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 sympy.assumptions.ask import ask, Q
+from sympy.core.relational import Eq
+from sympy.core.singleton import S
+from sympy.core.sympify import _sympify
+from sympy.functions.special.tensor_functions import KroneckerDelta
+from sympy.matrices.exceptions import NonInvertibleMatrixError
+from .matexpr import MatrixExpr
+
+
+class ZeroMatrix(MatrixExpr):
+    """The Matrix Zero 0 - additive identity
+
+    Examples
+    ========
+
+    >>> from sympy import MatrixSymbol, ZeroMatrix
+    >>> A = MatrixSymbol('A', 3, 5)
+    >>> Z = ZeroMatrix(3, 5)
+    >>> A + Z
+    A
+    >>> Z*A.T
+    0
+    """
+    is_ZeroMatrix = True
+
+    def __new__(cls, m, n):
+        m, n = _sympify(m), _sympify(n)
+        cls._check_dim(m)
+        cls._check_dim(n)
+
+        return super().__new__(cls, m, n)
+
+    @property
+    def shape(self):
+        return (self.args[0], self.args[1])
+
+    def _eval_power(self, exp):
+        # exp = -1, 0, 1 are already handled at this stage
+        if (exp < 0) == True:
+            raise NonInvertibleMatrixError("Matrix det == 0; not invertible")
+        return self
+
+    def _eval_transpose(self):
+        return ZeroMatrix(self.cols, self.rows)
+
+    def _eval_adjoint(self):
+        return ZeroMatrix(self.cols, self.rows)
+
+    def _eval_trace(self):
+        return S.Zero
+
+    def _eval_determinant(self):
+        return S.Zero
+
+    def _eval_inverse(self):
+        raise NonInvertibleMatrixError("Matrix det == 0; not invertible.")
+
+    def _eval_as_real_imag(self):
+        return (self, self)
+
+    def _eval_conjugate(self):
+        return self
+
+    def _entry(self, i, j, **kwargs):
+        return S.Zero
+
+
+class GenericZeroMatrix(ZeroMatrix):
+    """
+    A zero matrix without a specified shape
+
+    This exists primarily so MatAdd() with no arguments can return something
+    meaningful.
+    """
+    def __new__(cls):
+        # super(ZeroMatrix, cls) instead of super(GenericZeroMatrix, cls)
+        # because ZeroMatrix.__new__ doesn't have the same signature
+        return super(ZeroMatrix, cls).__new__(cls)
+
+    @property
+    def rows(self):
+        raise TypeError("GenericZeroMatrix does not have a specified shape")
+
+    @property
+    def cols(self):
+        raise TypeError("GenericZeroMatrix does not have a specified shape")
+
+    @property
+    def shape(self):
+        raise TypeError("GenericZeroMatrix does not have a specified shape")
+
+    # Avoid Matrix.__eq__ which might call .shape
+    def __eq__(self, other):
+        return isinstance(other, GenericZeroMatrix)
+
+    def __ne__(self, other):
+        return not (self == other)
+
+    def __hash__(self):
+        return super().__hash__()
+
+
+
+class Identity(MatrixExpr):
+    """The Matrix Identity I - multiplicative identity
+
+    Examples
+    ========
+
+    >>> from sympy import Identity, MatrixSymbol
+    >>> A = MatrixSymbol('A', 3, 5)
+    >>> I = Identity(3)
+    >>> I*A
+    A
+    """
+
+    is_Identity = True
+
+    def __new__(cls, n):
+        n = _sympify(n)
+        cls._check_dim(n)
+
+        return super().__new__(cls, n)
+
+    @property
+    def rows(self):
+        return self.args[0]
+
+    @property
+    def cols(self):
+        return self.args[0]
+
+    @property
+    def shape(self):
+        return (self.args[0], self.args[0])
+
+    @property
+    def is_square(self):
+        return True
+
+    def _eval_transpose(self):
+        return self
+
+    def _eval_trace(self):
+        return self.rows
+
+    def _eval_inverse(self):
+        return self
+
+    def _eval_as_real_imag(self):
+        return (self, ZeroMatrix(*self.shape))
+
+    def _eval_conjugate(self):
+        return self
+
+    def _eval_adjoint(self):
+        return self
+
+    def _entry(self, i, j, **kwargs):
+        eq = Eq(i, j)
+        if eq is S.true:
+            return S.One
+        elif eq is S.false:
+            return S.Zero
+        return KroneckerDelta(i, j, (0, self.cols-1))
+
+    def _eval_determinant(self):
+        return S.One
+
+    def _eval_power(self, exp):
+        return self
+
+
+class GenericIdentity(Identity):
+    """
+    An identity matrix without a specified shape
+
+    This exists primarily so MatMul() with no arguments can return something
+    meaningful.
