def setUp(self):
module = 'iris.fileformats.grib._load_convert'
patch = []
self.major = mock.sentinel.major
self.minor = mock.sentinel.minor
self.radius = mock.sentinel.radius
this = '{}.ellipsoid_geometry'.format(module)
return_value = (self.major, self.minor, self.radius)
patch.append(mock.patch(this, return_value=return_value))
this = '{}.ellipsoid'.format(module)
self.ellipsoid = mock.sentinel.ellipsoid
patch.append(mock.patch(this, return_value=self.ellipsoid))
this = '{}.grid_definition_template_4_and_5'.format(module)
self.coord = mock.sentinel.coord
self.dim = mock.sentinel.dim
item = (self.coord, self.dim)
func = lambda s, m, y, x, c: m['dim_coords_and_dims'].append(item)
patch.append(mock.patch(this, side_effect=func))
this = 'iris.coord_systems.RotatedGeogCS'
self.cs = mock.sentinel.cs
patch.append(mock.patch(this, return_value=self.cs))
self.metadata = {'factories': [], 'references': [],
'standard_name': None,
'long_name': None, 'units': None, 'attributes': {},
'cell_methods': [], 'dim_coords_and_dims': [],
'aux_coords_and_dims': []}
for p in patch:
p.start()
self.addCleanup(p.stop)
def setUp(self):
module = 'iris.fileformats.grib._load_convert'
patch = []
self.major = mock.sentinel.major
self.minor = mock.sentinel.minor
self.radius = mock.sentinel.radius
this = '{}.ellipsoid_geometry'.format(module)
return_value = (self.major, self.minor, self.radius)
patch.append(mock.patch(this, return_value=return_value))
this = '{}.ellipsoid'.format(module)
self.ellipsoid = mock.sentinel.ellipsoid
patch.append(mock.patch(this, return_value=self.ellipsoid))
this = '{}.grid_definition_template_4_and_5'.format(module)
self.coord = mock.sentinel.coord
self.dim = mock.sentinel.dim
item = (self.coord, self.dim)
func = lambda s, m, y, x, c: m['dim_coords_and_dims'].append(item)
patch.append(mock.patch(this, side_effect=func))
this = 'iris.coord_systems.RotatedGeogCS'
self.cs = mock.sentinel.cs
patch.append(mock.patch(this, return_value=self.cs))
self.metadata = {'factories': [], 'references': [],
'standard_name': None,
'long_name': None, 'units': None, 'attributes': {},
'cell_methods': [], 'dim_coords_and_dims': [],
'aux_coords_and_dims': []}
for p in patch:
p.start()
self.addCleanup(p.stop)
def test_hybrid_pressure_with_duplicate_references(self):
# Make a fake reference surface field.
pressure_field = self._field_with_data(10,
stash=iris.fileformats.pp.STASH(
1, 0, 409),
lbuser=[0, 0, 0, 409, 0, 0, 0])
# Make a fake data field which needs the reference surface.
model_level = 5678
sigma_lower, sigma, sigma_upper = 0.85, 0.9, 0.95
delta_lower, delta, delta_upper = 0.05, 0.1, 0.15
data_field = self._field_with_data(lbvc=9,
lblev=model_level,
bhlev=delta,
bhrlev=delta_lower,
blev=sigma,
brlev=sigma_lower,
brsvd=[sigma_upper, delta_upper])
# Convert both fields to cubes.
load = mock.Mock(
return_value=iter([data_field, pressure_field, pressure_field]))
msg = 'Multiple reference cubes for surface_air_pressure'
with mock.patch('iris.fileformats.pp.load',
new=load) as load, mock.patch('warnings.warn') as warn:
_, _, _ = iris.fileformats.pp.load_cubes('DUMMY')
warn.assert_called_with(msg)
def test_vectorise_call(self):
# Check that the function being passed through gets called with
# numpy.vectorize, before being applied to the points array.
# The reason we use numpy.vectorize is to support multi-dimensional
# coordinate points.
fn = lambda coord, v: v**2
with mock.patch('numpy.vectorize',
return_value=self.vectorised) as vectorise_patch:
with mock.patch('iris.coords.AuxCoord') as aux_coord_constructor:
add_categorised_coord(self.cube, 'foobar', self.coord, fn,
units=self.units)
# Check the constructor of AuxCoord gets called with the
# appropriate arguments.
# Start with the vectorised function.
vectorise_patch.assert_called_once_with(fn)
# Check the vectorize wrapper gets called with the appropriate args.
self.vectorised.assert_called_once_with(self.coord, self.coord.points)
# Check the AuxCoord constructor itself.
aux_coord_constructor.assert_called_once_with(
self.vectorised(self.coord, self.coord.points),
units=self.units,
attributes=self.coord.attributes.copy())
# And check adding the aux coord to the cube mock.
self.cube.add_aux_coord.assert_called_once_with(
aux_coord_constructor(), self.cube.coord_dims(self.coord))
def test_vectorise_call(self):
# Check that the function being passed through gets called with
# numpy.vectorize, before being applied to the points array.
# The reason we use numpy.vectorize is to support multi-dimensional
# coordinate points.
fn = lambda coord, v: v**2
with mock.patch('numpy.vectorize',
return_value=self.vectorised) as vectorise_patch:
with mock.patch('iris.coords.AuxCoord') as aux_coord_constructor:
add_categorised_coord(self.cube, 'foobar', self.coord, fn,
units=self.units)
