""" Basic methods common to all matrices to be used when creating more advanced matrices (e.g., matrices over rings, etc.). """ from __future__ import print_function, division from sympy.core.add import Add from sympy.core.basic import Basic, Atom from sympy.core.expr import Expr from sympy.core.symbol import Symbol from sympy.core.function import count_ops from sympy.core.singleton import S from sympy.core.sympify import sympify from sympy.core.compatibility import is_sequence, default_sort_key, range, \ NotIterable, Iterable from sympy.simplify import simplify as _simplify, signsimp, nsimplify from sympy.utilities.iterables import flatten from sympy.functions import Abs from sympy.core.compatibility import reduce, as_int, string_types from sympy.assumptions.refine import refine from sympy.core.decorators import call_highest_priority from types import FunctionType from collections import defaultdict class MatrixError(Exception): pass class ShapeError(ValueError, MatrixError): """Wrong matrix shape""" pass class NonSquareMatrixError(ShapeError): pass class MatrixRequired(object): """All subclasses of matrix objects must implement the required matrix properties listed here.""" rows = None cols = None shape = None _simplify = None @classmethod def _new(cls, *args, **kwargs): """`_new` must, at minimum, be callable as `_new(rows, cols, mat) where mat is a flat list of the elements of the matrix.""" raise NotImplementedError("Subclasses must implement this.") def __eq__(self, other): raise NotImplementedError("Subclasses must implement this.") def __getitem__(self, key): """Implementations of __getitem__ should accept ints, in which case the matrix is indexed as a flat list, tuples (i,j) in which case the (i,j) entry is returned, slices, or mixed tuples (a,b) where a and b are any combintion of slices and integers.""" raise NotImplementedError("Subclasses must implement this.") def __len__(self): """The total number of entries in the matrix.""" raise NotImplementedError("Subclasses must implement this.") class MatrixShaping(MatrixRequired): """Provides basic matrix shaping and extracting of submatrices""" def _eval_col_del(self, col): def entry(i, j): return self[i, j] if j < col else self[i, j + 1] return self._new(self.rows, self.cols - 1, entry) def _eval_col_insert(self, pos, other): cols = self.cols def entry(i, j): if j < pos: return self[i, j] elif pos <= j < pos + other.cols: return other[i, j - pos] return self[i, j - other.cols] return self._new(self.rows, self.cols + other.cols, lambda i, j: entry(i, j)) def _eval_col_join(self, other): rows = self.rows def entry(i, j): if i < rows: return self[i, j] return other[i - rows, j] return classof(self, other)._new(self.rows + other.rows, self.cols, lambda i, j: entry(i, j)) def _eval_extract(self, rowsList, colsList): mat = list(self) 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)) def _eval_get_diag_blocks(self): sub_blocks = [] def recurse_sub_blocks(M): i = 1 while i <= M.shape[0]: if i == 1: to_the_right = M[0, i:] to_the_bottom = M[i:, 0] else: to_the_right = M[:i, i:] to_the_bottom = M[i:, :i] if any(to_the_right) or any(to_the_bottom): i += 1 continue else: sub_blocks.append(M[:i, :i]) if M.shape == M[:i, :i].shape: return else: recurse_sub_blocks(M[i:, i:]) return recurse_sub_blocks(self) return sub_blocks def _eval_row_del(self, row): def entry(i, j): return self[i, j] if i < row else self[i + 1, j] return self._new(self.rows - 1, self.cols, entry) def _eval_row_insert(self, pos, other): entries = list(self) insert_pos = pos * self.cols entries[insert_pos:insert_pos] = list(other) return self._new(self.rows + other.rows, self.cols, entries) def _eval_row_join(self, other): cols = self.cols def entry(i, j): if j < cols: return self[i, j] return other[i, j - cols] return classof(self, other)._new(self.rows, self.cols + other.cols, lambda i, j: entry(i, j)) def _eval_tolist(self): return [list(self[i,:]) for i in range(self.rows)] def _eval_vec(self): rows = self.rows def entry(n, _): # we want to read off the columns first j = n // rows i = n - j * rows return self[i, j] return self._new(len(self), 1, entry) def col_del(self, col): """Delete the specified column.""" if col < 0: col += self.cols if not 0 <= col < self.cols: raise ValueError("Column {} out of range.".format(col)) return self._eval_col_del(col) def col_insert(self, pos, other): """Insert one or more columns at the given column position. Examples ======== >>> from sympy import zeros, ones >>> M = zeros(3) >>> V = ones(3, 1) >>> M.col_insert(1, V) Matrix([ [0, 1, 0, 0], [0, 1, 0, 0], [0, 1, 0, 0]]) See Also ======== col row_insert """ # Allows you to build a matrix even if it is null matrix if not self: return type(self)(other) pos = as_int(pos) if pos < 0: pos = self.cols + pos if pos < 0: pos = 0 elif pos > self.cols: pos = self.cols if self.rows != other.rows: raise ShapeError( "`self` and `other` must have the same number of rows.") return self._eval_col_insert(pos, other) def col_join(self, other): """Concatenates two matrices along self's last and other's first row. Examples ======== >>> from sympy import zeros, ones >>> M = zeros(3) >>> V = ones(1, 3) >>> M.col_join(V) Matrix([ [0, 0, 0], [0, 0, 0], [0, 0, 0], [1, 1, 1]]) See Also ======== col row_join """ # A null matrix can always be stacked (see #10770) if self.rows == 0 and self.cols != other.cols: return self._new(0, other.cols, []).col_join(other) if self.cols != other.cols: raise ShapeError( "`self` and `other` must have the same number of columns.") return self._eval_col_join(other) def col(self, j): """Elementary column selector. Examples ======== >>> from sympy import eye >>> eye(2).col(0) Matrix([ [1], [0]]) See Also ======== row col_op col_swap col_del col_join col_insert """ return self[:, j] def extract(self, rowsList, colsList): """Return a submatrix by specifying a list of rows and columns. Negative indices can be given. All indices must be in the range -n <= i < n where n is the number of rows or columns. Examples ======== >>> from sympy import Matrix >>> m = Matrix(4, 3, range(12)) >>> m Matrix([ [0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11]]) >>> m.extract([0, 1, 3], [0, 1]) Matrix([ [0, 1], [3, 4], [9, 10]]) Rows or columns can be