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 plot_sim_observed(hydrograph_fname, simulation_folder, file_Qobs,
outlet_ID):
'''
Parameters
----------
image_out: fname for image of hydrographs
file_Qobs: path to text file containing conserved value series
outlet_ID: outlet_ID
Returns
-------
list1 -> list, error checking parameters such as nash, rsme etc.
list2 -> list, Q_simulated
'''
import pytopkapi.utils as ut
from pytopkapi.results_analysis import plot_Qsim_Qobs_Rain as pt
import matplotlib.pyplot as plt
from matplotlib.dates import date2num
from datetime import datetime
file_Qsim = simulation_folder + "/results/results.h5"
group_name = 'sample_event'
Qobs = True
Pobs = False
nash = True
tab_col = ['k', 'r']
tab_style = ['-', '-']
tab_width = ['1', '1']
color_P = 'b'
transparency_P = 0.5 #(0 for invisible)
image_out = simulation_folder + '/results/calibration/' + hydrograph_fname + ".png"
if hydrograph_fname == '':
image_out = simulation_folder + '/results/calibration/Result_' + str(
datetime.now()).replace(':', '-')[:-7] + '.png'
#create path_out if it does'nt exist
ut.check_file_exist(image_out)
#Read the obs
#Qobs
ar_date, ar_Qobs = pt.read_observed_flow(file_Qobs)
delta = date2num(ar_date[1]) - date2num(ar_date[0])
# #Rain
# if Pobs:
# h5file = h5py.File(file_rain)
#
# dset_string = '/%s/rainfall' % group_name
# ndar_rain = h5file[dset_string][...]
#
# h5file.close()
# #Compute the mean catchment rainfall
# ar_rain=np.average(ndar_rain,axis=1)
#Read the simulated data Q
file_h5 = file_Qsim
ndar_Qc_out = ut.read_one_array_hdf(file_h5, 'Channel', 'Qc_out')
ar_Qsim = ndar_Qc_out[1:, outlet_ID]
##Graph
fig, ax = plt.subplots()
lines = []
tab_leg = []
if Qobs:
lines += ax.plot(ar_date,
ar_Qobs,
color=tab_col[-1],
linestyle=tab_style[-1],
linewidth=tab_width[-1])
tab_leg.append(('Observation'))
tab_leg = tab_leg[::-1] # extended slicing. This Reverses the order
lines += ax.plot(ar_date,
ar_Qsim,
color=tab_col[0],
linestyle=tab_style[0],
linewidth=tab_width[0])
tab_leg.append('Model')
if nash:
nash_value = ut.Nash(ar_Qsim, ar_Qobs)
lines += ax.plot(ar_date[0:1], ar_Qsim[0:1], 'w:')
tab_leg.append(('Eff = ' + str(nash_value)[0:5]))
RMSE = ut.RMSE(ar_Qsim, ar_Qobs)
lines += ax.plot(ar_date[0:1], ar_Qsim[0:1], 'w:')
tab_leg.append(('RMSE = ' + str(RMSE)[0:5]))
RMSE_norm = ut.RMSE_norm(ar_Qsim, ar_Qobs)
lines += ax.plot(ar_date[0:1], ar_Qsim[0:1], 'w:')
tab_leg.append(('RMSE_norm = ' + str(RMSE_norm)[0:5]))
Bias_cumul = ut.Bias_cumul(ar_Qsim, ar_Qobs)
lines += ax.plot(ar_date[0:1], ar_Qsim[0:1], 'w:')
tab_leg.append(('Bias_cumul = ' + str(Bias_cumul)[0:5]))
Diff_cumul = ut.Diff_cumul(ar_Qsim, ar_Qobs)
lines += ax.plot(ar_date[0:1], ar_Qsim[0:1], 'w:')
tab_leg.append(('Diff_cumul = ' + str(Diff_cumul)[0:5]))
Abs_cumul = ut.Abs_cumul(ar_Qsim, ar_Qobs)
lines += ax.plot(ar_date[0:1], ar_Qsim[0:1], 'w:')
tab_leg.append(('Abs_cumul = ' + str(Abs_cumul)[0:5]))
Err_cumul = ut.Err_cumul(ar_Qsim, ar_Qobs)
lines += ax.plot(ar_date[0:1], ar_Qsim[0:1], 'w:')
tab_leg.append(('Err_cumul = ' + str(Err_cumul)[0:5]))
# added to include all in the label
ax.set_xlim(ar_date[0], ar_date[-1])
ytitle = r'$Q \ (m^3/s)$'
ax.set_ylabel(ytitle, fontsize=18)
