373 lines
12 KiB
Python
373 lines
12 KiB
Python
#! /usr/bin/env python
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import sys
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import string
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import subprocess
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from getpass import getuser
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import numpy as np
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import netCDF4 as netcdf4
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import xarray as xr
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def mprint(mstr):
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vnum=sys.version_info[0]
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if vnum == 3:
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print(mstr)
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if vnum == 2:
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print mstr
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'''
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#------------------------------------------------------------------#
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#--------------------- Instructions -----------------------------#
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#------------------------------------------------------------------#
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After creating a single point surface data file from a global
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surface data file, use this script to overwrite some fields with
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site-specific data.
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'''
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myname = getuser()
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#-- Create single point CLM surface data file
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create_surfdata = True
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#-- specify site from which to extract data ----------
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sitename='US-Ha1'
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#-- Site level data directory ------------------------
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site_dir='../PTCLM/PTCLM_sitedata/'
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#-- Specify original file
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fsurf = '/glade/scratch/'+myname+'/single_point/surfdata_0.9x1.25_16pfts_CMIP6_simyr1850_287.8_42.5_c170706.nc'
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#-- Output directory ---------------------------------
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dir_output='/glade/scratch/'+myname+'/single_point/'
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#-- Specify new file name -----------------------------
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command='date "+%y%m%d"'
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x2=subprocess.Popen(command,stdout=subprocess.PIPE,shell='True')
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x=x2.communicate()
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timetag = x[0].strip()
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fsurf2 = dir_output + 'surfdata_0.9x1.25_16pfts_CMIP6_simyr2000_'+sitename+'_site.c'+timetag+'.nc'
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#== End User Mods =====================================
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#site_code,pft_f1,pft_c1,pft_f2,pft_c2,pft_f3,pft_c3,pft_f4,pft_c4,pft_f5,pft_c5
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pftfile =site_dir+'PTCLMDATA_pftdata.txt'
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#site_code,name,state,lon,lat,elev,startyear,endyear,alignyear,timestep,campaign
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sitefile=site_dir+'PTCLMDATA_sitedata.txt'
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#site_code,soil_depth,n_layers,layer_depth,layer_sand%,layer_clay%
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soilfile=site_dir+'PTCLMDATA_soildata.txt'
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#-- Raw datafiles ------------------------
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rawdatafile = '../mksurfdata_map/mksurfdata_map.namelist'
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mstr='Checking for data for site: '+sitename
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mprint(mstr)
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#-- Read raw datafiles ------------------------
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with open(rawdatafile, 'r') as t1:
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for tmp in t1:
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x=tmp.split('=')
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if x[0].strip() == 'mksrf_fsoitex':
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fsoitex = x[1].strip()
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fsoitex = fsoitex.strip("'")
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if x[0].strip() == 'mksrf_forganic':
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forganic = x[1].strip()
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forganic = forganic.strip("'")
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if x[0].strip() == 'mksrf_flakwat':
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flakwat= x[1].strip()
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flakwat= flakwat.strip("'")
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if x[0].strip() == 'mksrf_fwetlnd':
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fwetlnd = x[1].strip()
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fwetlnd = fwetlnd.strip("'")
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if x[0].strip() == 'mksrf_fmax':
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fmax = x[1].strip()
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fmax = fmax.strip("'")
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if x[0].strip() == 'mksrf_fglacier':
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fglacier= x[1].strip()
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fglacier= fglacier.strip("'")
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if x[0].strip() == 'mksrf_fglacierregion':
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fglacierregion= x[1].strip()
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fglacierregion= fglacierregion.strip("'")
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if x[0].strip() == 'mksrf_fvocef':
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fvocef= x[1].strip()
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fvocef= fvocef.strip("'")
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if x[0].strip() == 'mksrf_furbtopo':
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furbtopo = x[1].strip()
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furbtopo = furbtopo.strip("'")
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if x[0].strip() == 'mksrf_fgdp':
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fgdp = x[1].strip()
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fgdp = fgdp.strip("'")
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if x[0].strip() == 'mksrf_fpeat':
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fpeat = x[1].strip()
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fpeat = fpeat.strip("'")
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if x[0].strip() == 'mksrf_fsoildepth':
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fsoildepth= x[1].strip()
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fsoildepth= fsoildepth.strip("'")
