BoghdadyJR/codesearch_python · Datasets at Hugging Face

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def get_logw(ns_run, simulate=False): r"""Calculates the log posterior weights of the samples (using logarithms to avoid overflow errors with very large or small values). Uses the trapezium rule such that the weight of point i is .. math:: w_i = \mathcal{L}_i (X_{i-1} - X_{i+1}) / 2 Parameters ...
def get_w_rel(ns_run, simulate=False): """Get the relative posterior weights of the samples, normalised so the maximum sample weight is 1. This is calculated from get_logw with protection against numerical overflows. Parameters ---------- ns_run: dict Nested sampling run dict (see data_...
def get_logx(nlive, simulate=False): r"""Returns a logx vector showing the expected or simulated logx positions of points. The shrinkage factor between two points .. math:: t_i = X_{i-1} / X_{i} is distributed as the largest of :math:`n_i` uniform random variables between 1 and 0, where :math...
def log_subtract(loga, logb): r"""Numerically stable method for avoiding overflow errors when calculating :math:`\log (a-b)`, given :math:`\log (a)`, :math:`\log (a)` and that :math:`a > b`. See https://hips.seas.harvard.edu/blog/2013/01/09/computing-log-sum-exp/ for more details. Parameters ...
def check_ns_run(run, dup_assert=False, dup_warn=False): """Checks a nestcheck format nested sampling run dictionary has the expected properties (see the data_processing module docstring for more details). Parameters ---------- run: dict nested sampling run to check. dup_assert: boo...
def check_ns_run_members(run): """Check nested sampling run member keys and values. Parameters ---------- run: dict nested sampling run to check. Raises ------ AssertionError if run does not have expected properties. """ run_keys = list(run.keys()) # Mandatory k...
def check_ns_run_logls(run, dup_assert=False, dup_warn=False): """Check run logls are unique and in the correct order. Parameters ---------- run: dict nested sampling run to check. dup_assert: bool, optional Whether to raise and AssertionError if there are duplicate logl values. ...
def check_ns_run_threads(run): """Check thread labels and thread_min_max have expected properties. Parameters ---------- run: dict Nested sampling run to check. Raises ------ AssertionError If run does not have expected properties. """ assert run['thread_labels'].dt...
def count_samples(ns_run, **kwargs): r"""Number of samples in run. Unlike most estimators this does not require log weights, but for convenience will not throw an error if they are specified. Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module ...
def logz(ns_run, logw=None, simulate=False): r"""Natural log of Bayesian evidence :math:`\log \mathcal{Z}`. Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module docstring for more details). logw: None or 1d numpy array, optional Log wei...
def evidence(ns_run, logw=None, simulate=False): r"""Bayesian evidence :math:`\log \mathcal{Z}`. Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module docstring for more details). logw: None or 1d numpy array, optional Log weights of sam...
def param_mean(ns_run, logw=None, simulate=False, param_ind=0, handle_indexerror=False): """Mean of a single parameter (single component of theta). Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module docstring for more details). ...
def param_cred(ns_run, logw=None, simulate=False, probability=0.5, param_ind=0): """One-tailed credible interval on the value of a single parameter (component of theta). Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module docstr...
def param_squared_mean(ns_run, logw=None, simulate=False, param_ind=0): """Mean of the square of single parameter (second moment of its posterior distribution). Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module docstring for more details). ...
def r_mean(ns_run, logw=None, simulate=False): """Mean of the radial coordinate (magnitude of theta vector). Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module docstring for more details). logw: None or 1d numpy array, optional Log we...
def r_cred(ns_run, logw=None, simulate=False, probability=0.5): """One-tailed credible interval on the value of the radial coordinate (magnitude of theta vector). Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module docstring for more details)....
def get_latex_name(func_in, **kwargs): """ Produce a latex formatted name for each function for use in labelling results. Parameters ---------- func_in: function kwargs: dict, optional Kwargs for function. Returns ------- latex_name: str Latex formatted name for...
def weighted_quantile(probability, values, weights): """ Get quantile estimate for input probability given weighted samples using linear interpolation. Parameters ---------- probability: float Quantile to estimate - must be in open interval (0, 1). For example, use 0.5 for the m...
def write_run_output(run, **kwargs): """Writes PolyChord output files corresponding to the input nested sampling run. The file root is .. code-block:: python root = os.path.join(run['output']['base_dir'], run['output']['file_root']) Output files which can be made w...
def run_dead_birth_array(run, **kwargs): """Converts input run into an array of the format of a PolyChord <root>_dead-birth.txt file. Note that this in fact includes live points remaining at termination as well as dead points. Parameters ---------- ns_run: dict Nested sampling run dict ...
def write_stats_file(run_output_dict): """Writes a dummy PolyChord format .stats file for tests functions for processing stats files. This is written to: base_dir/file_root.stats Also returns the data in the file as a dict for comparison. Parameters ---------- run_output_dict: dict ...
def run_list_error_values(run_list, estimator_list, estimator_names, n_simulate=100, **kwargs): """Gets a data frame with calculation values and error diagnostics for each run in the input run list. NB when parallelised the results will not be produced in order (so results fro...
def estimator_values_df(run_list, estimator_list, **kwargs): """Get a dataframe of estimator values. NB when parallelised the results will not be produced in order (so results from some run number will not nessesarily correspond to that number run in run_list). Parameters ---------- run_li...
def error_values_summary(error_values, **summary_df_kwargs): """Get summary statistics about calculation errors, including estimated implementation errors. Parameters ---------- error_values: pandas DataFrame Of format output by run_list_error_values (look at it for more details). ...
