rgb

colorsynth.rgb(spd, wavelength=None, axis=-1, spd_min=None, spd_max=None, spd_norm=None, wavelength_min=None, wavelength_max=None, wavelength_norm=None)

Convert a given spectral power distribution into a RGB array that can be plotted with matplotlib.

Parameters:

• spd (ndarray) – a spectral power distribution to be converted into a RGB array

• wavelength (None | Quantity) – The wavelength array corresponding to the spectral power distribution. If None, the wavelength is assumed to be evenly sampled across the human visible color range.

• axis (int) – the logical axis corresponding to changing wavelength, or the axis along which to integrate the spectral power distribution

• spd_min (None | ndarray) – the value of the spectral power distribution representing minimum intensity.

• spd_max (None | ndarray) – the value of the spectral power distribution representing minimum intensity.

• spd_norm (None | Callable[[ndarray], ndarray]) – an optional function to transform the spectral power distribution values before mapping to RGB

• wavelength_min (None | Quantity) – the wavelength value that is mapped to the minimum wavelength of the human visible color range, 380 nm.

• wavelength_max (None | Quantity) – the wavelength value that is mapped to the maximum wavelength of the human visible color range, 700 nm

• wavelength_norm (None | Callable[[Quantity], Quantity]) – an optional function to transform the wavelength values before they are mapped into the human visible color range.

Examples

Colorize a random, 3D numpy array.

import numpy as np
import matplotlib.pyplot as plt
import colorsynth

# Create a uniform random 3D numpy array
a = np.random.uniform(low=0, high=1, size=(16, 16, 11))

# Colorize the 3D numpy array
rgb = colorsynth.rgb(a)

# Plot the resulting RGB image
fig, ax = plt.subplots(constrained_layout=True)
ax.imshow(rgb);