Merge branch 'master' of m:/git/pirata

example/hoff_muller_diagram.py

+import netCDF4 as nc
+
+ from netCDF4 import Dataset
+
+ import numpy as np
+
+ import matplotlib.pyplot as plt
+
+ import matplotlib.dates as dplt
+
+ from pylab import *
+
+ import os, sys
+
+ mask = 
+Dataset('/ccc/scratch/cont005/legos/nivertfl/VALIDATION_F01/INPUT_SAISON/HOFF-MULLER/total.nc', 
+mode='r')
+ trop = 
+Dataset('/ccc/scratch/cont005/legos/nivertfl/VALIDATION_F01/INPUT_SAISON/HOFF-MULLER/sst_tropflux_1d_2000-regrid.nc', 
+mode='r')
+ mask1 = mask.variables['votemper'][:,0,:,:]
+ trop1 = trop.variables['sst'][:,:,:]
+ zbla=np.zeros(shape=(301,1))
+ zblo=np.zeros(shape=(301,1))
+ zbla2=np.zeros(shape=(301,366))
+ zblo2=np.zeros(shape=(301,366))
+ time = mask.variables['time_counter'][:]
+
+ lon = mask.variables['nav_lon'][0,:]
+ mask1[mask1 == 0] = np.NaN
+ for y in range(0,365):
+      for x in range(0,300):
+          result1= np.nanmean(mask1[y,78:86,x])
+          zbla[x,:]=result1
+      zbla3=np.squeeze(zbla)
+      zbla2[:,y]=zbla3
+ zbla2=transpose(zbla2)
+
+ for y in range(0,365):
+     for x in range(0,300):
+         result2= np.nanmean(trop1[y,78:86,x])
+         zblo[x,:]=result2
+     zblo3=np.squeeze(zblo)
+     zblo2[:,y]=zblo3
+ zblo2=transpose(zblo2)
+ plt.clf()
+ 
+ units=mask.variables['time_counter'].units
+ 
+ from datetime import datetime
+ buf=mask.variables['time_centered'][:];
+ time=list()
+ time.extend(buf.tolist())
+ dates=nc.num2date(time,units,'gregorian').tolist()
+ 
+ nt=len(dates)
+ im1 = plt.figure(figsize=(6, 18))
+ im1 = plt.contourf(lon,dates,zbla2, np.linspace(23,30,21), 
+extend='both', cmap=cm.RdBu_r)
+ plt.colorbar(im1,orientation='horizontal')
+ im1 = plt.title("Modele annee 2000")
+ plt.savefig('modele2000.png')
+ plt.clf()
+ im2 = plt.figure(figsize=(6, 18))
+ im2 = plt.contourf(lon,dates,zblo2, np.linspace(23,30,21), 
+extend='both', cmap=cm.RdBu_r)
+ plt.colorbar(im2,orientation='horizontal')
+ im2 = plt.title("TropFlux annee 2000")
+ plt.savefig('obs2000.png')

example/various_modelisation_plot.py

+from netCDF4 import Dataset
+import numpy as np
+import matplotlib.pyplot as plt
+from pylab import *
+
+file1 = 
+'/ccc/scratch/cont005/legos/nivertfl/TEST_VALIDATION_TROPFLUX/MOYENNE-SAISON/grid_T_saison.nc'
+file2 = 
+'/ccc/scratch/cont005/legos/nivertfl/TEST_VALIDATION_TROPFLUX/MOYENNE-SAISON/grid_U_saison.nc'
+file3 = 
+'/ccc/scratch/cont005/legos/nivertfl/TEST_VALIDATION_TROPFLUX/MOYENNE-SAISON/grid_V_saison.nc'
+file4 = 
+'/ccc/scratch/cont005/legos/nivertfl/TEST_VALIDATION_TROPFLUX/MOYENNE-SAISON/MYWRF3D_regrid-saison.nc'
+
+trop1 = 
+'/ccc/scratch/cont005/legos/nivertfl/TEST_VALIDATION_TROPFLUX/MOYENNE-SAISON/sst_tropflux_1d_2000-regrid-saison.nc'
+trop2 = 
+'/ccc/scratch/cont005/legos/nivertfl/TEST_VALIDATION_TROPFLUX/MOYENNE-SAISON/taux_tropflux_1d_2000-regrid-saison.nc'
