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169,102 | import numpy as np
from numpy.core.numeric import normalize_axis_tuple
from numpy.core.overrides import array_function_dispatch, set_module
def _broadcast_shape(*args):
"""Returns the shape of the arrays that would result from broadcasting the
supplied arrays against each other.
"""
# use the old-iterator because np.nditer does not handle size 0 arrays
# consistently
b = np.broadcast(*args[:32])
# unfortunately, it cannot handle 32 or more arguments directly
for pos in range(32, len(args), 31):
# ironically, np.broadcast does not properly handle np.broadcast
# objects (it treats them as scalars)
# use broadcasting to avoid allocating the full array
b = broadcast_to(0, b.shape)
b = np.broadcast(b, *args[pos:(pos + 31)])
return b.shape
The provided code snippet includes necessary dependencies for implementing the `broadcast_shapes` function. Write a Python function `def broadcast_shapes(*args)` to solve the following problem:
Broadcast the input shapes into a single shape. :ref:`Learn more about broadcasting here <basics.broadcasting>`. .. versionadded:: 1.20.0 Parameters ---------- `*args` : tuples of ints, or ints The shapes to be broadcast against each other. Returns ------- tuple Broadcasted shape. Raises ------ ValueError If the shapes are not compatible and cannot be broadcast according to NumPy's broadcasting rules. See Also -------- broadcast broadcast_arrays broadcast_to Examples -------- >>> np.broadcast_shapes((1, 2), (3, 1), (3, 2)) (3, 2) >>> np.broadcast_shapes((6, 7), (5, 6, 1), (7,), (5, 1, 7)) (5, 6, 7)
Here is the function:
def broadcast_shapes(*args):
"""
Broadcast the input shapes into a single shape.
:ref:`Learn more about broadcasting here <basics.broadcasting>`.
.. versionadded:: 1.20.0
Parameters
----------
`*args` : tuples of ints, or ints
The shapes to be broadcast against each other.
Returns
-------
tuple
Broadcasted shape.
Raises
------
ValueError
If the shapes are not compatible and cannot be broadcast according
to NumPy's broadcasting rules.
See Also
--------
broadcast
broadcast_arrays
broadcast_to
Examples
--------
>>> np.broadcast_shapes((1, 2), (3, 1), (3, 2))
(3, 2)
>>> np.broadcast_shapes((6, 7), (5, 6, 1), (7,), (5, 1, 7))
(5, 6, 7)
"""
arrays = [np.empty(x, dtype=[]) for x in args]
return _broadcast_shape(*arrays) | Broadcast the input shapes into a single shape. :ref:`Learn more about broadcasting here <basics.broadcasting>`. .. versionadded:: 1.20.0 Parameters ---------- `*args` : tuples of ints, or ints The shapes to be broadcast against each other. Returns ------- tuple Broadcasted shape. Raises ------ ValueError If the shapes are not compatible and cannot be broadcast according to NumPy's broadcasting rules. See Also -------- broadcast broadcast_arrays broadcast_to Examples -------- >>> np.broadcast_shapes((1, 2), (3, 1), (3, 2)) (3, 2) >>> np.broadcast_shapes((6, 7), (5, 6, 1), (7,), (5, 1, 7)) (5, 6, 7) |
169,103 | import numpy as np
from numpy.core.numeric import normalize_axis_tuple
from numpy.core.overrides import array_function_dispatch, set_module
def _broadcast_arrays_dispatcher(*args, subok=None):
return args | null |
169,104 | 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(array, shape, subok, readonly):
shape = tuple(shape) if np.iterable(shape) else (shape,)
array = np.array(array, copy=False, subok=subok)
if not shape and array.shape:
raise ValueError('cannot broadcast a non-scalar to a scalar array')
if any(size < 0 for size in shape):
raise ValueError('all elements of broadcast shape must be non-'
'negative')
extras = []
it = np.nditer(
(array,), flags=['multi_index', 'refs_ok', 'zerosize_ok'] + extras,
op_flags=['readonly'], itershape=shape, order='C')
with it:
# never really has writebackifcopy semantics
broadcast = it.itviews[0]
result = _maybe_view_as_subclass(array, broadcast)
# In a future version this will go away
if not readonly and array.flags._writeable_no_warn:
result.flags.writeable = True
result.flags._warn_on_write = True
return result
def _broadcast_shape(*args):
"""Returns the shape of the arrays that would result from broadcasting the
supplied arrays against each other.
"""
# use the old-iterator because np.nditer does not handle size 0 arrays
# consistently
b = np.broadcast(*args[:32])
# unfortunately, it cannot handle 32 or more arguments directly
for pos in range(32, len(args), 31):
# ironically, np.broadcast does not properly handle np.broadcast
# objects (it treats them as scalars)
# use broadcasting to avoid allocating the full array
b = broadcast_to(0, b.shape)
b = np.broadcast(b, *args[pos:(pos + 31)])
return b.shape
The provided code snippet includes necessary dependencies for implementing the `broadcast_arrays` function. Write a Python function `def broadcast_arrays(*args, subok=False)` to solve the following problem:
Broadcast any number of arrays against each other. Parameters ---------- `*args` : array_likes The arrays to broadcast. subok : bool, optional If True, then sub-classes will be passed-through, otherwise the returned arrays will be forced to be a base-class array (default). Returns ------- broadcasted : list of arrays These arrays are views on the original arrays. They are typically not contiguous. Furthermore, more than one element of a broadcasted array may refer to a single memory location. If you need to write to the arrays, make copies first. While you can set the ``writable`` flag True, writing to a single output value may end up changing more than one location in the output array. .. deprecated:: 1.17 The output is currently marked so that if written to, a deprecation warning will be emitted. A future version will set the ``writable`` flag False so writing to it will raise an error. See Also -------- broadcast broadcast_to broadcast_shapes Examples -------- >>> x = np.array([[1,2,3]]) >>> y = np.array([[4],[5]]) >>> np.broadcast_arrays(x, y) [array([[1, 2, 3], [1, 2, 3]]), array([[4, 4, 4], [5, 5, 5]])] Here is a useful idiom for getting contiguous copies instead of non-contiguous views. >>> [np.array(a) for a in np.broadcast_arrays(x, y)] [array([[1, 2, 3], [1, 2, 3]]), array([[4, 4, 4], [5, 5, 5]])]
Here is the function:
def broadcast_arrays(*args, subok=False):
"""
Broadcast any number of arrays against each other.
Parameters
----------
`*args` : array_likes
The arrays to broadcast.
subok : bool, optional
If True, then sub-classes will be passed-through, otherwise
the returned arrays will be forced to be a base-class array (default).
Returns
-------
broadcasted : list of arrays
These arrays are views on the original arrays. They are typically
not contiguous. Furthermore, more than one element of a
broadcasted array may refer to a single memory location. If you need
to write to the arrays, make copies first. While you can set the
``writable`` flag True, writing to a single output value may end up
changing more than one location in the output array.
.. deprecated:: 1.17
The output is currently marked so that if written to, a deprecation
warning will be emitted. A future version will set the
``writable`` flag False so writing to it will raise an error.
See Also
--------
broadcast
broadcast_to
broadcast_shapes
Examples
--------
>>> x = np.array([[1,2,3]])
>>> y = np.array([[4],[5]])
>>> np.broadcast_arrays(x, y)
[array([[1, 2, 3],
[1, 2, 3]]), array([[4, 4, 4],
[5, 5, 5]])]
Here is a useful idiom for getting contiguous copies instead of
non-contiguous views.
>>> [np.array(a) for a in np.broadcast_arrays(x, y)]
[array([[1, 2, 3],
[1, 2, 3]]), array([[4, 4, 4],
[5, 5, 5]])]
"""
# nditer is not used here to avoid the limit of 32 arrays.
# Otherwise, something like the following one-liner would suffice:
# return np.nditer(args, flags=['multi_index', 'zerosize_ok'],
# order='C').itviews
args = [np.array(_m, copy=False, subok=subok) for _m in args]
shape = _broadcast_shape(*args)
if all(array.shape == shape for array in args):
# Common case where nothing needs to be broadcasted.
return args
return [_broadcast_to(array, shape, subok=subok, readonly=False)
for array in args] | Broadcast any number of arrays against each other. Parameters ---------- `*args` : array_likes The arrays to broadcast. subok : bool, optional If True, then sub-classes will be passed-through, otherwise the returned arrays will be forced to be a base-class array (default). Returns ------- broadcasted : list of arrays These arrays are views on the original arrays. They are typically not contiguous. Furthermore, more than one element of a broadcasted array may refer to a single memory location. If you need to write to the arrays, make copies first. While you can set the ``writable`` flag True, writing to a single output value may end up changing more than one location in the output array. .. deprecated:: 1.17 The output is currently marked so that if written to, a deprecation warning will be emitted. A future version will set the ``writable`` flag False so writing to it will raise an error. See Also -------- broadcast broadcast_to broadcast_shapes Examples -------- >>> x = np.array([[1,2,3]]) >>> y = np.array([[4],[5]]) >>> np.broadcast_arrays(x, y) [array([[1, 2, 3], [1, 2, 3]]), array([[4, 4, 4], [5, 5, 5]])] Here is a useful idiom for getting contiguous copies instead of non-contiguous views. >>> [np.array(a) for a in np.broadcast_arrays(x, y)] [array([[1, 2, 3], [1, 2, 3]]), array([[4, 4, 4], [5, 5, 5]])] |
169,122 | import os
import io
from numpy.core.overrides import set_module
The provided code snippet includes necessary dependencies for implementing the `_check_mode` function. Write a Python function `def _check_mode(mode, encoding, newline)` to solve the following problem:
Check mode and that encoding and newline are compatible. Parameters ---------- mode : str File open mode. encoding : str File encoding. newline : str Newline for text files.
Here is the function:
def _check_mode(mode, encoding, newline):
"""Check mode and that encoding and newline are compatible.
Parameters
----------
mode : str
File open mode.
encoding : str
File encoding.
newline : str
Newline for text files.
"""
if "t" in mode:
if "b" in mode:
raise ValueError("Invalid mode: %r" % (mode,))
else:
if encoding is not None:
raise ValueError("Argument 'encoding' not supported in binary mode")
if newline is not None:
raise ValueError("Argument 'newline' not supported in binary mode") | Check mode and that encoding and newline are compatible. Parameters ---------- mode : str File open mode. encoding : str File encoding. newline : str Newline for text files. |
169,123 | import functools
import sys
import math
import warnings
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, ScalarType, array, alltrue, cumprod, arange, ndim
)
from numpy.core.numerictypes import find_common_type, issubdtype
import numpy.matrixlib as matrixlib
from .function_base import diff
from numpy.core.multiarray import ravel_multi_index, unravel_index
from numpy.core.overrides import set_module
from numpy.core import overrides, linspace
from numpy.lib.stride_tricks import as_strided
def _ix__dispatcher(*args):
return args | null |
169,124 | import functools
import sys
import math
import warnings
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, ScalarType, array, alltrue, cumprod, arange, ndim
)
from numpy.core.numerictypes import find_common_type, issubdtype
import numpy.matrixlib as matrixlib
from .function_base import diff
from numpy.core.multiarray import ravel_multi_index, unravel_index
from numpy.core.overrides import set_module
from numpy.core import overrides, linspace
from numpy.lib.stride_tricks import as_strided
def issubdtype(arg1, arg2):
r"""
Returns True if first argument is a typecode lower/equal in type hierarchy.
This is like the builtin :func:`issubclass`, but for `dtype`\ s.
Parameters
----------
arg1, arg2 : dtype_like
`dtype` or object coercible to one
Returns
-------
out : bool
See Also
--------
:ref:`arrays.scalars` : Overview of the numpy type hierarchy.
issubsctype, issubclass_
Examples
--------
`issubdtype` can be used to check the type of arrays:
>>> ints = np.array([1, 2, 3], dtype=np.int32)
>>> np.issubdtype(ints.dtype, np.integer)
True
>>> np.issubdtype(ints.dtype, np.floating)
False
>>> floats = np.array([1, 2, 3], dtype=np.float32)
>>> np.issubdtype(floats.dtype, np.integer)
False
>>> np.issubdtype(floats.dtype, np.floating)
True
Similar types of different sizes are not subdtypes of each other:
>>> np.issubdtype(np.float64, np.float32)
False
>>> np.issubdtype(np.float32, np.float64)
False
but both are subtypes of `floating`:
>>> np.issubdtype(np.float64, np.floating)
True
>>> np.issubdtype(np.float32, np.floating)
True
For convenience, dtype-like objects are allowed too:
>>> np.issubdtype('S1', np.string_)
True
>>> np.issubdtype('i4', np.signedinteger)
True
"""
if not issubclass_(arg1, generic):
arg1 = dtype(arg1).type
if not issubclass_(arg2, generic):
arg2 = dtype(arg2).type
return issubclass(arg1, arg2)
The provided code snippet includes necessary dependencies for implementing the `ix_` function. Write a Python function `def ix_(*args)` to solve the following problem:
Construct an open mesh from multiple sequences. This function takes N 1-D sequences and returns N outputs with N dimensions each, such that the shape is 1 in all but one dimension and the dimension with the non-unit shape value cycles through all N dimensions. Using `ix_` one can quickly construct index arrays that will index the cross product. ``a[np.ix_([1,3],[2,5])]`` returns the array ``[[a[1,2] a[1,5]], [a[3,2] a[3,5]]]``. Parameters ---------- args : 1-D sequences Each sequence should be of integer or boolean type. Boolean sequences will be interpreted as boolean masks for the corresponding dimension (equivalent to passing in ``np.nonzero(boolean_sequence)``). Returns ------- out : tuple of ndarrays N arrays with N dimensions each, with N the number of input sequences. Together these arrays form an open mesh. See Also -------- ogrid, mgrid, meshgrid Examples -------- >>> a = np.arange(10).reshape(2, 5) >>> a array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]) >>> ixgrid = np.ix_([0, 1], [2, 4]) >>> ixgrid (array([[0], [1]]), array([[2, 4]])) >>> ixgrid[0].shape, ixgrid[1].shape ((2, 1), (1, 2)) >>> a[ixgrid] array([[2, 4], [7, 9]]) >>> ixgrid = np.ix_([True, True], [2, 4]) >>> a[ixgrid] array([[2, 4], [7, 9]]) >>> ixgrid = np.ix_([True, True], [False, False, True, False, True]) >>> a[ixgrid] array([[2, 4], [7, 9]])
Here is the function:
def ix_(*args):
"""
Construct an open mesh from multiple sequences.
This function takes N 1-D sequences and returns N outputs with N
dimensions each, such that the shape is 1 in all but one dimension
and the dimension with the non-unit shape value cycles through all
N dimensions.
Using `ix_` one can quickly construct index arrays that will index
the cross product. ``a[np.ix_([1,3],[2,5])]`` returns the array
``[[a[1,2] a[1,5]], [a[3,2] a[3,5]]]``.
Parameters
----------
args : 1-D sequences
Each sequence should be of integer or boolean type.
Boolean sequences will be interpreted as boolean masks for the
corresponding dimension (equivalent to passing in
``np.nonzero(boolean_sequence)``).
Returns
-------
out : tuple of ndarrays
N arrays with N dimensions each, with N the number of input
sequences. Together these arrays form an open mesh.
See Also
--------
ogrid, mgrid, meshgrid
Examples
--------
>>> a = np.arange(10).reshape(2, 5)
>>> a
array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
>>> ixgrid = np.ix_([0, 1], [2, 4])
>>> ixgrid
(array([[0],
[1]]), array([[2, 4]]))
>>> ixgrid[0].shape, ixgrid[1].shape
((2, 1), (1, 2))
>>> a[ixgrid]
array([[2, 4],
[7, 9]])
>>> ixgrid = np.ix_([True, True], [2, 4])
>>> a[ixgrid]
array([[2, 4],
[7, 9]])
>>> ixgrid = np.ix_([True, True], [False, False, True, False, True])
>>> a[ixgrid]
array([[2, 4],
[7, 9]])
"""
out = []
nd = len(args)
for k, new in enumerate(args):
if not isinstance(new, _nx.ndarray):
new = asarray(new)
if new.size == 0:
# Explicitly type empty arrays to avoid float default
new = new.astype(_nx.intp)
if new.ndim != 1:
raise ValueError("Cross index must be 1 dimensional")
if issubdtype(new.dtype, _nx.bool_):
new, = new.nonzero()
new = new.reshape((1,)*k + (new.size,) + (1,)*(nd-k-1))
out.append(new)
return tuple(out) | Construct an open mesh from multiple sequences. This function takes N 1-D sequences and returns N outputs with N dimensions each, such that the shape is 1 in all but one dimension and the dimension with the non-unit shape value cycles through all N dimensions. Using `ix_` one can quickly construct index arrays that will index the cross product. ``a[np.ix_([1,3],[2,5])]`` returns the array ``[[a[1,2] a[1,5]], [a[3,2] a[3,5]]]``. Parameters ---------- args : 1-D sequences Each sequence should be of integer or boolean type. Boolean sequences will be interpreted as boolean masks for the corresponding dimension (equivalent to passing in ``np.nonzero(boolean_sequence)``). Returns ------- out : tuple of ndarrays N arrays with N dimensions each, with N the number of input sequences. Together these arrays form an open mesh. See Also -------- ogrid, mgrid, meshgrid Examples -------- >>> a = np.arange(10).reshape(2, 5) >>> a array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]) >>> ixgrid = np.ix_([0, 1], [2, 4]) >>> ixgrid (array([[0], [1]]), array([[2, 4]])) >>> ixgrid[0].shape, ixgrid[1].shape ((2, 1), (1, 2)) >>> a[ixgrid] array([[2, 4], [7, 9]]) >>> ixgrid = np.ix_([True, True], [2, 4]) >>> a[ixgrid] array([[2, 4], [7, 9]]) >>> ixgrid = np.ix_([True, True], [False, False, True, False, True]) >>> a[ixgrid] array([[2, 4], [7, 9]]) |
169,125 | import functools
import sys
import math
import warnings
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, ScalarType, array, alltrue, cumprod, arange, ndim
)
from numpy.core.numerictypes import find_common_type, issubdtype
import numpy.matrixlib as matrixlib
from .function_base import diff
from numpy.core.multiarray import ravel_multi_index, unravel_index
from numpy.core.overrides import set_module
from numpy.core import overrides, linspace
from numpy.lib.stride_tricks import as_strided
def _fill_diagonal_dispatcher(a, val, wrap=None):
return (a,) | null |
169,126 | import functools
import sys
import math
import warnings
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, ScalarType, array, alltrue, cumprod, arange, ndim
)
from numpy.core.numerictypes import find_common_type, issubdtype
import numpy.matrixlib as matrixlib
from .function_base import diff
from numpy.core.multiarray import ravel_multi_index, unravel_index
from numpy.core.overrides import set_module
from numpy.core import overrides, linspace
from numpy.lib.stride_tricks import as_strided
def diff(a, n=1, axis=-1, prepend=np._NoValue, append=np._NoValue):
"""
Calculate the n-th discrete difference along the given axis.
The first difference is given by ``out[i] = a[i+1] - a[i]`` along
the given axis, higher differences are calculated by using `diff`
recursively.
Parameters
----------
a : array_like
Input array
n : int, optional
The number of times values are differenced. If zero, the input
is returned as-is.
axis : int, optional
The axis along which the difference is taken, default is the
last axis.
prepend, append : array_like, optional
Values to prepend or append to `a` along axis prior to
performing the difference. Scalar values are expanded to
arrays with length 1 in the direction of axis and the shape
of the input array in along all other axes. Otherwise the
dimension and shape must match `a` except along axis.
.. versionadded:: 1.16.0
Returns
-------
diff : ndarray
The n-th differences. The shape of the output is the same as `a`
except along `axis` where the dimension is smaller by `n`. The
type of the output is the same as the type of the difference
between any two elements of `a`. This is the same as the type of
`a` in most cases. A notable exception is `datetime64`, which
results in a `timedelta64` output array.
See Also
--------
gradient, ediff1d, cumsum
Notes
-----
Type is preserved for boolean arrays, so the result will contain
`False` when consecutive elements are the same and `True` when they
differ.
For unsigned integer arrays, the results will also be unsigned. This
should not be surprising, as the result is consistent with
calculating the difference directly:
>>> u8_arr = np.array([1, 0], dtype=np.uint8)
>>> np.diff(u8_arr)
array([255], dtype=uint8)
>>> u8_arr[1,...] - u8_arr[0,...]
255
If this is not desirable, then the array should be cast to a larger
integer type first:
>>> i16_arr = u8_arr.astype(np.int16)
>>> np.diff(i16_arr)
array([-1], dtype=int16)
Examples
--------
>>> x = np.array([1, 2, 4, 7, 0])
>>> np.diff(x)
array([ 1, 2, 3, -7])
>>> np.diff(x, n=2)
array([ 1, 1, -10])
>>> x = np.array([[1, 3, 6, 10], [0, 5, 6, 8]])
>>> np.diff(x)
array([[2, 3, 4],
[5, 1, 2]])
>>> np.diff(x, axis=0)
array([[-1, 2, 0, -2]])
>>> x = np.arange('1066-10-13', '1066-10-16', dtype=np.datetime64)
>>> np.diff(x)
array([1, 1], dtype='timedelta64[D]')
"""
if n == 0:
return a
if n < 0:
raise ValueError(
"order must be non-negative but got " + repr(n))
a = asanyarray(a)
nd = a.ndim
if nd == 0:
raise ValueError("diff requires input that is at least one dimensional")
axis = normalize_axis_index(axis, nd)
combined = []
if prepend is not np._NoValue:
prepend = np.asanyarray(prepend)
if prepend.ndim == 0:
shape = list(a.shape)
shape[axis] = 1
prepend = np.broadcast_to(prepend, tuple(shape))
combined.append(prepend)
combined.append(a)
if append is not np._NoValue:
append = np.asanyarray(append)
if append.ndim == 0:
shape = list(a.shape)
shape[axis] = 1
append = np.broadcast_to(append, tuple(shape))
combined.append(append)
if len(combined) > 1:
a = np.concatenate(combined, axis)
slice1 = [slice(None)] * nd
slice2 = [slice(None)] * nd
slice1[axis] = slice(1, None)
slice2[axis] = slice(None, -1)
slice1 = tuple(slice1)
slice2 = tuple(slice2)
op = not_equal if a.dtype == np.bool_ else subtract
for _ in range(n):
a = op(a[slice1], a[slice2])
return a
The provided code snippet includes necessary dependencies for implementing the `fill_diagonal` function. Write a Python function `def fill_diagonal(a, val, wrap=False)` to solve the following problem:
Fill the main diagonal of the given array of any dimensionality. For an array `a` with ``a.ndim >= 2``, the diagonal is the list of locations with indices ``a[i, ..., i]`` all identical. This function modifies the input array in-place, it does not return a value. Parameters ---------- a : array, at least 2-D. Array whose diagonal is to be filled, it gets modified in-place. val : scalar or array_like Value(s) to write on the diagonal. If `val` is scalar, the value is written along the diagonal. If array-like, the flattened `val` is written along the diagonal, repeating if necessary to fill all diagonal entries. wrap : bool For tall matrices in NumPy version up to 1.6.2, the diagonal "wrapped" after N columns. You can have this behavior with this option. This affects only tall matrices. See also -------- diag_indices, diag_indices_from Notes ----- .. versionadded:: 1.4.0 This functionality can be obtained via `diag_indices`, but internally this version uses a much faster implementation that never constructs the indices and uses simple slicing. Examples -------- >>> a = np.zeros((3, 3), int) >>> np.fill_diagonal(a, 5) >>> a array([[5, 0, 0], [0, 5, 0], [0, 0, 5]]) The same function can operate on a 4-D array: >>> a = np.zeros((3, 3, 3, 3), int) >>> np.fill_diagonal(a, 4) We only show a few blocks for clarity: >>> a[0, 0] array([[4, 0, 0], [0, 0, 0], [0, 0, 0]]) >>> a[1, 1] array([[0, 0, 0], [0, 4, 0], [0, 0, 0]]) >>> a[2, 2] array([[0, 0, 0], [0, 0, 0], [0, 0, 4]]) The wrap option affects only tall matrices: >>> # tall matrices no wrap >>> a = np.zeros((5, 3), int) >>> np.fill_diagonal(a, 4) >>> a array([[4, 0, 0], [0, 4, 0], [0, 0, 4], [0, 0, 0], [0, 0, 0]]) >>> # tall matrices wrap >>> a = np.zeros((5, 3), int) >>> np.fill_diagonal(a, 4, wrap=True) >>> a array([[4, 0, 0], [0, 4, 0], [0, 0, 4], [0, 0, 0], [4, 0, 0]]) >>> # wide matrices >>> a = np.zeros((3, 5), int) >>> np.fill_diagonal(a, 4, wrap=True) >>> a array([[4, 0, 0, 0, 0], [0, 4, 0, 0, 0], [0, 0, 4, 0, 0]]) The anti-diagonal can be filled by reversing the order of elements using either `numpy.flipud` or `numpy.fliplr`. >>> a = np.zeros((3, 3), int); >>> np.fill_diagonal(np.fliplr(a), [1,2,3]) # Horizontal flip >>> a array([[0, 0, 1], [0, 2, 0], [3, 0, 0]]) >>> np.fill_diagonal(np.flipud(a), [1,2,3]) # Vertical flip >>> a array([[0, 0, 3], [0, 2, 0], [1, 0, 0]]) Note that the order in which the diagonal is filled varies depending on the flip function.
Here is the function:
def fill_diagonal(a, val, wrap=False):
"""Fill the main diagonal of the given array of any dimensionality.
For an array `a` with ``a.ndim >= 2``, the diagonal is the list of
locations with indices ``a[i, ..., i]`` all identical. This function
modifies the input array in-place, it does not return a value.
Parameters
----------
a : array, at least 2-D.
Array whose diagonal is to be filled, it gets modified in-place.
val : scalar or array_like
Value(s) to write on the diagonal. If `val` is scalar, the value is
written along the diagonal. If array-like, the flattened `val` is
written along the diagonal, repeating if necessary to fill all
diagonal entries.
wrap : bool
For tall matrices in NumPy version up to 1.6.2, the
diagonal "wrapped" after N columns. You can have this behavior
with this option. This affects only tall matrices.
See also
--------
diag_indices, diag_indices_from
Notes
-----
.. versionadded:: 1.4.0
This functionality can be obtained via `diag_indices`, but internally
this version uses a much faster implementation that never constructs the
indices and uses simple slicing.
Examples
--------
>>> a = np.zeros((3, 3), int)
>>> np.fill_diagonal(a, 5)
>>> a
array([[5, 0, 0],
[0, 5, 0],
[0, 0, 5]])
The same function can operate on a 4-D array:
>>> a = np.zeros((3, 3, 3, 3), int)
>>> np.fill_diagonal(a, 4)
We only show a few blocks for clarity:
>>> a[0, 0]
array([[4, 0, 0],
[0, 0, 0],
[0, 0, 0]])
>>> a[1, 1]
array([[0, 0, 0],
[0, 4, 0],
[0, 0, 0]])
>>> a[2, 2]
array([[0, 0, 0],
[0, 0, 0],
[0, 0, 4]])
The wrap option affects only tall matrices:
>>> # tall matrices no wrap
>>> a = np.zeros((5, 3), int)
>>> np.fill_diagonal(a, 4)
>>> a
array([[4, 0, 0],
[0, 4, 0],
[0, 0, 4],
[0, 0, 0],
[0, 0, 0]])
>>> # tall matrices wrap
>>> a = np.zeros((5, 3), int)
>>> np.fill_diagonal(a, 4, wrap=True)
>>> a
array([[4, 0, 0],
[0, 4, 0],
[0, 0, 4],
[0, 0, 0],
[4, 0, 0]])
>>> # wide matrices
>>> a = np.zeros((3, 5), int)
>>> np.fill_diagonal(a, 4, wrap=True)
>>> a
array([[4, 0, 0, 0, 0],
[0, 4, 0, 0, 0],
[0, 0, 4, 0, 0]])
The anti-diagonal can be filled by reversing the order of elements
using either `numpy.flipud` or `numpy.fliplr`.
>>> a = np.zeros((3, 3), int);
>>> np.fill_diagonal(np.fliplr(a), [1,2,3]) # Horizontal flip
>>> a
array([[0, 0, 1],
[0, 2, 0],
[3, 0, 0]])
>>> np.fill_diagonal(np.flipud(a), [1,2,3]) # Vertical flip
>>> a
array([[0, 0, 3],
[0, 2, 0],
[1, 0, 0]])
Note that the order in which the diagonal is filled varies depending
on the flip function.
"""
if a.ndim < 2:
raise ValueError("array must be at least 2-d")
end = None
if a.ndim == 2:
# Explicit, fast formula for the common case. For 2-d arrays, we
# accept rectangular ones.
step = a.shape[1] + 1
# This is needed to don't have tall matrix have the diagonal wrap.
if not wrap:
end = a.shape[1] * a.shape[1]
else:
# For more than d=2, the strided formula is only valid for arrays with
# all dimensions equal, so we check first.
if not alltrue(diff(a.shape) == 0):
raise ValueError("All dimensions of input must be of equal length")
step = 1 + (cumprod(a.shape[:-1])).sum()
# Write the value out into the diagonal.
a.flat[:end:step] = val | Fill the main diagonal of the given array of any dimensionality. For an array `a` with ``a.ndim >= 2``, the diagonal is the list of locations with indices ``a[i, ..., i]`` all identical. This function modifies the input array in-place, it does not return a value. Parameters ---------- a : array, at least 2-D. Array whose diagonal is to be filled, it gets modified in-place. val : scalar or array_like Value(s) to write on the diagonal. If `val` is scalar, the value is written along the diagonal. If array-like, the flattened `val` is written along the diagonal, repeating if necessary to fill all diagonal entries. wrap : bool For tall matrices in NumPy version up to 1.6.2, the diagonal "wrapped" after N columns. You can have this behavior with this option. This affects only tall matrices. See also -------- diag_indices, diag_indices_from Notes ----- .. versionadded:: 1.4.0 This functionality can be obtained via `diag_indices`, but internally this version uses a much faster implementation that never constructs the indices and uses simple slicing. Examples -------- >>> a = np.zeros((3, 3), int) >>> np.fill_diagonal(a, 5) >>> a array([[5, 0, 0], [0, 5, 0], [0, 0, 5]]) The same function can operate on a 4-D array: >>> a = np.zeros((3, 3, 3, 3), int) >>> np.fill_diagonal(a, 4) We only show a few blocks for clarity: >>> a[0, 0] array([[4, 0, 0], [0, 0, 0], [0, 0, 0]]) >>> a[1, 1] array([[0, 0, 0], [0, 4, 0], [0, 0, 0]]) >>> a[2, 2] array([[0, 0, 0], [0, 0, 0], [0, 0, 4]]) The wrap option affects only tall matrices: >>> # tall matrices no wrap >>> a = np.zeros((5, 3), int) >>> np.fill_diagonal(a, 4) >>> a array([[4, 0, 0], [0, 4, 0], [0, 0, 4], [0, 0, 0], [0, 0, 0]]) >>> # tall matrices wrap >>> a = np.zeros((5, 3), int) >>> np.fill_diagonal(a, 4, wrap=True) >>> a array([[4, 0, 0], [0, 4, 0], [0, 0, 4], [0, 0, 0], [4, 0, 0]]) >>> # wide matrices >>> a = np.zeros((3, 5), int) >>> np.fill_diagonal(a, 4, wrap=True) >>> a array([[4, 0, 0, 0, 0], [0, 4, 0, 0, 0], [0, 0, 4, 0, 0]]) The anti-diagonal can be filled by reversing the order of elements using either `numpy.flipud` or `numpy.fliplr`. >>> a = np.zeros((3, 3), int); >>> np.fill_diagonal(np.fliplr(a), [1,2,3]) # Horizontal flip >>> a array([[0, 0, 1], [0, 2, 0], [3, 0, 0]]) >>> np.fill_diagonal(np.flipud(a), [1,2,3]) # Vertical flip >>> a array([[0, 0, 3], [0, 2, 0], [1, 0, 0]]) Note that the order in which the diagonal is filled varies depending on the flip function. |
169,127 | import functools
import sys
import math
import warnings
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, ScalarType, array, alltrue, cumprod, arange, ndim
)
from numpy.core.numerictypes import find_common_type, issubdtype
import numpy.matrixlib as matrixlib
from .function_base import diff
from numpy.core.multiarray import ravel_multi_index, unravel_index
from numpy.core.overrides import set_module
from numpy.core import overrides, linspace
from numpy.lib.stride_tricks import as_strided
def _diag_indices_from(arr):
return (arr,) | null |
169,128 | import functools
import sys
import math
import warnings
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, ScalarType, array, alltrue, cumprod, arange, ndim
)
from numpy.core.numerictypes import find_common_type, issubdtype
import numpy.matrixlib as matrixlib
from .function_base import diff
from numpy.core.multiarray import ravel_multi_index, unravel_index
from numpy.core.overrides import set_module
from numpy.core import overrides, linspace
from numpy.lib.stride_tricks import as_strided
def diag_indices(n, ndim=2):
"""
Return the indices to access the main diagonal of an array.
This returns a tuple of indices that can be used to access the main
diagonal of an array `a` with ``a.ndim >= 2`` dimensions and shape
(n, n, ..., n). For ``a.ndim = 2`` this is the usual diagonal, for
``a.ndim > 2`` this is the set of indices to access ``a[i, i, ..., i]``
for ``i = [0..n-1]``.
Parameters
----------
n : int
The size, along each dimension, of the arrays for which the returned
indices can be used.
ndim : int, optional
The number of dimensions.
See Also
--------
diag_indices_from
Notes
-----
.. versionadded:: 1.4.0
Examples
--------
Create a set of indices to access the diagonal of a (4, 4) array:
>>> di = np.diag_indices(4)
>>> di
(array([0, 1, 2, 3]), array([0, 1, 2, 3]))
>>> a = np.arange(16).reshape(4, 4)
>>> a
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11],
[12, 13, 14, 15]])
>>> a[di] = 100
>>> a
array([[100, 1, 2, 3],
[ 4, 100, 6, 7],
[ 8, 9, 100, 11],
[ 12, 13, 14, 100]])
Now, we create indices to manipulate a 3-D array:
>>> d3 = np.diag_indices(2, 3)
>>> d3
(array([0, 1]), array([0, 1]), array([0, 1]))
And use it to set the diagonal of an array of zeros to 1:
>>> a = np.zeros((2, 2, 2), dtype=int)
>>> a[d3] = 1
>>> a
array([[[1, 0],
[0, 0]],
[[0, 0],
[0, 1]]])
"""
idx = arange(n)
return (idx,) * ndim
def diff(a, n=1, axis=-1, prepend=np._NoValue, append=np._NoValue):
"""
Calculate the n-th discrete difference along the given axis.
The first difference is given by ``out[i] = a[i+1] - a[i]`` along
the given axis, higher differences are calculated by using `diff`
recursively.
Parameters
----------
a : array_like
Input array
n : int, optional
The number of times values are differenced. If zero, the input
is returned as-is.
axis : int, optional
The axis along which the difference is taken, default is the
last axis.
prepend, append : array_like, optional
Values to prepend or append to `a` along axis prior to
performing the difference. Scalar values are expanded to
arrays with length 1 in the direction of axis and the shape
of the input array in along all other axes. Otherwise the
dimension and shape must match `a` except along axis.
.. versionadded:: 1.16.0
Returns
-------
diff : ndarray
The n-th differences. The shape of the output is the same as `a`
except along `axis` where the dimension is smaller by `n`. The
type of the output is the same as the type of the difference
between any two elements of `a`. This is the same as the type of
`a` in most cases. A notable exception is `datetime64`, which
results in a `timedelta64` output array.
See Also
--------
gradient, ediff1d, cumsum
Notes
-----
Type is preserved for boolean arrays, so the result will contain
`False` when consecutive elements are the same and `True` when they
differ.
For unsigned integer arrays, the results will also be unsigned. This
should not be surprising, as the result is consistent with
calculating the difference directly:
>>> u8_arr = np.array([1, 0], dtype=np.uint8)
>>> np.diff(u8_arr)
array([255], dtype=uint8)
>>> u8_arr[1,...] - u8_arr[0,...]
255
If this is not desirable, then the array should be cast to a larger
integer type first:
>>> i16_arr = u8_arr.astype(np.int16)
>>> np.diff(i16_arr)
array([-1], dtype=int16)
Examples
--------
>>> x = np.array([1, 2, 4, 7, 0])
>>> np.diff(x)
array([ 1, 2, 3, -7])
>>> np.diff(x, n=2)
array([ 1, 1, -10])
>>> x = np.array([[1, 3, 6, 10], [0, 5, 6, 8]])
>>> np.diff(x)
array([[2, 3, 4],
[5, 1, 2]])
>>> np.diff(x, axis=0)
array([[-1, 2, 0, -2]])
>>> x = np.arange('1066-10-13', '1066-10-16', dtype=np.datetime64)
>>> np.diff(x)
array([1, 1], dtype='timedelta64[D]')
"""
if n == 0:
return a
if n < 0:
raise ValueError(
"order must be non-negative but got " + repr(n))
a = asanyarray(a)
nd = a.ndim
if nd == 0:
raise ValueError("diff requires input that is at least one dimensional")
axis = normalize_axis_index(axis, nd)
combined = []
if prepend is not np._NoValue:
prepend = np.asanyarray(prepend)
if prepend.ndim == 0:
shape = list(a.shape)
shape[axis] = 1
prepend = np.broadcast_to(prepend, tuple(shape))
combined.append(prepend)
combined.append(a)
if append is not np._NoValue:
append = np.asanyarray(append)
if append.ndim == 0:
shape = list(a.shape)
shape[axis] = 1
append = np.broadcast_to(append, tuple(shape))
combined.append(append)
if len(combined) > 1:
a = np.concatenate(combined, axis)
slice1 = [slice(None)] * nd
slice2 = [slice(None)] * nd
slice1[axis] = slice(1, None)
slice2[axis] = slice(None, -1)
slice1 = tuple(slice1)
slice2 = tuple(slice2)
op = not_equal if a.dtype == np.bool_ else subtract
for _ in range(n):
a = op(a[slice1], a[slice2])
return a
The provided code snippet includes necessary dependencies for implementing the `diag_indices_from` function. Write a Python function `def diag_indices_from(arr)` to solve the following problem:
Return the indices to access the main diagonal of an n-dimensional array. See `diag_indices` for full details. Parameters ---------- arr : array, at least 2-D See Also -------- diag_indices Notes ----- .. versionadded:: 1.4.0
Here is the function:
def diag_indices_from(arr):
"""
Return the indices to access the main diagonal of an n-dimensional array.
See `diag_indices` for full details.
Parameters
----------
arr : array, at least 2-D
See Also
--------
diag_indices
Notes
-----
.. versionadded:: 1.4.0
"""
if not arr.ndim >= 2:
raise ValueError("input array must be at least 2-d")
# For more than d=2, the strided formula is only valid for arrays with
# all dimensions equal, so we check first.
if not alltrue(diff(arr.shape) == 0):
raise ValueError("All dimensions of input must be of equal length")
return diag_indices(arr.shape[0], arr.ndim) | Return the indices to access the main diagonal of an n-dimensional array. See `diag_indices` for full details. Parameters ---------- arr : array, at least 2-D See Also -------- diag_indices Notes ----- .. versionadded:: 1.4.0 |
169,174 | import functools
import numbers
import sys
import numpy as np
from . import numerictypes as _nt
from .umath import absolute, isinf, isfinite, isnat
from . import multiarray
from .multiarray import (array, dragon4_positional, dragon4_scientific,
datetime_as_string, datetime_data, ndarray,
set_legacy_print_mode)
from .fromnumeric import any
from .numeric import concatenate, asarray, errstate
from .numerictypes import (longlong, intc, int_, float_, complex_, bool_,
flexible)
from .overrides import array_function_dispatch, set_module
import operator
import warnings
import contextlib
_format_options = {
'edgeitems': 3, # repr N leading and trailing items of each dimension
'threshold': 1000, # total items > triggers array summarization
'floatmode': 'maxprec',
'precision': 8, # precision of floating point representations
'suppress': False, # suppress printing small floating values in exp format
'linewidth': 75,
'nanstr': 'nan',
'infstr': 'inf',
'sign': '-',
'formatter': None,
# Internally stored as an int to simplify comparisons; converted from/to
# str/False on the way in/out.
'legacy': sys.maxsize}
The provided code snippet includes necessary dependencies for implementing the `_get_legacy_print_mode` function. Write a Python function `def _get_legacy_print_mode()` to solve the following problem:
Return the legacy print mode as an int.
Here is the function:
def _get_legacy_print_mode():
"""Return the legacy print mode as an int."""
return _format_options['legacy'] | Return the legacy print mode as an int. |
169,175 | import functools
import numbers
import sys
import numpy as np
from . import numerictypes as _nt
from .umath import absolute, isinf, isfinite, isnat
from . import multiarray
from .multiarray import (array, dragon4_positional, dragon4_scientific,
datetime_as_string, datetime_data, ndarray,
set_legacy_print_mode)
from .fromnumeric import any
from .numeric import concatenate, asarray, errstate
from .numerictypes import (longlong, intc, int_, float_, complex_, bool_,
flexible)
from .overrides import array_function_dispatch, set_module
import operator
import warnings
import contextlib
def set_printoptions(precision=None, threshold=None, edgeitems=None,
linewidth=None, suppress=None, nanstr=None, infstr=None,
formatter=None, sign=None, floatmode=None, *, legacy=None):
"""
Set printing options.
These options determine the way floating point numbers, arrays and
other NumPy objects are displayed.
Parameters
----------
precision : int or None, optional
Number of digits of precision for floating point output (default 8).
May be None if `floatmode` is not `fixed`, to print as many digits as
necessary to uniquely specify the value.
threshold : int, optional
Total number of array elements which trigger summarization
rather than full repr (default 1000).
