def test__extract_error_percentiles(error_threshold_cube,
error_percentile_cube):
"""Test the extraction of error percentiles from error-probability cube."""
result = ApplyRainForestsCalibrationLightGBM(
model_config_dict={})._extract_error_percentiles(
error_threshold_cube, 4)
assert result.long_name == error_percentile_cube.long_name
assert result.units == error_percentile_cube.units
assert result.coords() == error_percentile_cube.coords()
assert result.attributes == error_percentile_cube.attributes
# Test the case where error_threshold_cube has unit realization dimension
error_threshold_cube = error_threshold_cube.extract(
Constraint(realization=0))
error_threshold_cube = new_axis(error_threshold_cube, "realization")
error_threshold_cube.transpose([1, 0, 2, 3])
error_percentile_cube = error_percentile_cube.extract(
Constraint(realization=0))
error_percentile_cube = new_axis(error_percentile_cube, "realization")
result = ApplyRainForestsCalibrationLightGBM(
model_config_dict={})._extract_error_percentiles(
error_threshold_cube, 4)
assert result.long_name == error_percentile_cube.long_name
assert result.units == error_percentile_cube.units
assert result.coords() == error_percentile_cube.coords()
assert result.attributes == error_percentile_cube.attributes
def test_masked_unit_array(self):
cube = stock.simple_3d_mask()
test_cube = cube[0, 0, 0]
test_cube = new_axis(test_cube, 'longitude')
test_cube = new_axis(test_cube, 'latitude')
data_shape = test_cube.data.shape
mask_shape = test_cube.data.mask.shape
self.assertEqual(data_shape, mask_shape)
def test_masked_unit_array(self):
cube = stock.simple_3d_mask()
test_cube = cube[0, 0, 0]
test_cube = new_axis(test_cube, "longitude")
test_cube = new_axis(test_cube, "latitude")
data_shape = test_cube.data.shape
mask_shape = test_cube.data.mask.shape
self.assertEqual(data_shape, mask_shape)
def test_lazy_data(self):
cube = iris.cube.Cube(as_lazy_data(self.data))
cube.add_aux_coord(iris.coords.DimCoord([1], standard_name="time"))
res = new_axis(cube, "time")
self.assertTrue(cube.has_lazy_data())
self.assertTrue(res.has_lazy_data())
self.assertEqual(res.shape, (1, ) + cube.shape)
def test_lazy_data(self):
filename = tests.get_data_path(("PP", "globClim1", "theta.pp"))
cube = iris.load_cube(filename)
new_cube = new_axis(cube, "time")
self.assertTrue(cube.has_lazy_data())
self.assertTrue(new_cube.has_lazy_data())
self.assertEqual(new_cube.shape, (1, ) + cube.shape)
def _extract_error_percentiles(self, error_probability_cube,
error_percentiles_count):
"""Extract error percentile values from the error exceedence probabilities.
Args:
error_probability_cube:
A cube containing error exceedence probabilities.
error_percentiles_count:
The number of error percentiles to extract. The resulting percentiles
will be evenly spaced over the interval (0, 100).
Returns:
Cube containing percentile values for the error distributions.
"""
error_percentiles = choose_set_of_percentiles(
error_percentiles_count,
sampling="quantile",
)
error_percentiles_cube = ConvertProbabilitiesToPercentiles().process(
error_probability_cube, percentiles=error_percentiles)
if len(error_percentiles_cube.coord_dims("realization")) == 0:
error_percentiles_cube = new_axis(error_percentiles_cube,
"realization")
return error_percentiles_cube
def test_add_unit_dimension(input_cube, expected_cube):
"""Test case where added dimension is of length 1."""
