Updated loading logic

AstroM3Dataset.py

@@ -5,10 +5,8 @@ import pandas as pd
 import numpy as np
 import json
 from astropy.io import fits
-from tqdm import tqdm
 
 from utils.parallelzipfile import ParallelZipFile as ZipFile
-from datasets.utils.tqdm import enable_progress_bars, disable_progress_bars
 
 _DESCRIPTION = (
     "AstroM3 is a time-series astronomy dataset containing photometry, spectra, "
@@ -39,8 +37,6 @@ _CITATION = """
 class AstroM3Dataset(datasets.GeneratorBasedBuilder):
     """Hugging Face dataset for AstroM3 with configurable subsets and seeds."""
 
-    # HF_DATASETS_DISABLE_PROGRESS_BARS = True
-
     DEFAULT_CONFIG_NAME = "full_42"
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(name=f"{sub}_{seed}", version=_VERSION, data_dir=None)
@@ -118,25 +114,21 @@ class AstroM3Dataset(datasets.GeneratorBasedBuilder):
             "info": f"{_URL}/splits/{sub}/{seed}/info.json",
         }
         extracted_path = dl_manager.download(urls)
-        # print("Downloaded train.csv val.csv test.csv info.json")
 
-        # Load all spectra files
-        spectra = {}
         df1 = pd.read_csv(extracted_path["train"])
         df2 = pd.read_csv(extracted_path["val"])
         df3 = pd.read_csv(extracted_path["test"])
         df_combined = pd.concat([df1, df2, df3], ignore_index=True)
 
+        # Load all spectra files
+        spectra_urls = {}
         for _, row in df_combined.iterrows():
-            spectra_url = f"{_URL}/spectra/{row['target']}/{row['spec_filename']}"
-            spectra[row["spec_filename"]] = dl_manager.download(spectra_url)
-
-        # print("Downloaded spectra files")
+            spectra_urls[row["spec_filename"]] = f"{_URL}/spectra/{row['target']}/{row['spec_filename']}"
+        spectra = dl_manager.download(spectra_urls)
 
         # Load photometry and init reader
         photometry_path = dl_manager.download(f"{_URL}/photometry.zip")
         self.reader_v = ZipFile(photometry_path)
-        # print("Downloaded photometry")
 
         return [
             datasets.SplitGenerator(