# -*- Mode: python -*-
#!/usr/bin/env python3
### ===========================================================================
###
### Compute runoff weights.
### For LMDZ only. Not suitable for DYNAMICO
###
### ===========================================================================
##
##  Warning, to install, configure, run, use any of Olivier Marti's
##  software or to read the associated documentation you'll need at least
##  one (1) brain in a reasonably working order. Lack of this implement
##  will void any warranties (either express or implied).
##  O. Marti assumes no responsability for errors, omissions,
##  data loss, or any other consequences caused directly or indirectly by
##  the usage of his software by incorrectly or partially configured
##  personal.
##
# SVN information
__Author__   = "$Author$"
__Date__     = "$Date$"
__Revision__ = "$Revision$"
__Id__       = "$Id$"
__HeadURL__  = "$HeadURL$"
__SVN_Date__ = "$SVN_Date: $"
##

## Modules
import numpy as np, xarray as xr
import nemo
from scipy import ndimage
import sys, os, platform, argparse, textwrap, time

## Useful constants
zero    = np.dtype('float64').type(0.0)
zone    = np.dtype('float64').type(1.0)
epsfrac = np.dtype('float64').type(1.0E-10)
pi      = np.pi
rad     = pi/np.dtype('float64').type(180.0)  # Conversion from degrees to radian
ra      = np.dtype('float64').type(6371229.0) # Earth radius

## Functions
def geodist (plon1, plat1, plon2, plat2) :
      """Distance between two points (on sphere)"""
      zs = np.sin (rad*plat1) * np.sin (rad*plat2) +  np.cos (rad*plat1) * np.cos (rad*plat2) * np.cos(rad*(plon2-plon1))
      zs = np.maximum (-zone, np.minimum (zone, zs))
      geodist =  np.arccos (zs)
      return geodist

### ===== Reading command line parameters ==================================================
# Creating a parser
parser = argparse.ArgumentParser (
    description = """Compute calving weights""",
    epilog='-------- End of the help message --------')

# Adding arguments
parser.add_argument ('--oce'          , help='oce model name', type=str, default='eORCA1.2',
                         choices=['ORCA2.3', 'eORCA1.2', 'eORCA1.4', 'eORCA1.4.2', 'eORCA025', 'eORCA025.1', 'eORCA025.4'] )
parser.add_argument ('--atm'          , help='atm model name', type=str, default='LMD9695'    )
parser.add_argument ('--atmCoastWidth', help='width of the coastal band in atmosphere (in grid points)', type=int, default=1 )
parser.add_argument ('--oceCoastWidth', help='width of the coastal band in ocean (in grid points)'     , type=int, default=2 )
parser.add_argument ('--atmQuantity'  , help='Quantity if atm provides quantities (m/s), Surfacic if atm provided flux (m/s/m2)' , type=str,
                         default='Quantity', choices=['Quantity', 'Surfacic'] )
parser.add_argument ('--oceQuantity'  , help='Quantity if oce requires quantities (ks/s), Surfacic if oce requires flux (m/s/m2)', type=str,
                         default='Surfacic', choices=['Quantity', 'Surfacic'] )
parser.add_argument ('--searchRadius' , help='max distance to connect a land point to an ocean point (in km)', type=float, default=600.0 )
parser.add_argument ('--grids' , help='grids file name', default='grids.nc' )
parser.add_argument ('--areas' , help='masks file name', default='areas.nc' )
parser.add_argument ('--masks' , help='areas file name', default='masks.nc' )
parser.add_argument ('--o2a'   , help='o2a file name'  , default='o2a.nc'   )
parser.add_argument ('--output', help='output rmp file name', default='rmp_tlmd_to_torc_runoff.nc' )
parser.add_argument ('--fmt'   , help='NetCDF file format, using nco syntax', default='netcdf4',
                         choices=['classic', 'netcdf3', '64bit', '64bit_data', '64bit_data', 'netcdf4', 'netcdf4_classsic'] )
parser.add_argument ('--ocePerio'   , help='periodicity of ocean grid', type=float, default=0, choices=nemo.nperio_valid_range)

