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plotvert_canam4sims2.py
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plotvert_canam4sims2.py
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# plotvert_canam4sims2.py
# 2/19/2014
# Vertical plots for CanAM4 sea ice simulations
# 3/3/2014: taken from first version. Try and use new
# cccmaNC module to read and average netcdf data
#
#import numpy as np # for array handling
#import scipy as sp # scientific python
import scipy.stats
#import matplotlib.pyplot as plt # for basic plotting
import matplotlib.cm as cm
from subprocess import call # for doing system calls - not really needed
#from netCDF4 import Dataset
#from mpl_toolkits.basemap import Basemap # for maps
import datetime as datetime
import matplotlib.colors as col
import platform as platform
#import cccmaplots as cplt
import constants as con
import cccmautils as cutl
import matplotlib.font_manager as fm
#import cccmaNC as cnc
import cccmacmaps
#import cdo as cdo; cdo = cdo.Cdo()
#import os
# https://code.zmaw.de/projects/cdo/wiki/Cdo%7Brbpy%7D
# while I'm still creating these modules, have to reload to get changes
## cplt = reload(cplt)
## con = reload(con)
## cutl = reload(cutl)
## cnc = reload(cnc)
#os.system('rm -rf /tmp/cdoPy*')
plt.close("all")
plt.ion()
printtofile=1
allmos=0 # make monthly figures
bimos=0 # bi-monthly figures
seasonal=1 # seasonal figures
singleplots=0 # seasonal climo and mean diff
addcontlines=1 # want contour lines in addition to colors on figs
obssims=1 # this will overrule the simulation settings and set to kemhad*
obssimscomp=0 # compare hadpert to pert2 @@ not ready yet
# # # ######## set Field info ###############
field = 't' # t, u, gz
# # # ########### set Simulations #############
# Control run
casename = 'kemctl1'
timstr = '001-061'
timstr2 = '062-111'
# Pert run
#casenamep1 = 'kem1pert1' # 2002-2012 sic and sit
casenamep1 = 'kem1pert1b' # 2002-2012 sic and sit
casenamep2 = 'kem1pert2' # 2002-2012 sic, sit, adjusted sst
casenamep3 = 'kem1pert3' # 2002-2012 sic, adjusted sst. control sit
timstrp = '001-061'
timstrp2 = '062-121'
casenamepra = 'kem1rcp85a' # 2022-2032 sic, adjusted sst, sit from RCP8.5
######## set pert run ############
casenamep = casenamep1
######## set new control run ###########
#casename = casenamep3
########### OR SET OTHER RUNS ##############
if obssims:
casename = 'kemhadctl'
casenamep = 'kemhadpert'
timstr2 = '062-121'
timstrp2 = '062-121'
elif obssimscomp: # can't do this yet...need had runs to finish
casename = 'kemhadpert'
casenamep = 'kem1pert2'
seasons = 'SON','DJF','MAM','JJA'
model = 'CanAM4'
ftype = 'ts' # timeseries
## @@ really shouldn't use anymore now that code is all worked out #ftype = 'climo' # 12-month climatology
season = 'ANN'
#cmap = 'RdBu_r'
cmap = 'blue2red_20'
cmapclimo = 'Spectral_r'
conv = 1 # conversion factor to convert units, etc
if field == 't':
ncfield = 'TEMP'
units = 'K' # @@
cminc = 200; cmaxc = 310
cmin = -.5; cmax = .5
cminm = -.8; cmaxm = .8 # for monthly
cminsc = -2.5; cmaxsc = 2.5 # as Screen et al paper
cminp = -.5; cmaxp=.5 # for when a pert is 'control'
