sentence1 stringlengths 52 3.87M | sentence2 stringlengths 1 47.2k | label stringclasses 1
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def intersperse_hs_in_std_res(slice_, hs_dims, res):
"""Perform the insertions of place-holding rows and cols for insertions."""
for dim, inds in enumerate(slice_.inserted_hs_indices()):
if dim not in hs_dims:
continue
for i in inds:
res = np.insert(res, i, np.nan, axis=(... | Perform the insertions of place-holding rows and cols for insertions. | entailment |
def inflate_parameter_leaf(sub_parameter, base_year, inflator, unit_type = 'unit'):
"""
Inflate a Parameter leaf according to unit type
Basic unit type are supposed by default
Other admissible unit types are threshold_unit and rate_unit
"""
if isinstance(sub_parameter, Scale):
if unit_... | Inflate a Parameter leaf according to unit type
Basic unit type are supposed by default
Other admissible unit types are threshold_unit and rate_unit | entailment |
def calculate_variable(self, variable = None, period = None, use_baseline = False):
"""
Compute and return the variable values for period and baseline or reform tax_benefit_system
"""
if use_baseline:
assert self.baseline_simulation is not None, "self.baseline_simulation is N... | Compute and return the variable values for period and baseline or reform tax_benefit_system | entailment |
def filter_input_variables(self, input_data_frame = None, simulation = None):
"""
Filter the input data frame from variables that won't be used or are set to be computed
"""
assert input_data_frame is not None
assert simulation is not None
id_variable_by_entity_key = self... | Filter the input data frame from variables that won't be used or are set to be computed | entailment |
def init_from_data(self, calibration_kwargs = None, inflation_kwargs = None,
rebuild_input_data = False, rebuild_kwargs = None, data = None, memory_config = None):
'''Initialises a survey scenario from data.
:param rebuild_input_data: Whether or not to clean, format and save data.
... | Initialises a survey scenario from data.
:param rebuild_input_data: Whether or not to clean, format and save data.
Take a look at :func:`build_input_data`
:param data: Contains the data, or metadata needed to know where to find it. | entailment |
def init_entity(self, entity = None, input_data_frame = None, period = None, simulation = None):
"""
Initialize the simulation period with current input_data_frame
"""
assert entity is not None
assert input_data_frame is not None
assert period is not None
assert s... | Initialize the simulation period with current input_data_frame | entailment |
def init_simulation_with_data_frame(self, input_data_frame = None, period = None, simulation = None, entity = None):
"""
Initialize the simulation period with current input_data_frame for an entity if specified
"""
assert input_data_frame is not None
assert period is not None
... | Initialize the simulation period with current input_data_frame for an entity if specified | entailment |
def neutralize_variables(self, tax_benefit_system):
"""
Neutralizing input variables not in input dataframe and keep some crucial variables
"""
for variable_name, variable in tax_benefit_system.variables.items():
if variable.formulas:
continue
if s... | Neutralizing input variables not in input dataframe and keep some crucial variables | entailment |
def set_tax_benefit_systems(self, tax_benefit_system = None, baseline_tax_benefit_system = None):
"""
Set the tax and benefit system and eventually the baseline tax and benefit system
"""
assert tax_benefit_system is not None
self.tax_benefit_system = tax_benefit_system
i... | Set the tax and benefit system and eventually the baseline tax and benefit system | entailment |
def summarize_variable(self, variable = None, use_baseline = False, weighted = False, force_compute = False):
"""
Prints a summary of a variable including its memory usage.
:param string variable: the variable being summarized
:param bool use_baseline: the tax-benefit-system... | Prints a summary of a variable including its memory usage.
:param string variable: the variable being summarized
:param bool use_baseline: the tax-benefit-system considered
:param bool weighted: whether the produced statistics should be weigthted or not
:param bool force... | entailment |
def _set_id_variable_by_entity_key(self) -> Dict[str, str]:
'''Identify and set the good ids for the different entities'''
if self.id_variable_by_entity_key is None:
self.id_variable_by_entity_key = dict(
(entity.key, entity.key + '_id') for entity in self.tax_benefit_system.... | Identify and set the good ids for the different entities | entailment |
def _set_role_variable_by_entity_key(self) -> Dict[str, str]:
'''Identify and set the good roles for the different entities'''
if self.role_variable_by_entity_key is None:
self.role_variable_by_entity_key = dict(
(entity.key, entity.key + '_legacy_role') for entity in self.ta... | Identify and set the good roles for the different entities | entailment |
def _set_used_as_input_variables_by_entity(self) -> Dict[str, List[str]]:
'''Identify and set the good input variables for the different entities'''
if self.used_as_input_variables_by_entity is not None:
return
tax_benefit_system = self.tax_benefit_system
assert set(self.us... | Identify and set the good input variables for the different entities | entailment |
def _dimensions(self):
"""tuple of dimension objects in this collection.
This composed tuple is the source for the dimension objects in this
collection.
"""
return tuple(d for d in self._all_dimensions if d.dimension_type != DT.MR_CAT) | tuple of dimension objects in this collection.
This composed tuple is the source for the dimension objects in this
collection. | entailment |
def _iter_dimensions(self):
"""Generate Dimension object for each dimension dict."""
return (
Dimension(raw_dimension.dimension_dict, raw_dimension.dimension_type)
for raw_dimension in self._raw_dimensions
) | Generate Dimension object for each dimension dict. | entailment |
def _raw_dimensions(self):
"""Sequence of _RawDimension objects wrapping each dimension dict."""
return tuple(
_RawDimension(dimension_dict, self._dimension_dicts)
for dimension_dict in self._dimension_dicts
) | Sequence of _RawDimension objects wrapping each dimension dict. | entailment |
def dimension_type(self):
"""Return member of DIMENSION_TYPE appropriate to dimension_dict."""
base_type = self._base_type
if base_type == "categorical":
return self._resolve_categorical()
if base_type == "enum.variable":
return self._resolve_array_type()
... | Return member of DIMENSION_TYPE appropriate to dimension_dict. | entailment |
def _base_type(self):
"""Return str like 'enum.numeric' representing dimension type.
This string is a 'type.subclass' concatenation of the str keys
used to identify the dimension type in the cube response JSON.
The '.subclass' suffix only appears where a subtype is present.
"""
... | Return str like 'enum.numeric' representing dimension type.
This string is a 'type.subclass' concatenation of the str keys
used to identify the dimension type in the cube response JSON.
The '.subclass' suffix only appears where a subtype is present. | entailment |
def _next_raw_dimension(self):
"""_RawDimension for next *dimension_dict* in sequence or None for last.
Returns None if this dimension is the last in sequence for this cube.
"""
dimension_dicts = self._dimension_dicts
this_idx = dimension_dicts.index(self._dimension_dict)
... | _RawDimension for next *dimension_dict* in sequence or None for last.
Returns None if this dimension is the last in sequence for this cube. | entailment |
def _resolve_array_type(self):
"""Return one of the ARRAY_TYPES members of DIMENSION_TYPE.
This method distinguishes between CA and MR dimensions. The return
value is only meaningful if the dimension is known to be of array
type (i.e. either CA or MR, base-type 'enum.variable').
... | Return one of the ARRAY_TYPES members of DIMENSION_TYPE.
This method distinguishes between CA and MR dimensions. The return
value is only meaningful if the dimension is known to be of array
type (i.e. either CA or MR, base-type 'enum.variable'). | entailment |
def _resolve_categorical(self):
"""Return one of the categorical members of DIMENSION_TYPE.
This method distinguishes between CAT, CA_CAT, MR_CAT, and LOGICAL
dimension types, all of which have the base type 'categorical'. The
return value is only meaningful if the dimension is known to... | Return one of the categorical members of DIMENSION_TYPE.
This method distinguishes between CAT, CA_CAT, MR_CAT, and LOGICAL
dimension types, all of which have the base type 'categorical'. The
return value is only meaningful if the dimension is known to be one
of the categorical types (h... | entailment |
def hs_indices(self):
"""tuple of (anchor_idx, addend_idxs) pair for each subtotal.
Example::
(
(2, (0, 1, 2)),
(3, (3,)),
('bottom', (4, 5))
)
Note that the `anchor_idx` item in the first position of each pair
ca... | tuple of (anchor_idx, addend_idxs) pair for each subtotal.
Example::
(
(2, (0, 1, 2)),
(3, (3,)),
('bottom', (4, 5))
)
Note that the `anchor_idx` item in the first position of each pair
can be 'top' or 'bottom' as well as... | entailment |
def inserted_hs_indices(self):
"""list of int index of each inserted subtotal for the dimension.
Each value represents the position of a subtotal in the interleaved
sequence of elements and subtotals items.
"""
# ---don't do H&S insertions for CA and MR subvar dimensions---
... | list of int index of each inserted subtotal for the dimension.
Each value represents the position of a subtotal in the interleaved
sequence of elements and subtotals items. | entailment |
def is_marginable(self):
"""True if adding counts across this dimension axis is meaningful."""
return self.dimension_type not in {DT.CA, DT.MR, DT.MR_CAT, DT.LOGICAL} | True if adding counts across this dimension axis is meaningful. | entailment |
def labels(
self, include_missing=False, include_transforms=False, include_cat_ids=False
):
"""Return list of str labels for the elements of this dimension.
Returns a list of (label, element_id) pairs if *include_cat_ids* is
True. The `element_id` value in the second position of the... | Return list of str labels for the elements of this dimension.
Returns a list of (label, element_id) pairs if *include_cat_ids* is
True. The `element_id` value in the second position of the pair is
None for subtotal items (which don't have an element-id). | entailment |
def _iter_interleaved_items(self, elements):
"""Generate element or subtotal items in interleaved order.
This ordering corresponds to how value "rows" (or columns) are to
appear after subtotals have been inserted at their anchor locations.
Where more than one subtotal is anchored to the... | Generate element or subtotal items in interleaved order.
This ordering corresponds to how value "rows" (or columns) are to
appear after subtotals have been inserted at their anchor locations.
Where more than one subtotal is anchored to the same location, they
appear in their document or... | entailment |
def _subtotals(self):
"""_Subtotals sequence object for this dimension.
