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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
_init_worker | (counter) |
Switch to databases dedicated to this worker.
This helper lives at module-level because of the multiprocessing module's
requirements.
|
Switch to databases dedicated to this worker. | def _init_worker(counter):
"""
Switch to databases dedicated to this worker.
This helper lives at module-level because of the multiprocessing module's
requirements.
"""
global _worker_id
with counter.get_lock():
counter.value += 1
_worker_id = counter.value
for alias ... | [
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_run_subsuite | (args) |
Run a suite of tests with a RemoteTestRunner and return a RemoteTestResult.
This helper lives at module-level and its arguments are wrapped in a tuple
because of the multiprocessing module's requirements.
|
Run a suite of tests with a RemoteTestRunner and return a RemoteTestResult. | def _run_subsuite(args):
"""
Run a suite of tests with a RemoteTestRunner and return a RemoteTestResult.
This helper lives at module-level and its arguments are wrapped in a tuple
because of the multiprocessing module's requirements.
"""
runner_class, subsuite_index, subsuite, failfast = args
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is_discoverable | (label) |
Check if a test label points to a Python package or file directory.
Relative labels like "." and ".." are seen as directories.
|
Check if a test label points to a Python package or file directory. | def is_discoverable(label):
"""
Check if a test label points to a Python package or file directory.
Relative labels like "." and ".." are seen as directories.
"""
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reorder_suite | (suite, classes, reverse=False) |
Reorder a test suite by test type.
`classes` is a sequence of types
All tests of type classes[0] are placed first, then tests of type
classes[1], etc. Tests with no match in classes are placed last.
If `reverse` is True, sort tests within classes in opposite order but
don't reverse test clas... |
Reorder a test suite by test type. | def reorder_suite(suite, classes, reverse=False):
"""
Reorder a test suite by test type.
`classes` is a sequence of types
All tests of type classes[0] are placed first, then tests of type
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partition_suite_by_type | (suite, classes, bins, reverse=False) |
Partition a test suite by test type. Also prevent duplicated tests.
classes is a sequence of types
bins is a sequence of TestSuites, one more than classes
reverse changes the ordering of tests within bins
Tests of type classes[i] are added to bins[i],
tests with no match found in classes are ... |
Partition a test suite by test type. Also prevent duplicated tests. | def partition_suite_by_type(suite, classes, bins, reverse=False):
"""
Partition a test suite by test type. Also prevent duplicated tests.
classes is a sequence of types
bins is a sequence of TestSuites, one more than classes
reverse changes the ordering of tests within bins
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partition_suite_by_case | (suite) | Partition a test suite by test case, preserving the order of tests. | Partition a test suite by test case, preserving the order of tests. | def partition_suite_by_case(suite):
"""Partition a test suite by test case, preserving the order of tests."""
groups = []
suite_class = type(suite)
for test_type, test_group in itertools.groupby(suite, type):
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RemoteTestResult._confirm_picklable | (self, obj) |
Confirm that obj can be pickled and unpickled as multiprocessing will
need to pickle the exception in the child process and unpickle it in
the parent process. Let the exception rise, if not.
|
Confirm that obj can be pickled and unpickled as multiprocessing will
need to pickle the exception in the child process and unpickle it in
the parent process. Let the exception rise, if not.
| def _confirm_picklable(self, obj):
"""
Confirm that obj can be pickled and unpickled as multiprocessing will
need to pickle the exception in the child process and unpickle it in
the parent process. Let the exception rise, if not.
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ParallelTestSuite.run | (self, result) |
Distribute test cases across workers.
Return an identifier of each test case with its result in order to use
imap_unordered to show results as soon as they're available.
To minimize pickling errors when getting results from workers:
- pass back numeric indexes in self.subsuit... |
Distribute test cases across workers. | def run(self, result):
"""
Distribute test cases across workers.
Return an identifier of each test case with its result in order to use
imap_unordered to show results as soon as they're available.
To minimize pickling errors when getting results from workers:
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DiscoverRunner.teardown_databases | (self, old_config, **kwargs) | Destroy all the non-mirror databases. | Destroy all the non-mirror databases. | def teardown_databases(self, old_config, **kwargs):
"""Destroy all the non-mirror databases."""
_teardown_databases(
old_config,
verbosity=self.verbosity,
parallel=self.parallel,
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DiscoverRunner.run_tests | (self, test_labels, extra_tests=None, **kwargs) |
Run the unit tests for all the test labels in the provided list.
Test labels should be dotted Python paths to test modules, test
classes, or test methods.
A list of 'extra' tests may also be provided; these tests
will be added to the test suite.
Return the number of t... |
Run the unit tests for all the test labels in the provided list. | def run_tests(self, test_labels, extra_tests=None, **kwargs):
"""
Run the unit tests for all the test labels in the provided list.