To always use the full repr without summarization, pass `sys.maxsize`.
edgeitems : int, optional
Number of array items in summary at beginning and end of
each dimension (default 3).
linewidth : int, optional
The number of characters per line for the purpose of inserting
line breaks (default 75).
suppress : bool, optional
If True, always print floating point numbers using fixed point
notation, in which case numbers equal to zero in the current precision
will print as zero. If False, then scientific notation is used when
absolute value of the smallest number is < 1e-4 or the ratio of the
maximum absolute value to the minimum is > 1e3. The default is False.
nanstr : str, optional
String representation of floating point not-a-number (default nan).
infstr : str, optional
String representation of floating point infinity (default inf).
sign : string, either '-', '+', or ' ', optional
Controls printing of the sign of floating-point types. If '+', always
print the sign of positive values. If ' ', always prints a space
(whitespace character) in the sign position of positive values. If
'-', omit the sign character of positive values. (default '-')
formatter : dict of callables, optional
If not None, the keys should indicate the type(s) that the respective
formatting function applies to. Callables should return a string.
Types that are not specified (by their corresponding keys) are handled
by the default formatters. Individual types for which a formatter
can be set are:
- 'bool'
- 'int'
- 'timedelta' : a `numpy.timedelta64`
- 'datetime' : a `numpy.datetime64`
- 'float'
- 'longfloat' : 128-bit floats
- 'complexfloat'
- 'longcomplexfloat' : composed of two 128-bit floats
- 'numpystr' : types `numpy.string_` and `numpy.unicode_`
- 'object' : `np.object_` arrays
Other keys that can be used to set a group of types at once are:
- 'all' : sets all types
- 'int_kind' : sets 'int'
- 'float_kind' : sets 'float' and 'longfloat'
- 'complex_kind' : sets 'complexfloat' and 'longcomplexfloat'
- 'str_kind' : sets 'numpystr'
floatmode : str, optional
Controls the interpretation of the `precision` option for
floating-point types. Can take the following values
(default maxprec_equal):
* 'fixed': Always print exactly `precision` fractional digits,
even if this would print more or fewer digits than
necessary to specify the value uniquely.
* 'unique': Print the minimum number of fractional digits necessary
to represent each value uniquely. Different elements may
have a different number of digits. The value of the
`precision` option is ignored.
* 'maxprec': Print at most `precision` fractional digits, but if
an element can be uniquely represented with fewer digits
only print it with that many.
* 'maxprec_equal': Print at most `precision` fractional digits,
but if every element in the array can be uniquely
represented with an equal number of fewer digits, use that
many digits for all elements.
legacy : string or `False`, optional
If set to the string `'1.13'` enables 1.13 legacy printing mode. This
approximates numpy 1.13 print output by including a space in the sign
position of floats and different behavior for 0d arrays. This also
enables 1.21 legacy printing mode (described below).
If set to the string `'1.21'` enables 1.21 legacy printing mode. This
approximates numpy 1.21 print output of complex structured dtypes
by not inserting spaces after commas that separate fields and after
colons.
If set to `False`, disables legacy mode.
Unrecognized strings will be ignored with a warning for forward
compatibility.
.. versionadded:: 1.14.0
.. versionchanged:: 1.22.0
See Also
--------
get_printoptions, printoptions, set_string_function, array2string
Notes
-----
`formatter` is always reset with a call to `set_printoptions`.
Use `printoptions` as a context manager to set the values temporarily.
Examples
--------
Floating point precision can be set:
>>> np.set_printoptions(precision=4)
>>> np.array([1.123456789])
[1.1235]
Long arrays can be summarised:
>>> np.set_printoptions(threshold=5)
>>> np.arange(10)
array([0, 1, 2, ..., 7, 8, 9])
Small results can be suppressed:
>>> eps = np.finfo(float).eps
>>> x = np.arange(4.)
>>> x**2 - (x + eps)**2
array([-4.9304e-32, -4.4409e-16, 0.0000e+00, 0.0000e+00])
>>> np.set_printoptions(suppress=True)
>>> x**2 - (x + eps)**2
array([-0., -0., 0., 0.])
A custom formatter can be used to display array elements as desired:
>>> np.set_printoptions(formatter={'all':lambda x: 'int: '+str(-x)})
>>> x = np.arange(3)
>>> x
array([int: 0, int: -1, int: -2])
>>> np.set_printoptions() # formatter gets reset
>>> x
array([0, 1, 2])
To put back the default options, you can use:
>>> np.set_printoptions(edgeitems=3, infstr='inf',
... linewidth=75, nanstr='nan', precision=8,
... suppress=False, threshold=1000, formatter=None)
Also to temporarily override options, use `printoptions` as a context manager:
>>> with np.printoptions(precision=2, suppress=True, threshold=5):
... np.linspace(0, 10, 10)
array([ 0. , 1.11, 2.22, ..., 7.78, 8.89, 10. ])
"""
opt = _make_options_dict(precision, threshold, edgeitems, linewidth,
suppress, nanstr, infstr, sign, formatter,
floatmode, legacy)
# formatter is always reset
opt['formatter'] = formatter
_format_options.update(opt)
# set the C variable for legacy mode
if _format_options['legacy'] == 113:
set_legacy_print_mode(113)
# reset the sign option in legacy mode to avoid confusion
_format_options['sign'] = '-'
elif _format_options['legacy'] == 121:
set_legacy_print_mode(121)
elif _format_options['legacy'] == sys.maxsize:
set_legacy_print_mode(0)
def get_printoptions():
"""
Return the current print options.
Returns
-------
print_opts : dict
Dictionary of current print options with keys
- precision : int
- threshold : int
- edgeitems : int
- linewidth : int
- suppress : bool
- nanstr : str
- infstr : str
- formatter : dict of callables
- sign : str
For a full description of these options, see `set_printoptions`.
See Also
--------
set_printoptions, printoptions, set_string_function
"""
opts = _format_options.copy()
opts['legacy'] = {
113: '1.13', 121: '1.21', sys.maxsize: False,
}[opts['legacy']]
return opts
The provided code snippet includes necessary dependencies for implementing the `printoptions` function. Write a Python function `def printoptions(*args, **kwargs)` to solve the following problem:
Context manager for setting print options. Set print options for the scope of the `with` block, and restore the old options at the end. See `set_printoptions` for the full description of available options. Examples -------- >>> from numpy.testing import assert_equal >>> with np.printoptions(precision=2): ... np.array([2.0]) / 3 array([0.67]) The `as`-clause of the `with`-statement gives the current print options: >>> with np.printoptions(precision=2) as opts: ... assert_equal(opts, np.get_printoptions()) See Also -------- set_printoptions, get_printoptions
Here is the function:
def printoptions(*args, **kwargs):
"""Context manager for setting print options.
Set print options for the scope of the `with` block, and restore the old
options at the end. See `set_printoptions` for the full description of
available options.
Examples
--------
>>> from numpy.testing import assert_equal
>>> with np.printoptions(precision=2):
... np.array([2.0]) / 3
array([0.67])
The `as`-clause of the `with`-statement gives the current print options:
>>> with np.printoptions(precision=2) as opts:
... assert_equal(opts, np.get_printoptions())
See Also
--------
set_printoptions, get_printoptions
"""
opts = np.get_printoptions()
try:
np.set_printoptions(*args, **kwargs)
yield np.get_printoptions()
finally:
np.set_printoptions(**opts) | Context manager for setting print options. Set print options for the scope of the `with` block, and restore the old options at the end. See `set_printoptions` for the full description of available options. Examples -------- >>> from numpy.testing import assert_equal >>> with np.printoptions(precision=2): ... np.array([2.0]) / 3 array([0.67]) The `as`-clause of the `with`-statement gives the current print options: >>> with np.printoptions(precision=2) as opts: ... assert_equal(opts, np.get_printoptions()) See Also -------- set_printoptions, get_printoptions |
169,176 | import functools
import numbers
import sys
import numpy as np
from . import numerictypes as _nt
from .umath import absolute, isinf, isfinite, isnat
from . import multiarray
from .multiarray import (array, dragon4_positional, dragon4_scientific,
datetime_as_string, datetime_data, ndarray,
set_legacy_print_mode)
from .fromnumeric import any
from .numeric import concatenate, asarray, errstate
from .numerictypes import (longlong, intc, int_, float_, complex_, bool_,
flexible)
from .overrides import array_function_dispatch, set_module
import operator
import warnings
import contextlib
def get_ident() -> int: ...
def get_ident() -> int: ...
The provided code snippet includes necessary dependencies for implementing the `_recursive_guard` function. Write a Python function `def _recursive_guard(fillvalue='...')` to solve the following problem:
Like the python 3.2 reprlib.recursive_repr, but forwards *args and **kwargs Decorates a function such that if it calls itself with the same first argument, it returns `fillvalue` instead of recursing. Largely copied from reprlib.recursive_repr
Here is the function:
def _recursive_guard(fillvalue='...'):
"""
Like the python 3.2 reprlib.recursive_repr, but forwards *args and **kwargs
Decorates a function such that if it calls itself with the same first
argument, it returns `fillvalue` instead of recursing.
Largely copied from reprlib.recursive_repr
"""
def decorating_function(f):
repr_running = set()
@functools.wraps(f)
def wrapper(self, *args, **kwargs):
key = id(self), get_ident()
if key in repr_running:
return fillvalue
repr_running.add(key)
try:
return f(self, *args, **kwargs)
finally:
repr_running.discard(key)
return wrapper
return decorating_function | Like the python 3.2 reprlib.recursive_repr, but forwards *args and **kwargs Decorates a function such that if it calls itself with the same first argument, it returns `fillvalue` instead of recursing. Largely copied from reprlib.recursive_repr |
169,177 | import functools
import numbers
import sys
import numpy as np
from . import numerictypes as _nt
from .umath import absolute, isinf, isfinite, isnat
from . import multiarray
from .multiarray import (array, dragon4_positional, dragon4_scientific,
datetime_as_string, datetime_data, ndarray,
set_legacy_print_mode)
from .fromnumeric import any
from .numeric import concatenate, asarray, errstate
from .numerictypes import (longlong, intc, int_, float_, complex_, bool_,
flexible)
from .overrides import array_function_dispatch, set_module
import operator
import warnings
import contextlib
def _array2string_dispatcher(
a, max_line_width=None, precision=None,
suppress_small=None, separator=None, prefix=None,
style=None, formatter=None, threshold=None,
edgeitems=None, sign=None, floatmode=None, suffix=None,
*, legacy=None):
return (a,) | null |
169,178 | import functools
import numbers
import sys
import numpy as np
from . import numerictypes as _nt
from .umath import absolute, isinf, isfinite, isnat
from . import multiarray
from .multiarray import (array, dragon4_positional, dragon4_scientific,
datetime_as_string, datetime_data, ndarray,
set_legacy_print_mode)
from .fromnumeric import any
from .numeric import concatenate, asarray, errstate
from .numerictypes import (longlong, intc, int_, float_, complex_, bool_,
flexible)
from .overrides import array_function_dispatch, set_module
import operator
import warnings
import contextlib
_format_options = {
'edgeitems': 3, # repr N leading and trailing items of each dimension
'threshold': 1000, # total items > triggers array summarization
'floatmode': 'maxprec',
'precision': 8, # precision of floating point representations
'suppress': False, # suppress printing small floating values in exp format
'linewidth': 75,
'nanstr': 'nan',
'infstr': 'inf',
'sign': '-',
'formatter': None,
# Internally stored as an int to simplify comparisons; converted from/to
# str/False on the way in/out.
'legacy': sys.maxsize}
def _make_options_dict(precision=None, threshold=None, edgeitems=None,
linewidth=None, suppress=None, nanstr=None, infstr=None,
sign=None, formatter=None, floatmode=None, legacy=None):
"""
Make a dictionary out of the non-None arguments, plus conversion of
*legacy* and sanity checks.
"""
options = {k: v for k, v in locals().items() if v is not None}
if suppress is not None:
options['suppress'] = bool(suppress)
modes = ['fixed', 'unique', 'maxprec', 'maxprec_equal']
if floatmode not in modes + [None]:
raise ValueError("floatmode option must be one of " +
", ".join('"{}"'.format(m) for m in modes))
if sign not in [None, '-', '+', ' ']:
raise ValueError("sign option must be one of ' ', '+', or '-'")
if legacy == False:
options['legacy'] = sys.maxsize
elif legacy == '1.13':
options['legacy'] = 113
elif legacy == '1.21':
options['legacy'] = 121
elif legacy is None:
pass # OK, do nothing.
else:
warnings.warn(
"legacy printing option can currently only be '1.13', '1.21', or "
"`False`", stacklevel=3)
if threshold is not None:
# forbid the bad threshold arg suggested by stack overflow, gh-12351
if not isinstance(threshold, numbers.Number):
raise TypeError("threshold must be numeric")
if np.isnan(threshold):
raise ValueError("threshold must be non-NAN, try "
"sys.maxsize for untruncated representation")
if precision is not None:
# forbid the bad precision arg as suggested by issue #18254
try:
options['precision'] = operator.index(precision)
except TypeError as e:
raise TypeError('precision must be an integer') from e
return options
def _array2string(a, options, separator=' ', prefix=""):
# The formatter __init__s in _get_format_function cannot deal with
# subclasses yet, and we also need to avoid recursion issues in
# _formatArray with subclasses which return 0d arrays in place of scalars
data = asarray(a)
if a.shape == ():
a = data
if a.size > options['threshold']:
summary_insert = "..."
data = _leading_trailing(data, options['edgeitems'])
else:
summary_insert = ""
# find the right formatting function for the array
format_function = _get_format_function(data, **options)
# skip over "["
next_line_prefix = " "
# skip over array(
next_line_prefix += " "*len(prefix)
lst = _formatArray(a, format_function, options['linewidth'],
next_line_prefix, separator, options['edgeitems'],
summary_insert, options['legacy'])
return lst
The provided code snippet includes necessary dependencies for implementing the `array2string` function. Write a Python function `def array2string(a, max_line_width=None, precision=None, suppress_small=None, separator=' ', prefix="", style=np._NoValue, formatter=None, threshold=None, edgeitems=None, sign=None, floatmode=None, suffix="", *, legacy=None)` to solve the following problem:
Return a string representation of an array. Parameters ---------- a : ndarray Input array. max_line_width : int, optional Inserts newlines if text is longer than `max_line_width`. Defaults to ``numpy.get_printoptions()['linewidth']``. precision : int or None, optional Floating point precision. Defaults to ``numpy.get_printoptions()['precision']``. suppress_small : bool, optional Represent numbers "very close" to zero as zero; default is False. Very close is defined by precision: if the precision is 8, e.g., numbers smaller (in absolute value) than 5e-9 are represented as zero. Defaults to ``numpy.get_printoptions()['suppress']``. separator : str, optional Inserted between elements. prefix : str, optional suffix : str, optional The length of the prefix and suffix strings are used to respectively align and wrap the output. An array is typically printed as:: prefix + array2string(a) + suffix The output is left-padded by the length of the prefix string, and wrapping is forced at the column ``max_line_width - len(suffix)``. It should be noted that the content of prefix and suffix strings are not included in the output. style : _NoValue, optional Has no effect, do not use. .. deprecated:: 1.14.0 formatter : dict of callables, optional If not None, the keys should indicate the type(s) that the respective formatting function applies to. Callables should return a string. Types that are not specified (by their corresponding keys) are handled by the default formatters. Individual types for which a formatter can be set are: - 'bool' - 'int' - 'timedelta' : a `numpy.timedelta64` - 'datetime' : a `numpy.datetime64` - 'float' - 'longfloat' : 128-bit floats - 'complexfloat' - 'longcomplexfloat' : composed of two 128-bit floats - 'void' : type `numpy.void` - 'numpystr' : types `numpy.string_` and `numpy.unicode_` Other keys that can be used to set a group of types at once are: - 'all' : sets all types - 'int_kind' : sets 'int' - 'float_kind' : sets 'float' and 'longfloat' - 'complex_kind' : sets 'complexfloat' and 'longcomplexfloat' - 'str_kind' : sets 'numpystr' threshold : int, optional Total number of array elements which trigger summarization rather than full repr. Defaults to ``numpy.get_printoptions()['threshold']``. edgeitems : int, optional Number of array items in summary at beginning and end of each dimension. Defaults to ``numpy.get_printoptions()['edgeitems']``. sign : string, either '-', '+', or ' ', optional Controls printing of the sign of floating-point types. If '+', always print the sign of positive values. If ' ', always prints a space (whitespace character) in the sign position of positive values. If '-', omit the sign character of positive values. Defaults to ``numpy.get_printoptions()['sign']``. floatmode : str, optional Controls the interpretation of the `precision` option for floating-point types. Defaults to ``numpy.get_printoptions()['floatmode']``. Can take the following values: - 'fixed': Always print exactly `precision` fractional digits, even if this would print more or fewer digits than necessary to specify the value uniquely. - 'unique': Print the minimum number of fractional digits necessary to represent each value uniquely. Different elements may have a different number of digits. The value of the `precision` option is ignored. - 'maxprec': Print at most `precision` fractional digits, but if an element can be uniquely represented with fewer digits only print it with that many. - 'maxprec_equal': Print at most `precision` fractional digits, but if every element in the array can be uniquely represented with an equal number of fewer digits, use that many digits for all elements. legacy : string or `False`, optional If set to the string `'1.13'` enables 1.13 legacy printing mode. This approximates numpy 1.13 print output by including a space in the sign position of floats and different behavior for 0d arrays. If set to `False`, disables legacy mode. Unrecognized strings will be ignored with a warning for forward compatibility. .. versionadded:: 1.14.0 Returns ------- array_str : str String representation of the array. Raises ------ TypeError if a callable in `formatter` does not return a string. See Also -------- array_str, array_repr, set_printoptions, get_printoptions Notes ----- If a formatter is specified for a certain type, the `precision` keyword is ignored for that type. This is a very flexible function; `array_repr` and `array_str` are using `array2string` internally so keywords with the same name should work identically in all three functions. Examples -------- >>> x = np.array([1e-16,1,2,3]) >>> np.array2string(x, precision=2, separator=',', ... suppress_small=True) '[0.,1.,2.,3.]' >>> x = np.arange(3.) >>> np.array2string(x, formatter={'float_kind':lambda x: "%.2f" % x}) '[0.00 1.00 2.00]' >>> x = np.arange(3) >>> np.array2string(x, formatter={'int':lambda x: hex(x)}) '[0x0 0x1 0x2]'
Here is the function:
def array2string(a, max_line_width=None, precision=None,
suppress_small=None, separator=' ', prefix="",
style=np._NoValue, formatter=None, threshold=None,
edgeitems=None, sign=None, floatmode=None, suffix="",
*, legacy=None):
"""
Return a string representation of an array.
Parameters
----------
a : ndarray
Input array.
max_line_width : int, optional
Inserts newlines if text is longer than `max_line_width`.
Defaults to ``numpy.get_printoptions()['linewidth']``.
precision : int or None, optional
Floating point precision.
Defaults to ``numpy.get_printoptions()['precision']``.
suppress_small : bool, optional
Represent numbers "very close" to zero as zero; default is False.
Very close is defined by precision: if the precision is 8, e.g.,
numbers smaller (in absolute value) than 5e-9 are represented as
zero.
Defaults to ``numpy.get_printoptions()['suppress']``.
separator : str, optional
Inserted between elements.
prefix : str, optional
suffix : str, optional
The length of the prefix and suffix strings are used to respectively
align and wrap the output. An array is typically printed as::
prefix + array2string(a) + suffix
The output is left-padded by the length of the prefix string, and
wrapping is forced at the column ``max_line_width - len(suffix)``.
It should be noted that the content of prefix and suffix strings are
not included in the output.
style : _NoValue, optional
Has no effect, do not use.
.. deprecated:: 1.14.0
formatter : dict of callables, optional
If not None, the keys should indicate the type(s) that the respective
formatting function applies to. Callables should return a string.
Types that are not specified (by their corresponding keys) are handled
by the default formatters. Individual types for which a formatter
can be set are:
- 'bool'
- 'int'
- 'timedelta' : a `numpy.timedelta64`
- 'datetime' : a `numpy.datetime64`
- 'float'
- 'longfloat' : 128-bit floats
- 'complexfloat'
- 'longcomplexfloat' : composed of two 128-bit floats
- 'void' : type `numpy.void`
- 'numpystr' : types `numpy.string_` and `numpy.unicode_`
Other keys that can be used to set a group of types at once are:
- 'all' : sets all types
- 'int_kind' : sets 'int'
- 'float_kind' : sets 'float' and 'longfloat'
- 'complex_kind' : sets 'complexfloat' and 'longcomplexfloat'
- 'str_kind' : sets 'numpystr'
threshold : int, optional
Total number of array elements which trigger summarization
rather than full repr.
Defaults to ``numpy.get_printoptions()['threshold']``.
edgeitems : int, optional
Number of array items in summary at beginning and end of
each dimension.
Defaults to ``numpy.get_printoptions()['edgeitems']``.
sign : string, either '-', '+', or ' ', optional
Controls printing of the sign of floating-point types. If '+', always
print the sign of positive values. If ' ', always prints a space
(whitespace character) in the sign position of positive values. If
'-', omit the sign character of positive values.
Defaults to ``numpy.get_printoptions()['sign']``.
floatmode : str, optional
Controls the interpretation of the `precision` option for
floating-point types.
Defaults to ``numpy.get_printoptions()['floatmode']``.
Can take the following values:
- 'fixed': Always print exactly `precision` fractional digits,
even if this would print more or fewer digits than
necessary to specify the value uniquely.
- 'unique': Print the minimum number of fractional digits necessary
to represent each value uniquely. Different elements may
have a different number of digits. The value of the
`precision` option is ignored.
- 'maxprec': Print at most `precision` fractional digits, but if
an element can be uniquely represented with fewer digits
only print it with that many.
- 'maxprec_equal': Print at most `precision` fractional digits,
but if every element in the array can be uniquely
represented with an equal number of fewer digits, use that
many digits for all elements.
legacy : string or `False`, optional
If set to the string `'1.13'` enables 1.13 legacy printing mode. This
approximates numpy 1.13 print output by including a space in the sign
position of floats and different behavior for 0d arrays. If set to
`False`, disables legacy mode. Unrecognized strings will be ignored
with a warning for forward compatibility.
.. versionadded:: 1.14.0
Returns
-------
array_str : str
String representation of the array.
Raises
------
TypeError
if a callable in `formatter` does not return a string.
See Also
--------
array_str, array_repr, set_printoptions, get_printoptions
Notes
-----
If a formatter is specified for a certain type, the `precision` keyword is
ignored for that type.
This is a very flexible function; `array_repr` and `array_str` are using
`array2string` internally so keywords with the same name should work
identically in all three functions.
Examples
--------
>>> x = np.array([1e-16,1,2,3])
>>> np.array2string(x, precision=2, separator=',',
... suppress_small=True)
'[0.,1.,2.,3.]'
>>> x = np.arange(3.)
>>> np.array2string(x, formatter={'float_kind':lambda x: "%.2f" % x})
'[0.00 1.00 2.00]'
>>> x = np.arange(3)
>>> np.array2string(x, formatter={'int':lambda x: hex(x)})
'[0x0 0x1 0x2]'
"""
overrides = _make_options_dict(precision, threshold, edgeitems,
max_line_width, suppress_small, None, None,
sign, formatter, floatmode, legacy)
options = _format_options.copy()
options.update(overrides)
if options['legacy'] <= 113:
if style is np._NoValue:
style = repr
if a.shape == () and a.dtype.names is None:
return style(a.item())
elif style is not np._NoValue:
# Deprecation 11-9-2017 v1.14
warnings.warn("'style' argument is deprecated and no longer functional"
" except in 1.13 'legacy' mode",
DeprecationWarning, stacklevel=3)
if options['legacy'] > 113:
options['linewidth'] -= len(suffix)
# treat as a null array if any of shape elements == 0
if a.size == 0:
return "[]"
return _array2string(a, options, separator, prefix) | Return a string representation of an array. Parameters ---------- a : ndarray Input array. max_line_width : int, optional Inserts newlines if text is longer than `max_line_width`. Defaults to ``numpy.get_printoptions()['linewidth']``. precision : int or None, optional Floating point precision. Defaults to ``numpy.get_printoptions()['precision']``. suppress_small : bool, optional Represent numbers "very close" to zero as zero; default is False. Very close is defined by precision: if the precision is 8, e.g., numbers smaller (in absolute value) than 5e-9 are represented as zero. Defaults to ``numpy.get_printoptions()['suppress']``. separator : str, optional Inserted between elements. prefix : str, optional suffix : str, optional The length of the prefix and suffix strings are used to respectively align and wrap the output. An array is typically printed as:: prefix + array2string(a) + suffix The output is left-padded by the length of the prefix string, and wrapping is forced at the column ``max_line_width - len(suffix)``. It should be noted that the content of prefix and suffix strings are not included in the output. style : _NoValue, optional Has no effect, do not use. .. deprecated:: 1.14.0 formatter : dict of callables, optional If not None, the keys should indicate the type(s) that the respective formatting function applies to. Callables should return a string. Types that are not specified (by their corresponding keys) are handled by the default formatters. Individual types for which a formatter can be set are: - 'bool' - 'int' - 'timedelta' : a `numpy.timedelta64` - 'datetime' : a `numpy.datetime64` - 'float' - 'longfloat' : 128-bit floats - 'complexfloat' - 'longcomplexfloat' : composed of two 128-bit floats - 'void' : type `numpy.void` - 'numpystr' : types `numpy.string_` and `numpy.unicode_` Other keys that can be used to set a group of types at once are: - 'all' : sets all types - 'int_kind' : sets 'int' - 'float_kind' : sets 'float' and 'longfloat' - 'complex_kind' : sets 'complexfloat' and 'longcomplexfloat' - 'str_kind' : sets 'numpystr' threshold : int, optional Total number of array elements which trigger summarization rather than full repr. Defaults to ``numpy.get_printoptions()['threshold']``. edgeitems : int, optional Number of array items in summary at beginning and end of each dimension. Defaults to ``numpy.get_printoptions()['edgeitems']``. sign : string, either '-', '+', or ' ', optional Controls printing of the sign of floating-point types. If '+', always print the sign of positive values. If ' ', always prints a space (whitespace character) in the sign position of positive values. If '-', omit the sign character of positive values. Defaults to ``numpy.get_printoptions()['sign']``. floatmode : str, optional Controls the interpretation of the `precision` option for floating-point types. Defaults to ``numpy.get_printoptions()['floatmode']``. Can take the following values: - 'fixed': Always print exactly `precision` fractional digits, even if this would print more or fewer digits than necessary to specify the value uniquely. - 'unique': Print the minimum number of fractional digits necessary to represent each value uniquely. Different elements may have a different number of digits. The value of the `precision` option is ignored. - 'maxprec': Print at most `precision` fractional digits, but if an element can be uniquely represented with fewer digits only print it with that many. - 'maxprec_equal': Print at most `precision` fractional digits, but if every element in the array can be uniquely represented with an equal number of fewer digits, use that many digits for all elements. legacy : string or `False`, optional If set to the string `'1.13'` enables 1.13 legacy printing mode. This approximates numpy 1.13 print output by including a space in the sign position of floats and different behavior for 0d arrays. If set to `False`, disables legacy mode. Unrecognized strings will be ignored with a warning for forward compatibility. .. versionadded:: 1.14.0 Returns ------- array_str : str String representation of the array. Raises ------ TypeError if a callable in `formatter` does not return a string. See Also -------- array_str, array_repr, set_printoptions, get_printoptions Notes ----- If a formatter is specified for a certain type, the `precision` keyword is ignored for that type. This is a very flexible function; `array_repr` and `array_str` are using `array2string` internally so keywords with the same name should work identically in all three functions. Examples -------- >>> x = np.array([1e-16,1,2,3]) >>> np.array2string(x, precision=2, separator=',', ... suppress_small=True) '[0.,1.,2.,3.]' >>> x = np.arange(3.) >>> np.array2string(x, formatter={'float_kind':lambda x: "%.2f" % x}) '[0.00 1.00 2.00]' >>> x = np.arange(3) >>> np.array2string(x, formatter={'int':lambda x: hex(x)}) '[0x0 0x1 0x2]' |
169,179 | import functools
import numbers
import sys
import numpy as np
from . import numerictypes as _nt
from .umath import absolute, isinf, isfinite, isnat
from . import multiarray
from .multiarray import (array, dragon4_positional, dragon4_scientific,
datetime_as_string, datetime_data, ndarray,
set_legacy_print_mode)
from .fromnumeric import any
from .numeric import concatenate, asarray, errstate
from .numerictypes import (longlong, intc, int_, float_, complex_, bool_,
flexible)
from .overrides import array_function_dispatch, set_module
import operator
import warnings
import contextlib
def _none_or_positive_arg(x, name):
if x is None:
return -1
if x < 0:
raise ValueError("{} must be >= 0".format(name))
return x
The provided code snippet includes necessary dependencies for implementing the `format_float_scientific` function. Write a Python function `def format_float_scientific(x, precision=None, unique=True, trim='k', sign=False, pad_left=None, exp_digits=None, min_digits=None)` to solve the following problem:
Format a floating-point scalar as a decimal string in scientific notation. Provides control over rounding, trimming and padding. Uses and assumes IEEE unbiased rounding. Uses the "Dragon4" algorithm. Parameters ---------- x : python float or numpy floating scalar Value to format. precision : non-negative integer or None, optional Maximum number of digits to print. May be None if `unique` is `True`, but must be an integer if unique is `False`. unique : boolean, optional If `True`, use a digit-generation strategy which gives the shortest representation which uniquely identifies the floating-point number from other values of the same type, by judicious rounding. If `precision` is given fewer digits than necessary can be printed. If `min_digits` is given more can be printed, in which cases the last digit is rounded with unbiased rounding. If `False`, digits are generated as if printing an infinite-precision value and stopping after `precision` digits, rounding the remaining value with unbiased rounding trim : one of 'k', '.', '0', '-', optional Controls post-processing trimming of trailing digits, as follows: * 'k' : keep trailing zeros, keep decimal point (no trimming) * '.' : trim all trailing zeros, leave decimal point * '0' : trim all but the zero before the decimal point. Insert the zero if it is missing. * '-' : trim trailing zeros and any trailing decimal point sign : boolean, optional Whether to show the sign for positive values. pad_left : non-negative integer, optional Pad the left side of the string with whitespace until at least that many characters are to the left of the decimal point. exp_digits : non-negative integer, optional Pad the exponent with zeros until it contains at least this many digits. If omitted, the exponent will be at least 2 digits. min_digits : non-negative integer or None, optional Minimum number of digits to print. This only has an effect for `unique=True`. In that case more digits than necessary to uniquely identify the value may be printed and rounded unbiased. -- versionadded:: 1.21.0 Returns ------- rep : string The string representation of the floating point value See Also -------- format_float_positional Examples -------- >>> np.format_float_scientific(np.float32(np.pi)) '3.1415927e+00' >>> s = np.float32(1.23e24) >>> np.format_float_scientific(s, unique=False, precision=15) '1.230000071797338e+24' >>> np.format_float_scientific(s, exp_digits=4) '1.23e+0024'
Here is the function:
def format_float_scientific(x, precision=None, unique=True, trim='k',
sign=False, pad_left=None, exp_digits=None,
min_digits=None):
"""
Format a floating-point scalar as a decimal string in scientific notation.
Provides control over rounding, trimming and padding. Uses and assumes
IEEE unbiased rounding. Uses the "Dragon4" algorithm.
Parameters
----------
x : python float or numpy floating scalar
Value to format.
precision : non-negative integer or None, optional
Maximum number of digits to print. May be None if `unique` is
`True`, but must be an integer if unique is `False`.
unique : boolean, optional
If `True`, use a digit-generation strategy which gives the shortest
representation which uniquely identifies the floating-point number from
other values of the same type, by judicious rounding. If `precision`
is given fewer digits than necessary can be printed. If `min_digits`
is given more can be printed, in which cases the last digit is rounded
with unbiased rounding.
If `False`, digits are generated as if printing an infinite-precision
value and stopping after `precision` digits, rounding the remaining
value with unbiased rounding
trim : one of 'k', '.', '0', '-', optional
Controls post-processing trimming of trailing digits, as follows:
* 'k' : keep trailing zeros, keep decimal point (no trimming)
* '.' : trim all trailing zeros, leave decimal point
* '0' : trim all but the zero before the decimal point. Insert the
zero if it is missing.
* '-' : trim trailing zeros and any trailing decimal point
sign : boolean, optional
Whether to show the sign for positive values.
pad_left : non-negative integer, optional
Pad the left side of the string with whitespace until at least that
many characters are to the left of the decimal point.
exp_digits : non-negative integer, optional
Pad the exponent with zeros until it contains at least this many digits.
If omitted, the exponent will be at least 2 digits.
min_digits : non-negative integer or None, optional
Minimum number of digits to print. This only has an effect for
`unique=True`. In that case more digits than necessary to uniquely
identify the value may be printed and rounded unbiased.
-- versionadded:: 1.21.0
Returns
-------
rep : string
The string representation of the floating point value
See Also
--------
format_float_positional
Examples
--------
>>> np.format_float_scientific(np.float32(np.pi))
'3.1415927e+00'
>>> s = np.float32(1.23e24)
>>> np.format_float_scientific(s, unique=False, precision=15)
'1.230000071797338e+24'
>>> np.format_float_scientific(s, exp_digits=4)
'1.23e+0024'
"""
precision = _none_or_positive_arg(precision, 'precision')
pad_left = _none_or_positive_arg(pad_left, 'pad_left')
exp_digits = _none_or_positive_arg(exp_digits, 'exp_digits')
min_digits = _none_or_positive_arg(min_digits, 'min_digits')
if min_digits > 0 and precision > 0 and min_digits > precision:
raise ValueError("min_digits must be less than or equal to precision")
return dragon4_scientific(x, precision=precision, unique=unique,
trim=trim, sign=sign, pad_left=pad_left,
exp_digits=exp_digits, min_digits=min_digits) | Format a floating-point scalar as a decimal string in scientific notation. Provides control over rounding, trimming and padding. Uses and assumes IEEE unbiased rounding. Uses the "Dragon4" algorithm. Parameters ---------- x : python float or numpy floating scalar Value to format. precision : non-negative integer or None, optional Maximum number of digits to print. May be None if `unique` is `True`, but must be an integer if unique is `False`. unique : boolean, optional If `True`, use a digit-generation strategy which gives the shortest representation which uniquely identifies the floating-point number from other values of the same type, by judicious rounding. If `precision` is given fewer digits than necessary can be printed. If `min_digits` is given more can be printed, in which cases the last digit is rounded with unbiased rounding. If `False`, digits are generated as if printing an infinite-precision value and stopping after `precision` digits, rounding the remaining value with unbiased rounding trim : one of 'k', '.', '0', '-', optional Controls post-processing trimming of trailing digits, as follows: * 'k' : keep trailing zeros, keep decimal point (no trimming) * '.' : trim all trailing zeros, leave decimal point * '0' : trim all but the zero before the decimal point. Insert the zero if it is missing. * '-' : trim trailing zeros and any trailing decimal point sign : boolean, optional Whether to show the sign for positive values. pad_left : non-negative integer, optional Pad the left side of the string with whitespace until at least that many characters are to the left of the decimal point. exp_digits : non-negative integer, optional Pad the exponent with zeros until it contains at least this many digits. If omitted, the exponent will be at least 2 digits. min_digits : non-negative integer or None, optional Minimum number of digits to print. This only has an effect for `unique=True`. In that case more digits than necessary to uniquely identify the value may be printed and rounded unbiased. -- versionadded:: 1.21.0 Returns ------- rep : string The string representation of the floating point value See Also -------- format_float_positional Examples -------- >>> np.format_float_scientific(np.float32(np.pi)) '3.1415927e+00' >>> s = np.float32(1.23e24) >>> np.format_float_scientific(s, unique=False, precision=15) '1.230000071797338e+24' >>> np.format_float_scientific(s, exp_digits=4) '1.23e+0024' |
169,180 | import functools
import numbers
import sys
import numpy as np
from . import numerictypes as _nt
from .umath import absolute, isinf, isfinite, isnat
from . import multiarray
from .multiarray import (array, dragon4_positional, dragon4_scientific,
datetime_as_string, datetime_data, ndarray,
set_legacy_print_mode)
from .fromnumeric import any
from .numeric import concatenate, asarray, errstate
from .numerictypes import (longlong, intc, int_, float_, complex_, bool_,
flexible)
from .overrides import array_function_dispatch, set_module
import operator
import warnings
import contextlib
def _none_or_positive_arg(x, name):
if x is None:
return -1
if x < 0:
raise ValueError("{} must be >= 0".format(name))
return x
The provided code snippet includes necessary dependencies for implementing the `format_float_positional` function. Write a Python function `def format_float_positional(x, precision=None, unique=True, fractional=True, trim='k', sign=False, pad_left=None, pad_right=None, min_digits=None)` to solve the following problem:
Format a floating-point scalar as a decimal string in positional notation. Provides control over rounding, trimming and padding. Uses and assumes IEEE unbiased rounding. Uses the "Dragon4" algorithm. Parameters ---------- x : python float or numpy floating scalar Value to format. precision : non-negative integer or None, optional Maximum number of digits to print. May be None if `unique` is `True`, but must be an integer if unique is `False`. unique : boolean, optional If `True`, use a digit-generation strategy which gives the shortest representation which uniquely identifies the floating-point number from other values of the same type, by judicious rounding. If `precision` is given fewer digits than necessary can be printed, or if `min_digits` is given more can be printed, in which cases the last digit is rounded with unbiased rounding. If `False`, digits are generated as if printing an infinite-precision value and stopping after `precision` digits, rounding the remaining value with unbiased rounding fractional : boolean, optional If `True`, the cutoffs of `precision` and `min_digits` refer to the total number of digits after the decimal point, including leading zeros. If `False`, `precision` and `min_digits` refer to the total number of significant digits, before or after the decimal point, ignoring leading zeros. trim : one of 'k', '.', '0', '-', optional Controls post-processing trimming of trailing digits, as follows: * 'k' : keep trailing zeros, keep decimal point (no trimming) * '.' : trim all trailing zeros, leave decimal point * '0' : trim all but the zero before the decimal point. Insert the zero if it is missing. * '-' : trim trailing zeros and any trailing decimal point sign : boolean, optional Whether to show the sign for positive values. pad_left : non-negative integer, optional Pad the left side of the string with whitespace until at least that many characters are to the left of the decimal point. pad_right : non-negative integer, optional Pad the right side of the string with whitespace until at least that many characters are to the right of the decimal point. min_digits : non-negative integer or None, optional Minimum number of digits to print. Only has an effect if `unique=True` in which case additional digits past those necessary to uniquely identify the value may be printed, rounding the last additional digit. -- versionadded:: 1.21.0 Returns ------- rep : string The string representation of the floating point value See Also -------- format_float_scientific Examples -------- >>> np.format_float_positional(np.float32(np.pi)) '3.1415927' >>> np.format_float_positional(np.float16(np.pi)) '3.14' >>> np.format_float_positional(np.float16(0.3)) '0.3' >>> np.format_float_positional(np.float16(0.3), unique=False, precision=10) '0.3000488281'
Here is the function:
def format_float_positional(x, precision=None, unique=True,
fractional=True, trim='k', sign=False,
pad_left=None, pad_right=None, min_digits=None):
"""
Format a floating-point scalar as a decimal string in positional notation.
Provides control over rounding, trimming and padding. Uses and assumes
IEEE unbiased rounding. Uses the "Dragon4" algorithm.
Parameters
----------
x : python float or numpy floating scalar
Value to format.
precision : non-negative integer or None, optional
Maximum number of digits to print. May be None if `unique` is
`True`, but must be an integer if unique is `False`.
unique : boolean, optional
If `True`, use a digit-generation strategy which gives the shortest
representation which uniquely identifies the floating-point number from
other values of the same type, by judicious rounding. If `precision`
is given fewer digits than necessary can be printed, or if `min_digits`
is given more can be printed, in which cases the last digit is rounded
with unbiased rounding.
If `False`, digits are generated as if printing an infinite-precision
value and stopping after `precision` digits, rounding the remaining
value with unbiased rounding
fractional : boolean, optional
If `True`, the cutoffs of `precision` and `min_digits` refer to the
total number of digits after the decimal point, including leading
zeros.
If `False`, `precision` and `min_digits` refer to the total number of
significant digits, before or after the decimal point, ignoring leading
zeros.
trim : one of 'k', '.', '0', '-', optional
Controls post-processing trimming of trailing digits, as follows:
* 'k' : keep trailing zeros, keep decimal point (no trimming)
* '.' : trim all trailing zeros, leave decimal point
* '0' : trim all but the zero before the decimal point. Insert the
zero if it is missing.
* '-' : trim trailing zeros and any trailing decimal point
sign : boolean, optional
Whether to show the sign for positive values.
pad_left : non-negative integer, optional
Pad the left side of the string with whitespace until at least that
many characters are to the left of the decimal point.
pad_right : non-negative integer, optional
Pad the right side of the string with whitespace until at least that
many characters are to the right of the decimal point.
min_digits : non-negative integer or None, optional
Minimum number of digits to print. Only has an effect if `unique=True`
in which case additional digits past those necessary to uniquely
identify the value may be printed, rounding the last additional digit.
