# Pthon script to comput diagnostics
# L. Fita, LMD. CNR, UPMC-Jussieu, Paris, France
# File diagnostics.inf provides the combination of variables to get the desired diagnostic
#   To be used with module_ForDiagnostics.F90, module_ForDiagnosticsVars.F90, module_generic.F90
#      foudre: f2py -m module_ForDiagnostics --f90exec=/usr/bin/gfortran-4.7 -c module_generic.F90 module_ForDiagnosticsVars.F90 module_ForDiagnostics.F90 >& run_f2py.log
#      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

## e.g. # diagnostics.py -d 'Time@time,bottom_top@ZNU,south_north@XLAT,west_east@XLONG' -v 'clt|CLDFRA,cllmh|CLDFRA@WRFp,RAINTOT|RAINC@RAINNC@XTIME' -f WRF_LMDZ/NPv31/wrfout_d01_1980-03-01_00:00:00
## e.g. # diagnostics.py -f /home/lluis/PY/diagnostics.inf -d variable_combo -v WRFprc

from optparse import OptionParser
import numpy as np
from netCDF4 import Dataset as NetCDFFile
import os
import re
import nc_var_tools as ncvar
import generic_tools as gen
import datetime as dtime
import module_ForDiag as fdin

main = 'diagnostics.py'
errormsg = 'ERROR -- error -- ERROR -- error'
warnmsg = 'WARNING -- warning -- WARNING -- warning'

# Constants
grav = 9.81

# Gneral information
##
def reduce_spaces(string):
    """ Function to give words of a line of text removing any extra space
    """
    values = string.replace('\n','').split(' ')
    vals = []
    for val in values:
         if len(val) > 0:
             vals.append(val)

    return vals

def variable_combo(varn,combofile):
    """ Function to provide variables combination from a given variable name
      varn= name of the variable
      combofile= ASCII file with the combination of variables
        [varn] [combo]
          [combo]: '@' separated list of variables to use to generate [varn]
            [WRFdt] to get WRF time-step (from general attributes)
    >>> variable_combo('WRFprls','/home/lluis/PY/diagnostics.inf')
    deaccum@RAINNC@XTIME@prnc
    """
    fname = 'variable_combo'

    if varn == 'h':
        print fname + '_____________________________________________________________'
        print variable_combo.__doc__
        quit()

    if not os.path.isfile(combofile):
        print errormsg
        print '  ' + fname + ": file with combinations '" + combofile +              \
          "' does not exist!!"
        quit(-1)

    objf = open(combofile, 'r')

    found = False
    for line in objf:
        linevals = reduce_spaces(line)
        varnf = linevals[0]
        combo = linevals[1].replace('\n','')
        if varn == varnf: 
            found = True
            break

    if not found:
        print errormsg
        print '  ' + fname + ": variable '" + varn + "' not found in '" + combofile +\
          "' !!"
        combo='ERROR'

    objf.close()

    return combo

# Mathematical operators
##
def compute_accum(varv, dimns, dimvns):
    """ Function to compute the accumulation of a variable
    compute_accum(varv, dimnames, dimvns)
      [varv]= values to accum (assuming [t,])
      [dimns]= list of the name of the dimensions of the [varv]
      [dimvns]= list of the name of the variables with the values of the 
        dimensions of [varv]
    """
    fname = 'compute_accum'

    deacdims = dimns[:]
    deacvdims = dimvns[:]

    slicei = []
    slicee = []

    Ndims = len(varv.shape)
    for iid in range(0,Ndims):
        slicei.append(slice(0,varv.shape[iid]))
        slicee.append(slice(0,varv.shape[iid]))

    slicee[0] = np.arange(varv.shape[0])
    slicei[0] = np.arange(varv.shape[0])
    slicei[0][1:varv.shape[0]] = np.arange(varv.shape[0]-1)

    vari = varv[tuple(slicei)]
    vare = varv[tuple(slicee)]

    ac = vari*0.
    for it in range(1,varv.shape[0]):
        ac[it,] = ac[it-1,] + vare[it,]

    return ac, deacdims, deacvdims

def compute_deaccum(varv, dimns, dimvns):
    """ Function to compute the deaccumulation of a variable
    compute_deaccum(varv, dimnames, dimvns)
      [varv]= values to deaccum (assuming [t,])
      [dimns]= list of the name of the dimensions of the [varv]
      [dimvns]= list of the name of the variables with the values of the 
        dimensions of [varv]
    """
    fname = 'compute_deaccum'

    deacdims = dimns[:]
    deacvdims = dimvns[:]

    slicei = []
    slicee = []

    Ndims = len(varv.shape)
    for iid in range(0,Ndims):
        slicei.append(slice(0,varv.shape[iid]))
        slicee.append(slice(0,varv.shape[iid]))

    slicee[0] = np.arange(varv.shape[0])
    slicei[0] = np.arange(varv.shape[0])
    slicei[0][1:varv.shape[0]] = np.arange(varv.shape[0]-1)

    vari = varv[tuple(slicei)]
    vare = varv[tuple(slicee)]

    deac = vare - vari

    return deac, deacdims, deacvdims

def derivate_centered(var,dim,dimv):
    """ Function to compute the centered derivate of a given field
      centered derivate(n) = (var(n-1) + var(n+1))/(2*dn).
    [var]= variable
    [dim]= which dimension to compute the derivate
    [dimv]= dimension values (can be of different dimension of [var])
    >>> derivate_centered(np.arange(16).reshape(4,4)*1.,1,1.)
    [[  0.   1.   2.   0.]
     [  0.   5.   6.   0.]
     [  0.   9.  10.   0.]
     [  0.  13.  14.   0.]]
    """

    fname = 'derivate_centered'
    
    vark = var.dtype

    if hasattr(dimv, "__len__"):
# Assuming that the last dimensions of var [..., N, M] are the same of dimv [N, M]
        if len(var.shape) != len(dimv.shape):
            dimvals = np.zeros((var.shape), dtype=vark)
            if len(var.shape) - len(dimv.shape) == 1:
                for iz in range(var.shape[0]):
                    dimvals[iz,] = dimv
            elif len(var.shape) - len(dimv.shape) == 2:
                for it in range(var.shape[0]):
                    for iz in range(var.shape[1]):
                        dimvals[it,iz,] = dimv
            else:
                print errormsg
                print '  ' + fname + ': dimension difference between variable',      \
                  var.shape,'and variable with dimension values',dimv.shape,         \
                  ' not ready !!!'
                quit(-1)
        else:
            dimvals = dimv
    else:
# dimension values are identical everywhere! 
# from: http://stackoverflow.com/questions/16807011/python-how-to-identify-if-a-variable-is-an-array-or-a-scalar    
        dimvals = np.ones((var.shape), dtype=vark)*dimv

    derivate = np.zeros((var.shape), dtype=vark)
    if dim > len(var.shape) - 1:
        print errormsg
        print '  ' + fname + ': dimension',dim,' too big for given variable of ' +   \
          'shape:', var.shape,'!!!'
        quit(-1)

    slicebef = []
    sliceaft = []
    sliceder = []

    for id in range(len(var.shape)):
        if id == dim:
            slicebef.append(slice(0,var.shape[id]-2))
            sliceaft.append(slice(2,var.shape[id]))
            sliceder.append(slice(1,var.shape[id]-1))
        else:
            slicebef.append(slice(0,var.shape[id]))
            sliceaft.append(slice(0,var.shape[id]))
            sliceder.append(slice(0,var.shape[id]))

    if hasattr(dimv, "__len__"):
        derivate[tuple(sliceder)] = (var[tuple(slicebef)] + var[tuple(sliceaft)])/   \
          ((dimvals[tuple(sliceaft)] - dimvals[tuple(slicebef)]))
        print (dimvals[tuple(sliceaft)] - dimvals[tuple(slicebef)])
    else:
        derivate[tuple(sliceder)] = (var[tuple(slicebef)] + var[tuple(sliceaft)])/   \
          (2.*dimv)

#    print 'before________'
#    print var[tuple(slicebef)]

#    print 'after________'
#    print var[tuple(sliceaft)]

    return derivate

def rotational_z(Vx,Vy,pos):
    """ z-component of the rotatinoal of horizontal vectorial field
    \/ x (Vx,Vy,Vz) = \/xVy - \/yVx
    [Vx]= Variable component x
    [Vy]=  Variable component y
    [pos]= poisition of the grid points
    >>> rotational_z(np.arange(16).reshape(4,4)*1., np.arange(16).reshape(4,4)*1., 1.)
    [[  0.   1.   2.   0.]
     [ -4.   0.   0.  -7.]
     [ -8.   0.   0. -11.]
     [  0.  13.  14.   0.]]
    """

    fname =  'rotational_z'

    ndims = len(Vx.shape)
    rot1 = derivate_centered(Vy,ndims-1,pos)
    rot2 = derivate_centered(Vx,ndims-2,pos)

    rot = rot1 - rot2

    return rot

# Diagnostics
##

def var_clt(cfra):
    """ Function to compute the total cloud fraction following 'newmicro.F90' from 
      LMDZ using 1D vertical column values
      [cldfra]= cloud fraction values (assuming [[t],z,y,x])
    """
    ZEPSEC=1.0E-12

    fname = 'var_clt'

    zclear = 1.
    zcloud = 0.

