id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
168,731 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Rotate an array by 90 degrees in the plane specified by axes. Rotation direction is from the first towards the second axis. Parameters ---------- m : array_like Array of two or more dimensions. k : integer Number of times the array is rotated by 90 degrees. axes : (2,) array_like The array is rotated in the plane defin... |
168,732 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,733 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,734 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Convert the input to an array, checking for NaNs or Infs. Parameters ---------- a : array_like Input data, in any form that can be converted to an array. This includes lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays. Success requires no NaNs or Infs. dtype : data-type, optional By default... |
168,735 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,736 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,737 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Return an array drawn from elements in choicelist, depending on conditions. Parameters ---------- condlist : list of bool ndarrays The list of conditions which determine from which array in `choicelist` the output elements are taken. When multiple conditions are satisfied, the first one encountered in `condlist` is use... |
168,738 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,739 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,740 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Return the gradient of an N-dimensional array. The gradient is computed using second order accurate central differences in the interior points and either first or second order accurate one-sides (forward or backwards) differences at the boundaries. The returned gradient hence has the same shape as the input array. Para... |
168,741 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,742 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,743 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | One-dimensional linear interpolation for monotonically increasing sample points. Returns the one-dimensional piecewise linear interpolant to a function with given discrete data points (`xp`, `fp`), evaluated at `x`. Parameters ---------- x : array_like The x-coordinates at which to evaluate the interpolated values. xp ... |
168,744 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,745 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Return the angle of the complex argument. Parameters ---------- z : array_like A complex number or sequence of complex numbers. deg : bool, optional Return angle in degrees if True, radians if False (default). Returns ------- angle : ndarray or scalar The counterclockwise angle from the positive real axis on the comple... |
168,746 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,747 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | r""" Unwrap by taking the complement of large deltas with respect to the period. This unwraps a signal `p` by changing elements which have an absolute difference from their predecessor of more than ``max(discont, period/2)`` to their `period`-complementary values. For the default case where `period` is :math:`2\pi` and... |
168,748 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,749 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Sort a complex array using the real part first, then the imaginary part. Parameters ---------- a : array_like Input array Returns ------- out : complex ndarray Always returns a sorted complex array. Examples -------- >>> np.sort_complex([5, 3, 6, 2, 1]) array([1.+0.j, 2.+0.j, 3.+0.j, 5.+0.j, 6.+0.j]) >>> np.sort_comple... |
168,750 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,751 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Trim the leading and/or trailing zeros from a 1-D array or sequence. Parameters ---------- filt : 1-D array or sequence Input array. trim : str, optional A string with 'f' representing trim from front and 'b' to trim from back. Default is 'fb', trim zeros from both front and back of the array. Returns ------- trimmed :... |
168,752 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,753 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Return the elements of an array that satisfy some condition. This is equivalent to ``np.compress(ravel(condition), ravel(arr))``. If `condition` is boolean ``np.extract`` is equivalent to ``arr[condition]``. Note that `place` does the exact opposite of `extract`. Parameters ---------- condition : array_like An array wh... |
168,754 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,755 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Change elements of an array based on conditional and input values. Similar to ``np.copyto(arr, vals, where=mask)``, the difference is that `place` uses the first N elements of `vals`, where N is the number of True values in `mask`, while `copyto` uses the elements where `mask` is True. Note that `extract` does the exac... |