repeated: >>> m.extract([0, 0, 1], [-1]) Matrix([ [2], [2], [5]]) Every other row can be taken by using range to provide the indices: >>> m.extract(range(0, m.rows, 2), [-1]) Matrix([ [2], [8]]) RowsList or colsList can also be a list of booleans, in which case the rows or columns corresponding to the True values will be selected: >>> m.extract([0, 1, 2, 3], [True, False, True]) Matrix([ [0, 2], [3, 5], [6, 8], [9, 11]]) """ if not is_sequence(rowsList) or not is_sequence(colsList): raise TypeError("rowsList and colsList must be iterable") # ensure rowsList and colsList are lists of integers if rowsList and all(isinstance(i, bool) for i in rowsList): rowsList = [index for index, item in enumerate(rowsList) if item] if colsList and all(isinstance(i, bool) for i in colsList): colsList = [index for index, item in enumerate(colsList) if item] # ensure everything is in range rowsList = [a2idx(k, self.rows) for k in rowsList] colsList = [a2idx(k, self.cols) for k in colsList] return self._eval_extract(rowsList, colsList) def get_diag_blocks(self): """Obtains the square sub-matrices on the main diagonal of a square matrix. Useful for inverting symbolic matrices or solving systems of linear equations which may be decoupled by having a block diagonal structure. Examples ======== >>> from sympy import Matrix >>> from sympy.abc import x, y, z >>> A = Matrix([[1, 3, 0, 0], [y, z*z, 0, 0], [0, 0, x, 0], [0, 0, 0, 0]]) >>> a1, a2, a3 = A.get_diag_blocks() >>> a1 Matrix([ [1, 3], [y, z**2]]) >>> a2 Matrix([[x]]) >>> a3 Matrix([[0]]) """ return self._eval_get_diag_blocks() @classmethod def hstack(cls, *args): """Return a matrix formed by joining args horizontally (i.e. by repeated application of row_join). Examples ======== >>> from sympy.matrices import Matrix, eye >>> Matrix.hstack(eye(2), 2*eye(2)) Matrix([ [1, 0, 2, 0], [0, 1, 0, 2]]) """ if len(args) == 0: return cls._new() kls = type(args[0]) return reduce(kls.row_join, args) def reshape(self, rows, cols): """Reshape the matrix. Total number of elements must remain the same. Examples ======== >>> from sympy import Matrix >>> m = Matrix(2, 3, lambda i, j: 1) >>> m Matrix([ [1, 1, 1], [1, 1, 1]]) >>> m.reshape(1, 6) Matrix([[1, 1, 1, 1, 1, 1]]) >>> m.reshape(3, 2) Matrix([ [1, 1], [1, 1], [1, 1]]) """ if self.rows * self.cols != rows * cols: raise ValueError("Invalid reshape parameters %d %d" % (rows, cols)) return self._new(rows, cols, lambda i, j: self[i * cols + j]) def row_del(self, row): """Delete the specified row.""" if row < 0: row += self.rows if not 0 <= row < self.rows: raise ValueError("Row {} out of range.".format(row)) return self._eval_row_del(row) def row_insert(self, pos, other): """Insert one or more rows at the given row position. Examples ======== >>> from sympy import zeros, ones >>> M = zeros(3) >>> V = ones(1, 3) >>> M.row_insert(1, V) Matrix([ [0, 0, 0], [1, 1, 1], [0, 0, 0], [0, 0, 0]]) See Also ======== row col_insert """ from sympy.matrices import MutableMatrix # Allows you to build a matrix even if it is null matrix if not self: return self._new(other) pos = as_int(pos) if pos < 0: pos = self.rows + pos if pos < 0: pos = 0 elif pos > self.rows: pos = self.rows if self.cols != other.cols: raise ShapeError( "`self` and `other` must have the same number of columns.") return self._eval_row_insert(pos, other) def row_join(self, other): """Concatenates two matrices along self's last and rhs's first column Examples ======== >>> from sympy import zeros, ones >>> M = zeros(3) >>> V = ones(3, 1) >>> M.row_join(V) Matrix([ [0, 0, 0, 1], [0, 0, 0, 1], [0, 0, 0, 1]]) See Also ======== row col_join """ # A null matrix can always be stacked (see #10770) if self.cols == 0 and self.rows != other.rows: return self._new(other.rows, 0, []).row_join(other) if self.rows != other.rows: raise ShapeError( "`self` and `rhs` must have the same number of rows.") return self._eval_row_join(other) def row(self, i): """Elementary row selector. Examples ======== >>> from sympy import eye >>> eye(2).row(0) Matrix([[1, 0]]) See Also ======== col row_op row_swap row_del row_join row_insert """ return self[i, :] @property def shape(self): """The shape (dimensions) of the matrix as the 2-tuple (rows, cols). Examples ======== >>> from sympy.matrices import zeros >>> M = zeros(2, 3) >>> M.shape (2, 3) >>> M.rows 2 >>> M.cols 3 """ return (self.rows, self.cols) def tolist(self): """Return the Matrix as a nested Python list. Examples ======== >>> from sympy import Matrix, ones >>> m = Matrix(3, 3, range(9)) >>> m Matrix([ [0, 1, 2], [3, 4, 5], [6, 7, 8]]) >>> m.tolist() [[0, 1, 2], [3, 4, 5], [6, 7, 8]] >>> ones(3, 0).tolist() [[], [], []] When there are no rows then it will not be possible to tell how many columns were in the original matrix: >>> ones(0, 3).tolist() [] """ if not self.rows: return [] if not self.cols: return [[] for i in range(self.rows)] return self._eval_tolist() def vec(self): """Return the Matrix converted into a one column matrix by stacking columns Examples ======== >>> from sympy import Matrix >>> m=Matrix([[1, 3], [2, 4]]) >>> m Matrix([ [1, 3], [2, 4]]) >>> m.vec() Matrix([ [1], [2], [3], [4]]) See Also ======== vech """ return self._eval_vec() @classmethod def vstack(cls, *args): """Return a matrix formed by joining args vertically (i.e. by repeated application of col_join). Examples ======== >>> from sympy.matrices import Matrix, eye >>> Matrix.vstack(eye(2), 2*eye(2)) Matrix([ [1, 0], [0, 1], [2, 0], [0, 2]]) """ if len(args) == 0: return cls._new() kls = type(args[0]) return reduce(kls.col_join, args) class MatrixSpecial(MatrixRequired): """Construction of special matrices""" @classmethod def _eval_diag(cls, rows, cols, diag_dict): """diag_dict is a defaultdict containing all the entries of the diagonal matrix.""" def entry(i, j): return diag_dict[(i,j)] return cls._new(rows, cols, entry) @classmethod def _eval_eye(cls, rows, cols): def entry(i, j): return S.One if i == j else S.Zero return cls._new(rows, cols, entry) @classmethod def _eval_jordan_block(cls, rows, cols, eigenvalue, band='upper'): if band == 'lower': def entry(i, j): if i == j: return eigenvalue elif j + 1 == i: return S.One return S.Zero else: def entry(i, j): if i == j: return eigenvalue elif i + 1 == j: return S.One return S.Zero return cls._new(rows, cols, entry) @classmethod def _eval_ones(cls, rows, cols): def entry(i, j): return S.One return cls._new(rows, cols, entry) @classmethod def _eval_zeros(cls, rows, cols): def entry(i, j): return S.Zero return cls._new(rows, cols, entry) @classmethod def diag(kls, *args, **kwargs): """Returns a matrix with the specified diagonal. If matrices are passed, a block-diagonal matrix is created. kwargs ====== rows : rows of the resulting matrix; computed if not given. cols : columns of the resulting matrix; computed if not given. cls : class for the resulting matrix Examples ======== >>> from sympy.matrices import Matrix >>> Matrix.diag(1, 2, 3) Matrix([ [1, 0, 0], [0, 2, 0], [0, 0, 3]]) >>> Matrix.