    dz = cfra.shape[0]
    for iz in range(dz):
        zclear =zclear*(1.-np.max([cfra[iz],zcloud]))/(1.-np.min([zcloud,1.-ZEPSEC]))
        clt = 1. - zclear
        zcloud = cfra[iz]

    return clt

def compute_clt(cldfra, dimns, dimvns):
    """ Function to compute the total cloud fraction following 'newmicro.F90' from 
      LMDZ
    compute_clt(cldfra, dimnames)
      [cldfra]= cloud fraction values (assuming [[t],z,y,x])
      [dimns]= list of the name of the dimensions of [cldfra]
      [dimvns]= list of the name of the variables with the values of the 
        dimensions of [cldfra]
    """
    fname = 'compute_clt'

    cltdims = dimns[:]
    cltvdims = dimvns[:]

    if len(cldfra.shape) == 4:
        clt = np.zeros((cldfra.shape[0],cldfra.shape[2],cldfra.shape[3]),            \
          dtype=np.float)
        dx = cldfra.shape[3]
        dy = cldfra.shape[2]
        dz = cldfra.shape[1]
        dt = cldfra.shape[0]
        cltdims.pop(1)
        cltvdims.pop(1)

        for it in range(dt):
            for ix in range(dx):
                for iy in range(dy):
                    zclear = 1.
                    zcloud = 0.
                    ncvar.percendone(it*dx*dy + ix*dy + iy, dx*dy*dt, 5, 'diagnosted')
                    clt[it,iy,ix] = var_clt(cldfra[it,:,iy,ix])

    else:
        clt = np.zeros((cldfra.shape[1],cldfra.shape[2]), dtype=np.float)
        dx = cldfra.shape[2]
        dy = cldfra.shape[1]
        dy = cldfra.shape[0]
        cltdims.pop(0)
        cltvdims.pop(0)
        for ix in range(dx):
            for iy in range(dy):
                zclear = 1.
                zcloud = 0.
                ncvar.percendone(ix*dy + iy, dx*dy*dt, 5, 'diagnosted')
                clt[iy,ix] = var_clt(cldfra[:,iy,ix])

    return clt, cltdims, cltvdims

def Forcompute_clt(cldfra, dimns, dimvns):
    """ Function to compute the total cloud fraction following 'newmicro.F90' from 
      LMDZ via a Fortran module
    compute_clt(cldfra, dimnames)
      [cldfra]= cloud fraction values (assuming [[t],z,y,x])
      [dimns]= list of the name of the dimensions of [cldfra]
      [dimvns]= list of the name of the variables with the values of the 
        dimensions of [cldfra]
    """
    fname = 'Forcompute_clt'

    cltdims = dimns[:]
    cltvdims = dimvns[:]


    if len(cldfra.shape) == 4:
        clt = np.zeros((cldfra.shape[0],cldfra.shape[2],cldfra.shape[3]),            \
          dtype=np.float)
        dx = cldfra.shape[3]
        dy = cldfra.shape[2]
        dz = cldfra.shape[1]
        dt = cldfra.shape[0]
        cltdims.pop(1)
        cltvdims.pop(1)

        clt = fdin.module_fordiagnostics.compute_clt4d2(cldfra[:])

    else:
        clt = np.zeros((cldfra.shape[1],cldfra.shape[2]), dtype=np.float)
        dx = cldfra.shape[2]
        dy = cldfra.shape[1]
        dy = cldfra.shape[0]
        cltdims.pop(0)
        cltvdims.pop(0)

        clt = fdin.module_fordiagnostics.compute_clt3d1(cldfra[:])

    return clt, cltdims, cltvdims

def var_cllmh(cfra, p):
    """ Fcuntion to compute cllmh on a 1D column
    """

    fname = 'var_cllmh'

    ZEPSEC =1.0E-12
    prmhc = 440.*100.
    prmlc = 680.*100.

    zclearl = 1.
    zcloudl = 0.
    zclearm = 1.
    zcloudm = 0.
    zclearh = 1.
    zcloudh = 0.

    dvz = cfra.shape[0]

    cllmh = np.ones((3), dtype=np.float)

    for iz in range(dvz):
        if p[iz] < prmhc:
            cllmh[2] = cllmh[2]*(1.-np.max([cfra[iz], zcloudh]))/(1.-                \
              np.min([zcloudh,1.-ZEPSEC]))
            zcloudh = cfra[iz]
        elif p[iz] >= prmhc and p[iz] < prmlc:
            cllmh[1] = cllmh[1]*(1.-np.max([cfra[iz], zcloudm]))/(1.-                \
              np.min([zcloudm,1.-ZEPSEC]))
            zcloudm = cfra[iz]
        elif p[iz] >= prmlc:
            cllmh[0] = cllmh[0]*(1.-np.max([cfra[iz], zcloudl]))/(1.-                \
              np.min([zcloudl,1.-ZEPSEC]))
            zcloudl = cfra[iz]

    cllmh = 1.- cllmh

    return cllmh

def Forcompute_cllmh(cldfra, pres, dimns, dimvns):
    """ Function to compute cllmh: low/medium/hight cloud fraction following newmicro.F90 from LMDZ via Fortran subroutine
    compute_clt(cldfra, pres, dimns, dimvns)
      [cldfra]= cloud fraction values (assuming [[t],z,y,x])
      [pres] = pressure field
      [dimns]= list of the name of the dimensions of [cldfra]
      [dimvns]= list of the name of the variables with the values of the 
        dimensions of [cldfra]
    """
    fname = 'Forcompute_cllmh'

    cllmhdims = dimns[:]
    cllmhvdims = dimvns[:]

    if len(cldfra.shape) == 4:
        dx = cldfra.shape[3]
        dy = cldfra.shape[2]
        dz = cldfra.shape[1]
        dt = cldfra.shape[0]
        cllmhdims.pop(1)
        cllmhvdims.pop(1)

        cllmh = fdin.module_fordiagnostics.compute_cllmh4d2(cldfra[:], pres[:])
        
    else:
        dx = cldfra.shape[2]
        dy = cldfra.shape[1]
        dz = cldfra.shape[0]
        cllmhdims.pop(0)
        cllmhvdims.pop(0)

        cllmh = fdin.module_fordiagnostics.compute_cllmh3d1(cldfra[:], pres[:])

    return cllmh, cllmhdims, cllmhvdims

def compute_cllmh(cldfra, pres, dimns, dimvns):
    """ Function to compute cllmh: low/medium/hight cloud fraction following newmicro.F90 from LMDZ
    compute_clt(cldfra, pres, dimns, dimvns)
      [cldfra]= cloud fraction values (assuming [[t],z,y,x])
      [pres] = pressure field
      [dimns]= list of the name of the dimensions of [cldfra]
      [dimvns]= list of the name of the variables with the values of the 
        dimensions of [cldfra]
    """
    fname = 'compute_cllmh'

    cllmhdims = dimns[:]
    cllmhvdims = dimvns[:]

    if len(cldfra.shape) == 4:
        dx = cldfra.shape[3]
        dy = cldfra.shape[2]
        dz = cldfra.shape[1]
        dt = cldfra.shape[0]
        cllmhdims.pop(1)
        cllmhvdims.pop(1)

        cllmh = np.ones(tuple([3, dt, dy, dx]), dtype=np.float)

        for it in range(dt):
            for ix in range(dx):
                for iy in range(dy):
                    ncvar.percendone(it*dx*dy + ix*dy + iy, dx*dy*dt, 5, 'diagnosted')
                    cllmh[:,it,iy,ix] = var_cllmh(cldfra[it,:,iy,ix], pres[it,:,iy,ix])
        
    else:
        dx = cldfra.shape[2]
        dy = cldfra.shape[1]
        dz = cldfra.shape[0]
        cllmhdims.pop(0)
        cllmhvdims.pop(0)

        cllmh = np.ones(tuple([3, dy, dx]), dtype=np.float)

        for ix in range(dx):
            for iy in range(dy):
                ncvar.percendone(ix*dy + iy,dx*dy, 5, 'diagnosted')
                cllmh[:,iy,ix] = var_cllmh(cldfra[:,iy,ix], pres[:,iy,ix])

    return cllmh, cllmhdims, cllmhvdims

def var_virtualTemp (temp,rmix):
    """ This function returns virtual temperature in K, 
      temp: temperature [K]
      rmix: mixing ratio in [kgkg-1]
    """

    fname = 'var_virtualTemp'

    virtual=temp*(0.622+rmix)/(0.622*(1.+rmix))

    return virtual


def var_mslp(pres, psfc, ter, tk, qv):
    """ Function to compute mslp on a 1D column
    """

    fname = 'var_mslp'

    N = 1.0
    expon=287.04*.0065/9.81
    pref = 40000.

# First find where about 400 hPa is located
    dz=len(pres) 

    kref = -1
    pinc = pres[0] - pres[dz-1]

    if pinc < 0.:
        for iz in range(1,dz):
            if pres[iz-1] >= pref and pres[iz] < pref: 
                kref = iz
                break
    else:
        for iz in range(dz-1):
            if pres[iz] >= pref and pres[iz+1] < pref: 
                kref = iz
                break

    if kref == -1:
        print errormsg
        print '  ' + fname + ': no reference pressure:',pref,'found!!'
        print '    values:',pres[:]
        quit(-1)

    mslp = 0.

# We are below both the ground and the lowest data level.

# First, find the model level that is closest to a "target" pressure
# level, where the "target" pressure is delta-p less that the local
# value of a horizontally smoothed surface pressure field.  We use
# delta-p = 150 hPa here. A standard lapse rate temperature profile
# passing through the temperature at this model level will be used
# to define the temperature profile below ground.  This is similar
# to the Benjamin and Miller (1990) method, using  
# 700 hPa everywhere for the "target" pressure.

# ptarget = psfc - 15000.
    ptarget = 70000.
    dpmin=1.e4
    kupper = 0
    if pinc > 0.:
        for iz in range(dz-1,0,-1):
            kupper = iz
            dp=np.abs( pres[iz] - ptarget )
            if dp < dpmin: exit
            dpmin = np.min([dpmin, dp])
    else:
        for iz in range(dz):
            kupper = iz
            dp=np.abs( pres[iz] - ptarget )
            if dp < dpmin: exit
            dpmin = np.min([dpmin, dp])

    pbot=np.max([pres[0], psfc])
#    zbot=0.

#    tbotextrap=tk(i,j,kupper,itt)*(pbot/pres_field(i,j,kupper,itt))**expon
#    tvbotextrap=virtual(tbotextrap,qv(i,j,1,itt))

#    data_out(i,j,itt,1) = (zbot+tvbotextrap/.0065*(1.-(interp_levels(1)/pbot)**expon))
    tbotextrap = tk[kupper]*(psfc/ptarget)**expon
    tvbotextrap = var_virtualTemp(tbotextrap, qv[kupper])
    mslp = psfc*( (tvbotextrap+0.0065*ter)/tvbotextrap)**(1./expon)

    return mslp

def compute_mslp(pressure, psurface, terrain, temperature, qvapor, dimns, dimvns):
    """ Function to compute mslp: mean sea level pressure following p_interp.F90 from WRF
    var_mslp(pres, ter, tk, qv, dimns, dimvns)
      [pressure]= pressure field [Pa] (assuming [[t],z,y,x])
      [psurface]= surface pressure field [Pa]
      [terrain]= topography [m]
      [temperature]= temperature [K]
      [qvapor]= water vapour mixing ratio [kgkg-1]
      [dimns]= list of the name of the dimensions of [cldfra]
      [dimvns]= list of the name of the variables with the values of the 
        dimensions of [pres]
    """

    fname = 'compute_mslp'

    mslpdims = list(dimns[:])
    mslpvdims = list(dimvns[:])

    if len(pressure.shape) == 4:
        mslpdims.pop(1)
        mslpvdims.pop(1)
    else:
        mslpdims.pop(0)
        mslpvdims.pop(0)

    if len(pressure.shape) == 4:
        dx = pressure.shape[3]
        dy = pressure.shape[2]
        dz = pressure.shape[1]
        dt = pressure.shape[0]

        mslpv = np.zeros(tuple([dt, dy, dx]), dtype=np.float)

# Terrain... to 2D !
        terval = np.zeros(tuple([dy, dx]), dtype=np.float)
        if len(terrain.shape) == 3:
            terval = terrain[0,:,:]
        else:
            terval = terrain

        for ix in range(dx):
            for iy in range(dy):
                if terval[iy,ix] > 0.:
                    for it in range(dt):
                        mslpv[it,iy,ix] = var_mslp(pressure[it,:,iy,ix],             \
                          psurface[it,iy,ix], terval[iy,ix], temperature[it,:,iy,ix],\
                          qvapor[it,:,iy,ix])

                        ncvar.percendone(it*dx*dy + ix*dy + iy, dx*dy*dt, 5, 'diagnosted')
                else:
                    mslpv[:,iy,ix] = psurface[:,iy,ix]

    else:
        dx = pressure.shape[2]
        dy = pressure.shape[1]
        dz = pressure.shape[0]

        mslpv = np.zeros(tuple([dy, dx]), dtype=np.float)

# Terrain... to 2D !
        terval = np.zeros(tuple([dy, dx]), dtype=np.float)
        if len(terrain.shape) == 3:
            terval = terrain[0,:,:]
        else:
            terval = terrain

        for ix in range(dx):
            for iy in range(dy):
                ncvar.percendone(ix*dy + iy,dx*dy, 5, 'diagnosted')
                if terval[iy,ix] > 0.:
                    mslpv[iy,ix] = var_mslp(pressure[:,iy,ix], psurface[iy,ix],          \
                      terval[iy,ix], temperature[:,iy,ix], qvapor[:,iy,ix])
                else:
                    mslpv[iy,ix] = psfc[iy,ix]

    return mslpv, mslpdims, mslpvdims

def compute_OMEGAw(omega, p, t, dimns, dimvns):
    """ Function to transform OMEGA [Pas-1] to velocities [ms-1]
      tacking: https://www.ncl.ucar.edu/Document/Functions/Contributed/omega_to_w.shtml
      [omega] = vertical velocity [in ms-1] (assuming [t],z,y,x)
      [p] = pressure in [Pa] (assuming [t],z,y,x)
      [t] = temperature in [K] (assuming [t],z,y,x)
      [dimns]= list of the name of the dimensions of [q]
      [dimvns]= list of the name of the variables with the values of the 
        dimensions of [q]
    """
    fname = 'compute_OMEGAw'

    rgas = 287.058            # J/(kg-K) => m2/(s2 K)
    g    = 9.80665            # m/s2

    wdims = dimns[:]
    wvdims = dimvns[:]

    rho  = p/(rgas*t)         # density => kg/m3
    w    = -omega/(rho*g)     

    return w, wdims, wvdims

def compute_prw(dens, q, dimns, dimvns):
    """ Function to compute water vapour path (prw)
      [dens] = density [in kgkg-1] (assuming [t],z,y,x)
      [q] = mixing ratio in [kgkg-1] (assuming [t],z,y,x)
      [dimns]= list of the name of the dimensions of [q]
      [dimvns]= list of the name of the variables with the values of the 
        dimensions of [q]
    """
    fname = 'compute_prw'

    prwdims = dimns[:]
    prwvdims = dimvns[:]

    if len(q.shape) == 4:
        prwdims.pop(1)
        prwvdims.pop(1)
    else:
        prwdims.pop(0)
        prwvdims.pop(0)

    data1 = dens*q
    prw = np.sum(data1, axis=1)

    return prw, prwdims, prwvdims

def compute_rh(p, t, q, dimns, dimvns):
    """ Function to compute relative humidity following 'Tetens' equation (T,P) ...'
      [t]= temperature (assuming [[t],z,y,x] in [K])
      [p] = pressure field (assuming in [hPa])
      [q] = mixing ratio in [kgkg-1]
      [dimns]= list of the name of the dimensions of [t]
      [dimvns]= list of the name of the variables with the values of the 
        dimensions of [t]
    """
    fname = 'compute_rh'

    rhdims = dimns[:]
    rhvdims = dimvns[:]

    data1 = 10.*0.6112*np.exp(17.67*(t-273.16)/(t-29.65))
    data2 = 0.622*data1/(0.01*p-(1.-0.622)*data1)

    rh = q/data2

    return rh, rhdims, rhvdims

def compute_td(p, temp, qv, dimns, dimvns):
    """ Function to compute the dew point temperature
      [p]= pressure [Pa]
      [temp]= temperature [C]
      [qv]= mixing ratio [kgkg-1]
      [dimns]= list of the name of the dimensions of [p]
      [dimvns]= list of the name of the variables with the values of the 
        dimensions of [p]
    """
    fname = 'compute_td'

