Source code for sympy.matrices.dense

from __future__ import print_function, division

import random
from sympy import Derivative

from sympy.core import SympifyError
from sympy.core.basic import Basic
from sympy.core.expr import Expr
from sympy.core.compatibility import is_sequence, as_int, range, reduce
from sympy.core.function import count_ops
from sympy.core.singleton import S
from sympy.core.symbol import Symbol
from sympy.core.sympify import sympify
from sympy.functions.elementary.trigonometric import cos, sin
from sympy.functions.elementary.miscellaneous import sqrt
from sympy.simplify import simplify as _simplify
from sympy.utilities.misc import filldedent
from sympy.utilities.decorator import doctest_depends_on

from sympy.matrices.matrices import (MatrixBase,
                                     ShapeError, a2idx, classof)

def _iszero(x):
    """Returns True if x is zero."""
    return x.is_zero


def _compare_sequence(a, b):
    """Compares the elements of a list/tuple `a`
    and a list/tuple `b`.  `_compare_sequence((1,2), [1, 2])`
    is True, whereas `(1,2) == [1, 2]` is False"""
    if type(a) is type(b):
        # if they are the same type, compare directly
        return a == b
    # there is no overhead for calling `tuple` on a
    # tuple
    return tuple(a) == tuple(b)

class DenseMatrix(MatrixBase):

    is_MatrixExpr = False

    _op_priority = 10.01
    _class_priority = 4

    def __eq__(self, other):
        try:
            other = sympify(other)
            if self.shape != other.shape:
                return False
            if isinstance(other, Matrix):
                return _compare_sequence(self._mat,  other._mat)
            elif isinstance(other, MatrixBase):
                return _compare_sequence(self._mat, Matrix(other)._mat)
        except AttributeError:
            return False

    def __getitem__(self, key):
        """Return portion of self defined by key. If the key involves a slice
        then a list will be returned (if key is a single slice) or a matrix
        (if key was a tuple involving a slice).

        Examples
        ========

        >>> from sympy import Matrix, I
        >>> m = Matrix([
        ... [1, 2 + I],
        ... [3, 4    ]])

        If the key is a tuple that doesn't involve a slice then that element
        is returned:

        >>> m[1, 0]
        3

        When a tuple key involves a slice, a matrix is returned. Here, the
        first column is selected (all rows, column 0):

        >>> m[:, 0]
        Matrix([
        [1],
        [3]])

        If the slice is not a tuple then it selects from the underlying
        list of elements that are arranged in row order and a list is
        returned if a slice is involved:

        >>> m[0]
        1
        >>> m[::2]
        [1, 3]
        """
        if isinstance(key, tuple):
            i, j = key
            try:
                i, j = self.key2ij(key)
                return self._mat[i*self.cols + j]
            except (TypeError, IndexError):
                if (isinstance(i, Expr) and not i.is_number) or (isinstance(j, Expr) and not j.is_number):
                    if ((j < 0) is True) or ((j >= self.shape[1]) is True) or\
                       ((i < 0) is True) or ((i >= self.shape[0]) is True):
                        raise ValueError("index out of boundary")
                    from sympy.matrices.expressions.matexpr import MatrixElement
                    return MatrixElement(self, i, j)

                if isinstance(i, slice):
                    # XXX remove list() when PY2 support is dropped
                    i = list(range(self.rows))[i]
                elif is_sequence(i):
                    pass
                else:
                    i = [i]
                if isinstance(j, slice):
                    # XXX remove list() when PY2 support is dropped
                    j = list(range(self.cols))[j]
                elif is_sequence(j):
                    pass
                else:
                    j = [j]
                return self.extract(i, j)
        else:
            # row-wise decomposition of matrix
            if isinstance(key, slice):
                return self._mat[key]
            return self._mat[a2idx(key)]

    def __setitem__(self, key, value):
        raise NotImplementedError()

    def _cholesky(self):
        """Helper function of cholesky.
        Without the error checks.
        To be used privately. """
        L = zeros(self.rows, self.rows)
        for i in range(self.rows):
            for j in range(i):
                L[i, j] = (1 / L[j, j])*(self[i, j] -
                                         sum(L[i, k]*L[j, k] for k in range(j)))
            L[i, i] = sqrt(self[i, i] -
                           sum(L[i, k]**2 for k in range(i)))
        return self._new(L)

    def _diagonal_solve(self, rhs):
        """Helper function of function diagonal_solve,
        without the error checks, to be used privately.
        """
        return self._new(rhs.rows, rhs.cols, lambda i, j: rhs[i, j] / self[i, i])

    def _eval_add(self, other):
        # we assume both arguments are dense matrices since
        # sparse matrices have a higher priority
        mat = [a + b for a,b in zip(self._mat, other._mat)]
        return classof(self, other)._new(self.rows, self.cols, mat, copy=False)

    def _eval_extract(self, rowsList, colsList):
        mat = self._mat
        cols = self.cols
        indices = (i * cols + j for i in rowsList for j in colsList)
        return self._new(len(rowsList), len(colsList),
                         list(mat[i] for i in indices), copy=False)

    def _eval_matrix_mul(self, other):
        from sympy import Add
        # cache attributes for faster access
        self_rows, self_cols = self.rows, self.cols
        other_rows, other_cols = other.rows, other.cols
        other_len = other_rows * other_cols
        new_mat_rows = self.rows
        new_mat_cols = other.cols