ax.set_title("Calib:Param_" + hydrograph_fname)
ax2 = ax.twinx()
# ax2.set_ylabel(r'$Rainfall \ (mm)$', fontsize=18, color=color_P)
# ax2.bar(ar_date, ar_rain, width=delta,
# facecolor='blue', edgecolor='blue', alpha=transparency_P)
# ax2.set_ylim(max(ar_rain)*2, min(ar_rain))
ax2.legend(lines, tab_leg, loc='upper right', fancybox=True)
leg = ax2.get_legend()
leg.get_frame().set_alpha(0.75)
for label in leg.get_texts():
label.set_fontsize('8')
# rotate and align the tick labels so they look better,
# unfortunately autofmt_xdate doesn't work with twinx due to a bug
# in matplotlib <= 1.0.0 so we do it manually
## fig.autofmt_xdate()
bottom = 0.2
rotation = 30
ha = 'right'
for ax in fig.get_axes():
if hasattr(ax, 'is_last_row') and ax.is_last_row():
for label in ax.get_xticklabels():
label.set_ha(ha)
label.set_rotation(rotation)
else:
for label in ax.get_xticklabels():
label.set_visible(False)
ax.set_xlabel('')
fig.subplots_adjust(bottom=bottom)
fig.savefig(image_out)
#plt.show()
# RMSE= ut.RMSE(ar_Qsim,ar_Qobs)
# RMSE_norm = ut.RMSE_norm(ar_Qsim,ar_Qobs)
# Bias_cumul= ut.Bias_cumul(ar_Qsim,ar_Qobs)
# Diff_cumul= ut.Diff_cumul(ar_Qsim,ar_Qobs)
# Abs_cumul = ut.Abs_cumul(ar_Qsim,ar_Qobs)
# Err_cumul = ut.Err_cumul(ar_Qsim,ar_Qobs)
error_checking_param = [
str(nash_value)[0:5],
str(RMSE)[0:5],
str(RMSE_norm)[0:5],
str(Bias_cumul)[0:5],
str(Diff_cumul)[0:5],
str(Abs_cumul)[0:5],
str(Err_cumul)[0:5]
]
return error_checking_param, ar_Qsim
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 mean_simuVsi(ini_file='mean_simuVsi.ini'):
"""
* Objective
"""
### READ THE INI FILE ###
config.read(ini_file)
print('Read the file ',ini_file)
##~~~~~~ file_in ~~~~~~##
file_in=config.get('file_in','file_in')
file_in_global=config.get('file_in','file_in_global')
file_h5=config.get('file_in','file_h5')
##~~~~~~ file_out ~~~~~~##
file_out=config.get('file_out','file_out')
##~~~~~~ variables ~~~~~~##
mean_pVs_t0=config.getfloat('variables','mean_pVs_t0')
fac_L_simu=config.getfloat('variables','fac_L_simu')
fac_Ks_simu=config.getfloat('variables','fac_Ks_simu')
fac_n_o_simu=config.getfloat('variables','fac_n_o_simu')
fac_n_c_simu=config.getfloat('variables','fac_n_c_simu')
##~~~~~~ flags ~~~~~~##
nb_param=config.getfloat('flags','nb_param')
#--Read and compute the parameters to have the values of parameter and ar_Vsm
#~~~~Read Global parameters file
print('Pretreatment of input data')
#~~~~Read Global parameters file
X,Dt,alpha_s,alpha_o,alpha_c,A_thres,W_min,W_max\
=pm.read_global_parameters(file_in_global)
#~~~~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_L,ar_Ks,\
ar_theta_r,ar_theta_s,ar_n_o,ar_n_c,\
ar_cell_down,ar_pVs_t0,ar_Vo_t0,ar_Qc_t0,ar_kc\
=pm.read_cell_parameters(file_in)
#~~~~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_L1=ar_L*fac_L_simu
ar_Ks1=ar_Ks*fac_Ks_simu
ar_n_o1=ar_n_o*fac_n_o_simu
ar_n_c1=ar_n_c*fac_n_c_simu
#~~~~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_L1,\
ar_Ks1,ar_theta_r,ar_theta_s,ar_n_o1,ar_n_c1,\
ar_A_drained)
#Read the soil volume file
ndar_Vs=np.array(ut.read_one_array_hdf(file_h5,'Soil','V_s'))
#Read the file of catchment saturation rates
ndar_Vs_sat=ndar_Vs/ar_Vsm*100.