#    print '    ' + fname + ': computing dew-point temperature from TS as t and Tetens...'
# tacking from: http://en.wikipedia.org/wiki/Dew_point
    tk = temp
    data1 = 10.*0.6112*np.exp(17.67*(tk-273.16)/(tk-29.65))
    data2 = 0.622*data1/(0.01*p-(1.-0.622)*data1)

    rh = qv/data2
                
    pa = rh * data1
    td = 257.44*np.log(pa/6.1121)/(18.678-np.log(pa/6.1121))

    tddims = dimns[:]
    tdvdims = dimvns[:]

    return td, tddims, tdvdims

def turbulence_var(varv, dimvn, dimn):
    """ Function to compute the Taylor's decomposition turbulence term from a a given variable
      x*=<x^2>_t-(<X>_t)^2
    turbulence_var(varv,dimn)
      varv= values of the variable
      dimvn= names of the dimension of the variable
      dimn= names of the dimensions (as a dictionary with 'X', 'Y', 'Z', 'T')
    >>> turbulence_var(np.arange((27)).reshape(3,3,3),['time','y','x'],{'T':'time', 'Y':'y', 'X':'x'})
    [[ 54.  54.  54.]
     [ 54.  54.  54.]
     [ 54.  54.  54.]]
    """
    fname = 'turbulence_varv'

    timedimid = dimvn.index(dimn['T'])

    varv2 = varv*varv

    vartmean = np.mean(varv, axis=timedimid)
    var2tmean = np.mean(varv2, axis=timedimid)

    varvturb = var2tmean - (vartmean*vartmean)

    return varvturb

def compute_turbulence(v, dimns, dimvns):
    """ Function to compute the rubulence term of the Taylor's decomposition ...'
      x*=<x^2>_t-(<X>_t)^2
      [v]= variable (assuming [[t],z,y,x])
      [dimns]= list of the name of the dimensions of [v]
      [dimvns]= list of the name of the variables with the values of the 
        dimensions of [v]
    """
    fname = 'compute_turbulence'

    turbdims = dimns[:]
    turbvdims = dimvns[:]

    turbdims.pop(0)
    turbvdims.pop(0)

    v2 = v*v

    vartmean = np.mean(v, axis=0)
    var2tmean = np.mean(v2, axis=0)

    turb = var2tmean - (vartmean*vartmean)

    return turb, turbdims, turbvdims

def compute_wds(u, v, dimns, dimvns):
    """ Function to compute the wind direction
      [u]= W-E wind direction [ms-1, knot, ...]
      [v]= N-S wind direction [ms-1, knot, ...]
      [dimns]= list of the name of the dimensions of [u]
      [dimvns]= list of the name of the variables with the values of the 
        dimensions of [u]
    """
    fname = 'compute_wds'

#    print '    ' + fname + ': computing wind direction as ATAN2(v,u) ...'
    theta = np.arctan2(v,u)
    theta = np.where(theta < 0., theta + 2.*np.pi, theta)

    wds = 360.*theta/(2.*np.pi)

    wdsdims = dimns[:]
    wdsvdims = dimvns[:]

    return wds, wdsdims, wdsvdims

def compute_wss(u, v, dimns, dimvns):
    """ Function to compute the wind speed
      [u]= W-E wind direction [ms-1, knot, ...]
      [v]= N-S wind direction [ms-1, knot, ...]
      [dimns]= list of the name of the dimensions of [u]
      [dimvns]= list of the name of the variables with the values of the 
        dimensions of [u]
    """
    fname = 'compute_wss'

#    print '    ' + fname + ': computing wind speed as SQRT(v**2 + u**2) ...'
    wss = np.sqrt(u*u + v*v)

    wssdims = dimns[:]
    wssvdims = dimvns[:]

    return wss, wssdims, wssvdims

def timeunits_seconds(dtu):
    """ Function to transform a time units to seconds
    timeunits_seconds(timeuv)
      [dtu]= time units value to transform in seconds
    """
    fname='timunits_seconds'

    if dtu == 'years':
        times = 365.*24.*3600.
    elif dtu == 'weeks':
        times = 7.*24.*3600.
    elif dtu == 'days':
        times = 24.*3600.
    elif dtu == 'hours':
        times = 3600.
    elif dtu == 'minutes':
        times = 60.
    elif dtu == 'seconds':
        times = 1.
    elif dtu == 'miliseconds':
        times = 1./1000.
    else:
        print errormsg
        print '  ' + fname  + ": time units '" + dtu + "' not ready !!"
        quit(-1)

    return times

####### ###### ##### #### ### ## #
comboinf="\nIF -d 'variable_combo', provides information of the combination to obtain -v [varn] with the ASCII file with the combinations as -f [combofile]"

parser = OptionParser()
parser.add_option("-f", "--netCDF_file", dest="ncfile", help="file to use", metavar="FILE")
parser.add_option("-d", "--dimensions", dest="dimns",  
  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)" + comboinf, 
  metavar="LABELS")
parser.add_option("-v", "--variables", dest="varns", 
  help=" [varn1]|[var11]@[...[varN1]],[...,[varnM]|[var1M]@[...[varLM]]] ',' list of variables to compute [varnK] and its necessary ones [var1K]...[varPK]", metavar="VALUES")

(opts, args) = parser.parse_args()

#######    #######
## MAIN
    #######
availdiags = ['ACRAINTOT', 'accum', 'clt', 'cllmh', 'deaccum', 'LMDZrh', 'mslp',     \
  'OMEGAw', 'RAINTOT',                                                               \
  'rvors', 'td', 'turbulence', 'WRFgeop', 'WRFp', 'WRFrvors', 'ws', 'wds', 'wss',    \
  'WRFheight', 'WRFheightrel', 'WRFua', 'WRFva']

methods = ['accum', 'deaccum']

# Variables not to check
NONcheckingvars = ['cllmh', 'deaccum', 'TSrhs', 'TStd', 'TSwds', 'TSwss', 'WRFbils', \
  'WRFdens', 'WRFgeop',                                                              \
  'WRFp', 'WRFtd',                                                                   \
  'WRFpos', 'WRFprc', 'WRFprls', 'WRFrh', 'LMDZrh', 'LMDZrhs', 'WRFrhs', 'WRFrvors', \
  'WRFt', 'WRFtime', 'WRFua', 'WRFva', 'WRFwds', 'WRFwss', 'WRFheight']

NONchkvardims = ['WRFtime']

ofile = 'diagnostics.nc'

dimns = opts.dimns
varns = opts.varns

# Special method. knowing variable combination
##
if opts.dimns == 'variable_combo':
    print warnmsg
    print '  ' + main + ': knowing variable combination !!!'
    combination = variable_combo(opts.varns,opts.ncfile)
    print '     COMBO: ' + combination
    quit(-1)

if not os.path.isfile(opts.ncfile):
    print errormsg
    print '   ' + main + ": file '" + opts.ncfile + "' does not exist !!"
    quit(-1)

ncobj = NetCDFFile(opts.ncfile, 'r')

# Looking for specific variables that might be use in more than one diagnostic
WRFgeop_compute = False
WRFp_compute = False
WRFt_compute = False
WRFrh_compute = False
WRFght_compute = False
WRFdens_compute = False
WRFpos_compute = False
WRFtime_compute = False

# File creation
newnc = NetCDFFile(ofile,'w')

# dimensions
dimvalues = dimns.split(',')
dnames = []
dvnames = []

for dimval in dimvalues:
    dn = dimval.split('@')[0]
    dnv = dimval.split('@')[1]
    dnames.append(dn)
    dvnames.append(dnv)
    # Is there any dimension-variable which should be computed?
    if dnv == 'WRFgeop':WRFgeop_compute = True
    if dnv == 'WRFp': WRFp_compute = True
    if dnv == 'WRFt': WRFt_compute = True
    if dnv == 'WRFrh': WRFrh_compute = True
    if dnv == 'WRFght': WRFght_compute = True
    if dnv == 'WRFdens': WRFdens_compute = True
    if dnv == 'WRFpos': WRFpos_compute = True
    if dnv == 'WRFtime': WRFtime_compute = True

