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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
open_stream_to_socket_listener | (socket_listener) | Connect to the given :class:`~trio.SocketListener`.
This is particularly useful in tests when you want to let a server pick
its own port, and then connect to it::
listeners = await trio.open_tcp_listeners(0)
client = await trio.testing.open_stream_to_socket_listener(listeners[0])
Args:
... | Connect to the given :class:`~trio.SocketListener`. | async def open_stream_to_socket_listener(socket_listener):
"""Connect to the given :class:`~trio.SocketListener`.
This is particularly useful in tests when you want to let a server pick
its own port, and then connect to it::
listeners = await trio.open_tcp_listeners(0)
client = await trio.... | [
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Compile | (logger: logging.Logger, input_text: str,
cache: Optional[Cache]=None,
file_checker: Optional[IRFileChecker]=None,
secret_handler: Optional[SecretHandler]=None,
k8s=False, envoy_version="V2") |
Compile is a helper function to take a bunch of YAML and compile it into an
IR and, optionally, an Envoy config.
The output is a dictionary:
{
"ir": the IR data structure
}
IFF v2 is True, there will be a toplevel "v2" key whose value is the Envoy
V2 config.
:param input_tex... |
Compile is a helper function to take a bunch of YAML and compile it into an
IR and, optionally, an Envoy config. | def Compile(logger: logging.Logger, input_text: str,
cache: Optional[Cache]=None,
file_checker: Optional[IRFileChecker]=None,
secret_handler: Optional[SecretHandler]=None,
k8s=False, envoy_version="V2") -> Dict[str, Union[IR, EnvoyConfig]]:
"""
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simple_tmle | (y, w, q0w, q1w, p, alpha=0.0001) | Calculate the ATE and variances with the simplified TMLE method.
Args:
y (numpy.array): an outcome vector
w (numpy.array): a treatment vector
q0w (numpy.array): an outcome prediction vector given no treatment
q1w (numpy.array): an outcome prediction vector given treatment
p ... | Calculate the ATE and variances with the simplified TMLE method. | def simple_tmle(y, w, q0w, q1w, p, alpha=0.0001):
"""Calculate the ATE and variances with the simplified TMLE method.
Args:
y (numpy.array): an outcome vector
w (numpy.array): a treatment vector
q0w (numpy.array): an outcome prediction vector given no treatment
q1w (numpy.array)... | [
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TMLELearner.__init__ | (self, learner, ate_alpha=.05, control_name=0, cv=None, calibrate_propensity=True) | Initialize a TMLE learner.
Args:
learner: a model to estimate the outcome
ate_alpha (float, optional): the confidence level alpha of the ATE estimate
control_name (str or int, optional): the name of the control group
cv (sklearn.model_selection._BaseKFold, option... | Initialize a TMLE learner. | def __init__(self, learner, ate_alpha=.05, control_name=0, cv=None, calibrate_propensity=True):
"""Initialize a TMLE learner.
Args:
learner: a model to estimate the outcome
ate_alpha (float, optional): the confidence level alpha of the ATE estimate
control_name (str ... | [
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TMLELearner.estimate_ate | (self, X, treatment, y, p, segment=None, return_ci=False) | Estimate the Average Treatment Effect (ATE).
Args:
X (np.matrix or np.array or pd.Dataframe): a feature matrix
treatment (np.array or pd.Series): a treatment vector
y (np.array or pd.Series): an outcome vector
p (np.ndarray or pd.Series or dict): an array of prop... | Estimate the Average Treatment Effect (ATE). | def estimate_ate(self, X, treatment, y, p, segment=None, return_ci=False):
"""Estimate the Average Treatment Effect (ATE).
Args:
X (np.matrix or np.array or pd.Dataframe): a feature matrix
treatment (np.array or pd.Series): a treatment vector
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UpliftTreeClassifier.fit | (self, X, treatment, y) | Fit the uplift model.
Args
----
X : ndarray, shape = [num_samples, num_features]
An ndarray of the covariates used to train the uplift model.
treatment : array-like, shape = [num_samples]
An array containing the treatment group for each unit.
y : array... | Fit the uplift model. | def fit(self, X, treatment, y):
""" Fit the uplift model.
Args
----
X : ndarray, shape = [num_samples, num_features]
An ndarray of the covariates used to train the uplift model.
treatment : array-like, shape = [num_samples]
An array containing the treatm... | [
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UpliftTreeClassifier.prune | (self, X, treatment, y, minGain=0.0001, rule='maxAbsDiff') | Prune the uplift model.
Args
----
X : ndarray, shape = [num_samples, num_features]
An ndarray of the covariates used to train the uplift model.
treatment : array-like, shape = [num_samples]
An array containing the treatment group for each unit.
y : array-... | Prune the uplift model.
Args
----
X : ndarray, shape = [num_samples, num_features]
An ndarray of the covariates used to train the uplift model.
treatment : array-like, shape = [num_samples]
An array containing the treatment group for each unit.
y : array-... | def prune(self, X, treatment, y, minGain=0.0001, rule='maxAbsDiff'):
""" Prune the uplift model.
Args
----
X : ndarray, shape = [num_samples, num_features]
An ndarray of the covariates used to train the uplift model.
treatment : array-like, shape = [num_samples]
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UpliftTreeClassifier.pruneTree | (self, X, treatment, y, tree, rule='maxAbsDiff', minGain=0.,
evaluationFunction=None, notify=False, n_reg=0,
parentNodeSummary=None) | Prune one single tree node in the uplift model.
Args
----
X : ndarray, shape = [num_samples, num_features]
An ndarray of the covariates used to train the uplift model.
treatment : array-like, shape = [num_samples]
An array containing the treatment group for each u... | Prune one single tree node in the uplift model.
Args
----
X : ndarray, shape = [num_samples, num_features]
An ndarray of the covariates used to train the uplift model.
treatment : array-like, shape = [num_samples]
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evaluationFunction=None, notify=False, n_reg=0,
parentNodeSummary=None):
"""Prune one single tree node in the uplift model.
Args
----
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UpliftTreeClassifier.fill | (self, X, treatment, y) | Fill the data into an existing tree.
This is a higher-level function to transform the original data inputs
into lower level data inputs (list of list and tree).
Args
----
X : ndarray, shape = [num_samples, num_features]
An ndarray of the covariates used to train the... | Fill the data into an existing tree.
This is a higher-level function to transform the original data inputs
into lower level data inputs (list of list and tree). | def fill(self, X, treatment, y):
""" Fill the data into an existing tree.
This is a higher-level function to transform the original data inputs
into lower level data inputs (list of list and tree).
Args
----
X : ndarray, shape = [num_samples, num_features]
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UpliftTreeClassifier.fillTree | (self, X, treatment, y, tree) | Fill the data into an existing tree.
This is a lower-level function to execute on the tree filling task.
Args
----
X : ndarray, shape = [num_samples, num_features]
An ndarray of the covariates used to train the uplift model.
treatment : array-like, shape = [num_samp... | Fill the data into an existing tree.
This is a lower-level function to execute on the tree filling task. | def fillTree(self, X, treatment, y, tree):
""" Fill the data into an existing tree.
This is a lower-level function to execute on the tree filling task.
Args
----
X : ndarray, shape = [num_samples, num_features]
An ndarray of the covariates used to train the uplift mo... | [
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UpliftTreeClassifier.predict | (self, X, full_output=False) |
Returns the recommended treatment group and predicted optimal
probability conditional on using the recommended treatment group.