def run_list_error_summary(run_list, estimator_list, estimator_names, n_simulate, **kwargs): """Wrapper which runs run_list_error_values then applies error_values summary to the resulting dataframe. See the docstrings for those two funcions for more details and for descriptions of...
def bs_values_df(run_list, estimator_list, estimator_names, n_simulate, **kwargs): """Computes a data frame of bootstrap resampled values. Parameters ---------- run_list: list of dicts List of nested sampling run dicts. estimator_list: list of functions Estimators t...
def thread_values_df(run_list, estimator_list, estimator_names, **kwargs): """Calculates estimator values for the constituent threads of the input runs. Parameters ---------- run_list: list of dicts List of nested sampling run dicts. estimator_list: list of functions Estimators ...
def pairwise_dists_on_cols(df_in, earth_mover_dist=True, energy_dist=True): """Computes pairwise statistical distance measures. parameters ---------- df_in: pandas data frame Columns represent estimators and rows represent runs. Each data frane element is an array of values which are us...
def _backtick_columns(cols): """ Quote the column names """ def bt(s): b = '' if s == '*' or not s else '`' return [_ for _ in [b + (s or '') + b] if _] formatted = [] for c in cols: if c[0] == '#': formatted.append(c[1...
def _value_parser(self, value, columnname=False, placeholder='%s'): """ Input: {'c1': 'v', 'c2': None, '#c3': 'uuid()'} Output: ('%s, %s, uuid()', [None, 'v']) # insert; columnname=False ('`c2` = %s, `c1` = %s, `c3` = uuid()', [None, 'v']) # upd...
def _by_columns(self, columns): """ Allow select.group and select.order accepting string and list """ return columns if self.isstr(columns) else self._backtick_columns(columns)
def select(self, table, columns=None, join=None, where=None, group=None, having=None, order=None, limit=None, iterator=False, fetch=True): """ :type table: string :type columns: list :type join: dict :param join: {'[>]table1(t1)': {'user.id': 't1.user_id'}} -> "LEF...
def select_page(self, limit, offset=0, **kwargs): """ :type limit: int :param limit: The max row number for each page :type offset: int :param offset: The starting position of the page :return: """ start = offset while True: result = se...
def get(self, table, column, join=None, where=None, insert=False, ifnone=None): """ A simplified method of select, for getting the first result in one column only. A common case of using this method is getting id. :type table: string :type column: str :type join: dict ...
def insert(self, table, value, ignore=False, commit=True): """ Insert a dict into db. :type table: string :type value: dict :type ignore: bool :type commit: bool :return: int. The row id of the insert. """ value_q, _args = self._value_parser(value,...
def upsert(self, table, value, update_columns=None, commit=True): """ :type table: string :type value: dict :type update_columns: list :param update_columns: specify the columns which will be updated if record exists :type commit: bool """ if not isinstanc...
def insertmany(self, table, columns, value, ignore=False, commit=True): """ Insert multiple records within one query. :type table: string :type columns: list :type value: list\|tuple :param value: Doesn't support MySQL functions :param value: Example: [(value1_colu...
def update(self, table, value, where, join=None, commit=True): """ :type table: string :type value: dict :type where: dict :type join: dict :type commit: bool """ value_q, _value_args = self._value_parser(value, columnname=True) where_q, _where_a...
def delete(self, table, where=None, commit=True): """ :type table: string :type where: dict :type commit: bool """ where_q, _args = self._where_parser(where) alias = self._tablename_parser(table)['alias'] _sql = ''.join(['DELETE ', ...
def get_whitespace(txt): """ Returns a list containing the whitespace to the left and right of a string as its two elements """ # if the entire parameter is whitespace rall = re.search(r'^([\s])+$', txt) if rall: tmp = txt.split('\n', 1) if len(tmp) == 2: return ...
def find_whitespace_pattern(self): """ Try to find a whitespace pattern in the existing parameters to be applied to a newly added parameter """ name_ws = [] value_ws = [] for entry in self._entries: name_ws.append(get_whitespace(entry.name)) ...
def _path_for_file(self, project_name, date): """ Generate the path on disk for a specified project and date. :param project_name: the PyPI project name for the data :type project: str :param date: the date for the data :type date: datetime.datetime :return: path...
def get(self, project, date): """ Get the cache data for a specified project for the specified date. Returns None if the data cannot be found in the cache. :param project: PyPi project name to get data for :type project: str :param date: date to get data for :typ...
def set(self, project, date, data, data_ts): """ Set the cache data for a specified project for the specified date. :param project: project name to set data for :type project: str :param date: date to set data for :type date: datetime.datetime :param data: data t...
def get_dates_for_project(self, project): """ Return a list of the dates we have in cache for the specified project, sorted in ascending date order. :param project: project name :type project: str :return: list of datetime.datetime objects :rtype: datetime.dateti...
def parse_args(argv): """ Use Argparse to parse command-line arguments. :param argv: list of arguments to parse (``sys.argv[1:]``) :type argv: ``list`` :return: parsed arguments :rtype: :py:class:`argparse.Namespace` """ p = argparse.ArgumentParser( description='pypi-download-st...
def set_log_level_format(level, format): """ Set logger level and format. :param level: logging level; see the :py:mod:`logging` constants. :type level: int :param format: logging formatter format string :type format: str """ formatter = logging.Formatter(fmt=format) logger.handlers...
def _pypi_get_projects_for_user(username): """ Given the username of a PyPI user, return a list of all of the user's projects from the XMLRPC interface. See: https://wiki.python.org/moin/PyPIXmlRpc :param username: PyPI username :type username: str :return: list of string project names ...
def main(args=None): """ Main entry point """ # parse args if args is None: args = parse_args(sys.argv[1:]) # set logging level if args.verbose > 1: set_log_debug() elif args.verbose == 1: set_log_info() outpath = os.path.abspath(os.path.expanduser(args.out_...
def generate_graph(self): """ Generate the graph; return a 2-tuple of strings, script to place in the head of the HTML document and div content for the graph itself. :return: 2-tuple (script, div) :rtype: tuple """ logger.debug('Generating graph for %s', self._gr...