# diagnostics to compute
diags = varns.split(',')
Ndiags = len(diags)

for idiag in range(Ndiags):
    if diags[idiag].split('|')[1].find('@') == -1:
        depvars = diags[idiag].split('|')[1]
        if depvars == 'WRFgeop':WRFgeop_compute = True
        if depvars == 'WRFp': WRFp_compute = True
        if depvars == 'WRFt': WRFt_compute = True
        if depvars == 'WRFrh': WRFrh_compute = True
        if depvars == 'WRFght': WRFght_compute = True
        if depvars == 'WRFdens': WRFdens_compute = True
        if depvars == 'WRFpos': WRFpos_compute = True
        if depvars == 'WRFtime': WRFtime_compute = True
    else:
        depvars = diags[idiag].split('|')[1].split('@')
        if gen.searchInlist(depvars, 'WRFgeop'): WRFgeop_compute = True
        if gen.searchInlist(depvars, 'WRFp'): WRFp_compute = True
        if gen.searchInlist(depvars, 'WRFt'): WRFt_compute = True
        if gen.searchInlist(depvars, 'WRFrh'): WRFrh_compute = True
        if gen.searchInlist(depvars, 'WRFght'): WRFght_compute = True
        if gen.searchInlist(depvars, 'WRFdens'): WRFdens_compute = True
        if gen.searchInlist(depvars, 'WRFpos'): WRFpos_compute = True
        if gen.searchInlist(depvars, 'WRFtime'): WRFtime_compute = True

# Dictionary with the new computed variables to be able to add them
dictcompvars = {}
if WRFgeop_compute:
    print '  ' + main + ': Retrieving geopotential value from WRF as PH + PHB'
    dimv = ncobj.variables['PH'].shape
    WRFgeop = ncobj.variables['PH'][:] + ncobj.variables['PHB'][:]

    # Attributes of the variable
    Vvals = gen.variables_values('WRFgeop')
    dictcompvars['WRFgeop'] = {'name': Vvals[0], 'standard_name': Vvals[1],          \
      'long_name': Vvals[4].replace('|',' '), 'units': Vvals[5]}

if WRFp_compute:
    print '  ' + main + ': Retrieving pressure value from WRF as P + PB'
    dimv = ncobj.variables['P'].shape
    WRFp = ncobj.variables['P'][:] + ncobj.variables['PB'][:]

    # Attributes of the variable
    Vvals = gen.variables_values('WRFp')
    dictcompvars['WRFgeop'] = {'name': Vvals[0], 'standard_name': Vvals[1],          \
      'long_name': Vvals[4].replace('|',' '), 'units': Vvals[5]}

if WRFght_compute:
    print '    ' + main + ': computing geopotential height from WRF as PH + PHB ...' 
    WRFght = ncobj.variables['PH'][:] + ncobj.variables['PHB'][:]

    # Attributes of the variable
    Vvals = gen.variables_values('WRFght')
    dictcompvars['WRFgeop'] = {'name': Vvals[0], 'standard_name': Vvals[1],          \
      'long_name': Vvals[4].replace('|',' '), 'units': Vvals[5]}

if WRFrh_compute:
    print '    ' + main + ": computing relative humidity from WRF as 'Tetens'" +     \
      ' equation (T,P) ...'
    p0=100000.
    p=ncobj.variables['P'][:] + ncobj.variables['PB'][:]
    tk = (ncobj.variables['T'][:] + 300.)*(p/p0)**(2./7.)
    qv = ncobj.variables['QVAPOR'][:]

    data1 = 10.*0.6112*np.exp(17.67*(tk-273.16)/(tk-29.65))
    data2 = 0.622*data1/(0.01*p-(1.-0.622)*data1)

    WRFrh = qv/data2

    # Attributes of the variable
    Vvals = gen.variables_values('WRFrh')
    dictcompvars['WRFrh'] = {'name': Vvals[0], 'standard_name': Vvals[1],            \
      'long_name': Vvals[4].replace('|',' '), 'units': Vvals[5]}

if WRFt_compute:
    print '    ' + main + ': computing temperature from WRF as inv_potT(T + 300) ...'
    p0=100000.
    p=ncobj.variables['P'][:] + ncobj.variables['PB'][:]

    WRFt = (ncobj.variables['T'][:] + 300.)*(p/p0)**(2./7.)

    # Attributes of the variable
    Vvals = gen.variables_values('WRFt')
    dictcompvars['WRFt'] = {'name': Vvals[0], 'standard_name': Vvals[1],             \
      'long_name': Vvals[4].replace('|',' '), 'units': Vvals[5]}

if WRFdens_compute:
    print '    ' + main + ': computing air density from WRF as ((MU + MUB) * ' +     \
      'DNW)/g ...'

# Just we need in in absolute values: Size of the central grid cell
##    dxval = ncobj.getncattr('DX')
##    dyval = ncobj.getncattr('DY')
##    mapfac = ncobj.variables['MAPFAC_M'][:]
##    area = dxval*dyval*mapfac

    mu = (ncobj.variables['MU'][:] + ncobj.variables['MUB'][:])
    dnw = ncobj.variables['DNW'][:]

    WRFdens = np.zeros((mu.shape[0], dnw.shape[1], mu.shape[1], mu.shape[2]),        \
      dtype=np.float)
    levval = np.zeros((mu.shape[1], mu.shape[2]), dtype=np.float)

    for it in range(mu.shape[0]):
        for iz in range(dnw.shape[1]):
            levval.fill(np.abs(dnw[it,iz]))
            WRFdens[it,iz,:,:] = levval
            WRFdens[it,iz,:,:] = mu[it,:,:]*WRFdens[it,iz,:,:]/grav

    # Attributes of the variable
    Vvals = gen.variables_values('WRFdens')
    dictcompvars['WRFdens'] = {'name': Vvals[0], 'standard_name': Vvals[1],          \
      'long_name': Vvals[4].replace('|',' '), 'units': Vvals[5]}

if WRFpos_compute:
# WRF positions from the lowest-leftest corner of the matrix
    print '    ' + main + ': computing position from MAPFAC_M as sqrt(DY*j**2 + ' +  \
      'DX*x**2)*MAPFAC_M ...'

    mapfac = ncobj.variables['MAPFAC_M'][:]

    distx = np.float(ncobj.getncattr('DX'))
    disty = np.float(ncobj.getncattr('DY'))

    print 'distx:',distx,'disty:',disty

    dx = mapfac.shape[2]
    dy = mapfac.shape[1]
    dt = mapfac.shape[0]

    WRFpos = np.zeros((dt, dy, dx), dtype=np.float)

    for i in range(1,dx):
        WRFpos[0,0,i] = distx*i/mapfac[0,0,i]
    for j in range(1,dy):
        i=0
        WRFpos[0,j,i] = WRFpos[0,j-1,i] + disty/mapfac[0,j,i]
        for i in range(1,dx):
#            WRFpos[0,j,i] = np.sqrt((disty*j)**2. + (distx*i)**2.)/mapfac[0,j,i]
#            WRFpos[0,j,i] = np.sqrt((disty*j)**2. + (distx*i)**2.)
             WRFpos[0,j,i] = WRFpos[0,j,i-1] + distx/mapfac[0,j,i]

    for it in range(1,dt):
        WRFpos[it,:,:] = WRFpos[0,:,:]

if WRFtime_compute:
    print '    ' + main + ': computing time from WRF as CFtime(Times) ...'

    refdate='19491201000000'
    tunitsval='minutes'

    timeobj = ncobj.variables['Times']
    timewrfv = timeobj[:]

    yrref=refdate[0:4]
    monref=refdate[4:6]
    dayref=refdate[6:8]
    horref=refdate[8:10]
    minref=refdate[10:12]
    secref=refdate[12:14]

    refdateS = yrref + '-' + monref + '-' + dayref + ' ' + horref + ':' + minref +   \
      ':' + secref

    dt = timeobj.shape[0]
    WRFtime = np.zeros((dt), dtype=np.float)

    for it in range(dt):
        wrfdates = gen.datetimeStr_conversion(timewrfv[it,:],'WRFdatetime', 'matYmdHMS')
        WRFtime[it] = gen.realdatetime1_CFcompilant(wrfdates, refdate, tunitsval)

    tunits = tunitsval + ' since ' + refdateS

    # Attributes of the variable
    dictcompvars['WRFtime'] = {'name': 'time', 'standard_name': 'time',              \
      'long_name': 'time', 'units': tunits, 'calendar': 'gregorian'}

### ## #
# Going for the diagnostics
### ## #
print '  ' + main + ' ...'
varsadd = []

for idiag in range(Ndiags):
    print '    diagnostic:',diags[idiag]
    diag = diags[idiag].split('|')[0]
    depvars = diags[idiag].split('|')[1].split('@')
    if diags[idiag].split('|')[1].find('@') != -1:
        depvars = diags[idiag].split('|')[1].split('@')
        if depvars[0] == 'deaccum': diag='deaccum'
        if depvars[0] == 'accum': diag='accum'
        for depv in depvars:
            if not ncobj.variables.has_key(depv) and not                             \
              gen.searchInlist(NONcheckingvars, depv) and                            \
              not gen.searchInlist(methods, depv) and not depvars[0] == 'deaccum'    \
              and not depvars[0] == 'accum':
                print errormsg
                print '  ' + main + ": file '" + opts.ncfile +                       \
                  "' does not have variable '" + depv + "' !!"
                quit(-1)
    else:
        depvars = diags[idiag].split('|')[1]
        if not ncobj.variables.has_key(depvars) and not                              \
          gen.searchInlist(NONcheckingvars, depvars) and                             \
          not gen.searchInlist(methods, depvars):
            print errormsg
            print '  ' + main + ": file '" + opts.ncfile +                           \
              "' does not have variable '" + depvars + "' !!"
            quit(-1)

    print "\n    Computing '" + diag + "' from: ", depvars, '...'