realization_coord = DimCoord([0], standard_name="realization", units=1)
expected_cube = new_axis(expected_cube.extract(Constraint(realization=0)),
"realization")
output_cube = add_coordinate_to_cube(input_cube, realization_coord)
assert output_cube == expected_cube
def test_lazy_data(self):
filename = tests.get_data_path(('PP', 'globClim1', 'theta.pp'))
cube = iris.load_cube(filename)
new_cube = new_axis(cube, 'time')
self.assertTrue(cube.has_lazy_data())
self.assertTrue(new_cube.has_lazy_data())
self.assertEqual(new_cube.shape, (1, ) + cube.shape)
def test_lazy_data(self):
cube = iris.cube.Cube(NumpyArrayAdapter(self.data))
cube.add_aux_coord(iris.coords.DimCoord([1], standard_name='time'))
res = new_axis(cube, 'time')
self.assertTrue(cube.has_lazy_data())
self.assertTrue(res.has_lazy_data())
self.assertEqual(res.shape, (1,) + cube.shape)
def test_lazy_data(self):
filename = tests.get_data_path(('PP', 'globClim1', 'theta.pp'))
cube = iris.load_cube(filename)
new_cube = new_axis(cube, 'time')
self.assertTrue(cube.has_lazy_data())
self.assertTrue(new_cube.has_lazy_data())
self.assertEqual(new_cube.shape, (1,) + cube.shape)
def test_lazy_data(self):
cube = iris.cube.Cube(as_lazy_data(self.data))
cube.add_aux_coord(iris.coords.DimCoord([1], standard_name='time'))
res = new_axis(cube, 'time')
self.assertTrue(cube.has_lazy_data())
self.assertTrue(res.has_lazy_data())
self.assertEqual(res.shape, (1,) + cube.shape)
def test_maint_factory(self):
# Ensure that aux factory persists.
data = np.arange(12, dtype='i8').reshape((3, 4))
orography = iris.coords.AuxCoord([10, 25, 50, 5],
standard_name='surface_altitude',
units='m')
model_level = iris.coords.AuxCoord([2, 1, 0],
standard_name='model_level_number')
level_height = iris.coords.DimCoord([100, 50, 10],
long_name='level_height',
units='m',
attributes={'positive': 'up'},
bounds=[[150, 75], [75, 20],
[20, 0]])
sigma = iris.coords.AuxCoord([0.8, 0.9, 0.95],
long_name='sigma',
bounds=[[0.7, 0.85], [0.85, 0.97],
[0.97, 1.0]])
hybrid_height = iris.aux_factory.HybridHeightFactory(
level_height, sigma, orography)
cube = iris.cube.Cube(data,
standard_name='air_temperature',
units='K',
dim_coords_and_dims=[(level_height, 0)],
aux_coords_and_dims=[(orography, 1),
(model_level, 0),
(sigma, 0)],
aux_factories=[hybrid_height])
com = iris.cube.Cube(data[None],
standard_name='air_temperature',
units='K',
dim_coords_and_dims=[(copy.copy(level_height), 1)
],
aux_coords_and_dims=[(copy.copy(orography), 2),
(copy.copy(model_level), 1),
(copy.copy(sigma), 1)],
aux_factories=[copy.copy(hybrid_height)])
res = new_axis(cube)
self.assertEqual(res, com)
self._assert_cube_notis(res, cube)
# Check that factory dependencies are actual coords within the cube.
# Addresses a former bug : https://github.com/SciTools/iris/pull/3263
factory, = list(res.aux_factories)
deps = factory.dependencies
for dep_name, dep_coord in six.iteritems(deps):
coord_name = dep_coord.name()
msg = ('Factory dependency {!r} is a coord named {!r}, '
'but it is *not* the coord of that name in the new cube.')
self.assertIs(dep_coord, res.coord(coord_name),
msg.format(dep_name, coord_name))
def test_scalar_dimcoord(self):
# Providing a scalar coordinate to promote.
res = new_axis(self.cube, "time")
com = iris.cube.Cube(self.data[None])
com.add_dim_coord(self.coords["lat"].copy(), 1)
com.add_dim_coord(self.coords["lon"].copy(), 2)
com.add_aux_coord(self.coords["time"].copy(), 0)
com.add_aux_coord(self.coords["wibble"].copy(), None)
self.assertEqual(res, com)
self._assert_cube_notis(res, self.cube)
def test_scalar_auxcoord(self):
# Providing a scalar coordinate to promote.
res = new_axis(self.cube, 'wibble')
com = iris.cube.Cube(self.data[None])
com.add_dim_coord(self.coords['lat'].copy(), 1)
com.add_dim_coord(self.coords['lon'].copy(), 2)
com.add_aux_coord(self.coords['time'].copy(), None)
com.add_aux_coord(self.coords['wibble'].copy(), 0)
self.assertEqual(res, com)
self._assert_cube_notis(res, self.cube)
def test_scalar_auxcoord(self):