# Check the constructor of AuxCoord gets called with the
# appropriate arguments.
# Start with the vectorised function.
vectorise_patch.assert_called_once_with(fn)
# Check the vectorize wrapper gets called with the appropriate args.
self.vectorised.assert_called_once_with(self.coord, self.coord.points)
# Check the AuxCoord constructor itself.
aux_coord_constructor.assert_called_once_with(
self.vectorised(self.coord, self.coord.points),
units=self.units,
attributes=self.coord.attributes.copy())
# And check adding the aux coord to the cube mock.
self.cube.add_aux_coord.assert_called_once_with(
aux_coord_constructor(), self.cube.coord_dims(self.coord))
def setUp(self):
# Create dummy pyke engine.
self.engine = mock.Mock(
cube=mock.Mock(),
cf_var=mock.Mock(dimensions=('foo', 'bar')),
filename='DUMMY',
provides=dict(coordinates=[]))
# Create patch for deferred loading that prevents attempted
# file access. This assumes that self.cf_coord_var and
# self.cf_bounds_var are defined in the test case.
def patched__getitem__(proxy_self, keys):
for var in (self.cf_coord_var, self.cf_bounds_var):
if proxy_self.variable_name == var.cf_name:
return var[keys]
raise RuntimeError()
self.deferred_load_patch = mock.patch(
'iris.fileformats.netcdf.NetCDFDataProxy.__getitem__',
new=patched__getitem__)
# Patch the helper function that retrieves the bounds cf variable.
# This avoids the need for setting up further mocking of cf objects.
def get_cf_bounds_var(coord_var):
return self.cf_bounds_var
self.get_cf_bounds_var_patch = mock.patch(
'iris.fileformats._pyke_rules.compiled_krb.'
'fc_rules_cf_fc.get_cf_bounds_var',
new=get_cf_bounds_var)
def setUp(self):
self.delta = netcdf_variable('delta',
'height',
np.float,
bounds='delta_bnds')
self.delta_bnds = netcdf_variable('delta_bnds', 'height bnds',
np.float)
self.sigma = netcdf_variable('sigma',
'height',
np.float,
bounds='sigma_bnds')
self.sigma_bnds = netcdf_variable('sigma_bnds', 'height bnds',
np.float)
self.orography = netcdf_variable('orography', 'lat lon', np.float)
formula_terms = 'a: delta b: sigma orog: orography'
standard_name = 'atmosphere_hybrid_height_coordinate'
self.height = netcdf_variable('height',
'height',
np.float,
formula_terms=formula_terms,
bounds='height_bnds',
standard_name=standard_name)
# Over-specify the formula terms on the bounds variable,
# which will be ignored by the cf loader.
formula_terms = 'a: delta_bnds b: sigma_bnds orog: orography'
self.height_bnds = netcdf_variable('height_bnds',
'height bnds',
np.float,
formula_terms=formula_terms)
self.lat = netcdf_variable('lat', 'lat', np.float)
self.lon = netcdf_variable('lon', 'lon', np.float)
# Note that, only lat and lon are explicitly associated as coordinates.
self.temp = netcdf_variable('temp',
'height lat lon',
np.float,
coordinates='lat lon')
self.variables = dict(delta=self.delta,
sigma=self.sigma,
orography=self.orography,
height=self.height,
lat=self.lat,
lon=self.lon,
temp=self.temp,
delta_bnds=self.delta_bnds,
sigma_bnds=self.sigma_bnds,
height_bnds=self.height_bnds)
ncattrs = mock.Mock(return_value=[])
self.dataset = mock.Mock(file_format='NetCDF4',
variables=self.variables,
ncattrs=ncattrs)
# Restrict the CFReader functionality to only performing translations.
build_patch = mock.patch(
'iris.fileformats.cf.CFReader._build_cf_groups')
reset_patch = mock.patch('iris.fileformats.cf.CFReader._reset')
build_patch.start()
reset_patch.start()
self.addCleanup(build_patch.stop)
self.addCleanup(reset_patch.stop)
def test_hybrid_height_round_trip_no_reference(self):
# Use pp.load_cubes() to convert fake PPFields into Cubes.
# NB. Use MagicMock so that SplittableInt header items, such as
# LBCODE, support len().
# Make a fake data field which needs the reference surface.
model_level = 5678
sigma_lower, sigma, sigma_upper = 0.85, 0.9, 0.95
delta_lower, delta, delta_upper = 0.05, 0.1, 0.15
data_field = self._field_with_data(lbvc=65,
lblev=model_level,
bhlev=sigma,
bhrlev=sigma_lower,
blev=delta,
brlev=delta_lower,
brsvd=[delta_upper, sigma_upper])
# Convert field to a cube.
load = mock.Mock(return_value=iter([data_field]))
with mock.patch('iris.fileformats.pp.load', new=load) as load, \
mock.patch('warnings.warn') as warn:
data_cube, = iris.fileformats.pp.load_cubes('DUMMY')
msg = "Unable to create instance of HybridHeightFactory. " \
"The source data contains no field(s) for 'orography'."