def _line_for_patches(self, data, chart, renderer, series_name): """ Add a line along the top edge of a Patch in a stacked Area Chart; return the new Glyph for addition to HoverTool. :param data: original data for the graph :type data: dict :param chart: Chart to add the...
def _get_cache_dates(self): """ Get s list of dates (:py:class:`datetime.datetime`) present in cache, beginning with the longest contiguous set of dates that isn't missing more than one date in series. :return: list of datetime objects for contiguous dates in cache :rtyp...
def _is_empty_cache_record(self, rec): """ Return True if the specified cache record has no data, False otherwise. :param rec: cache record returned by :py:meth:`~._cache_get` :type rec: dict :return: True if record is empty, False otherwise :rtype: bool """ ...
def _cache_get(self, date): """ Return cache data for the specified day; cache locally in this class. :param date: date to get data for :type date: datetime.datetime :return: cache data for date :rtype: dict """ if date in self.cache_data: log...
def _compound_column_value(k1, k2): """ Like :py:meth:`~._column_value` but collapses two unknowns into one. :param k1: first (top-level) value :param k2: second (bottom-level) value :return: display key :rtype: str """ k1 = ProjectStats._column_value(k1)...
def _shorten_version(ver, num_components=2): """ If ``ver`` is a dot-separated string with at least (num_components +1) components, return only the first two. Else return the original string. :param ver: version string :type ver: str :return: shortened (major, minor) ver...
def per_version_data(self): """ Return download data by version. :return: dict of cache data; keys are datetime objects, values are dict of version (str) to count (int) :rtype: dict """ ret = {} for cache_date in self.cache_dates: data = sel...
def per_file_type_data(self): """ Return download data by file type. :return: dict of cache data; keys are datetime objects, values are dict of file type (str) to count (int) :rtype: dict """ ret = {} for cache_date in self.cache_dates: data...
def per_installer_data(self): """ Return download data by installer name and version. :return: dict of cache data; keys are datetime objects, values are dict of installer name/version (str) to count (int). :rtype: dict """ ret = {} for cache_date in sel...
def per_implementation_data(self): """ Return download data by python impelementation name and version. :return: dict of cache data; keys are datetime objects, values are dict of implementation name/version (str) to count (int). :rtype: dict """ ret = {} ...
def per_system_data(self): """ Return download data by system. :return: dict of cache data; keys are datetime objects, values are dict of system (str) to count (int) :rtype: dict """ ret = {} for cache_date in self.cache_dates: data = self._...
def per_country_data(self): """ Return download data by country. :return: dict of cache data; keys are datetime objects, values are dict of country (str) to count (int) :rtype: dict """ ret = {} for cache_date in self.cache_dates: data = sel...
def per_distro_data(self): """ Return download data by distro name and version. :return: dict of cache data; keys are datetime objects, values are dict of distro name/version (str) to count (int). :rtype: dict """ ret = {} for cache_date in self.cache_d...
def downloads_per_day(self): """ Return the number of downloads per day, averaged over the past 7 days of data. :return: average number of downloads per day :rtype: int """ count, num_days = self._downloads_for_num_days(7) res = ceil(count / num_days) ...
def downloads_per_week(self): """ Return the number of downloads in the last 7 days. :return: number of downloads in the last 7 days; if we have less than 7 days of data, returns None. :rtype: int """ if len(self.cache_dates) < 7: logger.error("Only...
def _downloads_for_num_days(self, num_days): """ Given a number of days of historical data to look at (starting with today and working backwards), return the total number of downloads for that time range, and the number of days of data we had (in cases where we had less data than...
def _get_project_id(self): """ Get our projectId from the ``GOOGLE_APPLICATION_CREDENTIALS`` creds JSON file. :return: project ID :rtype: str """ fpath = os.environ.get('GOOGLE_APPLICATION_CREDENTIALS', None) if fpath is None: raise Exception(...
def _get_bigquery_service(self): """ Connect to the BigQuery service. Calling ``GoogleCredentials.get_application_default`` requires that you either be running in the Google Cloud, or have the ``GOOGLE_APPLICATION_CREDENTIALS`` environment variable set to the path to a c...
def _get_download_table_ids(self): """ Get a list of PyPI downloads table (sharded per day) IDs. :return: list of table names (strings) :rtype: ``list`` """ all_table_names = [] # matching per-date table names logger.info('Querying for all tables in dataset') ...
def _datetime_for_table_name(self, table_name): """ Return a :py:class:`datetime.datetime` object for the date of the data in the specified table name. :param table_name: name of the table :type table_name: str :return: datetime that the table holds data for :rty...
def _run_query(self, query): """ Run one query against BigQuery and return the result. :param query: the query to run :type query: str :return: list of per-row response dicts (key => value) :rtype: ``list`` """ query_request = self.service.jobs() ...
def _get_newest_ts_in_table(self, table_name): """ Return the timestamp for the newest record in the given table. :param table_name: name of the table to query :type table_name: str :return: timestamp of newest row in table :rtype: int """ logger.debug( ...
def _query_by_installer(self, table_name): """ Query for download data broken down by installer, for one day. :param table_name: table name to query against :type table_name: str :return: dict of download information by installer; keys are project name, values are a di...
def _query_by_system(self, table_name): """ Query for download data broken down by system, for one day. :param table_name: table name to query against :type table_name: str :return: dict of download information by system; keys are project name, values are a dict of sys...
def _query_by_distro(self, table_name): """ Query for download data broken down by OS distribution, for one day. :param table_name: table name to query against :type table_name: str :return: dict of download information by distro; keys are project name, values are a di...