elif field == 'u':
ncfield = 'U'
units = 'm/s' #@@
cminc = -40; cmaxc = 40
cmin = -.5; cmax = .5
cminm = -1; cmaxm = 1
cminsc=cminm; cmaxsc=cmaxm
cminp = -.5; cmaxp = .5
elif field == 'gz':
ncfield = 'PHI'
units = 'm' # @@
conv = 1/con.get_g()
cminc = 0; cmaxc = 48000 # climo
cmin = -10; cmax = 10 # annual mean
cminm = -15; cmaxm = 15 # for monthly
cminsc = -25; cmaxsc = 25 # as Screen et al paper
cminp = -10; cmaxp=10
else:
print 'no such field: ' + field
# # # ########## Read NC data ###############
plat = platform.system()
if plat == 'Darwin': # means I'm on my mac
basepath = '/Users/kelly/CCCma/CanSISE/RUNS/'
subdir = '/'
else: # on linux workstation in Vic
basepath = '/home/rkm/work/DATA/' + model + '/'
subdir = '/ts/'
# The 3D data is split into 2 timeseries
fnamec = basepath + casename + subdir + casename + '_' + field + '_' + timstr + '_' + ftype + '.nc'
fnamep = basepath + casenamep + subdir + casenamep + '_' + field + '_' + timstrp + '_' + ftype + '.nc'
## fnamep2 = basepath + casenamep2 + subdir + casenamep2 + '_' + field + '_' + timstrp + '_' + ftype + '.nc'
## fnamep3 = basepath + casenamep3 + subdir + casenamep3 + '_' + field + '_' + timstrp + '_' + ftype + '.nc'
fnamec2 = basepath + casename + subdir + casename + '_' + field + '_' + timstr2 + '_' + ftype + '.nc'
fnamep2 = basepath + casenamep + subdir + casenamep + '_' + field + '_' + timstrp2 + '_' + ftype + '.nc'
## fnamep22 = basepath + casenamep2 + subdir + casenamep2 + '_' + field + '_' + timstrp2 + '_' + ftype + '.nc'
## fnamep32 = basepath + casenamep3 + subdir + casenamep3 + '_' + field + '_' + timstrp2 + '_' + ftype + '.nc'
## ############################
## ########## set pert! #######
## #fnamep = fnamep2; fnamep2 = fnamep22; casenamep = casenamep2
## #fnamep = fnamep1; fnamep2 = fnamep12; casenamep = casenamep1
## fnamep = fnamep3; fnamep2 = fnamep32; casenamep = casenamep3
print 'CONTROL: ' + casename
print 'PERT: ' + casenamep
if casename != 'kemctl1' and casename != 'kemhadctl':
cmin=cminp; cmax=cmaxp
cminm=cminp; cmaxm=cmaxp
cminsc=cminp; cmaxsc=cmaxp
lat = cnc.getNCvar(fnamec,'lat')
lev = cnc.getNCvar(fnamec,'plev')
# get the data already zonal-meaned
#fldczm = cnc.getNCvar(fnamec,ncfield,calc='zm',timechunk=((styr-1)*12,enyr*12+1) )*conv
#fldpzm = cnc.getNCvar(fnamep2,ncfield,calc='zm',timechunk=((styr-1)*12,enyr*12+1) )*conv
seasfldczm = np.append(cnc.getNCvar(fnamec,ncfield,timesel='0002-01-01,0061-12-31',seas=season,calc='zm')*conv,
cnc.getNCvar(fnamec2,ncfield,seas=season,calc='zm')*conv,
axis=0)
seasfldpzm = np.append(cnc.getNCvar(fnamep,ncfield,timesel='0002-01-01,0061-12-31',seas=season,calc='zm')*conv,
cnc.getNCvar(fnamep2,ncfield,seas=season,calc='zm')*conv,
axis=0)
#nt,nlev,nlat,nlon = fldc.shape # if nt == 12 then it's a climo
nt,nlev,nlat = seasfldczm.shape
print nt
# seasonalize
#seasfldczm = cutl.seasonalize_monthlyts(fldczm,timeavg)
#seasfldpzm = cutl.seasonalize_monthlyts(fldpzm,timeavg)
if ftype=='ts':
tstat,pval = sp.stats.ttest_ind(seasfldpzm,seasfldczm,axis=0)
# time-mean
seasfldczmtm=np.mean(seasfldczm,0)
seasfldpzmtm=np.mean(seasfldpzm,0)
lats,levs = np.meshgrid(lat,lev)
cmlen=15.
