JorgeVB/code_unlearning_dataset · Data Studio

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Computes the importance of each metadata by using tf * coverage (the percentage of the term occuring in a cluster) :param path_to_bag_of_genomes: The directory path :param cluster_no: cluster number :param preprocess: to remove the redundant information from the metadata :return: return...
Renders the cloud of words representation for a given dictionary of frequencies :param dictionary: the dictionary object that contains key-frequency pairs :param image_path: the path to the image mask, None if no masking is needed def visualize_cloud_of_words(dictionary, image_path=None): """ ...
Draws the cloud of words representation :param path_to_bog: path to bag of words :param cluster_no: the number of document to be visualized :param image_path: path to the image file for the masking, None if no masking is needed def cloud_of_words(path_to_bog, cluster_no, image_path=None): ...
Returns a sorted list of all of the files having the same extension under the same directory :param extension: the extension of the data files such as 'gdm' :param path: path to the folder containing the files :return: sorted list of files def _get_files(extension, path): """ Re...
Returns the schema file :param extension: extension of the schema file usually .schema :param path: path to the folder containing the schema file :return: the path to the schema file def _get_schema_file(extension, path): """ Returns the schema file :param extension: ext...
parses the schema file and returns the columns that are later going to represent the columns of the genometric space dataframe :param schema_file: the path to the schema file :return: the columns of the schema file def parse_schema(schema_file): """ parses the schema file and returns th...
Parses a single meta data file :param fname: name of the file :param selected_meta_data: If not none then only the specified columns of metadata are parsed :return: the resulting pandas series def parse_single_meta(self, fname, selected_meta_data): """ Parses a single meta data ...
Parses all of the metadata files :param selected_meta_data: if specified then only the columns that are contained here are going to be parsed :return: def parse_meta(self, selected_meta_data): """ Parses all of the metadata files :param selected_meta_data: if specified then only...
Parses a single region data file :param path: path to the file :param cols: the column names coming from the schema file :param selected_region_data: the selected of the region data to be parsed In most cases the analyst only needs a small subset of the region data :param selecte...
Parses all of the region data :param selected_region_data: the columns of region data that are needed :param selected_values: the selected values to be put in the matrix cells :param full_load: Specifies the method of parsing the data. If False then parser omits the parsing of zero(0) ...
Creates a GDataframe from a pandas dataframe of region and a pandas dataframe of metadata :param regs: a pandas Dataframe of regions that is coherent with the GMQL data model :param meta: (optional) a pandas Dataframe of metadata that is coherent with the regions :param chr_name: (optional) which column of...
Modifies a region dataframe to be coherent with the GMQL data model :param region_df: a pandas Dataframe of regions that is coherent with the GMQL data model :param chr_name: (optional) which column of :attr:`~.region_df` is the chromosome :param start_name: (optional) which column of :attr:`~.region_df` i...
Save the GDataframe to a local or remote location :param local_path: a local path to the folder in which the data must be saved :param remote_path: a remote dataset name that wants to be used for these data :return: None def to_dataset_files(self, local_path=None, remote_path=None): ""...
Converts the GDataframe in a GMQLDataset for later local or remote computation :return: a GMQLDataset def to_GMQLDataset(self, local_path=None, remote_path=None): """ Converts the GDataframe in a GMQLDataset for later local or remote computation :return: a GMQLDataset """ loca...
Projects the specified metadata attributes to new region fields :param attributes: a list of metadata attributes :return: a new GDataframe with additional region fields def project_meta(self, attributes): """ Projects the specified metadata attributes to new region fields :param attri...
Transforms the GDataframe to a pivot matrix having as index and columns the ones specified. This function is a wrapper around the pivot_table function of Pandas. :param index_regs: list of region fields to use as index :param index_meta: list of metadata attributes to use as index :para...
Charts Returns a list of Charts, ordered by creation date (newest first). A Chart is chosen by Pollster editors. One example is \"Obama job approval - Democrats\". It is always based upon a single Question. Users should strongly consider basing their analysis on Questions instead. Charts are derived data; Pol...
Chart A Chart is chosen by Pollster editors. One example is \"Obama job approval - Democrats\". It is always based upon a single Question. Users should strongly consider basing their analysis on Questions instead. Charts are derived data; Pollster editors publish them and change them as editorial priorities ch...
One row per poll plotted on a Chart Derived data presented on a Pollster Chart. Rules for which polls and responses are plotted on a chart can shift over time. Here are some examples of behaviors Pollster has used in the past: * We've omitted \"Registered Voters\" from a chart when \"Likely Voters\" respond...
Estimates of what the polls suggest about trends Derived data presented on a Pollster Chart. The trendlines on a Pollster chart don't add up to 100: we calculate each label's trendline separately. Use the `charts/{slug}` response's `chart.pollster_estimates[0].algorithm` to find the algorithm Pollster used to...
Polls A Poll on Pollster is a collection of questions and responses published by a reputable survey house. This endpoint provides raw data from the survey house, plus Pollster-provided metadata about each question. Pollster editors don't include every question when they enter Polls, and they don't necessarily ...
Poll A Poll on Pollster is a collection of questions and responses published by a reputable survey house. This endpoint provides raw data from the survey house, plus Pollster-provided metadata about each question. Pollster editors don't include every question when they enter Polls, and they don't necessarily e...
Questions Returns a list of Questions. A Question is chosen by Pollster editors. One example is \"Obama job approval\". Different survey houses may publish varying phrasings (\"Do you approve or disapprove\" vs \"What do you think of the job\") and prompt readers with varying responses (one poll might have \"...
