source: NEMO/branches/2020/dev_r13648_ASINTER-04_laurent_bulk_ice/tests/STATION_ASF/EXPREF/plot_station_asf_old.py @ 13675

#!/usr/bin/env python3
# -*- Mode: Python; coding: utf-8; indent-tabs-mode: nil; tab-width: 4 -*-

# Post-diagnostic of STATION_ASF /  L. Brodeau, 2020

import sys
from os import path as path
import math
import numpy as nmp
from netCDF4 import Dataset
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.dates as mdates

cy1     = '2018' ; # First year
cy2     = '2018' ; # Last year

dir_figs='.'
size_fig=(13,7)
fig_ext='png'

clr_red = '#AD0000'
clr_sat = '#ffed00'
clr_mod = '#008ab8'

rDPI=200.

L_ALGOS = [ 'COARE3p6' , 'ECMWF'   , 'NCAR' ]
l_xtrns = [ '-noskin'  , '-noskin' ,  ''    ] ; # string to add to algo name (L_ALGOS) to get version without skin params turned on
l_color = [  '#ffed00' , '#008ab8' , '0.4'  ] ; # colors to differentiate algos on the plot
l_width = [     3      ,    2      ,  1     ] ; # line-width to differentiate algos on the plot
l_style = [    '-'     ,   '-'     , '--'   ] ; # line-style

L_VNEM  = [   'qla'     ,     'qsb'     ,     'qt'     ,   'qlw'     ,  'taum'     ,    'dt_skin'         ]
L_VARO  = [   'Qlat'    ,    'Qsen'     ,     'Qnet'   ,   'Qlw'     ,  'Tau'      ,    'dT_skin'         ] ; # name of variable on figure
L_VARL  = [ r'$Q_{lat}$', r'$Q_{sens}$' , r'$Q_{net}$' , r'$Q_{lw}$' , r'$|\tau|$' , r'$\Delta T_{skin}$' ] ; # name of variable in latex mode
L_VUNT  = [ r'$W/m^2$'  , r'$W/m^2$'    , r'$W/m^2$'   , r'$W/m^2$'  , r'$N/m^2$'  ,      'K'             ]
L_VMAX  = [     75.     ,     75.       ,    800.      ,     25.     ,    1.2      ,       0.7            ]
L_VMIN  = [   -250.     ,   -125.       ,   -400.      ,   -150.     ,    0.       ,      -0.7            ]
L_ANOM  = [   True      ,    True       ,    True      ,    True     ,   True      ,      False           ]

#L_VNEM  = [   'qlw'      ]
#L_VARO  = [   'Qlw'      ] ; # name of variable on figure
#L_VARL  = [ r'$Q_{lw}$'  ] ; # name of variable in latex mode
#L_VUNT  = [ r'$W/m^2$'   ]
#L_VMAX  = [     25.      ]
#L_VMIN  = [   -150.      ]
#L_ANOM  = [    True      ]



nb_algos = len(L_ALGOS) ; print(nb_algos)

# Getting arguments:
narg = len(sys.argv)
if narg != 2:
    print('Usage: '+sys.argv[0]+' <DIR_OUT_SASF>'); sys.exit(0)
cdir_data = sys.argv[1]



# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
# Populating and checking existence of files to be read
# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
def chck4f(cf):
    cmesg = 'ERROR: File '+cf+' does not exist !!!'
    if not path.exists(cf): print(cmesg); sys.exit(0)

###cf_in = nmp.empty((), dtype="S10")
cf_in = [] ; cf_in_ns = []
for ja in range(nb_algos):
    cfi = cdir_data+'/output/'+'STATION_ASF-'+L_ALGOS[ja]+'_1h_'+cy1+'0101_'+cy2+'1231_gridT.nc'
    chck4f(cfi)
    cf_in.append(cfi)
# Same but without skin params:
for ja in range(nb_algos):
    cfi = cdir_data+'/output/'+'STATION_ASF-'+L_ALGOS[ja]+l_xtrns[ja]+'_1h_'+cy1+'0101_'+cy2+'1231_gridT.nc'
    chck4f(cfi)
    cf_in_ns.append(cfi)
print('Files we are goin to use:')
for ja in range(nb_algos): print(cf_in[ja])
print('   --- same without cool-skin/warm-layer:')
for ja in range(nb_algos): print(cf_in_ns[ja])
#-----------------------------------------------------------------


