switching to high quality piper tts and added label translations

2026-01-29 23:48:19 +01:00
commit d80c619df9
3934 changed files with 1451600 additions and 0 deletions
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+from .matexpr import MatrixExpr
+from .special import Identity
+from sympy.core import S
+from sympy.core.expr import ExprBuilder
+from sympy.core.cache import cacheit
+from sympy.core.power import Pow
+from sympy.core.sympify import _sympify
+from sympy.matrices import MatrixBase
+from sympy.matrices.exceptions import NonSquareMatrixError
+
+
+class MatPow(MatrixExpr):
+    def __new__(cls, base, exp, evaluate=False, **options):
+        base = _sympify(base)
+        if not base.is_Matrix:
+            raise TypeError("MatPow base should be a matrix")
+
+        if base.is_square is False:
+            raise NonSquareMatrixError("Power of non-square matrix %s" % base)
+
+        exp = _sympify(exp)
+        obj = super().__new__(cls, base, exp)
+
+        if evaluate:
+            obj = obj.doit(deep=False)
+
+        return obj
+
+    @property
+    def base(self):
+        return self.args[0]
+
+    @property
+    def exp(self):
+        return self.args[1]
+
+    @property
+    def shape(self):
+        return self.base.shape
+
+    @cacheit
+    def _get_explicit_matrix(self):
+        return self.base.as_explicit()**self.exp
+
+    def _entry(self, i, j, **kwargs):
+        from sympy.matrices.expressions import MatMul
+        A = self.doit()
+        if isinstance(A, MatPow):
+            # We still have a MatPow, make an explicit MatMul out of it.
+            if A.exp.is_Integer and A.exp.is_positive:
+                A = MatMul(*[A.base for k in range(A.exp)])
+            elif not self._is_shape_symbolic():
+                return A._get_explicit_matrix()[i, j]
+            else:
+                # Leave the expression unevaluated:
+                from sympy.matrices.expressions.matexpr import MatrixElement
+                return MatrixElement(self, i, j)
+        return A[i, j]
+
+    def doit(self, **hints):
+        if hints.get('deep', True):
+            base, exp = (arg.doit(**hints) for arg in self.args)
+        else:
+            base, exp = self.args
+
+        # combine all powers, e.g. (A ** 2) ** 3 -> A ** 6
+        while isinstance(base, MatPow):
+            exp *= base.args[1]
+            base = base.args[0]
+
+        if isinstance(base, MatrixBase):
+            # Delegate
+            return base ** exp
+
+        # Handle simple cases so that _eval_power() in MatrixExpr sub-classes can ignore them
+        if exp == S.One:
+            return base
+        if exp == S.Zero:
+            return Identity(base.rows)
+        if exp == S.NegativeOne:
+            from sympy.matrices.expressions import Inverse
+            return Inverse(base).doit(**hints)
+
+        eval_power = getattr(base, '_eval_power', None)
+        if eval_power is not None:
+            return eval_power(exp)
+
+        return MatPow(base, exp)
+
+    def _eval_transpose(self):
+        base, exp = self.args
+        return MatPow(base.transpose(), exp)
+
+    def _eval_adjoint(self):
+        base, exp = self.args
+        return MatPow(base.adjoint(), exp)
+
+    def _eval_conjugate(self):
+        base, exp = self.args
+        return MatPow(base.conjugate(), exp)
+
+    def _eval_derivative(self, x):
+        return Pow._eval_derivative(self, x)
+
+    def _eval_derivative_matrix_lines(self, x):
+        from sympy.tensor.array.expressions.array_expressions import ArrayContraction
+        from ...tensor.array.expressions.array_expressions import ArrayTensorProduct
+        from .matmul import MatMul
+        from .inverse import Inverse
+        exp = self.exp
+        if self.base.shape == (1, 1) and not exp.has(x):
+            lr = self.base._eval_derivative_matrix_lines(x)
+            for i in lr:
+                subexpr = ExprBuilder(
+                    ArrayContraction,
+                    [
+                        ExprBuilder(
+                            ArrayTensorProduct,
+                            [
+                                Identity(1),
+                                i._lines[0],
+                                exp*self.base**(exp-1),
+                                i._lines[1],
+                                Identity(1),
+                            ]
+                        ),
+                        (0, 3, 4), (5, 7, 8)
+                    ],
+                    validator=ArrayContraction._validate
+                )
+                i._first_pointer_parent = subexpr.args[0].args
+                i._first_pointer_index = 0
+                i._second_pointer_parent = subexpr.args[0].args
+                i._second_pointer_index = 4
+                i._lines = [subexpr]
+            return lr
+        if (exp > 0) == True:
+            newexpr = MatMul.fromiter([self.base for i in range(exp)])
+        elif (exp == -1) == True:
+            return Inverse(self.base)._eval_derivative_matrix_lines(x)
+        elif (exp < 0) == True:
+            newexpr = MatMul.fromiter([Inverse(self.base) for i in range(-exp)])
+        elif (exp == 0) == True:
+            return self.doit()._eval_derivative_matrix_lines(x)
+        else:
+            raise NotImplementedError("cannot evaluate %s derived by %s" % (self, x))
+        return newexpr._eval_derivative_matrix_lines(x)
+
+    def _eval_inverse(self):
+        return MatPow(self.base, -self.exp)