-- versionadded:: 1.21.0
Returns
-------
rep : string
The string representation of the floating point value
See Also
--------
format_float_scientific
Examples
--------
>>> np.format_float_positional(np.float32(np.pi))
'3.1415927'
>>> np.format_float_positional(np.float16(np.pi))
'3.14'
>>> np.format_float_positional(np.float16(0.3))
'0.3'
>>> np.format_float_positional(np.float16(0.3), unique=False, precision=10)
'0.3000488281'
"""
precision = _none_or_positive_arg(precision, 'precision')
pad_left = _none_or_positive_arg(pad_left, 'pad_left')
pad_right = _none_or_positive_arg(pad_right, 'pad_right')
min_digits = _none_or_positive_arg(min_digits, 'min_digits')
if not fractional and precision == 0:
raise ValueError("precision must be greater than 0 if "
"fractional=False")
if min_digits > 0 and precision > 0 and min_digits > precision:
raise ValueError("min_digits must be less than or equal to precision")
return dragon4_positional(x, precision=precision, unique=unique,
fractional=fractional, trim=trim,
sign=sign, pad_left=pad_left,
pad_right=pad_right, min_digits=min_digits) | Format a floating-point scalar as a decimal string in positional notation. Provides control over rounding, trimming and padding. Uses and assumes IEEE unbiased rounding. Uses the "Dragon4" algorithm. Parameters ---------- x : python float or numpy floating scalar Value to format. precision : non-negative integer or None, optional Maximum number of digits to print. May be None if `unique` is `True`, but must be an integer if unique is `False`. unique : boolean, optional If `True`, use a digit-generation strategy which gives the shortest representation which uniquely identifies the floating-point number from other values of the same type, by judicious rounding. If `precision` is given fewer digits than necessary can be printed, or if `min_digits` is given more can be printed, in which cases the last digit is rounded with unbiased rounding. If `False`, digits are generated as if printing an infinite-precision value and stopping after `precision` digits, rounding the remaining value with unbiased rounding fractional : boolean, optional If `True`, the cutoffs of `precision` and `min_digits` refer to the total number of digits after the decimal point, including leading zeros. If `False`, `precision` and `min_digits` refer to the total number of significant digits, before or after the decimal point, ignoring leading zeros. trim : one of 'k', '.', '0', '-', optional Controls post-processing trimming of trailing digits, as follows: * 'k' : keep trailing zeros, keep decimal point (no trimming) * '.' : trim all trailing zeros, leave decimal point * '0' : trim all but the zero before the decimal point. Insert the zero if it is missing. * '-' : trim trailing zeros and any trailing decimal point sign : boolean, optional Whether to show the sign for positive values. pad_left : non-negative integer, optional Pad the left side of the string with whitespace until at least that many characters are to the left of the decimal point. pad_right : non-negative integer, optional Pad the right side of the string with whitespace until at least that many characters are to the right of the decimal point. min_digits : non-negative integer or None, optional Minimum number of digits to print. Only has an effect if `unique=True` in which case additional digits past those necessary to uniquely identify the value may be printed, rounding the last additional digit. -- versionadded:: 1.21.0 Returns ------- rep : string The string representation of the floating point value See Also -------- format_float_scientific Examples -------- >>> np.format_float_positional(np.float32(np.pi)) '3.1415927' >>> np.format_float_positional(np.float16(np.pi)) '3.14' >>> np.format_float_positional(np.float16(0.3)) '0.3' >>> np.format_float_positional(np.float16(0.3), unique=False, precision=10) '0.3000488281' |
169,181 | import functools
import numbers
import sys
import numpy as np
from . import numerictypes as _nt
from .umath import absolute, isinf, isfinite, isnat
from . import multiarray
from .multiarray import (array, dragon4_positional, dragon4_scientific,
datetime_as_string, datetime_data, ndarray,
set_legacy_print_mode)
from .fromnumeric import any
from .numeric import concatenate, asarray, errstate
from .numerictypes import (longlong, intc, int_, float_, complex_, bool_,
flexible)
from .overrides import array_function_dispatch, set_module
import operator
import warnings
import contextlib
_format_options = {
'edgeitems': 3, # repr N leading and trailing items of each dimension
'threshold': 1000, # total items > triggers array summarization
'floatmode': 'maxprec',
'precision': 8, # precision of floating point representations
'suppress': False, # suppress printing small floating values in exp format
'linewidth': 75,
'nanstr': 'nan',
'infstr': 'inf',
'sign': '-',
'formatter': None,
# Internally stored as an int to simplify comparisons; converted from/to
# str/False on the way in/out.
'legacy': sys.maxsize}
class StructuredVoidFormat:
"""
Formatter for structured np.void objects.
This does not work on structured alias types like np.dtype(('i4', 'i2,i2')),
as alias scalars lose their field information, and the implementation
relies upon np.void.__getitem__.
"""
def __init__(self, format_functions):
self.format_functions = format_functions
def from_data(cls, data, **options):
"""
This is a second way to initialize StructuredVoidFormat, using the raw data
as input. Added to avoid changing the signature of __init__.
"""
format_functions = []
for field_name in data.dtype.names:
format_function = _get_format_function(data[field_name], **options)
if data.dtype[field_name].shape != ():
format_function = SubArrayFormat(format_function)
format_functions.append(format_function)
return cls(format_functions)
def __call__(self, x):
str_fields = [
format_function(field)
for field, format_function in zip(x, self.format_functions)
]
if len(str_fields) == 1:
return "({},)".format(str_fields[0])
else:
return "({})".format(", ".join(str_fields))
array.__module__ = 'numpy'
The provided code snippet includes necessary dependencies for implementing the `_void_scalar_repr` function. Write a Python function `def _void_scalar_repr(x)` to solve the following problem:
Implements the repr for structured-void scalars. It is called from the scalartypes.c.src code, and is placed here because it uses the elementwise formatters defined above.
Here is the function:
def _void_scalar_repr(x):
"""
Implements the repr for structured-void scalars. It is called from the
scalartypes.c.src code, and is placed here because it uses the elementwise
formatters defined above.
"""
return StructuredVoidFormat.from_data(array(x), **_format_options)(x) | Implements the repr for structured-void scalars. It is called from the scalartypes.c.src code, and is placed here because it uses the elementwise formatters defined above. |
169,182 | import functools
import numbers
import sys
import numpy as np
from . import numerictypes as _nt
from .umath import absolute, isinf, isfinite, isnat
from . import multiarray
from .multiarray import (array, dragon4_positional, dragon4_scientific,
datetime_as_string, datetime_data, ndarray,
set_legacy_print_mode)
from .fromnumeric import any
from .numeric import concatenate, asarray, errstate
from .numerictypes import (longlong, intc, int_, float_, complex_, bool_,
flexible)
from .overrides import array_function_dispatch, set_module
import operator
import warnings
import contextlib
def _array_repr_dispatcher(
arr, max_line_width=None, precision=None, suppress_small=None):
return (arr,) | null |
169,183 | import functools
import numbers
import sys
import numpy as np
from . import numerictypes as _nt
from .umath import absolute, isinf, isfinite, isnat
from . import multiarray
from .multiarray import (array, dragon4_positional, dragon4_scientific,
datetime_as_string, datetime_data, ndarray,
set_legacy_print_mode)
from .fromnumeric import any
from .numeric import concatenate, asarray, errstate
from .numerictypes import (longlong, intc, int_, float_, complex_, bool_,
flexible)
from .overrides import array_function_dispatch, set_module
import operator
import warnings
import contextlib
def _array_repr_implementation(
arr, max_line_width=None, precision=None, suppress_small=None,
array2string=array2string):
"""Internal version of array_repr() that allows overriding array2string."""
if max_line_width is None:
max_line_width = _format_options['linewidth']
if type(arr) is not ndarray:
class_name = type(arr).__name__
else:
class_name = "array"
skipdtype = dtype_is_implied(arr.dtype) and arr.size > 0
prefix = class_name + "("
suffix = ")" if skipdtype else ","
if (_format_options['legacy'] <= 113 and
arr.shape == () and not arr.dtype.names):
lst = repr(arr.item())
elif arr.size > 0 or arr.shape == (0,):
lst = array2string(arr, max_line_width, precision, suppress_small,
', ', prefix, suffix=suffix)
else: # show zero-length shape unless it is (0,)
lst = "[], shape=%s" % (repr(arr.shape),)
arr_str = prefix + lst + suffix
if skipdtype:
return arr_str
dtype_str = "dtype={})".format(dtype_short_repr(arr.dtype))
# compute whether we should put dtype on a new line: Do so if adding the
# dtype would extend the last line past max_line_width.
# Note: This line gives the correct result even when rfind returns -1.
last_line_len = len(arr_str) - (arr_str.rfind('\n') + 1)
spacer = " "
if _format_options['legacy'] <= 113:
if issubclass(arr.dtype.type, flexible):
spacer = '\n' + ' '*len(class_name + "(")
elif last_line_len + len(dtype_str) + 1 > max_line_width:
spacer = '\n' + ' '*len(class_name + "(")
return arr_str + spacer + dtype_str
The provided code snippet includes necessary dependencies for implementing the `array_repr` function. Write a Python function `def array_repr(arr, max_line_width=None, precision=None, suppress_small=None)` to solve the following problem:
Return the string representation of an array. Parameters ---------- arr : ndarray Input array. max_line_width : int, optional Inserts newlines if text is longer than `max_line_width`. Defaults to ``numpy.get_printoptions()['linewidth']``. precision : int, optional Floating point precision. Defaults to ``numpy.get_printoptions()['precision']``. suppress_small : bool, optional Represent numbers "very close" to zero as zero; default is False. Very close is defined by precision: if the precision is 8, e.g., numbers smaller (in absolute value) than 5e-9 are represented as zero. Defaults to ``numpy.get_printoptions()['suppress']``. Returns ------- string : str The string representation of an array. See Also -------- array_str, array2string, set_printoptions Examples -------- >>> np.array_repr(np.array([1,2])) 'array([1, 2])' >>> np.array_repr(np.ma.array([0.])) 'MaskedArray([0.])' >>> np.array_repr(np.array([], np.int32)) 'array([], dtype=int32)' >>> x = np.array([1e-6, 4e-7, 2, 3]) >>> np.array_repr(x, precision=6, suppress_small=True) 'array([0.000001, 0. , 2. , 3. ])'
Here is the function:
def array_repr(arr, max_line_width=None, precision=None, suppress_small=None):
"""
Return the string representation of an array.
Parameters
----------
arr : ndarray
Input array.
max_line_width : int, optional
Inserts newlines if text is longer than `max_line_width`.
Defaults to ``numpy.get_printoptions()['linewidth']``.
precision : int, optional
Floating point precision.
Defaults to ``numpy.get_printoptions()['precision']``.
suppress_small : bool, optional
Represent numbers "very close" to zero as zero; default is False.
Very close is defined by precision: if the precision is 8, e.g.,
numbers smaller (in absolute value) than 5e-9 are represented as
zero.
Defaults to ``numpy.get_printoptions()['suppress']``.
Returns
-------
string : str
The string representation of an array.
See Also
--------
array_str, array2string, set_printoptions
Examples
--------
>>> np.array_repr(np.array([1,2]))
'array([1, 2])'
>>> np.array_repr(np.ma.array([0.]))
'MaskedArray([0.])'
>>> np.array_repr(np.array([], np.int32))
'array([], dtype=int32)'
>>> x = np.array([1e-6, 4e-7, 2, 3])
>>> np.array_repr(x, precision=6, suppress_small=True)
'array([0.000001, 0. , 2. , 3. ])'
"""
return _array_repr_implementation(
arr, max_line_width, precision, suppress_small) | Return the string representation of an array. Parameters ---------- arr : ndarray Input array. max_line_width : int, optional Inserts newlines if text is longer than `max_line_width`. Defaults to ``numpy.get_printoptions()['linewidth']``. precision : int, optional Floating point precision. Defaults to ``numpy.get_printoptions()['precision']``. suppress_small : bool, optional Represent numbers "very close" to zero as zero; default is False. Very close is defined by precision: if the precision is 8, e.g., numbers smaller (in absolute value) than 5e-9 are represented as zero. Defaults to ``numpy.get_printoptions()['suppress']``. Returns ------- string : str The string representation of an array. See Also -------- array_str, array2string, set_printoptions Examples -------- >>> np.array_repr(np.array([1,2])) 'array([1, 2])' >>> np.array_repr(np.ma.array([0.])) 'MaskedArray([0.])' >>> np.array_repr(np.array([], np.int32)) 'array([], dtype=int32)' >>> x = np.array([1e-6, 4e-7, 2, 3]) >>> np.array_repr(x, precision=6, suppress_small=True) 'array([0.000001, 0. , 2. , 3. ])' |
169,184 | import functools
import numbers
import sys
import numpy as np
from . import numerictypes as _nt
from .umath import absolute, isinf, isfinite, isnat
from . import multiarray
from .multiarray import (array, dragon4_positional, dragon4_scientific,
datetime_as_string, datetime_data, ndarray,
set_legacy_print_mode)
from .fromnumeric import any
from .numeric import concatenate, asarray, errstate
from .numerictypes import (longlong, intc, int_, float_, complex_, bool_,
flexible)
from .overrides import array_function_dispatch, set_module
import operator
import warnings
import contextlib
def _array_str_dispatcher(
a, max_line_width=None, precision=None, suppress_small=None):
return (a,) | null |
169,185 | import functools
import numbers
import sys
import numpy as np
from . import numerictypes as _nt
from .umath import absolute, isinf, isfinite, isnat
from . import multiarray
from .multiarray import (array, dragon4_positional, dragon4_scientific,
datetime_as_string, datetime_data, ndarray,
set_legacy_print_mode)
from .fromnumeric import any
from .numeric import concatenate, asarray, errstate
from .numerictypes import (longlong, intc, int_, float_, complex_, bool_,
flexible)
from .overrides import array_function_dispatch, set_module
import operator
import warnings
import contextlib
def _array_str_implementation(
a, max_line_width=None, precision=None, suppress_small=None,
array2string=array2string):
"""Internal version of array_str() that allows overriding array2string."""
if (_format_options['legacy'] <= 113 and
a.shape == () and not a.dtype.names):
return str(a.item())
# the str of 0d arrays is a special case: It should appear like a scalar,
# so floats are not truncated by `precision`, and strings are not wrapped
# in quotes. So we return the str of the scalar value.
if a.shape == ():
# obtain a scalar and call str on it, avoiding problems for subclasses
# for which indexing with () returns a 0d instead of a scalar by using
# ndarray's getindex. Also guard against recursive 0d object arrays.
return _guarded_repr_or_str(np.ndarray.__getitem__(a, ()))
return array2string(a, max_line_width, precision, suppress_small, ' ', "")
The provided code snippet includes necessary dependencies for implementing the `array_str` function. Write a Python function `def array_str(a, max_line_width=None, precision=None, suppress_small=None)` to solve the following problem:
Return a string representation of the data in an array. The data in the array is returned as a single string. This function is similar to `array_repr`, the difference being that `array_repr` also returns information on the kind of array and its data type. Parameters ---------- a : ndarray Input array. max_line_width : int, optional Inserts newlines if text is longer than `max_line_width`. Defaults to ``numpy.get_printoptions()['linewidth']``. precision : int, optional Floating point precision. Defaults to ``numpy.get_printoptions()['precision']``. suppress_small : bool, optional Represent numbers "very close" to zero as zero; default is False. Very close is defined by precision: if the precision is 8, e.g., numbers smaller (in absolute value) than 5e-9 are represented as zero. Defaults to ``numpy.get_printoptions()['suppress']``. See Also -------- array2string, array_repr, set_printoptions Examples -------- >>> np.array_str(np.arange(3)) '[0 1 2]'
Here is the function:
def array_str(a, max_line_width=None, precision=None, suppress_small=None):
"""
Return a string representation of the data in an array.
The data in the array is returned as a single string. This function is
similar to `array_repr`, the difference being that `array_repr` also
returns information on the kind of array and its data type.
Parameters
----------
a : ndarray
Input array.
max_line_width : int, optional
Inserts newlines if text is longer than `max_line_width`.
Defaults to ``numpy.get_printoptions()['linewidth']``.
precision : int, optional
Floating point precision.
Defaults to ``numpy.get_printoptions()['precision']``.
suppress_small : bool, optional
Represent numbers "very close" to zero as zero; default is False.
Very close is defined by precision: if the precision is 8, e.g.,
numbers smaller (in absolute value) than 5e-9 are represented as
zero.
Defaults to ``numpy.get_printoptions()['suppress']``.
See Also
--------
array2string, array_repr, set_printoptions
Examples
--------
>>> np.array_str(np.arange(3))
'[0 1 2]'
"""
return _array_str_implementation(
a, max_line_width, precision, suppress_small) | Return a string representation of the data in an array. The data in the array is returned as a single string. This function is similar to `array_repr`, the difference being that `array_repr` also returns information on the kind of array and its data type. Parameters ---------- a : ndarray Input array. max_line_width : int, optional Inserts newlines if text is longer than `max_line_width`. Defaults to ``numpy.get_printoptions()['linewidth']``. precision : int, optional Floating point precision. Defaults to ``numpy.get_printoptions()['precision']``. suppress_small : bool, optional Represent numbers "very close" to zero as zero; default is False. Very close is defined by precision: if the precision is 8, e.g., numbers smaller (in absolute value) than 5e-9 are represented as zero. Defaults to ``numpy.get_printoptions()['suppress']``. See Also -------- array2string, array_repr, set_printoptions Examples -------- >>> np.array_str(np.arange(3)) '[0 1 2]' |
169,186 | import functools
import numbers
import sys
import numpy as np
from . import numerictypes as _nt
from .umath import absolute, isinf, isfinite, isnat
from . import multiarray
from .multiarray import (array, dragon4_positional, dragon4_scientific,
datetime_as_string, datetime_data, ndarray,
set_legacy_print_mode)
from .fromnumeric import any
from .numeric import concatenate, asarray, errstate
from .numerictypes import (longlong, intc, int_, float_, complex_, bool_,
flexible)
from .overrides import array_function_dispatch, set_module
import operator
import warnings
import contextlib
_default_array_str = functools.partial(_array_str_implementation,
array2string=_array2string_impl)
_default_array_repr = functools.partial(_array_repr_implementation,
array2string=_array2string_impl)
The provided code snippet includes necessary dependencies for implementing the `set_string_function` function. Write a Python function `def set_string_function(f, repr=True)` to solve the following problem:
Set a Python function to be used when pretty printing arrays. Parameters ---------- f : function or None Function to be used to pretty print arrays. The function should expect a single array argument and return a string of the representation of the array. If None, the function is reset to the default NumPy function to print arrays. repr : bool, optional If True (default), the function for pretty printing (``__repr__``) is set, if False the function that returns the default string representation (``__str__``) is set. See Also -------- set_printoptions, get_printoptions Examples -------- >>> def pprint(arr): ... return 'HA! - What are you going to do now?' ... >>> np.set_string_function(pprint) >>> a = np.arange(10) >>> a HA! - What are you going to do now? >>> _ = a >>> # [0 1 2 3 4 5 6 7 8 9] We can reset the function to the default: >>> np.set_string_function(None) >>> a array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) `repr` affects either pretty printing or normal string representation. Note that ``__repr__`` is still affected by setting ``__str__`` because the width of each array element in the returned string becomes equal to the length of the result of ``__str__()``. >>> x = np.arange(4) >>> np.set_string_function(lambda x:'random', repr=False) >>> x.__str__() 'random' >>> x.__repr__() 'array([0, 1, 2, 3])'
Here is the function:
def set_string_function(f, repr=True):
"""
Set a Python function to be used when pretty printing arrays.
Parameters
----------
f : function or None
Function to be used to pretty print arrays. The function should expect
a single array argument and return a string of the representation of
the array. If None, the function is reset to the default NumPy function
to print arrays.
repr : bool, optional
If True (default), the function for pretty printing (``__repr__``)
is set, if False the function that returns the default string
representation (``__str__``) is set.
See Also
--------
set_printoptions, get_printoptions
Examples
--------
>>> def pprint(arr):
... return 'HA! - What are you going to do now?'
...
>>> np.set_string_function(pprint)
>>> a = np.arange(10)
>>> a
HA! - What are you going to do now?
>>> _ = a
>>> # [0 1 2 3 4 5 6 7 8 9]
We can reset the function to the default:
>>> np.set_string_function(None)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
`repr` affects either pretty printing or normal string representation.
Note that ``__repr__`` is still affected by setting ``__str__``
because the width of each array element in the returned string becomes
equal to the length of the result of ``__str__()``.
>>> x = np.arange(4)
>>> np.set_string_function(lambda x:'random', repr=False)
>>> x.__str__()
'random'
>>> x.__repr__()
'array([0, 1, 2, 3])'
"""
if f is None:
if repr:
return multiarray.set_string_function(_default_array_repr, 1)
else:
return multiarray.set_string_function(_default_array_str, 0)
else:
return multiarray.set_string_function(f, repr) | Set a Python function to be used when pretty printing arrays. Parameters ---------- f : function or None Function to be used to pretty print arrays. The function should expect a single array argument and return a string of the representation of the array. If None, the function is reset to the default NumPy function to print arrays. repr : bool, optional If True (default), the function for pretty printing (``__repr__``) is set, if False the function that returns the default string representation (``__str__``) is set. See Also -------- set_printoptions, get_printoptions Examples -------- >>> def pprint(arr): ... return 'HA! - What are you going to do now?' ... >>> np.set_string_function(pprint) >>> a = np.arange(10) >>> a HA! - What are you going to do now? >>> _ = a >>> # [0 1 2 3 4 5 6 7 8 9] We can reset the function to the default: >>> np.set_string_function(None) >>> a array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) `repr` affects either pretty printing or normal string representation. Note that ``__repr__`` is still affected by setting ``__str__`` because the width of each array element in the returned string becomes equal to the length of the result of ``__str__()``. >>> x = np.arange(4) >>> np.set_string_function(lambda x:'random', repr=False) >>> x.__str__() 'random' >>> x.__repr__() 'array([0, 1, 2, 3])' |
169,187 | import os
import sys
import sysconfig
import pickle
import copy
import warnings
import textwrap
import glob
from os.path import join
from numpy.distutils import log
from numpy.distutils.msvccompiler import lib_opts_if_msvc
from distutils.dep_util import newer
from sysconfig import get_config_var
from numpy.compat import npy_load_module
from setup_common import *
NPY_RELAXED_STRIDES_DEBUG = (os.environ.get('NPY_RELAXED_STRIDES_DEBUG', "0") != "0")
NPY_RELAXED_STRIDES_DEBUG = NPY_RELAXED_STRIDES_DEBUG and NPY_RELAXED_STRIDES_CHECKING
class CallOnceOnly:
def __init__(self):
self._check_types = None
self._check_ieee_macros = None
self._check_complex = None
def check_types(self, *a, **kw):
if self._check_types is None:
out = check_types(*a, **kw)
self._check_types = pickle.dumps(out)
else:
out = copy.deepcopy(pickle.loads(self._check_types))
return out
def check_ieee_macros(self, *a, **kw):
if self._check_ieee_macros is None:
out = check_ieee_macros(*a, **kw)
self._check_ieee_macros = pickle.dumps(out)
else:
out = copy.deepcopy(pickle.loads(self._check_ieee_macros))
return out
def check_complex(self, *a, **kw):
if self._check_complex is None:
out = check_complex(*a, **kw)
self._check_complex = pickle.dumps(out)
else:
out = copy.deepcopy(pickle.loads(self._check_complex))
return out
def can_link_svml():
"""SVML library is supported only on x86_64 architecture and currently
only on linux
"""
if NPY_DISABLE_SVML:
return False
platform = sysconfig.get_platform()
return ("x86_64" in platform
and "linux" in platform
and sys.maxsize > 2**31)
def check_svml_submodule(svmlpath):
if not os.path.exists(svmlpath + "/README.md"):
raise RuntimeError("Missing `SVML` submodule! Run `git submodule "
"update --init` to fix this.")
return True
def is_npy_no_signal():
"""Return True if the NPY_NO_SIGNAL symbol must be defined in configuration
header."""
return sys.platform == 'win32'
def is_npy_no_smp():
"""Return True if the NPY_NO_SMP symbol must be defined in public
header (when SMP support cannot be reliably enabled)."""
# Perhaps a fancier check is in order here.
# so that threads are only enabled if there
# are actually multiple CPUS? -- but
# threaded code can be nice even on a single
# CPU so that long-calculating code doesn't
# block.
return 'NPY_NOSMP' in os.environ
def win32_checks(deflist):
from numpy.distutils.misc_util import get_build_architecture
a = get_build_architecture()
# Distutils hack on AMD64 on windows
print('BUILD_ARCHITECTURE: %r, os.name=%r, sys.platform=%r' %
(a, os.name, sys.platform))
if a == 'AMD64':
deflist.append('DISTUTILS_USE_SDK')
# On win32, force long double format string to be 'g', not
# 'Lg', since the MS runtime does not support long double whose
# size is > sizeof(double)
if a == "Intel" or a == "AMD64":
deflist.append('FORCE_NO_LONG_DOUBLE_FORMATTING')
def check_math_capabilities(config, ext, moredefs, mathlibs):
def check_func(
func_name,
decl=False,
headers=["feature_detection_math.h", "feature_detection_cmath.h"],
):
return config.check_func(
func_name,
libraries=mathlibs,
decl=decl,
call=True,
call_args=FUNC_CALL_ARGS[func_name],
headers=headers,
)
def check_funcs_once(
funcs_name,
headers=["feature_detection_math.h", "feature_detection_cmath.h"],
add_to_moredefs=True):
call = dict([(f, True) for f in funcs_name])
call_args = dict([(f, FUNC_CALL_ARGS[f]) for f in funcs_name])
st = config.check_funcs_once(
funcs_name,
libraries=mathlibs,
decl=False,
call=call,
call_args=call_args,
headers=headers,
)
if st and add_to_moredefs:
moredefs.extend([(fname2def(f), 1) for f in funcs_name])
return st
def check_funcs(
funcs_name,
headers=["feature_detection_math.h", "feature_detection_cmath.h"]):
# Use check_funcs_once first, and if it does not work, test func per
# func. Return success only if all the functions are available
if not check_funcs_once(funcs_name, headers=headers):
# Global check failed, check func per func
for f in funcs_name:
if check_func(f, headers=headers):
moredefs.append((fname2def(f), 1))
return 0
else:
return 1
# GH-14787: Work around GCC<8.4 bug when compiling with AVX512
# support on Windows-based platforms
def check_gh14787(fn):
if fn == 'attribute_target_avx512f':
if (sys.platform in ('win32', 'cygwin') and
config.check_compiler_gcc() and
not config.check_gcc_version_at_least(8, 4)):
ext.extra_compile_args.extend(
['-ffixed-xmm%s' % n for n in range(16, 32)])
#use_msvc = config.check_decl("_MSC_VER")
if not check_funcs_once(MANDATORY_FUNCS, add_to_moredefs=False):
raise SystemError("One of the required function to build numpy is not"
" available (the list is %s)." % str(MANDATORY_FUNCS))
# Standard functions which may not be available and for which we have a
# replacement implementation. Note that some of these are C99 functions.
# XXX: hack to circumvent cpp pollution from python: python put its
# config.h in the public namespace, so we have a clash for the common
# functions we test. We remove every function tested by python's
# autoconf, hoping their own test are correct
for f in OPTIONAL_FUNCS_MAYBE:
if config.check_decl(fname2def(f), headers=["Python.h"]):
OPTIONAL_FILE_FUNCS.remove(f)
check_funcs(OPTIONAL_FILE_FUNCS, headers=["feature_detection_stdio.h"])
check_funcs(OPTIONAL_MISC_FUNCS, headers=["feature_detection_misc.h"])
for h in OPTIONAL_HEADERS:
if config.check_func("", decl=False, call=False, headers=[h]):
h = h.replace(".", "_").replace(os.path.sep, "_")
moredefs.append((fname2def(h), 1))
# Try with both "locale.h" and "xlocale.h"
locale_headers = [
"stdlib.h",
"xlocale.h",
"feature_detection_locale.h",
]
if not check_funcs(OPTIONAL_LOCALE_FUNCS, headers=locale_headers):
# It didn't work with xlocale.h, maybe it will work with locale.h?
locale_headers[1] = "locale.h"
check_funcs(OPTIONAL_LOCALE_FUNCS, headers=locale_headers)
for tup in OPTIONAL_INTRINSICS:
headers = None
if len(tup) == 2:
f, args, m = tup[0], tup[1], fname2def(tup[0])
elif len(tup) == 3:
f, args, headers, m = tup[0], tup[1], [tup[2]], fname2def(tup[0])
else:
f, args, headers, m = tup[0], tup[1], [tup[2]], fname2def(tup[3])
if config.check_func(f, decl=False, call=True, call_args=args,
headers=headers):
moredefs.append((m, 1))
for dec, fn in OPTIONAL_FUNCTION_ATTRIBUTES:
if config.check_gcc_function_attribute(dec, fn):
moredefs.append((fname2def(fn), 1))
check_gh14787(fn)
platform = sysconfig.get_platform()
if ("x86_64" in platform):
for dec, fn in OPTIONAL_FUNCTION_ATTRIBUTES_AVX:
if config.check_gcc_function_attribute(dec, fn):
moredefs.append((fname2def(fn), 1))
check_gh14787(fn)
for dec, fn, code, header in (
OPTIONAL_FUNCTION_ATTRIBUTES_WITH_INTRINSICS_AVX):
if config.check_gcc_function_attribute_with_intrinsics(
dec, fn, code, header):
moredefs.append((fname2def(fn), 1))
for fn in OPTIONAL_VARIABLE_ATTRIBUTES:
if config.check_gcc_variable_attribute(fn):
m = fn.replace("(", "_").replace(")", "_")
moredefs.append((fname2def(m), 1))
def check_complex(config, mathlibs):
priv = []
pub = []
# Check for complex support
st = config.check_header('complex.h')
if st:
priv.append(('HAVE_COMPLEX_H', 1))
pub.append(('NPY_USE_C99_COMPLEX', 1))
for t in C99_COMPLEX_TYPES:
st = config.check_type(t, headers=["complex.h"])
if st:
pub.append(('NPY_HAVE_%s' % type2def(t), 1))
def check_prec(prec):
flist = [f + prec for f in C99_COMPLEX_FUNCS]
decl = dict([(f, True) for f in flist])
if not config.check_funcs_once(flist, call=decl, decl=decl,
libraries=mathlibs):
for f in flist:
if config.check_func(f, call=True, decl=True,
libraries=mathlibs):
priv.append((fname2def(f), 1))
else:
priv.extend([(fname2def(f), 1) for f in flist])
check_prec('')
check_prec('f')
check_prec('l')
return priv, pub
def check_ieee_macros(config):
priv = []
pub = []
macros = []
def _add_decl(f):
priv.append(fname2def("decl_%s" % f))
pub.append('NPY_%s' % fname2def("decl_%s" % f))
# XXX: hack to circumvent cpp pollution from python: python put its
# config.h in the public namespace, so we have a clash for the common
# functions we test. We remove every function tested by python's
# autoconf, hoping their own test are correct
_macros = ["isnan", "isinf", "signbit", "isfinite"]
for f in _macros:
py_symbol = fname2def("decl_%s" % f)
already_declared = config.check_decl(py_symbol,
headers=["Python.h", "math.h"])
if already_declared:
if config.check_macro_true(py_symbol,
headers=["Python.h", "math.h"]):
pub.append('NPY_%s' % fname2def("decl_%s" % f))
else:
macros.append(f)
# Normally, isnan and isinf are macro (C99), but some platforms only have
# func, or both func and macro version. Check for macro only, and define
# replacement ones if not found.
# Note: including Python.h is necessary because it modifies some math.h
# definitions
for f in macros:
st = config.check_decl(f, headers=["Python.h", "math.h"])
if st:
_add_decl(f)
return priv, pub
def check_types(config_cmd, ext, build_dir):
private_defines = []
public_defines = []
# Expected size (in number of bytes) for each type. This is an
# optimization: those are only hints, and an exhaustive search for the size
# is done if the hints are wrong.
expected = {'short': [2], 'int': [4], 'long': [8, 4],
'float': [4], 'double': [8], 'long double': [16, 12, 8],
'Py_intptr_t': [8, 4], 'PY_LONG_LONG': [8], 'long long': [8],
'off_t': [8, 4]}
# Check we have the python header (-dev* packages on Linux)
result = config_cmd.check_header('Python.h')
if not result:
python = 'python'
if '__pypy__' in sys.builtin_module_names:
python = 'pypy'
raise SystemError(
"Cannot compile 'Python.h'. Perhaps you need to "
"install {0}-dev|{0}-devel.".format(python))
res = config_cmd.check_header("endian.h")
if res:
private_defines.append(('HAVE_ENDIAN_H', 1))
public_defines.append(('NPY_HAVE_ENDIAN_H', 1))
res = config_cmd.check_header("sys/endian.h")
if res:
private_defines.append(('HAVE_SYS_ENDIAN_H', 1))
public_defines.append(('NPY_HAVE_SYS_ENDIAN_H', 1))
# Check basic types sizes
for type in ('short', 'int', 'long'):
res = config_cmd.check_decl("SIZEOF_%s" % sym2def(type), headers=["Python.h"])
if res:
public_defines.append(('NPY_SIZEOF_%s' % sym2def(type), "SIZEOF_%s" % sym2def(type)))
else:
res = config_cmd.check_type_size(type, expected=expected[type])
if res >= 0:
public_defines.append(('NPY_SIZEOF_%s' % sym2def(type), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % type)
for type in ('float', 'double', 'long double'):
already_declared = config_cmd.check_decl("SIZEOF_%s" % sym2def(type),
headers=["Python.h"])
res = config_cmd.check_type_size(type, expected=expected[type])
if res >= 0:
public_defines.append(('NPY_SIZEOF_%s' % sym2def(type), '%d' % res))
if not already_declared and not type == 'long double':
private_defines.append(('SIZEOF_%s' % sym2def(type), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % type)
# Compute size of corresponding complex type: used to check that our
# definition is binary compatible with C99 complex type (check done at
# build time in npy_common.h)
complex_def = "struct {%s __x; %s __y;}" % (type, type)
res = config_cmd.check_type_size(complex_def,
expected=[2 * x for x in expected[type]])
if res >= 0:
public_defines.append(('NPY_SIZEOF_COMPLEX_%s' % sym2def(type), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % complex_def)
for type in ('Py_intptr_t', 'off_t'):
res = config_cmd.check_type_size(type, headers=["Python.h"],
library_dirs=[pythonlib_dir()],
expected=expected[type])
if res >= 0:
private_defines.append(('SIZEOF_%s' % sym2def(type), '%d' % res))
public_defines.append(('NPY_SIZEOF_%s' % sym2def(type), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % type)
# We check declaration AND type because that's how distutils does it.
if config_cmd.check_decl('PY_LONG_LONG', headers=['Python.h']):
res = config_cmd.check_type_size('PY_LONG_LONG', headers=['Python.h'],
library_dirs=[pythonlib_dir()],
expected=expected['PY_LONG_LONG'])
if res >= 0:
private_defines.append(('SIZEOF_%s' % sym2def('PY_LONG_LONG'), '%d' % res))
public_defines.append(('NPY_SIZEOF_%s' % sym2def('PY_LONG_LONG'), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % 'PY_LONG_LONG')
res = config_cmd.check_type_size('long long',
expected=expected['long long'])
if res >= 0:
#private_defines.append(('SIZEOF_%s' % sym2def('long long'), '%d' % res))
public_defines.append(('NPY_SIZEOF_%s' % sym2def('long long'), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % 'long long')
if not config_cmd.check_decl('CHAR_BIT', headers=['Python.h']):
raise RuntimeError(
"Config wo CHAR_BIT is not supported"
", please contact the maintainers")
return private_defines, public_defines
def check_mathlib(config_cmd):
# Testing the C math library
mathlibs = []
mathlibs_choices = [[], ["m"], ["cpml"]]
mathlib = os.environ.get("MATHLIB")
if mathlib:
mathlibs_choices.insert(0, mathlib.split(","))
for libs in mathlibs_choices:
if config_cmd.check_func(
"log",
libraries=libs,
call_args="0",
decl="double log(double);",
call=True
):
mathlibs = libs
break
else:
raise RuntimeError(
"math library missing; rerun setup.py after setting the "
"MATHLIB env variable"
)
return mathlibs
def visibility_define(config):
"""Return the define value to use for NPY_VISIBILITY_HIDDEN (may be empty
string)."""
hide = '__attribute__((visibility("hidden")))'
if config.check_gcc_function_attribute(hide, 'hideme'):
return hide
else:
return ''
import os
del os
import sys
if 'setuptools' in sys.modules:
have_setuptools = True
from setuptools import setup as old_setup
# easy_install imports math, it may be picked up from cwd
from setuptools.command import easy_install
try:
# very old versions of setuptools don't have this
from setuptools.command import bdist_egg
except ImportError:
have_setuptools = False
else:
from distutils.core import setup as old_setup
have_setuptools = False
def lib_opts_if_msvc(build_cmd):
""" Add flags if we are using MSVC compiler
We can't see `build_cmd` in our scope, because we have not initialized
the distutils build command, so use this deferred calculation to run
when we are building the library.
"""
if build_cmd.compiler.compiler_type != 'msvc':
return []
# Explicitly disable whole-program optimization.
flags = ['/GL-']
# Disable voltbl section for vc142 to allow link using mingw-w64; see:
# https://github.com/matthew-brett/dll_investigation/issues/1#issuecomment-1100468171
if build_cmd.compiler_opt.cc_test_flags(['-d2VolatileMetadata-']):
flags.append('-d2VolatileMetadata-')
return flags
def dot_join(*args):
return '.'.join([a for a in args if a])
class Configuration:
_list_keys = ['packages', 'ext_modules', 'data_files', 'include_dirs',
'libraries', 'headers', 'scripts', 'py_modules',
'installed_libraries', 'define_macros']
_dict_keys = ['package_dir', 'installed_pkg_config']
_extra_keys = ['name', 'version']
numpy_include_dirs = []
def __init__(self,
package_name=None,
parent_name=None,
top_path=None,
package_path=None,
caller_level=1,
setup_name='setup.py',
**attrs):
"""Construct configuration instance of a package.
package_name -- name of the package
Ex.: 'distutils'
parent_name -- name of the parent package
Ex.: 'numpy'
top_path -- directory of the toplevel package
Ex.: the directory where the numpy package source sits
package_path -- directory of package. Will be computed by magic from the
directory of the caller module if not specified
Ex.: the directory where numpy.distutils is
caller_level -- frame level to caller namespace, internal parameter.
"""
self.name = dot_join(parent_name, package_name)
self.version = None
caller_frame = get_frame(caller_level)
self.local_path = get_path_from_frame(caller_frame, top_path)
# local_path -- directory of a file (usually setup.py) that
# defines a configuration() function.
# local_path -- directory of a file (usually setup.py) that
# defines a configuration() function.
if top_path is None:
top_path = self.local_path
self.local_path = ''
if package_path is None:
package_path = self.local_path
elif os.path.isdir(njoin(self.local_path, package_path)):
package_path = njoin(self.local_path, package_path)
if not os.path.isdir(package_path or '.'):
raise ValueError("%r is not a directory" % (package_path,))
self.top_path = top_path
self.package_path = package_path
# this is the relative path in the installed package
self.path_in_package = os.path.join(*self.name.split('.'))
self.list_keys = self._list_keys[:]
self.dict_keys = self._dict_keys[:]
for n in self.list_keys:
v = copy.copy(attrs.get(n, []))
setattr(self, n, as_list(v))
for n in self.dict_keys:
v = copy.copy(attrs.get(n, {}))
setattr(self, n, v)
known_keys = self.list_keys + self.dict_keys
self.extra_keys = self._extra_keys[:]
for n in attrs.keys():
if n in known_keys:
continue
a = attrs[n]
setattr(self, n, a)
if isinstance(a, list):
self.list_keys.append(n)
elif isinstance(a, dict):
self.dict_keys.append(n)
else:
self.extra_keys.append(n)
if os.path.exists(njoin(package_path, '__init__.py')):
self.packages.append(self.name)
self.package_dir[self.name] = package_path
self.options = dict(
ignore_setup_xxx_py = False,
assume_default_configuration = False,
delegate_options_to_subpackages = False,
quiet = False,
)
caller_instance = None
for i in range(1, 3):
try:
f = get_frame(i)
except ValueError:
break
try:
caller_instance = eval('self', f.f_globals, f.f_locals)
break
except NameError:
pass
if isinstance(caller_instance, self.__class__):
if caller_instance.options['delegate_options_to_subpackages']:
self.set_options(**caller_instance.options)
self.setup_name = setup_name
def todict(self):
"""
Return a dictionary compatible with the keyword arguments of distutils
setup function.
Examples
--------
>>> setup(**config.todict()) #doctest: +SKIP
"""
self._optimize_data_files()
d = {}
known_keys = self.list_keys + self.dict_keys + self.extra_keys
for n in known_keys:
a = getattr(self, n)
if a:
d[n] = a
return d
def info(self, message):
if not self.options['quiet']:
print(message)
def warn(self, message):
sys.stderr.write('Warning: %s\n' % (message,))
def set_options(self, **options):
"""
Configure Configuration instance.
The following options are available:
- ignore_setup_xxx_py
- assume_default_configuration
- delegate_options_to_subpackages
- quiet
"""
for key, value in options.items():
if key in self.options:
self.options[key] = value
else:
raise ValueError('Unknown option: '+key)
def get_distribution(self):
"""Return the distutils distribution object for self."""
from numpy.distutils.core import get_distribution
return get_distribution()
def _wildcard_get_subpackage(self, subpackage_name,
parent_name,
caller_level = 1):
l = subpackage_name.split('.')
subpackage_path = njoin([self.local_path]+l)
dirs = [_m for _m in sorted_glob(subpackage_path) if os.path.isdir(_m)]
config_list = []
for d in dirs:
if not os.path.isfile(njoin(d, '__init__.py')):
continue
if 'build' in d.split(os.sep):
continue
n = '.'.join(d.split(os.sep)[-len(l):])
c = self.get_subpackage(n,
parent_name = parent_name,
caller_level = caller_level+1)
config_list.extend(c)
return config_list
def _get_configuration_from_setup_py(self, setup_py,
subpackage_name,
subpackage_path,
parent_name,
caller_level = 1):
# In case setup_py imports local modules:
sys.path.insert(0, os.path.dirname(setup_py))
try:
setup_name = os.path.splitext(os.path.basename(setup_py))[0]
n = dot_join(self.name, subpackage_name, setup_name)
setup_module = exec_mod_from_location(
'_'.join(n.split('.')), setup_py)
if not hasattr(setup_module, 'configuration'):
if not self.options['assume_default_configuration']:
self.warn('Assuming default configuration '\
'(%s does not define configuration())'\
% (setup_module))
config = Configuration(subpackage_name, parent_name,
self.top_path, subpackage_path,
caller_level = caller_level + 1)
else:
pn = dot_join(*([parent_name] + subpackage_name.split('.')[:-1]))
args = (pn,)
if setup_module.configuration.__code__.co_argcount > 1:
args = args + (self.top_path,)
config = setup_module.configuration(*args)
if config.name!=dot_join(parent_name, subpackage_name):
self.warn('Subpackage %r configuration returned as %r' % \
(dot_join(parent_name, subpackage_name), config.name))
finally:
del sys.path[0]
return config
def get_subpackage(self,subpackage_name,
subpackage_path=None,
parent_name=None,
caller_level = 1):
"""Return list of subpackage configurations.
Parameters
----------
subpackage_name : str or None
Name of the subpackage to get the configuration. '*' in
subpackage_name is handled as a wildcard.
subpackage_path : str
If None, then the path is assumed to be the local path plus the
subpackage_name. If a setup.py file is not found in the
subpackage_path, then a default configuration is used.
parent_name : str
Parent name.
