def field_map_ndar(ndar_field,t,ar_coorx,ar_coory,X,image_out,variable):
ar_field=ndar_field[t,:]
max_val=int(np.max(ndar_field))
if variable==4:
max_val=100.
xmin=min(ar_coorx);xmax=max(ar_coorx)
ymin=min(ar_coory);ymax=max(ar_coory)
step=X
nx=(xmax-xmin)/step+1
ny=(ymax-ymin)/step+1
ar_indx=np.array((ar_coorx-xmin)/step,int)
ar_indy=np.array((ar_coory-ymin)/step,int)
ar_map=np.ones((ny,nx))*-99.9
ar_map[ar_indy,ar_indx]=ar_field
ar_map2 = M.masked_where(ar_map <0, ar_map)
ut.check_file_exist(image_out)
pl.clf()
pl.axes(axisbg='gray')
pl.imshow(ar_map2, cmap=pl.cm.RdBu,
interpolation='Nearest', origin='lower', vmax=max_val, vmin=0)
pl.title('time step= '+ut.string(t,len(str(t))))
pl.colorbar()
pl.savefig(image_out)
def field_map_ndar(ndar_field,t,ar_coorx,ar_coory,X,image_out,variable):
ar_field=ndar_field[t,:]
max_val=int(np.max(ndar_field))
if variable==4:
max_val=100.
xmin=min(ar_coorx);xmax=max(ar_coorx)
ymin=min(ar_coory);ymax=max(ar_coory)
step=X
nx=(xmax-xmin)/step+1
ny=(ymax-ymin)/step+1
ar_indx=np.array((ar_coorx-xmin)/step,int)
ar_indy=np.array((ar_coory-ymin)/step,int)
ar_map=np.ones((ny,nx))*-99.9
ar_map[ar_indy,ar_indx]=ar_field
ar_map2 = M.masked_where(ar_map <0, ar_map)
ut.check_file_exist(image_out)
pl.clf()
pl.axes(axisbg='gray')
pl.imshow(ar_map2, cmap=pl.cm.RdBu,
interpolation='Nearest', origin='lower', vmax=max_val, vmin=0)
pl.title('time step= '+ut.string(t,len(str(t))))
pl.colorbar()
pl.savefig(image_out)
def run(ini_file='plot_soil_moisture_maps.ini'):
config.read(ini_file)
print 'Read the file ',ini_file
file_global_param=config.get('files','file_global_param')
file_cell_param=config.get('files','file_cell_param')
file_sim=config.get('files','file_sim')
path_out=config.get('paths','path_out')
fac_L=config.getfloat('calib_params','fac_L')
fac_Ks=config.getfloat('calib_params','fac_Ks')
fac_n_o=config.getfloat('calib_params','fac_n_o')
fac_n_c=config.getfloat('calib_params','fac_n_c')
t1=config.getfloat('flags','t1')
t2=config.getfloat('flags','t2')
variable=config.getfloat('flags','variable')
##~~~~~~PROGRAM~~~~~##
# Create the folder if it doesn't exist
ut.check_folder_exist(path_out)
#~~~~Read Global parameters file
X,Dt,alpha_s,alpha_o,alpha_c,A_thres,W_min,W_max\
=pm.read_global_parameters(file_global_param)
#~~~~Read Cell parameters file
ar_cell_label, ar_coorx, \
ar_coory, ar_lambda, \
ar_Xc, ar_dam, \
ar_tan_beta, ar_tan_beta_channel, \
ar_L0, ar_Ks0, \
ar_theta_r, ar_theta_s, \
ar_n_o0, ar_n_c0, \
ar_cell_down, ar_pVs_t0, \
ar_Vo_t0, ar_Qc_t0, \
ar_kc, psi_b, lamda = pm.read_cell_parameters(file_cell_param)
#~~~~Number of cell in the catchment
nb_cell=len(ar_cell_label)
#~~~~Computation of cell order
ar_label_sort=pm.sort_cell(ar_cell_label,ar_cell_down)
#~~~~Computation of upcells
li_cell_up=pm.direct_up_cell(ar_cell_label,ar_cell_down,ar_label_sort)
#~~~~Computation of drained area
ar_A_drained=pm.drained_area(ar_label_sort,li_cell_up,X)
#~~~~Modifies the values of the parameters
ar_L=ar_L0*fac_L
ar_Ks=ar_Ks0*fac_Ks
ar_n_o=ar_n_o0*fac_n_o
ar_n_c=ar_n_c0*fac_n_c
#~~~~Computation of model parameters from physical parameters
ar_Vsm, ar_b_s, ar_b_o, ar_W, ar_b_c\
=pm.compute_cell_param(X,ar_Xc,Dt,alpha_s,alpha_o,alpha_c,nb_cell,\
A_thres,W_max,W_min,\
ar_lambda,ar_tan_beta,ar_tan_beta_channel,ar_L,\
ar_Ks,ar_theta_r,ar_theta_s,ar_n_o,ar_n_c,\
ar_A_drained)
#Read of data from the outputs of TOPKAPI in hdf5 format
ndar_Vs=np.array(ut.read_one_array_hdf(file_sim,'/Soil/','V_s'))
#Assign the variables
if variable==1:
im_out=os.path.join(path_out, 'field_Vs_')
tab=ndar_Vs
elif variable==2:
im_out=os.path.join(path_out, 'field_Vo_')
tab=ndar_Vo
elif variable==3:
im_out=os.path.join(path_out, 'field_Vo_bin_')
tab=ndar_Vo
tab[tab>0]=1.
