def test_duplicate(self):
paths = (
tests.get_data_path(['PP', 'aPPglob1', 'global.pp']),
tests.get_data_path(['PP', 'aPPglob1', 'gl?bal.pp'])
)
cubes = iris.load(paths)
self.assertEqual(len(cubes), 2)
def test_too_many(self):
paths = (
tests.get_data_path(["PP", "aPPglob1", "global.pp"]),
tests.get_data_path(["PP", "aPPglob1", "gl?bal.pp"]),
)
with self.assertRaises(iris.exceptions.ConstraintMismatchError):
iris.load_cube(paths)
def setUp(self):
self.dec_path = tests.get_data_path(['PP', 'globClim1', 'dec_subset.pp'])
self.theta_path = tests.get_data_path(['PP', 'globClim1', 'theta.pp'])
self.humidity = iris.Constraint(SN_SPECIFIC_HUMIDITY)
self.theta = iris.Constraint(SN_AIR_POTENTIAL_TEMPERATURE)
# Coord based constraints
self.level_10 = iris.Constraint(model_level_number=10)
self.level_22 = iris.Constraint(model_level_number=22)
# Value based coord constraint
self.level_30 = iris.Constraint(model_level_number=30)
self.level_gt_30_le_3 = iris.Constraint(model_level_number=lambda c: ( c > 30 ) | (c <= 3))
self.invalid_inequality = iris.Constraint(coord_values={'model_level_number': lambda c: c > 1000})
# bound based coord constraint
self.level_height_of_model_level_number_10 = iris.Constraint(level_height=1900)
self.model_level_number_10_22 = iris.Constraint(model_level_number=[10, 22])
# Invalid constraints
self.pressure_950 = iris.Constraint(model_level_number=950)
self.lat_30 = iris.Constraint(latitude=30)
self.lat_gt_45 = iris.Constraint(latitude=lambda c: c > 45)
def test_too_many(self):
paths = (
tests.get_data_path(['PP', 'aPPglob1', 'global.pp']),
tests.get_data_path(['PP', 'aPPglob1', 'gl?bal.pp'])
)
with self.assertRaises(iris.exceptions.ConstraintMismatchError):
iris.load_cube(paths)
def test_duplicate(self):
paths = (
tests.get_data_path(["PP", "aPPglob1", "global.pp"]),
tests.get_data_path(["PP", "aPPglob1", "gl?bal.pp"]),
)
cubes = iris.load(paths)
self.assertEqual(len(cubes), 2)
def test_no_file(self):
# Test an IOError is recieved when a filename is given which doesnt match any files
real_file = ['PP', 'globClim1', 'theta.pp']
non_existant_file = ['PP', 'globClim1', 'no_such_file*']
self.assertRaises(IOError, iris.load, tests.get_data_path(non_existant_file))
self.assertRaises(IOError, iris.load, [tests.get_data_path(non_existant_file), tests.get_data_path(real_file)])
self.assertRaises(IOError, iris.load, [tests.get_data_path(real_file), tests.get_data_path(non_existant_file)])
def test_pp_callback(self):
fname = tests.get_data_path(["PP", "trui", "air_temp_T24", "200812011200__qwqg12ff.T24.pp"])
cube = iris.load_strict(fname, callback=truipp_filename_callback)
self.assertCML(cube, ['uri_callback', 'trui_t24.cml'])
fname = tests.get_data_path(["PP", "trui", "air_temp_init", "200812011200__qwqu12ff.initanl.pp"])
cube = iris.load_strict(fname, callback=truipp_filename_callback)
self.assertCML(cube, ['uri_callback', 'trui_init.cml'])
def test_nonexist_wild(self):
paths = (
tests.get_data_path(["PP", "aPPglob1", "global.pp"]),
tests.get_data_path(["PP", "_guaranteed_non_exist_*.pp"]),
)
with self.assertRaises(IOError) as error_trap:
cubes = iris.load(paths)
self.assertTrue(error_trap.exception.message.startswith("One or more of the files specified did not exist"))
def test_nonexist_wild(self):
paths = (
tests.get_data_path(['PP', 'aPPglob1', 'global.pp']),
tests.get_data_path(['PP', '_guaranteed_non_exist_*.pp']),
)
with self.assertRaises(IOError) as error_trap:
cubes = iris.load(paths)
