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source: lmdz_wrf/trunk/tools/diagnostics.py @ 2595

Last change on this file since 2595 was 2390, checked in by lfita, 6 years ago

Adding:

`rhs_tas_tds': Computation of relative humidity from tas and tds

File size: 86.8 KB

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[1675]	1	# Python script to comput diagnostics
[1908]	2	# From L. Fita work in different places: CCRC (Australia), LMD (France)
	3	# More information at: http://www.xn--llusfb-5va.cat/python/PyNCplot
	4	#
	5	# pyNCplot and its component nc_var.py comes with ABSOLUTELY NO WARRANTY.
	6	# This work is licendes under a Creative Commons
	7	# Attribution-ShareAlike 4.0 International License (http://creativecommons.org/licenses/by-sa/4.0)
	8	#
	9	# L. Fita, CIMA. CONICET-UBA, CNRS UMI-IFAECI, C.A. Buenos Aires, Argentina
[365]	10	# File diagnostics.inf provides the combination of variables to get the desired diagnostic
[772]	11	# To be used with module_ForDiagnostics.F90, module_ForDiagnosticsVars.F90, module_generic.F90
[1150]	12	# foudre: f2py -m module_ForDiagnostics --f90exec=/usr/bin/gfortran-4.7 -c module_generic.F90 module_ForDiagnosticsVars.F90 module_ForDiagnostics.F90 >& run_f2py.log
	13	# ciclad: f2py --f90flags="-fPIC" --f90exec=/usr/bin/gfortran -L/opt/canopy-1.3.0/Canopy_64bit/System/lib/ -L/usr/lib64/ -L/opt/canopy-1.3.0/Canopy_64bit/System/lib/ -m module_ForDiagnostics -c module_generic.F90 module_ForDiagnosticsVars.F90 module_ForDiagnostics.F90 >& run_f2py.log
[1149]	14
[2095]	15	## e.g. # diagnostics.py -d 'Time@WRFtime,bottom_top@ZNU,south_north@XLAT,west_east@XLONG' -v 'clt\|CLDFRA,cllmh\|CLDFRA@WRFp,RAINTOT\|RAINC@RAINNC@RAINSH@XTIME' -f WRF_LMDZ/NPv31/wrfout_d01_1980-03-01_00:00:00
[413]	16	## e.g. # diagnostics.py -f /home/lluis/PY/diagnostics.inf -d variable_combo -v WRFprc
[365]	17
[1675]	18	# Available general pupose diagnostics (model independent) providing (varv1, varv2, ..., dimns, dimvns)
	19	# compute_accum: Function to compute the accumulation of a variable
	20	# compute_cllmh: Function to compute cllmh: low/medium/hight cloud fraction following
	21	# newmicro.F90 from LMDZ compute_clt(cldfra, pres, dimns, dimvns)
	22	# compute_clt: Function to compute the total cloud fraction following 'newmicro.F90' from LMDZ
	23	# compute_clivi: Function to compute cloud-ice water path (clivi)
	24	# compute_clwvl: Function to compute condensed water path (clwvl)
	25	# compute_deaccum: Function to compute the deaccumulation of a variable
	26	# compute_mslp: Function to compute mslp: mean sea level pressure following p_interp.F90 from WRF
	27	# compute_OMEGAw: Function to transform OMEGA [Pas-1] to velocities [ms-1]
	28	# compute_prw: Function to compute water vapour path (prw)
[2209]	29	# compute_range_faces: Function to compute faces [uphill, valley, downhill] of sections of a mountain
	30	# range, along a given face
[1675]	31	# compute_rh: Function to compute relative humidity following 'Tetens' equation (T,P) ...'
	32	# compute_td: Function to compute the dew point temperature
	33	# compute_turbulence: Function to compute the rubulence term of the Taylor's decomposition ...'
	34	# compute_wds: Function to compute the wind direction
	35	# compute_wss: Function to compute the wind speed
	36	# compute_WRFuava: Function to compute geographical rotated WRF 3D winds
	37	# compute_WRFuasvas: Fucntion to compute geographical rotated WRF 2-meter winds
	38	# derivate_centered: Function to compute the centered derivate of a given field
	39	# def Forcompute_cllmh: Function to compute cllmh: low/medium/hight cloud fraction following newmicro.F90 from LMDZ via Fortran subroutine
	40	# Forcompute_clt: Function to compute the total cloud fraction following 'newmicro.F90' from LMDZ via a Fortran module
[1758]	41	# Forcompute_psl_ptarget: Function to compute the sea-level pressure following target_pressure value found in `p_interp.F'
[1675]	42
	43	# Others just providing variable values
	44	# var_cllmh: Fcuntion to compute cllmh on a 1D column
	45	# var_clt: Function to compute the total cloud fraction following 'newmicro.F90' from LMDZ using 1D vertical column values
	46	# var_mslp: Fcuntion to compute mean sea-level pressure
	47	# var_virtualTemp: This function returns virtual temperature in K,
	48	# var_WRFtime: Function to copmute CFtimes from WRFtime variable
	49	# rotational_z: z-component of the rotatinoal of horizontal vectorial field
	50	# turbulence_var: Function to compute the Taylor's decomposition turbulence term from a a given variable
[2138]	51	# timeoverthres: When a given variable [varname] overpass a given [value]. Being [CFvarn] the name of the diagnostics in
	52	# variables_values.dat
[2141]	53	# timemax ([varname], time). When a given variable [varname] got its maximum
[1675]	54
[365]	55	from optparse import OptionParser
	56	import numpy as np
	57	from netCDF4 import Dataset as NetCDFFile
	58	import os
	59	import re
	60	import nc_var_tools as ncvar
[756]	61	import generic_tools as gen
[654]	62	import datetime as dtime
[1163]	63	import module_ForDiag as fdin
[1942]	64	import module_ForDef as fdef
[1675]	65	import diag_tools as diag
[365]	66
	67	main = 'diagnostics.py'
	68	errormsg = 'ERROR -- error -- ERROR -- error'
	69	warnmsg = 'WARNING -- warning -- WARNING -- warning'
	70
[654]	71	# Constants
	72	grav = 9.81
	73
[365]	74
	75	####### ###### ##### #### ### ## #
	76	comboinf="\nIF -d 'variable_combo', provides information of the combination to obtain -v [varn] with the ASCII file with the combinations as -f [combofile]"
	77
	78	parser = OptionParser()
	79	parser.add_option("-f", "--netCDF_file", dest="ncfile", help="file to use", metavar="FILE")
	80	parser.add_option("-d", "--dimensions", dest="dimns",
[1761]	81	help="[dimtn]@[dtvn],[dimzn]@[dzvn],[...,[dimxn]@[dxvn]], ',' list with the couples [dimDn]@[dDvn], [dimDn], name of the dimension D and name of the variable [dDvn] with the values of the dimension ('WRFtime', for WRF time copmutation). NOTE: same order as in file!!!!" + comboinf,
[365]	82	metavar="LABELS")
	83	parser.add_option("-v", "--variables", dest="varns",
	84	help=" [varn1]\|[var11]@[...[varN1]],[...,[varnM]\|[var1M]@[...[varLM]]] ',' list of variables to compute [varnK] and its necessary ones [var1K]...[varPK]", metavar="VALUES")
	85
	86	(opts, args) = parser.parse_args()
	87
	88	####### #######
	89	## MAIN
	90	#######
[2100]	91	availdiags = ['ACRAINTOT', 'accum', 'clt', 'cllmh', 'convini', 'deaccum', 'fog_K84', \
[2390]	92	'fog_RUC', 'rhs_tas_tds', 'LMDZrh', 'mslp', 'OMEGAw', 'RAINTOT', 'range_faces', \
	93	'rvors', 'td', 'timemax', 'timeoverthres', 'turbulence', 'tws', 'uavaFROMwswd', \
[2274]	94	'WRFbnds', 'WRFcape_afwa', 'WRFclivi', 'WRFclwvi', 'WRF_denszint', 'WRFgeop', \
[2206]	95	'WRFmrso', 'WRFmrsos', 'WRFpotevap_orPM', 'WRFp', 'WRFpsl_ecmwf', \
[1762]	96	'WRFpsl_ptarget', 'WRFrvors', 'WRFslw', 'ws', 'wds', 'wss', 'WRFheight', \
[2390]	97	'WRFheightrel', 'WRFtda', 'WRFtdas', 'WRFtws', 'WRFua', 'WRFva', 'WRFzwind', \
	98	'WRFzwind_log', 'WRFzwindMO']
[365]	99
[649]	100	methods = ['accum', 'deaccum']
	101
[365]	102	# Variables not to check
[2277]	103	NONcheckingvars = ['accum', 'cllmh', 'deaccum', 'face', 'LONLATdxdy', \
	104	'reglonlatbnds', 'TSrhs', 'TStd', 'TSwds', 'TSwss', \
[2274]	105	'WRFbils', 'WRFbnds', \
[2257]	106	'WRFclivi', 'WRFclwvi', 'WRFdens', 'WRFdx', 'WRFdxdy', 'WRFdxdywps', 'WRFdy', \
	107	'WRFgeop', 'WRFp', 'WRFtd', \
[1809]	108	'WRFpos', 'WRFprc', 'WRFprls', 'WRFrh', 'LMDZrh', 'LMDZrhs', \
[1806]	109	'WRFrhs', 'WRFrvors', \
[2215]	110	'WRFt', 'WRFtime', 'WRFua', 'WRFva', 'WRFwds', 'WRFwss', 'WRFheight', 'WRFz', \
	111	'WRFzg']
[365]	112
[1809]	113	# diagnostics not to check their dependeny
[2138]	114	NONcheckdepvars = ['accum', 'deaccum', 'timeoverthres', 'WRF_denszint', \
	115	'WRFzwind_log', 'WRFzwind', 'WRFzwindMO']
[1809]	116
[1351]	117	NONchkvardims = ['WRFtime']
	118
[365]	119	ofile = 'diagnostics.nc'
	120
	121	dimns = opts.dimns
	122	varns = opts.varns
	123
	124	# Special method. knowing variable combination
	125	##
	126	if opts.dimns == 'variable_combo':
	127	print warnmsg
	128	print ' ' + main + ': knowing variable combination !!!'
	129	combination = variable_combo(opts.varns,opts.ncfile)
	130	print ' COMBO: ' + combination
	131	quit(-1)
	132
[1833]	133	if opts.ncfile is None:
	134	print errormsg
	135	print ' ' + main + ": No file provided !!"
	136	print ' is mandatory to provide a file -f [filename]'
	137	quit(-1)
	138
	139	if opts.dimns is None:
	140	print errormsg
	141	print ' ' + main + ": No description of dimensions are provided !!"
	142	print ' is mandatory to provide description of dimensions as -d [dimn]@[vardimname],... '
	143	quit(-1)
	144
	145	if opts.varns is None:
	146	print errormsg
	147	print ' ' + main + ": No variable to diagnose is provided !!"
	148	print ' is mandatory to provide a variable to diagnose as -v [diagn]\|[varn1]@... '
	149	quit(-1)
	150
[365]	151	if not os.path.isfile(opts.ncfile):
	152	print errormsg
	153	print ' ' + main + ": file '" + opts.ncfile + "' does not exist !!"
	154	quit(-1)
	155
	156	ncobj = NetCDFFile(opts.ncfile, 'r')
	157
[1351]	158	# Looking for specific variables that might be use in more than one diagnostic
	159	WRFgeop_compute = False
	160	WRFp_compute = False
	161	WRFt_compute = False
	162	WRFrh_compute = False
	163	WRFght_compute = False
	164	WRFdens_compute = False
	165	WRFpos_compute = False
	166	WRFtime_compute = False
[1777]	167	WRFz_compute = False
[2215]	168	WRFdxdy_compute = False
[2257]	169	WRFdxdywps_compute = False
[2222]	170	LONLATdxdy_compute = False
[1351]	171
[365]	172	# File creation
	173	newnc = NetCDFFile(ofile,'w')
	174
	175	# dimensions
	176	dimvalues = dimns.split(',')
	177	dnames = []
	178	dvnames = []
	179
	180	for dimval in dimvalues:
[1351]	181	dn = dimval.split('@')[0]
	182	dnv = dimval.split('@')[1]
	183	dnames.append(dn)
	184	dvnames.append(dnv)
	185	# Is there any dimension-variable which should be computed?
	186	if dnv == 'WRFgeop':WRFgeop_compute = True
	187	if dnv == 'WRFp': WRFp_compute = True
	188	if dnv == 'WRFt': WRFt_compute = True
	189	if dnv == 'WRFrh': WRFrh_compute = True
	190	if dnv == 'WRFght': WRFght_compute = True
	191	if dnv == 'WRFdens': WRFdens_compute = True
	192	if dnv == 'WRFpos': WRFpos_compute = True
	193	if dnv == 'WRFtime': WRFtime_compute = True
[1777]	194	if dnv == 'WRFz':WRFz_compute = True
[2215]	195	if dnv == 'WRFdxdy':WRFdxdy_compute = True
[2257]	196	if dnv == 'WRFdxdywps':WRFdxdywps_compute = True
[2222]	197	if dnv == 'LONLATdxdy':LONLATdxdy_compute = True
[365]	198
	199	# diagnostics to compute
	200	diags = varns.split(',')
	201	Ndiags = len(diags)
	202
	203	for idiag in range(Ndiags):
	204	if diags[idiag].split('\|')[1].find('@') == -1:
	205	depvars = diags[idiag].split('\|')[1]
[654]	206	if depvars == 'WRFgeop':WRFgeop_compute = True
[365]	207	if depvars == 'WRFp': WRFp_compute = True
	208	if depvars == 'WRFt': WRFt_compute = True
	209	if depvars == 'WRFrh': WRFrh_compute = True
	210	if depvars == 'WRFght': WRFght_compute = True
	211	if depvars == 'WRFdens': WRFdens_compute = True
	212	if depvars == 'WRFpos': WRFpos_compute = True
[654]	213	if depvars == 'WRFtime': WRFtime_compute = True
[1777]	214	if depvars == 'WRFz': WRFz_compute = True
[365]	215	else:
	216	depvars = diags[idiag].split('\|')[1].split('@')
[756]	217	if gen.searchInlist(depvars, 'WRFgeop'): WRFgeop_compute = True
	218	if gen.searchInlist(depvars, 'WRFp'): WRFp_compute = True
	219	if gen.searchInlist(depvars, 'WRFt'): WRFt_compute = True
	220	if gen.searchInlist(depvars, 'WRFrh'): WRFrh_compute = True
	221	if gen.searchInlist(depvars, 'WRFght'): WRFght_compute = True
	222	if gen.searchInlist(depvars, 'WRFdens'): WRFdens_compute = True
	223	if gen.searchInlist(depvars, 'WRFpos'): WRFpos_compute = True
	224	if gen.searchInlist(depvars, 'WRFtime'): WRFtime_compute = True
[1777]	225	if gen.searchInlist(depvars, 'WRFz'): WRFz_compute = True
[2215]	226	if gen.searchInlist(depvars, 'WRFdxdy'): WRFdxdy_compute = True
[2257]	227	if gen.searchInlist(depvars, 'WRFdxdywps'): WRFdxdywps_compute = True
[2222]	228	if gen.searchInlist(depvars, 'LONLATdxdy'): LONLATdxdy_compute = True
[365]	229
[1351]	230	# Dictionary with the new computed variables to be able to add them
	231	dictcompvars = {}
[654]	232	if WRFgeop_compute:
	233	print ' ' + main + ': Retrieving geopotential value from WRF as PH + PHB'
	234	dimv = ncobj.variables['PH'].shape
	235	WRFgeop = ncobj.variables['PH'][:] + ncobj.variables['PHB'][:]
	236
[1351]	237	# Attributes of the variable
[1412]	238	Vvals = gen.variables_values('WRFgeop')
[1351]	239	dictcompvars['WRFgeop'] = {'name': Vvals[0], 'standard_name': Vvals[1], \
	240	'long_name': Vvals[4].replace('\|',' '), 'units': Vvals[5]}
	241
[365]	242	if WRFp_compute:
	243	print ' ' + main + ': Retrieving pressure value from WRF as P + PB'
	244	dimv = ncobj.variables['P'].shape
	245	WRFp = ncobj.variables['P'][:] + ncobj.variables['PB'][:]
	246
[1351]	247	# Attributes of the variable
	248	Vvals = gen.variables_values('WRFp')
	249	dictcompvars['WRFgeop'] = {'name': Vvals[0], 'standard_name': Vvals[1], \
	250	'long_name': Vvals[4].replace('\|',' '), 'units': Vvals[5]}
	251
[365]	252	if WRFght_compute:
	253	print ' ' + main + ': computing geopotential height from WRF as PH + PHB ...'
