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# Copyright 2022 - Barcelona Supercomputing Center
# Authors: Etienne Tourigny, Rodrigo Martín Posada
# MIT License
from netCDF4 import Dataset # pylint: disable=no-name-in-module
import logging
import numpy as np
from datetime import date, timedelta
from DataCouplerUtils import days_since_refyear, filename_from_pattern
import pyoasis
class DataCouplerVar:
def __init__(self, conf, fix_year, start_year):
self.id = conf['id']
self.oasis_name = conf['oasis_name']
self.grid_name = conf['grid_name']
self.file_pattern = conf['file_pattern']
self.var_name = conf['netcdf_variable']
self.ref_year = conf['yref_min']
self.timestep = conf['timestep']
self.scale_factor = conf['scale_factor']
self.offset = conf['offset']
self.min = conf['min'] if conf['min'] is not None else np.NINF
self.max = conf['max'] if conf['max'] is not None else np.Inf
self.interpolate = conf['interpolate']
self.fix_year = fix_year
# Initialization
self.__get_grids(start_year)
self.field = pyoasis.asarray(np.empty((2,self.grid_nx,self.grid_ny)))
# Field that will be sent
self.send_field = pyoasis.asarray(np.empty((self.grid_nx,self.grid_ny)))
# Current netCDF dataset
self.dataset = None
# Name of the current dataset file
self.current_file = ''
self.time_array = None
# Index in time_array
self.t_index = 0
# Dummy values
# Timesteps [days since refdate]
# Step before with data
self.t1 = -9999.
# Step after with data
self.t2 = -9999.
# Step at Jan 1st
self.t0101 = -9999.
# Step at Dec 31st
self.t1231 = -9999.
self.tnewyear = -9999.
def setup(self, yy, t_global, tnewyear_global):
# Dummy values
self.t1 = -9999.
self.t2 = -9999.
self.t_index = 0
self.t0101 = float(days_since_refyear(yy, 1, 1, self.ref_year))
self.t1231 = float(days_since_refyear(yy, 12, 31, self.ref_year))
# Current timestep
t_local = self.t0101 + t_global
self.tnewyear = self.t0101 + tnewyear_global
logging.debug('{}: INIT - t : {}'.format(self.id, t_local))
logging.debug('{}: INIT - t0101 : {}'.format(self.id, self.t0101))
logging.debug('{}: INIT - t1231 : {}'.format(self.id, self.t1231))
logging.debug('{}: INIT - tnewyear: {}'.format(self.id, self.tnewyear))
curr_date = date(self.ref_year, 1, 1) + timedelta(days=t_local)
file_name = filename_from_pattern(self.file_pattern, curr_date)
self.__open_file(file_name)
if t_local >= self.time_array[0]:
self.t_index = np.searchsorted(self.time_array, t_local, side='right')-1
self.t2 = self.time_array[self.t_index]
self.t1 = self.t2
self.__read_file()
def update(self, t_global):
t_local = self.t0101 + t_global
logging.debug('update var {} at time {}'.format(self.id, t_local))
while t_local > self.t2:
self.__read_next_timestep_from_files(t_local)
var_max = self.max
var_min = self.min
# Monthly interpolation
if self.timestep == 'monthly':
if self.interpolate:
if self.t1 < self.t2:
logging.debug('{}: time interpolation t1,t,t2 {} {} {}'.format(self.id, self.t1, t_local, self.t2))
raw_field = ((t_local-self.t1)*self.field[1] + (self.t2-t_local)*self.field[0])/(self.t2-self.t1)
else:
logging.debug('{}: no time interpolation t,t2 {} {}'.format(self.id, t_local, self.t2))
# Hardcoded to match original Fortran amip-forcing clipping avoidance
if self.oasis_name == 'AMIP_sst':
var_min = np.NINF
raw_field = self.field[1]
else:
# Nearest neighbour
if self.t1 < self.t2:
if np.floor(t_local)-np.floor(self.t1) < np.floor(self.t2)-np.floor(t_local):
nn = 0
else:
nn = 1
else:
nn = 1
logging.debug('{}: nearest neigbour nn,t1,t,t2 {} {} {} {}'.format(
self.id, nn, self.t1, t_local, self.t2))
# Hardcoded to match Fortran amip-forcing clipping avoidance
if self.oasis_name == 'AMIP_sst':
var_min = np.NINF
raw_field = self.field[nn]
# Daily (no interpolation)
else:
raw_field = self.field[1]
# Clipping required for CMIP6 BCs
self.send_field = np.maximum(var_min, np.minimum(var_max, raw_field))
def finalise(self):
self.__close_current_file()
def __get_grids(self, start_year):
'''
Sets `self.grid_lat` and `self.grid_lon` from the first input netCDF file.
