Tuple[Union[xr.DataArray, xr.Dataset], xr.DataArray, xr.DataArray]:\\n if mean is None:\\n mean = da.mean(dim)\\n if std is None:\\n std = da.std(dim)\\n with xr.set_options(keep_attrs=True):\\n return (da - mean) / std, mean, std\",\n \"def standardize_data(data):\\n return (data - np.mean(data, axis=0)) / (np.std(data, axis=0) + 10 ** -16)\",\n \"def normalize_data(x):\\n mvec = x.mean(0)\\n stdvec = x.std(axis=0)\\n \\n return (x - mvec)/stdvec\",\n \"def normalize_data(x):\\n mvec = x.mean(0)\\n stdvec = x.std(axis=0)\\n \\n return (x - mvec)/stdvec\",\n \"def _ss(data, data_mean=None):\\n if data_mean is None:\\n data_mean = mean(data)\\n return sum((x - data_mean)**2 for x in data)\",\n \"def error_dropoff(data):\\n # 2009-05-05 08:58 IJC: Adapted to Python from Matlab.\\n # 2006/06/06 IJC: Made it work with arrays of column vectors.\\n # Added the '--nomean' option.\\n \\n \\n# PARSE INPUTS\\n data = array(data).copy()\\n \\n#interval = max([1 round(extract_from_options('--interval=', 1, options))]);\\n \\n \\n if len(data)==len(data.ravel()):\\n data = data.ravel()\\n data = data.reshape(len(data),1)\\n \\n nsets = data.shape[1]\\n npts_vec = arange(data.shape[0]/2)+1.0\\n errors = zeros((data.shape[0]/2, nsets))\\n \\n# LOOP AND CALCULATE STUFF\\n for ii in range(len(npts_vec)):\\n npts = npts_vec[ii] # number of points we average over\\n nsamp = floor(data.shape[0]/npts) # number of subsamples\\n dre = reshape(data[0:nsamp*npts,:], (npts, nsamp, nsets))\\n error_values = std(dre.mean(1))\\n errors[ii,:] = error_values\\n \\n return (errors, npts_vec)\",\n \"def _ss(data):\\n c = mean(data)\\n ss = sum((x-c)**2 for x in data)\\n return ss\",\n \"def _ss(data):\\n c = mean(data)\\n ss = sum((x-c)**2 for x in data)\\n return ss\",\n \"def _ss(data):\\n c = mean(data)\\n ss = sum((x-c)**2 for x in data)\\n return ss\",\n \"def _ss(data):\\n c = mean(data)\\n ss = sum((x-c)**2 for x in data)\\n return ss\",\n \"def _ss(data):\\n c = mean(data)\\n ss = sum((x-c)**2 for x in data)\\n return ss\",\n \"def desv_estantar(datos,column):\\n var=sum([((int(i[column])-media(datos,column))**2) for i in datos])\\n var=var/(len(datos)-1)\\n desv=math.sqrt(var)\\n return desv\",\n \"def normalize(data):\\n data = numpy.asmatrix(data)\\n std_devs = numpy.std(data, axis=1)\\n std_devs[std_devs == 0] = 1 # prevent div by 0\\n return (data - numpy.mean(data, axis=1)) / std_devs\",\n \"def unscale_data(self, data):\\n return (data + self.mean)*self.std\",\n \"def overall_standard_deviation(individual, test_data, truth_data, name=None):\\r\\n test_data = np.array(test_data)\\r\\n truth_data = np.array(truth_data)\\r\\n return np.std(test_data - truth_data)\",\n \"def sd(x):\\n x_mean = mean(x)\\n return (\\n sum((x_i - x_mean) ** 2 for x_i in x) / (len(x) - 1)\\n ) ** 0.5\",\n \"def standard_variation(df):\\r\\n\\r\\n\\tdf_sdv_dict = dict()\\r\\n\\r\\n\\tfor i, col in enumerate(df.columns):\\r\\n\\t\\tdf_sdv_dict[col] = [df[col].std(), df[col].var()]\\r\\n\\r\\n\\tdf_sdv = pd.DataFrame(df_sdv_dict, index=['Standard Deviation', 'Variance'])\\r\\n\\tpd.set_option('precision', 2) # set output display precision in 2 decimal places\\r\\n\\r\\n\\treturn df_sdv\",\n \"def deviation(values, val):\\n\\tm = mean(values)\\n\\tdev = abs(val-m)\\n\\tsd = standard_deviation(values)\\n\\treturn float(dev)/sd if sd!=0 else 0.0\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67038697","0.65886843","0.65886843","0.6443919","0.6354485","0.62777156","0.62359643","0.6214864","0.6205753","0.61780286","0.6169963","0.61327285","0.6082657","0.6057745","0.6053649","0.60294616","0.6019821","0.60037726","0.5991898","0.5970491","0.5953077","0.5953077","0.5953077","0.5953077","0.59362096","0.59352803","0.5917337","0.5909379","0.5909235","0.5905733","0.5903049","0.5897858","0.5873047","0.5854915","0.58489096","0.584799","0.5840962","0.58244765","0.58229274","0.57940143","0.57742023","0.5767511","0.5765237","0.5765237","0.5745374","0.5740445","0.5740305","0.57348305","0.57195485","0.57195485","0.57064885","0.57007957","0.56795037","0.567579","0.56652075","0.56621397","0.5657146","0.5642384","0.56416106","0.5639651","0.5612224","0.5610319","0.56066304","0.5598549","0.55938935","0.55807066","0.557896","0.55768865","0.557412","0.55670667","0.55649847","0.55501086","0.5543329","0.55298716","0.5526244","0.5525052","0.5524703","0.55214655","0.55176806","0.5517064","0.5515157","0.55139995","0.54998296","0.5496364","0.5488994","0.5488994","0.5486601","0.54737425","0.5471101","0.5471101","0.5471101","0.5471101","0.5471101","0.5470875","0.5468998","0.5467295","0.5460731","0.5454925","0.5453592","0.54519856"],"string":"[\n \"0.67038697\",\n \"0.65886843\",\n \"0.65886843\",\n \"0.6443919\",\n \"0.6354485\",\n \"0.62777156\",\n \"0.62359643\",\n \"0.6214864\",\n \"0.6205753\",\n \"0.61780286\",\n \"0.6169963\",\n \"0.61327285\",\n \"0.6082657\",\n \"0.6057745\",\n \"0.6053649\",\n \"0.60294616\",\n \"0.6019821\",\n \"0.60037726\",\n \"0.5991898\",\n \"0.5970491\",\n \"0.5953077\",\n \"0.5953077\",\n \"0.5953077\",\n \"0.5953077\",\n \"0.59362096\",\n \"0.59352803\",\n \"0.5917337\",\n \"0.5909379\",\n \"0.5909235\",\n \"0.5905733\",\n \"0.5903049\",\n \"0.5897858\",\n \"0.5873047\",\n \"0.5854915\",\n \"0.58489096\",\n \"0.584799\",\n \"0.5840962\",\n \"0.58244765\",\n \"0.58229274\",\n \"0.57940143\",\n \"0.57742023\",\n \"0.5767511\",\n \"0.5765237\",\n \"0.5765237\",\n \"0.5745374\",\n \"0.5740445\",\n \"0.5740305\",\n \"0.57348305\",\n \"0.57195485\",\n \"0.57195485\",\n \"0.57064885\",\n \"0.57007957\",\n \"0.56795037\",\n \"0.567579\",\n \"0.56652075\",\n \"0.56621397\",\n \"0.5657146\",\n \"0.5642384\",\n \"0.56416106\",\n \"0.5639651\",\n \"0.5612224\",\n \"0.5610319\",\n \"0.56066304\",\n \"0.5598549\",\n \"0.55938935\",\n \"0.55807066\",\n \"0.557896\",\n \"0.55768865\",\n \"0.557412\",\n \"0.55670667\",\n \"0.55649847\",\n \"0.55501086\",\n \"0.5543329\",\n \"0.55298716\",\n \"0.5526244\",\n \"0.5525052\",\n \"0.5524703\",\n \"0.55214655\",\n \"0.55176806\",\n \"0.5517064\",\n \"0.5515157\",\n \"0.55139995\",\n \"0.54998296\",\n \"0.5496364\",\n \"0.5488994\",\n \"0.5488994\",\n \"0.5486601\",\n \"0.54737425\",\n \"0.5471101\",\n \"0.5471101\",\n \"0.5471101\",\n \"0.5471101\",\n \"0.5471101\",\n \"0.5470875\",\n \"0.5468998\",\n \"0.5467295\",\n \"0.5460731\",\n \"0.5454925\",\n \"0.5453592\",\n \"0.54519856\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.54657185"},"document_rank":{"kind":"string","value":"96"}}},{"rowIdx":22289,"cells":{"query":{"kind":"string","value":"Creates new task window"},"document":{"kind":"string","value":"def new_task(self, widget):\n my_task_window = taskwindow.TaskWindow(self)"},"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 create_task(event):\n manager = event.workbench.get_plugin('exopy.tasks')\n dialog = BuilderView(manager=manager,\n parent=event.parameters.get('parent_ui'),\n future_parent=event.parameters.get('future_parent'))\n result = dialog.exec_()\n if result:\n return dialog.config.build_task()\n else:\n return None","def open_create_task_dialog(self):\n color = self.app.theme_cls.primary_color\n\n create_btn = MDFlatButton(text=\"OK\", text_color=color)\n cancel_btn = MDFlatButton(text=\"CANCEL\", text_color=color)\n name_field = MDTextField(hint_text=\"Name\")\n\n dialog = MDDialog(title=\"Create a new Task\",\n type=\"custom\",\n content_cls=name_field,\n buttons=[cancel_btn, create_btn])\n dialog.auto_dismiss = False\n\n def create_task(*args):\n self.task_list.dismiss()\n dialog.dismiss()\n\n self.task_manager.save(name_field.text)\n\n create_btn.bind(on_press=create_task)\n cancel_btn.bind(on_press=lambda x: dialog.dismiss())\n\n dialog.open()","def addMainWindow(self,appendToTask):\n self.appendToTask = appendToTask","def createWindow():\n\n windowName = \"ObjectSpawner\"\n\n if cmds.window(windowName, query=True, exists=True):\n cmds.deleteUI(windowName)\n\n cmds.window(windowName)\n\n populateUI()\n enableEditorDrop()\n\n cmds.showWindow(windowName)","def init_task(self):\n all_tasks = self.db.get_tasks()\n scroll_parent = Window\n uw = self.ids.upcoming_wrapper\n\n if not all_tasks:\n new_btn = NewButton()\n new_btn.size_hint = [None, None]\n new_btn.size = [\n scroll_parent.width / 1.9,\n scroll_parent.height - (0.45 * scroll_parent.height),\n ]\n new_btn.bind(on_release=self.add_new)\n uw.add_widget(new_btn)\n else:\n for t in all_tasks:\n task = Task()\n task.name = t[1]\n task.details = t[2]\n date, time = t[3].rsplit(\" \", 1)\n x = self.compare_date(date)\n if x == \"today\":\n task.tsk_clr = (0.7, 0.45, 0.1, 0.6)\n elif x == \"past\":\n task.tsk_clr = (0.7, 0, 0, 0.6)\n\n task.time = time\n task.date = date\n\n task.size_hint = [None, None]\n task.size = [\n scroll_parent.width / 1.9,\n scroll_parent.height - (0.45 * scroll_parent.height),\n ]\n\n uw.add_widget(task)","def _create_example_window():\n return Window({\"warning\": False, \"state\": \"close\"})","def showUI(cls):\r\n win = cls()\r\n win.create()\r\n return win","def open_task_list(self):\n scrollable_task = ScrollView()\n scrollable_task.add_widget(self.get_task_list())\n\n new_task_btn = RightBottomFloatingButton(\n icon='plus',\n md_bg_color=self.app.theme_cls.accent_color,\n elevation_normal=8)\n new_task_btn.bind(on_press=lambda x: self.open_create_task_dialog())\n\n self.task_list_layout.add_widget(scrollable_task)\n self.task_list_layout.add_widget(new_task_btn)\n\n self.task_list.open()","def createWindow(self):\r\n\t\t# give the window a title\r\n\t\tself.parent.title( 'Acrobat Data Acquisition')\r\n\t\t# set the style\r\n\t\tself.style = ttk.Style()\r\n\t\tself.style.theme_use('default')\r\n\t\tself.pack(fill= tk.BOTH, expand=1)","def OnAddTask(self, event):\n task_dlg = AddEditTask(self)\n task_dlg.ShowModal()","def create(self):\n\n cv2.namedWindow(winname=self.title, flags=self.style)","def open_task(self, instance):\n self.task_manager.load(instance.text)\n\n # Build the task in editor\n for component in self.task_manager.task.graph.V:\n self.add_component(component)\n for tagged_link in self.task_manager.task.graph[component]:\n self.task_manager.add_editor_link(\n component,\n self.task_manager.task.graph.V[tagged_link.vertex_index],\n self.ids.edit_area,\n index=tagged_link.tag)\n self.task_list.dismiss()","def _create_window(self):\n wc = win32gui.WNDCLASS()\n wc.lpfnWndProc = self._on_message\n wc.lpszClassName = self.__class__.__name__\n wc.hInstance = win32api.GetModuleHandle(None)\n class_atom = win32gui.RegisterClass(wc)\n return win32gui.CreateWindow(class_atom, self.__class__.__name__, 0, 0, 0, 0, 0, 0, 0, wc.hInstance, None)","def create_window(session):\n def create_window():\n windows_before = session.handles\n name = session.execute_script(\"window.open()\")\n assert len(session.handles) == len(windows_before) + 1\n new_windows = list(set(session.handles) - set(windows_before))\n return new_windows.pop()\n return create_window","def click_add():\n # TODO: 1. In frontend_script.py, create function \"create_window()\" that takes a Toplevel() as a parameter.\n # TODO: 2. In this file, implement the code below\n # new_window = Toplevel(root)\n # frontend_script.create_window(new_window)","def _open_window(self):\r\n\t\t# Creating the window\r\n\t\tself._window = Window(self, Locations.RESTAL)","def create(self, verbose=False):\r\n # delete the window if its handle exists\r\n if cmds.window(self.window, exists=True):\r\n cmds.deleteUI(self.window)\r\n # initialize the window as a pane for docking\r\n self.window = cmds.loadUI(uiFile=self.uiFile, verbose=verbose)\r\n #layoutWin = cmds.paneLayout(configuration='single')\r\n # create a dockControl and parent the control to layoutWin\r\n cmds.dockControl(allowedArea='all', area='right', floating=False, \r\n height=cmds.window(self.window, query=True, height=True), \r\n content=self.window, label='Docked Cone Pointer Window')\r\n cmds.showWindow(self.window)","def see_tasks(self, widget):\n my_task_list = tasklistwindow.TaskListWindow(self.task_list)","def showUI(cls):\r\n win = cls(uiFile)\r\n win.create()\r\n return win","def createApp(self):\n app = self.app\n window = self.window\n window.show()\n app.exec()","def create_main_window_with_browser():\n main_win = create_main_window()\n return main_win.add_browser_tab()","def create_window(window_class):\n app_created = False\n app = QtCore.QCoreApplication.instance()\n if app is None:\n app = QtGui.QApplication(sys.argv)\n app_created = True\n app.references = set()\n window = window_class()\n app.references.add(window)\n window.show()\n if app_created:\n app.exec_()\n return window","def create_task():","def create_window(self, img, roi, name):\n\n self.window = SpinBalanceDialog()\n\n # call the user-implemented functionality\n self.window.main(img, roi)\n # show the window\n self.window.show()\n\n return self.window","def open_main_window(self):\r\n track_terms_dic = ''\r\n sg.theme(self.look)\r\n\r\n layout = [[sg.Text('Welcome to tweeet monitor ')],\r\n [sg.Text('Please enter Details ')],\r\n [sg.Text('User Mail', size=(15, 1)), sg.InputText()],\r\n [sg.Text('Timout', size=(15, 1)), sg.InputText('', enable_events=True, key='-DIGITS-')],\r\n [sg.Text('')],\r\n [sg.Text('You can select an existing list or create a new one '),\r\n sg.Combo(self.files, default_value='Select Track Terms List ', key='-COMBO1-')],\r\n [sg.Text('')],\r\n [sg.Button('Select Exists List'), sg.Button('Create a New List')],\r\n [sg.Text('\\n')],\r\n [sg.Button('Start Monitor'), sg.Button('Exit')]\r\n ]\r\n\r\n window = sg.Window('Monitor tweeter', layout)\r\n # Event Loop\r\n while True:\r\n event, values = window.read()\r\n\r\n if event == sg.WIN_CLOSED:\r\n exit()\r\n elif event == 'Select Exists List' or event == 'Create a New List' or event == 'Start Monitor':\r\n user_mail = values[0]\r\n timeout = values['-DIGITS-']\r\n list_dic = values['-COMBO1-']\r\n\r\n if self.check(user_mail) == 'Invalid Email':\r\n self.info_popup_window('You Enter not valid mail ', 'Info', self.look)\r\n elif event == 'Select Exists List':\r\n if list_dic == 'Select Track Terms List ':\r\n self.info_popup_window('Track Terms List ', 'Info', self.look)\r\n else:\r\n file_name = self.path + self.bachslash + list_dic\r\n os.system(file_name)\r\n track_terms_dic = list_dic\r\n elif event == 'Create a New List':\r\n track_terms_dic = self.open_window()\r\n track_terms_dic = track_terms_dic + '.txt'\r\n elif event == 'Start Monitor':\r\n if track_terms_dic == '':\r\n self.info_popup_window('Please, Create new Dictionary or select one ', 'Info', self.look)\r\n elif track_terms_dic != '':\r\n file_name = self.path + self.bachslash + track_terms_dic\r\n my_file = open(file_name, \"r\")\r\n content = my_file.read()\r\n content = content.split(\"\\n\")\r\n content = self.cleanList(content)\r\n # print(content)\r\n my_file.close()\r\n now = datetime.now()\r\n date_time = now.strftime(\"%m/%d/%Y, %H:%M:%S\")\r\n dict_list = {'User': user_mail,\r\n 'Timeout': timeout,\r\n 'Dictionary': list_dic,\r\n 'Create Date': date_time,\r\n 'track_terms_list': content\r\n }\r\n header = ['user_mail', 'Timeout', 'Dictionary', 'Create Date', 'list words']\r\n if os.path.isfile(self.file_track_terms_audit) == False:\r\n # check if the file exsist = if not: create file and print header to the file\r\n with open(self.file_track_terms_audit, 'a', newline='\\n') as file:\r\n try:\r\n write = csv.writer(file)\r\n write.writerow(header)\r\n write.writerows(self.values_list)\r\n file.close()\r\n except:\r\n print(\"Something went wrong when writing to the file\")\r\n else:\r\n self.values_list = list(dict_list.values())\r\n # print ('self.values_list :****',self.values_list)\r\n with open(self.file_track_terms_audit, 'a', newline='\\n') as file:\r\n try:\r\n write = csv.writer(file)\r\n self.values_list = [self.values_list]\r\n write.writerows(self.values_list)\r\n file.close()\r\n except:\r\n print(\"Something went wrong when writing to the file\")\r\n print('self.values_list:', self.values_list)\r\n\r\n window.close()\r\n\r\n print('track_terms_dic: ', track_terms_dic)\r\n print('dict_list:', dict_list)\r\n return (dict_list)\r\n\r\n # always check for closed window\r\n if event in (sg.WIN_CLOSED, 'Exit'):\r\n break\r\n\r\n if event == '-LIST-' and len(values['-LIST-']):\r\n sg.popup('Selected ', values['-LIST-'])\r\n\r\n if len(values['-DIGITS-']) and values['-DIGITS-'][-1] not in ('0123456789'):\r\n # delete last char from input\r\n window['-DIGITS-'].update(values['-DIGITS-'][:-1])\r\n\r\n window.close()","def createNewPeaPodWindow(**kwargs):\n\tglobal currentPeaWindow\n\tnewWindow=dataWindow(window,\"Create Pea Pod\")\n\t#Update the global variable\n\tcurrentPeaWindow=newWindow\n\tnewWindow.functionToRun=initiateNewPeaPod\n\t#Create Section\n\tallPodNames=masterPod.currentMasterPod.peas.keys()\n\tpodName=dataSection(newWindow.contentArea,advancedEntry,\"Pod Name\",cannotContain=allPodNames)\n\tpodName.pack()\n\ttemplateOption=dataSection(newWindow.contentArea,advancedOptionMenu,\"Template\",\n\t\t\t\t\t\t\t values=podTemplate.templates.keys(),default=\"Login\",\n\t\t\t\t\t\t\t optionCommand=changePopupColour)\n\ttemplateOption.pack()\n\n\t#Add the sections\n\tnewWindow.addDataSection(podName)\n\tnewWindow.addDataSection(templateOption)\n\t#Ititate colour\n\tchangePopupColour(\"Login\")","def createPrefWindow(self):\n if (not hasattr(self, \"pref_window\")):\n self.pref_window = PrefWindow(self, self.settings)\n self.pref_window.show()\n self.pref_window.button_apply.setEnabled(True)","def create_new_tab(default_script_content=\"\"):\n pass","def open_mwindow_timetable(self) -> None:\n self.mwindow_timetable.show()","def new_task(self):\n print \"Create a new task.\"\n\n # Collect new task info from user\n description = raw_input(\"Enter task (140 characters max) > \")\n due_date = raw_input(\"Enter due date as 'year-mm-dd' (optional). > \")\n tags = raw_input(\n \"Enter tags for the task (comma separated) (optional). > \")\n tag_list = [tag.strip() for tag in tags.split(',')]\n try:\n new_task = doto.Task(self.user, description, due_date, tag_list)\n except (NameError, ValueError) as e:\n # On error, print and return.\n print \"Task not created. Error: \", e\n raw_input(\"Press Enter to continue.\")\n return\n self.current_collection.add(new_task)\n return","def create_window(root, *args, **kwargs):\n global top, rt\n rt = root\n top = tk.Toplevel(root)\n ui_support.set_Tk_var()\n window = Window(top)\n ui_support.init(top, window, *args, **kwargs)\n return top, window","def __init__(self, root):\r\n self.root = root\r\n self.root.geometry(\"400x300\")\r\n \r\n self.button_rename = tk.Button(self.root, text = \"New window\",\r\n command= self.new_winF).pack()","def create_window(self, xoff: int, yoff: int, width: int, height: int, name: str = None):\n return Window(xoff, yoff, width, height, name)","def show(self, window):\r\n\r\n return","def createGameWindow():\n gameWindow = g.GraphWin(\"game\", 450, 800) #Window to show game\n\n return gameWindow","def build_second_window():\r\n\r\n new_window = tk.Tk()\r\n windows.append(new_window)\r\n new_window.protocol(\"WM_DELETE_WINDOW\", new_round(new_window))\r\n\r\n ask = tk.Label(new_window, text='Would You Like To Play Again?', bg='Cyan')\r\n ask.pack(fill=tk.X)\r\n\r\n frame = tk.Frame(new_window)\r\n frame.pack()\r\n\r\n yes_button = tk.Button(frame, text='Yes', bg='green',\r\n command=new_round(new_window))\r\n yes_button.pack(side=tk.LEFT)\r\n\r\n no_button = tk.Button(frame, text='No', bg='red',\r\n command=close)\r\n no_button.pack(side=tk.LEFT)","def createNewMasterPodWindow():\n\tnewWindow=dataWindow(window,\"Create Master Pod\")\n\tnewWindow.functionToRun=initiateNewMasterPod\n\t#Add the sections\n\tmasterPodNameSection=dataSection(newWindow.contentArea,advancedEntry,\"Master Pod Name\",cannotContain=masterPod.loadedPods.keys())\n\tmasterPodNameSection.pack()\n\tmasterPodPassword=dataSection(newWindow.contentArea,advancedEntry,\"Password\",hide=True,bannedSymbols=[\"£\"])\n\tmasterPodPassword.pack()\n\tmasterPodConfirm=dataSection(newWindow.contentArea,advancedEntry,\"Confirm\",mustBeSameAs=masterPodPassword,hide=True)\n\tmasterPodConfirm.pack()\n\n\tmasterHint=dataSection(newWindow.contentArea,advancedEntry,\"Hint\",checkNeeded=False)\n\tmasterHint.pack()\n\tmasterHint.dataValid=True\n\tmasterHint.changeColour=False\n\n\t#Add to window\n\tnewWindow.addDataSection(masterPodNameSection)\n\tnewWindow.addDataSection(masterPodPassword)\n\tnewWindow.addDataSection(masterPodConfirm)\n\tnewWindow.addDataSection(masterHint)","def window_tasks(self):\n if self._handle != win32gui.GetForegroundWindow():\n #print \"not in foreground\"\n self.restore_window()\n self.fix_ui()\n self.set_foreground()\n\n self._shell.AppActivate(self._handle)","def createWindow(self, type):\n # this = Browser(self.url())\n # this.show()\n\n self.popup = SequanixQWebView(**self.kwargs)\n self.popup.setObjectName(\"web_content\")\n self.popup.setWindowTitle(\"Sequana browser\")\n self.popup.page().windowCloseRequested.connect(self.popup.close)\n self.popup.show()\n return self.popup","def open_generatorWindow(self):\n self.window = generatorWindow(self)\n self.hide()","def run(self, action): \n active = self.root.get_full_property(self._ACTIVE_WINDOW, 0).value[0] \n curDesk = self.root.get_full_property(self._CURRENT_DESKTOP, 0).value[0]\n tasks = self.root.get_full_property(self._CLIENT_LIST, Xatom.WINDOW).value\n \n for task in tasks:\n win = self.display.create_resource_object('window', task)\n name = win.get_wm_name()\n desk = win.get_full_property(self._DESKTOP, 0).value[0]\n state = win.get_full_property(self._STATE, Xatom.ATOM).value \n if desk == curDesk: \n if int(task) != active:\n if action == 1: \n if not self._SHADED in state:\n self.doShade(win)\n else: \n if (self._SHADED in state):\n self.doUnShade(win)\n\n self.display.flush() # this magic command must be here !!!","def startWindow():\n\n m = mainWindow()\n\n # Show Window\n m.show()\n\n # Return to stay alive\n return m","def details_window(self, instance: Union[Nobleman, Location]):\n window = tk.Toplevel()\n window.title(instance.name)\n window.protocol(\"WM_DELETE_WINDOW\",\n partial(self.close_details_window, instance))\n self.register_extra_window(instance, window)\n self.generate_window_content(instance, window)","def showWindow(*args, **kwargs)->None:\n pass","def __init__(self, root):\n self.root = root\n self.root.geometry(\"400x300\")\n self.root[\"bg\"] = \"coral\"\n self.button_rename = tk.Button(self.root, text = \"New window\",\n command= lambda: self.new_window(Win2)).pack()","def into_new_window(self):\r\n t1 = time.time()\r\n try:\r\n all_handle = self.driver.window_handles\r\n flag = 0\r\n while len(all_handle) < 2:\r\n time.sleep(1)\r\n all_handle = self.driver.window_handles\r\n flag += 1\r\n if flag == 5:\r\n break\r\n self.driver.switch_to.window(all_handle[-1])\r\n self.my_print(\"{0} Switch to the new window,new window's url: {1}, Spend {2} seconds\".format(success,\r\n self.driver.current_url,time.time() - t1))\r\n except Exception:\r\n self.my_print(\"{0} Unable switch to the new window, Spend {1} seconds\".format(fail, time.time() - t1))\r\n raise","def new_workunit(self, name, labels=None, cmd=''):\r\n with self.run_tracker.new_workunit(name=name, labels=labels, cmd=cmd) as workunit:\r\n yield workunit","def start_task(display_window, daq, exp_type, parameters, file_save,\n signal_model=None, language_model=None, fake=True, auc_filename=None):\n task = make_task(display_window, daq, exp_type, parameters, file_save,\n signal_model, language_model, fake, auc_filename)\n task.execute()","def create(dlg):\n page = EditorCalltipsQScintillaPage()\n return page","def make_window(color, title):\n w = turtle.Screen()\n w.bgcolor(color)\n w.title(title)\n return w","def _show_task_list_panel(self):\n self.task_list_panel.show()\n self.task_list_panel.load_config()\n # set geometry\n _button_pos = self.task_frame.tasklist_button.pos()\n _button_pos = self.task_frame.mapTo(self, _button_pos)\n _button_height = self.task_frame.tasklist_button.height()\n _glo_pos = self.mapTo(tomaya.GetMayaMainWindowPoint(), _button_pos)\n self.task_list_panel.setGeometry(_glo_pos.x(), _glo_pos.y() + _button_height, self.task_frame.width(), tomaya.GetMayaMainWindowPoint().height()*1/2.0)","def newwindow(url):\n\n # Open the URL\n webbrowser.open_new(url)","def adv_new_window(self):\n adv=workflow.advancedoptions_w.ADialog()\n adv.exec_()","def newwin(self,name,sizeY,sizeX,offsetY,offsetX, border=False):\n\t\tself.windows[name]=Window(sizeY,sizeX,offsetY,offsetX,border)\n\t\treturn self.windows[name]","def new_workunit(self, name, labels=None, cmd=''):\r\n parent = self._threadlocal.current_workunit\r\n with self.new_workunit_under_parent(name, parent=parent, labels=labels, cmd=cmd) as workunit:\r\n self._threadlocal.current_workunit = workunit\r\n try:\r\n yield workunit\r\n finally:\r\n self._threadlocal.current_workunit = parent","def openWindow(self):\n # self.showSessionAct.setEnabled(False)\n self.musketeers_widget = MusketeersWidget(parent=self)\n self.setCentralWidget(self.musketeers_widget)\n self.saveGroupMenu = QAction('Save Group', self.fileMenu)\n self.fileMenu.addAction(self.saveGroupMenu)\n self.saveGroupMenu.triggered.connect(self.musketeers_widget.session_widget.save_group)","def _ConfigureNewWindow(self, pages, incognito=False):\n page_index = self._ActivateTabWithURL(pages[0])\n if not page_index:\n # This means the pages do not exist, load them\n if incognito:\n self.RunCommand(pyauto.IDC_NEW_INCOGNITO_WINDOW)\n else:\n self.OpenNewBrowserWindow(True)\n for url in pages:\n self.AppendTab(pyauto.GURL(url), self.GetBrowserWindowCount() - 1)\n # Cycle through the pages to make sure they render\n win = self.GetBrowserInfo()['windows'][self.GetBrowserWindowCount() - 1]\n for tab in win['tabs']:\n self.ActivateTab(tab['index'], self.GetBrowserWindowCount() - 1)\n # Give the plugin time to activate\n time.sleep(1.5)\n else:\n self.CloseBrowserWindow(page_index[1])","def create_blank_window(wm_name=None, wm_class=None):\n setup = xpybutil.conn.get_setup()\n window = xpybutil.conn.generate_id()\n xpybutil.conn.core.CreateWindow(\n setup.roots[0].root_depth,\n window,\n setup.roots[0].root,\n 0,\n 0,\n 640,\n 480,\n 0,\n xcffib.xproto.WindowClass.InputOutput,\n setup.roots[0].root_visual,\n xcffib.xproto.CW.BackPixel | xcffib.xproto.CW.EventMask,\n [\n setup.roots[0].white_pixel,\n xcffib.xproto.EventMask.Exposure | xcffib.xproto.EventMask.KeyPress,\n ],\n )\n xpybutil.conn.core.MapWindow(window)\n xpybutil.conn.flush()\n cookies = []\n if wm_class:\n cookies.append(set_wm_class_checked(window, wm_class[0], wm_class[1]))\n if wm_name:\n cookies.append(set_wm_name_checked(window, wm_name))\n for cookie in cookies:\n cookie.check()\n return window","def open_window(self):\r\n newdic = ''\r\n layout = [[sg.Text(\"Please enter a new name for your Dictionary\\n\\n\")],\r\n [sg.Text('New Dictionary name: ', size=(15, 1)),\r\n sg.InputText('', enable_events=True, key='-NEWDIC-')],\r\n [sg.Text(\"\\n\\n\")],\r\n [sg.Button(\"OK\"), sg.Button('Exit')]\r\n ]\r\n window = sg.Window(\"Create New Dictionary\", layout, modal=True)\r\n while True:\r\n event, values = window.read()\r\n if event == sg.WIN_CLOSED:\r\n break\r\n elif event == \"Exit\":\r\n window.close()\r\n\r\n elif event == \"OK\":\r\n newdic = values['-NEWDIC-']\r\n file_name = self.path + self.bachslash + newdic + '.txt'\r\n print(file_name)\r\n if not os.path.exists(file_name):\r\n with open(file_name, \"w\"):\r\n pass\r\n else:\r\n self.info_popup_window('The name you entered already exists ', 'Info', self.look)\r\n os.system(file_name)\r\n window.close()\r\n return newdic","def createHelpWindow(self):\n if (not hasattr(self, \"help_window\")):\n self.help_window = HelpWindow(self.settings, self)\n self.help_window.show()","def registrar(self):\n self.new_window = tk.Toplevel(self.acesso)\n Registrar(self.new_window)","def window(*args, width: int = 200, height: int = 200, autosize: bool = False,\n no_resize: bool = False, no_title_bar: bool = False, no_move: bool = False, no_scrollbar: bool = False,\n no_collapse: bool = False, horizontal_scrollbar: bool = False, no_focus_on_appearing: bool = False,\n no_bring_to_front_on_focus: bool = False, menubar: bool = False, no_close: bool = False,\n no_background: bool = False, label: str = '', show: bool = True, collapsed: bool = False,\n modal: bool = False, popup: bool = False,\n on_close: Callable = None, min_size: List[int]=[32, 32], max_size: List[int] = [30000, 30000], id:str=''):\n try:\n\n widget = internal_dpg.add_window(*args, width=width, height=height, autosize=autosize,\n no_resize=no_resize, no_title_bar=no_title_bar, no_move=no_move,\n no_scrollbar=no_scrollbar, no_collapse=no_collapse,\n horizontal_scrollbar=horizontal_scrollbar,\n no_focus_on_appearing=no_focus_on_appearing,\n no_bring_to_front_on_focus=no_bring_to_front_on_focus,\n menubar=menubar, no_close=no_close,\n no_background=no_background, label=label, show=show, \n collapsed=collapsed, on_close=on_close,\n min_size=min_size, max_size=max_size, id=id, modal=modal,\n popup=popup)\n internal_dpg.push_container_stack(widget)\n yield widget\n\n finally:\n internal_dpg.pop_container_stack()","def constructWindow(windowType):\n global LIVING_ID, DISPLAY_DURATION\n\n # add self destruction after DISPLAY_DURATION milliseconds\n LIVING_ID = GObject.timeout_add(\n DISPLAY_DURATION, destroyWindow, windowType)\n\n if windowType == \"volume\":\n open(MY_VOL_PATH, 'w').write(str(os.getpid()))\n window = VolDisplay()\n elif windowType == \"backlight\":\n open(MY_BACKLIGHT_PATH, 'w').write(str(os.getpid()))\n window = BacklightDisplay()\n\n window.connect(\"delete-event\", Gtk.main_quit)\n window.show_all()\n\n # poll volume all xxx milliseconds\n GObject.timeout_add(50, window.updateBar)\n\n Gtk.main()","def make_window(colr, ttle):\n w = turtle.Screen()\n w.bgcolor(colr)\n w.title(ttle)\n return w","def CreateConsole(self):\n lc = launcher.TextFrame('title')\n return lc","def _create_window(self):\n self.window = Gtk.Window()\n self.window.set_title(\"Yapsy Example\")\n self.window.set_default_size(400, 400)\n self.window.connect(\"destroy\", lambda w: Gtk.main_quit())\n # PluginList() is a composite widget that shows all installed plugins\n # in a Gtk.TreeView. See widgets.py\n self._plugin_list = PluginList(self.window)\n box = Gtk.Box(orientation=Gtk.Orientation.VERTICAL)\n box.pack_start(self._plugin_list, True, True, 0)\n box.show_all()\n self.window.add(box)","def add_task():\n # get values from user\n responses = accept_inputs([\"Task label\", \"Short task description\", \"Parent task label\"])\n # insert into db\n query_no_results(\"insert into task values(?, ?, ?)\",\n [responses[\"Task label\"], responses[\"Short task description\"], responses[\"Parent task label\"]])\n print(\"New task created\")","def create(\n cls,\n window, name, *,\n force_writes=False,\n follow_cursor=False,\n unlisted=False,\n **kwargs\n ):\n validate_view_options(kwargs)\n\n window.destroy_output_panel(name)\n view = window.create_output_panel(name, unlisted)\n set_view_options(view, **kwargs)\n\n return cls(window, name, force_writes=force_writes, follow_cursor=follow_cursor)","def _newFileWizard(self):\n\n dialogs = Dialogs(self.view)\n\n #get category\n category = dialogs.radioButtonDialog('Pick a category:' , \n self.model.project.config['NEW_FILE_CATEGORIES'])\n\n #get name of asset/shot (loop until valid input)\n name = dialogs.fileTextPrompt('Enter name:')\n\n #get task\n tasks = self.model.project.config['NEW_FILE_TASKS'][category][:]\n tasks.append('Other')\n\n task = dialogs.radioButtonDialog('Pick a task:' , tasks)\n\n if task == 'Other':\n task = dialogs.textPrompt('Enter task:')\n\n #get the path of the file we are creating\n category_dir = self.model.project.config['NEW_FILE_PATHS'][category]\n result = os.path.join(category_dir, name, '%s_%s.mb' % (name, task))\n\n #ask to overwrite\n print 'EXISTS:', result, os.path.exists(result)\n if os.path.exists(result):\n msg = 'The file \"%s\" already exists. Overwrite?' % result\n dialogs.confirmPrompt(msg)\n\n #confirm all settings\n msg = 'You have selected:'\n msg += '\\n\\n\\tCategory: %s' % category\n msg += '\\n\\tName: %s' % name\n msg += '\\n\\tTask: %s' % task\n msg += '\\n\\nThis will create a file in this location: '\n msg += '\\n%s' % result\n msg += '\\n\\nDoes this look okay?'\n dialogs.confirmPrompt(msg)\n\n return result","def createColorWindow():\n colorWindow = g.GraphWin(\"Color\", 150, 500) #Window to show colors to pick\n return colorWindow","def get_main_window():\n\n pass","def new(self):\r\n gen_name = lambda nb: self.tr(\"untitled\") + (\"%d.py\" % nb)\r\n nb = 0\r\n while osp.isfile(gen_name(nb)):\r\n nb += 1\r\n fname = gen_name(nb)\r\n self.emit(SIGNAL('redirect_stdio(bool)'), False)\r\n fname = QFileDialog.getSaveFileName(self, self.tr(\"New Python script\"),\r\n fname, self.tr(\"Python scripts\")+\" (*.py ; *.pyw)\")\r\n self.emit(SIGNAL('redirect_stdio(bool)'), True)\r\n if not fname.isEmpty():\r\n fname = unicode(fname)\r\n default = ['# -*- coding: utf-8 -*-',\r\n '\"\"\"', osp.basename(fname), '\"\"\"', '', '']\r\n text = os.linesep.join(default)\r\n encoding.write(unicode(text), fname, 'utf-8')\r\n self.load(fname)","def OnNew(self, e):\n self.mainparent.statusbar.SetStatusText(\"New Project\", 0)\n\n filename = \"__new_project__\"\n self.mainparent.input_file = InputFile(filename, read=False)\n self.mainparent.statusbar.SetStatusText(\"---New Project---\", 2)\n self.mainparent.file_loaded = True\n\n # reset menus and such\n self.mainparent.reset_namelist_menu()\n self.mainparent.nmlpanel.reset(unset_namelist=True)","def inserir(self):\n self.new_window = tk.Toplevel(self.menu)\n Inserir(self.new_window)","def handle_new_window(event):\n url = event.GetURL()\n webbrowser.open(url)","def create_utilities(self, n):\n f = Frame(n)\n f.pack(side=TOP, fill=BOTH, expand=Y)\n\n progs = Labelframe(f, text='Programs/Utilities')\n progs.pack(side=TOP, expand=Y, fill=BOTH)\n Grid.columnconfigure(progs, 0, weight=1)\n Grid.columnconfigure(progs, 1, weight=1)\n Grid.rowconfigure(progs, 2, weight=1)\n\n run_prog = Button(\n progs, text='Run Program', command=self.run_selected_utilities)\n create_tooltip(run_prog, 'Runs the selected program(s).')\n run_prog.grid(column=0, row=0, sticky=\"nsew\")\n open_utils = Button(\n progs, text='Open Utilities Folder', command=self.lnp.open_utils)\n create_tooltip(open_utils, 'Open the utilities folder')\n open_utils.grid(column=1, row=0, sticky=\"nsew\")\n Label(\n progs, text='Double-click on a program to launch it.').grid(\n column=0, row=1, columnspan=2)\n Label(\n progs, text='Right-click on a program to toggle auto-launch.').grid(\n column=0, row=2, columnspan=2)\n listframe = Frame(progs)\n listframe.grid(column=0, row=3, columnspan=2, sticky=\"nsew\")\n Grid.rowconfigure(listframe, 0, weight=1)\n Grid.columnconfigure(listframe, 0, weight=1)\n self.proglist = proglist = Treeview(\n listframe, columns=('exe', 'launch'), show=['headings'])\n proglist.column('exe', width=1, anchor='w')\n proglist.column('launch', width=35, anchor='e', stretch=NO)\n proglist.heading('exe', text='Executable')\n proglist.heading('launch', text='Auto')\n proglist.grid(column=0, row=0, sticky=\"nsew\")\n proglist.bind(\"<Double-1>\", lambda e: self.run_selected_utilities())\n if sys.platform == 'darwin':\n proglist.bind(\"<2>\", self.toggle_autorun)\n else:\n proglist.bind(\"<3>\", self.toggle_autorun)\n s = Scrollbar(listframe, orient=VERTICAL, command=proglist.yview)\n proglist['yscrollcommand'] = s.set\n s.grid(column=1, row=0, sticky=\"ns\")\n\n refresh = Button(\n progs, text='Refresh List', command=self.read_utilities)\n create_tooltip(refresh, 'Refresh the list of utilities')\n refresh.grid(column=0, row=4, columnspan=2, sticky=\"nsew\")\n return f","def __init__(self):\n self.root = Tk()\n self.root.title(\"Coursework 2\")\n\n mainframe = ttk.Frame(self.root, padding=\"3 3 12 12\")\n mainframe.grid(column=0, row=0, sticky=(N, W, E, S))\n mainframe.columnconfigure(0, weight=1)\n mainframe.rowconfigure(0, weight=1)\n\n self.visitor_uuid = StringVar()\n self.document_uuid = StringVar()\n self.task = StringVar()\n\n visitor_entry = ttk.Entry(mainframe, width=30, textvariable=self.visitor_uuid)\n visitor_entry.grid(column=2, row=1, sticky=(W, E))\n\n document_entry = ttk.Entry(mainframe, width=30, textvariable=self.document_uuid)\n document_entry.grid(column=2, row=2, sticky=(W, E))\n\n task_entry = ttk.Entry(mainframe, width=2, textvariable=self.task)\n task_entry.grid(column=2, row=3, sticky=(W, E))\n\n ttk.Button(mainframe, text=\"Do Task\", command=self.doTask).grid(column=2, row=4, sticky=W)\n\n ttk.Label(mainframe, text=\"Visitor UUID\").grid(column=1, row=1, sticky=W)\n ttk.Label(mainframe, text=\"Document UUID\").grid(column=1, row=2, sticky=W)\n ttk.Label(mainframe, text=\"Task\").grid(column=1, row=3, sticky=W)\n \n self.output = StringVar()\n self.w = ttk.Label( mainframe, textvariable = self.output)\n self.w.grid(column = 1, row = 5, sticky=S)\n\n for child in mainframe.winfo_children(): child.grid_configure(padx=5, pady=5)\n\n visitor_entry.focus()\n self.root.bind('<Return>', self.doTask)\n \n self.taskEx = TaskExecuter()\n self.root.mainloop()","def newtask_view(request, project_id):\n # Use to tell to the template that user want to creat a new task\n is_new = True\n\n # Retrieve the task, raise an error if the task does not exist\n project = get_object_or_404(Projet, id=project_id)\n\n # Check if the user is allowed to add a task to this project\n if request.user.has_perm('taskmanager.{}_project_permission'.format(project.id)):\n\n # Check if a form has been submitted\n if request.method == \"POST\":\n # Pass project to the form. Set the task's project fields with this project (initialize and never modify)\n form = TaskForm(project, request.POST)\n if form.is_valid():\n task = form.save(commit=True)\n task.last_modification = datetime.datetime.now() # it's probably not necessary\n task.save()\n\n return redirect(\"task\", task_id=task.id)\n else:\n # Pass project to the form. Set the task's project fields with this project (initialize and never modify)\n form = TaskForm(project)\n else:\n return redirect(\"projects\")\n return render(request, \"newtask.html\", locals())","def createAboutWindow(self):\n if (not hasattr(self, \"about_window\")):\n self.about_window = AboutWindow(self)\n self.about_window.show()","def open_transitWindow(self):\n self.window = surveyWindow(self, imaging=False)\n self.hide()","def window(main):\r\n main.title(\"BinCryptor 1.0\")\r\n main.update_idletasks()\r\n width = main.winfo_width() #Width of the current screen\r\n height = main.winfo_height() #Height of the current screen\r\n x = (main.winfo_screenwidth() // 2) - (width // 2)\r\n y = (main.winfo_screenheight() // 2) - (height // 2)\r\n main.geometry(f'{width}x{height}+{x}+{y}') #Adjusts the height and width\r","def add_window(self, window):\n if not self.valid_window(window):\n return False\n self.windows.append(window)\n window.tiler = self\n if window not in self.start_positions.keys():\n self.start_positions[window] = window.display_size\n\n rules = config.GET_RULES(window.classname)\n if rules is not None and re.search(rules[\"regex\"], window.title) is not None:\n if \"floating\" in rules:\n window.set_floating(rules[\"floating\"])\n if \"decorated\" in rules:\n if bool(rules[\"decorated\"]):\n window.enable_decoration()\n else:\n window.disable_decoration()\n if \"position\" in rules:\n window.move_to(tuple(rules[\"position\"]))\n\n print(\"Added window: {0}\".format(window))\n window.print_window_styles()\n return True","def add_window(self, window: AbstractView) -> None:\n self._logger.debug(\"running\")\n window.setParent(self)\n self.addSubWindow(window)\n window.show()\n window.restore_window()\n self._logger.debug(\"done\")","def create(self, parent):\n self.widget = _QMainWindow(parent)","def createAction(self):\n self.createProjectAction = QtGui.QAction(self.tr(\"&New Project\"), self)\n self.createProjectAction.setShortcut(QtGui.QKeySequence.New)\n self.createProjectAction.setStatusTip(self.tr(\"Create a new project\"))\n self.connect(self.createProjectAction, QtCore.SIGNAL(\"triggered()\"), self, QtCore.SLOT(\"newProject()\"))\n\n self.openProjectAction = QtGui.QAction(self.tr(\"&Open...\"), self)\n self.openProjectAction.setShortcut(QtGui.QKeySequence.Open)\n self.openProjectAction.setStatusTip(self.tr(\"Open an existing project\"))\n self.connect(self.openProjectAction, QtCore.SIGNAL(\"triggered()\"), self, QtCore.SLOT(\"openProject()\"))\n\n self.saveProjectAction = QtGui.QAction(self.tr(\"&Save\"), self)\n self.saveProjectAction.setShortcut(QtGui.QKeySequence.Save)\n self.saveProjectAction.setStatusTip(self.tr(\"Save the current project\"))\n self.connect(self.saveProjectAction, QtCore.SIGNAL(\"triggered()\"), self, QtCore.SLOT(\"save()\"))\n\n self.importVideoAction = QtGui.QAction(self.tr(\"&Import video...\"), self)\n self.importVideoAction.setStatusTip(self.tr(\"Import a video into your project\"))\n self.connect(self.importVideoAction, QtCore.SIGNAL(\"triggered()\"), self, QtCore.SLOT(\"importVideo()\"))\n\n self.aboutAction = QtGui.QAction(self.tr(\"&About\"), self)\n self.aboutAction.setStatusTip(self.tr(\"Show the credits and authors\"))\n self.connect(self.aboutAction, QtCore.SIGNAL(\"triggered()\"), self, QtCore.SLOT(\"showAbout()\"))","def new_tab(self):\n \n self.driver.execute_script(\"window.open('about:blank');\")","def __init__(self, threadID, name):\n\t\tThread.__init__(self)\n\t\tself.threadID = threadID\n\t\tself.name = name\n\t\tself.window = MainWindow(None)\n\t\tself.window.title(\"OpComms System IV Desktop Client\")","def generate_output(self, title):\n try:\n # First create the window inside the application used to hold it.\n # Then proceed to show the newly created window and fill it with\n # the data we want to display.\n self.title = title\n\n crea = self.create()\n\n if not crea:\n self.ClearLines()\n #self.add_line(\"hola manola\")\n self.refresh()\n else:\n #print \"1 OK...\"\n pass\n\n self.show()\n self.colorize() # This must be implemented in the\n # derived class.\n #self.refresh()\n\n except Exception, err:\n if self.debug:\n print format_exc()\n self.close()\n raise TextOutputMediaException(\n \"Error creating viewer called \\\"%s\\\"\" % title)","def create_task():\n client = RequestManager()\n client.set_method(\"POST\")\n client.set_endpoint(\"/projects/{0}/stories/{1}/tasks\".format(STORED_ID['project_id'], STORED_ID['story_id']))\n name = \"\".join(choices(string.ascii_letters, k=6))\n body = {\"description\": name}\n client.set_body(json.dumps(body))\n response = client.execute_request()\n try:\n STORED_ID['task_id'] = response.json()['id']\n except KeyError:\n LOGGER.info(response.json())","def taskdetail_create(name, tsk, td_id=None):\n return IMPL.taskdetail_create(name, tsk, td_id)","def add_create_pl_btn(self):\n self.create_pl = QPushButton(\"Add to playlist\")\n self.create_pl.clicked.connect(self.pl_btn_push)\n self.hbtnbox.addWidget(self.create_pl)","def new_tab(self, widget):\n print('new tab added')\n name = Gtk.Buildable.get_name(widget)\n if name == 'new':\n param = 'create'\n elif name == 'button_open':\n param = 'open'\n else:\n param = 'create'\n self.notebook.append_page(*self.create_tab(param))\n self.notebook.show_all()","def action_create_task(self):\n self.ensure_one()\n res = super(ProjectIssue, self).action_create_task()\n task_id = res.get('res_id', False)\n if task_id:\n task_obj = self.env['project.task'].browse(task_id)\n task_obj.description = self.description\n return res","def automatic_window(self):\n \n #Create window and label\n automatic_window = tk.Toplevel(self)\n windowtext = self.translate('How many days do you want the simulation to run for?') \n automatic_window.title(windowtext)\n automatic_window.config(bg=self.default_background)\n lbl_text = tk.Label(automatic_window, text=windowtext,\n bg=self.default_background)\n lbl_text.grid(column=0, row=0)\n \n #Create input box\n self.auto_var = tk.IntVar()\n self.auto_var.set(1)\n auto_menu = tk.Entry(automatic_window)\n auto_menu.insert(0,0)\n auto_menu.configure(width=5)\n auto_menu.grid(column=0, row=1)\n\n #Create button to initate the simulation\n auto_run_button = tk.Button(automatic_window, text=self.translate('Run Simulation'), \n command = lambda: self.auto_run(automatic_window, int(auto_menu.get())),\n bg=self.button_color,\n highlightbackground=self.highlight_color)\n auto_run_button.grid(column=0, row=2)\n \n #Center the window on the screen\n automatic_window.withdraw()\n automatic_window.update_idletasks() # Update \"requested size\" from geometry manager\n x = (self.screenwidth - automatic_window.winfo_reqwidth()) / 2\n y = (self.screenheight - automatic_window.winfo_reqheight()) / 2\n automatic_window.geometry(\"+%d+%d\" % (x, y))\n automatic_window.deiconify()","def new_round(new_window):\r\n\r\n global game_on\r\n game_on = False\r\n\r\n def func():\r\n new_window.destroy()\r\n windows.remove(new_window)\r\n window.after(1000)\r\n new_button = tk.Button(start_frame, text='Start', bg='orchid3')\r\n new_button.place(relheight=0.8, relwidth=0.1, relx=0.45, rely=0.1)\r\n new_button['command'] = cmd_start(new_button)\r\n return func","def create_help_window():\n global help_window\n help_window = Toplevel(root, relief=SUNKEN, bd=10, bg=\"light sky blue\")\n help_window.title(\"Справка\")\n help_window.minsize(width=1000, height=550)\n help_window.resizable(height=False, width=False)\n main_win_button = ttk.Button(help_window, text=\" Главное окно \", command=help_main_but)\n main_win_button.place(x=20, y=50)\n add_db_table = ttk.Button(help_window, text=\"Добавить базу данных\", command=help_add_db)\n add_db_table.place(x=10, y=120)\n add_string_button = ttk.Button(help_window, text=\" Добавить строку \", command=help_add_str)\n add_string_button.place(x=15, y=190)\n load_file_button = ttk.Button(help_window, text=\" Загрузить файл \", command=help_load_file)\n load_file_button.place(x=20, y=260)\n del_string_button = ttk.Button(help_window, text=\" Удалить строку \", command=help_del_str)\n del_string_button.place(x=20, y=330)\n download_file_button = ttk.Button(help_window, text=\" Выгрузить файл \", command=help_download_file)\n download_file_button.place(x=20, y=400)\n update_string_button = ttk.Button(help_window, text=\" Обновить строку \", command=help_update_str)\n update_string_button.place(x=20, y=470)","def set_window(self, handle):\n pass","def show_task1(self):\n self._show_task(self.controller.CURRENT)","def move_to_win(self):\n self.external_win = PlotWindow(plot=self.pw, parent=self)\n self.external_win.closeWin.connect(lambda: self.layout().takeAt(1))\n self.external_win.closeWin.connect(lambda: self.layout().insertWidget(1, self.pw))\n self.external_win.closeWin.connect(lambda: self.btn_open.setEnabled(True))\n self.external_win.show()","def create_new_tournament(self) -> None:\n # 1\n for elem in self.data:\n key = ''\n value = ''\n for k, v in elem.items():\n if k == 'name':\n key = v\n else:\n if key == 'Nombre de tours':\n if v.get() != '':\n value = v.get()\n else:\n value = cf.NUMBER_OF_ROUNDS\n else:\n value = v.get()\n self.attributs.update({key: value})\n # 2\n # order id a dictionary {'order': order, 'left_window_value': <value to display>}\n order = ct.Controls.verify_tournament_creation(self.attributs)\n # update the menus and the left window\n self.master.master.list_instances_menus_tournament = Menu.update_menus_tournament(order, self.master)\n self.master.master.left_window.update_and_display(self.master.master.list_instances_menus_tournament)\n # 3\n self.master.master.launch()\n self.destroy_window()"],"string":"[\n \"def create_task(event):\\n manager = event.workbench.get_plugin('exopy.tasks')\\n dialog = BuilderView(manager=manager,\\n parent=event.parameters.get('parent_ui'),\\n future_parent=event.parameters.get('future_parent'))\\n result = dialog.exec_()\\n if result:\\n return dialog.config.build_task()\\n else:\\n return None\",\n \"def open_create_task_dialog(self):\\n color = self.app.theme_cls.primary_color\\n\\n create_btn = MDFlatButton(text=\\\"OK\\\", text_color=color)\\n cancel_btn = MDFlatButton(text=\\\"CANCEL\\\", text_color=color)\\n name_field = MDTextField(hint_text=\\\"Name\\\")\\n\\n dialog = MDDialog(title=\\\"Create a new Task\\\",\\n type=\\\"custom\\\",\\n content_cls=name_field,\\n buttons=[cancel_btn, create_btn])\\n dialog.auto_dismiss = False\\n\\n def create_task(*args):\\n self.task_list.dismiss()\\n dialog.dismiss()\\n\\n self.task_manager.save(name_field.text)\\n\\n create_btn.bind(on_press=create_task)\\n cancel_btn.bind(on_press=lambda x: dialog.dismiss())\\n\\n dialog.open()\",\n \"def addMainWindow(self,appendToTask):\\n self.appendToTask = appendToTask\",\n \"def createWindow():\\n\\n windowName = \\\"ObjectSpawner\\\"\\n\\n if cmds.window(windowName, query=True, exists=True):\\n cmds.deleteUI(windowName)\\n\\n cmds.window(windowName)\\n\\n populateUI()\\n enableEditorDrop()\\n\\n cmds.showWindow(windowName)\",\n \"def init_task(self):\\n all_tasks = self.db.get_tasks()\\n scroll_parent = Window\\n uw = self.ids.upcoming_wrapper\\n\\n if not all_tasks:\\n new_btn = NewButton()\\n new_btn.size_hint = [None, None]\\n new_btn.size = [\\n scroll_parent.width / 1.9,\\n scroll_parent.height - (0.45 * scroll_parent.height),\\n ]\\n new_btn.bind(on_release=self.add_new)\\n uw.add_widget(new_btn)\\n else:\\n for t in all_tasks:\\n task = Task()\\n task.name = t[1]\\n task.details = t[2]\\n date, time = t[3].rsplit(\\\" \\\", 1)\\n x = self.compare_date(date)\\n if x == \\\"today\\\":\\n task.tsk_clr = (0.7, 0.45, 0.1, 0.6)\\n elif x == \\\"past\\\":\\n task.tsk_clr = (0.7, 0, 0, 0.6)\\n\\n task.time = time\\n task.date = date\\n\\n task.size_hint = [None, None]\\n task.size = [\\n scroll_parent.width / 1.9,\\n scroll_parent.height - (0.45 * scroll_parent.height),\\n ]\\n\\n uw.add_widget(task)\",\n \"def _create_example_window():\\n return Window({\\\"warning\\\": False, \\\"state\\\": \\\"close\\\"})\",\n \"def showUI(cls):\\r\\n win = cls()\\r\\n win.create()\\r\\n return win\",\n \"def open_task_list(self):\\n scrollable_task = ScrollView()\\n scrollable_task.add_widget(self.get_task_list())\\n\\n new_task_btn = RightBottomFloatingButton(\\n icon='plus',\\n md_bg_color=self.app.theme_cls.accent_color,\\n elevation_normal=8)\\n new_task_btn.bind(on_press=lambda x: self.open_create_task_dialog())\\n\\n self.task_list_layout.add_widget(scrollable_task)\\n self.task_list_layout.add_widget(new_task_btn)\\n\\n self.task_list.open()\",\n \"def createWindow(self):\\r\\n\\t\\t# give the window a title\\r\\n\\t\\tself.parent.title( 'Acrobat Data Acquisition')\\r\\n\\t\\t# set the style\\r\\n\\t\\tself.style = ttk.Style()\\r\\n\\t\\tself.style.theme_use('default')\\r\\n\\t\\tself.pack(fill= tk.BOTH, expand=1)\",\n \"def OnAddTask(self, event):\\n task_dlg = AddEditTask(self)\\n task_dlg.ShowModal()\",\n \"def create(self):\\n\\n cv2.namedWindow(winname=self.title, flags=self.style)\",\n \"def open_task(self, instance):\\n self.task_manager.load(instance.text)\\n\\n # Build the task in editor\\n for component in self.task_manager.task.graph.V:\\n self.add_component(component)\\n for tagged_link in self.task_manager.task.graph[component]:\\n self.task_manager.add_editor_link(\\n component,\\n self.task_manager.task.graph.V[tagged_link.vertex_index],\\n self.ids.edit_area,\\n index=tagged_link.tag)\\n self.task_list.dismiss()\",\n \"def _create_window(self):\\n wc = win32gui.WNDCLASS()\\n wc.lpfnWndProc = self._on_message\\n wc.lpszClassName = self.__class__.__name__\\n wc.hInstance = win32api.GetModuleHandle(None)\\n class_atom = win32gui.RegisterClass(wc)\\n return win32gui.CreateWindow(class_atom, self.__class__.__name__, 0, 0, 0, 0, 0, 0, 0, wc.hInstance, None)\",\n \"def create_window(session):\\n def create_window():\\n windows_before = session.handles\\n name = session.execute_script(\\\"window.open()\\\")\\n assert len(session.handles) == len(windows_before) + 1\\n new_windows = list(set(session.handles) - set(windows_before))\\n return new_windows.pop()\\n return create_window\",\n \"def click_add():\\n # TODO: 1. In frontend_script.py, create function \\\"create_window()\\\" that takes a Toplevel() as a parameter.\\n # TODO: 2. In this file, implement the code below\\n # new_window = Toplevel(root)\\n # frontend_script.create_window(new_window)\",\n \"def _open_window(self):\\r\\n\\t\\t# Creating the window\\r\\n\\t\\tself._window = Window(self, Locations.RESTAL)\",\n \"def create(self, verbose=False):\\r\\n # delete the window if its handle exists\\r\\n if cmds.window(self.window, exists=True):\\r\\n cmds.deleteUI(self.window)\\r\\n # initialize the window as a pane for docking\\r\\n self.window = cmds.loadUI(uiFile=self.uiFile, verbose=verbose)\\r\\n #layoutWin = cmds.paneLayout(configuration='single')\\r\\n # create a dockControl and parent the control to layoutWin\\r\\n cmds.dockControl(allowedArea='all', area='right', floating=False, \\r\\n height=cmds.window(self.window, query=True, height=True), \\r\\n content=self.window, label='Docked Cone Pointer Window')\\r\\n cmds.showWindow(self.window)\",\n \"def see_tasks(self, widget):\\n my_task_list = tasklistwindow.TaskListWindow(self.task_list)\",\n \"def showUI(cls):\\r\\n win = cls(uiFile)\\r\\n win.create()\\r\\n return win\",\n \"def createApp(self):\\n app = self.app\\n window = self.window\\n window.show()\\n app.exec()\",\n \"def create_main_window_with_browser():\\n main_win = create_main_window()\\n return main_win.add_browser_tab()\",\n \"def create_window(window_class):\\n app_created = False\\n app = QtCore.QCoreApplication.instance()\\n if app is None:\\n app = QtGui.QApplication(sys.argv)\\n app_created = True\\n app.references = set()\\n window = window_class()\\n app.references.add(window)\\n window.show()\\n if app_created:\\n app.exec_()\\n return window\",\n \"def create_task():\",\n \"def create_window(self, img, roi, name):\\n\\n self.window = SpinBalanceDialog()\\n\\n # call the user-implemented functionality\\n self.window.main(img, roi)\\n # show the window\\n self.window.show()\\n\\n return self.window\",\n \"def open_main_window(self):\\r\\n track_terms_dic = ''\\r\\n sg.theme(self.look)\\r\\n\\r\\n layout = [[sg.Text('Welcome to tweeet monitor ')],\\r\\n [sg.Text('Please enter Details ')],\\r\\n [sg.Text('User Mail', size=(15, 1)), sg.InputText()],\\r\\n [sg.Text('Timout', size=(15, 1)), sg.InputText('', enable_events=True, key='-DIGITS-')],\\r\\n [sg.Text('')],\\r\\n [sg.Text('You can select an existing list or create a new one '),\\r\\n sg.Combo(self.files, default_value='Select Track Terms List ', key='-COMBO1-')],\\r\\n [sg.Text('')],\\r\\n [sg.Button('Select Exists List'), sg.Button('Create a New List')],\\r\\n [sg.Text('\\\\n')],\\r\\n [sg.Button('Start Monitor'), sg.Button('Exit')]\\r\\n ]\\r\\n\\r\\n window = sg.Window('Monitor tweeter', layout)\\r\\n # Event Loop\\r\\n while True:\\r\\n event, values = window.read()\\r\\n\\r\\n if event == sg.WIN_CLOSED:\\r\\n exit()\\r\\n elif event == 'Select Exists List' or event == 'Create a New List' or event == 'Start Monitor':\\r\\n user_mail = values[0]\\r\\n timeout = values['-DIGITS-']\\r\\n list_dic = values['-COMBO1-']\\r\\n\\r\\n if self.check(user_mail) == 'Invalid Email':\\r\\n self.info_popup_window('You Enter not valid mail ', 'Info', self.look)\\r\\n elif event == 'Select Exists List':\\r\\n if list_dic == 'Select Track Terms List ':\\r\\n self.info_popup_window('Track Terms List ', 'Info', self.look)\\r\\n else:\\r\\n file_name = self.path + self.bachslash + list_dic\\r\\n os.system(file_name)\\r\\n track_terms_dic = list_dic\\r\\n elif event == 'Create a New List':\\r\\n track_terms_dic = self.open_window()\\r\\n track_terms_dic = track_terms_dic + '.txt'\\r\\n elif event == 'Start Monitor':\\r\\n if track_terms_dic == '':\\r\\n self.info_popup_window('Please, Create new Dictionary or select one ', 'Info', self.look)\\r\\n elif track_terms_dic != '':\\r\\n file_name = self.path + self.bachslash + track_terms_dic\\r\\n my_file = open(file_name, \\\"r\\\")\\r\\n content = my_file.read()\\r\\n content = content.split(\\\"\\\\n\\\")\\r\\n content = self.cleanList(content)\\r\\n # print(content)\\r\\n my_file.close()\\r\\n now = datetime.now()\\r\\n date_time = now.strftime(\\\"%m/%d/%Y, %H:%M:%S\\\")\\r\\n dict_list = {'User': user_mail,\\r\\n 'Timeout': timeout,\\r\\n 'Dictionary': list_dic,\\r\\n 'Create Date': date_time,\\r\\n 'track_terms_list': content\\r\\n }\\r\\n header = ['user_mail', 'Timeout', 'Dictionary', 'Create Date', 'list words']\\r\\n if os.path.isfile(self.file_track_terms_audit) == False:\\r\\n # check if the file exsist = if not: create file and print header to the file\\r\\n with open(self.file_track_terms_audit, 'a', newline='\\\\n') as file:\\r\\n try:\\r\\n write = csv.writer(file)\\r\\n write.writerow(header)\\r\\n write.writerows(self.values_list)\\r\\n file.close()\\r\\n except:\\r\\n print(\\\"Something went wrong when writing to the file\\\")\\r\\n else:\\r\\n self.values_list = list(dict_list.values())\\r\\n # print ('self.values_list :****',self.values_list)\\r\\n with open(self.file_track_terms_audit, 'a', newline='\\\\n') as file:\\r\\n try:\\r\\n write = csv.writer(file)\\r\\n self.values_list = [self.values_list]\\r\\n write.writerows(self.values_list)\\r\\n file.close()\\r\\n except:\\r\\n print(\\\"Something went wrong when writing to the file\\\")\\r\\n print('self.values_list:', self.values_list)\\r\\n\\r\\n window.close()\\r\\n\\r\\n print('track_terms_dic: ', track_terms_dic)\\r\\n print('dict_list:', dict_list)\\r\\n return (dict_list)\\r\\n\\r\\n # always check for closed window\\r\\n if event in (sg.WIN_CLOSED, 'Exit'):\\r\\n break\\r\\n\\r\\n if event == '-LIST-' and len(values['-LIST-']):\\r\\n sg.popup('Selected ', values['-LIST-'])\\r\\n\\r\\n if len(values['-DIGITS-']) and values['-DIGITS-'][-1] not in ('0123456789'):\\r\\n # delete last char from input\\r\\n window['-DIGITS-'].update(values['-DIGITS-'][:-1])\\r\\n\\r\\n window.close()\",\n \"def createNewPeaPodWindow(**kwargs):\\n\\tglobal currentPeaWindow\\n\\tnewWindow=dataWindow(window,\\\"Create Pea Pod\\\")\\n\\t#Update the global variable\\n\\tcurrentPeaWindow=newWindow\\n\\tnewWindow.functionToRun=initiateNewPeaPod\\n\\t#Create Section\\n\\tallPodNames=masterPod.currentMasterPod.peas.keys()\\n\\tpodName=dataSection(newWindow.contentArea,advancedEntry,\\\"Pod Name\\\",cannotContain=allPodNames)\\n\\tpodName.pack()\\n\\ttemplateOption=dataSection(newWindow.contentArea,advancedOptionMenu,\\\"Template\\\",\\n\\t\\t\\t\\t\\t\\t\\t values=podTemplate.templates.keys(),default=\\\"Login\\\",\\n\\t\\t\\t\\t\\t\\t\\t optionCommand=changePopupColour)\\n\\ttemplateOption.pack()\\n\\n\\t#Add the sections\\n\\tnewWindow.addDataSection(podName)\\n\\tnewWindow.addDataSection(templateOption)\\n\\t#Ititate colour\\n\\tchangePopupColour(\\\"Login\\\")\",\n \"def createPrefWindow(self):\\n if (not hasattr(self, \\\"pref_window\\\")):\\n self.pref_window = PrefWindow(self, self.settings)\\n self.pref_window.show()\\n self.pref_window.button_apply.setEnabled(True)\",\n \"def create_new_tab(default_script_content=\\\"\\\"):\\n pass\",\n \"def open_mwindow_timetable(self) -> None:\\n self.mwindow_timetable.show()\",\n \"def new_task(self):\\n print \\\"Create a new task.\\\"\\n\\n # Collect new task info from user\\n description = raw_input(\\\"Enter task (140 characters max) > \\\")\\n due_date = raw_input(\\\"Enter due date as 'year-mm-dd' (optional). > \\\")\\n tags = raw_input(\\n \\\"Enter tags for the task (comma separated) (optional). > \\\")\\n tag_list = [tag.strip() for tag in tags.split(',')]\\n try:\\n new_task = doto.Task(self.user, description, due_date, tag_list)\\n except (NameError, ValueError) as e:\\n # On error, print and return.\\n print \\\"Task not created. Error: \\\", e\\n raw_input(\\\"Press Enter to continue.\\\")\\n return\\n self.current_collection.add(new_task)\\n return\",\n \"def create_window(root, *args, **kwargs):\\n global top, rt\\n rt = root\\n top = tk.Toplevel(root)\\n ui_support.set_Tk_var()\\n window = Window(top)\\n ui_support.init(top, window, *args, **kwargs)\\n return top, window\",\n \"def __init__(self, root):\\r\\n self.root = root\\r\\n self.root.geometry(\\\"400x300\\\")\\r\\n \\r\\n self.button_rename = tk.Button(self.root, text = \\\"New window\\\",\\r\\n command= self.new_winF).pack()\",\n \"def create_window(self, xoff: int, yoff: int, width: int, height: int, name: str = None):\\n return Window(xoff, yoff, width, height, name)\",\n \"def show(self, window):\\r\\n\\r\\n return\",\n \"def createGameWindow():\\n gameWindow = g.GraphWin(\\\"game\\\", 450, 800) #Window to show game\\n\\n return gameWindow\",\n \"def build_second_window():\\r\\n\\r\\n new_window = tk.Tk()\\r\\n windows.append(new_window)\\r\\n new_window.protocol(\\\"WM_DELETE_WINDOW\\\", new_round(new_window))\\r\\n\\r\\n ask = tk.Label(new_window, text='Would You Like To Play Again?', bg='Cyan')\\r\\n ask.pack(fill=tk.X)\\r\\n\\r\\n frame = tk.Frame(new_window)\\r\\n frame.pack()\\r\\n\\r\\n yes_button = tk.Button(frame, text='Yes', bg='green',\\r\\n command=new_round(new_window))\\r\\n yes_button.pack(side=tk.LEFT)\\r\\n\\r\\n no_button = tk.Button(frame, text='No', bg='red',\\r\\n command=close)\\r\\n no_button.pack(side=tk.LEFT)\",\n \"def createNewMasterPodWindow():\\n\\tnewWindow=dataWindow(window,\\\"Create Master Pod\\\")\\n\\tnewWindow.functionToRun=initiateNewMasterPod\\n\\t#Add the sections\\n\\tmasterPodNameSection=dataSection(newWindow.contentArea,advancedEntry,\\\"Master Pod Name\\\",cannotContain=masterPod.loadedPods.keys())\\n\\tmasterPodNameSection.pack()\\n\\tmasterPodPassword=dataSection(newWindow.contentArea,advancedEntry,\\\"Password\\\",hide=True,bannedSymbols=[\\\"£\\\"])\\n\\tmasterPodPassword.pack()\\n\\tmasterPodConfirm=dataSection(newWindow.contentArea,advancedEntry,\\\"Confirm\\\",mustBeSameAs=masterPodPassword,hide=True)\\n\\tmasterPodConfirm.pack()\\n\\n\\tmasterHint=dataSection(newWindow.contentArea,advancedEntry,\\\"Hint\\\",checkNeeded=False)\\n\\tmasterHint.pack()\\n\\tmasterHint.dataValid=True\\n\\tmasterHint.changeColour=False\\n\\n\\t#Add to window\\n\\tnewWindow.addDataSection(masterPodNameSection)\\n\\tnewWindow.addDataSection(masterPodPassword)\\n\\tnewWindow.addDataSection(masterPodConfirm)\\n\\tnewWindow.addDataSection(masterHint)\",\n \"def window_tasks(self):\\n if self._handle != win32gui.GetForegroundWindow():\\n #print \\\"not in foreground\\\"\\n self.restore_window()\\n self.fix_ui()\\n self.set_foreground()\\n\\n self._shell.AppActivate(self._handle)\",\n \"def createWindow(self, type):\\n # this = Browser(self.url())\\n # this.show()\\n\\n self.popup = SequanixQWebView(**self.kwargs)\\n self.popup.setObjectName(\\\"web_content\\\")\\n self.popup.setWindowTitle(\\\"Sequana browser\\\")\\n self.popup.page().windowCloseRequested.connect(self.popup.close)\\n self.popup.show()\\n return self.popup\",\n \"def open_generatorWindow(self):\\n self.window = generatorWindow(self)\\n self.hide()\",\n \"def run(self, action): \\n active = self.root.get_full_property(self._ACTIVE_WINDOW, 0).value[0] \\n curDesk = self.root.get_full_property(self._CURRENT_DESKTOP, 0).value[0]\\n tasks = self.root.get_full_property(self._CLIENT_LIST, Xatom.WINDOW).value\\n \\n for task in tasks:\\n win = self.display.create_resource_object('window', task)\\n name = win.get_wm_name()\\n desk = win.get_full_property(self._DESKTOP, 0).value[0]\\n state = win.get_full_property(self._STATE, Xatom.ATOM).value \\n if desk == curDesk: \\n if int(task) != active:\\n if action == 1: \\n if not self._SHADED in state:\\n self.doShade(win)\\n else: \\n if (self._SHADED in state):\\n self.doUnShade(win)\\n\\n self.display.flush() # this magic command must be here !!!\",\n \"def startWindow():\\n\\n m = mainWindow()\\n\\n # Show Window\\n m.show()\\n\\n # Return to stay alive\\n return m\",\n \"def details_window(self, instance: Union[Nobleman, Location]):\\n window = tk.Toplevel()\\n window.title(instance.name)\\n window.protocol(\\\"WM_DELETE_WINDOW\\\",\\n partial(self.close_details_window, instance))\\n self.register_extra_window(instance, window)\\n self.generate_window_content(instance, window)\",\n \"def showWindow(*args, **kwargs)->None:\\n pass\",\n \"def __init__(self, root):\\n self.root = root\\n self.root.geometry(\\\"400x300\\\")\\n self.root[\\\"bg\\\"] = \\\"coral\\\"\\n self.button_rename = tk.Button(self.root, text = \\\"New window\\\",\\n command= lambda: self.new_window(Win2)).pack()\",\n \"def into_new_window(self):\\r\\n t1 = time.time()\\r\\n try:\\r\\n all_handle = self.driver.window_handles\\r\\n flag = 0\\r\\n while len(all_handle) < 2:\\r\\n time.sleep(1)\\r\\n all_handle = self.driver.window_handles\\r\\n flag += 1\\r\\n if flag == 5:\\r\\n break\\r\\n self.driver.switch_to.window(all_handle[-1])\\r\\n self.my_print(\\\"{0} Switch to the new window,new window's url: {1}, Spend {2} seconds\\\".format(success,\\r\\n self.driver.current_url,time.time() - t1))\\r\\n except Exception:\\r\\n self.my_print(\\\"{0} Unable switch to the new window, Spend {1} seconds\\\".format(fail, time.time() - t1))\\r\\n raise\",\n \"def new_workunit(self, name, labels=None, cmd=''):\\r\\n with self.run_tracker.new_workunit(name=name, labels=labels, cmd=cmd) as workunit:\\r\\n yield workunit\",\n \"def start_task(display_window, daq, exp_type, parameters, file_save,\\n signal_model=None, language_model=None, fake=True, auc_filename=None):\\n task = make_task(display_window, daq, exp_type, parameters, file_save,\\n signal_model, language_model, fake, auc_filename)\\n task.execute()\",\n \"def create(dlg):\\n page = EditorCalltipsQScintillaPage()\\n return page\",\n \"def make_window(color, title):\\n w = turtle.Screen()\\n w.bgcolor(color)\\n w.title(title)\\n return w\",\n \"def _show_task_list_panel(self):\\n self.task_list_panel.show()\\n self.task_list_panel.load_config()\\n # set geometry\\n _button_pos = self.task_frame.tasklist_button.pos()\\n _button_pos = self.task_frame.mapTo(self, _button_pos)\\n _button_height = self.task_frame.tasklist_button.height()\\n _glo_pos = self.mapTo(tomaya.GetMayaMainWindowPoint(), _button_pos)\\n self.task_list_panel.setGeometry(_glo_pos.x(), _glo_pos.y() + _button_height, self.task_frame.width(), tomaya.GetMayaMainWindowPoint().height()*1/2.0)\",\n \"def newwindow(url):\\n\\n # Open the URL\\n webbrowser.open_new(url)\",\n \"def adv_new_window(self):\\n adv=workflow.advancedoptions_w.ADialog()\\n adv.exec_()\",\n \"def newwin(self,name,sizeY,sizeX,offsetY,offsetX, border=False):\\n\\t\\tself.windows[name]=Window(sizeY,sizeX,offsetY,offsetX,border)\\n\\t\\treturn self.windows[name]\",\n \"def new_workunit(self, name, labels=None, cmd=''):\\r\\n parent = self._threadlocal.current_workunit\\r\\n with self.new_workunit_under_parent(name, parent=parent, labels=labels, cmd=cmd) as workunit:\\r\\n self._threadlocal.current_workunit = workunit\\r\\n try:\\r\\n yield workunit\\r\\n finally:\\r\\n self._threadlocal.current_workunit = parent\",\n \"def openWindow(self):\\n # self.showSessionAct.setEnabled(False)\\n self.musketeers_widget = MusketeersWidget(parent=self)\\n self.setCentralWidget(self.musketeers_widget)\\n self.saveGroupMenu = QAction('Save Group', self.fileMenu)\\n self.fileMenu.addAction(self.saveGroupMenu)\\n self.saveGroupMenu.triggered.connect(self.musketeers_widget.session_widget.save_group)\",\n \"def _ConfigureNewWindow(self, pages, incognito=False):\\n page_index = self._ActivateTabWithURL(pages[0])\\n if not page_index:\\n # This means the pages do not exist, load them\\n if incognito:\\n self.RunCommand(pyauto.IDC_NEW_INCOGNITO_WINDOW)\\n else:\\n self.OpenNewBrowserWindow(True)\\n for url in pages:\\n self.AppendTab(pyauto.GURL(url), self.GetBrowserWindowCount() - 1)\\n # Cycle through the pages to make sure they render\\n win = self.GetBrowserInfo()['windows'][self.GetBrowserWindowCount() - 1]\\n for tab in win['tabs']:\\n self.ActivateTab(tab['index'], self.GetBrowserWindowCount() - 1)\\n # Give the plugin time to activate\\n time.sleep(1.5)\\n else:\\n self.CloseBrowserWindow(page_index[1])\",\n \"def create_blank_window(wm_name=None, wm_class=None):\\n setup = xpybutil.conn.get_setup()\\n window = xpybutil.conn.generate_id()\\n xpybutil.conn.core.CreateWindow(\\n setup.roots[0].root_depth,\\n window,\\n setup.roots[0].root,\\n 0,\\n 0,\\n 640,\\n 480,\\n 0,\\n xcffib.xproto.WindowClass.InputOutput,\\n setup.roots[0].root_visual,\\n xcffib.xproto.CW.BackPixel | xcffib.xproto.CW.EventMask,\\n [\\n setup.roots[0].white_pixel,\\n xcffib.xproto.EventMask.Exposure | xcffib.xproto.EventMask.KeyPress,\\n ],\\n )\\n xpybutil.conn.core.MapWindow(window)\\n xpybutil.conn.flush()\\n cookies = []\\n if wm_class:\\n cookies.append(set_wm_class_checked(window, wm_class[0], wm_class[1]))\\n if wm_name:\\n cookies.append(set_wm_name_checked(window, wm_name))\\n for cookie in cookies:\\n cookie.check()\\n return window\",\n \"def open_window(self):\\r\\n newdic = ''\\r\\n layout = [[sg.Text(\\\"Please enter a new name for your Dictionary\\\\n\\\\n\\\")],\\r\\n [sg.Text('New Dictionary name: ', size=(15, 1)),\\r\\n sg.InputText('', enable_events=True, key='-NEWDIC-')],\\r\\n [sg.Text(\\\"\\\\n\\\\n\\\")],\\r\\n [sg.Button(\\\"OK\\\"), sg.Button('Exit')]\\r\\n ]\\r\\n window = sg.Window(\\\"Create New Dictionary\\\", layout, modal=True)\\r\\n while True:\\r\\n event, values = window.read()\\r\\n if event == sg.WIN_CLOSED:\\r\\n break\\r\\n elif event == \\\"Exit\\\":\\r\\n window.close()\\r\\n\\r\\n elif event == \\\"OK\\\":\\r\\n newdic = values['-NEWDIC-']\\r\\n file_name = self.path + self.bachslash + newdic + '.txt'\\r\\n print(file_name)\\r\\n if not os.path.exists(file_name):\\r\\n with open(file_name, \\\"w\\\"):\\r\\n pass\\r\\n else:\\r\\n self.info_popup_window('The name you entered already exists ', 'Info', self.look)\\r\\n os.system(file_name)\\r\\n window.close()\\r\\n return newdic\",\n \"def createHelpWindow(self):\\n if (not hasattr(self, \\\"help_window\\\")):\\n self.help_window = HelpWindow(self.settings, self)\\n self.help_window.show()\",\n \"def registrar(self):\\n self.new_window = tk.Toplevel(self.acesso)\\n Registrar(self.new_window)\",\n \"def window(*args, width: int = 200, height: int = 200, autosize: bool = False,\\n no_resize: bool = False, no_title_bar: bool = False, no_move: bool = False, no_scrollbar: bool = False,\\n no_collapse: bool = False, horizontal_scrollbar: bool = False, no_focus_on_appearing: bool = False,\\n no_bring_to_front_on_focus: bool = False, menubar: bool = False, no_close: bool = False,\\n no_background: bool = False, label: str = '', show: bool = True, collapsed: bool = False,\\n modal: bool = False, popup: bool = False,\\n on_close: Callable = None, min_size: List[int]=[32, 32], max_size: List[int] = [30000, 30000], id:str=''):\\n try:\\n\\n widget = internal_dpg.add_window(*args, width=width, height=height, autosize=autosize,\\n no_resize=no_resize, no_title_bar=no_title_bar, no_move=no_move,\\n no_scrollbar=no_scrollbar, no_collapse=no_collapse,\\n horizontal_scrollbar=horizontal_scrollbar,\\n no_focus_on_appearing=no_focus_on_appearing,\\n no_bring_to_front_on_focus=no_bring_to_front_on_focus,\\n menubar=menubar, no_close=no_close,\\n no_background=no_background, label=label, show=show, \\n collapsed=collapsed, on_close=on_close,\\n min_size=min_size, max_size=max_size, id=id, modal=modal,\\n popup=popup)\\n internal_dpg.push_container_stack(widget)\\n yield widget\\n\\n finally:\\n internal_dpg.pop_container_stack()\",\n \"def constructWindow(windowType):\\n global LIVING_ID, DISPLAY_DURATION\\n\\n # add self destruction after DISPLAY_DURATION milliseconds\\n LIVING_ID = GObject.timeout_add(\\n DISPLAY_DURATION, destroyWindow, windowType)\\n\\n if windowType == \\\"volume\\\":\\n open(MY_VOL_PATH, 'w').write(str(os.getpid()))\\n window = VolDisplay()\\n elif windowType == \\\"backlight\\\":\\n open(MY_BACKLIGHT_PATH, 'w').write(str(os.getpid()))\\n window = BacklightDisplay()\\n\\n window.connect(\\\"delete-event\\\", Gtk.main_quit)\\n window.show_all()\\n\\n # poll volume all xxx milliseconds\\n GObject.timeout_add(50, window.updateBar)\\n\\n Gtk.main()\",\n \"def make_window(colr, ttle):\\n w = turtle.Screen()\\n w.bgcolor(colr)\\n w.title(ttle)\\n return w\",\n \"def CreateConsole(self):\\n lc = launcher.TextFrame('title')\\n return lc\",\n \"def _create_window(self):\\n self.window = Gtk.Window()\\n self.window.set_title(\\\"Yapsy Example\\\")\\n self.window.set_default_size(400, 400)\\n self.window.connect(\\\"destroy\\\", lambda w: Gtk.main_quit())\\n # PluginList() is a composite widget that shows all installed plugins\\n # in a Gtk.TreeView. See widgets.py\\n self._plugin_list = PluginList(self.window)\\n box = Gtk.Box(orientation=Gtk.Orientation.VERTICAL)\\n box.pack_start(self._plugin_list, True, True, 0)\\n box.show_all()\\n self.window.add(box)\",\n \"def add_task():\\n # get values from user\\n responses = accept_inputs([\\\"Task label\\\", \\\"Short task description\\\", \\\"Parent task label\\\"])\\n # insert into db\\n query_no_results(\\\"insert into task values(?, ?, ?)\\\",\\n [responses[\\\"Task label\\\"], responses[\\\"Short task description\\\"], responses[\\\"Parent task label\\\"]])\\n print(\\\"New task created\\\")\",\n \"def create(\\n cls,\\n window, name, *,\\n force_writes=False,\\n follow_cursor=False,\\n unlisted=False,\\n **kwargs\\n ):\\n validate_view_options(kwargs)\\n\\n window.destroy_output_panel(name)\\n view = window.create_output_panel(name, unlisted)\\n set_view_options(view, **kwargs)\\n\\n return cls(window, name, force_writes=force_writes, follow_cursor=follow_cursor)\",\n \"def _newFileWizard(self):\\n\\n dialogs = Dialogs(self.view)\\n\\n #get category\\n category = dialogs.radioButtonDialog('Pick a category:' , \\n self.model.project.config['NEW_FILE_CATEGORIES'])\\n\\n #get name of asset/shot (loop until valid input)\\n name = dialogs.fileTextPrompt('Enter name:')\\n\\n #get task\\n tasks = self.model.project.config['NEW_FILE_TASKS'][category][:]\\n tasks.append('Other')\\n\\n task = dialogs.radioButtonDialog('Pick a task:' , tasks)\\n\\n if task == 'Other':\\n task = dialogs.textPrompt('Enter task:')\\n\\n #get the path of the file we are creating\\n category_dir = self.model.project.config['NEW_FILE_PATHS'][category]\\n result = os.path.join(category_dir, name, '%s_%s.mb' % (name, task))\\n\\n #ask to overwrite\\n print 'EXISTS:', result, os.path.exists(result)\\n if os.path.exists(result):\\n msg = 'The file \\\"%s\\\" already exists. Overwrite?' % result\\n dialogs.confirmPrompt(msg)\\n\\n #confirm all settings\\n msg = 'You have selected:'\\n msg += '\\\\n\\\\n\\\\tCategory: %s' % category\\n msg += '\\\\n\\\\tName: %s' % name\\n msg += '\\\\n\\\\tTask: %s' % task\\n msg += '\\\\n\\\\nThis will create a file in this location: '\\n msg += '\\\\n%s' % result\\n msg += '\\\\n\\\\nDoes this look okay?'\\n dialogs.confirmPrompt(msg)\\n\\n return result\",\n \"def createColorWindow():\\n colorWindow = g.GraphWin(\\\"Color\\\", 150, 500) #Window to show colors to pick\\n return colorWindow\",\n \"def get_main_window():\\n\\n pass\",\n \"def new(self):\\r\\n gen_name = lambda nb: self.tr(\\\"untitled\\\") + (\\\"%d.py\\\" % nb)\\r\\n nb = 0\\r\\n while osp.isfile(gen_name(nb)):\\r\\n nb += 1\\r\\n fname = gen_name(nb)\\r\\n self.emit(SIGNAL('redirect_stdio(bool)'), False)\\r\\n fname = QFileDialog.getSaveFileName(self, self.tr(\\\"New Python script\\\"),\\r\\n fname, self.tr(\\\"Python scripts\\\")+\\\" (*.py ; *.pyw)\\\")\\r\\n self.emit(SIGNAL('redirect_stdio(bool)'), True)\\r\\n if not fname.isEmpty():\\r\\n fname = unicode(fname)\\r\\n default = ['# -*- coding: utf-8 -*-',\\r\\n '\\\"\\\"\\\"', osp.basename(fname), '\\\"\\\"\\\"', '', '']\\r\\n text = os.linesep.join(default)\\r\\n encoding.write(unicode(text), fname, 'utf-8')\\r\\n self.load(fname)\",\n \"def OnNew(self, e):\\n self.mainparent.statusbar.SetStatusText(\\\"New Project\\\", 0)\\n\\n filename = \\\"__new_project__\\\"\\n self.mainparent.input_file = InputFile(filename, read=False)\\n self.mainparent.statusbar.SetStatusText(\\\"---New Project---\\\", 2)\\n self.mainparent.file_loaded = True\\n\\n # reset menus and such\\n self.mainparent.reset_namelist_menu()\\n self.mainparent.nmlpanel.reset(unset_namelist=True)\",\n \"def inserir(self):\\n self.new_window = tk.Toplevel(self.menu)\\n Inserir(self.new_window)\",\n \"def handle_new_window(event):\\n url = event.GetURL()\\n webbrowser.open(url)\",\n \"def create_utilities(self, n):\\n f = Frame(n)\\n f.pack(side=TOP, fill=BOTH, expand=Y)\\n\\n progs = Labelframe(f, text='Programs/Utilities')\\n progs.pack(side=TOP, expand=Y, fill=BOTH)\\n Grid.columnconfigure(progs, 0, weight=1)\\n Grid.columnconfigure(progs, 1, weight=1)\\n Grid.rowconfigure(progs, 2, weight=1)\\n\\n run_prog = Button(\\n progs, text='Run Program', command=self.run_selected_utilities)\\n create_tooltip(run_prog, 'Runs the selected program(s).')\\n run_prog.grid(column=0, row=0, sticky=\\\"nsew\\\")\\n open_utils = Button(\\n progs, text='Open Utilities Folder', command=self.lnp.open_utils)\\n create_tooltip(open_utils, 'Open the utilities folder')\\n open_utils.grid(column=1, row=0, sticky=\\\"nsew\\\")\\n Label(\\n progs, text='Double-click on a program to launch it.').grid(\\n column=0, row=1, columnspan=2)\\n Label(\\n progs, text='Right-click on a program to toggle auto-launch.').grid(\\n column=0, row=2, columnspan=2)\\n listframe = Frame(progs)\\n listframe.grid(column=0, row=3, columnspan=2, sticky=\\\"nsew\\\")\\n Grid.rowconfigure(listframe, 0, weight=1)\\n Grid.columnconfigure(listframe, 0, weight=1)\\n self.proglist = proglist = Treeview(\\n listframe, columns=('exe', 'launch'), show=['headings'])\\n proglist.column('exe', width=1, anchor='w')\\n proglist.column('launch', width=35, anchor='e', stretch=NO)\\n proglist.heading('exe', text='Executable')\\n proglist.heading('launch', text='Auto')\\n proglist.grid(column=0, row=0, sticky=\\\"nsew\\\")\\n proglist.bind(\\\"<Double-1>\\\", lambda e: self.run_selected_utilities())\\n if sys.platform == 'darwin':\\n proglist.bind(\\\"<2>\\\", self.toggle_autorun)\\n else:\\n proglist.bind(\\\"<3>\\\", self.toggle_autorun)\\n s = Scrollbar(listframe, orient=VERTICAL, command=proglist.yview)\\n proglist['yscrollcommand'] = s.set\\n s.grid(column=1, row=0, sticky=\\\"ns\\\")\\n\\n refresh = Button(\\n progs, text='Refresh List', command=self.read_utilities)\\n create_tooltip(refresh, 'Refresh the list of utilities')\\n refresh.grid(column=0, row=4, columnspan=2, sticky=\\\"nsew\\\")\\n return f\",\n \"def __init__(self):\\n self.root = Tk()\\n self.root.title(\\\"Coursework 2\\\")\\n\\n mainframe = ttk.Frame(self.root, padding=\\\"3 3 12 12\\\")\\n mainframe.grid(column=0, row=0, sticky=(N, W, E, S))\\n mainframe.columnconfigure(0, weight=1)\\n mainframe.rowconfigure(0, weight=1)\\n\\n self.visitor_uuid = StringVar()\\n self.document_uuid = StringVar()\\n self.task = StringVar()\\n\\n visitor_entry = ttk.Entry(mainframe, width=30, textvariable=self.visitor_uuid)\\n visitor_entry.grid(column=2, row=1, sticky=(W, E))\\n\\n document_entry = ttk.Entry(mainframe, width=30, textvariable=self.document_uuid)\\n document_entry.grid(column=2, row=2, sticky=(W, E))\\n\\n task_entry = ttk.Entry(mainframe, width=2, textvariable=self.task)\\n task_entry.grid(column=2, row=3, sticky=(W, E))\\n\\n ttk.Button(mainframe, text=\\\"Do Task\\\", command=self.doTask).grid(column=2, row=4, sticky=W)\\n\\n ttk.Label(mainframe, text=\\\"Visitor UUID\\\").grid(column=1, row=1, sticky=W)\\n ttk.Label(mainframe, text=\\\"Document UUID\\\").grid(column=1, row=2, sticky=W)\\n ttk.Label(mainframe, text=\\\"Task\\\").grid(column=1, row=3, sticky=W)\\n \\n self.output = StringVar()\\n self.w = ttk.Label( mainframe, textvariable = self.output)\\n self.w.grid(column = 1, row = 5, sticky=S)\\n\\n for child in mainframe.winfo_children(): child.grid_configure(padx=5, pady=5)\\n\\n visitor_entry.focus()\\n self.root.bind('<Return>', self.doTask)\\n \\n self.taskEx = TaskExecuter()\\n self.root.mainloop()\",\n \"def newtask_view(request, project_id):\\n # Use to tell to the template that user want to creat a new task\\n is_new = True\\n\\n # Retrieve the task, raise an error if the task does not exist\\n project = get_object_or_404(Projet, id=project_id)\\n\\n # Check if the user is allowed to add a task to this project\\n if request.user.has_perm('taskmanager.{}_project_permission'.format(project.id)):\\n\\n # Check if a form has been submitted\\n if request.method == \\\"POST\\\":\\n # Pass project to the form. Set the task's project fields with this project (initialize and never modify)\\n form = TaskForm(project, request.POST)\\n if form.is_valid():\\n task = form.save(commit=True)\\n task.last_modification = datetime.datetime.now() # it's probably not necessary\\n task.save()\\n\\n return redirect(\\\"task\\\", task_id=task.id)\\n else:\\n # Pass project to the form. Set the task's project fields with this project (initialize and never modify)\\n form = TaskForm(project)\\n else:\\n return redirect(\\\"projects\\\")\\n return render(request, \\\"newtask.html\\\", locals())\",\n \"def createAboutWindow(self):\\n if (not hasattr(self, \\\"about_window\\\")):\\n self.about_window = AboutWindow(self)\\n self.about_window.show()\",\n \"def open_transitWindow(self):\\n self.window = surveyWindow(self, imaging=False)\\n self.hide()\",\n \"def window(main):\\r\\n main.title(\\\"BinCryptor 1.0\\\")\\r\\n main.update_idletasks()\\r\\n width = main.winfo_width() #Width of the current screen\\r\\n height = main.winfo_height() #Height of the current screen\\r\\n x = (main.winfo_screenwidth() // 2) - (width // 2)\\r\\n y = (main.winfo_screenheight() // 2) - (height // 2)\\r\\n main.geometry(f'{width}x{height}+{x}+{y}') #Adjusts the height and width\\r\",\n \"def add_window(self, window):\\n if not self.valid_window(window):\\n return False\\n self.windows.append(window)\\n window.tiler = self\\n if window not in self.start_positions.keys():\\n self.start_positions[window] = window.display_size\\n\\n rules = config.GET_RULES(window.classname)\\n if rules is not None and re.search(rules[\\\"regex\\\"], window.title) is not None:\\n if \\\"floating\\\" in rules:\\n window.set_floating(rules[\\\"floating\\\"])\\n if \\\"decorated\\\" in rules:\\n if bool(rules[\\\"decorated\\\"]):\\n window.enable_decoration()\\n else:\\n window.disable_decoration()\\n if \\\"position\\\" in rules:\\n window.move_to(tuple(rules[\\\"position\\\"]))\\n\\n print(\\\"Added window: {0}\\\".format(window))\\n window.print_window_styles()\\n return True\",\n \"def add_window(self, window: AbstractView) -> None:\\n self._logger.debug(\\\"running\\\")\\n window.setParent(self)\\n self.addSubWindow(window)\\n window.show()\\n window.restore_window()\\n self._logger.debug(\\\"done\\\")\",\n \"def create(self, parent):\\n self.widget = _QMainWindow(parent)\",\n \"def createAction(self):\\n self.createProjectAction = QtGui.QAction(self.tr(\\\"&New Project\\\"), self)\\n self.createProjectAction.setShortcut(QtGui.QKeySequence.New)\\n self.createProjectAction.setStatusTip(self.tr(\\\"Create a new project\\\"))\\n self.connect(self.createProjectAction, QtCore.SIGNAL(\\\"triggered()\\\"), self, QtCore.SLOT(\\\"newProject()\\\"))\\n\\n self.openProjectAction = QtGui.QAction(self.tr(\\\"&Open...\\\"), self)\\n self.openProjectAction.setShortcut(QtGui.QKeySequence.Open)\\n self.openProjectAction.setStatusTip(self.tr(\\\"Open an existing project\\\"))\\n self.connect(self.openProjectAction, QtCore.SIGNAL(\\\"triggered()\\\"), self, QtCore.SLOT(\\\"openProject()\\\"))\\n\\n self.saveProjectAction = QtGui.QAction(self.tr(\\\"&Save\\\"), self)\\n self.saveProjectAction.setShortcut(QtGui.QKeySequence.Save)\\n self.saveProjectAction.setStatusTip(self.tr(\\\"Save the current project\\\"))\\n self.connect(self.saveProjectAction, QtCore.SIGNAL(\\\"triggered()\\\"), self, QtCore.SLOT(\\\"save()\\\"))\\n\\n self.importVideoAction = QtGui.QAction(self.tr(\\\"&Import video...\\\"), self)\\n self.importVideoAction.setStatusTip(self.tr(\\\"Import a video into your project\\\"))\\n self.connect(self.importVideoAction, QtCore.SIGNAL(\\\"triggered()\\\"), self, QtCore.SLOT(\\\"importVideo()\\\"))\\n\\n self.aboutAction = QtGui.QAction(self.tr(\\\"&About\\\"), self)\\n self.aboutAction.setStatusTip(self.tr(\\\"Show the credits and authors\\\"))\\n self.connect(self.aboutAction, QtCore.SIGNAL(\\\"triggered()\\\"), self, QtCore.SLOT(\\\"showAbout()\\\"))\",\n \"def new_tab(self):\\n \\n self.driver.execute_script(\\\"window.open('about:blank');\\\")\",\n \"def __init__(self, threadID, name):\\n\\t\\tThread.__init__(self)\\n\\t\\tself.threadID = threadID\\n\\t\\tself.name = name\\n\\t\\tself.window = MainWindow(None)\\n\\t\\tself.window.title(\\\"OpComms System IV Desktop Client\\\")\",\n \"def generate_output(self, title):\\n try:\\n # First create the window inside the application used to hold it.\\n # Then proceed to show the newly created window and fill it with\\n # the data we want to display.\\n self.title = title\\n\\n crea = self.create()\\n\\n if not crea:\\n self.ClearLines()\\n #self.add_line(\\\"hola manola\\\")\\n self.refresh()\\n else:\\n #print \\\"1 OK...\\\"\\n pass\\n\\n self.show()\\n self.colorize() # This must be implemented in the\\n # derived class.\\n #self.refresh()\\n\\n except Exception, err:\\n if self.debug:\\n print format_exc()\\n self.close()\\n raise TextOutputMediaException(\\n \\\"Error creating viewer called \\\\\\\"%s\\\\\\\"\\\" % title)\",\n \"def create_task():\\n client = RequestManager()\\n client.set_method(\\\"POST\\\")\\n client.set_endpoint(\\\"/projects/{0}/stories/{1}/tasks\\\".format(STORED_ID['project_id'], STORED_ID['story_id']))\\n name = \\\"\\\".join(choices(string.ascii_letters, k=6))\\n body = {\\\"description\\\": name}\\n client.set_body(json.dumps(body))\\n response = client.execute_request()\\n try:\\n STORED_ID['task_id'] = response.json()['id']\\n except KeyError:\\n LOGGER.info(response.json())\",\n \"def taskdetail_create(name, tsk, td_id=None):\\n return IMPL.taskdetail_create(name, tsk, td_id)\",\n \"def add_create_pl_btn(self):\\n self.create_pl = QPushButton(\\\"Add to playlist\\\")\\n self.create_pl.clicked.connect(self.pl_btn_push)\\n self.hbtnbox.addWidget(self.create_pl)\",\n \"def new_tab(self, widget):\\n print('new tab added')\\n name = Gtk.Buildable.get_name(widget)\\n if name == 'new':\\n param = 'create'\\n elif name == 'button_open':\\n param = 'open'\\n else:\\n param = 'create'\\n self.notebook.append_page(*self.create_tab(param))\\n self.notebook.show_all()\",\n \"def action_create_task(self):\\n self.ensure_one()\\n res = super(ProjectIssue, self).action_create_task()\\n task_id = res.get('res_id', False)\\n if task_id:\\n task_obj = self.env['project.task'].browse(task_id)\\n task_obj.description = self.description\\n return res\",\n \"def automatic_window(self):\\n \\n #Create window and label\\n automatic_window = tk.Toplevel(self)\\n windowtext = self.translate('How many days do you want the simulation to run for?') \\n automatic_window.title(windowtext)\\n automatic_window.config(bg=self.default_background)\\n lbl_text = tk.Label(automatic_window, text=windowtext,\\n bg=self.default_background)\\n lbl_text.grid(column=0, row=0)\\n \\n #Create input box\\n self.auto_var = tk.IntVar()\\n self.auto_var.set(1)\\n auto_menu = tk.Entry(automatic_window)\\n auto_menu.insert(0,0)\\n auto_menu.configure(width=5)\\n auto_menu.grid(column=0, row=1)\\n\\n #Create button to initate the simulation\\n auto_run_button = tk.Button(automatic_window, text=self.translate('Run Simulation'), \\n command = lambda: self.auto_run(automatic_window, int(auto_menu.get())),\\n bg=self.button_color,\\n highlightbackground=self.highlight_color)\\n auto_run_button.grid(column=0, row=2)\\n \\n #Center the window on the screen\\n automatic_window.withdraw()\\n automatic_window.update_idletasks() # Update \\\"requested size\\\" from geometry manager\\n x = (self.screenwidth - automatic_window.winfo_reqwidth()) / 2\\n y = (self.screenheight - automatic_window.winfo_reqheight()) / 2\\n automatic_window.geometry(\\\"+%d+%d\\\" % (x, y))\\n automatic_window.deiconify()\",\n \"def new_round(new_window):\\r\\n\\r\\n global game_on\\r\\n game_on = False\\r\\n\\r\\n def func():\\r\\n new_window.destroy()\\r\\n windows.remove(new_window)\\r\\n window.after(1000)\\r\\n new_button = tk.Button(start_frame, text='Start', bg='orchid3')\\r\\n new_button.place(relheight=0.8, relwidth=0.1, relx=0.45, rely=0.1)\\r\\n new_button['command'] = cmd_start(new_button)\\r\\n return func\",\n \"def create_help_window():\\n global help_window\\n help_window = Toplevel(root, relief=SUNKEN, bd=10, bg=\\\"light sky blue\\\")\\n help_window.title(\\\"Справка\\\")\\n help_window.minsize(width=1000, height=550)\\n help_window.resizable(height=False, width=False)\\n main_win_button = ttk.Button(help_window, text=\\\" Главное окно \\\", command=help_main_but)\\n main_win_button.place(x=20, y=50)\\n add_db_table = ttk.Button(help_window, text=\\\"Добавить базу данных\\\", command=help_add_db)\\n add_db_table.place(x=10, y=120)\\n add_string_button = ttk.Button(help_window, text=\\\" Добавить строку \\\", command=help_add_str)\\n add_string_button.place(x=15, y=190)\\n load_file_button = ttk.Button(help_window, text=\\\" Загрузить файл \\\", command=help_load_file)\\n load_file_button.place(x=20, y=260)\\n del_string_button = ttk.Button(help_window, text=\\\" Удалить строку \\\", command=help_del_str)\\n del_string_button.place(x=20, y=330)\\n download_file_button = ttk.Button(help_window, text=\\\" Выгрузить файл \\\", command=help_download_file)\\n download_file_button.place(x=20, y=400)\\n update_string_button = ttk.Button(help_window, text=\\\" Обновить строку \\\", command=help_update_str)\\n update_string_button.place(x=20, y=470)\",\n \"def set_window(self, handle):\\n pass\",\n \"def show_task1(self):\\n self._show_task(self.controller.CURRENT)\",\n \"def move_to_win(self):\\n self.external_win = PlotWindow(plot=self.pw, parent=self)\\n self.external_win.closeWin.connect(lambda: self.layout().takeAt(1))\\n self.external_win.closeWin.connect(lambda: self.layout().insertWidget(1, self.pw))\\n self.external_win.closeWin.connect(lambda: self.btn_open.setEnabled(True))\\n self.external_win.show()\",\n \"def create_new_tournament(self) -> None:\\n # 1\\n for elem in self.data:\\n key = ''\\n value = ''\\n for k, v in elem.items():\\n if k == 'name':\\n key = v\\n else:\\n if key == 'Nombre de tours':\\n if v.get() != '':\\n value = v.get()\\n else:\\n value = cf.NUMBER_OF_ROUNDS\\n else:\\n value = v.get()\\n self.attributs.update({key: value})\\n # 2\\n # order id a dictionary {'order': order, 'left_window_value': <value to display>}\\n order = ct.Controls.verify_tournament_creation(self.attributs)\\n # update the menus and the left window\\n self.master.master.list_instances_menus_tournament = Menu.update_menus_tournament(order, self.master)\\n self.master.master.left_window.update_and_display(self.master.master.list_instances_menus_tournament)\\n # 3\\n self.master.master.launch()\\n self.destroy_window()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7113246","0.70224667","0.6907756","0.68215317","0.6519921","0.6381383","0.637192","0.6319704","0.62721485","0.6223385","0.61893445","0.6168583","0.61627823","0.6148006","0.6140917","0.6093474","0.5996884","0.5986673","0.5972583","0.59595525","0.5941826","0.59259117","0.5908","0.59036815","0.58875144","0.5878807","0.58696157","0.5827885","0.5790684","0.57463086","0.57366014","0.57328963","0.5730838","0.5724751","0.5708972","0.569018","0.56851375","0.56690276","0.56659013","0.5661753","0.56592923","0.564755","0.564641","0.56396186","0.5635289","0.5635155","0.5632528","0.5617323","0.5613144","0.5607247","0.5596884","0.5574002","0.55705786","0.5560423","0.5551585","0.55492854","0.5538814","0.55329984","0.5530952","0.55188376","0.5501854","0.5492303","0.5479301","0.5476887","0.54738724","0.5470825","0.54703516","0.54362327","0.5419985","0.5407518","0.5402741","0.5399626","0.53904194","0.53816026","0.5368384","0.53618234","0.5359576","0.5353369","0.5352962","0.53461665","0.53427416","0.53412557","0.53411734","0.53367746","0.5331884","0.5330794","0.5321219","0.5320363","0.5319015","0.53165454","0.53156275","0.5315328","0.5310895","0.5310386","0.53021735","0.5296306","0.52876353","0.5282824","0.52808744","0.5280771"],"string":"[\n \"0.7113246\",\n \"0.70224667\",\n \"0.6907756\",\n \"0.68215317\",\n \"0.6519921\",\n \"0.6381383\",\n \"0.637192\",\n \"0.6319704\",\n \"0.62721485\",\n \"0.6223385\",\n \"0.61893445\",\n \"0.6168583\",\n \"0.61627823\",\n \"0.6148006\",\n \"0.6140917\",\n \"0.6093474\",\n \"0.5996884\",\n \"0.5986673\",\n \"0.5972583\",\n \"0.59595525\",\n \"0.5941826\",\n \"0.59259117\",\n \"0.5908\",\n \"0.59036815\",\n \"0.58875144\",\n \"0.5878807\",\n \"0.58696157\",\n \"0.5827885\",\n \"0.5790684\",\n \"0.57463086\",\n \"0.57366014\",\n \"0.57328963\",\n \"0.5730838\",\n \"0.5724751\",\n \"0.5708972\",\n \"0.569018\",\n \"0.56851375\",\n \"0.56690276\",\n \"0.56659013\",\n \"0.5661753\",\n \"0.56592923\",\n \"0.564755\",\n \"0.564641\",\n \"0.56396186\",\n \"0.5635289\",\n \"0.5635155\",\n \"0.5632528\",\n \"0.5617323\",\n \"0.5613144\",\n \"0.5607247\",\n \"0.5596884\",\n \"0.5574002\",\n \"0.55705786\",\n \"0.5560423\",\n \"0.5551585\",\n \"0.55492854\",\n \"0.5538814\",\n \"0.55329984\",\n \"0.5530952\",\n \"0.55188376\",\n \"0.5501854\",\n \"0.5492303\",\n \"0.5479301\",\n \"0.5476887\",\n \"0.54738724\",\n \"0.5470825\",\n \"0.54703516\",\n \"0.54362327\",\n \"0.5419985\",\n \"0.5407518\",\n \"0.5402741\",\n \"0.5399626\",\n \"0.53904194\",\n \"0.53816026\",\n \"0.5368384\",\n \"0.53618234\",\n \"0.5359576\",\n \"0.5353369\",\n \"0.5352962\",\n \"0.53461665\",\n \"0.53427416\",\n \"0.53412557\",\n \"0.53411734\",\n \"0.53367746\",\n \"0.5331884\",\n \"0.5330794\",\n \"0.5321219\",\n \"0.5320363\",\n \"0.5319015\",\n \"0.53165454\",\n \"0.53156275\",\n \"0.5315328\",\n \"0.5310895\",\n \"0.5310386\",\n \"0.53021735\",\n \"0.5296306\",\n \"0.52876353\",\n \"0.5282824\",\n \"0.52808744\",\n \"0.5280771\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.81975675"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22290,"cells":{"query":{"kind":"string","value":"Shows a window with all the tasks and alarms"},"document":{"kind":"string","value":"def see_tasks(self, widget):\n my_task_list = tasklistwindow.TaskListWindow(self.task_list)"},"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 dashboard_showall():\n tasks = Task.query.all()\n return render_template('home/taskshowall/dashboard_showall.html',\n tasks=tasks, title=\"Tasks\")","def show_tasks():\n\n task = Task(connection=connection, cursor=cursor)\n\n all_tasks = task.get_all_tasks()\n\n context = {\n 'all_tasks': all_tasks\n }\n\n return render_template('pages/tables/tasks.html', **context)","def open_mwindow_timetable(self) -> None:\n self.mwindow_timetable.show()","def open_mwindow_agenda(self) -> None:\n self.mwindow_agenda.show()","def show(self):\n i = 0\n print()\n for task in self.tasks:\n print(\"\\t\", i + 1, \". \", task.name, \"(\", task.priority, \")\")\n i += 1","def show_tasks():\n top_level_tasks = query_with_results(\"select label, description from task where parent = ''\", [])\n for task in top_level_tasks:\n _show_task(task)","def show_task1(self):\n self._show_task(self.controller.CURRENT)","def open_main_window(self):\r\n track_terms_dic = ''\r\n sg.theme(self.look)\r\n\r\n layout = [[sg.Text('Welcome to tweeet monitor ')],\r\n [sg.Text('Please enter Details ')],\r\n [sg.Text('User Mail', size=(15, 1)), sg.InputText()],\r\n [sg.Text('Timout', size=(15, 1)), sg.InputText('', enable_events=True, key='-DIGITS-')],\r\n [sg.Text('')],\r\n [sg.Text('You can select an existing list or create a new one '),\r\n sg.Combo(self.files, default_value='Select Track Terms List ', key='-COMBO1-')],\r\n [sg.Text('')],\r\n [sg.Button('Select Exists List'), sg.Button('Create a New List')],\r\n [sg.Text('\\n')],\r\n [sg.Button('Start Monitor'), sg.Button('Exit')]\r\n ]\r\n\r\n window = sg.Window('Monitor tweeter', layout)\r\n # Event Loop\r\n while True:\r\n event, values = window.read()\r\n\r\n if event == sg.WIN_CLOSED:\r\n exit()\r\n elif event == 'Select Exists List' or event == 'Create a New List' or event == 'Start Monitor':\r\n user_mail = values[0]\r\n timeout = values['-DIGITS-']\r\n list_dic = values['-COMBO1-']\r\n\r\n if self.check(user_mail) == 'Invalid Email':\r\n self.info_popup_window('You Enter not valid mail ', 'Info', self.look)\r\n elif event == 'Select Exists List':\r\n if list_dic == 'Select Track Terms List ':\r\n self.info_popup_window('Track Terms List ', 'Info', self.look)\r\n else:\r\n file_name = self.path + self.bachslash + list_dic\r\n os.system(file_name)\r\n track_terms_dic = list_dic\r\n elif event == 'Create a New List':\r\n track_terms_dic = self.open_window()\r\n track_terms_dic = track_terms_dic + '.txt'\r\n elif event == 'Start Monitor':\r\n if track_terms_dic == '':\r\n self.info_popup_window('Please, Create new Dictionary or select one ', 'Info', self.look)\r\n elif track_terms_dic != '':\r\n file_name = self.path + self.bachslash + track_terms_dic\r\n my_file = open(file_name, \"r\")\r\n content = my_file.read()\r\n content = content.split(\"\\n\")\r\n content = self.cleanList(content)\r\n # print(content)\r\n my_file.close()\r\n now = datetime.now()\r\n date_time = now.strftime(\"%m/%d/%Y, %H:%M:%S\")\r\n dict_list = {'User': user_mail,\r\n 'Timeout': timeout,\r\n 'Dictionary': list_dic,\r\n 'Create Date': date_time,\r\n 'track_terms_list': content\r\n }\r\n header = ['user_mail', 'Timeout', 'Dictionary', 'Create Date', 'list words']\r\n if os.path.isfile(self.file_track_terms_audit) == False:\r\n # check if the file exsist = if not: create file and print header to the file\r\n with open(self.file_track_terms_audit, 'a', newline='\\n') as file:\r\n try:\r\n write = csv.writer(file)\r\n write.writerow(header)\r\n write.writerows(self.values_list)\r\n file.close()\r\n except:\r\n print(\"Something went wrong when writing to the file\")\r\n else:\r\n self.values_list = list(dict_list.values())\r\n # print ('self.values_list :****',self.values_list)\r\n with open(self.file_track_terms_audit, 'a', newline='\\n') as file:\r\n try:\r\n write = csv.writer(file)\r\n self.values_list = [self.values_list]\r\n write.writerows(self.values_list)\r\n file.close()\r\n except:\r\n print(\"Something went wrong when writing to the file\")\r\n print('self.values_list:', self.values_list)\r\n\r\n window.close()\r\n\r\n print('track_terms_dic: ', track_terms_dic)\r\n print('dict_list:', dict_list)\r\n return (dict_list)\r\n\r\n # always check for closed window\r\n if event in (sg.WIN_CLOSED, 'Exit'):\r\n break\r\n\r\n if event == '-LIST-' and len(values['-LIST-']):\r\n sg.popup('Selected ', values['-LIST-'])\r\n\r\n if len(values['-DIGITS-']) and values['-DIGITS-'][-1] not in ('0123456789'):\r\n # delete last char from input\r\n window['-DIGITS-'].update(values['-DIGITS-'][:-1])\r\n\r\n window.close()","def _show_task_list_panel(self):\n self.task_list_panel.show()\n self.task_list_panel.load_config()\n # set geometry\n _button_pos = self.task_frame.tasklist_button.pos()\n _button_pos = self.task_frame.mapTo(self, _button_pos)\n _button_height = self.task_frame.tasklist_button.height()\n _glo_pos = self.mapTo(tomaya.GetMayaMainWindowPoint(), _button_pos)\n self.task_list_panel.setGeometry(_glo_pos.x(), _glo_pos.y() + _button_height, self.task_frame.width(), tomaya.GetMayaMainWindowPoint().height()*1/2.0)","def show_tasks(self):\n task_ids = [\n t and t['id'] for t in self.controller.selected_tasks\n ]\n\n if self._check_cluster():\n self.print_list(\n ('id', 'status'), self.controller.get_tasks(),\n lambda x: task_ids.index(x['id'])\n )","def show_all_tasks(self):\n tasks = self.session.query(self.Table).order_by(self.Table.deadline).all()\n print('All tasks:')\n if tasks:\n for n, task in enumerate(tasks, 1):\n print(f'{n}. {task.task}. {task.deadline.strftime(\"%d %b\")}')\n else:\n print('Nothing to do!')\n print()","def show(self, window):\r\n\r\n return","def list():\n manager = Actions()\n tasks_list = manager.get_tasks_list()\n console_utils.print_tree(manager, tasks_list)","def showtask(id):\n\n tasks = Task.query.filter_by(id=id)\n return render_template('home/taskshowall/dashboard_showtask.html',tasks=tasks,title=\"tasks\")","def show_weeks_tasks(self):\n for day in [datetime.today() + timedelta(days=i) for i in range(7)]:\n tasks = self.session.query(self.Table).filter(self.Table.deadline == day.strftime('%Y-%m-%d')).\\\n order_by(self.Table.deadline).all()\n print(f'{day.strftime(\"%A\")} {day.strftime(\"%d %b\")}:')\n if tasks:\n for n, task in enumerate(tasks, 1):\n print(f'{n}. {task.task}')\n else:\n print('Nothing to do!')\n print()","def list_tasks(self, toplevel=True, keys=None):\n\n message = {}\n # show all tasks by default\n if keys == None:\n keys = self.list_task_keys()\n\n for key in keys:\n try:\n last_run_instance = TaskInstance.objects.filter(task_key=key).exclude(completed=None).order_by('-completed').values_list('completed','task_key')[0]\n last_run = time.mktime(last_run_instance[0].timetuple())\n #no instances\n except (KeyError, IndexError):\n last_run = None\n\n # render the form if the task has one\n task = self.registry[key, None].tasks[key]\n if task.form:\n t = loader.get_template('task_parameter_form.html')\n c = Context ({'form':task.form()})\n rendered_form = t.render(c)\n else:\n rendered_form = None\n\n message[key] = {'description':task.description ,\n 'last_run':last_run,\n 'form':rendered_form}\n\n return message","def __show_all(self):\n print(\"\\nEvents:\\n\")\n self.__show_all_events()\n print(\"\\nMetrics:\\n\")\n self.__show_all_metrics()","def show(self,window):\n self.showFunctions(window)","def open_notifications(self):\n self.android_device_driver.adb.exec_adb_cmd(\n \"shell cmd statusbar expand-notifications\").wait()","def show_all(self):\n for UI_object in self.__UI_objects:\n UI_object.show()","def window_tasks(self):\n if self._handle != win32gui.GetForegroundWindow():\n #print \"not in foreground\"\n self.restore_window()\n self.fix_ui()\n self.set_foreground()\n\n self._shell.AppActivate(self._handle)","def run(self):\n while True:\n self.menu()\n if self.menu_choice == '1':\n self.show_today_tasks()\n elif self.menu_choice == '2':\n self.show_weeks_tasks()\n elif self.menu_choice == '3':\n self.show_all_tasks()\n elif self.menu_choice == '4':\n self.show_missed_tasks()\n elif self.menu_choice == '5':\n self.add_task()\n elif self.menu_choice == '6':\n self.delete_task()\n else:\n print('Bye!')\n break","def show_messages(self):\n console.alert(\n \"Info\",\n \"If StaSh does not launch anymore after you changed the config, run the 'launch_stash.py' script with \\n'--no-cfgfile'.\",\n \"Ok\",\n hide_cancel_button=True,\n )\n while True:\n self.wait_modal()\n if not self.subview_open:\n break\n console.alert(\n \"Info\",\n \"Some changes may only be visible after restarting StaSh and/or Pythonista.\",\n \"Ok\",\n hide_cancel_button=True,\n )","def show(self):\n self.present(orientations=ORIENTATIONS)\n # launch a background thread\n # we can not use ui.in_background here\n # because some dialogs would not open anymoe\n thr = threading.Thread(target=self.show_messages)\n thr.daemon = True\n thr.start()","def init_task(self):\n all_tasks = self.db.get_tasks()\n scroll_parent = Window\n uw = self.ids.upcoming_wrapper\n\n if not all_tasks:\n new_btn = NewButton()\n new_btn.size_hint = [None, None]\n new_btn.size = [\n scroll_parent.width / 1.9,\n scroll_parent.height - (0.45 * scroll_parent.height),\n ]\n new_btn.bind(on_release=self.add_new)\n uw.add_widget(new_btn)\n else:\n for t in all_tasks:\n task = Task()\n task.name = t[1]\n task.details = t[2]\n date, time = t[3].rsplit(\" \", 1)\n x = self.compare_date(date)\n if x == \"today\":\n task.tsk_clr = (0.7, 0.45, 0.1, 0.6)\n elif x == \"past\":\n task.tsk_clr = (0.7, 0, 0, 0.6)\n\n task.time = time\n task.date = date\n\n task.size_hint = [None, None]\n task.size = [\n scroll_parent.width / 1.9,\n scroll_parent.height - (0.45 * scroll_parent.height),\n ]\n\n uw.add_widget(task)","def new_task(self, widget):\n my_task_window = taskwindow.TaskWindow(self)","def show_window(self):\n self.show()","def __show_all_events(self):\n for event in self.events_list:\n self.__print_events_info(event)\n print()","def homescreen(self):\r\n self.root.ids.items_box.clear_widgets()\r\n homescreen_information = Label(\r\n text='Welcome.\\n\\n\\nThis was designed by Charles to calculate the hours different\\n'\r\n 'workgroups work at CIPS and WPS.\\n\\nTo begin, press the Load Data button on'\r\n ' the \\nleft hand side of this window.', font_size=20, halign='center')\r\n self.root.ids.items_box.add_widget(homescreen_information)","def addMainWindow(self,appendToTask):\n self.appendToTask = appendToTask","def _view_schedule(self):\n def plus_top_attach(f):\n\n def plus(*args, **kwargs):\n top_attach, left_attach = f(*args, **kwargs)\n return top_attach + 1, left_attach + 1\n\n return plus\n\n @plus_top_attach\n def create_label(text, left_attach, right_attach,\n top_attach, bottom_attach, align=None):\n label = gtk.Label('<span font=\"%s\">%s</span>' %\n (Params().get_default_font(), text))\n label.set_use_markup(True)\n if align == 'left':\n label.set_alignment(xalign=0.0, yalign=0.5)\n elif align == 'right':\n label.set_alignment(xalign=1.0, yalign=0.5)\n self.table.attach(label, left_attach, right_attach,\n top_attach, bottom_attach, xoptions=gtk.FILL, yoptions=False)\n label.show()\n return top_attach, left_attach\n\n @plus_top_attach\n def create_separator(left_attach, right_attach,\n top_attach, bottom_attach):\n separator = gtk.HSeparator()\n self.table.attach(separator, left_attach, right_attach,\n top_attach, bottom_attach, xoptions=gtk.FILL, yoptions=False)\n separator.show()\n return top_attach, left_attach\n\n tattach, tlen, view_sch = 0, 0, Params().get_view_sch()\n for i in view_sch:\n if i:\n tlen += 1\n for day in ['Monday', 'Tuesday', 'Wednesday',\n 'Thursday', 'Friday', 'Saturday']:\n tattach = create_label('<b><span color=\"%s\">%s</span></b>' %\n (Params().get_day_color(), day), 0, tlen,\n tattach, tattach + 1, 'left')[0]\n tattach = create_separator(0, tlen, tattach, tattach + 1)[0]\n\n schedule = Schedule().get_schedule(day,\n Schedule().get_current_week() - 1)\n for i in range(8):\n if not schedule[i][1] == '' and \\\n (schedule[i][0] == Schedule().get_subgroup() or\n schedule[i][0] == 2):\n if not schedule[i][2]:\n label_color = '%s' % str(Params().get_lecture_color())\n elif schedule[i][2] == 1:\n label_color = '%s' % \\\n str(Params().get_laboratory_color())\n elif schedule[i][2] == 2:\n label_color = '%s' % str(Params().get_practice_color())\n else:\n label_color = '%s' % str(Params().get_non_color())\n\n label_template = '<span color=\"%s\">%s</span>'\n lattach = 0\n if view_sch[0]:\n lattach = create_label('<span color=\"%s\">%d.</span>' %\n (label_color, i),\n lattach, lattach + 1, tattach, tattach + 1)[1]\n if view_sch[1]:\n lattach = create_label(label_template % (label_color,\n '-'.join(Schedule().get_lessons_time()[i])),\n lattach, lattach + 1, tattach, tattach + 1)[1]\n if view_sch[2]:\n lattach = create_label(label_template %\n (label_color, schedule[i][1]),\n lattach, lattach + 1,\n tattach, tattach + 1, 'left')[1]\n if view_sch[3]:\n lattach = create_label(label_template %\n (label_color, schedule[i][3]),\n lattach, lattach + 1, tattach, tattach + 1)[1]\n if view_sch[4]:\n create_label(label_template %\n (label_color, schedule[i][4]),\n lattach, lattach + 1,\n tattach, tattach + 1, 'right')\n tattach += 1","def run(self, action): \n active = self.root.get_full_property(self._ACTIVE_WINDOW, 0).value[0] \n curDesk = self.root.get_full_property(self._CURRENT_DESKTOP, 0).value[0]\n tasks = self.root.get_full_property(self._CLIENT_LIST, Xatom.WINDOW).value\n \n for task in tasks:\n win = self.display.create_resource_object('window', task)\n name = win.get_wm_name()\n desk = win.get_full_property(self._DESKTOP, 0).value[0]\n state = win.get_full_property(self._STATE, Xatom.ATOM).value \n if desk == curDesk: \n if int(task) != active:\n if action == 1: \n if not self._SHADED in state:\n self.doShade(win)\n else: \n if (self._SHADED in state):\n self.doUnShade(win)\n\n self.display.flush() # this magic command must be here !!!","def show_tasks(self, tasks=None, date_format=None):\n\n\t\tif not tasks:\n\t\t\ttasks = self.tasklist.tasks\n\n\t\tif len(tasks) > 0:\n\n\t\t\ttemplate = '{0:^3} {1:20} {2:^3} {3:20} {4:15} {5:20}'\n\t\t\tprint template.format('\\nID', 'Description', ' Pri', 'Due', 'Created', 'Tags')\n\t\t\tprint template.format('---', '--------------------', '---', '--------------------', '---------------',\n\t\t\t '--------------------')\n\t\t\tfor task in tasks:\n\t\t\t\tif task.priority == 'L':\n\t\t\t\t\tpriority = Fore.YELLOW + Style.BRIGHT + task.priority.center(3) + Fore.RESET + Style.NORMAL\n\t\t\t\telif task.priority == 'M':\n\t\t\t\t\tpriority = Fore.BLUE + Style.BRIGHT + task.priority.center(3) + Fore.RESET + Style.NORMAL\n\t\t\t\telif task.priority == 'H':\n\t\t\t\t\tpriority = Fore.RED + Style.BRIGHT + task.priority.center(3) + Fore.RESET + Style.NORMAL\n\t\t\t\telse:\n\t\t\t\t\tpriority = ''\n\n\t\t\t\tif task.due_date is None:\n\t\t\t\t\tdue_date = ''\n\t\t\t\telse:\n\t\t\t\t\tif date_format:\n\t\t\t\t\t\tdue_date = task.due_date.rsplit(' ', 1)[0].ljust(20)\n\t\t\t\t\telse:\n\t\t\t\t\t\tdue_date = (arrow.get(task.due_date, task.due_date_format).humanize()).ljust(20)\n\n\t\t\t\t\tif not task.completed:\n\t\t\t\t\t\ttoday = arrow.now()\n\t\t\t\t\t\tdiff = arrow.get(task.due_date, task.due_date_format) - today\n\t\t\t\t\t\tif diff.days >= 1 and diff.seconds > 0:\n\t\t\t\t\t\t\tdue_date = Fore.CYAN + Style.BRIGHT + due_date + Fore.RESET + Style.NORMAL\n\t\t\t\t\t\telif diff.days >= 0:\n\t\t\t\t\t\t\tdue_date = Fore.BLUE + Style.BRIGHT + due_date + Fore.RESET + Style.NORMAL\n\t\t\t\t\t\telif diff.days <= 0:\n\t\t\t\t\t\t\tdue_date = Fore.RED + Style.BRIGHT + due_date + Fore.RESET + Style.NORMAL\n\n\t\t\t\tif date_format:\n\t\t\t\t\tage = (str(task.creation_date).split()[0]).ljust(15) # drop the time zone\n\t\t\t\telse:\n\t\t\t\t\tage = (arrow.get(task.creation_date, 'MM/DD/YYYY h:mm:ss A ZZ').humanize()).ljust(15)\n\n\t\t\t\tif task.note:\n\t\t\t\t\tdesc = task.task + ' *'\n\t\t\t\telse:\n\t\t\t\t\tdesc = task.task\n\n\t\t\t\tif task.completed:\n\t\t\t\t\tif task.priority:\n\t\t\t\t\t\tpriority = task.priority\n\t\t\t\t\telse:\n\t\t\t\t\t\tpriority = ''\n\t\t\t\t\ttask_id = Fore.WHITE + Style.BRIGHT + Back.WHITE + str(task.id).center(3)\n\t\t\t\t\ttags = str(task.tags) + Fore.RESET + Style.NORMAL + Back.RESET\n\t\t\t\t\tprint template.format(task_id, desc, priority, due_date, age, tags)\n\t\t\t\telse:\n\t\t\t\t\tprint template.format(task.id, desc, priority, due_date, age, task.tags)\n\n\t\t\tprint self.legend\n\t\telse:\n\t\t\tprint('\\nThere are no tasks to display!\\n')","def show_jobs_from_hub_dialog(self):\n self.example_hub_jobs = get_hub_jobs()\n\n # Open a window, where desired job can be selected\n self.hub_jobs_window = QDialog(parent=self)\n hbox6 = QHBoxLayout()\n self.exampleJobBox = QListWidget()\n for job in self.example_hub_jobs:\n job_item = QListWidgetItem(self.exampleJobBox)\n job_item.setFlags(\n job_item.flags() | QtCore.Qt.ItemIsSelectable) # only one item can be selected this time\n job_item.setSelected(False)\n job_item.setText(job.title) # add Titles as QListWidgetItems\n\n self.exampleJobBox.setMinimumWidth(500)\n\n close_window_btn = QPushButton('Show process graph \\n and close window')\n hbox6.addWidget(self.exampleJobBox)\n hbox6.addWidget(close_window_btn)\n close_window_btn.clicked.connect(self.pick_job_from_hub)\n self.hub_jobs_window.setLayout(hbox6)\n self.hub_jobs_window.setWindowTitle('Select a Job')\n self.hub_jobs_window.show()","def print_tasks(self):\n unformatted_rows = self.db_link.get_tasks()\n formatted_rows = self.display.format_row(unformatted_rows)\n self.display.print_task_list_formatted(formatted_rows)","def displayWidgets(self):\n # Criando um label e um edit para o nome\n quest_lbl = QLabel(self)\n quest_lbl.setText(\"Em quais turnos você pode trabalhar? (Verifque antes de confirmar)\")\n quest_lbl.setWordWrap(True)\n quest_lbl.move(10, 10) # localiza o label na tela\n quest_lbl.resize(230, 60)\n\n # definindo os checkboxes\n manha_cbx = QCheckBox(\"Matutino [8:00 - 14:00]\", self)\n manha_cbx.move(20, 80)\n manha_cbx.toggle()\n manha_cbx.stateChanged.connect(self.printToTerminal)\n\n tarde_cbx = QCheckBox(\"Vespertino [14:00 - 20:00]\", self)\n tarde_cbx.move(20, 100)\n #tarde_cbx.toggle()\n tarde_cbx.stateChanged.connect(self.printToTerminal)\n\n noite_cbx = QCheckBox(\"Noturno [20:00 - 2:00]\", self)\n noite_cbx.move(20, 120)\n #noite_cbx.toggle()\n noite_cbx.stateChanged.connect(self.printToTerminal)","def show_main():\n db = get_db()\n now = datetime.datetime.now()\n rooms = app.config[\"HC_CONFIG\"].values()\n rooms.sort(key=config_lexer.Room.sortkey)\n # Get the three newest counts from the database, sorted by the\n # user-provided time\n newest_counts = db.get_newest_counts(3, hc_db.NewestSort.ENTERED_TIME)\n recent_counts = []\n for count in newest_counts:\n room_rows = db.get_roomdata_for_count_id(count[\"id\"])\n # I couldn't think of a short, descriptive name for this variable.