############################## SEASONAL MEAN ##########################
if singleplots:
# SEASONAL MEAN CLIMO (CONTROL)
cmlen=15.
incr = (cmaxc-cminc) / (cmlen)
conts = np.arange(cminc,cmaxc+incr,incr)
plotfld = seasfldczmtm
fig1 = plt.figure()
ax1 = fig1.add_subplot(111)
## plt.pcolormesh(lats,levs/100,plotfld,\
## cmap= plt.cm.get_cmap(cmapclimo),shading='gouraud',\
## vmin=cminc,vmax=cmaxc)
cf = plt.contourf(lats,levs/100.,plotfld,
cmap= plt.cm.get_cmap(cmapclimo),levels=conts,
vmin=cminc,vmax=cmaxc,extend='both')
if addcontlines:
plt.contour(lats,levs/100.,plotfld,levels=conts,colors='.3')
ax1.set_xlim(-90,90)
ax1.set_ylim(10,1000)
ax1.invert_yaxis()
ax1.set_yscale('log')
ax1.set_yticks([1000,800, 500, 300, 100, 10])
ax1.set_yticklabels((1000,800,500,300,100,10))
ax1.set_ylabel('Pressure (hPa)')
ax1.set_xlabel('Latitude')
ax1.set_xticks([-45, 0, 45])
ax1.set_xticklabels((-45, 0, 45))
ax1.set_title(ncfield + ' (' + units + ')')
cbar = fig1.colorbar(cf)
if printtofile:
plt.savefig(field + 'VERTzm_' + season + \
'_' + casename + '_shall2.pdf')
plt.savefig(field + 'VERTzm_' + season +\
'_' + casename + '_shall2.png')
# SEASONAL MEAN DIFF
# full height of atmosphere
cmlen=float( plt.cm.get_cmap(cmap).N) # or: from __future__ import division
incr = (cmax-cmin) / (cmlen)
conts = np.arange(cmin,cmax+incr,incr)
plotfld = seasfldpzmtm - seasfldczmtm
fig = plt.figure()
ax = fig.add_subplot(111)
## pc = plt.pcolormesh(lats,levs/100,plotfld,\
## cmap= plt.cm.get_cmap(cmap),shading='gouraud',\
## vmin=cmin,vmax=cmax)
pc = plt.contourf(lats,levs/100.,plotfld,
cmap=plt.cm.get_cmap(cmap),levels=conts,
vmin=cmin,vmax=cmax,extend='both')
if addcontlines:
plt.contour(lats,levs/100.,plotfld,levels=conts,colors='.3')
ax.invert_yaxis()
ax.set_xlim(-90,90)
ax.set_yscale('log')
ax.set_ylabel('Pressure (hPa)')
ax.set_xlabel('Latitude')
ax.set_yticks([1000,800, 500, 300, 100, 10])
ax.set_yticklabels((1000,800,500,300,100,10))
ax.set_xticks([-45, 0, 45])
ax.set_xticklabels((-45, 0, 45))
ax.set_title(ncfield + ' (' + units + ')')
cbar = fig.colorbar(pc)
if ftype == 'ts':
# add sig
cplt.addtsig(ax,pval,lat,lev/100.,type='hatch')
if printtofile:
if ftype=='ts':
plt.savefig(field + 'VERTzmdiff_' + season + '_' + casenamep + '_v_' + casename + '2.pdf')
plt.savefig(field + 'VERTzmdiffsig_' + season + '_' + casenamep + '_v_' + casename + '2.png')
else:
plt.savefig(field + 'VERTzmdiff_' + season + '_' + casenamep + '_v_' + casename + '2.pdf')
plt.savefig(field + 'VERTzmdiff_' + season + '_' + casenamep + '_v_' + casename + '2.png')
# shallow top (100hPa)
fig2 = plt.figure()
ax2 = fig2.add_subplot(111)
