我目前正在尝试傅里叶变换激光光谱。 原始数据是文本文件格式,我使用numpy导入数据。
由于数据是针对激光光谱的,因此X值是激光的波长,Y值代表激光的强度。
使用下面的代码,我将激光光谱绘制为
import numpy as np
import matplotlib.pyplot as plt
from pylab import genfromtxt
from matplotlib import pyplot
filelist = []
time = [0.00,-1.33,-2.67,-4.00,-5.33,-6.67]
offset = 0
index = 0
filelist.append("0.SSM")
for i in range (1,6):
filelist.append("-%s00.SSM" %i)
for fname in filelist:
data = np.genfromtxt(fname, delimiter=' ',unpack=True,skip_header=2)
X=data[0,:]
Y=data[1,:]
offset = offset + 500
plt.plot(X,Y+offset,label=str(time[index])+'ps')
index=index+1
plt.xlim([759,850])
axes=plt.gca
pyplot.title("Laser Spectrum of Both Pumps with Negative Time Delay(Pump 1 = 796nm, Pump 2 = 804nm)")
plt.xlabel("Wavelength[nm]")
plt.ylabel("Counts[arb.unit]")
plt.legend(loc='best')
plt.savefig('796and804nmminus')
plt.show()
这里的问题是当我尝试傅里叶变换这个图时。 我傅里叶变换了Y值并手动将X值(波长)转换为时间。 (通过使用电磁波的波长 - 频率关系)
我认为我处理X值的方式是错误的,因为它不是从0开始。当它们保存在数组中时,有没有办法根据X值对Y值进行傅里叶变换?
我用于傅立叶变换的代码如下。 由于这是试运行,因此只使用了一个文本文件来存储数据。
import numpy as np
import matplotlib.pyplot as plt
from pylab import genfromtxt
from matplotlib import pyplot
filelist = []
from scipy.fftpack import fft, ifft,rfft
sol = 299792458.00 #this is the speed of light in m/s
time = [0.00]
offset = 0
index = 0
filelist.append("0.SSM")
# for i in range (1,6):
# filelist.append("-%s00.SSM" %i)
for fname in filelist:
data = np.genfromtxt(fname, delimiter=' ',unpack=True,skip_header=2)
X=data[0,:]
F=sol/X
Y=data[1,:]
Z1=rfft(Y)
time1 = (1/F)*10**6
plt.plot(time1,Z1,label=str(time[index])+'ps')
index=index+1
plt.xlim([2.2,2.5])
axes=plt.gca
pyplot.title("Laser Spectrum of Both Pumps with Negative Time Delay(Pump 1 = 796nm, Pump 2 = 804nm)")
plt.xlabel("Time[ms]")
plt.ylabel("Counts[arb.unit]")
plt.legend(loc='best')
plt.savefig('796and804nmminus')
plt.show()
这就是我的"傅里叶变换"(至少我认为英国“金融时报”工作)图表的样子。
我的数学知识和编程知识都有点不稳定,所以非常感谢任何帮助/建议。
顺便说一句,这就是我的数据文件的样子
" File: Sabrina\20171212\796and804\0.SSM
" SCOPE-> Wave:814.54nm Pix:1050 Val:225.600 Time:5ms Avg:5 Sm:1 Sg:0 Tc:off Z Y Xt:1 Ch:1
688.00 2.113E+002
688.25 2.093E+002
688.50 2.116E+002
688.75 2.166E+002
689.00 2.125E+002
689.25 2.130E+002
689.50 2.096E+002
689.75 2.139E+002
690.00 2.138E+002
690.25 2.128E+002
690.50 2.117E+002
690.75 2.087E+002
691.00 2.079E+002
691.25 2.098E+002
691.50 2.114E+002
691.75 2.114E+002
692.00 2.116E+002
692.25 2.135E+002
692.50 2.150E+002
692.75 2.146E+002
693.00 2.131E+002
693.25 2.125E+002
693.50 2.115E+002
693.75 2.110E+002
694.00 2.126E+002
694.25 2.135E+002
694.50 2.135E+002
694.75 2.141E+002
695.00 2.154E+002
695.25 2.171E+002
695.50 2.176E+002
695.75 2.183E+002
696.00 2.181E+002
696.25 2.154E+002
696.50 2.141E+002
696.75 2.141E+002
697.00 2.160E+002
697.25 2.165E+002
697.50 2.168E+002
697.75 2.165E+002
698.00 2.164E+002
698.25 2.153E+002
698.50 2.155E+002
698.75 2.146E+002
699.00 2.115E+002
699.25 2.107E+002
699.50 2.124E+002
699.75 2.148E+002
700.00 2.164E+002
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700.50 2.182E+002
700.75 2.195E+002
701.00 2.196E+002
701.25 2.200E+002
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701.75 2.218E+002
702.00 2.217E+002
702.25 2.222E+002
702.50 2.203E+002
702.75 2.173E+002
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704.00 2.191E+002
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705.00 2.196E+002
705.25 2.206E+002
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706.00 2.198E+002
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706.75 2.210E+002
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707.75 2.204E+002
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