# acraintot: accumulated total precipitation from WRF RAINC, RAINNC
    if diag == 'ACRAINTOT':
            
        var0 = ncobj.variables[depvars[0]]
        var1 = ncobj.variables[depvars[1]]
        diagout = var0[:] + var1[:]

        dnamesvar = var0.dimensions
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        ncvar.insert_variable(ncobj, 'pracc', diagout, dnamesvar, dvnamesvar, newnc)

# accum: acumulation of any variable as (Variable, time [as [tunits] 
#   from/since ....], newvarname)
    elif diag == 'accum':

        var0 = ncobj.variables[depvars[0]]
        var1 = ncobj.variables[depvars[1]]

        dnamesvar = var0.dimensions
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        diagout, diagoutd, diagoutvd = compute_accum(var0,dnamesvar,dvnamesvar)

        CFvarn = ncvar.variables_values(depvars[0])[0]

# Removing the flux
        if depvars[1] == 'XTIME':
            dtimeunits = var1.getncattr('description')
            tunits = dtimeunits.split(' ')[0]
        else:
            dtimeunits = var1.getncattr('units')
            tunits = dtimeunits.split(' ')[0]

        dtime = (var1[1] - var1[0])*timeunits_seconds(tunits)

        ncvar.insert_variable(ncobj, CFvarn + 'acc', diagout*dtime, diagoutd, diagoutvd, newnc)

# cllmh with cldfra, pres
    elif diag == 'cllmh':
            
        var0 = ncobj.variables[depvars[0]]
        if depvars[1] == 'WRFp':
            var1 = WRFp
        else:
            var01 = ncobj.variables[depvars[1]]
            if len(size(var1.shape)) < len(size(var0.shape)):
                var1 = np.brodcast_arrays(var01,var0)[0]
            else:
                var1 = var01

        diagout, diagoutd, diagoutvd = Forcompute_cllmh(var0,var1,dnames,dvnames)

        # Removing the nonChecking variable-dimensions from the initial list
        varsadd = []
        for nonvd in NONchkvardims:
            if gen.searchInlist(diagoutvd,nonvd): diagoutvd.remove(nonvd)
            varsadd.append(nonvd)

        ncvar.insert_variable(ncobj, 'cll', diagout[0,:], diagoutd, diagoutvd, newnc)
        ncvar.insert_variable(ncobj, 'clm', diagout[1,:], diagoutd, diagoutvd, newnc)
        ncvar.insert_variable(ncobj, 'clh', diagout[2,:], diagoutd, diagoutvd, newnc)

# clt with cldfra
    elif diag == 'clt':
            
        var0 = ncobj.variables[depvars]
        diagout, diagoutd, diagoutvd = Forcompute_clt(var0,dnames,dvnames)

        # Removing the nonChecking variable-dimensions from the initial list
        varsadd = []
        for nonvd in NONchkvardims:
            if gen.searchInlist(diagoutvd,nonvd): diagoutvd.remove(nonvd)
            varsadd.append(nonvd)
            
        ncvar.insert_variable(ncobj, 'clt', diagout, diagoutd, diagoutvd, newnc)

# deaccum: deacumulation of any variable as (Variable, time [as [tunits] 
#   from/since ....], newvarname)
    elif diag == 'deaccum':

        var0 = ncobj.variables[depvars[1]]
        var1 = ncobj.variables[depvars[2]]

        dnamesvar = var0.dimensions
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        diagout, diagoutd, diagoutvd = compute_deaccum(var0,dnamesvar,dvnamesvar)

# Transforming to a flux
        if depvars[2] == 'XTIME':
            dtimeunits = var1.getncattr('description')
            tunits = dtimeunits.split(' ')[0]
        else:
            dtimeunits = var1.getncattr('units')
            tunits = dtimeunits.split(' ')[0]

        dtime = (var1[1] - var1[0])*timeunits_seconds(tunits)
        ncvar.insert_variable(ncobj, depvars[3], diagout/dtime, diagoutd, diagoutvd, newnc)

# LMDZrh (pres, t, r)
    elif diag == 'LMDZrh':
            
        var0 = ncobj.variables[depvars[0]][:]
        var1 = ncobj.variables[depvars[1]][:]
        var2 = ncobj.variables[depvars[2]][:]

        diagout, diagoutd, diagoutvd = compute_rh(var0,var1,var2,dnames,dvnames)
        ncvar.insert_variable(ncobj, 'hur', diagout, diagoutd, diagoutvd, newnc)

# LMDZrhs (psol, t2m, q2m)
    elif diag == 'LMDZrhs':
            
        var0 = ncobj.variables[depvars[0]][:]
        var1 = ncobj.variables[depvars[1]][:]
        var2 = ncobj.variables[depvars[2]][:]

        dnamesvar = ncobj.variables[depvars[0]].dimensions
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        diagout, diagoutd, diagoutvd = compute_rh(var0,var1,var2,dnamesvar,dvnamesvar)

        ncvar.insert_variable(ncobj, 'hurs', diagout, diagoutd, diagoutvd, newnc)

# mslp: mean sea level pressure (pres, psfc, terrain, temp, qv)
    elif diag == 'mslp' or diag == 'WRFmslp':
            
        var1 = ncobj.variables[depvars[1]][:]
        var2 = ncobj.variables[depvars[2]][:]
        var4 = ncobj.variables[depvars[4]][:]

        if diag == 'WRFmslp':
            var0 = WRFp
            var3 = WRFt
            dnamesvar = ncobj.variables['P'].dimensions
        else:
            var0 = ncobj.variables[depvars[0]][:]
            var3 = ncobj.variables[depvars[3]][:]
            dnamesvar = ncobj.variables[depvars[0]].dimensions

        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        diagout, diagoutd, diagoutvd = compute_mslp(var0, var1, var2, var3, var4,    \
          dnamesvar, dvnamesvar)

        ncvar.insert_variable(ncobj, 'psl', diagout, diagoutd, diagoutvd, newnc)

# OMEGAw (omega, p, t) from NCL formulation (https://www.ncl.ucar.edu/Document/Functions/Contributed/omega_to_w.shtml)
    elif diag == 'OMEGAw':
            
        var0 = ncobj.variables[depvars[0]][:]
        var1 = ncobj.variables[depvars[1]][:]
        var2 = ncobj.variables[depvars[2]][:]

        dnamesvar = ncobj.variables[depvars[0]].dimensions
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        diagout, diagoutd, diagoutvd = compute_OMEGAw(var0,var1,var2,dnamesvar,dvnamesvar)

        ncvar.insert_variable(ncobj, 'wa', diagout, diagoutd, diagoutvd, newnc)

# raintot: instantaneous total precipitation from WRF as (RAINC + RAINC) / dTime
    elif diag == 'RAINTOT':

        var0 = ncobj.variables[depvars[0]]
        var1 = ncobj.variables[depvars[1]]
        if depvars[2] != 'WRFtime':
            var2 = ncobj.variables[depvars[2]]
        else:
            var2 = np.arange(var0.shape[0], dtype=int)

        var = var0[:] + var1[:]

        dnamesvar = var0.dimensions
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        diagout, diagoutd, diagoutvd = compute_deaccum(var,dnamesvar,dvnamesvar)

# Transforming to a flux
        if var2.shape[0] > 1:
            if depvars[2] != 'WRFtime':
                dtimeunits = var2.getncattr('units')
                tunits = dtimeunits.split(' ')[0]
   
                dtime = (var2[1] - var2[0])*timeunits_seconds(tunits)
            else:
                var2 = ncobj.variables['Times']
                time1 = var2[0,:]
                time2 = var2[1,:]
                tmf1 = ''
                tmf2 = ''
                for ic in range(len(time1)):
                    tmf1 = tmf1 + time1[ic]
                    tmf2 = tmf2 + time2[ic]
                dtdate1 = dtime.datetime.strptime(tmf1,"%Y-%m-%d_%H:%M:%S")
                dtdate2 = dtime.datetime.strptime(tmf2,"%Y-%m-%d_%H:%M:%S")
                diffdate12 = dtdate2 - dtdate1
                dtime = diffdate12.total_seconds()
                print 'dtime:',dtime
        else:
            print warnmsg
            print '  ' + fname + ": only 1 time-step for '" + diag + "' !!"
            print '    leaving a zero value!'
            diagout = var0*0.
            dtime=1.

