plt.figure()
plt.hist(D.flatten(), bins=50, label='D')
#plt.hist(al.flatten(),bins= 50,label='al')
plt.hist(am1.flatten(), bins=50, label='am1')
# plt.xlim([0, 1])
plt.ylim([0, 3500])
plt.xlim([0, 2])
plt.legend()
plt.ylabel('count')
plt.xlabel('value')
#%% Comparison output
InputFolder_py = 'out/SICE_2020_py/'
InputFolder_fortran = 'out/SICE_2020_py - Kopi/'
#InputFolder_fortran = 'out/SICE_2020_py//'
tmp = bl.heatmap(
rio.open(InputFolder_py + 'diagnostic_retrieval.tif').read(1), 'isnow_py')
isnow = rio.open(InputFolder_py + 'diagnostic_retrieval.tif').read(1)
ind_all_clean = np.logical_or(isnow == 0, isnow == 7)
var_list = ('albedo_bb_planar_sw', 'albedo_bb_spherical_sw')
for i in range(len(var_list)):
var_py = rio.open(InputFolder_py + var_list[i] + '.tif').read(1)
var_f = rio.open(InputFolder_fortran + var_list[i] + '.tif').read(1)
diff = var_f - var_py
diff[~ind_all_clean] = np.nan
plt.figure(figsize=(10, 15))
bl.heatmap(diff,
'Approximation - Integration',
col_lim=(-0.01, 0.01),
cmap_in='seismic')
plt.title(var_list[i])
output_sice_f(OutputFolder+"boar.dat",'rBRR_'+str(i+1),4+i)
#%% plotting oulooks
try:
os.mkdir(OutputFolder+'plots')
except:
print('folder exist')
import matplotlib.pyplot as plt
# fig,ax=bl.heatmap_discrete(rio.open(OutputFolder+'diagnostic_retrieval.tif').read(1),
# 'diagnostic_retrieval ')
# ax.set_title(OutputFolder)
# fig.savefig(OutputFolder+'plots/diagnostic_retrieval.png',bbox_inches='tight')
var_list = ('albedo_bb_planar_sw','albedo_bb_spherical_sw')
for i in range(len(var_list)):
var_1 = rio.open(OutputFolder+var_list[i]+'.tif').read(1)
plt.figure(figsize=(10,15))
bl.heatmap(var_1,var_list[i], col_lim=(0, 1) ,cmap_in='jet')
plt.title(OutputFolder)
plt.savefig(OutputFolder+'plots/'+var_list[i]+'_diff.png',bbox_inches='tight')
plt.ioff()
for i in np.append(np.arange(11), np.arange(21)):
var_name = 'albedo_spectral_spherical_'+ str(i+1).zfill(2)
var_1 = rio.open(OutputFolder+var_name+'.tif').read(1)
plt.figure(figsize=(10,15))
bl.heatmap(var_1,var_name, col_lim=(0, 1) ,cmap_in='jet')
plt.title(OutputFolder)
plt.savefig(OutputFolder+'plots/'+var_name+'.png',bbox_inches='tight')
plt.ion()
os.mkdir(InputFolder + 'plots')
except:
print('folder exist')
fig, ax = bl.heatmap_discrete(
rio.open(InputFolder + 'diagnostic_retrieval.tif').read(1),
'diagnostic_retrieval ')
ax.set_title(InputFolder)
fig.savefig(InputFolder + 'plots/diagnostic_retrieval.png',
bbox_inches='tight')
var_list = ('albedo_bb_planar_sw', 'albedo_bb_spherical_sw', 'r0')
for i in range(len(var_list)):
var_1 = rio.open(InputFolder + var_list[i] + '.tif').read(1)
plt.figure(figsize=(10, 15))
bl.heatmap(var_1, var_list[i], col_lim=(0, 1), cmap_in='jet')
plt.title(InputFolder)
plt.savefig(InputFolder + 'plots/' + var_list[i] + '.png',
bbox_inches='tight')
plt.close()
var_list = ('O3_SICE', 'grain_diameter', 'snow_specific_area', 'al', 'conc')
for i in range(len(var_list)):
var_1 = rio.open(InputFolder + var_list[i] + '.tif').read(1)
plt.figure(figsize=(10, 15))
bl.heatmap(var_1, var_list[i], cmap_in='jet')
plt.title(InputFolder)
plt.savefig(InputFolder + 'plots/' + var_list[i] + '.png',
bbox_inches='tight')
plt.close()