# Providing a scalar coordinate to promote.
res = new_axis(self.cube, 'wibble')
com = iris.cube.Cube(self.data[None])
com.add_dim_coord(self.coords['lat'].copy(), 1)
com.add_dim_coord(self.coords['lon'].copy(), 2)
com.add_aux_coord(self.coords['time'].copy(), None)
com.add_aux_coord(self.coords['wibble'].copy(), 0)
self.assertEqual(res, com)
self._assert_cube_notis(res, self.cube)
def test_scalar_dimcoord(self):
# Providing a scalar coordinate to promote.
res = new_axis(self.cube, "time")
com = iris.cube.Cube(self.data[None])
com.add_dim_coord(self.coords["lat"].copy(), 1)
com.add_dim_coord(self.coords["lon"].copy(), 2)
com.add_aux_coord(self.coords["time"].copy(), 0)
com.add_aux_coord(self.coords["wibble"].copy(), None)
self.assertEqual(res, com)
self._assert_cube_notis(res, self.cube)
def test_maint_factory(self):
# Ensure that aux factory persists.
data = np.arange(12, dtype='i8').reshape((3, 4))
orography = iris.coords.AuxCoord([10, 25, 50, 5],
standard_name='surface_altitude',
units='m')
model_level = iris.coords.AuxCoord([2, 1, 0],
standard_name='model_level_number')
level_height = iris.coords.DimCoord([100, 50, 10],
long_name='level_height',
units='m',
attributes={'positive': 'up'},
bounds=[[150, 75], [75, 20],
[20, 0]])
sigma = iris.coords.AuxCoord([0.8, 0.9, 0.95],
long_name='sigma',
bounds=[[0.7, 0.85], [0.85, 0.97],
[0.97, 1.0]])
hybrid_height = iris.aux_factory.HybridHeightFactory(
level_height, sigma, orography)
cube = iris.cube.Cube(data,
standard_name='air_temperature',
units='K',
dim_coords_and_dims=[(level_height, 0)],
aux_coords_and_dims=[(orography, 1),
(model_level, 0),
(sigma, 0)],
aux_factories=[hybrid_height])
com = iris.cube.Cube(data[None],
standard_name='air_temperature',
units='K',
dim_coords_and_dims=[(copy.copy(level_height), 1)
],
aux_coords_and_dims=[(copy.copy(orography), 2),
(copy.copy(model_level), 1),
(copy.copy(sigma), 1)],
aux_factories=[copy.copy(hybrid_height)])
res = new_axis(cube)
self.assertEqual(res, com)
self._assert_cube_notis(res, cube)
def test_maint_factory(self):
# Ensure that aux factory persists.
data = np.arange(12, dtype="i8").reshape((3, 4))
orography = iris.coords.AuxCoord([10, 25, 50, 5], standard_name="surface_altitude", units="m")
model_level = iris.coords.AuxCoord([2, 1, 0], standard_name="model_level_number")
level_height = iris.coords.DimCoord(
[100, 50, 10],
long_name="level_height",
units="m",
attributes={"positive": "up"},
bounds=[[150, 75], [75, 20], [20, 0]],
)
sigma = iris.coords.AuxCoord(
[0.8, 0.9, 0.95], long_name="sigma", bounds=[[0.7, 0.85], [0.85, 0.97], [0.97, 1.0]]
)
hybrid_height = iris.aux_factory.HybridHeightFactory(level_height, sigma, orography)
cube = iris.cube.Cube(
data,
standard_name="air_temperature",
units="K",
dim_coords_and_dims=[(level_height, 0)],
aux_coords_and_dims=[(orography, 1), (model_level, 0), (sigma, 0)],
aux_factories=[hybrid_height],
)
com = iris.cube.Cube(
data[None],
standard_name="air_temperature",
units="K",
dim_coords_and_dims=[(copy.copy(level_height), 1)],
aux_coords_and_dims=[(copy.copy(orography), 2), (copy.copy(model_level), 1), (copy.copy(sigma), 1)],
aux_factories=[copy.copy(hybrid_height)],
)
res = new_axis(cube)
self.assertEqual(res, com)
self._assert_cube_notis(res, cube)
def create_data_object(self, filenames, variable, index_offset=1):
from cis.data_io.hdf_vd import get_data
from cis.data_io.hdf_vd import VDS
from pyhdf.error import HDF4Error
from cis.data_io import hdf_sd
from iris.coords import DimCoord, AuxCoord
from iris.cube import Cube, CubeList
from cis.data_io.gridded_data import GriddedData
from cis.time_util import cis_standard_time_unit
from datetime import datetime
from iris.util import new_axis
import numpy as np
logging.debug("Creating data object for variable " + variable)
variables = ["Pressure_Mean"]
logging.info("Listing coordinates: " + str(variables))
variables.append(variable)
# reading data from files
sdata = {}
for filename in filenames:
try:
sds_dict = hdf_sd.read(filename, variables)
except HDF4Error as e:
raise IOError(str(e))
for var in list(sds_dict.keys()):
utils.add_element_to_list_in_dict(sdata, var, sds_dict[var])
