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def utcnow(): """Better version of utcnow() that returns utcnow with a correct TZ.""" return timeutils.utcnow(True)
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def fetch_rows(cursor): """ Fetch and yield rows """ for row in cursor.fetchall(): yield row
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def test_imagehandler_extract_file_simple(tmp_path, caplog): """Extract a file from the tarfile and gets its info.""" caplog.set_level(logging.DEBUG, logger="charmcraft") # create a tar file with one file inside test_content = b"test content for the sample file" sample_file = tmp_path / "testfile.t...
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def build_profile(base_image, se_size=4, se_size_increment=2, num_openings_closings=4): """ Build the extended morphological profiles for a given set of images. Parameters: base_image: 3d matrix, each 'channel' is considered for applying the morphological profile. It is the spectral inf...
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def mujoco_env(env_id, nenvs=None, seed=None, summarize=True, normalize_obs=True, normalize_ret=True): """ Creates and wraps MuJoCo env. """ assert is_mujoco_id(env_id) seed = get_seed(nenvs, seed) if nenvs is not None: env = ParallelEnvBatch([ lambda s=s: mujoco_env(env_id, seed=s, s...
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def polar2cart(r, theta): """ Transform polar coordinates to Cartesian. Parameters ---------- r, theta : floats or arrays Polar coordinates Returns ------- [x, y] : floats or arrays Cartesian coordinates """ return torch.stack((r * theta.cos(), r * theta.sin()...
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def belongs_to(user, group_name): """ Check if the user belongs to the given group. :param user: :param group_name: :return: """ return user.groups.filter(name__iexact=group_name).exists()
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def test_setting_duplicated_config_flag_sets_only_desired_flag(mocker): """Test changing normal_init.type only affects the desired instance of the flag. This is a regression test as reuse of the same Dict object used to mean that setting any instance of normal_init.type would change all other instances too...
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def sum_log_loss(logits, mask, reduction='sum'): """ :param logits: reranking logits(B x C) or span loss(B x C x L) :param mask: reranking mask(B x C) or span mask(B x C x L) :return: sum log p_positive i over all candidates """ num_pos = mask.sum(-1) # B x C gold_s...
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def _get_iat_method(iatmethod): """Control routine for selecting the method used to calculate integrated autocorrelation times (iat) Parameters ---------- iat_method : string, optional Routine to use for calculating said iats. Accepts 'ipce', 'acor', and 'icce'. Returns ------...
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def setup_ubuntu(): """ Install the required Ubuntu dependencies from the package repository. These packages are for Ubuntu 14.04 """ sudo('apt-get -q -y install make gcc python-dev git mercurial nginx python-pip python-setuptools python-virtualenv') sudo('apt-get -q -y install postgresql-9.3 po...
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def test(model, eval_data): """ Computes the average loss on eval_data, which should be a Dataset. """ avg_loss = keras.metrics.Mean() for (labels, chars, sequence_length) in eval_data: predictions = model((chars, sequence_length), training=False) avg_loss.update_state(keras.losses...
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def run(): """Main entry point.""" return cli(obj={}, auto_envvar_prefix='IMPLANT') # noqa
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def test_kmeans(): """Test implementation of Kmeans.""" X_train, y_train = load_basic_motions(split="train") X_test, y_test = load_basic_motions(split="test") kmeans = TimeSeriesKMeans( averaging_method="mean", random_state=1, n_init=2, n_clusters=4, init_algorit...
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def writeFile(sentences, filename, maximum=-1, doLog=True): """Writes a list of strings to file, seperates by newline""" start = time.time() with io.open(filename, 'w+', encoding='utf8') as f: for i, s in enumerate(sentences): f.write(s + "\n") if i == maximum: ...
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def qtl_test_interaction_GxG(pheno, snps1, snps2=None, K=None, covs=None, test="lrt"): """ Epistasis test between two sets of SNPs Args: pheno: [N x 1] np.array of 1 phenotype for N individuals snps1: [N x S1] np.array of S1 SNPs for N individuals snps2: [N x S2] np.array of S2 S...
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def fixture_working_dir(): """A pytest fixture that creates a temporary working directory, config file, schema, and local user identity """ # Create temp dir config_file, temp_dir = _create_temp_work_dir() # Create user identity insert_cached_identity(temp_dir) # Create test client sch...
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def log_parser(log): """ This takes the EA task log file generated by e-prime and converts it into a set of numpy-friendly arrays (with mixed numeric and text fields.) pic -- 'Picture' lines, which contain the participant's ratings. res -- 'Response' lines, which contain their responses (unclear) ...
