def bom_ascii_to_nc(year,dates,froot):
debug = False
latvec = None
lonvec = None
ncobj = None
latslice = slice(None,None,None)
lonslice = slice(None,None,None)
missed_dates = []
adict = {}
latvec,lonvec,adict = get_basics()
miss = adict['missing']
for dti,dt in enumerate(dates):
index = (slice(dti,None,None),latslice,lonslice)
# Initialise the netcdf object
if ncobj is None:
if debug: print "Define ncobj:",dt
dni_var = 'solar_dni'
ghi_var = 'solar_ghi'
obs_var = 'obs_time'
ncobj = nb.nc_open(froot+'.nc','w',format='NETCDF4_CLASSIC')
nb.nc_set_timelatlon(ncobj,None,len(latvec),len(lonvec))
nb.nc_set_var(ncobj,dni_var,fill=miss,zlib=True)
nb.nc_set_var(ncobj,ghi_var,fill=miss,zlib=True)
nb.nc_set_var(ncobj,obs_var,fill=miss,zlib=True)
nb.nc_set_var(ncobj,'crs',dims=(),dtype="i4") # Grid mapping container
nb.nc_add_data(ncobj,'latitude',latvec)
nb.nc_add_data(ncobj,'longitude',lonvec)
dni_nc = ncobj.variables[dni_var]
ghi_nc = ncobj.variables[ghi_var]
obs_nc = ncobj.variables[obs_var]
# Calculate the observation time layer
obs_data = create_obs_time(latvec,lonvec,dt)
obs_nc[dti,:,:] = obs_data
# Add time values
nb.nc_add_time(ncobj,dates)
# Create an ordered metadata dictionary
meta = create_meta(dates[0],dates[-1],adict)
# Output the file-specific metadata to a JSON file regardless of the
# requested output formats
jh.json_dump(meta, froot+'.json')
# Setting attributes here is optional in this example because it is
# known that netcdf_json_wrapper.py will be called next with
# 'froot+".json"' as one of the input files.
nb.nc_set_attributes(ncobj,meta)
if debug: print "Added attributes"
nb.nc_close(ncobj)
print "Wrote: "+froot+".nc"
return froot
def bom_ascii_to_nc(year,dates,froot):
latvec = None
lonvec = None
ncobj = None
latslice = slice(None,None,None)
lonslice = slice(None,None,None)
missed_dates = []
for dti,dt in enumerate(dates):
dni,ghi = get_solar_files(str(year),dt)
index = (slice(dti,None,None),latslice,lonslice)
if dni is not None:
# Split the input file into metadata and data components
dni_head,dni_rows,dni_history = split_bom_file(dni)
# Resample the data
dni_data,dni_lat,dni_lon,dni_dict = resample_data(dni_rows,dni_head)
if ghi is not None:
# Split the input file into metadata and data components
ghi_head,ghi_rows,ghi_history = split_bom_file(ghi)
# Resample the data
ghi_data,ghi_lat,ghi_lon,ghi_dict = resample_data(ghi_rows,ghi_head)
# Skip initial dates until we get a valid file because we need lat,lon
if latvec is None and dni is not None:
latvec = dni_lat
lonvec = dni_lon
miss = dni_dict['missing']
if latvec is None and ghi is not None:
latvec = ghi_lat
lonvec = ghi_lon
miss = ghi_dict['missing']
if latvec is None:
missed_dates.append(dt)
continue
# Initialise the netcdf object
if ncobj is None:
dni_var = 'solar_dni'
ghi_var = 'solar_ghi'
obs_var = 'obs_time'
ncobj = nb.nc_open(froot+'.nc','w')
#ncobj = nb.nc_open(froot+'.nc','w',format='NETCDF4_CLASSIC')
nb.nc_set_timelatlon(ncobj,None,len(latvec),len(lonvec))
nb.nc_set_var(ncobj,dni_var,fill=miss)
nb.nc_set_var(ncobj,ghi_var,fill=miss)
nb.nc_set_var(ncobj,obs_var,fill=miss)
#nb.nc_set_var(ncobj,dni_var,fill=miss,zlib=True)
#nb.nc_set_var(ncobj,ghi_var,fill=miss,zlib=True)
#nb.nc_set_var(ncobj,obs_var,fill=miss,zlib=True)
nb.nc_set_var(ncobj,'crs',dims=(),dtype="i4") # Grid mapping container
nb.nc_add_data(ncobj,'latitude',latvec)
nb.nc_add_data(ncobj,'longitude',lonvec)
dni_nc = ncobj.variables[dni_var]
ghi_nc = ncobj.variables[ghi_var]
obs_nc = ncobj.variables[obs_var]
# Add observation time layers for any missed dates
for mi,md in enumerate(missed_dates):
obs_data = create_obs_time(latvec,lonvec,md)
#nb.nc_add_data(ncobj,obs_var,obs_data,
# index=(slice(mi,None,None),latslice,lonslice))
obs_nc[mi,:,:] = obs_data
missed_dates = []
# Calculate the observation time layer
obs_data = create_obs_time(latvec,lonvec,dt)
obs_nc[dti,:,:] = obs_data
# Add data
if dni is not None:
#nb.nc_add_data(ncobj,dni_var,dni_data,index=index)
dni_nc[dti,:,:] = dni_data
if ghi is not None:
#nb.nc_add_data(ncobj,ghi_var,ghi_data,index=index)
ghi_nc[dti,:,:] = ghi_data
# Add time values
nb.nc_add_time(ncobj,dates)
# Create an ordered metadata dictionary
meta = create_meta(dates[0],dates[-1],dni_dict)
# Output the file-specific metadata to a JSON file regardless of the
# requested output formats
jh.json_dump(meta, froot+'.json')
