def test_period_not_supported(self):
"""Test for time avg of a realistic time axis and 365 day calendar"""
data = np.ones((6, ))
times = np.array([15, 45, 74, 105, 135, 166])
bounds = np.array([[0, 31], [31, 59], [59, 90], [90, 120], [120, 151],
[151, 181]])
cube = self._create_cube(data, times, bounds)
with self.assertRaises(ValueError):
climate_statistics(cube, operator='mean', period='bad')
def test_climate_statistics_complex_cube():
"""Test climate statistics."""
cube = _make_cube()
new_cube = climate_statistics(cube, operator='sum', period='full')
assert cube.shape == (2, 1, 1, 3)
assert new_cube.shape == (1, 1, 3)
np.testing.assert_allclose(new_cube.data, [[[45.0, 45.0, 45.0]]])
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('********************************')
def test_time_median(self):
"""Test for time meadian of a 1D field."""
data = np.arange((3))
times = np.array([15., 45., 75.])
bounds = np.array([[0., 30.], [30., 60.], [60., 90.]])
cube = self._create_cube(data, times, bounds)
result = climate_statistics(cube, operator='median')
expected = np.array([1.])
assert_array_equal(result.data, expected)
def test_time_sum_uneven(self):
"""Test for time sum of a 1D field with uneven time boundaries."""
data = np.array([1., 5.])
times = np.array([5., 25.])
bounds = np.array([[0., 1.], [1., 4.]])
cube = self._create_cube(data, times, bounds)
result = climate_statistics(cube, operator='sum')
expected = np.array([16.0])
assert_array_equal(result.data, expected)
def test_day(self):
"""Test for time avg of a realistic time axis and 365 day calendar"""
data = np.ones((6, ))
times = np.array([0.5, 1.5, 2.5, 365.5, 366.5, 367.5])
bounds = np.array([[0, 1], [1, 2], [2, 3], [365, 366], [366, 367],
[367, 368]])
cube = self._create_cube(data, times, bounds)
result = climate_statistics(cube, operator='mean', period='day')
expected = np.array([1, 1, 1])
assert_array_equal(result.data, expected)
def test_monthly(self):
"""Test for time avg of a realistic time axis and 365 day calendar"""
data = np.ones((6, ))
times = np.array([15, 45, 74, 105, 135, 166])
bounds = np.array([[0, 31], [31, 59], [59, 90], [90, 120], [120, 151],
[151, 181]])
cube = self._create_cube(data, times, bounds)
result = climate_statistics(cube, operator='mean', period='mon')
expected = np.ones((6, ))
assert_array_equal(result.data, expected)
def test_time_sum(self):
"""Test for time sum of a 1D field."""
data = np.ones((3))
data[1] = 2.0
times = np.array([15., 45., 75.])
bounds = np.array([[0., 30.], [30., 60.], [60., 90.]])
cube = self._create_cube(data, times, bounds)
result = climate_statistics(cube, operator='sum')
expected = np.array([120.])
assert_array_equal(result.data, expected)
def test_time_sum_365_day(self):
"""Test for time sum of a realistic time axis and 365 day calendar"""
data = np.ones((6, ))
data[3] = 2.0
times = np.array([15, 45, 74, 105, 135, 166])
bounds = np.array([[0, 31], [31, 59], [59, 90], [90, 120], [120, 151],
[151, 181]])
cube = self._create_cube(data, times, bounds)
result = climate_statistics(cube, operator='sum')
expected = np.array([211.])
assert_array_equal(result.data, expected)
def test_climate_statistics_0d_time_1d_lon():
"""Test climate statistics."""
time = iris.coords.DimCoord([1.0], bounds=[[0.0, 2.0]], var_name='time',
standard_name='time',
units='days since 1850-01-01 00:00:00')
lons = get_lon_coord()
cube = iris.cube.Cube([[1.0, -1.0, 42.0]], var_name='x', units='K',
dim_coords_and_dims=[(time, 0), (lons, 1)])
new_cube = climate_statistics(cube, operator='sum', period='full')
assert cube.shape == (1, 3)
assert new_cube.shape == (3,)
np.testing.assert_allclose(new_cube.data, [1.0, -1.0, 42.0])
def compute(self):
print('----------- COMPUTE ----------')
# ---------------------------------------------------------------------
# Every dataset in the recipe is associated with an alias. We are going
# to use th:We alias and the group_metadata shared function to loop over
# the datasets.
#----------------------------------------------------------------------
data = group_metadata(self.cfg['input_data'].values(), 'alias')
ssp_trend = {}
ssp_clim = {}
hist_trend = {}
hist_clim = {}
# Loop over the datasets.
for alias in data:
exp = data[alias][0]['exp']
variables = group_metadata(data[alias], 'short_name')
# Returns the path to the preprocessed files.
tas_file = variables['tas'][0]['filename']
tas = iris.load(tas_file)[0]
tas.convert_units('degC')
# Calculate Trends
nlat = tas.coord('latitude').shape[0]
nlon = tas.coord('longitude').shape[0]
lat = tas.coord('latitude').points
lon = tas.coord('longitude').points
time_array = np.arange(1, tas.coord('time').shape[0] + 1, 1)
regr = np.zeros([nlat, nlon])
for j in range(nlat):
for k in range(nlon):
p = np.polyfit(time_array, tas[:, j, k].data, 1)
regr[j, k] = p[0] * 10 # the 10 is to convert to decadal
latitude = DimCoord(lat, standard_name='latitude', units='degrees')
longitude = DimCoord(lon,
standard_name='longitude',
units='degrees')
regr_cube = Cube(regr,
dim_coords_and_dims=[(latitude, 0),
(longitude, 1)])
