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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
DatabaseIntrospection.get_table_list | (self, cursor) | Return a list of table and view names in the current database. | Return a list of table and view names in the current database. | def get_table_list(self, cursor):
"""Return a list of table and view names in the current database."""
cursor.execute("SHOW FULL TABLES")
return [TableInfo(row[0], {'BASE TABLE': 't', 'VIEW': 'v'}.get(row[1]))
for row in cursor.fetchall()] | [
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DatabaseIntrospection.get_table_description | (self, cursor, table_name) |
Return a description of the table with the DB-API cursor.description
interface."
|
Return a description of the table with the DB-API cursor.description
interface."
| def get_table_description(self, cursor, table_name):
"""
Return a description of the table with the DB-API cursor.description
interface."
"""
json_constraints = {}
if self.connection.mysql_is_mariadb and self.connection.features.can_introspect_json_field:
# JS... | [
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DatabaseIntrospection.get_relations | (self, cursor, table_name) |
Return a dictionary of {field_name: (field_name_other_table, other_table)}
representing all relationships to the given table.
|
Return a dictionary of {field_name: (field_name_other_table, other_table)}
representing all relationships to the given table.
| def get_relations(self, cursor, table_name):
"""
Return a dictionary of {field_name: (field_name_other_table, other_table)}
representing all relationships to the given table.
"""
constraints = self.get_key_columns(cursor, table_name)
relations = {}
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DatabaseIntrospection.get_key_columns | (self, cursor, table_name) |
Return a list of (column_name, referenced_table_name, referenced_column_name)
for all key columns in the given table.
|
Return a list of (column_name, referenced_table_name, referenced_column_name)
for all key columns in the given table.
| def get_key_columns(self, cursor, table_name):
"""
Return a list of (column_name, referenced_table_name, referenced_column_name)
for all key columns in the given table.
"""
key_columns = []
cursor.execute("""
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DatabaseIntrospection.get_storage_engine | (self, cursor, table_name) |
Retrieve the storage engine for a given table. Return the default
storage engine if the table doesn't exist.
|
Retrieve the storage engine for a given table. Return the default
storage engine if the table doesn't exist.
| def get_storage_engine(self, cursor, table_name):
"""
Retrieve the storage engine for a given table. Return the default
storage engine if the table doesn't exist.
"""
cursor.execute(
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DatabaseIntrospection.get_constraints | (self, cursor, table_name) |
Retrieve any constraints or keys (unique, pk, fk, check, index) across
one or more columns.
|
Retrieve any constraints or keys (unique, pk, fk, check, index) across
one or more columns.
| def get_constraints(self, cursor, table_name):
"""
Retrieve any constraints or keys (unique, pk, fk, check, index) across
one or more columns.
"""
constraints = {}
# Get the actual constraint names and columns
name_query = """
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reconstruction_evaluation | (X_time_orig, X_time_recon, training_mode) | Reconstruction loss on evaluation set.
Given time major original and reconstructed features data and the training
mode, return loss and eval_metrics_ops.
Args:
X_time_orig: Time major original features data.
X_time_recon: Time major reconstructed features data.
training_mode: Current training mode.
... | Reconstruction loss on evaluation set. | def reconstruction_evaluation(X_time_orig, X_time_recon, training_mode):
"""Reconstruction loss on evaluation set.
Given time major original and reconstructed features data and the training
mode, return loss and eval_metrics_ops.
Args:
X_time_orig: Time major original features data.
X_time_recon: Time... | [
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RandomRec.__init__ | (self, train_file=None, test_file=None, output_file=None, rank_length=10, sep='\t', output_sep='\t') |
Random Recommender for Item Recommendation
This algorithm predicts a rank for each user using the count of number of feedback of users and items
Usage::
>> RandomRec(train).compute()
>> RandomRec(train, test, ranking).compute()
:param train_file: File which c... |
Random Recommender for Item Recommendation | def __init__(self, train_file=None, test_file=None, output_file=None, rank_length=10, sep='\t', output_sep='\t'):
"""
Random Recommender for Item Recommendation
This algorithm predicts a rank for each user using the count of number of feedback of users and items
Usage::
>>... | [
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RandomRec.predict | (self) |
Method to predict a rank for each user.
For each pair out of train set, predict a random score for it.
|
Method to predict a rank for each user. | def predict(self):
"""
Method to predict a rank for each user.
For each pair out of train set, predict a random score for it.