\n some_dict = {\"date\": count[\"entered_time\"], \"counts\": {}}\n for row in room_rows:\n some_dict[\"counts\"][row[\"room\"]] = row[\"people_count\"]\n some_dict[\"counts\"] = OrderedDict(\n sorted(some_dict[\"counts\"].items(), key=sort_count_data)\n )\n recent_counts.append(some_dict)\n if is_admin(session[\"username\"]):\n buttons = [\n NavButton(url_for(\"show_admin\"), \"Administration\"),\n NavButton(url_for(\"show_help\"), \"Help\"),\n NavButton(url_for(\"logout\"), \"Log Out\"),\n ]\n else:\n buttons = [\n NavButton(url_for(\"show_help\"), \"Help\"),\n NavButton(url_for(\"logout\"), \"Log Out\"),\n ]\n return render_template(\n \"main.html\",\n buttons=buttons,\n rooms=rooms,\n recent_counts=recent_counts,\n datewhen=now.strftime(\"%Y-%m-%d\"),\n timewhen=now.strftime(\"%H:%M\"),\n )","def view_tasks(self):\n if self.db_link.get_num_tasks() > 0:\n self.print_tasks()\n else:\n self.display.print_error('You don\\'t have any tasks! Add a task by calling `python-todo -a`.')","def open_task_list(self):\n scrollable_task = ScrollView()\n scrollable_task.add_widget(self.get_task_list())\n\n new_task_btn = RightBottomFloatingButton(\n icon='plus',\n md_bg_color=self.app.theme_cls.accent_color,\n elevation_normal=8)\n new_task_btn.bind(on_press=lambda x: self.open_create_task_dialog())\n\n self.task_list_layout.add_widget(scrollable_task)\n self.task_list_layout.add_widget(new_task_btn)\n\n self.task_list.open()","def show_task(self, task_id):\n\n\t\ttask_id = self._validate_task_id(task_id)\n\t\tif task_id:\n\t\t\ttask = self.tasklist.find_task(task_id)\n\t\t\tif task:\n\t\t\t\tif task.priority == 'L':\n\t\t\t\t\tpriority = Fore.YELLOW + Style.BRIGHT + ' ' + task.priority + ' ' + Fore.RESET + Style.NORMAL\n\t\t\t\telif task.priority == 'M':\n\t\t\t\t\tpriority = Fore.BLUE + Style.BRIGHT + ' ' + task.priority + ' ' + Fore.RESET + Style.NORMAL\n\t\t\t\telif task.priority == 'H':\n\t\t\t\t\tpriority = Fore.RED + Style.BRIGHT + ' ' + task.priority + ' ' + Fore.RESET + Style.NORMAL\n\t\t\t\telse:\n\t\t\t\t\tpriority = ''\n\t\t\t\ttemplate = '{0:^3} {1:^3} {2:20} {3:40}'\n\t\t\t\tprint template.format('\\nID', ' Pri', 'Description', 'Note')\n\t\t\t\tprint template.format('---', '---', '--------------------',\n\t\t\t\t '----------------------------------------')\n\t\t\t\tprint template.format(task.id, priority, task.task, task.note)","def showSettings(self):\n self.c.show()","def show(self):\n # * displays the window, after using either the iconify or the withdraw methods\n self.wm_deiconify()\n # * this method can be called after the event which needs to happen before the window event\n self.wait_window()","def show(self):\n self.wid.show()","def see_all_groups(self):\n self.all_group_window = AllGroups(self)\n self.all_group_window.show()\n self.object_created.append(self.all_group_window)","def show(self):\n if not self.init_run and self.flow_auto_update:\n self.run_all()\n self.init_run = True\n self.flow_view.show()","def dashboard():","def tentative_schedule(request):\n\n\tshows_dict = {\n\t\t0: [],\n\t\t1: [],\n\t\t2: [],\n\t\t3: [],\n\t\t4: [],\n\t\t5: [],\n\t\t6: []\n\t}\n\n\tfor i in range(7):\n\t\tfor show in Show.objects.filter(day=i).order_by('time'):\n\t\t\t\tshow_time = show.time\n\t\t\t\tdj = str(show.dj)\n\t\t\t\tif show.co_dj and str(show.co_dj) != \"Unknown Dj\":\n\t\t\t\t\tdj += \" & \" + str(show.co_dj)\n\t\t\t\tshows_dict[i].append([dj, show_time.strftime('%I:%M %p')])\n\n\treturn render(request, 'tentative_schedule.html', {\n\t\t\t'shows_dict': shows_dict\n\t})","def show(self):\n self.Show()","def show_notifications(run_time):\n # Initialize the d-bus connection and create the notification object\n notify2.init(\"News Indicator\")\n n = notify2.Notification(None, icon=ICON)\n\n # Set the urgency level and the timeout\n n.set_urgency(notify2.URGENCY_NORMAL)\n n.set_timeout(10000)\n formatted_time = run_time.time().strftime('%H:%M')\n message = 'Your {} news are here!'.format(formatted_time)\n\n n.update('News Indicator', message=message, icon=ICON)\n n.show()","def list(self):\n self.background_scheduler.print_jobs()","def show(self):\r\n self.wf.Show()","def list(self):\n\n for name in self.projects:\n self.projects[name].show()\n print(\"\\n\")","def show_today_tasks(self):\n today = datetime.today()\n tasks = self.session.query(self.Table).filter(self.Table.deadline == today.strftime('%Y-%m-%d')).all()\n print(f'Today {today.strftime(\"%d %b\")}:')\n if tasks:\n for n, task in enumerate(tasks, 1):\n print(f'{n}. {task.task}')\n else:\n print('Nothing to do!')\n print()","def main(to_be_scheduled):\n\n tasks = order_by_ftime(to_be_scheduled)\n print select_activity(tasks)","def display(self):\n self.tabbar.refresh()\n self.noact_pane.display()\n self.act_pane.display()\n self.statusbar.set_strings(self.act_pane.act_tab.build_status_line())\n self.statusbar.refresh()","def view_all(options, client):\n if options.show_events:\n return display_events(client.events())\n\n return \"\".join([\n display.DisplayServices().format(client.services()),\n '\\n',\n display.DisplayJobs(options).format(client.jobs())\n ])","def set_up_menu(self):\n self.app.title = \"work\"\n self.timer.start()","def showWindow(*args, **kwargs)->None:\n pass","def smart_alarm_homepage():\r\n s.run(blocking=False)\r\n try:\r\n delete_alarm()\r\n except:\r\n pass\r\n try:\r\n delete_notification()\r\n except:\r\n pass\r\n try:\r\n set_new_alarm()\r\n return redirect(request.path, code=302) \r\n except:\r\n pass\r\n return render_template('index.html', title='Alarm Clock', notifications=notifications, image=get_image(), alarms=alarms)","def showUI(cls):\r\n win = cls()\r\n win.create()\r\n return win","async def list_tasks():","def TeleopPeriodic(self):\n Scheduler.GetInstance().Run()\n LiveWindow.Run()","def show(self):\n QtGui.QGuiApplication.processEvents()","def show(self, opc=True):\n self.ventana.show_all()\n if opc:\n #mostrar la ventana\n self.ventana.show()\n else:\n #ocultarla\n self.ventana.hide()","def show(self):","def display_summary(self, *args):\n logger.debug(u\"{} Summary\".format(self.joueur))\n yield(self.remote.callRemote(\n \"display_summary\", self.currentperiod.todict()))\n self.joueur.info(\"Ok\")\n self.joueur.remove_waitmode()","def show(self) -> None:","def show(self):\n\n pass","def showBasic(self):\n self.setWindowIcon(QIcon(self.icon))\n self.setWindowTitle(self.title)\n self.setGeometry(*self.posXY, *self.windowSize)\n self.show()","def show(self) -> Any: # noqa: ANN401\n self.start()\n return self.join()","def schedule(request):\r\n\r\n return render(request, 'editorial/schedule.html', {})","def show_graphs(self):\n self.frequency_plot_graph.show()\n self.resistance_graph.show()\n self.temperature_plot_graph.show()\n self.pressure_plot_graph.show()\n self.humidity_plot_graph.show()\n self.overview_graph.show()\n self.overview_graph.setXRange(-1000, 5000)","def display_simple(self):\n print(\"\") \n print(\"Date: {}\".format(self.date))\n print(\" Task name: {}\".format(self.task_name))\n print(\" Time spent: {} minutes\".format(self.time_spent))\n print(\" Notes: {}\".format(self.notes))\n print(\" Task number: {}\".format(self.task_number))\n print(\"\")","def show(self):\n pass","def show(id, json):\n\n kargs={'host': c.cfg['host'], \"api_version\": c.cfg['api_version'], \"url_path\": \"/tasks\"}\n task = estask.Task(kargs)\n try:\n dict_resp= task.show(id)\n except Exception as e:\n sys.exit(\"Fail: %s\" %str(e))\n\n if dict_resp == None:\n click.echo(\"fail to get task list\")\n sys.exit(1)\n\n if json:\n print(jsn.dumps(dict_resp, sort_keys=True, indent=4))\n return\n\n try:\n task.print_show(dict_resp)\n except Exception as e:\n sys.exit(\"Fail: %s\" %str(e))","def openWindow(self):\n # self.showSessionAct.setEnabled(False)\n self.musketeers_widget = MusketeersWidget(parent=self)\n self.setCentralWidget(self.musketeers_widget)\n self.saveGroupMenu = QAction('Save Group', self.fileMenu)\n self.fileMenu.addAction(self.saveGroupMenu)\n self.saveGroupMenu.triggered.connect(self.musketeers_widget.session_widget.save_group)","def show_activities(self): \n database = Database('data/database.db')\n activities = database.read_activities()\n view = Template(filename=\"view/template.html\", lookup=lookup)\n \n \n return view.render(\n rows = [[item.number, item.name] for item in activities],\n pageTitle = \"Activités\",\n tableTitle = \"Liste de toutes les activités\",\n ths = [\"Numéro\", \"Nom\"]\n )","def command(task_id, tail, wip, limit):\n if task_id:\n task = storage.get_by_id(task_id)\n\n if not task:\n click.echo(f\"Task {task_id} not found.\")\n sys.exit(1)\n\n tasks = [task]\n else:\n tasks = storage.all(limit=limit, reverse=tail, wip=wip)\n\n print_header()\n for task in tasks:\n show_task(task)","def printSchedule(self):\n\t\tself.printWaiting()\n\t\tprint ' '.join(map(format,range(20),['2' for _ in range(20)]))\n\t\tprint \"\"","def show(self):\n self._window.show()","def showKey():\n\tnewWindow=topWindow(window)\n\t#Config Context Bar\n\tnewWindow.context.removePlaceholder(0)\n\t#Context commands\n\tnewWindow.context.updateContextButton(0,command=lambda n=newWindow: n.grab_release())\n\tnewWindow.context.updateContextButton(1,command=lambda n=newWindow: n.destroy())\n\t#Add Table\n\tnewTable=table(newWindow,\"Pod Templates\",False)\n\tnewTable.pack(fill=BOTH,expand=True)\n\t#Add the content to the table\n\tfor t in podTemplate.templateColours:\n\t\trowName=t+\" colour:\"\n\t\trowColour=podTemplate.templateColours[t]\n\t\tnewTable.addRow(rowName,t,rowColour)\n\n\tnewWindow.run()","def show(self):\r\n display(self.grid_part)","def display_all(self) -> None:\n self.display.draw_list(self.read_all_statuses())","def timesheet_all(request):\r\n return render(\r\n request,\r\n 'timesheet/timesheet_all.html'\r\n )","def print_schedule(self):\n for entry in self.entries:\n print(entry.get_entry_string())","def __call__(self):\n self.show()","def show(self):\n self.window.run_command(\"show_panel\", {\"panel\": self.full_name})","def do_standalone_display(self):\n stage = clutter.Stage()\n stage.connect('destroy', clutter.main_quit)\n stage.connect('key-press-event', lambda x,y: clutter.main_quit())\n stage.set_fullscreen(True)\n stage.set_color(clutter.color_from_string('black'))\n stage.add(self.group)\n stage.show_all()\n clutter.main()","def show_unfinished():\n\n cur = db.get_db().cursor()\n\n create_new_task(cur)\n\n cur.execute('SELECT * FROM todos WHERE completed = false')\n todos = cur.fetchall()\n cur.close()\n\n return render_template(\"index.html\", todos=todos)","def run(self):\n self.ui['main_window'].widgets['main'].show_all()\n gtk.main()","def general_timeline():\n return render_template('timeline.html', general=True, show_username=True)","def show_qp(self):\n\t\tpanel_items = []\n\t\tself.view.window().show_quick_panel(self.test_items.keys(), self.on_done)","async def list_tasks_periodically() -> None:\n while True:\n if sys.version_info >= (3, 8): # The task introspection API we use is not available before Python 3.8\n print(\n \"\\nRunning tasks:\\n\"\n + \"\\n\".join(f\"{i:4}: {t.get_coro()}\" for i, t in enumerate(asyncio.all_tasks())),\n file=sys.stderr,\n )\n else:\n print(f\"\\nRunning {len(asyncio.all_tasks())} tasks\")\n await asyncio.sleep(10)","def write_tasks_table(self):\n tasks = self._get_all_tasks()\n\n self.tasks_view.setRowCount(len(tasks))\n\n row_counter = 0\n for task in tasks:\n\n end_time = None\n start_time = None\n\n # Convert to display data\n if task.StartDate is not None:\n start_time = Time.date_time_format(int(task.StartDate))\n\n if task.EndDate is not None:\n end_time = Time.date_time_format(int(task.EndDate))\n\n # Project name header\n self.tasks_view.setItem(row_counter, 0, QtGui.QTableWidgetItem(str(task.Name)))\n self.tasks_view.setItem(row_counter, 1, QtGui.QTableWidgetItem(str(start_time)))\n self.tasks_view.setItem(row_counter, 2, QtGui.QTableWidgetItem(str(end_time)))\n self.tasks_view.setItem(row_counter, 4, QtGui.QTableWidgetItem(str(task.Assignee)))\n self.tasks_view.setItem(row_counter, 5, QtGui.QTableWidgetItem(str(self.get_project(task.Project))))\n self.tasks_view.setItem(row_counter, 6, QtGui.QTableWidgetItem(str(task.Description)))\n self.tasks_view.setItem(row_counter, 7, QtGui.QTableWidgetItem(str(task.Id)))\n\n # Status header\n if task.Status is None:\n task.Status = int(0)\n\n if int(task.Status) is 1:\n # TODO need translation\n display_status = \"In Progress\"\n\n elif int(task.Status) is 2:\n display_status = \"Not Started\"\n\n elif int(task.Status) is 3:\n display_status = \"Forecast\"\n\n else:\n # TODO need translation\n display_status = \"Done\"\n\n self.tasks_view.setItem(row_counter, 3, QtGui.QTableWidgetItem(str(display_status)))\n\n if task.Description is not None:\n self.tasks_view.setItem(row_counter, 6, QtGui.QTableWidgetItem(task.Description))\n\n row_counter += 1","def inicialUI(self):\r\n\r\n self.setGeometry(500, 500, 500, 500)\r\n self.setWindownTitle(\"Pesquisa\")\r\n self.displayWidgets()\r\n\r\n self.show()","def open_mwindow_my_subjects(self) -> None:\n self.mwindow_my_subjects.show()","def main_screen():\n if current_user.is_authenticated:\n\n activities = Run.query.filter_by(user_id=current_user.get_id())\n\n total_duration = sum([a.duration for a in activities.all()])\n hours = str(int(total_duration//3600))\n minutes = str(int((total_duration % 3600)//60)).rjust(2, '0')\n seconds = str(int(total_duration % 60)).rjust(2, '0')\n\n stats = {'count': len(activities.all()),\n 'total_distance': sum([a.distance for a in activities.all()]),\n 'total_hours': hours,\n 'total_minutes': minutes,\n 'total_seconds': seconds,\n 'total_calories': sum([a.calories for a in activities.all()])}\n\n return render_template('main.html', stats=stats)\n else:\n return render_template('main.html')","def show_events(usrservice,calservice):\r\n print(args.action, args.inuser, 'celendar events')","def show_main_buttons(self):\n pass","def alarm_page_clock():\n events.run(blocking=False)\n if request.method == 'POST':\n event_name = str(request.values.get('event_name'))\n event_date_time = str(request.values.get('event_date'))\n event_desc = str(request.values.get('event_desc'))\n event_log(\"user submitted data...\",\"event :\"+event_name+\" date :\"+event_date_time+\" description: \"+event_desc+\"\")\n if event_name != \"\" and event_date_time != \"\":#logic for setting boleans so the program can determine what data to display with each event as it expires\n if request.form.get(\"repeat\") is None:\n repeat = \"false\"\n else:\n repeat = \"true\"\n if request.form.get(\"weather\") is None:\n weather = \"false\"\n else:\n weather = \"true\"\n if request.form.get(\"news\") is None:\n news = \"false\"\n else:\n news = \"true\"\n set_alarm(event_date_time.replace(\"T\", \" \"), event_name, event_desc,repeat,weather,news)\n speak_output(event_name + \"has been added to upcomming alarms\")\n return render_template(\"alarm.html\", Events_list=Events_list, notification_list=notification_list)"],"string":"[\n \"def dashboard_showall():\\n tasks = Task.query.all()\\n return render_template('home/taskshowall/dashboard_showall.html',\\n tasks=tasks, title=\\\"Tasks\\\")\",\n \"def show_tasks():\\n\\n task = Task(connection=connection, cursor=cursor)\\n\\n all_tasks = task.get_all_tasks()\\n\\n context = {\\n 'all_tasks': all_tasks\\n }\\n\\n return render_template('pages/tables/tasks.html', **context)\",\n \"def open_mwindow_timetable(self) -> None:\\n self.mwindow_timetable.show()\",\n \"def open_mwindow_agenda(self) -> None:\\n self.mwindow_agenda.show()\",\n \"def show(self):\\n i = 0\\n print()\\n for task in self.tasks:\\n print(\\\"\\\\t\\\", i + 1, \\\". \\\", task.name, \\\"(\\\", task.priority, \\\")\\\")\\n i += 1\",\n \"def show_tasks():\\n top_level_tasks = query_with_results(\\\"select label, description from task where parent = ''\\\", [])\\n for task in top_level_tasks:\\n _show_task(task)\",\n \"def show_task1(self):\\n self._show_task(self.controller.CURRENT)\",\n \"def open_main_window(self):\\r\\n track_terms_dic = ''\\r\\n sg.theme(self.look)\\r\\n\\r\\n layout = [[sg.Text('Welcome to tweeet monitor ')],\\r\\n [sg.Text('Please enter Details ')],\\r\\n [sg.Text('User Mail', size=(15, 1)), sg.InputText()],\\r\\n [sg.Text('Timout', size=(15, 1)), sg.InputText('', enable_events=True, key='-DIGITS-')],\\r\\n [sg.Text('')],\\r\\n [sg.Text('You can select an existing list or create a new one '),\\r\\n sg.Combo(self.files, default_value='Select Track Terms List ', key='-COMBO1-')],\\r\\n [sg.Text('')],\\r\\n [sg.Button('Select Exists List'), sg.Button('Create a New List')],\\r\\n [sg.Text('\\\\n')],\\r\\n [sg.Button('Start Monitor'), sg.Button('Exit')]\\r\\n ]\\r\\n\\r\\n window = sg.Window('Monitor tweeter', layout)\\r\\n # Event Loop\\r\\n while True:\\r\\n event, values = window.read()\\r\\n\\r\\n if event == sg.WIN_CLOSED:\\r\\n exit()\\r\\n elif event == 'Select Exists List' or event == 'Create a New List' or event == 'Start Monitor':\\r\\n user_mail = values[0]\\r\\n timeout = values['-DIGITS-']\\r\\n list_dic = values['-COMBO1-']\\r\\n\\r\\n if self.check(user_mail) == 'Invalid Email':\\r\\n self.info_popup_window('You Enter not valid mail ', 'Info', self.look)\\r\\n elif event == 'Select Exists List':\\r\\n if list_dic == 'Select Track Terms List ':\\r\\n self.info_popup_window('Track Terms List ', 'Info', self.look)\\r\\n else:\\r\\n file_name = self.path + self.bachslash + list_dic\\r\\n os.system(file_name)\\r\\n track_terms_dic = list_dic\\r\\n elif event == 'Create a New List':\\r\\n track_terms_dic = self.open_window()\\r\\n track_terms_dic = track_terms_dic + '.txt'\\r\\n elif event == 'Start Monitor':\\r\\n if track_terms_dic == '':\\r\\n self.info_popup_window('Please, Create new Dictionary or select one ', 'Info', self.look)\\r\\n elif track_terms_dic != '':\\r\\n file_name = self.path + self.bachslash + track_terms_dic\\r\\n my_file = open(file_name, \\\"r\\\")\\r\\n content = my_file.read()\\r\\n content = content.split(\\\"\\\\n\\\")\\r\\n content = self.cleanList(content)\\r\\n # print(content)\\r\\n my_file.close()\\r\\n now = datetime.now()\\r\\n date_time = now.strftime(\\\"%m/%d/%Y, %H:%M:%S\\\")\\r\\n dict_list = {'User': user_mail,\\r\\n 'Timeout': timeout,\\r\\n 'Dictionary': list_dic,\\r\\n 'Create Date': date_time,\\r\\n 'track_terms_list': content\\r\\n }\\r\\n header = ['user_mail', 'Timeout', 'Dictionary', 'Create Date', 'list words']\\r\\n if os.path.isfile(self.file_track_terms_audit) == False:\\r\\n # check if the file exsist = if not: create file and print header to the file\\r\\n with open(self.file_track_terms_audit, 'a', newline='\\\\n') as file:\\r\\n try:\\r\\n write = csv.writer(file)\\r\\n write.writerow(header)\\r\\n write.writerows(self.values_list)\\r\\n file.close()\\r\\n except:\\r\\n print(\\\"Something went wrong when writing to the file\\\")\\r\\n else:\\r\\n self.values_list = list(dict_list.values())\\r\\n # print ('self.values_list :****',self.values_list)\\r\\n with open(self.file_track_terms_audit, 'a', newline='\\\\n') as file:\\r\\n try:\\r\\n write = csv.writer(file)\\r\\n self.values_list = [self.values_list]\\r\\n write.writerows(self.values_list)\\r\\n file.close()\\r\\n except:\\r\\n print(\\\"Something went wrong when writing to the file\\\")\\r\\n print('self.values_list:', self.values_list)\\r\\n\\r\\n window.close()\\r\\n\\r\\n print('track_terms_dic: ', track_terms_dic)\\r\\n print('dict_list:', dict_list)\\r\\n return (dict_list)\\r\\n\\r\\n # always check for closed window\\r\\n if event in (sg.WIN_CLOSED, 'Exit'):\\r\\n break\\r\\n\\r\\n if event == '-LIST-' and len(values['-LIST-']):\\r\\n sg.popup('Selected ', values['-LIST-'])\\r\\n\\r\\n if len(values['-DIGITS-']) and values['-DIGITS-'][-1] not in ('0123456789'):\\r\\n # delete last char from input\\r\\n window['-DIGITS-'].update(values['-DIGITS-'][:-1])\\r\\n\\r\\n window.close()\",\n \"def _show_task_list_panel(self):\\n self.task_list_panel.show()\\n self.task_list_panel.load_config()\\n # set geometry\\n _button_pos = self.task_frame.tasklist_button.pos()\\n _button_pos = self.task_frame.mapTo(self, _button_pos)\\n _button_height = self.task_frame.tasklist_button.height()\\n _glo_pos = self.mapTo(tomaya.GetMayaMainWindowPoint(), _button_pos)\\n self.task_list_panel.setGeometry(_glo_pos.x(), _glo_pos.y() + _button_height, self.task_frame.width(), tomaya.GetMayaMainWindowPoint().height()*1/2.0)\",\n \"def show_tasks(self):\\n task_ids = [\\n t and t['id'] for t in self.controller.selected_tasks\\n ]\\n\\n if self._check_cluster():\\n self.print_list(\\n ('id', 'status'), self.controller.get_tasks(),\\n lambda x: task_ids.index(x['id'])\\n )\",\n \"def show_all_tasks(self):\\n tasks = self.session.query(self.Table).order_by(self.Table.deadline).all()\\n print('All tasks:')\\n if tasks:\\n for n, task in enumerate(tasks, 1):\\n print(f'{n}. {task.task}. {task.deadline.strftime(\\\"%d %b\\\")}')\\n else:\\n print('Nothing to do!')\\n print()\",\n \"def show(self, window):\\r\\n\\r\\n return\",\n \"def list():\\n manager = Actions()\\n tasks_list = manager.get_tasks_list()\\n console_utils.print_tree(manager, tasks_list)\",\n \"def showtask(id):\\n\\n tasks = Task.query.filter_by(id=id)\\n return render_template('home/taskshowall/dashboard_showtask.html',tasks=tasks,title=\\\"tasks\\\")\",\n \"def show_weeks_tasks(self):\\n for day in [datetime.today() + timedelta(days=i) for i in range(7)]:\\n tasks = self.session.query(self.Table).filter(self.Table.deadline == day.strftime('%Y-%m-%d')).\\\\\\n order_by(self.Table.deadline).all()\\n print(f'{day.strftime(\\\"%A\\\")} {day.strftime(\\\"%d %b\\\")}:')\\n if tasks:\\n for n, task in enumerate(tasks, 1):\\n print(f'{n}. {task.task}')\\n else:\\n print('Nothing to do!')\\n print()\",\n \"def list_tasks(self, toplevel=True, keys=None):\\n\\n message = {}\\n # show all tasks by default\\n if keys == None:\\n keys = self.list_task_keys()\\n\\n for key in keys:\\n try:\\n last_run_instance = TaskInstance.objects.filter(task_key=key).exclude(completed=None).order_by('-completed').values_list('completed','task_key')[0]\\n last_run = time.mktime(last_run_instance[0].timetuple())\\n #no instances\\n except (KeyError, IndexError):\\n last_run = None\\n\\n # render the form if the task has one\\n task = self.registry[key, None].tasks[key]\\n if task.form:\\n t = loader.get_template('task_parameter_form.html')\\n c = Context ({'form':task.form()})\\n rendered_form = t.render(c)\\n else:\\n rendered_form = None\\n\\n message[key] = {'description':task.description ,\\n 'last_run':last_run,\\n 'form':rendered_form}\\n\\n return message\",\n \"def __show_all(self):\\n print(\\\"\\\\nEvents:\\\\n\\\")\\n self.__show_all_events()\\n print(\\\"\\\\nMetrics:\\\\n\\\")\\n self.__show_all_metrics()\",\n \"def show(self,window):\\n self.showFunctions(window)\",\n \"def open_notifications(self):\\n self.android_device_driver.adb.exec_adb_cmd(\\n \\\"shell cmd statusbar expand-notifications\\\").wait()\",\n \"def show_all(self):\\n for UI_object in self.__UI_objects:\\n UI_object.show()\",\n \"def window_tasks(self):\\n if self._handle != win32gui.GetForegroundWindow():\\n #print \\\"not in foreground\\\"\\n self.restore_window()\\n self.fix_ui()\\n self.set_foreground()\\n\\n self._shell.AppActivate(self._handle)\",\n \"def run(self):\\n while True:\\n self.menu()\\n if self.menu_choice == '1':\\n self.show_today_tasks()\\n elif self.menu_choice == '2':\\n self.show_weeks_tasks()\\n elif self.menu_choice == '3':\\n self.show_all_tasks()\\n elif self.menu_choice == '4':\\n self.show_missed_tasks()\\n elif self.menu_choice == '5':\\n self.add_task()\\n elif self.menu_choice == '6':\\n self.delete_task()\\n else:\\n print('Bye!')\\n break\",\n \"def show_messages(self):\\n console.alert(\\n \\\"Info\\\",\\n \\\"If StaSh does not launch anymore after you changed the config, run the 'launch_stash.py' script with \\\\n'--no-cfgfile'.\\\",\\n \\\"Ok\\\",\\n hide_cancel_button=True,\\n )\\n while True:\\n self.wait_modal()\\n if not self.subview_open:\\n break\\n console.alert(\\n \\\"Info\\\",\\n \\\"Some changes may only be visible after restarting StaSh and/or Pythonista.\\\",\\n \\\"Ok\\\",\\n hide_cancel_button=True,\\n )\",\n \"def show(self):\\n self.present(orientations=ORIENTATIONS)\\n # launch a background thread\\n # we can not use ui.in_background here\\n # because some dialogs would not open anymoe\\n thr = threading.Thread(target=self.show_messages)\\n thr.daemon = True\\n thr.start()\",\n \"def init_task(self):\\n all_tasks = self.db.get_tasks()\\n scroll_parent = Window\\n uw = self.ids.upcoming_wrapper\\n\\n if not all_tasks:\\n new_btn = NewButton()\\n new_btn.size_hint = [None, None]\\n new_btn.size = [\\n scroll_parent.width / 1.9,\\n scroll_parent.height - (0.45 * scroll_parent.height),\\n ]\\n new_btn.bind(on_release=self.add_new)\\n uw.add_widget(new_btn)\\n else:\\n for t in all_tasks:\\n task = Task()\\n task.name = t[1]\\n task.details = t[2]\\n date, time = t[3].rsplit(\\\" \\\", 1)\\n x = self.compare_date(date)\\n if x == \\\"today\\\":\\n task.tsk_clr = (0.7, 0.45, 0.1, 0.6)\\n elif x == \\\"past\\\":\\n task.tsk_clr = (0.7, 0, 0, 0.6)\\n\\n task.time = time\\n task.date = date\\n\\n task.size_hint = [None, None]\\n task.size = [\\n scroll_parent.width / 1.9,\\n scroll_parent.height - (0.45 * scroll_parent.height),\\n ]\\n\\n uw.add_widget(task)\",\n \"def new_task(self, widget):\\n my_task_window = taskwindow.TaskWindow(self)\",\n \"def show_window(self):\\n self.show()\",\n \"def __show_all_events(self):\\n for event in self.events_list:\\n self.__print_events_info(event)\\n print()\",\n \"def homescreen(self):\\r\\n self.root.ids.items_box.clear_widgets()\\r\\n homescreen_information = Label(\\r\\n text='Welcome.\\\\n\\\\n\\\\nThis was designed by Charles to calculate the hours different\\\\n'\\r\\n 'workgroups work at CIPS and WPS.\\\\n\\\\nTo begin, press the Load Data button on'\\r\\n ' the \\\\nleft hand side of this window.', font_size=20, halign='center')\\r\\n self.root.ids.items_box.add_widget(homescreen_information)\",\n \"def addMainWindow(self,appendToTask):\\n self.appendToTask = appendToTask\",\n \"def _view_schedule(self):\\n def plus_top_attach(f):\\n\\n def plus(*args, **kwargs):\\n top_attach, left_attach = f(*args, **kwargs)\\n return top_attach + 1, left_attach + 1\\n\\n return plus\\n\\n @plus_top_attach\\n def create_label(text, left_attach, right_attach,\\n top_attach, bottom_attach, align=None):\\n label = gtk.Label('<span font=\\\"%s\\\">%s</span>' %\\n (Params().get_default_font(), text))\\n label.set_use_markup(True)\\n if align == 'left':\\n label.set_alignment(xalign=0.0, yalign=0.5)\\n elif align == 'right':\\n label.set_alignment(xalign=1.0, yalign=0.5)\\n self.table.attach(label, left_attach, right_attach,\\n top_attach, bottom_attach, xoptions=gtk.FILL, yoptions=False)\\n label.show()\\n return top_attach, left_attach\\n\\n @plus_top_attach\\n def create_separator(left_attach, right_attach,\\n top_attach, bottom_attach):\\n separator = gtk.HSeparator()\\n self.table.attach(separator, left_attach, right_attach,\\n top_attach, bottom_attach, xoptions=gtk.FILL, yoptions=False)\\n separator.show()\\n return top_attach, left_attach\\n\\n tattach, tlen, view_sch = 0, 0, Params().get_view_sch()\\n for i in view_sch:\\n if i:\\n tlen += 1\\n for day in ['Monday', 'Tuesday', 'Wednesday',\\n 'Thursday', 'Friday', 'Saturday']:\\n tattach = create_label('<b><span color=\\\"%s\\\">%s</span></b>' %\\n (Params().get_day_color(), day), 0, tlen,\\n tattach, tattach + 1, 'left')[0]\\n tattach = create_separator(0, tlen, tattach, tattach + 1)[0]\\n\\n schedule = Schedule().get_schedule(day,\\n Schedule().get_current_week() - 1)\\n for i in range(8):\\n if not schedule[i][1] == '' and \\\\\\n (schedule[i][0] == Schedule().get_subgroup() or\\n schedule[i][0] == 2):\\n if not schedule[i][2]:\\n label_color = '%s' % str(Params().get_lecture_color())\\n elif schedule[i][2] == 1:\\n label_color = '%s' % \\\\\\n str(Params().get_laboratory_color())\\n elif schedule[i][2] == 2:\\n label_color = '%s' % str(Params().get_practice_color())\\n else:\\n label_color = '%s' % str(Params().get_non_color())\\n\\n label_template = '<span color=\\\"%s\\\">%s</span>'\\n lattach = 0\\n if view_sch[0]:\\n lattach = create_label('<span color=\\\"%s\\\">%d.</span>' %\\n (label_color, i),\\n lattach, lattach + 1, tattach, tattach + 1)[1]\\n if view_sch[1]:\\n lattach = create_label(label_template % (label_color,\\n '-'.join(Schedule().get_lessons_time()[i])),\\n lattach, lattach + 1, tattach, tattach + 1)[1]\\n if view_sch[2]:\\n lattach = create_label(label_template %\\n (label_color, schedule[i][1]),\\n lattach, lattach + 1,\\n tattach, tattach + 1, 'left')[1]\\n if view_sch[3]:\\n lattach = create_label(label_template %\\n (label_color, schedule[i][3]),\\n lattach, lattach + 1, tattach, tattach + 1)[1]\\n if view_sch[4]:\\n create_label(label_template %\\n (label_color, schedule[i][4]),\\n lattach, lattach + 1,\\n tattach, tattach + 1, 'right')\\n tattach += 1\",\n \"def run(self, action): \\n active = self.root.get_full_property(self._ACTIVE_WINDOW, 0).value[0] \\n curDesk = self.root.get_full_property(self._CURRENT_DESKTOP, 0).value[0]\\n tasks = self.root.get_full_property(self._CLIENT_LIST, Xatom.WINDOW).value\\n \\n for task in tasks:\\n win = self.display.create_resource_object('window', task)\\n name = win.get_wm_name()\\n desk = win.get_full_property(self._DESKTOP, 0).value[0]\\n state = win.get_full_property(self._STATE, Xatom.ATOM).value \\n if desk == curDesk: \\n if int(task) != active:\\n if action == 1: \\n if not self._SHADED in state:\\n self.doShade(win)\\n else: \\n if (self._SHADED in state):\\n self.doUnShade(win)\\n\\n self.display.flush() # this magic command must be here !!!\",\n \"def show_tasks(self, tasks=None, date_format=None):\\n\\n\\t\\tif not tasks:\\n\\t\\t\\ttasks = self.tasklist.tasks\\n\\n\\t\\tif len(tasks) > 0:\\n\\n\\t\\t\\ttemplate = '{0:^3} {1:20} {2:^3} {3:20} {4:15} {5:20}'\\n\\t\\t\\tprint template.format('\\\\nID', 'Description', ' Pri', 'Due', 'Created', 'Tags')\\n\\t\\t\\tprint template.format('---', '--------------------', '---', '--------------------', '---------------',\\n\\t\\t\\t '--------------------')\\n\\t\\t\\tfor task in tasks:\\n\\t\\t\\t\\tif task.priority == 'L':\\n\\t\\t\\t\\t\\tpriority = Fore.YELLOW + Style.BRIGHT + task.priority.center(3) + Fore.RESET + Style.NORMAL\\n\\t\\t\\t\\telif task.priority == 'M':\\n\\t\\t\\t\\t\\tpriority = Fore.BLUE + Style.BRIGHT + task.priority.center(3) + Fore.RESET + Style.NORMAL\\n\\t\\t\\t\\telif task.priority == 'H':\\n\\t\\t\\t\\t\\tpriority = Fore.RED + Style.BRIGHT + task.priority.center(3) + Fore.RESET + Style.NORMAL\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tpriority = ''\\n\\n\\t\\t\\t\\tif task.due_date is None:\\n\\t\\t\\t\\t\\tdue_date = ''\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tif date_format:\\n\\t\\t\\t\\t\\t\\tdue_date = task.due_date.rsplit(' ', 1)[0].ljust(20)\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tdue_date = (arrow.get(task.due_date, task.due_date_format).humanize()).ljust(20)\\n\\n\\t\\t\\t\\t\\tif not task.completed:\\n\\t\\t\\t\\t\\t\\ttoday = arrow.now()\\n\\t\\t\\t\\t\\t\\tdiff = arrow.get(task.due_date, task.due_date_format) - today\\n\\t\\t\\t\\t\\t\\tif diff.days >= 1 and diff.seconds > 0:\\n\\t\\t\\t\\t\\t\\t\\tdue_date = Fore.CYAN + Style.BRIGHT + due_date + Fore.RESET + Style.NORMAL\\n\\t\\t\\t\\t\\t\\telif diff.days >= 0:\\n\\t\\t\\t\\t\\t\\t\\tdue_date = Fore.BLUE + Style.BRIGHT + due_date + Fore.RESET + Style.NORMAL\\n\\t\\t\\t\\t\\t\\telif diff.days <= 0:\\n\\t\\t\\t\\t\\t\\t\\tdue_date = Fore.RED + Style.BRIGHT + due_date + Fore.RESET + Style.NORMAL\\n\\n\\t\\t\\t\\tif date_format:\\n\\t\\t\\t\\t\\tage = (str(task.creation_date).split()[0]).ljust(15) # drop the time zone\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tage = (arrow.get(task.creation_date, 'MM/DD/YYYY h:mm:ss A ZZ').humanize()).ljust(15)\\n\\n\\t\\t\\t\\tif task.note:\\n\\t\\t\\t\\t\\tdesc = task.task + ' *'\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tdesc = task.task\\n\\n\\t\\t\\t\\tif task.completed:\\n\\t\\t\\t\\t\\tif task.priority:\\n\\t\\t\\t\\t\\t\\tpriority = task.priority\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\tpriority = ''\\n\\t\\t\\t\\t\\ttask_id = Fore.WHITE + Style.BRIGHT + Back.WHITE + str(task.id).center(3)\\n\\t\\t\\t\\t\\ttags = str(task.tags) + Fore.RESET + Style.NORMAL + Back.RESET\\n\\t\\t\\t\\t\\tprint template.format(task_id, desc, priority, due_date, age, tags)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tprint template.format(task.id, desc, priority, due_date, age, task.tags)\\n\\n\\t\\t\\tprint self.legend\\n\\t\\telse:\\n\\t\\t\\tprint('\\\\nThere are no tasks to display!\\\\n')\",\n \"def show_jobs_from_hub_dialog(self):\\n self.example_hub_jobs = get_hub_jobs()\\n\\n # Open a window, where desired job can be selected\\n self.hub_jobs_window = QDialog(parent=self)\\n hbox6 = QHBoxLayout()\\n self.exampleJobBox = QListWidget()\\n for job in self.example_hub_jobs:\\n job_item = QListWidgetItem(self.exampleJobBox)\\n job_item.setFlags(\\n job_item.flags() | QtCore.Qt.ItemIsSelectable) # only one item can be selected this time\\n job_item.setSelected(False)\\n job_item.setText(job.title) # add Titles as QListWidgetItems\\n\\n self.exampleJobBox.setMinimumWidth(500)\\n\\n close_window_btn = QPushButton('Show process graph \\\\n and close window')\\n hbox6.addWidget(self.exampleJobBox)\\n hbox6.addWidget(close_window_btn)\\n close_window_btn.clicked.connect(self.pick_job_from_hub)\\n self.hub_jobs_window.setLayout(hbox6)\\n self.hub_jobs_window.setWindowTitle('Select a Job')\\n self.hub_jobs_window.show()\",\n \"def print_tasks(self):\\n unformatted_rows = self.db_link.get_tasks()\\n formatted_rows = self.display.format_row(unformatted_rows)\\n self.display.print_task_list_formatted(formatted_rows)\",\n \"def displayWidgets(self):\\n # Criando um label e um edit para o nome\\n quest_lbl = QLabel(self)\\n quest_lbl.setText(\\\"Em quais turnos você pode trabalhar? (Verifque antes de confirmar)\\\")\\n quest_lbl.setWordWrap(True)\\n quest_lbl.move(10, 10) # localiza o label na tela\\n quest_lbl.resize(230, 60)\\n\\n # definindo os checkboxes\\n manha_cbx = QCheckBox(\\\"Matutino [8:00 - 14:00]\\\", self)\\n manha_cbx.move(20, 80)\\n manha_cbx.toggle()\\n manha_cbx.stateChanged.connect(self.printToTerminal)\\n\\n tarde_cbx = QCheckBox(\\\"Vespertino [14:00 - 20:00]\\\", self)\\n tarde_cbx.move(20, 100)\\n #tarde_cbx.toggle()\\n tarde_cbx.stateChanged.connect(self.printToTerminal)\\n\\n noite_cbx = QCheckBox(\\\"Noturno [20:00 - 2:00]\\\", self)\\n noite_cbx.move(20, 120)\\n #noite_cbx.toggle()\\n noite_cbx.stateChanged.connect(self.printToTerminal)\",\n \"def show_main():\\n db = get_db()\\n now = datetime.datetime.now()\\n rooms = app.config[\\\"HC_CONFIG\\\"].values()\\n rooms.sort(key=config_lexer.Room.sortkey)\\n # Get the three newest counts from the database, sorted by the\\n # user-provided time\\n newest_counts = db.get_newest_counts(3, hc_db.NewestSort.ENTERED_TIME)\\n recent_counts = []\\n for count in newest_counts:\\n room_rows = db.get_roomdata_for_count_id(count[\\\"id\\\"])\\n # I couldn't think of a short, descriptive name for this variable.\\n some_dict = {\\\"date\\\": count[\\\"entered_time\\\"], \\\"counts\\\": {}}\\n for row in room_rows:\\n some_dict[\\\"counts\\\"][row[\\\"room\\\"]] = row[\\\"people_count\\\"]\\n some_dict[\\\"counts\\\"] = OrderedDict(\\n sorted(some_dict[\\\"counts\\\"].items(), key=sort_count_data)\\n )\\n recent_counts.append(some_dict)\\n if is_admin(session[\\\"username\\\"]):\\n buttons = [\\n NavButton(url_for(\\\"show_admin\\\"), \\\"Administration\\\"),\\n NavButton(url_for(\\\"show_help\\\"), \\\"Help\\\"),\\n NavButton(url_for(\\\"logout\\\"), \\\"Log Out\\\"),\\n ]\\n else:\\n buttons = [\\n NavButton(url_for(\\\"show_help\\\"), \\\"Help\\\"),\\n NavButton(url_for(\\\"logout\\\"), \\\"Log Out\\\"),\\n ]\\n return render_template(\\n \\\"main.html\\\",\\n buttons=buttons,\\n rooms=rooms,\\n recent_counts=recent_counts,\\n datewhen=now.strftime(\\\"%Y-%m-%d\\\"),\\n timewhen=now.strftime(\\\"%H:%M\\\"),\\n )\",\n \"def view_tasks(self):\\n if self.db_link.get_num_tasks() > 0:\\n self.print_tasks()\\n else:\\n self.display.print_error('You don\\\\'t have any tasks! Add a task by calling `python-todo -a`.')\",\n \"def open_task_list(self):\\n scrollable_task = ScrollView()\\n scrollable_task.add_widget(self.get_task_list())\\n\\n new_task_btn = RightBottomFloatingButton(\\n icon='plus',\\n md_bg_color=self.app.theme_cls.accent_color,\\n elevation_normal=8)\\n new_task_btn.bind(on_press=lambda x: self.open_create_task_dialog())\\n\\n self.task_list_layout.add_widget(scrollable_task)\\n self.task_list_layout.add_widget(new_task_btn)\\n\\n self.task_list.open()\",\n \"def show_task(self, task_id):\\n\\n\\t\\ttask_id = self._validate_task_id(task_id)\\n\\t\\tif task_id:\\n\\t\\t\\ttask = self.tasklist.find_task(task_id)\\n\\t\\t\\tif task:\\n\\t\\t\\t\\tif task.priority == 'L':\\n\\t\\t\\t\\t\\tpriority = Fore.YELLOW + Style.BRIGHT + ' ' + task.priority + ' ' + Fore.RESET + Style.NORMAL\\n\\t\\t\\t\\telif task.priority == 'M':\\n\\t\\t\\t\\t\\tpriority = Fore.BLUE + Style.BRIGHT + ' ' + task.priority + ' ' + Fore.RESET + Style.NORMAL\\n\\t\\t\\t\\telif task.priority == 'H':\\n\\t\\t\\t\\t\\tpriority = Fore.RED + Style.BRIGHT + ' ' + task.priority + ' ' + Fore.RESET + Style.NORMAL\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tpriority = ''\\n\\t\\t\\t\\ttemplate = '{0:^3} {1:^3} {2:20} {3:40}'\\n\\t\\t\\t\\tprint template.format('\\\\nID', ' Pri', 'Description', 'Note')\\n\\t\\t\\t\\tprint template.format('---', '---', '--------------------',\\n\\t\\t\\t\\t '----------------------------------------')\\n\\t\\t\\t\\tprint template.format(task.id, priority, task.task, task.note)\",\n \"def showSettings(self):\\n self.c.show()\",\n \"def show(self):\\n # * displays the window, after using either the iconify or the withdraw methods\\n self.wm_deiconify()\\n # * this method can be called after the event which needs to happen before the window event\\n self.wait_window()\",\n \"def show(self):\\n self.wid.show()\",\n \"def see_all_groups(self):\\n self.all_group_window = AllGroups(self)\\n self.all_group_window.show()\\n self.object_created.append(self.all_group_window)\",\n \"def show(self):\\n if not self.init_run and self.flow_auto_update:\\n self.run_all()\\n self.init_run = True\\n self.flow_view.show()\",\n \"def dashboard():\",\n \"def tentative_schedule(request):\\n\\n\\tshows_dict = {\\n\\t\\t0: [],\\n\\t\\t1: [],\\n\\t\\t2: [],\\n\\t\\t3: [],\\n\\t\\t4: [],\\n\\t\\t5: [],\\n\\t\\t6: []\\n\\t}\\n\\n\\tfor i in range(7):\\n\\t\\tfor show in Show.objects.filter(day=i).order_by('time'):\\n\\t\\t\\t\\tshow_time = show.time\\n\\t\\t\\t\\tdj = str(show.dj)\\n\\t\\t\\t\\tif show.co_dj and str(show.co_dj) != \\\"Unknown Dj\\\":\\n\\t\\t\\t\\t\\tdj += \\\" & \\\" + str(show.co_dj)\\n\\t\\t\\t\\tshows_dict[i].append([dj, show_time.strftime('%I:%M %p')])\\n\\n\\treturn render(request, 'tentative_schedule.html', {\\n\\t\\t\\t'shows_dict': shows_dict\\n\\t})\",\n \"def show(self):\\n self.Show()\",\n \"def show_notifications(run_time):\\n # Initialize the d-bus connection and create the notification object\\n notify2.init(\\\"News Indicator\\\")\\n n = notify2.Notification(None, icon=ICON)\\n\\n # Set the urgency level and the timeout\\n n.set_urgency(notify2.URGENCY_NORMAL)\\n n.set_timeout(10000)\\n formatted_time = run_time.time().strftime('%H:%M')\\n message = 'Your {} news are here!'.format(formatted_time)\\n\\n n.update('News Indicator', message=message, icon=ICON)\\n n.show()\",\n \"def list(self):\\n self.background_scheduler.print_jobs()\",\n \"def show(self):\\r\\n self.wf.Show()\",\n \"def list(self):\\n\\n for name in self.projects:\\n self.projects[name].show()\\n print(\\\"\\\\n\\\")\",\n \"def show_today_tasks(self):\\n today = datetime.today()\\n tasks = self.session.query(self.Table).filter(self.Table.deadline == today.strftime('%Y-%m-%d')).all()\\n print(f'Today {today.strftime(\\\"%d %b\\\")}:')\\n if tasks:\\n for n, task in enumerate(tasks, 1):\\n print(f'{n}. {task.task}')\\n else:\\n print('Nothing to do!')\\n print()\",\n \"def main(to_be_scheduled):\\n\\n tasks = order_by_ftime(to_be_scheduled)\\n print select_activity(tasks)\",\n \"def display(self):\\n self.tabbar.refresh()\\n self.noact_pane.display()\\n self.act_pane.display()\\n self.statusbar.set_strings(self.act_pane.act_tab.build_status_line())\\n self.statusbar.refresh()\",\n \"def view_all(options, client):\\n if options.show_events:\\n return display_events(client.events())\\n\\n return \\\"\\\".join([\\n display.DisplayServices().format(client.services()),\\n '\\\\n',\\n display.DisplayJobs(options).format(client.jobs())\\n ])\",\n \"def set_up_menu(self):\\n self.app.title = \\\"work\\\"\\n self.timer.start()\",\n \"def showWindow(*args, **kwargs)->None:\\n pass\",\n \"def smart_alarm_homepage():\\r\\n s.run(blocking=False)\\r\\n try:\\r\\n delete_alarm()\\r\\n except:\\r\\n pass\\r\\n try:\\r\\n delete_notification()\\r\\n except:\\r\\n pass\\r\\n try:\\r\\n set_new_alarm()\\r\\n return redirect(request.path, code=302) \\r\\n except:\\r\\n pass\\r\\n return render_template('index.html', title='Alarm Clock', notifications=notifications, image=get_image(), alarms=alarms)\",\n \"def showUI(cls):\\r\\n win = cls()\\r\\n win.create()\\r\\n return win\",\n \"async def list_tasks():\",\n \"def TeleopPeriodic(self):\\n Scheduler.GetInstance().Run()\\n LiveWindow.Run()\",\n \"def show(self):\\n QtGui.QGuiApplication.processEvents()\",\n \"def show(self, opc=True):\\n self.ventana.show_all()\\n if opc:\\n #mostrar la ventana\\n self.ventana.show()\\n else:\\n #ocultarla\\n self.ventana.hide()\",\n \"def show(self):\",\n \"def display_summary(self, *args):\\n logger.debug(u\\\"{} Summary\\\".format(self.joueur))\\n yield(self.remote.callRemote(\\n \\\"display_summary\\\", self.currentperiod.todict()))\\n self.joueur.info(\\\"Ok\\\")\\n self.joueur.remove_waitmode()\",\n \"def show(self) -> None:\",\n \"def show(self):\\n\\n pass\",\n \"def showBasic(self):\\n self.setWindowIcon(QIcon(self.icon))\\n self.setWindowTitle(self.title)\\n self.setGeometry(*self.posXY, *self.windowSize)\\n self.show()\",\n \"def show(self) -> Any: # noqa: ANN401\\n self.start()\\n return self.join()\",\n \"def schedule(request):\\r\\n\\r\\n return render(request, 'editorial/schedule.html', {})\",\n \"def show_graphs(self):\\n self.frequency_plot_graph.show()\\n self.resistance_graph.show()\\n self.temperature_plot_graph.show()\\n self.pressure_plot_graph.show()\\n self.humidity_plot_graph.show()\\n self.overview_graph.show()\\n self.overview_graph.setXRange(-1000, 5000)\",\n \"def display_simple(self):\\n print(\\\"\\\") \\n print(\\\"Date: {}\\\".format(self.date))\\n print(\\\" Task name: {}\\\".format(self.task_name))\\n print(\\\" Time spent: {} minutes\\\".format(self.time_spent))\\n print(\\\" Notes: {}\\\".format(self.notes))\\n print(\\\" Task number: {}\\\".format(self.task_number))\\n print(\\\"\\\")\",\n \"def show(self):\\n pass\",\n \"def show(id, json):\\n\\n kargs={'host': c.cfg['host'], \\\"api_version\\\": c.cfg['api_version'], \\\"url_path\\\": \\\"/tasks\\\"}\\n task = estask.Task(kargs)\\n try:\\n dict_resp= task.show(id)\\n except Exception as e:\\n sys.exit(\\\"Fail: %s\\\" %str(e))\\n\\n if dict_resp == None:\\n click.echo(\\\"fail to get task list\\\")\\n sys.exit(1)\\n\\n if json:\\n print(jsn.dumps(dict_resp, sort_keys=True, indent=4))\\n return\\n\\n try:\\n task.print_show(dict_resp)\\n except Exception as e:\\n sys.exit(\\\"Fail: %s\\\" %str(e))\",\n \"def openWindow(self):\\n # self.showSessionAct.setEnabled(False)\\n self.musketeers_widget = MusketeersWidget(parent=self)\\n self.setCentralWidget(self.musketeers_widget)\\n self.saveGroupMenu = QAction('Save Group', self.fileMenu)\\n self.fileMenu.addAction(self.saveGroupMenu)\\n self.saveGroupMenu.triggered.connect(self.musketeers_widget.session_widget.save_group)\",\n \"def show_activities(self): \\n database = Database('data/database.db')\\n activities = database.read_activities()\\n view = Template(filename=\\\"view/template.html\\\", lookup=lookup)\\n \\n \\n return view.render(\\n rows = [[item.number, item.name] for item in activities],\\n pageTitle = \\\"Activités\\\",\\n tableTitle = \\\"Liste de toutes les activités\\\",\\n ths = [\\\"Numéro\\\", \\\"Nom\\\"]\\n )\",\n \"def command(task_id, tail, wip, limit):\\n if task_id:\\n task = storage.get_by_id(task_id)\\n\\n if not task:\\n click.echo(f\\\"Task {task_id} not found.\\\")\\n sys.exit(1)\\n\\n tasks = [task]\\n else:\\n tasks = storage.all(limit=limit, reverse=tail, wip=wip)\\n\\n print_header()\\n for task in tasks:\\n show_task(task)\",\n \"def printSchedule(self):\\n\\t\\tself.printWaiting()\\n\\t\\tprint ' '.join(map(format,range(20),['2' for _ in range(20)]))\\n\\t\\tprint \\\"\\\"\",\n \"def show(self):\\n self._window.show()\",\n \"def showKey():\\n\\tnewWindow=topWindow(window)\\n\\t#Config Context Bar\\n\\tnewWindow.context.removePlaceholder(0)\\n\\t#Context commands\\n\\tnewWindow.context.updateContextButton(0,command=lambda n=newWindow: n.grab_release())\\n\\tnewWindow.context.updateContextButton(1,command=lambda n=newWindow: n.destroy())\\n\\t#Add Table\\n\\tnewTable=table(newWindow,\\\"Pod Templates\\\",False)\\n\\tnewTable.pack(fill=BOTH,expand=True)\\n\\t#Add the content to the table\\n\\tfor t in podTemplate.templateColours:\\n\\t\\trowName=t+\\\" colour:\\\"\\n\\t\\trowColour=podTemplate.templateColours[t]\\n\\t\\tnewTable.addRow(rowName,t,rowColour)\\n\\n\\tnewWindow.run()\",\n \"def show(self):\\r\\n display(self.grid_part)\",\n \"def display_all(self) -> None:\\n self.display.draw_list(self.read_all_statuses())\",\n \"def timesheet_all(request):\\r\\n return render(\\r\\n request,\\r\\n 'timesheet/timesheet_all.html'\\r\\n )\",\n \"def print_schedule(self):\\n for entry in self.entries:\\n print(entry.get_entry_string())\",\n \"def __call__(self):\\n self.show()\",\n \"def show(self):\\n self.window.run_command(\\\"show_panel\\\", {\\\"panel\\\": self.full_name})\",\n \"def do_standalone_display(self):\\n stage = clutter.Stage()\\n stage.connect('destroy', clutter.main_quit)\\n stage.connect('key-press-event', lambda x,y: clutter.main_quit())\\n stage.set_fullscreen(True)\\n stage.set_color(clutter.color_from_string('black'))\\n stage.add(self.group)\\n stage.show_all()\\n clutter.main()\",\n \"def show_unfinished():\\n\\n cur = db.get_db().cursor()\\n\\n create_new_task(cur)\\n\\n cur.execute('SELECT * FROM todos WHERE completed = false')\\n todos = cur.fetchall()\\n cur.close()\\n\\n return render_template(\\\"index.html\\\", todos=todos)\",\n \"def run(self):\\n self.ui['main_window'].widgets['main'].show_all()\\n gtk.main()\",\n \"def general_timeline():\\n return render_template('timeline.html', general=True, show_username=True)\",\n \"def show_qp(self):\\n\\t\\tpanel_items = []\\n\\t\\tself.view.window().show_quick_panel(self.test_items.keys(), self.on_done)\",\n \"async def list_tasks_periodically() -> None:\\n while True:\\n if sys.version_info >= (3, 8): # The task introspection API we use is not available before Python 3.8\\n print(\\n \\\"\\\\nRunning tasks:\\\\n\\\"\\n + \\\"\\\\n\\\".join(f\\\"{i:4}: {t.get_coro()}\\\" for i, t in enumerate(asyncio.all_tasks())),\\n file=sys.stderr,\\n )\\n else:\\n print(f\\\"\\\\nRunning {len(asyncio.all_tasks())} tasks\\\")\\n await asyncio.sleep(10)\",\n \"def write_tasks_table(self):\\n tasks = self._get_all_tasks()\\n\\n self.tasks_view.setRowCount(len(tasks))\\n\\n row_counter = 0\\n for task in tasks:\\n\\n end_time = None\\n start_time = None\\n\\n # Convert to display data\\n if task.StartDate is not None:\\n start_time = Time.date_time_format(int(task.StartDate))\\n\\n if task.EndDate is not None:\\n end_time = Time.date_time_format(int(task.EndDate))\\n\\n # Project name header\\n self.tasks_view.setItem(row_counter, 0, QtGui.QTableWidgetItem(str(task.Name)))\\n self.tasks_view.setItem(row_counter, 1, QtGui.QTableWidgetItem(str(start_time)))\\n self.tasks_view.setItem(row_counter, 2, QtGui.QTableWidgetItem(str(end_time)))\\n self.tasks_view.setItem(row_counter, 4, QtGui.QTableWidgetItem(str(task.Assignee)))\\n self.tasks_view.setItem(row_counter, 5, QtGui.QTableWidgetItem(str(self.get_project(task.Project))))\\n self.tasks_view.setItem(row_counter, 6, QtGui.QTableWidgetItem(str(task.Description)))\\n self.tasks_view.setItem(row_counter, 7, QtGui.QTableWidgetItem(str(task.Id)))\\n\\n # Status header\\n if task.Status is None:\\n task.Status = int(0)\\n\\n if int(task.Status) is 1:\\n # TODO need translation\\n display_status = \\\"In Progress\\\"\\n\\n elif int(task.Status) is 2:\\n display_status = \\\"Not Started\\\"\\n\\n elif int(task.Status) is 3:\\n display_status = \\\"Forecast\\\"\\n\\n else:\\n # TODO need translation\\n display_status = \\\"Done\\\"\\n\\n self.tasks_view.setItem(row_counter, 3, QtGui.QTableWidgetItem(str(display_status)))\\n\\n if task.Description is not None:\\n self.tasks_view.setItem(row_counter, 6, QtGui.QTableWidgetItem(task.Description))\\n\\n row_counter += 1\",\n \"def inicialUI(self):\\r\\n\\r\\n self.setGeometry(500, 500, 500, 500)\\r\\n self.setWindownTitle(\\\"Pesquisa\\\")\\r\\n self.displayWidgets()\\r\\n\\r\\n self.show()\",\n \"def open_mwindow_my_subjects(self) -> None:\\n self.mwindow_my_subjects.show()\",\n \"def main_screen():\\n if current_user.is_authenticated:\\n\\n activities = Run.query.filter_by(user_id=current_user.get_id())\\n\\n total_duration = sum([a.duration for a in activities.all()])\\n hours = str(int(total_duration//3600))\\n minutes = str(int((total_duration % 3600)//60)).rjust(2, '0')\\n seconds = str(int(total_duration % 60)).rjust(2, '0')\\n\\n stats = {'count': len(activities.all()),\\n 'total_distance': sum([a.distance for a in activities.all()]),\\n 'total_hours': hours,\\n 'total_minutes': minutes,\\n 'total_seconds': seconds,\\n 'total_calories': sum([a.calories for a in activities.all()])}\\n\\n return render_template('main.html', stats=stats)\\n else:\\n return render_template('main.html')\",\n \"def show_events(usrservice,calservice):\\r\\n print(args.action, args.inuser, 'celendar events')\",\n \"def show_main_buttons(self):\\n pass\",\n \"def alarm_page_clock():\\n events.run(blocking=False)\\n if request.method == 'POST':\\n event_name = str(request.values.get('event_name'))\\n event_date_time = str(request.values.get('event_date'))\\n event_desc = str(request.values.get('event_desc'))\\n event_log(\\\"user submitted data...\\\",\\\"event :\\\"+event_name+\\\" date :\\\"+event_date_time+\\\" description: \\\"+event_desc+\\\"\\\")\\n if event_name != \\\"\\\" and event_date_time != \\\"\\\":#logic for setting boleans so the program can determine what data to display with each event as it expires\\n if request.form.get(\\\"repeat\\\") is None:\\n repeat = \\\"false\\\"\\n else:\\n repeat = \\\"true\\\"\\n if request.form.get(\\\"weather\\\") is None:\\n weather = \\\"false\\\"\\n else:\\n weather = \\\"true\\\"\\n if request.form.get(\\\"news\\\") is None:\\n news = \\\"false\\\"\\n else:\\n news = \\\"true\\\"\\n set_alarm(event_date_time.replace(\\\"T\\\", \\\" \\\"), event_name, event_desc,repeat,weather,news)\\n speak_output(event_name + \\\"has been added to upcomming alarms\\\")\\n return render_template(\\\"alarm.html\\\", Events_list=Events_list, notification_list=notification_list)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6601088","0.6460697","0.628572","0.6235331","0.61510676","0.6129666","0.6097378","0.607427","0.6070758","0.6047063","0.59771895","0.59053344","0.58603084","0.58067805","0.58024603","0.57794356","0.57792646","0.5776163","0.57633907","0.5740602","0.5713612","0.567834","0.5635513","0.5630081","0.5627097","0.5603862","0.558525","0.55813694","0.55716866","0.5569356","0.5554481","0.55507946","0.5543955","0.55433756","0.55350536","0.55305684","0.55211633","0.5514374","0.5483187","0.54798347","0.54691315","0.5468575","0.54447407","0.5432809","0.5426288","0.54042685","0.5402276","0.5381292","0.53778213","0.5360421","0.5357786","0.53432095","0.5343177","0.5326026","0.53244776","0.5321899","0.5316232","0.530391","0.5290079","0.52866876","0.52739346","0.5272319","0.52713925","0.5256187","0.5242219","0.5222464","0.5216574","0.5215896","0.52070296","0.5206744","0.5200712","0.51941687","0.51908475","0.5185483","0.5184541","0.51745975","0.5174442","0.5161121","0.5160096","0.51592636","0.5154175","0.51483995","0.5133983","0.51274467","0.5125811","0.5124991","0.51184034","0.5112086","0.5109327","0.5105298","0.5088347","0.5085986","0.50830007","0.50808686","0.50729144","0.5069505","0.50673366","0.50670314","0.50659865","0.50650084"],"string":"[\n \"0.6601088\",\n \"0.6460697\",\n \"0.628572\",\n \"0.6235331\",\n \"0.61510676\",\n \"0.6129666\",\n \"0.6097378\",\n \"0.607427\",\n \"0.6070758\",\n \"0.6047063\",\n \"0.59771895\",\n \"0.59053344\",\n \"0.58603084\",\n \"0.58067805\",\n \"0.58024603\",\n \"0.57794356\",\n \"0.57792646\",\n \"0.5776163\",\n \"0.57633907\",\n \"0.5740602\",\n \"0.5713612\",\n \"0.567834\",\n \"0.5635513\",\n \"0.5630081\",\n \"0.5627097\",\n \"0.5603862\",\n \"0.558525\",\n \"0.55813694\",\n \"0.55716866\",\n \"0.5569356\",\n \"0.5554481\",\n \"0.55507946\",\n \"0.5543955\",\n \"0.55433756\",\n \"0.55350536\",\n \"0.55305684\",\n \"0.55211633\",\n \"0.5514374\",\n \"0.5483187\",\n \"0.54798347\",\n \"0.54691315\",\n \"0.5468575\",\n \"0.54447407\",\n \"0.5432809\",\n \"0.5426288\",\n \"0.54042685\",\n \"0.5402276\",\n \"0.5381292\",\n \"0.53778213\",\n \"0.5360421\",\n \"0.5357786\",\n \"0.53432095\",\n \"0.5343177\",\n \"0.5326026\",\n \"0.53244776\",\n \"0.5321899\",\n \"0.5316232\",\n \"0.530391\",\n \"0.5290079\",\n \"0.52866876\",\n \"0.52739346\",\n \"0.5272319\",\n \"0.52713925\",\n \"0.5256187\",\n \"0.5242219\",\n \"0.5222464\",\n \"0.5216574\",\n \"0.5215896\",\n \"0.52070296\",\n \"0.5206744\",\n \"0.5200712\",\n \"0.51941687\",\n \"0.51908475\",\n \"0.5185483\",\n \"0.5184541\",\n \"0.51745975\",\n \"0.5174442\",\n \"0.5161121\",\n \"0.5160096\",\n \"0.51592636\",\n \"0.5154175\",\n \"0.51483995\",\n \"0.5133983\",\n \"0.51274467\",\n \"0.5125811\",\n \"0.5124991\",\n \"0.51184034\",\n \"0.5112086\",\n \"0.5109327\",\n \"0.5105298\",\n \"0.5088347\",\n \"0.5085986\",\n \"0.50830007\",\n \"0.50808686\",\n \"0.50729144\",\n \"0.5069505\",\n \"0.50673366\",\n \"0.50670314\",\n \"0.50659865\",\n \"0.50650084\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69399023"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22291,"cells":{"query":{"kind":"string","value":"Delete the given profile from the server"},"document":{"kind":"string","value":"def delete_profile(subscription_key, profile_id):\r\n\r\n helper = VerificationServiceHttpClientHelper.VerificationServiceHttpClientHelper(subscription_key)\r\n\r\n helper.delete_profile(profile_id)\r\n\r\n print('Profile {0} has been successfully deleted.'.format(profile_id))"},"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 fusion_api_delete_server_profile(self, name=None, uri=None, param='', api=None, headers=None):\n return self.profile.delete(name=name, uri=uri, param=param, api=api, headers=headers)","def delete_network_profile(self, profile):\r\n return self.delete(self.network_profile_path % profile)","def test_delete_profile(self):\n self.cim.delete_profile(customer_profile_id=u\"123\")","def delete_profile(profile_id):\n \n profile = mongo.db.profiles\n profile.delete_one({'_id': ObjectId(profile_id)})\n flash('Your profile has been deleted.', 'success')\n return redirect(url_for('dashboard'))","def fusion_api_delete_server_profile_template(self, name=None, uri=None, api=None, headers=None):\n return self.profile_template.delete(name, uri, api, headers)","def delete(name: str):\n profiles = prefect.settings.load_profiles()\n if name not in profiles:\n exit_with_error(f\"Profile {name!r} not found.\")\n\n current_profile = prefect.context.get_settings_context().profile\n if current_profile.name == name:\n exit_with_error(\n f\"Profile {name!r} is the active profile. You must switch profiles before\"\n \" it can be deleted.\"\n )\n\n profiles.remove_profile(name)\n\n verb = \"Removed\"\n if name == \"default\":\n verb = \"Reset\"\n\n prefect.settings.save_profiles(profiles)\n exit_with_success(f\"{verb} profile {name!r}.\")","def delete(profile, name):\n client = boto3client.get(\"iam\", profile)\n params = {}\n params[\"InstanceProfileName\"] = name\n return client.delete_instance_profile(**params)","def delprofile(variable, account):\n stm = shared_morphene_instance()\n if mph.rpc is not None:\n mph.rpc.rpcconnect()\n\n if not account:\n account = mph.config[\"default_account\"]\n if not unlock_wallet(stm):\n return\n acc = Account(account, morphene_instance=stm)\n json_metadata = Profile(acc[\"json_metadata\"])\n\n for var in variable:\n json_metadata.remove(var)\n\n tx = acc.update_account_profile(json_metadata)\n tx = json.dumps(tx, indent=4)\n print(tx)","async def test_delete(self):\n rsps = respx.delete(f'{PROVISIONING_API_URL}/users/current/provisioning-profiles/id') \\\n .mock(return_value=Response(200))\n await provisioning_client.delete_provisioning_profile('id')\n assert rsps.calls[0].request.url == \\\n f'{PROVISIONING_API_URL}/users/current/provisioning-profiles/id'\n assert rsps.calls[0].request.headers['auth-token'] == 'header.payload.sign'","def sqdel_profile(self, profile_to_del):\r\n self.cursor.execute(\"DROP TABLE IF EXISTS \" + profile_to_del)","def remove_saved_profile(self, profile):\n\n self.profiles.remove(profile)\n gamedata.GameData._delete_game_data_file(path.join(self.save_dir, profile.player_name + '.yaml'))","def delete(self,\n tunnel_profile_id,\n ):\n return self._invoke('delete',\n {\n 'tunnel_profile_id': tunnel_profile_id,\n })","def delete(profile, name):\n # Make sure the instance profile exists.\n if not exists(profile, name):\n msg = \"No instance profile '\" + str(name) + \"'.\"\n raise ResourceDoesNotExist(msg)\n\n # Now try to delete it.\n params = {}\n params[\"profile\"] = profile\n params[\"name\"] = name\n response = utils.do_request(instanceprofile, \"delete\", params)\n\n # Check that it was, in fact, deleted.\n if exists(profile, name):\n msg = \"The instance profile '\" + str(name) + \"' was not deleted.\"\n raise ResourceNotDeleted(msg)","def remove_vpn_profile(**kwargs):\n proxy = kwargs['proxy']\n session_token = kwargs['sessiontoken']\n display_name = kwargs['display_name']\n profile_type = kwargs['profile_type']\n\n match profile_type:\n case \"ike\":\n profile = \"ipsec-vpn-ike-profiles\"\n case \"ipsec\":\n profile = \"ipsec-vpn-tunnel-profiles\"\n case \"dpd\":\n profile = \"ipsec-vpn-dpd-profiles\"\n case other:\n print(\"Invalid profile type\")\n sys.exit(1)\n\n json_response_status_code = delete_vpn_profile(proxy, session_token, display_name, profile)\n if json_response_status_code == 200:\n sys.exit(f\"Tier-1 VPN service {display_name} was deleted successfully\")\n else:\n print(f\"There was an error deleting Tier1 VPN service {display_name}\")\n sys.exit(1)","def delete(self, entity, schema):\n if schema == CoreProfile.__schema__:\n raise APIBadRequest('Cannot delete the core profile.')\n\n profile = entity.profiles.get_or_404(schema=schema)\n profile.delete()\n return '', 200","def test_delete_profile(self):\n url = self.url\n url = url + '{}/'.format(\n self.profile.pk\n )\n response = self.client.delete(url)\n self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED)\n self.assertEqual(Profile.objects.count(), 1)","def delete_profile(cls, id):\n return cls.objects.filter(id == id).delete()","def delete_network_profile(arn=None):\n pass","def test_delete_payment_profile(self):\n self.cim.delete_payment_profile(\n customer_profile_id=u\"123\",\n customer_payment_profile_id=u\"432\"\n )","def remove_profile(self, request, *args, **kwargs):\n context = {\n 'conversation': self.get_object(),\n 'request': request\n }\n serializer = RemoveProfileSerializer(data=request.data, context=context)\n serializer.is_valid(raise_exception=True)\n serializer.save()\n return Response(serializer.data, status=status.HTTP_202_ACCEPTED)","def test_pm_profile_remove(profile_manager, test_profile):\n profile_manager.activate(test_profile.name)\n profile_manager.delete(test_profile.name)\n assert not profile_manager.is_active(test_profile.name)\n assert not os.path.exists(test_profile.path)","def delete(self,\n ike_profile_id,\n ):\n return self._invoke('delete',\n {\n 'ike_profile_id': ike_profile_id,\n })","def delete(self,\n ipfix_l2_profile_id,\n ):\n return self._invoke('delete',\n {\n 'ipfix_l2_profile_id': ipfix_l2_profile_id,\n })","def people_delete(self, profiles=None, query_params=None, timezone_offset=None, ignore_alias=True, backup=True,\n backup_file=None):\n return self.people_operation('$delete', '', profiles=profiles, query_params=query_params,\n timezone_offset=timezone_offset, ignore_alias=ignore_alias, backup=backup,\n backup_file=backup_file)","def delete(self,\n dpd_profile_id,\n ):\n return self._invoke('delete',\n {\n 'dpd_profile_id': dpd_profile_id,\n })","def delete_profile():\n\n user = session['username']\n\n mongo.db.recipes.delete_many({'username': user})\n mongo.db.users.delete_one({'username': user})\n session.clear()\n flash('Your profile has been deleted', 'success')\n return redirect(url_for('index'))","def DeleteWiredNetworkProfile(self, profilename):\n profilename = misc.to_unicode(profilename)\n print \"Deleting wired profile for \" + str(profilename)\n config = ConfigParser.ConfigParser()\n config.read(self.wired_conf)\n if config.has_section(profilename):\n config.remove_section(profilename)\n else:\n return \"500: Profile does not exist\"\n config.write(open(self.wired_conf, \"w\"))\n return \"100: Profile Deleted\"","def fusion_api_delete_hypervisor_cluster_profile(self, name=None, uri=None, api=None, headers=None):\n return self.cluster_profile.delete(name, uri, api, headers)","def delete(self, request, flavor_profile_id):\n conn = get_sdk_connection(request)\n conn.load_balancer.delete_flavor_profile(flavor_profile_id,\n ignore_missing=True)","def on_remove_profile_action(self):\n profile = self.Manager.profile\n\n # TODO: while it can probably be safely assumed that currentData() and currentIndex() will refer to the same profile as Manager.profile, it is NOT guaranteed; we should either verify that they are indeed the same or search the model for the profile name (as pulled from the manager) to avoid the issue altogether\n if message('warning', 'Confirm Delete Profile',\n 'Delete \"' + profile.name + '\"?',\n 'Choosing \"Yes\" below will remove this profile '\n 'and all saved information within it, including '\n 'customized load-orders, ini-edits, etc. Note '\n 'that installed mods will not be affected. This '\n 'cannot be undone. Do you wish to continue?'):\n self.Manager.delete_profile(\n self._selector.currentData())\n self._selector.removeItem(\n self._selector.currentIndex())","def delete(self, user):\n q = \"DELETE FROM profiles WHERE user=?\"\n try:\n self._query(q, (user,), fetch='none')\n except Exception as e:\n raise e","async def test_not_delete_with_account_token(self):\n provisioning_client = ProvisioningProfileClient(httpClient, 'token')\n try:\n await provisioning_client.delete_provisioning_profile('id')\n except Exception as err:\n assert err.__str__() == 'You can not invoke delete_provisioning_profile method, because you ' + \\\n 'have connected with account access token. Please use API access token from ' + \\\n 'https://app.metaapi.cloud/token page to invoke this method.'","def delete_user_profile(IamUserArn=None):\n pass","def test_delete_profile(mocker, user):\n patched_delete_task = mocker.patch(\"search.search_index_helpers.deindex_document\")\n deindex_profile(user)\n assert patched_delete_task.called is True\n assert patched_delete_task.call_args[0] == (\n gen_profile_id(user.username),\n PROFILE_TYPE,\n )","def deleteThisPlayerProfile(self):\n #delete player from profiles.p\n filename = fileLocs.playerProfiles+r\"\\profiles.p\"\n playerList = []\n if self.__filename == \"\":\n self.__setPlayerFilename()\n f = None\n result = False\n try:\n f = open(filename, \"r\")\n playerList = pickle.load(f)\n f.close()\n try:\n playerList.remove(self.getPlayerName())\n f = open(filename, \"w\")\n pickle.dump(playerList, f)\n f.close()\n os.remove(self.getPlayerFilename())\n return True ## name found and removed -- return true\n except ValueError:\n return False #unsuccessful delete -- return false \n except IOError:\n return False\n except WindowsError:\n return False #file not found returned if player profile has never been saved to file \n except IOError:\n return False # no file found -- return false","def cleanup_profile(ipydir, profile):\n dir_to_rm = osp.join(ipydir, 'profile_' + profile)\n logger.info(\"removing '%s\", dir_to_rm)\n shutil.rmtree(dir_to_rm)","def delete_profile_details(request, pk=None):\n user = User.objects.get(email=request.user.email)\n profiledetails = UserProfile.objects.filter(user=request.user).first()\n\n if UserProfile.objects.filter(user=request.user or request.user.is_superuser):\n if request.method == \"POST\":\n profiledetails.delete()\n user.delete()\n return redirect(deleted_user)\n return render(request, \"profiledetailsdelete.html\", {'profiledetails': profiledetails})","def delete(self,\n ipfix_dfw_profile_id,\n ):\n return self._invoke('delete',\n {\n 'ipfix_dfw_profile_id': ipfix_dfw_profile_id,\n })","def delete_sp_profile(self,\n sp_profile_name,\n headers=None,\n **request_parameters):\n check_type(headers, dict)\n check_type(sp_profile_name, basestring,\n may_be_none=False)\n if headers is not None:\n if 'X-Auth-Token' in headers:\n check_type(headers.get('X-Auth-Token'),\n basestring, may_be_none=False)\n\n _params = {\n }\n _params.update(request_parameters)\n _params = dict_from_items_with_values(_params)\n\n path_params = {\n 'sp-profile-name': sp_profile_name,\n }\n\n with_custom_headers = False\n _headers = self._session.headers or {}\n if headers:\n _headers.update(dict_of_str(headers))\n with_custom_headers = True\n\n e_url = ('/dna/intent/api/v1/sp-profile/{sp-profile-name}')\n endpoint_full_url = apply_path_params(e_url, path_params)\n if with_custom_headers:\n json_data = self._session.delete(endpoint_full_url, params=_params,\n headers=_headers)\n else:\n json_data = self._session.delete(endpoint_full_url, params=_params)\n\n return self._object_factory('bpm_cc405e5a256e56788537e12f91de4029_v2_2_1', json_data)","def test_delete_driver_profile_in_list():\n tester = TestClass()\n driver_profiles = tester.populate_driver_profile_list()\n\n assert len(driver_profiles) == 2\n assert driver_profiles[1].get_driver_profile_name() == 'testDriverName'\n\n tester.delete_driver_profile()\n\n assert len(driver_profiles) == 1\n assert driver_profiles[0].get_driver_profile_name() == 'Default'","def people_remove(self, value, profiles=None, query_params=None, timezone_offset=None, ignore_alias=False,\n backup=True, backup_file=None):\n return self.people_operation('$remove', value=value, profiles=profiles, query_params=query_params,\n timezone_offset=timezone_offset, ignore_alias=ignore_alias, backup=backup,\n backup_file=backup_file)","def test_delete_spawning_profile_not_in_list():\n tester = TestClass()\n spawning_profiles = tester.delete_spawning_profile()\n\n assert spawning_profiles\n\n assert len(spawning_profiles) == 1\n\n assert spawning_profiles[0].get_spawning_profile_name() == 'Default'","def delete_profile_pic(sender, instance, **kwargs):\n if instance.profile_picture:\n if instance.profile_picture.name != \"default.png\":\n path = instance.profile_picture.path\n os.remove(path)","def remove_profile(request, profile, device=0):\n context = {}\n config = get_object_or_404(ConfigurationProfile, pk=profile)\n if device == 0:\n # Completely remove Configuration Profile from MDM\n mode = 'delete'\n if config.installed.all().count() == 0:\n config.delete()\n messages.success(request, \"Profile was successfully deleted\", extra_tags='success')\n return HttpResponseRedirect(reverse(\"mdm:list\"))\n else:\n context['form'] = ProfileRemovalForm(mode=mode)\n else:\n # Unlink profile from device\n laptop = get_object_or_404(Laptop, pk=device)\n if config in laptop.pending.all():\n laptop.pending.remove(config)\n record = InstallationRecord.objects.filter(profile=config, device=laptop, active=True, version=\"RM\").first()\n # If record exists (profile is already installed) reset to installed status\n if record is not None:\n laptop.installed.add(config)\n with open(config.profile) as profile:\n context = json.load(profile)\n record.version = str(context['data']['version'])\n record.save()\n messages.success(request, \"Removal request cancelled\", extra_tags='success')\n else:\n messages.success(request, \"Profile is no longer assigned to {}\".format(laptop.name),\n extra_tags='success')\n return HttpResponseRedirect(reverse(\"mdm:list\"))\n elif config in laptop.installed.all():\n mode = 'disassociate'\n context['form'] = ProfileRemovalForm(mode=mode)\n else:\n raise Http404\n\n # If auto-removal option presented, handle form data\n if request.method == 'POST':\n form = ProfileRemovalForm(request.POST, mode=mode)\n if form.is_valid():\n selected = form.cleaned_data['options']\n if selected == 'auto':\n if mode == 'disassociate':\n record = get_object_or_404(InstallationRecord, profile=config, device=laptop, active=True)\n record.version = \"RM\"\n record.save()\n laptop.installed.remove(config)\n laptop.pending.add(config)\n else:\n # Cancel all pending assignments first\n for laptop in config.pending_install.all():\n config.pending_install.remove(laptop)\n\n # Prepare MDM to remove profile from device\n for laptop in config.installed.all():\n record = get_object_or_404(InstallationRecord, profile=config, device=laptop, active=True)\n record.version = \"RM\"\n record.save()\n laptop.installed.remove(config)\n laptop.pending.add(config)\n messages.success(request, \"Profiles will be removed automatically at next checkin\",\n extra_tags='success')\n else:\n if mode == 'disassociate':\n record = get_object_or_404(InstallationRecord, profile=config, device=laptop, active=True)\n record.expires = timezone.now()\n record.active = False\n record.save()\n laptop.installed.remove(config)\n messages.success(request, \"Profile successfully removed from {}\".format(laptop.name),\n extra_tags='success')\n else:\n for laptop in config.installed.all():\n record = get_object_or_404(InstallationRecord, profile=config, device=laptop, active=True)\n record.expires = timezone.now()\n record.active = False\n record.save()\n config.delete()\n messages.success(request, \"Configuration profile deleted successfully\")\n return HttpResponseRedirect(reverse(\"mdm:list\"))\n else:\n context['form'] = form\n return render(request, 'form_crispy.html', context)","def test_delete_driver_profile_not_in_list():\n tester = TestClass()\n driver_profiles = tester.delete_driver_profile()\n\n assert driver_profiles\n assert len(driver_profiles) == 1\n assert driver_profiles[0].get_driver_profile_name() == 'Default'","def test_delete_spawning_profile_in_list():\n tester = TestClass()\n spawning_profiles = tester.populate_spawning_profile_list()\n\n assert len(spawning_profiles) == 2\n assert spawning_profiles[1].get_spawning_profile_name() == 'testSpawnName'\n\n tester.delete_spawning_profile()\n\n assert len(spawning_profiles) == 1\n assert spawning_profiles[0].get_spawning_profile_name() == 'Default'","def backwards(self, orm):\n \n user_type = orm['auth.User']\n user = user_type.objects.get(username=self.suUsername)\n \n profile_type = orm['mooi.Profile']\n userProfile = profile_type.objects.get(user=user)\n \n userProfile.delete()\n user.delete()","def delete(self,\n ipfix_l2_collector_profile_id,\n ):\n return self._invoke('delete',\n {\n 'ipfix_l2_collector_profile_id': ipfix_l2_collector_profile_id,\n })","def delete(stack, region, profile):\n ini_data = {}\n environment = {}\n\n environment['stack_name'] = stack\n if region:\n environment['region'] = region\n else:\n environment['region'] = find_myself()\n\n if profile:\n environment['profile'] = profile\n\n ini_data['environment'] = environment\n\n if start_smash(ini_data):\n sys.exit(0)\n else:\n sys.exit(1)","def test_delete_risk_profile_using_delete(self):\n pass","def delete(self,\n port_mirroring_profile_id,\n ):\n return self._invoke('delete',\n {\n 'port_mirroring_profile_id': port_mirroring_profile_id,\n })","def remove_skills_from_profile(profile_id=None, skills_id=None):\n # get specific objects\n profile = storage.get(\"Profile\", profile_id)\n skills = storage.get(\"Skills\", skills_id)\n if profile is not None and skills is not None:\n # check every skill in profile\n for profile_skill in profile.skills:\n # if the given skill matches skill in profile, remove it\n if profile_skill.id == skills.id:\n profile.skills.remove(skills)\n # save to update database\n profile.save()\n return jsonify({}), 200\n\n # if id not in database, abort\n abort(404)","def delete(cls, client, resource) :\n try :\n if type(resource) is not list :\n deleteresource = nshttpprofile()\n if type(resource) != type(deleteresource):\n deleteresource.name = resource\n else :\n deleteresource.name = resource.name\n return deleteresource.delete_resource(client)\n else :\n if type(resource[0]) != cls :\n if (resource and len(resource) > 0) :\n deleteresources = [ nshttpprofile() for _ in range(len(resource))]\n for i in range(len(resource)) :\n deleteresources[i].name = resource[i]\n else :\n if (resource and len(resource) > 0) :\n deleteresources = [ nshttpprofile() for _ in range(len(resource))]\n for i in range(len(resource)) :\n deleteresources[i].name = resource[i].name\n result = cls.delete_bulk_request(client, deleteresources)\n return result\n except Exception as e :\n raise e","def deleteSoftwareProfile(self, name):\n\n self._sp_db_api.deleteSoftwareProfile(name)\n\n # Remove all flags for software profile\n swProfileFlagPath = os.path.join(\n self._config_manager.getRoot(), 'var/run/actions/%s' % (name))\n if os.path.exists(swProfileFlagPath):\n shutil.rmtree(swProfileFlagPath)\n\n self.getLogger().info('Deleted software profile [%s]' % (name))","def delete(self,\n ipfix_collector_profile_id,\n ):\n return self._invoke('delete',\n {\n 'ipfix_collector_profile_id': ipfix_collector_profile_id,\n })","def test_delete_vehicle_profile_in_list():\n tester = TestClass()\n vehicle_profiles = tester.populate_vehicle_profile_list()\n\n assert len(vehicle_profiles) == 2\n assert vehicle_profiles[1].get_vehicle_profile_name() == 'testVehicleName'\n\n tester.delete_vehicle_profile()\n\n assert len(vehicle_profiles) == 1\n assert vehicle_profiles[0].get_vehicle_profile_name() == 'Default'","def delete(self, *args, **kwargs):\n # Delete the User and UserProfile objects associated with the\n # Member.\n user_profile = self.userprofile\n user = user_profile.user\n user_profile.delete()\n user.delete()\n # Delete the member itself\n super(Member, self).delete(*args, **kwargs)","def delete_user_profile(user_id, org_id):\r\n config_settings = config.get_config()\r\n db_client = boto3.resource('dynamodb', region_name=config_settings['region'])\r\n table = db_client.Table(config_settings['table_name'])\r\n\r\n try:\r\n response = table.delete_item(Key={'user_id': user_id, 'org_id': org_id})\r\n except ClientError as e:\r\n logger.error(\"Failed to delete profile for user {} and organization {}:{}\"\r\n .format(user_id, org_id, e.response['Error']['Message']))","def delete(profile, name):\n # Make sure the role exists.\n if not exists(profile, name):\n msg = \"No role '\" + str(name) + \"'.\"\n raise ResourceDoesNotExist(msg)\n\n # Now try to delete it.\n params = {}\n params[\"profile\"] = profile\n params[\"role\"] = name\n response = utils.do_request(role_lib, \"delete\", params)\n\n # Check that it was, in fact, deleted.\n if exists(profile, name):\n msg = \"The role '\" + str(name) + \"' was not deleted.\"\n raise ResourceNotDeleted(msg)","def test_delete_hyperflex_cluster_profile(self):\n pass","def delete(cls, client, resource) :\n\t\ttry :\n\t\t\tif type(resource) is not list :\n\t\t\t\tdeleteresource = lbprofile()\n\t\t\t\tif type(resource) != type(deleteresource):\n\t\t\t\t\tdeleteresource.lbprofilename = resource\n\t\t\t\telse :\n\t\t\t\t\tdeleteresource.lbprofilename = resource.lbprofilename\n\t\t\t\treturn deleteresource.delete_resource(client)\n\t\t\telse :\n\t\t\t\tif type(resource[0]) != cls :\n\t\t\t\t\tif (resource and len(resource) > 0) :\n\t\t\t\t\t\tdeleteresources = [ lbprofile() for _ in range(len(resource))]\n\t\t\t\t\t\tfor i in range(len(resource)) :\n\t\t\t\t\t\t\tdeleteresources[i].lbprofilename = resource[i]\n\t\t\t\telse :\n\t\t\t\t\tif (resource and len(resource) > 0) :\n\t\t\t\t\t\tdeleteresources = [ lbprofile() for _ in range(len(resource))]\n\t\t\t\t\t\tfor i in range(len(resource)) :\n\t\t\t\t\t\t\tdeleteresources[i].lbprofilename = resource[i].lbprofilename\n\t\t\t\tresult = cls.delete_bulk_request(client, deleteresources)\n\t\t\treturn result\n\t\texcept Exception as e :\n\t\t\traise e","def account_delete(request):\n fields = [\"email\", \"token\"]\n\n # serializes the quert string to a dict (neeto)\n args = request.args\n\n query_validation = validate_query_params(args, fields)\n # check that body validation succeeded\n if query_validation[1] != 200:\n return query_validation\n\n auth = azure_refresh_token(args[\"token\"])\n if not auth[0]:\n return http400(\"Not Authenticated\")\n\n account_db = Database(\"accounts\")\n storage = Storage(\"biit_profiles\")\n try:\n account_db.delete(args[\"email\"])\n storage.delete(args[\"email\"] + \".jpg\")\n return http200(\"Account deleted\")\n except:\n return http400(\"Error in account deletion\")","def bak_delete_all_appliance_server_profiles():\n selenium2lib = ui_lib.get_s2l()\n \"\"\" Navigate to Network Page \"\"\"\n if not selenium2lib._is_element_present(FusionServerProfilesPage.ID_PAGE_LABEL):\n navigate()\n\n # get the list of networks\n ui_lib.wait_for_element(FusionServerProfilesPage.ID_PROFILE_LIST)\n delete_server_profile([el.text for el in selenium2lib._element_find(FusionServerProfilesPage.ID_PROFILE_LIST_NAMES, False, False)])","def delete_user():","def delete_server_profile_template(profile_template_obj):\n FusionUIBase.navigate_to_section(SectionType.SERVER_PROFILE_TEMPLATES, time_for_loading=5)\n\n total = len(profile_template_obj)\n not_exists = 0\n deleted = 0\n\n for n, profile_template in enumerate(profile_template_obj):\n logger.info(\"{2} No: {0} --- Total: {1} {2}\".format((n + 1), total, '-' * 14))\n logger.info(\"deleting a server profile template named '%s'\" % profile_template.name)\n if not VerifyServerProfileTemplate.verify_server_profile_template_exist(profile_template.name, fail_if_false=False):\n logger.warn(\"server profile template '%s' does not exist\" % profile_template.name)\n not_exists += 1\n else:\n if delete_server_profile_template_by_name(profile_template.name) is False:\n logger.warn(\"server profile template '%s' is NOT deleted successfully, or 'Delete' action is not found in right-side-bar list.\" % profile_template.name)\n continue\n else:\n deleted += 1\n\n logger.info(\"{0} == Summary == {0}\".format('-' * 14))\n if total - not_exists == 0:\n logger.warn(\"no server profile template to delete! all %s server profile template(s) is NOT existing, keyword '%s' returns a 'False'\" % (not_exists, sys._getframe().f_code.co_name))\n return False\n else:\n if deleted < total:\n logger.warn(\"not all of the server profile template(s) is successfully deleted - %s out of %s deleted \" % (deleted, total))\n if deleted + not_exists == total:\n logger.warn(\"%s not-existing server profile template(s) is skipped, keyword '%s' returns a 'False'\" % (not_exists, sys._getframe().f_code.co_name))\n return False\n else:\n logger.warn(\"%s not-existing server profile template(s) is skipped, %s profile template(s) left is failed being deleted \" % (not_exists, total - deleted - not_exists))\n return False\n\n logger.info(\"all of the server profile template(s) is successfully deleted - %s out of %s \" % (deleted, total))\n return True","def deleteSocialAuthentication(self, network):\n\t\turl = \"https://habitica.com/api/v3/user/auth/social/\" + network\n\t\treturn(deleteUrl(url, self.credentials))","def delete(self,\n ipfix_dfw_collector_profile_id,\n ):\n return self._invoke('delete',\n {\n 'ipfix_dfw_collector_profile_id': ipfix_dfw_collector_profile_id,\n })","def deleted_user(request):\n auth.logout(request)\n messages.success(request, \"Your profile has been deleted. Please contact us if you want to undo this.\")\n return redirect(reverse('index'))","def delete_remote_project(profile, project):\n return delete_remote_project_worker.delay(profile_id=profile.id,\n project_id=project.id)","def test_delete_vehicle_profile_not_in_list():\n tester = TestClass()\n vehicle_profiles = tester.delete_vehicle_profile()\n\n assert vehicle_profiles\n assert len(vehicle_profiles) == 1\n assert vehicle_profiles[0].get_vehicle_profile_name() == 'Default'","def delete_login_profile(self, user_name):\r\n params = {'UserName' : user_name}\r\n return self.get_response('DeleteLoginProfile', params)","def confirm_delete(profile):\n profile_key = profile.key()\n\n program_ndb_key = ndb.Key.from_old_key(profile.program.key())\n user_ndb_key = ndb.Key.from_old_key(profile.parent_key())\n\n # Cannot delete the user entity if the user has other profiles, so set it\n # to False in that case.\n user_delete = not (profile_logic.hasOtherGCIProfiles(profile) or\n profile_logic.hasOtherGCIProfiles(profile))\n\n task_sub_q = task_model.GCITask.all().filter('subscribers', profile)\n task_sub_remove_list = []\n for task in task_sub_q.run():\n task_sub_remove_list.append(task)\n\n tasks_created_by_q = task_model.GCITask.all().filter('created_by', profile)\n task_created_list = []\n for task in tasks_created_by_q.run():\n task_created_list.append(task)\n\n tasks_modified_by_q = task_model.GCITask.all().filter('modified_by', profile)\n task_modified_list = []\n for task in tasks_modified_by_q.run():\n task_modified_list.append(task)\n\n tasks_student_q = task_model.GCITask.all().filter('student', profile)\n task_student_list = []\n for task in tasks_student_q.run():\n task_student_list.append(task)\n\n comments_created_by_q = comment_model.GCIComment.all().filter(\n 'created_by', profile.user)\n comments_created_by_list = []\n for comment in comments_created_by_q.run():\n comments_created_by_list.append(comment)\n\n conversations = conversation_logic.queryForProgramAndCreator(\n program_ndb_key, user_ndb_key)\n\n messages = message_logic.queryForUser(user_ndb_key)\n\n conversation_users = conversation_logic.queryForProgramAndUser(\n program_ndb_key, user_ndb_key)\n\n dummy_user = user_logic.getOrCreateDummyMelangeDeletedUser()\n dummy_profile = profile_logic.getOrCreateDummyMelangeDeletedProfile(\n profile.program)\n\n dummy_user_ndb_key = ndb.Key.from_old_key(dummy_user.key())\n\n options = db.create_transaction_options(xg=True)\n\n def delete_account_txn():\n entities_to_save = set([])\n entities_to_del = set([])\n\n # The batch size for query.run() is 20, in most of the cases we have\n # seen so far the user had a few tasks with subscriptions, created_by,\n # modified_by etc., so this should still be single datastore hits per\n # loop. Also, by running the query outside the transaction we may run\n # into situations of user subscribing to the task or creating or modifying\n # tasks or performing another activity after this batch fetch. However,\n # the chances of that happening is very low and can be traded-off for\n # the bigger problem of half run transactions.\n\n for conversation in conversations:\n conversation.creator = dummy_user_ndb_key\n conversation.put()\n\n for message in messages:\n message.author = dummy_user_ndb_key\n message.put()\n\n for conversation_user in conversation_users:\n conversation_user.key.delete()\n\n for task in task_sub_remove_list:\n task.subscribers.remove(profile_key)\n entities_to_save.add(task)\n\n for task in task_created_list:\n task.created_by = dummy_profile\n entities_to_save.add(task)\n\n for task in task_modified_list:\n task.modified_by = dummy_profile\n entities_to_save.add(task)\n\n for task in task_student_list:\n task.student = dummy_profile\n entities_to_save.add(task)\n\n for comment in comments_created_by_list:\n comment.created_by = dummy_user\n entities_to_save.add(comment)\n\n if profile.student_info:\n entities_to_del.add(profile.student_info)\n entities_to_del.add(profile)\n\n if user_delete:\n entities_to_del.add(profile.parent())\n\n db.put(entities_to_save)\n db.delete(entities_to_del)\n\n db.run_in_transaction_options(options, delete_account_txn)","def delete(profile):\n client = boto3client.get(\"ec2\", profile)\n params = {}\n params[\"InternetGatewayId\"] = vpc\n return client.delete_internet_gateway(**params)","def delete_empty_profile(face_profile_directory):\n for face_profile in os.listdir(face_profile_directory):\n if \".\" not in str(face_profile):\n face_profile = os.path.join(face_profile_directory, face_profile)\n index = 0\n for the_file in os.listdir(face_profile):\n file_path = os.path.join(face_profile, the_file)\n if file_path.endswith(\".png\") or file_path.endswith(\".jpg\") or file_path.endswith(\".jpeg\") or file_path.endswith(\".pgm\"):\n index += 1\n if index == 0 : \n shutil.rmtree(face_profile)\n print(\"\\nDeleted \", face_profile, \" because it contains no images\")\n if index < 2 : \n logging.error(\"\\nFace profile \" + str(face_profile) + \" contains too little images (At least 2 images are needed)\")","def reset_webprofileusername(username):\n try:\n url = HOST + '/profile/username'\n r = requests.delete(url, headers=headers)\n return r.json()\n except requests.exceptions.RequestException as e:\n print(\"Something went wrong. Could not delete webprofile username:\", e)","def delete(cls, client, resource) :\n\t\ttry :\n\t\t\tif type(resource) is not list :\n\t\t\t\tdeleteresource = lsntransportprofile()\n\t\t\t\tif type(resource) != type(deleteresource):\n\t\t\t\t\tdeleteresource.transportprofilename = resource\n\t\t\t\telse :\n\t\t\t\t\tdeleteresource.transportprofilename = resource.transportprofilename\n\t\t\t\treturn deleteresource.delete_resource(client)\n\t\t\telse :\n\t\t\t\tif type(resource[0]) != cls :\n\t\t\t\t\tif (resource and len(resource) > 0) :\n\t\t\t\t\t\tdeleteresources = [ lsntransportprofile() for _ in range(len(resource))]\n\t\t\t\t\t\tfor i in range(len(resource)) :\n\t\t\t\t\t\t\tdeleteresources[i].transportprofilename = resource[i]\n\t\t\t\telse :\n\t\t\t\t\tif (resource and len(resource) > 0) :\n\t\t\t\t\t\tdeleteresources = [ lsntransportprofile() for _ in range(len(resource))]\n\t\t\t\t\t\tfor i in range(len(resource)) :\n\t\t\t\t\t\t\tdeleteresources[i].transportprofilename = resource[i].transportprofilename\n\t\t\t\tresult = cls.delete_bulk_request(client, deleteresources)\n\t\t\treturn result\n\t\texcept Exception as e :\n\t\t\traise e","def delete_friend(request, id):\n user = request.user\n friend = get_object_or_404(User, id=id)\n user.profile.friends.remove(friend)\n friend.profile.friends.remove(user)\n messages.success(\n request,\n 'User deleted from your friends list'\n )\n return redirect('profiles:profile')","def people_unset(self, value, profiles=None, query_params=None, timezone_offset=None, ignore_alias=False,\n backup=True, backup_file=None):\n return self.people_operation('$unset', value=value, profiles=profiles, query_params=query_params,\n timezone_offset=timezone_offset, ignore_alias=ignore_alias, backup=backup,\n backup_file=backup_file)","def _unregister(self):\n try:\n self._profilemgr_proxy.proxy.UnregisterProfile(\n HFP_DBUS_PROFILE_ENDPOINT)\n logger.debug(\"Unregistered HFP profile.\")\n except Exception:\n logger.exception(\"Error unregistering profile endpoint.\")\n\n self._profile = None","def delete_user():\n del globalopts.appdata[request.user]\n del globalopts.users[request.user]\n return \"\", 200","def delete_upload(request):\r\n\tgame_id = request.GET['id']\r\n\tgame = Game.objects.get(id = game_id)\r\n\tif(request.user.profile == game.developer):\r\n\t\tif request.method == 'POST':\r\n\t\t\tgame.delete()\r\n\t\t\tprint('game deleted')\r\n\t\t\treturn redirect('developer_dashboard')\r\n\t\telse:\r\n\t\t\treturn render(request, 'confirm_delete.html', {'game':game})\r\n\telse:\r\n\t\treturn redirect('home')","def test_delete_hyperflex_node_profile(self):\n pass","def test_profile(profile_manager):\n name = 'test_profile'\n yield profile_manager.create(name)\n if profile_manager.has_profile(name):\n profile_manager.delete(name)","def delete_profile_post(request, pk=None):\n user = User.objects.get(email=request.user.email)\n profileposts = ProfilePost.objects.filter(user=request.user)\n profileposts = ProfilePost.objects.filter(published_date__lte=timezone.now()\n ).order_by('-published_date').all()\n contactuserposts = ContactUser.objects.all() \n profilepost = get_object_or_404(ProfilePost, pk=pk)\n if (request.user == profilepost.user or\n request.user.is_superuser):\n if request.method == \"POST\":\n profilepost.delete()\n messages.success(request, 'This information has been successfully deleted.')\n return render(request, 'profile.html', {'profileposts': profileposts, 'profile': user, 'contactuserposts': contactuserposts})\n return render(request, \"profilepostdelete.html\", {'profilepost': profilepost})","def remove(self, session: \"pwncat.manager.Session\"):","def delete(self, _uri):\n print(\"Deleting '%s'\"%(_uri))\n response = self.__httpsRequest('DELETE', _uri, '')","def del_user(self, username):\n pass","def delete_user(id):\n pass","def delete(bot, update):\n chatID = update.message.chat_id\n username = get_user_info(chatID)['PID']\n logger.info(\"Deleting user credentials for {}!\".format(username))\n Chat.query.filter(Chat.chatID == chatID).delete() # Delete the user's record referenced by their ChatID\n Misc.query.filter(Misc.chatID == chatID).delete()\n db_session.commit()\n messageContent = \"Your credentials have been deleted, {}\\nHope to see you back soon!\".format(username[3:-4].title())\n bot.sendMessage(chat_id=update.message.chat_id, text=messageContent)\n \n mp.track(username, 'User Left')\n mp.people_set(username, {'active': False })","def delete_my_account():\n # Remove user ownerships\n for p in current_user.projects:\n p.user_id = None\n p.save()\n # Delete user posts\n [ a.delete() for a in current_user.activities ]\n # Delete user account\n current_user.delete()\n logout_user()\n flash('We are sorry to see you go. Your profile has been deleted.', 'info')\n return redirect(url_for('public.home'))","def remove_spawning_profile(intersection, spawning_profile):\n return intersection.remove_spawning_profile(spawning_profile)","def _delete(\n session: \"Session\", url_tail: str, params: Optional[Dict[str, Any]] = None\n) -> None:\n url = session.get_base_url2() + url_tail\n response = _requests_session.delete(\n url,\n headers=_get_headers(session),\n params=params,\n verify=session.verify_ssl_certs,\n )\n _check_response_status(response)","def delete(self, data):\n url = self.base_url + '/v2/account/delete/'\n return self._call_vendasta(url, data)","def remove_user(self, username):\n\n row = self.c.execute(\"SELECT * FROM profiles WHERE name =?\",\n (username,))\n for i in row:\n user = i[1]\n print(user)\n if user == username:\n self.c.execute(\"SELECT id FROM profiles WHERE name=?\",\n (username,))\n i_d = self.c.fetchone()[0]\n self.c.execute(\"DELETE FROM events WHERE user_id=?\", (i_d,))\n self.c.execute(\"DELETE FROM profiles WHERE name=?\", (username,))\n self.conn.commit()\n return True\n else:\n print ('User not found.')","def delete():\n # Must be logged in to perform any delete commands.\n auth_required()\n pass","def onUserDeletion(event):\n client = getUtility(IAdminClient)\n xmpp_users = getUtility(IXMPPUsers)\n storage = getUtility(IPubSubStorage)\n\n principal_id = event.principal\n principal_jid = xmpp_users.getUserJID(principal_id)\n\n if principal_id in storage.leaf_nodes:\n storage.leaf_nodes.remove(principal_id)\n if principal_id in storage.publishers:\n del storage.publishers[principal_id]\n if principal_id in storage.node_items:\n del storage.node_items[principal_id]\n if principal_id in storage.collections['people']:\n storage.collections['people'].remove(principal_id)\n\n pass_storage = getUtility(IXMPPPasswordStorage)\n pass_storage.remove(principal_id)\n\n d = deletePrincipal(client, principal_jid)\n return d","def delete(self, path):\n client = self.connect(VAULT_TOKEN)\n client.delete(path)","def remove(self):\n self._switch.odlclient._request(self._path, method=\"delete\")","def delete(\n student: str = typer.Argument(..., help=\"Username of a student to be removed.\"),\n quiet: bool = typer.Option(\n False, \"-q\", \"--quiet\", help=\"Disable additional logging.\"\n ),\n):\n\n api = setup(quiet)\n result = 0 if api.delete(student) else 1\n sys.exit(result)","def get(self):\n\n try:\n health.UnlinkProfile()\n except gdata.service.RevokingOAuthTokenFailed:\n pass\n\n self.redirect('/')"],"string":"[\n \"def fusion_api_delete_server_profile(self, name=None, uri=None, param='', api=None, headers=None):\\n return self.profile.delete(name=name, uri=uri, param=param, api=api, headers=headers)\",\n \"def delete_network_profile(self, profile):\\r\\n return self.delete(self.network_profile_path % profile)\",\n \"def test_delete_profile(self):\\n self.cim.delete_profile(customer_profile_id=u\\\"123\\\")\",\n \"def delete_profile(profile_id):\\n \\n profile = mongo.db.profiles\\n profile.delete_one({'_id': ObjectId(profile_id)})\\n flash('Your profile has been deleted.', 'success')\\n return redirect(url_for('dashboard'))\",\n \"def fusion_api_delete_server_profile_template(self, name=None, uri=None, api=None, headers=None):\\n return self.profile_template.delete(name, uri, api, headers)\",\n \"def delete(name: str):\\n profiles = prefect.settings.load_profiles()\\n if name not in profiles:\\n exit_with_error(f\\\"Profile {name!r} not found.\\\")\\n\\n current_profile = prefect.context.get_settings_context().profile\\n if current_profile.name == name:\\n exit_with_error(\\n f\\\"Profile {name!r} is the active profile. You must switch profiles before\\\"\\n \\\" it can be deleted.\\\"\\n )\\n\\n profiles.remove_profile(name)\\n\\n verb = \\\"Removed\\\"\\n if name == \\\"default\\\":\\n verb = \\\"Reset\\\"\\n\\n prefect.settings.save_profiles(profiles)\\n exit_with_success(f\\\"{verb} profile {name!r}.\\\")\",\n \"def delete(profile, name):\\n client = boto3client.get(\\\"iam\\\", profile)\\n params = {}\\n params[\\\"InstanceProfileName\\\"] = name\\n return client.delete_instance_profile(**params)\",\n \"def delprofile(variable, account):\\n stm = shared_morphene_instance()\\n if mph.rpc is not None:\\n mph.rpc.rpcconnect()\\n\\n if not account:\\n account = mph.config[\\\"default_account\\\"]\\n if not unlock_wallet(stm):\\n return\\n acc = Account(account, morphene_instance=stm)\\n json_metadata = Profile(acc[\\\"json_metadata\\\"])\\n\\n for var in variable:\\n json_metadata.remove(var)\\n\\n tx = acc.update_account_profile(json_metadata)\\n tx = json.dumps(tx, indent=4)\\n print(tx)\",\n \"async def test_delete(self):\\n rsps = respx.delete(f'{PROVISIONING_API_URL}/users/current/provisioning-profiles/id') \\\\\\n .mock(return_value=Response(200))\\n await provisioning_client.delete_provisioning_profile('id')\\n assert rsps.calls[0].request.url == \\\\\\n f'{PROVISIONING_API_URL}/users/current/provisioning-profiles/id'\\n assert rsps.calls[0].request.headers['auth-token'] == 'header.payload.sign'\",\n \"def sqdel_profile(self, profile_to_del):\\r\\n self.cursor.execute(\\\"DROP TABLE IF EXISTS \\\" + profile_to_del)\",\n \"def remove_saved_profile(self, profile):\\n\\n self.profiles.remove(profile)\\n gamedata.GameData._delete_game_data_file(path.join(self.save_dir, profile.player_name + '.yaml'))\",\n \"def delete(self,\\n tunnel_profile_id,\\n ):\\n return self._invoke('delete',\\n {\\n 'tunnel_profile_id': tunnel_profile_id,\\n })\",\n \"def delete(profile, name):\\n # Make sure the instance profile exists.\\n if not exists(profile, name):\\n msg = \\\"No instance profile '\\\" + str(name) + \\\"'.\\\"\\n raise ResourceDoesNotExist(msg)\\n\\n # Now try to delete it.\\n params = {}\\n params[\\\"profile\\\"] = profile\\n params[\\\"name\\\"] = name\\n response = utils.do_request(instanceprofile, \\\"delete\\\", params)\\n\\n # Check that it was, in fact, deleted.\\n if exists(profile, name):\\n msg = \\\"The instance profile '\\\" + str(name) + \\\"' was not deleted.\\\"\\n raise ResourceNotDeleted(msg)\",\n \"def remove_vpn_profile(**kwargs):\\n proxy = kwargs['proxy']\\n session_token = kwargs['sessiontoken']\\n display_name = kwargs['display_name']\\n profile_type = kwargs['profile_type']\\n\\n match profile_type:\\n case \\\"ike\\\":\\n profile = \\\"ipsec-vpn-ike-profiles\\\"\\n case \\\"ipsec\\\":\\n profile = \\\"ipsec-vpn-tunnel-profiles\\\"\\n case \\\"dpd\\\":\\n profile = \\\"ipsec-vpn-dpd-profiles\\\"\\n case other:\\n print(\\\"Invalid profile type\\\")\\n sys.exit(1)\\n\\n json_response_status_code = delete_vpn_profile(proxy, session_token, display_name, profile)\\n if json_response_status_code == 200:\\n sys.exit(f\\\"Tier-1 VPN service {display_name} was deleted successfully\\\")\\n else:\\n print(f\\\"There was an error deleting Tier1 VPN service {display_name}\\\")\\n sys.exit(1)\",\n \"def delete(self, entity, schema):\\n if schema == CoreProfile.__schema__:\\n raise APIBadRequest('Cannot delete the core profile.')\\n\\n profile = entity.profiles.get_or_404(schema=schema)\\n profile.delete()\\n return '', 200\",\n \"def test_delete_profile(self):\\n url = self.url\\n url = url + '{}/'.format(\\n self.profile.pk\\n )\\n response = self.client.delete(url)\\n self.assertEqual(response.status_code, status.HTTP_405_METHOD_NOT_ALLOWED)\\n self.assertEqual(Profile.objects.count(), 1)\",\n \"def delete_profile(cls, id):\\n return cls.objects.filter(id == id).delete()\",\n \"def delete_network_profile(arn=None):\\n pass\",\n \"def test_delete_payment_profile(self):\\n self.cim.delete_payment_profile(\\n customer_profile_id=u\\\"123\\\",\\n customer_payment_profile_id=u\\\"432\\\"\\n )\",\n \"def remove_profile(self, request, *args, **kwargs):\\n context = {\\n 'conversation': self.get_object(),\\n 'request': request\\n }\\n serializer = RemoveProfileSerializer(data=request.data, context=context)\\n serializer.is_valid(raise_exception=True)\\n serializer.save()\\n return Response(serializer.data, status=status.HTTP_202_ACCEPTED)\",\n \"def test_pm_profile_remove(profile_manager, test_profile):\\n profile_manager.activate(test_profile.name)\\n profile_manager.delete(test_profile.name)\\n assert not profile_manager.is_active(test_profile.name)\\n assert not os.path.exists(test_profile.path)\",\n \"def delete(self,\\n ike_profile_id,\\n ):\\n return self._invoke('delete',\\n {\\n 'ike_profile_id': ike_profile_id,\\n })\",\n \"def delete(self,\\n ipfix_l2_profile_id,\\n ):\\n return self._invoke('delete',\\n {\\n 'ipfix_l2_profile_id': ipfix_l2_profile_id,\\n })\",\n \"def people_delete(self, profiles=None, query_params=None, timezone_offset=None, ignore_alias=True, backup=True,\\n backup_file=None):\\n return self.people_operation('$delete', '', profiles=profiles, query_params=query_params,\\n timezone_offset=timezone_offset, ignore_alias=ignore_alias, backup=backup,\\n backup_file=backup_file)\",\n \"def delete(self,\\n dpd_profile_id,\\n ):\\n return self._invoke('delete',\\n {\\n 'dpd_profile_id': dpd_profile_id,\\n })\",\n \"def delete_profile():\\n\\n user = session['username']\\n\\n mongo.db.recipes.delete_many({'username': user})\\n mongo.db.users.delete_one({'username': user})\\n session.clear()\\n flash('Your profile has been deleted', 'success')\\n return redirect(url_for('index'))\",\n \"def DeleteWiredNetworkProfile(self, profilename):\\n profilename = misc.to_unicode(profilename)\\n print \\\"Deleting wired profile for \\\" + str(profilename)\\n config = ConfigParser.ConfigParser()\\n config.read(self.wired_conf)\\n if config.has_section(profilename):\\n config.remove_section(profilename)\\n else:\\n return \\\"500: Profile does not exist\\\"\\n config.write(open(self.wired_conf, \\\"w\\\"))\\n return \\\"100: Profile Deleted\\\"\",\n \"def fusion_api_delete_hypervisor_cluster_profile(self, name=None, uri=None, api=None, headers=None):\\n return self.cluster_profile.delete(name, uri, api, headers)\",\n \"def delete(self, request, flavor_profile_id):\\n conn = get_sdk_connection(request)\\n conn.load_balancer.delete_flavor_profile(flavor_profile_id,\\n ignore_missing=True)\",\n \"def on_remove_profile_action(self):\\n profile = self.Manager.profile\\n\\n # TODO: while it can probably be safely assumed that currentData() and currentIndex() will refer to the same profile as Manager.profile, it is NOT guaranteed; we should either verify that they are indeed the same or search the model for the profile name (as pulled from the manager) to avoid the issue altogether\\n if message('warning', 'Confirm Delete Profile',\\n 'Delete \\\"' + profile.name + '\\\"?',\\n 'Choosing \\\"Yes\\\" below will remove this profile '\\n 'and all saved information within it, including '\\n 'customized load-orders, ini-edits, etc. Note '\\n 'that installed mods will not be affected. This '\\n 'cannot be undone. Do you wish to continue?'):\\n self.Manager.delete_profile(\\n self._selector.currentData())\\n self._selector.removeItem(\\n self._selector.currentIndex())\",\n \"def delete(self, user):\\n q = \\\"DELETE FROM profiles WHERE user=?\\\"\\n try:\\n self._query(q, (user,), fetch='none')\\n except Exception as e:\\n raise e\",\n \"async def test_not_delete_with_account_token(self):\\n provisioning_client = ProvisioningProfileClient(httpClient, 'token')\\n try:\\n await provisioning_client.delete_provisioning_profile('id')\\n except Exception as err:\\n assert err.__str__() == 'You can not invoke delete_provisioning_profile method, because you ' + \\\\\\n 'have connected with account access token. Please use API access token from ' + \\\\\\n 'https://app.metaapi.cloud/token page to invoke this method.'\",\n \"def delete_user_profile(IamUserArn=None):\\n pass\",\n \"def test_delete_profile(mocker, user):\\n patched_delete_task = mocker.patch(\\\"search.search_index_helpers.deindex_document\\\")\\n deindex_profile(user)\\n assert patched_delete_task.called is True\\n assert patched_delete_task.call_args[0] == (\\n gen_profile_id(user.username),\\n PROFILE_TYPE,\\n )\",\n \"def deleteThisPlayerProfile(self):\\n #delete player from profiles.p\\n filename = fileLocs.playerProfiles+r\\\"\\\\profiles.p\\\"\\n playerList = []\\n if self.__filename == \\\"\\\":\\n self.__setPlayerFilename()\\n f = None\\n result = False\\n try:\\n f = open(filename, \\\"r\\\")\\n playerList = pickle.load(f)\\n f.close()\\n try:\\n playerList.remove(self.getPlayerName())\\n f = open(filename, \\\"w\\\")\\n pickle.dump(playerList, f)\\n f.close()\\n os.remove(self.getPlayerFilename())\\n return True ## name found and removed -- return true\\n except ValueError:\\n return False #unsuccessful delete -- return false \\n except IOError:\\n return False\\n except WindowsError:\\n return False #file not found returned if player profile has never been saved to file \\n except IOError:\\n return False # no file found -- return false\",\n \"def cleanup_profile(ipydir, profile):\\n dir_to_rm = osp.join(ipydir, 'profile_' + profile)\\n logger.info(\\\"removing '%s\\\", dir_to_rm)\\n shutil.rmtree(dir_to_rm)\",\n \"def delete_profile_details(request, pk=None):\\n user = User.objects.get(email=request.user.email)\\n profiledetails = UserProfile.objects.filter(user=request.user).first()\\n\\n if UserProfile.objects.filter(user=request.user or request.user.is_superuser):\\n if request.method == \\\"POST\\\":\\n profiledetails.delete()\\n user.delete()\\n return redirect(deleted_user)\\n return render(request, \\\"profiledetailsdelete.html\\\", {'profiledetails': profiledetails})\",\n \"def delete(self,\\n ipfix_dfw_profile_id,\\n ):\\n return self._invoke('delete',\\n {\\n 'ipfix_dfw_profile_id': ipfix_dfw_profile_id,\\n })\",\n \"def delete_sp_profile(self,\\n sp_profile_name,\\n headers=None,\\n **request_parameters):\\n check_type(headers, dict)\\n check_type(sp_profile_name, basestring,\\n may_be_none=False)\\n if headers is not None:\\n if 'X-Auth-Token' in headers:\\n check_type(headers.get('X-Auth-Token'),\\n basestring, may_be_none=False)\\n\\n _params = {\\n }\\n _params.update(request_parameters)\\n _params = dict_from_items_with_values(_params)\\n\\n path_params = {\\n 'sp-profile-name': sp_profile_name,\\n }\\n\\n with_custom_headers = False\\n _headers = self._session.headers or {}\\n if headers:\\n _headers.update(dict_of_str(headers))\\n with_custom_headers = True\\n\\n e_url = ('/dna/intent/api/v1/sp-profile/{sp-profile-name}')\\n endpoint_full_url = apply_path_params(e_url, path_params)\\n if with_custom_headers:\\n json_data = self._session.delete(endpoint_full_url, params=_params,\\n headers=_headers)\\n else:\\n json_data = self._session.delete(endpoint_full_url, params=_params)\\n\\n return self._object_factory('bpm_cc405e5a256e56788537e12f91de4029_v2_2_1', json_data)\",\n \"def test_delete_driver_profile_in_list():\\n tester = TestClass()\\n driver_profiles = tester.populate_driver_profile_list()\\n\\n assert len(driver_profiles) == 2\\n assert driver_profiles[1].get_driver_profile_name() == 'testDriverName'\\n\\n tester.delete_driver_profile()\\n\\n assert len(driver_profiles) == 1\\n assert driver_profiles[0].get_driver_profile_name() == 'Default'\",\n \"def people_remove(self, value, profiles=None, query_params=None, timezone_offset=None, ignore_alias=False,\\n backup=True, backup_file=None):\\n return self.people_operation('$remove', value=value, profiles=profiles, query_params=query_params,\\n timezone_offset=timezone_offset, ignore_alias=ignore_alias, backup=backup,\\n backup_file=backup_file)\",\n \"def test_delete_spawning_profile_not_in_list():\\n tester = TestClass()\\n spawning_profiles = tester.delete_spawning_profile()\\n\\n assert spawning_profiles\\n\\n assert len(spawning_profiles) == 1\\n\\n assert spawning_profiles[0].get_spawning_profile_name() == 'Default'\",\n \"def delete_profile_pic(sender, instance, **kwargs):\\n if instance.profile_picture:\\n if instance.profile_picture.name != \\\"default.png\\\":\\n path = instance.profile_picture.path\\n os.remove(path)\",\n \"def remove_profile(request, profile, device=0):\\n context = {}\\n config = get_object_or_404(ConfigurationProfile, pk=profile)\\n if device == 0:\\n # Completely remove Configuration Profile from MDM\\n mode = 'delete'\\n if config.installed.all().count() == 0:\\n config.delete()\\n messages.success(request, \\\"Profile was successfully deleted\\\", extra_tags='success')\\n return HttpResponseRedirect(reverse(\\\"mdm:list\\\"))\\n else:\\n context['form'] = ProfileRemovalForm(mode=mode)\\n else:\\n # Unlink profile from device\\n laptop = get_object_or_404(Laptop, pk=device)\\n if config in laptop.pending.all():\\n laptop.pending.remove(config)\\n record = InstallationRecord.objects.filter(profile=config, device=laptop, active=True, version=\\\"RM\\\").first()\\n # If record exists (profile is already installed) reset to installed status\\n if record is not None:\\n laptop.installed.add(config)\\n with open(config.profile) as profile:\\n context = json.load(profile)\\n record.version = str(context['data']['version'])\\n record.save()\\n messages.success(request, \\\"Removal request cancelled\\\", extra_tags='success')\\n else:\\n messages.success(request, \\\"Profile is no longer assigned to {}\\\".format(laptop.name),\\n extra_tags='success')\\n return HttpResponseRedirect(reverse(\\\"mdm:list\\\"))\\n elif config in laptop.installed.all():\\n mode = 'disassociate'\\n context['form'] = ProfileRemovalForm(mode=mode)\\n else:\\n raise Http404\\n\\n # If auto-removal option presented, handle form data\\n if request.method == 'POST':\\n form = ProfileRemovalForm(request.POST, mode=mode)\\n if form.is_valid():\\n selected = form.cleaned_data['options']\\n if selected == 'auto':\\n if mode == 'disassociate':\\n record = get_object_or_404(InstallationRecord, profile=config, device=laptop, active=True)\\n record.version = \\\"RM\\\"\\n record.save()\\n laptop.installed.remove(config)\\n laptop.pending.add(config)\\n else:\\n # Cancel all pending assignments first\\n for laptop in config.pending_install.all():\\n config.pending_install.remove(laptop)\\n\\n # Prepare MDM to remove profile from device\\n for laptop in config.installed.all():\\n record = get_object_or_404(InstallationRecord, profile=config, device=laptop, active=True)\\n record.version = \\\"RM\\\"\\n record.save()\\n laptop.installed.remove(config)\\n laptop.pending.add(config)\\n messages.success(request, \\\"Profiles will be removed automatically at next checkin\\\",\\n extra_tags='success')\\n else:\\n if mode == 'disassociate':\\n record = get_object_or_404(InstallationRecord, profile=config, device=laptop, active=True)\\n record.expires = timezone.now()\\n record.active = False\\n record.save()\\n laptop.installed.remove(config)\\n messages.success(request, \\\"Profile successfully removed from {}\\\".format(laptop.name),\\n extra_tags='success')\\n else:\\n for laptop in config.installed.all():\\n record = get_object_or_404(InstallationRecord, profile=config, device=laptop, active=True)\\n record.expires = timezone.now()\\n record.active = False\\n record.save()\\n config.delete()\\n messages.success(request, \\\"Configuration profile deleted successfully\\\")\\n return HttpResponseRedirect(reverse(\\\"mdm:list\\\"))\\n else:\\n context['form'] = form\\n return render(request, 'form_crispy.html', context)\",\n \"def test_delete_driver_profile_not_in_list():\\n tester = TestClass()\\n driver_profiles = tester.delete_driver_profile()\\n\\n assert driver_profiles\\n assert len(driver_profiles) == 1\\n assert driver_profiles[0].get_driver_profile_name() == 'Default'\",\n \"def test_delete_spawning_profile_in_list():\\n tester = TestClass()\\n spawning_profiles = tester.populate_spawning_profile_list()\\n\\n assert len(spawning_profiles) == 2\\n assert spawning_profiles[1].get_spawning_profile_name() == 'testSpawnName'\\n\\n tester.delete_spawning_profile()\\n\\n assert len(spawning_profiles) == 1\\n assert spawning_profiles[0].get_spawning_profile_name() == 'Default'\",\n \"def backwards(self, orm):\\n \\n user_type = orm['auth.User']\\n user = user_type.objects.get(username=self.suUsername)\\n \\n profile_type = orm['mooi.Profile']\\n userProfile = profile_type.objects.get(user=user)\\n \\n userProfile.delete()\\n user.delete()\",\n \"def delete(self,\\n ipfix_l2_collector_profile_id,\\n ):\\n return self._invoke('delete',\\n {\\n 'ipfix_l2_collector_profile_id': ipfix_l2_collector_profile_id,\\n })\",\n \"def delete(stack, region, profile):\\n ini_data = {}\\n environment = {}\\n\\n environment['stack_name'] = stack\\n if region:\\n environment['region'] = region\\n else:\\n environment['region'] = find_myself()\\n\\n if profile:\\n environment['profile'] = profile\\n\\n ini_data['environment'] = environment\\n\\n if start_smash(ini_data):\\n sys.exit(0)\\n else:\\n sys.exit(1)\",\n \"def test_delete_risk_profile_using_delete(self):\\n pass\",\n \"def delete(self,\\n port_mirroring_profile_id,\\n ):\\n return self._invoke('delete',\\n {\\n 'port_mirroring_profile_id': port_mirroring_profile_id,\\n })\",\n \"def remove_skills_from_profile(profile_id=None, skills_id=None):\\n # get specific objects\\n profile = storage.get(\\\"Profile\\\", profile_id)\\n skills = storage.get(\\\"Skills\\\", skills_id)\\n if profile is not None and skills is not None:\\n # check every skill in profile\\n for profile_skill in profile.skills:\\n # if the given skill matches skill in profile, remove it\\n if profile_skill.id == skills.id:\\n profile.skills.remove(skills)\\n # save to update database\\n profile.save()\\n return jsonify({}), 200\\n\\n # if id not in database, abort\\n abort(404)\",\n \"def delete(cls, client, resource) :\\n try :\\n if type(resource) is not list :\\n deleteresource = nshttpprofile()\\n if type(resource) != type(deleteresource):\\n deleteresource.name = resource\\n else :\\n deleteresource.name = resource.name\\n return deleteresource.delete_resource(client)\\n else :\\n if type(resource[0]) != cls :\\n if (resource and len(resource) > 0) :\\n deleteresources = [ nshttpprofile() for _ in range(len(resource))]\\n for i in range(len(resource)) :\\n deleteresources[i].name = resource[i]\\n else :\\n if (resource and len(resource) > 0) :\\n deleteresources = [ nshttpprofile() for _ in range(len(resource))]\\n for i in range(len(resource)) :\\n deleteresources[i].name = resource[i].name\\n result = cls.delete_bulk_request(client, deleteresources)\\n return result\\n except Exception as e :\\n raise e\",\n \"def deleteSoftwareProfile(self, name):\\n\\n self._sp_db_api.deleteSoftwareProfile(name)\\n\\n # Remove all flags for software profile\\n swProfileFlagPath = os.path.join(\\n self._config_manager.getRoot(), 'var/run/actions/%s' % (name))\\n if os.path.exists(swProfileFlagPath):\\n shutil.rmtree(swProfileFlagPath)\\n\\n self.getLogger().info('Deleted software profile [%s]' % (name))\",\n \"def delete(self,\\n ipfix_collector_profile_id,\\n ):\\n return self._invoke('delete',\\n {\\n 'ipfix_collector_profile_id': ipfix_collector_profile_id,\\n })\",\n \"def test_delete_vehicle_profile_in_list():\\n tester = TestClass()\\n vehicle_profiles = tester.populate_vehicle_profile_list()\\n\\n assert len(vehicle_profiles) == 2\\n assert vehicle_profiles[1].get_vehicle_profile_name() == 'testVehicleName'\\n\\n tester.delete_vehicle_profile()\\n\\n assert len(vehicle_profiles) == 1\\n assert vehicle_profiles[0].get_vehicle_profile_name() == 'Default'\",\n \"def delete(self, *args, **kwargs):\\n # Delete the User and UserProfile objects associated with the\\n # Member.\\n user_profile = self.userprofile\\n user = user_profile.user\\n user_profile.delete()\\n user.delete()\\n # Delete the member itself\\n super(Member, self).delete(*args, **kwargs)\",\n \"def delete_user_profile(user_id, org_id):\\r\\n config_settings = config.get_config()\\r\\n db_client = boto3.resource('dynamodb', region_name=config_settings['region'])\\r\\n table = db_client.Table(config_settings['table_name'])\\r\\n\\r\\n try:\\r\\n response = table.delete_item(Key={'user_id': user_id, 'org_id': org_id})\\r\\n except ClientError as e:\\r\\n logger.error(\\\"Failed to delete profile for user {} and organization {}:{}\\\"\\r\\n .format(user_id, org_id, e.response['Error']['Message']))\",\n \"def delete(profile, name):\\n # Make sure the role exists.\\n if not exists(profile, name):\\n msg = \\\"No role '\\\" + str(name) + \\\"'.\\\"\\n raise ResourceDoesNotExist(msg)\\n\\n # Now try to delete it.\\n params = {}\\n params[\\\"profile\\\"] = profile\\n params[\\\"role\\\"] = name\\n response = utils.do_request(role_lib, \\\"delete\\\", params)\\n\\n # Check that it was, in fact, deleted.\\n if exists(profile, name):\\n msg = \\\"The role '\\\" + str(name) + \\\"' was not deleted.