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# Copyright 2016 Cambridge Environmental Research Consultants Ltd. 
# 
# This software is licensed under the terms of the Apache Licence Version 2.0 
# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0 
# 
# ************************************************************************** 
# Function      : compute_geopotential_on_ml_netcdf 
# 
# Author (date) : Mark Jackson (1/12/2016) 
# 
# Category      : COMPUTATION 
# 
# OneLineDesc   : Computes geopotential and height on model levels using netCDF files 
# 
# Description   : Computes geopotential on model levels using netCDF files. 
#                 Based on the Python script by Cristian Simarro which uses GRIB files: 
#                             https://softwareconfluence.ecmwf.int/wiki/display/GRIB/Compute+geopotential+on+model+levels 
#                 Which was based on the code of the Metview function mvl_geopotential_on_ml: 
#                             https://softwareconfluence.ecmwf.int/metview/mvl_geopotential_on_ml  
#                 This in turn was based on code from Nils Wedi, the IFS documentation: 
#                             https://softwareconfluence.ecmwf.int/wiki/display/IFS/CY41R1+Official+IFS+Documentation 
#                 part III. Dynamics and numerical procedures 
#                 and an optimised implementation by Dominique Lucas. 
#                 Ported to Python by Cristian Simarro 
# 
# Parameters    : FileA.nc  - netCDF file with the levelist of t and q. Does not require all levels  
#                             but does require a contiguous set of levels all the way to the bottom. 
#                 FileB.nc  - netCDF file with levelist 1 for params z and lnsp 
# 
# Return Value  : outputs CSV files 
#                 with geopotential and height (relative to terrain) on each model level.  
# 
# Dependencies  : netCDF4, numpy, scipy 
# 
 
 
from __future__ import print_function # make sure print behaves the same in Python 2.7 and 3.x 
import netCDF4 
from netCDF4 import num2date 
import numpy as np 
from scipy import interpolate 
import datetime 
import sys 
import io 
import math 
 
#Variable names in the netCDF 
#(File A - model levels) 
# level = model level numbers, possible values 1-60 
# t = temperature K 
# q = specific humidity kg/kg 
#(File B) 
# lnsp = log surface pressure  
# z = surface geopotential  
 
#arguments 
#ONEDAY - read these from the command line 
FILE_A_PATH="G:\\MiscProjects\\ERA-Interim-Python\\20160922-InvestigateProfiles-netCDF\\FileA.nc"  
FILE_B_PATH="G:\\MiscProjects\\ERA-Interim-Python\\20160922-InvestigateProfiles-netCDF\\FileB.nc"  
GRID_LAT=54.75   #(degrees N) 
GRID_LONG=-4.5    #(degrees E) 
OUT_DIR_PATH="G:\\MiscProjects\\ERA-Interim-Python\\20161101-Check MarkJ geopotentials against ECMWF geopotentials\\output\\" 
     
 
 
#Routine to Read File A data file for t, q values at a particular grid point 
def readfa(fileapath): 
    #Connect to data file for reading 
    print() 
    print("==================================================") 
    print("File A information") 
    print("Filename{}".format(fileapath)) 
    fa = netCDF4.Dataset(fileapath, 'r') 
 
    #Variables as netCDF variable objects 
    print() 
    print("--------------------------------------------------") 
    print("Variables") 
    print(fa.variables.keys()) # get all variable names 
 
    fanclongs = fa.variables['longitude']   
    print(fanclongs) 
    fanclats = fa.variables['latitude']   
    print(fanclats) 
    fanclevels = fa.variables['level']   
    print(fanclevels) 
    fanctimes = fa.variables['time']   
    print(fanctimes) 
    fancts = fa.variables['t']  
    print(fancts) 
    fancqs = fa.variables['q']  
    print(fancqs)  
 
    #Get level values (either model levels or pressure levels) and number of levels 
    falevels=fanclevels[:] 
    print() 
    print("--------------------------------------------------") 
    print("Model levels") 
    fanlevels=falevels.shape[0] 
    print("There are {} levels: {} - {}".format(fanlevels, falevels[0], falevels[fanlevels-1])) 
 