"""
if subpackage_name is None:
if subpackage_path is None:
raise ValueError(
"either subpackage_name or subpackage_path must be specified")
subpackage_name = os.path.basename(subpackage_path)
# handle wildcards
l = subpackage_name.split('.')
if subpackage_path is None and '*' in subpackage_name:
return self._wildcard_get_subpackage(subpackage_name,
parent_name,
caller_level = caller_level+1)
assert '*' not in subpackage_name, repr((subpackage_name, subpackage_path, parent_name))
if subpackage_path is None:
subpackage_path = njoin([self.local_path] + l)
else:
subpackage_path = njoin([subpackage_path] + l[:-1])
subpackage_path = self.paths([subpackage_path])[0]
setup_py = njoin(subpackage_path, self.setup_name)
if not self.options['ignore_setup_xxx_py']:
if not os.path.isfile(setup_py):
setup_py = njoin(subpackage_path,
'setup_%s.py' % (subpackage_name))
if not os.path.isfile(setup_py):
if not self.options['assume_default_configuration']:
self.warn('Assuming default configuration '\
'(%s/{setup_%s,setup}.py was not found)' \
% (os.path.dirname(setup_py), subpackage_name))
config = Configuration(subpackage_name, parent_name,
self.top_path, subpackage_path,
caller_level = caller_level+1)
else:
config = self._get_configuration_from_setup_py(
setup_py,
subpackage_name,
subpackage_path,
parent_name,
caller_level = caller_level + 1)
if config:
return [config]
else:
return []
def add_subpackage(self,subpackage_name,
subpackage_path=None,
standalone = False):
"""Add a sub-package to the current Configuration instance.
This is useful in a setup.py script for adding sub-packages to a
package.
Parameters
----------
subpackage_name : str
name of the subpackage
subpackage_path : str
if given, the subpackage path such as the subpackage is in
subpackage_path / subpackage_name. If None,the subpackage is
assumed to be located in the local path / subpackage_name.
standalone : bool
"""
if standalone:
parent_name = None
else:
parent_name = self.name
config_list = self.get_subpackage(subpackage_name, subpackage_path,
parent_name = parent_name,
caller_level = 2)
if not config_list:
self.warn('No configuration returned, assuming unavailable.')
for config in config_list:
d = config
if isinstance(config, Configuration):
d = config.todict()
assert isinstance(d, dict), repr(type(d))
self.info('Appending %s configuration to %s' \
% (d.get('name'), self.name))
self.dict_append(**d)
dist = self.get_distribution()
if dist is not None:
self.warn('distutils distribution has been initialized,'\
' it may be too late to add a subpackage '+ subpackage_name)
def add_data_dir(self, data_path):
"""Recursively add files under data_path to data_files list.
Recursively add files under data_path to the list of data_files to be
installed (and distributed). The data_path can be either a relative
path-name, or an absolute path-name, or a 2-tuple where the first
argument shows where in the install directory the data directory
should be installed to.
Parameters
----------
data_path : seq or str
Argument can be either
* 2-sequence (<datadir suffix>, <path to data directory>)
* path to data directory where python datadir suffix defaults
to package dir.
Notes
-----
Rules for installation paths::
foo/bar -> (foo/bar, foo/bar) -> parent/foo/bar
(gun, foo/bar) -> parent/gun
foo/* -> (foo/a, foo/a), (foo/b, foo/b) -> parent/foo/a, parent/foo/b
(gun, foo/*) -> (gun, foo/a), (gun, foo/b) -> gun
(gun/*, foo/*) -> parent/gun/a, parent/gun/b
/foo/bar -> (bar, /foo/bar) -> parent/bar
(gun, /foo/bar) -> parent/gun
(fun/*/gun/*, sun/foo/bar) -> parent/fun/foo/gun/bar
Examples
--------
For example suppose the source directory contains fun/foo.dat and
fun/bar/car.dat:
>>> self.add_data_dir('fun') #doctest: +SKIP
>>> self.add_data_dir(('sun', 'fun')) #doctest: +SKIP
>>> self.add_data_dir(('gun', '/full/path/to/fun'))#doctest: +SKIP
Will install data-files to the locations::
<package install directory>/
fun/
foo.dat
bar/
car.dat
sun/
foo.dat
bar/
car.dat
gun/
foo.dat
car.dat
"""
if is_sequence(data_path):
d, data_path = data_path
else:
d = None
if is_sequence(data_path):
[self.add_data_dir((d, p)) for p in data_path]
return
if not is_string(data_path):
raise TypeError("not a string: %r" % (data_path,))
if d is None:
if os.path.isabs(data_path):
return self.add_data_dir((os.path.basename(data_path), data_path))
return self.add_data_dir((data_path, data_path))
paths = self.paths(data_path, include_non_existing=False)
if is_glob_pattern(data_path):
if is_glob_pattern(d):
pattern_list = allpath(d).split(os.sep)
pattern_list.reverse()
# /a/*//b/ -> /a/*/b
rl = list(range(len(pattern_list)-1)); rl.reverse()
for i in rl:
if not pattern_list[i]:
del pattern_list[i]
#
for path in paths:
if not os.path.isdir(path):
print('Not a directory, skipping', path)
continue
rpath = rel_path(path, self.local_path)
path_list = rpath.split(os.sep)
path_list.reverse()
target_list = []
i = 0
for s in pattern_list:
if is_glob_pattern(s):
if i>=len(path_list):
raise ValueError('cannot fill pattern %r with %r' \
% (d, path))
target_list.append(path_list[i])
else:
assert s==path_list[i], repr((s, path_list[i], data_path, d, path, rpath))
target_list.append(s)
i += 1
if path_list[i:]:
self.warn('mismatch of pattern_list=%s and path_list=%s'\
% (pattern_list, path_list))
target_list.reverse()
self.add_data_dir((os.sep.join(target_list), path))
else:
for path in paths:
self.add_data_dir((d, path))
return
assert not is_glob_pattern(d), repr(d)
dist = self.get_distribution()
if dist is not None and dist.data_files is not None:
data_files = dist.data_files
else:
data_files = self.data_files
for path in paths:
for d1, f in list(general_source_directories_files(path)):
target_path = os.path.join(self.path_in_package, d, d1)
data_files.append((target_path, f))
def _optimize_data_files(self):
data_dict = {}
for p, files in self.data_files:
if p not in data_dict:
data_dict[p] = set()
for f in files:
data_dict[p].add(f)
self.data_files[:] = [(p, list(files)) for p, files in data_dict.items()]
def add_data_files(self,*files):
"""Add data files to configuration data_files.
Parameters
----------
files : sequence
Argument(s) can be either
* 2-sequence (<datadir prefix>,<path to data file(s)>)
* paths to data files where python datadir prefix defaults
to package dir.
Notes
-----
The form of each element of the files sequence is very flexible
allowing many combinations of where to get the files from the package
and where they should ultimately be installed on the system. The most
basic usage is for an element of the files argument sequence to be a
simple filename. This will cause that file from the local path to be
installed to the installation path of the self.name package (package
path). The file argument can also be a relative path in which case the
entire relative path will be installed into the package directory.
Finally, the file can be an absolute path name in which case the file
will be found at the absolute path name but installed to the package
path.
This basic behavior can be augmented by passing a 2-tuple in as the
file argument. The first element of the tuple should specify the
relative path (under the package install directory) where the
remaining sequence of files should be installed to (it has nothing to
do with the file-names in the source distribution). The second element
of the tuple is the sequence of files that should be installed. The
files in this sequence can be filenames, relative paths, or absolute
paths. For absolute paths the file will be installed in the top-level
package installation directory (regardless of the first argument).
Filenames and relative path names will be installed in the package
install directory under the path name given as the first element of
the tuple.
Rules for installation paths:
#. file.txt -> (., file.txt)-> parent/file.txt
#. foo/file.txt -> (foo, foo/file.txt) -> parent/foo/file.txt
#. /foo/bar/file.txt -> (., /foo/bar/file.txt) -> parent/file.txt
#. ``*``.txt -> parent/a.txt, parent/b.txt
#. foo/``*``.txt`` -> parent/foo/a.txt, parent/foo/b.txt
#. ``*/*.txt`` -> (``*``, ``*``/``*``.txt) -> parent/c/a.txt, parent/d/b.txt
#. (sun, file.txt) -> parent/sun/file.txt
#. (sun, bar/file.txt) -> parent/sun/file.txt
#. (sun, /foo/bar/file.txt) -> parent/sun/file.txt
#. (sun, ``*``.txt) -> parent/sun/a.txt, parent/sun/b.txt
#. (sun, bar/``*``.txt) -> parent/sun/a.txt, parent/sun/b.txt
#. (sun/``*``, ``*``/``*``.txt) -> parent/sun/c/a.txt, parent/d/b.txt
An additional feature is that the path to a data-file can actually be
a function that takes no arguments and returns the actual path(s) to
the data-files. This is useful when the data files are generated while
building the package.
Examples
--------
Add files to the list of data_files to be included with the package.
>>> self.add_data_files('foo.dat',
... ('fun', ['gun.dat', 'nun/pun.dat', '/tmp/sun.dat']),
... 'bar/cat.dat',
... '/full/path/to/can.dat') #doctest: +SKIP
will install these data files to::
<package install directory>/
foo.dat
fun/
gun.dat
nun/
pun.dat
sun.dat
bar/
car.dat
can.dat
where <package install directory> is the package (or sub-package)
directory such as '/usr/lib/python2.4/site-packages/mypackage' ('C:
\\Python2.4 \\Lib \\site-packages \\mypackage') or
'/usr/lib/python2.4/site- packages/mypackage/mysubpackage' ('C:
\\Python2.4 \\Lib \\site-packages \\mypackage \\mysubpackage').
"""
if len(files)>1:
for f in files:
self.add_data_files(f)
return
assert len(files)==1
if is_sequence(files[0]):
d, files = files[0]
else:
d = None
if is_string(files):
filepat = files
elif is_sequence(files):
if len(files)==1:
filepat = files[0]
else:
for f in files:
self.add_data_files((d, f))
return
else:
raise TypeError(repr(type(files)))
if d is None:
if hasattr(filepat, '__call__'):
d = ''
elif os.path.isabs(filepat):
d = ''
else:
d = os.path.dirname(filepat)
self.add_data_files((d, files))
return
paths = self.paths(filepat, include_non_existing=False)
if is_glob_pattern(filepat):
if is_glob_pattern(d):
pattern_list = d.split(os.sep)
pattern_list.reverse()
for path in paths:
path_list = path.split(os.sep)
path_list.reverse()
path_list.pop() # filename
target_list = []
i = 0
for s in pattern_list:
if is_glob_pattern(s):
target_list.append(path_list[i])
i += 1
else:
target_list.append(s)
target_list.reverse()
self.add_data_files((os.sep.join(target_list), path))
else:
self.add_data_files((d, paths))
return
assert not is_glob_pattern(d), repr((d, filepat))
dist = self.get_distribution()
if dist is not None and dist.data_files is not None:
data_files = dist.data_files
else:
data_files = self.data_files
data_files.append((os.path.join(self.path_in_package, d), paths))
### XXX Implement add_py_modules
def add_define_macros(self, macros):
"""Add define macros to configuration
Add the given sequence of macro name and value duples to the beginning
of the define_macros list This list will be visible to all extension
modules of the current package.
"""
dist = self.get_distribution()
if dist is not None:
if not hasattr(dist, 'define_macros'):
dist.define_macros = []
dist.define_macros.extend(macros)
else:
self.define_macros.extend(macros)
def add_include_dirs(self,*paths):
"""Add paths to configuration include directories.
Add the given sequence of paths to the beginning of the include_dirs
list. This list will be visible to all extension modules of the
current package.
"""
include_dirs = self.paths(paths)
dist = self.get_distribution()
if dist is not None:
if dist.include_dirs is None:
dist.include_dirs = []
dist.include_dirs.extend(include_dirs)
else:
self.include_dirs.extend(include_dirs)
def add_headers(self,*files):
"""Add installable headers to configuration.
Add the given sequence of files to the beginning of the headers list.
By default, headers will be installed under <python-
include>/<self.name.replace('.','/')>/ directory. If an item of files
is a tuple, then its first argument specifies the actual installation
location relative to the <python-include> path.
Parameters
----------
files : str or seq
Argument(s) can be either:
* 2-sequence (<includedir suffix>,<path to header file(s)>)
* path(s) to header file(s) where python includedir suffix will
default to package name.
"""
headers = []
for path in files:
if is_string(path):
[headers.append((self.name, p)) for p in self.paths(path)]
else:
if not isinstance(path, (tuple, list)) or len(path) != 2:
raise TypeError(repr(path))
[headers.append((path[0], p)) for p in self.paths(path[1])]
dist = self.get_distribution()
if dist is not None:
if dist.headers is None:
dist.headers = []
dist.headers.extend(headers)
else:
self.headers.extend(headers)
def paths(self,*paths,**kws):
"""Apply glob to paths and prepend local_path if needed.
Applies glob.glob(...) to each path in the sequence (if needed) and
pre-pends the local_path if needed. Because this is called on all
source lists, this allows wildcard characters to be specified in lists
of sources for extension modules and libraries and scripts and allows
path-names be relative to the source directory.
"""
include_non_existing = kws.get('include_non_existing', True)
return gpaths(paths,
local_path = self.local_path,
include_non_existing=include_non_existing)
def _fix_paths_dict(self, kw):
for k in kw.keys():
v = kw[k]
if k in ['sources', 'depends', 'include_dirs', 'library_dirs',
'module_dirs', 'extra_objects']:
new_v = self.paths(v)
kw[k] = new_v
def add_extension(self,name,sources,**kw):
"""Add extension to configuration.
Create and add an Extension instance to the ext_modules list. This
method also takes the following optional keyword arguments that are
passed on to the Extension constructor.
Parameters
----------
name : str
name of the extension
sources : seq
list of the sources. The list of sources may contain functions
(called source generators) which must take an extension instance
and a build directory as inputs and return a source file or list of
source files or None. If None is returned then no sources are
generated. If the Extension instance has no sources after
processing all source generators, then no extension module is
built.
include_dirs :
define_macros :
undef_macros :
library_dirs :
libraries :
runtime_library_dirs :
extra_objects :
extra_compile_args :
extra_link_args :
extra_f77_compile_args :
extra_f90_compile_args :
export_symbols :
swig_opts :
depends :
The depends list contains paths to files or directories that the
sources of the extension module depend on. If any path in the
depends list is newer than the extension module, then the module
will be rebuilt.
language :
f2py_options :
module_dirs :
extra_info : dict or list
dict or list of dict of keywords to be appended to keywords.
Notes
-----
The self.paths(...) method is applied to all lists that may contain
paths.
"""
ext_args = copy.copy(kw)
ext_args['name'] = dot_join(self.name, name)
ext_args['sources'] = sources
if 'extra_info' in ext_args:
extra_info = ext_args['extra_info']
del ext_args['extra_info']
if isinstance(extra_info, dict):
extra_info = [extra_info]
for info in extra_info:
assert isinstance(info, dict), repr(info)
dict_append(ext_args,**info)
self._fix_paths_dict(ext_args)
# Resolve out-of-tree dependencies
libraries = ext_args.get('libraries', [])
libnames = []
ext_args['libraries'] = []
for libname in libraries:
if isinstance(libname, tuple):
self._fix_paths_dict(libname[1])
# Handle library names of the form libname@relative/path/to/library
if '@' in libname:
lname, lpath = libname.split('@', 1)
lpath = os.path.abspath(njoin(self.local_path, lpath))
if os.path.isdir(lpath):
c = self.get_subpackage(None, lpath,
caller_level = 2)
if isinstance(c, Configuration):
c = c.todict()
for l in [l[0] for l in c.get('libraries', [])]:
llname = l.split('__OF__', 1)[0]
if llname == lname:
c.pop('name', None)
dict_append(ext_args,**c)
break
continue
libnames.append(libname)
ext_args['libraries'] = libnames + ext_args['libraries']
ext_args['define_macros'] = \
self.define_macros + ext_args.get('define_macros', [])
from numpy.distutils.core import Extension
ext = Extension(**ext_args)
self.ext_modules.append(ext)
dist = self.get_distribution()
if dist is not None:
self.warn('distutils distribution has been initialized,'\
' it may be too late to add an extension '+name)
return ext
def add_library(self,name,sources,**build_info):
"""
Add library to configuration.
Parameters
----------
name : str
Name of the extension.
sources : sequence
List of the sources. The list of sources may contain functions
(called source generators) which must take an extension instance
and a build directory as inputs and return a source file or list of
source files or None. If None is returned then no sources are
generated. If the Extension instance has no sources after
processing all source generators, then no extension module is
built.
build_info : dict, optional
The following keys are allowed:
* depends
* macros
* include_dirs
* extra_compiler_args
* extra_f77_compile_args
* extra_f90_compile_args
* f2py_options
* language
"""
self._add_library(name, sources, None, build_info)
dist = self.get_distribution()
if dist is not None:
self.warn('distutils distribution has been initialized,'\
' it may be too late to add a library '+ name)
def _add_library(self, name, sources, install_dir, build_info):
"""Common implementation for add_library and add_installed_library. Do
not use directly"""
build_info = copy.copy(build_info)
build_info['sources'] = sources
# Sometimes, depends is not set up to an empty list by default, and if
# depends is not given to add_library, distutils barfs (#1134)
if not 'depends' in build_info:
build_info['depends'] = []
self._fix_paths_dict(build_info)
# Add to libraries list so that it is build with build_clib
self.libraries.append((name, build_info))
def add_installed_library(self, name, sources, install_dir, build_info=None):
"""
Similar to add_library, but the specified library is installed.
Most C libraries used with `distutils` are only used to build python
extensions, but libraries built through this method will be installed
so that they can be reused by third-party packages.
Parameters
----------
name : str
Name of the installed library.
sources : sequence
List of the library's source files. See `add_library` for details.
install_dir : str
Path to install the library, relative to the current sub-package.
build_info : dict, optional
The following keys are allowed:
* depends
* macros
* include_dirs
* extra_compiler_args
* extra_f77_compile_args
* extra_f90_compile_args
* f2py_options
* language
Returns
-------
None
See Also
--------
add_library, add_npy_pkg_config, get_info
Notes
-----
The best way to encode the options required to link against the specified
C libraries is to use a "libname.ini" file, and use `get_info` to
retrieve the required options (see `add_npy_pkg_config` for more
information).
"""
if not build_info:
build_info = {}
install_dir = os.path.join(self.package_path, install_dir)
self._add_library(name, sources, install_dir, build_info)
self.installed_libraries.append(InstallableLib(name, build_info, install_dir))
def add_npy_pkg_config(self, template, install_dir, subst_dict=None):
"""
Generate and install a npy-pkg config file from a template.
The config file generated from `template` is installed in the
given install directory, using `subst_dict` for variable substitution.
Parameters
----------
template : str
The path of the template, relatively to the current package path.
install_dir : str
Where to install the npy-pkg config file, relatively to the current
package path.
subst_dict : dict, optional
If given, any string of the form ``@key@`` will be replaced by
``subst_dict[key]`` in the template file when installed. The install
prefix is always available through the variable ``@prefix@``, since the
install prefix is not easy to get reliably from setup.py.
See also
--------
add_installed_library, get_info
Notes
-----
This works for both standard installs and in-place builds, i.e. the
``@prefix@`` refer to the source directory for in-place builds.
Examples
--------
::
config.add_npy_pkg_config('foo.ini.in', 'lib', {'foo': bar})
Assuming the foo.ini.in file has the following content::
[meta]
Name=@foo@
Version=1.0
Description=dummy description
[default]
Cflags=-I@prefix@/include
Libs=
The generated file will have the following content::
[meta]
Name=bar
Version=1.0
Description=dummy description
[default]
Cflags=-Iprefix_dir/include
Libs=
and will be installed as foo.ini in the 'lib' subpath.
When cross-compiling with numpy distutils, it might be necessary to
use modified npy-pkg-config files. Using the default/generated files
will link with the host libraries (i.e. libnpymath.a). For
cross-compilation you of-course need to link with target libraries,
while using the host Python installation.
You can copy out the numpy/core/lib/npy-pkg-config directory, add a
pkgdir value to the .ini files and set NPY_PKG_CONFIG_PATH environment
variable to point to the directory with the modified npy-pkg-config
files.
Example npymath.ini modified for cross-compilation::
[meta]
Name=npymath
Description=Portable, core math library implementing C99 standard
Version=0.1
[variables]
pkgname=numpy.core
pkgdir=/build/arm-linux-gnueabi/sysroot/usr/lib/python3.7/site-packages/numpy/core
prefix=${pkgdir}
libdir=${prefix}/lib
includedir=${prefix}/include
[default]
Libs=-L${libdir} -lnpymath
Cflags=-I${includedir}
Requires=mlib
[msvc]
Libs=/LIBPATH:${libdir} npymath.lib
Cflags=/INCLUDE:${includedir}
Requires=mlib
"""
if subst_dict is None:
subst_dict = {}
template = os.path.join(self.package_path, template)
if self.name in self.installed_pkg_config:
self.installed_pkg_config[self.name].append((template, install_dir,
subst_dict))
else:
self.installed_pkg_config[self.name] = [(template, install_dir,
subst_dict)]
def add_scripts(self,*files):
"""Add scripts to configuration.
Add the sequence of files to the beginning of the scripts list.
Scripts will be installed under the <prefix>/bin/ directory.
"""
scripts = self.paths(files)
dist = self.get_distribution()
if dist is not None:
if dist.scripts is None:
dist.scripts = []
dist.scripts.extend(scripts)
else:
self.scripts.extend(scripts)
def dict_append(self,**dict):
for key in self.list_keys:
a = getattr(self, key)
a.extend(dict.get(key, []))
for key in self.dict_keys:
a = getattr(self, key)
a.update(dict.get(key, {}))
known_keys = self.list_keys + self.dict_keys + self.extra_keys
for key in dict.keys():
if key not in known_keys:
a = getattr(self, key, None)
if a and a==dict[key]: continue
self.warn('Inheriting attribute %r=%r from %r' \
% (key, dict[key], dict.get('name', '?')))
setattr(self, key, dict[key])
self.extra_keys.append(key)
elif key in self.extra_keys:
self.info('Ignoring attempt to set %r (from %r to %r)' \
% (key, getattr(self, key), dict[key]))
elif key in known_keys:
# key is already processed above
pass
else:
raise ValueError("Don't know about key=%r" % (key))
def __str__(self):
from pprint import pformat
known_keys = self.list_keys + self.dict_keys + self.extra_keys
s = '<'+5*'-' + '\n'
s += 'Configuration of '+self.name+':\n'
known_keys.sort()
for k in known_keys:
a = getattr(self, k, None)
if a:
s += '%s = %s\n' % (k, pformat(a))
s += 5*'-' + '>'
return s
def get_config_cmd(self):
"""
Returns the numpy.distutils config command instance.
"""
cmd = get_cmd('config')
cmd.ensure_finalized()
cmd.dump_source = 0
cmd.noisy = 0
old_path = os.environ.get('PATH')
if old_path:
path = os.pathsep.join(['.', old_path])
os.environ['PATH'] = path
return cmd
def get_build_temp_dir(self):
"""
Return a path to a temporary directory where temporary files should be
placed.
"""
cmd = get_cmd('build')
cmd.ensure_finalized()
return cmd.build_temp
def have_f77c(self):
"""Check for availability of Fortran 77 compiler.
Use it inside source generating function to ensure that
setup distribution instance has been initialized.
Notes
-----
True if a Fortran 77 compiler is available (because a simple Fortran 77
code was able to be compiled successfully).
"""
simple_fortran_subroutine = '''
subroutine simple
end
'''
config_cmd = self.get_config_cmd()
flag = config_cmd.try_compile(simple_fortran_subroutine, lang='f77')
return flag
def have_f90c(self):
"""Check for availability of Fortran 90 compiler.
Use it inside source generating function to ensure that
setup distribution instance has been initialized.
Notes
-----
True if a Fortran 90 compiler is available (because a simple Fortran
90 code was able to be compiled successfully)
"""
simple_fortran_subroutine = '''
subroutine simple
end
'''
config_cmd = self.get_config_cmd()
flag = config_cmd.try_compile(simple_fortran_subroutine, lang='f90')
return flag
def append_to(self, extlib):
"""Append libraries, include_dirs to extension or library item.
"""
if is_sequence(extlib):
lib_name, build_info = extlib
dict_append(build_info,
libraries=self.libraries,
include_dirs=self.include_dirs)
else:
from numpy.distutils.core import Extension
assert isinstance(extlib, Extension), repr(extlib)
extlib.libraries.extend(self.libraries)
extlib.include_dirs.extend(self.include_dirs)
def _get_svn_revision(self, path):
"""Return path's SVN revision number.
"""
try:
output = subprocess.check_output(['svnversion'], cwd=path)
except (subprocess.CalledProcessError, OSError):
pass
else:
m = re.match(rb'(?P<revision>\d+)', output)
if m:
return int(m.group('revision'))
if sys.platform=='win32' and os.environ.get('SVN_ASP_DOT_NET_HACK', None):
entries = njoin(path, '_svn', 'entries')
else:
entries = njoin(path, '.svn', 'entries')
if os.path.isfile(entries):
with open(entries) as f:
fstr = f.read()
if fstr[:5] == '<?xml': # pre 1.4
m = re.search(r'revision="(?P<revision>\d+)"', fstr)
if m:
return int(m.group('revision'))
else: # non-xml entries file --- check to be sure that
m = re.search(r'dir[\n\r]+(?P<revision>\d+)', fstr)
if m:
return int(m.group('revision'))
return None
def _get_hg_revision(self, path):
"""Return path's Mercurial revision number.
"""
try:
output = subprocess.check_output(
['hg', 'identify', '--num'], cwd=path)
except (subprocess.CalledProcessError, OSError):
pass
else:
m = re.match(rb'(?P<revision>\d+)', output)
if m:
return int(m.group('revision'))
branch_fn = njoin(path, '.hg', 'branch')
branch_cache_fn = njoin(path, '.hg', 'branch.cache')
if os.path.isfile(branch_fn):
branch0 = None
with open(branch_fn) as f:
revision0 = f.read().strip()
branch_map = {}
with open(branch_cache_fn, 'r') as f:
for line in f:
branch1, revision1 = line.split()[:2]
if revision1==revision0:
branch0 = branch1
try:
revision1 = int(revision1)
except ValueError:
continue
branch_map[branch1] = revision1
return branch_map.get(branch0)
return None
def get_version(self, version_file=None, version_variable=None):
"""Try to get version string of a package.
Return a version string of the current package or None if the version
information could not be detected.
Notes
-----
This method scans files named
__version__.py, <packagename>_version.py, version.py, and
__svn_version__.py for string variables version, __version__, and
<packagename>_version, until a version number is found.
"""
version = getattr(self, 'version', None)
if version is not None:
return version
# Get version from version file.
if version_file is None:
files = ['__version__.py',
self.name.split('.')[-1]+'_version.py',
'version.py',
'__svn_version__.py',
'__hg_version__.py']
else:
files = [version_file]
if version_variable is None:
version_vars = ['version',
'__version__',
self.name.split('.')[-1]+'_version']
else:
version_vars = [version_variable]
for f in files:
fn = njoin(self.local_path, f)
if os.path.isfile(fn):
info = ('.py', 'U', 1)
name = os.path.splitext(os.path.basename(fn))[0]
n = dot_join(self.name, name)
try:
version_module = exec_mod_from_location(
'_'.join(n.split('.')), fn)
except ImportError as e:
self.warn(str(e))
version_module = None
if version_module is None:
continue
for a in version_vars:
version = getattr(version_module, a, None)
if version is not None:
break
# Try if versioneer module
try:
version = version_module.get_versions()['version']
except AttributeError:
pass
if version is not None:
break
if version is not None:
self.version = version
return version
# Get version as SVN or Mercurial revision number
revision = self._get_svn_revision(self.local_path)
if revision is None:
revision = self._get_hg_revision(self.local_path)
if revision is not None:
version = str(revision)
self.version = version
return version
def make_svn_version_py(self, delete=True):
"""Appends a data function to the data_files list that will generate
__svn_version__.py file to the current package directory.
Generate package __svn_version__.py file from SVN revision number,
it will be removed after python exits but will be available
when sdist, etc commands are executed.
Notes
-----
If __svn_version__.py existed before, nothing is done.
This is
intended for working with source directories that are in an SVN
repository.
"""
target = njoin(self.local_path, '__svn_version__.py')
revision = self._get_svn_revision(self.local_path)
if os.path.isfile(target) or revision is None:
return
else:
def generate_svn_version_py():
if not os.path.isfile(target):
version = str(revision)
self.info('Creating %s (version=%r)' % (target, version))
with open(target, 'w') as f:
f.write('version = %r\n' % (version))
def rm_file(f=target,p=self.info):
if delete:
try: os.remove(f); p('removed '+f)
except OSError: pass
try: os.remove(f+'c'); p('removed '+f+'c')
except OSError: pass
atexit.register(rm_file)
return target
self.add_data_files(('', generate_svn_version_py()))
def make_hg_version_py(self, delete=True):
"""Appends a data function to the data_files list that will generate
__hg_version__.py file to the current package directory.
Generate package __hg_version__.py file from Mercurial revision,
it will be removed after python exits but will be available
when sdist, etc commands are executed.
Notes
-----
If __hg_version__.py existed before, nothing is done.
This is intended for working with source directories that are
in an Mercurial repository.
"""
target = njoin(self.local_path, '__hg_version__.py')
revision = self._get_hg_revision(self.local_path)
if os.path.isfile(target) or revision is None:
return
else:
def generate_hg_version_py():
if not os.path.isfile(target):
version = str(revision)
self.info('Creating %s (version=%r)' % (target, version))
with open(target, 'w') as f:
f.write('version = %r\n' % (version))
def rm_file(f=target,p=self.info):
if delete:
try: os.remove(f); p('removed '+f)
except OSError: pass
try: os.remove(f+'c'); p('removed '+f+'c')
except OSError: pass
atexit.register(rm_file)
return target
self.add_data_files(('', generate_hg_version_py()))
def make_config_py(self,name='__config__'):
"""Generate package __config__.py file containing system_info
information used during building the package.
This file is installed to the
package installation directory.
"""
self.py_modules.append((self.name, name, generate_config_py))
def get_info(self,*names):
"""Get resources information.
Return information (from system_info.get_info) for all of the names in
the argument list in a single dictionary.
"""
from .system_info import get_info, dict_append
info_dict = {}
for a in names:
dict_append(info_dict,**get_info(a))
return info_dict
def exec_mod_from_location(modname, modfile):
'''
Use importlib machinery to import a module `modname` from the file
`modfile`. Depending on the `spec.loader`, the module may not be
registered in sys.modules.
'''
spec = importlib.util.spec_from_file_location(modname, modfile)
foo = importlib.util.module_from_spec(spec)
spec.loader.exec_module(foo)
return foo
def get_info(name, notfound_action=0):
"""
notfound_action:
0 - do nothing
1 - display warning message
2 - raise error
"""
cl = {'armpl': armpl_info,
'blas_armpl': blas_armpl_info,
'lapack_armpl': lapack_armpl_info,
'fftw3_armpl': fftw3_armpl_info,
'atlas': atlas_info, # use lapack_opt or blas_opt instead
'atlas_threads': atlas_threads_info, # ditto
'atlas_blas': atlas_blas_info,
'atlas_blas_threads': atlas_blas_threads_info,
'lapack_atlas': lapack_atlas_info, # use lapack_opt instead
'lapack_atlas_threads': lapack_atlas_threads_info, # ditto
'atlas_3_10': atlas_3_10_info, # use lapack_opt or blas_opt instead
'atlas_3_10_threads': atlas_3_10_threads_info, # ditto
'atlas_3_10_blas': atlas_3_10_blas_info,
'atlas_3_10_blas_threads': atlas_3_10_blas_threads_info,
'lapack_atlas_3_10': lapack_atlas_3_10_info, # use lapack_opt instead
'lapack_atlas_3_10_threads': lapack_atlas_3_10_threads_info, # ditto
'flame': flame_info, # use lapack_opt instead
'mkl': mkl_info,
# openblas which may or may not have embedded lapack
'openblas': openblas_info, # use blas_opt instead
# openblas with embedded lapack
'openblas_lapack': openblas_lapack_info, # use blas_opt instead
'openblas_clapack': openblas_clapack_info, # use blas_opt instead
'blis': blis_info, # use blas_opt instead
'lapack_mkl': lapack_mkl_info, # use lapack_opt instead
'blas_mkl': blas_mkl_info, # use blas_opt instead
'accelerate': accelerate_info, # use blas_opt instead
'openblas64_': openblas64__info,
'openblas64__lapack': openblas64__lapack_info,
'openblas_ilp64': openblas_ilp64_info,
'openblas_ilp64_lapack': openblas_ilp64_lapack_info,
'x11': x11_info,
'fft_opt': fft_opt_info,
'fftw': fftw_info,
'fftw2': fftw2_info,
'fftw3': fftw3_info,
'dfftw': dfftw_info,
'sfftw': sfftw_info,
'fftw_threads': fftw_threads_info,
'dfftw_threads': dfftw_threads_info,
'sfftw_threads': sfftw_threads_info,
'djbfft': djbfft_info,
'blas': blas_info, # use blas_opt instead
'lapack': lapack_info, # use lapack_opt instead
'lapack_src': lapack_src_info,
'blas_src': blas_src_info,
'numpy': numpy_info,
'f2py': f2py_info,
'Numeric': Numeric_info,
'numeric': Numeric_info,
'numarray': numarray_info,
'numerix': numerix_info,
'lapack_opt': lapack_opt_info,
'lapack_ilp64_opt': lapack_ilp64_opt_info,
'lapack_ilp64_plain_opt': lapack_ilp64_plain_opt_info,
'lapack64__opt': lapack64__opt_info,
'blas_opt': blas_opt_info,
'blas_ilp64_opt': blas_ilp64_opt_info,
'blas_ilp64_plain_opt': blas_ilp64_plain_opt_info,
'blas64__opt': blas64__opt_info,
'boost_python': boost_python_info,
'agg2': agg2_info,
'wx': wx_info,
'gdk_pixbuf_xlib_2': gdk_pixbuf_xlib_2_info,
'gdk-pixbuf-xlib-2.0': gdk_pixbuf_xlib_2_info,
'gdk_pixbuf_2': gdk_pixbuf_2_info,
'gdk-pixbuf-2.0': gdk_pixbuf_2_info,
'gdk': gdk_info,
'gdk_2': gdk_2_info,
'gdk-2.0': gdk_2_info,
'gdk_x11_2': gdk_x11_2_info,
'gdk-x11-2.0': gdk_x11_2_info,
'gtkp_x11_2': gtkp_x11_2_info,
'gtk+-x11-2.0': gtkp_x11_2_info,
'gtkp_2': gtkp_2_info,
'gtk+-2.0': gtkp_2_info,
'xft': xft_info,
'freetype2': freetype2_info,
'umfpack': umfpack_info,
'amd': amd_info,
}.get(name.lower(), system_info)
return cl().get_info(notfound_action)
class lapack_opt_info(system_info):
notfounderror = LapackNotFoundError
# List of all known LAPACK libraries, in the default order
lapack_order = ['armpl', 'mkl', 'openblas', 'flame',
'accelerate', 'atlas', 'lapack']
order_env_var_name = 'NPY_LAPACK_ORDER'
def _calc_info_armpl(self):
info = get_info('lapack_armpl')
if info:
self.set_info(**info)
return True
return False
def _calc_info_mkl(self):
info = get_info('lapack_mkl')
if info:
self.set_info(**info)
return True
return False
def _calc_info_openblas(self):
info = get_info('openblas_lapack')
if info:
self.set_info(**info)
return True
info = get_info('openblas_clapack')
if info:
self.set_info(**info)
return True
return False
def _calc_info_flame(self):
info = get_info('flame')
if info:
self.set_info(**info)
return True
return False
def _calc_info_atlas(self):
info = get_info('atlas_3_10_threads')
if not info:
info = get_info('atlas_3_10')
if not info:
info = get_info('atlas_threads')
if not info:
info = get_info('atlas')
if info:
# Figure out if ATLAS has lapack...
# If not we need the lapack library, but not BLAS!
l = info.get('define_macros', [])
if ('ATLAS_WITH_LAPACK_ATLAS', None) in l \
or ('ATLAS_WITHOUT_LAPACK', None) in l:
# Get LAPACK (with possible warnings)
# If not found we don't accept anything
# since we can't use ATLAS with LAPACK!
lapack_info = self._get_info_lapack()
if not lapack_info:
return False
dict_append(info, **lapack_info)
self.set_info(**info)
return True
return False
def _calc_info_accelerate(self):
info = get_info('accelerate')
if info:
self.set_info(**info)
return True
return False
def _get_info_blas(self):
# Default to get the optimized BLAS implementation
info = get_info('blas_opt')
if not info:
warnings.warn(BlasNotFoundError.__doc__ or '', stacklevel=3)
info_src = get_info('blas_src')
if not info_src:
warnings.warn(BlasSrcNotFoundError.__doc__ or '', stacklevel=3)
return {}
dict_append(info, libraries=[('fblas_src', info_src)])
return info
def _get_info_lapack(self):
info = get_info('lapack')
if not info:
warnings.warn(LapackNotFoundError.__doc__ or '', stacklevel=3)
info_src = get_info('lapack_src')
if not info_src:
warnings.warn(LapackSrcNotFoundError.__doc__ or '', stacklevel=3)
return {}
dict_append(info, libraries=[('flapack_src', info_src)])
return info
def _calc_info_lapack(self):
info = self._get_info_lapack()
if info:
info_blas = self._get_info_blas()
dict_append(info, **info_blas)
dict_append(info, define_macros=[('NO_ATLAS_INFO', 1)])
self.set_info(**info)
return True
return False
def _calc_info_from_envvar(self):
info = {}
info['language'] = 'f77'
info['libraries'] = []
info['include_dirs'] = []
info['define_macros'] = []
info['extra_link_args'] = os.environ['NPY_LAPACK_LIBS'].split()
self.set_info(**info)
return True
def _calc_info(self, name):
return getattr(self, '_calc_info_{}'.format(name))()
def calc_info(self):
lapack_order, unknown_order = _parse_env_order(self.lapack_order, self.order_env_var_name)
if len(unknown_order) > 0:
raise ValueError("lapack_opt_info user defined "
"LAPACK order has unacceptable "
"values: {}".format(unknown_order))
if 'NPY_LAPACK_LIBS' in os.environ:
# Bypass autodetection, set language to F77 and use env var linker
# flags directly
self._calc_info_from_envvar()
return
for lapack in lapack_order:
if self._calc_info(lapack):
return
if 'lapack' not in lapack_order:
# Since the user may request *not* to use any library, we still need
# to raise warnings to signal missing packages!
warnings.warn(LapackNotFoundError.__doc__ or '', stacklevel=2)
warnings.warn(LapackSrcNotFoundError.__doc__ or '', stacklevel=2)
class blas_opt_info(system_info):
notfounderror = BlasNotFoundError
# List of all known BLAS libraries, in the default order
blas_order = ['armpl', 'mkl', 'blis', 'openblas',
'accelerate', 'atlas', 'blas']
order_env_var_name = 'NPY_BLAS_ORDER'
def _calc_info_armpl(self):
info = get_info('blas_armpl')
if info:
self.set_info(**info)
return True
return False
def _calc_info_mkl(self):
info = get_info('blas_mkl')
if info:
self.set_info(**info)
return True
return False
def _calc_info_blis(self):
info = get_info('blis')
if info:
self.set_info(**info)
return True
return False
def _calc_info_openblas(self):
info = get_info('openblas')
if info:
self.set_info(**info)
return True
return False
def _calc_info_atlas(self):
info = get_info('atlas_3_10_blas_threads')
if not info:
info = get_info('atlas_3_10_blas')
if not info:
info = get_info('atlas_blas_threads')
if not info:
info = get_info('atlas_blas')
if info:
self.set_info(**info)
return True
return False
def _calc_info_accelerate(self):
info = get_info('accelerate')
if info:
self.set_info(**info)
return True
return False
def _calc_info_blas(self):
# Warn about a non-optimized BLAS library
warnings.warn(BlasOptNotFoundError.__doc__ or '', stacklevel=3)
info = {}
dict_append(info, define_macros=[('NO_ATLAS_INFO', 1)])
blas = get_info('blas')
if blas:
dict_append(info, **blas)
else:
# Not even BLAS was found!
warnings.warn(BlasNotFoundError.__doc__ or '', stacklevel=3)
blas_src = get_info('blas_src')
if not blas_src:
warnings.warn(BlasSrcNotFoundError.__doc__ or '', stacklevel=3)
return False
dict_append(info, libraries=[('fblas_src', blas_src)])
self.set_info(**info)
return True
def _calc_info_from_envvar(self):
info = {}
info['language'] = 'f77'
info['libraries'] = []
info['include_dirs'] = []
info['define_macros'] = []
info['extra_link_args'] = os.environ['NPY_BLAS_LIBS'].split()
if 'NPY_CBLAS_LIBS' in os.environ:
info['define_macros'].append(('HAVE_CBLAS', None))
info['extra_link_args'].extend(
os.environ['NPY_CBLAS_LIBS'].split())
self.set_info(**info)
return True
def _calc_info(self, name):
return getattr(self, '_calc_info_{}'.format(name))()
def calc_info(self):
blas_order, unknown_order = _parse_env_order(self.blas_order, self.order_env_var_name)
if len(unknown_order) > 0:
raise ValueError("blas_opt_info user defined BLAS order has unacceptable values: {}".format(unknown_order))
if 'NPY_BLAS_LIBS' in os.environ:
# Bypass autodetection, set language to F77 and use env var linker
# flags directly
self._calc_info_from_envvar()
return
for blas in blas_order:
if self._calc_info(blas):
return
if 'blas' not in blas_order:
# Since the user may request *not* to use any library, we still need
# to raise warnings to signal missing packages!
warnings.warn(BlasNotFoundError.__doc__ or '', stacklevel=2)
warnings.warn(BlasSrcNotFoundError.__doc__ or '', stacklevel=2)
NPY_CXX_FLAGS = [
'-std=c++11', # Minimal standard version
'-D__STDC_VERSION__=0', # for compatibility with C headers
'-fno-exceptions', # no exception support
'-fno-rtti']
release = 'dev0' not in version and '+' not in version
def configuration(parent_package='',top_path=None):
from numpy.distutils.misc_util import (Configuration, dot_join,
exec_mod_from_location)
from numpy.distutils.system_info import (get_info, blas_opt_info,
lapack_opt_info)
from numpy.distutils.ccompiler_opt import NPY_CXX_FLAGS
from numpy.version import release as is_released
config = Configuration('core', parent_package, top_path)
local_dir = config.local_path
codegen_dir = join(local_dir, 'code_generators')
# Check whether we have a mismatch between the set C API VERSION and the
# actual C API VERSION. Will raise a MismatchCAPIError if so.
check_api_version(C_API_VERSION, codegen_dir)
generate_umath_py = join(codegen_dir, 'generate_umath.py')
n = dot_join(config.name, 'generate_umath')
generate_umath = exec_mod_from_location('_'.join(n.split('.')),
generate_umath_py)
header_dir = 'include/numpy' # this is relative to config.path_in_package
cocache = CallOnceOnly()
def generate_config_h(ext, build_dir):
target = join(build_dir, header_dir, 'config.h')
d = os.path.dirname(target)
if not os.path.exists(d):
os.makedirs(d)
if newer(__file__, target):
config_cmd = config.get_config_cmd()
log.info('Generating %s', target)
# Check sizeof
moredefs, ignored = cocache.check_types(config_cmd, ext, build_dir)
# Check math library and C99 math funcs availability
mathlibs = check_mathlib(config_cmd)
moredefs.append(('MATHLIB', ','.join(mathlibs)))
check_math_capabilities(config_cmd, ext, moredefs, mathlibs)
moredefs.extend(cocache.check_ieee_macros(config_cmd)[0])
moredefs.extend(cocache.check_complex(config_cmd, mathlibs)[0])
# Signal check
if is_npy_no_signal():
moredefs.append('__NPY_PRIVATE_NO_SIGNAL')
# Windows checks
if sys.platform == 'win32' or os.name == 'nt':
win32_checks(moredefs)
# C99 restrict keyword
moredefs.append(('NPY_RESTRICT', config_cmd.check_restrict()))
# Inline check
inline = config_cmd.check_inline()
if can_link_svml():
moredefs.append(('NPY_CAN_LINK_SVML', 1))
# Use bogus stride debug aid to flush out bugs where users use
# strides of dimensions with length 1 to index a full contiguous
# array.
if NPY_RELAXED_STRIDES_DEBUG:
moredefs.append(('NPY_RELAXED_STRIDES_DEBUG', 1))
else:
moredefs.append(('NPY_RELAXED_STRIDES_DEBUG', 0))
# Get long double representation
rep = check_long_double_representation(config_cmd)
moredefs.append(('HAVE_LDOUBLE_%s' % rep, 1))
if check_for_right_shift_internal_compiler_error(config_cmd):
moredefs.append('NPY_DO_NOT_OPTIMIZE_LONG_right_shift')
moredefs.append('NPY_DO_NOT_OPTIMIZE_ULONG_right_shift')
moredefs.append('NPY_DO_NOT_OPTIMIZE_LONGLONG_right_shift')
moredefs.append('NPY_DO_NOT_OPTIMIZE_ULONGLONG_right_shift')
# Generate the config.h file from moredefs
with open(target, 'w') as target_f:
if sys.platform == 'darwin':
target_f.write(
"/* may be overridden by numpyconfig.h on darwin */\n"
)
for d in moredefs:
if isinstance(d, str):
target_f.write('#define %s\n' % (d))
else:
target_f.write('#define %s %s\n' % (d[0], d[1]))
# define inline to our keyword, or nothing
target_f.write('#ifndef __cplusplus\n')
if inline == 'inline':
target_f.write('/* #undef inline */\n')
else:
target_f.write('#define inline %s\n' % inline)
target_f.write('#endif\n')
# add the guard to make sure config.h is never included directly,
# but always through npy_config.h
target_f.write(textwrap.dedent("""
#ifndef NUMPY_CORE_SRC_COMMON_NPY_CONFIG_H_
#error config.h should never be included directly, include npy_config.h instead
#endif
"""))
log.info('File: %s' % target)
with open(target) as target_f:
log.info(target_f.read())
log.info('EOF')
else:
mathlibs = []
with open(target) as target_f:
for line in target_f:
s = '#define MATHLIB'
if line.startswith(s):
value = line[len(s):].strip()
if value:
mathlibs.extend(value.split(','))
# Ugly: this can be called within a library and not an extension,
# in which case there is no libraries attributes (and none is
# needed).
if hasattr(ext, 'libraries'):
ext.libraries.extend(mathlibs)
incl_dir = os.path.dirname(target)
if incl_dir not in config.numpy_include_dirs:
config.numpy_include_dirs.append(incl_dir)
return target
def generate_numpyconfig_h(ext, build_dir):
"""Depends on config.h: generate_config_h has to be called before !"""
