| from herbie import FastHerbie |
| from concurrent.futures import ThreadPoolExecutor,as_completed |
| import numpy as np |
| import pandas as pd |
| from make_dataset import get_cropped_images |
| import matplotlib.pyplot as plt |
| import os |
| from datetime import datetime,timedelta |
| import json |
| from tqdm import tqdm |
| import warnings |
| import argparse |
| single_vars = ["msl", "2t", "10u", "10v"] |
| atmos_vars = ["hgtn", "u", "v", "t", "q"] |
| atmos_levels = [5000., 10000., 15000., 20000., |
| 25000., 30000., 40000., 50000., |
| 60000., 70000., 85000., 92500., |
| 100000.] |
|
|
| var_name = single_vars + [f"{v}_{int(p/100)}" for v in atmos_vars for p in atmos_levels] |
| var_name = np.array(var_name) |
| herbie_maps = {'hgtn': 'HGT', |
| 'u': 'UGRD', |
| 'v': 'VGRD', |
| 't': 'TMP', |
| 'q': 'SPFH', |
| 'msl': 'MSLMA:mean sea level', |
| '2t': 'TMP:2 m above', |
| '10u': 'UGRD:10 m above', |
| '10v': 'VGRD:10 m above'} |
| new_var_names = [] |
| for var in var_name: |
| if var in ['msl','2t','10u','10v']: |
| new_var_names.append(herbie_maps[var]) |
| else: |
| var,level_ = var.split('_') |
| searchString = f"{herbie_maps[var]}:{level_} mb" |
| new_var_names.append(searchString) |
| |
| def load_variable(H_pre,searchString): |
| return H_pre.xarray(searchString).to_array().values[0] |
|
|
| def multi_core(H_pre,var_name,max_workers=16): |
| results = {} |
| with ThreadPoolExecutor(max_workers=max_workers) as executor: |
| future_to_variable = {executor.submit(load_variable, H_pre, var): var for var in var_name} |
| for future in as_completed(future_to_variable): |
| variable = future_to_variable[future] |
| try: |
| data = future.result() |
| results[variable] = data |
| except Exception as exc: |
| print(f'{variable} generated an exception: {exc}') |
| return results |
|
|
|
|
| ''' |
| def load_files_herbie(filenames,new_var_names=new_var_names): |
| #### multi core version |
| filenames = [f.strftime("%Y%m%d%H")+'.npy' for f in filenames] |
| files = {} |
| #filenames = ['2019051108.npy', '2019051109.npy', '2019051110.npy'] |
| if len(filenames)==0: |
| return files |
| for f in filenames: |
| date_str = f.split('.')[0] |
| files[date_str] = np.zeros([1,69,1059,1799]) |
| date = pd.to_datetime(date_str, format='%Y%m%d%H') |
| H_pre = FastHerbie([date], model="hrrr", fxx=[1],product='prs',max_threads=50) |
| pre_array = multi_core(H_pre,new_var_names) |
| for i,var in enumerate(new_var_names): |
| files[date_str][0,i] = pre_array[var] |
| return files |
| ''' |
|
|
| def load_files_herbie(filenames,new_var_names=new_var_names): |
| |
| filenames = [f.strftime("%Y%m%d%H")+'.npy' for f in filenames] |
| files = {} |
| |
| if len(filenames)==0: |
| return files |
| for f in filenames: |
| date_str = f.split('.')[0] |
| files[date_str] = np.zeros([1,69,1059,1799]) |
| date = pd.to_datetime(date_str, format='%Y%m%d%H') |
| H_pre = FastHerbie([date], model="hrrr", fxx=[1],product='prs',max_threads=50) |
| |
| for i,var in enumerate(new_var_names): |
| files[date_str][0,i] = H_pre.xarray(var).to_array().values[0] |
| return files |
|
|
|
|
| if __name__=='__main__': |
| parser = argparse.ArgumentParser(description='Process an integer input.') |
|
|
| parser.add_argument('begin_datetime', type=str, help='The begin datetime in format YYYYMMDD') |
| parser.add_argument('end_datetime', type=str, help='The end datetime in format YYYYMMDD') |
| |
| args = parser.parse_args() |
| df = pd.read_csv('/kmsw/extreme_dataset/data_all202007_info.csv',parse_dates=['begin_time','end_time']) |
| |
| save_dir = "/kmsw/extreme_dataset/202007_nwp" |
| |
| df = df[(df.begin_time>=args.begin_datetime)&(df.begin_time<args.end_datetime)] |
| |
| |
| after = 2 |
| print('single core version') |
| for idx, row in tqdm(df.iterrows(),total=len(df)): |
| datetimes_all = pd.date_range(start=row['begin_time'],end=row['end_time'], freq='h') |
| x_min,y_min, x_max,y_max = [int(i) for i in row['bounding_box'].split('_')] |
| gap = 12 |
| for i in range(0,len(datetimes_all),gap): |
| datetimes = datetimes_all[i:i+gap] |
| need_save = False |
| for timestamp in datetimes: |
| save_name = '_'.join([timestamp.strftime("%Y%m%d%H"),'nwp',row['type'],row['bounding_box']]) + '.npz' |
| if not os.path.exists(os.path.join(save_dir,save_name)): |
| need_save = True |
|
|
| if need_save: |
| imgs = load_files_herbie(datetimes,new_var_names=new_var_names) |
| |
| for timestamp in datetimes: |
| save_name = '_'.join([timestamp.strftime("%Y%m%d%H"),'nwp',row['type'],row['bounding_box']]) + '.npz' |
| if not os.path.exists(os.path.join(save_dir,save_name)): |
| cropped_images,masks = get_cropped_images(imgs,[timestamp],x_min, x_max, y_min, y_max) |
| cropped_images = np.stack(cropped_images) |
| |
| masks = np.concatenate(masks,axis=0) |
| |
| np.savez(os.path.join(save_dir,save_name),targets=cropped_images,masks=masks) |
| else: |
| |
| 1 == 1 |
|
|
|
|
| |