warn.assert_called_once_with(msg)
# Check the data cube is set up to use hybrid height.
self._test_coord(data_cube,
model_level,
standard_name='model_level_number')
self._test_coord(data_cube,
delta, [delta_lower, delta_upper],
long_name='level_height')
self._test_coord(data_cube,
sigma, [sigma_lower, sigma_upper],
long_name='sigma')
# Check that no aux factory is created (due to missing
# reference surface).
aux_factories = data_cube.aux_factories
self.assertEqual(len(aux_factories), 0)
# Now use the save rules to convert the Cube back into a PPField.
data_field = iris.fileformats.pp.PPField3()
data_field.lbfc = 0
data_field.lbvc = 0
data_field.brsvd = [None, None]
data_field.lbuser = [None] * 7
data_field = verify(data_cube, data_field)
# Check the data field has the vertical coordinate as originally
# specified.
self.assertEqual(data_field.lbvc, 65)
self.assertEqual(data_field.lblev, model_level)
self.assertEqual(data_field.bhlev, sigma)
self.assertEqual(data_field.bhrlev, sigma_lower)
self.assertEqual(data_field.blev, delta)
self.assertEqual(data_field.brlev, delta_lower)
self.assertEqual(data_field.brsvd, [delta_upper, sigma_upper])
def test_formula_terms_ignore(self):
self.orography.dimensions = ['lat', 'wibble']
with mock.patch('netCDF4.Dataset', return_value=self.dataset), \
mock.patch('warnings.warn') as warn:
cf_group = CFReader('dummy').cf_group
group = cf_group.promoted
self.assertEqual(list(group.keys()), ['orography'])
self.assertIs(group['orography'].cf_data, self.orography)
self.assertEqual(warn.call_count, 1)
def test_formula_terms_ignore(self):
self.orography.dimensions = ['lat', 'wibble']
with mock.patch('netCDF4.Dataset', return_value=self.dataset), \
mock.patch('warnings.warn') as warn:
cf_group = CFReader('dummy').cf_group
group = cf_group.promoted
self.assertEqual(list(group.keys()), ['orography'])
self.assertIs(group['orography'].cf_data, self.orography)
self.assertEqual(warn.call_count, 1)
def test_valid_relative_path(self):
# Override the configuration value for System.dot_path
dummy_path = 'not_a_real_path' * 10
assert not os.path.exists(dummy_path)
with mock.patch('iris.config.get_option', return_value=dummy_path):
# Pretend we have a valid installation of dot
with mock.patch('subprocess.check_output'):
result = _dot_path()
self.assertEqual(result, dummy_path)
def test_unknown(self):
header = self._header(0)
with mock.patch('iris.fileformats.ff.NewDynamics',
mock.Mock(return_value=mock.sentinel.grid)):
with mock.patch('warnings.warn') as warn:
grid = header.grid()
warn.assert_called_with('Staggered grid type: 0 not currently'
' interpreted, assuming standard C-grid')
self.assertIs(grid, mock.sentinel.grid)
def test_valid_relative_path_broken_install(self):
# Override the configuration value for System.dot_path
dummy_path = 'not_a_real_path' * 10
assert not os.path.exists(dummy_path)
with mock.patch('iris.config.get_option', return_value=dummy_path):
# Pretend we have a broken installation of dot
error = subprocess.CalledProcessError(-5, 'foo', 'bar')
with mock.patch('subprocess.check_output', side_effect=error):
result = _dot_path()
self.assertIsNone(result)
def test_auxiliary_ignore(self):
self.x.dimensions = ['lat', 'wibble']
with mock.patch('netCDF4.Dataset', return_value=self.dataset), \
mock.patch('warnings.warn') as warn:
cf_group = CFReader('dummy').cf_group
promoted = ['x', 'orography']
group = cf_group.promoted
self.assertEqual(set(group.keys()), set(promoted))
for name in promoted:
self.assertIs(group[name].cf_data, getattr(self, name))
self.assertEqual(warn.call_count, 1)
def test_auxiliary_ignore(self):
self.x.dimensions = ['lat', 'wibble']
with mock.patch('netCDF4.Dataset', return_value=self.dataset), \
mock.patch('warnings.warn') as warn:
cf_group = CFReader('dummy').cf_group
promoted = ['x', 'orography']
group = cf_group.promoted
self.assertEqual(set(group.keys()), set(promoted))
for name in promoted:
self.assertIs(group[name].cf_data, getattr(self, name))
self.assertEqual(warn.call_count, 1)
def test_grib1(self):
sections = [{'editionNumber': 1}]
message = self._make_test_message(sections)
mfunc = 'iris.fileformats.grib.GribMessage.messages_from_filename'
mclass = 'iris.fileformats.grib.GribWrapper'
with mock.patch(mfunc, return_value=[message]) as mock_func:
with mock.patch(mclass, spec=GribWrapper) as mock_wrapper:
field = next(_load_generate(self.fname))
mock_func.assert_called_once_with(self.fname)
self.assertIsInstance(field, GribWrapper)
mock_wrapper.assert_called_once_with(self.message_id,
grib_fh=self.grib_fh)
def test_no_weights(self):
# Check we can use the regridder without weights.
src_grid = self.src_grid
target_grid = self.tgt_grid
regridder = Regridder(src_grid, target_grid)
with mock.patch(self.func_setup,
return_value=mock.sentinel.regrid_info) as patch_setup:
with mock.patch(
self.func_operate,
return_value=self.dummy_slice_result) as patch_operate:
result = regridder(src_grid)
patch_setup.assert_called_once_with(src_grid, None, target_grid)
def test_promoted_auxiliary_ignore(self):
self.wibble = netcdf_variable('wibble', 'lat wibble', np.float)
self.variables['wibble'] = self.wibble
self.orography.coordinates = 'wibble'
with mock.patch('netCDF4.Dataset', return_value=self.dataset), \
mock.patch('warnings.warn') as warn:
cf_group = CFReader('dummy').cf_group.promoted
promoted = ['wibble', 'orography']
self.assertEqual(set(cf_group.keys()), set(promoted))
for name in promoted:
self.assertIs(cf_group[name].cf_data, getattr(self, name))
self.assertEqual(warn.call_count, 2)
def test_promoted_auxiliary_ignore(self):
self.wibble = netcdf_variable('wibble', 'lat wibble', np.float)
self.variables['wibble'] = self.wibble
self.orography.coordinates = 'wibble'
with mock.patch('netCDF4.Dataset', return_value=self.dataset), \
mock.patch('warnings.warn') as warn:
cf_group = CFReader('dummy').cf_group.promoted
promoted = ['wibble', 'orography']
self.assertEqual(set(cf_group.keys()), set(promoted))
for name in promoted:
self.assertIs(cf_group[name].cf_data, getattr(self, name))
self.assertEqual(warn.call_count, 2)
def test_no_weights(self):
