neuralib.rastermap.core.RasterMapResult

class neuralib.rastermap.core.RasterMapResult

Bases: object

Container for storing the rastermap result, For both GUI load and customized plotting purpose

Dimension parameters:

N = Number of neurons/pixel

T = Number of samples

C = Number of clusters = N / binsize

__init__(filename, save_path, isort, embedding, ops, user_clusters=NOTHING, super_neurons=None)

Method generated by attrs for class RasterMapResult.

Parameters:

• filename (str | None)

• save_path (str | None)

• isort (np.ndarray)

• embedding (np.ndarray)

• ops (RasterOptions)

• user_clusters (list[UserCluster])

• super_neurons (np.ndarray | None)

Return type:

None

Methods

__init__(filename, save_path, isort, ...[, ...])	Method generated by attrs for class RasterMapResult.
asdict()	result as dict
load(path)	Load the results from rastermap output
save(path)	For GUI loading & cache computing for plotting in different time domains

Attributes

filename	Filename of the neural activity data (i.e., *.tif or *.avi for wfield activity; .npy Array[float, [N, T]] file for cellular)
save_path	filename for the rastermap result save
isort	Array[int, N]
embedding	Array[float, [N, 1]]
ops	RasterOptions
user_clusters	list of clusters UserCluster
super_neurons	super neuron activity.
n_clusters	number of clusters (super neurons)
n_samples	number of data samples (T)

filename: str | None

Filename of the neural activity data (i.e., *.tif or *.avi for wfield activity; .npy Array[float, [N, T]] file for cellular)

save_path: str | None

filename for the rastermap result save

isort: ndarray

Array[int, N]

embedding: ndarray

Array[float, [N, 1]]

ops: RasterOptions

RasterOptions

user_clusters: list[UserCluster]

list of clusters UserCluster

super_neurons: ndarray | None

super neuron activity. Array[float, [C, T]]

classmethod load(path)

Load the results from rastermap output

Parameters:

path (str | Path | PathLike[str]) – file path of the rastermap output

Returns:

RasterMapResult

Return type:

Self

save(path)

For GUI loading & cache computing for plotting in different time domains

Parameters:

path (str | Path | PathLike[str])

Return type:

None

asdict()

result as dict

Return type:

dict[str, Any]

property n_clusters: int

number of clusters (super neurons)

property n_samples: int

number of data samples (T)

__init__(filename, save_path, isort, embedding, ops, user_clusters=NOTHING, super_neurons=None)

Method generated by attrs for class RasterMapResult.

Parameters:

• filename (str | None)

• save_path (str | None)

• isort (np.ndarray)

• embedding (np.ndarray)

• ops (RasterOptions)

• user_clusters (list[UserCluster])

• super_neurons (np.ndarray | None)

Return type:

None