# work out size of data arrays
# the coordinate variables will be reshaped to match that.
# NOTE: This assumes that all Caliop_L1 files have the same altitudes.
# If this is not the case, then the following line will need to be changed
# to concatenate the data from all the files and not just arbitrarily pick
# the altitudes from the first file.
alt_data = self._get_calipso_data(hdf_sd.HDF_SDS(filenames[0], 'Altitude_Midpoint'))[0, :]
alt_coord = DimCoord(alt_data, standard_name='altitude', units='km')
alt_coord.convert_units('m')
lat_data = self._get_calipso_data(hdf_sd.HDF_SDS(filenames[0], 'Latitude_Midpoint'))[0, :]
lat_coord = DimCoord(lat_data, standard_name='latitude', units='degrees_north')
lon_data = self._get_calipso_data(hdf_sd.HDF_SDS(filenames[0], 'Longitude_Midpoint'))[0, :]
lon_coord = DimCoord(lon_data, standard_name='longitude', units='degrees_east')
cubes = CubeList()
for f in filenames:
t = get_data(VDS(f, "Nominal_Year_Month"), True)[0]
time_data = cis_standard_time_unit.date2num(datetime(int(t[0:4]), int(t[4:6]), 15))
time_coord = AuxCoord(time_data, long_name='Profile_Time', standard_name='time',
units=cis_standard_time_unit)
# retrieve data + its metadata
var = sdata[variable]
metadata = hdf.read_metadata(var, "SD")
data = self._get_calipso_data(hdf_sd.HDF_SDS(f, variable))
pres_data = self._get_calipso_data(hdf_sd.HDF_SDS(f, 'Pressure_Mean'))
pres_coord = AuxCoord(pres_data, standard_name='air_pressure', units='hPa')
if data.ndim == 2:
# pres_coord = new_axis()
cube = Cube(data, long_name=metadata.long_name or variable, units=self.clean_units(metadata.units),
dim_coords_and_dims=[(lat_coord, 0), (lon_coord, 1)],
aux_coords_and_dims=[(time_coord, ())])
# Promote the time scalar coord to a length one dimension
new_cube = new_axis(cube, 'time')
cubes.append(new_cube)
elif data.ndim == 3:
# pres_coord = new_axis()
cube = Cube(data, long_name=metadata.long_name or variable, units=self.clean_units(metadata.units),
dim_coords_and_dims=[(lat_coord, 0), (lon_coord, 1), (alt_coord, 2)],
aux_coords_and_dims=[(time_coord, ())])
# Promote the time scalar coord to a length one dimension
new_cube = new_axis(cube, 'time')
# Then add the (extended) pressure coord so that it is explicitly a function of time
new_cube.add_aux_coord(pres_coord[np.newaxis, ...], (0, 1, 2, 3))
cubes.append(new_cube)
else:
raise ValueError("Unexpected number of dimensions for CALIOP data: {}".format(data.ndim))
# Concatenate the cubes from each file into a single GriddedData object
gd = GriddedData.make_from_cube(cubes.concatenate_cube())
return gd
def test_1d_single_value_common_axis(self):
