add one more peak picker that might be more robust

pyproject.toml

@@ -61,6 +61,7 @@ dev = [
     "tifffile>=2024.2.12",
     "xmltodict",
     "licensecheck",
+    "ms-peak-picker"
 ]
 
 # potentially relevant in the future again:

src/depiction/spectrum/peak_picking/ms_peak_picker_wrapper.py

@@ -0,0 +1,42 @@
+# TODO better name
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import TYPE_CHECKING
+
+import loguru
+import ms_peak_picker
+import numpy as np
+import scipy
+
+from depiction.spectrum.peak_picking.basic_peak_picker import BasicPeakPicker
+
+if TYPE_CHECKING:
+    from numpy.typing import NDArray
+    from depiction.spectrum.peak_filtering import PeakFilteringType
+
+
+@dataclass
+class MSPeakPicker:
+    """Interfaces to the ms-peak-picker Python package.
+    TODO proper description and better name
+    """
+
+    fit_type: str = "quadratic"
+    peak_filtering: PeakFilteringType | None = None
+
+    def pick_peaks(self, mz_arr: NDArray[float], int_arr: NDArray[float]) -> tuple[NDArray[float], NDArray[float]]:
+        peak_list = ms_peak_picker.pick_peaks(mz_arr, int_arr, fit_type=self.fit_type)
+        peak_mz = np.array([peak.mz for peak in peak_list])
+        peak_int = np.array([peak.intensity for peak in peak_list])
+
+        if self.peak_filtering is not None:
+            peak_mz, peak_int = self.peak_filtering.filter_peaks(
+                spectrum_mz_arr=mz_arr,
+                spectrum_int_arr=int_arr,
+                peak_mz_arr=peak_mz,
+                peak_int_arr=peak_int,
+            )
+
+        return peak_mz, peak_int

src/depiction_targeted_preproc/pipeline_config/model.py

@@ -28,7 +28,15 @@ class PeakPickerBasicInterpolated(BaseModel):
     #  but postpone for later)
 
 
-PeakPicker = Annotated[None | PeakPickerBasicInterpolated, Field(discriminator="peak_picker_type")]
+class PeakPickerMSPeakPicker(BaseModel):
+    peak_picker_type: Literal["MSPeakPicker"]
+    fit_type: Literal["quadratic"] = "quadratic"
+
+
+PeakPicker = Annotated[
+    None | PeakPickerBasicInterpolated | PeakPickerMSPeakPicker,
+    Field(discriminator="peak_picker_type")
+]
 
 
 class CalibrationRegressShift(BaseModel):

src/depiction_targeted_preproc/workflow/proc/pick_peaks.py

@@ -6,11 +6,11 @@
 from loguru import logger
 
 from depiction.parallel_ops import ParallelConfig
+from depiction.persistence import ImzmlModeEnum, ImzmlWriteFile, ImzmlReadFile
 from depiction.spectrum.peak_filtering import FilterNHighestIntensityPartitioned
 from depiction.spectrum.peak_picking import BasicInterpolatedPeakPicker
-from depiction.persistence import ImzmlModeEnum, ImzmlWriteFile, ImzmlReadFile
+from depiction.spectrum.peak_picking.ms_peak_picker_wrapper import MSPeakPicker
 from depiction.tools.pick_peaks import PickPeaks
-
 from depiction_targeted_preproc.pipeline_config import model
 from depiction_targeted_preproc.pipeline_config.model import PipelineParameters
 
@@ -21,30 +21,34 @@ def proc_pick_peaks(
     output_imzml_path: Annotated[Path, typer.Option()],
 ) -> None:
     config = PipelineParameters.parse_yaml(config_path)
+
+    # TODO configurable
+    peak_filtering = FilterNHighestIntensityPartitioned(max_count=200, n_partitions=8),
+    parallel_config = ParallelConfig(n_jobs=config.n_jobs, task_size=None)
+
     match config.peak_picker:
         case None:
             logger.info("Peak picking is deactivated")
             shutil.copy(input_imzml_path, output_imzml_path)
             shutil.copy(input_imzml_path.with_suffix(".ibd"), output_imzml_path.with_suffix(".ibd"))
+            return
         case model.PeakPickerBasicInterpolated() as peak_picker_config:
             peak_picker = BasicInterpolatedPeakPicker(
                 min_prominence=peak_picker_config.min_prominence,
                 min_distance=peak_picker_config.min_distance,
                 min_distance_unit=peak_picker_config.min_distance_unit,
-                # TODO configurable
-                peak_filtering=FilterNHighestIntensityPartitioned(max_count=200, n_partitions=8),
-            )
-            parallel_config = ParallelConfig(n_jobs=config.n_jobs, task_size=None)
-            pick_peaks = PickPeaks(
-                peak_picker=peak_picker,
-                parallel_config=parallel_config,
+                peak_filtering=peak_filtering,
             )
-            read_file = ImzmlReadFile(input_imzml_path)
-            write_file = ImzmlWriteFile(output_imzml_path, imzml_mode=ImzmlModeEnum.PROCESSED)
-            pick_peaks.evaluate_file(read_file, write_file)
+        case model.PeakPickerMSPeakPicker() as peak_picker_config:
+            peak_picker = MSPeakPicker(fit_type=peak_picker_config.fit_type, peak_filtering=peak_filtering)
         case _:
             raise ValueError(f"Unsupported peak picker type: {config.peak_picker.peak_picker_type}")
 
+    pick_peaks = PickPeaks(peak_picker=peak_picker, parallel_config=parallel_config)
+    read_file = ImzmlReadFile(input_imzml_path)
+    write_file = ImzmlWriteFile(output_imzml_path, imzml_mode=ImzmlModeEnum.PROCESSED)
+    pick_peaks.evaluate_file(read_file, write_file)
+
 
 def main() -> None:
     typer.run(proc_pick_peaks)