id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
168,831 | import functools
import re
import warnings
import numpy.core.numeric as NX
from numpy.core import (isscalar, abs, finfo, atleast_1d, hstack, dot, array,
ones)
from numpy.core import overrides
from numpy.core.overrides import set_module
from numpy.lib.twodim_base import diag, vander
from numpy.li... | Find the product of two polynomials. .. note:: This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in `numpy.polynomial` is preferred. A summary of the differences can be found in the :doc:`transition guide </reference/routines.polynomials>`. Finds the polynomial resulting from ... |
168,832 | import functools
import re
import warnings
import numpy.core.numeric as NX
from numpy.core import (isscalar, abs, finfo, atleast_1d, hstack, dot, array,
ones)
from numpy.core import overrides
from numpy.core.overrides import set_module
from numpy.lib.twodim_base import diag, vander
from numpy.li... | null |
168,833 | import functools
import re
import warnings
import numpy.core.numeric as NX
from numpy.core import (isscalar, abs, finfo, atleast_1d, hstack, dot, array,
ones)
from numpy.core import overrides
from numpy.core.overrides import set_module
from numpy.lib.twodim_base import diag, vander
from numpy.li... | Returns the quotient and remainder of polynomial division. .. note:: This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in `numpy.polynomial` is preferred. A summary of the differences can be found in the :doc:`transition guide </reference/routines.polynomials>`. The input arra... |
168,834 | import functools
import re
import warnings
import numpy.core.numeric as NX
from numpy.core import (isscalar, abs, finfo, atleast_1d, hstack, dot, array,
ones)
from numpy.core import overrides
from numpy.core.overrides import set_module
from numpy.lib.twodim_base import diag, vander
from numpy.li... | null |
168,858 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | null |
168,860 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | null |
168,875 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def _recursive_fill_fields_dispatcher(input,... | null |
168,876 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
The provided code snippet includes necessary... | Returns the field names of the input datatype as a tuple. Input datatype must have fields otherwise error is raised. Parameters ---------- adtype : dtype Input datatype Examples -------- >>> from numpy.lib import recfunctions as rfn >>> rfn.get_names(np.empty((1,), dtype=[('A', int)]).dtype) ('A',) >>> rfn.get_names(np... |
168,877 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
The provided code snippet includes necessary... | Returns the field names of the input datatype as a tuple. Input datatype must have fields otherwise error is raised. Nested structure are flattened beforehand. Parameters ---------- adtype : dtype Input datatype Examples -------- >>> from numpy.lib import recfunctions as rfn >>> rfn.get_names_flat(np.empty((1,), dtype=... |
168,878 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def _zip_dtype(seqarrays, flatten=False):
... | Combine the dtype description of a series of arrays. Parameters ---------- seqarrays : sequence of arrays Sequence of arrays flatten : {boolean}, optional Whether to collapse nested descriptions. |
168,879 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def _merge_arrays_dispatcher(seqarrays, fill... | null |
168,880 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def _drop_fields_dispatcher(base, drop_names... | null |
168,881 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def _rec_drop_fields_dispatcher(base, drop_n... | null |
168,882 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def drop_fields(base, drop_names, usemask=Tru... | Returns a new numpy.recarray with fields in `drop_names` dropped. |
168,883 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def _rename_fields_dispatcher(base, namemapp... | null |
168,884 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
The provided code snippet includes necessary... | Rename the fields from a flexible-datatype ndarray or recarray. Nested fields are supported. Parameters ---------- base : ndarray Input array whose fields must be modified. namemapper : dictionary Dictionary mapping old field names to their new version. Examples -------- >>> from numpy.lib import recfunctions as rfn >>... |
168,885 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def _append_fields_dispatcher(base, names, d... | null |
168,886 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def _rec_append_fields_dispatcher(base, name... | null |
168,887 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def append_fields(base, names, data, dtypes=N... | Add new fields to an existing array. The names of the fields are given with the `names` arguments, the corresponding values with the `data` arguments. If a single field is appended, `names`, `data` and `dtypes` do not have to be lists but just values. Parameters ---------- base : array Input array to extend. names : st... |
