def _math_op_common(cube, operation_function, new_unit, new_dtype=None,
in_place=False):
_assert_is_cube(cube)
if in_place:
new_cube = cube
if cube.has_lazy_data():
new_cube.data = operation_function(cube.lazy_data())
else:
try:
operation_function(cube.data, out=cube.data)
except TypeError:
# Non ufunc function
operation_function(cube.data)
else:
new_cube = cube.copy(data=operation_function(cube.core_data()))
# If the result of the operation is scalar and masked, we need to fix up
# the dtype
if new_dtype is not None \
and not new_cube.has_lazy_data() \
and new_cube.data.shape == () \
and ma.is_masked(new_cube.data):
new_cube.data = ma.masked_array(0, 1, dtype=new_dtype)
iris.analysis.clear_phenomenon_identity(new_cube)
new_cube.units = new_unit
return new_cube
def _math_op_common(cube, operation_function, new_unit, new_dtype=None,
in_place=False):
_assert_is_cube(cube)
if in_place:
new_cube = cube
if cube.has_lazy_data():
new_cube.data = operation_function(cube.lazy_data())
else:
try:
operation_function(cube.data, out=cube.data)
except TypeError:
# Non ufunc function
operation_function(cube.data)
else:
new_cube = cube.copy(data=operation_function(cube.core_data()))
# If the result of the operation is scalar and masked, we need to fix up
# the dtype
if new_dtype is not None \
and not new_cube.has_lazy_data() \
and new_cube.data.shape == () \
and ma.is_masked(new_cube.data):
new_cube.data = ma.masked_array(0, 1, dtype=new_dtype)
iris.analysis.clear_phenomenon_identity(new_cube)
new_cube.units = new_unit
return new_cube
def _compute_anomalies(cube, reference, period, seasons):
cube_coord = _get_period_coord(cube, period, seasons)
ref_coord = _get_period_coord(reference, period, seasons)
data = cube.core_data()
cube_time = cube.coord('time')
ref = {}
for ref_slice in reference.slices_over(ref_coord):
ref[ref_slice.coord(ref_coord).points[0]] = ref_slice.core_data()
cube_coord_dim = cube.coord_dims(cube_coord)[0]
slicer = [slice(None)] * len(data.shape)
new_data = []
for i in range(cube_time.shape[0]):
slicer[cube_coord_dim] = i
new_data.append(data[tuple(slicer)] - ref[cube_coord.points[i]])
data = da.stack(new_data, axis=cube_coord_dim)
cube = cube.copy(data)
cube.remove_coord(cube_coord)
return cube
def _math_op_common(
cube,
operation_function,
new_unit,
new_dtype=None,
in_place=False,
skeleton_cube=False,
):
_assert_is_cube(cube)
if in_place and not skeleton_cube:
if cube.has_lazy_data():
cube.data = operation_function(cube.lazy_data())
else:
try:
operation_function(cube.data, out=cube.data)
except TypeError:
# Non-ufunc function
operation_function(cube.data)
new_cube = cube
else:
data = operation_function(cube.core_data())
if skeleton_cube:
# Simply wrap the resultant data in a cube, as no
# cube metadata is required by the caller.
new_cube = iris.cube.Cube(data)
else:
new_cube = cube.copy(data)
# If the result of the operation is scalar and masked, we need to fix-up the dtype.
if (new_dtype is not None and not new_cube.has_lazy_data()
and new_cube.data.shape == () and ma.is_masked(new_cube.data)):
new_cube.data = ma.masked_array(0, 1, dtype=new_dtype)
_sanitise_metadata(new_cube, new_unit)
return new_cube
def anomalies(cube,
period,
reference=None,
standardize=False,
seasons=('DJF', 'MAM', 'JJA', 'SON')):
"""Compute anomalies using a mean with the specified granularity.
Computes anomalies based on daily, monthly, seasonal or yearly means for
the full available period
Parameters
----------
cube: iris.cube.Cube
input cube.
period: str
Period to compute the statistic over.
Available periods: 'full', 'season', 'seasonal', 'monthly', 'month',
'mon', 'daily', 'day'
reference: list int, optional, default: None
Period of time to use a reference, as needed for the 'extract_time'
preprocessor function
If None, all available data is used as a reference
standardize: bool, optional
If True standardized anomalies are calculated
seasons: list or tuple of str, optional
Seasons to use if needed. Defaults to ('DJF', 'MAM', 'JJA', 'SON')
Returns
-------
iris.cube.Cube
Anomalies cube
"""
if reference is None:
reference_cube = cube
else:
reference_cube = extract_time(cube, **reference)
reference = climate_statistics(reference_cube,
period=period,
seasons=seasons)
if period in ['full']:
metadata = copy.deepcopy(cube.metadata)
cube = cube - reference
cube.metadata = metadata
if standardize:
cube_stddev = climate_statistics(cube,
operator='std_dev',
period=period,
seasons=seasons)
cube = cube / cube_stddev
cube.units = '1'
return cube
cube = _compute_anomalies(cube, reference, period, seasons)
# standardize the results if requested
if standardize:
cube_stddev = climate_statistics(cube,
operator='std_dev',
period=period)
tdim = cube.coord_dims('time')[0]
reps = cube.shape[tdim] / cube_stddev.shape[tdim]
if not reps % 1 == 0:
raise ValueError(
"Cannot safely apply preprocessor to this dataset, "
"since the full time period of this dataset is not "
f"a multiple of the period '{period}'")
cube.data = cube.core_data() / da.concatenate(
[cube_stddev.core_data() for _ in range(int(reps))], axis=tdim)
cube.units = '1'
return cube