# Check we can use the regridder without weights.
src_grid = self.src_grid
target_grid = self.tgt_grid
regridder = Regridder(src_grid, target_grid)
with mock.patch(self.func_setup,
return_value=mock.sentinel.regrid_info) as patch_setup:
with mock.patch(
self.func_operate,
return_value=self.dummy_slice_result) as patch_operate:
result = regridder(src_grid)
patch_setup.assert_called_once_with(
src_grid, None, target_grid)
def test_call_structure(self):
# Check that the load function calls the two necessary utility
# functions.
extract_result = mock.Mock()
interpret_patch = mock.patch('iris.fileformats.pp._interpret_fields',
autospec=True, return_value=iter([]))
field_gen_patch = mock.patch('iris.fileformats.pp._field_gen',
autospec=True,
return_value=extract_result)
with interpret_patch as interpret, field_gen_patch as field_gen:
pp.load('mock', read_data=True)
interpret.assert_called_once_with(extract_result)
field_gen.assert_called_once_with('mock', read_data_bytes=True)
def test_formula_terms_ignore(self):
self.orography.dimensions = ['lat', 'wibble']
for state in [False, True]:
with mock.patch('netCDF4.Dataset', return_value=self.dataset), \
iris.FUTURE.context(netcdf_promote=state), \
mock.patch('warnings.warn') as warn:
cf_group = CFReader('dummy').cf_group
if state:
group = cf_group.promoted
self.assertEqual(list(group.keys()), ['orography'])
self.assertIs(group['orography'].cf_data, self.orography)
else:
self.assertEqual(len(cf_group.promoted), 0)
self.assertEqual(warn.call_count, 1)
def test_formula_terms_ignore(self):
self.orography.dimensions = ['lat', 'wibble']
for state in [False, True]:
with mock.patch('netCDF4.Dataset', return_value=self.dataset), \
iris.FUTURE.context(netcdf_promote=state), \
mock.patch('warnings.warn') as warn:
cf_group = CFReader('dummy').cf_group
if state:
group = cf_group.promoted
self.assertEqual(list(group.keys()), ['orography'])
self.assertIs(group['orography'].cf_data, self.orography)
self.assertEqual(warn.call_count, 1)
else:
self.assertEqual(len(cf_group.promoted), 0)
self.assertEqual(warn.call_count, 2)
def _check(self, hours, minutes, request_warning, expect_warning=False):
# Setup the environment.
patch_target = 'iris.fileformats.grib._load_convert.options'
with mock.patch(patch_target) as options:
options.warn_on_unsupported = request_warning
with mock.patch('warnings.warn') as warn:
# The call being tested.
data_cutoff(hours, minutes)
# Check the result.
if expect_warning:
self.assertEqual(len(warn.mock_calls), 1)
args, kwargs = warn.call_args
self.assertIn('data cutoff', args[0])
else:
self.assertEqual(len(warn.mock_calls), 0)
def test_call_structure(self, _FFHeader):
# Check that the iter method calls the two necessary utility
# functions
extract_result = mock.Mock()
interpret_patch = mock.patch('iris.fileformats.pp._interpret_fields',
autospec=True, return_value=iter([]))
extract_patch = mock.patch('iris.fileformats.ff.FF2PP._extract_field',
autospec=True, return_value=extract_result)
FF2PP_instance = ff.FF2PP('mock')
with interpret_patch as interpret, extract_patch as extract:
list(iter(FF2PP_instance))
interpret.assert_called_once_with(extract_result)
extract.assert_called_once_with(FF2PP_instance)
def test_string_vectorised(self):
# Check that special case handling of a vectorized string returning
# function is taking place.
fn = lambda coord, v: '0123456789'[:v]
with mock.patch('numpy.vectorize',
return_value=self.vectorised) as vectorise_patch:
with mock.patch('iris.coords.AuxCoord') as aux_coord_constructor:
add_categorised_coord(self.cube, 'foobar', self.coord, fn,
units=self.units)
self.assertEqual(
aux_coord_constructor.call_args[0][0],
vectorise_patch(fn, otypes=[object])(self.coord, self.coord.points)
.astype('|S64'))
def test_call_structure(self):
# Check that the load function calls the two necessary utility
# functions.
extract_result = mock.Mock()
interpret_patch = mock.patch('iris.fileformats.pp._interpret_fields',
autospec=True,
return_value=iter([]))
field_gen_patch = mock.patch('iris.fileformats.pp._field_gen',
autospec=True,
return_value=extract_result)
with interpret_patch as interpret, field_gen_patch as field_gen:
pp.load('mock', read_data=True)
interpret.assert_called_once_with(extract_result)
field_gen.assert_called_once_with('mock', read_data_bytes=True)
def test_string_vectorised(self):