self.assertIn('One or more of the files specified did not exist',
str(error_trap.exception))
def test_nonexist(self):
paths = (
tests.get_data_path(['PP', 'aPPglob1', 'global.pp']),
tests.get_data_path(['PP', '_guaranteed_non_exist.pp']),
)
with self.assertRaises(IOError) as error_trap:
cubes = iris.load(paths)
self.assertTrue(error_trap.exception.message.startswith(
'One or more of the files specified did not exist'))
def test_no_time_coord_in_cubes(self):
path0 = tests.get_data_path(('PP', 'aPPglob1', 'global.pp'))
path1 = tests.get_data_path(('PP', 'aPPglob1', 'global_t_forecast.pp'))
cube0 = iris.load_cube(path0)
cube1 = iris.load_cube(path1)
cubes = iris.cube.CubeList([cube0, cube1])
result = copy.copy(cubes)
unify_time_units(result)
self.assertEqual(cubes, result)
def setUp(self):
filename = tests.get_data_path(
('NetCDF', 'label_and_climate',
'A1B-99999a-river-sep-2070-2099.nc'))
self.cfr_start = cf.CFReader(filename)
filename = tests.get_data_path(
('NetCDF', 'label_and_climate',
'small_FC_167_mon_19601101.nc'))
self.cfr_end = cf.CFReader(filename)
def test_load(self):
cubes = iris.load(tests.get_data_path(('GRIB', 'rotated_uk', "uk_wrongparam.grib1")))
self.assertCML(cubes, ("grib_load", "rotated.cml"))
cubes = iris.load(tests.get_data_path(('GRIB', "time_processed", "time_bound.grib2")))
self.assertCML(cubes, ("grib_load", "time_bound.cml"))
cubes = iris.load(tests.get_data_path(('GRIB', "3_layer_viz", "3_layer.grib2")))
cubes = iris.cube.CubeList([cubes[1], cubes[0], cubes[2]])
self.assertCML(cubes, ("grib_load", "3_layer.cml"))
def test_iris_loading(self):
ff32_fname = tests.get_data_path(('FF', 'n48_multi_field.ieee32'))
ff64_fname = tests.get_data_path(('FF', 'n48_multi_field'))
ff32_cubes = iris.load(ff32_fname)
ff64_cubes = iris.load(ff64_fname)
for ff32, ff64 in zip(ff32_cubes, ff64_cubes):
# load the data
_, _ = ff32.data, ff64.data
self.assertEqual(ff32, ff64)
def test_meanTrial_diff(self):
air_temp_T00_cube = iris.load_strict(tests.get_data_path(('PP', 'trui', 'air_temp_init', '*.pp')))
self.assertCML(air_temp_T00_cube, ['trui', 'air_temp_T00.cml'])
air_temp_T24_cube = iris.load_strict(tests.get_data_path(('PP', 'trui', 'air_temp_T24', '*.T24.pp')))
self.assertCML(air_temp_T24_cube, ['trui', 'air_temp_T24.cml'])
air_temp_T00_cube, air_temp_T24_cube = iris.analysis.maths.intersection_of_cubes(air_temp_T00_cube, air_temp_T24_cube)
delta_cube = air_temp_T00_cube - air_temp_T24_cube
self.assertCML(delta_cube, ('trui', 'air_temp_trial_diff_T00_to_T24.cml'))
mean_delta_cube = delta_cube.collapsed("time", iris.analysis.MEAN)
self.assertCML(mean_delta_cube, ('trui', 'mean_air_temp_trial_diff_T00_to_T24.cml'))
def test_deprecated_callbacks(self):
# Tests that callback functions that return values are still supported but that warnings are generated
def returns_cube(cube, field, filename):
return cube
def returns_no_cube(cube, field, filename):
return iris.io.NO_CUBE
fname = tests.get_data_path(["PP", "trui", "air_temp_init", "200812011200__qwqu12ff.initanl.pp"])
# Catch all warnings for returns_cube
with warnings.catch_warnings(record=True) as generated_warnings_cube:
warnings.simplefilter("always")
r = iris.load(fname, callback=returns_cube)
# Test that our warnings are present in the generated warnings:
gen_warnings_cube = [str(x.message) for x in generated_warnings_cube]
self.assertIn(iris.io.CALLBACK_DEPRECATION_MSG, gen_warnings_cube, "Callback deprecation warning message not issued.")