168,756 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Display a message on a device. Parameters ---------- mesg : str Message to display. device : object Device to write message. If None, defaults to ``sys.stdout`` which is very similar to ``print``. `device` needs to have ``write()`` and ``flush()`` methods. linefeed : bool, optional Option whether to print a line feed o... |
168,757 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Parse string signatures for a generalized universal function. Arguments --------- signature : string Generalized universal function signature, e.g., ``(m,n),(n,p)->(m,p)`` for ``np.matmul``. Returns ------- Tuple of input and output core dimensions parsed from the signature, each of the form List[Tuple[str, ...]]. |
168,758 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Parse broadcast and core dimensions for vectorize with a signature. Arguments --------- args : Tuple[ndarray, ...] Tuple of input arguments to examine. input_core_dims : List[Tuple[str, ...]] List of core dimensions corresponding to each input. Returns ------- broadcast_shape : Tuple[int, ...] Common shape to broadcast... |
168,759 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Helper for creating output arrays in vectorize. |
168,760 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,761 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,762 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Return Pearson product-moment correlation coefficients. Please refer to the documentation for `cov` for more detail. The relationship between the correlation coefficient matrix, `R`, and the covariance matrix, `C`, is .. math:: R_{ij} = \\frac{ C_{ij} } { \\sqrt{ C_{ii} C_{jj} } } The values of `R` are between -1 and 1... |
168,763 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Return the Blackman window. The Blackman window is a taper formed by using the first three terms of a summation of cosines. It was designed to have close to the minimal leakage possible. It is close to optimal, only slightly worse than a Kaiser window. Parameters ---------- M : int Number of points in the output window... |
168,764 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Return the Bartlett window. The Bartlett window is very similar to a triangular window, except that the end points are at zero. It is often used in signal processing for tapering a signal, without generating too much ripple in the frequency domain. Parameters ---------- M : int Number of points in the output window. If... |
168,765 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Return the Hanning window. The Hanning window is a taper formed by using a weighted cosine. Parameters ---------- M : int Number of points in the output window. If zero or less, an empty array is returned. Returns ------- out : ndarray, shape(M,) The window, with the maximum value normalized to one (the value one appea... |
168,766 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Return the Hamming window. The Hamming window is a taper formed by using a weighted cosine. Parameters ---------- M : int Number of points in the output window. If zero or less, an empty array is returned. Returns ------- out : ndarray The window, with the maximum value normalized to one (the value one appears only if ... |
168,767 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,768 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Return the Kaiser window. The Kaiser window is a taper formed by using a Bessel function. Parameters ---------- M : int Number of points in the output window. If zero or less, an empty array is returned. beta : float Shape parameter for window. Returns ------- out : array The window, with the maximum value normalized t... |
168,769 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,770 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | r""" Return the normalized sinc function. The sinc function is equal to :math:`\sin(\pi x)/(\pi x)` for any argument :math:`x\ne 0`. ``sinc(0)`` takes the limit value 1, making ``sinc`` not only everywhere continuous but also infinitely differentiable. .. note:: Note the normalization factor of ``pi`` used in the defin... |
168,771 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,772 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Return a copy of an array sorted along the first axis. .. deprecated:: 1.24 msort is deprecated, use ``np.sort(a, axis=0)`` instead. Parameters ---------- a : array_like Array to be sorted. Returns ------- sorted_array : ndarray Array of the same type and shape as `a`. See Also -------- sort Notes ----- ``np.msort(a)``... |
168,773 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,774 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,775 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Compute the q-th percentile of the data along the specified axis. Returns the q-th percentile(s) of the array elements. Parameters ---------- a : array_like Input array or object that can be converted to an array. q : array_like of float Percentile or sequence of percentiles to compute, which must be between 0 and 100 ... |