Question A Question is chosen by Pollster editors. One example is \"Obama job approval\". Different survey houses may publish varying phrasings (\"Do you approve or disapprove\" vs \"What do you think of the job\") and prompt readers with varying responses (one poll might have \"Approve\" and \"Disapprove\"; a...
One row of response values per PollQuestion+Subpopulation concerning the given Question We include one TSV column per response label. See `questions/{slug}` for the Question's list of response labels, which are chosen by Pollster editors. Each row represents a single PollQuestion+Subpopulation. The value for ea...
One row per PollQuestion+Subpopulation+Response concerning the given Question (Large) Raw data from which we derived `poll-responses-clean.tsv`. Each row represents a single PollQuestion+Subpopulation+Response. See the Poll API for a description of these terms. Group results by `(poll_slug, subpopulation, que...
Tags Returns the list of Tags. A Tag can apply to any number of Charts and Questions; Charts and Questions, in turn, can have any number of Tags. Tags all look `like-this`: lowercase letters, numbers and hyphens. This method makes a synchronous HTTP request by default. To make an asynchronou...
Builds a JSON POST object. If obj is None, return None. If obj is str, int, long, float, bool, return directly. If obj is datetime.datetime, datetime.date convert to string in iso8601 format. If obj is list, sanitize each element in the list. If obj is dict, return t...
Returns `Content-Type` based on an array of content_types provided. :param content_types: List of content-types. :return: Content-Type (e.g. application/json). def select_header_content_type(self, content_types): """ Returns `Content-Type` based on an array of content_types provided. ...
Deserializes list or dict to model. :param data: dict, list. :param klass: class literal. :return: model object. def __deserialize_model(self, data, klass): """ Deserializes list or dict to model. :param data: dict, list. :param klass: class literal. :r...
Public interface to the flat page view. Models: `flatpages.flatpages` Templates: Uses the template defined by the ``template_name`` field, or `flatpages/default.html` if template_name is not defined. Context: flatpage `flatpages.flatpages` object def flatpage(request, url): ...
Internal interface to the flat page view. def render_flatpage(request, f): """ Internal interface to the flat page view. """ # If registration is required for accessing this page, and the user isn't # logged in, redirect to the login page. if f.registration_required and not request.user.is_auth...
Get the absolute path of the pidfile. def pidfile(self): """Get the absolute path of the pidfile.""" return os.path.abspath( os.path.expandvars( os.path.expanduser( self._pidfile, ), ), )
Computes the square matrix of the argument :param argument: a dataset region field (dataset.field) or metadata (dataset['field']) def SQRT(argument): """ Computes the square matrix of the argument :param argument: a dataset region field (dataset.field) or metadata (dataset['field']) """ if isinst...
Enables the user to login to the remote GMQL service. If both username and password are None, the user will be connected as guest. def login(): """ Enables the user to login to the remote GMQL service. If both username and password are None, the user will be connected as guest. """ from .RemoteConn...
Any normal python logic which runs a loop. Can take arguments. def main(idle): """Any normal python logic which runs a loop. Can take arguments.""" while True: LOG.debug("Sleeping for {0} seconds.".format(idle)) time.sleep(idle)
Returns the metadata related to the current GMQLDataset. This function can be used only when a local dataset is loaded using the :meth:`~gmql.dataset.loaders.Loader.load_from_path` and no other operation has been called on the GMQLDataset. The metadata are returned in the form of a Pandas Dataf...
Creates an instance of a metadata field of the dataset. It can be used in building expressions or conditions for projection or selection. Notice that this function is equivalent to call:: dataset["name"] If the MetaField is used in a region projection (:meth:`~.reg_proj...
Wrapper of ``SELECT`` Selection operation. Enables to filter datasets on the basis of region features or metadata attributes. In addition it is possibile to perform a selection based on the existence of certain metadata :attr:`~.semiJoinMeta` attributes and the matching of their values with t...
Wrapper of ``SELECT`` Wrapper of the :meth:`~.select` function filtering samples only based on metadata. :param predicate: logical predicate on the values of the rows :param semiJoinDataset: an other GMQLDataset :param semiJoinMeta: a list of metadata :return: a new GMQLData...
Wrapper of ``SELECT`` Wrapper of the :meth:`~.select` function filtering regions only based on region attributes. :param predicate: logical predicate on the values of the regions :param semiJoinDataset: an other GMQLDataset :param semiJoinMeta: a list of metadata :return: a ...
Wrapper of ``PROJECT`` The PROJECT operator creates, from an existing dataset, a new dataset with all the samples (with their regions and region values) in the input one, but keeping for each sample in the input dataset only those metadata and/or region attributes expressed in the operator pa...
Wrapper of ``PROJECT`` Project the metadata based on a list of attribute names :param attr_list: list of the metadata fields to select :param all_but: list of metadata that must be excluded from the projection. :param new_attr_dict: an optional dictionary of the form {'new_fi...
Wrapper of ``PROJECT`` Project the region data based on a list of field names :param field_list: list of the fields to select :param all_but: keep only the region fields different from the ones specified :param new_field_dict: an optional dictionary of the form {'new_f...
Wrapper of ``EXTEND`` For each sample in an input dataset, the EXTEND operator builds new metadata attributes, assigns their values as the result of arithmetic and/or aggregate functions calculated on sample region attributes, and adds them to the existing metadata attribute-value pairs of ...
Wrapper of ``COVER`` COVER is a GMQL operator that takes as input a dataset (of usually, but not necessarily, multiple samples) and returns another dataset (with a single sample, if no groupby option is specified) by “collapsing” the input samples and their regions according to cert...
Wrapper of ``COVER`` The normal cover operation as described in :meth:`~.cover`. Equivalent to calling:: dataset.cover("normal", ...) def normal_cover(self, minAcc, maxAcc, groupBy=None, new_reg_fields=None): """ Wrapper of ``COVER`` The normal cover o...
Wrapper of ``COVER`` Variant of the function :meth:`~.cover` that returns the union of all the regions which contribute to the COVER. More precisely, it returns the contiguous regions that start from the first end and stop at the last end of the regions which would contribute to ea...