168,888 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def _repack_fields_dispatcher(a, align=None,... | null |
168,889 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
The provided code snippet includes necessary... | Re-pack the fields of a structured array or dtype in memory. The memory layout of structured datatypes allows fields at arbitrary byte offsets. This means the fields can be separated by padding bytes, their offsets can be non-monotonically increasing, and they can overlap. This method removes any overlaps and reorders ... |
168,890 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def _structured_to_unstructured_dispatcher(a... | null |
168,891 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def _unstructured_to_structured_dispatcher(a... | null |
168,892 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def _get_fields_and_offsets(dt, offset=0):
... | Converts an n-D unstructured array into an (n-1)-D structured array. The last dimension of the input array is converted into a structure, with number of field-elements equal to the size of the last dimension of the input array. By default all output fields have the input array's dtype, but an output structured dtype wi... |
168,893 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def _apply_along_fields_dispatcher(func, arr... | null |
168,894 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def structured_to_unstructured(arr, dtype=Non... | Apply function 'func' as a reduction across fields of a structured array. This is similar to `apply_along_axis`, but treats the fields of a structured array as an extra axis. The fields are all first cast to a common type following the type-promotion rules from `numpy.result_type` applied to the field's dtypes. Paramet... |
168,895 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def _assign_fields_by_name_dispatcher(dst, s... | null |
168,896 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def _require_fields_dispatcher(array, requir... | null |
168,897 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def assign_fields_by_name(dst, src, zero_unas... | Casts a structured array to a new dtype using assignment by field-name. This function assigns from the old to the new array by name, so the value of a field in the output array is the value of the field with the same name in the source array. This has the effect of creating a new ndarray containing only the fields "req... |
168,898 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def _stack_arrays_dispatcher(arrays, default... | null |
168,899 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def _get_fieldspec(dtype):
"""
Produc... | Superposes arrays fields by fields Parameters ---------- arrays : array or sequence Sequence of input arrays. defaults : dictionary, optional Dictionary mapping field names to the corresponding default values. usemask : {True, False}, optional Whether to return a MaskedArray (or MaskedRecords is `asrecarray==True`) or ... |
168,900 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def _find_duplicates_dispatcher(
a, ... | null |
168,901 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def get_fieldstructure(adtype, lastname=None,... | Find the duplicates in a structured array along a given key Parameters ---------- a : array-like Input array key : {string, None}, optional Name of the fields along which to check the duplicates. If None, the search is performed by records ignoremask : {True, False}, optional Whether masked data should be discarded or ... |
168,902 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def _join_by_dispatcher(
key, r1, r2... | null |
168,903 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def _rec_join_dispatcher(
key, r1, r... | null |
168,904 | import itertools
import numpy as np
import numpy.ma as ma
from numpy import ndarray, recarray
from numpy.ma import MaskedArray
from numpy.ma.mrecords import MaskedRecords
from numpy.core.overrides import array_function_dispatch
from numpy.lib._iotools import _is_string_like
def join_by(key, r1, r2, jointype='inner', r1... | Join arrays `r1` and `r2` on keys. Alternative to join_by, that always returns a np.recarray. See Also -------- join_by : equivalent function |
168,905 | import numpy
import warnings
from numpy.lib.utils import safe_eval
from numpy.compat import (
isfileobj, os_fspath, pickle
)
def _write_array_header(fp, d, version=None):
""" Write the header for an array and returns the version used
Parameters
----------
fp : filelike object
d : dict
... | Write the header for an array using the 1.0 format. Parameters ---------- fp : filelike object d : dict This has the appropriate entries for writing its string representation to the header of the file. |