# Check that special case handling of a vectorized string returning
# function is taking place.
fn = lambda coord, v: '0123456789'[:v]
with mock.patch('numpy.vectorize',
return_value=self.vectorised) as vectorise_patch:
with mock.patch('iris.coords.AuxCoord') as aux_coord_constructor:
add_categorised_coord(self.cube, 'foobar', self.coord, fn,
units=self.units)
self.assertEqual(
aux_coord_constructor.call_args[0][0],
vectorise_patch(fn, otypes=[object])(self.coord, self.coord.points)
.astype('|S64'))
def _check(self, hours, minutes, request_warning, expect_warning=False):
# Setup the environment.
patch_target = 'iris.fileformats.grib._load_convert.options'
with mock.patch(patch_target) as options:
options.warn_on_unsupported = request_warning
with mock.patch('warnings.warn') as warn:
# The call being tested.
data_cutoff(hours, minutes)
# Check the result.
if expect_warning:
self.assertEqual(len(warn.mock_calls), 1)
args, kwargs = warn.call_args
self.assertIn('data cutoff', args[0])
else:
self.assertEqual(len(warn.mock_calls), 0)
def test_33214(self):
with mock.patch('warnings.warn') as warn:
t = pp.BitwiseInt(33214)
self.assertEqual(warn.call_count, 1)
self.assertEqual(t[0], 4)
self.assertEqual(t.flag1, 0)
self.assertEqual(t.flag2, 1)
def test_soil_level_round_trip(self):
# Use pp.load_cubes() to convert a fake PPField into a Cube.
# NB. Use MagicMock so that SplittableInt header items, such as
# LBCODE, support len().
soil_level = 1234
field = mock.MagicMock(lbvc=6,
lblev=soil_level,
stash=iris.fileformats.pp.STASH(1, 0, 9),
lbuser=[0] * 7,
lbrsvd=[0] * 4,
brsvd=[0] * 4,
brlev=0)
load = mock.Mock(return_value=iter([field]))
with mock.patch('iris.fileformats.pp.load', new=load) as load:
cube = next(iris.fileformats.pp.load_cubes('DUMMY'))
self.assertIn('soil', cube.standard_name)
self._test_coord(cube, soil_level, long_name='soil_model_level_number')
# Now use the save rules to convert the Cube back into a PPField.
field = iris.fileformats.pp.PPField3()
field.lbfc = 0
field.lbvc = 0
field.brsvd = [None] * 4
field.brlev = None
run_save_rules(cube, field)
# Check the vertical coordinate is as originally specified.
self.assertEqual(field.lbvc, 6)
self.assertEqual(field.lblev, soil_level)
self.assertEqual(field.blev, soil_level)
self.assertEqual(field.brsvd[0], 0)
self.assertEqual(field.brlev, 0)
def setUp(self):
self.container = mock.Mock(name='container', attributes={})
self.data = np.array(1, dtype='int32')
patch = mock.patch('netCDF4.Dataset')
mock_netcdf_dataset = patch.start()
self.addCleanup(patch.stop)
def test_soil_depth_round_trip(self):
# Use pp.load_cubes() to convert a fake PPField into a Cube.
# NB. Use MagicMock so that SplittableInt header items, such as
# LBCODE, support len().
lower, point, upper = 1.2, 3.4, 5.6
brsvd = [lower, 0, 0, 0]
field = mock.MagicMock(lbvc=6, blev=point,
stash=iris.fileformats.pp.STASH(1, 0, 9),
lbuser=[0] * 7, lbrsvd=[0] * 4,
brsvd=brsvd, brlev=upper)
load = mock.Mock(return_value=iter([field]))
with mock.patch('iris.fileformats.pp.load', new=load) as load:
cube = next(iris.fileformats.pp.load_cubes('DUMMY'))
self.assertIn('soil', cube.standard_name)
self._test_coord(cube, point, bounds=[lower, upper],
standard_name='depth')
# Now use the save rules to convert the Cube back into a PPField.
field = iris.fileformats.pp.PPField3()
field.lbfc = 0
field.lbvc = 0
field.brlev = None
field.brsvd = [None] * 4
iris.fileformats.pp._ensure_save_rules_loaded()
iris.fileformats.pp._save_rules.verify(cube, field)
# Check the vertical coordinate is as originally specified.
self.assertEqual(field.lbvc, 6)
self.assertEqual(field.blev, point)
self.assertEqual(field.brsvd[0], lower)
self.assertEqual(field.brlev, upper)
def test_two_plots_with_independent_axes(self):
c1 = Contour(self.cube, self.axes)
levels = 5
other = _add_levels(self.cube, levels)[:, 0]
c2 = Contour(other, self.axes)
with mock.patch('cube_browser.Contour.__call__') as func:
browser = Browser([c1, c2])
browser.display()
# Check the initial render.
self.assertEqual(func.call_count, 2)
expected = [mock.call(time=self.value),
mock.call(model_level_number=self.value)]
func.assert_has_calls(expected)
# Now simulate a 'time' slider change.
slider = browser._slider_by_name['time']
change = dict(owner=slider)
browser.on_change(change)
self.assertEqual(func.call_count, 3)
expected.append(mock.call(time=self.value))
func.assert_has_calls(expected)
