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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
EasyProcess.is_alive | (self) |
poll process using :meth:`subprocess.Popen.poll`
:rtype: bool
|
poll process using :meth:`subprocess.Popen.poll` | def is_alive(self):
'''
poll process using :meth:`subprocess.Popen.poll`
:rtype: bool
'''
if self.popen:
return self.popen.poll() is None
else:
return False | [
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EasyProcess.wait | (self, timeout=None) | Wait for command to complete.
Timeout:
- discussion:
http://stackoverflow.com/questions/1191374/subprocess-with-timeout
- implementation: threading
:rtype: self
| Wait for command to complete. | def wait(self, timeout=None):
"""Wait for command to complete.
Timeout:
- discussion:
http://stackoverflow.com/questions/1191374/subprocess-with-timeout
- implementation: threading
:rtype: self
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EasyProcess.stop | (self) | Kill process and wait for command to complete.
same as:
1. :meth:`sendstop`
2. :meth:`wait`
:rtype: self
| Kill process and wait for command to complete. | def stop(self):
"""Kill process and wait for command to complete.
same as:
1. :meth:`sendstop`
2. :meth:`wait`
:rtype: self
"""
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EasyProcess.sendstop | (self) |
Kill process (:meth:`subprocess.Popen.terminate`).
Do not wait for command to complete.
:rtype: self
|
Kill process (:meth:`subprocess.Popen.terminate`).
Do not wait for command to complete. | def sendstop(self):
'''
Kill process (:meth:`subprocess.Popen.terminate`).
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EasyProcess.sleep | (self, sec) |
sleeping (same as :func:`time.sleep`)
:rtype: self
|
sleeping (same as :func:`time.sleep`) | def sleep(self, sec):
'''
sleeping (same as :func:`time.sleep`)
:rtype: self
'''
time.sleep(sec)
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EasyProcess.wrap | (self, func, delay=0) |
returns a function which:
1. start process
2. call func, save result
3. stop process
4. returns result
similar to :keyword:`with` statement
:rtype:
|
returns a function which:
1. start process
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3. stop process
4. returns result | def wrap(self, func, delay=0):
'''
returns a function which:
1. start process
2. call func, save result
3. stop process
4. returns result
similar to :keyword:`with` statement
:rtype:
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EasyProcess.__enter__ | (self) | used by the :keyword:`with` statement | used by the :keyword:`with` statement | def __enter__(self):
'''used by the :keyword:`with` statement'''
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EasyProcess.__exit__ | (self, *exc_info) | used by the :keyword:`with` statement | used by the :keyword:`with` statement | def __exit__(self, *exc_info):
'''used by the :keyword:`with` statement'''
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get_tagname_or_hash | () | return tagname if exists else hash | return tagname if exists else hash | def get_tagname_or_hash():
"""return tagname if exists else hash"""
# get hash
hash_cmd = ['git', 'rev-parse', '--short', 'HEAD']
hash_ = check_output(hash_cmd).decode('utf-8').strip()
# get tagname
tags_cmd = ['git', 'for-each-ref', '--points-at=HEAD', '--count=2', '--sort=-version:refname', '... | [
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SparkDFExecutionEngine.dataframe | (self) | If a batch has been loaded, returns a Spark Dataframe containing the data within the loaded batch | If a batch has been loaded, returns a Spark Dataframe containing the data within the loaded batch | def dataframe(self):
"""If a batch has been loaded, returns a Spark Dataframe containing the data within the loaded batch"""
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SparkDFExecutionEngine.guess_reader_method_from_path | (path) | Based on a given filepath, decides a reader method. Currently supports tsv, csv, and parquet. If none of these
file extensions are used, returns BatchKwargsError stating that it is unable to determine the current path.
Args:
path - A given file path
Returns:
A dictionar... | Based on a given filepath, decides a reader method. Currently supports tsv, csv, and parquet. If none of these
file extensions are used, returns BatchKwargsError stating that it is unable to determine the current path. | def guess_reader_method_from_path(path):
"""Based on a given filepath, decides a reader method. Currently supports tsv, csv, and parquet. If none of these
file extensions are used, returns BatchKwargsError stating that it is unable to determine the current path.
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SparkDFExecutionEngine._get_reader_fn | (self, reader, reader_method=None, path=None) | Static helper for providing reader_fn
Args:
reader: the base spark reader to use; this should have had reader_options applied already
reader_method: the name of the reader_method to use, if specified
path (str): the path to use to guess reader_method if it was not specified
... | Static helper for providing reader_fn | def _get_reader_fn(self, reader, reader_method=None, path=None):
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Args:
reader: the base spark reader to use; this should have had reader_options applied already
reader_method: the name of the reader_method to use, if specified
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SparkDFExecutionEngine.get_compute_domain | (
self,
domain_kwargs: dict,
domain_type: Union[str, MetricDomainTypes],
accessor_keys: Optional[Iterable[str]] = None,
) | Uses a given batch dictionary and domain kwargs (which include a row condition and a condition parser)
to obtain and/or query a batch. Returns in the format of a Pandas Series if only a single column is desired,
or otherwise a Data Frame.
Args:
domain_kwargs (dict) - A dictionary co... | Uses a given batch dictionary and domain kwargs (which include a row condition and a condition parser)
to obtain and/or query a batch. Returns in the format of a Pandas Series if only a single column is desired,
or otherwise a Data Frame. | def get_compute_domain(
self,
domain_kwargs: dict,
domain_type: Union[str, MetricDomainTypes],
accessor_keys: Optional[Iterable[str]] = None,
) -> Tuple["pyspark.sql.DataFrame", dict, dict]:
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SparkDFExecutionEngine.resolve_metric_bundle | (
self,
metric_fn_bundle: Iterable[Tuple[MetricConfiguration, Callable, dict]],
) | For each metric name in the given metric_fn_bundle, finds the domain of the metric and calculates it using a
metric function from the given provider class.
