1 files changed, 57 insertions, 0 deletions
diff --git a/array_utils.py b/array_utils.py
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+++ b/array_utils.py
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+import numpy as np
+
+def matrix_invert(mat):
+    """
+    Pointwise matrix inversion.
+
+    Given an array that stores many square matrices (e.g. the values of the
+    metric tensor at spacetime points), compute the array of corresponding
+    inverses.
+
+    :param array_like mat: N-D array with N>2 and each mat[i0, ..., :, :] a
+                           square matrix to be inverted.
+    :return: Array (same shape as mat) of inverses.
+    """
+    oldshape = mat.shape
+    newshape = oldshape[:2] + (np.product(mat.shape[2:]),)
+
+    mat = mat.reshape(newshape).transpose((2, 0, 1))
+    inv = np.linalg.inv(mat)
+    return inv.transpose((1, 2, 0)).reshape(oldshape)
+
+def matrix_det(mat):
+    """
+    Pointwise matrix determinant.
+
+    Given an array that stores many square matrices (e.g. the values of the
+    metric tensor at spacetime points), compute the array of corresponding
+    determinants.
+
+    :param array_like mat: N-D array with N>2 and each mat[i0, ..., :, :] a
+                           square matrix.
+    :return: Array of determinants.
+    """
+    oldshape = mat.shape
+    newshape = oldshape[:2] + (np.product(mat.shape[2:]),)
+
+    mat = mat.reshape(newshape).transpose((2, 0, 1))
+    return np.linalg.det(mat).reshape(oldshape[2:])
+
+def array_reflect(data, parity = 1.0, axis = -1):
+    """
+    Reflect an N-D array with respect to the specified axis. E.g. input
+    [0, 1, 2, 3] becomes [3, 2, 1, 0, 1, 2, 3] with parity=1.0 and
+    [-3, -2, -1, 0, 1, 2, 3] with parity=-1.0.
+
+    :param array_like data: The array to reflect.
+    :param float parity: The reflected portion is multiplied by this factor,
+                         typically 1.0 or -1.0.
+    :param int axis: Index of the axis to reflect along.
+    :return: Reflected array.
+    """
+    slices0 = [slice(None) for _ in data.shape]
+    slices1 = [slice(None) for _ in data.shape]
+    slices0[axis] = slice(None, None, -1)
+    slices1[axis] = slice(1, None)
+
+    return np.concatenate((parity * data[tuple(slices0)], data[tuple(slices1)]), axis)