Test labels should be dotted Python paths to test modules, test
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DatabaseOperations.fetch_returned_insert_rows | (self, cursor) |
Given a cursor object that has just performed an INSERT...RETURNING
statement into a table, return the tuple of returned data.
|
Given a cursor object that has just performed an INSERT...RETURNING
statement into a table, return the tuple of returned data.
| def fetch_returned_insert_rows(self, cursor):
"""
Given a cursor object that has just performed an INSERT...RETURNING
statement into a table, return the tuple of returned data.
"""
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DatabaseOperations.force_no_ordering | (self) |
"ORDER BY NULL" prevents MySQL from implicitly ordering by grouped
columns. If no ordering would otherwise be applied, we don't want any
implicit sorting going on.
|
"ORDER BY NULL" prevents MySQL from implicitly ordering by grouped
columns. If no ordering would otherwise be applied, we don't want any
implicit sorting going on.
| def force_no_ordering(self):
"""
"ORDER BY NULL" prevents MySQL from implicitly ordering by grouped
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"""
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MyPredictor.__init__ | (self, model) | Stores artifacts for prediction. Only initialized via `from_path`.
| Stores artifacts for prediction. Only initialized via `from_path`.
| def __init__(self, model):
"""Stores artifacts for prediction. Only initialized via `from_path`.
"""
self._model = model | [
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MyPredictor.predict | (self, instances, **kwargs) | Performs custom prediction.
Preprocesses inputs, then performs prediction using the trained
scikit-learn model.
Args:
instances: A list of prediction input instances.
**kwargs: A dictionary of keyword args provided as additional
fields on the predict req... | Performs custom prediction. | def predict(self, instances, **kwargs):
"""Performs custom prediction.
Preprocesses inputs, then performs prediction using the trained
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Args:
instances: A list of prediction input instances.
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MyPredictor.from_path | (cls, model_dir) | Creates an instance of MyPredictor using the given path.
This loads artifacts that have been copied from your model directory in
Cloud Storage. MyPredictor uses them during prediction.
Args:
model_dir: The local directory that contains the trained
scikit-learn model... | Creates an instance of MyPredictor using the given path. | def from_path(cls, model_dir):
"""Creates an instance of MyPredictor using the given path.
This loads artifacts that have been copied from your model directory in
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Command.sync_apps | (self, connection, app_labels) | Run the old syncdb-style operation on a list of app_labels. | Run the old syncdb-style operation on a list of app_labels. | def sync_apps(self, connection, app_labels):
"""Run the old syncdb-style operation on a list of app_labels."""
with connection.cursor() as cursor:
tables = connection.introspection.table_names(cursor)
# Build the manifest of apps and models that are to be synchronized.
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Command.describe_operation | (operation, backwards) | Return a string that describes a migration operation for --plan. | Return a string that describes a migration operation for --plan. | def describe_operation(operation, backwards):
"""Return a string that describes a migration operation for --plan."""
prefix = ''
is_error = False
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run | (argv=None) | The main function which creates the pipeline and runs it. | The main function which creates the pipeline and runs it. | def run(argv=None):
"""The main function which creates the pipeline and runs it."""
parser = argparse.ArgumentParser()
# Add the arguments needed for this specific Dataflow job.
parser.add_argument(
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RowTransformer.parse | (self, row) | This method translates a single delimited record into a dictionary
which can be loaded into BigQuery. It also adds filename and load_dt
fields to the dictionary. | This method translates a single delimited record into a dictionary
which can be loaded into BigQuery. It also adds filename and load_dt
fields to the dictionary. | def parse(self, row):
"""This method translates a single delimited record into a dictionary
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# Strip out the return characters and quote characters.
values = re.split(self.delimiter,... | [
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RandomRec.__init__ | (self, train_file, test_file, uniform=True, output_file=None, sep='\t', output_sep='\t', random_seed=None) |
Random recommendation for Rating Prediction
This algorithm predicts ratings for each user-item
Usage::
>> RandomRec(train, test).compute()
:param train_file: File which contains the train set. This file needs to have at least 3 columns
(user item feedba... |
Random recommendation for Rating Prediction
This algorithm predicts ratings for each user-item
Usage::
>> RandomRec(train, test).compute()
:param train_file: File which contains the train set. This file needs to have at least 3 columns
(user item feedba... | def __init__(self, train_file, test_file, uniform=True, output_file=None, sep='\t', output_sep='\t', random_seed=None):
"""
Random recommendation for Rating Prediction
This algorithm predicts ratings for each user-item
Usage::
>> RandomRec(train, test).compute()
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RandomRec.compute | (self, verbose=True, metrics=None, verbose_evaluation=True, as_table=False, table_sep='\t') |
Extends compute method from BaseRatingPrediction. Method to run recommender algorithm
:param verbose: Print recommender and database information
:type verbose: bool, default True
:param metrics: List of evaluation measures
:type metrics: list, default None
:p... |
Extends compute method from BaseRatingPrediction. Method to run recommender algorithm
:param verbose: Print recommender and database information
:type verbose: bool, default True
:param metrics: List of evaluation measures
:type metrics: list, default None
:p... | def compute(self, verbose=True, metrics=None, verbose_evaluation=True, as_table=False, table_sep='\t'):
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Extends compute method from BaseRatingPrediction. Method to run recommender algorithm
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abort | (status, *args, **kwargs) | Raises an :py:exc:`HTTPException` for the given status code or WSGI
application::
abort(404) # 404 Not Found
abort(Response('Hello World'))
Can be passed a WSGI application or a status code. If a status code is
given it's looked up in the list of exceptions and will raise that
except... | Raises an :py:exc:`HTTPException` for the given status code or WSGI
application:: | def abort(status, *args, **kwargs):
"""Raises an :py:exc:`HTTPException` for the given status code or WSGI
application::
abort(404) # 404 Not Found
abort(Response('Hello World'))
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MethodNotAllowed.__init__ | (self, valid_methods=None, description=None) | Takes an optional list of valid http methods
starting with werkzeug 0.3 the list will be mandatory. | Takes an optional list of valid http methods
starting with werkzeug 0.3 the list will be mandatory. | def __init__(self, valid_methods=None, description=None):
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347,
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351,
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RequestedRangeNotSatisfiable.__init__ | (self, length=None, units="bytes", description=None) | Takes an optional `Content-Range` header value based on ``length``
parameter.
