def test_incorrect_grid_attributes_removed(self):
"""Test grid attributes not present on the target cube are removed
after regridding"""
self.target_grid.attributes.pop("mosg__grid_domain")
result = StandardiseGridAndMetadata().process(
self.cube, target_grid=self.target_grid)
self.assertNotIn("mosg__grid_domain", result.attributes)
def test_default(self):
"""Test initialisation with default options"""
plugin = StandardiseGridAndMetadata()
self.assertEqual(plugin.regrid_mode, 'bilinear')
self.assertEqual(plugin.extrapolation_mode, 'nanmask')
self.assertIsNone(plugin.landmask_source_grid)
self.assertIsNone(plugin.landmask_vicinity)
self.assertEqual(plugin.landmask_name, 'land_binary_mask')
def test_error_regrid_with_incorrect_landmask(self):
"""Test an error is thrown if a landmask is provided that does not
match the source grid"""
landmask = self.target_grid.copy()
plugin = StandardiseGridAndMetadata(regrid_mode='nearest-with-mask',
landmask=landmask,
landmask_vicinity=90000)
msg = "Source landmask does not match input grid"
with self.assertRaisesRegex(ValueError, msg):
plugin.process(self.cube, target_grid=self.target_grid)
def test_access_regrid_with_landmask(self):
"""Test the RegridLandAndSea module is correctly called when using
landmask arguments. Diagnosed by identifiable error."""
msg = "Distance of 10000m gives zero cell extent"
with self.assertRaisesRegex(ValueError, msg):
StandardiseGridAndMetadata(regrid_mode='nearest-with-mask',
landmask=self.landmask,
landmask_vicinity=10000).process(
self.cube,
target_grid=self.target_grid)
def setUp(self):
"""Set up input cube"""
self.cube = set_up_variable_cube(
282 * np.ones((5, 5), dtype=np.float32),
spatial_grid='latlon',
standard_grid_metadata='gl_det',
time=datetime(2019, 10, 11),
time_bounds=[datetime(2019, 10, 10, 23),
datetime(2019, 10, 11)],
frt=datetime(2019, 10, 10, 18))
self.plugin = StandardiseGridAndMetadata()
def test_attribute_changes_with_regridding(self):
"""Test attributes inherited on regridding"""
expected_attributes = self.cube.attributes
expected_attributes["title"] = MANDATORY_ATTRIBUTE_DEFAULTS["title"]
for attr in [
"mosg__grid_domain", "mosg__grid_type", "mosg__grid_version"
]:
expected_attributes[attr] = self.target_grid.attributes[attr]
result = StandardiseGridAndMetadata().process(
self.cube, target_grid=self.target_grid)
self.assertDictEqual(result.attributes, expected_attributes)
def test_new_title(self):
"""Test new title can be set on regridding"""
new_title = "Global Model Forecast on UK 2km Standard Grid"
expected_attributes = self.cube.attributes
expected_attributes["title"] = new_title
for attr in [
"mosg__grid_domain", "mosg__grid_type", "mosg__grid_version"
]:
expected_attributes[attr] = self.target_grid.attributes[attr]
result = StandardiseGridAndMetadata().process(
self.cube, target_grid=self.target_grid, regridded_title=new_title)
self.assertDictEqual(result.attributes, expected_attributes)
def test_warning_source_not_landmask(self, warning_list=None):
"""Test warning is raised if landmask_source_grid is not a landmask"""
expected_data = 282 * np.ones((12, 12), dtype=np.float32)
self.landmask.rename("not_a_landmask")
result = StandardiseGridAndMetadata(regrid_mode='nearest-with-mask',
landmask=self.landmask,
landmask_vicinity=90000).process(
self.cube,
target_grid=self.target_grid)
msg = "Expected land_binary_mask in input_landmask cube"
self.assertTrue(any([msg in str(warning) for warning in warning_list]))
self.assertTrue(
any(item.category == UserWarning for item in warning_list))
self.assertArrayAlmostEqual(result.data, expected_data)
def test_attribute_changes_after_regridding(self):
"""Test attributes can be manually updated after regridding"""
attribute_changes = {
"institution": "Met Office",
"mosg__grid_version": "remove"
}
expected_attributes = {
"mosg__grid_domain": "uk_extended",
"mosg__grid_type": "standard",
"mosg__model_configuration": "gl_det",
"institution": "Met Office",
"title": MANDATORY_ATTRIBUTE_DEFAULTS["title"]
}
result = StandardiseGridAndMetadata().process(
self.cube,
target_grid=self.target_grid,
attributes_dict=attribute_changes)
self.assertDictEqual(result.attributes, expected_attributes)
def test_basic_regrid(self):
"""Test default regridding arguments return expected dimensionality
and updated grid-defining attributes"""
expected_data = 282 * np.ones((12, 12), dtype=np.float32)
expected_attributes = {
"mosg__model_configuration": "gl_det",
"title": MANDATORY_ATTRIBUTE_DEFAULTS["title"]
}
for attr in [
"mosg__grid_domain", "mosg__grid_type", "mosg__grid_version"
]:
expected_attributes[attr] = self.target_grid.attributes[attr]
result = StandardiseGridAndMetadata().process(self.cube,
self.target_grid.copy())
self.assertArrayAlmostEqual(result.data, expected_data)
for axis in ['x', 'y']:
self.assertEqual(result.coord(axis=axis),
self.target_grid.coord(axis=axis))
self.assertDictEqual(result.attributes, expected_attributes)
def test_run_regrid_with_landmask(self):
"""Test masked regridding (same expected values as basic, since input
points are all equal)"""
expected_data = 282 * np.ones((12, 12), dtype=np.float32)
expected_attributes = {
"mosg__model_configuration": "gl_det",
"title": MANDATORY_ATTRIBUTE_DEFAULTS["title"]
}
for attr in [
"mosg__grid_domain", "mosg__grid_type", "mosg__grid_version"
]:
expected_attributes[attr] = self.target_grid.attributes[attr]
result = StandardiseGridAndMetadata(
regrid_mode='nearest-with-mask',
landmask=self.landmask,
landmask_vicinity=90000).process(
self.cube, target_grid=self.target_grid.copy())
self.assertArrayAlmostEqual(result.data, expected_data)
for axis in ['x', 'y']:
self.assertEqual(result.coord(axis=axis),
self.target_grid.coord(axis=axis))
self.assertDictEqual(result.attributes, expected_attributes)
def process(cube: cli.inputcube,
target_grid: cli.inputcube = None,
land_sea_mask: cli.inputcube = None,
*,
regrid_mode='bilinear',
extrapolation_mode='nanmask',
land_sea_mask_vicinity: float = 25000,
regridded_title: str = None,
attributes_config: cli.inputjson = None,
coords_to_remove: cli.comma_separated_list = None,
new_name: str = None,
new_units: str = None,
fix_float64=False):
"""Standardises a cube by one or more of regridding, updating meta-data etc
Standardise a source cube. Available options are regridding (bi-linear or
nearest-neighbour, optionally with land-mask awareness), renaming,
converting units, updating attributes and / or converting float64 data to
float32.
Args:
cube (iris.cube.Cube):
Source cube to be standardised
target_grid (iris.cube.Cube):
If specified, then regridding of the source against the target
grid is enabled. If also using land_sea_mask-aware regridding then
this must be land_binary_mask data.
land_sea_mask (iris.cube.Cube):
A cube describing the land_binary_mask on the source-grid if
coastline-aware regridding is required.
regrid_mode (str):
Selects which regridding techniques to use. Default uses
iris.analysis.Linear(); "nearest" uses Nearest() (for less
continuous fields, e.g precipitation); "nearest-with-mask"
ensures that target data are sources from points with the same
mask value (for coast-line-dependant variables like temperature).
extrapolation_mode (str):
Mode to use for extrapolating data into regions beyond the limits
of the input cube domain. Refer to online documentation for
iris.analysis.
Modes are -
extrapolate - extrapolated points will take their values from the
nearest source point
nan - extrapolated points will be set to NaN
error - a ValueError will be raised notifying an attempt to
extrapolate
mask - extrapolated points will always be masked, even if
the source data is not a MaskedArray
nanmask - if the source data is a MaskedArray extrapolated points
will be masked; otherwise they will be set to NaN
land_sea_mask_vicinity (float):
Radius of vicinity to search for a coastline, in metres.
regridded_title (str):
New "title" attribute to be set if the field is being regridded
(since "title" may contain grid information). If None, a default
value is used.
attributes_config (dict):
Dictionary containing required changes that will be applied to
the attributes.
coords_to_remove (list):
List of names of scalar coordinates to remove.
new_name (str):
Name of output cube.
new_units (str):
Units to convert to.
fix_float64 (bool):
If True, checks and fixes cube for float64 data. Without this
option an exception will be raised if float64 data is found but no
fix applied.
Returns:
iris.cube.Cube:
Processed cube.
Raises:
ValueError:
If source land_sea_mask is supplied but regrid mode is not
"nearest-with-mask".
ValueError:
If regrid_mode is "nearest-with-mask" but no source land_sea_mask
is provided (from plugin).
"""
from improver.standardise import StandardiseGridAndMetadata
if (land_sea_mask and
"nearest-with-mask" not in regrid_mode):
msg = ("Land-mask file supplied without appropriate regrid-mode. "
"Use --regrid-mode nearest-with-mask.")
raise ValueError(msg)
plugin = StandardiseGridAndMetadata(
regrid_mode=regrid_mode, extrapolation_mode=extrapolation_mode,
landmask=land_sea_mask,
landmask_vicinity=land_sea_mask_vicinity)
output_data = plugin.process(
cube, target_grid, new_name=new_name, new_units=new_units,
regridded_title=regridded_title, coords_to_remove=coords_to_remove,
attributes_dict=attributes_config, fix_float64=fix_float64)
return output_data
def test_error_missing_landmask(self):
"""Test an error is thrown if no mask is provided for masked
regridding"""
msg = "requires an input landmask cube"
with self.assertRaisesRegex(ValueError, msg):
StandardiseGridAndMetadata(regrid_mode='nearest-with-mask')