# Setting attributes here is optional in this example because it is
# known that netcdf_json_wrapper.py will be called next with
# 'froot+".json"' as one of the input files.
nb.nc_set_attributes(ncobj,meta)
print "Added attributes"
nb.nc_close(ncobj)
print "Wrote: "+froot+".nc"
return froot
def bom_ascii_to_nc(year, dates, froot):
debug = False
latvec = None
lonvec = None
ncobj = None
latslice = slice(None, None, None)
lonslice = slice(None, None, None)
missed_dates = []
adict = {}
for dti, dt in enumerate(dates):
dni, ghi = get_solar_files(str(year), dt)
index = (slice(dti, None, None), latslice, lonslice)
if dni is not None:
# Split the input file into metadata and data components
dni_head, dni_rows, dni_history = split_bom_file(dni)
# Resample the data
dni_data, dni_lat, dni_lon, dni_dict = resample_data(
dni_rows, dni_head)
else:
if debug: print "No dni data:", dt
if ghi is not None:
# Split the input file into metadata and data components
ghi_head, ghi_rows, ghi_history = split_bom_file(ghi)
# Resample the data
ghi_data, ghi_lat, ghi_lon, ghi_dict = resample_data(
ghi_rows, ghi_head)
else:
if debug: print "No ghi data:", dt
# Skip initial dates until we get a valid file because we need lat,lon
if latvec is None and dni is not None:
if debug: print "Using dni_lat:", dt
latvec = dni_lat
lonvec = dni_lon
adict = dni_dict
miss = adict['missing']
if latvec is None and ghi is not None:
if debug: print "Using ghi_lat:", dt
latvec = ghi_lat
lonvec = ghi_lon
adict = ghi_dict
miss = adict['missing']
if latvec is None:
if debug: print "Save miss:", dt
missed_dates.append(dt)
continue
# Initialise the netcdf object
if ncobj is None:
if debug: print "Define ncobj:", dt
dni_var = 'solar_dni'
ghi_var = 'solar_ghi'
obs_var = 'obs_time'
ncobj = nb.nc_open(froot + '.nc', 'w', format='NETCDF4_CLASSIC')
nb.nc_set_timelatlon(ncobj, None, len(latvec), len(lonvec))
nb.nc_set_var(ncobj, dni_var, fill=miss, zlib=True)
nb.nc_set_var(ncobj, ghi_var, fill=miss, zlib=True)
nb.nc_set_var(ncobj, obs_var, fill=miss, zlib=True)
nb.nc_set_var(ncobj, 'crs', dims=(),
dtype="i4") # Grid mapping container
nb.nc_add_data(ncobj, 'latitude', latvec)
nb.nc_add_data(ncobj, 'longitude', lonvec)
dni_nc = ncobj.variables[dni_var]
ghi_nc = ncobj.variables[ghi_var]
obs_nc = ncobj.variables[obs_var]
# Add observation time layers for any missed dates
for mi, md in enumerate(missed_dates):
if debug: print "Add missed:", md
obs_data = create_obs_time(latvec, lonvec, md)
#nb.nc_add_data(ncobj,obs_var,obs_data,
# index=(slice(mi,None,None),latslice,lonslice))
obs_nc[mi, :, :] = obs_data
missed_dates = []
# Calculate the observation time layer
obs_data = create_obs_time(latvec, lonvec, dt)
obs_nc[dti, :, :] = obs_data
# Add data
if dni is not None:
#nb.nc_add_data(ncobj,dni_var,dni_data,index=index)
if debug: print "Add dni:", dni
dni_nc[dti, :, :] = dni_data
if ghi is not None:
if debug: print "Add ghi:", ghi
#nb.nc_add_data(ncobj,ghi_var,ghi_data,index=index)
ghi_nc[dti, :, :] = ghi_data
# Add time values
nb.nc_add_time(ncobj, dates)
# Create an ordered metadata dictionary
meta = create_meta(dates[0], dates[-1], adict)
# Output the file-specific metadata to a JSON file regardless of the
# requested output formats
jh.json_dump(meta, froot + '.json')
# Setting attributes here is optional in this example because it is
# known that netcdf_json_wrapper.py will be called next with
# 'froot+".json"' as one of the input files.
nb.nc_set_attributes(ncobj, meta)
if debug: print "Added attributes"
nb.nc_close(ncobj)
print "Wrote: " + froot + ".nc"
return froot