# Catch all warnings for returns_no_cube
with warnings.catch_warnings(record=True) as generated_warnings_no_cube:
warnings.simplefilter("always")
r = iris.load(fname, callback=returns_no_cube)
# Test that our warnings are present in the generated warnings:
gen_warnings_no_cube = [str(x.message) for x in generated_warnings_no_cube]
self.assertIn(iris.io.CALLBACK_DEPRECATION_MSG, gen_warnings_no_cube, "Callback deprecation warning message not issued.")
def test_grib_callback(self):
def grib_thing_getter(cube, field, filename):
cube.add_aux_coord(iris.coords.AuxCoord(field.extra_keys['_periodStartDateTime'], long_name='random element', units='no_unit'))
fname = tests.get_data_path(('GRIB', 'global_t', 'global.grib2'))
cube = iris.load_strict(fname, callback=grib_thing_getter)
self.assertCML(cube, ['uri_callback', 'grib_global.cml'])
def test_invalid_signature_callback(self):
def invalid_callback(cube, ):
# should never get here
pass
fname = tests.get_data_path(["PP", "trui", "air_temp_init", "200812011200__qwqu12ff.initanl.pp"])
self.assertRaises(TypeError, iris.load_strict, fname, callback=invalid_callback)
def test_load_stereographic_grid(self):
# Test loading a single CF-netCDF file with a stereographic
# grid_mapping.
cube = iris.load_cube(
tests.get_data_path(('NetCDF', 'stereographic',
'toa_brightness_temperature.nc')))
self.assertCML(cube, ('netcdf', 'netcdf_stereo.cml'))
def test_invalid_return_type_callback(self):
def invalid_callback(cube, field, filename):
return "Not valid to return a string"
fname = tests.get_data_path(["PP", "trui", "air_temp_init", "200812011200__qwqu12ff.initanl.pp"])
with self.assertRaises(TypeError):
iris.load_strict(fname, callback=invalid_callback)
def test_deferred_loading(self):
# Test exercising CF-netCDF deferred loading and deferred slicing.
# shape (31, 161, 320)
cube = iris.load_cube(tests.get_data_path(
('NetCDF', 'global', 'xyt', 'SMALL_total_column_co2.nc')))
# Consecutive index on same dimension.
self.assertCML(cube[0], ('netcdf', 'netcdf_deferred_index_0.cml'))
self.assertCML(cube[0][0], ('netcdf', 'netcdf_deferred_index_1.cml'))
self.assertCML(cube[0][0][0], ('netcdf',
'netcdf_deferred_index_2.cml'))
# Consecutive slice on same dimension.
self.assertCML(cube[0:20], ('netcdf', 'netcdf_deferred_slice_0.cml'))
self.assertCML(cube[0:20][0:10], ('netcdf',
'netcdf_deferred_slice_1.cml'))
self.assertCML(cube[0:20][0:10][0:5], ('netcdf',
'netcdf_deferred_slice_2.cml'))
# Consecutive tuple index on same dimension.
self.assertCML(cube[(0, 8, 4, 2, 14, 12), ],
('netcdf', 'netcdf_deferred_tuple_0.cml'))
self.assertCML(cube[(0, 8, 4, 2, 14, 12), ][(0, 2, 4, 1), ],
('netcdf', 'netcdf_deferred_tuple_1.cml'))
subcube = cube[(0, 8, 4, 2, 14, 12), ][(0, 2, 4, 1), ][(1, 3), ]
self.assertCML(subcube, ('netcdf', 'netcdf_deferred_tuple_2.cml'))