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if x[0].strip() == 'mksrf_fabm':
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fabm = x[1].strip()
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fabm = fabm.strip("'")
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if x[0].strip() == 'mksrf_ftopostats':
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ftopostats = x[1].strip()
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ftopostats = ftopostats.strip("'")
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if x[0].strip() == 'mksrf_fvic':
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fvic = x[1].strip()
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fvic = fvic.strip("'")
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if x[0].strip() == 'mksrf_furban':
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furban = x[1].strip()
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furban = furban.strip("'")
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if x[0].strip() == 'mksrf_fvegtyp':
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fvegtyp= x[1].strip()
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fvegtyp= fvegtyp.strip("'")
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if x[0].strip() == 'mksrf_fhrvtyp':
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fhrvtyp= x[1].strip()
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fhrvtyp= fhrvtyp.strip("'")
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if x[0].strip() == 'mksrf_fsoicol':
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fsoicol = x[1].strip()
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fsoicol = fsoicol.strip("'")
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if x[0].strip() == 'mksrf_flai':
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flai = x[1].strip()
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flai = flai.strip("'")
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#Open site file and extract data
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site_found = False
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with open(sitefile, 'r') as t1:
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for tmp in t1:
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x=tmp.split(',')
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if x[0] == sitename:
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site_found = True
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site_header = tmp
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name = x[1]
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state = x[2]
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plon = np.float32(x[3])
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plat = np.float32(x[4])
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elev = np.float32(x[5])
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startyear= np.int32(x[6])
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endyear = np.int32(x[7])
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alignyear= np.int32(x[8])
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timestep = np.float32(x[9])
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campaign = x[10]
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exit
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if not site_found:
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mprint('No site matching '+sitename+' was found')
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stop
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else:
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mprint(site_header)
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# convert longitude to east if needed
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if plon < 0:
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plon+=360.0
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#-- Open pft file and extract data ---------------------------------
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site_found = False
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with open(pftfile, 'r') as t1:
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for tmp in t1:
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x=tmp.split(',')
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if x[0] == sitename:
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site_found = True
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pft_f1= np.int32(x[1])
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pft_c1= np.int32(x[2])
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pft_f2= np.int32(x[3])
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pft_c2= np.int32(x[4])
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pft_f3= np.int32(x[5])
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pft_c3= np.int32(x[6])
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pft_f4= np.int32(x[7])
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pft_c4= np.int32(x[8])
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pft_f5= np.int32(x[9])
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pft_c5= np.int32(x[10])
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exit
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#-- Open soil file and extract data ---------------------------------
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site_found = False
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with open(soilfile, 'r') as t1:
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for tmp in t1:
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x=tmp.split(',')
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if x[0] == sitename:
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site_found = True
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soil_depth = np.float32(x[1])
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n_layers = np.int32(x[2])
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layer_depth = np.float32(x[3])
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layer_sand_pct = np.float32(x[4])
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layer_clay_pct = np.float32(x[5])
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exit
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#-- create surface data file --------------------------------------
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##2
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if create_surfdata:
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f1 = xr.open_dataset(fsurf)
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# expand dimensions
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#f1 = f1.expand_dims(['lsmlat','lsmlon'])
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# create 1d variables
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lon0=np.asarray(f1['LONGXY'][0,:])
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lon=xr.DataArray(lon0,name='lon',dims='lsmlon',coords={'lsmlon':lon0})
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lat0=np.asarray(f1['LATIXY'][:,0])
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lat=xr.DataArray(lat0,name='lat',dims='lsmlat',coords={'lsmlat':lat0})
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#f2=f1.assign({'lon':lon,'lat':lat})
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# the above doesn't work now
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f2=f1.assign()
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f2['lon'] = lon
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f2['lat'] = lat
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# make gridcell entirely natural vegetation *or* entirely crop
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new_pct_natveg = 0.
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new_pct_crop = 0.