tab_rate=np.average(ndar_Vs_sat,axis=1)
tab_rate_sort=np.sort(tab_rate)
indice_sort=np.argsort(tab_rate)
#Look for the rate closest to the expected mean value
if mean_pVs_t0<tab_rate_sort[0]:
ind=0
print('mean_pVs_t0 expected:', mean_pVs_t0,'effective:',tab_rate_sort[0])
elif mean_pVs_t0>tab_rate_sort[-1]:
ind=-1
print('mean_pVs_t0 expected:', mean_pVs_t0,' effective',tab_rate_sort[-1])
else:
loop=True
i=-1
while loop:
i=i+1
if mean_pVs_t0>=tab_rate_sort[i] and mean_pVs_t0<tab_rate_sort[i+1]:
ind=i
loop=False
print('mean_pVs_t0 expected:', mean_pVs_t0,' effective:',tab_rate_sort[i])
ind_end=indice_sort[ind]
print(ind,ind_end)
ar_Vs=ndar_Vs[ind_end,:]
ar_Vsi=ar_Vs/ar_Vsm*100.
print(ar_Vsi)
ar_pVs_t0=ar_Vsi
#~~~~~~Write parameter file~~~~~~#
tab_param=np.zeros((len(ar_cell_label),nb_param))
tab_param[:,0]=ar_cell_label
tab_param[:,1]=ar_coorx
tab_param[:,2]=ar_coory
tab_param[:,3]=ar_lambda
tab_param[:,4]=ar_Xc
tab_param[:,5]=ar_dam
tab_param[:,6]=ar_tan_beta
tab_param[:,7]=ar_tan_beta_channel
tab_param[:,8]=ar_L
tab_param[:,9]=ar_Ks
tab_param[:,10]=ar_theta_r
tab_param[:,11]=ar_theta_s
tab_param[:,12]=ar_n_o
tab_param[:,13]=ar_n_c
tab_param[:,14]=ar_cell_down
tab_param[:,15]=ar_pVs_t0
tab_param[:,16]=ar_Vo_t0
tab_param[:,17]=ar_Qc_t0
tab_param[:,18]=ar_kc
np.savetxt(file_out, tab_param)
def run(ini_file='plot_Qsim_Qobs_Rain.ini'):
config = SafeConfigParser()
config.read(ini_file)
print('Read the file ', ini_file)
file_Qsim = config.get('files', 'file_Qsim')
file_Qobs = config.get('files', 'file_Qobs')
file_rain = config.get('files', 'file_rain')
image_out = config.get('files', 'image_out')
group_name = config.get('groups', 'group_name')
Qobs = config.getboolean('flags', 'Qobs')
Pobs = config.getboolean('flags', 'Pobs')
nash = config.getboolean('flags', 'nash')
tab_col = ['k', 'r']
tab_style = ['-', '-']
tab_width = ['1', '1']
color_P = 'b'
transparency_P = 0.5 #(0 for invisible)
#create path_out if it does'nt exist
ut.check_file_exist(image_out)
#Read the obs
#Qobs
ar_date, ar_Qobs = read_observed_flow(file_Qobs)
delta = date2num(ar_date[1]) - date2num(ar_date[0])
#Rain
if Pobs:
h5file = h5py.File(file_rain)
dset_string = '/%s/rainfall' % group_name
ndar_rain = h5file[dset_string][...]
h5file.close()
#Compute the mean catchment rainfall
ar_rain = np.average(ndar_rain, axis=1)
#Read the simulated data Q
file_h5 = file_Qsim
ndar_Qc_out = ut.read_one_array_hdf(file_h5, 'Channel', 'Qc_out')
ar_Qsim = ndar_Qc_out[1:, 0]
##Graph
fig, ax = plt.subplots()
lines = []
tab_leg = []
if Qobs:
lines += ax.plot(ar_date,
ar_Qobs,
color=tab_col[-1],
linestyle=tab_style[-1],
linewidth=tab_width[-1])
tab_leg.append(('Observation'))
tab_leg = tab_leg[::-1]
lines += ax.plot(ar_date,
ar_Qsim,
color=tab_col[0],
linestyle=tab_style[0],
linewidth=tab_width[0])
tab_leg.append('Model')
if nash:
nash_value = ut.Nash(ar_Qsim, ar_Qobs)
lines += ax.plot(ar_date[0:1], ar_Qsim[0:1], 'w:')