# Parse command line
myargs = parser.parse_args()

#
grids = myargs.grids
areas = myargs.areas
masks = myargs.masks
o2a   = myargs.o2a

# Model Names
atm_Name = myargs.atm
oce_Name = myargs.oce
# Width of the coastal band (land points) in the atmopshere
atmCoastWidth = myargs.atmCoastWidth
# Width of the coastal band (ocean points) in the ocean 
oceCoastWidth = myargs.oceCoastWidth
searchRadius  = myargs.searchRadius * 1000.0 # From km to meters
# Netcdf format
if myargs.fmt == 'classic'         : FmtNetcdf = 'CLASSIC'
if myargs.fmt == 'netcdf3'         : FmtNetcdf = 'CLASSIC'
if myargs.fmt == '64bit'           : FmtNetcdf = 'NETCDF3_64BIT_OFFSET'
if myargs.fmt == '64bit_data'      : FmtNetcdf = 'NETCDF3_64BIT_DATA'
if myargs.fmt == '64bit_offset'    : FmtNetcdf = 'NETCDF3_64BIT_OFFSET'
if myargs.fmt == 'netcdf4'         : FmtNetcdf = 'NETCDF4'
if myargs.fmt == 'netcdf4_classic' : FmtNetcdf = 'NETCDF4_CLASSIC'

#
if atm_Name.find('LMD') >= 0 : atm_n = 'lmd' ; atmDomainType = 'rectilinear'
if atm_Name.find('ICO') >= 0 : atm_n = 'ico' ; atmDomainType = 'unstructured'

print ('atmQuantity : ' + str (myargs.atmQuantity) )
print ('oceQuantity : ' + str (myargs.oceQuantity) )

# Ocean grid periodicity
oce_perio = myargs.ocePerio

### Read coordinates of all models
###

diaFile    = xr.open_dataset ( o2a   )
gridFile   = xr.open_dataset ( grids )
areaFile   = xr.open_dataset ( areas )
maskFile   = xr.open_dataset ( masks )

o2aFrac             = diaFile ['OceFrac'].squeeze()
o2aFrac = np.where ( np.abs(o2aFrac) < 1E10, o2aFrac, 0.0)

(atm_nvertex, atm_jpj, atm_jpi) = gridFile['t'+atm_n+'.clo'][:].shape
atm_grid_size = atm_jpj*atm_jpi
atm_grid_rank = len(gridFile['t'+atm_n+'.lat'][:].shape)

atm_grid_center_lat = gridFile['t'+atm_n+'.lat'].squeeze()
atm_grid_center_lon = gridFile['t'+atm_n+'.lon'].squeeze()
atm_grid_corner_lat = gridFile['t'+atm_n+'.cla'].squeeze()
atm_grid_corner_lon = gridFile['t'+atm_n+'.clo'].squeeze()
atm_grid_area       = areaFile['t'+atm_n+'.srf'].squeeze()
atm_grid_imask      = 1-maskFile['t'+atm_n+'.msk'][:].squeeze()
atm_grid_dims       = gridFile['t'+atm_n+'.lat'][:].shape

if atmDomainType == 'unstructured' :
    atm_grid_center_lat = atm_grid_center_lat.rename ({'ycell':'cell'})
    atm_grid_center_lon = atm_grid_center_lon.rename ({'ycell':'cell'})
    atm_grid_corner_lat = atm_grid_corner_lat.rename ({'ycell':'cell'})
    atm_grid_corner_lon = atm_grid_corner_lon.rename ({'ycell':'cell'})
    atm_grid_area       = atm_grid_area.rename  ({'ycell':'cell'})
    atm_grid_imask      = atm_grid_imask.rename ({'ycell':'cell'})
    
if atmDomainType == 'rectilinear' :
    atm_grid_center_lat = atm_grid_center_lat.stack (cell=['y', 'x'])
    atm_grid_center_lon = atm_grid_center_lon.stack (cell=['y', 'x'])
    atm_grid_corner_lat = atm_grid_corner_lat.stack (cell=['y', 'x']).rename({'nvertex_lmd':'nvertex'})
    atm_grid_corner_lon = atm_grid_corner_lon.stack (cell=['y', 'x']).rename({'nvertex_lmd':'nvertex'})
    atm_grid_area       = atm_grid_area.stack       (cell=['y', 'x'])
    atm_grid_imask      = atm_grid_imask.stack      (cell=['y', 'x'])

atm_perio = 0
atm_grid_pmask = atm_grid_imask
atm_address = np.arange(atm_jpj*atm_jpi)

