csp2: CSP2/CSP2_env/env-d9b9114564458d9d-741b3de822f2aaca6c6caa4325c4afce/lib/python3.8/site-packages/numpy/matlib.py comparison

comparison CSP2/CSP2_env/env-d9b9114564458d9d-741b3de822f2aaca6c6caa4325c4afce/lib/python3.8/site-packages/numpy/matlib.py @ 69:33d812a61356

planemo upload commit 2e9511a184a1ca667c7be0c6321a36dc4e3d116d

author	jpayne
date	Tue, 18 Mar 2025 17:55:14 -0400
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-:0e9998148a16
+:33d812a61356
+import warnings
+# 2018-05-29, PendingDeprecationWarning added to matrix.__new__
+# 2020-01-23, numpy 1.19.0 PendingDeprecatonWarning
+warnings.warn("Importing from numpy.matlib is deprecated since 1.19.0. "
+"The matrix subclass is not the recommended way to represent "
+"matrices or deal with linear algebra (see "
+"https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). "
+"Please adjust your code to use regular ndarray. ",
+PendingDeprecationWarning, stacklevel=2)
+import numpy as np
+from numpy.matrixlib.defmatrix import matrix, asmatrix
+# Matlib.py contains all functions in the numpy namespace with a few
+# replacements. See doc/source/reference/routines.matlib.rst for details.
+# Need * as we're copying the numpy namespace.
+from numpy import *  # noqa: F403
+__version__ = np.__version__
+__all__ = np.__all__[:] # copy numpy namespace
+__all__ += ['rand', 'randn', 'repmat']
+def empty(shape, dtype=None, order='C'):
+"""Return a new matrix of given shape and type, without initializing entries.
+Parameters
+----------
+shape : int or tuple of int
+Shape of the empty matrix.
+dtype : data-type, optional
+Desired output data-type.
+order : {'C', 'F'}, optional
+Whether to store multi-dimensional data in row-major
+(C-style) or column-major (Fortran-style) order in
+memory.
+See Also
+--------
+empty_like, zeros
+Notes
+-----
+`empty`, unlike `zeros`, does not set the matrix values to zero,
+and may therefore be marginally faster.  On the other hand, it requires
+the user to manually set all the values in the array, and should be
+used with caution.
+Examples
+--------
+>>> import numpy.matlib
+>>> np.matlib.empty((2, 2))    # filled with random data
+matrix([[  6.76425276e-320,   9.79033856e-307], # random
+[  7.39337286e-309,   3.22135945e-309]])
+>>> np.matlib.empty((2, 2), dtype=int)
+matrix([[ 6600475,        0], # random
+[ 6586976, 22740995]])
+"""
+return ndarray.__new__(matrix, shape, dtype, order=order)
+def ones(shape, dtype=None, order='C'):
+"""
+Matrix of ones.
+Return a matrix of given shape and type, filled with ones.
+Parameters
+----------
+shape : {sequence of ints, int}
+Shape of the matrix
+dtype : data-type, optional
+The desired data-type for the matrix, default is np.float64.
+order : {'C', 'F'}, optional
+Whether to store matrix in C- or Fortran-contiguous order,
+default is 'C'.
+Returns
+-------
+out : matrix
+Matrix of ones of given shape, dtype, and order.
+See Also
+--------
+ones : Array of ones.
+matlib.zeros : Zero matrix.
+Notes
+-----
+If `shape` has length one i.e. ``(N,)``, or is a scalar ``N``,
+`out` becomes a single row matrix of shape ``(1,N)``.
+Examples
+--------
+>>> np.matlib.ones((2,3))
+matrix([[1.,  1.,  1.],
+[1.,  1.,  1.]])
+>>> np.matlib.ones(2)
+matrix([[1.,  1.]])
+"""