'''
yy = self.fix_year if self.fix_year > 0 else start_year
file_name = filename_from_pattern(self.file_pattern, date(yy, 1, 1))
ds = Dataset(file_name, 'r')
variable_names = ds.variables.keys()
# Look for lat and lon variables to create a 2d grid.
# If not found, look for sector to greate a 1d grid (only 3 sectors for co2 supported for now).
if 'lat' in variable_names:
lat = ds['lat'][:]
elif 'latitude' in variable_names:
lat = ds['latitude'][:]
else:
lat = None
if 'lon' in variable_names:
lon = ds['lon'][:]
elif 'longitude' in variable_names:
lon = ds['longitude'][:]
else:
lon = None
if lat is None or lon is None:
if 'sector' in variable_names:
num_sectors = len(ds['sector'][:])
if num_sectors == 3:
self.grid_ny = 2
self.grid_nx = 1
lat=[-45,45]
lon=0
self.dy=lat[1]-lat[0]
self.dx=0
# Create 2-D grid for OASIS
self.grid_lon = np.tile(lon, (self.grid_ny, 1)).T
self.grid_lat = np.tile(lat, (self.grid_nx, 1))
else:
raise Exception(
'DataCouplerVar: {}: Could not find sector variable of length 3. Invalid input netCDF file {}.'.format(self.id, file_name))
else:
raise Exception(
'DataCouplerVar: {}: Could not find lon or longitude variable name. Invalid input netCDF file {}.'.format(self.id, file_name))
else:
self.grid_ny = lat.size
self.grid_nx = lon.size
self.dy=lat[1]-lat[0]
self.dx=lon[1]-lon[0]
# Create 2-D grid for OASIS
self.grid_lon = np.tile(lon, (self.grid_ny, 1)).T
self.grid_lat = np.tile(lat, (self.grid_nx, 1))
ds.close()
def __read_next_timestep_from_files(self, t_local):
self.t1 = self.t2
self.field[0] = self.field[1]
if t_local > np.max(self.time_array):
old_file_name = self.current_file
self.__close_current_file()
curr_date = date(self.ref_year, 1, 1) + timedelta(days=t_local)
file_name = filename_from_pattern(self.file_pattern, curr_date)
if file_name == old_file_name:
raise Exception('DataCouplerVar: {}: Could not find the next file for date {}.'.format(self.id, curr_date))
self.__open_file(file_name)
self.t_index = -1
self.t_index += 1
self.t2 = self.time_array[self.t_index]
self.__read_file()
def __open_file(self, file_name):
self.current_file = file_name
logging.debug('{}: open file {}'.format(self.id, self.current_file))
self.dataset = Dataset(file_name, 'r')
self.time_array = self.dataset['time'][:]
def __read_file(self):
if self.fix_year > 0 and self.time_array[self.t_index] >= self.tnewyear:
logging.debug('{}:...use first BC data for timestep {}'.format(self.id, self.time_array[self.t_index]))
# Assign BC only if values have been assigned previously
first_t_index = np.searchsorted(self.time_array, self.t0101, side='right')-1
raw_field = self.dataset[self.var_name][first_t_index+1]
elif self.fix_year > 0 and self.time_array[self.t_index] < self.t0101:
logging.debug('{}:...use last BC data for timestep {}'.format(self.id, self.time_array[self.t_index]))
# Assign BC only if values have been assigned previously
last_t_index = np.searchsorted(self.time_array, self.t1231, side='right')-1
raw_field = self.dataset[self.var_name][last_t_index]
else:
logging.debug('{}: ...read timestep {} {:<018}'.format(
self.id, self.t_index, self.time_array[self.t_index]))
raw_field = self.dataset[self.var_name][self.t_index]
# This section can be "hardcoded" by the user
# Hardcoded CO2 emissions data.
logging.debug('reading {}'.format(self.var_name))
if self.var_name == 'CO2_em_anthro' or self.var_name == 'CO2_em_AIR_anthro':
#Read sum of all sectors / levels
raw_field = np.sum(raw_field,axis=0)
elif self.var_name == 'mole_fraction_of_carbon_dioxide_in_air':
raw_field = raw_field[1:]
logging.debug('reading co2 raw_field={} raw_field.shape={} field.shape={}'.format(str(raw_field),str(raw_field.shape),str(self.field[1].shape)))
self.field[1] = pyoasis.asarray(np.transpose(raw_field)).astype('float64')*self.scale_factor+self.offset
def __close_current_file(self):
logging.debug('{}: close file {}'.format(self.id, self.current_file))
self.dataset.close()