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plotcase.py
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#!/usr/bin/python
import os, sys, csv, glob
import numpy, scipy
from scipy.io import netcdf
from optparse import OptionParser
import matplotlib as mpl
def getvar(fname, varname, npf, index, scale_factor):
usescipy = False
try:
import Scientific.IO.NetCDF as netcdf
except ImportError:
import scipy
from scipy.io import netcdf
usescipy = True
if (usescipy):
nffile = netcdf.netcdf_file(fname,"r",mmap=False)
var = nffile.variables[varname]
varvals = var[0:npf,index].copy() * scale_factor #works for vector only?
nffile.close()
else:
nffile = netcdf.NetCDFFile(fname,"r")
var = nffile.variables[varname]
varvals = var.getValue()[0:npf,index] * scale_factor
nffile.close()
return varvals
parser = OptionParser()
parser.add_option("--ad_Pinit", dest="ad_Pinit", default=False, action="store_true",\
help="Initialize AD spinup with P pools and use CNP mode")
parser.add_option("--csmdir", dest="mycsmdir", default='', \
help = 'Base CESM directory (default = ..)')
parser.add_option("--cases", dest="mycase", default='', \
help = "name of case id prefixs to plot (comma delmited)")
parser.add_option("--compset", dest="compset", default="I20TRCLM45CN", \
help = "Compset to plot")
parser.add_option("--titles", dest="titles", default='', \
help = "titles of case to plot (for legend)")
parser.add_option("--obs", action="store_true", default=False, \
help = "plot observations", dest="myobs")
parser.add_option("--sites", dest="site", default="none", \
help = 'site (to plot observations)')
parser.add_option("--timezone", dest="timezone", default=0, \
help = 'time zone (relative to UTC')
parser.add_option("--varfile", dest="myvarfile", default='varfile', \
help = 'file containing list of variables to plot')
parser.add_option("--vars", dest="myvar", default='', \
help="variable to plot (overrides varfile, " \
+"sends plot to screen")
parser.add_option("--avpd", dest="myavpd", default=1, \
help = 'averaging period in # of output timesteps' \
+' (default = 1)')
parser.add_option("--hist_mfilt", dest="myhist_mfilt", default=-999, \
help = 'beginning model year to plot')
parser.add_option("--hist_nhtfrq", dest="myhist_nhtfrq", default=-999, \
help = 'beginning model year to plot')
parser.add_option("--ystart", dest="myystart", default=1, \
help = 'beginning model year to plot')
parser.add_option("--yend", dest="myyend", default=9999, \
help = 'final model year to plot')
parser.add_option("--ystart_obs", dest="ystart_obs", default=0, \
help = 'beginning model year to plot')
parser.add_option("--yend_obs", dest="yend_obs", default=0, \
help = 'final model year to plot')
parser.add_option("--diurnal", dest="mydiurnal", default=False, \
action="store_true", help = 'plot diurnal cycle')
parser.add_option("--dstart", dest="dstart", default=1, \
help = 'beginning model DOY to plot (for diruanl average)')
parser.add_option("--dend", dest="dend", default=365, \
help = 'final model DOY to plot (for diurnal average)')
parser.add_option("--seasonal", dest="myseasonal", default=False, \
action="store_true", help = 'plot seasonal cycle')
parser.add_option("--h1", dest="h1", default=False, \
