added example, cleaned content

README.md

@@ -39,11 +39,9 @@ extra_gated_fields:
   - [Changelog](#changelog)
   - [Dataset Summary](#dataset-summary)
   - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
 - [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
   - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
+  - [Usage Example](#usage-example)
 - [Dataset Creation](#dataset-creation)
 - [Considerations for Using the Data](#considerations-for-using-the-data)
 - [Additional Information](#additional-information)
@@ -85,6 +83,74 @@ As na example of aggregation operation on The Stack, the image above shows conce
 
 The meta data will allow you to reconstruct repository directory structures. For this, for each repository form `ri` tabele it is needed to take all its files from `fi` table, find them in The Stack by file's `hexsha` and  save those files' content under its path for a repository from `fi` table. For speed it is preferable to index The Stack by hexsha first.
 
+### Usage Example
+
+Restore folder structure for python files in numpy repository
+```
+import datasets
+from pathlib import Path
+from tqdm.auto import tqdm
+import pandas as pd
+
+# assuming metadata is cloned into the local folder /data/hf_repos/the-stack-metadata
+#          the stack is cloned into the local folder /data/hf_repos/the-stack-v1.1
+#          destination folder is in  /repo_workdir/numpy_restored
+the_stack_meta_path = Path('/data/hf_repos/the-stack-metadata')
+the_stack_path = Path('/data/hf_repos/the-stack-v1.1')
+repo_dst_root = Path('/repo_workdir/numpy_restored')
+repo_name = 'numpy/numpy'
+
+# Get bucket with numpy repo info 
+# meta_bucket_path = None
+#for fn in tqdm(list((the_stack_meta_path/'data').glob('*/ri.parquet'))):
+#    df = pd.read_parquet(fn)
+#    if any(df['name'] == repo_name):
+#        meta_bucket_path = fn
+#        break
+meta_bucket_path = the_stack_meta_path / 'data/255_944'
+
+
+# Get repository id from repo name
+ri_id = pd.read_parquet(
+    meta_bucket_path / 'ri.parquet'
+).query(
+    f'`name` == "{repo_name}"'
+)['id'].to_list()[0]
+
+# Get files information for the reopository
+files_info = pd.read_parquet(
+    meta_bucket_path / 'fi.parquet'
+).query(
+    f'`ri_id` == {ri_id} and `size` != 0 and `is_deleted` == False'
+)
+
+# Convert DF with files information to a dictionary by language and then file hexsha
+#   there can be more than one file with the same  hexsha in the repo so we gather
+#   all instances per unique hexsha
+files_info_dict = {
+    k: v[['hexsha', 'path']].groupby('hexsha').apply(lambda x: list(x['path'])).to_dict()
+    for k, v in files_info.groupby('lang_ex')
+}
+
+# Load Python part of The Stack
+ds = datasets.load_dataset(
+    str(the_stack_path/'data/python'),
+    num_proc=10, ignore_verifications=True
+)
+
+# Save file content of the python files in the numpy reposirotry in their appropriate locations
+def save_file_content(example, files_info_dict, repo_dst_root):
+    if example['hexsha'] in  files_info_dict:
+        for el in files_info_dict[example['hexsha']]:
+            path = repo_dst_root / el
+            path.parent.mkdir(parents=True, exist_ok=True)
+            path.write_text(example['content'])
+ds.map(
+    save_file_content,
+    fn_kwargs={'files_info_dict': files_info_dict['Python'], 'repo_dst_root': repo_dst_root},
+    num_proc=10
+)
+```
 
 ## Dataset Creation