\\\"\\n raise ResourceNotDeleted(msg)\",\n \"def test_delete_hyperflex_cluster_profile(self):\\n pass\",\n \"def delete(cls, client, resource) :\\n\\t\\ttry :\\n\\t\\t\\tif type(resource) is not list :\\n\\t\\t\\t\\tdeleteresource = lbprofile()\\n\\t\\t\\t\\tif type(resource) != type(deleteresource):\\n\\t\\t\\t\\t\\tdeleteresource.lbprofilename = resource\\n\\t\\t\\t\\telse :\\n\\t\\t\\t\\t\\tdeleteresource.lbprofilename = resource.lbprofilename\\n\\t\\t\\t\\treturn deleteresource.delete_resource(client)\\n\\t\\t\\telse :\\n\\t\\t\\t\\tif type(resource[0]) != cls :\\n\\t\\t\\t\\t\\tif (resource and len(resource) > 0) :\\n\\t\\t\\t\\t\\t\\tdeleteresources = [ lbprofile() for _ in range(len(resource))]\\n\\t\\t\\t\\t\\t\\tfor i in range(len(resource)) :\\n\\t\\t\\t\\t\\t\\t\\tdeleteresources[i].lbprofilename = resource[i]\\n\\t\\t\\t\\telse :\\n\\t\\t\\t\\t\\tif (resource and len(resource) > 0) :\\n\\t\\t\\t\\t\\t\\tdeleteresources = [ lbprofile() for _ in range(len(resource))]\\n\\t\\t\\t\\t\\t\\tfor i in range(len(resource)) :\\n\\t\\t\\t\\t\\t\\t\\tdeleteresources[i].lbprofilename = resource[i].lbprofilename\\n\\t\\t\\t\\tresult = cls.delete_bulk_request(client, deleteresources)\\n\\t\\t\\treturn result\\n\\t\\texcept Exception as e :\\n\\t\\t\\traise e\",\n \"def account_delete(request):\\n fields = [\\\"email\\\", \\\"token\\\"]\\n\\n # serializes the quert string to a dict (neeto)\\n args = request.args\\n\\n query_validation = validate_query_params(args, fields)\\n # check that body validation succeeded\\n if query_validation[1] != 200:\\n return query_validation\\n\\n auth = azure_refresh_token(args[\\\"token\\\"])\\n if not auth[0]:\\n return http400(\\\"Not Authenticated\\\")\\n\\n account_db = Database(\\\"accounts\\\")\\n storage = Storage(\\\"biit_profiles\\\")\\n try:\\n account_db.delete(args[\\\"email\\\"])\\n storage.delete(args[\\\"email\\\"] + \\\".jpg\\\")\\n return http200(\\\"Account deleted\\\")\\n except:\\n return http400(\\\"Error in account deletion\\\")\",\n \"def bak_delete_all_appliance_server_profiles():\\n selenium2lib = ui_lib.get_s2l()\\n \\\"\\\"\\\" Navigate to Network Page \\\"\\\"\\\"\\n if not selenium2lib._is_element_present(FusionServerProfilesPage.ID_PAGE_LABEL):\\n navigate()\\n\\n # get the list of networks\\n ui_lib.wait_for_element(FusionServerProfilesPage.ID_PROFILE_LIST)\\n delete_server_profile([el.text for el in selenium2lib._element_find(FusionServerProfilesPage.ID_PROFILE_LIST_NAMES, False, False)])\",\n \"def delete_user():\",\n \"def delete_server_profile_template(profile_template_obj):\\n FusionUIBase.navigate_to_section(SectionType.SERVER_PROFILE_TEMPLATES, time_for_loading=5)\\n\\n total = len(profile_template_obj)\\n not_exists = 0\\n deleted = 0\\n\\n for n, profile_template in enumerate(profile_template_obj):\\n logger.info(\\\"{2} No: {0} --- Total: {1} {2}\\\".format((n + 1), total, '-' * 14))\\n logger.info(\\\"deleting a server profile template named '%s'\\\" % profile_template.name)\\n if not VerifyServerProfileTemplate.verify_server_profile_template_exist(profile_template.name, fail_if_false=False):\\n logger.warn(\\\"server profile template '%s' does not exist\\\" % profile_template.name)\\n not_exists += 1\\n else:\\n if delete_server_profile_template_by_name(profile_template.name) is False:\\n logger.warn(\\\"server profile template '%s' is NOT deleted successfully, or 'Delete' action is not found in right-side-bar list.\\\" % profile_template.name)\\n continue\\n else:\\n deleted += 1\\n\\n logger.info(\\\"{0} == Summary == {0}\\\".format('-' * 14))\\n if total - not_exists == 0:\\n logger.warn(\\\"no server profile template to delete! all %s server profile template(s) is NOT existing, keyword '%s' returns a 'False'\\\" % (not_exists, sys._getframe().f_code.co_name))\\n return False\\n else:\\n if deleted < total:\\n logger.warn(\\\"not all of the server profile template(s) is successfully deleted - %s out of %s deleted \\\" % (deleted, total))\\n if deleted + not_exists == total:\\n logger.warn(\\\"%s not-existing server profile template(s) is skipped, keyword '%s' returns a 'False'\\\" % (not_exists, sys._getframe().f_code.co_name))\\n return False\\n else:\\n logger.warn(\\\"%s not-existing server profile template(s) is skipped, %s profile template(s) left is failed being deleted \\\" % (not_exists, total - deleted - not_exists))\\n return False\\n\\n logger.info(\\\"all of the server profile template(s) is successfully deleted - %s out of %s \\\" % (deleted, total))\\n return True\",\n \"def deleteSocialAuthentication(self, network):\\n\\t\\turl = \\\"https://habitica.com/api/v3/user/auth/social/\\\" + network\\n\\t\\treturn(deleteUrl(url, self.credentials))\",\n \"def delete(self,\\n ipfix_dfw_collector_profile_id,\\n ):\\n return self._invoke('delete',\\n {\\n 'ipfix_dfw_collector_profile_id': ipfix_dfw_collector_profile_id,\\n })\",\n \"def deleted_user(request):\\n auth.logout(request)\\n messages.success(request, \\\"Your profile has been deleted. Please contact us if you want to undo this.\\\")\\n return redirect(reverse('index'))\",\n \"def delete_remote_project(profile, project):\\n return delete_remote_project_worker.delay(profile_id=profile.id,\\n project_id=project.id)\",\n \"def test_delete_vehicle_profile_not_in_list():\\n tester = TestClass()\\n vehicle_profiles = tester.delete_vehicle_profile()\\n\\n assert vehicle_profiles\\n assert len(vehicle_profiles) == 1\\n assert vehicle_profiles[0].get_vehicle_profile_name() == 'Default'\",\n \"def delete_login_profile(self, user_name):\\r\\n params = {'UserName' : user_name}\\r\\n return self.get_response('DeleteLoginProfile', params)\",\n \"def confirm_delete(profile):\\n profile_key = profile.key()\\n\\n program_ndb_key = ndb.Key.from_old_key(profile.program.key())\\n user_ndb_key = ndb.Key.from_old_key(profile.parent_key())\\n\\n # Cannot delete the user entity if the user has other profiles, so set it\\n # to False in that case.\\n user_delete = not (profile_logic.hasOtherGCIProfiles(profile) or\\n profile_logic.hasOtherGCIProfiles(profile))\\n\\n task_sub_q = task_model.GCITask.all().filter('subscribers', profile)\\n task_sub_remove_list = []\\n for task in task_sub_q.run():\\n task_sub_remove_list.append(task)\\n\\n tasks_created_by_q = task_model.GCITask.all().filter('created_by', profile)\\n task_created_list = []\\n for task in tasks_created_by_q.run():\\n task_created_list.append(task)\\n\\n tasks_modified_by_q = task_model.GCITask.all().filter('modified_by', profile)\\n task_modified_list = []\\n for task in tasks_modified_by_q.run():\\n task_modified_list.append(task)\\n\\n tasks_student_q = task_model.GCITask.all().filter('student', profile)\\n task_student_list = []\\n for task in tasks_student_q.run():\\n task_student_list.append(task)\\n\\n comments_created_by_q = comment_model.GCIComment.all().filter(\\n 'created_by', profile.user)\\n comments_created_by_list = []\\n for comment in comments_created_by_q.run():\\n comments_created_by_list.append(comment)\\n\\n conversations = conversation_logic.queryForProgramAndCreator(\\n program_ndb_key, user_ndb_key)\\n\\n messages = message_logic.queryForUser(user_ndb_key)\\n\\n conversation_users = conversation_logic.queryForProgramAndUser(\\n program_ndb_key, user_ndb_key)\\n\\n dummy_user = user_logic.getOrCreateDummyMelangeDeletedUser()\\n dummy_profile = profile_logic.getOrCreateDummyMelangeDeletedProfile(\\n profile.program)\\n\\n dummy_user_ndb_key = ndb.Key.from_old_key(dummy_user.key())\\n\\n options = db.create_transaction_options(xg=True)\\n\\n def delete_account_txn():\\n entities_to_save = set([])\\n entities_to_del = set([])\\n\\n # The batch size for query.run() is 20, in most of the cases we have\\n # seen so far the user had a few tasks with subscriptions, created_by,\\n # modified_by etc., so this should still be single datastore hits per\\n # loop. Also, by running the query outside the transaction we may run\\n # into situations of user subscribing to the task or creating or modifying\\n # tasks or performing another activity after this batch fetch. However,\\n # the chances of that happening is very low and can be traded-off for\\n # the bigger problem of half run transactions.\\n\\n for conversation in conversations:\\n conversation.creator = dummy_user_ndb_key\\n conversation.put()\\n\\n for message in messages:\\n message.author = dummy_user_ndb_key\\n message.put()\\n\\n for conversation_user in conversation_users:\\n conversation_user.key.delete()\\n\\n for task in task_sub_remove_list:\\n task.subscribers.remove(profile_key)\\n entities_to_save.add(task)\\n\\n for task in task_created_list:\\n task.created_by = dummy_profile\\n entities_to_save.add(task)\\n\\n for task in task_modified_list:\\n task.modified_by = dummy_profile\\n entities_to_save.add(task)\\n\\n for task in task_student_list:\\n task.student = dummy_profile\\n entities_to_save.add(task)\\n\\n for comment in comments_created_by_list:\\n comment.created_by = dummy_user\\n entities_to_save.add(comment)\\n\\n if profile.student_info:\\n entities_to_del.add(profile.student_info)\\n entities_to_del.add(profile)\\n\\n if user_delete:\\n entities_to_del.add(profile.parent())\\n\\n db.put(entities_to_save)\\n db.delete(entities_to_del)\\n\\n db.run_in_transaction_options(options, delete_account_txn)\",\n \"def delete(profile):\\n client = boto3client.get(\\\"ec2\\\", profile)\\n params = {}\\n params[\\\"InternetGatewayId\\\"] = vpc\\n return client.delete_internet_gateway(**params)\",\n \"def delete_empty_profile(face_profile_directory):\\n for face_profile in os.listdir(face_profile_directory):\\n if \\\".\\\" not in str(face_profile):\\n face_profile = os.path.join(face_profile_directory, face_profile)\\n index = 0\\n for the_file in os.listdir(face_profile):\\n file_path = os.path.join(face_profile, the_file)\\n if file_path.endswith(\\\".png\\\") or file_path.endswith(\\\".jpg\\\") or file_path.endswith(\\\".jpeg\\\") or file_path.endswith(\\\".pgm\\\"):\\n index += 1\\n if index == 0 : \\n shutil.rmtree(face_profile)\\n print(\\\"\\\\nDeleted \\\", face_profile, \\\" because it contains no images\\\")\\n if index < 2 : \\n logging.error(\\\"\\\\nFace profile \\\" + str(face_profile) + \\\" contains too little images (At least 2 images are needed)\\\")\",\n \"def reset_webprofileusername(username):\\n try:\\n url = HOST + '/profile/username'\\n r = requests.delete(url, headers=headers)\\n return r.json()\\n except requests.exceptions.RequestException as e:\\n print(\\\"Something went wrong. Could not delete webprofile username:\\\", e)\",\n \"def delete(cls, client, resource) :\\n\\t\\ttry :\\n\\t\\t\\tif type(resource) is not list :\\n\\t\\t\\t\\tdeleteresource = lsntransportprofile()\\n\\t\\t\\t\\tif type(resource) != type(deleteresource):\\n\\t\\t\\t\\t\\tdeleteresource.transportprofilename = resource\\n\\t\\t\\t\\telse :\\n\\t\\t\\t\\t\\tdeleteresource.transportprofilename = resource.transportprofilename\\n\\t\\t\\t\\treturn deleteresource.delete_resource(client)\\n\\t\\t\\telse :\\n\\t\\t\\t\\tif type(resource[0]) != cls :\\n\\t\\t\\t\\t\\tif (resource and len(resource) > 0) :\\n\\t\\t\\t\\t\\t\\tdeleteresources = [ lsntransportprofile() for _ in range(len(resource))]\\n\\t\\t\\t\\t\\t\\tfor i in range(len(resource)) :\\n\\t\\t\\t\\t\\t\\t\\tdeleteresources[i].transportprofilename = resource[i]\\n\\t\\t\\t\\telse :\\n\\t\\t\\t\\t\\tif (resource and len(resource) > 0) :\\n\\t\\t\\t\\t\\t\\tdeleteresources = [ lsntransportprofile() for _ in range(len(resource))]\\n\\t\\t\\t\\t\\t\\tfor i in range(len(resource)) :\\n\\t\\t\\t\\t\\t\\t\\tdeleteresources[i].transportprofilename = resource[i].transportprofilename\\n\\t\\t\\t\\tresult = cls.delete_bulk_request(client, deleteresources)\\n\\t\\t\\treturn result\\n\\t\\texcept Exception as e :\\n\\t\\t\\traise e\",\n \"def delete_friend(request, id):\\n user = request.user\\n friend = get_object_or_404(User, id=id)\\n user.profile.friends.remove(friend)\\n friend.profile.friends.remove(user)\\n messages.success(\\n request,\\n 'User deleted from your friends list'\\n )\\n return redirect('profiles:profile')\",\n \"def people_unset(self, value, profiles=None, query_params=None, timezone_offset=None, ignore_alias=False,\\n backup=True, backup_file=None):\\n return self.people_operation('$unset', value=value, profiles=profiles, query_params=query_params,\\n timezone_offset=timezone_offset, ignore_alias=ignore_alias, backup=backup,\\n backup_file=backup_file)\",\n \"def _unregister(self):\\n try:\\n self._profilemgr_proxy.proxy.UnregisterProfile(\\n HFP_DBUS_PROFILE_ENDPOINT)\\n logger.debug(\\\"Unregistered HFP profile.\\\")\\n except Exception:\\n logger.exception(\\\"Error unregistering profile endpoint.\\\")\\n\\n self._profile = None\",\n \"def delete_user():\\n del globalopts.appdata[request.user]\\n del globalopts.users[request.user]\\n return \\\"\\\", 200\",\n \"def delete_upload(request):\\r\\n\\tgame_id = request.GET['id']\\r\\n\\tgame = Game.objects.get(id = game_id)\\r\\n\\tif(request.user.profile == game.developer):\\r\\n\\t\\tif request.method == 'POST':\\r\\n\\t\\t\\tgame.delete()\\r\\n\\t\\t\\tprint('game deleted')\\r\\n\\t\\t\\treturn redirect('developer_dashboard')\\r\\n\\t\\telse:\\r\\n\\t\\t\\treturn render(request, 'confirm_delete.html', {'game':game})\\r\\n\\telse:\\r\\n\\t\\treturn redirect('home')\",\n \"def test_delete_hyperflex_node_profile(self):\\n pass\",\n \"def test_profile(profile_manager):\\n name = 'test_profile'\\n yield profile_manager.create(name)\\n if profile_manager.has_profile(name):\\n profile_manager.delete(name)\",\n \"def delete_profile_post(request, pk=None):\\n user = User.objects.get(email=request.user.email)\\n profileposts = ProfilePost.objects.filter(user=request.user)\\n profileposts = ProfilePost.objects.filter(published_date__lte=timezone.now()\\n ).order_by('-published_date').all()\\n contactuserposts = ContactUser.objects.all() \\n profilepost = get_object_or_404(ProfilePost, pk=pk)\\n if (request.user == profilepost.user or\\n request.user.is_superuser):\\n if request.method == \\\"POST\\\":\\n profilepost.delete()\\n messages.success(request, 'This information has been successfully deleted.')\\n return render(request, 'profile.html', {'profileposts': profileposts, 'profile': user, 'contactuserposts': contactuserposts})\\n return render(request, \\\"profilepostdelete.html\\\", {'profilepost': profilepost})\",\n \"def remove(self, session: \\\"pwncat.manager.Session\\\"):\",\n \"def delete(self, _uri):\\n print(\\\"Deleting '%s'\\\"%(_uri))\\n response = self.__httpsRequest('DELETE', _uri, '')\",\n \"def del_user(self, username):\\n pass\",\n \"def delete_user(id):\\n pass\",\n \"def delete(bot, update):\\n chatID = update.message.chat_id\\n username = get_user_info(chatID)['PID']\\n logger.info(\\\"Deleting user credentials for {}!\\\".format(username))\\n Chat.query.filter(Chat.chatID == chatID).delete() # Delete the user's record referenced by their ChatID\\n Misc.query.filter(Misc.chatID == chatID).delete()\\n db_session.commit()\\n messageContent = \\\"Your credentials have been deleted, {}\\\\nHope to see you back soon!\\\".format(username[3:-4].title())\\n bot.sendMessage(chat_id=update.message.chat_id, text=messageContent)\\n \\n mp.track(username, 'User Left')\\n mp.people_set(username, {'active': False })\",\n \"def delete_my_account():\\n # Remove user ownerships\\n for p in current_user.projects:\\n p.user_id = None\\n p.save()\\n # Delete user posts\\n [ a.delete() for a in current_user.activities ]\\n # Delete user account\\n current_user.delete()\\n logout_user()\\n flash('We are sorry to see you go. Your profile has been deleted.', 'info')\\n return redirect(url_for('public.home'))\",\n \"def remove_spawning_profile(intersection, spawning_profile):\\n return intersection.remove_spawning_profile(spawning_profile)\",\n \"def _delete(\\n session: \\\"Session\\\", url_tail: str, params: Optional[Dict[str, Any]] = None\\n) -> None:\\n url = session.get_base_url2() + url_tail\\n response = _requests_session.delete(\\n url,\\n headers=_get_headers(session),\\n params=params,\\n verify=session.verify_ssl_certs,\\n )\\n _check_response_status(response)\",\n \"def delete(self, data):\\n url = self.base_url + '/v2/account/delete/'\\n return self._call_vendasta(url, data)\",\n \"def remove_user(self, username):\\n\\n row = self.c.execute(\\\"SELECT * FROM profiles WHERE name =?\\\",\\n (username,))\\n for i in row:\\n user = i[1]\\n print(user)\\n if user == username:\\n self.c.execute(\\\"SELECT id FROM profiles WHERE name=?\\\",\\n (username,))\\n i_d = self.c.fetchone()[0]\\n self.c.execute(\\\"DELETE FROM events WHERE user_id=?\\\", (i_d,))\\n self.c.execute(\\\"DELETE FROM profiles WHERE name=?\\\", (username,))\\n self.conn.commit()\\n return True\\n else:\\n print ('User not found.')\",\n \"def delete():\\n # Must be logged in to perform any delete commands.\\n auth_required()\\n pass\",\n \"def onUserDeletion(event):\\n client = getUtility(IAdminClient)\\n xmpp_users = getUtility(IXMPPUsers)\\n storage = getUtility(IPubSubStorage)\\n\\n principal_id = event.principal\\n principal_jid = xmpp_users.getUserJID(principal_id)\\n\\n if principal_id in storage.leaf_nodes:\\n storage.leaf_nodes.remove(principal_id)\\n if principal_id in storage.publishers:\\n del storage.publishers[principal_id]\\n if principal_id in storage.node_items:\\n del storage.node_items[principal_id]\\n if principal_id in storage.collections['people']:\\n storage.collections['people'].remove(principal_id)\\n\\n pass_storage = getUtility(IXMPPPasswordStorage)\\n pass_storage.remove(principal_id)\\n\\n d = deletePrincipal(client, principal_jid)\\n return d\",\n \"def delete(self, path):\\n client = self.connect(VAULT_TOKEN)\\n client.delete(path)\",\n \"def remove(self):\\n self._switch.odlclient._request(self._path, method=\\\"delete\\\")\",\n \"def delete(\\n student: str = typer.Argument(..., help=\\\"Username of a student to be removed.\\\"),\\n quiet: bool = typer.Option(\\n False, \\\"-q\\\", \\\"--quiet\\\", help=\\\"Disable additional logging.\\\"\\n ),\\n):\\n\\n api = setup(quiet)\\n result = 0 if api.delete(student) else 1\\n sys.exit(result)\",\n \"def get(self):\\n\\n try:\\n health.UnlinkProfile()\\n except gdata.service.RevokingOAuthTokenFailed:\\n pass\\n\\n self.redirect('/')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8142084","0.80761915","0.7869769","0.7786373","0.76816595","0.7514914","0.7442937","0.7420244","0.74095243","0.739577","0.7388937","0.7387069","0.7378266","0.737173","0.73160154","0.72956455","0.72497237","0.7233755","0.7171392","0.7163262","0.7143254","0.70918286","0.69732976","0.6917448","0.69001377","0.68919677","0.68602335","0.68385106","0.68194526","0.6785866","0.6776873","0.6755161","0.67247725","0.6647321","0.6581248","0.65553725","0.6508326","0.65033835","0.6460328","0.6399572","0.6395834","0.63725334","0.6368723","0.63625985","0.6354759","0.6276307","0.62691945","0.6267918","0.6214599","0.6202639","0.6196568","0.6196025","0.61912817","0.6133376","0.6097302","0.6077619","0.60729307","0.6063385","0.60579294","0.6054931","0.604663","0.604127","0.6038107","0.6004333","0.59794295","0.59757966","0.5973865","0.59421146","0.5928095","0.59062415","0.59034085","0.5890709","0.58852863","0.5878921","0.5876342","0.58745956","0.58157486","0.58073014","0.5796873","0.5787956","0.5774087","0.5751076","0.57321155","0.571373","0.5707372","0.56808937","0.5678906","0.5666083","0.566355","0.5660391","0.56546074","0.56511843","0.5651168","0.5648144","0.56228614","0.56047213","0.559465","0.5581884","0.55727977","0.55717576"],"string":"[\n \"0.8142084\",\n \"0.80761915\",\n \"0.7869769\",\n \"0.7786373\",\n \"0.76816595\",\n \"0.7514914\",\n \"0.7442937\",\n \"0.7420244\",\n \"0.74095243\",\n \"0.739577\",\n \"0.7388937\",\n \"0.7387069\",\n \"0.7378266\",\n \"0.737173\",\n \"0.73160154\",\n \"0.72956455\",\n \"0.72497237\",\n \"0.7233755\",\n \"0.7171392\",\n \"0.7163262\",\n \"0.7143254\",\n \"0.70918286\",\n \"0.69732976\",\n \"0.6917448\",\n \"0.69001377\",\n \"0.68919677\",\n \"0.68602335\",\n \"0.68385106\",\n \"0.68194526\",\n \"0.6785866\",\n \"0.6776873\",\n \"0.6755161\",\n \"0.67247725\",\n \"0.6647321\",\n \"0.6581248\",\n \"0.65553725\",\n \"0.6508326\",\n \"0.65033835\",\n \"0.6460328\",\n \"0.6399572\",\n \"0.6395834\",\n \"0.63725334\",\n \"0.6368723\",\n \"0.63625985\",\n \"0.6354759\",\n \"0.6276307\",\n \"0.62691945\",\n \"0.6267918\",\n \"0.6214599\",\n \"0.6202639\",\n \"0.6196568\",\n \"0.6196025\",\n \"0.61912817\",\n \"0.6133376\",\n \"0.6097302\",\n \"0.6077619\",\n \"0.60729307\",\n \"0.6063385\",\n \"0.60579294\",\n \"0.6054931\",\n \"0.604663\",\n \"0.604127\",\n \"0.6038107\",\n \"0.6004333\",\n \"0.59794295\",\n \"0.59757966\",\n \"0.5973865\",\n \"0.59421146\",\n \"0.5928095\",\n \"0.59062415\",\n \"0.59034085\",\n \"0.5890709\",\n \"0.58852863\",\n \"0.5878921\",\n \"0.5876342\",\n \"0.58745956\",\n \"0.58157486\",\n \"0.58073014\",\n \"0.5796873\",\n \"0.5787956\",\n \"0.5774087\",\n \"0.5751076\",\n \"0.57321155\",\n \"0.571373\",\n \"0.5707372\",\n \"0.56808937\",\n \"0.5678906\",\n \"0.5666083\",\n \"0.566355\",\n \"0.5660391\",\n \"0.56546074\",\n \"0.56511843\",\n \"0.5651168\",\n \"0.5648144\",\n \"0.56228614\",\n \"0.56047213\",\n \"0.559465\",\n \"0.5581884\",\n \"0.55727977\",\n \"0.55717576\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7901775"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":22292,"cells":{"query":{"kind":"string","value":"Calculates maximum likelihood estimates"},"document":{"kind":"string","value":"def aicmle(timeSeries, distribution):\n mlevals = {} \n if distribution == 'pareto':\n mlevals['xmin'] = np.min(timeSeries)\n mlevals['mu'] = 1 - timeSeries.shape[0] / (timeSeries.shape[0] * np.log(mlevals['xmin']) - np.sum(np.log(timeSeries)))\n \n elif distribution == 'lognormal':\n mlevals['mu'] = np.sum(np.log(timeSeries)) / timeSeries.shape[0]\n mlevals['sigma'] = np.sqrt(np.sum( (np.log(timeSeries) - mlevals['mu'])**2) / timeSeries.shape[0])\n \n elif distribution == 'normal':\n mlevals['mu'] = np.mean(timeSeries)\n mlevals['sigma'] = np.sqrt(sum((timeSeries - np.mean(timeSeries))**2) / timeSeries.shape[0])\n \n elif distribution == 'exponential':\n mlevals['lambda'] = 1.0 / np.mean(timeSeries)\n \n elif distribution == 'boundedpl':\n mlevals['xmin'] = np.min(timeSeries)\n mlevals['xmax'] = np.max(timeSeries)\n minmuEstimate = 1.1\n mlevals['mu'] = fmin(lambda mu: -len(timeSeries) * np.log( (mu - 1) / (np.min(timeSeries)**(1 - mu) - np.max(timeSeries)**(1 - mu))) + mu * np.sum(np.log(timeSeries)), minmuEstimate, disp=0)[0]\n\n return mlevals"},"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 maxlikelihood(self):\n\n chi2 = lambda *args: -2 * lnlikelihood.lnlike(*args) \n # print('***DEBUGGING*** chi2 = ', chi2)\n # print('***DEBUGGING*** self.theta_guess = ', self.theta_guess)\n # print('***DEBUGGING*** self.transinfo = ', self.transinfo)\n # print('***DEBUGGING*** self.wave_b = ', self.wave_b)\n # print('***DEBUGGING*** self.flux_b = ', self.flux_b)\n # print('***DEBUGGING*** self.err_b = ', self.err_b)\n # print('***DEBUGGING*** self.wave_r = ', self.wave_r)\n # print('***DEBUGGING*** self.flux_r = ', self.flux_r)\n # print('***DEBUGGING*** self.err_r = ', self.err_r)\n # print('***DEBUGGING*** self.velres = ', self.velres)\n result = op.minimize(chi2, self.theta_guess,\n args=(self.transinfo, self.wave_b, self.flux_b, self.err_b,\n self.wave_r, self.flux_r, self.err_r, self.velres))\n\n self.theta_ml = result[\"x\"]","def max_log_likelihood(data):\n # Assume data is given as counts\n tot = sum(data)\n return _np.sum([nlogp(n, n / tot) for n in data if n > 0])","def maximum_likelihood_loss(z, log_det_J, **args):\n\n return tf.reduce_mean(input_tensor=0.5 * tf.square(tf.norm(tensor=z, axis=-1)) - log_det_J)","def maximum_likelihood_loss(z, log_det_J):\n\n return tf.reduce_mean(0.5 * tf.square(tf.norm(z, axis=-1)) - log_det_J)","def evaluate_GMM_log_likelihood(model, x, y):\n y_pred = model.predict(x)\n \n num_datapoints = len(x)\n output_dim = y.shape[-1]\n num_comp = int(y_pred.shape[-1] / (3*output_dim))\n\n mix_comp_logits = y_pred[:, :num_comp]\n mus = y_pred[:, num_comp:(1+output_dim)*num_comp]\n sigmas = y_pred[:, (1+output_dim)*num_comp:]\n \n # convert logits to categorical distribution - need to itterate through all points\n mix_comp = np.zeros((num_datapoints, num_comp))\n for i in range(num_datapoints):\n mix_comp[i,:] = get_mixture_dist(mix_comp_logits[i,:], num_comp)\n \n log_likelihood = 0\n for i in range(num_comp):\n for j in range(output_dim):\n mse = -0.5*np.sum(mix_comp[:,i]*np.square((y[:,j]-mus[:,(i*output_dim)+j])/sigmas[:,(i*output_dim)+j]))\n sigma_trace = -np.sum(mix_comp[:,i]*np.log(sigmas[:,(i*output_dim)+j]))\n log2pi = -np.sum(mix_comp[:,i]*0.5*output_dim*np.log(2*np.pi))\n\n log_likelihood += mse + sigma_trace + log2pi\n \n avg_log_likelihood = np.round(log_likelihood / num_datapoints, 2)\n print(f'Log likelihood: {avg_log_likelihood}')\n return avg_log_likelihood","def maximum_likelihood_estimation(head_count, tail_count):\n return head_count / (head_count + tail_count)","def compute_log_likelihood(self,params: ndarray) -> float:\n \n pred_mag = self._pred_mag(params,self.times)\n sigma_2 = self.sd_mags**2 \n ln_likelihood = -0.5*np.sum((pred_mag - self.mags)**2 / sigma_2+ np.log(sigma_2))\n\n return ln_likelihood","def __log_likelihood(self, params, *args):\n\t\tX, y, feature_set, lambda_reg, empirical_weights, verbose, sign = args\n\n\t\tno_example = len(X)\n\t\ttotal_logZ = 0\n\t\ttotal_logProb = 0\n\t\texpected_weights = np.zeros(len(feature_set))\n\t\tfor t in range(len(X)):\n\t\t\t# example_features = X[t], example_labels = y[t]\n\n\t\t\tpotential = np.zeros(len(X[t]))\n\t\t\tfor i in range(len(X[t])):\n\t\t\t\t#candidate_features = X[t][i], candidate_label = y[t][i]\n\t\t\t\tpotential[i] = feature_set.calc_inner_product(X[t][i], params)\n\n\t\t\t#scaling\n\t\t\tpotential = potential - np.max(potential, keepdims=True)\n\n\t\t\tfor i in range(len(X[t])):\n\t\t\t\ttotal_logProb += potential[i] * y[t][i]\n\n\t\t\tpotential, Z = self.__softmax(potential)\n\n\t\t\tfor i in range(len(X[t])):\n\t\t\t\tfeature_set.calc_inner_sum(expected_weights, X[t][i], potential[i])\n\n\t\t\ttotal_logZ += log(Z)\n\n\t\t# _params = feature_set.get_regularized_params(params, 'bias')\n\t\t_params = params\n\t\tlog_likelihood = total_logProb - total_logZ - (lambda_reg/2) * np.sum(np.multiply(_params,_params))\n\t\tgradients = empirical_weights - expected_weights - lambda_reg * _params\n\n\t\tglobal SUB_ITERATION_NUM\n\t\tif verbose:\n\t\t\tsub_iteration_str = ' '\n\t\t\tif SUB_ITERATION_NUM > 0:\n\t\t\t\tsub_iteration_str = '(' + '{0:02d}'.format(SUB_ITERATION_NUM) + ')'\n\t\t\tprint(' ', '{0:03d}'.format(ITERATION_NUM), sub_iteration_str, ':', log_likelihood * sign)\n\n\t\tSUB_ITERATION_NUM += 1\n\n\t\treturn sign * log_likelihood, sign * gradients","def log_marginal_likelihood(X_train,y_train,phi,tau=1.,Ve=1.e-10):","def compute_maximisation( self, X, Z, O ):\n\n raise NotImplementedError","def likelihood(params,data):\n spec, isnflux, igalflux = data\n chi2=0\n modflux = (params[0]*isnflux + params[1]*igalflux)\n chi2 += sum((spec.flux - modflux)**2)/((0.05*sum(spec.var)**2)/2.0)\n return np.exp(-chi2/2.0)","def log_likelihood(self, y_list):\n if self.lambda_mat is None:\n raise ValueError(\"Can't compute model likelihood before fitting!\")\n\n # precision prior distribution given precision hyper-parameters\n prec_distr = stats.gamma(a=self.prec_distr[0],\n scale=self.prec_distr[1] ** -1.0)\n\n # likelihood of projection matrix precision priors given\n # precision hyper-parameters\n lambda_logl = np.sum(\n prec_distr.logpdf(self.lambda_mat['alpha']\n / self.lambda_mat['beta'])\n )\n\n # likelihood of projection matrix values given their precision priors\n a_logl = np.sum(\n stats.norm(loc=0, scale=(self.lambda_mat['beta']\n / self.lambda_mat['alpha']))\n .logpdf(self.A_mat['mu'])\n )\n\n # likelihood of latent feature matrix given kernel matrix,\n # projection matrix, and standard deviation hyper-parameter\n h_logl = np.sum(\n stats.norm(loc=self.A_mat['mu'].transpose() @ self.kernel_mat,\n scale=self.sigma_h)\n .logpdf(self.H_mat['mu'])\n )\n\n # likelihood of bias parameter precision priors given\n # precision hyper-parameters\n weight_prior_logl = np.sum(\n prec_distr.logpdf(np.array(self.weight_priors['alpha'])\n / np.array(self.weight_priors['beta']))\n )\n\n # likelihood of bias parameters given their precision priors\n weight_logl = np.sum(\n stats.norm(loc=0, scale=(np.array(self.weight_priors['beta'])\n / np.array(self.weight_priors['alpha'])))\n .logpdf(self.weight_mat['mu'])\n )\n\n # likelihood of predicted outputs given latent features, bias\n # parameters, and latent feature weight parameters\n f_logl = np.sum(\n stats.norm(\n loc=(self.weight_mat['mu'][1:, :].transpose()\n @ self.H_mat['mu']\n + np.vstack(self.weight_mat['mu'][0, :])),\n scale=1).logpdf(self.output_mat['mu'])\n )\n\n # likelihood of actual output labels given class separation margin\n # and predicted output labels\n y_logl = np.sum(self.get_y_logl(y_list))\n\n return (lambda_logl + a_logl + h_logl\n + weight_prior_logl + weight_logl + f_logl + y_logl)","def max_log_likelihood_fit(self) -> ag.FitQuantity:\r\n\r\n return self.analysis.fit_quantity_for_instance(instance=self.instance)","def calculate_likelihood(self, number_of_topics):\n print(\"Start of calculate_likelihood...\")\n maxNumberOfWrd = 0\n for docValue in range(0,self.number_of_documents):\n for wordValue in range(0,self.vocabulary_size):\n maxTopicVal = 0\n for topicValue in range(0,number_of_topics):\n maxTopicVal = maxTopicVal + ((self.document_topic_prob[docValue][topicValue]*self.topic_word_prob[topicValue][wordValue]))\n if maxTopicVal > 0:\n maxNumberOfWrd = maxNumberOfWrd + (np.log(maxTopicVal) * self.term_doc_matrix[docValue][wordValue])\n\t\t#print(\"Value of maxNumberOfWrd >>> \"+maxNumberOfWrd)\n self.likelihoods.append(maxNumberOfWrd)\n\t\t#print(\"End of calculate_likelihood...\")\n return","def MVN_log_likelihood(X, model):\n D, M = X.shape\n X_normalized = normalize_log_likelihoods(X.copy())\n mvn = multivariate_normal(mean=model.mean, cov=model.cov)\n return mvn.logpdf(X_normalized.T).sum()\n # log_2pi = D * np.log(2 * np.pi)\n # log_det = np.log(np.linalg.det(model.cov))\n # residuals = calc_residuals(X_normalized, model.mean, \"minus\")\n # mahalanobis_distance = np.dot(np.dot(residuals.T, np.linalg.inv(model.cov)), residuals)\n # return -0.5 * (log_2pi + log_det + mahalanobis_distance).sum()","def GUE0_lambda_max_estimate(d, num_samples):\n return np.mean([GUE0_lambda_max(d) for _ in range(num_samples)])","def relative_likelihood(self):\n \n if self.num_hidden == 0:\n \n return T.exp(-self.compute_energy(self.x, self.batch_size))\n \n if self.num_hidden > 0:\n \n return T.exp(-self.compute_free_energy(self.x))","def likelihood(self):\n \n raise NotImplementedError()","def log_likelihood(self, params):\n # extract the parameters\n m1 = params['m1']\n m2 = params['m2']\n DL = params['DL']\n Tc = params['Tc']\n iota = params['iota']\n phic = params['phic']\n psi = params['psi']\n thetaS = params['thetaS']\n phiS = params['phiS']\n\n # calculate the model\n model = self._model(time, m1, m2, DL, Tc, iota, phic, psi, thetaS, phiS)\n\n# # normalisation\n# norm = -0.5*self._ndata*LN2PI - self._ndata*self._logsigma\n\n# # chi-squared\n# chisq = np.sum(((self._data - model)/(self._sigma))**2)\n\n return -np.vdot(self._data - model,self._data - model)","def loglikelihood(y: float64, m: float64, sigma: float64) -> float64:\n\n # -log(sqrt(2*pi)) = -0.9189385332046727\n\n return -0.9189385332046727 - np.log(sigma) - (y - m) * (y - m) / (2.0 * sigma * sigma)","def auxmaxf2(x):\n # Sum over data points\n f = 0.0\n for m_ind in range(cfg.ntrain):\n f += auxmaxrho1(x,m_ind) + auxmaxrho2(x,m_ind) \n \n return f","def likelihood_prediction():\n # Get info\n selected_word = prompt_tech_selection()\n article_json = get_json_from_file()\n\n # Calculate results\n total_word_counter, selected_word_counter = count_occurrences(article_json, selected_word)\n probability = selected_word_counter / total_word_counter\n total_time = article_json[-1]['time'] - article_json[0]['time'] # unix subtraction = seconds\n months_in_train_set = total_time / SECONDS_IN_MONTH\n expected_posts_per_month = int(total_word_counter / months_in_train_set)\n\n # Show results\n print_text_results(expected_posts_per_month, probability, selected_word)\n plot_likelihood(expected_posts_per_month, probability)","def log_marginal_likelihood(self, eval_gradient=False):\n A, B, mu_tilda, gamma_tilda, r_grid, n_grid = self.calc_tau(return_all=True)\n\n gamma = self.GAMMA\n gamma_y = self.GAMMA_Y\n\n A1 = np.copy(self.baseTau0)\n A1[np.diag_indices_from(A1)] = 2 * self.ndim\n\n # A1 = (A - B) / gamma_y\n A2 = np.diag(n_grid)\n\n B_inv = np.linalg.inv(B)\n A_inv = np.linalg.inv(A)\n\n M_lambda = B - B.dot(np.linalg.inv(A).dot(B))\n\n # log_likelihood = mu_tilda.dot(M_lambda).dot(\n # mu_tilda[:, np.newaxis]) - np.nan_to_num(np.log(np.linalg.det(M_lambda)))\n log_likelihood = mu_tilda.dot(M_lambda).dot(mu_tilda[:, np.newaxis])\n\n # log_likelihood = -log_likelihood\n print('log_likelihood: %f' % log_likelihood)\n\n Lambda = B - B.dot(A_inv.dot(B))\n Lambda_inv = np.linalg.inv(Lambda)\n\n M_gamma = A2 - A2.dot(2 * gamma * A_inv - (gamma ** 2) * A_inv.dot(A2).dot(A_inv)).dot(A2)\n\n mean_r_grid = np.nan_to_num(np.array([r.sum() for r in r_grid]) / n_grid)\n\n log_likelihood_grad_gamma = mean_r_grid.dot(M_gamma.dot(mean_r_grid[:, np.newaxis]))[0]\n\n tmpMat = A2.dot(A_inv).dot(A1).dot(A_inv).dot(A2)\n\n log_likelihood_grad_gamma_y = (gamma ** 2) * (\n np.trace(Lambda_inv.dot(tmpMat) + mean_r_grid.dot(tmpMat.dot(mean_r_grid[:, np.newaxis]))[0]))\n\n log_likelihood_gradient = np.array([log_likelihood_grad_gamma, log_likelihood_grad_gamma_y])\n print('log_likelihood_grad: %s' % log_likelihood_gradient)\n\n if eval_gradient:\n return log_likelihood, log_likelihood_gradient\n else:\n return log_likelihood","def MAXED(N, sigma2, R, f_def, params):\n\n # pull out algorithm-specific parameters\n Omega = params['Omega']\n\n # create the function that we will maximize, Z\n def Z(lam, N, sigma2, R, f_def, Omega):\n \"\"\"A function, the maximization of which is equivalent to the\n maximization of \"\"\"\n\n A = - np.sum(f_def * np.exp(- np.sum((lam * R.T).T, axis=0)))\n B = - (Omega * np.sum(lam**2 * sigma2))**(0.5)\n C = - np.sum(N * lam)\n\n # negate because it's a minimization\n return - (A + B + C)\n\n # create a lambda\n lam = np.ones(len(N))\n\n # apply the simulated annealing to the Z\n mk = {'args': (N, sigma2, R, f_def, Omega)}\n lam = basinhopping(Z, lam, minimizer_kwargs=mk).x\n\n # back out the spectrum values from the lam\n return f_def * np.exp(-np.sum((lam * R.T).T, axis=0))","def p_logl_max(self):\n sampler = self.__sampler\n if sampler == \"EnsembleSampler\":\n chain = self.sampler.chain\n lnlike = self.sampler.lnprobability\n elif sampler == \"PTSampler\":\n chain = self.sampler.chain[0, ...]\n lnlike = self.sampler.lnlikelihood[0, ...]\n else:\n raise ValueError(\"[linfit]: The sampler type ({0}) is unrecognised!\".format(sampler))\n idx = lnlike.ravel().argmax()\n p = chain.reshape(-1, self.ndim)[idx]\n return p","def _getCurrentPosteriorLikelihood(self): \n likelihood = 0\n T = self.T\n K= self.K \n final_likelihood = 0\n total_log_lik = 0\n \n for n in range(1,self.N+1):\n # Compute total Likelihood for all Instances P(x1...xn / theta) \n tot_lik = 0\n tot_scale_factor = 0\n \n for i in range(1,self.K+1): \n likelihood = self.posterior_state_trellis[n][(T,i)]\n tot_lik = tot_lik + likelihood\n\n try:\n total_log_lik = math.log(tot_lik) \n except ValueError:\n ipdb.set_trace()\n \n for t in range(1,self.T):\n scale_factor = self.forward_scaling_vector[n][t] \n tot_scale_factor = tot_scale_factor + math.log(scale_factor)\n\n final_likelihood = final_likelihood + (total_log_lik - tot_scale_factor)\n\n return final_likelihood","def log_likelihood(self, data, reward_model, bias_params):","def loss_maxL(y, tx, w):\n # to avoid problems when 0 in log\n epsilon=0.00000001\n sig=sigmoid(tx.dot(w))\n # calculated probability\n p=y.T.dot(np.log(sig+epsilon)) + (1-y).T.dot(np.log(1-sig+epsilon))\n #divides with number of samples so that learning rate is not dependant on number of samples\n p=p/len(y)\n return np.squeeze(- p)","def calculate_ln_posterior(thetas, posterior_stats):\n\tln_posterior = np.dot(posterior_stats['fisher'], (thetas - posterior_stats['mean']))\n\tln_posterior = - np.dot((thetas - posterior_stats['mean']).transpose(), ln_posterior) /2\n\n\treturn ln_posterior","def log_likelihood_exp(self, x):\n predictions = self.get_predictions(x)\n ll = 0.\n for measurement in self.get_measurements:\n m_obj = flavio.Measurement[measurement]\n m_obs = m_obj.all_parameters\n exclude_observables = set(m_obs) - set(self.observables)\n prob_dict = m_obj.get_logprobability_all(predictions, exclude_parameters=exclude_observables)\n ll += sum(prob_dict.values())\n return ll","def _expectation_maximization(self, y, responsibility=None, **kwargs): \n\n # Calculate log-likelihood and initial expectation step.\n __init_responsibility, ll, dl = self._expectation(y, **kwargs)\n if responsibility is None:\n responsibility = __init_responsibility\n\n ll_dl = [(ll.sum(), dl)]\n\n meta = dict(warnflag=False)\n for iteration in range(self.max_em_iterations):\n\n # M-step.\n self._maximization(y, responsibility, **kwargs)\n\n # E-step.\n responsibility, ll, dl = self._expectation(y, **kwargs)\n\n # Check for convergence.\n lls = ll.sum()\n prev_ll, prev_dl = ll_dl[-1]\n change = (lls - prev_ll)/prev_ll\n ll_dl.append([lls, dl])\n\n #print(\"E-M\", iteration, change, self.threshold)\n\n if abs(change) <= self.threshold:\n break\n\n else:\n meta.update(warnflag=True)\n logger.warn(\"Maximum number of E-M iterations reached ({})\"\\\n .format(self.max_em_iterations))\n\n meta.update(log_likelihood=lls, message_length=dl)\n\n return (responsibility, meta)","def auxmaxf1(x):\n \n# Sum over data points\n f = 0.0\n for m_ind in range(cfg.ntrain):\n f += auxmax_f1_part_i(x,m_ind) \n \n return f","def maximize_marginal_likelihood(kernel, model, optimizer, output_directory,\n testing_data, feature_names, plot_ARD, plot_params):\n\n # run the optimiser with a callback function if user wants to track the parameters\n if plot_params:\n parameter_log = []\n opt_logs = optimizer.minimize(closure=model.training_loss, variables=model.trainable_variables,\n step_callback=(lambda x,y,z: parameter_log.append([x, z])),\n options=dict(maxiter=ci_niter(250)))\n\n else:\n # run the optimiser without a callback function otherwise\n opt_logs = optimizer.minimize(closure=model.training_loss, variables=model.trainable_variables,\n options=dict(maxiter=ci_niter(250)))\n\n # set data against which to validate the model\n features_test = testing_data['features']\n affinity_test = testing_data['affinity']\n\n # calculate the predictions and Pearson's R, Spearman's R as well as RMSE\n mean, var = model.predict_f(features_test)\n pearsonsr, pvalue = pearsonr(mean.numpy().flatten(), affinity_test.values)\n spearmansr, spvalue = spearmanr(a=mean.numpy().flatten(), b=affinity_test.values)\n rmse = np.sqrt(mean_squared_error(affinity_test.values, mean.numpy().flatten()))\n\n # write the results to a file\n filename = f'{model.name}_{kernel.name}'+'.csv'\n\n with open(output_directory+'/'+filename, 'w') as out_file:\n out_file.write(f'%Gaussian process regression with a Gaussian likelihood\\n')\n out_file.write(f'%model: {model.name}, kernel: {kernel.name}\\n')\n out_file.write(f'Optimization success: {opt_logs.get(\"success\")} in {opt_logs.get(\"nit\")} iterations, {opt_logs.get(\"message\")}\\n')\n for key, value in gp.utilities.read_values(model).items():\n out_file.write(f'%{key}: {value}\\n')\n out_file.write(f'%loglikelihood: {model.log_marginal_likelihood()}\\n')\n out_file.write(f'%RMSE:{rmse:.3f}\\n')\n out_file.write(f'%Pearson_correlation_coefficient:{pearsonsr:.3f},P-value:{pvalue:.3f}\\n')\n out_file.write(f'%Spearman_correlation_coefficient:{spearmansr:.3f},P-value:{spvalue:.3f}\\n')\n out_file.write('%%%%%%PREDICTIONS%%%%%\\n')\n out_file.write(f'name,f_pred_mean,f_pred_var,y_true,{\",\".join(feature_names)}\\n')\n for i in range(0, len(mean)):\n out_file.write(f'{affinity_test.index.values[i]},{mean.numpy()[i][0]:.4f},{var.numpy()[i][0]:.4f},{affinity_test.values[i]:.4f},{\"\".join(str(i)+\",\" for i in features_test[i].round(4).tolist())[:-1]}\\n')\n out_file.close()\n\n # create the plots that were specified in the arguments to the specified output directory\n if plot_ARD:\n plot_feature_rankings(lengthscales=model.kernel.kernels[0].lengthscales.numpy(),\n feature_names=feature_names, figpath=output_directory+'/feature_relevance.png')\n if plot_params:\n plot_parameter_change(parameter_log=parameter_log, figpath=output_directory+'/parameter_change.png')","def max_log_likelihood_fit(self) -> FitInterferometer:\r\n return self.analysis.fit_interferometer_via_instance_from(\r\n instance=self.instance\r\n )","def log_marg_likelihood(self):\n self.A = np.linalg.inv(self.Sn)\n term1 = self.t - self.design_matrix@self.mn\n self.Evidence_mN = (self.beta/2)*np.linalg.norm(term1)+ (self.alpha/2)*self.mn.T@self.mn\n A_abs = np.linalg.eigvals(self.A)\n A_abs = np.prod(A_abs)\n\n self.marg_lik = ((self.p)/2)*np.log(self.alpha) + (self.n/2)*np.log(self.beta) - self.Evidence_mN - (1/2)*np.log(A_abs) - (self.n/2)*np.log(2*np.pi)\n\n return self.marg_lik","def log_likelihood(mu, sigma, y, T):\n ll = 0.\n for yi, Ti in zip(y, T):\n d = yi.size\n log_det_cov = np.linalg.slogdet(sigma[Ti])[1]\n y_minus_mean = yi - mu[Ti]\n term3 = np.dot(y_minus_mean.T.ravel(),\n np.linalg.solve(sigma[Ti], y_minus_mean.T).ravel())\n ll += (-0.5 * d * np.log(2 * np.pi) - 0.5 * log_det_cov - 0.5 * term3)\n return ll","def mamajek08_logRpHK_max():\n return -3.8918287373004357","def compute_prob_mle(X: np.ndarray) -> float:\n\n Geometric._check_input_data(X=X)\n Geometric._check_support(X=X)\n\n prob = 1 / X.mean()\n return prob","def _get_max_likelihood_genus(self, words,\n word_posteriors, word_idxs):\n #Argmax prod( p(vi|G) )\n row_idxs = filter(not_none, map(word_idxs.get, words))\n likelihoods = word_posteriors[row_idxs].prod(0)\n # avoid .argmax() to solve tie problem.\n return (likelihoods == likelihoods.max()).nonzero()[0]","def _compute_log_likelihood(self, parameters):\n raise NotImplementedError('')","def __findBestLogProbability(self):\n best_model = None\n highest_log_probability = -sys.maxsize# (np.finfo(float).eps)\n\n # Find the highest model\n for item in self.data_array:\n if item[1] > highest_log_probability:\n best_model = item\n highest_log_probability = item[1]\n\n return best_model","def log_likelihood(self):\r\n if self.likelihood.is_heteroscedastic:\r\n A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)\r\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self._A))\r\n else:\r\n A = -0.5 * self.num_data * self.output_dim * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT\r\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self._A))\r\n C = -self.output_dim * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.num_inducing * np.log(sf2))\r\n D = 0.5 * self.data_fit\r\n self._A_part, self._B_part, self._C_part, self._D_part = A, B, C, D\r\n return A + B + C + D + self.likelihood.Z","def _calculate_ll(self, x):\n observation_log_probs = self._observation_log_probs(x, mask=None)\n forward_log_probs = self._forward(observation_log_probs)\n log_likelihood = logsumexp(\n forward_log_probs[forward_log_probs.shape[0] - 1, :].numpy())\n return log_likelihood","def log_likelihood(self):\r\n return (-0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) -\r\n 0.5 * self.output_dim * self.K_logdet + self._model_fit_term() + self.likelihood.Z)","def log_likelihood(self):\r\n A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.beta_star)) - 0.5 * np.sum(self.V_star * self.likelihood.Y)\r\n C = -self.output_dim * (np.sum(np.log(np.diag(self.LB))))\r\n D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))\r\n return A + C + D + self.likelihood.Z","def Likelihood(self, data, hypo):\n p_correct = hypo\n score = data\n\n k = self.exam.Reverse(score)\n n = self.exam.max_score\n like = thinkbayes2.EvalBinomialPmf(k, n, p_correct)\n return like","def maximum_likelyhood_estimate(mc_model):\n m = pymc.MAP(mc_model)\n m.fit()\n return (m.values['theta'],) + tuple(m.values[p] for p in m.params)","def run_MH(input_datapath, points_datapath, M, filename, calEstimate=False):\n\n #input data\n t = read_data(input_datapath, ',')\n\n #2D data points\n r = read_data(points_datapath, ',')\n\n #mean and variance for prior\n mu = np.array([0,0,4])\n var = 10*np.identity(3)\n\n #variance for likelihood\n co_var = 0.0025*np.identity(2)\n\n #random initial and final 3D points \n pi,pf = np.random.multivariate_normal(mu, var, 2)\n\n #initializing corresponding initial & final 2D points\n qi,qf = [0]*2\n '''\n piArray =np.empty([n+1,3])\n pfArray =np.empty([n+1,3])\n piArray =np.array(piArray,pi)\n pfArray =np.append(pfArray,pf)\n '''\n piArray=np.array(pi)\n pfArray=np.array(pf)\n\n for i in range(0,n):\n pa=np.append(pi, 1)\n pb=np.append(pf, 1)\n\n xi = M.dot(pa)\n xf = M.dot(pb)\n\n xi = xi/xi[2]\n xf = xf/xf[2]\n\n qi = xi[0:2]\n qf = xf[0:2]\n\n qs =np.zeros((t.shape[0],2)) \n\n #Calculating qs which is the mean for likelihood\n for k in range(0,t.shape[0]):\n qs[k]=np.add(qi, np.subtract(qf,qi)*t[k])\n\n ##Calculating posterior for old points\n prior = multivariate_normal.logpdf([pi,pf], mean=mu, cov=var)\n likelihood = np.zeros((t.shape[0],1))\n\n for j in range(0, t.shape[0]):\n #likelihood[j] =multivariate_normal.logpdf(r[j,:], mean=qs[j,:], cov=co_var)\n likelihood[j] =multivariate_normal.logpdf(r[j], mean=qs[j], cov=co_var)\n \n #p_old - posterior for old points\n p_old = np.sum(prior)+np.sum(likelihood)\n\n ##new points\n pi_new= np.random.multivariate_normal(pi,var)\n pf_new= np.random.multivariate_normal(pf,var)\n\n #pi_new +=pi\n #pf_new +=pf\n piArray =np.append(piArray,pi_new)\n pfArray =np.append(pfArray,pf_new)\n\n pa=np.append(pi_new, 1)\n pb=np.append(pf_new, 1)\n\n xi = M.dot(pa)\n xf = M.dot(pb)\n\n xi = xi/xi[2]\n xf = xf/xf[2]\n\n qi_new = xi[0:2]\n qf_new = xf[0:2]\n\n qs =np.zeros((t.shape[0],2)) \n\n for k in range(0,t.shape[0]):\n qs[k]=np.add(qi_new, np.subtract(qf_new,qi_new)*t[k])\n\n #Calculating posterior for new points\n \n prior = multivariate_normal.logpdf([pi_new,pf_new], mean=mu, cov=var)\n likelihood = np.zeros((t.shape[0],1))\n\n for j in range(0, t.shape[0]):\n #likelihood[j] =multivariate_normal.logpdf(r[j,:], mean=qs[j,:], cov=co_var)\n likelihood[j] =multivariate_normal.logpdf(r[j], mean=qs[j], cov=co_var)\n \n\n #p_new - posterior for new points\n p_new = np.sum(prior)+np.sum(likelihood)\n \n if(p_new-p_old>=0):\n pi=pi_new\n pf=pf_new\n qi=qi_new\n qf=qf_new\n else:\n u = np.random.uniform(0.0,1.0,1)\n if(np.log(u)<p_new-p_old):\n pi=pi_new\n pf=pf_new\n qi=qi_new\n qf=qf_new\n\n print '\\n----------------------\\nResults ('+filename+')'\n print '\\nMAP estimate of the 3D line segment (pi&pf):{0},{1}'.format(pi,pf)\n print '\\nCorresponding 2D points (qi&qf):{0},{1}'.format(qi,qf)\n\n if(calEstimate):\n print '\\n Mean of posterior distribution (E[pi],E[pf]):{0},{1}'.format(pi,pf)\n qs = qi+(qf-qi)*1.5\n print '\\n Monte Carlo estimate of the predicted 2D output point:{0}'.format(qs)\n \n plot_data(r, qi,qf,piArray,pfArray,filename)","def GSM_log_likelihood(X, model):\n D, M = X.shape\n k = model.mix.shape[0]\n log_likelihood = 0\n for i in range(M):\n logpdf_X = 0\n for j in range(k):\n mvn = multivariate_normal(cov=model.cov[j, :])\n logpdf_X = mvn.logpdf(x=X[:, i]) * model.mix[j]\n log_likelihood += logpdf_X\n return log_likelihood","def search_for_maximum(self):\n return self.maximise_aquisition(self.expected_improvement)","def softmax_loss(x, y):\n\n eps = 1e-5\n \n N,C = x.shape\n p = softmax(x)\n llikelihood = -np.log(p[range(N),y] + eps)\n# print(llikelihood)\n loss = np.sum(llikelihood) / N\n\n dx = p\n dx[range(N),y] -= 1\n dx = dx/N\n \n return loss, dx","def negative_log_likelihood(self):\n # y.shape[0] is (symbolically) the number of rows in y, i.e.,\n # number of examples (call it n) in the minibatch\n # T.arange(y.shape[0]) is a symbolic vector which will contain\n # [0,1,2,... n-1] T.log(self.p_y_given_x) is a matrix of\n # Log-Probabilities (call it LP) with one row per example and\n # one column per class LP[T.arange(y.shape[0]),y] is a vector\n # v containing [LP[0,y[0]], LP[1,y[1]], LP[2,y[2]], ...,\n # LP[n-1,y[n-1]]] and T.mean(LP[T.arange(y.shape[0]),y]) is\n # the mean (across minibatch examples) of the elements in v,\n # i.e., the mean log-likelihood across the minibatch.\n return -T.log(self.p_y_given_x)[T.arange(self.y.shape[0]), self.y]","def loglikelihood(self, y):\n raise NotImplementedError","def likelihood(self,x,params = None,**kwargs):\n return np.exp(self.log_likelihood(x,params=params,**kwargs))","def log_likelihood(self,samples,times):\n prior_mu = np.ones(2*len(self.A)+1) \n prior_var = np.eye(2*len(self.A)+1)*0.7\n prior_p = np.log(self.prior_pdf())\n #prior_p = np.log(self.normal_prior(prior_mu,prior_var))\n xform = [self.sum_exp(t) for t in times]\n lp = scipy.stats.norm(xform,np.sqrt(self.var)).pdf(samples)\n sample_p =np.sum(np.log(lp))\n ll = prior_p + sample_p\n\n if np.isnan(ll):\n return -np.infty\n return ll","def get_log_marginal_likelihood(self, mode='BIC'):\n if mode == 'BIC':\n if not self.isOptimized:\n print('Parameters have not been optimized; training now')\n self.train()\n if self.BICscore is None:\n BIC = 0\n for i, model in enumerate(self.models):\n n = model.n \n k = model.m.num_params\n L = model.m.log_likelihood()\n BIC += L - k/2*np.log(n)\n self.BICscore = BIC\n return self.BICscore\n elif mode in ['laplace', 'Laplace']:\n raise NotImplementedError('Laplace approximation is not yet implemented')\n elif mode == 'AIS':\n raise NotImplementedError('Annealed importance sampling is not yet implemented')\n else:\n raise NotImplementedError('Unrecognized marginal likelihood approximation {:s}'.format(mode))","def get_mle(self, warmup=1):\n detfs = self.detfs[warmup:]\n sigmas = self.sigmas[warmup:]\n nobs = np.sum(self.observation_count[warmup:])\n mle = nobs * np.log(2 * np.pi) + np.sum(detfs) + np.sum(sigmas)\n return mle","def likelihood(mean, logs, x):\n return -0.5 * (logs * 2. + ((x - mean) ** 2) / np.exp(logs * 2.) + GaussianDiag.Log2PI)","def negative_loglikelihood(targets, estimated_distribution):\n return -estimated_distribution.log_prob(targets)","def negative_log_likelihood(self, y):\r\n \r\n return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])\r\n # end-snippet-2\r","def log_likelihood(self):\r\n assert not self.likelihood.is_heteroscedastic\r\n A = -0.5*self.batchsize*self.output_dim*(np.log(2.*np.pi) - np.log(self.likelihood.precision))\r\n B = -0.5*self.likelihood.precision*self.output_dim*self.trace_K\r\n Kmm_logdet = 2.*np.sum(np.log(np.diag(self.Lm)))\r\n C = -0.5*self.output_dim*self.data_prop*(Kmm_logdet-self.q_u_logdet - self.num_inducing)\r\n C += -0.5*np.sum(self.LQL * self.B)\r\n D = -0.5*self.likelihood.precision*self.likelihood.trYYT\r\n E = np.sum(self.V*self.projected_mean)\r\n return (A+B+C+D+E)/self.data_prop","def compute_prob_mle(X: np.ndarray) -> float:\n\n Bernoulli._check_input_data(X=X)\n Bernoulli._check_support(X=X)\n\n prob = X.mean()\n return prob","def negative_log_likelihood(self, y):\r\n return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])","def negative_log_likelihood(self, y):\r\n return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])","def pwm_max_score(self):\n if self.max_score is None:\n score = 0\n for row in self.pwm:\n score += log(max(row) / 0.25 + 0.01)\n self.max_score = score\n \n return self.max_score","def log_likelihood(self, x):\n # set nuisance parameters to their central values!\n predictions = self.get_predictions(self.shortarray_to_array(x), nuisance=False)\n m_obj = flavio.Measurement['Pseudo-measurement for FastFit instance: ' + self.name]\n m_obs = m_obj.all_parameters\n prob_dict = m_obj.get_logprobability_all(predictions)\n ll = sum(prob_dict.values())\n return ll","def compute_log_likelihood(self, indicators, weights, l2):\n scores, _ = self.predict_probability(self.train_feature_x, weights)\n probs = self.predict_probability(self.train_feature_x, weights)\n lp = np.sum((indicators-1)*scores + np.log(probs)) - l2* np.sum(weights[1:]**2)\n return lp","def _log_likelihood(self, theta, f, x, y, yerr):\n sigma2 = yerr**2\n return -0.5*np.sum((y - f(theta, x))**2 / sigma2 + 2*np.log(sigma2))","def neg_log_likelihood(self,params: ndarray) -> float:\n\n return -self.compute_log_likelihood(params)","def log_likelihood(self):\n return -0.5*self.D*self.K_logdet + self._model_fit_term() + self.likelihood.Z","def likelihood(ts,w,Phi):\n a = Phi.dot(w)\n return np.exp(a*ts)*sigmoid(-a)","def get_bprop_maximum(self):\n input_grad = G.MaximumGrad()\n\n def bprop(x, y, out, dout):\n dx, dy = input_grad(x, y, dout)\n return dx, dy\n return bprop","def compute_log_prior(self,params: ndarray) -> float:\n ln_tE = params[0]\n ln_A0 = params[1]\n ln_deltaT = params[2]\n fbl = params[3]\n mb = params[4]\n\n # Equation (16,15,17) (note that Albrow uses \"log\" for log10)\n log10e = np.log10(np.exp(1))\n ln_pr_ln_tE = np.log(0.476) - ((log10e*ln_tE - 1.333)**2 / 0.330) + np.log(log10e)\n ln_pr_ln_A0 = np.log(0.660) - (1.289*log10e*ln_A0) + np.log(log10e)\n ln_pr_ln_deltaT = np.log(0.156) - ((log10e*ln_deltaT - 1.432)**2 / 0.458) +\\\n np.log(log10e)\n \n # Paper doesnt mention the prior used, but I assume it to be uniform\n ln_pr_fbl = uniform.logpdf(fbl,0.0,1.0)\n\n # Paper doesnr mention the prior used but I will asuumed it to be uniform\n ln_pr_mb = uniform.logpdf(mb,self.mag_min - 1.0, self.mag_max + 1.0)\n \n \n return ln_pr_fbl + ln_pr_ln_A0 + ln_pr_ln_deltaT + ln_pr_ln_tE + ln_pr_mb","def Maxwellian_velocity_distribution_fit(guesses, velocity_component_arr, N_balls_histogram, mass, temperature):\r\n\tdef Gaussian(v, A, mu):\r\n\t\treturn A*np.exp(-(mass*(v-mu)**2/(2*k_B*temperature)))\r\n\r\n\tfit \t = curve_fit(Gaussian, velocity_component_arr, N_balls_histogram, p0 = guesses)\r\n\tdata_fit = Gaussian(velocity_component_arr, *fit[0])\r\n\treturn data_fit, fit[0][1], np.sqrt(np.diag(fit[1]))[1]","def _compute_likelihood(self, mus, pmfs):\n expected_counts = pmfs.copy()\n for mu, _p_bin_source in zip(mus, expected_counts):\n _p_bin_source *= mu # Works because of numpy view magic...\n expected_total = np.sum(expected_counts, axis=0)\n\n observed_counts = self.data_events_per_bin.histogram\n\n ret = observed_counts * np.log(expected_total) - expected_total - gammaln(observed_counts + 1.).real\n return np.sum(ret)","def lnlike(pars, data, massdata=None, bulk_alpha=False, include_ti=False):\n\n pars = np.asarray(pars)\n\n if massdata is not None:\n mgas, mstar, mstarerr = massdata\n\n #Observational data to use in the likelihood determination\n n_obs = len(data['fe_h'])\n\n #Based on the GCE model used, check if the number of parameters is correct\n n_pars = len(pars)\n w = np.where(pars < 0.0)[0]\n if len(w) > 0: return np.inf\n if gce_params.name == 'Zwind':\n if n_pars == 7:\n if pars[6] > 1.0: return np.inf\n else:\n if pars[5] > 1.0: return np.inf\n\n #Run the GCE model for this given set of model parameters\n model,_ = gce.gce_model(pars)\n n_model = len(model['t'])\n\n #Initialize variables to be used in the likelihood calculation\n norm = np.sqrt(2*np.pi)\n likelihood = 0.\n\n #Consider only timesteps after 7 Myr in the galaxy's evolution, and check that\n #there are a sufficient number of finite values in the abundances returned by the\n #GCE model\n where_greater = model['t'] > 0.007\n\n where_finite = np.isfinite(model['eps'][:,mg_index]) &\\\n np.isfinite(model['eps'][:,si_index]) &\\\n np.isfinite(model['eps'][:,ca_index]) &\\\n np.isfinite(model['eps'][:,fe_index])\n if include_ti:\n where_finite = where_finite & np.isfinite(model['eps'][:,ti_index])\n\n where_good = where_greater & where_finite\n if len(model['t'][where_good]) < 10:\n return np.inf\n\n #If the model passes the above criteria, then construct the abundance ratios for\n #[Fe/H], [Mg/Fe], [Si/Fe], [Ca/Fe]\n feh = model['eps'][:,fe_index][where_good]\n mgfe = model['eps'][:,mg_index][where_good] - model['eps'][:,fe_index][where_good]\n sife = model['eps'][:,si_index][where_good] - model['eps'][:,fe_index][where_good]\n cafe = model['eps'][:,ca_index][where_good] - model['eps'][:,fe_index][where_good]\n tife = model['eps'][:,ti_index][where_good] - model['eps'][:,ti_index][where_good]\n\n #Timesteps to consider from the model\n t = model['t'][where_good]\n\n #Calculate the probability with respect to time that a star forms\n dp_dt = model['mdot'][where_good]/model['mstar'][n_model-1]\n dp_dt = dp_dt[dp_dt > 0.]\n dp_dt /= simps(dp_dt, t) #note that the original IDL uses Netwon-Cotes integration here\n\n \"\"\"\n #Do not consider the last 3 Myr of the evolution of the galaxy\n maxt = n_model - 3\n\n #Calculate the integrated ejecta in each element\n ejecta = np.zeros(len(model['eps'][0]))\n for j in range(len(model['eps'][0])):\n\n w = np.where((model['mout'][:,j][7:maxt] > 0.)&(np.isfinite(model['mout'][:,j][7:maxt])))[0] + 7\n if len(w) > 50:\n ejecta[j] = simps(model['mout'][:,j][w], model['t'][w])\n else: ejecta[j] = -999.\n\n #Calculate the total outflow based on the ejecta in each element\n total_outflow = np.sum(ejecta[0:1]) + np.sum(ejecta[3:6])*10**(1.31) + ejecta[7]*10**(0.03)\n\n #Now calculate the integrated mass influx\n w = np.where((model['f_in'][7:maxt] > 0.)&(np.isfinite(model['f_in'][7:maxt])))[0] + 7\n if len(w) > 50:\n integrated_mass = simps(model['f_in'][w], model['t'][w]) + model['mgas'][0]\n else: integrated_mass = -999\n \"\"\"\n\n #Calculate the total mass based on the luminosity and the mass-to-light ratio\n #assumed for the observed dwarf galaxies\n #bdm_ratio = integrated_mass/total_mass\n\n #Now loop through the observational data dictionary provided in the input\n #parameters, where i is the index for a given red giant star in the galaxy,\n #and determine the likelihood as compared to the model for each\n #instance based on simultaneously using [Fe/H], [Mg/Fe], [Si/Fe], and [Ca/Fe]\n\n print(massdata, bulk_alpha, include_ti)\n for i in range(n_obs):\n\n feh_dist = ((feh - data['fe_h'][i])/data['e_fe_h'][i])**2.\n\n if bulk_alpha:\n\n if include_ti:\n alphafe = (mgfe + cafe + sife + tife)/4.\n else:\n alphafe = (mgfe + cafe + sife)/3.\n\n alphafe_dist = ((alphafe - data['alpha_fe'][i])/data['e_alpha_fe'][i])**2.\n\n dist_arr = [feh_dist, alphafe_dist]\n err_arr = [data['e_fe_h'][i], data['e_alpha_fe'][i]]\n\n else:\n\n mgfe_dist = ((mgfe - data['mg_fe'][i])/data['e_mg_fe'][i])**2.\n cafe_dist = ((cafe - data['ca_fe'][i])/data['e_ca_fe'][i])**2.\n sife_dist = ((sife - data['si_fe'][i])/data['e_si_fe'][i])**2.\n\n dist_arr = [feh_dist, mgfe_dist, sife_dist, cafe_dist]\n\n err_arr = [data['e_fe_h'][i], data['e_mg_fe'][i], data['e_si_fe'][i],\n data['e_ca_fe'][i]]\n\n if include_ti:\n tife_dist = ((tife - data['ti_fe'][i])/data['e_ti_fe'][i])**2.\n dist_arr.append(tife_dist)\n err_arr.append(data['e_ti_fe'][i])\n\n err_mult = np.nanprod(err_arr)\n dist = np.nansum(dist_arr, axis=0)\n\n lfunc = np.exp(-0.5*dist) / (err_mult * norm**4.)\n\n int_i = simps(lfunc*dp_dt, t)\n if (np.isfinite(int_i)) and (int_i >= 0.):\n likelihood -= np.log(int_i)\n else:\n likelihood += 5.\n\n #Now add terms to the likelihood based on the stellar mass in the GCE model and the\n #observed gas mass as described in Eq. 17 of Kirby et al 2011b\n if massdata is not None:\n\n #Now calculate the amount of gas remaining in the galaxy at the end of the GCE model\n remaining_gas = model['mgas'][n_model-1]\n if np.abs(model['mgas'][n_model-2] - remaining_gas) > 0.5*remaining_gas:\n remaining_gas = 0.\n if remaining_gas < 0.: remaining_gas = 0.\n\n z0_mstar_term = 0.5*((model['mstar'][n_model-1] - mstar)/mstarerr)**2.\n delta_mgas = 1.e3\n z0_mgas_term = 0.5*((remaining_gas - mgas)/delta_mgas)**2. #assuming observed gas mass of 0 Msun at z = 0\n\n likelihood += 0.1*n_obs*(z0_mstar_term + z0_mgas_term + np.log(norm**2. * mstarerr * delta_mgas))\n\n if (model['mstar'][n_model-1] < delta_mgas): likelihood += 3.e6\n\n return likelihood","def Maxwell_Boltzmann_distribution_fit(guesses, speeds_arr, N_balls_histogram, mass, temperature):\r\n\tdef Max_Boltz_distrib(v, A):\r\n\t\treturn A*v*np.exp(-(mass*v*v/(2*k_B*temperature)))\r\n\r\n\tfit \t = curve_fit(Max_Boltz_distrib, speeds_arr, N_balls_histogram, p0 = guesses)\r\n\tdata_fit = Max_Boltz_distrib(speeds_arr, *fit[0])\r\n\treturn list(data_fit), fit[0][0], np.sqrt(np.diag(fit[1]))[0]","def perform_bayesian_optimization(self):\n bounds = {'hunits': (self.hunits_lower, self.hunits_upper),\n 'embedding_dim': (self.embedding_dim_lower, self.embedding_dim_upper)}\n optimizer = BayesianOptimization(f=self.lstm_score, pbounds=bounds, random_state=1)\n optimizer.maximize(init_points=2, n_iter=self.iterations)\n print(optimizer.max)\n print(optimizer.res)","def _build_likelihood(self):\n\n # Get prior KL.\n KL = self.build_prior_KL()\n\n # Get conditionals\n fmean, fvar = self._build_predict(self.X, full_cov=False)\n\n # Get variational expectations.\n var_exp = self.likelihood.variational_expectations(fmean, fvar, self.Y) * self.obs_weight\n\n # re-scale for minibatch size\n scale = tf.cast(self.num_data, gp.settings.float_type) / tf.cast(tf.shape(self.X)[0], gp.settings.float_type)\n scale = scale / tf.reduce_mean(self.obs_weight)\n return tf.reduce_sum(var_exp) * scale - KL","def loglikelihood(self):\n raise NotImplementedError(\"To be implemented\")","def BMA_Marginal_Likelihood_exh(df_train, df_test):\n\n # number of individual forecasts and number of periods\n K = df_test.shape[1]\n T = df_train.shape[0]\n T_oos = df_test.shape[0]\n # g-prior parameter\n c = float(K**2)\n # separate real value and individual forecasts (for both train and oos)\n y = df_train.iloc[:, 0].values\n multid_y = y[:, np.newaxis]\n F_all = np.concatenate((df_train.iloc[:, 1:].values, df_test.values),\n axis=0)\n # list of variables\n var_list = np.hsplit(F_all, K)\n # list containing vectors of all model by the number of variables\n model_list = []\n # null model\n model_list += [np.full((1, T+T_oos, 1), 1, dtype=float)]\n # single variable models\n model_list += [np.insert(np.squeeze(list(itertools.combinations(\n var_list, 1)), axis=3)[:, :, np.newaxis], 0, 1, axis=2)]\n # 2-(K-1) variable combinations models\n for i in range(2, K):\n model_list += [np.insert(np.swapaxes(np.squeeze(list(\n itertools.combinations(var_list, i)),\n axis=3), 1, 2), 0, 1, axis=2)]\n # K variable model\n model_list += [np.insert(F_all[np.newaxis, :, :], 0, 1, axis=2)]\n\n # initializations\n marginal_lik = np.full((2**K, 1), fill_value=np.nan, dtype=float)\n oos_fcts = np.full((df_test.shape[0], 2**K), np.nan, dtype=float)\n\n # compute marginal likelihoods\n # precompute the right part (same in every case)\n y_bar = np.mean(y)\n S_right = sum((y - y_bar)**2)/(c+1)\n # start indices for sets with different numbers of variables\n start_ind = 0\n # compute the marginal likelihoods for all sets of models\n for i in range(len(model_list)):\n # unpack the training design matrix\n Z = model_list[i][:, :T, :]\n Z_t = np.swapaxes(Z, 1, 2)\n # unpack out-of-sample testing matrix\n Z_oos = model_list[i][:, T:, :]\n # estimate the model in sample\n theta_hat = np.matmul(np.linalg.inv(np.matmul(Z_t, Z)),\n np.matmul(Z_t, multid_y))\n # sum of squared residuals\n SSR = np.sum((multid_y - np.matmul(Z, theta_hat))**2, axis=1)\n S_left = (c/(c+1))*SSR\n S = S_left + S_right\n\n # scale to first decimal place for numerical reasons\n # find the scaling factor\n if i == 0:\n S_rescaled = np.copy(S)\n scaling_factor = 1\n while (S_rescaled < 0.1):\n S_rescaled *= 10\n scaling_factor *= 10\n # rescale S (to prevent overflow)\n S *= scaling_factor\n\n # update set end index\n end_ind = start_ind + Z.shape[0]\n # compute and save marginal likelihood for the given set of models\n marginal_lik[start_ind:end_ind, :] = ((c+1)**(-i))*(S**(-((T-1)/2)))\n # out-of-sample forecasts\n oos_fcts[:, start_ind:end_ind] = np.transpose(\n np.squeeze(np.matmul(Z_oos, theta_hat), axis=2))\n # update set start index\n start_ind = end_ind\n\n # model posterior probabilities\n marginal_lik_sum = np.sum(marginal_lik)\n posterior_prob = marginal_lik / marginal_lik_sum\n\n pred = np.squeeze(np.dot(oos_fcts, posterior_prob), axis=1)\n\n df_pred = pd.DataFrame(\n {\"BMA (Marginal Likelihood)\": pred},\n index=df_test.index)\n\n return df_pred","def calculate_likelihood_probability(measurement, predicted_measurement, covariance):\n \n return None","def negative_log_likelihood(self, y):\n return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])","def negative_log_likelihood(self, y):\n return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])","def gmmloglik(log_emlik, weights):","def gmmloglik(log_emlik, weights):","def train_MLE(self) -> None:\n result = differential_evolution(self.neg_log_likelihood,\n bounds=self.initial_params_bounds)\n self.mle_params = result['x']\n print(result)","def get_parameters_max(self):\n maxValues = numpy.zeros(self.get_num_parameters())\n i = 0\n for p in self.parameters:\n maxValues[i] = p.get_max_value()\n i += 1\n return maxValues","def log_likelihood_function(self, instance: af.ModelInstance) -> float:\r\n model_data = self.model_data_from_instance(instance=instance)\r\n fit = self.fit_from_model_data(model_data=model_data)\r\n return fit.log_likelihood","def negative_log_likelihood(self, y):\r\n # y.shape[0] is (symbolically) the number of rows in y, i.e.,\r\n # number of examples (call it n) in the minibatch\r\n # T.arange(y.shape[0]) is a symbolic vector which will contain\r\n # [0,1,2,... n-1] T.log(self.p_y_given_x) is a matrix of\r\n # Log-Probabilities (call it LP) with one row per example and\r\n # one column per class LP[T.arange(y.shape[0]),y] is a vector\r\n # v containing [LP[0,y[0]], LP[1,y[1]], LP[2,y[2]], ...,\r\n # LP[n-1,y[n-1]]] and T.mean(LP[T.arange(y.shape[0]),y]) is\r\n # the mean (across minibatch examples) of the elements in v,\r\n # i.e., the mean log-likelihood across the minibatch.\r\n return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])","def backward(log_emlik, log_startprob, log_transmat):\n #print(log_transmat)\n beta = np.zeros(log_emlik.shape)\n n = len(log_emlik)-2\n log_transmat = log_transmat[0:-1,0:-1];\n\n\n\n while n >= 0:\n\n for j in range(0,len(log_emlik[0])):\n\n beta[n,j] = logsumexp(log_transmat[j,:] + log_emlik[n+1,:] + beta[n+1,:])\n\n n = n -1\n #print(beta[n,:])\n #print(beta)\n return beta","def negative_log_likelihood(self, y):\r\n # start-snippet-2\r\n # y.shape[0] is (symbolically) the number of rows in y, i.e. number of examples (call it n) in the minibatch\r\n # T.arange(y.shape[0]) is a symbolic vector which will contain [0,1,2,... n-1]\r\n # T.log(self.p_y_given_x) is a matrix of Log-Probabilities (call it LP) with one row per example and one column per class\r\n # LP[T.arange(y.shape[0]),y] is a vector v containing [LP[0,y[0]], LP[1,y[1]], LP[2,y[2]], ..., LP[n-1,y[n-1]]] and\r\n # T.mean(LP[T.arange(y.shape[0]),y]) is the mean (across minibatch examples) of the elements in v, i.e., the mean log-likelihood across the minibatch.\r\n\r\n #print \"y.ndim = \",y.ndim\r\n return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])\r\n # end-snippet-2\r","def _compute_log_likelihood(self, X, S):\n log_likelihood = 0\n for n in range(self.n_col):\n likelihood = 1\n for k in range(self.n_components):\n likelihood *= self.weights[k] \\\n * multivariate_normal(self.means[k], self.covs[k]).pdf(X[n]) \\\n * poisson(self.rates[k]).pmf(S[n])\n log_likelihood += np.log(likelihood)\n\n return log_likelihood","def negative_log_likelihood(self, y):\r\n # y.shape[0] is (symbolically) the number of rows in y, i.e., number of examples (call it n) in the minibatch\r\n # T.arange(y.shape[0]) is a symbolic vector which will contain [0,1,2,... n-1]\r\n # T.log(self.p_y_given_x) is a matrix of Log-Probabilities (call it LP) with one row per example and one column per class\r\n # LP[T.arange(y.shape[0]),y] is a vector v containing [LP[0,y[0]], LP[1,y[1]], LP[2,y[2]], ..., LP[n-1,y[n-1]]]\r\n # and T.mean(LP[T.arange(y.shape[0]),y]) is the mean (across minibatch examples) of the elements in v,\r\n # i.e., the mean log-likelihood across the minibatch.\r\n return T.log(self.p_y_given_x)[T.arange(y.shape[0]),y]","def get_lip_max(self) -> float:\n if self._fitted:\n return self._model.get_lip_max()\n else:\n raise ValueError(\"call ``fit`` before calling ``get_lip_max``\")","def max_evidence(self):\n self.A = np.linalg.inv(self.Sn)\n A_eigval = np.linalg.eigvals(self.A)\n gamma = 0\n for i in range(len(A_eigval)):\n gamma += A_eigval[i]/(self.alpha + A_eigval[i])\n new_alpha = gamma/(self.mn.T@self.mn)\n\n sum = 0\n for i in range(self.n):\n sum +=(self.t[i]-self.mn.T@self.design_matrix[i])**2\n new_beta = 1/((1/(self.n-gamma))*sum)\n\n return new_alpha, new_beta","def lnlike(theta, model, x, y, yerr):\n return -np.nansum(0.5 * np.log([2 * np.pi] * len(y)))\\\n -np.nansum(np.log(yerr))\\\n -0.5*np.nansum(((y-model(x, *theta))/yerr)**2)","def log_likelihood(y_true, y_pred):\n ll = np.sum(y_true * np.log(y_pred) - y_pred)\n return ll","def eval_likelihood_estimator(self, val_loader):\n likelihood_list = []\n self.observation_encoder.eval()\n self.likelihood_estimator.eval()\n\n for i, (sta, obs, act) in enumerate(val_loader):\n if self.use_cuda:\n sta = sta.cuda()\n obs = obs.cuda()\n w = self.get_likelihood(sta, obs)\n\n # calculate the likelihood\n correct_item = 0\n incorrect_item = 0\n for batch_ind in range(w.size()[0]):\n correct_samples = torch.diag(w[batch_ind])\n incorrect_samples = w[batch_ind] - torch.diag(torch.diag(w[batch_ind]))\n correct_item += torch.sum(torch.log(correct_samples))\n incorrect_item += torch.sum(torch.log(1.0 - incorrect_samples))\n likelihood = correct_item / w.size()[0] + incorrect_item / (w.size()[0] * (w.size()[0] - 1))\n likelihood_list.append(math.exp(likelihood))\n\n # visualize the output of the model\n if self.visualize:\n w = w.data.cpu().numpy()\n for j in range(w.shape[0]):\n plot_measurement(w[i], save_image=True,\n outdir='eval_vis/measurement',\n batch=i, ind=j)\n\n likelihood = sum(likelihood_list) / len(likelihood_list)\n return likelihood","def mamajek08_logRpHK_Ro_max():\n return mamajek08_Ro_logRpHK(-5.0)","def compute_maximisation( self, X, Z, O ):\n N, d = X.shape\n\n M, sigma, w = O\n\n # Cluster weights (smoothed)\n # Pseudo counts\n w = Z.sum(axis=0) + 1e-5\n\n # Get new means\n M = (Z.T.dot( X ).T / w)\n sigma = sc.sqrt(1e-7+( cdist(X, M.T, 'sqeuclidean') * Z).sum(0)/(N))\n w /= w.sum()\n\n return M, sigma, w"],"string":"[\n \"def maxlikelihood(self):\\n\\n chi2 = lambda *args: -2 * lnlikelihood.lnlike(*args) \\n # print('***DEBUGGING*** chi2 = ', chi2)\\n # print('***DEBUGGING*** self.theta_guess = ', self.theta_guess)\\n # print('***DEBUGGING*** self.transinfo = ', self.transinfo)\\n # print('***DEBUGGING*** self.wave_b = ', self.wave_b)\\n # print('***DEBUGGING*** self.flux_b = ', self.flux_b)\\n # print('***DEBUGGING*** self.err_b = ', self.err_b)\\n # print('***DEBUGGING*** self.wave_r = ', self.wave_r)\\n # print('***DEBUGGING*** self.flux_r = ', self.flux_r)\\n # print('***DEBUGGING*** self.err_r = ', self.err_r)\\n # print('***DEBUGGING*** self.velres = ', self.velres)\\n result = op.minimize(chi2, self.theta_guess,\\n args=(self.transinfo, self.wave_b, self.flux_b, self.err_b,\\n self.wave_r, self.flux_r, self.err_r, self.velres))\\n\\n self.theta_ml = result[\\\"x\\\"]\",\n \"def max_log_likelihood(data):\\n # Assume data is given as counts\\n tot = sum(data)\\n return _np.sum([nlogp(n, n / tot) for n in data if n > 0])\",\n \"def maximum_likelihood_loss(z, log_det_J, **args):\\n\\n return tf.reduce_mean(input_tensor=0.5 * tf.square(tf.norm(tensor=z, axis=-1)) - log_det_J)\",\n \"def maximum_likelihood_loss(z, log_det_J):\\n\\n return tf.reduce_mean(0.5 * tf.square(tf.norm(z, axis=-1)) - log_det_J)\",\n \"def evaluate_GMM_log_likelihood(model, x, y):\\n y_pred = model.predict(x)\\n \\n num_datapoints = len(x)\\n output_dim = y.shape[-1]\\n num_comp = int(y_pred.shape[-1] / (3*output_dim))\\n\\n mix_comp_logits = y_pred[:, :num_comp]\\n mus = y_pred[:, num_comp:(1+output_dim)*num_comp]\\n sigmas = y_pred[:, (1+output_dim)*num_comp:]\\n \\n # convert logits to categorical distribution - need to itterate through all points\\n mix_comp = np.zeros((num_datapoints, num_comp))\\n for i in range(num_datapoints):\\n mix_comp[i,:] = get_mixture_dist(mix_comp_logits[i,:], num_comp)\\n \\n log_likelihood = 0\\n for i in range(num_comp):\\n for j in range(output_dim):\\n mse = -0.5*np.sum(mix_comp[:,i]*np.square((y[:,j]-mus[:,(i*output_dim)+j])/sigmas[:,(i*output_dim)+j]))\\n sigma_trace = -np.sum(mix_comp[:,i]*np.log(sigmas[:,(i*output_dim)+j]))\\n log2pi = -np.sum(mix_comp[:,i]*0.5*output_dim*np.log(2*np.pi))\\n\\n log_likelihood += mse + sigma_trace + log2pi\\n \\n avg_log_likelihood = np.round(log_likelihood / num_datapoints, 2)\\n print(f'Log likelihood: {avg_log_likelihood}')\\n return avg_log_likelihood\",\n \"def maximum_likelihood_estimation(head_count, tail_count):\\n return head_count / (head_count + tail_count)\",\n \"def compute_log_likelihood(self,params: ndarray) -> float:\\n \\n pred_mag = self._pred_mag(params,self.times)\\n sigma_2 = self.sd_mags**2 \\n ln_likelihood = -0.5*np.sum((pred_mag - self.mags)**2 / sigma_2+ np.log(sigma_2))\\n\\n return ln_likelihood\",\n \"def __log_likelihood(self, params, *args):\\n\\t\\tX, y, feature_set, lambda_reg, empirical_weights, verbose, sign = args\\n\\n\\t\\tno_example = len(X)\\n\\t\\ttotal_logZ = 0\\n\\t\\ttotal_logProb = 0\\n\\t\\texpected_weights = np.zeros(len(feature_set))\\n\\t\\tfor t in range(len(X)):\\n\\t\\t\\t# example_features = X[t], example_labels = y[t]\\n\\n\\t\\t\\tpotential = np.zeros(len(X[t]))\\n\\t\\t\\tfor i in range(len(X[t])):\\n\\t\\t\\t\\t#candidate_features = X[t][i], candidate_label = y[t][i]\\n\\t\\t\\t\\tpotential[i] = feature_set.calc_inner_product(X[t][i], params)\\n\\n\\t\\t\\t#scaling\\n\\t\\t\\tpotential = potential - np.max(potential, keepdims=True)\\n\\n\\t\\t\\tfor i in range(len(X[t])):\\n\\t\\t\\t\\ttotal_logProb += potential[i] * y[t][i]\\n\\n\\t\\t\\tpotential, Z = self.__softmax(potential)\\n\\n\\t\\t\\tfor i in range(len(X[t])):\\n\\t\\t\\t\\tfeature_set.calc_inner_sum(expected_weights, X[t][i], potential[i])\\n\\n\\t\\t\\ttotal_logZ += log(Z)\\n\\n\\t\\t# _params = feature_set.get_regularized_params(params, 'bias')\\n\\t\\t_params = params\\n\\t\\tlog_likelihood = total_logProb - total_logZ - (lambda_reg/2) * np.sum(np.multiply(_params,_params))\\n\\t\\tgradients = empirical_weights - expected_weights - lambda_reg * _params\\n\\n\\t\\tglobal SUB_ITERATION_NUM\\n\\t\\tif verbose:\\n\\t\\t\\tsub_iteration_str = ' '\\n\\t\\t\\tif SUB_ITERATION_NUM > 0:\\n\\t\\t\\t\\tsub_iteration_str = '(' + '{0:02d}'.format(SUB_ITERATION_NUM) + ')'\\n\\t\\t\\tprint(' ', '{0:03d}'.format(ITERATION_NUM), sub_iteration_str, ':', log_likelihood * sign)\\n\\n\\t\\tSUB_ITERATION_NUM += 1\\n\\n\\t\\treturn sign * log_likelihood, sign * gradients\",\n \"def log_marginal_likelihood(X_train,y_train,phi,tau=1.,Ve=1.e-10):\",\n \"def compute_maximisation( self, X, Z, O ):\\n\\n raise NotImplementedError\",\n \"def likelihood(params,data):\\n spec, isnflux, igalflux = data\\n chi2=0\\n modflux = (params[0]*isnflux + params[1]*igalflux)\\n chi2 += sum((spec.flux - modflux)**2)/((0.05*sum(spec.var)**2)/2.0)\\n return np.exp(-chi2/2.0)\",\n \"def log_likelihood(self, y_list):\\n if self.lambda_mat is None:\\n raise ValueError(\\\"Can't compute model likelihood before fitting!\\\")\\n\\n # precision prior distribution given precision hyper-parameters\\n prec_distr = stats.gamma(a=self.prec_distr[0],\\n scale=self.prec_distr[1] ** -1.0)\\n\\n # likelihood of projection matrix precision priors given\\n # precision hyper-parameters\\n lambda_logl = np.sum(\\n prec_distr.logpdf(self.lambda_mat['alpha']\\n / self.lambda_mat['beta'])\\n )\\n\\n # likelihood of projection matrix values given their precision priors\\n a_logl = np.sum(\\n stats.norm(loc=0, scale=(self.lambda_mat['beta']\\n / self.lambda_mat['alpha']))\\n .logpdf(self.A_mat['mu'])\\n )\\n\\n # likelihood of latent feature matrix given kernel matrix,\\n # projection matrix, and standard deviation hyper-parameter\\n h_logl = np.sum(\\n stats.norm(loc=self.A_mat['mu'].transpose() @ self.kernel_mat,\\n scale=self.sigma_h)\\n .logpdf(self.H_mat['mu'])\\n )\\n\\n # likelihood of bias parameter precision priors given\\n # precision hyper-parameters\\n weight_prior_logl = np.sum(\\n prec_distr.logpdf(np.array(self.weight_priors['alpha'])\\n / np.array(self.weight_priors['beta']))\\n )\\n\\n # likelihood of bias parameters given their precision priors\\n weight_logl = np.sum(\\n stats.norm(loc=0, scale=(np.array(self.weight_priors['beta'])\\n / np.array(self.weight_priors['alpha'])))\\n .logpdf(self.weight_mat['mu'])\\n )\\n\\n # likelihood of predicted outputs given latent features, bias\\n # parameters, and latent feature weight parameters\\n f_logl = np.sum(\\n stats.norm(\\n loc=(self.weight_mat['mu'][1:, :].transpose()\\n @ self.H_mat['mu']\\n + np.vstack(self.weight_mat['mu'][0, :])),\\n scale=1).logpdf(self.output_mat['mu'])\\n )\\n\\n # likelihood of actual output labels given class separation margin\\n # and predicted output labels\\n y_logl = np.sum(self.get_y_logl(y_list))\\n\\n return (lambda_logl + a_logl + h_logl\\n + weight_prior_logl + weight_logl + f_logl + y_logl)\",\n \"def max_log_likelihood_fit(self) -> ag.FitQuantity:\\r\\n\\r\\n return self.analysis.fit_quantity_for_instance(instance=self.instance)\",\n \"def calculate_likelihood(self, number_of_topics):\\n print(\\\"Start of calculate_likelihood...\\\")\\n maxNumberOfWrd = 0\\n for docValue in range(0,self.number_of_documents):\\n for wordValue in range(0,self.vocabulary_size):\\n maxTopicVal = 0\\n for topicValue in range(0,number_of_topics):\\n maxTopicVal = maxTopicVal + ((self.document_topic_prob[docValue][topicValue]*self.topic_word_prob[topicValue][wordValue]))\\n if maxTopicVal > 0:\\n maxNumberOfWrd = maxNumberOfWrd + (np.log(maxTopicVal) * self.term_doc_matrix[docValue][wordValue])\\n\\t\\t#print(\\\"Value of maxNumberOfWrd >>> \\\"+maxNumberOfWrd)\\n self.likelihoods.append(maxNumberOfWrd)\\n\\t\\t#print(\\\"End of calculate_likelihood...\\\")\\n return\",\n \"def MVN_log_likelihood(X, model):\\n D, M = X.shape\\n X_normalized = normalize_log_likelihoods(X.copy())\\n mvn = multivariate_normal(mean=model.mean, cov=model.cov)\\n return mvn.logpdf(X_normalized.T).sum()\\n # log_2pi = D * np.log(2 * np.pi)\\n # log_det = np.log(np.linalg.det(model.cov))\\n # residuals = calc_residuals(X_normalized, model.mean, \\\"minus\\\")\\n # mahalanobis_distance = np.dot(np.dot(residuals.T, np.linalg.inv(model.cov)), residuals)\\n # return -0.5 * (log_2pi + log_det + mahalanobis_distance).sum()\",\n \"def GUE0_lambda_max_estimate(d, num_samples):\\n return np.mean([GUE0_lambda_max(d) for _ in range(num_samples)])\",\n \"def relative_likelihood(self):\\n \\n if self.num_hidden == 0:\\n \\n return T.exp(-self.compute_energy(self.x, self.batch_size))\\n \\n if self.num_hidden > 0:\\n \\n return T.exp(-self.compute_free_energy(self.x))\",\n \"def likelihood(self):\\n \\n raise NotImplementedError()\",\n \"def log_likelihood(self, params):\\n # extract the parameters\\n m1 = params['m1']\\n m2 = params['m2']\\n DL = params['DL']\\n Tc = params['Tc']\\n iota = params['iota']\\n phic = params['phic']\\n psi = params['psi']\\n thetaS = params['thetaS']\\n phiS = params['phiS']\\n\\n # calculate the model\\n model = self._model(time, m1, m2, DL, Tc, iota, phic, psi, thetaS, phiS)\\n\\n# # normalisation\\n# norm = -0.5*self._ndata*LN2PI - self._ndata*self._logsigma\\n\\n# # chi-squared\\n# chisq = np.sum(((self._data - model)/(self._sigma))**2)\\n\\n return -np.vdot(self._data - model,self._data - model)\",\n \"def loglikelihood(y: float64, m: float64, sigma: float64) -> float64:\\n\\n # -log(sqrt(2*pi)) = -0.9189385332046727\\n\\n return -0.9189385332046727 - np.log(sigma) - (y - m) * (y - m) / (2.0 * sigma * sigma)\",\n \"def auxmaxf2(x):\\n # Sum over data points\\n f = 0.0\\n for m_ind in range(cfg.ntrain):\\n f += auxmaxrho1(x,m_ind) + auxmaxrho2(x,m_ind) \\n \\n return f\",\n \"def likelihood_prediction():\\n # Get info\\n selected_word = prompt_tech_selection()\\n article_json = get_json_from_file()\\n\\n # Calculate results\\n total_word_counter, selected_word_counter = count_occurrences(article_json, selected_word)\\n probability = selected_word_counter / total_word_counter\\n total_time = article_json[-1]['time'] - article_json[0]['time'] # unix subtraction = seconds\\n months_in_train_set = total_time / SECONDS_IN_MONTH\\n expected_posts_per_month = int(total_word_counter / months_in_train_set)\\n\\n # Show results\\n print_text_results(expected_posts_per_month, probability, selected_word)\\n plot_likelihood(expected_posts_per_month, probability)\",\n \"def log_marginal_likelihood(self, eval_gradient=False):\\n A, B, mu_tilda, gamma_tilda, r_grid, n_grid = self.calc_tau(return_all=True)\\n\\n gamma = self.GAMMA\\n gamma_y = self.GAMMA_Y\\n\\n A1 = np.copy(self.baseTau0)\\n A1[np.diag_indices_from(A1)] = 2 * self.ndim\\n\\n # A1 = (A - B) / gamma_y\\n A2 = np.diag(n_grid)\\n\\n B_inv = np.linalg.inv(B)\\n A_inv = np.linalg.inv(A)\\n\\n M_lambda = B - B.dot(np.linalg.inv(A).dot(B))\\n\\n # log_likelihood = mu_tilda.dot(M_lambda).dot(\\n # mu_tilda[:, np.newaxis]) - np.nan_to_num(np.log(np.linalg.det(M_lambda)))\\n log_likelihood = mu_tilda.dot(M_lambda).dot(mu_tilda[:, np.newaxis])\\n\\n # log_likelihood = -log_likelihood\\n print('log_likelihood: %f' % log_likelihood)\\n\\n Lambda = B - B.dot(A_inv.dot(B))\\n Lambda_inv = np.linalg.inv(Lambda)\\n\\n M_gamma = A2 - A2.dot(2 * gamma * A_inv - (gamma ** 2) * A_inv.dot(A2).dot(A_inv)).dot(A2)\\n\\n mean_r_grid = np.nan_to_num(np.array([r.sum() for r in r_grid]) / n_grid)\\n\\n log_likelihood_grad_gamma = mean_r_grid.dot(M_gamma.dot(mean_r_grid[:, np.newaxis]))[0]\\n\\n tmpMat = A2.dot(A_inv).dot(A1).dot(A_inv).dot(A2)\\n\\n log_likelihood_grad_gamma_y = (gamma ** 2) * (\\n np.trace(Lambda_inv.dot(tmpMat) + mean_r_grid.dot(tmpMat.dot(mean_r_grid[:, np.newaxis]))[0]))\\n\\n log_likelihood_gradient = np.array([log_likelihood_grad_gamma, log_likelihood_grad_gamma_y])\\n print('log_likelihood_grad: %s' % log_likelihood_gradient)\\n\\n if eval_gradient:\\n return log_likelihood, log_likelihood_gradient\\n else:\\n return log_likelihood\",\n \"def MAXED(N, sigma2, R, f_def, params):\\n\\n # pull out algorithm-specific parameters\\n Omega = params['Omega']\\n\\n # create the function that we will maximize, Z\\n def Z(lam, N, sigma2, R, f_def, Omega):\\n \\\"\\\"\\\"A function, the maximization of which is equivalent to the\\n maximization of \\\"\\\"\\\"\\n\\n A = - np.sum(f_def * np.exp(- np.sum((lam * R.T).T, axis=0)))\\n B = - (Omega * np.sum(lam**2 * sigma2))**(0.5)\\n C = - np.sum(N * lam)\\n\\n # negate because it's a minimization\\n return - (A + B + C)\\n\\n # create a lambda\\n lam = np.ones(len(N))\\n\\n # apply the simulated annealing to the Z\\n mk = {'args': (N, sigma2, R, f_def, Omega)}\\n lam = basinhopping(Z, lam, minimizer_kwargs=mk).x\\n\\n # back out the spectrum values from the lam\\n return f_def * np.exp(-np.sum((lam * R.T).T, axis=0))\",\n \"def p_logl_max(self):\\n sampler = self.__sampler\\n if sampler == \\\"EnsembleSampler\\\":\\n chain = self.sampler.chain\\n lnlike = self.sampler.lnprobability\\n elif sampler == \\\"PTSampler\\\":\\n chain = self.sampler.chain[0, ...]\\n lnlike = self.sampler.lnlikelihood[0, ...]\\n else:\\n raise ValueError(\\\"[linfit]: The sampler type ({0}) is unrecognised!\\\".format(sampler))\\n idx = lnlike.ravel().argmax()\\n p = chain.reshape(-1, self.ndim)[idx]\\n return p\",\n \"def _getCurrentPosteriorLikelihood(self): \\n likelihood = 0\\n T = self.T\\n K= self.K \\n final_likelihood = 0\\n total_log_lik = 0\\n \\n for n in range(1,self.N+1):\\n # Compute total Likelihood for all Instances P(x1...xn / theta) \\n tot_lik = 0\\n tot_scale_factor = 0\\n \\n for i in range(1,self.K+1): \\n likelihood = self.posterior_state_trellis[n][(T,i)]\\n tot_lik = tot_lik + likelihood\\n\\n try:\\n total_log_lik = math.log(tot_lik) \\n except ValueError:\\n ipdb.set_trace()\\n \\n for t in range(1,self.T):\\n scale_factor = self.forward_scaling_vector[n][t] \\n tot_scale_factor = tot_scale_factor + math.log(scale_factor)\\n\\n final_likelihood = final_likelihood + (total_log_lik - tot_scale_factor)\\n\\n return final_likelihood\",\n \"def log_likelihood(self, data, reward_model, bias_params):\",\n \"def loss_maxL(y, tx, w):\\n # to avoid problems when 0 in log\\n epsilon=0.00000001\\n sig=sigmoid(tx.dot(w))\\n # calculated probability\\n p=y.T.dot(np.log(sig+epsilon)) + (1-y).T.dot(np.log(1-sig+epsilon))\\n #divides with number of samples so that learning rate is not dependant on number of samples\\n p=p/len(y)\\n return np.squeeze(- p)\",\n \"def calculate_ln_posterior(thetas, posterior_stats):\\n\\tln_posterior = np.dot(posterior_stats['fisher'], (thetas - posterior_stats['mean']))\\n\\tln_posterior = - np.dot((thetas - posterior_stats['mean']).transpose(), ln_posterior) /2\\n\\n\\treturn ln_posterior\",\n \"def log_likelihood_exp(self, x):\\n predictions = self.get_predictions(x)\\n ll = 0.\\n for measurement in self.get_measurements:\\n m_obj = flavio.Measurement[measurement]\\n m_obs = m_obj.all_parameters\\n exclude_observables = set(m_obs) - set(self.observables)\\n prob_dict = m_obj.get_logprobability_all(predictions, exclude_parameters=exclude_observables)\\n ll += sum(prob_dict.values())\\n return ll\",\n \"def _expectation_maximization(self, y, responsibility=None, **kwargs): \\n\\n # Calculate log-likelihood and initial expectation step.\\n __init_responsibility, ll, dl = self._expectation(y, **kwargs)\\n if responsibility is None:\\n responsibility = __init_responsibility\\n\\n ll_dl = [(ll.sum(), dl)]\\n\\n meta = dict(warnflag=False)\\n for iteration in range(self.max_em_iterations):\\n\\n # M-step.\\n self._maximization(y, responsibility, **kwargs)\\n\\n # E-step.\\n responsibility, ll, dl = self._expectation(y, **kwargs)\\n\\n # Check for convergence.\\n lls = ll.sum()\\n prev_ll, prev_dl = ll_dl[-1]\\n change = (lls - prev_ll)/prev_ll\\n ll_dl.append([lls, dl])\\n\\n #print(\\\"E-M\\\", iteration, change, self.threshold)\\n\\n if abs(change) <= self.threshold:\\n break\\n\\n else:\\n meta.update(warnflag=True)\\n logger.warn(\\\"Maximum number of E-M iterations reached ({})\\\"\\\\\\n .format(self.max_em_iterations))\\n\\n meta.update(log_likelihood=lls, message_length=dl)\\n\\n return (responsibility, meta)\",\n \"def auxmaxf1(x):\\n \\n# Sum over data points\\n f = 0.0\\n for m_ind in range(cfg.ntrain):\\n f += auxmax_f1_part_i(x,m_ind) \\n \\n return f\",\n \"def maximize_marginal_likelihood(kernel, model, optimizer, output_directory,\\n testing_data, feature_names, plot_ARD, plot_params):\\n\\n # run the optimiser with a callback function if user wants to track the parameters\\n if plot_params:\\n parameter_log = []\\n opt_logs = optimizer.minimize(closure=model.training_loss, variables=model.trainable_variables,\\n step_callback=(lambda x,y,z: parameter_log.append([x, z])),\\n options=dict(maxiter=ci_niter(250)))\\n\\n else:\\n # run the optimiser without a callback function otherwise\\n opt_logs = optimizer.minimize(closure=model.training_loss, variables=model.trainable_variables,\\n options=dict(maxiter=ci_niter(250)))\\n\\n # set data against which to validate the model\\n features_test = testing_data['features']\\n affinity_test = testing_data['affinity']\\n\\n # calculate the predictions and Pearson's R, Spearman's R as well as RMSE\\n mean, var = model.predict_f(features_test)\\n pearsonsr, pvalue = pearsonr(mean.numpy().flatten(), affinity_test.values)\\n spearmansr, spvalue = spearmanr(a=mean.numpy().flatten(), b=affinity_test.values)\\n rmse = np.sqrt(mean_squared_error(affinity_test.values, mean.numpy().flatten()))\\n\\n # write the results to a file\\n filename = f'{model.name}_{kernel.name}'+'.csv'\\n\\n with open(output_directory+'/'+filename, 'w') as out_file:\\n out_file.write(f'%Gaussian process regression with a Gaussian likelihood\\\\n')\\n out_file.write(f'%model: {model.name}, kernel: {kernel.name}\\\\n')\\n out_file.write(f'Optimization success: {opt_logs.get(\\\"success\\\")} in {opt_logs.get(\\\"nit\\\")} iterations, {opt_logs.get(\\\"message\\\")}\\\\n')\\n for key, value in gp.utilities.read_values(model).items():\\n out_file.write(f'%{key}: {value}\\\\n')\\n out_file.write(f'%loglikelihood: {model.log_marginal_likelihood()}\\\\n')\\n out_file.write(f'%RMSE:{rmse:.3f}\\\\n')\\n out_file.write(f'%Pearson_correlation_coefficient:{pearsonsr:.3f},P-value:{pvalue:.3f}\\\\n')\\n out_file.write(f'%Spearman_correlation_coefficient:{spearmansr:.3f},P-value:{spvalue:.3f}\\\\n')\\n out_file.write('%%%%%%PREDICTIONS%%%%%\\\\n')\\n out_file.write(f'name,f_pred_mean,f_pred_var,y_true,{\\\",\\\".join(feature_names)}\\\\n')\\n for i in range(0, len(mean)):\\n out_file.write(f'{affinity_test.index.values[i]},{mean.numpy()[i][0]:.4f},{var.numpy()[i][0]:.4f},{affinity_test.values[i]:.4f},{\\\"\\\".join(str(i)+\\\",\\\" for i in features_test[i].round(4).tolist())[:-1]}\\\\n')\\n out_file.close()\\n\\n # create the plots that were specified in the arguments to the specified output directory\\n if plot_ARD:\\n plot_feature_rankings(lengthscales=model.kernel.kernels[0].lengthscales.numpy(),\\n feature_names=feature_names, figpath=output_directory+'/feature_relevance.png')\\n if plot_params:\\n plot_parameter_change(parameter_log=parameter_log, figpath=output_directory+'/parameter_change.png')\",\n \"def max_log_likelihood_fit(self) -> FitInterferometer:\\r\\n return self.analysis.fit_interferometer_via_instance_from(\\r\\n instance=self.instance\\r\\n )\",\n \"def log_marg_likelihood(self):\\n self.A = np.linalg.inv(self.Sn)\\n term1 = self.t - self.design_matrix@self.mn\\n self.Evidence_mN = (self.beta/2)*np.linalg.norm(term1)+ (self.alpha/2)*self.mn.T@self.mn\\n A_abs = np.linalg.eigvals(self.A)\\n A_abs = np.prod(A_abs)\\n\\n self.marg_lik = ((self.p)/2)*np.log(self.alpha) + (self.n/2)*np.log(self.beta) - self.Evidence_mN - (1/2)*np.log(A_abs) - (self.n/2)*np.log(2*np.pi)\\n\\n return self.marg_lik\",\n \"def log_likelihood(mu, sigma, y, T):\\n ll = 0.\\n for yi, Ti in zip(y, T):\\n d = yi.size\\n log_det_cov = np.linalg.slogdet(sigma[Ti])[1]\\n y_minus_mean = yi - mu[Ti]\\n term3 = np.dot(y_minus_mean.T.ravel(),\\n np.linalg.solve(sigma[Ti], y_minus_mean.T).ravel())\\n ll += (-0.5 * d * np.log(2 * np.pi) - 0.5 * log_det_cov - 0.5 * term3)\\n return ll\",\n \"def mamajek08_logRpHK_max():\\n return -3.8918287373004357\",\n \"def compute_prob_mle(X: np.ndarray) -> float:\\n\\n Geometric._check_input_data(X=X)\\n Geometric._check_support(X=X)\\n\\n prob = 1 / X.mean()\\n return prob\",\n \"def _get_max_likelihood_genus(self, words,\\n word_posteriors, word_idxs):\\n #Argmax prod( p(vi|G) )\\n row_idxs = filter(not_none, map(word_idxs.get, words))\\n likelihoods = word_posteriors[row_idxs].prod(0)\\n # avoid .argmax() to solve tie problem.\\n return (likelihoods == likelihoods.max()).nonzero()[0]\",\n \"def _compute_log_likelihood(self, parameters):\\n raise NotImplementedError('')\",\n \"def __findBestLogProbability(self):\\n best_model = None\\n highest_log_probability = -sys.maxsize# (np.finfo(float).eps)\\n\\n # Find the highest model\\n for item in self.data_array:\\n if item[1] > highest_log_probability:\\n best_model = item\\n highest_log_probability = item[1]\\n\\n return best_model\",\n \"def log_likelihood(self):\\r\\n if self.likelihood.is_heteroscedastic:\\r\\n A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)\\r\\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self._A))\\r\\n else:\\r\\n A = -0.5 * self.num_data * self.output_dim * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT\\r\\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self._A))\\r\\n C = -self.output_dim * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.num_inducing * np.log(sf2))\\r\\n D = 0.5 * self.data_fit\\r\\n self._A_part, self._B_part, self._C_part, self._D_part = A, B, C, D\\r\\n return A + B + C + D + self.likelihood.Z\",\n \"def _calculate_ll(self, x):\\n observation_log_probs = self._observation_log_probs(x, mask=None)\\n forward_log_probs = self._forward(observation_log_probs)\\n log_likelihood = logsumexp(\\n forward_log_probs[forward_log_probs.shape[0] - 1, :].numpy())\\n return log_likelihood\",\n \"def log_likelihood(self):\\r\\n return (-0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) -\\r\\n 0.5 * self.output_dim * self.K_logdet + self._model_fit_term() + self.likelihood.Z)\",\n \"def log_likelihood(self):\\r\\n A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.beta_star)) - 0.5 * np.sum(self.V_star * self.likelihood.Y)\\r\\n C = -self.output_dim * (np.sum(np.log(np.diag(self.LB))))\\r\\n D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))\\r\\n return A + C + D + self.likelihood.Z\",\n \"def Likelihood(self, data, hypo):\\n p_correct = hypo\\n score = data\\n\\n k = self.exam.Reverse(score)\\n n = self.exam.max_score\\n like = thinkbayes2.EvalBinomialPmf(k, n, p_correct)\\n return like\",\n \"def maximum_likelyhood_estimate(mc_model):\\n m = pymc.MAP(mc_model)\\n m.fit()\\n return (m.values['theta'],) + tuple(m.values[p] for p in m.params)\",\n \"def run_MH(input_datapath, points_datapath, M, filename, calEstimate=False):\\n\\n #input data\\n t = read_data(input_datapath, ',')\\n\\n #2D data points\\n r = read_data(points_datapath, ',')\\n\\n #mean and variance for prior\\n mu = np.array([0,0,4])\\n var = 10*np.identity(3)\\n\\n #variance for likelihood\\n co_var = 0.0025*np.identity(2)\\n\\n #random initial and final 3D points \\n pi,pf = np.random.multivariate_normal(mu, var, 2)\\n\\n #initializing corresponding initial & final 2D points\\n qi,qf = [0]*2\\n '''\\n piArray =np.empty([n+1,3])\\n pfArray =np.empty([n+1,3])\\n piArray =np.array(piArray,pi)\\n pfArray =np.append(pfArray,pf)\\n '''\\n piArray=np.array(pi)\\n pfArray=np.array(pf)\\n\\n for i in range(0,n):\\n pa=np.append(pi, 1)\\n pb=np.append(pf, 1)\\n\\n xi = M.dot(pa)\\n xf = M.dot(pb)\\n\\n xi = xi/xi[2]\\n xf = xf/xf[2]\\n\\n qi = xi[0:2]\\n qf = xf[0:2]\\n\\n qs =np.zeros((t.shape[0],2)) \\n\\n #Calculating qs which is the mean for likelihood\\n for k in range(0,t.shape[0]):\\n qs[k]=np.add(qi, np.subtract(qf,qi)*t[k])\\n\\n ##Calculating posterior for old points\\n prior = multivariate_normal.logpdf([pi,pf], mean=mu, cov=var)\\n likelihood = np.zeros((t.shape[0],1))\\n\\n for j in range(0, t.shape[0]):\\n #likelihood[j] =multivariate_normal.logpdf(r[j,:], mean=qs[j,:], cov=co_var)\\n likelihood[j] =multivariate_normal.logpdf(r[j], mean=qs[j], cov=co_var)\\n \\n #p_old - posterior for old points\\n p_old = np.sum(prior)+np.sum(likelihood)\\n\\n ##new points\\n pi_new= np.random.multivariate_normal(pi,var)\\n pf_new= np.random.multivariate_normal(pf,var)\\n\\n #pi_new +=pi\\n #pf_new +=pf\\n piArray =np.append(piArray,pi_new)\\n pfArray =np.append(pfArray,pf_new)\\n\\n pa=np.append(pi_new, 1)\\n pb=np.append(pf_new, 1)\\n\\n xi = M.dot(pa)\\n xf = M.dot(pb)\\n\\n xi = xi/xi[2]\\n xf = xf/xf[2]\\n\\n qi_new = xi[0:2]\\n qf_new = xf[0:2]\\n\\n qs =np.zeros((t.shape[0],2)) \\n\\n for k in range(0,t.shape[0]):\\n qs[k]=np.add(qi_new, np.subtract(qf_new,qi_new)*t[k])\\n\\n #Calculating posterior for new points\\n \\n prior = multivariate_normal.logpdf([pi_new,pf_new], mean=mu, cov=var)\\n likelihood = np.zeros((t.shape[0],1))\\n\\n for j in range(0, t.shape[0]):\\n #likelihood[j] =multivariate_normal.logpdf(r[j,:], mean=qs[j,:], cov=co_var)\\n likelihood[j] =multivariate_normal.logpdf(r[j], mean=qs[j], cov=co_var)\\n \\n\\n #p_new - posterior for new points\\n p_new = np.sum(prior)+np.sum(likelihood)\\n \\n if(p_new-p_old>=0):\\n pi=pi_new\\n pf=pf_new\\n qi=qi_new\\n qf=qf_new\\n else:\\n u = np.random.uniform(0.0,1.0,1)\\n if(np.log(u)<p_new-p_old):\\n pi=pi_new\\n pf=pf_new\\n qi=qi_new\\n qf=qf_new\\n\\n print '\\\\n----------------------\\\\nResults ('+filename+')'\\n print '\\\\nMAP estimate of the 3D line segment (pi&pf):{0},{1}'.format(pi,pf)\\n print '\\\\nCorresponding 2D points (qi&qf):{0},{1}'.format(qi,qf)\\n\\n if(calEstimate):\\n print '\\\\n Mean of posterior distribution (E[pi],E[pf]):{0},{1}'.format(pi,pf)\\n qs = qi+(qf-qi)*1.5\\n print '\\\\n Monte Carlo estimate of the predicted 2D output point:{0}'.format(qs)\\n \\n plot_data(r, qi,qf,piArray,pfArray,filename)\",\n \"def GSM_log_likelihood(X, model):\\n D, M = X.shape\\n k = model.mix.shape[0]\\n log_likelihood = 0\\n for i in range(M):\\n logpdf_X = 0\\n for j in range(k):\\n mvn = multivariate_normal(cov=model.cov[j, :])\\n logpdf_X = mvn.logpdf(x=X[:, i]) * model.mix[j]\\n log_likelihood += logpdf_X\\n return log_likelihood\",\n \"def search_for_maximum(self):\\n return self.maximise_aquisition(self.expected_improvement)\",\n \"def softmax_loss(x, y):\\n\\n eps = 1e-5\\n \\n N,C = x.shape\\n p = softmax(x)\\n llikelihood = -np.log(p[range(N),y] + eps)\\n# print(llikelihood)\\n loss = np.sum(llikelihood) / N\\n\\n dx = p\\n dx[range(N),y] -= 1\\n dx = dx/N\\n \\n return loss, dx\",\n \"def negative_log_likelihood(self):\\n # y.shape[0] is (symbolically) the number of rows in y, i.e.,\\n # number of examples (call it n) in the minibatch\\n # T.arange(y.shape[0]) is a symbolic vector which will contain\\n # [0,1,2,... n-1] T.log(self.p_y_given_x) is a matrix of\\n # Log-Probabilities (call it LP) with one row per example and\\n # one column per class LP[T.arange(y.shape[0]),y] is a vector\\n # v containing [LP[0,y[0]], LP[1,y[1]], LP[2,y[2]], ...,\\n # LP[n-1,y[n-1]]] and T.mean(LP[T.arange(y.shape[0]),y]) is\\n # the mean (across minibatch examples) of the elements in v,\\n # i.e., the mean log-likelihood across the minibatch.\\n return -T.log(self.p_y_given_x)[T.arange(self.y.shape[0]), self.y]\",\n \"def loglikelihood(self, y):\\n raise NotImplementedError\",\n \"def likelihood(self,x,params = None,**kwargs):\\n return np.exp(self.log_likelihood(x,params=params,**kwargs))\",\n \"def log_likelihood(self,samples,times):\\n prior_mu = np.ones(2*len(self.A)+1) \\n prior_var = np.eye(2*len(self.A)+1)*0.7\\n prior_p = np.log(self.prior_pdf())\\n #prior_p = np.log(self.normal_prior(prior_mu,prior_var))\\n xform = [self.sum_exp(t) for t in times]\\n lp = scipy.stats.norm(xform,np.sqrt(self.var)).pdf(samples)\\n sample_p =np.sum(np.log(lp))\\n ll = prior_p + sample_p\\n\\n if np.isnan(ll):\\n return -np.infty\\n return ll\",\n \"def get_log_marginal_likelihood(self, mode='BIC'):\\n if mode == 'BIC':\\n if not self.isOptimized:\\n print('Parameters have not been optimized; training now')\\n self.train()\\n if self.BICscore is None:\\n BIC = 0\\n for i, model in enumerate(self.models):\\n n = model.n \\n k = model.m.num_params\\n L = model.m.log_likelihood()\\n BIC += L - k/2*np.log(n)\\n self.BICscore = BIC\\n return self.BICscore\\n elif mode in ['laplace', 'Laplace']:\\n raise NotImplementedError('Laplace approximation is not yet implemented')\\n elif mode == 'AIS':\\n raise NotImplementedError('Annealed importance sampling is not yet implemented')\\n else:\\n raise NotImplementedError('Unrecognized marginal likelihood approximation {:s}'.format(mode))\",\n \"def get_mle(self, warmup=1):\\n detfs = self.detfs[warmup:]\\n sigmas = self.sigmas[warmup:]\\n nobs = np.sum(self.observation_count[warmup:])\\n mle = nobs * np.log(2 * np.pi) + np.sum(detfs) + np.sum(sigmas)\\n return mle\",\n \"def likelihood(mean, logs, x):\\n return -0.5 * (logs * 2. + ((x - mean) ** 2) / np.exp(logs * 2.) + GaussianDiag.Log2PI)\",\n \"def negative_loglikelihood(targets, estimated_distribution):\\n return -estimated_distribution.log_prob(targets)\",\n \"def negative_log_likelihood(self, y):\\r\\n \\r\\n return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])\\r\\n # end-snippet-2\\r\",\n \"def log_likelihood(self):\\r\\n assert not self.likelihood.is_heteroscedastic\\r\\n A = -0.5*self.batchsize*self.output_dim*(np.log(2.*np.pi) - np.log(self.likelihood.precision))\\r\\n B = -0.5*self.likelihood.precision*self.output_dim*self.trace_K\\r\\n Kmm_logdet = 2.*np.sum(np.log(np.diag(self.Lm)))\\r\\n C = -0.5*self.output_dim*self.data_prop*(Kmm_logdet-self.q_u_logdet - self.num_inducing)\\r\\n C += -0.5*np.sum(self.LQL * self.B)\\r\\n D = -0.5*self.likelihood.precision*self.likelihood.trYYT\\r\\n E = np.sum(self.V*self.projected_mean)\\r\\n return (A+B+C+D+E)/self.data_prop\",\n \"def compute_prob_mle(X: np.ndarray) -> float:\\n\\n Bernoulli._check_input_data(X=X)\\n Bernoulli._check_support(X=X)\\n\\n prob = X.mean()\\n return prob\",\n \"def negative_log_likelihood(self, y):\\r\\n return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])\",\n \"def negative_log_likelihood(self, y):\\r\\n return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])\",\n \"def pwm_max_score(self):\\n if self.max_score is None:\\n score = 0\\n for row in self.pwm:\\n score += log(max(row) / 0.25 + 0.01)\\n self.max_score = score\\n \\n return self.max_score\",\n \"def log_likelihood(self, x):\\n # set nuisance parameters to their central values!\\n predictions = self.get_predictions(self.shortarray_to_array(x), nuisance=False)\\n m_obj = flavio.Measurement['Pseudo-measurement for FastFit instance: ' + self.name]\\n m_obs = m_obj.all_parameters\\n prob_dict = m_obj.get_logprobability_all(predictions)\\n ll = sum(prob_dict.values())\\n return ll\",\n \"def compute_log_likelihood(self, indicators, weights, l2):\\n scores, _ = self.predict_probability(self.train_feature_x, weights)\\n probs = self.predict_probability(self.train_feature_x, weights)\\n lp = np.sum((indicators-1)*scores + np.log(probs)) - l2* np.sum(weights[1:]**2)\\n return lp\",\n \"def _log_likelihood(self, theta, f, x, y, yerr):\\n sigma2 = yerr**2\\n return -0.5*np.sum((y - f(theta, x))**2 / sigma2 + 2*np.log(sigma2))\",\n \"def neg_log_likelihood(self,params: ndarray) -> float:\\n\\n return -self.compute_log_likelihood(params)\",\n \"def log_likelihood(self):\\n return -0.5*self.D*self.K_logdet + self._model_fit_term() + self.likelihood.Z\",\n \"def likelihood(ts,w,Phi):\\n a = Phi.dot(w)\\n return np.exp(a*ts)*sigmoid(-a)\",\n \"def get_bprop_maximum(self):\\n input_grad = G.MaximumGrad()\\n\\n def bprop(x, y, out, dout):\\n dx, dy = input_grad(x, y, dout)\\n return dx, dy\\n return bprop\",\n \"def compute_log_prior(self,params: ndarray) -> float:\\n ln_tE = params[0]\\n ln_A0 = params[1]\\n ln_deltaT = params[2]\\n fbl = params[3]\\n mb = params[4]\\n\\n # Equation (16,15,17) (note that Albrow uses \\\"log\\\" for log10)\\n log10e = np.log10(np.exp(1))\\n ln_pr_ln_tE = np.log(0.476) - ((log10e*ln_tE - 1.333)**2 / 0.330) + np.log(log10e)\\n ln_pr_ln_A0 = np.log(0.660) - (1.289*log10e*ln_A0) + np.log(log10e)\\n ln_pr_ln_deltaT = np.log(0.156) - ((log10e*ln_deltaT - 1.432)**2 / 0.458) +\\\\\\n np.log(log10e)\\n \\n # Paper doesnt mention the prior used, but I assume it to be uniform\\n ln_pr_fbl = uniform.logpdf(fbl,0.0,1.0)\\n\\n # Paper doesnr mention the prior used but I will asuumed it to be uniform\\n ln_pr_mb = uniform.logpdf(mb,self.mag_min - 1.0, self.mag_max + 1.0)\\n \\n \\n return ln_pr_fbl + ln_pr_ln_A0 + ln_pr_ln_deltaT + ln_pr_ln_tE + ln_pr_mb\",\n \"def Maxwellian_velocity_distribution_fit(guesses, velocity_component_arr, N_balls_histogram, mass, temperature):\\r\\n\\tdef Gaussian(v, A, mu):\\r\\n\\t\\treturn A*np.exp(-(mass*(v-mu)**2/(2*k_B*temperature)))\\r\\n\\r\\n\\tfit \\t = curve_fit(Gaussian, velocity_component_arr, N_balls_histogram, p0 = guesses)\\r\\n\\tdata_fit = Gaussian(velocity_component_arr, *fit[0])\\r\\n\\treturn data_fit, fit[0][1], np.sqrt(np.diag(fit[1]))[1]\",\n \"def _compute_likelihood(self, mus, pmfs):\\n expected_counts = pmfs.copy()\\n for mu, _p_bin_source in zip(mus, expected_counts):\\n _p_bin_source *= mu # Works because of numpy view magic...\\n expected_total = np.sum(expected_counts, axis=0)\\n\\n observed_counts = self.data_events_per_bin.histogram\\n\\n ret = observed_counts * np.log(expected_total) - expected_total - gammaln(observed_counts + 1.).real\\n return np.sum(ret)\",\n \"def lnlike(pars, data, massdata=None, bulk_alpha=False, include_ti=False):\\n\\n pars = np.asarray(pars)\\n\\n if massdata is not None:\\n mgas, mstar, mstarerr = massdata\\n\\n #Observational data to use in the likelihood determination\\n n_obs = len(data['fe_h'])\\n\\n #Based on the GCE model used, check if the number of parameters is correct\\n n_pars = len(pars)\\n w = np.where(pars < 0.0)[0]\\n if len(w) > 0: return np.inf\\n if gce_params.name == 'Zwind':\\n if n_pars == 7:\\n if pars[6] > 1.0: return np.inf\\n else:\\n if pars[5] > 1.0: return np.inf\\n\\n #Run the GCE model for this given set of model parameters\\n model,_ = gce.gce_model(pars)\\n n_model = len(model['t'])\\n\\n #Initialize variables to be used in the likelihood calculation\\n norm = np.sqrt(2*np.pi)\\n likelihood = 0.\\n\\n #Consider only timesteps after 7 Myr in the galaxy's evolution, and check that\\n #there are a sufficient number of finite values in the abundances returned by the\\n #GCE model\\n where_greater = model['t'] > 0.007\\n\\n where_finite = np.isfinite(model['eps'][:,mg_index]) &\\\\\\n np.isfinite(model['eps'][:,si_index]) &\\\\\\n np.isfinite(model['eps'][:,ca_index]) &\\\\\\n np.isfinite(model['eps'][:,fe_index])\\n if include_ti:\\n where_finite = where_finite & np.isfinite(model['eps'][:,ti_index])\\n\\n where_good = where_greater & where_finite\\n if len(model['t'][where_good]) < 10:\\n return np.inf\\n\\n #If the model passes the above criteria, then construct the abundance ratios for\\n #[Fe/H], [Mg/Fe], [Si/Fe], [Ca/Fe]\\n feh = model['eps'][:,fe_index][where_good]\\n mgfe = model['eps'][:,mg_index][where_good] - model['eps'][:,fe_index][where_good]\\n sife = model['eps'][:,si_index][where_good] - model['eps'][:,fe_index][where_good]\\n cafe = model['eps'][:,ca_index][where_good] - model['eps'][:,fe_index][where_good]\\n tife = model['eps'][:,ti_index][where_good] - model['eps'][:,ti_index][where_good]\\n\\n #Timesteps to consider from the model\\n t = model['t'][where_good]\\n\\n #Calculate the probability with respect to time that a star forms\\n dp_dt = model['mdot'][where_good]/model['mstar'][n_model-1]\\n dp_dt = dp_dt[dp_dt > 0.]\\n dp_dt /= simps(dp_dt, t) #note that the original IDL uses Netwon-Cotes integration here\\n\\n \\\"\\\"\\\"\\n #Do not consider the last 3 Myr of the evolution of the galaxy\\n maxt = n_model - 3\\n\\n #Calculate the integrated ejecta in each element\\n ejecta = np.zeros(len(model['eps'][0]))\\n for j in range(len(model['eps'][0])):\\n\\n w = np.where((model['mout'][:,j][7:maxt] > 0.)&(np.isfinite(model['mout'][:,j][7:maxt])))[0] + 7\\n if len(w) > 50:\\n ejecta[j] = simps(model['mout'][:,j][w], model['t'][w])\\n else: ejecta[j] = -999.\\n\\n #Calculate the total outflow based on the ejecta in each element\\n total_outflow = np.sum(ejecta[0:1]) + np.sum(ejecta[3:6])*10**(1.31) + ejecta[7]*10**(0.03)\\n\\n #Now calculate the integrated mass influx\\n w = np.where((model['f_in'][7:maxt] > 0.)&(np.isfinite(model['f_in'][7:maxt])))[0] + 7\\n if len(w) > 50:\\n integrated_mass = simps(model['f_in'][w], model['t'][w]) + model['mgas'][0]\\n else: integrated_mass = -999\\n \\\"\\\"\\\"\\n\\n #Calculate the total mass based on the luminosity and the mass-to-light ratio\\n #assumed for the observed dwarf galaxies\\n #bdm_ratio = integrated_mass/total_mass\\n\\n #Now loop through the observational data dictionary provided in the input\\n #parameters, where i is the index for a given red giant star in the galaxy,\\n #and determine the likelihood as compared to the model for each\\n #instance based on simultaneously using [Fe/H], [Mg/Fe], [Si/Fe], and [Ca/Fe]\\n\\n print(massdata, bulk_alpha, include_ti)\\n for i in range(n_obs):\\n\\n feh_dist = ((feh - data['fe_h'][i])/data['e_fe_h'][i])**2.\\n\\n if bulk_alpha:\\n\\n if include_ti:\\n alphafe = (mgfe + cafe + sife + tife)/4.\\n else:\\n alphafe = (mgfe + cafe + sife)/3.\\n\\n alphafe_dist = ((alphafe - data['alpha_fe'][i])/data['e_alpha_fe'][i])**2.\\n\\n dist_arr = [feh_dist, alphafe_dist]\\n err_arr = [data['e_fe_h'][i], data['e_alpha_fe'][i]]\\n\\n else:\\n\\n mgfe_dist = ((mgfe - data['mg_fe'][i])/data['e_mg_fe'][i])**2.\\n cafe_dist = ((cafe - data['ca_fe'][i])/data['e_ca_fe'][i])**2.\\n sife_dist = ((sife - data['si_fe'][i])/data['e_si_fe'][i])**2.\\n\\n dist_arr = [feh_dist, mgfe_dist, sife_dist, cafe_dist]\\n\\n err_arr = [data['e_fe_h'][i], data['e_mg_fe'][i], data['e_si_fe'][i],\\n data['e_ca_fe'][i]]\\n\\n if include_ti:\\n tife_dist = ((tife - data['ti_fe'][i])/data['e_ti_fe'][i])**2.\\n dist_arr.append(tife_dist)\\n err_arr.append(data['e_ti_fe'][i])\\n\\n err_mult = np.nanprod(err_arr)\\n dist = np.nansum(dist_arr, axis=0)\\n\\n lfunc = np.exp(-0.5*dist) / (err_mult * norm**4.)\\n\\n int_i = simps(lfunc*dp_dt, t)\\n if (np.isfinite(int_i)) and (int_i >= 0.):\\n likelihood -= np.log(int_i)\\n else:\\n likelihood += 5.\\n\\n #Now add terms to the likelihood based on the stellar mass in the GCE model and the\\n #observed gas mass as described in Eq. 17 of Kirby et al 2011b\\n if massdata is not None:\\n\\n #Now calculate the amount of gas remaining in the galaxy at the end of the GCE model\\n remaining_gas = model['mgas'][n_model-1]\\n if np.abs(model['mgas'][n_model-2] - remaining_gas) > 0.5*remaining_gas:\\n remaining_gas = 0.\\n if remaining_gas < 0.: remaining_gas = 0.\\n\\n z0_mstar_term = 0.5*((model['mstar'][n_model-1] - mstar)/mstarerr)**2.\\n delta_mgas = 1.e3\\n z0_mgas_term = 0.5*((remaining_gas - mgas)/delta_mgas)**2. #assuming observed gas mass of 0 Msun at z = 0\\n\\n likelihood += 0.1*n_obs*(z0_mstar_term + z0_mgas_term + np.log(norm**2. * mstarerr * delta_mgas))\\n\\n if (model['mstar'][n_model-1] < delta_mgas): likelihood += 3.e6\\n\\n return likelihood\",\n \"def Maxwell_Boltzmann_distribution_fit(guesses, speeds_arr, N_balls_histogram, mass, temperature):\\r\\n\\tdef Max_Boltz_distrib(v, A):\\r\\n\\t\\treturn A*v*np.exp(-(mass*v*v/(2*k_B*temperature)))\\r\\n\\r\\n\\tfit \\t = curve_fit(Max_Boltz_distrib, speeds_arr, N_balls_histogram, p0 = guesses)\\r\\n\\tdata_fit = Max_Boltz_distrib(speeds_arr, *fit[0])\\r\\n\\treturn list(data_fit), fit[0][0], np.sqrt(np.diag(fit[1]))[0]\",\n \"def perform_bayesian_optimization(self):\\n bounds = {'hunits': (self.hunits_lower, self.hunits_upper),\\n 'embedding_dim': (self.embedding_dim_lower, self.embedding_dim_upper)}\\n optimizer = BayesianOptimization(f=self.lstm_score, pbounds=bounds, random_state=1)\\n optimizer.maximize(init_points=2, n_iter=self.iterations)\\n print(optimizer.max)\\n print(optimizer.res)\",\n \"def _build_likelihood(self):\\n\\n # Get prior KL.\\n KL = self.build_prior_KL()\\n\\n # Get conditionals\\n fmean, fvar = self._build_predict(self.X, full_cov=False)\\n\\n # Get variational expectations.\\n var_exp = self.likelihood.variational_expectations(fmean, fvar, self.Y) * self.obs_weight\\n\\n # re-scale for minibatch size\\n scale = tf.cast(self.num_data, gp.settings.float_type) / tf.cast(tf.shape(self.X)[0], gp.settings.float_type)\\n scale = scale / tf.reduce_mean(self.obs_weight)\\n return tf.reduce_sum(var_exp) * scale - KL\",\n \"def loglikelihood(self):\\n raise NotImplementedError(\\\"To be implemented\\\")\",\n \"def BMA_Marginal_Likelihood_exh(df_train, df_test):\\n\\n # number of individual forecasts and number of periods\\n K = df_test.shape[1]\\n T = df_train.shape[0]\\n T_oos = df_test.shape[0]\\n # g-prior parameter\\n c = float(K**2)\\n # separate real value and individual forecasts (for both train and oos)\\n y = df_train.iloc[:, 0].values\\n multid_y = y[:, np.newaxis]\\n F_all = np.concatenate((df_train.iloc[:, 1:].values, df_test.values),\\n axis=0)\\n # list of variables\\n var_list = np.hsplit(F_all, K)\\n # list containing vectors of all model by the number of variables\\n model_list = []\\n # null model\\n model_list += [np.full((1, T+T_oos, 1), 1, dtype=float)]\\n # single variable models\\n model_list += [np.insert(np.squeeze(list(itertools.combinations(\\n var_list, 1)), axis=3)[:, :, np.newaxis], 0, 1, axis=2)]\\n # 2-(K-1) variable combinations models\\n for i in range(2, K):\\n model_list += [np.insert(np.swapaxes(np.squeeze(list(\\n itertools.combinations(var_list, i)),\\n axis=3), 1, 2), 0, 1, axis=2)]\\n # K variable model\\n model_list += [np.insert(F_all[np.newaxis, :, :], 0, 1, axis=2)]\\n\\n # initializations\\n marginal_lik = np.full((2**K, 1), fill_value=np.nan, dtype=float)\\n oos_fcts = np.full((df_test.shape[0], 2**K), np.nan, dtype=float)\\n\\n # compute marginal likelihoods\\n # precompute the right part (same in every case)\\n y_bar = np.mean(y)\\n S_right = sum((y - y_bar)**2)/(c+1)\\n # start indices for sets with different numbers of variables\\n start_ind = 0\\n # compute the marginal likelihoods for all sets of models\\n for i in range(len(model_list)):\\n # unpack the training design matrix\\n Z = model_list[i][:, :T, :]\\n Z_t = np.swapaxes(Z, 1, 2)\\n # unpack out-of-sample testing matrix\\n Z_oos = model_list[i][:, T:, :]\\n # estimate the model in sample\\n theta_hat = np.matmul(np.linalg.inv(np.matmul(Z_t, Z)),\\n np.matmul(Z_t, multid_y))\\n # sum of squared residuals\\n SSR = np.sum((multid_y - np.matmul(Z, theta_hat))**2, axis=1)\\n S_left = (c/(c+1))*SSR\\n S = S_left + S_right\\n\\n # scale to first decimal place for numerical reasons\\n # find the scaling factor\\n if i == 0:\\n S_rescaled = np.copy(S)\\n scaling_factor = 1\\n while (S_rescaled < 0.1):\\n S_rescaled *= 10\\n scaling_factor *= 10\\n # rescale S (to prevent overflow)\\n S *= scaling_factor\\n\\n # update set end index\\n end_ind = start_ind + Z.shape[0]\\n # compute and save marginal likelihood for the given set of models\\n marginal_lik[start_ind:end_ind, :] = ((c+1)**(-i))*(S**(-((T-1)/2)))\\n # out-of-sample forecasts\\n oos_fcts[:, start_ind:end_ind] = np.transpose(\\n np.squeeze(np.matmul(Z_oos, theta_hat), axis=2))\\n # update set start index\\n start_ind = end_ind\\n\\n # model posterior probabilities\\n marginal_lik_sum = np.sum(marginal_lik)\\n posterior_prob = marginal_lik / marginal_lik_sum\\n\\n pred = np.squeeze(np.dot(oos_fcts, posterior_prob), axis=1)\\n\\n df_pred = pd.DataFrame(\\n {\\\"BMA (Marginal Likelihood)\\\": pred},\\n index=df_test.index)\\n\\n return df_pred\",\n \"def calculate_likelihood_probability(measurement, predicted_measurement, covariance):\\n \\n return None\",\n \"def negative_log_likelihood(self, y):\\n return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])\",\n \"def negative_log_likelihood(self, y):\\n return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])\",\n \"def gmmloglik(log_emlik, weights):\",\n \"def gmmloglik(log_emlik, weights):\",\n \"def train_MLE(self) -> None:\\n result = differential_evolution(self.neg_log_likelihood,\\n bounds=self.initial_params_bounds)\\n self.mle_params = result['x']\\n print(result)\",\n \"def get_parameters_max(self):\\n maxValues = numpy.zeros(self.get_num_parameters())\\n i = 0\\n for p in self.parameters:\\n maxValues[i] = p.get_max_value()\\n i += 1\\n return maxValues\",\n \"def log_likelihood_function(self, instance: af.ModelInstance) -> float:\\r\\n model_data = self.model_data_from_instance(instance=instance)\\r\\n fit = self.fit_from_model_data(model_data=model_data)\\r\\n return fit.log_likelihood\",\n \"def negative_log_likelihood(self, y):\\r\\n # y.shape[0] is (symbolically) the number of rows in y, i.e.,\\r\\n # number of examples (call it n) in the minibatch\\r\\n # T.arange(y.shape[0]) is a symbolic vector which will contain\\r\\n # [0,1,2,... n-1] T.log(self.p_y_given_x) is a matrix of\\r\\n # Log-Probabilities (call it LP) with one row per example and\\r\\n # one column per class LP[T.arange(y.shape[0]),y] is a vector\\r\\n # v containing [LP[0,y[0]], LP[1,y[1]], LP[2,y[2]], ...,\\r\\n # LP[n-1,y[n-1]]] and T.mean(LP[T.arange(y.shape[0]),y]) is\\r\\n # the mean (across minibatch examples) of the elements in v,\\r\\n # i.e., the mean log-likelihood across the minibatch.