def dailyAve():
from nco import Nco
import datetime
nco = Nco()
for d in range(365):
dp = datetime.date(startY,1,1)+datetime.timedelta(d)
print "Averaging TRMM 3B42 for day "+dp.strftime('%j')+"..."
ifile = ' '.join("3B42_daily."+str(year)+"."+dp.strftime('%m')+"."+dp.strftime('%d')+".7.nc" for year in range(startY,endY))
ofile = "3B42_aver."+dp.strftime('%j')+".nc"
if not os.path.isfile(ofile):
nco.ncra(input=ifile, output=ofile)
nco.ncrcat(input="3B42_aver.*.nc", output="3B42_cat.nc", options="-d time,1,365")
nco.ncwa(input="3B42_cat.nc", output="3B42_MAP.nc", options='-N -a time')
return None
def dailyAve():
from nco import Nco
import datetime
nco = Nco()
for d in range(365):
dp = datetime.date(startY, 1, 1) + datetime.timedelta(d)
print "Averaging TRMM 3B42 for day " + dp.strftime('%j') + "..."
ifile = ' '.join("3B42_daily." + str(year) + "." + dp.strftime('%m') +
"." + dp.strftime('%d') + ".7.nc"
for year in range(startY, endY))
ofile = "3B42_aver." + dp.strftime('%j') + ".nc"
if not os.path.isfile(ofile):
nco.ncra(input=ifile, output=ofile)
nco.ncrcat(input="3B42_aver.*.nc",
output="3B42_cat.nc",
options="-d time,1,365")
nco.ncwa(input="3B42_cat.nc", output="3B42_MAP.nc", options='-N -a time')
return None
def run(self, latidx, lonidx):
try:
inputfile_dir = self.config.get_dict(self.translator_type,
'inputfile_dir',
default=os.path.join(
'..', '..'))
inputfiles = self.config.get_dict(self.translator_type,
'inputfile',
default='1.soil.tile.nc4')
inputfiles = param_to_list(inputfiles)
latdelta, londelta = [
double(d) / 60 for d in self.config.get('delta').split(',')
] # convert to degrees
nco = Nco()
outputfiles = self.config.get_dict(
self.translator_type,
'outputfile',
default=inputs_to_outputs(inputfiles))
outputfiles = param_to_list(outputfiles)
inputfiles = apply_prefix(inputfiles, inputfile_dir)
for i in range(len(inputfiles)):
inputfile = inputfiles[i]
outputfile = outputfiles[i]
with nc(inputfile) as f:
variables = f.variables.keys()
soil_id = f.getncattr('soil_id')
# get latitude, longitude limits
minlat = 90 - latdelta * latidx
maxlat = minlat + latdelta
minlon = -180 + londelta * (lonidx - 1)
maxlon = minlon + londelta
# additional options
options = '-h -a lat,lon -d lat,%f,%f -d lon,%f,%f --rd' % (
minlat, maxlat, minlon, maxlon)
if 'cropland' in variables:
options += ' -w cropland'
# perform aggregation
nco.ncwa(input=inputfile, output=outputfile, options=options)
# add degenerate profile dimension
nco.ncecat(input=outputfile,
output=outputfile,
options='-O -h -u profile')
nco.ncap2(input=outputfile,
output=outputfile,
options='-O -h -s profile[profile]=1')
# add soil_id variable
nco.ncap2(input=outputfile,
output=outputfile,
options='-O -h -s soil_id[profile,lat,lon]=1')
nco.ncatted(input=outputfile,
output=outputfile,
options='-O -h -a units,soil_id,c,c,"mapping"')
nco.ncatted(input=outputfile,
output=outputfile,
options='-O -h -a long_name,soil_id,c,c,"%s"' %
str(soil_id))
nco.ncatted(input=outputfile,
output=outputfile,
options='-O -h -a soil_id,global,d,c,""')
# change latitude, longitude to simulated point
with nc(outputfile, 'a') as f:
latv = f.variables['lat']
latv[:] = 90 - latdelta * (latidx - 0.5)
lonv = f.variables['lon']
lonv[:] = -180 + londelta * (lonidx - 0.5)
return True
except:
print "[%s]: %s" % (os.path.basename(__file__),
traceback.format_exc())
return False
def run(self, latidx, lonidx):
try:
inputfile_dir = self.config.get_dict(self.translator_type,
'inputfile_dir',
default=os.path.join(
'..', '..'))
