我試圖寫(xiě)一個(gè)簡(jiǎn)單的python代碼,用于給定溫度T = 200K的強(qiáng)度與波長(zhǎng)的關(guān)系圖.

到目前為止我有這個(gè)……

import scipy as sp

import math

import matplotlib.pyplot as plt

import numpy as np

pi = np.pi

h = 6.626e-34

c = 3.0e+8

k = 1.38e-23

def planck(wav, T):

a = 2.0*h*pi*c**2

b = h*c/(wav*k*T)

intensity = a/ ( (wav**5)*(math.e**b - 1.0) )

return intensity

我不知道如何定義波長(zhǎng)(wav),從而產(chǎn)生普朗克公式的圖.任何幫助,將不勝感激.

解決方法:

這是一個(gè)基本情節(jié).要使用plt.plot(x,y,fmt)進(jìn)行繪圖,您需要兩個(gè)相同大小的數(shù)組x和y,其中x是要繪制的每個(gè)點(diǎn)的x坐標(biāo),y是y坐標(biāo),fmt是描述如何的字符串繪制數(shù)字.

因此,您需要做的就是創(chuàng)建一個(gè)均勻間隔的波長(zhǎng)陣列(我稱(chēng)之為波長(zhǎng)的np.array).這可以通過(guò)范圍(開(kāi)始,結(jié)束,間隔)來(lái)完成,這將創(chuàng)建從開(kāi)始到結(jié)束(不包括)間隔開(kāi)的數(shù)組.

然后使用函數(shù)在數(shù)組中的每個(gè)點(diǎn)處計(jì)算強(qiáng)度(將存儲(chǔ)在另一個(gè)np.array中),然后調(diào)用plt.plot繪制它們.注意numpy讓你在vectorized form中快速對(duì)數(shù)組進(jìn)行數(shù)學(xué)運(yùn)算,這在計(jì)算上是有效的.

import matplotlib.pyplot as plt

import numpy as np

h = 6.626e-34

c = 3.0e+8

k = 1.38e-23

def planck(wav, T):

a = 2.0*h*c**2

b = h*c/(wav*k*T)

intensity = a/ ( (wav**5) * (np.exp(b) - 1.0) )

return intensity

# generate x-axis in increments from 1nm to 3 micrometer in 1 nm increments

# starting at 1 nm to avoid wav = 0, which would result in division by zero.

wavelengths = np.arange(1e-9, 3e-6, 1e-9)

# intensity at 4000K, 5000K, 6000K, 7000K

intensity4000 = planck(wavelengths, 4000.)

intensity5000 = planck(wavelengths, 5000.)

intensity6000 = planck(wavelengths, 6000.)

intensity7000 = planck(wavelengths, 7000.)

plt.hold(True) # doesn't erase plots on subsequent calls of plt.plot()

plt.plot(wavelengths*1e9, intensity4000, 'r-')

# plot intensity4000 versus wavelength in nm as a red line

plt.plot(wavelengths*1e9, intensity5000, 'g-') # 5000K green line

plt.plot(wavelengths*1e9, intensity6000, 'b-') # 6000K blue line

plt.plot(wavelengths*1e9, intensity7000, 'k-') # 7000K black line

# show the plot

plt.show()

而你看到：

您可能需要清理軸標(biāo)簽,添加圖例,在同一圖表上繪制多個(gè)溫度下的強(qiáng)度等.咨詢(xún)r(jià)elevant matplotlib documentation.

標(biāo)簽：python,numpy,scipy,matplotlib

來(lái)源： https://codeday.me/bug/20190831/1772064.html

總結(jié)

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