canns.analyzer.data.asa.config

Exceptions

CANN2DError	Base exception for CANN2D analysis errors.
DataLoadError	Raised when data loading fails.
ProcessingError	Raised when data processing fails.

Classes

CANN2DPlotConfig	Specialized PlotConfig for CANN2D visualizations.
Constants	Constants used throughout CANN2D analysis.
SpikeEmbeddingConfig	Configuration for spike train embedding.
TDAConfig	Configuration for Topological Data Analysis (TDA).

Module Contents

exception canns.analyzer.data.asa.config.CANN2DError

Bases: Exception

Base exception for CANN2D analysis errors.

Examples

>>> try:
...     raise CANN2DError("boom")
... except CANN2DError:
...     pass

Initialize self. See help(type(self)) for accurate signature.

exception canns.analyzer.data.asa.config.DataLoadError

Bases: CANN2DError

Raised when data loading fails.

Examples

>>> try:
...     raise DataLoadError("missing data")
... except DataLoadError:
...     pass

Initialize self. See help(type(self)) for accurate signature.

exception canns.analyzer.data.asa.config.ProcessingError

Bases: CANN2DError

Raised when data processing fails.

Examples

>>> try:
...     raise ProcessingError("processing failed")
... except ProcessingError:
...     pass

Initialize self. See help(type(self)) for accurate signature.

class canns.analyzer.data.asa.config.CANN2DPlotConfig

Bases: canns.analyzer.visualization.PlotConfig

Specialized PlotConfig for CANN2D visualizations.

Extends canns.analyzer.visualization.PlotConfig with fields that control 3D projection and torus animation parameters.

Examples

>>> from canns.analyzer.data import CANN2DPlotConfig
>>> cfg = CANN2DPlotConfig.for_projection_3d(title="Projection")
>>> cfg.zlabel
'Component 3'

classmethod for_projection_3d(**kwargs)

Create configuration for 3D projection plots.

Examples

>>> cfg = CANN2DPlotConfig.for_projection_3d(figsize=(6, 5))
>>> cfg.figsize
(6, 5)

classmethod for_torus_animation(**kwargs)

Create configuration for 3D torus bump animations.

Examples

>>> cfg = CANN2DPlotConfig.for_torus_animation(fps=10, n_frames=50)
>>> cfg.fps, cfg.n_frames
(10, 50)

dpi: int = 300

frame_step: int = 5

n_frames: int = 20

numangsint: int = 51

r1: float = 1.5

r2: float = 1.0

window_size: int = 300

zlabel: str = 'Component 3'

class canns.analyzer.data.asa.config.Constants

Constants used throughout CANN2D analysis.

Examples

>>> from canns.analyzer.data import Constants
>>> Constants.DEFAULT_DPI
300

DEFAULT_DPI = 300

DEFAULT_FIGSIZE = (10, 8)

GAUSSIAN_SIGMA_FACTOR = 100

MULTIPROCESSING_CORES = 4

SPEED_CONVERSION_FACTOR = 100

TIME_CONVERSION_FACTOR = 0.01

class canns.analyzer.data.asa.config.SpikeEmbeddingConfig

Configuration for spike train embedding.

res

Time scaling factor that converts seconds to integer bins.

Type:

int

dt

Bin width in the same scaled units as res.

Type:

int

sigma

Gaussian smoothing width (scaled units).

Type:

int

smooth

Whether to apply temporal smoothing to spike counts.

Type:

bool

speed_filter

Whether to filter by animal speed (requires x/y/t in the input).

Type:

bool

min_speed

Minimum speed threshold for speed_filter (cm/s by convention).

Type:

float

Examples

>>> from canns.analyzer.data import SpikeEmbeddingConfig
>>> cfg = SpikeEmbeddingConfig(smooth=False, speed_filter=False)
>>> cfg.min_speed
2.5

dt: int = 1000

min_speed: float = 2.5

res: int = 100000

sigma: int = 5000

smooth: bool = True

speed_filter: bool = True

class canns.analyzer.data.asa.config.TDAConfig

Configuration for Topological Data Analysis (TDA).

dim

Target PCA dimension before TDA.

Type:

int

num_times

Downsampling stride in time.

Type:

int

active_times

Number of most active time points to keep.

Type:

int

k

Number of neighbors used in denoising.

Type:

int

n_points

Number of points sampled for persistent homology.

Type:

int

metric

Distance metric for point cloud (e.g., “cosine”).

Type:

str

nbs

Number of neighbors for distance matrix construction.

Type:

int

maxdim

Maximum homology dimension for persistence.

Type:

int

coeff

Field coefficient for persistent homology.

Type:

int

show

Whether to show barcode plots.

Type:

bool

do_shuffle

Whether to run shuffle analysis.

Type:

bool

num_shuffles

Number of shuffles for null distribution.

Type:

int

progress_bar

Whether to show progress bars.

Type:

bool

standardize

Whether to standardize data before PCA (z-score).

Type:

bool

Examples

>>> from canns.analyzer.data import TDAConfig
>>> cfg = TDAConfig(maxdim=1, do_shuffle=False, show=False)
>>> cfg.maxdim
1

active_times: int = 15000

coeff: int = 47

dim: int = 6

do_shuffle: bool = False

k: int = 1000

maxdim: int = 1

metric: str = 'cosine'

n_points: int = 1200

nbs: int = 800

num_shuffles: int = 1000

num_times: int = 5

progress_bar: bool = True

show: bool = True

standardize: bool = True