Args
----
X : ndarray, shape = [num_samples, num_features]
An ndarray of the covariates used to train the uplift model.
full_outp... |
Returns the recommended treatment group and predicted optimal
probability conditional on using the recommended treatment group. | def predict(self, X, full_output=False):
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Returns the recommended treatment group and predicted optimal
probability conditional on using the recommended treatment group.
Args
----
X : ndarray, shape = [num_samples, num_features]
An ndarray of the covariate... | [
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UpliftTreeClassifier.divideSet | (X, treatment, y, column, value) |
Tree node split.
Args
----
X : ndarray, shape = [num_samples, num_features]
An ndarray of the covariates used to train the uplift model.
treatment : array-like, shape = [num_samples]
An array containing the treatment group for each unit.
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Tree node split. | def divideSet(X, treatment, y, column, value):
'''
Tree node split.
Args
----
X : ndarray, shape = [num_samples, num_features]
An ndarray of the covariates used to train the uplift model.
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An array cont... | [
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UpliftTreeClassifier.group_uniqueCounts | (self, treatment, y) |
Count sample size by experiment group.
Args
----
treatment : array-like, shape = [num_samples]
An array containing the treatment group for each unit.
y : array-like, shape = [num_samples]
An array containing the outcome of interest for each unit.
... |
Count sample size by experiment group. | def group_uniqueCounts(self, treatment, y):
'''
Count sample size by experiment group.
Args
----
treatment : array-like, shape = [num_samples]
An array containing the treatment group for each unit.
y : array-like, shape = [num_samples]
An array co... | [
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UpliftTreeClassifier.kl_divergence | (pk, qk) |
Calculate KL Divergence for binary classification.
sum(np.array(pk) * np.log(np.array(pk) / np.array(qk)))
Args
----
pk : float
The probability of 1 in one distribution.
qk : float
The probability of 1 in the other distribution.
Returns... |
Calculate KL Divergence for binary classification. | def kl_divergence(pk, qk):
'''
Calculate KL Divergence for binary classification.
sum(np.array(pk) * np.log(np.array(pk) / np.array(qk)))
Args
----
pk : float
The probability of 1 in one distribution.
qk : float
The probability of 1 in th... | [
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UpliftTreeClassifier.evaluate_KL | (self, nodeSummary, control_name) |
Calculate KL Divergence as split evaluation criterion for a given node.
Args
----
nodeSummary : dictionary
The tree node summary statistics, produced by tree_node_summary()
method.
control_name : string
The control group name.
Retur... |
Calculate KL Divergence as split evaluation criterion for a given node. | def evaluate_KL(self, nodeSummary, control_name):
'''
Calculate KL Divergence as split evaluation criterion for a given node.
Args
----
nodeSummary : dictionary
The tree node summary statistics, produced by tree_node_summary()
method.
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UpliftTreeClassifier.evaluate_ED | (nodeSummary, control_name) |
Calculate Euclidean Distance as split evaluation criterion for a given node.
Args
----
nodeSummary : dictionary
The tree node summary statistics, produced by tree_node_summary()
method.
control_name : string
The control group name.
... |
Calculate Euclidean Distance as split evaluation criterion for a given node. | def evaluate_ED(nodeSummary, control_name):
'''
Calculate Euclidean Distance as split evaluation criterion for a given node.
Args
----
nodeSummary : dictionary
The tree node summary statistics, produced by tree_node_summary()
method.
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UpliftTreeClassifier.evaluate_Chi | (nodeSummary, control_name) |
Calculate Chi-Square statistic as split evaluation criterion for a given node.
Args
----
nodeSummary : dictionary
The tree node summary statistics, produced by tree_node_summary() method.
control_name : string
The control group name.
Returns
... |
Calculate Chi-Square statistic as split evaluation criterion for a given node. | def evaluate_Chi(nodeSummary, control_name):
'''
Calculate Chi-Square statistic as split evaluation criterion for a given node.
Args
----
nodeSummary : dictionary
The tree node summary statistics, produced by tree_node_summary() method.
control_name : string... | [
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UpliftTreeClassifier.evaluate_DDP | (nodeSummary, control_name) |
Calculate Delta P as split evaluation criterion for a given node.
Args
----
nodeSummary : dictionary
The tree node summary statistics, produced by tree_node_summary() method.
control_name : string
The control group name.
Returns
-------... |
Calculate Delta P as split evaluation criterion for a given node. | def evaluate_DDP(nodeSummary, control_name):
'''
Calculate Delta P as split evaluation criterion for a given node.
Args
----
nodeSummary : dictionary
The tree node summary statistics, produced by tree_node_summary() method.
control_name : string
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UpliftTreeClassifier.evaluate_CTS | (currentNodeSummary) |
Calculate CTS (conditional treatment selection) as split evaluation criterion for a given node.
Args
----
nodeSummary : dictionary
The tree node summary statistics, produced by tree_node_summary() method.
control_name : string
The control group name.
... |
Calculate CTS (conditional treatment selection) as split evaluation criterion for a given node. | def evaluate_CTS(currentNodeSummary):
'''
Calculate CTS (conditional treatment selection) as split evaluation criterion for a given node.
Args
----
nodeSummary : dictionary
The tree node summary statistics, produced by tree_node_summary() method.
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UpliftTreeClassifier.entropyH | (p, q=None) |
Entropy
Entropy calculation for normalization.
Args
----
p : float
The probability used in the entropy calculation.
q : float, optional, (default = None)
The second probability used in the entropy calculation.
Returns
-------
... |
Entropy | def entropyH(p, q=None):
'''
Entropy
Entropy calculation for normalization.
Args
----
p : float
The probability used in the entropy calculation.
q : float, optional, (default = None)
The second probability used in the entropy calculation... | [
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UpliftTreeClassifier.normI | (self, currentNodeSummary, leftNodeSummary, rightNodeSummary, control_name, alpha=0.9) |
Normalization factor.
Args
----
currentNodeSummary : dictionary
The summary statistics of the current tree node.
leftNodeSummary : dictionary
The summary statistics of the left tree node.
rightNodeSummary : dictionary
The summary st... |
Normalization factor. | def normI(self, currentNodeSummary, leftNodeSummary, rightNodeSummary, control_name, alpha=0.9):
'''
Normalization factor.
Args
----
currentNodeSummary : dictionary
The summary statistics of the current tree node.
leftNodeSummary : dictionary
The... | [
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UpliftTreeClassifier.tree_node_summary | (self, treatment, y, min_samples_treatment=10, n_reg=100, parentNodeSummary=None) |
Tree node summary statistics.
Args
----
treatment : array-like, shape = [num_samples]
An array containing the treatment group for each unit.
y : array-like, shape = [num_samples]
An array containing the outcome of interest for each unit.
min_samp... |
Tree node summary statistics. | def tree_node_summary(self, treatment, y, min_samples_treatment=10, n_reg=100, parentNodeSummary=None):
'''
Tree node summary statistics.
Args
----
treatment : array-like, shape = [num_samples]
An array containing the treatment group for each unit.
y : array-... | [
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UpliftTreeClassifier.uplift_classification_results | (self, treatment, y) |
Classification probability for each treatment in the tree node.
Args
----
treatment : array-like, shape = [num_samples]
An array containing the treatment group for each unit.
y : array-like, shape = [num_samples]
An array containing the outcome of intere... |
Classification probability for each treatment in the tree node. | def uplift_classification_results(self, treatment, y):
'''
Classification probability for each treatment in the tree node.
