seaborn_image.filterplot

seaborn_image.filterplot(data, filt='gaussian', *, ax=None, cmap=None, vmin=None, vmax=None, alpha=None, origin=None, interpolation=None, robust=False, perc=(2, 98), dx=None, units=None, dimension=None, describe=False, cbar=True, orientation='v', cbar_log=False, cbar_label=None, cbar_ticks=None, showticks=False, despine=None, **kwargs)

Apply N-dimensional filters and plot the filterd data as 2-D image with options to add scalebar, colorbar, titles and configure similar to imgplot

Parameters

  • data (array-like) – Image data. Supported array shapes are all matplotlib.pyplot.imshow array shapes

  • filt (str or callable, optional) – Filter name or function to be applied. Filter name can be a string from seaborn_image.implemented_filters or a callable filter. Defaults to “gaussian”.

  • ax (matplotlib.axes.Axes, optional) – Matplotlib axes to plot image on. If None, figure and axes are auto-generated, by default None

  • cmap (str or matplotlib.colors.Colormap, optional) – Colormap for image. Can be a seaborn-image colormap or default matplotlib colormaps or any other colormap converted to a matplotlib colormap, by default None

  • gray (bool, optional) – If True and data is RGB image, it will be converted to grayscale. If True and cmap is None, cmap will be set to “gray”, by default None

  • vmin (float, optional) – Minimum data value that colormap covers, by default None

  • vmax (float, optional) – Maximum data value that colormap covers, by default None

  • alpha (float or array-like, optional) – matplotlib.pyplot.imshow alpha blending value from 0 (transparent) to 1 (opaque), by default None

  • origin (str, optional) – Image origin, by default None

  • interpolation (str, optional) – matplotlib.pyplot.imshow interpolation method used, by default None

  • robust (bool, optional) – If True and vmin or vmax are None, colormap range is calculated based on the percentiles defined in perc parameter, by default False

  • perc (tuple or list, optional) – If robust is True, colormap range is calculated based on the percentiles specified instead of the extremes, by default (2, 98) - 2nd and 98th percentiles for min and max values

  • dx (float, optional) – Size per pixel of the image data. Specifying dx and units adds a scalebar to the image, by default None

  • units (str, optional) – Units of dx, by default None

  • dimension (str, optional) –

    dimension of dx and units, by default None Options include (similar to matplotlib_scalebar):

    • ”si” : scale bar showing km, m, cm, etc.

    • ”imperial” : scale bar showing in, ft, yd, mi, etc.

    • ”si-reciprocal” : scale bar showing 1/m, 1/cm, etc.

    • ”angle” : scale bar showing °, ʹ (minute of arc) or ʹʹ (second of arc)

    • ”pixel” : scale bar showing px, kpx, Mpx, etc.

  • describe (bool, optional) – Brief statistical description of the data, by default True

  • cbar (bool, optional) – Specify if a colorbar is to be added to the image, by default True. If data is RGB image, cbar is False

  • orientation (str, optional) –

    Specify the orientaion of colorbar, by default “v”. Options include :

    • ’h’ or ‘horizontal’ for a horizontal colorbar to the bottom of the image.

    • ’v’ or ‘vertical’ for a vertical colorbar to the right of the image.

  • cbar_log (bool, optional) – Log scale colormap and colorbar

  • cbar_label (str, optional) – Colorbar label, by default None

  • cbar_ticks (list, optional) – List of colorbar ticks, by default None

  • showticks (bool, optional) – Show image x-y axis ticks, by default False

  • despine (bool, optional) – Remove axes spines from image axes as well as colorbar axes, by default None

  • **kwargs (optional) – Any additional parameters to be passed to the specific filt chosen. For instance, “sigma” or “size” or “mode” etc.

Returns

Matplotlib axes where the image is drawn.

Return type

matplotlib.axes.Axes

Raises

  • TypeError – if filt is not a string type or callable function

  • NotImplementedError – if a filt that is not implemented is specified

  • TypeError – if describe is not a bool

Examples

Use default gaussian filter

>>> import seaborn_image as isns
>>> img = isns.load_image("polymer")
>>> isns.filterplot(img, sigma=3)
../_images/filterplot-1.png

Specify an image filter with specific parameters

>>> isns.filterplot(img, "percentile", percentile=35, size=10)
../_images/filterplot-2.png

filter can also be a function

>>> import scipy.ndimage as ndi
>>> isns.filterplot(img, ndi.gaussian_filter, sigma=2.5)
../_images/filterplot-3.png

Specify other image parameters for visualization along with the filter

>>> isns.filterplot(img, "sobel", dx=15, units="nm", cmap="acton")
../_images/filterplot-4.png