| Takes an optional `Content-Range` header value based on ``length``
parameter.
| def __init__(self, length=None, units="bytes", description=None):
"""Takes an optional `Content-Range` header value based on ``length``
parameter.
"""
HTTPException.__init__(self, description)
self.length = length
self.units = units | [
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before_nothing | (retry_state: "RetryCallState") | Before call strategy that does nothing. | Before call strategy that does nothing. | def before_nothing(retry_state: "RetryCallState") -> None:
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before_log | (logger: "logging.Logger", log_level: int) | Before call strategy that logs to some logger the attempt. | Before call strategy that logs to some logger the attempt. | def before_log(logger: "logging.Logger", log_level: int) -> typing.Callable[["RetryCallState"], None]:
"""Before call strategy that logs to some logger the attempt."""
def log_it(retry_state: "RetryCallState") -> None:
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CurrentThreadExecutor.run_until_future | (self, future) |
Runs the code in the work queue until a result is available from the future.
Should be run from the thread the executor is initialised in.
|
Runs the code in the work queue until a result is available from the future.
Should be run from the thread the executor is initialised in.
| def run_until_future(self, future):
"""
Runs the code in the work queue until a result is available from the future.
Should be run from the thread the executor is initialised in.
"""
# Check we're in the right thread
if threading.current_thread() != self._work_thread:
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Wheel.__init__ | (self, filename: str) |
:raises InvalidWheelFilename: when the filename is invalid for a wheel
|
:raises InvalidWheelFilename: when the filename is invalid for a wheel
| def __init__(self, filename: str) -> None:
"""
:raises InvalidWheelFilename: when the filename is invalid for a wheel
"""
wheel_info = self.wheel_file_re.match(filename)
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Wheel.get_formatted_file_tags | (self) | Return the wheel's tags as a sorted list of strings. | Return the wheel's tags as a sorted list of strings. | def get_formatted_file_tags(self) -> List[str]:
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Wheel.support_index_min | (self, tags: List[Tag]) | Return the lowest index that one of the wheel's file_tag combinations
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For example, if there are 8 supported tags and one of the file tags
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Wheel.find_most_preferred_tag | (
self, tags: List[Tag], tag_to_priority: Dict[Tag, int]
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tag combinations achieves in the given list of supported tags using the given
tag_to_priority mapping, where lower priorities are more-preferred.
This is used in place of support_index_min in some cases in order to avoid... | Return the priority of the most preferred tag that one of the wheel's file
tag combinations achieves in the given list of supported tags using the given
tag_to_priority mapping, where lower priorities are more-preferred. | def find_most_preferred_tag(
self, tags: List[Tag], tag_to_priority: Dict[Tag, int]
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Wheel.supported | (self, tags: Iterable[Tag]) | Return whether the wheel is compatible with one of the given tags.
:param tags: the PEP 425 tags to check the wheel against.
| Return whether the wheel is compatible with one of the given tags. | def supported(self, tags: Iterable[Tag]) -> bool:
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dc | (result, reference) | r"""
Dice coefficient
Computes the Dice coefficient (also known as Sorensen index) between the binary
objects in two images.
The metric is defined as
.. math::
DC=\frac{2|A\cap B|}{|A|+|B|}
, where :math:`A` is the first and :math:`B` the second set of samples (here: binary objects)... | r"""
Dice coefficient | def dc(result, reference):
r"""
Dice coefficient
Computes the Dice coefficient (also known as Sorensen index) between the binary
objects in two images.