"""
for user in set(self.users):
predictions = list()
for item in self.train_set['items_unobserved'].get(user, []):
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RandomRec.compute | (self, verbose=True, metrics=None, verbose_evaluation=True, as_table=False, table_sep='\t') |
Extends compute method from BaseItemRecommendation. Method to run recommender algorithm
:param verbose: Print recommender and database information
:type verbose: bool, default True
:param metrics: List of evaluation metrics
:type metrics: list, default None
:param ver... |
Extends compute method from BaseItemRecommendation. Method to run recommender algorithm | def compute(self, verbose=True, metrics=None, verbose_evaluation=True, as_table=False, table_sep='\t'):
"""
Extends compute method from BaseItemRecommendation. Method to run recommender algorithm
:param verbose: Print recommender and database information
:type verbose: bool, default Tru... | [
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tune_anomaly_thresholds_unsupervised_training | (
cur_batch_size,
time_anom_thresh_var,
mahalanobis_dist_time,
count_thresh_time_var,
mean_thresh_time_var,
var_thresh_time_var,
feat_anom_thresh_var,
mahalanobis_dist_feat,
count_thresh_feat_var,
mean_thresh_feat_var,
var_thresh_feat_var,
params,
dummy_var) | Tunes anomaly thresholds during unsupervised training mode.
Given dimensions of inputs, mahalanobis distances, and variables tracking
counts, means, and variances of mahalanobis distance, returns loss and
train_op.
Args:
cur_batch_size: Current batch size, could be partially filled.
time_anom_thresh_v... | Tunes anomaly thresholds during unsupervised training mode. | def tune_anomaly_thresholds_unsupervised_training(
cur_batch_size,
time_anom_thresh_var,
mahalanobis_dist_time,
count_thresh_time_var,
mean_thresh_time_var,
var_thresh_time_var,
feat_anom_thresh_var,
mahalanobis_dist_feat,
count_thresh_feat_var,
mean_thresh_feat_var,
var_thre... | [
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7,
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125,
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tune_anomaly_thresholds_unsupervised_eval | (
cur_batch_size,
time_anom_thresh_var,
mahalanobis_dist_time,
feat_anom_thresh_var,
mahalanobis_dist_feat) | Checks tuned anomaly thresholds during supervised evaluation mode.
Given dimensions of inputs, mahalanobis distances, and variables tracking
counts, means, and variances of mahalanobis distance, returns loss and
train_op.
Args:
cur_batch_size: Current batch size, could be partially filled.
time_anom_t... | Checks tuned anomaly thresholds during supervised evaluation mode. | def tune_anomaly_thresholds_unsupervised_eval(
cur_batch_size,
time_anom_thresh_var,
mahalanobis_dist_time,
feat_anom_thresh_var,
mahalanobis_dist_feat):
"""Checks tuned anomaly thresholds during supervised evaluation mode.
Given dimensions of inputs, mahalanobis distances, and variables tracki... | [
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BaseManager.__str__ | (self) | Return "app_label.model_label.manager_name". | Return "app_label.model_label.manager_name". | def __str__(self):
"""Return "app_label.model_label.manager_name"."""
return '%s.%s' % (self.model._meta.label, self.name) | [
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BaseManager.deconstruct | (self) |
Return a 5-tuple of the form (as_manager (True), manager_class,
queryset_class, args, kwargs).
Raise a ValueError if the manager is dynamically generated.
|
Return a 5-tuple of the form (as_manager (True), manager_class,
queryset_class, args, kwargs). | def deconstruct(self):
"""
Return a 5-tuple of the form (as_manager (True), manager_class,
queryset_class, args, kwargs).
Raise a ValueError if the manager is dynamically generated.
"""
qs_class = self._queryset_class
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BaseManager._set_creation_counter | (self) |
Set the creation counter value for this instance and increment the
class-level copy.
|
Set the creation counter value for this instance and increment the
class-level copy.
| def _set_creation_counter(self):
"""
Set the creation counter value for this instance and increment the
class-level copy.
"""
self.creation_counter = BaseManager.creation_counter
BaseManager.creation_counter += 1 | [
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BaseManager.get_queryset | (self) |
Return a new QuerySet object. Subclasses can override this method to
customize the behavior of the Manager.
|
Return a new QuerySet object. Subclasses can override this method to
customize the behavior of the Manager.
| def get_queryset(self):
"""
Return a new QuerySet object. Subclasses can override this method to
customize the behavior of the Manager.
"""
return self._queryset_class(model=self.model, using=self._db, hints=self._hints) | [
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task_log_audit | (self: Task, user_id: int, verb: AuditVerbEnum, app_label: str, model: str, object_id: int) | log changes into audit | log changes into audit | def task_log_audit(self: Task, user_id: int, verb: AuditVerbEnum, app_label: str, model: str, object_id: int):
""" log changes into audit """
try:
# get object using content type
obj_type = ContentType.objects.get(app_label=app_label, model=model)
user = User.objects.get(id=user_id)
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optimize | (node, environment) | The context hint can be used to perform an static optimization
based on the context given. | The context hint can be used to perform an static optimization
based on the context given. | def optimize(node, environment):
"""The context hint can be used to perform an static optimization
based on the context given."""
optimizer = Optimizer(environment)
return optimizer.visit(node) | [
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Optimizer.fold | (self, node, eval_ctx=None) | Do constant folding. | Do constant folding. | def fold(self, node, eval_ctx=None):
"""Do constant folding."""
node = self.generic_visit(node)
try:
return nodes.Const.from_untrusted(node.as_const(eval_ctx),
lineno=node.lineno,
environment=... | [
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MultipleObjectMixin.get_queryset | (self) |
Return the list of items for this view.
