Search is not available for this dataset
identifier stringlengths 1 155 | parameters stringlengths 2 6.09k | docstring stringlengths 11 63.4k | docstring_summary stringlengths 0 63.4k | function stringlengths 29 99.8k | function_tokens list | start_point list | end_point list | language stringclasses 1
value | docstring_language stringlengths 2 7 | docstring_language_predictions stringlengths 18 23 | is_langid_reliable stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|---|---|---|
Configuration.set_value | (self, key, value) | Modify a value in the configuration.
| Modify a value in the configuration.
| def set_value(self, key, value):
# type: (str, Any) -> None
"""Modify a value in the configuration.
"""
self._ensure_have_load_only()
assert self.load_only
fname, parser = self._get_parser_to_modify()
if parser is not None:
section, name = _disassemb... | [
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Configuration.unset_value | (self, key) | Unset a value in the configuration. | Unset a value in the configuration. | def unset_value(self, key):
# type: (str) -> None
"""Unset a value in the configuration."""
self._ensure_have_load_only()
assert self.load_only
if key not in self._config[self.load_only]:
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Configuration.save | (self) | Save the current in-memory state.
| Save the current in-memory state.
| def save(self):
# type: () -> None
"""Save the current in-memory state.
"""
self._ensure_have_load_only()
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Configuration._dictionary | (self) | A dictionary representing the loaded configuration.
| A dictionary representing the loaded configuration.
| def _dictionary(self):
# type: () -> Dict[str, Any]
"""A dictionary representing the loaded configuration.
"""
# NOTE: Dictionaries are not populated if not loaded. So, conditionals
# are not needed here.
retval = {}
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Configuration._load_config_files | (self) | Loads configuration from configuration files
| Loads configuration from configuration files
| def _load_config_files(self):
# type: () -> None
"""Loads configuration from configuration files
"""
config_files = dict(self.iter_config_files())
if config_files[kinds.ENV][0:1] == [os.devnull]:
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Configuration._load_environment_vars | (self) | Loads configuration from environment variables
| Loads configuration from environment variables
| def _load_environment_vars(self):
# type: () -> None
"""Loads configuration from environment variables
"""
self._config[kinds.ENV_VAR].update(
self._normalized_keys(":env:", self.get_environ_vars())
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Configuration._normalized_keys | (self, section, items) | Normalizes items to construct a dictionary with normalized keys.
This routine is where the names become keys and are made the same
regardless of source - configuration files or environment.
| Normalizes items to construct a dictionary with normalized keys. | def _normalized_keys(self, section, items):
# type: (str, Iterable[Tuple[str, Any]]) -> Dict[str, Any]
"""Normalizes items to construct a dictionary with normalized keys.
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Configuration.get_environ_vars | (self) | Returns a generator with all environmental vars with prefix PIP_ | Returns a generator with all environmental vars with prefix PIP_ | def get_environ_vars(self):
# type: () -> Iterable[Tuple[str, str]]
"""Returns a generator with all environmental vars with prefix PIP_"""
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Configuration.iter_config_files | (self) | Yields variant and configuration files associated with it.
This should be treated like items of a dictionary.
| Yields variant and configuration files associated with it. | def iter_config_files(self):
# type: () -> Iterable[Tuple[Kind, List[str]]]
"""Yields variant and configuration files associated with it.
This should be treated like items of a dictionary.
"""
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Configuration.get_values_in_config | (self, variant) | Get values present in a config file | Get values present in a config file | def get_values_in_config(self, variant):
# type: (Kind) -> Dict[str, Any]
"""Get values present in a config file"""
return self._config[variant] | [
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bs_progress_bar | (*args, **kwargs) | A Standard Bootstrap Progress Bar.
http://getbootstrap.com/components/#progress
param args (Array of Numbers: 0-100): Percent of Progress Bars
param context (String): Adds 'progress-bar-{context} to the class attribute
param contexts (Array of Strings): Cycles through contexts for stacked bars
par... | A Standard Bootstrap Progress Bar. | def bs_progress_bar(*args, **kwargs):
"""A Standard Bootstrap Progress Bar.
http://getbootstrap.com/components/#progress
param args (Array of Numbers: 0-100): Percent of Progress Bars
param context (String): Adds 'progress-bar-{context} to the class attribute
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MultiRef.process | (self, body) |
Process the specified soap envelope body and replace I{multiref} node
references with the contents of the referenced node.
@param body: A soap envelope body node.
@type body: L{Element}
@return: The processed I{body}
@rtype: L{Element}
|
Process the specified soap envelope body and replace I{multiref} node
references with the contents of the referenced node.
| def process(self, body):
"""
Process the specified soap envelope body and replace I{multiref} node
references with the contents of the referenced node.
@param body: A soap envelope body node.
@type body: L{Element}
@return: The processed I{body}
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MultiRef.update | (self, node) |
Update the specified I{node} by replacing the I{multiref} references with
the contents of the referenced nodes and remove the I{href} attribute.
@param node: A node to update.
@type node: L{Element}
@return: The updated node
@rtype: L{Element}
|
Update the specified I{node} by replacing the I{multiref} references with
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| def update(self, node):
"""
Update the specified I{node} by replacing the I{multiref} references with
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@param node: A node to update.
@type node: L{Element}
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MultiRef.replace_references | (self, node) |
Replacing the I{multiref} references with the contents of the
referenced nodes and remove the I{href} attribute. Warning: since
the I{ref} is not cloned,
@param node: A node to update.
