def test_complex_condition(self):
"""Test with a condition that uses an operator"""
query = {
"rain_or_snow": self.dummy_queries["significant_precipitation"],
}
query["rain_or_snow"]["diagnostic_fields"] = [
[
"probability_of_lwe_sleetfall_rate_above_threshold",
"-",
"probability_of_rainfall_rate_above_threshold",
],
[
"probability_of_lwe_sleetfall_rate_above_threshold",
"-",
"probability_of_lwe_snowfall_rate_above_threshold",
],
]
query["rain_or_snow"]["diagnostic_thresholds"] = [
[AuxCoord(0.1, units="mm hr-1"), AuxCoord(0.1, units="mm hr-1")],
[AuxCoord(0.1, units="mm hr-1"), AuxCoord(0.1, units="mm hr-1")],
]
query["rain_or_snow"]["diagnostic_conditions"] = [
["above", "above"],
["above", "above"],
]
plugin = WeatherSymbols()
test_condition = query["rain_or_snow"]
result = plugin.create_condition_chain(test_condition)
expected = (
"(( cubes.extract(iris.Constraint(name='probability_of_"
"lwe_sleetfall_rate_above_threshold', lwe_sleetfall_rate="
"lambda cell: 0.1 * {t_min} < cell < 0.1 * {t_max})"
")[0].data - cubes.extract(iris.Constraint("
"name='probability_of_rainfall_rate_above_threshold', "
"rainfall_rate=lambda cell: 0.1 * {t_min} < cell < "
"0.1 * {t_max}))[0].data) >= 0.5) | "
"(( cubes.extract(iris.Constraint(name="
"'probability_of_lwe_sleetfall_rate_above_threshold', "
"lwe_sleetfall_rate=lambda cell: 0.1 * {t_min} < cell "
"< 0.1 * {t_max}))[0].data - "
"cubes.extract(iris.Constraint(name="
"'probability_of_lwe_snowfall_rate_above_threshold', "
"lwe_snowfall_rate=lambda cell: 0.1 * {t_min} < cell "
"< 0.1 * {t_max}))[0].data) >= 0.5)".format(
t_min=(1.0 - WeatherSymbols().float_tolerance),
t_max=(1.0 + WeatherSymbols().float_tolerance),
)
)
self.assertIsInstance(result, list)
self.assertTrue(all([isinstance(s, str) for s in result]))
self.assertEqual(len(result), 1)
self.assertEqual(result[0], expected)
def test_basic(self):
"""Test create_condition_chain returns a list of strings."""
plugin = WeatherSymbols()
test_condition = self.dummy_queries['significant_precipitation']
result = plugin.create_condition_chain(test_condition)
expected = ("(cubes.extract(iris.Constraint(name='probability_of_"
"rainfall_rate', threshold=lambda cell: 0.03 * {t_min} < "
"cell < 0.03 * {t_max}))[0].data >= 0.5) | (cubes.extract"
"(iris.Constraint(name='probability_of_lwe_snowfall_rate',"
" threshold=lambda cell: 0.03 * {t_min} < cell < 0.03 * "
"{t_max}))[0].data >= 0.5)".format(
t_min=(1. - WeatherSymbols().float_tolerance),
t_max=(1. + WeatherSymbols().float_tolerance)))
self.assertIsInstance(result, list)
self.assertIsInstance(result[0], str)
self.assertEqual(result[0], expected)
def test_old_naming_convention(self):
"""Test create_condition_chain can return conditions using old
threshold coordinate name"""
plugin = WeatherSymbols()
plugin.coord_named_threshold = True
test_condition = self.dummy_queries['significant_precipitation']
result = plugin.create_condition_chain(test_condition)
expected = ("(cubes.extract(iris.Constraint(name='probability_of_"
"rainfall_rate_above_threshold', threshold=lambda "
"cell: 0.03 * {t_min} < "
"cell < 0.03 * {t_max}))[0].data >= 0.5) | (cubes.extract"
"(iris.Constraint("
"name='probability_of_lwe_snowfall_rate_above_threshold',"
" threshold=lambda cell: 0.03 * {t_min} < cell < "
"0.03 * {t_max}))[0].data >= 0.5)".format(
t_min=(1. - WeatherSymbols().float_tolerance),
t_max=(1. + WeatherSymbols().float_tolerance)))
self.assertIsInstance(result, list)
self.assertIsInstance(result[0], str)
self.assertEqual(result[0], expected)
def test_basic(self):
"""Test create_condition_chain returns a list of strings."""
plugin = WeatherSymbols()
test_condition = self.dummy_queries["significant_precipitation"]
result = plugin.create_condition_chain(test_condition)
expected = ("(cubes.extract(iris.Constraint(name='probability_of_"
"rainfall_rate_above_threshold', rainfall_rate=lambda "
"cell: 0.03 * {t_min} < "
"cell < 0.03 * {t_max}))[0].data >= 0.5) | (cubes.extract"
"(iris.Constraint("
"name='probability_of_lwe_snowfall_rate_above_threshold',"
" lwe_snowfall_rate=lambda cell: 0.03 * {t_min} < cell < "
"0.03 * {t_max}))[0].data >= 0.5)".format(
t_min=(1.0 - WeatherSymbols().float_tolerance),
t_max=(1.0 + WeatherSymbols().float_tolerance),
))
self.assertIsInstance(result, list)
self.assertTrue(all([isinstance(s, str) for s in result]))
self.assertEqual(len(result), 1)
self.assertEqual(result[0], expected)