        # preallocate the array
        new_mat = [S.Zero]*new_mat_rows*new_mat_cols

        # if we multiply an n x 0 with a 0 x m, the
        # expected behavior is to produce an n x m matrix of zeros
        if self.cols != 0 and other.rows != 0:
            # cache self._mat and other._mat for performance
            mat = self._mat
            other_mat = other._mat
            for i in range(len(new_mat)):
                row, col = i // new_mat_cols, i % new_mat_cols
                row_indices = range(self_cols*row, self_cols*(row+1))
                col_indices = range(col, other_len, other_cols)
                vec = (mat[a]*other_mat[b] for a,b in zip(row_indices, col_indices))
                try:
                    new_mat[i] = Add(*vec)
                except (TypeError, SympifyError):
                    # Block matrices don't work with `sum` or `Add` (ISSUE #11599)
                    # They don't work with `sum` because `sum` tries to add `0`
                    # initially, and for a matrix, that is a mix of a scalar and
                    # a matrix, which raises a TypeError. Fall back to a
                    # block-matrix-safe way to multiply if the `sum` fails.
                    vec = (mat[a]*other_mat[b] for a,b in zip(row_indices, col_indices))
                    new_mat[i] = reduce(lambda a,b: a + b, vec)
        return classof(self, other)._new(new_mat_rows, new_mat_cols, new_mat, copy=False)

    def _eval_matrix_mul_elementwise(self, other):
        mat = [a*b for a,b in zip(self._mat, other._mat)]
        return classof(self, other)._new(self.rows, self.cols, mat, copy=False)

    def _eval_diff(self, *args, **kwargs):
        if kwargs.pop("evaluate", True):
            return self.diff(*args)
        else:
            return Derivative(self, *args, **kwargs)

    def _eval_inverse(self, **kwargs):
        """Return the matrix inverse using the method indicated (default
        is Gauss elimination).

        kwargs
        ======

        method : ('GE', 'LU', or 'ADJ')
        iszerofunc
        try_block_diag

        Notes
        =====

        According to the ``method`` keyword, it calls the appropriate method:

          GE .... inverse_GE(); default
          LU .... inverse_LU()
          ADJ ... inverse_ADJ()

        According to the ``try_block_diag`` keyword, it will try to form block
        diagonal matrices using the method get_diag_blocks(), invert these
        individually, and then reconstruct the full inverse matrix.

        Note, the GE and LU methods may require the matrix to be simplified
        before it is inverted in order to properly detect zeros during
        pivoting. In difficult cases a custom zero detection function can
        be provided by setting the ``iszerosfunc`` argument to a function that
        should return True if its argument is zero. The ADJ routine computes
        the determinant and uses that to detect singular matrices in addition
        to testing for zeros on the diagonal.

        See Also
        ========

        inverse_LU
        inverse_GE
        inverse_ADJ
        """
        from sympy.matrices import diag

        method = kwargs.get('method', 'GE')
        iszerofunc = kwargs.get('iszerofunc', _iszero)
        if kwargs.get('try_block_diag', False):
            blocks = self.get_diag_blocks()
            r = []
            for block in blocks:
                r.append(block.inv(method=method, iszerofunc=iszerofunc))
            return diag(*r)

        M = self.as_mutable()
        if method == "GE":
            rv = M.inverse_GE(iszerofunc=iszerofunc)
        elif method == "LU":
            rv = M.inverse_LU(iszerofunc=iszerofunc)
        elif method == "ADJ":
            rv = M.inverse_ADJ(iszerofunc=iszerofunc)
        else:
            # make sure to add an invertibility check (as in inverse_LU)
            # if a new method is added.
            raise ValueError("Inversion method unrecognized")
        return self._new(rv)

    def _eval_scalar_mul(self, other):
        mat = [other*a for a in self._mat]
        return self._new(self.rows, self.cols, mat, copy=False)

    def _eval_scalar_rmul(self, other):
        mat = [a*other for a in self._mat]
        return self._new(self.rows, self.cols, mat, copy=False)

    def _eval_tolist(self):
        mat = list(self._mat)
        cols = self.cols
        return [mat[i*cols:(i + 1)*cols] for i in range(self.rows)]