\\r\\n return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])\",\n \"def backward(log_emlik, log_startprob, log_transmat):\\n #print(log_transmat)\\n beta = np.zeros(log_emlik.shape)\\n n = len(log_emlik)-2\\n log_transmat = log_transmat[0:-1,0:-1];\\n\\n\\n\\n while n >= 0:\\n\\n for j in range(0,len(log_emlik[0])):\\n\\n beta[n,j] = logsumexp(log_transmat[j,:] + log_emlik[n+1,:] + beta[n+1,:])\\n\\n n = n -1\\n #print(beta[n,:])\\n #print(beta)\\n return beta\",\n \"def negative_log_likelihood(self, y):\\r\\n # start-snippet-2\\r\\n # y.shape[0] is (symbolically) the number of rows in y, i.e. number of examples (call it n) in the minibatch\\r\\n # T.arange(y.shape[0]) is a symbolic vector which will contain [0,1,2,... n-1]\\r\\n # T.log(self.p_y_given_x) is a matrix of Log-Probabilities (call it LP) with one row per example and one column per class\\r\\n # LP[T.arange(y.shape[0]),y] is a vector v containing [LP[0,y[0]], LP[1,y[1]], LP[2,y[2]], ..., LP[n-1,y[n-1]]] and\\r\\n # T.mean(LP[T.arange(y.shape[0]),y]) is the mean (across minibatch examples) of the elements in v, i.e., the mean log-likelihood across the minibatch.\\r\\n\\r\\n #print \\\"y.ndim = \\\",y.ndim\\r\\n return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])\\r\\n # end-snippet-2\\r\",\n \"def _compute_log_likelihood(self, X, S):\\n log_likelihood = 0\\n for n in range(self.n_col):\\n likelihood = 1\\n for k in range(self.n_components):\\n likelihood *= self.weights[k] \\\\\\n * multivariate_normal(self.means[k], self.covs[k]).pdf(X[n]) \\\\\\n * poisson(self.rates[k]).pmf(S[n])\\n log_likelihood += np.log(likelihood)\\n\\n return log_likelihood\",\n \"def negative_log_likelihood(self, y):\\r\\n # y.shape[0] is (symbolically) the number of rows in y, i.e., number of examples (call it n) in the minibatch\\r\\n # T.arange(y.shape[0]) is a symbolic vector which will contain [0,1,2,... n-1]\\r\\n # T.log(self.p_y_given_x) is a matrix of Log-Probabilities (call it LP) with one row per example and one column per class\\r\\n # LP[T.arange(y.shape[0]),y] is a vector v containing [LP[0,y[0]], LP[1,y[1]], LP[2,y[2]], ..., LP[n-1,y[n-1]]]\\r\\n # and T.mean(LP[T.arange(y.shape[0]),y]) is the mean (across minibatch examples) of the elements in v,\\r\\n # i.e., the mean log-likelihood across the minibatch.\\r\\n return T.log(self.p_y_given_x)[T.arange(y.shape[0]),y]\",\n \"def get_lip_max(self) -> float:\\n if self._fitted:\\n return self._model.get_lip_max()\\n else:\\n raise ValueError(\\\"call ``fit`` before calling ``get_lip_max``\\\")\",\n \"def max_evidence(self):\\n self.A = np.linalg.inv(self.Sn)\\n A_eigval = np.linalg.eigvals(self.A)\\n gamma = 0\\n for i in range(len(A_eigval)):\\n gamma += A_eigval[i]/(self.alpha + A_eigval[i])\\n new_alpha = gamma/(self.mn.T@self.mn)\\n\\n sum = 0\\n for i in range(self.n):\\n sum +=(self.t[i]-self.mn.T@self.design_matrix[i])**2\\n new_beta = 1/((1/(self.n-gamma))*sum)\\n\\n return new_alpha, new_beta\",\n \"def lnlike(theta, model, x, y, yerr):\\n return -np.nansum(0.5 * np.log([2 * np.pi] * len(y)))\\\\\\n -np.nansum(np.log(yerr))\\\\\\n -0.5*np.nansum(((y-model(x, *theta))/yerr)**2)\",\n \"def log_likelihood(y_true, y_pred):\\n ll = np.sum(y_true * np.log(y_pred) - y_pred)\\n return ll\",\n \"def eval_likelihood_estimator(self, val_loader):\\n likelihood_list = []\\n self.observation_encoder.eval()\\n self.likelihood_estimator.eval()\\n\\n for i, (sta, obs, act) in enumerate(val_loader):\\n if self.use_cuda:\\n sta = sta.cuda()\\n obs = obs.cuda()\\n w = self.get_likelihood(sta, obs)\\n\\n # calculate the likelihood\\n correct_item = 0\\n incorrect_item = 0\\n for batch_ind in range(w.size()[0]):\\n correct_samples = torch.diag(w[batch_ind])\\n incorrect_samples = w[batch_ind] - torch.diag(torch.diag(w[batch_ind]))\\n correct_item += torch.sum(torch.log(correct_samples))\\n incorrect_item += torch.sum(torch.log(1.0 - incorrect_samples))\\n likelihood = correct_item / w.size()[0] + incorrect_item / (w.size()[0] * (w.size()[0] - 1))\\n likelihood_list.append(math.exp(likelihood))\\n\\n # visualize the output of the model\\n if self.visualize:\\n w = w.data.cpu().numpy()\\n for j in range(w.shape[0]):\\n plot_measurement(w[i], save_image=True,\\n outdir='eval_vis/measurement',\\n batch=i, ind=j)\\n\\n likelihood = sum(likelihood_list) / len(likelihood_list)\\n return likelihood\",\n \"def mamajek08_logRpHK_Ro_max():\\n return mamajek08_Ro_logRpHK(-5.0)\",\n \"def compute_maximisation( self, X, Z, O ):\\n N, d = X.shape\\n\\n M, sigma, w = O\\n\\n # Cluster weights (smoothed)\\n # Pseudo counts\\n w = Z.sum(axis=0) + 1e-5\\n\\n # Get new means\\n M = (Z.T.dot( X ).T / w)\\n sigma = sc.sqrt(1e-7+( cdist(X, M.T, 'sqeuclidean') * Z).sum(0)/(N))\\n w /= w.sum()\\n\\n return M, sigma, w\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.70915174","0.66576946","0.6527788","0.64514744","0.6386824","0.6367023","0.6366287","0.63379896","0.631658","0.62785214","0.61752355","0.60306334","0.6018936","0.5992277","0.59862584","0.5976006","0.59672964","0.59648114","0.5962802","0.5960479","0.59505874","0.59472877","0.5946401","0.5930292","0.59297544","0.5926541","0.5912855","0.589245","0.5890731","0.5888754","0.58834535","0.58822","0.58648384","0.5864131","0.58579284","0.58335394","0.5827808","0.58253217","0.5816467","0.581366","0.5807918","0.5805739","0.58049756","0.578543","0.57813406","0.5778481","0.5777616","0.5761333","0.5758537","0.57461333","0.5739769","0.57394606","0.5736131","0.572937","0.57265395","0.5725818","0.57243747","0.57215154","0.5711564","0.57043767","0.5697074","0.5694082","0.5692679","0.5692679","0.5685948","0.5684743","0.56539327","0.56437784","0.5635749","0.5635551","0.562974","0.5629337","0.56213236","0.5603128","0.5600945","0.5600644","0.5593619","0.5592864","0.55879027","0.5586571","0.5584056","0.55804384","0.5579867","0.5579867","0.55777365","0.55777365","0.5577685","0.5575963","0.5575377","0.55660987","0.5565725","0.5564903","0.5563643","0.55625397","0.5562478","0.5562096","0.5556447","0.55508626","0.55506086","0.5549952","0.55414456"],"string":"[\n \"0.70915174\",\n \"0.66576946\",\n \"0.6527788\",\n \"0.64514744\",\n \"0.6386824\",\n \"0.6367023\",\n \"0.6366287\",\n \"0.63379896\",\n \"0.631658\",\n \"0.62785214\",\n \"0.61752355\",\n \"0.60306334\",\n \"0.6018936\",\n \"0.5992277\",\n \"0.59862584\",\n \"0.5976006\",\n \"0.59672964\",\n \"0.59648114\",\n \"0.5962802\",\n \"0.5960479\",\n \"0.59505874\",\n \"0.59472877\",\n \"0.5946401\",\n \"0.5930292\",\n \"0.59297544\",\n \"0.5926541\",\n \"0.5912855\",\n \"0.589245\",\n \"0.5890731\",\n \"0.5888754\",\n \"0.58834535\",\n \"0.58822\",\n \"0.58648384\",\n \"0.5864131\",\n \"0.58579284\",\n \"0.58335394\",\n \"0.5827808\",\n \"0.58253217\",\n \"0.5816467\",\n \"0.581366\",\n \"0.5807918\",\n \"0.5805739\",\n \"0.58049756\",\n \"0.578543\",\n \"0.57813406\",\n \"0.5778481\",\n \"0.5777616\",\n \"0.5761333\",\n \"0.5758537\",\n \"0.57461333\",\n \"0.5739769\",\n \"0.57394606\",\n \"0.5736131\",\n \"0.572937\",\n \"0.57265395\",\n \"0.5725818\",\n \"0.57243747\",\n \"0.57215154\",\n \"0.5711564\",\n \"0.57043767\",\n \"0.5697074\",\n \"0.5694082\",\n \"0.5692679\",\n \"0.5692679\",\n \"0.5685948\",\n \"0.5684743\",\n \"0.56539327\",\n \"0.56437784\",\n \"0.5635749\",\n \"0.5635551\",\n \"0.562974\",\n \"0.5629337\",\n \"0.56213236\",\n \"0.5603128\",\n \"0.5600945\",\n \"0.5600644\",\n \"0.5593619\",\n \"0.5592864\",\n \"0.55879027\",\n \"0.5586571\",\n \"0.5584056\",\n \"0.55804384\",\n \"0.5579867\",\n \"0.5579867\",\n \"0.55777365\",\n \"0.55777365\",\n \"0.5577685\",\n \"0.5575963\",\n \"0.5575377\",\n \"0.55660987\",\n \"0.5565725\",\n \"0.5564903\",\n \"0.5563643\",\n \"0.55625397\",\n \"0.5562478\",\n \"0.5562096\",\n \"0.5556447\",\n \"0.55508626\",\n \"0.55506086\",\n \"0.5549952\",\n \"0.55414456\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":22293,"cells":{"query":{"kind":"string","value":"Calculates natural log likelihood values"},"document":{"kind":"string","value":"def aiclike(timeSeries, params, distribution):\n if distribution == 'pareto':\n nloglval = -(timeSeries.shape[0] * np.log(params['mu']) + timeSeries.shape[0] * params['mu'] * np.log(params['xmin']) - (params['xmin']+1) * np.sum(np.log(timeSeries)))\n return nloglval\n \n elif distribution == 'lognormal':\n nloglval = np.sum(np.log(timeSeries * params['sigma'] * np.sqrt(2*np.pi)) + (np.log(timeSeries) - params['mu'])**2 / (2 * params['sigma']**2))\n return nloglval\n \n elif distribution == 'normal':\n nloglval = np.sum(np.log( params['sigma'] * np.sqrt(2*np.pi) ) + (timeSeries - params['mu'])**2 / (2 * params['sigma']**2))\n return nloglval\n \n elif distribution == 'exponential':\n nloglval = np.sum(params['lambda'] * timeSeries - np.log(params['lambda']))\n return nloglval\n \n elif distribution == 'boundedpl':\n nloglval = -len(timeSeries) * np.log( (params['mu'] - 1) / (np.min(timeSeries)**(1 - params['mu']) - np.max(timeSeries)**(1 - params['mu']))) + params['mu'] * np.sum(np.log(timeSeries))\n return nloglval"},"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 log_likelihood(self):\r\n return (-0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) -\r\n 0.5 * self.output_dim * self.K_logdet + self._model_fit_term() + self.likelihood.Z)","def nloglikeobs(self, params):\n lambda_ = params[0]\n\n ll_output = self._LL(self.endog, rate=lambda_)\n\n return -np.log(ll_output)","def log_likelihood(self, x):\n # set nuisance parameters to their central values!\n predictions = self.get_predictions(self.shortarray_to_array(x), nuisance=False)\n m_obj = flavio.Measurement['Pseudo-measurement for FastFit instance: ' + self.name]\n m_obs = m_obj.all_parameters\n prob_dict = m_obj.get_logprobability_all(predictions)\n ll = sum(prob_dict.values())\n return ll","def likelihood(mean, logs, x):\n return -0.5 * (logs * 2. + ((x - mean) ** 2) / np.exp(logs * 2.) + GaussianDiag.Log2PI)","def _calculate_ll(self, x):\n observation_log_probs = self._observation_log_probs(x, mask=None)\n forward_log_probs = self._forward(observation_log_probs)\n log_likelihood = logsumexp(\n forward_log_probs[forward_log_probs.shape[0] - 1, :].numpy())\n return log_likelihood","def log_likelihood(parameters):\n if len(copula.bounds_param) == 1:\n params = [parameters]\n else:\n param1, param2 = parameters\n params = [param1, param2]\n logl = -np.sum(np.log(copula.get_pdf(psd_obs[0], psd_obs[1], params)))\n return logl","def _compute_log_likelihood(self, parameters):\n raise NotImplementedError('')","def nloglikeobs(self, params):\n #print len(params),\n beta = params[:-2]\n df = params[-2]\n scale = params[-1]\n loc = np.dot(self.exog, beta)\n endog = self.endog\n x = (endog - loc)/scale\n #next part is stats.t._logpdf\n lPx = sps_gamln((df+1)/2) - sps_gamln(df/2.)\n lPx -= 0.5*np_log(df*np_pi) + (df+1)/2.*np_log(1+(x**2)/df)\n lPx -= np_log(scale) # correction for scale\n return -lPx","def loglikelihood(self):\n raise NotImplementedError(\"To be implemented\")","def log_likelihood(self):\n return -0.5*self.D*self.K_logdet + self._model_fit_term() + self.likelihood.Z","def log_likelihood(self, x):\n return self.log_likelihood_exp(x) + self.log_prior_nuisance_parameters(x)","def log_likelihood(self,samples,times):\n prior_mu = np.ones(2*len(self.A)+1) \n prior_var = np.eye(2*len(self.A)+1)*0.7\n prior_p = np.log(self.prior_pdf())\n #prior_p = np.log(self.normal_prior(prior_mu,prior_var))\n xform = [self.sum_exp(t) for t in times]\n lp = scipy.stats.norm(xform,np.sqrt(self.var)).pdf(samples)\n sample_p =np.sum(np.log(lp))\n ll = prior_p + sample_p\n\n if np.isnan(ll):\n return -np.infty\n return ll","def log_marginal_likelihood(X_train,y_train,phi,tau=1.,Ve=1.e-10):","def log_likelihood(y_true, y_pred):\n ll = np.sum(y_true * np.log(y_pred) - y_pred)\n return ll","def neglogli(preds, actual):\n preds = np.reshape(preds, (-1,))\n nll = np.sum((preds - actual*np.log(preds + 1e-07) + np.log(sp.special.factorial(actual))))\n return nll","def log_prob(self):","def log_likelihood(self, X, Y):\n\t\tr,c = twod(Y).shape\n\t\tif r == 1 and c != 1:\n\t\t\tY = twod(Y).T\n\n\t\tsoft = self.predict_soft(X)\n\t\treturn np.mean(np.sum(np.log(np.power(soft, Y, )), 1), 0)","def log_likelihood(self):\r\n A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.beta_star)) - 0.5 * np.sum(self.V_star * self.likelihood.Y)\r\n C = -self.output_dim * (np.sum(np.log(np.diag(self.LB))))\r\n D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))\r\n return A + C + D + self.likelihood.Z","def log_likelihood_exp(self, x):\n predictions = self.get_predictions(x)\n ll = 0.\n for measurement in self.get_measurements:\n m_obj = flavio.Measurement[measurement]\n m_obs = m_obj.all_parameters\n exclude_observables = set(m_obs) - set(self.observables)\n prob_dict = m_obj.get_logprobability_all(predictions, exclude_parameters=exclude_observables)\n ll += sum(prob_dict.values())\n return ll","def log_likelihood(self, params):\n # extract the parameters\n m1 = params['m1']\n m2 = params['m2']\n DL = params['DL']\n Tc = params['Tc']\n iota = params['iota']\n phic = params['phic']\n psi = params['psi']\n thetaS = params['thetaS']\n phiS = params['phiS']\n\n # calculate the model\n model = self._model(time, m1, m2, DL, Tc, iota, phic, psi, thetaS, phiS)\n\n# # normalisation\n# norm = -0.5*self._ndata*LN2PI - self._ndata*self._logsigma\n\n# # chi-squared\n# chisq = np.sum(((self._data - model)/(self._sigma))**2)\n\n return -np.vdot(self._data - model,self._data - model)","def log_likelihood(self, log_occr_array=None):\n\n if log_occr_array is not None:\n # Catch invalid occurrence rates for zero likelihood\n try:\n self.log_occr = log_occr_array\n except InvalidOccurrenceRate:\n return -np.inf\n\n # N_exp\n N_exp = self.calc_integral() * self._N_stars\n\n # Product terms\n # TODO:Check that the array broadcasting works here\n # Shape of s_terms should be [N_planets, NR, NP]\n s_terms = self.H_array * self.F_array * self.occr\n\n if tf.is_tensor(self.occr):\n ps_terms = tf.reduce_sum(s_terms, axis=(-1, -2))\n product_term = tf.reduce_sum(tf.math.log(ps_terms))\n log_ll_value = product_term - N_exp\n else:\n product_term = np.log(s_terms.sum(axis=(-1, -2))).sum()\n log_ll_value = product_term - N_exp\n\n # BUG TODO\n if np.isnan(log_ll_value):\n warnings.warn(\".likelihood value is nan.\")\n import pdb; pdb.set_trace()\n\n # A nan value is possible when some of the occr are too high\n log_ll_value = -np.inf if np.isnan(log_ll_value) else log_ll_value\n\n return log_ll_value","def log_likelihood(self, log_occr_array=None):\n\n if log_occr_array is not None:\n # Catch invalid occurrence rates for zero likelihood\n try:\n self.log_occr = log_occr_array\n except InvalidOccurrenceRate:\n return -np.inf\n\n # N_exp\n N_exp = self.calc_integral() * self._N_stars\n\n # Product terms\n # TODO:Check that the array broadcasting works here\n # Shape of s_terms should be [N_planets, NR, NP]\n s_terms = self.H_array * self.F_array * self.occr_grid\n\n if tf.is_tensor(self.occr):\n ps_terms = tf.reduce_sum(s_terms, axis=(-1, -2))\n product_term = tf.reduce_sum(tf.math.log(ps_terms))\n log_ll_value = product_term - N_exp\n else:\n product_term = np.log(s_terms.sum(axis=(-1, -2))).sum()\n log_ll_value = product_term - N_exp\n\n # BUG TODO\n if np.isnan(log_ll_value):\n warnings.warn(\".likelihood value is nan.\")\n import pdb; pdb.set_trace()\n\n # A nan value is possible when some of the occr are too high\n log_ll_value = -np.inf if np.isnan(log_ll_value) else log_ll_value\n\n return log_ll_value","def poisson_log_likelihood(x, log_rate):\n return x * log_rate - np.exp(log_rate) - lax.lgamma(x + 1.0)","def log_likelihood_function(self, instance):\r\n\r\n xvalues = np.arange(self.data.shape[0])\r\n model_data = instance.profile_from_xvalues(xvalues=xvalues)\r\n residual_map = self.data - model_data\r\n chi_squared_map = (residual_map / self.noise_map) ** 2.0\r\n log_likelihood = -0.5 * sum(chi_squared_map)\r\n\r\n return log_likelihood","def likelihood(self, log_occr_array=None):\n\n if log_occr_array is not None:\n # Catch invalid occurrence rates for zero likelihood\n try:\n self.log_occr = log_occr_array\n except InvalidOccurrenceRate:\n return 0.0\n\n # Procedure: calculate N_exp in all cases, calculate detection\n # term if there are detections\n\n # N_exp\n N_exp = self.calc_integral() * self._N_stars\n\n # Product terms\n # TODO:Check that the array broadcasting works here\n # Shape of s_terms should be [N_planets, NR, NP]\n s_terms = self.H_array * self.F_array * self.occr\n\n if tf.is_tensor(self.occr):\n ps_terms = tf.reduce_sum(s_terms, axis=(-1, -2))\n product_term = tf.reduce_prod(ps_terms)\n ll_value = product_term * tf.exp(product_term)\n else:\n product_term = s_terms.sum(axis=(-1, -2)).prod()\n ll_value = product_term * np.exp(-N_exp)\n\n # BUG\n if np.isnan(ll_value):\n warnings.warn(\".likelihood value is nan.\")\n import pdb; pdb.set_trace()\n\n # A nan value is possible when some of the occr are too high\n return ll_value if not np.isnan(ll_value) else 0.0","def compute_log_likelihood(self,params: ndarray) -> float:\n \n pred_mag = self._pred_mag(params,self.times)\n sigma_2 = self.sd_mags**2 \n ln_likelihood = -0.5*np.sum((pred_mag - self.mags)**2 / sigma_2+ np.log(sigma_2))\n\n return ln_likelihood","def log_likelihood(self):\r\n assert not self.likelihood.is_heteroscedastic\r\n A = -0.5*self.batchsize*self.output_dim*(np.log(2.*np.pi) - np.log(self.likelihood.precision))\r\n B = -0.5*self.likelihood.precision*self.output_dim*self.trace_K\r\n Kmm_logdet = 2.*np.sum(np.log(np.diag(self.Lm)))\r\n C = -0.5*self.output_dim*self.data_prop*(Kmm_logdet-self.q_u_logdet - self.num_inducing)\r\n C += -0.5*np.sum(self.LQL * self.B)\r\n D = -0.5*self.likelihood.precision*self.likelihood.trYYT\r\n E = np.sum(self.V*self.projected_mean)\r\n return (A+B+C+D+E)/self.data_prop","def compute_log_prob(self,params: ndarray) -> float:\n return self.compute_log_prior(params) + self.compute_log_likelihood(params)","def log_likelihood_function(self, instance) -> float:\n return self.prior.factor(instance[0])","def compute_log_likelihood(self, indicators, weights, l2):\n scores, _ = self.predict_probability(self.train_feature_x, weights)\n probs = self.predict_probability(self.train_feature_x, weights)\n lp = np.sum((indicators-1)*scores + np.log(probs)) - l2* np.sum(weights[1:]**2)\n return lp","def log_likelihood(self):\r\n if self.likelihood.is_heteroscedastic:\r\n A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)\r\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self._A))\r\n else:\r\n A = -0.5 * self.num_data * self.output_dim * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT\r\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self._A))\r\n C = -self.output_dim * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.num_inducing * np.log(sf2))\r\n D = 0.5 * self.data_fit\r\n self._A_part, self._B_part, self._C_part, self._D_part = A, B, C, D\r\n return A + B + C + D + self.likelihood.Z","def log_likelihood(X, parameters):\n check_data_type_column_data(X)\n check_model_params_dict(parameters)\n\n sigma = (1.0/parameters['rho'])**.5\n\n log_likelihood = numpy.sum(norm.logpdf(X,parameters['mu'],sigma))\n\n return log_likelihood","def loglikelihood(self, y):\n raise NotImplementedError","def log_likelihood(self) -> tf.Tensor:\n # K⁻¹ + GᵀΣ⁻¹G = LLᵀ.\n l_post = self._k_inv_post.cholesky\n num_data = self.observations_index.shape[0]\n\n # Hμ [..., num_transitions + 1, output_dim]\n marginal = self.emission.project_state_to_f(self.prior_ssm.marginal_means)\n marginal = self._drop_batch_shape(marginal)\n\n # y = obs - Hμ [..., num_transitions + 1, output_dim]\n disp = self.observations - marginal\n disp_data = self.sparse_observations - self.dense_to_sparse(marginal)\n\n # cst is the constant term for a gaussian log likelihood\n cst = (\n -0.5 * np.log(2 * np.pi) * tf.cast(self.emission.output_dim * num_data, default_float())\n )\n\n term1 = -0.5 * tf.reduce_sum(\n input_tensor=tf.einsum(\"...op,...p,...o->...o\", self._r_inv_data, disp_data, disp_data), axis=[-1, -2]\n )\n\n # term 2 is: ½|L⁻¹(GᵀΣ⁻¹)y|²\n # (GᵀΣ⁻¹)y [..., num_transitions + 1, state_dim]\n obs_proj = self._back_project_y_to_state(disp)\n\n # ½|L⁻¹(GᵀΣ⁻¹)y|² [...]\n term2 = 0.5 * tf.reduce_sum(\n input_tensor=tf.square(l_post.solve(obs_proj, transpose_left=False)), axis=[-1, -2]\n )\n\n ## term 3 is: ½log |K⁻¹| - log |L| + ½ log |Σ⁻¹|\n # where log |Σ⁻¹| = num_data * log|R⁻¹|\n term3 = (\n 0.5 * self.prior_ssm.log_det_precision()\n - l_post.abs_log_det()\n + 0.5 * self._log_det_observation_precision\n )\n\n return tf.reduce_sum(cst + term1 + term2 + term3)","def logprob(predictions, labels):\n predictions[predictions < 1e-10] = 1e-10\n return np.sum(np.multiply(labels, -np.log(predictions))) / labels.shape[0]","def logprob(predictions, labels):\n predictions[predictions < 1e-10] = 1e-10\n return np.sum(np.multiply(labels, -np.log(predictions))) / labels.shape[0]","def log_likelihood_function(self, instance: af.ModelInstance) -> float:\r\n model_data = self.model_data_from_instance(instance=instance)\r\n fit = self.fit_from_model_data(model_data=model_data)\r\n return fit.log_likelihood","def lnlike(params, observables, nDraws=1000000):\n #print('checking type ({}) and length ({}) of params in lnlikefxn'.format(type(params),len(params)))\n evalData=generateModelData(params, distance_standoffMid, nDraws)\n evalHist, evalBinEdges = np.histogram(evalData[:,3], tof_nBins, tof_range,\n density=True)\n logEvalHist = np.log(evalHist)\n #print(logEvalHist)\n # find what TOFs have zero observed data\n # we'll use this to handle cases where we might wind up with -inf*0\n # likelihood is fine if PDF is 0 somewhere where no data is found\n # without checks though, ln(PDF=0)=-inf, -inf*0 = nan\n # however, if PDF is 0 (lnPDF=-inf) where there IS data, lnL should be -inf\n zeroObservedIndices = np.where(observables == 0)[0]\n for idx in zeroObservedIndices:\n if logEvalHist[idx] == -inf:\n logEvalHist[zeroObservedIndices] = 0\n \n loglike = np.dot(logEvalHist,observables)\n return loglike","def log_likelihood(self, points):\n\t\tpoint_set = list(points)\n\t\tlog_probabilities = [np.log(self.density(point)) for point in point_set]\n\t\treturn sum(log_probabilities)","def lnlike(theta, dtarray, dmagarray, sigmaarray):\n gamma, A = theta\n\n aux=np.sum(np.log(like_one(theta,dtarray,dmagarray,sigmaarray)))\n\n return aux","def _compute_log_likelihood(self, X, S):\n log_likelihood = 0\n for n in range(self.n_col):\n likelihood = 1\n for k in range(self.n_components):\n likelihood *= self.weights[k] \\\n * multivariate_normal(self.means[k], self.covs[k]).pdf(X[n]) \\\n * poisson(self.rates[k]).pmf(S[n])\n log_likelihood += np.log(likelihood)\n\n return log_likelihood","def likelihood(self,x,params = None,**kwargs):\n return np.exp(self.log_likelihood(x,params=params,**kwargs))","def likelihood_prediction():\n # Get info\n selected_word = prompt_tech_selection()\n article_json = get_json_from_file()\n\n # Calculate results\n total_word_counter, selected_word_counter = count_occurrences(article_json, selected_word)\n probability = selected_word_counter / total_word_counter\n total_time = article_json[-1]['time'] - article_json[0]['time'] # unix subtraction = seconds\n months_in_train_set = total_time / SECONDS_IN_MONTH\n expected_posts_per_month = int(total_word_counter / months_in_train_set)\n\n # Show results\n print_text_results(expected_posts_per_month, probability, selected_word)\n plot_likelihood(expected_posts_per_month, probability)","def MVN_log_likelihood(X, model):\n D, M = X.shape\n X_normalized = normalize_log_likelihoods(X.copy())\n mvn = multivariate_normal(mean=model.mean, cov=model.cov)\n return mvn.logpdf(X_normalized.T).sum()\n # log_2pi = D * np.log(2 * np.pi)\n # log_det = np.log(np.linalg.det(model.cov))\n # residuals = calc_residuals(X_normalized, model.mean, \"minus\")\n # mahalanobis_distance = np.dot(np.dot(residuals.T, np.linalg.inv(model.cov)), residuals)\n # return -0.5 * (log_2pi + log_det + mahalanobis_distance).sum()","def lnprior(theta):\n gamma, A = theta\n\n if 0.0 < gamma and 0.0 < A < 2.0 :\n return ( np.log(1.0/A) + np.log(1.0/(1.0+(gamma**2.0))) )\n\n return -np.inf","def log_likelihood(data, probs):\n # Assume data is given as counts\n return _np.sum([nlogp(n, p) for n, p in zip(data, probs) if n > 0])","def logprob(predictions, labels):\n predictions[predictions < 1e-10] = 1e-10\n return np.sum(np.multiply(labels, -np.log(predictions)))","def likelihood(self):\n \n raise NotImplementedError()","def log_likelihood(self):\n\n if self._log_likelihood is None:\n self._log_likelihood = logpdf(x=self.y, cov=self.S)\n return self._log_likelihood","def log_likelihood(X, Z, variable_types):\n\tk = Z['pi_unconstrained'].shape[1]+1 # the number of mixture components\n\t## We gather the log probabilities of each indiv in batch for each mixture component into\n\t## a matrix of size (B x k), where B is the batch size.\n\tlogps = torch.zeros([len(X), k])\n\t## First insert the mixture weight contribution to the array\n\tlogps += logsoftmax(Z['pi_unconstrained'], dim=-1)\n\t## Next loop over the features and sum the contributions to logps\n\tfor i, (key, z) in enumerate(Z.items()):\n\t\tif key not in ['pi_unconstrained']:\n\t\t\tdata = torch.Tensor(X[key].values).unsqueeze(-1)\n\t\t\tdist = variable_types[key]\n\t\t\tif dist == 'Categorical':\n\t\t\t\talpha = softmax(z, dim=-1, additional=-50.)\n\t\t\t\tlogps += Categorical(probs = alpha).log_prob(data)\n\t\t\telif dist == 'Bernoulli':\n\t\t\t\ttheta = z\n\t\t\t\tlogps += Bernoulli(logits = theta).log_prob(data)\n\t\t\telif dist == 'Beta':\n\t\t\t\talpha, beta = torch.exp(z).transpose(0,1)\n\t\t\t\tlogps += Beta(alpha, beta).log_prob(data)\n\t## Compute logsumexp over the mixture components and return the sum over data elements.\n\tlogp = torch.logsumexp(logps, dim=-1)\n\treturn logp.sum()","def _log_linear_interpolation(predictions):\n log_probs = utils.average_arrays([mx.nd.log(p) for p in predictions])\n return -mx.nd.log(mx.nd.softmax(log_probs))","def ln(x):\n return log(x, const.e)","def get_log_likelihood(response_probability, response):\n pass","def log_likelihood(self, theta, x, **kwargs):\n\n u, logdet_dudx, log_a = self.forward(theta, x, **kwargs)\n\n constant = float(-0.5 * self.n_inputs * np.log(2.0 * np.pi))\n # log_likelihood = torch.log(torch.sum(torch.exp(log_a - 0.5 * u ** 2 + logdet_dudx), dim=2))\n log_likelihood = torch.logsumexp(log_a - 0.5 * u**2 + logdet_dudx, dim=2)\n log_likelihood = constant + torch.sum(log_likelihood, dim=1)\n\n return u, log_likelihood","def logprob(predictions, labels):\n # prevent negative probability\n predictions[predictions < 1e-10] = 1e-10\n return np.sum(np.multiply(labels, -np.log(predictions))) / labels.shape[0]","def nllfun(x):\n mu = x[0]\n logsigma = x[1]\n return logsigma + 0.5*np.sum((mu/(np.exp(logsigma) + 0.01))**2) + 0.1/np.exp(logsigma)","def nllfun(x):\n mu = x[0]\n logsigma = x[1]\n return logsigma + 0.5*np.sum((mu/(np.exp(logsigma) + 0.01))**2) + 0.1/np.exp(logsigma)","def max_log_likelihood(data):\n # Assume data is given as counts\n tot = sum(data)\n return _np.sum([nlogp(n, n / tot) for n in data if n > 0])","def __log_likelihood(self, params, *args):\n\t\tX, y, feature_set, lambda_reg, empirical_weights, verbose, sign = args\n\n\t\tno_example = len(X)\n\t\ttotal_logZ = 0\n\t\ttotal_logProb = 0\n\t\texpected_weights = np.zeros(len(feature_set))\n\t\tfor t in range(len(X)):\n\t\t\t# example_features = X[t], example_labels = y[t]\n\n\t\t\tpotential = np.zeros(len(X[t]))\n\t\t\tfor i in range(len(X[t])):\n\t\t\t\t#candidate_features = X[t][i], candidate_label = y[t][i]\n\t\t\t\tpotential[i] = feature_set.calc_inner_product(X[t][i], params)\n\n\t\t\t#scaling\n\t\t\tpotential = potential - np.max(potential, keepdims=True)\n\n\t\t\tfor i in range(len(X[t])):\n\t\t\t\ttotal_logProb += potential[i] * y[t][i]\n\n\t\t\tpotential, Z = self.__softmax(potential)\n\n\t\t\tfor i in range(len(X[t])):\n\t\t\t\tfeature_set.calc_inner_sum(expected_weights, X[t][i], potential[i])\n\n\t\t\ttotal_logZ += log(Z)\n\n\t\t# _params = feature_set.get_regularized_params(params, 'bias')\n\t\t_params = params\n\t\tlog_likelihood = total_logProb - total_logZ - (lambda_reg/2) * np.sum(np.multiply(_params,_params))\n\t\tgradients = empirical_weights - expected_weights - lambda_reg * _params\n\n\t\tglobal SUB_ITERATION_NUM\n\t\tif verbose:\n\t\t\tsub_iteration_str = ' '\n\t\t\tif SUB_ITERATION_NUM > 0:\n\t\t\t\tsub_iteration_str = '(' + '{0:02d}'.format(SUB_ITERATION_NUM) + ')'\n\t\t\tprint(' ', '{0:03d}'.format(ITERATION_NUM), sub_iteration_str, ':', log_likelihood * sign)\n\n\t\tSUB_ITERATION_NUM += 1\n\n\t\treturn sign * log_likelihood, sign * gradients","def log_prior(x):\n logp = (-0.5 * x.pow(2) - torch.tensor(2 * math.pi).sqrt().log()).sum(dim=1)\n return logp","def calculate_log_perplexity(self, output, flat_labels): #completed, expensive, should be compiled\n return -np.sum(np.log2(np.clip(output, a_min=1E-12, a_max=1.0))[np.arange(flat_labels.shape[0]), flat_labels[:,1]])","def log_poisson(k, l):\n return k*np.log(l) -l - gammaln(k+1)","def likelihood(self, w, class_words):\n return log((class_words.count(w) + 1)/(len(class_words) + self.N))","def likelihood(ts,w,Phi):\n a = Phi.dot(w)\n return np.exp(a*ts)*sigmoid(-a)","def NormLog(self,X,Y,parameterValues, independentValues):\n lgX = numpy.log10(X)\n D = numpy.around(lgX[1] - lgX[0],2)\n bins = 10**(lgX+D/2.) - 10**(lgX-D/2.)\n return Y/sum(Y*bins)","def get_total_log_likelihood(self, x, **kwargs):\n pass","def log_marginal_likelihood(self) -> tf.Tensor:\n L = tf.linalg.cholesky(self.likelihood.add_to(self.KXX))\n return tf.reduce_sum(multivariate_normal(self._Y, self._mean, L))","def log_prob_parameters(self, parameters):\n lp = 0.0\n parameters_model = self.get_parameters_model\n index = 0\n\n for parameter in parameters_model:\n dimension = parameter.dimension\n lp += parameter.log_prior(parameters[index: index + dimension])\n\n index += dimension\n\n if not np.isinf(lp):\n lp += self.log_likelihood(parameters[0], parameters[1], parameters[2:])\n\n return lp","def calculate_negative_log_likelihood(self):\n data = self.played_points_hist[:self.t]\n kernel_matrix = self.kernel_fn(data, data, self.best_ard_params)\n c_matrix = kernel_matrix + (self.noise_sigma ** 2) * np.eye(data.shape[0])\n c_matrix_inv = np.linalg.inv(c_matrix)\n first_term = np.matmul(self.rews_hist[:self.t].T, np.matmul(c_matrix_inv, self.rews_hist[:self.t]))\n second_term = np.log(np.linalg.det(c_matrix))\n return first_term + second_term","def ICA_log_likelihood(X, model):\n\n # TODO: YOUR CODE HERE","def log_marginal_likelihood(self) -> tf.Tensor:\n X, Y = self.data\n Y = Y[..., :-1]\n K = self.kernel(X)\n ks = self._add_noise_cov(K)\n L = tf.linalg.cholesky(ks)\n m = self.mean_function(X)\n\n # [R,] log-likelihoods for each independent dimension of Y\n log_prob = gpflow.logdensities.multivariate_normal(Y, m, L)\n return tf.reduce_sum(log_prob)","def NLL(sample, params):\n mu = params[:,:,0]\n logsigma = params[:,:,1]\n \n c = normalization.to(mu.device)\n inv_sigma = torch.exp(-logsigma)\n tmp = (sample - mu) * inv_sigma\n return torch.mean(0.5 * (tmp * tmp + 2 * logsigma + c))","def logrels(rets):\n return np.log(rets + 1)","def likelihood(self):\n if self._likelihood is None:\n self._likelihood = exp(self.log_likelihood)\n if self._likelihood == 0:\n self._likelihood = sys.float_info.min\n return self._likelihood","def loglnumpy(store):\n xbeta = dot(store['xmat'], store['beta'])\n lamb = exp(xbeta)\n return sum(store['yvec'] * xbeta - lamb)","def calculate_likelihood(truth, log_forecast):\n\n return tf.reduce_sum(truth * log_forecast) # Dimensions [batch_size, N_LABEL_TIMESTEPS, N_LABEL_CLASSES]","def log_prob_from_logits(x):\n axis = len(x.shape) - 1\n m = x.max(dim=axis, keepdim=True)[0]\n return x - m - torch.log(torch.exp(x - m).sum(dim=axis, keepdim=True))","def log_likelihood(self, X, Y, theta):\n \n alphas, logZ = self.alpha_chain(X, theta)\n total = 0\n \n s_prev = self.S\n \n for t,s in enumerate(Y):\n total += self.log_psi(theta, t, s_prev, s, X[t]) \n s_prev = s\n \n total -= logZ\n return total / X.shape[0]","def pseudolikelihood(self, data):\n data = toPM(data); # interface glue: convert {0,1} to {-1,+1}\n r = self.L.dot(data)\n r += self.h.reshape(-1,1) if len(data.shape)==2 else self.h\n lnp = -np.log(1+np.exp(-2*data*r)) # ln p(x_i^(s)|x_{-i}^(s)) for all vars i, samples s\n return lnp.sum(axis=0) # sum over i => pseudo-log-likelihood of each x^(s)","def get_log_likelihood(phi, pred, t, dot_product, weight, reg= 1):\n prior = -0.5* np.sum(np.multiply(weight, weight))\n likelihood = np.multiply(t, np.log(pred+TOLERANCE)) + np.multiply(1.0- t, np.log(1.0-pred+TOLERANCE))\n likelihood = np.sum(likelihood)\n\n return prior + likelihood","def likelihood(params,data):\n spec, isnflux, igalflux = data\n chi2=0\n modflux = (params[0]*isnflux + params[1]*igalflux)\n chi2 += sum((spec.flux - modflux)**2)/((0.05*sum(spec.var)**2)/2.0)\n return np.exp(-chi2/2.0)","def __calc_likelihood(self, *args):\n params = {}\n for i, p in enumerate(self._par_names):\n if self._par_islog[p]:\n params[p] = np.power(10., args[i])\n else:\n params[p] = args[i]\n return self.return_likelihood(params)","def NLL(self,y):\n return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])","def make_ln_func(variable):\n def safe_ln_queryset(qs):\n \"\"\"Takes the natural log of a queryset's values and handles zeros\"\"\"\n vals = qs.values_list(variable, flat=True)\n ret = np.log(vals)\n ret[ret == -np.inf] = 0\n return ret\n return safe_ln_queryset","def lnprobability(self):\n return","def log_likelihood(self, data, reward_model, bias_params):","def log_prior(self, params):\n # log likelihood function, see:\n # https://en.wikipedia.org/wiki/Multivariate_normal_distribution#Likelihood_function\n variance = self.std ** 2\n ndim = params.ndim\n mean_diff = params - self.mean\n scaled_sq_err = jnp.dot(mean_diff, mean_diff) / variance\n # log determinant of covariance matrix\n log_det_cov = 2 * ndim * jnp.log(self.std)\n norm_term = ndim * jnp.log(2 * jnp.pi)\n return -0.5 * (log_det_cov + scaled_sq_err + norm_term)","def likelihood(self):\n\n # assert the Gaussian process is up to date\n self._gp_up_to_date()\n\n noise_penalization_term = -1 / 2 * np.log(\n np.linalg.det(self.cov_matrix))\n\n y = np.linalg.solve(self.cov_matrix, self.list_y)\n y = np.array(self.list_y) @ y\n data_fidelity_term = -1 / 2 * y\n\n nbr_obs_term = - self.n_observation * np.log(2 * np.pi)\n likelihood = (\n noise_penalization_term + data_fidelity_term + nbr_obs_term\n )\n return likelihood","def normalize_log_likelihoods(X):\n h, w = np.shape(X)\n return X - np.tile(logsumexp(X, axis=0), (h, 1))\n # return X - np.matlib.repmat(logsumexp(X, axis=0), h, 1)","def negative_log_likelihood(self):\n # y.shape[0] is (symbolically) the number of rows in y, i.e.,\n # number of examples (call it n) in the minibatch\n # T.arange(y.shape[0]) is a symbolic vector which will contain\n # [0,1,2,... n-1] T.log(self.p_y_given_x) is a matrix of\n # Log-Probabilities (call it LP) with one row per example and\n # one column per class LP[T.arange(y.shape[0]),y] is a vector\n # v containing [LP[0,y[0]], LP[1,y[1]], LP[2,y[2]], ...,\n # LP[n-1,y[n-1]]] and T.mean(LP[T.arange(y.shape[0]),y]) is\n # the mean (across minibatch examples) of the elements in v,\n # i.e., the mean log-likelihood across the minibatch.\n return -T.log(self.p_y_given_x)[T.arange(self.y.shape[0]), self.y]","def log_likelihood(self, theta=None, phi=None):\n theta = theta if theta is not None else self.theta\n phi = phi if phi is not None else self.phi\n ret = 0.\n for m, dm in enumerate(self.corpus):\n for n, w_mn in enumerate(dm):\n tp = 0.\n for k in range(self.n_components):\n tp += theta[m, k] * phi[k, w_mn]\n ret += np.log(tp)\n return ret","def nll_logprobs(self, input, target_idx):\n raise NotImplementedError()","def compute_log_marginal_likelihood(\n K_i: torch.Tensor,\n logDetK: torch.Tensor,\n y: torch.Tensor,\n normalize: bool = True,\n log_prior_dist=None,\n):\n lml = (\n -0.5 * y.t() @ K_i @ y\n + 0.5 * logDetK\n - y.shape[0]\n / 2.0\n * torch.log(\n 2\n * torch.tensor(\n np.pi,\n )\n )\n )\n if log_prior_dist is not None:\n lml -= log_prior_dist\n return lml / y.shape[0] if normalize else lml","def loglikelihood(self, net):\n\n adjmat = net.edges.adjacency_matrix\n\n # if any of the mustexist or mustnotexist constraints are violated,\n # return negative infinity\n if (not (adjmat | self.mustexist).all()) or \\\n (adjmat & self.mustnotexist).any():\n return NEGINF\n\n # if any custom constraints are violated, return negative infinity\n if self.constraints and not all(c(adjmat) for c in self.constraints):\n return NEGINF\n\n loglike = 0.0\n if self.energy_matrix != None:\n energy = N.sum(adjmat * self.energy_matrix) \n loglike = -self.weight * energy\n\n return loglike","def loglike(self, params, *args, **kwargs):\n return np.sum(self.loglikeobs(params, *args, **kwargs))","def log_prob(self):\n res = -self.L_h/2*np.log(2*np.pi*self.la)\n res = res + self.L_h*(self.L_h-1)/2*self.a\n\n\n res = res - 1/(2*self.la)*np.square(np.linalg.norm(self.e*self.pie))\n\n res = res - 1/(2*self.la)*np.sum(self.e2*self.pie_var)\n\n res = res - self.L_h/2*np.log(2*np.pi*self.sigma2)\n res = res - 1/(2*self.sigma2)*(np.square(np.linalg.norm(self.w))+np.trace(self.R))\n\n print(\"Log-probability difference = {}\".format(res - self.LP), file=self.logfile)\n self.LP = res\n return res","def lognormexp(values, dim=0):\n\n log_denominator = torch.logsumexp(values, dim=dim, keepdim=True)\n # log_numerator = values\n return values - log_denominator","def lognormexp(values, dim=0):\n\n log_denominator = torch.logsumexp(values, dim=dim, keepdim=True)\n # log_numerator = values\n return values - log_denominator","def log_marg_likelihood(self):\n self.A = np.linalg.inv(self.Sn)\n term1 = self.t - self.design_matrix@self.mn\n self.Evidence_mN = (self.beta/2)*np.linalg.norm(term1)+ (self.alpha/2)*self.mn.T@self.mn\n A_abs = np.linalg.eigvals(self.A)\n A_abs = np.prod(A_abs)\n\n self.marg_lik = ((self.p)/2)*np.log(self.alpha) + (self.n/2)*np.log(self.beta) - self.Evidence_mN - (1/2)*np.log(A_abs) - (self.n/2)*np.log(2*np.pi)\n\n return self.marg_lik","def sentence_logprob(self, sentence):\n line = get_ngrams(sentence,3)\n log_por = 0.0\n for item in line:\n raw_por = self.smoothed_trigram_probability(item)\n log_por = log_por+math.log2(raw_por)\n\n return float(log_por)","def log_likelihood_loss(y, tx, w):\n p_1 = sigmoid(tx.dot(w))\n p_0 = np.log(1-p_1)\n p_1 = np.log(p_1)\n return -np.sum((y == 1)*p_1+(y == 0)*p_0)"],"string":"[\n \"def log_likelihood(self):\\r\\n return (-0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) -\\r\\n 0.5 * self.output_dim * self.K_logdet + self._model_fit_term() + self.likelihood.Z)\",\n \"def nloglikeobs(self, params):\\n lambda_ = params[0]\\n\\n ll_output = self._LL(self.endog, rate=lambda_)\\n\\n return -np.log(ll_output)\",\n \"def log_likelihood(self, x):\\n # set nuisance parameters to their central values!\\n predictions = self.get_predictions(self.shortarray_to_array(x), nuisance=False)\\n m_obj = flavio.Measurement['Pseudo-measurement for FastFit instance: ' + self.name]\\n m_obs = m_obj.all_parameters\\n prob_dict = m_obj.get_logprobability_all(predictions)\\n ll = sum(prob_dict.values())\\n return ll\",\n \"def likelihood(mean, logs, x):\\n return -0.5 * (logs * 2. + ((x - mean) ** 2) / np.exp(logs * 2.) + GaussianDiag.Log2PI)\",\n \"def _calculate_ll(self, x):\\n observation_log_probs = self._observation_log_probs(x, mask=None)\\n forward_log_probs = self._forward(observation_log_probs)\\n log_likelihood = logsumexp(\\n forward_log_probs[forward_log_probs.shape[0] - 1, :].numpy())\\n return log_likelihood\",\n \"def log_likelihood(parameters):\\n if len(copula.bounds_param) == 1:\\n params = [parameters]\\n else:\\n param1, param2 = parameters\\n params = [param1, param2]\\n logl = -np.sum(np.log(copula.get_pdf(psd_obs[0], psd_obs[1], params)))\\n return logl\",\n \"def _compute_log_likelihood(self, parameters):\\n raise NotImplementedError('')\",\n \"def nloglikeobs(self, params):\\n #print len(params),\\n beta = params[:-2]\\n df = params[-2]\\n scale = params[-1]\\n loc = np.dot(self.exog, beta)\\n endog = self.endog\\n x = (endog - loc)/scale\\n #next part is stats.t._logpdf\\n lPx = sps_gamln((df+1)/2) - sps_gamln(df/2.)\\n lPx -= 0.5*np_log(df*np_pi) + (df+1)/2.*np_log(1+(x**2)/df)\\n lPx -= np_log(scale) # correction for scale\\n return -lPx\",\n \"def loglikelihood(self):\\n raise NotImplementedError(\\\"To be implemented\\\")\",\n \"def log_likelihood(self):\\n return -0.5*self.D*self.K_logdet + self._model_fit_term() + self.likelihood.Z\",\n \"def log_likelihood(self, x):\\n return self.log_likelihood_exp(x) + self.log_prior_nuisance_parameters(x)\",\n \"def log_likelihood(self,samples,times):\\n prior_mu = np.ones(2*len(self.A)+1) \\n prior_var = np.eye(2*len(self.A)+1)*0.7\\n prior_p = np.log(self.prior_pdf())\\n #prior_p = np.log(self.normal_prior(prior_mu,prior_var))\\n xform = [self.sum_exp(t) for t in times]\\n lp = scipy.stats.norm(xform,np.sqrt(self.var)).pdf(samples)\\n sample_p =np.sum(np.log(lp))\\n ll = prior_p + sample_p\\n\\n if np.isnan(ll):\\n return -np.infty\\n return ll\",\n \"def log_marginal_likelihood(X_train,y_train,phi,tau=1.,Ve=1.e-10):\",\n \"def log_likelihood(y_true, y_pred):\\n ll = np.sum(y_true * np.log(y_pred) - y_pred)\\n return ll\",\n \"def neglogli(preds, actual):\\n preds = np.reshape(preds, (-1,))\\n nll = np.sum((preds - actual*np.log(preds + 1e-07) + np.log(sp.special.factorial(actual))))\\n return nll\",\n \"def log_prob(self):\",\n \"def log_likelihood(self, X, Y):\\n\\t\\tr,c = twod(Y).shape\\n\\t\\tif r == 1 and c != 1:\\n\\t\\t\\tY = twod(Y).T\\n\\n\\t\\tsoft = self.predict_soft(X)\\n\\t\\treturn np.mean(np.sum(np.log(np.power(soft, Y, )), 1), 0)\",\n \"def log_likelihood(self):\\r\\n A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.beta_star)) - 0.5 * np.sum(self.V_star * self.likelihood.Y)\\r\\n C = -self.output_dim * (np.sum(np.log(np.diag(self.LB))))\\r\\n D = 0.5 * np.sum(np.square(self._LBi_Lmi_psi1V))\\r\\n return A + C + D + self.likelihood.Z\",\n \"def log_likelihood_exp(self, x):\\n predictions = self.get_predictions(x)\\n ll = 0.\\n for measurement in self.get_measurements:\\n m_obj = flavio.Measurement[measurement]\\n m_obs = m_obj.all_parameters\\n exclude_observables = set(m_obs) - set(self.observables)\\n prob_dict = m_obj.get_logprobability_all(predictions, exclude_parameters=exclude_observables)\\n ll += sum(prob_dict.values())\\n return ll\",\n \"def log_likelihood(self, params):\\n # extract the parameters\\n m1 = params['m1']\\n m2 = params['m2']\\n DL = params['DL']\\n Tc = params['Tc']\\n iota = params['iota']\\n phic = params['phic']\\n psi = params['psi']\\n thetaS = params['thetaS']\\n phiS = params['phiS']\\n\\n # calculate the model\\n model = self._model(time, m1, m2, DL, Tc, iota, phic, psi, thetaS, phiS)\\n\\n# # normalisation\\n# norm = -0.5*self._ndata*LN2PI - self._ndata*self._logsigma\\n\\n# # chi-squared\\n# chisq = np.sum(((self._data - model)/(self._sigma))**2)\\n\\n return -np.vdot(self._data - model,self._data - model)\",\n \"def log_likelihood(self, log_occr_array=None):\\n\\n if log_occr_array is not None:\\n # Catch invalid occurrence rates for zero likelihood\\n try:\\n self.log_occr = log_occr_array\\n except InvalidOccurrenceRate:\\n return -np.inf\\n\\n # N_exp\\n N_exp = self.calc_integral() * self._N_stars\\n\\n # Product terms\\n # TODO:Check that the array broadcasting works here\\n # Shape of s_terms should be [N_planets, NR, NP]\\n s_terms = self.H_array * self.F_array * self.occr\\n\\n if tf.is_tensor(self.occr):\\n ps_terms = tf.reduce_sum(s_terms, axis=(-1, -2))\\n product_term = tf.reduce_sum(tf.math.log(ps_terms))\\n log_ll_value = product_term - N_exp\\n else:\\n product_term = np.log(s_terms.sum(axis=(-1, -2))).sum()\\n log_ll_value = product_term - N_exp\\n\\n # BUG TODO\\n if np.isnan(log_ll_value):\\n warnings.warn(\\\".likelihood value is nan.\\\")\\n import pdb; pdb.set_trace()\\n\\n # A nan value is possible when some of the occr are too high\\n log_ll_value = -np.inf if np.isnan(log_ll_value) else log_ll_value\\n\\n return log_ll_value\",\n \"def log_likelihood(self, log_occr_array=None):\\n\\n if log_occr_array is not None:\\n # Catch invalid occurrence rates for zero likelihood\\n try:\\n self.log_occr = log_occr_array\\n except InvalidOccurrenceRate:\\n return -np.inf\\n\\n # N_exp\\n N_exp = self.calc_integral() * self._N_stars\\n\\n # Product terms\\n # TODO:Check that the array broadcasting works here\\n # Shape of s_terms should be [N_planets, NR, NP]\\n s_terms = self.H_array * self.F_array * self.occr_grid\\n\\n if tf.is_tensor(self.occr):\\n ps_terms = tf.reduce_sum(s_terms, axis=(-1, -2))\\n product_term = tf.reduce_sum(tf.math.log(ps_terms))\\n log_ll_value = product_term - N_exp\\n else:\\n product_term = np.log(s_terms.sum(axis=(-1, -2))).sum()\\n log_ll_value = product_term - N_exp\\n\\n # BUG TODO\\n if np.isnan(log_ll_value):\\n warnings.warn(\\\".likelihood value is nan.\\\")\\n import pdb; pdb.set_trace()\\n\\n # A nan value is possible when some of the occr are too high\\n log_ll_value = -np.inf if np.isnan(log_ll_value) else log_ll_value\\n\\n return log_ll_value\",\n \"def poisson_log_likelihood(x, log_rate):\\n return x * log_rate - np.exp(log_rate) - lax.lgamma(x + 1.0)\",\n \"def log_likelihood_function(self, instance):\\r\\n\\r\\n xvalues = np.arange(self.data.shape[0])\\r\\n model_data = instance.profile_from_xvalues(xvalues=xvalues)\\r\\n residual_map = self.data - model_data\\r\\n chi_squared_map = (residual_map / self.noise_map) ** 2.0\\r\\n log_likelihood = -0.5 * sum(chi_squared_map)\\r\\n\\r\\n return log_likelihood\",\n \"def likelihood(self, log_occr_array=None):\\n\\n if log_occr_array is not None:\\n # Catch invalid occurrence rates for zero likelihood\\n try:\\n self.log_occr = log_occr_array\\n except InvalidOccurrenceRate:\\n return 0.0\\n\\n # Procedure: calculate N_exp in all cases, calculate detection\\n # term if there are detections\\n\\n # N_exp\\n N_exp = self.calc_integral() * self._N_stars\\n\\n # Product terms\\n # TODO:Check that the array broadcasting works here\\n # Shape of s_terms should be [N_planets, NR, NP]\\n s_terms = self.H_array * self.F_array * self.occr\\n\\n if tf.is_tensor(self.occr):\\n ps_terms = tf.reduce_sum(s_terms, axis=(-1, -2))\\n product_term = tf.reduce_prod(ps_terms)\\n ll_value = product_term * tf.exp(product_term)\\n else:\\n product_term = s_terms.sum(axis=(-1, -2)).prod()\\n ll_value = product_term * np.exp(-N_exp)\\n\\n # BUG\\n if np.isnan(ll_value):\\n warnings.warn(\\\".likelihood value is nan.\\\")\\n import pdb; pdb.set_trace()\\n\\n # A nan value is possible when some of the occr are too high\\n return ll_value if not np.isnan(ll_value) else 0.0\",\n \"def compute_log_likelihood(self,params: ndarray) -> float:\\n \\n pred_mag = self._pred_mag(params,self.times)\\n sigma_2 = self.sd_mags**2 \\n ln_likelihood = -0.5*np.sum((pred_mag - self.mags)**2 / sigma_2+ np.log(sigma_2))\\n\\n return ln_likelihood\",\n \"def log_likelihood(self):\\r\\n assert not self.likelihood.is_heteroscedastic\\r\\n A = -0.5*self.batchsize*self.output_dim*(np.log(2.*np.pi) - np.log(self.likelihood.precision))\\r\\n B = -0.5*self.likelihood.precision*self.output_dim*self.trace_K\\r\\n Kmm_logdet = 2.*np.sum(np.log(np.diag(self.Lm)))\\r\\n C = -0.5*self.output_dim*self.data_prop*(Kmm_logdet-self.q_u_logdet - self.num_inducing)\\r\\n C += -0.5*np.sum(self.LQL * self.B)\\r\\n D = -0.5*self.likelihood.precision*self.likelihood.trYYT\\r\\n E = np.sum(self.V*self.projected_mean)\\r\\n return (A+B+C+D+E)/self.data_prop\",\n \"def compute_log_prob(self,params: ndarray) -> float:\\n return self.compute_log_prior(params) + self.compute_log_likelihood(params)\",\n \"def log_likelihood_function(self, instance) -> float:\\n return self.prior.factor(instance[0])\",\n \"def compute_log_likelihood(self, indicators, weights, l2):\\n scores, _ = self.predict_probability(self.train_feature_x, weights)\\n probs = self.predict_probability(self.train_feature_x, weights)\\n lp = np.sum((indicators-1)*scores + np.log(probs)) - l2* np.sum(weights[1:]**2)\\n return lp\",\n \"def log_likelihood(self):\\r\\n if self.likelihood.is_heteroscedastic:\\r\\n A = -0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) + 0.5 * np.sum(np.log(self.likelihood.precision)) - 0.5 * np.sum(self.likelihood.V * self.likelihood.Y)\\r\\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision.flatten() * self.psi0) - np.trace(self._A))\\r\\n else:\\r\\n A = -0.5 * self.num_data * self.output_dim * (np.log(2.*np.pi) - np.log(self.likelihood.precision)) - 0.5 * self.likelihood.precision * self.likelihood.trYYT\\r\\n B = -0.5 * self.output_dim * (np.sum(self.likelihood.precision * self.psi0) - np.trace(self._A))\\r\\n C = -self.output_dim * (np.sum(np.log(np.diag(self.LB)))) # + 0.5 * self.num_inducing * np.log(sf2))\\r\\n D = 0.5 * self.data_fit\\r\\n self._A_part, self._B_part, self._C_part, self._D_part = A, B, C, D\\r\\n return A + B + C + D + self.likelihood.Z\",\n \"def log_likelihood(X, parameters):\\n check_data_type_column_data(X)\\n check_model_params_dict(parameters)\\n\\n sigma = (1.0/parameters['rho'])**.5\\n\\n log_likelihood = numpy.sum(norm.logpdf(X,parameters['mu'],sigma))\\n\\n return log_likelihood\",\n \"def loglikelihood(self, y):\\n raise NotImplementedError\",\n \"def log_likelihood(self) -> tf.Tensor:\\n # K⁻¹ + GᵀΣ⁻¹G = LLᵀ.\\n l_post = self._k_inv_post.cholesky\\n num_data = self.observations_index.shape[0]\\n\\n # Hμ [..., num_transitions + 1, output_dim]\\n marginal = self.emission.project_state_to_f(self.prior_ssm.marginal_means)\\n marginal = self._drop_batch_shape(marginal)\\n\\n # y = obs - Hμ [..., num_transitions + 1, output_dim]\\n disp = self.observations - marginal\\n disp_data = self.sparse_observations - self.dense_to_sparse(marginal)\\n\\n # cst is the constant term for a gaussian log likelihood\\n cst = (\\n -0.5 * np.log(2 * np.pi) * tf.cast(self.emission.output_dim * num_data, default_float())\\n )\\n\\n term1 = -0.5 * tf.reduce_sum(\\n input_tensor=tf.einsum(\\\"...op,...p,...o->...o\\\", self._r_inv_data, disp_data, disp_data), axis=[-1, -2]\\n )\\n\\n # term 2 is: ½|L⁻¹(GᵀΣ⁻¹)y|²\\n # (GᵀΣ⁻¹)y [..., num_transitions + 1, state_dim]\\n obs_proj = self._back_project_y_to_state(disp)\\n\\n # ½|L⁻¹(GᵀΣ⁻¹)y|² [...]\\n term2 = 0.5 * tf.reduce_sum(\\n input_tensor=tf.square(l_post.solve(obs_proj, transpose_left=False)), axis=[-1, -2]\\n )\\n\\n ## term 3 is: ½log |K⁻¹| - log |L| + ½ log |Σ⁻¹|\\n # where log |Σ⁻¹| = num_data * log|R⁻¹|\\n term3 = (\\n 0.5 * self.prior_ssm.log_det_precision()\\n - l_post.abs_log_det()\\n + 0.5 * self._log_det_observation_precision\\n )\\n\\n return tf.reduce_sum(cst + term1 + term2 + term3)\",\n \"def logprob(predictions, labels):\\n predictions[predictions < 1e-10] = 1e-10\\n return np.sum(np.multiply(labels, -np.log(predictions))) / labels.shape[0]\",\n \"def logprob(predictions, labels):\\n predictions[predictions < 1e-10] = 1e-10\\n return np.sum(np.multiply(labels, -np.log(predictions))) / labels.shape[0]\",\n \"def log_likelihood_function(self, instance: af.ModelInstance) -> float:\\r\\n model_data = self.model_data_from_instance(instance=instance)\\r\\n fit = self.fit_from_model_data(model_data=model_data)\\r\\n return fit.log_likelihood\",\n \"def lnlike(params, observables, nDraws=1000000):\\n #print('checking type ({}) and length ({}) of params in lnlikefxn'.format(type(params),len(params)))\\n evalData=generateModelData(params, distance_standoffMid, nDraws)\\n evalHist, evalBinEdges = np.histogram(evalData[:,3], tof_nBins, tof_range,\\n density=True)\\n logEvalHist = np.log(evalHist)\\n #print(logEvalHist)\\n # find what TOFs have zero observed data\\n # we'll use this to handle cases where we might wind up with -inf*0\\n # likelihood is fine if PDF is 0 somewhere where no data is found\\n # without checks though, ln(PDF=0)=-inf, -inf*0 = nan\\n # however, if PDF is 0 (lnPDF=-inf) where there IS data, lnL should be -inf\\n zeroObservedIndices = np.where(observables == 0)[0]\\n for idx in zeroObservedIndices:\\n if logEvalHist[idx] == -inf:\\n logEvalHist[zeroObservedIndices] = 0\\n \\n loglike = np.dot(logEvalHist,observables)\\n return loglike\",\n \"def log_likelihood(self, points):\\n\\t\\tpoint_set = list(points)\\n\\t\\tlog_probabilities = [np.log(self.density(point)) for point in point_set]\\n\\t\\treturn sum(log_probabilities)\",\n \"def lnlike(theta, dtarray, dmagarray, sigmaarray):\\n gamma, A = theta\\n\\n aux=np.sum(np.log(like_one(theta,dtarray,dmagarray,sigmaarray)))\\n\\n return aux\",\n \"def _compute_log_likelihood(self, X, S):\\n log_likelihood = 0\\n for n in range(self.n_col):\\n likelihood = 1\\n for k in range(self.n_components):\\n likelihood *= self.weights[k] \\\\\\n * multivariate_normal(self.means[k], self.covs[k]).pdf(X[n]) \\\\\\n * poisson(self.rates[k]).pmf(S[n])\\n log_likelihood += np.log(likelihood)\\n\\n return log_likelihood\",\n \"def likelihood(self,x,params = None,**kwargs):\\n return np.exp(self.log_likelihood(x,params=params,**kwargs))\",\n \"def likelihood_prediction():\\n # Get info\\n selected_word = prompt_tech_selection()\\n article_json = get_json_from_file()\\n\\n # Calculate results\\n total_word_counter, selected_word_counter = count_occurrences(article_json, selected_word)\\n probability = selected_word_counter / total_word_counter\\n total_time = article_json[-1]['time'] - article_json[0]['time'] # unix subtraction = seconds\\n months_in_train_set = total_time / SECONDS_IN_MONTH\\n expected_posts_per_month = int(total_word_counter / months_in_train_set)\\n\\n # Show results\\n print_text_results(expected_posts_per_month, probability, selected_word)\\n plot_likelihood(expected_posts_per_month, probability)\",\n \"def MVN_log_likelihood(X, model):\\n D, M = X.shape\\n X_normalized = normalize_log_likelihoods(X.copy())\\n mvn = multivariate_normal(mean=model.mean, cov=model.cov)\\n return mvn.logpdf(X_normalized.T).sum()\\n # log_2pi = D * np.log(2 * np.pi)\\n # log_det = np.log(np.linalg.det(model.cov))\\n # residuals = calc_residuals(X_normalized, model.mean, \\\"minus\\\")\\n # mahalanobis_distance = np.dot(np.dot(residuals.T, np.linalg.inv(model.cov)), residuals)\\n # return -0.5 * (log_2pi + log_det + mahalanobis_distance).sum()\",\n \"def lnprior(theta):\\n gamma, A = theta\\n\\n if 0.0 < gamma and 0.0 < A < 2.0 :\\n return ( np.log(1.0/A) + np.log(1.0/(1.0+(gamma**2.0))) )\\n\\n return -np.inf\",\n \"def log_likelihood(data, probs):\\n # Assume data is given as counts\\n return _np.sum([nlogp(n, p) for n, p in zip(data, probs) if n > 0])\",\n \"def logprob(predictions, labels):\\n predictions[predictions < 1e-10] = 1e-10\\n return np.sum(np.multiply(labels, -np.log(predictions)))\",\n \"def likelihood(self):\\n \\n raise NotImplementedError()\",\n \"def log_likelihood(self):\\n\\n if self._log_likelihood is None:\\n self._log_likelihood = logpdf(x=self.y, cov=self.S)\\n return self._log_likelihood\",\n \"def log_likelihood(X, Z, variable_types):\\n\\tk = Z['pi_unconstrained'].shape[1]+1 # the number of mixture components\\n\\t## We gather the log probabilities of each indiv in batch for each mixture component into\\n\\t## a matrix of size (B x k), where B is the batch size.\\n\\tlogps = torch.zeros([len(X), k])\\n\\t## First insert the mixture weight contribution to the array\\n\\tlogps += logsoftmax(Z['pi_unconstrained'], dim=-1)\\n\\t## Next loop over the features and sum the contributions to logps\\n\\tfor i, (key, z) in enumerate(Z.items()):\\n\\t\\tif key not in ['pi_unconstrained']:\\n\\t\\t\\tdata = torch.Tensor(X[key].values).unsqueeze(-1)\\n\\t\\t\\tdist = variable_types[key]\\n\\t\\t\\tif dist == 'Categorical':\\n\\t\\t\\t\\talpha = softmax(z, dim=-1, additional=-50.)\\n\\t\\t\\t\\tlogps += Categorical(probs = alpha).log_prob(data)\\n\\t\\t\\telif dist == 'Bernoulli':\\n\\t\\t\\t\\ttheta = z\\n\\t\\t\\t\\tlogps += Bernoulli(logits = theta).log_prob(data)\\n\\t\\t\\telif dist == 'Beta':\\n\\t\\t\\t\\talpha, beta = torch.exp(z).transpose(0,1)\\n\\t\\t\\t\\tlogps += Beta(alpha, beta).log_prob(data)\\n\\t## Compute logsumexp over the mixture components and return the sum over data elements.\\n\\tlogp = torch.logsumexp(logps, dim=-1)\\n\\treturn logp.sum()\",\n \"def _log_linear_interpolation(predictions):\\n log_probs = utils.average_arrays([mx.nd.log(p) for p in predictions])\\n return -mx.nd.log(mx.nd.softmax(log_probs))\",\n \"def ln(x):\\n return log(x, const.e)\",\n \"def get_log_likelihood(response_probability, response):\\n pass\",\n \"def log_likelihood(self, theta, x, **kwargs):\\n\\n u, logdet_dudx, log_a = self.forward(theta, x, **kwargs)\\n\\n constant = float(-0.5 * self.n_inputs * np.log(2.0 * np.pi))\\n # log_likelihood = torch.log(torch.sum(torch.exp(log_a - 0.5 * u ** 2 + logdet_dudx), dim=2))\\n log_likelihood = torch.logsumexp(log_a - 0.5 * u**2 + logdet_dudx, dim=2)\\n log_likelihood = constant + torch.sum(log_likelihood, dim=1)\\n\\n return u, log_likelihood\",\n \"def logprob(predictions, labels):\\n # prevent negative probability\\n predictions[predictions < 1e-10] = 1e-10\\n return np.sum(np.multiply(labels, -np.log(predictions))) / labels.shape[0]\",\n \"def nllfun(x):\\n mu = x[0]\\n logsigma = x[1]\\n return logsigma + 0.5*np.sum((mu/(np.exp(logsigma) + 0.01))**2) + 0.1/np.exp(logsigma)\",\n \"def nllfun(x):\\n mu = x[0]\\n logsigma = x[1]\\n return logsigma + 0.5*np.sum((mu/(np.exp(logsigma) + 0.01))**2) + 0.1/np.exp(logsigma)\",\n \"def max_log_likelihood(data):\\n # Assume data is given as counts\\n tot = sum(data)\\n return _np.sum([nlogp(n, n / tot) for n in data if n > 0])\",\n \"def __log_likelihood(self, params, *args):\\n\\t\\tX, y, feature_set, lambda_reg, empirical_weights, verbose, sign = args\\n\\n\\t\\tno_example = len(X)\\n\\t\\ttotal_logZ = 0\\n\\t\\ttotal_logProb = 0\\n\\t\\texpected_weights = np.zeros(len(feature_set))\\n\\t\\tfor t in range(len(X)):\\n\\t\\t\\t# example_features = X[t], example_labels = y[t]\\n\\n\\t\\t\\tpotential = np.zeros(len(X[t]))\\n\\t\\t\\tfor i in range(len(X[t])):\\n\\t\\t\\t\\t#candidate_features = X[t][i], candidate_label = y[t][i]\\n\\t\\t\\t\\tpotential[i] = feature_set.calc_inner_product(X[t][i], params)\\n\\n\\t\\t\\t#scaling\\n\\t\\t\\tpotential = potential - np.max(potential, keepdims=True)\\n\\n\\t\\t\\tfor i in range(len(X[t])):\\n\\t\\t\\t\\ttotal_logProb += potential[i] * y[t][i]\\n\\n\\t\\t\\tpotential, Z = self.__softmax(potential)\\n\\n\\t\\t\\tfor i in range(len(X[t])):\\n\\t\\t\\t\\tfeature_set.calc_inner_sum(expected_weights, X[t][i], potential[i])\\n\\n\\t\\t\\ttotal_logZ += log(Z)\\n\\n\\t\\t# _params = feature_set.get_regularized_params(params, 'bias')\\n\\t\\t_params = params\\n\\t\\tlog_likelihood = total_logProb - total_logZ - (lambda_reg/2) * np.sum(np.multiply(_params,_params))\\n\\t\\tgradients = empirical_weights - expected_weights - lambda_reg * _params\\n\\n\\t\\tglobal SUB_ITERATION_NUM\\n\\t\\tif verbose:\\n\\t\\t\\tsub_iteration_str = ' '\\n\\t\\t\\tif SUB_ITERATION_NUM > 0:\\n\\t\\t\\t\\tsub_iteration_str = '(' + '{0:02d}'.format(SUB_ITERATION_NUM) + ')'\\n\\t\\t\\tprint(' ', '{0:03d}'.format(ITERATION_NUM), sub_iteration_str, ':', log_likelihood * sign)\\n\\n\\t\\tSUB_ITERATION_NUM += 1\\n\\n\\t\\treturn sign * log_likelihood, sign * gradients\",\n \"def log_prior(x):\\n logp = (-0.5 * x.pow(2) - torch.tensor(2 * math.pi).sqrt().log()).sum(dim=1)\\n return logp\",\n \"def calculate_log_perplexity(self, output, flat_labels): #completed, expensive, should be compiled\\n return -np.sum(np.log2(np.clip(output, a_min=1E-12, a_max=1.0))[np.arange(flat_labels.shape[0]), flat_labels[:,1]])\",\n \"def log_poisson(k, l):\\n return k*np.log(l) -l - gammaln(k+1)\",\n \"def likelihood(self, w, class_words):\\n return log((class_words.count(w) + 1)/(len(class_words) + self.N))\",\n \"def likelihood(ts,w,Phi):\\n a = Phi.dot(w)\\n return np.exp(a*ts)*sigmoid(-a)\",\n \"def NormLog(self,X,Y,parameterValues, independentValues):\\n lgX = numpy.log10(X)\\n D = numpy.around(lgX[1] - lgX[0],2)\\n bins = 10**(lgX+D/2.) - 10**(lgX-D/2.)\\n return Y/sum(Y*bins)\",\n \"def get_total_log_likelihood(self, x, **kwargs):\\n pass\",\n \"def log_marginal_likelihood(self) -> tf.Tensor:\\n L = tf.linalg.cholesky(self.likelihood.add_to(self.KXX))\\n return tf.reduce_sum(multivariate_normal(self._Y, self._mean, L))\",\n \"def log_prob_parameters(self, parameters):\\n lp = 0.0\\n parameters_model = self.get_parameters_model\\n index = 0\\n\\n for parameter in parameters_model:\\n dimension = parameter.dimension\\n lp += parameter.log_prior(parameters[index: index + dimension])\\n\\n index += dimension\\n\\n if not np.isinf(lp):\\n lp += self.log_likelihood(parameters[0], parameters[1], parameters[2:])\\n\\n return lp\",\n \"def calculate_negative_log_likelihood(self):\\n data = self.played_points_hist[:self.t]\\n kernel_matrix = self.kernel_fn(data, data, self.best_ard_params)\\n c_matrix = kernel_matrix + (self.noise_sigma ** 2) * np.eye(data.shape[0])\\n c_matrix_inv = np.linalg.inv(c_matrix)\\n first_term = np.matmul(self.rews_hist[:self.t].T, np.matmul(c_matrix_inv, self.rews_hist[:self.t]))\\n second_term = np.log(np.linalg.det(c_matrix))\\n return first_term + second_term\",\n \"def ICA_log_likelihood(X, model):\\n\\n # TODO: YOUR CODE HERE\",\n \"def log_marginal_likelihood(self) -> tf.Tensor:\\n X, Y = self.data\\n Y = Y[..., :-1]\\n K = self.kernel(X)\\n ks = self._add_noise_cov(K)\\n L = tf.linalg.cholesky(ks)\\n m = self.mean_function(X)\\n\\n # [R,] log-likelihoods for each independent dimension of Y\\n log_prob = gpflow.logdensities.multivariate_normal(Y, m, L)\\n return tf.reduce_sum(log_prob)\",\n \"def NLL(sample, params):\\n mu = params[:,:,0]\\n logsigma = params[:,:,1]\\n \\n c = normalization.to(mu.device)\\n inv_sigma = torch.exp(-logsigma)\\n tmp = (sample - mu) * inv_sigma\\n return torch.mean(0.5 * (tmp * tmp + 2 * logsigma + c))\",\n \"def logrels(rets):\\n return np.log(rets + 1)\",\n \"def likelihood(self):\\n if self._likelihood is None:\\n self._likelihood = exp(self.log_likelihood)\\n if self._likelihood == 0:\\n self._likelihood = sys.float_info.min\\n return self._likelihood\",\n \"def loglnumpy(store):\\n xbeta = dot(store['xmat'], store['beta'])\\n lamb = exp(xbeta)\\n return sum(store['yvec'] * xbeta - lamb)\",\n \"def calculate_likelihood(truth, log_forecast):\\n\\n return tf.reduce_sum(truth * log_forecast) # Dimensions [batch_size, N_LABEL_TIMESTEPS, N_LABEL_CLASSES]\",\n \"def log_prob_from_logits(x):\\n axis = len(x.shape) - 1\\n m = x.max(dim=axis, keepdim=True)[0]\\n return x - m - torch.log(torch.exp(x - m).sum(dim=axis, keepdim=True))\",\n \"def log_likelihood(self, X, Y, theta):\\n \\n alphas, logZ = self.alpha_chain(X, theta)\\n total = 0\\n \\n s_prev = self.S\\n \\n for t,s in enumerate(Y):\\n total += self.log_psi(theta, t, s_prev, s, X[t]) \\n s_prev = s\\n \\n total -= logZ\\n return total / X.shape[0]\",\n \"def pseudolikelihood(self, data):\\n data = toPM(data); # interface glue: convert {0,1} to {-1,+1}\\n r = self.L.dot(data)\\n r += self.h.reshape(-1,1) if len(data.shape)==2 else self.h\\n lnp = -np.log(1+np.exp(-2*data*r)) # ln p(x_i^(s)|x_{-i}^(s)) for all vars i, samples s\\n return lnp.sum(axis=0) # sum over i => pseudo-log-likelihood of each x^(s)\",\n \"def get_log_likelihood(phi, pred, t, dot_product, weight, reg= 1):\\n prior = -0.5* np.sum(np.multiply(weight, weight))\\n likelihood = np.multiply(t, np.log(pred+TOLERANCE)) + np.multiply(1.0- t, np.log(1.0-pred+TOLERANCE))\\n likelihood = np.sum(likelihood)\\n\\n return prior + likelihood\",\n \"def likelihood(params,data):\\n spec, isnflux, igalflux = data\\n chi2=0\\n modflux = (params[0]*isnflux + params[1]*igalflux)\\n chi2 += sum((spec.flux - modflux)**2)/((0.05*sum(spec.var)**2)/2.0)\\n return np.exp(-chi2/2.0)\",\n \"def __calc_likelihood(self, *args):\\n params = {}\\n for i, p in enumerate(self._par_names):\\n if self._par_islog[p]:\\n params[p] = np.power(10., args[i])\\n else:\\n params[p] = args[i]\\n return self.return_likelihood(params)\",\n \"def NLL(self,y):\\n return -T.mean(T.log(self.p_y_given_x)[T.arange(y.shape[0]), y])\",\n \"def make_ln_func(variable):\\n def safe_ln_queryset(qs):\\n \\\"\\\"\\\"Takes the natural log of a queryset's values and handles zeros\\\"\\\"\\\"\\n vals = qs.values_list(variable, flat=True)\\n ret = np.log(vals)\\n ret[ret == -np.inf] = 0\\n return ret\\n return safe_ln_queryset\",\n \"def lnprobability(self):\\n return\",\n \"def log_likelihood(self, data, reward_model, bias_params):\",\n \"def log_prior(self, params):\\n # log likelihood function, see:\\n # https://en.wikipedia.org/wiki/Multivariate_normal_distribution#Likelihood_function\\n variance = self.std ** 2\\n ndim = params.ndim\\n mean_diff = params - self.mean\\n scaled_sq_err = jnp.dot(mean_diff, mean_diff) / variance\\n # log determinant of covariance matrix\\n log_det_cov = 2 * ndim * jnp.log(self.std)\\n norm_term = ndim * jnp.log(2 * jnp.pi)\\n return -0.5 * (log_det_cov + scaled_sq_err + norm_term)\",\n \"def likelihood(self):\\n\\n # assert the Gaussian process is up to date\\n self._gp_up_to_date()\\n\\n noise_penalization_term = -1 / 2 * np.log(\\n np.linalg.det(self.cov_matrix))\\n\\n y = np.linalg.solve(self.cov_matrix, self.list_y)\\n y = np.array(self.list_y) @ y\\n data_fidelity_term = -1 / 2 * y\\n\\n nbr_obs_term = - self.n_observation * np.log(2 * np.pi)\\n likelihood = (\\n noise_penalization_term + data_fidelity_term + nbr_obs_term\\n )\\n return likelihood\",\n \"def normalize_log_likelihoods(X):\\n h, w = np.shape(X)\\n return X - np.tile(logsumexp(X, axis=0), (h, 1))\\n # return X - np.matlib.repmat(logsumexp(X, axis=0), h, 1)\",\n \"def negative_log_likelihood(self):\\n # y.shape[0] is (symbolically) the number of rows in y, i.e.,\\n # number of examples (call it n) in the minibatch\\n # T.arange(y.shape[0]) is a symbolic vector which will contain\\n # [0,1,2,... n-1] T.log(self.p_y_given_x) is a matrix of\\n # Log-Probabilities (call it LP) with one row per example and\\n # one column per class LP[T.arange(y.shape[0]),y] is a vector\\n # v containing [LP[0,y[0]], LP[1,y[1]], LP[2,y[2]], ...,\\n # LP[n-1,y[n-1]]] and T.mean(LP[T.arange(y.shape[0]),y]) is\\n # the mean (across minibatch examples) of the elements in v,\\n # i.e., the mean log-likelihood across the minibatch.\\n return -T.log(self.p_y_given_x)[T.arange(self.y.shape[0]), self.y]\",\n \"def log_likelihood(self, theta=None, phi=None):\\n theta = theta if theta is not None else self.theta\\n phi = phi if phi is not None else self.phi\\n ret = 0.\\n for m, dm in enumerate(self.corpus):\\n for n, w_mn in enumerate(dm):\\n tp = 0.\\n for k in range(self.n_components):\\n tp += theta[m, k] * phi[k, w_mn]\\n ret += np.log(tp)\\n return ret\",\n \"def nll_logprobs(self, input, target_idx):\\n raise NotImplementedError()\",\n \"def compute_log_marginal_likelihood(\\n K_i: torch.Tensor,\\n logDetK: torch.Tensor,\\n y: torch.Tensor,\\n normalize: bool = True,\\n log_prior_dist=None,\\n):\\n lml = (\\n -0.5 * y.t() @ K_i @ y\\n + 0.5 * logDetK\\n - y.shape[0]\\n / 2.0\\n * torch.log(\\n 2\\n * torch.tensor(\\n np.pi,\\n )\\n )\\n )\\n if log_prior_dist is not None:\\n lml -= log_prior_dist\\n return lml / y.shape[0] if normalize else lml\",\n \"def loglikelihood(self, net):\\n\\n adjmat = net.edges.adjacency_matrix\\n\\n # if any of the mustexist or mustnotexist constraints are violated,\\n # return negative infinity\\n if (not (adjmat | self.mustexist).all()) or \\\\\\n (adjmat & self.mustnotexist).any():\\n return NEGINF\\n\\n # if any custom constraints are violated, return negative infinity\\n if self.constraints and not all(c(adjmat) for c in self.constraints):\\n return NEGINF\\n\\n loglike = 0.0\\n if self.energy_matrix != None:\\n energy = N.sum(adjmat * self.energy_matrix) \\n loglike = -self.weight * energy\\n\\n return loglike\",\n \"def loglike(self, params, *args, **kwargs):\\n return np.sum(self.loglikeobs(params, *args, **kwargs))\",\n \"def log_prob(self):\\n res = -self.L_h/2*np.log(2*np.pi*self.la)\\n res = res + self.L_h*(self.L_h-1)/2*self.a\\n\\n\\n res = res - 1/(2*self.la)*np.square(np.linalg.norm(self.e*self.pie))\\n\\n res = res - 1/(2*self.la)*np.sum(self.e2*self.pie_var)\\n\\n res = res - self.L_h/2*np.log(2*np.pi*self.sigma2)\\n res = res - 1/(2*self.sigma2)*(np.square(np.linalg.norm(self.w))+np.trace(self.R))\\n\\n print(\\\"Log-probability difference = {}\\\".format(res - self.LP), file=self.logfile)\\n self.LP = res\\n return res\",\n \"def lognormexp(values, dim=0):\\n\\n log_denominator = torch.logsumexp(values, dim=dim, keepdim=True)\\n # log_numerator = values\\n return values - log_denominator\",\n \"def lognormexp(values, dim=0):\\n\\n log_denominator = torch.logsumexp(values, dim=dim, keepdim=True)\\n # log_numerator = values\\n return values - log_denominator\",\n \"def log_marg_likelihood(self):\\n self.A = np.linalg.inv(self.Sn)\\n term1 = self.t - self.design_matrix@self.mn\\n self.Evidence_mN = (self.beta/2)*np.linalg.norm(term1)+ (self.alpha/2)*self.mn.T@self.mn\\n A_abs = np.linalg.eigvals(self.A)\\n A_abs = np.prod(A_abs)\\n\\n self.marg_lik = ((self.p)/2)*np.log(self.alpha) + (self.n/2)*np.log(self.beta) - self.Evidence_mN - (1/2)*np.log(A_abs) - (self.n/2)*np.log(2*np.pi)\\n\\n return self.marg_lik\",\n \"def sentence_logprob(self, sentence):\\n line = get_ngrams(sentence,3)\\n log_por = 0.0\\n for item in line:\\n raw_por = self.smoothed_trigram_probability(item)\\n log_por = log_por+math.log2(raw_por)\\n\\n return float(log_por)\",\n \"def log_likelihood_loss(y, tx, w):\\n p_1 = sigmoid(tx.dot(w))\\n p_0 = np.log(1-p_1)\\n p_1 = np.log(p_1)\\n return -np.sum((y == 1)*p_1+(y == 0)*p_0)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.73833865","0.7282104","0.72731364","0.7224237","0.72090906","0.72088003","0.7207167","0.71788365","0.7166103","0.7160064","0.7155805","0.71335495","0.71213716","0.7114622","0.7103507","0.70852166","0.70822245","0.70803833","0.70540184","0.7014505","0.7005979","0.69746643","0.6971167","0.69698966","0.69688666","0.696493","0.6962561","0.6946454","0.6937759","0.69121677","0.6878745","0.6850562","0.6847932","0.6845611","0.68401355","0.68401355","0.6833356","0.6826192","0.68014884","0.67998576","0.6799148","0.6797497","0.67969257","0.6796705","0.6792722","0.6789459","0.67764425","0.6774893","0.67715544","0.67427105","0.67294073","0.6724474","0.6722698","0.6718627","0.67174935","0.671424","0.671424","0.67030245","0.66779345","0.66695565","0.66630656","0.66620547","0.6656266","0.66547835","0.66423106","0.66267914","0.6610428","0.6610133","0.66067916","0.66061735","0.6601277","0.6599658","0.65978855","0.6590921","0.6588399","0.6587864","0.65867037","0.65836775","0.65826815","0.6574082","0.6571237","0.65692264","0.656658","0.6556656","0.6556297","0.65562934","0.65532583","0.6550737","0.6541781","0.6540285","0.6538422","0.65296024","0.652856","0.6521024","0.6508734","0.65042853","0.65010285","0.65010285","0.6500285","0.64980996","0.6493873"],"string":"[\n \"0.73833865\",\n \"0.7282104\",\n \"0.72731364\",\n \"0.7224237\",\n \"0.72090906\",\n \"0.72088003\",\n \"0.7207167\",\n \"0.71788365\",\n \"0.7166103\",\n \"0.7160064\",\n \"0.7155805\",\n \"0.71335495\",\n \"0.71213716\",\n \"0.7114622\",\n \"0.7103507\",\n \"0.70852166\",\n \"0.70822245\",\n \"0.70803833\",\n \"0.70540184\",\n \"0.7014505\",\n \"0.7005979\",\n \"0.69746643\",\n \"0.6971167\",\n \"0.69698966\",\n \"0.69688666\",\n \"0.696493\",\n \"0.6962561\",\n \"0.6946454\",\n \"0.6937759\",\n \"0.69121677\",\n \"0.6878745\",\n \"0.6850562\",\n \"0.6847932\",\n \"0.6845611\",\n \"0.68401355\",\n \"0.68401355\",\n \"0.6833356\",\n \"0.6826192\",\n \"0.68014884\",\n \"0.67998576\",\n \"0.6799148\",\n \"0.6797497\",\n \"0.67969257\",\n \"0.6796705\",\n \"0.6792722\",\n \"0.6789459\",\n \"0.67764425\",\n \"0.6774893\",\n \"0.67715544\",\n \"0.67427105\",\n \"0.67294073\",\n \"0.6724474\",\n \"0.6722698\",\n \"0.6718627\",\n \"0.67174935\",\n \"0.671424\",\n \"0.671424\",\n \"0.67030245\",\n \"0.66779345\",\n \"0.66695565\",\n \"0.66630656\",\n \"0.66620547\",\n \"0.6656266\",\n \"0.66547835\",\n \"0.66423106\",\n \"0.66267914\",\n \"0.6610428\",\n \"0.6610133\",\n \"0.66067916\",\n \"0.66061735\",\n \"0.6601277\",\n \"0.6599658\",\n \"0.65978855\",\n \"0.6590921\",\n \"0.6588399\",\n \"0.6587864\",\n \"0.65867037\",\n \"0.65836775\",\n \"0.65826815\",\n \"0.6574082\",\n \"0.6571237\",\n \"0.65692264\",\n \"0.656658\",\n \"0.6556656\",\n \"0.6556297\",\n \"0.65562934\",\n \"0.65532583\",\n \"0.6550737\",\n \"0.6541781\",\n \"0.6540285\",\n \"0.6538422\",\n \"0.65296024\",\n \"0.652856\",\n \"0.6521024\",\n \"0.6508734\",\n \"0.65042853\",\n \"0.65010285\",\n \"0.65010285\",\n \"0.6500285\",\n \"0.64980996\",\n \"0.6493873\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":22294,"cells":{"query":{"kind":"string","value":"Generates the values for the probability distributions"},"document":{"kind":"string","value":"def aicpdf(xvals, distribution, params):\n if distribution == 'pareto':\n pvals = (params['xmin'] * params['mu'] ** params['xmin']) / (xvals ** (params['xmin'] + 1))\n return pvals\n \n elif distribution == 'lognormal':\n #import pdb; pdb.set_trace()\n pvals = np.exp(-(np.log(xvals) - params['mu'])**2 / (2 * params['sigma']**2)) / (xvals * params['sigma'] * np.sqrt(2*np.pi))\n return pvals\n \n elif distribution == 'normal':\n pvals = np.exp(-(xvals - params['mu'])**2 / (2 * params['sigma']**2)) / (params['sigma'] * np.sqrt(2*np.pi))\n return pvals\n \n elif distribution == 'exponential':\n pvals = params['lambda'] * np.exp(-params['lambda'] * xvals)\n return pvals \n \n elif distribution == 'boundedpl':\n #pvals = (params['mu'] * (params['mu'] ** params['xmax'] - params['xmin'] ** params['xmax'])) / (xvals ** (params['mu'] + 1))\n #mu * (xmax ^ mu - xmin ^ mu) / x ^ (mu+1)\n pvals = (params['mu'] * (params['xmax'] ** params['mu'] - params['xmin'] ** params['mu'])) / (xvals ** (params['mu'] + 1))\n return pvals"},"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_samples(self):\n self.analytic_probability()","def generate_probabilities(self):\n k = 1\n v= 10\n for g in self.class_probabilities:\n curr_list = self.class_probabilities[g]\n for l in range(0,28):\n for w in range(0,28):\n total = float(curr_list[l][w][0] + curr_list[l][w][1] + curr_list[l][w][2])\n curr_list[l][w][0] = (float(curr_list[l][w][0])+k)/(total + k*v) \n curr_list[l][w][1] = (float(curr_list[l][w][1])+k)/(total + k*v)\n curr_list[l][w][2] = (float(curr_list[l][w][2])+k)/(total + k*v)\n curr_list[l][w][3] = curr_list[l][w][0] + curr_list[l][w][1] + curr_list[l][w][2]","def generate_values(num_points: int, distribution: str, dist_params, sort: bool = False) -> np.ndarray:\n vals = method_map[distribution](*dist_params, size=num_points)\n if sort:\n vals.sort()\n return vals","def _generate_distribution_samples(self, set_count, parameter_count):\n self._samples = numpy.zeros((set_count, parameter_count))\n for i, distribution in enumerate(self.parameter_distributions.values()):\n self._samples[:, i] = distribution.ppf(self._quantiles[:, i])","def probability(self, samples):\n pass","def p(self) -> Probability:\n ...","def probabilities(self):\n raise NotImplementedError","def outputGenerationProbability(self):\n self.b = zeros((self.noOfEmmittingStates, self.T))\n for row in range(self.noOfEmmittingStates):\n for col in range(self.T):\n self.b[row, col] = self.gaussianDist(self.observationSequence[0, col],\n self.outputProbabilities[row, 0],\n self.outputProbabilities[row, 1])","def custom_pdf(self, cum_probs, values):\n rnd_num = random()\n for p in range(len(cum_probs)):\n if rnd_num < cum_probs[p]:\n return values[p]","def example():\n\tN=9000\n\t#true distribution -> uniform between 0 and 3\n\ttrue = np.zeros(N)\n\ttrue[:N/3-1] = 0.5\n\ttrue[N/3:2*N/3-1] = 1.5\n\ttrue[2*N/3:] = 2.5\n\t# measured distribution -> fixed normal distribution with sigma=.5 and mean=1.5\n\tmeasured = np.zeros(N)\n\tmeasured[:1300-1] = 0.5\n\tmeasured[1300:1300+6000-1] = 1.5\n\tmeasured[1300+6000:] = 2.5\n\t# Also return the generating true pdf\n\tx = np.linspace(-1, 5, 500)\n\tpdf = np.zeros([2, len(x)])\n\tpdf[0] = x\n\tpdf[1] = scs.uniform.pdf(x, 0, 3)\n\n\treturn measured, true, pdf, [0,1,2,3]","def _generate_p(self):\n self._values, weights = zip(*self._weights.items())\n cumsum = list(itertools.accumulate(weights))\n total = cumsum[-1]\n self._p = [i / total for i in cumsum]","def prob4():\n\n\n N = 500000\n random_draws = np.random.multivariate_normal(mean = [-1,1], cov =[[1,0],[0,1]], size = N)\n\n h = lambda x: x[0] < -1 and x[1] > 1\n f = lambda x: stats.multivariate_normal(mean = [ 0, 0]).pdf(x)\n g = lambda x: stats.multivariate_normal(mean = [-1, 1]).pdf(x)\n\n probability = [h(random_draws[i]) * f(random_draws[i]) / g(random_draws[i]) for i in range(N)]\n\n return 1./N * np.sum(probability)","def generate_distribution(p1=[1.0,0.1,10000],p2=[-1.0,0.6,30000]):\n mu1,sig1,num1 = p1\n mu2,sig2,num2 = p2\n g1 = scipy.stats.norm(mu1,sig1).rvs(num1)\n g2 = scipy.stats.norm(mu2,sig2).rvs(num2)\n\n samples = np.concatenate([g1,g2])\n return samples","def _gen_pert(self, count, **kwargs):\n self._check_pert(**kwargs)\n pert = FairBetaPert(**kwargs)\n rvs = pert.random_variates(count)\n return rvs","def compute_empirical_distribution(values):\n distribution = {}\n\n # -------------------------------------------------------------------------\n # YOUR CODE HERE\n #\n for value in values:\n if value not in distribution:\n distribution[value] = 1\n else:\n distribution[value] += 1\n \n total = len(values)\n for v in distribution.keys():\n distribution[v] /= total\n \n\n #\n # END OF YOUR CODE\n # -------------------------------------------------------------------------\n\n return distribution","def get_prob_for_distributions(p):\n w1 = p[0]\n mu1 = p[1]\n sigma1 = p[2]\n w2 = p[3]\n mu2 = p[4]\n sigma2 = p[5]\n w3 = p[6]\n mu3 = p[7]\n sigma3 = p[8]\n dist_range = (0, 4.330310991999920844e+01)\n x = np.linspace(dist_range[0], dist_range[1], 1000)\n A1 = np.array(w1 * mlab.normpdf(x, mu1, sigma1)).sum()\n A2 = np.array(w2 * mlab.normpdf(x, mu2, sigma2)).sum()\n A3 = np.array(w3 * mlab.normpdf(x, mu3, sigma3)).sum()\n p1 = A1 / (A1 + A2 + A3)\n p2 = A2 / (A1 + A2 + A3)\n p3 = A3 / (A1 + A2 + A3)\n return p1, p2, p3","def prob4():\n#raise NotImplementedError(\"Problem 4 Incomplete\")\n h = lambda x : x[0] < -1 and x[1] > 1\n f = lambda x : stats.multivariate_normal.pdf(x,mean=np.array([0,0]),cov=np.eye(2))\n g = lambda x : stats.multivariate_normal.pdf(x,mean=np.array([-1,1]),cov=np.eye(2))\n X = np.random.multivariate_normal(mean=np.array([-1,1]),cov=np.eye(2),size=10000)\n return 1./10000*np.sum(np.apply_along_axis(h,1,X)*np.apply_along_axis(f,1,X)/np.apply_along_axis(g,1,X))","def generate_random_numbers(self):\r\n #random.seed(seed=self.seed)\r\n #err = random.random((3,1))\r\n #f = open('test_res', 'a')\r\n #f.write('probability - %s' %self.seed)\r\n #f.write(str(list(err[:3,:])))\r\n #f.write('\\n')\r\n #f.close()\r\n\r\n dist = RandomDistribution(self.seed)\r\n rand_numbers = dist.return_random_variables(self.num_agents)\r\n return rand_numbers","def __init__(self, num_values, probability):\n self.num_values = num_values\n self.probability = probability\n self.register_bounds = GeometricDistribution._compute_register_bounds(num_values, probability)\n self._register_probs = GeometricDistribution._compute_register_probs(num_values, probability)","def generate_probabilities(self):\n dists = np.copy(self.distances)\n\n for i in range(self.number_towers):\n for j in range(self.number_towers):\n if self.method == 'distance_distribution':\n dists[i][j] = (\n -1 *\n (dists[i][j] ** 2) *\n xamtfos(dists[i][j] ** 2, self.sigma) *\n self.expander\n )\n elif self.method == 'distance_square':\n dists[i][j] = -1 * (dists[i][j] + 1) ** self.distance_power\n\n normalizer = dists.max().max() / 2\n dists -= normalizer\n\n return np.array([\n softmax(dists[i])\n for i in range(self.number_towers)\n ])","def define_pdf(self, values: torch.Tensor, weights: torch.Tensor, inds: torch.Tensor) -> Distribution:\n\n raise NotImplementedError()","def get_Sample(self, values, probabilities):\r\n return choices(values,probabilities)\r\n # return np.random.choice(values,p=probabilities)\r","def sample(self, probabilities):\n return self.sample_bernoulli(probabilities)","def generic_distribution(target, seeds, func):\n seeds = target[seeds]\n value = func.ppf(seeds)\n return value","def dealer_probs():\n # Pdf of any current hand (value, hard) and final value; p(v_f | v_c) where v_f = final value, v_c = current value\n probabilities = {}\n\n # End nodes: (value, True) for value >= 17 and (value, False) for value > 17\n # Dependencies (in order of increasing requirements):\n # Hard values, value >= 11, possiblity of bust, no possibility of going soft with an ace (value, True) depends on (value', True) for 17 > value' > value\n # Soft values, 17 >= value >= 11 (value, False) depends on (value', False) for 17 >= value' > value, (value', True) for 17 > value' > 11\n # Hard values, 11 > value >= 2 , no possibility of bust, possibility of going soft with an ace (value, True) depends on (value', True) for 17 > value' > value and (value', False) for 17 >= value' > 13\n\n\n # End nodes\n for value in xrange(17, 22):\n probabilities[(value, True)] = {value: 1.0}\n if value == 17: continue # on soft 17, dealer will still hit\n probabilities[(value, False)] = {value: 1.0}\n\n # Hard values, 17 > value >= 11, possibility of bust, no possibility of going soft with an ace\n for value in xrange(16, 10, -1):\n probabilities[(value, True)] = {}\n current_prob = probabilities[(value, True)]\n for next_card in xrange(1, min(10, 21-value)+1):\n next_prob = probabilities[(value + next_card, True)]\n for end_val in next_prob:\n current_prob[end_val] = current_prob.get(end_val, 0) + next_prob[end_val] * PROBABILITIES[next_card]\n\n # Soft values, 17 >= value >= 11\n for value in xrange(17, 10, -1):\n probabilities[(value, False)] = {}\n current_prob = probabilities[(value, False)]\n for next_card in xrange(1, 11):\n next_value = value + next_card\n hard = False\n if next_value > 21:\n next_value -= 10\n hard = True\n next_prob = probabilities[(next_value, hard)]\n for end_val in next_prob:\n current_prob[end_val] = current_prob.get(end_val, 0) + next_prob[end_val] * PROBABILITIES[next_card]\n\n # Hard values, 11 > value >= 2, no possibility of bust, possibility of going soft with an ace\n for value in xrange(10, 1, -1):\n probabilities[(value, True)] = {}\n current_prob = probabilities[(value, True)]\n for next_card in xrange(2, 12):\n next_value = value + next_card\n hard = (next_card != 11)\n next_prob = probabilities[(next_value, hard)]\n for end_val in next_prob:\n current_prob[end_val] = current_prob.get(end_val, 0) + next_prob[end_val] * PROBABILITIES[next_card]\n\n return probabilities","def _compute_register_probs(cls, num_values, probability):\n bits = np.arange(1, num_values + 1)\n probs = scipy.stats.geom.pmf(bits, probability)\n\n return probs / sum(probs)","def probability(series, params):\n\n prob = 1\n\n for result in series:\n\n prob *= params[result]\n\n return prob * params[\"die\"]","def get_output_distr(self, stat_list):\n out_probs = [[np.zeros(500) for _ in stat_list]\n for _ in range(self.task_count)]\n\n # for each task, get the posterior distribution for each predicted\n # output label\n for i in range(self.task_count):\n distr_vec = [\n stats.norm(loc=mu, scale=sigma) for mu, sigma in\n zip(self.output_mat['mu'][i, :],\n self.output_mat['sigma'][i, :])\n ]\n\n # calculate the range of possible predicted output values\n min_range = min(distr.ppf(0.01) for distr in distr_vec)\n max_range = max(distr.ppf(0.99) for distr in distr_vec)\n x_vals = np.linspace(min_range, max_range, 1000)\n\n # calculate the cumulative probability density function across all\n # label distributions at each possible predicted value\n for j, stat in enumerate(stat_list):\n out_probs[i][j] = [\n (x,\n np.mean([distr.pdf(x) for distr, st in\n zip(distr_vec, stat) if st])\n ) for x in x_vals\n ]\n\n return out_probs","def calcProbability(self):\n for attribute in self.attributes:\n index = self.F2I[attribute]\n features = set([self.train[i][0][index] for i in range(len(self.train))])\n for feature in features:\n #all the true and false\n result_t = list(filter(lambda x: x[1]== True, self.train))\n total_t = len(result_t)\n result_f = list(filter(lambda x: x[1]== False, self.train))\n total_f= len(result_f)\n #the probability for the feature if its true or false\n t = len(list(filter(lambda x: x[0][index] == feature, result_t)))\n f = len(list(filter(lambda x: x[0][index] == feature, result_f)))\n prob_yes= t/total_t\n prob_no = f/total_f\n #assign the probabilities to the dictionaries\n self.probs_yes[(index,feature)] = prob_yes\n self.probs_no[(index,feature)] = prob_no","def __GenerateVariantsDistribution(self):\n np.random.seed(self.random_seed_parametr)\n try:\n Students = pd.read_excel(self.student_path)\n print('Load {}'.format(self.student_path))\n students_number = len(Students)\n\n self.__create_ALL_LR()\n Course_structure, variants_numbers = self.__generate_stracture()\n print('Generate stracture')\n Number_of_weaks = len(Course_structure)\n\n number_of_distribution = 0\n for WeakNumber in range(Number_of_weaks):\n for TaskNumber in range(Course_structure[WeakNumber]):\n Students['Week {0} Task {1}'.format(WeakNumber + 1, TaskNumber + 1)] = np.random.randint(\n variants_numbers[number_of_distribution], size=students_number)\n number_of_distribution += 1\n\n writer = pd.ExcelWriter(self.students_with_variants_path)\n print('Save {}'.format(self.students_with_variants_path))\n Students.to_excel(writer)\n writer.save()\n except:\n print('File with students doesnot exist')","def prob_distr(self, x):\n return 1.0/x","def probability_from_internal(internal_values, constr):\n return internal_values / internal_values.sum()","def initializeDistribution(self):\n if self.nPoints is None:\n self.xArray = np.arange(self.lowerBound,self.upperBound+1)\n else:\n self.xArray = np.linspace(self.lowerBound,self.upperBound,self.nPoints)\n\n # Here the actual calculation of discrete distribution parameters is performed\n self.pdfArray = 1.0/self.xArray.size * np.ones(self.xArray.size)\n paramsDict={}\n paramsDict['outcome'] = self.xArray\n paramsDict['state'] = self.pdfArray\n\n self.categoricalDist = Categorical()\n self.categoricalDist.initializeFromDict(paramsDict)\n initialPerm = randomUtils.randomPermutation(self.xArray.tolist(),self)\n self.pot = np.asarray(initialPerm)","def init_probability_dict(self):\n for x in xrange(0,10):\n self.class_probabilities[x] = self.init_probability_2d()","def random_distribution():\n b = np.random.uniform(0.0, 1.0, size=[1, vocabulary_size])\n return b/np.sum(b, 1)[:,None]","def random_distribution():\n b = np.random.uniform(0.0, 1.0, size=[1, vocabulary_size])\n return b/np.sum(b, 1)[:,None]","def frequency_to_probability_distribution(\n index_num, node_indv_vals, prop_or_freq, test=False\n):\n\n # Checks for NaN values\n if np.isnan(node_indv_vals).any():\n raise Exception('NaN value encountered in {} array'.format(prop_or_freq))\n\n if prop_or_freq == 'propensity':\n node_indv_vals = np.array(range(1, (node_indv_vals.shape[0]+1)))\n\n # Converts fitness scores into probabilities\n total = node_indv_vals.sum()\n\n node_probabilities = np.full(node_indv_vals.shape, np.nan)\n for index, val in np.ndenumerate(node_indv_vals):\n index = index[0] # Numpy array indices are tuples\n node_probabilities[index] = val / total\n\n # Randomly shuffles probability array, in order to avoid smallest\n # probabilities being grouped together at the beginning of the range\n if test is False:\n (index_num, node_indv_vals, node_probabilities) = random_shuffle(\n index_num, node_indv_vals, node_probabilities\n )\n\n return index_num, node_indv_vals, node_probabilities","def probability_density(dic):\n\n var = dic['var']\n par = dic['par']\n y1 = dic['y']\n y = y1.conjugate() * y\n return dic_result(var,par,y)","def generate_data(params, N, rng=(-7, 7)):\n hp = np.array(params)\n print(\"parameters for data generated from gp are : {0}\".format(hp))\n print(\"using a ExpSquared kernel\")\n gp = george.GP(hp[0] * kernels.ExpSquaredKernel(hp[1]))\n t = rng[0] + np.diff(rng) * np.sort(np.random.rand(N))\n\n #y = model(params, t)\n y = gp.sample(t)\n yerr = 1.e-5 #1 + 0.1 * np.random.randn(N)\n y += yerr\n\n return t, y, yerr","def probability(structure,seq, react=None):\n return energy_to_proba(get_ens_energy(seq,react),get_stru_energy(structure,seq,react))","def random_distribution():\n b = np.random.uniform(0.0, 1.0, size=[1, vocabulary_size])\n return b / np.sum(b, 1)[:, None]","def generate_data(params, sigma):\n rng = random.PRNGKey(0)\n k = len(params) // 2\n a_array = params[:k]\n b_array = params[k:]\n n = 20 * k\n xs = sample_our_uniform(n, 1, rng).reshape((n,))\n ys = onp.zeros(n)\n all_indices = set(onp.arange(n))\n for i in range(k):\n i_idxs = onp.random.choice(list(all_indices), 20, replace=False)\n all_indices = set(all_indices) - set(i_idxs)\n ys[i_idxs] = xs[i_idxs] * a_array[i] + b_array[i] + onp.random.normal(0, sigma, size=(20,))\n return xs, ys","def generate_parameters(self):\n self.parameters = np.zeros(self.D)\n for l in range(self.D):\n if self.p_l[l] >= np.random.uniform(0,1):\n self.parameters[l] = 1","def create_probability_object(self):\n self.update_frequencies(self)\n prob_dict = {}\n for symbol in self.all_symbols.keys():\n prob_dict[symbol] = self.all_symbols[symbol] / self.total_symbols\n return prob_dict\n # self.prob_dict = prob_dict","def pdf(self, test_vectors: np.ndarray) -> np.ndarray:\n probability = np.zeros((len(test_vectors)), float)\n for i in range(len(self.weights_)):\n curve = multivariate_normal(mean=self.means_[i], cov=self.covariances_[i], allow_singular=True)\n probability += self.weights_[i] * curve.pdf(test_vectors)\n return probability","def probability_array(self):\n q = self.apply_weights()\n return np.exp(q)/(1 + np.exp(q))","def create_model_outputs_prob(target_names,features):\n # Create a probabilistic output (Normal distribution), and use the `Dense` layer\n # to produce the parameters of the distribution.\n # We set units=2 to learn both the mean and the variance of the Normal distribution.\n\n outputs = []\n for target_name in target_names:\n distribution_params = layers.Dense(units=2, name = target_name+'_params')(features)\n outputs.append(tfp.layers.IndependentNormal(1,name=target_name,)(distribution_params))\n return outputs","def homogenous_poisson_gen():\n pass","def generatePopulations(num_pops):\n distGenerator = DistributionGenerator()\n populations = []\n for i in range(num_pops):\n dist_type = random.sample(config.dist_types, 1)[0]\n populations.append(distGenerator.generateDistributions(dist_type, config.MaxDistributionSize))\n return populations","def gen_values(self):","def prob(x):\n\treturn 1. * bivariate_normal(x, (0., 1.2), (1., 1.), .8) + \\\n\t 1.05 * bivariate_normal(x, (.6, -1.), (1.3, .7), -.6)","def test_sampling(distribution):\n print(\"TESTING: sampling for %s distribution\" % distribution)\n params = dist.DISTRIBUTIONS[distribution][dist.KEY_TEST_PARAMS]\n print(\" input parameters: %s\" % dist.get_params(params, distribution))\n print(\" creating pdf\")\n test_pmf = dist.pmf(distribution, params)\n print(\" generating samples\")\n test_sample_pmf = dist.get_sample_pmf(dist.samples(distribution, params))\n test_distribution = []\n for i in range(len(test_pmf)):\n if i < len(test_sample_pmf):\n test_distribution.append([i, test_pmf[i], test_sample_pmf[i]])\n else:\n test_distribution.append([i, test_pmf[i], 0.0])\n test_output = 'TEST-SAMPLE.CSV'\n print(\" printing results in %s\" % test_output)\n utils.print_csv(test_output, ['value', 'probability', 'relative_frequency'], test_distribution)","def test_discrete_distribution():\n rng = utils.RandomState(0)\n distribution = dist.DiscreteDistribution(rng)\n with pytest.raises(NotImplementedError):\n distribution.sample([])\n with pytest.raises(NotImplementedError):\n distribution.log_probability([], None)\n with pytest.raises(NotImplementedError):\n distribution.support([])","def getValues(self):\n return [self._rng.normal(25,1)]","def prob(self, feature_index, feature_value, class_):\r\n\r\n deviation = self.conditional_prob[class_][feature_index][1]\r\n mean = self.conditional_prob[class_][feature_index][0]\r\n\r\n val1 = math.pow((feature_value - mean), 2)\r\n val1 = val1/math.pow(deviation, 2)\r\n\r\n val2 = 2*math.pi*math.pow(deviation, 2)\r\n val2 = 1/(math.sqrt(val2))\r\n\r\n probability = val2 * math.exp(-val1)\r\n\r\n return probability","def generate_perfomances(x):\n\n\ty=OrderedDict()\n\n\t# Cycles through both the values and the keys in the dictionary, creating a new dictionary which has within it\n\t# The names coupled with their normal distribution values\n\n\tfor xValues,names in zip(x.values(),x): y[names]=random.normalvariate(xValues[0],xValues[1])\n\treturn y","def _make_distribution(series):\n arr = series.values\n return arr / np.sum(arr)","def build_distributions(self):\n res = {}\n n_partitions = self.partition_num\n partition_num = 1\n # each part size\n partition_size = int(math.floor(self.size / n_partitions))\n\n for n in range(int(partition_size), self.size + 1, int(partition_size)):\n if self.learn_start <= n <= self.priority_size:\n distribution = {}\n # P(i) = (rank i) ^ (-alpha) / sum ((rank i) ^ (-alpha))\n pdf = list(\n map(lambda x: math.pow(x, -self.alpha), range(1, n + 1))\n )\n pdf_sum = math.fsum(pdf)\n distribution['pdf'] = list(map(lambda x: x / pdf_sum, pdf))\n # split to k segment, and than uniform sample in each k\n # set k = batch_size, each segment has total probability is 1 / batch_size\n # strata_ends keep each segment start pos and end pos\n cdf = np.cumsum(distribution['pdf'])\n strata_ends = {1: 0, self.batch_size + 1: n}\n step = 1.0 / self.batch_size\n index = 1\n for s in range(2, self.batch_size + 1):\n while cdf[index] < step:\n index += 1\n strata_ends[s] = index\n step += 1.0 / self.batch_size\n\n distribution['strata_ends'] = strata_ends\n\n res[partition_num] = distribution\n\n partition_num += 1\n\n return res","def predictProbabilities(self,density ='Gaussian'):\n\t\ttestingProbs = pd.DataFrame(index=self.testing.index.values,\n\t\t\t\t\t\t\t\t\tcolumns=self.trainingMeans.index.values)\n\n\t\ttesting = self.testing.copy().drop(self.classLabel,1)\n\n\t\tdef calculateGaussian(x, mean, stdev):\n\t\t\t\"\"\"\n\t\t\tReturns the density value of a Gaussian distribution\n\t\t\t\"\"\"\n\t\t\texponent = math.exp(-(math.pow(x-mean,2)/(2*math.pow(stdev,2))))\n\t\t\tvalue= (1 / (math.sqrt(2*math.pi) * stdev)) * exponent\n\t\t\tif value==0:\n\t\t\t\treturn np.nan\n\t\t\telse:\n\t\t\t\treturn math.log(value)\n\n\t\tdef calculateBernoulli(x, mean, stdev):\n\t\t\t\"\"\"\n\t\t\tReturns the density value of a Bernoulli distribution\n\t\t\t\"\"\"\n\t\t\tif x:\n\t\t\t\tprob = mean\n\t\t\telse:\n\t\t\t\tprob = 1-mean\n\t\t\treturn prob\n\n\t\tdef calculateMultinoulli(x, *series):\n\t\t\t\"\"\"\n\t\t\tReturns the density value of a Multinoulli distribution\n\t\t\t\"\"\"\n\t\t\tseries= series[0]\n\t\t\treturn series.ix[x]/float(series.sum())\n\n\t\tif density=='Multinoulli':\n\t\t\t#Redefine the parameters to be conditional means\n\t\t\tfor each in self.params.columns:\n\t\t\t\tfor el in self.params.index:\n\t\t\t\t\tmultiDF = pd.Series(index=self.data[each].unique())\n\t\t\t\t\tcounts = self.training[self.training[self.classLabel]==el][each].value_counts()\n\t\t\t\t\tself.params.ix[el][each] = (pd.concat([multiDF,counts],1).drop(0,1),)\n\t\t\tpdf = calculateMultinoulli\n\t\telif density == 'Bernoulli':\n\t\t\tpdf =calculateBernoulli\n\t\telse:\n\t\t\tpdf = calculateGaussian\n\n\t\tprint \"Note: Assuming features follow a \"+density+\" distribution\"\n\n\t\tfor el in testingProbs.columns:\n\t\t\t#Retrieve parameters of distribution\n\t\t\tparameters = self.params.ix[el]\n\t\t\tprobabilities = self.testing.copy().drop(self.classLabel,1)\n\n\t\t\t#For each feature, compute the likelihood of class being el\n\t\t\tfor each in probabilities.columns:\n\t\t\t\t#Skip features with 0 standard deviation\n\t\t\t\tif each in self.useless_features:\n\t\t\t\t\tcontinue\n\t\t\t\tprobabilities[each] = probabilities[each].apply(lambda x: pdf(x,*parameters[each]))\n\n\t\t\t#Multiply features together with prior\n\t\t\ttestingProbs[el] = math.log(self.priors.ix[el])+probabilities.sum(1)\n\t\t\t#testingProbs[el] = self.priors.ix[el]*probabilities.prod(1)\n\t\t#Use log-sum-exp trick. We need the offsetting factor as max among classLabels\n\t\tB = testingProbs.max(1)\n\t\t#Compute log_sum = log(\\sigma_c' exp(b_c' - B)) + B\n\t\tlog_sum = testingProbs.apply(lambda t: (t-B)).applymap(lambda u: math.exp(u)).sum(1).apply(math.log)+B\n\t\tself.testingProbs = testingProbs.apply(lambda x: x-log_sum)\n\t\t#self.testingProbs = testingProbs","def generateStats(self):\n\t\tn = float(self.n)\n\t\tm = float(self.m)\n\t\tk = float(self.k)\n\t\tp_fp = math.pow(1.0 - math.exp(-(k*n)/m), k)\n\t\tprint \"Probability of false positives: \", p_fp\n\t\tprint \"Predicted false positive rate: \", p_fp * 100.0\n\t\tprint \"Number of elements entered in filter: \", n\n\t\tprint \"Number of bits in filter: \", m\n\t\tprint \"Number of hashes in filter: \", k","def _process(self, data: np.ndarray) -> np.ndarray:\n probabilities = np.empty(data.size, dtype=object)\n\n for idx, counts_dict in enumerate(data):\n shots = sum(counts_dict.values())\n freq = counts_dict.get(self._outcome, 0)\n alpha_posterior = [freq + self._alpha_prior[0], shots - freq + self._alpha_prior[1]]\n alpha_sum = sum(alpha_posterior)\n\n p_mean = alpha_posterior[0] / alpha_sum\n p_var = p_mean * (1 - p_mean) / (alpha_sum + 1)\n\n probabilities[idx] = ufloat(nominal_value=p_mean, std_dev=np.sqrt(p_var))\n\n return probabilities","def probability(self, xvalue):\n # Initialize key variables\n probability = {}\n bayesian = {}\n classes = self.classes()\n\n # Calculate the principal components of the individual xvalue\n p1p2 = self.pca_object.pc_of_x(xvalue, self.components)\n\n # Get probability of each class\n for cls in classes:\n # Initialize values for the loop\n sample_count = len(self.pca_object.xvalues(cls))\n\n # Get values for calculating gaussian parameters\n dimensions = len(p1p2)\n x_mu = p1p2 - self.meanvector(cls)\n covariance = self.covariance(cls)\n inverse_cov = np.linalg.inv(covariance)\n determinant_cov = np.linalg.det(covariance)\n\n # Work on the exponent part of the bayesian classifer\n power = -0.5 * np.dot(np.dot(x_mu, inverse_cov), x_mu.T)\n exponent = math.pow(math.e, power)\n\n # Determine the constant value\n pipart = math.pow(2 * math.pi, dimensions / 2)\n constant = pipart * math.sqrt(determinant_cov)\n\n # Determine final bayesian\n bayesian[cls] = (sample_count * exponent) / constant\n\n # Calculate bayesian probability\n denominator = bayesian[classes[0]] + bayesian[classes[1]]\n for cls in classes:\n probability[cls] = bayesian[cls] / denominator\n\n # Return\n return probability","def evaluate(self, xs):\n # ps = 1. / (np.sqrt(2. * np.pi) * self.sigma) * \\\n # np.exp(-0.5 * (self.mean - xs) * self.invvar * (self.mean - xs))\n # ps = np.zeros_like(xs)\n # for n, x in enumerate(xs):\n # ps[n] += self.evaluate_one(x)\n ps = self.dist.probability(xs)\n return ps","def distribution(vals):\n tot = 0\n rv = []\n for v in vals:\n tot += v\n for v in vals:\n rv.append(v * 100 // tot)\n # rv.extend(sorted(rv))\n return tuple(rv)","def sample_probabilities(pvals: np.ndarray) -> Callable[[], int]:\n\n return Sampler(np.cumsum(pvals))","def data_gen(size, p):\n #print(np.random.get_state()[1][0])\n random_table = np.random.binomial(size = size, p = p, n = 1)\n test_array = np.zeros((size, 2), dtype = int)\n for i in range(size):\n test_array[i,0] = i\n test_array[i,1] = random_table[i]\n return test_array","def build_distributions(self):\n res = {}\n n_partitions = self.partition_num\n partition_num = 1\n # each part size\n partition_size = int(math.floor(self.size / n_partitions))\n\n for n in range(partition_size, self.size + 1, partition_size):\n if self.learning_starts <= n <= self.priority_size:\n distribution = {}\n # P(i) = (rank i) ^ (-alpha) / sum ((rank i) ^ (-alpha))\n pdf = list(\n map(lambda x: math.pow(x, -self.alpha), range(1, n + 1))\n )\n pdf_sum = math.fsum(pdf)\n distribution['pdf'] = list(map(lambda x: x / pdf_sum, pdf))\n # split to k segment, and than uniform sample in each k\n # set k = batch_size, each segment has total probability is 1 / batch_size\n # strata_ends keep each segment start pos and end pos\n cdf = np.cumsum(distribution['pdf'])\n strata_ends = {1: 0, self.batch_size + 1: n}\n step = 1 / float(self.batch_size)\n index = 1\n for s in range(2, self.batch_size + 1):\n while cdf[index] < step:\n index += 1\n strata_ends[s] = index\n step += 1 / float(self.batch_size)\n\n distribution['strata_ends'] = strata_ends\n\n res[partition_num] = distribution\n\n partition_num += 1\n\n return res","def entropy_permutation_test(ordered_pitch_types, single_pitch_pdf, conditional_joint_probabilities, total_transitions,\n n=1000):\n pitch_types, pitch_probabilities = zip(*single_pitch_pdf.items())\n permutation_entropies = []\n progress = progressbar.ProgressBar()\n\n for test_number in progress(xrange(n)):\n # create the new matrix\n permutation_counts = {}\n for first_pitch_type in ordered_pitch_types:\n permutation_counts[first_pitch_type] = {}\n for second_pitch_type in ordered_pitch_types:\n permutation_counts[first_pitch_type][second_pitch_type] = 0\n\n pitch_permutation = numpy.random.choice(pitch_types, total_transitions, p=pitch_probabilities)\n current_pitch = numpy.random.choice(pitch_types, p=pitch_probabilities)\n for next_pitch in pitch_permutation:\n permutation_counts[current_pitch][next_pitch] += 1\n current_pitch = next_pitch\n\n joint_probabilities, _, _ = joint_probabilities_from_transitions(ordered_pitch_types, permutation_counts)\n permutation_entropies.append(entropy_from_probability_matrix(joint_probabilities))\n\n joint_entropy = entropy_from_probability_matrix(conditional_joint_probabilities)\n # print 'Mean', numpy.mean(permutation_entropies)\n # print 'Standard deviation', numpy.std(permutation_entropies)\n # tdof, tloc, tscale = stats.t.fit(permutation_entropies)\n # print 'DF', tdof, 'Loc (mean)', tloc, 'Scale (SD)', tscale\n # t_score = (joint_entropy - tloc) / tscale\n # print stats.t.cdf(joint_entropy, df=tdof, loc=tloc, scale=tscale)\n\n mean, stddev = stats.norm.fit(permutation_entropies)\n print 'Mean = {mean}\\t StdDev = {stddev}'.format(mean=mean, stddev=stddev)\n z_score = (joint_entropy - mean) / stddev\n p_value = stats.norm.cdf(joint_entropy, mean, stddev)\n print 'The joint entropy has a Z-score of {z_score} which gives a P-value of {p_value}'.format(z_score=z_score,\n p_value=p_value)\n return z_score, p_value","def prob3():\n\n h = lambda x: x > 10\n\n N = range(5000,500001, 5000)\n\n estimates = []\n\n for n in N:\n random_draw = np.random.gamma(9, scale = 0.5, size = n)\n\n estimate = 1./n * np.sum(h(random_draw))\n estimates.append(estimate)\n\n # arrayify it\n estimates = np.array(estimates)\n\n m = 1 - stats.gamma(a = 9, scale = 0.5).cdf(10)\n \n y = abs(estimates - m)\n y_2 = abs(prob2() - m)\n\n plt.plot(N,y)\n plt.plot(N,y_2)\n\n plt.show()","def generate_distribution(self, rng: RNG, filtration_range, scale=None):\n if scale is None:\n scale = self.scale\n if self.delayed_coordinates:\n points = self.generate_3D_points_delayed(rng, self.number_of_points, scale)\n else:\n points = self.generate_points(rng, self.number_of_points, self.dimension, scale)\n # distance_matrix = pairwise_distances(points)\n if self.gpu:\n # TODO add in GPU calculation.\n raise NotImplementedError(\"GPU calculation of distance matrix not implemented yet.\")\n else:\n sparse_distance_matrix = self.make_sparse_dm(points, filtration_range[-1])\n if self.f is not None:\n output_string = \"[Points]\\n\"\n output_string += str(points)\n self.f.write(output_string)\n # An attempt to reduce memory usage, might not work\n del points\n diagrams = self.generate_diagrams(sparse_distance_matrix, threshold=filtration_range[-1])\n homology = self.generate_homology(diagrams, filtration_range)\n distribution = homology[self.homology_dimension]\n if self.f is not None:\n self.f.write(\"\\n[Distribution]\\n\" + str(distribution) + \"\\n\")\n return distribution","def sdd(events,probs):\n \n import random\n nprobs=[x*1000 for x in probs] #so, here i multiply each float in 'probs' by 1000 and store the products in 'nprobs'\n newlist=[]\n for a in range(len(events)) : #then, in this loop, i create a list (newlist), in which each event appears 1000*its probability times\n b=nprobs[a]\n b=int(b)\n for c in range(b) :\n newlist.append(events[a]) \n return (random.choice(newlist)) #and finally, i ramdonly sample ","def initializeDistribution(self):\n self.convertToDistrDict['Jacobi'] = self.convertJacobiToBeta\n self.convertToQuadDict ['Jacobi'] = self.convertBetaToJacobi\n self.measureNormDict ['Jacobi'] = self.stdProbabilityNorm\n #this \"if\" section can only be called if distribution not generated using readMoreXML\n if (not self.upperBoundUsed) and (not self.lowerBoundUsed):\n self._distribution = distribution1D.BasicBetaDistribution(self.alpha,self.beta,self.high-self.low,self.low)\n else:\n if self.lowerBoundUsed == False:\n a = 0.0\n else:\n a = self.lowerBound\n if self.upperBoundUsed == False:\n b = sys.float_info.max\n else:\n b = self.upperBound\n self._distribution = distribution1D.BasicBetaDistribution(self.alpha,self.beta,self.high-self.low,a,b,self.low)\n self.preferredPolynomials = 'Jacobi'\n self.compatibleQuadrature.append('Jacobi')\n self.compatibleQuadrature.append('ClenshawCurtis')","def propensity_to_probability_distribution(\n index_num, node_indv_propensities, test=False\n):\n\n # Checks for NaN values\n if np.isnan(node_indv_propensities).any():\n raise Exception('NaN value encountered in propensity array')\n\n # Converts fitness scores into probabilities\n node_eqm_constant_values = np.exp(np.negative(node_indv_propensities))\n total = np.sum(node_eqm_constant_values)\n\n node_probabilities = np.full(node_indv_propensities.shape, np.nan)\n for index, eqm_constant in np.ndenumerate(node_eqm_constant_values):\n index = index[0] # Numpy array indices are tuples\n node_probabilities[index] = eqm_constant / total\n\n # Randomly shuffles probability array, in order to avoid smallest\n # probabilities being grouped together at the beginning of the range\n if test is False:\n (index_num, node_indv_propensities, node_probabilities\n ) = random_shuffle(\n index_num, node_indv_propensities, node_probabilities\n )\n\n return index_num, node_indv_propensities, node_probabilities","def initPmf(self, values):\n for value, prob in values.items():\n self.set(value, prob)","def probability(distances):\n v = [1.0/(d + 1) for d in distances]\n s = sum(v)\n return [i/s for i in v]","def random_values():\n while True:\n yield random()","def get_distribution(counts, n_qubits):\n\tprobabilities = np.zeros(2 ** n_qubits) # Array of zeros and with the correct size for the measured qubits\n\tfor key in counts.keys(): # Iterate over the measured outputs\n\t\t# Transform the key from binary to decimal, and them save the probability\n\t\tprobabilities[int(key, 2)] = counts[key] / NUM_SHOTS\n\treturn probabilities","def get_hybrid_distribution():\n return get_discrete_distribution() + get_standard_normal_distribution()","def prob1(n):\n#raise NotImplementedError(\"Problem 1 Incomplete\")\n if n == 0 :\n raise ValueError(\"Sampling 0 points is not defined.\")\n total = 0\n for i in xrange(n) :\n if np.random.normal() > 3 :\n total += 1\n return float(total)/n","def probability(prods, prod_dict_As, count_dict):\n for p in prods:\n if p not in prod_dict_As:\n raise Exception(\"Think we cannot make the product {}.\".format(p))\n # Argh, Python, this is a reference!\n #possible_As = prod_dict_As[prods[0]]\n possible_As = set( prod_dict_As[prods[0]] )\n for p in prods[1:]:\n possible_As &= prod_dict_As[p]\n ret = []\n for A in possible_As:\n count = 1\n for p in prods:\n count *= count_dict[(p,A)]\n ret.append((A,count))\n return ret","def make_experimental_distribution(zshots):\n N,L = zshots.shape\n pvals = np.zeros(2**L)\n for j in range(N):\n m = to_int(zshots[j, :])\n pvals[m] += 1/N\n return pvals","def tabulate_histogram(self):\n\n # Generate a table of uniform variates\n from mitsuba.core import Float, Vector2f, Vector2u, Float32, \\\n UInt64, PCG32\n\n rng = PCG32(initseq=ek.arange(UInt64, self.sample_count))\n\n samples_in = getattr(mitsuba.core, 'Vector%if' % self.sample_dim)()\n for i in range(self.sample_dim):\n samples_in[i] = rng.next_float32() if Float is Float32 \\\n else rng.next_float64()\n\n self.pdf_start = time.time()\n\n # Invoke sampling strategy\n samples_out = self.sample_func(samples_in)\n\n if type(samples_out) is tuple:\n weights_out = samples_out[1]\n samples_out = samples_out[0]\n else:\n weights_out = Float(1.0)\n\n # Map samples into the parameter domain\n xy = self.domain.map_backward(samples_out)\n\n # Sanity check\n eps = self.bounds.extents() * 1e-4\n in_domain = ek.all((xy >= self.bounds.min - eps) &\n (xy <= self.bounds.max + eps))\n if not ek.all(in_domain):\n self._log('Encountered samples outside of the specified '\n 'domain: %s' % str(ek.compress(xy, ~in_domain)))\n self.fail = True\n\n # Normalize position values\n xy = (xy - self.bounds.min) / self.bounds.extents()\n xy = Vector2u(ek.clamp(xy * Vector2f(self.res), 0,\n Vector2f(self.res - 1)))\n\n # Compute a histogram of the positions in the parameter domain\n self.histogram = ek.zero(Float, ek.hprod(self.res))\n\n ek.scatter_add(\n target=self.histogram,\n index=xy.x + xy.y * self.res.x,\n source=weights_out\n )\n\n self.pdf_end = time.time()\n\n histogram_min = ek.hmin(self.histogram)\n if not histogram_min >= 0:\n self._log('Encountered a cell with negative sample '\n 'weights: %f' % histogram_min)\n self.fail = True\n\n self.histogram_sum = ek.hsum(self.histogram) / self.sample_count\n if self.histogram_sum > 1.1:\n self._log('Sample weights add up to a value greater '\n 'than 1.0: %f' % self.histogram_sum)\n self.fail = True","def build_probdist(prob_filename):\n fits_file = fits.open(prob_filename)\n \n z_hdu = fits_file[1]\n p_hdu = fits_file[2]\n \n z_data = z_hdu.data\n p_data = np.exp(p_hdu.data)\n \n return ProbDist(z_data, p_data)","def pdf(self,x):\n return self.categoricalDist.pdf(x)","def distribution(self):\n \n #external_distribution serves both the purpose of external setting of distribution and the caching of distribution()\n if self.external_distribution:\n return self.external_distribution_array;\n else:\n energy_vector = []\n superset = self.generate_superset(0) \n \n for i in superset:\n state = self.ket(i)\n \n norm_squared = np.dot(state.T, state)\n \n if norm_squared > 0: #zero is appended at the end\n energy = np.dot(state.T, np.dot( self.epsilon, state))\n interaction = np.dot(state.T, np.dot( self.u, state))/2.0 #divide by two. Otherwise, <l r| U |l r > = U_LR + U_RL = 2U\n #print state, np.dot(self.u, state) \n #print interaction\n energy_vector.append( energy + interaction )\n \n energy_vector.insert(0, 0.0) \n probability = np.exp( np.multiply(-self.beta, energy_vector)) \n probability /= probability.sum() \n return probability","def create_distribution(sample_size):\n if sample_size < 0:\n raise ValueError(\"Unable to generate negative number of distributions \", sample_size)\n result = []\n for _ in range(sample_size):\n result.append(get_hybrid_distribution())\n return result","def probabilities(self, x, y):\n return self.feed_and_return(x, y, self.network.a)","def process_simulation(self):\n for i in range(self._n):\n probability = self._alpha / float(self._alpha + i - 1)\n tmp = np.random.uniform(size=(1,))\n if tmp < probability:\n self._results.append(np.random.normal(1))\n else:\n self._results.append(np.random.choice(self._results[:i-1], 1)[0])","def data_generate_process():\n\n a = 0.8\n b = 0.4\n c = 0.1\n d = 0.3\n e = 0.7\n y_0 = 0.0\n a_0 = 0.2\n sigma_0 = 0.35\n\n data_len = 10000\n y_series = pd.Series([np.nan] * data_len)\n a_series = pd.Series([np.nan] * data_len)\n sigma_series = pd.Series([np.nan] * data_len)\n\n epsilon_normal = np.random.normal(loc=0.0, scale=1.0, size=data_len)\n\n y_series[0] = y_0\n a_series[0] = a_0\n sigma_series[0] = sigma_0\n\n for idx in range(1, data_len):\n epsilon_t = epsilon_normal[idx]\n sigma_t = np.sqrt(c + d * a_series[idx - 1] ** 2 + e * sigma_series[idx - 1] ** 2)\n a_t = epsilon_t * sigma_t\n y_series[idx] = a * y_series[idx - 1] + b + a_t\n a_series[idx] = a_t\n sigma_series[idx] = sigma_t\n\n return y_series, a_series, sigma_series","def quantitative_distribution(child, N): \n binned = np.histogram(child.dropna(), bins=10) # Value counts (first line)\n prop = binned[0] / binned[0].sum() # proportion of bins\n bins = binned[1] # N + 1 bin boundaries\n # print(prop)\n indices = np.random.choice(a=10, p=prop, size=N)\n # print(np.sum(indices == 8) / 10000)\n samples = []\n for ind in indices: # Loop through samples\n lower, upper = bins[ind], bins[ind+1] # Upper and lower bounds of bin\n samples.append(np.random.uniform(low=lower, high=upper))\n return np.array(samples)","def sample_discrete(probs):\r\n q = np.random.rand()\r\n i = 0\r\n p_sum = 0.0\r\n while p_sum < q:\r\n p_sum += probs[i]\r\n i += 1\r\n return i - 1","def uniform(N=10000, left=0., right=3., n_bins_meas=10):\n\t# true distribution\n\ttrue = np.random.uniform(left, right, N)\n\t# measured distribution\n\tmeasured = true\n\t# Also return the generating true pdf\n\tx = np.linspace(-1, 5, 500)\n\tpdf = np.zeros([2, len(x)])\n\tpdf[0] = x\n\tpdf[1] = scs.uniform.pdf(x, left, right-left)\n\t# Default binning\n\tdefault_binning = np.linspace(np.amin(measured), np.amax(measured), n_bins_meas)\n\n\treturn measured, true, pdf, default_binning","def generate_data(self,\n num_batches,\n num_samples,\n percentile):\n\n xs = []\n ys = []\n ws = []\n\n for j in range(num_batches):\n\n # sample designs from the prior\n z = tf.random.normal([num_samples, self.latent_size])\n q_dx = self.q_vae.decoder.get_distribution(z, training=False)\n p_dx = self.p_vae.decoder.get_distribution(z, training=False)\n\n # evaluate the score and importance weights\n x = q_dx.sample()\n y = self.ensemble.get_distribution(x).mean()\n log_w = p_dx.log_prob(x)[..., tf.newaxis] - \\\n q_dx.log_prob(x)[..., tf.newaxis]\n while len(log_w.shape) > 2:\n log_w = tf.reduce_sum(log_w, axis=1)\n\n xs.append(x)\n ys.append(y)\n ws.append(tf.math.exp(log_w))\n\n # locate the cutoff for the scores below the percentile\n gamma = tfp.stats.percentile(ys, percentile)\n\n for j in range(num_batches):\n\n # re-weight by the cumulative probability of the score\n d = self.ensemble.get_distribution(xs[j])\n ws[j] *= 1.0 - d.cdf(tf.fill([num_samples, 1], gamma))\n\n return tf.concat(xs, axis=0), \\\n tf.concat(ys, axis=0), \\\n tf.concat(ws, axis=0)","def test_correct_build_distribution_values(self):\n timeseries_with_distribution_values = self.build_distribution_value()\n\n distribution_value = main.build_distribution_value(timeseries_with_distribution_values[\"points\"][0][\"value\"][\"distributionValue\"])\n expected_distribution_value = self.build_expected_distribution_value()\n self.assertEqual(distribution_value,expected_distribution_value)","def __init__(self, variables=[], values=[], probabilities=[]):\n self.variables = variables\n self.values = values\n self.probabilities = probabilities","def occupation_distribution(data):","def generate():","def probability_density(self, X):\n raise NotImplementedError","def distribution_probability(self, game):\n dist_probability = {}\n\n total_visits = sum(self.root.n_a.values())\n\n for action, visits in self.root.n_a.items():\n dist_probability[action] = visits/total_visits\n return dist_probability","def calc_fitness_by_gen(self):\r\n f_sum = 0\r\n # first loop gives us the sum of the fitness\r\n for c, _ in self.temp_hist_by_gen.items():\r\n f_sum += c.fitness()\r\n # now we calc the chances by fitness of each one\r\n for c, _ in self.temp_hist_by_gen.items():\r\n self.temp_hist_by_gen[c] = c.fitness() / f_sum","def _make_gp_list(self, mean, samples):\n width = np.sqrt(mean)\n k_min = max([min([0, mean - 3 * width]), 0])\n k_max = mean + 3 * width + 10\n k_arr = np.arange(k_min, k_max)\n gp_prob = _general_poisson(k_arr, mean, self.lamb)\n gp_prob = gp_prob / np.sum(gp_prob) ## Additional normalization\n dist = stats.rv_discrete('GeneralizedPoisson', values=(k_arr, gp_prob))\n return dist.rvs(size=samples)"],"string":"[\n \"def generate_samples(self):\\n self.analytic_probability()\",\n \"def generate_probabilities(self):\\n k = 1\\n v= 10\\n for g in self.class_probabilities:\\n curr_list = self.class_probabilities[g]\\n for l in range(0,28):\\n for w in range(0,28):\\n total = float(curr_list[l][w][0] + curr_list[l][w][1] + curr_list[l][w][2])\\n curr_list[l][w][0] = (float(curr_list[l][w][0])+k)/(total + k*v) \\n curr_list[l][w][1] = (float(curr_list[l][w][1])+k)/(total + k*v)\\n curr_list[l][w][2] = (float(curr_list[l][w][2])+k)/(total + k*v)\\n curr_list[l][w][3] = curr_list[l][w][0] + curr_list[l][w][1] + curr_list[l][w][2]\",\n \"def generate_values(num_points: int, distribution: str, dist_params, sort: bool = False) -> np.ndarray:\\n vals = method_map[distribution](*dist_params, size=num_points)\\n if sort:\\n vals.sort()\\n return vals\",\n \"def _generate_distribution_samples(self, set_count, parameter_count):\\n self._samples = numpy.zeros((set_count, parameter_count))\\n for i, distribution in enumerate(self.parameter_distributions.values()):\\n self._samples[:, i] = distribution.ppf(self._quantiles[:, i])\",\n \"def probability(self, samples):\\n pass\",\n \"def p(self) -> Probability:\\n ...