The metric is defined as
.. math::
DC=\frac{2|A\cap B|}{|A|+|B|}
, where :math:`A` is the first and :math:`B` the second set of... | [
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jc | (result, reference) |
Jaccard coefficient
Computes the Jaccard coefficient between the binary objects in two images.
Parameters
----------
result: array_like
Input data containing objects. Can be any type but will be converted
into binary: background where 0, object everywhere else.
referen... |
Jaccard coefficient | def jc(result, reference):
"""
Jaccard coefficient
Computes the Jaccard coefficient between the binary objects in two images.
Parameters
----------
result: array_like
Input data containing objects. Can be any type but will be converted
into binary: background where 0, o... | [
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precision | (result, reference) |
Precison.
Parameters
----------
result : array_like
Input data containing objects. Can be any type but will be converted
into binary: background where 0, object everywhere else.
reference : array_like
Input data containing objects. Can be any type but will be converted
... |
Precison. | def precision(result, reference):
"""
Precison.
Parameters
----------
result : array_like
Input data containing objects. Can be any type but will be converted
into binary: background where 0, object everywhere else.
reference : array_like
Input data containing objects. C... | [
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recall | (result, reference) |
Recall.
Parameters
----------
result : array_like
Input data containing objects. Can be any type but will be converted
into binary: background where 0, object everywhere else.
reference : array_like
Input data containing objects. Can be any type but will be converted
... |
Recall. | def recall(result, reference):
"""
Recall.
Parameters
----------
result : array_like
Input data containing objects. Can be any type but will be converted
into binary: background where 0, object everywhere else.
reference : array_like
Input data containing objects. Can be... | [
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sensitivity | (result, reference) |
Sensitivity.
Same as :func:`recall`, see there for a detailed description.
See also
--------
:func:`specificity`
|
Sensitivity.
Same as :func:`recall`, see there for a detailed description. | def sensitivity(result, reference):
"""
Sensitivity.
Same as :func:`recall`, see there for a detailed description.
See also
--------
:func:`specificity`
"""
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specificity | (result, reference) |
Specificity.
Parameters
----------
result : array_like
Input data containing objects. Can be any type but will be converted
into binary: background where 0, object everywhere else.
reference : array_like
Input data containing objects. Can be any type but will be converted
... |
Specificity. | def specificity(result, reference):
"""
Specificity.
Parameters
----------
result : array_like
Input data containing objects. Can be any type but will be converted
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reference : array_like
Input data containing objec... | [
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true_negative_rate | (result, reference) |
True negative rate.
Same as :func:`sensitivity`, see there for a detailed description.
See also
--------
:func:`true_positive_rate`
:func:`positive_predictive_value`
|
True negative rate.
Same as :func:`sensitivity`, see there for a detailed description. | def true_negative_rate(result, reference):
"""
True negative rate.
Same as :func:`sensitivity`, see there for a detailed description.
See also
--------
:func:`true_positive_rate`
:func:`positive_predictive_value`
"""
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true_positive_rate | (result, reference) |
True positive rate.
Same as :func:`recall`, see there for a detailed description.
See also
--------
:func:`positive_predictive_value`
:func:`true_negative_rate`
|
True positive rate.
Same as :func:`recall`, see there for a detailed description. | def true_positive_rate(result, reference):
"""
True positive rate.
Same as :func:`recall`, see there for a detailed description.
See also
--------
:func:`positive_predictive_value`
:func:`true_negative_rate`
"""
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positive_predictive_value | (result, reference) |
Positive predictive value.
Same as :func:`precision`, see there for a detailed description.
See also
--------
:func:`true_positive_rate`
:func:`true_negative_rate`
|
Positive predictive value.
Same as :func:`precision`, see there for a detailed description. | def positive_predictive_value(result, reference):
"""
Positive predictive value.
Same as :func:`precision`, see there for a detailed description.
See also
--------
:func:`true_positive_rate`
:func:`true_negative_rate`
"""
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hd | (result, reference, voxelspacing=None, connectivity=1) |
Hausdorff Distance.
Computes the (symmetric) Hausdorff Distance (HD) between the binary objects in two
images. It is defined as the maximum surface distance between the objects.
Parameters
----------
result : array_like
Input data containing objects. Can be any type but will be conver... |
Hausdorff Distance. | def hd(result, reference, voxelspacing=None, connectivity=1):
"""
Hausdorff Distance.
Computes the (symmetric) Hausdorff Distance (HD) between the binary objects in two
images. It is defined as the maximum surface distance between the objects.
Parameters
----------
result : array_like
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assd | (result, reference, voxelspacing=None, connectivity=1) |
Average symmetric surface distance.
Computes the average symmetric surface distance (ASD) between the binary objects in
two images.
Parameters
----------
result : array_like
Input data containing objects. Can be any type but will be converted
into binary: background where 0, o... |
Average symmetric surface distance. | def assd(result, reference, voxelspacing=None, connectivity=1):
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Average symmetric surface distance.