+trop3 = 
+'/ccc/scratch/cont005/legos/nivertfl/TEST_VALIDATION_TROPFLUX/MOYENNE-SAISON/tauy_tropflux_1d_2000-regrid-saison.nc'
+trop4 = 
+'/ccc/scratch/cont005/legos/nivertfl/TEST_VALIDATION_TROPFLUX/MOYENNE-SAISON/swr_tropflux_1d_2000-regrid-saison.nc'
+trop5 = 
+'/ccc/scratch/cont005/legos/nivertfl/TEST_VALIDATION_TROPFLUX/MOYENNE-SAISON/lwr_tropflux_1d_2000-regrid-saison.nc'
+
+pluie1 = 
+'/ccc/scratch/cont005/legos/nivertfl/TEST_VALIDATION_TROPFLUX/MOYENNE-SAISON/precip-2000-regrid-saison.nc'
+
+meshmask = 
+'/ccc/scratch/cont005/legos/nivertfl/TEST_VALIDATION_TROPFLUX/MOYENNE-SAISON/mesh_mask.nc'
+
+mask = Dataset(file1, mode='r')
+masku = Dataset(file2, mode='r')
+maskv = Dataset(file3, mode='r')
+wrf = Dataset(file4, mode='r')
+pluie = Dataset(pluie1, mode='r')
+meshmask1 = Dataset(meshmask, mode='r')
+
+tropsst= Dataset(trop1, mode='r')
+troptaux= Dataset(trop2, mode='r')
+troptauy= Dataset(trop3, mode='r')
+tropswr= Dataset(trop4, mode='r')
+troplwr= Dataset(trop5, mode='r')
+
+lat = mask.variables['nav_lat'][:]
+lon = mask.variables['nav_lon'][:]
+lat1 = mask.variables['nav_lat'][:,0]
+
+meshmask2 = meshmask1.variables['tmask'][:,0,:,:]
+
+mask1 = mask.variables['votemper'][:,0,:,:]
+mask11 = tropsst.variables['sst'][:,:,:]
+
+vari2 = mask.variables['qsr'][:,:,:]
+vari22 = tropswr.variables['swr'][:,:,:]
+
+vari31 = wrf.variables['RAIN'][:,:,:]
+vari3 = vari31[:,:,:] * 40 * 24
+vari33 = pluie.variables['precipitation'][:,:,:]
+
+vari4 = masku.variables['utau'][:,:,:]
+vari44 = troptaux.variables['taux'][:,:,:]
+
+vari51 = wrf.variables['GLW'][:,:,:]
+vari5 = vari51[:,:,:] * meshmask2[:,:,:]
+vari555 = troplwr.variables['lwr'][:,:,:]
+mask112 = mask11[:,:,:] + 273.15
+vari55 = vari555[:,:,:] + 
+5.67e-8*(mask112[:,:,:])*(mask112[:,:,:])*(mask112[:,:,:])*(mask112[:,:,:])
+
+
+vari6 = maskv.variables['vtau'][:,:,:]
+vari6[vari6 == 0] = np.NaN
+vari66 = troptauy.variables['tauy'][:,:,:]
+
+list1 = ['JFM' , 'AMJ' , 'JAS' , 'OND']
+
+
+
+'''Difference modele'''
+for x in range(0,4):
+    print "We're on time %d" % (x)
+    plt.clf()
+    result1 = mask1[x,:,:] - mask11[x,:,:]
+    result2 = vari2[x,:,:] - vari22[x,:,:]
+    result3 = vari3[x,:,:] - vari33[x,:,:]
+    result4 = vari4[x,:,:] - vari44[x,:,:]
+    result5 = vari5[x,:,:] - vari55[x,:,:]
+    result6 = vari6[x,:,:] - vari66[x,:,:]
+    fig, cs = plt.subplots(3, 2, figsize=(9, 6))
+    fig.subplots_adjust(left=0.125, bottom=0.1, right=0.9, top=0.9, 
+wspace=0.3, hspace=0.3)
+    im1 = cs[0, 0].contourf(lon,lat,result1, np.linspace(-2.5,2.5,21), 
+extend='both', cmap=cm.RdBu_r)
+    cs[0, 0].set_title('Difference VOTEMPER MODELE - OBS  Saison ' + 
+list1[x],fontsize=10)
+    fig.colorbar(im1, ax=cs[0, 0], orientation='horizontal')
+    im2 = cs[0, 1].contourf(lon,lat,result2, np.linspace(-100,100,21), 
+extend='both', cmap=cm.RdBu_r)
+    cs[0, 1].set_title('Difference QSR MODELE - OBS  Saison ' + 
+list1[x],fontsize=10)
+    fig.colorbar(im2, ax=cs[0, 1], orientation='horizontal')
+    im3 = cs[1, 0].contourf(lon,lat,result3, np.linspace(-16,16,21), 
+extend='both', cmap=cm.RdBu_r)
+    cs[1, 0].set_title('Difference PRECIP MODELE - OBS  Saison ' + 
+list1[x],fontsize=10)
+    fig.colorbar(im3, ax=cs[1, 0], orientation='horizontal')
+    im4 = cs[1, 1].contourf(lon,lat,result4, np.linspace(-0.1,0.1,21), 
+extend='both', cmap=cm.RdBu_r)
+    cs[1, 1].set_title('Difference U TAU MODELE - OBS  Saison ' + 
+list1[x],fontsize=10)
+    fig.colorbar(im4, ax=cs[1, 1], orientation='horizontal', format='%.0e')
+    im5 = cs[2, 0].contourf(lon,lat,result5, np.linspace(-50,50,21), 
+extend='both', cmap=cm.RdBu_r)
+    cs[2, 0].set_title('Difference LW MODELE - OBS  Saison ' + 
+list1[x],fontsize=10)
+    fig.colorbar(im5, ax=cs[2, 0], orientation='horizontal')
+    im6 = cs[2, 1].contourf(lon,lat,result6, np.linspace(-0.1,0.1,21), 
+extend='both', cmap=cm.RdBu_r)
+    cs[2, 1].set_title('Difference V TAU MODELE - OBS  Saison ' + 
+list1[x],fontsize=10)
+    fig.colorbar(im6, ax=cs[2, 1], orientation='horizontal')
+    plt.savefig('all_var_diff_saison_' + list1[x] + '.png')
+
+'''plot modele'''
+for x in range(0,4):
+    print "We're on time %d" % (x)
+    plt.clf()
+    result1 = mask1[x,:,:]
+    result2 = vari2[x,:,:]
+    result3 = vari3[x,:,:]
+    result4 = vari4[x,:,:]
+    result5 = vari5[x,:,:]
+    result6 = vari6[x,:,:]
+    fig, cs = plt.subplots(3, 2, figsize=(9, 6))
+    fig.subplots_adjust(left=0.125, bottom=0.1, right=0.9, top=0.9, 
+wspace=0.3, hspace=0.3)
+    im1 = cs[0, 0].contourf(lon,lat,result1, np.linspace(20,30,21), 
+extend='both', cmap=cm.jet)
+    cs[0, 0].set_title('VOTEMPER MODELE Saison ' + list1[x],fontsize=10)
+    fig.colorbar(im1, ax=cs[0, 0], orientation='horizontal')
+    im2 = cs[0, 1].contourf(lon,lat,result2, np.linspace(100,380,15), 
+extend='both', cmap=cm.jet)
+    cs[0, 1].set_title('QSR MODELE Saison ' + list1[x],fontsize=10)
+    fig.colorbar(im2, ax=cs[0, 1], orientation='horizontal')
+    im3 = cs[1, 0].contourf(lon,lat,result3, np.linspace(0,20,21), 
+extend='both', cmap=cm.jet)
+    cs[1, 0].set_title('PRECIP MODELE Saison ' + list1[x],fontsize=10)
+    fig.colorbar(im3, ax=cs[1, 0], orientation='horizontal')
+    im4 = cs[1, 1].contourf(lon,lat,result4, 
+np.linspace(-0.13,0.05,21), extend='both', cmap=cm.jet)
+    cs[1, 1].set_title('U TAU MODELE Saison ' + list1[x],fontsize=10)
+    fig.colorbar(im4, ax=cs[1, 1], orientation='horizontal', format='%.0e')
+    im5 = cs[2, 0].contourf(lon,lat,result5, np.linspace(300,460,21), 
+extend='both', cmap=cm.jet)