Args:
metric_fn_bundle - A batch containing MetricEdgeKeys and their corresponding functions
metric... | For each metric name in the given metric_fn_bundle, finds the domain of the metric and calculates it using a
metric function from the given provider class. | def resolve_metric_bundle(
self,
metric_fn_bundle: Iterable[Tuple[MetricConfiguration, Callable, dict]],
) -> dict:
"""For each metric name in the given metric_fn_bundle, finds the domain of the metric and calculates it using a
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SparkDFExecutionEngine.head | (self, n=5) | Returns dataframe head. Default is 5 | Returns dataframe head. Default is 5 | def head(self, n=5):
"""Returns dataframe head. Default is 5"""
return self.dataframe.limit(n).toPandas() | [
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SparkDFExecutionEngine._split_on_divided_integer | (
df, column_name: str, divisor: int, batch_identifiers: dict
) | Divide the values in the named column by `divisor`, and split on that | Divide the values in the named column by `divisor`, and split on that | def _split_on_divided_integer(
df, column_name: str, divisor: int, batch_identifiers: dict
):
"""Divide the values in the named column by `divisor`, and split on that"""
matching_divisor = batch_identifiers[column_name]
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SparkDFExecutionEngine._split_on_mod_integer | (df, column_name: str, mod: int, batch_identifiers: dict) | Divide the values in the named column by `divisor`, and split on that | Divide the values in the named column by `divisor`, and split on that | def _split_on_mod_integer(df, column_name: str, mod: int, batch_identifiers: dict):
"""Divide the values in the named column by `divisor`, and split on that"""
matching_mod_value = batch_identifiers[column_name]
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SparkDFExecutionEngine._split_on_multi_column_values | (df, column_names: list, batch_identifiers: dict) | Split on the joint values in the named columns | Split on the joint values in the named columns | def _split_on_multi_column_values(df, column_names: list, batch_identifiers: dict):
"""Split on the joint values in the named columns"""
for column_name in column_names:
value = batch_identifiers.get(column_name)
if not value:
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... | [
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SparkDFExecutionEngine._split_on_hashed_column | (
df,
column_name: str,
hash_digits: int,
batch_identifiers: dict,
hash_function_name: str = "sha256",
) | Split on the hashed value of the named column | Split on the hashed value of the named column | def _split_on_hashed_column(
df,
column_name: str,
hash_digits: int,
batch_identifiers: dict,
hash_function_name: str = "sha256",
):
"""Split on the hashed value of the named column"""
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SparkDFExecutionEngine._sample_using_random | (df, p: float = 0.1, seed: int = 1) | Take a random sample of rows, retaining proportion p | Take a random sample of rows, retaining proportion p | def _sample_using_random(df, p: float = 0.1, seed: int = 1):
"""Take a random sample of rows, retaining proportion p"""
res = (
df.withColumn("rand", F.rand(seed=seed))
.filter(F.col("rand") < p)
.drop("rand")
)
return res | [
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SparkDFExecutionEngine._sample_using_mod | (
df,
column_name: str,
mod: int,
value: int,
) | Take the mod of named column, and only keep rows that match the given value | Take the mod of named column, and only keep rows that match the given value | def _sample_using_mod(
df,
column_name: str,
mod: int,
value: int,
):
"""Take the mod of named column, and only keep rows that match the given value"""
res = (
df.withColumn("mod_temp", (F.col(column_name) % mod).cast(IntegerType()))
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SparkDFExecutionEngine._sample_using_a_list | (
df,
column_name: str,
value_list: list,
) | Match the values in the named column against value_list, and only keep the matches | Match the values in the named column against value_list, and only keep the matches | def _sample_using_a_list(
df,
column_name: str,
value_list: list,
):
"""Match the values in the named column against value_list, and only keep the matches"""
return df.where(F.col(column_name).isin(value_list)) | [
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regex_opt_inner | (strings, open_paren) | Return a regex that matches any string in the sorted list of strings. | Return a regex that matches any string in the sorted list of strings. | def regex_opt_inner(strings, open_paren):
"""Return a regex that matches any string in the sorted list of strings."""
close_paren = open_paren and ')' or ''
# print strings, repr(open_paren)
if not strings:
# print '-> nothing left'
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regex_opt | (strings, prefix='', suffix='') | Return a compiled regex that matches any string in the given list.
The strings to match must be literal strings, not regexes. They will be
regex-escaped.
*prefix* and *suffix* are pre- and appended to the final regex.
| Return a compiled regex that matches any string in the given list. | def regex_opt(strings, prefix='', suffix=''):
"""Return a compiled regex that matches any string in the given list.
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82,
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91,
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datasource | (ctx) | Datasource operations | Datasource operations | def datasource(ctx):
"""Datasource operations"""
directory: str = toolkit.parse_cli_config_file_location(
config_file_location=ctx.obj.config_file_location
).get("directory")
context: DataContext = toolkit.load_data_context_with_error_handling(
directory=directory,
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datasource_new | (ctx, name, jupyter) | Add a new Datasource to the data context. | Add a new Datasource to the data context. | def datasource_new(ctx, name, jupyter):
"""Add a new Datasource to the data context."""
context: DataContext = ctx.obj.data_context
usage_event_end: str = ctx.obj.usage_event_end
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delete_datasource | (ctx, datasource) | Delete the datasource specified as an argument | Delete the datasource specified as an argument | def delete_datasource(ctx, datasource):
"""Delete the datasource specified as an argument"""
context: DataContext = ctx.obj.data_context
usage_event_end: str = ctx.obj.usage_event_end
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datasource_list | (ctx) | List known Datasources. | List known Datasources. | def datasource_list(ctx):
"""List known Datasources."""
context = ctx.obj.data_context
usage_event_end: str = ctx.obj.usage_event_end
try:
datasources = context.list_datasources()
cli_message(_build_datasource_intro_string(datasources))
for datasource in datasources:
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sanitize_yaml_and_save_datasource | (
context: DataContext, datasource_yaml: str, overwrite_existing: bool = False
) | A convenience function used in notebooks to help users save secrets. | A convenience function used in notebooks to help users save secrets. | def sanitize_yaml_and_save_datasource(
context: DataContext, datasource_yaml: str, overwrite_existing: bool = False
) -> None:
"""A convenience function used in notebooks to help users save secrets."""