Wrapper of ``COVER`` Variant of the function :meth:`~.cover` that returns only those portions of the COVER result where the maximum number of regions overlap (this is done by returning only regions that start from a position after which the number of overlaps does not increase, and s...
Wrapper of ``COVER`` Variant of the function :meth:`~.cover` that returns all regions contributing to the COVER divided in different (contiguous) parts according to their accumulation index value (one part for each different accumulation value), which is assigned to the AccIndex re...
Wrapper of ``JOIN`` The JOIN operator takes in input two datasets, respectively known as anchor (the first/left one) and experiment (the second/right one) and returns a dataset of samples consisting of regions extracted from the operands according to the specified condition (known as...
Wrapper of ``MAP`` MAP is a non-symmetric operator over two datasets, respectively called reference and experiment. The operation computes, for each sample in the experiment dataset, aggregates over the values of the experiment regions that intersect with a region in a reference sam...
Wrapper of ``ORDER`` The ORDER operator is used to order either samples, sample regions, or both, in a dataset according to a set of metadata and/or region attributes, and/or region coordinates. The number of samples and their regions in the output dataset is as in the input dataset...
Wrapper of ``DIFFERENCE`` DIFFERENCE is a binary, non-symmetric operator that produces one sample in the result for each sample of the first operand, by keeping the same metadata of the first operand sample and only those regions (with their schema and values) of the first operand sam...
Wrapper of ``UNION`` The UNION operation is used to integrate homogeneous or heterogeneous samples of two datasets within a single dataset; for each sample of either one of the input datasets, a sample is created in the result as follows: * its metadata are the same as in...
Wrapper of ``MERGE`` The MERGE operator builds a new dataset consisting of a single sample having * as regions all the regions of all the input samples, with the same attributes and values * as metadata the union of all the metadata attribute-values ...
Wrapper of ``GROUP`` The GROUP operator is used for grouping both regions and/or metadata of input dataset samples according to distinct values of certain attributes (known as grouping attributes); new grouping attributes are added to samples in the output dataset, storing the results...
Wrapper of ``GROUP`` Group operation only for metadata. For further information check :meth:`~.group` def meta_group(self, meta, meta_aggregates=None): """ Wrapper of ``GROUP`` Group operation only for metadata. For further information check :meth:`~.group` """ ret...
Wrapper of ``GROUP`` Group operation only for region data. For further information check :meth:`~.group` def regs_group(self, regs, regs_aggregates=None): """ Wrapper of ``GROUP`` Group operation only for region data. For further information check :meth:`~.group` """ ...
Wrapper of ``MATERIALIZE`` Starts the execution of the operations for the GMQLDataset. PyGMQL implements lazy execution and no operation is performed until the materialization of the results is requestd. This operation can happen both locally or remotely. * Local mode: if the GMQLDat...
Grab a new message and dispatch it to the handler. This method should not be extended or overwritten. Instead, implementations of this daemon should implement the 'get_message()' and 'handle_message()' methods. def step(self): """Grab a new message and dispatch it to the handler. ...
Initialize application configuration def init_app(self, app=None): """Initialize application configuration""" config = getattr(app, 'config', app) self.team_id = config.get('TEAM_ID')
A decorator used to register a command. Example:: @slack.command('your_command', token='your_token', team_id='your_team_id', methods=['POST']) def your_method(**kwargs): text = kwargs.get('text') return slack.response(text) ...
Dispatch http request to registerd commands. Example:: slack = Slack(app) app.add_url_rule('/', view_func=slack.dispatch) def dispatch(self): """Dispatch http request to registerd commands. Example:: slack = Slack(app) app.add_url_rule('/', view...
Validate request queries with registerd commands :param command: command parameter from request :param token: token parameter from request :param team_id: team_id parameter from request :param method: the request method def validate(self, command, token, team_id, method): """Va...
Return a response with json format :param text: the text returned to the client :param response_type: optional. When `in_channel` is assigned, both the response message and the initial message typed by the user will be shared ...
Get the pid which represents a daemonized process. The result should be None if the process is not running. def pid(self): """Get the pid which represents a daemonized process. The result should be None if the process is not running. """ try: with open(self.pidfil...
Set the pid for a running process. def pid(self, pidnum): """Set the pid for a running process.""" try: with open(self.pidfile, "w+") as pidfile: pidfile.write("{0}\n".format(pidnum)) except IOError: LOG.exception("Failed to write pidfile {0}).".forma...
Stop managing the current pid. def pid(self): """Stop managing the current pid.""" try: os.remove(self.pidfile) except IOError: if not os.path.isfile(self.pidfile): return None LOG.exception("Failed to clear pidfile {0}).".format(self.pid...
Get an gevent pool used to dispatch requests. def pool(self): """Get an gevent pool used to dispatch requests.""" self._pool = self._pool or gevent.pool.Pool(size=self.pool_size) return self._pool
Set the ESSID of a given interface Arguments: interface - device to work on (e.g. eth1, wlan0). essid - ESSID to set. Must be no longer than IW_ESSID_MAX_SIZE (typically 32 characters). def set_essid(interface, essid): """ Set the ESSID of a given interface Arguments: interfac...
Reads an edge list in csv format and returns the adjacency matrix in SciPy Sparse COOrdinate format. Inputs: - file_path: The path where the adjacency matrix is stored. - separator: The delimiter among values (e.g. ",", "\t", " ") Outputs: - adjacency_matrix: The adjacency matrix in SciPy Sparse...
Reads node-label pairs in csv format and returns a list of tuples and a node-label matrix. Inputs: - file_path: The path where the node-label matrix is stored. - separator: The delimiter among values (e.g. ",", "\t", " ") - number_of_nodes: The number of nodes of the full graph. It is po...
Ensure that the Garuda directory and files def ensure_data(): ''' Ensure that the Garuda directory and files ''' if not os.path.exists(GARUDA_DIR): os.makedirs(GARUDA_DIR) Path(f'{GARUDA_DIR}/__init__.py').touch()
Construct protobuf compiler arguments def protoc_arguments(): ''' Construct protobuf compiler arguments ''' proto_include = resource_filename('grpc_tools', '_proto') return [ protoc.__file__, '-I', GARUDA_DIR, f'--python_out={GARUDA_DIR}', f'--grpc_python_out={GARUDA_DIR}', GARUDA_P...
