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(ar_field, ar_coorx, ar_coory, X, picture_out, title, flip=0):
"""
Plot an overview depicting the location of the subcatchment.
"""
max_val=max(ar_field)
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
if flip==1:
ar_map=np.flipud(ar_map)
ar_map2 = ma.masked_where(ar_map <0, ar_map)
ut.check_file_exist(picture_out)
pl.clf()
pl.imshow(ar_map2, interpolation='Nearest',
origin='lower', vmax=max_val,vmin=0)
pl.title(title)
pl.colorbar()
pl.savefig(picture_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 field_map(ar_field,ar_coorx,ar_coory,X,image_out,title,flip=0,min_val=0.,max_val=0.):
import pylab as pl
import numpy.ma as M
#max_val=max(ar_field)
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
if flip==1:
ar_map=np.flipud(ar_map)
ar_map2 = M.masked_where(ar_map <0, ar_map)
ut.check_file_exist(image_out)
pl.clf()
pl.imshow(ar_map2,interpolation='Nearest',origin='lower',vmax=max_val,vmin=min_val)
pl.title(title)
pl.colorbar()
pl.savefig(image_out)
def field_map2(ar_field,
ar_coorx,
ar_coory,
X,
image_out,
title,
flip=0,
min_val=0.,
max_val=0.):
import pylab as pl
import numpy.ma as M
#max_val=max(ar_field)
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
if flip == 1:
ar_map = np.flipud(ar_map)
ar_map2 = ar_map
ut.check_file_exist(image_out)
pl.clf()
pl.imshow(ar_map2,
interpolation='Nearest',
origin='lower',
vmax=max_val,
vmin=min_val)
pl.title(title)
pl.colorbar()
pl.savefig(image_out)
def field_map(ar_field, ar_coorx, ar_coory, X, picture_out, title, flip=0):
"""
Plot an overview depicting the location of the subcatchment.
"""
max_val = max(ar_field)
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
if flip == 1:
ar_map = np.flipud(ar_map)
ar_map2 = ma.masked_where(ar_map < 0, ar_map)
ut.check_file_exist(picture_out)
pl.clf()
pl.imshow(ar_map2,
interpolation='Nearest',
origin='lower',
vmax=max_val,
vmin=0)
pl.title(title)
pl.colorbar()
pl.savefig(picture_out)
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_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 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 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