if not datasource_yaml:
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check_if_datasource_name_exists | (context: DataContext, datasource_name: str) |
Check if a Datasource name already exists in the on-disk version of the given DataContext and if so raise an error
Args:
context: DataContext to check for existing Datasource
datasource_name: name of the proposed Datasource
Returns:
boolean True if datasource name exists in on-disk ... |
Check if a Datasource name already exists in the on-disk version of the given DataContext and if so raise an error
Args:
context: DataContext to check for existing Datasource
datasource_name: name of the proposed Datasource
Returns:
boolean True if datasource name exists in on-disk ... | def check_if_datasource_name_exists(context: DataContext, datasource_name: str) -> bool:
"""
Check if a Datasource name already exists in the on-disk version of the given DataContext and if so raise an error
Args:
context: DataContext to check for existing Datasource
datasource_name: name of... | [
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BaseDatasourceNewYamlHelper.verify_libraries_installed | (self) | Used in the interactive CLI to help users install dependencies. | Used in the interactive CLI to help users install dependencies. | def verify_libraries_installed(self) -> bool:
"""Used in the interactive CLI to help users install dependencies."""
raise NotImplementedError | [
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BaseDatasourceNewYamlHelper.create_notebook | (self, context: DataContext) | Create a datasource_new notebook and save it to disk. | Create a datasource_new notebook and save it to disk. | def create_notebook(self, context: DataContext) -> str:
"""Create a datasource_new notebook and save it to disk."""
renderer = self.get_notebook_renderer(context)
notebook_path = os.path.join(
context.root_directory,
context.GE_UNCOMMITTED_DIR,
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BaseDatasourceNewYamlHelper.get_notebook_renderer | (self, context) | Get a renderer specifically constructed for the datasource type. | Get a renderer specifically constructed for the datasource type. | def get_notebook_renderer(self, context) -> DatasourceNewNotebookRenderer:
"""Get a renderer specifically constructed for the datasource type."""
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BaseDatasourceNewYamlHelper.prompt | (self) | Optional prompt if more information is needed before making a notebook. | Optional prompt if more information is needed before making a notebook. | def prompt(self) -> None:
"""Optional prompt if more information is needed before making a notebook."""
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BaseDatasourceNewYamlHelper.yaml_snippet | (self) | Override to create the yaml for the notebook. | Override to create the yaml for the notebook. | def yaml_snippet(self) -> str:
"""Override to create the yaml for the notebook."""
raise NotImplementedError | [
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FilesYamlHelper.yaml_snippet | (self) |
Note the InferredAssetFilesystemDataConnector was selected to get users
to data assets with minimal configuration. Other DataConnectors are
available.
|
Note the InferredAssetFilesystemDataConnector was selected to get users
to data assets with minimal configuration. Other DataConnectors are
available.
| def yaml_snippet(self) -> str:
"""
Note the InferredAssetFilesystemDataConnector was selected to get users
to data assets with minimal configuration. Other DataConnectors are
available.
"""
return f'''f"""
name: {{datasource_name}}
class_name: Datasource
execution_engine:... | [
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292,
4
] | [
315,
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SQLCredentialYamlHelper._yaml_innards | (self) | Override if needed. | Override if needed. | def _yaml_innards(self) -> str:
"""Override if needed."""
return """
credentials:
host: {host}
port: '{port}'
username: {username}
password: {password}
database: {database}""" | [
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] | [
426,
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] | [
434,
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] | python | en | ['en', 'nl', 'en'] | True |
check_one_way_stream | (stream_maker, clogged_stream_maker) | Perform a number of generic tests on a custom one-way stream
implementation.
Args:
stream_maker: An async (!) function which returns a connected
(:class:`~trio.abc.SendStream`, :class:`~trio.abc.ReceiveStream`)
pair.
clogged_stream_maker: Either None, or an async function simila... | Perform a number of generic tests on a custom one-way stream
implementation. | async def check_one_way_stream(stream_maker, clogged_stream_maker):
"""Perform a number of generic tests on a custom one-way stream
implementation.
Args:
stream_maker: An async (!) function which returns a connected
(:class:`~trio.abc.SendStream`, :class:`~trio.abc.ReceiveStream`)
... | [
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374,
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check_two_way_stream | (stream_maker, clogged_stream_maker) | Perform a number of generic tests on a custom two-way stream
implementation.
This is similar to :func:`check_one_way_stream`, except that the maker
functions are expected to return objects implementing the
:class:`~trio.abc.Stream` interface.
This function tests a *superset* of what :func:`check_o... | Perform a number of generic tests on a custom two-way stream
implementation. | async def check_two_way_stream(stream_maker, clogged_stream_maker):
"""Perform a number of generic tests on a custom two-way stream
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This is similar to :func:`check_one_way_stream`, except that the maker
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377,
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445,
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check_half_closeable_stream | (stream_maker, clogged_stream_maker) | Perform a number of generic tests on a custom half-closeable stream
implementation.
This is similar to :func:`check_two_way_stream`, except that the maker
functions are expected to return objects that implement the
:class:`~trio.abc.HalfCloseableStream` interface.
This function tests a *superset* ... | Perform a number of generic tests on a custom half-closeable stream
implementation. | async def check_half_closeable_stream(stream_maker, clogged_stream_maker):
"""Perform a number of generic tests on a custom half-closeable stream
implementation.
This is similar to :func:`check_two_way_stream`, except that the maker
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free_calldef_t._get__cmp__call_items | (self) | implementation details | implementation details | def _get__cmp__call_items(self):
"""implementation details"""
return [] | [
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free_calldef_t.function_type | (self) | returns function type. See :class:`type_t` hierarchy | returns function type. See :class:`type_t` hierarchy | def function_type(self):
"""returns function type. See :class:`type_t` hierarchy"""
return cpptypes.free_function_type_t(
return_type=self.return_type,
arguments_types=[
arg.decl_type for arg in self.arguments]) | [
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free_calldef_t.guess_calling_convention | (self) | This function should be overriden in the derived classes and return
more-or-less successfull guess about calling convention | This function should be overriden in the derived classes and return
more-or-less successfull guess about calling convention | def guess_calling_convention(self):
"""This function should be overriden in the derived classes and return
more-or-less successfull guess about calling convention"""
return calldef_types.CALLING_CONVENTION_TYPES.UNKNOWN | [
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free_operator_t.class_types | (self) | list of class/class declaration types, extracted from the
operator arguments | list of class/class declaration types, extracted from the
operator arguments | def class_types(self):
"""list of class/class declaration types, extracted from the
operator arguments"""
if None is self.__class_types:
self.__class_types = []
for type_ in self.argument_types:
decl = None
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test_profiler_init_no_config | (
cardinality_dataset,
) |
What does this test do and why?
Confirms that profiler can initialize with no config.
|
What does this test do and why?