@type node: L{Element}
|
Replacing the I{multiref} references with the contents of the
referenced nodes and remove the I{href} attribute. Warning: since
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| def replace_references(self, node):
"""
Replacing the I{multiref} references with the contents of the
referenced nodes and remove the I{href} attribute. Warning: since
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@param node: A node to update.
@type node: L{Element}
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MultiRef.build_catalog | (self, body) |
Create the I{catalog} of multiref nodes by id and the list of
non-multiref nodes.
@param body: A soap envelope body node.
@type body: L{Element}
|
Create the I{catalog} of multiref nodes by id and the list of
non-multiref nodes.
| def build_catalog(self, body):
"""
Create the I{catalog} of multiref nodes by id and the list of
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@param body: A soap envelope body node.
@type body: L{Element}
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MultiRef.soaproot | (self, node) |
Get whether the specified I{node} is a soap encoded root.
This is determined by examining @soapenc:root='1'.
The node is considered to be a root when the attribute
is not specified.
@param node: A node to evaluate.
@type node: L{Element}
@return: True if a soap e... |
Get whether the specified I{node} is a soap encoded root.
This is determined by examining | def soaproot(self, node):
"""
Get whether the specified I{node} is a soap encoded root.
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The node is considered to be a root when the attribute
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@param node: A node to evaluate.
@type node: L{Eleme... | [
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with_metaclass | (meta, *bases) |
Create a base class with a metaclass.
|
Create a base class with a metaclass.
| def with_metaclass(meta, *bases):
# type: (Type[Any], Tuple[Type[Any], ...]) -> Any
"""
Create a base class with a metaclass.
"""
# This requires a bit of explanation: the basic idea is to make a dummy
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tenant_quota_usages | (request, tenant_id=None, targets=None) | Get our quotas and construct our usage object.
:param tenant_id: Target tenant ID. If no tenant_id is provided,
a the request.user.project_id is assumed to be used.
:param targets: A tuple of quota names to be retrieved.
If unspecified, all quota and usage information is retrieved.
| Get our quotas and construct our usage object. | def tenant_quota_usages(request, tenant_id=None, targets=None):
"""Get our quotas and construct our usage object.
:param tenant_id: Target tenant ID. If no tenant_id is provided,
a the request.user.project_id is assumed to be used.
:param targets: A tuple of quota names to be retrieved.
If ... | [
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enabled_quotas | (request) | Returns the list of quotas available minus those that are disabled | Returns the list of quotas available minus those that are disabled | def enabled_quotas(request):
"""Returns the list of quotas available minus those that are disabled"""
return QUOTA_FIELDS - get_disabled_quotas(request) | [
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QuotaUsage.add_quota | (self, quota) | Adds an internal tracking reference for the given quota. | Adds an internal tracking reference for the given quota. | def add_quota(self, quota):
"""Adds an internal tracking reference for the given quota."""
if quota.limit in (None, -1, float('inf')):
# Handle "unlimited" quotas.
self.usages[quota.name]['quota'] = float("inf")
self.usages[quota.name]['available'] = float("inf")
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QuotaUsage.tally | (self, name, value) | Adds to the "used" metric for the given quota. | Adds to the "used" metric for the given quota. | def tally(self, name, value):
"""Adds to the "used" metric for the given quota."""
value = value or 0 # Protection against None.
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QuotaUsage.update_available | (self, name) | Updates the "available" metric for the given quota. | Updates the "available" metric for the given quota. | def update_available(self, name):
"""Updates the "available" metric for the given quota."""
quota = self.usages.get(name, {}).get('quota', float('inf'))
available = quota - self.usages[name]['used']
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BaseFeature.data | (self) | To be overrode. | To be overrode. | def data(self):
"""To be overrode."""
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BaseFeature.mapping | (self) | To be overrode. | To be overrode. | def mapping(self):
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CategoricalFeature.__init__ | (self, series, name, description, imputed_category, transformed=False, mapping=None) | Construct new CategoricalFeature object.
Additionally create raw_mapping and mapped_series attributes.
Args:
series (pandas.Series): Series holding the data (copy)
name (str): name of the Feature
description (str): description of the Feature
imputed_cate... | Construct new CategoricalFeature object. | def __init__(self, series, name, description, imputed_category, transformed=False, mapping=None):
"""Construct new CategoricalFeature object.
Additionally create raw_mapping and mapped_series attributes.
Args:
series (pandas.Series): Series holding the data (copy)
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CategoricalFeature.data | (self) | Return mapped_series property. | Return mapped_series property. | def data(self):
"""Return mapped_series property."""
return self.mapped_series | [
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CategoricalFeature.original_data | (self) | Return original Series. | Return original Series. | def original_data(self):
"""Return original Series."""
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CategoricalFeature.mapping | (self) | Return _descriptive_mapping attribute and if it's None, create it with _create_descriptive_mapping method. | Return _descriptive_mapping attribute and if it's None, create it with _create_descriptive_mapping method. | def mapping(self):
"""Return _descriptive_mapping attribute and if it's None, create it with _create_descriptive_mapping method."""
if not self._descriptive_mapping:
self._descriptive_mapping = self._create_descriptive_mapping()
return self._descriptive_mapping | [
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CategoricalFeature._create_mapped_series | (self) | Return series property with it's content replaced with raw_mapping dictionary. | Return series property with it's content replaced with raw_mapping dictionary. | def _create_mapped_series(self):
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CategoricalFeature._create_raw_mapping | (self) | Return dictionary of 'unique value': number pairs.
