+\"'\")\r\n\r\n quoted_keyvals = []\r\n for key, val in inputdictionary.items():\r\n quoted_keyvals.append(\"%s=%s\" % (urllib_quote(key), urllib_quote(val)))\r\n\r\n return \"&\".join(quoted_keyvals)","def quote(value):\n return DoubleQuotedScalarString(value)","def escape_variables(environ):\n return {key: environ[key].replace('%', '%%') for key in environ}","def _StringQuote(s, quote='\"', escape='\\\\'):\n entity = {'\\f': '\\\\f', '\\n': '\\\\n', '\\r': '\\\\r', '\\t': '\\\\t'}\n chars = []\n if quote:\n chars.append(quote)\n for c in s:\n if c in (escape, quote):\n chars.append(escape)\n elif c in entity:\n c = entity[c]\n chars.append(c)\n if quote:\n chars.append(quote)\n return ''.join(chars)","def Environ(envstring):\n try:\n envint = int(envstring)\n except ValueError:\n return os.environ.get(envstring, \"\")\n # Is an integer - need to get the envint'th value\n try:\n return \"%s=%s\" % (list(os.environ.keys())[envint], list(os.environ.values())[envint])\n except IndexError:\n return \"\"","def _dict_to_args(self, arg_dict):\n if arg_dict:\n yield \"--{}=data:application/json;charset=utf-8,{}\".format(\n self._CONFIG_FLAG.name,\n urllib.parse.quote(json_encode(arg_dict, pretty=False), encoding=\"utf-8\")\n )","def environ_expansions():\r\n global _environ_expansions\r\n\r\n if _environ_expansions:\r\n return _environ_expansions\r\n\r\n _environ_expansions = {}\r\n for key, value in os.environ.items():\r\n _environ_expansions['ENV_%s' % key] = value\r\n\r\n return _environ_expansions","def standardise_quotes(self, val):\n if val.startswith(self.altquote) and val.endswith(self.altquote):\n middle = val[1:-1]\n val = \"%s%s%s\" % (self.quote, middle, self.quote)\n\n val = self.escape_quotes(val)\n\n return val","def quote(*a, **kw):\n return quote(*a, **kw)","def get_execution_envs_str(self):\n\n return ' '.join(str(x) for x in self.execution_envs)","def export_vars(env_vars_dict):\n env_vars = ['{}={}'.format(k, env_vars_dict[k])\n for k in env_vars_dict.keys()]\n with open(\"vars.env\", \"w\") as file:\n for item in env_vars[:-1]:\n file.write(\"{}\\n\".format(item))\n file.write(\"{}\".format(env_vars[-1]))","def overwrite_environment_variable(self, key, value):\n if value is not None:\n self._printer(\"$env:{0} = \\\"{1}\\\"\".format(key, value))\n else:\n self._printer(\"$env:{0} = \\\"\\\"\".format(key))","def _escape_string(s, surrounding_quote='\"'):\n s = s.replace('\\\\', '\\\\\\\\')\n if surrounding_quote == '\"':\n s = s.replace('\"', r'\\\"')\n if surrounding_quote == \"'\":\n s = s.replace(\"'\", r\"\\'\")\n return s","def quote_arguments(args):\n if isinstance(args, str):\n args_list = [args]\n else:\n args_list = args\n\n quoted_args = []\n for a in args_list:\n qa = _double_up_quotes(a)\n if \" \" in qa or \"'\" in qa:\n qa = \"'\" + qa + \"'\"\n quoted_args.append(qa)\n return \" \".join(quoted_args)","def augmented_environment(self):\n env = os.environ.copy()\n env.update(self.environ)\n return env","def quote(s):\n return unescape(quoteattr(s))","def _wrap_ssm_env(self, e):\n # Value should be serialized JSON\n ssm_e = {\"Parameter\": {\"Name\": \"environment\", \"Value\": json.dumps(e)}}\n return ssm_e","def argument_quote(argument):\n argument = argument.replace('\"', '\"\"')\n if ' ' in argument:\n argument = argument.replace(\"'\", \"''\")\n argument = \"'\" + argument + \"'\"\n return argument","def escape_quotes(self, val):\n if val.startswith(self.quote) and val.endswith(self.quote):\n # make sure any previously escaped quotes are not re-escaped\n middle = val[1:-1].replace(\"\\\\\" + self.quote, self.quote)\n middle = middle.replace(self.quote, \"\\\\\" + self.quote)\n val = \"%s%s%s\" % (self.quote, middle, self.quote)\n\n return val","def test_get_environment_string(self):\n pass","def save_envs_to_file(file_path=constants.ENV_FILE_DEFAULT_PATH.value):\n #pylint: disable=unspecified-encoding\n with open(file_path, \"w\") as file:\n for key, value in environ.items():\n if key in constants.ENV_FILE_ALLOWED_KEYS.value:\n file.write(\"{}={}\\n\".format(key, value))","def shQuote(text):\n\treturn \"'%s'\" % text.replace(\"'\", r\"'\\''\")","def _env(env):\n return dict((key.strip(), val)\n for line in env.strip().splitlines()\n for key, _, val in [line.partition('=')])","def update_env_from_string(env_string):\n excluded_keys = [\"_\", \"SHLVL\", \"PWD\", \"OLDPWD\"]\n env = os.environ\n for line in env_string.split(\"\\n\"):\n (key, _, value) = line.partition(\"=\")\n if key and value and key not in excluded_keys:\n env[key] = value\n return env","def __construct_mail_env(self, env):\n\n if isinstance(env, dict):\n for k, v in env.items():\n self.env_content += \"\"\"\n <tr><td>{}</td><td>{}</td></tr>\n \"\"\".format(k, v)\n return self.env_content","def get_environ():\n # Manually set environment.\n if FLAGS.env is not None:\n return BasicEnvironment.from_json(open(FLAGS.env, \"r\").read())\n\n if FLAGS.data_folder is None:\n data_folder = FLAGS.dataset\n else:\n data_folder = FLAGS.data_folder\n exp_id = \"exp_\" + FLAGS.dataset + \"_\" + FLAGS.model\n if FLAGS.id is None:\n exp_id = gen_id(exp_id)\n else:\n exp_id = FLAGS.id\n return BasicEnvironment(\n device=get_device(FLAGS.gpu),\n dataset=FLAGS.dataset,\n data_folder=data_folder,\n logs_folder=FLAGS.logs,\n save_folder=FLAGS.results,\n run_validation=FLAGS.validation,\n verbose=FLAGS.verbose,\n exp_id=exp_id,\n description=FLAGS.description,\n valid_num_fold=FLAGS.valid_num_fold,\n valid_fold_id=FLAGS.valid_fold_id)","def argument_list_quote(arguments):\n args = []\n for arg in arguments:\n args.append(argument_quote(arg))\n return '\"%s\"' % ' '.join(args)","def environment_setting_format(value):\n try:\n env_name, env_value = value.split('=')\n except ValueError:\n message = (\"Incorrectly formatted enviroment settings. \"\n \"Argmuent values should be in the format a=b c=d\")\n raise ValueError(message)\n else:\n return {'name': env_name, 'value': env_value}","def env_str(name: str, default: str) -> str:\n value = stringify(env.get(name))\n return default if value is None else value","def _quote_arguments(args):\n return map(lambda x: '\"{}\"'.format(x) if ' ' in x else '{}'.format(x), args)","def test_environment_str_conversion():\n for l in list(environments.data):\n e = environments[l]\n assert str(e) == e.name","def quote(value):\n single = value.find(\"'\")\n double = value.find('\"')\n multiline = value.find('\\n') != -1\n if multiline or ((single != -1) and (double != -1)):\n if value.find('\"\"\"') == -1 and value[0] != '\"' and value[-1] != '\"':\n s = '\"\"\"%s\"\"\"' % value\n else:\n s = \"'''%s'''\" % value\n elif (single != -1) and (double == -1):\n s = '\"%s\"' % value\n else:\n s = \"'%s'\" % value\n return s","def prepend_environment_variable(self, key, value):\n script_keys = {\n \"k\": key,\n \"v\": value\n }\n script = \"$env:{k} = \\\"{v};$env:{k}\\\"\".format(**script_keys)\n self._printer(script)","def test_env_top_list(self, monkeypatch):\n with open(\".scuba.yml\", \"w\") as f:\n f.write(\n r\"\"\"\n image: na\n environment:\n - FOO=This is foo # No quotes\n - FOO_WITH_QUOTES=\"Quoted foo\" # Quotes included in value\n - BAR=This is bar\n - MAGIC=42\n - SWITCH_2=true\n - EMPTY=\n - EXTERNAL # Comes from os env\n - EXTERNAL_NOTSET # Missing in os env\n \"\"\"\n )\n\n monkeypatch.setenv(\"EXTERNAL\", \"Outside world\")\n monkeypatch.delenv(\"EXTERNAL_NOTSET\", raising=False)\n\n config = scuba.config.load_config(\".scuba.yml\")\n\n expect = dict(\n FOO=\"This is foo\",\n FOO_WITH_QUOTES='\"Quoted foo\"',\n BAR=\"This is bar\",\n MAGIC=\"42\", # N.B. string\n SWITCH_2=\"true\",\n EMPTY=\"\",\n EXTERNAL=\"Outside world\",\n EXTERNAL_NOTSET=\"\",\n )\n assert expect == config.environment","def generate_activate_content(self):\n content = \"\\nimport os\"\n for name, value in self.items():\n content += \"\\nos.environ['{name}'] = '{value}'\".format(\n name=name, value=value\n )\n return content","def set_environment_variables(env_dict, session):\n for key, value in env_dict.items():\n session.env[key] = value","def quote(m):\n return '\"' + m + '\"'","def env(name, value):\n import os\n os.environ[name] = value","def getenv(space, var):\n e = os.environ.get(var)\n if e is None:\n return space.w_False\n return space.newstr(e)","def environ_to_dict(environ):\n return dict((k, v) for k, v in environ.iteritems()\n if k.isupper())","def generic_env_configure_vars(self, verbose=False):\n\n if self.settings.os == \"Windows\":\n self.output.fatal(\"Cannot build on Windows, sorry!\")\n return\n\n if self.settings.os == \"Linux\" or self.settings.os == \"Macos\":\n libs = 'LIBS=\"%s\"' % \" \".join([\"-l%s\" % lib for lib in self.deps_cpp_info.libs])\n ldflags = 'LDFLAGS=\"%s\"' % \" \".join([\"-L%s\" % lib for lib in self.deps_cpp_info.lib_paths]) \n archflag = \"-m32\" if self.settings.arch == \"x86\" else \"\"\n cflags = 'CFLAGS=\"-fPIC %s %s\"' % (archflag, \" \".join(self.deps_cpp_info.cflags))\n cpp_flags = 'CPPFLAGS=\"%s %s\"' % (archflag, \" \".join(self.deps_cpp_info.cppflags))\n command = \"env %s %s %s %s\" % (libs, ldflags, cflags, cpp_flags)\n # elif self.settings.os == \"Windows\" and self.settings.compiler == \"Visual Studio\":\n # cl_args = \" \".join(['/I\"%s\"' % lib for lib in self.deps_cpp_info.include_paths])\n # lib_paths= \";\".join(['\"%s\"' % lib for lib in self.deps_cpp_info.lib_paths])\n # command = \"SET LIB=%s;%%LIB%% && SET CL=%s\" % (lib_paths, cl_args)\n # if verbose:\n # command += \" && SET LINK=/VERBOSE\"\n \n return command","def embeded_triple_quotes():\n pass","def escape(orig):\n return '\"{}\"'.format(orig.replace('\\\\', '\\\\\\\\').replace('\"', '\\\\\"'))","def quote_spaces(arg):\n if ' ' in arg or '\\t' in arg:\n return '\"%s\"' % arg\n else:\n return str(arg)","def set_magic_quotes_runtime():\n raise NotImplementedError()","def _create_environment(config):\n if isinstance(config.env, str):\n env = gym.make(config.env)\n else:\n env = config.env()\n if config.max_length:\n env = tools.wrappers.LimitDuration(env, config.max_length)\n env = tools.wrappers.RangeNormalize(env)\n env = tools.wrappers.ClipAction(env)\n env = tools.wrappers.ConvertTo32Bit(env)\n return env","def json_escape(context, value):\n\n return json.dumps(value).strip('\"')","def quote_all(parameters: Dict[str, Any]) -> Dict[str, Any]:\n return {key: quote_plus(value) if isinstance(value, str) else value for key, value in parameters.items()}","def set_environment_variables(args):\n credentials = args.gcp_application_credentials\n try:\n json_credentials = json.loads(credentials)\n fd, path = tempfile.mkstemp()\n print(f'Storing json credentials temporarily at {path}')\n with os.fdopen(fd, 'w') as tmp:\n tmp.write(credentials)\n os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = path\n return path\n except Exception:\n print('Using specified json credentials file')\n os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = credentials\n return","def quoteString(s):\n if s is None:\n return None\n quoted = str(s).replace(\"\\\\\", \"\\\\\\\\\").replace(\"'\", \"\\\\'\")\n return \"'{}'\".format(quoted)","def env_lines_to_dict(self, env_lines):\n env_dict = {}\n for env_line in env_lines:\n split_env_line = shlex.split(env_line)\n if split_env_line[0] == \"export\":\n split_env_line = split_env_line[1:]\n for item in split_env_line:\n if \"=\" in item:\n k, v = item.split(\"=\", 1)\n env_dict[k] = v\n return env_dict","def environment_variable_string(self, name):\n return \"$(\" + name + \")\"","def quot(string):\r\n return string.replace('\"', \"'\")","def safe_quoted_string(value):\n validate_safe_string(value)\n return u'\\'{}\\''.format(value)","def dumpenv(self):\n\n print('-------------------------------')\n pprint.pprint(dict(os.environ))\n print('-------------------------------')","def env(\n self,\n env: Dict[str, Optional[str]],\n ) -> Iterator[None]:\n old_env: Dict[str, Optional[str]] = {}\n\n for key, value in env.items():\n old_env[key] = os.environ.get(key)\n\n if value is None:\n os.environ.pop(key, None)\n else:\n os.environ[key] = value\n\n try:\n yield\n finally:\n for key, value in old_env.items():\n if value is None:\n os.environ.pop(key, None)\n else:\n os.environ[key] = value","def _wrap_lambda_env(self, e):\n # Value should be a dict (NOT a string)\n lam_e = {\"Environment\": {\"Variables\": e}}\n return lam_e","def _get_environ(args=None):\n if args is not None:\n if not isinstance(args, list):\n args = [args]\n args += ['>', 'nul', '&&']\n args += ['set']\n\n popen = subprocess.Popen(args, shell=True, stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n stdout, stderr = popen.communicate()\n if popen.returncode != 0:\n raise subprocess.CalledProcessError(popen.returncode, ' '.join(args),\n output=stderr.decode('cp866'))\n result = {}\n for line in stdout.decode('cp866').splitlines():\n key, value = line.strip().split('=', 1)\n try:\n result[str(key).upper()] = str(value)\n except UnicodeEncodeError:\n pass\n return result","def env_config():\n # setup\n env = {'ELB_GCP_PROJECT': 'expected-gcp-project',\n 'ELB_GCP_REGION': 'expected-gcp-region',\n 'ELB_GCP_ZONE': 'expected-gcp-zone',\n 'ELB_BATCH_LEN': '93',\n 'ELB_CLUSTER_NAME': 'expected-cluster-name',\n 'ELB_RESULTS': 'gs://expected-results',\n 'ELB_USE_PREEMPTIBLE': 'true',\n 'ELB_BID_PERCENTAGE': '91'}\n\n for var_name in env:\n os.environ[var_name] = str(env[var_name])\n\n yield env\n\n # cleanup\n for var_name in env:\n # os.unsetenv does not work on every system\n del os.environ[var_name]","def env(config, args):\n print config.template(\"scripts/env.sh\", project=args.project)","def c_stringify(cls, st):\n return '\"{0}\"'.format(st.value.replace('\"', '\\\\\"'))","def env(key, default=None, required=False):\n try:\n value = os.environ[key]\n return ast.literal_eval(value)\n except (SyntaxError, ValueError):\n return value\n except KeyError:\n if default or not required:\n return default\n raise ImproperlyConfigured(\n \"Missing required environment variable '%s'\" % key)","def send_env(self, keys):\n if keys is None:\n return self.env\n return dict((key, self.env.get(key, '')) for key in keys)","def write_config_file(config_dict: dict, env: str):\n file_path = f'./CloudFormation/configuration_{env}.json'\n config_dict['Parameters']['Environment'] = env\n with open(file_path, 'w') as config_fh:\n config_fh.write(json.dumps(config_dict))","def write_config_string(input_dict, entry_char='>', attribution_char='=',\n usekeys=None):\n # Selects the desired entries of the input_dict\n if usekeys is not None:\n input_dict = {key: input_dict[key] for key in usekeys}\n\n result_str = \"\"\n\n for key, value in input_dict.items():\n result_str += entry_string(key, value, entry_char, attribution_char)\n\n return result_str","def quote_escape(value, lf='&mjf-lf;', quot='&mjf-quot;'):\n if '\\n' in value:\n value = value.replace('\\n', lf)\n if '\\'' in value and '\\\"' in value:\n value = value.replace('\"', quot)\n return value","def dict_to_json_str(d):\n\n json_entry = '\"{}\":{}'\n json_entry_str = '\"{}\":\"{}\"'\n entries = []\n\n for entry in d:\n key = entry\n value = d[entry]\n\n try:\n basestring\n except NameError:\n basestring = str\n\n if not value:\n value = False\n\n elif isinstance(value, basestring):\n value = value.replace(u'\"', u'\\\\\"')\n entries.append(json_entry_str.format(key, value))\n\n elif isinstance(value, bool):\n value = 'true' if value else 'false'\n entries.append(json_entry.format(key, value))\n\n elif isinstance(value, dict):\n entries.append(json_entry.format(key, dict_to_json_str(value)))\n\n else:\n entries.append(json_entry.format(key, value))\n\n return '{} {} {}'.format('{', ','.join(entries), '}')","def quote(s):\n # Based on shlex.quote. Bun unlike shlex, it quotes every string and\n # not just the ones that contain unsafe characters.\n return \"'\" + s.replace(\"'\", \"'\\\"'\\\"'\") + \"'\"","def quote(s):\n if not s:\n return \"''\"\n if _find_unsafe(s) is None:\n return s\n\n # use single quotes, and put single quotes into double quotes\n # the string $'b is then quoted as '$'\"'\"'b'\n return \"'\" + s.replace(\"'\", \"'\\\"'\\\"'\") + \"'\"","def sh_quote_safe(arg):\n return (\"'\" + str(arg).replace(\"'\", r\"'\\''\") + \"'\")","def _dequote(value: str) -> str:\n if value[0] == '\"' and value[-1] == '\"':\n return value[1:-1]\n return value","def environ(self):\n return dict(page='environ', environment=request.environ)","def environ(self):\n return dict(page='environ', environment=request.environ)"],"string":"[\n \"def _build_environment(envdict):\\n lines = []\\n for k, v in envdict.iteritems():\\n if \\\" \\\" in v: # NOTE: per the spec, one might want to handle all 'whitespace' chars.