168,906 | import numpy
import warnings
from numpy.lib.utils import safe_eval
from numpy.compat import (
isfileobj, os_fspath, pickle
)
def _write_array_header(fp, d, version=None):
""" Write the header for an array and returns the version used
Parameters
----------
fp : filelike object
d : dict
... | Write the header for an array using the 2.0 format. The 2.0 format allows storing very large structured arrays. .. versionadded:: 1.9.0 Parameters ---------- fp : filelike object d : dict This has the appropriate entries for writing its string representation to the header of the file. |
168,907 | import numpy
import warnings
from numpy.lib.utils import safe_eval
from numpy.compat import (
isfileobj, os_fspath, pickle
)
_MAX_HEADER_SIZE = 10000
def _read_array_header(fp, version, max_header_size=_MAX_HEADER_SIZE):
"""
see read_array_header_1_0
"""
# Read an unsigned, little-endian short i... | Read an array header from a filelike object using the 1.0 file format version. This will leave the file object located just after the header. Parameters ---------- fp : filelike object A file object or something with a `.read()` method like a file. Returns ------- shape : tuple of int The shape of the array. fortran_or... |
168,908 | import numpy
import warnings
from numpy.lib.utils import safe_eval
from numpy.compat import (
isfileobj, os_fspath, pickle
)
_MAX_HEADER_SIZE = 10000
def _read_array_header(fp, version, max_header_size=_MAX_HEADER_SIZE):
"""
see read_array_header_1_0
"""
# Read an unsigned, little-endian short i... | Read an array header from a filelike object using the 2.0 file format version. This will leave the file object located just after the header. .. versionadded:: 1.9.0 Parameters ---------- fp : filelike object A file object or something with a `.read()` method like a file. max_header_size : int, optional Maximum allowed... |
168,909 | import contextlib
import functools
import operator
import warnings
import numpy as np
from numpy.core import overrides
def _ptp(x):
"""Peak-to-peak value of x.
This implementation avoids the problem of signed integer arrays having a
peak-to-peak value that cannot be represented with the array's data type.
... | Square root histogram bin estimator. Bin width is inversely proportional to the data size. Used by many programs for its simplicity. Parameters ---------- x : array_like Input data that is to be histogrammed, trimmed to range. May not be empty. Returns ------- h : An estimate of the optimal bin width for the given data... |
168,910 | import contextlib
import functools
import operator
import warnings
import numpy as np
from numpy.core import overrides
def _ptp(x):
"""Peak-to-peak value of x.
This implementation avoids the problem of signed integer arrays having a
peak-to-peak value that cannot be represented with the array's data type.
... | Rice histogram bin estimator. Another simple estimator with no normality assumption. It has better performance for large data than Sturges, but tends to overestimate the number of bins. The number of bins is proportional to the cube root of data size (asymptotically optimal). The estimate depends only on size of the da... |
168,911 | import contextlib
import functools
import operator
import warnings
import numpy as np
from numpy.core import overrides
The provided code snippet includes necessary dependencies for implementing the `_hist_bin_scott` function. Write a Python function `def _hist_bin_scott(x, range)` to solve the following problem:
Scott... | Scott histogram bin estimator. The binwidth is proportional to the standard deviation of the data and inversely proportional to the cube root of data size (asymptotically optimal). Parameters ---------- x : array_like Input data that is to be histogrammed, trimmed to range. May not be empty. Returns ------- h : An esti... |
168,912 | import contextlib
import functools
import operator
import warnings
import numpy as np
from numpy.core import overrides
_range = range
def _ptp(x):
"""Peak-to-peak value of x.
This implementation avoids the problem of signed integer arrays having a
peak-to-peak value that cannot be represented with the array... | Histogram bin estimator based on minimizing the estimated integrated squared error (ISE). The number of bins is chosen by minimizing the estimated ISE against the unknown true distribution. The ISE is estimated using cross-validation and can be regarded as a generalization of Scott's rule. https://en.wikipedia.org/wiki... |
168,913 | import contextlib
import functools
import operator
import warnings
import numpy as np
from numpy.core import overrides
def _ptp(x):
"""Peak-to-peak value of x.
This implementation avoids the problem of signed integer arrays having a
peak-to-peak value that cannot be represented with the array's data type.