(oce_nvertex, oce_jpj, oce_jpi) = gridFile['torc.cla'][:].shape ; jpon=oce_jpj*oce_jpj
oce_grid_size = oce_jpj*oce_jpi
oce_grid_rank = len(gridFile['torc.lat'][:].shape)

oce_grid_center_lat = gridFile['torc.lat'].stack(oce_grid_size=['y_grid_T', 'x_grid_T'])
oce_grid_center_lon = gridFile['torc.lon'].stack(oce_grid_size=['y_grid_T', 'x_grid_T'])
oce_grid_corner_lat = gridFile['torc.cla'].squeeze().stack(oce_grid_size=['y_grid_T', 'x_grid_T'])
oce_grid_corner_lon = gridFile['torc.clo'].squeeze().stack(oce_grid_size=['y_grid_T', 'x_grid_T'])
oce_grid_area       = areaFile['torc.srf'].stack(oce_grid_size=['y_grid_T', 'x_grid_T'])
oce_grid_imask      = 1-maskFile['torc.msk'].stack(oce_grid_size=['y_grid_T', 'x_grid_T'])
oce_grid_dims       = gridFile['torc.lat'][:].shape

if oce_perio == 0 :
    if oce_jpi ==  182 : oce_perio = 4 # ORCA 2
    if oce_jpi ==  362 : oce_perio = 6 # ORCA 1
    if oce_jpi == 1442 : oce_perio = 6 # ORCA 025
        
print ("Oce NPERIO parameter : {:}".format(oce_perio))
oce_grid_pmask = nemo.lbc_mask (np.reshape(oce_grid_imask.values, (oce_jpj,oce_jpi)), nperio=oce_perio, cd_type='T', sval=0).ravel()
oce_address = np.arange(oce_jpj*oce_jpi)

print ("Fill closed sea with image processing library")
oce_grid_imask2D = np.reshape(oce_grid_pmask,(oce_jpj,oce_jpi))
oce_grid_imask2D = nemo.lbc_mask ( 1-ndimage.binary_fill_holes (1-nemo.lbc(oce_grid_imask2D, nperio=oce_perio, cd_type='T')),
                                       nperio=oce_perio, cd_type='T', sval=0 )
oce_grid_imask = oce_grid_imask2D.ravel()
## 
print ("Computes an ocean coastal band")

oceLand2D  = np.reshape ( np.where (oce_grid_pmask < 0.5, True, False), (oce_jpj, oce_jpi) )
oceOcean2D = np.reshape ( np.where (oce_grid_pmask > 0.5, True, False), (oce_jpj, oce_jpi) )

NNocean = 1+2*oceCoastWidth
oceOceanFiltered2D = ndimage.uniform_filter(oceOcean2D.astype(float), size=NNocean)
oceCoast2D = np.where (oceOceanFiltered2D<(1.0-0.5/(NNocean**2)),True,False) & oceOcean2D
oceCoast2D = nemo.lbc_mask (np.reshape(oceCoast2D,(oce_jpj,oce_jpi)), nperio=oce_perio, cd_type='T').ravel()

oceOceanFiltered = oceOceanFiltered2D.ravel()
oceLand  = oceLand2D.ravel ()
oceOcean = oceOcean2D.ravel()
oceCoast = oceCoast2D.ravel()

print ('Number of points in oceLand  : {:8d}'.format (oceLand.sum())  )
print ('Number of points in oceOcean : {:8d}'.format (oceOcean.sum()) )
print ('Number of points in oceCoast : {:8d}'.format (oceCoast.sum()) )

# Arrays with coastal points only
oceCoast_grid_center_lon = oce_grid_center_lon[oceCoast]
oceCoast_grid_center_lat = oce_grid_center_lat[oceCoast]
oceCoast_grid_area       = oce_grid_area      [oceCoast]
oceCoast_grid_imask      = oce_grid_imask     [oceCoast]
oceCoast_grid_pmask      = oce_grid_pmask     [oceCoast]
oceCoast_address         = oce_address        [oceCoast]

print ("Computes an atmosphere coastal band " )
atmLand      = np.where (o2aFrac[:] < epsfrac       , True, False)
atmLandFrac  = np.where (o2aFrac[:] < zone-epsfrac  , True, False)
atmFrac      = np.where (o2aFrac[:] > epsfrac       , True, False) & np.where (o2aFrac[:] < (zone-epsfrac), True, False)
atmOcean     = np.where (o2aFrac[:] < (zone-epsfrac), True, False)
atmOceanFrac = np.where (o2aFrac[:] > epsfrac       , True, False)