# Now simulate a 'model_level_number' slider change.
slider = browser._slider_by_name['model_level_number']
change = dict(owner=slider)
browser.on_change(change)
self.assertEqual(func.call_count, 4)
expected.append(mock.call(model_level_number=self.value))
func.assert_has_calls(expected)
def test_33214(self):
with mock.patch('warnings.warn') as warn:
t = pp.BitwiseInt(33214)
self.assertEqual(warn.call_count, 1)
self.assertEqual(t[0], 4)
self.assertEqual(t.flag1, 0)
self.assertEqual(t.flag2, 1)
def test_standard_operators(self):
with mock.patch('warnings.warn') as warn:
t = pp.BitwiseInt(323)
self.assertEqual(warn.call_count, 1)
self.assertTrue(t == 323)
self.assertFalse(t == 324)
self.assertFalse(t != 323)
self.assertTrue(t != 324)
self.assertTrue(t >= 323)
self.assertFalse(t >= 324)
self.assertFalse(t > 323)
self.assertTrue(t > 322)
self.assertTrue(t <= 323)
self.assertFalse(t <= 322)
self.assertFalse(t < 323)
self.assertTrue(t < 324)
self.assertTrue(t in [323])
self.assertFalse(t in [324])
def test_many(self):
for i in range(100):
lbproc = _LBProc(i)
with mock.patch('warnings.warn') as warn:
flag = lbproc.flag2
self.assertEqual(warn.call_count, 1)
self.assertEqual(flag, bool(i & 2))
def check_mdtol(self, mdtol=None):
src_grid, target_grid = self.grids()
if mdtol is None:
regridder = AreaWeightedRegridder(src_grid, target_grid)
mdtol = 1
else:
regridder = AreaWeightedRegridder(src_grid,
target_grid,
mdtol=mdtol)
# Make a new cube to regrid with different data so we can
# distinguish between regridding the original src grid
# definition cube and the cube passed to the regridder.
src = src_grid.copy()
src.data += 10
with mock.patch(
'iris.experimental.regrid.'
'regrid_area_weighted_rectilinear_src_and_grid',
return_value=mock.sentinel.result) as regrid:
result = regridder(src)
self.assertEqual(regrid.call_count, 1)
_, args, kwargs = regrid.mock_calls[0]
self.assertEqual(args[0], src)
self.assertEqual(self.extract_grid(args[1]),
self.extract_grid(target_grid))
self.assertEqual(kwargs, {'mdtol': mdtol})
self.assertIs(result, mock.sentinel.result)
def _check(self, request_warning):
this = 'iris.fileformats.grib._load_convert.options'
with mock.patch(this, warn_on_unsupported=request_warning):
metadata = deepcopy(self.metadata)
perturbationNumber = 666
section = {'perturbationNumber': perturbationNumber}
forecast_reference_time = mock.sentinel.forecast_reference_time
# The called being tested.
product_definition_template_1(section, metadata,
forecast_reference_time)
expected = deepcopy(self.metadata)
expected['cell_methods'].append(self.cell_method)
realization = DimCoord(perturbationNumber,
standard_name='realization',
units='no_unit')
expected['aux_coords_and_dims'].append((realization, None))
self.assertEqual(metadata, expected)
if request_warning:
warn_msgs = [mcall[1][0] for mcall in warnings.warn.mock_calls]
expected_msgs = ['type of ensemble', 'number of forecasts']
for emsg in expected_msgs:
matches = [wmsg for wmsg in warn_msgs if emsg in wmsg]
self.assertEqual(len(matches), 1)
warn_msgs.remove(matches[0])
else:
self.assertEqual(len(warnings.warn.mock_calls), 0)
def setUp(self):
# Create coordinate cf variables and pyke engine.
points = np.arange(6).reshape(2, 3)
self.cf_coord_var = mock.Mock(
dimensions=('foo', 'bar'),
cf_name='wibble',
standard_name=None,
long_name='wibble',
units='m',
shape=points.shape,
dtype=points.dtype,
__getitem__=lambda self, key: points[key])
self.engine = mock.Mock(
cube=mock.Mock(),
cf_var=mock.Mock(dimensions=('foo', 'bar')),
filename='DUMMY',
provides=dict(coordinates=[]))
# Create patch for deferred loading that prevents attempted
# file access. This assumes that self.cf_bounds_var is
# defined in the test case.
def patched__getitem__(proxy_self, keys):
variable = None
for var in (self.cf_coord_var, self.cf_bounds_var):
if proxy_self.variable_name == var.cf_name:
return var[keys]
raise RuntimeError()
self.deferred_load_patch = mock.patch(
'iris.fileformats.netcdf.NetCDFDataProxy.__getitem__',
new=patched__getitem__)
def test_fastest_varying_vertex_dim(self):
bounds = np.arange(24).reshape(2, 3, 4)
self.cf_bounds_var = mock.Mock(
dimensions=('foo', 'bar', 'nv'),
cf_name='wibble_bnds',
shape=bounds.shape,
dtype=bounds.dtype,
__getitem__=lambda self, key: bounds[key])
expected_coord = AuxCoord(
self.cf_coord_var[:],
long_name=self.cf_coord_var.long_name,
var_name=self.cf_coord_var.cf_name,
units=self.cf_coord_var.units,
bounds=bounds)
get_cf_bounds_var_patch = mock.patch(
'iris.fileformats._pyke_rules.compiled_krb.'