# Consecutive mixture on same dimension.
self.assertCML(cube[0:20:2][(9, 5, 8, 0), ][3],
('netcdf', 'netcdf_deferred_mix_0.cml'))
self.assertCML(cube[(2, 7, 3, 4, 5, 0, 9, 10), ][2:6][3],
('netcdf', 'netcdf_deferred_mix_0.cml'))
self.assertCML(cube[0][(0, 2), (1, 3)],
('netcdf', 'netcdf_deferred_mix_1.cml'))
def test_load_tmerc_grid_and_clim_bounds(self):
# Test loading a single CF-netCDF file with a transverse Mercator
# grid_mapping and a time variable with climatology.
cube = iris.load_cube(
tests.get_data_path(('NetCDF', 'transverse_mercator',
'tmean_1910_1910.nc')))
self.assertCML(cube, ('netcdf', 'netcdf_tmerc_and_climatology.cml'))
def test_load_lcc_grid(self):
# Test loading a single CF-netCDF file with Lambert conformal conic
# grid mapping.
cube = iris.load_cube(
tests.get_data_path(('NetCDF', 'lambert_conformal',
'test_lcc.nc')))
self.assertCML(cube, ('netcdf', 'netcdf_lcc.cml'))
def test_load_rotated_xy_land(self):
# Test loading single xy rotated pole CF-netCDF file.
cube = iris.load_cube(tests.get_data_path(
('NetCDF', 'rotated', 'xy', 'rotPole_landAreaFraction.nc')))
# Make sure the AuxCoords have lazy data.
self.assertTrue(is_lazy_data(cube.coord('latitude').core_points()))
self.assertCML(cube, ('netcdf', 'netcdf_rotated_xy_land.cml'))
def test_load_rotated_xyt_precipitation(self):
# Test loading single xyt rotated pole CF-netCDF file.
cube = iris.load_cube(
tests.get_data_path(('NetCDF', 'rotated', 'xyt',
'small_rotPole_precipitation.nc')))
self.assertCML(cube, ('netcdf',
'netcdf_rotated_xyt_precipitation.cml'))
def test_plot_tmerc(self):
filename = tests.get_data_path(('NetCDF', 'transverse_mercator',
'tmean_1910_1910.nc'))
self.cube = iris.load_cube(filename)
iplt.pcolormesh(self.cube[0])
plt.gca().coastlines()
self.check_graphic()
def test_load_file_with_captured_warnings(self):
test_filename = tests.get_data_path(('NetCDF', 'testing', 'units.nc'))
with warnings.catch_warnings(record=True) as filtered_warnings:
with suppress_unit_warnings():
iris.load(test_filename)
filtered_warnings_list = [str(w.message) for w in filtered_warnings]
self.assertEqual(len(filtered_warnings_list), 0)
def test_lots_of_extra_data(self):
r = pp.load(tests.get_data_path(('PP', 'cf_processing', 'HadCM2_ts_SAT_ann_18602100.b.pp')))
r = list(r)
self.assertEqual(r[0].lbcode.ix, 13)
self.assertEqual(r[0].lbcode.iy, 23)
self.assertEqual(len(r[0].lbcode), 5)
self.check_pp(r, ('PP', 'extra_data_time_series.pp.txt'))
def test_unmasked(self):
tif_header = 'SMALL_total_column_co2.nc.tif_header.txt'
fin = tests.get_data_path(('NetCDF', 'global', 'xyt',
'SMALL_total_column_co2.nc'))
cube = iris.load_cube(fin)[0]
# PIL doesn't support float64
cube.data = cube.data.astype('f4')
# Ensure longitude values are continuous and monotonically increasing,
# and discard the 'half cells' at the top and bottom of the UM output
# by extracting a subset.
east = iris.Constraint(longitude=lambda cell: cell < 180)
non_edge = iris.Constraint(latitude=lambda cell: -90 < cell < 90)
cube = cube.extract(east & non_edge)
cube.coord('longitude').guess_bounds()
cube.coord('latitude').guess_bounds()
self.check_tiff(cube, tif_header)
# Check again with the latitude coordinate (and the corresponding
# cube.data) inverted. The output should be the same as before.
coord = cube.coord('latitude')
coord.points = coord.points[::-1]
coord.bounds = None
coord.guess_bounds()
cube.data = cube.data[::-1, :]
self.check_tiff(cube, tif_header)
def realistic_4d_w_missing_data():
data_path = tests.get_data_path(('stock', 'stock_mdi_arrays.npz'))
if not os.path.isfile(data_path):
raise IOError('Test data is not available at {}.'.format(data_path))
data_archive = np.load(data_path)
data = ma.masked_array(data_archive['arr_0'], mask=data_archive['arr_1'])