\\n v = v.replace(\\\"'\\\", \\\"''\\\")\\n v = \\\"'%s'\\\" % v\\n v = v.replace('\\\"', '\\\"\\\"')\\n lines.append('%s=%s' % (k, v))\\n return '\\\"%s\\\"' % ' '.join(lines)\",\n \"def add_to_environment(v):\\n return \\\"Environment='{}'\\\".format(\\n \\\"\\\\nEnvironment=\\\".join(\\n \\\"{}={}\\\".format(k, \\\"\\\".join(map(str, v))) for k, v in iteritems(v)\\n )\\n if isinstance(v, dict)\\n else v\\n )\",\n \"def test_single_quoted(self):\\n e = yaenv.core.EnvVar(\\\"key = 'value'\\\\n\\\")\\n assert e.key == 'key'\\n assert e.value == 'value'\\n assert not e._interpolate\",\n \"def test_double_quoted(self):\\n e = yaenv.core.EnvVar('key = \\\"value\\\"\\\\n')\\n assert e.key == 'key'\\n assert e.value == 'value'\\n assert e._interpolate\",\n \"def quote_json(_json: dict) -> str:\\n stringified = json.dumps(_json)\\n return quote(stringified)\",\n \"def __MakeEnvironment(self):\\n environment= os.environ.copy()\\n\\n for key, value in self.__context.items():\\n if type(value) is str:\\n name = \\\"QMV_\\\" + key.replace(\\\".\\\", \\\"__\\\")\\n environment[name]= value\\n\\n return environment\",\n \"def MakeEnvironment(self, context):\\n\\n # Start with any environment variables that are already present\\n # in the environment.\\n environment = os.environ.copy()\\n # Copy context variables into the environment.\\n for key, value in context.items():\\n name = \\\"QMV_\\\" + key\\n environment[name] = value\\n # Extract additional environment variable assignments from the\\n # 'Environment' field.\\n for assignment in self.environment:\\n if \\\"=\\\" in assignment:\\n # Break the assignment at the first equals sign.\\n variable, value = string.split(assignment, \\\"=\\\", 1)\\n environment[variable] = value\\n else:\\n raise ValueError, \\\\\\n qm.error(\\\"invalid environment assignment\\\",\\n assignment=assignment)\\n return environment\",\n \"def MakeEnvironment(self, context):\\n\\n # Start with any environment variables that are already present\\n # in the environment.\\n environment = os.environ.copy()\\n # Copy context variables into the environment.\\n for key, value in context.items():\\n name = \\\"QMV_\\\" + key\\n environment[name] = value\\n return environment\",\n \"def flatten_and_write(dotenv_path, dotenv_as_dict, quote_mode='always'):\\n with open(dotenv_path, 'w') as f:\\n for k, v in dotenv_as_dict.items():\\n str_format = _get_format(v, quote_mode)\\n f.write(str_format.format(key=k, value=v))\\n return True\",\n \"def _quote(v):\\n return '\\\"' + v + '\\\"' if ' ' in v else v\",\n \"def test_unquoted(self):\\n e = yaenv.core.EnvVar('key = value\\\\n')\\n assert e.key == 'key'\\n assert e.value == 'value'\\n assert e._interpolate\",\n \"def wrapper_environment(args):\\n\\n return {\\n ENVIRONMENT_KEY: json.dumps({\\n 'verbose': args.verbose,\\n 'cc': shlex.split(args.cc),\\n 'cxx': shlex.split(args.cxx)\\n })\\n }\",\n \"def to_env(self, d):\\n return {k.upper(): str(v) for k, v in d.items()}\",\n \"def get_formatted_env_vars() -> str:\\n res = \\\"\\\"\\n for k, v in os.environ.items():\\n res += '{0}={1}\\\\n'.format(k, v)\\n return res\",\n \"def putenv(space, envstr):\\n try:\\n key, value = envstr.split(\\\"=\\\")\\n except ValueError:\\n return space.w_True\\n os.environ[key] = value\\n return space.w_True\",\n \"def quote(value, *args, **kwargs):\\n return parse.quote(encode(value, *args, **kwargs))\",\n \"def test_env_top_dict(self, monkeypatch):\\n with open(\\\".scuba.yml\\\", \\\"w\\\") as f:\\n f.write(\\n r\\\"\\\"\\\"\\n image: na\\n environment:\\n FOO: This is foo\\n FOO_WITH_QUOTES: \\\"\\\\\\\"Quoted foo\\\\\\\"\\\" # Quotes included in value\\n BAR: \\\"This is bar\\\"\\n MAGIC: 42\\n SWITCH_1: true # YAML boolean\\n SWITCH_2: \\\"true\\\" # YAML string\\n EMPTY: \\\"\\\"\\n EXTERNAL: # Comes from os env\\n EXTERNAL_NOTSET: # Missing in os env\\n \\\"\\\"\\\"\\n )\\n\\n monkeypatch.setenv(\\\"EXTERNAL\\\", \\\"Outside world\\\")\\n monkeypatch.delenv(\\\"EXTERNAL_NOTSET\\\", raising=False)\\n\\n config = scuba.config.load_config(\\\".scuba.yml\\\")\\n\\n expect = dict(\\n FOO=\\\"This is foo\\\",\\n FOO_WITH_QUOTES='\\\"Quoted foo\\\"',\\n BAR=\\\"This is bar\\\",\\n MAGIC=\\\"42\\\", # N.B. string\\n SWITCH_1=\\\"True\\\", # Unfortunately this is due to str(bool(1))\\n SWITCH_2=\\\"true\\\",\\n EMPTY=\\\"\\\",\\n EXTERNAL=\\\"Outside world\\\",\\n EXTERNAL_NOTSET=\\\"\\\",\\n )\\n assert expect == config.environment\",\n \"def env_dict(self): # type: () -> t.Dict[str, str]\\n return dict((item[0], item[1]) for item in [e.split('=', 1) for e in self.env])\",\n \"def _quote(self, arg):\\n arg = arg.replace('\\\\\\\\', '\\\\\\\\\\\\\\\\')\\n arg = arg.replace('\\\"', '\\\\\\\\\\\"')\\n return '\\\"%s\\\"' % arg\",\n \"def _escapeString(self, value):\\n if '\\\"' in value and \\\"'\\\" in value:\\n substrings = value.split(\\\"\\\\\\\"\\\")\\n result = [\\\"concat(\\\"]\\n for substring in substrings:\\n result.append(\\\"\\\\\\\"%s\\\\\\\"\\\" % substring)\\n result.append(\\\", '\\\\\\\"', \\\")\\n result = result[0:-1]\\n if value.endswith('\\\"'):\\n result.append(\\\", '\\\\\\\"'\\\")\\n return \\\"\\\".join(result) + \\\")\\\"\\n\\n if '\\\"' in value:\\n return \\\"'%s'\\\" % value\\n return \\\"\\\\\\\"%s\\\\\\\"\\\" % value\",\n \"def _make_env(environment):\\n env = []\\n for key, value in environment.items():\\n env.append({\\\"name\\\": key, \\\"value\\\": value})\\n\\n return env\",\n \"def esc_quotes(strng):\\n\\n return strng.replace('\\\"','\\\\\\\\\\\"').replace(\\\"'\\\",\\\"\\\\\\\\'\\\")\",\n \"def patch_environment(**kwargs):\\n for key, value in kwargs.items():\\n os.environ[key.upper()] = str(value)\\n\\n yield\\n\\n for key in kwargs:\\n if key.upper() in os.environ:\\n del os.environ[key.upper()]\",\n \"def quoted(val: str) -> str:\\n return f'\\\"{val}\\\"' if ' ' in val else val\",\n \"def _generate_environment(self):\\n envvars = {}\\n for key in self.envvars:\\n try:\\n envvars[key] = os.environ[key]\\n except KeyError:\\n continue\\n\\n # Warn the user that we cannot support secrets\\n if envvars:\\n logger.warning(\\\"This API does not support environment secrets.\\\")\\n return envvars\",\n \"def shellquote(s):\\n return '\\\"' + s.replace(\\\"'\\\", \\\"'\\\\\\\\''\\\") + '\\\"'\",\n \"def create_dictionary_string(\\n dict_,\\n open_dict=\\\"<<\\\",\\n close_dict=\\\">>\\\",\\n field_join=\\\"\\\\n\\\",\\n key_value_join=\\\" \\\",\\n has_empty_fields=False,\\n):\\n if has_empty_fields:\\n dict_ = clear_empty_fields(dict_)\\n\\n return \\\"\\\".join(\\n [\\n open_dict,\\n field_join.join([key_value_join.join(f) for f in dict_.items()]),\\n close_dict,\\n ]\\n )\",\n \"def urllib_quote_parameters(inputdictionary):\\r\\n if type(inputdictionary) is not dict:\\r\\n raise TypeError(\\\"urllib_quote_parameters' inputstringdictionary parameter must be a dict, not '\\\"+str(type(inputstring))+\\\"'\\\")\\r\\n\\r\\n quoted_keyvals = []\\r\\n for key, val in inputdictionary.items():\\r\\n quoted_keyvals.append(\\\"%s=%s\\\" % (urllib_quote(key), urllib_quote(val)))\\r\\n\\r\\n return \\\"&\\\".join(quoted_keyvals)\",\n \"def quote(value):\\n return DoubleQuotedScalarString(value)\",\n \"def escape_variables(environ):\\n return {key: environ[key].replace('%', '%%') for key in environ}\",\n \"def _StringQuote(s, quote='\\\"', escape='\\\\\\\\'):\\n entity = {'\\\\f': '\\\\\\\\f', '\\\\n': '\\\\\\\\n', '\\\\r': '\\\\\\\\r', '\\\\t': '\\\\\\\\t'}\\n chars = []\\n if quote:\\n chars.append(quote)\\n for c in s:\\n if c in (escape, quote):\\n chars.append(escape)\\n elif c in entity:\\n c = entity[c]\\n chars.append(c)\\n if quote:\\n chars.append(quote)\\n return ''.join(chars)\",\n \"def Environ(envstring):\\n try:\\n envint = int(envstring)\\n except ValueError:\\n return os.environ.get(envstring, \\\"\\\")\\n # Is an integer - need to get the envint'th value\\n try:\\n return \\\"%s=%s\\\" % (list(os.environ.keys())[envint], list(os.environ.values())[envint])\\n except IndexError:\\n return \\\"\\\"\",\n \"def _dict_to_args(self, arg_dict):\\n if arg_dict:\\n yield \\\"--{}=data:application/json;charset=utf-8,{}\\\".format(\\n self._CONFIG_FLAG.name,\\n urllib.parse.quote(json_encode(arg_dict, pretty=False), encoding=\\\"utf-8\\\")\\n )\",\n \"def environ_expansions():\\r\\n global _environ_expansions\\r\\n\\r\\n if _environ_expansions:\\r\\n return _environ_expansions\\r\\n\\r\\n _environ_expansions = {}\\r\\n for key, value in os.environ.items():\\r\\n _environ_expansions['ENV_%s' % key] = value\\r\\n\\r\\n return _environ_expansions\",\n \"def standardise_quotes(self, val):\\n if val.startswith(self.altquote) and val.endswith(self.altquote):\\n middle = val[1:-1]\\n val = \\\"%s%s%s\\\" % (self.quote, middle, self.quote)\\n\\n val = self.escape_quotes(val)\\n\\n return val\",\n \"def quote(*a, **kw):\\n return quote(*a, **kw)\",\n \"def get_execution_envs_str(self):\\n\\n return ' '.join(str(x) for x in self.execution_envs)\",\n \"def export_vars(env_vars_dict):\\n env_vars = ['{}={}'.format(k, env_vars_dict[k])\\n for k in env_vars_dict.keys()]\\n with open(\\\"vars.env\\\", \\\"w\\\") as file:\\n for item in env_vars[:-1]:\\n file.write(\\\"{}\\\\n\\\".format(item))\\n file.write(\\\"{}\\\".format(env_vars[-1]))\",\n \"def overwrite_environment_variable(self, key, value):\\n if value is not None:\\n self._printer(\\\"$env:{0} = \\\\\\\"{1}\\\\\\\"\\\".format(key, value))\\n else:\\n self._printer(\\\"$env:{0} = \\\\\\\"\\\\\\\"\\\".format(key))\",\n \"def _escape_string(s, surrounding_quote='\\\"'):\\n s = s.replace('\\\\\\\\', '\\\\\\\\\\\\\\\\')\\n if surrounding_quote == '\\\"':\\n s = s.replace('\\\"', r'\\\\\\\"')\\n if surrounding_quote == \\\"'\\\":\\n s = s.replace(\\\"'\\\", r\\\"\\\\'\\\")\\n return s\",\n \"def quote_arguments(args):\\n if isinstance(args, str):\\n args_list = [args]\\n else:\\n args_list = args\\n\\n quoted_args = []\\n for a in args_list:\\n qa = _double_up_quotes(a)\\n if \\\" \\\" in qa or \\\"'\\\" in qa:\\n qa = \\\"'\\\" + qa + \\\"'\\\"\\n quoted_args.append(qa)\\n return \\\" \\\".join(quoted_args)\",\n \"def augmented_environment(self):\\n env = os.environ.copy()\\n env.update(self.environ)\\n return env\",\n \"def quote(s):\\n return unescape(quoteattr(s))\",\n \"def _wrap_ssm_env(self, e):\\n # Value should be serialized JSON\\n ssm_e = {\\\"Parameter\\\": {\\\"Name\\\": \\\"environment\\\", \\\"Value\\\": json.dumps(e)}}\\n return ssm_e\",\n \"def argument_quote(argument):\\n argument = argument.replace('\\\"', '\\\"\\\"')\\n if ' ' in argument:\\n argument = argument.replace(\\\"'\\\", \\\"''\\\")\\n argument = \\\"'\\\" + argument + \\\"'\\\"\\n return argument\",\n \"def escape_quotes(self, val):\\n if val.startswith(self.quote) and val.endswith(self.quote):\\n # make sure any previously escaped quotes are not re-escaped\\n middle = val[1:-1].replace(\\\"\\\\\\\\\\\" + self.quote, self.quote)\\n middle = middle.replace(self.quote, \\\"\\\\\\\\\\\" + self.quote)\\n val = \\\"%s%s%s\\\" % (self.quote, middle, self.quote)\\n\\n return val\",\n \"def test_get_environment_string(self):\\n pass\",\n \"def save_envs_to_file(file_path=constants.ENV_FILE_DEFAULT_PATH.value):\\n #pylint: disable=unspecified-encoding\\n with open(file_path, \\\"w\\\") as file:\\n for key, value in environ.items():\\n if key in constants.ENV_FILE_ALLOWED_KEYS.value:\\n file.write(\\\"{}={}\\\\n\\\".format(key, value))\",\n \"def shQuote(text):\\n\\treturn \\\"'%s'\\\" % text.replace(\\\"'\\\", r\\\"'\\\\''\\\")\",\n \"def _env(env):\\n return dict((key.strip(), val)\\n for line in env.strip().splitlines()\\n for key, _, val in [line.partition('=')])\",\n \"def update_env_from_string(env_string):\\n excluded_keys = [\\\"_\\\", \\\"SHLVL\\\", \\\"PWD\\\", \\\"OLDPWD\\\"]\\n env = os.environ\\n for line in env_string.split(\\\"\\\\n\\\"):\\n (key, _, value) = line.partition(\\\"=\\\")\\n if key and value and key not in excluded_keys:\\n env[key] = value\\n return env\",\n \"def __construct_mail_env(self, env):\\n\\n if isinstance(env, dict):\\n for k, v in env.items():\\n self.env_content += \\\"\\\"\\\"\\n <tr><td>{}</td><td>{}</td></tr>\\n \\\"\\\"\\\".format(k, v)\\n return self.env_content\",\n \"def get_environ():\\n # Manually set environment.\\n if FLAGS.env is not None:\\n return BasicEnvironment.from_json(open(FLAGS.env, \\\"r\\\").read())\\n\\n if FLAGS.data_folder is None:\\n data_folder = FLAGS.dataset\\n else:\\n data_folder = FLAGS.data_folder\\n exp_id = \\\"exp_\\\" + FLAGS.dataset + \\\"_\\\" + FLAGS.model\\n if FLAGS.id is None:\\n exp_id = gen_id(exp_id)\\n else:\\n exp_id = FLAGS.id\\n return BasicEnvironment(\\n device=get_device(FLAGS.gpu),\\n dataset=FLAGS.dataset,\\n data_folder=data_folder,\\n logs_folder=FLAGS.logs,\\n save_folder=FLAGS.results,\\n run_validation=FLAGS.validation,\\n verbose=FLAGS.verbose,\\n exp_id=exp_id,\\n description=FLAGS.description,\\n valid_num_fold=FLAGS.valid_num_fold,\\n valid_fold_id=FLAGS.valid_fold_id)\",\n \"def argument_list_quote(arguments):\\n args = []\\n for arg in arguments:\\n args.append(argument_quote(arg))\\n return '\\\"%s\\\"' % ' '.join(args)\",\n \"def environment_setting_format(value):\\n try:\\n env_name, env_value = value.split('=')\\n except ValueError:\\n message = (\\\"Incorrectly formatted enviroment settings. \\\"\\n \\\"Argmuent values should be in the format a=b c=d\\\")\\n raise ValueError(message)\\n else:\\n return {'name': env_name, 'value': env_value}\",\n \"def env_str(name: str, default: str) -> str:\\n value = stringify(env.get(name))\\n return default if value is None else value\",\n \"def _quote_arguments(args):\\n return map(lambda x: '\\\"{}\\\"'.format(x) if ' ' in x else '{}'.format(x), args)\",\n \"def test_environment_str_conversion():\\n for l in list(environments.data):\\n e = environments[l]\\n assert str(e) == e.name\",\n \"def quote(value):\\n single = value.find(\\\"'\\\")\\n double = value.find('\\\"')\\n multiline = value.find('\\\\n') != -1\\n if multiline or ((single != -1) and (double != -1)):\\n if value.find('\\\"\\\"\\\"') == -1 and value[0] != '\\\"' and value[-1] != '\\\"':\\n s = '\\\"\\\"\\\"%s\\\"\\\"\\\"' % value\\n else:\\n s = \\\"'''%s'''\\\" % value\\n elif (single != -1) and (double == -1):\\n s = '\\\"%s\\\"' % value\\n else:\\n s = \\\"'%s'\\\" % value\\n return s\",\n \"def prepend_environment_variable(self, key, value):\\n script_keys = {\\n \\\"k\\\": key,\\n \\\"v\\\": value\\n }\\n script = \\\"$env:{k} = \\\\\\\"{v};$env:{k}\\\\\\\"\\\".format(**script_keys)\\n self._printer(script)\",\n \"def test_env_top_list(self, monkeypatch):\\n with open(\\\".scuba.yml\\\", \\\"w\\\") as f:\\n f.write(\\n r\\\"\\\"\\\"\\n image: na\\n environment:\\n - FOO=This is foo # No quotes\\n - FOO_WITH_QUOTES=\\\"Quoted foo\\\" # Quotes included in value\\n - BAR=This is bar\\n - MAGIC=42\\n - SWITCH_2=true\\n - EMPTY=\\n - EXTERNAL # Comes from os env\\n - EXTERNAL_NOTSET # Missing in os env\\n \\\"\\\"\\\"\\n )\\n\\n monkeypatch.setenv(\\\"EXTERNAL\\\", \\\"Outside world\\\")\\n monkeypatch.delenv(\\\"EXTERNAL_NOTSET\\\", raising=False)\\n\\n config = scuba.config.load_config(\\\".scuba.yml\\\")\\n\\n expect = dict(\\n FOO=\\\"This is foo\\\",\\n FOO_WITH_QUOTES='\\\"Quoted foo\\\"',\\n BAR=\\\"This is bar\\\",\\n MAGIC=\\\"42\\\", # N.B. string\\n SWITCH_2=\\\"true\\\",\\n EMPTY=\\\"\\\",\\n EXTERNAL=\\\"Outside world\\\",\\n EXTERNAL_NOTSET=\\\"\\\",\\n )\\n assert expect == config.environment\",\n \"def generate_activate_content(self):\\n content = \\\"\\\\nimport os\\\"\\n for name, value in self.items():\\n content += \\\"\\\\nos.environ['{name}'] = '{value}'\\\".format(\\n name=name, value=value\\n )\\n return content\",\n \"def set_environment_variables(env_dict, session):\\n for key, value in env_dict.items():\\n session.env[key] = value\",\n \"def quote(m):\\n return '\\\"' + m + '\\\"'\",\n \"def env(name, value):\\n import os\\n os.environ[name] = value\",\n \"def getenv(space, var):\\n e = os.environ.get(var)\\n if e is None:\\n return space.w_False\\n return space.newstr(e)\",\n \"def environ_to_dict(environ):\\n return dict((k, v) for k, v in environ.iteritems()\\n if k.isupper())\",\n \"def generic_env_configure_vars(self, verbose=False):\\n\\n if self.settings.os == \\\"Windows\\\":\\n self.output.fatal(\\\"Cannot build on Windows, sorry!