\",\n \"def probabilities(self):\\n raise NotImplementedError\",\n \"def outputGenerationProbability(self):\\n self.b = zeros((self.noOfEmmittingStates, self.T))\\n for row in range(self.noOfEmmittingStates):\\n for col in range(self.T):\\n self.b[row, col] = self.gaussianDist(self.observationSequence[0, col],\\n self.outputProbabilities[row, 0],\\n self.outputProbabilities[row, 1])\",\n \"def custom_pdf(self, cum_probs, values):\\n rnd_num = random()\\n for p in range(len(cum_probs)):\\n if rnd_num < cum_probs[p]:\\n return values[p]\",\n \"def example():\\n\\tN=9000\\n\\t#true distribution -> uniform between 0 and 3\\n\\ttrue = np.zeros(N)\\n\\ttrue[:N/3-1] = 0.5\\n\\ttrue[N/3:2*N/3-1] = 1.5\\n\\ttrue[2*N/3:] = 2.5\\n\\t# measured distribution -> fixed normal distribution with sigma=.5 and mean=1.5\\n\\tmeasured = np.zeros(N)\\n\\tmeasured[:1300-1] = 0.5\\n\\tmeasured[1300:1300+6000-1] = 1.5\\n\\tmeasured[1300+6000:] = 2.5\\n\\t# Also return the generating true pdf\\n\\tx = np.linspace(-1, 5, 500)\\n\\tpdf = np.zeros([2, len(x)])\\n\\tpdf[0] = x\\n\\tpdf[1] = scs.uniform.pdf(x, 0, 3)\\n\\n\\treturn measured, true, pdf, [0,1,2,3]\",\n \"def _generate_p(self):\\n self._values, weights = zip(*self._weights.items())\\n cumsum = list(itertools.accumulate(weights))\\n total = cumsum[-1]\\n self._p = [i / total for i in cumsum]\",\n \"def prob4():\\n\\n\\n N = 500000\\n random_draws = np.random.multivariate_normal(mean = [-1,1], cov =[[1,0],[0,1]], size = N)\\n\\n h = lambda x: x[0] < -1 and x[1] > 1\\n f = lambda x: stats.multivariate_normal(mean = [ 0, 0]).pdf(x)\\n g = lambda x: stats.multivariate_normal(mean = [-1, 1]).pdf(x)\\n\\n probability = [h(random_draws[i]) * f(random_draws[i]) / g(random_draws[i]) for i in range(N)]\\n\\n return 1./N * np.sum(probability)\",\n \"def generate_distribution(p1=[1.0,0.1,10000],p2=[-1.0,0.6,30000]):\\n mu1,sig1,num1 = p1\\n mu2,sig2,num2 = p2\\n g1 = scipy.stats.norm(mu1,sig1).rvs(num1)\\n g2 = scipy.stats.norm(mu2,sig2).rvs(num2)\\n\\n samples = np.concatenate([g1,g2])\\n return samples\",\n \"def _gen_pert(self, count, **kwargs):\\n self._check_pert(**kwargs)\\n pert = FairBetaPert(**kwargs)\\n rvs = pert.random_variates(count)\\n return rvs\",\n \"def compute_empirical_distribution(values):\\n distribution = {}\\n\\n # -------------------------------------------------------------------------\\n # YOUR CODE HERE\\n #\\n for value in values:\\n if value not in distribution:\\n distribution[value] = 1\\n else:\\n distribution[value] += 1\\n \\n total = len(values)\\n for v in distribution.keys():\\n distribution[v] /= total\\n \\n\\n #\\n # END OF YOUR CODE\\n # -------------------------------------------------------------------------\\n\\n return distribution\",\n \"def get_prob_for_distributions(p):\\n w1 = p[0]\\n mu1 = p[1]\\n sigma1 = p[2]\\n w2 = p[3]\\n mu2 = p[4]\\n sigma2 = p[5]\\n w3 = p[6]\\n mu3 = p[7]\\n sigma3 = p[8]\\n dist_range = (0, 4.330310991999920844e+01)\\n x = np.linspace(dist_range[0], dist_range[1], 1000)\\n A1 = np.array(w1 * mlab.normpdf(x, mu1, sigma1)).sum()\\n A2 = np.array(w2 * mlab.normpdf(x, mu2, sigma2)).sum()\\n A3 = np.array(w3 * mlab.normpdf(x, mu3, sigma3)).sum()\\n p1 = A1 / (A1 + A2 + A3)\\n p2 = A2 / (A1 + A2 + A3)\\n p3 = A3 / (A1 + A2 + A3)\\n return p1, p2, p3\",\n \"def prob4():\\n#raise NotImplementedError(\\\"Problem 4 Incomplete\\\")\\n h = lambda x : x[0] < -1 and x[1] > 1\\n f = lambda x : stats.multivariate_normal.pdf(x,mean=np.array([0,0]),cov=np.eye(2))\\n g = lambda x : stats.multivariate_normal.pdf(x,mean=np.array([-1,1]),cov=np.eye(2))\\n X = np.random.multivariate_normal(mean=np.array([-1,1]),cov=np.eye(2),size=10000)\\n return 1./10000*np.sum(np.apply_along_axis(h,1,X)*np.apply_along_axis(f,1,X)/np.apply_along_axis(g,1,X))\",\n \"def generate_random_numbers(self):\\r\\n #random.seed(seed=self.seed)\\r\\n #err = random.random((3,1))\\r\\n #f = open('test_res', 'a')\\r\\n #f.write('probability - %s' %self.seed)\\r\\n #f.write(str(list(err[:3,:])))\\r\\n #f.write('\\\\n')\\r\\n #f.close()\\r\\n\\r\\n dist = RandomDistribution(self.seed)\\r\\n rand_numbers = dist.return_random_variables(self.num_agents)\\r\\n return rand_numbers\",\n \"def __init__(self, num_values, probability):\\n self.num_values = num_values\\n self.probability = probability\\n self.register_bounds = GeometricDistribution._compute_register_bounds(num_values, probability)\\n self._register_probs = GeometricDistribution._compute_register_probs(num_values, probability)\",\n \"def generate_probabilities(self):\\n dists = np.copy(self.distances)\\n\\n for i in range(self.number_towers):\\n for j in range(self.number_towers):\\n if self.method == 'distance_distribution':\\n dists[i][j] = (\\n -1 *\\n (dists[i][j] ** 2) *\\n xamtfos(dists[i][j] ** 2, self.sigma) *\\n self.expander\\n )\\n elif self.method == 'distance_square':\\n dists[i][j] = -1 * (dists[i][j] + 1) ** self.distance_power\\n\\n normalizer = dists.max().max() / 2\\n dists -= normalizer\\n\\n return np.array([\\n softmax(dists[i])\\n for i in range(self.number_towers)\\n ])\",\n \"def define_pdf(self, values: torch.Tensor, weights: torch.Tensor, inds: torch.Tensor) -> Distribution:\\n\\n raise NotImplementedError()\",\n \"def get_Sample(self, values, probabilities):\\r\\n return choices(values,probabilities)\\r\\n # return np.random.choice(values,p=probabilities)\\r\",\n \"def sample(self, probabilities):\\n return self.sample_bernoulli(probabilities)\",\n \"def generic_distribution(target, seeds, func):\\n seeds = target[seeds]\\n value = func.ppf(seeds)\\n return value\",\n \"def dealer_probs():\\n # Pdf of any current hand (value, hard) and final value; p(v_f | v_c) where v_f = final value, v_c = current value\\n probabilities = {}\\n\\n # End nodes: (value, True) for value >= 17 and (value, False) for value > 17\\n # Dependencies (in order of increasing requirements):\\n # Hard values, value >= 11, possiblity of bust, no possibility of going soft with an ace (value, True) depends on (value', True) for 17 > value' > value\\n # Soft values, 17 >= value >= 11 (value, False) depends on (value', False) for 17 >= value' > value, (value', True) for 17 > value' > 11\\n # Hard values, 11 > value >= 2 , no possibility of bust, possibility of going soft with an ace (value, True) depends on (value', True) for 17 > value' > value and (value', False) for 17 >= value' > 13\\n\\n\\n # End nodes\\n for value in xrange(17, 22):\\n probabilities[(value, True)] = {value: 1.0}\\n if value == 17: continue # on soft 17, dealer will still hit\\n probabilities[(value, False)] = {value: 1.0}\\n\\n # Hard values, 17 > value >= 11, possibility of bust, no possibility of going soft with an ace\\n for value in xrange(16, 10, -1):\\n probabilities[(value, True)] = {}\\n current_prob = probabilities[(value, True)]\\n for next_card in xrange(1, min(10, 21-value)+1):\\n next_prob = probabilities[(value + next_card, True)]\\n for end_val in next_prob:\\n current_prob[end_val] = current_prob.get(end_val, 0) + next_prob[end_val] * PROBABILITIES[next_card]\\n\\n # Soft values, 17 >= value >= 11\\n for value in xrange(17, 10, -1):\\n probabilities[(value, False)] = {}\\n current_prob = probabilities[(value, False)]\\n for next_card in xrange(1, 11):\\n next_value = value + next_card\\n hard = False\\n if next_value > 21:\\n next_value -= 10\\n hard = True\\n next_prob = probabilities[(next_value, hard)]\\n for end_val in next_prob:\\n current_prob[end_val] = current_prob.get(end_val, 0) + next_prob[end_val] * PROBABILITIES[next_card]\\n\\n # Hard values, 11 > value >= 2, no possibility of bust, possibility of going soft with an ace\\n for value in xrange(10, 1, -1):\\n probabilities[(value, True)] = {}\\n current_prob = probabilities[(value, True)]\\n for next_card in xrange(2, 12):\\n next_value = value + next_card\\n hard = (next_card != 11)\\n next_prob = probabilities[(next_value, hard)]\\n for end_val in next_prob:\\n current_prob[end_val] = current_prob.get(end_val, 0) + next_prob[end_val] * PROBABILITIES[next_card]\\n\\n return probabilities\",\n \"def _compute_register_probs(cls, num_values, probability):\\n bits = np.arange(1, num_values + 1)\\n probs = scipy.stats.geom.pmf(bits, probability)\\n\\n return probs / sum(probs)\",\n \"def probability(series, params):\\n\\n prob = 1\\n\\n for result in series:\\n\\n prob *= params[result]\\n\\n return prob * params[\\\"die\\\"]\",\n \"def get_output_distr(self, stat_list):\\n out_probs = [[np.zeros(500) for _ in stat_list]\\n for _ in range(self.task_count)]\\n\\n # for each task, get the posterior distribution for each predicted\\n # output label\\n for i in range(self.task_count):\\n distr_vec = [\\n stats.norm(loc=mu, scale=sigma) for mu, sigma in\\n zip(self.output_mat['mu'][i, :],\\n self.output_mat['sigma'][i, :])\\n ]\\n\\n # calculate the range of possible predicted output values\\n min_range = min(distr.ppf(0.01) for distr in distr_vec)\\n max_range = max(distr.ppf(0.99) for distr in distr_vec)\\n x_vals = np.linspace(min_range, max_range, 1000)\\n\\n # calculate the cumulative probability density function across all\\n # label distributions at each possible predicted value\\n for j, stat in enumerate(stat_list):\\n out_probs[i][j] = [\\n (x,\\n np.mean([distr.pdf(x) for distr, st in\\n zip(distr_vec, stat) if st])\\n ) for x in x_vals\\n ]\\n\\n return out_probs\",\n \"def calcProbability(self):\\n for attribute in self.attributes:\\n index = self.F2I[attribute]\\n features = set([self.train[i][0][index] for i in range(len(self.train))])\\n for feature in features:\\n #all the true and false\\n result_t = list(filter(lambda x: x[1]== True, self.train))\\n total_t = len(result_t)\\n result_f = list(filter(lambda x: x[1]== False, self.train))\\n total_f= len(result_f)\\n #the probability for the feature if its true or false\\n t = len(list(filter(lambda x: x[0][index] == feature, result_t)))\\n f = len(list(filter(lambda x: x[0][index] == feature, result_f)))\\n prob_yes= t/total_t\\n prob_no = f/total_f\\n #assign the probabilities to the dictionaries\\n self.probs_yes[(index,feature)] = prob_yes\\n self.probs_no[(index,feature)] = prob_no\",\n \"def __GenerateVariantsDistribution(self):\\n np.random.seed(self.random_seed_parametr)\\n try:\\n Students = pd.read_excel(self.student_path)\\n print('Load {}'.format(self.student_path))\\n students_number = len(Students)\\n\\n self.__create_ALL_LR()\\n Course_structure, variants_numbers = self.__generate_stracture()\\n print('Generate stracture')\\n Number_of_weaks = len(Course_structure)\\n\\n number_of_distribution = 0\\n for WeakNumber in range(Number_of_weaks):\\n for TaskNumber in range(Course_structure[WeakNumber]):\\n Students['Week {0} Task {1}'.format(WeakNumber + 1, TaskNumber + 1)] = np.random.randint(\\n variants_numbers[number_of_distribution], size=students_number)\\n number_of_distribution += 1\\n\\n writer = pd.ExcelWriter(self.students_with_variants_path)\\n print('Save {}'.format(self.students_with_variants_path))\\n Students.to_excel(writer)\\n writer.save()\\n except:\\n print('File with students doesnot exist')\",\n \"def prob_distr(self, x):\\n return 1.0/x\",\n \"def probability_from_internal(internal_values, constr):\\n return internal_values / internal_values.sum()\",\n \"def initializeDistribution(self):\\n if self.nPoints is None:\\n self.xArray = np.arange(self.lowerBound,self.upperBound+1)\\n else:\\n self.xArray = np.linspace(self.lowerBound,self.upperBound,self.nPoints)\\n\\n # Here the actual calculation of discrete distribution parameters is performed\\n self.pdfArray = 1.0/self.xArray.size * np.ones(self.xArray.size)\\n paramsDict={}\\n paramsDict['outcome'] = self.xArray\\n paramsDict['state'] = self.pdfArray\\n\\n self.categoricalDist = Categorical()\\n self.categoricalDist.initializeFromDict(paramsDict)\\n initialPerm = randomUtils.randomPermutation(self.xArray.tolist(),self)\\n self.pot = np.asarray(initialPerm)\",\n \"def init_probability_dict(self):\\n for x in xrange(0,10):\\n self.class_probabilities[x] = self.init_probability_2d()\",\n \"def random_distribution():\\n b = np.random.uniform(0.0, 1.0, size=[1, vocabulary_size])\\n return b/np.sum(b, 1)[:,None]\",\n \"def random_distribution():\\n b = np.random.uniform(0.0, 1.0, size=[1, vocabulary_size])\\n return b/np.sum(b, 1)[:,None]\",\n \"def frequency_to_probability_distribution(\\n index_num, node_indv_vals, prop_or_freq, test=False\\n):\\n\\n # Checks for NaN values\\n if np.isnan(node_indv_vals).any():\\n raise Exception('NaN value encountered in {} array'.format(prop_or_freq))\\n\\n if prop_or_freq == 'propensity':\\n node_indv_vals = np.array(range(1, (node_indv_vals.shape[0]+1)))\\n\\n # Converts fitness scores into probabilities\\n total = node_indv_vals.sum()\\n\\n node_probabilities = np.full(node_indv_vals.shape, np.nan)\\n for index, val in np.ndenumerate(node_indv_vals):\\n index = index[0] # Numpy array indices are tuples\\n node_probabilities[index] = val / total\\n\\n # Randomly shuffles probability array, in order to avoid smallest\\n # probabilities being grouped together at the beginning of the range\\n if test is False:\\n (index_num, node_indv_vals, node_probabilities) = random_shuffle(\\n index_num, node_indv_vals, node_probabilities\\n )\\n\\n return index_num, node_indv_vals, node_probabilities\",\n \"def probability_density(dic):\\n\\n var = dic['var']\\n par = dic['par']\\n y1 = dic['y']\\n y = y1.conjugate() * y\\n return dic_result(var,par,y)\",\n \"def generate_data(params, N, rng=(-7, 7)):\\n hp = np.array(params)\\n print(\\\"parameters for data generated from gp are : {0}\\\".format(hp))\\n print(\\\"using a ExpSquared kernel\\\")\\n gp = george.GP(hp[0] * kernels.ExpSquaredKernel(hp[1]))\\n t = rng[0] + np.diff(rng) * np.sort(np.random.rand(N))\\n\\n #y = model(params, t)\\n y = gp.sample(t)\\n yerr = 1.e-5 #1 + 0.1 * np.random.randn(N)\\n y += yerr\\n\\n return t, y, yerr\",\n \"def probability(structure,seq, react=None):\\n return energy_to_proba(get_ens_energy(seq,react),get_stru_energy(structure,seq,react))\",\n \"def random_distribution():\\n b = np.random.uniform(0.0, 1.0, size=[1, vocabulary_size])\\n return b / np.sum(b, 1)[:, None]\",\n \"def generate_data(params, sigma):\\n rng = random.PRNGKey(0)\\n k = len(params) // 2\\n a_array = params[:k]\\n b_array = params[k:]\\n n = 20 * k\\n xs = sample_our_uniform(n, 1, rng).reshape((n,))\\n ys = onp.zeros(n)\\n all_indices = set(onp.arange(n))\\n for i in range(k):\\n i_idxs = onp.random.choice(list(all_indices), 20, replace=False)\\n all_indices = set(all_indices) - set(i_idxs)\\n ys[i_idxs] = xs[i_idxs] * a_array[i] + b_array[i] + onp.random.normal(0, sigma, size=(20,))\\n return xs, ys\",\n \"def generate_parameters(self):\\n self.parameters = np.zeros(self.D)\\n for l in range(self.D):\\n if self.p_l[l] >= np.random.uniform(0,1):\\n self.parameters[l] = 1\",\n \"def create_probability_object(self):\\n self.update_frequencies(self)\\n prob_dict = {}\\n for symbol in self.all_symbols.keys():\\n prob_dict[symbol] = self.all_symbols[symbol] / self.total_symbols\\n return prob_dict\\n # self.prob_dict = prob_dict\",\n \"def pdf(self, test_vectors: np.ndarray) -> np.ndarray:\\n probability = np.zeros((len(test_vectors)), float)\\n for i in range(len(self.weights_)):\\n curve = multivariate_normal(mean=self.means_[i], cov=self.covariances_[i], allow_singular=True)\\n probability += self.weights_[i] * curve.pdf(test_vectors)\\n return probability\",\n \"def probability_array(self):\\n q = self.apply_weights()\\n return np.exp(q)/(1 + np.exp(q))\",\n \"def create_model_outputs_prob(target_names,features):\\n # Create a probabilistic output (Normal distribution), and use the `Dense` layer\\n # to produce the parameters of the distribution.\\n # We set units=2 to learn both the mean and the variance of the Normal distribution.\\n\\n outputs = []\\n for target_name in target_names:\\n distribution_params = layers.Dense(units=2, name = target_name+'_params')(features)\\n outputs.append(tfp.layers.IndependentNormal(1,name=target_name,)(distribution_params))\\n return outputs\",\n \"def homogenous_poisson_gen():\\n pass\",\n \"def generatePopulations(num_pops):\\n distGenerator = DistributionGenerator()\\n populations = []\\n for i in range(num_pops):\\n dist_type = random.sample(config.dist_types, 1)[0]\\n populations.append(distGenerator.generateDistributions(dist_type, config.MaxDistributionSize))\\n return populations\",\n \"def gen_values(self):\",\n \"def prob(x):\\n\\treturn 1. * bivariate_normal(x, (0., 1.2), (1., 1.), .8) + \\\\\\n\\t 1.05 * bivariate_normal(x, (.6, -1.), (1.3, .7), -.6)\",\n \"def test_sampling(distribution):\\n print(\\\"TESTING: sampling for %s distribution\\\" % distribution)\\n params = dist.DISTRIBUTIONS[distribution][dist.KEY_TEST_PARAMS]\\n print(\\\" input parameters: %s\\\" % dist.get_params(params, distribution))\\n print(\\\" creating pdf\\\")\\n test_pmf = dist.pmf(distribution, params)\\n print(\\\" generating samples\\\")\\n test_sample_pmf = dist.get_sample_pmf(dist.samples(distribution, params))\\n test_distribution = []\\n for i in range(len(test_pmf)):\\n if i < len(test_sample_pmf):\\n test_distribution.append([i, test_pmf[i], test_sample_pmf[i]])\\n else:\\n test_distribution.append([i, test_pmf[i], 0.0])\\n test_output = 'TEST-SAMPLE.CSV'\\n print(\\\" printing results in %s\\\" % test_output)\\n utils.print_csv(test_output, ['value', 'probability', 'relative_frequency'], test_distribution)\",\n \"def test_discrete_distribution():\\n rng = utils.RandomState(0)\\n distribution = dist.DiscreteDistribution(rng)\\n with pytest.raises(NotImplementedError):\\n distribution.sample([])\\n with pytest.raises(NotImplementedError):\\n distribution.log_probability([], None)\\n with pytest.raises(NotImplementedError):\\n distribution.support([])\",\n \"def getValues(self):\\n return [self._rng.normal(25,1)]\",\n \"def prob(self, feature_index, feature_value, class_):\\r\\n\\r\\n deviation = self.conditional_prob[class_][feature_index][1]\\r\\n mean = self.conditional_prob[class_][feature_index][0]\\r\\n\\r\\n val1 = math.pow((feature_value - mean), 2)\\r\\n val1 = val1/math.pow(deviation, 2)\\r\\n\\r\\n val2 = 2*math.pi*math.pow(deviation, 2)\\r\\n val2 = 1/(math.sqrt(val2))\\r\\n\\r\\n probability = val2 * math.exp(-val1)\\r\\n\\r\\n return probability\",\n \"def generate_perfomances(x):\\n\\n\\ty=OrderedDict()\\n\\n\\t# Cycles through both the values and the keys in the dictionary, creating a new dictionary which has within it\\n\\t# The names coupled with their normal distribution values\\n\\n\\tfor xValues,names in zip(x.values(),x): y[names]=random.normalvariate(xValues[0],xValues[1])\\n\\treturn y\",\n \"def _make_distribution(series):\\n arr = series.values\\n return arr / np.sum(arr)\",\n \"def build_distributions(self):\\n res = {}\\n n_partitions = self.partition_num\\n partition_num = 1\\n # each part size\\n partition_size = int(math.floor(self.size / n_partitions))\\n\\n for n in range(int(partition_size), self.size + 1, int(partition_size)):\\n if self.learn_start <= n <= self.priority_size:\\n distribution = {}\\n # P(i) = (rank i) ^ (-alpha) / sum ((rank i) ^ (-alpha))\\n pdf = list(\\n map(lambda x: math.pow(x, -self.alpha), range(1, n + 1))\\n )\\n pdf_sum = math.fsum(pdf)\\n distribution['pdf'] = list(map(lambda x: x / pdf_sum, pdf))\\n # split to k segment, and than uniform sample in each k\\n # set k = batch_size, each segment has total probability is 1 / batch_size\\n # strata_ends keep each segment start pos and end pos\\n cdf = np.cumsum(distribution['pdf'])\\n strata_ends = {1: 0, self.batch_size + 1: n}\\n step = 1.0 / self.batch_size\\n index = 1\\n for s in range(2, self.batch_size + 1):\\n while cdf[index] < step:\\n index += 1\\n strata_ends[s] = index\\n step += 1.0 / self.batch_size\\n\\n distribution['strata_ends'] = strata_ends\\n\\n res[partition_num] = distribution\\n\\n partition_num += 1\\n\\n return res\",\n \"def predictProbabilities(self,density ='Gaussian'):\\n\\t\\ttestingProbs = pd.DataFrame(index=self.testing.index.values,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\tcolumns=self.trainingMeans.index.values)\\n\\n\\t\\ttesting = self.testing.copy().drop(self.classLabel,1)\\n\\n\\t\\tdef calculateGaussian(x, mean, stdev):\\n\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\tReturns the density value of a Gaussian distribution\\n\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\texponent = math.exp(-(math.pow(x-mean,2)/(2*math.pow(stdev,2))))\\n\\t\\t\\tvalue= (1 / (math.sqrt(2*math.pi) * stdev)) * exponent\\n\\t\\t\\tif value==0:\\n\\t\\t\\t\\treturn np.nan\\n\\t\\t\\telse:\\n\\t\\t\\t\\treturn math.log(value)\\n\\n\\t\\tdef calculateBernoulli(x, mean, stdev):\\n\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\tReturns the density value of a Bernoulli distribution\\n\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\tif x:\\n\\t\\t\\t\\tprob = mean\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprob = 1-mean\\n\\t\\t\\treturn prob\\n\\n\\t\\tdef calculateMultinoulli(x, *series):\\n\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\tReturns the density value of a Multinoulli distribution\\n\\t\\t\\t\\\"\\\"\\\"\\n\\t\\t\\tseries= series[0]\\n\\t\\t\\treturn series.ix[x]/float(series.sum())\\n\\n\\t\\tif density=='Multinoulli':\\n\\t\\t\\t#Redefine the parameters to be conditional means\\n\\t\\t\\tfor each in self.params.columns:\\n\\t\\t\\t\\tfor el in self.params.index:\\n\\t\\t\\t\\t\\tmultiDF = pd.Series(index=self.data[each].unique())\\n\\t\\t\\t\\t\\tcounts = self.training[self.training[self.classLabel]==el][each].value_counts()\\n\\t\\t\\t\\t\\tself.params.ix[el][each] = (pd.concat([multiDF,counts],1).drop(0,1),)\\n\\t\\t\\tpdf = calculateMultinoulli\\n\\t\\telif density == 'Bernoulli':\\n\\t\\t\\tpdf =calculateBernoulli\\n\\t\\telse:\\n\\t\\t\\tpdf = calculateGaussian\\n\\n\\t\\tprint \\\"Note: Assuming features follow a \\\"+density+\\\" distribution\\\"\\n\\n\\t\\tfor el in testingProbs.columns:\\n\\t\\t\\t#Retrieve parameters of distribution\\n\\t\\t\\tparameters = self.params.ix[el]\\n\\t\\t\\tprobabilities = self.testing.copy().drop(self.classLabel,1)\\n\\n\\t\\t\\t#For each feature, compute the likelihood of class being el\\n\\t\\t\\tfor each in probabilities.columns:\\n\\t\\t\\t\\t#Skip features with 0 standard deviation\\n\\t\\t\\t\\tif each in self.useless_features:\\n\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\tprobabilities[each] = probabilities[each].apply(lambda x: pdf(x,*parameters[each]))\\n\\n\\t\\t\\t#Multiply features together with prior\\n\\t\\t\\ttestingProbs[el] = math.log(self.priors.ix[el])+probabilities.sum(1)\\n\\t\\t\\t#testingProbs[el] = self.priors.ix[el]*probabilities.prod(1)\\n\\t\\t#Use log-sum-exp trick. We need the offsetting factor as max among classLabels\\n\\t\\tB = testingProbs.max(1)\\n\\t\\t#Compute log_sum = log(\\\\sigma_c' exp(b_c' - B)) + B\\n\\t\\tlog_sum = testingProbs.apply(lambda t: (t-B)).applymap(lambda u: math.exp(u)).sum(1).apply(math.log)+B\\n\\t\\tself.testingProbs = testingProbs.apply(lambda x: x-log_sum)\\n\\t\\t#self.testingProbs = testingProbs\",\n \"def generateStats(self):\\n\\t\\tn = float(self.n)\\n\\t\\tm = float(self.m)\\n\\t\\tk = float(self.k)\\n\\t\\tp_fp = math.pow(1.0 - math.exp(-(k*n)/m), k)\\n\\t\\tprint \\\"Probability of false positives: \\\", p_fp\\n\\t\\tprint \\\"Predicted false positive rate: \\\", p_fp * 100.0\\n\\t\\tprint \\\"Number of elements entered in filter: \\\", n\\n\\t\\tprint \\\"Number of bits in filter: \\\", m\\n\\t\\tprint \\\"Number of hashes in filter: \\\", k\",\n \"def _process(self, data: np.ndarray) -> np.ndarray:\\n probabilities = np.empty(data.size, dtype=object)\\n\\n for idx, counts_dict in enumerate(data):\\n shots = sum(counts_dict.values())\\n freq = counts_dict.get(self._outcome, 0)\\n alpha_posterior = [freq + self._alpha_prior[0], shots - freq + self._alpha_prior[1]]\\n alpha_sum = sum(alpha_posterior)\\n\\n p_mean = alpha_posterior[0] / alpha_sum\\n p_var = p_mean * (1 - p_mean) / (alpha_sum + 1)\\n\\n probabilities[idx] = ufloat(nominal_value=p_mean, std_dev=np.sqrt(p_var))\\n\\n return probabilities\",\n \"def probability(self, xvalue):\\n # Initialize key variables\\n probability = {}\\n bayesian = {}\\n classes = self.classes()\\n\\n # Calculate the principal components of the individual xvalue\\n p1p2 = self.pca_object.pc_of_x(xvalue, self.components)\\n\\n # Get probability of each class\\n for cls in classes:\\n # Initialize values for the loop\\n sample_count = len(self.pca_object.xvalues(cls))\\n\\n # Get values for calculating gaussian parameters\\n dimensions = len(p1p2)\\n x_mu = p1p2 - self.meanvector(cls)\\n covariance = self.covariance(cls)\\n inverse_cov = np.linalg.inv(covariance)\\n determinant_cov = np.linalg.det(covariance)\\n\\n # Work on the exponent part of the bayesian classifer\\n power = -0.5 * np.dot(np.dot(x_mu, inverse_cov), x_mu.T)\\n exponent = math.pow(math.e, power)\\n\\n # Determine the constant value\\n pipart = math.pow(2 * math.pi, dimensions / 2)\\n constant = pipart * math.sqrt(determinant_cov)\\n\\n # Determine final bayesian\\n bayesian[cls] = (sample_count * exponent) / constant\\n\\n # Calculate bayesian probability\\n denominator = bayesian[classes[0]] + bayesian[classes[1]]\\n for cls in classes:\\n probability[cls] = bayesian[cls] / denominator\\n\\n # Return\\n return probability\",\n \"def evaluate(self, xs):\\n # ps = 1. / (np.sqrt(2. * np.pi) * self.sigma) * \\\\\\n # np.exp(-0.5 * (self.mean - xs) * self.invvar * (self.mean - xs))\\n # ps = np.zeros_like(xs)\\n # for n, x in enumerate(xs):\\n # ps[n] += self.evaluate_one(x)\\n ps = self.dist.probability(xs)\\n return ps\",\n \"def distribution(vals):\\n tot = 0\\n rv = []\\n for v in vals:\\n tot += v\\n for v in vals:\\n rv.append(v * 100 // tot)\\n # rv.extend(sorted(rv))\\n return tuple(rv)\",\n \"def sample_probabilities(pvals: np.ndarray) -> Callable[[], int]:\\n\\n return Sampler(np.cumsum(pvals))\",\n \"def data_gen(size, p):\\n #print(np.random.get_state()[1][0])\\n random_table = np.random.binomial(size = size, p = p, n = 1)\\n test_array = np.zeros((size, 2), dtype = int)\\n for i in range(size):\\n test_array[i,0] = i\\n test_array[i,1] = random_table[i]\\n return test_array\",\n \"def build_distributions(self):\\n res = {}\\n n_partitions = self.partition_num\\n partition_num = 1\\n # each part size\\n partition_size = int(math.floor(self.size / n_partitions))\\n\\n for n in range(partition_size, self.size + 1, partition_size):\\n if self.learning_starts <= n <= self.priority_size:\\n distribution = {}\\n # P(i) = (rank i) ^ (-alpha) / sum ((rank i) ^ (-alpha))\\n pdf = list(\\n map(lambda x: math.pow(x, -self.alpha), range(1, n + 1))\\n )\\n pdf_sum = math.fsum(pdf)\\n distribution['pdf'] = list(map(lambda x: x / pdf_sum, pdf))\\n # split to k segment, and than uniform sample in each k\\n # set k = batch_size, each segment has total probability is 1 / batch_size\\n # strata_ends keep each segment start pos and end pos\\n cdf = np.cumsum(distribution['pdf'])\\n strata_ends = {1: 0, self.batch_size + 1: n}\\n step = 1 / float(self.batch_size)\\n index = 1\\n for s in range(2, self.batch_size + 1):\\n while cdf[index] < step:\\n index += 1\\n strata_ends[s] = index\\n step += 1 / float(self.batch_size)\\n\\n distribution['strata_ends'] = strata_ends\\n\\n res[partition_num] = distribution\\n\\n partition_num += 1\\n\\n return res\",\n \"def entropy_permutation_test(ordered_pitch_types, single_pitch_pdf, conditional_joint_probabilities, total_transitions,\\n n=1000):\\n pitch_types, pitch_probabilities = zip(*single_pitch_pdf.items())\\n permutation_entropies = []\\n progress = progressbar.ProgressBar()\\n\\n for test_number in progress(xrange(n)):\\n # create the new matrix\\n permutation_counts = {}\\n for first_pitch_type in ordered_pitch_types:\\n permutation_counts[first_pitch_type] = {}\\n for second_pitch_type in ordered_pitch_types:\\n permutation_counts[first_pitch_type][second_pitch_type] = 0\\n\\n pitch_permutation = numpy.random.choice(pitch_types, total_transitions, p=pitch_probabilities)\\n current_pitch = numpy.random.choice(pitch_types, p=pitch_probabilities)\\n for next_pitch in pitch_permutation:\\n permutation_counts[current_pitch][next_pitch] += 1\\n current_pitch = next_pitch\\n\\n joint_probabilities, _, _ = joint_probabilities_from_transitions(ordered_pitch_types, permutation_counts)\\n permutation_entropies.append(entropy_from_probability_matrix(joint_probabilities))\\n\\n joint_entropy = entropy_from_probability_matrix(conditional_joint_probabilities)\\n # print 'Mean', numpy.mean(permutation_entropies)\\n # print 'Standard deviation', numpy.std(permutation_entropies)\\n # tdof, tloc, tscale = stats.t.fit(permutation_entropies)\\n # print 'DF', tdof, 'Loc (mean)', tloc, 'Scale (SD)', tscale\\n # t_score = (joint_entropy - tloc) / tscale\\n # print stats.t.cdf(joint_entropy, df=tdof, loc=tloc, scale=tscale)\\n\\n mean, stddev = stats.norm.fit(permutation_entropies)\\n print 'Mean = {mean}\\\\t StdDev = {stddev}'.format(mean=mean, stddev=stddev)\\n z_score = (joint_entropy - mean) / stddev\\n p_value = stats.norm.cdf(joint_entropy, mean, stddev)\\n print 'The joint entropy has a Z-score of {z_score} which gives a P-value of {p_value}'.format(z_score=z_score,\\n p_value=p_value)\\n return z_score, p_value\",\n \"def prob3():\\n\\n h = lambda x: x > 10\\n\\n N = range(5000,500001, 5000)\\n\\n estimates = []\\n\\n for n in N:\\n random_draw = np.random.gamma(9, scale = 0.5, size = n)\\n\\n estimate = 1./n * np.sum(h(random_draw))\\n estimates.append(estimate)\\n\\n # arrayify it\\n estimates = np.array(estimates)\\n\\n m = 1 - stats.gamma(a = 9, scale = 0.5).cdf(10)\\n \\n y = abs(estimates - m)\\n y_2 = abs(prob2() - m)\\n\\n plt.plot(N,y)\\n plt.plot(N,y_2)\\n\\n plt.show()\",\n \"def generate_distribution(self, rng: RNG, filtration_range, scale=None):\\n if scale is None:\\n scale = self.scale\\n if self.delayed_coordinates:\\n points = self.generate_3D_points_delayed(rng, self.number_of_points, scale)\\n else:\\n points = self.generate_points(rng, self.number_of_points, self.dimension, scale)\\n # distance_matrix = pairwise_distances(points)\\n if self.gpu:\\n # TODO add in GPU calculation.\\n raise NotImplementedError(\\\"GPU calculation of distance matrix not implemented yet.\\\")\\n else:\\n sparse_distance_matrix = self.make_sparse_dm(points, filtration_range[-1])\\n if self.f is not None:\\n output_string = \\\"[Points]\\\\n\\\"\\n output_string += str(points)\\n self.f.write(output_string)\\n # An attempt to reduce memory usage, might not work\\n del points\\n diagrams = self.generate_diagrams(sparse_distance_matrix, threshold=filtration_range[-1])\\n homology = self.generate_homology(diagrams, filtration_range)\\n distribution = homology[self.homology_dimension]\\n if self.f is not None:\\n self.f.write(\\\"\\\\n[Distribution]\\\\n\\\" + str(distribution) + \\\"\\\\n\\\")\\n return distribution\",\n \"def sdd(events,probs):\\n \\n import random\\n nprobs=[x*1000 for x in probs] #so, here i multiply each float in 'probs' by 1000 and store the products in 'nprobs'\\n newlist=[]\\n for a in range(len(events)) : #then, in this loop, i create a list (newlist), in which each event appears 1000*its probability times\\n b=nprobs[a]\\n b=int(b)\\n for c in range(b) :\\n newlist.append(events[a]) \\n return (random.choice(newlist)) #and finally, i ramdonly sample \",\n \"def initializeDistribution(self):\\n self.convertToDistrDict['Jacobi'] = self.convertJacobiToBeta\\n self.convertToQuadDict ['Jacobi'] = self.convertBetaToJacobi\\n self.measureNormDict ['Jacobi'] = self.stdProbabilityNorm\\n #this \\\"if\\\" section can only be called if distribution not generated using readMoreXML\\n if (not self.upperBoundUsed) and (not self.lowerBoundUsed):\\n self._distribution = distribution1D.BasicBetaDistribution(self.alpha,self.beta,self.high-self.low,self.low)\\n else:\\n if self.lowerBoundUsed == False:\\n a = 0.0\\n else:\\n a = self.lowerBound\\n if self.upperBoundUsed == False:\\n b = sys.float_info.max\\n else:\\n b = self.upperBound\\n self._distribution = distribution1D.BasicBetaDistribution(self.alpha,self.beta,self.high-self.low,a,b,self.low)\\n self.preferredPolynomials = 'Jacobi'\\n self.compatibleQuadrature.append('Jacobi')\\n self.compatibleQuadrature.append('ClenshawCurtis')\",\n \"def propensity_to_probability_distribution(\\n index_num, node_indv_propensities, test=False\\n):\\n\\n # Checks for NaN values\\n if np.isnan(node_indv_propensities).any():\\n raise Exception('NaN value encountered in propensity array')\\n\\n # Converts fitness scores into probabilities\\n node_eqm_constant_values = np.exp(np.negative(node_indv_propensities))\\n total = np.sum(node_eqm_constant_values)\\n\\n node_probabilities = np.full(node_indv_propensities.shape, np.nan)\\n for index, eqm_constant in np.ndenumerate(node_eqm_constant_values):\\n index = index[0] # Numpy array indices are tuples\\n node_probabilities[index] = eqm_constant / total\\n\\n # Randomly shuffles probability array, in order to avoid smallest\\n # probabilities being grouped together at the beginning of the range\\n if test is False:\\n (index_num, node_indv_propensities, node_probabilities\\n ) = random_shuffle(\\n index_num, node_indv_propensities, node_probabilities\\n )\\n\\n return index_num, node_indv_propensities, node_probabilities\",\n \"def initPmf(self, values):\\n for value, prob in values.items():\\n self.set(value, prob)\",\n \"def probability(distances):\\n v = [1.0/(d + 1) for d in distances]\\n s = sum(v)\\n return [i/s for i in v]\",\n \"def random_values():\\n while True:\\n yield random()\",\n \"def get_distribution(counts, n_qubits):\\n\\tprobabilities = np.zeros(2 ** n_qubits) # Array of zeros and with the correct size for the measured qubits\\n\\tfor key in counts.keys(): # Iterate over the measured outputs\\n\\t\\t# Transform the key from binary to decimal, and them save the probability\\n\\t\\tprobabilities[int(key, 2)] = counts[key] / NUM_SHOTS\\n\\treturn probabilities\",\n \"def get_hybrid_distribution():\\n return get_discrete_distribution() + get_standard_normal_distribution()\",\n \"def prob1(n):\\n#raise NotImplementedError(\\\"Problem 1 Incomplete\\\")\\n if n == 0 :\\n raise ValueError(\\\"Sampling 0 points is not defined.\\\")\\n total = 0\\n for i in xrange(n) :\\n if np.random.normal() > 3 :\\n total += 1\\n return float(total)/n\",\n \"def probability(prods, prod_dict_As, count_dict):\\n for p in prods:\\n if p not in prod_dict_As:\\n raise Exception(\\\"Think we cannot make the product {}.\\\".format(p))\\n # Argh, Python, this is a reference!\\n #possible_As = prod_dict_As[prods[0]]\\n possible_As = set( prod_dict_As[prods[0]] )\\n for p in prods[1:]:\\n possible_As &= prod_dict_As[p]\\n ret = []\\n for A in possible_As:\\n count = 1\\n for p in prods:\\n count *= count_dict[(p,A)]\\n ret.append((A,count))\\n return ret\",\n \"def make_experimental_distribution(zshots):\\n N,L = zshots.shape\\n pvals = np.zeros(2**L)\\n for j in range(N):\\n m = to_int(zshots[j, :])\\n pvals[m] += 1/N\\n return pvals\",\n \"def tabulate_histogram(self):\\n\\n # Generate a table of uniform variates\\n from mitsuba.core import Float, Vector2f, Vector2u, Float32, \\\\\\n UInt64, PCG32\\n\\n rng = PCG32(initseq=ek.arange(UInt64, self.sample_count))\\n\\n samples_in = getattr(mitsuba.core, 'Vector%if' % self.sample_dim)()\\n for i in range(self.sample_dim):\\n samples_in[i] = rng.next_float32() if Float is Float32 \\\\\\n else rng.next_float64()\\n\\n self.pdf_start = time.time()\\n\\n # Invoke sampling strategy\\n samples_out = self.sample_func(samples_in)\\n\\n if type(samples_out) is tuple:\\n weights_out = samples_out[1]\\n samples_out = samples_out[0]\\n else:\\n weights_out = Float(1.0)\\n\\n # Map samples into the parameter domain\\n xy = self.domain.map_backward(samples_out)\\n\\n # Sanity check\\n eps = self.bounds.extents() * 1e-4\\n in_domain = ek.all((xy >= self.bounds.min - eps) &\\n (xy <= self.bounds.max + eps))\\n if not ek.all(in_domain):\\n self._log('Encountered samples outside of the specified '\\n 'domain: %s' % str(ek.compress(xy, ~in_domain)))\\n self.fail = True\\n\\n # Normalize position values\\n xy = (xy - self.bounds.min) / self.bounds.extents()\\n xy = Vector2u(ek.clamp(xy * Vector2f(self.res), 0,\\n Vector2f(self.res - 1)))\\n\\n # Compute a histogram of the positions in the parameter domain\\n self.histogram = ek.zero(Float, ek.hprod(self.res))\\n\\n ek.scatter_add(\\n target=self.histogram,\\n index=xy.x + xy.y * self.res.x,\\n source=weights_out\\n )\\n\\n self.pdf_end = time.time()\\n\\n histogram_min = ek.hmin(self.histogram)\\n if not histogram_min >= 0:\\n self._log('Encountered a cell with negative sample '\\n 'weights: %f' % histogram_min)\\n self.fail = True\\n\\n self.histogram_sum = ek.hsum(self.histogram) / self.sample_count\\n if self.histogram_sum > 1.1:\\n self._log('Sample weights add up to a value greater '\\n 'than 1.0: %f' % self.histogram_sum)\\n self.fail = True\",\n \"def build_probdist(prob_filename):\\n fits_file = fits.open(prob_filename)\\n \\n z_hdu = fits_file[1]\\n p_hdu = fits_file[2]\\n \\n z_data = z_hdu.data\\n p_data = np.exp(p_hdu.data)\\n \\n return ProbDist(z_data, p_data)\",\n \"def pdf(self,x):\\n return self.categoricalDist.pdf(x)\",\n \"def distribution(self):\\n \\n #external_distribution serves both the purpose of external setting of distribution and the caching of distribution()\\n if self.external_distribution:\\n return self.external_distribution_array;\\n else:\\n energy_vector = []\\n superset = self.generate_superset(0) \\n \\n for i in superset:\\n state = self.ket(i)\\n \\n norm_squared = np.dot(state.T, state)\\n \\n if norm_squared > 0: #zero is appended at the end\\n energy = np.dot(state.T, np.dot( self.epsilon, state))\\n interaction = np.dot(state.T, np.dot( self.u, state))/2.0 #divide by two. Otherwise, <l r| U |l r > = U_LR + U_RL = 2U\\n #print state, np.dot(self.u, state) \\n #print interaction\\n energy_vector.append( energy + interaction )\\n \\n energy_vector.insert(0, 0.0) \\n probability = np.exp( np.multiply(-self.beta, energy_vector)) \\n probability /= probability.sum() \\n return probability\",\n \"def create_distribution(sample_size):\\n if sample_size < 0:\\n raise ValueError(\\\"Unable to generate negative number of distributions \\\", sample_size)\\n result = []\\n for _ in range(sample_size):\\n result.append(get_hybrid_distribution())\\n return result\",\n \"def probabilities(self, x, y):\\n return self.feed_and_return(x, y, self.network.a)\",\n \"def process_simulation(self):\\n for i in range(self._n):\\n probability = self._alpha / float(self._alpha + i - 1)\\n tmp = np.random.uniform(size=(1,))\\n if tmp < probability:\\n self._results.append(np.random.normal(1))\\n else:\\n self._results.append(np.random.choice(self._results[:i-1], 1)[0])\",\n \"def data_generate_process():\\n\\n a = 0.8\\n b = 0.4\\n c = 0.1\\n d = 0.3\\n e = 0.7\\n y_0 = 0.0\\n a_0 = 0.2\\n sigma_0 = 0.35\\n\\n data_len = 10000\\n y_series = pd.Series([np.nan] * data_len)\\n a_series = pd.Series([np.nan] * data_len)\\n sigma_series = pd.Series([np.nan] * data_len)\\n\\n epsilon_normal = np.random.normal(loc=0.0, scale=1.0, size=data_len)\\n\\n y_series[0] = y_0\\n a_series[0] = a_0\\n sigma_series[0] = sigma_0\\n\\n for idx in range(1, data_len):\\n epsilon_t = epsilon_normal[idx]\\n sigma_t = np.sqrt(c + d * a_series[idx - 1] ** 2 + e * sigma_series[idx - 1] ** 2)\\n a_t = epsilon_t * sigma_t\\n y_series[idx] = a * y_series[idx - 1] + b + a_t\\n a_series[idx] = a_t\\n sigma_series[idx] = sigma_t\\n\\n return y_series, a_series, sigma_series\",\n \"def quantitative_distribution(child, N): \\n binned = np.histogram(child.dropna(), bins=10) # Value counts (first line)\\n prop = binned[0] / binned[0].sum() # proportion of bins\\n bins = binned[1] # N + 1 bin boundaries\\n # print(prop)\\n indices = np.random.choice(a=10, p=prop, size=N)\\n # print(np.sum(indices == 8) / 10000)\\n samples = []\\n for ind in indices: # Loop through samples\\n lower, upper = bins[ind], bins[ind+1] # Upper and lower bounds of bin\\n samples.append(np.random.uniform(low=lower, high=upper))\\n return np.array(samples)\",\n \"def sample_discrete(probs):\\r\\n q = np.random.rand()\\r\\n i = 0\\r\\n p_sum = 0.0\\r\\n while p_sum < q:\\r\\n p_sum += probs[i]\\r\\n i += 1\\r\\n return i - 1\",\n \"def uniform(N=10000, left=0., right=3., n_bins_meas=10):\\n\\t# true distribution\\n\\ttrue = np.random.uniform(left, right, N)\\n\\t# measured distribution\\n\\tmeasured = true\\n\\t# Also return the generating true pdf\\n\\tx = np.linspace(-1, 5, 500)\\n\\tpdf = np.zeros([2, len(x)])\\n\\tpdf[0] = x\\n\\tpdf[1] = scs.uniform.pdf(x, left, right-left)\\n\\t# Default binning\\n\\tdefault_binning = np.linspace(np.amin(measured), np.amax(measured), n_bins_meas)\\n\\n\\treturn measured, true, pdf, default_binning\",\n \"def generate_data(self,\\n num_batches,\\n num_samples,\\n percentile):\\n\\n xs = []\\n ys = []\\n ws = []\\n\\n for j in range(num_batches):\\n\\n # sample designs from the prior\\n z = tf.random.normal([num_samples, self.latent_size])\\n q_dx = self.q_vae.decoder.get_distribution(z, training=False)\\n p_dx = self.p_vae.decoder.get_distribution(z, training=False)\\n\\n # evaluate the score and importance weights\\n x = q_dx.sample()\\n y = self.ensemble.get_distribution(x).mean()\\n log_w = p_dx.log_prob(x)[..., tf.newaxis] - \\\\\\n q_dx.log_prob(x)[..., tf.newaxis]\\n while len(log_w.shape) > 2:\\n log_w = tf.reduce_sum(log_w, axis=1)\\n\\n xs.append(x)\\n ys.append(y)\\n ws.append(tf.math.exp(log_w))\\n\\n # locate the cutoff for the scores below the percentile\\n gamma = tfp.stats.percentile(ys, percentile)\\n\\n for j in range(num_batches):\\n\\n # re-weight by the cumulative probability of the score\\n d = self.ensemble.get_distribution(xs[j])\\n ws[j] *= 1.0 - d.cdf(tf.fill([num_samples, 1], gamma))\\n\\n return tf.concat(xs, axis=0), \\\\\\n tf.concat(ys, axis=0), \\\\\\n tf.concat(ws, axis=0)\",\n \"def test_correct_build_distribution_values(self):\\n timeseries_with_distribution_values = self.build_distribution_value()\\n\\n distribution_value = main.build_distribution_value(timeseries_with_distribution_values[\\\"points\\\"][0][\\\"value\\\"][\\\"distributionValue\\\"])\\n expected_distribution_value = self.build_expected_distribution_value()\\n self.assertEqual(distribution_value,expected_distribution_value)\",\n \"def __init__(self, variables=[], values=[], probabilities=[]):\\n self.variables = variables\\n self.values = values\\n self.probabilities = probabilities\",\n \"def occupation_distribution(data):\",\n \"def generate():\",\n \"def probability_density(self, X):\\n raise NotImplementedError\",\n \"def distribution_probability(self, game):\\n dist_probability = {}\\n\\n total_visits = sum(self.root.n_a.values())\\n\\n for action, visits in self.root.n_a.items():\\n dist_probability[action] = visits/total_visits\\n return dist_probability\",\n \"def calc_fitness_by_gen(self):\\r\\n f_sum = 0\\r\\n # first loop gives us the sum of the fitness\\r\\n for c, _ in self.temp_hist_by_gen.items():\\r\\n f_sum += c.fitness()\\r\\n # now we calc the chances by fitness of each one\\r\\n for c, _ in self.temp_hist_by_gen.items():\\r\\n self.temp_hist_by_gen[c] = c.fitness() / f_sum\",\n \"def _make_gp_list(self, mean, samples):\\n width = np.sqrt(mean)\\n k_min = max([min([0, mean - 3 * width]), 0])\\n k_max = mean + 3 * width + 10\\n k_arr = np.arange(k_min, k_max)\\n gp_prob = _general_poisson(k_arr, mean, self.lamb)\\n gp_prob = gp_prob / np.sum(gp_prob) ## Additional normalization\\n dist = stats.rv_discrete('GeneralizedPoisson', values=(k_arr, gp_prob))\\n return dist.rvs(size=samples)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7003688","0.6883407","0.6792984","0.67818236","0.666771","0.66444635","0.6615368","0.6597994","0.65978616","0.6579974","0.6578962","0.6567796","0.6556646","0.6516561","0.64934367","0.64649326","0.640872","0.6397725","0.6374798","0.6366242","0.6345269","0.6336321","0.63108695","0.63001585","0.6274997","0.62501216","0.62367755","0.6189194","0.6183141","0.6151431","0.6133644","0.6126278","0.6122597","0.61203724","0.61014456","0.61014456","0.60997266","0.60944796","0.6093238","0.6088409","0.60852134","0.6076633","0.6055794","0.60339034","0.6033869","0.6028959","0.6023084","0.60197276","0.6015337","0.60143936","0.6010529","0.60022503","0.5990474","0.5986178","0.59858865","0.595721","0.59529227","0.59520316","0.5948035","0.59428215","0.59398127","0.5939734","0.5935416","0.59088016","0.58971345","0.5894472","0.5883361","0.58757263","0.5870696","0.5861638","0.58603454","0.5856267","0.58472705","0.583814","0.58255744","0.58228105","0.5822636","0.5821562","0.5820383","0.5816854","0.58164984","0.5815384","0.5815374","0.5814319","0.5813384","0.58129096","0.5812305","0.5803804","0.5798425","0.57909244","0.57857984","0.5774531","0.5768874","0.5768548","0.57613736","0.57613105","0.5757442","0.5745872","0.5740246","0.57374245","0.5729726"],"string":"[\n \"0.7003688\",\n \"0.6883407\",\n \"0.6792984\",\n \"0.67818236\",\n \"0.666771\",\n \"0.66444635\",\n \"0.6615368\",\n \"0.6597994\",\n \"0.65978616\",\n \"0.6579974\",\n \"0.6578962\",\n \"0.6567796\",\n \"0.6556646\",\n \"0.6516561\",\n \"0.64934367\",\n \"0.64649326\",\n \"0.640872\",\n \"0.6397725\",\n \"0.6374798\",\n \"0.6366242\",\n \"0.6345269\",\n \"0.6336321\",\n \"0.63108695\",\n \"0.63001585\",\n \"0.6274997\",\n \"0.62501216\",\n \"0.62367755\",\n \"0.6189194\",\n \"0.6183141\",\n \"0.6151431\",\n \"0.6133644\",\n \"0.6126278\",\n \"0.6122597\",\n \"0.61203724\",\n \"0.61014456\",\n \"0.61014456\",\n \"0.60997266\",\n \"0.60944796\",\n \"0.6093238\",\n \"0.6088409\",\n \"0.60852134\",\n \"0.6076633\",\n \"0.6055794\",\n \"0.60339034\",\n \"0.6033869\",\n \"0.6028959\",\n \"0.6023084\",\n \"0.60197276\",\n \"0.6015337\",\n \"0.60143936\",\n \"0.6010529\",\n \"0.60022503\",\n \"0.5990474\",\n \"0.5986178\",\n \"0.59858865\",\n \"0.595721\",\n \"0.59529227\",\n \"0.59520316\",\n \"0.5948035\",\n \"0.59428215\",\n \"0.59398127\",\n \"0.5939734\",\n \"0.5935416\",\n \"0.59088016\",\n \"0.58971345\",\n \"0.5894472\",\n \"0.5883361\",\n \"0.58757263\",\n \"0.5870696\",\n \"0.5861638\",\n \"0.58603454\",\n \"0.5856267\",\n \"0.58472705\",\n \"0.583814\",\n \"0.58255744\",\n \"0.58228105\",\n \"0.5822636\",\n \"0.5821562\",\n \"0.5820383\",\n \"0.5816854\",\n \"0.58164984\",\n \"0.5815384\",\n \"0.5815374\",\n \"0.5814319\",\n \"0.5813384\",\n \"0.58129096\",\n \"0.5812305\",\n \"0.5803804\",\n \"0.5798425\",\n \"0.57909244\",\n \"0.57857984\",\n \"0.5774531\",\n \"0.5768874\",\n \"0.5768548\",\n \"0.57613736\",\n \"0.57613105\",\n \"0.5757442\",\n \"0.5745872\",\n \"0.5740246\",\n \"0.57374245\",\n \"0.5729726\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":22295,"cells":{"query":{"kind":"string","value":"aic(timeSeries, ssc=0) > data, max_weight, max_weight_params"},"document":{"kind":"string","value":"def aic(timeSeries, ssc=0): \n if np.min(timeSeries) <= 0:\n timeSeries = timeSeries + -np.min(timeSeries) + .01\n \n # create histogram to determine plot values\n # note that the original uses hist centers, this uses edges. It may matter\n counts, plotvals_edges = np.histogram(timeSeries, 50)\n plotvals = np.array([np.mean([plotvals_edges[i], plotvals_edges[i+1]]) for i in range(plotvals_edges.shape[0]-1)])\n \n distributions = ['normal', 'lognormal', 'exponential', 'pareto', 'boundedpl'] #no gamma currently\n #pdfs = [dict(name=dist) for dist in distributions]\n pdfs = defaultdict(dict)\n aicvals = defaultdict(dict)\n \n # calculate maximum likelihood for core distributions\n # calculate log likelihood value at maximum\n # find k (number of params)\n # generate probability density function using parameters\n kvals = dict()\n for dist in distributions:\n aicvals[dist]['mle'] = aicmle(timeSeries, dist)\n aicvals[dist]['nll'] = aiclike(timeSeries, aicvals[dist]['mle'], dist)\n kvals[dist] = len(aicvals[dist]['mle'])\n pdfs[dist]['vals'] = aicpdf(plotvals, dist, aicvals[dist]['mle'])\n \n # plot histogram and mle pdf\n # note: only creats the data to make a plot, does not actually generate it\n for dist in distributions:\n scaling = np.sum(counts) / np.sum(pdfs[dist]['vals'])\n aicvals[dist]['plots'] = {}\n aicvals[dist]['plots']['xvals'] = plotvals\n aicvals[dist]['plots']['datay'] = counts\n aicvals[dist]['plots']['aicy'] = pdfs[dist]['vals'] * scaling\n \n # check for small sample correction\n if timeSeries.shape[0] / np.max(kvals.values()) < 40:\n ssc = 1\n \n # calculate akaike information criteria\n for dist in distributions:\n aicvals[dist]['aic'] = 2 * aicvals[dist]['nll'] + 2 * kvals[dist]\n if ssc == 1:\n aicvals[dist]['aic'] = aicvals[dist]['aic'] + 2 * kvals[dist] * (kvals[dist] + 1) / (timeSeries.shape[0] - kvals[dist] -1)\n \n # calculate AIC differences and akaike weights\n aicmin = np.min([aicvals[dist]['aic'] for dist in distributions])\n for dist in distributions:\n aicvals[dist]['aicdiff'] = aicvals[dist]['aic'] - aicmin\n \n aicsum = 0\n for dist in distributions:\n aicsum = aicsum + np.exp(-aicvals[dist]['aicdiff'] / 2)\n \n for dist in distributions:\n aicvals[dist]['weight'] = np.exp(-aicvals[dist]['aicdiff'] / 2) / aicsum\n \n max_weight_val = np.max([aicvals[dist]['weight'] for dist in distributions])\n max_weight = [key for key, value in aicvals.items() if value['weight'] == max_weight_val][0]\n max_weight_params = aicvals[max_weight]['mle']\n \n return aicvals, max_weight, max_weight_params"},"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 test_aic_ms(distribution):\n print(\"TESTING: AIC model selection for %s distribution\" % distribution.upper())\n params = dist.DISTRIBUTIONS[distribution][dist.KEY_TEST_PARAMS]\n print(\" creating sample\")\n test_sample = dist.samples(distribution, params)\n print(\" calculating AIC for all distributions\")\n fit_results = {}\n aic = {}\n for d in dist.get():\n fit_results[d] = fit.fit_mle(d, test_sample)\n aic[d] = me.aic_measure(dist.log_likelihood(d, fit_results[d]['params'], test_sample, nonzero_only=True),\n len(fit_results[d]['params']))\n delta_aic = {d: aic[d]-min(aic.values()) for d in aic}\n weights = {d: float(exp(-delta_aic[d]/2)) for d in delta_aic}\n best_model = dist.get()[0]\n print(\" input parameters: %s\" % dist.get_params(params, distribution))\n for d in dist.get():\n if weights[d] > weights[best_model]:\n best_model = d\n weights[d] /= sum(weights.values())\n print(\" %s:\" % d.upper())\n print(\" %s\" % dist.get_params(fit_results[d]['params'], d))\n print(\" AIC = %.0f\" % aic[d])\n print(\" dAIC = %.0f\" % delta_aic[d])\n print(\" w = %r\" % weights[d])\n print(\" Most likely model: %s\" % best_model.upper())\n print_pmfs(test_sample, fit_results, 'TEST-AIC.CSV')","def is_good_qualtiative_example(iaa_score, ann1_total, ann2_total):\n return iaa_score > .3 and iaa_score < 1 and ann1_total > 3 and ann2_total > 3","def aiclike(timeSeries, params, distribution):\n if distribution == 'pareto':\n nloglval = -(timeSeries.shape[0] * np.log(params['mu']) + timeSeries.shape[0] * params['mu'] * np.log(params['xmin']) - (params['xmin']+1) * np.sum(np.log(timeSeries)))\n return nloglval\n \n elif distribution == 'lognormal':\n nloglval = np.sum(np.log(timeSeries * params['sigma'] * np.sqrt(2*np.pi)) + (np.log(timeSeries) - params['mu'])**2 / (2 * params['sigma']**2))\n return nloglval\n \n elif distribution == 'normal':\n nloglval = np.sum(np.log( params['sigma'] * np.sqrt(2*np.pi) ) + (timeSeries - params['mu'])**2 / (2 * params['sigma']**2))\n return nloglval\n \n elif distribution == 'exponential':\n nloglval = np.sum(params['lambda'] * timeSeries - np.log(params['lambda']))\n return nloglval\n \n elif distribution == 'boundedpl':\n nloglval = -len(timeSeries) * np.log( (params['mu'] - 1) / (np.min(timeSeries)**(1 - params['mu']) - np.max(timeSeries)**(1 - params['mu']))) + params['mu'] * np.sum(np.log(timeSeries))\n return nloglval","def aic(self):\n if hasattr(self, '_aic'):\n return self._aic\n else:\n self._aic = 2 * self.k + 2 * self.neg_ll()\n return self._aic","def adaptive_selection(p_values, data, alpha):\n HC_stat, max_idx_HC, HC_vec = HC_update(p_values, alpha)\n cscshm_stat, max_idx_cscshm, cscshm_vec = CsCsHM_update(p_values, alpha)\n thresh_HC = data[max_idx_HC]\n thresh_cscshm = data[max_idx_cscshm]\n nonnull_HC = np.where(data < thresh_HC, 0, data)\n nonnull_cscshm = np.where(data < thresh_cscshm, 0, data)\n return nonnull_HC, nonnull_cscshm","def AIC(y,yhat,k):\r\n residuals = y-yhat\r\n sse = np.sum(residuals**2) #sum of squared errors\r\n N = len(y)\r\n Lhat = sse/N\r\n return(2*k + N*np.log(Lhat))","def test_adf(self):\n\n dftest = adfuller(self.ts_df['y'], autolag='AIC')\n dfoutput = pd.Series(dftest[0:4],\n index=['Test Statistic', 'p-value', '#Lags Used', 'Number of Observations Used'])\n for key, value in dftest[4].items():\n dfoutput['Critical Value (%s)' % key] = value\n print(dfoutput)\n if dftest[0] > dftest[4]['5%']:\n print(\n \"Test statistic greater than critical value at 5% --> series seems to be not stationary. \"\n \"Look at critical values at 1% and 10% too, ideally they also should be less than test statistic.\")\n else:\n print(\n \"Test statistic less than critical value at 5% --> series seems to be stationary. \"\n \"Look at critical values at 1% and 10% too, ideally they also should be greater than test statistic.\")","def opt_optical_absorption(aref, asig, traw, awl, aoff, tcal, tbins, ta_arr,\n cpd_ts, cwl, T, PS, rwlngth=715.):\n # reset shapes of input arguments\n # using np.array ndmin=# seems faster than using np.atleast_#d\n aref = np.array(aref, ndmin=2)\n asig = np.array(asig, ndmin=2)\n traw = np.array(traw, ndmin=1)\n awl = np.around(np.array(awl, ndmin=2), decimals=1)\n aoff = np.array(aoff, ndmin=2)\n tcal = np.array(tcal, ndmin=1)\n tbins = np.array(tbins, ndmin=2)\n # note, np.atleast_3d appends the extra dimension;\n # np.array using ndmin prepends the extra dimension.\n ta_arr = np.array(ta_arr, ndmin=3)\n cpd_ts = np.array(cpd_ts, ndmin=2)\n cwl = np.array(cwl, ndmin=2)\n T = np.array(T, ndmin=1)\n PS = np.array(PS, ndmin=1)\n\n # size up inputs\n npackets = awl.shape[0]\n nwavelengths = awl.shape[1]\n # initialize output array\n apd_ts_s = np.zeros([npackets, nwavelengths])\n\n for ii in range(npackets):\n\n # calculate the internal instrument temperature [deg_C]\n tintrn = opt_internal_temp(traw[ii])\n\n # calculate the uncorrected optical absorption coefficient [m^-1]\n apd, _ = opt_pd_calc(aref[ii, :], asig[ii, :], aoff[ii, :], tintrn,\n tbins[ii, :], ta_arr[ii, :, :])\n\n # correct the optical absorption coefficient for temperature and salinity.\n apd_ts = opt_tempsal_corr('a', apd, awl[ii, :], tcal[ii], T[ii], PS[ii])\n\n # correct the optical absorption coefficient for scattering effects\n apd_ts_s_row = opt_scatter_corr(apd_ts, awl[ii, :], cpd_ts[ii, :], cwl[ii, :], rwlngth)\n apd_ts_s[ii, :] = apd_ts_s_row\n\n # return the temperature, salinity and scattering corrected optical\n # absorption coefficient OPTABSN_L2 [m^-1]\n return apd_ts_s","def utility_sarimax_predict(series,nforecast=1,loga=False,plot_figures_flag=False,show_mdl_detail=False):\n \n from sklearn.metrics import r2_score, median_absolute_error, mean_absolute_error\n from sklearn.metrics import median_absolute_error, mean_squared_error, mean_squared_log_error\n from scipy.optimize import minimize\n import statsmodels.tsa.api as smt\n import statsmodels.api as sm\n import numpy as np\n import pandas as pd\n from tqdm import tqdm \n from itertools import product\n import warnings\n \n np.random.seed(25)\n \n ## SARIMA\n def mean_absolute_percentage_error(y_true, y_pred):\n return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n \n warnings.filterwarnings('ignore')\n \n def optimize_SARIMA(data_column,parameters_list):\n \"\"\"\n Return dataframe with parameters and corresponding AIC\n \n parameters_list - list with (ps,d, qs, Ps,D, Qs,s) tuples\n\n \"\"\" \n results = []\n best_aic = float('inf') \n \n #for param in tqdm(parameters_list):\n for param in parameters_list:\n try: model = sm.tsa.statespace.SARIMAX(data_column, order=(param[0], param[1],param[2]),\n seasonal_order=(param[3], param[4], param[5], param[5])).fit(disp=-1)\n # try: model = sm.tsa.statespace.SARIMAX(data_column, order=(param[0], d, param[1])).fit(disp=-1)\n except:\n #print(\"model not built with params\".format(param))\n pass\n continue \n aic = model.aic \n #Save best model, AIC and parameters\n if aic < best_aic:\n best_model = model\n best_aic = aic\n best_param = param\n results.append([param, model.aic]) \n result_table = pd.DataFrame(results)\n result_table.columns = ['parameters', 'aic']\n #Sort in ascending order, lower AIC is better\n result_table = result_table.sort_values(by='aic', ascending=True).reset_index(drop=True) \n return result_table\n \n #Set initial values and some bounds\n ps = range(0, 3)\n d = range(0,1)\n qs = range(0, 3)\n Ps = range(0, 3)\n D = range(0,1)\n Qs = range(0, 3)\n s = [3,5,7]\n #Create a list with all possible combinations of parameters\n parameters = product(ps,d, qs, Ps,D, Qs,s)\n parameters_list = list(parameters)\n len(parameters_list)\n if loga==True:\n series=series.apply(np.log1p)\n series_diff=series\n result_table = optimize_SARIMA(series_diff,parameters_list)\n \n #Set parameters that give the lowest AIC (Akaike Information Criteria) \n p, d, q, P, D, Q,s = result_table.parameters[0] \n best_model = sm.tsa.statespace.SARIMAX(series_diff, order=(p, d, q),seasonal_order=(P, D, Q, s)).fit(disp=-1)\n if show_mdl_detail==True:\n print(best_model.summary())\n if plot_figures_flag==True:\n best_model.plot_diagnostics(figsize=(15, 12)) \n #Forecast on n_steps forward\n forecast = best_model.forecast(nforecast)\n if loga==True:\n forecast=np.exp(forecast)-1 #log1p\n forecast=pd.Series(forecast.values,name='sarimax_model')\n lenn=series[:].shape[0]\n new_index=pd.Index(list(range(series[-1:].index.values[0]+1,series[-1:].index.values[0]+1+nforecast)))\n forecast.index=new_index\n return forecast","def stop_on_low_ais_ess(trial_id, result):\n return result[\"ais_effective_sample_size\"] < 0.1","def aic_c(self):\n if hasattr(self, '_aic_c'):\n return self._aic_c\n else:\n k = len(self.params)\n n = self.data['n'].sum()\n self._aic_c = self.aic() + (2*k**2 + 2*k)/(n - k - 1)\n return self._aic_c","def optimize_SARIMA(data_column,parameters_list): \n results = []\n best_aic = float('inf') \n \n #for param in tqdm(parameters_list):\n for param in parameters_list:\n try: model = sm.tsa.statespace.SARIMAX(data_column, order=(param[0], param[1],param[2]),\n seasonal_order=(param[3], param[4], param[5], param[5])).fit(disp=-1)\n # try: model = sm.tsa.statespace.SARIMAX(data_column, order=(param[0], d, param[1])).fit(disp=-1)\n except:\n #print(\"model not built with params\".format(param))\n pass\n continue \n aic = model.aic \n #Save best model, AIC and parameters\n if aic < best_aic:\n best_model = model\n best_aic = aic\n best_param = param\n results.append([param, model.aic]) \n result_table = pd.DataFrame(results)\n result_table.columns = ['parameters', 'aic']\n #Sort in ascending order, lower AIC is better\n result_table = result_table.sort_values(by='aic', ascending=True).reset_index(drop=True) \n return result_table","def test_am_threshold(Simulator, plt, seed, rng):\n d = 64\n vocab = Vocabulary(d, pointer_gen=rng)\n vocab.populate('A; B; C; D')\n\n d2 = int(d / 2)\n vocab2 = Vocabulary(d2, pointer_gen=rng)\n vocab2.populate('A; B; C; D')\n\n def input_func(t):\n return '0.49 * A' if t < 0.1 else '0.8 * B'\n\n with spa.Network('model', seed=seed) as m:\n m.am = ThresholdingAssocMem(\n threshold=0.5, input_vocab=vocab, output_vocab=vocab2,\n function=filtered_step_fn, mapping='by-key')\n m.stimulus = spa.Transcode(input_func, output_vocab=vocab)\n m.stimulus >> m.am\n\n in_p = nengo.Probe(m.am.input)\n out_p = nengo.Probe(m.am.output, synapse=0.03)\n\n with Simulator(m) as sim:\n sim.run(0.3)\n t = sim.trange()\n below_th = t < 0.1\n above_th = t > 0.25\n\n plt.subplot(2, 1, 1)\n plt.plot(t, similarity(sim.data[in_p], vocab))\n plt.ylabel(\"Input\")\n plt.subplot(2, 1, 2)\n plt.plot(t, similarity(sim.data[out_p], vocab2))\n plt.plot(t[above_th], np.ones(t.shape)[above_th] * 0.9, c='g', lw=2)\n plt.ylabel(\"Output\")\n\n assert np.mean(sim.data[out_p][below_th]) < 0.01\n assert_sp_close(t, sim.data[out_p], vocab2['B'], skip=0.25, duration=0.05)","def ransac(data, hypothesis, metric, sample_size, num_iter, inlier_thresh):\n N,d = data.shape\n best_frac, best_hypothesis, best_mask = 0, None, None\n for i in range(num_iter):\n js = np.random.choice(N,size=sample_size,replace=False)\n hypothesis_elements = data[js,:]\n H = hypothesis(hypothesis_elements)\n badness = np.array([metric(row,H) for row in data])\n inlier_mask = (badness<inlier_thresh)\n inlier_frac = inlier_mask.mean()\n if inlier_frac>best_frac:\n best_frac, best_hypothesis, best_mask = inlier_frac,H,inlier_mask\n # print(H)\n # print(inlier_mask)\n return best_hypothesis, best_mask","def akaike_info_criterion(log_likelihood, n_params, n_samples):\n # Correction in case of small number of observations\n if n_samples / float(n_params) >= 40.0:\n aic = 2.0 * (n_params - log_likelihood)\n else:\n aic = 2.0 * (n_params - log_likelihood) + 2.0 * n_params * (n_params + 1.0) / (\n n_samples - n_params - 1.0\n )\n return aic","def test_conditional():\n # verify that conditioning increases the likelihood of getting a sample with the specified\n # categorical value","def binarize(adata, copy=False):\n threshold, upper, lower = 1.0, 1.0, 0.0\n admatrix = adata.X\n admatrix = np.where(admatrix>threshold, upper, lower)\n if copy:\n adata2 = adata.copy()\n adata2.X = admatrix\n return(adata2)\n else:\n adata.X = admatrix","def ks_test(timeseries):\r\n\r\n hour_ago = time() - 3600\r\n ten_minutes_ago = time() - 600\r\n reference = scipy.array([x[1] for x in timeseries if x[0] >= hour_ago and x[0] < ten_minutes_ago])\r\n probe = scipy.array([x[1] for x in timeseries if x[0] >= ten_minutes_ago])\r\n\r\n if reference.size < 20 or probe.size < 20:\r\n return False\r\n\r\n ks_d,ks_p_value = scipy.stats.ks_2samp(reference, probe)\r\n\r\n if ks_p_value < 0.05 and ks_d > 0.5:\r\n adf = sm.tsa.stattools.adfuller(reference, 10)\r\n if adf[1] < 0.05:\r\n return True\r\n\r\n return False","def early_warnings_sensitivity_analysis(series,\n indicators=['var','ac'],\n winsizerange = [0.10, 0.8],\n incrwinsize = 0.10,\n smooth = \"Gaussian\",\n bandwidthrange = [0.05, 1.],\n spanrange = [0.05, 1.1],\n incrbandwidth = 0.2,\n incrspanrange = 0.1):\n\n results_kendal_tau = []\n for winsize in np.arange(winsizerange[0],winsizerange[1]+0.01,incrwinsize):\n\n winsize = round(winsize,3)\n if smooth == \"Gaussian\":\n\n for bw in np.arange(bandwidthrange[0], bandwidthrange[1]+0.01, incrbandwidth):\n\n bw = round(bw, 3)\n ews_dic_veg = ewstools.core.ews_compute(series.dropna(),\n roll_window=winsize,\n smooth=smooth,\n lag_times=[1, 2],\n ews=indicators,\n band_width=bw)\n\n result = ews_dic_veg['Kendall tau']\n result['smooth'] = bw\n result['winsize'] = winsize\n\n results_kendal_tau.append(result)\n\n\n elif smooth ==\"Lowess\":\n\n for span in np.arange(spanrange[0], spanrange[1]+0.01, incrspanrange):\n\n span = round(span,2)\n ews_dic_veg = ewstools.core.ews_compute(series.dropna(),\n roll_window=winsize,\n smooth=smooth,\n lag_times=[1, 2],\n ews=indicators,\n span=span)\n\n result = ews_dic_veg['Kendall tau']\n result['smooth'] = bw\n result['winsize'] = winsize\n\n results_kendal_tau.append(result)\n\n else:\n\n ews_dic_veg = ewstools.core.ews_compute(series.dropna(),\n roll_window=winsize,\n smooth='None',\n lag_times=[1, 2],\n ews=indicators)\n\n result = ews_dic_veg['Kendall tau']\n result['smooth'] = 0\n result['winsize'] = winsize\n\n results_kendal_tau.append(result)\n\n sensitivity_df = pd.concat(results_kendal_tau)\n\n return sensitivity_df","def early_warnings_null_hypothesis(series,\n indicators=['var', 'ac'],\n roll_window=0.4,\n smooth='Lowess',\n span=0.1,\n band_width=0.2,\n lag_times=[1],\n n_simulations=1000):\n\n ews_dic = ewstools.core.ews_compute(series,\n roll_window=roll_window,\n smooth=smooth,\n span=span,\n band_width=band_width,\n ews=indicators,\n lag_times=lag_times)\n\n from statsmodels.tsa.arima_model import ARIMA\n from statsmodels.tsa.arima_process import ArmaProcess\n\n # Use the short_series EWS if smooth='None'. Otherwise use reiduals.\n eval_series = ews_dic['EWS metrics']['Residuals']\n\n # Fit ARMA model based on AIC\n aic_max = 10000\n\n for i in range(0, 2):\n for j in range(0, 2):\n\n model = ARIMA(eval_series, order=(i, j, 0))\n model_fit = model.fit()\n aic = model_fit.aic\n\n print(\"AR\", \"MA\", \"AIC\")\n print(i, j, aic)\n\n if aic < aic_max:\n aic_max = aic\n result = model_fit\n\n def compute_indicators(series):\n \"\"\"\n Rolling window indicators computation based on the ewstools.core.ews_compute function from\n ewstools\n \"\"\"\n\n df_ews = pd.DataFrame()\n # Compute the rolling window size (integer value)\n rw_size = int(np.floor(roll_window * series.shape[0]))\n\n # ------------ Compute temporal EWS---------------#\n\n # Compute standard deviation as a Series and add to the DataFrame\n if 'sd' in indicators:\n roll_sd = series.rolling(window=rw_size).std()\n df_ews['Standard deviation'] = roll_sd\n\n # Compute variance as a Series and add to the DataFrame\n if 'var' in indicators:\n roll_var = series.rolling(window=rw_size).var()\n df_ews['Variance'] = roll_var\n\n # Compute autocorrelation for each lag in lag_times and add to the DataFrame\n if 'ac' in indicators:\n for i in range(len(lag_times)):\n roll_ac = series.rolling(window=rw_size).apply(\n func=lambda x: pd.Series(x).autocorr(lag=lag_times[i]),\n raw=True)\n df_ews['Lag-' + str(lag_times[i]) + ' AC'] = roll_ac\n\n # Compute Coefficient of Variation (C.V) and add to the DataFrame\n if 'cv' in indicators:\n # mean of raw_series\n roll_mean = series.rolling(window=rw_size).mean()\n # standard deviation of residuals\n roll_std = series.rolling(window=rw_size).std()\n # coefficient of variation\n roll_cv = roll_std.divide(roll_mean)\n df_ews['Coefficient of variation'] = roll_cv\n\n # Compute skewness and add to the DataFrame\n if 'skew' in indicators:\n roll_skew = series.rolling(window=rw_size).skew()\n df_ews['Skewness'] = roll_skew\n\n # Compute Kurtosis and add to DataFrame\n if 'kurt' in indicators:\n roll_kurt = series.rolling(window=rw_size).kurt()\n df_ews['Kurtosis'] = roll_kurt\n\n # ------------Compute Kendall tau coefficients----------------#\n\n ''' In this section we compute the kendall correlation coefficients for each EWS\n with respect to time. Values close to one indicate high correlation (i.e. EWS\n increasing with time), values close to zero indicate no significant correlation,\n and values close to negative one indicate high negative correlation (i.e. EWS\n decreasing with time).'''\n\n # Put time values as their own series for correlation computation\n time_vals = pd.Series(df_ews.index, index=df_ews.index)\n\n # List of EWS that can be used for Kendall tau computation\n ktau_metrics = ['Variance', 'Standard deviation', 'Skewness', 'Kurtosis', 'Coefficient of variation', 'Smax',\n 'Smax/Var', 'Smax/Mean'] + ['Lag-' + str(i) + ' AC' for i in lag_times]\n # Find intersection with this list and EWS computed\n ews_list = df_ews.columns.values.tolist()\n ktau_metrics = list(set(ews_list) & set(ktau_metrics))\n\n # Find Kendall tau for each EWS and store in a DataFrame\n dic_ktau = {x: df_ews[x].corr(time_vals, method='kendall') for x in ktau_metrics} # temporary dictionary\n df_ktau = pd.DataFrame(dic_ktau, index=[0]) # DataFrame (easier for concatenation purposes)\n\n # -------------Organise final output and return--------------#\n\n # Ouptut a dictionary containing EWS DataFrame, power spectra DataFrame, and Kendall tau values\n output_dic = {'EWS metrics': df_ews, 'Kendall tau': df_ktau}\n\n return output_dic\n\n process = ArmaProcess.from_estimation(result)\n\n # run simulations on best fitted ARIMA process and get values\n kendall_tau = []\n for i in range(n_simulations):\n ts = process.generate_sample(len(eval_series))\n\n kendall_tau.append(compute_indicators(pd.Series(ts))['Kendall tau'])\n\n surrogates_kendall_tau_df = pd.concat(kendall_tau)\n surrogates_kendall_tau_df['true_data'] = False\n\n # get results for true data\n data_kendall_tau_df = compute_indicators(eval_series)['Kendall tau']\n data_kendall_tau_df['true_data'] = True\n\n # return dataframe with both surrogates and true data\n kendall_tau_df = pd.concat([data_kendall_tau_df,surrogates_kendall_tau_df])\n\n return kendall_tau_df","def aic(self, tmin=None, tmax=None):\n noise = self.ml.noise(tmin=tmin, tmax=tmax)\n nparam = len(self.ml.parameters[self.ml.parameters.vary == True])\n aic = -2.0 * np.log(sum(noise ** 2.0)) + 2.0 * nparam\n return aic","def test02a(self):\n a = np.arange(1, 11)\n b = bcolz.carray(a)\n wt = [v for v in a if v <= 5]\n cwt = [v for v in b.where(a <= 5)]\n # print \"numpy ->\", [v for v in a if v<=5]\n # print \"where ->\", [v for v in b.where(a<=5)]\n self.assertTrue(wt == cwt, \"where() does not work correctly\")","def test10(self):\n a = np.arange(1, 11)\n b = bcolz.carray(a)\n bi = b.where(a <= 5)\n ai = (v for v in a if v <= 5)\n self.assertEqual([i for i in ai], [i for i in bi])\n self.assertEqual([i for i in ai], [i for i in bi])","def A_weight(signal, fs):\n\n b, a = A_weighting(fs)\n return lfilter(b, a, signal)","def info_criteria(indep,\n dep,\n models,\n add_aicc=False):\n num_data = len(indep)\n\n bic_calc = BicCalculator(bic_type=BIC_TYPE.STANDARD)\n bic_calc_bkpt = BicCalculator(bic_type=BIC_TYPE.HYBRID)\n\n #bic_calc_bkpt = bic_calc\n #####bic_calc_bkpt = BicCalculator(bic_type = BIC_TYPE.HOS)\n\n bics = []\n aics = []\n aiccs = []\n for model in models:\n\n if model in [Model.ONE_BKPT, Model.TWO_BKPT]:\n bic_calc_to_use = bic_calc_bkpt\n else:\n bic_calc_to_use = bic_calc\n\n estimator = model.estimator(num_end_to_skip=NUM_END_TO_SKIP,\n num_between_to_skip=NUM_BETWEEN_TO_SKIP)\n estimator.fit(indep, dep)\n\n loglikelihood = estimator.loglikelihood\n num_params = estimator.num_params\n\n bic = bic_calc_to_use.bic(num_params=num_params,\n loglikelihood=loglikelihood,\n num_data=num_data)\n aic = stats_util.aic(num_params=num_params,\n loglikelihood=loglikelihood)\n aicc = stats_util.aicc(num_params=num_params,\n loglikelihood=loglikelihood,\n num_data=num_data)\n\n bics.append(bic)\n aics.append(aic)\n aiccs.append(aicc)\n ic_df = pd.DataFrame({\"BIC\": bics, \"AIC\": aics}, index=models)\n if add_aicc:\n ic_df[\"AICC\"] = aiccs\n\n wts_df = ic_df.apply(stats_util.bma_weights, axis=0)\n wts_cols = [x + \" Model Wt\" for x in wts_df.columns]\n wts_df.columns = wts_cols\n both = pd.concat([ic_df, wts_df], join=\"outer\", axis=1)\n return both","def test_stationarity(dataframe, time_window):\n timeseries = dataframe.iloc[:, 0]\n # Determing rolling statistics\n rolmean = timeseries.rolling(time_window).mean()\n rolstd = timeseries.rolling(time_window).std()\n\n # Plot rolling statistics:\n plt.figure(figsize=(18, 9))\n plt.plot(timeseries, color=\"blue\", label=\"Original\")\n plt.plot(rolmean, color=\"red\", label=\"Rolling Mean\")\n plt.plot(rolstd, color=\"black\", label=\"Rolling Std\")\n plt.legend(loc=\"best\")\n plt.title(\"Rolling Mean & Standard Deviation\")\n plt.show(block=False)\n\n # Perform Dickey-Fuller test:\n print(\"Results of Dickey-Fuller Test:\")\n dftest = adfuller(timeseries, autolag=\"AIC\")\n dfoutput = pd.Series(\n dftest[0:4],\n index=[\n \"Test Statistic\",\n \"p-value\",\n \"#Lags Used\",\n \"Number of Observations Used\",\n ],\n )\n for key, value in dftest[4].items():\n dfoutput[\"Critical Value (%s)\" % key] = value\n print(dfoutput)","def activation_channels_apoz(activation):\n if activation.ndim == 4:\n featuremap_apoz_mat = (np.abs(activation) > 0).sum(axis=(2, 3)) / (activation.shape[2] * activation.shape[3])\n elif activation.ndim == 2:\n featuremap_apoz_mat = (np.abs(activation) > 0).sum(axis=1) / activation.shape[1] # batch x 1\n else:\n raise ValueError(\"activation_channels_apoz: Unsupported shape: \".format(activation.shape))\n return 100 - featuremap_apoz_mat.mean(axis=0)*100","def MyFilter(data, window_width=10, beta=2.0, draw_graph=False):\n\n #read data and change the format\n if 'time' in data.columns:\n date_list = []\n for i in data.index:\n date_parse = parse(str(data.ix[i].time))\n date_list.append(date_parse)\n data['date'] = date_list\n data_use = data\n data_use.index = data_use['date'].tolist()\n data_use = data_use.drop(['date','time'], axis=1)\n data_use.index.name = 'time'\n else:\n data_use = data\n #design filter, use the kaiser window here\n window = signal.kaiser(window_width, beta=beta)\n data_use['close_filtered'] = signal.convolve(data_use['close'], window, mode='same') / sum(window)\n data_use['high_frequency'] = data_use['close'] - data_use['close_filtered']\n\n #delete the distortion datas after filtered\n if window_width % 2 == 0:\n data_changed = data_use[window_width/2: -(window_width/2 - 1)]\n else:\n data_changed = data_use[(window_width-1)/2: -(window_width-1)/2]\n\n #draw graph\n if (draw_graph == True) :\n fig = plt.figure()\n ax1 = plt.subplot2grid((3,1), (0,0), rowspan=2)\n data_changed.loc[:,'close'].plot(style='r', label='original')\n data_changed.loc[:,'close_filtered'].plot(style='k', label='filtered')\n plt.title('Kaiser window_width = %d , const = %d' % (window_width, beta))\n plt.legend(loc='best')\n\n ax2 = plt.subplot2grid((3,1), (2,0))\n data_changed.loc[:,'high_frequency'].plot(label='high_frequency')\n ax2.set_ylim([-150, 150])\n plt.title('High Frequency')\n plt.legend(loc='best')\n plt.show()\n # print data_use\n # print data_changed\n data_out = data_changed['close_filtered']\n return np.array(data_out.tolist())","def check_stationarity(time_series, window, figsize=(10,6)): \n # Calculating rolling mean and standard deviation\n rolling_mn = time_series.rolling(window).mean()\n rolling_std = time_series.rolling(window).std()\n \n plt.figure(figsize=figsize)\n plt.plot(time_series, color = 'blue',label = 'Original TS')\n plt.plot(rolling_mn, color = 'red', label = 'Rolling Mean')\n plt.plot(rolling_std, color = 'black', label = 'Rolling St.Dev.')\n plt.legend(loc = 'best')\n plt.grid(True, color = 'lightgrey')\n plt.title('Rolling Mean & Standard Deviation of the Trade Value of Vaccines', fontsize = 10)\n \n # Dickey-Fuller test:\n print('Results of Dickey-Fuller Test:')\n fuller_test = adfuller(time_series, autolag = 'AIC')\n results_ts = pd.Series(fuller_test[0:4], index = ['Test Statistic','P-value','#Lags Used','Number of Observations Used'])\n for key,value in fuller_test[4].items():\n results_ts['Critical Value (%s)'%key] = value\n print(results_ts)","def test08(self):\n a = np.arange(1, 11)\n b = bcolz.carray(a)\n ul = [v for v in a if v <= 5]\n u = b.where(a <= 5)\n wl = [v for v in a if v <= 6]\n w = b.where(a <= 6)\n self.assertEqual(ul, list(u))\n self.assertEqual(wl, list(w))","def test_sag_adaptive():\n np.random.seed(0)\n X = sparse.rand(100, 10, density=.5, random_state=0).tocsr()\n y = np.random.randint(0, high=2, size=100)\n for alpha in np.logspace(-3, 1, 5):\n clf_adaptive = SAGClassifier(\n eta='line-search', random_state=0, alpha=alpha)\n clf_adaptive.fit(X, y)\n clf = SAGClassifier(\n eta='auto', random_state=0, alpha=alpha)\n clf.fit(X, y)\n assert_almost_equal(clf_adaptive.score(X, y), clf.score(X, y), 1)\n\n clf_adaptive = SAGAClassifier(\n eta='line-search', loss='log', random_state=0, alpha=alpha, max_iter=20)\n clf_adaptive.fit(X, y)\n assert np.isnan(clf_adaptive.coef_.sum()) == False\n clf = SAGAClassifier(\n eta='auto', loss='log', random_state=0, alpha=alpha, max_iter=20)\n clf.fit(X, y)\n assert_almost_equal(clf_adaptive.score(X, y), clf.score(X, y), 1)","def quality_data(self, s):\n known_symbols = np.mod(range(176),48)>=32\n print('quality_data',np.sum(np.real(s[known_symbols])<0))\n success = np.sum(np.real(s[known_symbols])<0) < 20\n return success,0 ## no doppler estimate for data frames","def test01(self):\n a = np.arange(1, 11)\n b = bcolz.carray(a)\n wt = [v for v in a if v < 0]\n cwt = [v for v in b.where(a < 0)]\n # print \"numpy ->\", [v for v in a if v<0]\n # print \"where ->\", [v for v in b.where(a<0)]\n self.assertTrue(wt == cwt, \"where() does not work correctly\")","def cooling_system_is_ac(bpr):\n\n if bpr.hvac['type_cs'] in {'T2', 'T3'}: # mini-split ac and central ac\n return True\n elif bpr.hvac['type_cs'] in {'T0', 'T1'}:\n return False\n else:\n print('Error: Unknown cooling system')\n return False","def spec_to_peaks(data, value, fp = iterate_structure(generate_binary_structure(rank = 2, connectivity=2), 10)):\n\n max_arr = maximum_filter(data, footprint = fp)\n return (data == max_arr) & (data > value)","def A_weighting(fs):\n b, a = A_weighting_analog()\n\n # Use the bilinear transformation to get the digital filter.\n return bilinear(b, a, fs)","def optaa_benthic(ds, cal_file):\n # check to see if there is more than one deployment in the data set\n if len(np.unique(ds['deployment'].values)) > 1:\n raise ValueError('More than one deployment in the data set. Please structure processing request to process '\n 'one deployment at a time.')\n\n # drop some of the variables:\n # internal_timestamp == time, redundant so can remove\n # pressure_counts == none of the OOI OPTAAs have a pressure sensor\n # serial_number == available in the global attributes\n # meter_type == always the same, not needed\n # packet_type == always the same, not needed\n # record_length == always the same, not needed\n # checksum == not needed, used in data parsing\n ds = ds.drop(['internal_timestamp', 'pressure_counts', 'serial_number', 'meter_type', 'packet_type',\n 'record_length', 'checksum'])\n\n # check for data from a co-located CTD, if not present create the variables using NaN's as the fill value\n if 'sea_water_temperature' not in ds.variables:\n ds['sea_water_temperature'] = ('time', ds['deployment'].data * np.nan)\n ds['sea_water_practical_salinity'] = ('time', ds['deployment'].data * np.nan)\n\n # pull out the number of wavelengths and serial number and then drop the variables (part of the metadata)\n num_wavelengths = ds.num_wavelengths.values[0].astype(int)\n serial_number = int(re.sub('[^0-9]', '', ds.attrs['SerialNumber']))\n ds = ds.drop('num_wavelengths')\n\n # load the calibration coefficients\n uid = ds.attrs['AssetUniqueID']\n start_time = ds['time'][0].values.astype(float) / 10 ** 9\n cal = load_cal_coefficients(cal_file, uid, start_time)\n\n # check the calibration coefficients against the deployment data\n if cal.coeffs['serial_number'] != serial_number:\n raise Exception('Serial Number mismatch between ac-s data and the device file.')\n if cal.coeffs['num_wavelengths'] != num_wavelengths:\n raise Exception('Number of wavelengths mismatch between ac-s data and the device file.')\n\n # remove the units from the variable names\n rename = {\n 'a_signal_dark_counts': 'a_signal_dark',\n 'a_reference_dark_counts': 'a_reference_dark',\n 'a_signal_counts': 'a_signal',\n 'a_reference_counts': 'a_reference',\n 'c_signal_dark_counts': 'c_signal_dark',\n 'c_reference_dark_counts': 'c_reference_dark',\n 'c_signal_counts': 'c_signal',\n 'c_reference_counts': 'c_reference',\n 'wavelength': 'wavelength_number'\n }\n ds = ds.rename(rename)\n\n # Delete the first 45 seconds of the data record per recommendation from the vendor. Note, originally the vendor\n # recommended deleting the first 45 seconds, then 60 seconds and then 120 seconds. They never provided a data\n # based reason for the change in recommendation. Within OOI, instruments were programmed to run for 60 seconds,\n # then 120 seconds and then 240 seconds ... and it is all mixed up across the various data sets. So, we are\n # going to use the 45-second recommendation and apply it to all data sets. If the vendor ever provides an analysis\n # justifying the change in recommendation, we can revisit this.\n ds.elapsed_run_time.values = ds.elapsed_run_time.where(ds.elapsed_run_time / 1000 > 45)\n ds = ds.dropna(dim='time', subset=['elapsed_run_time'])\n\n # convert internal and external temperature sensors from raw counts to degrees Celsius\n ds['internal_temp'] = opt_internal_temp(ds['internal_temp_raw'])\n ds['external_temp'] = opt_external_temp(ds['external_temp_raw'])\n\n # calculate the median of the remaining data per burst measurement (configured to run hourly for 3 minutes)\n # calculate the median of the remaining data per burst measurement\n print('Calculating burst averages ...')\n start_time = time.time()\n burst = ds.resample(time='3600s', base=1800, loffset='1800s', skipna=True).reduce(np.median, dim='time',\n keep_attrs=True)\n burst = burst.where(~np.isnan(burst.deployment), drop=True)\n stop_time = time.time()\n elapsed_time = stop_time - start_time\n print('... burst averaging complete. Elapsed time: %f seconds' % elapsed_time)\n\n # re-process the raw data in order to create the intermediate variables, correcting for the holographic\n # grating, applying the temperature and salinity corrections and applying a baseline scatter correction\n # to the absorption data. All intermediate processing outputs are added to the data set.\n burst = apply_dev(burst, cal.coeffs)\n burst = apply_tscorr(burst, cal.coeffs, burst.sea_water_temperature, burst.sea_water_practical_salinity)\n burst = apply_scatcorr(burst, cal.coeffs)\n\n # add the jump offsets as NaN's if the grating index correction was not used\n if 'a_jump_offsets' not in ds.variables:\n ds['a_jump_offsets'] = ('time', ds['deployment'].data * np.nan)\n ds['c_jump_offsets'] = ('time', ds['deployment'].data * np.nan)\n\n # estimate chlorophyll and POC and calculate select absorption ratios\n burst = estimate_chl_poc(burst, cal.coeffs)\n burst = calculate_ratios(burst)\n\n # create a xarray dataset of the 2D variables, padding the number of wavelengths to a consistent\n # length of 100 using fill values.\n wavelength_number = np.arange(100).astype(int) # used as a dimensional variable\n pad = 100 - num_wavelengths\n fill_nan = np.tile(np.ones(pad) * np.nan, (len(burst.time), 1))\n fill_int = np.tile(np.ones(pad) * FILL_INT, (len(burst.time), 1))\n\n wavelength_a = np.concatenate([burst.wavelength_a.values, fill_nan], axis=1)\n wavelength_c = np.concatenate([burst.wavelength_c.values, fill_nan], axis=1)\n\n ac = xr.Dataset({\n 'wavelength_a': (['time', 'wavelength_number'], wavelength_a),\n 'a_signal': (['time', 'wavelength_number'], np.concatenate([burst.a_signal, fill_int], axis=1).astype(int)),\n 'a_reference': (['time', 'wavelength_number'], np.concatenate([burst.a_reference, fill_int],\n axis=1).astype(int)),\n 'optical_absorption': (['time', 'wavelength_number'], np.concatenate([burst.optical_absorption, fill_nan],\n axis=1)),\n 'apg': (['time', 'wavelength_number'], np.concatenate([burst.apg, fill_nan], axis=1)),\n 'apg_ts': (['time', 'wavelength_number'], np.concatenate([burst.apg_ts, fill_nan], axis=1)),\n 'apg_ts_s': (['time', 'wavelength_number'], np.concatenate([burst.apg_ts_s, fill_nan], axis=1)),\n 'wavelength_c': (['time', 'wavelength_number'], wavelength_c),\n 'c_signal': (['time', 'wavelength_number'], np.concatenate([burst.c_signal, fill_int], axis=1).astype(int)),\n 'c_reference': (['time', 'wavelength_number'], np.concatenate([burst.c_reference, fill_int],\n axis=1).astype(int)),\n 'beam_attenuation': (['time', 'wavelength_number'], np.concatenate([burst.beam_attenuation, fill_nan], axis=1)),\n 'cpg': (['time', 'wavelength_number'], np.concatenate([burst.cpg, fill_nan], axis=1)),\n 'cpg_ts': (['time', 'wavelength_number'], np.concatenate([burst.cpg_ts, fill_nan], axis=1)),\n }, coords={'time': (['time'], burst.time.values), 'wavelength_number': wavelength_number})\n\n # drop the original 2D variables from the burst data set\n drop = burst.drop(['wavelength_number', 'wavelength_a', 'a_signal', 'a_reference',\n 'optical_absorption', 'apg', 'apg_ts', 'apg_ts_s',\n 'wavelength_c', 'c_signal', 'c_reference',\n 'beam_attenuation', 'cpg', 'cpg_ts'])\n\n # reset the data type for the 'a' and 'c' signal and reference dark values, and the other raw parameters\n int_arrays = ['a_signal_dark', 'a_reference_dark', 'c_signal_dark', 'c_reference_dark',\n 'internal_temp_raw', 'external_temp_raw', 'deployment']\n for k in drop.variables:\n if k in int_arrays:\n drop[k] = drop[k].astype(int)\n\n # recombine the two datasets\n optaa = xr.merge([drop, ac])\n\n # reset the attributes, which the merging drops\n optaa.attrs = burst.attrs\n for v in optaa.variables:\n optaa[v].attrs = burst[v].attrs\n\n # reset some attributes\n for key, value in ATTRS.items():\n for atk, atv in value.items():\n if key in optaa.variables:\n optaa[key].attrs[atk] = atv\n\n # add the original variable name as an attribute, if renamed\n for key, value in rename.items():\n optaa[value].attrs['ooinet_variable_name'] = key\n\n # add the actual number of wavelengths to the dataset as an attribute\n optaa['wavelength_number'].attrs['actual_wavelengths'] = num_wavelengths\n\n # if the filter index was used to adjust the spectral jumps, add that attribute to the data set\n if cal.coeffs['grate_index']:\n optaa['a_jump_offsets'].attrs['grate_index'] = cal.coeffs['grate_index']\n optaa['c_jump_offsets'].attrs['grate_index'] = cal.coeffs['grate_index']\n\n return optaa","def calculate_AIC(self): \n hmm_ll_calculator = LikelihoodInfEngineHMM(\n dbn=self.model.dbn, hidden_node_index=0, check_dbn=False)\n ll_full = hmm_ll_calculator.calc_ll(self.seq_list, self.mismask_list)\n return 2 * ll_full - 2 * self._get_parameter_count()","def bearish_engulfing(self):\n self.data['bearish_engulfing'] = ((self.data['Close'].shift(1) > self.data['Open'].shift(1)) &\\\n (self.data['Open'] > self.data['Close']) & \\\n (self.data['Open'] >= self.data['Close'].shift(1)) & \\\n (self.data['Open'].shift(1) >= self.data['Close']) & \\\n ((self.data['Open']-self.data['Close']) > (self.data['Close'].shift(1)-self.data['Open'].shift(1))))","def first_peak_auc_rahman(datas):\n fig, ax = plt.subplots(figsize=(8, 8))\n columns = ('A_1', 'tau_1', 'gamma_1', 'A_2', 'tau_2', 'gamma_2')\n index = [i[\"name\"] for i in datas]\n df = pd.DataFrame(index=index, columns=columns)\n for i in range(1, 2):\n data = datas[i-1]\n r = data['r'] * 10 # convert from angstroms to nm\n t = data['t']\n g = data['g']\n\n I = np.empty_like(t)\n I[:] = np.nan\n\n # Get area under the curve\n #for i in range(0, t.shape[0], 2):\n for i in range(0, t.shape[0]):\n I[i] = get_auc(data, i)\n ls = '--'\n\n ax.semilogy(t, I, marker='.', linestyle=ls, label=data['name'])\n\n # Get finite values\n I_idx = np.where(~np.isnan(I))\n I = I[np.isfinite(I)]\n t = t[I_idx]\n \n print(t.shape)\n print(I.shape)\n \n\n #upper_limit = np.where(t < 0.28)[0][-1]\n if data[\"name\"] != \"IXS (11/18)\":\n print(data[\"name\"])\n upper_limit = np.where(t < 0.95)[0][-1]\n t = t[:upper_limit]\n I = I[:upper_limit]\n \n\n # Calling `compute_fit` to get the compressed exponential function fit\n try:\n fit, popt = compute_fit(t, I)\n except:\n print(f\"Fit for {data['name']} has failed\")\n continue\n if (1 / popt[1]) < (1 / popt[4]):\n df.loc[data[\"name\"]][\"A_1\"] = popt[0]\n df.loc[data[\"name\"]][\"tau_1\"] = 1 / popt[1]\n df.loc[data[\"name\"]][\"gamma_1\"] = popt[2]\n df.loc[data[\"name\"]][\"A_2\"] = popt[3]\n df.loc[data[\"name\"]][\"tau_2\"] = 1 / popt[4]\n df.loc[data[\"name\"]][\"gamma_2\"] = popt[5]\n print(data[\"name\"])\n print(f\"tau_1 is: {1/popt[1]}\")\n print(f\"A_1 is: {popt[0]}\")\n print(f\"gamma_1 is: {popt[2]}\")\n print(f\"tau_2 is: {1/popt[4]}\")\n print(f\"A_2 is: {popt[3]}\")\n print(f\"gamma_2 is: {popt[5]}\")\n else:\n df.loc[data[\"name\"]][\"A_1\"] = popt[3]\n df.loc[data[\"name\"]][\"tau_1\"] = 1 / popt[4]\n df.loc[data[\"name\"]][\"gamma_1\"] = popt[5]\n df.loc[data[\"name\"]][\"A_2\"] = popt[0]\n df.loc[data[\"name\"]][\"tau_2\"] = 1 / popt[1]\n df.loc[data[\"name\"]][\"gamma_2\"] = popt[2]\n print(data[\"name\"])\n print(f\"tau_1 is: {1/popt[4]}\")\n print(f\"A_1 is: {popt[3]}\")\n print(f\"gamma_1 is: {popt[5]}\")\n print(f\"tau_2 is: {1/popt[1]}\")\n print(f\"A_2 is: {popt[0]}\")\n print(f\"gamma_2 is: {popt[2]}\")\n ax.semilogy(t, fit, linestyle=ls, color='k', label=f\"{data['name']}_fit\")\n ax.set_title(data['name'], fontsize=12)\n\n # Plot the compressed exponential functions given from 2018 Phys. Rev.\n #A_t = 0.42*(np.exp(-(t/0.12)**1.57)) + 0.026*(np.exp(-(t/0.4)**4.1))\n #ax.plot(t, A_t, label=\"IXS fit (2018 Phys. Rev.) at 310 K\")\n #A_t = 0.45*(np.exp(-(t/0.12)**1.57)) + 0.018*(np.exp(-(t/0.43)**12.8))\n #ax.plot(t, A_t, label=\"IXS fit (2018 Phys. Rev.) at 295 K K\")\n ax.xaxis.set_major_locator(MultipleLocator(0.25))\n ax.set_xlim((0.00, 1.0))\n ax.set_ylim((5e-3, 1.0))\n ax.set_ylabel(r'$A(t)$')","def filterHighs( adata, bandlimit = 5000) :\n\n # TODO: compute Fourier representation of data\n\n fig = plt.figure()\n fig.add_subplot(2, 1, 1)\n plt.plot( np.real(fdata))\n plt.xlim( [0,adata.size])\n\n # TODO: filter data\n\n fig.add_subplot(2, 1, 2)\n plt.plot( np.real(fdata))\n plt.xlim( [0,adata.size])\n plt.show()\n\n # TODO: restore time domain representation of data\n\n return adata_filtered","def test_meta_fit_performance(meta_res, signal_masks, simulatedata_cbma):\n _, (ground_truth_foci, _) = simulatedata_cbma\n mask = meta_res.masker.mask_img\n ground_truth_foci_ijks = [tuple(mm2vox(focus, mask.affine)) for focus in ground_truth_foci]\n sig_idx, nonsig_idx = [\n meta_res.masker.transform(img).astype(bool).squeeze() for img in signal_masks\n ]\n\n # all estimators generate p-values\n p_array = meta_res.get_map(\"p\", return_type=\"array\")\n\n # poor performer(s)\n if (\n isinstance(meta_res.estimator, ale.ALE)\n and isinstance(meta_res.estimator.kernel_transformer, kernel.KDAKernel)\n and meta_res.estimator.get_params().get(\"null_method\") == \"approximate\"\n ):\n good_sensitivity = True\n good_specificity = False\n elif (\n isinstance(meta_res.estimator, ale.ALE)\n and isinstance(meta_res.estimator.kernel_transformer, kernel.KDAKernel)\n and \"montecarlo\" in meta_res.estimator.get_params().get(\"null_method\")\n ):\n good_sensitivity = False\n good_specificity = True\n elif (\n isinstance(meta_res.estimator, ale.ALE)\n and type(meta_res.estimator.kernel_transformer) == kernel.KDAKernel\n and \"montecarlo\" in meta_res.estimator.get_params().get(\"null_method\")\n ):\n good_sensitivity = False\n good_specificity = True\n elif (\n isinstance(meta_res.estimator, ale.ALE)\n and type(meta_res.estimator.kernel_transformer) == kernel.KDAKernel\n and meta_res.estimator.get_params().get(\"null_method\") == \"approximate\"\n ):\n good_sensitivity = True\n good_specificity = False\n elif (\n isinstance(meta_res.estimator, mkda.MKDADensity)\n and isinstance(meta_res.estimator.kernel_transformer, kernel.ALEKernel)\n and meta_res.estimator.get_params().get(\"null_method\") != \"reduced_montecarlo\"\n ):\n good_sensitivity = False\n good_specificity = True\n else:\n good_sensitivity = True\n good_specificity = True\n\n _check_p_values(\n p_array,\n meta_res.masker,\n sig_idx,\n nonsig_idx,\n ALPHA,\n ground_truth_foci_ijks,\n n_iters=None,\n good_sensitivity=good_sensitivity,\n good_specificity=good_specificity,\n )","def test_AFQ_anisotropic():\n _, bids_path, _ = get_temp_hardi()\n myafq = api.AFQ(\n bids_path=bids_path,\n dmriprep='vistasoft',\n min_bval=1990,\n max_bval=2010,\n b0_threshold=50,\n reg_template=\"mni_T1\",\n reg_subject=\"power_map\")\n\n gtab = myafq.gtab[\"01\"]\n\n # check the b0s mask is correct\n b0s_mask = np.zeros(160, dtype=bool)\n b0s_mask[0:10] = True\n npt.assert_equal(gtab.b0s_mask, b0s_mask)\n\n # check that only b values in the b val range passed\n bvals_in_range = \\\n np.logical_and((gtab.bvals > 1990), (gtab.bvals < 2010))\n bvals_in_range_or_0 = \\\n np.logical_or(bvals_in_range, gtab.b0s_mask)\n npt.assert_equal(bvals_in_range_or_0, np.ones(160, dtype=bool))\n\n # check that the apm map was made\n myafq.mapping\n assert op.exists(op.join(\n myafq.results_dir[\"01\"],\n 'sub-01_ses-01_dwi_model-CSD_APM.nii.gz'))","def is_increasing(data):\n pass","def bipartite_matching(data=None, is_ascend=_Null, threshold=_Null, topk=_Null, out=None, name=None, **kwargs):\n return (0,)","def BIC(y,yhat,k):\r\n y = np.asarray(y)\r\n yhat = np.asarray(yhat)\r\n \r\n N = yhat.size\r\n y = y.reshape(N,)\r\n yhat = yhat.reshape(N,)\r\n residuals = y-yhat\r\n sse = np.sum(residuals**2) #sum of squared errors\r\n N = len(y)\r\n Lhat = sse/(N-k)\r\n return(N*np.log(Lhat) + k*np.log(N))","def test_am_threshold(Simulator, plt, seed, rng):\n D = 64\n vocab = Vocabulary(D, rng=rng)\n vocab.parse('A+B+C+D')\n\n D2 = int(D / 2)\n vocab2 = Vocabulary(D2, rng=rng)\n vocab2.parse('A+B+C+D')\n\n def input_func(t):\n return vocab.parse('0.49*A').v if t < 0.1 else vocab.parse('0.8*A').v\n\n with nengo.Network('model', seed=seed) as m:\n am = AssociativeMemory(vocab, vocab2, threshold=0.5)\n in_node = nengo.Node(output=input_func, label='input')\n nengo.Connection(in_node, am.input)\n\n in_p = nengo.Probe(in_node)\n out_p = nengo.Probe(am.output, synapse=0.03)\n\n sim = Simulator(m)\n sim.run(0.3)\n t = sim.trange()\n below_th = t < 0.1\n above_th = t > 0.25\n\n plt.subplot(2, 1, 1)\n plt.plot(t, nengo.spa.similarity(sim.data[in_p], vocab))\n plt.ylabel(\"Input\")\n plt.legend(vocab.keys, loc='best')\n plt.subplot(2, 1, 2)\n plt.plot(t, nengo.spa.similarity(sim.data[out_p], vocab2))\n plt.plot(t[above_th], np.ones(t.shape)[above_th] * 0.8, c='g', lw=2)\n plt.ylabel(\"Output\")\n plt.legend(vocab.keys, loc='best')\n\n assert similarity(sim.data[in_p][below_th], vocab.parse(\"A\").v) > 0.48\n assert similarity(sim.data[in_p][above_th], vocab.parse(\"A\").v) > 0.79\n assert similarity(sim.data[out_p][below_th], vocab2.parse(\"0\").v) < 0.01\n assert similarity(sim.data[out_p][above_th], vocab2.parse(\"A\").v) > 0.79","def decide(el, il, model, threshold):\n\n if model == 0:\n return el >= threshold[0] and il >=threshold[1]\n elif model == 1:\n return el >= threshold[0] or il >= threshold[1]\n elif model == 2:\n return harmonic_mean([el, il]) >= harmonic_mean(threshold)\n else:\n return bool(round(random.random()))","def test_ICA(epochs):\n ep = [epochs.epo1, epochs.epo2]\n icas = prep.ICA_fit(ep, n_components=15, method='infomax', fit_params=dict(extended=True),\n random_state=97)\n # check that the number of componenents is similar between the two participants\n for i in range(0, len(icas)-1):\n mne.preprocessing.ICA.get_components(\n icas[i]).shape == mne.preprocessing.ICA.get_components(icas[i+1]).shape\n # cleaned_epochs_ICA = prep.ICA_choice_comp(icas, ep) # pb interactive window\n # check signal better after than before\n # check bad channels are not deleted\n # assert epochs.epo1.info['ch_names'] == cleaned_epochs_ICA[0].info['ch_names']\n # assert epochs.epo2.info['ch_names'] == cleaned_epochs_ICA[1].info['ch_names']","def asy_ei(gp, acq_optimiser, anc_data):\n curr_best = anc_data.curr_max_val\n def _ei_acq(x):\n mu, sigma = gp.eval(x, uncert_form='std')\n Z = (mu - curr_best) / sigma\n return (mu - curr_best)*normal_distro.cdf(Z) + sigma*normal_distro.pdf(Z)\n return _optimise_acquisition(_ei_acq, acq_optimiser, anc_data)","def learn_ICA(X, k):\n\n # TODO: YOUR CODE HERE","def modelStats(model,data, azSpacing=0.5,zenSpacing=0.5):\n az = np.linspace(0,360.,int(360./azSpacing)+1)\n zz = np.linspace(0,90,int(90./zenSpacing)+1)\n\n azCtr = 0\n iCtr = 0\n chi = 0\n SS_tot = 0\n SS_res = 0\n SS_reg = 0\n test_ctr = 0\n reg_ctr = 0\n\n for i in az:\n if(i - azSpacing/2. < 0) :\n criterion = (data[:,0] < (i + azSpacing/2.)) | (data[:,0] > (360. - azSpacing/2.) )\n else:\n criterion = (data[:,0] < (i + azSpacing/2.)) & (data[:,0] > (i - azSpacing/2.) )\n\n ind = np.array(np.where(criterion))\n \n if ind.size > 0:\n tmp = data[ind,:]\n tmp = tmp.reshape(tmp.shape[1],tmp.shape[2])\n jCtr = 0\n for j in zz:\n # disregard any observation above 80 in zenith, too noisy\n if j < 80. + zenSpacing:\n criterion = (tmp[:,1] < (j + zenSpacing/2.)) & (tmp[:,1] > (j - zenSpacing/2.) ) \n tmpZ = np.array( tmp[indZ[:],2] )\n if indZ.size > 3 and not np.isnan(model[iCtr,jCtr]): # and (model[iCtr,jCtr] > 0.00001 or model[iCtr,jCtr] < -0.00001):\n test_data = reject_outliers(reject_abs( tmp[indZ,2],70. ),5.)\n #print(i,j,test_data,model[iCtr,jCtr])\n if test_data.size > 0:\n y_mean = np.mean(test_data)\n SS_reg += (model[iCtr,jCtr] - y_mean)**2\n reg_ctr += 1\n for obs in test_data:\n #chi += (obs - model[iCtr,jCtr]) ** 2 / model[iCtr,jCtr]\n SS_tot += (obs - y_mean) ** 2\n SS_res += (obs - model[iCtr,jCtr])**2\n test_ctr += 1\n jCtr += 1\n iCtr += 1\n\n rr = 1. - SS_res/SS_tot\n\n rms = np.sqrt(SS_res) * 1./test_ctr\n\n # gives an indication of how different the models would be between the test and training data set \n rms2 = np.sqrt(SS_reg) * 1./reg_ctr\n\n # Used in matlab instead of rr\n norm_res = np.sqrt(SS_res)\n mse = 1./(2.*test_ctr) * SS_res\n return mse,rr,rms,rms2","def ct_bis(env, a, median, mad):\n\n\tb1 = (a.T < median - env.threshold * mad).T\n\tb2 = a[:, :a.shape[1] - 1] < a[:, 1:]\n\tb3 = a[:, 1:] < a[:, :a.shape[1] - 1]\n\tb1[:, :a.shape[1] - 1] = b1[:, :a.shape[1] - 1] & b2\n\tb1[:, 1:] = b1[:, 1:] & b3\n\treturn (b1)","def filtfilt_gust_opt_func(ics, b, a, x):\n m = max(len(a), len(b)) - 1\n z0f = ics[:m]\n z0b = ics[m:]\n y_f = lfilter(b, a, x, zi=z0f)[0]\n y_fb = lfilter(b, a, y_f[::-1], zi=z0b)[0][::-1]\n\n y_b = lfilter(b, a, x[::-1], zi=z0b)[0][::-1]\n y_bf = lfilter(b, a, y_b, zi=z0f)[0]\n value = np.sum((y_fb - y_bf)**2)\n return value","def test_matching_knots(kind, al, au, tl, tu, bl, bu, cnt):\n prior = SmoothingPrior()\n prior_set = dict(\n prior_type=kind,\n age_lower=al,\n age_upper=au,\n time_lower=tl,\n time_upper=tu,\n born_lower=bl,\n born_upper=bu\n )\n for elem, value in prior_set.items():\n setattr(prior, elem, value)\n prior.validate_and_normalize()\n grid = SimpleNamespace()\n grid.ages = np.arange(20)\n grid.times = np.array([1990, 2000, 2005, 2010, 2015])\n age_time = list(matching_knots(grid, prior))\n assert len(age_time) == cnt\n # uniqueness\n assert len({(a, t) for (a, t) in age_time}) == len(age_time)","def adf_test(series,title=''):\n print(f'Augmented Dickey-Fuller Test: {title}')\n result = adfuller(series.dropna(),autolag='AIC') # .dropna() handles differenced data\n \n labels = ['ADF test statistic','p-value','# lags used','# observations']\n out = pd.Series(result[0:4],index=labels)\n\n for key,val in result[4].items():\n out[f'critical value ({key})']=val\n \n print(out.to_string()) # .to_string() removes the line \"dtype: float64\"\n \n if result[1] <= 0.05:\n print(\"Strong evidence against the null hypothesis\")\n print(\"Reject the null hypothesis\")\n print(\"Data has no unit root and is stationary\")\n else:\n print(\"Weak evidence against the null hypothesis\")\n print(\"Fail to reject the null hypothesis\")\n print(\"Data has a unit root and is non-stationary\")","def maks2(a, b, c):\n m = a\n if b > m:\n m = b\n if c > m:\n m = c\n \n return m","def test_weight_is_positive_carn(self):\n nt.assert_greater(self.carn.weight, 0)","def test05(self):\n a = np.arange(1, 11)\n b = bcolz.carray(a)\n wt = [v for v in a if v <= 5][2:]\n cwt = [v for v in b.where(bcolz.carray(a <= 5), skip=2)]\n # print \"numpy ->\", [v for v in a if v<=5][2:]\n # print \"where ->\", [v for v in b.where(bcolz.carray(a<=5), skip=2)]\n self.assertTrue(wt == cwt, \"where() does not work correctly\")","def anoise(this, *args, **kargs):\n\t\t\n\t\t# Arguments\n\t\tif not args: args = [50]\n\t\t\n\t\t# Kernel's retrieval\n\t\tanoisek = this._ANOISEK\n\t\tif anoisek is None: return None\n\t\t\n\t\t# More magic\n\t\tbin = this._BINARY\n\t\tfor thresh in args:\n\t\t\tbin[:,:] = (cv2.filter2D(bin, -1, anoisek) / 2.55 > thresh) * 255\n\t\treturn True","def test_continuity():\n dc = cs[:,0:Nr-1]-cs[:,1:Nr]\n assert dc.any < cmax","def is_stationary(self) -> bool:\n ad_fuller_result = adfuller(self.y.dropna(), autolag='AIC')\n p_value = ad_fuller_result[1]\n return p_value <= 0.5","def control_chart_analysis(value, c_l, param, anal_type=None):\n try:\n w_w = param[\"ww\"]\n w_c = param[\"wc\"]\n # val = np.array(value, dtype=pd.Series)\n if anal_type == \"lower\":\n lwl = c_l.loc[c_l[\"Kind\"] == \"LWL\", \"Value\"].values[0]\n lcl = c_l.loc[c_l[\"Kind\"] == \"LCL\", \"Value\"].values[0]\n len_w = len(value[(value < lwl) & (value >= lcl)])\n len_c = len(value[(value < lcl)])\n else:\n uwl = c_l.loc[c_l[\"Kind\"] == \"UWL\", \"Value\"].values[0]\n ucl = c_l.loc[c_l[\"Kind\"] == \"UCL\", \"Value\"].values[0]\n len_w = len(value[(value > uwl) & (value <= ucl)])\n len_c = len(value[(value > ucl)])\n beta = (w_w * len_w + w_c * len_c) / (max(w_w, w_c) * len(value))\n return [beta, len_w / len(value), len_c / len(value)]\n except:\n raise","def maCrossoverStrategy(df, i:int):\n\n\tif not df.__contains__('50_ema') and not df.__contains__('200_ema'):\n\t\tIndicators.AddIndicator(df, indicator_name=\"ema\", col_name=\"50_ema\", args=50)\n\t\tIndicators.AddIndicator(df, indicator_name=\"ema\", col_name=\"200_ema\", args=200)\n\n\tif i > 0 and df['50_ema'][i-1] <= df['200_ema'][i-1] and \\\n\t\tdf['50_ema'][i] > df['200_ema'][i]:\n\t\treturn df['close'][i]\n\n\treturn False","def AIC(y_true, y_pred, n_features):\n ll = log_likelihood(y_true, y_pred)\n AIC = 2 * n_features - 2 * ll\n return AIC","def test00(self):\n a = np.arange(1, 11)\n b = bcolz.carray(a)\n wt = [v for v in a if v > 0]\n cwt = [v for v in b.where(a > 0)]\n # print \"numpy ->\", [v for v in a if v>0]\n # print \"where ->\", [v for v in b.where(a>0)]\n self.assertTrue(wt == cwt, \"where() does not work correctly\")","def __gt__(self, *args):\n return _ida_hexrays.cif_t___gt__(self, *args)","def baseline(data):\n weights = weighting(data)\n return np.inner(weights,data['clicks'])/weights.sum()","def on(series, on_power_threshold=DEFAULT_ON_POWER_THRESHOLD):\n # TODO: replace this evil hack to handle dataframes(!)\n if isinstance(series, pd.DataFrame):\n series = series.icol(0)\n\n when_on = series >= on_power_threshold\n return when_on","def maxAlk(H0,C,Pt,Sit,parm):\n keys = parm.dtype.names\n\n bc = np.broadcast(parm,C,Pt,Sit)\n res = np.empty(bc.shape)\n resf = res.flat\n for i,(p,C,Pt,Sit) in enumerate(bc):\n d = dict((k,p[k]) for k in keys)\n try:\n H = solveACpF(H0,C,Pt,Sit,**d)\n except ValueError:\n print i\n raise\n resf[i] = ACoC(H,**d)*C - F(H0,Pt,Sit,**d)\n\n return res","def calculate_top_cond(counts, dos, cov_mat, rsid1, \n return_rsid1=False):\n acov_mat = cov_mat.copy(deep=True)\n acov_mat[rsid1] = dos.ix[rsid1, acov_mat.index]\n\n def _calc_model(geno, counts, cov_mat):\n t = cov_mat.copy(deep=True)\n t['ts'] = geno\n return(sm.OLS(counts, t).fit().pvalues['ts'])\n\n def _calc_model_two(geno, counts, cov_mat, rsid1):\n t = cov_mat.copy(deep=True)\n t['ts'] = geno\n fit = sm.OLS(counts, t).fit()\n return(pd.Series({'aic': fit.aic,\n 'rsid2': fit.pvalues['ts']}))\n\n '''\n pvalues = dos.apply(_calc_model, axis=1, args=(counts, \n acov_mat))\n '''\n pvalues = dos.apply(_calc_model_two, axis=1, args=(counts, \n acov_mat, rsid1))\n # Mask collinearity\n pvalues.ix[rsid1, 'rsid2'] = 1\n return(pvalues)","def ica(data, k, c, svdmethod=\"direct\", maxiter=100, tol=0.000001, seed=0):\n # get count\n n = data.count()\n\n # reduce dimensionality\n scores, latent, comps = svd(data, k, meansubtract=0, method=svdmethod)\n\n # whiten data\n whtmat = real(dot(inv(diag(latent/sqrt(n))), comps))\n unwhtmat = real(dot(transpose(comps), diag(latent/sqrt(n))))\n wht = data.mapValues(lambda x: dot(whtmat, x))\n\n # do multiple independent component extraction\n if seed != 0:\n random.seed(seed)\n b = orth(random.randn(k, c))\n b_old = zeros((k, c))\n iter = 0\n minabscos = 0\n errvec = zeros(maxiter)\n\n while (iter < maxiter) & ((1 - minabscos) > tol):\n iter += 1\n # update rule for pow3 non-linearity (TODO: add others)\n b = wht.map(lambda (_, v): v).map(lambda x: outer(x, dot(x, b) ** 3)).sum() / n - 3 * b\n # make orthogonal\n b = dot(b, real(sqrtm(inv(dot(transpose(b), b)))))\n # evaluate error\n minabscos = min(abs(diag(dot(transpose(b), b_old))))\n # store results\n b_old = b\n errvec[iter-1] = (1 - minabscos)\n\n # get un-mixing matrix\n w = dot(transpose(b), whtmat)\n\n # get components\n sigs = data.mapValues(lambda x: dot(w, x))\n\n return w, sigs","def ICA_Denoise(Y, ica_model, noise_std):\n\n # TODO: YOUR CODE HERE","def test04(self):\n a = np.arange(1, 11)\n b = bcolz.carray(a)\n wt = [v for v in a if v <= 5][:3]\n cwt = [v for v in b.where(bcolz.carray(a <= 5), limit=3)]\n # print \"numpy ->\", [v for v in a if v<=5][:3]\n # print \"where ->\", [v for v in b.where(bcolz.carray(a<=5), limit=3)]\n self.assertTrue(wt == cwt, \"where() does not work correctly\")","def bearish_harami(self):\n self.data['bearish_harami'] = ((self.data['Close'].shift(1) > self.data['Open'].shift(1)) & \\\n (self.data['Open'] > self.data['Close']) & \\\n (self.data['Open'] <= self.data['Close'].shift(1)) &\\\n (self.data['Open'].shift(1) <= self.data['Close']) & \\\n ((self.data['Open'] - self.data['Close']) < (self.data['Close'].shift(1) - self.data['Open'].shift(1))))","def a_test_predict_is_nonconstant():\n model = ARIMAX(formula=\"y ~ x1\", data=data, ar=1, ma=1, family=Exponential())\n x = model.fit('BBVI',iterations=200)\n predictions = model.predict_is(h=10, fit_method='BBVI', intervals=False)\n assert(not np.all(predictions.values==predictions.values[0]))","def test03(self):\n a = np.arange(1, 11)\n b = bcolz.carray(a)\n wt = [v for v in a if v <= 5]\n cwt = [v for v in b.where(bcolz.carray(a <= 5))]\n # print \"numpy ->\", [v for v in a if v<=5]\n # print \"where ->\", [v for v in b.where(bcolz.carray(a<=5))]\n self.assertTrue(wt == cwt, \"where() does not work correctly\")","def is_artificial(self):\n\t\treturn 0","def detect_beats(ecg, rate, ransac_window_size=5.0, lowfreq=5.0, highfreq=15.0):\n\n ransac_window_size = int(ransac_window_size * rate)\n\n lowpass = scipy.signal.butter(1, highfreq / (rate / 2.0), 'low')\n highpass = scipy.signal.butter(1, lowfreq / (rate / 2.0), 'high')\n # TODO: Could use an actual bandpass filter\n ecg_low = scipy.signal.filtfilt(*lowpass, x=ecg)\n ecg_band = scipy.signal.filtfilt(*highpass, x=ecg_low)\n\n # Square (=signal power) of the first difference of the signal\n decg = np.diff(ecg_band)\n decg_power = decg ** 2\n\n # Robust threshold and normalizator estimation\n thresholds = []\n max_powers = []\n for i in range(int(len(decg_power) / ransac_window_size)):\n sample = slice(i * ransac_window_size, (i + 1) * ransac_window_size)\n d = decg_power[sample]\n thresholds.append(0.5 * np.std(d))\n max_powers.append(np.max(d))\n\n threshold = np.median(thresholds)\n max_power = np.median(max_powers)\n decg_power[decg_power < threshold] = 0\n\n decg_power /= max_power\n decg_power[decg_power > 1.0] = 1.0\n square_decg_power = decg_power ** 2\n square_decg_power[square_decg_power < 1e-12] = 1e-12\n\n shannon_energy = -square_decg_power * np.log(square_decg_power)\n shannon_energy[~np.isfinite(shannon_energy)] = 0.0\n\n mean_window_len = int(rate * 0.125 + 1)\n lp_energy = np.convolve(shannon_energy, [1.0 / mean_window_len] * mean_window_len, mode='same')\n\n lp_energy = scipy.ndimage.gaussian_filter1d(lp_energy, rate / 8.0)\n lp_energy_diff = np.diff(lp_energy)\n\n zero_crossings = (lp_energy_diff[:-1] > 0) & (lp_energy_diff[1:] < 0)\n zero_crossings = np.flatnonzero(zero_crossings)\n zero_crossings -= 1\n return zero_crossings","def ecg_diagnose(apn):\n # Total minutes of apnea\n apnea_total = sum(apn)\n\n # Maximum hourly Apnea Index\n total_hour = int(len(apn) / 60)\n y_pred_hourly = np.reshape(apn[: total_hour * 60], (total_hour, 60))\n AI_hourly = y_pred_hourly.sum(axis=1)\n # If data in the last hour exceed 30 minutes, then convert to hourly result\n y_pred_left = apn[total_hour * 60 :]\n # if len(y_pred_left) >= 30:\n # AI_hourly = np.append(AI_hourly, sum(y_pred_left) * 60 / len(y_pred_left))\n # total_hour += 1\n AI_max = AI_hourly.max()\n\n if AI_max >= 10 and apnea_total >= 100:\n return \"A\"\n elif AI_max >= 5 and apnea_total >= 5:\n return \"B\"\n else:\n return \"C\"","def prior(old_params,params):\n \n for s in range(len(params)):\n if params[s] < 0.0 or params[s] > 2:\n return 0\n return 1","def test02b(self):\n a = np.arange(1, 11)\n b = bcolz.carray(a)\n wt = [v for v in a if v <= 5 and v > 2]\n cwt = [v for v in b.where((a <= 5) & (a > 2))]\n # print \"numpy ->\", [v for v in a if v<=5 and v>2]\n # print \"where ->\", [v for v in b.where((a<=5) & (a>2))]\n self.assertTrue(wt == cwt, \"where() does not work correctly\")","def kA_func(self):\n\n i1 = self.inl[0].to_flow()\n i2 = self.inl[1].to_flow()\n o1 = self.outl[0].to_flow()\n o2 = self.outl[1].to_flow()\n\n T_i1 = T_bp_p(i1)\n T_i2 = T_mix_ph(i2, T0=self.inl[1].T.val_SI)\n T_o1 = T_mix_ph(o1, T0=self.outl[0].T.val_SI)\n T_o2 = T_mix_ph(o2, T0=self.outl[1].T.val_SI)\n\n if T_i1 <= T_o2 and not self.inl[0].T.val_set:\n T_i1 = T_o2 + 0.5\n if T_i1 <= T_o2 and not self.outl[1].T.val_set:\n T_o2 = T_i1 - 0.5\n\n if T_o1 <= T_i2 and not self.outl[0].T.val_set:\n T_o1 = T_i2 + 1\n if T_o1 <= T_i2 and not self.inl[1].T.val_set:\n T_i2 = T_o1 - 1\n\n td_log = ((T_o1 - T_i2 - T_i1 + T_o2) /\n np.log((T_o1 - T_i2) / (T_i1 - T_o2)))\n\n return i1[0] * (o1[2] - i1[2]) + self.kA.val * td_log","def grubbs(timeseries):\r\n\r\n series = scipy.array([x[1] for x in timeseries])\r\n stdDev = scipy.std(series)\r\n mean = np.mean(series)\r\n tail_average = tail_avg(timeseries)\r\n z_score = (tail_average - mean) / stdDev\r\n len_series = len(series)\r\n threshold = scipy.stats.t.isf(.05 / (2 * len_series) , len_series - 2)\r\n threshold_squared = threshold * threshold\r\n grubbs_score = ((len_series - 1) / np.sqrt(len_series)) * np.sqrt(threshold_squared / (len_series - 2 + threshold_squared))\r\n\r\n return z_score > grubbs_score","def asy_hei(gp, acq_optimiser, anc_data):\n return _halluc_ei(gp, acq_optimiser, anc_data)","def compare_CII_w_models(**kwargs):\n\n p = copy.copy(params)\n for key,val in kwargs.items():\n setattr(p,key,val)\n\n if not p.xlim: p.xlim = [-4,2]\n if not p.ylim: p.ylim = [4,10]\n\n fig,ax = plt.subplots(figsize=(8,6))\n ax.set_ylim(p.ylim)\n ax.set_xlim(p.xlim)\n\n # SIGAME Simba-100 ext ON (default run)\n GR = glo.global_results(sim_run=p.sim_runs[1],nGal=p.nGals[1],grid_ext=p.grid_exts[1])\n L_line = getattr(GR,'L_[CII]158_sun')\n SFR = getattr(GR,'SFR')\n # Phantom points to close contour lines...\n L_line = np.append(L_line,np.array([1e8,10**9.3]))\n SFR = np.append(SFR,np.array([0.1,10**0.85]))\n lL_line = np.log10(L_line)\n lSFR = np.log10(SFR)\n lSFR = lSFR[L_line > 0]\n lL_line = lL_line[L_line > 0]\n # ax.plot(np.log10(SFR),np.log10(L_line),'o',ms=4,color='midnightblue',\\\n # alpha=0.7,label='SIGAME with Simba-100',zorder=10)\n # Evaluate a gaussian kde on a regular grid of nbins x nbins over data extents\n nbins = 100\n k = kde.gaussian_kde(np.column_stack([lSFR,lL_line]).T)\n x, y = np.mgrid[lSFR.min():lSFR.max():nbins*1j,4:lL_line.max():nbins*1j]\n z = k(np.vstack([x.flatten(), y.flatten()]))\n CS = ax.contour(x, y, z.reshape(x.shape),colors='forestgreen',levels=6,zorder=10)\n CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF')\n\n # Select only MS galaxies\n L_line = getattr(GR,'L_[CII]158_sun')\n indices = aux.select_salim18(GR.M_star[L_line > 0],GR.SFR[L_line > 0])\n print('With MS selection criteria: only %i galaxies' % (len(L_line)))\n lSFR = lSFR[indices]\n lL_line = lL_line[indices]\n lSFR = np.append(lSFR,np.array([-1,0.85,1.5]))\n lL_line = np.append(lL_line,np.array([8,9.3,8.4]))\n nbins = 100\n k = kde.gaussian_kde(np.column_stack([lSFR,lL_line]).T)\n x, y = np.mgrid[lSFR.min():lSFR.max():nbins*1j,4:lL_line.max():nbins*1j]\n z = k(np.vstack([x.flatten(), y.flatten()]))\n CS = ax.contour(x, y, z.reshape(x.shape),colors='purple',linestyles='dashed',levels=6,zorder=10)\n CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF (MS)')\n\n\n # SIGAME Simba-100 ext OFF\n # GR = glo.global_results(sim_run=p.sim_runs[1],nGal=p.nGals[1],grid_ext=p.grid_exts[0])\n # L_line = getattr(GR,'L_[CII]158_sun')\n # SFR = getattr(GR,'SFR')\n # # Phantom points to close contour lines...