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inference_to_nc_iterative_era5.py
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import os
import numpy as np
import onnx
import onnxruntime as ort
import xarray as xr
import argparse
from pathlib import Path
import datetime
# Define the coordinates
lat = np.linspace(90, -90, 721)
lon = np.linspace(0, 359.75, 1440)
upper_air_channels = [
'z1000', 'z925', 'z850', 'z700', 'z600', 'z500', 'z400', 'z300', 'z250', 'z200', 'z150', 'z100', 'z50', 'q1000',
'q925', 'q850', 'q700', 'q600', 'q500', 'q400', 'q300', 'q250', 'q200', 'q150', 'q100', 'q50', 't1000', 't925',
't850', 't700', 't600', 't500', 't400', 't300', 't250', 't200', 't150', 't100', 't50', 'u1000', 'u925', 'u850',
'u700', 'u600', 'u500', 'u400', 'u300', 'u250', 'u200', 'u150', 'u100', 'u50', 'v1000', 'v925', 'v850', 'v700',
'v600', 'v500', 'v400', 'v300', 'v250', 'v200', 'v150', 'v100', 'v50'
]
surface_channels = ['msl', 'u10m', 'v10m', 't2m']
def parse_arguments():
parser = argparse.ArgumentParser(description='Run inference for a date range')
parser.add_argument('--start_date', type=str, required=True,
help='Start date in YYYYMMDD format')
parser.add_argument('--end_date', type=str, required=True,
help='End date in YYYYMMDD format')
parser.add_argument('--inference_input_dir', type=str, required=True,
help='Directory containing input files')
parser.add_argument('--inference_output_dir', type=str, required=True,
help='Directory for saving inference results')
parser.add_argument('--model_dir', type=str, default='models',
help='Directory containing ONNX models')
return parser.parse_args()
def generate_time_list(start_date, end_date):
start = datetime.datetime.strptime(start_date, "%Y%m%d")
end = datetime.datetime.strptime(end_date, "%Y%m%d")
current = start
time_list = []
while current <= end:
time_list.append(current)
current += datetime.timedelta(days=1)
return time_list
def ensure_directory_exists(path):
Path(path).mkdir(parents=True, exist_ok=True)
def setup_model_sessions(model_dir):
# Load models
model_24 = onnx.load(os.path.join(model_dir, 'pangu_weather_24.onnx'))
model_6 = onnx.load(os.path.join(model_dir, 'pangu_weather_6.onnx'))
# Set the behavior of onnxruntime
options = ort.SessionOptions()
options.enable_cpu_mem_arena = False
options.enable_mem_pattern = False
options.enable_mem_reuse = False
options.intra_op_num_threads = 4
# Set the behavior of cuda provider
cuda_provider_options = {'arena_extend_strategy': 'kSameAsRequested'}
# Initialize sessions
ort_session_24 = ort.InferenceSession(
os.path.join(model_dir, 'pangu_weather_24.onnx'),
sess_options=options,
providers=[('CUDAExecutionProvider', cuda_provider_options)]
)
ort_session_6 = ort.InferenceSession(
os.path.join(model_dir, 'pangu_weather_6.onnx'),
sess_options=options,
providers=[('CUDAExecutionProvider', cuda_provider_options)]
)
return ort_session_24, ort_session_6
def run_inference(date, input_dir, output_dir, ort_session_24, ort_session_6):
# Define level lists
# zlevels = list(reversed(['z1000', 'z925', 'z850', 'z700', 'z600', 'z500', 'z400', 'z300', 'z250', 'z200', 'z150', 'z100', 'z50']))
# qlevels = list(reversed(['q1000', 'q925', 'q850', 'q700', 'q600', 'q500', 'q400', 'q300', 'q250', 'q200', 'q150', 'q100', 'q50']))
# tlevels = list(reversed(['t1000', 't925', 't850', 't700', 't600', 't500', 't400', 't300', 't250', 't200', 't150', 't100', 't50']))
# ulevels = list(reversed(['u1000', 'u925', 'u850', 'u700', 'u600', 'u500', 'u400', 'u300', 'u250', 'u200', 'u150', 'u100', 'u50']))
# vlevels = list(reversed(['v1000', 'v925', 'v850', 'v700', 'v600', 'v500', 'v400', 'v300', 'v250', 'v200', 'v150', 'v100', 'v50']))
zlevels = ['z1000', 'z925', 'z850', 'z700', 'z600', 'z500', 'z400', 'z300', 'z250', 'z200', 'z150', 'z100', 'z50',]
qlevels = ['q1000','q925', 'q850', 'q700', 'q600', 'q500', 'q400', 'q300', 'q250', 'q200', 'q150', 'q100', 'q50',]
tlevels = ['t1000', 't925','t850', 't700', 't600', 't500', 't400', 't300', 't250', 't200', 't150', 't100', 't50']
ulevels = ['u1000', 'u925', 'u850','u700', 'u600', 'u500', 'u400', 'u300', 'u250', 'u200', 'u150', 'u100', 'u50',]
vlevels = ['v1000', 'v925', 'v850', 'v700','v600', 'v500', 'v400', 'v300', 'v250', 'v200', 'v150', 'v100', 'v50',]
surfaces = ['msl', 'u10m', 'v10m', 't2m']
# Create output directory for this date
date_output_dir = os.path.join(output_dir, date.strftime('%Y%m%d%H'))
ensure_directory_exists(date_output_dir)
# Check if all outputs already exist
all_exists = True
for i in range(40):
nc_file = os.path.join(date_output_dir, f'pangu_era5_pred_{str((i+1)*6).zfill(3)}.nc')
if not os.path.exists(nc_file):
all_exists = False
break
if all_exists:
print(f"All outputs already exist for {date.strftime('%Y-%m-%d')}, skipping...")