elif variable==4:
im_out=os.path.join(path_out, 'field_SSI_')
tab=ndar_Vs/ar_Vsm*100.
# Plot the maps
for t in np.arange(int(t1),int(t2+1)):
print 'Map time-step ', t
image_out=im_out+ut.string(t,len(str(t2)))+'.png'
field_map_ndar(tab,t,ar_coorx,ar_coory,X,image_out,variable)
def run(ini_file='plot_soil_moisture_maps.ini'):
config.read(ini_file)
print('Read the file ',ini_file)
file_global_param=config.get('files','file_global_param')
file_cell_param=config.get('files','file_cell_param')
file_sim=config.get('files','file_sim')
path_out=config.get('paths','path_out')
fac_L=config.getfloat('calib_params','fac_L')
fac_Ks=config.getfloat('calib_params','fac_Ks')
fac_n_o=config.getfloat('calib_params','fac_n_o')
fac_n_c=config.getfloat('calib_params','fac_n_c')
t1=config.getfloat('flags','t1')
t2=config.getfloat('flags','t2')
variable=config.getfloat('flags','variable')
##~~~~~~PROGRAM~~~~~##
# Create the folder if it doesn't exist
ut.check_folder_exist(path_out)
#~~~~Read Global parameters file
X,Dt,alpha_s,alpha_o,alpha_c,A_thres,W_min,W_max\
=pm.read_global_parameters(file_global_param)
#~~~~Read Cell parameters file
ar_cell_label, ar_coorx, \
ar_coory, ar_lambda, \
ar_Xc, ar_dam, \
ar_tan_beta, ar_tan_beta_channel, \
ar_L0, ar_Ks0, \
ar_theta_r, ar_theta_s, \
ar_n_o0, ar_n_c0, \
ar_cell_down, ar_pVs_t0, \
ar_Vo_t0, ar_Qc_t0, \
ar_kc, psi_b, lamda = pm.read_cell_parameters(file_cell_param)
#~~~~Number of cell in the catchment
nb_cell=len(ar_cell_label)
#~~~~Computation of cell order
ar_label_sort=pm.sort_cell(ar_cell_label,ar_cell_down)
#~~~~Computation of upcells
li_cell_up=pm.direct_up_cell(ar_cell_label,ar_cell_down,ar_label_sort)
#~~~~Computation of drained area
ar_A_drained=pm.drained_area(ar_label_sort,li_cell_up,X)
#~~~~Modifies the values of the parameters
ar_L=ar_L0*fac_L
ar_Ks=ar_Ks0*fac_Ks
ar_n_o=ar_n_o0*fac_n_o
ar_n_c=ar_n_c0*fac_n_c
#~~~~Computation of model parameters from physical parameters
ar_Vsm, ar_b_s, ar_b_o, ar_W, ar_b_c\
=pm.compute_cell_param(X,ar_Xc,Dt,alpha_s,alpha_o,alpha_c,nb_cell,\
A_thres,W_max,W_min,\
ar_lambda,ar_tan_beta,ar_tan_beta_channel,ar_L,\
ar_Ks,ar_theta_r,ar_theta_s,ar_n_o,ar_n_c,\
ar_A_drained)
#Read of data from the outputs of TOPKAPI in hdf5 format
ndar_Vs=np.array(ut.read_one_array_hdf(file_sim,'Soil','V_s'))
#Assign the variables
if variable==1:
im_out=os.path.join(path_out, 'field_Vs_')
tab=ndar_Vs
elif variable==2:
im_out=os.path.join(path_out, 'field_Vo_')
tab=ndar_Vo
elif variable==3:
im_out=os.path.join(path_out, 'field_Vo_bin_')
tab=ndar_Vo
tab[tab>0]=1.
elif variable==4:
im_out=os.path.join(path_out, 'field_SSI_')
tab=ndar_Vs/ar_Vsm*100.
# Plot the maps
for t in np.arange(int(t1),int(t2+1)):
print('Map time-step ', t)
image_out=im_out+ut.string(t,len(str(t2)))+'.png'
field_map_ndar(tab,t,ar_coorx,ar_coory,X,image_out,variable)