+    cs[2, 0].set_title('GLW MODELE Saison ' + list1[x],fontsize=10)
+    fig.colorbar(im5, ax=cs[2, 0], orientation='horizontal')
+    im6 = cs[2, 1].contourf(lon,lat,result6, 
+np.linspace(-0.15,0.13,21), extend='both', cmap=cm.jet)
+    cs[2, 1].set_title('V TAU MODELE Saison ' + list1[x],fontsize=10)
+    fig.colorbar(im6, ax=cs[2, 1], orientation='horizontal')
+    plt.savefig('all_var_modele_saison_' + list1[x] + '.png')
+
+'''plot obs Trop et TRMM'''
+for x in range(0,4):
+    print "We're on time %d" % (x)
+    plt.clf()
+    result1 = mask11[x,:,:]
+    result2 = vari22[x,:,:]
+    result3 = vari33[x,:,:]
+    result4 = vari44[x,:,:]
+    result5 = vari55[x,:,:]
+    result6 = vari66[x,:,:]
+    fig, cs = plt.subplots(3, 2, figsize=(9, 6))
+    fig.subplots_adjust(left=0.125, bottom=0.1, right=0.9, top=0.9, 
+wspace=0.3, hspace=0.3)
+    im1 = cs[0, 0].contourf(lon,lat,result1, np.linspace(20,30,21), 
+extend='both', cmap=cm.jet)
+    cs[0, 0].set_title('VOTEMPER OBS  Saison ' + list1[x],fontsize=10)
+    fig.colorbar(im1, ax=cs[0, 0], orientation='horizontal')
+    im2 = cs[0, 1].contourf(lon,lat,result2, np.linspace(100,380,15), 
+extend='both', cmap=cm.jet)
+    cs[0, 1].set_title('QSR OBS  Saison ' + list1[x],fontsize=10)
+    fig.colorbar(im2, ax=cs[0, 1], orientation='horizontal')
+    im3 = cs[1, 0].contourf(lon,lat,result3, np.linspace(0,20,21), 
+extend='both', cmap=cm.jet)
+    cs[1, 0].set_title('PRECIP OBS  Saison ' + list1[x],fontsize=10)
+    fig.colorbar(im3, ax=cs[1, 0], orientation='horizontal')
+    im4 = cs[1, 1].contourf(lon,lat,result4, 
+np.linspace(-0.13,0.05,21), extend='both', cmap=cm.jet)
+    cs[1, 1].set_title('U TAU OBS  Saison ' + list1[x],fontsize=10)
+    fig.colorbar(im4, ax=cs[1, 1], orientation='horizontal', format='%.0e')
+    im5 = cs[2, 0].contourf(lon,lat,result5, np.linspace(300,460,21), 
+extend='both', cmap=cm.jet)
+    cs[2, 0].set_title('LW OBS  Saison ' + list1[x],fontsize=10)
+    fig.colorbar(im5, ax=cs[2, 0], orientation='horizontal')
+    im6 = cs[2, 1].contourf(lon,lat,result6, 
+np.linspace(-0.15,0.13,21), extend='both', cmap=cm.jet)
+    cs[2, 1].set_title('V TAU OBS  Saison ' + list1[x],fontsize=10)
+    fig.colorbar(im6, ax=cs[2, 1], orientation='horizontal')
+    plt.savefig('all_var_obs_saison_' + list1[x] + '.png')
+
+
+zbla=np.zeros(shape=(165,1))
+zbla2=np.zeros(shape=(165,1))
+zbla3=np.zeros(shape=(165,1))
+zbla4=np.zeros(shape=(165,1))
+zbla5=np.zeros(shape=(165,1))
+zbla6=np.zeros(shape=(165,1))
+zbla7=np.zeros(shape=(165,1))
+zbla8=np.zeros(shape=(165,1))
+zbla9=np.zeros(shape=(165,1))
+zbla10=np.zeros(shape=(165,1))
+zbla11=np.zeros(shape=(165,1))
+zbla12=np.zeros(shape=(165,1))
+
+mask1[mask1 == 0] = np.NaN
+vari2[vari2 == 0] = np.NaN
+vari4[vari4 == 0] = np.NaN
+vari5[vari5 == 0] = np.NaN
+