    #Get time values and number of times 
    fatimes=fanctimes[:] 
    print() 
    print("--------------------------------------------------") 
    print("File A Times") 
    print(fatimes) 
    fantimes=fatimes.shape[0] 
 
    #Get python datetime for each time 
    fapydts=num2date(fatimes, fanctimes.units) 
 
    #Output first 10 datetimes 
    print() 
    print("First 10 times as date-time") 
    print([pydt.strftime('%Y-%m-%d %H:%M:%S') for pydt in fapydts[:10]]) 
 
    #Get lat and long values 
    falats = fanclats[:] 
    print() 
    print("--------------------------------------------------") 
    print("File A Latitudes") 
    print(falats) 
    falongs = fanclongs[:] 
    print() 
    print("--------------------------------------------------") 
    print("File A Longitudes") 
    print(falongs) 
 
    #Get index of grid point of interest 
    failat=np.where(falats==GRID_LAT)[0] 
    failong=np.where(falongs==GRID_LONG)[0] 
    print() 
    print("==================================================") 
    print("Grid point location: latitude and longitude indexes for lat {} and long {}".format(GRID_LAT, GRID_LONG)) 
    print(failat) 
    print(failong) 
 
    #Get t, q values for specified grid point for all levels (slicing) 
    #The result is still a 4D array with 1 latitude and 1 longitude 
    print() 
    print("==================================================") 
    print("Get t, q values for all levels") 
    fats=fancts[range(fantimes),range(fanlevels),failat,failong] 
    faqs=fancqs[range(fantimes),range(fanlevels),failat,failong] 
 
    return (fats, faqs, falevels, fapydts) 
 
#Routine to Read File B data file for z, lnsp values at a particular grid point 
def readfb(fbfilepath): 
    #Connect to file for reading 
    print() 
    print("==================================================") 
    print("fb File information") 
    print("Filename{}".format(fbfilepath)) 
    fbf = netCDF4.Dataset(fbfilepath, 'r') 
 
    print(fbf) 
    print() 
 
    #Variables as netCDF variable objects 
    print() 
    print("--------------------------------------------------") 
    print("Variables") 
    print(fbf.variables.keys()) # get all variable names 
 
    fbnclongs = fbf.variables['longitude']   
    print(fbnclongs) 
    fbnclats = fbf.variables['latitude']   
    print(fbnclats) 
    fbnctimes = fbf.variables['time']   
    print(fbnctimes) 
    fbnczs = fbf.variables['z']  
    print(fbnczs) 
    fbnclnsps = fbf.variables['lnsp']  
    print(fbnclnsps)  
 
    #Get time values and number of times 
    fbtimes=fbnctimes[:] 
    print() 
    print("--------------------------------------------------") 
    print("fb Times") 
    print(fbtimes) 
    fbntimes=fbtimes.shape[0] 
 
    #Get python datetime for each time 
    fbpydts=num2date(fbtimes, fbnctimes.units) 
 
    #Output first 10 datetimes 
    print() 
    print("fb First 10 times as date-time") 
    print([pydt.strftime('%Y-%m-%d %H:%M:%S') for pydt in fbpydts[:10]]) 
 
    #Get lat and long values 
    fblats = fbnclats[:] 
    print() 
    print("--------------------------------------------------") 
    print("fb Latitudes") 
    print(fblats) 
    fblongs = fbnclongs[:] 
    print() 
    print("--------------------------------------------------") 
    print("fb Longitudes") 
    print(fblongs) 
 
 
    #Get index of grid point of interest 
    fbilat=np.where(fblats==GRID_LAT)[0] 
    fbilong=np.where(fblongs==GRID_LONG)[0] 
    print() 
    print("==================================================") 
    print("Grid point location: fb latitude and longitude indexes for lat {} and long {}".format(GRID_LAT, GRID_LONG)) 
    print(fbilat) 
    print(fbilong) 
 