The return value must be an iterable and may be an instance of
`QuerySet` in which case `QuerySet` specific behavior will be enabled.
|
Return the list of items for this view. | def get_queryset(self):
"""
Return the list of items for this view.
The return value must be an iterable and may be an instance of
`QuerySet` in which case `QuerySet` specific behavior will be enabled.
"""
if self.queryset is not None:
queryset = self.queryse... | [
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MultipleObjectMixin.get_ordering | (self) | Return the field or fields to use for ordering the queryset. | Return the field or fields to use for ordering the queryset. | def get_ordering(self):
"""Return the field or fields to use for ordering the queryset."""
return self.ordering | [
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MultipleObjectMixin.paginate_queryset | (self, queryset, page_size) | Paginate the queryset, if needed. | Paginate the queryset, if needed. | def paginate_queryset(self, queryset, page_size):
"""Paginate the queryset, if needed."""
paginator = self.get_paginator(
queryset, page_size, orphans=self.get_paginate_orphans(),
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page_kwarg = self.page_kwarg
page = s... | [
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MultipleObjectMixin.get_paginate_by | (self, queryset) |
Get the number of items to paginate by, or ``None`` for no pagination.
|
Get the number of items to paginate by, or ``None`` for no pagination.
| def get_paginate_by(self, queryset):
"""
Get the number of items to paginate by, or ``None`` for no pagination.
"""
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] | python | en | ['en', 'error', 'th'] | False |
MultipleObjectMixin.get_paginator | (self, queryset, per_page, orphans=0,
allow_empty_first_page=True, **kwargs) | Return an instance of the paginator for this view. | Return an instance of the paginator for this view. | def get_paginator(self, queryset, per_page, orphans=0,
allow_empty_first_page=True, **kwargs):
"""Return an instance of the paginator for this view."""
return self.paginator_class(
queryset, per_page, orphans=orphans,
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MultipleObjectMixin.get_paginate_orphans | (self) |
Return the maximum number of orphans extend the last page by when
paginating.
|
Return the maximum number of orphans extend the last page by when
paginating.
| def get_paginate_orphans(self):
"""
Return the maximum number of orphans extend the last page by when
paginating.
"""
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MultipleObjectMixin.get_allow_empty | (self) |
Return ``True`` if the view should display empty lists and ``False``
if a 404 should be raised instead.
|
Return ``True`` if the view should display empty lists and ``False``
if a 404 should be raised instead.
| def get_allow_empty(self):
"""
Return ``True`` if the view should display empty lists and ``False``
if a 404 should be raised instead.
"""
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MultipleObjectMixin.get_context_object_name | (self, object_list) | Get the name of the item to be used in the context. | Get the name of the item to be used in the context. | def get_context_object_name(self, object_list):
"""Get the name of the item to be used in the context."""
if self.context_object_name:
return self.context_object_name
elif hasattr(object_list, 'model'):
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MultipleObjectMixin.get_context_data | (self, *, object_list=None, **kwargs) | Get the context for this view. | Get the context for this view. | def get_context_data(self, *, object_list=None, **kwargs):
"""Get the context for this view."""
queryset = object_list if object_list is not None else self.object_list
page_size = self.get_paginate_by(queryset)
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MultipleObjectTemplateResponseMixin.get_template_names | (self) |
Return a list of template names to be used for the request. Must return
a list. May not be called if render_to_response is overridden.
|
Return a list of template names to be used for the request. Must return
a list. May not be called if render_to_response is overridden.
| def get_template_names(self):
"""
Return a list of template names to be used for the request. Must return
a list. May not be called if render_to_response is overridden.
"""
try:
names = super().get_template_names()
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# If... | [
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image_resize2square | (image, desired_size=None) |
Resize image to a square by specific resolution(desired_size).
|
Resize image to a square by specific resolution(desired_size).
| def image_resize2square(image, desired_size=None):
'''
Resize image to a square by specific resolution(desired_size).
'''
assert (image is not None), 'Image cannot be None.'
# Initialize the dimensions of the image to be resized and
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create_path | (data_dir) |
Create a specific path to store result images.
- Under the data directory, two separated folders will store image and masking files
- Example:
- data_dir-
|- IMAGE_FOLDER
|- MASK_FOLDER
|
Create a specific path to store result images.
- Under the data directory, two separated folders will store image and masking files
- Example:
- data_dir-
|- IMAGE_FOLDER
|- MASK_FOLDER
| def create_path(data_dir):
'''
Create a specific path to store result images.
- Under the data directory, two separated folders will store image and masking files
- Example:
- data_dir-
|- IMAGE_FOLDER
|- MASK_FOLDER
'''
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main | (args) |
The main entry point of the program
- This program will download image from MS COCO 2017 (Microsoft Common Objects in Context) repo
and generate annotation to the specific object classes.
|
The main entry point of the program
- This program will download image from MS COCO 2017 (Microsoft Common Objects in Context) repo
and generate annotation to the specific object classes.
| def main(args):
'''
The main entry point of the program
- This program will download image from MS COCO 2017 (Microsoft Common Objects in Context) repo
and generate annotation to the specific object classes.