\n # L_line = np.append(L_line,np.array([10.**9.2,1e8]))\n # SFR = np.append(SFR,np.array([0.9,0.1]))\n # lL_line = np.log10(L_line)\n # lSFR = np.log10(SFR)\n # lSFR = lSFR[L_line > 0]\n # lL_line = lL_line[L_line > 0]\n # # ax.plot(np.log10(SFR),np.log10(L_line),'o',ms=4,color='midnightblue',\\\n # # alpha=0.7,label='SIGAME with Simba-100',zorder=10)\n # # Evaluate a gaussian kde on a regular grid of nbins x nbins over data extents\n # nbins = 100\n # k = kde.gaussian_kde(np.column_stack([lSFR,\\\n # lL_line]).T)\n\n # x, y = np.mgrid[lSFR.min():lSFR.max():nbins*1j, \\\n # 4:lL_line.max():nbins*1j]\n # z = k(np.vstack([x.flatten(), y.flatten()]))\n # CS = ax.contour(x, y, z.reshape(x.shape),colors='brown',levels=6,linestyles='dotted')\n # CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF_no_ext')\n\n # SIGAME Simba-25\n # GR = glo.global_results(sim_run=p.sim_runs[0],nGal=p.nGals[0])\n # L_line = getattr(GR,'L_[CII]158_sun')\n # SFR = getattr(GR,'SFR')\n # ax.plot(np.log10(SFR),np.log10(L_line),'^',ms=6,color='darkorchid',alpha=0.7,label='SIGAME with Simba-25')\n\n # Observations in background\n add_line_SFR_obs('[CII]158',L_line,ax,plot_fit=False)\n\n # Popping 2019\n G19 = pd.read_csv(p.d_data + 'models/Popping2019.csv',skiprows=1,sep=' ',\\\n names=['logSFR', 'logLCII', 'log LCII 14th percentile', 'log LCII 86th percentile'])\n ax.plot(G19.logSFR,G19.logLCII,'k-',label='Popping 2019',alpha=0.8)\n ax.fill_between(G19.logSFR,G19['log LCII 14th percentile'].values,\\\n G19['log LCII 86th percentile'].values,color='grey',alpha=0.4)\n\n\n # Padilla 2020\n P20 = pd.read_csv(p.d_data + 'models/DataFig6.csv',skiprows=1,sep=',',\\\n names=['IDSim','GalID','SFR','LCIITotal'])\n P20['logLCII'] = np.log10(P20['LCIITotal'])\n P20['logSFR'] = np.log10(P20['SFR'])\n\n\n colors = ['cyan','orange','midnightblue']\n nbins = 100\n IDSims = ['Ref25','Recal25','Ref100']\n for i,IDSim in enumerate(IDSims):\n # Evaluate a gaussian kde on a regular grid of nbins x nbins over data extents\n k = kde.gaussian_kde(np.column_stack([P20.logSFR.values[P20.IDSim == IDSim],\\\n P20.logLCII.values[P20.IDSim == IDSim]]).T)\n\n xP20, yP20 = np.mgrid[P20.logSFR.min():P20.logSFR.max():nbins*1j, \\\n 4:P20.logLCII.max():nbins*1j]\n zP20 = k(np.vstack([xP20.flatten(), yP20.flatten()]))\n # To remove weird contour line:\n zP20.reshape(xP20.shape)[(xP20 > -1) & (yP20 < 5.5)] = 1e-5\n zP20.reshape(xP20.shape)[(xP20 < -3)] = 1e-5\n zP20.reshape(xP20.shape)[(xP20 < -2) & (yP20 > 8)] = 1e-5\n zP20.reshape(xP20.shape)[(xP20 > 1) & (yP20 < 7)] = 1e-5\n CS = ax.contour(xP20, yP20, zP20.reshape(xP20.shape),colors=colors[i],levels=5)\n CS.collections[0].set_label('Ramos Padilla 2020 '+IDSim)\n\n\n ax.set_xlabel('log '+getlabel('SFR'))\n ax.set_ylabel('log '+getlabel('[CII]158'))\n handles,labels = ax.get_legend_handles_labels()\n handles = [handles[_] for _ in [8,9,10,11,12,7,0,1,2,3,4,5,6]]\n labels = [labels[_] for _ in [8,9,10,11,12,7,0,1,2,3,4,5,6]]\n plt.legend(handles,labels,fontsize=9,loc='upper left')\n if p.savefig:\n if not os.path.isdir(p.d_plot + 'luminosity/'): os.mkdir(p.d_plot + 'luminosity/') \n plt.savefig(p.d_plot + 'luminosity/CII_SFR_w_models.png', format='png', dpi=300)","def part1a_0():\n xs = exampleInput\n ys = exampleTags\n ys_ = submission.computeViterbi(simpleCRF, xs)\n grader.requireIsEqual( ys, ys_ )","def get_acis_fp_limits(states, acis_s=-111.0, acis_i=-112.0):\n\n states_acis_limits = np.zeros(len(states)) + 20 # Set limit initially to 20C\n ind_acis_s = (states['simpos'] > 0) & (states['simpos'] < 80669) & (states['clocking'] == 1) & (\n states['vid_board'] == 1)\n ind_acis_i = (states['simpos'] > 83826) & (states['clocking'] == 1) & (states['vid_board'] == 1)\n states_acis_limits[ind_acis_s] = acis_s\n states_acis_limits[ind_acis_i] = acis_i\n\n return states_acis_limits","def cn_filter(df, binary_cutoff=12):\n del_df = (df.ix['Deletion'].dropna(1) < 0).astype(int)\n del_df = del_df[del_df.sum(1) >= binary_cutoff]\n del_df.index = del_df.index.droplevel(1)\n del_df = del_df.T\n amp_df = (df.ix['Amplification'].dropna(1) > 0).astype(int)\n amp_df = amp_df[amp_df.sum(1) >= binary_cutoff]\n amp_df.index = amp_df.index.droplevel(1)\n amp_df = amp_df.T\n return amp_df, del_df","def test_ap_hs20_aka(dev, apdev):\n if not hlr_auc_gw_available():\n return \"skip\"\n hs20_simulated_sim(dev[0], apdev[0], \"AKA\")","def test06(self):\n a = np.arange(1, 11)\n b = bcolz.carray(a)\n wt = [v for v in a if v <= 5][1:4]\n cwt = [v for v in b.where(bcolz.carray(a <= 5), limit=3, skip=1)]\n # print \"numpy ->\", [v for v in a if v<=5][1:4]\n # print \"where ->\", [v for v in b.where(bcolz.carray(a<=5),\n # limit=3, skip=1)]\n self.assertTrue(wt == cwt, \"where() does not work correctly\")","def test_warning_is_triggered(caplog):\n pytest.importorskip('transformers')\n apply_alibi(\n model=torch.nn.Sequential(torch.nn.Linear(20, 10), torch.nn.Linear(10, 5)),\n max_sequence_length=64,\n )\n assert encountered_alibi_warning(caplog), 'A warning should be generated when Alibi has no effect.'","def weighted_estimation(self) -> bool:\n pass","def _analyse(self, source='sdf', alpha = 0.05, n_bootstrap = 2000, \n\t\tbiphase_split_point = 0.5, biphase_select_resp = None):\n\t\t\n\t\t\n\t\t# Need to add capacity to handle two things:\n\t\t# Qualitative conditions ne\n\t\t# Conditions split - need to to deal with splitting a single dataset, where one part\n\t\t# is qualitiative and the other quantitative\n\t\t\n\t\t# For qualitative, the self.cond_tuning array is numerical. Replace with record\n\t\t# see Initial Chrom Analysis. Keep conditions as strings, and convert to numerical\n\t\t# for plotting (?). Where qualitative, only use bar plot, where mixed, split.\n\t\t## Add parameters to parameters dictionary\n\n\t\tself.parameters['biphase_split_point'] = biphase_split_point\n\t\tself.parameters['biphase_select_resp'] = biphase_select_resp\n\t\t\n\t\t# Organising source selection - raw and mov_avg not develoepd fully yet.\n\t\tsources = {'sdf': (self.spike_dens_func, self.CI_pos, self.CI_neg), \n\t\t\t\t 'mov_avg': 'doesnt exist yet, call it self.spike_mov_avg', \n\t\t\t\t 'raw': (self.conditions_hist_mean, \n\t\t\t\t\t\t self.conditions_hist_mean + 2*self.conditions_hist_stderr, \n\t\t\t\t\t\t self.conditions_hist_mean - 2*self.conditions_hist_stderr)}\n\t\t\t\t \n\t\tassert source.lower() in sources.keys(), ('Tuning source data \"%s\" is invalid '\n\t\t\t\t\t\t\t\t\t\t\t\t\t'select one of %s' %(source, sources.keys())) \n\t \n\t\t## Need to expand this functionality to the mean and CI_pos and CI_neg. Doing so for\n\t # raw and moving average is not a priority, using sdf and bootstrap is pretty good.\n\t # overall aim is to clean this function up to accomadte a number of tuning functinos\n\t # in a clear and easy to use fasion.\n\t \n\t\tn_con = self.parameters['conditions']\n\t\t\n\t\t# values for transient bar responses\n\t\tif self.parameters['stimulus'] == 'bar':\n\t\t\t\n\t\t\tresp, CI_pos, CI_neg = sources[source.lower()]\n\t\t\t\n\t\t\t\n\t\t\tif self.parameters['biphasic']:\n\t\t\t\t\n\t\t\t\t# Take max response for each half of each PSTH, including Conf Intvls\n\t\t\t\thalf = int(self.bins.size * biphase_split_point)\n\n\t\t\t\tmax_val_arg = (resp[:, :half].argmax(axis=1),\n\t\t\t\t\t\t\t resp[:, half:].argmax(axis=1)+half)\n\t\t\t\t\t\t\t\t\t\n\t\t\t\tmax_val = (resp[:, :half].max(axis=1),\n\t\t\t\t\t\t resp[:, half:].max(axis=1))\n\t\t\t\t\t\t \n\t\t\t\t\t\t\t \n\t\t\t\tmax_val_CI_neg = (CI_neg[np.arange(n_con), max_val_arg[0]],\n\t\t\t\t\t\t\t\t CI_neg[np.arange(n_con), max_val_arg[1]])\n\t\t\t\t\t\t\t\t \n\t\t\t\tmax_val_CI_pos = (CI_pos[np.arange(n_con), max_val_arg[0]],\n\t\t\t\t\t\t\t\t CI_pos[np.arange(n_con), max_val_arg[1]])\n\n\t\t\t\t# encode which of the two responses the data is attached to\n\t\t\t\tbiphas_id = np.zeros_like(np.hstack((self.conditions, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.conditions2)))\n\t\t\t\tbiphas_id[:self.conditions.size] = 1\n\t\t\t\tbiphas_id[self.conditions2.size:] = 2\n\n\n\t\t\t\t\t\t\t\t \n\t\t\t\tself.cond_tuning = np.vstack((np.hstack((self.conditions, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.conditions2)),\n\t\t\t\t\t\t\t\t\t\t\t np.hstack(max_val),\n\t\t\t\t\t\t\t\t\t\t\t np.hstack(max_val_CI_neg),\n\t\t\t\t\t\t\t\t\t\t\t np.hstack(max_val_CI_pos),\n\t\t\t\t\t\t\t\t\t\t\t biphas_id))\n\t\t\t\t\t\t\t\t\t\t\t \n\t\t\t\t# Convert to Hertz - design choice is to keep all PSTH datasets as raw average spike\n\t\t\t\t# counts, with easy option of seeing frequency in the plotting, but converting to \n\t\t\t\t# Hertz for all condition tuning data.\n\t\t\t\tself.cond_tuning[1:-1,:] *= (1/self.bin_width)\n\t\t\t\t\t\t\t\t\t\t\t \n\t\t\t\t\t\t\t \n\t\t\t\t# Column labels for pd.dataframe of tuning data\n\t\t\t\t# Percentage of confidence intervals\n\t\t\t\t# ci_perc = (100 * (1 - self.parameters['sdf_alpha']))\n\t\t\t\t\n\t\t\t\t# Labels\n\t\t\t\tidx = ['condition', 'max_resp', 'neg_CI', 'pos_CI', 'biphas_id']\n\t\t\t\t\n\t\t\t\t# Pandas object, with transpose of tuning array to data frame object \n\t\t\t\tself.cond_tuning_pd = pd.DataFrame(self.cond_tuning.transpose(), columns=idx)\n\t\t\n\t\t\t#non biphasic version of above\n\t\t\tif not self.parameters['biphasic']:\n\n\t\t\t\tmax_val_arg = resp[:, :].argmax(axis=1)\n\t\t\t\t\t\t\t\t\t\n\t\t\t\tmax_val = resp[:, :].max(axis=1)\n\t\t\t\t\t\t \n\t\t\t\t\t\t\t \n\t\t\t\tmax_val_CI_neg = CI_neg[np.arange(n_con), max_val_arg]\n\t\t\t\t\t\t\t\t \n\t\t\t\tmax_val_CI_pos = CI_pos[np.arange(n_con), max_val_arg]\n\t\t\t\t\t\t\t\t \n\t\t\t\tself.cond_tuning = np.vstack((self.conditions,\n\t\t\t\t\t\t\t\t\t\t\t max_val,\n\t\t\t\t\t\t\t\t\t\t\t max_val_CI_neg,\n\t\t\t\t\t\t\t\t\t\t\t max_val_CI_pos))\n\t\t\t\t\t\t\t\t\t\t\t \n\t\t\t\t# Convert to Hertz - design choice is to keep all PSTH datasets as raw average spike\n\t\t\t\t# counts, with easy option of seeing frequency in the plotting, but converting to \n\t\t\t\t# Hertz for all condition tuning data.\n\t\t\t\tself.cond_tuning[1:,:] *= (1/self.bin_width)\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t# Column labels for pd.dataframe of tuning data\n\t\t\t\t# ci_perc = (100 * (1 - self.parameters['sdf_alpha']))\n\n\t\t\t\tidx = ['condition', 'max_resp', 'neg_CI', 'pos_CI']\n\n\t\t\t\t\t \n\t\t\t\t# transpose of tuning array to data frame object \n\t\t\t\tself.cond_tuning_pd = pd.DataFrame(self.cond_tuning.transpose(), columns=idx)\n\t\t\n\t\t\n\t\t# values for sinusoids/gratings\n\t\t## Note issue of temporal frequency tuning - need variable tf.\n\t\tif self.parameters['stimulus'] == 'grating':\n\t\t\t\n\t\t\tself.parameters['fft_alpha'] = alpha\n\t\t\tself.parameters['fft_number_bootstrap'] = n_bootstrap\n\t\t\t\n\t\t\tif source == 'sdf':\n\t\t\t\tprint ('WARNING, using a smoothed/filtered dataset will artificially increase'\n\t\t\t\t\t 'the amplitude of the DC component and decrease that of the F1') \n\t\t\t\n\t\t\tsources = {'sdf': self.conditions_trials_sdf,\n\t\t\t\t\t 'mov_avg': \"doesn't exist yet (?)\",\n\t\t\t\t\t 'raw': self.conditions_trials_hist}\n\t\t\t\n\t\t\tresp = sources[source]\n\t\t\t\n\t\t\ttemp_freq = self.parameters['temp_freq']\n\t\t\tstim_len = self.parameters['stimulus_length']\n\t\t\t\n\t\t\t# ensuring that the temp_freq is measured in the FFT whilst taking the maximum time.\n\t\t\t# on the basis of delt-f = 1 / n*del-t; stim_len*F1=factor; 1/(bin_width*F1)=min bins\n\t\t\t# number times greater than minimum can fit in stim_length \n\t\t\tfactor = np.floor(stim_len * temp_freq).astype('int')\n\t\t\t\n\t\t\t# number of bins to take - the window size necessary for temp_freq to be measured\n\t\t\tbins_take = np.floor(factor / (self.bin_width * temp_freq)).astype('int')\n\n\t\t\t# Frequency axis generation\n\t\t\tself.freq = fft.rfftfreq(bins_take, self.bin_width)\n\t\t\t\n\t\t\t#Checkign whether the temp_freq is in the FFT.\n\t\t\tassert self.freq[factor] == temp_freq, ('The calculated FFT F1 frequency (%s)'\n\t\t\t\t\t\t\t\t\t\t\t\t\t 'does not equal the Stimulus temp_freq (%s)'\n\t\t\t\t\t\t\t\t\t\t\t\t\t %(self.freq[bins_take], temp_freq))\n\n\t\t\t# Fourier Transform\n\t\t\tself.conditions_trials_fourier = fft.rfft(resp[:,:,:bins_take], axis=2)\n\t\t\t\n\t\t\t# Amplitude (peak-to-peak)\n\t\t\tself.conditions_trials_ampl = np.abs(self.conditions_trials_fourier)\n\t\t\t\n\t\t\t# normalising to dataset size, except the DC.\n\t\t\tself.conditions_trials_ampl[:,:,0] *= 1 / float(bins_take)\n\t\t\tself.conditions_trials_ampl[:,:,1:] *= 2 / float(bins_take)\n\t\t\t\n\t\t\t\n\t\t\t# Mean amplitudes and bootstrapped CI_intervals \n\t\t\tself.conditions_ampl_mean = np.mean(self.conditions_trials_ampl, axis=1)\n\t\t\t\n\t\t\tCI_pos, CI_neg = bootstrap(self.conditions_trials_ampl, alpha=alpha, \n\t\t\t\t\t\t\t\t\t n_bootstrap=n_bootstrap)\n\t\t\tself.conditions_ampl_CI_pos, self.conditions_ampl_CI_neg = CI_pos, CI_neg\n\t\t\t\n\t\t\t# isolating F0, F1, and F2 responses and compiling into a single table.\n\t\t\tconditions_f0 = self.conditions_ampl_mean[:,0]\n\t\t\tconditions_f1 = self.conditions_ampl_mean[:,factor]\n\t\t\tconditions_f2 = self.conditions_ampl_mean[:,2*factor]\n\t\t\t\n\t\t\t# Condition Tuning array\n\t\t\tself.cond_tuning = np.vstack((self.conditions,\n\t\t\t\t\t\t\t\t\t\t conditions_f0, CI_pos[:,0], CI_neg[:,0],\n\t\t\t\t\t\t\t\t\t\t conditions_f1, CI_pos[:,factor], CI_neg[:,factor],\n\t\t\t\t\t\t\t\t\t\t conditions_f2, CI_pos[:,2*factor], CI_neg[:,2*factor],\n\t\t\t\t\t\t\t\t\t\t conditions_f1/conditions_f0))\n\t\t\t\n\t\t\t# Convert to Hertz - design choice is to keep all PSTH datasets as raw average spike\n\t\t\t# counts, with easy option of seeing frequency in the plotting, but converting to \n\t\t\t# Hertz for all condition tuning data.\n\t\t\t\n\t\t\tself.cond_tuning[1:-1,:] *= (1/self.bin_width)\n\t\t\t\n\t\t\t# Column labels for pd.dataframe of tuning data\n\t\t\t# ci_perc = (100 * (1 - self.parameters['fft_alpha']))\n\t\t\tidx = ['conditions', \n\t\t\t\t 'F0', 'F0_pos_CI', 'F0_neg_CI', \n\t\t\t\t 'F1', 'F1_pos_CI', 'F1_neg_CI',\n\t\t\t\t 'F2', 'F2_pos_CI', 'F2_neg_CI',\n\t\t\t\t 'F1/F0_ratio']\n\t\t\t# transpose of tuning array to data frame object \n\t\t\tself.cond_tuning_pd = pd.DataFrame(self.cond_tuning.transpose(), columns=idx)\n\t\t\n\t\t\n\t\t\t \n\t\t# for orientation data, the orientation angles can get scrambled due to the circ() function\n\t\t# rotating the angles around. This orders them numerically in the final cond_tuning\n\t\t\n\t\tif self.parameters['condition_type'] == 'orientation':\n\t\t\tself.cond_tuning = self.cond_tuning[:,self.cond_tuning[0].argsort()]\n\t\t\tself.cond_tuning_pd.sort_values(self.cond_tuning_pd.columns[0], inplace=True)\n\n\n\t\t# \n\t\t# cond_tuning cleaning up and inserting important meta data / columns\n\t\t# \n\n\t\tif biphase_select_resp is not None:\n\t\t\tassert isinstance(biphase_select_resp, int) and biphase_select_resp in [1,2], \\\n\t\t\tf'biphase_select_resp ({biphase_select_resp}) must be an integer of 1 or 2'\n\n\t\t\tassert self.parameters['biphasic'], 'Stimulus not analysed as biphasic'\n\n\t\t\t# cond tuning array\n\t\t\tcond_tuning_biphase_mask = self.cond_tuning[4,:] == biphase_select_resp\n\t\t\tself.cond_tuning = self.cond_tuning[:, cond_tuning_biphase_mask]\n\n\t\t\t# cond tuning pandas dataframe\n\t\t\tself.cond_tuning_pd = self.cond_tuning_pd.query('biphas_id == @biphase_select_resp')\n\n\n\n\t\tassert hasattr(self, 'CELL_ID'), 'Make Cell ID first'\n\n\n\t\tself.cond_tuning_pd.insert(0, 'run_key', self.RUN_KEY)\n\t\tself.cond_tuning_pd.insert(0, 'cell_key', self.CELL_KEY)\n\t\tself.cond_tuning_pd.set_index(['cell_key', 'run_key'], inplace=True)\n\n\t\tself.cond_tuning_pd.insert(0, 'cond_type', self.parameters['condition_type'])\n\t\tself.cond_tuning_pd.insert(1, 'cond_unit', self.parameters['condition_unit'])","def ofi(b, b_v, a, a_v):\n b_, b_v_, a_, a_v_ = b.shift(1), b_v.shift(1), a.shift(1), a_v.shift(1)\n ofi = i(b >= b_) * b_v - i(b <= b_) * b_v_ - i(a <= a_) * a_v + i(a >= a_) * a_v_\n return ofi.fillna(0)","def test05(self):\n a = np.arange(1, 110)\n b = bcolz.carray(a, chunklen=10)\n wt = a[a < 5]\n cwt = b[a < 5]\n # print \"numpy ->\", a[a<5]\n # print \"where ->\", b[a<5]\n assert_array_equal(wt, cwt, \"where() does not work correctly\")","def test01(self):\n a = np.arange(1, 11) < 0\n b = bcolz.carray(a)\n wt = a.nonzero()[0].tolist()\n cwt = [i for i in b.wheretrue()]\n # print \"numpy ->\", a.nonzero()[0].tolist()\n # print \"where ->\", [i for i in b.wheretrue()]\n self.assertTrue(wt == cwt, \"wheretrue() does not work correctly\")","def SpatiallyAdaptiveBinaryThresholding(image, kernel_sigma, a, b): \n\n if kernel_sigma >= 1:\n image = Denoising(image, kernel_sigma); \n \n nr, nc = image.shape;\n mask = np.zeros(image.shape);\n \n for ii in range(1, nr - 1):\n for jj in range(1, nc - 1):\n nb = image[ii - 1:ii + 2, jj - 1:jj + 2];\n nb = nb.reshape(3, 3);\n nb_std = np.std(nb);\n nb_mean = np.mean(nb);\n \n if image[ii, jj] > a * nb_std and image[ii, jj] > b * nb_mean:\n mask[ii, jj] = 1;\n\n return mask;","def FDA_test(X_test, w_star, w_0):\n\n y_proj = X_test.dot(w_star)[:, np.newaxis]\n\n #1-of-K code scheme\n y_1 = y_proj >= w_0\n y_2 = y_proj < w_0\n y_pred = np.hstack((y_1, y_2)).astype(int)\n\n return y_pred","def maStrategy(df, i:int):\n\n\tif not df.__contains__('slow_sma'):\n\t\tIndicators.AddIndicator(df, indicator_name=\"sma\", col_name=\"slow_sma\", args=30)\n\n\tbuy_price = 0.96 * df['slow_sma'][i]\n\tif buy_price >= df['close'][i]:\n\t\treturn min(buy_price, df['high'][i])\n\n\treturn False"],"string":"[\n \"def test_aic_ms(distribution):\\n print(\\\"TESTING: AIC model selection for %s distribution\\\" % distribution.upper())\\n params = dist.DISTRIBUTIONS[distribution][dist.KEY_TEST_PARAMS]\\n print(\\\" creating sample\\\")\\n test_sample = dist.samples(distribution, params)\\n print(\\\" calculating AIC for all distributions\\\")\\n fit_results = {}\\n aic = {}\\n for d in dist.get():\\n fit_results[d] = fit.fit_mle(d, test_sample)\\n aic[d] = me.aic_measure(dist.log_likelihood(d, fit_results[d]['params'], test_sample, nonzero_only=True),\\n len(fit_results[d]['params']))\\n delta_aic = {d: aic[d]-min(aic.values()) for d in aic}\\n weights = {d: float(exp(-delta_aic[d]/2)) for d in delta_aic}\\n best_model = dist.get()[0]\\n print(\\\" input parameters: %s\\\" % dist.get_params(params, distribution))\\n for d in dist.get():\\n if weights[d] > weights[best_model]:\\n best_model = d\\n weights[d] /= sum(weights.values())\\n print(\\\" %s:\\\" % d.upper())\\n print(\\\" %s\\\" % dist.get_params(fit_results[d]['params'], d))\\n print(\\\" AIC = %.0f\\\" % aic[d])\\n print(\\\" dAIC = %.0f\\\" % delta_aic[d])\\n print(\\\" w = %r\\\" % weights[d])\\n print(\\\" Most likely model: %s\\\" % best_model.upper())\\n print_pmfs(test_sample, fit_results, 'TEST-AIC.CSV')\",\n \"def is_good_qualtiative_example(iaa_score, ann1_total, ann2_total):\\n return iaa_score > .3 and iaa_score < 1 and ann1_total > 3 and ann2_total > 3\",\n \"def aiclike(timeSeries, params, distribution):\\n if distribution == 'pareto':\\n nloglval = -(timeSeries.shape[0] * np.log(params['mu']) + timeSeries.shape[0] * params['mu'] * np.log(params['xmin']) - (params['xmin']+1) * np.sum(np.log(timeSeries)))\\n return nloglval\\n \\n elif distribution == 'lognormal':\\n nloglval = np.sum(np.log(timeSeries * params['sigma'] * np.sqrt(2*np.pi)) + (np.log(timeSeries) - params['mu'])**2 / (2 * params['sigma']**2))\\n return nloglval\\n \\n elif distribution == 'normal':\\n nloglval = np.sum(np.log( params['sigma'] * np.sqrt(2*np.pi) ) + (timeSeries - params['mu'])**2 / (2 * params['sigma']**2))\\n return nloglval\\n \\n elif distribution == 'exponential':\\n nloglval = np.sum(params['lambda'] * timeSeries - np.log(params['lambda']))\\n return nloglval\\n \\n elif distribution == 'boundedpl':\\n nloglval = -len(timeSeries) * np.log( (params['mu'] - 1) / (np.min(timeSeries)**(1 - params['mu']) - np.max(timeSeries)**(1 - params['mu']))) + params['mu'] * np.sum(np.log(timeSeries))\\n return nloglval\",\n \"def aic(self):\\n if hasattr(self, '_aic'):\\n return self._aic\\n else:\\n self._aic = 2 * self.k + 2 * self.neg_ll()\\n return self._aic\",\n \"def adaptive_selection(p_values, data, alpha):\\n HC_stat, max_idx_HC, HC_vec = HC_update(p_values, alpha)\\n cscshm_stat, max_idx_cscshm, cscshm_vec = CsCsHM_update(p_values, alpha)\\n thresh_HC = data[max_idx_HC]\\n thresh_cscshm = data[max_idx_cscshm]\\n nonnull_HC = np.where(data < thresh_HC, 0, data)\\n nonnull_cscshm = np.where(data < thresh_cscshm, 0, data)\\n return nonnull_HC, nonnull_cscshm\",\n \"def AIC(y,yhat,k):\\r\\n residuals = y-yhat\\r\\n sse = np.sum(residuals**2) #sum of squared errors\\r\\n N = len(y)\\r\\n Lhat = sse/N\\r\\n return(2*k + N*np.log(Lhat))\",\n \"def test_adf(self):\\n\\n dftest = adfuller(self.ts_df['y'], autolag='AIC')\\n dfoutput = pd.Series(dftest[0:4],\\n index=['Test Statistic', 'p-value', '#Lags Used', 'Number of Observations Used'])\\n for key, value in dftest[4].items():\\n dfoutput['Critical Value (%s)' % key] = value\\n print(dfoutput)\\n if dftest[0] > dftest[4]['5%']:\\n print(\\n \\\"Test statistic greater than critical value at 5% --> series seems to be not stationary. \\\"\\n \\\"Look at critical values at 1% and 10% too, ideally they also should be less than test statistic.\\\")\\n else:\\n print(\\n \\\"Test statistic less than critical value at 5% --> series seems to be stationary. \\\"\\n \\\"Look at critical values at 1% and 10% too, ideally they also should be greater than test statistic.\\\")\",\n \"def opt_optical_absorption(aref, asig, traw, awl, aoff, tcal, tbins, ta_arr,\\n cpd_ts, cwl, T, PS, rwlngth=715.):\\n # reset shapes of input arguments\\n # using np.array ndmin=# seems faster than using np.atleast_#d\\n aref = np.array(aref, ndmin=2)\\n asig = np.array(asig, ndmin=2)\\n traw = np.array(traw, ndmin=1)\\n awl = np.around(np.array(awl, ndmin=2), decimals=1)\\n aoff = np.array(aoff, ndmin=2)\\n tcal = np.array(tcal, ndmin=1)\\n tbins = np.array(tbins, ndmin=2)\\n # note, np.atleast_3d appends the extra dimension;\\n # np.array using ndmin prepends the extra dimension.\\n ta_arr = np.array(ta_arr, ndmin=3)\\n cpd_ts = np.array(cpd_ts, ndmin=2)\\n cwl = np.array(cwl, ndmin=2)\\n T = np.array(T, ndmin=1)\\n PS = np.array(PS, ndmin=1)\\n\\n # size up inputs\\n npackets = awl.shape[0]\\n nwavelengths = awl.shape[1]\\n # initialize output array\\n apd_ts_s = np.zeros([npackets, nwavelengths])\\n\\n for ii in range(npackets):\\n\\n # calculate the internal instrument temperature [deg_C]\\n tintrn = opt_internal_temp(traw[ii])\\n\\n # calculate the uncorrected optical absorption coefficient [m^-1]\\n apd, _ = opt_pd_calc(aref[ii, :], asig[ii, :], aoff[ii, :], tintrn,\\n tbins[ii, :], ta_arr[ii, :, :])\\n\\n # correct the optical absorption coefficient for temperature and salinity.\\n apd_ts = opt_tempsal_corr('a', apd, awl[ii, :], tcal[ii], T[ii], PS[ii])\\n\\n # correct the optical absorption coefficient for scattering effects\\n apd_ts_s_row = opt_scatter_corr(apd_ts, awl[ii, :], cpd_ts[ii, :], cwl[ii, :], rwlngth)\\n apd_ts_s[ii, :] = apd_ts_s_row\\n\\n # return the temperature, salinity and scattering corrected optical\\n # absorption coefficient OPTABSN_L2 [m^-1]\\n return apd_ts_s\",\n \"def utility_sarimax_predict(series,nforecast=1,loga=False,plot_figures_flag=False,show_mdl_detail=False):\\n \\n from sklearn.metrics import r2_score, median_absolute_error, mean_absolute_error\\n from sklearn.metrics import median_absolute_error, mean_squared_error, mean_squared_log_error\\n from scipy.optimize import minimize\\n import statsmodels.tsa.api as smt\\n import statsmodels.api as sm\\n import numpy as np\\n import pandas as pd\\n from tqdm import tqdm \\n from itertools import product\\n import warnings\\n \\n np.random.seed(25)\\n \\n ## SARIMA\\n def mean_absolute_percentage_error(y_true, y_pred):\\n return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\\n \\n warnings.filterwarnings('ignore')\\n \\n def optimize_SARIMA(data_column,parameters_list):\\n \\\"\\\"\\\"\\n Return dataframe with parameters and corresponding AIC\\n \\n parameters_list - list with (ps,d, qs, Ps,D, Qs,s) tuples\\n\\n \\\"\\\"\\\" \\n results = []\\n best_aic = float('inf') \\n \\n #for param in tqdm(parameters_list):\\n for param in parameters_list:\\n try: model = sm.tsa.statespace.SARIMAX(data_column, order=(param[0], param[1],param[2]),\\n seasonal_order=(param[3], param[4], param[5], param[5])).fit(disp=-1)\\n # try: model = sm.tsa.statespace.SARIMAX(data_column, order=(param[0], d, param[1])).fit(disp=-1)\\n except:\\n #print(\\\"model not built with params\\\".format(param))\\n pass\\n continue \\n aic = model.aic \\n #Save best model, AIC and parameters\\n if aic < best_aic:\\n best_model = model\\n best_aic = aic\\n best_param = param\\n results.append([param, model.aic]) \\n result_table = pd.DataFrame(results)\\n result_table.columns = ['parameters', 'aic']\\n #Sort in ascending order, lower AIC is better\\n result_table = result_table.sort_values(by='aic', ascending=True).reset_index(drop=True) \\n return result_table\\n \\n #Set initial values and some bounds\\n ps = range(0, 3)\\n d = range(0,1)\\n qs = range(0, 3)\\n Ps = range(0, 3)\\n D = range(0,1)\\n Qs = range(0, 3)\\n s = [3,5,7]\\n #Create a list with all possible combinations of parameters\\n parameters = product(ps,d, qs, Ps,D, Qs,s)\\n parameters_list = list(parameters)\\n len(parameters_list)\\n if loga==True:\\n series=series.apply(np.log1p)\\n series_diff=series\\n result_table = optimize_SARIMA(series_diff,parameters_list)\\n \\n #Set parameters that give the lowest AIC (Akaike Information Criteria) \\n p, d, q, P, D, Q,s = result_table.parameters[0] \\n best_model = sm.tsa.statespace.SARIMAX(series_diff, order=(p, d, q),seasonal_order=(P, D, Q, s)).fit(disp=-1)\\n if show_mdl_detail==True:\\n print(best_model.summary())\\n if plot_figures_flag==True:\\n best_model.plot_diagnostics(figsize=(15, 12)) \\n #Forecast on n_steps forward\\n forecast = best_model.forecast(nforecast)\\n if loga==True:\\n forecast=np.exp(forecast)-1 #log1p\\n forecast=pd.Series(forecast.values,name='sarimax_model')\\n lenn=series[:].shape[0]\\n new_index=pd.Index(list(range(series[-1:].index.values[0]+1,series[-1:].index.values[0]+1+nforecast)))\\n forecast.index=new_index\\n return forecast\",\n \"def stop_on_low_ais_ess(trial_id, result):\\n return result[\\\"ais_effective_sample_size\\\"] < 0.1\",\n \"def aic_c(self):\\n if hasattr(self, '_aic_c'):\\n return self._aic_c\\n else:\\n k = len(self.params)\\n n = self.data['n'].sum()\\n self._aic_c = self.aic() + (2*k**2 + 2*k)/(n - k - 1)\\n return self._aic_c\",\n \"def optimize_SARIMA(data_column,parameters_list): \\n results = []\\n best_aic = float('inf') \\n \\n #for param in tqdm(parameters_list):\\n for param in parameters_list:\\n try: model = sm.tsa.statespace.SARIMAX(data_column, order=(param[0], param[1],param[2]),\\n seasonal_order=(param[3], param[4], param[5], param[5])).fit(disp=-1)\\n # try: model = sm.tsa.statespace.SARIMAX(data_column, order=(param[0], d, param[1])).fit(disp=-1)\\n except:\\n #print(\\\"model not built with params\\\".format(param))\\n pass\\n continue \\n aic = model.aic \\n #Save best model, AIC and parameters\\n if aic < best_aic:\\n best_model = model\\n best_aic = aic\\n best_param = param\\n results.append([param, model.aic]) \\n result_table = pd.DataFrame(results)\\n result_table.columns = ['parameters', 'aic']\\n #Sort in ascending order, lower AIC is better\\n result_table = result_table.sort_values(by='aic', ascending=True).reset_index(drop=True) \\n return result_table\",\n \"def test_am_threshold(Simulator, plt, seed, rng):\\n d = 64\\n vocab = Vocabulary(d, pointer_gen=rng)\\n vocab.populate('A; B; C; D')\\n\\n d2 = int(d / 2)\\n vocab2 = Vocabulary(d2, pointer_gen=rng)\\n vocab2.populate('A; B; C; D')\\n\\n def input_func(t):\\n return '0.49 * A' if t < 0.1 else '0.8 * B'\\n\\n with spa.Network('model', seed=seed) as m:\\n m.am = ThresholdingAssocMem(\\n threshold=0.5, input_vocab=vocab, output_vocab=vocab2,\\n function=filtered_step_fn, mapping='by-key')\\n m.stimulus = spa.Transcode(input_func, output_vocab=vocab)\\n m.stimulus >> m.am\\n\\n in_p = nengo.Probe(m.am.input)\\n out_p = nengo.Probe(m.am.output, synapse=0.03)\\n\\n with Simulator(m) as sim:\\n sim.run(0.3)\\n t = sim.trange()\\n below_th = t < 0.1\\n above_th = t > 0.25\\n\\n plt.subplot(2, 1, 1)\\n plt.plot(t, similarity(sim.data[in_p], vocab))\\n plt.ylabel(\\\"Input\\\")\\n plt.subplot(2, 1, 2)\\n plt.plot(t, similarity(sim.data[out_p], vocab2))\\n plt.plot(t[above_th], np.ones(t.shape)[above_th] * 0.9, c='g', lw=2)\\n plt.ylabel(\\\"Output\\\")\\n\\n assert np.mean(sim.data[out_p][below_th]) < 0.01\\n assert_sp_close(t, sim.data[out_p], vocab2['B'], skip=0.25, duration=0.05)\",\n \"def ransac(data, hypothesis, metric, sample_size, num_iter, inlier_thresh):\\n N,d = data.shape\\n best_frac, best_hypothesis, best_mask = 0, None, None\\n for i in range(num_iter):\\n js = np.random.choice(N,size=sample_size,replace=False)\\n hypothesis_elements = data[js,:]\\n H = hypothesis(hypothesis_elements)\\n badness = np.array([metric(row,H) for row in data])\\n inlier_mask = (badness<inlier_thresh)\\n inlier_frac = inlier_mask.mean()\\n if inlier_frac>best_frac:\\n best_frac, best_hypothesis, best_mask = inlier_frac,H,inlier_mask\\n # print(H)\\n # print(inlier_mask)\\n return best_hypothesis, best_mask\",\n \"def akaike_info_criterion(log_likelihood, n_params, n_samples):\\n # Correction in case of small number of observations\\n if n_samples / float(n_params) >= 40.0:\\n aic = 2.0 * (n_params - log_likelihood)\\n else:\\n aic = 2.0 * (n_params - log_likelihood) + 2.0 * n_params * (n_params + 1.0) / (\\n n_samples - n_params - 1.0\\n )\\n return aic\",\n \"def test_conditional():\\n # verify that conditioning increases the likelihood of getting a sample with the specified\\n # categorical value\",\n \"def binarize(adata, copy=False):\\n threshold, upper, lower = 1.0, 1.0, 0.0\\n admatrix = adata.X\\n admatrix = np.where(admatrix>threshold, upper, lower)\\n if copy:\\n adata2 = adata.copy()\\n adata2.X = admatrix\\n return(adata2)\\n else:\\n adata.X = admatrix\",\n \"def ks_test(timeseries):\\r\\n\\r\\n hour_ago = time() - 3600\\r\\n ten_minutes_ago = time() - 600\\r\\n reference = scipy.array([x[1] for x in timeseries if x[0] >= hour_ago and x[0] < ten_minutes_ago])\\r\\n probe = scipy.array([x[1] for x in timeseries if x[0] >= ten_minutes_ago])\\r\\n\\r\\n if reference.size < 20 or probe.size < 20:\\r\\n return False\\r\\n\\r\\n ks_d,ks_p_value = scipy.stats.ks_2samp(reference, probe)\\r\\n\\r\\n if ks_p_value < 0.05 and ks_d > 0.5:\\r\\n adf = sm.tsa.stattools.adfuller(reference, 10)\\r\\n if adf[1] < 0.05:\\r\\n return True\\r\\n\\r\\n return False\",\n \"def early_warnings_sensitivity_analysis(series,\\n indicators=['var','ac'],\\n winsizerange = [0.10, 0.8],\\n incrwinsize = 0.10,\\n smooth = \\\"Gaussian\\\",\\n bandwidthrange = [0.05, 1.],\\n spanrange = [0.05, 1.1],\\n incrbandwidth = 0.2,\\n incrspanrange = 0.1):\\n\\n results_kendal_tau = []\\n for winsize in np.arange(winsizerange[0],winsizerange[1]+0.01,incrwinsize):\\n\\n winsize = round(winsize,3)\\n if smooth == \\\"Gaussian\\\":\\n\\n for bw in np.arange(bandwidthrange[0], bandwidthrange[1]+0.01, incrbandwidth):\\n\\n bw = round(bw, 3)\\n ews_dic_veg = ewstools.core.ews_compute(series.dropna(),\\n roll_window=winsize,\\n smooth=smooth,\\n lag_times=[1, 2],\\n ews=indicators,\\n band_width=bw)\\n\\n result = ews_dic_veg['Kendall tau']\\n result['smooth'] = bw\\n result['winsize'] = winsize\\n\\n results_kendal_tau.append(result)\\n\\n\\n elif smooth ==\\\"Lowess\\\":\\n\\n for span in np.arange(spanrange[0], spanrange[1]+0.01, incrspanrange):\\n\\n span = round(span,2)\\n ews_dic_veg = ewstools.core.ews_compute(series.dropna(),\\n roll_window=winsize,\\n smooth=smooth,\\n lag_times=[1, 2],\\n ews=indicators,\\n span=span)\\n\\n result = ews_dic_veg['Kendall tau']\\n result['smooth'] = bw\\n result['winsize'] = winsize\\n\\n results_kendal_tau.append(result)\\n\\n else:\\n\\n ews_dic_veg = ewstools.core.ews_compute(series.dropna(),\\n roll_window=winsize,\\n smooth='None',\\n lag_times=[1, 2],\\n ews=indicators)\\n\\n result = ews_dic_veg['Kendall tau']\\n result['smooth'] = 0\\n result['winsize'] = winsize\\n\\n results_kendal_tau.append(result)\\n\\n sensitivity_df = pd.concat(results_kendal_tau)\\n\\n return sensitivity_df\",\n \"def early_warnings_null_hypothesis(series,\\n indicators=['var', 'ac'],\\n roll_window=0.4,\\n smooth='Lowess',\\n span=0.1,\\n band_width=0.2,\\n lag_times=[1],\\n n_simulations=1000):\\n\\n ews_dic = ewstools.core.ews_compute(series,\\n roll_window=roll_window,\\n smooth=smooth,\\n span=span,\\n band_width=band_width,\\n ews=indicators,\\n lag_times=lag_times)\\n\\n from statsmodels.tsa.arima_model import ARIMA\\n from statsmodels.tsa.arima_process import ArmaProcess\\n\\n # Use the short_series EWS if smooth='None'. Otherwise use reiduals.\\n eval_series = ews_dic['EWS metrics']['Residuals']\\n\\n # Fit ARMA model based on AIC\\n aic_max = 10000\\n\\n for i in range(0, 2):\\n for j in range(0, 2):\\n\\n model = ARIMA(eval_series, order=(i, j, 0))\\n model_fit = model.fit()\\n aic = model_fit.aic\\n\\n print(\\\"AR\\\", \\\"MA\\\", \\\"AIC\\\")\\n print(i, j, aic)\\n\\n if aic < aic_max:\\n aic_max = aic\\n result = model_fit\\n\\n def compute_indicators(series):\\n \\\"\\\"\\\"\\n Rolling window indicators computation based on the ewstools.core.ews_compute function from\\n ewstools\\n \\\"\\\"\\\"\\n\\n df_ews = pd.DataFrame()\\n # Compute the rolling window size (integer value)\\n rw_size = int(np.floor(roll_window * series.shape[0]))\\n\\n # ------------ Compute temporal EWS---------------#\\n\\n # Compute standard deviation as a Series and add to the DataFrame\\n if 'sd' in indicators:\\n roll_sd = series.rolling(window=rw_size).std()\\n df_ews['Standard deviation'] = roll_sd\\n\\n # Compute variance as a Series and add to the DataFrame\\n if 'var' in indicators:\\n roll_var = series.rolling(window=rw_size).var()\\n df_ews['Variance'] = roll_var\\n\\n # Compute autocorrelation for each lag in lag_times and add to the DataFrame\\n if 'ac' in indicators:\\n for i in range(len(lag_times)):\\n roll_ac = series.rolling(window=rw_size).apply(\\n func=lambda x: pd.Series(x).autocorr(lag=lag_times[i]),\\n raw=True)\\n df_ews['Lag-' + str(lag_times[i]) + ' AC'] = roll_ac\\n\\n # Compute Coefficient of Variation (C.V) and add to the DataFrame\\n if 'cv' in indicators:\\n # mean of raw_series\\n roll_mean = series.rolling(window=rw_size).mean()\\n # standard deviation of residuals\\n roll_std = series.rolling(window=rw_size).std()\\n # coefficient of variation\\n roll_cv = roll_std.divide(roll_mean)\\n df_ews['Coefficient of variation'] = roll_cv\\n\\n # Compute skewness and add to the DataFrame\\n if 'skew' in indicators:\\n roll_skew = series.rolling(window=rw_size).skew()\\n df_ews['Skewness'] = roll_skew\\n\\n # Compute Kurtosis and add to DataFrame\\n if 'kurt' in indicators:\\n roll_kurt = series.rolling(window=rw_size).kurt()\\n df_ews['Kurtosis'] = roll_kurt\\n\\n # ------------Compute Kendall tau coefficients----------------#\\n\\n ''' In this section we compute the kendall correlation coefficients for each EWS\\n with respect to time. Values close to one indicate high correlation (i.e. EWS\\n increasing with time), values close to zero indicate no significant correlation,\\n and values close to negative one indicate high negative correlation (i.e. EWS\\n decreasing with time).'''\\n\\n # Put time values as their own series for correlation computation\\n time_vals = pd.Series(df_ews.index, index=df_ews.index)\\n\\n # List of EWS that can be used for Kendall tau computation\\n ktau_metrics = ['Variance', 'Standard deviation', 'Skewness', 'Kurtosis', 'Coefficient of variation', 'Smax',\\n 'Smax/Var', 'Smax/Mean'] + ['Lag-' + str(i) + ' AC' for i in lag_times]\\n # Find intersection with this list and EWS computed\\n ews_list = df_ews.columns.values.tolist()\\n ktau_metrics = list(set(ews_list) & set(ktau_metrics))\\n\\n # Find Kendall tau for each EWS and store in a DataFrame\\n dic_ktau = {x: df_ews[x].corr(time_vals, method='kendall') for x in ktau_metrics} # temporary dictionary\\n df_ktau = pd.DataFrame(dic_ktau, index=[0]) # DataFrame (easier for concatenation purposes)\\n\\n # -------------Organise final output and return--------------#\\n\\n # Ouptut a dictionary containing EWS DataFrame, power spectra DataFrame, and Kendall tau values\\n output_dic = {'EWS metrics': df_ews, 'Kendall tau': df_ktau}\\n\\n return output_dic\\n\\n process = ArmaProcess.from_estimation(result)\\n\\n # run simulations on best fitted ARIMA process and get values\\n kendall_tau = []\\n for i in range(n_simulations):\\n ts = process.generate_sample(len(eval_series))\\n\\n kendall_tau.append(compute_indicators(pd.Series(ts))['Kendall tau'])\\n\\n surrogates_kendall_tau_df = pd.concat(kendall_tau)\\n surrogates_kendall_tau_df['true_data'] = False\\n\\n # get results for true data\\n data_kendall_tau_df = compute_indicators(eval_series)['Kendall tau']\\n data_kendall_tau_df['true_data'] = True\\n\\n # return dataframe with both surrogates and true data\\n kendall_tau_df = pd.concat([data_kendall_tau_df,surrogates_kendall_tau_df])\\n\\n return kendall_tau_df\",\n \"def aic(self, tmin=None, tmax=None):\\n noise = self.ml.noise(tmin=tmin, tmax=tmax)\\n nparam = len(self.ml.parameters[self.ml.parameters.vary == True])\\n aic = -2.0 * np.log(sum(noise ** 2.0)) + 2.0 * nparam\\n return aic\",\n \"def test02a(self):\\n a = np.arange(1, 11)\\n b = bcolz.carray(a)\\n wt = [v for v in a if v <= 5]\\n cwt = [v for v in b.where(a <= 5)]\\n # print \\\"numpy ->\\\", [v for v in a if v<=5]\\n # print \\\"where ->\\\", [v for v in b.where(a<=5)]\\n self.assertTrue(wt == cwt, \\\"where() does not work correctly\\\")\",\n \"def test10(self):\\n a = np.arange(1, 11)\\n b = bcolz.carray(a)\\n bi = b.where(a <= 5)\\n ai = (v for v in a if v <= 5)\\n self.assertEqual([i for i in ai], [i for i in bi])\\n self.assertEqual([i for i in ai], [i for i in bi])\",\n \"def A_weight(signal, fs):\\n\\n b, a = A_weighting(fs)\\n return lfilter(b, a, signal)\",\n \"def info_criteria(indep,\\n dep,\\n models,\\n add_aicc=False):\\n num_data = len(indep)\\n\\n bic_calc = BicCalculator(bic_type=BIC_TYPE.STANDARD)\\n bic_calc_bkpt = BicCalculator(bic_type=BIC_TYPE.HYBRID)\\n\\n #bic_calc_bkpt = bic_calc\\n #####bic_calc_bkpt = BicCalculator(bic_type = BIC_TYPE.HOS)\\n\\n bics = []\\n aics = []\\n aiccs = []\\n for model in models:\\n\\n if model in [Model.ONE_BKPT, Model.TWO_BKPT]:\\n bic_calc_to_use = bic_calc_bkpt\\n else:\\n bic_calc_to_use = bic_calc\\n\\n estimator = model.estimator(num_end_to_skip=NUM_END_TO_SKIP,\\n num_between_to_skip=NUM_BETWEEN_TO_SKIP)\\n estimator.fit(indep, dep)\\n\\n loglikelihood = estimator.loglikelihood\\n num_params = estimator.num_params\\n\\n bic = bic_calc_to_use.bic(num_params=num_params,\\n loglikelihood=loglikelihood,\\n num_data=num_data)\\n aic = stats_util.aic(num_params=num_params,\\n loglikelihood=loglikelihood)\\n aicc = stats_util.aicc(num_params=num_params,\\n loglikelihood=loglikelihood,\\n num_data=num_data)\\n\\n bics.append(bic)\\n aics.append(aic)\\n aiccs.append(aicc)\\n ic_df = pd.DataFrame({\\\"BIC\\\": bics, \\\"AIC\\\": aics}, index=models)\\n if add_aicc:\\n ic_df[\\\"AICC\\\"] = aiccs\\n\\n wts_df = ic_df.apply(stats_util.bma_weights, axis=0)\\n wts_cols = [x + \\\" Model Wt\\\" for x in wts_df.columns]\\n wts_df.columns = wts_cols\\n both = pd.concat([ic_df, wts_df], join=\\\"outer\\\", axis=1)\\n return both\",\n \"def test_stationarity(dataframe, time_window):\\n timeseries = dataframe.iloc[:, 0]\\n # Determing rolling statistics\\n rolmean = timeseries.rolling(time_window).mean()\\n rolstd = timeseries.rolling(time_window).std()\\n\\n # Plot rolling statistics:\\n plt.figure(figsize=(18, 9))\\n plt.plot(timeseries, color=\\\"blue\\\", label=\\\"Original\\\")\\n plt.plot(rolmean, color=\\\"red\\\", label=\\\"Rolling Mean\\\")\\n plt.plot(rolstd, color=\\\"black\\\", label=\\\"Rolling Std\\\")\\n plt.legend(loc=\\\"best\\\")\\n plt.title(\\\"Rolling Mean & Standard Deviation\\\")\\n plt.show(block=False)\\n\\n # Perform Dickey-Fuller test:\\n print(\\\"Results of Dickey-Fuller Test:\\\")\\n dftest = adfuller(timeseries, autolag=\\\"AIC\\\")\\n dfoutput = pd.Series(\\n dftest[0:4],\\n index=[\\n \\\"Test Statistic\\\",\\n \\\"p-value\\\",\\n \\\"#Lags Used\\\",\\n \\\"Number of Observations Used\\\",\\n ],\\n )\\n for key, value in dftest[4].items():\\n dfoutput[\\\"Critical Value (%s)\\\" % key] = value\\n print(dfoutput)\",\n \"def activation_channels_apoz(activation):\\n if activation.ndim == 4:\\n featuremap_apoz_mat = (np.abs(activation) > 0).sum(axis=(2, 3)) / (activation.shape[2] * activation.shape[3])\\n elif activation.ndim == 2:\\n featuremap_apoz_mat = (np.abs(activation) > 0).sum(axis=1) / activation.shape[1] # batch x 1\\n else:\\n raise ValueError(\\\"activation_channels_apoz: Unsupported shape: \\\".format(activation.shape))\\n return 100 - featuremap_apoz_mat.mean(axis=0)*100\",\n \"def MyFilter(data, window_width=10, beta=2.0, draw_graph=False):\\n\\n #read data and change the format\\n if 'time' in data.columns:\\n date_list = []\\n for i in data.index:\\n date_parse = parse(str(data.ix[i].time))\\n date_list.append(date_parse)\\n data['date'] = date_list\\n data_use = data\\n data_use.index = data_use['date'].tolist()\\n data_use = data_use.drop(['date','time'], axis=1)\\n data_use.index.name = 'time'\\n else:\\n data_use = data\\n #design filter, use the kaiser window here\\n window = signal.kaiser(window_width, beta=beta)\\n data_use['close_filtered'] = signal.convolve(data_use['close'], window, mode='same') / sum(window)\\n data_use['high_frequency'] = data_use['close'] - data_use['close_filtered']\\n\\n #delete the distortion datas after filtered\\n if window_width % 2 == 0:\\n data_changed = data_use[window_width/2: -(window_width/2 - 1)]\\n else:\\n data_changed = data_use[(window_width-1)/2: -(window_width-1)/2]\\n\\n #draw graph\\n if (draw_graph == True) :\\n fig = plt.figure()\\n ax1 = plt.subplot2grid((3,1), (0,0), rowspan=2)\\n data_changed.loc[:,'close'].plot(style='r', label='original')\\n data_changed.loc[:,'close_filtered'].plot(style='k', label='filtered')\\n plt.title('Kaiser window_width = %d , const = %d' % (window_width, beta))\\n plt.legend(loc='best')\\n\\n ax2 = plt.subplot2grid((3,1), (2,0))\\n data_changed.loc[:,'high_frequency'].plot(label='high_frequency')\\n ax2.set_ylim([-150, 150])\\n plt.title('High Frequency')\\n plt.legend(loc='best')\\n plt.show()\\n # print data_use\\n # print data_changed\\n data_out = data_changed['close_filtered']\\n return np.array(data_out.tolist())\",\n \"def check_stationarity(time_series, window, figsize=(10,6)): \\n # Calculating rolling mean and standard deviation\\n rolling_mn = time_series.rolling(window).mean()\\n rolling_std = time_series.rolling(window).std()\\n \\n plt.figure(figsize=figsize)\\n plt.plot(time_series, color = 'blue',label = 'Original TS')\\n plt.plot(rolling_mn, color = 'red', label = 'Rolling Mean')\\n plt.plot(rolling_std, color = 'black', label = 'Rolling St.Dev.')\\n plt.legend(loc = 'best')\\n plt.grid(True, color = 'lightgrey')\\n plt.title('Rolling Mean & Standard Deviation of the Trade Value of Vaccines', fontsize = 10)\\n \\n # Dickey-Fuller test:\\n print('Results of Dickey-Fuller Test:')\\n fuller_test = adfuller(time_series, autolag = 'AIC')\\n results_ts = pd.Series(fuller_test[0:4], index = ['Test Statistic','P-value','#Lags Used','Number of Observations Used'])\\n for key,value in fuller_test[4].items():\\n results_ts['Critical Value (%s)'%key] = value\\n print(results_ts)\",\n \"def test08(self):\\n a = np.arange(1, 11)\\n b = bcolz.carray(a)\\n ul = [v for v in a if v <= 5]\\n u = b.where(a <= 5)\\n wl = [v for v in a if v <= 6]\\n w = b.where(a <= 6)\\n self.assertEqual(ul, list(u))\\n self.assertEqual(wl, list(w))\",\n \"def test_sag_adaptive():\\n np.random.seed(0)\\n X = sparse.rand(100, 10, density=.5, random_state=0).tocsr()\\n y = np.random.randint(0, high=2, size=100)\\n for alpha in np.logspace(-3, 1, 5):\\n clf_adaptive = SAGClassifier(\\n eta='line-search', random_state=0, alpha=alpha)\\n clf_adaptive.fit(X, y)\\n clf = SAGClassifier(\\n eta='auto', random_state=0, alpha=alpha)\\n clf.fit(X, y)\\n assert_almost_equal(clf_adaptive.score(X, y), clf.score(X, y), 1)\\n\\n clf_adaptive = SAGAClassifier(\\n eta='line-search', loss='log', random_state=0, alpha=alpha, max_iter=20)\\n clf_adaptive.fit(X, y)\\n assert np.isnan(clf_adaptive.coef_.sum()) == False\\n clf = SAGAClassifier(\\n eta='auto', loss='log', random_state=0, alpha=alpha, max_iter=20)\\n clf.fit(X, y)\\n assert_almost_equal(clf_adaptive.score(X, y), clf.score(X, y), 1)\",\n \"def quality_data(self, s):\\n known_symbols = np.mod(range(176),48)>=32\\n print('quality_data',np.sum(np.real(s[known_symbols])<0))\\n success = np.sum(np.real(s[known_symbols])<0) < 20\\n return success,0 ## no doppler estimate for data frames\",\n \"def test01(self):\\n a = np.arange(1, 11)\\n b = bcolz.carray(a)\\n wt = [v for v in a if v < 0]\\n cwt = [v for v in b.where(a < 0)]\\n # print \\\"numpy ->\\\", [v for v in a if v<0]\\n # print \\\"where ->\\\", [v for v in b.where(a<0)]\\n self.assertTrue(wt == cwt, \\\"where() does not work correctly\\\")\",\n \"def cooling_system_is_ac(bpr):\\n\\n if bpr.hvac['type_cs'] in {'T2', 'T3'}: # mini-split ac and central ac\\n return True\\n elif bpr.hvac['type_cs'] in {'T0', 'T1'}:\\n return False\\n else:\\n print('Error: Unknown cooling system')\\n return False\",\n \"def spec_to_peaks(data, value, fp = iterate_structure(generate_binary_structure(rank = 2, connectivity=2), 10)):\\n\\n max_arr = maximum_filter(data, footprint = fp)\\n return (data == max_arr) & (data > value)\",\n \"def A_weighting(fs):\\n b, a = A_weighting_analog()\\n\\n # Use the bilinear transformation to get the digital filter.\\n return bilinear(b, a, fs)\",\n \"def optaa_benthic(ds, cal_file):\\n # check to see if there is more than one deployment in the data set\\n if len(np.unique(ds['deployment'].values)) > 1:\\n raise ValueError('More than one deployment in the data set. Please structure processing request to process '\\n 'one deployment at a time.')\\n\\n # drop some of the variables:\\n # internal_timestamp == time, redundant so can remove\\n # pressure_counts == none of the OOI OPTAAs have a pressure sensor\\n # serial_number == available in the global attributes\\n # meter_type == always the same, not needed\\n # packet_type == always the same, not needed\\n # record_length == always the same, not needed\\n # checksum == not needed, used in data parsing\\n ds = ds.drop(['internal_timestamp', 'pressure_counts', 'serial_number', 'meter_type', 'packet_type',\\n 'record_length', 'checksum'])\\n\\n # check for data from a co-located CTD, if not present create the variables using NaN's as the fill value\\n if 'sea_water_temperature' not in ds.variables:\\n ds['sea_water_temperature'] = ('time', ds['deployment'].data * np.nan)\\n ds['sea_water_practical_salinity'] = ('time', ds['deployment'].data * np.nan)\\n\\n # pull out the number of wavelengths and serial number and then drop the variables (part of the metadata)\\n num_wavelengths = ds.num_wavelengths.values[0].astype(int)\\n serial_number = int(re.sub('[^0-9]', '', ds.attrs['SerialNumber']))\\n ds = ds.drop('num_wavelengths')\\n\\n # load the calibration coefficients\\n uid = ds.attrs['AssetUniqueID']\\n start_time = ds['time'][0].values.astype(float) / 10 ** 9\\n cal = load_cal_coefficients(cal_file, uid, start_time)\\n\\n # check the calibration coefficients against the deployment data\\n if cal.coeffs['serial_number'] != serial_number:\\n raise Exception('Serial Number mismatch between ac-s data and the device file.')\\n if cal.coeffs['num_wavelengths'] != num_wavelengths:\\n raise Exception('Number of wavelengths mismatch between ac-s data and the device file.')\\n\\n # remove the units from the variable names\\n rename = {\\n 'a_signal_dark_counts': 'a_signal_dark',\\n 'a_reference_dark_counts': 'a_reference_dark',\\n 'a_signal_counts': 'a_signal',\\n 'a_reference_counts': 'a_reference',\\n 'c_signal_dark_counts': 'c_signal_dark',\\n 'c_reference_dark_counts': 'c_reference_dark',\\n 'c_signal_counts': 'c_signal',\\n 'c_reference_counts': 'c_reference',\\n 'wavelength': 'wavelength_number'\\n }\\n ds = ds.rename(rename)\\n\\n # Delete the first 45 seconds of the data record per recommendation from the vendor. Note, originally the vendor\\n # recommended deleting the first 45 seconds, then 60 seconds and then 120 seconds. They never provided a data\\n # based reason for the change in recommendation. Within OOI, instruments were programmed to run for 60 seconds,\\n # then 120 seconds and then 240 seconds ... and it is all mixed up across the various data sets. So, we are\\n # going to use the 45-second recommendation and apply it to all data sets. If the vendor ever provides an analysis\\n # justifying the change in recommendation, we can revisit this.\\n ds.elapsed_run_time.values = ds.elapsed_run_time.where(ds.elapsed_run_time / 1000 > 45)\\n ds = ds.dropna(dim='time', subset=['elapsed_run_time'])\\n\\n # convert internal and external temperature sensors from raw counts to degrees Celsius\\n ds['internal_temp'] = opt_internal_temp(ds['internal_temp_raw'])\\n ds['external_temp'] = opt_external_temp(ds['external_temp_raw'])\\n\\n # calculate the median of the remaining data per burst measurement (configured to run hourly for 3 minutes)\\n # calculate the median of the remaining data per burst measurement\\n print('Calculating burst averages ...')\\n start_time = time.time()\\n burst = ds.resample(time='3600s', base=1800, loffset='1800s', skipna=True).reduce(np.median, dim='time',\\n keep_attrs=True)\\n burst = burst.where(~np.isnan(burst.deployment), drop=True)\\n stop_time = time.time()\\n elapsed_time = stop_time - start_time\\n print('... burst averaging complete. Elapsed time: %f seconds' % elapsed_time)\\n\\n # re-process the raw data in order to create the intermediate variables, correcting for the holographic\\n # grating, applying the temperature and salinity corrections and applying a baseline scatter correction\\n # to the absorption data. All intermediate processing outputs are added to the data set.\\n burst = apply_dev(burst, cal.coeffs)\\n burst = apply_tscorr(burst, cal.coeffs, burst.sea_water_temperature, burst.sea_water_practical_salinity)\\n burst = apply_scatcorr(burst, cal.coeffs)\\n\\n # add the jump offsets as NaN's if the grating index correction was not used\\n if 'a_jump_offsets' not in ds.variables:\\n ds['a_jump_offsets'] = ('time', ds['deployment'].data * np.nan)\\n ds['c_jump_offsets'] = ('time', ds['deployment'].data * np.nan)\\n\\n # estimate chlorophyll and POC and calculate select absorption ratios\\n burst = estimate_chl_poc(burst, cal.coeffs)\\n burst = calculate_ratios(burst)\\n\\n # create a xarray dataset of the 2D variables, padding the number of wavelengths to a consistent\\n # length of 100 using fill values.\\n wavelength_number = np.arange(100).astype(int) # used as a dimensional variable\\n pad = 100 - num_wavelengths\\n fill_nan = np.tile(np.ones(pad) * np.nan, (len(burst.time), 1))\\n fill_int = np.tile(np.ones(pad) * FILL_INT, (len(burst.time), 1))\\n\\n wavelength_a = np.concatenate([burst.wavelength_a.values, fill_nan], axis=1)\\n wavelength_c = np.concatenate([burst.wavelength_c.values, fill_nan], axis=1)\\n\\n ac = xr.Dataset({\\n 'wavelength_a': (['time', 'wavelength_number'], wavelength_a),\\n 'a_signal': (['time', 'wavelength_number'], np.concatenate([burst.a_signal, fill_int], axis=1).astype(int)),\\n 'a_reference': (['time', 'wavelength_number'], np.concatenate([burst.a_reference, fill_int],\\n axis=1).astype(int)),\\n 'optical_absorption': (['time', 'wavelength_number'], np.concatenate([burst.optical_absorption, fill_nan],\\n axis=1)),\\n 'apg': (['time', 'wavelength_number'], np.concatenate([burst.apg, fill_nan], axis=1)),\\n 'apg_ts': (['time', 'wavelength_number'], np.concatenate([burst.apg_ts, fill_nan], axis=1)),\\n 'apg_ts_s': (['time', 'wavelength_number'], np.concatenate([burst.apg_ts_s, fill_nan], axis=1)),\\n 'wavelength_c': (['time', 'wavelength_number'], wavelength_c),\\n 'c_signal': (['time', 'wavelength_number'], np.concatenate([burst.c_signal, fill_int], axis=1).astype(int)),\\n 'c_reference': (['time', 'wavelength_number'], np.concatenate([burst.c_reference, fill_int],\\n axis=1).astype(int)),\\n 'beam_attenuation': (['time', 'wavelength_number'], np.concatenate([burst.beam_attenuation, fill_nan], axis=1)),\\n 'cpg': (['time', 'wavelength_number'], np.concatenate([burst.cpg, fill_nan], axis=1)),\\n 'cpg_ts': (['time', 'wavelength_number'], np.concatenate([burst.cpg_ts, fill_nan], axis=1)),\\n }, coords={'time': (['time'], burst.time.values), 'wavelength_number': wavelength_number})\\n\\n # drop the original 2D variables from the burst data set\\n drop = burst.drop(['wavelength_number', 'wavelength_a', 'a_signal', 'a_reference',\\n 'optical_absorption', 'apg', 'apg_ts', 'apg_ts_s',\\n 'wavelength_c', 'c_signal', 'c_reference',\\n 'beam_attenuation', 'cpg', 'cpg_ts'])\\n\\n # reset the data type for the 'a' and 'c' signal and reference dark values, and the other raw parameters\\n int_arrays = ['a_signal_dark', 'a_reference_dark', 'c_signal_dark', 'c_reference_dark',\\n 'internal_temp_raw', 'external_temp_raw', 'deployment']\\n for k in drop.variables:\\n if k in int_arrays:\\n drop[k] = drop[k].astype(int)\\n\\n # recombine the two datasets\\n optaa = xr.merge([drop, ac])\\n\\n # reset the attributes, which the merging drops\\n optaa.attrs = burst.attrs\\n for v in optaa.variables:\\n optaa[v].attrs = burst[v].attrs\\n\\n # reset some attributes\\n for key, value in ATTRS.items():\\n for atk, atv in value.items():\\n if key in optaa.variables:\\n optaa[key].attrs[atk] = atv\\n\\n # add the original variable name as an attribute, if renamed\\n for key, value in rename.items():\\n optaa[value].attrs['ooinet_variable_name'] = key\\n\\n # add the actual number of wavelengths to the dataset as an attribute\\n optaa['wavelength_number'].attrs['actual_wavelengths'] = num_wavelengths\\n\\n # if the filter index was used to adjust the spectral jumps, add that attribute to the data set\\n if cal.coeffs['grate_index']:\\n optaa['a_jump_offsets'].attrs['grate_index'] = cal.coeffs['grate_index']\\n optaa['c_jump_offsets'].attrs['grate_index'] = cal.coeffs['grate_index']\\n\\n return optaa\",\n \"def calculate_AIC(self): \\n hmm_ll_calculator = LikelihoodInfEngineHMM(\\n dbn=self.model.dbn, hidden_node_index=0, check_dbn=False)\\n ll_full = hmm_ll_calculator.calc_ll(self.seq_list, self.mismask_list)\\n return 2 * ll_full - 2 * self._get_parameter_count()\",\n \"def bearish_engulfing(self):\\n self.data['bearish_engulfing'] = ((self.data['Close'].shift(1) > self.data['Open'].shift(1)) &\\\\\\n (self.data['Open'] > self.data['Close']) & \\\\\\n (self.data['Open'] >= self.data['Close'].shift(1)) & \\\\\\n (self.data['Open'].shift(1) >= self.data['Close']) & \\\\\\n ((self.data['Open']-self.data['Close']) > (self.data['Close'].shift(1)-self.data['Open'].shift(1))))\",\n \"def first_peak_auc_rahman(datas):\\n fig, ax = plt.subplots(figsize=(8, 8))\\n columns = ('A_1', 'tau_1', 'gamma_1', 'A_2', 'tau_2', 'gamma_2')\\n index = [i[\\\"name\\\"] for i in datas]\\n df = pd.DataFrame(index=index, columns=columns)\\n for i in range(1, 2):\\n data = datas[i-1]\\n r = data['r'] * 10 # convert from angstroms to nm\\n t = data['t']\\n g = data['g']\\n\\n I = np.empty_like(t)\\n I[:] = np.nan\\n\\n # Get area under the curve\\n #for i in range(0, t.shape[0], 2):\\n for i in range(0, t.shape[0]):\\n I[i] = get_auc(data, i)\\n ls = '--'\\n\\n ax.semilogy(t, I, marker='.', linestyle=ls, label=data['name'])\\n\\n # Get finite values\\n I_idx = np.where(~np.isnan(I))\\n I = I[np.isfinite(I)]\\n t = t[I_idx]\\n \\n print(t.shape)\\n print(I.shape)\\n \\n\\n #upper_limit = np.where(t < 0.28)[0][-1]\\n if data[\\\"name\\\"] != \\\"IXS (11/18)\\\":\\n print(data[\\\"name\\\"])\\n upper_limit = np.where(t < 0.95)[0][-1]\\n t = t[:upper_limit]\\n I = I[:upper_limit]\\n \\n\\n # Calling `compute_fit` to get the compressed exponential function fit\\n try:\\n fit, popt = compute_fit(t, I)\\n except:\\n print(f\\\"Fit for {data['name']} has failed\\\")\\n continue\\n if (1 / popt[1]) < (1 / popt[4]):\\n df.loc[data[\\\"name\\\"]][\\\"A_1\\\"] = popt[0]\\n df.loc[data[\\\"name\\\"]][\\\"tau_1\\\"] = 1 / popt[1]\\n df.loc[data[\\\"name\\\"]][\\\"gamma_1\\\"] = popt[2]\\n df.loc[data[\\\"name\\\"]][\\\"A_2\\\"] = popt[3]\\n df.loc[data[\\\"name\\\"]][\\\"tau_2\\\"] = 1 / popt[4]\\n df.loc[data[\\\"name\\\"]][\\\"gamma_2\\\"] = popt[5]\\n print(data[\\\"name\\\"])\\n print(f\\\"tau_1 is: {1/popt[1]}\\\")\\n print(f\\\"A_1 is: {popt[0]}\\\")\\n print(f\\\"gamma_1 is: {popt[2]}\\\")\\n print(f\\\"tau_2 is: {1/popt[4]}\\\")\\n print(f\\\"A_2 is: {popt[3]}\\\")\\n print(f\\\"gamma_2 is: {popt[5]}\\\")\\n else:\\n df.loc[data[\\\"name\\\"]][\\\"A_1\\\"] = popt[3]\\n df.loc[data[\\\"name\\\"]][\\\"tau_1\\\"] = 1 / popt[4]\\n df.loc[data[\\\"name\\\"]][\\\"gamma_1\\\"] = popt[5]\\n df.loc[data[\\\"name\\\"]][\\\"A_2\\\"] = popt[0]\\n df.loc[data[\\\"name\\\"]][\\\"tau_2\\\"] = 1 / popt[1]\\n df.loc[data[\\\"name\\\"]][\\\"gamma_2\\\"] = popt[2]\\n print(data[\\\"name\\\"])\\n print(f\\\"tau_1 is: {1/popt[4]}\\\")\\n print(f\\\"A_1 is: {popt[3]}\\\")\\n print(f\\\"gamma_1 is: {popt[5]}\\\")\\n print(f\\\"tau_2 is: {1/popt[1]}\\\")\\n print(f\\\"A_2 is: {popt[0]}\\\")\\n print(f\\\"gamma_2 is: {popt[2]}\\\")\\n ax.semilogy(t, fit, linestyle=ls, color='k', label=f\\\"{data['name']}_fit\\\")\\n ax.set_title(data['name'], fontsize=12)\\n\\n # Plot the compressed exponential functions given from 2018 Phys. Rev.\\n #A_t = 0.42*(np.exp(-(t/0.12)**1.57)) + 0.026*(np.exp(-(t/0.4)**4.1))\\n #ax.plot(t, A_t, label=\\\"IXS fit (2018 Phys. Rev.) at 310 K\\\")\\n #A_t = 0.45*(np.exp(-(t/0.12)**1.57)) + 0.018*(np.exp(-(t/0.43)**12.8))\\n #ax.plot(t, A_t, label=\\\"IXS fit (2018 Phys. Rev.) at 295 K K\\\")\\n ax.xaxis.set_major_locator(MultipleLocator(0.25))\\n ax.set_xlim((0.00, 1.0))\\n ax.set_ylim((5e-3, 1.0))\\n ax.set_ylabel(r'$A(t)$')\",\n \"def filterHighs( adata, bandlimit = 5000) :\\n\\n # TODO: compute Fourier representation of data\\n\\n fig = plt.figure()\\n fig.add_subplot(2, 1, 1)\\n plt.plot( np.real(fdata))\\n plt.xlim( [0,adata.size])\\n\\n # TODO: filter data\\n\\n fig.add_subplot(2, 1, 2)\\n plt.plot( np.real(fdata))\\n plt.xlim( [0,adata.size])\\n plt.show()\\n\\n # TODO: restore time domain representation of data\\n\\n return adata_filtered\",\n \"def test_meta_fit_performance(meta_res, signal_masks, simulatedata_cbma):\\n _, (ground_truth_foci, _) = simulatedata_cbma\\n mask = meta_res.masker.mask_img\\n ground_truth_foci_ijks = [tuple(mm2vox(focus, mask.affine)) for focus in ground_truth_foci]\\n sig_idx, nonsig_idx = [\\n meta_res.masker.transform(img).astype(bool).squeeze() for img in signal_masks\\n ]\\n\\n # all estimators generate p-values\\n p_array = meta_res.get_map(\\\"p\\\", return_type=\\\"array\\\")\\n\\n # poor performer(s)\\n if (\\n isinstance(meta_res.estimator, ale.ALE)\\n and isinstance(meta_res.estimator.kernel_transformer, kernel.KDAKernel)\\n and meta_res.estimator.get_params().get(\\\"null_method\\\") == \\\"approximate\\\"\\n ):\\n good_sensitivity = True\\n good_specificity = False\\n elif (\\n isinstance(meta_res.estimator, ale.ALE)\\n and isinstance(meta_res.estimator.kernel_transformer, kernel.KDAKernel)\\n and \\\"montecarlo\\\" in meta_res.estimator.get_params().get(\\\"null_method\\\")\\n ):\\n good_sensitivity = False\\n good_specificity = True\\n elif (\\n isinstance(meta_res.estimator, ale.ALE)\\n and type(meta_res.estimator.kernel_transformer) == kernel.KDAKernel\\n and \\\"montecarlo\\\" in meta_res.estimator.get_params().get(\\\"null_method\\\")\\n ):\\n good_sensitivity = False\\n good_specificity = True\\n elif (\\n isinstance(meta_res.estimator, ale.ALE)\\n and type(meta_res.estimator.kernel_transformer) == kernel.KDAKernel\\n and meta_res.estimator.get_params().get(\\\"null_method\\\") == \\\"approximate\\\"\\n ):\\n good_sensitivity = True\\n good_specificity = False\\n elif (\\n isinstance(meta_res.estimator, mkda.MKDADensity)\\n and isinstance(meta_res.estimator.kernel_transformer, kernel.ALEKernel)\\n and meta_res.estimator.get_params().get(\\\"null_method\\\") != \\\"reduced_montecarlo\\\"\\n ):\\n good_sensitivity = False\\n good_specificity = True\\n else:\\n good_sensitivity = True\\n good_specificity = True\\n\\n _check_p_values(\\n p_array,\\n meta_res.masker,\\n sig_idx,\\n nonsig_idx,\\n ALPHA,\\n ground_truth_foci_ijks,\\n n_iters=None,\\n good_sensitivity=good_sensitivity,\\n good_specificity=good_specificity,\\n )\",\n \"def test_AFQ_anisotropic():\\n _, bids_path, _ = get_temp_hardi()\\n myafq = api.AFQ(\\n bids_path=bids_path,\\n dmriprep='vistasoft',\\n min_bval=1990,\\n max_bval=2010,\\n b0_threshold=50,\\n reg_template=\\\"mni_T1\\\",\\n reg_subject=\\\"power_map\\\")\\n\\n gtab = myafq.gtab[\\\"01\\\"]\\n\\n # check the b0s mask is correct\\n b0s_mask = np.zeros(160, dtype=bool)\\n b0s_mask[0:10] = True\\n npt.assert_equal(gtab.b0s_mask, b0s_mask)\\n\\n # check that only b values in the b val range passed\\n bvals_in_range = \\\\\\n np.logical_and((gtab.bvals > 1990), (gtab.bvals < 2010))\\n bvals_in_range_or_0 = \\\\\\n np.logical_or(bvals_in_range, gtab.b0s_mask)\\n npt.assert_equal(bvals_in_range_or_0, np.ones(160, dtype=bool))\\n\\n # check that the apm map was made\\n myafq.mapping\\n assert op.exists(op.join(\\n myafq.results_dir[\\\"01\\\"],\\n 'sub-01_ses-01_dwi_model-CSD_APM.nii.gz'))\",\n \"def is_increasing(data):\\n pass\",\n \"def bipartite_matching(data=None, is_ascend=_Null, threshold=_Null, topk=_Null, out=None, name=None, **kwargs):\\n return (0,)\",\n \"def BIC(y,yhat,k):\\r\\n y = np.asarray(y)\\r\\n yhat = np.asarray(yhat)\\r\\n \\r\\n N = yhat.size\\r\\n y = y.reshape(N,)\\r\\n yhat = yhat.reshape(N,)\\r\\n residuals = y-yhat\\r\\n sse = np.sum(residuals**2) #sum of squared errors\\r\\n N = len(y)\\r\\n Lhat = sse/(N-k)\\r\\n return(N*np.log(Lhat) + k*np.log(N))\",\n \"def test_am_threshold(Simulator, plt, seed, rng):\\n D = 64\\n vocab = Vocabulary(D, rng=rng)\\n vocab.parse('A+B+C+D')\\n\\n D2 = int(D / 2)\\n vocab2 = Vocabulary(D2, rng=rng)\\n vocab2.parse('A+B+C+D')\\n\\n def input_func(t):\\n return vocab.parse('0.49*A').v if t < 0.1 else vocab.parse('0.8*A').v\\n\\n with nengo.Network('model', seed=seed) as m:\\n am = AssociativeMemory(vocab, vocab2, threshold=0.5)\\n in_node = nengo.Node(output=input_func, label='input')\\n nengo.Connection(in_node, am.input)\\n\\n in_p = nengo.Probe(in_node)\\n out_p = nengo.Probe(am.output, synapse=0.03)\\n\\n sim = Simulator(m)\\n sim.run(0.3)\\n t = sim.trange()\\n below_th = t < 0.1\\n above_th = t > 0.25\\n\\n plt.subplot(2, 1, 1)\\n plt.plot(t, nengo.spa.similarity(sim.data[in_p], vocab))\\n plt.ylabel(\\\"Input\\\")\\n plt.legend(vocab.keys, loc='best')\\n plt.subplot(2, 1, 2)\\n plt.plot(t, nengo.spa.similarity(sim.data[out_p], vocab2))\\n plt.plot(t[above_th], np.ones(t.shape)[above_th] * 0.8, c='g', lw=2)\\n plt.ylabel(\\\"Output\\\")\\n plt.legend(vocab.keys, loc='best')\\n\\n assert similarity(sim.data[in_p][below_th], vocab.parse(\\\"A\\\").v) > 0.48\\n assert similarity(sim.data[in_p][above_th], vocab.parse(\\\"A\\\").v) > 0.79\\n assert similarity(sim.data[out_p][below_th], vocab2.parse(\\\"0\\\").v) < 0.01\\n assert similarity(sim.data[out_p][above_th], vocab2.parse(\\\"A\\\").v) > 0.79\",\n \"def decide(el, il, model, threshold):\\n\\n if model == 0:\\n return el >= threshold[0] and il >=threshold[1]\\n elif model == 1:\\n return el >= threshold[0] or il >= threshold[1]\\n elif model == 2:\\n return harmonic_mean([el, il]) >= harmonic_mean(threshold)\\n else:\\n return bool(round(random.random()))\",\n \"def test_ICA(epochs):\\n ep = [epochs.epo1, epochs.epo2]\\n icas = prep.ICA_fit(ep, n_components=15, method='infomax', fit_params=dict(extended=True),\\n random_state=97)\\n # check that the number of componenents is similar between the two participants\\n for i in range(0, len(icas)-1):\\n mne.preprocessing.ICA.get_components(\\n icas[i]).shape == mne.preprocessing.ICA.get_components(icas[i+1]).shape\\n # cleaned_epochs_ICA = prep.ICA_choice_comp(icas, ep) # pb interactive window\\n # check signal better after than before\\n # check bad channels are not deleted\\n # assert epochs.epo1.info['ch_names'] == cleaned_epochs_ICA[0].info['ch_names']\\n # assert epochs.epo2.info['ch_names'] == cleaned_epochs_ICA[1].info['ch_names']\",\n \"def asy_ei(gp, acq_optimiser, anc_data):\\n curr_best = anc_data.curr_max_val\\n def _ei_acq(x):\\n mu, sigma = gp.eval(x, uncert_form='std')\\n Z = (mu - curr_best) / sigma\\n return (mu - curr_best)*normal_distro.cdf(Z) + sigma*normal_distro.pdf(Z)\\n return _optimise_acquisition(_ei_acq, acq_optimiser, anc_data)\",\n \"def learn_ICA(X, k):\\n\\n # TODO: YOUR CODE HERE\",\n \"def modelStats(model,data, azSpacing=0.5,zenSpacing=0.5):\\n az = np.linspace(0,360.,int(360./azSpacing)+1)\\n zz = np.linspace(0,90,int(90./zenSpacing)+1)\\n\\n azCtr = 0\\n iCtr = 0\\n chi = 0\\n SS_tot = 0\\n SS_res = 0\\n SS_reg = 0\\n test_ctr = 0\\n reg_ctr = 0\\n\\n for i in az:\\n if(i - azSpacing/2. < 0) :\\n criterion = (data[:,0] < (i + azSpacing/2.)) | (data[:,0] > (360. - azSpacing/2.) )\\n else:\\n criterion = (data[:,0] < (i + azSpacing/2.)) & (data[:,0] > (i - azSpacing/2.) )\\n\\n ind = np.array(np.where(criterion))\\n \\n if ind.size > 0:\\n tmp = data[ind,:]\\n tmp = tmp.reshape(tmp.shape[1],tmp.shape[2])\\n jCtr = 0\\n for j in zz:\\n # disregard any observation above 80 in zenith, too noisy\\n if j < 80. + zenSpacing:\\n criterion = (tmp[:,1] < (j + zenSpacing/2.)) & (tmp[:,1] > (j - zenSpacing/2.) ) \\n tmpZ = np.array( tmp[indZ[:],2] )\\n if indZ.size > 3 and not np.isnan(model[iCtr,jCtr]): # and (model[iCtr,jCtr] > 0.00001 or model[iCtr,jCtr] < -0.00001):\\n test_data = reject_outliers(reject_abs( tmp[indZ,2],70. ),5.)\\n #print(i,j,test_data,model[iCtr,jCtr])\\n if test_data.size > 0:\\n y_mean = np.mean(test_data)\\n SS_reg += (model[iCtr,jCtr] - y_mean)**2\\n reg_ctr += 1\\n for obs in test_data:\\n #chi += (obs - model[iCtr,jCtr]) ** 2 / model[iCtr,jCtr]\\n SS_tot += (obs - y_mean) ** 2\\n SS_res += (obs - model[iCtr,jCtr])**2\\n test_ctr += 1\\n jCtr += 1\\n iCtr += 1\\n\\n rr = 1. - SS_res/SS_tot\\n\\n rms = np.sqrt(SS_res) * 1./test_ctr\\n\\n # gives an indication of how different the models would be between the test and training data set \\n rms2 = np.sqrt(SS_reg) * 1./reg_ctr\\n\\n # Used in matlab instead of rr\\n norm_res = np.sqrt(SS_res)\\n mse = 1./(2.*test_ctr) * SS_res\\n return mse,rr,rms,rms2\",\n \"def ct_bis(env, a, median, mad):\\n\\n\\tb1 = (a.T < median - env.threshold * mad).T\\n\\tb2 = a[:, :a.shape[1] - 1] < a[:, 1:]\\n\\tb3 = a[:, 1:] < a[:, :a.shape[1] - 1]\\n\\tb1[:, :a.shape[1] - 1] = b1[:, :a.shape[1] - 1] & b2\\n\\tb1[:, 1:] = b1[:, 1:] & b3\\n\\treturn (b1)\",\n \"def filtfilt_gust_opt_func(ics, b, a, x):\\n m = max(len(a), len(b)) - 1\\n z0f = ics[:m]\\n z0b = ics[m:]\\n y_f = lfilter(b, a, x, zi=z0f)[0]\\n y_fb = lfilter(b, a, y_f[::-1], zi=z0b)[0][::-1]\\n\\n y_b = lfilter(b, a, x[::-1], zi=z0b)[0][::-1]\\n y_bf = lfilter(b, a, y_b, zi=z0f)[0]\\n value = np.sum((y_fb - y_bf)**2)\\n return value\",\n \"def test_matching_knots(kind, al, au, tl, tu, bl, bu, cnt):\\n prior = SmoothingPrior()\\n prior_set = dict(\\n prior_type=kind,\\n age_lower=al,\\n age_upper=au,\\n time_lower=tl,\\n time_upper=tu,\\n born_lower=bl,\\n born_upper=bu\\n )\\n for elem, value in prior_set.items():\\n setattr(prior, elem, value)\\n prior.validate_and_normalize()\\n grid = SimpleNamespace()\\n grid.ages = np.arange(20)\\n grid.times = np.array([1990, 2000, 2005, 2010, 2015])\\n age_time = list(matching_knots(grid, prior))\\n assert len(age_time) == cnt\\n # uniqueness\\n assert len({(a, t) for (a, t) in age_time}) == len(age_time)\",\n \"def adf_test(series,title=''):\\n print(f'Augmented Dickey-Fuller Test: {title}')\\n result = adfuller(series.dropna(),autolag='AIC') # .dropna() handles differenced data\\n \\n labels = ['ADF test statistic','p-value','# lags used','# observations']\\n out = pd.Series(result[0:4],index=labels)\\n\\n for key,val in result[4].items():\\n out[f'critical value ({key})']=val\\n \\n print(out.to_string()) # .to_string() removes the line \\\"dtype: float64\\\"\\n \\n if result[1] <= 0.05:\\n print(\\\"Strong evidence against the null hypothesis\\\")\\n print(\\\"Reject the null hypothesis\\\")\\n print(\\\"Data has no unit root and is stationary\\\")\\n else:\\n print(\\\"Weak evidence against the null hypothesis\\\")\\n print(\\\"Fail to reject the null hypothesis\\\")\\n print(\\\"Data has a unit root and is non-stationary\\\")\",\n \"def maks2(a, b, c):\\n m = a\\n if b > m:\\n m = b\\n if c > m:\\n m = c\\n \\n return m\",\n \"def test_weight_is_positive_carn(self):\\n nt.assert_greater(self.carn.weight, 0)\",\n \"def test05(self):\\n a = np.arange(1, 11)\\n b = bcolz.carray(a)\\n wt = [v for v in a if v <= 5][2:]\\n cwt = [v for v in b.where(bcolz.carray(a <= 5), skip=2)]\\n # print \\\"numpy ->\\\", [v for v in a if v<=5][2:]\\n # print \\\"where ->\\\", [v for v in b.where(bcolz.carray(a<=5), skip=2)]\\n self.assertTrue(wt == cwt, \\\"where() does not work correctly\\\")\",\n \"def anoise(this, *args, **kargs):\\n\\t\\t\\n\\t\\t# Arguments\\n\\t\\tif not args: args = [50]\\n\\t\\t\\n\\t\\t# Kernel's retrieval\\n\\t\\tanoisek = this._ANOISEK\\n\\t\\tif anoisek is None: return None\\n\\t\\t\\n\\t\\t# More magic\\n\\t\\tbin = this._BINARY\\n\\t\\tfor thresh in args:\\n\\t\\t\\tbin[:,:] = (cv2.filter2D(bin, -1, anoisek) / 2.55 > thresh) * 255\\n\\t\\treturn True\",\n \"def test_continuity():\\n dc = cs[:,0:Nr-1]-cs[:,1:Nr]\\n assert dc.any < cmax\",\n \"def is_stationary(self) -> bool:\\n ad_fuller_result = adfuller(self.y.dropna(), autolag='AIC')\\n p_value = ad_fuller_result[1]\\n return p_value <= 0.5\",\n \"def control_chart_analysis(value, c_l, param, anal_type=None):\\n try:\\n w_w = param[\\\"ww\\\"]\\n w_c = param[\\\"wc\\\"]\\n # val = np.array(value, dtype=pd.Series)\\n if anal_type == \\\"lower\\\":\\n lwl = c_l.loc[c_l[\\\"Kind\\\"] == \\\"LWL\\\", \\\"Value\\\"].values[0]\\n lcl = c_l.loc[c_l[\\\"Kind\\\"] == \\\"LCL\\\", \\\"Value\\\"].values[0]\\n len_w = len(value[(value < lwl) & (value >= lcl)])\\n len_c = len(value[(value < lcl)])\\n else:\\n uwl = c_l.loc[c_l[\\\"Kind\\\"] == \\\"UWL\\\", \\\"Value\\\"].values[0]\\n ucl = c_l.loc[c_l[\\\"Kind\\\"] == \\\"UCL\\\", \\\"Value\\\"].values[0]\\n len_w = len(value[(value > uwl) & (value <= ucl)])\\n len_c = len(value[(value > ucl)])\\n beta = (w_w * len_w + w_c * len_c) / (max(w_w, w_c) * len(value))\\n return [beta, len_w / len(value), len_c / len(value)]\\n except:\\n raise\",\n \"def maCrossoverStrategy(df, i:int):\\n\\n\\tif not df.__contains__('50_ema') and not df.__contains__('200_ema'):\\n\\t\\tIndicators.AddIndicator(df, indicator_name=\\\"ema\\\", col_name=\\\"50_ema\\\", args=50)\\n\\t\\tIndicators.AddIndicator(df, indicator_name=\\\"ema\\\", col_name=\\\"200_ema\\\", args=200)\\n\\n\\tif i > 0 and df['50_ema'][i-1] <= df['200_ema'][i-1] and \\\\\\n\\t\\tdf['50_ema'][i] > df['200_ema'][i]:\\n\\t\\treturn df['close'][i]\\n\\n\\treturn False\",\n \"def AIC(y_true, y_pred, n_features):\\n ll = log_likelihood(y_true, y_pred)\\n AIC = 2 * n_features - 2 * ll\\n return AIC\",\n \"def test00(self):\\n a = np.arange(1, 11)\\n b = bcolz.carray(a)\\n wt = [v for v in a if v > 0]\\n cwt = [v for v in b.where(a > 0)]\\n # print \\\"numpy ->\\\", [v for v in a if v>0]\\n # print \\\"where ->\\\", [v for v in b.where(a>0)]\\n self.assertTrue(wt == cwt, \\\"where() does not work correctly\\\")\",\n \"def __gt__(self, *args):\\n return _ida_hexrays.cif_t___gt__(self, *args)\",\n \"def baseline(data):\\n weights = weighting(data)\\n return np.inner(weights,data['clicks'])/weights.sum()\",\n \"def on(series, on_power_threshold=DEFAULT_ON_POWER_THRESHOLD):\\n # TODO: replace this evil hack to handle dataframes(!)\\n if isinstance(series, pd.DataFrame):\\n series = series.icol(0)\\n\\n when_on = series >= on_power_threshold\\n return when_on\",\n \"def maxAlk(H0,C,Pt,Sit,parm):\\n keys = parm.dtype.names\\n\\n bc = np.broadcast(parm,C,Pt,Sit)\\n res = np.empty(bc.shape)\\n resf = res.flat\\n for i,(p,C,Pt,Sit) in enumerate(bc):\\n d = dict((k,p[k]) for k in keys)\\n try:\\n H = solveACpF(H0,C,Pt,Sit,**d)\\n except ValueError:\\n print i\\n raise\\n resf[i] = ACoC(H,**d)*C - F(H0,Pt,Sit,**d)\\n\\n return res\",\n \"def calculate_top_cond(counts, dos, cov_mat, rsid1, \\n return_rsid1=False):\\n acov_mat = cov_mat.copy(deep=True)\\n acov_mat[rsid1] = dos.ix[rsid1, acov_mat.index]\\n\\n def _calc_model(geno, counts, cov_mat):\\n t = cov_mat.copy(deep=True)\\n t['ts'] = geno\\n return(sm.OLS(counts, t).fit().pvalues['ts'])\\n\\n def _calc_model_two(geno, counts, cov_mat, rsid1):\\n t = cov_mat.copy(deep=True)\\n t['ts'] = geno\\n fit = sm.OLS(counts, t).fit()\\n return(pd.Series({'aic': fit.aic,\\n 'rsid2': fit.pvalues['ts']}))\\n\\n '''\\n pvalues = dos.apply(_calc_model, axis=1, args=(counts, \\n acov_mat))\\n '''\\n pvalues = dos.apply(_calc_model_two, axis=1, args=(counts, \\n acov_mat, rsid1))\\n # Mask collinearity\\n pvalues.ix[rsid1, 'rsid2'] = 1\\n return(pvalues)\",\n \"def ica(data, k, c, svdmethod=\\\"direct\\\", maxiter=100, tol=0.000001, seed=0):\\n # get count\\n n = data.count()\\n\\n # reduce dimensionality\\n scores, latent, comps = svd(data, k, meansubtract=0, method=svdmethod)\\n\\n # whiten data\\n whtmat = real(dot(inv(diag(latent/sqrt(n))), comps))\\n unwhtmat = real(dot(transpose(comps), diag(latent/sqrt(n))))\\n wht = data.mapValues(lambda x: dot(whtmat, x))\\n\\n # do multiple independent component extraction\\n if seed != 0:\\n random.seed(seed)\\n b = orth(random.randn(k, c))\\n b_old = zeros((k, c))\\n iter = 0\\n minabscos = 0\\n errvec = zeros(maxiter)\\n\\n while (iter < maxiter) & ((1 - minabscos) > tol):\\n iter += 1\\n # update rule for pow3 non-linearity (TODO: add others)\\n b = wht.map(lambda (_, v): v).map(lambda x: outer(x, dot(x, b) ** 3)).sum() / n - 3 * b\\n # make orthogonal\\n b = dot(b, real(sqrtm(inv(dot(transpose(b), b)))))\\n # evaluate error\\n minabscos = min(abs(diag(dot(transpose(b), b_old))))\\n # store results\\n b_old = b\\n errvec[iter-1] = (1 - minabscos)\\n\\n # get un-mixing matrix\\n w = dot(transpose(b), whtmat)\\n\\n # get components\\n sigs = data.mapValues(lambda x: dot(w, x))\\n\\n return w, sigs\",\n \"def ICA_Denoise(Y, ica_model, noise_std):\\n\\n # TODO: YOUR CODE HERE\",\n \"def test04(self):\\n a = np.arange(1, 11)\\n b = bcolz.carray(a)\\n wt = [v for v in a if v <= 5][:3]\\n cwt = [v for v in b.where(bcolz.carray(a <= 5), limit=3)]\\n # print \\\"numpy ->\\\", [v for v in a if v<=5][:3]\\n # print \\\"where ->\\\", [v for v in b.where(bcolz.carray(a<=5), limit=3)]\\n self.assertTrue(wt == cwt, \\\"where() does not work correctly\\\")\",\n \"def bearish_harami(self):\\n self.data['bearish_harami'] = ((self.data['Close'].shift(1) > self.data['Open'].shift(1)) & \\\\\\n (self.data['Open'] > self.data['Close']) & \\\\\\n (self.data['Open'] <= self.data['Close'].shift(1)) &\\\\\\n (self.data['Open'].shift(1) <= self.data['Close']) & \\\\\\n ((self.data['Open'] - self.data['Close']) < (self.data['Close'].shift(1) - self.data['Open'].shift(1))))\",\n \"def a_test_predict_is_nonconstant():\\n model = ARIMAX(formula=\\\"y ~ x1\\\", data=data, ar=1, ma=1, family=Exponential())\\n x = model.fit('BBVI',iterations=200)\\n predictions = model.predict_is(h=10, fit_method='BBVI', intervals=False)\\n assert(not np.all(predictions.values==predictions.values[0]))\",\n \"def test03(self):\\n a = np.arange(1, 11)\\n b = bcolz.carray(a)\\n wt = [v for v in a if v <= 5]\\n cwt = [v for v in b.where(bcolz.carray(a <= 5))]\\n # print \\\"numpy ->\\\", [v for v in a if v<=5]\\n # print \\\"where ->\\\", [v for v in b.where(bcolz.carray(a<=5))]\\n self.assertTrue(wt == cwt, \\\"where() does not work correctly\\\")\",\n \"def is_artificial(self):\\n\\t\\treturn 0\",\n \"def detect_beats(ecg, rate, ransac_window_size=5.0, lowfreq=5.0, highfreq=15.0):\\n\\n ransac_window_size = int(ransac_window_size * rate)\\n\\n lowpass = scipy.signal.butter(1, highfreq / (rate / 2.0), 'low')\\n highpass = scipy.signal.butter(1, lowfreq / (rate / 2.0), 'high')\\n # TODO: Could use an actual bandpass filter\\n ecg_low = scipy.signal.filtfilt(*lowpass, x=ecg)\\n ecg_band = scipy.signal.filtfilt(*highpass, x=ecg_low)\\n\\n # Square (=signal power) of the first difference of the signal\\n decg = np.diff(ecg_band)\\n decg_power = decg ** 2\\n\\n # Robust threshold and normalizator estimation\\n thresholds = []\\n max_powers = []\\n for i in range(int(len(decg_power) / ransac_window_size)):\\n sample = slice(i * ransac_window_size, (i + 1) * ransac_window_size)\\n d = decg_power[sample]\\n thresholds.append(0.5 * np.std(d))\\n max_powers.append(np.max(d))\\n\\n threshold = np.median(thresholds)\\n max_power = np.median(max_powers)\\n decg_power[decg_power < threshold] = 0\\n\\n decg_power /= max_power\\n decg_power[decg_power > 1.0] = 1.0\\n square_decg_power = decg_power ** 2\\n square_decg_power[square_decg_power < 1e-12] = 1e-12\\n\\n shannon_energy = -square_decg_power * np.log(square_decg_power)\\n shannon_energy[~np.isfinite(shannon_energy)] = 0.0\\n\\n mean_window_len = int(rate * 0.125 + 1)\\n lp_energy = np.convolve(shannon_energy, [1.0 / mean_window_len] * mean_window_len, mode='same')\\n\\n lp_energy = scipy.ndimage.gaussian_filter1d(lp_energy, rate / 8.0)\\n lp_energy_diff = np.diff(lp_energy)\\n\\n zero_crossings = (lp_energy_diff[:-1] > 0) & (lp_energy_diff[1:] < 0)\\n zero_crossings = np.flatnonzero(zero_crossings)\\n zero_crossings -= 1\\n return zero_crossings\",\n \"def ecg_diagnose(apn):\\n # Total minutes of apnea\\n apnea_total = sum(apn)\\n\\n # Maximum hourly Apnea Index\\n total_hour = int(len(apn) / 60)\\n y_pred_hourly = np.reshape(apn[: total_hour * 60], (total_hour, 60))\\n AI_hourly = y_pred_hourly.sum(axis=1)\\n # If data in the last hour exceed 30 minutes, then convert to hourly result\\n y_pred_left = apn[total_hour * 60 :]\\n # if len(y_pred_left) >= 30:\\n # AI_hourly = np.append(AI_hourly, sum(y_pred_left) * 60 / len(y_pred_left))\\n # total_hour += 1\\n AI_max = AI_hourly.max()\\n\\n if AI_max >= 10 and apnea_total >= 100:\\n return \\\"A\\\"\\n elif AI_max >= 5 and apnea_total >= 5:\\n return \\\"B\\\"\\n else:\\n return \\\"C\\\"\",\n \"def prior(old_params,params):\\n \\n for s in range(len(params)):\\n if params[s] < 0.0 or params[s] > 2:\\n return 0\\n return 1\",\n \"def test02b(self):\\n a = np.arange(1, 11)\\n b = bcolz.carray(a)\\n wt = [v for v in a if v <= 5 and v > 2]\\n cwt = [v for v in b.where((a <= 5) & (a > 2))]\\n # print \\\"numpy ->\\\", [v for v in a if v<=5 and v>2]\\n # print \\\"where ->\\\", [v for v in b.where((a<=5) & (a>2))]\\n self.assertTrue(wt == cwt, \\\"where() does not work correctly\\\")\",\n \"def kA_func(self):\\n\\n i1 = self.inl[0].to_flow()\\n i2 = self.inl[1].to_flow()\\n o1 = self.outl[0].to_flow()\\n o2 = self.outl[1].to_flow()\\n\\n T_i1 = T_bp_p(i1)\\n T_i2 = T_mix_ph(i2, T0=self.inl[1].T.val_SI)\\n T_o1 = T_mix_ph(o1, T0=self.outl[0].T.val_SI)\\n T_o2 = T_mix_ph(o2, T0=self.outl[1].T.val_SI)\\n\\n if T_i1 <= T_o2 and not self.inl[0].T.val_set:\\n T_i1 = T_o2 + 0.5\\n if T_i1 <= T_o2 and not self.outl[1].T.val_set:\\n T_o2 = T_i1 - 0.5\\n\\n if T_o1 <= T_i2 and not self.outl[0].T.val_set:\\n T_o1 = T_i2 + 1\\n if T_o1 <= T_i2 and not self.inl[1].T.val_set:\\n T_i2 = T_o1 - 1\\n\\n td_log = ((T_o1 - T_i2 - T_i1 + T_o2) /\\n np.log((T_o1 - T_i2) / (T_i1 - T_o2)))\\n\\n return i1[0] * (o1[2] - i1[2]) + self.kA.val * td_log\",\n \"def grubbs(timeseries):\\r\\n\\r\\n series = scipy.array([x[1] for x in timeseries])\\r\\n stdDev = scipy.std(series)\\r\\n mean = np.mean(series)\\r\\n tail_average = tail_avg(timeseries)\\r\\n z_score = (tail_average - mean) / stdDev\\r\\n len_series = len(series)\\r\\n threshold = scipy.stats.t.isf(.05 / (2 * len_series) , len_series - 2)\\r\\n threshold_squared = threshold * threshold\\r\\n grubbs_score = ((len_series - 1) / np.sqrt(len_series)) * np.sqrt(threshold_squared / (len_series - 2 + threshold_squared))\\r\\n\\r\\n return z_score > grubbs_score\",\n \"def asy_hei(gp, acq_optimiser, anc_data):\\n return _halluc_ei(gp, acq_optimiser, anc_data)\",\n \"def compare_CII_w_models(**kwargs):\\n\\n p = copy.copy(params)\\n for key,val in kwargs.items():\\n setattr(p,key,val)\\n\\n if not p.xlim: p.xlim = [-4,2]\\n if not p.ylim: p.ylim = [4,10]\\n\\n fig,ax = plt.subplots(figsize=(8,6))\\n ax.set_ylim(p.ylim)\\n ax.set_xlim(p.xlim)\\n\\n # SIGAME Simba-100 ext ON (default run)\\n GR = glo.global_results(sim_run=p.sim_runs[1],nGal=p.nGals[1],grid_ext=p.grid_exts[1])\\n L_line = getattr(GR,'L_[CII]158_sun')\\n SFR = getattr(GR,'SFR')\\n # Phantom points to close contour lines...\\n L_line = np.append(L_line,np.array([1e8,10**9.3]))\\n SFR = np.append(SFR,np.array([0.1,10**0.85]))\\n lL_line = np.log10(L_line)\\n lSFR = np.log10(SFR)\\n lSFR = lSFR[L_line > 0]\\n lL_line = lL_line[L_line > 0]\\n # ax.plot(np.log10(SFR),np.log10(L_line),'o',ms=4,color='midnightblue',\\\\\\n # alpha=0.7,label='SIGAME with Simba-100',zorder=10)\\n # Evaluate a gaussian kde on a regular grid of nbins x nbins over data extents\\n nbins = 100\\n k = kde.gaussian_kde(np.column_stack([lSFR,lL_line]).T)\\n x, y = np.mgrid[lSFR.min():lSFR.max():nbins*1j,4:lL_line.max():nbins*1j]\\n z = k(np.vstack([x.flatten(), y.flatten()]))\\n CS = ax.contour(x, y, z.reshape(x.shape),colors='forestgreen',levels=6,zorder=10)\\n CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF')\\n\\n # Select only MS galaxies\\n L_line = getattr(GR,'L_[CII]158_sun')\\n indices = aux.select_salim18(GR.M_star[L_line > 0],GR.SFR[L_line > 0])\\n print('With MS selection criteria: only %i galaxies' % (len(L_line)))\\n lSFR = lSFR[indices]\\n lL_line = lL_line[indices]\\n lSFR = np.append(lSFR,np.array([-1,0.85,1.5]))\\n lL_line = np.append(lL_line,np.array([8,9.3,8.4]))\\n nbins = 100\\n k = kde.gaussian_kde(np.column_stack([lSFR,lL_line]).T)\\n x, y = np.mgrid[lSFR.min():lSFR.max():nbins*1j,4:lL_line.max():nbins*1j]\\n z = k(np.vstack([x.flatten(), y.flatten()]))\\n CS = ax.contour(x, y, z.reshape(x.shape),colors='purple',linestyles='dashed',levels=6,zorder=10)\\n CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF (MS)')\\n\\n\\n # SIGAME Simba-100 ext OFF\\n # GR = glo.global_results(sim_run=p.sim_runs[1],nGal=p.nGals[1],grid_ext=p.grid_exts[0])\\n # L_line = getattr(GR,'L_[CII]158_sun')\\n # SFR = getattr(GR,'SFR')\\n # # Phantom points to close contour lines...\\n # L_line = np.append(L_line,np.array([10.**9.2,1e8]))\\n # SFR = np.append(SFR,np.array([0.9,0.1]))\\n # lL_line = np.log10(L_line)\\n # lSFR = np.log10(SFR)\\n # lSFR = lSFR[L_line > 0]\\n # lL_line = lL_line[L_line > 0]\\n # # ax.plot(np.log10(SFR),np.log10(L_line),'o',ms=4,color='midnightblue',\\\\\\n # # alpha=0.7,label='SIGAME with Simba-100',zorder=10)\\n # # Evaluate a gaussian kde on a regular grid of nbins x nbins over data extents\\n # nbins = 100\\n # k = kde.gaussian_kde(np.column_stack([lSFR,\\\\\\n # lL_line]).T)\\n\\n # x, y = np.mgrid[lSFR.min():lSFR.max():nbins*1j, \\\\\\n # 4:lL_line.max():nbins*1j]\\n # z = k(np.vstack([x.flatten(), y.flatten()]))\\n # CS = ax.contour(x, y, z.reshape(x.shape),colors='brown',levels=6,linestyles='dotted')\\n # CS.collections[0].set_label('SIGAME 100Mpc_arepoPDF_no_ext')\\n\\n # SIGAME Simba-25\\n # GR = glo.global_results(sim_run=p.sim_runs[0],nGal=p.nGals[0])\\n # L_line = getattr(GR,'L_[CII]158_sun')\\n # SFR = getattr(GR,'SFR')\\n # ax.plot(np.log10(SFR),np.log10(L_line),'^',ms=6,color='darkorchid',alpha=0.7,label='SIGAME with Simba-25')\\n\\n # Observations in background\\n add_line_SFR_obs('[CII]158',L_line,ax,plot_fit=False)\\n\\n # Popping 2019\\n G19 = pd.read_csv(p.d_data + 'models/Popping2019.csv',skiprows=1,sep=' ',\\\\\\n names=['logSFR', 'logLCII', 'log LCII 14th percentile', 'log LCII 86th percentile'])\\n ax.plot(G19.logSFR,G19.logLCII,'k-',label='Popping 2019',alpha=0.8)\\n ax.fill_between(G19.logSFR,G19['log LCII 14th percentile'].values,\\\\\\n G19['log LCII 86th percentile'].values,color='grey',alpha=0.4)\\n\\n\\n # Padilla 2020\\n P20 = pd.read_csv(p.d_data + 'models/DataFig6.csv',skiprows=1,sep=',',\\\\\\n names=['IDSim','GalID','SFR','LCIITotal'])\\n P20['logLCII'] = np.log10(P20['LCIITotal'])\\n P20['logSFR'] = np.log10(P20['SFR'])\\n\\n\\n colors = ['cyan','orange','midnightblue']\\n nbins = 100\\n IDSims = ['Ref25','Recal25','Ref100']\\n for i,IDSim in enumerate(IDSims):\\n # Evaluate a gaussian kde on a regular grid of nbins x nbins over data extents\\n k = kde.gaussian_kde(np.column_stack([P20.logSFR.values[P20.IDSim == IDSim],\\\\\\n P20.logLCII.values[P20.IDSim == IDSim]]).T)\\n\\n xP20, yP20 = np.mgrid[P20.logSFR.min():P20.logSFR.max():nbins*1j, \\\\\\n 4:P20.logLCII.max():nbins*1j]\\n zP20 = k(np.vstack([xP20.flatten(), yP20.flatten()]))\\n # To remove weird contour line:\\n zP20.reshape(xP20.shape)[(xP20 > -1) & (yP20 < 5.5)] = 1e-5\\n zP20.reshape(xP20.shape)[(xP20 < -3)] = 1e-5\\n zP20.reshape(xP20.shape)[(xP20 < -2) & (yP20 > 8)] = 1e-5\\n zP20.reshape(xP20.shape)[(xP20 > 1) & (yP20 < 7)] = 1e-5\\n CS = ax.contour(xP20, yP20, zP20.reshape(xP20.shape),colors=colors[i],levels=5)\\n CS.collections[0].set_label('Ramos Padilla 2020 '+IDSim)\\n\\n\\n ax.set_xlabel('log '+getlabel('SFR'))\\n ax.set_ylabel('log '+getlabel('[CII]158'))\\n handles,labels = ax.get_legend_handles_labels()\\n handles = [handles[_] for _ in [8,9,10,11,12,7,0,1,2,3,4,5,6]]\\n labels = [labels[_] for _ in [8,9,10,11,12,7,0,1,2,3,4,5,6]]\\n plt.legend(handles,labels,fontsize=9,loc='upper left')\\n if p.savefig:\\n if not os.path.isdir(p.d_plot + 'luminosity/'): os.mkdir(p.d_plot + 'luminosity/') \\n plt.savefig(p.d_plot + 'luminosity/CII_SFR_w_models.png', format='png', dpi=300)\",\n \"def part1a_0():\\n xs = exampleInput\\n ys = exampleTags\\n ys_ = submission.computeViterbi(simpleCRF, xs)\\n grader.requireIsEqual( ys, ys_ )\",\n \"def get_acis_fp_limits(states, acis_s=-111.0, acis_i=-112.0):\\n\\n states_acis_limits = np.zeros(len(states)) + 20 # Set limit initially to 20C\\n ind_acis_s = (states['simpos'] > 0) & (states['simpos'] < 80669) & (states['clocking'] == 1) & (\\n states['vid_board'] == 1)\\n ind_acis_i = (states['simpos'] > 83826) & (states['clocking'] == 1) & (states['vid_board'] == 1)\\n states_acis_limits[ind_acis_s] = acis_s\\n states_acis_limits[ind_acis_i] = acis_i\\n\\n return states_acis_limits\",\n \"def cn_filter(df, binary_cutoff=12):\\n del_df = (df.ix['Deletion'].dropna(1) < 0).astype(int)\\n del_df = del_df[del_df.sum(1) >= binary_cutoff]\\n del_df.index = del_df.index.droplevel(1)\\n del_df = del_df.T\\n amp_df = (df.ix['Amplification'].dropna(1) > 0).astype(int)\\n amp_df = amp_df[amp_df.sum(1) >= binary_cutoff]\\n amp_df.index = amp_df.index.droplevel(1)\\n amp_df = amp_df.T\\n return amp_df, del_df\",\n \"def test_ap_hs20_aka(dev, apdev):\\n if not hlr_auc_gw_available():\\n return \\\"skip\\\"\\n hs20_simulated_sim(dev[0], apdev[0], \\\"AKA\\\")\",\n \"def test06(self):\\n a = np.arange(1, 11)\\n b = bcolz.carray(a)\\n wt = [v for v in a if v <= 5][1:4]\\n cwt = [v for v in b.where(bcolz.carray(a <= 5), limit=3, skip=1)]\\n # print \\\"numpy ->\\\", [v for v in a if v<=5][1:4]\\n # print \\\"where ->\\\", [v for v in b.where(bcolz.carray(a<=5),\\n # limit=3, skip=1)]\\n self.assertTrue(wt == cwt, \\\"where() does not work correctly\\\")\",\n \"def test_warning_is_triggered(caplog):\\n pytest.importorskip('transformers')\\n apply_alibi(\\n model=torch.nn.Sequential(torch.nn.Linear(20, 10), torch.nn.Linear(10, 5)),\\n max_sequence_length=64,\\n )\\n assert encountered_alibi_warning(caplog), 'A warning should be generated when Alibi has no effect.'\",\n \"def weighted_estimation(self) -> bool:\\n pass\",\n \"def _analyse(self, source='sdf', alpha = 0.05, n_bootstrap = 2000, \\n\\t\\tbiphase_split_point = 0.5, biphase_select_resp = None):\\n\\t\\t\\n\\t\\t\\n\\t\\t# Need to add capacity to handle two things:\\n\\t\\t# Qualitative conditions ne\\n\\t\\t# Conditions split - need to to deal with splitting a single dataset, where one part\\n\\t\\t# is qualitiative and the other quantitative\\n\\t\\t\\n\\t\\t# For qualitative, the self.cond_tuning array is numerical. Replace with record\\n\\t\\t# see Initial Chrom Analysis. Keep conditions as strings, and convert to numerical\\n\\t\\t# for plotting (?). Where qualitative, only use bar plot, where mixed, split.\\n\\t\\t## Add parameters to parameters dictionary\\n\\n\\t\\tself.parameters['biphase_split_point'] = biphase_split_point\\n\\t\\tself.parameters['biphase_select_resp'] = biphase_select_resp\\n\\t\\t\\n\\t\\t# Organising source selection - raw and mov_avg not develoepd fully yet.\\n\\t\\tsources = {'sdf': (self.spike_dens_func, self.CI_pos, self.CI_neg), \\n\\t\\t\\t\\t 'mov_avg': 'doesnt exist yet, call it self.spike_mov_avg', \\n\\t\\t\\t\\t 'raw': (self.conditions_hist_mean, \\n\\t\\t\\t\\t\\t\\t self.conditions_hist_mean + 2*self.conditions_hist_stderr, \\n\\t\\t\\t\\t\\t\\t self.conditions_hist_mean - 2*self.conditions_hist_stderr)}\\n\\t\\t\\t\\t \\n\\t\\tassert source.lower() in sources.keys(), ('Tuning source data \\\"%s\\\" is invalid '\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t'select one of %s' %(source, sources.keys())) \\n\\t \\n\\t\\t## Need to expand this functionality to the mean and CI_pos and CI_neg. Doing so for\\n\\t # raw and moving average is not a priority, using sdf and bootstrap is pretty good.\\n\\t # overall aim is to clean this function up to accomadte a number of tuning functinos\\n\\t # in a clear and easy to use fasion.\\n\\t \\n\\t\\tn_con = self.parameters['conditions']\\n\\t\\t\\n\\t\\t# values for transient bar responses\\n\\t\\tif self.parameters['stimulus'] == 'bar':\\n\\t\\t\\t\\n\\t\\t\\tresp, CI_pos, CI_neg = sources[source.lower()]\\n\\t\\t\\t\\n\\t\\t\\t\\n\\t\\t\\tif self.parameters['biphasic']:\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# Take max response for each half of each PSTH, including Conf Intvls\\n\\t\\t\\t\\thalf = int(self.bins.size * biphase_split_point)\\n\\n\\t\\t\\t\\tmax_val_arg = (resp[:, :half].argmax(axis=1),\\n\\t\\t\\t\\t\\t\\t\\t resp[:, half:].argmax(axis=1)+half)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\tmax_val = (resp[:, :half].max(axis=1),\\n\\t\\t\\t\\t\\t\\t resp[:, half:].max(axis=1))\\n\\t\\t\\t\\t\\t\\t \\n\\t\\t\\t\\t\\t\\t\\t \\n\\t\\t\\t\\tmax_val_CI_neg = (CI_neg[np.arange(n_con), max_val_arg[0]],\\n\\t\\t\\t\\t\\t\\t\\t\\t CI_neg[np.arange(n_con), max_val_arg[1]])\\n\\t\\t\\t\\t\\t\\t\\t\\t \\n\\t\\t\\t\\tmax_val_CI_pos = (CI_pos[np.arange(n_con), max_val_arg[0]],\\n\\t\\t\\t\\t\\t\\t\\t\\t CI_pos[np.arange(n_con), max_val_arg[1]])\\n\\n\\t\\t\\t\\t# encode which of the two responses the data is attached to\\n\\t\\t\\t\\tbiphas_id = np.zeros_like(np.hstack((self.conditions, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tself.conditions2)))\\n\\t\\t\\t\\tbiphas_id[:self.conditions.size] = 1\\n\\t\\t\\t\\tbiphas_id[self.conditions2.size:] = 2\\n\\n\\n\\t\\t\\t\\t\\t\\t\\t\\t \\n\\t\\t\\t\\tself.cond_tuning = np.vstack((np.hstack((self.conditions, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\tself.conditions2)),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t np.hstack(max_val),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t np.hstack(max_val_CI_neg),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t np.hstack(max_val_CI_pos),\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t biphas_id))\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \\n\\t\\t\\t\\t# Convert to Hertz - design choice is to keep all PSTH datasets as raw average spike\\n\\t\\t\\t\\t# counts, with easy option of seeing frequency in the plotting, but converting to \\n\\t\\t\\t\\t# Hertz for all condition tuning data.\\n\\t\\t\\t\\tself.cond_tuning[1:-1,:] *= (1/self.bin_width)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \\n\\t\\t\\t\\t\\t\\t\\t \\n\\t\\t\\t\\t# Column labels for pd.dataframe of tuning data\\n\\t\\t\\t\\t# Percentage of confidence intervals\\n\\t\\t\\t\\t# ci_perc = (100 * (1 - self.parameters['sdf_alpha']))\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# Labels\\n\\t\\t\\t\\tidx = ['condition', 'max_resp', 'neg_CI', 'pos_CI', 'biphas_id']\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# Pandas object, with transpose of tuning array to data frame object \\n\\t\\t\\t\\tself.cond_tuning_pd = pd.DataFrame(self.cond_tuning.transpose(), columns=idx)\\n\\t\\t\\n\\t\\t\\t#non biphasic version of above\\n\\t\\t\\tif not self.parameters['biphasic']:\\n\\n\\t\\t\\t\\tmax_val_arg = resp[:, :].argmax(axis=1)\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\tmax_val = resp[:, :].max(axis=1)\\n\\t\\t\\t\\t\\t\\t \\n\\t\\t\\t\\t\\t\\t\\t \\n\\t\\t\\t\\tmax_val_CI_neg = CI_neg[np.arange(n_con), max_val_arg]\\n\\t\\t\\t\\t\\t\\t\\t\\t \\n\\t\\t\\t\\tmax_val_CI_pos = CI_pos[np.arange(n_con), max_val_arg]\\n\\t\\t\\t\\t\\t\\t\\t\\t \\n\\t\\t\\t\\tself.cond_tuning = np.vstack((self.conditions,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t max_val,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t max_val_CI_neg,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t max_val_CI_pos))\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t \\n\\t\\t\\t\\t# Convert to Hertz - design choice is to keep all PSTH datasets as raw average spike\\n\\t\\t\\t\\t# counts, with easy option of seeing frequency in the plotting, but converting to \\n\\t\\t\\t\\t# Hertz for all condition tuning data.\\n\\t\\t\\t\\tself.cond_tuning[1:,:] *= (1/self.bin_width)\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# Column labels for pd.dataframe of tuning data\\n\\t\\t\\t\\t# ci_perc = (100 * (1 - self.parameters['sdf_alpha']))\\n\\n\\t\\t\\t\\tidx = ['condition', 'max_resp', 'neg_CI', 'pos_CI']\\n\\n\\t\\t\\t\\t\\t \\n\\t\\t\\t\\t# transpose of tuning array to data frame object \\n\\t\\t\\t\\tself.cond_tuning_pd = pd.DataFrame(self.cond_tuning.transpose(), columns=idx)\\n\\t\\t\\n\\t\\t\\n\\t\\t# values for sinusoids/gratings\\n\\t\\t## Note issue of temporal frequency tuning - need variable tf.\\n\\t\\tif self.parameters['stimulus'] == 'grating':\\n\\t\\t\\t\\n\\t\\t\\tself.parameters['fft_alpha'] = alpha\\n\\t\\t\\tself.parameters['fft_number_bootstrap'] = n_bootstrap\\n\\t\\t\\t\\n\\t\\t\\tif source == 'sdf':\\n\\t\\t\\t\\tprint ('WARNING, using a smoothed/filtered dataset will artificially increase'\\n\\t\\t\\t\\t\\t 'the amplitude of the DC component and decrease that of the F1') \\n\\t\\t\\t\\n\\t\\t\\tsources = {'sdf': self.conditions_trials_sdf,\\n\\t\\t\\t\\t\\t 'mov_avg': \\\"doesn't exist yet (?)\\\",\\n\\t\\t\\t\\t\\t 'raw': self.conditions_trials_hist}\\n\\t\\t\\t\\n\\t\\t\\tresp = sources[source]\\n\\t\\t\\t\\n\\t\\t\\ttemp_freq = self.parameters['temp_freq']\\n\\t\\t\\tstim_len = self.parameters['stimulus_length']\\n\\t\\t\\t\\n\\t\\t\\t# ensuring that the temp_freq is measured in the FFT whilst taking the maximum time.\\n\\t\\t\\t# on the basis of delt-f = 1 / n*del-t; stim_len*F1=factor; 1/(bin_width*F1)=min bins\\n\\t\\t\\t# number times greater than minimum can fit in stim_length \\n\\t\\t\\tfactor = np.floor(stim_len * temp_freq).astype('int')\\n\\t\\t\\t\\n\\t\\t\\t# number of bins to take - the window size necessary for temp_freq to be measured\\n\\t\\t\\tbins_take = np.floor(factor / (self.bin_width * temp_freq)).astype('int')\\n\\n\\t\\t\\t# Frequency axis generation\\n\\t\\t\\tself.freq = fft.rfftfreq(bins_take, self.bin_width)\\n\\t\\t\\t\\n\\t\\t\\t#Checkign whether the temp_freq is in the FFT.\\n\\t\\t\\tassert self.freq[factor] == temp_freq, ('The calculated FFT F1 frequency (%s)'\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t 'does not equal the Stimulus temp_freq (%s)'\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t %(self.freq[bins_take], temp_freq))\\n\\n\\t\\t\\t# Fourier Transform\\n\\t\\t\\tself.conditions_trials_fourier = fft.rfft(resp[:,:,:bins_take], axis=2)\\n\\t\\t\\t\\n\\t\\t\\t# Amplitude (peak-to-peak)\\n\\t\\t\\tself.conditions_trials_ampl = np.abs(self.conditions_trials_fourier)\\n\\t\\t\\t\\n\\t\\t\\t# normalising to dataset size, except the DC.\\n\\t\\t\\tself.conditions_trials_ampl[:,:,0] *= 1 / float(bins_take)\\n\\t\\t\\tself.conditions_trials_ampl[:,:,1:] *= 2 / float(bins_take)\\n\\t\\t\\t\\n\\t\\t\\t\\n\\t\\t\\t# Mean amplitudes and bootstrapped CI_intervals \\n\\t\\t\\tself.conditions_ampl_mean = np.mean(self.conditions_trials_ampl, axis=1)\\n\\t\\t\\t\\n\\t\\t\\tCI_pos, CI_neg = bootstrap(self.conditions_trials_ampl, alpha=alpha, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t n_bootstrap=n_bootstrap)\\n\\t\\t\\tself.conditions_ampl_CI_pos, self.conditions_ampl_CI_neg = CI_pos, CI_neg\\n\\t\\t\\t\\n\\t\\t\\t# isolating F0, F1, and F2 responses and compiling into a single table.\\n\\t\\t\\tconditions_f0 = self.conditions_ampl_mean[:,0]\\n\\t\\t\\tconditions_f1 = self.conditions_ampl_mean[:,factor]\\n\\t\\t\\tconditions_f2 = self.conditions_ampl_mean[:,2*factor]\\n\\t\\t\\t\\n\\t\\t\\t# Condition Tuning array\\n\\t\\t\\tself.cond_tuning = np.vstack((self.conditions,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t conditions_f0, CI_pos[:,0], CI_neg[:,0],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t conditions_f1, CI_pos[:,factor], CI_neg[:,factor],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t conditions_f2, CI_pos[:,2*factor], CI_neg[:,2*factor],\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t conditions_f1/conditions_f0))\\n\\t\\t\\t\\n\\t\\t\\t# Convert to Hertz - design choice is to keep all PSTH datasets as raw average spike\\n\\t\\t\\t# counts, with easy option of seeing frequency in the plotting, but converting to \\n\\t\\t\\t# Hertz for all condition tuning data.\\n\\t\\t\\t\\n\\t\\t\\tself.cond_tuning[1:-1,:] *= (1/self.bin_width)\\n\\t\\t\\t\\n\\t\\t\\t# Column labels for pd.dataframe of tuning data\\n\\t\\t\\t# ci_perc = (100 * (1 - self.parameters['fft_alpha']))\\n\\t\\t\\tidx = ['conditions', \\n\\t\\t\\t\\t 'F0', 'F0_pos_CI', 'F0_neg_CI', \\n\\t\\t\\t\\t 'F1', 'F1_pos_CI', 'F1_neg_CI',\\n\\t\\t\\t\\t 'F2', 'F2_pos_CI', 'F2_neg_CI',\\n\\t\\t\\t\\t 'F1/F0_ratio']\\n\\t\\t\\t# transpose of tuning array to data frame object \\n\\t\\t\\tself.cond_tuning_pd = pd.DataFrame(self.cond_tuning.transpose(), columns=idx)\\n\\t\\t\\n\\t\\t\\n\\t\\t\\t \\n\\t\\t# for orientation data, the orientation angles can get scrambled due to the circ() function\\n\\t\\t# rotating the angles around. This orders them numerically in the final cond_tuning\\n\\t\\t\\n\\t\\tif self.parameters['condition_type'] == 'orientation':\\n\\t\\t\\tself.cond_tuning = self.cond_tuning[:,self.cond_tuning[0].argsort()]\\n\\t\\t\\tself.cond_tuning_pd.sort_values(self.cond_tuning_pd.columns[0], inplace=True)\\n\\n\\n\\t\\t# \\n\\t\\t# cond_tuning cleaning up and inserting important meta data / columns\\n\\t\\t# \\n\\n\\t\\tif biphase_select_resp is not None:\\n\\t\\t\\tassert isinstance(biphase_select_resp, int) and biphase_select_resp in [1,2], \\\\\\n\\t\\t\\tf'biphase_select_resp ({biphase_select_resp}) must be an integer of 1 or 2'\\n\\n\\t\\t\\tassert self.parameters['biphasic'], 'Stimulus not analysed as biphasic'\\n\\n\\t\\t\\t# cond tuning array\\n\\t\\t\\tcond_tuning_biphase_mask = self.cond_tuning[4,:] == biphase_select_resp\\n\\t\\t\\tself.cond_tuning = self.cond_tuning[:, cond_tuning_biphase_mask]\\n\\n\\t\\t\\t# cond tuning pandas dataframe\\n\\t\\t\\tself.cond_tuning_pd = self.cond_tuning_pd.query('biphas_id == @biphase_select_resp')\\n\\n\\n\\n\\t\\tassert hasattr(self, 'CELL_ID'), 'Make Cell ID first'\\n\\n\\n\\t\\tself.cond_tuning_pd.insert(0, 'run_key', self.RUN_KEY)\\n\\t\\tself.cond_tuning_pd.insert(0, 'cell_key', self.CELL_KEY)\\n\\t\\tself.cond_tuning_pd.set_index(['cell_key', 'run_key'], inplace=True)\\n\\n\\t\\tself.cond_tuning_pd.insert(0, 'cond_type', self.parameters['condition_type'])\\n\\t\\tself.cond_tuning_pd.insert(1, 'cond_unit', self.parameters['condition_unit'])\",\n \"def ofi(b, b_v, a, a_v):\\n b_, b_v_, a_, a_v_ = b.shift(1), b_v.shift(1), a.shift(1), a_v.shift(1)\\n ofi = i(b >= b_) * b_v - i(b <= b_) * b_v_ - i(a <= a_) * a_v + i(a >= a_) * a_v_\\n return ofi.fillna(0)\",\n \"def test05(self):\\n a = np.arange(1, 110)\\n b = bcolz.carray(a, chunklen=10)\\n wt = a[a < 5]\\n cwt = b[a < 5]\\n # print \\\"numpy ->\\\", a[a<5]\\n # print \\\"where ->\\\", b[a<5]\\n assert_array_equal(wt, cwt, \\\"where() does not work correctly\\\")\",\n \"def test01(self):\\n a = np.arange(1, 11) < 0\\n b = bcolz.carray(a)\\n wt = a.nonzero()[0].tolist()\\n cwt = [i for i in b.wheretrue()]\\n # print \\\"numpy ->\\\", a.nonzero()[0].tolist()\\n # print \\\"where ->\\\", [i for i in b.wheretrue()]\\n self.assertTrue(wt == cwt, \\\"wheretrue() does not work correctly\\\")\",\n \"def SpatiallyAdaptiveBinaryThresholding(image, kernel_sigma, a, b): \\n\\n if kernel_sigma >= 1:\\n image = Denoising(image, kernel_sigma); \\n \\n nr, nc = image.shape;\\n mask = np.zeros(image.shape);\\n \\n for ii in range(1, nr - 1):\\n for jj in range(1, nc - 1):\\n nb = image[ii - 1:ii + 2, jj - 1:jj + 2];\\n nb = nb.reshape(3, 3);\\n nb_std = np.std(nb);\\n nb_mean = np.mean(nb);\\n \\n if image[ii, jj] > a * nb_std and image[ii, jj] > b * nb_mean:\\n mask[ii, jj] = 1;\\n\\n return mask;\",\n \"def FDA_test(X_test, w_star, w_0):\\n\\n y_proj = X_test.dot(w_star)[:, np.newaxis]\\n\\n #1-of-K code scheme\\n y_1 = y_proj >= w_0\\n y_2 = y_proj < w_0\\n y_pred = np.hstack((y_1, y_2)).astype(int)\\n\\n return y_pred\",\n \"def maStrategy(df, i:int):\\n\\n\\tif not df.__contains__('slow_sma'):\\n\\t\\tIndicators.AddIndicator(df, indicator_name=\\\"sma\\\", col_name=\\\"slow_sma\\\", args=30)\\n\\n\\tbuy_price = 0.96 * df['slow_sma'][i]\\n\\tif buy_price >= df['close'][i]:\\n\\t\\treturn min(buy_price, df['high'][i])\\n\\n\\treturn False\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.52783775","0.5277361","0.51554257","0.5079259","0.5041149","0.5019891","0.49585706","0.49330664","0.49314007","0.4872369","0.4855907","0.48519504","0.48410013","0.4838592","0.47834566","0.47363517","0.47308505","0.47119272","0.47084534","0.47058246","0.46982104","0.46889034","0.46841618","0.46496356","0.46467048","0.46394926","0.46243754","0.46002617","0.45861483","0.45795107","0.45647624","0.4557393","0.4557282","0.45536667","0.45421398","0.45381424","0.45240238","0.45045745","0.44979048","0.44764894","0.44756415","0.44719929","0.4471928","0.4471743","0.44629183","0.44595706","0.44503617","0.4445045","0.44353136","0.44341716","0.44339356","0.443327","0.4432451","0.44299263","0.44279605","0.44262394","0.44257948","0.4425667","0.44236532","0.4422433","0.44139647","0.44035828","0.4401739","0.4394367","0.43916607","0.43904394","0.43862963","0.43797117","0.437826","0.43782094","0.43768618","0.43759295","0.43614006","0.43588355","0.43577784","0.43546852","0.4354167","0.43537113","0.43530467","0.43528798","0.4350336","0.4347228","0.4344988","0.4344738","0.43419334","0.43382654","0.43371755","0.43356502","0.43316552","0.43278584","0.43266165","0.4323688","0.43223712","0.43208057","0.43178418","0.43164805","0.4310847","0.4307433","0.43072838","0.4306911"],"string":"[\n \"0.52783775\",\n \"0.5277361\",\n \"0.51554257\",\n \"0.5079259\",\n \"0.5041149\",\n \"0.5019891\",\n \"0.49585706\",\n \"0.49330664\",\n \"0.49314007\",\n \"0.4872369\",\n \"0.4855907\",\n \"0.48519504\",\n \"0.48410013\",\n \"0.4838592\",\n \"0.47834566\",\n \"0.47363517\",\n \"0.47308505\",\n \"0.47119272\",\n \"0.47084534\",\n \"0.47058246\",\n \"0.46982104\",\n \"0.46889034\",\n \"0.46841618\",\n \"0.46496356\",\n \"0.46467048\",\n \"0.46394926\",\n \"0.46243754\",\n \"0.46002617\",\n \"0.45861483\",\n \"0.45795107\",\n \"0.45647624\",\n \"0.4557393\",\n \"0.4557282\",\n \"0.45536667\",\n \"0.45421398\",\n \"0.45381424\",\n \"0.45240238\",\n \"0.45045745\",\n \"0.44979048\",\n \"0.44764894\",\n \"0.44756415\",\n \"0.44719929\",\n \"0.4471928\",\n \"0.4471743\",\n \"0.44629183\",\n \"0.44595706\",\n \"0.44503617\",\n \"0.4445045\",\n \"0.44353136\",\n \"0.44341716\",\n \"0.44339356\",\n \"0.443327\",\n \"0.4432451\",\n \"0.44299263\",\n \"0.44279605\",\n \"0.44262394\",\n \"0.44257948\",\n \"0.4425667\",\n \"0.44236532\",\n \"0.4422433\",\n \"0.44139647\",\n \"0.44035828\",\n \"0.4401739\",\n \"0.4394367\",\n \"0.43916607\",\n \"0.43904394\",\n \"0.43862963\",\n \"0.43797117\",\n \"0.437826\",\n \"0.43782094\",\n \"0.43768618\",\n \"0.43759295\",\n \"0.43614006\",\n \"0.43588355\",\n \"0.43577784\",\n \"0.43546852\",\n \"0.4354167\",\n \"0.43537113\",\n \"0.43530467\",\n \"0.43528798\",\n \"0.4350336\",\n \"0.4347228\",\n \"0.4344988\",\n \"0.4344738\",\n \"0.43419334\",\n \"0.43382654\",\n \"0.43371755\",\n \"0.43356502\",\n \"0.43316552\",\n \"0.43278584\",\n \"0.43266165\",\n \"0.4323688\",\n \"0.43223712\",\n \"0.43208057\",\n \"0.43178418\",\n \"0.43164805\",\n \"0.4310847\",\n \"0.4307433\",\n \"0.43072838\",\n \"0.4306911\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5706766"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22296,"cells":{"query":{"kind":"string","value":"Add item to heap"},"document":{"kind":"string","value":"def add_item(self, new_value):\n\n # Allocate more memory if necessary\n # This keeps add_item to O(1), generally.\n # Otherwise, have to duplicate ndarray every time\n # last_item is an index, heap_size is a limit (index + 1)\n if self.last_item >= self.heap_size - 1:\n # Allocate double the memory\n new_heap_list = self.heap.tolist() + [0] * self.heap_size\n self.heap = np.array(new_heap_list, dtype=np.int)\n self.heap_size *= 2\n\n # Add item index and value\n # to already allocated memory\n self.last_item = self.last_item + 1\n self.heap[self.last_item] = new_value\n\n # Update heap level\n self.level = np.floor(np.log(self.last_item + 1) / np.log(2))"},"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 push(self, item):\n heapq.heappush(self.heap, item)","def heappush(heap, item):\n heap.append(item)\n Heap.siftdown(heap, 0, len(heap) - 1)","def heappush(heap, item):\n pass","def push(self, item: tuple):\n self.__heap.append(item)\n self.__sift_up(self.__len__() - 1)","def heappush(heap, item):\n heap.append(item)\n _siftdown(heap, 0, len(heap)-1)","def insert(self, item):\n self.heaplist.append(item)\n self.currentsize += 1\n self.shift_item_up(self.currentsize)","def insert(self, item):\n self._heap.append(item)\n self._perc_up(len(self._heap) - 1)","def append(self, item):\n\t\theapq.heappush(self.heap, (self.f(item), item))","def push(self, item):\n self.heap.append(self.m * item)\n self._sift_up()","def add(self, key, value):\n self._data.append(self._Item(key, value))\n self._upheap(len(self._data) - 1) # upheap newly added position","def add(self, key, value):\n self._data.append(self._Item(key, value))\n self._upheap(len(self._data) - 1) # upheap newly added position","def add(self, item):\n\n assert self.size < self.max_size, \"Cannot add item! The MaxHeap is full!\"\n self.items[self.size] = item\n self.size += 1\n self._shift_up(self.size - 1)","def enqueue(self, item, priority):\n # TODO: Insert given item into heap\n ...","def add(self, item):\n if not (item in self.set):\n self.set[item] = True\n heapq.heappush(self.heap, item)","def insert(self, key, value):\n self.heap.append(None)\n hi = HeapItem(key,value)\n self.siftup(hi, len(self.heap)-1)\n return hi","def push(self, val):\n self._heap_list.append(val)\n self._build_heap()","def add(self, value):\n self.heap.append(value)\n self.heapify_up()","def push(self, priority: float, item):\n heappush(self._heap, (-1 * priority, item))","def heap_push(self, value):\n if self.find(value) is None:\n self.table.append(value)\n self.percolate_up(self.get_size() - 1)","def push(self, key, value):\r\n if len(self.heap)<self.depth:\r\n heapq.heappush(self.heap, key)\r\n self.elements[key] = value\r\n else:\r\n oldkey = heapq.heappushpop(self.heap, key)\r\n self.elements[key] = value\r\n del self.elements[oldkey]","def add(self, node: object) -> None:\n end_index=self.heap.length()\n parent_index=((end_index-1)//2)\n if end_index==0:\n self.heap.append(node)\n else:\n self.heap.append(node)\n while self.heap.get_at_index(end_index)<self.heap.get_at_index(parent_index):\n self.heap.swap(end_index,parent_index)\n end_index=parent_index\n if end_index==0:\n break\n parent_index=(end_index-1)//2","def add(self, item, priority=0):\n if item in self.set:\n self.remove(item)\n count = next(self.counter)\n entry = [priority, count, item]\n self.set[item] = entry\n hpq.heappush(self.heap, entry)","def add(self,key,value):\r\n try:\r\n self._data.append(self.Item(key,value)) # insert as Item class object with key value.\r\n self._heapify_after_add(len(self._data) - 1 )\r\n return self._data\r\n except Exception, e:\r\n print \"Error occurred in HeapDistance: add method\", e","def insert(self, data):\n # add data to list'end\n self.heap_list.append(data)\n # adjust max-heap from bottom to top\n self.sift_up(len(self.heap_list)-1)","def heap_push_max(heap, item):\n heap.append(item)\n heapq._siftdown_max(heap, 0, len(heap)-1)","def __heappush(heap, nodes, node):\n pos = len(heap)\n heap.append(node)\n nodes[node[1]] = pos\n Graph.__siftup(heap, nodes, pos)","def push(self, priority, key):\n index = len(self.__heap)\n self.__position[key] = index\n self.__heap.append([priority, key])\n self.__bubble_up(index)","def append(self, key):\n if key is None:\n raise ValueError('Cannot insert None in the queue')\n\n i = len(self.heap)\n self.heap.append(key)\n while i > 1:\n parent = i // 2\n if key < self.heap[parent]:\n self.heap[i], self.heap[parent] = self.heap[parent], key\n i = parent\n else:\n break","def insert(self, key, value):\r\n self._data.append(self._Item(key, value))\r\n self._upheap(len(self._data) - 1) # upheap newly added position\r","def push(self, item):\n\t\tself.top+=1;\n\t\tself.arr.insert(self.top, item);","def put(self, element):\n self.heap.append(element)\n # sift up the element append before\n self.sift_up(self.size() - 1)","def push(self, x):\n heapq.heappush(self.array, x)","def add(self, element):\n # add element to the heap\n self.heap.append(element)\n\n # get index of added element and parent of added element\n index = len(self.heap) - 1\n parentIndex = (index - 1) // 2\n\n # swap parents and childs while needed\n while index >= 1 and self.heap[parentIndex] < self.heap[index]:\n\n # swap parent and child\n self.swap(parentIndex, index)\n\n # update parent and child indexes\n index = parentIndex\n parentIndex = (index - 1) // 2","def push(self, x):\n assert x not in self.rank\n i = len(self.heap)\n self.heap.append(x) # add a new leaf\n self.rank[x] = i\n self.up(i) # maintain heap order","def push(self, x):\n assert x not in self.rank\n i = len(self.heap)\n self.heap.append(x) # add a new leaf\n self.rank[x] = i\n self.up(i) # maintain heap order","def push(self, val):\n if type(val) == int:\n if val in self._heap:\n raise ValueError('Cannot have duplicate values in list')\n if not self._heap:\n self._heap.append(val)\n else:\n self._heap.append(val)\n self._sort(len(self._heap) - 1)\n else:\n raise TypeError('Must add an integer')","def insert(self, k): \r\n self.heap_array.append(k)\r\n\r\n current_index = len(self.heap_array) - 1\r\n while (current_index > 0):\r\n parent_index = ((current_index-1)//2)\r\n\r\n if int(self.heap_array[current_index]) > int(self.heap_array[parent_index]): # if no vialation of the min heap property \r\n return\r\n else: # if heap property is broken then swap the parent and child that are breaking the prop \r\n self.heap_array[parent_index], self.heap_array[current_index] = self.heap_array[current_index], self.heap_array[parent_index]\r\n current_index = parent_index","def add(self, element):\n # add element to the heap\n self.heap.append(element)\n\n # get index of added element and parent of added element\n index = len(self.heap) - 1\n parentIndex = (index - 1) // 2\n\n # swap parents and childs while needed\n while index >= 1 and self.heap[parentIndex][1] > self.heap[index][1]:\n\n # swap parent and child\n swap = self.heap[parentIndex]\n self.heap[parentIndex] = self.heap[index]\n self.heap[index] = swap\n\n # update parent and child indexes\n index = parentIndex\n parentIndex = (index - 1) // 2","def build_heap(self, items):\n for key in items:\n self.insert(key)","def addItem(self, item, rank):\n with self.lock:\n if self.ItemHashList.get(item, -1) == -1:\n self.ItemHashList[item] = None\n if rank < 0:\n rank = 0\n heapq.heappush(self.ItemList, (rank, item))","def insert(self, value):\n self.heap.append(value)\n index = len(self.heap) - 1\n self._percolate_up(index)","def insert(self, k):\n self.heapList.append(k)\n self.currentSize = self.currentSize + 1\n self.percUp(self.currentSize)","def add(self, key, value):\n self._data.append(self._Item(key, value))\n self._up_heap(len(self) - 1)","def enqueue(self, priority, value, key=None):\n key = key if key else value\n if key in self._index:\n self._update(priority, key)\n return\n self._heap.append(Item(priority, value, key))\n self._size = len(self._heap)\n self._index[key] = self._size - 1\n self._sift_up(self._size - 1)","def add(self, node: object) -> None:\n # add node to end of array\n self.heap.append(node)\n\n # find index where node was inserted\n end = self.heap.length() - 1\n\n # find parent index\n parent = (end - 1) // 2\n\n # while the value at the parent index is greater than the value and parent index is\n # not less than 0, swap nodes, set new end index to parent index and find new parent\n while self.heap.get_at_index(parent) > node and parent >= 0:\n self.heap.swap(parent, end)\n end = parent\n parent = (end - 1) // 2\n return","def insert(self, value):\n self.heap.append(value)\n self._shiftUp()","def push(self, x):\n self.elements.append(x)\n self._heapify()","def push(self, x):\n assert self._data is not None\n if len(self._data) < self._n:\n heapq.heappush(self._data, x)\n else:\n heapq.heappushpop(self._data, x)","def insert(self, key):\n\n self.heap.append(key)\n\n self.bubble_up(len(self.heap) - 1)","def insert(self, k):\n #Append the element to the min heap\n self.heap_list.append(k)\n #Increase the size of the min heap\n self.current_size += 1\n #Move the value to its appropriate position in the min heap (following the definition of a min heap)\n self.sift_up(self.current_size)","def push(self,item):\n self.items.append(item)","def add(self, item, priority=0) -> None:\n if item in self.entry_finder:\n self.remove(item)\n count = next(self.counter)\n entry = (priority, count, [item])\n self.entry_finder[item] = entry\n heapq.heappush(self.priority_queue, entry)","def push(self, item):\n\t\tself.items.append(item)","def push(self, new_node):\n # Add a new node into the data list\n self.data.append(new_node)\n # Maintain the min heap\n self.heapify_up(len(self.data) - 1)","def add_to_heap(self, key, count=0):\n entry = [1 + count, next(self.counter), key, HeapItemStatus.ACTIVE]\n self.map[key] = entry\n heappush(self.heap, entry)","def push(self, item):\n self.items.append(item)","def push(self, item):\n self.items.append(item)","def push(self, item):\n self.items.append(item)","def push(self, item):\n self.items.append(item)","def push(self, item):\n self.items.append(item)","def add(self, key, value=None):\n if value is None:\n value = key\n node = HeapNode(key, value)\n self.store.append(node)\n self.heap_up(len(self.store)-1)\n return None","def insert(self, item):\n index = self.insert_at_next_index(item)\n self.items[index] = item\n while index > 1:\n parent_index = index / 2 # Truncate, e.g. 4 and 5 have parent 2.