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if np.logical_and(
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np.any([pft_f1,pft_f2,pft_f3,pft_f4,pft_f5]),
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np.any([(pft_c1 < 15),(pft_c2 < 15),(pft_c3 < 15),(pft_c4 < 15),(pft_c5 < 15)])):
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new_pct_natveg = 100.
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if np.logical_and(
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np.any([pft_f1,pft_f2,pft_f3,pft_f4,pft_f5]),
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np.any([(pft_c1 >= 15),(pft_c2 >= 15),(pft_c3 >= 15),(pft_c4 >= 15),(pft_c5 >= 15)])):
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new_pct_crop = 100.
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if new_pct_natveg == new_pct_crop:
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print 'both natveg and crop set to 100, exiting'
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stop
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#-- Remove non-vegetated landunits ---------------------------------
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f2['PCT_NATVEG'] = new_pct_natveg
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f2['PCT_CROP'] = new_pct_crop
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f2['PCT_LAKE'] = 0.
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f2['PCT_WETLAND'] = 0.
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f2['PCT_URBAN'] = 0.
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f2['PCT_GLACIER'] = 0.
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f2['PCT_OCEAN'] = 0.
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#-- Overwrite global data with raw data ----------------------------
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f2['LONGXY'] = plon
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f2['LATIXY'] = plat
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#-- Soil texture
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r1 = xr.open_dataset(fsoitex)
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plonc = plon
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if plonc > 180.0:
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plonc -= 360.0
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# set coordinates (seems to require 'lon' and 'lat' to recognize...
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r1=r1.rename({'LON':'lon','LAT':'lat'})
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r1.set_coords(['lon','lat'],inplace=True)
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# extract gridcell closest to plonc/plat
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r2 = r1.sel(lon=plonc,lat=plat,method='nearest')
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# extract sand/clay profiles for given mapunit
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mapunit = np.int32(r2['MAPUNITS'])
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f2['PCT_SAND'][:,0,0] = np.asarray(r2['PCT_SAND'][:,mapunit])
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f2['PCT_CLAY'][:,0,0] = np.asarray(r2['PCT_CLAY'][:,mapunit])
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r1.close() ; r2.close()
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#-- Organic
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r1 = xr.open_dataset(forganic)
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r1=r1.rename({'LON':'lon','LAT':'lat'})
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r1.set_coords(['lon','lat'],inplace=True)
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# extract gridcell closest to plonc/plat
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r2 = r1.sel(lon=plonc,lat=plat,method='nearest')
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f2['ORGANIC'][:,0,0] = np.asarray(r2['ORGANIC'])
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r1.close() ; r2.close()
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#-- Fmax
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r1 = xr.open_dataset(fmax)
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# extract gridcell closest to plonc/plat
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r2 = r1.sel(lon=plonc,lat=plat,method='nearest')
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f2['FMAX'] = np.asarray(r2['FMAX'])
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r1.close() ; r2.close()
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#-- VOC
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r1 = xr.open_dataset(fvocef)
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# extract gridcell closest to plonc/plat
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r2 = r1.sel(lon=plonc,lat=plat,method='nearest')
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f2['EF1_BTR'] = np.asarray(r2['ef_btr'])
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f2['EF1_CRP'] = np.asarray(r2['ef_crp'])
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f2['EF1_FDT'] = np.asarray(r2['ef_fdt'])
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f2['EF1_FET'] = np.asarray(r2['ef_fet'])
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f2['EF1_GRS'] = np.asarray(r2['ef_grs'])
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f2['EF1_SHR'] = np.asarray(r2['ef_shr'])
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r1.close() ; r2.close()
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#-- GDP
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r1 = xr.open_dataset(fgdp)
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# extract gridcell closest to plonc/plat
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r2 = r1.sel(lon=plonc,lat=plat,method='nearest')
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f2['gdp'] = np.asarray(r2['gdp'])
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r1.close() ; r2.close()
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#-- Peat
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r1 = xr.open_dataset(fpeat)
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# extract gridcell closest to plonc/plat