# put common include directory in build_dir on search path
# allows using code generation in headers
config.add_include_dirs(join(build_dir, "src", "common"))
config.add_include_dirs(join(build_dir, "src", "npymath"))
target = join(build_dir, header_dir, '_numpyconfig.h')
d = os.path.dirname(target)
if not os.path.exists(d):
os.makedirs(d)
if newer(__file__, target):
config_cmd = config.get_config_cmd()
log.info('Generating %s', target)
# Check sizeof
ignored, moredefs = cocache.check_types(config_cmd, ext, build_dir)
if is_npy_no_signal():
moredefs.append(('NPY_NO_SIGNAL', 1))
if is_npy_no_smp():
moredefs.append(('NPY_NO_SMP', 1))
else:
moredefs.append(('NPY_NO_SMP', 0))
mathlibs = check_mathlib(config_cmd)
moredefs.extend(cocache.check_ieee_macros(config_cmd)[1])
moredefs.extend(cocache.check_complex(config_cmd, mathlibs)[1])
if NPY_RELAXED_STRIDES_DEBUG:
moredefs.append(('NPY_RELAXED_STRIDES_DEBUG', 1))
# Check whether we can use inttypes (C99) formats
if config_cmd.check_decl('PRIdPTR', headers=['inttypes.h']):
moredefs.append(('NPY_USE_C99_FORMATS', 1))
# visibility check
hidden_visibility = visibility_define(config_cmd)
moredefs.append(('NPY_VISIBILITY_HIDDEN', hidden_visibility))
# Add the C API/ABI versions
moredefs.append(('NPY_ABI_VERSION', '0x%.8X' % C_ABI_VERSION))
moredefs.append(('NPY_API_VERSION', '0x%.8X' % C_API_VERSION))
# Add moredefs to header
with open(target, 'w') as target_f:
for d in moredefs:
if isinstance(d, str):
target_f.write('#define %s\n' % (d))
else:
target_f.write('#define %s %s\n' % (d[0], d[1]))
# Define __STDC_FORMAT_MACROS
target_f.write(textwrap.dedent("""
#ifndef __STDC_FORMAT_MACROS
#define __STDC_FORMAT_MACROS 1
#endif
"""))
# Dump the numpyconfig.h header to stdout
log.info('File: %s' % target)
with open(target) as target_f:
log.info(target_f.read())
log.info('EOF')
config.add_data_files((header_dir, target))
return target
def generate_api_func(module_name):
def generate_api(ext, build_dir):
script = join(codegen_dir, module_name + '.py')
sys.path.insert(0, codegen_dir)
try:
m = __import__(module_name)
log.info('executing %s', script)
h_file, c_file, doc_file = m.generate_api(os.path.join(build_dir, header_dir))
finally:
del sys.path[0]
config.add_data_files((header_dir, h_file),
(header_dir, doc_file))
return (h_file,)
return generate_api
generate_numpy_api = generate_api_func('generate_numpy_api')
generate_ufunc_api = generate_api_func('generate_ufunc_api')
config.add_include_dirs(join(local_dir, "src", "common"))
config.add_include_dirs(join(local_dir, "src"))
config.add_include_dirs(join(local_dir))
config.add_data_dir('include/numpy')
config.add_include_dirs(join('src', 'npymath'))
config.add_include_dirs(join('src', 'multiarray'))
config.add_include_dirs(join('src', 'umath'))
config.add_include_dirs(join('src', 'npysort'))
config.add_include_dirs(join('src', '_simd'))
config.add_define_macros([("NPY_INTERNAL_BUILD", "1")]) # this macro indicates that Numpy build is in process
config.add_define_macros([("HAVE_NPY_CONFIG_H", "1")])
if sys.platform[:3] == "aix":
config.add_define_macros([("_LARGE_FILES", None)])
else:
config.add_define_macros([("_FILE_OFFSET_BITS", "64")])
config.add_define_macros([('_LARGEFILE_SOURCE', '1')])
config.add_define_macros([('_LARGEFILE64_SOURCE', '1')])
config.numpy_include_dirs.extend(config.paths('include'))
deps = [join('src', 'npymath', '_signbit.c'),
join('include', 'numpy', '*object.h'),
join(codegen_dir, 'genapi.py'),
]
#######################################################################
# npymath library #
#######################################################################
subst_dict = dict([("sep", os.path.sep), ("pkgname", "numpy.core")])
def get_mathlib_info(*args):
# Another ugly hack: the mathlib info is known once build_src is run,
# but we cannot use add_installed_pkg_config here either, so we only
# update the substitution dictionary during npymath build
config_cmd = config.get_config_cmd()
# Check that the toolchain works, to fail early if it doesn't
# (avoid late errors with MATHLIB which are confusing if the
# compiler does not work).
for lang, test_code, note in (
('c', 'int main(void) { return 0;}', ''),
('c++', (
'int main(void)'
'{ auto x = 0.0; return static_cast<int>(x); }'
), (
'note: A compiler with support for C++11 language '
'features is required.'
)
),
):
is_cpp = lang == 'c++'
if is_cpp:
# this a workaround to get rid of invalid c++ flags
# without doing big changes to config.
# c tested first, compiler should be here
bk_c = config_cmd.compiler
config_cmd.compiler = bk_c.cxx_compiler()
# Check that Linux compiler actually support the default flags
if hasattr(config_cmd.compiler, 'compiler'):
config_cmd.compiler.compiler.extend(NPY_CXX_FLAGS)
config_cmd.compiler.compiler_so.extend(NPY_CXX_FLAGS)
st = config_cmd.try_link(test_code, lang=lang)
if not st:
# rerun the failing command in verbose mode
config_cmd.compiler.verbose = True
config_cmd.try_link(test_code, lang=lang)
raise RuntimeError(
f"Broken toolchain: cannot link a simple {lang.upper()} "
f"program. {note}"
)
if is_cpp:
config_cmd.compiler = bk_c
mlibs = check_mathlib(config_cmd)
posix_mlib = ' '.join(['-l%s' % l for l in mlibs])
msvc_mlib = ' '.join(['%s.lib' % l for l in mlibs])
subst_dict["posix_mathlib"] = posix_mlib
subst_dict["msvc_mathlib"] = msvc_mlib
npymath_sources = [join('src', 'npymath', 'npy_math_internal.h.src'),
join('src', 'npymath', 'npy_math.c'),
# join('src', 'npymath', 'ieee754.cpp'),
join('src', 'npymath', 'ieee754.c.src'),
join('src', 'npymath', 'npy_math_complex.c.src'),
join('src', 'npymath', 'halffloat.c'),
]
config.add_installed_library('npymath',
sources=npymath_sources + [get_mathlib_info],
install_dir='lib',
build_info={
'include_dirs' : [], # empty list required for creating npy_math_internal.h
'extra_compiler_args': [lib_opts_if_msvc],
})
config.add_npy_pkg_config("npymath.ini.in", "lib/npy-pkg-config",
subst_dict)
config.add_npy_pkg_config("mlib.ini.in", "lib/npy-pkg-config",
subst_dict)
#######################################################################
# multiarray_tests module #
#######################################################################
config.add_extension('_multiarray_tests',
sources=[join('src', 'multiarray', '_multiarray_tests.c.src'),
join('src', 'common', 'mem_overlap.c'),
join('src', 'common', 'npy_argparse.c'),
join('src', 'common', 'npy_hashtable.c')],
depends=[join('src', 'common', 'mem_overlap.h'),
join('src', 'common', 'npy_argparse.h'),
join('src', 'common', 'npy_hashtable.h'),
join('src', 'common', 'npy_extint128.h')],
libraries=['npymath'])
#######################################################################
# _multiarray_umath module - common part #
#######################################################################
common_deps = [
join('src', 'common', 'dlpack', 'dlpack.h'),
join('src', 'common', 'array_assign.h'),
join('src', 'common', 'binop_override.h'),
join('src', 'common', 'cblasfuncs.h'),
join('src', 'common', 'lowlevel_strided_loops.h'),
join('src', 'common', 'mem_overlap.h'),
join('src', 'common', 'npy_argparse.h'),
join('src', 'common', 'npy_cblas.h'),
join('src', 'common', 'npy_config.h'),
join('src', 'common', 'npy_ctypes.h'),
join('src', 'common', 'npy_dlpack.h'),
join('src', 'common', 'npy_extint128.h'),
join('src', 'common', 'npy_import.h'),
join('src', 'common', 'npy_hashtable.h'),
join('src', 'common', 'npy_longdouble.h'),
join('src', 'common', 'npy_svml.h'),
join('src', 'common', 'templ_common.h.src'),
join('src', 'common', 'ucsnarrow.h'),
join('src', 'common', 'ufunc_override.h'),
join('src', 'common', 'umathmodule.h'),
join('src', 'common', 'numpyos.h'),
join('src', 'common', 'npy_cpu_dispatch.h'),
join('src', 'common', 'simd', 'simd.h'),
]
common_src = [
join('src', 'common', 'array_assign.c'),
join('src', 'common', 'mem_overlap.c'),
join('src', 'common', 'npy_argparse.c'),
join('src', 'common', 'npy_hashtable.c'),
join('src', 'common', 'npy_longdouble.c'),
join('src', 'common', 'templ_common.h.src'),
join('src', 'common', 'ucsnarrow.c'),
join('src', 'common', 'ufunc_override.c'),
join('src', 'common', 'numpyos.c'),
join('src', 'common', 'npy_cpu_features.c'),
]
if os.environ.get('NPY_USE_BLAS_ILP64', "0") != "0":
blas_info = get_info('blas_ilp64_opt', 2)
else:
blas_info = get_info('blas_opt', 0)
have_blas = blas_info and ('HAVE_CBLAS', None) in blas_info.get('define_macros', [])
if have_blas:
extra_info = blas_info
# These files are also in MANIFEST.in so that they are always in
# the source distribution independently of HAVE_CBLAS.
common_src.extend([join('src', 'common', 'cblasfuncs.c'),
join('src', 'common', 'python_xerbla.c'),
])
else:
extra_info = {}
#######################################################################
# _multiarray_umath module - multiarray part #
#######################################################################
multiarray_deps = [
join('src', 'multiarray', 'abstractdtypes.h'),
join('src', 'multiarray', 'arrayobject.h'),
join('src', 'multiarray', 'arraytypes.h.src'),
join('src', 'multiarray', 'arrayfunction_override.h'),
join('src', 'multiarray', 'array_coercion.h'),
join('src', 'multiarray', 'array_method.h'),
join('src', 'multiarray', 'npy_buffer.h'),
join('src', 'multiarray', 'calculation.h'),
join('src', 'multiarray', 'common.h'),
join('src', 'multiarray', 'common_dtype.h'),
join('src', 'multiarray', 'convert_datatype.h'),
join('src', 'multiarray', 'convert.h'),
join('src', 'multiarray', 'conversion_utils.h'),
join('src', 'multiarray', 'ctors.h'),
join('src', 'multiarray', 'descriptor.h'),
join('src', 'multiarray', 'dtypemeta.h'),
join('src', 'multiarray', 'dtype_transfer.h'),
join('src', 'multiarray', 'dragon4.h'),
join('src', 'multiarray', 'einsum_debug.h'),
join('src', 'multiarray', 'einsum_sumprod.h'),
join('src', 'multiarray', 'experimental_public_dtype_api.h'),
join('src', 'multiarray', 'getset.h'),
join('src', 'multiarray', 'hashdescr.h'),
join('src', 'multiarray', 'iterators.h'),
join('src', 'multiarray', 'legacy_dtype_implementation.h'),
join('src', 'multiarray', 'mapping.h'),
join('src', 'multiarray', 'methods.h'),
join('src', 'multiarray', 'multiarraymodule.h'),
join('src', 'multiarray', 'nditer_impl.h'),
join('src', 'multiarray', 'number.h'),
join('src', 'multiarray', 'refcount.h'),
join('src', 'multiarray', 'scalartypes.h'),
join('src', 'multiarray', 'sequence.h'),
join('src', 'multiarray', 'shape.h'),
join('src', 'multiarray', 'strfuncs.h'),
join('src', 'multiarray', 'typeinfo.h'),
join('src', 'multiarray', 'usertypes.h'),
join('src', 'multiarray', 'vdot.h'),
join('src', 'multiarray', 'textreading', 'readtext.h'),
join('include', 'numpy', 'arrayobject.h'),
join('include', 'numpy', '_neighborhood_iterator_imp.h'),
join('include', 'numpy', 'npy_endian.h'),
join('include', 'numpy', 'arrayscalars.h'),
join('include', 'numpy', 'noprefix.h'),
join('include', 'numpy', 'npy_interrupt.h'),
join('include', 'numpy', 'npy_3kcompat.h'),
join('include', 'numpy', 'npy_math.h'),
join('include', 'numpy', 'halffloat.h'),
join('include', 'numpy', 'npy_common.h'),
join('include', 'numpy', 'npy_os.h'),
join('include', 'numpy', 'utils.h'),
join('include', 'numpy', 'ndarrayobject.h'),
join('include', 'numpy', 'npy_cpu.h'),
join('include', 'numpy', 'numpyconfig.h'),
join('include', 'numpy', 'ndarraytypes.h'),
join('include', 'numpy', 'npy_1_7_deprecated_api.h'),
# add library sources as distuils does not consider libraries
# dependencies
] + npymath_sources
multiarray_src = [
join('src', 'multiarray', 'abstractdtypes.c'),
join('src', 'multiarray', 'alloc.c'),
join('src', 'multiarray', 'arrayobject.c'),
join('src', 'multiarray', 'arraytypes.h.src'),
join('src', 'multiarray', 'arraytypes.c.src'),
join('src', 'multiarray', 'argfunc.dispatch.c.src'),
join('src', 'multiarray', 'array_coercion.c'),
join('src', 'multiarray', 'array_method.c'),
join('src', 'multiarray', 'array_assign_scalar.c'),
join('src', 'multiarray', 'array_assign_array.c'),
join('src', 'multiarray', 'arrayfunction_override.c'),
join('src', 'multiarray', 'buffer.c'),
join('src', 'multiarray', 'calculation.c'),
join('src', 'multiarray', 'compiled_base.c'),
join('src', 'multiarray', 'common.c'),
join('src', 'multiarray', 'common_dtype.c'),
join('src', 'multiarray', 'convert.c'),
join('src', 'multiarray', 'convert_datatype.c'),
join('src', 'multiarray', 'conversion_utils.c'),
join('src', 'multiarray', 'ctors.c'),
join('src', 'multiarray', 'datetime.c'),
join('src', 'multiarray', 'datetime_strings.c'),
join('src', 'multiarray', 'datetime_busday.c'),
join('src', 'multiarray', 'datetime_busdaycal.c'),
join('src', 'multiarray', 'descriptor.c'),
join('src', 'multiarray', 'dlpack.c'),
join('src', 'multiarray', 'dtypemeta.c'),
join('src', 'multiarray', 'dragon4.c'),
join('src', 'multiarray', 'dtype_transfer.c'),
join('src', 'multiarray', 'einsum.c.src'),
join('src', 'multiarray', 'einsum_sumprod.c.src'),
join('src', 'multiarray', 'experimental_public_dtype_api.c'),
join('src', 'multiarray', 'flagsobject.c'),
join('src', 'multiarray', 'getset.c'),
join('src', 'multiarray', 'hashdescr.c'),
join('src', 'multiarray', 'item_selection.c'),
join('src', 'multiarray', 'iterators.c'),
join('src', 'multiarray', 'legacy_dtype_implementation.c'),
join('src', 'multiarray', 'lowlevel_strided_loops.c.src'),
join('src', 'multiarray', 'mapping.c'),
join('src', 'multiarray', 'methods.c'),
join('src', 'multiarray', 'multiarraymodule.c'),
join('src', 'multiarray', 'nditer_templ.c.src'),
join('src', 'multiarray', 'nditer_api.c'),
join('src', 'multiarray', 'nditer_constr.c'),
join('src', 'multiarray', 'nditer_pywrap.c'),
join('src', 'multiarray', 'number.c'),
join('src', 'multiarray', 'refcount.c'),
join('src', 'multiarray', 'sequence.c'),
join('src', 'multiarray', 'shape.c'),
join('src', 'multiarray', 'scalarapi.c'),
join('src', 'multiarray', 'scalartypes.c.src'),
join('src', 'multiarray', 'strfuncs.c'),
join('src', 'multiarray', 'temp_elide.c'),
join('src', 'multiarray', 'typeinfo.c'),
join('src', 'multiarray', 'usertypes.c'),
join('src', 'multiarray', 'vdot.c'),
join('src', 'common', 'npy_sort.h.src'),
join('src', 'npysort', 'x86-qsort.dispatch.cpp'),
join('src', 'npysort', 'quicksort.cpp'),
join('src', 'npysort', 'mergesort.cpp'),
join('src', 'npysort', 'timsort.cpp'),
join('src', 'npysort', 'heapsort.cpp'),
join('src', 'npysort', 'radixsort.cpp'),
join('src', 'common', 'npy_partition.h'),
join('src', 'npysort', 'selection.cpp'),
join('src', 'common', 'npy_binsearch.h'),
join('src', 'npysort', 'binsearch.cpp'),
join('src', 'multiarray', 'textreading', 'conversions.c'),
join('src', 'multiarray', 'textreading', 'field_types.c'),
join('src', 'multiarray', 'textreading', 'growth.c'),
join('src', 'multiarray', 'textreading', 'readtext.c'),
join('src', 'multiarray', 'textreading', 'rows.c'),
join('src', 'multiarray', 'textreading', 'stream_pyobject.c'),
join('src', 'multiarray', 'textreading', 'str_to_int.c'),
join('src', 'multiarray', 'textreading', 'tokenize.cpp'),
# Remove this once scipy macos arm64 build correctly
# links to the arm64 npymath library,
# see gh-22673
join('src', 'npymath', 'arm64_exports.c'),
]
#######################################################################
# _multiarray_umath module - umath part #
#######################################################################
def generate_umath_c(ext, build_dir):
target = join(build_dir, header_dir, '__umath_generated.c')
dir = os.path.dirname(target)
if not os.path.exists(dir):
os.makedirs(dir)
script = generate_umath_py
if newer(script, target):
with open(target, 'w') as f:
f.write(generate_umath.make_code(generate_umath.defdict,
generate_umath.__file__))
return []
def generate_umath_doc_header(ext, build_dir):
from numpy.distutils.misc_util import exec_mod_from_location
target = join(build_dir, header_dir, '_umath_doc_generated.h')
dir = os.path.dirname(target)
if not os.path.exists(dir):
os.makedirs(dir)
generate_umath_doc_py = join(codegen_dir, 'generate_umath_doc.py')
if newer(generate_umath_doc_py, target):
n = dot_join(config.name, 'generate_umath_doc')
generate_umath_doc = exec_mod_from_location(
'_'.join(n.split('.')), generate_umath_doc_py)
generate_umath_doc.write_code(target)
umath_src = [
join('src', 'umath', 'umathmodule.c'),
join('src', 'umath', 'reduction.c'),
join('src', 'umath', 'funcs.inc.src'),
join('src', 'umath', 'simd.inc.src'),
join('src', 'umath', 'loops.h.src'),
join('src', 'umath', 'loops_utils.h.src'),
join('src', 'umath', 'loops.c.src'),
join('src', 'umath', 'loops_unary_fp.dispatch.c.src'),
join('src', 'umath', 'loops_arithm_fp.dispatch.c.src'),
join('src', 'umath', 'loops_arithmetic.dispatch.c.src'),
join('src', 'umath', 'loops_minmax.dispatch.c.src'),
join('src', 'umath', 'loops_trigonometric.dispatch.c.src'),
join('src', 'umath', 'loops_umath_fp.dispatch.c.src'),
join('src', 'umath', 'loops_exponent_log.dispatch.c.src'),
join('src', 'umath', 'loops_hyperbolic.dispatch.c.src'),
join('src', 'umath', 'loops_modulo.dispatch.c.src'),
join('src', 'umath', 'loops_comparison.dispatch.c.src'),
join('src', 'umath', 'matmul.h.src'),
join('src', 'umath', 'matmul.c.src'),
join('src', 'umath', 'clip.h'),
join('src', 'umath', 'clip.cpp'),
join('src', 'umath', 'dispatching.c'),
join('src', 'umath', 'legacy_array_method.c'),
join('src', 'umath', 'wrapping_array_method.c'),
join('src', 'umath', 'ufunc_object.c'),
join('src', 'umath', 'extobj.c'),
join('src', 'umath', 'scalarmath.c.src'),
join('src', 'umath', 'ufunc_type_resolution.c'),
join('src', 'umath', 'override.c'),
join('src', 'umath', 'string_ufuncs.cpp'),
# For testing. Eventually, should use public API and be separate:
join('src', 'umath', '_scaled_float_dtype.c'),
]
umath_deps = [
generate_umath_py,
join('include', 'numpy', 'npy_math.h'),
join('include', 'numpy', 'halffloat.h'),
join('src', 'multiarray', 'common.h'),
join('src', 'multiarray', 'number.h'),
join('src', 'common', 'templ_common.h.src'),
join('src', 'umath', 'simd.inc.src'),
join('src', 'umath', 'override.h'),
join(codegen_dir, 'generate_ufunc_api.py'),
join(codegen_dir, 'ufunc_docstrings.py'),
]
svml_path = join('numpy', 'core', 'src', 'umath', 'svml')
svml_objs = []
# we have converted the following into universal intrinsics
# so we can bring the benefits of performance for all platforms
# not just for avx512 on linux without performance/accuracy regression,
# actually the other way around, better performance and
# after all maintainable code.
svml_filter = (
)
if can_link_svml() and check_svml_submodule(svml_path):
svml_objs = glob.glob(svml_path + '/**/*.s', recursive=True)
svml_objs = [o for o in svml_objs if not o.endswith(svml_filter)]
# The ordering of names returned by glob is undefined, so we sort
# to make builds reproducible.
svml_objs.sort()
config.add_extension('_multiarray_umath',
# Forcing C language even though we have C++ sources.
# It forces the C linker and don't link C++ runtime.
language = 'c',
sources=multiarray_src + umath_src +
common_src +
[generate_config_h,
generate_numpyconfig_h,
generate_numpy_api,
join(codegen_dir, 'generate_numpy_api.py'),
join('*.py'),
generate_umath_c,
generate_umath_doc_header,
generate_ufunc_api,
],
depends=deps + multiarray_deps + umath_deps +
common_deps,
libraries=['npymath'],
extra_objects=svml_objs,
extra_info=extra_info,
extra_cxx_compile_args=NPY_CXX_FLAGS)
#######################################################################
# umath_tests module #
#######################################################################
config.add_extension('_umath_tests', sources=[
join('src', 'umath', '_umath_tests.c.src'),
join('src', 'umath', '_umath_tests.dispatch.c'),
join('src', 'common', 'npy_cpu_features.c'),
])
#######################################################################
# custom rational dtype module #
#######################################################################
config.add_extension('_rational_tests',
sources=[join('src', 'umath', '_rational_tests.c')])
#######################################################################
# struct_ufunc_test module #
#######################################################################
config.add_extension('_struct_ufunc_tests',
sources=[join('src', 'umath', '_struct_ufunc_tests.c')])
#######################################################################
# operand_flag_tests module #
#######################################################################
config.add_extension('_operand_flag_tests',
sources=[join('src', 'umath', '_operand_flag_tests.c')])
#######################################################################
# SIMD module #
#######################################################################
config.add_extension('_simd',
sources=[
join('src', 'common', 'npy_cpu_features.c'),
join('src', '_simd', '_simd.c'),
join('src', '_simd', '_simd_inc.h.src'),
join('src', '_simd', '_simd_data.inc.src'),
join('src', '_simd', '_simd.dispatch.c.src'),
], depends=[
join('src', 'common', 'npy_cpu_dispatch.h'),
join('src', 'common', 'simd', 'simd.h'),
join('src', '_simd', '_simd.h'),
join('src', '_simd', '_simd_inc.h.src'),
join('src', '_simd', '_simd_data.inc.src'),
join('src', '_simd', '_simd_arg.inc'),
join('src', '_simd', '_simd_convert.inc'),
join('src', '_simd', '_simd_easyintrin.inc'),
join('src', '_simd', '_simd_vector.inc'),
],
libraries=['npymath']
)
config.add_subpackage('tests')
config.add_data_dir('tests/data')
config.add_data_dir('tests/examples')
config.add_data_files('*.pyi')
config.make_svn_version_py()
return config | null |
169,188 | from .overrides import (
array_function_dispatch,
set_array_function_like_doc,
set_module,
)
from .multiarray import array, asanyarray
def _require_dispatcher(a, dtype=None, requirements=None, *, like=None):
return (like,) | null |
169,189 | from .overrides import (
array_function_dispatch,
set_array_function_like_doc,
set_module,
)
from .multiarray import array, asanyarray
POSSIBLE_FLAGS = {
'C': 'C', 'C_CONTIGUOUS': 'C', 'CONTIGUOUS': 'C',
'F': 'F', 'F_CONTIGUOUS': 'F', 'FORTRAN': 'F',
'A': 'A', 'ALIGNED': 'A',
'W': 'W', 'WRITEABLE': 'W',
'O': 'O', 'OWNDATA': 'O',
'E': 'E', 'ENSUREARRAY': 'E'
}
_require_with_like = array_function_dispatch(
_require_dispatcher
)(require)
array.__module__ = 'numpy'
asanyarray.__module__ = 'numpy'
The provided code snippet includes necessary dependencies for implementing the `require` function. Write a Python function `def require(a, dtype=None, requirements=None, *, like=None)` to solve the following problem:
Return an ndarray of the provided type that satisfies requirements. This function is useful to be sure that an array with the correct flags is returned for passing to compiled code (perhaps through ctypes). Parameters ---------- a : array_like The object to be converted to a type-and-requirement-satisfying array. dtype : data-type The required data-type. If None preserve the current dtype. If your application requires the data to be in native byteorder, include a byteorder specification as a part of the dtype specification. requirements : str or sequence of str The requirements list can be any of the following * 'F_CONTIGUOUS' ('F') - ensure a Fortran-contiguous array * 'C_CONTIGUOUS' ('C') - ensure a C-contiguous array * 'ALIGNED' ('A') - ensure a data-type aligned array * 'WRITEABLE' ('W') - ensure a writable array * 'OWNDATA' ('O') - ensure an array that owns its own data * 'ENSUREARRAY', ('E') - ensure a base array, instead of a subclass ${ARRAY_FUNCTION_LIKE} .. versionadded:: 1.20.0 Returns ------- out : ndarray Array with specified requirements and type if given. See Also -------- asarray : Convert input to an ndarray. asanyarray : Convert to an ndarray, but pass through ndarray subclasses. ascontiguousarray : Convert input to a contiguous array. asfortranarray : Convert input to an ndarray with column-major memory order. ndarray.flags : Information about the memory layout of the array. Notes ----- The returned array will be guaranteed to have the listed requirements by making a copy if needed. Examples -------- >>> x = np.arange(6).reshape(2,3) >>> x.flags C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : False WRITEABLE : True ALIGNED : True WRITEBACKIFCOPY : False >>> y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F']) >>> y.flags C_CONTIGUOUS : False F_CONTIGUOUS : True OWNDATA : True WRITEABLE : True ALIGNED : True WRITEBACKIFCOPY : False
Here is the function:
def require(a, dtype=None, requirements=None, *, like=None):
"""
Return an ndarray of the provided type that satisfies requirements.
This function is useful to be sure that an array with the correct flags
is returned for passing to compiled code (perhaps through ctypes).
Parameters
----------
a : array_like
The object to be converted to a type-and-requirement-satisfying array.
dtype : data-type
The required data-type. If None preserve the current dtype. If your
application requires the data to be in native byteorder, include
a byteorder specification as a part of the dtype specification.
requirements : str or sequence of str
The requirements list can be any of the following
* 'F_CONTIGUOUS' ('F') - ensure a Fortran-contiguous array
* 'C_CONTIGUOUS' ('C') - ensure a C-contiguous array
* 'ALIGNED' ('A') - ensure a data-type aligned array
* 'WRITEABLE' ('W') - ensure a writable array
* 'OWNDATA' ('O') - ensure an array that owns its own data
* 'ENSUREARRAY', ('E') - ensure a base array, instead of a subclass
${ARRAY_FUNCTION_LIKE}
.. versionadded:: 1.20.0
Returns
-------
out : ndarray
Array with specified requirements and type if given.
See Also
--------
asarray : Convert input to an ndarray.
asanyarray : Convert to an ndarray, but pass through ndarray subclasses.
ascontiguousarray : Convert input to a contiguous array.
asfortranarray : Convert input to an ndarray with column-major
memory order.
ndarray.flags : Information about the memory layout of the array.
Notes
-----
The returned array will be guaranteed to have the listed requirements
by making a copy if needed.
Examples
--------
>>> x = np.arange(6).reshape(2,3)
>>> x.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
>>> y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F'])
>>> y.flags
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
"""
if like is not None:
return _require_with_like(
a,
dtype=dtype,
requirements=requirements,
like=like,
)
if not requirements:
return asanyarray(a, dtype=dtype)
requirements = {POSSIBLE_FLAGS[x.upper()] for x in requirements}
if 'E' in requirements:
requirements.remove('E')
subok = False
else:
subok = True
order = 'A'
if requirements >= {'C', 'F'}:
raise ValueError('Cannot specify both "C" and "F" order')
elif 'F' in requirements:
order = 'F'
requirements.remove('F')
elif 'C' in requirements:
order = 'C'
requirements.remove('C')
arr = array(a, dtype=dtype, order=order, copy=False, subok=subok)
for prop in requirements:
if not arr.flags[prop]:
return arr.copy(order)
return arr | Return an ndarray of the provided type that satisfies requirements. This function is useful to be sure that an array with the correct flags is returned for passing to compiled code (perhaps through ctypes). Parameters ---------- a : array_like The object to be converted to a type-and-requirement-satisfying array. dtype : data-type The required data-type. If None preserve the current dtype. If your application requires the data to be in native byteorder, include a byteorder specification as a part of the dtype specification. requirements : str or sequence of str The requirements list can be any of the following * 'F_CONTIGUOUS' ('F') - ensure a Fortran-contiguous array * 'C_CONTIGUOUS' ('C') - ensure a C-contiguous array * 'ALIGNED' ('A') - ensure a data-type aligned array * 'WRITEABLE' ('W') - ensure a writable array * 'OWNDATA' ('O') - ensure an array that owns its own data * 'ENSUREARRAY', ('E') - ensure a base array, instead of a subclass ${ARRAY_FUNCTION_LIKE} .. versionadded:: 1.20.0 Returns ------- out : ndarray Array with specified requirements and type if given. See Also -------- asarray : Convert input to an ndarray. asanyarray : Convert to an ndarray, but pass through ndarray subclasses. ascontiguousarray : Convert input to a contiguous array. asfortranarray : Convert input to an ndarray with column-major memory order. ndarray.flags : Information about the memory layout of the array. Notes ----- The returned array will be guaranteed to have the listed requirements by making a copy if needed. Examples -------- >>> x = np.arange(6).reshape(2,3) >>> x.flags C_CONTIGUOUS : True F_CONTIGUOUS : False OWNDATA : False WRITEABLE : True ALIGNED : True WRITEBACKIFCOPY : False >>> y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F']) >>> y.flags C_CONTIGUOUS : False F_CONTIGUOUS : True OWNDATA : True WRITEABLE : True ALIGNED : True WRITEBACKIFCOPY : False |
169,190 | import os
import genapi
from genapi import \
TypeApi, GlobalVarApi, FunctionApi, BoolValuesApi
import numpy_api
def do_generate_api(targets, sources):
import os
del os
def generate_api(output_dir, force=False):
basename = 'multiarray_api'
h_file = os.path.join(output_dir, '__%s.h' % basename)
c_file = os.path.join(output_dir, '__%s.c' % basename)
d_file = os.path.join(output_dir, '%s.txt' % basename)
targets = (h_file, c_file, d_file)
sources = numpy_api.multiarray_api
if (not force and not genapi.should_rebuild(targets, [numpy_api.__file__, __file__])):
return targets
else:
do_generate_api(targets, sources)
return targets | null |
169,191 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _binary_op_dispatcher(x1, x2):
return (x1, x2) | null |
169,192 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
The provided code snippet includes necessary dependencies for implementing the `equal` function. Write a Python function `def equal(x1, x2)` to solve the following problem:
Return (x1 == x2) element-wise. Unlike `numpy.equal`, this comparison is performed by first stripping whitespace characters from the end of the string. This behavior is provided for backward-compatibility with numarray. Parameters ---------- x1, x2 : array_like of str or unicode Input arrays of the same shape. Returns ------- out : ndarray Output array of bools. See Also -------- not_equal, greater_equal, less_equal, greater, less
Here is the function:
def equal(x1, x2):
"""
Return (x1 == x2) element-wise.
Unlike `numpy.equal`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray
Output array of bools.
See Also
--------
not_equal, greater_equal, less_equal, greater, less
"""
return compare_chararrays(x1, x2, '==', True) | Return (x1 == x2) element-wise. Unlike `numpy.equal`, this comparison is performed by first stripping whitespace characters from the end of the string. This behavior is provided for backward-compatibility with numarray. Parameters ---------- x1, x2 : array_like of str or unicode Input arrays of the same shape. Returns ------- out : ndarray Output array of bools. See Also -------- not_equal, greater_equal, less_equal, greater, less |
169,193 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
The provided code snippet includes necessary dependencies for implementing the `not_equal` function. Write a Python function `def not_equal(x1, x2)` to solve the following problem:
Return (x1 != x2) element-wise. Unlike `numpy.not_equal`, this comparison is performed by first stripping whitespace characters from the end of the string. This behavior is provided for backward-compatibility with numarray. Parameters ---------- x1, x2 : array_like of str or unicode Input arrays of the same shape. Returns ------- out : ndarray Output array of bools. See Also -------- equal, greater_equal, less_equal, greater, less
Here is the function:
def not_equal(x1, x2):
"""
Return (x1 != x2) element-wise.
Unlike `numpy.not_equal`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray
Output array of bools.
See Also
--------
equal, greater_equal, less_equal, greater, less
"""
return compare_chararrays(x1, x2, '!=', True) | Return (x1 != x2) element-wise. Unlike `numpy.not_equal`, this comparison is performed by first stripping whitespace characters from the end of the string. This behavior is provided for backward-compatibility with numarray. Parameters ---------- x1, x2 : array_like of str or unicode Input arrays of the same shape. Returns ------- out : ndarray Output array of bools. See Also -------- equal, greater_equal, less_equal, greater, less |
169,194 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
The provided code snippet includes necessary dependencies for implementing the `greater_equal` function. Write a Python function `def greater_equal(x1, x2)` to solve the following problem:
Return (x1 >= x2) element-wise. Unlike `numpy.greater_equal`, this comparison is performed by first stripping whitespace characters from the end of the string. This behavior is provided for backward-compatibility with numarray. Parameters ---------- x1, x2 : array_like of str or unicode Input arrays of the same shape. Returns ------- out : ndarray Output array of bools. See Also -------- equal, not_equal, less_equal, greater, less
Here is the function:
def greater_equal(x1, x2):
"""
Return (x1 >= x2) element-wise.
Unlike `numpy.greater_equal`, this comparison is performed by
first stripping whitespace characters from the end of the string.
This behavior is provided for backward-compatibility with
numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray
Output array of bools.
See Also
--------
equal, not_equal, less_equal, greater, less
"""
return compare_chararrays(x1, x2, '>=', True) | Return (x1 >= x2) element-wise. Unlike `numpy.greater_equal`, this comparison is performed by first stripping whitespace characters from the end of the string. This behavior is provided for backward-compatibility with numarray. Parameters ---------- x1, x2 : array_like of str or unicode Input arrays of the same shape. Returns ------- out : ndarray Output array of bools. See Also -------- equal, not_equal, less_equal, greater, less |
169,195 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
The provided code snippet includes necessary dependencies for implementing the `less_equal` function. Write a Python function `def less_equal(x1, x2)` to solve the following problem:
Return (x1 <= x2) element-wise. Unlike `numpy.less_equal`, this comparison is performed by first stripping whitespace characters from the end of the string. This behavior is provided for backward-compatibility with numarray. Parameters ---------- x1, x2 : array_like of str or unicode Input arrays of the same shape. Returns ------- out : ndarray Output array of bools. See Also -------- equal, not_equal, greater_equal, greater, less
Here is the function:
def less_equal(x1, x2):
"""
Return (x1 <= x2) element-wise.
Unlike `numpy.less_equal`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray
Output array of bools.
See Also
--------
equal, not_equal, greater_equal, greater, less
"""
return compare_chararrays(x1, x2, '<=', True) | Return (x1 <= x2) element-wise. Unlike `numpy.less_equal`, this comparison is performed by first stripping whitespace characters from the end of the string. This behavior is provided for backward-compatibility with numarray. Parameters ---------- x1, x2 : array_like of str or unicode Input arrays of the same shape. Returns ------- out : ndarray Output array of bools. See Also -------- equal, not_equal, greater_equal, greater, less |
169,196 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
The provided code snippet includes necessary dependencies for implementing the `greater` function. Write a Python function `def greater(x1, x2)` to solve the following problem:
Return (x1 > x2) element-wise. Unlike `numpy.greater`, this comparison is performed by first stripping whitespace characters from the end of the string. This behavior is provided for backward-compatibility with numarray. Parameters ---------- x1, x2 : array_like of str or unicode Input arrays of the same shape. Returns ------- out : ndarray Output array of bools. See Also -------- equal, not_equal, greater_equal, less_equal, less
Here is the function:
def greater(x1, x2):
"""
Return (x1 > x2) element-wise.