# Manually promote scalar time cube to be a 1d cube.
single = CubeSignature(new_axis(self.scalar_cube, 'time'))
self.assertEqual(self.series_inc.dim_metadata, single.dim_metadata)
self.assertEqual(self.series_dec.dim_metadata, single.dim_metadata)
def create_data_object(self, filenames, variable, index_offset=1):
from cis.data_io.hdf_vd import get_data
from cis.data_io.hdf_vd import VDS
from pyhdf.error import HDF4Error
from cis.data_io import hdf_sd
from iris.coords import DimCoord, AuxCoord
from iris.cube import Cube, CubeList
from cis.data_io.gridded_data import GriddedData
from cis.time_util import cis_standard_time_unit
from datetime import datetime
from iris.util import new_axis
import numpy as np
logging.debug("Creating data object for variable " + variable)
variables = ["Pressure_Mean"]
logging.info("Listing coordinates: " + str(variables))
variables.append(variable)
# reading data from files
sdata = {}
for filename in filenames:
try:
sds_dict = hdf_sd.read(filename, variables)
except HDF4Error as e:
raise IOError(str(e))
for var in list(sds_dict.keys()):
utils.add_element_to_list_in_dict(sdata, var, sds_dict[var])
# work out size of data arrays
# the coordinate variables will be reshaped to match that.
# NOTE: This assumes that all Caliop_L1 files have the same altitudes.
# If this is not the case, then the following line will need to be changed
# to concatenate the data from all the files and not just arbitrarily pick
# the altitudes from the first file.
alt_data = self._get_calipso_data(
hdf_sd.HDF_SDS(filenames[0], 'Altitude_Midpoint'))[0, :]
alt_coord = DimCoord(alt_data, standard_name='altitude', units='km')
alt_coord.convert_units('m')
lat_data = self._get_calipso_data(
hdf_sd.HDF_SDS(filenames[0], 'Latitude_Midpoint'))[0, :]
lat_coord = DimCoord(lat_data,
standard_name='latitude',
units='degrees_north')
lon_data = self._get_calipso_data(
hdf_sd.HDF_SDS(filenames[0], 'Longitude_Midpoint'))[0, :]
lon_coord = DimCoord(lon_data,
standard_name='longitude',
units='degrees_east')
cubes = CubeList()
for f in filenames:
t = get_data(VDS(f, "Nominal_Year_Month"), True)[0]
time_data = cis_standard_time_unit.date2num(
datetime(int(t[0:4]), int(t[4:6]), 15))
time_coord = AuxCoord(time_data,
long_name='Profile_Time',
standard_name='time',
units=cis_standard_time_unit)
# retrieve data + its metadata
var = sdata[variable]
metadata = hdf.read_metadata(var, "SD")
data = self._get_calipso_data(hdf_sd.HDF_SDS(f, variable))
pres_data = self._get_calipso_data(
hdf_sd.HDF_SDS(f, 'Pressure_Mean'))
pres_coord = AuxCoord(pres_data,
standard_name='air_pressure',
units='hPa')
if data.ndim == 2:
# pres_coord = new_axis()
cube = Cube(data,
long_name=metadata.long_name or variable,
units=self.clean_units(metadata.units),
dim_coords_and_dims=[(lat_coord, 0),
(lon_coord, 1)],
aux_coords_and_dims=[(time_coord, ())])
# Promote the time scalar coord to a length one dimension
new_cube = new_axis(cube, 'time')
cubes.append(new_cube)
elif data.ndim == 3:
# pres_coord = new_axis()
cube = Cube(data,
long_name=metadata.long_name or variable,
units=self.clean_units(metadata.units),
dim_coords_and_dims=[(lat_coord, 0),
(lon_coord, 1),
(alt_coord, 2)],
aux_coords_and_dims=[(time_coord, ())])
# Promote the time scalar coord to a length one dimension
new_cube = new_axis(cube, 'time')
# Then add the (extended) pressure coord so that it is explicitly a function of time
new_cube.add_aux_coord(pres_coord[np.newaxis, ...],
(0, 1, 2, 3))
cubes.append(new_cube)
else:
raise ValueError(
"Unexpected number of dimensions for CALIOP data: {}".
