gegoftime/Mako-DeepThink-RU · Datasets at Hugging Face

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def get_posts_data(self): """ given a WordPress xml export file, will return list of dictionaries with keys that match the expected json keys of a wordpress API call >>> xp = XML_parser('example_export.xml') >>> json_vals = {"slug","ID", "title","description", "content", ...
def get_comments_data(self, slug): """ Returns a flat list of all comments in XML dump. Formatted as the JSON output from Wordpress API. Keys: ('content', 'slug', 'date', 'status', 'author', 'ID', 'parent') date format: '%Y-%m-%dT%H:%M:%S' ...
def _format_content(self, content): """Returns `content` with consecutive spaces converted to non-break spaces, and linebreak converted into HTML br elements. :param content: text to format :type content: `str` :rtype: `str` """ content = re.sub(r'\n', '<br/>\n'...
def _prepare_text(self, text): """Returns `text` with each consituent token wrapped in HTML markup for later match annotation. :param text: text to be marked up :type text: `str` :rtype: `str` """ # Remove characters that should be escaped for XML input (but ...
def generate(self, output_dir, work, ngrams, labels, minus_ngrams): """Generates HTML reports for each witness to `work`, showing its text with the n-grams in `ngrams` highlighted. Any n-grams in `minus_ngrams` have any highlighting of them (or subsets of them) removed. :param ...
def _highlight(self, content, ngrams, highlight): """Returns `content` with its n-grams from `ngrams` highlighted (if `add_class` is True) or unhighlighted. :param content: text to be modified :type content: `str` :param ngrams: n-grams to modify :type ngrams: `list` of ...
def generate(self, output_dir, work, matches_filename): """Generates HTML reports showing the text of each witness to `work` with its matches in `matches` highlighted. :param output_dir: directory to write report to :type output_dir: `str` :param work: name of work to highlight ...
def packet_meta_data(self): """Pull out the metadata about each packet from the input_stream Args: None Returns: generator (dictionary): a generator that contains packet meta data in the form of a dictionary""" # For each packet in the pcap process th...
def print_flow_info(flow): """Print a summary of the flow information""" print('Flow %s (%s)-- Packets:%d Bytes:%d Payload: %s...' % (flow['flow_id'], flow['direction'], len(flow['packet_list']), len(flow['payload']), repr(flow['payload'])[:30]))
def packets_to_flows(self): """Combine packets into flows""" # For each packet, place it into either an existing flow or a new flow for packet in self.input_stream: # Compute flow tuple and add the packet to the flow flow_id = flow_utils.flow_tuple(packet) s...
def _copy_static_assets(self, output_dir): """Copy assets for the report to `output_dir`. :param output_dir: directory to output assets to :type output_dir: `str` """ base_directory = 'assets/{}'.format(self._report_name) for asset in resource_listdir(self._package_name...
def _get_template(self): """Returns a template for this report. :rtype: `jinja2.Template` """ loader = PackageLoader(self._package_name, 'assets/templates') env = Environment(extensions=['jinja2.ext.with_'], loader=loader) return env.get_template('{}.html'.format(self._...
def _write(self, context, report_dir, report_name, assets_dir=None, template=None): """Writes the data in `context` in the report's template to `report_name` in `report_dir`. If `assets_dir` is supplied, copies all assets for this report to the specified directory. ...
def pull(self): """Print out summary information about each packet from the input_stream""" # For each packet in the pcap process the contents for item in self.input_stream: # Print out the timestamp in UTC print('%s -' % item['timestamp'], end='') # Transp...
def pull(self): """Print out information about each packet from the input_stream""" # For each packet in the pcap process the contents for item in self.input_stream: # Print out the timestamp in UTC print('Timestamp: %s' % item['timestamp']) # Unpack the Et...
def load_backend(**orm_config): """ Load the client adaptor module of django_peeringdb Assumes config is valid. """ settings = {} settings['SECRET_KEY'] = orm_config.get('secret_key', '') db_config = orm_config['database'] if db_config: settings['DATABASES'] = { 'def...