tab_leg.append(('Eff = ' + str(nash_value)[0:5]))
ax.set_xlim(ar_date[0], ar_date[-1])
ytitle = r'$Q \ (m^3/s)$'
ax.set_ylabel(ytitle, fontsize=18)
ax.set_title(group_name)
ax2 = ax.twinx()
ax2.set_ylabel(r'$Rainfall \ (mm)$', fontsize=18, color=color_P)
ax2.bar(ar_date,
ar_rain,
width=delta,
facecolor='blue',
edgecolor='blue',
alpha=transparency_P)
ax2.set_ylim(max(ar_rain) * 2, min(ar_rain))
ax2.legend(lines, tab_leg, loc='upper right', fancybox=True)
leg = ax2.get_legend()
leg.get_frame().set_alpha(0.75)
# rotate and align the tick labels so they look better,
# unfortunately autofmt_xdate doesn't work with twinx due to a bug
# in matplotlib <= 1.0.0 so we do it manually
## fig.autofmt_xdate()
bottom = 0.2
rotation = 30
ha = 'right'
for ax in fig.get_axes():
if hasattr(ax, 'is_last_row') and ax.is_last_row():
for label in ax.get_xticklabels():
label.set_ha(ha)
label.set_rotation(rotation)
else:
for label in ax.get_xticklabels():
label.set_visible(False)
ax.set_xlabel('')
fig.subplots_adjust(bottom=bottom)
fig.savefig(image_out)
plt.show()
def initial_pVs_Vo_Qc_from_simu(ini_file='initial_pVs_Vo_Qc_from_simu.ini'):
"""
* Objective
"""
### READ THE INI FILE ###
config.read(ini_file)
print('Read the file ',ini_file)
##~~~~~~ file_in ~~~~~~##
file_in=config.get('file_in','file_in')
file_in_global=config.get('file_in','file_in_global')
file_h5=config.get('file_in','file_h5')
##~~~~~~ file_out ~~~~~~##
file_out=config.get('file_out','file_out')
##~~~~~~ variables ~~~~~~##
time_step=config.getint('variables','time_step')
fac_L_simu=config.getfloat('variables','fac_L_simu')
fac_Ks_simu=config.getfloat('variables','fac_Ks_simu')
fac_n_o_simu=config.getfloat('variables','fac_n_o_simu')
fac_n_c_simu=config.getfloat('variables','fac_n_c_simu')
##~~~~~~ flags ~~~~~~##
nb_param=config.getfloat('flags','nb_param')
#--Read and compute the parameters to have the values of parameter and ar_Vsm
#~~~~Read Global parameters file
#~~~~Read Global parameters file
X,Dt,alpha_s,alpha_o,alpha_c,A_thres,W_min,W_max\
=pm.read_global_parameters(file_in_global)
#~~~~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_L,ar_Ks,\
ar_theta_r,ar_theta_s,ar_n_o,ar_n_c,\
ar_cell_down,ar_pVs_t0,ar_Vo_t0,ar_Qc_t0,ar_kc\
=pm.read_cell_parameters(file_in)
#~~~~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_L1=ar_L*fac_L_simu
ar_Ks1=ar_Ks*fac_Ks_simu
ar_n_o1=ar_n_o*fac_n_o_simu
ar_n_c1=ar_n_c*fac_n_c_simu
#~~~~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_L1,\
ar_Ks1,ar_theta_r,ar_theta_s,ar_n_o1,ar_n_c1,\
ar_A_drained)
#Read the soil volume file
ndar_Vs=np.array(ut.read_one_array_hdf(file_h5,'Soil','V_s'))
#Read the overland volume file
ndar_Vo=np.array(ut.read_one_array_hdf(file_h5,'Overland','V_o'))
#Read the channel dischargefile
ndar_Qc=np.array(ut.read_one_array_hdf(file_h5,'Channel','Qc_out'))
ar_Vs=ndar_Vs[time_step,:]
ar_pVs_t0=ar_Vs/ar_Vsm*100.
