minimal aggregation of value sets

src/nmdc_submission_schema/scripts/aggregate_value_sets.py

@@ -0,0 +1,56 @@
+import os
+import pandas as pd
+import click
+
+
+@click.command()
+@click.option(
+    '--file', '-f',
+    type=click.Path(exists=True),
+    multiple=True,
+    help="Paths to the input TSV files. This option can be used multiple times to specify multiple files."
+)
+@click.option(
+    '--output', '-o',
+    type=click.Path(),
+    default="Combined_Environmental_Context_Data.tsv",
+    help="Path to the output TSV file. Defaults to 'Combined_Environmental_Context_Data.tsv'."
+)
+def combine_context_files(file, output):
+    """Combine environmental context files into a single TSV file with Extension, Context Field, ENVO Class CURIE, and Label columns."""
+    combined_data = []
+
+    for file_path in file:
+        # Parse extension and context field from the filename
+        filename = os.path.basename(file_path)
+        parts = filename.split("_")
+        extension = parts[3].capitalize()  # e.g., "soil" or "water"
+
+        # Correctly map context fields to their full names
+        if "broad" in filename:
+            context_field = "env_broad_scale"
+        elif "local" in filename:
+            context_field = "env_local_scale"
+        elif "medium" in filename:
+            context_field = "env_medium"
+        else:
+            raise ValueError(f"Could not determine context field from filename: {filename}")
+
+        # Load the file into a DataFrame
+        df = pd.read_csv(file_path, sep="\t")
+
+        # Extract CURIEs and labels from the "id" and "label" columns
+        if "id" in df.columns and "label" in df.columns:
+            for curie, label in zip(df["id"].dropna(), df["label"].dropna()):
+                combined_data.append((extension, context_field, curie, label))
+
+    # Convert the combined data into a DataFrame
+    final_df = pd.DataFrame(combined_data, columns=["extension", "env_context_field", "class_curie", "label"])
+
+    # Save the DataFrame to a TSV file
+    final_df.to_csv(output, sep="\t", index=False)
+    click.echo(f"Combined data saved to: {output}")
+
+
+if __name__ == "__main__":
+    combine_context_files()