\\\")\\n return\\n\\n if self.settings.os == \\\"Linux\\\" or self.settings.os == \\\"Macos\\\":\\n libs = 'LIBS=\\\"%s\\\"' % \\\" \\\".join([\\\"-l%s\\\" % lib for lib in self.deps_cpp_info.libs])\\n ldflags = 'LDFLAGS=\\\"%s\\\"' % \\\" \\\".join([\\\"-L%s\\\" % lib for lib in self.deps_cpp_info.lib_paths]) \\n archflag = \\\"-m32\\\" if self.settings.arch == \\\"x86\\\" else \\\"\\\"\\n cflags = 'CFLAGS=\\\"-fPIC %s %s\\\"' % (archflag, \\\" \\\".join(self.deps_cpp_info.cflags))\\n cpp_flags = 'CPPFLAGS=\\\"%s %s\\\"' % (archflag, \\\" \\\".join(self.deps_cpp_info.cppflags))\\n command = \\\"env %s %s %s %s\\\" % (libs, ldflags, cflags, cpp_flags)\\n # elif self.settings.os == \\\"Windows\\\" and self.settings.compiler == \\\"Visual Studio\\\":\\n # cl_args = \\\" \\\".join(['/I\\\"%s\\\"' % lib for lib in self.deps_cpp_info.include_paths])\\n # lib_paths= \\\";\\\".join(['\\\"%s\\\"' % lib for lib in self.deps_cpp_info.lib_paths])\\n # command = \\\"SET LIB=%s;%%LIB%% && SET CL=%s\\\" % (lib_paths, cl_args)\\n # if verbose:\\n # command += \\\" && SET LINK=/VERBOSE\\\"\\n \\n return command\",\n \"def embeded_triple_quotes():\\n pass\",\n \"def escape(orig):\\n return '\\\"{}\\\"'.format(orig.replace('\\\\\\\\', '\\\\\\\\\\\\\\\\').replace('\\\"', '\\\\\\\\\\\"'))\",\n \"def quote_spaces(arg):\\n if ' ' in arg or '\\\\t' in arg:\\n return '\\\"%s\\\"' % arg\\n else:\\n return str(arg)\",\n \"def set_magic_quotes_runtime():\\n raise NotImplementedError()\",\n \"def _create_environment(config):\\n if isinstance(config.env, str):\\n env = gym.make(config.env)\\n else:\\n env = config.env()\\n if config.max_length:\\n env = tools.wrappers.LimitDuration(env, config.max_length)\\n env = tools.wrappers.RangeNormalize(env)\\n env = tools.wrappers.ClipAction(env)\\n env = tools.wrappers.ConvertTo32Bit(env)\\n return env\",\n \"def json_escape(context, value):\\n\\n return json.dumps(value).strip('\\\"')\",\n \"def quote_all(parameters: Dict[str, Any]) -> Dict[str, Any]:\\n return {key: quote_plus(value) if isinstance(value, str) else value for key, value in parameters.items()}\",\n \"def set_environment_variables(args):\\n credentials = args.gcp_application_credentials\\n try:\\n json_credentials = json.loads(credentials)\\n fd, path = tempfile.mkstemp()\\n print(f'Storing json credentials temporarily at {path}')\\n with os.fdopen(fd, 'w') as tmp:\\n tmp.write(credentials)\\n os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = path\\n return path\\n except Exception:\\n print('Using specified json credentials file')\\n os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = credentials\\n return\",\n \"def quoteString(s):\\n if s is None:\\n return None\\n quoted = str(s).replace(\\\"\\\\\\\\\\\", \\\"\\\\\\\\\\\\\\\\\\\").replace(\\\"'\\\", \\\"\\\\\\\\'\\\")\\n return \\\"'{}'\\\".format(quoted)\",\n \"def env_lines_to_dict(self, env_lines):\\n env_dict = {}\\n for env_line in env_lines:\\n split_env_line = shlex.split(env_line)\\n if split_env_line[0] == \\\"export\\\":\\n split_env_line = split_env_line[1:]\\n for item in split_env_line:\\n if \\\"=\\\" in item:\\n k, v = item.split(\\\"=\\\", 1)\\n env_dict[k] = v\\n return env_dict\",\n \"def environment_variable_string(self, name):\\n return \\\"$(\\\" + name + \\\")\\\"\",\n \"def quot(string):\\r\\n return string.replace('\\\"', \\\"'\\\")\",\n \"def safe_quoted_string(value):\\n validate_safe_string(value)\\n return u'\\\\'{}\\\\''.format(value)\",\n \"def dumpenv(self):\\n\\n print('-------------------------------')\\n pprint.pprint(dict(os.environ))\\n print('-------------------------------')\",\n \"def env(\\n self,\\n env: Dict[str, Optional[str]],\\n ) -> Iterator[None]:\\n old_env: Dict[str, Optional[str]] = {}\\n\\n for key, value in env.items():\\n old_env[key] = os.environ.get(key)\\n\\n if value is None:\\n os.environ.pop(key, None)\\n else:\\n os.environ[key] = value\\n\\n try:\\n yield\\n finally:\\n for key, value in old_env.items():\\n if value is None:\\n os.environ.pop(key, None)\\n else:\\n os.environ[key] = value\",\n \"def _wrap_lambda_env(self, e):\\n # Value should be a dict (NOT a string)\\n lam_e = {\\\"Environment\\\": {\\\"Variables\\\": e}}\\n return lam_e\",\n \"def _get_environ(args=None):\\n if args is not None:\\n if not isinstance(args, list):\\n args = [args]\\n args += ['>', 'nul', '&&']\\n args += ['set']\\n\\n popen = subprocess.Popen(args, shell=True, stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE)\\n stdout, stderr = popen.communicate()\\n if popen.returncode != 0:\\n raise subprocess.CalledProcessError(popen.returncode, ' '.join(args),\\n output=stderr.decode('cp866'))\\n result = {}\\n for line in stdout.decode('cp866').splitlines():\\n key, value = line.strip().split('=', 1)\\n try:\\n result[str(key).upper()] = str(value)\\n except UnicodeEncodeError:\\n pass\\n return result\",\n \"def env_config():\\n # setup\\n env = {'ELB_GCP_PROJECT': 'expected-gcp-project',\\n 'ELB_GCP_REGION': 'expected-gcp-region',\\n 'ELB_GCP_ZONE': 'expected-gcp-zone',\\n 'ELB_BATCH_LEN': '93',\\n 'ELB_CLUSTER_NAME': 'expected-cluster-name',\\n 'ELB_RESULTS': 'gs://expected-results',\\n 'ELB_USE_PREEMPTIBLE': 'true',\\n 'ELB_BID_PERCENTAGE': '91'}\\n\\n for var_name in env:\\n os.environ[var_name] = str(env[var_name])\\n\\n yield env\\n\\n # cleanup\\n for var_name in env:\\n # os.unsetenv does not work on every system\\n del os.environ[var_name]\",\n \"def env(config, args):\\n print config.template(\\\"scripts/env.sh\\\", project=args.project)\",\n \"def c_stringify(cls, st):\\n return '\\\"{0}\\\"'.format(st.value.replace('\\\"', '\\\\\\\\\\\"'))\",\n \"def env(key, default=None, required=False):\\n try:\\n value = os.environ[key]\\n return ast.literal_eval(value)\\n except (SyntaxError, ValueError):\\n return value\\n except KeyError:\\n if default or not required:\\n return default\\n raise ImproperlyConfigured(\\n \\\"Missing required environment variable '%s'\\\" % key)\",\n \"def send_env(self, keys):\\n if keys is None:\\n return self.env\\n return dict((key, self.env.get(key, '')) for key in keys)\",\n \"def write_config_file(config_dict: dict, env: str):\\n file_path = f'./CloudFormation/configuration_{env}.json'\\n config_dict['Parameters']['Environment'] = env\\n with open(file_path, 'w') as config_fh:\\n config_fh.write(json.dumps(config_dict))\",\n \"def write_config_string(input_dict, entry_char='>', attribution_char='=',\\n usekeys=None):\\n # Selects the desired entries of the input_dict\\n if usekeys is not None:\\n input_dict = {key: input_dict[key] for key in usekeys}\\n\\n result_str = \\\"\\\"\\n\\n for key, value in input_dict.items():\\n result_str += entry_string(key, value, entry_char, attribution_char)\\n\\n return result_str\",\n \"def quote_escape(value, lf='&mjf-lf;', quot='&mjf-quot;'):\\n if '\\\\n' in value:\\n value = value.replace('\\\\n', lf)\\n if '\\\\'' in value and '\\\\\\\"' in value:\\n value = value.replace('\\\"', quot)\\n return value\",\n \"def dict_to_json_str(d):\\n\\n json_entry = '\\\"{}\\\":{}'\\n json_entry_str = '\\\"{}\\\":\\\"{}\\\"'\\n entries = []\\n\\n for entry in d:\\n key = entry\\n value = d[entry]\\n\\n try:\\n basestring\\n except NameError:\\n basestring = str\\n\\n if not value:\\n value = False\\n\\n elif isinstance(value, basestring):\\n value = value.replace(u'\\\"', u'\\\\\\\\\\\"')\\n entries.append(json_entry_str.format(key, value))\\n\\n elif isinstance(value, bool):\\n value = 'true' if value else 'false'\\n entries.append(json_entry.format(key, value))\\n\\n elif isinstance(value, dict):\\n entries.append(json_entry.format(key, dict_to_json_str(value)))\\n\\n else:\\n entries.append(json_entry.format(key, value))\\n\\n return '{} {} {}'.format('{', ','.join(entries), '}')\",\n \"def quote(s):\\n # Based on shlex.quote. Bun unlike shlex, it quotes every string and\\n # not just the ones that contain unsafe characters.\\n return \\\"'\\\" + s.replace(\\\"'\\\", \\\"'\\\\\\\"'\\\\\\\"'\\\") + \\\"'\\\"\",\n \"def quote(s):\\n if not s:\\n return \\\"''\\\"\\n if _find_unsafe(s) is None:\\n return s\\n\\n # use single quotes, and put single quotes into double quotes\\n # the string $'b is then quoted as '$'\\\"'\\\"'b'\\n return \\\"'\\\" + s.replace(\\\"'\\\", \\\"'\\\\\\\"'\\\\\\\"'\\\") + \\\"'\\\"\",\n \"def sh_quote_safe(arg):\\n return (\\\"'\\\" + str(arg).replace(\\\"'\\\", r\\\"'\\\\''\\\") + \\\"'\\\")\",\n \"def _dequote(value: str) -> str:\\n if value[0] == '\\\"' and value[-1] == '\\\"':\\n return value[1:-1]\\n return value\",\n \"def environ(self):\\n return dict(page='environ', environment=request.environ)\",\n \"def environ(self):\\n return dict(page='environ', environment=request.environ)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.75373447","0.6221324","0.6093788","0.5934985","0.5808252","0.57019943","0.5597145","0.5515628","0.5512391","0.547237","0.5445413","0.5308214","0.5276308","0.52133214","0.51868063","0.5152444","0.50912315","0.5079086","0.50783825","0.5009073","0.50075024","0.49856868","0.49751547","0.49726507","0.4965494","0.4965239","0.49500945","0.49487448","0.49354243","0.4933097","0.4926953","0.4896521","0.48883694","0.48759475","0.48570096","0.4843289","0.48421013","0.48215556","0.4813205","0.48113915","0.47980917","0.47943956","0.47873142","0.47819254","0.4780532","0.47802106","0.4757813","0.47545385","0.47412848","0.47313017","0.4725096","0.47237766","0.47234464","0.47229898","0.471049","0.4707578","0.47068942","0.4697391","0.46834463","0.4680963","0.46790802","0.46693525","0.46676034","0.46445066","0.46392375","0.46365413","0.4636126","0.46353707","0.4624378","0.46219292","0.4609449","0.46093044","0.46018934","0.45909557","0.4590793","0.45855907","0.4581781","0.45786083","0.45755106","0.45667243","0.45638973","0.45393297","0.45309168","0.45258012","0.45118248","0.450957","0.450095","0.4499479","0.44989243","0.4485668","0.44855237","0.44847018","0.4476897","0.4475178","0.44718948","0.44703832","0.44686145","0.44657823","0.4465486","0.4465486"],"string":"[\n \"0.75373447\",\n \"0.6221324\",\n \"0.6093788\",\n \"0.5934985\",\n \"0.5808252\",\n \"0.57019943\",\n \"0.5597145\",\n \"0.5515628\",\n \"0.5512391\",\n \"0.547237\",\n \"0.5445413\",\n \"0.5308214\",\n \"0.5276308\",\n \"0.52133214\",\n \"0.51868063\",\n \"0.5152444\",\n \"0.50912315\",\n \"0.5079086\",\n \"0.50783825\",\n \"0.5009073\",\n \"0.50075024\",\n \"0.49856868\",\n \"0.49751547\",\n \"0.49726507\",\n \"0.4965494\",\n \"0.4965239\",\n \"0.49500945\",\n \"0.49487448\",\n \"0.49354243\",\n \"0.4933097\",\n \"0.4926953\",\n \"0.4896521\",\n \"0.48883694\",\n \"0.48759475\",\n \"0.48570096\",\n \"0.4843289\",\n \"0.48421013\",\n \"0.48215556\",\n \"0.4813205\",\n \"0.48113915\",\n \"0.47980917\",\n \"0.47943956\",\n \"0.47873142\",\n \"0.47819254\",\n \"0.4780532\",\n \"0.47802106\",\n \"0.4757813\",\n \"0.47545385\",\n \"0.47412848\",\n \"0.47313017\",\n \"0.4725096\",\n \"0.47237766\",\n \"0.47234464\",\n \"0.47229898\",\n \"0.471049\",\n \"0.4707578\",\n \"0.47068942\",\n \"0.4697391\",\n \"0.46834463\",\n \"0.4680963\",\n \"0.46790802\",\n \"0.46693525\",\n \"0.46676034\",\n \"0.46445066\",\n \"0.46392375\",\n \"0.46365413\",\n \"0.4636126\",\n \"0.46353707\",\n \"0.4624378\",\n \"0.46219292\",\n \"0.4609449\",\n \"0.46093044\",\n \"0.46018934\",\n \"0.45909557\",\n \"0.4590793\",\n \"0.45855907\",\n \"0.4581781\",\n \"0.45786083\",\n \"0.45755106\",\n \"0.45667243\",\n \"0.45638973\",\n \"0.45393297\",\n \"0.45309168\",\n \"0.45258012\",\n \"0.45118248\",\n \"0.450957\",\n \"0.450095\",\n \"0.4499479\",\n \"0.44989243\",\n \"0.4485668\",\n \"0.44855237\",\n \"0.44847018\",\n \"0.4476897\",\n \"0.4475178\",\n \"0.44718948\",\n \"0.44703832\",\n \"0.44686145\",\n \"0.44657823\",\n \"0.4465486\",\n \"0.4465486\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8446683"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":855,"numItemsPerPage":100,"numTotalItems":95772,"offset":85500,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjE0NTA5MiwianRpIjoiYjEyN2IyZWEtZjRlOC00NzU2LWJhMjctYWNlZDFmZjRlOWQwIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODYxNDg2OTIsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.NHb1lY0bqBiKE3AsVUv0JG3mGa0uCy9DXXkouHmcNkiAlvlsgVRlJKj7lf04LTIwBu6n35ECzto0Nw4A4u2RAw","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
This generalization of kron can be used in 2 ways. It can straight forwardly take the tensor product of operators given the arguments i.e superkron(I,Z,X) will return the tensor product of I, Z and X. It can also do something more general. It can accept a dictionary of operators and a string variable that specifies in ... | def superkron(*args, val=0, string=''):
out = 1
if val == 0:
for i in range(len(args)):
out = kron(out, args[i])
else:
for digit in string:
out = kron(out, args[0][digit])
return out | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def wrapped_kronecker(operator_1, operator_2):\n return scipy.sparse.kron(operator_1, operator_2, 'csc')",
"def _kron_core(*ops, stype=None, coo_build=False, parallel=False):\n tmp_stype = \"coo\" if coo_build or stype == \"coo\" else None\n reducer = par_reduce if parallel else functools.reduce\n re... | [
"0.6527015",
"0.65184176",
"0.6495759",
"0.6454937",
"0.64209217",
"0.6300958",
"0.62514806",
"0.6235155",
"0.5904813",
"0.58663565",
"0.57320786",
"0.5712016",
"0.5634301",
"0.5531212",
"0.55011654",
"0.5418882",
"0.5387492",
"0.53842014",
"0.53497887",
"0.5278386",
"0.52774... | 0.6138414 | 8 |
Gets a complex vector in C^d and produces a real vector in R^2d | def realify(vec):
realified = []
a = vec.tolist()
for i in a[0]:
real_part = i.real
realified.append(real_part)
for i in a[0]:
imag_part = i.imag
realified.append(imag_part)
realified = np.array(realified)
return realified | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def complex_magnitude(c):\n return (c * c.conjugate()) ** 0.5",
"def complex(real, imag):",
"def _complex(real, imag):\n real = np.asarray(real)\n imag = np.asarray(imag)\n cplx = 1j * imag \n return cplx + real",
"def complex_inverse(c1,cr):",
"def real_of_complex(z):\n return np.vstack... | [
"0.7253624",
"0.7182984",
"0.71656555",
"0.70819247",
"0.70349497",
"0.6909833",
"0.67579955",
"0.6634494",
"0.65789676",
"0.6569811",
"0.6563385",
"0.6507573",
"0.64937395",
"0.64186794",
"0.6414348",
"0.64124167",
"0.6404053",
"0.63613313",
"0.6354115",
"0.6311204",
"0.6250... | 0.5668276 | 69 |
Useful when constructing unitaries to make quantum states. It needs to be input into gram schmidt orthogonalization method | def construct_preunitary(arr, array_list):
s = array_list[1].shape
preunitary = np.array([])
preunitary = np.append(preunitary, arr)
preunitary = preunitary.reshape(1, s[1])
for i in range(1, len(array_list)):
preunitary = np.hstack((preunitary, array_list[i]))
preunitary = preunitary.re... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__( self , power , the_phis = None ) :\n\n ## check the arguments \n assert isinstance ( power , num_types ) and int ( power ) == power and 0 <= power, \\\n \"Phases: invalid type/value for ``power''-parameter: %s/%s\" % ( power , type(power) )\n power = int ( powe... | [
"0.5909385",
"0.5810368",
"0.5763187",
"0.57423675",
"0.5733273",
"0.5709202",
"0.56170774",
"0.56161934",
"0.55760515",
"0.55707914",
"0.54700404",
"0.5450743",
"0.53737944",
"0.5371641",
"0.53648967",
"0.535579",
"0.5338217",
"0.53352964",
"0.53315026",
"0.53248763",
"0.532... | 0.0 | -1 |
Removes specific words from files and returns the file with same name but bad lines are removed | def remove_line(file, string=[]):
with open(file, 'r') as f, open('tmp.txt', '+a') as new_f:
for line in f:
clean = True
for word in string:
if word in line:
clean = False
if clean is True:
new_f.write(line)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_GOs(self):\n\n file = open(self.filename, 'r')\n new_file = open(self.temp_filename, 'w')\n for line in file:\n if \"GO\" not in line and \"go\" not in line:\n new_file.write(line)\n file.close()\n new_file.close()\n self.overwrite_file... | [
"0.6530023",
"0.64687544",
"0.64657426",
"0.643936",
"0.63439274",
"0.6255501",
"0.6159948",
"0.6086295",
"0.6086085",
"0.60616916",
"0.60099834",
"0.5958632",
"0.5916732",
"0.58726555",
"0.58483857",
"0.5817161",
"0.5817054",
"0.5814065",
"0.58092576",
"0.5801109",
"0.580010... | 0.6500035 | 1 |
Write data to file in column format. Each keyword is a column | def save_to_file(name='', **kwargs):
string = ''
for k in kwargs:
string += '{' + k + '}' + ' '
string += '\n'
file = open(name, 'a')
file.write(string.format(**kwargs))
file.close() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_data(self, filename,\n columns=('Q', 'R', 'dR'),\n header=None):\n if header is None:\n header = \"# %s\\n\"%' '.join(columns)\n with open(filename, 'wb') as fid:\n fid.write(asbytes(header))\n data = np.vstack([getattr(se... | [
"0.6221027",
"0.6078891",
"0.60711247",
"0.6039951",
"0.60351264",
"0.60065895",
"0.5969383",
"0.5945531",
"0.5918868",
"0.59003776",
"0.58955956",
"0.588802",
"0.58851725",
"0.58790314",
"0.5869868",
"0.5849799",
"0.5838432",
"0.58361983",
"0.5770194",
"0.5768405",
"0.576294... | 0.0 | -1 |
Internal tag stripping utility used by strip_tags. | def _strip_once(value):
s = MLStripper()
s.feed(value)
s.close()
return s.get_data() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_tag(args):",
"def strip_tags(src):\n res = ''.join(BeautifulSoup(src).findAll(text=True))\n res = re.sub(r\"\\s+\", \" \", res).strip()\n return res",
"def _strip_tags(value):\r\n return re.sub(r'<[^>]*?>', ' ', force_unicode(value))",
"def _preprocess(self, tagged: List[Tuple]) -> Tup... | [
"0.6422434",
"0.6422135",
"0.6417006",
"0.64156944",
"0.63390356",
"0.6240141",
"0.6196964",
"0.61389947",
"0.61058277",
"0.6091044",
"0.6082325",
"0.60043335",
"0.5998592",
"0.5998592",
"0.59793687",
"0.5949377",
"0.59187335",
"0.5878682",
"0.58657026",
"0.5850112",
"0.58437... | 0.0 | -1 |
Return the given HTML with all tags stripped. | def strip_tags(value):
