def test_from_light_curves_no_meta():
lcs = []
for idx in range(5):
lc = make_light_curve(f'test_{idx}')
lc.meta = {}
lcs.append(lc)
dataset = lcdata.from_light_curves(lcs)
assert len(dataset.meta) == 5
def make_dataset(start_idx=0, end_idx=10):
lcs = [make_light_curve(f'test_{i}') for i in range(start_idx, end_idx)]
dataset = lcdata.from_light_curves(lcs)
dataset.meta['maskedvar'] = np.ma.masked_array(
np.arange(start_idx, end_idx),
mask=np.arange(start_idx, end_idx) % 2 == 0)
return dataset
def test_from_light_curves_order():
# We sort the light curves in alphabetical order. Make sure that this works
# correctly.
lc1 = make_light_curve(object_id='1')
lc2 = make_light_curve(object_id='2')
lc3 = make_light_curve(object_id='3')
# Add a flag to the fluxes so that we can track them.
lc1['flux'][0] = 1.
lc2['flux'][0] = 2.
lc3['flux'][0] = 3.
dataset = lcdata.from_light_curves([lc2, lc3, lc1])
assert np.all(dataset.meta['object_id'] == ['1', '2', '3'])
assert dataset.light_curves[0]['flux'][0] == 1.
assert dataset.light_curves[1]['flux'][0] == 2.
assert dataset.light_curves[2]['flux'][0] == 3.
def test_from_light_curves():
lc = make_light_curve()
dataset = lcdata.from_light_curves([lc])
assert len(dataset) == 1
def test_from_light_curves_duplicate_object_ids():
lcs = [make_light_curve('same_object_id') for i in range(2)]
with pytest.raises(ValueError):
lcdata.from_light_curves(lcs)