	254	WRFght = ncobj.variables['PH'][:] + ncobj.variables['PHB'][:]
	255
[1351]	256	# Attributes of the variable
	257	Vvals = gen.variables_values('WRFght')
	258	dictcompvars['WRFgeop'] = {'name': Vvals[0], 'standard_name': Vvals[1], \
	259	'long_name': Vvals[4].replace('\|',' '), 'units': Vvals[5]}
	260
[365]	261	if WRFrh_compute:
	262	print ' ' + main + ": computing relative humidity from WRF as 'Tetens'" + \
	263	' equation (T,P) ...'
	264	p0=100000.
	265	p=ncobj.variables['P'][:] + ncobj.variables['PB'][:]
	266	tk = (ncobj.variables['T'][:] + 300.)(p/p0)*(2./7.)
	267	qv = ncobj.variables['QVAPOR'][:]
	268
	269	data1 = 10.0.6112np.exp(17.67*(tk-273.16)/(tk-29.65))
	270	data2 = 0.622data1/(0.01p-(1.-0.622)*data1)
	271
	272	WRFrh = qv/data2
	273
[1351]	274	# Attributes of the variable
	275	Vvals = gen.variables_values('WRFrh')
	276	dictcompvars['WRFrh'] = {'name': Vvals[0], 'standard_name': Vvals[1], \
	277	'long_name': Vvals[4].replace('\|',' '), 'units': Vvals[5]}
	278
[365]	279	if WRFt_compute:
	280	print ' ' + main + ': computing temperature from WRF as inv_potT(T + 300) ...'
	281	p0=100000.
	282	p=ncobj.variables['P'][:] + ncobj.variables['PB'][:]
	283
	284	WRFt = (ncobj.variables['T'][:] + 300.)(p/p0)*(2./7.)
	285
[1351]	286	# Attributes of the variable
	287	Vvals = gen.variables_values('WRFt')
	288	dictcompvars['WRFt'] = {'name': Vvals[0], 'standard_name': Vvals[1], \
	289	'long_name': Vvals[4].replace('\|',' '), 'units': Vvals[5]}
	290
[365]	291	if WRFdens_compute:
	292	print ' ' + main + ': computing air density from WRF as ((MU + MUB) * ' + \
	293	'DNW)/g ...'
	294
	295	# Just we need in in absolute values: Size of the central grid cell
	296	## dxval = ncobj.getncattr('DX')
	297	## dyval = ncobj.getncattr('DY')
	298	## mapfac = ncobj.variables['MAPFAC_M'][:]
	299	## area = dxvaldyvalmapfac
	300
	301	mu = (ncobj.variables['MU'][:] + ncobj.variables['MUB'][:])
	302	dnw = ncobj.variables['DNW'][:]
	303
	304	WRFdens = np.zeros((mu.shape[0], dnw.shape[1], mu.shape[1], mu.shape[2]), \
	305	dtype=np.float)
	306	levval = np.zeros((mu.shape[1], mu.shape[2]), dtype=np.float)
	307
	308	for it in range(mu.shape[0]):
	309	for iz in range(dnw.shape[1]):
	310	levval.fill(np.abs(dnw[it,iz]))
	311	WRFdens[it,iz,:,:] = levval
	312	WRFdens[it,iz,:,:] = mu[it,:,:]*WRFdens[it,iz,:,:]/grav
	313
[1351]	314	# Attributes of the variable
	315	Vvals = gen.variables_values('WRFdens')
	316	dictcompvars['WRFdens'] = {'name': Vvals[0], 'standard_name': Vvals[1], \
	317	'long_name': Vvals[4].replace('\|',' '), 'units': Vvals[5]}
	318
[365]	319	if WRFpos_compute:
	320	# WRF positions from the lowest-leftest corner of the matrix
	321	print ' ' + main + ': computing position from MAPFAC_M as sqrt(DYj*2 + ' + \
	322	'DXx2)MAPFAC_M ...'
	323
	324	mapfac = ncobj.variables['MAPFAC_M'][:]
	325
	326	distx = np.float(ncobj.getncattr('DX'))
	327	disty = np.float(ncobj.getncattr('DY'))
	328
	329	print 'distx:',distx,'disty:',disty
	330
	331	dx = mapfac.shape[2]
	332	dy = mapfac.shape[1]
	333	dt = mapfac.shape[0]
	334
	335	WRFpos = np.zeros((dt, dy, dx), dtype=np.float)
	336
	337	for i in range(1,dx):
	338	WRFpos[0,0,i] = distx*i/mapfac[0,0,i]
	339	for j in range(1,dy):
	340	i=0
	341	WRFpos[0,j,i] = WRFpos[0,j-1,i] + disty/mapfac[0,j,i]
	342	for i in range(1,dx):
	343	# WRFpos[0,j,i] = np.sqrt((distyj)2. + (distxi)**2.)/mapfac[0,j,i]
	344	# WRFpos[0,j,i] = np.sqrt((distyj)2. + (distxi)**2.)
	345	WRFpos[0,j,i] = WRFpos[0,j,i-1] + distx/mapfac[0,j,i]
	346
	347	for it in range(1,dt):
	348	WRFpos[it,:,:] = WRFpos[0,:,:]
	349
[654]	350	if WRFtime_compute:
	351	print ' ' + main + ': computing time from WRF as CFtime(Times) ...'
	352
	353	refdate='19491201000000'
	354	tunitsval='minutes'
	355
	356	timeobj = ncobj.variables['Times']
	357	timewrfv = timeobj[:]
	358
	359	yrref=refdate[0:4]
	360	monref=refdate[4:6]
	361	dayref=refdate[6:8]
	362	horref=refdate[8:10]
	363	minref=refdate[10:12]
	364	secref=refdate[12:14]
	365
	366	refdateS = yrref + '-' + monref + '-' + dayref + ' ' + horref + ':' + minref + \
	367	':' + secref
	368
[2065]	369
	370	if len(timeobj.shape) == 2:
	371	dt = timeobj.shape[0]
	372	else:
	373	dt = 1
[654]	374	WRFtime = np.zeros((dt), dtype=np.float)
	375
[2065]	376	if len(timeobj.shape) == 2:
	377	for it in range(dt):
	378	wrfdates = gen.datetimeStr_conversion(timewrfv[it,:],'WRFdatetime', \
	379	'matYmdHMS')
	380	WRFtime[it] = gen.realdatetime1_CFcompilant(wrfdates, refdate, tunitsval)
	381	else:
	382	wrfdates = gen.datetimeStr_conversion(timewrfv[:],'WRFdatetime', \
	383	'matYmdHMS')
[2072]	384	WRFtime[0] = gen.realdatetime1_CFcompilant(wrfdates, refdate, tunitsval)
[654]	385
	386	tunits = tunitsval + ' since ' + refdateS
	387
[1351]	388	# Attributes of the variable
	389	dictcompvars['WRFtime'] = {'name': 'time', 'standard_name': 'time', \
	390	'long_name': 'time', 'units': tunits, 'calendar': 'gregorian'}
	391
[1777]	392	if WRFz_compute:
	393	print ' ' + main + ': Retrieving z: height above surface value from WRF as ' + \
	394	'unstagger(PH + PHB)/9.8-hgt'
	395	dimv = ncobj.variables['PH'].shape
	396	WRFzg = (ncobj.variables['PH'][:] + ncobj.variables['PHB'][:])/9.8
	397
	398	unzgd = (dimv[0], dimv[1]-1, dimv[2], dimv[3])
	399	unzg = np.zeros(unzgd, dtype=np.float)
	400	unzg = 0.5*(WRFzg[:,0:dimv[1]-1,:,:] + WRFzg[:,1:dimv[1],:,:])
	401
	402	WRFz = np.zeros(unzgd, dtype=np.float)
	403	for iz in range(dimv[1]-1):
	404	WRFz[:,iz,:,:] = unzg[:,iz,:,:] - ncobj.variables['HGT'][:]
	405
	406	# Attributes of the variable
	407	Vvals = gen.variables_values('WRFz')
[2215]	408	dictcompvars['WRFz'] = {'name': Vvals[0], 'standard_name': Vvals[1], \
[1777]	409	'long_name': Vvals[4].replace('\|',' '), 'units': Vvals[5]}
	410
[2215]	411	if WRFdxdy_compute:
	412	print ' ' + main + ': Retrieving dxdy: real distance between grid points ' + \
	413	'from WRF as dx=(XLONG(i+1)-XLONG(i))DX/MAPFAC_M, dy=(XLAT(j+1)-XLAT(i))DY/'+\
	414	'MAPFAC_M, ds=sqrt(dx2+dy2)'
	415	dimv = ncobj.variables['XLONG'].shape
	416	WRFlon = ncobj.variables['XLONG'][0,:,:]
	417	WRFlat = ncobj.variables['XLAT'][0,:,:]
	418	WRFmapfac_m = ncobj.variables['MAPFAC_M'][0,:,:]
	419	DX = ncobj.DX
	420	DY = ncobj.DY
	421
	422	dimx = dimv[2]
	423	dimy = dimv[1]
	424
	425	WRFdx = np.zeros((dimy,dimx), dtype=np.float)
	426	WRFdy = np.zeros((dimy,dimx), dtype=np.float)
	427
	428	WRFdx[:,0:dimx-1]=(WRFlon[:,1:dimx]-WRFlon[:,0:dimx-1])*DX/WRFmapfac_m[:,0:dimx-1]
	429	WRFdy[0:dimy-1,:]=(WRFlat[1:dimy,:]-WRFlat[0:dimy-1,:])*DY/WRFmapfac_m[0:dimy-1,:]
	430	WRFds = np.sqrt(WRFdx2 + WRFdy2)
	431
	432	# Attributes of the variable
	433	Vvals = gen.variables_values('WRFdx')
	434	dictcompvars['WRFdx'] = {'name': Vvals[0], 'standard_name': Vvals[1], \
	435	'long_name': Vvals[4].replace('\|',' '), 'units': Vvals[5]}
	436	Vvals = gen.variables_values('WRFdy')
	437	dictcompvars['WRFdy'] = {'name': Vvals[0], 'standard_name': Vvals[1], \
	438	'long_name': Vvals[4].replace('\|',' '), 'units': Vvals[5]}
	439	Vvals = gen.variables_values('WRFds')
[2222]	440	dictcompvars['WRFds'] = {'name': Vvals[0], 'standard_name': Vvals[1], \
	441	'long_name': Vvals[4].replace('\|',' '), 'units': Vvals[5]}
	442
[2257]	443	if WRFdxdywps_compute:
	444	print ' ' + main + ': Retrieving dxdy: real distance between grid points ' + \
	445	'from wpsWRF as dx=(XLONG_M(i+1)-XLONG_M(i))*DX/MAPFAC_M, ' + \
	446	'dy=(XLAT_M(j+1)-XLAT_M(i))DY/MAPFAC_M, ds=sqrt(dx2+dy*2)'
	447	dimv = ncobj.variables['XLONG_M'].shape
	448	WRFlon = ncobj.variables['XLONG_M'][0,:,:]
	449	WRFlat = ncobj.variables['XLAT_M'][0,:,:]
	450	WRFmapfac_m = ncobj.variables['MAPFAC_M'][0,:,:]
	451	DX = ncobj.DX
	452	DY = ncobj.DY
	453
	454	dimx = dimv[2]
	455	dimy = dimv[1]
	456
	457	WRFdx = np.zeros((dimy,dimx), dtype=np.float)
	458	WRFdy = np.zeros((dimy,dimx), dtype=np.float)
	459
	460	WRFdx[:,0:dimx-1]=(WRFlon[:,1:dimx]-WRFlon[:,0:dimx-1])*DX/WRFmapfac_m[:,0:dimx-1]
	461	WRFdy[0:dimy-1,:]=(WRFlat[1:dimy,:]-WRFlat[0:dimy-1,:])*DY/WRFmapfac_m[0:dimy-1,:]
	462	WRFds = np.sqrt(WRFdx2 + WRFdy2)
	463
	464	# Attributes of the variable
	465	Vvals = gen.variables_values('WRFdx')
	466	dictcompvars['WRFdx'] = {'name': Vvals[0], 'standard_name': Vvals[1], \
	467	'long_name': Vvals[4].replace('\|',' '), 'units': Vvals[5]}
	468	Vvals = gen.variables_values('WRFdy')
	469	dictcompvars['WRFdy'] = {'name': Vvals[0], 'standard_name': Vvals[1], \
	470	'long_name': Vvals[4].replace('\|',' '), 'units': Vvals[5]}
	471	Vvals = gen.variables_values('WRFds')
	472	dictcompvars['WRFds'] = {'name': Vvals[0], 'standard_name': Vvals[1], \
	473	'long_name': Vvals[4].replace('\|',' '), 'units': Vvals[5]}
	474
[2222]	475	if LONLATdxdy_compute:
	476	print ' ' + main + ': Retrieving dxdy: real distance between grid points ' + \
	477	'from a regular lonlat projection as dx=(lon[i+1]-lon[i])raddegRearth*' + \
	478	'cos(abs(lat[i])); dy=(lat[j+1]-lat[i])raddegRearth; ds=sqrt(dx2+dy2); '+\
	479	'raddeg = pi/180; Rearth=6370.0e03'
	480	dimv = ncobj.variables['lon'].shape
	481	lon = ncobj.variables['lon'][:]
	482	lat = ncobj.variables['lat'][:]
	483
	484	WRFlon, WRFlat = gen.lonlat2D(lon,lat)
	485
	486	dimx = WRFlon.shape[1]
	487	dimy = WRFlon.shape[0]
	488
	489	WRFdx = np.zeros((dimy,dimx), dtype=np.float)
	490	WRFdy = np.zeros((dimy,dimx), dtype=np.float)
	491
	492	raddeg = np.pi/180.
