Add visualization step

.github/workflows/ci-test.yml

@@ -6,6 +6,10 @@ on:
     branches:
       - main
 
+env:  #  For the sortingview backend
+  KACHERY_CLOUD_CLIENT_ID: ${{ secrets.KACHERY_CLOUD_CLIENT_ID }}
+  KACHERY_CLOUD_PRIVATE_KEY: ${{ secrets.KACHERY_CLOUD_PRIVATE_KEY }}
+
 concurrency:  # Cancel previous workflows on the same pull request
   group: ${{ github.workflow }}-${{ github.ref }}
   cancel-in-progress: true
@@ -27,6 +31,7 @@ jobs:
         run: |
           python -m pip install -U pip  # Official recommended way
           pip install pytest
+          pip install pyvips
           pip install -e .
       - name: Test pipeline with pytest
         run: |

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "spikeinterface_pipelines"
-version = "0.0.6"
+version = "0.0.8"
 description = "Collection of standardized analysis pipelines based on SpikeInterfacee."
 readme = "README.md"
 authors = [
@@ -9,7 +9,11 @@ authors = [
     { name = "Luiz Tauffer", email = "[email protected]" },
 ]
 requires-python = ">=3.8"
-dependencies = ["spikeinterface[full]", "neo>=0.12.0", "pydantic>=2.4.2"]
+dependencies = [
+    "spikeinterface[full,widgets]>=0.100.0",
+    "neo>=0.12.0",
+    "pydantic>=2.4.2",
+]
 keywords = [
     "spikeinterface",
     "spike sorting",

src/spikeinterface_pipelines/curation/curation.py

@@ -4,7 +4,7 @@
 
 import numpy as np
 
-import spikeinterface.full as si
+import spikeinterface as si
 import spikeinterface.curation as sc
 
 from ..logger import logger

src/spikeinterface_pipelines/pipeline.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 from pathlib import Path
+import re
 from typing import Tuple
 import spikeinterface as si
 
@@ -8,6 +9,8 @@
 from .preprocessing import preprocess, PreprocessingParams
 from .spikesorting import spikesort, SpikeSortingParams
 from .postprocessing import postprocess, PostprocessingParams
+from .curation import curate, CurationParams
+from .visualization import visualize, VisualizationParams
 
 
 def run_pipeline(
@@ -18,13 +21,19 @@ def run_pipeline(
     preprocessing_params: PreprocessingParams | dict = PreprocessingParams(),
     spikesorting_params: SpikeSortingParams | dict = SpikeSortingParams(),
     postprocessing_params: PostprocessingParams | dict = PostprocessingParams(),
+    curation_params: CurationParams | dict = CurationParams(),
+    visualization_params: VisualizationParams | dict = VisualizationParams(),
     run_preprocessing: bool = True,
     run_spikesorting: bool = True,
     run_postprocessing: bool = True,
+    run_curation: bool = True,
+    run_visualization: bool = True,
 ) -> Tuple[
     si.BaseRecording | None,
     si.BaseSorting | None,
-    si.WaveformExtractor | None
+    si.WaveformExtractor | None,
+    si.BaseSorting | None,
+    dict | None,
 ]:
     # Create folders
     results_folder = Path(results_folder)
@@ -36,6 +45,8 @@ def run_pipeline(
     results_folder_preprocessing = results_folder / "preprocessing"
     results_folder_spikesorting = results_folder / "spikesorting"
     results_folder_postprocessing = results_folder / "postprocessing"
+    results_folder_curation = results_folder / "curation"
+    results_folder_visualization = results_folder / "visualization"
 
     # Arguments Models validation, in case of dict
     if isinstance(job_kwargs, dict):
@@ -46,6 +57,10 @@ def run_pipeline(
         spikesorting_params = SpikeSortingParams(**spikesorting_params)
     if isinstance(postprocessing_params, dict):
         postprocessing_params = PostprocessingParams(**postprocessing_params)
+    if isinstance(curation_params, dict):
+        curation_params = CurationParams(**curation_params)
+    if isinstance(visualization_params, dict):
+        visualization_params = VisualizationParams(**visualization_params)
 
     # set global job kwargs
     si.set_global_job_kwargs(**job_kwargs.model_dump())
@@ -77,6 +92,7 @@ def run_pipeline(
             raise Exception("Spike sorting failed")
 
         # Postprocessing
+        sorting_curated = sorting
         if run_postprocessing:
             logger.info("Postprocessing sorting")
             waveform_extractor = postprocess(
@@ -86,16 +102,40 @@ def run_pipeline(
                 scratch_folder=scratch_folder,
                 results_folder=results_folder_postprocessing,
             )
+
+            # Curation
+            if run_curation:
+                logger.info("Curating sorting")
+                sorting_curated = curate(
+                    waveform_extractor=waveform_extractor,
+                    curation_params=curation_params,
+                    scratch_folder=scratch_folder,
+                    results_folder=results_folder_curation,
+                )
+            else:
+                logger.info("Skipping curation")
         else:
             logger.info("Skipping postprocessing")
             waveform_extractor = None
+
     else:
         logger.info("Skipping spike sorting")
         sorting = None
         waveform_extractor = None
+        sorting_curated = None
+
 