    #Get z, lnsp values for specified grid point (slicing) 
    #The result is still a 3D array with 1 latitude and 1 longitude 
    print() 
    print("==================================================") 
    print("Get z, lnsp values ") 
    fbzs=fbnczs[range(fbntimes),fbilat,fbilong] 
    print('shape of z slice: %s' % repr(fbzs.shape)) 
    fblnsps=fbnclnsps[range(fbntimes),fbilat,fbilong] 
 
    return (fbzs, fblnsps, fbpydts) 
 
 
#Read File A file  
fats, faqs, falevels, fapydts = readfa(FILE_A_PATH) 
fanlevels=falevels.shape[0] 
fantimes=fapydts.shape[0] 
 
#Read File B file  
fbzs, fblnsps, fbpydts = readfb(FILE_B_PATH) 
 
 
print() 
print("==================================================") 
print("Running...") 
 
#Calculation of geopotential and height 
def calculategeoh(z, lnsp, ts, qs, levels): 
    heighttoreturn=np.full(ts.shape[0], -999, np.double) 
    geotoreturn=np.full(ts.shape[0], -999, np.double) 
 
    Rd = 287.06 
 
    z_h = 0 
     
    #surface pressure 
    sp = math.exp(lnsp) 
 
    # A and B parameters to calculate pressures for model levels,  
    #  extracted from an ECMWF ERA-Interim GRIB file and then hardcoded here 
    pv =  [ 
      0.0000000000e+000, 2.0000000000e+001, 3.8425338745e+001, 6.3647796631e+001, 9.5636962891e+001, 
      1.3448330688e+002, 1.8058435059e+002, 2.3477905273e+002, 2.9849584961e+002, 3.7397192383e+002, 
      4.6461816406e+002, 5.7565112305e+002, 7.1321801758e+002, 8.8366040039e+002, 1.0948347168e+003, 
      1.3564746094e+003, 1.6806403809e+003, 2.0822739258e+003, 2.5798886719e+003, 3.1964216309e+003, 
      3.9602915039e+003, 4.9067070313e+003, 6.0180195313e+003, 7.3066328125e+003, 8.7650546875e+003, 
      1.0376125000e+004, 1.2077445313e+004, 1.3775324219e+004, 1.5379804688e+004, 1.6819472656e+004, 
      1.8045183594e+004, 1.9027695313e+004, 1.9755109375e+004, 2.0222203125e+004, 2.0429863281e+004, 
      2.0384480469e+004, 2.0097402344e+004, 1.9584328125e+004, 1.8864750000e+004, 1.7961359375e+004, 
      1.6899468750e+004, 1.5706449219e+004, 1.4411125000e+004, 1.3043218750e+004, 1.1632757813e+004, 
      1.0209500000e+004, 8.8023554688e+003, 7.4388046875e+003, 6.1443164063e+003, 4.9417773438e+003, 
      3.8509133301e+003, 2.8876965332e+003, 2.0637797852e+003, 1.3859125977e+003, 8.5536181641e+002, 
      4.6733349609e+002, 2.1039389038e+002, 6.5889236450e+001, 7.3677425385e+000, 0.0000000000e+000, 
      0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 
      0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 
      0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 
      0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 
      0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 
      7.5823496445e-005, 4.6139489859e-004, 1.8151560798e-003, 5.0811171532e-003, 1.1142909527e-002, 
      2.0677875727e-002, 3.4121163189e-002, 5.1690407097e-002, 7.3533833027e-002, 9.9674701691e-002, 
      1.3002252579e-001, 1.6438430548e-001, 2.0247590542e-001, 2.4393314123e-001, 2.8832298517e-001, 
      3.3515489101e-001, 3.8389211893e-001, 4.3396294117e-001, 4.8477154970e-001, 5.3570991755e-001, 
      5.8616840839e-001, 6.3554745913e-001, 6.8326860666e-001, 7.2878581285e-001, 7.7159661055e-001, 
      8.1125342846e-001, 8.4737491608e-001, 8.7965691090e-001, 9.0788388252e-001, 9.3194031715e-001, 
      9.5182150602e-001, 9.6764522791e-001, 9.7966271639e-001, 9.8827010393e-001, 9.9401944876e-001, 
      9.9763011932e-001, 1.0000000000e+000 ] 
    levelSize=60 
    A = pv[0:levelSize+1] 
    B = pv[levelSize+1:] 
 