	493
	494	Rearth = fdef.module_definitions.earthradii
	495
	496	WRFdx[:,0:dimx-1]=(WRFlon[:,1:dimx]-WRFlon[:,0:dimx-1])raddegRearth* \
	497	np.cos(np.abs(WRFlat[:,0:dimx-1]*raddeg))
	498	WRFdy[0:dimy-1,:]=(WRFlat[1:dimy,:]-WRFlat[0:dimy-1,:])raddegRearth
	499	WRFds = np.sqrt(WRFdx2 + WRFdy2)
	500
	501	# Attributes of the variable
	502	Vvals = gen.variables_values('WRFdx')
	503	dictcompvars['WRFdx'] = {'name': Vvals[0], 'standard_name': Vvals[1], \
	504	'long_name': Vvals[4].replace('\|',' '), 'units': Vvals[5]}
	505	Vvals = gen.variables_values('WRFdy')
	506	dictcompvars['WRFdy'] = {'name': Vvals[0], 'standard_name': Vvals[1], \
	507	'long_name': Vvals[4].replace('\|',' '), 'units': Vvals[5]}
	508	Vvals = gen.variables_values('WRFds')
[2215]	509	dictcompvars['WRFds'] = {'name': Vvals[0], 'standard_name': Vvals[1], \
	510	'long_name': Vvals[4].replace('\|',' '), 'units': Vvals[5]}
	511
[365]	512	### ## #
	513	# Going for the diagnostics
	514	### ## #
	515	print ' ' + main + ' ...'
[1404]	516	varsadd = []
[365]	517
[2390]	518	Varns = ncobj.variables.keys()
	519	Varns.sort()
	520
[365]	521	for idiag in range(Ndiags):
	522	print ' diagnostic:',diags[idiag]
[1758]	523	diagn = diags[idiag].split('\|')[0]
[365]	524	depvars = diags[idiag].split('\|')[1].split('@')
[1809]	525	if not gen.searchInlist(NONcheckdepvars, diagn):
	526	if diags[idiag].split('\|')[1].find('@') != -1:
	527	depvars = diags[idiag].split('\|')[1].split('@')
	528	if depvars[0] == 'deaccum': diagn='deaccum'
	529	if depvars[0] == 'accum': diagn='accum'
	530	for depv in depvars:
[2212]	531	# Checking without extra arguments of a depending variable (':', separated)
	532	if depv.find(':') != -1: depv=depv.split(':')[0]
[1809]	533	if not ncobj.variables.has_key(depv) and not \
	534	gen.searchInlist(NONcheckingvars, depv) and \
	535	not gen.searchInlist(methods, depv) and not depvars[0] == 'deaccum'\
	536	and not depvars[0] == 'accum' and not depv[0:2] == 'z=':
	537	print errormsg
	538	print ' ' + main + ": file '" + opts.ncfile + \
	539	"' does not have variable '" + depv + "' !!"
[2390]	540	print ' available ones:', Varns
[1809]	541	quit(-1)
	542	else:
	543	depvars = diags[idiag].split('\|')[1]
	544	if not ncobj.variables.has_key(depvars) and not \
	545	gen.searchInlist(NONcheckingvars, depvars) and \
	546	not gen.searchInlist(methods, depvars):
[365]	547	print errormsg
	548	print ' ' + main + ": file '" + opts.ncfile + \
[1809]	549	"' does not have variable '" + depvars + "' !!"
[2390]	550	print ' available ones:', Varns
[365]	551	quit(-1)
	552
[1758]	553	print "\n Computing '" + diagn + "' from: ", depvars, '...'
[365]	554
[2095]	555	# acraintot: accumulated total precipitation from WRF RAINC, RAINNC, RAINSH
[1758]	556	if diagn == 'ACRAINTOT':
[365]	557
	558	var0 = ncobj.variables[depvars[0]]
	559	var1 = ncobj.variables[depvars[1]]
[2095]	560	var2 = ncobj.variables[depvars[2]]
[365]	561
[2095]	562	diagout = var0[:] + var1[:] + var2[:]
	563
[365]	564	dnamesvar = var0.dimensions
	565	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	566
[1647]	567	# Removing the nonChecking variable-dimensions from the initial list
	568	varsadd = []
	569	for nonvd in NONchkvardims:
	570	if gen.searchInlist(dvnamesvar,nonvd): dvnamesvar.remove(nonvd)
	571	varsadd.append(nonvd)
	572
[649]	573	ncvar.insert_variable(ncobj, 'pracc', diagout, dnamesvar, dvnamesvar, newnc)
[365]	574
[649]	575	# accum: acumulation of any variable as (Variable, time [as [tunits]
	576	# from/since ....], newvarname)
[1758]	577	elif diagn == 'accum':
[649]	578
	579	var0 = ncobj.variables[depvars[0]]
	580	var1 = ncobj.variables[depvars[1]]
	581
	582	dnamesvar = var0.dimensions
	583	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	584
[1675]	585	diagout, diagoutd, diagoutvd = diag.compute_accum(var0,dnamesvar,dvnamesvar)
[1825]	586	# Removing the nonChecking variable-dimensions from the initial list
	587	varsadd = []
	588	diagoutvd = list(dvnames)
	589	for nonvd in NONchkvardims:
	590	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	591	varsadd.append(nonvd)
[649]	592
	593	CFvarn = ncvar.variables_values(depvars[0])[0]
	594
	595	# Removing the flux
	596	if depvars[1] == 'XTIME':
	597	dtimeunits = var1.getncattr('description')
	598	tunits = dtimeunits.split(' ')[0]
	599	else:
	600	dtimeunits = var1.getncattr('units')
	601	tunits = dtimeunits.split(' ')[0]
	602
[1825]	603	dtime = (var1[1] - var1[0])*diag.timeunits_seconds(tunits)
[649]	604
	605	ncvar.insert_variable(ncobj, CFvarn + 'acc', diagout*dtime, diagoutd, diagoutvd, newnc)
	606
[365]	607	# cllmh with cldfra, pres
[1758]	608	elif diagn == 'cllmh':
[365]	609
	610	var0 = ncobj.variables[depvars[0]]
	611	if depvars[1] == 'WRFp':
	612	var1 = WRFp
	613	else:
	614	var01 = ncobj.variables[depvars[1]]
	615	if len(size(var1.shape)) < len(size(var0.shape)):
	616	var1 = np.brodcast_arrays(var01,var0)[0]
	617	else:
	618	var1 = var01
	619
[1675]	620	diagout, diagoutd, diagoutvd = diag.Forcompute_cllmh(var0,var1,dnames,dvnames)
[772]	621
[1351]	622	# Removing the nonChecking variable-dimensions from the initial list
	623	varsadd = []
	624	for nonvd in NONchkvardims:
	625	if gen.searchInlist(diagoutvd,nonvd): diagoutvd.remove(nonvd)
	626	varsadd.append(nonvd)
	627
[365]	628	ncvar.insert_variable(ncobj, 'cll', diagout[0,:], diagoutd, diagoutvd, newnc)
	629	ncvar.insert_variable(ncobj, 'clm', diagout[1,:], diagoutd, diagoutvd, newnc)
	630	ncvar.insert_variable(ncobj, 'clh', diagout[2,:], diagoutd, diagoutvd, newnc)
	631
	632	# clt with cldfra
[1758]	633	elif diagn == 'clt':
[365]	634
	635	var0 = ncobj.variables[depvars]
[1675]	636	diagout, diagoutd, diagoutvd = diag.Forcompute_clt(var0,dnames,dvnames)
[1351]	637
	638	# Removing the nonChecking variable-dimensions from the initial list
	639	varsadd = []
	640	for nonvd in NONchkvardims:
	641	if gen.searchInlist(diagoutvd,nonvd): diagoutvd.remove(nonvd)
	642	varsadd.append(nonvd)
	643
[365]	644	ncvar.insert_variable(ncobj, 'clt', diagout, diagoutd, diagoutvd, newnc)
	645
[2100]	646	# convini (pr, time)
	647	elif diagn == 'convini':
	648
	649	var0 = ncobj.variables[depvars[0]][:]
	650	var1 = ncobj.variables[depvars[1]][:]
	651	otime = ncobj.variables[depvars[1]]
	652
	653	dnamesvar = ncobj.variables[depvars[0]].dimensions
	654	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	655
	656	diagout, diagoutd, diagoutvd = diag.var_convini(var0, var1, dnames, dvnames)
	657
	658	ncvar.insert_variable(ncobj, 'convini', diagout, diagoutd, diagoutvd, newnc, \
	659	fill=gen.fillValueF)
	660	# Getting the right units
	661	ovar = newnc.variables['convini']
	662	if gen.searchInlist(otime.ncattrs(), 'units'):
	663	tunits = otime.getncattr('units')
	664	ncvar.set_attribute(ovar, 'units', tunits)
	665	newnc.sync()
	666
[365]	667	# deaccum: deacumulation of any variable as (Variable, time [as [tunits]
	668	# from/since ....], newvarname)
[1758]	669	elif diagn == 'deaccum':
[365]	670
[1825]	671	var0 = ncobj.variables[depvars[0]]
	672	var1 = ncobj.variables[depvars[1]]
[365]	673
	674	dnamesvar = var0.dimensions
	675	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	676
[1675]	677	diagout, diagoutd, diagoutvd = diag.compute_deaccum(var0,dnamesvar,dvnamesvar)
[1825]	678	# Removing the nonChecking variable-dimensions from the initial list
	679	varsadd = []
	680	diagoutvd = list(dvnames)
	681	for nonvd in NONchkvardims:
	682	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	683	varsadd.append(nonvd)
[365]	684
	685	# Transforming to a flux
[1825]	686	if depvars[1] == 'XTIME':
[365]	687	dtimeunits = var1.getncattr('description')
	688	tunits = dtimeunits.split(' ')[0]
	689	else:
	690	dtimeunits = var1.getncattr('units')
	691	tunits = dtimeunits.split(' ')[0]
	692
[1825]	693	dtime = (var1[1] - var1[0])*diag.timeunits_seconds(tunits)
[1908]	694	ncvar.insert_variable(ncobj, depvars[2], diagout/dtime, diagoutd, diagoutvd, \
	695	newnc)
[365]	696
[1909]	697	# fog_K84: Computation of fog and visibility following Kunkel, (1984) as QCLOUD, QICE
[1908]	698	elif diagn == 'fog_K84':
	699
	700	var0 = ncobj.variables[depvars[0]]
	701	var1 = ncobj.variables[depvars[1]]
	702
	703	dnamesvar = list(var0.dimensions)
	704	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	705
	706	diag1, diag2, diagoutd, diagoutvd = diag.Forcompute_fog_K84(var0, var1, \
	707	dnamesvar, dvnamesvar)
	708	# Removing the nonChecking variable-dimensions from the initial list
	709	varsadd = []
	710	diagoutvd = list(dvnames)
	711	for nonvd in NONchkvardims:
	712	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	713	varsadd.append(nonvd)
	714	ncvar.insert_variable(ncobj, 'fog', diag1, diagoutd, diagoutvd, newnc)
	715	ncvar.insert_variable(ncobj, 'fogvisblty', diag2, diagoutd, diagoutvd, newnc)
	716
[1909]	717	# fog_RUC: Computation of fog and visibility following Kunkel, (1984) as QVAPOR,
	718	# WRFt, WRFp or Q2, T2, PSFC
[1908]	719	elif diagn == 'fog_RUC':
	720
	721	var0 = ncobj.variables[depvars[0]]
[1909]	722	print gen.infmsg
	723	if depvars[1] == 'WRFt':
	724	print ' ' + main + ": computing '" + diagn + "' using 3D variables !!"
	725	var1 = WRFt
	726	var2 = WRFp
	727	else:
	728	print ' ' + main + ": computing '" + diagn + "' using 2D variables !!"
	729	var1 = ncobj.variables[depvars[1]]
	730	var2 = ncobj.variables[depvars[2]]
[1908]	731
	732	dnamesvar = list(var0.dimensions)
	733	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	734
[1909]	735	diag1, diag2, diagoutd, diagoutvd = diag.Forcompute_fog_RUC(var0, var1, var2,\
[1908]	736	dnamesvar, dvnamesvar)
	737	# Removing the nonChecking variable-dimensions from the initial list
	738	varsadd = []
	739	diagoutvd = list(dvnames)
	740	for nonvd in NONchkvardims:
	741	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	742	varsadd.append(nonvd)
	743	ncvar.insert_variable(ncobj, 'fog', diag1, diagoutd, diagoutvd, newnc)
	744	ncvar.insert_variable(ncobj, 'fogvisblty', diag2, diagoutd, diagoutvd, newnc)
	745
[1909]	746	# fog_FRAML50: Computation of fog and visibility following Gultepe, I. and
	747	# J.A. Milbrandt, 2010 as QVAPOR, WRFt, WRFp or Q2, T2, PSFC
	748	elif diagn == 'fog_FRAML50':
	749
	750	var0 = ncobj.variables[depvars[0]]
	751	print gen.infmsg
	752	if depvars[1] == 'WRFt':
	753	print ' ' + main + ": computing '" + diagn + "' using 3D variables !!"
	754	var1 = WRFt
	755	var2 = WRFp
	756	else:
	757	print ' ' + main + ": computing '" + diagn + "' using 2D variables !!"