inputfiles = self.config.get_dict(self.translator_type,
'inputfile')
variables = self.config.get_dict(self.translator_type,
'variables',
default=None)
slicefirst = self.config.get_dict(self.translator_type,
'slicefirst',
default=False)
latdelta, londelta = [
double(d) / 60 for d in self.config.get('delta').split(',')
] # convert to degrees
refyear = self.config.get('ref_year')
numyears = self.config.get('num_years')
if inputfiles:
inputfiles = param_to_list(inputfiles)
inputfiles = apply_prefix(inputfiles, inputfile_dir)
else:
inputfiles = list_tiles(
os.path.join(inputfile_dir, '*.clim.tile.nc4'))
outputfiles = self.config.get_dict(
self.translator_type,
'outputfile',
default=inputs_to_outputs(inputfiles))
outputfiles = param_to_list(outputfiles)
if variables:
variables = variables.split(',')
nco = Nco()
for i in range(len(inputfiles)):
inputfile = inputfiles[i]
outputfile = outputfiles[i]
with nc(inputfile) as f:
lats, lons = f.variables['lat'][:], f.variables['lon'][:]
varkeys = array(f.variables.keys())
flatdelta = abs(diff(lats)[0]) # assume uniform grid
flondelta = abs(diff(lons)[0])
if slicefirst and not i:
time = f.variables['time'][:].astype(
int) # convert to integers
refyeari = int(
findall(r'\d+', f.variables['time'].units)[0])
ndays0 = (datetime(refyear, 1, 1) -
datetime(refyeari, 1, 1)).days
ndays1 = (datetime(refyear + numyears, 12, 31) -
datetime(refyeari, 1, 1)).days
tidx0 = where(
time == ndays0)[0][0] if ndays0 in time else 0
tidx1 = where(
time ==
ndays1)[0][0] if ndays1 in time else len(time) - 1
if flatdelta > latdelta:
minlat = 90 - latdelta * (latidx - 0.5)
maxlat = minlat
else:
minlat = 90 - latdelta * latidx
maxlat = minlat + latdelta
if flondelta > londelta:
minlon = -180 + londelta * (lonidx - 0.5)
maxlon = minlon
else:
minlon = -180 + londelta * (lonidx - 1)
maxlon = minlon + londelta
# lookup variables
if variables:
varnames = [0] * len(variables)
for j in range(len(variables)):
idx = foundVar(varkeys, variables[j])
if not len(idx):
raise Exception(
'Variable %s not found in file %s' %
(variables[j], inputfile))
varnames[j] = str(','.join(varkeys[idx]))
# basic options
options = '-h -a lat,lon -d lat,%f,%f -d lon,%f,%f --rd' % (
minlat, maxlat, minlon, maxlon)
# select time
if slicefirst and not i and (tidx0 != 0
or tidx1 != len(time) - 1):
options += ' -d time,%d,%d' % (tidx0, tidx1)
# select variables
if variables:
options += ' -v %s' % ','.join(varnames)
# average over space
if logical_and(lats >= minlat,
lats <= maxlat).sum() > 1 or logical_and(
lons >= minlon, lons <= maxlon).sum() > 1:
options += ' -w cropland'
# average over latitude, longitude
nco.ncwa(input=inputfile, output=outputfile, options=options)
# change latitude, longitude to simulated point
with nc(outputfile, 'a') as f:
latv = f.variables['lat']
latv[:] = 90 - latdelta * (latidx - 0.5)
lonv = f.variables['lon']
lonv[:] = -180 + londelta * (lonidx - 0.5)
return True
except:
print "[%s]: %s" % (os.path.basename(__file__),
traceback.format_exc())
return False
from cdo import Cdo
from nco import Nco
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
import sys
import metpy.calc as mpcalc
from metpy.interpolate import cross_section
cdo = Cdo()
cdo.timmean(input='PFile.nc', output='AFile.nc')
nco = Nco()
nco.ncwa(input='AFile.nc', output='BFile.nc', options=['-a', 'bnds'])
nco.ncks(input='BFile.nc',
output='CFile.nc',
options=['-C', '-O', '-x', '-v', 'time_bnds'])
cdo.timmean(input='rhumFile.nc', output='dFile.nc')
nco.ncwa(input='dFile.nc', output='eFile.nc', options=['-a', 'bnds'])
nco.ncks(input='eFile.nc',
output='fFile.nc',
options=['-C', '-O', '-x', '-v', 'time_bnds'])
fFile = Dataset('fFile.nc', 'r')
rhum = fFile.variables['rhum'][:]
z = np.zeros((1, 9, 73, 144), dtype=rhum.dtype)
c = np.concatenate((rhum, z), axis=1)
logger.info("merge PR, TAS, and SMB")
nco.ncks(input=smb_merged_file_time_mean,
output=merged_file_time_mean,
overwrite=True)
nco.ncks(input=pr_merged_file_time_mean,
output=merged_file_time_mean,
append=True)
nco.ncks(input=tas_merged_file_time_mean,
output=merged_file_time_mean,
append=True)
# add topo file
logger.info("removing height dimension of topo file")
topo_file_tmp_1 = "tmp1_topo_geog.nc"
topo_file_tmp_2 = "tmp2_topo_geog.nc"
nco.ncwa(input=topo_file, output=topo_file_tmp_1, average="height")
nco.ncwa(input=topo_file_tmp_1, output=topo_file_tmp_2, average="time")
logger.info("renaming variables and dimensions")
rDict = {"var6": "usurf"}
dDict = {"x": "rlon", "y": "rlat"}
nco.ncrename(
input=topo_file_tmp_2,
options=[c.Rename("variable", rDict),
c.Rename("dimension", dDict)])
logger.info("add topo file {} to {}".format(topo_file, merged_file_time_mean))
nco.ncks(input=topo_file_tmp_2,
output=merged_file_time_mean,
append=True,
variable="usurf")
opt = [