\n if self.is_heap_order(self.items[parent_index], self.items[index]):\n # The item does not need to bubble up anymore. Done.\n return\n else:\n # Swap items at index and parent_index\n temp = self.items[index]\n self.items[index] = self.items[parent_index]\n self.items[parent_index] = temp\n index = parent_index\n # The item bubbled all the way to the root. Done.\n return","def push(self, obj):\n # wrap the object to allow for correct pop operation\n # remember that in python it's a min-heap (not max!)\n wrap_obj = (obj.minus_priority, len(self), obj)\n # use insertion number to ensure we never compare based on obj itself!\n # additionally resolves ties by popping earliest-inserted object\n heapq.heappush(self._heap, wrap_obj)","def push(self, item):\n array = self.array\n compare = self.compare\n array.append(item)\n self.pos[item] = len(array) - 1\n high = len(array) - 1\n while high > 0:\n low = (high-1)/2\n if compare(array[low], array[high]) <= 0:\n break\n self.pos[array[high]] = low\n self.pos[array[low]] = high\n array[low], array[high] = array[high], array[low]\n high = low","def push(self, item):\n pass","def add(self, item, priority):\n heappush(self.contents, (priority, item))","def insert(self, element):\n if self.size >= self.maxsize : \n return\n self.size+= 1\n self.Heap[self.size] = element \n \n current = self.size \n \n while self.Heap[current] < self.Heap[self.parent(current)]: \n self.swap(current, self.parent(current)) \n current = self.parent(current)","def push(self, item) -> None:\n self.items.append(item)","def insert(self, element):\n if self.size >= self.maxsize:\n return\n self.size += 1\n self.heap[self.size] = element\n\n current = self.size\n\n while self.heap[current] < self.heap[self.parent(current)]:\n self.swap(current, self.parent(current))\n current = self.parent(current)","def push(self, item):\n self.stack.append(item)\n\n if not self.max or item >= self.max[-1]: # add if empty or if greater\n self.max.append(item)","def pushPriorityQueue(self, node):\r\n if self.priority == \"bin\":\r\n heappush(self.unvisited, node)\r\n elif self.priority == \"fib\":\r\n fheappush(self.unvisited, node)\r\n else: # list\r\n self.unvisited.append(node)","def add(self, path, value):\n self.frontier_index += 1 # get a new unique index\n heapq.heappush(self.frontierpq,(value, -self.frontier_index, path))","def push(self, item):\r\n self.stack.insert(0, item)","def push(self, node):\n try:\n self._load(True)\n\n # Stow the new node at our head and increment it\n self.db[self.head] = node\n self.head = self.head + 1\n if self.head >= self.size:\n self.head -= self.size\n self.db['head'] = self.head\n\n # If we haven't just also pushed out an old item,\n # increment the count of items in our db.\n if self.count < self.size:\n self.count += 1\n self.db['count'] = self.count\n except KeyError:\n # HACK\n self.clear()","def push(self, item): # 05:27 Lecture Week 2 \"Stacks\" (16:24)\n oldfirst = self.first # Save a link to the list\n self.first = self._Node(item, oldfirst) # first points to most recent Node\n self.N += 1","def push(self, item):\n if len(self._data) == self.MAX_SIZE:\n # full we have to pop the oldest item (head)\n self._data.pop(0)\n self._data.append(item)","def insert_at_end(self, new_node):\n self.heap.append(new_node)\n self.heap_size += 1","def test_insert(self):\n self.minheap.heap = [0, 1, 4, 6, 9]\n self.minheap.insert(2)\n assert self.minheap.heap == [0, 1, 2, 6, 9, 4]","def push(self, item):\n self._items.append(item)","def push(self, item):\n self.stack.append(item)","def push(self, item):\n self.stack.append(item)","def push(self, value):\n idx = self.__capacity - 1 + self.__size\n self.__tree[idx] = value\n self.__update(idx)\n self.__size += 1","def insert(self, item):\n for h_num in xrange(self.k):\n val = self.hash_value(item, h_num)\n self.arr[val] = True","def add(self, value):\r\n # Does the underlying \"array\" have enough space? If not\r\n # increase its size. Note, we are really using a Python list\r\n # as our underlying data structure, so any \"doubling\" is\r\n # handled for us.\r\n if len(self) == len(self._data): self._data.append(None)\r\n self._data[self._size] = value # insert new item at end of arry\r\n self.up_heap(self._size) # \"bubble\" the new item up.\r\n self._size += 1 # bump the sze\r","def push(self, item) -> None:\n self._items.append(item)","def push(self, item):\n if item not in self._items:\n self._items.append(item)","def push(self, item):\n # Reassign the link to top of the stack and update the\n self.top = [item] + self.top","def push(self, new_item):\n self.items.append(new_item)","def push(self, item):\n super().add_item_to_front(item)","def Push(self, item):\n self.list.append(item)","def append(self, x):\n if self.even:\n min_heap_push(self.right, x)\n max_heap_push(self.left, min_heap_pop(self.right))\n else:\n max_heap_push(self.left, x)\n min_heap_push(self.right, max_heap_pop(self.left))\n self.even = not self.even","def push(self, Item):\n self.data_container.insert(0, Item)","def add(self, elem: T):\n if elem not in self._unique_values:\n if len(self._heap) < self.maxsize:\n heapq.heappush(self._heap, HeapObj(elem))\n elif elem < self._heap[0].val:\n heapq.heappushpop(self._heap, HeapObj(elem))\n self._unique_values.add(elem)","def insert(self, to_insert: Article) -> None:\n heapq.heappush(self.heap, to_insert)","def push(self, from_node, to_node, value):\n heap = self.heap\n heap.append([from_node, to_node, value])\n if len(heap) == 1:\n return\n\n index = len(heap) - 1\n while True:\n # parent of k is at (k-1)/2 [int division]\n parent_index = max(floor((index-1)/2), 0)\n parent_value = heap[parent_index][self.VALUE]\n\n if value < parent_value:\n self.__swap(parent_index, index)\n index = parent_index\n else:\n return","def push(self, edge):\n ew = EdgeWrapper(edge)\n heapq.heappush(self.__heap, (ew.weight(), ew))","def push(self, item):\n self._items.append(item)\n # This operation increments the number of items\n # in the stack, we need to update the count variable\n self._update_count()","def test_insert_increases_size(sample_priorityq):\n assert len(sample_priorityq.heap_list) == 0\n sample_priorityq.insert([5, 1])\n assert len(sample_priorityq.heap_list) == 1\n sample_priorityq.insert([6, 2])\n assert len(sample_priorityq.heap_list) == 2","def push(self, item: Any) -> None:\n self._items.append(item)","def push(self, item):\n self._data.append(item)"],"string":"[\n \"def push(self, item):\\n heapq.heappush(self.heap, item)\",\n \"def heappush(heap, item):\\n heap.append(item)\\n Heap.siftdown(heap, 0, len(heap) - 1)\",\n \"def heappush(heap, item):\\n pass\",\n \"def push(self, item: tuple):\\n self.__heap.append(item)\\n self.__sift_up(self.__len__() - 1)\",\n \"def heappush(heap, item):\\n heap.append(item)\\n _siftdown(heap, 0, len(heap)-1)\",\n \"def insert(self, item):\\n self.heaplist.append(item)\\n self.currentsize += 1\\n self.shift_item_up(self.currentsize)\",\n \"def insert(self, item):\\n self._heap.append(item)\\n self._perc_up(len(self._heap) - 1)\",\n \"def append(self, item):\\n\\t\\theapq.heappush(self.heap, (self.f(item), item))\",\n \"def push(self, item):\\n self.heap.append(self.m * item)\\n self._sift_up()\",\n \"def add(self, key, value):\\n self._data.append(self._Item(key, value))\\n self._upheap(len(self._data) - 1) # upheap newly added position\",\n \"def add(self, key, value):\\n self._data.append(self._Item(key, value))\\n self._upheap(len(self._data) - 1) # upheap newly added position\",\n \"def add(self, item):\\n\\n assert self.size < self.max_size, \\\"Cannot add item! The MaxHeap is full!\\\"\\n self.items[self.size] = item\\n self.size += 1\\n self._shift_up(self.size - 1)\",\n \"def enqueue(self, item, priority):\\n # TODO: Insert given item into heap\\n ...\",\n \"def add(self, item):\\n if not (item in self.set):\\n self.set[item] = True\\n heapq.heappush(self.heap, item)\",\n \"def insert(self, key, value):\\n self.heap.append(None)\\n hi = HeapItem(key,value)\\n self.siftup(hi, len(self.heap)-1)\\n return hi\",\n \"def push(self, val):\\n self._heap_list.append(val)\\n self._build_heap()\",\n \"def add(self, value):\\n self.heap.append(value)\\n self.heapify_up()\",\n \"def push(self, priority: float, item):\\n heappush(self._heap, (-1 * priority, item))\",\n \"def heap_push(self, value):\\n if self.find(value) is None:\\n self.table.append(value)\\n self.percolate_up(self.get_size() - 1)\",\n \"def push(self, key, value):\\r\\n if len(self.heap)<self.depth:\\r\\n heapq.heappush(self.heap, key)\\r\\n self.elements[key] = value\\r\\n else:\\r\\n oldkey = heapq.heappushpop(self.heap, key)\\r\\n self.elements[key] = value\\r\\n del self.elements[oldkey]\",\n \"def add(self, node: object) -> None:\\n end_index=self.heap.length()\\n parent_index=((end_index-1)//2)\\n if end_index==0:\\n self.heap.append(node)\\n else:\\n self.heap.append(node)\\n while self.heap.get_at_index(end_index)<self.heap.get_at_index(parent_index):\\n self.heap.swap(end_index,parent_index)\\n end_index=parent_index\\n if end_index==0:\\n break\\n parent_index=(end_index-1)//2\",\n \"def add(self, item, priority=0):\\n if item in self.set:\\n self.remove(item)\\n count = next(self.counter)\\n entry = [priority, count, item]\\n self.set[item] = entry\\n hpq.heappush(self.heap, entry)\",\n \"def add(self,key,value):\\r\\n try:\\r\\n self._data.append(self.Item(key,value)) # insert as Item class object with key value.\\r\\n self._heapify_after_add(len(self._data) - 1 )\\r\\n return self._data\\r\\n except Exception, e:\\r\\n print \\\"Error occurred in HeapDistance: add method\\\", e\",\n \"def insert(self, data):\\n # add data to list'end\\n self.heap_list.append(data)\\n # adjust max-heap from bottom to top\\n self.sift_up(len(self.heap_list)-1)\",\n \"def heap_push_max(heap, item):\\n heap.append(item)\\n heapq._siftdown_max(heap, 0, len(heap)-1)\",\n \"def __heappush(heap, nodes, node):\\n pos = len(heap)\\n heap.append(node)\\n nodes[node[1]] = pos\\n Graph.__siftup(heap, nodes, pos)\",\n \"def push(self, priority, key):\\n index = len(self.__heap)\\n self.__position[key] = index\\n self.__heap.append([priority, key])\\n self.__bubble_up(index)\",\n \"def append(self, key):\\n if key is None:\\n raise ValueError('Cannot insert None in the queue')\\n\\n i = len(self.heap)\\n self.heap.append(key)\\n while i > 1:\\n parent = i // 2\\n if key < self.heap[parent]:\\n self.heap[i], self.heap[parent] = self.heap[parent], key\\n i = parent\\n else:\\n break\",\n \"def insert(self, key, value):\\r\\n self._data.append(self._Item(key, value))\\r\\n self._upheap(len(self._data) - 1) # upheap newly added position\\r\",\n \"def push(self, item):\\n\\t\\tself.top+=1;\\n\\t\\tself.arr.insert(self.top, item);\",\n \"def put(self, element):\\n self.heap.append(element)\\n # sift up the element append before\\n self.sift_up(self.size() - 1)\",\n \"def push(self, x):\\n heapq.heappush(self.array, x)\",\n \"def add(self, element):\\n # add element to the heap\\n self.heap.append(element)\\n\\n # get index of added element and parent of added element\\n index = len(self.heap) - 1\\n parentIndex = (index - 1) // 2\\n\\n # swap parents and childs while needed\\n while index >= 1 and self.heap[parentIndex] < self.heap[index]:\\n\\n # swap parent and child\\n self.swap(parentIndex, index)\\n\\n # update parent and child indexes\\n index = parentIndex\\n parentIndex = (index - 1) // 2\",\n \"def push(self, x):\\n assert x not in self.rank\\n i = len(self.heap)\\n self.heap.append(x) # add a new leaf\\n self.rank[x] = i\\n self.up(i) # maintain heap order\",\n \"def push(self, x):\\n assert x not in self.rank\\n i = len(self.heap)\\n self.heap.append(x) # add a new leaf\\n self.rank[x] = i\\n self.up(i) # maintain heap order\",\n \"def push(self, val):\\n if type(val) == int:\\n if val in self._heap:\\n raise ValueError('Cannot have duplicate values in list')\\n if not self._heap:\\n self._heap.append(val)\\n else:\\n self._heap.append(val)\\n self._sort(len(self._heap) - 1)\\n else:\\n raise TypeError('Must add an integer')\",\n \"def insert(self, k): \\r\\n self.heap_array.append(k)\\r\\n\\r\\n current_index = len(self.heap_array) - 1\\r\\n while (current_index > 0):\\r\\n parent_index = ((current_index-1)//2)\\r\\n\\r\\n if int(self.heap_array[current_index]) > int(self.heap_array[parent_index]): # if no vialation of the min heap property \\r\\n return\\r\\n else: # if heap property is broken then swap the parent and child that are breaking the prop \\r\\n self.heap_array[parent_index], self.heap_array[current_index] = self.heap_array[current_index], self.heap_array[parent_index]\\r\\n current_index = parent_index\",\n \"def add(self, element):\\n # add element to the heap\\n self.heap.append(element)\\n\\n # get index of added element and parent of added element\\n index = len(self.heap) - 1\\n parentIndex = (index - 1) // 2\\n\\n # swap parents and childs while needed\\n while index >= 1 and self.heap[parentIndex][1] > self.heap[index][1]:\\n\\n # swap parent and child\\n swap = self.heap[parentIndex]\\n self.heap[parentIndex] = self.heap[index]\\n self.heap[index] = swap\\n\\n # update parent and child indexes\\n index = parentIndex\\n parentIndex = (index - 1) // 2\",\n \"def build_heap(self, items):\\n for key in items:\\n self.insert(key)\",\n \"def addItem(self, item, rank):\\n with self.lock:\\n if self.ItemHashList.get(item, -1) == -1:\\n self.ItemHashList[item] = None\\n if rank < 0:\\n rank = 0\\n heapq.heappush(self.ItemList, (rank, item))\",\n \"def insert(self, value):\\n self.heap.append(value)\\n index = len(self.heap) - 1\\n self._percolate_up(index)\",\n \"def insert(self, k):\\n self.heapList.append(k)\\n self.currentSize = self.currentSize + 1\\n self.percUp(self.currentSize)\",\n \"def add(self, key, value):\\n self._data.append(self._Item(key, value))\\n self._up_heap(len(self) - 1)\",\n \"def enqueue(self, priority, value, key=None):\\n key = key if key else value\\n if key in self._index:\\n self._update(priority, key)\\n return\\n self._heap.append(Item(priority, value, key))\\n self._size = len(self._heap)\\n self._index[key] = self._size - 1\\n self._sift_up(self._size - 1)\",\n \"def add(self, node: object) -> None:\\n # add node to end of array\\n self.heap.append(node)\\n\\n # find index where node was inserted\\n end = self.heap.length() - 1\\n\\n # find parent index\\n parent = (end - 1) // 2\\n\\n # while the value at the parent index is greater than the value and parent index is\\n # not less than 0, swap nodes, set new end index to parent index and find new parent\\n while self.heap.get_at_index(parent) > node and parent >= 0:\\n self.heap.swap(parent, end)\\n end = parent\\n parent = (end - 1) // 2\\n return\",\n \"def insert(self, value):\\n self.heap.append(value)\\n self._shiftUp()\",\n \"def push(self, x):\\n self.elements.append(x)\\n self._heapify()\",\n \"def push(self, x):\\n assert self._data is not None\\n if len(self._data) < self._n:\\n heapq.heappush(self._data, x)\\n else:\\n heapq.heappushpop(self._data, x)\",\n \"def insert(self, key):\\n\\n self.heap.append(key)\\n\\n self.bubble_up(len(self.heap) - 1)\",\n \"def insert(self, k):\\n #Append the element to the min heap\\n self.heap_list.append(k)\\n #Increase the size of the min heap\\n self.current_size += 1\\n #Move the value to its appropriate position in the min heap (following the definition of a min heap)\\n self.sift_up(self.current_size)\",\n \"def push(self,item):\\n self.items.append(item)\",\n \"def add(self, item, priority=0) -> None:\\n if item in self.entry_finder:\\n self.remove(item)\\n count = next(self.counter)\\n entry = (priority, count, [item])\\n self.entry_finder[item] = entry\\n heapq.heappush(self.priority_queue, entry)\",\n \"def push(self, item):\\n\\t\\tself.items.append(item)\",\n \"def push(self, new_node):\\n # Add a new node into the data list\\n self.data.append(new_node)\\n # Maintain the min heap\\n self.heapify_up(len(self.data) - 1)\",\n \"def add_to_heap(self, key, count=0):\\n entry = [1 + count, next(self.counter), key, HeapItemStatus.ACTIVE]\\n self.map[key] = entry\\n heappush(self.heap, entry)\",\n \"def push(self, item):\\n self.items.append(item)\",\n \"def push(self, item):\\n self.items.append(item)\",\n \"def push(self, item):\\n self.items.append(item)\",\n \"def push(self, item):\\n self.items.append(item)\",\n \"def push(self, item):\\n self.items.append(item)\",\n \"def add(self, key, value=None):\\n if value is None:\\n value = key\\n node = HeapNode(key, value)\\n self.store.append(node)\\n self.heap_up(len(self.store)-1)\\n return None\",\n \"def insert(self, item):\\n index = self.insert_at_next_index(item)\\n self.items[index] = item\\n while index > 1:\\n parent_index = index / 2 # Truncate, e.g. 4 and 5 have parent 2.\\n if self.is_heap_order(self.items[parent_index], self.items[index]):\\n # The item does not need to bubble up anymore. Done.\\n return\\n else:\\n # Swap items at index and parent_index\\n temp = self.items[index]\\n self.items[index] = self.items[parent_index]\\n self.items[parent_index] = temp\\n index = parent_index\\n # The item bubbled all the way to the root. Done.\\n return\",\n \"def push(self, obj):\\n # wrap the object to allow for correct pop operation\\n # remember that in python it's a min-heap (not max!)\\n wrap_obj = (obj.minus_priority, len(self), obj)\\n # use insertion number to ensure we never compare based on obj itself!\\n # additionally resolves ties by popping earliest-inserted object\\n heapq.heappush(self._heap, wrap_obj)\",\n \"def push(self, item):\\n array = self.array\\n compare = self.compare\\n array.append(item)\\n self.pos[item] = len(array) - 1\\n high = len(array) - 1\\n while high > 0:\\n low = (high-1)/2\\n if compare(array[low], array[high]) <= 0:\\n break\\n self.pos[array[high]] = low\\n self.pos[array[low]] = high\\n array[low], array[high] = array[high], array[low]\\n high = low\",\n \"def push(self, item):\\n pass\",\n \"def add(self, item, priority):\\n heappush(self.contents, (priority, item))\",\n \"def insert(self, element):\\n if self.size >= self.maxsize : \\n return\\n self.size+= 1\\n self.Heap[self.size] = element \\n \\n current = self.size \\n \\n while self.Heap[current] < self.Heap[self.parent(current)]: \\n self.swap(current, self.parent(current)) \\n current = self.parent(current)\",\n \"def push(self, item) -> None:\\n self.items.append(item)\",\n \"def insert(self, element):\\n if self.size >= self.maxsize:\\n return\\n self.size += 1\\n self.heap[self.size] = element\\n\\n current = self.size\\n\\n while self.heap[current] < self.heap[self.parent(current)]:\\n self.swap(current, self.parent(current))\\n current = self.parent(current)\",\n \"def push(self, item):\\n self.stack.append(item)\\n\\n if not self.max or item >= self.max[-1]: # add if empty or if greater\\n self.max.append(item)\",\n \"def pushPriorityQueue(self, node):\\r\\n if self.priority == \\\"bin\\\":\\r\\n heappush(self.unvisited, node)\\r\\n elif self.priority == \\\"fib\\\":\\r\\n fheappush(self.unvisited, node)\\r\\n else: # list\\r\\n self.unvisited.append(node)\",\n \"def add(self, path, value):\\n self.frontier_index += 1 # get a new unique index\\n heapq.heappush(self.frontierpq,(value, -self.frontier_index, path))\",\n \"def push(self, item):\\r\\n self.stack.insert(0, item)\",\n \"def push(self, node):\\n try:\\n self._load(True)\\n\\n # Stow the new node at our head and increment it\\n self.db[self.head] = node\\n self.head = self.head + 1\\n if self.head >= self.size:\\n self.head -= self.size\\n self.db['head'] = self.head\\n\\n # If we haven't just also pushed out an old item,\\n # increment the count of items in our db.\\n if self.count < self.size:\\n self.count += 1\\n self.db['count'] = self.count\\n except KeyError:\\n # HACK\\n self.clear()\",\n \"def push(self, item): # 05:27 Lecture Week 2 \\\"Stacks\\\" (16:24)\\n oldfirst = self.first # Save a link to the list\\n self.first = self._Node(item, oldfirst) # first points to most recent Node\\n self.N += 1\",\n \"def push(self, item):\\n if len(self._data) == self.MAX_SIZE:\\n # full we have to pop the oldest item (head)\\n self._data.pop(0)\\n self._data.append(item)\",\n \"def insert_at_end(self, new_node):\\n self.heap.append(new_node)\\n self.heap_size += 1\",\n \"def test_insert(self):\\n self.minheap.heap = [0, 1, 4, 6, 9]\\n self.minheap.insert(2)\\n assert self.minheap.heap == [0, 1, 2, 6, 9, 4]\",\n \"def push(self, item):\\n self._items.append(item)\",\n \"def push(self, item):\\n self.stack.append(item)\",\n \"def push(self, item):\\n self.stack.append(item)\",\n \"def push(self, value):\\n idx = self.__capacity - 1 + self.__size\\n self.__tree[idx] = value\\n self.__update(idx)\\n self.__size += 1\",\n \"def insert(self, item):\\n for h_num in xrange(self.k):\\n val = self.hash_value(item, h_num)\\n self.arr[val] = True\",\n \"def add(self, value):\\r\\n # Does the underlying \\\"array\\\" have enough space? If not\\r\\n # increase its size. Note, we are really using a Python list\\r\\n # as our underlying data structure, so any \\\"doubling\\\" is\\r\\n # handled for us.\\r\\n if len(self) == len(self._data): self._data.append(None)\\r\\n self._data[self._size] = value # insert new item at end of arry\\r\\n self.up_heap(self._size) # \\\"bubble\\\" the new item up.\\r\\n self._size += 1 # bump the sze\\r\",\n \"def push(self, item) -> None:\\n self._items.append(item)\",\n \"def push(self, item):\\n if item not in self._items:\\n self._items.append(item)\",\n \"def push(self, item):\\n # Reassign the link to top of the stack and update the\\n self.top = [item] + self.top\",\n \"def push(self, new_item):\\n self.items.append(new_item)\",\n \"def push(self, item):\\n super().add_item_to_front(item)\",\n \"def Push(self, item):\\n self.list.append(item)\",\n \"def append(self, x):\\n if self.even:\\n min_heap_push(self.right, x)\\n max_heap_push(self.left, min_heap_pop(self.right))\\n else:\\n max_heap_push(self.left, x)\\n min_heap_push(self.right, max_heap_pop(self.left))\\n self.even = not self.even\",\n \"def push(self, Item):\\n self.data_container.insert(0, Item)\",\n \"def add(self, elem: T):\\n if elem not in self._unique_values:\\n if len(self._heap) < self.maxsize:\\n heapq.heappush(self._heap, HeapObj(elem))\\n elif elem < self._heap[0].val:\\n heapq.heappushpop(self._heap, HeapObj(elem))\\n self._unique_values.add(elem)\",\n \"def insert(self, to_insert: Article) -> None:\\n heapq.heappush(self.heap, to_insert)\",\n \"def push(self, from_node, to_node, value):\\n heap = self.heap\\n heap.append([from_node, to_node, value])\\n if len(heap) == 1:\\n return\\n\\n index = len(heap) - 1\\n while True:\\n # parent of k is at (k-1)/2 [int division]\\n parent_index = max(floor((index-1)/2), 0)\\n parent_value = heap[parent_index][self.VALUE]\\n\\n if value < parent_value:\\n self.__swap(parent_index, index)\\n index = parent_index\\n else:\\n return\",\n \"def push(self, edge):\\n ew = EdgeWrapper(edge)\\n heapq.heappush(self.__heap, (ew.weight(), ew))\",\n \"def push(self, item):\\n self._items.append(item)\\n # This operation increments the number of items\\n # in the stack, we need to update the count variable\\n self._update_count()\",\n \"def test_insert_increases_size(sample_priorityq):\\n assert len(sample_priorityq.heap_list) == 0\\n sample_priorityq.insert([5, 1])\\n assert len(sample_priorityq.heap_list) == 1\\n sample_priorityq.insert([6, 2])\\n assert len(sample_priorityq.heap_list) == 2\",\n \"def push(self, item: Any) -> None:\\n self._items.append(item)\",\n \"def push(self, item):\\n self._data.append(item)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.86743045","0.85555136","0.84952396","0.8408714","0.83693314","0.83544827","0.8334501","0.82352614","0.8180887","0.79542404","0.7924978","0.7850471","0.78375816","0.7834717","0.7763897","0.7657202","0.764088","0.7628112","0.7622526","0.76175183","0.761704","0.759219","0.7567667","0.7536136","0.75343525","0.7530992","0.75178003","0.7509038","0.74912417","0.74838465","0.7442104","0.7397425","0.7380301","0.73628926","0.73628926","0.7359167","0.73576885","0.7353426","0.7351651","0.7340112","0.7320866","0.7318282","0.7299963","0.72596484","0.7248803","0.72475195","0.7241882","0.7238748","0.7223088","0.7203318","0.7189","0.71869636","0.71678555","0.71489185","0.7136154","0.71183723","0.71183723","0.71183723","0.71183723","0.71183723","0.7100176","0.7086722","0.70741135","0.70706296","0.70389724","0.7037934","0.70195496","0.70177716","0.699618","0.6982584","0.6972198","0.6964439","0.69610727","0.6955881","0.694618","0.6943176","0.69384325","0.69366467","0.692825","0.69182825","0.69182825","0.6906888","0.69062674","0.6903988","0.6885989","0.6879134","0.68663394","0.6862902","0.6853461","0.684058","0.6837938","0.68297875","0.6825046","0.68096536","0.68086886","0.6804733","0.68018115","0.67824334","0.6772109","0.6768384"],"string":"[\n \"0.86743045\",\n \"0.85555136\",\n \"0.84952396\",\n \"0.8408714\",\n \"0.83693314\",\n \"0.83544827\",\n \"0.8334501\",\n \"0.82352614\",\n \"0.8180887\",\n \"0.79542404\",\n \"0.7924978\",\n \"0.7850471\",\n \"0.78375816\",\n \"0.7834717\",\n \"0.7763897\",\n \"0.7657202\",\n \"0.764088\",\n \"0.7628112\",\n \"0.7622526\",\n \"0.76175183\",\n \"0.761704\",\n \"0.759219\",\n \"0.7567667\",\n \"0.7536136\",\n \"0.75343525\",\n \"0.7530992\",\n \"0.75178003\",\n \"0.7509038\",\n \"0.74912417\",\n \"0.74838465\",\n \"0.7442104\",\n \"0.7397425\",\n \"0.7380301\",\n \"0.73628926\",\n \"0.73628926\",\n \"0.7359167\",\n \"0.73576885\",\n \"0.7353426\",\n \"0.7351651\",\n \"0.7340112\",\n \"0.7320866\",\n \"0.7318282\",\n \"0.7299963\",\n \"0.72596484\",\n \"0.7248803\",\n \"0.72475195\",\n \"0.7241882\",\n \"0.7238748\",\n \"0.7223088\",\n \"0.7203318\",\n \"0.7189\",\n \"0.71869636\",\n \"0.71678555\",\n \"0.71489185\",\n \"0.7136154\",\n \"0.71183723\",\n \"0.71183723\",\n \"0.71183723\",\n \"0.71183723\",\n \"0.71183723\",\n \"0.7100176\",\n \"0.7086722\",\n \"0.70741135\",\n \"0.70706296\",\n \"0.70389724\",\n \"0.7037934\",\n \"0.70195496\",\n \"0.70177716\",\n \"0.699618\",\n \"0.6982584\",\n \"0.6972198\",\n \"0.6964439\",\n \"0.69610727\",\n \"0.6955881\",\n \"0.694618\",\n \"0.6943176\",\n \"0.69384325\",\n \"0.69366467\",\n \"0.692825\",\n \"0.69182825\",\n \"0.69182825\",\n \"0.6906888\",\n \"0.69062674\",\n \"0.6903988\",\n \"0.6885989\",\n \"0.6879134\",\n \"0.68663394\",\n \"0.6862902\",\n \"0.6853461\",\n \"0.684058\",\n \"0.6837938\",\n \"0.68297875\",\n \"0.6825046\",\n \"0.68096536\",\n \"0.68086886\",\n \"0.6804733\",\n \"0.68018115\",\n \"0.67824334\",\n \"0.6772109\",\n \"0.6768384\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7952556"},"document_rank":{"kind":"string","value":"10"}}},{"rowIdx":22297,"cells":{"query":{"kind":"string","value":"Show line of asterisks to demarcate bounds of heap"},"document":{"kind":"string","value":"def demarcate_heap(hgt=self.level, cell_wid=minimum_cell):\n # Number of nodes on bottom is 2^hgt\n max_nodes = int(np.power(2, hgt))\n print (''.center(cell_wid * max_nodes, '*'))"},"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 print_heap(self):\n for i in range(1, (self.size//2)+1): \n print(\" PARENT : \"+ str(self.Heap[i])+\" LEFT CHILD : \"+ \n str(self.Heap[2 * i])+\" RIGHT CHILD : \"+\n str(self.Heap[2 * i + 1]))","def min_heap(self): \n \n for pos in range(self.size//2, 0, -1): \n self.min_heapify(pos)","def print_heap(L):\n accum = \"\"\n powToBeat = -1\n for index, item in enumerate(L):\n log = math.log(index + 1, 2)\n if int(log) > powToBeat:\n #print accum\n accum += \"\\n\"\n #powToBeat = int(log)\n powToBeat += 1\n else:\n pass\n #accum += \" \" + str(item)\n accum += \" \" + str(item)\n return accum","def __repr__(self):\n return \"heap:[\" + ','.join(map(str, self.ar[:self.n])) + \"]\"","def for_X():\r\n for row in range(7):\r\n for col in range(7):\r\n if row-col==0 or row+col==6:\r\n print(\"*\",end=\" \")\r\n else:\r\n print(\" \",end=\" \")\r\n print()","def print_heap(self):\n print self.queue[:self.size:]","def Print(self):\n for i in range(1, (self.size // 2) + 1):\n print(f\" PARENT : {str(self.heap[i])}\"\n f\" LEFT CHILD : {str(self.heap[2 * i])}\"\n f\" RIGHT CHILD : {str(self.heap[2 * i + 1])}\")","def Print(self):\n for i in range(1, (self.size // 2) + 1):\n print(f\" PARENT : {str(self.heap[i])}\"\n f\" LEFT CHILD : {str(self.heap[2 * i])}\"\n f\" RIGHT CHILD : {str(self.heap[2 * i + 1])}\")","def print_mines(self):\n\t\tfor y in range(self.height):\n\t\t\tfor x in range(self.width):\n\t\t\t\tprint(\"#\" if self.mines[x][y] else \".\", end=\"\")\n\t\t\tprint(\"\")","def print_stars():\n for i in range(2):\n for j in range(35):\n print(\"*\", end = '')\n print('')","def for_P():\r\n for row in range(7):\r\n for col in range(4):\r\n if col==0 or row in (0,3) and col!=3 or col==3 and row in(1,2):\r\n print(\"*\",end=\" \")\r\n else:\r\n print(\" \",end=\" \")\r\n print()","def heappop(heap):\n pass","def minHeap(self):\n for pos in range(self.size // 2, 0, -1):\n self.minHeapify(pos)","def belt():\r\n\tfor i in range(SIZE*2+2):\r\n\t\tif i == 0 or i == SIZE*2+1:\r\n\t\t\tprint('+', end='')\r\n\t\telse:\r\n\t\t\tprint('=', end='')\r\n\tprint('')","def annotate_major_heap(self):\n # TODO: we could provide a fallback path by manually taking the proper bytes as\n # the ml_heap command does\n if heap_chunk_head_p is None:\n self.set_inaccurate(\"major heap info\")\n return\n\n heap_chunk_ptr = get_value_safe(\"caml_heap_start\", heap_chunk_head_p)\n try:\n while heap_chunk_ptr is not None and heap_chunk_ptr != 0:\n heap_chunk_head_ptr = heap_chunk_ptr - 1\n heap_chunk_head = heap_chunk_head_ptr.dereference()\n\n block = heap_chunk_head[\"block\"]\n size = heap_chunk_head[\"size\"]\n\n memrange = self.get_range(heap_chunk_head_ptr)\n if memrange is not None:\n self.annotate_split_range(heap_chunk_ptr.cast(size_t), size, MemoryType.MajorHeap, \"Major heap\")\n else:\n new_range = MemoryRange(heap_chunk_ptr.cast(size_t), size, \"gdb\", \"Major Heap\", MemoryType.MajorHeap)\n assert(false) # This shouldn't happen\n self.tentative_add_range(new_range)\n\n heap_chunk_ptr = heap_chunk_head[\"next\"].cast(heap_chunk_head_p)\n except gdb.MemoryError:\n print(\"OCaml major heap linked list is corrupt: last entry = 0x%08X\" % (int(heap_chunk_ptr.cast(size_t))))\n\n gray_vals = get_value_safe(\"gray_vals\", size_t)\n gray_vals_cur = get_value_safe(\"gray_vals_cur\", size_t)\n gray_vals_size = get_value_safe(\"gray_vals_size\", size_t)\n gray_vals_end = get_value_safe(\"gray_vals_end\", size_t)\n if gray_vals is not None and gray_vals_size is not None:\n self.annotate_split_range(gray_vals, gray_vals_size, MemoryType.GC_Metadata, \"major GC's gray values\")\n if gray_vals_cur is not None and gray_vals_end is not None:\n self.annotate_split_range(gray_vals_cur, gray_vals_end - gray_vals_cur, MemoryType.GC_Metadata, \"major GC's current gray values\")","def __str__(self) -> str:\n return 'HEAP ' + str(self.heap)","def __str__(self) -> str:\n return 'HEAP ' + str(self.heap)","def __str__(self):\n\n string = \"[\"\n for i in range(1, self.i , 1):\n try:\n string += str(self.heap[i]) + \"\\n\"\n except:\n string += \"Nan \"\n return string + \"]\"","def starbox(width, height):\n print(\"*\" * width) # print top edge of the box\n # print sides of the box\n for _ in range(height - 2):\n print(\"*\" + \" \" * (width - 2) + \"*\")\n print(\"*\" * width) # print bottom edge of the box","def print_grid(grid, score):\n print(\"\")\n print(score)\n wall = \"+------\"*len(grid[0])+\"+\"\n print(wall)\n for row in grid:\n meat = \"|\".join(\"{:^6}\".format(val) for val in row)\n print(\"|{}|\".format(meat))\n print(wall)","def tower_of_hanoi_stack(n, beg, aux, end):","def annotate_minor_heap(self):\n minor_start = get_value_safe(\"caml_young_base\", size_t)\n minor_end = get_value_safe(\"caml_young_end\", size_t)\n if minor_start is None or minor_end is None:\n return\n minor_size = minor_end - minor_start\n\n memrange = self.get_range(minor_start)\n if memrange is not None:\n self.annotate_split_range(minor_start, minor_size, MemoryType.MinorHeap, \"Minor heap\")\n else:\n new_range = MemoryRange(minor_start, minor_size, \"gdb\", \"Minor Heap\", MemoryType.MinorHeap)\n self.set_inaccurate(\"minor heap memory map info\")\n bisect.insort(self.ranges, new_range)","def for_L():\r\n\r\n for row in range(6):\r\n for col in range(4):\r\n if col==0 or row==5:\r\n print('*', end = ' ')\r\n else:\r\n print(' ', end = ' ')\r\n print()","def __init__(self, parent, populator):\n ListHeap.__init__(self, parent, populator, 6)\n self.propagate(0)\n self.config(width=10, height=100)","def print_grid(grid):\r\n print(\"+\",'-'*len(grid[0]*5),'+',sep='')# top line of box\r\n for i in range(len(grid)):\r\n grid_str = ''\r\n for j in range(len(grid[i])):\r\n if grid[i][j] == 0:\r\n grid_str += \"{:<5}\".format(' ')\r\n else:\r\n grid_str += \"{:<5}\".format(grid[i][j])#append a 5-width column\r\n print('|',grid_str,'|',sep='')\r\n print(\"+\",'-'*len(grid[0]*5),'+',sep='')# bottom line of box\r","def for_reverse_triange():\r\n\r\n for row in range(6,0,-1):\r\n print(' '*(6-row), '* '*row)","def __init__(self):\n self.max_heap = [] # to contain left smaller half, or + 1\n self.min_heap = [] # to contain right bigger half","def a_star(start, end, board):\n board_n = board\n closed_set = deque()\n open_set = deque()\n open_set.append(start)\n\n path = list()\n\n while open_set:\n lowest_f_index = 0\n for i, node in enumerate(open_set):\n if open_set[i].f < open_set[lowest_f_index].f:\n lowest_f_index = i\n # Adds an additional check in case the f values are similar. Then we compare the g score instead\n # and find the lowest\n if open_set[i].f == open_set[lowest_f_index].f:\n if open_set[i].g < open_set[lowest_f_index].g:\n lowest_f_index = i\n\n current_node = open_set[lowest_f_index]\n\n if current_node == end:\n tmp = current_node\n path.append(tmp)\n while tmp.previous:\n path.append(tmp.previous)\n tmp = tmp.previous\n for elem in path[1:-1]: \n elem.symbol = '▪'\n draw_4k(board_n, wait = True)\n\n open_set.remove(current_node)\n closed_set.append(current_node)\n\n neighbors = current_node.neighbors\n for nb in neighbors:\n if nb in closed_set: #Doesnt check walls here since there is no walls\n continue\n \n tmp_g = current_node.g + nb.cost # Adds the cost of the neighbor cell to the tentative g score instead of just 1\n\n if nb not in open_set:\n open_set.append(nb)\n \n elif tmp_g >= nb.g:\n continue\n\n nb.previous = current_node \n nb.g = tmp_g \n nb.h = calculate_manhattan(nb, end)\n nb.f = nb.g + nb.h","def prints(self):\r\n\r\n for i in range(len(self.heap_array)):\r\n print(self.heap_array[i])","def show( self):\n def symbol( i):\n return i<0 and (i==-2 and ' ' or '0') or chr(ord('a') + i)\n \n X, Y = np.max( self.board.positions, 0)\n # -2 to indicate outside board.\n display = np.zeros( (X+1,Y+1), dtype=int) - 2 \n for x, y in self.board.positions:\n display[x, y] = -1 # -1 to indicate unoccupied\n for p, i in self.occupation.items():\n x, y = self.board.positions[p]\n display[x, y] = i\n for x in xrange(X+1):\n s = ''.join( [ symbol( display[x, y]) for y in xrange(Y+1) ])\n print s","def heapify(x):\n pass","def print_grid (grid):\r\n print('+--------------------+')\r\n for o in range(len(grid)):\r\n print('|',end='')\r\n for e in range(len(grid[o])):\r\n j=grid[o][e]\r\n if j==0:\r\n g=' '\r\n else:\r\n g=j\r\n print(g,end=' '*(5-len(str(grid[o][e]))))\r\n print('|')\r\n print('+--------------------+')","def print_grid (grid):\r\n print('+--------------------+')\r\n for i in range(4):\r\n print('|',end='')\r\n for j in range(4):\r\n if grid[i][j] == 0:\r\n print(' '*5,end='')\r\n else:\r\n print('{:<5}'.format(grid[i][j]),end='')\r\n print('|') \r\n print('+--------------------+')","def draw_2(n: int):\n\n for row in range(n):\n for col in range(n - row):\n print('*', end='')\n print()","def pprint(self):\n pad = 4\n upper = [x/10 for x in range(GRID_SIZE)]\n lower = [x%10 for x in range(GRID_SIZE)]\n print(' ' * pad + ' '+' '.join(map(str,upper)))\n print(' ' * pad + ' '+' '.join(map(str,lower)))\n print(' ' * pad + pprint_header())\n for row in range(GRID_SIZE):\n print(' {0:2}'.format(row), end=' ')\n for col in range(GRID_SIZE):\n cell = self.get_cell_rc(row, col)\n if cell is None:\n print('.', end=' ')\n elif cell == 0:\n print(' ', end=' ')\n else: # cell == 1\n print('#', end=' ')\n print() # end line\n print(' ' * pad + pprint_header(delim='+', pad='-'))","def heapify(self, not_a_heap, show_details=False):\n self._heap = not_a_heap[:]\n cur_idx = len(self._heap) // 2 - 1\n while cur_idx >= 0:\n self._perc_down(cur_idx)\n cur_idx = cur_idx - 1\n if show_details:\n print(self._heap)","def print_hint_board(self):\n board = \"\"\n count = 1 \n for i in range(3):#need to change this in the future\n for j in range(3):#need to change this in the future\n board += str(count) \n count += 1\n if j != 2:#need to change this in the future\n board += \" | \"\n board += \"\\n\"\n return board","def build_heap(data):\n size = len(data)\n for i in range(size//2, -1,-1):\n shiftDown(data, i)","def star():\n print('*', end='')","def diamond(height):\n mid = height // 2\n lines = []\n for l in height:\n if l + 1 < mid:\n lines[1] = \" \" * (mid - (l + 1)) + \"/\" * (1 + 1) + \"\\\" * (1 + 1) + \"\\n\"\"\n return \"\".join(lines)\n #pass","def printSpace(x0,y0):\n\t\t\tprint\n\n\t\t\t# keep track of the island ids\n\t\t\tids = set()\n\n\t\t\t# convert id to a letter\n\t\t\tdef idChr(_id):\n\t\t\t\treturn chr(_id+97)\n\n\t\t\t# print the map\n\t\t\ti = 0\n\t\t\tfor y in xrange(h):\n\t\t\t\tfor x in xrange(w):\n\t\t\t\t\tif (x,y) == (x0,y0):\n\t\t\t\t\t\t# show current location\n\t\t\t\t\t\tprint \"O\",\n\t\t\t\t\telse:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t# show island label if available\n\t\t\t\t\t\t\t_id = findIslands.pixelToIslandId[i]\n\t\t\t\t\t\t\tids.add(_id)\n\t\t\t\t\t\t\tprint idChr(_id),\n\t\t\t\t\t\texcept KeyError:\n\t\t\t\t\t\t\t# just display character if no island info available\n\t\t\t\t\t\t\tprint space[i],\n\t\t\t\t\ti += 1\n\t\t\t\tprint\n\n\t\t\t# print \n\t\t\tprint \"island -> parent -> global owner\"\n\t\t\tfor _id in ids:\n\t\t\t\tprint \"%s -> %s -> %s\" % (\n\t\t\t\t\tidChr(_id),\n\t\t\t\t\tidChr(findIslands.islandIdToParent[_id]._id),\n\t\t\t\t\tidChr(findIslands.getIslandOwner(_id)._id))","def glow_boundary(bound):\n assert bound < 4\n global layout\n temp = len(layout) - 1\n for i in range(bound, bound + len_square(bound)):\n for j in range(bound, bound + len_square(bound)): # TODO: assign this to a variable\t\n layout[i][j] = 1","def draw_1(n: int):\n \n for row in range(n):\n\n for col in range(n - row - 1):\n print(' ', end='')\n\n for col in range(2 * row + 1):\n print('*', end='')\n \n print()","def display(board):\n for i in range(height-1, -1, -1):\n print(' '.join(['O' if at(board, i * width + j) else '-' for j in range(1, width+1)]))\n print(\"\")","def _heapify(self):\n for _ in range(len(self.elements)):\n for i in range(len(self.elements)-1, 0, -1):\n parentPosition = (i-1)/2 # defaults to int i.e. 7/2=3, and 6/2=3\n if parentPosition < 0:\n parentPosition = 0\n \n # change this condition to '>' if coding for max-heap. This is for min-heap.\n if self.elements[i] < self.elements[parentPosition]:\n self.elements[i], self.elements[parentPosition] = self.elements[parentPosition], self.elements[i]","def xtile(lst, lo=0, hi=1, width=10,\n chops=[0.1, 0.3, 0.5, 0.7, 0.9],\n marks=[\"-\", \" \", \" \", \"-\", \" \"],\n bar=\"|\", star=\"*\", show=\" %3.0f\"):\n\n def pos(p):\n return ordered[int(len(lst) * p)]\n\n def place(x):\n return int(width * float((x - lo)) / (hi - lo + 0.00001))\n\n def pretty(lst):\n return ', '.join([show % x for x in lst])\n\n ordered = sorted(lst)\n lo = min(lo, ordered[0])\n hi = max(hi, ordered[-1])\n what = [pos(p) for p in chops]\n where = [place(n) for n in what]\n out = [\" \"] * width\n for one, two in pairs(where):\n for i in range(one, two):\n out[i] = marks[0]\n marks = marks[1:]\n out[int(width / 2)] = bar\n out[place(pos(0.5))] = star\n return '(' + ''.join(out) + \"),\" + pretty(what)","def _heapify(self):\n start = self._parent(len(self) - 1)\n for i in range(start, -1, -1):\n self._down_heap(i)","def heapify(seq):\n minheap = [0] + seq\n for i in range(len(seq)//2, 0, -1): #len(seq)//2 -= 1 to index 1\n minHeapify(minheap, i, seq)\n seq[:] = minheap[1:]\n return seq","def show_trunk(height=2):\n for k in range(height):\n print(\"|\".center(GROUND_WIDTH))","def slab_print(slab):\n for ir, r in enumerate(slab):\n print('{:2d}¦'.format(ir), end='')\n for val in r:\n if val < 0.0: print('-', end='')\n elif val < .15: print(' ', end=''),\n elif val < .35: print('░', end=''),\n elif val < .65: print('▒', end=''),\n elif val < .85: print('▓', end=''),\n elif val <= 1.: print('█', end=''),\n else: print('+', end='')\n print('¦')","def display_cli(self) -> None:\n if len(self.living_cells.keys()) == 0:\n print('.')\n return\n min_x, min_y = math.inf, math.inf\n max_x, max_y = -math.inf, -math.inf\n for x, y in self.living_cells.keys():\n min_x = min(min_x, x)\n min_y = min(min_y, y)\n max_x = max(max_x, x)\n max_y = max(max_y, y)\n for y in range(min_y, max_y + 1):\n chars = \"\"\n for x in range(min_x, max_x + 1):\n chars += '*' if (x, y) in self.living_cells.keys() else '.'\n print(chars)\n print()","def print_column():\n print('+----+----+')","def display(self):\n prow = self.__width * '#'\n nstr = self.y * \"\\n\"\n for x in range(self.__height):\n nstr += self.x * \" \"\n nstr += prow\n if x == (self.__height - 1):\n break\n nstr += \"\\n\"\n print(nstr)","def gaps(self):\n return self.gaps_L + self.gaps_R","def prime_gap_plots(maxp, gap_sizes):\n P = prime_range(maxp + 1)\n v = [[(0, 0)] for i in gap_sizes]\n k = dict([(g, i) for i, g in enumerate(gap_sizes)])\n for i in range(len(P) - 1):\n g = P[i + 1] - P[i]\n if g in k:\n w = v[k[g]]\n w.append((P[i + 1], w[-1][1]))\n w.append((P[i + 1], w[-1][1] + 1))\n return v","def while_X():\r\n i=0\r\n while i<6:\r\n j=0\r\n while j<6:\r\n if i-j==0 or i+j==5:\r\n print(\"*\",end=\" \")\r\n else:\r\n print(\" \",end=\" \")\r\n j+=1 \r\n print()\r\n i+=1","def flotar(heap, indice):\n padre = (indice-1)//2\n while(indice > 0) and (heap.vector[padre][0] > heap.vector[indice][0]):\n heap.vector[padre], heap.vector[indice] = heap.vector[indice], heap.vector[padre]\n indice = padre\n padre = (padre-1)//2","def show_board(self): \n for row in range(self.n):\n for col in range(self.n):\n if [row, col] in self.queens:\n print (' Q ', end = '')\n else:\n print (' - ', end = '')\n print()\n print()","def __repr__(self):\n ncols, nrows = self.ncols, self.nrows # easier to read\n\n rep = \" \".join([str(n-1) if n else \" \" for n in xrange(self.ncols+1)])\n for piece in self.squares:\n if piece in [i for i in xrange(0, ncols * nrows, ncols)]:\n rep += \"\\n{0} \".format(piece // ncols)\n\n if self.squares[piece]:\n rep += \"{0} \".format(self.squares[piece])\n else:\n rep += \"- \"\n\n return rep","def display_board(x):\n\n # For b in i in x\n c = ''\n for i in x:\n\n a = 0\n p = ''\n for b in i:\n ilen = len(b)\n a += 1\n if b == ' ':\n p = p + \"_\"\n elif b == '*':\n p = p + \"*\"\n elif a == ilen:\n p = p + '\\n'\n else:\n raise False\n c = c + p\n print(p)","def while_L():\r\n\r\n for row in range(6):\r\n for col in range(4):\r\n if col==0 or row==5:\r\n print('*', end = ' ')\r\n else:\r\n print(' ', end = ' ')\r\n print()","def __repr__(self):\n rows = ''\n for y in range(self.height):\n for x in range(self.width):\n rows += '*' if self.pixels[y * self.width + x] else ' '\n rows += '\\n'\n return rows","def display(self):\n lines = []\n for y in range(1, self.height+1):\n line = [\".\"] * self.width\n for x in range(1, self.width+1):\n if self.array[y][x]:\n line[x-1] = \"#\"\n lines.append(\"\".join(line))\n return \"\\n\".join(lines)","def main():\n heap = MinHeap()\n for i in range(10):\n heap.add(i)\n print(heap.peek())\n for i in range(4):\n heap.poll()\n print(heap.peek())","def __repr__(self) -> str:\n return self.gap","def for_o():\r\n for row in range(5):\r\n for col in range(5):\r\n if row%4 ==0 and col %4 !=0 or row%4 !=0 and col %4 ==0:\r\n print('*',end=' ')\r\n else:\r\n print(' ',end=' ')\r\n print()","def printgrid_1():\n print(plusminusrow + newline + piperow*4 + plusminusrow + newline + piperow*4 +plusminusrow)","def while_reverse_triange():\r\n row = 6\r\n while row>0:\r\n \r\n print(' '*(6-row), '* '*row)\r\n row -= 1","def description(self) -> str:\n return f\"Maximize number of {colour_name(self.colour)} unit cells \" \\\n f\"that form a blob by touching sides. \" \\\n f\"Touching corners doesn't count\"","def print_grid(grid):\n\tprint(\"\")\n\twall = \"+------\"*len(grid[0])+\"+\"\n\tprint(wall)\n\tfor row in grid:\n\t\tmeat = \"|\".join(COLORS[val] if val else \" \"*6 for val in row)\n\t\tprint(\"|{}|\".format(meat))\n\t\tprint(wall)","def pprint_grid(grid):\n print(\"\\n\".join(\" \".join(str(r) for r in g) for g in grid))","def render(board):\n #The top of the board render, which gives enough space for each square based on max square value\n spot_length = len(str(len(board)))\n rv = '-'*((spot_length+1)*len(board))+'-\\n' \n\n #Create a print statement for each line\n for i in range(len(board)): \n line = '|' \n\n #For each number in line, add to print line \n for j in range(len(board[i])): \n spot = str(board[i][j]) \n #If spot is a zero, replace for visuals with '_' \n if spot == '0': \n spot = '_'*spot_length\n #Add number to print line with a separator '|'\n line += ' '*(spot_length-len(spot)) + spot + '|' \n\n #Print the rendered line\n rv += line + '\\n' \n\n #Print the bottom of the board\n rv += '-'*((spot_length+1)*len(board))+'-' \n return rv","def print(self):\n for index in range(0, len(self.heap)):\n print(self.heap[index])\n print()","def printgrid(\n rows=2,\n columns=2,\n cell_width=8,\n cell_height=4,\n corner_symbol=\"+\",\n horizontal_symbol=\"-\",\n vertical_symbol=\"|\",\n):\n horizontal_boundary = (\n corner_symbol + ((horizontal_symbol * cell_width) + corner_symbol) * columns\n ) + \"\\n\"\n horizontal_middle = horizontal_boundary.replace(horizontal_symbol, \" \").replace(\n corner_symbol, vertical_symbol\n )\n vertical_cells = (horizontal_middle * cell_height + horizontal_boundary) * rows\n print(\"\\n\")\n print(horizontal_boundary + vertical_cells)","def flotar(heap, indice):\n padre = (indice-1)//2\n while (padre >= 0) and (heap.vector[padre] > heap.vector[indice]):\n heap.vector[padre], heap.vector[indice] = heap.vector[indice], heap.vector[padre]\n indice = padre\n padre = (padre - 1) // 2","def __print_ruler():\n print(\"{:s}-+-{:s}-+-{:s}-+-{:s}-+-{:s}-+-{:s}\".format(\n \"-\"*__collen[\"id\"],\n \"-\"*__collen[\"name\"],\n \"-\"*MAX_SIZE,\n \"-\"*__collen[\"used_lim\"],\n \"-\"*MAX_SIZE,\n \"-\"*__collen[\"excl_lim\"]))","def head():\r\n\tfor i in range(SIZE):\r\n\t\tfor j in range(SIZE*2+2):\r\n\t\t\tif j == (SIZE-i):\r\n\t\t\t\tfor k in range(i+1):\r\n\t\t\t\t\tprint('/', end='')\r\n\t\t\t\tfor k in range(i+1):\r\n\t\t\t\t\tprint('\\\\', end='')\r\n\t\t\tprint(' ', end='')\r\n\t\tprint('')","def my_print(self):\n if self.__size > 0:\n for k in range(self.__position[1]):\n print()\n for i in range(self.__size):\n for j in range(self.__position[0]):\n print(\" \", end='')\n print(\"#\" * self.__size)\n else:\n print()","def __str__(self):\r\n out = \"##\"*(self.width+1)+\"\\n\"\r\n for i in range(self.height):\r\n out += \"#\"\r\n for j in range(self.width):\r\n if self.grid[i][j] == 0:\r\n out += \"##\"\r\n else:\r\n if not self.showSolution:\r\n out += \" \"\r\n elif (i,j) in self.solution:\r\n out += \"**\"\r\n else:\r\n out += \" \"\r\n out += \"#\\n\"\r\n return out + \"##\"*(self.width+1)","def __str__(self):\n return str(self._heap)","def insert(self, k): \r\n self.heap_array.append(k)\r\n\r\n current_index = len(self.heap_array) - 1\r\n while (current_index > 0):\r\n parent_index = ((current_index-1)//2)\r\n\r\n if int(self.heap_array[current_index]) > int(self.heap_array[parent_index]): # if no vialation of the min heap property \r\n return\r\n else: # if heap property is broken then swap the parent and child that are breaking the prop \r\n self.heap_array[parent_index], self.heap_array[current_index] = self.heap_array[current_index], self.heap_array[parent_index]\r\n current_index = parent_index","def print_seg_row_col(sp) : \n s, r, c = src_from_rc8x8(sp.peak_y_raw, sp.peak_x_raw)\n print('seg: %d, row: %.1f, col: %.1f' % (s, r, c))","def _build_pop_segment(self):\n value = self.value\n label = str(self.segment)\n return dedent(\n f\"\"\"\n // D = offset\n @{value}\n D=A\n // D = offset + segmentPointer\n @{label}\n D=D+M\n // SP--\n @SP\n M=M-1\n A=M\n D=D+M // addr = addr + RAM[SP]\n A=D-M // A = addr - RAM[SP] \n M=D-A // RAM[A] = addr - A\n \"\"\"\n )","def draw_5(n: int):\n\n # Top half + middle\n for row in range(n // 2 + (n % 2)):\n cols = (row) * 2 + (n % 2)\n \n for col in range((n - cols) // 2):\n print(' ', end='')\n\n for col in range(cols):\n print('*', end='')\n \n print()\n\n # Bottom half\n for row in range(n // 2):\n cols = n - (row + 1) * 2\n \n for col in range((n - cols) // 2):\n print(' ', end='')\n\n for col in range(cols):\n print('*', end='')\n \n print()","def prettyprint(board):\n print(\"\")\n for row in range(3,0,-1):\n print(\"\" + str(row) + \" |\" + board[(row-1)*3:(row)*3].replace(\"\",\" \"))\n print(\" +------\")\n print(\" A B C\")","def for_isosceles_triangle():\r\n\r\n for row in range(7):\r\n print(' '*(7-row), '* '*row)","def my_print(self):\n if self.size == 0:\n print(\"\")\n return\n for j in range(self.__position[1]):\n print(\"\")\n for i in range(self.size):\n if self.__position[0] > 0:\n print(\" \" * self.__position[0], end=\"\")\n print('#' * self.size)","def pretty_print_board(board):\n board_rows = str(board).split('\\n')\n annotated = [str(8 - i) + \"\\t\" + x for i, x in enumerate(board_rows)]\n print '\\n'.join(annotated)\n print '\\n\\ta b c d e f g h'\n\n return","def build_heap(arr):\n for i in range(len(arr)-1, -1, -1):\n down_heapify(arr, len(arr), i)","def plot_insertsize():","def rectangle(height,width):\n for row in range(height):\n for column in range(width):\n print(CHAR, end = '')\n print()","def FullDump(reader, heap):\n def dump_region(reader, start, size, location):\n print()\n while start & 3 != 0:\n start += 1\n size -= 1\n location += 1\n is_executable = reader.IsProbableExecutableRegion(location, size)\n is_ascii = reader.IsProbableASCIIRegion(location, size)\n\n if is_executable is not False:\n lines = reader.GetDisasmLines(start, size)\n for line in lines:\n print(FormatDisasmLine(start, heap, line))\n print()\n\n if is_ascii is not False:\n # Output in the same format as the Unix hd command\n addr = start\n for i in range(0, size, 16):\n slot = i + location\n hex_line = \"\"\n asc_line = \"\"\n for i in range(16):\n if slot + i < location + size:\n byte = ctypes.c_uint8.from_buffer(reader.minidump, slot + i).value\n if byte >= 0x20 and byte < 0x7f:\n asc_line += chr(byte)\n else:\n asc_line += \".\"\n hex_line += \" %02x\" % (byte)\n else:\n hex_line += \" \"\n if i == 7:\n hex_line += \" \"\n print(\"%s %s |%s|\" % (reader.FormatIntPtr(addr),\n hex_line,\n asc_line))\n addr += 16\n\n if is_executable is not True and is_ascii is not True:\n print(\"%s - %s\" % (reader.FormatIntPtr(start),\n reader.FormatIntPtr(start + size)))\n print(start + size + 1);\n for i in range(0, size, reader.PointerSize()):\n slot = start + i\n maybe_address = reader.ReadUIntPtr(slot)\n heap_object = heap.FindObject(maybe_address)\n print(\"%s: %s\" % (reader.FormatIntPtr(slot),\n reader.FormatIntPtr(maybe_address)))\n if heap_object:\n heap_object.Print(Printer())\n print()\n\n reader.ForEachMemoryRegion(dump_region)","def display(self):\n for _jumpline in range(self.y):\n print(end=\"\\n\")\n for _height in range(self.height):\n for _space in range(self.x):\n print(\" \", end=\"\")\n for _width in range(self.width):\n print(\"#\", end=\"\")\n print(end=\"\\n\")","def display(self):\n print(\"\\n\" * self.y, end='')\n for i in range(self.height):\n for j in range(self.width + self.x):\n if j < self.x:\n print(' ', end='')\n else:\n print('#', end='')\n print('')","def my_print(self):\n if self.size is 0:\n print(\"\")\n for i in range(0, self.size):\n for j in range(0, self.size):\n print(\"#\", end=\"\")\n print(\"\")","def tiles(rxs):\n\n lo = math.inf\n hi = -math.inf\n\n for rx in rxs:\n lo = min(lo, rx[\"has\"][0])\n hi = max(hi, rx[\"has\"][len(rx[\"has\"]) - 1])\n\n for rx in rxs:\n t, u = rx['has'], []\n\n def of(x, most):\n return int(max(1, min(most, x)))\n\n def at(x):\n return t[of(len(t)*x//1, len(t))]\n\n def pos(x):\n return math.floor(of(40*(x-lo)/(hi-lo+1E-32)//1, 40))\n\n for _ in range(globals.the[\"width\"] + 1):\n u.append(\" \")\n\n for i in range(pos(at(.1)), pos(at(.3)) + 1):\n u[i] = \"-\"\n\n for i in range(pos(at(.7)), pos(at(.9)) + 1):\n u[i] = \"-\"\n\n u[globals.the[\"width\"] // 2] = \"|\"\n u[pos(at(.5))] = \"*\"\n\n rx[\"show\"] = \"\".join(u) + \" {\" + globals.the[\"Fmt\"].format(at(.1))\n\n for x in [at(.3), at(.5), at(.7), at(.9)]:\n rx[\"show\"] += \", \" + globals.the[\"Fmt\"].format(x)\n\n rx[\"show\"] += \" }\"\n\n return rxs","def printBoard(self,board):\n print(\" A B C D E F G H \")\n print(\" - - - - - - - - - - - - - - - - -\")\n for r in range(0,8,1):\n row = \"\"\n for c in range(0,8,1):\n value = \"| \"\n if board[c][r] == -1: value = \"| X \"\n if board[c][r] == 1: value = \"| O \"\n row = row+value\n print(str(r+1)+row+\"|\")\n print(\" - - - - - - - - - - - - - - - - -\")","def __init__(self):\n # min heap for right part, max heap for left part\n self.minHeap_right = []\n self.maxHeap_left = []\n self.tot_num = 0","def draw_4(n: int):\n\n # Top half + middle\n for row in range(n // 2 + (n % 2)):\n cols = n - row * 2\n \n for col in range((n - cols) // 2):\n print(' ', end='')\n\n for col in range(cols):\n print('*', end='')\n \n print()\n\n # Bottom half\n for row in range(n // 2):\n cols = (row + 1) * 2 + (n % 2)\n \n for col in range((n - cols) // 2):\n print(' ', end='')\n\n for col in range(cols):\n print('*', end='')\n \n print()","def print_column():\n print('+----+----+----+----+')"],"string":"[\n \"def print_heap(self):\\n for i in range(1, (self.size//2)+1): \\n print(\\\" PARENT : \\\"+ str(self.Heap[i])+\\\" LEFT CHILD : \\\"+ \\n str(self.Heap[2 * i])+\\\" RIGHT CHILD : \\\"+\\n str(self.Heap[2 * i + 1]))\",\n \"def min_heap(self): \\n \\n for pos in range(self.size//2, 0, -1): \\n self.min_heapify(pos)\",\n \"def print_heap(L):\\n accum = \\\"\\\"\\n powToBeat = -1\\n for index, item in enumerate(L):\\n log = math.log(index + 1, 2)\\n if int(log) > powToBeat:\\n #print accum\\n accum += \\\"\\\\n\\\"\\n #powToBeat = int(log)\\n powToBeat += 1\\n else:\\n pass\\n #accum += \\\" \\\" + str(item)\\n accum += \\\" \\\" + str(item)\\n return accum\",\n \"def __repr__(self):\\n return \\\"heap:[\\\" + ','.join(map(str, self.ar[:self.n])) + \\\"]\\\"\",\n \"def for_X():\\r\\n for row in range(7):\\r\\n for col in range(7):\\r\\n if row-col==0 or row+col==6:\\r\\n print(\\\"*\\\",end=\\\" \\\")\\r\\n else:\\r\\n print(\\\" \\\",end=\\\" \\\")\\r\\n print()\",\n \"def print_heap(self):\\n print self.queue[:self.size:]\",\n \"def Print(self):\\n for i in range(1, (self.size // 2) + 1):\\n print(f\\\" PARENT : {str(self.heap[i])}\\\"\\n f\\\" LEFT CHILD : {str(self.heap[2 * i])}\\\"\\n f\\\" RIGHT CHILD : {str(self.heap[2 * i + 1])}\\\")\",\n \"def Print(self):\\n for i in range(1, (self.size // 2) + 1):\\n print(f\\\" PARENT : {str(self.heap[i])}\\\"\\n f\\\" LEFT CHILD : {str(self.heap[2 * i])}\\\"\\n f\\\" RIGHT CHILD : {str(self.heap[2 * i + 1])}\\\")\",\n \"def print_mines(self):\\n\\t\\tfor y in range(self.height):\\n\\t\\t\\tfor x in range(self.width):\\n\\t\\t\\t\\tprint(\\\"#\\\" if self.mines[x][y] else \\\".\\\", end=\\\"\\\")\\n\\t\\t\\tprint(\\\"\\\")\",\n \"def print_stars():\\n for i in range(2):\\n for j in range(35):\\n print(\\\"*\\\", end = '')\\n print('')\",\n \"def for_P():\\r\\n for row in range(7):\\r\\n for col in range(4):\\r\\n if col==0 or row in (0,3) and col!=3 or col==3 and row in(1,2):\\r\\n print(\\\"*\\\",end=\\\" \\\")\\r\\n else:\\r\\n print(\\\" \\\",end=\\\" \\\")\\r\\n print()\",\n \"def heappop(heap):\\n pass\",\n \"def minHeap(self):\\n for pos in range(self.size // 2, 0, -1):\\n self.minHeapify(pos)\",\n \"def belt():\\r\\n\\tfor i in range(SIZE*2+2):\\r\\n\\t\\tif i == 0 or i == SIZE*2+1:\\r\\n\\t\\t\\tprint('+', end='')\\r\\n\\t\\telse:\\r\\n\\t\\t\\tprint('=', end='')\\r\\n\\tprint('')\",\n \"def annotate_major_heap(self):\\n # TODO: we could provide a fallback path by manually taking the proper bytes as\\n # the ml_heap command does\\n if heap_chunk_head_p is None:\\n self.set_inaccurate(\\\"major heap info\\\")\\n return\\n\\n heap_chunk_ptr = get_value_safe(\\\"caml_heap_start\\\", heap_chunk_head_p)\\n try:\\n while heap_chunk_ptr is not None and heap_chunk_ptr != 0:\\n heap_chunk_head_ptr = heap_chunk_ptr - 1\\n heap_chunk_head = heap_chunk_head_ptr.dereference()\\n\\n block = heap_chunk_head[\\\"block\\\"]\\n size = heap_chunk_head[\\\"size\\\"]\\n\\n memrange = self.get_range(heap_chunk_head_ptr)\\n if memrange is not None:\\n self.annotate_split_range(heap_chunk_ptr.cast(size_t), size, MemoryType.MajorHeap, \\\"Major heap\\\")\\n else:\\n new_range = MemoryRange(heap_chunk_ptr.cast(size_t), size, \\\"gdb\\\", \\\"Major Heap\\\", MemoryType.MajorHeap)\\n assert(false) # This shouldn't happen\\n self.tentative_add_range(new_range)\\n\\n heap_chunk_ptr = heap_chunk_head[\\\"next\\\"].cast(heap_chunk_head_p)\\n except gdb.MemoryError:\\n print(\\\"OCaml major heap linked list is corrupt: last entry = 0x%08X\\\" % (int(heap_chunk_ptr.cast(size_t))))\\n\\n gray_vals = get_value_safe(\\\"gray_vals\\\", size_t)\\n gray_vals_cur = get_value_safe(\\\"gray_vals_cur\\\", size_t)\\n gray_vals_size = get_value_safe(\\\"gray_vals_size\\\", size_t)\\n gray_vals_end = get_value_safe(\\\"gray_vals_end\\\", size_t)\\n if gray_vals is not None and gray_vals_size is not None:\\n self.annotate_split_range(gray_vals, gray_vals_size, MemoryType.GC_Metadata, \\\"major GC's gray values\\\")\\n if gray_vals_cur is not None and gray_vals_end is not None:\\n self.annotate_split_range(gray_vals_cur, gray_vals_end - gray_vals_cur, MemoryType.GC_Metadata, \\\"major GC's current gray values\\\")\",\n \"def __str__(self) -> str:\\n return 'HEAP ' + str(self.heap)\",\n \"def __str__(self) -> str:\\n return 'HEAP ' + str(self.heap)\",\n \"def __str__(self):\\n\\n string = \\\"[\\\"\\n for i in range(1, self.i , 1):\\n try:\\n string += str(self.heap[i]) + \\\"\\\\n\\\"\\n except:\\n string += \\\"Nan \\\"\\n return string + \\\"]\\\"\",\n \"def starbox(width, height):\\n print(\\\"*\\\" * width) # print top edge of the box\\n # print sides of the box\\n for _ in range(height - 2):\\n print(\\\"*\\\" + \\\" \\\" * (width - 2) + \\\"*\\\")\\n print(\\\"*\\\" * width) # print bottom edge of the box\",\n \"def print_grid(grid, score):\\n print(\\\"\\\")\\n print(score)\\n wall = \\\"+------\\\"*len(grid[0])+\\\"+\\\"\\n print(wall)\\n for row in grid:\\n meat = \\\"|\\\".join(\\\"{:^6}\\\".format(val) for val in row)\\n print(\\\"|{}|\\\".format(meat))\\n print(wall)\",\n \"def tower_of_hanoi_stack(n, beg, aux, end):\",\n \"def annotate_minor_heap(self):\\n minor_start = get_value_safe(\\\"caml_young_base\\\", size_t)\\n minor_end = get_value_safe(\\\"caml_young_end\\\", size_t)\\n if minor_start is None or minor_end is None:\\n return\\n minor_size = minor_end - minor_start\\n\\n memrange = self.get_range(minor_start)\\n if memrange is not None:\\n self.annotate_split_range(minor_start, minor_size, MemoryType.MinorHeap, \\\"Minor heap\\\")\\n else:\\n new_range = MemoryRange(minor_start, minor_size, \\\"gdb\\\", \\\"Minor Heap\\\", MemoryType.MinorHeap)\\n self.set_inaccurate(\\\"minor heap memory map info\\\")\\n bisect.insort(self.ranges, new_range)\",\n \"def for_L():\\r\\n\\r\\n for row in range(6):\\r\\n for col in range(4):\\r\\n if col==0 or row==5:\\r\\n print('*', end = ' ')\\r\\n else:\\r\\n print(' ', end = ' ')\\r\\n print()\",\n \"def __init__(self, parent, populator):\\n ListHeap.__init__(self, parent, populator, 6)\\n self.propagate(0)\\n self.config(width=10, height=100)\",\n \"def print_grid(grid):\\r\\n print(\\\"+\\\",'-'*len(grid[0]*5),'+',sep='')# top line of box\\r\\n for i in range(len(grid)):\\r\\n grid_str = ''\\r\\n for j in range(len(grid[i])):\\r\\n if grid[i][j] == 0:\\r\\n grid_str += \\\"{:<5}\\\".format(' ')\\r\\n else:\\r\\n grid_str += \\\"{:<5}\\\".format(grid[i][j])#append a 5-width column\\r\\n print('|',grid_str,'|',sep='')\\r\\n print(\\\"+\\\",'-'*len(grid[0]*5),'+',sep='')# bottom line of box\\r\",\n \"def for_reverse_triange():\\r\\n\\r\\n for row in range(6,0,-1):\\r\\n print(' '*(6-row), '* '*row)\",\n \"def __init__(self):\\n self.max_heap = [] # to contain left smaller half, or + 1\\n self.min_heap = [] # to contain right bigger half\",\n \"def a_star(start, end, board):\\n board_n = board\\n closed_set = deque()\\n open_set = deque()\\n open_set.append(start)\\n\\n path = list()\\n\\n while open_set:\\n lowest_f_index = 0\\n for i, node in enumerate(open_set):\\n if open_set[i].f < open_set[lowest_f_index].f:\\n lowest_f_index = i\\n # Adds an additional check in case the f values are similar. Then we compare the g score instead\\n # and find the lowest\\n if open_set[i].f == open_set[lowest_f_index].f:\\n if open_set[i].g < open_set[lowest_f_index].g:\\n lowest_f_index = i\\n\\n current_node = open_set[lowest_f_index]\\n\\n if current_node == end:\\n tmp = current_node\\n path.append(tmp)\\n while tmp.previous:\\n path.append(tmp.previous)\\n tmp = tmp.previous\\n for elem in path[1:-1]: \\n elem.symbol = '▪'\\n draw_4k(board_n, wait = True)\\n\\n open_set.remove(current_node)\\n closed_set.append(current_node)\\n\\n neighbors = current_node.neighbors\\n for nb in neighbors:\\n if nb in closed_set: #Doesnt check walls here since there is no walls\\n continue\\n \\n tmp_g = current_node.g + nb.cost # Adds the cost of the neighbor cell to the tentative g score instead of just 1\\n\\n if nb not in open_set:\\n open_set.append(nb)\\n \\n elif tmp_g >= nb.g:\\n continue\\n\\n nb.previous = current_node \\n nb.g = tmp_g \\n nb.h = calculate_manhattan(nb, end)\\n nb.f = nb.g + nb.h\",\n \"def prints(self):\\r\\n\\r\\n for i in range(len(self.heap_array)):\\r\\n print(self.heap_array[i])\",\n \"def show( self):\\n def symbol( i):\\n return i<0 and (i==-2 and ' ' or '0') or chr(ord('a') + i)\\n \\n X, Y = np.max( self.board.positions, 0)\\n # -2 to indicate outside board.\\n display = np.zeros( (X+1,Y+1), dtype=int) - 2 \\n for x, y in self.board.positions:\\n display[x, y] = -1 # -1 to indicate unoccupied\\n for p, i in self.occupation.items():\\n x, y = self.board.positions[p]\\n display[x, y] = i\\n for x in xrange(X+1):\\n s = ''.join( [ symbol( display[x, y]) for y in xrange(Y+1) ])\\n print s\",\n \"def heapify(x):\\n pass\",\n \"def print_grid (grid):\\r\\n print('+--------------------+')\\r\\n for o in range(len(grid)):\\r\\n print('|',end='')\\r\\n for e in range(len(grid[o])):\\r\\n j=grid[o][e]\\r\\n if j==0:\\r\\n g=' '\\r\\n else:\\r\\n g=j\\r\\n print(g,end=' '*(5-len(str(grid[o][e]))))\\r\\n print('|')\\r\\n print('+--------------------+')\",\n \"def print_grid (grid):\\r\\n print('+--------------------+')\\r\\n for i in range(4):\\r\\n print('|',end='')\\r\\n for j in range(4):\\r\\n if grid[i][j] == 0:\\r\\n print(' '*5,end='')\\r\\n else:\\r\\n print('{:<5}'.format(grid[i][j]),end='')\\r\\n print('|') \\r\\n print('+--------------------+')\",\n \"def draw_2(n: int):\\n\\n for row in range(n):\\n for col in range(n - row):\\n print('*', end='')\\n print()\",\n \"def pprint(self):\\n pad = 4\\n upper = [x/10 for x in range(GRID_SIZE)]\\n lower = [x%10 for x in range(GRID_SIZE)]\\n print(' ' * pad + ' '+' '.join(map(str,upper)))\\n print(' ' * pad + ' '+' '.join(map(str,lower)))\\n print(' ' * pad + pprint_header())\\n for row in range(GRID_SIZE):\\n print(' {0:2}'.format(row), end=' ')\\n for col in range(GRID_SIZE):\\n cell = self.get_cell_rc(row, col)\\n if cell is None:\\n print('.', end=' ')\\n elif cell == 0:\\n print(' ', end=' ')\\n else: # cell == 1\\n print('#', end=' ')\\n print() # end line\\n print(' ' * pad + pprint_header(delim='+', pad='-'))\",\n \"def heapify(self, not_a_heap, show_details=False):\\n self._heap = not_a_heap[:]\\n cur_idx = len(self._heap) // 2 - 1\\n while cur_idx >= 0:\\n self._perc_down(cur_idx)\\n cur_idx = cur_idx - 1\\n if show_details:\\n print(self._heap)\",\n \"def print_hint_board(self):\\n board = \\\"\\\"\\n count = 1 \\n for i in range(3):#need to change this in the future\\n for j in range(3):#need to change this in the future\\n board += str(count) \\n count += 1\\n if j != 2:#need to change this in the future\\n board += \\\" | \\\"\\n board += \\\"\\\\n\\\"\\n return board\",\n \"def build_heap(data):\\n size = len(data)\\n for i in range(size//2, -1,-1):\\n shiftDown(data, i)\",\n \"def star():\\n print('*', end='')\",\n \"def diamond(height):\\n mid = height // 2\\n lines = []\\n for l in height:\\n if l + 1 < mid:\\n lines[1] = \\\" \\\" * (mid - (l + 1)) + \\\"/\\\" * (1 + 1) + \\\"\\\\\\\" * (1 + 1) + \\\"\\\\n\\\"\\\"\\n return \\\"\\\".join(lines)\\n #pass\",\n \"def printSpace(x0,y0):\\n\\t\\t\\tprint\\n\\n\\t\\t\\t# keep track of the island ids\\n\\t\\t\\tids = set()\\n\\n\\t\\t\\t# convert id to a letter\\n\\t\\t\\tdef idChr(_id):\\n\\t\\t\\t\\treturn chr(_id+97)\\n\\n\\t\\t\\t# print the map\\n\\t\\t\\ti = 0\\n\\t\\t\\tfor y in xrange(h):\\n\\t\\t\\t\\tfor x in xrange(w):\\n\\t\\t\\t\\t\\tif (x,y) == (x0,y0):\\n\\t\\t\\t\\t\\t\\t# show current location\\n\\t\\t\\t\\t\\t\\tprint \\\"O\\\",\\n\\t\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t\\t\\t# show island label if available\\n\\t\\t\\t\\t\\t\\t\\t_id = findIslands.pixelToIslandId[i]\\n\\t\\t\\t\\t\\t\\t\\tids.add(_id)\\n\\t\\t\\t\\t\\t\\t\\tprint idChr(_id),\\n\\t\\t\\t\\t\\t\\texcept KeyError:\\n\\t\\t\\t\\t\\t\\t\\t# just display character if no island info available\\n\\t\\t\\t\\t\\t\\t\\tprint space[i],\\n\\t\\t\\t\\t\\ti += 1\\n\\t\\t\\t\\tprint\\n\\n\\t\\t\\t# print \\n\\t\\t\\tprint \\\"island -> parent -> global owner\\\"\\n\\t\\t\\tfor _id in ids:\\n\\t\\t\\t\\tprint \\\"%s -> %s -> %s\\\" % (\\n\\t\\t\\t\\t\\tidChr(_id),\\n\\t\\t\\t\\t\\tidChr(findIslands.islandIdToParent[_id]._id),\\n\\t\\t\\t\\t\\tidChr(findIslands.getIslandOwner(_id)._id))\",\n \"def glow_boundary(bound):\\n assert bound < 4\\n global layout\\n temp = len(layout) - 1\\n for i in range(bound, bound + len_square(bound)):\\n for j in range(bound, bound + len_square(bound)): # TODO: assign this to a variable\\t\\n layout[i][j] = 1\",\n \"def draw_1(n: int):\\n \\n for row in range(n):\\n\\n for col in range(n - row - 1):\\n print(' ', end='')\\n\\n for col in range(2 * row + 1):\\n print('*', end='')\\n \\n print()\",\n \"def display(board):\\n for i in range(height-1, -1, -1):\\n print(' '.join(['O' if at(board, i * width + j) else '-' for j in range(1, width+1)]))\\n print(\\\"\\\")\",\n \"def _heapify(self):\\n for _ in range(len(self.elements)):\\n for i in range(len(self.elements)-1, 0, -1):\\n parentPosition = (i-1)/2 # defaults to int i.e. 7/2=3, and 6/2=3\\n if parentPosition < 0:\\n parentPosition = 0\\n \\n # change this condition to '>' if coding for max-heap. This is for min-heap.\\n if self.elements[i] < self.elements[parentPosition]:\\n self.elements[i], self.elements[parentPosition] = self.elements[parentPosition], self.elements[i]\",\n \"def xtile(lst, lo=0, hi=1, width=10,\\n chops=[0.1, 0.3, 0.5, 0.7, 0.9],\\n marks=[\\\"-\\\", \\\" \\\", \\\" \\\", \\\"-\\\", \\\" \\\"],\\n bar=\\\"|\\\", star=\\\"*\\\", show=\\\" %3.0f\\\"):\\n\\n def pos(p):\\n return ordered[int(len(lst) * p)]\\n\\n def place(x):\\n return int(width * float((x - lo)) / (hi - lo + 0.00001))\\n\\n def pretty(lst):\\n return ', '.join([show % x for x in lst])\\n\\n ordered = sorted(lst)\\n lo = min(lo, ordered[0])\\n hi = max(hi, ordered[-1])\\n what = [pos(p) for p in chops]\\n where = [place(n) for n in what]\\n out = [\\\" \\\"] * width\\n for one, two in pairs(where):\\n for i in range(one, two):\\n out[i] = marks[0]\\n marks = marks[1:]\\n out[int(width / 2)] = bar\\n out[place(pos(0.5))] = star\\n return '(' + ''.join(out) + \\\"),\\\" + pretty(what)\",\n \"def _heapify(self):\\n start = self._parent(len(self) - 1)\\n for i in range(start, -1, -1):\\n self._down_heap(i)\",\n \"def heapify(seq):\\n minheap = [0] + seq\\n for i in range(len(seq)//2, 0, -1): #len(seq)//2 -= 1 to index 1\\n minHeapify(minheap, i, seq)\\n seq[:] = minheap[1:]\\n return seq\",\n \"def show_trunk(height=2):\\n for k in range(height):\\n print(\\\"|\\\".center(GROUND_WIDTH))\",\n \"def slab_print(slab):\\n for ir, r in enumerate(slab):\\n print('{:2d}¦'.format(ir), end='')\\n for val in r:\\n if val < 0.0: print('-', end='')\\n elif val < .15: print(' ', end=''),\\n elif val < .35: print('░', end=''),\\n elif val < .65: print('▒', end=''),\\n elif val < .85: print('▓', end=''),\\n elif val <= 1.: print('█', end=''),\\n else: print('+', end='')\\n print('¦')\",\n \"def display_cli(self) -> None:\\n if len(self.living_cells.keys()) == 0:\\n print('.')\\n return\\n min_x, min_y = math.inf, math.inf\\n max_x, max_y = -math.inf, -math.inf\\n for x, y in self.living_cells.keys():\\n min_x = min(min_x, x)\\n min_y = min(min_y, y)\\n max_x = max(max_x, x)\\n max_y = max(max_y, y)\\n for y in range(min_y, max_y + 1):\\n chars = \\\"\\\"\\n for x in range(min_x, max_x + 1):\\n chars += '*' if (x, y) in self.living_cells.keys() else '.'\\n print(chars)\\n print()\",\n \"def print_column():\\n print('+----+----+')\",\n \"def display(self):\\n prow = self.__width * '#'\\n nstr = self.y * \\\"\\\\n\\\"\\n for x in range(self.__height):\\n nstr += self.x * \\\" \\\"\\n nstr += prow\\n if x == (self.__height - 1):\\n break\\n nstr += \\\"\\\\n\\\"\\n print(nstr)\",\n \"def gaps(self):\\n return self.gaps_L + self.gaps_R\",\n \"def prime_gap_plots(maxp, gap_sizes):\\n P = prime_range(maxp + 1)\\n v = [[(0, 0)] for i in gap_sizes]\\n k = dict([(g, i) for i, g in enumerate(gap_sizes)])\\n for i in range(len(P) - 1):\\n g = P[i + 1] - P[i]\\n if g in k:\\n w = v[k[g]]\\n w.append((P[i + 1], w[-1][1]))\\n w.append((P[i + 1], w[-1][1] + 1))\\n return v\",\n \"def while_X():\\r\\n i=0\\r\\n while i<6:\\r\\n j=0\\r\\n while j<6:\\r\\n if i-j==0 or i+j==5:\\r\\n print(\\\"*\\\",end=\\\" \\\")\\r\\n else:\\r\\n print(\\\" \\\",end=\\\" \\\")\\r\\n j+=1 \\r\\n print()\\r\\n i+=1\",\n \"def flotar(heap, indice):\\n padre = (indice-1)//2\\n while(indice > 0) and (heap.vector[padre][0] > heap.vector[indice][0]):\\n heap.vector[padre], heap.vector[indice] = heap.vector[indice], heap.vector[padre]\\n indice = padre\\n padre = (padre-1)//2\",\n \"def show_board(self): \\n for row in range(self.n):\\n for col in range(self.n):\\n if [row, col] in self.queens:\\n print (' Q ', end = '')\\n else:\\n print (' - ', end = '')\\n print()\\n print()\",\n \"def __repr__(self):\\n ncols, nrows = self.ncols, self.nrows # easier to read\\n\\n rep = \\\" \\\".join([str(n-1) if n else \\\" \\\" for n in xrange(self.ncols+1)])\\n for piece in self.squares:\\n if piece in [i for i in xrange(0, ncols * nrows, ncols)]:\\n rep += \\\"\\\\n{0} \\\".format(piece // ncols)\\n\\n if self.squares[piece]:\\n rep += \\\"{0} \\\".format(self.squares[piece])\\n else:\\n rep += \\\"- \\\"\\n\\n return rep\",\n \"def display_board(x):\\n\\n # For b in i in x\\n c = ''\\n for i in x:\\n\\n a = 0\\n p = ''\\n for b in i:\\n ilen = len(b)\\n a += 1\\n if b == ' ':\\n p = p + \\\"_\\\"\\n elif b == '*':\\n p = p + \\\"*\\\"\\n elif a == ilen:\\n p = p + '\\\\n'\\n else:\\n raise False\\n c = c + p\\n print(p)\",\n \"def while_L():\\r\\n\\r\\n for row in range(6):\\r\\n for col in range(4):\\r\\n if col==0 or row==5:\\r\\n print('*', end = ' ')\\r\\n else:\\r\\n print(' ', end = ' ')\\r\\n print()\",\n \"def __repr__(self):\\n rows = ''\\n for y in range(self.height):\\n for x in range(self.width):\\n rows += '*' if self.pixels[y * self.width + x] else ' '\\n rows += '\\\\n'\\n return rows\",\n \"def display(self):\\n lines = []\\n for y in range(1, self.height+1):\\n line = [\\\".\\\"] * self.width\\n for x in range(1, self.width+1):\\n if self.array[y][x]:\\n line[x-1] = \\\"#\\\"\\n lines.append(\\\"\\\".join(line))\\n return \\\"\\\\n\\\".join(lines)\",\n \"def main():\\n heap = MinHeap()\\n for i in range(10):\\n heap.add(i)\\n print(heap.peek())\\n for i in range(4):\\n heap.poll()\\n print(heap.peek())\",\n \"def __repr__(self) -> str:\\n return self.gap\",\n \"def for_o():\\r\\n for row in range(5):\\r\\n for col in range(5):\\r\\n if row%4 ==0 and col %4 !=0 or row%4 !=0 and col %4 ==0:\\r\\n print('*',end=' ')\\r\\n else:\\r\\n print(' ',end=' ')\\r\\n print()\",\n \"def printgrid_1():\\n print(plusminusrow + newline + piperow*4 + plusminusrow + newline + piperow*4 +plusminusrow)\",\n \"def while_reverse_triange():\\r\\n row = 6\\r\\n while row>0:\\r\\n \\r\\n print(' '*(6-row), '* '*row)\\r\\n row -= 1\",\n \"def description(self) -> str:\\n return f\\\"Maximize number of {colour_name(self.colour)} unit cells \\\" \\\\\\n f\\\"that form a blob by touching sides. \\\" \\\\\\n f\\\"Touching corners doesn't count\\\"\",\n \"def print_grid(grid):\\n\\tprint(\\\"\\\")\\n\\twall = \\\"+------\\\"*len(grid[0])+\\\"+\\\"\\n\\tprint(wall)\\n\\tfor row in grid:\\n\\t\\tmeat = \\\"|\\\".join(COLORS[val] if val else \\\" \\\"*6 for val in row)\\n\\t\\tprint(\\\"|{}|\\\".format(meat))\\n\\t\\tprint(wall)\",\n \"def pprint_grid(grid):\\n print(\\\"\\\\n\\\".join(\\\" \\\".join(str(r) for r in g) for g in grid))\",\n \"def render(board):\\n #The top of the board render, which gives enough space for each square based on max square value\\n spot_length = len(str(len(board)))\\n rv = '-'*((spot_length+1)*len(board))+'-\\\\n' \\n\\n #Create a print statement for each line\\n for i in range(len(board)): \\n line = '|' \\n\\n #For each number in line, add to print line \\n for j in range(len(board[i])): \\n spot = str(board[i][j]) \\n #If spot is a zero, replace for visuals with '_' \\n if spot == '0': \\n spot = '_'*spot_length\\n #Add number to print line with a separator '|'\\n line += ' '*(spot_length-len(spot)) + spot + '|' \\n\\n #Print the rendered line\\n rv += line + '\\\\n' \\n\\n #Print the bottom of the board\\n rv += '-'*((spot_length+1)*len(board))+'-' \\n return rv\",\n \"def print(self):\\n for index in range(0, len(self.heap)):\\n print(self.heap[index])\\n print()\",\n \"def printgrid(\\n rows=2,\\n columns=2,\\n cell_width=8,\\n cell_height=4,\\n corner_symbol=\\\"+\\\",\\n horizontal_symbol=\\\"-\\\",\\n vertical_symbol=\\\"|\\\",\\n):\\n horizontal_boundary = (\\n corner_symbol + ((horizontal_symbol * cell_width) + corner_symbol) * columns\\n ) + \\\"\\\\n\\\"\\n horizontal_middle = horizontal_boundary.replace(horizontal_symbol, \\\" \\\").replace(\\n corner_symbol, vertical_symbol\\n )\\n vertical_cells = (horizontal_middle * cell_height + horizontal_boundary) * rows\\n print(\\\"\\\\n\\\")\\n print(horizontal_boundary + vertical_cells)\",\n \"def flotar(heap, indice):\\n padre = (indice-1)//2\\n while (padre >= 0) and (heap.vector[padre] > heap.vector[indice]):\\n heap.vector[padre], heap.vector[indice] = heap.vector[indice], heap.vector[padre]\\n indice = padre\\n padre = (padre - 1) // 2\",\n \"def __print_ruler():\\n print(\\\"{:s}-+-{:s}-+-{:s}-+-{:s}-+-{:s}-+-{:s}\\\".format(\\n \\\"-\\\"*__collen[\\\"id\\\"],\\n \\\"-\\\"*__collen[\\\"name\\\"],\\n \\\"-\\\"*MAX_SIZE,\\n \\\"-\\\"*__collen[\\\"used_lim\\\"],\\n \\\"-\\\"*MAX_SIZE,\\n \\\"-\\\"*__collen[\\\"excl_lim\\\"]))\",\n \"def head():\\r\\n\\tfor i in range(SIZE):\\r\\n\\t\\tfor j in range(SIZE*2+2):\\r\\n\\t\\t\\tif j == (SIZE-i):\\r\\n\\t\\t\\t\\tfor k in range(i+1):\\r\\n\\t\\t\\t\\t\\tprint('/', end='')\\r\\n\\t\\t\\t\\tfor k in range(i+1):\\r\\n\\t\\t\\t\\t\\tprint('\\\\\\\\', end='')\\r\\n\\t\\t\\tprint(' ', end='')\\r\\n\\t\\tprint('')\",\n \"def my_print(self):\\n if self.__size > 0:\\n for k in range(self.__position[1]):\\n print()\\n for i in range(self.__size):\\n for j in range(self.__position[0]):\\n print(\\\" \\\", end='')\\n print(\\\"#\\\" * self.__size)\\n else:\\n print()\",\n \"def __str__(self):\\r\\n out = \\\"##\\\"*(self.width+1)+\\\"\\\\n\\\"\\r\\n for i in range(self.height):\\r\\n out += \\\"#\\\"\\r\\n for j in range(self.width):\\r\\n if self.grid[i][j] == 0:\\r\\n out += \\\"##\\\"\\r\\n else:\\r\\n if not self.showSolution:\\r\\n out += \\\" \\\"\\r\\n elif (i,j) in self.solution:\\r\\n out += \\\"**\\\"\\r\\n else:\\r\\n out += \\\" \\\"\\r\\n out += \\\"#\\\\n\\\"\\r\\n return out + \\\"##\\\"*(self.width+1)\",\n \"def __str__(self):\\n return str(self._heap)\",\n \"def insert(self, k): \\r\\n self.heap_array.append(k)\\r\\n\\r\\n current_index = len(self.heap_array) - 1\\r\\n while (current_index > 0):\\r\\n parent_index = ((current_index-1)//2)\\r\\n\\r\\n if int(self.heap_array[current_index]) > int(self.heap_array[parent_index]): # if no vialation of the min heap property \\r\\n return\\r\\n else: # if heap property is broken then swap the parent and child that are breaking the prop \\r\\n self.heap_array[parent_index], self.heap_array[current_index] = self.heap_array[current_index], self.heap_array[parent_index]\\r\\n current_index = parent_index\",\n \"def print_seg_row_col(sp) : \\n s, r, c = src_from_rc8x8(sp.peak_y_raw, sp.peak_x_raw)\\n print('seg: %d, row: %.1f, col: %.1f' % (s, r, c))\",\n \"def _build_pop_segment(self):\\n value = self.value\\n label = str(self.segment)\\n return dedent(\\n f\\\"\\\"\\\"\\n // D = offset\\n @{value}\\n D=A\\n // D = offset + segmentPointer\\n @{label}\\n D=D+M\\n // SP--\\n @SP\\n M=M-1\\n A=M\\n D=D+M // addr = addr + RAM[SP]\\n A=D-M // A = addr - RAM[SP] \\n M=D-A // RAM[A] = addr - A\\n \\\"\\\"\\\"\\n )\",\n \"def draw_5(n: int):\\n\\n # Top half + middle\\n for row in range(n // 2 + (n % 2)):\\n cols = (row) * 2 + (n % 2)\\n \\n for col in range((n - cols) // 2):\\n print(' ', end='')\\n\\n for col in range(cols):\\n print('*', end='')\\n \\n print()\\n\\n # Bottom half\\n for row in range(n // 2):\\n cols = n - (row + 1) * 2\\n \\n for col in range((n - cols) // 2):\\n print(' ', end='')\\n\\n for col in range(cols):\\n print('*', end='')\\n \\n print()\",\n \"def prettyprint(board):\\n print(\\\"\\\")\\n for row in range(3,0,-1):\\n print(\\\"\\\" + str(row) + \\\" |\\\" + board[(row-1)*3:(row)*3].replace(\\\"\\\",\\\" \\\"))\\n print(\\\" +------\\\")\\n print(\\\" A B C\\\")\",\n \"def for_isosceles_triangle():\\r\\n\\r\\n for row in range(7):\\r\\n print(' '*(7-row), '* '*row)\",\n \"def my_print(self):\\n if self.size == 0:\\n print(\\\"\\\")\\n return\\n for j in range(self.__position[1]):\\n print(\\\"\\\")\\n for i in range(self.size):\\n if self.__position[0] > 0:\\n print(\\\" \\\" * self.__position[0], end=\\\"\\\")\\n print('#' * self.size)\",\n \"def pretty_print_board(board):\\n board_rows = str(board).split('\\\\n')\\n annotated = [str(8 - i) + \\\"\\\\t\\\" + x for i, x in enumerate(board_rows)]\\n print '\\\\n'.join(annotated)\\n print '\\\\n\\\\ta b c d e f g h'\\n\\n return\",\n \"def build_heap(arr):\\n for i in range(len(arr)-1, -1, -1):\\n down_heapify(arr, len(arr), i)\",\n \"def plot_insertsize():\",\n \"def rectangle(height,width):\\n for row in range(height):\\n for column in range(width):\\n print(CHAR, end = '')\\n print()\",\n \"def FullDump(reader, heap):\\n def dump_region(reader, start, size, location):\\n print()\\n while start & 3 != 0:\\n start += 1\\n size -= 1\\n location += 1\\n is_executable = reader.IsProbableExecutableRegion(location, size)\\n is_ascii = reader.IsProbableASCIIRegion(location, size)\\n\\n if is_executable is not False:\\n lines = reader.GetDisasmLines(start, size)\\n for line in lines:\\n print(FormatDisasmLine(start, heap, line))\\n print()\\n\\n if is_ascii is not False:\\n # Output in the same format as the Unix hd command\\n addr = start\\n for i in range(0, size, 16):\\n slot = i + location\\n hex_line = \\\"\\\"\\n asc_line = \\\"\\\"\\n for i in range(16):\\n if slot + i < location + size:\\n byte = ctypes.c_uint8.from_buffer(reader.minidump, slot + i).value\\n if byte >= 0x20 and byte < 0x7f:\\n asc_line += chr(byte)\\n else:\\n asc_line += \\\".\\\"\\n hex_line += \\\" %02x\\\" % (byte)\\n else:\\n hex_line += \\\" \\\"\\n if i == 7:\\n hex_line += \\\" \\\"\\n print(\\\"%s %s |%s|\\\" % (reader.FormatIntPtr(addr),\\n hex_line,\\n asc_line))\\n addr += 16\\n\\n if is_executable is not True and is_ascii is not True:\\n print(\\\"%s - %s\\\" % (reader.FormatIntPtr(start),\\n reader.FormatIntPtr(start + size)))\\n print(start + size + 1);\\n for i in range(0, size, reader.PointerSize()):\\n slot = start + i\\n maybe_address = reader.ReadUIntPtr(slot)\\n heap_object = heap.FindObject(maybe_address)\\n print(\\\"%s: %s\\\" % (reader.FormatIntPtr(slot),\\n reader.FormatIntPtr(maybe_address)))\\n if heap_object:\\n heap_object.Print(Printer())\\n print()\\n\\n reader.ForEachMemoryRegion(dump_region)\",\n \"def display(self):\\n for _jumpline in range(self.y):\\n print(end=\\\"\\\\n\\\")\\n for _height in range(self.height):\\n for _space in range(self.x):\\n print(\\\" \\\", end=\\\"\\\")\\n for _width in range(self.width):\\n print(\\\"#\\\", end=\\\"\\\")\\n print(end=\\\"\\\\n\\\")\",\n \"def display(self):\\n print(\\\"\\\\n\\\" * self.y, end='')\\n for i in range(self.height):\\n for j in range(self.width + self.x):\\n if j < self.x:\\n print(' ', end='')\\n else:\\n print('#', end='')\\n print('')\",\n \"def my_print(self):\\n if self.size is 0:\\n print(\\\"\\\")\\n for i in range(0, self.size):\\n for j in range(0, self.size):\\n print(\\\"#\\\", end=\\\"\\\")\\n print(\\\"\\\")\",\n \"def tiles(rxs):\\n\\n lo = math.inf\\n hi = -math.inf\\n\\n for rx in rxs:\\n lo = min(lo, rx[\\\"has\\\"][0])\\n hi = max(hi, rx[\\\"has\\\"][len(rx[\\\"has\\\"]) - 1])\\n\\n for rx in rxs:\\n t, u = rx['has'], []\\n\\n def of(x, most):\\n return int(max(1, min(most, x)))\\n\\n def at(x):\\n return t[of(len(t)*x//1, len(t))]\\n\\n def pos(x):\\n return math.floor(of(40*(x-lo)/(hi-lo+1E-32)//1, 40))\\n\\n for _ in range(globals.the[\\\"width\\\"] + 1):\\n u.append(\\\" \\\")\\n\\n for i in range(pos(at(.1)), pos(at(.3)) + 1):\\n u[i] = \\\"-\\\"\\n\\n for i in range(pos(at(.7)), pos(at(.9)) + 1):\\n u[i] = \\\"-\\\"\\n\\n u[globals.the[\\\"width\\\"] // 2] = \\\"|\\\"\\n u[pos(at(.5))] = \\\"*\\\"\\n\\n rx[\\\"show\\\"] = \\\"\\\".join(u) + \\\" {\\\" + globals.the[\\\"Fmt\\\"].format(at(.1))\\n\\n for x in [at(.3), at(.5), at(.7), at(.9)]:\\n rx[\\\"show\\\"] += \\\", \\\" + globals.the[\\\"Fmt\\\"].format(x)\\n\\n rx[\\\"show\\\"] += \\\" }\\\"\\n\\n return rxs\",\n \"def printBoard(self,board):\\n print(\\\" A B C D E F G H \\\")\\n print(\\\" - - - - - - - - - - - - - - - - -\\\")\\n for r in range(0,8,1):\\n row = \\\"\\\"\\n for c in range(0,8,1):\\n value = \\\"| \\\"\\n if board[c][r] == -1: value = \\\"| X \\\"\\n if board[c][r] == 1: value = \\\"| O \\\"\\n row = row+value\\n print(str(r+1)+row+\\\"|\\\")\\n print(\\\" - - - - - - - - - - - - - - - - -\\\")\",\n \"def __init__(self):\\n # min heap for right part, max heap for left part\\n self.minHeap_right = []\\n self.maxHeap_left = []\\n self.tot_num = 0\",\n \"def draw_4(n: int):\\n\\n # Top half + middle\\n for row in range(n // 2 + (n % 2)):\\n cols = n - row * 2\\n \\n for col in range((n - cols) // 2):\\n print(' ', end='')\\n\\n for col in range(cols):\\n print('*', end='')\\n \\n print()\\n\\n # Bottom half\\n for row in range(n // 2):\\n cols = (row + 1) * 2 + (n % 2)\\n \\n for col in range((n - cols) // 2):\\n print(' ', end='')\\n\\n for col in range(cols):\\n print('*', end='')\\n \\n print()\",\n \"def print_column():\\n print('+----+----+----+----+')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6224625","0.6115098","0.60877067","0.6073852","0.58567894","0.5810459","0.57655525","0.57655525","0.5722967","0.5565458","0.55614096","0.55261725","0.55241466","0.550935","0.5506681","0.54333556","0.54333556","0.54319435","0.5421225","0.54208","0.5418962","0.54035205","0.53875935","0.5385004","0.5374566","0.5344161","0.5337163","0.5331037","0.532872","0.5325107","0.5301962","0.53013647","0.52967757","0.52947986","0.52892923","0.5287405","0.5286875","0.5263393","0.5229365","0.5222368","0.52211094","0.5216734","0.52128434","0.52105474","0.51930773","0.51918864","0.51845133","0.51838076","0.51573396","0.5148861","0.5144476","0.51442605","0.51418716","0.5131762","0.5125983","0.51189375","0.5112449","0.5106837","0.51028717","0.5090805","0.5090071","0.5079199","0.5076414","0.5062965","0.5057853","0.5057476","0.50564855","0.50546527","0.50535154","0.50530076","0.5042533","0.50421715","0.5036602","0.50357056","0.5035583","0.50354356","0.5034651","0.5033992","0.50321996","0.5031743","0.5025423","0.50213385","0.5020483","0.50162125","0.50141144","0.50084114","0.50061464","0.50060093","0.5005621","0.5003105","0.49986124","0.49915266","0.49908495","0.49885622","0.49859205","0.49828076","0.49780127","0.49777767","0.4973493","0.49714488"],"string":"[\n \"0.6224625\",\n \"0.6115098\",\n \"0.60877067\",\n \"0.6073852\",\n \"0.58567894\",\n \"0.5810459\",\n \"0.57655525\",\n \"0.57655525\",\n \"0.5722967\",\n \"0.5565458\",\n \"0.55614096\",\n \"0.55261725\",\n \"0.55241466\",\n \"0.550935\",\n \"0.5506681\",\n \"0.54333556\",\n \"0.54333556\",\n \"0.54319435\",\n \"0.5421225\",\n \"0.54208\",\n \"0.5418962\",\n \"0.54035205\",\n \"0.53875935\",\n \"0.5385004\",\n \"0.5374566\",\n \"0.5344161\",\n \"0.5337163\",\n \"0.5331037\",\n \"0.532872\",\n \"0.5325107\",\n \"0.5301962\",\n \"0.53013647\",\n \"0.52967757\",\n \"0.52947986\",\n \"0.52892923\",\n \"0.5287405\",\n \"0.5286875\",\n \"0.5263393\",\n \"0.5229365\",\n \"0.5222368\",\n \"0.52211094\",\n \"0.5216734\",\n \"0.52128434\",\n \"0.52105474\",\n \"0.51930773\",\n \"0.51918864\",\n \"0.51845133\",\n \"0.51838076\",\n \"0.51573396\",\n \"0.5148861\",\n \"0.5144476\",\n \"0.51442605\",\n \"0.51418716\",\n \"0.5131762\",\n \"0.5125983\",\n \"0.51189375\",\n \"0.5112449\",\n \"0.5106837\",\n \"0.51028717\",\n \"0.5090805\",\n \"0.5090071\",\n \"0.5079199\",\n \"0.5076414\",\n \"0.5062965\",\n \"0.5057853\",\n \"0.5057476\",\n \"0.50564855\",\n \"0.50546527\",\n \"0.50535154\",\n \"0.50530076\",\n \"0.5042533\",\n \"0.50421715\",\n \"0.5036602\",\n \"0.50357056\",\n \"0.5035583\",\n \"0.50354356\",\n \"0.5034651\",\n \"0.5033992\",\n \"0.50321996\",\n \"0.5031743\",\n \"0.5025423\",\n \"0.50213385\",\n \"0.5020483\",\n \"0.50162125\",\n \"0.50141144\",\n \"0.50084114\",\n \"0.50061464\",\n \"0.50060093\",\n \"0.5005621\",\n \"0.5003105\",\n \"0.49986124\",\n \"0.49915266\",\n \"0.49908495\",\n \"0.49885622\",\n \"0.49859205\",\n \"0.49828076\",\n \"0.49780127\",\n \"0.49777767\",\n \"0.4973493\",\n \"0.49714488\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6630077"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":22298,"cells":{"query":{"kind":"string","value":"Get index of parent node"},"document":{"kind":"string","value":"def get_parent_index(i):\n # Indexing for i_parent == i // 2 is NOT ZERO-INDEXED\n pos = i + 1\n parent_pos = pos // 2\n parent_index = parent_pos - 1\n return parent_index"},"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_parent_index(self):\n return (self.index - 1) // 2","def get_left_child_index(self, parent):\n return 2*parent+1","def parent(self, index):\n if index == 0:\n print(\"index 0 has no parent\")\n return None\n return (index - 1) // 2","def get_parent_index(self, child):\n return (child-1)//2","def parent_idx(idx):\n return (idx - 1) >> 1","def parent(self, index):\n return index / 2","def _parent(self, index):\r\n # Declaring the \"root\" its own parent, otherwise usual math\r\n return (index - 1) // 2 if index else 0","def get_parent_index(index):\n if index == 0:\n return 0\n if index % 2 == 0:\n return int(index / 2 - 1)\n return int(index / 2)","def parent(self, index):\n if index == 0:\n return -1\n return self.adjacency_list[index][0]","def get_parent(self, index):\n return (index - 1) // (2)","def _get_parent_index(self, child_index: int) -> int:\n index = (child_index - 1) // 2\n\n return index if index >= 0 else 0","def get_right_child_index(self, parent):\n return 2*parent+2","def parent(self, pos):\n if pos == 0: \n return None\n return int(math.ceil(pos / self.dary) - 1)","def row(self):\n\t\tif self._parent != None:\n\t\t\treturn self._parent._children.index(self)\n\t\telse:\n\t\t\treturn 0","def get_left_child_index(self):\n return (2 * self.index) + 1","def parent(self, pos):\n return pos // 2","def parent(self, pos):\n return pos // 2","def get_index(self, child):\n for _index, item in enumerate(self.children):\n if item == child:\n return _index\n\n return -1","def _parent(node):\n if node == _root():\n return _root()\n return (node + 1) // 2 - 1","def parent(self, pos): \n return pos//2","def parent(self, i):\n return (i - 1) // 2","def _get_parent(self, child_ix):\n if child_ix == 0:\n return None\n t = 1 if child_ix & 1 else 2\n return (child_ix - t) / 2","def position(self):\n # (this to be able to let the model know my 'row')\n if self.parent and self in self.parent.children:\n return self.parent.children.index(self)\n return 0","def find_root(self):\r\n self.find_parents()\r\n index = 0\r\n while len(self.vertices[index].parents)>0:\r\n index = self.vertices[index].parents[0]\r\n return index","def left_child_idx(idx):\n return (idx << 1) + 1","def _parent(self, j):\n return (j - 1) // 2","def _parent(self, j):\n return (j - 1) // 2","def parent(i):\r\n return math.floor((i - 1) / 2)","def get_parent(self, index):\n return self.heap[self.get_parent_index(index)]","def index(self, row, column, parent):\n if not self._nodes:\n return QModelIndex()\n node = parent.internalPointer()\n if not node:\n index = self.createIndex(row, column, self._nodes[row])\n return index\n else:\n return self.createIndex(row, column, node.get_child(row))","def get_parent_idx(self, view_idx):\n a, b = view_idx\n R, C = self.shape\n i = self.p_rows[a]\n j = self.p_cols[b]\n return (i, j)","def parent_id(self) -> pulumi.Output[int]:\n return pulumi.get(self, \"parent_id\")","def fm_get_parent(self, idx):\n return self._relation_lst[self.PARENT][idx]","def get_curpos(self):\n for i in range(len(self.tree)):\n if self.path == self.tree[i][2]:\n return i\n else:\n return -1","def GetIndexOfItem(self, item):\n \n parent = self._window.GetItemParent(item)\n if parent:\n parentIndices = self.GetIndexOfItem(parent)\n ownIndex = self.GetItemChildren(parent).index(item)\n return parentIndices + (ownIndex,)\n else:\n return ()","def parent_id(self) -> int:\n return self._parent_id","def get_parent_link_index(self, link_idx):\n indexes = []\n for i in range(len(info_populator.InfoPopulator.LINK_PARENT_CHILD_RELATION)):\n if link_idx == info_populator.InfoPopulator.LINK_PARENT_CHILD_RELATION[\n i][1]:\n return self.get_link_index(\n info_populator.InfoPopulator.LINK_PARENT_CHILD_RELATION[i][0])\n return None","def parent(self, node):\n self._validate_node(node)\n idx = node._index\n if idx == 0:\n return None # Root node has no parent\n if idx % 2 == 0:\n return self._array[(idx-2)//2] # Right child (even number)\n return self._array[(idx-1)//2] # left child (odd number)","def left_child(self, index):\n return 2 * index + 1","def return_parent(self):\n # Return parent if completed\n if self.completed:\n return self.father\n return -1","def find_parent(self,\r\n index):\r\n\r\n if not index.level() > 1:\r\n return False\r\n go_on = True\r\n while not index.is_top() or index.level() < 1:\r\n\r\n\r\n if str(index.parent()) in self.indexes():\r\n return index.parent()\r\n index = index.parent()\r\n\r\n return False","def assign_index(self) -> int:\n return self._parent_node.assign_index()","def find(self,i):\r\n if self.parent[i]<0:\r\n return i\r\n return self.find(self.parent[i])","def parent_id(neuron, selected_index):\n parent_id = np.array([], dtype=int)\n for i in selected_index:\n p = neuron.parent_index[i]\n while(~np.any(selected_index == p)):\n p = neuron.parent_index[p]\n (ind,) = np.where(selected_index == p)\n parent_id = np.append(parent_id, ind)\n return parent_id","def get_right_child_index(self):\n return (2 * self.index) + 2","def getIndex(self, child):\n \n if child in self._children:\n return self._children.index(child)\n else:\n return -1","def parent(self, p):\n node = self._validate(p)\n return self._make_position(node._parent)","def parent_id(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"parent_id\")","def parent_id(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"parent_id\")","def parent(self, index):\n if not index.isValid():\n return QModelIndex()\n\n parent_item = index.internalPointer().parent\n if parent_item.parent is None:\n return QModelIndex()\n\n parent_index_in_grandparent = parent_item.parent.children.index(parent_item)\n return self.createIndex(parent_index_in_grandparent, 0, parent_item)","def parent(self, n):\n\n return None if n == 0 else (n - 1) // 2","def parent_id(self):\n return self._parent_id","def parent_id(self):\n return self._parent_id","def parent_index(self, parent, parent_names):\n if parent == 'mother' or parent == 'm':\n return [np.where(parent_names == x)[0][0] for x in self.mothers]\n if parent == 'father' or parent == 'f':\n return [np.where(parent_names == x)[0][0] for x in self.fathers]\n if parent == 'parents' or parent == 'p':\n return [np.where(parent_names == x)[0][0] for x in self.parents]\n else:\n raise ValueError(\"parent must be 'mother', 'father', or 'parents'.\")","def parent(self, node):\r\n return self.find_node(node).parent.content","def left_child(self, index):\n return 2 * index","def parent(self,p):\n node = self._validate(p)\n return self._make_position(node._parent)","def indexOfChild(self, child):\n self.__initChild()\n return self.__child.index(child)","def increasing_parent(self, v):\n parent = None\n for i in range(self.size(), v, -1):\n if self.le(v, i):\n parent = i\n return parent","def parent(self, p):\n node = self._validate(p)\n return self._make_position(node.parent)","def parent(self, n):\n return n._parent","def get_parent_id(self):\n return self._parent_id","def get_p_idx(self, node_idx):\n idx = (node_idx + 1) / 2 - 1\n return idx","def root_index(self):\n if self.is_binary_tree:\n return 0\n return -1","def find(self, parent, i):\n if parent[i] == i:\n return i\n return self.find(parent, parent[i])","def index(self, row, column, parent_index):\n \n if not self.hasIndex(row, column, parent_index):\n return QModelIndex()\n\n parent_item = self.root_item if not parent_index.isValid() else parent_index.internalPointer()\n child_item = parent_item.children[row]\n return self.createIndex(row, column, child_item)","def get_parentnode(taxonomy, nodeName): \n for entry in taxonomy:\n if entry['nodeName'] == nodeName:\n return entry['parentId']\n print \"Node Name {} is invalid !\" .format(nodeName)\n return -1","def right_child_idx(idx):\n return (idx + 1) << 1","def _get_children_ixs(self, parent_ix):\n ix_left = (parent_ix * 2) + 1\n ix_right = ix_left + 1\n if ix_left >= len(self):\n ix_left = None\n if ix_right >= len(self):\n ix_right = None\n return ix_left, ix_right","def get_view_idx(self, parent_idx):\n a = self.p_rows.index(i)\n b = self.p_cols.index(j)\n return (a, b)","def parent(self, p):\n node = self._validate(p)\n return self._make_position(node._parent)","def parent(self, p):\n node = self._validate(p)\n return self._make_position(node._parent)","def parent(self, p):\n node = self._validate(p)\n return self._make_position(node._parent)","def index(self):\n return self._ll_tree.get_index()","def parent(i):\n return i // 2","def parent(self, A, i):\n if i == 0:\n return None\n div = float(i)/2\n if div.is_integer():\n div -= 1\n return int(div)","def parent(self, A, i):\n if i == 0:\n return None\n div = float(i)/2\n if div.is_integer():\n div -= 1\n return int(div)","def parent(self, p):\n node = self._validate_position(p)\n return self._make_position(node)","def side_of_parent(self):\n if self._node_empty(self._parent):\n return -1\n\n if self._parent.left is self:\n return 0\n elif self._parent.right is self:\n return 1\n else:\n raise ValueError('Node invalid.')","def parent(self, _):\n return qtc.QModelIndex()","def parent_id(self) -> str:\n return self._db_data.parent_id","def get_child_index(self, child: \"AbstractNode\") -> int:\n return self._children.index(child)","def get_node_loc(node):\n lineno = node.lineno\n end_lineno = get_last_deep_child(node).lineno\n return end_lineno - lineno","def rowCount(self, parent):\n if parent.column() > 0:\n return 0\n parent_item = self.root_item if not parent.isValid() else parent.internalPointer()\n return len(parent_item.children)","def dist_from_root(self, index):\n if index == 0:\n return 0\n return self.dist_from_root(self.parent(index)) + 1","def getIdentifyingParent(self):\n return _libsbml.SpeciesTypeComponentIndex_getIdentifyingParent(self)","def _get_level(self, node):\n level = 0\n while node.parent:\n node = node.parent\n level += 1\n return level","def rowCount(self, parent):\n parent_item = parent.internalPointer()\n if parent.isValid():\n parent_item = parent.internalPointer() # noqa\n return 0\n else:\n return len(self._nodes)","def pdelements_parent_pd_element(self) -> int:\n return self.dss_obj.PDElementsI(ctypes.c_int32(6), ctypes.c_int32(0))","def parent_ref_id(self) -> EntityId:\n raise NotImplementedError(\"\"\"Needs to be implemented.\"\"\")","def parent_ref_id(self) -> EntityId:\n raise NotImplementedError(\"\"\"Needs to be implemented.\"\"\")","def parent_ref_id(self) -> EntityId:\n raise NotImplementedError(\"\"\"Needs to be implemented.\"\"\")","def parent_ref_id(self) -> EntityId:\n raise NotImplementedError(\"\"\"Needs to be implemented.\"\"\")","def parent(self):\n return self.key().parent()","def get_index(self, action, column=0):\n if action == self.menuAction():\n return QtCore.QModelIndex()\n # find all parents to get their index\n parents = self._get_parents(action)\n index = QtCore.QModelIndex()\n # Move through indexes down the chain\n for a in reversed(parents):\n parent = a.parent()\n # if parent of action is its own menu, get parent of that menu.\n # We want to know which row the action is in. For that we need\n # The real parent menu.\n if parent is a.menu():\n parent = parent.parent()\n row = parent.actions().index(a)\n index = self._model.index(row, 0, index)\n parent = action.parent()\n if parent is None:\n return index\n if parent is action.menu():\n parent = parent.parent()\n row = parent.actions().index(action)\n index = self._model.index(row, column, index)\n return index","def find_parent_of(self, *args):\n return _ida_hexrays.citem_t_find_parent_of(self, *args)","def find_parent(self):\n pass","def get_parentID(self):\n parent = Collection.find(self.resource.container)\n return parent.uuid","def parent(self):\n result = self.get_parent(\n identifier=DEFAULT_PARENT_IDENTIFIER,\n relationship=CommCareCaseIndexSQL.CHILD\n )\n return result[0] if result else None","def parent_count(self):\n return len(self._parent_ids)"],"string":"[\n \"def get_parent_index(self):\\n return (self.index - 1) // 2\",\n \"def get_left_child_index(self, parent):\\n return 2*parent+1\",\n \"def parent(self, index):\\n if index == 0:\\n print(\\\"index 0 has no parent\\\")\\n return None\\n return (index - 1) // 2\",\n \"def get_parent_index(self, child):\\n return (child-1)//2\",\n \"def parent_idx(idx):\\n return (idx - 1) >> 1\",\n \"def parent(self, index):\\n return index / 2\",\n \"def _parent(self, index):\\r\\n # Declaring the \\\"root\\\" its own parent, otherwise usual math\\r\\n return (index - 1) // 2 if index else 0\",\n \"def get_parent_index(index):\\n if index == 0:\\n return 0\\n if index % 2 == 0:\\n return int(index / 2 - 1)\\n return int(index / 2)\",\n \"def parent(self, index):\\n if index == 0:\\n return -1\\n return self.adjacency_list[index][0]\",\n \"def get_parent(self, index):\\n return (index - 1) // (2)\",\n \"def _get_parent_index(self, child_index: int) -> int:\\n index = (child_index - 1) // 2\\n\\n return index if index >= 0 else 0\",\n \"def get_right_child_index(self, parent):\\n return 2*parent+2\",\n \"def parent(self, pos):\\n if pos == 0: \\n return None\\n return int(math.ceil(pos / self.dary) - 1)\",\n \"def row(self):\\n\\t\\tif self._parent != None:\\n\\t\\t\\treturn self._parent._children.index(self)\\n\\t\\telse:\\n\\t\\t\\treturn 0\",\n \"def get_left_child_index(self):\\n return (2 * self.index) + 1\",\n \"def parent(self, pos):\\n return pos // 2\",\n \"def parent(self, pos):\\n return pos // 2\",\n \"def get_index(self, child):\\n for _index, item in enumerate(self.children):\\n if item == child:\\n return _index\\n\\n return -1\",\n \"def _parent(node):\\n if node == _root():\\n return _root()\\n return (node + 1) // 2 - 1\",\n \"def parent(self, pos): \\n return pos//2\",\n \"def parent(self, i):\\n return (i - 1) // 2\",\n \"def _get_parent(self, child_ix):\\n if child_ix == 0:\\n return None\\n t = 1 if child_ix & 1 else 2\\n return (child_ix - t) / 2\",\n \"def position(self):\\n # (this to be able to let the model know my 'row')\\n if self.parent and self in self.parent.children:\\n return self.parent.children.index(self)\\n return 0\",\n \"def find_root(self):\\r\\n self.find_parents()\\r\\n index = 0\\r\\n while len(self.vertices[index].parents)>0:\\r\\n index = self.vertices[index].parents[0]\\r\\n return index\",\n \"def left_child_idx(idx):\\n return (idx << 1) + 1\",\n \"def _parent(self, j):\\n return (j - 1) // 2\",\n \"def _parent(self, j):\\n return (j - 1) // 2\",\n \"def parent(i):\\r\\n return math.floor((i - 1) / 2)\",\n \"def get_parent(self, index):\\n return self.heap[self.get_parent_index(index)]\",\n \"def index(self, row, column, parent):\\n if not self._nodes:\\n return QModelIndex()\\n node = parent.internalPointer()\\n if not node:\\n index = self.createIndex(row, column, self._nodes[row])\\n return index\\n else:\\n return self.createIndex(row, column, node.get_child(row))\",\n \"def get_parent_idx(self, view_idx):\\n a, b = view_idx\\n R, C = self.shape\\n i = self.p_rows[a]\\n j = self.p_cols[b]\\n return (i, j)\",\n \"def parent_id(self) -> pulumi.Output[int]:\\n return pulumi.get(self, \\\"parent_id\\\")\",\n \"def fm_get_parent(self, idx):\\n return self._relation_lst[self.PARENT][idx]\",\n \"def get_curpos(self):\\n for i in range(len(self.tree)):\\n if self.path == self.tree[i][2]:\\n return i\\n else:\\n return -1\",\n \"def GetIndexOfItem(self, item):\\n \\n parent = self._window.GetItemParent(item)\\n if parent:\\n parentIndices = self.GetIndexOfItem(parent)\\n ownIndex = self.GetItemChildren(parent).index(item)\\n return parentIndices + (ownIndex,)\\n else:\\n return ()\",\n \"def parent_id(self) -> int:\\n return self._parent_id\",\n \"def get_parent_link_index(self, link_idx):\\n indexes = []\\n for i in range(len(info_populator.InfoPopulator.LINK_PARENT_CHILD_RELATION)):\\n if link_idx == info_populator.InfoPopulator.LINK_PARENT_CHILD_RELATION[\\n i][1]:\\n return self.get_link_index(\\n info_populator.InfoPopulator.LINK_PARENT_CHILD_RELATION[i][0])\\n return None\",\n \"def parent(self, node):\\n self._validate_node(node)\\n idx = node._index\\n if idx == 0:\\n return None # Root node has no parent\\n if idx % 2 == 0:\\n return self._array[(idx-2)//2] # Right child (even number)\\n return self._array[(idx-1)//2] # left child (odd number)\",\n \"def left_child(self, index):\\n return 2 * index + 1\",\n \"def return_parent(self):\\n # Return parent if completed\\n if self.completed:\\n return self.father\\n return -1\",\n \"def find_parent(self,\\r\\n index):\\r\\n\\r\\n if not index.level() > 1:\\r\\n return False\\r\\n go_on = True\\r\\n while not index.is_top() or index.level() < 1:\\r\\n\\r\\n\\r\\n if str(index.parent()) in self.indexes():\\r\\n return index.parent()\\r\\n index = index.parent()\\r\\n\\r\\n return False\",\n \"def assign_index(self) -> int:\\n return self._parent_node.assign_index()\",\n \"def find(self,i):\\r\\n if self.parent[i]<0:\\r\\n return i\\r\\n return self.find(self.parent[i])\",\n \"def parent_id(neuron, selected_index):\\n parent_id = np.array([], dtype=int)\\n for i in selected_index:\\n p = neuron.parent_index[i]\\n while(~np.any(selected_index == p)):\\n p = neuron.parent_index[p]\\n (ind,) = np.where(selected_index == p)\\n parent_id = np.append(parent_id, ind)\\n return parent_id\",\n \"def get_right_child_index(self):\\n return (2 * self.index) + 2\",\n \"def getIndex(self, child):\\n \\n if child in self._children:\\n return self._children.index(child)\\n else:\\n return -1\",\n \"def parent(self, p):\\n node = self._validate(p)\\n return self._make_position(node._parent)\",\n \"def parent_id(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"parent_id\\\")\",\n \"def parent_id(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"parent_id\\\")\",\n \"def parent(self, index):\\n if not index.isValid():\\n return QModelIndex()\\n\\n parent_item = index.internalPointer().parent\\n if parent_item.parent is None:\\n return QModelIndex()\\n\\n parent_index_in_grandparent = parent_item.parent.children.index(parent_item)\\n return self.createIndex(parent_index_in_grandparent, 0, parent_item)\",\n \"def parent(self, n):\\n\\n return None if n == 0 else (n - 1) // 2\",\n \"def parent_id(self):\\n return self._parent_id\",\n \"def parent_id(self):\\n return self._parent_id\",\n \"def parent_index(self, parent, parent_names):\\n if parent == 'mother' or parent == 'm':\\n return [np.where(parent_names == x)[0][0] for x in self.mothers]\\n if parent == 'father' or parent == 'f':\\n return [np.where(parent_names == x)[0][0] for x in self.fathers]\\n if parent == 'parents' or parent == 'p':\\n return [np.where(parent_names == x)[0][0] for x in self.parents]\\n else:\\n raise ValueError(\\\"parent must be 'mother', 'father', or 'parents'.\\\")\",\n \"def parent(self, node):\\r\\n return self.find_node(node).parent.content\",\n \"def left_child(self, index):\\n return 2 * index\",\n \"def parent(self,p):\\n node = self._validate(p)\\n return self._make_position(node._parent)\",\n \"def indexOfChild(self, child):\\n self.__initChild()\\n return self.__child.index(child)\",\n \"def increasing_parent(self, v):\\n parent = None\\n for i in range(self.size(), v, -1):\\n if self.le(v, i):\\n parent = i\\n return parent\",\n \"def parent(self, p):\\n node = self._validate(p)\\n return self._make_position(node.parent)\",\n \"def parent(self, n):\\n return n._parent\",\n \"def get_parent_id(self):\\n return self._parent_id\",\n \"def get_p_idx(self, node_idx):\\n idx = (node_idx + 1) / 2 - 1\\n return idx\",\n \"def root_index(self):\\n if self.is_binary_tree:\\n return 0\\n return -1\",\n \"def find(self, parent, i):\\n if parent[i] == i:\\n return i\\n return self.find(parent, parent[i])\",\n \"def index(self, row, column, parent_index):\\n \\n if not self.hasIndex(row, column, parent_index):\\n return QModelIndex()\\n\\n parent_item = self.root_item if not parent_index.isValid() else parent_index.internalPointer()\\n child_item = parent_item.children[row]\\n return self.createIndex(row, column, child_item)\",\n \"def get_parentnode(taxonomy, nodeName): \\n for entry in taxonomy:\\n if entry['nodeName'] == nodeName:\\n return entry['parentId']\\n print \\\"Node Name {} is invalid !\\\" .format(nodeName)\\n return -1\",\n \"def right_child_idx(idx):\\n return (idx + 1) << 1\",\n \"def _get_children_ixs(self, parent_ix):\\n ix_left = (parent_ix * 2) + 1\\n ix_right = ix_left + 1\\n if ix_left >= len(self):\\n ix_left = None\\n if ix_right >= len(self):\\n ix_right = None\\n return ix_left, ix_right\",\n \"def get_view_idx(self, parent_idx):\\n a = self.p_rows.index(i)\\n b = self.p_cols.index(j)\\n return (a, b)\",\n \"def parent(self, p):\\n node = self._validate(p)\\n return self._make_position(node._parent)\",\n \"def parent(self, p):\\n node = self._validate(p)\\n return self._make_position(node._parent)\",\n \"def parent(self, p):\\n node = self._validate(p)\\n return self._make_position(node._parent)\",\n \"def index(self):\\n return self._ll_tree.get_index()\",\n \"def parent(i):\\n return i // 2\",\n \"def parent(self, A, i):\\n if i == 0:\\n return None\\n div = float(i)/2\\n if div.is_integer():\\n div -= 1\\n return int(div)\",\n \"def parent(self, A, i):\\n if i == 0:\\n return None\\n div = float(i)/2\\n if div.is_integer():\\n div -= 1\\n return int(div)\",\n \"def parent(self, p):\\n node = self._validate_position(p)\\n return self._make_position(node)\",\n \"def side_of_parent(self):\\n if self._node_empty(self._parent):\\n return -1\\n\\n if self._parent.left is self:\\n return 0\\n elif self._parent.right is self:\\n return 1\\n else:\\n raise ValueError('Node invalid.')\",\n \"def parent(self, _):\\n return qtc.QModelIndex()\",\n \"def parent_id(self) -> str:\\n return self._db_data.parent_id\",\n \"def get_child_index(self, child: \\\"AbstractNode\\\") -> int:\\n return self._children.index(child)\",\n \"def get_node_loc(node):\\n lineno = node.lineno\\n end_lineno = get_last_deep_child(node).lineno\\n return end_lineno - lineno\",\n \"def rowCount(self, parent):\\n if parent.column() > 0:\\n return 0\\n parent_item = self.root_item if not parent.isValid() else parent.internalPointer()\\n return len(parent_item.children)\",\n \"def dist_from_root(self, index):\\n if index == 0:\\n return 0\\n return self.dist_from_root(self.parent(index)) + 1\",\n \"def getIdentifyingParent(self):\\n return _libsbml.SpeciesTypeComponentIndex_getIdentifyingParent(self)\",\n \"def _get_level(self, node):\\n level = 0\\n while node.parent:\\n node = node.parent\\n level += 1\\n return level\",\n \"def rowCount(self, parent):\\n parent_item = parent.internalPointer()\\n if parent.isValid():\\n parent_item = parent.internalPointer() # noqa\\n return 0\\n else:\\n return len(self._nodes)\",\n \"def pdelements_parent_pd_element(self) -> int:\\n return self.dss_obj.PDElementsI(ctypes.c_int32(6), ctypes.c_int32(0))\",\n \"def parent_ref_id(self) -> EntityId:\\n raise NotImplementedError(\\\"\\\"\\\"Needs to be implemented.\\\"\\\"\\\")\",\n \"def parent_ref_id(self) -> EntityId:\\n raise NotImplementedError(\\\"\\\"\\\"Needs to be implemented.\\\"\\\"\\\")\",\n \"def parent_ref_id(self) -> EntityId:\\n raise NotImplementedError(\\\"\\\"\\\"Needs to be implemented.\\\"\\\"\\\")\",\n \"def parent_ref_id(self) -> EntityId:\\n raise NotImplementedError(\\\"\\\"\\\"Needs to be implemented.\\\"\\\"\\\")\",\n \"def parent(self):\\n return self.key().parent()\",\n \"def get_index(self, action, column=0):\\n if action == self.menuAction():\\n return QtCore.QModelIndex()\\n # find all parents to get their index\\n parents = self._get_parents(action)\\n index = QtCore.QModelIndex()\\n # Move through indexes down the chain\\n for a in reversed(parents):\\n parent = a.parent()\\n # if parent of action is its own menu, get parent of that menu.\\n # We want to know which row the action is in. For that we need\\n # The real parent menu.