Unlike `numpy.greater`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray
Output array of bools.
See Also
--------
equal, not_equal, greater_equal, less_equal, less
"""
return compare_chararrays(x1, x2, '>', True) | Return (x1 > x2) element-wise. Unlike `numpy.greater`, this comparison is performed by first stripping whitespace characters from the end of the string. This behavior is provided for backward-compatibility with numarray. Parameters ---------- x1, x2 : array_like of str or unicode Input arrays of the same shape. Returns ------- out : ndarray Output array of bools. See Also -------- equal, not_equal, greater_equal, less_equal, less |
169,197 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
The provided code snippet includes necessary dependencies for implementing the `less` function. Write a Python function `def less(x1, x2)` to solve the following problem:
Return (x1 < x2) element-wise. Unlike `numpy.greater`, this comparison is performed by first stripping whitespace characters from the end of the string. This behavior is provided for backward-compatibility with numarray. Parameters ---------- x1, x2 : array_like of str or unicode Input arrays of the same shape. Returns ------- out : ndarray Output array of bools. See Also -------- equal, not_equal, greater_equal, less_equal, greater
Here is the function:
def less(x1, x2):
"""
Return (x1 < x2) element-wise.
Unlike `numpy.greater`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray
Output array of bools.
See Also
--------
equal, not_equal, greater_equal, less_equal, greater
"""
return compare_chararrays(x1, x2, '<', True) | Return (x1 < x2) element-wise. Unlike `numpy.greater`, this comparison is performed by first stripping whitespace characters from the end of the string. This behavior is provided for backward-compatibility with numarray. Parameters ---------- x1, x2 : array_like of str or unicode Input arrays of the same shape. Returns ------- out : ndarray Output array of bools. See Also -------- equal, not_equal, greater_equal, less_equal, greater |
169,198 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _unary_op_dispatcher(a):
return (a,) | null |
169,199 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
The provided code snippet includes necessary dependencies for implementing the `str_len` function. Write a Python function `def str_len(a)` to solve the following problem:
Return len(a) element-wise. Parameters ---------- a : array_like of str or unicode Returns ------- out : ndarray Output array of integers See Also -------- builtins.len Examples -------- >>> a = np.array(['Grace Hopper Conference', 'Open Source Day']) >>> np.char.str_len(a) array([23, 15]) >>> a = np.array([u'\u0420', u'\u043e']) >>> np.char.str_len(a) array([1, 1]) >>> a = np.array([['hello', 'world'], [u'\u0420', u'\u043e']]) >>> np.char.str_len(a) array([[5, 5], [1, 1]])
Here is the function:
def str_len(a):
"""
Return len(a) element-wise.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of integers
See Also
--------
builtins.len
Examples
--------
>>> a = np.array(['Grace Hopper Conference', 'Open Source Day'])
>>> np.char.str_len(a)
array([23, 15])
>>> a = np.array([u'\u0420', u'\u043e'])
>>> np.char.str_len(a)
array([1, 1])
>>> a = np.array([['hello', 'world'], [u'\u0420', u'\u043e']])
>>> np.char.str_len(a)
array([[5, 5], [1, 1]])
"""
# Note: __len__, etc. currently return ints, which are not C-integers.
# Generally intp would be expected for lengths, although int is sufficient
# due to the dtype itemsize limitation.
return _vec_string(a, int_, '__len__') | Return len(a) element-wise. Parameters ---------- a : array_like of str or unicode Returns ------- out : ndarray Output array of integers See Also -------- builtins.len Examples -------- >>> a = np.array(['Grace Hopper Conference', 'Open Source Day']) >>> np.char.str_len(a) array([23, 15]) >>> a = np.array([u'\u0420', u'\u043e']) >>> np.char.str_len(a) array([1, 1]) >>> a = np.array([['hello', 'world'], [u'\u0420', u'\u043e']]) >>> np.char.str_len(a) array([[5, 5], [1, 1]]) |
169,200 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _use_unicode(*args):
"""
Helper function for determining the output type of some string
operations.
For an operation on two ndarrays, if at least one is unicode, the
result should be unicode.
"""
for x in args:
if (isinstance(x, str) or
issubclass(numpy.asarray(x).dtype.type, unicode_)):
return unicode_
return string_
def _get_num_chars(a):
"""
Helper function that returns the number of characters per field in
a string or unicode array. This is to abstract out the fact that
for a unicode array this is itemsize / 4.
"""
if issubclass(a.dtype.type, unicode_):
return a.itemsize // 4
return a.itemsize
def asarray(obj, itemsize=None, unicode=None, order=None):
"""
Convert the input to a `chararray`, copying the data only if
necessary.
Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
when indexed
2) comparison operators automatically remove whitespace from the
end when comparing values
3) vectorized string operations are provided as methods
(e.g. `str.endswith`) and infix operators (e.g. ``+``, ``*``,``%``)
Parameters
----------
obj : array of str or unicode-like
itemsize : int, optional
`itemsize` is the number of characters per scalar in the
resulting array. If `itemsize` is None, and `obj` is an
object array or a Python list, the `itemsize` will be
automatically determined. If `itemsize` is provided and `obj`
is of type str or unicode, then the `obj` string will be
chunked into `itemsize` pieces.
unicode : bool, optional
When true, the resulting `chararray` can contain Unicode
characters, when false only 8-bit characters. If unicode is
None and `obj` is one of the following:
- a `chararray`,
- an ndarray of type `str` or 'unicode`
- a Python str or unicode object,
then the unicode setting of the output array will be
automatically determined.
order : {'C', 'F'}, optional
Specify the order of the array. If order is 'C' (default), then the
array will be in C-contiguous order (last-index varies the
fastest). If order is 'F', then the returned array
will be in Fortran-contiguous order (first-index varies the
fastest).
"""
return array(obj, itemsize, copy=False,
unicode=unicode, order=order)
The provided code snippet includes necessary dependencies for implementing the `add` function. Write a Python function `def add(x1, x2)` to solve the following problem:
Return element-wise string concatenation for two arrays of str or unicode. Arrays `x1` and `x2` must have the same shape. Parameters ---------- x1 : array_like of str or unicode Input array. x2 : array_like of str or unicode Input array. Returns ------- add : ndarray Output array of `string_` or `unicode_`, depending on input types of the same shape as `x1` and `x2`.
Here is the function:
def add(x1, x2):
"""
Return element-wise string concatenation for two arrays of str or unicode.
Arrays `x1` and `x2` must have the same shape.
Parameters
----------
x1 : array_like of str or unicode
Input array.
x2 : array_like of str or unicode
Input array.
Returns
-------
add : ndarray
Output array of `string_` or `unicode_`, depending on input types
of the same shape as `x1` and `x2`.
"""
arr1 = numpy.asarray(x1)
arr2 = numpy.asarray(x2)
out_size = _get_num_chars(arr1) + _get_num_chars(arr2)
dtype = _use_unicode(arr1, arr2)
return _vec_string(arr1, (dtype, out_size), '__add__', (arr2,)) | Return element-wise string concatenation for two arrays of str or unicode. Arrays `x1` and `x2` must have the same shape. Parameters ---------- x1 : array_like of str or unicode Input array. x2 : array_like of str or unicode Input array. Returns ------- add : ndarray Output array of `string_` or `unicode_`, depending on input types of the same shape as `x1` and `x2`. |
169,201 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _multiply_dispatcher(a, i):
return (a,) | null |
169,202 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _get_num_chars(a):
"""
Helper function that returns the number of characters per field in
a string or unicode array. This is to abstract out the fact that
for a unicode array this is itemsize / 4.
"""
if issubclass(a.dtype.type, unicode_):
return a.itemsize // 4
return a.itemsize
def asarray(obj, itemsize=None, unicode=None, order=None):
"""
Convert the input to a `chararray`, copying the data only if
necessary.
Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
when indexed
2) comparison operators automatically remove whitespace from the
end when comparing values
3) vectorized string operations are provided as methods
(e.g. `str.endswith`) and infix operators (e.g. ``+``, ``*``,``%``)
Parameters
----------
obj : array of str or unicode-like
itemsize : int, optional
`itemsize` is the number of characters per scalar in the
resulting array. If `itemsize` is None, and `obj` is an
object array or a Python list, the `itemsize` will be
automatically determined. If `itemsize` is provided and `obj`
is of type str or unicode, then the `obj` string will be
chunked into `itemsize` pieces.
unicode : bool, optional
When true, the resulting `chararray` can contain Unicode
characters, when false only 8-bit characters. If unicode is
None and `obj` is one of the following:
- a `chararray`,
- an ndarray of type `str` or 'unicode`
- a Python str or unicode object,
then the unicode setting of the output array will be
automatically determined.
order : {'C', 'F'}, optional
Specify the order of the array. If order is 'C' (default), then the
array will be in C-contiguous order (last-index varies the
fastest). If order is 'F', then the returned array
will be in Fortran-contiguous order (first-index varies the
fastest).
"""
return array(obj, itemsize, copy=False,
unicode=unicode, order=order)
The provided code snippet includes necessary dependencies for implementing the `multiply` function. Write a Python function `def multiply(a, i)` to solve the following problem:
Return (a * i), that is string multiple concatenation, element-wise. Values in `i` of less than 0 are treated as 0 (which yields an empty string). Parameters ---------- a : array_like of str or unicode i : array_like of ints Returns ------- out : ndarray Output array of str or unicode, depending on input types Examples -------- >>> a = np.array(["a", "b", "c"]) >>> np.char.multiply(x, 3) array(['aaa', 'bbb', 'ccc'], dtype='<U3') >>> i = np.array([1, 2, 3]) >>> np.char.multiply(a, i) array(['a', 'bb', 'ccc'], dtype='<U3') >>> np.char.multiply(np.array(['a']), i) array(['a', 'aa', 'aaa'], dtype='<U3') >>> a = np.array(['a', 'b', 'c', 'd', 'e', 'f']).reshape((2, 3)) >>> np.char.multiply(a, 3) array([['aaa', 'bbb', 'ccc'], ['ddd', 'eee', 'fff']], dtype='<U3') >>> np.char.multiply(a, i) array([['a', 'bb', 'ccc'], ['d', 'ee', 'fff']], dtype='<U3')
Here is the function:
def multiply(a, i):
"""
Return (a * i), that is string multiple concatenation,
element-wise.
Values in `i` of less than 0 are treated as 0 (which yields an
empty string).
Parameters
----------
a : array_like of str or unicode
i : array_like of ints
Returns
-------
out : ndarray
Output array of str or unicode, depending on input types
Examples
--------
>>> a = np.array(["a", "b", "c"])
>>> np.char.multiply(x, 3)
array(['aaa', 'bbb', 'ccc'], dtype='<U3')
>>> i = np.array([1, 2, 3])
>>> np.char.multiply(a, i)
array(['a', 'bb', 'ccc'], dtype='<U3')
>>> np.char.multiply(np.array(['a']), i)
array(['a', 'aa', 'aaa'], dtype='<U3')
>>> a = np.array(['a', 'b', 'c', 'd', 'e', 'f']).reshape((2, 3))
>>> np.char.multiply(a, 3)
array([['aaa', 'bbb', 'ccc'],
['ddd', 'eee', 'fff']], dtype='<U3')
>>> np.char.multiply(a, i)
array([['a', 'bb', 'ccc'],
['d', 'ee', 'fff']], dtype='<U3')
"""
a_arr = numpy.asarray(a)
i_arr = numpy.asarray(i)
if not issubclass(i_arr.dtype.type, integer):
raise ValueError("Can only multiply by integers")
out_size = _get_num_chars(a_arr) * max(int(i_arr.max()), 0)
return _vec_string(
a_arr, (a_arr.dtype.type, out_size), '__mul__', (i_arr,)) | Return (a * i), that is string multiple concatenation, element-wise. Values in `i` of less than 0 are treated as 0 (which yields an empty string). Parameters ---------- a : array_like of str or unicode i : array_like of ints Returns ------- out : ndarray Output array of str or unicode, depending on input types Examples -------- >>> a = np.array(["a", "b", "c"]) >>> np.char.multiply(x, 3) array(['aaa', 'bbb', 'ccc'], dtype='<U3') >>> i = np.array([1, 2, 3]) >>> np.char.multiply(a, i) array(['a', 'bb', 'ccc'], dtype='<U3') >>> np.char.multiply(np.array(['a']), i) array(['a', 'aa', 'aaa'], dtype='<U3') >>> a = np.array(['a', 'b', 'c', 'd', 'e', 'f']).reshape((2, 3)) >>> np.char.multiply(a, 3) array([['aaa', 'bbb', 'ccc'], ['ddd', 'eee', 'fff']], dtype='<U3') >>> np.char.multiply(a, i) array([['a', 'bb', 'ccc'], ['d', 'ee', 'fff']], dtype='<U3') |
169,203 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _mod_dispatcher(a, values):
return (a, values) | null |
169,204 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _to_string_or_unicode_array(result):
"""
Helper function to cast a result back into a string or unicode array
if an object array must be used as an intermediary.
"""
return numpy.asarray(result.tolist())
The provided code snippet includes necessary dependencies for implementing the `mod` function. Write a Python function `def mod(a, values)` to solve the following problem:
Return (a % i), that is pre-Python 2.6 string formatting (interpolation), element-wise for a pair of array_likes of str or unicode. Parameters ---------- a : array_like of str or unicode values : array_like of values These values will be element-wise interpolated into the string. Returns ------- out : ndarray Output array of str or unicode, depending on input types See Also -------- str.__mod__
Here is the function:
def mod(a, values):
"""
Return (a % i), that is pre-Python 2.6 string formatting
(interpolation), element-wise for a pair of array_likes of str
or unicode.
Parameters
----------
a : array_like of str or unicode
values : array_like of values
These values will be element-wise interpolated into the string.
Returns
-------
out : ndarray
Output array of str or unicode, depending on input types
See Also
--------
str.__mod__
"""
return _to_string_or_unicode_array(
_vec_string(a, object_, '__mod__', (values,))) | Return (a % i), that is pre-Python 2.6 string formatting (interpolation), element-wise for a pair of array_likes of str or unicode. Parameters ---------- a : array_like of str or unicode values : array_like of values These values will be element-wise interpolated into the string. Returns ------- out : ndarray Output array of str or unicode, depending on input types See Also -------- str.__mod__ |
169,205 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def asarray(obj, itemsize=None, unicode=None, order=None):
"""
Convert the input to a `chararray`, copying the data only if
necessary.
Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
when indexed
2) comparison operators automatically remove whitespace from the
end when comparing values
3) vectorized string operations are provided as methods
(e.g. `str.endswith`) and infix operators (e.g. ``+``, ``*``,``%``)
Parameters
----------
obj : array of str or unicode-like
itemsize : int, optional
`itemsize` is the number of characters per scalar in the
resulting array. If `itemsize` is None, and `obj` is an
object array or a Python list, the `itemsize` will be
automatically determined. If `itemsize` is provided and `obj`
is of type str or unicode, then the `obj` string will be
chunked into `itemsize` pieces.
unicode : bool, optional
When true, the resulting `chararray` can contain Unicode
characters, when false only 8-bit characters. If unicode is
None and `obj` is one of the following:
- a `chararray`,
- an ndarray of type `str` or 'unicode`
- a Python str or unicode object,
then the unicode setting of the output array will be
automatically determined.
order : {'C', 'F'}, optional
Specify the order of the array. If order is 'C' (default), then the
array will be in C-contiguous order (last-index varies the
fastest). If order is 'F', then the returned array
will be in Fortran-contiguous order (first-index varies the
fastest).
"""
return array(obj, itemsize, copy=False,
unicode=unicode, order=order)
The provided code snippet includes necessary dependencies for implementing the `capitalize` function. Write a Python function `def capitalize(a)` to solve the following problem:
Return a copy of `a` with only the first character of each element capitalized. Calls `str.capitalize` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like of str or unicode Input array of strings to capitalize. Returns ------- out : ndarray Output array of str or unicode, depending on input types See Also -------- str.capitalize Examples -------- >>> c = np.array(['a1b2','1b2a','b2a1','2a1b'],'S4'); c array(['a1b2', '1b2a', 'b2a1', '2a1b'], dtype='|S4') >>> np.char.capitalize(c) array(['A1b2', '1b2a', 'B2a1', '2a1b'], dtype='|S4')
Here is the function:
def capitalize(a):
"""
Return a copy of `a` with only the first character of each element
capitalized.
Calls `str.capitalize` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Input array of strings to capitalize.
Returns
-------
out : ndarray
Output array of str or unicode, depending on input
types
See Also
--------
str.capitalize
Examples
--------
>>> c = np.array(['a1b2','1b2a','b2a1','2a1b'],'S4'); c
array(['a1b2', '1b2a', 'b2a1', '2a1b'],
dtype='|S4')
>>> np.char.capitalize(c)
array(['A1b2', '1b2a', 'B2a1', '2a1b'],
dtype='|S4')
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'capitalize') | Return a copy of `a` with only the first character of each element capitalized. Calls `str.capitalize` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like of str or unicode Input array of strings to capitalize. Returns ------- out : ndarray Output array of str or unicode, depending on input types See Also -------- str.capitalize Examples -------- >>> c = np.array(['a1b2','1b2a','b2a1','2a1b'],'S4'); c array(['a1b2', '1b2a', 'b2a1', '2a1b'], dtype='|S4') >>> np.char.capitalize(c) array(['A1b2', '1b2a', 'B2a1', '2a1b'], dtype='|S4') |
169,206 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _center_dispatcher(a, width, fillchar=None):
return (a,) | null |
169,207 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def asarray(obj, itemsize=None, unicode=None, order=None):
"""
Convert the input to a `chararray`, copying the data only if
necessary.
Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
when indexed
2) comparison operators automatically remove whitespace from the
end when comparing values
3) vectorized string operations are provided as methods
(e.g. `str.endswith`) and infix operators (e.g. ``+``, ``*``,``%``)
Parameters
----------
obj : array of str or unicode-like
itemsize : int, optional
`itemsize` is the number of characters per scalar in the
resulting array. If `itemsize` is None, and `obj` is an
object array or a Python list, the `itemsize` will be
automatically determined. If `itemsize` is provided and `obj`
is of type str or unicode, then the `obj` string will be
chunked into `itemsize` pieces.
unicode : bool, optional
When true, the resulting `chararray` can contain Unicode
characters, when false only 8-bit characters. If unicode is
None and `obj` is one of the following:
- a `chararray`,
- an ndarray of type `str` or 'unicode`
- a Python str or unicode object,
then the unicode setting of the output array will be
automatically determined.
order : {'C', 'F'}, optional
Specify the order of the array. If order is 'C' (default), then the
array will be in C-contiguous order (last-index varies the
fastest). If order is 'F', then the returned array
will be in Fortran-contiguous order (first-index varies the
fastest).
"""
return array(obj, itemsize, copy=False,
unicode=unicode, order=order)
The provided code snippet includes necessary dependencies for implementing the `center` function. Write a Python function `def center(a, width, fillchar=' ')` to solve the following problem:
Return a copy of `a` with its elements centered in a string of length `width`. Calls `str.center` element-wise. Parameters ---------- a : array_like of str or unicode width : int The length of the resulting strings fillchar : str or unicode, optional The padding character to use (default is space). Returns ------- out : ndarray Output array of str or unicode, depending on input types See Also -------- str.center Notes ----- This function is intended to work with arrays of strings. The fill character is not applied to numeric types. Examples -------- >>> c = np.array(['a1b2','1b2a','b2a1','2a1b']); c array(['a1b2', '1b2a', 'b2a1', '2a1b'], dtype='<U4') >>> np.char.center(c, width=9) array([' a1b2 ', ' 1b2a ', ' b2a1 ', ' 2a1b '], dtype='<U9') >>> np.char.center(c, width=9, fillchar='*') array(['***a1b2**', '***1b2a**', '***b2a1**', '***2a1b**'], dtype='<U9') >>> np.char.center(c, width=1) array(['a', '1', 'b', '2'], dtype='<U1')
Here is the function:
def center(a, width, fillchar=' '):
"""
Return a copy of `a` with its elements centered in a string of
length `width`.
Calls `str.center` element-wise.
Parameters
----------
a : array_like of str or unicode
width : int
The length of the resulting strings
fillchar : str or unicode, optional
The padding character to use (default is space).
Returns
-------
out : ndarray
Output array of str or unicode, depending on input
types
See Also
--------
str.center
Notes
-----
This function is intended to work with arrays of strings. The
fill character is not applied to numeric types.
Examples
--------
>>> c = np.array(['a1b2','1b2a','b2a1','2a1b']); c
array(['a1b2', '1b2a', 'b2a1', '2a1b'], dtype='<U4')
>>> np.char.center(c, width=9)
array([' a1b2 ', ' 1b2a ', ' b2a1 ', ' 2a1b '], dtype='<U9')
>>> np.char.center(c, width=9, fillchar='*')
array(['***a1b2**', '***1b2a**', '***b2a1**', '***2a1b**'], dtype='<U9')
>>> np.char.center(c, width=1)
array(['a', '1', 'b', '2'], dtype='<U1')
"""
a_arr = numpy.asarray(a)
width_arr = numpy.asarray(width)
size = int(numpy.max(width_arr.flat))
if numpy.issubdtype(a_arr.dtype, numpy.string_):
fillchar = asbytes(fillchar)
return _vec_string(
a_arr, (a_arr.dtype.type, size), 'center', (width_arr, fillchar)) | Return a copy of `a` with its elements centered in a string of length `width`. Calls `str.center` element-wise. Parameters ---------- a : array_like of str or unicode width : int The length of the resulting strings fillchar : str or unicode, optional The padding character to use (default is space). Returns ------- out : ndarray Output array of str or unicode, depending on input types See Also -------- str.center Notes ----- This function is intended to work with arrays of strings. The fill character is not applied to numeric types. Examples -------- >>> c = np.array(['a1b2','1b2a','b2a1','2a1b']); c array(['a1b2', '1b2a', 'b2a1', '2a1b'], dtype='<U4') >>> np.char.center(c, width=9) array([' a1b2 ', ' 1b2a ', ' b2a1 ', ' 2a1b '], dtype='<U9') >>> np.char.center(c, width=9, fillchar='*') array(['***a1b2**', '***1b2a**', '***b2a1**', '***2a1b**'], dtype='<U9') >>> np.char.center(c, width=1) array(['a', '1', 'b', '2'], dtype='<U1') |
169,208 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _count_dispatcher(a, sub, start=None, end=None):
return (a,) | null |
169,209 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _clean_args(*args):
"""
Helper function for delegating arguments to Python string
functions.
Many of the Python string operations that have optional arguments
do not use 'None' to indicate a default value. In these cases,
we need to remove all None arguments, and those following them.
"""
newargs = []
for chk in args:
if chk is None:
break
newargs.append(chk)
return newargs
The provided code snippet includes necessary dependencies for implementing the `count` function. Write a Python function `def count(a, sub, start=0, end=None)` to solve the following problem:
Returns an array with the number of non-overlapping occurrences of substring `sub` in the range [`start`, `end`]. Calls `str.count` element-wise. Parameters ---------- a : array_like of str or unicode sub : str or unicode The substring to search for. start, end : int, optional Optional arguments `start` and `end` are interpreted as slice notation to specify the range in which to count. Returns ------- out : ndarray Output array of ints. See Also -------- str.count Examples -------- >>> c = np.array(['aAaAaA', ' aA ', 'abBABba']) >>> c array(['aAaAaA', ' aA ', 'abBABba'], dtype='<U7') >>> np.char.count(c, 'A') array([3, 1, 1]) >>> np.char.count(c, 'aA') array([3, 1, 0]) >>> np.char.count(c, 'A', start=1, end=4) array([2, 1, 1]) >>> np.char.count(c, 'A', start=1, end=3) array([1, 0, 0])
Here is the function:
def count(a, sub, start=0, end=None):
"""
Returns an array with the number of non-overlapping occurrences of
substring `sub` in the range [`start`, `end`].
Calls `str.count` element-wise.
Parameters
----------
a : array_like of str or unicode
sub : str or unicode
The substring to search for.
start, end : int, optional
Optional arguments `start` and `end` are interpreted as slice
notation to specify the range in which to count.
Returns
-------
out : ndarray
Output array of ints.
See Also
--------
str.count
Examples
--------
>>> c = np.array(['aAaAaA', ' aA ', 'abBABba'])
>>> c
array(['aAaAaA', ' aA ', 'abBABba'], dtype='<U7')
>>> np.char.count(c, 'A')
array([3, 1, 1])
>>> np.char.count(c, 'aA')
array([3, 1, 0])
>>> np.char.count(c, 'A', start=1, end=4)
array([2, 1, 1])
>>> np.char.count(c, 'A', start=1, end=3)
array([1, 0, 0])
"""
return _vec_string(a, int_, 'count', [sub, start] + _clean_args(end)) | Returns an array with the number of non-overlapping occurrences of substring `sub` in the range [`start`, `end`]. Calls `str.count` element-wise. Parameters ---------- a : array_like of str or unicode sub : str or unicode The substring to search for. start, end : int, optional Optional arguments `start` and `end` are interpreted as slice notation to specify the range in which to count. Returns ------- out : ndarray Output array of ints. See Also -------- str.count Examples -------- >>> c = np.array(['aAaAaA', ' aA ', 'abBABba']) >>> c array(['aAaAaA', ' aA ', 'abBABba'], dtype='<U7') >>> np.char.count(c, 'A') array([3, 1, 1]) >>> np.char.count(c, 'aA') array([3, 1, 0]) >>> np.char.count(c, 'A', start=1, end=4) array([2, 1, 1]) >>> np.char.count(c, 'A', start=1, end=3) array([1, 0, 0]) |
169,210 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _code_dispatcher(a, encoding=None, errors=None):
return (a,) | null |
169,211 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _to_string_or_unicode_array(result):
"""
Helper function to cast a result back into a string or unicode array
if an object array must be used as an intermediary.
"""
return numpy.asarray(result.tolist())
def _clean_args(*args):
"""
Helper function for delegating arguments to Python string
functions.
Many of the Python string operations that have optional arguments
do not use 'None' to indicate a default value. In these cases,
we need to remove all None arguments, and those following them.
"""
newargs = []
for chk in args:
if chk is None:
break
newargs.append(chk)
return newargs
The provided code snippet includes necessary dependencies for implementing the `decode` function. Write a Python function `def decode(a, encoding=None, errors=None)` to solve the following problem:
r""" Calls ``bytes.decode`` element-wise. The set of available codecs comes from the Python standard library, and may be extended at runtime. For more information, see the :mod:`codecs` module. Parameters ---------- a : array_like of str or unicode encoding : str, optional The name of an encoding errors : str, optional Specifies how to handle encoding errors Returns ------- out : ndarray See Also -------- :py:meth:`bytes.decode` Notes ----- The type of the result will depend on the encoding specified. Examples -------- >>> c = np.array([b'\x81\xc1\x81\xc1\x81\xc1', b'@@\x81\xc1@@', ... b'\x81\x82\xc2\xc1\xc2\x82\x81']) >>> c array([b'\x81\xc1\x81\xc1\x81\xc1', b'@@\x81\xc1@@', ... b'\x81\x82\xc2\xc1\xc2\x82\x81'], dtype='|S7') >>> np.char.decode(c, encoding='cp037') array(['aAaAaA', ' aA ', 'abBABba'], dtype='<U7')
Here is the function:
def decode(a, encoding=None, errors=None):
r"""
Calls ``bytes.decode`` element-wise.
The set of available codecs comes from the Python standard library,
and may be extended at runtime. For more information, see the
:mod:`codecs` module.
Parameters
----------
a : array_like of str or unicode
encoding : str, optional
The name of an encoding
errors : str, optional
Specifies how to handle encoding errors
Returns
-------
out : ndarray
See Also
--------
:py:meth:`bytes.decode`
Notes
-----
The type of the result will depend on the encoding specified.
Examples
--------
>>> c = np.array([b'\x81\xc1\x81\xc1\x81\xc1', b'@@\x81\xc1@@',
... b'\x81\x82\xc2\xc1\xc2\x82\x81'])
>>> c
array([b'\x81\xc1\x81\xc1\x81\xc1', b'@@\x81\xc1@@',
... b'\x81\x82\xc2\xc1\xc2\x82\x81'], dtype='|S7')
>>> np.char.decode(c, encoding='cp037')
array(['aAaAaA', ' aA ', 'abBABba'], dtype='<U7')
"""
return _to_string_or_unicode_array(
_vec_string(a, object_, 'decode', _clean_args(encoding, errors))) | r""" Calls ``bytes.decode`` element-wise. The set of available codecs comes from the Python standard library, and may be extended at runtime. For more information, see the :mod:`codecs` module. Parameters ---------- a : array_like of str or unicode encoding : str, optional The name of an encoding errors : str, optional Specifies how to handle encoding errors Returns ------- out : ndarray See Also -------- :py:meth:`bytes.decode` Notes ----- The type of the result will depend on the encoding specified. Examples -------- >>> c = np.array([b'\x81\xc1\x81\xc1\x81\xc1', b'@@\x81\xc1@@', ... b'\x81\x82\xc2\xc1\xc2\x82\x81']) >>> c array([b'\x81\xc1\x81\xc1\x81\xc1', b'@@\x81\xc1@@', ... b'\x81\x82\xc2\xc1\xc2\x82\x81'], dtype='|S7') >>> np.char.decode(c, encoding='cp037') array(['aAaAaA', ' aA ', 'abBABba'], dtype='<U7') |
169,212 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _to_string_or_unicode_array(result):
"""
Helper function to cast a result back into a string or unicode array
if an object array must be used as an intermediary.
"""
return numpy.asarray(result.tolist())
def _clean_args(*args):
"""
Helper function for delegating arguments to Python string
functions.
Many of the Python string operations that have optional arguments
do not use 'None' to indicate a default value. In these cases,
we need to remove all None arguments, and those following them.
"""
newargs = []
for chk in args:
if chk is None:
break
newargs.append(chk)
return newargs
The provided code snippet includes necessary dependencies for implementing the `encode` function. Write a Python function `def encode(a, encoding=None, errors=None)` to solve the following problem:
Calls `str.encode` element-wise. The set of available codecs comes from the Python standard library, and may be extended at runtime. For more information, see the codecs module. Parameters ---------- a : array_like of str or unicode encoding : str, optional The name of an encoding errors : str, optional Specifies how to handle encoding errors Returns ------- out : ndarray See Also -------- str.encode Notes ----- The type of the result will depend on the encoding specified.
Here is the function:
def encode(a, encoding=None, errors=None):
"""
Calls `str.encode` element-wise.
The set of available codecs comes from the Python standard library,
and may be extended at runtime. For more information, see the codecs
module.
Parameters
----------
a : array_like of str or unicode
encoding : str, optional
The name of an encoding
errors : str, optional
Specifies how to handle encoding errors
Returns
-------
out : ndarray
See Also
--------
str.encode
Notes
-----
The type of the result will depend on the encoding specified.
"""
return _to_string_or_unicode_array(
_vec_string(a, object_, 'encode', _clean_args(encoding, errors))) | Calls `str.encode` element-wise. The set of available codecs comes from the Python standard library, and may be extended at runtime. For more information, see the codecs module. Parameters ---------- a : array_like of str or unicode encoding : str, optional The name of an encoding errors : str, optional Specifies how to handle encoding errors Returns ------- out : ndarray See Also -------- str.encode Notes ----- The type of the result will depend on the encoding specified. |
169,213 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _endswith_dispatcher(a, suffix, start=None, end=None):
return (a,) | null |
169,214 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _clean_args(*args):
"""
Helper function for delegating arguments to Python string
functions.
Many of the Python string operations that have optional arguments
do not use 'None' to indicate a default value. In these cases,
we need to remove all None arguments, and those following them.
"""
newargs = []
for chk in args:
if chk is None:
break
newargs.append(chk)
return newargs
The provided code snippet includes necessary dependencies for implementing the `endswith` function. Write a Python function `def endswith(a, suffix, start=0, end=None)` to solve the following problem:
Returns a boolean array which is `True` where the string element in `a` ends with `suffix`, otherwise `False`. Calls `str.endswith` element-wise. Parameters ---------- a : array_like of str or unicode suffix : str start, end : int, optional With optional `start`, test beginning at that position. With optional `end`, stop comparing at that position. Returns ------- out : ndarray Outputs an array of bools. See Also -------- str.endswith Examples -------- >>> s = np.array(['foo', 'bar']) >>> s[0] = 'foo' >>> s[1] = 'bar' >>> s array(['foo', 'bar'], dtype='<U3') >>> np.char.endswith(s, 'ar') array([False, True]) >>> np.char.endswith(s, 'a', start=1, end=2) array([False, True])
Here is the function:
def endswith(a, suffix, start=0, end=None):
"""
Returns a boolean array which is `True` where the string element
in `a` ends with `suffix`, otherwise `False`.
Calls `str.endswith` element-wise.
Parameters
----------
a : array_like of str or unicode
suffix : str
start, end : int, optional
With optional `start`, test beginning at that position. With
optional `end`, stop comparing at that position.
Returns
-------
out : ndarray
Outputs an array of bools.
See Also
--------
str.endswith
Examples
--------
>>> s = np.array(['foo', 'bar'])
>>> s[0] = 'foo'
>>> s[1] = 'bar'
>>> s
array(['foo', 'bar'], dtype='<U3')
>>> np.char.endswith(s, 'ar')
array([False, True])
>>> np.char.endswith(s, 'a', start=1, end=2)
array([False, True])
"""
return _vec_string(
a, bool_, 'endswith', [suffix, start] + _clean_args(end)) | Returns a boolean array which is `True` where the string element in `a` ends with `suffix`, otherwise `False`. Calls `str.endswith` element-wise. Parameters ---------- a : array_like of str or unicode suffix : str start, end : int, optional With optional `start`, test beginning at that position. With optional `end`, stop comparing at that position. Returns ------- out : ndarray Outputs an array of bools. See Also -------- str.endswith Examples -------- >>> s = np.array(['foo', 'bar']) >>> s[0] = 'foo' >>> s[1] = 'bar' >>> s array(['foo', 'bar'], dtype='<U3') >>> np.char.endswith(s, 'ar') array([False, True]) >>> np.char.endswith(s, 'a', start=1, end=2) array([False, True]) |
169,215 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _expandtabs_dispatcher(a, tabsize=None):
return (a,) | null |
169,216 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _to_string_or_unicode_array(result):
"""
Helper function to cast a result back into a string or unicode array
if an object array must be used as an intermediary.
"""
return numpy.asarray(result.tolist())
The provided code snippet includes necessary dependencies for implementing the `expandtabs` function. Write a Python function `def expandtabs(a, tabsize=8)` to solve the following problem:
Return a copy of each string element where all tab characters are replaced by one or more spaces. Calls `str.expandtabs` element-wise. Return a copy of each string element where all tab characters are replaced by one or more spaces, depending on the current column and the given `tabsize`. The column number is reset to zero after each newline occurring in the string. This doesn't understand other non-printing characters or escape sequences. Parameters ---------- a : array_like of str or unicode Input array tabsize : int, optional Replace tabs with `tabsize` number of spaces. If not given defaults to 8 spaces. Returns ------- out : ndarray Output array of str or unicode, depending on input type See Also -------- str.expandtabs
Here is the function:
def expandtabs(a, tabsize=8):
"""
Return a copy of each string element where all tab characters are
replaced by one or more spaces.
Calls `str.expandtabs` element-wise.
Return a copy of each string element where all tab characters are
replaced by one or more spaces, depending on the current column
and the given `tabsize`. The column number is reset to zero after
each newline occurring in the string. This doesn't understand other
non-printing characters or escape sequences.
Parameters
----------
a : array_like of str or unicode
Input array
tabsize : int, optional
Replace tabs with `tabsize` number of spaces. If not given defaults
to 8 spaces.
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See Also
--------
str.expandtabs
"""
return _to_string_or_unicode_array(
_vec_string(a, object_, 'expandtabs', (tabsize,))) | Return a copy of each string element where all tab characters are replaced by one or more spaces. Calls `str.expandtabs` element-wise. Return a copy of each string element where all tab characters are replaced by one or more spaces, depending on the current column and the given `tabsize`. The column number is reset to zero after each newline occurring in the string. This doesn't understand other non-printing characters or escape sequences. Parameters ---------- a : array_like of str or unicode Input array tabsize : int, optional Replace tabs with `tabsize` number of spaces. If not given defaults to 8 spaces. Returns ------- out : ndarray Output array of str or unicode, depending on input type See Also -------- str.expandtabs |
169,217 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _clean_args(*args):
"""
Helper function for delegating arguments to Python string
functions.
Many of the Python string operations that have optional arguments
do not use 'None' to indicate a default value. In these cases,
we need to remove all None arguments, and those following them.
"""
newargs = []
for chk in args:
if chk is None:
break
newargs.append(chk)
return newargs
The provided code snippet includes necessary dependencies for implementing the `find` function. Write a Python function `def find(a, sub, start=0, end=None)` to solve the following problem:
For each element, return the lowest index in the string where substring `sub` is found. Calls `str.find` element-wise. For each element, return the lowest index in the string where substring `sub` is found, such that `sub` is contained in the range [`start`, `end`]. Parameters ---------- a : array_like of str or unicode sub : str or unicode start, end : int, optional Optional arguments `start` and `end` are interpreted as in slice notation. Returns ------- out : ndarray or int Output array of ints. Returns -1 if `sub` is not found. See Also -------- str.find Examples -------- >>> a = np.array(["NumPy is a Python library"]) >>> np.char.find(a, "Python", start=0, end=None) array([11])
Here is the function:
def find(a, sub, start=0, end=None):
"""
For each element, return the lowest index in the string where
substring `sub` is found.
Calls `str.find` element-wise.
For each element, return the lowest index in the string where
substring `sub` is found, such that `sub` is contained in the
range [`start`, `end`].
Parameters
----------
a : array_like of str or unicode
sub : str or unicode
start, end : int, optional
Optional arguments `start` and `end` are interpreted as in
slice notation.
Returns
-------
out : ndarray or int
Output array of ints. Returns -1 if `sub` is not found.
See Also
--------
str.find
Examples
--------
>>> a = np.array(["NumPy is a Python library"])
>>> np.char.find(a, "Python", start=0, end=None)
array([11])
"""
return _vec_string(
a, int_, 'find', [sub, start] + _clean_args(end)) | For each element, return the lowest index in the string where substring `sub` is found. Calls `str.find` element-wise. For each element, return the lowest index in the string where substring `sub` is found, such that `sub` is contained in the range [`start`, `end`]. Parameters ---------- a : array_like of str or unicode sub : str or unicode start, end : int, optional Optional arguments `start` and `end` are interpreted as in slice notation. Returns ------- out : ndarray or int Output array of ints. Returns -1 if `sub` is not found. See Also -------- str.find Examples -------- >>> a = np.array(["NumPy is a Python library"]) >>> np.char.find(a, "Python", start=0, end=None) array([11]) |
169,218 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _clean_args(*args):
"""
Helper function for delegating arguments to Python string
functions.
Many of the Python string operations that have optional arguments
do not use 'None' to indicate a default value. In these cases,
we need to remove all None arguments, and those following them.
"""
newargs = []
for chk in args:
if chk is None:
break
newargs.append(chk)
return newargs
The provided code snippet includes necessary dependencies for implementing the `index` function. Write a Python function `def index(a, sub, start=0, end=None)` to solve the following problem:
Like `find`, but raises `ValueError` when the substring is not found. Calls `str.index` element-wise. Parameters ---------- a : array_like of str or unicode sub : str or unicode start, end : int, optional Returns ------- out : ndarray Output array of ints. Returns -1 if `sub` is not found. See Also -------- find, str.find Examples -------- >>> a = np.array(["Computer Science"]) >>> np.char.index(a, "Science", start=0, end=None) array([9])
Here is the function:
def index(a, sub, start=0, end=None):
"""
Like `find`, but raises `ValueError` when the substring is not found.
Calls `str.index` element-wise.
Parameters
----------
a : array_like of str or unicode
sub : str or unicode
start, end : int, optional
Returns
-------
out : ndarray
Output array of ints. Returns -1 if `sub` is not found.
See Also
--------
find, str.find
Examples
--------
>>> a = np.array(["Computer Science"])
>>> np.char.index(a, "Science", start=0, end=None)
array([9])
"""
return _vec_string(
a, int_, 'index', [sub, start] + _clean_args(end)) | Like `find`, but raises `ValueError` when the substring is not found. Calls `str.index` element-wise. Parameters ---------- a : array_like of str or unicode sub : str or unicode start, end : int, optional Returns ------- out : ndarray Output array of ints. Returns -1 if `sub` is not found. See Also -------- find, str.find Examples -------- >>> a = np.array(["Computer Science"]) >>> np.char.index(a, "Science", start=0, end=None) array([9]) |
169,219 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
The provided code snippet includes necessary dependencies for implementing the `isalnum` function. Write a Python function `def isalnum(a)` to solve the following problem:
Returns true for each element if all characters in the string are alphanumeric and there is at least one character, false otherwise. Calls `str.isalnum` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like of str or unicode Returns ------- out : ndarray Output array of str or unicode, depending on input type See Also -------- str.isalnum
Here is the function:
def isalnum(a):
"""
Returns true for each element if all characters in the string are
alphanumeric and there is at least one character, false otherwise.
Calls `str.isalnum` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See Also
--------
str.isalnum
"""
return _vec_string(a, bool_, 'isalnum') | Returns true for each element if all characters in the string are alphanumeric and there is at least one character, false otherwise. Calls `str.isalnum` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like of str or unicode Returns ------- out : ndarray Output array of str or unicode, depending on input type See Also -------- str.isalnum |
169,220 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
The provided code snippet includes necessary dependencies for implementing the `isalpha` function. Write a Python function `def isalpha(a)` to solve the following problem:
Returns true for each element if all characters in the string are alphabetic and there is at least one character, false otherwise. Calls `str.isalpha` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like of str or unicode Returns ------- out : ndarray Output array of bools See Also -------- str.isalpha
Here is the function:
def isalpha(a):
"""
Returns true for each element if all characters in the string are
alphabetic and there is at least one character, false otherwise.