Computes the average symmetric surface distance (ASD) between the binary objects in
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result : array_like
Input data containing objects. Can be an... | [
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asd | (result, reference, voxelspacing=None, connectivity=1) |
Average surface distance metric.
Computes the average surface distance (ASD) between the binary objects in two images.
Parameters
----------
result : array_like
Input data containing objects. Can be any type but will be converted
into binary: background where 0, object everywhere ... |
Average surface distance metric. | def asd(result, reference, voxelspacing=None, connectivity=1):
"""
Average surface distance metric.
Computes the average surface distance (ASD) between the binary objects in two images.
Parameters
----------
result : array_like
Input data containing objects. Can be any type but will be... | [
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ravd | (result, reference) |
Relative absolute volume difference.
Compute the relative absolute volume difference between the (joined) binary objects
in the two images.
Parameters
----------
result : array_like
Input data containing objects. Can be any type but will be converted
into binary: background wh... |
Relative absolute volume difference. | def ravd(result, reference):
"""
Relative absolute volume difference.
Compute the relative absolute volume difference between the (joined) binary objects
in the two images.
Parameters
----------
result : array_like
Input data containing objects. Can be any type but will be converte... | [
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volume_correlation | (results, references) | r"""
Volume correlation.
Computes the linear correlation in binary object volume between the
contents of the successive binary images supplied. Measured through
the Pearson product-moment correlation coefficient.
Parameters
----------
results : sequence of array_like
Ordered list o... | r"""
Volume correlation. | def volume_correlation(results, references):
r"""
Volume correlation.
Computes the linear correlation in binary object volume between the
contents of the successive binary images supplied. Measured through
the Pearson product-moment correlation coefficient.
Parameters
----------
result... | [
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volume_change_correlation | (results, references) | r"""
Volume change correlation.
Computes the linear correlation of change in binary object volume between
the contents of the successive binary images supplied. Measured through
the Pearson product-moment correlation coefficient.
Parameters
----------
results : sequence of array_like
... | r"""
Volume change correlation. | def volume_change_correlation(results, references):
r"""
Volume change correlation.
Computes the linear correlation of change in binary object volume between
the contents of the successive binary images supplied. Measured through
the Pearson product-moment correlation coefficient.
Parameters
... | [
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obj_assd | (result, reference, voxelspacing=None, connectivity=1) |
Average symmetric surface distance.
Computes the average symmetric surface distance (ASSD) between the binary objects in
two images.
Parameters
----------
result : array_like
Input data containing objects. Can be any type but will be converted
into binary: background where 0, ... |
Average symmetric surface distance. | def obj_assd(result, reference, voxelspacing=None, connectivity=1):
"""
Average symmetric surface distance.
Computes the average symmetric surface distance (ASSD) between the binary objects in
two images.
Parameters
----------
result : array_like
Input data containing objects. Can ... | [
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obj_asd | (result, reference, voxelspacing=None, connectivity=1) |
Average surface distance between objects.
First correspondences between distinct binary objects in reference and result are
established. Then the average surface distance is only computed between corresponding
objects. Correspondence is defined as unique and at least one voxel overlap.
Parameters... |
Average surface distance between objects. | def obj_asd(result, reference, voxelspacing=None, connectivity=1):
"""
Average surface distance between objects.
First correspondences between distinct binary objects in reference and result are
established. Then the average surface distance is only computed between corresponding
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obj_fpr | (result, reference, connectivity=1) |
The false positive rate of distinct binary object detection.
The false positive rates gives a percentage measure of how many distinct binary
objects in the second array do not exists in the first array. A partial overlap
(of minimum one voxel) is here considered sufficient.
In cases where two dis... |
The false positive rate of distinct binary object detection. | def obj_fpr(result, reference, connectivity=1):
"""
The false positive rate of distinct binary object detection.
The false positive rates gives a percentage measure of how many distinct binary
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obj_tpr | (result, reference, connectivity=1) |
The true positive rate of distinct binary object detection.
The true positive rates gives a percentage measure of how many distinct binary
objects in the first array also exists in the second array. A partial overlap
(of minimum one voxel) is here considered sufficient.
In cases where two distinc... |
The true positive rate of distinct binary object detection. | def obj_tpr(result, reference, connectivity=1):
"""
The true positive rate of distinct binary object detection.