## plt.pcolormesh(lats,levs/100,plotfld,\
## cmap= plt.cm.get_cmap(cmap),shading='gouraud',\
## vmin=cmin,vmax=cmax)
cf = plt.contourf(lats,levs/100.,plotfld,
cmap=plt.cm.get_cmap(cmap),levels=conts,
vmin=cmin,vmax=cmax,extend='both')
if addcontlines:
plt.contour(lats,levs/100.,plotfld,levels=conts,colors='.3')
ax2.set_xlim(-90,90)
ax2.set_ylim(10,1000)
ax2.invert_yaxis()
ax2.set_yscale('log')
ax2.set_ylabel('Pressure (hPa)')
ax2.set_xlabel('Latitude')
ax2.set_yticks([1000,800, 500, 300, 100, 10])
ax2.set_yticklabels((1000,800,500,300,100,10))
ax2.set_xticks([-45, 0, 45])
ax2.set_xticklabels((-45, 0, 45))
ax2.set_title(ncfield + ' (' + units + ')')
cbar = fig2.colorbar(cf)
if ftype == 'ts':
# add sig
cplt.addtsig(ax2,pval,lat,lev/100.,type='hatch')
if printtofile:
if ftype=='ts':
plt.savefig(field + 'VERTzmdiff_' + season + '_' + casenamep +\
'_v_' + casename + '_shall2.pdf') # sig doesn't plot to pdf
plt.savefig(field + 'VERTzmdiffsig_' + season + '_' + casenamep +\
'_v_' + casename + '_shall2.png')
else:
plt.savefig(field + 'VERTzmdiff_' + season + '_' + casenamep +\
'_v_' + casename + '_shall2.pdf')
plt.savefig(field + 'VERTzmdiff_' + season + '_' + casenamep +\
'_v_' + casename + '_shall2.png')
######################## ALL MONTHS #######################
if allmos:
# ALL MONTHS
cmlen=float( plt.cm.get_cmap(cmap).N) # or: from __future__ import division
incr = (cmaxm-cminm) / (cmlen)
conts = np.arange(cminm,cmaxm+incr,incr)
months = con.get_mon()
# these are suitable for plotting, but not tstat calcs. they come back as climos
## fldczm = np.append(cnc.getNCvar(fnamec,ncfield,timesel='0002-01-01,0061-12-31',seas='climo',calc='zm')*conv,
## cnc.getNCvar(fnamec2,ncfield,seas='climo',calc='zm')*conv,
## axis=0)
## fldpzm = np.append(cnc.getNCvar(fnamep,ncfield,timesel='0002-01-01,0061-12-31',seas='climo',calc='zm')*conv,
## cnc.getNCvar(fnamep2,ncfield,seas='climo',calc='zm')*conv,
## axis=0)
tstat = np.zeros((12,nlev,nlat))
pval = np.zeros((12,nlev,nlat))
fldczmmos = np.zeros((12,nlev,nlat))
fldpzmmos = np.zeros((12,nlev,nlat))
midx=0
fig4,ax4 = plt.subplots(2,6)
fig4.set_size_inches(12,6)
fig4.subplots_adjust(hspace=.15,wspace=.05)
for ax in ax4.flat:
fldczmmo = np.append(cnc.getNCvar(fnamec,ncfield,timesel='0002-01-01,0061-12-31',seas=midx+1,calc='zm')*conv,
cnc.getNCvar(fnamec2,ncfield,seas=midx+1,calc='zm')*conv,
axis=0)
fldpzmmo = np.append(cnc.getNCvar(fnamep,ncfield,timesel='0002-01-01,0061-12-31',seas=midx+1,calc='zm')*conv,
cnc.getNCvar(fnamep2,ncfield,seas=midx+1,calc='zm')*conv,
axis=0)
tstat[midx,:,:],pval[midx,:,:] = sp.stats.ttest_ind(fldpzmmo,fldczmmo,axis=0)