    Ph_levplusone = A[levelSize] + (B[levelSize]*sp) 
 
    #Get a list of level numbers in reverse order 
    reversedlevels=np.full(levels.shape[0], -999, np.int32) 
    for iLev in list(reversed(range(levels.shape[0]))): 
        reversedlevels[levels.shape[0] - 1 - iLev] = levels[iLev] 
             
    #Integrate up into the atmosphere from lowest level 
    for lev in reversedlevels: 
        #lev is the level number 1-60, we need a corresponding index into ts and qs 
        ilevel=np.where(levels==lev)[0] 
        t_level=ts[ilevel] 
        q_level=qs[ilevel] 
 
        #compute moist temperature 
        t_level = t_level * (1.+0.609133*q_level) 
 
        #compute the pressures (on half-levels) 
        Ph_lev = A[lev-1] + (B[lev-1] * sp) 
  
        if lev == 1: 
            dlogP = math.log(Ph_levplusone/0.1) 
            alpha = math.log(2) 
        else: 
            dlogP = math.log(Ph_levplusone/Ph_lev) 
            dP    = Ph_levplusone-Ph_lev 
            alpha = 1. - ((Ph_lev/dP)*dlogP) 
  
        TRd = t_level*Rd 
  
        # z_f is the geopotential of this full level 
        # integrate from previous (lower) half-level z_h to the full level 
        z_f = z_h + (TRd*alpha)  
 
        #Convert geopotential to height  
        heighttoreturn[ilevel] = z_f / 9.80665 
         
        #Geopotential (add in surface geopotential) 
        geotoreturn[ilevel] = z_f + z 
  
        # z_h is the geopotential of 'half-levels' 
        # integrate z_h to next half level 
        z_h=z_h+(TRd*dlogP)  
  
        Ph_levplusone = Ph_lev 
 
    return geotoreturn, heighttoreturn 
     
#Output all the values for the specified grid point 
#Iterate over all the times and write out the data 
for itime in range(fantimes): 
    if fapydts[itime]!=fbpydts[itime]: 
        print("ERROR! Mismatching times in the files. Time number {}: File A = {} and File B = {}".format( 
            itime, fapydts[itime].strftime('%Y-%m-%d %H:%M:%S'), fbpydts[itime].strftime('%Y-%m-%d %H:%M:%S'))) 
        sys.exit() 
               
    pydt=fapydts[itime] 
    print(" Processing {}/{} : {} ...".format(itime, fantimes, pydt.strftime('%Y-%m-%d %H:%M:%S'))) 
    z, lnsp = fbzs[itime,0,0], fblnsps[itime,0,0] 
    sline="{}, {}, {}".format(pydt.strftime('%Y-%m-%d %H:%M:%S'), z, lnsp) 
 
    #Calculate geopotentials and heights for the model levels 
    geo, h = calculategeoh(z, lnsp, fats[itime,range(fanlevels),0,0], 
        faqs[itime,range(fanlevels),0,0], falevels) 
 
    with io.open(OUT_DIR_PATH + pydt.strftime('%Y%m%d-%H') + ".csv", 'w', newline='\r\n') as writer: 
        sheader1 = "lat, long, geopotential, h\n" 
        writer.write(unicode(sheader1)) 
 
        #Iterate over levels for this time 
        for ilevel in range(fanlevels): 
            sline="{}, {}, {}, {}".format( 
                GRID_LAT, GRID_LONG, geo[ilevel], h[ilevel]) 
            sline+="\n" 
            writer.write(unicode(sline)) 
 
 
print("Finished")