def construct_condition(extract_constraint, condition,
probability_threshold):
"""
Create a string representing a comparison condition.
Args:
extract_constraint (str or list of str):
A string, or list of strings, encoding iris constraints
that will be used to extract the correct diagnostic cube
(by name) from the input cube list and the correct threshold
from that cube.
A list should contain only cube names, operators and numbers,
e.g. ["probability_of_lwe_snowfall_rate_above_threshold", "-",
"probability_of_rainfall_rate_above_threshold", "*", "0.7"]
condition (str):
The condition statement (e.g. greater than, >).
probability_threshold (float):
The probability value to use in the comparison.
Returns:
string:
The formatted condition statement,
e.g.::
cubes.extract(Constraint(
name='probability_of_rainfall_rate_above_threshold',
coord_values={'threshold': 0.03})
)[0].data < 0.5)
"""
if isinstance(extract_constraint, list):
formatted_str = ""
for item in extract_constraint:
if is_variable(item):
formatted_str += f" cubes.extract({item})[0].data"
else:
formatted_str += " " + item
return f"({formatted_str}) {condition} {probability_threshold}"
return "cubes.extract({})[0].data {} {}".format(
extract_constraint, condition, probability_threshold)
def create_condition_chain(self, test_conditions: Dict) -> List:
"""
Construct a list of all the conditions specified in a single query.
Args:
test_conditions:
A query from the decision tree.
Returns:
A valid condition chain is defined recursively:
(1) If each a_1, ..., a_n is an extract expression (i.e. a
constraint, or a list of constraints,
operator strings and floats), and b is either "AND", "OR" or "",
then [[a1, ..., an], b] is a valid condition chain.
(2) If a1, ..., an are each valid conditions chain, and b is
either "AND" or "OR", then [[a1, ..., an], b] is a valid
condition chain.
"""
conditions = []
loop = 0
for diagnostic, p_threshold, d_threshold in zip(
test_conditions["diagnostic_fields"],
test_conditions["probability_thresholds"],
test_conditions["diagnostic_thresholds"],
):
loop += 1
if isinstance(diagnostic, list):
# We have a list which could contain variable names, operators and
# numbers. The variable names need converting into Iris Constraint
# syntax while operators and numbers remain unchanged.
# We expect an entry in p_threshold for each variable name, so
# d_threshold_index is used to track these.
d_threshold_index = -1
extract_constraint = []
for item in diagnostic:
if is_variable(item):
# Add a constraint from the variable name and threshold value
d_threshold_index += 1
extract_constraint.append(
self.construct_extract_constraint(
item,
d_threshold[d_threshold_index],
self.coord_named_threshold,
)
)
else:
# Add this operator or variable as-is
extract_constraint.append(item)
else:
# Non-lists are assumed to be constraints on a single variable.
extract_constraint = self.construct_extract_constraint(
diagnostic, d_threshold, self.coord_named_threshold
)
conditions.append(
[
extract_constraint,
test_conditions["threshold_condition"],
p_threshold,
]
)
condition_chain = [conditions, test_conditions["condition_combination"]]
return condition_chain
def create_condition_chain(self, test_conditions):
"""
A wrapper to call the construct_condition function for all the
conditions specified in a single query.
Args:
test_conditions (dict):
A query from the decision tree.
Returns:
list of str:
A list of strings that describe the conditions comprising the
query.
e.g.::
[
"(cubes.extract(Constraint(
name='probability_of_rainfall_rate_above_threshold',
coord_values={'threshold': 0.03})
)[0].data < 0.5) |
(cubes.extract(Constraint(
name=
'probability_of_lwe_snowfall_rate_above_threshold',
coord_values={'threshold': 0.03})
)[0].data < 0.5)"
]
"""
conditions = []
loop = 0
for diagnostic, p_threshold, d_threshold in zip(
test_conditions["diagnostic_fields"],
test_conditions["probability_thresholds"],
test_conditions["diagnostic_thresholds"],
):
loop += 1
if isinstance(diagnostic, list):
# We have a list which could contain variable names, operators and
# numbers. The variable names need converting into Iris Constraint
# syntax while operators and numbers remain unchanged.
# We expect an entry in p_threshold for each variable name, so
# d_threshold_index is used to track these.
d_threshold_index = -1
extract_constraint = []
for item in diagnostic:
if is_variable(item):
# Add a constraint from the variable name and threshold value
d_threshold_index += 1
extract_constraint.append(
self.construct_extract_constraint(
item,
d_threshold[d_threshold_index],
self.coord_named_threshold,
))
else:
# Add this operator or variable as-is
extract_constraint.append(item)
else:
# Non-lists are assumed to be strings containing one variable name.
extract_constraint = self.construct_extract_constraint(
diagnostic, d_threshold, self.coord_named_threshold)
conditions.append(
self.construct_condition(
extract_constraint,
test_conditions["threshold_condition"],
p_threshold,
))
condition_chain = WeatherSymbols.format_condition_chain(
conditions,
condition_combination=test_conditions["condition_combination"])
return [condition_chain]