ar_Vo_t0=ndar_Vo[time_step,:]
ar_Qc_t0=ndar_Qc[time_step,:]
#~~~~~~Write parameter file~~~~~~#
tab_param=np.zeros((len(ar_cell_label),nb_param))
tab_param[:,0]=ar_cell_label
tab_param[:,1]=ar_coorx
tab_param[:,2]=ar_coory
tab_param[:,3]=ar_lambda
tab_param[:,4]=ar_Xc
tab_param[:,5]=ar_dam
tab_param[:,6]=ar_tan_beta
tab_param[:,7]=ar_tan_beta_channel
tab_param[:,8]=ar_L
tab_param[:,9]=ar_Ks
tab_param[:,10]=ar_theta_r
tab_param[:,11]=ar_theta_s
tab_param[:,12]=ar_n_o
tab_param[:,13]=ar_n_c
tab_param[:,14]=ar_cell_down
tab_param[:,15]=ar_pVs_t0
tab_param[:,16]=ar_Vo_t0
tab_param[:,17]=ar_Qc_t0
tab_param[:,18]=ar_kc
np.savetxt(file_out, tab_param)
def run(ini_file='plot_Qsim_Qobs_Rain.ini'):
config = SafeConfigParser()
config.read(ini_file)
print 'Read the file ',ini_file
file_Qsim=config.get('files','file_Qsim')
file_Qobs=config.get('files','file_Qobs')
file_rain=config.get('files','file_rain')
image_out=config.get('files','image_out')
group_name=config.get('groups','group_name')
Qobs=config.getboolean('flags','Qobs')
Pobs=config.getboolean('flags','Pobs')
nash=config.getboolean('flags','nash')
tab_col=['k','r']
tab_style=['-','-']
tab_width=['1','1']
color_P='b'
transparency_P=0.5#(0 for invisible)
#create path_out if it does'nt exist
ut.check_file_exist(image_out)
#Read the obs
#Qobs
ar_date, ar_Qobs = read_observed_flow(file_Qobs)
delta = date2num(ar_date[1]) - date2num(ar_date[0])
#Rain
if Pobs:
h5file_in=h5.openFile(file_rain,mode='r')
group='/'+group_name+'/'
node = h5file_in.getNode(group+'rainfall')
ndar_rain=node.read()
h5file_in.close()
#Compute the mean catchment rainfall
ar_rain=np.average(ndar_rain,axis=1)
#Read the simulated data Q
file_h5=file_Qsim
ndar_Qc_out=ut.read_one_array_hdf(file_h5,'/Channel/','Qc_out')
ar_Qsim=ndar_Qc_out[1:,0]
##Graph
fig, ax = plt.subplots()
lines = []
tab_leg = []
if Qobs:
lines += ax.plot(ar_date, ar_Qobs,
color=tab_col[-1],
linestyle=tab_style[-1], linewidth=tab_width[-1])
tab_leg.append(('Observation'))
tab_leg = tab_leg[::-1]
lines += ax.plot(ar_date, ar_Qsim,
color=tab_col[0],
linestyle=tab_style[0], linewidth=tab_width[0])
tab_leg.append('Model')
if nash:
nash_value = ut.Nash(ar_Qsim,ar_Qobs)
lines += ax.plot(ar_date[0:1], ar_Qsim[0:1], 'w:')
tab_leg.append(('Eff = '+str(nash_value)[0:5]))
ax.set_xlim(ar_date[0], ar_date[-1])
ytitle=r'$Q \ (m^3/s)$'
ax.set_ylabel(ytitle, fontsize=18)
ax.set_title(group_name)
ax2 = ax.twinx()
ax2.set_ylabel(r'$Rainfall \ (mm)$', fontsize=18, color=color_P)
ax2.bar(ar_date, ar_rain, width=delta,
facecolor='blue', edgecolor='blue', alpha=transparency_P)
ax2.set_ylim(max(ar_rain)*2, min(ar_rain))
ax2.legend(lines, tab_leg, loc='upper right', fancybox=True)
leg = ax2.get_legend()
leg.get_frame().set_alpha(0.75)
# rotate and align the tick labels so they look better,
# unfortunately autofmt_xdate doesn't work with twinx due to a bug
# in matplotlib <= 1.0.0 so we do it manually
## fig.autofmt_xdate()
bottom=0.2
rotation=30
ha='right'
for ax in fig.get_axes():
if hasattr(ax, 'is_last_row') and ax.is_last_row():
for label in ax.get_xticklabels():
label.set_ha(ha)
label.set_rotation(rotation)
else:
for label in ax.get_xticklabels():
label.set_visible(False)
ax.set_xlabel('')
fig.subplots_adjust(bottom=bottom)
fig.savefig(image_out)
plt.show()
def mean_simuVsi(ini_file='mean_simuVsi.ini'):
"""
* Objective
"""
### READ THE INI FILE ###
config.read(ini_file)
print 'Read the file ', ini_file
##~~~~~~ file_in ~~~~~~##
file_in = config.get('file_in', 'file_in')
file_in_global = config.get('file_in', 'file_in_global')
file_h5 = config.get('file_in', 'file_h5')
##~~~~~~ file_out ~~~~~~##
file_out = config.get('file_out', 'file_out')
##~~~~~~ variables ~~~~~~##
mean_pVs_t0 = config.getfloat('variables', 'mean_pVs_t0')
fac_L_simu = config.getfloat('variables', 'fac_L_simu')
fac_Ks_simu = config.getfloat('variables', 'fac_Ks_simu')
fac_n_o_simu = config.getfloat('variables', 'fac_n_o_simu')
fac_n_c_simu = config.getfloat('variables', 'fac_n_c_simu')
##~~~~~~ flags ~~~~~~##
nb_param = config.getfloat('flags', 'nb_param')
#--Read and compute the parameters to have the values of parameter and ar_Vsm
#~~~~Read Global parameters file
print 'Pretreatment of input data'
#~~~~Read Global parameters file
X,Dt,alpha_s,alpha_o,alpha_c,A_thres,W_min,W_max\
=pm.read_global_parameters(file_in_global)
#~~~~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_L,ar_Ks,\
ar_theta_r,ar_theta_s,ar_n_o,ar_n_c,\
ar_cell_down,ar_pVs_t0,ar_Vo_t0,ar_Qc_t0,ar_kc\
=pm.read_cell_parameters(file_in)
#~~~~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_L1 = ar_L * fac_L_simu
ar_Ks1 = ar_Ks * fac_Ks_simu
ar_n_o1 = ar_n_o * fac_n_o_simu
ar_n_c1 = ar_n_c * fac_n_c_simu
#~~~~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_L1,\
ar_Ks1,ar_theta_r,ar_theta_s,ar_n_o1,ar_n_c1,\
ar_A_drained)
#Read the soil volume file
ndar_Vs = np.array(ut.read_one_array_hdf(file_h5, '/Soil/', 'V_s'))
#Read the file of catchment saturation rates
ndar_Vs_sat = ndar_Vs / ar_Vsm * 100.