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def line_assign_z_to_vertexes(line_2d: ogr.Geometry, dem: DEM, allowed_input_types: List[int] = None) -> ogr.Geometry: """ Assign Z dimension to vertices of line based on raster value of `dem`. The values from `dem` are interpolated using bilinear ...
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def test_calibration_ols(): """Testing ordinary least squares procedure. And compare with device calibrated temperature. The measurements were calibrated by the device using only section 8--17.m. Those temperatures are compared up to 2 decimals. Silixa only uses a single calibration constant (I think they ...
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def before_scenario(context, scenario): """Environment preparation before other cli tests are run. Installs kedro by running pip in the top level directory. """ # make a venv kedro_install_venv_dir = _create_new_venv() context.venv_dir = kedro_install_venv_dir context = _setup_context_with_...
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def test_featurecount(): """Test star index creation and mapping.""" map_dir = os.path.join("tests/test_count", "processes", "mapping", "samp5") if os.path.exists(map_dir) is False: os.makedirs(map_dir) cp_cmd = ["tests/data/test_prok/processes/mapping/samp5/samp5_srt.bam", map_dir] cp[cp_c...
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def backoff_linear(n): """ backoff_linear(n) -> float Linear backoff implementation. This returns n. See ReconnectingWebSocket for details. """ return n
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def _doc(): """ :rtype: str """ return pkg_resources.resource_string( 'dcoscli', 'data/help/config.txt').decode('utf-8')
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def sk_algo(U, gates, n): """Solovay-Kitaev Algorithm.""" if n == 0: return find_closest_u(gates, U) else: U_next = sk_algo(U, gates, n-1) V, W = gc_decomp(U @ U_next.adjoint()) V_next = sk_algo(V, gates, n-1) W_next = sk_algo(W, gates, n-1) return V_next @ W_next @ V_next.adjoint() @ W...
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def get_movie_list(): """ Returns: A list of populated media.Movie objects """ print("Generating movie list...") movie_list = [] movie_list.append(media.Movie( title='Four Brothers', summary='Mark Wahlberg takes on a crime syndicate with his brothers.', trailer_yo...
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def check_dna_sequence(sequence): """Check if a given sequence contains only the allowed letters A, C, T, G.""" return len(sequence) != 0 and all(base.upper() in ['A', 'C', 'T', 'G'] for base in sequence)
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def test_inner_scalar_mod_args_length(): """ Feature: Check the length of input of inner scalar mod. Description: The length of input of inner scalar mod should not less than 2. Expectation: The length of input of inner scalar mod should not less than 2. """ class Net(Cell): def __init__...
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def zip_list_files(url): """ cd = central directory eocd = end of central directory refer to zip rfcs for further information :sob: -Erica """ # get blog representing the maximum size of a EOBD # that is 22 bytes of fixed-sized EOCD fields # plus the max comment len...
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def project_ball(tensor, epsilon=1, ord=2): """ Compute the orthogonal projection of the input tensor (as vector) onto the L_ord epsilon-ball. **Assumes the first dimension to be batch dimension, which is preserved.** :param tensor: variable or tensor :type tensor: torch.autograd.Variable or torch...
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def decode_json(filepath="stocks.json"): """ Description: Generates a pathname to the service account json file needed to access the google calendar """ # Check for stocks file if os.path.exists(filepath): return filepath creds = os.environ.get("GOOGLE_SERVICE_CREDS") if creds ...
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def load_model(filename): """ Loads the specified Keras model from a file. Parameters ---------- filename : string The name of the file to read from Returns ------- Keras model The Keras model loaded from a file """ return load_keras_model(__construct_path(file...
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def get_ffmpeg_executable_path(ffmpeg_folder_path): """ Get's ffmpeg's executable path for current system, given the folder. :param ffmpeg_folder_path: Folder path for the ffmpeg and ffprobe executable. :return: ffmpeg executable path as absolute path. """ return os.path.join(ffmpeg_folder_path...
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def testjob(request): """ handler for test job request Actual result from beanstalk instance: * testjob triggerd at 2019-11-14 01:02:00.105119 [headers] - Content-Type : application/json - User-Agent : aws-sqsd/2.4 - X-Aws-Sqsd-Msgid : 6998edf8-3f19-4c69-92cf-7c919241b957 - X-Aws-Sq...