The true positive rates gives a percentage measure of how many distinct binary
objects in the first array also exists in the second array. A partial overlap
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__distinct_binary_object_correspondences | (reference, result, connectivity=1) |
Determines all distinct (where connectivity is defined by the connectivity parameter
passed to scipy's `generate_binary_structure`) binary objects in both of the input
parameters and returns a 1to1 mapping from the labelled objects in reference to the
corresponding (whereas a one-voxel overlap suffices... |
Determines all distinct (where connectivity is defined by the connectivity parameter
passed to scipy's `generate_binary_structure`) binary objects in both of the input
parameters and returns a 1to1 mapping from the labelled objects in reference to the
corresponding (whereas a one-voxel overlap suffices... | def __distinct_binary_object_correspondences(reference, result, connectivity=1):
"""
Determines all distinct (where connectivity is defined by the connectivity parameter
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__surface_distances | (result, reference, voxelspacing=None, connectivity=1) |
The distances between the surface voxel of binary objects in result and their
nearest partner surface voxel of a binary object in reference.
|
The distances between the surface voxel of binary objects in result and their
nearest partner surface voxel of a binary object in reference.
| def __surface_distances(result, reference, voxelspacing=None, connectivity=1):
"""
The distances between the surface voxel of binary objects in result and their
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__combine_windows | (w1, w2) |
Joins two windows (defined by tuple of slices) such that their maximum
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|
Joins two windows (defined by tuple of slices) such that their maximum
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| def __combine_windows(w1, w2):
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Joins two windows (defined by tuple of slices) such that their maximum
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"""
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LineString.__init__ | (self, *args, **kwargs) |
Initialize on the given sequence -- may take lists, tuples, NumPy arrays
of X,Y pairs, or Point objects. If Point objects are used, ownership is
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Examples:
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ls = LineString([(1, 1), (2, 2)])
... |
Initialize on the given sequence -- may take lists, tuples, NumPy arrays
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_not_ transferred to the LineString object. | def __init__(self, *args, **kwargs):
"""
Initialize on the given sequence -- may take lists, tuples, NumPy arrays
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LineString.__iter__ | (self) | Allow iteration over this LineString. | Allow iteration over this LineString. | def __iter__(self):
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LineString.__len__ | (self) | Return the number of points in this LineString. | Return the number of points in this LineString. | def __len__(self):
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LineString.tuple | (self) | Return a tuple version of the geometry from the coordinate sequence. | Return a tuple version of the geometry from the coordinate sequence. | def tuple(self):
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LineString._listarr | (self, func) |
Return a sequence (list) corresponding with the given function.
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|
Return a sequence (list) corresponding with the given function.
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| def _listarr(self, func):
"""
Return a sequence (list) corresponding with the given function.
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LineString.array | (self) | Return a numpy array for the LineString. | Return a numpy array for the LineString. | def array(self):
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CacheAdapter.get | (self, public_id, type, resource_type, transformation, format) |
Gets value specified by parameters
:param public_id: The public ID of the resource
:param type: The storage type
:param resource_type: The type of the resource
:param transformation: The transformation string
:param format: The format of the... |
Gets value specified by parameters | def get(self, public_id, type, resource_type, transformation, format):
"""
Gets value specified by parameters
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CacheAdapter.set | (self, public_id, type, resource_type, transformation, format, value) |
Sets value specified by parameters
:param public_id: The public ID of the resource
:param type: The storage type
:param resource_type: The type of the resource
:param transformation: The transformation string
:param format: The format of the... |
Sets value specified by parameters | def set(self, public_id, type, resource_type, transformation, format, value):
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Sets value specified by parameters
:param public_id: The public ID of the resource
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CacheAdapter.delete | (self, public_id, type, resource_type, transformation, format) |
Deletes entry specified by parameters
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Deletes entry specified by parameters | def delete(self, public_id, type, resource_type, transformation, format):
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Deletes entry specified by parameters
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CacheAdapter.flush_all | (self) |
Flushes all entries from cache
:return: bool True on success or False on failure
|
Flushes all entries from cache | def flush_all(self):
"""
Flushes all entries from cache
:return: bool True on success or False on failure
"""
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formset_factory | (form, formset=BaseFormSet, extra=1, can_order=False,
can_delete=False, max_num=None, validate_max=False,
min_num=None, validate_min=False, absolute_max=None,
can_delete_extra=True) | Return a FormSet for the given form class. | Return a FormSet for the given form class. | def formset_factory(form, formset=BaseFormSet, extra=1, can_order=False,
can_delete=False, max_num=None, validate_max=False,
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all_valid | (formsets) | Validate every formset and return True if all are valid. | Validate every formset and return True if all are valid. | def all_valid(formsets):
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make_model_tuple | (model) |
Take a model or a string of the form "app_label.ModelName" and return a
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|
Take a model or a string of the form "app_label.ModelName" and return a
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resolve_callables | (mapping) |
Generate key/value pairs for the given mapping where the values are
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|
Generate key/value pairs for the given mapping where the values are
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| def resolve_callables(mapping):
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EmailBackend.send_messages | (self, email_messages) | Write all messages to the stream in a thread-safe way. | Write all messages to the stream in a thread-safe way. | def send_messages(self, email_messages):
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DatabaseWrapper.disable_constraint_checking | (self) |
Disable foreign key checks, primarily for use in adding rows with
forward references. Always return True to indicate constraint checks
need to be re-enabled.
|
Disable foreign key checks, primarily for use in adding rows with
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281,
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DatabaseWrapper.enable_constraint_checking | (self) |
Re-enable foreign key checks after they have been disabled.
|
Re-enable foreign key checks after they have been disabled.
| def enable_constraint_checking(self):
"""
Re-enable foreign key checks after they have been disabled.
