def setUp(self):
self.mean_bias = metrics.TemporalMeanBias()
# Initialize reference dataset
self.reference_lat = np.array([10, 12, 14, 16, 18])
self.reference_lon = np.array([100, 102, 104, 106, 108])
self.reference_time = np.array(
[dt.datetime(2000, x, 1) for x in range(1, 13)])
flat_array = np.array(range(300))
self.reference_value = flat_array.reshape(12, 5, 5)
self.reference_variable = 'prec'
self.reference_dataset = Dataset(self.reference_lat,
self.reference_lon,
self.reference_time,
self.reference_value,
self.reference_variable)
# Initialize target dataset
self.target_lat = np.array([1, 2, 4, 6, 8])
self.target_lon = np.array([10, 12, 14, 16, 18])
self.target_time = np.array(
[dt.datetime(2001, x, 1) for x in range(1, 13)])
flat_array = np.array(range(300, 600))
self.target_value = flat_array.reshape(12, 5, 5)
self.target_variable = 'tasmax'
self.target_dataset = Dataset(self.target_lat,
self.target_lon,
self.target_time,
self.target_value,
self.target_variable)
def Map_plot_bias_of_multiyear_climatology(obs_dataset,
obs_name,
model_datasets,
model_names,
file_name,
row,
column,
map_projection=None):
'''Draw maps of observed multi-year climatology and biases of models"'''
# calculate climatology of observation data
obs_clim = utils.calc_temporal_mean(obs_dataset)
# determine the metrics
map_of_bias = metrics.TemporalMeanBias()
# create the Evaluation object
bias_evaluation = Evaluation(
obs_dataset, # Reference dataset for the evaluation
model_datasets, # list of target datasets for the evaluation
[map_of_bias, map_of_bias])
# run the evaluation (bias calculation)
bias_evaluation.run()
rcm_bias = bias_evaluation.results[0]
fig = plt.figure()
lat_min = obs_dataset.lats.min()
lat_max = obs_dataset.lats.max()
lon_min = obs_dataset.lons.min()
lon_max = obs_dataset.lons.max()
string_list = list(string.ascii_lowercase)
ax = fig.add_subplot(row, column, 1)
if map_projection == 'npstere':
m = Basemap(ax=ax,
projection='npstere',
boundinglat=lat_min,
lon_0=0,
resolution='l',
fix_aspect=False)
else:
m = Basemap(ax=ax,
projection='cyl',
llcrnrlat=lat_min,
urcrnrlat=lat_max,
llcrnrlon=lon_min,
urcrnrlon=lon_max,
resolution='l',
fix_aspect=False)
lons, lats = np.meshgrid(obs_dataset.lons, obs_dataset.lats)
x, y = m(lons, lats)
m.drawcoastlines(linewidth=1)
m.drawcountries(linewidth=1)
m.drawstates(linewidth=0.5, color='w')
max = m.contourf(x,
y,
obs_clim,
levels=plotter._nice_intervals(obs_dataset.values, 10),
extend='both',
cmap='rainbow')
ax.annotate('(a) \n' + obs_name, xy=(lon_min, lat_min))
cax = fig.add_axes([0.02, 1. - float(1. / row), 0.01, 1. / row * 0.6])
plt.colorbar(max, cax=cax)
clevs = plotter._nice_intervals(rcm_bias, 11)
for imodel in np.arange(len(model_datasets)):
ax = fig.add_subplot(row, column, 2 + imodel)
if map_projection == 'npstere':
m = Basemap(ax=ax,
projection='npstere',
boundinglat=lat_min,
lon_0=0,
resolution='l',
fix_aspect=False)
else:
m = Basemap(ax=ax,
projection='cyl',
llcrnrlat=lat_min,
urcrnrlat=lat_max,
llcrnrlon=lon_min,
urcrnrlon=lon_max,
resolution='l',
fix_aspect=False)
m.drawcoastlines(linewidth=1)
m.drawcountries(linewidth=1)
m.drawstates(linewidth=0.5, color='w')
max = m.contourf(x,
y,
rcm_bias[imodel, :],
levels=clevs,
extend='both',
cmap='RdBu_r')
ax.annotate('(' + string_list[imodel + 1] + ') \n ' +
model_names[imodel],
xy=(lon_min, lat_min))
cax = fig.add_axes([0.91, 0.1, 0.015, 0.8])
plt.colorbar(max, cax=cax)
plt.subplots_adjust(hspace=0.01, wspace=0.05)
fig.savefig(file_name, dpi=600, bbox_inches='tight')