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def args_parser_test(): """ returns argument parser object used while testing a model """ parser = argparse.ArgumentParser() parser.add_argument('--architecture', type=str, metavar='arch', required=True, help='neural network architecture [vgg19, resnet50]') parser.add_argument('--dataset',type=str, required=True...
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def todatetime(mydate): """ Convert the given thing to a datetime.datetime. This is intended mainly to be used with the mx.DateTime that psycopg sometimes returns, but could be extended in the future to take other types. """ if isinstance(mydate, datetime.datetime): return my...
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def generate_datetime(time: str) -> datetime: """生成时间戳""" today: str = datetime.now().strftime("%Y%m%d") timestamp: str = f"{today} {time}" dt: datetime = parse_datetime(timestamp) return dt
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def get_rgb_scores(arr_2d=None, truth=None): """ Returns a rgb image of pixelwise separation between ground truth and arr_2d (predicted image) with different color codes Easy when needed to inspect segmentation result against ground truth. :param arr_2d: :param truth: :return: """ ar...
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def calClassMemProb(param, expVars, classAv): """ Function that calculates the class membership probabilities for each observation in the dataset. Parameters ---------- param : 1D numpy array of size nExpVars. Contains parameter values of class membership model. expVars : 2D num...
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def detect_statistical_outliers( cloud_xyz: np.ndarray, k: int, std_factor: float = 3.0 ) -> List[int]: """ Determine the indexes of the points of cloud_xyz to filter. The removed points have mean distances with their k nearest neighbors that are greater than a distance threshold (dist_thresh). ...
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def describe_image_builders(Names=None, MaxResults=None, NextToken=None): """ Retrieves a list that describes one or more specified image builders, if the image builder names are provided. Otherwise, all image builders in the account are described. See also: AWS API Documentation Exceptions ...
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def collinear(cell1, cell2, column_test): """Determines whether the given cells are collinear along a dimension. Returns True if the given cells are in the same row (column_test=False) or in the same column (column_test=True). Args: cell1: The first geocell string. cell2: The second geocell string. ...
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def plasma_fractal(mapsize=512, wibbledecay=3): """Generate a heightmap using diamond-square algorithm. Modification of the algorithm in https://github.com/FLHerne/mapgen/blob/master/diamondsquare.py Args: mapsize: side length of the heightmap, must be a power of two. wibbledecay: integer, decay facto...
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def stat_to_longname(st, filename): """ Some clients (FileZilla, I'm looking at you!) require 'longname' field of SSH2_FXP_NAME to be 'alike' to the output of ls -l. So, let's build it! Encoding side: unicode sandwich. """ try: n_link = str(st.st_nlink) except: # Some stat...
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def driver(): """ Make sure this driver returns the result. :return: result - Result of computation. """ _n = int(input()) arr = [] for i in range(_n): arr.append(input()) result = solve(_n, arr) print(result) return result
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def synapses_vs_weights(filename="distal", param_list=None): """Clump synapses and vary synapse numbers and synapse weights. param_list contains tuples of (exc_syn, inh_syn, exc_weight, inh_weight, clump) """ import itertools syn_list = param_list if not syn_list: syn_list = itertools....
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def load_element_different(properties, data): """ Load elements which include lists of different lengths based on the element's property-definitions. Parameters ------------ properties : dict Property definitions encoded in a dict where the property name is the key and the property ...
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def start_survey(): """clears the session and starts the survey""" # QUESTION: flask session is used to store temporary information. for permanent data, use a database. # So what's the difference between using an empty list vs session. Is it just for non sens. data like user logged in or not? # QUESTI...
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def test_scan_and_find_dependencies_maven(): """Test scan_and_find_dependencies function for Maven.""" manifests = [{ "filename": "dependencies.txt", "filepath": "/bin/local", "content": open(str(Path(__file__).parent / "data/dependencies.txt")).read() }] res = DependencyFinder()...
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def page(token): """``page`` property validation.""" if token.type == 'ident': return 'auto' if token.lower_value == 'auto' else token.value
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def adjust_learning_rate(optimizer, epoch): """ 調整學習率 """ global lr if epoch % 30 == 0 and epoch != 0: lr = lr * 0.9 if (lr < min_lr): lr = min_lr for param_group in optimizer.param_groups: param_group['lr'] = lr
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def main(): """ Challenge 09: Using ImageDraw to draw polygon from points http://www.pythonchallenge.com/pc/return/good.html """ # Read text files and gather numberical points pattern = re.compile(r'\d+') dots1 = re.findall(pattern, open("text/lv9-dots1.txt").read()) dots2 = re.findall(p...
