Download scripts/review_rows.py from kazeevn/LeMat-Bulk-fixed: direct link, hf CLI and curl.
- Browser
- Download file 665 Bytes
-
https://huggingface.co/datasets/kazeevn/LeMat-Bulk-fixed/resolve/main/scripts/review_rows.py
- Command line
-
hf download hf://datasets/kazeevn/LeMat-Bulk-fixed/scripts/review_rows.py
-
curl -L -o review_rows.py https://huggingface.co/datasets/kazeevn/LeMat-Bulk-fixed/resolve/main/scripts/review_rows.py
665 Bytes
| """Full upstream rows for every MP row under review.""" | |
| import sys, os, glob, pandas as pd, pyarrow as pa, pyarrow.parquet as pq, pyarrow.compute as pc | |
| W=sys.argv[1] | |
| S=os.path.expanduser("~/.cache/huggingface/hub/datasets--LeMaterial--LeMat-Bulk/snapshots/0dc17eea904b860ad7288141e9870f67f8e6bb2c/compatible_pbe") | |
| f=pd.read_parquet(W+"/identify.parquet"); ids=pa.array(f[f.review!=""].immutable_id.tolist()) | |
| t=pa.concat_tables([pq.read_table(fn).filter(pc.is_in(pq.read_table(fn,columns=["immutable_id"]).column("immutable_id"),value_set=ids)) for fn in sorted(glob.glob(S+"/*.parquet"))]) | |
| pq.write_table(t,W+"/lemat_review_rows.parquet"); print("rows",t.num_rows) | |