action="store_true", help = 'Use h1 history files')
parser.add_option("--h2", dest="h2", default=False, \
action="store_true", help = 'Use h2 history files')
parser.add_option("--h3", dest="h3", default=False, \
action="store_true", help = 'Use h3 history files')
parser.add_option("--h4", dest="h4", default=False, \
action="store_true", help = 'Use h4 history files')
parser.add_option("--index", dest="index", help = 'index (site or pft)', \
default=0)
parser.add_option("--spinup", dest="spinup", help = 'plot Ad and final spinup', \
default=False, action="store_true")
parser.add_option("--scale_factor", dest="scale_factor", help = 'scale factor', \
default=-999)
parser.add_option("--ylog", dest="ylog", help="log scale for Y axis", \
action="store_true", default=False)
parser.add_option("--pdf", dest="pdf", help="save plot to pdf", \
action="store_true", default=False)
parser.add_option("--noplot", dest="noplot", help="Do not make plots", \
action="store_true", default=False)
(options,args) = parser.parse_args()
cesmdir=os.path.abspath(options.mycsmdir)
if (options.pdf):
mpl.use('Agg')
import matplotlib.pyplot as plt
mycases = options.mycase.split(',')
mysites = options.site.split(',')
mycompsets = options.compset.split(',')
ncases = 1
if (len(mycases) > 1):
ncases = len(mycases)
mysites=[]
for c in range(0,ncases):
mysites.append(options.site)
#if (len(mycompsets) == 1):
mycompsets.append(options.compset)
mytitles = mycases
elif (len(mysites) > 1):
ncases = len(mysites)
mycases=[]
mycompsets=[]
for c in range(0,ncases):
mycases.append(options.mycase)
mycompsets.append(options.compset)
mytitles = mysites
elif (len(mycompsets) > 1):
ncases = len(mycompsets)
mycases=[]
mysites=[]
for c in range(0,ncases):
mycases.append(options.mycase)
mysites.append(options.site)
mytitles = mycompsets
else:
mytitles=[]
mytitles.append(mysites[0])
if (options.titles != ''):
mytitles = options.titles.split(',')
obs = options.myobs
myobsdir = '/lustre/or-hydra/cades-ccsi/scratch/dmricciuto/fluxnet'
#get list of variables from varfile
myvars=[]
if (options.myvar == ''):
if os.path.isfile('./'+options.myvarfile):
input = open('./'+options.myvarfile)
for s in input:
myvars.append(s.strip())
else:
print('Error: invalid varfile')
sys.exit()
terminal = 'postscript'
else:
terminal=''
myvars = options.myvar.split(',')
avpd = int(options.myavpd) # desired averaging period in output timestep
ystart = int(options.myystart) # beginning year to plot/average
yend = int(options.myyend) # final year to plot/average
yend_all = yend # keep track of last year for which datasets exist
mylat_vals =[]
mylon_vals= []
avtype = 'default'
if (options.mydiurnal):
avtype = 'diurnal'
avpd=1
if (options.myseasonal):
avtype = 'seasonal'
#------------------------------------------------------------------------------
#site = options.site
#compset = options.compset
#dirs=[]
nvar = len(myvars)
x_toplot = numpy.zeros([ncases, 2000000], numpy.float)
data_toplot = numpy.zeros([ncases, nvar, 2000000], numpy.float)
obs_toplot = numpy.zeros([ncases, nvar, 2000000], numpy.float)+numpy.NaN
err_toplot = numpy.zeros([ncases, nvar, 2000000], numpy.float)+numpy.NaN
snum = numpy.zeros([ncases], numpy.int)
