add log sum of intensities

spectrum_fundamentals/annotation/annotation.py

@@ -1,4 +1,5 @@
 import logging
+import math
 from typing import Dict, List, Optional, Tuple, Union
 
 import numpy as np
@@ -28,11 +29,11 @@ def match_peaks(
     :param unmod_sequence: Unmodified peptide sequence
     :param charge: Precursor charge
     :return: List of matched/annotated peaks
-    :return: float with sum of experimental peaks intensities
+    :return: Float with log10 of sum of labeled experimental peaks intensities
     """
     start_peak = 0
     no_of_peaks = len(peaks_intensity)
-    sum_intensities = sum(peaks_intensity)
+    sum_intensities = 0.0
     max_intensity = 1.0
     row_list = []
     temp_list = []
@@ -71,6 +72,7 @@ def match_peaks(
                         "intensity": peak_intensity,
                     }
                 )
+                sum_intensities += peak_intensity
                 if peak_intensity > max_intensity and fragment_no < seq_len:
                     max_intensity = float(peak_intensity)
             matched_peak = True
@@ -79,7 +81,12 @@ def match_peaks(
     for row in row_list:
         row["intensity"] = float(row["intensity"]) / max_intensity
         temp_list.append(row)
-    return temp_list, sum_intensities
+    if sum_intensities == 0:
+        log_sum_intensities = 0 # todo check ists okay?
+    else:
+        log_sum_intensities = math.log(sum_intensities)
+
+    return temp_list, log_sum_intensities
 
 
 def handle_multiple_matches(
@@ -151,7 +158,7 @@ def annotate_spectra(
             continue
         raw_file_annotations.append(results)
     results_df = pd.DataFrame(raw_file_annotations)
-    results_df.columns = ["INTENSITIES", "MZ", "CALCULATED_MASS", "removed_peaks", "SUM_INTENSITIES"]
+    results_df.columns = ["INTENSITIES", "MZ", "CALCULATED_MASS", "removed_peaks", "LOG_SUM_INTENSITIES"]
     logger.info(f"Removed {results_df['removed_peaks'].describe()} redundant peaks")
 
     return results_df
@@ -247,7 +254,7 @@ def parallel_annotate(
     )
     if not unmod_sequence:
         return None
-    matched_peaks, sum_intensities = match_peaks(
+    matched_peaks, log_sum_intensities = match_peaks(
         fragments_meta_data,
         spectrum[index_columns["INTENSITIES"]],
         spectrum[index_columns["MZ"]],
@@ -258,9 +265,9 @@ def parallel_annotate(
     if len(matched_peaks) == 0:
         intensity = np.full(174, 0.0)
         mass = np.full(174, 0.0)
-        return intensity, mass, calc_mass, 0, sum_intensities
+        return intensity, mass, calc_mass, 0, log_sum_intensities
     matched_peaks, removed_peaks = handle_multiple_matches(matched_peaks)
     intensities, mass = generate_annotation_matrix(
         matched_peaks, unmod_sequence, spectrum[index_columns["PRECURSOR_CHARGE"]]
     )
-    return intensities, mass, calc_mass, removed_peaks, sum_intensities
+    return intensities, mass, calc_mass, removed_peaks, log_sum_intensities

tests/unit_tests/data/spectrum_output.csv

@@ -1,4 +1,4 @@
-,INTENSITIES,MZ,CALCULATED_MASS,removed_peaks,SUM_INTENSITIES
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+,INTENSITIES,MZ,CALCULATED_MASS,removed_peaks,LOG_SUM_INTENSITIES
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tests/unit_tests/test_annotation.py

@@ -27,6 +27,7 @@ def test_annotate_spectra(self):
         spectrum_input["MZ"] = spectrum_input["MZ"].map(lambda mz: np.array(mz))
 
         result = annotation.annotate_spectra(spectrum_input)
+        print(result)
         pd.testing.assert_frame_equal(expected_result, result)
 
     def test_handle_multiple_matches(self):