"""
# Override needs_rollback in case constraint_checks_disabled is
# nested inside transaction.atomic.
self.needs_rollback, needs_rollback = False, self.needs_rollback
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DatabaseWrapper.check_constraints | (self, table_names=None) |
Check each table name in `table_names` for rows with invalid foreign
key references. This method is intended to be used in conjunction with
`disable_constraint_checking()` and `enable_constraint_checking()`, to
determine if rows with invalid references were entered while constraint
... |
Check each table name in `table_names` for rows with invalid foreign
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DatabaseWrapper.check_constraints | (self, table_names=None) |
Check constraints by setting them to immediate. Return them to deferred
afterward.
|
Check constraints by setting them to immediate. Return them to deferred
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| def check_constraints(self, table_names=None):
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Check constraints by setting them to immediate. Return them to deferred
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update_proxy_model_permissions | (apps, schema_editor, reverse=False) |
Update the content_type of proxy model permissions to use the ContentType
of the proxy model.
|
Update the content_type of proxy model permissions to use the ContentType
of the proxy model.
| def update_proxy_model_permissions(apps, schema_editor, reverse=False):
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Update the content_type of proxy model permissions to use the ContentType
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style = color_style()
Permission = apps.get_model('auth', 'Permission')
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revert_proxy_model_permissions | (apps, schema_editor) |
Update the content_type of proxy model permissions to use the ContentType
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|
Update the content_type of proxy model permissions to use the ContentType
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| def revert_proxy_model_permissions(apps, schema_editor):
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Update the content_type of proxy model permissions to use the ContentType
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StatementSplitter._reset | (self) | Set the filter attributes to its default values | Set the filter attributes to its default values | def _reset(self):
"""Set the filter attributes to its default values"""
self._in_declare = False
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StatementSplitter._change_splitlevel | (self, ttype, value) | Get the new split level (increase, decrease or remain equal) | Get the new split level (increase, decrease or remain equal) | def _change_splitlevel(self, ttype, value):
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StatementSplitter.process | (self, stream) | Process the stream | Process the stream | def process(self, stream):
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EOS_TTYPE = T.Whitespace, T.Comment.Single
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build_networks | (
state_shape, action_size, learning_rate,
critic_weight, hidden_neurons, entropy) | Creates Actor Critic Neural Networks.
Creates a two hidden-layer Policy Gradient Neural Network. The loss
function is altered to be a log-likelihood function weighted
by an action's advantage.
Args:
space_shape: a tuple of ints representing the observation space.
action_size (int): the... | Creates Actor Critic Neural Networks. | def build_networks(
state_shape, action_size, learning_rate,
critic_weight, hidden_neurons, entropy):
"""Creates Actor Critic Neural Networks.
Creates a two hidden-layer Policy Gradient Neural Network. The loss
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Memory.add | (self, experience) | Adds an experience into the memory buffer.
Args:
experience: (state, action, reward, state_prime_value, done) tuple.
| Adds an experience into the memory buffer. | def add(self, experience):
"""Adds an experience into the memory buffer.
Args:
experience: (state, action, reward, state_prime_value, done) tuple.
"""
self.buffer.append(experience) | [
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Memory.sample | (self) | Returns formated experiences and clears the buffer.
Returns:
(list): A tuple of lists with structure [
[states], [actions], [rewards], [state_prime_values], [dones]
]
| Returns formated experiences and clears the buffer. | def sample(self):
"""Returns formated experiences and clears the buffer.
Returns:
(list): A tuple of lists with structure [
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Agent.__init__ | (self, actor, critic, policy, memory, action_size) | Initializes the agent with DQN and memory sub-classes.
Args:
network: A neural network created from deep_q_network().
memory: A Memory class object.
epsilon_decay (float): The rate at which to decay random actions.
action_size (int): The number of possible action... | Initializes the agent with DQN and memory sub-classes. | def __init__(self, actor, critic, policy, memory, action_size):
"""Initializes the agent with DQN and memory sub-classes.
Args:
network: A neural network created from deep_q_network().
memory: A Memory class object.
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Agent.act | (self, state) | Selects an action for the agent to take given a game state.
Args:
state (list of numbers): The state of the environment to act on.
traning (bool): True if the agent is training.
Returns:
(int) The index of the action to take.
| Selects an action for the agent to take given a game state. | def act(self, state):
"""Selects an action for the agent to take given a game state.
Args:
state (list of numbers): The state of the environment to act on.
traning (bool): True if the agent is training.
