def test_water_flux_unit_conversion_swe(self):
     ''' Tests variable values in returned dataset '''
     self.ten_year_dataset.variable = 'swe'
     self.ten_year_dataset.units = 'm'
     values = self.ten_year_dataset.values + 999
     dp.variable_unit_conversion(self.ten_year_dataset)
     np.testing.assert_array_equal(self.ten_year_dataset.values, values)
 def test_returned_variable_unit_swe(self):
     ''' Tests returned dataset unit if original dataset unit is swe '''
     self.ten_year_dataset.variable = 'swe'
     self.ten_year_dataset.units = 'm'
     dp.variable_unit_conversion(self.ten_year_dataset)
     self.assertEqual(self.ten_year_dataset.variable, 'swe')
     self.assertEqual(self.ten_year_dataset.units, 'km')
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 def test_water_flux_unit_conversion_swe(self):
     ''' Tests variable values in returned dataset '''
     self.ten_year_dataset.variable = 'swe'
     self.ten_year_dataset.units = 'm'
     values = self.ten_year_dataset.values + 999
     dp.variable_unit_conversion(self.ten_year_dataset)
     np.testing.assert_array_equal(self.ten_year_dataset.values, values)
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 def test_water_flux_unit_conversion_pr(self):
     ''' Tests variable values in returned dataset '''
     self.ten_year_dataset.variable = 'pr'
     self.ten_year_dataset.units = 'kg m-2 s-1'
     values = self.ten_year_dataset.values + 86399
     dp.variable_unit_conversion(self.ten_year_dataset)
     np.testing.assert_array_equal(self.ten_year_dataset.values, values)
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 def test_returned_variable_unit_swe(self):
     ''' Tests returned dataset unit if original dataset unit is swe '''
     self.ten_year_dataset.variable = 'swe'
     self.ten_year_dataset.units = 'm'
     dp.variable_unit_conversion(self.ten_year_dataset)
     self.assertEqual(self.ten_year_dataset.variable, 'swe')
     self.assertEqual(self.ten_year_dataset.units, 'km')
 def test_water_flux_unit_conversion_pr(self):
     ''' Tests variable values in returned dataset '''
     self.ten_year_dataset.variable = 'pr'
     self.ten_year_dataset.units = 'kg m-2 s-1'
     values = self.ten_year_dataset.values + 86399
     dp.variable_unit_conversion(self.ten_year_dataset)
     np.testing.assert_array_equal(self.ten_year_dataset.values, values)
 def test_returned_variable_unit_pr(self):
     '''
     Tests returned dataset unit if original dataset unit is kgm^-2s^-1
     '''
     self.ten_year_dataset.variable = 'pr'
     self.ten_year_dataset.units = 'kg m-2 s-1'
     dp.variable_unit_conversion(self.ten_year_dataset)
     self.assertEqual(self.ten_year_dataset.variable, 'pr')
     self.assertEqual(self.ten_year_dataset.units, 'mm/day')
 def test_temp_unit_conversion(self):
     ''' Tests returned dataset temp values '''
     self.ten_year_dataset.values = np.ones([
         len(self.ten_year_dataset.times),
         len(self.ten_year_dataset.lats),
         len(self.ten_year_dataset.lons)])
     values = self.ten_year_dataset.values + 273.15
     dp.variable_unit_conversion(self.ten_year_dataset)
     np.testing.assert_array_equal(self.ten_year_dataset.values, values)
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 def test_returned_variable_unit_pr(self):
     '''
     Tests returned dataset unit if original dataset unit is kgm^-2s^-1
     '''
     self.ten_year_dataset.variable = 'pr'
     self.ten_year_dataset.units = 'kg m-2 s-1'
     dp.variable_unit_conversion(self.ten_year_dataset)
     self.assertEqual(self.ten_year_dataset.variable, 'pr')
     self.assertEqual(self.ten_year_dataset.units, 'mm/day')
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 def test_temp_unit_conversion(self):
     ''' Tests returned dataset temp values '''
     self.ten_year_dataset.values = np.ones([
         len(self.ten_year_dataset.times),
         len(self.ten_year_dataset.lats),
         len(self.ten_year_dataset.lons)])
     values = self.ten_year_dataset.values + 273.15
     dp.variable_unit_conversion(self.ten_year_dataset)
     np.testing.assert_array_equal(self.ten_year_dataset.values, values)
 def test_returned_variable_unit_kelvin(self):
     ''' Tests returned dataset unit if original dataset unit is kelvin '''
     self.ten_year_dataset.units = 'K'
     another_dataset = dp.variable_unit_conversion(self.ten_year_dataset)
     self.assertEqual(another_dataset.units, self.ten_year_dataset.units)
 def test_returned_variable_unit_celsius(self):
     ''' Tests returned dataset unit if original dataset unit is celcius '''
     dp.variable_unit_conversion(self.ten_year_dataset)
     self.assertEqual(self.ten_year_dataset.units, 'K')
print('Regridding datasets: {}'.format(config['regrid']))
if not config['regrid']['regrid_on_reference']:
    reference_dataset = dsp.spatial_regrid(reference_dataset, new_lat, new_lon)
    print('Reference dataset has been regridded')
for i, dataset in enumerate(target_datasets):
    target_datasets[i] = dsp.spatial_regrid(dataset, new_lat, new_lon,
                                           boundary_check=boundary_check)
    print('{} has been regridded'.format(target_names[i]))
print('Propagating missing data information')
datasets = dsp.mask_missing_data([reference_dataset]+target_datasets)
reference_dataset = datasets[0]
target_datasets = datasets[1:]