168,776 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,777 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Compute the q-th quantile of the data along the specified axis. .. versionadded:: 1.15.0 Parameters ---------- a : array_like Input array or object that can be converted to an array. q : array_like of float Quantile or sequence of quantiles to compute, which must be between 0 and 1 inclusive. axis : {int, tuple of int,... |
168,778 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Compute the floating point indexes of an array for the linear interpolation of quantiles. n : array_like The sample sizes. quantiles : array_like The quantiles values. alpha : float A constant used to correct the index computed. beta : float A constant used to correct the index computed. alpha and beta values depend on... |
168,779 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,780 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,781 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,782 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | r""" Integrate along the given axis using the composite trapezoidal rule. If `x` is provided, the integration happens in sequence along its elements - they are not sorted. Integrate `y` (`x`) along each 1d slice on the given axis, compute :math:`\int y(x) dx`. When `x` is specified, this integrates along the parametric... |
168,783 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,784 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Return coordinate matrices from coordinate vectors. Make N-D coordinate arrays for vectorized evaluations of N-D scalar/vector fields over N-D grids, given one-dimensional coordinate arrays x1, x2,..., xn. .. versionchanged:: 1.9 1-D and 0-D cases are allowed. Parameters ---------- x1, x2,..., xn : array_like 1-D array... |
168,785 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,786 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Return a new array with sub-arrays along an axis deleted. For a one dimensional array, this returns those entries not returned by `arr[obj]`. Parameters ---------- arr : array_like Input array. obj : slice, int or array of ints Indicate indices of sub-arrays to remove along the specified axis. .. versionchanged:: 1.19.... |
168,787 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,788 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,789 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | null |
168,790 | import collections.abc
import functools
import re
import sys
import warnings
import numpy as np
import numpy.core.numeric as _nx
from numpy.core import transpose
from numpy.core.numeric import (
ones, zeros_like, arange, concatenate, array, asarray, asanyarray, empty,
ndarray, take, dot, where, intp, integer, i... | Return the indices of the bins to which each value in input array belongs. ========= ============= ============================ `right` order of bins returned index `i` satisfies ========= ============= ============================ ``False`` increasing ``bins[i-1] <= x < bins[i]`` ``True`` increasing ``bins[i-1] < x <=... |
168,791 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | null |
168,792 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | Take values from the input array by matching 1d index and data slices. This iterates over matching 1d slices oriented along the specified axis in the index and data arrays, and uses the former to look up values in the latter. These slices can be different lengths. Functions returning an index along an axis, like `argso... |
168,793 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | null |
168,794 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | Put values into the destination array by matching 1d index and data slices. This iterates over matching 1d slices oriented along the specified axis in the index and data arrays, and uses the former to place values into the latter. These slices can be different lengths. Functions returning an index along an axis, like `... |
168,795 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | null |
168,796 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | Apply a function to 1-D slices along the given axis. Execute `func1d(a, *args, **kwargs)` where `func1d` operates on 1-D arrays and `a` is a 1-D slice of `arr` along `axis`. This is equivalent to (but faster than) the following use of `ndindex` and `s_`, which sets each of ``ii``, ``jj``, and ``kk`` to a tuple of indic... |
168,797 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | null |
168,798 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | Apply a function repeatedly over multiple axes. `func` is called as `res = func(a, axis)`, where `axis` is the first element of `axes`. The result `res` of the function call must have either the same dimensions as `a` or one less dimension. If `res` has one less dimension than `a`, a dimension is inserted before `axis`... |
168,799 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | null |
168,800 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | null |