    def _LDLdecomposition(self):
        """Helper function of LDLdecomposition.
        Without the error checks.
        To be used privately.
        """
        D = zeros(self.rows, self.rows)
        L = eye(self.rows)
        for i in range(self.rows):
            for j in range(i):
                L[i, j] = (1 / D[j, j])*(self[i, j] - sum(
                    L[i, k]*L[j, k]*D[k, k] for k in range(j)))
            D[i, i] = self[i, i] - sum(L[i, k]**2*D[k, k]
                                       for k in range(i))
        return self._new(L), self._new(D)

    def _lower_triangular_solve(self, rhs):
        """Helper function of function lower_triangular_solve.
        Without the error checks.
        To be used privately.
        """
        X = zeros(self.rows, rhs.cols)
        for j in range(rhs.cols):
            for i in range(self.rows):
                if self[i, i] == 0:
                    raise TypeError("Matrix must be non-singular.")
                X[i, j] = (rhs[i, j] - sum(self[i, k]*X[k, j]
                                           for k in range(i))) / self[i, i]
        return self._new(X)

    def _upper_triangular_solve(self, rhs):
        """Helper function of function upper_triangular_solve.
        Without the error checks, to be used privately. """
        X = zeros(self.rows, rhs.cols)
        for j in range(rhs.cols):
            for i in reversed(range(self.rows)):
                if self[i, i] == 0:
                    raise ValueError("Matrix must be non-singular.")
                X[i, j] = (rhs[i, j] - sum(self[i, k]*X[k, j]
                                           for k in range(i + 1, self.rows))) / self[i, i]
        return self._new(X)

    def as_immutable(self):
        """Returns an Immutable version of this Matrix
        """
        from .immutable import ImmutableDenseMatrix as cls
        if self.rows and self.cols:
            return cls._new(self.tolist())
        return cls._new(self.rows, self.cols, [])

    def as_mutable(self):
        """Returns a mutable version of this matrix

        Examples
        ========

        >>> from sympy import ImmutableMatrix
        >>> X = ImmutableMatrix([[1, 2], [3, 4]])
        >>> Y = X.as_mutable()
        >>> Y[1, 1] = 5 # Can set values in Y
        >>> Y
        Matrix([
        [1, 2],
        [3, 5]])
        """
        return Matrix(self)

    def equals(self, other, failing_expression=False):
        """Applies ``equals`` to corresponding elements of the matrices,
        trying to prove that the elements are equivalent, returning True
        if they are, False if any pair is not, and None (or the first
        failing expression if failing_expression is True) if it cannot
        be decided if the expressions are equivalent or not. This is, in
        general, an expensive operation.

        Examples
        ========

        >>> from sympy.matrices import Matrix
        >>> from sympy.abc import x
        >>> from sympy import cos
        >>> A = Matrix([x*(x - 1), 0])
        >>> B = Matrix([x**2 - x, 0])
        >>> A == B
        False
        >>> A.simplify() == B.simplify()
        True
        >>> A.equals(B)
        True
        >>> A.equals(2)
        False

        See Also
        ========
        sympy.core.expr.equals
        """
        try:
            if self.shape != other.shape:
                return False
            rv = True
            for i in range(self.rows):
                for j in range(self.cols):
                    ans = self[i, j].equals(other[i, j], failing_expression)
                    if ans is False:
                        return False
                    elif ans is not True and rv is True:
                        rv = ans
            return rv
        except AttributeError:
            return False


def _force_mutable(x):
    """Return a matrix as a Matrix, otherwise return x."""
    if getattr(x, 'is_Matrix', False):
        return x.as_mutable()
    elif isinstance(x, Basic):
        return x
    elif hasattr(x, '__array__'):
        a = x.__array__()
        if len(a.shape) == 0:
            return sympify(a)
        return Matrix(x)
    return x