'fc_rules_cf_fc.get_cf_bounds_var',
return_value=self.cf_bounds_var)
# Asserts must lie within context manager because of deferred loading.
with self.deferred_load_patch, get_cf_bounds_var_patch:
build_auxiliary_coordinate(self.engine, self.cf_coord_var)
# Test that expected coord is built and added to cube.
self.engine.cube.add_aux_coord.assert_called_with(
expected_coord, [0, 1])
# Test that engine.provides container is correctly populated.
expected_list = [(expected_coord, self.cf_coord_var.cf_name)]
self.assertEqual(self.engine.provides['coordinates'],
expected_list)
def setUp(self):
self.container = mock.Mock(name='container', attributes={})
self.data = np.array(1, dtype='int32')
patch = mock.patch('netCDF4.Dataset')
mock_netcdf_dataset = patch.start()
self.addCleanup(patch.stop)
def test_negative_number(self):
with mock.patch('warnings.warn') as warn:
try:
_ = pp.BitwiseInt(-5)
except ValueError as err:
self.assertEqual(str(err), 'Negative numbers not supported with splittable integers object')
self.assertEqual(warn.call_count, 1)
def test_fractional_hours(self, mock_set):
cell_method = CellMethod("sum", "time", "25.9 hours")
with mock.patch("warnings.warn") as warn:
set_time_increment(cell_method, mock.sentinel.grib)
warn.assert_called_once_with("Truncating floating point timeIncrement " "25.9 to integer value 25")
mock_set.assert_any_call(mock.sentinel.grib, "indicatorOfUnitForTimeIncrement", 1)
mock_set.assert_any_call(mock.sentinel.grib, "timeIncrement", 25)
def test_float64(self):
# Tests down-casting of >f8 data to >f4.
def field_checksum(data):
field = TestPPField()
field.dummy1 = 0
field.dummy2 = 0
field.dummy3 = 0
field.dummy4 = 0
field.lbtim = 0
field.lblrec = 0
field.lbrow = 0
field.lbext = 0
field.lbpack = 0
field.lbuser = 0
field.brsvd = 0
field.bdatum = 0
field.data = data
with self.temp_filename('.pp') as temp_filename:
with open(temp_filename, 'wb') as pp_file:
field.save(pp_file)
checksum = self.file_checksum(temp_filename)
return checksum
data_64 = np.linspace(0, 1, num=10, endpoint=False).reshape(2, 5)
checksum_32 = field_checksum(data_64.astype('>f4'))
with mock.patch('warnings.warn') as warn:
checksum_64 = field_checksum(data_64.astype('>f8'))
self.assertEqual(checksum_32, checksum_64)
warn.assert_called_once_with(
'Downcasting array precision from float64 to float32 for save.'
'If float64 precision is required then please save in a '
'different format')
def test_standard_operators(self):
with mock.patch('warnings.warn') as warn:
t = pp.BitwiseInt(323)
self.assertEqual(warn.call_count, 1)
self.assertTrue(t == 323)
self.assertFalse(t == 324)
self.assertFalse(t != 323)
self.assertTrue(t != 324)
self.assertTrue(t >= 323)
self.assertFalse(t >= 324)
self.assertFalse(t > 323)
self.assertTrue(t > 322)
self.assertTrue(t <= 323)
self.assertFalse(t <= 322)
self.assertFalse(t < 323)
self.assertTrue(t < 324)
self.assertTrue(t in [323])
self.assertFalse(t in [324])
def check_mdtol(self, mdtol=None):
src_grid, target_grid = self.grids()
if mdtol is None:
regridder = AreaWeightedRegridder(src_grid, target_grid)
mdtol = 1
else:
regridder = AreaWeightedRegridder(src_grid, target_grid,
mdtol=mdtol)
# Make a new cube to regrid with different data so we can
# distinguish between regridding the original src grid
# definition cube and the cube passed to the regridder.
src = src_grid.copy()
src.data += 10
with mock.patch('iris.experimental.regrid.'
'regrid_area_weighted_rectilinear_src_and_grid',
return_value=mock.sentinel.result) as regrid:
result = regridder(src)
self.assertEqual(regrid.call_count, 1)
_, args, kwargs = regrid.mock_calls[0]
self.assertEqual(args[0], src)
self.assertEqual(self.extract_grid(args[1]),
self.extract_grid(target_grid))
self.assertEqual(kwargs, {'mdtol': mdtol})
self.assertIs(result, mock.sentinel.result)
def test_two_plots_with_independent_axes(self):
c1 = Contour(self.cube, self.axes)
levels = 5
other = _add_levels(self.cube, levels)[:, 0]
c2 = Contour(other, self.axes)
with mock.patch('cube_browser.Contour.__call__') as func:
browser = Browser([c1, c2])
browser.display()
# Check the initial render.
self.assertEqual(func.call_count, 2)
expected = [
mock.call(time=self.value),
mock.call(model_level_number=self.value)
]
func.assert_has_calls(expected)
# Now simulate a 'time' slider change.
slider = browser._slider_by_name['time']
change = dict(owner=slider)
browser.on_change(change)
self.assertEqual(func.call_count, 3)
expected.append(mock.call(time=self.value))
func.assert_has_calls(expected)
# Now simulate a 'model_level_number' slider change.
slider = browser._slider_by_name['model_level_number']
change = dict(owner=slider)
browser.on_change(change)
self.assertEqual(func.call_count, 4)
expected.append(mock.call(model_level_number=self.value))
func.assert_has_calls(expected)
def test_deferred_bytes(self):