Snippet to fix the gRPC import path def fix_grpc_import(): ''' Snippet to fix the gRPC import path ''' with open(GARUDA_GRPC_PATH, 'r') as f: filedata = f.read() filedata = filedata.replace( 'import garuda_pb2 as garuda__pb2', f'import {GARUDA_DIR}.garuda_pb2 as garuda__pb2'...
Simple utility function that writes an average score row in a file designated by a file pointer. Inputs: - fp: A file pointer. - score_name: What it says on the tin. - scores: An array of average score values corresponding to each of the training set percentages. def write_average_score...
Catches specific errors in admin actions and shows a friendly error. def catch_errors(f): """ Catches specific errors in admin actions and shows a friendly error. """ @functools.wraps(f) def wrapper(self, request, args, kwargs): try: return f(self, request, args, **kwargs) ...
Performs augmented TF-IDF normalization on a bag-of-words vector representation of data. Augmented TF-IDF introduced in: Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval (Vol. 1, p. 6). Cambridge: Cambr...
Reads an separated value file row by row. Inputs: - file_path: The path of the separated value format file. - separator: The delimiter among values (e.g. ",", "\t", " ") - encoding: The encoding used in the stored text. Yields: - words: A list of strings corresponding to each of the fi...
Pickle some data to a given path. Inputs: - file_path: Target file path. - data: The python object to be serialized via pickle. def store_pickle(file_path, data): """ Pickle some data to a given path. Inputs: - file_path: Target file path. - data: The python object to be seria...
Unpickle some data from a given path. Input: - file_path: Target file path. Output: - data: The python object that was serialized and stored in disk. def load_pickle(file_path): """ Unpickle some data from a given path. Input: - file_path: Target file path. Output: - data: The python obje...
Given the current ID, returns a packet to set the servo to a new ID def makeServoIDPacket(curr_id, new_id): """ Given the current ID, returns a packet to set the servo to a new ID """ pkt = Packet.makeWritePacket(curr_id, xl320.XL320_ID, [new_id]) return pkt
This performs row normalization to 1 of community embedding features. Input: - X in R^(nxC_n): The community indicator matrix. Output: - X_norm in R^(nxC_n): The row normalized community indicator matrix. def normalize_rows(features): """ This performs row normalization to 1 of community embedding f...
This performs column normalization of community embedding features. Input: - X in R^(nxC_n): The community indicator matrix. Output: - X_norm in R^(nxC_n): The tf-idf + row normalized community indicator matrix. def normalize_columns(features): """ This performs column normalization of community emb...
Performs a random step without a self-loop. def pagerank_limit_push(s, r, w_i, a_i, push_node, rho): """ Performs a random step without a self-loop. """ # Calculate the A and B quantities to infinity A_inf = rhor[push_node] B_inf = (1-rho)r[push_node] # Update approximate Pagerank and re...
Performs a random step with a self-loop. Introduced in: Andersen, R., Chung, F., & Lang, K. (2006, October). Local graph partitioning using pagerank vectors. In Foundations of Computer Science, 2006. FOCS'06. 47th Annual IEEE Symposium on (pp. 475-486). IEEE. def pagerank_laz...
Performs a random step without a self-loop. Inputs: - s: A NumPy array that contains the approximate absorbing random walk cumulative probabilities. - r: A NumPy array that contains the residual probability distribution. - w_i: A NumPy array of probability transition weights from the see...
Include pyramid_multiauth into a pyramid configurator. This function provides a hook for pyramid to include the default settings for auth via pyramid_multiauth. Activate it like so: config.include("pyramid_multiauth") This will pull the list of registered authn policies from the deployment s...
Create a policy factory that works by config.include()'ing a module. This function does some trickery with the Pyramid config system. Loosely, it does config.include(module), and then sucks out information about the authn policy that was registered. It's complicated by pyramid's delayed- commit system...
Find all multiauth policy definitions from the settings dict. This function processes the paster deployment settings looking for items that start with "multiauth.policy.<policyname>.". It pulls them all out into a dict indexed by the policy name. def get_policy_definitions(settings): """Find all mult...
Read file to memory. For stdin sourced files, this function does something super duper incredibly hacky and shameful. So so shameful. I'm obtaining the original source code of the target module from the only instance of pycodestyle.Checker through the Python garbage collector. Flake8's API doesn't give me ...
Should this code be ignored. :param str code: Error code (e.g. D201). :return: True if code should be ignored, False otherwise. :rtype: bool def ignore(code): """Should this code be ignored. :param str code: Error code (e.g. D201). :return: True if code should be ignored, False otherwise. ...
Add options to flake8. :param parser: optparse.OptionParser from pycodestyle. def add_options(cls, parser): """Add options to flake8. :param parser: optparse.OptionParser from pycodestyle. """ parser.add_option('--show-pydocstyle', action='store_true', help='show explanation o...
Read parsed options from flake8. :param options: Options to add to flake8's command line options. def parse_options(cls, options): """Read parsed options from flake8. :param options: Options to add to flake8's command line options. """ # Handle flake8 options. cls.opti...
Run analysis on a single file. def run(self): """Run analysis on a single file.""" pydocstyle.Error.explain = self.options['explain'] filename, source = load_file(self.filename) for error in pydocstyle.PEP257Checker().check_source(source, filename): if not hasattr(error, 'co...
Returns the barcode's number without the verification digit. :return: list(int) def numbers(self): """" Returns the barcode's number without the verification digit. :return: list(int) """ numstring = '{:011d}{:02d}{:04d}{}{}'.format( self._receipt.point_of_...
Returns the verification digit for a given numbre. The verification digit is calculated as follows: * A = sum of all even-positioned numbers * B = A * 3 * C = sum of all odd-positioned numbers * D = B + C * The results is the smallset number N, such that (D + N) % 10 ==...
Returns the full number including the verification digit. :return: str def full_number(self): """ Returns the full number including the verification digit. :return: str """ return '{}{}'.format( ''.join(str(n) for n in self.numbers), ReceiptBarc...
Reads an edge list in csv format and returns the adjacency matrix in SciPy Sparse COOrdinate format. Inputs: - file_path: The path where the adjacency matrix is stored. - separator: The delimiter among values (e.g. ",", "\t", " ") - numbering: Array numbering style: * "matlab" ...