[docs]class MutableDenseMatrix(DenseMatrix, MatrixBase): def __new__(cls, *args, **kwargs): return cls._new(*args, **kwargs) @classmethod def _new(cls, *args, **kwargs): # if the `copy` flag is set to False, the input # was rows, cols, [list]. It should be used directly # without creating a copy. if kwargs.get('copy', True) is False: if len(args) != 3: raise TypeError("'copy=False' requires a matrix be initialized as rows,cols,[list]") rows, cols, flat_list = args else: rows, cols, flat_list = cls._handle_creation_inputs(*args, **kwargs) flat_list = list(flat_list) # create a shallow copy self = object.__new__(cls) self.rows = rows self.cols = cols self._mat = flat_list return self def __setitem__(self, key, value): """ Examples ======== >>> from sympy import Matrix, I, zeros, ones >>> m = Matrix(((1, 2+I), (3, 4))) >>> m Matrix([ [1, 2 + I], [3, 4]]) >>> m[1, 0] = 9 >>> m Matrix([ [1, 2 + I], [9, 4]]) >>> m[1, 0] = [[0, 1]] To replace row r you assign to position r*m where m is the number of columns: >>> M = zeros(4) >>> m = M.cols >>> M[3*m] = ones(1, m)*2; M Matrix([ [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [2, 2, 2, 2]]) And to replace column c you can assign to position c: >>> M[2] = ones(m, 1)*4; M Matrix([ [0, 0, 4, 0], [0, 0, 4, 0], [0, 0, 4, 0], [2, 2, 4, 2]]) """ rv = self._setitem(key, value) if rv is not None: i, j, value = rv self._mat[i*self.cols + j] = value def as_mutable(self): return self.copy()
[docs] def col_del(self, i): """Delete the given column. Examples ======== >>> from sympy.matrices import eye >>> M = eye(3) >>> M.col_del(1) >>> M Matrix([ [1, 0], [0, 0], [0, 1]]) See Also ======== col row_del """ if i < -self.cols or i >= self.cols: raise IndexError("Index out of range: 'i=%s', valid -%s <= i < %s" % (i, self.cols, self.cols)) for j in range(self.rows - 1, -1, -1): del self._mat[i + j*self.cols] self.cols -= 1
[docs] def col_op(self, j, f): """In-place operation on col j using two-arg functor whose args are interpreted as (self[i, j], i). Examples ======== >>> from sympy.matrices import eye >>> M = eye(3) >>> M.col_op(1, lambda v, i: v + 2*M[i, 0]); M Matrix([ [1, 2, 0], [0, 1, 0], [0, 0, 1]]) See Also ======== col row_op """ self._mat[j::self.cols] = [f(*t) for t in list(zip(self._mat[j::self.cols], list(range(self.rows))))]
[docs] def col_swap(self, i, j): """Swap the two given columns of the matrix in-place. Examples ======== >>> from sympy.matrices import Matrix >>> M = Matrix([[1, 0], [1, 0]]) >>> M Matrix([ [1, 0], [1, 0]]) >>> M.col_swap(0, 1) >>> M Matrix([ [0, 1], [0, 1]]) See Also ======== col row_swap """ for k in range(0, self.rows): self[k, i], self[k, j] = self[k, j], self[k, i]
[docs] def copyin_list(self, key, value): """Copy in elements from a list. Parameters ========== key : slice The section of this matrix to replace. value : iterable The iterable to copy values from. Examples ======== >>> from sympy.matrices import eye >>> I = eye(3) >>> I[:2, 0] = [1, 2] # col >>> I Matrix([ [1, 0, 0], [2, 1, 0], [0, 0, 1]]) >>> I[1, :2] = [[3, 4]] >>> I Matrix([ [1, 0, 0], [3, 4, 0], [0, 0, 1]]) See Also ======== copyin_matrix """ if not is_sequence(value): raise TypeError("`value` must be an ordered iterable, not %s." % type(value)) return self.copyin_matrix(key, Matrix(value))
[docs] def copyin_matrix(self, key, value): """Copy in values from a matrix into the given bounds. Parameters ========== key : slice The section of this matrix to replace. value : Matrix The matrix to copy values from. Examples ======== >>> from sympy.matrices import Matrix, eye >>> M = Matrix([[0, 1], [2, 3], [4, 5]]) >>> I = eye(3) >>> I[:3, :2] = M >>> I Matrix([ [0, 1, 0], [2, 3, 0], [4, 5, 1]]) >>> I[0, 1] = M >>> I Matrix([ [0, 0, 1], [2, 2, 3], [4, 4, 5]]) See Also ======== copyin_list """ rlo, rhi, clo, chi = self.key2bounds(key) shape = value.shape dr, dc = rhi - rlo, chi - clo if shape != (dr, dc): raise ShapeError(filldedent("The Matrix `value` doesn't have the " "same dimensions " "as the in sub-Matrix given by `key`.")) for i in range(value.rows): for j in range(value.cols): self[i + rlo, j + clo] = value[i, j]
