# Tools for the compute of diagnostics
# L. Fita, CIMA. CONICET-UBA, CNRS UMI-IFAECI, Buenos Aires, Argentina

# Available general pupose diagnostics (model independent) providing (varv1, varv2, ..., dimns, dimvns)
# compute_accum: Function to compute the accumulation of a variable
# compute_cllmh: Function to compute cllmh: low/medium/hight cloud fraction following 
#   newmicro.F90 from LMDZ compute_clt(cldfra, pres, dimns, dimvns)
# compute_clt: Function to compute the total cloud fraction following 'newmicro.F90' from LMDZ
# compute_clivi: Function to compute cloud-ice water path (clivi)
# compute_clwvl: Function to compute condensed water path (clwvl)
# compute_deaccum: Function to compute the deaccumulation of a variable
# compute_mslp: Function to compute mslp: mean sea level pressure following p_interp.F90 from WRF
# compute_OMEGAw: Function to transform OMEGA [Pas-1] to velocities [ms-1]
# compute_prw: Function to compute water vapour path (prw)
# compute_rh: Function to compute relative humidity following 'Tetens' equation (T,P) ...'
# compute_td: Function to compute the dew point temperature
# compute_turbulence: Function to compute the rubulence term of the Taylor's decomposition ...'
# compute_wds: Function to compute the wind direction
# compute_wss: Function to compute the wind speed
# compute_WRFta: Function to compute WRF air temperature
# compute_WRFtd: Function to compute WRF dew-point air temperature
# compute_WRFuava: Function to compute geographical rotated WRF 3D winds
# compute_WRFuasvas: Fucntion to compute geographical rotated WRF 2-meter winds
# derivate_centered: Function to compute the centered derivate of a given field
# def Forcompute_cllmh: Function to compute cllmh: low/medium/hight cloud fraction following newmicro.F90 from LMDZ via Fortran subroutine
# Forcompute_clt: Function to compute the total cloud fraction following 'newmicro.F90' from LMDZ via a Fortran module

# Others just providing variable values
# var_cllmh: Fcuntion to compute cllmh on a 1D column
# var_clt: Function to compute the total cloud fraction following 'newmicro.F90' from LMDZ using 1D vertical column values
# var_mslp: Fcuntion to compute mean sea-level pressure
# var_virtualTemp: This function returns virtual temperature in K, 
# var_WRFtime: Function to copmute CFtimes from WRFtime variable
# rotational_z: z-component of the rotatinoal of horizontal vectorial field
# turbulence_var: Function to compute the Taylor's decomposition turbulence term from a a given variable

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
import module_ForDef as fdef

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

# Constants
grav = fdef.module_definitions.grav

# 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.
                    gen.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.
                gen.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):
                    gen.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):
                gen.percendone(ix*dy + iy,dx*dy, 5, 'diagnosted')
                cllmh[:,iy,ix] = var_cllmh(cldfra[:,iy,ix], pres[:,iy,ix])

    return cllmh, cllmhdims, cllmhvdims

def compute_clivi(dens, qtot, dimns, dimvns):
    """ Function to compute cloud-ice water path (clivi)
      [dens] = density [in kgkg-1] (assuming [t],z,y,x)
      [qtot] = added mixing ratio of all cloud-ice species 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_clivi'

    clividims = dimns[:]
    clivivdims = dimvns[:]

    if len(qtot.shape) == 4:
        clividims.pop(1)
        clivivdims.pop(1)
    else:
        clividims.pop(0)
        clivivdims.pop(0)

    data1 = dens*qtot
    clivi = np.sum(data1, axis=1)

    return clivi, clividims, clivivdims


def compute_clwvl(dens, qtot, dimns, dimvns):
    """ Function to compute condensed water path (clwvl)
      [dens] = density [in kgkg-1] (assuming [t],z,y,x)
      [qtot] = added mixing ratio of all cloud-water species 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_clwvl'

    clwvldims = dimns[:]
    clwvlvdims = dimvns[:]

    if len(qtot.shape) == 4:
        clwvldims.pop(1)
        clwvlvdims.pop(1)
    else:
        clwvldims.pop(0)
        clwvlvdims.pop(0)

    data1 = dens*qtot
    clwvl = np.sum(data1, axis=1)

    return clwvl, clwvldims, clwvlvdims

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])

                        gen.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):
                gen.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 var_WRFtime(timewrfv, refdate='19491201000000', tunitsval='minutes'):
    """ Function to copmute CFtimes from WRFtime variable
      refdate= [YYYYMMDDMIHHSS] format of reference date
      tunitsval= CF time units
      timewrfv= matrix string values of WRF 'Times' variable
    """
    fname = 'var_WRFtime'

    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 = timewrfv.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

    return WRFtime, tunits

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

def compute_WRFuava(u, v, sina, cosa, dimns, dimvns):
    """ Function to compute geographical rotated WRF 3D winds
      u= orginal WRF x-wind
      v= orginal WRF y-wind
      sina= original WRF local sinus of map rotation
      cosa= original WRF local cosinus of map rotation
      formula:
        ua = u*cosa-va*sina
        va = u*sina+va*cosa
    """
    fname = 'compute_WRFuava'

    var0 = u
    var1 = v
    var2 = sina
    var3 = cosa

    # 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)
    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]):
        ua[:,iz,:,:] = unstgvar0[:,iz,:,:]*var3 - unstgvar1[:,iz,:,:]*var2
        va[:,iz,:,:] = unstgvar0[:,iz,:,:]*var2 + unstgvar1[:,iz,:,:]*var3

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

    return ua, va, dnamesvar, dvnamesvar

def compute_WRFuasvas(u10, v10, sina, cosa, dimns, dimvns):
    """ Function to compute geographical rotated WRF 2-meter winds
      u10= orginal WRF 10m x-wind
      v10= orginal WRF 10m y-wind
      sina= original WRF local sinus of map rotation
      cosa= original WRF local cosinus of map rotation
      formula:
        uas = u10*cosa-va10*sina
        vas = u10*sina+va10*cosa
    """
    fname = 'compute_WRFuasvas'

    var0 = u10
    var1 = v10
    var2 = sina
    var3 = cosa

    uas = np.zeros(var0.shape, dtype=np.float)
    vas = np.zeros(var0.shape, dtype=np.float)

    uas = var0*var3 - var1*var2
    vas = var0*var2 + var1*var3

    dnamesvar = ['Time','south_north','west_east']
    dvnamesvar = ncvar.var_dim_dimv(dnamesvar,dimns,dimvns)

    return uas, vas, dnamesvar, dvnamesvar

def compute_WRFta(t, p, dimns, dimvns):
    """ Function to compute WRF air temperature
      t= orginal WRF temperature
      p= original WRF pressure (P + PB)
      formula:
          temp = theta*(p/p0)**(R/Cp)

    """
    fname = 'compute_WRFta'

    ta = (t+300.)*(p/fdef.module_definitions.p0ref)**(fdef.module_definitions.rcp)

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

    return ta, dnamesvar, dvnamesvar

def compute_WRFtd(t, p, qv, dimns, dimvns):
    """ Function to compute WRF dew-point air temperature
      t= orginal WRF temperature
      p= original WRF pressure (P + PB)
      formula:
          temp = theta*(p/p0)**(R/Cp)

    """
    fname = 'compute_WRFtd'

    tk = (t+300.)*(p/fdef.module_definitions.p0ref)**(fdef.module_definitions.rcp)
    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))

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

    return td, dnamesvar, dvnamesvar