-
Notifications
You must be signed in to change notification settings - Fork 0
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #12 from catalystneuro/dev
Dev
- Loading branch information
Showing
24 changed files
with
2,108 additions
and
237 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
|
|
@@ -16,6 +16,7 @@ jobs: | |
| app_dir: | ||
| - si_kilosort25 | ||
| - si_kilosort3 | ||
| - si_mountainsort5 | ||
|
|
||
| steps: | ||
| - name: Checkout repository | ||
|
|
||
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,86 @@ | ||
| from pydantic import BaseModel, Field | ||
| from typing import List, Union | ||
|
|
||
|
|
||
| class Kilosort25SortingContext(BaseModel): | ||
| detect_threshold: float = Field(default=6, description="Threshold for spike detection") | ||
| projection_threshold: List[int] = Field(default=[10, 4], description="Threshold on projections") | ||
| preclust_threshold: float = Field(default=8, description="Threshold crossings for pre-clustering (in PCA projection space)") | ||
| car: bool = Field(default=True, description="Enable or disable common reference") | ||
| minFR: float = Field(default=0.1, description="Minimum spike rate (Hz), if a cluster falls below this for too long it gets removed") | ||
| minfr_goodchannels: float = Field(default=0.1, description="Minimum firing rate on a 'good' channel") | ||
| nblocks: int = Field(default=5, description="blocks for registration. 0 turns it off, 1 does rigid registration. Replaces 'datashift' option.") | ||
| sig: float = Field(default=20, description="spatial smoothness constant for registration") | ||
| freq_min: float = Field(default=150, description="High-pass filter cutoff frequency") | ||
| sigmaMask: float = Field(default=30, description="Spatial constant in um for computing residual variance of spike") | ||
| lam: float = Field(default=10.0, description="The importance of the amplitude penalty (like in Kilosort1: 0 means not used, 10 is average, 50 is a lot)") | ||
| nPCs: int = Field(default=3, description="Number of PCA dimensions") | ||
| ntbuff: int = Field(default=64, description="Samples of symmetrical buffer for whitening and spike detection") | ||
| nfilt_factor: int = Field(default=4, description="Max number of clusters per good channel (even temporary ones) 4") | ||
| AUCsplit: float = Field(default=0.9, description="Threshold on the area under the curve (AUC) criterion for performing a split in the final step") | ||
| do_correction: bool = Field(default=True, description="If True drift registration is applied") | ||
| wave_length: float = Field(default=61, description="size of the waveform extracted around each detected peak, (Default 61, maximum 81)") | ||
| keep_good_only: bool = Field(default=False, description="If True only 'good' units are returned") | ||
| skip_kilosort_preprocessing: bool = Field(default=False, description="Can optionaly skip the internal kilosort preprocessing") | ||
|
|
||
|
|
||
| class Kilosort3SortingContext(BaseModel): | ||
| detect_threshold: float = Field(default=6, description="Threshold for spike detection") | ||
| projection_threshold: List[int] = Field(default=[9, 9], description="Threshold on projections") | ||
| preclust_threshold: float = Field(default=8, description="Threshold crossings for pre-clustering (in PCA projection space)") | ||
| car: bool = Field(default=True, description="Enable or disable common reference") | ||
| minFR: float = Field(default=0.2, description="Minimum spike rate (Hz), if a cluster falls below this for too long it gets removed") | ||
| minfr_goodchannels: float = Field(default=0.2, description="Minimum firing rate on a 'good' channel") | ||
| nblocks: int = Field(default=5, description="blocks for registration. 0 turns it off, 1 does rigid registration. Replaces 'datashift' option.") | ||
| sig: float = Field(default=20, description="spatial smoothness constant for registration") | ||
| freq_min: float = Field(default=300, description="High-pass filter cutoff frequency") | ||
| sigmaMask: float = Field(default=30, description="Spatial constant in um for computing residual variance of spike") | ||
| lam: float = Field(default=20.0, description="The importance of the amplitude penalty (like in Kilosort1: 0 means not used, 10 is average, 50 is a lot)") | ||
| nPCs: int = Field(default=3, description="Number of PCA dimensions") | ||
| ntbuff: int = Field(default=64, description="Samples of symmetrical buffer for whitening and spike detection") | ||
| nfilt_factor: int = Field(default=4, description="Max number of clusters per good channel (even temporary ones) 4") | ||
| AUCsplit: float = Field(default=0.8, description="Threshold on the area under the curve (AUC) criterion for performing a split in the final step") | ||
| do_correction: bool = Field(default=True, description="If True drift registration is applied") | ||
| wave_length: float = Field(default=61, description="size of the waveform extracted around each detected peak, (Default 61, maximum 81)") | ||
| keep_good_only: bool = Field(default=False, description="If True only 'good' units are returned") | ||
| skip_kilosort_preprocessing: bool = Field(default=False, description="Can optionaly skip the internal kilosort preprocessing") | ||
|
|
||
|
|
||
| class MountainSort5SortingContext(BaseModel): | ||
| 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 SpykingCircusModel(BaseModel): | ||
| detect_sign: int = Field(default=-1, description="Sign of the peak") | ||
| adjacency_radius: int = Field(default=100, description="Adjacency radius") | ||
| detect_threshold: float = Field(default=6, description="Threshold for spike detection") | ||
| template_width_ms: int = Field(default=3, description="Template width in milliseconds") | ||
| filter: bool = Field(default=True, description="Enable or disable filter") | ||
| merge_spikes: bool = Field(default=True, description="Enable or disable merge spikes") | ||
| auto_merge: float = Field(default=0.75, description="Auto merge") | ||
| num_workers: Union[int, None] = Field(default=None, description="Number of workers") | ||
| whitening_max_elts: int = Field(default=1000, description="Whitening max elements") | ||
| clustering_max_elts: int = Field(default=10000, description="Clustering max elements") |
Oops, something went wrong.