Replace every categorical value with a number starting from 1 (sorted alphabetically). Starting with 1
to be consistent with "count" obtained with .describe() methods on dataframes.
Returns:
dict: 'unique value': number pairs dict.... | Return dictionary of 'unique value': number pairs. | def _create_raw_mapping(self):
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CategoricalFeature._create_descriptive_mapping | (self) | Create and return dictionary mapping for unique values present in series.
Key is the "new" value provided with enumerating unique values in raw_mapping. Value is either the description
of the category taken from original descriptions or the original value (if description dict is None).
Returns... | Create and return dictionary mapping for unique values present in series. | def _create_descriptive_mapping(self):
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NumericalFeature.__init__ | (self, series, name, description, imputed_category, transformed=False) | Construct new NumericalFeature object.
Args:
series (pandas.Series): Series holding the data (copy)
name (str): name of the Feature
description (str): description of the Feature
imputed_category (bool): flag indicating if the category of the Feature was provided ... | Construct new NumericalFeature object. | def __init__(self, series, name, description, imputed_category, transformed=False):
"""Construct new NumericalFeature object.
Args:
series (pandas.Series): Series holding the data (copy)
name (str): name of the Feature
description (str): description of the Feature
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NumericalFeature.data | (self) | Return series attribute. | Return series attribute. | def data(self):
"""Return series attribute."""
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NumericalFeature.mapping | (self) | Return None, as NumericalFeature has no mapping. | Return None, as NumericalFeature has no mapping. | def mapping(self):
"""Return None, as NumericalFeature has no mapping."""
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Features.__init__ | (self, X, y, descriptor=None, transformed_features=None) | Construct Features object from passed arguments.
Automatically analyze provided DataFrame (X + y) and assess their types.
Args:
X (pandas.DataFrame): DataFrame of features (columns), from which Models will learn
y (pandas.Series): Series of target variable data
desc... | Construct Features object from passed arguments. | def __init__(self, X, y, descriptor=None, transformed_features=None):
"""Construct Features object from passed arguments.
Automatically analyze provided DataFrame (X + y) and assess their types.
Args:
X (pandas.DataFrame): DataFrame of features (columns), from which Models will lea... | [
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Features._analyze_features | (self, descriptor) | Analyze original_dataframe attribute and assess type of each column (Numerical or Categorical).
Every column present in the original_dataframe will be checked and appropriate FeatureClass will be created
for it. Every FeatureClass will also have mapping and description attributes specific to them.
... | Analyze original_dataframe attribute and assess type of each column (Numerical or Categorical). | def _analyze_features(self, descriptor):
"""Analyze original_dataframe attribute and assess type of each column (Numerical or Categorical).
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Features._impute_column_type | (self, series) | Impute column type based on the data included in provided series.
Args:
series (pandas.Series): Series which column type is checked
Returns:
str: one of _categorical, _numerical_ or _date str attributes
Raises:
Exception: raised when all conversions fail
... | Impute column type based on the data included in provided series. | def _impute_column_type(self, series):
"""Impute column type based on the data included in provided series.
Args:
series (pandas.Series): Series which column type is checked
Returns:
str: one of _categorical, _numerical_ or _date str attributes
Raises:
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Features.features | (self, drop_target=False, exclude_transformed=False) | Return list of features names present in _all_features attribute.
If _all_features attribute is None, feature list is first created and assigned to that attribute.
Args:
drop_target (bool, optional): flag indicating if returned list should exclude target name or not, defaults
... | Return list of features names present in _all_features attribute. | def features(self, drop_target=False, exclude_transformed=False):
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Features.categorical_features | (self, drop_target=False, exclude_transformed=False) | Return list of categorical features names present in _categorical_features attribute.
If _categorical_features attribute is None, categorical feature list is first created and assigned to
that attribute.
Args:
drop_target (bool, optional): flag indicating if returned list should ex... | Return list of categorical features names present in _categorical_features attribute. | def categorical_features(self, drop_target=False, exclude_transformed=False):
"""Return list of categorical features names present in _categorical_features attribute.
If _categorical_features attribute is None, categorical feature list is first created and assigned to
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Features.numerical_features | (self, drop_target=False, exclude_transformed=False) | Return list of numerical features names present in _numerical_features attribute.
If _numerical_features attribute is None, numerical feature list is first created and assigned to
that attribute.
Args:
drop_target (bool, optional): flag indicating if returned list should exclude ta... | Return list of numerical features names present in _numerical_features attribute. | def numerical_features(self, drop_target=False, exclude_transformed=False):
"""Return list of numerical features names present in _numerical_features attribute.
If _numerical_features attribute is None, numerical feature list is first created and assigned to
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Args:
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Features.raw_data | (self, drop_target=False, exclude_transformed=False) | Return pandas DataFrame present in _raw_dataframe attribute.
If _raw_dataframe attribute is None, raw DataFrame is first created and assigned to that attribute.
Args:
drop_target (bool, optional): flag indicating if returned DataFrame should exclude target from columns,
def... | Return pandas DataFrame present in _raw_dataframe attribute. | def raw_data(self, drop_target=False, exclude_transformed=False):
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If _raw_dataframe attribute is None, raw DataFrame is first created and assigned to that attribute.
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Features.data | (self, drop_target=False, exclude_transformed=False) | Return pandas DataFrame present in _mapped_dataframe attribute.
If _mapped_dataframe attribute is None, mapped DataFrame is first created and assigned to that attribute.