... | Doane's histogram bin estimator. Improved version of Sturges' formula which works better for non-normal data. See stats.stackexchange.com/questions/55134/doanes-formula-for-histogram-binning Parameters ---------- x : array_like Input data that is to be histogrammed, trimmed to range. May not be empty. Returns ------- h... |
168,914 | import contextlib
import functools
import operator
import warnings
import numpy as np
from numpy.core import overrides
def _hist_bin_sturges(x, range):
"""
Sturges histogram bin estimator.
A very simplistic estimator based on the assumption of normality of
the data. This estimator has poor performance f... | Histogram bin estimator that uses the minimum width of the Freedman-Diaconis and Sturges estimators if the FD bin width is non-zero. If the bin width from the FD estimator is 0, the Sturges estimator is used. The FD estimator is usually the most robust method, but its width estimate tends to be too large for small `x` ... |
168,915 | import contextlib
import functools
import operator
import warnings
import numpy as np
from numpy.core import overrides
def _histogram_bin_edges_dispatcher(a, bins=None, range=None, weights=None):
return (a, bins, weights) | null |
168,916 | import contextlib
import functools
import operator
import warnings
import numpy as np
from numpy.core import overrides
def _ravel_and_check_weights(a, weights):
""" Check a and weights have matching shapes, and ravel both """
a = np.asarray(a)
# Ensure that the array is a "subtractable" dtype
if a.dtype... | r""" Function to calculate only the edges of the bins used by the `histogram` function. Parameters ---------- a : array_like Input data. The histogram is computed over the flattened array. bins : int or sequence of scalars or str, optional If `bins` is an int, it defines the number of equal-width bins in the given rang... |
168,917 | import contextlib
import functools
import operator
import warnings
import numpy as np
from numpy.core import overrides
def _histogram_dispatcher(
a, bins=None, range=None, density=None, weights=None):
return (a, bins, weights) | null |
168,918 | import contextlib
import functools
import operator
import warnings
import numpy as np
from numpy.core import overrides
def _histogramdd_dispatcher(sample, bins=None, range=None, density=None,
weights=None):
if hasattr(sample, 'shape'): # same condition as used in histogramdd
yi... | null |
168,919 | import contextlib
import functools
import operator
import warnings
import numpy as np
from numpy.core import overrides
_range = range
def _get_outer_edges(a, range):
"""
Determine the outer bin edges to use, from either the data or the range
argument
"""
if range is not None:
first_edge, las... | Compute the multidimensional histogram of some data. Parameters ---------- sample : (N, D) array, or (N, D) array_like The data to be histogrammed. Note the unusual interpretation of sample when an array_like: * When an array, each row is a coordinate in a D-dimensional space - such as ``histogramdd(np.array([p1, p2, p... |
168,920 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
The provided code snippet includes necessary dependencies for implementing the `_nan_mask` function. Write a Python function `def _nan_mask(a, out=None)` to solve the following problem:
Parameters -... | Parameters ---------- a : array-like Input array with at least 1 dimension. out : ndarray, optional Alternate output array in which to place the result. The default is ``None``; if provided, it must have the same shape as the expected output and will prevent the allocation of a new array. Returns ------- y : bool ndarr... |
168,921 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _nanmin_dispatcher(a, axis=None, out=None, keepdims=None,
initial=None, where=None):
return (a, out) | null |
168,922 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _replace_nan(a, val):
"""
If `a` is of inexact type, make a copy of `a`, replace NaNs with
the `val` value, and return the copy together with a boolean mask
marking the locations ... | Return minimum of an array or minimum along an axis, ignoring any NaNs. When all-NaN slices are encountered a ``RuntimeWarning`` is raised and Nan is returned for that slice. Parameters ---------- a : array_like Array containing numbers whose minimum is desired. If `a` is not an array, a conversion is attempted. axis :... |
168,923 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _nanmax_dispatcher(a, axis=None, out=None, keepdims=None,
initial=None, where=None):
return (a, out) | null |
168,924 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _replace_nan(a, val):
"""
If `a` is of inexact type, make a copy of `a`, replace NaNs with
the `val` value, and return the copy together with a boolean mask
marking the locations ... | Return the maximum of an array or maximum along an axis, ignoring any NaNs. When all-NaN slices are encountered a ``RuntimeWarning`` is raised and NaN is returned for that slice. Parameters ---------- a : array_like Array containing numbers whose maximum is desired. If `a` is not an array, a conversion is attempted. ax... |