# sort the arrays based on the order they were originally given.
# The names given are of the form 'arr_1' or 'arr_10'
ll_cs = GeogCS(6371229)
lat = DimCoord(np.arange(20, dtype=np.float32),
standard_name='grid_latitude',
units='degrees', coord_system=ll_cs)
lon = DimCoord(np.arange(20, dtype=np.float32),
standard_name='grid_longitude',
units='degrees', coord_system=ll_cs)
time = DimCoord([1000., 1003., 1006.], standard_name='time',
units='hours since 1970-01-01 00:00:00')
forecast_period = DimCoord([0.0, 3.0, 6.0],
standard_name='forecast_period',
units='hours')
pressure = DimCoord(np.array([800., 900., 1000.], dtype=np.float32),
long_name='pressure', units='hPa')
cube = iris.cube.Cube(data, long_name='missing data test data', units='K',
dim_coords_and_dims=[(time, 0), (pressure, 1),
(lat, 2), (lon, 3)],
aux_coords_and_dims=[(forecast_period, 0)],
attributes={'source': 'Iris test case'})
return cube
def setUp(self):
self.original_pp_filepath = tests.get_data_path(
("PP", "ukV1", "ukVpmslont.pp")
)
self.r = list(pp.load(self.original_pp_filepath))[0:5]
def test_NAMEII_timeseries(self):
cubes = iris.load(
tests.get_data_path(('NAME', 'NAMEII_timeseries.txt')))
self.assertCMLApproxData(cubes, ('name', 'NAMEII_timeseries.cml'))
def test_NAMEII_trajectory(self):
cubes = iris.load(
tests.get_data_path(('NAME', 'NAMEIII_trajectory.txt')))
self.assertCML(cubes[0], ('name', 'NAMEIII_trajectory0.cml'))
self.assertCML(cubes, ('name', 'NAMEIII_trajectory.cml'),
checksum=False)
def setUp(self):
self._data_path = tests.get_data_path(("PP", "globClim1", "theta.pp"))
self._num_cubes = 1
self._prefix = "theta"
def setUp(self):
self._data_path = tests.get_data_path(
("PP", "globClim1", "dec_subset.pp")
)
self._num_cubes = 4
self._prefix = "dec"
def test_load_time_processed(self):
cubes = iris.load(tests.get_data_path(('GRIB', "time_processed",
"time_bound.grib2")))
self.assertCML(cubes, ("grib_load", "time_bound_grib2.cml"))
def setUp(self):
self.subcubes = iris.cube.CubeList()
filename = tests.get_data_path(('PP', 'aPPglob1', 'global.pp'))
self.template = iris.fileformats.pp.load(filename).next()
def test_regular_gg_grib2(self):
cube = iris.load_cube(tests.get_data_path(
('GRIB', 'gaussian', 'regular_gg.grib2')))
self.assertCML(cube, ('grib_load', 'regular_gg_grib2.cml'))
def test_reduced_gg(self):
cube = iris.load_cube(tests.get_data_path(
("GRIB", "reduced", "reduced_gg.grib2")))
self.assertCML(cube, ("grib_load", "reduced_gg_grib2.cml"))
def test_reduced_missing(self):
cube = iris.load_cube(tests.get_data_path(
("GRIB", "reduced", "reduced_ll_missing.grib1")))
self.assertCML(cube, ("grib_load", "reduced_ll_missing_grib1.cml"))
def _old_compat_load(name):
cube = iris.load(tests.get_data_path(('GRIB', 'ij_directions',
name)))[0]
return [cube]
def _old_compat_load(name):
cube = iris.load(tests.get_data_path(('GRIB', 'shape_of_earth',
name)))[0]
return cube
def setUp(self):
self.original_pp_filepath = tests.get_data_path(
("PP", "aPPglob1", "global.pp")
)
self.r = list(pp.load(self.original_pp_filepath))
def setUp(self):