Args
----
treatment : array-like, shape = [num_samples]
An array containing the treatment group for each unit.
y : array-like, shape = [... | [
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UpliftTreeClassifier.growDecisionTreeFrom | (self, X, treatment, y, evaluationFunction, max_depth=10,
min_samples_leaf=100, depth=1,
min_samples_treatment=10, n_reg=100,
parentNodeSummary=None) |
Train the uplift decision tree.
Args
----
X : ndarray, shape = [num_samples, num_features]
An ndarray of the covariates used to train the uplift model.
treatment : array-like, shape = [num_samples]
An array containing the treatment group for each unit.
... |
Train the uplift decision tree. | def growDecisionTreeFrom(self, X, treatment, y, evaluationFunction, max_depth=10,
min_samples_leaf=100, depth=1,
min_samples_treatment=10, n_reg=100,
parentNodeSummary=None):
'''
Train the uplift decision tree.
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UpliftTreeClassifier.classify | (observations, tree, dataMissing=False) |
Classifies (prediction) the observations according to the tree.
Args
----
observations : list of list
The internal data format for the training data (combining X, Y, treatment).
dataMissing: boolean, optional (default = False)
An indicator for if data a... |
Classifies (prediction) the observations according to the tree. | def classify(observations, tree, dataMissing=False):
'''
Classifies (prediction) the observations according to the tree.
Args
----
observations : list of list
The internal data format for the training data (combining X, Y, treatment).
dataMissing: boolean, o... | [
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UpliftRandomForestClassifier.__init__ | (self,
n_estimators=10,
max_features=10,
random_state=2019,
max_depth=5,
min_samples_leaf=100,
min_samples_treatment=10,
n_reg=10,
evaluationFunction=None,
control_nam... |
Initialize the UpliftRandomForestClassifier class.
|
Initialize the UpliftRandomForestClassifier class.
| def __init__(self,
n_estimators=10,
max_features=10,
random_state=2019,
max_depth=5,
min_samples_leaf=100,
min_samples_treatment=10,
n_reg=10,
evaluationFunction=None,
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UpliftRandomForestClassifier.fit | (self, X, treatment, y) |
Fit the UpliftRandomForestClassifier.
Args
----
X : ndarray, shape = [num_samples, num_features]
An ndarray of the covariates used to train the uplift model.
treatment : array-like, shape = [num_samples]
An array containing the treatment group for each ... |
Fit the UpliftRandomForestClassifier. | def fit(self, X, treatment, y):
"""
Fit the UpliftRandomForestClassifier.
Args
----
X : ndarray, shape = [num_samples, num_features]
An ndarray of the covariates used to train the uplift model.
treatment : array-like, shape = [num_samples]
An arr... | [
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UpliftRandomForestClassifier.predict | (self, X, full_output=False) |
Returns the recommended treatment group and predicted optimal
probability conditional on using the recommended treatment group.
Args
----
X : ndarray, shape = [num_samples, num_features]
An ndarray of the covariates used to train the uplift model.
full_outp... |
Returns the recommended treatment group and predicted optimal
probability conditional on using the recommended treatment group. | def predict(self, X, full_output=False):
'''
Returns the recommended treatment group and predicted optimal
probability conditional on using the recommended treatment group.
Args
----
X : ndarray, shape = [num_samples, num_features]
An ndarray of the covariate... | [
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num_to_str | (f, precision=DEFAULT_PRECISION, use_locale=False, no_scientific=False) | Convert the given float to a string, centralizing standards for precision and decisions about scientific
notation. Adds an approximately equal sign in the event precision loss (e.g. rounding) has occurred.
There's a good discussion of related issues here:
https://stackoverflow.com/questions/38847690/co... | Convert the given float to a string, centralizing standards for precision and decisions about scientific
notation. Adds an approximately equal sign in the event precision loss (e.g. rounding) has occurred. | def num_to_str(f, precision=DEFAULT_PRECISION, use_locale=False, no_scientific=False):
"""Convert the given float to a string, centralizing standards for precision and decisions about scientific
notation. Adds an approximately equal sign in the event precision loss (e.g. rounding) has occurred.
There's a g... | [
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ordinal | (num) | Convert a number to ordinal | Convert a number to ordinal | def ordinal(num):
"""Convert a number to ordinal"""
# Taken from https://codereview.stackexchange.com/questions/41298/producing-ordinal-numbers/41301
# Consider a library like num2word when internationalization comes
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# the second paramete... | [
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substitute_none_for_missing | (kwargs, kwarg_list) | Utility function to plug Nones in when optional parameters are not specified in expectation kwargs.
Example:
Input:
kwargs={"a":1, "b":2},
kwarg_list=["c", "d"]
Output: {"a":1, "b":2, "c": None, "d": None}
This is helpful for standardizing the input objects for renderi... | Utility function to plug Nones in when optional parameters are not specified in expectation kwargs. | def substitute_none_for_missing(kwargs, kwarg_list):
"""Utility function to plug Nones in when optional parameters are not specified in expectation kwargs.
Example:
Input:
kwargs={"a":1, "b":2},
kwarg_list=["c", "d"]
Output: {"a":1, "b":2, "c": None, "d": None}
Thi... | [
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handle_strict_min_max | (params: dict) |
Utility function for the at least and at most conditions based on strictness.
Args:
params: dictionary containing "strict_min" and "strict_max" booleans.
Returns:
tuple of strings to use for the at least condition and the at most condition
|
Utility function for the at least and at most conditions based on strictness. | def handle_strict_min_max(params: dict) -> (str, str):
"""
Utility function for the at least and at most conditions based on strictness.
Args:
params: dictionary containing "strict_min" and "strict_max" booleans.
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DataConnector.__init__ | (
self,
name: str,
datasource_name: str,
execution_engine: Optional[ExecutionEngine] = None,
batch_spec_passthrough: Optional[dict] = None,
) |
Base class for DataConnectors
Args:
name (str): required name for DataConnector
datasource_name (str): required name for datasource
execution_engine (ExecutionEngine): optional reference to ExecutionEngine
batch_spec_passthrough (dict): dictionary with k... |
Base class for DataConnectors | def __init__(
self,
name: str,
datasource_name: str,
execution_engine: Optional[ExecutionEngine] = None,
batch_spec_passthrough: Optional[dict] = None,
):
"""
Base class for DataConnectors
Args:
name (str): required name for DataConnector
... | [
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DataConnector.get_batch_data_and_metadata | (
self,
batch_definition: BatchDefinition,
) |
Uses batch_definition to retrieve batch_data and batch_markers by building a batch_spec from batch_definition,
then using execution_engine to return batch_data and batch_markers
Args:
batch_definition (BatchDefinition): required batch_definition parameter for retrieval
|
Uses batch_definition to retrieve batch_data and batch_markers by building a batch_spec from batch_definition,
then using execution_engine to return batch_data and batch_markers | def get_batch_data_and_metadata(
self,
batch_definition: BatchDefinition,
) -> Tuple[Any, BatchSpec, BatchMarkers,]: # batch_data
"""
Uses batch_definition to retrieve batch_data and batch_markers by building a batch_spec from batch_definition,
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DataConnector.build_batch_spec | (self, batch_definition: BatchDefinition) |
Builds batch_spec from batch_definition by generating batch_spec params and adding any pass_through params
Args:
batch_definition (BatchDefinition): required batch_definition parameter for retrieval
Returns:
BatchSpec object built from BatchDefinition
|
Builds batch_spec from batch_definition by generating batch_spec params and adding any pass_through params | def build_batch_spec(self, batch_definition: BatchDefinition) -> BatchSpec:
"""
Builds batch_spec from batch_definition by generating batch_spec params and adding any pass_through params
Args:
batch_definition (BatchDefinition): required batch_definition parameter for retrieval
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DataConnector._get_data_reference_list | (
self, data_asset_name: Optional[str] = None
) |
List objects in the underlying data store to create a list of data_references.