fldczmmos[midx,:,:] = np.mean(fldczmmo,axis=0)
fldpzmmos[midx,:,:] = np.mean(fldpzmmo,axis=0)
# plotfld = fldpzm[midx,:,:] - fldczm[midx,:,:]
plotfld = fldpzmmos[midx,:,:] - fldczmmos[midx,:,:]
## pc = ax.pcolormesh(lats,levs/100,plotfld,\
## cmap= plt.cm.get_cmap(cmap),shading='gouraud',\
## vmin=cminm,vmax=cmaxm)
pc = ax.contourf(lats,levs/100.,plotfld,
cmap=plt.cm.get_cmap(cmap),levels=conts,
vmin=cminm,vmax=cmaxm,extend='both')
if addcontlines:
ax.contour(lats,levs/100.,plotfld,levels=conts,colors='.3')
if ftype == 'ts':
# add sig
cplt.addtsig(ax,pval[midx,:,:],lat,lev/100.,type='hatch')
ax.set_title(months[midx])
ax.set_xlim(-90,90)
ax.set_ylim(10,1000)
ax.invert_yaxis()
ax.set_yscale('log')
if midx == 0 or midx == 6:
ax.set_ylabel('Pressure (hPa)')
ax.set_yticks([1000,800, 500, 300, 100, 10])
ax.set_yticklabels((1000,800,500,300,100,10))
else:
ax.set_yticklabels('')
if midx in range(6,12):
ax.set_xlabel('Latitude')
ax.set_xticks([-45, 0, 45])
ax.set_xticklabels((-45, 0, 45))
else:
ax.set_xticklabels('')
midx = midx+1
#cbar_ax = fig4.add_axes([.2, .02, .7, .03])
#fig4.colorbar(pc,cax=cbar_ax,orientation='horizontal')
cbar_ax = fig4.add_axes([.91,.15, .02,.7])
fig4.colorbar(pc,cax=cbar_ax)
plt.suptitle(ncfield + ' (' + units + ')')
if printtofile:
if ftype=='ts':
plt.savefig(field + 'VERTzmdiff_' + casenamep +\
'_v_' + casename + '_shall_allmos2.pdf') # sig doesn't plot to pdf
plt.savefig(field + 'VERTzmdiffsig_' + casenamep +\
'_v_' + casename + '_shall_allmos2.png')
else:
plt.savefig(field + 'VERTzmdiff_' + casenamep +\
'_v_' + casename + '_shall_allmos2.pdf')
plt.savefig(field + 'VERTzmdiff_' + casenamep +\
'_v_' + casename + '_shall_allmos2.png')
# ALL MONTHS As Screen et al 2013, ClimDyn
incr = (cmaxsc-cminsc) / (cmlen)
conts = np.arange(cminsc,cmaxsc+incr,incr)
midx=0
fig5,ax5 = plt.subplots(2,6)
fig5.set_size_inches(12,4.5)
fig5.subplots_adjust(hspace=.15,wspace=.05)
for ax in ax5.flat:
plotfld = fldpzmmos[midx,:,:] - fldczmmos[midx,:,:]
## pc = ax.pcolormesh(lats,levs/100,plotfld,\
## cmap= plt.cm.get_cmap(cmap),shading='gouraud',\
## vmin=cminsc,vmax=cmaxsc)
pc = ax.contourf(lats,levs/100.,plotfld,
cmap=plt.cm.get_cmap(cmap),levels=conts,
vmin=cminsc,vmax=cmaxsc, extend='both')
if addcontlines:
ax.contour(lats,levs/100.,plotfld,levels=conts,colors='.3')
if ftype == 'ts':
# add sig
cplt.addtsig(ax,pval[midx,:,:],lat,lev/100.,type='hatch')
ax.set_title(months[midx])
ax.set_xlim(20,90)
ax.set_ylim(300,1000)
ax.invert_yaxis()
#ax.set_yscale('log')
if midx == 0 or midx == 6:
ax.set_ylabel('Pressure (hPa)')
ax.set_yticks([900,700, 500, 300])
ax.set_yticklabels((900,700,500,300))
else:
ax.set_yticklabels('')
if midx in range(6,12):
ax.set_xlabel('Latitude')
ax.set_xticks([40, 60, 80])
ax.set_xticklabels((40,60,80))
else:
ax.set_xticklabels('')
midx = midx+1
#cbar_ax = fig4.add_axes([.2, .02, .7, .03])
#fig4.colorbar(pc,cax=cbar_ax,orientation='horizontal')
cbar_ax = fig5.add_axes([.91,.15, .02,.7])
fig5.colorbar(pc,cax=cbar_ax)
plt.suptitle(ncfield + ' (' + units + ')')
if printtofile:
if ftype == 'ts':
plt.savefig(field + 'VERTzmdiff_'+ casenamep +\
'_v_' + casename + '_screen_allmos2.pdf') # sig doesn't plot to pdf
plt.savefig(field + 'VERTzmdiffsig_' + casenamep +\
'_v_' + casename + '_screen_allmos2.png')
else:
plt.savefig(field + 'VERTzmdiff_'+ casenamep +\
'_v_' + casename + '_screen_allmos2.pdf')
plt.savefig(field + 'VERTzmdiff_' + casenamep +\
'_v_' + casename + '_screen_allmos2.png')
if bimos:
print 'bimos'
if seasonal:
tstat = np.zeros((len(seasons),nlev,nlat))
pval = np.zeros((len(seasons),nlev,nlat))
fldczmallseas = np.zeros((len(seasons),nlev,nlat))
fldpzmallseas = np.zeros((len(seasons),nlev,nlat))
cmlen=float( plt.cm.get_cmap(cmap).N)
incr = (cmaxsc-cminsc) / (cmlen)
conts = np.arange(cminsc,cmaxsc+incr,incr)
midx=0
fig6,ax6 = plt.subplots(1,4)
fig6.set_size_inches(12,2.5)
fig6.subplots_adjust(hspace=.15,wspace=.05)
for ax in ax6.flat:
fldczmsea = np.append(cnc.getNCvar(fnamec,ncfield,timesel='0002-01-01,0061-12-31',
seas=seasons[midx],calc='zm')*conv,
cnc.getNCvar(fnamec2,ncfield,seas=seasons[midx],calc='zm')*conv,
axis=0)
fldpzmsea = np.append(cnc.getNCvar(fnamep,ncfield,timesel='0002-01-01,0061-12-31',
seas=seasons[midx],calc='zm')*conv,
cnc.getNCvar(fnamep2,ncfield,seas=seasons[midx],calc='zm')*conv,
axis=0)
tstat[midx,:,:],pval[midx,:,:] = sp.stats.ttest_ind(fldpzmsea,fldczmsea,axis=0)
fldczmallseas[midx,:,:] = np.mean(fldczmsea,axis=0)
fldpzmallseas[midx,:,:] = np.mean(fldpzmsea,axis=0)
plotfld = fldpzmallseas[midx,:,:] - fldczmallseas[midx,:,:]
pc = ax.contourf(lats,levs/100.,plotfld,
cmap=plt.cm.get_cmap(cmap),levels=conts,
vmin=cminsc,vmax=cmaxsc, extend='both')
if addcontlines:
ax.contour(lats,levs/100.,plotfld,levels=conts,colors='.3')
if ftype == 'ts':
# add sig
cplt.addtsig(ax,pval[midx,:,:],lat,lev/100.,type='hatch')
ax.set_title(seasons[midx])
ax.set_xlim(20,90)
ax.set_ylim(300,1000)
ax.invert_yaxis()
#ax.set_yscale('log')
if midx == 0:
ax.set_ylabel('Pressure (hPa)')
ax.set_yticks([900,700, 500, 300])
ax.set_yticklabels((900,700,500,300))
else:
ax.set_yticklabels('')
ax.set_xlabel('Latitude')
ax.set_xticks([40, 60, 80])
ax.set_xticklabels((40,60,80))
midx = midx+1
#cbar_ax = fig4.add_axes([.2, .02, .7, .03])
#fig4.colorbar(pc,cax=cbar_ax,orientation='horizontal')