""" Step 4: Checking and converting variable units """
print('Checking and converting variable units')
reference_dataset = dsp.variable_unit_conversion(reference_dataset)
for i, dataset in enumerate(target_datasets):
    target_datasets[i] = dsp.variable_unit_conversion(dataset)

print('Generating multi-model ensemble')
if len(target_datasets) >= 2.:
    target_datasets.append(dsp.ensemble(target_datasets))
    target_names.append('ENS')

""" Step 5: Generate subregion average and standard deviation """
if config['use_subregions']:
    # sort the subregion by region names and make a list
    subregions= sorted(config['subregions'].items(),key=operator.itemgetter(0))

    # number of subregions
    nsubregion = len(subregions)
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 def test_returned_variable_unit_kelvin(self):
     ''' Tests returned dataset unit if original dataset unit is kelvin '''
     self.ten_year_dataset.units = 'K'
     another_dataset = dp.variable_unit_conversion(self.ten_year_dataset)
     self.assertEqual(another_dataset.units, self.ten_year_dataset.units)
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 def test_returned_variable_unit_celsius(self):
     ''' Tests returned dataset unit if original dataset unit is celcius '''
     dp.variable_unit_conversion(self.ten_year_dataset)
     self.assertEqual(self.ten_year_dataset.units, 'K')
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""" Step 4: Spatial regriding of the reference datasets """
print 'Regridding datasets: ', config['regrid']
if not config['regrid']['regrid_on_reference']:
    ref_dataset = dsp.spatial_regrid(ref_dataset, new_lat, new_lon)
    print 'Reference dataset has been regridded'
for idata,dataset in enumerate(model_datasets):
    model_datasets[idata] = dsp.spatial_regrid(dataset, new_lat, new_lon, boundary_check = boundary_check_model)
    print model_names[idata]+' has been regridded'
print 'Propagating missing data information'
ref_dataset = dsp.mask_missing_data([ref_dataset]+model_datasets)[0]
model_datasets = dsp.mask_missing_data([ref_dataset]+model_datasets)[1:]

""" Step 5: Checking and converting variable units """
print 'Checking and converting variable units'
ref_dataset = dsp.variable_unit_conversion(ref_dataset)
for idata,dataset in enumerate(model_datasets):
    model_datasets[idata] = dsp.variable_unit_conversion(dataset)
    

print 'Generating multi-model ensemble'
if len(model_datasets) >= 2.:
    model_datasets.append(dsp.ensemble(model_datasets))
    model_names.append('ENS')

""" Step 6: Generate subregion average and standard deviation """
if config['use_subregions']:
    # sort the subregion by region names and make a list
    subregions= sorted(config['subregions'].items(),key=operator.itemgetter(0))

    # number of subregions
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print 'Regridding datasets: ', config['regrid']
if not config['regrid']['regrid_on_reference']:
    obs_dataset = dsp.spatial_regrid(obs_dataset, new_lat, new_lon)
    print 'Reference dataset has been regridded'
for i, dataset in enumerate(model_datasets):
    model_datasets[i] = dsp.spatial_regrid(dataset,
                                           new_lat,
                                           new_lon,
                                           boundary_check=boundary_check)
    print model_names[i] + ' has been regridded'
print 'Propagating missing data information'
obs_dataset = dsp.mask_missing_data([obs_dataset] + model_datasets)[0]
model_datasets = dsp.mask_missing_data([obs_dataset] + model_datasets)[1:]
""" Step 5: Checking and converting variable units """
print 'Checking and converting variable units'
obs_dataset = dsp.variable_unit_conversion(obs_dataset)
for idata, dataset in enumerate(model_datasets):
    model_datasets[idata] = dsp.variable_unit_conversion(dataset)

print 'Generating multi-model ensemble'
if len(model_datasets) >= 2.:
    model_datasets.append(dsp.ensemble(model_datasets))
    model_names.append('ENS')
""" Step 6: Generate subregion average and standard deviation """
if config['use_subregions']:
    # sort the subregion by region names and make a list
    subregions = sorted(config['subregions'].items(),
                        key=operator.itemgetter(0))

    # number of subregions
    nsubregion = len(subregions)
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if not config['regrid']['regrid_on_reference']:
    reference_dataset = dsp.spatial_regrid(reference_dataset, new_lat, new_lon)
    print('Reference dataset has been regridded')
for i, dataset in enumerate(target_datasets):
    target_datasets[i] = dsp.spatial_regrid(dataset,
                                            new_lat,
                                            new_lon,
                                            boundary_check=boundary_check)
    print('{} has been regridded'.format(target_names[i]))
print('Propagating missing data information')
datasets = dsp.mask_missing_data([reference_dataset] + target_datasets)
reference_dataset = datasets[0]
target_datasets = datasets[1:]
""" Step 4: Checking and converting variable units """
print('Checking and converting variable units')
reference_dataset = dsp.variable_unit_conversion(reference_dataset)
for i, dataset in enumerate(target_datasets):
    target_datasets[i] = dsp.variable_unit_conversion(dataset)

print('Generating multi-model ensemble')
if len(target_datasets) >= 2.:
    target_datasets.append(dsp.ensemble(target_datasets))
    target_names.append('ENS')
""" Step 5: Generate subregion average and standard deviation """
if config['use_subregions']:
    # sort the subregion by region names and make a list
    subregions = sorted(config['subregions'].items(),
                        key=operator.itemgetter(0))

    # number of subregions
    nsubregion = len(subregions)