for c in range(0,ncases):
if (mycases[c] == ''):
mydir = cesmdir+'/'+mysites[c]+'_'+mycompsets[c]+'/run/'
else:
mydir = cesmdir+'/'+mycases[c]+'_'+mysites[c]+'_'+mycompsets[c]+'/run/'
print('Processing '+mydir)
#query lnd_in file for output file information
if ((options.myhist_mfilt == -999 or options.myhist_nhtfrq == -999)):
#print('Obtaining output resolution information from lnd_in')
input = open(mydir+"/lnd_in")
npf=-999
tstep=-999
input = open(mydir+"/lnd_in")
for s in input:
if ('hist_mfilt' in s):
mfiltinfo = s.split()[2]
npf = int(mfiltinfo.split(',')[0])
if (options.h1):
npf = int(mfiltinfo.split(',')[1])
if (options.h2):
npf = int(mfiltinfo.split(',')[2])
if (options.h3):
npf = int(mfiltinfo.split(',')[3])
if (options.h4):
npf = int(mfiltinfo.split(',')[4])
if ('hist_nhtfrq' in s):
nhtfrqinfo = s.split()[2]
tstep = int(nhtfrqinfo.split(',')[0])
if (options.h1):
tstep = int(nhtfrqinfo.split(',')[1])
if (options.h2):
tstep = int(nhtfrqinfo.split(',')[2])
if (options.h3):
tstep = int(nhtfrqinfo.split(',')[3])
if (options.h4):
tstep = int(nhtfrqinfo.split(',')[4])
input.close()
else:
npf = int(options.myhist_mfilt)
tstep = int(options.myhist_nhtfrq)
if (npf == -999 or tstep == -999):
print('Unable to obtain output file information from lnd_in. Exiting')
sys.exit()
yststr=str(100000+ystart)
#determine type of file to plot
if (options.h4):
hst = 'h4'
elif (options.h3):
hst = 'h3'
elif (options.h2):
hst = 'h2'
elif (options.h1):
hst = 'h1'
else:
hst = 'h0'
if (tstep == 0):
ftype = 'default'
mytstep = 'monthly'
npy=12
else:
ftype = 'custom'
if (abs(npf) == 8760):
mytstep = 'halfhourly'
npy=8760
elif (abs(npf) == 365):
mytstep = 'daily'
npy=365
elif (abs(npf) == 1):
mytstep = 'annual'
npy=1
nhtot=-1*tstep*npf
nypf = max(1, nhtot/8760)
#initialize data arrays
mydata = numpy.zeros([nvar,2000000], numpy.float)
myobs = numpy.zeros([nvar,2000000], numpy.float)+numpy.NaN
myerr = numpy.zeros([nvar,2000000], numpy.float)+numpy.NaN
x = numpy.zeros([2000000], numpy.float)
nsteps=0
if (c == 0):
var_units=[]
var_long_names=[]
myscalefactors=[]
#Get observations
if (obs):
myobsfiles = os.listdir(myobsdir+'/'+mytstep+'/')
for f in myobsfiles:
if mysites[c] in f and '.csv' in f:
myobsfile = myobsdir+'/'+mytstep+'/'+f
avpd_obs = 1
if (mytstep == 'halfhourly' and '_HH_' in myobsfile):
avpd_obs = 2
if (os.path.exists(myobsfile) and ystart < 1900):
print ('Getting start and end year information from observation file')
thisrow=0
myobs_input = open(myobsfile)
for s in myobs_input:
if thisrow == 1:
ystart = int(s[0:4])
elif (thisrow > 1):
if (int(options.myyend) > 9000):
yend = min(int(s[0:4]), int(options.myyend))
thisrow=thisrow+1
myobs_input.close
for v in range(0,nvar):
if (os.path.exists(myobsfile)):
myobs_in = open(myobsfile)
thisrow=0
thisob=0
for s in myobs_in:
if (thisrow == 0):
header = s.split(',')
else:
myvals = s.split(',')
thiscol=0
if int(myvals[0][0:4]) >= ystart and int(myvals[0][0:4]) <= yend:
if (thisob == 0):
thisob = (int(myvals[0][0:4])-ystart)*npy*avpd_obs
if (thisob % avpd_obs == 0):
myobs[v,thisob/avpd_obs] = 0.0
myerr[v,thisob/avpd_obs] = 0.0
for h in header:
if (h.strip() == 'NEE_CUT_REF' and 'NEE' in myvars[v]):