Args:
drop_target (bool, optional): flag indicating if returned DataFrame should exclude target from columns,
... | Return pandas DataFrame present in _mapped_dataframe attribute. | def data(self, drop_target=False, exclude_transformed=False):
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Features.mapping | (self) | Return _mapping attribute and if it's None, create it.
Returns:
dict: 'feature name': mapping dict pairs
| Return _mapping attribute and if it's None, create it. | def mapping(self):
"""Return _mapping attribute and if it's None, create it.
Returns:
dict: 'feature name': mapping dict pairs
"""
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Features.descriptions | (self) | Return _descriptions attribute and if it's None, create it.
Returns:
dict: 'feature name': description pairs
| Return _descriptions attribute and if it's None, create it. | def descriptions(self):
"""Return _descriptions attribute and if it's None, create it.
Returns:
dict: 'feature name': description pairs
"""
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self._descriptions = self._create_descriptions()
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Features.unused_features | (self) | Return _unused_columns attribute. | Return _unused_columns attribute. | def unused_features(self):
"""Return _unused_columns attribute."""
return self._unused_columns | [
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Features._create_features | (self) | Return list of names as taken from name attribute of every FeatureClass present in _features.
Returns:
list: list of features names
| Return list of names as taken from name attribute of every FeatureClass present in _features. | def _create_features(self):
"""Return list of names as taken from name attribute of every FeatureClass present in _features.
Returns:
list: list of features names
"""
output = []
for feature in self._features.values():
output.append(feature.name)
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Features._create_categorical_features | (self) | Return list of names of features (name attribute) if a given FeatureClass is an instance of
CategoricalFeature.
Returns:
list: list of categorical features names
| Return list of names of features (name attribute) if a given FeatureClass is an instance of
CategoricalFeature. | def _create_categorical_features(self):
"""Return list of names of features (name attribute) if a given FeatureClass is an instance of
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Returns:
list: list of categorical features names
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Features._create_numerical_features | (self) | Return list of names of features (name attribute) if a given FeatureClass is an instance of
NumericalFeature.
Returns:
list: list of numerical features names
| Return list of names of features (name attribute) if a given FeatureClass is an instance of
NumericalFeature. | def _create_numerical_features(self):
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Returns:
list: list of numerical features names
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for feature in self._features.values():
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Features._create_mapped_dataframe | (self) | Return pandas.Dataframe made from single mapped series (where appropriate) of every FeatureClass
(data method).
Returns:
pandas.DataFrame: dataframe consisting of mapped series
| Return pandas.Dataframe made from single mapped series (where appropriate) of every FeatureClass
(data method). | def _create_mapped_dataframe(self):
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Returns:
pandas.DataFrame: dataframe consisting of mapped series
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Features._create_raw_dataframe | (self) | Return pandas.DataFrame made from original series data of every FeatureClass.
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Returns:
pandas.DataFrame: original DataFrame constructed from serie... | Return pandas.DataFrame made from original series data of every FeatureClass. | def _create_raw_dataframe(self):
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Features._create_mapping | (self) | Create dictionary of 'feature name': mapping dict pairs, where mapping dict is taken from mapping method
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Returns:
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] | python | en | ['en', 'en', 'en'] | True |
Features._create_descriptions | (self) | Create dictionary of 'feature name': description pairs, where description is taken from description attribute
of every FeatureClass.
Returns:
dict: 'feature name': description pairs
| Create dictionary of 'feature name': description pairs, where description is taken from description attribute
of every FeatureClass. | def _create_descriptions(self):
"""Create dictionary of 'feature name': description pairs, where description is taken from description attribute
of every FeatureClass.
Returns:
dict: 'feature name': description pairs
"""
output = {}
for feature in self.featur... | [
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Features.__getitem__ | (self, arg) | Return arg item from _features attribute dictionary.
Args:
arg (str, Hashable): str representing the name of the feature
Returns:
Feature: FeatureClass present in _features attribute dictionary
Raises:
KeyError: when arg is not in _features
| Return arg item from _features attribute dictionary. | def __getitem__(self, arg):
"""Return arg item from _features attribute dictionary.
Args:
arg (str, Hashable): str representing the name of the feature
Returns:
Feature: FeatureClass present in _features attribute dictionary
Raises:
KeyError: when a... | [
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_cf_data_from_bytes | (bytestring) |
Given a bytestring, create a CFData object from it. This CFData object must
be CFReleased by the caller.
|
Given a bytestring, create a CFData object from it. This CFData object must
be CFReleased by the caller.
| def _cf_data_from_bytes(bytestring):
"""
Given a bytestring, create a CFData object from it. This CFData object must
be CFReleased by the caller.
"""
return CoreFoundation.CFDataCreate(
CoreFoundation.kCFAllocatorDefault, bytestring, len(bytestring)
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33,
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_cf_dictionary_from_tuples | (tuples) |
Given a list of Python tuples, create an associated CFDictionary.
|
Given a list of Python tuples, create an associated CFDictionary.
| def _cf_dictionary_from_tuples(tuples):
"""
Given a list of Python tuples, create an associated CFDictionary.
"""
dictionary_size = len(tuples)
# We need to get the dictionary keys and values out in the same order.
keys = (t[0] for t in tuples)
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cf_keys = ... | [
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_cf_string_to_unicode | (value) |
Creates a Unicode string from a CFString object. Used entirely for error
reporting.
Yes, it annoys me quite a lot that this function is this complex.
|
Creates a Unicode string from a CFString object. Used entirely for error
reporting. | def _cf_string_to_unicode(value):
"""
Creates a Unicode string from a CFString object. Used entirely for error
reporting.