def add_label_count(self): """Adds to each result row a count of the number of occurrences of that n-gram across all works within the label. This count uses the highest witness count for each work. """ self._logger.info('Adding label count') def add_label_count(df): ...
def add_label_work_count(self): """Adds to each result row a count of the number of works within the label contain that n-gram. This counts works that have at least one witness carrying the n-gram. This correctly handles cases where an n-gram has only zero counts for a ...
def _annotate_bifurcated_extend_data(self, row, smaller, larger, tokenize, join): """Returns `row` annotated with whether it should be deleted or not. An n-gram is marked for deletion if: * its label count is 1 and its constituent (n-1)-grams also ...
def bifurcated_extend(self, corpus, max_size): """Replaces the results with those n-grams that contain any of the original n-grams, and that represent points at which an n-gram is a constituent of multiple larger n-grams with a lower label count. :param corpus: corpus of works t...
def collapse_witnesses(self): """Groups together witnesses for the same n-gram and work that has the same count, and outputs a single row for each group. This output replaces the siglum field with a sigla field that provides a comma separated list of the witness sigla. Due to th...
def csv(self, fh): """Writes the results data to `fh` in CSV format and returns `fh`. :param fh: file to write data to :type fh: file object :rtype: file object """ self._matches.to_csv(fh, encoding='utf-8', float_format='%d', index=False) ...
def excise(self, ngram): """Removes all rows whose n-gram contains `ngram`. This operation uses simple string containment matching. For tokens that consist of multiple characters, this means that `ngram` may be part of one or two tokens; eg, "he m" would match on "she may". ...
def extend(self, corpus): """Adds rows for all longer forms of n-grams in the results that are present in the witnesses. This works with both diff and intersect results. :param corpus: corpus of works to which results belong :type corpus: `Corpus` """ self._log...
def _generate_extended_matches(self, extended_ngrams, highest_n, work, siglum, label): """Returns extended match data derived from `extended_ngrams`. This extended match data are the counts for all intermediate n-grams within each extended n-gram. :pa...
def _generate_extended_ngrams(self, matches, work, siglum, label, corpus, highest_n): """Returns the n-grams of the largest size that exist in `siglum` witness to `work` under `label`, generated from adding together overlapping n-grams in `matches`. :pa...
def _generate_filter_ngrams(self, data, min_size): """Returns the n-grams in `data` that do not contain any other n-gram in `data`. :param data: n-gram results data :type data: `pandas.DataFrame` :param min_size: minimum n-gram size in `data` :type min_size: `int` ...
def _generate_substrings(self, ngram, size): """Returns a list of all substrings of `ngram`. :param ngram: n-gram to generate substrings of :type ngram: `str` :param size: size of `ngram` :type size: `int` :rtype: `list` """ text = Text(ngram, self._toke...
def group_by_ngram(self, labels): """Groups result rows by n-gram and label, providing a single summary field giving the range of occurrences across each work's witnesses. Results are sorted by n-gram then by label (in the order given in `labels`). :param labels: labels to order...
def group_by_witness(self): """Groups results by witness, providing a single summary field giving the n-grams found in it, a count of their number, and the count of their combined occurrences.""" if self._matches.empty: # Ensure that the right columns are used, even though th...
def _is_intersect_results(results): """Returns False if `results` has an n-gram that exists in only one label, True otherwise. :param results: results to analyze :type results: `pandas.DataFrame` :rtype: `bool` """ sample = results.iloc[0] ngram = sample...
def prune_by_ngram(self, ngrams): """Removes results rows whose n-gram is in `ngrams`. :param ngrams: n-grams to remove :type ngrams: `list` of `str` """ self._logger.info('Pruning results by n-gram') self._matches = self._matches[ ~self._matches[constants.N...
def prune_by_ngram_count(self, minimum=None, maximum=None, label=None): """Removes results rows whose total n-gram count (across all works bearing this n-gram) is outside the range specified by `minimum` and `maximum`. For each text, the count used as part of the sum across all ...