-    # TODO: Curation
-
-    # TODO: Visualization
+    # Visualization
+    visualization_output = None
+    if run_visualization:
+        logger.info("Visualizing results")
+        visualization_output = visualize(
+            recording=recording_preprocessed,
+            sorting_curated=sorting_curated,
+            waveform_extractor=waveform_extractor,
+            visualization_params=visualization_params,
+            scratch_folder=scratch_folder,
+            results_folder=results_folder_visualization,
+        )
 
-    return (recording_preprocessed, sorting, waveform_extractor)
+    return (recording_preprocessed, sorting, waveform_extractor, sorting_curated, visualization_output)

src/spikeinterface_pipelines/visualization/__init__.py

@@ -0,0 +1,2 @@
+from .visualization import visualize
+from .params import VisualizationParams

src/spikeinterface_pipelines/visualization/params.py

@@ -0,0 +1,108 @@
+from cProfile import label
+from pydantic import BaseModel, Field
+from typing import Literal, Union
+
+from spikeinterface.widgets import sorting_summary
+
+
+class TracesParams(BaseModel):
+    """
+    Traces parameters.
+    """
+    n_snippets_per_segment: int = Field(default=2, description="Number of snippets per segment to visualize.")
+    snippet_duration_s: float = Field(default=0.5, description="Duration of each snippet in seconds.")
+    skip: bool = Field(default=False, description="Skip traces visualization.")
+
+
+class DetectionParams(BaseModel):
+    """
+    Detection parameters.
+    """
+    peak_sign: Literal["neg", "pos", "both"] = Field(default="neg", description="Peak sign for peak detection.")
+    detect_threshold: float = Field(default=5.0, description="Threshold for peak detection.")
+    exclude_sweep_ms: float = Field(default=0.1, description="Exclude sweep in ms around peak detection.")
+
+
+class LocalizationParams(BaseModel):
+    """
+    Localization parameters.
+    """
+    ms_before: float = Field(default=0.1, description="Time before peak in ms.")
+    ms_after: float = Field(default=0.3, description="Time after peak in ms.")
+    radius_um: float = Field(default=100.0, description="Radius in um for sparsifying waveforms before localization.")
+
+
+class DriftParams(BaseModel):
+    """
+    Drift parameters.
+    """
+    detection: DetectionParams = Field(
+        default=DetectionParams(),
+        description="Detection parameters (only used if spike localization was not performed in postprocessing)"
+    )
+    localization: LocalizationParams = Field(
+        default=LocalizationParams(),
+        description="Localization parameters (only used if spike localization was not performed in postprocessing)"
+    )
+    decimation_factor: int = Field(
+        default=30,
+        description="The decimation factor for drift visualization. E.g. 30 means that 1 out of 30 spikes is plotted."
+    )
+    alpha: float = Field(default=0.15, description="Alpha for scatter plot.")
+    vmin: float = Field(default=-200, description="Min value for colormap.")
+    vmax: float = Field(default=0, description="Max value for colormap.")
+    cmap: str = Field(default="Greys_r", description="Matplotlib colormap for drift visualization.")
+    figsize: Union[list, tuple] = Field(default=(10, 10), description="Figure size for drift visualization.")
+
+
+class SortingSummaryVisualizationParams(BaseModel):
+    """
+    Sorting summary visualization parameters.
+    """
+    unit_table_properties: list = Field(
+        default=["default_qc"],
+        description="List of properties to show in the unit table."
+    )
+    curation: bool = Field(
+        default=True,
+        description="Whether to show curation buttons."
+    )
+    label_choices: list = Field(
+        default=["SUA", "MUA", "noise"],
+        description="List of labels to choose from (if `curation=True`)"
+    )
+    label: str = Field(
+        default="Sorting summary from SI pipelines",
+        description="Label for the sorting summary."
+    )
+
+
+class RecordingVisualizationParams(BaseModel):
+    """
+    Recording visualization parameters.
+    """
+    timeseries: TracesParams = Field(
+        default=TracesParams(),
+        description="Traces visualization parameters."
+    )
+    drift: DriftParams = Field(
+        default=DriftParams(),
+        description="Drift visualization parameters."   
+    )
+    label: str = Field(
+        default="Recording visualization from SI pipelines",
+        description="Label for the recording."
+    )
+
+class VisualizationParams(BaseModel):
+    """
+    Visualization parameters.
+    """
+    recording: RecordingVisualizationParams = Field(
+        default=RecordingVisualizationParams(),
+        description="Recording visualization parameters."
+    )
+    sorting_summary: SortingSummaryVisualizationParams = Field(
+        default=SortingSummaryVisualizationParams(),
+        description="Sorting summary visualization parameters."
+    )