Commit d0a77a17 authored by Matthieu Tristram's avatar Matthieu Tristram
Browse files

Add Symm class to store hermitian matrices (not usde yet)

parent da2d72f3
......@@ -368,3 +368,55 @@ def IsInvertible(F):
# P *= binsnorm
# return P, Q, ell, ellval
class Sym(np.ndarray):
# wrapper class for numpy array for symmetric matrices. New attribute can pack matrix to optimize storage.
# Usage:
# If you have a symmetric matrix A as a shape (n,n) numpy ndarray, Sym(A).packed is a shape (n(n+1)/2,) numpy array
# that is a packed version of A. To convert it back, just wrap the flat list in Sym(). Note that Sym(Sym(A).packed)
def __new__(cls, input_array):
obj = np.asarray(input_array).view(cls)
if len(obj.shape) == 1:
l = obj.copy()
p = obj.copy()
m = int((np.sqrt(8 * len(obj) + 1) - 1) / 2)
sqrt_m = np.sqrt(m)
if np.isclose(sqrt_m, np.round(sqrt_m)):
A = np.zeros((m, m))
for i in range(m):
A[i, i:] = l[:(m-i)]
A[i:, i] = l[:(m-i)]
l = l[(m-i):]
obj = np.asarray(A).view(cls)
obj.packed = p
raise ValueError('One dimensional input length must be a triangular number.')
elif len(obj.shape) == 2:
if obj.shape[0] != obj.shape[1]:
raise ValueError('Two dimensional input must be a square matrix.')
packed_out = []
for i in range(obj.shape[0]):
packed_out.append(obj[i, i:])
obj.packed = np.concatenate(packed_out)
raise ValueError('Input array must be 1 or 2 dimensional.')
return obj
def __array_finalize__(self, obj):
if obj is None: return
self.packed = getattr(obj, 'packed', None)
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