def test_multi_d(self):
cube = iris.tests.stock.realistic_4d()
# TODO: Re-instate surface_altitude & hybrid-height once we're
# using the post-CF test results.
cube.remove_aux_factory(cube.aux_factories[0])
cube.remove_coord('surface_altitude')
self.assertCML(cube, ('cube_collapsed', 'original.cml'))
# Compare 2-stage collapsing with a single stage collapse over 2 Coords.
self.collapse_test_common(cube, 'grid_latitude', 'grid_longitude', decimal=1)
self.collapse_test_common(cube, 'grid_longitude', 'grid_latitude', decimal=1)
self.collapse_test_common(cube, 'time', 'grid_latitude', decimal=1)
self.collapse_test_common(cube, 'grid_latitude', 'time', decimal=1)
self.collapse_test_common(cube, 'time', 'grid_longitude', decimal=1)
self.collapse_test_common(cube, 'grid_longitude', 'time', decimal=1)
self.collapse_test_common(cube, 'grid_latitude', 'model_level_number', decimal=1)
self.collapse_test_common(cube, 'model_level_number', 'grid_latitude', decimal=1)
self.collapse_test_common(cube, 'grid_longitude', 'model_level_number', decimal=1)
self.collapse_test_common(cube, 'model_level_number', 'grid_longitude', decimal=1)
self.collapse_test_common(cube, 'time', 'model_level_number', decimal=1)
self.collapse_test_common(cube, 'model_level_number', 'time', decimal=1)
self.collapse_test_common(cube, 'model_level_number', 'time', decimal=1)
self.collapse_test_common(cube, 'time', 'model_level_number', decimal=1)
# Collapse 3 things at once.
triple_collapse = cube.collapsed(['model_level_number', 'time', 'grid_longitude'], iris.analysis.MEAN)
self.assertCMLApproxData(triple_collapse, ('cube_collapsed', 'triple_collapse_ml_pt_lon.cml'), decimal=1)
triple_collapse = cube.collapsed(['grid_latitude', 'model_level_number', 'time'], iris.analysis.MEAN)
self.assertCMLApproxData(triple_collapse, ('cube_collapsed', 'triple_collapse_lat_ml_pt.cml'), decimal=1)
# Ensure no side effects
self.assertCML(cube, ('cube_collapsed', 'original.cml'))
def test_multi_d(self):
cube = iris.tests.stock.realistic_4d()
# TODO: Re-instate surface_altitude & hybrid-height once we're
# using the post-CF test results.
cube.remove_aux_factory(cube.aux_factories[0])
cube.remove_coord('surface_altitude')
self.assertCML(cube, ('cube_collapsed', 'original.cml'))
# Compare 2-stage collapsing with a single stage collapse over 2 Coords.
self.collapse_test_common(cube, 'grid_latitude', 'grid_longitude', decimal=1)
self.collapse_test_common(cube, 'grid_longitude', 'grid_latitude', decimal=1)
self.collapse_test_common(cube, 'time', 'grid_latitude', decimal=1)
self.collapse_test_common(cube, 'grid_latitude', 'time', decimal=1)
self.collapse_test_common(cube, 'time', 'grid_longitude', decimal=1)
self.collapse_test_common(cube, 'grid_longitude', 'time', decimal=1)
self.collapse_test_common(cube, 'grid_latitude', 'model_level_number', decimal=1)
self.collapse_test_common(cube, 'model_level_number', 'grid_latitude', decimal=1)
self.collapse_test_common(cube, 'grid_longitude', 'model_level_number', decimal=1)
self.collapse_test_common(cube, 'model_level_number', 'grid_longitude', decimal=1)
self.collapse_test_common(cube, 'time', 'model_level_number', decimal=1)
self.collapse_test_common(cube, 'model_level_number', 'time', decimal=1)
self.collapse_test_common(cube, 'model_level_number', 'time', decimal=1)
self.collapse_test_common(cube, 'time', 'model_level_number', decimal=1)
# Collapse 3 things at once.
triple_collapse = cube.collapsed(['model_level_number', 'time', 'grid_longitude'], iris.analysis.MEAN)
self.assertCMLApproxData(triple_collapse, ('cube_collapsed', 'triple_collapse_ml_pt_lon.cml'), decimal=1)
triple_collapse = cube.collapsed(['grid_latitude', 'model_level_number', 'time'], iris.analysis.MEAN)
self.assertCMLApproxData(triple_collapse, ('cube_collapsed', 'triple_collapse_lat_ml_pt.cml'), decimal=1)
# Ensure no side effects
self.assertCML(cube, ('cube_collapsed', 'original.cml'))
def test_multi_d(self):
cube = iris.tests.stock.realistic_4d()
# TODO: Re-instate surface_altitude & hybrid-height once we're
# using the post-CF test results.
cube.remove_aux_factory(cube.aux_factories[0])
cube.remove_coord('surface_altitude')
self.assertCML(cube, ('cube_collapsed', 'original.cml'))