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]]) >>> Matrix.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]]) A given band off the diagonal can be made by padding with a vertical or horizontal "kerning" vector: >>> hpad = Matrix(0, 2, []) >>> vpad = Matrix(2, 0, []) >>> Matrix.diag(vpad, 1, 2, 3, hpad) + Matrix.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 of the resulting matrix can be affected with the ``cls`` keyword. >>> type(Matrix.diag(1)) >>> from sympy.matrices import ImmutableMatrix >>> type(Matrix.diag(1, cls=ImmutableMatrix)) """ klass = kwargs.get('cls', kls) # allow a sequence to be passed in as the only argument if len(args) == 1 and is_sequence(args[0]) and not getattr(args[0], 'is_Matrix', False): args = args[0] def size(m): """Compute the size of the diagonal block""" if hasattr(m, 'rows'): return m.rows, m.cols return 1, 1 diag_rows = sum(size(m)[0] for m in args) diag_cols = sum(size(m)[1] for m in args) rows = kwargs.get('rows', diag_rows) cols = kwargs.get('cols', diag_cols) if rows < diag_rows or cols < diag_cols: raise ValueError("A {} x {} diagnal matrix cannot accommodate a" "diagonal of size at least {} x {}.".format(rows, cols, diag_rows, diag_cols)) # fill a default dict with the diagonal entries diag_entries = defaultdict(lambda: S.Zero) row_pos, col_pos = 0, 0 for m in args: if hasattr(m, 'rows'): # in this case, we're a matrix for i in range(m.rows): for j in range(m.cols): diag_entries[(i + row_pos, j + col_pos)] = m[i, j] row_pos += m.rows col_pos += m.cols else: # in this case, we're a single value diag_entries[(row_pos, col_pos)] = m row_pos += 1 col_pos += 1 return klass._eval_diag(rows, cols, diag_entries) @classmethod def eye(kls, rows, cols=None, **kwargs): """Returns an identity matrix. Args ==== rows : rows of the matrix cols : cols of the matrix (if None, cols=rows) kwargs ====== cls : class of the returned matrix """ if cols is None: cols = rows klass = kwargs.get('cls', kls) rows, cols = as_int(rows), as_int(cols) return klass._eval_eye(rows, cols) @classmethod def jordan_block(kls, *args, **kwargs): """Returns a Jordan block with the specified size and eigenvalue. You may call `jordan_block` with two args (size, eigenvalue) or with keyword arguments. kwargs ====== size : rows and columns of the matrix rows : rows of the matrix (if None, rows=size) cols : cols of the matrix (if None, cols=size) eigenvalue : value on the diagonal of the matrix band : position of off-diagonal 1s. May be 'upper' or 'lower'. (Default: 'upper') cls : class of the returned matrix Examples ======== >>> from sympy import Matrix >>> from sympy.abc import x >>> Matrix.jordan_block(4, x) Matrix([ [x, 1, 0, 0], [0, x, 1, 0], [0, 0, x, 1], [0, 0, 0, x]]) >>> Matrix.jordan_block(4, x, band='lower') Matrix([ [x, 0, 0, 0], [1, x, 0, 0], [0, 1, x, 0], [0, 0, 1, x]]) >>> Matrix.jordan_block(size=4, eigenvalue=x) Matrix([ [x, 1, 0, 0], [0, x, 1, 0], [0, 0, x, 1], [0, 0, 0, x]]) """ klass = kwargs.get('cls', kls) size, eigenvalue = None, None if len(args) == 2: size, eigenvalue = args elif len(args) == 1: size = args[0] elif len(args) != 0: raise ValueError("'jordan_block' accepts 0, 1, or 2 arguments, not {}".format(len(args))) rows, cols = kwargs.get('rows', None), kwargs.get('cols', None) size = kwargs.get('size', size) band = kwargs.get('band', 'upper') # allow for a shortened form of `eigenvalue` eigenvalue = kwargs.get('eigenval', eigenvalue) eigenvalue = kwargs.get('eigenvalue', eigenvalue) if eigenvalue is None: raise ValueError("Must supply an eigenvalue") if (size, rows, cols) == (None, None, None): raise ValueError("Must supply a matrix size") if size is not None: rows, cols = size, size elif rows is not None and cols is None: cols = rows elif cols is not None and rows is None: rows = cols rows, cols = as_int(rows), as_int(cols) return klass._eval_jordan_block(rows, cols, eigenvalue, band) @classmethod def ones(kls, rows, cols=None, **kwargs): """Returns a matrix of ones. Args ==== rows : rows of the matrix cols : cols of the matrix (if None, cols=rows) kwargs ====== cls : class of the returned matrix """ if cols is None: cols = rows klass = kwargs.get('cls', kls) rows, cols = as_int(rows), as_int(cols) return klass._eval_ones(rows, cols) @classmethod def zeros(kls, rows, cols=None, **kwargs): """Returns a matrix of zeros. Args ==== rows : rows of the matrix cols : cols of the matrix (if None, cols=rows) kwargs ====== cls : class of the returned matrix """ if cols is None: cols = rows klass = kwargs.get('cls', kls) rows, cols = as_int(rows), as_int(cols) return klass._eval_zeros(rows, cols) class MatrixProperties(MatrixRequired): """Provides basic properties of a matrix.""" def _eval_atoms(self, *types): result = set() for i in self: result.update(i.atoms(*types)) return result def _eval_free_symbols(self): return set().union(*(i.free_symbols for i in self)) def _eval_has(self, *patterns): return any(a.has(*patterns) for a in self) def _eval_is_anti_symmetric(self, simpfunc): if not all(simpfunc(self[i, j] + self[j, i]).is_zero for i in range(self.rows) for j in range(self.cols)): return False return True def _eval_is_diagonal(self): for i in range(self.rows): for j in range(self.cols): if i != j and self[i, j]: return False return True # _eval_is_hermitian is called by some general sympy # routines and has a different *args signature. Make # sure the names don't clash by adding `_matrix_` in name. def _eval_is_matrix_hermitian(self, simpfunc): mat = self._new(self.rows, self.cols, lambda i, j: simpfunc(self[i, j] - self[j, i].conjugate())) return mat.is_zero def _eval_is_Identity(self): def dirac(i, j): if i == j: return 1 return 0 return all(self[i, j] == dirac(i, j) for i in range(self.rows) for j in range(self.cols)) def _eval_is_lower_hessenberg(self): return all(self[i, j].is_zero for i in range(self.rows) for j in range(i + 2, self.cols)) def _eval_is_lower(self): return all(self[i, j].is_zero for i in range(self.rows) for j in range(i + 1, self.cols)) def _eval_is_symbolic(self): return self.has(Symbol) def _eval_is_symmetric(self, simpfunc): mat = self._new(self.rows, self.cols, lambda i, j: simpfunc(self[i, j] - self[j, i])) return mat.is_zero def _eval_is_zero(self): if any(i.is_zero == False for i in self): return False if any(i.is_zero == None for i in self): return None return True def _eval_is_upper_hessenberg(self): return all(self[i, j].is_zero for i in range(2, self.rows) for j in range(min(self.cols, (i - 1)))) def _eval_values(self): return [i for i in self if not i.is_zero] def atoms(self, *types): """Returns the atoms that form the current object. Examples ======== >>> from sympy.abc import x, y >>> from sympy.matrices import Matrix >>> Matrix([[x]]) Matrix([[x]]) >>> _.atoms() {x} """ types = tuple(t if isinstance(t, type) else type(t) for t in types) if not types: types = (Atom,) return self._eval_atoms(*types) @property def free_symbols(self): """Returns the free symbols within the matrix. Examples ======== >>> from sympy.abc import x >>> from sympy.matrices import Matrix >>> Matrix([[x], [1]]).free_symbols {x} """ return self._eval_free_symbols() def has(self, *patterns): """Test whether any subexpression matches any of the patterns. Examples ======== >>> from sympy import Matrix, SparseMatrix, Float >>> from sympy.abc import x, y >>> A = Matrix(((1, x), (0.2, 3))) >>> B = SparseMatrix(((1, x), (0.2, 3))) >>> A.has(x) True >>> A.has(y) False >>> A.has(Float) True >>> B.has(x) True >>> B.has(y) False >>> B.has(Float) True """ return self._eval_has(*patterns) def is_anti_symmetric(self, simplify=True): """Check if matrix M is an antisymmetric matrix, that is, M is a square matrix with all M[i, j] == -M[j, i]. When ``simplify=True`` (default), the sum M[i, j] + M[j, i] is simplified before testing to see if it is zero. By default, the SymPy simplify function is used. To use a custom function set simplify to a function that accepts a single argument which returns a simplified expression. To skip simplification, set simplify to False but note that although this will be faster, it may induce false negatives. Examples ======== >>> from sympy import Matrix, symbols >>> m = Matrix(2, 2, [0, 1, -1, 0]) >>> m Matrix([ [ 0, 1], [-1, 0]]) >>> m.is_anti_symmetric() True >>> x, y = symbols('x y') >>> m = Matrix(2, 3, [0, 0, x, -y, 0, 0]) >>> m Matrix([ [ 0, 0, x], [-y, 0, 0]]) >>> m.is_anti_symmetric() False >>> from sympy.abc import x, y >>> m = Matrix(3, 3, [0, x**2 + 2*x + 1, y, ... -(x + 1)**2 , 0, x*y, ... -y, -x*y, 0]) Simplification of matrix elements is done by default so even though two elements which should be equal and opposite wouldn't pass an equality test, the matrix is still reported as anti-symmetric: >>> m[0, 1] == -m[1, 0] False >>> m.is_anti_symmetric() True If 'simplify=False' is used for the case when a Matrix is already simplified, this will speed things up. Here, we see that without simplification the matrix does not appear anti-symmetric: >>> m.is_anti_symmetric(simplify=False) False But if the matrix were already expanded, then it would appear anti-symmetric and simplification in the is_anti_symmetric routine is not needed: >>> m = m.expand() >>> m.is_anti_symmetric(simplify=False) True """ # accept custom simplification simpfunc = simplify if not isinstance(simplify, FunctionType): simpfunc = _simplify if simplify else lambda x: x if not self.is_square: return False return self._eval_is_anti_symmetric(simpfunc) def is_diagonal(self): """Check if matrix is diagonal, that is matrix in which the entries outside the main diagonal are all zero. Examples ======== >>> from sympy import Matrix, diag >>> m = Matrix(2, 2, [1, 0, 0, 2]) >>> m Matrix([ [1, 0], [0, 2]]) >>> m.is_diagonal() True >>> m = Matrix(2, 2, [1, 1, 0, 2]) >>> m Matrix([ [1, 1], [0, 2]]) >>> m.is_diagonal() False >>> m = diag(1, 2, 3) >>> m Matrix([ [1, 0, 0], [0, 2, 0], [0, 0, 3]]) >>> m.is_diagonal() True See Also ======== is_lower is_upper is_diagonalizable diagonalize """ return self._eval_is_diagonal() @property def is_hermitian(self, simplify=True): """Checks if the matrix is Hermitian. In a Hermitian matrix element i,j is the complex conjugate of element j,i. Examples ======== >>> from sympy.matrices import Matrix >>> from sympy import I >>> from sympy.abc import x >>> a = Matrix([[1, I], [-I, 1]]) >>> a Matrix([ [ 1, I], [-I, 1]]) >>> a.is_hermitian True >>> a[0, 0] = 2*I >>> a.is_hermitian False >>> a[0, 0] = x >>> a.is_hermitian >>> a[0, 1] = a[1, 0]*I >>> a.is_hermitian False """ if not self.is_square: return False simpfunc = simplify if not isinstance(simplify, FunctionType): simpfunc = _simplify if simplify else lambda x: x return self._eval_is_matrix_hermitian(simpfunc) @property def is_Identity(self): if not self.is_square: return False return self._eval_is_Identity() @property def is_lower_hessenberg(self): r"""Checks if the matrix is in the lower-Hessenberg form. The lower hessenberg matrix has zero entries above the first superdiagonal. Examples ======== >>> from sympy.matrices import Matrix >>> a = Matrix([[1, 2, 0, 0], [5, 2, 3, 0], [3, 4, 3, 7], [5, 6, 1, 1]]) >>> a Matrix([ [1, 2, 0, 0], [5, 2, 3, 0], [3, 4, 3, 7], [5, 6, 1, 1]]) >>> a.is_lower_hessenberg True See Also ======== is_upper_hessenberg is_lower """ return self._eval_is_lower_hessenberg() @property def is_lower(self): """Check if matrix is a lower triangular matrix. True can be returned even if the matrix is not square. Examples ======== >>> from sympy import Matrix >>> m = Matrix(2, 2, [1, 0, 0, 1]) >>> m Matrix([ [1, 0], [0, 1]]) >>> m.is_lower True >>> m = Matrix(4, 3, [0, 0, 0, 2, 0, 0, 1, 4 , 0, 6, 6, 5]) >>> m Matrix([ [0, 0, 0], [2, 0, 0], [1, 4, 0], [6, 6, 5]]) >>> m.is_lower True >>> from sympy.abc import x, y >>> m = Matrix(2, 2, [x**2 + y, y**2 + x, 0, x + y]) >>> m