Computes the importance of each metadata by using tf * coverage (the percentage of the term occuring in a cluster) :param path_to_bag_of_genomes: The directory path :param cluster_no: cluster number :param preprocess: to remove the redundant information from the metadata :return: return...

Renders the cloud of words representation for a given dictionary of frequencies :param dictionary: the dictionary object that contains key-frequency pairs :param image_path: the path to the image mask, None if no masking is needed def visualize_cloud_of_words(dictionary, image_path=None): """ ...

Draws the cloud of words representation :param path_to_bog: path to bag of words :param cluster_no: the number of document to be visualized :param image_path: path to the image file for the masking, None if no masking is needed def cloud_of_words(path_to_bog, cluster_no, image_path=None): ...

Returns a sorted list of all of the files having the same extension under the same directory :param extension: the extension of the data files such as 'gdm' :param path: path to the folder containing the files :return: sorted list of files def _get_files(extension, path): """ Re...

Returns the schema file :param extension: extension of the schema file usually .schema :param path: path to the folder containing the schema file :return: the path to the schema file def _get_schema_file(extension, path): """ Returns the schema file :param extension: ext...

parses the schema file and returns the columns that are later going to represent the columns of the genometric space dataframe :param schema_file: the path to the schema file :return: the columns of the schema file def parse_schema(schema_file): """ parses the schema file and returns th...

Parses a single meta data file :param fname: name of the file :param selected_meta_data: If not none then only the specified columns of metadata are parsed :return: the resulting pandas series def parse_single_meta(self, fname, selected_meta_data): """ Parses a single meta data ...

Parses all of the metadata files :param selected_meta_data: if specified then only the columns that are contained here are going to be parsed :return: def parse_meta(self, selected_meta_data): """ Parses all of the metadata files :param selected_meta_data: if specified then only...

Parses a single region data file :param path: path to the file :param cols: the column names coming from the schema file :param selected_region_data: the selected of the region data to be parsed In most cases the analyst only needs a small subset of the region data :param selecte...

Parses all of the region data :param selected_region_data: the columns of region data that are needed :param selected_values: the selected values to be put in the matrix cells :param full_load: Specifies the method of parsing the data. If False then parser omits the parsing of zero(0) ...

Creates a GDataframe from a pandas dataframe of region and a pandas dataframe of metadata :param regs: a pandas Dataframe of regions that is coherent with the GMQL data model :param meta: (optional) a pandas Dataframe of metadata that is coherent with the regions :param chr_name: (optional) which column of...

Modifies a region dataframe to be coherent with the GMQL data model :param region_df: a pandas Dataframe of regions that is coherent with the GMQL data model :param chr_name: (optional) which column of :attr:`~.region_df` is the chromosome :param start_name: (optional) which column of :attr:`~.region_df` i...

Save the GDataframe to a local or remote location :param local_path: a local path to the folder in which the data must be saved :param remote_path: a remote dataset name that wants to be used for these data :return: None def to_dataset_files(self, local_path=None, remote_path=None): ""...

Converts the GDataframe in a GMQLDataset for later local or remote computation :return: a GMQLDataset def to_GMQLDataset(self, local_path=None, remote_path=None): """ Converts the GDataframe in a GMQLDataset for later local or remote computation :return: a GMQLDataset """ loca...

Projects the specified metadata attributes to new region fields :param attributes: a list of metadata attributes :return: a new GDataframe with additional region fields def project_meta(self, attributes): """ Projects the specified metadata attributes to new region fields :param attri...

Transforms the GDataframe to a pivot matrix having as index and columns the ones specified. This function is a wrapper around the pivot_table function of Pandas. :param index_regs: list of region fields to use as index :param index_meta: list of metadata attributes to use as index :para...

Charts Returns a list of Charts, ordered by creation date (newest first). A Chart is chosen by Pollster editors. One example is \"Obama job approval - Democrats\". It is always based upon a single Question. Users should strongly consider basing their analysis on Questions instead. Charts are derived data; Pol...

Chart A Chart is chosen by Pollster editors. One example is \"Obama job approval - Democrats\". It is always based upon a single Question. Users should strongly consider basing their analysis on Questions instead. Charts are derived data; Pollster editors publish them and change them as editorial priorities ch...

One row per poll plotted on a Chart Derived data presented on a Pollster Chart. Rules for which polls and responses are plotted on a chart can shift over time. Here are some examples of behaviors Pollster has used in the past: * We've omitted \"Registered Voters\" from a chart when \"Likely Voters\" respond...

Estimates of what the polls suggest about trends Derived data presented on a Pollster Chart. The trendlines on a Pollster chart don't add up to 100: we calculate each label's trendline separately. Use the `charts/{slug}` response's `chart.pollster_estimates[0].algorithm` to find the algorithm Pollster used to...

Polls A Poll on Pollster is a collection of questions and responses published by a reputable survey house. This endpoint provides raw data from the survey house, plus Pollster-provided metadata about each question. Pollster editors don't include every question when they enter Polls, and they don't necessarily ...

Poll A Poll on Pollster is a collection of questions and responses published by a reputable survey house. This endpoint provides raw data from the survey house, plus Pollster-provided metadata about each question. Pollster editors don't include every question when they enter Polls, and they don't necessarily e...

Questions Returns a list of Questions. A Question is chosen by Pollster editors. One example is \"Obama job approval\". Different survey houses may publish varying phrasings (\"Do you approve or disapprove\" vs \"What do you think of the job\") and prompt readers with varying responses (one poll might have \"...

Question A Question is chosen by Pollster editors. One example is \"Obama job approval\". Different survey houses may publish varying phrasings (\"Do you approve or disapprove\" vs \"What do you think of the job\") and prompt readers with varying responses (one poll might have \"Approve\" and \"Disapprove\"; a...

One row of response values per PollQuestion+Subpopulation concerning the given Question We include one TSV column per response label. See `questions/{slug}` for the Question's list of response labels, which are chosen by Pollster editors. Each row represents a single PollQuestion+Subpopulation. The value for ea...