168,925 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _nanargmin_dispatcher(a, axis=None, out=None, *, keepdims=None):
return (a,) | null |
168,926 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _replace_nan(a, val):
"""
If `a` is of inexact type, make a copy of `a`, replace NaNs with
the `val` value, and return the copy together with a boolean mask
marking the locations ... | Return the indices of the minimum values in the specified axis ignoring NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the results cannot be trusted if a slice contains only NaNs and Infs. Parameters ---------- a : array_like Input data. axis : int, optional Axis along which to operate. By default flattene... |
168,927 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _nanargmax_dispatcher(a, axis=None, out=None, *, keepdims=None):
return (a,) | null |
168,928 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _replace_nan(a, val):
"""
If `a` is of inexact type, make a copy of `a`, replace NaNs with
the `val` value, and return the copy together with a boolean mask
marking the locations ... | Return the indices of the maximum values in the specified axis ignoring NaNs. For all-NaN slices ``ValueError`` is raised. Warning: the results cannot be trusted if a slice contains only NaNs and -Infs. Parameters ---------- a : array_like Input data. axis : int, optional Axis along which to operate. By default flatten... |
168,929 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _nansum_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None,
initial=None, where=None):
return (a, out) | null |
168,930 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _replace_nan(a, val):
"""
If `a` is of inexact type, make a copy of `a`, replace NaNs with
the `val` value, and return the copy together with a boolean mask
marking the locations ... | Return the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero. In NumPy versions <= 1.9.0 Nan is returned for slices that are all-NaN or empty. In later versions zero is returned. Parameters ---------- a : array_like Array containing numbers whose sum is desired. If `a` is not an array, a con... |
168,931 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _nanprod_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None,
initial=None, where=None):
return (a, out) | null |
168,932 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _replace_nan(a, val):
"""
If `a` is of inexact type, make a copy of `a`, replace NaNs with
the `val` value, and return the copy together with a boolean mask
marking the locations ... | Return the product of array elements over a given axis treating Not a Numbers (NaNs) as ones. One is returned for slices that are all-NaN or empty. .. versionadded:: 1.10.0 Parameters ---------- a : array_like Array containing numbers whose product is desired. If `a` is not an array, a conversion is attempted. axis : {... |
168,933 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _nancumsum_dispatcher(a, axis=None, dtype=None, out=None):
return (a, out) | null |
168,934 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _replace_nan(a, val):
"""
If `a` is of inexact type, make a copy of `a`, replace NaNs with
the `val` value, and return the copy together with a boolean mask
marking the locations ... | Return the cumulative sum of array elements over a given axis treating Not a Numbers (NaNs) as zero. The cumulative sum does not change when NaNs are encountered and leading NaNs are replaced by zeros. Zeros are returned for slices that are all-NaN or empty. .. versionadded:: 1.12.0 Parameters ---------- a : array_like... |
168,935 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _nancumprod_dispatcher(a, axis=None, dtype=None, out=None):
return (a, out) | null |
168,936 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _replace_nan(a, val):
"""
If `a` is of inexact type, make a copy of `a`, replace NaNs with
the `val` value, and return the copy together with a boolean mask
marking the locations ... | Return the cumulative product of array elements over a given axis treating Not a Numbers (NaNs) as one. The cumulative product does not change when NaNs are encountered and leading NaNs are replaced by ones. Ones are returned for slices that are all-NaN or empty. .. versionadded:: 1.12.0 Parameters ---------- a : array... |
168,937 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _nanmean_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None,
*, where=None):
return (a, out) | null |
168,938 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _nanmedian_dispatcher(
a, axis=None, out=None, overwrite_input=None, keepdims=None):
return (a, out) | null |
168,939 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def nanmean(a, axis=None, dtype=None, out=None, keepdims=np._NoValue,
*, where=np._NoValue):
"""
Compute the arithmetic mean along the specified axis, ignoring NaNs.