[docs] def fill(self, value): """Fill the matrix with the scalar value. See Also ======== zeros ones """ self._mat = [value]*len(self)
[docs] def row_del(self, i): """Delete the given row. Examples ======== >>> from sympy.matrices import eye >>> M = eye(3) >>> M.row_del(1) >>> M Matrix([ [1, 0, 0], [0, 0, 1]]) See Also ======== row col_del """ if i < -self.rows or i >= self.rows: raise IndexError("Index out of range: 'i = %s', valid -%s <= i" " < %s" % (i, self.rows, self.rows)) if i < 0: i += self.rows del self._mat[i*self.cols:(i+1)*self.cols] self.rows -= 1
[docs] def row_op(self, i, f): """In-place operation on row ``i`` using two-arg functor whose args are interpreted as ``(self[i, j], j)``. Examples ======== >>> from sympy.matrices import eye >>> M = eye(3) >>> M.row_op(1, lambda v, j: v + 2*M[0, j]); M Matrix([ [1, 0, 0], [2, 1, 0], [0, 0, 1]]) See Also ======== row zip_row_op col_op """ i0 = i*self.cols ri = self._mat[i0: i0 + self.cols] self._mat[i0: i0 + self.cols] = [f(x, j) for x, j in zip(ri, list(range(self.cols)))]
[docs] def row_swap(self, i, j): """Swap the two given rows of the matrix in-place. Examples ======== >>> from sympy.matrices import Matrix >>> M = Matrix([[0, 1], [1, 0]]) >>> M Matrix([ [0, 1], [1, 0]]) >>> M.row_swap(0, 1) >>> M Matrix([ [1, 0], [0, 1]]) See Also ======== row col_swap """ for k in range(0, self.cols): self[i, k], self[j, k] = self[j, k], self[i, k]
[docs] def simplify(self, ratio=1.7, measure=count_ops): """Applies simplify to the elements of a matrix in place. This is a shortcut for M.applyfunc(lambda x: simplify(x, ratio, measure)) See Also ======== sympy.simplify.simplify.simplify """ for i in range(len(self._mat)): self._mat[i] = _simplify(self._mat[i], ratio=ratio, measure=measure)
[docs] def zip_row_op(self, i, k, f): """In-place operation on row ``i`` using two-arg functor whose args are interpreted as ``(self[i, j], self[k, j])``. Examples ======== >>> from sympy.matrices import eye >>> M = eye(3) >>> M.zip_row_op(1, 0, lambda v, u: v + 2*u); M Matrix([ [1, 0, 0], [2, 1, 0], [0, 0, 1]]) See Also ======== row row_op col_op """ i0 = i*self.cols k0 = k*self.cols ri = self._mat[i0: i0 + self.cols] rk = self._mat[k0: k0 + self.cols] self._mat[i0: i0 + self.cols] = [f(x, y) for x, y in zip(ri, rk)]
# Utility functions MutableMatrix = Matrix = MutableDenseMatrix ########### # Numpy Utility Functions: # list2numpy, matrix2numpy, symmarray, rot_axis[123] ###########
[docs]def list2numpy(l, dtype=object): # pragma: no cover """Converts python list of SymPy expressions to a NumPy array. See Also ======== matrix2numpy """ from numpy import empty a = empty(len(l), dtype) for i, s in enumerate(l): a[i] = s return a
[docs]def matrix2numpy(m, dtype=object): # pragma: no cover """Converts SymPy's matrix to a NumPy array. See Also ======== list2numpy """ from numpy import empty a = empty(m.shape, dtype) for i in range(m.rows): for j in range(m.cols): a[i, j] = m[i, j] return a
[docs]def rot_axis3(theta): """Returns a rotation matrix for a rotation of theta (in radians) about the 3-axis. Examples ======== >>> from sympy import pi >>> from sympy.matrices import rot_axis3 A rotation of pi/3 (60 degrees): >>> theta = pi/3 >>> rot_axis3(theta) Matrix([ [ 1/2, sqrt(3)/2, 0], [-sqrt(3)/2, 1/2, 0], [ 0, 0, 1]]) If we rotate by pi/2 (90 degrees): >>> rot_axis3(pi/2) Matrix([ [ 0, 1, 0], [-1, 0, 0], [ 0, 0, 1]]) See Also ======== rot_axis1: Returns a rotation matrix for a rotation of theta (in radians) about the 1-axis rot_axis2: Returns a rotation matrix for a rotation of theta (in radians) about the 2-axis """ ct = cos(theta) st = sin(theta) lil = ((ct, st, 0), (-st, ct, 0), (0, 0, 1)) return Matrix(lil)
[docs]def rot_axis2(theta): """Returns a rotation matrix for a rotation of theta (in radians) about the 2-axis. Examples ======== >>> from sympy import pi >>> from sympy.matrices import rot_axis2 A rotation of pi/3 (60 degrees): >>> theta = pi/3 >>> rot_axis2(theta) Matrix([ [ 1/2, 0, -sqrt(3)/2], [ 0, 1, 0], [sqrt(3)/2, 0, 1/2]]) If we rotate by pi/2 (90 degrees): >>> rot_axis2(pi/2) Matrix([ [0, 0, -1], [0, 1, 0], [1, 0, 0]]) See Also ======== rot_axis1: Returns a rotation matrix for a rotation of theta (in radians) about the 1-axis rot_axis3: Returns a rotation matrix for a rotation of theta (in radians) about the 3-axis """ ct = cos(theta) st = sin(theta) lil = ((ct, 0, -st), (0, 1, 0), (st, 0, ct)) return Matrix(lil)