The subtotals sequence provides access to any subtotal insertions
defined on this dimension.
"""
view = self._dimension_dict.get("references", {}).get("view", {})
# ---view can be both None and {}, t... | _Subtotals sequence object for this dimension.
The subtotals sequence provides access to any subtotal insertions
defined on this dimension. | entailment |
def _element_makings(self):
"""(ElementCls, element_dicts) pair for this dimension's elements.
All the elements of a given dimension are the same type. This method
determines the type (class) and source dicts for the elements of this
dimension and provides them for the element factory.
... | (ElementCls, element_dicts) pair for this dimension's elements.
All the elements of a given dimension are the same type. This method
determines the type (class) and source dicts for the elements of this
dimension and provides them for the element factory. | entailment |
def _elements(self):
"""Composed tuple storing actual sequence of element objects."""
ElementCls, element_dicts = self._element_makings
return tuple(
ElementCls(element_dict, idx, element_dicts)
for idx, element_dict in enumerate(element_dicts)
) | Composed tuple storing actual sequence of element objects. | entailment |
def numeric_value(self):
"""Numeric value assigned to element by user, np.nan if absent."""
numeric_value = self._element_dict.get("numeric_value")
return np.nan if numeric_value is None else numeric_value | Numeric value assigned to element by user, np.nan if absent. | entailment |
def label(self):
"""str display-name for this element, '' when absent from cube response.
This property handles numeric, datetime and text variables, but also
subvar dimensions
"""
value = self._element_dict.get("value")
type_name = type(value).__name__
if type_... | str display-name for this element, '' when absent from cube response.
This property handles numeric, datetime and text variables, but also
subvar dimensions | entailment |
def iter_for_anchor(self, anchor):
"""Generate each subtotal having matching *anchor*."""
return (subtotal for subtotal in self._subtotals if subtotal.anchor == anchor) | Generate each subtotal having matching *anchor*. | entailment |
def _iter_valid_subtotal_dicts(self):
"""Generate each insertion dict that represents a valid subtotal."""
for insertion_dict in self._insertion_dicts:
# ---skip any non-dicts---
if not isinstance(insertion_dict, dict):
continue
# ---skip any non-subt... | Generate each insertion dict that represents a valid subtotal. | entailment |
def _subtotals(self):
"""Composed tuple storing actual sequence of _Subtotal objects."""
return tuple(
_Subtotal(subtotal_dict, self.valid_elements)
for subtotal_dict in self._iter_valid_subtotal_dicts()
) | Composed tuple storing actual sequence of _Subtotal objects. | entailment |
def anchor(self):
"""int or str indicating element under which to insert this subtotal.
An int anchor is the id of the dimension element (category or
subvariable) under which to place this subtotal. The return value can
also be one of 'top' or 'bottom'.
The return value default... | int or str indicating element under which to insert this subtotal.
An int anchor is the id of the dimension element (category or
subvariable) under which to place this subtotal. The return value can
also be one of 'top' or 'bottom'.
The return value defaults to 'bottom' for an anchor r... | entailment |
def anchor_idx(self):
"""int or str representing index of anchor element in dimension.
When the anchor is an operation, like 'top' or 'bottom'
"""
anchor = self.anchor
if anchor in ["top", "bottom"]:
return anchor
return self.valid_elements.get_by_id(anchor).... | int or str representing index of anchor element in dimension.
When the anchor is an operation, like 'top' or 'bottom' | entailment |
def addend_ids(self):
"""tuple of int ids of elements contributing to this subtotal.
Any element id not present in the dimension or present but
representing missing data is excluded.
"""
return tuple(
arg
for arg in self._subtotal_dict.get("args", [])
... | tuple of int ids of elements contributing to this subtotal.
Any element id not present in the dimension or present but
representing missing data is excluded. | entailment |
def addend_idxs(self):
"""tuple of int index of each addend element for this subtotal.
The length of the tuple is the same as that for `.addend_ids`, but
each value repesents the offset of that element within the dimension,
rather than its element id.
"""
return tuple(
... | tuple of int index of each addend element for this subtotal.
The length of the tuple is the same as that for `.addend_ids`, but
each value repesents the offset of that element within the dimension,
rather than its element id. | entailment |
def create_data_file_by_format(directory_path = None):
"""
Browse subdirectories to extract stata and sas files
"""
stata_files = []
sas_files = []
for root, subdirs, files in os.walk(directory_path):
for file_name in files:
file_path = os.path.join(root, file_name)
... | Browse subdirectories to extract stata and sas files | entailment |
def as_array(
self,
include_missing=False,
weighted=True,
include_transforms_for_dims=None,
prune=False,
):
"""Return `ndarray` representing cube values.
Returns the tabular representation of the crunch cube. The returned
array has the same number of ... | Return `ndarray` representing cube values.
Returns the tabular representation of the crunch cube. The returned
array has the same number of dimensions as the cube. E.g. for
a cross-tab representation of a categorical and numerical variable,
the resulting cube will have two dimensions.
