def test_samples():
rng = numpy.random.default_rng(seed=8001)
# Build a bogus covariance matrix
m = numpy.zeros(10, dtype=float)
c = numpy.diag(numpy.full(10-2, 0.2, dtype=float), k=-2) \
+ numpy.diag(numpy.full(10-1, 0.5, dtype=float), k=-1) \
+ numpy.diag(numpy.full(10, 1.0, dtype=float), k=0) \
+ numpy.diag(numpy.full(10-1, 0.5, dtype=float), k=1) \
+ numpy.diag(numpy.full(10-2, 0.2, dtype=float), k=2)
# Draw samples
s = rng.multivariate_normal(m, c, size=100000)
# Instantiate
covar = Covariance.from_samples(s.T, cov_tol=0.1)
# Check the values are very nearly the same as the input
assert numpy.all(numpy.absolute(c - covar.toarray()) < 0.02), 'Covariances are too different'
# Check that `find` returns the same indices
# NOTE: For some reason the ordering of numpy.where and
# scipy.sparse.find are different. So I run lexsort before checking
# if the arrays are the same.
coo = numpy.array([ [i,j] for i,j in zip(*numpy.where(c > 0))])
srt = numpy.lexsort((coo[:,0], coo[:,1]))
coo = coo[srt,:]
_coo = numpy.array([ [i,j] for i,j,k in zip(*covar.find())])
srt = numpy.lexsort((_coo[:,0], _coo[:,1]))
_coo = _coo[srt,:]
assert numpy.array_equal(coo, _coo), 'Did not find the same indices'
def test_io():
rng = numpy.random.default_rng(seed=8001)
# Clean up in case of a failure
ofile = 'test_covar_io.fits'
if os.path.isfile(ofile):
os.remove(ofile)
# Build a bogus covariance matrix
m = numpy.zeros(10, dtype=float)
c = numpy.diag(numpy.full(10-2, 0.2, dtype=float), k=-2) \
+ numpy.diag(numpy.full(10-1, 0.5, dtype=float), k=-1) \
+ numpy.diag(numpy.full(10, 1.0, dtype=float), k=0) \
+ numpy.diag(numpy.full(10-1, 0.5, dtype=float), k=1) \
+ numpy.diag(numpy.full(10-2, 0.2, dtype=float), k=2)
# Draw samples
s = rng.multivariate_normal(m, c, size=100000)
# Instantiate
covar = Covariance.from_samples(s.T, cov_tol=0.1)
# Write
covar.write(ofile)
# Read
_covar = Covariance.from_fits(ofile)
# Should be the same
assert numpy.allclose(covar.toarray(), _covar.toarray()), 'Bad I/O'
# Clean-up
os.remove(ofile)