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def create_fsns_label(image_dir, anno_file_dirs): """Get image path and annotation.""" if not os.path.isdir(image_dir): raise ValueError(f'Cannot find {image_dir} dataset path.') image_files_dict = {} image_anno_dict = {} images = [] img_id = 0 for anno_file_dir in anno_file_dirs:...
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def index(): """ Gets the the weight data and displays it to the user. """ # Create a base query weight_data_query = Weight.query.filter_by(member=current_user).order_by(Weight.id.desc()) # Get all the weight data. all_weight_data = weight_data_query.all() # Get the last 5 data points f...
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def git_pull(remote='origin', branch='prod'): """Updates the repository.""" with cd(env.directory): run('git fetch --all') run('git reset --hard %s/%s' % (remote, branch))
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def upload_binified_data(binified_data, error_handler, survey_id_dict): """ Takes in binified csv data and handles uploading/downloading+updating older data to/from S3 for each chunk. Returns a set of concatenations that have succeeded and can be removed. Returns the number of failed FTP...
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def system_get_enum_values(enum): """Gets all values from a System.Enum instance. Parameters ---------- enum: System.Enum A Enum instance. Returns ------- list A list containing the values of the Enum instance """ return list(Enum.GetValues(enum))
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def skip_leading_ws_with_indent(s,i,tab_width): """Skips leading whitespace and returns (i, indent), - i points after the whitespace - indent is the width of the whitespace, assuming tab_width wide tabs.""" count = 0 ; n = len(s) while i < n: ch = s[i] if ch == ' ': c...
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def testContactPerson(): """ @tests: ContactPerson.__init__ ContactPerson.getFirstName ContactPerson.setFirstName ContactPerson.getLastName ContactPerson.setLastName ContactPerson.setName ContactPerson.getInstitution ContactPerson.setInstitution ContactPerson.getEmai...
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def streamTap(source, tap): """ DEPRECATED! """ #print("******** Stream TAP is deprecated. Use streamConnect instead. ********") streamConnect(source,tap)
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def linkcode_resolve(domain, info): """ Determine the URL corresponding to Python object """ if domain != 'py': return None modname = info['module'] fullname = info['fullname'] submod = sys.modules.get(modname) if submod is None: return None obj = submod for pa...
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def get_minibam_bed(bamfile, bedfile, minibam=None): """ samtools view -L could do the work, but it is NOT random access. Here we are processing multiple regions sequentially. See also: https://www.biostars.org/p/49306/ """ pf = op.basename(bedfile).split(".")[0] minibamfile = minibam or op.bas...
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def test_2(): """ database - simple test with sqlalchemy/elixir unicode test """ file_path = os.path.join(os.path.dirname(__file__), 'helloworld.csv') try: f = open(file_path, 'r', encoding='utf-8') except TypeError: f = open(file_path, 'r') reader = csv.reader(f) res = {} f...
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def create_app(config_object="tigerhacks_api.settings"): """Create application factory, as explained here: http://flask.pocoo.org/docs/patterns/appfactories/. :param config_object: The configuration object to use. """ app = Flask(__name__.split(".")[0]) logger.info("Flask app initialized") app...
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def dest_in_spiral(data): """ The map of the circuit consists of square cells. The first element in the center is marked as 1, and continuing in a clockwise spiral, the other elements are marked in ascending order ad infinitum. On the map, you can move (connect cells) vertically and horizontally....
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def get_current_time(): """ returns current time w.r.t to the timezone defined in Returns ------- : str time string of now() """ import pytz from datetime import datetime srv = get_server() if srv.time_zone is None: time_zone = 'UTC' else: time_zone = srv...
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async def _update_google_domains(hass, session, domain, user, password, timeout): """Update Google Domains.""" url = f"https://{user}:{password}@domains.google.com/nic/update" params = {"hostname": domain} try: async with async_timeout.timeout(timeout): resp = await session.get(url...
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def smoothen_over_time(lane_lines): """ Smooth the lane line inference over a window of frames and returns the average lines. """ avg_line_lt = np.zeros((len(lane_lines), 4)) avg_line_rt = np.zeros((len(lane_lines), 4)) for t in range(0, len(lane_lines)): avg_line_lt[t] += lane_lines[t...
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def select_random(nodes: List[DiscoveredNode]) -> Optional[DiscoveredNode]: """ Return a random node. """ return random.choice(nodes)
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def convert(from_path, ingestor, to_path, egestor, select_only_known_labels, filter_images_without_labels): """ Converts between data formats, validating that the converted data matches `IMAGE_DETECTION_SCHEMA` along the way. :param from_path: '/path/to/read/from' :param ingestor: `Ingestor` to rea...