## For LMDZ only !!
if atmDomainType == 'rectilinear' :
    print ("Extend coastal band " )
    NNatm = 1+2*atmCoastWidth
    atmLand2D = np.reshape ( atmLand, ( atm_jpj, atm_jpi) )

    atmLandFiltered2D = ndimage.uniform_filter(atmLand2D.astype(float), size=NNatm)
    atmCoast2D = np.where (atmLandFiltered2D<(1.0-0.5/(NNatm**2)),True,False) & atmLandFrac
    
    atmLandFiltered = atmLandFiltered2D.ravel()
    atmCoast = atmCoast2D.ravel()

    print ('Number of points in atmLand  : {:8d}'.format (atmLand.sum())  )
    print ('Number of points in atmOcean : {:8d}'.format (atmOcean.sum()) )
    print ('Number of points in atmCoast : {:8d}'.format (atmCoast.sum()) )

else :
    atmCoast = atmFrac
    
    
# Arrays with coastal points only
atmCoast_grid_center_lon = atm_grid_center_lon[atmCoast]
atmCoast_grid_center_lat = atm_grid_center_lat[atmCoast]
atmCoast_grid_area       = atm_grid_area      [atmCoast]
atmCoast_grid_imask      = atm_grid_imask     [atmCoast]
atmCoast_grid_pmask      = atm_grid_pmask     [atmCoast]
atmCoast_address         = atm_address        [atmCoast]

# Initialisations before the loop
remap_matrix = np.empty ( shape=(0), dtype=np.float64 )
atm_address  = np.empty ( shape=(0), dtype=np.int32   )
oce_address  = np.empty ( shape=(0), dtype=np.int32   )

## Loop on atmosphere coastal points
if searchRadius > 0. :
    print ("Loop on atmosphere coastal points")
    for ja in np.arange(len(atmCoast_grid_pmask)) :
        z_dist = geodist ( atmCoast_grid_center_lon[ja], atmCoast_grid_center_lat[ja], oceCoast_grid_center_lon, oceCoast_grid_center_lat)
        z_mask = np.where (z_dist*ra < searchRadius, True, False)
        num_links = int(z_mask.sum())
        if num_links == 0 : continue
        z_area = oceCoast_grid_area[z_mask].sum().values
        poids = np.ones ((num_links),dtype=np.float64) / z_area
        if myargs.atmQuantity == 'Surfacic' : poids = poids * atm_grid_area[ja]
        if myargs.oceQuantity == 'Quantity' : poids = poids * oceCoast_grid_area[z_mask]
        if  ja % (len(atmCoast_grid_pmask)//50) == 0 : # Control print
            print ( 'ja:{:8d}, num_links:{:8d},  z_area:{:8.4e},  atm area:{:8.4e},  weights sum:{:8.4e}  '
                        .format(ja, num_links, z_area, atm_grid_area[ja].values, poids.sum() ) )
        #
        matrix_local = poids
        atm_address_local = np.ones(num_links, dtype=np.int32 ) * atmCoast_address[ja]
        # Address on destination grid
        oce_address_local = oceCoast_address[z_mask]
        # Append to global arrays
        remap_matrix = np.append ( remap_matrix, matrix_local      )
        atm_address  = np.append ( atm_address , atm_address_local )
        oce_address  = np.append ( oce_address , oce_address_local )

    print ('End of loop')

num_links = remap_matrix.shape[0]

print ("Write output file")
runoff = myargs.output
print ('Output file: ' + runoff )


remap_matrix = xr.DataArray ( np.reshape(remap_matrix, (num_links, 1)), dims = ['num_links', 'num_wgts'] )