'''
plt.ioff()
data_dir = args.data_root_dir
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PostGISAdapter.__init__ | (self, obj, geography=False) |
Initialize on the spatial object.
|
Initialize on the spatial object.
| def __init__(self, obj, geography=False):
"""
Initialize on the spatial object.
"""
self.is_geometry = isinstance(obj, (GEOSGeometry, PostGISAdapter))
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PostGISAdapter.__conform__ | (self, proto) | Does the given protocol conform to what Psycopg2 expects? | Does the given protocol conform to what Psycopg2 expects? | def __conform__(self, proto):
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PostGISAdapter.prepare | (self, conn) |
This method allows escaping the binary in the style required by the
server's `standard_conforming_string` setting.
|
This method allows escaping the binary in the style required by the
server's `standard_conforming_string` setting.
| def prepare(self, conn):
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This method allows escaping the binary in the style required by the
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PostGISAdapter.getquoted | (self) |
Return a properly quoted string for use in PostgreSQL/PostGIS.
|
Return a properly quoted string for use in PostgreSQL/PostGIS.
| def getquoted(self):
"""
Return a properly quoted string for use in PostgreSQL/PostGIS.
"""
if self.is_geometry:
# Psycopg will figure out whether to use E'\\000' or '\000'.
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upsample | (base_model, x, layers, mode: XModelMode) |
Feature extraction upsampling/generation before regression and heatmap heads.
:param base_model: tf.keras.Model object
:param x: output from last layer of base_model
:param layers: layer names from backbone
:param mode: upsampling mode for features
:return: base_model and features
|
Feature extraction upsampling/generation before regression and heatmap heads.
:param base_model: tf.keras.Model object
:param x: output from last layer of base_model
:param layers: layer names from backbone
:param mode: upsampling mode for features
:return: base_model and features
| def upsample(base_model, x, layers, mode: XModelMode):
"""
Feature extraction upsampling/generation before regression and heatmap heads.
:param base_model: tf.keras.Model object
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ApplicationCommunicator.wait | (self, timeout=1) |
Waits for the application to stop itself and returns any exceptions.
|
Waits for the application to stop itself and returns any exceptions.
| async def wait(self, timeout=1):
"""
Waits for the application to stop itself and returns any exceptions.
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ApplicationCommunicator.send_input | (self, message) |
Sends a single message to the application
|
Sends a single message to the application
| async def send_input(self, message):
"""
Sends a single message to the application
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ApplicationCommunicator.receive_output | (self, timeout=1) |
Receives a single message from the application, with optional timeout.
|
Receives a single message from the application, with optional timeout.
| async def receive_output(self, timeout=1):
"""
Receives a single message from the application, with optional timeout.
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# Make sure there's not an exception to raise from the task
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ApplicationCommunicator.receive_nothing | (self, timeout=0.1, interval=0.01) |
Checks that there is no message to receive in the given time.
|
Checks that there is no message to receive in the given time.
| async def receive_nothing(self, timeout=0.1, interval=0.01):
"""
Checks that there is no message to receive in the given time.
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# `interval` has precedence over `timeout`
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video_and_frame_level_model | (features, labels, mode, params) |
features['video_id'].shape = (batch_size, 1)
features['mean_rgb'].shape = (batch_size, 1024)
features['mean_audio'].shape = (batch_size, 128)
features['rgb'].shape = (batch_size, MAX_FRAMES, 1024)
features['audio'].shape = (batch_size, MAX_FRAMES, 128)
|
features['video_id'].shape = (batch_size, 1)
features['mean_rgb'].shape = (batch_size, 1024)
features['mean_audio'].shape = (batch_size, 128)
features['rgb'].shape = (batch_size, MAX_FRAMES, 1024)
features['audio'].shape = (batch_size, MAX_FRAMES, 128)
| def video_and_frame_level_model(features, labels, mode, params):
'''
features['video_id'].shape = (batch_size, 1)
features['mean_rgb'].shape = (batch_size, 1024)
features['mean_audio'].shape = (batch_size, 128)
features['rgb'].shape = (batch_size, MAX_FRAMES, 1024)
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FileSystemKeyValueStorage.__init__ | (self, root_path) |
Create a new Storage object.
All files will be stored under the root_path location
:param root_path: The base folder for all storage files
|
Create a new Storage object. | def __init__(self, root_path):
"""
Create a new Storage object.
