plot_modelspace_aggr.py

Go to the documentation of this file.

import os
import sys
import numpy
import numpy as np
from netCDF4 import Dataset
import matplotlib
matplotlib.use('pdf')
import matplotlib.cm as cm
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from copy import deepcopy
import basic_plot_functions as BasicPF
import plot_utils as pu
import var_utils as vu
import modelsp_utils as mu
 
def readdata():
 
   for metrics in mu.allFileStats:
       for varName in mu.varNames3d:
           for latBand in mu.latBands:
             for iexp, expName in enumerate(mu.expNames):
               xlabeltime  = []
               alldata = []
               alldiffdata = []
               alldiffdata_rmsdiv = []
               nc_file =  mu.expDirectory+'/'+mu.expLongNames[iexp]+'/FC2DIAG/expmgfs.nc'
               nc_fid = Dataset(nc_file, "r", format="NETCDF4")
               for fcTDelta in np.arange(0,mu.fcRange+mu.interval,mu.interval):
                   varNamesList = ['expmgfs_day'+str(fcTDelta)+'_'+ latBand +'_'+ varName + '_' + metrics]
                   # data: exp-GFSANA
                   data = np.array( nc_fid.variables[''.join(varNamesList)][:])
                   alldata = np.append(alldata, data)
                   xlabeltime = np.append(xlabeltime,fcTDelta)
 
               alldata = alldata.reshape(len(xlabeltime),len(data)).T
               ylevels = list(np.arange(0,len(data),1))
               varNamesListUse = 'expmgfs_fc_'+ latBand +'_'+ varName + '_' + metrics 
               if (iexp == 0):
                   arraylist = [alldata]
               else:
                   arraylist= arraylist + [alldata]
             dmin = np.amin(arraylist) #min(arraylist.all()) #np.amin(arraylist)
             dmax = np.amax(arraylist)
             nx = mu.nExp
             ny = 1 #nVars
             nsubplots = nx * ny
             subplot_size = 2.4
             aspect = 0.55
             FULL_SUBPLOT_LABELS =True
             interiorLabels = True
             fig = pu.setup_fig(nx, ny, subplot_size, aspect, FULL_SUBPLOT_LABELS)
             title_tmp = varNamesListUse #''.join(varNamesListAll[i])
             region = ''.join(title_tmp.split("_")[2:][:-2])
             var    = ''.join(title_tmp.split("_")[3:][:-1])
             stats  = ''.join(title_tmp.split("_")[4:])
             statDiagLabel = stats
             if (stats == 'Mean'):
                 signDefinite = False
             else:
                 signDefinite = True
 
             #print(region,var,stats)
             indepLabel = 'Vertical level'
             sciTicks = False
             invert_ind_axis = False
             iplot = 0
 
             for k in arraylist:
                valuemin = np.amin(k)
                valuemax = np.amax(k)
                title = mu.expNames[iplot]  +' var:'+vu.varDictModel[var][1]+'('+ vu.varDictModel[var][0]+')\
 '+region+' min=' +str(round(valuemin,3))+' max='+str(round(valuemax,3))
 
                BasicPF.plotTimeSeries2D( fig, \
                            xlabeltime,ylevels, k, \
                            title, statDiagLabel, \
                            sciTicks, signDefinite, \
                            indepLabel, invert_ind_axis, \
                            ny, nx, nsubplots, iplot, \
                            dmin = dmin, dmax = dmax, \
                            interiorLabels = interiorLabels )
                iplot = iplot +1
                filename = varNamesListUse+'_TS_2d'
                pu.finalize_fig(fig, filename, 'png', FULL_SUBPLOT_LABELS, True)
 
             #plot diff between target and control exp for RMS:
             if (mu.diff2exp == 'True' and varNamesListUse[-3:] == 'RMS'):
                 for iexp in range(1,mu.nExp):
                     #             target_exp - control_exp
                     alldiffdata = arraylist[iexp]-arraylist[0]
                     BasicPF.plotTimeserial2D(alldiffdata,xlabeltime,ylevels,varNamesListUse+mu.expNames[iexp]+'-RMS'+mu.expNames[0])
 
def main():
    readdata()
 
if __name__ == '__main__': main()