imghist docstring is off
kamilc opened this issue · 1 comments
kamilc commented
The docstring for imghist
lists a different set of arguments than what the function accepts:
def imghist(
data,
cmap=None,
bins=None,
vmin=None,
vmax=None,
alpha=None,
origin=None,
interpolation=None,
norm=None,
robust=False,
perc=(2, 98),
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,
height=5,
aspect=1.75,
**kwargs,
):
"""Plot data as a 2-D image with histogram showing the distribution of
the data. Options to add scalebar, colorbar, title.
Parameters
----------
data : array-like
Image data. Supported array shapes are all `matplotlib.pyplot.imshow` array shapes
bins : int, optional
Histogram bins, by default None. If None, `auto` is used.
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
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
height : int or float, optional
Size of the individual images, by default 5.
aspect : int or float, optional
Aspect ratio of individual images, by default 1.75.
I can't propose a fix now, but creating this ticket so that at least it's tracked.
SarthakJariwala commented
Good catch - Thanks for reporting.