def test_equal_number_of_plots_and_old_shape(self):
nplots = 4
oldshape = (2, 2)
expected_shape = (2, 2)
new_shape = plotter._best_grid_shape(nplots, oldshape)
self.assertEqual(new_shape, expected_shape)
def test_returned_shape_large(self):
nplots = 57
oldshape = (220, 12)
expected_shape = (5, 12)
new_shape = plotter._best_grid_shape(nplots, oldshape)
self.assertEqual(new_shape, expected_shape)
def test_invalid_shape(self):
nplots = 2532
oldshape = (22, 12)
with self.assertRaises(ValueError):
plotter._best_grid_shape(nplots, oldshape)
def test_equal_number_of_plots_and_old_shape(self):
nplots = 4
oldshape = (2, 2)
expected_shape = (2, 2)
new_shape = plotter._best_grid_shape(nplots, oldshape)
self.assertEqual(new_shape, expected_shape)
def test_returned_shape_small(self):
nplots = 2
oldshape = (2, 2)
expected_shape = (1, 2)
new_shape = plotter._best_grid_shape(nplots, oldshape)
self.assertEqual(new_shape, expected_shape)
def test_invalid_shape(self):
nplots = 2532
oldshape = (22, 12)
with self.assertRaises(ValueError):
plotter._best_grid_shape(nplots, oldshape)
def test_returned_shape_large(self):
nplots = 57
oldshape = (12, 220)
expected_shape = (12, 5)
new_shape = plotter._best_grid_shape(nplots, oldshape)
self.assertEqual(new_shape, expected_shape)
def test_returned_shape_small(self):
nplots = 2
oldshape = (2, 2)
expected_shape = (2, 1)
new_shape = plotter._best_grid_shape(nplots, oldshape)
self.assertEqual(new_shape, expected_shape)
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)
nmodels = len(model_datasets)
row, column = plotter._best_grid_shape((row, column), nmodels + 1)
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=True)
else:
m = Basemap(ax=ax, projection ='cyl', llcrnrlat = lat_min, urcrnrlat = lat_max,
llcrnrlon = lon_min, urcrnrlon = lon_max, resolution = 'l', fix_aspect=True)
if obs_dataset.lons.ndim == 1 and obs_dataset.lats.ndim == 1:
lons, lats = np.meshgrid(obs_dataset.lons, obs_dataset.lats)
if obs_dataset.lons.ndim == 2 and obs_dataset.lats.ndim == 2:
lons = obs_dataset.lons
lats = 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)+1./row*0.25, 0.01, 1./row*0.5])
plt.colorbar(max, cax = cax)
clevs = plotter._nice_intervals(rcm_bias, 11)
for imodel in np.arange(nmodels):
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=True)
else:
m = Basemap(ax=ax, projection ='cyl', llcrnrlat = lat_min, urcrnrlat = lat_max,
llcrnrlon = lon_min, urcrnrlon = lon_max, resolution = 'l', fix_aspect=True)
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, 0.5, 0.015, 0.4])
plt.colorbar(max, cax = cax)
plt.subplots_adjust(hspace=0.01,wspace=0.05)
fig.savefig(file_name,dpi=600,bbox_inches='tight')
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)
nmodels = len(model_datasets)
row, column = plotter._best_grid_shape(nmodels + 1, (row, column))
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=True)
else:
m = Basemap(ax=ax, projection ='cyl', llcrnrlat = lat_min, urcrnrlat = lat_max,
llcrnrlon = lon_min, urcrnrlon = lon_max, resolution = 'l', fix_aspect=True)
if obs_dataset.lons.ndim == 1 and obs_dataset.lats.ndim == 1:
lons, lats = np.meshgrid(obs_dataset.lons, obs_dataset.lats)
if obs_dataset.lons.ndim == 2 and obs_dataset.lats.ndim == 2:
lons = obs_dataset.lons
lats = 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)+1./row*0.25, 0.01, 1./row*0.5])
plt.colorbar(max, cax = cax)
clevs = plotter._nice_intervals(rcm_bias, 11)
for imodel in np.arange(nmodels):
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=True)
else:
m = Basemap(ax=ax, projection ='cyl', llcrnrlat = lat_min, urcrnrlat = lat_max,
llcrnrlon = lon_min, urcrnrlon = lon_max, resolution = 'l', fix_aspect=True)
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, 0.5, 0.015, 0.4])
plt.colorbar(max, cax = cax)
plt.subplots_adjust(hspace=0.01,wspace=0.05)
fig.savefig(file_name,dpi=600,bbox_inches='tight')