Calls `str.isalpha` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of bools
See Also
--------
str.isalpha
"""
return _vec_string(a, bool_, 'isalpha') | Returns true for each element if all characters in the string are alphabetic and there is at least one character, false otherwise. Calls `str.isalpha` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like of str or unicode Returns ------- out : ndarray Output array of bools See Also -------- str.isalpha |
169,221 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
The provided code snippet includes necessary dependencies for implementing the `isdigit` function. Write a Python function `def isdigit(a)` to solve the following problem:
Returns true for each element if all characters in the string are digits and there is at least one character, false otherwise. Calls `str.isdigit` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like of str or unicode Returns ------- out : ndarray Output array of bools See Also -------- str.isdigit Examples -------- >>> a = np.array(['a', 'b', '0']) >>> np.char.isdigit(a) array([False, False, True]) >>> a = np.array([['a', 'b', '0'], ['c', '1', '2']]) >>> np.char.isdigit(a) array([[False, False, True], [False, True, True]])
Here is the function:
def isdigit(a):
"""
Returns true for each element if all characters in the string are
digits and there is at least one character, false otherwise.
Calls `str.isdigit` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of bools
See Also
--------
str.isdigit
Examples
--------
>>> a = np.array(['a', 'b', '0'])
>>> np.char.isdigit(a)
array([False, False, True])
>>> a = np.array([['a', 'b', '0'], ['c', '1', '2']])
>>> np.char.isdigit(a)
array([[False, False, True], [False, True, True]])
"""
return _vec_string(a, bool_, 'isdigit') | Returns true for each element if all characters in the string are digits and there is at least one character, false otherwise. Calls `str.isdigit` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like of str or unicode Returns ------- out : ndarray Output array of bools See Also -------- str.isdigit Examples -------- >>> a = np.array(['a', 'b', '0']) >>> np.char.isdigit(a) array([False, False, True]) >>> a = np.array([['a', 'b', '0'], ['c', '1', '2']]) >>> np.char.isdigit(a) array([[False, False, True], [False, True, True]]) |
169,222 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
The provided code snippet includes necessary dependencies for implementing the `islower` function. Write a Python function `def islower(a)` to solve the following problem:
Returns true for each element if all cased characters in the string are lowercase and there is at least one cased character, false otherwise. Calls `str.islower` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like of str or unicode Returns ------- out : ndarray Output array of bools See Also -------- str.islower
Here is the function:
def islower(a):
"""
Returns true for each element if all cased characters in the
string are lowercase and there is at least one cased character,
false otherwise.
Calls `str.islower` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of bools
See Also
--------
str.islower
"""
return _vec_string(a, bool_, 'islower') | Returns true for each element if all cased characters in the string are lowercase and there is at least one cased character, false otherwise. Calls `str.islower` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like of str or unicode Returns ------- out : ndarray Output array of bools See Also -------- str.islower |
169,223 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
The provided code snippet includes necessary dependencies for implementing the `isspace` function. Write a Python function `def isspace(a)` to solve the following problem:
Returns true for each element if there are only whitespace characters in the string and there is at least one character, false otherwise. Calls `str.isspace` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like of str or unicode Returns ------- out : ndarray Output array of bools See Also -------- str.isspace
Here is the function:
def isspace(a):
"""
Returns true for each element if there are only whitespace
characters in the string and there is at least one character,
false otherwise.
Calls `str.isspace` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of bools
See Also
--------
str.isspace
"""
return _vec_string(a, bool_, 'isspace') | Returns true for each element if there are only whitespace characters in the string and there is at least one character, false otherwise. Calls `str.isspace` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like of str or unicode Returns ------- out : ndarray Output array of bools See Also -------- str.isspace |
169,224 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
The provided code snippet includes necessary dependencies for implementing the `istitle` function. Write a Python function `def istitle(a)` to solve the following problem:
Returns true for each element if the element is a titlecased string and there is at least one character, false otherwise. Call `str.istitle` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like of str or unicode Returns ------- out : ndarray Output array of bools See Also -------- str.istitle
Here is the function:
def istitle(a):
"""
Returns true for each element if the element is a titlecased
string and there is at least one character, false otherwise.
Call `str.istitle` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of bools
See Also
--------
str.istitle
"""
return _vec_string(a, bool_, 'istitle') | Returns true for each element if the element is a titlecased string and there is at least one character, false otherwise. Call `str.istitle` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like of str or unicode Returns ------- out : ndarray Output array of bools See Also -------- str.istitle |
169,225 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
The provided code snippet includes necessary dependencies for implementing the `isupper` function. Write a Python function `def isupper(a)` to solve the following problem:
Return true for each element if all cased characters in the string are uppercase and there is at least one character, false otherwise. Call `str.isupper` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like of str or unicode Returns ------- out : ndarray Output array of bools See Also -------- str.isupper Examples -------- >>> str = "GHC" >>> np.char.isupper(str) array(True) >>> a = np.array(["hello", "HELLO", "Hello"]) >>> np.char.isupper(a) array([False, True, False])
Here is the function:
def isupper(a):
"""
Return true for each element if all cased characters in the
string are uppercase and there is at least one character, false
otherwise.
Call `str.isupper` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of bools
See Also
--------
str.isupper
Examples
--------
>>> str = "GHC"
>>> np.char.isupper(str)
array(True)
>>> a = np.array(["hello", "HELLO", "Hello"])
>>> np.char.isupper(a)
array([False, True, False])
"""
return _vec_string(a, bool_, 'isupper') | Return true for each element if all cased characters in the string are uppercase and there is at least one character, false otherwise. Call `str.isupper` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like of str or unicode Returns ------- out : ndarray Output array of bools See Also -------- str.isupper Examples -------- >>> str = "GHC" >>> np.char.isupper(str) array(True) >>> a = np.array(["hello", "HELLO", "Hello"]) >>> np.char.isupper(a) array([False, True, False]) |
169,226 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _join_dispatcher(sep, seq):
return (sep, seq) | null |
169,227 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _to_string_or_unicode_array(result):
"""
Helper function to cast a result back into a string or unicode array
if an object array must be used as an intermediary.
"""
return numpy.asarray(result.tolist())
The provided code snippet includes necessary dependencies for implementing the `join` function. Write a Python function `def join(sep, seq)` to solve the following problem:
Return a string which is the concatenation of the strings in the sequence `seq`. Calls `str.join` element-wise. Parameters ---------- sep : array_like of str or unicode seq : array_like of str or unicode Returns ------- out : ndarray Output array of str or unicode, depending on input types See Also -------- str.join Examples -------- >>> np.char.join('-', 'osd') array('o-s-d', dtype='<U5') >>> np.char.join(['-', '.'], ['ghc', 'osd']) array(['g-h-c', 'o.s.d'], dtype='<U5')
Here is the function:
def join(sep, seq):
"""
Return a string which is the concatenation of the strings in the
sequence `seq`.
Calls `str.join` element-wise.
Parameters
----------
sep : array_like of str or unicode
seq : array_like of str or unicode
Returns
-------
out : ndarray
Output array of str or unicode, depending on input types
See Also
--------
str.join
Examples
--------
>>> np.char.join('-', 'osd')
array('o-s-d', dtype='<U5')
>>> np.char.join(['-', '.'], ['ghc', 'osd'])
array(['g-h-c', 'o.s.d'], dtype='<U5')
"""
return _to_string_or_unicode_array(
_vec_string(sep, object_, 'join', (seq,))) | Return a string which is the concatenation of the strings in the sequence `seq`. Calls `str.join` element-wise. Parameters ---------- sep : array_like of str or unicode seq : array_like of str or unicode Returns ------- out : ndarray Output array of str or unicode, depending on input types See Also -------- str.join Examples -------- >>> np.char.join('-', 'osd') array('o-s-d', dtype='<U5') >>> np.char.join(['-', '.'], ['ghc', 'osd']) array(['g-h-c', 'o.s.d'], dtype='<U5') |
169,228 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _just_dispatcher(a, width, fillchar=None):
return (a,) | null |
169,229 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def asarray(obj, itemsize=None, unicode=None, order=None):
"""
Convert the input to a `chararray`, copying the data only if
necessary.
Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
when indexed
2) comparison operators automatically remove whitespace from the
end when comparing values
3) vectorized string operations are provided as methods
(e.g. `str.endswith`) and infix operators (e.g. ``+``, ``*``,``%``)
Parameters
----------
obj : array of str or unicode-like
itemsize : int, optional
`itemsize` is the number of characters per scalar in the
resulting array. If `itemsize` is None, and `obj` is an
object array or a Python list, the `itemsize` will be
automatically determined. If `itemsize` is provided and `obj`
is of type str or unicode, then the `obj` string will be
chunked into `itemsize` pieces.
unicode : bool, optional
When true, the resulting `chararray` can contain Unicode
characters, when false only 8-bit characters. If unicode is
None and `obj` is one of the following:
- a `chararray`,
- an ndarray of type `str` or 'unicode`
- a Python str or unicode object,
then the unicode setting of the output array will be
automatically determined.
order : {'C', 'F'}, optional
Specify the order of the array. If order is 'C' (default), then the
array will be in C-contiguous order (last-index varies the
fastest). If order is 'F', then the returned array
will be in Fortran-contiguous order (first-index varies the
fastest).
"""
return array(obj, itemsize, copy=False,
unicode=unicode, order=order)
The provided code snippet includes necessary dependencies for implementing the `ljust` function. Write a Python function `def ljust(a, width, fillchar=' ')` to solve the following problem:
Return an array with the elements of `a` left-justified in a string of length `width`. Calls `str.ljust` element-wise. Parameters ---------- a : array_like of str or unicode width : int The length of the resulting strings fillchar : str or unicode, optional The character to use for padding Returns ------- out : ndarray Output array of str or unicode, depending on input type See Also -------- str.ljust
Here is the function:
def ljust(a, width, fillchar=' '):
"""
Return an array with the elements of `a` left-justified in a
string of length `width`.
Calls `str.ljust` element-wise.
Parameters
----------
a : array_like of str or unicode
width : int
The length of the resulting strings
fillchar : str or unicode, optional
The character to use for padding
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See Also
--------
str.ljust
"""
a_arr = numpy.asarray(a)
width_arr = numpy.asarray(width)
size = int(numpy.max(width_arr.flat))
if numpy.issubdtype(a_arr.dtype, numpy.string_):
fillchar = asbytes(fillchar)
return _vec_string(
a_arr, (a_arr.dtype.type, size), 'ljust', (width_arr, fillchar)) | Return an array with the elements of `a` left-justified in a string of length `width`. Calls `str.ljust` element-wise. Parameters ---------- a : array_like of str or unicode width : int The length of the resulting strings fillchar : str or unicode, optional The character to use for padding Returns ------- out : ndarray Output array of str or unicode, depending on input type See Also -------- str.ljust |
169,230 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def asarray(obj, itemsize=None, unicode=None, order=None):
"""
Convert the input to a `chararray`, copying the data only if
necessary.
Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
when indexed
2) comparison operators automatically remove whitespace from the
end when comparing values
3) vectorized string operations are provided as methods
(e.g. `str.endswith`) and infix operators (e.g. ``+``, ``*``,``%``)
Parameters
----------
obj : array of str or unicode-like
itemsize : int, optional
`itemsize` is the number of characters per scalar in the
resulting array. If `itemsize` is None, and `obj` is an
object array or a Python list, the `itemsize` will be
automatically determined. If `itemsize` is provided and `obj`
is of type str or unicode, then the `obj` string will be
chunked into `itemsize` pieces.
unicode : bool, optional
When true, the resulting `chararray` can contain Unicode
characters, when false only 8-bit characters. If unicode is
None and `obj` is one of the following:
- a `chararray`,
- an ndarray of type `str` or 'unicode`
- a Python str or unicode object,
then the unicode setting of the output array will be
automatically determined.
order : {'C', 'F'}, optional
Specify the order of the array. If order is 'C' (default), then the
array will be in C-contiguous order (last-index varies the
fastest). If order is 'F', then the returned array
will be in Fortran-contiguous order (first-index varies the
fastest).
"""
return array(obj, itemsize, copy=False,
unicode=unicode, order=order)
The provided code snippet includes necessary dependencies for implementing the `lower` function. Write a Python function `def lower(a)` to solve the following problem:
Return an array with the elements converted to lowercase. Call `str.lower` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like, {str, unicode} Input array. Returns ------- out : ndarray, {str, unicode} Output array of str or unicode, depending on input type See Also -------- str.lower Examples -------- >>> c = np.array(['A1B C', '1BCA', 'BCA1']); c array(['A1B C', '1BCA', 'BCA1'], dtype='<U5') >>> np.char.lower(c) array(['a1b c', '1bca', 'bca1'], dtype='<U5')
Here is the function:
def lower(a):
"""
Return an array with the elements converted to lowercase.
Call `str.lower` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like, {str, unicode}
Input array.
Returns
-------
out : ndarray, {str, unicode}
Output array of str or unicode, depending on input type
See Also
--------
str.lower
Examples
--------
>>> c = np.array(['A1B C', '1BCA', 'BCA1']); c
array(['A1B C', '1BCA', 'BCA1'], dtype='<U5')
>>> np.char.lower(c)
array(['a1b c', '1bca', 'bca1'], dtype='<U5')
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'lower') | Return an array with the elements converted to lowercase. Call `str.lower` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like, {str, unicode} Input array. Returns ------- out : ndarray, {str, unicode} Output array of str or unicode, depending on input type See Also -------- str.lower Examples -------- >>> c = np.array(['A1B C', '1BCA', 'BCA1']); c array(['A1B C', '1BCA', 'BCA1'], dtype='<U5') >>> np.char.lower(c) array(['a1b c', '1bca', 'bca1'], dtype='<U5') |
169,231 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def asarray(obj, itemsize=None, unicode=None, order=None):
"""
Convert the input to a `chararray`, copying the data only if
necessary.
Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
when indexed
2) comparison operators automatically remove whitespace from the
end when comparing values
3) vectorized string operations are provided as methods
(e.g. `str.endswith`) and infix operators (e.g. ``+``, ``*``,``%``)
Parameters
----------
obj : array of str or unicode-like
itemsize : int, optional
`itemsize` is the number of characters per scalar in the
resulting array. If `itemsize` is None, and `obj` is an
object array or a Python list, the `itemsize` will be
automatically determined. If `itemsize` is provided and `obj`
is of type str or unicode, then the `obj` string will be
chunked into `itemsize` pieces.
unicode : bool, optional
When true, the resulting `chararray` can contain Unicode
characters, when false only 8-bit characters. If unicode is
None and `obj` is one of the following:
- a `chararray`,
- an ndarray of type `str` or 'unicode`
- a Python str or unicode object,
then the unicode setting of the output array will be
automatically determined.
order : {'C', 'F'}, optional
Specify the order of the array. If order is 'C' (default), then the
array will be in C-contiguous order (last-index varies the
fastest). If order is 'F', then the returned array
will be in Fortran-contiguous order (first-index varies the
fastest).
"""
return array(obj, itemsize, copy=False,
unicode=unicode, order=order)
The provided code snippet includes necessary dependencies for implementing the `lstrip` function. Write a Python function `def lstrip(a, chars=None)` to solve the following problem:
For each element in `a`, return a copy with the leading characters removed. Calls `str.lstrip` element-wise. Parameters ---------- a : array-like, {str, unicode} Input array. chars : {str, unicode}, optional The `chars` argument is a string specifying the set of characters to be removed. If omitted or None, the `chars` argument defaults to removing whitespace. The `chars` argument is not a prefix; rather, all combinations of its values are stripped. Returns ------- out : ndarray, {str, unicode} Output array of str or unicode, depending on input type See Also -------- str.lstrip Examples -------- >>> c = np.array(['aAaAaA', ' aA ', 'abBABba']) >>> c array(['aAaAaA', ' aA ', 'abBABba'], dtype='<U7') The 'a' variable is unstripped from c[1] because whitespace leading. >>> np.char.lstrip(c, 'a') array(['AaAaA', ' aA ', 'bBABba'], dtype='<U7') >>> np.char.lstrip(c, 'A') # leaves c unchanged array(['aAaAaA', ' aA ', 'abBABba'], dtype='<U7') >>> (np.char.lstrip(c, ' ') == np.char.lstrip(c, '')).all() ... # XXX: is this a regression? This used to return True ... # np.char.lstrip(c,'') does not modify c at all. False >>> (np.char.lstrip(c, ' ') == np.char.lstrip(c, None)).all() True
Here is the function:
def lstrip(a, chars=None):
"""
For each element in `a`, return a copy with the leading characters
removed.
Calls `str.lstrip` element-wise.
Parameters
----------
a : array-like, {str, unicode}
Input array.
chars : {str, unicode}, optional
The `chars` argument is a string specifying the set of
characters to be removed. If omitted or None, the `chars`
argument defaults to removing whitespace. The `chars` argument
is not a prefix; rather, all combinations of its values are
stripped.
Returns
-------
out : ndarray, {str, unicode}
Output array of str or unicode, depending on input type
See Also
--------
str.lstrip
Examples
--------
>>> c = np.array(['aAaAaA', ' aA ', 'abBABba'])
>>> c
array(['aAaAaA', ' aA ', 'abBABba'], dtype='<U7')
The 'a' variable is unstripped from c[1] because whitespace leading.
>>> np.char.lstrip(c, 'a')
array(['AaAaA', ' aA ', 'bBABba'], dtype='<U7')
>>> np.char.lstrip(c, 'A') # leaves c unchanged
array(['aAaAaA', ' aA ', 'abBABba'], dtype='<U7')
>>> (np.char.lstrip(c, ' ') == np.char.lstrip(c, '')).all()
... # XXX: is this a regression? This used to return True
... # np.char.lstrip(c,'') does not modify c at all.
False
>>> (np.char.lstrip(c, ' ') == np.char.lstrip(c, None)).all()
True
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'lstrip', (chars,)) | For each element in `a`, return a copy with the leading characters removed. Calls `str.lstrip` element-wise. Parameters ---------- a : array-like, {str, unicode} Input array. chars : {str, unicode}, optional The `chars` argument is a string specifying the set of characters to be removed. If omitted or None, the `chars` argument defaults to removing whitespace. The `chars` argument is not a prefix; rather, all combinations of its values are stripped. Returns ------- out : ndarray, {str, unicode} Output array of str or unicode, depending on input type See Also -------- str.lstrip Examples -------- >>> c = np.array(['aAaAaA', ' aA ', 'abBABba']) >>> c array(['aAaAaA', ' aA ', 'abBABba'], dtype='<U7') The 'a' variable is unstripped from c[1] because whitespace leading. >>> np.char.lstrip(c, 'a') array(['AaAaA', ' aA ', 'bBABba'], dtype='<U7') >>> np.char.lstrip(c, 'A') # leaves c unchanged array(['aAaAaA', ' aA ', 'abBABba'], dtype='<U7') >>> (np.char.lstrip(c, ' ') == np.char.lstrip(c, '')).all() ... # XXX: is this a regression? This used to return True ... # np.char.lstrip(c,'') does not modify c at all. False >>> (np.char.lstrip(c, ' ') == np.char.lstrip(c, None)).all() True |
169,232 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _partition_dispatcher(a, sep):
return (a,) | null |
169,233 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _to_string_or_unicode_array(result):
"""
Helper function to cast a result back into a string or unicode array
if an object array must be used as an intermediary.
"""
return numpy.asarray(result.tolist())
The provided code snippet includes necessary dependencies for implementing the `partition` function. Write a Python function `def partition(a, sep)` to solve the following problem:
Partition each element in `a` around `sep`. Calls `str.partition` element-wise. For each element in `a`, split the element as the first occurrence of `sep`, and return 3 strings containing the part before the separator, the separator itself, and the part after the separator. If the separator is not found, return 3 strings containing the string itself, followed by two empty strings. Parameters ---------- a : array_like, {str, unicode} Input array sep : {str, unicode} Separator to split each string element in `a`. Returns ------- out : ndarray, {str, unicode} Output array of str or unicode, depending on input type. The output array will have an extra dimension with 3 elements per input element. See Also -------- str.partition
Here is the function:
def partition(a, sep):
"""
Partition each element in `a` around `sep`.
Calls `str.partition` element-wise.
For each element in `a`, split the element as the first
occurrence of `sep`, and return 3 strings containing the part
before the separator, the separator itself, and the part after
the separator. If the separator is not found, return 3 strings
containing the string itself, followed by two empty strings.
Parameters
----------
a : array_like, {str, unicode}
Input array
sep : {str, unicode}
Separator to split each string element in `a`.
Returns
-------
out : ndarray, {str, unicode}
Output array of str or unicode, depending on input type.
The output array will have an extra dimension with 3
elements per input element.
See Also
--------
str.partition
"""
return _to_string_or_unicode_array(
_vec_string(a, object_, 'partition', (sep,))) | Partition each element in `a` around `sep`. Calls `str.partition` element-wise. For each element in `a`, split the element as the first occurrence of `sep`, and return 3 strings containing the part before the separator, the separator itself, and the part after the separator. If the separator is not found, return 3 strings containing the string itself, followed by two empty strings. Parameters ---------- a : array_like, {str, unicode} Input array sep : {str, unicode} Separator to split each string element in `a`. Returns ------- out : ndarray, {str, unicode} Output array of str or unicode, depending on input type. The output array will have an extra dimension with 3 elements per input element. See Also -------- str.partition |
169,234 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _replace_dispatcher(a, old, new, count=None):
return (a,) | null |
169,235 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _to_string_or_unicode_array(result):
"""
Helper function to cast a result back into a string or unicode array
if an object array must be used as an intermediary.
"""
return numpy.asarray(result.tolist())
def _clean_args(*args):
"""
Helper function for delegating arguments to Python string
functions.
Many of the Python string operations that have optional arguments
do not use 'None' to indicate a default value. In these cases,
we need to remove all None arguments, and those following them.
"""
newargs = []
for chk in args:
if chk is None:
break
newargs.append(chk)
return newargs
The provided code snippet includes necessary dependencies for implementing the `replace` function. Write a Python function `def replace(a, old, new, count=None)` to solve the following problem:
For each element in `a`, return a copy of the string with all occurrences of substring `old` replaced by `new`. Calls `str.replace` element-wise. Parameters ---------- a : array-like of str or unicode old, new : str or unicode count : int, optional If the optional argument `count` is given, only the first `count` occurrences are replaced. Returns ------- out : ndarray Output array of str or unicode, depending on input type See Also -------- str.replace Examples -------- >>> a = np.array(["That is a mango", "Monkeys eat mangos"]) >>> np.char.replace(a, 'mango', 'banana') array(['That is a banana', 'Monkeys eat bananas'], dtype='<U19') >>> a = np.array(["The dish is fresh", "This is it"]) >>> np.char.replace(a, 'is', 'was') array(['The dwash was fresh', 'Thwas was it'], dtype='<U19')
Here is the function:
def replace(a, old, new, count=None):
"""
For each element in `a`, return a copy of the string with all
occurrences of substring `old` replaced by `new`.
Calls `str.replace` element-wise.
Parameters
----------
a : array-like of str or unicode
old, new : str or unicode
count : int, optional
If the optional argument `count` is given, only the first
`count` occurrences are replaced.
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See Also
--------
str.replace
Examples
--------
>>> a = np.array(["That is a mango", "Monkeys eat mangos"])
>>> np.char.replace(a, 'mango', 'banana')
array(['That is a banana', 'Monkeys eat bananas'], dtype='<U19')
>>> a = np.array(["The dish is fresh", "This is it"])
>>> np.char.replace(a, 'is', 'was')
array(['The dwash was fresh', 'Thwas was it'], dtype='<U19')
"""
return _to_string_or_unicode_array(
_vec_string(
a, object_, 'replace', [old, new] + _clean_args(count))) | For each element in `a`, return a copy of the string with all occurrences of substring `old` replaced by `new`. Calls `str.replace` element-wise. Parameters ---------- a : array-like of str or unicode old, new : str or unicode count : int, optional If the optional argument `count` is given, only the first `count` occurrences are replaced. Returns ------- out : ndarray Output array of str or unicode, depending on input type See Also -------- str.replace Examples -------- >>> a = np.array(["That is a mango", "Monkeys eat mangos"]) >>> np.char.replace(a, 'mango', 'banana') array(['That is a banana', 'Monkeys eat bananas'], dtype='<U19') >>> a = np.array(["The dish is fresh", "This is it"]) >>> np.char.replace(a, 'is', 'was') array(['The dwash was fresh', 'Thwas was it'], dtype='<U19') |
169,236 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _clean_args(*args):
"""
Helper function for delegating arguments to Python string
functions.
Many of the Python string operations that have optional arguments
do not use 'None' to indicate a default value. In these cases,
we need to remove all None arguments, and those following them.
"""
newargs = []
for chk in args:
if chk is None:
break
newargs.append(chk)
return newargs
The provided code snippet includes necessary dependencies for implementing the `rfind` function. Write a Python function `def rfind(a, sub, start=0, end=None)` to solve the following problem:
For each element in `a`, return the highest index in the string where substring `sub` is found, such that `sub` is contained within [`start`, `end`]. Calls `str.rfind` element-wise. Parameters ---------- a : array-like of str or unicode sub : str or unicode start, end : int, optional Optional arguments `start` and `end` are interpreted as in slice notation. Returns ------- out : ndarray Output array of ints. Return -1 on failure. See Also -------- str.rfind
Here is the function:
def rfind(a, sub, start=0, end=None):
"""
For each element in `a`, return the highest index in the string
where substring `sub` is found, such that `sub` is contained
within [`start`, `end`].
Calls `str.rfind` element-wise.
Parameters
----------
a : array-like of str or unicode
sub : str or unicode
start, end : int, optional
Optional arguments `start` and `end` are interpreted as in
slice notation.
Returns
-------
out : ndarray
Output array of ints. Return -1 on failure.
See Also
--------
str.rfind
"""
return _vec_string(
a, int_, 'rfind', [sub, start] + _clean_args(end)) | For each element in `a`, return the highest index in the string where substring `sub` is found, such that `sub` is contained within [`start`, `end`]. Calls `str.rfind` element-wise. Parameters ---------- a : array-like of str or unicode sub : str or unicode start, end : int, optional Optional arguments `start` and `end` are interpreted as in slice notation. Returns ------- out : ndarray Output array of ints. Return -1 on failure. See Also -------- str.rfind |
169,237 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _clean_args(*args):
"""
Helper function for delegating arguments to Python string
functions.
Many of the Python string operations that have optional arguments
do not use 'None' to indicate a default value. In these cases,
we need to remove all None arguments, and those following them.
"""
newargs = []
for chk in args:
if chk is None:
break
newargs.append(chk)
return newargs
The provided code snippet includes necessary dependencies for implementing the `rindex` function. Write a Python function `def rindex(a, sub, start=0, end=None)` to solve the following problem:
Like `rfind`, but raises `ValueError` when the substring `sub` is not found. Calls `str.rindex` element-wise. Parameters ---------- a : array-like of str or unicode sub : str or unicode start, end : int, optional Returns ------- out : ndarray Output array of ints. See Also -------- rfind, str.rindex
Here is the function:
def rindex(a, sub, start=0, end=None):
"""
Like `rfind`, but raises `ValueError` when the substring `sub` is
not found.
Calls `str.rindex` element-wise.
Parameters
----------
a : array-like of str or unicode
sub : str or unicode
start, end : int, optional
Returns
-------
out : ndarray
Output array of ints.
See Also
--------
rfind, str.rindex
"""
return _vec_string(
a, int_, 'rindex', [sub, start] + _clean_args(end)) | Like `rfind`, but raises `ValueError` when the substring `sub` is not found. Calls `str.rindex` element-wise. Parameters ---------- a : array-like of str or unicode sub : str or unicode start, end : int, optional Returns ------- out : ndarray Output array of ints. See Also -------- rfind, str.rindex |
169,238 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def asarray(obj, itemsize=None, unicode=None, order=None):
"""
Convert the input to a `chararray`, copying the data only if
necessary.
Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
when indexed
2) comparison operators automatically remove whitespace from the
end when comparing values
3) vectorized string operations are provided as methods
(e.g. `str.endswith`) and infix operators (e.g. ``+``, ``*``,``%``)
Parameters
----------
obj : array of str or unicode-like
itemsize : int, optional
`itemsize` is the number of characters per scalar in the
resulting array. If `itemsize` is None, and `obj` is an
object array or a Python list, the `itemsize` will be
automatically determined. If `itemsize` is provided and `obj`
is of type str or unicode, then the `obj` string will be
chunked into `itemsize` pieces.
unicode : bool, optional
When true, the resulting `chararray` can contain Unicode
characters, when false only 8-bit characters. If unicode is
None and `obj` is one of the following:
- a `chararray`,
- an ndarray of type `str` or 'unicode`
- a Python str or unicode object,
then the unicode setting of the output array will be
automatically determined.
order : {'C', 'F'}, optional
Specify the order of the array. If order is 'C' (default), then the
array will be in C-contiguous order (last-index varies the
fastest). If order is 'F', then the returned array
will be in Fortran-contiguous order (first-index varies the
fastest).
"""
return array(obj, itemsize, copy=False,
unicode=unicode, order=order)
The provided code snippet includes necessary dependencies for implementing the `rjust` function. Write a Python function `def rjust(a, width, fillchar=' ')` to solve the following problem:
Return an array with the elements of `a` right-justified in a string of length `width`. Calls `str.rjust` element-wise. Parameters ---------- a : array_like of str or unicode width : int The length of the resulting strings fillchar : str or unicode, optional The character to use for padding Returns ------- out : ndarray Output array of str or unicode, depending on input type See Also -------- str.rjust
Here is the function:
def rjust(a, width, fillchar=' '):
"""
Return an array with the elements of `a` right-justified in a
string of length `width`.
Calls `str.rjust` element-wise.
Parameters
----------
a : array_like of str or unicode
width : int
The length of the resulting strings
fillchar : str or unicode, optional
The character to use for padding
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See Also
--------
str.rjust
"""
a_arr = numpy.asarray(a)
width_arr = numpy.asarray(width)
size = int(numpy.max(width_arr.flat))
if numpy.issubdtype(a_arr.dtype, numpy.string_):
fillchar = asbytes(fillchar)
return _vec_string(
a_arr, (a_arr.dtype.type, size), 'rjust', (width_arr, fillchar)) | Return an array with the elements of `a` right-justified in a string of length `width`. Calls `str.rjust` element-wise. Parameters ---------- a : array_like of str or unicode width : int The length of the resulting strings fillchar : str or unicode, optional The character to use for padding Returns ------- out : ndarray Output array of str or unicode, depending on input type See Also -------- str.rjust |
169,239 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _to_string_or_unicode_array(result):
"""
Helper function to cast a result back into a string or unicode array
if an object array must be used as an intermediary.
"""
return numpy.asarray(result.tolist())
The provided code snippet includes necessary dependencies for implementing the `rpartition` function. Write a Python function `def rpartition(a, sep)` to solve the following problem:
Partition (split) each element around the right-most separator. Calls `str.rpartition` element-wise. For each element in `a`, split the element as the last occurrence of `sep`, and return 3 strings containing the part before the separator, the separator itself, and the part after the separator. If the separator is not found, return 3 strings containing the string itself, followed by two empty strings. Parameters ---------- a : array_like of str or unicode Input array sep : str or unicode Right-most separator to split each element in array. Returns ------- out : ndarray Output array of string or unicode, depending on input type. The output array will have an extra dimension with 3 elements per input element. See Also -------- str.rpartition
Here is the function:
def rpartition(a, sep):
"""
Partition (split) each element around the right-most separator.
Calls `str.rpartition` element-wise.
For each element in `a`, split the element as the last
occurrence of `sep`, and return 3 strings containing the part
before the separator, the separator itself, and the part after
the separator. If the separator is not found, return 3 strings
containing the string itself, followed by two empty strings.
Parameters
----------
a : array_like of str or unicode
Input array
sep : str or unicode
Right-most separator to split each element in array.
Returns
-------
out : ndarray
Output array of string or unicode, depending on input
type. The output array will have an extra dimension with
3 elements per input element.
See Also
--------
str.rpartition
"""
return _to_string_or_unicode_array(
_vec_string(a, object_, 'rpartition', (sep,))) | Partition (split) each element around the right-most separator. Calls `str.rpartition` element-wise. For each element in `a`, split the element as the last occurrence of `sep`, and return 3 strings containing the part before the separator, the separator itself, and the part after the separator. If the separator is not found, return 3 strings containing the string itself, followed by two empty strings. Parameters ---------- a : array_like of str or unicode Input array sep : str or unicode Right-most separator to split each element in array. Returns ------- out : ndarray Output array of string or unicode, depending on input type. The output array will have an extra dimension with 3 elements per input element. See Also -------- str.rpartition |
169,240 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _split_dispatcher(a, sep=None, maxsplit=None):
return (a,) | null |
169,241 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _clean_args(*args):
"""
Helper function for delegating arguments to Python string
functions.
Many of the Python string operations that have optional arguments
do not use 'None' to indicate a default value. In these cases,
we need to remove all None arguments, and those following them.
"""
newargs = []
for chk in args:
if chk is None:
break
newargs.append(chk)
return newargs
The provided code snippet includes necessary dependencies for implementing the `rsplit` function. Write a Python function `def rsplit(a, sep=None, maxsplit=None)` to solve the following problem:
For each element in `a`, return a list of the words in the string, using `sep` as the delimiter string. Calls `str.rsplit` element-wise. Except for splitting from the right, `rsplit` behaves like `split`. Parameters ---------- a : array_like of str or unicode sep : str or unicode, optional If `sep` is not specified or None, any whitespace string is a separator. maxsplit : int, optional If `maxsplit` is given, at most `maxsplit` splits are done, the rightmost ones. Returns ------- out : ndarray Array of list objects See Also -------- str.rsplit, split
Here is the function:
def rsplit(a, sep=None, maxsplit=None):
"""
For each element in `a`, return a list of the words in the
string, using `sep` as the delimiter string.
Calls `str.rsplit` element-wise.
Except for splitting from the right, `rsplit`
behaves like `split`.
Parameters
----------
a : array_like of str or unicode
sep : str or unicode, optional
If `sep` is not specified or None, any whitespace string
is a separator.
maxsplit : int, optional
If `maxsplit` is given, at most `maxsplit` splits are done,
the rightmost ones.
Returns
-------
out : ndarray
Array of list objects
See Also
--------
str.rsplit, split
"""
# This will return an array of lists of different sizes, so we
# leave it as an object array
return _vec_string(
a, object_, 'rsplit', [sep] + _clean_args(maxsplit)) | For each element in `a`, return a list of the words in the string, using `sep` as the delimiter string. Calls `str.rsplit` element-wise. Except for splitting from the right, `rsplit` behaves like `split`. Parameters ---------- a : array_like of str or unicode sep : str or unicode, optional If `sep` is not specified or None, any whitespace string is a separator. maxsplit : int, optional If `maxsplit` is given, at most `maxsplit` splits are done, the rightmost ones. Returns ------- out : ndarray Array of list objects See Also -------- str.rsplit, split |
169,242 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def asarray(obj, itemsize=None, unicode=None, order=None):
"""
Convert the input to a `chararray`, copying the data only if
necessary.
Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
when indexed
2) comparison operators automatically remove whitespace from the
end when comparing values
3) vectorized string operations are provided as methods
(e.g. `str.endswith`) and infix operators (e.g. ``+``, ``*``,``%``)
Parameters
----------
obj : array of str or unicode-like
itemsize : int, optional
`itemsize` is the number of characters per scalar in the
resulting array. If `itemsize` is None, and `obj` is an
object array or a Python list, the `itemsize` will be
automatically determined. If `itemsize` is provided and `obj`
is of type str or unicode, then the `obj` string will be
chunked into `itemsize` pieces.
unicode : bool, optional
When true, the resulting `chararray` can contain Unicode
characters, when false only 8-bit characters. If unicode is
None and `obj` is one of the following:
- a `chararray`,
- an ndarray of type `str` or 'unicode`
- a Python str or unicode object,
then the unicode setting of the output array will be
automatically determined.
order : {'C', 'F'}, optional
Specify the order of the array. If order is 'C' (default), then the
array will be in C-contiguous order (last-index varies the
fastest). If order is 'F', then the returned array
will be in Fortran-contiguous order (first-index varies the
fastest).
"""
return array(obj, itemsize, copy=False,
unicode=unicode, order=order)
The provided code snippet includes necessary dependencies for implementing the `rstrip` function. Write a Python function `def rstrip(a, chars=None)` to solve the following problem:
For each element in `a`, return a copy with the trailing characters removed. Calls `str.rstrip` element-wise. Parameters ---------- a : array-like of str or unicode chars : str or unicode, optional The `chars` argument is a string specifying the set of characters to be removed. If omitted or None, the `chars` argument defaults to removing whitespace. The `chars` argument is not a suffix; rather, all combinations of its values are stripped. Returns ------- out : ndarray Output array of str or unicode, depending on input type See Also -------- str.rstrip Examples -------- >>> c = np.array(['aAaAaA', 'abBABba'], dtype='S7'); c array(['aAaAaA', 'abBABba'], dtype='|S7') >>> np.char.rstrip(c, b'a') array(['aAaAaA', 'abBABb'], dtype='|S7') >>> np.char.rstrip(c, b'A') array(['aAaAa', 'abBABba'], dtype='|S7')
Here is the function:
def rstrip(a, chars=None):
"""
For each element in `a`, return a copy with the trailing
characters removed.
Calls `str.rstrip` element-wise.
Parameters
----------
a : array-like of str or unicode
chars : str or unicode, optional
The `chars` argument is a string specifying the set of
characters to be removed. If omitted or None, the `chars`
argument defaults to removing whitespace. The `chars` argument
is not a suffix; rather, all combinations of its values are
stripped.
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See Also
--------
str.rstrip
Examples
--------
>>> c = np.array(['aAaAaA', 'abBABba'], dtype='S7'); c
array(['aAaAaA', 'abBABba'],
dtype='|S7')
>>> np.char.rstrip(c, b'a')
array(['aAaAaA', 'abBABb'],
dtype='|S7')
>>> np.char.rstrip(c, b'A')
array(['aAaAa', 'abBABba'],
dtype='|S7')
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'rstrip', (chars,)) | For each element in `a`, return a copy with the trailing characters removed. Calls `str.rstrip` element-wise. Parameters ---------- a : array-like of str or unicode chars : str or unicode, optional The `chars` argument is a string specifying the set of characters to be removed. If omitted or None, the `chars` argument defaults to removing whitespace. The `chars` argument is not a suffix; rather, all combinations of its values are stripped. Returns ------- out : ndarray Output array of str or unicode, depending on input type See Also -------- str.rstrip Examples -------- >>> c = np.array(['aAaAaA', 'abBABba'], dtype='S7'); c array(['aAaAaA', 'abBABba'], dtype='|S7') >>> np.char.rstrip(c, b'a') array(['aAaAaA', 'abBABb'], dtype='|S7') >>> np.char.rstrip(c, b'A') array(['aAaAa', 'abBABba'], dtype='|S7') |
169,243 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _clean_args(*args):
"""
Helper function for delegating arguments to Python string
functions.
Many of the Python string operations that have optional arguments
do not use 'None' to indicate a default value. In these cases,
we need to remove all None arguments, and those following them.
"""
newargs = []
for chk in args:
if chk is None:
break
newargs.append(chk)
return newargs
The provided code snippet includes necessary dependencies for implementing the `split` function. Write a Python function `def split(a, sep=None, maxsplit=None)` to solve the following problem:
For each element in `a`, return a list of the words in the string, using `sep` as the delimiter string. Calls `str.split` element-wise. Parameters ---------- a : array_like of str or unicode sep : str or unicode, optional If `sep` is not specified or None, any whitespace string is a separator. maxsplit : int, optional If `maxsplit` is given, at most `maxsplit` splits are done. Returns ------- out : ndarray Array of list objects See Also -------- str.split, rsplit
Here is the function:
def split(a, sep=None, maxsplit=None):
"""
For each element in `a`, return a list of the words in the
string, using `sep` as the delimiter string.
Calls `str.split` element-wise.
Parameters
----------
a : array_like of str or unicode
sep : str or unicode, optional
If `sep` is not specified or None, any whitespace string is a
separator.
maxsplit : int, optional
If `maxsplit` is given, at most `maxsplit` splits are done.
Returns
-------
out : ndarray
Array of list objects
See Also
--------
str.split, rsplit
"""
# This will return an array of lists of different sizes, so we
# leave it as an object array
return _vec_string(
a, object_, 'split', [sep] + _clean_args(maxsplit)) | For each element in `a`, return a list of the words in the string, using `sep` as the delimiter string. Calls `str.split` element-wise. Parameters ---------- a : array_like of str or unicode sep : str or unicode, optional If `sep` is not specified or None, any whitespace string is a separator. maxsplit : int, optional If `maxsplit` is given, at most `maxsplit` splits are done. Returns ------- out : ndarray Array of list objects See Also -------- str.split, rsplit |
169,244 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _splitlines_dispatcher(a, keepends=None):
return (a,) | null |
169,245 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _clean_args(*args):
"""
Helper function for delegating arguments to Python string
functions.
Many of the Python string operations that have optional arguments
do not use 'None' to indicate a default value. In these cases,
we need to remove all None arguments, and those following them.
"""
newargs = []
for chk in args:
if chk is None:
break
newargs.append(chk)
return newargs
The provided code snippet includes necessary dependencies for implementing the `splitlines` function. Write a Python function `def splitlines(a, keepends=None)` to solve the following problem:
For each element in `a`, return a list of the lines in the element, breaking at line boundaries. Calls `str.splitlines` element-wise. Parameters ---------- a : array_like of str or unicode keepends : bool, optional Line breaks are not included in the resulting list unless keepends is given and true. Returns ------- out : ndarray Array of list objects See Also -------- str.splitlines
Here is the function:
def splitlines(a, keepends=None):
"""
For each element in `a`, return a list of the lines in the
element, breaking at line boundaries.
Calls `str.splitlines` element-wise.
Parameters
----------
a : array_like of str or unicode
keepends : bool, optional
Line breaks are not included in the resulting list unless
keepends is given and true.
Returns
-------
out : ndarray
Array of list objects
See Also
--------
str.splitlines
"""
return _vec_string(
a, object_, 'splitlines', _clean_args(keepends)) | For each element in `a`, return a list of the lines in the element, breaking at line boundaries. Calls `str.splitlines` element-wise. Parameters ---------- a : array_like of str or unicode keepends : bool, optional Line breaks are not included in the resulting list unless keepends is given and true. Returns ------- out : ndarray Array of list objects See Also -------- str.splitlines |
169,246 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _startswith_dispatcher(a, prefix, start=None, end=None):
return (a,) | null |
169,247 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _clean_args(*args):
"""
Helper function for delegating arguments to Python string
functions.