This method is used to refresh the cache by classes that extend this base DataConnector class
Args:
data_asset_name (str): optional data_asset_name to retrieve more specific results
|
List objects in the underlying data store to create a list of data_references.
This method is used to refresh the cache by classes that extend this base DataConnector class | def _get_data_reference_list(
self, data_asset_name: Optional[str] = None
) -> List[str]:
"""
List objects in the underlying data store to create a list of data_references.
This method is used to refresh the cache by classes that extend this base DataConnector class
Args:
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DataConnector._get_data_reference_list_from_cache_by_data_asset_name | (
self, data_asset_name: str
) |
Fetch data_references corresponding to data_asset_name from the cache.
|
Fetch data_references corresponding to data_asset_name from the cache.
| def _get_data_reference_list_from_cache_by_data_asset_name(
self, data_asset_name: str
) -> List[Any]:
"""
Fetch data_references corresponding to data_asset_name from the cache.
"""
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DataConnector.get_available_data_asset_names | (self) | Return the list of asset names known by this data connector.
Returns:
A list of available names
| Return the list of asset names known by this data connector. | def get_available_data_asset_names(self) -> List[str]:
"""Return the list of asset names known by this data connector.
Returns:
A list of available names
"""
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DataConnector.self_check | (self, pretty_print=True, max_examples=3) |
Checks the configuration of the current DataConnector by doing the following :
1. refresh or create data_reference_cache
2. print batch_definition_count and example_data_references for each data_asset_names
3. also print unmatched data_references, and allow the user to modify the regex... |
Checks the configuration of the current DataConnector by doing the following : | def self_check(self, pretty_print=True, max_examples=3):
"""
Checks the configuration of the current DataConnector by doing the following :
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DataConnector._self_check_fetch_batch | (
self,
pretty_print: bool,
example_data_reference: Any,
data_asset_name: str,
) |
Helper function for self_check() to retrieve batch using example_data_reference and data_asset_name,
all while printing helpful messages. First 5 rows of batch_data are printed by default.
Args:
pretty_print (bool): print to console?
example_data_reference (Any): data_r... |
Helper function for self_check() to retrieve batch using example_data_reference and data_asset_name,
all while printing helpful messages. First 5 rows of batch_data are printed by default. | def _self_check_fetch_batch(
self,
pretty_print: bool,
example_data_reference: Any,
data_asset_name: str,
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"""
Helper function for self_check() to retrieve batch using example_data_reference and data_asset_name,
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DataConnector._validate_batch_request | (self, batch_request: BatchRequest) |
Validate batch_request by checking:
1. if configured datasource_name matches batch_request's datasource_name
2. if current data_connector_name matches batch_request's data_connector_name
Args:
batch_request (BatchRequest): batch_request to validate
|
Validate batch_request by checking:
1. if configured datasource_name matches batch_request's datasource_name
2. if current data_connector_name matches batch_request's data_connector_name
Args:
batch_request (BatchRequest): batch_request to validate | def _validate_batch_request(self, batch_request: BatchRequest):
"""
Validate batch_request by checking:
1. if configured datasource_name matches batch_request's datasource_name
2. if current data_connector_name matches batch_request's data_connector_name
Args:
... | [
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set_data_source | (context, data_source_type=None) |
TODO: Needs a docstring and tests.
|
TODO: Needs a docstring and tests.
| def set_data_source(context, data_source_type=None):
"""
TODO: Needs a docstring and tests.
"""
data_source_name = None
if not data_source_type:
configured_datasources = [
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setup_notebook_logging | (logger=None, log_level=logging.INFO) | Set up the provided logger for the GE default logging configuration.
Args:
logger - the logger to configure
| Set up the provided logger for the GE default logging configuration. | def setup_notebook_logging(logger=None, log_level=logging.INFO):
"""Set up the provided logger for the GE default logging configuration.
Args:
logger - the logger to configure
"""
def posix2local(timestamp, tz=tzlocal.get_localzone()):
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show_available_data_asset_names | (context, data_source_name=None) | List asset names found in the current context. | List asset names found in the current context. | def show_available_data_asset_names(context, data_source_name=None):
"""List asset names found in the current context."""
# TODO: Needs tests.
styles = """
<style type='text/css'>
ul.data-assets {
margin-top: 0px;
}
ul.data-assets li {
line-height: 1.2em;
list-style-t... | [
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display_column_expectations_as_section | (
expectation_suite,
column,
include_styling=True,
return_without_displaying=False,
) | This is a utility function to render all of the Expectations in an ExpectationSuite with the same column name as an HTML block.
By default, the HTML block is rendered using ExpectationSuiteColumnSectionRenderer and the view is rendered using DefaultJinjaSectionView.
Therefore, it should look exactly the same a... | This is a utility function to render all of the Expectations in an ExpectationSuite with the same column name as an HTML block. | def display_column_expectations_as_section(
expectation_suite,
column,
include_styling=True,
return_without_displaying=False,
):
"""This is a utility function to render all of the Expectations in an ExpectationSuite with the same column name as an HTML block.
By default, the HTML block is rende... | [
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display_profiled_column_evrs_as_section | (
evrs,
column,
include_styling=True,
return_without_displaying=False,
) | This is a utility function to render all of the EVRs in an ExpectationSuite with the same column name as an HTML block.
By default, the HTML block is rendered using ExpectationSuiteColumnSectionRenderer and the view is rendered using DefaultJinjaSectionView.
Therefore, it should look exactly the same as the de... | This is a utility function to render all of the EVRs in an ExpectationSuite with the same column name as an HTML block. | def display_profiled_column_evrs_as_section(
evrs,
column,
include_styling=True,
return_without_displaying=False,
):
"""This is a utility function to render all of the EVRs in an ExpectationSuite with the same column name as an HTML block.
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display_column_evrs_as_section | (
evrs,
column,
include_styling=True,
return_without_displaying=False,
) |
Display validation results for a single column as a section.
WARNING: This method is experimental.
|
Display validation results for a single column as a section. | def display_column_evrs_as_section(
evrs,
column,
include_styling=True,
return_without_displaying=False,
):
"""
Display validation results for a single column as a section.
WARNING: This method is experimental.
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ACPragma.__init__ | (self, rkey: str, location: str="-pragma-", *,
name: str="-pragma-",
kind: str="Pragma",
apiVersion: Optional[str]=None,
serialization: Optional[str]=None,
**kwargs) |
Initialize an ACPragma from the raw fields of its ACResource.
|
Initialize an ACPragma from the raw fields of its ACResource.
| def __init__(self, rkey: str, location: str="-pragma-", *,
name: str="-pragma-",
kind: str="Pragma",
apiVersion: Optional[str]=None,
serialization: Optional[str]=None,
**kwargs) -> None:
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test_func | () | My cool test.name | My cool test.name | def test_func():
""" My cool test.name """
assert True | [
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0,
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declaration_matcher_t.__init__ | (
self,
name=None,
decl_type=None,
header_dir=None,
header_file=None) |
:param decl_type: declaration type to match by. For example
:class:`enumeration_t`.
:type decl_type: any class that derives from :class:`declaration_t`
class
:param name: declaration name, could be full name.
:type name: str
:param header_dir: absolute director... |
:param decl_type: declaration type to match by. For example
:class:`enumeration_t`.
:type decl_type: any class that derives from :class:`declaration_t`
class | def __init__(
self,
name=None,
decl_type=None,
header_dir=None,
header_file=None):
"""
:param decl_type: declaration type to match by. For example
:class:`enumeration_t`.