Confirms that profiler can initialize with no config.
| def test_profiler_init_no_config(
cardinality_dataset,
):
"""
What does this test do and why?
Confirms that profiler can initialize with no config.
"""
profiler = UserConfigurableProfiler(cardinality_dataset)
assert profiler.primary_or_compound_key == []
assert profiler.ignored_columns =... | [
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test_profiler_init_full_config_no_semantic_types | (cardinality_dataset) |
What does this test do and why?
Confirms that profiler initializes properly with a full config, without a semantic_types dict
|
What does this test do and why?
Confirms that profiler initializes properly with a full config, without a semantic_types dict
| def test_profiler_init_full_config_no_semantic_types(cardinality_dataset):
"""
What does this test do and why?
Confirms that profiler initializes properly with a full config, without a semantic_types dict
"""
profiler = UserConfigurableProfiler(
cardinality_dataset,
primary_or_compo... | [
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test_init_with_semantic_types | (cardinality_dataset) |
What does this test do and why?
Confirms that profiler initializes properly with a full config and a semantic_types dict
|
What does this test do and why?
Confirms that profiler initializes properly with a full config and a semantic_types dict
| def test_init_with_semantic_types(cardinality_dataset):
"""
What does this test do and why?
Confirms that profiler initializes properly with a full config and a semantic_types dict
"""
semantic_types = {
"numeric": ["col_few", "col_many", "col_very_many"],
"value_set": ["col_two", "... | [
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173,
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test__validate_config | (cardinality_dataset) |
What does this test do and why?
Tests the validate config function on the profiler
|
What does this test do and why?
Tests the validate config function on the profiler
| def test__validate_config(cardinality_dataset):
"""
What does this test do and why?
Tests the validate config function on the profiler
"""
with pytest.raises(AssertionError) as e:
UserConfigurableProfiler(cardinality_dataset, ignored_columns="col_name")
assert e.typename == "AssertionEr... | [
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test_value_set_threshold | (cardinality_dataset) |
What does this test do and why?
Tests the value_set_threshold logic on the profiler works as expected.
|
What does this test do and why?
Tests the value_set_threshold logic on the profiler works as expected.
| def test_value_set_threshold(cardinality_dataset):
"""
What does this test do and why?
Tests the value_set_threshold logic on the profiler works as expected.
"""
# Test that when value_set_threshold is unset, it will default to "MANY"
profiler = UserConfigurableProfiler(cardinality_dataset)
... | [
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test__validate_semantic_types_dict | (cardinality_dataset) |
What does this test do and why?
Tests that _validate_semantic_types_dict function errors when not formatted correctly
|
What does this test do and why?
Tests that _validate_semantic_types_dict function errors when not formatted correctly
| def test__validate_semantic_types_dict(cardinality_dataset):
"""
What does this test do and why?
Tests that _validate_semantic_types_dict function errors when not formatted correctly
"""
bad_semantic_types_dict_type = {"value_set": "col_few"}
with pytest.raises(AssertionError) as e:
Use... | [
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"UserConfigurableProfiler"... | [
219,
0
] | [
256,
5
] | python | en | ['en', 'error', 'th'] | False |
test_build_suite_no_config | (titanic_dataset, possible_expectations_set) |
What does this test do and why?
Tests that the build_suite function works as expected with no config
|
What does this test do and why?
Tests that the build_suite function works as expected with no config
| def test_build_suite_no_config(titanic_dataset, possible_expectations_set):
"""
What does this test do and why?
Tests that the build_suite function works as expected with no config
"""
profiler = UserConfigurableProfiler(titanic_dataset)
suite = profiler.build_suite()
expectations_from_suite... | [
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259,
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269,
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test_build_suite_with_config_and_no_semantic_types_dict | (
titanic_dataset, possible_expectations_set
) |
What does this test do and why?
Tests that the build_suite function works as expected with a config and without a semantic_types dict
|
What does this test do and why?
Tests that the build_suite function works as expected with a config and without a semantic_types dict
| def test_build_suite_with_config_and_no_semantic_types_dict(
titanic_dataset, possible_expectations_set
):
"""
What does this test do and why?
Tests that the build_suite function works as expected with a config and without a semantic_types dict
"""
profiler = UserConfigurableProfiler(
ti... | [
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272,
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297,
40
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test_build_suite_with_semantic_types_dict | (
cardinality_dataset,
possible_expectations_set,
) |
What does this test do and why?
Tests that the build_suite function works as expected with a semantic_types dict
|
What does this test do and why?
Tests that the build_suite function works as expected with a semantic_types dict
| def test_build_suite_with_semantic_types_dict(
cardinality_dataset,
possible_expectations_set,
):
"""
What does this test do and why?
Tests that the build_suite function works as expected with a semantic_types dict
"""
semantic_types = {
"numeric": ["col_few", "col_many", "col_very_... | [
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... | [
300,
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342,
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test_build_suite_when_suite_already_exists | (cardinality_dataset) |
What does this test do and why?
Confirms that creating a new suite on an existing profiler wipes the previous suite
|
What does this test do and why?
Confirms that creating a new suite on an existing profiler wipes the previous suite
| def test_build_suite_when_suite_already_exists(cardinality_dataset):
"""
What does this test do and why?
Confirms that creating a new suite on an existing profiler wipes the previous suite
"""
profiler = UserConfigurableProfiler(
cardinality_dataset,
table_expectations_only=True,
... | [
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345,
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test_primary_or_compound_key_not_found_in_columns | (cardinality_dataset) |
What does this test do and why?
Confirms that an error is raised if a primary_or_compound key is specified with a column not found in the dataset
|
What does this test do and why?
Confirms that an error is raised if a primary_or_compound key is specified with a column not found in the dataset
| def test_primary_or_compound_key_not_found_in_columns(cardinality_dataset):
"""
What does this test do and why?
Confirms that an error is raised if a primary_or_compound key is specified with a column not found in the dataset
"""
# regular case, should pass
working_profiler = UserConfigurablePro... | [
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368,
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] | [
396,
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test_config_with_not_null_only | (nulls_dataset, possible_expectations_set) |
What does this test do and why?
Confirms that the not_null_only key in config works as expected.
|
What does this test do and why?
Confirms that the not_null_only key in config works as expected.
| def test_config_with_not_null_only(nulls_dataset, possible_expectations_set):
"""
What does this test do and why?
