⚡ Bolt: Streaming File Loading Optimization

.gitignore

@@ -1,3 +1,4 @@
 .gitignore
 /venv
-/.pytest_cache
+/.pytest_cache
+__pycache__/

.jules/bolt.md

@@ -1,3 +1,7 @@
 ## 2024-05-23 - [Regex Pre-compilation in Loops]
 **Learning:** Pre-compiling regular expressions (`re.compile`) at the module level provides a significant performance boost (measured ~1.8x speedup) when the regex is used inside a tight loop or a pandas `apply` function, compared to compiling it repeatedly or implicitly inside the loop. Vectorized string operations in Pandas are usually faster, but in complex logic cases (multiple prioritized regex groups + fallback logic), a simple pre-compiled regex with `apply` can sometimes be cleaner and sufficiently fast, or even faster if the vectorized approach requires multiple passes or expensive intermediate structures.
 **Action:** Always check for regex usage in loops or `apply` calls. If found, refactor to use module-level pre-compiled patterns. When considering vectorization, benchmark against the optimized loop version, as the overhead of complex vectorization might outweigh the benefits for moderate dataset sizes.
+
+## 2025-01-29 - [Streaming File Decoding with TextIOWrapper]
+**Learning:** `streamlit.UploadedFile` is a binary stream (`io.BytesIO` subclass). Reading it fully with `.read().decode()` to create a string for `io.StringIO` doubles memory usage (bytes + string) and is O(N) in allocation. Wrapping the binary stream directly with `io.TextIOWrapper` allows streaming decoding, which is compatible with `pyteomics` parsers (MGF and mzTab) and significantly reduces memory footprint for large files.
+**Action:** When handling `streamlit.UploadedFile` (or any binary stream) for text-based parsers, prefer `io.TextIOWrapper(file, encoding='utf-8')` over `io.StringIO(file.read().decode('utf-8'))`. Ensure the downstream parser accepts a file-like object (and `use_index=False` for MGF if needed).

app.py

@@ -33,8 +33,9 @@ def run_streamlit_app():
     if mgf_file and mztab_file:
         # Decode uploaded file contents (Streamlit files are bytes by default)
         # Use StringIO to create file-like objects for pyteomics parsers
-        spectra = load_mgf(io.StringIO(mgf_file.read().decode('utf-8')))
-        psm_df = load_mztab(io.StringIO(mztab_file.read().decode('utf-8')))
+        # ⚡ OPTIMIZATION: Use io.TextIOWrapper to stream decoding instead of loading whole file into memory
+        spectra = load_mgf(io.TextIOWrapper(mgf_file, encoding='utf-8'))
+        psm_df = load_mztab(io.TextIOWrapper(mztab_file, encoding='utf-8'))
 
         # Create mappings between PSMs and spectra
         mapped = map_psms_to_spectra(spectra, psm_df)

tests/test_streaming_io.py

@@ -0,0 +1,58 @@
+
+import io
+import unittest
+from pyteomics import mgf, mztab
+import pandas as pd
+
+class TestStreamingIO(unittest.TestCase):
+    def test_mgf_streaming(self):
+        # Create a mock MGF content as bytes
+        mgf_content = b"""BEGIN IONS
+TITLE=Spectrum 1
+PEPMASS=1000.0
+CHARGE=2+
+100.0 1000.0
+200.0 500.0
+END IONS
+"""
+        # Wrap in BytesIO to simulate Streamlit's UploadedFile
+        binary_stream = io.BytesIO(mgf_content)
+
+        # Wrap in TextIOWrapper for streaming decoding
+        text_stream = io.TextIOWrapper(binary_stream, encoding='utf-8')
+
+        # Verify pyteomics can read from it
+        with mgf.read(text_stream, use_index=False) as reader:
+            spectra = list(reader)
+
+        self.assertEqual(len(spectra), 1)
+        self.assertEqual(spectra[0]['params']['title'], 'Spectrum 1')
+
+        # Check if underlying stream is closed
+        # TextIOWrapper might define closed property
+        self.assertFalse(binary_stream.closed, "Binary stream should not be closed implicitly if possible, but strict ownership might vary")
+
+    def test_mztab_streaming(self):
+        # Create a mock mzTab content as bytes
+        mztab_content = b"""MTD\tmzTab-version\t1.0.0
+MTD\tmzTab-mode\tSummary
+MTD\tmzTab-type\tIdentification
+PSH\tsequence\tPSM_ID\taccession\tunique\tdatabase\tdatabase_version\tsearch_engine\tsearch_engine_score[1]\tmodifications\tretention_time\tcharge\texp_mass_to_charge\tcalc_mass_to_charge\tspectra_ref\tpre\tpost\tstart\tend\topt_global_cv_MS:1002217_decoy_peptide\topt_global_cv_MS:1000889_peptidoform_sequence\topt_global_spec_evalue
+PSM\tPEPTIDE\t1\tACC\t0\tDB\t1.0\tSE\t0.99\tnull\t100.0\t2\t1000.0\t1000.0\tindex=1\t-\t-\t1\t10\t0\tPEPTIDE\t0.01
+"""
+        # Wrap in BytesIO to simulate Streamlit's UploadedFile
+        binary_stream = io.BytesIO(mztab_content)
+
+        # Wrap in TextIOWrapper for streaming decoding
+        text_stream = io.TextIOWrapper(binary_stream, encoding='utf-8')
+
+        # Verify pyteomics can read from it
+        # pyteomics.mztab.MzTab reads the whole file, but it should accept a file-like object
+        tab = mztab.MzTab(text_stream)
+        psm_df = pd.DataFrame(tab['psm'])
+
+        self.assertEqual(len(psm_df), 1)
+        self.assertEqual(psm_df.iloc[0]['sequence'], 'PEPTIDE')
+
+if __name__ == '__main__':
+    unittest.main()