:type decl_type: any class that derives from :class:`decl... | [
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] | [
78,
77
] | python | en | ['en', 'error', 'th'] | False |
variable_matcher_t.__init__ | (
self,
name=None,
decl_type=None,
header_dir=None,
header_file=None) |
:param decl_type: variable type
:type decl_type: string or instance of :class:`type_t` derived class
|
:param decl_type: variable type
:type decl_type: string or instance of :class:`type_t` derived class
| def __init__(
self,
name=None,
decl_type=None,
header_dir=None,
header_file=None):
"""
:param decl_type: variable type
:type decl_type: string or instance of :class:`type_t` derived class
"""
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189,
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206,
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calldef_matcher_t.__init__ | (
self,
name=None,
return_type=None,
arg_types=None,
decl_type=None,
header_dir=None,
header_file=None) |
:param return_type: callable return type
:type return_type: string or instance of :class:`type_t` derived class
:type arg_types: list
:param arg_types: list of function argument types. `arg_types` can
contain.
Any item within the list... |
:param return_type: callable return type
:type return_type: string or instance of :class:`type_t` derived class | def __init__(
self,
name=None,
return_type=None,
arg_types=None,
decl_type=None,
header_dir=None,
header_file=None):
"""
:param return_type: callable return type
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299,
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operator_matcher_t.__init__ | (
self,
name=None,
symbol=None,
return_type=None,
arg_types=None,
decl_type=None,
header_dir=None,
header_file=None) |
:param symbol: operator symbol
:type symbol: str
|
:param symbol: operator symbol
:type symbol: str
| def __init__(
self,
name=None,
symbol=None,
return_type=None,
arg_types=None,
decl_type=None,
header_dir=None,
header_file=None):
"""
:param symbol: operator symbol
:type symbol: str
"""
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358,
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381,
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current_default_thread_limiter | () | Get the default `~trio.CapacityLimiter` used by
`trio.to_thread.run_sync`.
The most common reason to call this would be if you want to modify its
:attr:`~trio.CapacityLimiter.total_tokens` attribute.
| Get the default `~trio.CapacityLimiter` used by
`trio.to_thread.run_sync`. | def current_default_thread_limiter():
"""Get the default `~trio.CapacityLimiter` used by
`trio.to_thread.run_sync`.
The most common reason to call this would be if you want to modify its
:attr:`~trio.CapacityLimiter.total_tokens` attribute.
"""
try:
limiter = _limiter_local.get()
e... | [
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to_thread_run_sync | (sync_fn, *args, cancellable=False, limiter=None) | Convert a blocking operation into an async operation using a thread.
These two lines are equivalent::
sync_fn(*args)
await trio.to_thread.run_sync(sync_fn, *args)
except that if ``sync_fn`` takes a long time, then the first line will
block the Trio loop while it runs, while the second lin... | Convert a blocking operation into an async operation using a thread. | async def to_thread_run_sync(sync_fn, *args, cancellable=False, limiter=None):
"""Convert a blocking operation into an async operation using a thread.
These two lines are equivalent::
sync_fn(*args)
await trio.to_thread.run_sync(sync_fn, *args)
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_run_fn_as_system_task | (cb, fn, *args, trio_token=None) | Helper function for from_thread.run and from_thread.run_sync.
Since this internally uses TrioToken.run_sync_soon, all warnings about
raised exceptions canceling all tasks should be noted.
| Helper function for from_thread.run and from_thread.run_sync. | def _run_fn_as_system_task(cb, fn, *args, trio_token=None):
"""Helper function for from_thread.run and from_thread.run_sync.
Since this internally uses TrioToken.run_sync_soon, all warnings about
raised exceptions canceling all tasks should be noted.
"""
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from_thread_run | (afn, *args, trio_token=None) | Run the given async function in the parent Trio thread, blocking until it
is complete.
Returns:
Whatever ``afn(*args)`` returns.
Returns or raises whatever the given function returns or raises. It
can also raise exceptions of its own:
Raises:
RunFinishedError: if the corresponding c... | Run the given async function in the parent Trio thread, blocking until it
is complete. | def from_thread_run(afn, *args, trio_token=None):
"""Run the given async function in the parent Trio thread, blocking until it
is complete.
Returns:
Whatever ``afn(*args)`` returns.
Returns or raises whatever the given function returns or raises. It
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from_thread_run_sync | (fn, *args, trio_token=None) | Run the given sync function in the parent Trio thread, blocking until it
is complete.
Returns:
Whatever ``fn(*args)`` returns.
Returns or raises whatever the given function returns or raises. It
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Raises:
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ExpectColumnDistinctValuesToBeInSet.validate_configuration | (self, configuration: Optional[ExpectationConfiguration]) | Validating that user has inputted a value set and that configuration has been initialized | Validating that user has inputted a value set and that configuration has been initialized | def validate_configuration(self, configuration: Optional[ExpectationConfiguration]):
"""Validating that user has inputted a value set and that configuration has been initialized"""
super().validate_configuration(configuration)
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bind_aliases | (decls) |
This function binds between class and it's typedefs.
Deprecated since 1.9.0, will be removed in 2.0.0
:param decls: list of all declarations
:rtype: None
|
This function binds between class and it's typedefs. | def bind_aliases(decls):
"""
This function binds between class and it's typedefs.
Deprecated since 1.9.0, will be removed in 2.0.0
:param decls: list of all declarations
:rtype: None
"""
warnings.warn(
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source_reader_t.__init__ | (self, configuration, cache=None, decl_factory=None) |
:param configuration:
Instance of :class:`xml_generator_configuration_t`
class, that contains GCC-XML or CastXML configuration.
:param cache: Reference to cache object, that will be updated after a
file has been parsed.
:type ... |
:param configuration:
Instance of :class:`xml_generator_configuration_t`
class, that contains GCC-XML or CastXML configuration. | def __init__(self, configuration, cache=None, decl_factory=None):
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:param configuration:
Instance of :class:`xml_generator_configuration_t`
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source_reader_t.xml_generator_from_xml_file | (self) |
Configuration object containing information about the xml generator
read from the xml file.
Returns:
utils.xml_generators: configuration object
|
Configuration object containing information about the xml generator
read from the xml file. | def xml_generator_from_xml_file(self):
"""
Configuration object containing information about the xml generator
read from the xml file.
Returns:
utils.xml_generators: configuration object
"""
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82,
49
] | python | en | ['en', 'error', 'th'] | False |
source_reader_t.__create_command_line | (self, source_file, xml_file) |
Generate the command line used to build xml files.
Depending on the chosen xml_generator a different command line
is built. The gccxml option may be removed once gccxml
support is dropped (this was the original c++ xml_generator,
castxml is replacing it now).
|
Generate the command line used to build xml files. | def __create_command_line(self, source_file, xml_file):
"""
Generate the command line used to build xml files.
Depending on the chosen xml_generator a different command line
is built. The gccxml option may be removed once gccxml
support is dropped (this was the original c++ xml_... | [
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source_reader_t.__add_symbols | (self, cmd) |
Add all additional defined and undefined symbols.
|
Add all additional defined and undefined symbols. | def __add_symbols(self, cmd):
"""
Add all additional defined and undefined symbols.
"""
if self.__config.define_symbols:
symbols = self.__config.define_symbols
cmd.append(''.join(
[' -D"%s"' % def_symbol for def_symbol in symbols]))
if s... | [
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239,
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source_reader_t.create_xml_file | (self, source_file, destination=None) |
This method will generate a xml file using an external tool.