Confirms that the not_null_only key in config works as expected.
"""
excluded_expectations = [i for i in possible_expectations_set if "null" not in i]
batch_df = nulls_dataset
... | [
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] | [
433,
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test_profiled_dataset_passes_own_validation | (
cardinality_dataset, titanic_data_context
) |
What does this test do and why?
Confirms that a suite created on a dataset with no config will pass when validated against itself
|
What does this test do and why?
Confirms that a suite created on a dataset with no config will pass when validated against itself
| def test_profiled_dataset_passes_own_validation(
cardinality_dataset, titanic_data_context
):
"""
What does this test do and why?
Confirms that a suite created on a dataset with no config will pass when validated against itself
"""
context = titanic_data_context
profiler = UserConfigurablePr... | [
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452,
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470,
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test_profiler_all_expectation_types | (
titanic_data_context, possible_expectations_set
) |
What does this test do and why?
Ensures that all available expectation types work as expected
|
What does this test do and why?
Ensures that all available expectation types work as expected
| def test_profiler_all_expectation_types(
titanic_data_context, possible_expectations_set
):
"""
What does this test do and why?
Ensures that all available expectation types work as expected
"""
context = titanic_data_context
df = ge.read_csv(
file_relative_path(
__file__,... | [
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"\"../test_sets/taxi_yellow_trip... | [
473,
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] | [
551,
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] | python | en | ['en', 'error', 'th'] | False |
regression_errors | (y, y_hat, smoothing_window=0.01, smooth=True) | Compute an array of absolute errors comparing predictions and expected output.
If smooth is True, apply EWMA to the resulting array of errors.
Args:
y (ndarray):
Ground truth.
y_hat (ndarray):
Predicted values.
smoothing_window (float):
Optional. Siz... | Compute an array of absolute errors comparing predictions and expected output. | def regression_errors(y, y_hat, smoothing_window=0.01, smooth=True):
"""Compute an array of absolute errors comparing predictions and expected output.
If smooth is True, apply EWMA to the resulting array of errors.
Args:
y (ndarray):
Ground truth.
y_hat (ndarray):
P... | [
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_point_wise_error | (y, y_hat) | Compute point-wise error between predicted and expected values.
The computed error is calculated as the difference between predicted
and expected values with a rolling smoothing factor.
Args:
y (ndarray):
Ground truth.
y_hat (ndarray):
Predicted values.
Returns... | Compute point-wise error between predicted and expected values. | def _point_wise_error(y, y_hat):
"""Compute point-wise error between predicted and expected values.
The computed error is calculated as the difference between predicted
and expected values with a rolling smoothing factor.
Args:
y (ndarray):
Ground truth.
y_hat (ndarray):
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_area_error | (y, y_hat, score_window=10) | Compute area error between predicted and expected values.
The computed error is calculated as the area difference between predicted
and expected values with a smoothing factor.
Args:
y (ndarray):
Ground truth.
y_hat (ndarray):
Predicted values.
score_window ... | Compute area error between predicted and expected values. | def _area_error(y, y_hat, score_window=10):
"""Compute area error between predicted and expected values.
The computed error is calculated as the area difference between predicted
and expected values with a smoothing factor.
Args:
y (ndarray):
Ground truth.
y_hat (ndarray):
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_dtw_error | (y, y_hat, score_window=10) | Compute dtw error between predicted and expected values.
The computed error is calculated as the dynamic time warping distance
between predicted and expected values with a smoothing factor.
Args:
y (ndarray):
Ground truth.
y_hat (ndarray):
Predicted values.
... | Compute dtw error between predicted and expected values. | def _dtw_error(y, y_hat, score_window=10):
"""Compute dtw error between predicted and expected values.
The computed error is calculated as the dynamic time warping distance
between predicted and expected values with a smoothing factor.
Args:
y (ndarray):
Ground truth.
y_hat... | [
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reconstruction_errors | (y, y_hat, step_size=1, score_window=10, smoothing_window=0.01,
smooth=True, rec_error_type='point') | Compute an array of reconstruction errors.
Compute the discrepancies between the expected and the
predicted values according to the reconstruction error type.
Args:
y (ndarray):
Ground truth.
y_hat (ndarray):
Predicted values. Each timestamp has multiple predictions... | Compute an array of reconstruction errors. | def reconstruction_errors(y, y_hat, step_size=1, score_window=10, smoothing_window=0.01,
smooth=True, rec_error_type='point'):
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Compute the discrepancies between the expected and the
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mpjpe | (predicted, target) |
Mean per-joint position error (i.e. mean Euclidean distance),
often referred to as "Protocol #1" in many papers.
|
Mean per-joint position error (i.e. mean Euclidean distance),
often referred to as "Protocol #1" in many papers.
| def mpjpe(predicted, target):
"""
Mean per-joint position error (i.e. mean Euclidean distance),
often referred to as "Protocol #1" in many papers.
"""
assert predicted.shape == target.shape
#l2_error = torch.mean(torch.norm((predicted - target), dim=len(target.shape) - 1), -1).squeeze()
#pri... | [
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13,
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] | [
24,
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mpjae | (predicted, target) |
Mean per-joint angle error (3d bone vector angle error between gt and predicted one)
|
Mean per-joint angle error (3d bone vector angle error between gt and predicted one)
| def mpjae(predicted, target):
"""
Mean per-joint angle error (3d bone vector angle error between gt and predicted one)
"""
assert predicted.shape == target.shape # [B,T, K]
joint_error = torch.mean(torch.abs(predicted - target).cuda(), dim=0) # Calculate each joint angle
print('each bone angle... | [
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mpjpe_smooth | (predicted, target, threshold, mi, L1) |
Referred in triangulation 3d pose paper
|
Referred in triangulation 3d pose paper
| def mpjpe_smooth(predicted, target, threshold, mi, L1):
"""
Referred in triangulation 3d pose paper
"""
assert predicted.shape == target.shape
if L1:
diff_norm = torch.abs((predicted - target), dim=len(target.shape) - 1)
diff = diff_norm.clone()
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diff = (predi... | [
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p_mpjpe | (predicted, target) |
Pose error: MPJPE after rigid alignment (scale, rotation, and translation),
often referred to as "Protocol #2" in many papers.
|
Pose error: MPJPE after rigid alignment (scale, rotation, and translation),
often referred to as "Protocol #2" in many papers.
| def p_mpjpe(predicted, target):
"""
Pose error: MPJPE after rigid alignment (scale, rotation, and translation),
often referred to as "Protocol #2" in many papers.