myobs[v,thisob/avpd_obs] = myobs[v,thisob/avpd_obs] + float(myvals[thiscol])/avpd_obs
elif (h.strip () == 'NEE_CUT_REF_JOINTUNC' and \
'NEE' in myvars[v]):
myerr[v,thisob/avpd_obs] = myerr[v,thisob/avpd_obs] +float(myvals[thiscol])/avpd_obs
elif (h.strip() == 'GPP_NT_CUT_REF' and 'GPP' in myvars[v]):
myobs[v,thisob/avpd_obs] = myobs[v,thisob/avpd_obs] +float(myvals[thiscol])/avpd_obs
elif (h.strip() == 'GPP_NT_CUT_SE' and 'GPP' in myvars[v]):
myerr[v,thisob/avpd_obs] = myerr[v,thisob/avpd_obs] +float(myvals[thiscol])/avpd_obs
elif (h.strip() == 'RECO_NT_CUT_REF' and 'ER' in myvars[v]):
myobs[v,thisob/avpd_obs] = myobs[v,thisob/avpd_obs] +float(myvals[thiscol])/avpd_obs
elif (h.strip() == 'RECO_NET_CUT_SE' and 'ER' in myvars[v]):
myerr[v,thisob/avpd_obs] = myerr[v,thisob/avpd_obs] +float(myvals[thiscol])/avpd_obs
elif (h.strip() == 'LE_F_MDS' and 'EFLX_LH_TOT' in myvars[v]):
myobs[v,thisob/avpd_obs] = myobs[v,thisob/avpd_obs] +float(myvals[thiscol])/avpd_obs
elif (h.strip() == 'LE_RANDUNC' and 'EFLX_LH_TOT' in myvars[v]):
myerr[v,thisob/avpd_obs] = myerr[v,thisob/avpd_obs] +float(myvals[thiscol])/avpd_obs
elif (h.strip() == 'H_F_MDS' and 'FSH' in myvars[v]):
myobs[v,thisob/avpd_obs] = myobs[v,thisob/avpd_obs] +float(myvals[thiscol])/avpd_obs
elif (h.strip() == 'H_RANDUNC' and 'FSH' in myvars[v]):
myerr[v,thisob/avpd_obs] = myerr[v,thisob/avpd_obs] +float(myvals[thiscol])/avpd_obs
elif (h.strip() == 'TA_F_MDS' and 'TBOT' in myvars[v]):
myobs[v,thisob/avpd_obs] = myobs[v,thisob/avpd_obs] +float(myvals[thiscol])/avpd_obs
elif (h.strip() == 'SWIN_F_MDS' and 'FSDS' in myvars[v]):
myobs[v,thisob/avpd_obs] = myobs[v,thisob/avpd_obs] +float(myvals[thiscol])/avpd_obs
elif (h.strip() == 'WS_F' and 'WIND' in myvars[v]):
myobs[v,thisob/avpd_obs] = myobs[v,thisob/avpd_obs] +float(myvals[thiscol])/avpd_obs
elif (h.strip() == 'P_F' and 'RAIN' in myvars[v]):
myobs[v,thisob/avpd_obs] = myobs[v,thisob/avpd_obs] +float(myvals[thiscol])/avpd_obs
if myobs[v,thisob/avpd_obs] < -4000:
myobs[v,thisob/avpd_obs] = numpy.NaN
thiscol=thiscol+1
thisob=thisob+1
thisrow = thisrow+1
myobs_in.close()
else:
print 'Error reading observations for '+mysites[c]
#read monthly .nc files (default output)
if (ftype == 'default'):
for v in range(0,nvar):
nsteps = 0
for y in range(ystart,yend+1):
yst=str(10000+y)[1:5]
for m in range(0,12):
mst=str(101+m)[1:3]
myfile = os.path.abspath(mydir+'/'+mycases[c]+'_'+mysites[c]+'_'+mycompsets[c]+ \
".clm2."+hst+"."+yst+"-"+mst+".nc")
#get units/long names from first file
if (os.path.exists(myfile)):
if (y == ystart and m == 0 and c == 0):
nffile = netcdf.netcdf_file(myfile,"r")
varout=nffile.variables[myvars[v]]
var_long_names.append(varout.long_name)
nffile.close()
if (float(options.scale_factor) < -900):
if ('gC/m^2/s' in varout.units):
myscalefactors.append(3600*24)
var_units.append('g.C/m2/day')
else:
myscalefactors.append(1.0)
var_units.append(varout.units.replace('^',''))
else:
myscalefactors.append(float(options.scale_factor))
var_units.append(varout.units.replace('^',''))
if (y == ystart and m == 0 and v == 0): # get lat/lon info
nffile = netcdf.netcdf_file(myfile,"r")
mylat_vals.append(nffile.variables['lat'][0])
mylon_vals.append(nffile.variables['lon'][0])