# Compare 2-stage collapsing with a single stage collapse
# over 2 Coords.
self.collapse_test_common(cube, 'grid_latitude', 'grid_longitude',
rtol=1e-05)
self.collapse_test_common(cube, 'grid_longitude', 'grid_latitude',
rtol=1e-05)
self.collapse_test_common(cube, 'time', 'grid_latitude', rtol=1e-05)
self.collapse_test_common(cube, 'grid_latitude', 'time', rtol=1e-05)
self.collapse_test_common(cube, 'time', 'grid_longitude', rtol=1e-05)
self.collapse_test_common(cube, 'grid_longitude', 'time', rtol=1e-05)
self.collapse_test_common(cube, 'grid_latitude', 'model_level_number',
rtol=5e-04)
self.collapse_test_common(cube, 'model_level_number', 'grid_latitude',
rtol=5e-04)
self.collapse_test_common(cube, 'grid_longitude', 'model_level_number',
rtol=5e-04)
self.collapse_test_common(cube, 'model_level_number', 'grid_longitude',
rtol=5e-04)
self.collapse_test_common(cube, 'time', 'model_level_number',
rtol=5e-04)
self.collapse_test_common(cube, 'model_level_number', 'time',
rtol=5e-04)
self.collapse_test_common(cube, 'model_level_number', 'time',
rtol=5e-04)
self.collapse_test_common(cube, 'time', 'model_level_number',
rtol=5e-04)
# Collapse 3 things at once.
triple_collapse = cube.collapsed(['model_level_number',
'time', 'grid_longitude'],
iris.analysis.MEAN)
self.assertCMLApproxData(triple_collapse, ('cube_collapsed',
('triple_collapse_ml_pt_'
'lon.cml')),
rtol=5e-04)
triple_collapse = cube.collapsed(['grid_latitude',
'model_level_number', 'time'],
iris.analysis.MEAN)
self.assertCMLApproxData(triple_collapse, ('cube_collapsed',
('triple_collapse_lat_ml'
'_pt.cml')),
rtol=0.05)
# KNOWN PROBLEM: the previous 'rtol' is very large.
# Numpy 1.10 and 1.11 give significantly different results here.
# This may relate to known problems with summing over large arrays,
# which were largely fixed in numpy 1.9 but still occur in some cases,
# as-of numpy 1.11.
# Ensure no side effects
self.assertCML(cube, ('cube_collapsed', 'original.cml'))
def test_multi_d(self):
cube = iris.tests.stock.realistic_4d()
# TODO: Re-instate surface_altitude & hybrid-height once we're
# using the post-CF test results.
cube.remove_aux_factory(cube.aux_factories[0])
cube.remove_coord('surface_altitude')
self.assertCML(cube, ('cube_collapsed', 'original.cml'))
# Compare 2-stage collapsing with a single stage collapse
# over 2 Coords.
self.collapse_test_common(cube, 'grid_latitude', 'grid_longitude',
rtol=1e-05)
self.collapse_test_common(cube, 'grid_longitude', 'grid_latitude',
rtol=1e-05)
self.collapse_test_common(cube, 'time', 'grid_latitude', rtol=1e-05)
self.collapse_test_common(cube, 'grid_latitude', 'time', rtol=1e-05)
self.collapse_test_common(cube, 'time', 'grid_longitude', rtol=1e-05)
self.collapse_test_common(cube, 'grid_longitude', 'time', rtol=1e-05)
self.collapse_test_common(cube, 'grid_latitude', 'model_level_number',
rtol=5e-04)
self.collapse_test_common(cube, 'model_level_number', 'grid_latitude',
rtol=5e-04)
self.collapse_test_common(cube, 'grid_longitude', 'model_level_number',
rtol=5e-04)
self.collapse_test_common(cube, 'model_level_number', 'grid_longitude',
rtol=5e-04)
self.collapse_test_common(cube, 'time', 'model_level_number',
rtol=5e-04)
self.collapse_test_common(cube, 'model_level_number', 'time',
rtol=5e-04)
self.collapse_test_common(cube, 'model_level_number', 'time',
rtol=5e-04)
self.collapse_test_common(cube, 'time', 'model_level_number',
rtol=5e-04)
# Collapse 3 things at once.
triple_collapse = cube.collapsed(['model_level_number',
'time', 'grid_longitude'],
iris.analysis.MEAN)
self.assertCMLApproxData(triple_collapse, ('cube_collapsed',
('triple_collapse_ml_pt_'
'lon.cml')),
rtol=5e-04)
triple_collapse = cube.collapsed(['grid_latitude',
'model_level_number', 'time'],
iris.analysis.MEAN)
self.assertCMLApproxData(triple_collapse, ('cube_collapsed',
('triple_collapse_lat_ml'
'_pt.cml')),
rtol=0.05)
# KNOWN PROBLEM: the previous 'rtol' is very large.
# Numpy 1.10 and 1.11 give significantly different results here.
# This may relate to known problems with summing over large arrays,
# which were largely fixed in numpy 1.9 but still occur in some cases,
# as-of numpy 1.11.
# Ensure no side effects
self.assertCML(cube, ('cube_collapsed', 'original.cml'))
