From c4b3001e1861e0a8294449b39a94c2f3f17bd5d1 Mon Sep 17 00:00:00 2001 From: kmario23 Date: Sun, 20 Oct 2024 15:04:42 +0000 Subject: [PATCH] comply with ruff for download_direct.py --- pdebench/data_download/download_direct.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/pdebench/data_download/download_direct.py b/pdebench/data_download/download_direct.py index 0dc4b39..35393dd 100644 --- a/pdebench/data_download/download_direct.py +++ b/pdebench/data_download/download_direct.py @@ -2,6 +2,7 @@ import argparse import os +from pathlib import Path import pandas as pd from torchvision.datasets.utils import download_url @@ -51,14 +52,13 @@ def parse_metadata(pde_names): ] assert all( - [name.lower() in pde_list for name in pde_names] + name.lower() in pde_list for name in pde_names ), "PDE name not defined." # Filter the files to be downloaded meta_df["PDE"] = meta_df["PDE"].str.lower() - pde_df = meta_df[meta_df["PDE"].isin(pde_names)] - return pde_df + return meta_df[meta_df["PDE"].isin(pde_names)] def download_data(root_folder, pde_name): @@ -70,14 +70,14 @@ def download_data(root_folder, pde_name): pde_name : The name of the PDE for which the data to be downloaded """ - print(f"Downloading data for {pde_name} ...") + # print(f"Downloading data for {pde_name} ...") # Load and parse metadata csv file pde_df = parse_metadata(pde_name) # Iterate filtered dataframe and download the files - for index, row in tqdm(pde_df.iterrows(), total=pde_df.shape[0]): - file_path = os.path.join(root_folder, row["Path"]) + for _, row in tqdm(pde_df.iterrows(), total=pde_df.shape[0]): + file_path = Path(root_folder) / row["Path"] download_url(row["URL"], file_path, row["Filename"], md5=row["MD5"])