nffile.close()
x[nsteps] = y+m/12.0
myvar_temp = getvar(myfile, myvars[v],npf,int(options.index), \
myscalefactors[v])
mydata[v,nsteps] = myvar_temp
if (myvars[v] == 'RAIN'):
myvar_temp2 = getvar(myfile,'SNOW',npf,int(options.index), \
myscalefactors[v])
mydata[v,nsteps] = mydata[v,nsteps]+myvar_temp2
else:
if (v == 0 and m == 0):
print 'Warning: '+myfile+' does not exist'
x[nsteps] = y+m/12.0
mydata[v,nsteps] = numpy.NaN
if (y-1 < yend_all):
yend_all = y-1
nsteps = nsteps+1
#read annual .nc files
if (ftype == 'custom'):
for v in range(0,nvar):
nsteps=0
nfiles = (yend-ystart)/nypf
nc=1
starti=0
ylast=0
if (options.spinup):
nc=2
if (npf == 1):
starti = 1
for n in range(0,nc):
if ((options.spinup)and n== 0):
if (mycases[c] == ''):
if (options.ad_Pinit):
mydir = cesmdir+'/'+mysites[c]+'_'+mycompsets[c]+'_ad_spinup/run/'
else:
mydir = cesmdir+'/'+mysites[c]+'_'+mycompsets[c].replace('CNP','CN')+ \
'_ad_spinup/run/'
else:
if (options.ad_Pinit):
mydir = cesmdir+'/'+mycases[c]+'_'+mysites[c]+'_'+ \
mycompsets[c]+'_ad_spinup/run/'
thiscompset = mycompsets[c]+'_ad_spinup'
else:
mydir = cesmdir+'/'+mycases[c]+'_'+mysites[c]+'_'+ \
mycompsets[c].replace('CNP','CN')+'_ad_spinup/run/'
thiscompset = mycompsets[c].replace('CNP','CN')+'_ad_spinup'
else:
if (mycases[c] == ''):
mydir = cesmdir+'/'+mysites[c]+'_'+mycompsets[c]+'/run/'
else:
mydir = cesmdir+'/'+mycases[c]+'_'+mysites[c]+'_'+ \
mycompsets[c]+'/run/'
thiscompset = mycompsets[c]
for y in range(starti,nfiles+1): #skip first file in spinup
yst=str(10000+ystart+(y*nypf))[1:5]
if (mycases[c].strip() == ''):
myfile = os.path.abspath(mydir+'/'+mycases[c]+'_'+thiscompset+".clm2."+hst+ \
"."+yst+"-01-01-00000.nc")
else:
myfile = os.path.abspath(mydir+'/'+mycases[c]+"_"+mysites[c]+'_'+thiscompset+ \
".clm2."+hst+"."+yst+"-01-01-00000.nc")
if (os.path.exists(myfile)):
if (n == 0):
ylast = y
if (y == starti and n == 0 and c == 0):
nffile = netcdf.netcdf_file(myfile,"r")
varout=nffile.variables[myvars[v]]
var_long_names.append(varout.long_name)
if (float(options.scale_factor) < -900):
if ('gC/m^2/s' in varout.units):
if (npf >= 365):
myscalefactors.append(3600*24)
var_units.append('g.C/m2/day')
else:
myscalefactors.append(3600*24*365)
var_units.append('g.C/m2/yr')
else:
myscalefactors.append(1.0)
var_units.append(varout.units.replace('^',''))
else:
myscalefactors.append(float(options.scale_factor))
var_units.append(varout.units.replace('^',''))
nffile.close()
if (y == starti and n == 0 and v == 0): # get lat/lon info
nffile = netcdf.netcdf_file(myfile,"r")
mylat_vals.append(nffile.variables['lat'][0])
mylon_vals.append(nffile.variables['lon'][0])
nffile.close()
myvar_temp = getvar(myfile,myvars[v],npf,int(options.index), \
myscalefactors[v])
if (myvars[v] == 'RAIN'):
myvar_temp2 = getvar(myfile,'SNOW',npf,int(options.index), \
myscalefactors[v])
if len(myvar_temp) == npf:
for i in range(0,npf):
myind = ylast*n*npf+y*npf+i
x[nsteps] = ystart+(ylast*n*nypf+y*nypf) + nypf*(i*1.0-0.5)/npf
mydata[v,nsteps] = myvar_temp[i]
if (myvars[v] == 'RAIN'): #add snow for total precip
mydata[v,nsteps] = mydata[v,nsteps]+myvar_temp2[i]
nsteps=nsteps+1
else:
for i in range(0,npf):
myind=ylast*n*npf+(y-1)*npf+i
