def test_1d_load(comm):
# load plaintext format
with pytest.warns(FutureWarning):
ds1 = BinnedStatistic.from_plaintext(['k'], os.path.join(data_dir, 'dataset_1d_deprecated.dat'))
# wrong dimensions
with pytest.raises(ValueError):
ds1 = BinnedStatistic.from_plaintext(['k', 'mu'], os.path.join(data_dir, 'dataset_1d_deprecated.dat'))
# load from JSON
ds2 = BinnedStatistic.from_json(os.path.join(data_dir, 'dataset_1d.json'))
# same data?
for name in ds1:
testing.assert_almost_equal(ds1[name], ds2[name])
def _load(kind, box=None):
"""
Internal function to load the QPM results.
"""
d = os.environ['THESIS_DIR']
d = os.path.join(d, 'boss_dr12_mocks', 'Results', 'QPM', 'nbodykit',
'redshift', kind)
# the pattern
box = "*" if box is None else "%04d" % box
if kind == 'power':
files = glob(
os.path.join(d,
f"pkmu_qpm_unscaled_{box}_0.6452_dk005_Nmu100.dat"))
dims = ['k', 'mu']
names = ['k', 'mu', 'power', 'modes']
else:
files = glob(
os.path.join(d,
f"poles_qpm_unscaled_{box}_0.6452_dk005_Nmu100.dat"))
dims = ['k']
names = ['k', 'power_0', 'power_2', 'power_4', 'modes']
toret = []
for f in files:
d = BinnedStatistic.from_plaintext(dims, f)
if kind == 'poles':
for i, name in enumerate(names):
d.rename_variable('col_%d' % i, name)
toret.append(d)
return toret
def test_1d_load(comm):
# load plaintext format
with pytest.warns(FutureWarning):
ds1 = BinnedStatistic.from_plaintext(['k'], os.path.join(data_dir, 'dataset_1d_deprecated.dat'))
# wrong dimensions
with pytest.raises(ValueError):
ds1 = BinnedStatistic.from_plaintext(['k', 'mu'], os.path.join(data_dir, 'dataset_1d_deprecated.dat'))
# load from JSON
ds2 = BinnedStatistic.from_json(os.path.join(data_dir, 'dataset_1d.json'))
# same data?
for name in ds1:
testing.assert_almost_equal(ds1[name], ds2[name])
def _load(box=None, los=None):
"""
Internal function to load the N-cubic results.
"""
d = os.environ['THESIS_DIR']
d = os.path.join(d, 'boss_dr12_mocks', 'Results', 'ChallengeMocks',
'nbodykit', 'power')
if los is not None:
assert los in "xyz"
# the pattern
box = "*" if box is None else "%d" % box
los = "*" if los is None else los
files = glob(
os.path.join(
d, f"pkmu_challenge_boxN{box}_unscaled_dk005_Nmu100_{los}los.dat"))
toret = []
for f in files:
toret.append(BinnedStatistic.from_plaintext(['k', 'mu'], f))
return toret