imshow

fcp.imshow(**kwargs)

Image show plotting function.

Parameters:

df –

• Single image only:

  • 2D numpy array of pixel data

  • OR a 2D pd.DataFrame of pixel data

• Multiple images:

  • pd.DataFrame with 1 row per image

  • row index value must match a key in the kwargs[‘imgs’] dict

  • other columns in this DataFrame are grouping columns

Keyword Arguments:

• imgs –

  • Single image only:

    • Not defined or used

  • Multiple images:

    • dict of the actual image data; dict key must match a row index value in kwargs[‘df’]

• cfa (str) – Color-filter array pattern that is used to split data from a Bayer image into separate color planes. Defaults to None. More details

• cmap (bool) – Name of a color map to apply to the plot. Defaults to gray. More details

• imshow_interp|interp (str) – imshow interpolation scheme [see matplotlib docs for more details]. Defaults to ‘none’.

• stretch (float|list) – Calculate “stretch” times the standard deviation above and below the mean to set new z-limits. Can be a single value used as +/- limits or a two-value list for the lower/upper multiplier values. Defaults to None. More details

Examples

Basic:

>>> import fivecentplots as fcp
>>> from pathlib import Path
>>> import pandas as pd
>>> import imageio.v3 as imageio
>>> # Read an image from the world-wide web
>>> url = 'https://imagesvc.meredithcorp.io/v3/mm/image?q=85&c=sc&rect=0%2C214%2C2000%2C1214&'         >>>       + 'poi=%5B920%2C546%5D&w=2000&h=1000&url=https%3A%2F%2Fstatic.onecms.io%2Fwp-content%2Fuploads'         >>>       + '%2Fsites%2F47%2F2020%2F10%2F07%2Fcat-in-pirate-costume-380541532-2000.jpg'
>>> imgr = imageio.imread(url)
>>> # Convert to grayscale
>>> img = fcp.utilities.img_grayscale(imgr)
>>> fcp.imshow(img, ax_size=[600, 600])

With +/- 3 sigma contrast stretching:

>>> uu = img.stack().mean()
>>> ss = img.stack().std()
>>> fcp.imshow(img, cmap='inferno', cbar=True, ax_size=[600, 600], zmin=uu-3*ss, zmax=uu+3*ss)