update sorting models

src/spikeinterface_pipelines/pipeline.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 from pathlib import Path
-import re
 from typing import Tuple
 import spikeinterface as si
 
@@ -19,7 +18,7 @@ def run_pipeline(
     results_folder: Path | str = Path("./results/"),
     job_kwargs: JobKwargs | dict = JobKwargs(),
     preprocessing_params: PreprocessingParams | dict = PreprocessingParams(),
-    spikesorting_params: SpikeSortingParams | dict = SpikeSortingParams(),
+    spikesorting_params: SpikeSortingParams | dict = dict(),
     postprocessing_params: PostprocessingParams | dict = PostprocessingParams(),
     curation_params: CurationParams | dict = CurationParams(),
     visualization_params: VisualizationParams | dict = VisualizationParams(),
@@ -54,7 +53,10 @@ def run_pipeline(
     if isinstance(preprocessing_params, dict):
         preprocessing_params = PreprocessingParams(**preprocessing_params)
     if isinstance(spikesorting_params, dict):
-        spikesorting_params = SpikeSortingParams(**spikesorting_params)
+        spikesorting_params = SpikeSortingParams(
+            sorter_name=spikesorting_params['sorter_name'],
+            sorter_kwargs=spikesorting_params['sorter_kwargs']
+        )
     if isinstance(postprocessing_params, dict):
         postprocessing_params = PostprocessingParams(**postprocessing_params)
     if isinstance(curation_params, dict):
@@ -117,13 +119,13 @@ def run_pipeline(
         else:
             logger.info("Skipping postprocessing")
             waveform_extractor = None
-            
+
     else:
         logger.info("Skipping spike sorting")
         sorting = None
         waveform_extractor = None
         sorting_curated = None
-        
+
 
     # Visualization
     visualization_output = None

src/spikeinterface_pipelines/spikesorting/params.py

@@ -1,4 +1,4 @@
-from pydantic import BaseModel, Field
+from pydantic import BaseModel, Field, ConfigDict
 from typing import Union, List
 from enum import Enum
 
@@ -11,6 +11,7 @@ class SorterName(str, Enum):
 
 
 class Kilosort25Model(BaseModel):
+    model_config = ConfigDict(extra='forbid')
     detect_threshold: float = Field(default=6, description="Threshold for spike detection")
     projection_threshold: List[float] = Field(default=[10, 4], description="Threshold on projections")
     preclust_threshold: float = Field(
@@ -49,20 +50,47 @@ class Kilosort25Model(BaseModel):
 
 
 class Kilosort3Model(BaseModel):
+    model_config = ConfigDict(extra='forbid')
     pass
 
 
 class IronClustModel(BaseModel):
+    model_config = ConfigDict(extra='forbid')
     pass
 
 
 class MountainSort5Model(BaseModel):
-    pass
+    model_config = ConfigDict(extra='forbid')
+    scheme: str = Field(
+        default='2',
+        description="Sorting scheme",
+        json_schema_extra={'options': ["1", "2", "3"]}
+    )
+    detect_threshold: float = Field(default=5.5, description="Threshold for spike detection")
+    detect_sign: int = Field(default=-1, description="Sign of the peak")
+    detect_time_radius_msec: float = Field(default=0.5, description="Time radius in milliseconds")
+    snippet_T1: int = Field(default=20, description="Snippet T1")
+    snippet_T2: int = Field(default=20, description="Snippet T2")
+    npca_per_channel: int = Field(default=3, description="Number of PCA per channel")
+    npca_per_subdivision: int = Field(default=10, description="Number of PCA per subdivision")
+    snippet_mask_radius: int = Field(default=250, description="Snippet mask radius")
+    scheme1_detect_channel_radius: int = Field(default=150, description="Scheme 1 detect channel radius")
+    scheme2_phase1_detect_channel_radius: int = Field(default=200, description="Scheme 2 phase 1 detect channel radius")
+    scheme2_detect_channel_radius: int = Field(default=50, description="Scheme 2 detect channel radius")
+    scheme2_max_num_snippets_per_training_batch: int = Field(default=200, description="Scheme 2 max number of snippets per training batch")
+    scheme2_training_duration_sec: int = Field(default=300, description="Scheme 2 training duration in seconds")
+    scheme2_training_recording_sampling_mode: str = Field(default='uniform', description="Scheme 2 training recording sampling mode")
+    scheme3_block_duration_sec: int = Field(default=1800, description="Scheme 3 block duration in seconds")
+    freq_min: int = Field(default=300, description="High-pass filter cutoff frequency")
+    freq_max: int = Field(default=6000, description="Low-pass filter cutoff frequency")
+    filter: bool = Field(default=True, description="Enable or disable filter")
+    whiten: bool = Field(default=True, description="Enable or disable whiten")
 
 
 class SpikeSortingParams(BaseModel):
-    sorter_name: SorterName = Field(default="kilosort2_5", description="Name of the sorter to use.")
-    spikesort_by_group: bool = Field(default=False, description="If True, spike sorting is run for each group separately.")
+    sorter_name: SorterName = Field(description="Name of the sorter to use.")
     sorter_kwargs: Union[Kilosort25Model, Kilosort3Model, IronClustModel, MountainSort5Model] = Field(
-        default=Kilosort25Model(), description="Sorter specific kwargs."
+        description="Sorter specific kwargs.",
+        union_mode='left_to_right'
     )
+    spikesort_by_group: bool = Field(default=False, description="If True, spike sorting is run for each group separately.")

src/spikeinterface_pipelines/spikesorting/spikesorting.py

@@ -12,7 +12,7 @@
 
 def spikesort(
     recording: si.BaseRecording,
-    spikesorting_params: SpikeSortingParams = SpikeSortingParams(),
+    spikesorting_params: SpikeSortingParams,
     scratch_folder: Path = Path("./scratch/"),
     results_folder: Path = Path("./results/spikesorting/"),
 ) -> si.BaseSorting | None:
@@ -39,13 +39,6 @@ def spikesort(
 
     try:
         logger.info(f"[Spikesorting] \tStarting {spikesorting_params.sorter_name} spike sorter")
-
-
-        ## TEST ONLY - REMOVE LATER ##
-        # si.get_default_sorter_params('kilosort2_5')
-        # params_kilosort2_5 = {'do_correction': False}
-        ## --------------------------##
-
         if spikesorting_params.spikesort_by_group and len(np.unique(recording.get_channel_groups())) > 1:
             logger.info(f"[Spikesorting] \tSorting by channel groups")
             sorting = si.run_sorter_by_property(