| | from mpmath.matrices.matrices import _matrix |
| |
|
| | from sympy.core import Basic, Dict, Tuple |
| | from sympy.core.numbers import Integer |
| | from sympy.core.cache import cacheit |
| | from sympy.core.sympify import _sympy_converter as sympify_converter, _sympify |
| | from sympy.matrices.dense import DenseMatrix |
| | from sympy.matrices.expressions import MatrixExpr |
| | from sympy.matrices.matrixbase import MatrixBase |
| | from sympy.matrices.repmatrix import RepMatrix |
| | from sympy.matrices.sparse import SparseRepMatrix |
| | from sympy.multipledispatch import dispatch |
| |
|
| |
|
| | def sympify_matrix(arg): |
| | return arg.as_immutable() |
| |
|
| |
|
| | sympify_converter[MatrixBase] = sympify_matrix |
| |
|
| |
|
| | def sympify_mpmath_matrix(arg): |
| | mat = [_sympify(x) for x in arg] |
| | return ImmutableDenseMatrix(arg.rows, arg.cols, mat) |
| |
|
| |
|
| | sympify_converter[_matrix] = sympify_mpmath_matrix |
| |
|
| |
|
| | class ImmutableRepMatrix(RepMatrix, MatrixExpr): |
| | """Immutable matrix based on RepMatrix |
| | |
| | Uses DomainMAtrix as the internal representation. |
| | """ |
| |
|
| | |
| | |
| | |
| | |
| | |
| |
|
| | def __new__(cls, *args, **kwargs): |
| | return cls._new(*args, **kwargs) |
| |
|
| | __hash__ = MatrixExpr.__hash__ |
| |
|
| | def copy(self): |
| | return self |
| |
|
| | @property |
| | def cols(self): |
| | return self._cols |
| |
|
| | @property |
| | def rows(self): |
| | return self._rows |
| |
|
| | @property |
| | def shape(self): |
| | return self._rows, self._cols |
| |
|
| | def as_immutable(self): |
| | return self |
| |
|
| | def _entry(self, i, j, **kwargs): |
| | return self[i, j] |
| |
|
| | def __setitem__(self, *args): |
| | raise TypeError("Cannot set values of {}".format(self.__class__)) |
| |
|
| | def is_diagonalizable(self, reals_only=False, **kwargs): |
| | return super().is_diagonalizable( |
| | reals_only=reals_only, **kwargs) |
| |
|
| | is_diagonalizable.__doc__ = SparseRepMatrix.is_diagonalizable.__doc__ |
| | is_diagonalizable = cacheit(is_diagonalizable) |
| |
|
| | def analytic_func(self, f, x): |
| | return self.as_mutable().analytic_func(f, x).as_immutable() |
| |
|
| |
|
| | class ImmutableDenseMatrix(DenseMatrix, ImmutableRepMatrix): |
| | """Create an immutable version of a matrix. |
| | |
| | Examples |
| | ======== |
| | |
| | >>> from sympy import eye, ImmutableMatrix |
| | >>> ImmutableMatrix(eye(3)) |
| | Matrix([ |
| | [1, 0, 0], |
| | [0, 1, 0], |
| | [0, 0, 1]]) |
| | >>> _[0, 0] = 42 |
| | Traceback (most recent call last): |
| | ... |
| | TypeError: Cannot set values of ImmutableDenseMatrix |
| | """ |
| |
|
| | |
| | |
| | _iterable = True |
| | _class_priority = 8 |
| | _op_priority = 10.001 |
| |
|
| | @classmethod |
| | def _new(cls, *args, **kwargs): |
| | if len(args) == 1 and isinstance(args[0], ImmutableDenseMatrix): |
| | return args[0] |
| | 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) |
| |
|
| | rep = cls._flat_list_to_DomainMatrix(rows, cols, flat_list) |
| |
|
| | return cls._fromrep(rep) |
| |
|
| | @classmethod |
| | def _fromrep(cls, rep): |
| | rows, cols = rep.shape |
| | flat_list = rep.to_sympy().to_list_flat() |
| | obj = Basic.__new__(cls, |
| | Integer(rows), |
| | Integer(cols), |
| | Tuple(*flat_list, sympify=False)) |
| | obj._rows = rows |
| | obj._cols = cols |
| | obj._rep = rep |
| | return obj |
| |
|
| |
|
| | |
| | ImmutableMatrix = ImmutableDenseMatrix |
| |
|
| |
|
| | class ImmutableSparseMatrix(SparseRepMatrix, ImmutableRepMatrix): |
| | """Create an immutable version of a sparse matrix. |
| | |
| | Examples |
| | ======== |
| | |
| | >>> from sympy import eye, ImmutableSparseMatrix |
| | >>> ImmutableSparseMatrix(1, 1, {}) |
| | Matrix([[0]]) |
| | >>> ImmutableSparseMatrix(eye(3)) |
| | Matrix([ |
| | [1, 0, 0], |
| | [0, 1, 0], |
| | [0, 0, 1]]) |
| | >>> _[0, 0] = 42 |
| | Traceback (most recent call last): |
| | ... |
| | TypeError: Cannot set values of ImmutableSparseMatrix |
| | >>> _.shape |
| | (3, 3) |
| | """ |
| | is_Matrix = True |
| | _class_priority = 9 |
| |
|
| | @classmethod |
| | def _new(cls, *args, **kwargs): |
| | rows, cols, smat = cls._handle_creation_inputs(*args, **kwargs) |
| |
|
| | rep = cls._smat_to_DomainMatrix(rows, cols, smat) |
| |
|
| | return cls._fromrep(rep) |
| |
|
| | @classmethod |
| | def _fromrep(cls, rep): |
| | rows, cols = rep.shape |
| | smat = rep.to_sympy().to_dok() |
| | obj = Basic.__new__(cls, Integer(rows), Integer(cols), Dict(smat)) |
| | obj._rows = rows |
| | obj._cols = cols |
| | obj._rep = rep |
| | return obj |
| |
|
| |
|
| | @dispatch(ImmutableDenseMatrix, ImmutableDenseMatrix) |
| | def _eval_is_eq(lhs, rhs): |
| | """Helper method for Equality with matrices.sympy. |
| | |
| | Relational automatically converts matrices to ImmutableDenseMatrix |
| | instances, so this method only applies here. Returns True if the |
| | matrices are definitively the same, False if they are definitively |
| | different, and None if undetermined (e.g. if they contain Symbols). |
| | Returning None triggers default handling of Equalities. |
| | |
| | """ |
| | if lhs.shape != rhs.shape: |
| | return False |
| | return (lhs - rhs).is_zero_matrix |
| |
|