Returns t... | Compute the median along the specified axis, while ignoring NaNs. Returns the median of the array elements. .. versionadded:: 1.9.0 Parameters ---------- a : array_like Input array or object that can be converted to an array. axis : {int, sequence of int, None}, optional Axis or axes along which the medians are compute... |
168,940 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _nanpercentile_dispatcher(
a, q, axis=None, out=None, overwrite_input=None,
method=None, keepdims=None, *, interpolation=None):
return (a, q, out) | null |
168,941 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _nanquantile_unchecked(
a,
q,
axis=None,
out=None,
overwrite_input=False,
method="linear",
keepdims=np._NoValue,
):
"""Assumes that q i... | Compute the qth percentile of the data along the specified axis, while ignoring nan values. Returns the qth percentile(s) of the array elements. .. versionadded:: 1.9.0 Parameters ---------- a : array_like Input array or object that can be converted to an array, containing nan values to be ignored. q : array_like of fl... |
168,942 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _nanquantile_dispatcher(a, q, axis=None, out=None, overwrite_input=None,
method=None, keepdims=None, *, interpolation=None):
return (a, q, out) | null |
168,943 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _nanquantile_unchecked(
a,
q,
axis=None,
out=None,
overwrite_input=False,
method="linear",
keepdims=np._NoValue,
):
"""Assumes that q i... | Compute the qth quantile of the data along the specified axis, while ignoring nan values. Returns the qth quantile(s) of the array elements. .. versionadded:: 1.15.0 Parameters ---------- a : array_like Input array or object that can be converted to an array, containing nan values to be ignored q : array_like of float ... |
168,944 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _nanvar_dispatcher(a, axis=None, dtype=None, out=None, ddof=None,
keepdims=None, *, where=None):
return (a, out) | null |
168,945 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def _nanstd_dispatcher(a, axis=None, dtype=None, out=None, ddof=None,
keepdims=None, *, where=None):
return (a, out) | null |
168,946 | import functools
import warnings
import numpy as np
from numpy.lib import function_base
from numpy.core import overrides
def nanvar(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue,
*, where=np._NoValue):
"""
Compute the variance along the specified axis, while ignoring NaNs.
Retu... | Compute the standard deviation along the specified axis, while ignoring NaNs. Returns the standard deviation, a measure of the spread of a distribution, of the non-NaN array elements. The standard deviation is computed for the flattened array by default, otherwise over the specified axis. For all-NaN slices or slices w... |
168,952 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
169,026 | import numpy as np
from numpy.core.overrides import array_function_dispatch
from numpy.lib.index_tricks import ndindex
def _pad_dispatcher(array, pad_width, mode=None, **kwargs):
return (array,) | null |
169,027 | import numpy as np
from numpy.core.overrides import array_function_dispatch
from numpy.lib.index_tricks import ndindex
def _view_roi(array, original_area_slice, axis):
"""
Get a view of the current region of interest during iterative padding.
