the walrus

mrmustard/math/lattice/strategies/compactFock/inputValidation.py

@@ -18,7 +18,39 @@
 from mrmustard.math.lattice.strategies.compactFock.singleLeftoverMode_grad import (
     fock_representation_1leftoverMode_grad,
 )
-from thewalrus._hafnian import input_validation
+
+def input_validation(A, rtol=1e-05, atol=1e-08):
+    """Checks that the matrix A satisfies the requirements for Hafnian calculation.
+    These include:
+    * That the ``A`` is a NumPy array
+    * That ``A`` is square
+    * That ``A`` does not contain any NaNs
+    * That ``A`` is symmetric
+
+    Args:
+        A (array): a NumPy array.
+        rtol (float): the relative tolerance parameter used in ``np.allclose``
+        atol (float): the absolute tolerance parameter used in ``np.allclose``
+
+    Returns:
+        bool: returns ``True`` if the matrix satisfies all requirements
+    """
+
+    if not isinstance(A, np.ndarray):
+        raise TypeError("Input matrix must be a NumPy array.")
+
+    n = A.shape
+
+    if n[0] != n[1]:
+        raise ValueError("Input matrix must be square.")
+
+    if np.isnan(A).any():
+        raise ValueError("Input matrix must not contain NaNs.")
+
+    if not np.allclose(A, A.T, rtol=rtol, atol=atol):
+        raise ValueError("Input matrix must be symmetric.")
+
+    return True
 
 
 def hermite_multidimensional_diagonal(A, B, G0, cutoffs, rtol=1e-05, atol=1e-08):

mrmustard/physics/gaussian.py

@@ -18,8 +18,6 @@
 
 from typing import Any, Optional, Sequence, Tuple, Union
 
-from thewalrus.quantum import is_pure_cov
-
 from mrmustard import math, settings
 from mrmustard.math.tensor_wrappers.xptensor import XPMatrix, XPVector
 from mrmustard.utils.typing import Matrix, Scalar, Vector
@@ -660,11 +658,6 @@ def number_cov(cov: Matrix, means: Vector) -> Matrix:
     )
 
 
-def is_mixed_cov(cov: Matrix) -> bool:  # TODO: deprecate
-    r"""Returns ``True`` if the covariance matrix is mixed, ``False`` otherwise."""
-    return not is_pure_cov(math.asnumpy(cov))
-
-
 def trace(cov: Matrix, means: Vector, Bmodes: Sequence[int]) -> Tuple[Matrix, Vector]:
     r"""Returns the covariances and means after discarding the specified modes.
 

pyproject.toml

@@ -31,7 +31,7 @@ grpcio = "1.60.0"
 numpy = "^1.23.5"
 scipy = "^1.8.0"
 numba = "^0.59"
-thewalrus = "^0.21.0"
+thewalrus = "^0.19.0"
 rich = "^10.15.1"
 matplotlib = "^3.5.0"
 ray = { version = "^2.5.0", extras = ["tune"], optional = true }

tests/test_about.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
-Unit tests for the :mod:`thewalrus` configuration class :class:`Configuration`.
+Unit tests for the ``about`` method.
 """
 
 import contextlib