168,801 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | Stack 1-D arrays as columns into a 2-D array. Take a sequence of 1-D arrays and stack them as columns to make a single 2-D array. 2-D arrays are stacked as-is, just like with `hstack`. 1-D arrays are turned into 2-D columns first. Parameters ---------- tup : sequence of 1-D or 2-D arrays. Arrays to stack. All of them m... |
168,802 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | null |
168,803 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | Stack arrays in sequence depth wise (along third axis). This is equivalent to concatenation along the third axis after 2-D arrays of shape `(M,N)` have been reshaped to `(M,N,1)` and 1-D arrays of shape `(N,)` have been reshaped to `(1,N,1)`. Rebuilds arrays divided by `dsplit`. This function makes most sense for array... |
168,804 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | null |
168,805 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | null |
168,806 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | null |
168,807 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | null |
168,808 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | Split an array into multiple sub-arrays horizontally (column-wise). Please refer to the `split` documentation. `hsplit` is equivalent to `split` with ``axis=1``, the array is always split along the second axis except for 1-D arrays, where it is split at ``axis=0``. See Also -------- split : Split an array into multiple... |
168,809 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | Split an array into multiple sub-arrays vertically (row-wise). Please refer to the ``split`` documentation. ``vsplit`` is equivalent to ``split`` with `axis=0` (default), the array is always split along the first axis regardless of the array dimension. See Also -------- split : Split an array into multiple sub-arrays o... |
168,810 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | Split array into multiple sub-arrays along the 3rd axis (depth). Please refer to the `split` documentation. `dsplit` is equivalent to `split` with ``axis=2``, the array is always split along the third axis provided the array dimension is greater than or equal to 3. See Also -------- split : Split an array into multiple... |
168,811 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | Find the wrapper for the array with the highest priority. In case of ties, leftmost wins. If no wrapper is found, return None |
168,812 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | Find the wrapper for the array with the highest priority. In case of ties, leftmost wins. If no wrapper is found, return None |
168,813 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | null |
168,814 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | Kronecker product of two arrays. Computes the Kronecker product, a composite array made of blocks of the second array scaled by the first. Parameters ---------- a, b : array_like Returns ------- out : ndarray See Also -------- outer : The outer product Notes ----- The function assumes that the number of dimensions of `... |
168,815 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | null |
168,816 | import functools
import numpy.core.numeric as _nx
from numpy.core.numeric import (
asarray, zeros, outer, concatenate, array, asanyarray
)
from numpy.core.fromnumeric import reshape, transpose
from numpy.core.multiarray import normalize_axis_index
from numpy.core import overrides
from numpy.core import vstack, ... | Construct an array by repeating A the number of times given by reps. If `reps` has length ``d``, the result will have dimension of ``max(d, A.ndim)``. If ``A.ndim < d``, `A` is promoted to be d-dimensional by prepending new axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication, or shape (1, 1, 3) for 3-... |
168,817 | import functools
import re
import warnings
import numpy.core.numeric as NX
from numpy.core import (isscalar, abs, finfo, atleast_1d, hstack, dot, array,
ones)
from numpy.core import overrides
from numpy.core.overrides import set_module
from numpy.lib.twodim_base import diag, vander
from numpy.li... | null |
168,818 | import functools
import re
import warnings
import numpy.core.numeric as NX
from numpy.core import (isscalar, abs, finfo, atleast_1d, hstack, dot, array,
ones)
from numpy.core import overrides
from numpy.core.overrides import set_module
from numpy.lib.twodim_base import diag, vander
from numpy.li... | Find the coefficients of a polynomial with the given sequence of roots. .. note:: This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in `numpy.polynomial` is preferred. A summary of the differences can be found in the :doc:`transition guide </reference/routines.polynomials>`. R... |
168,819 | import functools
import re
import warnings
import numpy.core.numeric as NX
from numpy.core import (isscalar, abs, finfo, atleast_1d, hstack, dot, array,
ones)
from numpy.core import overrides
from numpy.core.overrides import set_module
from numpy.lib.twodim_base import diag, vander
from numpy.li... | null |
168,820 | import functools
import re
import warnings
import numpy.core.numeric as NX
from numpy.core import (isscalar, abs, finfo, atleast_1d, hstack, dot, array,