When padding multiple dimensions iteratively corner values are
... | Pad an array. Parameters ---------- array : array_like of rank N The array to pad. pad_width : {sequence, array_like, int} Number of values padded to the edges of each axis. ``((before_1, after_1), ... (before_N, after_N))`` unique pad widths for each axis. ``(before, after)`` or ``((before, after),)`` yields same befo... |
169,028 | import functools
import warnings
import numpy.core.numeric as _nx
from numpy.core.numeric import asarray, asanyarray, isnan, zeros
from numpy.core.overrides import set_module
from numpy.core import overrides
from .ufunclike import isneginf, isposinf
def _asfarray_dispatcher(a, dtype=None):
return (a,) | null |
169,029 | import functools
import warnings
import numpy.core.numeric as _nx
from numpy.core.numeric import asarray, asanyarray, isnan, zeros
from numpy.core.overrides import set_module
from numpy.core import overrides
from .ufunclike import isneginf, isposinf
The provided code snippet includes necessary dependencies for impleme... | Return an array converted to a float type. Parameters ---------- a : array_like The input array. dtype : str or dtype object, optional Float type code to coerce input array `a`. If `dtype` is one of the 'int' dtypes, it is replaced with float64. Returns ------- out : ndarray The input `a` as a float ndarray. Examples -... |
169,030 | import functools
import warnings
import numpy.core.numeric as _nx
from numpy.core.numeric import asarray, asanyarray, isnan, zeros
from numpy.core.overrides import set_module
from numpy.core import overrides
from .ufunclike import isneginf, isposinf
def _real_dispatcher(val):
return (val,) | null |
169,031 | import functools
import warnings
import numpy.core.numeric as _nx
from numpy.core.numeric import asarray, asanyarray, isnan, zeros
from numpy.core.overrides import set_module
from numpy.core import overrides
from .ufunclike import isneginf, isposinf
def _imag_dispatcher(val):
return (val,) | null |
169,032 | import functools
import warnings
import numpy.core.numeric as _nx
from numpy.core.numeric import asarray, asanyarray, isnan, zeros
from numpy.core.overrides import set_module
from numpy.core import overrides
from .ufunclike import isneginf, isposinf
def _is_type_dispatcher(x):
return (x,) | null |
169,033 | import functools
import warnings
import numpy.core.numeric as _nx
from numpy.core.numeric import asarray, asanyarray, isnan, zeros
from numpy.core.overrides import set_module
from numpy.core import overrides
from .ufunclike import isneginf, isposinf
def iscomplexobj(x):
"""
Check for a complex type or an array ... | Return True if x is a not complex type or an array of complex numbers. The type of the input is checked, not the value. So even if the input has an imaginary part equal to zero, `isrealobj` evaluates to False if the data type is complex. Parameters ---------- x : any The input can be of any type and shape. Returns ----... |
169,034 | import functools
import warnings
import numpy.core.numeric as _nx
from numpy.core.numeric import asarray, asanyarray, isnan, zeros
from numpy.core.overrides import set_module
from numpy.core import overrides
from .ufunclike import isneginf, isposinf
def _nan_to_num_dispatcher(x, copy=None, nan=None, posinf=None, negin... | null |
169,035 | import functools
import warnings
import numpy.core.numeric as _nx
from numpy.core.numeric import asarray, asanyarray, isnan, zeros
from numpy.core.overrides import set_module
from numpy.core import overrides
from .ufunclike import isneginf, isposinf
def real(val):
"""
Return the real part of the complex argumen... | Replace NaN with zero and infinity with large finite numbers (default behaviour) or with the numbers defined by the user using the `nan`, `posinf` and/or `neginf` keywords. If `x` is inexact, NaN is replaced by zero or by the user defined value in `nan` keyword, infinity is replaced by the largest finite floating point... |
169,036 | import functools
import warnings
import numpy.core.numeric as _nx
from numpy.core.numeric import asarray, asanyarray, isnan, zeros
from numpy.core.overrides import set_module
from numpy.core import overrides
from .ufunclike import isneginf, isposinf
def _real_if_close_dispatcher(a, tol=None):
return (a,) | null |
169,037 | import functools
import warnings
import numpy.core.numeric as _nx
from numpy.core.numeric import asarray, asanyarray, isnan, zeros
from numpy.core.overrides import set_module
from numpy.core import overrides