# Getting time array from the first file:
id_in = Dataset(cf_in[0])
vt = id_in.variables['time_counter'][:]
cunit_t = id_in.variables['time_counter'].units ; print(' "time_counter" is in "'+cunit_t+'"')
id_in.close()
nbr = len(vt)




vtime = nmp.zeros(nbr)

vt = vt + 1036800. + 30.*60. # BUG!??? don't get why false in epoch to date conversion, and yet ncview gets it right!
for jt in range(nbr): vtime[jt] = mdates.epoch2num(vt[jt])

ii=nbr/300
ib=max(ii-ii%10,1)
xticks_d=int(30*ib)

font_inf = { 'fontname':'Open Sans', 'fontweight':'normal', 'fontsize':14 }

nb_var = len(L_VNEM)

xF  = nmp.zeros((nbr,nb_algos))
xFa = nmp.zeros((nbr,nb_algos))


for ctest in ['skin','noskin']:

    for jv in range(nb_var):
        print('\n *** Treating variable: '+L_VARO[jv]+' ('+ctest+') !')

        for ja in range(nb_algos):
            #
            if ctest == 'skin':   id_in = Dataset(cf_in[ja])
            if ctest == 'noskin': id_in = Dataset(cf_in_ns[ja])
            xF[:,ja] = id_in.variables[L_VNEM[jv]][:,1,1] # only the center point of the 3x3 spatial domain!
            if ja == 0: cvar_lnm = id_in.variables[L_VNEM[jv]].long_name
            id_in.close()

        fig = plt.figure(num = jv, figsize=size_fig, facecolor='w', edgecolor='k')

        ax1 = plt.axes([0.07, 0.22, 0.9, 0.75])

        ax1.set_xticks(vtime[::xticks_d])
        ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M:%S'))
        plt.xticks(rotation='60')

        for ja in range(nb_algos):
            plt.plot(vtime, xF[:,ja], '-', color=l_color[ja], linestyle=l_style[ja], linewidth=l_width[ja], label=L_ALGOS[ja], zorder=10+ja)

        ax1.set_ylim(L_VMIN[jv], L_VMAX[jv]) ; ax1.set_xlim(vtime[0],vtime[nbr-1])
        plt.ylabel(L_VARL[jv]+' ['+L_VUNT[jv]+']')

        ax1.grid(color='k', linestyle='-', linewidth=0.3)
        plt.legend(bbox_to_anchor=(0.45, 0.2), ncol=1, shadow=True, fancybox=True)
        ax1.annotate(cvar_lnm+' ('+ctest+')', xy=(0.3, 0.97), xycoords='axes fraction',  bbox={'facecolor':'w', 'alpha':1., 'pad':10}, zorder=50, **font_inf)
        plt.savefig(L_VARO[jv]+'_'+ctest+'.'+fig_ext, dpi=int(rDPI), transparent=False)
        plt.close(jv)



        if L_ANOM[jv]:

            for ja in range(nb_algos): xFa[:,ja] = xF[:,ja] - nmp.mean(xF,axis=1)

            if nmp.sum(xFa[:,:]) == 0.0:
                print('     Well! Seems that for variable '+L_VARO[jv]+', choice of algo has no impact a all!')
                print('          ==> skipping anomaly plot...')

            else:

                # Want a symetric y-range that makes sense for the anomaly we're looking at:
                rmax = nmp.max(xFa) ; rmin = nmp.min(xFa)
                rmax = max( abs(rmax) , abs(rmin) )
                romagn = math.floor(math.log(rmax, 10)) ; # order of magnitude of the anomaly  we're dealing with
                rmlt = 10.**(int(romagn)) / 2.
                yrng = math.copysign( math.ceil(abs(rmax)/rmlt)*rmlt , rmax)

                fig = plt.figure(num = 10+jv, figsize=size_fig, facecolor='w', edgecolor='k')
                ax1 = plt.axes([0.07, 0.22, 0.9, 0.75])

                ax1.set_xticks(vtime[::xticks_d])
                ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M:%S'))
                plt.xticks(rotation='60')