def query_one_table(self, table_name): """ Run all queries for the given table name (date) and update the cache. :param table_name: table name to query against :type table_name: str """ table_date = self._datetime_for_table_name(table_name) logger.info('Running a...
def _have_cache_for_date(self, dt): """ Return True if we have cached data for all projects for the specified datetime. Return False otherwise. :param dt: datetime to find cache for :type dt: datetime.datetime :return: True if we have cache for all projects for this date...
def backfill_history(self, num_days, available_table_names): """ Backfill historical data for days that are missing. :param num_days: number of days of historical data to backfill, if missing :type num_days: int :param available_table_names: names of available per-date...
def run_queries(self, backfill_num_days=7): """ Run the data queries for the specified projects. :param backfill_num_days: number of days of historical data to backfill, if missing :type backfill_num_days: int """ available_tables = self._get_download_table_ids...
def filter_data_columns(data): """ Given a dict of data such as those in :py:class:`~.ProjectStats` attributes, made up of :py:class:`datetime.datetime` keys and values of dicts of column keys to counts, return a list of the distinct column keys in sorted order. :param data: data dict as returned b...
def _generate_html(self): """ Generate the HTML for the specified graphs. :return: :rtype: """ logger.debug('Generating templated HTML') env = Environment( loader=PackageLoader('pypi_download_stats', 'templates'), extensions=['jinja2.ext.l...
def _data_dict_to_bokeh_chart_data(self, data): """ Take a dictionary of data, as returned by the :py:class:`~.ProjectStats` per_*_data properties, return a 2-tuple of data dict and x labels list usable by bokeh.charts. :param data: data dict from :py:class:`~.ProjectStats` prop...
def _limit_data(self, data): """ Find the per-day average of each series in the data over the last 7 days; drop all but the top 10. :param data: original graph data :type data: dict :return: dict containing only the top 10 series, based on average over the last...
def _generate_graph(self, name, title, stats_data, y_name): """ Generate a downloads graph; append it to ``self._graphs``. :param name: HTML name of the graph, also used in ``self.GRAPH_KEYS`` :type name: str :param title: human-readable title for the graph :type title: ...
def _generate_badges(self): """ Generate download badges. Append them to ``self._badges``. """ daycount = self._stats.downloads_per_day day = self._generate_badge('Downloads', '%d/day' % daycount) self._badges['per-day'] = day weekcount = self._stats.downloads_per...
def _generate_badge(self, subject, status): """ Generate SVG for one badge via shields.io. :param subject: subject; left-hand side of badge :type subject: str :param status: status; right-hand side of badge :type status: str :return: badge SVG :rtype: str...
def generate(self): """ Generate all output types and write to disk. """ logger.info('Generating graphs') self._generate_graph( 'by-version', 'Downloads by Version', self._stats.per_version_data, 'Version' ) self._ge...
def datetime_format(desired_format, datetime_instance=None, args, *kwargs): """ Replaces format style phrases (listed in the dt_exps dictionary) with this datetime instance's information. .. code :: python reusables.datetime_format("Hey, it's {month-full} already!") "Hey, it's March...
def datetime_from_iso(iso_string): """ Create a DateTime object from a ISO string .. code :: python reusables.datetime_from_iso('2017-03-10T12:56:55.031863') datetime.datetime(2017, 3, 10, 12, 56, 55, 31863) :param iso_string: string of an ISO datetime :return: DateTime object ...
def now(utc=False, tz=None): """ Get a current DateTime object. By default is local. .. code:: python reusables.now() # DateTime(2016, 12, 8, 22, 5, 2, 517000) reusables.now().format("It's {24-hour}:{min}") # "It's 22:05" :param utc: bool, default False, UTC time not ...
def run(command, input=None, stdout=subprocess.PIPE, stderr=subprocess.PIPE, timeout=None, copy_local_env=False, **kwargs): """ Cross platform compatible subprocess with CompletedProcess return. No formatting or encoding is performed on the output of subprocess, so it's output will appear the s...
def run_in_pool(target, iterable, threaded=True, processes=4, asynchronous=False, target_kwargs=None): """ Run a set of iterables to a function in a Threaded or MP Pool. .. code: python def func(a): return a + a reusables.run_in_pool(func, [1,2,3,4,5]) # [1...
def tree_view(dictionary, level=0, sep="\| "): """ View a dictionary as a tree. """ return "".join(["{0}{1}\n{2}".format(sep * level, k, tree_view(v, level + 1, sep=sep) if isinstance(v, dict) else "") for k, v in dictionary.items()])
def to_dict(self, in_dict=None): """ Turn the Namespace and sub Namespaces back into a native python dictionary. :param in_dict: Do not use, for self recursion :return: python dictionary of this Namespace """ in_dict = in_dict if in_dict else self out_dic...
def list(self, item, default=None, spliter=",", strip=True, mod=None): """ Return value of key as a list :param item: key of value to transform :param mod: function to map against list :param default: value to return if item does not exist :param spliter: character to split str ...
def download(url, save_to_file=True, save_dir=".", filename=None, block_size=64000, overwrite=False, quiet=False): """ Download a given URL to either file or memory :param url: Full url (with protocol) of path to download :param save_to_file: boolean if it should be saved to file or not ...
def url_to_ips(url, port=None, ipv6=False, connect_type=socket.SOCK_STREAM, proto=socket.IPPROTO_TCP, flags=0): """ Provide a list of IP addresses, uses `socket.getaddrinfo` .. code:: python reusables.url_to_ips("example.com", ipv6=True) # ['2606:2800:220:1:248:1893:25c8:194...
def ip_to_url(ip_addr): """ Resolve a hostname based off an IP address. This is very limited and will probably not return any results if it is a shared IP address or an address with improperly setup DNS records. .. code:: python reusables.ip_to_url('93.184.216.34') # example.com ...
def start(self): """Create a background thread for httpd and serve 'forever'""" self._process = threading.Thread(target=self._background_runner) self._process.start()
def get_stream_handler(stream=sys.stderr, level=logging.INFO, log_format=log_formats.easy_read): """ Returns a set up stream handler to add to a logger. :param stream: which stream to use, defaults to sys.stderr :param level: logging level to set handler at :param log_format:...

