utils: move code to the math_utils package

Nothing about this is axisymmetry-specific.

1 files changed, 3 insertions, 56 deletions

diff --git a/utils.py b/utils.py
index e16dca1..8d82f49 100644
--- a/utils.py
+++ b/utils.py
@@ -1,57 +1,4 @@
-import numpy as np
+import warnings
+warnings.warn('This module is deprecated, use math_utils.array_utils', DeprecationWarning)
 
-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)
+from math_utils.array_utils import matrix_invert, matrix_det, array_reflect