--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/CSP2/CSP2_env/env-d9b9114564458d9d-741b3de822f2aaca6c6caa4325c4afce/lib/python3.8/site-packages/numpy/matlib.py	Tue Mar 18 17:55:14 2025 -0400
@@ -0,0 +1,378 @@
+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)