def get_logw(ns_run, simulate=False): r"""Calculates the log posterior weights of the samples (using logarithms to avoid overflow errors with very large or small values). Uses the trapezium rule such that the weight of point i is .. math:: w_i = \mathcal{L}_i (X_{i-1} - X_{i+1}) / 2 Parameters ...

def get_w_rel(ns_run, simulate=False): """Get the relative posterior weights of the samples, normalised so the maximum sample weight is 1. This is calculated from get_logw with protection against numerical overflows. Parameters ---------- ns_run: dict Nested sampling run dict (see data_...

def get_logx(nlive, simulate=False): r"""Returns a logx vector showing the expected or simulated logx positions of points. The shrinkage factor between two points .. math:: t_i = X_{i-1} / X_{i} is distributed as the largest of :math:`n_i` uniform random variables between 1 and 0, where :math...

def log_subtract(loga, logb): r"""Numerically stable method for avoiding overflow errors when calculating :math:`\log (a-b)`, given :math:`\log (a)`, :math:`\log (a)` and that :math:`a > b`. See https://hips.seas.harvard.edu/blog/2013/01/09/computing-log-sum-exp/ for more details. Parameters ...

def check_ns_run(run, dup_assert=False, dup_warn=False): """Checks a nestcheck format nested sampling run dictionary has the expected properties (see the data_processing module docstring for more details). Parameters ---------- run: dict nested sampling run to check. dup_assert: boo...

def check_ns_run_members(run): """Check nested sampling run member keys and values. Parameters ---------- run: dict nested sampling run to check. Raises ------ AssertionError if run does not have expected properties. """ run_keys = list(run.keys()) # Mandatory k...

def check_ns_run_logls(run, dup_assert=False, dup_warn=False): """Check run logls are unique and in the correct order. Parameters ---------- run: dict nested sampling run to check. dup_assert: bool, optional Whether to raise and AssertionError if there are duplicate logl values. ...

def check_ns_run_threads(run): """Check thread labels and thread_min_max have expected properties. Parameters ---------- run: dict Nested sampling run to check. Raises ------ AssertionError If run does not have expected properties. """ assert run['thread_labels'].dt...

def count_samples(ns_run, **kwargs): r"""Number of samples in run. Unlike most estimators this does not require log weights, but for convenience will not throw an error if they are specified. Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module ...

def logz(ns_run, logw=None, simulate=False): r"""Natural log of Bayesian evidence :math:`\log \mathcal{Z}`. Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module docstring for more details). logw: None or 1d numpy array, optional Log wei...

def evidence(ns_run, logw=None, simulate=False): r"""Bayesian evidence :math:`\log \mathcal{Z}`. Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module docstring for more details). logw: None or 1d numpy array, optional Log weights of sam...

def param_mean(ns_run, logw=None, simulate=False, param_ind=0, handle_indexerror=False): """Mean of a single parameter (single component of theta). Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module docstring for more details). ...

def param_cred(ns_run, logw=None, simulate=False, probability=0.5, param_ind=0): """One-tailed credible interval on the value of a single parameter (component of theta). Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module docstr...

def param_squared_mean(ns_run, logw=None, simulate=False, param_ind=0): """Mean of the square of single parameter (second moment of its posterior distribution). Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module docstring for more details). ...

def r_mean(ns_run, logw=None, simulate=False): """Mean of the radial coordinate (magnitude of theta vector). Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module docstring for more details). logw: None or 1d numpy array, optional Log we...

def r_cred(ns_run, logw=None, simulate=False, probability=0.5): """One-tailed credible interval on the value of the radial coordinate (magnitude of theta vector). Parameters ---------- ns_run: dict Nested sampling run dict (see the data_processing module docstring for more details)....

def get_latex_name(func_in, **kwargs): """ Produce a latex formatted name for each function for use in labelling results. Parameters ---------- func_in: function kwargs: dict, optional Kwargs for function. Returns ------- latex_name: str Latex formatted name for...

def weighted_quantile(probability, values, weights): """ Get quantile estimate for input probability given weighted samples using linear interpolation. Parameters ---------- probability: float Quantile to estimate - must be in open interval (0, 1). For example, use 0.5 for the m...

def write_run_output(run, **kwargs): """Writes PolyChord output files corresponding to the input nested sampling run. The file root is .. code-block:: python root = os.path.join(run['output']['base_dir'], run['output']['file_root']) Output files which can be made w...

def run_dead_birth_array(run, **kwargs): """Converts input run into an array of the format of a PolyChord <root>_dead-birth.txt file. Note that this in fact includes live points remaining at termination as well as dead points. Parameters ---------- ns_run: dict Nested sampling run dict ...

def write_stats_file(run_output_dict): """Writes a dummy PolyChord format .stats file for tests functions for processing stats files. This is written to: base_dir/file_root.stats Also returns the data in the file as a dict for comparison. Parameters ---------- run_output_dict: dict ...

def run_list_error_values(run_list, estimator_list, estimator_names, n_simulate=100, **kwargs): """Gets a data frame with calculation values and error diagnostics for each run in the input run list. NB when parallelised the results will not be produced in order (so results fro...

def estimator_values_df(run_list, estimator_list, **kwargs): """Get a dataframe of estimator values. NB when parallelised the results will not be produced in order (so results from some run number will not nessesarily correspond to that number run in run_list). Parameters ---------- run_li...

def error_values_summary(error_values, **summary_df_kwargs): """Get summary statistics about calculation errors, including estimated implementation errors. Parameters ---------- error_values: pandas DataFrame Of format output by run_list_error_values (look at it for more details). ...

def run_list_error_summary(run_list, estimator_list, estimator_names, n_simulate, **kwargs): """Wrapper which runs run_list_error_values then applies error_values summary to the resulting dataframe. See the docstrings for those two funcions for more details and for descriptions of...