... | entailment |
def count(self, weighted=True):
"""Return numberic count of rows considered for cube response."""
return self._measures.weighted_n if weighted else self._measures.unweighted_n | Return numberic count of rows considered for cube response. | entailment |
def get_slices(self, ca_as_0th=False):
"""Return list of :class:`.CubeSlice` objects.
The number of slice objects in the returned list depends on the
dimensionality of this cube. A 1D or 2D cube will return a list
containing one slice object. A 3D cube will return a list of slices
... | Return list of :class:`.CubeSlice` objects.
The number of slice objects in the returned list depends on the
dimensionality of this cube. A 1D or 2D cube will return a list
containing one slice object. A 3D cube will return a list of slices
the same length as the first dimension. | entailment |
def index(self, weighted=True, prune=False):
"""Return cube index measurement.
This function is deprecated. Use index_table from CubeSlice.
"""
warnings.warn(
"CrunchCube.index() is deprecated. Use CubeSlice.index_table().",
DeprecationWarning,
)
... | Return cube index measurement.
This function is deprecated. Use index_table from CubeSlice. | entailment |
def inserted_hs_indices(self, prune=False):
"""Get indices of the inserted H&S (for formatting purposes)."""
if self.ndim == 2 and prune:
# If pruning is applied, we need to subtract from the H&S indes
# the number of pruned rows (cols) that come before that index.
pr... | Get indices of the inserted H&S (for formatting purposes). | entailment |
def is_univariate_ca(self):
"""True if cube only contains a CA dimension-pair, in either order."""
return self.ndim == 2 and set(self.dim_types) == {DT.CA_SUBVAR, DT.CA_CAT} | True if cube only contains a CA dimension-pair, in either order. | entailment |
def labels(self, include_missing=False, include_transforms_for_dims=False):
"""Gets labels for each cube's dimension.
Args
include_missing (bool): Include labels for missing values
Returns
labels (list of lists): Labels for each dimension
"""
return [
... | Gets labels for each cube's dimension.
Args
include_missing (bool): Include labels for missing values
Returns
labels (list of lists): Labels for each dimension | entailment |
def margin(
self,
axis=None,
weighted=True,
include_missing=False,
include_transforms_for_dims=None,
prune=False,
include_mr_cat=False,
):
"""Get margin for the selected axis.
the selected axis. For MR variables, this is the sum of the selecte... | Get margin for the selected axis.
the selected axis. For MR variables, this is the sum of the selected
and non-selected slices.
Args
axis (int): Axis across the margin is calculated. If no axis is
provided the margin is calculated across all axis.
... | entailment |
def mr_dim_ind(self):
"""Return int, tuple of int, or None, representing MR indices.
The return value represents the index of each multiple-response (MR)
dimension in this cube. Return value is None if there are no MR
dimensions, and int if there is one MR dimension, and a tuple of int
... | Return int, tuple of int, or None, representing MR indices.
The return value represents the index of each multiple-response (MR)
dimension in this cube. Return value is None if there are no MR
dimensions, and int if there is one MR dimension, and a tuple of int
when there are more than ... | entailment |
def population_counts(
self,
population_size,
weighted=True,
include_missing=False,
include_transforms_for_dims=None,
prune=False,
):
"""Return counts scaled in proportion to overall population.
The return value is a numpy.ndarray object. Count values... | Return counts scaled in proportion to overall population.
The return value is a numpy.ndarray object. Count values are scaled
proportionally to approximate their value if the entire population
had been sampled. This calculation is based on the estimated size of
the population provided a... | entailment |
def proportions(
self,
axis=None,
weighted=True,
include_transforms_for_dims=None,
include_mr_cat=False,
prune=False,
):
"""Return percentage values for cube as `numpy.ndarray`.
This function calculates the proportions across the selected axis
... | Return percentage values for cube as `numpy.ndarray`.
This function calculates the proportions across the selected axis
of a crunch cube. For most variable types, it means the value divided
by the margin value. For a multiple-response variable, the value is
divided by the sum of selecte... | entailment |
def _denominator(self, weighted, include_transforms_for_dims, axis):
"""Calculate denominator for percentages.
Only include those H&S dimensions, across which we DON'T sum. These H&S
are needed because of the shape, when dividing. Those across dims
which are summed across MUST NOT be in... | Calculate denominator for percentages.
Only include those H&S dimensions, across which we DON'T sum. These H&S
are needed because of the shape, when dividing. Those across dims
which are summed across MUST NOT be included, because they would
change the result. | entailment |
def scale_means(self, hs_dims=None, prune=False):
"""Get cube means."""
slices_means = [ScaleMeans(slice_).data for slice_ in self.slices]
if hs_dims and self.ndim > 1:
# Intersperse scale means with nans if H&S specified, and 2D. No
# need to modify 1D, as only one mean... | Get cube means. | entailment |
def zscore(self, weighted=True, prune=False, hs_dims=None):
"""Return ndarray with cube's zscore measurements.
Zscore is a measure of statistical significance of observed vs.
expected counts. It's only applicable to a 2D contingency tables.
For 3D cubes, the measures of separate slices ... | Return ndarray with cube's zscore measurements.
Zscore is a measure of statistical significance of observed vs.
expected counts. It's only applicable to a 2D contingency tables.