# Note: in typical case this loop executes _strip_once once. Loop condition
# is redundant, but helps to reduce number of executions of _strip_once.
value = str(value)
while "<" in value and ">" in value:
new_value = _strip_once(value)
if value.count("<") == new_va... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stripHTMLTags (html):\r\n import re\r\n text = html\r\n \r\n # apply rules in given order!\r\n rules = [\r\n { r'>\\s+' : u'>'}, # remove spaces after a tag opens or closes\r\n { r'\\s+' : u' '}, # replace consecutive spaces\r\n { r'\\s*<br\\s*/?>\\s*' : u'\\n'},... | [
"0.82184535",
"0.81930643",
"0.81557626",
"0.8141694",
"0.81045276",
"0.81004316",
"0.803043",
"0.79539573",
"0.7940033",
"0.7934653",
"0.7933371",
"0.79037124",
"0.78986967",
"0.7886259",
"0.7878473",
"0.7878473",
"0.7841912",
"0.78334934",
"0.78334934",
"0.78146344",
"0.780... | 0.0 | -1 |
Searches the directory recursively for files with the passedin file name (not file path) set. Returns the file names of any matches. | def _FindFileNamesInDirectory(input_api, dir_path, search_file_names):
matches = []
for _, _, file_names in input_api.os_walk(dir_path):
for file_name in file_names:
if file_name in search_file_names:
matches.append(file_name)
return matches | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _recursive_file_search(self, path, pattern):\n matches = []\n for root, dirnames, filenames in os.walk(path):\n for filename in fnmatch.filter(filenames, pattern):\n matches.append(os.path.join(root, filename))\n\n return matches",
"def find_files(directory, fil... | [
"0.7796083",
"0.7477516",
"0.7372066",
"0.733775",
"0.72947305",
"0.72270644",
"0.7131149",
"0.71299195",
"0.7121892",
"0.71138424",
"0.7109738",
"0.7094641",
"0.7090112",
"0.7039987",
"0.70312285",
"0.6994089",
"0.69901896",
"0.6963479",
"0.69572544",
"0.69526935",
"0.693415... | 0.7325236 | 4 |
Check that if |request_update_for_shell_apk_version| is updated it is the only change in the CL. | def _CheckChromeUpdateTriggerRule(input_api, output_api):
if _CheckVersionVariableChanged(input_api,
REQUEST_UPDATE_FOR_VERSION_LOCAL_PATH,
REQUEST_UPDATE_FOR_VERSION_VARIABLE):
if (len(input_api.AffectedFiles()) != 1 or
len(input_api.Aff... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_for_updates():\n last_version = str(request.urlopen(__source__).read().decode(\"utf8\"))\n if str(open(__file__).read()) != last_version:\n log.warning(\"Theres new Version available!, Update from \" + __source__)\n else:\n log.info(\"No new updates!,You have the lastest version of... | [
"0.69817704",
"0.68537796",
"0.67906266",
"0.6604481",
"0.6522289",
"0.64818054",
"0.64153945",
"0.63426113",
"0.6202474",
"0.6198753",
"0.61828953",
"0.61085165",
"0.6076537",
"0.60127264",
"0.6009001",
"0.60024965",
"0.5982071",
"0.59345007",
"0.59335583",
"0.59049",
"0.589... | 0.6769555 | 3 |
Check that if a file in $WAM_MINT_TRIGGER_LOCAL_PATHS is updated that |template_shell_apk_version| is updated as well. | def _CheckCurrentVersionIncreaseRule(input_api, output_api):
files_requiring_version_increase = []
for f in input_api.AffectedFiles():
if f.ChangedContents():
local_path = input_api.os_path.relpath(
f.AbsoluteLocalPath(),
input_api.PresubmitLocalPath()).replace('\\', '/')
for tri... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_for_updates():\n last_version = str(request.urlopen(__source__).read().decode(\"utf8\"))\n if str(open(__file__).read()) != last_version:\n log.warning(\"Theres new Version available!, Update from \" + __source__)\n else:\n log.info(\"No new updates!,You have the lastest version of... | [
"0.59480643",
"0.58289295",
"0.57964",
"0.57759136",
"0.53680897",
"0.5334255",
"0.5307752",
"0.5295217",
"0.528286",
"0.525433",
"0.52535343",
"0.52270526",
"0.5215952",
"0.52041125",
"0.5189144",
"0.51874846",
"0.5187396",
"0.51703596",
"0.5161194",
"0.51581484",
"0.5148415... | 0.5407697 | 4 |
Checks that if a file has been added to a res/ directory that its file name is unique to the res/ directory. res/values/dimens.xml and res/valuesv17/dimens.xml > OK res/values/dimens.xml and libs/common/res_splash/values/dimens.xml > BAD | def _CheckNoOverlappingFileNamesInResourceDirsRule(input_api, output_api):
res_dir_file_names_map = {}
for f in input_api.AffectedFiles():
local_path = input_api.os_path.relpath(
f.AbsoluteLocalPath(),
input_api.PresubmitLocalPath()).replace('\\', '/')
for res_dir_local_path in RES_DIR_LOCAL... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def valid_resfile(listname):\r\n global results_file, directory_res\r\n try:\r\n results_file = open(directory_res+listname+\".output\", \"r\")\r\n return True\r\n except:\r\n return False",
"def validate(self, spec):\n d = spec.directory\n for file_name in os.listdir(... | [
"0.6195069",
"0.61837715",
"0.6021968",
"0.5920074",
"0.583933",
"0.58312166",
"0.56605995",
"0.5639456",
"0.5594713",
"0.55712736",
"0.55012786",
"0.55012786",
"0.5501188",
"0.54754484",
"0.54694206",
"0.5450779",
"0.5419939",
"0.54080826",
"0.5407323",
"0.53906304",
"0.5371... | 0.5596819 | 8 |
Checks common to both upload and commit. | def _CommonChecks(input_api, output_api):
result = []
result.extend(_CheckChromeUpdateTriggerRule(input_api, output_api))
result.extend(_CheckCurrentVersionIncreaseRule(input_api, output_api))
result.extend(_CheckNoOverlappingFileNamesInResourceDirsRule(input_api,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testCheckChangeOnUploadWithEmptyAffectedFileList(self, _):\n diff_file_chromium1_h = ['some diff']\n diff_file_chromium2_h = ['another diff']\n diff_file_layout_test_html = ['more diff']\n mock_input_api = MockInputApi()\n mock_input_api.files = []\n # Access to a prot... | [
"0.61919343",
"0.6017219",
"0.5985389",
"0.59435034",
"0.59289926",
"0.5904462",
"0.58712673",
"0.58421326",
"0.5817588",
"0.5799402",
"0.5788161",
"0.5749811",
"0.5734672",
"0.5721772",
"0.57161516",
"0.5686996",
"0.5659645",
"0.56544584",
"0.5618077",
"0.5618063",
"0.559031... | 0.0 | -1 |
Pickles the raw images | def dump(self, img_labels, images_base_directory, destination_pickle_path, destination_pickle_file_name, preprocessing_transformer):
# Load images_base_directory
# files_per_pickle = len(img_labels) // parts
# pickle_part_num = 1
result = None
images = []
for i in range(l... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getimgs():",
"def Read_Raw_Images(path_data,path_labels):\n \n data = skimage.io.imread(path_data).astype(np.float32)\n for i in range(data.shape[0]):\n data[i,...] = skimage.exposure.rescale_intensity(data[i,...], out_range=(0,1))\n data_labels = skimage.io.imread(path_labels) > 0\n \n... | [
"0.6534259",
"0.62466824",
"0.61905247",
"0.61871415",
"0.6170488",
"0.60333925",
"0.6026292",
"0.6017525",
"0.5964287",
"0.5951983",
"0.59493524",
"0.59432364",
"0.5941363",
"0.5899673",
"0.5880692",
"0.58698577",
"0.58665174",
"0.5857296",
"0.5857296",
"0.5857296",
"0.58539... | 0.6040779 | 5 |
Checks if parameter is output parameter. Returns True if parameter has key 'gisprompt.age' == 'new', False otherwise. | def isOutput(parameter):
try:
if '@age' in parameter['gisprompt'].keys():
return (parameter['gisprompt']['@age'] == 'new')
else:
return False
except KeyError:
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def verify_output(self, output):\n return output == self.output",
"def check(self) -> Tuple[bool, str]:\n if self[\"changed\"]:\n return True, \"\"\n else:\n return False, \"The \" + self[\"name\"] + \" parameter have to be set\"",
"def params_ok(): \n \n if ... | [
"0.5807495",
"0.56992525",
"0.557156",
"0.5501243",
"0.54481214",
"0.5438",
"0.5403741",
"0.53788143",
"0.53770506",
"0.5369349",
"0.53541017",
"0.5340148",
"0.5340148",
"0.5340148",
"0.530699",
"0.52968436",
"0.5277953",
"0.5249121",
"0.5176042",
"0.5163241",
"0.5161659",
... | 0.924517 | 0 |
Parses output of GRASS interfacedescription and returns openEO process object | def ParseInterfaceDescription(xml_string, keys=None):
gm_dict = xmltodict.parse(xml_string)['task']
module_id = gm_dict['@name']
description = gm_dict['description']
categories = gm_dict['keywords'].replace(' ', '').split(',')
categories.append('grass-module')
parameters = {}
returns = {}
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process():\n reader = owslib.wps.WPSDescribeProcessReader()\n root = reader.readFromString(open(resource_file(\"process_description.xml\")).read())\n xml = root.findall(\"ProcessDescription\")[0]\n return owslib.wps.Process(xml)",
"def parse_os_info(self):\n pipe = subprocess.Popen([self.c... | [
"0.5585331",
"0.55455714",
"0.55155593",
"0.5485775",
"0.5353875",
"0.52453214",
"0.5239776",
"0.515878",
"0.5139114",
"0.51333976",
"0.5119332",
"0.5111082",
"0.5097562",
"0.5060103",
"0.5051946",
"0.50517285",
"0.5047649",
"0.50408345",
"0.50263286",
"0.50263286",
"0.502632... | 0.47916546 | 52 |
From the game result string, extract the winner's id. | def _determine_winner(game_result):
return next(line for line in game_result.splitlines()
if re.compile(_WINNING_RANK_STRING).search(line)).split(_SPACE_DELIMITER)[_BOT_ID_POSITION] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_uniprot_id(uniprot_result):\n id_lines = [l for l in uniprot_result.split('\\n') if l.startswith('ID')]\n\n for id_line in id_lines:\n return id_line.split()[1]\n\n return None",
"def get_run_id_from_result(model_result):\n if 'ml_flow' not in model_result:\n return None... | [
"0.6389786",
"0.6329488",
"0.61289394",
"0.5958591",
"0.5893738",
"0.5817661",
"0.5805153",
"0.58014286",
"0.5724729",
"0.56584716",
"0.56151605",
"0.5602205",
"0.55870014",
"0.55432886",
"0.55321854",
"0.54670435",
"0.54463226",
"0.54409176",
"0.5419892",
"0.53860986",
"0.53... | 0.7051588 | 0 |
Plays one game considering the specified bots and the game and map constraints. | def _play_game(binary, map_width, map_height, bot_commands):
game_run_command = '\"{}\" -d "{} {}" -t'.format(binary, map_width, map_height)
for bot_command in bot_commands:
game_run_command += " \"{}\"".format(bot_command)
return subprocess.check_output(game_run_command, shell=True).decode() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_game(map_settings, players, **kwargs) -> Union[Result, List[Optional[Result]]]:\n if sum(isinstance(p, (Human, Bot)) for p in players) > 1:\n host_only_args = [\"save_replay_as\", \"rgb_render_config\", \"random_seed\", \"sc2_version\", \"disable_fog\"]\n join_kwargs = {k: v for k, v in kw... | [
"0.64811915",
"0.6312781",
"0.60999507",
"0.60926616",
"0.6040818",
"0.59653705",
"0.59286845",
"0.5846582",
"0.5824571",
"0.5776197",
"0.576185",
"0.57472235",
"0.5744895",
"0.57314324",
"0.56971264",
"0.56946355",
"0.56720424",
"0.5665085",
"0.56599295",
"0.56463945",
"0.56... | 0.5764885 | 10 |
Runs number_of_runs games using the designated bots and binary, recording the tally of wins per player | def play_games(binary, map_width, map_height, bot_commands, number_of_runs):
print("Comparing Bots!")
result = {}
if not(len(bot_commands) == 4 or len(bot_commands) == 2):
raise IndexError("The number of bots specified must be either 2 or 4.")
for current_run in range(0, number_of_runs):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _run_match(bots, nr_of_games) -> dict:\n\n start, game_results = datetime.now().timestamp(), _run_games(nr_of_games, bots)\n match_duration = datetime.now().timestamp() - start\n\n match_json = _match_result(game_results, match_duration)\n _print_match_end_score(match_json, match_duration)\n ret... | [
"0.6457276",
"0.6405058",
"0.63816977",
"0.6365274",
"0.6318773",
"0.62803453",
"0.6180857",
"0.6159016",
"0.6089046",
"0.59996665",
"0.59805876",
"0.5962676",
"0.59359556",
"0.59175366",
"0.59158796",
"0.5890217",
"0.5871049",
"0.5856832",
"0.58529985",
"0.58124524",
"0.5800... | 0.7698103 | 0 |
r"""Verify a simple DAG pipeline runs and captures logs as expected. | def test_run_and_logs(self, registered_model):
create_standard_model = functools.partial(
registered_model.create_standard_model,
code_dependencies=[],
environment=Python([]),
)
echo_model_ver = create_standard_model(Echo)
double_model_ver = create_sta... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_build_pipeline_six(self):\n args = \"Test_APP FIVE A B\".split(\" \")\n task_list = build_pipeline(args, False)\n self.assertEqual(1, len(task_list))",
"def test_pipeline_runner_main():\n working_dir = os.path.join(\n os.getcwd(),\n 'tests')\n pypyr.pipelinerunne... | [
"0.65219057",
"0.64891356",
"0.64445233",
"0.64214414",
"0.6386529",
"0.6375167",
"0.62957263",
"0.6271578",
"0.6222027",
"0.61659265",
"0.60646236",
"0.60298234",
"0.60103154",
"0.5998008",
"0.5974212",
"0.5972701",
"0.59685767",
"0.594035",
"0.58930403",
"0.58778256",
"0.58... | 0.6119972 | 10 |
Save a PNG plot visualizing posterior uncertainty on heldout data. | def plot_heldout_prediction(input_val,
y_val,
mu_val,
sigma_val,
fname=None,
n=1,
title=""):
fig = figure.Figure(figsize=(9, 3 * n))
canvas = ba... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cb_save(event):\n fig.savefig('sample.univariate_discrete.py.png', dpi=300, format='png', transparent=True)",
"def save(file_name):\n setup()\n plt.savefig(file_name)",
"def export(self, path):\r\n # Save plot as png\r\n dt = m.get_instance().dt\r\n self.perception_history = m... | [
"0.72815305",
"0.6994909",
"0.68869275",
"0.65535516",
"0.6454215",
"0.6431693",
"0.6421337",
"0.63826656",
"0.63084036",
"0.6303676",
"0.6301993",
"0.629871",
"0.6241234",
"0.6240071",
"0.6239843",
"0.6228209",
"0.62094927",
"0.611023",
"0.6079249",
"0.60417473",
"0.60310334... | 0.6270795 | 12 |
Construct a Game bean beanContext is an instance of nova.beanContext.BeanContext | def createBean(self, beanContext):
currentTime = DateTime.now()
return game.Game().construct(self.serialNum(), self.startTime(), self.endTime(), currentTime, nextProductionTime(currentTime, self), \
self.deathProbeCost(), self.spyProbeCost(), self.factoryCost(), self.speedCost(), self.rangeCost(), self.pro... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self,context=''):\n if context=='': \n context_name=self.__class__.__name__\n else: \n context_name=context+'.'+self.__class__.__name__ \n if not (context_name in self.__class__._nInstancesContextList):\n self.__class__._nInstancesContextList.appen... | [
"0.5429802",
"0.5398456",
"0.52547485",
"0.51466453",
"0.5145113",
"0.5114401",
"0.5002459",
"0.49618447",
"0.49233636",
"0.49038294",
"0.4899187",
"0.48675564",
"0.48630452",
"0.48493588",
"0.4817039",
"0.4813379",
"0.48067135",
"0.48053434",
"0.48048058",
"0.480347",
"0.479... | 0.77193683 | 0 |
Return the 'Nobody' player for the game | def nobody(self):
return self._mk_store.fetchObjectsOfClass(Player, clauses="WHERE gameId = %d AND name = 'Nobody'" % self.sqlObjRef())[0] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def opponent(self, player):\r\n # player = core.BLACK (can do this for any static var)\r\n if player == core.BLACK:\r\n return core.WHITE\r\n else:\r\n return core.BLACK",
"def get_opponent(self):\n for cell in self.__state.board.find_workers():\n play... | [
"0.7179082",
"0.71346235",
"0.70811814",
"0.7037287",
"0.6971815",
"0.69505286",
"0.6927338",
"0.68758464",
"0.68403226",
"0.6769221",
"0.67114675",
"0.6703061",
"0.6703061",
"0.6701386",
"0.6669454",
"0.66185004",
"0.6609078",
"0.6556283",
"0.6543647",
"0.6519385",
"0.651381... | 0.80251557 | 0 |
Draw something to a surface. The offset is a x and y tuple which offsets the final position. | def draw(self, screen, offsets: tuple):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def draw(self, offset: IntegerPosition2D, canvas: Canvas) -> None:\n canvas_position: IntegerPosition2D\n if not self.buffer:\n canvas_position = IntegerPosition2D(0, 0)\n else:\n row: int = self.buffer.get_row(self.index)\n column: int = self.buffer.get_column... | [
"0.71174616",
"0.6813651",
"0.6532218",
"0.6521753",
"0.6519349",
"0.6490653",
"0.6319898",
"0.6258449",
"0.6215199",
"0.6161956",
"0.6161956",
"0.61448157",
"0.6084019",
"0.60630345",
"0.60574555",
"0.6041663",
"0.59853274",
"0.5980514",
"0.59275645",
"0.5915207",
"0.5913053... | 0.70662737 | 1 |
Gets called 60 times a second. | def tick(self):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def everytime(self):\n return True",
"async def _timein_refresh(self):\n\t\t\n\t\tawait self.refresh_cache()",
"def _inst_run(self):\r\n self._inst_get_img_info_from_db()\r\n print(\"run method: \", time.ctime())\r\n th = threading.Timer(\r\n 10,\r\n self._inst... | [
"0.6385242",
"0.63584125",
"0.6151215",
"0.6067275",
"0.6057214",
"0.605076",
"0.60446185",
"0.60382575",
"0.60161215",
"0.59875643",
"0.5986476",
"0.59559673",
"0.58786386",
"0.5877851",
"0.58549607",
"0.5854388",
"0.58293724",
"0.58263654",
"0.57705706",
"0.57678723",
"0.57... | 0.5468217 | 63 |