def bs_values_df(run_list, estimator_list, estimator_names, n_simulate, **kwargs): """Computes a data frame of bootstrap resampled values. Parameters ---------- run_list: list of dicts List of nested sampling run dicts. estimator_list: list of functions Estimators t...

def thread_values_df(run_list, estimator_list, estimator_names, **kwargs): """Calculates estimator values for the constituent threads of the input runs. Parameters ---------- run_list: list of dicts List of nested sampling run dicts. estimator_list: list of functions Estimators ...

def pairwise_dists_on_cols(df_in, earth_mover_dist=True, energy_dist=True): """Computes pairwise statistical distance measures. parameters ---------- df_in: pandas data frame Columns represent estimators and rows represent runs. Each data frane element is an array of values which are us...

def _backtick_columns(cols): """ Quote the column names """ def bt(s): b = '' if s == '*' or not s else '`' return [_ for _ in [b + (s or '') + b] if _] formatted = [] for c in cols: if c[0] == '#': formatted.append(c[1...

def _value_parser(self, value, columnname=False, placeholder='%s'): """ Input: {'c1': 'v', 'c2': None, '#c3': 'uuid()'} Output: ('%s, %s, uuid()', [None, 'v']) # insert; columnname=False ('`c2` = %s, `c1` = %s, `c3` = uuid()', [None, 'v']) # upd...

def _by_columns(self, columns): """ Allow select.group and select.order accepting string and list """ return columns if self.isstr(columns) else self._backtick_columns(columns)

def select(self, table, columns=None, join=None, where=None, group=None, having=None, order=None, limit=None, iterator=False, fetch=True): """ :type table: string :type columns: list :type join: dict :param join: {'[>]table1(t1)': {'user.id': 't1.user_id'}} -> "LEF...

def select_page(self, limit, offset=0, **kwargs): """ :type limit: int :param limit: The max row number for each page :type offset: int :param offset: The starting position of the page :return: """ start = offset while True: result = se...

def get(self, table, column, join=None, where=None, insert=False, ifnone=None): """ A simplified method of select, for getting the first result in one column only. A common case of using this method is getting id. :type table: string :type column: str :type join: dict ...

def insert(self, table, value, ignore=False, commit=True): """ Insert a dict into db. :type table: string :type value: dict :type ignore: bool :type commit: bool :return: int. The row id of the insert. """ value_q, _args = self._value_parser(value,...

def upsert(self, table, value, update_columns=None, commit=True): """ :type table: string :type value: dict :type update_columns: list :param update_columns: specify the columns which will be updated if record exists :type commit: bool """ if not isinstanc...

def insertmany(self, table, columns, value, ignore=False, commit=True): """ Insert multiple records within one query. :type table: string :type columns: list :type value: list|tuple :param value: Doesn't support MySQL functions :param value: Example: [(value1_colu...

def update(self, table, value, where, join=None, commit=True): """ :type table: string :type value: dict :type where: dict :type join: dict :type commit: bool """ value_q, _value_args = self._value_parser(value, columnname=True) where_q, _where_a...

def delete(self, table, where=None, commit=True): """ :type table: string :type where: dict :type commit: bool """ where_q, _args = self._where_parser(where) alias = self._tablename_parser(table)['alias'] _sql = ''.join(['DELETE ', ...

def get_whitespace(txt): """ Returns a list containing the whitespace to the left and right of a string as its two elements """ # if the entire parameter is whitespace rall = re.search(r'^([\s])+$', txt) if rall: tmp = txt.split('\n', 1) if len(tmp) == 2: return ...

def find_whitespace_pattern(self): """ Try to find a whitespace pattern in the existing parameters to be applied to a newly added parameter """ name_ws = [] value_ws = [] for entry in self._entries: name_ws.append(get_whitespace(entry.name)) ...

def _path_for_file(self, project_name, date): """ Generate the path on disk for a specified project and date. :param project_name: the PyPI project name for the data :type project: str :param date: the date for the data :type date: datetime.datetime :return: path...

def get(self, project, date): """ Get the cache data for a specified project for the specified date. Returns None if the data cannot be found in the cache. :param project: PyPi project name to get data for :type project: str :param date: date to get data for :typ...

def set(self, project, date, data, data_ts): """ Set the cache data for a specified project for the specified date. :param project: project name to set data for :type project: str :param date: date to set data for :type date: datetime.datetime :param data: data t...

def get_dates_for_project(self, project): """ Return a list of the dates we have in cache for the specified project, sorted in ascending date order. :param project: project name :type project: str :return: list of datetime.datetime objects :rtype: datetime.dateti...

def parse_args(argv): """ Use Argparse to parse command-line arguments. :param argv: list of arguments to parse (``sys.argv[1:]``) :type argv: ``list`` :return: parsed arguments :rtype: :py:class:`argparse.Namespace` """ p = argparse.ArgumentParser( description='pypi-download-st...

def set_log_level_format(level, format): """ Set logger level and format. :param level: logging level; see the :py:mod:`logging` constants. :type level: int :param format: logging formatter format string :type format: str """ formatter = logging.Formatter(fmt=format) logger.handlers...

def _pypi_get_projects_for_user(username): """ Given the username of a PyPI user, return a list of all of the user's projects from the XMLRPC interface. See: https://wiki.python.org/moin/PyPIXmlRpc :param username: PyPI username :type username: str :return: list of string project names ...

def main(args=None): """ Main entry point """ # parse args if args is None: args = parse_args(sys.argv[1:]) # set logging level if args.verbose > 1: set_log_debug() elif args.verbose == 1: set_log_info() outpath = os.path.abspath(os.path.expanduser(args.out_...

def generate_graph(self): """ Generate the graph; return a 2-tuple of strings, script to place in the head of the HTML document and div content for the graph itself. :return: 2-tuple (script, div) :rtype: tuple """ logger.debug('Generating graph for %s', self._gr...