All files will be stored under the root_path location
:param root_path: The base folder for all storage files
"""
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root_path = gettempdir()
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FileSystemKeyValueStorage._get_key_full_path | (self, key) |
Generate the file path for the key
:param key: The key
:return: The absolute path of the value file associated with the key
|
Generate the file path for the key | def _get_key_full_path(self, key):
"""
Generate the file path for the key
:param key: The key
:return: The absolute path of the value file associated with the key
"""
return os.path.join(self._root_path, key + self._item_ext) | [
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FileSystemKeyValueStorage._exists | (self, key) |
Indicate whether key exists
:param key: The key
:return: bool True if the file for the given key exists
|
Indicate whether key exists | def _exists(self, key):
"""
Indicate whether key exists
:param key: The key
:return: bool True if the file for the given key exists
"""
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api_client | () | api client without authentication | api client without authentication | def api_client() -> Generator[APIClient, None, None]:
""" api client without authentication """
from rest_framework.test import APIClient
yield APIClient() | [
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auth_api_client | (db, api_client) | APIClient with authentication factory | APIClient with authentication factory | def auth_api_client(db, api_client):
""" APIClient with authentication factory """
def make_auth_client(user: AbstractUser) -> APIClient:
# api_client.force_login(user=user)
# login
api_client.force_authenticate(user=user)
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create_user | (db, django_user_model: AbstractUser, test_password: str) | factory for creating users | factory for creating users | def create_user(db, django_user_model: AbstractUser, test_password: str):
""" factory for creating users """
def make_user(username, email, first_name, last_name) -> AbstractUser:
new_user: AbstractUser = User.objects.create(username=username, email=email,
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create_image | () | image file factory | image file factory | def create_image():
""" image file factory """
with tempfile.NamedTemporaryFile(suffix=".jpg", delete=False) as tf:
image = Image.new("RGB", (100, 100), color="#ddd")
image.save(tf, format="JPEG")
tf.close()
yield tf
# clean up
image.close() | [
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delete_selected | (modeladmin, request, queryset) |
Default action which deletes the selected objects.
This action first displays a confirmation page which shows all the
deletable objects, or, if the user has no permission one of the related
childs (foreignkeys), a "permission denied" message.
Next, it deletes all selected objects and redirects ba... |
Default action which deletes the selected objects. | def delete_selected(modeladmin, request, queryset):
"""
Default action which deletes the selected objects.
This action first displays a confirmation page which shows all the
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GroupBasedRecommender.__init__ | (self, train_files=None, test_file=None, output_file=None, similarity_metric="cosine", rank_length=10,
k_groups=3, recommender='UserKNN', as_binary=False, sep='\t', output_sep='\t', max_int_kmedoids=1000,
parser='', user_weights=False) |
Group-Based for Item Recommendation
This algorithm predicts a rank for each user using a co-clustering algorithm
Usage::
>> GroupBasedRecommender([train_history], test).compute()
>> GroupBasedRecommender([train_history, train_rating], test, as_binary=True).compute()
... |
Group-Based for Item Recommendation | def __init__(self, train_files=None, test_file=None, output_file=None, similarity_metric="cosine", rank_length=10,
k_groups=3, recommender='UserKNN', as_binary=False, sep='\t', output_sep='\t', max_int_kmedoids=1000,
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GroupBasedRecommender.read_files | (self) |
Method to initialize recommender algorithm.
|
Method to initialize recommender algorithm. | def read_files(self):
"""
Method to initialize recommender algorithm.
"""
self.n_files = len(self.train_files)
self.users = []
self.items = []
for train_file in self.train_files:
train_set = ReadFile(train_file, sep=self.sep, as_binary=self.as_bina... | [
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GroupBasedRecommender.compute_distance | (self) |
Method to compute a distance matrix from train set
|
Method to compute a distance matrix from train set | def compute_distance(self):
"""
Method to compute a distance matrix from train set
"""
# Calculate distance matrix
distance_matrix = np.float32(squareform(pdist(self.matrix, self.similarity_metric)))
# Remove NaNs
distance_matrix[np.isnan(distance_matrix)] = 1.0... | [
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SelectionPreferences.__init__ | (
self,
allow_yanked: bool,
allow_all_prereleases: bool = False,
format_control: Optional[FormatControl] = None,
prefer_binary: bool = False,
ignore_requires_python: Optional[bool] = None,
) | Create a SelectionPreferences object.
:param allow_yanked: Whether files marked as yanked (in the sense
of PEP 592) are permitted to be candidates for install.