Add a thing to a specific section. Leaving out index will append it, otherwise will insert at specific index. | def add_to_drawn(section: str, thing: Base, index: int=None):
if index is None:
drawn[section].append(thing)
else:
drawn[section].insert(index, thing) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def add_new_section(self, name, context=...):\n ...",
"def add_section(self, section, lyrics):\n self.sections[section] = lyrics",
"def add_section(self, section_name: str) -> None:\n pass",
"def add_section(self, section_name: str) -> None:\n pass",
"def add_section(self, secti... | [
"0.6761237",
"0.66035444",
"0.6566462",
"0.6566462",
"0.6247197",
"0.61579007",
"0.61553264",
"0.60965484",
"0.6061076",
"0.6059241",
"0.6051624",
"0.604102",
"0.6035189",
"0.60212517",
"0.6017513",
"0.6003912",
"0.599046",
"0.59664154",
"0.5942525",
"0.59222454",
"0.58979625... | 0.76561177 | 0 |
Remove a thing from a specific section using a specific index. | def remove_from_drawn(section: str, index: int):
del drawn[section][index] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove(self, index):\n raise NotImplementedError()",
"def delete_at_index(self, index: int) -> T:\n pass",
"def delete_at_index(self, index: int) -> T:\n pass",
"def removeRow(self, index: int) -> None:\n ...",
"def __delitem__(self, index: Any) -> None:\n del self.co... | [
"0.70469296",
"0.69429576",
"0.69429576",
"0.6858212",
"0.67361236",
"0.66979665",
"0.6678134",
"0.66500795",
"0.6619056",
"0.6598444",
"0.6516929",
"0.6505775",
"0.6504128",
"0.6432966",
"0.6431581",
"0.6431434",
"0.63970387",
"0.63944894",
"0.639437",
"0.6385715",
"0.631306... | 0.7609031 | 0 |
This will execute all the operations selection according to the instance. | def ks_execute_operation(self):
if self.ks_sync_orders or self.ks_sync_customers or self.ks_sync_coupons or self.ks_sync_products or \
self.ks_sync_attributes or self.ks_sync_product_tags or self.ks_sync_product_category or \
self.ks_sync_payment_gateways or self.ks_publish_produ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def execute(self):",
"def execute(self):",
"def execute(self):",
"def execute(self):",
"def execute(self):\r\n pass",
"def execute(self):\n\t\tpass",
"def execute(self):\n\n pass",
"def execute(self):\n\n pass",
"def execute(self):\n\n pass",
"def execute(self):\n\n ... | [
"0.662009",
"0.662009",
"0.662009",
"0.662009",
"0.6608843",
"0.6564755",
"0.655838",
"0.655838",
"0.655838",
"0.655838",
"0.6540609",
"0.6540609",
"0.6540609",
"0.6540609",
"0.6540609",
"0.6540609",
"0.6421845",
"0.6409058",
"0.63194174",
"0.63194174",
"0.6228255",
"0.6196... | 0.0 | -1 |
Tests whether network rejects invalid hidden_layers inputted from user | def test_user_hidden_layers_input_rejections():
inputs_that_should_fail = [[["linearr", 33]], [["linear", 12, 33]], [["gru", 2, 33]], [["lstm", 2, 33]], [["lstmr", 33]],
[["gruu", 33]], [["gru", 33], ["xxx", 33]], [["linear", 33], ["gru", 12], ["gru", 33]] ]
for input in inputs_th... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_RNN_layers_valid(self):\n error_msg_layer_type = \"First element in a layer specification must be one of {}\".format(self.valid_RNN_hidden_layer_types)\n error_msg_layer_form = \"Layer must be of form [layer_name, hidden_units]\"\n error_msg_layer_list = \"Layers must be provided as ... | [
"0.7171822",
"0.63561493",
"0.610897",
"0.6082803",
"0.6039953",
"0.6018404",
"0.57547784",
"0.5728571",
"0.57113945",
"0.5701364",
"0.5636148",
"0.5581209",
"0.55617243",
"0.5515967",
"0.55017126",
"0.5476648",
"0.54517835",
"0.54494756",
"0.5427705",
"0.5413505",
"0.5391189... | 0.69881386 | 1 |
Tests whether network rejects invalid hidden_layers inputted from user | def test_user_hidden_layers_input_acceptances():
inputs_that_should_work = [[["linear", 33]], [["linear", 12]], [["gru", 2]], [["lstm", 2]], [["lstm", 1]],
[["gru", 330]], [["gru", 33], ["linear", 2]] ]
for input in inputs_that_should_work:
assert RNN(input_dim=1, layers_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_RNN_layers_valid(self):\n error_msg_layer_type = \"First element in a layer specification must be one of {}\".format(self.valid_RNN_hidden_layer_types)\n error_msg_layer_form = \"Layer must be of form [layer_name, hidden_units]\"\n error_msg_layer_list = \"Layers must be provided as ... | [
"0.7171822",
"0.69881386",
"0.63561493",
"0.6082803",
"0.6039953",
"0.6018404",
"0.57547784",
"0.5728571",
"0.57113945",
"0.5701364",
"0.5636148",
"0.5581209",
"0.55617243",
"0.5515967",
"0.55017126",
"0.5476648",
"0.54517835",
"0.54494756",
"0.5427705",
"0.5413505",
"0.53911... | 0.610897 | 3 |
Tests that create_hidden_layers works correctly | def test_hidden_layers_created_correctly():
layers = [["gru", 25], ["lstm", 23], ["linear", 5], ["linear", 10]]
rnn = RNN(input_dim=5, layers_info=layers, hidden_activations="relu",
output_activation="relu")
assert type(rnn.hidden_layers[0]) == nn.GRU
assert rnn.hidden_layers[0].input_si... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_visibility(self, data, visible):\n layer = Points(data)\n assert layer.visible is True\n\n layer = Points(data, visible=visible)\n assert layer.visible is visible\n\n layer.visible = not visible\n assert layer.visible is not visible",
"def build_layers(self):\n ... | [
"0.6285836",
"0.619946",
"0.617445",
"0.6167124",
"0.5950911",
"0.5936817",
"0.58561146",
"0.5794933",
"0.5788519",
"0.5737324",
"0.5709296",
"0.570217",
"0.56971806",
"0.5679431",
"0.56654364",
"0.5664339",
"0.56596035",
"0.56536275",
"0.56391835",
"0.56333864",
"0.56308633"... | 0.7149164 | 0 |
Tests that create_output_layers works correctly | def test_output_layers_created_correctly():
layers = [["gru", 25], ["lstm", 23], ["linear", 5], ["linear", 10]]
rnn = RNN(input_dim=5, layers_info=layers, hidden_activations="relu", output_activation="relu")
assert rnn.output_layers[0].in_features == 5
assert rnn.output_layers[0].out_features == 10
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_output_head_layers():\n for output_dim in [[[\"linear\", 3],[\"linear\", 9]], [[\"linear\", 4], [\"linear\", 20]], [[\"linear\", 1], [\"linear\", 1]]]:\n nn_instance = RNN(input_dim=5, layers_info=[[\"gru\", 20], [\"lstm\", 8], output_dim],\n hidden_activations=\"relu\",... | [
"0.70297605",
"0.69095945",
"0.67008126",
"0.6463796",
"0.64326644",
"0.62291646",
"0.615556",
"0.615226",
"0.60517156",
"0.6034557",
"0.60170466",
"0.6016979",
"0.60113925",
"0.60038334",
"0.5999134",
"0.5999134",
"0.5973484",
"0.5926574",
"0.59118354",
"0.5879324",
"0.58793... | 0.7599407 | 0 |
Tests whether network rejects an invalid output_dim input from user | def test_output_dim_user_input():
inputs_that_should_fail = [-1, "aa", ["dd"], [2], 0, 2.5, {2}]
for input_value in inputs_that_should_fail:
with pytest.raises(AssertionError):
RNN(input_dim=3, layers_info=[2, input_value], hidden_activations="relu", output_activation="relu")
with p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def error(input_, output):\n global number_of_neurons_by_layer\n if len(output) != number_of_neurons_by_layer[-1]:\n raise IndexError(\n f\"\\033[91mDesired output length is incorrect. It must be {number_of_neurons_by_layer[-1]}.\\033[m\")\n output = np.array(output).reshape(len(output),... | [
"0.6838607",
"0.6454841",
"0.63032913",
"0.6283632",
"0.6166759",
"0.61523336",
"0.6115839",
"0.608904",
"0.6088475",
"0.6049992",
"0.60479885",
"0.60479844",
"0.6031168",
"0.6017515",
"0.601701",
"0.5978786",
"0.59750605",
"0.5965413",
"0.59618676",
"0.5956854",
"0.59553415"... | 0.71197593 | 0 |
Tests whether network rejects an invalid hidden_activations or output_activation from user | def test_activations_user_input():
inputs_that_should_fail = [-1, "aa", ["dd"], [2], 0, 2.5, {2}, "Xavier_"]
for input_value in inputs_that_should_fail:
with pytest.raises(AssertionError):
RNN(input_dim=4, layers_info=[["linear", 2]], hidden_activations=input_value,
output_ac... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_user_hidden_layers_input_rejections():\n inputs_that_should_fail = [[[\"linearr\", 33]], [[\"linear\", 12, 33]], [[\"gru\", 2, 33]], [[\"lstm\", 2, 33]], [[\"lstmr\", 33]],\n [[\"gruu\", 33]], [[\"gru\", 33], [\"xxx\", 33]], [[\"linear\", 33], [\"gru\", 12], [\"gru\", 33]]... | [
"0.646115",
"0.588715",
"0.5790413",
"0.5729977",
"0.5634677",
"0.56261045",
"0.5624805",
"0.56081295",
"0.55690366",
"0.55218625",
"0.5504632",
"0.5499097",
"0.5493358",
"0.54845136",
"0.54714316",
"0.5470243",
"0.54539776",
"0.544579",
"0.5414394",
"0.53987086",
"0.53792995... | 0.6299433 | 1 |
Tests whether network rejects an invalid initialiser from user | def test_initialiser_user_input():
inputs_that_should_fail = [-1, "aa", ["dd"], [2], 0, 2.5, {2}, "Xavier_"]
for input_value in inputs_that_should_fail:
with pytest.raises(AssertionError):
RNN(input_dim=4, layers_info=[["linear", 2]], hidden_activations="relu",
output_activat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_parameter_net_invalid(self, mock_ghn, mock_grnam, mock_pwnam):\n # Should pass\n self.driver.check_for_setup_error()\n # Should throw exceptions\n self._fail_network_list = True\n self.configuration.hgst_net = 'Fred'\n self.assertRaises(exception.VolumeDriverExcep... | [
"0.6485824",
"0.6225917",
"0.61696994",
"0.6149689",
"0.6108564",
"0.60041595",
"0.5883205",
"0.5870985",
"0.5809604",
"0.5742367",
"0.5724536",
"0.5699671",
"0.5689126",
"0.5671148",
"0.56684226",
"0.56614745",
"0.56337553",
"0.5632013",
"0.5615396",
"0.56135267",
"0.5600927... | 0.5697767 | 12 |
Tests whether batch_norm_layers method works correctly | def test_batch_norm_layers():
layers = [["gru", 20], ["lstm", 3], ["linear", 4], ["linear", 10]]
rnn = RNN(layers_info=layers, hidden_activations="relu", input_dim=5,
output_activation="relu", initialiser="xavier", batch_norm=True)
assert len(rnn.batch_norm_layers) == 3
assert rnn.batch_no... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_cnn_batchnorm_dim(self):\n model = modelgen.generate_CNN_model((None, 20, 3), 2, [32, 32], 100)\n batchnormlay = model.layers[2]\n assert batchnormlay.output_shape == (None, 20, 32)",
"def test_cnn_enough_batchnorm(self):\n model = modelgen.generate_CNN_model((None, 20, 3), 2... | [
"0.8106033",
"0.80667454",
"0.7956286",
"0.78110313",
"0.77823645",
"0.74154824",
"0.73335415",
"0.7272949",
"0.7086378",
"0.70444816",
"0.70052063",
"0.6950574",
"0.6918558",
"0.6895405",
"0.6852374",
"0.6791033",
"0.6783299",
"0.6750765",
"0.67006516",
"0.6681655",
"0.66285... | 0.79899555 | 2 |
Tests that it throws an error if user tries to provide list of hidden layers that include linear layers where they don't only come at the end | def test_linear_layers_only_come_at_end():
layers = [["gru", 20], ["linear", 4], ["lstm", 3], ["linear", 10]]
with pytest.raises(AssertionError):
rnn = RNN(layers_info=layers, hidden_activations="relu", input_dim=4,
output_activation="relu", initialiser="xavier", batch_norm=True)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_RNN_layers_valid(self):\n error_msg_layer_type = \"First element in a layer specification must be one of {}\".format(self.valid_RNN_hidden_layer_types)\n error_msg_layer_form = \"Layer must be of form [layer_name, hidden_units]\"\n error_msg_layer_list = \"Layers must be provided as ... | [
"0.74692565",
"0.69579005",
"0.6833342",
"0.6695402",
"0.6636951",
"0.66085696",
"0.6465696",
"0.64110804",
"0.63803566",
"0.6348933",
"0.6182347",
"0.616117",
"0.61357015",
"0.60021096",
"0.59823257",
"0.5962591",
"0.59096926",
"0.59012794",
"0.58661574",
"0.5831734",
"0.582... | 0.6448731 | 7 |
Tests whether network outputs data that has gone through correct activation function | def test_output_activation():
RANDOM_ITERATIONS = 20
input_dim = 100
for _ in range(RANDOM_ITERATIONS):
data = torch.randn((25, 10, 100))
RNN_instance = RNN(layers_info=[["lstm", 20], ["gru", 5], ["linear", 10], ["linear", 3]],
hidden_activations="relu", input_dim=... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_sigmoid_activation(self):\n self.assertEqual([0.5, 0.5], list(\n af.Sigmoid().output(np.array([0, 0]))))\n self.assertEqual([0.25, 0.25], list(\n af.Sigmoid().derivative(np.array([0, 0]))))",
"def test_output_activation_return_return_final_seq_only_off():\n RANDOM_... | [
"0.68730205",
"0.67846847",
"0.66975826",
"0.6651164",
"0.661632",
"0.6461441",
"0.63807184",
"0.63212496",
"0.6313192",
"0.63074267",
"0.62492007",
"0.62348425",
"0.6229038",
"0.6172399",
"0.61524916",
"0.61336595",
"0.61072147",
"0.60924554",
"0.60870665",
"0.6061939",
"0.6... | 0.72898424 | 0 |
Tests whether network outputs data that has gone through correct activation function | def test_output_activation_return_return_final_seq_only_off():
RANDOM_ITERATIONS = 20
input_dim = 100
for _ in range(RANDOM_ITERATIONS):
data = torch.randn((25, 10, 100))
RNN_instance = RNN(layers_info=[["lstm", 20], ["gru", 5], ["linear", 10], ["linear", 3]],
hidd... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_output_activation():\n RANDOM_ITERATIONS = 20\n input_dim = 100\n for _ in range(RANDOM_ITERATIONS):\n data = torch.randn((25, 10, 100))\n RNN_instance = RNN(layers_info=[[\"lstm\", 20], [\"gru\", 5], [\"linear\", 10], [\"linear\", 3]],\n hidden_activations... | [
"0.72898424",
"0.68730205",
"0.66975826",
"0.6651164",
"0.661632",
"0.6461441",
"0.63807184",
"0.63212496",
"0.6313192",
"0.63074267",
"0.62492007",
"0.62348425",
"0.6229038",
"0.6172399",
"0.61524916",
"0.61336595",
"0.61072147",
"0.60924554",
"0.60870665",
"0.6061939",
"0.6... | 0.67846847 | 2 |
Tests whether setting a y range works correctly | def test_y_range():
for _ in range(100):
val1 = random.random() - 3.0*random.random()
val2 = random.random() + 2.0*random.random()
lower_bound = min(val1, val2)
upper_bound = max(val1, val2)
rnn = RNN(layers_info=[["lstm", 20], ["gru", 5], ["lstm", 25]],
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_y_range_user_input():\n invalid_y_range_inputs = [ (4, 1), (2, 4, 8), [2, 4], (np.array(2.0), 6.9)]\n for y_range_value in invalid_y_range_inputs:\n with pytest.raises(AssertionError):\n print(y_range_value)\n rnn = RNN(layers_info=[[\"lstm\", 20], [\"gru\", 5], [\"lstm\... | [
"0.69887966",
"0.6787549",
"0.6728513",
"0.6704412",
"0.66581345",
"0.6623556",
"0.65083843",
"0.6419491",
"0.64088297",
"0.6408696",
"0.6345011",
"0.6308929",
"0.62689686",
"0.62398374",
"0.62286705",
"0.61892277",
"0.6184476",
"0.6139843",
"0.6135049",
"0.6135049",
"0.61283... | 0.7210425 | 0 |
Tests whether is able to handle user inputting None as output activation | def test_deals_with_None_activation():
assert RNN(layers_info=[["lstm", 20], ["gru", 5], ["lstm", 25]],
hidden_activations="relu", output_activation=None,
initialiser="xavier", input_dim=5) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_none_input(self):\n eq_(None, output())",
"def noinput():\n env.prompt = False",
"def NoPrompt(self) -> bool:",
"def __init__(self):\n self.the_input = raw_input().strip().replace(' ', '')\n if self.the_input == '':\n print ('No input detected')\n exit(1... | [
"0.79208875",
"0.6521303",
"0.6440907",
"0.63686097",
"0.6247845",
"0.6149467",
"0.614875",
"0.60871434",
"0.6086204",
"0.6085143",
"0.60612893",
"0.60589445",
"0.59811735",
"0.5976643",
"0.59697825",
"0.59590137",
"0.59576726",
"0.5948883",
"0.5945956",
"0.5917923",
"0.58935... | 0.0 | -1 |
Tests that check_input_data_into_forward_once method only runs once | def test_check_input_data_into_forward_once():
rnn = RNN(layers_info=[["lstm", 20], ["gru", 5], ["lstm", 25]],
hidden_activations="relu", input_dim=5,
output_activation="relu", initialiser="xavier")
data_not_to_throw_error = torch.randn((1, 4, 5))
data_to_throw... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_forward(self):\n validate_forward()",
"def check_Data(self):\r\n \r\n if self._target_data is None:\r\n self.processData()",
"def has_buffered_inputs(self):",
"def forward_once(self, x):\n\t\t#x = F.normalize(self.network(x), p=2)\n\t\tx = self.network(x)\n\t\treturn ... | [
"0.61968815",
"0.58582073",
"0.5682138",
"0.5575721",
"0.5539609",
"0.5531701",
"0.5507432",
"0.5409996",
"0.53948504",
"0.5367557",
"0.5359325",
"0.533804",
"0.5321757",
"0.52892125",
"0.5246141",
"0.52032685",
"0.51893234",
"0.51822394",
"0.5180504",
"0.5171261",
"0.5169993... | 0.6379902 | 0 |
Tests whether network rejects invalid y_range inputs | def test_y_range_user_input():
invalid_y_range_inputs = [ (4, 1), (2, 4, 8), [2, 4], (np.array(2.0), 6.9)]
for y_range_value in invalid_y_range_inputs:
with pytest.raises(AssertionError):
print(y_range_value)
rnn = RNN(layers_info=[["lstm", 20], ["gru", 5], ["lstm", 25]],
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_invalid_target(self):\n y_valid = np.random.randint(2, size=100)\n y_invalid = np.random.uniform(size=100)\n\n oz = ClassBalance()\n\n with pytest.raises(YellowbrickValueError):\n oz.fit(y_invalid)\n\n with pytest.raises(YellowbrickValueError):\n oz... | [
