def run(self, latidx, lonidx):
try:
inputfile_dir = self.config.get_dict(self.translator_type,
'inputfile_dir',
default=os.path.join(
'..', '..'))
inputfiles = self.config.get_dict(self.translator_type,
'inputfile',
default='1.soil.tile.nc4')
inputfiles = param_to_list(inputfiles)
latdelta, londelta = [
double(d) / 60 for d in self.config.get('delta').split(',')
] # convert to degrees
nco = Nco()
outputfiles = self.config.get_dict(
self.translator_type,
'outputfile',
default=inputs_to_outputs(inputfiles))
outputfiles = param_to_list(outputfiles)
inputfiles = apply_prefix(inputfiles, inputfile_dir)
for i in range(len(inputfiles)):
inputfile = inputfiles[i]
outputfile = outputfiles[i]
with nc(inputfile) as f:
variables = f.variables.keys()
soil_id = f.getncattr('soil_id')
# get latitude, longitude limits
minlat = 90 - latdelta * latidx
maxlat = minlat + latdelta
minlon = -180 + londelta * (lonidx - 1)
maxlon = minlon + londelta
# additional options
options = '-h -a lat,lon -d lat,%f,%f -d lon,%f,%f --rd' % (
minlat, maxlat, minlon, maxlon)
if 'cropland' in variables:
options += ' -w cropland'
# perform aggregation
nco.ncwa(input=inputfile, output=outputfile, options=options)
# add degenerate profile dimension
nco.ncecat(input=outputfile,
output=outputfile,
options='-O -h -u profile')
nco.ncap2(input=outputfile,
output=outputfile,
options='-O -h -s profile[profile]=1')
# add soil_id variable
nco.ncap2(input=outputfile,
output=outputfile,
options='-O -h -s soil_id[profile,lat,lon]=1')
nco.ncatted(input=outputfile,
output=outputfile,
options='-O -h -a units,soil_id,c,c,"mapping"')
nco.ncatted(input=outputfile,
output=outputfile,
options='-O -h -a long_name,soil_id,c,c,"%s"' %
str(soil_id))
nco.ncatted(input=outputfile,
output=outputfile,
options='-O -h -a soil_id,global,d,c,""')
# change latitude, longitude to simulated point
with nc(outputfile, 'a') as f:
latv = f.variables['lat']
latv[:] = 90 - latdelta * (latidx - 0.5)
lonv = f.variables['lon']
lonv[:] = -180 + londelta * (lonidx - 0.5)
return True
except:
print "[%s]: %s" % (os.path.basename(__file__),
traceback.format_exc())
return False
def write_means_to_netcdf_file(files,
ens_means,
variables,
start_date,
end_date,
argv,
test=False):
"""
Write means computed in netcdf files
:param files: initial files
:param ens_means: ensemble means calculated calling function compute_average
:param variables: list of variables
:param start_date: start date list in [day, month, year] format
:param end_date: end date list in [day, month, year] format
:param argv: string containing command line arguments used
:param test: if test is true, make some changes specific to files on my pc
:return: None, files created in folder analysis/ensemble_means
"""
# Initialise Nco
nco = Nco()
# Start and end date string
start_end_str = str(start_date[0]) + "-" + str(start_date[1]) + "-" + str(start_date[2]) + " and " + \
str(end_date[0]) + "-" +str(end_date[1]) + "-" + str(end_date[2])
# Get path
path = directories.CLIMATE_DATA
# Get normal and files in absolute path saved in ensemble groups
ens_files = [[] for _ in range(len(ens_means))]
abs_files = [[] for _ in range(len(ens_means))]
# Get absolute path of each file
for i in range(len(files)):
# Get file ensemble number index
ens_indx = ens_to_indx(get_ens_num(files[i]))
# save in ens_files
ens_files[ens_indx].append(files[i])
# Get absolute path
joined = os.path.abspath(os.path.join(path, files[i]))
if test:
joined = joined.replace("Adanna Akwataghibe", "Adanna")
# save in ens_files
abs_files[ens_indx].append(joined)
# Get folder to store ensemble means
results = directories.ANALYSIS
mean_folder = os.path.abspath(os.path.join(results, directories.MEANS))
if test:
mean_folder = mean_folder.replace("Adanna Akwataghibe", "Adanna")
# Go through ensembles, merge files to get output and write to output
for i in range(len(ens_means)):
# Get first file name in specific ensemble and add last year to name - use as output file name
output_file = ""
if ens_files[i][0].endswith(".nc"):
output_file = ens_files[i][0][:-3] + '_' + str(end_date[2]) + '.nc'
output_file = os.path.join(mean_folder, output_file)
# Merge files in ensemble in output_file
nco.ncecat(input=abs_files[i], output=output_file)
# Write means to file
with Dataset(abs_files[i][0], 'r') as src, Dataset(output_file,
'a') as dest:
for var in variables:
# create dataset identical to original variable in file
mean_var_name = var + '_mean'
datatype = src.variables[var].datatype
# Get dimensions without time
dims = src.variables[var].dimensions[1:]
mean_var = dest.createVariable(mean_var_name, datatype, dims)
# save means in variable
mean_var[:] = ens_means[i][var][:]
mean_var.setncatts(src[var].__dict__)
mean_var.long_name = mean_var.long_name + ' averaged between ' + start_end_str
# Write to description and history of file
desc_str = "Added averages of variables " + ', '.join(variables) + " within time period " + \
start_end_str
if 'description' in dest.ncattrs():
dest.description = desc_str + ' \n' + dest.description
else:
dest.description = desc_str
dest.history = time.ctime(time.time()) + ': Commands used to produce file: ' + argv + ' \n' + \
time.ctime(time.time()) + ': Functions used: extract_data, compute_average,' \
' write_means_to_netcdf_file' + ' \n' + dest.history
print("Mean ensemble files created in " +
os.path.join(directories.ANALYSIS, directories.MEANS) + "folder.")