format(data.ndim))
# Concatenate the cubes from each file into a single GriddedData object
gd = GriddedData.make_from_cube(cubes.concatenate_cube())
return gd
def segmentation_2D(track,
field,
dxy,
threshold=0,
target='maximum',
method='watershed',
max_distance=None):
"""
Function using watershedding or random walker to determine cloud volumes associated with tracked updrafts
Parameters:
track: pandas.DataFrame
output from trackpy/maketrack
field_in: iris.cube.Cube
containing the 3D (time,x,y) field to perform the watershedding on
threshold: float
threshold for the watershedding field to be used for the mask
target: string
Switch to determine if algorithm looks strating from maxima or minima in input field (maximum: starting from maxima (default), minimum: starting from minima)
method: str ('method')
flag determining the algorithm to use (currently watershedding implemented)
Output:
segmentation_out: iris.cube.Cube
Cloud mask, 0 outside and integer numbers according to track inside the clouds
"""
import numpy as np
from skimage.morphology import watershed
# from skimage.segmentation import random_walker
import logging
from iris.cube import CubeList
from iris.util import new_axis
from scipy.ndimage import distance_transform_edt
logging.info('Start wateshedding 2D')
# CubeList to store individual segmentation masks
segmentation_out_list = CubeList()
track['ncells'] = 0
if max_distance is not None:
max_distance_pixel = np.ceil(max_distance / dxy)
field_time = field.slices_over('time')
for i, field_i in enumerate(field_time):
# Create cube of the same dimensions and coordinates as input data to store mask:
segmentation_out_i = 1 * field_i
segmentation_out_i.rename('segmentation_mask')
segmentation_out_i.units = 1
data_i = field_i.core_data()
time_i = field_i.coord('time').units.num2date(
field_i.coord('time').points[0])
tracks_i = track[track['time'] == time_i]
# mask data outside region above/below threshold and invert data if tracking maxima:
if target == 'maximum':
unmasked = data_i > threshold
data_i_segmentation = -1 * data_i
elif target == 'minimum':
unmasked = data_i < threshold
data_i_segmentation = data_i
else:
raise ValueError('unknown type of target')
markers = np.zeros_like(unmasked).astype(np.int32)
for index, row in tracks_i.iterrows():
markers[int(row['hdim_1']), int(row['hdim_2'])] = row['feature']
markers[~unmasked] = 0
if method == 'watershed':
segmentation_mask_i = watershed(data_i_segmentation,
markers.astype(np.int32),
mask=unmasked)
# elif method=='random_walker':
# #res1 = random_walker(Mask, markers,mode='cg')
# res1=random_walker(data_i_segmentation, markers.astype(np.int32),
# beta=130, mode='bf', tol=0.001, copy=True, multichannel=False, return_full_prob=False, spacing=None)
else:
raise ValueError('unknown method, must be watershed')
# remove everything from the individual masks that is more than max_distance_pixel away from the markers
if max_distance is not None:
for feature in tracks_i['feature']:
D = distance_transform_edt((markers != feature).astype(int))
segmentation_mask_i[np.bitwise_and(
segmentation_mask_i == feature,
D > max_distance_pixel)] = 0
segmentation_out_i.data = segmentation_mask_i
# using merge throws error, so cubes with time promoted to DimCoord and using concatenate:
# segmentation_out_list.append(segmentation_out_i)
segmentation_out_i_temp = new_axis(segmentation_out_i,
scalar_coord='time')
segmentation_out_list.append(segmentation_out_i_temp)
# count number of grid cells asoociated to each tracked cell and write that into DataFrame:
values, count = np.unique(segmentation_mask_i, return_counts=True)
counts = dict(zip(values, count))
for index, row in tracks_i.iterrows():
if row['feature'] in counts.keys():
track.loc[index, 'ncells'] = counts[row['feature']]
logging.debug('Finished segmentation 2D for ' +
time_i.strftime('%Y-%m-%d_%H:%M:%S'))
#merge individual masks in CubeList into one Cube:
# using merge throws error, so cubes with time promoted to DimCoord and using concatenate:
# segmentation_out=segmentation_out_list.merge_cube()
segmentation_out = segmentation_out_list.concatenate_cube()
logging.debug('Finished segmentation 2D')
return segmentation_out, track
def test_1d_single_value_common_axis(self):
# Manually promote scalar time cube to be a 1d cube.
single = CubeSignature(new_axis(self.scalar_cube, 'time'))
self.assertEqual(self.series_inc.dim_metadata, single.dim_metadata)
self.assertEqual(self.series_dec.dim_metadata, single.dim_metadata)