                for ja in range(nb_algos):
                    plt.plot(vtime, xFa[:,ja], '-', color=l_color[ja], linewidth=l_width[ja], label=L_ALGOS[ja], zorder=10+ja)

                ax1.set_ylim(-yrng,yrng) ; ax1.set_xlim(vtime[0],vtime[nbr-1])
                plt.ylabel(L_VARL[jv]+' ['+L_VUNT[jv]+']')
                ax1.grid(color='k', linestyle='-', linewidth=0.3)
                plt.legend(bbox_to_anchor=(0.45, 0.2), ncol=1, shadow=True, fancybox=True)
                ax1.annotate('Anomaly of '+cvar_lnm+' ('+ctest+')', xy=(0.3, 0.97), xycoords='axes fraction',  bbox={'facecolor':'w', 'alpha':1., 'pad':10}, zorder=50, **font_inf)
                plt.savefig(L_VARO[jv]+'_'+ctest+'_anomaly.'+fig_ext, dpi=int(rDPI), transparent=False)
                plt.close(10+jv)




# Difference skin vs noskin:
xFns = nmp.zeros((nbr,nb_algos))

for jv in range(nb_var-1):
    print('\n *** Treating variable: '+L_VARO[jv]+' ('+ctest+') !')

    for ja in range(nb_algos-1):
        id_in = Dataset(cf_in[ja])
        xF[:,ja]   = id_in.variables[L_VNEM[jv]][:,1,1] # only the center point of the 3x3 spatial domain!
        if ja == 0: cvar_lnm = id_in.variables[L_VNEM[jv]].long_name
        id_in.close()
        #
        id_in = Dataset(cf_in_ns[ja])
        xFns[:,ja] = id_in.variables[L_VNEM[jv]][:,1,1] # only the center point of the 3x3 spatial domain!
        if ja == 0: cvar_lnm = id_in.variables[L_VNEM[jv]].long_name
        id_in.close()

        xFa[:,ja] = xF[:,ja] - xFns[:,ja]  ; # difference!


    # Want a symetric y-range that makes sense for the anomaly we're looking at:
    rmax = nmp.max(xFa) ; rmin = nmp.min(xFa)
    rmax = max( abs(rmax) , abs(rmin) )
    romagn = math.floor(math.log(rmax, 10)) ; # order of magnitude of the anomaly  we're dealing with
    rmlt = 10.**(int(romagn)) / 2.
    yrng = math.copysign( math.ceil(abs(rmax)/rmlt)*rmlt , rmax)

    
    for ja in range(nb_algos-1):

        calgo = L_ALGOS[ja]

        if nmp.sum(xFa[:,ja]) == 0.0:
            print('     Well! Seems that for variable '+L_VARO[jv]+', and algo '+calgo+', skin param has no impact')
            print('          ==> skipping difference plot...')

        else:

            fig = plt.figure(num = jv, figsize=size_fig, facecolor='w', edgecolor='k')
            ax1 = plt.axes([0.07, 0.22, 0.9, 0.75])

            ax1.set_xticks(vtime[::xticks_d])
            ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M:%S'))
            plt.xticks(rotation='60')

            plt.plot(vtime, xFa[:,ja], '-', color=l_color[ja], linestyle=l_style[ja], linewidth=l_width[ja], label=None, zorder=10+ja)

            ax1.set_ylim(-yrng,yrng) ; ax1.set_xlim(vtime[0],vtime[nbr-1])
            plt.ylabel(L_VARL[jv]+' ['+L_VUNT[jv]+']')

            ax1.grid(color='k', linestyle='-', linewidth=0.3)
            #plt.legend(bbox_to_anchor=(0.45, 0.2), ncol=1, shadow=True, fancybox=True)
            ax1.annotate(cvar_lnm+' ('+ctest+')', xy=(0.3, 0.97), xycoords='axes fraction',  bbox={'facecolor':'w', 'alpha':1., 'pad':10}, zorder=50, **font_inf)
            plt.savefig('diff_skin-noskin_'+L_VARO[jv]+'_'+calgo+'_'+ctest+'.'+fig_ext, dpi=int(rDPI), transparent=False)
            plt.close(jv)