Yes, it annoys me quite a lot that this function is this complex.
"""
value_as_void_p = ctypes.cast(value, ctypes.POINTER(ctypes.c_void_p))
string = CoreFoundation.CFSt... | [
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58,
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_assert_no_error | (error, exception_class=None) |
Checks the return code and throws an exception if there is an error to
report
|
Checks the return code and throws an exception if there is an error to
report
| def _assert_no_error(error, exception_class=None):
"""
Checks the return code and throws an exception if there is an error to
report
"""
if error == 0:
return
cf_error_string = Security.SecCopyErrorMessageString(error, None)
output = _cf_string_to_unicode(cf_error_string)
CoreFo... | [
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101,
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_cert_array_from_pem | (pem_bundle) |
Given a bundle of certs in PEM format, turns them into a CFArray of certs
that can be used to validate a cert chain.
|
Given a bundle of certs in PEM format, turns them into a CFArray of certs
that can be used to validate a cert chain.
| def _cert_array_from_pem(pem_bundle):
"""
Given a bundle of certs in PEM format, turns them into a CFArray of certs
that can be used to validate a cert chain.
"""
# Normalize the PEM bundle's line endings.
pem_bundle = pem_bundle.replace(b"\r\n", b"\n")
der_certs = [
base64.b64decod... | [
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104,
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] | [
146,
21
] | python | en | ['en', 'error', 'th'] | False |
_is_cert | (item) |
Returns True if a given CFTypeRef is a certificate.
|
Returns True if a given CFTypeRef is a certificate.
| def _is_cert(item):
"""
Returns True if a given CFTypeRef is a certificate.
"""
expected = Security.SecCertificateGetTypeID()
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154,
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_is_identity | (item) |
Returns True if a given CFTypeRef is an identity.
|
Returns True if a given CFTypeRef is an identity.
| def _is_identity(item):
"""
Returns True if a given CFTypeRef is an identity.
"""
expected = Security.SecIdentityGetTypeID()
return CoreFoundation.CFGetTypeID(item) == expected | [
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157,
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162,
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] | python | en | ['en', 'error', 'th'] | False |
_temporary_keychain | () |
This function creates a temporary Mac keychain that we can use to work with
credentials. This keychain uses a one-time password and a temporary file to
store the data. We expect to have one keychain per socket. The returned
SecKeychainRef must be freed by the caller, including calling
SecKeychainDe... |
This function creates a temporary Mac keychain that we can use to work with
credentials. This keychain uses a one-time password and a temporary file to
store the data. We expect to have one keychain per socket. The returned
SecKeychainRef must be freed by the caller, including calling
SecKeychainDe... | def _temporary_keychain():
"""
This function creates a temporary Mac keychain that we can use to work with
credentials. This keychain uses a one-time password and a temporary file to
store the data. We expect to have one keychain per socket. The returned
SecKeychainRef must be freed by the caller, i... | [
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"# This filename will be 8 r... | [
165,
0
] | [
197,
34
] | python | en | ['en', 'error', 'th'] | False |
_load_items_from_file | (keychain, path) |
Given a single file, loads all the trust objects from it into arrays and
the keychain.
Returns a tuple of lists: the first list is a list of identities, the
second a list of certs.
|
Given a single file, loads all the trust objects from it into arrays and
the keychain.
Returns a tuple of lists: the first list is a list of identities, the
second a list of certs.
| def _load_items_from_file(keychain, path):
"""
Given a single file, loads all the trust objects from it into arrays and
the keychain.
Returns a tuple of lists: the first list is a list of identities, the
second a list of certs.
"""
certificates = []
identities = []
result_array = Non... | [
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200,
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252,
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] | python | en | ['en', 'error', 'th'] | False |
_load_client_cert_chain | (keychain, *paths) |
Load certificates and maybe keys from a number of files. Has the end goal
of returning a CFArray containing one SecIdentityRef, and then zero or more
SecCertificateRef objects, suitable for use as a client certificate trust
chain.
|
Load certificates and maybe keys from a number of files. Has the end goal
of returning a CFArray containing one SecIdentityRef, and then zero or more
SecCertificateRef objects, suitable for use as a client certificate trust
chain.
| def _load_client_cert_chain(keychain, *paths):
"""
Load certificates and maybe keys from a number of files. Has the end goal
of returning a CFArray containing one SecIdentityRef, and then zero or more
SecCertificateRef objects, suitable for use as a client certificate trust
chain.
"""
# Ok, ... | [
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255,
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] | [
327,
41
] | python | en | ['en', 'error', 'th'] | False |
_fixup_find_links | (find_links) | Ensure find-links option end-up being a list of strings. | Ensure find-links option end-up being a list of strings. | def _fixup_find_links(find_links):
"""Ensure find-links option end-up being a list of strings."""
if isinstance(find_links, str):
return find_links.split()
assert isinstance(find_links, (tuple, list))
return find_links | [
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_legacy_fetch_build_egg | (dist, req) | Fetch an egg needed for building.
Legacy path using EasyInstall.
| Fetch an egg needed for building. | def _legacy_fetch_build_egg(dist, req):
"""Fetch an egg needed for building.
Legacy path using EasyInstall.
"""
tmp_dist = dist.__class__({'script_args': ['easy_install']})
opts = tmp_dist.get_option_dict('easy_install')
opts.clear()
opts.update(
(k, v)
for k, v in dist.get_... | [
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fetch_build_egg | (dist, req) | Fetch an egg needed for building.