	758	var1 = ncobj.variables[depvars[1]]
	759	var2 = ncobj.variables[depvars[2]]
	760
	761	dnamesvar = list(var0.dimensions)
	762	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	763
	764	diag1, diag2, diagoutd, diagoutvd = diag.Forcompute_fog_FRAML50(var0, var1, \
	765	var2, dnamesvar, dvnamesvar)
	766	# Removing the nonChecking variable-dimensions from the initial list
	767	varsadd = []
	768	diagoutvd = list(dvnames)
	769	for nonvd in NONchkvardims:
	770	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	771	varsadd.append(nonvd)
	772	ncvar.insert_variable(ncobj, 'fog', diag1, diagoutd, diagoutvd, newnc)
	773	ncvar.insert_variable(ncobj, 'fogvisblty', diag2, diagoutd, diagoutvd, newnc)
	774
[365]	775	# LMDZrh (pres, t, r)
[1758]	776	elif diagn == 'LMDZrh':
[365]	777
	778	var0 = ncobj.variables[depvars[0]][:]
	779	var1 = ncobj.variables[depvars[1]][:]
	780	var2 = ncobj.variables[depvars[2]][:]
	781
[1675]	782	diagout, diagoutd, diagoutvd = diag.compute_rh(var0,var1,var2,dnames,dvnames)
[1079]	783	ncvar.insert_variable(ncobj, 'hur', diagout, diagoutd, diagoutvd, newnc)
[365]	784
	785	# LMDZrhs (psol, t2m, q2m)
[1758]	786	elif diagn == 'LMDZrhs':
[365]	787
	788	var0 = ncobj.variables[depvars[0]][:]
	789	var1 = ncobj.variables[depvars[1]][:]
	790	var2 = ncobj.variables[depvars[2]][:]
	791
	792	dnamesvar = ncobj.variables[depvars[0]].dimensions
	793	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	794
[1675]	795	diagout, diagoutd, diagoutvd = diag.compute_rh(var0,var1,var2,dnamesvar,dvnamesvar)
[365]	796
[1079]	797	ncvar.insert_variable(ncobj, 'hurs', diagout, diagoutd, diagoutvd, newnc)
[365]	798
[2215]	799	# range_faces: LON, LAT, HGT, WRFdxdy, 'face:['WE'/'SN']:[dsfilt]:[dsnewrange]:[hvalleyrange]'
[2208]	800	elif diagn == 'range_faces':
	801
	802	var0 = ncobj.variables[depvars[0]][:]
	803	var1 = ncobj.variables[depvars[1]][:]
	804	var2 = ncobj.variables[depvars[2]][:]
[2215]	805	face = depvars[4].split(':')[1]
	806	dsfilt = np.float(depvars[4].split(':')[2])
	807	dsnewrange = np.float(depvars[4].split(':')[3])
	808	hvalleyrange = np.float(depvars[4].split(':')[4])
[2208]	809
[2222]	810	dnamesvar = list(ncobj.variables[depvars[2]].dimensions)
[2212]	811	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
[2209]	812	lon, lat = gen.lonlat2D(var0, var1)
	813	if len(var2.shape) == 3:
	814	print warnmsg
	815	print ' ' + diagn + ": shapping to 2D variable '" + depvars[2] + "' !!"
	816	hgt = var2[0,:,:]
[2212]	817	dnamesvar.pop(0)
	818	dvnamesvar.pop(0)
[2209]	819	else:
	820	hgt = var2[:]
	821
[2213]	822	orogmax, ptorogmax, dhgt, peaks, valleys, origfaces, diagout, diagoutd, \
[2223]	823	diagoutvd, rng, rngorogmax, ptrngorogmax= diag.Forcompute_range_faces(lon, \
[2260]	824	lat, hgt, WRFdx, WRFdy, WRFds, face, dsfilt, dsnewrange, hvalleyrange, \
	825	dnamesvar, dvnamesvar)
[2208]	826
	827	# Removing the nonChecking variable-dimensions from the initial list
	828	varsadd = []
	829	diagoutvd = list(dvnames)
	830	for nonvd in NONchkvardims:
	831	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	832	varsadd.append(nonvd)
[2212]	833
[2215]	834	ncvar.insert_variable(ncobj, 'dx', WRFdx, diagoutd, diagoutvd, newnc)
	835	ncvar.insert_variable(ncobj, 'dy', WRFdy, diagoutd, diagoutvd, newnc)
	836	ncvar.insert_variable(ncobj, 'ds', WRFds, diagoutd, diagoutvd, newnc)
	837
[2213]	838	# adding variables to output file
	839	if face == 'WE': axis = 'lon'
	840	elif face == 'SN': axis = 'lat'
	841
[2223]	842	ncvar.insert_variable(ncobj, 'range', rng, diagoutd, diagoutvd, newnc, \
	843	fill=gen.fillValueI)
	844	ovar = newnc.variables['range']
	845	ncvar.set_attribute(ovar, 'deriv', axis)
	846
[2214]	847	ncvar.insert_variable(ncobj, 'orogmax', rngorogmax, diagoutd, diagoutvd, \
[2222]	848	newnc, fill=gen.fillValueF)
[2214]	849	newnc.renameVariable('orogmax', 'rangeorogmax')
	850	ovar = newnc.variables['rangeorogmax']
	851	ncvar.set_attribute(ovar, 'deriv', axis)
	852	stdn = ovar.standard_name
	853	ncvar.set_attribute(ovar, 'standard_name', 'range_' + stdn)
	854	Ln = ovar.long_name
	855	ncvar.set_attribute(ovar, 'long_name', 'range ' + stdn)
	856
	857	ncvar.insert_variable(ncobj, 'ptorogmax', ptrngorogmax, diagoutd, diagoutvd, \
	858	newnc)
	859	newnc.renameVariable('ptorogmax', 'rangeptorogmax')
	860	ovar = newnc.variables['rangeptorogmax']
	861	ncvar.set_attribute(ovar, 'deriv', axis)
	862	stdn = ovar.standard_name
	863	ncvar.set_attribute(ovar, 'standard_name', 'range_' + stdn)
	864	Ln = ovar.long_name
	865	ncvar.set_attribute(ovar, 'long_name', 'range ' + stdn)
	866
[2213]	867	ncvar.insert_variable(ncobj, 'orogmax', orogmax, diagoutd, diagoutvd, \
	868	newnc)
	869	ovar = newnc.variables['orogmax']
	870	ncvar.set_attribute(ovar, 'deriv', axis)
	871
	872	ncvar.insert_variable(ncobj, 'ptorogmax', ptorogmax, diagoutd, diagoutvd, \
	873	newnc)
	874	ovar = newnc.variables['ptorogmax']
	875	ncvar.set_attribute(ovar, 'deriv', axis)
	876
[2215]	877	ncvar.insert_variable(ncobj, 'orogderiv', dhgt, diagoutd, diagoutvd, newnc)
	878	ovar = newnc.variables['orogderiv']
[2212]	879	ncvar.set_attribute(ovar, 'deriv', axis)
[2208]	880
[2212]	881	ncvar.insert_variable(ncobj, 'peak', peaks, diagoutd, diagoutvd, newnc)
	882	ncvar.insert_variable(ncobj, 'valley', valleys, diagoutd, diagoutvd, newnc)
	883
	884	ncvar.insert_variable(ncobj, 'rangefaces', diagout, diagoutd, diagoutvd, \
	885	newnc)
	886	newnc.renameVariable('rangefaces', 'rangefacesfilt')
	887	ovar = newnc.variables['rangefacesfilt']
	888	ncvar.set_attribute(ovar, 'face', face)
[2215]	889	ncvar.set_attributek(ovar, 'dist_filter', dsfilt, 'F')
[2212]	890
	891	ncvar.insert_variable(ncobj, 'rangefaces', origfaces, diagoutd, diagoutvd, \
[2215]	892	newnc, fill=gen.fillValueI)
[2212]	893	ovar = newnc.variables['rangefaces']
	894	ncvar.set_attribute(ovar, 'face', face)
[2215]	895	ncvar.set_attributek(ovar, 'dist_newrange', dsnewrange, 'F')
	896	ncvar.set_attributek(ovar, 'h_valley_newrange', hvalleyrange, 'F')
[2212]	897
[2277]	898	# cell_bnds: grid cell bounds from lon, lat from a reglar lon/lat projection as
	899	# intersection of their related parallels and meridians
	900	elif diagn == 'reglonlatbnds':
	901
	902	var00 = ncobj.variables[depvars[0]][:]
	903	var01 = ncobj.variables[depvars[1]][:]
	904
	905	var0, var1 = gen.lonlat2D(var00,var01)
	906
	907	dnamesvar = []
	908	dnamesvar.append('bnds')
	909	if (len(var00.shape) == 3):
	910	dnamesvar.append(ncobj.variables[depvars[0]].dimensions[1])
	911	dnamesvar.append(ncobj.variables[depvars[0]].dimensions[2])
	912	elif (len(var00.shape) == 2):
	913	dnamesvar.append(ncobj.variables[depvars[0]].dimensions[0])
	914	dnamesvar.append(ncobj.variables[depvars[0]].dimensions[1])
	915	elif (len(var00.shape) == 1):
	916	dnamesvar.append(ncobj.variables[depvars[0]].dimensions[0])
	917	dnamesvar.append(ncobj.variables[depvars[1]].dimensions[0])
	918	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	919
	920	cellbndsx, cellbndsy, diagoutd, diagoutvd = diag.Forcompute_cellbndsreg(var0,\
	921	var1, dnamesvar, dvnamesvar)
	922
	923	# Removing the nonChecking variable-dimensions from the initial list
	924	varsadd = []
	925	diagoutvd = list(dvnames)
	926	for nonvd in NONchkvardims:
	927	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	928	varsadd.append(nonvd)
	929	# creation of bounds dimension
	930	newdim = newnc.createDimension('bnds', 4)
	931
	932	ncvar.insert_variable(ncobj, 'lon_bnds', cellbndsx, diagoutd, diagoutvd, newnc)
	933	ncvar.insert_variable(ncobj, 'lat_bnds', cellbndsy, diagoutd, diagoutvd, newnc)
	934
[2390]	935	# tws: Bet Wulb temperature following Stull 2011 (tas, hurs)
	936	elif diagn == 'tws':
[2387]	937
	938	var0 = ncobj.variables[depvars[0]][:]
	939	var1 = ncobj.variables[depvars[1]][:]
	940
[2390]	941	dnamesvar = list(ncobj.variables[depvars[0]].dimensions)
	942	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	943	diagoutd = dnames
	944	diagoutvd = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	945
	946	diagout = diag.var_tws_S11(var0,var1)
[2387]	947	ncvar.insert_variable(ncobj, 'tws', diagout, diagoutd, diagoutvd, newnc)
	948
[2274]	949	# cell_bnds: grid cell bounds from XLONG_U, XLAT_U, XLONG_V, XLAT_V as intersection
	950	# of their related parallels and meridians
	951	elif diagn == 'WRFbnds':
	952
	953	var0 = ncobj.variables[depvars[0]][0,:,:]
	954	var1 = ncobj.variables[depvars[1]][0,:,:]
	955	var2 = ncobj.variables[depvars[2]][0,:,:]
	956	var3 = ncobj.variables[depvars[3]][0,:,:]
	957
	958	dnamesvar = []
[2276]	959	dnamesvar.append('bnds')
	960	dnamesvar.append(ncobj.variables[depvars[0]].dimensions[1])
[2274]	961	dnamesvar.append(ncobj.variables[depvars[2]].dimensions[2])
	962	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	963
	964	cellbndsx, cellbndsy, diagoutd, diagoutvd = diag.Forcompute_cellbnds(var0, \
	965	var1, var2, var3, dnamesvar, dvnamesvar)
	966
	967	# Removing the nonChecking variable-dimensions from the initial list
	968	varsadd = []
	969	diagoutvd = list(dvnames)
	970	for nonvd in NONchkvardims:
	971	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	972	varsadd.append(nonvd)
	973	# creation of bounds dimension
	974	newdim = newnc.createDimension('bnds', 4)
	975
	976	ncvar.insert_variable(ncobj, 'lon_bnds', cellbndsx, diagoutd, diagoutvd, newnc)
[2285]	977	newnc.sync()
[2274]	978	ncvar.insert_variable(ncobj, 'lat_bnds', cellbndsy, diagoutd, diagoutvd, newnc)
[2285]	979	newnc.sync()
[2274]	980
[2346]	981	if newnc.variables.has_key('XLONG'):
	982	ovar = newnc.variables['XLONG']
	983	ovar.setncattr('bounds', 'lon_bnds lat_bnds')
	984	ovar = newnc.variables['XLAT']
	985	ovar.setncattr('bounds', 'lon_bnds lat_bnds')
	986	elif newnc.variables.has_key('XLONG_M'):
	987	ovar = newnc.variables['XLONG_M']
	988	ovar.setncattr('bounds', 'lon_bnds lat_bnds')
	989	ovar = newnc.variables['XLAT_M']
	990	ovar.setncattr('bounds', 'lon_bnds lat_bnds')
	991	else:
	992	print errormsg
	993	print ' ' + fname + ": error computing diagnostic '" + diagn + "' !!"
	994	print " neither: 'XLONG', 'XLONG_M' have been found"
	995	quit(-1)
[2283]	996
[1762]	997	# mrso: total soil moisture SMOIS, DZS
	998	elif diagn == 'WRFmrso':
	999
	1000	var0 = ncobj.variables[depvars[0]][:]
	1001	var10 = ncobj.variables[depvars[1]][:]
	1002	dnamesvar = list(ncobj.variables[depvars[0]].dimensions)
	1003	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1004
	1005	var1 = var0.copy()*0.
	1006	var2 = var0.copy()*0.+1.
	1007	# Must be a better way....
	1008	for j in range(var0.shape[2]):
	1009	for i in range(var0.shape[3]):
	1010	var1[:,:,j,i] = var10
	1011
	1012	diagout, diagoutd, diagoutvd = diag.Forcompute_zint(var0, var1, var2, \
	1013	dnamesvar, dvnamesvar)
	1014
	1015	# Removing the nonChecking variable-dimensions from the initial list
	1016	varsadd = []
	1017	diagoutvd = list(dvnames)
	1018	for nonvd in NONchkvardims:
	1019	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1020	varsadd.append(nonvd)
	1021	ncvar.insert_variable(ncobj, 'mrso', diagout, diagoutd, diagoutvd, newnc)
	1022
[2206]	1023	# mrsos: First layer soil moisture SMOIS, DZS
	1024	elif diagn == 'WRFmrsos':
	1025
	1026	var0 = ncobj.variables[depvars[0]][:]
	1027	var1 = ncobj.variables[depvars[1]][:]
	1028	diagoutd = list(ncobj.variables[depvars[0]].dimensions)
	1029	diagoutvd = ncvar.var_dim_dimv(diagoutd,dnames,dvnames)
	1030
	1031	diagoutd.pop(1)
	1032	diagoutvd.pop(1)
	1033
	1034	diagout= np.zeros((var0.shape[0],var0.shape[2],var0.shape[3]), dtype=np.float)
	1035
	1036	# Must be a better way....