# Check that a field with deferred array bytes in core_data gets a
# dask array.
fname = mock.sentinel.fname
position = mock.sentinel.position
n_bytes = mock.sentinel.n_bytes
newbyteorder = mock.Mock(return_value=mock.sentinel.dtype)
dtype = mock.Mock(newbyteorder=newbyteorder)
deferred_bytes = (fname, position, n_bytes, dtype)
core_data = mock.MagicMock(return_value=deferred_bytes)
field = mock.Mock(core_data=core_data)
data_shape = (100, 120)
land_mask = mock.Mock()
proxy = mock.Mock(dtype=np.dtype('f4'),
shape=data_shape,
spec=pp.PPDataProxy)
# We can't directly inspect the concrete data source underlying
# the dask array, so instead we patch the proxy creation and check it's
# being created and invoked correctly.
with mock.patch('iris.fileformats.pp.PPDataProxy') as PPDataProxy:
PPDataProxy.return_value = proxy
pp._create_field_data(field, data_shape, land_mask)
# The data should be assigned via field.data. As this is a mock object
# we can check the attribute directly.
self.assertEqual(field.data.shape, data_shape)
self.assertEqual(field.data.dtype, np.dtype('f4'))
# Is it making use of a correctly configured proxy?
# NB. We know it's *using* the result of this call because
# that's where the dtype came from above.
PPDataProxy.assert_called_once_with(
(data_shape), dtype, fname, position, n_bytes, field.raw_lbpack,
field.boundary_packing, field.bmdi, land_mask)
def test_invalid_units(self):
# Mock converter() function that returns an invalid
# units string amongst the collection of other elements.
factories = None
references = None
standard_name = None
long_name = None
units = 'wibble' # Invalid unit.
attributes = dict(source='test')
cell_methods = None
dim_coords_and_dims = None
aux_coords_and_dims = None
metadata = iris.fileformats.rules.ConversionMetadata(
factories, references, standard_name, long_name, units, attributes,
cell_methods, dim_coords_and_dims, aux_coords_and_dims)
converter = mock.Mock(return_value=metadata)
field = mock.Mock()
with mock.patch('warnings.warn') as warn:
cube, factories, references = _make_cube(field, converter)
# Check attributes dictionary is correctly populated.
expected_attributes = attributes.copy()
expected_attributes['invalid_units'] = units
self.assertEqual(cube.attributes, expected_attributes)
# Check warning was raised.
self.assertEqual(warn.call_count, 1)
warning_msg = warn.call_args[0][0]
self.assertIn('invalid units', warning_msg)
def test_potential_temperature_level_round_trip(self):
# Check save+load for data on 'potential temperature' levels.
# Use pp.load_cubes() to convert a fake PPField into a Cube.
# NB. Use MagicMock so that SplittableInt header items, such as
# LBCODE, support len().
potm_value = 22.5
field = mock.MagicMock(lbvc=19,
blev=potm_value,
lbuser=[0] * 7,
lbrsvd=[0] * 4)
load = mock.Mock(return_value=iter([field]))
with mock.patch('iris.fileformats.pp.load', new=load) as load:
cube = next(iris.fileformats.pp.load_cubes('DUMMY'))
self._test_coord(cube,
potm_value,
standard_name='air_potential_temperature')
# Now use the save rules to convert the Cube back into a PPField.
field = iris.fileformats.pp.PPField3()
field.lbfc = 0
field.lbvc = 0
run_save_rules(cube, field)
# Check the vertical coordinate is as originally specified.
self.assertEqual(field.lbvc, 19)
self.assertEqual(field.blev, potm_value)
def _check(self, request_warning):
this = 'iris.fileformats.grib._load_convert.options'
with mock.patch(this, warn_on_unsupported=request_warning):
metadata = deepcopy(self.metadata)
perturbationNumber = 666
section = {'perturbationNumber': perturbationNumber}
forecast_reference_time = mock.sentinel.forecast_reference_time
# The called being tested.
product_definition_template_1(section, metadata,
forecast_reference_time)
expected = deepcopy(self.metadata)
expected['cell_methods'].append(self.cell_method)
realization = DimCoord(perturbationNumber,
standard_name='realization',
units='no_unit')
expected['aux_coords_and_dims'].append((realization, None))
self.assertEqual(metadata, expected)
if request_warning:
warn_msgs = [mcall[1][0] for mcall in warnings.warn.mock_calls]
expected_msgs = ['type of ensemble', 'number of forecasts']
for emsg in expected_msgs:
matches = [wmsg for wmsg in warn_msgs if emsg in wmsg]
self.assertEqual(len(matches), 1)
warn_msgs.remove(matches[0])
else:
self.assertEqual(len(warnings.warn.mock_calls), 0)
def check_non_trivial_coordinate_warning(self, field):
field.lbegin = 0
field.lbrow = 10
field.lbnpt = 12
# stash m01s31i020
field.lbuser = [None, None, 121416, 20, None, None, 1]
orig_bdx, orig_bdy = field.bdx, field.bdy
x = np.array([1, 2, 6])
y = np.array([1, 2, 6])
with self.mock_for_extract_field([field], x, y) as ff2pp:
ff2pp._ff_header.dataset_type = 5
with mock.patch('warnings.warn') as warn:
list(ff2pp._extract_field())
# Check the values are unchanged.
self.assertEqual(field.bdy, orig_bdy)
self.assertEqual(field.bdx, orig_bdx)
# Check a warning was raised with a suitable message.
warn_error_tmplt = 'Unexpected warning message: {}'
non_trivial_coord_warn_msg = warn.call_args[0][0]
msg = ('The x or y coordinates of your boundary condition field may '
'be incorrect, not having taken into account the boundary '
'size.')
self.assertTrue(non_trivial_coord_warn_msg.startswith(msg),
warn_error_tmplt.format(non_trivial_coord_warn_msg))