def _line_for_patches(self, data, chart, renderer, series_name): """ Add a line along the top edge of a Patch in a stacked Area Chart; return the new Glyph for addition to HoverTool. :param data: original data for the graph :type data: dict :param chart: Chart to add the...

def _get_cache_dates(self): """ Get s list of dates (:py:class:`datetime.datetime`) present in cache, beginning with the longest contiguous set of dates that isn't missing more than one date in series. :return: list of datetime objects for contiguous dates in cache :rtyp...

def _is_empty_cache_record(self, rec): """ Return True if the specified cache record has no data, False otherwise. :param rec: cache record returned by :py:meth:`~._cache_get` :type rec: dict :return: True if record is empty, False otherwise :rtype: bool """ ...

def _cache_get(self, date): """ Return cache data for the specified day; cache locally in this class. :param date: date to get data for :type date: datetime.datetime :return: cache data for date :rtype: dict """ if date in self.cache_data: log...

def _compound_column_value(k1, k2): """ Like :py:meth:`~._column_value` but collapses two unknowns into one. :param k1: first (top-level) value :param k2: second (bottom-level) value :return: display key :rtype: str """ k1 = ProjectStats._column_value(k1)...

def _shorten_version(ver, num_components=2): """ If ``ver`` is a dot-separated string with at least (num_components +1) components, return only the first two. Else return the original string. :param ver: version string :type ver: str :return: shortened (major, minor) ver...

def per_version_data(self): """ Return download data by version. :return: dict of cache data; keys are datetime objects, values are dict of version (str) to count (int) :rtype: dict """ ret = {} for cache_date in self.cache_dates: data = sel...

def per_file_type_data(self): """ Return download data by file type. :return: dict of cache data; keys are datetime objects, values are dict of file type (str) to count (int) :rtype: dict """ ret = {} for cache_date in self.cache_dates: data...

def per_installer_data(self): """ Return download data by installer name and version. :return: dict of cache data; keys are datetime objects, values are dict of installer name/version (str) to count (int). :rtype: dict """ ret = {} for cache_date in sel...

def per_implementation_data(self): """ Return download data by python impelementation name and version. :return: dict of cache data; keys are datetime objects, values are dict of implementation name/version (str) to count (int). :rtype: dict """ ret = {} ...

def per_system_data(self): """ Return download data by system. :return: dict of cache data; keys are datetime objects, values are dict of system (str) to count (int) :rtype: dict """ ret = {} for cache_date in self.cache_dates: data = self._...

def per_country_data(self): """ Return download data by country. :return: dict of cache data; keys are datetime objects, values are dict of country (str) to count (int) :rtype: dict """ ret = {} for cache_date in self.cache_dates: data = sel...

def per_distro_data(self): """ Return download data by distro name and version. :return: dict of cache data; keys are datetime objects, values are dict of distro name/version (str) to count (int). :rtype: dict """ ret = {} for cache_date in self.cache_d...

def downloads_per_day(self): """ Return the number of downloads per day, averaged over the past 7 days of data. :return: average number of downloads per day :rtype: int """ count, num_days = self._downloads_for_num_days(7) res = ceil(count / num_days) ...

def downloads_per_week(self): """ Return the number of downloads in the last 7 days. :return: number of downloads in the last 7 days; if we have less than 7 days of data, returns None. :rtype: int """ if len(self.cache_dates) < 7: logger.error("Only...

def _downloads_for_num_days(self, num_days): """ Given a number of days of historical data to look at (starting with today and working backwards), return the total number of downloads for that time range, and the number of days of data we had (in cases where we had less data than...

def _get_project_id(self): """ Get our projectId from the ``GOOGLE_APPLICATION_CREDENTIALS`` creds JSON file. :return: project ID :rtype: str """ fpath = os.environ.get('GOOGLE_APPLICATION_CREDENTIALS', None) if fpath is None: raise Exception(...

def _get_bigquery_service(self): """ Connect to the BigQuery service. Calling ``GoogleCredentials.get_application_default`` requires that you either be running in the Google Cloud, or have the ``GOOGLE_APPLICATION_CREDENTIALS`` environment variable set to the path to a c...

def _get_download_table_ids(self): """ Get a list of PyPI downloads table (sharded per day) IDs. :return: list of table names (strings) :rtype: ``list`` """ all_table_names = [] # matching per-date table names logger.info('Querying for all tables in dataset') ...

def _datetime_for_table_name(self, table_name): """ Return a :py:class:`datetime.datetime` object for the date of the data in the specified table name. :param table_name: name of the table :type table_name: str :return: datetime that the table holds data for :rty...

def _run_query(self, query): """ Run one query against BigQuery and return the result. :param query: the query to run :type query: str :return: list of per-row response dicts (key => value) :rtype: ``list`` """ query_request = self.service.jobs() ...

def _get_newest_ts_in_table(self, table_name): """ Return the timestamp for the newest record in the given table. :param table_name: name of the table to query :type table_name: str :return: timestamp of newest row in table :rtype: int """ logger.debug( ...

def _query_by_installer(self, table_name): """ Query for download data broken down by installer, for one day. :param table_name: table name to query against :type table_name: str :return: dict of download information by installer; keys are project name, values are a di...

def _query_by_system(self, table_name): """ Query for download data broken down by system, for one day. :param table_name: table name to query against :type table_name: str :return: dict of download information by system; keys are project name, values are a dict of sys...

def _query_by_distro(self, table_name): """ Query for download data broken down by OS distribution, for one day. :param table_name: table name to query against :type table_name: str :return: dict of download information by distro; keys are project name, values are a di...

def query_one_table(self, table_name): """ Run all queries for the given table name (date) and update the cache. :param table_name: table name to query against :type table_name: str """ table_date = self._datetime_for_table_name(table_name) logger.info('Running a...

def _have_cache_for_date(self, dt): """ Return True if we have cached data for all projects for the specified datetime. Return False otherwise. :param dt: datetime to find cache for :type dt: datetime.datetime :return: True if we have cache for all projects for this date...