# OASIS uses Fortran style indexing, starting at one
src_address  = xr.DataArray ( atm_address.astype(np.int32)+1, dims = ['num_links'],
                                 attrs={"convention": "Fortran style addressing, starting at 1"}) 
dst_address  = xr.DataArray ( oce_address.astype(np.int32)+1, dims = ['num_links'],
                                 attrs={"convention": "Fortran style addressing, starting at 1"})

src_grid_dims       = xr.DataArray (np.array(atm_grid_dims, dtype=np.int32), dims = ['src_grid_rank',] )
src_grid_center_lon = xr.DataArray (atm_grid_center_lon.values , dims = ['src_grid_size',] )
src_grid_center_lat = xr.DataArray (atm_grid_center_lat.values , dims = ['src_grid_size',] )

src_grid_center_lon.attrs['units']='degrees_east'  ; src_grid_center_lon.attrs['long_name']='Longitude'
src_grid_center_lon.attrs['long_name']='longitude' ; src_grid_center_lon.attrs['bounds']="src_grid_corner_lon"
src_grid_center_lat.attrs['units']='degrees_north' ; src_grid_center_lat.attrs['long_name']='Latitude'
src_grid_center_lat.attrs['long_name']='latitude ' ; src_grid_center_lat.attrs['bounds']="src_grid_corner_lat"
 
src_grid_corner_lon = xr.DataArray (atm_grid_corner_lon.values.transpose(), dims = [ 'src_grid_size', 'src_grid_corners'] )
src_grid_corner_lat = xr.DataArray (atm_grid_corner_lat.values.transpose(), dims = [ 'src_grid_size', 'src_grid_corners'] )
src_grid_corner_lon.attrs['units']="degrees_east"
src_grid_corner_lat.attrs['units']="degrees_north"

src_grid_area       =  xr.DataArray (atm_grid_area.values, dims = ['src_grid_size',] ) 
src_grid_area.attrs['long_name']="Grid area" ; src_grid_area.attrs['standard_name']="cell_area" ; src_grid_area.attrs['units']="m2"

src_grid_imask      =  xr.DataArray (atm_grid_imask.values, dims = ['src_grid_size',] ) 
src_grid_imask.attrs['long_name']="Land-sea mask" ; src_grid_imask.attrs['units']="Land:1, Ocean:0"

src_grid_pmask      =  xr.DataArray (atm_grid_pmask.values, dims = ['src_grid_size',] ) 
src_grid_pmask.attrs['long_name']="Land-sea mask (periodicity removed)" ; src_grid_pmask.attrs['units']="Land:1, Ocean:0"

# --
dst_grid_dims       = xr.DataArray (np.array(oce_grid_dims, dtype=np.int32), dims = ['dst_grid_rank',] )
dst_grid_center_lon = xr.DataArray (oce_grid_center_lon.values, dims = ['dst_grid_size',] )
dst_grid_center_lat = xr.DataArray (oce_grid_center_lat.values, dims = ['dst_grid_size',] )

dst_grid_center_lon.attrs['units']='degrees_east'  ; dst_grid_center_lon.attrs['long_name']='Longitude'
dst_grid_center_lon.attrs['long_name']='longitude' ; dst_grid_center_lon.attrs['bounds']="dst_grid_corner_lon"
dst_grid_center_lat.attrs['units']='degrees_north' ; dst_grid_center_lat.attrs['long_name']='Latitude'
dst_grid_center_lat.attrs['long_name']='latitude ' ; dst_grid_center_lat.attrs['bounds']="dst_grid_corner_lat"

dst_grid_corner_lon = xr.DataArray (np.transpose(oce_grid_corner_lon.values), dims = [ 'dst_grid_size', 'dst_grid_corners'] )
dst_grid_corner_lat = xr.DataArray (np.transpose(oce_grid_corner_lat.values), dims = [ 'dst_grid_size', 'dst_grid_corners'] )
dst_grid_corner_lon.attrs['units']="degrees_east"
dst_grid_corner_lat.attrs['units']="degrees_north"

dst_grid_area       =  xr.DataArray (oce_grid_area.values, dims = ['dst_grid_size',] ) 
dst_grid_area.attrs['long_name']="Grid area" ; dst_grid_area.attrs['standard_name']="cell_area" ; dst_grid_area.attrs['units']="m2"

dst_grid_imask      =  xr.DataArray (oce_grid_imask.astype(np.int32), dims = ['dst_grid_size',] ) 
dst_grid_imask.attrs['long_name']="Land-sea mask" ; dst_grid_imask.attrs['units']="Land:1, Ocean:0"