For 3D cubes, the measures of separate slices are stacked together
and returned as the result.
:para... | entailment |
def wishart_pairwise_pvals(self, axis=0):
"""Return matrices of column-comparison p-values as list of numpy.ndarrays.
Square, symmetric matrix along *axis* of pairwise p-values for the
null hypothesis that col[i] = col[j] for each pair of columns.
*axis* (int): axis along which to perf... | Return matrices of column-comparison p-values as list of numpy.ndarrays.
Square, symmetric matrix along *axis* of pairwise p-values for the
null hypothesis that col[i] = col[j] for each pair of columns.
*axis* (int): axis along which to perform comparison. Only columns (0)
are implemen... | entailment |
def _adjust_axis(self, axis):
"""Return raw axis/axes corresponding to apparent axis/axes.
This method adjusts user provided 'axis' parameter, for some of the
cube operations, mainly 'margin'. The user never sees the MR selections
dimension, and treats all MRs as single dimensions. Thus... | Return raw axis/axes corresponding to apparent axis/axes.
This method adjusts user provided 'axis' parameter, for some of the
cube operations, mainly 'margin'. The user never sees the MR selections
dimension, and treats all MRs as single dimensions. Thus we need to
adjust the values of ... | entailment |
def _adjust_inserted_indices(inserted_indices_list, prune_indices_list):
"""Adjust inserted indices, if there are pruned elements."""
# Created a copy, to preserve cached property
updated_inserted = [[i for i in dim_inds] for dim_inds in inserted_indices_list]
pruned_and_inserted = zip(p... | Adjust inserted indices, if there are pruned elements. | entailment |
def _apply_missings(self, res, include_missing=False):
"""Return ndarray with missing and insertions as specified.
The return value is the result of the following operations on *res*,
which is a raw cube value array (raw meaning it has shape of original
cube response).
* Remove... | Return ndarray with missing and insertions as specified.
The return value is the result of the following operations on *res*,
which is a raw cube value array (raw meaning it has shape of original
cube response).
* Remove vectors (rows/cols) for missing elements if *include_missin*
... | entailment |
def _apply_subtotals(self, res, include_transforms_for_dims):
"""* Insert subtotals (and perhaps other insertions later) for
dimensions having their apparent dimension-idx in
*include_transforms_for_dims*.
"""
if not include_transforms_for_dims:
return res
... | * Insert subtotals (and perhaps other insertions later) for
dimensions having their apparent dimension-idx in
*include_transforms_for_dims*. | entailment |
def _as_array(
self,
include_missing=False,
get_non_selected=False,
weighted=True,
include_transforms_for_dims=False,
):
"""Get crunch cube as ndarray.
Args
include_missing (bool): Include rows/cols for missing values.
get_non_selected... | Get crunch cube as ndarray.
Args
include_missing (bool): Include rows/cols for missing values.
get_non_selected (bool): Get non-selected slices for MR vars.
weighted (bool): Take weighted or unweighted counts.
include_transforms_for_dims (list): For which dims to... | entailment |
def _calculate_constraints_sum(cls, prop_table, prop_margin, axis):
"""Calculate sum of constraints (part of the standard error equation).
This method calculates the sum of the cell proportions multiplied by
row (or column) marginal proportions (margins divide by the total
count). It do... | Calculate sum of constraints (part of the standard error equation).
This method calculates the sum of the cell proportions multiplied by
row (or column) marginal proportions (margins divide by the total
count). It does this by utilizing the matrix multiplication, which
directly translat... | entailment |
def _counts(self, weighted):
"""Return _BaseMeasure subclass for *weighted* counts.
The return value is a _WeightedCountMeasure object if *weighted* is
True and the cube response is weighted. Otherwise it is an
_UnweightedCountMeasure object. Any means measure that may be present
... | Return _BaseMeasure subclass for *weighted* counts.
The return value is a _WeightedCountMeasure object if *weighted* is
True and the cube response is weighted. Otherwise it is an
_UnweightedCountMeasure object. Any means measure that may be present
is not considered. Contrast with `._me... | entailment |
def _cube_dict(self):
"""dict containing raw cube response, parsed from JSON payload."""
try:
cube_response = self._cube_response_arg
# ---parse JSON to a dict when constructed with JSON---
cube_dict = (
cube_response
if isinstance(cube... | dict containing raw cube response, parsed from JSON payload. | entailment |
def _drop_mr_cat_dims(self, array, fix_valids=False):
"""Return ndarray reflecting *array* with MR_CAT dims dropped.
If any (except 1st) dimension has a single element, it is
flattened in the resulting array (which is more convenient for the
users of the CrunchCube).
If the ori... | Return ndarray reflecting *array* with MR_CAT dims dropped.
If any (except 1st) dimension has a single element, it is
flattened in the resulting array (which is more convenient for the
users of the CrunchCube).
If the original shape of the cube is needed (e.g. to calculate the
... | entailment |
def _fix_valid_indices(cls, valid_indices, insertion_index, dim):
"""Add indices for H&S inserted elements."""