def backfill_history(self, num_days, available_table_names): """ Backfill historical data for days that are missing. :param num_days: number of days of historical data to backfill, if missing :type num_days: int :param available_table_names: names of available per-date...

def run_queries(self, backfill_num_days=7): """ Run the data queries for the specified projects. :param backfill_num_days: number of days of historical data to backfill, if missing :type backfill_num_days: int """ available_tables = self._get_download_table_ids...

def filter_data_columns(data): """ Given a dict of data such as those in :py:class:`~.ProjectStats` attributes, made up of :py:class:`datetime.datetime` keys and values of dicts of column keys to counts, return a list of the distinct column keys in sorted order. :param data: data dict as returned b...

def _generate_html(self): """ Generate the HTML for the specified graphs. :return: :rtype: """ logger.debug('Generating templated HTML') env = Environment( loader=PackageLoader('pypi_download_stats', 'templates'), extensions=['jinja2.ext.l...

def _data_dict_to_bokeh_chart_data(self, data): """ Take a dictionary of data, as returned by the :py:class:`~.ProjectStats` per_*_data properties, return a 2-tuple of data dict and x labels list usable by bokeh.charts. :param data: data dict from :py:class:`~.ProjectStats` prop...

def _limit_data(self, data): """ Find the per-day average of each series in the data over the last 7 days; drop all but the top 10. :param data: original graph data :type data: dict :return: dict containing only the top 10 series, based on average over the last...

def _generate_graph(self, name, title, stats_data, y_name): """ Generate a downloads graph; append it to ``self._graphs``. :param name: HTML name of the graph, also used in ``self.GRAPH_KEYS`` :type name: str :param title: human-readable title for the graph :type title: ...

def _generate_badges(self): """ Generate download badges. Append them to ``self._badges``. """ daycount = self._stats.downloads_per_day day = self._generate_badge('Downloads', '%d/day' % daycount) self._badges['per-day'] = day weekcount = self._stats.downloads_per...

def _generate_badge(self, subject, status): """ Generate SVG for one badge via shields.io. :param subject: subject; left-hand side of badge :type subject: str :param status: status; right-hand side of badge :type status: str :return: badge SVG :rtype: str...

def generate(self): """ Generate all output types and write to disk. """ logger.info('Generating graphs') self._generate_graph( 'by-version', 'Downloads by Version', self._stats.per_version_data, 'Version' ) self._ge...

def datetime_format(desired_format, datetime_instance=None, *args, **kwargs): """ Replaces format style phrases (listed in the dt_exps dictionary) with this datetime instance's information. .. code :: python reusables.datetime_format("Hey, it's {month-full} already!") "Hey, it's March...

def datetime_from_iso(iso_string): """ Create a DateTime object from a ISO string .. code :: python reusables.datetime_from_iso('2017-03-10T12:56:55.031863') datetime.datetime(2017, 3, 10, 12, 56, 55, 31863) :param iso_string: string of an ISO datetime :return: DateTime object ...

def now(utc=False, tz=None): """ Get a current DateTime object. By default is local. .. code:: python reusables.now() # DateTime(2016, 12, 8, 22, 5, 2, 517000) reusables.now().format("It's {24-hour}:{min}") # "It's 22:05" :param utc: bool, default False, UTC time not ...

def run(command, input=None, stdout=subprocess.PIPE, stderr=subprocess.PIPE, timeout=None, copy_local_env=False, **kwargs): """ Cross platform compatible subprocess with CompletedProcess return. No formatting or encoding is performed on the output of subprocess, so it's output will appear the s...

def run_in_pool(target, iterable, threaded=True, processes=4, asynchronous=False, target_kwargs=None): """ Run a set of iterables to a function in a Threaded or MP Pool. .. code: python def func(a): return a + a reusables.run_in_pool(func, [1,2,3,4,5]) # [1...

def tree_view(dictionary, level=0, sep="| "): """ View a dictionary as a tree. """ return "".join(["{0}{1}\n{2}".format(sep * level, k, tree_view(v, level + 1, sep=sep) if isinstance(v, dict) else "") for k, v in dictionary.items()])

def to_dict(self, in_dict=None): """ Turn the Namespace and sub Namespaces back into a native python dictionary. :param in_dict: Do not use, for self recursion :return: python dictionary of this Namespace """ in_dict = in_dict if in_dict else self out_dic...

def list(self, item, default=None, spliter=",", strip=True, mod=None): """ Return value of key as a list :param item: key of value to transform :param mod: function to map against list :param default: value to return if item does not exist :param spliter: character to split str ...

def download(url, save_to_file=True, save_dir=".", filename=None, block_size=64000, overwrite=False, quiet=False): """ Download a given URL to either file or memory :param url: Full url (with protocol) of path to download :param save_to_file: boolean if it should be saved to file or not ...

def url_to_ips(url, port=None, ipv6=False, connect_type=socket.SOCK_STREAM, proto=socket.IPPROTO_TCP, flags=0): """ Provide a list of IP addresses, uses `socket.getaddrinfo` .. code:: python reusables.url_to_ips("example.com", ipv6=True) # ['2606:2800:220:1:248:1893:25c8:194...

def ip_to_url(ip_addr): """ Resolve a hostname based off an IP address. This is very limited and will probably not return any results if it is a shared IP address or an address with improperly setup DNS records. .. code:: python reusables.ip_to_url('93.184.216.34') # example.com ...

def start(self): """Create a background thread for httpd and serve 'forever'""" self._process = threading.Thread(target=self._background_runner) self._process.start()

def get_stream_handler(stream=sys.stderr, level=logging.INFO, log_format=log_formats.easy_read): """ Returns a set up stream handler to add to a logger. :param stream: which stream to use, defaults to sys.stderr :param level: logging level to set handler at :param log_format:...