Returns:
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Agent.learn | (self) | Trains the Deep Q Network based on stored experiences. | Trains the Deep Q Network based on stored experiences. | def learn(self):
"""Trains the Deep Q Network based on stored experiences."""
gamma = self.memory.gamma
experiences = self.memory.sample()
state_mb, action_mb, reward_mb, dones_mb, next_value = experiences
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observations_to_float_rgb | (scene: np.ndarray,
user_input: Tuple[Tuple[int, int], ...] = (),
is_solved: Optional[bool] = None) | Convert an observation as returned by a simulator to an image. | Convert an observation as returned by a simulator to an image. | def observations_to_float_rgb(scene: np.ndarray,
user_input: Tuple[Tuple[int, int], ...] = (),
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observations_to_uint8_rgb | (scene: np.ndarray,
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is_solved: Optional[bool] = None) | Convert an observation as returned by a simulator to an image. | Convert an observation as returned by a simulator to an image. | def observations_to_uint8_rgb(scene: np.ndarray,
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save_observation_series_to_gif | (batched_observation_series_rows,
fpath,
solved_states=None,
solved_wrt_step=False,
pad_frames=True,
fps=10) | Saves a list of arrays of intermediate scenes as a gif.
Args:
batched_observation_series_rows:
[[[video1, video2, ..., videoB]], (B = batch size)
[another row of frames, typically corresponding to earlier one]
]
Each video is TxHxW, in the PHYRE format (not R... | Saves a list of arrays of intermediate scenes as a gif.
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batched_observation_series_rows:
[[[video1, video2, ..., videoB]], (B = batch size)
[another row of frames, typically corresponding to earlier one]
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Each video is TxHxW, in the PHYRE format (not R... | def save_observation_series_to_gif(batched_observation_series_rows,
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compose_gifs_compact | (input_fpathes, output_fpath) | Create progressin for first and last frames over time. | Create progressin for first and last frames over time. | def compose_gifs_compact(input_fpathes, output_fpath):
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first_and_last_per_batch_id = []
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compose_gifs | (input_fpathes, output_fpath) | Concatenate and sync all gifs. | Concatenate and sync all gifs. | def compose_gifs(input_fpathes, output_fpath):
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timesince | (d, now=None, reversed=False, time_strings=None, depth=2) |
Take two datetime objects and return the time between d and now as a nicely
formatted string, e.g. "10 minutes". If d occurs after now, return
"0 minutes".
Units used are years, months, weeks, days, hours, and minutes.
Seconds and microseconds are ignored. Up to `depth` adjacent units will be
... |
Take two datetime objects and return the time between d and now as a nicely
formatted string, e.g. "10 minutes". If d occurs after now, return
"0 minutes". | def timesince(d, now=None, reversed=False, time_strings=None, depth=2):
"""
Take two datetime objects and return the time between d and now as a nicely
formatted string, e.g. "10 minutes". If d occurs after now, return
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timeuntil | (d, now=None, time_strings=None, depth=2) |
Like timesince, but return a string measuring the time until the given time.
|
Like timesince, but return a string measuring the time until the given time.
| def timeuntil(d, now=None, time_strings=None, depth=2):
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Like timesince, but return a string measuring the time until the given time.
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compress_kml | (kml) | Return compressed KMZ from the given KML string. | Return compressed KMZ from the given KML string. | def compress_kml(kml):
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kmz = BytesIO()
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render_to_kml | (*args, **kwargs) | Render the response as KML (using the correct MIME type). | Render the response as KML (using the correct MIME type). | def render_to_kml(*args, **kwargs):
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... | [
23,
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28,
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render_to_kmz | (*args, **kwargs) |
Compress the KML content and return as KMZ (using the correct
MIME type).
|
Compress the KML content and return as KMZ (using the correct
MIME type).
| def render_to_kmz(*args, **kwargs):
"""
Compress the KML content and return as KMZ (using the correct
MIME type).
"""
return HttpResponse(
compress_kml(loader.render_to_string(*args, **kwargs)),
content_type='application/vnd.google-earth.kmz',
) | [
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BitString.asNumbers | (self) | Get |ASN.1| value as a sequence of 8-bit integers.
If |ASN.1| object length is not a multiple of 8, result
will be left-padded with zeros.
| Get |ASN.1| value as a sequence of 8-bit integers. | def asNumbers(self):
"""Get |ASN.1| value as a sequence of 8-bit integers.
If |ASN.1| object length is not a multiple of 8, result
will be left-padded with zeros.
"""
return tuple(octets.octs2ints(self.asOctets())) | [
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BitString.asOctets | (self) | Get |ASN.1| value as a sequence of octets.
If |ASN.1| object length is not a multiple of 8, result
will be left-padded with zeros.
| Get |ASN.1| value as a sequence of octets. | def asOctets(self):
"""Get |ASN.1| value as a sequence of octets.
If |ASN.1| object length is not a multiple of 8, result
will be left-padded with zeros.
"""
return integer.to_bytes(self._value, length=len(self)) | [
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BitString.asInteger | (self) | Get |ASN.1| value as a single integer value.
| Get |ASN.1| value as a single integer value.
| def asInteger(self):
"""Get |ASN.1| value as a single integer value.
"""
return self._value | [
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