tab_rate = np.average(ndar_Vs_sat, axis=1)
tab_rate_sort = np.sort(tab_rate)
indice_sort = np.argsort(tab_rate)
#Look for the rate closest to the expected mean value
if mean_pVs_t0 < tab_rate_sort[0]:
ind = 0
print 'mean_pVs_t0 expected:', mean_pVs_t0, 'effective:', tab_rate_sort[
0]
elif mean_pVs_t0 > tab_rate_sort[-1]:
ind = -1
print 'mean_pVs_t0 expected:', mean_pVs_t0, ' effective', tab_rate_sort[
-1]
else:
loop = True
i = -1
while loop:
i = i + 1
if mean_pVs_t0 >= tab_rate_sort[i] and mean_pVs_t0 < tab_rate_sort[
i + 1]:
ind = i
loop = False
print 'mean_pVs_t0 expected:', mean_pVs_t0, ' effective:', tab_rate_sort[
i]
ind_end = indice_sort[ind]
print ind, ind_end
ar_Vs = ndar_Vs[ind_end, :]
ar_Vsi = ar_Vs / ar_Vsm * 100.
print ar_Vsi
ar_pVs_t0 = ar_Vsi
#~~~~~~Write parameter file~~~~~~#
tab_param = np.zeros((len(ar_cell_label), nb_param))
tab_param[:, 0] = ar_cell_label
tab_param[:, 1] = ar_coorx
tab_param[:, 2] = ar_coory
tab_param[:, 3] = ar_lambda
tab_param[:, 4] = ar_Xc
tab_param[:, 5] = ar_dam
tab_param[:, 6] = ar_tan_beta
tab_param[:, 7] = ar_tan_beta_channel
tab_param[:, 8] = ar_L
tab_param[:, 9] = ar_Ks
tab_param[:, 10] = ar_theta_r
tab_param[:, 11] = ar_theta_s
tab_param[:, 12] = ar_n_o
tab_param[:, 13] = ar_n_c
tab_param[:, 14] = ar_cell_down
tab_param[:, 15] = ar_pVs_t0
tab_param[:, 16] = ar_Vo_t0
tab_param[:, 17] = ar_Qc_t0
tab_param[:, 18] = ar_kc
np.savetxt(file_out, tab_param)
def initial_pVs_Vo_Qc_from_simu(ini_file='initial_pVs_Vo_Qc_from_simu.ini'):
"""
* Objective
"""
### READ THE INI FILE ###
config.read(ini_file)
print 'Read the file ', ini_file
##~~~~~~ file_in ~~~~~~##
file_in = config.get('file_in', 'file_in')
file_in_global = config.get('file_in', 'file_in_global')
file_h5 = config.get('file_in', 'file_h5')
##~~~~~~ file_out ~~~~~~##
file_out = config.get('file_out', 'file_out')
##~~~~~~ variables ~~~~~~##
time_step = config.getint('variables', 'time_step')
fac_L_simu = config.getfloat('variables', 'fac_L_simu')
fac_Ks_simu = config.getfloat('variables', 'fac_Ks_simu')
fac_n_o_simu = config.getfloat('variables', 'fac_n_o_simu')
fac_n_c_simu = config.getfloat('variables', 'fac_n_c_simu')
##~~~~~~ flags ~~~~~~##
nb_param = config.getfloat('flags', 'nb_param')
#--Read and compute the parameters to have the values of parameter and ar_Vsm
#~~~~Read Global parameters file
#~~~~Read Global parameters file
X,Dt,alpha_s,alpha_o,alpha_c,A_thres,W_min,W_max\
=pm.read_global_parameters(file_in_global)
#~~~~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_L,ar_Ks,\
ar_theta_r,ar_theta_s,ar_n_o,ar_n_c,\
ar_cell_down,ar_pVs_t0,ar_Vo_t0,ar_Qc_t0,ar_kc\
