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Update mtdna_backend.py
Browse files- mtdna_backend.py +6 -13
mtdna_backend.py
CHANGED
@@ -236,10 +236,10 @@ def summarize_results(accession, stop_flag=None):
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"Predicted Sample Type":pred_sample or "unknown",
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"Sample Type Explanation":sample_explanation or "unknown",
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"Sources": "\n".join(outputs[key]["source"]) or "No Links",
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"Query_cost": outputs[key]["query_cost"],
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"Time cost": outputs[key]["time_cost"],
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"file_chunk":outputs[key]["file_chunk"],
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"file_all_output":outputs[key]["file_all_output"]
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}
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#row_score.append(row)
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save_rows.append(list(save_row.values()))
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@@ -315,23 +315,16 @@ def summarize_results(accession, stop_flag=None):
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if existing_data:
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df_old = pd.DataFrame(existing_data[1:], columns=existing_data[0])
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else:
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required_columns = [
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"Sample ID", "Actual_country", "Actual_sample_type", "Country Explanation",
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"Match_country", "Match_sample_type", "Predicted Country", "Predicted Sample Type",
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"Query_cost", "Sample Type Explanation", "Sources", "Time cost", "file_chunk", "file_all_output"
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]
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for col in required_columns:
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if col not in df_old.columns:
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df_old[col] = "" # create empty column
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df_old = pd.DataFrame(columns=[
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"Sample ID", "Actual_country", "Actual_sample_type", "Country Explanation",
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"Match_country", "Match_sample_type", "Predicted Country", "Predicted Sample Type",
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"Query_cost", "Sample Type Explanation", "Sources", "Time cost", "file_chunk", "file_all_output"
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])
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# ✅ Index by Sample ID
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df_old.set_index("Sample ID", inplace=True)
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df_new.set_index("Sample ID", inplace=True)
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"Predicted Sample Type":pred_sample or "unknown",
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"Sample Type Explanation":sample_explanation or "unknown",
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"Sources": "\n".join(outputs[key]["source"]) or "No Links",
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"Query_cost": outputs[key]["query_cost"] or "",
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"Time cost": outputs[key]["time_cost"] or "",
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"file_chunk":outputs[key]["file_chunk"] or "",
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"file_all_output":outputs[key]["file_all_output"] or ""
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}
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#row_score.append(row)
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save_rows.append(list(save_row.values()))
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if existing_data:
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df_old = pd.DataFrame(existing_data[1:], columns=existing_data[0])
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else:
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df_old = pd.DataFrame(columns=[
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"Sample ID", "Actual_country", "Actual_sample_type", "Country Explanation",
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"Match_country", "Match_sample_type", "Predicted Country", "Predicted Sample Type",
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"Query_cost", "Sample Type Explanation", "Sources", "Time cost", "file_chunk", "file_all_output"
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])
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# ✅ Index by Sample ID
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df_old.set_index("Sample ID", inplace=True)
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df_new.set_index("Sample ID", inplace=True)
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