def test_season_max(self):
"""Test for season max of a 1D field."""
data = np.arange(12)
times = np.arange(15, 360, 30)
cube = self._create_cube(data, times)
result = seasonal_statistics(cube, 'max')
expected = np.array([4., 7., 10.])
assert_array_equal(result.data, expected)
def test_season_sum(self):
"""Test for season sum of a 1D field."""
data = np.arange(12)
times = np.arange(15, 360, 30)
cube = self._create_cube(data, times)
result = seasonal_statistics(cube, 'sum')
expected = np.array([9., 18., 27.])
assert_array_equal(result.data, expected)
def test_season_mean(self):
"""Test for season average of a 1D field."""
data = np.arange(12)
times = np.arange(15, 360, 30)
cube = self._create_cube(data, times)
result = seasonal_statistics(cube, 'mean')
expected = np.array([3., 6., 9.])
assert_array_equal(result.data, expected)
def compute(self):
# ---------------------------------------------------------------------
# Every dataset in the recipe is associated with an alias. We are going
# to use the alias and the group_metadata shared function to loop over
# the datasets.
#----------------------------------------------------------------------
data = group_metadata(self.cfg['input_data'].values(), 'alias')
total = {}
# Loop over the datasets.
for alias in data:
# -----------------------------------------------------------------
# Use the group_metadata function again so that, for each dataset,
# the metadata dictionary is organised by the variables'
# short name.
# -----------------------------------------------------------------
variables = group_metadata(data[alias], 'short_name')
# Returns the path to the preprocessed files.
tas_file = variables['tas'][0]['filename']
# tos_file = variables['tos'][0]['filename']
# -----------------------------------------------------------------
# Now it is up to you to load the data and work with it with your
# preferred libraries and methods. We are going to continue the
# example using Iris and ESMValTool functions.
# Note that all preprocessor routines can be called inside the
# diag_script by adding the corresponding imports.
# -----------------------------------------------------------------
tas = iris.load(tas_file)[0]
tas.convert_units('degC')
y_mn = seasonal_statistics(tas, 'mean') #[0]
# Returns the path to the preprocessed files.
mn = climate_statistics(tas, 'mean', 'season')[0]
std = climate_statistics(tas, 'std_dev', 'season')[0]
#anom = anomalies(y_mn, 'season')
with open("/esarchive/scratch/jcos/esmvaltool/scripts/sample.txt",
"w") as f:
f.write(str(tas))
f.write('******************************** \\ y_mean******')
f.write(str(y_mn[0]))
f.write('********************************\\ mn******')
f.write(str(y_mn[1]))
f.write('******************************\\ std*******')
f.write(str(std))
f.write('********************************')
f.write(str(0))
f.write('********************************')