def get_posts_data(self): """ given a WordPress xml export file, will return list of dictionaries with keys that match the expected json keys of a wordpress API call >>> xp = XML_parser('example_export.xml') >>> json_vals = {"slug","ID", "title","description", "content", ...

def get_comments_data(self, slug): """ Returns a flat list of all comments in XML dump. Formatted as the JSON output from Wordpress API. Keys: ('content', 'slug', 'date', 'status', 'author', 'ID', 'parent') date format: '%Y-%m-%dT%H:%M:%S' ...

def _format_content(self, content): """Returns `content` with consecutive spaces converted to non-break spaces, and linebreak converted into HTML br elements. :param content: text to format :type content: `str` :rtype: `str` """ content = re.sub(r'\n', '<br/>\n'...

def _prepare_text(self, text): """Returns `text` with each consituent token wrapped in HTML markup for later match annotation. :param text: text to be marked up :type text: `str` :rtype: `str` """ # Remove characters that should be escaped for XML input (but ...

def generate(self, output_dir, work, ngrams, labels, minus_ngrams): """Generates HTML reports for each witness to `work`, showing its text with the n-grams in `ngrams` highlighted. Any n-grams in `minus_ngrams` have any highlighting of them (or subsets of them) removed. :param ...

def _highlight(self, content, ngrams, highlight): """Returns `content` with its n-grams from `ngrams` highlighted (if `add_class` is True) or unhighlighted. :param content: text to be modified :type content: `str` :param ngrams: n-grams to modify :type ngrams: `list` of ...

def generate(self, output_dir, work, matches_filename): """Generates HTML reports showing the text of each witness to `work` with its matches in `matches` highlighted. :param output_dir: directory to write report to :type output_dir: `str` :param work: name of work to highlight ...

def packet_meta_data(self): """Pull out the metadata about each packet from the input_stream Args: None Returns: generator (dictionary): a generator that contains packet meta data in the form of a dictionary""" # For each packet in the pcap process th...

def print_flow_info(flow): """Print a summary of the flow information""" print('Flow %s (%s)-- Packets:%d Bytes:%d Payload: %s...' % (flow['flow_id'], flow['direction'], len(flow['packet_list']), len(flow['payload']), repr(flow['payload'])[:30]))

def packets_to_flows(self): """Combine packets into flows""" # For each packet, place it into either an existing flow or a new flow for packet in self.input_stream: # Compute flow tuple and add the packet to the flow flow_id = flow_utils.flow_tuple(packet) s...

def _copy_static_assets(self, output_dir): """Copy assets for the report to `output_dir`. :param output_dir: directory to output assets to :type output_dir: `str` """ base_directory = 'assets/{}'.format(self._report_name) for asset in resource_listdir(self._package_name...

def _get_template(self): """Returns a template for this report. :rtype: `jinja2.Template` """ loader = PackageLoader(self._package_name, 'assets/templates') env = Environment(extensions=['jinja2.ext.with_'], loader=loader) return env.get_template('{}.html'.format(self._...

def _write(self, context, report_dir, report_name, assets_dir=None, template=None): """Writes the data in `context` in the report's template to `report_name` in `report_dir`. If `assets_dir` is supplied, copies all assets for this report to the specified directory. ...

def pull(self): """Print out summary information about each packet from the input_stream""" # For each packet in the pcap process the contents for item in self.input_stream: # Print out the timestamp in UTC print('%s -' % item['timestamp'], end='') # Transp...

def pull(self): """Print out information about each packet from the input_stream""" # For each packet in the pcap process the contents for item in self.input_stream: # Print out the timestamp in UTC print('Timestamp: %s' % item['timestamp']) # Unpack the Et...

def load_backend(**orm_config): """ Load the client adaptor module of django_peeringdb Assumes config is valid. """ settings = {} settings['SECRET_KEY'] = orm_config.get('secret_key', '') db_config = orm_config['database'] if db_config: settings['DATABASES'] = { 'def...