Use pip/wheel to fetch/build a wheel. | Fetch an egg needed for building. | def fetch_build_egg(dist, req):
"""Fetch an egg needed for building.
Use pip/wheel to fetch/build a wheel."""
# Check pip is available.
try:
pkg_resources.get_distribution('pip')
except pkg_resources.DistributionNotFound:
dist.announce(
'WARNING: The pip package is not a... | [
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135,
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] | python | en | ['en', 'en', 'en'] | True |
strip_marker | (req) |
Return a new requirement without the environment marker to avoid
calling pip with something like `babel; extra == "i18n"`, which
would always be ignored.
|
Return a new requirement without the environment marker to avoid
calling pip with something like `babel; extra == "i18n"`, which
would always be ignored.
| def strip_marker(req):
"""
Return a new requirement without the environment marker to avoid
calling pip with something like `babel; extra == "i18n"`, which
would always be ignored.
"""
# create a copy to avoid mutating the input
req = pkg_resources.Requirement.parse(str(req))
req.marker ... | [
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BotTest.test_bot_add_subscription | (self) |
Calling POST /json/users/me/subscriptions should successfully add
streams, and a stream to the
list of subscriptions and confirm the right number of events
are generated.
When 'principals' has a bot, no notification message event or invitation email
is sent when add_subs... |
Calling POST /json/users/me/subscriptions should successfully add
streams, and a stream to the
list of subscriptions and confirm the right number of events
are generated.
When 'principals' has a bot, no notification message event or invitation email
is sent when add_subs... | def test_bot_add_subscription(self) -> None:
"""
Calling POST /json/users/me/subscriptions should successfully add
streams, and a stream to the
list of subscriptions and confirm the right number of events
are generated.
When 'principals' has a bot, no notification message... | [
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BotTest.test_deactivate_bogus_bot | (self) | Deleting a bogus bot will succeed silently. | Deleting a bogus bot will succeed silently. | def test_deactivate_bogus_bot(self) -> None:
"""Deleting a bogus bot will succeed silently."""
self.login("hamlet")
self.assert_num_bots_equal(0)
self.create_bot()
self.assert_num_bots_equal(1)
invalid_user_id = 1000
result = self.client_delete(f"/json/bots/{inval... | [
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BotTest.test_bot_deactivation_attacks | (self) | You cannot deactivate somebody else's bot. | You cannot deactivate somebody else's bot. | def test_bot_deactivation_attacks(self) -> None:
"""You cannot deactivate somebody else's bot."""
self.login("hamlet")
self.assert_num_bots_equal(0)
self.create_bot()
self.assert_num_bots_equal(1)
# Have Othello try to deactivate both Hamlet and
# Hamlet's bot.
... | [
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BotTest.test_patch_bogus_bot | (self) | Deleting a bogus bot will succeed silently. | Deleting a bogus bot will succeed silently. | def test_patch_bogus_bot(self) -> None:
"""Deleting a bogus bot will succeed silently."""
self.login("hamlet")
self.create_bot()
bot_info = {
"full_name": "Fred",
}
invalid_user_id = 1000
result = self.client_patch(f"/json/bots/{invalid_user_id}", bot_... | [
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OptionsSpecParser.get_section_type | (line) |
Example section header: [TableOptions/BlockBasedTable "default"]
Here ConfigurationOptimizer returned would be
'TableOptions.BlockBasedTable'
|
Example section header: [TableOptions/BlockBasedTable "default"]
Here ConfigurationOptimizer returned would be
'TableOptions.BlockBasedTable'
| def get_section_type(line):
'''
Example section header: [TableOptions/BlockBasedTable "default"]
Here ConfigurationOptimizer returned would be
'TableOptions.BlockBasedTable'
'''
section_path = line.strip()[1:-1].split()[0]
section_type = '.'.join(section_path.spli... | [
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test_output_path_to_file | (output, output_directory, filename) | Testing if creating filepaths with provided output_directory works correctly. | Testing if creating filepaths with provided output_directory works correctly. | def test_output_path_to_file(output, output_directory, filename):
"""Testing if creating filepaths with provided output_directory works correctly."""
output.output_directory = output_directory
actual = output._path_to_file(filename)
expected = os.path.join(output_directory, filename)
assert actual ... | [
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test_output_write_html | (output, filename, template, tmpdir) | Testing if writing content to the file works correctly. | Testing if writing content to the file works correctly. | def test_output_write_html(output, filename, template, tmpdir):
"""Testing if writing content to the file works correctly."""
output._write_html(filename, template)
created_file = os.path.join(tmpdir, filename)
assert os.path.exists(created_file)
with open(created_file) as f:
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test_models_view_creator | (output, problem_type, expected_result) | Testing if output creates a correct ModelsView based on a provided problem type. | Testing if output creates a correct ModelsView based on a provided problem type. | def test_models_view_creator(output, problem_type, expected_result):
"""Testing if output creates a correct ModelsView based on a provided problem type."""
if problem_type == "classification":
problem = output.model_finder._classification
elif problem_type == "regression":
problem = output.m... | [
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test_models_view_creator_error | (output, incorrect_problem_type) | Testing if _models_view_creator raises an Exception when an incorrect problem type is provided. | Testing if _models_view_creator raises an Exception when an incorrect problem type is provided. | def test_models_view_creator_error(output, incorrect_problem_type):
"""Testing if _models_view_creator raises an Exception when an incorrect problem type is provided."""