super(TestPlotDimAndAuxCoordsKwarg, self).setUp()
filename = tests.get_data_path(
('NetCDF', 'rotated', 'xy', 'rotPole_landAreaFraction.nc'))
self.cube = iris.load_cube(filename)
def test_2d_positive_up(self):
path = tests.get_data_path(
('NetCDF', 'testing', 'small_theta_colpex.nc'))
cube = iris.load_cube(path, 'air_potential_temperature')[0, :, 42, :]
qplt.pcolormesh(cube)
self.check_graphic()
def test_no_std_name(self):
fname = tests.get_data_path(['PP', 'simple_pp', 'bad_global.pp'])
cube = iris.load_cube(fname)
self.assertCML([cube], ['cube_io', 'pp', 'no_std_name.cml'])
def setUp(self):
tests.GraphicsTest.setUp(self)
self.cube = iris.load_cube(
tests.get_data_path(('NAME', 'NAMEIII_trajectory.txt')),
'Temperature')
self.draw_method = qplt.scatter
def test_2d_positive_down(self):
path = tests.get_data_path(('NetCDF', 'ORCA2', 'votemper.nc'))
cube = iris.load_cube(path)[0, :, 42, :]
qplt.pcolormesh(cube)
self.check_graphic()
def test_polar_stereo_grib1(self):
cube = iris.load_cube(tests.get_data_path(
("GRIB", "polar_stereo", "ST4.2013052210.01h")))
self.assertCML(cube, ("grib_load", "polar_stereo_grib1.cml"))
def test_1d_positive_down(self):
path = tests.get_data_path(('NetCDF', 'ORCA2', 'votemper.nc'))
cube = iris.load_cube(path)
qplt.plot(cube[0, :, 60, 80], cube.coord('depth'))
self.check_graphic()
def test_load_masked(self):
gribfile = tests.get_data_path(
('GRIB', 'missing_values', 'missing_values.grib2'))
cubes = iris.load(gribfile)
self.assertCML(cubes, ('grib_load', 'missing_values_grib2.cml'))
def setUp(self):
super(Test1dScatter, self).setUp()
self.cube = iris.load_cube(
tests.get_data_path(('NAME', 'NAMEIII_trajectory.txt')),
'Temperature')
self.draw_method = iplt.scatter
def setUp(self):
self.filename = tests.get_data_path(("PP", "aPPglob1", "global.pp"))
def test_y_fastest(self):
cubes = iris.load(tests.get_data_path(("GRIB", "y_fastest",
"y_fast.grib2")))
self.assertCML(cubes, ("grib_load", "y_fastest.cml"))
def test_lambert_grib2(self):
cube = iris.load_cube(tests.get_data_path(
("GRIB", "lambert", "lambert.grib2")))
self.assertCML(cube, ("grib_load", "lambert_grib2.cml"))
def test_load_3_layer(self):
cubes = iris.load(tests.get_data_path(('GRIB', "3_layer_viz",
"3_layer.grib2")))
cubes = iris.cube.CubeList([cubes[1], cubes[0], cubes[2]])
self.assertCML(cubes, ("grib_load", "3_layer.cml"))
def test_pp_no_constraint(self):
filenames = [tests.get_data_path(("PP", "globClim1", "dec_subset.pp"))]
pp_constraints = pp._convert_constraints(None)
pp_loader = iris.fileformats.rules.Loader(pp.load, {}, convert)
cubes = list(load_cubes(filenames, None, pp_loader, pp_constraints))
self.assertEqual(len(cubes), 152)
def setUp(self):
self._data_path = tests.get_data_path(
("PP", "COLPEX", "small_colpex_theta_p_alt.pp")
)
def test_normal(self):
paths = (tests.get_data_path(['PP', 'aPPglob1', 'global.pp']), )
cubes = iris.load_cubes(paths)
self.assertEqual(len(cubes), 1)
def test_load_rotated(self):
cubes = iris.load(tests.get_data_path(('GRIB', 'rotated_uk',
"uk_wrongparam.grib1")))
self.assertCML(cubes, ("grib_load", "rotated.cml"))