One row per PollQuestion+Subpopulation+Response concerning the given Question (Large) Raw data from which we derived `poll-responses-clean.tsv`. Each row represents a single PollQuestion+Subpopulation+Response. See the Poll API for a description of these terms. Group results by `(poll_slug, subpopulation, que...

Tags Returns the list of Tags. A Tag can apply to any number of Charts and Questions; Charts and Questions, in turn, can have any number of Tags. Tags all look `like-this`: lowercase letters, numbers and hyphens. This method makes a synchronous HTTP request by default. To make an asynchronou...

Builds a JSON POST object. If obj is None, return None. If obj is str, int, long, float, bool, return directly. If obj is datetime.datetime, datetime.date convert to string in iso8601 format. If obj is list, sanitize each element in the list. If obj is dict, return t...

Returns `Content-Type` based on an array of content_types provided. :param content_types: List of content-types. :return: Content-Type (e.g. application/json). def select_header_content_type(self, content_types): """ Returns `Content-Type` based on an array of content_types provided. ...

Deserializes list or dict to model. :param data: dict, list. :param klass: class literal. :return: model object. def __deserialize_model(self, data, klass): """ Deserializes list or dict to model. :param data: dict, list. :param klass: class literal. :r...

Public interface to the flat page view. Models: `flatpages.flatpages` Templates: Uses the template defined by the ``template_name`` field, or `flatpages/default.html` if template_name is not defined. Context: flatpage `flatpages.flatpages` object def flatpage(request, url): ...

Internal interface to the flat page view. def render_flatpage(request, f): """ Internal interface to the flat page view. """ # If registration is required for accessing this page, and the user isn't # logged in, redirect to the login page. if f.registration_required and not request.user.is_auth...

Get the absolute path of the pidfile. def pidfile(self): """Get the absolute path of the pidfile.""" return os.path.abspath( os.path.expandvars( os.path.expanduser( self._pidfile, ), ), )

Computes the square matrix of the argument :param argument: a dataset region field (dataset.field) or metadata (dataset['field']) def SQRT(argument): """ Computes the square matrix of the argument :param argument: a dataset region field (dataset.field) or metadata (dataset['field']) """ if isinst...

Enables the user to login to the remote GMQL service. If both username and password are None, the user will be connected as guest. def login(): """ Enables the user to login to the remote GMQL service. If both username and password are None, the user will be connected as guest. """ from .RemoteConn...

Any normal python logic which runs a loop. Can take arguments. def main(idle): """Any normal python logic which runs a loop. Can take arguments.""" while True: LOG.debug("Sleeping for {0} seconds.".format(idle)) time.sleep(idle)

Returns the metadata related to the current GMQLDataset. This function can be used only when a local dataset is loaded using the :meth:`~gmql.dataset.loaders.Loader.load_from_path` and no other operation has been called on the GMQLDataset. The metadata are returned in the form of a Pandas Dataf...

Creates an instance of a metadata field of the dataset. It can be used in building expressions or conditions for projection or selection. Notice that this function is equivalent to call:: dataset["name"] If the MetaField is used in a region projection (:meth:`~.reg_proj...

*Wrapper of* ``SELECT`` Selection operation. Enables to filter datasets on the basis of region features or metadata attributes. In addition it is possibile to perform a selection based on the existence of certain metadata :attr:`~.semiJoinMeta` attributes and the matching of their values with t...

*Wrapper of* ``SELECT`` Wrapper of the :meth:`~.select` function filtering samples only based on metadata. :param predicate: logical predicate on the values of the rows :param semiJoinDataset: an other GMQLDataset :param semiJoinMeta: a list of metadata :return: a new GMQLData...

*Wrapper of* ``SELECT`` Wrapper of the :meth:`~.select` function filtering regions only based on region attributes. :param predicate: logical predicate on the values of the regions :param semiJoinDataset: an other GMQLDataset :param semiJoinMeta: a list of metadata :return: a ...

*Wrapper of* ``PROJECT`` The PROJECT operator creates, from an existing dataset, a new dataset with all the samples (with their regions and region values) in the input one, but keeping for each sample in the input dataset only those metadata and/or region attributes expressed in the operator pa...

*Wrapper of* ``PROJECT`` Project the metadata based on a list of attribute names :param attr_list: list of the metadata fields to select :param all_but: list of metadata that must be excluded from the projection. :param new_attr_dict: an optional dictionary of the form {'new_fi...

*Wrapper of* ``PROJECT`` Project the region data based on a list of field names :param field_list: list of the fields to select :param all_but: keep only the region fields different from the ones specified :param new_field_dict: an optional dictionary of the form {'new_f...

*Wrapper of* ``EXTEND`` For each sample in an input dataset, the EXTEND operator builds new metadata attributes, assigns their values as the result of arithmetic and/or aggregate functions calculated on sample region attributes, and adds them to the existing metadata attribute-value pairs of ...

*Wrapper of* ``COVER`` COVER is a GMQL operator that takes as input a dataset (of usually, but not necessarily, multiple samples) and returns another dataset (with a single sample, if no groupby option is specified) by “collapsing” the input samples and their regions according to cert...

*Wrapper of* ``COVER`` The normal cover operation as described in :meth:`~.cover`. Equivalent to calling:: dataset.cover("normal", ...) def normal_cover(self, minAcc, maxAcc, groupBy=None, new_reg_fields=None): """ *Wrapper of* ``COVER`` The normal cover o...

*Wrapper of* ``COVER`` Variant of the function :meth:`~.cover` that returns the union of all the regions which contribute to the COVER. More precisely, it returns the contiguous regions that start from the first end and stop at the last end of the regions which would contribute to ea...

*Wrapper of* ``COVER`` Variant of the function :meth:`~.cover` that returns only those portions of the COVER result where the maximum number of regions overlap (this is done by returning only regions that start from a position after which the number of overlaps does not increase, and s...

*Wrapper of* ``COVER`` Variant of the function :meth:`~.cover` that returns all regions contributing to the COVER divided in different (contiguous) parts according to their accumulation index value (one part for each different accumulation value), which is assigned to the AccIndex re...

