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4 changes: 3 additions & 1 deletion .gitignore
Original file line number Diff line number Diff line change
@@ -1,3 +1,5 @@
.gitignore
/venv
/.pytest_cache
/.pytest_cache
__pycache__/
*.pyc
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15 changes: 8 additions & 7 deletions processing.py
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Expand Up @@ -100,15 +100,16 @@ def map_psms_to_spectra(spectra: List[Dict], psm_df: pd.DataFrame) -> pd.DataFra
# Original: Multiple apply calls (4x iteration over full dataset)

# Convert matched Series to list, replacing NaNs with empty dicts for DataFrame construction
specs_list = [x if isinstance(x, dict) else {} for x in matched_spec_series]
specs_df = pd.DataFrame(specs_list)
# ⚡ OPTIMIZATION: Use tolist() for faster iteration and pre-allocate dict structure
empty_spec = {'title': None, 'mz_array': None, 'intensity_array': None, 'pepmass': None}
specs_list = [x if isinstance(x, dict) else empty_spec for x in matched_spec_series.tolist()]

# ⚡ OPTIMIZATION: Explicit columns prevents schema inference (~2x speedup)
# Note: We only include columns that are actually used in the final 'mappings' DataFrame.
# Any extra metadata in the input spectra is intentionally dropped here.
specs_df = pd.DataFrame(specs_list, columns=['title', 'mz_array', 'intensity_array', 'pepmass'])
specs_df.index = psm_df.index # Align index with original DataFrame

# Ensure required columns exist (if no spectra matched or mock data missing keys)
for col in ['title', 'mz_array', 'intensity_array', 'pepmass']:
if col not in specs_df.columns:
specs_df[col] = None

mappings = pd.DataFrame({
'psm_index': psm_df.index,
'sequence': psm_df['sequence'].astype(str),
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