Plank辐射公式中对数范围图有限范围

  • Z时代
  • 2024-01-10
  • 分类：问答

我想绘制普朗克辐射方程，如下所示。当我使用Mathematica时，绘图效果很好，但是当我尝试用Python来完成时，我无法正确理解它。它基本不会低于〜1.0微米波长。请参阅附件图片和代码。如果你能提供帮助，这将是非常可观的。先谢谢你。Plank辐射公式中对数范围图有限范围

import matplotlib.pyplot as plt 
from matplotlib import pyplot 
from matplotlib import pylab 
import numpy as np 
h = 6.626e-34 
c = 2.9979e+8 
k = 1.38e-23 
def planck(wav, T): 
    a = 2.0*3.14*h*c**2 
    b = h*c/(wav*1e3*1e-9*k*T) 
    intensity = a/ (((wav*1e3*1e-9)**5) * (np.exp(b) - 1.0))*1e-6 
    return intensity 
wavelengths = np.logspace(1e-2, 1e2, 1e4, endpoint=False) 
intensity310 = planck(wavelengths, 310.) 
intensity3000 = planck(wavelengths, 3000.) 
intensity5800 = planck(wavelengths, 5800.) 
intensity15000 = planck(wavelengths, 15000.) 
plt.plot(wavelengths, intensity310, 'k-') # 5000K Black line 
plt.plot(wavelengths, intensity3000, 'r-') # 5000K green line 
plt.plot(wavelengths, intensity5800, 'y-') # 6000K blue line 
plt.plot(wavelengths, intensity15000, 'b-') # 7000K Red line 
pyplot.xscale('log') 
pyplot.yscale('log') 
pylab.xlim([1e-2,1e2]) 
pylab.ylim([1,1e10]) 
plt.show()

使用Python

使用Mathematica

一个计谋的计策

回答：

。在你的np.logspace()用法是错误的。如果您需要1e-2到1e2之间的值，则应该分别使用参数-2和2（即np.logspace(-2, 2, 1e4)）调用该函数。此外，如果您想创建一个日志缩放图，则可以使用pyplot.loglog()函数。没有必要导入pylab。请参见下面的代码：

import matplotlib.pyplot as plt 
import numpy as np 
h = 6.626e-34 
c = 2.9979e+8 
k = 1.38e-23 
def planck(wav, T): 
    a = 2.0*3.14*h*c**2 
    b = h*c/(wav*1e3*1e-9*k*T) 
    intensity = a/ (((wav*1e3*1e-9)**5) * (np.exp(b) - 1.0))*1e-6 
    return intensity 
wavelengths = np.logspace(-2, 2, 1e4, endpoint=False) 
intensity310 = planck(wavelengths, 310.) 
intensity3000 = planck(wavelengths, 3000.) 
intensity5800 = planck(wavelengths, 5800.) 
intensity15000 = planck(wavelengths, 15000.) 
plt.loglog(wavelengths, intensity310, 'k-') # 5000K Black line 
plt.loglog(wavelengths, intensity3000, 'r-') # 5000K green line 
plt.loglog(wavelengths, intensity5800, 'y-') # 6000K blue line 
plt.loglog(wavelengths, intensity15000, 'b-') # 7000K Red line 
plt.xlim([1e-2,1e2]) 
plt.ylim([1,1e10]) 
plt.show()

我碰到下面的情节：

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