*Wrapper of* ``JOIN`` The JOIN operator takes in input two datasets, respectively known as anchor (the first/left one) and experiment (the second/right one) and returns a dataset of samples consisting of regions extracted from the operands according to the specified condition (known as...

*Wrapper of* ``MAP`` MAP is a non-symmetric operator over two datasets, respectively called reference and experiment. The operation computes, for each sample in the experiment dataset, aggregates over the values of the experiment regions that intersect with a region in a reference sam...

*Wrapper of* ``ORDER`` The ORDER operator is used to order either samples, sample regions, or both, in a dataset according to a set of metadata and/or region attributes, and/or region coordinates. The number of samples and their regions in the output dataset is as in the input dataset...

*Wrapper of* ``DIFFERENCE`` DIFFERENCE is a binary, non-symmetric operator that produces one sample in the result for each sample of the first operand, by keeping the same metadata of the first operand sample and only those regions (with their schema and values) of the first operand sam...

*Wrapper of* ``UNION`` The UNION operation is used to integrate homogeneous or heterogeneous samples of two datasets within a single dataset; for each sample of either one of the input datasets, a sample is created in the result as follows: * its metadata are the same as in...

*Wrapper of* ``MERGE`` The MERGE operator builds a new dataset consisting of a single sample having * as regions all the regions of all the input samples, with the same attributes and values * as metadata the union of all the metadata attribute-values ...

*Wrapper of* ``GROUP`` The GROUP operator is used for grouping both regions and/or metadata of input dataset samples according to distinct values of certain attributes (known as grouping attributes); new grouping attributes are added to samples in the output dataset, storing the results...

*Wrapper of* ``GROUP`` Group operation only for metadata. For further information check :meth:`~.group` def meta_group(self, meta, meta_aggregates=None): """ *Wrapper of* ``GROUP`` Group operation only for metadata. For further information check :meth:`~.group` """ ret...

*Wrapper of* ``GROUP`` Group operation only for region data. For further information check :meth:`~.group` def regs_group(self, regs, regs_aggregates=None): """ *Wrapper of* ``GROUP`` Group operation only for region data. For further information check :meth:`~.group` """ ...

*Wrapper of* ``MATERIALIZE`` Starts the execution of the operations for the GMQLDataset. PyGMQL implements lazy execution and no operation is performed until the materialization of the results is requestd. This operation can happen both locally or remotely. * Local mode: if the GMQLDat...

Grab a new message and dispatch it to the handler. This method should not be extended or overwritten. Instead, implementations of this daemon should implement the 'get_message()' and 'handle_message()' methods. def step(self): """Grab a new message and dispatch it to the handler. ...

Initialize application configuration def init_app(self, app=None): """Initialize application configuration""" config = getattr(app, 'config', app) self.team_id = config.get('TEAM_ID')

A decorator used to register a command. Example:: @slack.command('your_command', token='your_token', team_id='your_team_id', methods=['POST']) def your_method(**kwargs): text = kwargs.get('text') return slack.response(text) ...

Dispatch http request to registerd commands. Example:: slack = Slack(app) app.add_url_rule('/', view_func=slack.dispatch) def dispatch(self): """Dispatch http request to registerd commands. Example:: slack = Slack(app) app.add_url_rule('/', view...

Validate request queries with registerd commands :param command: command parameter from request :param token: token parameter from request :param team_id: team_id parameter from request :param method: the request method def validate(self, command, token, team_id, method): """Va...

Return a response with json format :param text: the text returned to the client :param response_type: optional. When `in_channel` is assigned, both the response message and the initial message typed by the user will be shared ...

Get the pid which represents a daemonized process. The result should be None if the process is not running. def pid(self): """Get the pid which represents a daemonized process. The result should be None if the process is not running. """ try: with open(self.pidfil...

Set the pid for a running process. def pid(self, pidnum): """Set the pid for a running process.""" try: with open(self.pidfile, "w+") as pidfile: pidfile.write("{0}\n".format(pidnum)) except IOError: LOG.exception("Failed to write pidfile {0}).".forma...

Stop managing the current pid. def pid(self): """Stop managing the current pid.""" try: os.remove(self.pidfile) except IOError: if not os.path.isfile(self.pidfile): return None LOG.exception("Failed to clear pidfile {0}).".format(self.pid...

Get an gevent pool used to dispatch requests. def pool(self): """Get an gevent pool used to dispatch requests.""" self._pool = self._pool or gevent.pool.Pool(size=self.pool_size) return self._pool

Set the ESSID of a given interface Arguments: interface - device to work on (e.g. eth1, wlan0). essid - ESSID to set. Must be no longer than IW_ESSID_MAX_SIZE (typically 32 characters). def set_essid(interface, essid): """ Set the ESSID of a given interface Arguments: interfac...

Reads an edge list in csv format and returns the adjacency matrix in SciPy Sparse COOrdinate format. Inputs: - file_path: The path where the adjacency matrix is stored. - separator: The delimiter among values (e.g. ",", "\t", " ") Outputs: - adjacency_matrix: The adjacency matrix in SciPy Sparse...

Reads node-label pairs in csv format and returns a list of tuples and a node-label matrix. Inputs: - file_path: The path where the node-label matrix is stored. - separator: The delimiter among values (e.g. ",", "\t", " ") - number_of_nodes: The number of nodes of the full graph. It is po...

Ensure that the Garuda directory and files def ensure_data(): ''' Ensure that the Garuda directory and files ''' if not os.path.exists(GARUDA_DIR): os.makedirs(GARUDA_DIR) Path(f'{GARUDA_DIR}/__init__.py').touch()

Construct protobuf compiler arguments def protoc_arguments(): ''' Construct protobuf compiler arguments ''' proto_include = resource_filename('grpc_tools', '_proto') return [ protoc.__file__, '-I', GARUDA_DIR, f'--python_out={GARUDA_DIR}', f'--grpc_python_out={GARUDA_DIR}', GARUDA_P...

Snippet to fix the gRPC import path def fix_grpc_import(): ''' Snippet to fix the gRPC import path ''' with open(GARUDA_GRPC_PATH, 'r') as f: filedata = f.read() filedata = filedata.replace( 'import garuda_pb2 as garuda__pb2', f'import {GARUDA_DIR}.garuda_pb2 as garuda__pb2'...