"0.69277006",
"0.67759",
"0.66113496",
"0.6391974",
"0.6369389",
"0.63201374",
"0.63130033",
"0.62934816",
"0.626229",
"0.624285",
"0.62060905",
"0.6107401",
"0.60435826",
"0.6005684",
"0.5997749",
"0.5965224",
"0.59253037",
"0.59052044",
"0.5887826",
"0.58822834",
"0.5843428... | 0.81492263 | 0 |
Checks if a given network is able to solve a simple problem | def solves_simple_problem(X, y, nn_instance):
optimizer = optim.Adam(nn_instance.parameters(), lr=0.15)
for ix in range(800):
out = nn_instance.forward(X)
loss = torch.sum((out.squeeze() - y) ** 2) / N
optimizer.zero_grad()
loss.backward()
optimizer.step()
print("LOSS... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_whole_network(self):\n if not self.network.check_network():\n # check_network has failed, issue error\n self._display_semantic_error(\"network\")",
"def test_check_network(network_with_devices):\n network = network_with_devices\n devices = network.devices\n names ... | [
"0.7131779",
"0.7025503",
"0.6738139",
"0.63704544",
"0.63352126",
"0.63034153",
"0.62601763",
"0.6208311",
"0.6193372",
"0.61778873",
"0.61509526",
"0.61064",
"0.60851043",
"0.60542005",
"0.60393006",
"0.601454",
"0.6002187",
"0.5981219",
"0.5966142",
"0.59387124",
"0.591576... | 0.0 | -1 |
Tests whether a small range of networks can solve a simple task | def test_model_trains():
for output_activation in ["sigmoid", "None"]:
rnn = RNN(layers_info=[["gru", 20], ["lstm", 8], ["linear", 1]], input_dim=15,
hidden_activations="relu", output_activation=output_activation,
initialiser="xavier")
assert sol... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def solve(self, network):\n # Convert to a network if it is not.\n if not isinstance(network, NeuralNetwork):\n network = NeuralNetwork(network)\n \n steps, _, _ = self._loop(network, max_steps=100000)\n if steps < 100000:\n print((\"Failed 100k test with %d... | [
"0.6783458",
"0.64321506",
"0.62441313",
"0.61896414",
"0.61744946",
"0.612199",
"0.6099211",
"0.6083165",
"0.60603386",
"0.6048442",
"0.60126317",
"0.59663063",
"0.595257",
"0.58757645",
"0.5870619",
"0.58563685",
"0.585211",
"0.585142",
"0.5799482",
"0.57957184",
"0.5793637... | 0.54056114 | 83 |
Tests that it raises an error if we try to do an embedding on negative data | def test_error_when_provide_negative_data_for_embedding():
N = 250
X = torch.randn((N, 5, 15))
X[0:125, 0, 3] += 20.0
y = X[:, 0, 3] > 5.0
y = y.float()
with pytest.raises(AssertionError):
rnn = RNN(layers_info=[["gru", 20], ["lstm", 8], ["linear", 1]], input_dim=15,
hi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_extract_incorrect_embeddings():\n with pytest.raises(ValueError):\n model = BERTopic(language=\"Unknown language\")\n model._extract_embeddings([\"Some document\"])",
"def test_embedder_raises_exception_for_incorrect_input_type(self):\n embed = layers.Embed(num_embeddings=10, feature... | [
"0.7797407",
"0.69551307",
"0.6756526",
"0.6376604",
"0.620987",
"0.61232376",
"0.6023977",
"0.59729695",
"0.59117335",
"0.58505356",
"0.5842247",
"0.5827982",
"0.5789328",
"0.5751712",
"0.56799704",
"0.5669994",
"0.56527793",
"0.56489265",
"0.56377167",
"0.5577644",
"0.55776... | 0.7570276 | 1 |
Tests whether create_embedding_layers method works correctly | def test_embedding_layers():
for embedding_in_dim_1, embedding_out_dim_1, embedding_in_dim_2, embedding_out_dim_2 in zip(range(5, 8), range(3, 6), range(1, 4), range(24, 27)):
nn_instance = RNN(input_dim=15, layers_info=[["gru", 20], ["lstm", 8], ["linear", 1]],
embedding_dimensions... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_embedding_matrix_shape(self):\n num_embeddings = 10\n features = 5\n embed = layers.Embed(num_embeddings=num_embeddings, features=features)\n inputs = np.expand_dims(np.arange(features, dtype=np.int64), 1)\n variables = embed.init(jax.random.PRNGKey(0), inputs)\n embedding_matrix = varia... | [
"0.6754671",
"0.66972744",
"0.6680853",
"0.65963113",
"0.65625614",
"0.64607567",
"0.6458213",
"0.643751",
"0.64041454",
"0.6341409",
"0.62876004",
"0.62862927",
"0.6283644",
"0.6279857",
"0.61914355",
"0.61818564",
"0.61757904",
"0.617194",
"0.6163812",
"0.6151228",
"0.61408... | 0.7215121 | 0 |
Tests that model trains when using embeddings | def test_model_trains_with_embeddings():
N = 250
X = torch.randn((N, 5, 15))
X[0:125, 0, 3] += 20.0
y = X[:, 0, 3] > 5.0
y = y.float()
Z = copy.deepcopy(X)
Z[:, :, 0] = abs(Z[:, :, 0]).long()
rnn = RNN(layers_info=[["gru", 20], ["lstm", 8], ["linear", 1]], input_dim=15,
hi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_build_with_embeddings(self):\n # Train a very small model\n dataset = KDDCupDataset()\n dataset.create_fixed_samples(\n *self.data, samples_num=1, partition_sizes=self.partition_sizes)\n dataset.set_current_sample(0)\n sentences = [[str(x) for x in numpy.arang... | [
"0.72196376",
"0.71237755",
"0.704444",
"0.6993473",
"0.68587327",
"0.677294",
"0.675533",
"0.6684885",
"0.66717297",
"0.6624322",
"0.6594463",
"0.6589527",
"0.6489075",
"0.6450095",
"0.63827366",
"0.6311518",
"0.62798834",
"0.62602514",
"0.6243312",
"0.6233273",
"0.62169766"... | 0.6352863 | 15 |
Tests whether dropout layer reads in probability correctly | def test_dropout():
rnn = RNN(layers_info=[["lstm", 20], ["gru", 10], ["linear", 20], ["linear", 1]],
hidden_activations="relu", output_activation="sigmoid", dropout=0.9999,
initialiser="xavier", input_dim=15)
assert rnn.dropout_layer.p == 0.9999
assert ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _Dropout(self, name, drop_prob):\n return super()._Dropout(name, keep_prob=1.0 - drop_prob)",
"def compute_dropout(self, activations, dropout_prob = 0.5):\n # handle error\n if dropout_prob < 0 or dropout_prob > 1:\n dropout_prob = 0.5\n # scale the activations (see http://... | [
"0.67629886",
"0.6578518",
"0.6430722",
"0.62942684",
"0.62148696",
"0.62001896",
"0.61766964",
"0.616204",
"0.6118928",
"0.6095642",
"0.6074857",
"0.60608095",
"0.6050995",
"0.60476774",
"0.60386544",
"0.59819704",
"0.5975472",
"0.59751296",
"0.5924714",
"0.5882051",
"0.5881... | 0.6646074 | 1 |
Tests that all activations get accepted | def test_all_activations_work():
nn_instance = RNN(layers_info=[["lstm", 20], ["gru", 10], ["linear", 20], ["linear", 1]],
hidden_activations="relu", output_activation=None, dropout=0.0000001,
initialiser="xavier", input_dim=15)
for key in nn_instance.str_to... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_accept_or_reject(self):\n for decision, expected_status in [(1, Transfer.ACCEPTED), (0, Transfer.REJECTED)]:\n transfer = random.choice(Transfer.objects.filter(process_status=Transfer.VALIDATED))\n self.assert_status_code(\n \"get\", reverse(\"appraise:list\"), ... | [
"0.6415789",
"0.6383503",
"0.6199055",
"0.61586833",
"0.6149722",
"0.6138494",
"0.6132158",
"0.60875577",
"0.60807586",
"0.6079302",
"0.6033272",
"0.6018975",
"0.601579",
"0.5988945",
"0.59615004",
"0.59433323",
"0.5895086",
"0.58526635",
"0.5849961",
"0.5830023",
"0.58249617... | 0.56912756 | 54 |
Tests that all initialisers get accepted | def test_all_initialisers_work():
nn_instance = RNN(layers_info=[["lstm", 20], ["gru", 10], ["linear", 20], ["linear", 1]],
hidden_activations="relu", output_activation=None, dropout=0.0000001,
initialiser="xavier", input_dim=15)
for key in nn_instance.str_t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_01_Init(self):\n pass",
"def test_init(self):\n self.assertEqual(self.ing_mgr.ingredient_list, [])\n self.assertEqual(self.ing_mgr.user_input, True)",
"def test_init_default(self):\n self._test_init_default()",
"def test_initialization(self):\n self.assertEqual(sel... | [
"0.70216167",
"0.6774803",
"0.66703606",
"0.64539313",
"0.6438114",
"0.63028103",
"0.6296848",
"0.62899536",
"0.6286861",
"0.6273079",
"0.6268029",
"0.6249012",
"0.62255603",
"0.62252647",
"0.6224343",
"0.61891216",
"0.6178358",
"0.6172544",
"0.61631143",
"0.61234355",
"0.611... | 0.592791 | 55 |
Tests whether network outputs of correct shape | def test_output_shapes():
rnn = RNN(layers_info=[["gru", 20], ["lstm", 8], ["linear", 3]],
hidden_activations="relu", initialiser="xavier", input_dim=15)
output = rnn(X)
assert output.shape == (N, 3)
rnn = RNN(layers_info=[["gru", 20], ["lstm", 8], ["linear", 7]],
hidden_act... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_unet_verify_output_shape(simple_unet_data):\n unet = models.UNet()\n output = unet(simple_unet_data)\n print(\"Input shape:\", simple_unet_data.shape)\n print(\"Output shape:\", output.shape)\n assert simple_unet_data.shape == output.shape",
"def dimension_check():\n print(\"### DIMENS... | [
"0.7367588",
"0.7063788",
"0.69999754",
"0.69562316",
"0.6948631",
"0.68892664",
"0.68857855",
"0.6879901",
"0.6879901",
"0.68292147",
"0.6785833",
"0.6736189",
"0.6731217",
"0.6698011",
"0.6661939",
"0.6600723",
"0.6569839",
"0.6545449",
"0.6511452",
"0.6490843",
"0.6480389"... | 0.7374896 | 0 |
Checks whether network only accepts a valid boolean value for return_final_seq_only | def test_return_final_seq_user_input_valid():
for valid_case in [True, False]:
assert RNN(layers_info=[["gru", 20], ["lstm", 8], ["linear", 7]],
hidden_activations="relu", initialiser="xavier", return_final_seq_only=valid_case, input_dim=15)
for invalid_case in [[True], 22, [1, 3], (T... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __bool__(self):\r\n return self.valid",
"def check_for_bool(check):",
"def validate(self):\n\t\trVal = False\n\t\t#return rVal\n\t\treturn True",
"def __bool__(self):\n return self.is_valid",
"def validate(self):\n # rVal = False\n # return rVal\n return True",
"def... | [
"0.6146068",
"0.6059029",
"0.59578097",
"0.5924273",
"0.5922708",
"0.5791276",
"0.57778454",
"0.5729236",
"0.57164973",
"0.5700221",
"0.56962913",
"0.56714386",
"0.5657782",
"0.5627528",
"0.56212014",
"0.5613088",
"0.5606647",
"0.5603606",
"0.55852324",
"0.5579543",
"0.556653... | 0.5744099 | 7 |
Tests that having multiple output heads catches errors from user inputs | def test_output_heads_error_catching():
output_dims_that_should_break = [["linear", 2, 2, "SAME", "conv", 3, 4, "SAME"], [[["lstm", 3], ["gru", 4]]],
[[2, 8]], [-33, 33, 33, 33, 33]]
for output_dim in output_dims_that_should_break:
with pytest.raises(AssertionError):... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_bad_input():\n\n for arg in ['5', 'ch']:\n rv, out = getstatusoutput('{} {}'.format(prg, arg))\n assert rv == 0\n expected = 'I do not know \"{}\".'.format(arg)\n assert out.strip() == expected",
"def test_001(self):\n user_input = [\"0\",\"0\",\"1\"]\n with ... | [
"0.7316071",
"0.685618",
"0.6823957",
"0.6820864",
"0.6810941",
"0.6792938",
"0.6758819",
"0.6752919",
"0.66592836",
"0.6565772",
"0.6560659",
"0.6541746",
"0.6538069",
"0.6533875",
"0.65189856",
"0.6461399",
"0.64344615",
"0.6359316",
"0.63488054",
"0.6324248",
"0.63083804",... | 0.64847004 | 15 |
Tests whether the output head layers get created properly | def test_output_head_layers():
for output_dim in [[["linear", 3],["linear", 9]], [["linear", 4], ["linear", 20]], [["linear", 1], ["linear", 1]]]:
nn_instance = RNN(input_dim=5, layers_info=[["gru", 20], ["lstm", 8], output_dim],
hidden_activations="relu", output_activation=["softm... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_gz_batch(self):\n assert self.design.layout.layers[0].name == 'top'",
"def test_output_head_shapes_correct():\n N = 20\n X = torch.randn((N, 10, 4)) * -20.0\n for _ in range(25):\n nn_instance = RNN(input_dim=4,\n layers_info=[[\"gru\", 20], [\"lstm\", 8], [\"linear\", ... | [
"0.64709437",
"0.6327445",
"0.62262976",
"0.6176358",
"0.61338794",
"0.6049259",
"0.5949284",
"0.5947901",
"0.5946794",
"0.594015",
"0.5908161",
"0.5864121",
"0.582288",
"0.58142173",
"0.5809196",
"0.5793423",
"0.57716125",
"0.5731328",
"0.57262856",
"0.5711052",
"0.569529",
... | 0.73129505 | 0 |
Tests that output head activations work properly | def test_output_head_activations_work():
output_dim = [["linear", 5], ["linear", 10], ["linear", 3]]
nn_instance = RNN(input_dim=5, layers_info=[["gru", 20], ["lstm", 8], output_dim],
hidden_activations="relu", output_activation=["softmax", None, "relu"])
x = torch.randn((20, 12,... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_trn_head():\n from mmaction.models.heads.trn_head import (RelationModule,\n RelationModuleMultiScale)\n trn_head = TRNHead(num_classes=4, in_channels=2048, relation_type='TRN')\n trn_head.init_weights()\n\n assert trn_head.num_classes == 4\n as... | [
"0.6236734",
"0.6213275",
"0.6145977",
"0.5907997",
"0.5812824",
"0.57191104",
"0.56966907",
"0.5668249",
"0.5625803",
"0.5543424",
"0.55362344",
"0.5510609",
"0.5454253",
"0.544644",
"0.5443262",
"0.54215544",
"0.5415511",
"0.54015803",
"0.5390789",
"0.538465",
"0.5355284",
... | 0.5559491 | 9 |
Tests that the output shape of network is correct when using multiple outpout heads | def test_output_head_shapes_correct():
N = 20
X = torch.randn((N, 10, 4)) * -20.0
for _ in range(25):
nn_instance = RNN(input_dim=4,
layers_info=[["gru", 20], ["lstm", 8], ["linear", 1], ["linear", 12]],
hidden_activations="relu")
out = nn_instance(X)
assert o... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_shapes_coupling_out(self):\n\n out_single = self.coupling_net_odd(self.x_single_odd, self.y_single)\n out_batch = self.coupling_net_odd(self.x_batch_odd, self.y_batch)\n\n self.assertEqual(out_single.shape[0], 1,\n 'Batch shape mismatch on single instance in Co... | [
"0.7296105",
"0.7296105",
"0.70709145",
"0.70210224",
"0.6994198",
"0.6923519",
"0.6618199",
"0.65257",
"0.65257",
"0.6494162",
"0.6463666",
"0.6409217",
"0.62905854",
"0.62666196",
"0.61876714",
"0.61641824",
"0.61295414",
"0.61267626",
"0.61267626",
"0.61154383",
"0.6108088... | 0.7155083 | 2 |
Decorator to set waiting cursor while function is running. | def waiting_cursor(function):
def new_function(self):
QApplication.setOverrideCursor(QCursor(Qt.WaitCursor))
try:
function(self)
except Exception as e:
raise e
print("Error {}".format(e.args[0]))
finally:
QApplication.restoreOverrideCu... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def wait_cursor(win):\n win.app.setOverrideCursor(gui.QCursor(core.Qt.WaitCursor))\n yield\n win.app.restoreOverrideCursor()",
"def wait(func):\n def decorator(self, pkt: str) -> None:\n init_time = time.perf_counter()\n func(self, pkt)\n while time.perf_counter()... | [
"0.621839",
"0.6173325",
"0.60418564",
"0.5873782",
"0.58133435",
"0.5802555",
"0.56343734",
"0.5622056",
"0.55775356",
"0.5572662",
"0.55615383",
"0.5531714",
"0.54737693",
"0.5402359",
"0.5394669",
"0.5377479",
"0.5330632",
"0.5328029",
"0.53188837",
"0.53006744",
"0.530067... | 0.7422905 | 0 |
Slot triggered when the button is clicked. Creates a new .csl file. | def on_pushButtonHelp_clicked(self, checked):
help = Help.MainApp()
help.setWindowTitle('Help for ' + TITLE)
help.show()
help.textEdit.setText(helpText) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cb_new(self, button):\n print(\"New File callback\")\n self.cb_save_as(button)",
"def CreateNewFile(self):\n\t\tself.acad.Documents.Add()",
"def newFile(self):\n self.open_file_name = None\n self.ui.main_edit.setText(\"\")\n self.saveEnabled(False)",
"def new(self):\r\n... | [
"0.6893947",
"0.66023135",
"0.6593208",
"0.65121573",
"0.6401062",
"0.63207823",
"0.62781936",
"0.62067664",
"0.6142108",
"0.61093783",
"0.6075374",
"0.6015218",
"0.58974504",
"0.58847827",
"0.5734388",
"0.5707925",
"0.56786156",
"0.56684",
"0.5659722",
"0.56219506",
"0.56217... | 0.0 | -1 |
Slot triggered when the button is clicked. Creates a new .csl file. | def on_pushButtonCheck_clicked(self, checked):
# get the file name to open
#cslDir = self.settings.value('CslDir', '') # default = ''
#options = QFileDialog.Options()
#fileName, _ = QFileDialog.getOpenFileName(self,
# "Open csl file",
# cslDir,
# ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cb_new(self, button):\n print(\"New File callback\")\n self.cb_save_as(button)",
"def CreateNewFile(self):\n\t\tself.acad.Documents.Add()",
"def newFile(self):\n self.open_file_name = None\n self.ui.main_edit.setText(\"\")\n self.saveEnabled(False)",
"def new(self):\r\n... | [
"0.6895971",
"0.6606282",
"0.65964454",
"0.6513772",
"0.64039904",
"0.63240844",
"0.62802327",
"0.6209494",
"0.61432064",
"0.61116624",
"0.60776496",
"0.6018341",
"0.5897397",
"0.58868194",
"0.57367384",
"0.5708123",
"0.56786484",
"0.56706613",
"0.5660706",
"0.5622602",
"0.56... | 0.52957624 | 44 |
Slot triggered when the thread issues signal finishedSig. | def onThreadFinished(self):
# reenable the button now thread method is finished
self.pushButtonCheck.setEnabled(True)
self.debug('Thread Method Finished!')