# TODO: make this accept an immutable sequence for valid_indices
# (a tuple) and return an immutable sequence rather than mutating an
# argument.
indices = np.array(sort... | Add indices for H&S inserted elements. | entailment |
def _insertions(self, result, dimension, dimension_index):
"""Return list of (idx, sum) pairs representing subtotals.
*idx* is the int offset at which to insert the ndarray subtotal
in *sum*.
"""
def iter_insertions():
for anchor_idx, addend_idxs in dimension.hs_ind... | Return list of (idx, sum) pairs representing subtotals.
*idx* is the int offset at which to insert the ndarray subtotal
in *sum*. | entailment |
def _is_axis_allowed(self, axis):
"""Check if axis are allowed.
In case the calculation is requested over CA items dimension, it is not
valid. It's valid in all other cases.
"""
if axis is None:
# If table direction was requested, we must ensure that each slice
... | Check if axis are allowed.
In case the calculation is requested over CA items dimension, it is not
valid. It's valid in all other cases. | entailment |
def _measure(self, weighted):
"""_BaseMeasure subclass representing primary measure for this cube.
If the cube response includes a means measure, the return value is
means. Otherwise it is counts, with the choice between weighted or
unweighted determined by *weighted*.
Note tha... | _BaseMeasure subclass representing primary measure for this cube.
If the cube response includes a means measure, the return value is
means. Otherwise it is counts, with the choice between weighted or
unweighted determined by *weighted*.
Note that weighted counts are provided on an "as-... | entailment |
def _prune_3d_body(self, res, transforms):
"""Return masked array where mask indicates pruned vectors.
*res* is an ndarray (result). *transforms* is a list of ...
"""
mask = np.zeros(res.shape)
mr_dim_idxs = self.mr_dim_ind
for i, prune_inds in enumerate(self.prune_indi... | Return masked array where mask indicates pruned vectors.
*res* is an ndarray (result). *transforms* is a list of ... | entailment |
def _prune_body(self, res, transforms=None):
"""Return a masked version of *res* where pruned rows/cols are masked.
Return value is an `np.ma.MaskedArray` object. Pruning is the removal
of rows or columns whose corresponding marginal elements are either
0 or not defined (np.nan).
... | Return a masked version of *res* where pruned rows/cols are masked.
Return value is an `np.ma.MaskedArray` object. Pruning is the removal
of rows or columns whose corresponding marginal elements are either
0 or not defined (np.nan). | entailment |
def prune_indices(self, transforms=None):
"""Return indices of pruned rows and columns as list.
The return value has one of three possible forms:
* a 1-element list of row indices (in case of 1D cube)
* 2-element list of row and col indices (in case of 2D cube)
* n-element list... | Return indices of pruned rows and columns as list.
The return value has one of three possible forms:
* a 1-element list of row indices (in case of 1D cube)
* 2-element list of row and col indices (in case of 2D cube)
* n-element list of tuples of 2 elements (if it's 3D cube).
... | entailment |
def _pruning_base(self, axis=None, hs_dims=None):
"""Gets margin if across CAT dimension. Gets counts if across items.
Categorical variables are pruned based on their marginal values. If the
marginal is a 0 or a NaN, the corresponding row/column is pruned. In
case of a subvars (items) d... | Gets margin if across CAT dimension. Gets counts if across items.
Categorical variables are pruned based on their marginal values. If the
marginal is a 0 or a NaN, the corresponding row/column is pruned. In
case of a subvars (items) dimension, we only prune if all the counts
of the corr... | entailment |
def _update_result(self, result, insertions, dimension_index):
"""Insert subtotals into resulting ndarray."""
for j, (ind_insertion, value) in enumerate(insertions):
result = np.insert(
result, ind_insertion + j + 1, value, axis=dimension_index
)
return re... | Insert subtotals into resulting ndarray. | entailment |
def is_weighted(self):
"""True if weights have been applied to the measure(s) for this cube.
Unweighted counts are available for all cubes. Weighting applies to
any other measures provided by the cube.
"""
cube_dict = self._cube_dict
if cube_dict.get("query", {}).get("we... | True if weights have been applied to the measure(s) for this cube.
Unweighted counts are available for all cubes. Weighting applies to
any other measures provided by the cube. | entailment |
def means(self):
"""_MeanMeasure object providing access to means values.
None when the cube response does not contain a mean measure.
"""
mean_measure_dict = (
self._cube_dict.get("result", {}).get("measures", {}).get("mean")
)
if mean_measure_dict is None:
... | _MeanMeasure object providing access to means values.
None when the cube response does not contain a mean measure. | entailment |
def missing_count(self):
"""numeric representing count of missing rows in cube response."""
if self.means:
return self.means.missing_count
return self._cube_dict["result"].get("missing", 0) | numeric representing count of missing rows in cube response. | entailment |
def population_fraction(self):
"""The filtered/unfiltered ratio for cube response.
This value is required for properly calculating population on a cube
where a filter has been applied. Returns 1.0 for an unfiltered cube.
Returns `np.nan` if the unfiltered count is zero, which would
... | The filtered/unfiltered ratio for cube response.
This value is required for properly calculating population on a cube
where a filter has been applied. Returns 1.0 for an unfiltered cube.