[docs]def rot_axis1(theta): """Returns a rotation matrix for a rotation of theta (in radians) about the 1-axis. Examples ======== >>> from sympy import pi >>> from sympy.matrices import rot_axis1 A rotation of pi/3 (60 degrees): >>> theta = pi/3 >>> rot_axis1(theta) Matrix([ [1, 0, 0], [0, 1/2, sqrt(3)/2], [0, -sqrt(3)/2, 1/2]]) If we rotate by pi/2 (90 degrees): >>> rot_axis1(pi/2) Matrix([ [1, 0, 0], [0, 0, 1], [0, -1, 0]]) See Also ======== rot_axis2: Returns a rotation matrix for a rotation of theta (in radians) about the 2-axis rot_axis3: Returns a rotation matrix for a rotation of theta (in radians) about the 3-axis """ ct = cos(theta) st = sin(theta) lil = ((1, 0, 0), (0, ct, st), (0, -st, ct)) return Matrix(lil)
[docs]@doctest_depends_on(modules=('numpy',)) def symarray(prefix, shape, **kwargs): # pragma: no cover r"""Create a numpy ndarray of symbols (as an object array). The created symbols are named ``prefix_i1_i2_``... You should thus provide a non-empty prefix if you want your symbols to be unique for different output arrays, as SymPy symbols with identical names are the same object. Parameters ---------- prefix : string A prefix prepended to the name of every symbol. shape : int or tuple Shape of the created array. If an int, the array is one-dimensional; for more than one dimension the shape must be a tuple. \*\*kwargs : dict keyword arguments passed on to Symbol Examples ======== These doctests require numpy. >>> from sympy import symarray >>> symarray('', 3) [_0 _1 _2] If you want multiple symarrays to contain distinct symbols, you *must* provide unique prefixes: >>> a = symarray('', 3) >>> b = symarray('', 3) >>> a[0] == b[0] True >>> a = symarray('a', 3) >>> b = symarray('b', 3) >>> a[0] == b[0] False Creating symarrays with a prefix: >>> symarray('a', 3) [a_0 a_1 a_2] For more than one dimension, the shape must be given as a tuple: >>> symarray('a', (2, 3)) [[a_0_0 a_0_1 a_0_2] [a_1_0 a_1_1 a_1_2]] >>> symarray('a', (2, 3, 2)) [[[a_0_0_0 a_0_0_1] [a_0_1_0 a_0_1_1] [a_0_2_0 a_0_2_1]] <BLANKLINE> [[a_1_0_0 a_1_0_1] [a_1_1_0 a_1_1_1] [a_1_2_0 a_1_2_1]]] For setting assumptions of the underlying Symbols: >>> [s.is_real for s in symarray('a', 2, real=True)] [True, True] """ from numpy import empty, ndindex arr = empty(shape, dtype=object) for index in ndindex(shape): arr[index] = Symbol('%s_%s' % (prefix, '_'.join(map(str, index))), **kwargs) return arr
############### # Functions ###############
[docs]def casoratian(seqs, n, zero=True): """Given linear difference operator L of order 'k' and homogeneous equation Ly = 0 we want to compute kernel of L, which is a set of 'k' sequences: a(n), b(n), ... z(n). Solutions of L are linearly independent iff their Casoratian, denoted as C(a, b, ..., z), do not vanish for n = 0. Casoratian is defined by k x k determinant:: + a(n) b(n) . . . z(n) + | a(n+1) b(n+1) . . . z(n+1) | | . . . . | | . . . . | | . . . . | + a(n+k-1) b(n+k-1) . . . z(n+k-1) + It proves very useful in rsolve_hyper() where it is applied to a generating set of a recurrence to factor out linearly dependent solutions and return a basis: >>> from sympy import Symbol, casoratian, factorial >>> n = Symbol('n', integer=True) Exponential and factorial are linearly independent: >>> casoratian([2**n, factorial(n)], n) != 0 True """ from .dense import Matrix seqs = list(map(sympify, seqs)) if not zero: f = lambda i, j: seqs[j].subs(n, n + i) else: f = lambda i, j: seqs[j].subs(n, i) k = len(seqs) return Matrix(k, k, f).det()
[docs]def eye(*args, **kwargs): """Create square identity matrix n x n See Also ======== diag zeros ones """ from .dense import Matrix return Matrix.eye(*args, **kwargs)