The external tool can be either gccxml or castxml. The method will
return the file path of the generated xml file.
:param source_file: path to the source file that should be parsed.
:type source_file: str
:p... |
This method will generate a xml file using an external tool. | def create_xml_file(self, source_file, destination=None):
"""
This method will generate a xml file using an external tool.
The external tool can be either gccxml or castxml. The method will
return the file path of the generated xml file.
:param source_file: path to the source f... | [
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source_reader_t.create_xml_file_from_string | (self, content, destination=None) |
Creates XML file from text.
:param content: C++ source code
:type content: str
:param destination: file name for GCC-XML generated file
:type destination: str
:rtype: returns file name of GCC-XML generated file
|
Creates XML file from text. | def create_xml_file_from_string(self, content, destination=None):
"""
Creates XML file from text.
:param content: C++ source code
:type content: str
:param destination: file name for GCC-XML generated file
:type destination: str
:rtype: returns file name of GCC... | [
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source_reader_t.read_cpp_source_file | (self, source_file) |
Reads C++ source file and returns declarations tree
:param source_file: path to C++ source file
:type source_file: str
|
Reads C++ source file and returns declarations tree | def read_cpp_source_file(self, source_file):
"""
Reads C++ source file and returns declarations tree
:param source_file: path to C++ source file
:type source_file: str
"""
xml_file = ''
try:
ffname = self.__file_full_name(source_file)
se... | [
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] | [
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source_reader_t.read_xml_file | (self, xml_file) |
Read generated XML file.
:param xml_file: path to xml file
:type xml_file: str
:rtype: declarations tree
|
Read generated XML file. | def read_xml_file(self, xml_file):
"""
Read generated XML file.
:param xml_file: path to xml file
:type xml_file: str
:rtype: declarations tree
"""
assert self.__config is not None
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source_reader_t.read_string | (self, content) |
Reads a Python string that contains C++ code, and return
the declarations tree.
|
Reads a Python string that contains C++ code, and return
the declarations tree. | def read_string(self, content):
"""
Reads a Python string that contains C++ code, and return
the declarations tree.
"""
header_file = utils.create_temp_file_name(suffix='.h')
with open(header_file, "w+") as f:
f.write(content)
try:
decls... | [
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Load | (build_files, format, default_variables={},
includes=[], depth='.', params=None, check=False,
circular_check=True, duplicate_basename_check=True) |
Loads one or more specified build files.
default_variables and includes will be copied before use.
Returns the generator for the specified format and the
data returned by loading the specified build files.
|
Loads one or more specified build files.
default_variables and includes will be copied before use.
Returns the generator for the specified format and the
data returned by loading the specified build files.
| def Load(build_files, format, default_variables={},
includes=[], depth='.', params=None, check=False,
circular_check=True, duplicate_basename_check=True):
"""
Loads one or more specified build files.
default_variables and includes will be copied before use.
Returns the generator for the specif... | [
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NameValueListToDict | (name_value_list) |
Takes an array of strings of the form 'NAME=VALUE' and creates a dictionary
of the pairs. If a string is simply NAME, then the value in the dictionary
is set to True. If VALUE can be converted to an integer, it is.
|
Takes an array of strings of the form 'NAME=VALUE' and creates a dictionary
of the pairs. If a string is simply NAME, then the value in the dictionary
is set to True. If VALUE can be converted to an integer, it is.
| def NameValueListToDict(name_value_list):
"""
Takes an array of strings of the form 'NAME=VALUE' and creates a dictionary
of the pairs. If a string is simply NAME, then the value in the dictionary
is set to True. If VALUE can be converted to an integer, it is.
"""
result = { }
for item in name_value_lis... | [
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152,
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RegenerateAppendFlag | (flag, values, predicate, env_name, options) | Regenerate a list of command line flags, for an option of action='append'.
The |env_name|, if given, is checked in the environment and used to generate
an initial list of options, then the options that were specified on the
command line (given in |values|) are appended. This matches the handling of
environmen... | Regenerate a list of command line flags, for an option of action='append'. | def RegenerateAppendFlag(flag, values, predicate, env_name, options):
"""Regenerate a list of command line flags, for an option of action='append'.
The |env_name|, if given, is checked in the environment and used to generate
an initial list of options, then the options that were specified on the
command line (... | [
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RegenerateFlags | (options) | Given a parsed options object, and taking the environment variables into
account, returns a list of flags that should regenerate an equivalent options
object (even in the absence of the environment variables.)
Any path options will be normalized relative to depth.
The format flag is not included, as it is ass... | Given a parsed options object, and taking the environment variables into
account, returns a list of flags that should regenerate an equivalent options
object (even in the absence of the environment variables.) | def RegenerateFlags(options):
"""Given a parsed options object, and taking the environment variables into
account, returns a list of flags that should regenerate an equivalent options
object (even in the absence of the environment variables.)
Any path options will be normalized relative to depth.
The format... | [
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RegeneratableOptionParser.add_option | (self, *args, **kw) | Add an option to the parser.
This accepts the same arguments as OptionParser.add_option, plus the
following:
regenerate: can be set to False to prevent this option from being included
in regeneration.
env_name: name of environment variable that additional values for this
... | Add an option to the parser. | def add_option(self, *args, **kw):
"""Add an option to the parser.
This accepts the same arguments as OptionParser.add_option, plus the
following:
regenerate: can be set to False to prevent this option from being included
in regeneration.
env_name: name of environment variable... | [
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aws_credentials | () | Mocked AWS Credentials for moto. | Mocked AWS Credentials for moto. | def aws_credentials():
"""Mocked AWS Credentials for moto."""
os.environ["AWS_ACCESS_KEY_ID"] = "testing"
os.environ["AWS_SECRET_ACCESS_KEY"] = "testing"
os.environ["AWS_SECURITY_TOKEN"] = "testing"
os.environ["AWS_SESSION_TOKEN"] = "testing" | [
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LatexFormatter.get_style_defs | (self, arg='') |
Return the command sequences needed to define the commands
used to format text in the verbatim environment. ``arg`` is ignored.
|
Return the command sequences needed to define the commands
used to format text in the verbatim environment. ``arg`` is ignored.
| def get_style_defs(self, arg=''):
"""
Return the command sequences needed to define the commands
used to format text in the verbatim environment. ``arg`` is ignored.
"""
cp = self.commandprefix
styles = []
for name, definition in iteritems(self.cmd2def):
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lone_object_filter | (image, min_size=2, connectivity=1, kernel_size=3) |
Replaces isolated, contiguous regions of values in a raster with values
representing the surrounding pixels.
More specifically, this reduces noise in a raster by setting
contiguous regions of values greater than a specified minimum size to
the modal value in a specified neighborhood.
The defa... |
Replaces isolated, contiguous regions of values in a raster with values
representing the surrounding pixels. | def lone_object_filter(image, min_size=2, connectivity=1, kernel_size=3):
"""
Replaces isolated, contiguous regions of values in a raster with values
representing the surrounding pixels.
More specifically, this reduces noise in a raster by setting
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apply_filter | (statistic, filter_output, padded_arr, filter_shape) |
Creates a mean, median, or standard deviation filtered version
of an `xarray.DataArray`.
Parameters
----------
filter_output: xarray.DataArray
The `xarray.DataArray` to store the filtered values in.
Must contain the values to filter. This object is modified.**
statistic: string... |
Creates a mean, median, or standard deviation filtered version
of an `xarray.DataArray`. | def apply_filter(statistic, filter_output, padded_arr, filter_shape):
"""
Creates a mean, median, or standard deviation filtered version
of an `xarray.DataArray`.