Returns `np.nan` if the unfiltered count is zero, which would
otherwise result in a divide-by-zero e... | entailment |
def weighted_counts(self):
"""_WeightedCountMeasure object for this cube.
This object provides access to weighted counts for this cube, if
available. If the cube response is not weighted, the
_UnweightedCountMeasure object for this cube is returned.
"""
if not self.is_we... | _WeightedCountMeasure object for this cube.
This object provides access to weighted counts for this cube, if
available. If the cube response is not weighted, the
_UnweightedCountMeasure object for this cube is returned. | entailment |
def weighted_n(self):
"""float count of returned rows adjusted for weighting."""
if not self.is_weighted:
return float(self.unweighted_n)
return float(sum(self._cube_dict["result"]["measures"]["count"]["data"])) | float count of returned rows adjusted for weighting. | entailment |
def raw_cube_array(self):
"""Return read-only ndarray of measure values from cube-response.
The shape of the ndarray mirrors the shape of the (raw) cube
response. Specifically, it includes values for missing elements, any
MR_CAT dimensions, and any prunable rows and columns.
"""... | Return read-only ndarray of measure values from cube-response.
The shape of the ndarray mirrors the shape of the (raw) cube
response. Specifically, it includes values for missing elements, any
MR_CAT dimensions, and any prunable rows and columns. | entailment |
def _flat_values(self):
"""Return tuple of mean values as found in cube response.
Mean data may include missing items represented by a dict like
{'?': -1} in the cube response. These are replaced by np.nan in the
returned value.
"""
return tuple(
np.nan if ty... | Return tuple of mean values as found in cube response.
Mean data may include missing items represented by a dict like
{'?': -1} in the cube response. These are replaced by np.nan in the
returned value. | entailment |
def make_input_dataframe_by_entity(tax_benefit_system, nb_persons, nb_groups):
"""
Generate a dictionnary of dataframes containing nb_persons persons spread in nb_groups groups.
:param TaxBenefitSystem tax_benefit_system: the tax_benefit_system to use
:param int nb_persons: the number of pe... | Generate a dictionnary of dataframes containing nb_persons persons spread in nb_groups groups.
:param TaxBenefitSystem tax_benefit_system: the tax_benefit_system to use
:param int nb_persons: the number of persons in the system
:param int nb_groups: the number of collective entities in the syst... | entailment |
def randomly_init_variable(tax_benefit_system, input_dataframe_by_entity, variable_name, max_value, condition = None, seed = None):
"""
Initialise a variable with random values (from 0 to max_value).
If a condition vector is provided, only set the value of persons or groups for which condition is Tr... | Initialise a variable with random values (from 0 to max_value).
If a condition vector is provided, only set the value of persons or groups for which condition is True.
Exemple:
>>> from openfisca_survey_manager.input_dataframe_generator import make_input_dataframe_by_entity
>>> from op... | entailment |
def get_value(self, variable = None, table = None):
"""
Get value
Parameters
----------
variable : string
name of the variable
table : string, default None
name of the table hosting the variable
Returns
-------
df... | Get value
Parameters
----------
variable : string
name of the variable
table : string, default None
name of the table hosting the variable
Returns
-------
df : DataFrame, default None
A DataFrame containing the varia... | entailment |
def get_values(self, variables = None, table = None, lowercase = False, rename_ident = True):
"""
Get values
Parameters
----------
variables : list of strings, default None
list of variables names, if None return the whole table
table : string, defaul... | Get values
Parameters
----------
variables : list of strings, default None
list of variables names, if None return the whole table
table : string, default None
name of the table hosting the variables
lowercase : boolean, deflault True
... | entailment |
def insert_table(self, label = None, name = None, **kwargs):
"""
Insert a table in the Survey object
"""
data_frame = kwargs.pop('data_frame', None)
if data_frame is None:
data_frame = kwargs.pop('dataframe', None)
to_hdf_kwargs = kwargs.pop('to_hdf_kwargs',... | Insert a table in the Survey object | entailment |
def quantile(q, variable, weight_variable = None, filter_variable = None):
"""
Return quantile of a variable with weight provided by a specific wieght variable potentially filtered
"""
def formula(entity, period):
value = entity(variable, period)
if weight_variable is not None:
... | Return quantile of a variable with weight provided by a specific wieght variable potentially filtered | entailment |
def _get_version():
"""Get the version from package itself."""
with open("../waliki/__init__.py") as fh:
for line in fh:
if line.startswith("__version__ = "):
return line.split("=")[-1].strip().strip("'").strip('"') | Get the version from package itself. | entailment |
def clean_meta(rst_content):
"""remove moinmoin metada from the top of the file"""
rst = rst_content.split('\n')
for i, line in enumerate(rst):
if line.startswith('#'):
continue
break
return '\n'.join(rst[i:]) | remove moinmoin metada from the top of the file | entailment |
def entry_point(context, block_name):
"""include an snippet at the bottom of a block, if it exists
For example, if the plugin with slug 'attachments' is registered
waliki/attachments_edit_content.html will be included with
{% entry_point 'edit_content' %}
which is declared at the bottom ... | include an snippet at the bottom of a block, if it exists
For example, if the plugin with slug 'attachments' is registered
waliki/attachments_edit_content.html will be included with
{% entry_point 'edit_content' %}
which is declared at the bottom of the block 'content' in edit.html | entailment |
def check_perms(parser, token):
"""
Returns a list of permissions (as ``codename`` strings) for a given
``user``/``group`` and ``obj`` (Model instance).