x[myind] = ystart+(ylast*n*nypf+y*nypf) + nypf*(i*1.0-0.5)/npf
mydata[v,myind] = numpy.NaN
nsteps=nsteps+1
else:
if (v == 0):
print('Warning: '+myfile+' does not exist')
if (y-1 < yend_all):
yend_all = y-1
for i in range(0,npf):
if (n == nc-1):
myind=ylast*n*npf+y*npf+i
x[myind] = ystart+(ylast*n*nypf+y*nypf) + nypf*(i*1.0-0.5)/npf
mydata[v,myind] = numpy.NaN
nsteps=nsteps+1
#perform averaging and write output files
if (avtype == 'default'):
for v in range(0,nvar):
snum[c] = 0
for s in range(0,int(nsteps/avpd)):
x_toplot[c, snum[c]] = sum(x[s*avpd:(s+1)*avpd])/avpd
data_toplot[c, v, snum[c]] = sum(mydata[v,s*avpd:(s+1)*avpd])/avpd
obs_toplot[c, v, snum[c]] = sum(myobs[v,s*avpd:(s+1)*avpd])/avpd
if (min(myerr[v,s*avpd:(s+1)*avpd]) < -9000):
err_toplot[c,v,snum[c]] = 0
else:
err_toplot[c, v, snum[c]] = sum(myerr[v,s*avpd:(s+1)*avpd])/avpd
snum[c] = snum[c]+1
#diurnal average (must have hourly output)
if (avtype == 'diurnal'):
snum[c]=24
for v in range(0,nvar):
mysum = numpy.zeros(snum[c], numpy.float)
mysum_obs = numpy.zeros(snum[c], numpy.float)
myct = numpy.zeros(snum[c],numpy.float)
myct_obs = numpy.zeros(snum[c],numpy.float)
for y in range(0,(yend_all-ystart+1)):
for d in range (int(options.dstart),int(options.dend)):
for s in range(0,snum[c]):
h=s
if (h >= 24):
h=h-24
mysum[s] = mysum[s]+mydata[v,y*8760+(d-1)*24+h-int(options.timezone)+1]
myct[s] = myct[s]+1
if (myobs[v,y*8760+(d-1)*24+h] > -900):
mysum_obs[s] = mysum_obs[s]+myobs[v,y*8760+(d-1)*24+h]
myct_obs[s] = myct_obs[s]+1
for s in range(0,snum[c]):
if (myct_obs[s] > 0):
mysum_obs[s] = mysum_obs[s]/myct_obs[s]
else:
mysum_obs[s] = numpy.NaN
x_toplot[c,s] = s+1
obs_toplot[c, v, s] = mysum_obs[s]
data_toplot[c, v, s] = mysum[s]/myct[s]
#seasonal average (assumes default monthly output)
if (avtype == 'seasonal'):
for v in range(0,nvar):
snum[c] = 12
mysum=numpy.zeros(snum[c], numpy.float)
mysum_obs = numpy.zeros(snum[c], numpy.float)
mycount_obs = numpy.zeros(snum[c], numpy.int)
for y in range(0,(yend_all-ystart+1)):
for s in range(0,snum[c]):
mysum[s]=mysum[s]+mydata[v,(y*12+s)]/float(yend_all-ystart+1)
if (myobs[v,(y*12+s)] > -900):
mysum_obs[s]=mysum_obs[s]+myobs[v,(y*12+s)]
mycount_obs[s] = mycount_obs[s]+1
for s in range(0,snum[c]):
if (mycount_obs[s] > 0):
mysum_obs[s] = mysum_obs[s]/mycount_obs[s]
else:
mysum_obs[s] = numpy.NaN
x_toplot[c,s] = s+1.5
obs_toplot[c,v,s] = mysum_obs[s]
data_toplot[c,v,s] = mysum[s]
#diagnostics, outputs and plots
if (options.spinup):
analysis_type = 'spinup'
elif (options.mydiurnal):
analysis_type = 'diurnalcycle_'+str(options.dstart)+'_'+str(options.dend)
elif (options.myseasonal):
analysis_type = 'seasonalcycle'
elif (mytstep == 'halfhourly'):
analysis_type = 'hourly'
else:
analysis_type=mytstep
rmse = numpy.zeros([len(myvars),ncases],numpy.float)
bias = numpy.zeros([len(myvars),ncases],numpy.float)
corr = numpy.zeros([len(myvars),ncases],numpy.float)
for v in range(0,len(myvars)):
if (not options.noplot):
fig = plt.figure()
ax = plt.subplot(111)
colors=['b','g','r','c','m','y','k','b','g','r','c','m','y','k','b','g','r','c','m','y','k']
styles=['-','-','-','-','-','-','-','--','--','--','--','--','--','--','-.','-.','-.','-.','-.','-.','-.']
