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)
Beispiel #2
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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)
Beispiel #3
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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)
Beispiel #4
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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)
Beispiel #5
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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)
Beispiel #6
0
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