        ncvar.insert_variable(ncobj, 'pr', diagout/dtime, diagoutd, diagoutvd, newnc)

# rhs (psfc, t, q) from TimeSeries files
    elif diag == 'TSrhs':
            
        p0=100000.
        var0 = ncobj.variables[depvars[0]][:]
        var1 = (ncobj.variables[depvars[1]][:])*(var0/p0)**(2./7.)
        var2 = ncobj.variables[depvars[2]][:]

        dnamesvar = ncobj.variables[depvars[0]].dimensions
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        diagout, diagoutd, diagoutvd = compute_rh(var0,var1,var2,dnamesvar,dvnamesvar)

        ncvar.insert_variable(ncobj, 'hurs', diagout, diagoutd, diagoutvd, newnc)

# td (psfc, t, q) from TimeSeries files
    elif diag == 'TStd' or diag == 'td':
            
        var0 = ncobj.variables[depvars[0]][:]
        var1 = ncobj.variables[depvars[1]][:] - 273.15
        var2 = ncobj.variables[depvars[2]][:]

        dnamesvar = ncobj.variables[depvars[0]].dimensions
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        diagout, diagoutd, diagoutvd = compute_td(var0,var1,var2,dnamesvar,dvnamesvar)

        ncvar.insert_variable(ncobj, 'tds', diagout, diagoutd, diagoutvd, newnc)

# td (psfc, t, q) from TimeSeries files
    elif diag == 'TStdC' or diag == 'tdC':
            
        var0 = ncobj.variables[depvars[0]][:]
# Temperature is already in degrees Celsius
        var1 = ncobj.variables[depvars[1]][:]
        var2 = ncobj.variables[depvars[2]][:]

        dnamesvar = ncobj.variables[depvars[0]].dimensions
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        diagout, diagoutd, diagoutvd = compute_td(var0,var1,var2,dnamesvar,dvnamesvar)

        ncvar.insert_variable(ncobj, 'tds', diagout, diagoutd, diagoutvd, newnc)

# wds (u, v)
    elif diag == 'TSwds' or diag == 'wds' :
 
        var0 = ncobj.variables[depvars[0]][:]
        var1 = ncobj.variables[depvars[1]][:]

        dnamesvar = ncobj.variables[depvars[0]].dimensions
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        diagout, diagoutd, diagoutvd = compute_wds(var0,var1,dnamesvar,dvnamesvar)

        ncvar.insert_variable(ncobj, 'wds', diagout, diagoutd, diagoutvd, newnc)

# wss (u, v)
    elif diag == 'TSwss' or diag == 'wss':
            
        var0 = ncobj.variables[depvars[0]][:]
        var1 = ncobj.variables[depvars[1]][:]

        dnamesvar = ncobj.variables[depvars[0]].dimensions
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        diagout, diagoutd, diagoutvd = compute_wss(var0,var1,dnamesvar,dvnamesvar)

        ncvar.insert_variable(ncobj, 'wss', diagout, diagoutd, diagoutvd, newnc)

# turbulence (var)
    elif diag == 'turbulence':

        var0 = ncobj.variables[depvars][:]

        dnamesvar = list(ncobj.variables[depvars].dimensions)
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        diagout, diagoutd, diagoutvd = compute_turbulence(var0,dnamesvar,dvnamesvar)
        valsvar = gen.variables_values(depvars)

        newvarn = depvars + 'turb'
        print main + '; Lluis newvarn:', newvarn
        ncvar.insert_variable(ncobj, newvarn, diagout, diagoutd, 
          diagoutvd, newnc)
        print main + '; Lluis variables:', newnc.variables.keys()
        varobj = newnc.variables[newvarn]
        attrv = varobj.long_name
        attr = varobj.delncattr('long_name')
        newattr = ncvar.set_attribute(varobj, 'long_name', attrv +                   \
          " Taylor decomposition turbulence term")

# WRFbils fom WRF as HFX + LH
    elif diag == 'WRFbils':
            
        var0 = ncobj.variables[depvars[0]][:]
        var1 = ncobj.variables[depvars[1]][:]

        diagout = var0 + var1
        dnamesvar = list(ncobj.variables[depvars[0]].dimensions)
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        ncvar.insert_variable(ncobj, 'bils', diagout, dnamesvar, dvnamesvar, newnc)

# WRFgeop geopotential from WRF as PH + PHB
    elif diag == 'WRFgeop':
        var0 = ncobj.variables[depvars[0]][:]
        var1 = ncobj.variables[depvars[1]][:]

        # de-staggering geopotential
        diagout0 = var0 + var1
        dt = diagout0.shape[0]
        dz = diagout0.shape[1]
        dy = diagout0.shape[2]
        dx = diagout0.shape[3]

        diagout = np.zeros((dt,dz-1,dy,dx), dtype=np.float)
        diagout = 0.5*(diagout0[:,1:dz,:,:]+diagout0[:,0:dz-1,:,:])

        # Removing the nonChecking variable-dimensions from the initial list
        varsadd = []
        diagoutvd = list(dvnames)
        for nonvd in NONchkvardims:
            if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
            varsadd.append(nonvd)

        ncvar.insert_variable(ncobj, 'zg', diagout, dnames, diagoutvd, newnc)

# WRFp pressure from WRF as P + PB
    elif diag == 'WRFp':
            
        diagout = WRFp

        ncvar.insert_variable(ncobj, 'pres', diagout, dnames, dvnames, newnc)

# WRFpos 
    elif diag == 'WRFpos':
            
        dnamesvar = ncobj.variables['MAPFAC_M'].dimensions
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        ncvar.insert_variable(ncobj, 'WRFpos', WRFpos, dnamesvar, dvnamesvar, newnc)

# WRFprw WRF water vapour path WRFdens, QVAPOR
    elif diag == 'WRFprw':
            
        var0 = WRFdens
        var1 = ncobj.variables[depvars[1]]

        dnamesvar = list(var1.dimensions)
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        diagout, diagoutd, diagoutvd = compute_prw(var0, var1, dnamesvar,dvnamesvar)

        ncvar.insert_variable(ncobj, 'prw', diagout, diagoutd, diagoutvd, newnc)

# WRFrh (P, T, QVAPOR)
    elif diag == 'WRFrh':
            
        dnamesvar = list(ncobj.variables[depvars[2]].dimensions)
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        ncvar.insert_variable(ncobj, 'hur', WRFrh, dnames, dvnames, newnc)

# WRFrhs (PSFC, T2, Q2)
    elif diag == 'WRFrhs':
            
        var0 = ncobj.variables[depvars[0]][:]
        var1 = ncobj.variables[depvars[1]][:]
        var2 = ncobj.variables[depvars[2]][:]

        dnamesvar = list(ncobj.variables[depvars[2]].dimensions)
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        diagout, diagoutd, diagoutvd = compute_rh(var0,var1,var2,dnamesvar,dvnamesvar)
        ncvar.insert_variable(ncobj, 'hurs', diagout, diagoutd, diagoutvd, newnc)

# rvors (u10, v10, WRFpos)
    elif diag == 'WRFrvors':
            
        var0 = ncobj.variables[depvars[0]]
        var1 = ncobj.variables[depvars[1]]

        diagout = rotational_z(var0, var1, distx)

        dnamesvar = ncobj.variables[depvars[0]].dimensions
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        ncvar.insert_variable(ncobj, 'rvors', diagout, dnamesvar, dvnamesvar, newnc)

# WRFt (T, P, PB)
    elif diag == 'WRFt':
        var0 = ncobj.variables[depvars[0]][:]
        var1 = ncobj.variables[depvars[1]][:]
        var2 = ncobj.variables[depvars[2]][:]

        p0=100000.
        p=var1 + var2

        WRFt = (var0 + 300.)*(p/p0)**(2./7.)

        dnamesvar = list(ncobj.variables[depvars[0]].dimensions)
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        # Removing the nonChecking variable-dimensions from the initial list
        varsadd = []
        diagoutvd = list(dvnames)
        for nonvd in NONchkvardims:
            if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
            varsadd.append(nonvd)

        ncvar.insert_variable(ncobj, 'ta', WRFt, dnames, diagoutvd, newnc)

# WRFua (U, V, SINALPHA, COSALPHA) to be rotated !!
    elif diag == 'WRFua':
        var0 = ncobj.variables[depvars[0]][:]
        var1 = ncobj.variables[depvars[1]][:]
        var2 = ncobj.variables[depvars[2]][:]
        var3 = ncobj.variables[depvars[3]][:]