def add_label_count(self): """Adds to each result row a count of the number of occurrences of that n-gram across all works within the label. This count uses the highest witness count for each work. """ self._logger.info('Adding label count') def add_label_count(df): ...

def add_label_work_count(self): """Adds to each result row a count of the number of works within the label contain that n-gram. This counts works that have at least one witness carrying the n-gram. This correctly handles cases where an n-gram has only zero counts for a ...

def _annotate_bifurcated_extend_data(self, row, smaller, larger, tokenize, join): """Returns `row` annotated with whether it should be deleted or not. An n-gram is marked for deletion if: * its label count is 1 and its constituent (n-1)-grams also ...

def bifurcated_extend(self, corpus, max_size): """Replaces the results with those n-grams that contain any of the original n-grams, and that represent points at which an n-gram is a constituent of multiple larger n-grams with a lower label count. :param corpus: corpus of works t...

def collapse_witnesses(self): """Groups together witnesses for the same n-gram and work that has the same count, and outputs a single row for each group. This output replaces the siglum field with a sigla field that provides a comma separated list of the witness sigla. Due to th...

def csv(self, fh): """Writes the results data to `fh` in CSV format and returns `fh`. :param fh: file to write data to :type fh: file object :rtype: file object """ self._matches.to_csv(fh, encoding='utf-8', float_format='%d', index=False) ...

def excise(self, ngram): """Removes all rows whose n-gram contains `ngram`. This operation uses simple string containment matching. For tokens that consist of multiple characters, this means that `ngram` may be part of one or two tokens; eg, "he m" would match on "she may". ...

def extend(self, corpus): """Adds rows for all longer forms of n-grams in the results that are present in the witnesses. This works with both diff and intersect results. :param corpus: corpus of works to which results belong :type corpus: `Corpus` """ self._log...

def _generate_extended_matches(self, extended_ngrams, highest_n, work, siglum, label): """Returns extended match data derived from `extended_ngrams`. This extended match data are the counts for all intermediate n-grams within each extended n-gram. :pa...

def _generate_extended_ngrams(self, matches, work, siglum, label, corpus, highest_n): """Returns the n-grams of the largest size that exist in `siglum` witness to `work` under `label`, generated from adding together overlapping n-grams in `matches`. :pa...

def _generate_filter_ngrams(self, data, min_size): """Returns the n-grams in `data` that do not contain any other n-gram in `data`. :param data: n-gram results data :type data: `pandas.DataFrame` :param min_size: minimum n-gram size in `data` :type min_size: `int` ...

def _generate_substrings(self, ngram, size): """Returns a list of all substrings of `ngram`. :param ngram: n-gram to generate substrings of :type ngram: `str` :param size: size of `ngram` :type size: `int` :rtype: `list` """ text = Text(ngram, self._toke...

def group_by_ngram(self, labels): """Groups result rows by n-gram and label, providing a single summary field giving the range of occurrences across each work's witnesses. Results are sorted by n-gram then by label (in the order given in `labels`). :param labels: labels to order...

def group_by_witness(self): """Groups results by witness, providing a single summary field giving the n-grams found in it, a count of their number, and the count of their combined occurrences.""" if self._matches.empty: # Ensure that the right columns are used, even though th...

def _is_intersect_results(results): """Returns False if `results` has an n-gram that exists in only one label, True otherwise. :param results: results to analyze :type results: `pandas.DataFrame` :rtype: `bool` """ sample = results.iloc[0] ngram = sample...

def prune_by_ngram(self, ngrams): """Removes results rows whose n-gram is in `ngrams`. :param ngrams: n-grams to remove :type ngrams: `list` of `str` """ self._logger.info('Pruning results by n-gram') self._matches = self._matches[ ~self._matches[constants.N...

def prune_by_ngram_count(self, minimum=None, maximum=None, label=None): """Removes results rows whose total n-gram count (across all works bearing this n-gram) is outside the range specified by `minimum` and `maximum`. For each text, the count used as part of the sum across all ...