from .ufunclike import isneginf, isposinf
def real(val):
"""
Return the real part of the complex argumen... | If input is complex with all imaginary parts close to zero, return real parts. "Close to zero" is defined as `tol` * (machine epsilon of the type for `a`). Parameters ---------- a : array_like Input array. tol : float Tolerance in machine epsilons for the complex part of the elements in the array. Returns ------- out :... |
169,038 | import functools
import warnings
import numpy.core.numeric as _nx
from numpy.core.numeric import asarray, asanyarray, isnan, zeros
from numpy.core.overrides import set_module
from numpy.core import overrides
from .ufunclike import isneginf, isposinf
_namefromtype = {'S1': 'character',
'?': 'bool',
... | Return a description for the given data type code. Parameters ---------- char : str Data type code. Returns ------- out : str Description of the input data type code. See Also -------- dtype, typecodes Examples -------- >>> typechars = ['S1', '?', 'B', 'D', 'G', 'F', 'I', 'H', 'L', 'O', 'Q', ... 'S', 'U', 'V', 'b', 'd'... |
169,039 | import functools
import warnings
import numpy.core.numeric as _nx
from numpy.core.numeric import asarray, asanyarray, isnan, zeros
from numpy.core.overrides import set_module
from numpy.core import overrides
from .ufunclike import isneginf, isposinf
def _common_type_dispatcher(*arrays):
return arrays | null |
169,040 | import functools
import warnings
import numpy.core.numeric as _nx
from numpy.core.numeric import asarray, asanyarray, isnan, zeros
from numpy.core.overrides import set_module
from numpy.core import overrides
from .ufunclike import isneginf, isposinf
def iscomplexobj(x):
"""
Check for a complex type or an array ... | Return a scalar type which is common to the input arrays. The return type will always be an inexact (i.e. floating point) scalar type, even if all the arrays are integer arrays. If one of the inputs is an integer array, the minimum precision type that is returned is a 64-bit floating point dtype. All input arrays excep... |
169,057 | import numpy.core.numeric as nx
from numpy.core.overrides import (
array_function_dispatch, ARRAY_FUNCTION_ENABLED,
)
import warnings
import functools
def _deprecate_out_named_y(f):
"""
Allow the out argument to be passed as the name `y` (deprecated)
In future, this decorator should be removed.
"""
... | Use the appropriate decorator, depending upon if dispatching is being used. |
169,058 | import numpy.core.numeric as nx
from numpy.core.overrides import (
array_function_dispatch, ARRAY_FUNCTION_ENABLED,
)
import warnings
import functools
def _dispatcher(x, out=None):
return (x, out) | null |
169,059 | import numpy.core.numeric as nx
from numpy.core.overrides import (
array_function_dispatch, ARRAY_FUNCTION_ENABLED,
)
import warnings
import functools
The provided code snippet includes necessary dependencies for implementing the `fix` function. Write a Python function `def fix(x, out=None)` to solve the following... | Round to nearest integer towards zero. Round an array of floats element-wise to nearest integer towards zero. The rounded values are returned as floats. Parameters ---------- x : array_like An array of floats to be rounded out : ndarray, optional A location into which the result is stored. If provided, it must have a s... |
169,099 | import numpy as np
from numpy.core.numeric import normalize_axis_tuple
from numpy.core.overrides import array_function_dispatch, set_module
def _sliding_window_view_dispatcher(x, window_shape, axis=None, *,
subok=None, writeable=None):
return (x,) | null |
169,100 | import numpy as np
from numpy.core.numeric import normalize_axis_tuple
from numpy.core.overrides import array_function_dispatch, set_module
def as_strided(x, shape=None, strides=None, subok=False, writeable=True):
"""
Create a view into the array with the given shape and strides.
.. warning:: This function ... | Create a sliding window view into the array with the given window shape. Also known as rolling or moving window, the window slides across all dimensions of the array and extracts subsets of the array at all window positions. .. versionadded:: 1.20.0 Parameters ---------- x : array_like Array to create the sliding windo... |
169,101 | import numpy as np
from numpy.core.numeric import normalize_axis_tuple
from numpy.core.overrides import array_function_dispatch, set_module
def _broadcast_to_dispatcher(array, shape, subok=None):
return (array,) | null |
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