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"""This module defines the utilities required for wxcode plugin """
from collections import OrderedDict
import iris
import numpy as np
from iris.exceptions import CoordinateNotFoundError
import improver.utilities.solar as solar
from improver.wxcode.wxcode_decision_tree import wxcode_decision_tree
from improver.wxcode.wxcode_decision_tree_global import (
wxcode_decision_tree_global)
_WX_DICT_IN = {0: 'Clear_Night',
1: 'Sunny_Day',
2: 'Partly_Cloudy_Night',
3: 'Partly_Cloudy_Day',
4: 'Dust',
5: 'Mist',
6: 'Fog',
7: 'Cloudy',
8: 'Overcast',
9: 'Light_Shower_Night',
10: 'Light_Shower_Day',
11: 'Drizzle',
12: 'Light_Rain',
13: 'Heavy_Shower_Night',
14: 'Heavy_Shower_Day',
15: 'Heavy_Rain',
16: 'Sleet_Shower_Night',
17: 'Sleet_Shower_Day',
18: 'Sleet',
19: 'Hail_Shower_Night',
20: 'Hail_Shower_Day',
21: 'Hail',
22: 'Light_Snow_Shower_Night',
23: 'Light_Snow_Shower_Day',
24: 'Light_Snow',
25: 'Heavy_Snow_Shower_Night',
26: 'Heavy_Snow_Shower_Day',
27: 'Heavy_Snow',
28: 'Thunder_Shower_Night',
29: 'Thunder_Shower_Day',
30: 'Thunder'}
WX_DICT = OrderedDict(sorted(_WX_DICT_IN.items(), key=lambda t: t[0]))
DAYNIGHT_CODES = [1, 3, 10, 14, 17, 20, 23, 26, 29]
[docs]def expand_nested_lists(query, key):
"""
Produce flat lists from list and nested lists.
Args:
query (dict):
A single query from the decision tree.
key (str):
A string denoting the field to be taken from the dict.
Returns:
list:
A 1D list containing all the values for a given key.
"""
items = []
for item in query[key]:
if isinstance(item, list):
items.extend(item)
else:
items.extend([item])
return items
[docs]def update_daynight(cubewx):
""" Update weather cube depending on whether it is day or night
Args:
cubewx(iris.cube.Cube):
Cube containing only daytime weather symbols.
Returns:
iris.cube.Cube:
Cube containing day and night weather symbols
Raises:
CoordinateNotFoundError : cube must have time coordinate.
"""
if not cubewx.coords("time"):
msg = ("cube must have time coordinate ")
raise CoordinateNotFoundError(msg)
time_dim = cubewx.coord_dims('time')
if not time_dim:
cubewx_daynight = iris.util.new_axis(cubewx.copy(), 'time')
else:
cubewx_daynight = cubewx.copy()
daynightplugin = solar.DayNightMask()
daynight_mask = daynightplugin.process(cubewx_daynight)
# Loop over the codes which decrease by 1 if a night time value
# e.g. 1 - sunny day becomes 0 - clear night.
for val in DAYNIGHT_CODES:
index = np.where(cubewx_daynight.data == val)
# Where day leave as is, where night correct weather
# code to value - 1.
cubewx_daynight.data[index] = np.where(
daynight_mask.data[index] == daynightplugin.day,
cubewx_daynight.data[index],
cubewx_daynight.data[index] - 1)
if not time_dim:
cubewx_daynight = iris.util.squeeze(cubewx_daynight)
return cubewx_daynight
[docs]def interrogate_decision_tree(wxtree):
"""
Obtain a list of necessary inputs from the decision tree as it is currently
defined. Return a formatted string that contains the diagnostic names, the
thresholds needed, and whether they are thresholded above or below these
values. This output is used to create the CLI help, informing the user of
the necessary inputs.
Args:
wxtree (str):
The weather symbol tree that is to be interrogated.
Returns:
list of str:
Returns a formatted string descring the diagnostics required,
including threshold details.
"""
# Get current weather symbol decision tree and populate a list of
# required inputs for printing.
if wxtree == 'high_resolution':
queries = wxcode_decision_tree()
elif wxtree == 'global':
queries = wxcode_decision_tree_global()
else:
raise ValueError('Unknown decision tree name provided.')
# Diagnostic names and threshold values.
requirements = {}
# How the data has been thresholded relative to these thresholds.
relative = {}
for query in queries.values():
diagnostics = expand_nested_lists(query, 'diagnostic_fields')
for index, diagnostic in enumerate(diagnostics):
if diagnostic not in requirements:
requirements[diagnostic] = []
relative[diagnostic] = []
requirements[diagnostic].extend(
expand_nested_lists(query, 'diagnostic_thresholds')[index])
relative[diagnostic].append(
expand_nested_lists(query, 'diagnostic_conditions')[index])
# Create a list of formatted strings that will be printed as part of the
# CLI help.
output = []
for requirement in requirements:
entries = np.array([entry for entry in requirements[requirement]])
relations = np.array([entry for entry in relative[requirement]])
_, thresholds = np.unique(np.array([item.points.item()
for item in entries]),
return_index=True)
output.append('{}; thresholds: {}'.format(
requirement, ', '.join([
'{} ({})'.format(str(threshold.points.item()),
str(threshold.units))
for threshold, relation in
zip(entries[thresholds], relations[thresholds])])))
n_files = len(output)
formatted_string = ('{}\n'*n_files)
formatted_output = formatted_string.format(*output)
return formatted_output