[docs]def diag(*values, **kwargs): """Create a sparse, diagonal matrix from a list of diagonal values. Notes ===== When arguments are matrices they are fitted in resultant matrix. The returned matrix is a mutable, dense matrix. To make it a different type, send the desired class for keyword ``cls``. Examples ======== >>> from sympy.matrices import diag, Matrix, ones >>> diag(1, 2, 3) Matrix([ [1, 0, 0], [0, 2, 0], [0, 0, 3]]) >>> diag(*[1, 2, 3]) Matrix([ [1, 0, 0], [0, 2, 0], [0, 0, 3]]) The diagonal elements can be matrices; diagonal filling will continue on the diagonal from the last element of the matrix: >>> from sympy.abc import x, y, z >>> a = Matrix([x, y, z]) >>> b = Matrix([[1, 2], [3, 4]]) >>> c = Matrix([[5, 6]]) >>> diag(a, 7, b, c) Matrix([ [x, 0, 0, 0, 0, 0], [y, 0, 0, 0, 0, 0], [z, 0, 0, 0, 0, 0], [0, 7, 0, 0, 0, 0], [0, 0, 1, 2, 0, 0], [0, 0, 3, 4, 0, 0], [0, 0, 0, 0, 5, 6]]) When diagonal elements are lists, they will be treated as arguments to Matrix: >>> diag([1, 2, 3], 4) Matrix([ [1, 0], [2, 0], [3, 0], [0, 4]]) >>> diag([[1, 2, 3]], 4) Matrix([ [1, 2, 3, 0], [0, 0, 0, 4]]) A given band off the diagonal can be made by padding with a vertical or horizontal "kerning" vector: >>> hpad = ones(0, 2) >>> vpad = ones(2, 0) >>> diag(vpad, 1, 2, 3, hpad) + diag(hpad, 4, 5, 6, vpad) Matrix([ [0, 0, 4, 0, 0], [0, 0, 0, 5, 0], [1, 0, 0, 0, 6], [0, 2, 0, 0, 0], [0, 0, 3, 0, 0]]) The type is mutable by default but can be made immutable by setting the ``mutable`` flag to False: >>> type(diag(1)) <class 'sympy.matrices.dense.MutableDenseMatrix'> >>> from sympy.matrices import ImmutableMatrix >>> type(diag(1, cls=ImmutableMatrix)) <class 'sympy.matrices.immutable.ImmutableDenseMatrix'> See Also ======== eye """ from .dense import Matrix # diag assumes any lists passed in are to be interpreted # as arguments to Matrix, so apply Matrix to any list arguments def normalize(m): if is_sequence(m) and not isinstance(m, MatrixBase): return Matrix(m) return m values = (normalize(m) for m in values) return Matrix.diag(*values, **kwargs)
[docs]def GramSchmidt(vlist, orthonormal=False): """ Apply the Gram-Schmidt process to a set of vectors. see: http://en.wikipedia.org/wiki/Gram%E2%80%93Schmidt_process """ out = [] m = len(vlist) for i in range(m): tmp = vlist[i] for j in range(i): tmp -= vlist[i].project(out[j]) if not tmp.values(): raise ValueError( "GramSchmidt: vector set not linearly independent") out.append(tmp) if orthonormal: for i in range(len(out)): out[i] = out[i].normalized() return out
[docs]def hessian(f, varlist, constraints=[]): """Compute Hessian matrix for a function f wrt parameters in varlist which may be given as a sequence or a row/column vector. A list of constraints may optionally be given. Examples ======== >>> from sympy import Function, hessian, pprint >>> from sympy.abc import x, y >>> f = Function('f')(x, y) >>> g1 = Function('g')(x, y) >>> g2 = x**2 + 3*y >>> pprint(hessian(f, (x, y), [g1, g2])) [ d d ] [ 0 0 --(g(x, y)) --(g(x, y)) ] [ dx dy ] [ ] [ 0 0 2*x 3 ] [ ] [ 2 2 ] [d d d ] [--(g(x, y)) 2*x ---(f(x, y)) -----(f(x, y))] [dx 2 dy dx ] [ dx ] [ ] [ 2 2 ] [d d d ] [--(g(x, y)) 3 -----(f(x, y)) ---(f(x, y)) ] [dy dy dx 2 ] [ dy ] References ========== http://en.wikipedia.org/wiki/Hessian_matrix See Also ======== sympy.matrices.mutable.Matrix.jacobian wronskian """ # f is the expression representing a function f, return regular matrix if isinstance(varlist, MatrixBase): if 1 not in varlist.shape: raise ShapeError("`varlist` must be a column or row vector.") if varlist.cols == 1: varlist = varlist.T varlist = varlist.tolist()[0] if is_sequence(varlist): n = len(varlist) if not n: raise ShapeError("`len(varlist)` must not be zero.") else: raise ValueError("Improper variable list in hessian function") if not getattr(f, 'diff'): # check differentiability raise ValueError("Function `f` (%s) is not differentiable" % f) m = len(constraints) N = m + n out = zeros(N) for k, g in enumerate(constraints): if not getattr(g, 'diff'): # check differentiability raise ValueError("Function `f` (%s) is not differentiable" % f) for i in range(n): out[k, i + m] = g.diff(varlist[i]) for i in range(n): for j in range(i, n): out[i + m, j + m] = f.diff(varlist[i]).diff(varlist[j]) for i in range(N): for j in range(i + 1, N): out[j, i] = out[i, j] return out