ones)
from numpy.core import overrides
from numpy.core.overrides import set_module
from numpy.lib.twodim_base import diag, vander
from numpy.li... | null |
168,821 | import functools
import re
import warnings
import numpy.core.numeric as NX
from numpy.core import (isscalar, abs, finfo, atleast_1d, hstack, dot, array,
ones)
from numpy.core import overrides
from numpy.core.overrides import set_module
from numpy.lib.twodim_base import diag, vander
from numpy.li... | Return an antiderivative (indefinite integral) of a polynomial. .. note:: This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in `numpy.polynomial` is preferred. A summary of the differences can be found in the :doc:`transition guide </reference/routines.polynomials>`. The retur... |
168,822 | import functools
import re
import warnings
import numpy.core.numeric as NX
from numpy.core import (isscalar, abs, finfo, atleast_1d, hstack, dot, array,
ones)
from numpy.core import overrides
from numpy.core.overrides import set_module
from numpy.lib.twodim_base import diag, vander
from numpy.li... | null |
168,823 | import functools
import re
import warnings
import numpy.core.numeric as NX
from numpy.core import (isscalar, abs, finfo, atleast_1d, hstack, dot, array,
ones)
from numpy.core import overrides
from numpy.core.overrides import set_module
from numpy.lib.twodim_base import diag, vander
from numpy.li... | Return the derivative of the specified order of a polynomial. .. note:: This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in `numpy.polynomial` is preferred. A summary of the differences can be found in the :doc:`transition guide </reference/routines.polynomials>`. Parameters ... |
168,824 | import functools
import re
import warnings
import numpy.core.numeric as NX
from numpy.core import (isscalar, abs, finfo, atleast_1d, hstack, dot, array,
ones)
from numpy.core import overrides
from numpy.core.overrides import set_module
from numpy.lib.twodim_base import diag, vander
from numpy.li... | null |
168,825 | import functools
import re
import warnings
import numpy.core.numeric as NX
from numpy.core import (isscalar, abs, finfo, atleast_1d, hstack, dot, array,
ones)
from numpy.core import overrides
from numpy.core.overrides import set_module
from numpy.lib.twodim_base import diag, vander
from numpy.li... | Least squares polynomial fit. .. note:: This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in `numpy.polynomial` is preferred. A summary of the differences can be found in the :doc:`transition guide </reference/routines.polynomials>`. Fit a polynomial ``p(x) = p[0] * x**deg + .... |
168,826 | import functools
import re
import warnings
import numpy.core.numeric as NX
from numpy.core import (isscalar, abs, finfo, atleast_1d, hstack, dot, array,
ones)
from numpy.core import overrides
from numpy.core.overrides import set_module
from numpy.lib.twodim_base import diag, vander
from numpy.li... | null |
168,827 | import functools
import re
import warnings
import numpy.core.numeric as NX
from numpy.core import (isscalar, abs, finfo, atleast_1d, hstack, dot, array,
ones)
from numpy.core import overrides
from numpy.core.overrides import set_module
from numpy.lib.twodim_base import diag, vander
from numpy.li... | Evaluate a polynomial at specific values. .. note:: This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in `numpy.polynomial` is preferred. A summary of the differences can be found in the :doc:`transition guide </reference/routines.polynomials>`. If `p` is of length N, this fun... |
168,828 | import functools
import re
import warnings
import numpy.core.numeric as NX
from numpy.core import (isscalar, abs, finfo, atleast_1d, hstack, dot, array,
ones)
from numpy.core import overrides
from numpy.core.overrides import set_module
from numpy.lib.twodim_base import diag, vander
from numpy.li... | null |
168,829 | import functools
import re
import warnings
import numpy.core.numeric as NX
from numpy.core import (isscalar, abs, finfo, atleast_1d, hstack, dot, array,
ones)
from numpy.core import overrides
from numpy.core.overrides import set_module
from numpy.lib.twodim_base import diag, vander
from numpy.li... | Find the sum of two polynomials. .. note:: This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in `numpy.polynomial` is preferred. A summary of the differences can be found in the :doc:`transition guide </reference/routines.polynomials>`. Returns the polynomial resulting from th... |
168,830 | import functools
import re
import warnings
import numpy.core.numeric as NX
from numpy.core import (isscalar, abs, finfo, atleast_1d, hstack, dot, array,
ones)
from numpy.core import overrides
from numpy.core.overrides import set_module
from numpy.lib.twodim_base import diag, vander
from numpy.li... | Difference (subtraction) of two polynomials. .. note:: This forms part of the old polynomial API. Since version 1.4, the new polynomial API defined in `numpy.polynomial` is preferred. A summary of the differences can be found in the :doc:`transition guide </reference/routines.polynomials>`. Given two polynomials `a1` a... |
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