from prep_forcing_utils import (to_netcdf_forcing_file_compress, setup_output_dirs,
remap_con, add_gridlines, edges_from_centers)
# ======================================================= #
# Process command line argument
# ======================================================= #
cfg = read_configobj(sys.argv[1])
# ============================================================ #
# Setup output subdirs
# ============================================================ #
output_dir = cfg['OUTPUT']['out_dir']
output_subdir_plots = setup_output_dirs(output_dir, mkdirs=['plots'])['plots']
output_subdir_data_remapped = setup_output_dirs(
output_dir, mkdirs=['prec_only'])['prec_only']
output_subdir_tmp = setup_output_dirs(output_dir, mkdirs=['tmp'])['tmp']
# ======================================================= #
# Process data for each VIC timestep
# ======================================================= #
# --- Loop over each VIC timestep (if > hourly, each VIC timestep --- #
# --- will contain multiple hourly NLDAS-2 data) --- #
vic_times = pd.date_range(
cfg['TIME']['start_time'],
cfg['TIME']['end_time'],
freq='{}H'.format(cfg['VIC']['time_step']))
# Process command line argument
# ======================================================= #
cfg = read_configobj(sys.argv[1])
start_time = pd.to_datetime(sys.argv[2])
end_time = pd.to_datetime(sys.argv[3])
start_year = start_time.year
end_year = end_time.year
# ============================================================ #
# Setup output subdirs
# ============================================================ #
output_dir = cfg['OUTPUT']['out_dir']
output_subdir_plots = setup_output_dirs(output_dir, mkdirs=['plots'])['plots']
output_subdir_data = setup_output_dirs(
output_dir, mkdirs=['data'])['data']
output_subdir_tmp = setup_output_dirs(output_dir, mkdirs=['tmp'])['tmp']
# ======================================================= #
# Generate remap weight file, if not input
# ======================================================= #
# --- Load the first GPM timestep --- #
filename = os.path.join(
cfg['GPM']['gpm_dir'],
'{}'.format(start_time.year), '{:02d}'.format(start_time.month),
'{:02d}'.format(start_time.day),
'{}.{:02d}00.nc'.format(start_time.strftime('%Y%m%d'), start_time.hour))
da = xr.open_dataset(filename)['precipitationCal']
from tonic.io import read_config, read_configobj
from prep_forcing_utils import (to_netcdf_forcing_file_compress,
setup_output_dirs, remap_con, add_gridlines,
edges_from_centers)
# ======================================================= #
# Process command line argument
# ======================================================= #
cfg = read_configobj(sys.argv[1])
# ============================================================ #
# Setup output subdirs
# ============================================================ #
output_dir = cfg['OUTPUT']['out_dir']
output_subdir_plots = setup_output_dirs(output_dir, mkdirs=['plots'])['plots']
output_subdir_data_remapped = setup_output_dirs(output_dir,
mkdirs=['prec_only'
])['prec_only']
output_subdir_tmp = setup_output_dirs(output_dir, mkdirs=['tmp'])['tmp']
# ======================================================= #
# Process data for each VIC timestep
# ======================================================= #
# --- Loop over each VIC timestep (if > hourly, each VIC timestep --- #
# --- will contain multiple hourly NLDAS-2 data) --- #
vic_times = pd.date_range(cfg['TIME']['start_time'],
cfg['TIME']['end_time'],
freq='{}H'.format(cfg['VIC']['time_step']))
# Initialize a dictionary to store VIC-timestep forcing data
from tonic.io import read_config, read_configobj
from prep_forcing_utils import to_netcdf_forcing_file_compress, setup_output_dirs
# ======================================================= #
# Process command line argument
# ======================================================= #
cfg = read_configobj(sys.argv[1])
# ============================================================ #
# Setup output subdirs
# ============================================================ #
output_dir = cfg['OUTPUT']['out_dir']
output_subdir_data_prec = setup_output_dirs(
output_dir, mkdirs=['prec_only'])['prec_only']
output_subdir_data_force = setup_output_dirs(
output_dir, mkdirs=['force_with_NLDAS2'])['force_with_NLDAS2']
# ======================================================= #
# For each year, load GPM and NLDAS-2 forcing data and put together, and save to file
# ======================================================= #
# Initialize a dictionary to store final forcing data
start_time = pd.to_datetime(cfg['TIME']['start_time'])
end_time = pd.to_datetime(cfg['TIME']['end_time'])
start_year = start_time.year
end_year = end_time.year
dict_force_yearly = {} # This dict will save forcing data
for year in range(start_year, end_year+1):
# Load GPM data
cfg = read_configobj(sys.argv[1])
# Start and end time of the 12-hour timesteps
# The 12-hour time intervals would be the SMPA 6AM - 6PM local time
start_date = pd.to_datetime(sys.argv[2])
end_date = pd.to_datetime(sys.argv[3])
start_year = start_date.year
end_year = end_date.year
# ============================================================ #
# Setup output subdirs
# ============================================================ #
output_dir = cfg['OUTPUT']['out_dir']
output_subdir_data = setup_output_dirs(output_dir, mkdirs=['data'])['data']
output_12hour = cfg['OUTPUT']['out_dir_12hour']
output_subdir_plots = setup_output_dirs(output_12hour,
mkdirs=['plots'])['plots']
# ======================================================= #
# Aggregating GPM 30min timestep to 12 hourly (SMAP LST 6:00 - 18:00, 18:00 - 6:00)
# NOTE: different UCT times of 30-min GPM will be extracted for the 12H-intervals local time
# ======================================================= #
# --- Construct final timestamps --- #
# LST 6am and 6pm timestamps; timestamps are time-beginnig for precipitation
times_12h = pd.date_range(start_date + pd.DateOffset(hours=6),
end_date + pd.DateOffset(hours=18),
freq='12H')
cfg = read_configobj(sys.argv[1])
# Start and end time of the 12-hour timesteps to concatenate
# The 12-hour time intervals would be the SMPA 6AM - 6PM local time
start_date = pd.to_datetime(sys.argv[2])
end_date = pd.to_datetime(sys.argv[3])
start_year = start_date.year
end_year = end_date.year
# ============================================================ #
# Setup output subdirs
# ============================================================ #
output_dir = cfg['OUTPUT']['out_dir']
output_subdir_data = setup_output_dirs(output_dir, mkdirs=['data'])['data']
output_12hour = cfg['OUTPUT']['out_dir_12hour']
# ======================================================= #
# Concatenate 12-hourly GPM data (SMAP LST 6:00 - 18:00, 18:00 - 6:00)
# ======================================================= #
# --- Construct timestamps to concatenate --- #
# LST 6am and 6pm timestamps; timestamps are time-beginnig for precipitation
times_12h = pd.date_range(start_date + pd.DateOffset(hours=6),
end_date + pd.DateOffset(hours=18),
freq='12H')
# --- Load all 12-hour GPM data (LST interval) --- #
list_12h_alltime = []
for time_12h in times_12h: