seaborn_image.imgplot#

seaborn_image.imgplot(data, ax=None, cmap=None, gray=None, vmin=None, vmax=None, alpha=None, origin=None, interpolation=None, norm=None, robust=False, perc=(2, 98), diverging=False, dx=None, units=None, dimension=None, describe=False, map_func=None, cbar=True, orientation='v', cbar_log=False, cbar_label=None, cbar_ticks=None, showticks=False, despine=None, extent=None, **kwargs)#

Plot data as a 2-D image with options to ignore outliers, add scalebar, colorbar, title.

Parameters:

data (array-like) – Image data. Supported array shapes are all matplotlib.pyplot.imshow array shapes
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
norm (matplotlib.colors.Normalize, optional) – matplotlib Normalize instance used to scale scalar data before mapping to colors using cmap
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
diverging (bool, optional) – If True, vmax and vmin are adjusted so they have the same absolute value, making the diverging color maps show 0 at the middle.
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 False
map_func (callable, optional) – Transform input image data using this function. All function arguments must be passed as kwargs.
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
extent (floats (left, right, bottom, top), optional) – The bounding box in data coordinates that the image will fill. The image is stretched individually along x and y to fill the box.

Returns:

Matplotlib axes where the image is drawn.

Return type:

matplotlib.axes.Axes

Raises:

TypeError – if cmap is not str or matplotlib.colors.Colormap
TypeError – if ax is not matplotlib.axes.Axes
TypeError – if describe is not bool
TypeError – if robust is not bool
TypeError – if cbar is not bool
TypeError – if orientation is not str
TypeError – if cbar_label is not str
TypeError – if showticks is not bool
TypeError – if despine is not bool
AssertionError – if len(perc) is not equal to 2
AssertionError – if the first element of perc is greater than the second

Examples

Plot image

>>> import seaborn_image as isns
>>> img = isns.load_image("polymer")
>>> isns.imgplot(img)

Get image statistics

>>> isns.imgplot(img, describe=True)

Add a scalebar

>>> isns.imgplot(img, dx=15, units="nm")

Change colormap

>>> isns.imgplot(img, cmap="deep")

Rescale colormap to exclude outliers while plotting

Image with outliers

>>> img_out = isns.load_image("polymer outliers")
>>> isns.imgplot(img_out)

Rescale colormap using robust parameter

>>> isns.imgplot(img_out, robust=True)

Change percentile for robust parameter

>>> isns.imgplot(img_out, robust=True, perc=(0.5,99.5))

Rescale colormap using the diverging parameter

>>> img_standard = img - img.mean()
>>> isns.imgplot(img_standard, diverging=True, cmap="seismic")

Map a function to transform input image

>>> from skimage.exposure import adjust_gamma
>>> cells = isns.load_image("cells")[:, :, 32]
>>> isns.imgplot(cells, map_func=adjust_gamma, gamma=0.5)

Convert RGB image to grayscale

>>> from skimage.data import astronaut
>>> isns.imgplot(astronaut(), gray=True)

Change colorbar orientation

>>> isns.imgplot(img, orientation="h") # horizontal

>>> isns.imgplot(img, orientation="v") # vertical

Add colorbar label

>>> isns.imgplot(img, cbar_label="Height (nm)")

Despine image and colorbar

>>> isns.imgplot(img, despine=True)

Change colorbar and colormap to log scale

>>> pl = isns.load_image("fluorescence")
>>> isns.imgplot(pl, cbar_log=True)

Change image transparency

>>> isns.imgplot(pl, alpha=0.75)