python - Matplotlib - 基于光谱颜色的曲线下颜色

Question

我想绘制一个光谱图，其中曲线下方的区域将根据相应的光颜色进行着色。很像这个情节:

我试图在 matplotlib 中模拟这一点，方法是使用 imshow 和 spectral 颜色图来绘制颜色，并使用白色 fill_between 来绘制颜色掩盖曲线上方的区域。我对结果相当满意，除了两件事:

1) 我绘制的颜色与可见光谱不太一致。例如，当它是红色时，我将 700 nm 显示为黄色/橙色。我对有点程式化的表示感到满意(例如，我认为第二个答案 here 中显示的准确颜色很无聊)，但总的来说，我希望波长与其可见颜色对齐。

2) 我喜欢上面的光谱如何将可见区域之外的区域着色为 alpha<1.0。我不确定如何实现这一目标。

这是我目前所拥有的:

import numpy as np
import matplotlib.pyplot as plt

fig, axs = plt.subplots(1, 1, figsize=(8,4), tight_layout=True)

wavelengths = np.linspace(200, 1000, 1000)
spectrum = (5 + np.sin(wavelengths*0.1)**2) * np.exp(-0.00002*(wavelengths-600)**2)
plt.plot(wavelengths, spectrum, color='darkred')

y = np.linspace(0, 6, 100)
X,Y = np.meshgrid(wavelengths, y)
X[X<400] = 400
extent=(np.min(wavelengths), np.max(wavelengths), np.min(y), np.max(y))

plt.imshow(X, clim=(350,820),  extent=extent, cmap=plt.get_cmap('spectral'), aspect='auto')
plt.xlabel('Wavelength (nm)')
plt.ylabel('Intensity')

plt.fill_between(wavelengths, spectrum, 8, color='w')
plt.savefig('WavelengthColors.png', dpi=200)

plt.show()

Answer 1

首先，您需要一个将波长作为输入并返回 RGB 颜色的函数。可以找到这样的功能here .可以调整它以返回一个 alpha 值，该值在可见颜色范围之外小于 1。

此函数可用于创建颜色图。使用适当的归一化可以将波长范围映射到 0 到 1 之间的范围，这样就可以在 imshow 图中使用该颜色图。

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


def wavelength_to_rgb(wavelength, gamma=0.8):
    ''' taken from http://www.noah.org/wiki/Wavelength_to_RGB_in_Python
    This converts a given wavelength of light to an 
    approximate RGB color value. The wavelength must be given
    in nanometers in the range from 380 nm through 750 nm
    (789 THz through 400 THz).

    Based on code by Dan Bruton
    http://www.physics.sfasu.edu/astro/color/spectra.html
    Additionally alpha value set to 0.5 outside range
    '''
    wavelength = float(wavelength)
    if wavelength >= 380 and wavelength <= 750:
        A = 1.
    else:
        A=0.5
    if wavelength < 380:
        wavelength = 380.
    if wavelength >750:
        wavelength = 750.
    if wavelength >= 380 and wavelength <= 440:
        attenuation = 0.3 + 0.7 * (wavelength - 380) / (440 - 380)
        R = ((-(wavelength - 440) / (440 - 380)) * attenuation) ** gamma
        G = 0.0
        B = (1.0 * attenuation) ** gamma
    elif wavelength >= 440 and wavelength <= 490:
        R = 0.0
        G = ((wavelength - 440) / (490 - 440)) ** gamma
        B = 1.0
    elif wavelength >= 490 and wavelength <= 510:
        R = 0.0
        G = 1.0
        B = (-(wavelength - 510) / (510 - 490)) ** gamma
    elif wavelength >= 510 and wavelength <= 580:
        R = ((wavelength - 510) / (580 - 510)) ** gamma
        G = 1.0
        B = 0.0
    elif wavelength >= 580 and wavelength <= 645:
        R = 1.0
        G = (-(wavelength - 645) / (645 - 580)) ** gamma
        B = 0.0
    elif wavelength >= 645 and wavelength <= 750:
        attenuation = 0.3 + 0.7 * (750 - wavelength) / (750 - 645)
        R = (1.0 * attenuation) ** gamma
        G = 0.0
        B = 0.0
    else:
        R = 0.0
        G = 0.0
        B = 0.0
    return (R,G,B,A)

clim=(350,780)
norm = plt.Normalize(*clim)
wl = np.arange(clim[0],clim[1]+1,2)
colorlist = list(zip(norm(wl),[wavelength_to_rgb(w) for w in wl]))
spectralmap = matplotlib.colors.LinearSegmentedColormap.from_list("spectrum", colorlist)

fig, axs = plt.subplots(1, 1, figsize=(8,4), tight_layout=True)

wavelengths = np.linspace(200, 1000, 1000)
spectrum = (5 + np.sin(wavelengths*0.1)**2) * np.exp(-0.00002*(wavelengths-600)**2)
plt.plot(wavelengths, spectrum, color='darkred')

y = np.linspace(0, 6, 100)
X,Y = np.meshgrid(wavelengths, y)

extent=(np.min(wavelengths), np.max(wavelengths), np.min(y), np.max(y))

plt.imshow(X, clim=clim,  extent=extent, cmap=spectralmap, aspect='auto')
plt.xlabel('Wavelength (nm)')
plt.ylabel('Intensity')

plt.fill_between(wavelengths, spectrum, 8, color='w')
plt.savefig('WavelengthColors.png', dpi=200)

plt.show()