+'''Coupe zonale'''
+for y in range(0,4):
+    print "We're on time %d" % (y)
+    for x in range(0,165):
+        result1= np.nanmean(mask1[y,x,80:160])
+        result2= np.nanmean(mask11[y,x,80:160])
+        zbla[x,:]=result1
+        zbla2[x,:]=result2
+
+    for z in range(0,165):
+        result3= np.nanmean(vari2[y,z,80:160])
+        result4= np.nanmean(vari22[y,z,80:160])
+        zbla3[z,:]=result3
+        zbla4[z,:]=result4
+
+    for ii in range(0,165):
+        result5= np.nanmean(vari3[y,ii,80:160])
+        result6= np.nanmean(vari33[y,ii,80:160])
+        zbla5[ii,:]=result5
+        zbla6[ii,:]=result6
+
+    for jj in range(0,165):
+        result7= np.nanmean(vari4[y,jj,80:160])
+        result8= np.nanmean(vari44[y,jj,80:160])
+        zbla7[jj,:]=result7
+        zbla8[jj,:]=result8
+
+    for zz in range(0,165):
+        result9= np.nanmean(vari5[y,zz,80:160])
+        result10= np.nanmean(vari55[y,zz,80:160])
+        zbla9[zz,:]=result9
+        zbla10[zz,:]=result10
+
+    for hh in range(0,165):
+        result11= np.nanmean(vari6[y,hh,80:160])
+        result12= np.nanmean(vari66[y,hh,80:160])
+        zbla11[hh,:]=result11
+        zbla12[hh,:]=result12
+
+    fig, cs = plt.subplots(3, 2, figsize=(9, 6))
+    fig.subplots_adjust(left=0.125, bottom=0.1, right=0.9, top=0.9, 
+wspace=0.3, hspace=0.3)
+    im1 = cs[0, 0].plot(lat1,zbla)
+    im1 = cs[0, 0].plot(lat1,zbla2)
+    cs[0, 0].set_title('Comparaison SST MODELE et OBS  Saison ' + 
+list1[y],fontsize=10)
+    cs[0, 0].set_ylim([18,30])
+    cs[0, 0].set_xlim([-20,20])
+    im2 = cs[0, 1].plot(lat1,zbla3)
+    im2 = cs[0, 1].plot(lat1,zbla4)
+    cs[0, 1].set_title('Comparaison QSR MODELE et OBS  Saison ' + 
+list1[y],fontsize=10)
+    cs[0, 1].set_ylim([100,380])
+    cs[0, 1].set_xlim([-20,20])
+    im3 = cs[1, 0].plot(lat1,zbla5)
+    im3 = cs[1, 0].plot(lat1,zbla6)
+    cs[1, 0].set_title('Comparaison PRECIP MODELE et OBS  Saison ' + 
+list1[y],fontsize=10)
+    cs[1, 0].set_ylim([0, 20])
+    cs[1, 0].set_xlim([-20,20])
+    im4 = cs[1, 1].plot(lat1,zbla7)
+    im4 = cs[1, 1].plot(lat1,zbla8)
+    cs[1, 1].set_title('Comparaison U TAU MODELE et OBS  Saison ' + 
+list1[y],fontsize=10)
+    cs[1, 1].set_ylim([-0.13,0.05])
+    cs[1, 1].set_xlim([-20,20])
+    im5 = cs[2, 0].plot(lat1,zbla9)
+    im5 = cs[2, 0].plot(lat1,zbla10)
+    cs[2, 0].set_title('Comparaison LW MODELE et OBS  Saison ' + 
+list1[y],fontsize=10)
+    cs[2, 0].set_ylim([300,430])
+    cs[2, 0].set_xlim([-20,20])
+    im6 = cs[2, 1].plot(lat1,zbla11)
+    im6 = cs[2, 1].plot(lat1,zbla12)
+    cs[2, 1].set_title('Comparaison V TAU MODELE et OBS  Saison ' + 
+list1[y],fontsize=10)
+    cs[2, 1].set_ylim([-0.15,0.13])
+    cs[2, 1].set_xlim([-20,20])
+    plt.savefig('coupe_zonale_saison_' + list1[y] + '.png')