:param format_control: A FormatControl object or None. Used to control
the selection of source packages / binary packag... | Create a SelectionPreferences object. | def __init__(
self,
allow_yanked: bool,
allow_all_prereleases: bool = False,
format_control: Optional[FormatControl] = None,
prefer_binary: bool = False,
ignore_requires_python: Optional[bool] = None,
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45,
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] | python | en | ['en', 'en', 'en'] | True |
ValidationError.__init__ | (self, message, code=None, params=None) |
The `message` argument can be a single error, a list of errors, or a
dictionary that maps field names to lists of errors. What we define as
an "error" can be either a simple string or an instance of
ValidationError with its message attribute set, and what we define as
list or di... |
The `message` argument can be a single error, a list of errors, or a
dictionary that maps field names to lists of errors. What we define as
an "error" can be either a simple string or an instance of
ValidationError with its message attribute set, and what we define as
list or di... | def __init__(self, message, code=None, params=None):
"""
The `message` argument can be a single error, a list of errors, or a
dictionary that maps field names to lists of errors. What we define as
an "error" can be either a simple string or an instance of
ValidationError with its... | [
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convert_parquet_to_iceberg | (parquet_file: ParquetFile) |
Given two Iceberg schema's returns a list of column_names for all id's in the
file schema that are projected in the expected schema
Parameters
----------
parquet_file : pyarrow.parquet.ParquetFile
The Parquet File to use to extract the iceberg schema
Returns
-------
iceberg.ap... |
Given two Iceberg schema's returns a list of column_names for all id's in the
file schema that are projected in the expected schema | def convert_parquet_to_iceberg(parquet_file: ParquetFile) -> Schema: # noqa: ignore=C901
"""
Given two Iceberg schema's returns a list of column_names for all id's in the
file schema that are projected in the expected schema
Parameters
----------
parquet_file : pyarrow.parquet.ParquetFile
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arrow_to_iceberg | (arrow_schema: pa.Schema) |
Use an arrow schema, which contains the field_id metadata, to create an equivalent iceberg Schema
Parameters
----------
arrow_schema : pyarrow.Schema
An Arrow schema with the parquet field_id metadata
Returns
-------
iceberg.api.Schema
returns an equivalent iceberg Schema ... |
Use an arrow schema, which contains the field_id metadata, to create an equivalent iceberg Schema | def arrow_to_iceberg(arrow_schema: pa.Schema) -> Schema:
"""
Use an arrow schema, which contains the field_id metadata, to create an equivalent iceberg Schema
Parameters
----------
arrow_schema : pyarrow.Schema
An Arrow schema with the parquet field_id metadata
Returns
-------
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train_and_evaluate | (args) | Train and evaluate custom Estimator with three training modes.
Given the dictionary of parameters, create custom Estimator and run up to
three training modes then return Estimator object.
Args:
args: Dictionary of parameters.
Returns:
Estimator object.
| Train and evaluate custom Estimator with three training modes. | def train_and_evaluate(args):
"""Train and evaluate custom Estimator with three training modes.
Given the dictionary of parameters, create custom Estimator and run up to
three training modes then return Estimator object.
Args:
args: Dictionary of parameters.
Returns:
Estimator object.
"""
# Cre... | [
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ImagePalette.getdata | (self) |
Get palette contents in format suitable for the low-level
``im.putpalette`` primitive.
.. warning:: This method is experimental.
|
Get palette contents in format suitable for the low-level
``im.putpalette`` primitive. | def getdata(self):
"""
Get palette contents in format suitable for the low-level
``im.putpalette`` primitive.
.. warning:: This method is experimental.
"""
if self.rawmode:
return self.rawmode, self.palette
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ImagePalette.tobytes | (self) | Convert palette to bytes.
.. warning:: This method is experimental.
| Convert palette to bytes. | def tobytes(self):
"""Convert palette to bytes.
.. warning:: This method is experimental.
"""
if self.rawmode:
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ImagePalette.getcolor | (self, color, image=None) | Given an rgb tuple, allocate palette entry.
.. warning:: This method is experimental.
| Given an rgb tuple, allocate palette entry. | def getcolor(self, color, image=None):
"""Given an rgb tuple, allocate palette entry.
.. warning:: This method is experimental.
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ImagePalette.save | (self, fp) | Save palette to text file.
.. warning:: This method is experimental.
| Save palette to text file. | def save(self, fp):
"""Save palette to text file.
.. warning:: This method is experimental.
"""
if self.rawmode:
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load_coco_classes | (coco) | Loads the class to label mapping (and inverse) for COCO.
| Loads the class to label mapping (and inverse) for COCO.
| def load_coco_classes(coco):
""" Loads the class to label mapping (and inverse) for COCO.
"""
# load class names (name -> label)
categories = coco.loadCats(coco.getCatIds())
categories.sort(key=lambda x: x["id"])
classes = {}
coco_labels = {}
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_execfile | (filename, globals, locals=None) |
Python 3 implementation of execfile.
|
Python 3 implementation of execfile.
| def _execfile(filename, globals, locals=None):
"""
Python 3 implementation of execfile.
"""
mode = 'rb'
with open(filename, mode) as stream:
script = stream.read()
if locals is None:
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override_temp | (replacement) |
Monkey-patch tempfile.tempdir with replacement, ensuring it exists
|
Monkey-patch tempfile.tempdir with replacement, ensuring it exists
| def override_temp(replacement):
"""
Monkey-patch tempfile.tempdir with replacement, ensuring it exists
"""
pkg_resources.py31compat.makedirs(replacement, exist_ok=True)
saved = tempfile.tempdir
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save_modules | () |
Context in which imported modules are saved.
Translates exceptions internal to the context into the equivalent exception
outside the context.
|
Context in which imported modules are saved. | def save_modules():
"""
Context in which imported modules are saved.