	1037	for j in range(var0.shape[2]):
	1038	for i in range(var0.shape[3]):
	1039	diagout[:,j,i] = var0[:,0,j,i]*var1[:,0]
	1040
	1041	# Removing the nonChecking variable-dimensions from the initial list
	1042	varsadd = []
	1043	diagoutvd = list(dvnames)
	1044	for nonvd in NONchkvardims:
	1045	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1046	varsadd.append(nonvd)
	1047	ncvar.insert_variable(ncobj, 'mrsos', diagout, diagoutd, diagoutvd, newnc)
	1048
[365]	1049	# mslp: mean sea level pressure (pres, psfc, terrain, temp, qv)
[1758]	1050	elif diagn == 'mslp' or diagn == 'WRFmslp':
[365]	1051
	1052	var1 = ncobj.variables[depvars[1]][:]
	1053	var2 = ncobj.variables[depvars[2]][:]
	1054	var4 = ncobj.variables[depvars[4]][:]
	1055
[1758]	1056	if diagn == 'WRFmslp':
[365]	1057	var0 = WRFp
	1058	var3 = WRFt
	1059	dnamesvar = ncobj.variables['P'].dimensions
	1060	else:
	1061	var0 = ncobj.variables[depvars[0]][:]
	1062	var3 = ncobj.variables[depvars[3]][:]
	1063	dnamesvar = ncobj.variables[depvars[0]].dimensions
	1064
	1065	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1066
[1675]	1067	diagout, diagoutd, diagoutvd = diag.compute_mslp(var0, var1, var2, var3, var4, \
[365]	1068	dnamesvar, dvnamesvar)
	1069
[1581]	1070	# Removing the nonChecking variable-dimensions from the initial list
	1071	varsadd = []
	1072	diagoutvd = list(dvnames)
	1073	for nonvd in NONchkvardims:
	1074	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1075	varsadd.append(nonvd)
[365]	1076	ncvar.insert_variable(ncobj, 'psl', diagout, diagoutd, diagoutvd, newnc)
	1077
[2387]	1078	# WRFtws: Bet Wulb temperature following Stull 2011 (PSFC, T2, Q2)
	1079	elif diagn == 'WRFtws':
	1080
	1081	var0 = ncobj.variables[depvars[0]][:]
	1082	var1 = ncobj.variables[depvars[1]][:]
	1083	var2 = ncobj.variables[depvars[2]][:]
	1084
	1085	dnamesvar = list(ncobj.variables[depvars[2]].dimensions)
	1086	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1087
	1088	hurs, diagoutd, diagoutvd = diag.compute_rh(var0,var1,var2,dnamesvar,dvnamesvar)
	1089
	1090	diagout = diag.var_tws_S11(var1, hurs)
	1091	# Removing the nonChecking variable-dimensions from the initial list
	1092	varsadd = []
	1093	diagoutvd = list(dvnames)
	1094	for nonvd in NONchkvardims:
	1095	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1096	varsadd.append(nonvd)
	1097
	1098	ncvar.insert_variable(ncobj, 'tws', diagout, diagoutd, diagoutvd, newnc)
	1099
[642]	1100	# OMEGAw (omega, p, t) from NCL formulation (https://www.ncl.ucar.edu/Document/Functions/Contributed/omega_to_w.shtml)
[1758]	1101	elif diagn == 'OMEGAw':
[642]	1102
	1103	var0 = ncobj.variables[depvars[0]][:]
	1104	var1 = ncobj.variables[depvars[1]][:]
[643]	1105	var2 = ncobj.variables[depvars[2]][:]
[642]	1106
	1107	dnamesvar = ncobj.variables[depvars[0]].dimensions
	1108	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1109
[1675]	1110	diagout, diagoutd, diagoutvd = diag.compute_OMEGAw(var0,var1,var2,dnamesvar,dvnamesvar)
[642]	1111
	1112	ncvar.insert_variable(ncobj, 'wa', diagout, diagoutd, diagoutvd, newnc)
	1113
[2095]	1114	# raintot: instantaneous total precipitation from WRF as (RAINC + RAINC + RAINSH) / dTime
[1758]	1115	elif diagn == 'RAINTOT':
[365]	1116
	1117	var0 = ncobj.variables[depvars[0]]
	1118	var1 = ncobj.variables[depvars[1]]
[2095]	1119	var2 = ncobj.variables[depvars[2]]
	1120
	1121	if depvars[3] != 'WRFtime':
	1122	var3 = ncobj.variables[depvars[3]]
[654]	1123	else:
[2095]	1124	var3 = np.arange(var0.shape[0], dtype=int)
[365]	1125
[2095]	1126	var = var0[:] + var1[:] + var2[:]
[365]	1127
	1128	dnamesvar = var0.dimensions
	1129	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1130
[1675]	1131	diagout, diagoutd, diagoutvd = diag.compute_deaccum(var,dnamesvar,dvnamesvar)
[365]	1132
	1133	# Transforming to a flux
[2095]	1134	if var3.shape[0] > 1:
	1135	if depvars[3] != 'WRFtime':
	1136	dtimeunits = var3.getncattr('units')
[600]	1137	tunits = dtimeunits.split(' ')[0]
	1138
[2095]	1139	dtime = (var3[1] - var3[0])*diag.timeunits_seconds(tunits)
[600]	1140	else:
[2095]	1141	var3 = ncobj.variables['Times']
	1142	time1 = var3[0,:]
	1143	time2 = var3[1,:]
[600]	1144	tmf1 = ''
	1145	tmf2 = ''
	1146	for ic in range(len(time1)):
	1147	tmf1 = tmf1 + time1[ic]
	1148	tmf2 = tmf2 + time2[ic]
[654]	1149	dtdate1 = dtime.datetime.strptime(tmf1,"%Y-%m-%d_%H:%M:%S")
	1150	dtdate2 = dtime.datetime.strptime(tmf2,"%Y-%m-%d_%H:%M:%S")
[600]	1151	diffdate12 = dtdate2 - dtdate1
	1152	dtime = diffdate12.total_seconds()
	1153	print 'dtime:',dtime
[442]	1154	else:
[600]	1155	print warnmsg
[1645]	1156	print ' ' + main + ": only 1 time-step for '" + diag + "' !!"
[600]	1157	print ' leaving a zero value!'
[1646]	1158	diagout = var0[:]*0.
[600]	1159	dtime=1.
[442]	1160
[1644]	1161	# Removing the nonChecking variable-dimensions from the initial list
	1162	varsadd = []
	1163	for nonvd in NONchkvardims:
	1164	if gen.searchInlist(diagoutvd,nonvd): diagoutvd.remove(nonvd)
	1165	varsadd.append(nonvd)
	1166
[365]	1167	ncvar.insert_variable(ncobj, 'pr', diagout/dtime, diagoutd, diagoutvd, newnc)
	1168
[2140]	1169	# timemax ([varname], time). When a given variable [varname] got its maximum
	1170	elif diagn == 'timemax':
	1171
	1172	var0 = ncobj.variables[depvars[0]][:]
	1173	var1 = ncobj.variables[depvars[1]][:]
	1174
	1175	otime = ncobj.variables[depvars[1]]
	1176
	1177	dnamesvar = ncobj.variables[depvars[0]].dimensions
	1178	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1179
	1180	diagout, diagoutd, diagoutvd = diag.var_timemax(var0, var1, dnames, \
	1181	dvnames)
	1182
	1183	ncvar.insert_variable(ncobj, 'timemax', diagout, diagoutd, diagoutvd, newnc, \
	1184	fill=gen.fillValueF)
	1185	# Getting the right units
	1186	ovar = newnc.variables['timemax']
	1187	if gen.searchInlist(otime.ncattrs(), 'units'):
	1188	tunits = otime.getncattr('units')
	1189	ncvar.set_attribute(ovar, 'units', tunits)
	1190	newnc.sync()
	1191	ncvar.set_attribute(ovar, 'variable', depvars[0])
	1192
[2138]	1193	# timeoverthres ([varname], time, [value], [CFvarn]). When a given variable [varname]
	1194	# overpass a given [value]. Being [CFvarn] the name of the diagnostics in
	1195	# variables_values.dat
	1196	elif diagn == 'timeoverthres':
	1197
	1198	var0 = ncobj.variables[depvars[0]][:]
	1199	var1 = ncobj.variables[depvars[1]][:]
	1200	var2 = np.float(depvars[2])
	1201	var3 = depvars[3]
	1202
	1203	otime = ncobj.variables[depvars[1]]
	1204
	1205	dnamesvar = ncobj.variables[depvars[0]].dimensions
	1206	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1207
	1208	diagout, diagoutd, diagoutvd = diag.var_timeoverthres(var0, var1, var2, \
	1209	dnames, dvnames)
	1210
	1211	ncvar.insert_variable(ncobj, var3, diagout, diagoutd, diagoutvd, newnc, \
	1212	fill=gen.fillValueF)
	1213	# Getting the right units
	1214	ovar = newnc.variables[var3]
	1215	if gen.searchInlist(otime.ncattrs(), 'units'):
	1216	tunits = otime.getncattr('units')
	1217	ncvar.set_attribute(ovar, 'units', tunits)
	1218	newnc.sync()
	1219	ncvar.set_attribute(ovar, 'overpassed_threshold', var2)
	1220
[612]	1221	# rhs (psfc, t, q) from TimeSeries files
[1758]	1222	elif diagn == 'TSrhs':
[612]	1223
	1224	p0=100000.
	1225	var0 = ncobj.variables[depvars[0]][:]
	1226	var1 = (ncobj.variables[depvars[1]][:])(var0/p0)*(2./7.)
	1227	var2 = ncobj.variables[depvars[2]][:]
	1228
	1229	dnamesvar = ncobj.variables[depvars[0]].dimensions
	1230	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1231
[1675]	1232	diagout, diagoutd, diagoutvd = diag.compute_rh(var0,var1,var2,dnamesvar,dvnamesvar)
[612]	1233
[1079]	1234	ncvar.insert_variable(ncobj, 'hurs', diagout, diagoutd, diagoutvd, newnc)
[612]	1235
[2390]	1236	# rhs (psfc, t, q) from tas, tds
	1237	elif diagn == 'rhs_tas_tds':
	1238
	1239	var0 = ncobj.variables[depvars[0]][:]
	1240	var1 = ncobj.variables[depvars[1]][:]
	1241
	1242	dnamesvar = ncobj.variables[depvars[0]].dimensions
	1243	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1244
	1245	diagout, diagoutd, diagoutvd = diag.var_hur_tas_tds(var0,var1,dnamesvar, \
	1246	dvnamesvar)
	1247
	1248	ncvar.insert_variable(ncobj, 'hurs', diagout, diagoutd, diagoutvd, newnc)
	1249
[1762]	1250	# slw: total soil liquid water SH2O, DZS
	1251	elif diagn == 'WRFslw':
	1252
	1253	var0 = ncobj.variables[depvars[0]][:]
	1254	var10 = ncobj.variables[depvars[1]][:]
	1255	dnamesvar = list(ncobj.variables[depvars[0]].dimensions)
	1256	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1257
	1258	var1 = var0.copy()*0.
	1259	var2 = var0.copy()*0.+1.
	1260	# Must be a better way....
	1261	for j in range(var0.shape[2]):
	1262	for i in range(var0.shape[3]):
	1263	var1[:,:,j,i] = var10
	1264
	1265	diagout, diagoutd, diagoutvd = diag.Forcompute_zint(var0, var1, var2, \
	1266	dnamesvar, dvnamesvar)
	1267
	1268	# Removing the nonChecking variable-dimensions from the initial list
	1269	varsadd = []
	1270	diagoutvd = list(dvnames)
	1271	for nonvd in NONchkvardims:
	1272	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1273	varsadd.append(nonvd)
	1274	ncvar.insert_variable(ncobj, 'slw', diagout, diagoutd, diagoutvd, newnc)
	1275
[612]	1276	# td (psfc, t, q) from TimeSeries files
[1758]	1277	elif diagn == 'TStd' or diagn == 'td':
[612]	1278
	1279	var0 = ncobj.variables[depvars[0]][:]
	1280	var1 = ncobj.variables[depvars[1]][:] - 273.15
	1281	var2 = ncobj.variables[depvars[2]][:]
	1282
	1283	dnamesvar = ncobj.variables[depvars[0]].dimensions
	1284	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1285
[1675]	1286	diagout, diagoutd, diagoutvd = diag.compute_td(var0,var1,var2,dnamesvar,dvnamesvar)
[612]	1287
[1966]	1288	ncvar.insert_variable(ncobj, 'tdas', diagout, diagoutd, diagoutvd, newnc)
[612]	1289
	1290	# td (psfc, t, q) from TimeSeries files
[1758]	1291	elif diagn == 'TStdC' or diagn == 'tdC':
[612]	1292
	1293	var0 = ncobj.variables[depvars[0]][:]
	1294	# Temperature is already in degrees Celsius
	1295	var1 = ncobj.variables[depvars[1]][:]
	1296	var2 = ncobj.variables[depvars[2]][:]
	1297
	1298	dnamesvar = ncobj.variables[depvars[0]].dimensions
	1299	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1300
[1675]	1301	diagout, diagoutd, diagoutvd = diag.compute_td(var0,var1,var2,dnamesvar,dvnamesvar)
[612]	1302
[1999]	1303	# Removing the nonChecking variable-dimensions from the initial list
	1304	varsadd = []
	1305	for nonvd in NONchkvardims:
	1306	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1307	varsadd.append(nonvd)
	1308
[1966]	1309	ncvar.insert_variable(ncobj, 'tdas', diagout, diagoutd, diagoutvd, newnc)
[612]	1310
	1311	# wds (u, v)
[1758]	1312	elif diagn == 'TSwds' or diagn == 'wds' :
[612]	1313
	1314	var0 = ncobj.variables[depvars[0]][:]
	1315	var1 = ncobj.variables[depvars[1]][:]
	1316
	1317	dnamesvar = ncobj.variables[depvars[0]].dimensions
	1318	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1319
[1675]	1320	diagout, diagoutd, diagoutvd = diag.compute_wds(var0,var1,dnamesvar,dvnamesvar)
[612]	1321
[1999]	1322	# Removing the nonChecking variable-dimensions from the initial list
	1323	varsadd = []
	1324	for nonvd in NONchkvardims:
	1325	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1326	varsadd.append(nonvd)
	1327
[612]	1328	ncvar.insert_variable(ncobj, 'wds', diagout, diagoutd, diagoutvd, newnc)
	1329
	1330	# wss (u, v)
[1758]	1331	elif diagn == 'TSwss' or diagn == 'wss':
[612]	1332
	1333	var0 = ncobj.variables[depvars[0]][:]
	1334	var1 = ncobj.variables[depvars[1]][:]
	1335
	1336	dnamesvar = ncobj.variables[depvars[0]].dimensions
	1337	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1338
[1675]	1339	diagout, diagoutd, diagoutvd = diag.compute_wss(var0,var1,dnamesvar,dvnamesvar)
[612]	1340
[1999]	1341	# Removing the nonChecking variable-dimensions from the initial list
	1342	varsadd = []
	1343	for nonvd in NONchkvardims:
	1344	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1345	varsadd.append(nonvd)
	1346
[612]	1347	ncvar.insert_variable(ncobj, 'wss', diagout, diagoutd, diagoutvd, newnc)
	1348
[365]	1349	# turbulence (var)
[1758]	1350	elif diagn == 'turbulence':
[365]	1351
	1352	var0 = ncobj.variables[depvars][:]
	1353
	1354	dnamesvar = list(ncobj.variables[depvars].dimensions)
	1355	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1356
[1675]	1357	diagout, diagoutd, diagoutvd = diag.compute_turbulence(var0,dnamesvar,dvnamesvar)
[959]	1358	valsvar = gen.variables_values(depvars)
[365]	1359
[959]	1360	newvarn = depvars + 'turb'
	1361	ncvar.insert_variable(ncobj, newvarn, diagout, diagoutd,
[365]	1362	diagoutvd, newnc)
[959]	1363	varobj = newnc.variables[newvarn]
[365]	1364	attrv = varobj.long_name
	1365	attr = varobj.delncattr('long_name')
	1366	newattr = ncvar.set_attribute(varobj, 'long_name', attrv + \
	1367	" Taylor decomposition turbulence term")
	1368
[1927]	1369	# ua va from ws wd (deg)
	1370	elif diagn == 'uavaFROMwswd':
	1371
	1372	var0 = ncobj.variables[depvars[0]][:]
	1373	var1 = ncobj.variables[depvars[1]][:]
	1374
	1375	ua = var0np.cos(var1np.pi/180.)
