Move to peptdeep

alpharaw/dia/normal_dia.py

@@ -1,87 +1,5 @@
-import typing
-from collections import defaultdict
-
-import numpy as np
-from alphatims.alphatims_wrapper import convert_to_alphatims, remove_unused_peaks
-from alphatims.bruker import TimsTOF
-
-from alpharaw.ms_data_base import MSData_Base
-
-
 class NormalDIAGrouper:
-    def __init__(self, ms_data: MSData_Base):
-        self.ms_data = ms_data
-        self.ms_data.spectrum_df["dia_group"] = ms_data.spectrum_df.precursor_mz.astype(
-            int
+    def __init__(self, *args, **kwargs):
+        raise DeprecationWarning(
+            "NormalDIAGrouper has been moved to alphapeptdeep and will be removed from alpharaw in a future version."
         )
-
-        self.dia_group_dfs = self.ms_data.spectrum_df.groupby("dia_group")
-        self.dia_isolation_dict = {}
-        for dia_group, df in self.dia_group_dfs:
-            if dia_group == -1:
-                continue
-            self.dia_isolation_dict[dia_group] = (
-                df.isolation_lower_mz.values[0],
-                df.isolation_upper_mz.values[0],
-            )
-        self.dia_groups = np.sort(list(self.dia_isolation_dict.keys()))
-
-    def get_ms_data_for_a_group(
-        self,
-        dia_group: int = -1,
-        return_alpharaw_data: bool = True,
-        return_alphatims_data: bool = True,
-    ) -> typing.Union[MSData_Base, TimsTOF, typing.Tuple[MSData_Base, TimsTOF]]:
-        """Get compressed MS data for isolation window `dia_group`.
-
-        Args:
-            dia_group (int, optional): The DIA group, -1 means ms1. Defaults to -1.
-            return_alphatims_data (bool, optional): If return `MSData_Base`. Defaults to True
-            return_alphatims_data (bool, optional): If return alphatims object. Defaults to True.
-
-        Returns:
-            MSData_Base: Compressed MS data, if `return_alpharaw_data==True`
-            TimsTOF: Alphatims object for the window, if `return_alphatims_data==True`
-        """
-
-        spec_df = self.dia_group_dfs.get_group(dia_group)
-
-        if return_alphatims_data:
-            ms_data, ms_tims = convert_to_alphatims(
-                spec_df, self.ms_data.peak_df, dda=False
-            )
-            if return_alpharaw_data:
-                return ms_data, ms_tims
-            else:
-                return ms_tims
-        else:
-            ms_data = MSData_Base()
-
-            spec_df, peak_df = remove_unused_peaks(spec_df, self.ms_data.peak_df)
-
-            ms_data.spectrum_df = spec_df
-            ms_data.peak_df = peak_df
-            return ms_data
-
-    def assign_dia_groups(
-        self, precursor_mzs
-    ) -> typing.DefaultDict[typing.List, typing.List]:
-        dia_precursor_groups = defaultdict(list)
-        for i, mz in enumerate(precursor_mzs):
-            i_group = np.searchsorted(self.dia_groups, int(mz))
-            if i_group == 0:
-                i_group = 1
-            elif i_group == len(self.dia_groups):
-                i_group -= 1
-            dia_group = self.dia_groups[i_group]
-            if dia_group in self.dia_isolation_dict:
-                isolation_lower, isolation_upper = self.dia_isolation_dict[dia_group]
-                if mz >= isolation_lower and mz <= isolation_upper:
-                    dia_precursor_groups[dia_group].append(i)
-                    continue
-            dia_group = self.dia_groups[i_group - 1]
-            if dia_group in self.dia_isolation_dict:
-                isolation_lower, isolation_upper = self.dia_isolation_dict[dia_group]
-                if mz >= isolation_lower and mz <= isolation_upper:
-                    dia_precursor_groups[dia_group].append(i)
-        return dia_precursor_groups

alpharaw/match/match_utils.py

@@ -82,6 +82,7 @@ def match_batch_spec(
     return matched_mzs, matched_intens
 
 
+# TODO move to peptdeep?
 @numba.njit
 def match_closest_peaks(
     spec_mzs: np.ndarray,
@@ -133,6 +134,7 @@ def match_closest_peaks(
     return ret_indices.reshape(query_mzs.shape)
 
 
+# TODO move to peptdeep?
 @numba.njit
 def match_highest_peaks(
     spec_mzs: np.ndarray,