\\n if parent is a.menu():\\n parent = parent.parent()\\n row = parent.actions().index(a)\\n index = self._model.index(row, 0, index)\\n parent = action.parent()\\n if parent is None:\\n return index\\n if parent is action.menu():\\n parent = parent.parent()\\n row = parent.actions().index(action)\\n index = self._model.index(row, column, index)\\n return index\",\n \"def find_parent_of(self, *args):\\n return _ida_hexrays.citem_t_find_parent_of(self, *args)\",\n \"def find_parent(self):\\n pass\",\n \"def get_parentID(self):\\n parent = Collection.find(self.resource.container)\\n return parent.uuid\",\n \"def parent(self):\\n result = self.get_parent(\\n identifier=DEFAULT_PARENT_IDENTIFIER,\\n relationship=CommCareCaseIndexSQL.CHILD\\n )\\n return result[0] if result else None\",\n \"def parent_count(self):\\n return len(self._parent_ids)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8496504","0.82781035","0.8254789","0.82179976","0.81770974","0.81386656","0.8075585","0.8044404","0.8016533","0.79629314","0.79252255","0.7716487","0.74751145","0.7472065","0.7414298","0.7355776","0.7355776","0.7342362","0.7312784","0.7292973","0.7262405","0.7234606","0.72295654","0.7215185","0.7133951","0.7108991","0.7108991","0.7093205","0.7070273","0.70525324","0.70402294","0.7039712","0.698839","0.697995","0.6979867","0.69454825","0.6913868","0.68843025","0.68627405","0.68518454","0.6827707","0.6826437","0.6820811","0.6815422","0.6783303","0.67525935","0.6741618","0.6725589","0.6725589","0.67243665","0.67202836","0.67162853","0.67162853","0.66926026","0.669188","0.6652739","0.66425884","0.65921396","0.6588841","0.65850747","0.6574857","0.65731245","0.656263","0.6551499","0.6550784","0.65479237","0.6546761","0.65407604","0.6537731","0.65290976","0.6522513","0.6522513","0.6522513","0.6522357","0.6484313","0.64507043","0.64507043","0.644312","0.64404345","0.64147115","0.6413796","0.63904285","0.63848656","0.63698196","0.63688654","0.6352124","0.63490725","0.63472533","0.634605","0.6344882","0.6344882","0.6344882","0.6344882","0.63406616","0.6321326","0.63104457","0.62985003","0.6298097","0.6284665","0.6260003"],"string":"[\n \"0.8496504\",\n \"0.82781035\",\n \"0.8254789\",\n \"0.82179976\",\n \"0.81770974\",\n \"0.81386656\",\n \"0.8075585\",\n \"0.8044404\",\n \"0.8016533\",\n \"0.79629314\",\n \"0.79252255\",\n \"0.7716487\",\n \"0.74751145\",\n \"0.7472065\",\n \"0.7414298\",\n \"0.7355776\",\n \"0.7355776\",\n \"0.7342362\",\n \"0.7312784\",\n \"0.7292973\",\n \"0.7262405\",\n \"0.7234606\",\n \"0.72295654\",\n \"0.7215185\",\n \"0.7133951\",\n \"0.7108991\",\n \"0.7108991\",\n \"0.7093205\",\n \"0.7070273\",\n \"0.70525324\",\n \"0.70402294\",\n \"0.7039712\",\n \"0.698839\",\n \"0.697995\",\n \"0.6979867\",\n \"0.69454825\",\n \"0.6913868\",\n \"0.68843025\",\n \"0.68627405\",\n \"0.68518454\",\n \"0.6827707\",\n \"0.6826437\",\n \"0.6820811\",\n \"0.6815422\",\n \"0.6783303\",\n \"0.67525935\",\n \"0.6741618\",\n \"0.6725589\",\n \"0.6725589\",\n \"0.67243665\",\n \"0.67202836\",\n \"0.67162853\",\n \"0.67162853\",\n \"0.66926026\",\n \"0.669188\",\n \"0.6652739\",\n \"0.66425884\",\n \"0.65921396\",\n \"0.6588841\",\n \"0.65850747\",\n \"0.6574857\",\n \"0.65731245\",\n \"0.656263\",\n \"0.6551499\",\n \"0.6550784\",\n \"0.65479237\",\n \"0.6546761\",\n \"0.65407604\",\n \"0.6537731\",\n \"0.65290976\",\n \"0.6522513\",\n \"0.6522513\",\n \"0.6522513\",\n \"0.6522357\",\n \"0.6484313\",\n \"0.64507043\",\n \"0.64507043\",\n \"0.644312\",\n \"0.64404345\",\n \"0.64147115\",\n \"0.6413796\",\n \"0.63904285\",\n \"0.63848656\",\n \"0.63698196\",\n \"0.63688654\",\n \"0.6352124\",\n \"0.63490725\",\n \"0.63472533\",\n \"0.634605\",\n \"0.6344882\",\n \"0.6344882\",\n \"0.6344882\",\n \"0.6344882\",\n \"0.63406616\",\n \"0.6321326\",\n \"0.63104457\",\n \"0.62985003\",\n \"0.6298097\",\n \"0.6284665\",\n \"0.6260003\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.81710243"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":22299,"cells":{"query":{"kind":"string","value":"Get index of left child"},"document":{"kind":"string","value":"def get_left_index(i):\n pos = i + 1\n left_pos = 2 * pos\n left_index = left_pos - 1\n return left_index"},"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_left_child_index(self):\n return (2 * self.index) + 1","def left_child(self, index):\n return 2 * index + 1","def left_child(self, index):\n return 2 * index","def get_left_child_index(self, parent):\n return 2*parent+1","def left_child_idx(idx):\n return (idx << 1) + 1","def left_child(self, pos): \n return 2 * pos","def leftChild(self, pos):\n return 2 * pos","def leftChild(self, pos):\n return 2 * pos","def get_right_child_index(self):\n return (2 * self.index) + 2","def _left(self, index):\r\n return 2*index + 1","def left_child(self, position):\n child = 2 * position + 1\n if child > len(self.table) - 1:\n return None\n return child","def right_child(self, index):\n return 2 * index + 1","def left(self, index):\n try:\n if index == self.root_index():\n index = self.adjacency_list[index][0]\n else:\n index = self.adjacency_list[index][1]\n return index\n except IndexError:\n return -1","def get_right_child_index(self, parent):\n return 2*parent+2","def right_child(self, index):\n return 2 * index + 2","def get_parent_index(self):\n return (self.index - 1) // 2","def right_child(self, pos): \n return (2 * pos) + 1","def right_child_idx(idx):\n return (idx + 1) << 1","def parent(self, index):\n return index / 2","def get_parent_index(self, child):\n return (child-1)//2","def parent_idx(idx):\n return (idx - 1) >> 1","def get_parent(self, index):\n return (index - 1) // (2)","def parent(self, pos): \n return pos//2","def rightChild(self, pos):\n return (2 * pos) + 1","def rightChild(self, pos):\n return (2 * pos) + 1","def parent(self, pos):\n return pos // 2","def parent(self, pos):\n return pos // 2","def get_left_child(self, index):\n return self.heap[self.get_left_child_index(index)]","def parent(self, index):\n if index == 0:\n print(\"index 0 has no parent\")\n return None\n return (index - 1) // 2","def get_leftchild(self):\n return self._leftchild","def position(self):\n # (this to be able to let the model know my 'row')\n if self.parent and self in self.parent.children:\n return self.parent.children.index(self)\n return 0","def get_parent_index(index):\n if index == 0:\n return 0\n if index % 2 == 0:\n return int(index / 2 - 1)\n return int(index / 2)","def get_index(self, child):\n for _index, item in enumerate(self.children):\n if item == child:\n return _index\n\n return -1","def _left(self, j):\n return 2 * j + 1","def _left(self, j):\n return 2 * j + 1","def get_left(self):\n return self.left","def parent(self, pos):\n if pos == 0: \n return None\n return int(math.ceil(pos / self.dary) - 1)","def max_child(self, index):\n # left and right child\n left = self._data[(index*2)+1] if (index*2)+1 < len(self) else None\n right = self._data[(index*2)+2] if (index*2)+2 < len(self) else None\n\n # if has both children\n if left and right:\n if left < right:\n return (index*2)+2\n return (index*2)+1\n\n # if only has left child\n elif left and right is None:\n return (index*2)+1\n\n # if only has right child\n elif right and left is None:\n return (index*2)+2\n\n # no children\n return None","def _parent(self, index):\r\n # Declaring the \"root\" its own parent, otherwise usual math\r\n return (index - 1) // 2 if index else 0","def row(self):\n\t\tif self._parent != None:\n\t\t\treturn self._parent._children.index(self)\n\t\telse:\n\t\t\treturn 0","def left(self, node):\r\n if self._col(node.count) > 0:\r\n return self.nodes[node.count - 1]\r\n else:\r\n return None","def get_parent_index(i):\n # Indexing for i_parent == i // 2 is NOT ZERO-INDEXED\n pos = i + 1\n parent_pos = pos // 2\n parent_index = parent_pos - 1\n return parent_index","def x(self):\n return self.left","def _get_parent_index(self, child_index: int) -> int:\n index = (child_index - 1) // 2\n\n return index if index >= 0 else 0","def parent(self, i):\n return (i - 1) // 2","def getLeft(self):\n return self.left","def _get_children_ixs(self, parent_ix):\n ix_left = (parent_ix * 2) + 1\n ix_right = ix_left + 1\n if ix_left >= len(self):\n ix_left = None\n if ix_right >= len(self):\n ix_right = None\n return ix_left, ix_right","def left(self, A, i):\n if 2*i +1 < len(A):\n return 2*i +1\n return None","def left(self, A, i):\n if 2*i +1 < len(A):\n return 2*i +1\n return None","def left(self):\n\t\treturn self._left","def _get_parent(self, child_ix):\n if child_ix == 0:\n return None\n t = 1 if child_ix & 1 else 2\n return (child_ix - t) / 2","def left(self):\n x, y = (self.loc[0] - 1, self.loc[1])\n\n if x < 0:\n return None # None\n\n return self.garden.cells[y][x]","def GetIndexOfItem(self, item):\n \n parent = self._window.GetItemParent(item)\n if parent:\n parentIndices = self.GetIndexOfItem(parent)\n ownIndex = self.GetItemChildren(parent).index(item)\n return parentIndices + (ownIndex,)\n else:\n return ()","def left(self):\n return self.l","def parent(i):\r\n return math.floor((i - 1) / 2)","def get_left(self):\n return self.__left","def get_leftmost_child(self):\n\t\tif self.left_child == None:\n\t\t\treturn self\n\t\telse:\n\t\t\treturn self.left_child.get_leftmost_child()","def min(self):\n return self.root.leftmost","def indexOfChild(self, child):\n self.__initChild()\n return self.__child.index(child)","def getContainerLeft(self):\n return self.containerLeft","def _left(node):\n return 2 * node + 1","def _get_child_indices(self, current_index: int) -> List[int]:\n multiplier = current_index * 2\n left_index = multiplier + 1\n right_index = multiplier + 2\n\n return [left_index, right_index]","def getLeftChild(self):\n return _libsbml.ASTNode_getLeftChild(self)","def left(self):\n return self.points['topLeft'].x","def left(self):\n return self.points['topLeft'].x","def span(self):\n return self.right - self.left","def right_child(self, position):\n child = 2 * position + 2\n if child > len(self.table) - 1:\n return None\n return child","def right(self, index):\n try:\n if index == self.root_index():\n index = self.adjacency_list[index][1]\n else:\n index = self.adjacency_list[index][2]\n return index\n except IndexError:\n return -1","def left_child(self, n):\n\n if 2 * n + 1 >= len(self.heap):\n return None\n else:\n return 2 * n + 1","def getIndex(self, child):\n \n if child in self._children:\n return self._children.index(child)\n else:\n return -1","def root_index(self):\n if self.is_binary_tree:\n return 0\n return -1","def left(self):\n return self._left","def left(self):\n return self._left","def get_pos_index(self):\n return [self.row-1, self.col-1]","def position(element1, root=None):\n \n position = [] \n current = element1\n while (current.getparent() is not None) and (current is not root):\n parent = current.getparent()\n #find the index of current under parent\n index = 0\n for i in parent:\n if i is current: break\n index += 1\n position.insert(0, index + 1)\n current = parent\n \n position.insert(0, 1) # for the root element\n return position","def parent(self, index):\n if index == 0:\n return -1\n return self.adjacency_list[index][0]","def get_curpos(self):\n for i in range(len(self.tree)):\n if self.path == self.tree[i][2]:\n return i\n else:\n return -1","def leftmost_leaf_descendant_indices(self, node_list):\r\n # Cf. Zhang & Shasha:p.1249:\r\n # \"l(i) is the number of the leftmost leaf descendant of the subtree\r\n # rooted at T[i]. When T[i] is a leaf, l(i)=i.\"\r\n def get_leftmost_leaf(node):\r\n if not node.is_leaf():\r\n return get_leftmost_leaf(node.left_child())\r\n else:\r\n return node\r\n \r\n indices = []\r\n for node in node_list:\r\n leftmost_node = get_leftmost_leaf(node)\r\n for i in range(len(node_list)):\r\n if id(node_list[i]) == id(leftmost_node):\r\n indices.append(i)\r\n break\r\n return indices","def get_node_left(self, n: MazeCell) -> MazeCell:\n if n.x == 0:\n return None\n else:\n return self.get_node(n.x - 1, n.y)","def _add_left(self, p, e):\n node = self._validate_position(p)\n if node.left is not None:\n raise ValueError(\"left child exists\")\n self._size += 1\n node.left = self._Node(e, parent=node)\n return self._make_position(node.left)","def left(self, n):\n return n._left","def _get_start_render_index(self):\n\n for ol in self.overlay_list[-1::-1]:\n # FIXME There may be even no 'label' in a label's name, so we need use other method to recognize a label.\n if \"label\" not in ol.get_name() and ol.get_alpha() == 1. and ol.is_visible()\\\n and ol.get_min() <= np.min(ol.get_data()):\n return self.overlay_list.index(ol)\n\n # 0 means that the render will start with the bottom overlay.\n return 0","def find(self,i):\r\n if self.parent[i]<0:\r\n return i\r\n return self.find(self.parent[i])","def left_distance(self):\n return self.x","def _parent(self, j):\n return (j - 1) // 2","def _parent(self, j):\n return (j - 1) // 2","def _add_left(self, p, e):\n node = self._validate(p)\n if node._left is not None:\n raise ValueError('Left child exists')\n self._size += 1\n node._left = self._Node(e, node) # node is its parent\n return self._make_position(node._left)","def findMin(self):\n curr = self\n while curr.hasLeftChild():\n curr = curr.leftChild\n return curr","def element_index(self):\n return self._index","def get_right_index(i):\n pos = i + 1\n right_pos = 2 * pos + 1\n right_index = right_pos - 1\n return right_index","def left_child(self, u):\n return self._ll_tree.get_left_child(u)","def min_child(self, index):\n if self.empty():\n return None\n if self._has_left(index):\n left = self._left(index)\n small_child = left\n if self._has_right(index):\n right = self._right(index)\n if self._data[right] < self._data[left]:\n small_child = right\n if self._data[right] == self._data[left]:\n small_child = right\n return small_child\n return None","def _add_left(self, p, e):\n node = self._validate(p)\n if node._left is not None:\n raise ValueError('Left child exists')\n self._size += 1\n node._left = self._Node(e, node) # node is its parent\n return self._make_position(node._left)","def _root(self, ind):\n while (ind != self._id[ind]):\n ind = self._id[ind]\n return ind","def left(self, i):\n return (i + self.seats - 1) % self.seats","def left_root(self):\n return self.left_child(self.virtual_root)","def get_parent_idx(self, view_idx):\n a, b = view_idx\n R, C = self.shape\n i = self.p_rows[a]\n j = self.p_cols[b]\n return (i, j)","def _add_left(self, p, e):\n node = self._validate(p)\n\n if node.left is not None:\n raise ValueError('Left child exists')\n\n self._size += 1\n node.left = self._Node(e, node)\n return self._make_position(node.left)","def XPGetNthChildWidget(inWidget, inIndex):\n pass","def get_child_index(self, child: \"AbstractNode\") -> int:\n return self._children.index(child)"],"string":"[\n \"def get_left_child_index(self):\\n return (2 * self.index) + 1\",\n \"def left_child(self, index):\\n return 2 * index + 1\",\n \"def left_child(self, index):\\n return 2 * index\",\n \"def get_left_child_index(self, parent):\\n return 2*parent+1\",\n \"def left_child_idx(idx):\\n return (idx << 1) + 1\",\n \"def left_child(self, pos): \\n return 2 * pos\",\n \"def leftChild(self, pos):\\n return 2 * pos\",\n \"def leftChild(self, pos):\\n return 2 * pos\",\n \"def get_right_child_index(self):\\n return (2 * self.index) + 2\",\n \"def _left(self, index):\\r\\n return 2*index + 1\",\n \"def left_child(self, position):\\n child = 2 * position + 1\\n if child > len(self.table) - 1:\\n return None\\n return child\",\n \"def right_child(self, index):\\n return 2 * index + 1\",\n \"def left(self, index):\\n try:\\n if index == self.root_index():\\n index = self.adjacency_list[index][0]\\n else:\\n index = self.adjacency_list[index][1]\\n return index\\n except IndexError:\\n return -1\",\n \"def get_right_child_index(self, parent):\\n return 2*parent+2\",\n \"def right_child(self, index):\\n return 2 * index + 2\",\n \"def get_parent_index(self):\\n return (self.index - 1) // 2\",\n \"def right_child(self, pos): \\n return (2 * pos) + 1\",\n \"def right_child_idx(idx):\\n return (idx + 1) << 1\",\n \"def parent(self, index):\\n return index / 2\",\n \"def get_parent_index(self, child):\\n return (child-1)//2\",\n \"def parent_idx(idx):\\n return (idx - 1) >> 1\",\n \"def get_parent(self, index):\\n return (index - 1) // (2)\",\n \"def parent(self, pos): \\n return pos//2\",\n \"def rightChild(self, pos):\\n return (2 * pos) + 1\",\n \"def rightChild(self, pos):\\n return (2 * pos) + 1\",\n \"def parent(self, pos):\\n return pos // 2\",\n \"def parent(self, pos):\\n return pos // 2\",\n \"def get_left_child(self, index):\\n return self.heap[self.get_left_child_index(index)]\",\n \"def parent(self, index):\\n if index == 0:\\n print(\\\"index 0 has no parent\\\")\\n return None\\n return (index - 1) // 2\",\n \"def get_leftchild(self):\\n return self._leftchild\",\n \"def position(self):\\n # (this to be able to let the model know my 'row')\\n if self.parent and self in self.parent.children:\\n return self.parent.children.index(self)\\n return 0\",\n \"def get_parent_index(index):\\n if index == 0:\\n return 0\\n if index % 2 == 0:\\n return int(index / 2 - 1)\\n return int(index / 2)\",\n \"def get_index(self, child):\\n for _index, item in enumerate(self.children):\\n if item == child:\\n return _index\\n\\n return -1\",\n \"def _left(self, j):\\n return 2 * j + 1\",\n \"def _left(self, j):\\n return 2 * j + 1\",\n \"def get_left(self):\\n return self.left\",\n \"def parent(self, pos):\\n if pos == 0: \\n return None\\n return int(math.ceil(pos / self.dary) - 1)\",\n \"def max_child(self, index):\\n # left and right child\\n left = self._data[(index*2)+1] if (index*2)+1 < len(self) else None\\n right = self._data[(index*2)+2] if (index*2)+2 < len(self) else None\\n\\n # if has both children\\n if left and right:\\n if left < right:\\n return (index*2)+2\\n return (index*2)+1\\n\\n # if only has left child\\n elif left and right is None:\\n return (index*2)+1\\n\\n # if only has right child\\n elif right and left is None:\\n return (index*2)+2\\n\\n # no children\\n return None\",\n \"def _parent(self, index):\\r\\n # Declaring the \\\"root\\\" its own parent, otherwise usual math\\r\\n return (index - 1) // 2 if index else 0\",\n \"def row(self):\\n\\t\\tif self._parent != None:\\n\\t\\t\\treturn self._parent._children.index(self)\\n\\t\\telse:\\n\\t\\t\\treturn 0\",\n \"def left(self, node):\\r\\n if self._col(node.count) > 0:\\r\\n return self.nodes[node.count - 1]\\r\\n else:\\r\\n return None\",\n \"def get_parent_index(i):\\n # Indexing for i_parent == i // 2 is NOT ZERO-INDEXED\\n pos = i + 1\\n parent_pos = pos // 2\\n parent_index = parent_pos - 1\\n return parent_index\",\n \"def x(self):\\n return self.left\",\n \"def _get_parent_index(self, child_index: int) -> int:\\n index = (child_index - 1) // 2\\n\\n return index if index >= 0 else 0\",\n \"def parent(self, i):\\n return (i - 1) // 2\",\n \"def getLeft(self):\\n return self.left\",\n \"def _get_children_ixs(self, parent_ix):\\n ix_left = (parent_ix * 2) + 1\\n ix_right = ix_left + 1\\n if ix_left >= len(self):\\n ix_left = None\\n if ix_right >= len(self):\\n ix_right = None\\n return ix_left, ix_right\",\n \"def left(self, A, i):\\n if 2*i +1 < len(A):\\n return 2*i +1\\n return None\",\n \"def left(self, A, i):\\n if 2*i +1 < len(A):\\n return 2*i +1\\n return None\",\n \"def left(self):\\n\\t\\treturn self._left\",\n \"def _get_parent(self, child_ix):\\n if child_ix == 0:\\n return None\\n t = 1 if child_ix & 1 else 2\\n return (child_ix - t) / 2\",\n \"def left(self):\\n x, y = (self.loc[0] - 1, self.loc[1])\\n\\n if x < 0:\\n return None # None\\n\\n return self.garden.cells[y][x]\",\n \"def GetIndexOfItem(self, item):\\n \\n parent = self._window.GetItemParent(item)\\n if parent:\\n parentIndices = self.GetIndexOfItem(parent)\\n ownIndex = self.GetItemChildren(parent).index(item)\\n return parentIndices + (ownIndex,)\\n else:\\n return ()\",\n \"def left(self):\\n return self.l\",\n \"def parent(i):\\r\\n return math.floor((i - 1) / 2)\",\n \"def get_left(self):\\n return self.__left\",\n \"def get_leftmost_child(self):\\n\\t\\tif self.left_child == None:\\n\\t\\t\\treturn self\\n\\t\\telse:\\n\\t\\t\\treturn self.left_child.get_leftmost_child()\",\n \"def min(self):\\n return self.root.leftmost\",\n \"def indexOfChild(self, child):\\n self.__initChild()\\n return self.__child.index(child)\",\n \"def getContainerLeft(self):\\n return self.containerLeft\",\n \"def _left(node):\\n return 2 * node + 1\",\n \"def _get_child_indices(self, current_index: int) -> List[int]:\\n multiplier = current_index * 2\\n left_index = multiplier + 1\\n right_index = multiplier + 2\\n\\n return [left_index, right_index]\",\n \"def getLeftChild(self):\\n return _libsbml.ASTNode_getLeftChild(self)\",\n \"def left(self):\\n return self.points['topLeft'].x\",\n \"def left(self):\\n return self.points['topLeft'].x\",\n \"def span(self):\\n return self.right - self.left\",\n \"def right_child(self, position):\\n child = 2 * position + 2\\n if child > len(self.table) - 1:\\n return None\\n return child\",\n \"def right(self, index):\\n try:\\n if index == self.root_index():\\n index = self.adjacency_list[index][1]\\n else:\\n index = self.adjacency_list[index][2]\\n return index\\n except IndexError:\\n return -1\",\n \"def left_child(self, n):\\n\\n if 2 * n + 1 >= len(self.heap):\\n return None\\n else:\\n return 2 * n + 1\",\n \"def getIndex(self, child):\\n \\n if child in self._children:\\n return self._children.index(child)\\n else:\\n return -1\",\n \"def root_index(self):\\n if self.is_binary_tree:\\n return 0\\n return -1\",\n \"def left(self):\\n return self._left\",\n \"def left(self):\\n return self._left\",\n \"def get_pos_index(self):\\n return [self.row-1, self.col-1]\",\n \"def position(element1, root=None):\\n \\n position = [] \\n current = element1\\n while (current.getparent() is not None) and (current is not root):\\n parent = current.getparent()\\n #find the index of current under parent\\n index = 0\\n for i in parent:\\n if i is current: break\\n index += 1\\n position.insert(0, index + 1)\\n current = parent\\n \\n position.insert(0, 1) # for the root element\\n return position\",\n \"def parent(self, index):\\n if index == 0:\\n return -1\\n return self.adjacency_list[index][0]\",\n \"def get_curpos(self):\\n for i in range(len(self.tree)):\\n if self.path == self.tree[i][2]:\\n return i\\n else:\\n return -1\",\n \"def leftmost_leaf_descendant_indices(self, node_list):\\r\\n # Cf. Zhang & Shasha:p.1249:\\r\\n # \\\"l(i) is the number of the leftmost leaf descendant of the subtree\\r\\n # rooted at T[i]. When T[i] is a leaf, l(i)=i.\\\"\\r\\n def get_leftmost_leaf(node):\\r\\n if not node.is_leaf():\\r\\n return get_leftmost_leaf(node.left_child())\\r\\n else:\\r\\n return node\\r\\n \\r\\n indices = []\\r\\n for node in node_list:\\r\\n leftmost_node = get_leftmost_leaf(node)\\r\\n for i in range(len(node_list)):\\r\\n if id(node_list[i]) == id(leftmost_node):\\r\\n indices.append(i)\\r\\n break\\r\\n return indices\",\n \"def get_node_left(self, n: MazeCell) -> MazeCell:\\n if n.x == 0:\\n return None\\n else:\\n return self.get_node(n.x - 1, n.y)\",\n \"def _add_left(self, p, e):\\n node = self._validate_position(p)\\n if node.left is not None:\\n raise ValueError(\\\"left child exists\\\")\\n self._size += 1\\n node.left = self._Node(e, parent=node)\\n return self._make_position(node.left)\",\n \"def left(self, n):\\n return n._left\",\n \"def _get_start_render_index(self):\\n\\n for ol in self.overlay_list[-1::-1]:\\n # FIXME There may be even no 'label' in a label's name, so we need use other method to recognize a label.\\n if \\\"label\\\" not in ol.get_name() and ol.get_alpha() == 1. and ol.is_visible()\\\\\\n and ol.get_min() <= np.min(ol.get_data()):\\n return self.overlay_list.index(ol)\\n\\n # 0 means that the render will start with the bottom overlay.\\n return 0\",\n \"def find(self,i):\\r\\n if self.parent[i]<0:\\r\\n return i\\r\\n return self.find(self.parent[i])\",\n \"def left_distance(self):\\n return self.x\",\n \"def _parent(self, j):\\n return (j - 1) // 2\",\n \"def _parent(self, j):\\n return (j - 1) // 2\",\n \"def _add_left(self, p, e):\\n node = self._validate(p)\\n if node._left is not None:\\n raise ValueError('Left child exists')\\n self._size += 1\\n node._left = self._Node(e, node) # node is its parent\\n return self._make_position(node._left)\",\n \"def findMin(self):\\n curr = self\\n while curr.hasLeftChild():\\n curr = curr.leftChild\\n return curr\",\n \"def element_index(self):\\n return self._index\",\n \"def get_right_index(i):\\n pos = i + 1\\n right_pos = 2 * pos + 1\\n right_index = right_pos - 1\\n return right_index\",\n \"def left_child(self, u):\\n return self._ll_tree.get_left_child(u)\",\n \"def min_child(self, index):\\n if self.empty():\\n return None\\n if self._has_left(index):\\n left = self._left(index)\\n small_child = left\\n if self._has_right(index):\\n right = self._right(index)\\n if self._data[right] < self._data[left]:\\n small_child = right\\n if self._data[right] == self._data[left]:\\n small_child = right\\n return small_child\\n return None\",\n \"def _add_left(self, p, e):\\n node = self._validate(p)\\n if node._left is not None:\\n raise ValueError('Left child exists')\\n self._size += 1\\n node._left = self._Node(e, node) # node is its parent\\n return self._make_position(node._left)\",\n \"def _root(self, ind):\\n while (ind != self._id[ind]):\\n ind = self._id[ind]\\n return ind\",\n \"def left(self, i):\\n return (i + self.seats - 1) % self.seats\",\n \"def left_root(self):\\n return self.left_child(self.virtual_root)\",\n \"def get_parent_idx(self, view_idx):\\n a, b = view_idx\\n R, C = self.shape\\n i = self.p_rows[a]\\n j = self.p_cols[b]\\n return (i, j)\",\n \"def _add_left(self, p, e):\\n node = self._validate(p)\\n\\n if node.left is not None:\\n raise ValueError('Left child exists')\\n\\n self._size += 1\\n node.left = self._Node(e, node)\\n return self._make_position(node.left)\",\n \"def XPGetNthChildWidget(inWidget, inIndex):\\n pass\",\n \"def get_child_index(self, child: \\\"AbstractNode\\\") -> int:\\n return self._children.index(child)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.9122641","0.88522995","0.8762248","0.86230975","0.83617294","0.8189093","0.80934316","0.80934316","0.79510504","0.77594656","0.76739126","0.76264703","0.7587339","0.7570154","0.750047","0.7485209","0.74505705","0.74247074","0.72877926","0.7251152","0.72425985","0.71855646","0.71615875","0.7139462","0.7139462","0.7117249","0.7117249","0.70185685","0.697145","0.6952395","0.6932111","0.6879224","0.6807672","0.6790416","0.6790416","0.6775985","0.6771376","0.6693774","0.6688283","0.6684442","0.66839844","0.6679956","0.664594","0.6644122","0.66082525","0.6603414","0.6596323","0.6566398","0.6566398","0.65299296","0.6506461","0.6504272","0.6502878","0.6500916","0.6482116","0.647566","0.64756536","0.6462241","0.64476305","0.64401805","0.6411828","0.64046043","0.63800025","0.6369883","0.6369883","0.63640285","0.63464475","0.6344579","0.63387245","0.63338447","0.6321773","0.6313995","0.6313995","0.62916714","0.62743","0.6267607","0.62624323","0.6254151","0.6252978","0.6201912","0.62008655","0.6200412","0.61984134","0.6196452","0.6190072","0.6190072","0.6166647","0.6153381","0.614806","0.61465526","0.6142386","0.61391836","0.61384","0.6134814","0.6099831","0.6081734","0.60783184","0.60744864","0.60719085","0.60648566"],"string":"[\n \"0.9122641\",\n \"0.88522995\",\n \"0.8762248\",\n \"0.86230975\",\n \"0.83617294\",\n \"0.8189093\",\n \"0.80934316\",\n \"0.80934316\",\n \"0.79510504\",\n \"0.77594656\",\n \"0.76739126\",\n \"0.76264703\",\n \"0.7587339\",\n \"0.7570154\",\n \"0.750047\",\n \"0.7485209\",\n \"0.74505705\",\n \"0.74247074\",\n \"0.72877926\",\n \"0.7251152\",\n \"0.72425985\",\n \"0.71855646\",\n \"0.71615875\",\n \"0.7139462\",\n \"0.7139462\",\n \"0.7117249\",\n \"0.7117249\",\n \"0.70185685\",\n \"0.697145\",\n \"0.6952395\",\n \"0.6932111\",\n \"0.6879224\",\n \"0.6807672\",\n \"0.6790416\",\n \"0.6790416\",\n \"0.6775985\",\n \"0.6771376\",\n \"0.6693774\",\n \"0.6688283\",\n \"0.6684442\",\n \"0.66839844\",\n \"0.6679956\",\n \"0.664594\",\n \"0.6644122\",\n \"0.66082525\",\n \"0.6603414\",\n \"0.6596323\",\n \"0.6566398\",\n \"0.6566398\",\n \"0.65299296\",\n \"0.6506461\",\n \"0.6504272\",\n \"0.6502878\",\n \"0.6500916\",\n \"0.6482116\",\n \"0.647566\",\n \"0.64756536\",\n \"0.6462241\",\n \"0.64476305\",\n \"0.64401805\",\n \"0.6411828\",\n \"0.64046043\",\n \"0.63800025\",\n \"0.6369883\",\n \"0.6369883\",\n \"0.63640285\",\n \"0.63464475\",\n \"0.6344579\",\n \"0.63387245\",\n \"0.63338447\",\n \"0.6321773\",\n \"0.6313995\",\n \"0.6313995\",\n \"0.62916714\",\n \"0.62743\",\n \"0.6267607\",\n \"0.62624323\",\n \"0.6254151\",\n \"0.6252978\",\n \"0.6201912\",\n \"0.62008655\",\n \"0.6200412\",\n \"0.61984134\",\n \"0.6196452\",\n \"0.6190072\",\n \"0.6190072\",\n \"0.6166647\",\n \"0.6153381\",\n \"0.614806\",\n \"0.61465526\",\n \"0.6142386\",\n \"0.61391836\",\n \"0.61384\",\n \"0.6134814\",\n \"0.6099831\",\n \"0.6081734\",\n \"0.60783184\",\n \"0.60744864\",\n \"0.60719085\",\n \"0.60648566\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.75896484"},"document_rank":{"kind":"string","value":"12"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":222,"numItemsPerPage":100,"numTotalItems":95772,"offset":22200,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjY0ODQ3MiwianRpIjoiOGU3NzZjMDgtYzcwMC00N2QxLTk5ZDItMTI5YWZjYzZlZmQ5Iiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODY2NTIwNzIsImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.uzPHK6AS36ch_Fnux9iE5M9DyMY0Co1aDcqZKwSo1PnYIc0sEWkwddnPKse-Dxzz8Tn2JhWFpCxpN7HTVJuqDA","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 |
|---|---|---|---|---|---|---|
Checks the passwords entered are the same Checks they meet the complexity criteria | def check_pass(password, confirmed):
# Requires at least one digit, a lower case letter,
# an upper case letter and has at least 6 characters
password_regex = r"^(?=.*?\d)(?=.*?[a-z])(?=.*?[A-Z])[a-zA-Z\d]{6,}$"
regex = re.compile(password_regex)
if password == confirmed:
if regex.match(pas)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_check_password():\n assert check_password('Longpassword') == False\n assert check_password('123456') == False\n assert check_password('short') == False\n assert check_password('C0rect') == False\n assert check_password('Correct8') == True",
"def do_passwords_match(self, password1, passwor... | [
"0.72476536",
"0.7238956",
"0.7209978",
"0.7175978",
"0.71610385",
"0.7142391",
"0.7134592",
"0.7098522",
"0.70844126",
"0.7058614",
"0.7052501",
"0.70388466",
"0.7036679",
"0.7034364",
"0.7020793",
"0.69643396",
"0.69606173",
"0.693962",
"0.6920598",
"0.6911224",
"0.69087875... | 0.0 | -1 |
Performs dijsktras algorithm from a certain node | def dijsktra(graph, initial):
# Sets initial node score to 10
visited = {initial: 10}
nodes = set(graph.nodes)
max_weight = graph.distances[max(graph.distances, key=graph.distances.get)]
min_weight = graph.distances[min(graph.distances, key=graph.distances.get)]
# Defines the number of nodes t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def label_correcting_algo(dt, ori_node, des_node, do_return=False):\n # Convert all labels to string\n ori = str(ori_node)\n des = str(des_node)\n dt[[\"start\", \"end\"]] = dt[[\"start\", \"end\"]].astype(str) \n \n # Initialization\n nodes = set(dt.loc[:,\"start\"].unique()) | set(dt.loc[:,\... | [
"0.59614277",
"0.5681578",
"0.56544733",
"0.55270684",
"0.5521709",
"0.5521709",
"0.5468644",
"0.54065055",
"0.5363926",
"0.53576124",
"0.5327712",
"0.529251",
"0.5275726",
"0.5270906",
"0.5267602",
"0.52594185",
"0.5239558",
"0.5176163",
"0.5163248",
"0.51169175",
"0.5099423... | 0.5816856 | 1 |
Serves the home page at the '/' route Loads notifications for the logged in user | def index():
try:
with Database() as db:
notifs = db.getNotifs(session['username'])
b_notifs = []
for i in range(len(notifs) - 1, -1, -1):
b_notifs.append(notifs[i])
session['notifs'] = b_notifs
return render_template(
'inde... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def home_view(request):\n if request.authenticated_userid:\n return HTTPFound(location=request.route_url('app_view')) # pragma no cover\n return {} # pragma no cover",
"def get(self):\n if self.logged_in:\n self.render('home.html', {\n 'name': self.current_user.name,\n 'server... | [
"0.66011",
"0.65371966",
"0.6510675",
"0.6460462",
"0.636571",
"0.63617945",
"0.63198596",
"0.6269688",
"0.62398434",
"0.6231268",
"0.62178016",
"0.61766225",
"0.6167033",
"0.61527145",
"0.6149676",
"0.61176664",
"0.61155385",
"0.6103278",
"0.61006826",
"0.6091367",
"0.607030... | 0.0 | -1 |
Upload link for files Creates analyser object Serves upload text display page. | def upload_file():
try:
global current_file
if request.method == "POST":
# Validates a file has been uploaded
if 'file' not in request.files:
flash("No file submitted")
return redirect(url_for('index'))
f = request.files['file']
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def upload():\n\treturn render_template(\"upload.html\", title=\"Upload a file\")",
"def raw_text_upload():\n try:\n global current_file\n if request.method == \"POST\":\n raw_text = request.form['raw_text']\n # Checks text is not empty\n raw_text = raw_text.stri... | [
"0.67696977",
"0.66535026",
"0.64683944",
"0.6226196",
"0.61673456",
"0.61646247",
"0.6144482",
"0.6115935",
"0.6083226",
"0.60172004",
"0.6000657",
"0.5965749",
"0.5825415",
"0.5809112",
"0.58016574",
"0.57651275",
"0.57315755",
"0.56884503",
"0.56718403",
"0.5638984",
"0.56... | 0.6194034 | 4 |
Upload path for raw text, creates a text file with the text in Creates analyser object | def raw_text_upload():
try:
global current_file
if request.method == "POST":
raw_text = request.form['raw_text']
# Checks text is not empty
raw_text = raw_text.strip('<>')
if raw_text != '':
if app.config['UPLOAD_FOLDER'] == UPLOAD_FOLD... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_text_file(text, path):\n os.makedirs(os.path.dirname(path), exist_ok=True)\n with open(path, \"w\") as f:\n f.write(text)",
"def store_lyrics_text(target_path, track_id, text, extension=\".txt\"):\n file_path = os.path.join(target_path, track_id + extension)\n print(file_path)\n wi... | [
"0.63974303",
"0.6382405",
"0.6246426",
"0.62317955",
"0.61909753",
"0.61618316",
"0.6158124",
"0.60989183",
"0.6093532",
"0.6090002",
"0.6025201",
"0.59735364",
"0.59291154",
"0.59190583",
"0.5890567",
"0.5857344",
"0.58509505",
"0.5835025",
"0.5823908",
"0.58041954",
"0.579... | 0.77678305 | 0 |
Allows the user to change their password, changes password in database | def changepassword():
try:
if request.method == 'POST':
# Makes sure the passwords match and that it meets complexity
validate = check_pass(
request.form['newpass'], request.form['connewpass'])
if validate == "Passed":
data = [request.form[... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def change_password(self):\n self.test_user.set_password(self.create_user_data()['password1'])\n self.test_user.save()",
"def change_password(change_account):\n change_data(change_account, changed_data='password')",
"def change_password(self, new_pass):\n self.manager.change_user_passwo... | [
"0.8027422",
"0.7864155",
"0.7834404",
"0.77842826",
"0.7754724",
"0.77413195",
"0.7683205",
"0.7681768",
"0.76722574",
"0.7665951",
"0.76569355",
"0.7640851",
"0.76029825",
"0.760133",
"0.7560938",
"0.7558102",
"0.7539255",
"0.75238895",
"0.75107753",
"0.7507648",
"0.7468965... | 0.7694745 | 6 |
Gets the users texts from the database and shows them on their profile page | def profile(username):
try:
with Database() as database:
# Makes sure the user exists
user = database.checkForUser(username)
if user == session['username']:
if session['username'] == username:
session['id'] = database.getID(session['use... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def profile(request):\n user = Info.objects.all()\n return render(request, 'kvent/profile.html',{user:'user'})",
"def users_page(request):",
"def retrieve_user_page():\n users = hl.getUsers()\n groups = hl.getAllGroups()\n requests = hl.retrieveRequests()\n nodes = hl.getAllNodes()\n retur... | [
"0.660658",
"0.65475494",
"0.6475246",
"0.64415663",
"0.6368816",
"0.6345084",
"0.63256514",
"0.62959826",
"0.6294599",
"0.6272487",
"0.6256409",
"0.624308",
"0.6236545",
"0.6234458",
"0.6203177",
"0.61630565",
"0.61494046",
"0.61314356",
"0.6126782",
"0.61165947",
"0.6114926... | 0.60989845 | 21 |
Search through texts with titles that contain a specified string | def search_titles(username):
try:
with Database() as database:
user = database.checkForUser(username)
if user == session['username']:
if session['username'] == username:
session['id'] = database.getID(session['username'])
search... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def search_by_contains(self, tl):\n print(\"Search by string\")\n string = input(\"Please enter search string: \")\n return tl.findall_contains(string)",
"def search(self, title):\n close_matches = self.get_close_matches_by_title(title)\n count = 0\n for item in self.it... | [
"0.6895403",
"0.68408966",
"0.67932504",
"0.6696992",
"0.6536303",
"0.6526159",
"0.64328575",
"0.6326639",
"0.63163996",
"0.6315477",
"0.6232018",
"0.6192571",
"0.61624473",
"0.6138239",
"0.6131031",
"0.61125135",
"0.6083743",
"0.607554",
"0.6075036",
"0.6062325",
"0.60302883... | 0.0 | -1 |
Looks for texts with a certain keyword and similar keywords | def search_keywords(username):
try:
with Database() as database:
user = database.checkForUser(username)
if user == session['username']:
if session['username'] == username:
session['id'] = database.getID(session['username'])
sear... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def match_keywords(text, keywords):\n for keyword in keywords:\n if re.search(r\"\\b\" + keyword + r\"\\b\", text, flags=re.IGNORECASE):\n return True\n return False",
"def _keyword_search(id_to_text, raw_keywords, modified_keywords):\n\t# The raw keywords and modified keywords should be ... | [
"0.712303",
"0.68108404",
"0.67155206",
"0.668099",
"0.66752326",
"0.66552484",
"0.6584532",
"0.6532232",
"0.6453535",
"0.6453535",
"0.64387393",
"0.64295256",
"0.6406779",
"0.635997",
"0.6331261",
"0.6328947",
"0.6284383",
"0.61962557",
"0.61889714",
"0.6178077",
"0.6120695"... | 0.0 | -1 |
Gets texts within a certain category | def search_category(username):
try:
with Database() as database:
user = database.checkForUser(username)
if user == session['username']:
if session['username'] == username:
session['id'] = database.getID(session['username'])
cate... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extract_text(self, categories, entries):\n text = \" \".join(\n [\n self.podcast.title,\n self.podcast.description,\n self.podcast.keywords,\n self.podcast.authors,\n ]\n + [c.name for c in categories]\n ... | [
"0.72024417",
"0.656723",
"0.65608114",
"0.6502506",
"0.64198023",
"0.6408471",
"0.63583153",
"0.6300433",
"0.62832785",
"0.60738915",
"0.6068554",
"0.60593975",
"0.5932416",
"0.5892128",
"0.58820987",
"0.5877177",
"0.5874937",
"0.58415854",
"0.583352",
"0.582695",
"0.5822817... | 0.0 | -1 |
Search for texts that have a certain language feature Reading age and sentiment are in a certain range | def search_values(username):
try:
with Database() as database:
user = database.checkForUser(username)
if user == session['username']:
if session['username'] == username:
features = ["Alliteration", "Antithesis", "Juxtaposition"]
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_eligible(text, n, lng):\n for language in detect_langs(text):\n if language.lang == lng:\n probability = language.prob\n word_count = len(text.split(\" \"))\n if probability * word_count > n:\n return True\n else:\n break\n ... | [
"0.57004565",
"0.55651575",
"0.55386007",
"0.5538032",
"0.55267215",
"0.55050963",
"0.549548",
"0.5475534",
"0.54684144",
"0.5442134",
"0.5429395",
"0.54142946",
"0.5403086",
"0.5392609",
"0.53638613",
"0.5359004",
"0.53550684",
"0.5329151",
"0.5324816",
"0.53167015",
"0.5301... | 0.0 | -1 |
Loads a different analysis of a certain text | def changeview(analysis):
try:
analysed_texts = current_file.analysed_texts
text_facts = current_file.stats
with Database() as db:
categories = db.loadCategories()
keywords = ''
for word in text_facts['Key Words']:
keywords += word[0] + ", "
ke... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def webtext(analysis):\n global current_file\n try:\n if request.form[\"url\"] == \"\":\n flash(\"No URL given\")\n return redirect(url_for('index'))\n url = request.form['url']\n current_file = main.Analyser(url)\n analysed_texts = current_file.analysed_text... | [
"0.60592824",
"0.59292406",
"0.592167",
"0.58063495",
"0.57186705",
"0.5711558",
"0.5694043",
"0.56910104",
"0.5678257",
"0.56741613",
"0.564287",
"0.56425405",
"0.5625102",
"0.5608563",
"0.56067073",
"0.55877876",
"0.558111",
"0.5556692",
"0.5546069",
"0.55426407",
"0.554239... | 0.5290827 | 60 |
Creates a pdf with the raw text in from a html template | def download(texttitle):
try:
body = current_file.analysed_texts['Regular']
rendered = render_template('pdf_template.html', title=texttitle, body=body)
options = {'encoding': "UTF-8"}
pdf = pdfkit.from_string(rendered, False, options=options)
response = make_response(pdf)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def render_to_pdf(template_src, context_dict={}):\n template = get_template(template_src)\n html = template.render(context_dict)\n result = BytesIO()\n pdf = pisa.pisaDocument(BytesIO(html.encode(\"ISO-8859-1\")), result)\n if not pdf.err:\n return HttpResponse(result.getvalue(), content_type... | [
"0.7403683",
"0.71494794",
"0.71099365",
"0.7091731",
"0.6857262",
"0.6822364",
"0.6765777",
"0.6644698",
"0.65659434",
"0.654277",
"0.6541064",
"0.65084815",
"0.63685113",
"0.63370645",
"0.6314567",
"0.6307673",
"0.6296097",
"0.62153006",
"0.6158231",
"0.61504066",
"0.613498... | 0.6280712 | 17 |
Shares the text with the user, by creating link in databse | def share_text(texttitle, username):
message = session['username'] + \
" shared the text " + texttitle + " with you."
with Database() as database:
database.share_text(texttitle, username, session["username"])
database.sendNotif(username, message)
flash("Text Shared")
return redir... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def share_link(cls, user, link):",
"def share_link(cls, user, link):",
"def insert_link(self, text, href):\n self.insert_text('\\n<a href=\"%s\">%s</a>' % (href, text))",
"def paste_text(text, language=\"text\", paste_expire=8640, paste_user=\"paste.py\",\n return_link=True):\n # costruct ur... | [
"0.6997458",
"0.6997458",
"0.6394542",
"0.6387845",
"0.63727754",
"0.6336196",
"0.62323594",
"0.59182006",
"0.59175485",
"0.5866517",
"0.585186",
"0.5835037",
"0.58334965",
"0.57938176",
"0.5787896",
"0.5775299",
"0.57594526",
"0.5759272",
"0.5726179",
"0.57058954",
"0.569536... | 0.7278323 | 0 |
Displays the text on upload and allows the analysis to be selected | def textdisplay(textTitle, analysis):
try:
global current_file
with Database() as database:
text_owner = database.getTextOwner(textTitle, session['username'])
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER + text_owner
path = app.config['UPLOAD_FOLDER'] + '/objects/' + textT... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def raw_text_upload():\n try:\n global current_file\n if request.method == \"POST\":\n raw_text = request.form['raw_text']\n # Checks text is not empty\n raw_text = raw_text.strip('<>')\n if raw_text != '':\n if app.config['UPLOAD_FOLDER']... | [
"0.7159925",
"0.70026785",
"0.6800656",
"0.6480692",
"0.6478881",
"0.63340646",
"0.6268505",
"0.62629604",
"0.6173394",
"0.61475825",
"0.6066088",
"0.60618997",
"0.60281646",
"0.5980928",
"0.5969213",
"0.5954582",
"0.5953171",
"0.59319204",
"0.59178823",
"0.5866974",
"0.58288... | 0.7547743 | 0 |
Deletes text file and wipes any records from the database | def deletetext(texttitle):
try:
with Database() as database:
canDelete = database.checkDelete(texttitle, session['id'])
if canDelete:
if app.config['UPLOAD_FOLDER'] == UPLOAD_FOLDER:
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER + \
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def erase_db(file):\n open(file, 'w').close()",
"def delete_db(self):\n import os.path\n os.remove(self.filepath)",
"def delete(self, filename):\n pass",
"def TextDelete(texttitle):\n\n path = app.config['UPLOAD_FOLDER'] + \\\n '/objects/' + texttitle + '.txt'\n with ... | [
"0.7497814",
"0.7277273",
"0.6885682",
"0.680071",
"0.6764736",
"0.6670453",
"0.66297805",
"0.6629244",
"0.66218156",
"0.65419096",
"0.6515271",
"0.6503777",
"0.6454652",
"0.6429588",
"0.64249486",
"0.64119023",
"0.64052826",
"0.6397928",
"0.6392429",
"0.63903683",
"0.6355564... | 0.0 | -1 |
Saves the text object in a text file and saves it in db | def save_text():
try:
global current_file
if request.method == "POST":
current_file.title = request.form['title'].replace(' ', '')
with Database() as database:
category = database.getCategory(request.form['Category'])
current_file.category = ca... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def save_text(self):\n if self.tab_control.index(\"current\") == 0:\n text = self.textbox.get(\"1.0\", tk.END)\n if text is not None:\n files = [('Text Document', '*.txt')]\n text_file = asksaveasfile(title=\"Save your text as .txt\", filetypes=files,\n ... | [
"0.7108115",
"0.69423765",
"0.6938336",
"0.68713033",
"0.677555",
"0.6758945",
"0.6651387",
"0.659884",
"0.65734136",
"0.6515245",
"0.6488028",
"0.64555424",
"0.64316326",
"0.6394388",
"0.6353932",
"0.63499653",
"0.6315648",
"0.6307501",
"0.630571",
"0.62945545",
"0.62737465"... | 0.7057494 | 1 |
Gets a text from the internet using an API | def webtext(analysis):
global current_file
try:
if request.form["url"] == "":
flash("No URL given")
return redirect(url_for('index'))
url = request.form['url']
current_file = main.Analyser(url)
analysed_texts = current_file.analysed_texts
text_fact... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_text(self, url, *, timeout, headers):",
"def get_text(url):\n try:\n result = requests.get(url, headers=DEFAULT_REQUEST_HEADERS)\n result.raise_for_status()\n except requests.HTTPError as err:\n raise URLGetTextError(err)\n\n return result.text",
"def read_text(self, url: ... | [
"0.7869953",
"0.730019",
"0.70340216",
"0.6941515",
"0.676543",
"0.6755698",
"0.66908175",
"0.6630932",
"0.6549851",
"0.6511394",
"0.6500466",
"0.64911705",
"0.6463328",
"0.64559066",
"0.6447297",
"0.6443701",
"0.6441239",
"0.6439431",
"0.6432376",
"0.6432075",
"0.6420207",
... | 0.0 | -1 |
Deletes the users account, their files and texts from the database | def deleteaccount():
try:
if app.config['UPLOAD_FOLDER'] == UPLOAD_FOLDER:
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER + \
session['username']
with Database() as db:
texts = db.getOwnedTexts(session['id'])
for text in texts:
TextDel... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def delete_user():",
"def db_delete_user_data(self):\n util.log(\"Clearing all user data\", util.LogLevel.Info)\n self.db.db_clear_data_user()\n util.log(\"Done\", util.LogLevel.Info)",
"def delete_user():\n #TODO user delete\n pass",
"def delete_user(self) -> None:\n table_... | [
"0.75881016",
"0.7571063",
"0.73386544",
"0.72856486",
"0.71325874",
"0.70421135",
"0.70142746",
"0.6962373",
"0.6958195",
"0.69209486",
"0.6919454",
"0.6880083",
"0.68616986",
"0.67589813",
"0.6717374",
"0.6716456",
"0.66849625",
"0.6683709",
"0.6657697",
"0.66400325",
"0.66... | 0.76813954 | 0 |
Logs the user out | def logout():
try:
session.clear()
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
flash("You have been logged out")
return redirect(url_for('index'))
except Exception as e:
flash("Oops, something went wrong... Try again.")
return render_template('index.html') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def logout_user():\n pass",
"def log_out_user(self):\n flask_login.logout_user()",
"def logout():",
"def logOut(self):\n self.client.logout()",
"def logout(self):\n pass",
"def logout():\n login()",
"def logout():\n return logout_user()",
"def sign_out(self):\n se... | [
"0.8616682",
"0.8555834",
"0.8535574",
"0.8505401",
"0.84060234",
"0.8363244",
"0.8331417",
"0.83193165",
"0.828638",
"0.8264839",
"0.8237121",
"0.8207617",
"0.8191595",
"0.81912225",
"0.8185386",
"0.8086846",
"0.8070375",
"0.79742473",
"0.79740614",
"0.7971824",
"0.7926226",... | 0.0 | -1 |
Allows the user to login | def login_page():
try:
if request.method == "POST":
with Database() as database:
db_password = database.checkPass(request.form['username'])
if len(db_password) > 0:
db_password = db_password[0][0]
if pbkdf2_sha256.verify(req... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def login():",
"def login():",
"def login_user():\n pass",
"def login(self):\n\t\treturn",
"def login(self):",
"def login():\n pass",
"def login_menu(self):\n print(\"\\nPlease enter your email and password\")\n email = self.validate_email()\n password = self.validate_passwor... | [
"0.8591797",
"0.8591797",
"0.8469158",
"0.8464104",
"0.8455766",
"0.8266615",
"0.8212561",
"0.8142589",
"0.80873966",
"0.7950496",
"0.7835791",
"0.780167",
"0.7779753",
"0.77034426",
"0.76880175",
"0.7650659",
"0.75441074",
"0.7543309",
"0.7531979",
"0.7497269",
"0.7492912",
... | 0.0 | -1 |
Allows the user to register | def register_page():
try:
if request.method == "POST":
form = reg_form(request.form)
validation = form.validate()
if validation == "Passed":
# Hashes the passwords using sha256
password_hash = pbkdf2_sha256.encrypt(form.password.data, round... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def register_user():\n pass",
"def register(self):\n first_name = self.first_name_entry.get()\n insertion = self.insertion_entry.get()\n last_name = self.last_name_entry.get()\n zip_code = self.zip_entry.get()\n streetnumber = self.streetnumber_entry.get()\n email = s... | [
"0.8532799",
"0.7963627",
"0.78872436",
"0.7882183",
"0.7834774",
"0.7823601",
"0.7792079",
"0.77843577",
"0.77785784",
"0.7773236",
"0.77628666",
"0.7750897",
"0.7720143",
"0.7708544",
"0.7683669",
"0.7662307",
"0.7659337",
"0.7658292",
"0.764016",
"0.76397055",
"0.7622419",... | 0.0 | -1 |
Initializes a block object. | def __init__(self, raw, style_cls):
super(Base, self).__init__()
self.raw = raw
self.style_cls = style_cls | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self):\n\n pyxel.init(windowWidth, windowHeight)\n\n # generates randomly ordered list of [0, 1, 2, 3, 4, 5, 6, 7]\n self.bag = sample(list(range(7)), 7)\n\n # generates a block from last element of self.bag into self.blocks\n self.block = Block(blockData[self.bag.po... | [
"0.7198606",
"0.713938",
"0.70537674",
"0.6927632",
"0.6839594",
"0.6788867",
"0.67439294",
"0.6693654",
"0.6693117",
"0.66663164",
"0.66554767",
"0.6590122",
"0.6525242",
"0.6498737",
"0.64744353",
"0.64673245",
"0.64261454",
"0.64225394",
"0.641554",
"0.64104724",
"0.640030... | 0.0 | -1 |
Parses the given raw text and return a block object representation, if applicable. | def parse(cls, raw, style_cls):
return cls(raw, style_cls) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parseBlock(self, text, prevLineData):\n return self.parser.parseBlock(text, prevLineData)",
"def parseChunk(self, parent, text):\r\n self.parseBlocks(parent, text.split('\\n\\n'))",
"async def parse(self, raw: str) -> dict:",
"def parse_block(\n string: str,\n vars: Dict,\n neg: bo... | [
"0.72058433",
"0.6476995",
"0.6294301",
"0.6247581",
"0.62070143",
"0.61923724",
"0.6171336",
"0.6068363",
"0.6008469",
"0.59850705",
"0.59833205",
"0.59809935",
"0.5980157",
"0.5961037",
"0.59007657",
"0.58809936",
"0.5860813",
"0.58484924",
"0.58467954",
"0.5825048",
"0.578... | 0.55218345 | 38 |
204 responses must not return some entity headers | def get204(self):
bad = ('content-length', 'content-type')
for h in bad:
bottle.response.set_header(h, 'foo')
bottle.status = 204
for h, v in bottle.response.headerlist:
self.assertFalse(h.lower() in bad, "Header %s not deleted" % h) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_204_response() -> bytes:\n date = datetime.datetime.now(datetime.timezone.utc).strftime(\"%a, %d %b %Y %H:%M:%S GMT\")\n\n header = \"HTTP/1.1 204 No Content\" + \"\\r\\nDate: \" + date + \"\\r\\n\\r\\n\"\n\n print(header)\n return header.encode(HttpServer.FORMAT)",
"def as... | [
"0.7504536",
"0.7456752",
"0.70702946",
"0.7007137",
"0.6838798",
"0.6740226",
"0.66936654",
"0.66851264",
"0.66362447",
"0.65761197",
"0.648202",
"0.6477876",
"0.64216065",
"0.6242951",
"0.623777",
"0.623777",
"0.6185526",
"0.61398613",
"0.61072254",
"0.6086671",
"0.60757756... | 0.83296347 | 0 |
304 responses must not return entity headers | def get304(self):
bad = ('allow', 'content-encoding', 'content-language',
'content-length', 'content-md5', 'content-range',
'content-type', 'last-modified') # + c-location, expires?
for h in bad:
bottle.response.set_header(h, 'foo')
bottle.status = 304
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_304_response() -> bytes:\n content_data = HttpServer.get_content_data(\"/not_modified.html\")\n date = datetime.datetime.now(datetime.timezone.utc).strftime(\"%a, %d %b %Y %H:%M:%S GMT\")\n\n header = \"HTTP/1.1 304 Not Modified\" + \"\\r\\nDate: \" + date + \"\\r\\n\" + content_dat... | [
"0.75092345",
"0.7419917",
"0.72726953",
"0.7164188",
"0.7087629",
"0.6714187",
"0.66934466",
"0.6550176",
"0.65363",
"0.6527774",
"0.65198684",
"0.6495292",
"0.6457777",
"0.63518214",
"0.6306506",
"0.6283255",
"0.6283255",
"0.6245512",
"0.6245413",
"0.6172141",
"0.6148629",
... | 0.82604736 | 0 |
Commandline interface to this module. | def main() -> None:
cache: Dict[str, Any] = {}
datadir = util.get_abspath(sys.argv[1])
for yaml_path in glob.glob(os.path.join(datadir, "*.yaml")):
with open(yaml_path) as yaml_stream:
cache_key = os.path.relpath(yaml_path, datadir)
cache[cache_key] = yaml.load(yaml_stream)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cli():\n pass",
"def cli():\r\n pass",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"def cli():",
"d... | [
"0.8111833",
"0.7890598",
"0.77864957",
"0.77864957",
"0.77864957",
"0.77864957",
"0.77864957",
"0.77864957",
"0.77864957",
"0.77864957",
"0.77864957",
"0.77864957",
"0.77864957",
"0.77864957",
"0.77864957",
"0.77864957",
"0.77864957",
"0.77864957",
"0.77864957",
"0.77864957",
... | 0.0 | -1 |
Returns an amended copy of the proxies dictionary used by `requests`, it will disable the proxy if the uri provided is to be reached directly. | def config_proxy_skip(proxies, uri, skip_proxy=False):
parsed_uri = urlparse(uri)
# disable proxy if necessary
if skip_proxy:
if 'http' in proxies:
proxies.pop('http')
if 'https' in proxies:
proxies.pop('https')
elif proxies.get('no'):
urls = []
i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_proxies(self) -> dict:\n return self._proxies.copy() if self._proxies else None",
"def _proxies_dict(proxy):\r\n if not proxy:\r\n return None\r\n return {'http': proxy, 'https': proxy}",
"def proxies(self):\n\n proxies = APIConsumer.get(\"/proxies\").json()\n proxies... | [
"0.66343987",
"0.6391098",
"0.6165451",
"0.6008237",
"0.55292976",
"0.5497907",
"0.5492077",
"0.5427683",
"0.5362546",
"0.5328795",
"0.52967983",
"0.52951497",
"0.52471906",
"0.5230866",
"0.52142024",
"0.5200751",
"0.5157269",
"0.5149122",
"0.5092851",
"0.5078898",
"0.5063718... | 0.6825937 | 0 |
Construct an ThothOnixTelescope instance. | def __init__(
self,
*,
dag_id: str,
cloud_workspace: CloudWorkspace,
publisher_id: str,
format_specification: str,
bq_dataset_id: str = "onix",
bq_table_name: str = "onix",
bq_dataset_description: str = "Thoth ONIX Feed",
bq_table_descripti... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, world: Optional[SimWorld] = None, **kwargs: Any):\n BaseTelescope.__init__(self, **kwargs, motion_status_interfaces=[\"ITelescope\", \"IFocuser\", \"IFilters\"])\n FitsNamespaceMixin.__init__(self, **kwargs)\n\n # init world and get telescope\n from pyobs.utils.simula... | [
"0.6043081",
"0.5964881",
"0.57800967",
"0.57065284",
"0.5576632",
"0.5284193",
"0.5260236",
"0.5221126",
"0.52082175",
"0.51896495",
"0.5188457",
"0.5174516",
"0.5166596",
"0.51366943",
"0.5117554",
"0.51174974",
"0.5112744",
"0.5068512",
"0.50668824",
"0.5044727",
"0.504393... | 0.0 | -1 |
Creates a new Thoth release instance | def make_release(self, **kwargs) -> ThothRelease:
snapshot_date = make_snapshot_date(**kwargs)
release = ThothRelease(dag_id=self.dag_id, run_id=kwargs["run_id"], snapshot_date=snapshot_date)
return release | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_release(self, **kwargs) -> CrossrefEventsRelease:\n\n start_date, end_date, first_release = self.get_release_info(**kwargs)\n\n release = CrossrefEventsRelease(\n self.dag_id, start_date, end_date, first_release, self.mailto, self.max_threads, self.max_processes\n )\n ... | [
"0.64280033",
"0.63345087",
"0.6219137",
"0.62178385",
"0.59829724",
"0.5792777",
"0.5772973",
"0.56713486",
"0.5646336",
"0.56411284",
"0.556297",
"0.55392003",
"0.5535768",
"0.55193186",
"0.54974574",
"0.54897374",
"0.54813004",
"0.5434569",
"0.5398685",
"0.5379647",
"0.537... | 0.74462247 | 0 |
Task to download the ONIX release from Thoth. | def download(self, release: ThothRelease, **kwargs) -> None:
thoth_download_onix(
publisher_id=self.publisher_id,
format_spec=self.format_specification,
download_path=release.download_path,
) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _download(self):\n self._system.download(\"http://geant4.web.cern.ch/geant4/support/source/\" + self._tar_name)",
"def thoth_download_onix(\n publisher_id: str,\n download_path: str,\n format_spec: str,\n host_name: str = DEFAULT_HOST_NAME,\n num_retries: int = 3,\n) -> None:\n url =... | [
"0.6697182",
"0.6280148",
"0.62222373",
"0.61106557",
"0.6080942",
"0.5996796",
"0.5958443",
"0.59322697",
"0.5895125",
"0.5894148",
"0.58753026",
"0.580345",
"0.57798254",
"0.57530046",
"0.57020843",
"0.5632765",
"0.5632071",
"0.5620086",
"0.56162405",
"0.5600442",
"0.558787... | 0.69096416 | 0 |
Upload the downloaded thoth onix XML to google cloud bucket | def upload_downloaded(self, release: ThothRelease, **kwargs) -> None:
success = gcs_upload_files(bucket_name=self.cloud_workspace.download_bucket, file_paths=[release.download_path])
set_task_state(success, kwargs["ti"].task_id, release=release) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def upload_finish(self, cloud_file):",
"def upload_progress(self, cloud_file, size, uploaded):",
"def upload_svg(filename, xml_string):\n s3 = boto3.client('s3')\n response = s3.put_object(\n ACL='public-read',\n Body=xml_string,\n Bucket=BUCKET,\n Key=filename,\n Stora... | [
"0.6021707",
"0.56937593",
"0.56745994",
"0.56631327",
"0.56376046",
"0.5610331",
"0.5573977",
"0.55667204",
"0.5515031",
"0.5492787",
"0.5467463",
"0.5440767",
"0.5401655",
"0.538869",
"0.5375465",
"0.5358939",
"0.5349745",
"0.5332269",
"0.5331079",
"0.53214574",
"0.52899987... | 0.0 | -1 |
Task to transform the Thoth ONIX data | def transform(self, release: ThothRelease, **kwargs) -> None:
success, parser_path = onix_parser_download()
set_task_state(success, task_id=kwargs["ti"].task_id, release=release)
success = onix_parser_execute(
parser_path, input_dir=release.download_folder, output_dir=release.transfo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def transform():",
"def transform(self, data):",
"def transform():\n pass",
"def transform(self):",
"def transform(self, X):\n ...",
"def transform(self, X):\n ...",
"def transform(self, X):\n ...",
"def transform(self, X):\n ...",
"def transform(self, X):\n ... | [
"0.6583367",
"0.63111854",
"0.62925464",
"0.58286405",
"0.58054906",
"0.58054906",
"0.58054906",
"0.58054906",
"0.58054906",
"0.58054906",
"0.58054906",
"0.5745898",
"0.569793",
"0.5667463",
"0.5654679",
"0.5575874",
"0.5546051",
"0.5525497",
"0.5441114",
"0.5440563",
"0.5440... | 0.59375197 | 3 |
Upload the downloaded thoth onix .jsonl to google cloud bucket | def upload_transformed(self, release: ThothRelease, **kwargs) -> None:
success = gcs_upload_files(
bucket_name=self.cloud_workspace.transform_bucket, file_paths=[release.transform_path]
)
set_task_state(success, kwargs["ti"].task_id, release=release) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def upload(jsonfiles):\n # clear S3 Bucket\n bucket = S3Bucket()\n bucket.clear()\n for jsonfile in jsonfiles:\n filename = os.path.basename(jsonfile)\n key = build_key(filename)\n logging.info(\"%s %s\", filename, key)\n # store json in S3 object\n bucket.store(key, ... | [
"0.65068454",
"0.6342714",
"0.61773777",
"0.61429673",
"0.61228466",
"0.61177385",
"0.608761",
"0.6081685",
"0.6045406",
"0.6026031",
"0.5962328",
"0.59582734",
"0.5925211",
"0.5922055",
"0.59198475",
"0.5882836",
"0.58755136",
"0.5874109",
"0.58695334",
"0.5841015",
"0.58316... | 0.0 | -1 |
Task to load the transformed ONIX jsonl file to BigQuery. | def bq_load(self, release: ThothRelease, **kwargs) -> None:
bq_create_dataset(
project_id=self.cloud_workspace.project_id,
dataset_id=self.bq_dataset_id,
location=self.cloud_workspace.data_location,
description=self.bq_dataset_description,
)
uri = ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_raw_to_bq(event, context):\n\n import os\n\n\n print(f\"Processing .....\")\n\n file = event\n project = os.environ.get('ENV_PROJECT')\n dataset = os.environ.get('ENV_DATASET')\n bucket = file.get(\"bucket\")\n tableCsv = file.get(\"name\")\n tableDestList = tableCsv.split(\".\")\... | [
"0.6239616",
"0.5627366",
"0.5611043",
"0.5270496",
"0.5261157",
"0.52611226",
"0.52270687",
"0.5213831",
"0.5192585",
"0.5190901",
"0.5186956",
"0.5167473",
"0.51594496",
"0.51577294",
"0.5153345",
"0.5153345",
"0.5149505",
"0.51362944",
"0.5136233",
"0.51066357",
"0.5101144... | 0.52878547 | 3 |
Adds release information to API. | def add_new_dataset_releases(self, release: ThothRelease, **kwargs) -> None:
dataset_release = DatasetRelease(
dag_id=self.dag_id,
dataset_id=self.api_dataset_id,
dag_run_id=release.run_id,
snapshot_date=release.snapshot_date,
data_interval_start=kwarg... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def publish_release(ctx):\n rel = _get_release()\n rel.update_release(rel.title, rel.raw_data[\"body\"], draft=False)",
"def get_release_info(self):\r\n return self.detail_info.get_release_info(self.version)",
"def add_new_dataset_releases(self, release: CrossrefMetadataRelease, **kwargs) -> None:... | [
"0.603399",
"0.5955066",
"0.5925179",
"0.5867882",
"0.5854016",
"0.5843925",
"0.58369064",
"0.5833375",
"0.5751572",
"0.5749097",
"0.5744806",
"0.5743118",
"0.5721889",
"0.5713864",
"0.5705936",
"0.5648544",
"0.56480837",
"0.55830395",
"0.55755025",
"0.5569443",
"0.55320984",... | 0.58169043 | 8 |
Delete all files, folders and XComs associated with this release. | def cleanup(self, release: ThothRelease, **kwargs) -> None:
cleanup(dag_id=self.dag_id, execution_date=kwargs["execution_date"], workflow_folder=release.workflow_folder) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def cleanup():\n if len(env.releases) > 3:\n directories = env.releases\n directories.reverse()\n del directories[:3]\n env.directories = ' '.join([ '%(releases_path)s/%(release)s' % { 'releases_path':env.releases_path, 'release':release } for release in directories ])\n run('... | [
"0.705404",
"0.6923961",
"0.6872746",
"0.68182623",
"0.6804205",
"0.67814773",
"0.6733583",
"0.6713712",
"0.667929",
"0.66577154",
"0.665055",
"0.6616377",
"0.6591545",
"0.65912616",
"0.6561124",
"0.6556905",
"0.6555388",
"0.65534294",
"0.65193635",
"0.65015835",
"0.65008426"... | 0.0 | -1 |
Hits the Thoth API and requests the ONIX feed for a particular publisher. Creates a file called onix.xml at the specified location | def thoth_download_onix(
publisher_id: str,
download_path: str,
format_spec: str,
host_name: str = DEFAULT_HOST_NAME,
num_retries: int = 3,
) -> None:
url = THOTH_URL.format(host_name=host_name, format_specification=format_spec, publisher_id=publisher_id)
logging.info(f"Downloading ONIX XML ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def oai_harvest(basic_url, metadata_prefix=None, oai_set=None, processing=None, out_file_suffix=None):\n collection_xpath = \".//oai_2_0:metadata//intact:collection\"\n record_xpath = \".//oai_2_0:record\"\n identifier_xpath = \".//oai_2_0:header//oai_2_0:identifier\"\n token_xpath = \".//oai_2_0:resum... | [
"0.50976276",
"0.50819564",
"0.5062179",
"0.505473",
"0.49140215",
"0.47646818",
"0.47354826",
"0.4707565",
"0.46446255",
"0.46394545",
"0.46037316",
"0.4576128",
"0.45600936",
"0.4554681",
"0.45524687",
"0.4548541",
"0.45293865",
"0.4528018",
"0.4522511",
"0.45112112",
"0.45... | 0.5873819 | 0 |
Generates a JSON file with all the Video action based features | def merge_vaxn_features(vaxn_feat_root_path, vaxn_feat_cache_path):
if os.path.exists(vaxn_feat_cache_path):
print("Found vaxn cache, loading ...")
return load_json(vaxn_feat_cache_path)
show_names = ["bbt"]
vaxn_features = {}
for sn in show_names:
cur_base_path = os.path.join(v... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_output():\n\n input_data = \"{}/{}.json\".format(TRANSCRIPTS_VIDEOS_PATH, request.form[\"name\"])\n duration = \"0,{}\".format(int(float(request.form[\"duration\"])))\n movie = \"{}/{}\".format(VIDEOS_PATH, request.form[\"movie\"]) # videos/movie.mp4\n movie_data = \"{}/{}.json\".format(TRANSCRIPTS_... | [
"0.634132",
"0.5869211",
"0.58267266",
"0.566327",
"0.5597393",
"0.55778354",
"0.5547687",
"0.5519301",
"0.54381615",
"0.54166013",
"0.5411057",
"0.5408507",
"0.53804255",
"0.53419405",
"0.53226936",
"0.53097594",
"0.53077525",
"0.53019357",
"0.52878267",
"0.5259834",
"0.5254... | 0.0 | -1 |
This function will import all of the example images sets in the data folder. Returns ======= | def preprocess_images():
# Set up the lists to collect the images and measurements
images = []
measurements = []
# Set up the path to the data files
data_sets_path = 'data'
data_sets = [os.path.join(data_sets_path, i) for i
in os.listdir(data_sets_path)]
# St... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load_data(self):\n for set_name in self.image_dir_path:\n if self.verbose:\n print('\\n> Loading data files for the set: ' + set_name)\n\n # image dir\n image_dir = os.path.join(self.data_path, self.image_dir_path[set_name])\n\n # annotation fil... | [
"0.71541685",
"0.71112",
"0.7007383",
"0.69861394",
"0.69643354",
"0.6875159",
"0.687149",
"0.6793674",
"0.6761374",
"0.6747905",
"0.67314863",
"0.67229164",
"0.6722691",
"0.668295",
"0.6682827",
"0.6676145",
"0.66268",
"0.65782994",
"0.65700173",
"0.654787",
"0.65367633",
... | 0.0 | -1 |
This function will produce a batch of features and labels for each epoch step to reduce the memory usage. | def generator(features, labels, batch_size):
# Create empty arrays to contain batch of features and labels#
batch_features = np.zeros((batch_size, 160, 320, 3))
batch_labels = np.zeros((batch_size, 1))
while True:
for i in range(batch_size):
# choose random index in features
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def batch_features_labels(features, labels, batch_size):\n for start in range(0, len(features), batch_size):\n end = min(start + batch_size, len(features))\n #print(labels[start:end])\n yield features[start:end], labels[start:end]",
"def batch_features_labels(features, labels, batch_size)... | [
"0.76270497",
"0.7307724",
"0.7307669",
"0.7307669",
"0.7307669",
"0.72704774",
"0.70929223",
"0.69568336",
"0.69184935",
"0.68820596",
"0.6748234",
"0.67144907",
"0.66818917",
"0.66654253",
"0.66291654",
"0.6627662",
"0.66152316",
"0.6594451",
"0.6584446",
"0.6561161",
"0.65... | 0.7333913 | 1 |
Validate and update field value against validator. Raise NoValidatorError if no validator was set. | def validate(self):
if self.validator is None:
raise NoValidatorError('Field %s has no validator assigned.' %
self.id)
self.value = self.validator(self.value) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def validator(self, value: Optional[Dict[str, Any]]):\n self._validator = value",
"def validate(self):\n for field in self.fields:\n if field.validate():\n self.model.set(field.name, field.model_value)\n else:\n self.errors.append(field.error())\n... | [
"0.62739575",
"0.608497",
"0.6071332",
"0.6071332",
"0.6062489",
"0.60375136",
"0.59934586",
"0.5940005",
"0.59261507",
"0.5883836",
"0.5870587",
"0.5867741",
"0.58644885",
"0.5827615",
"0.5827615",
"0.57252115",
"0.5696865",
"0.565482",
"0.5627075",
"0.5613289",
"0.5612839",... | 0.83141893 | 0 |
Shortcut for field.renderer.render(). Raise NoRendererError if no renderer is set. | def render(self):
if not self.renderer:
raise NoRendererError('Field %s has no renderer assigned.' %
self.id)
return self.renderer.render(self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def render(self):\n try:\n if self.permit():\n return self.renderer.render(self)\n except AttributeError:\n if self.renderer is None:\n raise NotImplementedError(\"Should have implemented a renderer for {0}\".format(self.name))\n else:\n ... | [
"0.7004743",
"0.65256923",
"0.64937174",
"0.63230234",
"0.6244154",
"0.6143011",
"0.60672754",
"0.60345244",
"0.5998662",
"0.59685975",
"0.5790698",
"0.57647145",
"0.57602614",
"0.57167757",
"0.5699508",
"0.5687103",
"0.56720227",
"0.5661371",
"0.563945",
"0.5615098",
"0.5605... | 0.77539104 | 0 |
Set the coordinate system for the GeoSeries. | def set_crs(self, crs):
crs = _validate_crs(crs)
self._crs = crs | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setcoordsys(self, csys):\n return _image.image_setcoordsys(self, csys)",
"def setGxLocation(self):\n if self.xyz is None:\n gxobs = self.obs.get(None, {}).get(\"GX\")\n if gxobs is not None:\n gxyz = np.array((0.0, 0.0, 0.0))\n ... | [
"0.6976951",
"0.6533349",
"0.64059937",
"0.62488025",
"0.6244008",
"0.6237713",
"0.621931",
"0.6195711",
"0.61874264",
"0.61691636",
"0.61650574",
"0.6104936",
"0.60478747",
"0.6043912",
"0.6025414",
"0.6002747",
"0.598583",
"0.59691477",
"0.5968583",
"0.5968583",
"0.5968583"... | 0.58906555 | 25 |
Set the coordinate system for the GeoSeries. | def crs(self, crs):
self.set_crs(crs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setcoordsys(self, csys):\n return _image.image_setcoordsys(self, csys)",
"def setGxLocation(self):\n if self.xyz is None:\n gxobs = self.obs.get(None, {}).get(\"GX\")\n if gxobs is not None:\n gxyz = np.array((0.0, 0.0, 0.0))\n ... | [
"0.69760185",
"0.6532696",
"0.6406236",
"0.6248199",
"0.6238903",
"0.62181735",
"0.6195803",
"0.61864096",
"0.616831",
"0.61657757",
"0.6105004",
"0.6046242",
"0.604405",
"0.6025245",
"0.60031265",
"0.59855217",
"0.5968437",
"0.5968437",
"0.5968437",
"0.5968437",
"0.59671736"... | 0.62427175 | 4 |
Test whether two objects contain the same elements. This function allows two GeoSeries to be compared against each other to see if they have the same shape and geometries (same wkb bytes). NaNs in the same location are considered equal. The column headers do not need to have the same type, but the elements within the c... | def equals(self, other):
if not isinstance(other, GeoSeries):
return False
return self._data.equals(other._data) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_identical(self, other):\n return (self.compounddatatype == other.compounddatatype and\n self.min_row == other.min_row and\n self.max_row == other.max_row)",
"def similar(self, other):\r\n if self.rows == other.rows and self.columns == other.columns:\r\n ... | [
"0.713626",
"0.70874375",
"0.6902014",
"0.6831683",
"0.68126047",
"0.67645264",
"0.66774386",
"0.6625861",
"0.6573688",
"0.65623236",
"0.6527109",
"0.6523672",
"0.65229714",
"0.6514099",
"0.6510609",
"0.6509781",
"0.6501745",
"0.64968944",
"0.64894736",
"0.6446339",
"0.644061... | 0.6488795 | 19 |
Fill NA values with a geometry, which can be WKT or WKB formed. | def fillna(
self,
value=None,
method=None,
axis=None,
inplace=False,
limit=None,
downcast=None,
):
return super().fillna(value, method, axis, inplace, limit, downcast) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_170612_nullgeom(self):\n spc = parser(get_file('PTSD48_nullgeom.txt'))\n # spc.draw_outlooks()\n spc.sql(self.txn)\n outlook = spc.get_outlook('ANY SEVERE', '0.15', 4)\n self.assertAlmostEqual(outlook.geometry.area, 56.84, 2)",
"def test_insert_empty_geometry():\n e... | [
"0.56847125",
"0.56693435",
"0.56686133",
"0.56586635",
"0.561771",
"0.55634767",
"0.55186915",
"0.54937625",
"0.54811686",
"0.53563386",
"0.53505456",
"0.5311615",
"0.52947694",
"0.52927846",
"0.52743405",
"0.52210534",
"0.5193054",
"0.5190897",
"0.5188896",
"0.51856804",
"0... | 0.46377096 | 93 |
Detect missing values. NA value in GeoSeries is represented as None. | def isna(self):
return super().isna() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pd_isnan(val):\n return val is None or val != val",
"def ISNA(value):\n return isinstance(value, float) and math.isnan(value)",
"def checkNaN(data):\n if data.isnull().values.any():\n N = data.isnull().sum().sum()\n print(\"There are {} missing values.\".format(N))",
"def nan_value(d... | [
"0.7382185",
"0.7306854",
"0.719385",
"0.71861726",
"0.71788853",
"0.7176771",
"0.70606303",
"0.6982853",
"0.6982853",
"0.69516784",
"0.6938889",
"0.6934969",
"0.68899995",
"0.68127024",
"0.68127024",
"0.6808158",
"0.678505",
"0.67573625",
"0.6751283",
"0.67491585",
"0.671536... | 0.6863134 | 13 |
Detect nonmissing values. Inverse of isna. | def notna(self):
return super().notna() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pd_isnan(val):\n return val is None or val != val",
"def ISNA(value):\n return isinstance(value, float) and math.isnan(value)",
"def nan_value(data):\n return data.isnull().any()",
"def isna(self):\n return super().isna()",
"def isna(self):\n # type: () -> np.ndarray\n retur... | [
"0.7560518",
"0.74665135",
"0.7452157",
"0.7438255",
"0.73962665",
"0.73962665",
"0.73709446",
"0.7326189",
"0.7220557",
"0.7139181",
"0.7064639",
"0.69654924",
"0.69546664",
"0.6941255",
"0.68872374",
"0.68794274",
"0.6848532",
"0.68473583",
"0.68365127",
"0.682528",
"0.6777... | 0.7091962 | 10 |
Check if each geometry is of valid geometry format. | def is_valid(self):
return _property_op(arctern.ST_IsValid, self).astype(bool, copy=False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def checkGeom(geodataframe):\n for geometry in geodataframe.geometry:\n if explain_validity(geometry) != 'Valid Geometry':\n print(explain_validity(geometry))",
"def is_valid_geometry(self, value: List) -> bool:\n\n def check_geom(geom):\n if isinstance(geom, (Point, MultiP... | [
"0.7904392",
"0.690688",
"0.6685605",
"0.66615665",
"0.6612329",
"0.64486986",
"0.64352995",
"0.6389536",
"0.6370707",
"0.6149703",
"0.6111263",
"0.6095195",
"0.6012014",
"0.59583324",
"0.5873552",
"0.58691776",
"0.58599246",
"0.5856493",
"0.58545256",
"0.58147585",
"0.581045... | 0.0 | -1 |
Calculate the length of each geometry. | def length(self):
return _property_op(arctern.ST_Length, self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Lengths(self):\n\n self.__do_essential_memebers_exist__()\n\n if self.element_type == \"line\":\n coords = self.points[self.elements[:,:2],:]\n lengths = np.linalg.norm(coords[:,1,:] - coords[:,0,:],axis=1)\n else:\n # self.GetEdges()\n # coords ... | [
"0.75420904",
"0.72353274",
"0.70973814",
"0.6988344",
"0.6942517",
"0.6910893",
"0.6852218",
"0.6681032",
"0.6670038",
"0.6617122",
"0.6555902",
"0.6541204",
"0.6533393",
"0.6511183",
"0.65023154",
"0.6492468",
"0.63879216",
"0.6387602",
"0.6379638",
"0.6348502",
"0.63365",
... | 0.6313044 | 23 |
Check whether each geometry is "simple". "Simple" here means that a geometry has no anomalous geometric points, such as self intersection or self tangency. | def is_simple(self):
return _property_op(arctern.ST_IsSimple, self).astype(bool, copy=False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_simple(geometry, **kwargs):\n return lib.is_simple(geometry, **kwargs)",
"def is_simple(self):\n if not self.is_compact(): return False\n\n for v in self.vertex_generator():\n adj = [a for a in v.neighbors()]\n if len(adj) != self.dim():\n return False... | [
"0.7666004",
"0.64723015",
"0.6392264",
"0.62469393",
"0.6124172",
"0.6097932",
"0.60693663",
"0.59628433",
"0.59498405",
"0.59405744",
"0.58801895",
"0.5861422",
"0.5823311",
"0.5766822",
"0.5688275",
"0.560034",
"0.55730313",
"0.5571647",
"0.5562339",
"0.5434933",
"0.541820... | 0.6210017 | 4 |
Calculate the 2D Cartesian (planar) area of each geometry. | def area(self):
return _property_op(arctern.ST_Area, self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def area(self):\n if isinstance(self.crs, GeographicalCRS):\n major_axis = self.crs.ellipsoid.a\n minor_axis = self.crs.ellipsoid.b\n\n area = 0.0\n if major_axis == minor_axis: # Sphere\n for seg in self.segment_tuples:\n x1, ... | [
"0.7063971",
"0.6625418",
"0.65976596",
"0.659629",
"0.6554382",
"0.6529425",
"0.65187824",
"0.6507955",
"0.64807117",
"0.6467007",
"0.6453874",
"0.6421093",
"0.6408375",
"0.6345457",
"0.6292591",
"0.6268464",
"0.6240269",
"0.6230155",
"0.61827403",
"0.6177272",
"0.6141594",
... | 0.0 | -1 |
For each geometry in geometries, return a string that indicates is type. | def geom_type(self):
return _property_op(arctern.ST_GeometryType, self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def geometry_type(self) -> ir.StringValue:\n return ops.GeoGeometryType(self).to_expr()",
"def getGeometryType(restGeom):\n if \"Polygon\" in restGeom:\n return \"POLYGON\"\n elif \"Polyline\" in restGeom:\n return \"POLYLINE\"\n elif \"Point\" in restGeom:\n return \"POINT\"... | [
"0.6874476",
"0.6696634",
"0.6290559",
"0.62605953",
"0.6224997",
"0.619825",
"0.5985244",
"0.575593",
"0.569537",
"0.56879616",
"0.55618614",
"0.54778594",
"0.54542613",
"0.5450805",
"0.54445904",
"0.5382666",
"0.53738856",
"0.53256947",
"0.5278076",
"0.5276421",
"0.52664536... | 0.626888 | 3 |
Compute the centroid of each geometry. | def centroid(self):
return _property_geo(arctern.ST_Centroid, self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calcCentroid(self):\n size = len(self.vectors)\n # zip all features together\n zipped = zip(*self.vectors)\n # Calculate the mean for each feature/column\n centroid = [math.fsum(column)/size for column in zipped]\n \n return centroid",
"def centroid(self): # ... | [
"0.80138916",
"0.7795813",
"0.7621316",
"0.7526914",
"0.74279696",
"0.72885245",
"0.7254839",
"0.7190571",
"0.7184448",
"0.7174818",
"0.71524346",
"0.71008474",
"0.70810723",
"0.70650667",
"0.7009923",
"0.69806325",
"0.69658154",
"0.6955261",
"0.6946786",
"0.68795633",
"0.678... | 0.72300625 | 7 |
For each geometry, compute the smallest convex geometry that encloses all geometries in it. | def convex_hull(self):
return _property_geo(arctern.ST_ConvexHull, self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_geometry(self):\n geometry = self._geometry\n for geo in self._holes:\n geometry = geometry.difference(geo) \n return geometry",
"def concave_hull(hull:list, points:list, max_iterations:int=None, min_length_fraction:float=0, min_angle:float=90)->list:\n tweet.info(\"Cre... | [
"0.60205543",
"0.5530485",
"0.5502788",
"0.54940367",
"0.54893094",
"0.5410566",
"0.5404145",
"0.5397154",
"0.5388533",
"0.53502923",
"0.5344877",
"0.5302913",
"0.5264694",
"0.5254407",
"0.5252312",
"0.52518874",
"0.5245833",
"0.5226095",
"0.5222082",
"0.5200354",
"0.5196485"... | 0.5312225 | 11 |
Calculates the points number for each geometry. | def npoints(self):
return _property_op(arctern.ST_NPoints, self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_points_number(self):\n ncontour = self.get_contours_number\n npoints = []\n for i in range(0, ncontour):\n npoints.append(len(self.x[i]))\n return npoints",
"def GetNumberOfPoints(self):\n return self.GetNumberOfElements(ArrayAssociation.POINT)",
"def nr_po... | [
"0.6741213",
"0.65841794",
"0.6553323",
"0.65361804",
"0.65322703",
"0.62996763",
"0.6286703",
"0.6274652",
"0.6244351",
"0.618118",
"0.6174774",
"0.61558765",
"0.61459893",
"0.60894793",
"0.6071493",
"0.60674316",
"0.60643",
"0.6039086",
"0.60236716",
"0.58761185",
"0.586858... | 0.6183094 | 9 |
Compute the doubleprecision minimum bounding box geometry for each geometry. | def envelope(self):
return _property_geo(arctern.ST_Envelope, self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_minimal_bounding_box(self):\n\n big = (95.06, -11.0, 141.0, 5.9)\n mid = [103.28, -8.46, 109.67, -4.68]\n sml = (106.818998, -6.18585170, 106.82264510, -6.1810)\n\n min_res = 0.008333333333000\n eps = 1.0e-4\n\n # Check that sml box is actually too small\n ... | [
"0.69018716",
"0.643876",
"0.6386188",
"0.6372415",
"0.62286246",
"0.61563987",
"0.6153013",
"0.59914505",
"0.59569925",
"0.59569925",
"0.59372354",
"0.5932275",
"0.5924669",
"0.5921674",
"0.5911022",
"0.5888883",
"0.58672947",
"0.5848557",
"0.5837018",
"0.5832062",
"0.579750... | 0.0 | -1 |
Convert curves in each geometry to approximate linear representation, e.g., CIRCULAR STRING to regular LINESTRING, CURVEPOLYGON to POLYGON, and MULTISURFACE to MULTIPOLYGON. Useful for outputting to devices that can't support CIRCULARSTRING geometry types. | def curve_to_line(self):
return _unary_geo(arctern.ST_CurveToLine, self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_segments(segments):\n polygons = []\n interiors = []\n linestrings = []\n for segment in segments:\n ls = LineString(segment)\n if segment[0][0] == segment[-1][0] and segment[0][1] == segment[-1][1]:\n lr = LinearRing(ls)\n if not lr.is_ccw:\n ... | [
"0.5612656",
"0.5605624",
"0.55674744",
"0.55128264",
"0.54048735",
"0.5354755",
"0.53211635",
"0.52772313",
"0.52101445",
"0.5138058",
"0.5118836",
"0.5081327",
"0.5062001",
"0.5044732",
"0.50275433",
"0.49676812",
"0.49635187",
"0.4959512",
"0.49567842",
"0.4953524",
"0.494... | 0.51644486 | 9 |
Transform each geometry to a different coordinate reference system. The ``crs`` attribute on the current GeoSeries must be set. | def to_crs(self, crs):
if crs is None:
raise ValueError("Can not transform with invalid crs")
if self.crs is None:
raise ValueError("Can not transform geometries without crs. Set crs for this GeoSeries first.")
if self.crs == crs:
return self
return _u... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def transform_geometries(datasource, src_epsg, dst_epsg):\n # Part 1\n src_srs = osr.SpatialReference()\n src_srs.ImportFromEPSG(src_epsg)\n dst_srs = osr.SpatialReference()\n dst_srs.ImportFromEPSG(dst_epsg)\n transformation = osr.CoordinateTransformation(src_srs, dst_srs)\n layer = datasource.GetLa... | [
"0.6502866",
"0.62526053",
"0.61256564",
"0.6086267",
"0.60835266",
"0.6010616",
"0.5969945",
"0.5863563",
"0.5861438",
"0.5789306",
"0.5703812",
"0.56789464",
"0.5665309",
"0.5656348",
"0.56265503",
"0.5570779",
"0.5527477",
"0.5501872",
"0.5500846",
"0.5494727",
"0.5475225"... | 0.7170733 | 0 |
Returns a "simplified" version for each geometry using the DouglasPeucker algorithm. | def simplify(self, tolerance):
return _unary_geo(arctern.ST_SimplifyPreserveTopology, self, tolerance) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def simplify(self, tolerance, preserve_topology=...): # -> BaseGeometry:\n ...",
"def simplify(uniques, intersections, tolerance):\n uniques_sm = [u.simplify(tolerance=tolerance) for u in uniques]\n\n intersections_sm = [[None for i in range(len(uniques))] for j in range(len(uniques))]\n for i,s... | [
"0.637332",
"0.6203799",
"0.5494519",
"0.54518986",
"0.541968",
"0.5412575",
"0.54017067",
"0.5362468",
"0.5342239",
"0.53284794",
"0.53053784",
"0.52785295",
"0.5266336",
"0.5264212",
"0.52319866",
"0.5184626",
"0.51542795",
"0.5133027",
"0.5126322",
"0.51219654",
"0.5120376... | 0.5490384 | 3 |
For each geometry, returns a geometry that represents all points whose distance from this geos is less than or equal to "distance". | def buffer(self, distance):
return _unary_geo(arctern.ST_Buffer, self, distance) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def edges_dwithin(\n self, lon: float, lat: float, distance: float, sort: bool = False\n ) -> Iterable[EdgeTuple]:\n # TODO: document self.network.edges instead?\n return self.network.edges.dwithin_edges(lon, lat, distance, sort=sort)",
"def compute_signed_distance_and_closest_geometry(sc... | [
"0.56591654",
"0.54377896",
"0.5347388",
"0.5315113",
"0.5289874",
"0.52756774",
"0.5231627",
"0.5228826",
"0.522027",
"0.5200996",
"0.5199149",
"0.51547587",
"0.51416624",
"0.51387566",
"0.5124852",
"0.5111418",
"0.510608",
"0.50760573",
"0.5072427",
"0.50680065",
"0.5067088... | 0.0 | -1 |
For the coordinates of each geometry, reduce the number of significant digits to the given number. The last decimal place will be rounded. | def precision_reduce(self, precision):
return _unary_geo(arctern.ST_PrecisionReduce, self, precision) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def roundSigfigs(num, sigfigs):\n if num != 0:\n return str(round(num, -int(math.floor(math.log10(abs(num))) - (sigfigs - 1))))\n else:\n return str(0.0) # Can't take the log of 0",
"def roundSigfigs(num, sigfigs):\n if num != 0:\n return str(round(num, -int(math.floor(math.log10(a... | [
"0.60132486",
"0.60132486",
"0.58656776",
"0.573517",
"0.5606471",
"0.5581084",
"0.55775976",
"0.54481006",
"0.54399276",
"0.5426035",
"0.54122615",
"0.54122615",
"0.53650385",
"0.535975",
"0.5348461",
"0.5318411",
"0.5297865",
"0.52699935",
"0.52645403",
"0.52614075",
"0.524... | 0.52493805 | 20 |
Create a valid representation of each geometry without losing any of the input vertices. If the geometry is alreadyvalid, then nothing will be done. If the geometry can't be made to valid, it will be set to None value. | def make_valid(self):
return _unary_geo(arctern.ST_MakeValid, self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def geometry():\n return Geometry()",
"def fromVertices(cls,\n xp0, yp0, zp0, xp1, yp1, zp1,\n xp2, yp2, zp2, xp3, yp3, zp3,\n origin,\n group_index=None,\n reference=None):\n if len(xp0) == len(yp0) == le... | [
"0.55397016",
"0.547042",
"0.54656315",
"0.53561056",
"0.53190964",
"0.5298444",
"0.52899414",
"0.5283208",
"0.52193624",
"0.517035",
"0.5165746",
"0.51519984",
"0.5132783",
"0.51110107",
"0.51100355",
"0.50842196",
"0.50837195",
"0.50727284",
"0.507113",
"0.50376564",
"0.503... | 0.5022376 | 21 |
Return a geometry that represents the union of all geometries in the GeoSeries. | def unary_union(self):
return GeoSeries(arctern.ST_Union_Aggr(self)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mergeGeometries(self):\n self.geometry = reduce(lambda p1,p2 : p1.union(p2) ,map(lambda tax : tax.biomeGeometry,self.taxonomies))\n return self.geometry",
"def union(self, other):\n return self._geomgen(capi.geom_union, other)",
"def unary_union(self) -> ir.GeoSpatialScalar:\n r... | [
"0.73016036",
"0.685745",
"0.6835243",
"0.6767258",
"0.67599726",
"0.64897877",
"0.63472724",
"0.634694",
"0.60967267",
"0.60640794",
"0.60346997",
"0.5986849",
"0.59392625",
"0.58512485",
"0.5828639",
"0.57456577",
"0.5710652",
"0.568876",
"0.5681035",
"0.56726",
"0.5660516"... | 0.7909888 | 0 |
Compute the doubleprecision minimum bounding box geometry for the union of all geometries. | def envelope_aggr(self):
return GeoSeries(arctern.ST_Envelope_Aggr(self)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_bounding_box(self):\n if len(self.elements) == 0:\n return None\n if not (self._bb_valid and\n all(ref._bb_valid for ref in self.get_dependencies(True))):\n bb = numpy.array(((1e300, 1e300), (-1e300, -1e300)))\n all_polygons = []\n fo... | [
"0.64559424",
"0.63894683",
"0.63460636",
"0.6325376",
"0.62859964",
"0.62129617",
"0.60120475",
"0.60054564",
"0.5887245",
"0.58749264",
"0.5857979",
"0.58571696",
"0.5831913",
"0.58012956",
"0.5794952",
"0.57558835",
"0.57558835",
"0.5743555",
"0.57128584",
"0.5684636",
"0.... | 0.0 | -1 |
Check whether each geometry intersects other (elementwise). | def intersects(self, other):
return _binary_op(arctern.ST_Intersects, self, other).astype(bool, copy=False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def intersects(self, other): # -> bool:\n ...",
"def intersects(self, *__args): # real signature unknown; restored from __doc__ with multiple overloads\r\n return False",
"def doBoundingBoxesIntersect(self, other):\n if(self.upperLeft.x <= other.lowerRight.x and\n self.lowerRig... | [
"0.75880086",
"0.74986064",
"0.73812044",
"0.72191447",
"0.7136789",
"0.71249735",
"0.701634",
"0.69970363",
"0.68745273",
"0.6840998",
"0.6757996",
"0.67431086",
"0.6726097",
"0.6692108",
"0.6664353",
"0.6642848",
"0.663825",
"0.66000044",
"0.6524565",
"0.6524129",
"0.651469... | 0.68560857 | 9 |
Check whether each geometry is within other (elementwise). | def within(self, other):
return _binary_op(arctern.ST_Within, self, other).astype(bool, copy=False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def doBoundingBoxesIntersect(self, other):\n if(self.upperLeft.x <= other.lowerRight.x and\n self.lowerRight.x >= other.upperLeft.x and\n self.upperLeft.y >= other.lowerRight.y and\n self.lowerRight.y <= other.upperLeft.y):\n return True\n return False"... | [
"0.7524902",
"0.73729897",
"0.71685326",
"0.7139274",
"0.7030789",
"0.702235",
"0.70122725",
"0.6991214",
"0.6973276",
"0.6951702",
"0.69121456",
"0.6908521",
"0.6871371",
"0.6854054",
"0.6852921",
"0.6798403",
"0.6786245",
"0.6746447",
"0.6740402",
"0.6731472",
"0.67281044",... | 0.6281432 | 82 |
Check whether each geometry contains other (elementwise). | def contains(self, other):
return _binary_op(arctern.ST_Contains, self, other).astype(bool, copy=False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def doBoundingBoxesIntersect(self, other):\n if(self.upperLeft.x <= other.lowerRight.x and\n self.lowerRight.x >= other.upperLeft.x and\n self.upperLeft.y >= other.lowerRight.y and\n self.lowerRight.y <= other.upperLeft.y):\n return True\n return False"... | [
"0.6927342",
"0.6813174",
"0.67808735",
"0.6683514",
"0.6608095",
"0.66048783",
"0.6543079",
"0.651822",
"0.6502066",
"0.6419477",
"0.64123785",
"0.6408629",
"0.6377291",
"0.63678026",
"0.63514185",
"0.63224965",
"0.6319783",
"0.6301619",
"0.6301403",
"0.62778205",
"0.6274065... | 0.0 | -1 |
Check whether each geometry and other(elementwise) "spatially cross". "Spatially cross" here means two geometries have some, but not all interior points in common. The intersection of the interiors of the geometries must not be the empty set and must have a dimensionality less than the maximum dimension of the two inpu... | def crosses(self, other):
return _binary_op(arctern.ST_Crosses, self, other).astype(bool, copy=False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def intersects(*args):\r\n if len(args) == 2:\r\n p0, p1, p2, p3 = *args[0], *args[1]\r\n elif len(args) == 4:\r\n p0, p1, p2, p3 = args\r\n else:\r\n raise AttributeError(\"Pass 2, 2-pnt lines or 4 points to the function\")\r\n #\r\n # ---- First check ---- np.cross(p1-p0, p3... | [
"0.6487575",
"0.63430226",
"0.63197684",
"0.63048565",
"0.62401897",
"0.62110454",
"0.60857844",
"0.60824424",
"0.60203683",
"0.6004619",
"0.60024345",
"0.5978805",
"0.595224",
"0.59431404",
"0.5899379",
"0.5882534",
"0.5864912",
"0.5863718",
"0.5834981",
"0.58330286",
"0.583... | 0.0 | -1 |
Check whether each geometry is "spatially equal" to other. "Spatially equal" means two geometries represent the same geometry structure. | def geom_equals(self, other):
from pandas.api.types import is_scalar
if is_scalar(other):
other = self.__class__([other] * len(self), index=self.index)
this = self
if not this.index.equals(other.index):
warn("The indices of the two GeoSeries are different.")
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def are_equal(self, sp1, sp2):\n return True",
"def are_equal(self, sp1, sp2):\n for s1 in sp1.keys():\n spin1 = getattr(s1, \"spin\", 0)\n oxi1 = getattr(s1, \"oxi_state\", 0)\n for s2 in sp2.keys():\n spin2 = getattr(s2, \"spin\", 0)\n ... | [
"0.6802465",
"0.67805123",
"0.66960746",
"0.66589355",
"0.6498028",
"0.6484803",
"0.62827766",
"0.6278034",
"0.6247545",
"0.6240349",
"0.6240165",
"0.62319565",
"0.6208261",
"0.6191864",
"0.61298805",
"0.6103202",
"0.6102807",
"0.6097758",
"0.6073104",
"0.6059349",
"0.6056827... | 0.6046562 | 21 |
Check whether each geometry "touches" other. "Touch" means two geometries have common points, and the common points locate only on their boundaries. | def touches(self, other):
return _binary_op(arctern.ST_Touches, self, other).astype(bool, copy=False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def intersects(self):\n match = False\n for i in range(len(self.__points) - 1):\n p1 = self.__points[i]\n p2 = self.__points[i + 1]\n bounds = self.__line_segment(p1, p2)\n if not bounds is None:\n xmin = bounds[0]\n ymin = bounds[1]\n xmax = bounds[0]\n ymax =... | [
"0.65426964",
"0.6534798",
"0.64519405",
"0.6406436",
"0.6399988",
"0.637896",
"0.6334022",
"0.6320897",
"0.6273946",
"0.6238817",
"0.6082561",
"0.60700196",
"0.6025307",
"0.601121",
"0.59974545",
"0.59330696",
"0.59228545",
"0.59096324",
"0.58627254",
"0.5842866",
"0.5815128... | 0.65202117 | 2 |
Check whether each geometry "spatially overlaps" other. "Spatially overlap" here means two geometries intersect but one does not completely contain another. | def overlaps(self, other):
return _binary_op(arctern.ST_Overlaps, self, other).astype(bool, copy=False) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def can_overlap(self):\n return False",
"def span_overlap(a: Tuple[int, int], b: Tuple[int, int]) -> bool:\n return not (a[0] > b[1] or a[1] < b[0])",
"def overlaps(self, other): # -> bool:\n ...",
"def overlap(component1, component2):\n if component1[0].start <= component2[0].stop a... | [
"0.7119898",
"0.69237816",
"0.6911162",
"0.68759036",
"0.6854764",
"0.6843278",
"0.6818101",
"0.6797722",
"0.67557263",
"0.67337745",
"0.67266196",
"0.670879",
"0.6697069",
"0.6681085",
"0.6661396",
"0.66502994",
"0.6640487",
"0.66175556",
"0.66064024",
"0.6606194",
"0.660260... | 0.68331456 | 6 |
Calculates the minimum 2D Cartesian (planar) distance between each geometry and other. | def distance(self, other):
return _binary_op(arctern.ST_Distance, self, other) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def minimum_distance(object_1, object_2):\n\n # package import\n import numpy as np\n\n # main algorithm\n minimum_distance = 100000\n\n for coord_1 in object_1:\n for coord_2 in object_2:\n distance_btwn_coords = np.linalg.norm(coord_1 - coord_2)\n if distance_btwn_coor... | [
"0.6431486",
"0.63372993",
"0.61613613",
"0.6061822",
"0.6057627",
"0.60562485",
"0.6043775",
"0.60295826",
"0.60089844",
"0.6001629",
"0.5990504",
"0.59392023",
"0.5931772",
"0.5912168",
"0.5911987",
"0.59024376",
"0.58987397",
"0.588519",
"0.5879089",
"0.5869567",
"0.586037... | 0.0 | -1 |
Return minimum distance in meters between two lon/lat points. Uses a spherical earth and radius derived from the spheroid defined by the SRID. | def distance_sphere(self, other):
if not self.crs == getattr(other, "crs", "EPSG:4326") == "EPSG:4326":
raise ValueError("Only can calculate spherical distance with 'EPSG:4326' crs.")
return _binary_op(arctern.ST_DistanceSphere, self, other) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_spherical_distance(lat1,lat2,long1,long2):\n lat1,lat2,long1,long2= float(lat1),float(lat2),float(long1),float(long2)\n q=radians(lat2-lat1)\n r=radians(long2-long1)\n lat2r=radians(lat2)\n lat1r=radians(lat1)\n a=sin(q/2)*sin(q/2)+cos(lat1r)*cos(lat2r)*sin(r/2)*si... | [
"0.68833476",
"0.68543684",
"0.6664104",
"0.66225034",
"0.6614609",
"0.6551876",
"0.6545566",
"0.6468671",
"0.64259017",
"0.63936806",
"0.63863176",
"0.63863176",
"0.63863176",
"0.63863176",
"0.63863176",
"0.638405",
"0.6378784",
"0.6377177",
"0.63747877",
"0.63671356",
"0.63... | 0.6405948 | 9 |
Returns the Hausdorff distance between each geometry and other. This is a measure of how similar or dissimilar 2 geometries are. | def hausdorff_distance(self, other):
return _binary_op(arctern.ST_HausdorffDistance, self, other) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def hausdorff_distance(self, other):\n ...",
"def hausdorff_distance(image1, image2):\n image1_int = image1.clone(\"unsigned int\")\n image2_int = image2.clone(\"unsigned int\")\n\n libfn = utils.get_lib_fn(\"hausdorffDistance%iD\" % image1_int.dimension)\n d = libfn(image1_int.pointer, image2... | [
"0.759283",
"0.7179202",
"0.7101328",
"0.689909",
"0.6858431",
"0.6837336",
"0.67604476",
"0.6683294",
"0.66194665",
"0.66013527",
"0.6569919",
"0.65585876",
"0.6527269",
"0.6473583",
"0.6433952",
"0.6392746",
"0.63679177",
"0.63628966",
"0.6316012",
"0.63065404",
"0.6305005"... | 0.76239663 | 0 |
Calculate the point set intersection between each geometry and other. | def intersection(self, other):
return _binary_geo(arctern.ST_Intersection, self, other) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def intersection(self, other): # -> BaseGeometry:\n ...",
"def intersection(self, other):\n return self._geomgen(capi.geom_intersection, other)",
"def intersection(x, y, f, p):",
"def intersection(*entities):\n from entity import GeometryEntity\n\n entities = GeometryEntity.extract_entit... | [
"0.7651218",
"0.74701786",
"0.7083056",
"0.6893054",
"0.67865145",
"0.67679137",
"0.67574686",
"0.6725878",
"0.665778",
"0.6639866",
"0.65870994",
"0.65796703",
"0.65796703",
"0.65796703",
"0.65796703",
"0.6557968",
"0.6526289",
"0.6521376",
"0.65168005",
"0.65129095",
"0.647... | 0.69138277 | 3 |
Transform each geometry to WKT formed string. | def to_wkt(self):
return _property_op(arctern.ST_AsText, self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_list_to_wkt(self, geom):\n if geom['type'] == \"Polygon\":\n coords = [f\"{coord[0]} {coord[1]}\" for coord in geom['coordinates'][0]]\n return f\"POLYGON (( {', '.join(coords)} ))\"\n else:\n raise Exception(f\"Unknown type of Geometry in GeoJSON of {geom... | [
"0.6284619",
"0.5979369",
"0.588133",
"0.5870684",
"0.58419377",
"0.5840937",
"0.5709922",
"0.5570101",
"0.5550173",
"0.5397603",
"0.5344564",
"0.534022",
"0.5335962",
"0.53175837",
"0.5316207",
"0.52969074",
"0.5270357",
"0.5260585",
"0.5257257",
"0.5244518",
"0.52068967",
... | 0.52084917 | 20 |
Transform each geometry to WKB formed bytes object. | def to_wkb(self):
return _property_op(lambda x: x, self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def wkb(self): # -> bytes:\n ...",
"def ToBytes (self):\n return zlib.compress (json.dumps (self.containments, 2).encode ('utf-8'), 9)",
"def test_geotransform2bbox(self):\n\n M = 5\n N = 10\n for gt in GEOTRANSFORMS:\n bbox = geotransform2bbox(gt, M, N)\n\n ... | [
"0.615493",
"0.54587793",
"0.5230626",
"0.52266526",
"0.5186636",
"0.513073",
"0.51119035",
"0.5024733",
"0.502031",
"0.5013838",
"0.5013339",
"0.49796233",
"0.49733862",
"0.49601117",
"0.4927707",
"0.49172413",
"0.49162441",
"0.49131826",
"0.49010167",
"0.48981664",
"0.48796... | 0.53376216 | 2 |
Transform each to GeoJSON format string. | def as_geojson(self):
return _property_op(arctern.ST_AsGeoJSON, self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def geojson(self):\n return {\n \"type\": \"FeatureCollection\",\n \"features\": [f.geojson(i) for i, f in self._features.items()]\n }",
"def get_geojson(self, sql, context):\n result = self.db.query(sql).getresult()\n geo_objects = []\n\n for poly in resu... | [
"0.62960416",
"0.6181512",
"0.59290135",
"0.589605",
"0.58446467",
"0.5824667",
"0.57942975",
"0.5702228",
"0.569644",
"0.56767356",
"0.566967",
"0.56581604",
"0.5653806",
"0.5648496",
"0.56391734",
"0.5633548",
"0.5613218",
"0.561292",
"0.55785376",
"0.55779",
"0.5575015",
... | 0.5802877 | 6 |
Transform each arctern GeoSeries to GeoPandas GeoSeries. | def to_geopandas(self):
import geopandas
import shapely
return geopandas.GeoSeries(self.apply(lambda x: shapely.wkb.loads(x) if x is not None else None), crs=self.crs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def raster_to_geodataframe(*a, **kw) -> gpd.GeoDataFrame:\n kw[\"geo\"] = True\n return raster_to_dataframe(*a, **kw)",
"def convert_to_geopandas(df):\n df['geometry'] = [Point(xy) for xy in zip(df.latitude, df.longitude)]\n crs = {'init': 'epsg:4326'}\n df = gpd.GeoDataFrame(df, crs=crs, geometry... | [
"0.63202363",
"0.6096996",
"0.603656",
"0.598663",
"0.59274143",
"0.5879244",
"0.5840413",
"0.5799306",
"0.5705398",
"0.56461185",
"0.5643855",
"0.55626243",
"0.55617464",
"0.5537935",
"0.5530619",
"0.5516425",
"0.5465296",
"0.5460588",
"0.54439163",
"0.5427812",
"0.5421134",... | 0.6296897 | 1 |
Construct polygon(rectangle) geometries from arr_min_x, arr_min_y, arr_max_x, arr_max_y and special coordinate system. The edges of polygon are parallel to coordinate axis. | def polygon_from_envelope(cls, min_x, min_y, max_x, max_y, crs=None):
crs = _validate_crs(crs)
return cls(arctern.ST_PolygonFromEnvelope(min_x, min_y, max_x, max_y), crs=crs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generatePolygons():",
"def draw_polygon(\n i, sx, tx, sy, ty, xmin, xmax, ymin, ymax,\n offsets, values, xs, ys, yincreasing, eligible,\n *aggs_and_cols\n ):\n # Initialize values of pre-allocated buffers\n xs.fill(np.nan)\n ys.fill(np.nan)\n yi... | [
"0.6298787",
"0.6130665",
"0.5995941",
"0.59403425",
"0.59305",
"0.5916149",
"0.58777076",
"0.58749014",
"0.5831342",
"0.5804383",
"0.57978517",
"0.578452",
"0.5773578",
"0.5768583",
"0.5755375",
"0.57550496",
"0.57409936",
"0.5726229",
"0.57090235",
"0.5684887",
"0.5683477",... | 0.6215077 | 1 |
Construct Point geometries according to the coordinates. | def point(cls, x, y, crs=None):
crs = _validate_crs(crs)
return cls(arctern.ST_Point(x, y), crs=crs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_points(data):\n #TODO list comprehension\n for row in data:\n\n if row[\"x\"] and row[\"y\"]:\n try:\n row[\"geometry\"] = point.Point(float(row[\"x\"]), float(row[\"y\"]))\n except:\n row[\"geometry\"] = None\n... | [
"0.6530858",
"0.6457091",
"0.64059484",
"0.640297",
"0.6357891",
"0.6344461",
"0.6337387",
"0.6316078",
"0.62294465",
"0.6183091",
"0.6170689",
"0.613722",
"0.613722",
"0.60727257",
"0.60714775",
"0.6061231",
"0.60591716",
"0.59994185",
"0.59989065",
"0.59790605",
"0.5955864"... | 0.5411405 | 95 |
Construct geometry from the GeoJSON representation string. | def geom_from_geojson(cls, json, crs=None):
crs = _validate_crs(crs)
return cls(arctern.ST_GeomFromGeoJSON(json), crs=crs) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_geometry(self, val):\n g = OGRGeometry(val)\n return json.loads(g.json)",
"def json2polygon(geojson_str):\n geojson_object = geojson.loads(geojson_str)\n return geometry.shape(geojson_object)",
"def get_geojson_feature(id, raw_bbox_string, properties_dict):\n coords = raw_bbox_s... | [
"0.64928246",
"0.6445108",
"0.62764484",
"0.62726635",
"0.6204472",
"0.61289346",
"0.59531116",
"0.5860503",
"0.5817557",
"0.5810382",
"0.5739932",
"0.5705384",
"0.56882906",
"0.5613755",
"0.56050944",
"0.5557749",
"0.5536399",
"0.55300456",
"0.5526873",
"0.5519496",
"0.54749... | 0.6517472 | 0 |
Construct geometries from geopandas GeoSeries. | def from_geopandas(cls, data):
import geopandas as gpd
import shapely.wkb
if not isinstance(data, gpd.GeoSeries):
raise TypeError(f"data must be {gpd.GeoSeries}, got {type(data)}")
if data.crs is not None:
crs = data.crs.to_authority() or data.crs.source_crs.to_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def build_geoseries(self, dataframe):\n geo_list = []\n with click.progressbar(dataframe.iterrows(), label='Pulling site plans and geographic title data', length=len(dataframe)) as d:\n for index, row in d:\n geo_list.append(self.map_property(row['linc']))\n\n geo_ser... | [
"0.6760645",
"0.66406184",
"0.6611926",
"0.65941155",
"0.65144163",
"0.63927484",
"0.6314211",
"0.62846774",
"0.62605417",
"0.61809194",
"0.5986733",
"0.5974453",
"0.59539646",
"0.5952975",
"0.59197783",
"0.5875279",
"0.5856764",
"0.5832553",
"0.58124745",
"0.58063805",
"0.57... | 0.663567 | 2 |
Decorator to check and update session attributes. | def check_session(wrapped):
def check(request, *arg, **kwargs):
collection = request.GET.get('collection', None)
journal = request.GET.get('journal', None)
document = request.GET.get('document', None)
range_start = request.GET.get('range_start', None)
under_development = req... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_modify_authenticated_session_var(self):\r\n cursor = self.db.cursor()\r\n cursor.execute(\"INSERT INTO session VALUES ('john', 1, 0)\")\r\n cursor.execute(\"INSERT INTO session_attribute VALUES \"\r\n \"('john', 1, 'foo', 'bar')\")\r\n\r\n req = Mock(authn... | [
"0.68283325",
"0.6451606",
"0.61932784",
"0.61213243",
"0.60973513",
"0.597331",
"0.5921812",
"0.5765885",
"0.5700388",
"0.5672152",
"0.5666347",
"0.5607734",
"0.5568922",
"0.5488824",
"0.5479005",
"0.5472188",
"0.54597175",
"0.54507554",
"0.53897274",
"0.53520393",
"0.531647... | 0.5884659 | 7 |
Decorator to load common data used by all views | def base_data_manager(wrapped):
@check_session
def wrapper(request, *arg, **kwargs):
@cache_region.cache_on_arguments()
def get_data_manager(collection, journal, document, range_start, range_end):
code = document or journal or collection
data = {}
xylose_do... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def data():\n return app_views",
"def data_for_all(request):\n data = common_data(request)\n data.update({\"tags\": Tag.used_tags(),\n \"archive_qualifier\": \"\",\n \"recent_active_months\": Blog.recent_active_months()})\n return data",
"def common_context(request):... | [
"0.6341154",
"0.5741006",
"0.5648095",
"0.559915",
"0.5597389",
"0.55743283",
"0.5551376",
"0.5519761",
"0.5490871",
"0.54798126",
"0.5479319",
"0.5315314",
"0.5302181",
"0.5232341",
"0.522839",
"0.52071565",
"0.5182045",
"0.5180877",
"0.51765704",
"0.51497936",
"0.51259214",... | 0.5870001 | 1 |
Constructor. Unless otherwise specified it has a perfect quantum efficiency, samples at a rate of once per second and has a 0.1s integration time | def __init__(self, quantum_efficiency=1.0,
sample_rate_times_per_second=1.0,
integration_time_seconds=0.1):
self.quantum_efficiency = quantum_efficiency
self.sample_rate_times_per_second = sample_rate_times_per_second
self.integration_time_seconds = integration_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, time_constant: float, sampling_time: float):\n self.alpha = sampling_time / (time_constant + sampling_time)\n self.state = None",
"def __init__(self, timer=120, rate=1, percent=0):\n self.timer = timer\n self.rate = rate\n self.percent = percent",
"def __init__(sel... | [
"0.6873452",
"0.6581009",
"0.6519729",
"0.6232511",
"0.6232355",
"0.6188222",
"0.616987",
"0.61652625",
"0.6155164",
"0.61493057",
"0.6139855",
"0.6118475",
"0.610823",
"0.6098916",
"0.6088264",
"0.60863924",
"0.60642815",
"0.6009342",
"0.60072887",
"0.60061246",
"0.59945565"... | 0.80746883 | 0 |
Demeans data assuming that each row is a timecourse and divides the row by it's standard deviation to enforce unit variance. | def scale_timecourse_data(V):
# Get the mean of each row
V_mean = V.mean(axis=1)
# Get the std of each row
V_std = V.std(axis=1)
# Change zeros to ones (avoid dividing zero columns by zero)
V_std[V_std==0] = 1
V_std = V_std.T[:, np.newaxis]
# Feature scale the rows of V
V = np.di... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stdev(headers, data):\n\tcolumn_matrix=data.get_data(headers)\n\tmean_values=column_matrix.std(0)\n\tstd_values=mean_values.tolist()\n\treturn std_values",
"def standard_deviation(data):\n\n return np.sqrt(variance(data))",
"def standard_deviation(data):\n\n return np.sqrt(variance(data))",
"def ge... | [
"0.67038697",
"0.65886843",
"0.65886843",
"0.6443919",
"0.6354485",
"0.62777156",
"0.62359643",
"0.6214864",
"0.6205753",
"0.61780286",
"0.6169963",
"0.61327285",
"0.6082657",
"0.6057745",
"0.6053649",
"0.60294616",
"0.6019821",
"0.60037726",
"0.5991898",
"0.5970491",
"0.5953... | 0.54657185 | 96 |
Creates new task window | def new_task(self, widget):
my_task_window = taskwindow.TaskWindow(self) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_task(event):\n manager = event.workbench.get_plugin('exopy.tasks')\n dialog = BuilderView(manager=manager,\n parent=event.parameters.get('parent_ui'),\n future_parent=event.parameters.get('future_parent'))\n result = dialog.exec_()\n if result:... | [
"0.7113246",
"0.70224667",
"0.6907756",
"0.68215317",
"0.6519921",
"0.6381383",
"0.637192",
"0.6319704",
"0.62721485",
"0.6223385",
"0.61893445",
"0.6168583",
"0.61627823",
"0.6148006",
"0.6140917",
"0.6093474",
"0.5996884",
"0.5986673",
"0.5972583",
"0.59595525",
"0.5941826"... | 0.81975675 | 0 |
Shows a window with all the tasks and alarms | def see_tasks(self, widget):
my_task_list = tasklistwindow.TaskListWindow(self.task_list) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dashboard_showall():\n tasks = Task.query.all()\n return render_template('home/taskshowall/dashboard_showall.html',\n tasks=tasks, title=\"Tasks\")",
"def show_tasks():\n\n task = Task(connection=connection, cursor=cursor)\n\n all_tasks = task.get_all_tasks()\n\n cont... | [
"0.6601088",
"0.6460697",
"0.628572",
"0.6235331",
"0.61510676",
"0.6129666",
"0.6097378",
"0.607427",
"0.6070758",
"0.6047063",
"0.59771895",
"0.59053344",
"0.58603084",
"0.58067805",
"0.58024603",
"0.57794356",
"0.57792646",
"0.5776163",
"0.57633907",
"0.5740602",
"0.571361... | 0.69399023 | 0 |
Delete the given profile from the server | def delete_profile(subscription_key, profile_id):
helper = VerificationServiceHttpClientHelper.VerificationServiceHttpClientHelper(subscription_key)
helper.delete_profile(profile_id)
print('Profile {0} has been successfully deleted.'.format(profile_id)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fusion_api_delete_server_profile(self, name=None, uri=None, param='', api=None, headers=None):\n return self.profile.delete(name=name, uri=uri, param=param, api=api, headers=headers)",
"def delete_network_profile(self, profile):\r\n return self.delete(self.network_profile_path % profile)",
... | [
"0.8142084",
"0.80761915",
"0.7869769",
"0.7786373",
"0.76816595",
"0.7514914",
"0.7442937",
"0.7420244",
"0.74095243",
"0.739577",
"0.7388937",
"0.7387069",
"0.7378266",
"0.737173",
"0.73160154",
"0.72956455",
"0.72497237",
"0.7233755",
"0.7171392",
"0.7163262",
"0.7143254",... | 0.7901775 | 2 |
Calculates maximum likelihood estimates | def aicmle(timeSeries, distribution):
mlevals = {}
if distribution == 'pareto':
mlevals['xmin'] = np.min(timeSeries)
mlevals['mu'] = 1 - timeSeries.shape[0] / (timeSeries.shape[0] * np.log(mlevals['xmin']) - np.sum(np.log(timeSeries)))
elif distribution == 'lognormal':
mlev... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def maxlikelihood(self):\n\n chi2 = lambda *args: -2 * lnlikelihood.lnlike(*args) \n # print('***DEBUGGING*** chi2 = ', chi2)\n # print('***DEBUGGING*** self.theta_guess = ', self.theta_guess)\n # print('***DEBUGGING*** self.transinfo = ', self.transinfo)\n # print('***D... | [
"0.70915174",
"0.66576946",
"0.6527788",
"0.64514744",
"0.6386824",
"0.6367023",
"0.6366287",
"0.63379896",
"0.631658",
"0.62785214",
"0.61752355",
"0.60306334",
"0.6018936",
"0.5992277",
"0.59862584",
"0.5976006",
"0.59672964",
"0.59648114",
"0.5962802",
"0.5960479",
"0.5950... | 0.0 | -1 |
Calculates natural log likelihood values | def aiclike(timeSeries, params, distribution):
if distribution == 'pareto':
nloglval = -(timeSeries.shape[0] * np.log(params['mu']) + timeSeries.shape[0] * params['mu'] * np.log(params['xmin']) - (params['xmin']+1) * np.sum(np.log(timeSeries)))
return nloglval
elif distribution == 'logn... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log_likelihood(self):\r\n return (-0.5 * self.num_data * self.output_dim * np.log(2.*np.pi) -\r\n 0.5 * self.output_dim * self.K_logdet + self._model_fit_term() + self.likelihood.Z)",
"def nloglikeobs(self, params):\n lambda_ = params[0]\n\n ll_output = self._LL(self.endog, rate=lambd... | [
"0.73833865",
"0.7282104",
"0.72731364",
"0.7224237",
"0.72090906",
"0.72088003",
"0.7207167",
"0.71788365",
"0.7166103",
"0.7160064",
"0.7155805",
"0.71335495",
"0.71213716",
"0.7114622",
"0.7103507",
"0.70852166",
"0.70822245",
"0.70803833",
"0.70540184",
"0.7014505",
"0.70... | 0.0 | -1 |
Generates the values for the probability distributions | def aicpdf(xvals, distribution, params):
if distribution == 'pareto':
pvals = (params['xmin'] * params['mu'] ** params['xmin']) / (xvals ** (params['xmin'] + 1))
return pvals
elif distribution == 'lognormal':
#import pdb; pdb.set_trace()
pvals = np.exp(-(np.log(xvals) - para... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_samples(self):\n self.analytic_probability()",
"def generate_probabilities(self):\n k = 1\n v= 10\n for g in self.class_probabilities:\n curr_list = self.class_probabilities[g]\n for l in range(0,28):\n for w in range(0,28):\n ... | [
"0.7003688",
"0.6883407",
"0.6792984",
"0.67818236",
"0.666771",
"0.66444635",
"0.6615368",
"0.6597994",
"0.65978616",
"0.6579974",
"0.6578962",
"0.6567796",
"0.6556646",
"0.6516561",
"0.64934367",
"0.64649326",
"0.640872",
"0.6397725",
"0.6374798",
"0.6366242",
"0.6345269",
... | 0.0 | -1 |
aic(timeSeries, ssc=0) > data, max_weight, max_weight_params | def aic(timeSeries, ssc=0):
if np.min(timeSeries) <= 0:
timeSeries = timeSeries + -np.min(timeSeries) + .01
# create histogram to determine plot values
# note that the original uses hist centers, this uses edges. It may matter
counts, plotvals_edges = np.histogram(timeSeries, 50)
plot... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_aic_ms(distribution):\n print(\"TESTING: AIC model selection for %s distribution\" % distribution.upper())\n params = dist.DISTRIBUTIONS[distribution][dist.KEY_TEST_PARAMS]\n print(\" creating sample\")\n test_sample = dist.samples(distribution, params)\n print(\" calculating AIC for all ... | [
"0.52783775",
"0.5277361",
"0.51554257",
"0.5079259",
"0.5041149",
"0.5019891",
"0.49585706",
"0.49330664",
"0.49314007",
"0.4872369",
"0.4855907",
"0.48519504",
"0.48410013",
"0.4838592",
"0.47834566",
"0.47363517",
"0.47308505",
"0.47119272",
"0.47084534",
"0.47058246",
"0.... | 0.5706766 | 0 |
Add item to heap | def add_item(self, new_value):
# Allocate more memory if necessary
# This keeps add_item to O(1), generally.
# Otherwise, have to duplicate ndarray every time
# last_item is an index, heap_size is a limit (index + 1)
if self.last_item >= self.heap_size - 1:
# Allocat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def push(self, item):\n heapq.heappush(self.heap, item)",
"def heappush(heap, item):\n heap.append(item)\n Heap.siftdown(heap, 0, len(heap) - 1)",
"def heappush(heap, item):\n pass",
"def push(self, item: tuple):\n self.__heap.append(item)\n self.__sift_up(self.__len__()... | [
"0.86743045",
"0.85555136",
"0.84952396",
"0.8408714",
"0.83693314",
"0.83544827",
"0.8334501",
"0.82352614",
"0.8180887",
"0.79542404",
"0.7924978",
"0.7850471",
"0.78375816",
"0.7834717",
"0.7763897",
"0.7657202",
"0.764088",
"0.7628112",
"0.7622526",
"0.76175183",
"0.76170... | 0.7952556 | 10 |
Show line of asterisks to demarcate bounds of heap | def demarcate_heap(hgt=self.level, cell_wid=minimum_cell):
# Number of nodes on bottom is 2^hgt
max_nodes = int(np.power(2, hgt))
print (''.center(cell_wid * max_nodes, '*')) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def print_heap(self):\n for i in range(1, (self.size//2)+1): \n print(\" PARENT : \"+ str(self.Heap[i])+\" LEFT CHILD : \"+ \n str(self.Heap[2 * i])+\" RIGHT CHILD : \"+\n str(self.Heap[2 * i + 1]))",
"def min_heap(self): \n \n ... | [
"0.6224625",
"0.6115098",
"0.60877067",
"0.6073852",
"0.58567894",
"0.5810459",
"0.57655525",
"0.57655525",
"0.5722967",
"0.5565458",
"0.55614096",
"0.55261725",
"0.55241466",
"0.550935",
"0.5506681",
"0.54333556",
"0.54333556",
"0.54319435",
"0.5421225",
"0.54208",
"0.541896... | 0.6630077 | 0 |
Get index of parent node | def get_parent_index(i):
# Indexing for i_parent == i // 2 is NOT ZERO-INDEXED
pos = i + 1
parent_pos = pos // 2
parent_index = parent_pos - 1
return parent_index | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_parent_index(self):\n return (self.index - 1) // 2",
"def get_left_child_index(self, parent):\n return 2*parent+1",
"def parent(self, index):\n if index == 0:\n print(\"index 0 has no parent\")\n return None\n return (index - 1) // 2",
"def get_parent... | [
"0.8496504",
"0.82781035",
"0.8254789",
"0.82179976",
"0.81770974",
"0.81386656",
"0.8075585",
"0.8044404",
"0.8016533",
"0.79629314",
"0.79252255",
"0.7716487",
"0.74751145",
"0.7472065",
"0.7414298",
"0.7355776",
"0.7355776",
"0.7342362",
"0.7312784",
"0.7292973",
"0.726240... | 0.81710243 | 5 |
Get index of left child | def get_left_index(i):
pos = i + 1
left_pos = 2 * pos
left_index = left_pos - 1
return left_index | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_left_child_index(self):\n return (2 * self.index) + 1",
"def left_child(self, index):\n return 2 * index + 1",
"def left_child(self, index):\n return 2 * index",
"def get_left_child_index(self, parent):\n return 2*parent+1",
"def left_child_idx(idx):\n return (idx << ... | [
"0.9122641",
"0.88522995",
"0.8762248",
"0.86230975",
"0.83617294",
"0.8189093",
"0.80934316",
"0.80934316",
"0.79510504",
"0.77594656",
"0.76739126",
"0.76264703",
"0.7587339",
"0.7570154",
"0.750047",
"0.7485209",
"0.74505705",
"0.74247074",
"0.72877926",
"0.7251152",
"0.72... | 0.75896484 | 12 |
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