def test_anomalies_preserve_metadata(period, reference, standardize=False):
cube = make_map_data(number_years=2)
cube.var_name = "si"
cube.units = "m"
metadata = copy.deepcopy(cube.metadata)
result = anomalies(cube, period, reference, standardize=standardize)
assert result.metadata == metadata
for coord_cube, coord_res in zip(cube.coords(), result.coords()):
if coord_cube.has_bounds() and coord_res.has_bounds():
assert_array_equal(coord_cube.bounds, coord_res.bounds)
assert coord_cube == coord_res
def test_anomalies(period, reference, standardize=False):
cube = make_map_data(number_years=2)
result = anomalies(cube, period, reference, standardize=standardize)
if reference is None:
if period == 'full':
anom = np.arange(-359.5, 360)
elif period == 'day':
anom = np.concatenate((np.ones(360) * -180, np.ones(360) * 180))
elif period == 'month':
anom1 = np.concatenate(
[np.arange(-194.5, -165) for x in range(12)])
anom2 = np.concatenate([np.arange(165.5, 195) for x in range(12)])
anom = np.concatenate((anom1, anom2))
elif period == 'season':
anom = np.concatenate((
np.arange(-314.5, -255),
np.arange(-224.5, -135),
np.arange(-224.5, -135),
np.arange(-224.5, -135),
np.arange(15.5, 105),
np.arange(135.5, 225),
np.arange(135.5, 225),
np.arange(135.5, 225),
np.arange(375.5, 405),
))
else:
if period == 'full':
anom = np.arange(-179.5, 540)
elif period == 'day':
anom = np.concatenate((np.zeros(360), np.ones(360) * 360))
elif period == 'month':
anom1 = np.concatenate([np.arange(-14.5, 15) for x in range(12)])
anom2 = np.concatenate([np.arange(345.5, 375) for x in range(12)])
anom = np.concatenate((anom1, anom2))
elif period == 'season':
anom = np.concatenate((
np.arange(-374.5, -315),
np.arange(-44.5, 45),
np.arange(-44.5, 45),
np.arange(-44.5, 45),
np.arange(-44.5, 45),
np.arange(315.5, 405),
np.arange(315.5, 405),
np.arange(315.5, 405),
np.arange(315.5, 345),
))
zeros = np.zeros_like(anom)
print(anom)
print(result.data[0, 1, ...])
assert_array_equal(result.data, np.array([[zeros, anom], [anom, zeros]]))
assert_array_equal(result.coord('time').points, cube.coord('time').points)
def test_standardized_anomalies(period, standardize=True):
cube = make_map_data(number_years=2)
result = anomalies(cube, period, standardize=standardize)
if period == 'full':
expected_anomalies = (cube.data -
np.mean(cube.data, axis=2, keepdims=True))
if standardize:
# NB: default behaviour for np.std is ddof=0, whereas
# default behaviour for iris.analysis.STD_DEV is ddof=1
expected_stdanomalies = expected_anomalies / np.std(
expected_anomalies, axis=2, keepdims=True, ddof=1)
expected = np.ma.masked_invalid(expected_stdanomalies)
assert_array_equal(result.data, expected)
else:
expected = np.ma.masked_invalid(expected_anomalies)
assert_array_equal(result.data, expected)
def test_anomalies(period):
cube = make_map_data(number_years=2)
result = anomalies(cube, period)
if period == 'full':
anom = np.arange(-359.5, 360, 1)
zeros = np.zeros_like(anom)
assert_array_equal(result.data, np.array([[zeros, anom], [anom,
zeros]]))
elif period == 'day':
anom = np.concatenate((np.ones(360) * -180, np.ones(360) * 180))
zeros = np.zeros_like(anom)
assert_array_equal(result.data, np.array([[zeros, anom], [anom,
zeros]]))
elif period == 'month':
anom1 = np.concatenate([np.arange(-194.5, -165) for x in range(12)])
anom2 = np.concatenate([np.arange(165.5, 195) for x in range(12)])
anom = np.concatenate((anom1, anom2))
zeros = np.zeros_like(anom)
print(result.data[0, 1])
assert_array_equal(result.data, np.array([[zeros, anom], [anom,
zeros]]))
elif period == 'season':
anom = np.concatenate((
np.arange(-314.5, -255),
np.arange(-224.5, -135),
np.arange(-224.5, -135),
np.arange(-224.5, -135),
np.arange(15.5, 105),
np.arange(135.5, 225),
np.arange(135.5, 225),
np.arange(135.5, 225),
np.arange(375.5, 405),
))
zeros = np.zeros_like(anom)
print(result.data[0, 1])
assert_array_equal(result.data, np.array([[zeros, anom], [anom,
zeros]]))
assert_array_equal(result.coord('time').points, cube.coord('time').points)
