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src/01_process_csv.py

@@ -1,15 +1,15 @@
 #!/usr/bin/env python3
 """
-Clean FireProtDB 2.0 CSV into ML-ready canonical table.
+Clean FireProtDB 2.0 CSV into ML-ready table with some reformatting.
 
 Outputs:
 - A canonical row-per-experiment table with parsed mutation fields and normalized columns.
 - Optionally writes Parquet for speed.
 
 Usage:
-  python scripts/clean_fireprotdb.py \
-    --input fireprotdb_2_0.csv \
-    --output data/fireprotdb_clean.parquet
+  python 01_process_csv.py \
+    --input ../data/fireprotdb_20251015_16.csv \
+    --output ../data/fireprotdb_clean.parquet
 
 Notes:
 - This script is conservative: it does NOT impute missing ddg/dtm.
@@ -178,6 +178,11 @@ def clean_numeric_columns(df: pd.DataFrame) -> pd.DataFrame:
             df[col.lower()] = df[col].map(to_float)
         else:
             df[col.lower()] = pd.NA
+    # fitness
+    if "DOMAINOME_FITNESS" in df.columns:
+        df['fitness'] = df["DOMAINOME_FITNESS"].map(to_float)
+    else:
+        df['fitness'] = pd.NA
 
     # pH
     if "PH" in df.columns:
@@ -230,6 +235,7 @@ def select_and_rename(df: pd.DataFrame) -> pd.DataFrame:
         "SOURCE_DATASET": "source_dataset",
         "REFERENCING_DATASET": "referencing_dataset",
         "WWPDB": "wwpdb_raw",
+
     }
 
     out = pd.DataFrame()
@@ -239,7 +245,7 @@ def select_and_rename(df: pd.DataFrame) -> pd.DataFrame:
     # numeric & normalized categorical fields added earlier
     extra_cols = [
         "ddg", "domainome_ddg", "dg", "dh", "dhvh",
-        "tm", "dtm", "exp_temperature",
+        "tm", "dtm", "exp_temperature", "fitness",
         "ph",
         "buffer_norm", "method_norm", "measure_norm",
         "stabilizing",
@@ -257,8 +263,8 @@ def select_and_rename(df: pd.DataFrame) -> pd.DataFrame:
 
 def main():
     ap = argparse.ArgumentParser()
-    ap.add_argument("--input", required=True, help="Path to raw FireProtDB 2.0 CSV")
-    ap.add_argument("--output", required=True, help="Path to output .parquet or .csv")
+    ap.add_argument("--input", help="Path to raw FireProtDB 2.0 CSV", default='../data/fireprotdb_20251015-164116.csv')
+    ap.add_argument("--output", help="Path to output .parquet or .csv", default='../data/fireprotdb_cleaned.parquet')
     ap.add_argument("--min_seq_len", type=int, default=1, help="Drop sequences shorter than this")
     ap.add_argument("--drop_no_label", action="store_true", help="Drop rows with neither ddg nor dtm")
     args = ap.parse_args()

src/02_add_uniprot_sequences.py

@@ -1,15 +1,18 @@
 #!/usr/bin/env python3
 """
-Add UniProt sequences to FireProtDB canonical parquet.
+Add UniProt sequences to FireProtDB parquet/csv.
 
-Input:  data/fireprotdb_cleaned.parquet
-Output: data/fireprotdb_with_sequences.parquet
-Cache:  data/uniprot_cache.tsv  (acc \t sequence)
+Outputs:
+- Parquet file containing a newly appended sequence column
 
-Features:
-- Caches sequences locally (resume-friendly)
-- Uses UniProt REST FASTA endpoint for single accessions
-- Verifies sequence length against FireProtDB SEQUENCE_LENGTH
+Usage:
+    python 02_add_uniprot_sequences.py \
+        --input ../data/fireprotdb_cleaned.parquet \
+        --output ../data/fireprotdb_with_sequences.parquet \
+        --cache ../data/uniprot_cache.tsv
+
+Notes:
+- This script generates a cache file, which can be used to restart failed or paused runs.
 """
 
 from __future__ import annotations