### ---------- remask -------------- ###
# finding the trends turns the remask usseless as pyplot doesn't care about masked arrays
# Ergo another remask is needed.
output_trend = mask_landsea(regr_cube,
['/blablabla/where/the/fx/at/'], 'sea',
True)
# Save the output trends in the cube dict
output_trend.standard_name = None
output_trend.long_name = 'tas_trend_med'
output_trend.short_name = 'tastrend'
# Calculate Climatology
output_clim = climate_statistics(tas)
output_clim.standard_name = None
output_clim.long_name = 'tas_clim_med'
output_clim.short_name = 'tasclim'
# Save diagnosed dataset to dict. TODO: what about averaging first?
if exp == 'historical':
hist_trend[alias] = output_trend
hist_clim[alias] = output_clim
if exp == 'ssp585':
ssp_trend[alias] = output_trend
ssp_clim[alias] = output_clim
# Save the outputs for each dataset.
#self.save(output, alias, data)
# Plot the results.
#self.plot_2D(total, data)
print(ssp_trend.variables)
print(len(ssp_trend))
def compute(self):
print('----------- COMPUTE ----------')
# ---------------------------------------------------------------------
# Every dataset in the recipe is associated with an alias. We are going
# to use th:We alias and the group_metadata shared function to loop over
# the datasets.
#----------------------------------------------------------------------
data = group_metadata(self.cfg['input_data'].values(), 'alias')
ssp_ts = {}
hist_ts = {}
rean_ts = {}
hist = 0
ssp = 0
rean = 0
# Loop over the datasets.
for i, alias in enumerate(data):
exp = data[alias][0]['exp']
variables = group_metadata(data[alias], 'short_name')
# Returns the path to the preprocessed files.
tas_file = variables['tas'][0]['filename']
tas = iris.load(tas_file)[0]
tas.convert_units('degC')
if i == 0:
climatology = self.ref_clim(tas, 1960, 1962)
#anomaly = tas - climatology
#timeseries = anomaly.collapsed(['longitude', 'latitude'], iris.analysis.MEAN)
timeseries = tas.collapsed(['longitude', 'latitude'], iris.analysis.MEAN)
#timeseries.long_name = 'med_r_timeseries_tas'
# Calculate Trends
nlat = tas.coord('latitude').shape[0]
nlon = tas.coord('longitude').shape[0]
lat = tas.coord('latitude').points
lon = tas.coord('longitude').points
time_array = np.arange(1,tas.coord('time').shape[0]+1,1)
regr = np.zeros([nlat, nlon])
for j in range(nlat):
for k in range(nlon):
p = np.polyfit(time_array, tas[:,j,k].data, 1)
regr[j, k] = p[0]*10 # the 10 is to convert to decadal
latitude = DimCoord(lat, standard_name='latitude', units='degrees')
longitude = DimCoord(lon, standard_name='longitude', units='degrees')
regr_cube = Cube(regr, dim_coords_and_dims=[(latitude, 0), (longitude, 1)])
### ---------- remask -------------- ###
# finding the trends turns the remask usseless as pyplot doesn't care about masked arrays
# Ergo another remask is needed.
output_trend = mask_landsea(regr_cube, ['/blablabla/where/the/fx/at/'] ,'sea', True)
# Save the output trends in the cube dict
output_trend.standard_name = None
output_trend.long_name = 'tas_trend_med'
output_trend.short_name = 'tastrend'
# Calculate Climatology
output_clim = climate_statistics(tas)
output_clim.standard_name = None
output_clim.long_name = 'tas_clim_med'
output_clim.short_name = 'tasclim'
# Save diagnosed dataset to dict. TODO: what about averaging first?
if exp == 'historical':
hist_ts[alias] = timeseries
if hist == 0:
mean_hist_trend = output_trend
mean_hist_clim = output_clim
hist += 1
else:
mean_hist_trend = (mean_hist_trend + output_trend)
mean_hist_clim = (mean_hist_clim + output_clim)
hist += 1
if exp == 'ssp585':
ssp_ts[alias] = timeseries
if ssp == 0:
mean_ssp_trend = output_trend
mean_ssp_clim = output_clim
ssp += 1
else:
mean_ssp_trend = (mean_ssp_trend + output_trend)
mean_ssp_clim = (mean_ssp_clim + output_clim)
ssp += 1
if exp == 'reanaly':
rean_ts[alias] = timeseries
if rean == 0:
mean_rean_trend = output_trend
mean_rean_clim = output_clim
rean += 1
else:
mean_rean_trend = (mean_rean_trend + output_trend)
mean_rean_clim = (mean_rean_clim + output_clim)
rean += 1
mean_hist_trend = mean_hist_trend/hist
mean_hist_clim = mean_hist_clim/hist
mean_ssp_trend = mean_ssp_trend/ssp
mean_ssp_clim = mean_ssp_clim/ssp
#mean_rean_trend = mean_rean_trend/ssp
#mean_rean_clim = mean_rean_clim/ssp
mean_ssp_trend.long_name = 'ssp_trend_Med'
mean_ssp_clim.long_name = 'ssp_clim_Med'
mean_hist_trend.long_name = 'hist_trend_Med'
mean_hist_clim.long_name = 'hist_clim_Med'
# mean_rean_trend.long_name = 'rean_trend_Med'
# mean_rean_clim.long_name = 'rean_clim_Med'
##### Biases #####
#trend_bias = mean_hist_trend - mean_rean_trend
#clim_bias = mean_hist_clim - mean_rean_clim
# Save the outputs for each dataset.
#self.save(output, alias, data)
# Plot the results.
self.plot_2D(mean_ssp_trend)
self.plot_1D(timeseries)
print(mean_ssp_trend)