[docs]def jordan_cell(eigenval, n): """ Create a Jordan block: Examples ======== >>> from sympy.matrices import jordan_cell >>> from sympy.abc import x >>> jordan_cell(x, 4) Matrix([ [x, 1, 0, 0], [0, x, 1, 0], [0, 0, x, 1], [0, 0, 0, x]]) """ from .dense import Matrix return Matrix.jordan_block(size=n, eigenvalue=eigenval)
[docs]def matrix_multiply_elementwise(A, B): """Return the Hadamard product (elementwise product) of A and B >>> from sympy.matrices import matrix_multiply_elementwise >>> from sympy.matrices import Matrix >>> A = Matrix([[0, 1, 2], [3, 4, 5]]) >>> B = Matrix([[1, 10, 100], [100, 10, 1]]) >>> matrix_multiply_elementwise(A, B) Matrix([ [ 0, 10, 200], [300, 40, 5]]) See Also ======== __mul__ """ if A.shape != B.shape: raise ShapeError() shape = A.shape return classof(A, B)._new(shape[0], shape[1], lambda i, j: A[i, j]*B[i, j])
[docs]def ones(*args, **kwargs): """Returns a matrix of ones with ``rows`` rows and ``cols`` columns; if ``cols`` is omitted a square matrix will be returned. See Also ======== zeros eye diag """ if 'c' in kwargs: kwargs['cols'] = kwargs.pop('c') from .dense import Matrix return Matrix.ones(*args, **kwargs)
[docs]def randMatrix(r, c=None, min=0, max=99, seed=None, symmetric=False, percent=100, prng=None): """Create random matrix with dimensions ``r`` x ``c``. If ``c`` is omitted the matrix will be square. If ``symmetric`` is True the matrix must be square. If ``percent`` is less than 100 then only approximately the given percentage of elements will be non-zero. The pseudo-random number generator used to generate matrix is chosen in the following way. * If ``prng`` is supplied, it will be used as random number generator. It should be an instance of :class:`random.Random`, or at least have ``randint`` and ``shuffle`` methods with same signatures. * if ``prng`` is not supplied but ``seed`` is supplied, then new :class:`random.Random` with given ``seed`` will be created; * otherwise, a new :class:`random.Random` with default seed will be used. Examples ======== >>> from sympy.matrices import randMatrix >>> randMatrix(3) # doctest:+SKIP [25, 45, 27] [44, 54, 9] [23, 96, 46] >>> randMatrix(3, 2) # doctest:+SKIP [87, 29] [23, 37] [90, 26] >>> randMatrix(3, 3, 0, 2) # doctest:+SKIP [0, 2, 0] [2, 0, 1] [0, 0, 1] >>> randMatrix(3, symmetric=True) # doctest:+SKIP [85, 26, 29] [26, 71, 43] [29, 43, 57] >>> A = randMatrix(3, seed=1) >>> B = randMatrix(3, seed=2) >>> A == B # doctest:+SKIP False >>> A == randMatrix(3, seed=1) True >>> randMatrix(3, symmetric=True, percent=50) # doctest:+SKIP [0, 68, 43] [0, 68, 0] [0, 91, 34] """ if c is None: c = r # Note that ``Random()`` is equivalent to ``Random(None)`` prng = prng or random.Random(seed) if symmetric and r != c: raise ValueError( 'For symmetric matrices, r must equal c, but %i != %i' % (r, c)) if not symmetric: m = Matrix._new(r, c, lambda i, j: prng.randint(min, max)) else: m = zeros(r) for i in range(r): for j in range(i, r): m[i, j] = prng.randint(min, max) for i in range(r): for j in range(i): m[i, j] = m[j, i] if percent == 100: return m else: z = int(r*c*percent // 100) m._mat[:z] = [S.Zero]*z prng.shuffle(m._mat) return m
[docs]def wronskian(functions, var, method='bareiss'): """ Compute Wronskian for [] of functions :: | f1 f2 ... fn | | f1' f2' ... fn' | | . . . . | W(f1, ..., fn) = | . . . . | | . . . . | | (n) (n) (n) | | D (f1) D (f2) ... D (fn) | see: http://en.wikipedia.org/wiki/Wronskian See Also ======== sympy.matrices.mutable.Matrix.jacobian hessian """ from .dense import Matrix for index in range(0, len(functions)): functions[index] = sympify(functions[index]) n = len(functions) if n == 0: return 1 W = Matrix(n, n, lambda i, j: functions[i].diff(var, j)) return W.det(method)
[docs]def zeros(*args, **kwargs): """Returns a matrix of zeros with ``rows`` rows and ``cols`` columns; if ``cols`` is omitted a square matrix will be returned. See Also ======== ones eye diag """ if 'c' in kwargs: kwargs['cols'] = kwargs.pop('c') from .dense import Matrix return Matrix.zeros(*args, **kwargs)