=pm.read_cell_parameters(file_in)
#~~~~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_L1 = ar_L * fac_L_simu
ar_Ks1 = ar_Ks * fac_Ks_simu
ar_n_o1 = ar_n_o * fac_n_o_simu
ar_n_c1 = ar_n_c * fac_n_c_simu
#~~~~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_L1,\
ar_Ks1,ar_theta_r,ar_theta_s,ar_n_o1,ar_n_c1,\
ar_A_drained)
#Read the soil volume file
ndar_Vs = np.array(ut.read_one_array_hdf(file_h5, '/Soil/', 'V_s'))
#Read the overland volume file
ndar_Vo = np.array(ut.read_one_array_hdf(file_h5, '/Overland/', 'V_o'))
#Read the channel dischargefile
ndar_Qc = np.array(ut.read_one_array_hdf(file_h5, '/Channel/', 'Qc_out'))
ar_Vs = ndar_Vs[time_step, :]
ar_pVs_t0 = ar_Vs / ar_Vsm * 100.
ar_Vo_t0 = ndar_Vo[time_step, :]
ar_Qc_t0 = ndar_Qc[time_step, :]
#~~~~~~Write parameter file~~~~~~#
tab_param = np.zeros((len(ar_cell_label), nb_param))
tab_param[:, 0] = ar_cell_label
tab_param[:, 1] = ar_coorx
tab_param[:, 2] = ar_coory
tab_param[:, 3] = ar_lambda
tab_param[:, 4] = ar_Xc
tab_param[:, 5] = ar_dam
tab_param[:, 6] = ar_tan_beta
tab_param[:, 7] = ar_tan_beta_channel
tab_param[:, 8] = ar_L
tab_param[:, 9] = ar_Ks
tab_param[:, 10] = ar_theta_r
tab_param[:, 11] = ar_theta_s
tab_param[:, 12] = ar_n_o
tab_param[:, 13] = ar_n_c
tab_param[:, 14] = ar_cell_down
tab_param[:, 15] = ar_pVs_t0
tab_param[:, 16] = ar_Vo_t0
tab_param[:, 17] = ar_Qc_t0
tab_param[:, 18] = ar_kc
np.savetxt(file_out, tab_param)
def plot_sim_observed(hydrograph_fname, simulation_folder, file_Qobs, outlet_ID):
'''
Parameters
----------
image_out: fname for image of hydrographs
file_Qobs: path to text file containing conserved value series
outlet_ID: outlet_ID
Returns
-------
list1 -> list, error checking parameters such as nash, rsme etc.
list2 -> list, Q_simulated
'''
import pytopkapi.utils as ut
from pytopkapi.results_analysis import plot_Qsim_Qobs_Rain as pt
import matplotlib.pyplot as plt
from matplotlib.dates import date2num
from datetime import datetime
file_Qsim= simulation_folder + "/results/results.h5"
group_name= 'sample_event'
Qobs= True
Pobs= False
nash= True
tab_col=['k','r']
tab_style=['-','-']
tab_width=['1','1']
color_P='b'
transparency_P=0.5#(0 for invisible)
image_out = simulation_folder +'/results/calibration/'+ hydrograph_fname+".png"
if hydrograph_fname == '':
image_out = simulation_folder+'/results/calibration/Result_'+ str(datetime.now()).replace(':','-')[:-7] + '.png'
#create path_out if it does'nt exist
ut.check_file_exist(image_out)
#Read the obs
#Qobs
ar_date, ar_Qobs = pt.read_observed_flow(file_Qobs)
delta = date2num(ar_date[1]) - date2num(ar_date[0])