qApp.restoreOverrideCursor() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __finish(self):\n self.finished.emit()",
"def thread_finished(self):\n # self.worker.join()\n self.worker = None\n self.want_to_abort = False",
"def finished(self, reply):\n pass",
"def _finished(self) -> None:",
"def finishThread(self):\n logging.info(\"Fin Th... | [
"0.7433008",
"0.69723034",
"0.67958206",
"0.66644305",
"0.6616974",
"0.6565671",
"0.6560136",
"0.6429806",
"0.6407112",
"0.6362226",
"0.63549864",
"0.63026565",
"0.62754816",
"0.6253748",
"0.6246546",
"0.62232757",
"0.6211996",
"0.61976945",
"0.6186162",
"0.6116034",
"0.60947... | 0.6425185 | 8 |
Override inherited QMainWindow resize event. Saves the window geometry. | def resizeEvent(self, event):
self.settings.setValue("geometry", self.saveGeometry())
super().resizeEvent(event) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def resizeEvent(self, *args, **kwargs):\n self.windowMoved.emit()",
"def resizeEvent(self, event):\r\n QDialog.resizeEvent(self, event)\r\n self.emit(SIGNAL(\"size_change(QSize)\"), self.size())",
"def resizeEvent(self, event):\n self.resized.emit()\n return super(PiWndow, se... | [
"0.7568738",
"0.73874474",
"0.73514515",
"0.7263355",
"0.7208239",
"0.7027689",
"0.6980282",
"0.6977833",
"0.6970731",
"0.6908289",
"0.6868141",
"0.682982",
"0.6793767",
"0.6758908",
"0.6676213",
"0.6672818",
"0.6643312",
"0.64994204",
"0.6469828",
"0.6464748",
"0.64348876",
... | 0.78121006 | 0 |
Override inherited QMainWindow move event. Saves the window geometry. | def moveEvent(self, event):
self.settings.setValue("geometry", self.saveGeometry())
super().moveEvent(event) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def moveEvent(self, *args, **kwargs):\n self.windowMoved.emit()",
"def resizeEvent(self, *args, **kwargs):\n self.windowMoved.emit()",
"def ev_windowmoved(self, event: WindowMoved) -> None:",
"def _positionWindow(self):\n\t\tscreen = QtGui.QDesktopWidget().screenGeometry()\n\t\tself.setGeometry... | [
"0.7606414",
"0.71723086",
"0.71219075",
"0.6784356",
"0.66384774",
"0.663542",
"0.65639585",
"0.65360874",
"0.6531752",
"0.6499311",
"0.64266163",
"0.6333455",
"0.63130426",
"0.62280285",
"0.6225158",
"0.62186563",
"0.61726123",
"0.6161794",
"0.6124457",
"0.61130536",
"0.599... | 0.7580476 | 1 |
Gets a random dad joke. | async def dadjoke(self, ctx):
author = ctx.message.author
joke = await self.get_joke()
data = Embed.create(self, ctx, title='Demaratus Dad Jokes :joy:',
description=joke)
image = (f"https://media.discordapp.net/attachments/745608075670585344/770068453502877716... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_joke():\n joke = None\n\n while joke is None:\n service_num = randint(1, NUM_SERVICES)\n joke = load_joke(service_num)\n return joke",
"def get_joke(self):\n if not self.jokes:\n self.__init__()\n index = random.randint(0, len(self.jokes) - 1)\n joke... | [
"0.7188688",
"0.6922877",
"0.68233687",
"0.6660566",
"0.6432047",
"0.6295852",
"0.59438914",
"0.58900243",
"0.5767906",
"0.5690085",
"0.56747085",
"0.56361866",
"0.5629127",
"0.5591978",
"0.55778074",
"0.55778074",
"0.55778074",
"0.55380374",
"0.5513841",
"0.5504545",
"0.5501... | 0.5504928 | 19 |
Pop some bubble wrap! | async def bubblewrap(self, ctx):
data = Embed.create(
self, ctx, title="Bubblewrap!",
description=(
"||pop||||pop||||pop||||pop||||pop||||pop||||pop||||pop||||pop||||pop||\n"
"||pop||||pop||||pop||||pop||||pop||||pop||||pop||||pop||||pop||||pop||\n"
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pop_bubble(self):\n i = self.cursor_bubble_collide()\n if i != -1:\n bubble = self.all_bubbles.sprites()[i]\n bubble.bubblekill()\n self.increase_score(bubble.get_value() * Settings.points_multiplier)",
"def pop():",
"def pop(self):",
"def pop(self):",
"de... | [
"0.6925461",
"0.66646165",
"0.6537013",
"0.6537013",
"0.6212795",
"0.6212795",
"0.61346465",
"0.612005",
"0.59507227",
"0.5943642",
"0.59259933",
"0.5899823",
"0.58830035",
"0.58816",
"0.5865467",
"0.584214",
"0.58349234",
"0.5826333",
"0.580838",
"0.57985026",
"0.5770079",
... | 0.58795863 | 14 |
Replace acauses with those in the bridge map. | def get_computed_dataframe(self, df):
df = add_nid_metadata(df, ['data_type_id'], **self.cache_options)
has_verbal_autopsy = self.VA in df['data_type_id'].unique()
if self.needs_bridging(has_verbal_autopsy):
sheet_name = self.get_sheet_name(has_verbal_autopsy)
map_df = p... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def acause_to_bridge_code(self, df):\n df['swap'] = 0\n df.loc[\n (df['acause'] != df['bridge_code']) &\n (df['bridge_code'].notnull()),\n 'swap'\n ] = 1\n df.loc[df['swap'] == 1, 'acause'] = df['bridge_code']\n self.causes_not_in_bridge_map(df)\n... | [
"0.61886996",
"0.57108057",
"0.55985564",
"0.55602914",
"0.5543052",
"0.544156",
"0.53855085",
"0.53791714",
"0.52379787",
"0.51952946",
"0.51637304",
"0.5134801",
"0.51123565",
"0.51105",
"0.5107196",
"0.50854534",
"0.50727737",
"0.5067354",
"0.5023531",
"0.5018238",
"0.4986... | 0.0 | -1 |
Check data type and source to see if the bridge map is needed. | def needs_bridging(self, has_verbal_autopsy):
sources_to_bridge_map = [
"India_SCD_states_rural", "India_CRS",
"India_MCCD_states_ICD9", "India_MCCD_states_ICD10",
"India_Maharashtra_SCD", "India_MCCD_Orissa_ICD10",
"India_MCCD_Delhi_ICD10", "ICD9_BTL", "Russia_FM... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_base_type(cls, data):\n return _mapping_resolver.get_type(data) == \"MAPPING\"",
"def checkMap(self):\n return True",
"def _sanity_check_datasource(ds):\n if len(ds) != 1:\n raise SanityCheckError('GeoJSON should have only 1 layer.')\n # TODO: add more checks",
"def check_Da... | [
"0.5886268",
"0.5826029",
"0.5767964",
"0.57115424",
"0.5651457",
"0.5650617",
"0.56332195",
"0.5626689",
"0.5591905",
"0.5586229",
"0.556025",
"0.55439883",
"0.55269843",
"0.5517739",
"0.5505605",
"0.5470142",
"0.54573935",
"0.5434472",
"0.5417162",
"0.5383413",
"0.5376976",... | 0.60287815 | 0 |
Determine the sheet name needed based on the source. | def get_sheet_name(self, has_verbal_autopsy):
source_to_sheet = {
"India_MCCD_Orissa_ICD10": "India_MCCD_states_ICD10",
"India_MCCD_Delhi_ICD10": "India_MCCD_states_ICD10",
"Thailand_Public_Health_Statistics": "ICD10_tabulated",
"India_SRS_states_report": "India_S... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_sheet_name(self):\n\n xl = pd.ExcelFile(self.excel_file)\n sheet_names = xl.sheet_names\n for item in sheet_names:\n if re.match('(.*)Current primers', item, re.IGNORECASE): # Only extracts most recent primers.\n sheet_name = item\n return shee... | [
"0.7164989",
"0.6308564",
"0.61821693",
"0.603946",
"0.59906214",
"0.5879506",
"0.58683854",
"0.5754971",
"0.57401884",
"0.5708304",
"0.57011294",
"0.55058926",
"0.54911",
"0.5414342",
"0.5403765",
"0.53900576",
"0.5371769",
"0.5352899",
"0.53527546",
"0.5343347",
"0.5297994"... | 0.7110151 | 1 |
Replace the acause with the bridge code. | def acause_to_bridge_code(self, df):
df['swap'] = 0
df.loc[
(df['acause'] != df['bridge_code']) &
(df['bridge_code'].notnull()),
'swap'
] = 1
df.loc[df['swap'] == 1, 'acause'] = df['bridge_code']
self.causes_not_in_bridge_map(df)
return... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def reset_cause():",
"def _fix_up(self, cls, code_name):",
"def replacement(exception):\n assert exception.__class__.__name__ == \"LinterFailure\"\n return exception.replacement",
"def errorCodeCause(self, cause):\n pass",
"def fixup(self):\n raise Exception(\"Fixup not implemented yet!... | [
"0.56187624",
"0.5339114",
"0.53007525",
"0.5278467",
"0.51992387",
"0.5144127",
"0.5144127",
"0.4979441",
"0.4972489",
"0.49498683",
"0.48308125",
"0.48117915",
"0.47808516",
"0.47630504",
"0.4756997",
"0.47563353",
"0.47268003",
"0.46959338",
"0.46171212",
"0.4606202",
"0.4... | 0.63376826 | 0 |
Print causes that aren't in the bridge map, but are in the data. | def causes_not_in_bridge_map(self, df):
check = set(df.loc[df['bridge_code'].isnull(), 'acause'])
if len(check) > 0:
print("These acauses are not in the bridge map: {}".format(check)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_out_unexplained(self):\n for node in self.vertices:\n for arc in self.out_arcs_lists[node]:\n s = self.arc_info[arc]['start']\n t = self.arc_info[arc]['destin']\n w = self.arc_info[arc]['unexplained_flow']\n print(\"({} {}) une... | [
"0.5635357",
"0.5168932",
"0.5168312",
"0.51084465",
"0.50421125",
"0.5012694",
"0.49809542",
"0.49573722",
"0.49084926",
"0.4888484",
"0.48796296",
"0.48676422",
"0.48665074",
"0.4809346",
"0.47892943",
"0.47808835",
"0.47711042",
"0.47605914",
"0.47398275",
"0.4732302",
"0.... | 0.7070158 | 0 |
Replace the actual data cause under certain conditions. This essentially allows mapping based on not just the cause and code system but based on other information like the location, NID, year, etc. | def special_cause_reassignment(self, df, code_system_id):
cache_args = {
'force_rerun': False,
'block_rerun': True,
'cache_dir': 'standard',
'cache_results': False
}
# Some SRS codes get redistributed differently than
# other ICD10 dataset... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def assert_valid_mappings(self, df, code_system_id):\n # add code value from cached code map\n print(\"Adding value\")\n df = add_code_metadata(\n df, ['value'], code_system_id,\n force_rerun=False,\n block_rerun=True,\n cache_dir=self.cache_dir\n ... | [
"0.61165214",
"0.58322775",
"0.5648111",
"0.56048506",
"0.5507958",
"0.55043066",
"0.54737777",
"0.543537",
"0.53194505",
"0.5253456",
"0.5244946",
"0.5241888",
"0.5216187",
"0.5211156",
"0.5194944",
"0.51515746",
"0.5127338",
"0.5109179",
"0.51009274",
"0.50683755",
"0.50682... | 0.57788795 | 2 |
Group by final columns, summing across deaths. | def collapse_and_sum_by_deaths(self, df):
df = df.groupby(self.id_cols, as_index=False)[self.data_col].sum()
self.assert_unique_cols_unique(df)
return df | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _aggregate(group_df):\n out = {}\n for col in data_columns:\n # The name of the error column (if it exists)\n error_col = f\"{col}_moe\"\n\n # remove any NaN rows\n subset = group_df.dropna(subset=[col], how=\"any\")\n\n # aggregat if we had ... | [
"0.5318467",
"0.5297273",
"0.5217793",
"0.5203764",
"0.5133742",
"0.50570774",
"0.5016997",
"0.49657285",
"0.49467602",
"0.4945261",
"0.49407688",
"0.49390042",
"0.48924458",
"0.48900697",
"0.4883349",
"0.48651448",
"0.48445007",
"0.4817701",
"0.48097548",
"0.47966254",
"0.47... | 0.75622433 | 0 |
Test that the mapping worked. Runs a suite of assertions to make sure that mapping was successful. | def assert_valid_mappings(self, df, code_system_id):
# add code value from cached code map
print("Adding value")
df = add_code_metadata(
df, ['value'], code_system_id,
force_rerun=False,
block_rerun=True,
cache_dir=self.cache_dir
)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_check_map(self):\r\n\r\n header, mapping_data = check_map(self.valid_mapping_data_golay)\r\n\r\n expected_header =\\\r\n ['SampleID',\r\n 'BarcodeSequence',\r\n 'LinkerPrimerSequence',\r\n 'Description']\r\n expected_mapping_data =\\\r\n ... | [
"0.7310833",
"0.6937956",
"0.6737042",
"0.67209184",
"0.6715749",
"0.67143923",
"0.66619116",
"0.66569936",
"0.66303176",
"0.6584895",
"0.6484949",
"0.6411683",
"0.6405239",
"0.6371015",
"0.63441306",
"0.6323892",
"0.6319045",
"0.6312411",
"0.62868655",
"0.62857336",
"0.62508... | 0.0 | -1 |
Test that columns that should uniquely identify the dataframe do. | def assert_unique_cols_unique(self, df):
assert not df.duplicated(self.unique_cols).any() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def checkcolumnstest(chosen_columns, chosen_df):\n if not all([item in chosen_columns for item in chosen_df.columns]):\n raise ValueError('Columns do not match')",
"def verify_columns_in_dataframe(df, columns):\n\n if not isinstance(columns, list):\n columns = [columns]\n return set(column... | [
"0.7065261",
"0.68916535",
"0.67887884",
"0.6769533",
"0.67213285",
"0.6651365",
"0.6554256",
"0.64366084",
"0.64201415",
"0.64169556",
"0.640995",
"0.63906235",
"0.63551354",
"0.63454103",
"0.6342936",
"0.63194436",
"0.6296456",
"0.62885845",
"0.626959",
"0.6255762",
"0.6241... | 0.81844956 | 0 |
Filter out mutation features, ensuring that a feature is not entirely an artifact of mutation rate. | def mut_filter(df, rate, binary_cutoff=12):
get_min_count = lambda s: s.value_counts().min() if len(s.unique()) > 1 else -1
df = df[df.apply(get_min_count, axis=1) > binary_cutoff]
cc = H.screen_feature(rate, rev_kruskal, df)
fc_apply = lambda s: fc(s, rate)
direction = df.apply(fc_apply, axis=1)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_filtered_features(self, features):\n return [\n feature\n for feature in features\n if \"\".join(feature.qualifiers.get(\"is_edit\", \"false\")) != \"true\"\n ]",
"def apply_feature_filter(self):\n self.features = set()\n for language in se... | [
"0.6798122",
"0.65159315",
"0.6311767",
"0.6206602",
"0.6020291",
"0.6020286",
"0.59741116",
"0.5803234",
"0.57466453",
"0.56200033",
"0.5560362",
"0.5550866",
"0.5536949",
"0.55036086",
"0.5501796",
"0.54453415",
"0.54438686",
"0.5440273",
"0.53379816",
"0.5326922",
"0.53198... | 0.0 | -1 |
Extract copy number features. | def cn_filter(df, binary_cutoff=12):
del_df = (df.ix['Deletion'].dropna(1) < 0).astype(int)
del_df = del_df[del_df.sum(1) >= binary_cutoff]
del_df.index = del_df.index.droplevel(1)
del_df = del_df.T
amp_df = (df.ix['Amplification'].dropna(1) > 0).astype(int)
amp_df = amp_df[amp_df.sum(1) >= bina... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _extract_features(self):\n # print(os.getpid())\n return {n:self._extract_feature(f) for (n,f) in self.features.items()}",
"def extract_features(self, inputs):\n pass",
"def make_returnn_audio_features_func():\n return _extract",
"def extract_features(self):\n self.extract_fe... | [
"0.6355945",
"0.6265867",
"0.60493034",
"0.5984363",
"0.58230263",
"0.5735201",
"0.57213664",
"0.57068473",
"0.5680347",
"0.5680347",
"0.5655088",
"0.560756",
"0.55590224",
"0.55192435",
"0.54978615",
"0.54792076",
"0.5478675",
"0.54740584",
"0.5422775",
"0.541428",
"0.537273... | 0.0 | -1 |
Process real valued feature into binary feature. | def process_real(df):
df_c = df.copy()
df_c = df_c.apply(lambda s: H.to_quants(s, std=1), axis=1)
df_c = df_c > 0
if type(df.index) == pd.MultiIndex:
df_c.index = map(lambda s: '_'.join(s), df_c.index)
return df_c.T | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process_feature(self, feature):\n self.num_features += 1\n\n def create_int_feature(values):\n feature = tf.train.Feature(\n int64_list=tf.train.Int64List(value=list(values)))\n return feature\n\n features = collections.OrderedDict()\n features[\... | [
"0.6582357",
"0.64743507",
"0.6151538",
"0.6109762",
"0.60528004",
"0.6031506",
"0.60027516",
"0.59822625",
"0.58153456",
"0.5803053",
"0.5775498",
"0.57638526",
"0.5762423",
"0.5737403",
"0.5713473",
"0.570815",
"0.56762433",
"0.56180775",
"0.560164",
"0.5593572",
"0.5569730... | 0.0 | -1 |
Binarize a feature to minimize the difference in sum of squares between the two resulting groups. | def binarize_feature(f):
f = f - f.mean()
f2 = (f.order() ** 2)
split = f.ix[(f2.cumsum() - (f2.sum() / 2.)).abs().idxmin()]
return f > split | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def binarize(self):\n # Loop through the ratings and binarize based on overall average rating\n rating_sum = np.sum(self.ratings)\n rating_count = np.count_nonzero(self.ratings)\n rating_avg = (1.0 * rating_sum) / rating_count\n\n def binary_transform(x, rating_avg):\n if x == 0.0:\... | [
"0.5834405",
"0.5714596",
"0.56099087",
"0.53564245",
"0.5345524",
"0.5343635",
"0.53198165",
"0.5301293",
"0.5279895",
"0.52382255",
"0.52295107",
"0.5213143",
"0.5212723",
"0.51760197",
"0.51498497",
"0.51463294",
"0.5144221",
"0.5110803",
"0.5091475",
"0.50752646",
"0.5073... | 0.64920145 | 0 |
Screens out redundant pathways with high correlation above _cutoff_. Pathways are ranked based on lack of correlation to the background signal. Then if two pathways have high correlation the lower ranked pathway is removed. | def remove_redundant_pathways(pathways, rna, cutoff=.7, binarize=False):
#bg = H.screen_feature(background, spearman_pandas, pathways)
dx = pd.DataFrame({p: ttest_rel(rna.df.ix[l.index].T.dot(l)) for p,l in
rna.loadings.iteritems()}).T
dx = dx.t.abs()
dd = pathways.ix[dx.index[::-1]].T.c... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __generate_all_shortest_paths(self,cutoff = 10):\n if cutoff < 1:\n cutoff = 10\n self.__logger.info(\"cutoff value must be a positive integer. Set back to default value: 10\")\n\n all_pair_shortest_paths = nx.all_pairs_shortest_path(self.G, cutoff=cutoff)\n for item ... | [
"0.58749056",
"0.5758001",
"0.5598225",
"0.5390486",
"0.5309527",
"0.5248357",
"0.5210141",
"0.5127525",
"0.5095731",
"0.5082875",
"0.5073413",
"0.5005037",
"0.498891",
"0.4978641",
"0.49535555",
"0.49215704",
"0.492009",
"0.48521635",
"0.48403457",
"0.48393774",
"0.4787763",... | 0.6945232 | 0 |
Pull the most differentially expressed genes from the rna expression object. | def extract_diff_exp_rna(rna, n=300, binarize=False):
genes = rna.features.ix[['real', 'binary']].index.get_level_values(1)
dd = rna.df.ix[genes].dropna()
rr = dd.apply(exp_change, 1)
d2 = dd.ix[rr.sort('F').index[-n:]].xs('01', 1, 1)
if binarize is False:
return d2
else:
real_ge... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def differential_gene_expression(phenotypes,\n gene_expression,\n output_filename,\n max_number_of_genes_to_show=20,\n number_of_permutations=10,\n title=N... | [
"0.5540566",
"0.55107176",
"0.55078834",
"0.5460858",
"0.5407551",
"0.5332774",
"0.5270649",
"0.5269659",
"0.5216537",
"0.52059203",
"0.51554185",
"0.51399195",
"0.51355946",
"0.51238877",
"0.51074076",
"0.50921005",
"0.5082791",
"0.50656515",
"0.5041371",
"0.5040676",
"0.503... | 0.5214804 | 9 |
Correct pvalues multiple ways along multiindex. | def corrections(vec):
bonf_all = vec * len(vec)
bonf_within = vec.groupby(level=0).apply(lambda s: s * len(s))
bh_all = H.bhCorrection(vec)
bh_within = vec.groupby(level=0).apply(H.bhCorrection).order()
two_step = bh_within * len(vec.groupby(level=0).size())
q = pd.concat([vec, bh_within, bh_a... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _pval_pairs(self, idx0, idx1):\n pass",
"def test_correct_p_values_large_correction(self):\r\n exp = [1, None, 0.03, 0.03]\r\n obs = self.mc._correct_p_values([0.5, None, 0.01, 0.01])\r\n self.compare_multiple_level_array(obs, exp)",
"def test_showalter_index():\n pressure = ... | [
"0.59434146",
"0.5774727",
"0.52260697",
"0.5221227",
"0.5216993",
"0.5191264",
"0.5181901",
"0.5160022",
"0.50624126",
"0.5048196",
"0.5009357",
"0.49917948",
"0.49787354",
"0.49680746",
"0.4957835",
"0.4956589",
"0.49506983",
"0.49300522",
"0.49235076",
"0.49085063",
"0.490... | 0.0 | -1 |
Run before each test method to initialize test environment. | def setUp(self):
super(TestCase, self).setUp()
# Change the default directory that the tempfile
# module places temporary files and directories in
self.useFixture(fixtures.NestedTempfile())