dst_grid_pmask      =  xr.DataArray (oce_grid_pmask, dims = ['dst_grid_size',] ) 
dst_grid_pmask.attrs['long_name']="Land-sea mask (periodicity removed)" ; dst_grid_pmask.attrs['units']="Land:1, Ocean:0"

src_lon_addressed   =  xr.DataArray (atm_grid_center_lon.values[atm_address]                  , dims = ['num_links'] )
src_lat_addressed   =  xr.DataArray (atm_grid_center_lat.values[atm_address]                  , dims = ['num_links'] )
src_area_addressed  =  xr.DataArray (atm_grid_area      .values[atm_address]                  , dims = ['num_links'] )
src_imask_addressed =  xr.DataArray (1-atm_grid_imask   .values[atm_address].astype(np.int32) , dims = ['num_links'] )
src_pmask_addressed =  xr.DataArray (1-atm_grid_pmask   .values[atm_address].astype(np.int32) , dims = ['num_links'] )

dst_lon_addressed   =  xr.DataArray (oce_grid_center_lon.values[atm_address], dims = ['num_links'] )
dst_lat_addressed   =  xr.DataArray (oce_grid_center_lat.values[oce_address], dims = ['num_links'] )
dst_area_addressed  =  xr.DataArray (oce_grid_area.values[oce_address].astype(np.int32)      , dims = ['num_links'] )
dst_imask_addressed =  xr.DataArray (1-oce_grid_imask[oce_address].astype(np.int32)   , dims = ['num_links'] )
dst_pmask_addressed =  xr.DataArray (1-oce_grid_pmask[oce_address].astype(np.int32)   , dims = ['num_links'] )

src_lon_addressed.attrs['long_name']="Longitude" ; src_lon_addressed.attrs['standard_name']="longitude" ; src_lon_addressed.attrs['units']="degrees_east"
src_lat_addressed.attrs['long_name']="Latitude"  ; src_lat_addressed.attrs['standard_name']="latitude"  ; src_lat_addressed.attrs['units']="degrees_north"

dst_lon_addressed.attrs['long_name']="Longitude" ; dst_lon_addressed.attrs['standard_name']="longitude" ; dst_lon_addressed.attrs['units']="degrees_east"
dst_lat_addressed.attrs['long_name']="Latitude"  ; dst_lat_addressed.attrs['standard_name']="latitude"  ; dst_lat_addressed.attrs['units']="degrees_north"

if atmDomainType == 'rectilinear' :
    atmLand         = xr.DataArray ( atmLand.ravel()     , dims = ['src_grid_size',] )
    atmLandFiltered = xr.DataArray ( atmLandFrac.ravel() , dims = ['src_grid_size',] )
    atmLandFrac     = xr.DataArray ( atmFrac.ravel()     , dims = ['src_grid_size',] )
    atmFrac         = xr.DataArray ( atmFrac.ravel()     , dims = ['src_grid_size',] )
    atmOcean        = xr.DataArray ( atmOcean.ravel()    , dims = ['src_grid_size',] )
    atmOceanFrac    = xr.DataArray ( atmOceanFrac.ravel(), dims = ['src_grid_size',] )

atmCoast         = xr.DataArray (atmCoast.astype(np.int32)          , dims = ['src_grid_size',])
oceLand          = xr.DataArray (oceLand.astype(np.int32)           , dims = ['dst_grid_size',])
oceOcean         = xr.DataArray (oceOcean.astype(np.int32)          , dims = ['dst_grid_size',])
oceOceanFiltered = xr.DataArray (oceOceanFiltered.astype(np.float32), dims = ['dst_grid_size',])
oceCoast         = xr.DataArray (oceCoast.astype(np.int32)          , dims = ['dst_grid_size',])


