colorbar#

colorsynth.colorbar(spd, wavelength=None, axis=-1, axis_intensity=0, axis_wavelength=1, spd_min=None, spd_max=None, spd_norm=None, wavelength_min=None, wavelength_max=None, wavelength_norm=None, squeeze=True)[source]#

Calculate the colorbar corresponding to calling rgb() with these same arguments.

The return value from this function is designed to be used directly by matplotlib.pyplot.pcolormesh().

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
axis_intensity (int) – The index of new logical axis in the result which corresponds to changing spectral radiance.
axis_wavelength (int) – The index of a new logical axis in the result which corresponds to changing wavelength.
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.
squeeze (bool) – A boolean flag indicating whether to remove singleton dimensions from the result. If you’re just making a single colorbar, this should be True (the default) so matplotlib.pyplot.pcolormesh() will work correctly. If you’re making a stack of colorbars, you might want to set this to False so that you don’t lose track of axis meanings.

Return type:

tuple[ndarray, ndarray, ndarray]

Examples

Plot the colorbar corresponding to a random, 3D cube.

import numpy as np
import matplotlib.pyplot as plt
import astropy.units as u
import astropy.visualization
import colorsynth

# Define a random 3d cube
a = np.random.uniform(
    low=0,
    high=1000,
    size=(16, 16, 11),
) * u.photon

# Define wavelength axis
wavelength = np.linspace(
    start=100 * u.AA,
    stop=200 * u.AA,
    num=a.shape[~0],
)

# Compute the colorbar corresponding to the random 3d cube.
colorbar = colorsynth.colorbar(
    spd=a,
    wavelength=wavelength,
    axis=~0,
)

# Plot the colorbar
with astropy.visualization.quantity_support():
    fig, ax = plt.subplots()
    plt.pcolormesh(*colorbar)

colorbar#

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