"""
assert predicted.shape == target.shape # (3071, 17, 3)
muX = np.mean(target, axis=1, keepdims=True)
muY = np.mean(predicted,... | [
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n_mpjpe | (predicted, target) |
Normalized MPJPE (scale only), adapted from:
https://github.com/hrhodin/UnsupervisedGeometryAwareRepresentationLearning/blob/master/losses/poses.py
|
Normalized MPJPE (scale only), adapted from:
https://github.com/hrhodin/UnsupervisedGeometryAwareRepresentationLearning/blob/master/losses/poses.py
| def n_mpjpe(predicted, target):
"""
Normalized MPJPE (scale only), adapted from:
https://github.com/hrhodin/UnsupervisedGeometryAwareRepresentationLearning/blob/master/losses/poses.py
"""
assert predicted.shape == target.shape # [1, 1703, 17, 3]
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mean_velocity_error | (predicted, target) |
Mean per-joint velocity error (i.e. mean Euclidean distance of the 1st derivative)
|
Mean per-joint velocity error (i.e. mean Euclidean distance of the 1st derivative)
| def mean_velocity_error(predicted, target):
"""
Mean per-joint velocity error (i.e. mean Euclidean distance of the 1st derivative)
"""
assert predicted.shape == target.shape
velocity_predicted = np.diff(predicted, axis=0)
velocity_target = np.diff(target, axis=0)
return np.mean(np.linalg.nor... | [
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WindowGenerator.example | (self) | Get and cache an example batch of `inputs, labels` for plotting. | Get and cache an example batch of `inputs, labels` for plotting. | def example(self):
"""Get and cache an example batch of `inputs, labels` for plotting."""
result = getattr(self, '_example', None)
if result is None:
# No example batch was found, so get one from the `.train` dataset
result = next(iter(self.train))
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datasource | () | Datasource operations | Datasource operations | def datasource():
"""Datasource operations"""
pass | [
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datasource_new | (directory) | Add a new datasource to the data context. | Add a new datasource to the data context. | def datasource_new(directory):
"""Add a new datasource to the data context."""
context = toolkit.load_data_context_with_error_handling(directory)
datasource_name, data_source_type = add_datasource(context)
if datasource_name:
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delete_datasource | (directory, datasource) | Delete the datasource specified as an argument | Delete the datasource specified as an argument | def delete_datasource(directory, datasource):
"""Delete the datasource specified as an argument"""
context = toolkit.load_data_context_with_error_handling(directory)
try:
context.delete_datasource(datasource)
except ValueError:
cli_message(
"<red>{}</red>".format(
... | [
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datasource_list | (directory) | List known datasources. | List known datasources. | def datasource_list(directory):
"""List known datasources."""
context = toolkit.load_data_context_with_error_handling(directory)
datasources = context.list_datasources()
datasource_count = len(datasources)
if datasource_count == 0:
list_intro_string = "No Datasources found"
else:
... | [
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datasource_profile | (
datasource,
batch_kwargs_generator_name,
data_assets,
profile_all_data_assets,
directory,
view,
additional_batch_kwargs,
assume_yes,
) |
Profile a datasource (Experimental)
If the optional data_assets and profile_all_data_assets arguments are not specified, the profiler will check
if the number of data assets in the datasource exceeds the internally defined limit. If it does, it will
prompt the user to either specify the list of data a... |
Profile a datasource (Experimental) | def datasource_profile(
datasource,
batch_kwargs_generator_name,
data_assets,
profile_all_data_assets,
directory,
view,
additional_batch_kwargs,
assume_yes,
):
"""
Profile a datasource (Experimental)
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296,
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add_datasource | (context, choose_one_data_asset=False) |
Interactive flow for adding a datasource to an existing context.
:param context:
:param choose_one_data_asset: optional - if True, this signals the method that the intent
is to let user choose just one data asset (e.g., a file) and there is no need
to configure a batch kwargs gener... |
Interactive flow for adding a datasource to an existing context. | def add_datasource(context, choose_one_data_asset=False):
"""
Interactive flow for adding a datasource to an existing context.
:param context:
:param choose_one_data_asset: optional - if True, this signals the method that the intent
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] | [
355,
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] | python | en | ['en', 'error', 'th'] | False |
_should_hide_input | () |
This is a workaround to help identify Windows and adjust the prompts accordingly
since hidden prompts may freeze in certain Windows terminals
|
This is a workaround to help identify Windows and adjust the prompts accordingly
since hidden prompts may freeze in certain Windows terminals
| def _should_hide_input():
"""
This is a workaround to help identify Windows and adjust the prompts accordingly
since hidden prompts may freeze in certain Windows terminals
"""
if "windows" in platform.platform().lower():
return False
return True | [
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get_batch_kwargs | (
context,
datasource_name=None,
batch_kwargs_generator_name=None,
data_asset_name=None,
additional_batch_kwargs=None,
) |
This method manages the interaction with user necessary to obtain batch_kwargs for a batch of a data asset.
In order to get batch_kwargs this method needs datasource_name, batch_kwargs_generator_name and data_asset_name
to combine them into a fully-qualified data asset identifier(datasource_name/batch_kwa... |
This method manages the interaction with user necessary to obtain batch_kwargs for a batch of a data asset. | def get_batch_kwargs(
context,
datasource_name=None,
batch_kwargs_generator_name=None,
data_asset_name=None,
additional_batch_kwargs=None,
):
"""
This method manages the interaction with user necessary to obtain batch_kwargs for a batch of a data asset.