	1376	va = var0np.sin(var1np.pi/180.)
	1377
	1378	dnamesvar = ncobj.variables[depvars[0]].dimensions
	1379	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1380
	1381	ncvar.insert_variable(ncobj, 'ua', ua, dnamesvar, dvnamesvar, newnc)
	1382	ncvar.insert_variable(ncobj, 'va', va, dnamesvar, dvnamesvar, newnc)
	1383
[2033]	1384	# ua va from obs ws wd (deg)
	1385	elif diagn == 'uavaFROMobswswd':
	1386
	1387	var0 = ncobj.variables[depvars[0]][:]
	1388	var1 = ncobj.variables[depvars[1]][:]
[1927]	1389
[2033]	1390	ua = var0np.cos((var1+180.)np.pi/180.)
	1391	va = var0np.sin((var1+180.)np.pi/180.)
	1392
	1393	dnamesvar = ncobj.variables[depvars[0]].dimensions
	1394	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1395
	1396	ncvar.insert_variable(ncobj, 'ua', ua, dnamesvar, dvnamesvar, newnc)
	1397	ncvar.insert_variable(ncobj, 'va', va, dnamesvar, dvnamesvar, newnc)
	1398
[390]	1399	# WRFbils fom WRF as HFX + LH
[1758]	1400	elif diagn == 'WRFbils':
[390]	1401
	1402	var0 = ncobj.variables[depvars[0]][:]
	1403	var1 = ncobj.variables[depvars[1]][:]
	1404
	1405	diagout = var0 + var1
[867]	1406	dnamesvar = list(ncobj.variables[depvars[0]].dimensions)
	1407	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
[390]	1408
[867]	1409	ncvar.insert_variable(ncobj, 'bils', diagout, dnamesvar, dvnamesvar, newnc)
[390]	1410
[1759]	1411	# WRFcape_afwa CAPE, CIN, ZLFC, PLFC, LI following WRF 'phys/module_diaf_afwa.F'
	1412	# methodology as WRFt, WRFrh, WRFp, WRFgeop, HGT
	1413	elif diagn == 'WRFcape_afwa':
	1414	var0 = WRFt
	1415	var1 = WRFrh
	1416	var2 = WRFp
	1417	dz = WRFgeop.shape[1]
	1418	# de-staggering
[1833]	1419	var3 = 0.5*(WRFgeop[:,0:dz-1,:,:]+WRFgeop[:,1:dz,:,:])/9.8
[1759]	1420	var4 = ncobj.variables[depvars[4]][0,:,:]
	1421
	1422	dnamesvar = list(ncobj.variables['T'].dimensions)
	1423	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1424
	1425	diagout = np.zeros(var0.shape, dtype=np.float)
	1426	diagout1, diagout2, diagout3, diagout4, diagout5, diagoutd, diagoutvd = \
	1427	diag.Forcompute_cape_afwa(var0, var1, var2, var3, var4, 3, dnamesvar, \
	1428	dvnamesvar)
	1429
	1430	# Removing the nonChecking variable-dimensions from the initial list
	1431	varsadd = []
	1432	for nonvd in NONchkvardims:
	1433	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1434	varsadd.append(nonvd)
	1435
	1436	ncvar.insert_variable(ncobj, 'cape', diagout1, diagoutd, diagoutvd, newnc)
	1437	ncvar.insert_variable(ncobj, 'cin', diagout2, diagoutd, diagoutvd, newnc)
	1438	ncvar.insert_variable(ncobj, 'zlfc', diagout3, diagoutd, diagoutvd, newnc)
	1439	ncvar.insert_variable(ncobj, 'plfc', diagout4, diagoutd, diagoutvd, newnc)
	1440	ncvar.insert_variable(ncobj, 'li', diagout5, diagoutd, diagoutvd, newnc)
	1441
[1581]	1442	# WRFclivi WRF water vapour path WRFdens, QICE, QGRAUPEL, QHAIL
[1758]	1443	elif diagn == 'WRFclivi':
[1581]	1444
	1445	var0 = WRFdens
	1446	qtot = ncobj.variables[depvars[1]]
	1447	qtotv = qtot[:]
	1448	Nspecies = len(depvars) - 2
	1449	for iv in range(Nspecies):
[1585]	1450	if ncobj.variables.has_key(depvars[iv+2]):
	1451	var1 = ncobj.variables[depvars[iv+2]][:]
	1452	qtotv = qtotv + var1
[1581]	1453
	1454	dnamesvar = list(qtot.dimensions)
	1455	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1456
[1675]	1457	diagout, diagoutd, diagoutvd = diag.compute_clivi(var0, qtotv, dnamesvar,dvnamesvar)
[1581]	1458
	1459	# Removing the nonChecking variable-dimensions from the initial list
	1460	varsadd = []
	1461	diagoutvd = list(dvnames)
	1462	for nonvd in NONchkvardims:
	1463	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1464	varsadd.append(nonvd)
	1465	ncvar.insert_variable(ncobj, 'clivi', diagout, diagoutd, diagoutvd, newnc)
	1466
[1803]	1467	# WRFclwvi WRF water cloud-condensed path WRFdens, QCLOUD, QICE, QGRAUPEL, QHAIL
	1468	elif diagn == 'WRFclwvi':
[1581]	1469
	1470	var0 = WRFdens
	1471	qtot = ncobj.variables[depvars[1]]
	1472	qtotv = ncobj.variables[depvars[1]]
	1473	Nspecies = len(depvars) - 2
	1474	for iv in range(Nspecies):
[1585]	1475	if ncobj.variables.has_key(depvars[iv+2]):
	1476	var1 = ncobj.variables[depvars[iv+2]]
	1477	qtotv = qtotv + var1[:]
[1581]	1478
	1479	dnamesvar = list(qtot.dimensions)
	1480	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1481
[1675]	1482	diagout, diagoutd, diagoutvd = diag.compute_clwvl(var0, qtotv, dnamesvar,dvnamesvar)
[1581]	1483
	1484	# Removing the nonChecking variable-dimensions from the initial list
	1485	varsadd = []
	1486	diagoutvd = list(dvnames)
	1487	for nonvd in NONchkvardims:
	1488	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1489	varsadd.append(nonvd)
[1803]	1490	ncvar.insert_variable(ncobj, 'clwvi', diagout, diagoutd, diagoutvd, newnc)
[1581]	1491
[1809]	1492	# WRF_denszint WRF vertical integration as WRFdens, sum(Q[water species1], ..., Q[water speciesN]), varn=[varN]
	1493	elif diagn == 'WRF_denszint':
	1494
	1495	var0 = WRFdens
	1496	varn = depvars[1].split('=')[1]
	1497	qtot = ncobj.variables[depvars[2]]
	1498	qtotv = ncobj.variables[depvars[2]]
	1499	Nspecies = len(depvars) - 2
	1500	for iv in range(Nspecies):
	1501	if ncobj.variables.has_key(depvars[iv+2]):
	1502	var1 = ncobj.variables[depvars[iv+2]]
	1503	qtotv = qtotv + var1[:]
	1504
	1505	dnamesvar = list(qtot.dimensions)
	1506	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1507
	1508	diagout, diagoutd, diagoutvd = diag.compute_clwvl(var0, qtotv, dnamesvar,dvnamesvar)
	1509
	1510	# Removing the nonChecking variable-dimensions from the initial list
	1511	varsadd = []
	1512	diagoutvd = list(dvnames)
	1513	for nonvd in NONchkvardims:
	1514	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1515	varsadd.append(nonvd)
	1516	ncvar.insert_variable(ncobj, varn, diagout, diagoutd, diagoutvd, newnc)
	1517
[654]	1518	# WRFgeop geopotential from WRF as PH + PHB
[1758]	1519	elif diagn == 'WRFgeop':
[1382]	1520	var0 = ncobj.variables[depvars[0]][:]
	1521	var1 = ncobj.variables[depvars[1]][:]
[654]	1522
[1382]	1523	# de-staggering geopotential
	1524	diagout0 = var0 + var1
	1525	dt = diagout0.shape[0]
	1526	dz = diagout0.shape[1]
	1527	dy = diagout0.shape[2]
	1528	dx = diagout0.shape[3]
	1529
	1530	diagout = np.zeros((dt,dz-1,dy,dx), dtype=np.float)
	1531	diagout = 0.5*(diagout0[:,1:dz,:,:]+diagout0[:,0:dz-1,:,:])
	1532
	1533	# Removing the nonChecking variable-dimensions from the initial list
	1534	varsadd = []
[1389]	1535	diagoutvd = list(dvnames)
[1382]	1536	for nonvd in NONchkvardims:
[1389]	1537	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
[1382]	1538	varsadd.append(nonvd)
	1539
[1389]	1540	ncvar.insert_variable(ncobj, 'zg', diagout, dnames, diagoutvd, newnc)
[654]	1541
[1804]	1542	# WRFpotevap_orPM potential evapotranspiration following Penman-Monteith formulation
[1833]	1543	# implemented in ORCHIDEE (in src_sechiba/enerbil.f90) as: WRFdens, UST, U10, V10, T2, PSFC, QVAPOR
[1804]	1544	elif diagn == 'WRFpotevap_orPM':
	1545	var0 = WRFdens[:,0,:,:]
	1546	var1 = ncobj.variables[depvars[1]][:]
	1547	var2 = ncobj.variables[depvars[2]][:]
	1548	var3 = ncobj.variables[depvars[3]][:]
	1549	var4 = ncobj.variables[depvars[4]][:]
	1550	var5 = ncobj.variables[depvars[5]][:]
	1551	var6 = ncobj.variables[depvars[6]][:,0,:,:]
	1552
	1553	dnamesvar = list(ncobj.variables[depvars[1]].dimensions)
	1554	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1555
	1556	diagout = np.zeros(var1.shape, dtype=np.float)
	1557	diagout, diagoutd, diagoutvd = diag.Forcompute_potevap_orPM(var0, var1, var2,\
	1558	var3, var4, var5, var6, dnamesvar, dvnamesvar)
	1559
	1560	# Removing the nonChecking variable-dimensions from the initial list
	1561	varsadd = []
	1562	for nonvd in NONchkvardims:
	1563	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1564	varsadd.append(nonvd)
	1565
	1566	ncvar.insert_variable(ncobj, 'evspsblpot', diagout, diagoutd, diagoutvd, newnc)
	1567
[1795]	1568	# WRFmslp_ptarget sea-level pressure following ECMWF method as PSFC, HGT, WRFt, WRFp, ZNU, ZNW
	1569	elif diagn == 'WRFpsl_ecmwf':
	1570	var0 = ncobj.variables[depvars[0]][:]
	1571	var1 = ncobj.variables[depvars[1]][0,:,:]
	1572	var2 = WRFt[:,0,:,:]
	1573	var4 = WRFp[:,0,:,:]
	1574	var5 = ncobj.variables[depvars[4]][0,:]
	1575	var6 = ncobj.variables[depvars[5]][0,:]
	1576
	1577	# This is quite too appriximate!! passing pressure at half-levels to 2nd full
	1578	# level, using eta values at full (ZNW) and half (ZNU) mass levels
	1579	var3 = WRFp[:,0,:,:] + (var6[1] - var5[0])*(WRFp[:,1,:,:] - WRFp[:,0,:,:])/ \
	1580	(var5[1]-var5[0])
	1581
	1582	dnamesvar = list(ncobj.variables[depvars[0]].dimensions)
	1583	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1584
	1585	diagout = np.zeros(var0.shape, dtype=np.float)
	1586	diagout, diagoutd, diagoutvd = diag.Forcompute_psl_ecmwf(var0, var1, var2, \
	1587	var3, var4, dnamesvar, dvnamesvar)
	1588
	1589	# Removing the nonChecking variable-dimensions from the initial list
	1590	varsadd = []
	1591	for nonvd in NONchkvardims:
	1592	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1593	varsadd.append(nonvd)
	1594
	1595	ncvar.insert_variable(ncobj, 'psl', diagout, diagoutd, diagoutvd, newnc)
	1596
[1758]	1597	# WRFmslp_ptarget sea-level pressure following ptarget method as WRFp, PSFC, WRFt, HGT, QVAPOR
	1598	elif diagn == 'WRFpsl_ptarget':
	1599	var0 = WRFp
	1600	var1 = ncobj.variables[depvars[1]][:]
	1601	var2 = WRFt
	1602	var3 = ncobj.variables[depvars[3]][0,:,:]
	1603	var4 = ncobj.variables[depvars[4]][:]
	1604
	1605	dnamesvar = list(ncobj.variables[depvars[4]].dimensions)
	1606	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1607
	1608	diagout = np.zeros(var0.shape, dtype=np.float)
	1609	diagout, diagoutd, diagoutvd = diag.Forcompute_psl_ptarget(var0, var1, var2, \
	1610	var3, var4, 700000., dnamesvar, dvnamesvar)
	1611
	1612	# Removing the nonChecking variable-dimensions from the initial list
	1613	varsadd = []
	1614	for nonvd in NONchkvardims:
	1615	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1616	varsadd.append(nonvd)
	1617
	1618	ncvar.insert_variable(ncobj, 'psl', diagout, diagoutd, diagoutvd, newnc)
	1619
[390]	1620	# WRFp pressure from WRF as P + PB
[1758]	1621	elif diagn == 'WRFp':
[1944]	1622	var0 = ncobj.variables[depvars[0]][:]
	1623	var1 = ncobj.variables[depvars[1]][:]
[365]	1624
[1944]	1625	diagout = var0 + var1
[1999]	1626	diagoutd = list(ncobj.variables[depvars[0]].dimensions)
	1627	diagoutvd = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
[365]	1628
[1999]	1629	# Removing the nonChecking variable-dimensions from the initial list
	1630	varsadd = []
	1631	for nonvd in NONchkvardims:
	1632	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1633	varsadd.append(nonvd)
[365]	1634
[1999]	1635	ncvar.insert_variable(ncobj, 'pres', diagout, diagoutd, diagoutvd, newnc)
	1636
[365]	1637	# WRFpos
[1758]	1638	elif diagn == 'WRFpos':
[365]	1639
	1640	dnamesvar = ncobj.variables['MAPFAC_M'].dimensions
	1641	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1642
	1643	ncvar.insert_variable(ncobj, 'WRFpos', WRFpos, dnamesvar, dvnamesvar, newnc)
	1644
	1645	# WRFprw WRF water vapour path WRFdens, QVAPOR
[1758]	1646	elif diagn == 'WRFprw':
[365]	1647
	1648	var0 = WRFdens
	1649	var1 = ncobj.variables[depvars[1]]
	1650
	1651	dnamesvar = list(var1.dimensions)
	1652	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1653
[1675]	1654	diagout, diagoutd, diagoutvd = diag.compute_prw(var0, var1, dnamesvar,dvnamesvar)
[365]	1655
[1586]	1656	# Removing the nonChecking variable-dimensions from the initial list
	1657	varsadd = []
	1658	diagoutvd = list(dvnames)
	1659	for nonvd in NONchkvardims:
	1660	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1661	varsadd.append(nonvd)
[365]	1662	ncvar.insert_variable(ncobj, 'prw', diagout, diagoutd, diagoutvd, newnc)
	1663
	1664	# WRFrh (P, T, QVAPOR)
[1758]	1665	elif diagn == 'WRFrh':
[365]	1666
	1667	dnamesvar = list(ncobj.variables[depvars[2]].dimensions)
	1668	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1669
[878]	1670	ncvar.insert_variable(ncobj, 'hur', WRFrh, dnames, dvnames, newnc)
[365]	1671
	1672	# WRFrhs (PSFC, T2, Q2)
[1758]	1673	elif diagn == 'WRFrhs':
[365]	1674
	1675	var0 = ncobj.variables[depvars[0]][:]
	1676	var1 = ncobj.variables[depvars[1]][:]
	1677	var2 = ncobj.variables[depvars[2]][:]
	1678
	1679	dnamesvar = list(ncobj.variables[depvars[2]].dimensions)
	1680	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1681
[1675]	1682	diagout, diagoutd, diagoutvd = diag.compute_rh(var0,var1,var2,dnamesvar,dvnamesvar)
[878]	1683	ncvar.insert_variable(ncobj, 'hurs', diagout, diagoutd, diagoutvd, newnc)
[365]	1684
	1685	# rvors (u10, v10, WRFpos)
[1758]	1686	elif diagn == 'WRFrvors':
[365]	1687
	1688	var0 = ncobj.variables[depvars[0]]
	1689	var1 = ncobj.variables[depvars[1]]
	1690
	1691	diagout = rotational_z(var0, var1, distx)
	1692
	1693	dnamesvar = ncobj.variables[depvars[0]].dimensions
	1694	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1695
	1696	ncvar.insert_variable(ncobj, 'rvors', diagout, dnamesvar, dvnamesvar, newnc)
	1697
[884]	1698	# WRFt (T, P, PB)
[1758]	1699	elif diagn == 'WRFt':
[884]	1700	var0 = ncobj.variables[depvars[0]][:]
	1701	var1 = ncobj.variables[depvars[1]][:]
	1702	var2 = ncobj.variables[depvars[2]][:]
[654]	1703
[884]	1704	p0=100000.