+    """
+    def __new__(cls):
+        # super(Identity, cls) instead of super(GenericIdentity, cls) because
+        # Identity.__new__ doesn't have the same signature
+        return super(Identity, cls).__new__(cls)
+
+    @property
+    def rows(self):
+        raise TypeError("GenericIdentity does not have a specified shape")
+
+    @property
+    def cols(self):
+        raise TypeError("GenericIdentity does not have a specified shape")
+
+    @property
+    def shape(self):
+        raise TypeError("GenericIdentity does not have a specified shape")
+
+    @property
+    def is_square(self):
+        return True
+
+    # Avoid Matrix.__eq__ which might call .shape
+    def __eq__(self, other):
+        return isinstance(other, GenericIdentity)
+
+    def __ne__(self, other):
+        return not (self == other)
+
+    def __hash__(self):
+        return super().__hash__()
+
+
+class OneMatrix(MatrixExpr):
+    """
+    Matrix whose all entries are ones.
+    """
+    def __new__(cls, m, n, evaluate=False):
+        m, n = _sympify(m), _sympify(n)
+        cls._check_dim(m)
+        cls._check_dim(n)
+
+        if evaluate:
+            condition = Eq(m, 1) & Eq(n, 1)
+            if condition == True:
+                return Identity(1)
+
+        obj = super().__new__(cls, m, n)
+        return obj
+
+    @property
+    def shape(self):
+        return self._args
+
+    @property
+    def is_Identity(self):
+        return self._is_1x1() == True
+
+    def as_explicit(self):
+        from sympy.matrices.immutable import ImmutableDenseMatrix
+        return ImmutableDenseMatrix.ones(*self.shape)
+
+    def doit(self, **hints):
+        args = self.args
+        if hints.get('deep', True):
+            args = [a.doit(**hints) for a in args]
+        return self.func(*args, evaluate=True)
+
+    def _eval_power(self, exp):
+        # exp = -1, 0, 1 are already handled at this stage
+        if self._is_1x1() == True:
+            return Identity(1)
+        if (exp < 0) == True:
+            raise NonInvertibleMatrixError("Matrix det == 0; not invertible")
+        if ask(Q.integer(exp)):
+            return self.shape[0] ** (exp - 1) * OneMatrix(*self.shape)
+        return super()._eval_power(exp)
+
+    def _eval_transpose(self):
+        return OneMatrix(self.cols, self.rows)
+
+    def _eval_adjoint(self):
+        return OneMatrix(self.cols, self.rows)
+
+    def _eval_trace(self):
+        return S.One*self.rows
+
+    def _is_1x1(self):
+        """Returns true if the matrix is known to be 1x1"""
+        shape = self.shape
+        return Eq(shape[0], 1) & Eq(shape[1], 1)
+
+    def _eval_determinant(self):
+        condition = self._is_1x1()
+        if condition == True:
+            return S.One
+        elif condition == False:
+            return S.Zero
+        else:
+            from sympy.matrices.expressions.determinant import Determinant
+            return Determinant(self)
+
+    def _eval_inverse(self):
+        condition = self._is_1x1()
+        if condition == True:
+            return Identity(1)
+        elif condition == False:
+            raise NonInvertibleMatrixError("Matrix det == 0; not invertible.")
+        else:
+            from .inverse import Inverse
+            return Inverse(self)
+
+    def _eval_as_real_imag(self):
+        return (self, ZeroMatrix(*self.shape))
+
+    def _eval_conjugate(self):
+        return self
+
+    def _entry(self, i, j, **kwargs):
+        return S.One