with pytest.raises(ValueError) as excinfo:
_ = output._models_view_creator(incorrect_problem_type)
assert str(incorrect_problem_ty... | [
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test_models_plot_output | (
output, model_finder_classification_fitted, model_finder_regression_fitted, model_finder_multiclass_fitted,
problem_type, expected_result, fixture_features_multiclass, output_multiclass
) | Testing if output creates output of a correct type based on a provided problem type. | Testing if output creates output of a correct type based on a provided problem type. | def test_models_plot_output(
output, model_finder_classification_fitted, model_finder_regression_fitted, model_finder_multiclass_fitted,
problem_type, expected_result, fixture_features_multiclass, output_multiclass
):
"""Testing if output creates output of a correct type based on a provided problem ... | [
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test_models_plot_output_error | (output, incorrect_problem_type) | Testing if _models_view_creator raises an Exception when an incorrect problem type is provided. | Testing if _models_view_creator raises an Exception when an incorrect problem type is provided. | def test_models_plot_output_error(output, incorrect_problem_type):
"""Testing if _models_view_creator raises an Exception when an incorrect problem type is provided."""
with pytest.raises(ValueError) as excinfo:
_ = output._models_plot_output(incorrect_problem_type)
assert str(incorrect_problem_type... | [
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test_output_static_path | (output, tmpdir) | Testing if static directory is created in the provided output_directory. | Testing if static directory is created in the provided output_directory. | def test_output_static_path(output, tmpdir):
"""Testing if static directory is created in the provided output_directory."""
assert output.static_path() == os.path.join(tmpdir, "static") | [
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test_output_assets_path | (output, tmpdir) | Testing if assets directory is created in the provided output_directory. | Testing if assets directory is created in the provided output_directory. | def test_output_assets_path(output, tmpdir):
"""Testing if assets directory is created in the provided output_directory."""
assert output.assets_path() == os.path.join(tmpdir, "assets") | [
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test_output_logs_path | (output, tmpdir) | Testing if logs directory is created in the provided output_directory. | Testing if logs directory is created in the provided output_directory. | def test_output_logs_path(output, tmpdir):
"""Testing if logs directory is created in the provided output_directory."""
assert output.logs_path() == os.path.join(tmpdir, "logs") | [
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test_output_create_logs_directory | (output, tmpdir, input_time, expected_directory_name) | Testing if subdirectory in logs is created correctly and with a correct name based on a provided time. | Testing if subdirectory in logs is created correctly and with a correct name based on a provided time. | def test_output_create_logs_directory(output, tmpdir, input_time, expected_directory_name):
"""Testing if subdirectory in logs is created correctly and with a correct name based on a provided time."""
expected_result = os.path.join(tmpdir, "logs", expected_directory_name)
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test_output_write_logs_files_created | (output, tmpdir) | Testing if writing log csv log files works correctly. | Testing if writing log csv log files works correctly. | def test_output_write_logs_files_created(output, tmpdir):
"""Testing if writing log csv log files works correctly."""
test_date = datetime.datetime(2020, 3, 1, 14, 0, 34)
log_dir = "01032020140034"
filenames = [output._search_results_csv, output._quicksearch_results_csv, output._gridsearch_results_csv]
... | [
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test_output_write_logs_csv_content | (output, tmpdir, model_finder_classification_fitted) | Testing if csv files written as logs are the same as those in model_finder properties. | Testing if csv files written as logs are the same as those in model_finder properties. | def test_output_write_logs_csv_content(output, tmpdir, model_finder_classification_fitted):
"""Testing if csv files written as logs are the same as those in model_finder properties."""
test_date = datetime.datetime(2020, 3, 1, 14, 0, 34)
log_dir = "01032020140034"
filenames = [output._search_results_csv... | [
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test_output_write_logs_one_df_missing | (output, tmpdir, model_finder_classification_fitted) | Testing that csv files are not created when appropriate result df is None. | Testing that csv files are not created when appropriate result df is None. | def test_output_write_logs_one_df_missing(output, tmpdir, model_finder_classification_fitted):
"""Testing that csv files are not created when appropriate result df is None."""
test_date = datetime.datetime(2020, 3, 1, 14, 0, 34)
log_dir = "01032020140034"
filenames = [output._search_results_csv, output.... | [
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test_output_create_output_directory | (output, tmpdir, input_directory) | Testing if creating output_directory works in case it doesn't exist. | Testing if creating output_directory works in case it doesn't exist. | def test_output_create_output_directory(output, tmpdir, input_directory):
"""Testing if creating output_directory works in case it doesn't exist."""
directory = os.path.join(tmpdir, input_directory)
output.output_directory = directory
output._create_output_directory()
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test_dashboard_output_directory_exists | (output, tmpdir, input_directory) | Testing if create_output_directory does not interfere when the directory already exists. | Testing if create_output_directory does not interfere when the directory already exists. | def test_dashboard_output_directory_exists(output, tmpdir, input_directory):
"""Testing if create_output_directory does not interfere when the directory already exists."""
directory = os.path.join(tmpdir, input_directory)
os.makedirs(directory)
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test_output_create_subdirectories | (output, tmpdir) | Testing if static and assets subdirectories are created correctly. | Testing if static and assets subdirectories are created correctly. | def test_output_create_subdirectories(output, tmpdir):
"""Testing if static and assets subdirectories are created correctly."""
directories = ["static", "assets"]
expected_directories = [os.path.join(tmpdir, d) for d in directories]
for d in expected_directories:
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... | [
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test_output_copy_static | (output, tmpdir, root_path_to_package) | Testing if static files are copied correctly to the output_directory folder. | Testing if static files are copied correctly to the output_directory folder. | def test_output_copy_static(output, tmpdir, root_path_to_package):
"""Testing if static files are copied correctly to the output_directory folder."""
