pyckmeans.utils package

Submodules

pyckmeans.utils.plotting module

Plotting utitlies

pyckmeans.utils.plotting.plot_ckmeans_result(ckm_res: pyckmeans.core.ckmeans.CKmeansResult, names: Optional[Iterable[str]] = None, order: Optional[Union[str, numpy.ndarray]] = 'GW', cmap_cm: Union[str, matplotlib.colors.Colormap] = 'Blues', cmap_clbar: Union[str, matplotlib.colors.Colormap] = 'tab20', figsize: Tuple[float, float] = (7, 7)) → matplotlib.figure.Figure

Plot pyckmeans result consensus matrix with consensus clusters.

Parameters

ckm_resCKmeansResult

CKmeansResult as returned from CKmeans.predict.

namesOptional[Iterable[str]]

Sample names to be plotted.

orderOptional[Union[str, numpy.ndarray]]

Sample Plotting order. Either a string, determining the oder method to use (see CKmeansResult.order), or a numpy.ndarray giving the sample order, or None to apply no reordering.

cmap_cmUnion[str, matplotlib.colors.Colormap], optional

Colormap for the consensus matrix, by default ‘Blues’

cmap_clbarUnion[str, matplotlib.colors.Colormap], optional

Colormap for the cluster bar, by default ‘tab20’

figsizeTuple[float, float], optional

Figure size for the matplotlib figure, by default (7, 7).

Returns

matplotlib.figure.Figure

Matplotlib figure.

pyckmeans.utils.plotting.plot_cmatrix(cmatrix: numpy.ndarray, cl: numpy.ndarray, names: Optional[Iterable[str]] = None, order: Optional[Union[str, numpy.ndarray]] = 'GW', cmap_cm: Union[str, matplotlib.colors.Colormap] = 'Blues', cmap_clbar: Union[str, matplotlib.colors.Colormap] = 'tab20', figsize: Tuple[float, float] = (7, 7)) → Tuple[matplotlib.figure.Figure, matplotlib.axes._axes.Axes, matplotlib.axes._axes.Axes, matplotlib.axes._axes.Axes]

Plot consensus matrix and consensus clustering.

Parameters

cmatrixnumpy.ndarray

Consensus matrix.

clnumpy.ndarray

Cluster membership.

namesOptional[Iterable[str]]

Sample names to be plotted.

orderOptional[Union[str, numpy.ndarray]]

Sample Plotting order. Either a string, or a numpy.ndarray giving the sample order, or None to apply no reordering.

cmap_cmUnion[str, matplotlib.colors.Colormap], optional

Colormap for the consensus matrix, by default ‘Blues’

cmap_clbarUnion[str, matplotlib.colors.Colormap], optional

Colormap for the cluster bar, by default ‘tab20’

figsizeTuple[float, float], optional

Figure size for the matplotlib figure, by default (7, 7).

Returns

Tuple[matplotlib.figure.Figure, matplotlib.axes.Axes, matplotlib.axes.Axes, matplotlib.axes.Axes]

Figure, consensus matrix Axes, cluster membership Axes, colorbar Axes.

pyckmeans.utils.plotting.plot_multickmeans_metrics(mckm_res: pyckmeans.core.multickmeans.MultiCKmeansResult, figsize: Tuple[float, float] = (7, 7)) → matplotlib.figure.Figure

Plot MultiCKMeansResult metrics.

Parameters

mckm_resMultiCKmeansResult

MultiCKmeansResult object

figsizeTuple[float, float], optional

Figure size for the matplotlib figure, by default (7, 7).

Returns

matplotlib.figure.Figure

Matplotlib Figure of the metrics plot.

pyckmeans.utils.plotting.plot_wecr_result(wecr_res: pyckmeans.core.wecr.WECRResult, k: int, names: Optional[Iterable[str]] = None, order: Optional[Union[str, numpy.ndarray]] = 'GW', cmap_cm: Union[str, matplotlib.colors.Colormap] = 'Blues', cmap_clbar: Union[str, matplotlib.colors.Colormap] = 'tab20', figsize: Tuple[float, float] = (7, 7)) → matplotlib.figure.Figure

Plot wecr result consensus matrix with consensus clusters.

Parameters

wecr_respyckmeans.core.WECRResult

WECRResult as returned from pyckmeans.core.WECR.predict.

k: int

The number of clusters k to use for plotting.

namesOptional[Iterable[str]]

Sample names to be plotted.

orderOptional[Union[str, numpy.ndarray]]

Sample Plotting order. Either a string, determining the oder method to use (see WECRResult.order), or a numpy.ndarray giving the sample order, or None to apply no reordering.

cmap_cmUnion[str, matplotlib.colors.Colormap], optional

Colormap for the consensus matrix, by default ‘Blues’

cmap_clbarUnion[str, matplotlib.colors.Colormap], optional

Colormap for the cluster bar, by default ‘tab20’

figsizeTuple[float, float], optional

Figure size for the matplotlib figure, by default (7, 7).

Returns

matplotlib.figure.Figure

Matplotlib figure.

Raises

wecr.InvalidKError

Raised if an invalid k argument is provided.

pyckmeans.utils.plotting.plot_wecr_result_metrics(wecr_res: pyckmeans.core.wecr.WECRResult, figsize: Tuple[float, float] = (7, 7)) → matplotlib.figure.Figure

Plot WECRResult metrics.

Parameters

wecr_resWECRResult

WECRResult object

figsizeTuple[float, float], optional

Figure size for the matplotlib figure, by default (7, 7).

Returns

matplotlib.figure.Figure

Matplotlib Figure of the metrics plot.

pyckmeans.utils.progressbar module

Progress bar utilities

class pyckmeans.utils.progressbar.MultiCKMeansProgressBars(mckm: pyckmeans.core.multickmeans.MultiCKMeans, **kwargs: Dict[str, Any])

Bases: object

Context Manager for a MultiCKMeans progress bars.

Parameters

mckmMultiCKMeans

MultiCKMeans object to display progress bars for.

kwargsDict[str, Any]

Additional keyword arguments passed to tqdm.tqdm.

Methods

update([n])

Update progress by n iterations.

update(n: int = 1)

Update progress by n iterations.

Parameters

nint, optional

Progress increment in iterations, by default 1

Module contents

Utilties module