In order to get batch_kwargs this... | [
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profile_datasource | (
context,
datasource_name,
batch_kwargs_generator_name=None,
data_assets=None,
profile_all_data_assets=False,
max_data_assets=20,
additional_batch_kwargs=None,
open_docs=False,
skip_prompt_flag=False,
) | Profile a named datasource using the specified context | Profile a named datasource using the specified context | def profile_datasource(
context,
datasource_name,
batch_kwargs_generator_name=None,
data_assets=None,
profile_all_data_assets=False,
max_data_assets=20,
additional_batch_kwargs=None,
open_docs=False,
skip_prompt_flag=False,
):
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_fn_matches | (fn, glob) | Return whether the supplied file name fn matches pattern filename. | Return whether the supplied file name fn matches pattern filename. | def _fn_matches(fn, glob):
"""Return whether the supplied file name fn matches pattern filename."""
if glob not in _pattern_cache:
pattern = _pattern_cache[glob] = re.compile(fnmatch.translate(glob))
return pattern.match(fn)
return _pattern_cache[glob].match(fn) | [
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_load_lexers | (module_name) | Load a lexer (and all others in the module too). | Load a lexer (and all others in the module too). | def _load_lexers(module_name):
"""Load a lexer (and all others in the module too)."""
mod = __import__(module_name, None, None, ['__all__'])
for lexer_name in mod.__all__:
cls = getattr(mod, lexer_name)
_lexer_cache[cls.name] = cls | [
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get_all_lexers | () | Return a generator of tuples in the form ``(name, aliases,
filenames, mimetypes)`` of all know lexers.
| Return a generator of tuples in the form ``(name, aliases,
filenames, mimetypes)`` of all know lexers.
| def get_all_lexers():
"""Return a generator of tuples in the form ``(name, aliases,
filenames, mimetypes)`` of all know lexers.
"""
for item in itervalues(LEXERS):
yield item[1:]
for lexer in find_plugin_lexers():
yield lexer.name, lexer.aliases, lexer.filenames, lexer.mimetypes | [
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find_lexer_class | (name) | Lookup a lexer class by name.
Return None if not found.
| Lookup a lexer class by name. | def find_lexer_class(name):
"""Lookup a lexer class by name.
Return None if not found.
"""
if name in _lexer_cache:
return _lexer_cache[name]
# lookup builtin lexers
for module_name, lname, aliases, _, _ in itervalues(LEXERS):
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get_lexer_by_name | (_alias, **options) | Get a lexer by an alias.
Raises ClassNotFound if not found.
| Get a lexer by an alias. | def get_lexer_by_name(_alias, **options):
"""Get a lexer by an alias.
Raises ClassNotFound if not found.
"""
if not _alias:
raise ClassNotFound('no lexer for alias %r found' % _alias)
# lookup builtin lexers
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if _alias.... | [
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find_lexer_class_for_filename | (_fn, code=None) | Get a lexer for a filename.
If multiple lexers match the filename pattern, use ``analyse_text()`` to
figure out which one is more appropriate.
Returns None if not found.
| Get a lexer for a filename. | def find_lexer_class_for_filename(_fn, code=None):
"""Get a lexer for a filename.
If multiple lexers match the filename pattern, use ``analyse_text()`` to
figure out which one is more appropriate.
Returns None if not found.
"""
matches = []
fn = basename(_fn)
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get_lexer_for_filename | (_fn, code=None, **options) | Get a lexer for a filename.
If multiple lexers match the filename pattern, use ``analyse_text()`` to
figure out which one is more appropriate.
Raises ClassNotFound if not found.
| Get a lexer for a filename. | def get_lexer_for_filename(_fn, code=None, **options):
"""Get a lexer for a filename.
If multiple lexers match the filename pattern, use ``analyse_text()`` to
figure out which one is more appropriate.
Raises ClassNotFound if not found.
"""
res = find_lexer_class_for_filename(_fn, code)
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get_lexer_for_mimetype | (_mime, **options) | Get a lexer for a mimetype.
Raises ClassNotFound if not found.
| Get a lexer for a mimetype. | def get_lexer_for_mimetype(_mime, **options):
"""Get a lexer for a mimetype.
Raises ClassNotFound if not found.
"""
for modname, name, _, _, mimetypes in itervalues(LEXERS):
if _mime in mimetypes:
if name not in _lexer_cache:
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_iter_lexerclasses | (plugins=True) | Return an iterator over all lexer classes. | Return an iterator over all lexer classes. | def _iter_lexerclasses(plugins=True):
"""Return an iterator over all lexer classes."""
for key in sorted(LEXERS):
module_name, name = LEXERS[key][:2]
if name not in _lexer_cache:
_load_lexers(module_name)
yield _lexer_cache[name]
if plugins:
for lexer in find_plug... | [
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guess_lexer_for_filename | (_fn, _text, **options) |
Lookup all lexers that handle those filenames primary (``filenames``)
or secondary (``alias_filenames``). Then run a text analysis for those
lexers and choose the best result.
usage::
>>> from pygments.lexers import guess_lexer_for_filename
>>> guess_lexer_for_filename('hello.html', '... |
Lookup all lexers that handle those filenames primary (``filenames``)
or secondary (``alias_filenames``). Then run a text analysis for those
lexers and choose the best result. | def guess_lexer_for_filename(_fn, _text, **options):
"""
Lookup all lexers that handle those filenames primary (``filenames``)
or secondary (``alias_filenames``). Then run a text analysis for those
lexers and choose the best result.
usage::
>>> from pygments.lexers import guess_lexer_for_f... | [
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guess_lexer | (_text, **options) | Guess a lexer by strong distinctions in the text (eg, shebang). | Guess a lexer by strong distinctions in the text (eg, shebang). | def guess_lexer(_text, **options):
"""Guess a lexer by strong distinctions in the text (eg, shebang)."""
# try to get a vim modeline first
ft = get_filetype_from_buffer(_text)
if ft is not None:
try:
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XvfbDisplay.__init__ | (self, size=(1024, 768), color_depth=24,
bgcolor='black', fbdir=None) |
:param bgcolor: 'black' or 'white'
:param fbdir: If non-null, the virtual screen is memory-mapped
to a file in the given directory ('-fbdir' option)
|
:param bgcolor: 'black' or 'white'
:param fbdir: If non-null, the virtual screen is memory-mapped
to a file in the given directory ('-fbdir' option)
| def __init__(self, size=(1024, 768), color_depth=24,
bgcolor='black', fbdir=None):
'''
:param bgcolor: 'black' or 'white'
:param fbdir: If non-null, the virtual screen is memory-mapped
to a file in the given directory ('-fbdir' option)
'''
self.screen... | [
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TIMESAT_stats | (dataarray, time_dim='time') |
For a 1D array of values for a vegetation index - for which higher values tend to
indicate more vegetation - determine several statistics:
1. Beginning of Season (BOS): The time index of the beginning of the growing season.
(The downward inflection point before the maximum vegetation index value)
... |
For a 1D array of values for a vegetation index - for which higher values tend to
indicate more vegetation - determine several statistics:
1. Beginning of Season (BOS): The time index of the beginning of the growing season.
(The downward inflection point before the maximum vegetation index value)
... | def TIMESAT_stats(dataarray, time_dim='time'):
"""
For a 1D array of values for a vegetation index - for which higher values tend to
indicate more vegetation - determine several statistics:
1. Beginning of Season (BOS): The time index of the beginning of the growing season.
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IRIPAllowDeny.__init__ | (self, ir: 'IR', aconf: Config,
rkey: str="ir.ipallowdeny",
name: str="ir.ipallowdeny",
kind: str="IRIPAllowDeny",
parent: IRResource=None,
action: str=None,
**kwargs) |
Initialize an IRIPAllowDeny. In addition to the usual IRFilter parameters,
parent and action are required:
parent is the IRResource in which the IRIPAllowDeny is defined; at present,
this will be the Ambassador module. It's required because it's where errors
should be posted.
... |
Initialize an IRIPAllowDeny. In addition to the usual IRFilter parameters,
parent and action are required: | def __init__(self, ir: 'IR', aconf: Config,
rkey: str="ir.ipallowdeny",
name: str="ir.ipallowdeny",
kind: str="IRIPAllowDeny",
parent: IRResource=None,
action: str=None,
**kwargs) -> None:
"""
Initializ... | [
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IRIPAllowDeny.setup | (self, ir: 'IR', aconf: Config) |
Set up an IRIPAllowDeny based on the action and principals passed into
__init__.
|
Set up an IRIPAllowDeny based on the action and principals passed into
__init__.
| def setup(self, ir: 'IR', aconf: Config) -> bool:
"""
Set up an IRIPAllowDeny based on the action and principals passed into
__init__.
"""
assert self.parent
# These pops will crash if the action or principals are missing. That's
# OK -- they're required element... | [
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"action",
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"... | [
54,
4
] | [
119,
24
] | python | en | ['en', 'error', 'th'] | False |
complex_flat_schema | () | This includes some descriptions. | This includes some descriptions. | def complex_flat_schema():
"""This includes some descriptions."""
return {
"$id": "https://example.com/address.schema.json",
"$schema": "http://json-schema.org/draft-07/schema#",
"description": "An address",
"type": "object",
"properties": {
"post-office-box":... | [
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",",
"\"description\"",
":",
"\"An address\"",
",",
"\"type\"",
":",
"\"object\... | [
22,
0
] | [
42,
5
] | python | en | ['en', 'en', 'en'] | True |
string_lengths_schema | () |
This fixture has various combinations string lengths.
https://json-schema.org/understanding-json-schema/reference/string.html#length
|
This fixture has various combinations string lengths.
https://json-schema.org/understanding-json-schema/reference/string.html#length
| def string_lengths_schema():
"""
This fixture has various combinations string lengths.
https://json-schema.org/understanding-json-schema/reference/string.html#length
"""
return {
"$id": "https://example.com/address.schema.json",
"$schema": "http://json-schema.org/draft-07/schema#",
... | [
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",",
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":",
"\"object\"",
",",
"\"properties\"",
":",
"{",
"\"com... | [
95,
0
] | [
126,
5
] | python | en | ['en', 'error', 'th'] | False |
integer_ranges_schema | () |
This fixture has various combinations of integer ranges.
https://json-schema.org/understanding-json-schema/reference/numeric.html#range
|
This fixture has various combinations of integer ranges.
https://json-schema.org/understanding-json-schema/reference/numeric.html#range
| def integer_ranges_schema():
"""
This fixture has various combinations of integer ranges.
https://json-schema.org/understanding-json-schema/reference/numeric.html#range
"""
return {
"$id": "https://example.com/address.schema.json",
"$schema": "http://json-schema.org/draft-07/schema#"... | [
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",",
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":",
"\"An address similar to http://microformats.org/wiki/h-... | [
130,
0
] | [
165,
5
] | python | en | ['en', 'error', 'th'] | False |
number_ranges_schema | () |
This fixture has various combinations of number ranges.
https://json-schema.org/understanding-json-schema/reference/numeric.html#range
|
This fixture has various combinations of number ranges.
https://json-schema.org/understanding-json-schema/reference/numeric.html#range
| def number_ranges_schema():
"""
This fixture has various combinations of number ranges.
https://json-schema.org/understanding-json-schema/reference/numeric.html#range
"""
return {
"$id": "https://example.com/address.schema.json",
"$schema": "http://json-schema.org/draft-07/schema#",
... | [
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",",
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":",
"\"An address similar to http://microformats.org/wiki/h-c... | [
169,
0
] | [
204,
5
] | python | en | ['en', 'error', 'th'] | False |
null_fields_schema | () |
This fixture has null fields.
https://json-schema.org/understanding-json-schema/reference/null.html
|
This fixture has null fields.
https://json-schema.org/understanding-json-schema/reference/null.html
| def null_fields_schema():
"""
This fixture has null fields.
https://json-schema.org/understanding-json-schema/reference/null.html
"""
return {
"$id": "https://example.com/null.schema.json",
"$schema": "http://json-schema.org/draft-07/schema#",
"type": "object",
"prope... | [
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",",
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"\"object\"",
",",
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":",
"{",
"\"null\"",
... | [
208,
0
] | [
224,
5
] | python | en | ['en', 'error', 'th'] | False |
open_tcp_listeners | (port, *, host=None, backlog=None) | Create :class:`SocketListener` objects to listen for TCP connections.
Args:
port (int): The port to listen on.
If you use 0 as your port, then the kernel will automatically pick
an arbitrary open port. But be careful: if you use this feature when
binding to multiple IP address... | Create :class:`SocketListener` objects to listen for TCP connections. | async def open_tcp_listeners(port, *, host=None, backlog=None):
"""Create :class:`SocketListener` objects to listen for TCP connections.
Args:
port (int): The port to listen on.
If you use 0 as your port, then the kernel will automatically pick
an arbitrary open port. But be careful... | [
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"# getaddrinfo sometimes allows port=None, sometimes not (depending on",
"# whether host=None). And on some systems it treats \"\" as 0, others it",
"# ... | [
44,
0
] | [
142,
20
] | python | en | ['en', 'en', 'en'] | True |
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