# Create a temporary directory and set it as $HOME in the environment.
self.useFix... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setUp(self):\n test_env_setup()",
"def setUp(self):\n\n BaseTest.setUp(self)",
"def setUp(self):\n MainTests.setUp(self)",
"def before_run_tests(cls):\n pass",
"def setUp(self):\r\n # nothing to do, all tests use different things\r\n pass",
"def setUp(self):\... | [
"0.84060824",
"0.7896673",
"0.7857576",
"0.77827775",
"0.76289546",
"0.76258075",
"0.7622942",
"0.7581788",
"0.7581788",
"0.75668246",
"0.75593936",
"0.75593936",
"0.7546393",
"0.75383157",
"0.7528227",
"0.74921465",
"0.74873954",
"0.7485794",
"0.7485794",
"0.7485794",
"0.748... | 0.0 | -1 |
Replace a function for the duration of the test. Use the monkey patch fixture to replace a function for the duration of a test. Useful when you want to provide fake methods instead of mocks during testing. This should be used instead of self.stubs.Set (which is based on mox) going forward. | def stub_out(self, old, new):
self.useFixture(fixtures.MonkeyPatch(old, new)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def patch(original_module):\n def wrap(override_fn):\n original_fn = getattr(original_module, override_fn.__name__)\n setattr(original_module, override_fn.__name__, partial(override_fn, original_fn))\n\n # Don't actually modify the function being decorated.\n return override_fn\n ... | [
"0.66126996",
"0.6246913",
"0.59958524",
"0.59808385",
"0.5970251",
"0.5970251",
"0.59676784",
"0.5964168",
"0.5732706",
"0.5707334",
"0.568837",
"0.56609994",
"0.5626188",
"0.56159544",
"0.5610572",
"0.55929077",
"0.555881",
"0.55573034",
"0.55572486",
"0.5480746",
"0.547858... | 0.698745 | 1 |
Use python mock to mock an object attribute Mocks the specified objects attribute with the given value. Automatically performs 'addCleanup' for the mock. | def mock_object(self, obj, attr_name, new_attr=None, **kwargs):
if not new_attr:
new_attr = mock.Mock()
patcher = mock.patch.object(obj, attr_name, new_attr, **kwargs)
patcher.start()
self.addCleanup(patcher.stop)
return new_attr | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def patch_object(mocker):\n\n def _object(obj_to_patch, attribute, return_value):\n mock_func = mocker.patch.object(obj_to_patch, attribute)\n if return_value is not None:\n mock_func.return_value = return_value\n\n return _object",
"def mock_side_effect(self, original_object, attr... | [
"0.6999446",
"0.64985067",
"0.6480005",
"0.61804056",
"0.6107706",
"0.6030694",
"0.59762293",
"0.58769643",
"0.58677036",
"0.58640003",
"0.5860096",
"0.585139",
"0.5840659",
"0.58258325",
"0.5818307",
"0.58063465",
"0.58023256",
"0.57815766",
"0.5776021",
"0.5776021",
"0.5748... | 0.7388397 | 1 |
ROI on the surface or volume with a given centre and radius | def py_SurfStatROI(centre, radius, surf):
sys.exit("Function py_SurfStatROI is under development") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def objects_radius(self, centre, radius):",
"def extract_roi_from_volume(volume, in_center, output_shape, fill = 'random'):\n input_shape = volume.shape \n if(fill == 'random'):\n output = np.random.normal(0, 1, size = output_shape)\n else:\n output = np.zeros(output_shape)\n r0max = ... | [
"0.6983759",
"0.6608037",
"0.6608037",
"0.6565604",
"0.6497947",
"0.6497947",
"0.6379288",
"0.6208821",
"0.61896527",
"0.6177499",
"0.61722773",
"0.6139702",
"0.607727",
"0.6039256",
"0.59929496",
"0.5988569",
"0.5976138",
"0.597191",
"0.5934677",
"0.5894657",
"0.5884144",
... | 0.7116994 | 0 |
Check that the games data is correct. | def _data_integrity_check(df):
if df is None or len(df) == 0:
logging.info("Dataframe is empty! No games data recorded.")
return
if not isinstance(df, pd.DataFrame):
raise ValueError(f"Type of df is {type(df)}, it should be pandas.DataFrame")
logging.info(f"Checking integrity of ga... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def check_games(self, ctx):\n print(self.data)\n print(self.games_info)",
"def isValid(self, game):\n return True",
"def check_game_sanity(game_val, log):\n\n global __problem_deck_index__\n\n supply = game_val.get_supply()\n if game_val.any_resigned():\n return True ... | [
"0.75368327",
"0.70760256",
"0.69226396",
"0.6563712",
"0.65609324",
"0.6393713",
"0.63804436",
"0.637144",
"0.6327626",
"0.6323198",
"0.6308842",
"0.6299205",
"0.6270144",
"0.62564266",
"0.622065",
"0.6184035",
"0.61409825",
"0.61319643",
"0.6127695",
"0.6106813",
"0.6062647... | 0.66139734 | 3 |
Gets the latest season and season type in the games data | def _get_latest_season_and_type(df):
if df is None or len(df) == 0:
return (None, None)
if not isinstance(df, pd.DataFrame):
raise ValueError(f"Type of df is {type(df)}, it should be pandas.DataFrame")
df = df[df['state_of_game'] == 'POST']
latest_season = df['season'].max()
season... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def latestGamePack(team):\n lgr= get('schedule', {'ver':'v1', 'sportId':1, 'date':today, 'teamId':team, 'fields':['dates','games','gamePk'] })\n return lgr['dates'][0]['games'][0]['gamePk']",
"def getseason(data):\n ## Season key is the most reliable\n season = data.get(\"season\")\n if season:\n ... | [
"0.6553931",
"0.6546608",
"0.6442716",
"0.64363",
"0.6434008",
"0.6398952",
"0.62399364",
"0.6105547",
"0.6082319",
"0.6062433",
"0.59988546",
"0.59488773",
"0.59089303",
"0.5888597",
"0.5820921",
"0.5803751",
"0.57766825",
"0.5746373",
"0.57297796",
"0.5726727",
"0.5723362",... | 0.7470444 | 0 |
Uses the semantic ordering of season types to get the latest/max | def _get_latest_season_type(season_types_list):
if not isinstance(season_types_list, (list, tuple)):
raise ValueError(f"type of {type(season_types_list)} for season_type_list not list or tuple")
latest_season_type = season_types_list[0]
if len(season_types_list) > 1:
for season_type in seas... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_latest_season_and_type(df):\n if df is None or len(df) == 0:\n return (None, None)\n\n if not isinstance(df, pd.DataFrame):\n raise ValueError(f\"Type of df is {type(df)}, it should be pandas.DataFrame\")\n\n df = df[df['state_of_game'] == 'POST']\n latest_season = df['season'].m... | [
"0.672219",
"0.5860014",
"0.57655185",
"0.57589823",
"0.55447674",
"0.5494647",
"0.5436334",
"0.5394017",
"0.53333473",
"0.5325742",
"0.52458227",
"0.52447414",
"0.52128184",
"0.5152866",
"0.51410383",
"0.5102868",
"0.5095066",
"0.5065986",
"0.5064631",
"0.50637007",
"0.50637... | 0.71224105 | 0 |
Removes the latest season and season type from df. | def _truncate_games_df(df, season, season_type):
return df[(df['season'] != season) | (df['type'] != season_type)] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _drop_last_season_championship_matches(self, df: pd.DataFrame) -> pd.DataFrame:\n max_season = df[\"season\"].max()\n\n if self._resume:\n # Check if max season changed\n if max_season != self._model_settings[\"max_season\"]:\n raise ValueError(\"Current max s... | [
"0.6965733",
"0.63223386",
"0.6169938",
"0.5764446",
"0.5585531",
"0.5310778",
"0.5301593",
"0.52592826",
"0.5258071",
"0.52488506",
"0.5140651",
"0.5111419",
"0.50804186",
"0.5079051",
"0.5058062",
"0.49890277",
"0.4945572",
"0.4936485",
"0.49139735",
"0.48739588",
"0.481331... | 0.7332864 | 0 |
Drops rows from the games table that are of season and season_type. | def _truncate_games_table(db_conn, season, season_type):
delete_statement = f"DELETE FROM games WHERE season = {season} and type = '{season_type}'"
logging.info(f"Truncating games table with statement: {delete_statement}")
db_conn.execute(delete_statement) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _truncate_games_df(df, season, season_type):\n return df[(df['season'] != season) | (df['type'] != season_type)]",
"def update_season_table(session, sched_tbl, season_df):\n date = datetime.date(datetime.now())\n update_query = session.query(sched_tbl).filter(sched_tbl.start_time < date,\n ... | [
"0.762441",
"0.6251675",
"0.61136353",
"0.5862718",
"0.56220615",
"0.54519236",
"0.5399716",
"0.537986",
"0.53599685",
"0.5343498",
"0.5125028",
"0.51231134",
"0.50927806",
"0.5061084",
"0.50600594",
"0.5034044",
"0.502096",
"0.49832642",
"0.4961268",
"0.4951709",
"0.49427244... | 0.7035129 | 1 |
Enumerates all combos of season and season types | def _get_seasons_grid(start_season, start_season_type):
if start_season not in list(range(config.START_SEASON, config.CURRENT_SEASON + 1)):
raise ValueError(f"Start season of {start_season} not valid.")
if start_season_type not in config.SEASON_TYPES:
raise ValueError(f"Start season type {start... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def season_choices():\n return [(s, s) for s in range(0, 3)]",
"def distributeSeason(self):\n i = 1\n for day in self.daylist:\n if i >= monthbeg[5] and i < monthbeg[9]: #june through SEpt as per SCE\n day.season = 'summer' #https://www.sce.com/resident... | [
"0.7313621",
"0.60445046",
"0.59210426",
"0.5904497",
"0.5747831",
"0.57103455",
"0.5611361",
"0.55891776",
"0.5507902",
"0.5506329",
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"0.5421824",
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"0.5385352",
"0.53806645",
"0.5351801",
"0.5346506",
"0.5319504",
"0.52745056",
"0.5232151... | 0.6033037 | 2 |
Runs the nflscrapr R script for the given season and type | def _extract_games_data(season, season_type):
nflscrapr.run(
'games',
season=season,
season_type=season_type
)
nflscrapr_output = etl_tools.extract_from_csv(config.GAMES_DUMP_CSV_PATH)
return nflscrapr_output | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def scrape():\n league_year = Config.get_property(\"league_year\")\n\n # Create table\n season_data = client.season_schedule(league_year)\n season_data = br_enum_to_string(season_data)\n return season_data",
"def get_schedules_for_season(self, season, season_type=\"REG\"):\n try:\n ... | [
"0.58400744",
"0.5825069",
"0.5700488",
"0.56795895",
"0.5633705",
"0.550612",
"0.5492933",
"0.54907745",
"0.5442901",
"0.5320366",
"0.52981234",
"0.5222515",
"0.51775146",
"0.5153715",
"0.5135699",
"0.5109447",
"0.50783205",
"0.5008098",
"0.50059414",
"0.5002093",
"0.4990885... | 0.61678904 | 0 |
Runs the workflow for extracting and loading games data. Finds the starting point for extracting new data Extracts new data using the nflscrapr module Loads to database | def run():
games_db_conn = db.get_db_eng()
games_query = "SELECT * FROM GAMES"
games_data = etl_tools.extract_from_db(games_db_conn, games_query)
_data_integrity_check(games_data)
latest_season, latest_season_type = _get_latest_season_and_type(games_data)
logging.info(f"Latest season and type i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def download_and_preprocess(self):\n print('Preparing steering angle database.')\n print('Downloading...')\n self.download()\n print('Preprocessing...')\n self.preprocess()",
"def scrape_all():\n\n # Scrape team information by season\n for team in scrape_utils.team_names(... | [
"0.6121077",
"0.6028184",
"0.59892666",
"0.59821534",
"0.5883398",
"0.585514",
"0.58488256",
"0.583428",
"0.5776301",
"0.577098",
"0.57684034",
"0.5755793",
"0.57253456",
"0.5703388",
"0.56687576",
"0.56505823",
"0.56160414",
"0.560584",
"0.56016344",
"0.5601598",
"0.55712277... | 0.72591263 | 0 |
Convert the labelme annotations to coco format and validate the annotations with randomly picked 5 images. If the annotation is incorrect, the program terminates. | def convert_labelme_to_coco(path_to_data):
# convert labelme annotations to coco
labelme2coco.convert(path_to_data, path_to_data + r'\coco_annotation.json')
# Open the coco format data
with open(path_to_data + r'\coco_annotation.json') as f:
coco_d = json.load(f)
# Get the category... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_annotation(target, images, bbox_path):\n good_image_paths = []\n bad_image_paths = []\n bb = read_annotation_yolov5(bbox_path)\n for path in images:\n basename = os.path.basename(path) # extract file name only (e.g., bear_013.jpg)\n basename_no_ext = os.path.splitext(basenam... | [
"0.6922452",
"0.6743097",
"0.67311317",
"0.6724994",
"0.64523804",
"0.6366201",
"0.6221984",
"0.6215864",
"0.6204359",
"0.6179897",
"0.61457837",
"0.613554",
"0.6132579",
"0.60457397",
"0.602493",
"0.5998027",
"0.5937625",
"0.5865713",
"0.5820261",
"0.5809766",
"0.5806412",
... | 0.6996364 | 0 |
Update the prediction model with the new data. | def update_model(path_to_data, path_to_model):
# Open the annotation files.
with open(path_to_data + r'\coco_annotation.json') as f:
coco_d = json.load(f)
# Get the categories.
categories = []
for cat in coco_d['categories']:
categories.append(cat['name'])
# Register th... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_predictions(data):\n # TODO: Priority 1 - update predictions with inference results\n # TODO: Understand from a research team exactly what the data is going to look like\n trackID = data[0]\n prediction = data[1]\n confidence = data[2]\n to_Insert_Array = [trackID, prediction, confiden... | [
"0.72333366",
"0.7135157",
"0.7065093",
"0.7044976",
"0.67507654",
"0.66787064",
"0.66733956",
"0.66451544",
"0.66342115",
"0.6553452",
"0.6528085",
"0.6525013",
"0.64743006",
"0.64256346",
"0.6359152",
"0.6347298",
"0.6312711",
"0.62738705",
"0.6263622",
"0.62602943",
"0.623... | 0.5957653 | 58 |
Read the poses_bounds.npy file produced by LLFF imgs2poses.py. | def read_meta(in_dir, use_ndc):
poses_bounds = np.load(os.path.join(in_dir, 'poses_bounds.npy')) # (N_images, 17)
c2ws = poses_bounds[:, :15].reshape(-1, 3, 5) # (N_images, 3, 5)
bounds = poses_bounds[:, -2:] # (N_images, 2)
H, W, focal = c2ws[0, :, -1]
# correct c2ws: original c2ws has rotatio... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_las_bounds(fpath):\n with laspy.file.File(fpath) as f:\n return Bounds(*(f.header.min + f.header.max))",
"def load_poses(self):\n print('Loading poses for sequence ' + self.sequence + '...')\n\n pose_file = os.path.join(self.pose_path, self.sequence + '.txt')\n\n # Read an... | [
"0.66865915",
"0.61436146",
"0.60371596",
"0.5696726",
"0.5668809",
"0.5654928",
"0.56231886",
"0.5586926",
"0.5579912",
"0.55704826",
"0.5489618",
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"0.54122",
"0.5406048",
"0.5370545",
"0.53350437",
"0.5314571",
"0.5290624",
"0.52806646",
"0.5278963... | 0.59269446 | 3 |
Overload this method when doing extra feature processes or data augmentation. | def __getitem__(self, idx):
record = self.records[idx]
return np.array(record['feat']), np.array(record['label'], dtype=np.int64) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def feature():\n pass",
"def feat():\n pass",
"def augment(self, *args, **kwargs):\n pass",
"def _data_augmentation(feature_dict):\n image_features = feature_dict[_transformed_name(constants.IMAGE_KEY)]\n image_features = _image_augmentation(image_features)\n feature_dict[_transformed_n... | [
"0.67851365",
"0.6742683",
"0.6596476",
"0.64233947",
"0.6299479",
"0.62270296",
"0.61860555",
"0.61621183",
"0.6025776",
"0.6020395",
"0.5981607",
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"0.59114283",
"0.5909907",
"0.59008974",
"0.5896712",
"0.5865747",
"0.58578086",
"0.5854... | 0.0 | -1 |
Convert an array of export statements (what we get from envextra in the config) into a dict | def env_lines_to_dict(self, env_lines):
env_dict = {}
for env_line in env_lines:
split_env_line = shlex.split(env_line)
if split_env_line[0] == "export":
split_env_line = split_env_line[1:]
for item in split_env_line:
if "=" in item:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def export_configurations():\n pass",
"def export(self) -> Dict[str, Any]:\n return {\n \"name\": self.name,\n \"channels\": [channel for channel in self.channels],\n \"packages\": self.packages.export(),\n \"logs\": [log for log in self.logs],\n \... | [
"0.59988654",
"0.59330225",
"0.5903955",
"0.56446517",
"0.5643221",
"0.56416047",
"0.54888237",
"0.5403373",
"0.53202504",
"0.52638626",
"0.5262855",
"0.5257865",
"0.5199736",
"0.5194403",
"0.5183345",
"0.5182272",
"0.51688004",
"0.5123766",
"0.5117179",
"0.5108427",
"0.51010... | 0.6357385 | 0 |
Construct a job submission script | def job_script(self):
quoted_arguments = quote_arguments([self._command_template])
quoted_environment = quote_environment(self.env_dict)
job_header_lines = "\n".join(
"%s = %s" % (k, v) for k, v in self.job_header_dict.items()
)
return self._script_template % {
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_submission_script(path,\n script_name,\n save_history=True,\n walltime=10,\n allocation='p30653',\n cores=1,\n memory=4... | [
"0.7447285",
"0.70335376",
"0.6996281",
"0.68916833",
"0.68905133",
"0.6690032",
"0.65249985",
"0.6521264",
"0.6519338",
"0.6451723",
"0.64462316",
"0.64395326",
"0.63563967",
"0.63540643",
"0.6347614",
"0.63037384",
"0.6277216",
"0.6258536",
"0.6221125",
"0.6196768",
"0.6189... | 0.64803964 | 9 |
Quote a string or list of strings using the Condor submit file "new" argument quoting rules. Returns str The arguments in a quoted form. Warnings You will need to surround the result in doublequotes before using it in the Arguments attribute. Examples >>> quote_arguments(["3", "simple", "arguments"]) '3 simple argument... | def quote_arguments(args):
if isinstance(args, str):
args_list = [args]
else:
args_list = args
quoted_args = []
for a in args_list:
qa = _double_up_quotes(a)
if " " in qa or "'" in qa:
qa = "'" + qa + "'"
quoted_args.append(qa)
return " ".join(quo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def argument_list_quote(arguments):\n args = []\n for arg in arguments:\n args.append(argument_quote(arg))\n return '\"%s\"' % ' '.join(args)",
"def _quote_arguments(args):\n return map(lambda x: '\"{}\"'.format(x) if ' ' in x else '{}'.format(x), args)",
"def argument_quote(argument):\n... | [
"0.7215548",
"0.6580144",
"0.6086504",
"0.5937315",
"0.5752094",
"0.56268567",
"0.55315644",
"0.544126",
"0.54088515",
"0.533452",
"0.5203533",
"0.5165263",
"0.51386887",
"0.51292366",
"0.5123674",
"0.50810844",
"0.5078379",
"0.5072677",
"0.50487226",
"0.50456464",
"0.5013752... | 0.74514025 | 0 |
Quote a dict of strings using the Condor submit file "new" environment quoting rules. Returns str The environment in quoted form. Warnings You will need to surround the result in doublequotes before using it in the Environment attribute. Examples >>> from collections import OrderedDict >>> quote_environment(OrderedDict... | def quote_environment(env):
if not isinstance(env, dict):
raise TypeError("env must be a dict")
entries = []
for k, v in env.items():
qv = _double_up_quotes(str(v))
if " " in qv or "'" in qv:
qv = "'" + qv + "'"
entries.append("%s=%s" % (k, qv))
return " ".j... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _build_environment(envdict):\n lines = []\n for k, v in envdict.iteritems():\n if \" \" in v: # NOTE: per the spec, one might want to handle all 'whitespace' chars.\n v = v.replace(\"'\", \"''\")\n v = \"'%s'\" % v\n v = v.replace('\"', '\"\"')\n lines.append('%... | [
"0.75373447",
"0.6221324",
"0.6093788",
"0.5934985",
"0.5808252",
"0.57019943",
"0.5597145",
"0.5515628",
"0.5512391",
"0.547237",
"0.5445413",
"0.5308214",
"0.5276308",
"0.52133214",
"0.51868063",
"0.5152444",
"0.50912315",
"0.5079086",
"0.50783825",
"0.5009073",
"0.50075024... | 0.8446683 | 0 |
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