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def timer(method): """ Method decorator to capture and print total run time in seconds :param method: The method or function to time :return: A function """ @wraps(method) def wrapped(*args, **kw): timer_start = timeit.default_timer() result = method(*args, **kw) time...
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def save_figure(df, filename, output, events, eventtimes): """ Takes in a pandas dataframe and saves the graph of the data. Also labels events on the graph the user defines. Arguments: df {pd.DataFrame} -- a dataframe to generate a graph from filename {str} -- the name to sav...
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def macro_states(macro_df, style, roll_window): """ Function to convert macro factors into binary states Args: macro_df (pd.DataFrame): contains macro factors data style (str): specify method used to classify. Accepted values: 'naive' roll_window (int): specify rolling...
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def test_zsp_gets_right_roles_for_methods(): """ >>> from AccessControl.unauthorized import Unauthorized >>> from AccessControl.ZopeSecurityPolicy import ZopeSecurityPolicy >>> zsp = ZopeSecurityPolicy() >>> from ExtensionClass import Base >>> class C(Base): ... def foo(self): ... ...
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def bcd7(num): """Converts num to a BCD representation""" GPIO.output(36, GPIO.HIGH if (num & 0x00000001) > 0 else GPIO.LOW ) GPIO.output(38, GPIO.HIGH if (num & 0x00000002) > 0 else GPIO.LOW ) GPIO.output(40, GPIO.HIGH if (num & 0x00000004) > 0 else GPIO.LOW ) GPIO.output(37, GPIO.HIGH if (num & 0x00000008) ...
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def get_sparsity(lat): """Return percentage of nonzero slopes in lat. Args: lat (Lattice): instance of Lattice class """ # Initialize operators placeholder_input = torch.tensor([[0., 0]]) op = Operators(lat, placeholder_input) # convert z, L, H to np.float64 (simplex requires this) ...
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def XYZ_to_Kim2009( XYZ: ArrayLike, XYZ_w: ArrayLike, L_A: FloatingOrArrayLike, media: MediaParameters_Kim2009 = MEDIA_PARAMETERS_KIM2009["CRT Displays"], surround: InductionFactors_Kim2009 = VIEWING_CONDITIONS_KIM2009["Average"], discount_illuminant: Boolean = False, n_c: Floating = 0.57, )...
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def load_capsule(path: Union[str, Path], source_path: Optional[Path] = None, key: Optional[str] = None, inference_mode: bool = True) -> BaseCapsule: """Load a capsule from the filesystem. :param path: The path to the capsule file :param source_path: The pa...
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def geodetic2ecef(lat, lon, alt): """Convert geodetic coordinates to ECEF.""" lat, lon = radians(lat), radians(lon) xi = sqrt(1 - esq * sin(lat)) x = (a / xi + alt) * cos(lat) * cos(lon) y = (a / xi + alt) * cos(lat) * sin(lon) z = (a / xi * (1 - esq) + alt) * sin(lat) return x, y, z
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def processor_group_size(nprocs, number_of_tasks): """ Find the number of groups to divide `nprocs` processors into to tackle `number_of_tasks` tasks. When `number_of_tasks` > `nprocs` the smallest integer multiple of `nprocs` is returned that equals or exceeds `number_of_tasks` is returned. When ...
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def skin_base_url(skin, variables): """ Returns the skin_base_url associated to the skin. """ return variables \ .get('skins', {}) \ .get(skin, {}) \ .get('base_url', '')
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def test_output_path_snapshot_local_add_output(tmpdir, version, mode): """ Test of output path for local snapshot output created with 'lttng snapshot add-output'. """ nb_loop = 10 datetime_re = "[0-9]{8}-[0-9]{6}" snapshot_re = "snapshot-1-{}-0".format(datetime_re) trailing = "{}/ust/{}"...
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def check_is_author_logged_in(author_name: str) -> None: """ Check if current user's name equals to item's author. :param author_name: str item author username. :raise ClickException: if username and author's name are not equal. :return: None. """ resp = request_api("GET", "/rest-auth/user...
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def load( name: str, device: Union[str, torch.device] = 'cuda' if torch.cuda.is_available() else 'cpu', jit: bool = False, download_root: str = None, ): """Load a CLIP model Parameters ---------- name : str A model name listed by `clip.available_models()`, or the path to a model...