Simple utility function that writes an average score row in a file designated by a file pointer. Inputs: - fp: A file pointer. - score_name: What it says on the tin. - scores: An array of average score values corresponding to each of the training set percentages. def write_average_score...

Catches specific errors in admin actions and shows a friendly error. def catch_errors(f): """ Catches specific errors in admin actions and shows a friendly error. """ @functools.wraps(f) def wrapper(self, request, *args, **kwargs): try: return f(self, request, *args, **kwargs) ...

Performs augmented TF-IDF normalization on a bag-of-words vector representation of data. Augmented TF-IDF introduced in: Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to information retrieval (Vol. 1, p. 6). Cambridge: Cambr...

Reads an separated value file row by row. Inputs: - file_path: The path of the separated value format file. - separator: The delimiter among values (e.g. ",", "\t", " ") - encoding: The encoding used in the stored text. Yields: - words: A list of strings corresponding to each of the fi...

Pickle some data to a given path. Inputs: - file_path: Target file path. - data: The python object to be serialized via pickle. def store_pickle(file_path, data): """ Pickle some data to a given path. Inputs: - file_path: Target file path. - data: The python object to be seria...

Unpickle some data from a given path. Input: - file_path: Target file path. Output: - data: The python object that was serialized and stored in disk. def load_pickle(file_path): """ Unpickle some data from a given path. Input: - file_path: Target file path. Output: - data: The python obje...

Given the current ID, returns a packet to set the servo to a new ID def makeServoIDPacket(curr_id, new_id): """ Given the current ID, returns a packet to set the servo to a new ID """ pkt = Packet.makeWritePacket(curr_id, xl320.XL320_ID, [new_id]) return pkt

This performs row normalization to 1 of community embedding features. Input: - X in R^(nxC_n): The community indicator matrix. Output: - X_norm in R^(nxC_n): The row normalized community indicator matrix. def normalize_rows(features): """ This performs row normalization to 1 of community embedding f...

This performs column normalization of community embedding features. Input: - X in R^(nxC_n): The community indicator matrix. Output: - X_norm in R^(nxC_n): The tf-idf + row normalized community indicator matrix. def normalize_columns(features): """ This performs column normalization of community emb...

Performs a random step without a self-loop. def pagerank_limit_push(s, r, w_i, a_i, push_node, rho): """ Performs a random step without a self-loop. """ # Calculate the A and B quantities to infinity A_inf = rho*r[push_node] B_inf = (1-rho)*r[push_node] # Update approximate Pagerank and re...

Performs a random step with a self-loop. Introduced in: Andersen, R., Chung, F., & Lang, K. (2006, October). Local graph partitioning using pagerank vectors. In Foundations of Computer Science, 2006. FOCS'06. 47th Annual IEEE Symposium on (pp. 475-486). IEEE. def pagerank_laz...

Performs a random step without a self-loop. Inputs: - s: A NumPy array that contains the approximate absorbing random walk cumulative probabilities. - r: A NumPy array that contains the residual probability distribution. - w_i: A NumPy array of probability transition weights from the see...

Include pyramid_multiauth into a pyramid configurator. This function provides a hook for pyramid to include the default settings for auth via pyramid_multiauth. Activate it like so: config.include("pyramid_multiauth") This will pull the list of registered authn policies from the deployment s...

Create a policy factory that works by config.include()'ing a module. This function does some trickery with the Pyramid config system. Loosely, it does config.include(module), and then sucks out information about the authn policy that was registered. It's complicated by pyramid's delayed- commit system...

Find all multiauth policy definitions from the settings dict. This function processes the paster deployment settings looking for items that start with "multiauth.policy.<policyname>.". It pulls them all out into a dict indexed by the policy name. def get_policy_definitions(settings): """Find all mult...

Read file to memory. For stdin sourced files, this function does something super duper incredibly hacky and shameful. So so shameful. I'm obtaining the original source code of the target module from the only instance of pycodestyle.Checker through the Python garbage collector. Flake8's API doesn't give me ...

Should this code be ignored. :param str code: Error code (e.g. D201). :return: True if code should be ignored, False otherwise. :rtype: bool def ignore(code): """Should this code be ignored. :param str code: Error code (e.g. D201). :return: True if code should be ignored, False otherwise. ...

Add options to flake8. :param parser: optparse.OptionParser from pycodestyle. def add_options(cls, parser): """Add options to flake8. :param parser: optparse.OptionParser from pycodestyle. """ parser.add_option('--show-pydocstyle', action='store_true', help='show explanation o...

Read parsed options from flake8. :param options: Options to add to flake8's command line options. def parse_options(cls, options): """Read parsed options from flake8. :param options: Options to add to flake8's command line options. """ # Handle flake8 options. cls.opti...

Run analysis on a single file. def run(self): """Run analysis on a single file.""" pydocstyle.Error.explain = self.options['explain'] filename, source = load_file(self.filename) for error in pydocstyle.PEP257Checker().check_source(source, filename): if not hasattr(error, 'co...

Returns the barcode's number without the verification digit. :return: list(int) def numbers(self): """" Returns the barcode's number without the verification digit. :return: list(int) """ numstring = '{:011d}{:02d}{:04d}{}{}'.format( self._receipt.point_of_...

Returns the verification digit for a given numbre. The verification digit is calculated as follows: * A = sum of all even-positioned numbers * B = A * 3 * C = sum of all odd-positioned numbers * D = B + C * The results is the smallset number N, such that (D + N) % 10 ==...

Returns the full number including the verification digit. :return: str def full_number(self): """ Returns the full number including the verification digit. :return: str """ return '{}{}'.format( ''.join(str(n) for n in self.numbers), ReceiptBarc...

Reads an edge list in csv format and returns the adjacency matrix in SciPy Sparse COOrdinate format. Inputs: - file_path: The path where the adjacency matrix is stored. - separator: The delimiter among values (e.g. ",", "\t", " ") - numbering: Array numbering style: * "matlab" ...