return
# Load input data
input_file = os.path.join(input_dir, f"pangu_era5_init_{date.strftime('%Y%m%d%H')}.nc")
ds_in = xr.open_dataarray(input_file)
# Prepare input data
input_upper = np.stack([
ds_in.sel(channel=zlevels).to_numpy().squeeze(),
ds_in.sel(channel=qlevels).to_numpy().squeeze(),
ds_in.sel(channel=tlevels).to_numpy().squeeze(),
ds_in.sel(channel=ulevels).to_numpy().squeeze(),
ds_in.sel(channel=vlevels).to_numpy().squeeze()
], axis=0).astype(np.float32)
input_surface = ds_in.sel(channel=surfaces).to_numpy().squeeze().astype(np.float32)
# Modified inference loop
input_24, input_surface_24 = input_upper, input_surface
input, input_surface = input_upper, input_surface
for i in range(40):
print(f'Processing {date.strftime("%Y-%m-%d")} - {(i+1)*6} hour')
if (i+1) % 4 == 0:
output, output_surface = ort_session_24.run(None, {
'input': input_24,
'input_surface': input_surface_24
})
input_24, input_surface_24 = output, output_surface
else:
output, output_surface = ort_session_6.run(None, {
'input': input,
'input_surface': input_surface
})
input, input_surface = output, output_surface
# Save results
# np.save(os.path.join(date_output_dir, f'output_upper_{str((i+1)*6).zfill(3)}'), output)
# np.save(os.path.join(date_output_dir, f'output_surface_{str((i+1)*6).zfill(3)}'), output_surface)
# Create prediction timedelta
pred_timedelta = np.timedelta64((i+1)*6, 'h').astype('timedelta64[ns]')
# Reshape upper air output to combine variables and pressure levels
output_reshaped = output.reshape(65, 721, 1440) # 5 variables * 13 pressure levels = 65 channels
# Create xarray DataArrays with proper dimensions
da_upper = xr.DataArray(
data=np.expand_dims(np.expand_dims(output_reshaped, axis=0), axis=0),
coords={
'init_time': [date],
'prediction_timedelta': [pred_timedelta],
'channel': upper_air_channels,
'lat': lat,
'lon': lon
},
dims=['init_time', 'prediction_timedelta', 'channel', 'lat', 'lon']
)
da_surface = xr.DataArray(
data=np.expand_dims(np.expand_dims(output_surface, axis=0), axis=0),
coords={
'init_time': [date],
'prediction_timedelta': [pred_timedelta],
'channel': surface_channels,
'lat': lat,
'lon': lon
},
dims=['init_time', 'prediction_timedelta', 'channel', 'lat', 'lon']
)
# Combine upper air and surface data
combined_channels = upper_air_channels + surface_channels
combined_data = xr.concat([da_upper, da_surface], dim='channel').sel(lat=slice(60,20), lon=slice(220,300))
# Save as netCDF
output_filename = os.path.join(date_output_dir, f'pangu_era5_pred_{str((i+1)*6).zfill(3)}.nc')
combined_data.to_netcdf(output_filename)
def main():
args = parse_arguments()
# Ensure output directory exists
ensure_directory_exists(args.inference_output_dir)
# Setup model sessions
ort_session_24, ort_session_6 = setup_model_sessions(args.model_dir)
# Generate list of dates to process
dates = generate_time_list(args.start_date, args.end_date)
# Process each date
for date in dates:
try:
run_inference(date, args.inference_input_dir, args.inference_output_dir,
ort_session_24, ort_session_6)
except Exception as e:
print(f"Error processing {date.strftime('%Y-%m-%d')}: {str(e)}")
if __name__ == "__main__":
main()