Matrix([ [x**2 + y, x + y**2], [ 0, x + y]]) >>> m.is_lower False See Also ======== is_upper is_diagonal is_lower_hessenberg """ return self._eval_is_lower() @property def is_square(self): """Checks if a matrix is square. A matrix is square if the number of rows equals the number of columns. The empty matrix is square by definition, since the number of rows and the number of columns are both zero. Examples ======== >>> from sympy import Matrix >>> a = Matrix([[1, 2, 3], [4, 5, 6]]) >>> b = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) >>> c = Matrix([]) >>> a.is_square False >>> b.is_square True >>> c.is_square True """ return self.rows == self.cols def is_symbolic(self): """Checks if any elements contain Symbols. Examples ======== >>> from sympy.matrices import Matrix >>> from sympy.abc import x, y >>> M = Matrix([[x, y], [1, 0]]) >>> M.is_symbolic() True """ return self._eval_is_symbolic() def is_symmetric(self, simplify=True): """Check if matrix is symmetric matrix, that is square matrix and is equal to its transpose. By default, simplifications occur before testing symmetry. They can be skipped using 'simplify=False'; while speeding things a bit, this may however induce false negatives. Examples ======== >>> from sympy import Matrix >>> m = Matrix(2, 2, [0, 1, 1, 2]) >>> m Matrix([ [0, 1], [1, 2]]) >>> m.is_symmetric() True >>> m = Matrix(2, 2, [0, 1, 2, 0]) >>> m Matrix([ [0, 1], [2, 0]]) >>> m.is_symmetric() False >>> m = Matrix(2, 3, [0, 0, 0, 0, 0, 0]) >>> m Matrix([ [0, 0, 0], [0, 0, 0]]) >>> m.is_symmetric() False >>> from sympy.abc import x, y >>> m = Matrix(3, 3, [1, x**2 + 2*x + 1, y, (x + 1)**2 , 2, 0, y, 0, 3]) >>> m Matrix([ [ 1, x**2 + 2*x + 1, y], [(x + 1)**2, 2, 0], [ y, 0, 3]]) >>> m.is_symmetric() True If the matrix is already simplified, you may speed-up is_symmetric() test by using 'simplify=False'. >>> bool(m.is_symmetric(simplify=False)) False >>> m1 = m.expand() >>> m1.is_symmetric(simplify=False) True """ simpfunc = simplify if not isinstance(simplify, FunctionType): simpfunc = _simplify if simplify else lambda x: x if not self.is_square: return False return self._eval_is_symmetric(simpfunc) @property def is_upper_hessenberg(self): """Checks if the matrix is the upper-Hessenberg form. The upper hessenberg matrix has zero entries below the first subdiagonal. Examples ======== >>> from sympy.matrices import Matrix >>> a = Matrix([[1, 4, 2, 3], [3, 4, 1, 7], [0, 2, 3, 4], [0, 0, 1, 3]]) >>> a Matrix([ [1, 4, 2, 3], [3, 4, 1, 7], [0, 2, 3, 4], [0, 0, 1, 3]]) >>> a.is_upper_hessenberg True See Also ======== is_lower_hessenberg is_upper """ return self._eval_is_upper_hessenberg() @property def is_upper(self): """Check if matrix is an upper triangular matrix. True can be returned even if the matrix is not square. Examples ======== >>> from sympy import Matrix >>> m = Matrix(2, 2, [1, 0, 0, 1]) >>> m Matrix([ [1, 0], [0, 1]]) >>> m.is_upper True >>> m = Matrix(4, 3, [5, 1, 9, 0, 4 , 6, 0, 0, 5, 0, 0, 0]) >>> m Matrix([ [5, 1, 9], [0, 4, 6], [0, 0, 5], [0, 0, 0]]) >>> m.is_upper True >>> m = Matrix(2, 3, [4, 2, 5, 6, 1, 1]) >>> m Matrix([ [4, 2, 5], [6, 1, 1]]) >>> m.is_upper False See Also ======== is_lower is_diagonal is_upper_hessenberg """ return all(self[i, j].is_zero for i in range(1, self.rows) for j in range(min(i, self.cols))) @property def is_zero(self): """Checks if a matrix is a zero matrix. A matrix is zero if every element is zero. A matrix need not be square to be considered zero. The empty matrix is zero by the principle of vacuous truth. For a matrix that may or may not be zero (e.g. contains a symbol), this will be None Examples ======== >>> from sympy import Matrix, zeros >>> from sympy.abc import x >>> a = Matrix([[0, 0], [0, 0]]) >>> b = zeros(3, 4) >>> c = Matrix([[0, 1], [0, 0]]) >>> d = Matrix([]) >>> e = Matrix([[x, 0], [0, 0]]) >>> a.is_zero True >>> b.is_zero True >>> c.is_zero False >>> d.is_zero True >>> e.is_zero """ return self._eval_is_zero() def values(self): """Return non-zero values of self.""" return self._eval_values() class MatrixOperations(MatrixRequired): """Provides basic matrix shape and elementwise operations. Should not be instantiated directly.""" def _eval_adjoint(self): return self.transpose().conjugate() def _eval_applyfunc(self, f): out = self._new(self.rows, self.cols, [f(x) for x in self]) return out def _eval_as_real_imag(self): from sympy.functions.elementary.complexes import re, im return (self.applyfunc(re), self.applyfunc(im)) def _eval_conjugate(self): return self.applyfunc(lambda x: x.conjugate()) def _eval_permute_cols(self, perm): # apply the permutation to a list mapping = list(perm) def entry(i, j): return self[i, mapping[j]] return self._new(self.rows, self.cols, entry) def _eval_permute_rows(self, perm): # apply the permutation to a list mapping = list(perm) def entry(i, j): return self[mapping[i], j] return self._new(self.rows, self.cols, entry) def _eval_trace(self): return sum(self[i, i] for i in range(self.rows)) def _eval_transpose(self): return self._new(self.cols, self.rows, lambda i, j: self[j, i]) def adjoint(self): """Conjugate transpose or Hermitian conjugation.""" return self._eval_adjoint() def applyfunc(self, f): """Apply a function to each element of the matrix. Examples ======== >>> from sympy import Matrix >>> m = Matrix(2, 2, lambda i, j: i*2+j) >>> m Matrix([ [0, 1], [2, 3]]) >>> m.applyfunc(lambda i: 2*i) Matrix([ [0, 2], [4, 6]]) """ if not callable(f): raise TypeError("`f` must be callable.") return self._eval_applyfunc(f) def as_real_imag(self): """Returns a tuple containing the (real, imaginary) part of matrix.""" return self._eval_as_real_imag() def conjugate(self): """Return the by-element conjugation. Examples ======== >>> from sympy.matrices import SparseMatrix >>> from sympy import I >>> a = SparseMatrix(((1, 2 + I), (3, 4), (I, -I))) >>> a Matrix([ [1, 2 + I], [3, 4], [I, -I]]) >>> a.C Matrix([ [ 1, 2 - I], [ 3, 4], [-I, I]]) See Also ======== transpose: Matrix transposition H: Hermite conjugation D: Dirac conjugation """ return self._eval_conjugate() def doit(self, **kwargs): return self.applyfunc(lambda x: x.doit()) def evalf(self, prec=None, **options): """Apply evalf() to each element of self.""" return self.applyfunc(lambda i: i.evalf(prec, **options)) def expand(self, deep=True, modulus=None, power_base=True, power_exp=True, mul=True, log=True, multinomial=True, basic=True, **hints): """Apply core.function.expand to each entry of the matrix. Examples ======== >>> from sympy.abc import x >>> from sympy.matrices import Matrix >>> Matrix(1, 1, [x*(x+1)]) Matrix([[x*(x + 1)]]) >>> _.expand() Matrix([[x**2 + x]]) """ return self.applyfunc(lambda x: x.expand( deep, modulus, power_base, power_exp, mul, log, multinomial, basic, **hints)) @property def H(self): """Return Hermite conjugate. Examples ======== >>> from sympy import Matrix, I >>> m = Matrix((0, 1 + I, 2, 3)) >>> m Matrix([ [ 0], [1 + I], [ 2], [ 3]]) >>> m.H Matrix([[0, 1 - I, 2, 3]]) See Also ======== conjugate: By-element conjugation D: Dirac conjugation """ return self.T.C def permute(self, perm, orientation='rows', direction='forward'): """Permute the rows or columns of a matrix by the given list of swaps. Parameters ========== perm : a permutation. This may be a list swaps (e.g., `[[1, 2], [0, 3]]`), or any valid input to the `Permutation` constructor, including a `Permutation()` itself. If `perm` is given explicitly as a list of indices or a `Permutation`, `direction` has no effect. orientation : ('rows' or 'cols') whether to permute the rows or the columns direction : ('forward', 'backward') whether to apply the permutations from the start of the list first, or from the back of the list first Examples ======== >>> from sympy.matrices import eye >>> M = eye(3) >>> M.permute([[0, 1], [0, 2]], orientation='rows', direction='forward') Matrix([ [0, 0, 1], [1, 0, 0], [0, 1, 0]]) >>> from sympy.matrices import eye >>> M = eye(3) >>> M.permute([[0, 1], [0, 2]], orientation='rows', direction='backward') Matrix([ [0, 1, 0], [0, 0, 1], [1, 0, 0]]) """ # allow british variants and `columns` if direction == 'forwards': direction = 'forward' if direction == 'backwards': direction = 'backward' if orientation == 'columns': orientation = 'cols' if direction not in ('forward', 'backward'): raise TypeError("direction='{}' is an invalid kwarg. " "Try 'forward' or 'backward'".format(direction)) if orientation not in ('rows', 'cols'): raise TypeError("orientation='{}' is an invalid kwarg. " "Try 'rows' or 'cols'".format(orientation)) # ensure all swaps are in range max_index = self.rows if orientation == 'rows' else self.cols if not all(0 <= t <= max_index for t in flatten(list(perm))): raise IndexError("`swap` indices out of range.") # see if we are a list of pairs try: assert len(perm[0]) == 2 # we are a list of swaps, so `direction` matters if direction == 'backward': perm = reversed(perm) # since Permutation doesn't let us have non-disjoint cycles, # we'll construct the explicit mapping ourselves XXX Bug #12479 mapping = list(range(max_index)) for (i, j) in perm: mapping[i], mapping[j] = mapping[j], mapping[i] perm = mapping except (TypeError, AssertionError, IndexError): pass from sympy.combinatorics import Permutation perm = Permutation(perm, size=max_index) if orientation == 'rows': return self._eval_permute_rows(perm) if orientation == 'cols': return self._eval_permute_cols(perm) def permute_cols(self, swaps, direction='forward'): """Alias for `self.permute(swaps, orientation='cols', direction=direction)` See Also ======== permute """ return self.permute(swaps, orientation='cols', direction=direction) def permute_rows(self, swaps, direction='forward'): """Alias for `self.permute(swaps, orientation='rows', direction=direction)` See Also ======== permute """ return self.permute(swaps, orientation='rows', direction=direction) def refine(self, assumptions=True): """Apply refine to each element of the matrix. Examples ======== >>> from sympy import Symbol, Matrix, Abs, sqrt, Q >>> x = Symbol('x') >>> Matrix([[Abs(x)**2, sqrt(x**2)],[sqrt(x**2), Abs(x)**2]]) Matrix([ [ Abs(x)**2, sqrt(x**2)], [sqrt(x**2), Abs(x)**2]]) >>> _.refine(Q.real(x)) Matrix([ [ x**2, Abs(x)], [Abs(x), x**2]]) """ return self.applyfunc(lambda x: refine(x, assumptions)) def replace(self, F, G, map=False): """Replaces Function F in Matrix entries with Function G. Examples ======== >>> from sympy import symbols, Function, Matrix >>> F, G = symbols('F, G', cls=Function) >>> M = Matrix(2, 2, lambda i, j: F(i+j)) ; M Matrix([ [F(0), F(1)], [F(1), F(2)]]) >>> N = M.replace(F,G) >>> N Matrix([ [G(0), G(1)], [G(1), G(2)]]) """ return self.applyfunc(lambda x: x.replace(F, G, map)) def simplify(self, ratio=1.7, measure=count_ops, rational=False, inverse=False): """Apply simplify to each element of the matrix. Examples ======== >>> from sympy.abc import x, y >>> from sympy import sin, cos >>> from sympy.matrices import SparseMatrix >>> SparseMatrix(1, 1, [x*sin(y)**2 + x*cos(y)**2]) Matrix([[x*sin(y)**2 + x*cos(y)**2]]) >>> _.simplify() Matrix([[x]]) """ return self.applyfunc(lambda x: x.simplify(ratio=ratio, measure=measure, rational=rational, inverse=inverse)) def subs(self, *args, **kwargs): # should mirror core.basic.subs """Return a new matrix with subs applied to each entry. Examples ======== >>> from sympy.abc import x, y >>> from sympy.matrices import SparseMatrix, Matrix >>> SparseMatrix(1, 1, [x]) Matrix([[x]]) >>> _.subs(x, y) Matrix([[y]]) >>> Matrix(_).subs(y, x) Matrix([[x]]) """ return self.applyfunc(lambda x: x.subs(*args, **kwargs)) def trace(self): """ Returns the trace of a square matrix i.e. the sum of the diagonal elements. Examples ======== >>> from sympy import Matrix >>> A = Matrix(2, 2, [1, 2, 3, 4]) >>> A.trace() 5 """ if not self.rows == self.cols: raise NonSquareMatrixError() return self._eval_trace() def transpose(self): """ Returns the transpose of the matrix. Examples ======== >>> from sympy import Matrix >>> A = Matrix(2, 2, [1, 2, 3, 4]) >>> A.transpose() Matrix([ [1, 3], [2, 4]]) >>> from sympy import Matrix, I >>> m=Matrix(((1, 2+I), (3, 4))) >>> m Matrix([ [1, 2 + I], [3, 4]]) >>> m.transpose() Matrix([ [ 1, 3], [2 + I, 4]]) >>> m.T == m.transpose() True See Also ======== conjugate: By-element conjugation """ return self._eval_transpose() T = property(transpose, None, None, "Matrix transposition.") C = property(conjugate, None, None, "By-element conjugation.") n = evalf def xreplace(self, rule): # should mirror core.basic.xreplace """Return a new matrix with xreplace applied to each entry. Examples ======== >>> from sympy.abc import x, y >>> from sympy.matrices import SparseMatrix, Matrix >>> SparseMatrix(1, 1, [x]) Matrix([[x]]) >>> _.xreplace({x: y}) Matrix([[y]]) >>> Matrix(_).xreplace({y: x}) Matrix([[x]]) """ return self.applyfunc(lambda x: x.xreplace(rule)) _eval_simplify = simplify def _eval_trigsimp(self, **opts): from sympy.simplify import trigsimp return self.applyfunc(lambda x: trigsimp(x, **opts)) class MatrixArithmetic(MatrixRequired): """Provides basic matrix arithmetic operations. Should not be instantiated directly.""" _op_priority = 10.01 def _eval_Abs(self): return self._new(self.rows, self.cols, lambda i, j: Abs(self[i, j])) def _eval_add(self, other): return self._new(self.rows, self.cols, lambda i, j: self[i, j] + other[i, j]) def _eval_matrix_mul(self, other): def entry(i, j): try: return sum(self[i,k]*other[k,j] for k in range(self.cols)) except TypeError: # 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. ret = self[i, 0]*other[0, j] for k in range(1, self.cols): ret += self[i, k]*other[k, j] return ret return self._new(self.rows, other.cols, entry) def _eval_matrix_mul_elementwise(self, other): return self._new(self.rows, self.cols, lambda i, j: self[i,j]*other[i,j]) def _eval_matrix_rmul(self, other): def entry(i, j): return sum(other[i,k]*self[k,j] for k in range(other.cols)) return self._new(other.rows, self.cols, entry) def _eval_pow_by_recursion(self, num): if num == 1: return self if num % 2 == 1: return self * self._eval_pow_by_recursion(num - 1) ret = self._eval_pow_by_recursion(num // 2) return ret * ret def _eval_scalar_mul(self, other): return self._new(self.rows, self.cols, lambda i, j: self[i,j]*other) def _eval_scalar_rmul(self, other): return self._new(self.rows, self.cols, lambda i, j: other*self[i,j]) def _eval_Mod(self, other): from sympy import Mod return self._new(self.rows, self.cols, lambda i, j: Mod(self[i, j], other)) # python arithmetic functions def __abs__(self): """Returns a new matrix with entry-wise absolute values.""" return self._eval_Abs() @call_highest_priority('__radd__') def __add__(self, other): """Return self + other, raising ShapeError if shapes don't match.""" other = _matrixify(other) # matrix-like objects can have shapes. This is # our first sanity check. if hasattr(other, 'shape'): if self.shape != other.shape: raise ShapeError("Matrix size mismatch: %s + %s" % ( self.shape, other.shape)) # honest sympy matrices defer to their class's routine if getattr(other, 'is_Matrix', False): # call the highest-priority class's _eval_add a, b = self, other if a.__class__ != classof(a, b): b, a = a, b return a._eval_add(b) # Matrix-like objects can be passed to CommonMatrix routines directly. if getattr(other, 'is_MatrixLike', False): return MatrixArithmetic._eval_add(self, other) raise TypeError('cannot add %s and %s' % (type(self), type(other))) @call_highest_priority('__rdiv__') def __div__(self, other): return self * (S.One / other) @call_highest_priority('__rmatmul__') def __matmul__(self, other): other = _matrixify(other) if not getattr(other, 'is_Matrix', False) and not getattr(other, 'is_MatrixLike', False): return NotImplemented return self.__mul__(other) @call_highest_priority('__rmul__') def __mul__(self, other): """Return self*other where other is either a scalar or a matrix of compatible dimensions. Examples ======== >>> from sympy.matrices import Matrix >>> A = Matrix([[1, 2, 3], [4, 5, 6]]) >>> 2*A == A*2 == Matrix([[2, 4, 6], [8, 10, 12]]) True >>> B = Matrix([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) >>> A*B Matrix([ [30, 36, 42], [66, 81, 96]]) >>> B*A Traceback (most recent call last): ... ShapeError: Matrices size mismatch. >>> See Also ======== matrix_multiply_elementwise """ other = _matrixify(other) # matrix-like objects can have shapes. This is # our first sanity check. if hasattr(other, 'shape') and len(other.shape) == 2: if self.shape[1] != other.shape[0]: raise ShapeError("Matrix size mismatch: %s * %s." % ( self.shape, other.shape)) # honest sympy matrices defer to their class's routine if getattr(other, 'is_Matrix', False): return self._eval_matrix_mul(other) # Matrix-like objects can be passed to CommonMatrix routines directly. if getattr(other, 'is_MatrixLike', False): return MatrixArithmetic._eval_matrix_mul(self, other) # if 'other' is not iterable then scalar multiplication. if not isinstance(other, Iterable): try: return self._eval_scalar_mul(other) except TypeError: pass return NotImplemented def __neg__(self): return self._eval_scalar_mul(-1) @call_highest_priority('__rpow__') def __pow__(self, num): if not self.rows == self.cols: raise NonSquareMatrixError() try: a = self num = sympify(num) if num.is_Number and num % 1 == 0: if a.rows == 1: return a._new([[a[0]**num]]) if num == 0: return self._new(self.rows, self.cols, lambda i, j: int(i == j)) if num < 0: num = -num a = a.inv() # When certain conditions are met, # Jordan block algorithm is faster than # computation by recursion. elif a.rows == 2 and num > 100000: try: return a._matrix_pow_by_jordan_blocks(num) except (AttributeError, MatrixError): pass return a._eval_pow_by_recursion(num) elif isinstance(num, (Expr, float)): return a._matrix_pow_by_jordan_blocks(num) else: raise TypeError( "Only SymPy expressions or integers are supported as exponent for matrices") except AttributeError: raise TypeError("Don't know how to raise {} to {}".format(self.__class__, num)) @call_highest_priority('__add__') def __radd__(self, other): return self + other @call_highest_priority('__matmul__') def __rmatmul__(self, other): other = _matrixify(other) if not getattr(other, 'is_Matrix', False) and not getattr(other, 'is_MatrixLike', False): return NotImplemented return self.