Parameters
----------
filter_output: xarray.DataArray
The `xarray.DataArray` to store the filtered values in.
Must c... | [
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stats_filter_3d_composite_2d | (dataarray, statistic, filter_size=1,
time_dim='time') |
Returns a mean, median, or standard deviation filter composite
of a 3D `xarray.DataArray` with a time dimension. This makes a 2D composite
of a 3D array by stretching the filter kernel across time.
This function is more accurate than using SciPy or scikit-image methods, because
those don't handle t... |
Returns a mean, median, or standard deviation filter composite
of a 3D `xarray.DataArray` with a time dimension. This makes a 2D composite
of a 3D array by stretching the filter kernel across time.
This function is more accurate than using SciPy or scikit-image methods, because
those don't handle t... | def stats_filter_3d_composite_2d(dataarray, statistic, filter_size=1,
time_dim='time'):
"""
Returns a mean, median, or standard deviation filter composite
of a 3D `xarray.DataArray` with a time dimension. This makes a 2D composite
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stats_filter_2d | (dataarray, statistic, filter_size=3) |
Returns a mean, median, or standard deviation filter of a 2D `xarray.DataArray`.
This function is more accurate than using SciPy or scikit-image methods, because
those don't handle the extremities ideally (edges and corners).
Specifically, only values actually inside the filter should be considered,
... |
Returns a mean, median, or standard deviation filter of a 2D `xarray.DataArray`.
This function is more accurate than using SciPy or scikit-image methods, because
those don't handle the extremities ideally (edges and corners).
Specifically, only values actually inside the filter should be considered,
... | def stats_filter_2d(dataarray, statistic, filter_size=3):
"""
Returns a mean, median, or standard deviation filter of a 2D `xarray.DataArray`.
This function is more accurate than using SciPy or scikit-image methods, because
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AdminSiteTests.test_users_listed | (self) | Test that users are listed on user page | Test that users are listed on user page | def test_users_listed(self):
"""Test that users are listed on user page"""
url = reverse('admin:core_user_changelist')
res = self.client.get(url)
self.assertContains(res, self.user.name)
self.assertContains(res, self.user.email) | [
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AdminSiteTests.test_user_change_page | (self) | Test that the user edit page works | Test that the user edit page works | def test_user_change_page(self):
"""Test that the user edit page works"""
url = reverse('admin:core_user_change', args=[self.user.id])
res = self.client.get(url)
self.assertEqual(res.status_code, 200) | [
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AdminSiteTests.test_create_user_page | (self) | Test that the create user page works | Test that the create user page works | def test_create_user_page(self):
"""Test that the create user page works"""
url = reverse('admin:core_user_add')
res = self.client.get(url)
self.assertEqual(res.status_code, 200) | [
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] | python | en | ['en', 'en', 'en'] | True |
TeamcityServiceMessages.testStarted | (self, testName, captureStandardOutput=None, flowId=None, metainfo=None) |
:param metainfo: Used to pass any payload from test runner to Intellij. See IDEA-176950
| def testStarted(self, testName, captureStandardOutput=None, flowId=None, metainfo=None):
"""
:param metainfo: Used to pass any payload from test runner to Intellij. See IDEA-176950
"""
self.message('testStarted', name=testName, captureStandardOutput=captureStandardOutput, flowId=flowId,... | [
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144,
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] | python | en | ['en', 'error', 'th'] | False | |
_library_not_loaded_test | (
tmp_path_factory,
cli_input,
library_name,
library_import_name,
my_caplog,
monkeypatch,
) |
This test requires that a library is NOT installed. It tests that:
- a helpful error message is returned to install the missing library
- the expected tree structure is in place
- the config yml contains an empty dict in its datasource entry
|
This test requires that a library is NOT installed. It tests that:
- a helpful error message is returned to install the missing library
- the expected tree structure is in place
- the config yml contains an empty dict in its datasource entry
| def _library_not_loaded_test(
tmp_path_factory,
cli_input,
library_name,
library_import_name,
my_caplog,
monkeypatch,
):
"""
This test requires that a library is NOT installed. It tests that:
- a helpful error message is returned to install the missing library
- the expected tree... | [
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14,
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102,
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] | python | en | ['en', 'error', 'th'] | False |
test_init_install_sqlalchemy | (caplog, tmp_path_factory, monkeypatch) | WARNING: THIS TEST IS AWFUL AND WE HATE IT. | WARNING: THIS TEST IS AWFUL AND WE HATE IT. | def test_init_install_sqlalchemy(caplog, tmp_path_factory, monkeypatch):
"""WARNING: THIS TEST IS AWFUL AND WE HATE IT."""
# This test is as much about changing the entire test environment with side effects as it is about actually testing
# the observed behavior.
library_import_name = "sqlalchemy"
l... | [
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"# the observed behavior.",
"library_import_name",
"=",
"\"s... | [
114,
0
] | [
142,
77
] | python | en | ['en', 'en', 'en'] | True |
MetaPandasDataset.column_map_expectation | (cls, func) | Constructs an expectation using column-map semantics.
The MetaPandasDataset implementation replaces the "column" parameter supplied by the user with a pandas Series
object containing the actual column from the relevant pandas dataframe. This simplifies the implementing expectation
logic while ... | Constructs an expectation using column-map semantics. | def column_map_expectation(cls, func):
"""Constructs an expectation using column-map semantics.
The MetaPandasDataset implementation replaces the "column" parameter supplied by the user with a pandas Series
object containing the actual column from the relevant pandas dataframe. This simplifies... | [
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43,
4
] | [
159,
28
] | python | en | ['en', 'lb', 'en'] | True |
MetaPandasDataset.column_pair_map_expectation | (cls, func) |
The column_pair_map_expectation decorator handles boilerplate issues surrounding the common pattern of evaluating
truthiness of some condition on a per row basis across a pair of columns.
|
The column_pair_map_expectation decorator handles boilerplate issues surrounding the common pattern of evaluating
truthiness of some condition on a per row basis across a pair of columns.
| def column_pair_map_expectation(cls, func):
"""
The column_pair_map_expectation decorator handles boilerplate issues surrounding the common pattern of evaluating
truthiness of some condition on a per row basis across a pair of columns.
"""
argspec = inspect.getfullargspec(func)[0... | [
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"f... | [
162,
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] | [
264,
28
] | python | en | ['en', 'error', 'th'] | False |
MetaPandasDataset.multicolumn_map_expectation | (cls, func) |
The multicolumn_map_expectation decorator handles boilerplate issues surrounding the common pattern of
evaluating truthiness of some condition on a per row basis across a set of columns.
|
The multicolumn_map_expectation decorator handles boilerplate issues surrounding the common pattern of
evaluating truthiness of some condition on a per row basis across a set of columns.
| def multicolumn_map_expectation(cls, func):
"""
The multicolumn_map_expectation decorator handles boilerplate issues surrounding the common pattern of
evaluating truthiness of some condition on a per row basis across a set of columns.
"""
argspec = inspect.getfullargspec(func)[0]... | [
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267,
4
] | [
336,
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] | python | en | ['en', 'error', 'th'] | False |
PandasDataset.get_crosstab | (
self,
column_A,
column_B,
bins_A=None,
bins_B=None,
n_bins_A=None,
n_bins_B=None,
) | Get crosstab of column_A and column_B, binning values if necessary | Get crosstab of column_A and column_B, binning values if necessary | def get_crosstab(
self,
column_A,
column_B,
bins_A=None,
bins_B=None,
n_bins_A=None,
n_bins_B=None,
):
"""Get crosstab of column_A and column_B, binning values if necessary"""
series_A = self.get_binned_values(self[column_A], bins_A, n_bins_A)
... | [
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... | [
542,
4
] | [
554,
54
] | python | en | ['en', 'en', 'en'] | True |
PandasDataset.get_binned_values | (self, series, bins, n_bins) |
Get binned values of series.