Parses ``check_perms`` tag which should be in format::
{% check_perms "perm1[, perm2, ...]" for user in slug as "context_var" %}
or
... | Returns a list of permissions (as ``codename`` strings) for a given
``user``/``group`` and ``obj`` (Model instance).
Parses ``check_perms`` tag which should be in format::
{% check_perms "perm1[, perm2, ...]" for user in slug as "context_var" %}
or
{% check_perms "perm1[, perm2, ...]" fo... | entailment |
def waliki_box(context, slug, show_edit=True, *args, **kwargs):
"""
A templatetag to render a wiki page content as a box in any webpage,
and allow rapid edition if you have permission.
It's inspired in `django-boxes`_
.. _django-boxes: https://github.com/eldarion/django-boxes
"""
request ... | A templatetag to render a wiki page content as a box in any webpage,
and allow rapid edition if you have permission.
It's inspired in `django-boxes`_
.. _django-boxes: https://github.com/eldarion/django-boxes | entailment |
def check_perms(perms, user, slug, raise_exception=False):
"""a helper user to check if a user has the permissions
for a given slug"""
if isinstance(perms, string_types):
perms = {perms}
else:
perms = set(perms)
allowed_users = ACLRule.get_users_for(perms, slug)
if allowed_user... | a helper user to check if a user has the permissions
for a given slug | entailment |
def permission_required(perms, login_url=None, raise_exception=False, redirect_field_name=REDIRECT_FIELD_NAME):
"""
this is analog to django's builtin ``permission_required`` decorator, but
improved to check per slug ACLRules and default permissions for
anonymous and logged in users
if there is a r... | this is analog to django's builtin ``permission_required`` decorator, but
improved to check per slug ACLRules and default permissions for
anonymous and logged in users
if there is a rule affecting a slug, the user needs to be part of the
rule's allowed users. If there isn't a matching rule, defaults pe... | entailment |
def get_module(app, modname, verbose=False, failfast=False):
"""
Internal function to load a module from a single app.
taken from https://github.com/ojii/django-load.
"""
module_name = '%s.%s' % (app, modname)
try:
module = import_module(module_name)
except ImportError as e:
... | Internal function to load a module from a single app.
taken from https://github.com/ojii/django-load. | entailment |
def load(modname, verbose=False, failfast=False):
"""
Loads all modules with name 'modname' from all installed apps.
If verbose is True, debug information will be printed to stdout.
If failfast is True, import errors will not be surpressed.
"""
for app in settings.INSTALLED_APPS:
get_mod... | Loads all modules with name 'modname' from all installed apps.
If verbose is True, debug information will be printed to stdout.
If failfast is True, import errors will not be surpressed. | entailment |
def register(PluginClass):
"""
Register a plugin class. This function will call back your plugin's
constructor.
"""
if PluginClass in _cache.keys():
raise Exception("Plugin class already registered")
plugin = PluginClass()
_cache[PluginClass] = plugin
if getattr(PluginClass, 'ex... | Register a plugin class. This function will call back your plugin's
constructor. | entailment |
def render_form(form):
"""same than {{ form|crispy }} if crispy_forms is installed.
render using a bootstrap3 templating otherwise"""
if 'crispy_forms' in settings.INSTALLED_APPS:
from crispy_forms.templatetags.crispy_forms_filters import as_crispy_form
return as_crispy_form(form)
tem... | same than {{ form|crispy }} if crispy_forms is installed.
render using a bootstrap3 templating otherwise | entailment |
def settings(request):
"""inject few waliki's settings to the context to be used in templates"""
from waliki.settings import WALIKI_USE_MATHJAX # NOQA
return {k: v for (k, v) in locals().items() if k.startswith('WALIKI')} | inject few waliki's settings to the context to be used in templates | entailment |
def smart_encode_str(s):
"""Create a UTF-16 encoded PDF string literal for `s`."""
try:
utf16 = s.encode('utf_16_be')
except AttributeError: # ints and floats
utf16 = str(s).encode('utf_16_be')
safe = utf16.replace(b'\x00)', b'\x00\\)').replace(b'\x00(', b'\x00\\(')
return b''.join(... | Create a UTF-16 encoded PDF string literal for `s`. | entailment |
def forge_fdf(pdf_form_url=None, fdf_data_strings=[], fdf_data_names=[],
fields_hidden=[], fields_readonly=[],
checkbox_checked_name=b"Yes"):
"""Generates fdf string from fields specified
* pdf_form_url (default: None): just the url for the form.
* fdf_data_strings (default: [])... | Generates fdf string from fields specified
* pdf_form_url (default: None): just the url for the form.
* fdf_data_strings (default: []): array of (string, value) tuples for the
form fields (or dicts). Value is passed as a UTF-16 encoded string,
unless True/False, in which case it is assumed to be a ... | entailment |
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