        # un-staggering variables
        unstgdims = [var0.shape[0], var0.shape[1], var0.shape[2], var0.shape[3]-1]
        ua = np.zeros(tuple(unstgdims), dtype=np.float)
        unstgvar0 = np.zeros(tuple(unstgdims), dtype=np.float)
        unstgvar1 = np.zeros(tuple(unstgdims), dtype=np.float)
        unstgvar0 = 0.5*(var0[:,:,:,0:var0.shape[3]-1] + var0[:,:,:,1:var0.shape[3]])
        unstgvar1 = 0.5*(var1[:,:,0:var1.shape[2]-1,:] + var1[:,:,1:var1.shape[2],:])

        for iz in range(var0.shape[1]):
            ua[:,iz,:,:] = unstgvar0[:,iz,:,:]*var3 - unstgvar1[:,iz,:,:]*var2

        dnamesvar = ['Time','bottom_top','south_north','west_east']
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        # Removing the nonChecking variable-dimensions from the initial list
        varsadd = []
        diagoutvd = list(dvnames)
        for nonvd in NONchkvardims:
            if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
            varsadd.append(nonvd)

        ncvar.insert_variable(ncobj, 'ua', ua, dnames, diagoutvd, newnc)

# WRFua (U, V, SINALPHA, COSALPHA) to be rotated !!
    elif diag == 'WRFva':
        var0 = ncobj.variables[depvars[0]][:]
        var1 = ncobj.variables[depvars[1]][:]
        var2 = ncobj.variables[depvars[2]][:]
        var3 = ncobj.variables[depvars[3]][:]

        # un-staggering variables
        unstgdims = [var0.shape[0], var0.shape[1], var0.shape[2], var0.shape[3]-1]
        va = np.zeros(tuple(unstgdims), dtype=np.float)
        unstgvar0 = np.zeros(tuple(unstgdims), dtype=np.float)
        unstgvar1 = np.zeros(tuple(unstgdims), dtype=np.float)
        unstgvar0 = 0.5*(var0[:,:,:,0:var0.shape[3]-1] + var0[:,:,:,1:var0.shape[3]])
        unstgvar1 = 0.5*(var1[:,:,0:var1.shape[2]-1,:] + var1[:,:,1:var1.shape[2],:])
        for iz in range(var0.shape[1]):
            va[:,iz,:,:] = unstgvar0[:,iz,:,:]*var2 + unstgvar1[:,iz,:,:]*var3

        dnamesvar = ['Time','bottom_top','south_north','west_east']
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        # Removing the nonChecking variable-dimensions from the initial list
        varsadd = []
        diagoutvd = list(dvnames)
        for nonvd in NONchkvardims:
            if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
            varsadd.append(nonvd)
        ncvar.insert_variable(ncobj, 'va', va, dnames, diagoutvd, newnc)

# WRFtime
    elif diag == 'WRFtime':
            
        diagout = WRFtime

        dnamesvar = ['Time']
        dvnamesvar = ['Times']

        ncvar.insert_variable(ncobj, 'time', diagout, dnamesvar, dvnamesvar, newnc)

# ws (U, V)
    elif diag == 'ws':
            
        var0 = ncobj.variables[depvars[0]][:]
        var1 = ncobj.variables[depvars[1]][:]
        # un-staggering variables
        unstgdims = [var0.shape[0], var0.shape[1], var0.shape[2], var0.shape[3]-1]
        va = np.zeros(tuple(unstgdims), dtype=np.float)
        unstgvar0 = np.zeros(tuple(unstgdims), dtype=np.float)
        unstgvar1 = np.zeros(tuple(unstgdims), dtype=np.float)
        unstgvar0 = 0.5*(var0[:,:,:,0:var0.shape[3]-1] + var0[:,:,:,1:var0.shape[3]])
        unstgvar1 = 0.5*(var1[:,:,0:var1.shape[2]-1,:] + var1[:,:,1:var1.shape[2],:])

        dnamesvar = ['Time','bottom_top','south_north','west_east']
        diagout = np.sqrt(unstgvar0*unstgvar0 + unstgvar1*unstgvar1)

#        dnamesvar = ncobj.variables[depvars[0]].dimensions
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        # Removing the nonChecking variable-dimensions from the initial list
        varsadd = []
        diagoutvd = list(dvnamesvar)
        for nonvd in NONchkvardims:
            if gen.searchInlist(dvnamesvar,nonvd): diagoutvd.remove(nonvd)
            varsadd.append(nonvd)
        ncvar.insert_variable(ncobj, 'ws', diagout, dnamesvar, diagoutvd, newnc)

# wss (u10, v10)
    elif diag == 'wss':
            
        var0 = ncobj.variables[depvars[0]][:]
        var1 = ncobj.variables[depvars[1]][:]

        diagout = np.sqrt(var0*var0 + var1*var1)

        dnamesvar = ncobj.variables[depvars[0]].dimensions
        dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dnames,dvnames)

        ncvar.insert_variable(ncobj, 'wss', diagout, dnamesvar, dvnamesvar, newnc)

# WRFheight height from WRF geopotential as WRFGeop/g
    elif diag == 'WRFheight':
            
        diagout = WRFgeop/grav

        # Removing the nonChecking variable-dimensions from the initial list
        varsadd = []
        diagoutvd = list(dvnames)
        for nonvd in NONchkvardims:
            if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
            varsadd.append(nonvd)

        ncvar.insert_variable(ncobj, 'zhgt', diagout, dnames, diagoutvd, newnc)

# WRFheightrel relative-height from WRF geopotential as WRFgeop(PH + PHB)/g-HGT 'WRFheightrel|PH@PHB@HGT
    elif diag == 'WRFheightrel':
        var0 = ncobj.variables[depvars[0]][:]
        var1 = ncobj.variables[depvars[1]][:]
        var2 = ncobj.variables[depvars[2]][:]

        dimz = var0.shape[1]
        diagout = np.zeros(tuple(var0.shape), dtype=np.float)
        for iz in range(dimz):
            diagout[:,iz,:,:] = (var0[:,iz,:,:]+ var1[:,iz,:,:])/grav - var2

        # Removing the nonChecking variable-dimensions from the initial list
        varsadd = []
        diagoutvd = list(dvnames)
        for nonvd in NONchkvardims:
            if gen.searchInlist(dvnames,nonvd): diagoutvd.remove(nonvd)
            varsadd.append(nonvd)

        ncvar.insert_variable(ncobj, 'zhgtrel', diagout, dnames, diagoutvd, newnc)

    else:
        print errormsg
        print '  ' + main + ": diagnostic '" + diag + "' not ready!!!"
        print '    available diagnostics: ', availdiags
        quit(-1)

    newnc.sync()
    # Adding that additional variables required to compute some diagnostics which
    #   where not in the original file
    for vadd in varsadd:
        if not gen.searchInlist(newnc.variables.keys(),vadd):
            attrs = dictcompvars[vadd]
            vvn = attrs['name']
            if not gen.searchInlist(newnc.variables.keys(), vvn):
                iidvn = dvnames.index(vadd)
                dnn = dnames[iidvn]
                if vadd == 'WRFtime':
                    dvarvals = WRFtime[:]
                newvar = newnc.createVariable(vvn, 'f8', (dnn))
                newvar[:] = dvarvals
                for attn in attrs.keys():
                    if attn != 'name':
                        attv = attrs[attn]
                        ncvar.set_attribute(newvar, attn, attv)

#   end of diagnostics

# Global attributes
##
atvar = ncvar.set_attribute(newnc, 'program', 'diagnostics.py')
atvar = ncvar.set_attribute(newnc, 'version', '1.0')
atvar = ncvar.set_attribute(newnc, 'author', 'Fita Borrell, Lluis')
atvar = ncvar.set_attribute(newnc, 'institution', 'Laboratoire Meteorologie ' +      \
  'Dynamique')
atvar = ncvar.set_attribute(newnc, 'university', 'Universite Pierre et Marie ' +     \
  'Curie -- Jussieu')
atvar = ncvar.set_attribute(newnc, 'centre', 'Centre national de la recherche ' +    \
  'scientifique')
atvar = ncvar.set_attribute(newnc, 'city', 'Paris')
atvar = ncvar.set_attribute(newnc, 'original_file', opts.ncfile)

gorigattrs = ncobj.ncattrs()

for attr in gorigattrs:
    attrv = ncobj.getncattr(attr)
    atvar = ncvar.set_attribute(newnc, attr, attrv)

ncobj.close()
newnc.close()

print '\n' + main + ': successfull writting of diagnostics file "' + ofile + '" !!!'