cbar_ax = fig6.add_axes([.91,.15, .02,.7])
fig6.colorbar(pc,cax=cbar_ax)
plt.suptitle(ncfield + ' (' + units + ')')
if printtofile:
if ftype == 'ts':
plt.savefig(field + 'VERTzmdiff_'+ casenamep +\
'_v_' + casename + '_screen_seas2.pdf') # sig doesn't plot to pdf
plt.savefig(field + 'VERTzmdiffsig_' + casenamep +\
'_v_' + casename + '_screen_seas2.png')
else:
plt.savefig(field + 'VERTzmdiff_'+ casenamep +\
'_v_' + casename + '_screen_seas2.pdf')
plt.savefig(field + 'VERTzmdiff_' + casenamep +\
'_v_' + casename + '_screen_seas2.png')
incr = (cmaxm-cminm) / (cmlen)
conts = np.arange(cminm,cmaxm+incr,incr)
midx=0
fig6,ax6 = plt.subplots(1,4)
fig6.set_size_inches(12,3.5)
fig6.subplots_adjust(hspace=.15,wspace=.05)
for ax in ax6.flat:
## fldczmsea[midx,:,:] = np.append(cnc.getNCvar(fnamec,ncfield,timesel='0002-01-01,0061-12-31',
## seas=seasons[midx],calc='zm')*conv,
## cnc.getNCvar(fnamec2,ncfield,seas=seasons[midx],calc='zm')*conv,
## axis=0)
## fldpzmsea[midx,:,:] = np.append(cnc.getNCvar(fnamep,ncfield,timesel='0002-01-01,0061-12-31',
## seas=seasons[midx],calc='zm')*conv,
## cnc.getNCvar(fnamep2,ncfield,seas=seasons[midx],calc='zm')*conv,
## axis=0)
## tstat[midx,:,:],pval[midx,:,:] = sp.stats.ttest_ind(fldpzmsea,fldczmsea,axis=0)
plotfld = fldpzmallseas[midx,:,:] - fldczmallseas[midx,:,:]
pc = ax.contourf(lats,levs/100.,plotfld,
cmap=plt.cm.get_cmap(cmap),levels=conts,
vmin=cminm,vmax=cmaxm, extend='both')
if addcontlines:
ax.contour(lats,levs/100.,plotfld,levels=conts,colors='.3')
if ftype == 'ts':
# add sig
cplt.addtsig(ax,pval[midx,:,:],lat,lev/100.,type='hatch')
ax.set_title(seasons[midx])
ax.set_xlim(-90,90)
ax.set_ylim(10,1000)
ax.invert_yaxis()
#ax.set_yscale('log')
if midx == 0:
ax.set_ylabel('Pressure (hPa)')
ax.set_yticks([1000,800, 500, 300, 100, 10])
ax.set_yticklabels((1000,800,500,300,100,10))
else:
ax.set_yticklabels('')
ax.set_xlabel('Latitude')
ax.set_xticks([-45, 0, 45])
ax.set_xticklabels((-45, 0, 45))
midx = midx+1
#cbar_ax = fig4.add_axes([.2, .02, .7, .03])
#fig4.colorbar(pc,cax=cbar_ax,orientation='horizontal')
cbar_ax = fig6.add_axes([.91,.15, .02,.7])
fig6.colorbar(pc,cax=cbar_ax)
plt.suptitle(ncfield + ' (' + units + ')')
if printtofile:
if ftype == 'ts':
plt.savefig(field + 'VERTzmdiff_'+ casenamep +\
'_v_' + casename + '_shall_seas2.pdf') # sig doesn't plot to pdf
plt.savefig(field + 'VERTzmdiffsig_' + casenamep +\
'_v_' + casename + '_shall_seas2.png')
else:
plt.savefig(field + 'VERTzmdiff_'+ casenamep +\
'_v_' + casename + '_shall_seas2.pdf')
plt.savefig(field + 'VERTzmdiff_' + casenamep +\
'_v_' + casename + '_shall_seas2.png')