for c in range(0,ncases):
#Output data in netcdf format
if (c == 0):
if (v == 0):
ftype_suffix=['model','obs']
os.system('mkdir -p ./plots/'+mycases[0]+'/'+analysis_type)
for ftype in range(0,2):
outdata = netcdf.netcdf_file('./plots/'+mycases[0]+'/'+analysis_type+'/'+mycases[0]+"_"+mysites[0]+'_'+ftype_suffix[ftype]+".nc","w",mmap=False)
outdata.createDimension('time',snum[c])
#outdata.createDimension('lat',ncases)
#outdata.createDimension('lon',ncases)
outdata.createDimension('gridcell',ncases)
outdata.createDimension('strlen',6)
mylat = outdata.createVariable('lat','f',('gridcell',))
mylat.long_name='coordinate latitude'
mylat.units='degrees_north'
mylon = outdata.createVariable('lon','f',('gridcell',))
mylon.long_name='coordinate longitude'
mylon.units='degrees_east'
mytime = outdata.createVariable('time','f',('time',))
mytime.long_name='time'
mytime.units='days since '+str(ystart)+'-01-01 00:00:00'
mytime.calendar='noleap'
mytime[:] = (x_toplot[0,0:snum[c]]-ystart)*365
#myname = outdata.createVariable('site_name','c',('lat','lon','strlen'))
myname = outdata.createVariable('site_name','c',('gridcell','strlen'))
myname[:,:] = '' #changed for gridcell
outdata.close()
for ftype in range(0,2):
outdata = netcdf.netcdf_file('./plots/'+mycases[0]+'/'+analysis_type+'/'+mycases[0]+"_"+mysites[0]+'_'+ftype_suffix[ftype]+".nc","a",mmap=False)
if (c == 0):
#myvar = outdata.createVariable(myvars[v],'f',('time','lat','lon'))
myvar = outdata.createVariable(myvars[v],'f',('time','gridcell'))
myvar.units=var_units[v]
myvar.missing_value=1e36
myvar[:,:]=myvar.missing_value #changed for gridcell
else:
myvar=outdata.variables[myvars[v]]
scalefac = 1.0
if (var_units[v] == 'g.C/m2/day'):
myvar.units = 'kg.C/m2/s'
scalefac = 1.0 / (3600*24*1000.0)
if (ftype == 0):
myvar[:,c] = data_toplot[c,v,0:snum[c]]*scalefac #changed for gridcell
if (ftype == 1):
myvar[:,c] = obs_toplot[c,v,0:snum[c]]*scalefac #changed for gridcell
if (v == 0):
myname = outdata.variables['site_name']
myname[c,0:6] = str(mysites[c])[0:6] #changed for gridcell
mylat = outdata.variables['lat']
mylat[c] = mylat_vals[c]
mylon = outdata.variables['lon']
mylon[c] = mylon_vals[c]
outdata.close()
#----------------------- plotting ---------------------------------
if (options.noplot == False):
gind=[]
for i in range(0,snum[c]):
if (obs_toplot[c,v,i] < -900):
obs_toplot[c,v,i] = numpy.nan
else:
gind.append(i)
rmse[v,c] = rmse[v,c] + (data_toplot[c,v,i]-obs_toplot[c,v,i])**2.0
bias[v,c] = bias[v,c] + (data_toplot[c,v,i]-obs_toplot[c,v,i])
rmse[v,c] = (rmse[v,c]/len(gind))**0.5
bias[v,c] = bias[v,c]/len(gind)
corr[v,c] = numpy.corrcoef(data_toplot[c,v,gind],obs_toplot[c,v,gind])[1,0]
if (options.ylog):
ax.plot(x_toplot[c, 0:snum[c]], abs(data_toplot[c,v,0:snum[c]]), label=mytitles[c], color=colors[c], \
linestyle=styles[c], linewidth=3)
else:
ax.plot(x_toplot[c, 0:snum[c]], (data_toplot[c,v,0:snum[c]]), label=mytitles[c], color=colors[c], \
linestyle=styles[c], linewidth=3)
if (options.myobs and c == 0):
ax.errorbar(x_toplot[c, 0:snum[c]], obs_toplot[c,v,0:snum[c]], yerr = err_toplot[c,v,0:snum[c]], \
color=colors[c], fmt='o')
if (options.noplot == False):
if (avtype == 'seasonal'):
plt.xlabel('Model Month')
elif (avtype == 'diurnal'):
plt.xlabel('Model Hour (LST)')
else:
plt.xlabel('Model Year')
plt.ylabel(myvars[v]+' ('+var_units[v]+')')
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.legend(loc='center left', bbox_to_anchor=(1, 0.5),prop={'size': 10})
plt.title(var_long_names[v]+' at '+mysites[0])
if (options.ylog):
plt.yscale('log')
os.system('mkdir -p ./plots/'+mycases[0]+'/'+analysis_type)
if (options.pdf):
fig_filename = './plots/'+mycases[0]+'/'+analysis_type+'/'+mysites[0]+'_'+myvars[v]
fig_filename = fig_filename+'_'+analysis_type
fig.savefig(fig_filename+'.pdf')
if (not options.pdf and not options.noplot):
plt.show()