+a = ndarray.__new__(matrix, shape, dtype, order=order)
+a.fill(1)
+return a
+def zeros(shape, dtype=None, order='C'):
+"""
+Return a matrix of given shape and type, filled with zeros.
+Parameters
+----------
+shape : int or sequence of ints
+Shape of the matrix
+dtype : data-type, optional
+The desired data-type for the matrix, default is float.
+order : {'C', 'F'}, optional
+Whether to store the result in C- or Fortran-contiguous order,
+default is 'C'.
+Returns
+-------
+out : matrix
+Zero matrix of given shape, dtype, and order.
+See Also
+--------
+numpy.zeros : Equivalent array function.
+matlib.ones : Return a matrix of ones.
+Notes
+-----
+If `shape` has length one i.e. ``(N,)``, or is a scalar ``N``,
+`out` becomes a single row matrix of shape ``(1,N)``.
+Examples
+--------
+>>> import numpy.matlib
+>>> np.matlib.zeros((2, 3))
+matrix([[0.,  0.,  0.],
+[0.,  0.,  0.]])
+>>> np.matlib.zeros(2)
+matrix([[0.,  0.]])
+"""
+a = ndarray.__new__(matrix, shape, dtype, order=order)
+a.fill(0)
+return a
+def identity(n,dtype=None):
+"""
+Returns the square identity matrix of given size.
+Parameters
+----------
+n : int
+Size of the returned identity matrix.
+dtype : data-type, optional
+Data-type of the output. Defaults to ``float``.
+Returns
+-------
+out : matrix
+`n` x `n` matrix with its main diagonal set to one,
+and all other elements zero.
+See Also
+--------
+numpy.identity : Equivalent array function.
+matlib.eye : More general matrix identity function.
+Examples
+--------
+>>> import numpy.matlib
+>>> np.matlib.identity(3, dtype=int)
+matrix([[1, 0, 0],
+[0, 1, 0],
+[0, 0, 1]])
+"""
+a = array([1]+n*[0], dtype=dtype)
+b = empty((n, n), dtype=dtype)
+b.flat = a
+return b
+def eye(n,M=None, k=0, dtype=float, order='C'):
+"""
+Return a matrix with ones on the diagonal and zeros elsewhere.
+Parameters
+----------
+n : int
+Number of rows in the output.
+M : int, optional
+Number of columns in the output, defaults to `n`.
+k : int, optional
+Index of the diagonal: 0 refers to the main diagonal,
+a positive value refers to an upper diagonal,
+and a negative value to a lower diagonal.
+dtype : dtype, optional
+Data-type of the returned matrix.
+order : {'C', 'F'}, optional
+Whether the output should be stored in row-major (C-style) or
+column-major (Fortran-style) order in memory.
+.. versionadded:: 1.14.0
+Returns
+-------
+I : matrix
+A `n` x `M` matrix where all elements are equal to zero,
+except for the `k`-th diagonal, whose values are equal to one.
+See Also
+--------
+numpy.eye : Equivalent array function.
+identity : Square identity matrix.
+Examples
+--------
+>>> import numpy.matlib
+>>> np.matlib.eye(3, k=1, dtype=float)
+matrix([[0.,  1.,  0.],
+[0.,  0.,  1.],
+[0.,  0.,  0.]])
+"""
+return asmatrix(np.eye(n, M=M, k=k, dtype=dtype, order=order))
+def rand(*args):
+"""
+Return a matrix of random values with given shape.
+Create a matrix of the given shape and propagate it with
+random samples from a uniform distribution over ``[0, 1)``.
+Parameters
+----------
+\\*args : Arguments
+Shape of the output.
+If given as N integers, each integer specifies the size of one
+dimension.
+If given as a tuple, this tuple gives the complete shape.
+Returns
+-------
+out : ndarray
+The matrix of random values with shape given by `\\*args`.