f_runoff = xr.Dataset ( {
    'remap_matrix'            : remap_matrix,
	'src_address'         : src_address,
	'dst_address'         : dst_address,
	'src_grid_dims'       : src_grid_dims,
	'src_grid_center_lon' : src_grid_center_lon,
	'src_grid_center_lat' : src_grid_center_lat,
    'src_grid_corner_lon' : src_grid_corner_lon,
    'src_grid_corner_lat' : src_grid_corner_lat,
	'src_grid_area'       : src_grid_area,
	'src_grid_area'       : src_grid_area,
	'src_grid_pmask'      : src_grid_pmask,
	'dst_grid_dims'       : dst_grid_dims,
	'dst_grid_center_lon' : dst_grid_center_lon,
	'st_grid_center_lat'  : dst_grid_center_lat,
	'dst_grid_corner_lon' : dst_grid_corner_lon,
	'dst_grid_corner_lat' : dst_grid_corner_lat,
	'dst_grid_area'       : dst_grid_area,
	'dst_grid_imask'      : dst_grid_imask,
	'dst_grid_pmask'      : dst_grid_pmask,
	'src_lon_addressed'   : src_lon_addressed,
	'src_lat_addressed'   : src_lat_addressed,
	'src_area_addressed'  : src_area_addressed,
	'dst_lon_addressed'   : dst_lon_addressed,
	'dst_lat_addressed'   : dst_lat_addressed,
	'dst_area_addressed'  : dst_area_addressed,
	'dst_imask_addressed' : dst_imask_addressed,
	'dst_pmask_addressed' : dst_pmask_addressed,
	'atmCoast'            : atmCoast,
	'oceLand'             : oceLand,
	'oceOcean'            : oceOcean,
	'oceOceanFiltered'    : oceOceanFiltered,
	'oceCoast'            : oceCoast
    } )

f_runoff.attrs['Conventions']     = "CF-1.6"
f_runoff.attrs['source']          = "IPSL Earth system model"
f_runoff.attrs['group']           = "ICMC IPSL Climate Modelling Center"
f_runoff.attrs['Institution']     = "IPSL https.//www.ipsl.fr"
f_runoff.attrs['Ocean']           = oce_Name + " https://www.nemo-ocean.eu"
f_runoff.attrs['Atmosphere']      = atm_Name + " http://lmdz.lmd.jussieu.fr"
f_runoff.attrs['associatedFiles'] = grids + " " + areas + " " + masks
f_runoff.attrs['description']     = "Generated with RunoffWeights.py"
f_runoff.attrs['title']           = runoff
f_runoff.attrs['Program']         = "Generated by " + sys.argv[0] + " with flags " + ' '.join (sys.argv[1:]) 
f_runoff.attrs['atmCoastWidth']   = "{:d} grid points".format(atmCoastWidth)
f_runoff.attrs['oceCoastWidth']   = "{:d} grid points".format(oceCoastWidth)
f_runoff.attrs['searchRadius']    = "{:.0f} km".format(searchRadius/1000.)
f_runoff.attrs['atmQuantity']     = myargs.atmQuantity
f_runoff.attrs['oceQuantity']     = myargs.oceQuantity
f_runoff.attrs['gridsFile']       = grids
f_runoff.attrs['areasFile']       = areas
f_runoff.attrs['masksFile']       = masks
f_runoff.attrs['o2aFile']         = o2a
f_runoff.attrs['timeStamp']       = time.asctime ()
try    : f_calving.attrs['directory'] = os.getcwd ()
except : pass
try    : f_runoff.attrs['HOSTNAME'] = platform.node ()
except : pass
try    : f_runoff.attrs['LOGNAME']  = os.getlogin ()
except : pass
try    : f_runoff.attrs['Python']   = "Python version " +  platform.python_version ()
except : pass
try    : f_runoff.attrs['OS']       = platform.system ()
except : pass
try    : f_runoff.attrs['release']  = platform.release ()
except : pass
try    : f_runoff.attrs['hardware'] = platform.machine ()
except : pass
f_runoff.attrs['conventions']     = "SCRIP"
f_runoff.attrs['source_grid']     = "curvilinear"
f_runoff.attrs['dest_grid']       = "curvilinear"
f_runoff.attrs['Model']           = "IPSL CM6"
f_runoff.attrs['SVN_Author']      = "$Author$"
f_runoff.attrs['SVN_Date']        = "$Date$"
f_runoff.attrs['SVN_Revision']    = "$Revision$"
f_runoff.attrs['SVN_Id']          = "$Id$"
f_runoff.attrs['SVN_HeadURL']     = "$HeadURL$"

f_runoff.to_netcdf ( runoff, mode='w', format=FmtNetcdf )
f_runoff.close ()

##

print ('That''s all folks !')
## ======================================================================================