	1705	p=var1 + var2
	1706
	1707	WRFt = (var0 + 300.)(p/p0)*(2./7.)
	1708
	1709	dnamesvar = list(ncobj.variables[depvars[0]].dimensions)
	1710	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1711
[1382]	1712	# Removing the nonChecking variable-dimensions from the initial list
	1713	varsadd = []
[1389]	1714	diagoutvd = list(dvnames)
[1382]	1715	for nonvd in NONchkvardims:
[1389]	1716	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
[1382]	1717	varsadd.append(nonvd)
	1718
[1389]	1719	ncvar.insert_variable(ncobj, 'ta', WRFt, dnames, diagoutvd, newnc)
[884]	1720
[1942]	1721	# WRFtda (WRFrh, WRFt)
	1722	elif diagn == 'WRFtda':
	1723	ARM2 = fdef.module_definitions.arm2
	1724	ARM3 = fdef.module_definitions.arm3
	1725
	1726	gammatarh = np.log(WRFrh) + ARM2*(WRFt-273.15)/((WRFt-273.15)+ARM3)
[1943]	1727	td = ARM3*gammatarh/(ARM2-gammatarh)
[1942]	1728
	1729	dnamesvar = list(ncobj.variables['T'].dimensions)
	1730	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1731
	1732	# Removing the nonChecking variable-dimensions from the initial list
	1733	varsadd = []
	1734	diagoutvd = list(dvnames)
	1735	for nonvd in NONchkvardims:
	1736	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1737	varsadd.append(nonvd)
	1738
	1739	ncvar.insert_variable(ncobj, 'tda', td, dnames, diagoutvd, newnc)
	1740
[1966]	1741	# WRFtdas (PSFC, T2, Q2)
	1742	elif diagn == 'WRFtdas':
[1962]	1743	ARM2 = fdef.module_definitions.arm2
	1744	ARM3 = fdef.module_definitions.arm3
	1745
	1746	var0 = ncobj.variables[depvars[0]][:]
	1747	var1 = ncobj.variables[depvars[1]][:]
	1748	var2 = ncobj.variables[depvars[2]][:]
	1749
	1750	dnamesvar = list(ncobj.variables[depvars[1]].dimensions)
	1751	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1752
	1753	rhs, diagoutd, diagoutvd = diag.compute_rh(var0,var1,var2,dnamesvar,dvnamesvar)
	1754
	1755	gammatarhs = np.log(rhs) + ARM2*(var1-273.15)/((var1-273.15)+ARM3)
[1970]	1756	tdas = ARM3*gammatarhs/(ARM2-gammatarhs) + 273.15
[1962]	1757
	1758	# Removing the nonChecking variable-dimensions from the initial list
	1759	varsadd = []
	1760	diagoutvd = list(dvnames)
	1761	for nonvd in NONchkvardims:
	1762	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1763	varsadd.append(nonvd)
	1764
[1966]	1765	ncvar.insert_variable(ncobj, 'tdas', tdas, dnames, diagoutvd, newnc)
[1962]	1766
[914]	1767	# WRFua (U, V, SINALPHA, COSALPHA) to be rotated !!
[1758]	1768	elif diagn == 'WRFua':
[914]	1769	var0 = ncobj.variables[depvars[0]][:]
	1770	var1 = ncobj.variables[depvars[1]][:]
	1771	var2 = ncobj.variables[depvars[2]][:]
	1772	var3 = ncobj.variables[depvars[3]][:]
	1773
	1774	# un-staggering variables
[1999]	1775	if len(var0.shape) == 4:
	1776	unstgdims = [var0.shape[0], var0.shape[1], var0.shape[2], var0.shape[3]-1]
	1777	elif len(var0.shape) == 3:
	1778	# Asuming sunding point (dimt, dimz, dimstgx)
	1779	unstgdims = [var0.shape[0], var0.shape[1]]
	1780
[914]	1781	ua = np.zeros(tuple(unstgdims), dtype=np.float)
	1782	unstgvar0 = np.zeros(tuple(unstgdims), dtype=np.float)
	1783	unstgvar1 = np.zeros(tuple(unstgdims), dtype=np.float)
	1784
[1999]	1785	if len(var0.shape) == 4:
	1786	unstgvar0 = 0.5*(var0[:,:,:,0:var0.shape[3]-1] + var0[:,:,:,1:var0.shape[3]])
	1787	unstgvar1 = 0.5*(var1[:,:,0:var1.shape[2]-1,:] + var1[:,:,1:var1.shape[2],:])
[914]	1788
[1999]	1789	for iz in range(var0.shape[1]):
	1790	ua[:,iz,:,:] = unstgvar0[:,iz,:,:]var3 - unstgvar1[:,iz,:,:]var2
	1791
	1792	dnamesvar = ['Time','bottom_top','south_north','west_east']
	1793
	1794	elif len(var0.shape) == 3:
	1795	unstgvar0 = 0.5*(var0[:,:,0] + var0[:,:,1])
	1796	unstgvar1 = 0.5*(var1[:,:,0] + var1[:,:,1])
	1797	for iz in range(var0.shape[1]):
	1798	ua[:,iz] = unstgvar0[:,iz]var3 - unstgvar1[:,iz]var2
	1799
	1800	dnamesvar = ['Time','bottom_top']
	1801
[914]	1802	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1803
[1404]	1804	# Removing the nonChecking variable-dimensions from the initial list
	1805	varsadd = []
	1806	diagoutvd = list(dvnames)
	1807	for nonvd in NONchkvardims:
	1808	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1809	varsadd.append(nonvd)
[914]	1810
[1404]	1811	ncvar.insert_variable(ncobj, 'ua', ua, dnames, diagoutvd, newnc)
	1812
[914]	1813	# WRFua (U, V, SINALPHA, COSALPHA) to be rotated !!
[1758]	1814	elif diagn == 'WRFva':
[914]	1815	var0 = ncobj.variables[depvars[0]][:]
	1816	var1 = ncobj.variables[depvars[1]][:]
	1817	var2 = ncobj.variables[depvars[2]][:]
	1818	var3 = ncobj.variables[depvars[3]][:]
	1819
	1820	# un-staggering variables
[1999]	1821	if len(var0.shape) == 4:
	1822	unstgdims = [var0.shape[0], var0.shape[1], var0.shape[2], var0.shape[3]-1]
	1823	elif len(var0.shape) == 3:
	1824	# Asuming sunding point (dimt, dimz, dimstgx)
	1825	unstgdims = [var0.shape[0], var0.shape[1]]
	1826
[914]	1827	va = np.zeros(tuple(unstgdims), dtype=np.float)
	1828	unstgvar0 = np.zeros(tuple(unstgdims), dtype=np.float)
	1829	unstgvar1 = np.zeros(tuple(unstgdims), dtype=np.float)
	1830
[1999]	1831	if len(var0.shape) == 4:
	1832	unstgvar0 = 0.5*(var0[:,:,:,0:var0.shape[3]-1] + var0[:,:,:,1:var0.shape[3]])
	1833	unstgvar1 = 0.5*(var1[:,:,0:var1.shape[2]-1,:] + var1[:,:,1:var1.shape[2],:])
	1834
	1835	for iz in range(var0.shape[1]):
	1836	va[:,iz,:,:] = unstgvar0[:,iz,:,:]var2 + unstgvar1[:,iz,:,:]var3
	1837
	1838	dnamesvar = ['Time','bottom_top','south_north','west_east']
	1839
	1840	elif len(var0.shape) == 3:
	1841	unstgvar0 = 0.5*(var0[:,:,0] + var0[:,:,1])
	1842	unstgvar1 = 0.5*(var1[:,:,0] + var1[:,:,1])
	1843	for iz in range(var0.shape[1]):
	1844	va[:,iz] = unstgvar0[:,iz]var2 + unstgvar1[:,iz]var3
	1845
	1846	dnamesvar = ['Time','bottom_top']
	1847
[914]	1848	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1849
[1404]	1850	# Removing the nonChecking variable-dimensions from the initial list
	1851	varsadd = []
	1852	diagoutvd = list(dvnames)
	1853	for nonvd in NONchkvardims:
	1854	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1855	varsadd.append(nonvd)
	1856	ncvar.insert_variable(ncobj, 'va', va, dnames, diagoutvd, newnc)
[914]	1857
[1980]	1858
	1859	# WRFwd (U, V, SINALPHA, COSALPHA) to be rotated !!
