def test_diagnostic_len_match(self):
"""Test diagnostic fields, thresholds and conditions are same len."""
tree = wxcode_decision_tree_global()
for node in tree:
query = tree[node]
diag_len = len(expand_nested_lists(query, 'diagnostic_fields'))
thres_len = len(expand_nested_lists(query,
'diagnostic_thresholds'))
cond_len = len(expand_nested_lists(query, 'diagnostic_conditions'))
self.assertEqual(diag_len, thres_len)
self.assertEqual(diag_len, cond_len)
def test_list_of_lists(self):
"""Returns a expanded list if given a list of lists."""
result = expand_nested_lists(self.dictionary, "list_of_lists")
for val in result:
self.assertEqual(val, "a")
def test_simple_list(self):
"""Testexpand_nested_lists returns a expanded list if given a list."""
result = expand_nested_lists(self.dictionary, "list")
for val in result:
self.assertEqual(val, "a")
def test_basic(self):
"""Test that the expand_nested_lists returns a list."""
result = expand_nested_lists(self.dictionary, "list")
self.assertIsInstance(result, list)
def check_input_cubes(self, cubes: CubeList) -> Optional[Dict[str, Any]]:
"""
Check that the input cubes contain all the diagnostics and thresholds
required by the decision tree. Sets self.coord_named_threshold to
"True" if threshold-type coordinates have the name "threshold" (as
opposed to the standard name of the diagnostic), for backward
compatibility.
Args:
cubes:
A CubeList containing the input diagnostic cubes.
Returns:
A dictionary of (keyword) nodes names where the diagnostic
data is missing and (values) node associated with
diagnostic_missing_action.
Raises:
IOError:
Raises an IOError if any of the required input data is missing.
The error includes details of which fields are missing.
"""
optional_node_data_missing = {}
missing_data = []
for key, query in self.queries.items():
diagnostics = get_parameter_names(
expand_nested_lists(query, "diagnostic_fields")
)
thresholds = expand_nested_lists(query, "diagnostic_thresholds")
conditions = expand_nested_lists(query, "diagnostic_conditions")
for diagnostic, threshold, condition in zip(
diagnostics, thresholds, conditions
):
# First we check the diagnostic name and units, performing
# a conversion is required and possible.
test_condition = iris.Constraint(name=diagnostic)
matched_cube = cubes.extract(test_condition)
if not matched_cube:
if "diagnostic_missing_action" in query:
optional_node_data_missing.update(
{key: query[query["diagnostic_missing_action"]]}
)
else:
missing_data.append([diagnostic, threshold, condition])
continue
cube_threshold_units = find_threshold_coordinate(matched_cube[0]).units
threshold.convert_units(cube_threshold_units)
# Then we check if the required threshold is present in the
# cube, and that the thresholding is relative to it correctly.
threshold = threshold.points.item()
threshold_name = find_threshold_coordinate(matched_cube[0]).name()
# Set flag to check for old threshold coordinate names
if threshold_name == "threshold" and not self.coord_named_threshold:
self.coord_named_threshold = True
# Check threshold == 0.0
if abs(threshold) < self.float_abs_tolerance:
coord_constraint = {
threshold_name: lambda cell: np.isclose(
cell.point, 0, rtol=0, atol=self.float_abs_tolerance
)
}
else:
coord_constraint = {
threshold_name: lambda cell: np.isclose(
cell.point, threshold, rtol=self.float_tolerance, atol=0
)
}
# Checks whether the spp__relative_to_threshold attribute is above
# or below a threshold and and compares to the diagnostic_condition.
test_condition = iris.Constraint(
coord_values=coord_constraint,
cube_func=lambda cube: (
probability_is_above_or_below(cube) == condition
),
)
matched_threshold = matched_cube.extract(test_condition)
if not matched_threshold:
missing_data.append([diagnostic, threshold, condition])
if missing_data:
msg = (
"Weather Symbols input cubes are missing"
" the following required"
" input fields:\n"
)
dyn_msg = "name: {}, threshold: {}, " "spp__relative_to_threshold: {}\n"
for item in missing_data:
msg = msg + dyn_msg.format(*item)
raise IOError(msg)
if not optional_node_data_missing:
optional_node_data_missing = None
return optional_node_data_missing
def check_input_cubes(self, cubes):
"""
Check that the input cubes contain all the diagnostics and thresholds
required by the decision tree. Sets self.coord_named_threshold to
"True" if threshold-type coordinates have the name "threshold" (as
opposed to the standard name of the diagnostic), for backward
compatibility.
Args:
cubes (iris.cube.CubeList):
A CubeList containing the input diagnostic cubes.
Returns:
dict or None:
A dictionary of (keyword) nodes names where the diagnostic
data is missing and (values) node associated with
diagnostic_missing_action.
Raises:
IOError:
Raises an IOError if any of the required input data is missing.
The error includes details of which fields are missing.
"""
optional_node_data_missing = {}
missing_data = []
for key, query in self.queries.items():
diagnostics = expand_nested_lists(query, 'diagnostic_fields')
thresholds = expand_nested_lists(query, 'diagnostic_thresholds')
conditions = expand_nested_lists(query, 'diagnostic_conditions')
for diagnostic, threshold, condition in zip(
diagnostics, thresholds, conditions):
# First we check the diagnostic name and units, performing
# a conversion is required and possible.
test_condition = (iris.Constraint(name=diagnostic))
matched_cube = cubes.extract(test_condition)
if not matched_cube:
if 'diagnostic_missing_action' in query:
optional_node_data_missing.update(
{key: query[query['diagnostic_missing_action']]})
else:
missing_data.append([diagnostic, threshold, condition])
continue
else:
cube_threshold_units = (find_threshold_coordinate(
matched_cube[0]).units)
threshold.convert_units(cube_threshold_units)
# Then we check if the required threshold is present in the
# cube, and that the thresholding is relative to it correctly.
threshold = threshold.points.item()
threshold_name = find_threshold_coordinate(
matched_cube[0]).name()
# Check cube and threshold coordinate names match according to
# expected convention. If not, add to exception dictionary.
if extract_diagnostic_name(diagnostic) != threshold_name:
self.threshold_coord_names[diagnostic] = (threshold_name)
# Set flag to check for old threshold coordinate names
if (threshold_name == "threshold"
and not self.coord_named_threshold):
self.coord_named_threshold = True
# Check threshold == 0.0
if abs(threshold) < self.float_abs_tolerance:
coord_constraint = {
threshold_name:
lambda cell: (-self.float_abs_tolerance < cell < self.
float_abs_tolerance)
}
else:
coord_constraint = {
threshold_name:
lambda cell:
(threshold *
(1. - self.float_tolerance) < cell < threshold *
(1. + self.float_tolerance))
}
test_condition = (iris.Constraint(
coord_values=coord_constraint,
cube_func=lambda cube:
(find_threshold_coordinate(cube).attributes[
'spp__relative_to_threshold'] == condition)))
matched_threshold = matched_cube.extract(test_condition)
if not matched_threshold:
missing_data.append([diagnostic, threshold, condition])
if missing_data:
msg = ('Weather Symbols input cubes are missing'
' the following required'
' input fields:\n')
dyn_msg = ('name: {}, threshold: {}, '
'spp__relative_to_threshold: {}\n')
for item in missing_data:
msg = msg + dyn_msg.format(*item)
raise IOError(msg)
if not optional_node_data_missing:
optional_node_data_missing = None
return optional_node_data_missing
