Internal pipeline 0.4.4

spectrum_fundamentals/metrics/percolator.py

@@ -64,9 +64,6 @@ def __init__(
         self.all_features_flag = all_features_flag
         self.regression_method = regression_method
         self.fdr_cutoff = fdr_cutoff
-        self.internal = True # TODO: Get this parameter from the caller
-
-        self._resolve_percolator_compatibility(percolator_version)
         super().__init__(pred_intensities, true_intensities, mz)
 
     @staticmethod
@@ -159,29 +156,6 @@ def get_aligned_predicted_retention_times(
 
         return aligned_rts_predicted
 
-    @staticmethod
-    def get_scannr(metadata_subset: Union[pd.Series, Tuple[str, int]]) -> int:
-        """
-        Creates a hash of the raw_file and scan number to use as a unique scan number in percolator.
-
-        :param metadata_subset: tuple of (raw_file, scan_number)
-        :return: hashed unique id
-        """
-        raw_file, scan_number = metadata_subset
-        s = f"{raw_file}{scan_number}".encode()
-        return int(hashlib.sha224(s).hexdigest()[:6], 16)
-
-    @staticmethod
-    def get_scannr_internal(metadata_subset: Union[pd.Series, Tuple[str, int]]) -> int:
-        """
-        Creates a hash of the raw_file and scan number to use as a unique scan number in percolator.
-
-        :param metadata_subset: tuple of (raw_file, scan_number)
-        :return: hashed unique id
-        """
-        scan_event_number = metadata_subset
-        return int(scan_event_number)
-
     @staticmethod
     def get_delta_score(scores_df: pd.DataFrame, scoring_feature: str) -> np.ndarray:
         """
@@ -296,11 +270,7 @@ def add_percolator_metadata_columns(self):
             spec_id_cols.append("SCAN_EVENT_NUMBER")
         self.metrics_val["SpecId"] = self.metadata[spec_id_cols].apply(Percolator.get_specid, axis=1)
         self.metrics_val["Label"] = self.target_decoy_labels
-
-        if self.internal:
-            self.metrics_val["ScanNr"] = self.metadata[["SCAN_EVENT_NUMBER"]].apply(Percolator.get_scannr_internal, axis=1)
-        else:
-            self.metrics_val["ScanNr"] = self.metadata["SCAN_NUMBER"]
+        self.metrics_val["ScanNr"] = self.metadata["SCAN_EVENT_NUMBER"]
         self.metrics_val["filename"] = self.metadata["RAW_FILE"]
         self.metrics_val["Peptide"] = self.metadata["MODIFIED_SEQUENCE"].apply(lambda x: "_." + x + "._")
 
@@ -510,4 +480,4 @@ def spline(knots: int, x: np.ndarray, y: np.ndarray):
 def logistic(x: Union[pd.Series, np.ndarray], a: float, b: float, c: float, d: float):
     """Calculates logistic regression function."""
     exponent = np.clip(-c * (x - d), -700, 700)  # make this stable, i.e. avoid 0.0 or inf
-    return a / (1.0 + np.exp(exponent)) + b
+    return a / (1.0 + np.exp(exponent)) + b