Translates exceptions internal to the context into the equivalent exception
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"""
saved = sys.modules.copy()
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_needs_hiding | (mod_name) |
>>> _needs_hiding('setuptools')
True
>>> _needs_hiding('pkg_resources')
True
>>> _needs_hiding('setuptools_plugin')
False
>>> _needs_hiding('setuptools.__init__')
True
>>> _needs_hiding('distutils')
True
>>> _needs_hiding('os')
False
>>> _needs_hiding('Cython')
T... |
>>> _needs_hiding('setuptools')
True
>>> _needs_hiding('pkg_resources')
True
>>> _needs_hiding('setuptools_plugin')
False
>>> _needs_hiding('setuptools.__init__')
True
>>> _needs_hiding('distutils')
True
>>> _needs_hiding('os')
False
>>> _needs_hiding('Cython')
T... | def _needs_hiding(mod_name):
"""
>>> _needs_hiding('setuptools')
True
>>> _needs_hiding('pkg_resources')
True
>>> _needs_hiding('setuptools_plugin')
False
>>> _needs_hiding('setuptools.__init__')
True
>>> _needs_hiding('distutils')
True
>>> _needs_hiding('os')
False
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hide_setuptools | () |
Remove references to setuptools' modules from sys.modules to allow the
invocation to import the most appropriate setuptools. This technique is
necessary to avoid issues such as #315 where setuptools upgrading itself
would fail to find a function declared in the metadata.
|
Remove references to setuptools' modules from sys.modules to allow the
invocation to import the most appropriate setuptools. This technique is
necessary to avoid issues such as #315 where setuptools upgrading itself
would fail to find a function declared in the metadata.
| def hide_setuptools():
"""
Remove references to setuptools' modules from sys.modules to allow the
invocation to import the most appropriate setuptools. This technique is
necessary to avoid issues such as #315 where setuptools upgrading itself
would fail to find a function declared in the metadata.
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run_setup | (setup_script, args) | Run a distutils setup script, sandboxed in its directory | Run a distutils setup script, sandboxed in its directory | def run_setup(setup_script, args):
"""Run a distutils setup script, sandboxed in its directory"""
setup_dir = os.path.abspath(os.path.dirname(setup_script))
with setup_context(setup_dir):
try:
sys.argv[:] = [setup_script] + list(args)
sys.path.insert(0, setup_dir)
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UnpickleableException.dump | (type, exc) |
Always return a dumped (pickled) type and exc. If exc can't be pickled,
wrap it in UnpickleableException first.
|
Always return a dumped (pickled) type and exc. If exc can't be pickled,
wrap it in UnpickleableException first.
| def dump(type, exc):
"""
Always return a dumped (pickled) type and exc. If exc can't be pickled,
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"""
try:
return pickle.dumps(type), pickle.dumps(exc)
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ExceptionSaver.resume | (self) | restore and re-raise any exception | restore and re-raise any exception | def resume(self):
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AbstractSandbox.run | (self, func) | Run 'func' under os sandboxing | Run 'func' under os sandboxing | def run(self, func):
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AbstractSandbox._validate_path | (self, path) | Called to remap or validate any path, whether input or output | Called to remap or validate any path, whether input or output | def _validate_path(self, path):
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AbstractSandbox._remap_input | (self, operation, path, *args, **kw) | Called for path inputs | Called for path inputs | def _remap_input(self, operation, path, *args, **kw):
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AbstractSandbox._remap_output | (self, operation, path) | Called for path outputs | Called for path outputs | def _remap_output(self, operation, path):
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AbstractSandbox._remap_pair | (self, operation, src, dst, *args, **kw) | Called for path pairs like rename, link, and symlink operations | Called for path pairs like rename, link, and symlink operations | def _remap_pair(self, operation, src, dst, *args, **kw):
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DirectorySandbox._remap_input | (self, operation, path, *args, **kw) | Called for path inputs | Called for path inputs | def _remap_input(self, operation, path, *args, **kw):
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DirectorySandbox.open | (self, file, flags, mode=0o777, *args, **kw) | Called for low-level os.open() | Called for low-level os.open() | def open(self, file, flags, mode=0o777, *args, **kw):
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transform | (data, ops=[]) | transform | transform | def transform(data, ops=[]):
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wsgi_to_bytes | (data) | coerce wsgi unicode represented bytes to real ones | coerce wsgi unicode represented bytes to real ones | def wsgi_to_bytes(data):
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quote_header_value | (value, extra_chars="", allow_token=True) | Quote a header value if necessary.
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unquote_header_value | (value, is_filename=False) | r"""Unquotes a header value. (Reversal of :func:`quote_header_value`).
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dump_options_header | (header, options) | The reverse function to :func:`parse_options_header`.
:param header: the header to dump
:param options: a dict of options to append.
| The reverse function to :func:`parse_options_header`. | def dump_options_header(header, options):
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dump_header | (iterable, allow_token=True) | Dump an HTTP header again. This is the reversal of
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parse_list_header | (value) | Parse lists as described by RFC 2068 Section 2.
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middle. Quotes are removed automatically after parsing.
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parse_dict_header | (value, cls=dict) | Parse lists of key, value pairs as described by RFC 2068 Section 2 and
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parse_options_header | (value, multiple=False) | Parse a ``Content-Type`` like header into a tuple with the content
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('text/html', {'charset': 'utf8'})
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parse_accept_header | (value, cls=None) | Parses an HTTP Accept-* header. This does not implement a complete
valid algorithm but one that supports at least value and quality
extraction.
