SpikeAnalysisResult#

class neurodent.visualization.SpikeAnalysisResult(result_sas: list[SortingAnalyzer], result_mne: RawArray | None = None, animal_id: str | None = None, genotype: str | None = None, animal_day: str | None = None, bin_folder_name: str | None = None, metadata: DDFBinaryMetadata | None = None, channel_names: list[str] | None = None, assume_from_number=False) → None[source]#

Bases: AnimalFeatureParser

Parameters:

  • result_sas (list[SortingAnalyzer])

  • result_mne (RawArray)

  • animal_id (str)

  • genotype (str)

  • animal_day (str)

  • bin_folder_name (str)

  • metadata (DDFBinaryMetadata)

  • channel_names (list[str])

__init__(result_sas: list[SortingAnalyzer], result_mne: RawArray | None = None, animal_id: str | None = None, genotype: str | None = None, animal_day: str | None = None, bin_folder_name: str | None = None, metadata: DDFBinaryMetadata | None = None, channel_names: list[str] | None = None, assume_from_number=False) → None[source]#
Parameters:

  • result (list[si.SortingAnalyzer]) – Result comes from AnimalOrganizer.compute_spike_analysis(). Each SortingAnalyzer is a single channel.

  • animal_id (str, optional) – Identifier for the animal where result was computed from. Defaults to None.

  • genotype (str, optional) – Genotype of animal. Defaults to None.

  • channel_names (list[str], optional) – List of channel names. Defaults to None.

  • assume_channels (bool, optional) – If true, assumes channel names according to AnimalFeatureParser.DEFAULT_CHNUM_TO_NAME. Defaults to False.

  • result_sas (list[SortingAnalyzer])

  • result_mne (RawArray | None)

  • animal_day (str | None)

  • bin_folder_name (str | None)

  • metadata (DDFBinaryMetadata | None)

Return type:

None

convert_to_mne(chunk_len: float = 60, save_raw=True) → RawArray[source]#
Return type:

RawArray

Parameters:

chunk_len (float)

save_fif_and_json(folder: str | Path, convert_to_mne=True, make_folder=True, slugify_filebase=True, save_abbrevs_as_chnames=False, overwrite=False)[source]#

Archive spike analysis result into the folder specified, as a fif and json file.

Parameters:

  • folder (str | Path) – Destination folder to save results to

  • convert_to_mne (bool, optional) – If True, convert the SortingAnalyzers to a MNE RawArray if self.result_mne is None. Defaults to True.

  • make_folder (bool, optional) – If True, create the folder if it doesn’t exist. Defaults to True.

  • slugify_filebase (bool, optional) – If True, slugify the filebase (replace special characters). Defaults to True.

  • save_abbrevs_as_chnames (bool, optional) – If True, save the channel abbreviations as the channel names in the json file. Defaults to False.

  • overwrite (bool, optional) – If True, overwrite the existing files. Defaults to False.

classmethod load_fif_and_json(folder: str | Path)[source]#
Parameters:

folder (str | Path)

static convert_sas_to_mne(sas: list[SortingAnalyzer], chunk_len: float = 60) → RawArray[source]#

Convert a list of SortingAnalyzers to a MNE RawArray.

Parameters:

  • sas (list[si.SortingAnalyzer]) – The list of SortingAnalyzers to convert

  • chunk_len (float, optional) – The length of the chunks to use for the conversion. Defaults to 60.

Returns:

The converted RawArray, with spikes labeled as annotations

Return type:

mne.io.RawArray

static convert_sa_to_np(sa: SortingAnalyzer, chunk_len: float = 60) → ndarray[source]#

Convert a SortingAnalyzer to an MNE RawArray.

Parameters:

  • sa (si.SortingAnalyzer) – The SortingAnalyzer to convert. Must have only 1 channel.

  • chunk_len (float, optional) – The length of the chunks to use for the conversion. Defaults to 60.

Returns:

The converted traces

Return type:

np.ndarray