__rmul__(other) @call_highest_priority('__mul__') def __rmul__(self, other): other = _matrixify(other) # matrix-like objects can have shapes. This is # our first sanity check. if hasattr(other, 'shape') and len(other.shape) == 2: if self.shape[0] != other.shape[1]: raise ShapeError("Matrix size mismatch.") # honest sympy matrices defer to their class's routine if getattr(other, 'is_Matrix', False): return other._new(other.as_mutable() * self) # Matrix-like objects can be passed to CommonMatrix routines directly. if getattr(other, 'is_MatrixLike', False): return MatrixArithmetic._eval_matrix_rmul(self, other) # if 'other' is not iterable then scalar multiplication. if not isinstance(other, Iterable): try: return self._eval_scalar_rmul(other) except TypeError: pass return NotImplemented @call_highest_priority('__sub__') def __rsub__(self, a): return (-self) + a @call_highest_priority('__rsub__') def __sub__(self, a): return self + (-a) @call_highest_priority('__rtruediv__') def __truediv__(self, other): return self.__div__(other) def multiply_elementwise(self, other): """Return the Hadamard product (elementwise product) of A and B Examples ======== >>> from sympy.matrices import Matrix >>> A = Matrix([[0, 1, 2], [3, 4, 5]]) >>> B = Matrix([[1, 10, 100], [100, 10, 1]]) >>> A.multiply_elementwise(B) Matrix([ [ 0, 10, 200], [300, 40, 5]]) See Also ======== cross dot multiply """ if self.shape != other.shape: raise ShapeError("Matrix shapes must agree {} != {}".format(self.shape, other.shape)) return self._eval_matrix_mul_elementwise(other) class MatrixCommon(MatrixArithmetic, MatrixOperations, MatrixProperties, MatrixSpecial, MatrixShaping): """All common matrix operations including basic arithmetic, shaping, and special matrices like `zeros`, and `eye`.""" _diff_wrt = True class _MinimalMatrix(object): """Class providing the minimum functionality for a matrix-like object and implementing every method required for a `MatrixRequired`. This class does not have everything needed to become a full-fledged sympy object, but it will satisfy the requirements of anything inheriting from `MatrixRequired`. If you wish to make a specialized matrix type, make sure to implement these methods and properties with the exception of `__init__` and `__repr__` which are included for convenience.""" is_MatrixLike = True _sympify = staticmethod(sympify) _class_priority = 3 is_Matrix = True is_MatrixExpr = False @classmethod def _new(cls, *args, **kwargs): return cls(*args, **kwargs) def __init__(self, rows, cols=None, mat=None): if isinstance(mat, FunctionType): # if we passed in a function, use that to populate the indices mat = list(mat(i, j) for i in range(rows) for j in range(cols)) try: if cols is None and mat is None: mat = rows rows, cols = mat.shape except AttributeError: pass try: # if we passed in a list of lists, flatten it and set the size if cols is None and mat is None: mat = rows cols = len(mat[0]) rows = len(mat) mat = [x for l in mat for x in l] except (IndexError, TypeError): pass self.mat = tuple(self._sympify(x) for x in mat) self.rows, self.cols = rows, cols if self.rows is None or self.cols is None: raise NotImplementedError("Cannot initialize matrix with given parameters") def __getitem__(self, key): def _normalize_slices(row_slice, col_slice): """Ensure that row_slice and col_slice don't have `None` in their arguments. Any integers are converted to slices of length 1""" if not isinstance(row_slice, slice): row_slice = slice(row_slice, row_slice + 1, None) row_slice = slice(*row_slice.indices(self.rows)) if not isinstance(col_slice, slice): col_slice = slice(col_slice, col_slice + 1, None) col_slice = slice(*col_slice.indices(self.cols)) return (row_slice, col_slice) def _coord_to_index(i, j): """Return the index in _mat corresponding to the (i,j) position in the matrix. """ return i * self.cols + j if isinstance(key, tuple): i, j = key if isinstance(i, slice) or isinstance(j, slice): # if the coordinates are not slices, make them so # and expand the slices so they don't contain `None` i, j = _normalize_slices(i, j) rowsList, colsList = list(range(self.rows))[i], \ list(range(self.cols))[j] indices = (i * self.cols + j for i in rowsList for j in colsList) return self._new(len(rowsList), len(colsList), list(self.mat[i] for i in indices)) # if the key is a tuple of ints, change # it to an array index key = _coord_to_index(i, j) return self.mat[key] def __eq__(self, other): return self.shape == other.shape and list(self) == list(other) def __len__(self): return self.rows*self.cols def __repr__(self): return "_MinimalMatrix({}, {}, {})".format(self.rows, self.cols, self.mat) @property def shape(self): return (self.rows, self.cols) class _MatrixWrapper(object): """Wrapper class providing the minimum functionality for a matrix-like object: .rows, .cols, .shape, indexability, and iterability. CommonMatrix math operations should work on matrix-like objects. For example, wrapping a numpy matrix in a MatrixWrapper allows it to be passed to CommonMatrix. """ is_MatrixLike = True def __init__(self, mat, shape=None): self.mat = mat self.rows, self.cols = mat.shape if shape is None else shape def __getattr__(self, attr): """Most attribute access is passed straight through to the stored matrix""" return getattr(self.mat, attr) def __getitem__(self, key): return self.mat.__getitem__(key) def _matrixify(mat): """If `mat` is a Matrix or is matrix-like, return a Matrix or MatrixWrapper object. Otherwise `mat` is passed through without modification.""" if getattr(mat, 'is_Matrix', False): return mat if hasattr(mat, 'shape'): if len(mat.shape) == 2: return _MatrixWrapper(mat) return mat def a2idx(j, n=None): """Return integer after making positive and validating against n.""" if type(j) is not int: try: j = j.__index__() except AttributeError: raise IndexError("Invalid index a[%r]" % (j,)) if n is not None: if j < 0: j += n if not (j >= 0 and j < n): raise IndexError("Index out of range: a[%s]" % (j,)) return int(j) def classof(A, B): """ Get the type of the result when combining matrices of different types. Currently the strategy is that immutability is contagious. Examples ======== >>> from sympy import Matrix, ImmutableMatrix >>> from sympy.matrices.common import classof >>> M = Matrix([[1, 2], [3, 4]]) # a Mutable Matrix >>> IM = ImmutableMatrix([[1, 2], [3, 4]]) >>> classof(M, IM) """ try: if A._class_priority > B._class_priority: return A.__class__ else: return B.__class__ except AttributeError: pass try: import numpy if isinstance(A, numpy.ndarray): return B.__class__ if isinstance(B, numpy.ndarray): return A.__class__ except (AttributeError, ImportError): pass raise TypeError("Incompatible classes %s, %s" % (A.__class__, B.__class__))