directory, pkg_name = root_path_to_package[0], root_path_to_package[1]
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test_output_overview_path | (output, tmpdir) | Testing if overview HTML file path is created correctly. | Testing if overview HTML file path is created correctly. | def test_output_overview_path(output, tmpdir):
"""Testing if overview HTML file path is created correctly."""
expected_path = os.path.join(tmpdir, "overview.html")
actual_path = output.overview_file()
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test_output_features_path | (output, tmpdir) | Testing if features HTML file path is created correctly. | Testing if features HTML file path is created correctly. | def test_output_features_path(output, tmpdir):
"""Testing if features HTML file path is created correctly."""
expected_path = os.path.join(tmpdir, "features.html")
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test_output_models_path | (output, tmpdir) | Testing if models HTML file path is created correctly. | Testing if models HTML file path is created correctly. | def test_output_models_path(output, tmpdir):
"""Testing if models HTML file path is created correctly."""
expected_path = os.path.join(tmpdir, "models.html")
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user_passes_test | (
test_func: Callable[[HttpResponse], bool],
login_url: Optional[str] = None,
redirect_field_name: str = REDIRECT_FIELD_NAME,
) |
Decorator for views that checks that the user passes the given test,
redirecting to the log-in page if necessary. The test should be a callable
that takes the user object and returns True if the user passes.
|
Decorator for views that checks that the user passes the given test,
redirecting to the log-in page if necessary. The test should be a callable
that takes the user object and returns True if the user passes.
| def user_passes_test(
test_func: Callable[[HttpResponse], bool],
login_url: Optional[str] = None,
redirect_field_name: str = REDIRECT_FIELD_NAME,
) -> Callable[[ViewFuncT], ViewFuncT]:
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do_login | (request: HttpRequest, user_profile: UserProfile) | Creates a session, logging in the user, using the Django method,
and also adds helpful data needed by our server logs.
| Creates a session, logging in the user, using the Django method,
and also adds helpful data needed by our server logs.
| def do_login(request: HttpRequest, user_profile: UserProfile) -> None:
"""Creates a session, logging in the user, using the Django method,
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web_public_view | (
view_func: ViewFuncT,
redirect_field_name: str = REDIRECT_FIELD_NAME,
login_url: str = settings.HOME_NOT_LOGGED_IN,
) |
This wrapper adds client info for unauthenticated users but
forces authenticated users to go through 2fa.
NOTE: This function == zulip_login_required in a production environment as
web_public_view path has only been enabled for development purposes
currently.
|
This wrapper adds client info for unauthenticated users but
forces authenticated users to go through 2fa. | def web_public_view(
view_func: ViewFuncT,
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internal_notify_view | (is_tornado_view: bool) | Used for situations where something running on the Zulip server
needs to make a request to the (other) Django/Tornado processes running on
the server. | Used for situations where something running on the Zulip server
needs to make a request to the (other) Django/Tornado processes running on
the server. | def internal_notify_view(is_tornado_view: bool) -> Callable[[ViewFuncT], ViewFuncT]:
# The typing here could be improved by using the extended Callable types:
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statsd_increment | (counter: str, val: int = 1) | Increments a statsd counter on completion of the
decorated function.
Pass the name of the counter to this decorator-returning function. | Increments a statsd counter on completion of the
decorated function. | def statsd_increment(counter: str, val: int = 1) -> Callable[[FuncT], FuncT]:
"""Increments a statsd counter on completion of the
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rate_limit_user | (request: HttpRequest, user: UserProfile, domain: str) | Returns whether or not a user was rate limited. Will raise a RateLimited exception
if the user has been rate limited, otherwise returns and modifies request to contain
the rate limit information | Returns whether or not a user was rate limited. Will raise a RateLimited exception
if the user has been rate limited, otherwise returns and modifies request to contain
the rate limit information | def rate_limit_user(request: HttpRequest, user: UserProfile, domain: str) -> None:
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... | [
838,
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] | [
843,
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rate_limit | (domain: str = "api_by_user") | Rate-limits a view. Takes an optional 'domain' param if you wish to
rate limit different types of API calls independently.
Returns a decorator | Rate-limits a view. Takes an optional 'domain' param if you wish to
rate limit different types of API calls independently. | def rate_limit(domain: str = "api_by_user") -> Callable[[ViewFuncT], ViewFuncT]:
"""Rate-limits a view. Takes an optional 'domain' param if you wish to
rate limit different types of API calls independently.
Returns a decorator"""
def wrapper(func: ViewFuncT) -> ViewFuncT:
@wraps(func)
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zulip_otp_required | (
redirect_field_name: str = "next",
login_url: str = settings.HOME_NOT_LOGGED_IN,
) |
The reason we need to create this function is that the stock
otp_required decorator doesn't play well with tests. We cannot
enable/disable if_configured parameter during tests since the decorator
retains its value due to closure.
Similar to :func:`~django.contrib.auth.decorators.login_required`, b... |
The reason we need to create this function is that the stock
otp_required decorator doesn't play well with tests. We cannot
enable/disable if_configured parameter during tests since the decorator
retains its value due to closure. | def zulip_otp_required(
redirect_field_name: str = "next",
login_url: str = settings.HOME_NOT_LOGGED_IN,
) -> Callable[[ViewFuncT], ViewFuncT]:
"""
The reason we need to create this function is that the stock
otp_required decorator doesn't play well with tests. We cannot
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