	1860	elif diagn == 'WRFwd':
	1861	var0 = ncobj.variables[depvars[0]][:]
	1862	var1 = ncobj.variables[depvars[1]][:]
	1863	var2 = ncobj.variables[depvars[2]][:]
	1864	var3 = ncobj.variables[depvars[3]][:]
	1865
	1866	# un-staggering variables
[1999]	1867	if len(var0.shape) == 4:
	1868	unstgdims = [var0.shape[0], var0.shape[1], var0.shape[2], var0.shape[3]-1]
	1869	elif len(var0.shape) == 3:
	1870	# Asuming sunding point (dimt, dimz, dimstgx)
	1871	unstgdims = [var0.shape[0], var0.shape[1]]
	1872
[1980]	1873	ua = np.zeros(tuple(unstgdims), dtype=np.float)
	1874	va = np.zeros(tuple(unstgdims), dtype=np.float)
	1875	unstgvar0 = np.zeros(tuple(unstgdims), dtype=np.float)
	1876	unstgvar1 = np.zeros(tuple(unstgdims), dtype=np.float)
	1877
[1999]	1878	if len(var0.shape) == 4:
	1879	unstgvar0 = 0.5*(var0[:,:,:,0:var0.shape[3]-1] + var0[:,:,:,1:var0.shape[3]])
	1880	unstgvar1 = 0.5*(var1[:,:,0:var1.shape[2]-1,:] + var1[:,:,1:var1.shape[2],:])
[1980]	1881
[1999]	1882	for iz in range(var0.shape[1]):
	1883	ua[:,iz,:,:] = unstgvar0[:,iz,:,:]var3 - unstgvar1[:,iz,:,:]var2
	1884	va[:,iz,:,:] = unstgvar0[:,iz,:,:]var2 + unstgvar1[:,iz,:,:]var3
	1885
	1886	dnamesvar = ['Time','bottom_top','south_north','west_east']
	1887
	1888	elif len(var0.shape) == 3:
	1889	unstgvar0 = 0.5*(var0[:,:,0] + var0[:,:,1])
	1890	unstgvar1 = 0.5*(var1[:,:,0] + var1[:,:,1])
	1891	for iz in range(var0.shape[1]):
	1892	ua[:,iz] = unstgvar0[:,iz]var3 - unstgvar1[:,iz]var2
	1893	va[:,iz] = unstgvar0[:,iz]var2 + unstgvar1[:,iz]var3
	1894
	1895	dnamesvar = ['Time','bottom_top']
	1896
[1980]	1897	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1898
	1899	wd, dnames, dvnames = diag.compute_wd(ua, va, dnamesvar, dvnamesvar)
	1900
	1901	# Removing the nonChecking variable-dimensions from the initial list
	1902	varsadd = []
	1903	diagoutvd = list(dvnames)
	1904	for nonvd in NONchkvardims:
	1905	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1906	varsadd.append(nonvd)
	1907
	1908	ncvar.insert_variable(ncobj, 'wd', wd, dnames, diagoutvd, newnc)
	1909
[914]	1910	# WRFtime
[1758]	1911	elif diagn == 'WRFtime':
[654]	1912
	1913	diagout = WRFtime
	1914
	1915	dnamesvar = ['Time']
	1916	dvnamesvar = ['Times']
	1917
	1918	ncvar.insert_variable(ncobj, 'time', diagout, dnamesvar, dvnamesvar, newnc)
	1919
[959]	1920	# ws (U, V)
[1758]	1921	elif diagn == 'ws':
[959]	1922
	1923	# un-staggering variables
[1999]	1924	if len(var0.shape) == 4:
	1925	unstgdims = [var0.shape[0], var0.shape[1], var0.shape[2], var0.shape[3]-1]
	1926	elif len(var0.shape) == 3:
	1927	# Asuming sunding point (dimt, dimz, dimstgx)
	1928	unstgdims = [var0.shape[0], var0.shape[1]]
	1929
	1930	ua = np.zeros(tuple(unstgdims), dtype=np.float)
[959]	1931	va = np.zeros(tuple(unstgdims), dtype=np.float)
	1932	unstgvar0 = np.zeros(tuple(unstgdims), dtype=np.float)
	1933	unstgvar1 = np.zeros(tuple(unstgdims), dtype=np.float)
	1934
[1999]	1935	if len(var0.shape) == 4:
	1936	unstgvar0 = 0.5*(var0[:,:,:,0:var0.shape[3]-1] + var0[:,:,:,1:var0.shape[3]])
	1937	unstgvar1 = 0.5*(var1[:,:,0:var1.shape[2]-1,:] + var1[:,:,1:var1.shape[2],:])
	1938
	1939	for iz in range(var0.shape[1]):
	1940	ua[:,iz,:,:] = unstgvar0[:,iz,:,:]var3 - unstgvar1[:,iz,:,:]var2
	1941	va[:,iz,:,:] = unstgvar0[:,iz,:,:]var2 + unstgvar1[:,iz,:,:]var3
	1942
	1943	dnamesvar = ['Time','bottom_top','south_north','west_east']
	1944
	1945	elif len(var0.shape) == 3:
	1946	unstgvar0 = 0.5*(var0[:,:,0] + var0[:,:,1])
	1947	unstgvar1 = 0.5*(var1[:,:,0] + var1[:,:,1])
	1948	for iz in range(var0.shape[1]):
	1949	ua[:,iz] = unstgvar0[:,iz]var3 - unstgvar1[:,iz]var2
	1950	va[:,iz] = unstgvar0[:,iz]var2 + unstgvar1[:,iz]var3
	1951
	1952	dnamesvar = ['Time','bottom_top']
	1953
[959]	1954	diagout = np.sqrt(unstgvar0unstgvar0 + unstgvar1unstgvar1)
	1955
	1956	# dnamesvar = ncobj.variables[depvars[0]].dimensions
	1957	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1958
[1408]	1959	# Removing the nonChecking variable-dimensions from the initial list
	1960	varsadd = []
	1961	diagoutvd = list(dvnamesvar)
	1962	for nonvd in NONchkvardims:
	1963	if gen.searchInlist(dvnamesvar,nonvd): diagoutvd.remove(nonvd)
	1964	varsadd.append(nonvd)
	1965	ncvar.insert_variable(ncobj, 'ws', diagout, dnamesvar, diagoutvd, newnc)
[959]	1966
[365]	1967	# wss (u10, v10)
[1758]	1968	elif diagn == 'wss':
[365]	1969
	1970	var0 = ncobj.variables[depvars[0]][:]
	1971	var1 = ncobj.variables[depvars[1]][:]
	1972
	1973	diagout = np.sqrt(var0var0 + var1var1)
	1974
	1975	dnamesvar = ncobj.variables[depvars[0]].dimensions
	1976	dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)
	1977
	1978	ncvar.insert_variable(ncobj, 'wss', diagout, dnamesvar, dvnamesvar, newnc)
	1979
[654]	1980	# WRFheight height from WRF geopotential as WRFGeop/g
[1758]	1981	elif diagn == 'WRFheight':
[654]	1982
	1983	diagout = WRFgeop/grav
	1984
[1412]	1985	# Removing the nonChecking variable-dimensions from the initial list
	1986	varsadd = []
	1987	diagoutvd = list(dvnames)
	1988	for nonvd in NONchkvardims:
	1989	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	1990	varsadd.append(nonvd)
[654]	1991
[1412]	1992	ncvar.insert_variable(ncobj, 'zhgt', diagout, dnames, diagoutvd, newnc)
	1993
[1413]	1994	# WRFheightrel relative-height from WRF geopotential as WRFgeop(PH + PHB)/g-HGT 'WRFheightrel\|PH@PHB@HGT
[1758]	1995	elif diagn == 'WRFheightrel':
[1413]	1996	var0 = ncobj.variables[depvars[0]][:]
	1997	var1 = ncobj.variables[depvars[1]][:]
	1998	var2 = ncobj.variables[depvars[2]][:]
	1999
	2000	dimz = var0.shape[1]
	2001	diagout = np.zeros(tuple(var0.shape), dtype=np.float)
	2002	for iz in range(dimz):
[1419]	2003	diagout[:,iz,:,:] = (var0[:,iz,:,:]+ var1[:,iz,:,:])/grav - var2
[1413]	2004
	2005	# Removing the nonChecking variable-dimensions from the initial list
	2006	varsadd = []
	2007	diagoutvd = list(dvnames)
	2008	for nonvd in NONchkvardims:
	2009	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	2010	varsadd.append(nonvd)
	2011
	2012	ncvar.insert_variable(ncobj, 'zhgtrel', diagout, dnames, diagoutvd, newnc)
	2013
[1773]	2014	# WRFzmla_gen generic boundary layer hieght computation from WRF theta, QVAPOR, WRFgeop, HGT,
	2015	elif diagn == 'WRFzmlagen':
	2016	var0 = ncobj.variables[depvars[0]][:]+300.
	2017	var1 = ncobj.variables[depvars[1]][:]
	2018	dimz = var0.shape[1]
	2019	var2 = WRFgeop[:,1:dimz+1,:,:]/9.8
	2020	var3 = ncobj.variables[depvars[3]][0,:,:]
	2021
	2022	diagout, diagoutd, diagoutvd = diag.Forcompute_zmla_gen(var0,var1,var2,var3, \
	2023	dnames,dvnames)
	2024
	2025	# Removing the nonChecking variable-dimensions from the initial list
	2026	varsadd = []
	2027	for nonvd in NONchkvardims:
	2028	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	2029	varsadd.append(nonvd)
	2030
	2031	ncvar.insert_variable(ncobj, 'zmla', diagout, diagoutd, diagoutvd, newnc)
	2032
[1784]	2033	# WRFzwind wind extrapolation at a given height using power law computation from WRF
	2034	# U, V, WRFz, U10, V10, SINALPHA, COSALPHA, z=[zval]
[1776]	2035	elif diagn == 'WRFzwind':
	2036	var0 = ncobj.variables[depvars[0]][:]
	2037	var1 = ncobj.variables[depvars[1]][:]
[1777]	2038	var2 = WRFz
[1776]	2039	var3 = ncobj.variables[depvars[3]][:]
	2040	var4 = ncobj.variables[depvars[4]][:]
	2041	var5 = ncobj.variables[depvars[5]][0,:,:]
	2042	var6 = ncobj.variables[depvars[6]][0,:,:]
[1777]	2043	var7 = np.float(depvars[7].split('=')[1])
[1776]	2044
	2045	# un-staggering 3D winds
	2046	unstgdims = [var0.shape[0], var0.shape[1], var0.shape[2], var0.shape[3]-1]
	2047	va = np.zeros(tuple(unstgdims), dtype=np.float)
	2048	unvar0 = np.zeros(tuple(unstgdims), dtype=np.float)
	2049	unvar1 = np.zeros(tuple(unstgdims), dtype=np.float)
	2050	unvar0 = 0.5*(var0[:,:,:,0:var0.shape[3]-1] + var0[:,:,:,1:var0.shape[3]])
	2051	unvar1 = 0.5*(var1[:,:,0:var1.shape[2]-1,:] + var1[:,:,1:var1.shape[2],:])
	2052
	2053	diagout1, diagout2, diagoutd, diagoutvd = diag.Forcompute_zwind(unvar0, \
[1777]	2054	unvar1, var2, var3, var4, var5, var6, var7, dnames, dvnames)
[1776]	2055
	2056	# Removing the nonChecking variable-dimensions from the initial list
	2057	varsadd = []
	2058	for nonvd in NONchkvardims:
	2059	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	2060	varsadd.append(nonvd)
	2061
	2062	ncvar.insert_variable(ncobj, 'uaz', diagout1, diagoutd, diagoutvd, newnc)
	2063	ncvar.insert_variable(ncobj, 'vaz', diagout2, diagoutd, diagoutvd, newnc)
	2064
[1784]	2065	# WRFzwind wind extrapolation at a given hieght using logarithmic law computation
	2066	# from WRF U, V, WRFz, U10, V10, SINALPHA, COSALPHA, z=[zval]
	2067	elif diagn == 'WRFzwind_log':
	2068	var0 = ncobj.variables[depvars[0]][:]
	2069	var1 = ncobj.variables[depvars[1]][:]
	2070	var2 = WRFz
	2071	var3 = ncobj.variables[depvars[3]][:]
	2072	var4 = ncobj.variables[depvars[4]][:]
	2073	var5 = ncobj.variables[depvars[5]][0,:,:]
	2074	var6 = ncobj.variables[depvars[6]][0,:,:]
	2075	var7 = np.float(depvars[7].split('=')[1])
	2076
	2077	# un-staggering 3D winds
	2078	unstgdims = [var0.shape[0], var0.shape[1], var0.shape[2], var0.shape[3]-1]
	2079	va = np.zeros(tuple(unstgdims), dtype=np.float)
	2080	unvar0 = np.zeros(tuple(unstgdims), dtype=np.float)
	2081	unvar1 = np.zeros(tuple(unstgdims), dtype=np.float)
	2082	unvar0 = 0.5*(var0[:,:,:,0:var0.shape[3]-1] + var0[:,:,:,1:var0.shape[3]])
	2083	unvar1 = 0.5*(var1[:,:,0:var1.shape[2]-1,:] + var1[:,:,1:var1.shape[2],:])
	2084
	2085	diagout1, diagout2, diagoutd, diagoutvd = diag.Forcompute_zwind_log(unvar0, \
	2086	unvar1, var2, var3, var4, var5, var6, var7, dnames, dvnames)
	2087
	2088	# Removing the nonChecking variable-dimensions from the initial list
	2089	varsadd = []
	2090	for nonvd in NONchkvardims:
	2091	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	2092	varsadd.append(nonvd)
	2093
	2094	ncvar.insert_variable(ncobj, 'uaz', diagout1, diagoutd, diagoutvd, newnc)
	2095	ncvar.insert_variable(ncobj, 'vaz', diagout2, diagoutd, diagoutvd, newnc)
	2096
[1783]	2097	# WRFzwindMO wind extrapolation at a given height computation using Monin-Obukhow
	2098	# theory from WRF UST, ZNT, RMOL, U10, V10, SINALPHA, COSALPHA, z=[zval]
[1784]	2099	# NOTE: only useful for [zval] < 80. m
[1783]	2100	elif diagn == 'WRFzwindMO':
	2101	var0 = ncobj.variables[depvars[0]][:]
	2102	var1 = ncobj.variables[depvars[1]][:]
	2103	var2 = ncobj.variables[depvars[2]][:]
	2104	var3 = ncobj.variables[depvars[3]][:]
	2105	var4 = ncobj.variables[depvars[4]][:]
	2106	var5 = ncobj.variables[depvars[5]][0,:,:]
	2107	var6 = ncobj.variables[depvars[6]][0,:,:]
	2108	var7 = np.float(depvars[7].split('=')[1])
	2109
	2110	diagout1, diagout2, diagoutd, diagoutvd = diag.Forcompute_zwindMO(var0, var1,\
	2111	var2, var3, var4, var5, var6, var7, dnames, dvnames)
	2112
	2113	# Removing the nonChecking variable-dimensions from the initial list
	2114	varsadd = []
	2115	for nonvd in NONchkvardims:
	2116	if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
	2117	varsadd.append(nonvd)
	2118
	2119	ncvar.insert_variable(ncobj, 'uaz', diagout1, diagoutd, diagoutvd, newnc)
	2120	ncvar.insert_variable(ncobj, 'vaz', diagout2, diagoutd, diagoutvd, newnc)
	2121
[365]	2122	else:
	2123	print errormsg
[1758]	2124	print ' ' + main + ": diagnostic '" + diagn + "' not ready!!!"
[365]	2125	print ' available diagnostics: ', availdiags
	2126	quit(-1)
	2127
	2128	newnc.sync()
[1351]	2129	# Adding that additional variables required to compute some diagnostics which
	2130	# where not in the original file
[1944]	2131	print ' adding additional variables...'
[1351]	2132	for vadd in varsadd:
[1942]	2133	if not gen.searchInlist(newnc.variables.keys(),vadd) and \
	2134	dictcompvars.has_key(vadd):
[1351]	2135	attrs = dictcompvars[vadd]
	2136	vvn = attrs['name']
	2137	if not gen.searchInlist(newnc.variables.keys(), vvn):
	2138	iidvn = dvnames.index(vadd)
	2139	dnn = dnames[iidvn]
	2140	if vadd == 'WRFtime':
	2141	dvarvals = WRFtime[:]
	2142	newvar = newnc.createVariable(vvn, 'f8', (dnn))
	2143	newvar[:] = dvarvals
	2144	for attn in attrs.keys():
	2145	if attn != 'name':
	2146	attv = attrs[attn]
	2147	ncvar.set_attribute(newvar, attn, attv)
[365]	2148
	2149	# end of diagnostics
	2150
	2151	# Global attributes
	2152	##
[1758]	2153	ncvar.add_global_PyNCplot(newnc, main, None, '2.0')
[365]	2154
	2155	gorigattrs = ncobj.ncattrs()
	2156	for attr in gorigattrs:
	2157	attrv = ncobj.getncattr(attr)
	2158	atvar = ncvar.set_attribute(newnc, attr, attrv)
	2159
	2160	ncobj.close()
	2161	newnc.close()
	2162
	2163	print '\n' + main + ': successfull writting of diagnostics file "' + ofile + '" !!!'

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