# #Rain
# if Pobs:
# h5file = h5py.File(file_rain)
#
# dset_string = '/%s/rainfall' % group_name
# ndar_rain = h5file[dset_string][...]
#
# h5file.close()
# #Compute the mean catchment rainfall
# ar_rain=np.average(ndar_rain,axis=1)
#Read the simulated data Q
file_h5=file_Qsim
ndar_Qc_out=ut.read_one_array_hdf(file_h5,'Channel','Qc_out')
ar_Qsim=ndar_Qc_out[1:,outlet_ID]
##Graph
fig, ax = plt.subplots()
lines = []
tab_leg = []
if Qobs:
lines += ax.plot(ar_date, ar_Qobs,
color=tab_col[-1],
linestyle=tab_style[-1], linewidth=tab_width[-1])
tab_leg.append(('Observation'))
tab_leg = tab_leg[::-1] # extended slicing. This Reverses the order
lines += ax.plot(ar_date, ar_Qsim,
color=tab_col[0],
linestyle=tab_style[0], linewidth=tab_width[0])
tab_leg.append('Model')
if nash:
nash_value = ut.Nash(ar_Qsim,ar_Qobs)
lines += ax.plot(ar_date[0:1], ar_Qsim[0:1], 'w:')
tab_leg.append(('Eff = '+str(nash_value)[0:5]))
RMSE = ut.RMSE(ar_Qsim, ar_Qobs) ;lines += ax.plot(ar_date[0:1], ar_Qsim[0:1], 'w:') ; tab_leg.append(('RMSE = '+str(RMSE)[0:5]))
RMSE_norm = ut.RMSE_norm(ar_Qsim, ar_Qobs) ;lines += ax.plot(ar_date[0:1], ar_Qsim[0:1], 'w:') ; tab_leg.append(('RMSE_norm = '+str(RMSE_norm)[0:5]))
Bias_cumul = ut.Bias_cumul(ar_Qsim, ar_Qobs) ; lines += ax.plot(ar_date[0:1], ar_Qsim[0:1], 'w:') ; tab_leg.append(('Bias_cumul = '+str(Bias_cumul)[0:5]))
Diff_cumul = ut.Diff_cumul(ar_Qsim, ar_Qobs) ; lines += ax.plot(ar_date[0:1], ar_Qsim[0:1], 'w:'); tab_leg.append(('Diff_cumul = '+str(Diff_cumul)[0:5]))
Abs_cumul = ut.Abs_cumul(ar_Qsim, ar_Qobs) ; lines += ax.plot(ar_date[0:1], ar_Qsim[0:1], 'w:'); tab_leg.append(('Abs_cumul = '+str(Abs_cumul)[0:5]))
Err_cumul = ut.Err_cumul(ar_Qsim, ar_Qobs) ; lines += ax.plot(ar_date[0:1], ar_Qsim[0:1], 'w:'); tab_leg.append(('Err_cumul = '+str(Err_cumul)[0:5]))
# added to include all in the label
ax.set_xlim(ar_date[0], ar_date[-1])
ytitle=r'$Q \ (m^3/s)$'
ax.set_ylabel(ytitle, fontsize=18)
ax.set_title("Calib:Param_"+hydrograph_fname)
ax2 = ax.twinx()
# ax2.set_ylabel(r'$Rainfall \ (mm)$', fontsize=18, color=color_P)
# ax2.bar(ar_date, ar_rain, width=delta,
# facecolor='blue', edgecolor='blue', alpha=transparency_P)
# ax2.set_ylim(max(ar_rain)*2, min(ar_rain))
ax2.legend(lines, tab_leg, loc='upper right', fancybox=True)
leg = ax2.get_legend()
leg.get_frame().set_alpha(0.75)
for label in leg.get_texts():
label.set_fontsize('8')
# rotate and align the tick labels so they look better,
# unfortunately autofmt_xdate doesn't work with twinx due to a bug
# in matplotlib <= 1.0.0 so we do it manually
## fig.autofmt_xdate()
bottom=0.2
rotation=30
ha='right'
for ax in fig.get_axes():
if hasattr(ax, 'is_last_row') and ax.is_last_row():
for label in ax.get_xticklabels():
label.set_ha(ha)
label.set_rotation(rotation)
else:
for label in ax.get_xticklabels():
label.set_visible(False)
ax.set_xlabel('')
fig.subplots_adjust(bottom=bottom)
fig.savefig(image_out)
#plt.show()
# RMSE= ut.RMSE(ar_Qsim,ar_Qobs)
# RMSE_norm = ut.RMSE_norm(ar_Qsim,ar_Qobs)
# Bias_cumul= ut.Bias_cumul(ar_Qsim,ar_Qobs)
# Diff_cumul= ut.Diff_cumul(ar_Qsim,ar_Qobs)
# Abs_cumul = ut.Abs_cumul(ar_Qsim,ar_Qobs)
# Err_cumul = ut.Err_cumul(ar_Qsim,ar_Qobs)
error_checking_param = [str(nash_value)[0:5], str(RMSE)[0:5],str(RMSE_norm)[0:5],str(Bias_cumul)[0:5],str(Diff_cumul)[0:5],str(Abs_cumul)[0:5],str(Err_cumul)[0:5]]
return error_checking_param, ar_Qsim