Args:
Series (pd.Series): Input series
bins (list):
Bins for the series. List of numeric if series is numeric or list of list
of series values else.
n_bins (int): Number of bins. Ignored if bins is not Non... |
Get binned values of series. | def get_binned_values(self, series, bins, n_bins):
"""
Get binned values of series.
Args:
Series (pd.Series): Input series
bins (list):
Bins for the series. List of numeric if series is numeric or list of list
of series values else.
... | [
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... | [
556,
4
] | [
617,
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] | python | en | ['en', 'error', 'th'] | False |
PandasDataset.expect_column_values_to_be_of_type | (
self,
column,
type_,
**kwargs
# Since we've now received the default arguments *before* the expectation decorator, we need to
# ensure we only pass what we actually received. Hence, we'll use kwargs
# mostly=None,
# result_format=None,
# row_cond... |
The pandas implementation of this expectation takes kwargs mostly, result_format, include_config,
catch_exceptions, and meta as other expectations, however it declares **kwargs because it needs to
be able to fork into either aggregate or map semantics depending on the column type (see below).
... |
The pandas implementation of this expectation takes kwargs mostly, result_format, include_config,
catch_exceptions, and meta as other expectations, however it declares **kwargs because it needs to
be able to fork into either aggregate or map semantics depending on the column type (see below). | def expect_column_values_to_be_of_type(
self,
column,
type_,
**kwargs
# Since we've now received the default arguments *before* the expectation decorator, we need to
# ensure we only pass what we actually received. Hence, we'll use kwargs
# mostly=None,
# ... | [
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"# ensure we only pass what we actually received. Hence, we'll use kwargs",
"... | [
671,
4
] | [
782,
18
] | python | en | ['en', 'error', 'th'] | False |
PandasDataset.expect_column_values_to_be_in_type_list | (
self,
column,
type_list,
**kwargs
# Since we've now received the default arguments *before* the expectation decorator, we need to
# ensure we only pass what we actually received. Hence, we'll use kwargs
# mostly=None,
# result_format = None,
# ro... |
The pandas implementation of this expectation takes kwargs mostly, result_format, include_config,
catch_exceptions, and meta as other expectations, however it declares **kwargs because it needs to
be able to fork into either aggregate or map semantics depending on the column type (see below).
... |
The pandas implementation of this expectation takes kwargs mostly, result_format, include_config,
catch_exceptions, and meta as other expectations, however it declares **kwargs because it needs to
be able to fork into either aggregate or map semantics depending on the column type (see below). | def expect_column_values_to_be_in_type_list(
self,
column,
type_list,
**kwargs
# Since we've now received the default arguments *before* the expectation decorator, we need to
# ensure we only pass what we actually received. Hence, we'll use kwargs
# mostly=None,
... | [
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"# Since we've now received the default arguments *before* the expectation decorator, we need to",
"# ensure we only pass what we actually received. Hence, we'll use kwa... | [
899,
4
] | [
1007,
18
] | python | en | ['en', 'error', 'th'] | False |
PandasDataset.expect_multicolumn_sum_to_equal | (
self,
column_list,
sum_total,
result_format=None,
include_config=True,
catch_exceptions=None,
meta=None,
) | Multi-Column Map Expectation
Expects that sum of all rows for a set of columns is equal to a specific value
Args:
column_list (List[str]): \
Set of columns to be checked
sum_total (int): \
expected sum of columns
| Multi-Column Map Expectation | def expect_multicolumn_sum_to_equal(
self,
column_list,
sum_total,
result_format=None,
include_config=True,
catch_exceptions=None,
meta=None,
):
""" Multi-Column Map Expectation
Expects that sum of all rows for a set of columns is equal to a s... | [
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"return",
"column... | [
1856,
4
] | [
1875,
51
] | python | en | ['es', 'en', 'en'] | True |
LegacyDatasource.from_configuration | (cls, **kwargs) |
Build a new datasource from a configuration dictionary.
Args:
**kwargs: configuration key-value pairs
Returns:
datasource (Datasource): the newly-created datasource
|
Build a new datasource from a configuration dictionary. | def from_configuration(cls, **kwargs):
"""
Build a new datasource from a configuration dictionary.
Args:
**kwargs: configuration key-value pairs
Returns:
datasource (Datasource): the newly-created datasource
"""
return cls(**kwargs) | [
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"kwargs",
")"
] | [
113,
4
] | [
124,
28
] | python | en | ['en', 'error', 'th'] | False |
LegacyDatasource.build_configuration | (
cls,
class_name,
module_name="great_expectations.datasource",
data_asset_type=None,
batch_kwargs_generators=None,
**kwargs
) |
Build a full configuration object for a datasource, potentially including batch kwargs generators with defaults.
Args:
class_name: The name of the class for which to build the config
module_name: The name of the module in which the datasource class is located
data_a... |
Build a full configuration object for a datasource, potentially including batch kwargs generators with defaults. | def build_configuration(
cls,
class_name,
module_name="great_expectations.datasource",
data_asset_type=None,
batch_kwargs_generators=None,
**kwargs
):
"""
Build a full configuration object for a datasource, potentially including batch kwargs generators... | [
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":",
"verify_dynamic_loading_supp... | [
127,
4
] | [
156,
28
] | python | en | ['en', 'error', 'th'] | False |
LegacyDatasource.__init__ | (
self,
name,
data_context=None,
data_asset_type=None,
batch_kwargs_generators=None,
**kwargs
) |
Build a new datasource.
Args:
name: the name for the datasource
data_context: data context to which to connect
data_asset_type (ClassConfig): the type of DataAsset to produce
batch_kwargs_generators: BatchKwargGenerators to add to the datasource
|
Build a new datasource. | def __init__(
self,
name,
data_context=None,
data_asset_type=None,
batch_kwargs_generators=None,
**kwargs
):
"""
Build a new datasource.
Args:
name: the name for the datasource
data_context: data context to which to con... | [
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"self",
"... | [
158,
4
] | [
188,
88
] | python | en | ['en', 'error', 'th'] | False |
LegacyDatasource.name | (self) |
Property for datasource name
|
Property for datasource name
| def name(self):
"""
Property for datasource name
"""
return self._name | [
"def",
"name",
"(",
"self",
")",
":",
"return",
"self",
".",
"_name"
] | [
191,
4
] | [
195,
25
] | python | en | ['en', 'error', 'th'] | False |
LegacyDatasource.data_context | (self) |
Property for attached DataContext
|
Property for attached DataContext
| def data_context(self):
"""
Property for attached DataContext
"""
return self._data_context | [
"def",
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] | [
202,
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] | [
206,
33
] | python | en | ['en', 'error', 'th'] | False |
LegacyDatasource._build_generators | (self) |
Build batch kwargs generator objects from the datasource configuration.
Returns:
None
|
Build batch kwargs generator objects from the datasource configuration. | def _build_generators(self):
"""
Build batch kwargs generator objects from the datasource configuration.
Returns:
None
"""
try:
for generator in self._datasource_config["batch_kwargs_generators"].keys():
self.get_batch_kwargs_generator(gen... | [
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"... | [
208,
4
] | [
219,
16
] | python | en | ['en', 'error', 'th'] | False |
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