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def default_pre_training_callbacks( logger=default_logger, with_lr_finder=False, with_export_augmentations=True, with_reporting_server=True, with_profiler=False, additional_callbacks=None): """ Default callbacks to be performed before the fitting of the model ...
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def validate_dumpling(dumpling_json): """ Validates a dumpling received from (or about to be sent to) the dumpling hub. Validation involves ensuring that it's valid JSON and that it includes a ``metadata.chef`` key. :param dumpling_json: The dumpling JSON. :raise: :class:`netdumplings.exception...
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def check_gradient(func,atol=1e-8,rtol=1e-5,quiet=False): """ Test gradient function with a set of MC photons. This works with either LCPrimitive or LCTemplate objects. TODO -- there is trouble with the numerical gradient when a for the location-related parameters when the finite st...
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def _recurse_to_best_estimate( lower_bound, upper_bound, num_entities, sample_sizes ): """Recursively finds the best estimate of population size by identifying which half of [lower_bound, upper_bound] contains the best estimate. Parameters ---------- lower_bound: int The lower bound...
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def betwix(iterable, start=None, stop=None, inc=False): """ Extract selected elements from an iterable. But unlike `islice`, extract based on the element's value instead of its position. Args: iterable (iter): The initial sequence start (str): The fragment to begin with (inclusive) ...
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def map_configuration(config: dict) -> tp.List[MeterReaderNode]: # noqa MC0001 """ Parsed configuration :param config: dict from :return: """ # pylint: disable=too-many-locals, too-many-nested-blocks meter_reader_nodes = [] if 'devices' in config and 'middleware' in config: try:...
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def places(client, query, location=None, radius=None, language=None, min_price=None, max_price=None, open_now=False, type=None, region=None, page_token=None): """ Places search. :param query: The text string on which to search, for example: "restaurant". :type query: string :...
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def load_data(_file, pct_split): """Load test and train data into a DataFrame :return pd.DataFrame with ['test'/'train', features]""" # load train and test data data = pd.read_csv(_file) # split into train and test using pct_split # data_train = ... # data_test = ... # concat and labe...
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def sorted_items(d, key=None, reverse=False): """Given a dictionary `d` return items: (k1, v1), (k2, v2)... sorted in ascending order according to key. :param dict d: dictionary :param key: optional function remapping key :param bool reverse: If True return in descending order instead of default as...
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def factorize(eri_full, rank): """ Do single factorization of the ERI tensor Args: eri_full (np.ndarray) - 4D (N x N x N x N) full ERI tensor rank (int) - number of vectors to retain in ERI rank-reduction procedure Returns: eri_rr (np.ndarray) - 4D approximate ERI tensor reconstructed...
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def randint_population(shape, max_value, min_value=0): """Generate a random population made of Integers Args: (set of ints): shape of the population. Its of the form (num_chromosomes, chromosome_dim_1, .... chromesome_dim_n) max_value (int): Maximum value taken by a given gene. ...
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def simplex_creation( mean_value: np.array, sigma_variation: np.array, rng: RandomNumberGenerator = None ) -> np.array: """ Creation of the simplex @return: """ ctrl_par_number = mean_value.shape[0] ################## # Scale matrix: # Explain what the scale matrix means here ##...
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def _scale_db(out, data, mask, vmins, vmaxs, scale=1.0, offset=0.0): # pylint: disable=too-many-arguments """ decibel data scaling. """ vmins = [0.1*v for v in vmins] vmaxs = [0.1*v for v in vmaxs] return _scale_log10(out, data, mask, vmins, vmaxs, scale, offset)
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def make_tree(anime): """ Creates anime tree :param anime: Anime :return: AnimeTree """ tree = AnimeTree(anime) # queue for BFS queue = deque() root = tree.root queue.appendleft(root) # set for keeping track of visited anime visited = {anime} # BFS downwards while len(queue) > 0: current = queue.pop()...
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def draw_bboxes(images, # type: thelper.typedefs.InputType preds=None, # type: Optional[thelper.typedefs.AnyPredictionType] bboxes=None, # type: Optional[thelper.typedefs.AnyTargetType] color_map=None, # type: Optional[thelpe...
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def html_table_from_dict(data, ordering): """ >>> ordering = ['administrators', 'key', 'leader', 'project'] >>> data = [ \ {'key': 'DEMO', 'project': 'Demonstration', 'leader': 'leader@example.com', 'administrators': ['admin1@example.com', 'admin2@example.com']}, \ {'key': 'FOO', 'project': ...
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