Many of the Python string operations that have optional arguments
do not use 'None' to indicate a default value. In these cases,
we need to remove all None arguments, and those following them.
"""
newargs = []
for chk in args:
if chk is None:
break
newargs.append(chk)
return newargs
The provided code snippet includes necessary dependencies for implementing the `startswith` function. Write a Python function `def startswith(a, prefix, start=0, end=None)` to solve the following problem:
Returns a boolean array which is `True` where the string element in `a` starts with `prefix`, otherwise `False`. Calls `str.startswith` element-wise. Parameters ---------- a : array_like of str or unicode prefix : str start, end : int, optional With optional `start`, test beginning at that position. With optional `end`, stop comparing at that position. Returns ------- out : ndarray Array of booleans See Also -------- str.startswith
Here is the function:
def startswith(a, prefix, start=0, end=None):
"""
Returns a boolean array which is `True` where the string element
in `a` starts with `prefix`, otherwise `False`.
Calls `str.startswith` element-wise.
Parameters
----------
a : array_like of str or unicode
prefix : str
start, end : int, optional
With optional `start`, test beginning at that position. With
optional `end`, stop comparing at that position.
Returns
-------
out : ndarray
Array of booleans
See Also
--------
str.startswith
"""
return _vec_string(
a, bool_, 'startswith', [prefix, start] + _clean_args(end)) | Returns a boolean array which is `True` where the string element in `a` starts with `prefix`, otherwise `False`. Calls `str.startswith` element-wise. Parameters ---------- a : array_like of str or unicode prefix : str start, end : int, optional With optional `start`, test beginning at that position. With optional `end`, stop comparing at that position. Returns ------- out : ndarray Array of booleans See Also -------- str.startswith |
169,248 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _clean_args(*args):
"""
Helper function for delegating arguments to Python string
functions.
Many of the Python string operations that have optional arguments
do not use 'None' to indicate a default value. In these cases,
we need to remove all None arguments, and those following them.
"""
newargs = []
for chk in args:
if chk is None:
break
newargs.append(chk)
return newargs
def asarray(obj, itemsize=None, unicode=None, order=None):
"""
Convert the input to a `chararray`, copying the data only if
necessary.
Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
when indexed
2) comparison operators automatically remove whitespace from the
end when comparing values
3) vectorized string operations are provided as methods
(e.g. `str.endswith`) and infix operators (e.g. ``+``, ``*``,``%``)
Parameters
----------
obj : array of str or unicode-like
itemsize : int, optional
`itemsize` is the number of characters per scalar in the
resulting array. If `itemsize` is None, and `obj` is an
object array or a Python list, the `itemsize` will be
automatically determined. If `itemsize` is provided and `obj`
is of type str or unicode, then the `obj` string will be
chunked into `itemsize` pieces.
unicode : bool, optional
When true, the resulting `chararray` can contain Unicode
characters, when false only 8-bit characters. If unicode is
None and `obj` is one of the following:
- a `chararray`,
- an ndarray of type `str` or 'unicode`
- a Python str or unicode object,
then the unicode setting of the output array will be
automatically determined.
order : {'C', 'F'}, optional
Specify the order of the array. If order is 'C' (default), then the
array will be in C-contiguous order (last-index varies the
fastest). If order is 'F', then the returned array
will be in Fortran-contiguous order (first-index varies the
fastest).
"""
return array(obj, itemsize, copy=False,
unicode=unicode, order=order)
The provided code snippet includes necessary dependencies for implementing the `strip` function. Write a Python function `def strip(a, chars=None)` to solve the following problem:
For each element in `a`, return a copy with the leading and trailing characters removed. Calls `str.strip` element-wise. Parameters ---------- a : array-like of str or unicode chars : str or unicode, optional The `chars` argument is a string specifying the set of characters to be removed. If omitted or None, the `chars` argument defaults to removing whitespace. The `chars` argument is not a prefix or suffix; rather, all combinations of its values are stripped. Returns ------- out : ndarray Output array of str or unicode, depending on input type See Also -------- str.strip Examples -------- >>> c = np.array(['aAaAaA', ' aA ', 'abBABba']) >>> c array(['aAaAaA', ' aA ', 'abBABba'], dtype='<U7') >>> np.char.strip(c) array(['aAaAaA', 'aA', 'abBABba'], dtype='<U7') >>> np.char.strip(c, 'a') # 'a' unstripped from c[1] because whitespace leads array(['AaAaA', ' aA ', 'bBABb'], dtype='<U7') >>> np.char.strip(c, 'A') # 'A' unstripped from c[1] because (unprinted) ws trails array(['aAaAa', ' aA ', 'abBABba'], dtype='<U7')
Here is the function:
def strip(a, chars=None):
"""
For each element in `a`, return a copy with the leading and
trailing characters removed.
Calls `str.strip` element-wise.
Parameters
----------
a : array-like of str or unicode
chars : str or unicode, optional
The `chars` argument is a string specifying the set of
characters to be removed. If omitted or None, the `chars`
argument defaults to removing whitespace. The `chars` argument
is not a prefix or suffix; rather, all combinations of its
values are stripped.
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See Also
--------
str.strip
Examples
--------
>>> c = np.array(['aAaAaA', ' aA ', 'abBABba'])
>>> c
array(['aAaAaA', ' aA ', 'abBABba'], dtype='<U7')
>>> np.char.strip(c)
array(['aAaAaA', 'aA', 'abBABba'], dtype='<U7')
>>> np.char.strip(c, 'a') # 'a' unstripped from c[1] because whitespace leads
array(['AaAaA', ' aA ', 'bBABb'], dtype='<U7')
>>> np.char.strip(c, 'A') # 'A' unstripped from c[1] because (unprinted) ws trails
array(['aAaAa', ' aA ', 'abBABba'], dtype='<U7')
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'strip', _clean_args(chars)) | For each element in `a`, return a copy with the leading and trailing characters removed. Calls `str.strip` element-wise. Parameters ---------- a : array-like of str or unicode chars : str or unicode, optional The `chars` argument is a string specifying the set of characters to be removed. If omitted or None, the `chars` argument defaults to removing whitespace. The `chars` argument is not a prefix or suffix; rather, all combinations of its values are stripped. Returns ------- out : ndarray Output array of str or unicode, depending on input type See Also -------- str.strip Examples -------- >>> c = np.array(['aAaAaA', ' aA ', 'abBABba']) >>> c array(['aAaAaA', ' aA ', 'abBABba'], dtype='<U7') >>> np.char.strip(c) array(['aAaAaA', 'aA', 'abBABba'], dtype='<U7') >>> np.char.strip(c, 'a') # 'a' unstripped from c[1] because whitespace leads array(['AaAaA', ' aA ', 'bBABb'], dtype='<U7') >>> np.char.strip(c, 'A') # 'A' unstripped from c[1] because (unprinted) ws trails array(['aAaAa', ' aA ', 'abBABba'], dtype='<U7') |
169,249 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def asarray(obj, itemsize=None, unicode=None, order=None):
"""
Convert the input to a `chararray`, copying the data only if
necessary.
Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
when indexed
2) comparison operators automatically remove whitespace from the
end when comparing values
3) vectorized string operations are provided as methods
(e.g. `str.endswith`) and infix operators (e.g. ``+``, ``*``,``%``)
Parameters
----------
obj : array of str or unicode-like
itemsize : int, optional
`itemsize` is the number of characters per scalar in the
resulting array. If `itemsize` is None, and `obj` is an
object array or a Python list, the `itemsize` will be
automatically determined. If `itemsize` is provided and `obj`
is of type str or unicode, then the `obj` string will be
chunked into `itemsize` pieces.
unicode : bool, optional
When true, the resulting `chararray` can contain Unicode
characters, when false only 8-bit characters. If unicode is
None and `obj` is one of the following:
- a `chararray`,
- an ndarray of type `str` or 'unicode`
- a Python str or unicode object,
then the unicode setting of the output array will be
automatically determined.
order : {'C', 'F'}, optional
Specify the order of the array. If order is 'C' (default), then the
array will be in C-contiguous order (last-index varies the
fastest). If order is 'F', then the returned array
will be in Fortran-contiguous order (first-index varies the
fastest).
"""
return array(obj, itemsize, copy=False,
unicode=unicode, order=order)
The provided code snippet includes necessary dependencies for implementing the `swapcase` function. Write a Python function `def swapcase(a)` to solve the following problem:
Return element-wise a copy of the string with uppercase characters converted to lowercase and vice versa. Calls `str.swapcase` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like, {str, unicode} Input array. Returns ------- out : ndarray, {str, unicode} Output array of str or unicode, depending on input type See Also -------- str.swapcase Examples -------- >>> c=np.array(['a1B c','1b Ca','b Ca1','cA1b'],'S5'); c array(['a1B c', '1b Ca', 'b Ca1', 'cA1b'], dtype='|S5') >>> np.char.swapcase(c) array(['A1b C', '1B cA', 'B cA1', 'Ca1B'], dtype='|S5')
Here is the function:
def swapcase(a):
"""
Return element-wise a copy of the string with
uppercase characters converted to lowercase and vice versa.
Calls `str.swapcase` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like, {str, unicode}
Input array.
Returns
-------
out : ndarray, {str, unicode}
Output array of str or unicode, depending on input type
See Also
--------
str.swapcase
Examples
--------
>>> c=np.array(['a1B c','1b Ca','b Ca1','cA1b'],'S5'); c
array(['a1B c', '1b Ca', 'b Ca1', 'cA1b'],
dtype='|S5')
>>> np.char.swapcase(c)
array(['A1b C', '1B cA', 'B cA1', 'Ca1B'],
dtype='|S5')
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'swapcase') | Return element-wise a copy of the string with uppercase characters converted to lowercase and vice versa. Calls `str.swapcase` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like, {str, unicode} Input array. Returns ------- out : ndarray, {str, unicode} Output array of str or unicode, depending on input type See Also -------- str.swapcase Examples -------- >>> c=np.array(['a1B c','1b Ca','b Ca1','cA1b'],'S5'); c array(['a1B c', '1b Ca', 'b Ca1', 'cA1b'], dtype='|S5') >>> np.char.swapcase(c) array(['A1b C', '1B cA', 'B cA1', 'Ca1B'], dtype='|S5') |
169,250 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def asarray(obj, itemsize=None, unicode=None, order=None):
"""
Convert the input to a `chararray`, copying the data only if
necessary.
Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
when indexed
2) comparison operators automatically remove whitespace from the
end when comparing values
3) vectorized string operations are provided as methods
(e.g. `str.endswith`) and infix operators (e.g. ``+``, ``*``,``%``)
Parameters
----------
obj : array of str or unicode-like
itemsize : int, optional
`itemsize` is the number of characters per scalar in the
resulting array. If `itemsize` is None, and `obj` is an
object array or a Python list, the `itemsize` will be
automatically determined. If `itemsize` is provided and `obj`
is of type str or unicode, then the `obj` string will be
chunked into `itemsize` pieces.
unicode : bool, optional
When true, the resulting `chararray` can contain Unicode
characters, when false only 8-bit characters. If unicode is
None and `obj` is one of the following:
- a `chararray`,
- an ndarray of type `str` or 'unicode`
- a Python str or unicode object,
then the unicode setting of the output array will be
automatically determined.
order : {'C', 'F'}, optional
Specify the order of the array. If order is 'C' (default), then the
array will be in C-contiguous order (last-index varies the
fastest). If order is 'F', then the returned array
will be in Fortran-contiguous order (first-index varies the
fastest).
"""
return array(obj, itemsize, copy=False,
unicode=unicode, order=order)
The provided code snippet includes necessary dependencies for implementing the `title` function. Write a Python function `def title(a)` to solve the following problem:
Return element-wise title cased version of string or unicode. Title case words start with uppercase characters, all remaining cased characters are lowercase. Calls `str.title` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like, {str, unicode} Input array. Returns ------- out : ndarray Output array of str or unicode, depending on input type See Also -------- str.title Examples -------- >>> c=np.array(['a1b c','1b ca','b ca1','ca1b'],'S5'); c array(['a1b c', '1b ca', 'b ca1', 'ca1b'], dtype='|S5') >>> np.char.title(c) array(['A1B C', '1B Ca', 'B Ca1', 'Ca1B'], dtype='|S5')
Here is the function:
def title(a):
"""
Return element-wise title cased version of string or unicode.
Title case words start with uppercase characters, all remaining cased
characters are lowercase.
Calls `str.title` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like, {str, unicode}
Input array.
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See Also
--------
str.title
Examples
--------
>>> c=np.array(['a1b c','1b ca','b ca1','ca1b'],'S5'); c
array(['a1b c', '1b ca', 'b ca1', 'ca1b'],
dtype='|S5')
>>> np.char.title(c)
array(['A1B C', '1B Ca', 'B Ca1', 'Ca1B'],
dtype='|S5')
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'title') | Return element-wise title cased version of string or unicode. Title case words start with uppercase characters, all remaining cased characters are lowercase. Calls `str.title` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like, {str, unicode} Input array. Returns ------- out : ndarray Output array of str or unicode, depending on input type See Also -------- str.title Examples -------- >>> c=np.array(['a1b c','1b ca','b ca1','ca1b'],'S5'); c array(['a1b c', '1b ca', 'b ca1', 'ca1b'], dtype='|S5') >>> np.char.title(c) array(['A1B C', '1B Ca', 'B Ca1', 'Ca1B'], dtype='|S5') |
169,251 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _translate_dispatcher(a, table, deletechars=None):
return (a,) | null |
169,252 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _clean_args(*args):
"""
Helper function for delegating arguments to Python string
functions.
Many of the Python string operations that have optional arguments
do not use 'None' to indicate a default value. In these cases,
we need to remove all None arguments, and those following them.
"""
newargs = []
for chk in args:
if chk is None:
break
newargs.append(chk)
return newargs
def asarray(obj, itemsize=None, unicode=None, order=None):
"""
Convert the input to a `chararray`, copying the data only if
necessary.
Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
when indexed
2) comparison operators automatically remove whitespace from the
end when comparing values
3) vectorized string operations are provided as methods
(e.g. `str.endswith`) and infix operators (e.g. ``+``, ``*``,``%``)
Parameters
----------
obj : array of str or unicode-like
itemsize : int, optional
`itemsize` is the number of characters per scalar in the
resulting array. If `itemsize` is None, and `obj` is an
object array or a Python list, the `itemsize` will be
automatically determined. If `itemsize` is provided and `obj`
is of type str or unicode, then the `obj` string will be
chunked into `itemsize` pieces.
unicode : bool, optional
When true, the resulting `chararray` can contain Unicode
characters, when false only 8-bit characters. If unicode is
None and `obj` is one of the following:
- a `chararray`,
- an ndarray of type `str` or 'unicode`
- a Python str or unicode object,
then the unicode setting of the output array will be
automatically determined.
order : {'C', 'F'}, optional
Specify the order of the array. If order is 'C' (default), then the
array will be in C-contiguous order (last-index varies the
fastest). If order is 'F', then the returned array
will be in Fortran-contiguous order (first-index varies the
fastest).
"""
return array(obj, itemsize, copy=False,
unicode=unicode, order=order)
The provided code snippet includes necessary dependencies for implementing the `translate` function. Write a Python function `def translate(a, table, deletechars=None)` to solve the following problem:
For each element in `a`, return a copy of the string where all characters occurring in the optional argument `deletechars` are removed, and the remaining characters have been mapped through the given translation table. Calls `str.translate` element-wise. Parameters ---------- a : array-like of str or unicode table : str of length 256 deletechars : str Returns ------- out : ndarray Output array of str or unicode, depending on input type See Also -------- str.translate
Here is the function:
def translate(a, table, deletechars=None):
"""
For each element in `a`, return a copy of the string where all
characters occurring in the optional argument `deletechars` are
removed, and the remaining characters have been mapped through the
given translation table.
Calls `str.translate` element-wise.
Parameters
----------
a : array-like of str or unicode
table : str of length 256
deletechars : str
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See Also
--------
str.translate
"""
a_arr = numpy.asarray(a)
if issubclass(a_arr.dtype.type, unicode_):
return _vec_string(
a_arr, a_arr.dtype, 'translate', (table,))
else:
return _vec_string(
a_arr, a_arr.dtype, 'translate', [table] + _clean_args(deletechars)) | For each element in `a`, return a copy of the string where all characters occurring in the optional argument `deletechars` are removed, and the remaining characters have been mapped through the given translation table. Calls `str.translate` element-wise. Parameters ---------- a : array-like of str or unicode table : str of length 256 deletechars : str Returns ------- out : ndarray Output array of str or unicode, depending on input type See Also -------- str.translate |
169,253 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def asarray(obj, itemsize=None, unicode=None, order=None):
"""
Convert the input to a `chararray`, copying the data only if
necessary.
Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
when indexed
2) comparison operators automatically remove whitespace from the
end when comparing values
3) vectorized string operations are provided as methods
(e.g. `str.endswith`) and infix operators (e.g. ``+``, ``*``,``%``)
Parameters
----------
obj : array of str or unicode-like
itemsize : int, optional
`itemsize` is the number of characters per scalar in the
resulting array. If `itemsize` is None, and `obj` is an
object array or a Python list, the `itemsize` will be
automatically determined. If `itemsize` is provided and `obj`
is of type str or unicode, then the `obj` string will be
chunked into `itemsize` pieces.
unicode : bool, optional
When true, the resulting `chararray` can contain Unicode
characters, when false only 8-bit characters. If unicode is
None and `obj` is one of the following:
- a `chararray`,
- an ndarray of type `str` or 'unicode`
- a Python str or unicode object,
then the unicode setting of the output array will be
automatically determined.
order : {'C', 'F'}, optional
Specify the order of the array. If order is 'C' (default), then the
array will be in C-contiguous order (last-index varies the
fastest). If order is 'F', then the returned array
will be in Fortran-contiguous order (first-index varies the
fastest).
"""
return array(obj, itemsize, copy=False,
unicode=unicode, order=order)
The provided code snippet includes necessary dependencies for implementing the `upper` function. Write a Python function `def upper(a)` to solve the following problem:
Return an array with the elements converted to uppercase. Calls `str.upper` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like, {str, unicode} Input array. Returns ------- out : ndarray, {str, unicode} Output array of str or unicode, depending on input type See Also -------- str.upper Examples -------- >>> c = np.array(['a1b c', '1bca', 'bca1']); c array(['a1b c', '1bca', 'bca1'], dtype='<U5') >>> np.char.upper(c) array(['A1B C', '1BCA', 'BCA1'], dtype='<U5')
Here is the function:
def upper(a):
"""
Return an array with the elements converted to uppercase.
Calls `str.upper` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like, {str, unicode}
Input array.
Returns
-------
out : ndarray, {str, unicode}
Output array of str or unicode, depending on input type
See Also
--------
str.upper
Examples
--------
>>> c = np.array(['a1b c', '1bca', 'bca1']); c
array(['a1b c', '1bca', 'bca1'], dtype='<U5')
>>> np.char.upper(c)
array(['A1B C', '1BCA', 'BCA1'], dtype='<U5')
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'upper') | Return an array with the elements converted to uppercase. Calls `str.upper` element-wise. For 8-bit strings, this method is locale-dependent. Parameters ---------- a : array_like, {str, unicode} Input array. Returns ------- out : ndarray, {str, unicode} Output array of str or unicode, depending on input type See Also -------- str.upper Examples -------- >>> c = np.array(['a1b c', '1bca', 'bca1']); c array(['a1b c', '1bca', 'bca1'], dtype='<U5') >>> np.char.upper(c) array(['A1B C', '1BCA', 'BCA1'], dtype='<U5') |
169,254 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _zfill_dispatcher(a, width):
return (a,) | null |
169,255 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def asarray(obj, itemsize=None, unicode=None, order=None):
"""
Convert the input to a `chararray`, copying the data only if
necessary.
Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
when indexed
2) comparison operators automatically remove whitespace from the
end when comparing values
3) vectorized string operations are provided as methods
(e.g. `str.endswith`) and infix operators (e.g. ``+``, ``*``,``%``)
Parameters
----------
obj : array of str or unicode-like
itemsize : int, optional
`itemsize` is the number of characters per scalar in the
resulting array. If `itemsize` is None, and `obj` is an
object array or a Python list, the `itemsize` will be
automatically determined. If `itemsize` is provided and `obj`
is of type str or unicode, then the `obj` string will be
chunked into `itemsize` pieces.
unicode : bool, optional
When true, the resulting `chararray` can contain Unicode
characters, when false only 8-bit characters. If unicode is
None and `obj` is one of the following:
- a `chararray`,
- an ndarray of type `str` or 'unicode`
- a Python str or unicode object,
then the unicode setting of the output array will be
automatically determined.
order : {'C', 'F'}, optional
Specify the order of the array. If order is 'C' (default), then the
array will be in C-contiguous order (last-index varies the
fastest). If order is 'F', then the returned array
will be in Fortran-contiguous order (first-index varies the
fastest).
"""
return array(obj, itemsize, copy=False,
unicode=unicode, order=order)
The provided code snippet includes necessary dependencies for implementing the `zfill` function. Write a Python function `def zfill(a, width)` to solve the following problem:
Return the numeric string left-filled with zeros Calls `str.zfill` element-wise. Parameters ---------- a : array_like, {str, unicode} Input array. width : int Width of string to left-fill elements in `a`. Returns ------- out : ndarray, {str, unicode} Output array of str or unicode, depending on input type See Also -------- str.zfill
Here is the function:
def zfill(a, width):
"""
Return the numeric string left-filled with zeros
Calls `str.zfill` element-wise.
Parameters
----------
a : array_like, {str, unicode}
Input array.
width : int
Width of string to left-fill elements in `a`.
Returns
-------
out : ndarray, {str, unicode}
Output array of str or unicode, depending on input type
See Also
--------
str.zfill
"""
a_arr = numpy.asarray(a)
width_arr = numpy.asarray(width)
size = int(numpy.max(width_arr.flat))
return _vec_string(
a_arr, (a_arr.dtype.type, size), 'zfill', (width_arr,)) | Return the numeric string left-filled with zeros Calls `str.zfill` element-wise. Parameters ---------- a : array_like, {str, unicode} Input array. width : int Width of string to left-fill elements in `a`. Returns ------- out : ndarray, {str, unicode} Output array of str or unicode, depending on input type See Also -------- str.zfill |
169,256 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _use_unicode(*args):
"""
Helper function for determining the output type of some string
operations.
For an operation on two ndarrays, if at least one is unicode, the
result should be unicode.
"""
for x in args:
if (isinstance(x, str) or
issubclass(numpy.asarray(x).dtype.type, unicode_)):
return unicode_
return string_
The provided code snippet includes necessary dependencies for implementing the `isnumeric` function. Write a Python function `def isnumeric(a)` to solve the following problem:
For each element, return True if there are only numeric characters in the element. Calls `unicode.isnumeric` element-wise. Numeric characters include digit characters, and all characters that have the Unicode numeric value property, e.g. ``U+2155, VULGAR FRACTION ONE FIFTH``. Parameters ---------- a : array_like, unicode Input array. Returns ------- out : ndarray, bool Array of booleans of same shape as `a`. See Also -------- unicode.isnumeric Examples -------- >>> np.char.isnumeric(['123', '123abc', '9.0', '1/4', 'VIII']) array([ True, False, False, False, False])
Here is the function:
def isnumeric(a):
"""
For each element, return True if there are only numeric
characters in the element.
Calls `unicode.isnumeric` element-wise.
Numeric characters include digit characters, and all characters
that have the Unicode numeric value property, e.g. ``U+2155,
VULGAR FRACTION ONE FIFTH``.
Parameters
----------
a : array_like, unicode
Input array.
Returns
-------
out : ndarray, bool
Array of booleans of same shape as `a`.
See Also
--------
unicode.isnumeric
Examples
--------
>>> np.char.isnumeric(['123', '123abc', '9.0', '1/4', 'VIII'])
array([ True, False, False, False, False])
"""
if _use_unicode(a) != unicode_:
raise TypeError("isnumeric is only available for Unicode strings and arrays")
return _vec_string(a, bool_, 'isnumeric') | For each element, return True if there are only numeric characters in the element. Calls `unicode.isnumeric` element-wise. Numeric characters include digit characters, and all characters that have the Unicode numeric value property, e.g. ``U+2155, VULGAR FRACTION ONE FIFTH``. Parameters ---------- a : array_like, unicode Input array. Returns ------- out : ndarray, bool Array of booleans of same shape as `a`. See Also -------- unicode.isnumeric Examples -------- >>> np.char.isnumeric(['123', '123abc', '9.0', '1/4', 'VIII']) array([ True, False, False, False, False]) |
169,257 | import functools
from .numerictypes import (
string_, unicode_, integer, int_, object_, bool_, character)
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.core.overrides import set_module
from numpy.core import overrides
from numpy.compat import asbytes
import numpy
def _use_unicode(*args):
"""
Helper function for determining the output type of some string
operations.
For an operation on two ndarrays, if at least one is unicode, the
result should be unicode.
"""
for x in args:
if (isinstance(x, str) or
issubclass(numpy.asarray(x).dtype.type, unicode_)):
return unicode_
return string_
The provided code snippet includes necessary dependencies for implementing the `isdecimal` function. Write a Python function `def isdecimal(a)` to solve the following problem:
For each element, return True if there are only decimal characters in the element. Calls `unicode.isdecimal` element-wise. Decimal characters include digit characters, and all characters that can be used to form decimal-radix numbers, e.g. ``U+0660, ARABIC-INDIC DIGIT ZERO``. Parameters ---------- a : array_like, unicode Input array. Returns ------- out : ndarray, bool Array of booleans identical in shape to `a`. See Also -------- unicode.isdecimal Examples -------- >>> np.char.isdecimal(['12345', '4.99', '123ABC', '']) array([ True, False, False, False])
Here is the function:
def isdecimal(a):
"""
For each element, return True if there are only decimal
characters in the element.
Calls `unicode.isdecimal` element-wise.
Decimal characters include digit characters, and all characters
that can be used to form decimal-radix numbers,
e.g. ``U+0660, ARABIC-INDIC DIGIT ZERO``.
Parameters
----------
a : array_like, unicode
Input array.
Returns
-------
out : ndarray, bool
Array of booleans identical in shape to `a`.
See Also
--------
unicode.isdecimal
Examples
--------
>>> np.char.isdecimal(['12345', '4.99', '123ABC', ''])
array([ True, False, False, False])
"""
if _use_unicode(a) != unicode_:
raise TypeError("isnumeric is only available for Unicode strings and arrays")
return _vec_string(a, bool_, 'isdecimal') | For each element, return True if there are only decimal characters in the element. Calls `unicode.isdecimal` element-wise. Decimal characters include digit characters, and all characters that can be used to form decimal-radix numbers, e.g. ``U+0660, ARABIC-INDIC DIGIT ZERO``. Parameters ---------- a : array_like, unicode Input array. Returns ------- out : ndarray, bool Array of booleans identical in shape to `a`. See Also -------- unicode.isdecimal Examples -------- >>> np.char.isdecimal(['12345', '4.99', '123ABC', '']) array([ True, False, False, False]) |
169,258 | import functools
import types
import warnings
import numpy as np
from . import multiarray as mu
from . import overrides
from . import umath as um
from . import numerictypes as nt
from .multiarray import asarray, array, asanyarray, concatenate
from . import _methods
def _take_dispatcher(a, indices, axis=None, out=None, mode=None):
return (a, out) | null |
169,259 | import functools
import types
import warnings
import numpy as np
from . import multiarray as mu
from . import overrides
from . import umath as um
from . import numerictypes as nt
from .multiarray import asarray, array, asanyarray, concatenate
from . import _methods
def _reshape_dispatcher(a, newshape, order=None):
return (a,) | null |
169,260 | import functools
import types
import warnings
import numpy as np
from . import multiarray as mu
from . import overrides
from . import umath as um
from . import numerictypes as nt
from .multiarray import asarray, array, asanyarray, concatenate
from . import _methods
def _choose_dispatcher(a, choices, out=None, mode=None):
yield a
yield from choices
yield out | null |
169,261 | import functools
import types
import warnings
import numpy as np
from . import multiarray as mu
from . import overrides
from . import umath as um
from . import numerictypes as nt
from .multiarray import asarray, array, asanyarray, concatenate
from . import _methods
def _wrapfunc(obj, method, *args, **kwds):
bound = getattr(obj, method, None)
if bound is None:
return _wrapit(obj, method, *args, **kwds)
try:
return bound(*args, **kwds)
except TypeError:
# A TypeError occurs if the object does have such a method in its
# class, but its signature is not identical to that of NumPy's. This
# situation has occurred in the case of a downstream library like
# 'pandas'.
#
# Call _wrapit from within the except clause to ensure a potential
# exception has a traceback chain.
return _wrapit(obj, method, *args, **kwds)
The provided code snippet includes necessary dependencies for implementing the `choose` function. Write a Python function `def choose(a, choices, out=None, mode='raise')` to solve the following problem:
Construct an array from an index array and a list of arrays to choose from. First of all, if confused or uncertain, definitely look at the Examples - in its full generality, this function is less simple than it might seem from the following code description (below ndi = `numpy.lib.index_tricks`): ``np.choose(a,c) == np.array([c[a[I]][I] for I in ndi.ndindex(a.shape)])``. But this omits some subtleties. Here is a fully general summary: Given an "index" array (`a`) of integers and a sequence of ``n`` arrays (`choices`), `a` and each choice array are first broadcast, as necessary, to arrays of a common shape; calling these *Ba* and *Bchoices[i], i = 0,...,n-1* we have that, necessarily, ``Ba.shape == Bchoices[i].shape`` for each ``i``. Then, a new array with shape ``Ba.shape`` is created as follows: * if ``mode='raise'`` (the default), then, first of all, each element of ``a`` (and thus ``Ba``) must be in the range ``[0, n-1]``; now, suppose that ``i`` (in that range) is the value at the ``(j0, j1, ..., jm)`` position in ``Ba`` - then the value at the same position in the new array is the value in ``Bchoices[i]`` at that same position; * if ``mode='wrap'``, values in `a` (and thus `Ba`) may be any (signed) integer; modular arithmetic is used to map integers outside the range `[0, n-1]` back into that range; and then the new array is constructed as above; * if ``mode='clip'``, values in `a` (and thus ``Ba``) may be any (signed) integer; negative integers are mapped to 0; values greater than ``n-1`` are mapped to ``n-1``; and then the new array is constructed as above. Parameters ---------- a : int array This array must contain integers in ``[0, n-1]``, where ``n`` is the number of choices, unless ``mode=wrap`` or ``mode=clip``, in which cases any integers are permissible. choices : sequence of arrays Choice arrays. `a` and all of the choices must be broadcastable to the same shape. If `choices` is itself an array (not recommended), then its outermost dimension (i.e., the one corresponding to ``choices.shape[0]``) is taken as defining the "sequence". out : array, optional If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype. Note that `out` is always buffered if ``mode='raise'``; use other modes for better performance. mode : {'raise' (default), 'wrap', 'clip'}, optional Specifies how indices outside ``[0, n-1]`` will be treated: * 'raise' : an exception is raised * 'wrap' : value becomes value mod ``n`` * 'clip' : values < 0 are mapped to 0, values > n-1 are mapped to n-1 Returns ------- merged_array : array The merged result. Raises ------ ValueError: shape mismatch If `a` and each choice array are not all broadcastable to the same shape. See Also -------- ndarray.choose : equivalent method numpy.take_along_axis : Preferable if `choices` is an array Notes ----- To reduce the chance of misinterpretation, even though the following "abuse" is nominally supported, `choices` should neither be, nor be thought of as, a single array, i.e., the outermost sequence-like container should be either a list or a tuple. Examples -------- >>> choices = [[0, 1, 2, 3], [10, 11, 12, 13], ... [20, 21, 22, 23], [30, 31, 32, 33]] >>> np.choose([2, 3, 1, 0], choices ... # the first element of the result will be the first element of the ... # third (2+1) "array" in choices, namely, 20; the second element ... # will be the second element of the fourth (3+1) choice array, i.e., ... # 31, etc. ... ) array([20, 31, 12, 3]) >>> np.choose([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1) array([20, 31, 12, 3]) >>> # because there are 4 choice arrays >>> np.choose([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4) array([20, 1, 12, 3]) >>> # i.e., 0 A couple examples illustrating how choose broadcasts: >>> a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]] >>> choices = [-10, 10] >>> np.choose(a, choices) array([[ 10, -10, 10], [-10, 10, -10], [ 10, -10, 10]]) >>> # With thanks to Anne Archibald >>> a = np.array([0, 1]).reshape((2,1,1)) >>> c1 = np.array([1, 2, 3]).reshape((1,3,1)) >>> c2 = np.array([-1, -2, -3, -4, -5]).reshape((1,1,5)) >>> np.choose(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2 array([[[ 1, 1, 1, 1, 1], [ 2, 2, 2, 2, 2], [ 3, 3, 3, 3, 3]], [[-1, -2, -3, -4, -5], [-1, -2, -3, -4, -5], [-1, -2, -3, -4, -5]]])
Here is the function:
def choose(a, choices, out=None, mode='raise'):
"""
Construct an array from an index array and a list of arrays to choose from.
First of all, if confused or uncertain, definitely look at the Examples -
in its full generality, this function is less simple than it might
seem from the following code description (below ndi =
`numpy.lib.index_tricks`):
``np.choose(a,c) == np.array([c[a[I]][I] for I in ndi.ndindex(a.shape)])``.
But this omits some subtleties. Here is a fully general summary:
Given an "index" array (`a`) of integers and a sequence of ``n`` arrays
(`choices`), `a` and each choice array are first broadcast, as necessary,
to arrays of a common shape; calling these *Ba* and *Bchoices[i], i =
0,...,n-1* we have that, necessarily, ``Ba.shape == Bchoices[i].shape``
for each ``i``. Then, a new array with shape ``Ba.shape`` is created as
follows:
* if ``mode='raise'`` (the default), then, first of all, each element of
``a`` (and thus ``Ba``) must be in the range ``[0, n-1]``; now, suppose
that ``i`` (in that range) is the value at the ``(j0, j1, ..., jm)``
position in ``Ba`` - then the value at the same position in the new array
is the value in ``Bchoices[i]`` at that same position;
* if ``mode='wrap'``, values in `a` (and thus `Ba`) may be any (signed)
integer; modular arithmetic is used to map integers outside the range
`[0, n-1]` back into that range; and then the new array is constructed
as above;
* if ``mode='clip'``, values in `a` (and thus ``Ba``) may be any (signed)
integer; negative integers are mapped to 0; values greater than ``n-1``
are mapped to ``n-1``; and then the new array is constructed as above.
Parameters
----------
a : int array
This array must contain integers in ``[0, n-1]``, where ``n`` is the
number of choices, unless ``mode=wrap`` or ``mode=clip``, in which
cases any integers are permissible.
choices : sequence of arrays
Choice arrays. `a` and all of the choices must be broadcastable to the
same shape. If `choices` is itself an array (not recommended), then
its outermost dimension (i.e., the one corresponding to
``choices.shape[0]``) is taken as defining the "sequence".
out : array, optional
If provided, the result will be inserted into this array. It should
be of the appropriate shape and dtype. Note that `out` is always
buffered if ``mode='raise'``; use other modes for better performance.
mode : {'raise' (default), 'wrap', 'clip'}, optional
Specifies how indices outside ``[0, n-1]`` will be treated:
* 'raise' : an exception is raised
* 'wrap' : value becomes value mod ``n``
* 'clip' : values < 0 are mapped to 0, values > n-1 are mapped to n-1
Returns
-------
merged_array : array
The merged result.
Raises
------
ValueError: shape mismatch
If `a` and each choice array are not all broadcastable to the same
shape.
See Also
--------
ndarray.choose : equivalent method
numpy.take_along_axis : Preferable if `choices` is an array
Notes
-----
To reduce the chance of misinterpretation, even though the following
"abuse" is nominally supported, `choices` should neither be, nor be
thought of as, a single array, i.e., the outermost sequence-like container
should be either a list or a tuple.
Examples
--------
>>> choices = [[0, 1, 2, 3], [10, 11, 12, 13],
... [20, 21, 22, 23], [30, 31, 32, 33]]
>>> np.choose([2, 3, 1, 0], choices
... # the first element of the result will be the first element of the
... # third (2+1) "array" in choices, namely, 20; the second element
... # will be the second element of the fourth (3+1) choice array, i.e.,
... # 31, etc.
... )
array([20, 31, 12, 3])
>>> np.choose([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1)
array([20, 31, 12, 3])
>>> # because there are 4 choice arrays
>>> np.choose([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4)
array([20, 1, 12, 3])
>>> # i.e., 0
A couple examples illustrating how choose broadcasts:
>>> a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]
>>> choices = [-10, 10]
>>> np.choose(a, choices)
array([[ 10, -10, 10],
[-10, 10, -10],
[ 10, -10, 10]])
>>> # With thanks to Anne Archibald
>>> a = np.array([0, 1]).reshape((2,1,1))
>>> c1 = np.array([1, 2, 3]).reshape((1,3,1))
>>> c2 = np.array([-1, -2, -3, -4, -5]).reshape((1,1,5))
>>> np.choose(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2
array([[[ 1, 1, 1, 1, 1],
[ 2, 2, 2, 2, 2],
[ 3, 3, 3, 3, 3]],
[[-1, -2, -3, -4, -5],
[-1, -2, -3, -4, -5],
[-1, -2, -3, -4, -5]]])
"""
return _wrapfunc(a, 'choose', choices, out=out, mode=mode) | Construct an array from an index array and a list of arrays to choose from. First of all, if confused or uncertain, definitely look at the Examples - in its full generality, this function is less simple than it might seem from the following code description (below ndi = `numpy.lib.index_tricks`): ``np.choose(a,c) == np.array([c[a[I]][I] for I in ndi.ndindex(a.shape)])``. But this omits some subtleties. Here is a fully general summary: Given an "index" array (`a`) of integers and a sequence of ``n`` arrays (`choices`), `a` and each choice array are first broadcast, as necessary, to arrays of a common shape; calling these *Ba* and *Bchoices[i], i = 0,...,n-1* we have that, necessarily, ``Ba.shape == Bchoices[i].shape`` for each ``i``. Then, a new array with shape ``Ba.shape`` is created as follows: * if ``mode='raise'`` (the default), then, first of all, each element of ``a`` (and thus ``Ba``) must be in the range ``[0, n-1]``; now, suppose that ``i`` (in that range) is the value at the ``(j0, j1, ..., jm)`` position in ``Ba`` - then the value at the same position in the new array is the value in ``Bchoices[i]`` at that same position; * if ``mode='wrap'``, values in `a` (and thus `Ba`) may be any (signed) integer; modular arithmetic is used to map integers outside the range `[0, n-1]` back into that range; and then the new array is constructed as above; * if ``mode='clip'``, values in `a` (and thus ``Ba``) may be any (signed) integer; negative integers are mapped to 0; values greater than ``n-1`` are mapped to ``n-1``; and then the new array is constructed as above. Parameters ---------- a : int array This array must contain integers in ``[0, n-1]``, where ``n`` is the number of choices, unless ``mode=wrap`` or ``mode=clip``, in which cases any integers are permissible. choices : sequence of arrays Choice arrays. `a` and all of the choices must be broadcastable to the same shape. If `choices` is itself an array (not recommended), then its outermost dimension (i.e., the one corresponding to ``choices.shape[0]``) is taken as defining the "sequence". out : array, optional If provided, the result will be inserted into this array. It should be of the appropriate shape and dtype. Note that `out` is always buffered if ``mode='raise'``; use other modes for better performance. mode : {'raise' (default), 'wrap', 'clip'}, optional Specifies how indices outside ``[0, n-1]`` will be treated: * 'raise' : an exception is raised * 'wrap' : value becomes value mod ``n`` * 'clip' : values < 0 are mapped to 0, values > n-1 are mapped to n-1 Returns ------- merged_array : array The merged result. Raises ------ ValueError: shape mismatch If `a` and each choice array are not all broadcastable to the same shape. See Also -------- ndarray.choose : equivalent method numpy.take_along_axis : Preferable if `choices` is an array Notes ----- To reduce the chance of misinterpretation, even though the following "abuse" is nominally supported, `choices` should neither be, nor be thought of as, a single array, i.e., the outermost sequence-like container should be either a list or a tuple. Examples -------- >>> choices = [[0, 1, 2, 3], [10, 11, 12, 13], ... [20, 21, 22, 23], [30, 31, 32, 33]] >>> np.choose([2, 3, 1, 0], choices ... # the first element of the result will be the first element of the ... # third (2+1) "array" in choices, namely, 20; the second element ... # will be the second element of the fourth (3+1) choice array, i.e., ... # 31, etc. ... ) array([20, 31, 12, 3]) >>> np.choose([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1) array([20, 31, 12, 3]) >>> # because there are 4 choice arrays >>> np.choose([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4) array([20, 1, 12, 3]) >>> # i.e., 0 A couple examples illustrating how choose broadcasts: >>> a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]] >>> choices = [-10, 10] >>> np.choose(a, choices) array([[ 10, -10, 10], [-10, 10, -10], [ 10, -10, 10]]) >>> # With thanks to Anne Archibald >>> a = np.array([0, 1]).reshape((2,1,1)) >>> c1 = np.array([1, 2, 3]).reshape((1,3,1)) >>> c2 = np.array([-1, -2, -3, -4, -5]).reshape((1,1,5)) >>> np.choose(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2 array([[[ 1, 1, 1, 1, 1], [ 2, 2, 2, 2, 2], [ 3, 3, 3, 3, 3]], [[-1, -2, -3, -4, -5], [-1, -2, -3, -4, -5], [-1, -2, -3, -4, -5]]]) |
169,262 | import functools
import types
import warnings
import numpy as np
from . import multiarray as mu
from . import overrides
from . import umath as um
from . import numerictypes as nt
from .multiarray import asarray, array, asanyarray, concatenate
from . import _methods
def _repeat_dispatcher(a, repeats, axis=None):
return (a,) | null |
169,263 | import functools
import types
import warnings
import numpy as np
from . import multiarray as mu
from . import overrides
from . import umath as um
from . import numerictypes as nt
from .multiarray import asarray, array, asanyarray, concatenate
from . import _methods
def _put_dispatcher(a, ind, v, mode=None):
return (a, ind, v) | null |
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