+See Also
+--------
+randn, numpy.random.RandomState.rand
+Examples
+--------
+>>> np.random.seed(123)
+>>> import numpy.matlib
+>>> np.matlib.rand(2, 3)
+matrix([[0.69646919, 0.28613933, 0.22685145],
+[0.55131477, 0.71946897, 0.42310646]])
+>>> np.matlib.rand((2, 3))
+matrix([[0.9807642 , 0.68482974, 0.4809319 ],
+[0.39211752, 0.34317802, 0.72904971]])
+If the first argument is a tuple, other arguments are ignored:
+>>> np.matlib.rand((2, 3), 4)
+matrix([[0.43857224, 0.0596779 , 0.39804426],
+[0.73799541, 0.18249173, 0.17545176]])
+"""
+if isinstance(args[0], tuple):
+args = args[0]
+return asmatrix(np.random.rand(*args))
+def randn(*args):
+"""
+Return a random matrix with data from the "standard normal" distribution.
+`randn` generates a matrix filled with random floats sampled from a
+univariate "normal" (Gaussian) distribution of mean 0 and variance 1.
+Parameters
+----------
+\\*args : Arguments
+Shape of the output.
+If given as N integers, each integer specifies the size of one
+dimension. If given as a tuple, this tuple gives the complete shape.
+Returns
+-------
+Z : matrix of floats
+A matrix of floating-point samples drawn from the standard normal
+distribution.
+See Also
+--------
+rand, numpy.random.RandomState.randn
+Notes
+-----
+For random samples from the normal distribution with mean ``mu`` and
+standard deviation ``sigma``, use::
+sigma * np.matlib.randn(...) + mu
+Examples
+--------
+>>> np.random.seed(123)
+>>> import numpy.matlib
+>>> np.matlib.randn(1)
+matrix([[-1.0856306]])
+>>> np.matlib.randn(1, 2, 3)
+matrix([[ 0.99734545,  0.2829785 , -1.50629471],
+[-0.57860025,  1.65143654, -2.42667924]])
+Two-by-four matrix of samples from the normal distribution with
+mean 3 and standard deviation 2.5:
+>>> 2.5 * np.matlib.randn((2, 4)) + 3
+matrix([[1.92771843, 6.16484065, 0.83314899, 1.30278462],
+[2.76322758, 6.72847407, 1.40274501, 1.8900451 ]])
+"""
+if isinstance(args[0], tuple):
+args = args[0]
+return asmatrix(np.random.randn(*args))
+def repmat(a, m, n):
+"""
+Repeat a 0-D to 2-D array or matrix MxN times.
+Parameters
+----------
+a : array_like
+The array or matrix to be repeated.
+m, n : int
+The number of times `a` is repeated along the first and second axes.
+Returns
+-------
+out : ndarray
+The result of repeating `a`.
+Examples
+--------
+>>> import numpy.matlib
+>>> a0 = np.array(1)
+>>> np.matlib.repmat(a0, 2, 3)
+array([[1, 1, 1],
+[1, 1, 1]])
+>>> a1 = np.arange(4)
+>>> np.matlib.repmat(a1, 2, 2)
+array([[0, 1, 2, 3, 0, 1, 2, 3],
+[0, 1, 2, 3, 0, 1, 2, 3]])
+>>> a2 = np.asmatrix(np.arange(6).reshape(2, 3))
+>>> np.matlib.repmat(a2, 2, 3)
+matrix([[0, 1, 2, 0, 1, 2, 0, 1, 2],
+[3, 4, 5, 3, 4, 5, 3, 4, 5],
+[0, 1, 2, 0, 1, 2, 0, 1, 2],
+[3, 4, 5, 3, 4, 5, 3, 4, 5]])
+"""
+a = asanyarray(a)
+ndim = a.ndim
+if ndim == 0:
+origrows, origcols = (1, 1)
+elif ndim == 1:
+origrows, origcols = (1, a.shape[0])
+else:
+origrows, origcols = a.shape
+rows = origrows * m
+cols = origcols * n
+c = a.reshape(1, a.size).repeat(m, 0).reshape(rows, origcols).repeat(n, 0)
+return c.reshape(rows, cols)

Mercurial > repos > rliterman > csp2

comparison CSP2/CSP2_env/env-d9b9114564458d9d-741b3de822f2aaca6c6caa4325c4afce/lib/python3.8/site-packages/numpy/matlib.py @ 69:33d812a61356