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r2 = r1.sel(lon=plonc,lat=plat,method='nearest')
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f2['peatf'] = np.asarray(r2['peatf'])
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r1.close() ; r2.close()
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#-- Soil Depth
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r1 = xr.open_dataset(fsoildepth)
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# create 1d variables
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lon0=np.asarray(r1['LONGXY'][0,:])
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lon=xr.DataArray(lon0,name='lon',dims='lon',coords={'lon':lon0})
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lat0=np.asarray(r1['LATIXY'][:,0])
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lat=xr.DataArray(lat0,name='lat',dims='lat',coords={'lat':lat0})
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r1['lon'] = lon
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r1['lat'] = lat
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r1=r1.rename({'lsmlon':'lon','lsmlat':'lat'})
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# extract gridcell closest to plonc/plat
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r2 = r1.sel(lon=plonc,lat=plat,method='nearest')
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f2['zbedrock'] = np.asarray(r2['Avg_Depth_Median'])
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#f2['zbedrock'] = np.asarray(r2['Avg_Depth_Mean'])
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r1.close() ; r2.close()
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#-- ABM
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r1 = xr.open_dataset(fabm)
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# extract gridcell closest to plonc/plat
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r2 = r1.sel(lon=plonc,lat=plat,method='nearest')
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f2['abm'] = np.asarray(r2['abm'])
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r1.close() ; r2.close()
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#-- SLOPE
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r1 = xr.open_dataset(ftopostats)
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rlon=np.asarray(r1['LONGITUDE'])
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rlat=np.asarray(r1['LATITUDE'])
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# extract gridcell closest to plon/plat (data are 1-d) (lon [0,360])
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k1 = np.argmin(np.power(rlon - plon,2)+np.power(rlat - plat,2))
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f2['SLOPE'] = np.asarray(r1['SLOPE'][k1])
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r1.close() ; r2.close()
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#-- VIC
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r1 = xr.open_dataset(fvic)
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r1=r1.rename({'lsmlon':'lon','lsmlat':'lat'})
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# extract gridcell closest to plonc/plat
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r2 = r1.sel(lon=plonc,lat=plat,method='nearest')
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f2['Ws'] = np.asarray(r2['Ws'])
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f2['Dsmax'] = np.asarray(r2['Dsmax'])
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f2['Ds'] = np.asarray(r2['Ds'])
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r1.close() ; r2.close()
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#-- Soil Color
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r1 = xr.open_dataset(fsoicol)
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r1=r1.rename({'LON':'lon','LAT':'lat'})
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r1.set_coords(['lon','lat'],inplace=True)
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# extract gridcell closest to plonc/plat
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r2 = r1.sel(lon=plonc,lat=plat,method='nearest')
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f2['SOIL_COLOR'] = np.asarray(r2['SOIL_COLOR'])
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r1.close() ; r2.close()
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#-- LAI / Height
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r1 = xr.open_dataset(flai)
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r1=r1.rename({'LON':'lon','LAT':'lat'})
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r1.set_coords(['lon','lat'],inplace=True)
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# extract gridcell closest to plonc/plat
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r2 = r1.sel(lon=plonc,lat=plat,method='nearest')
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# ignoring crop, i.e. excluding index 15
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f2['MONTHLY_HEIGHT_BOT'][:,0:15,0,0] = np.asarray(r2['MONTHLY_HEIGHT_BOT'][:,0:15])
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f2['MONTHLY_HEIGHT_TOP'][:,0:15,0,0] = np.asarray(r2['MONTHLY_HEIGHT_TOP'][:,0:15])
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f2['MONTHLY_LAI'][:,0:15,0,0] = np.asarray(r2['MONTHLY_LAI'][:,0:15])
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f2['MONTHLY_SAI'][:,0:15,0,0] = np.asarray(r2['MONTHLY_SAI'][:,0:15])
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r1.close() ; r2.close()
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#-- Close output file
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# mode 'w' overwrites file
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f2.to_netcdf(path=fsurf2, mode='w')
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mprint('created file '+fsurf2)
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f1.close(); f2.close(); f2.close()
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mprint('successfully created surface data file: '+fsurf2)
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