Returns a new :class:`Accept` object (basically a list of ``(value, quality)``
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parse_cache_control_header | (value, on_update=None, cls=None) | Parse a cache control header. The RFC differs between response and
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.. versionadded:: 0.5
The `cls` was added. If not specified an immutable
:class:`~werkzeug.datastructures.Reques... | Parse a cache control header. The RFC differs between response and
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parse_set_header | (value, on_update=None) | Parse a set-like header and return a
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parse_authorization_header | (value) | Parse an HTTP basic/digest authorization header transmitted by the web
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:param value: the authorization header to parse.
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parse_www_authenticate_header | (value, on_update=None) | Parse an HTTP WWW-Authenticate header into a
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:param value: a WWW-Authenticate header to parse.
:param on_update: an optional callable that is called every time a value
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parse_if_range_header | (value) | Parses an if-range header which can be an etag or a date. Returns
a :class:`~werkzeug.datastructures.IfRange` object.
.. versionadded:: 0.7
| Parses an if-range header which can be an etag or a date. Returns
a :class:`~werkzeug.datastructures.IfRange` object. | def parse_if_range_header(value):
"""Parses an if-range header which can be an etag or a date. Returns
a :class:`~werkzeug.datastructures.IfRange` object.
.. versionadded:: 0.7
"""
if not value:
return IfRange()
date = parse_date(value)
if date is not None:
return IfRange(d... | [
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parse_range_header | (value, make_inclusive=True) | Parses a range header into a :class:`~werkzeug.datastructures.Range`
object. If the header is missing or malformed `None` is returned.
`ranges` is a list of ``(start, stop)`` tuples where the ranges are
non-inclusive.
.. versionadded:: 0.7
| Parses a range header into a :class:`~werkzeug.datastructures.Range`
object. If the header is missing or malformed `None` is returned.
`ranges` is a list of ``(start, stop)`` tuples where the ranges are
non-inclusive. | def parse_range_header(value, make_inclusive=True):
"""Parses a range header into a :class:`~werkzeug.datastructures.Range`
object. If the header is missing or malformed `None` is returned.
`ranges` is a list of ``(start, stop)`` tuples where the ranges are
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.. versionadded:: 0.7
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660,
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parse_content_range_header | (value, on_update=None) | Parses a range header into a
:class:`~werkzeug.datastructures.ContentRange` object or `None` if
parsing is not possible.
.. versionadded:: 0.7
:param value: a content range header to be parsed.
:param on_update: an optional callable that is called every time a value
on the :c... | Parses a range header into a
:class:`~werkzeug.datastructures.ContentRange` object or `None` if
parsing is not possible. | def parse_content_range_header(value, on_update=None):
"""Parses a range header into a
:class:`~werkzeug.datastructures.ContentRange` object or `None` if
parsing is not possible.
.. versionadded:: 0.7
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705,
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quote_etag | (etag, weak=False) | Quote an etag.
:param etag: the etag to quote.
:param weak: set to `True` to tag it "weak".
| Quote an etag. | def quote_etag(etag, weak=False):
"""Quote an etag.
:param etag: the etag to quote.
:param weak: set to `True` to tag it "weak".
"""
if '"' in etag:
raise ValueError("invalid etag")
etag = '"%s"' % etag
if weak:
etag = "W/" + etag
return etag | [
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708,
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719,
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unquote_etag | (etag) | Unquote a single etag:
>>> unquote_etag('W/"bar"')
('bar', True)
>>> unquote_etag('"bar"')
('bar', False)
:param etag: the etag identifier to unquote.
:return: a ``(etag, weak)`` tuple.
| Unquote a single etag: | def unquote_etag(etag):
"""Unquote a single etag:
>>> unquote_etag('W/"bar"')
('bar', True)
>>> unquote_etag('"bar"')
('bar', False)
:param etag: the etag identifier to unquote.
:return: a ``(etag, weak)`` tuple.
"""
if not etag:
return None, None
etag = etag.strip()
... | [
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722,
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] | [
742,
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] | python | fr | ['fr', 'it', 'pt'] | False |
parse_etags | (value) | Parse an etag header.
:param value: the tag header to parse
:return: an :class:`~werkzeug.datastructures.ETags` object.
| Parse an etag header. | def parse_etags(value):
"""Parse an etag header.
:param value: the tag header to parse
:return: an :class:`~werkzeug.datastructures.ETags` object.
"""
if not value:
return ETags()
strong = []
weak = []
end = len(value)
pos = 0
while pos < end:
match = _etag_re.ma... | [
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771,
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] | python | br | ['br', 'gd', 'es'] | False |
generate_etag | (data) | Generate an etag for some data. | Generate an etag for some data. | def generate_etag(data):
"""Generate an etag for some data."""
return md5(data).hexdigest() | [
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] | [
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] | [
776,
32
] | python | en | ['no', 'en', 'es'] | False |
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