0

I need to analyze some data taken in my lab but it goes beyond my limited programming skills. I have multiple sets of similar data and I need to take the average of all of these and plot that with error bars. The idea is to plot the area of an optical signal (which is recorded by a photomultiplier tube) versus the angle of the diffuser that emits the optical signal. To collect the data, the diffuser is rotated from -60 to 60 degrees and stops every 3 degrees for the photomultiplier tube (PMT) to record the area optical waveform. I have three individual sets of data that look like PMT = (angle, area). (Ignore the diode data. That is used to normalize the area values for the PMT so essentially what is plotted is PMT = (angle, normalizedarea.) )Instead of three individual graphs in one plot (like the picture attached has) I need to plot them all as one average of the three data sets combined and with error bars to show the deviation between data sets. Thank you in advance! Here is my code:

 from pathlib import Path
 import numpy as np
 import matplotlib.pyplot as plt 
 import matplotlib as mpl

 PMT1 = ([-60,-57,-54,-51,-48,-45,-42,-39,-36,-33,-30,-27,-24,-21,-18,-15,-12,-9,-6,-3,0,3,6,9,12,15,18,21,24,27,30,33,36,39,42,45,48,51,54,57,60],[6.751,7.115,7.40,7.646,7.839,8.101,8.319,8.452,8.843,8.954,9.202,9.270,9.521,9.612,9.632,9.740,9.928,9.991,10.06,10.11,10.13,10.07,9.998,10.08,9.952,9.847,9.668,9.413,9.465,9.322,9.017,8.815,8.716,8.385,8.257,8.093,7.880,7.623,7.477,7.379,6.968])

 diode1 = ([-60,-57,-54,-51,-48,-45,-42,-39,-36,-33,-30,-27,-24,-21,-18,-15,-12,-9,-6,-3,0,3,6,9,12,15,18,21 
,24,27,30,33,36,39,42,45,48,51,54,57,60],[0.01021,0.01016,0.01020,0.01011,0.01013,0.01019,0.01015,0.01020,0.01022,0.01027,0.01037,0.01024,0.010170,0.01028,0.01022,0.01032,0.01026,0.01028,0.01022,0.01026,0.01036,0.01033,0.01021,0.01036,0.01030,0.01012,0.01014,0.01024,0.01019,0.01020,0.01028,0.01035,0.01021,0.01023,0.01017,0.01017,0.01025,0.01026,0.01011,0.01017,0.01006])


 PMT2 = ([-60,-57,-54,-51,-48,-45,-42,-39,-36,-33,-30,-27,-24,-21,-18,-15,-12,-9,-6,-3,0,3,6,9,12,15,18,21,24,27,30,33,36,39,42,45,48,51,54,57,60],[7.012,7.157,7.420,7.587,7.867,8.070,8.271,8.473,8.836,9.114,9.228,9.392,9.462,9.592,9.688,9.798,9.945,9.892,10.03,9.869,10.19,10.06,9.928,9.988,9.850,9.753,9.601,9.589,9.459,9.302,9.078,8.960,8.744,8.596,8.415,8.096,8.002,7.757,7.498,7.276,6.973])

 diode2 = ([-60,-57,-54,-51,-48,-45,-42,-39,-36,-33,-30,-27,-24,-21,-18,-15,-12,-9,-6,-3,0,3,6,9,12,15,18,21,24,27,30,33,36,39,42,45,48,51,54,57,60],[0.009975,0.01014,0.01018,0.01016,0.01019,0.01010,0.01029,0.01029,0.01027,0.01028,0.01031,0.01016,0.01012,0.01038,0.01022,0.01037,0.01029,0.01020,0.01031,0.01041,0.01030,0.01031,0.01019,0.01031,0.01034,0.01027,0.01033,0.01037,0.01024,0.01026,0.01032,0.01035,0.01034,0.01022,0.01025,0.01007,0.01004,0.01024,0.01012,0.01023,0.01014])

 PMT4 = ([-60,-57,-54,-51,-48,-45,-42,-39,-36,-33,-30,-27,-24,-21,-18,-15,-12,-9,-6,-3,0,3,6,9,12,15,18,21,24,27,30,33,36,39,42,45,48,51,54,57,60],[7.043,7.222,7.462,7.746,7.974,8.181,8.285,8.542,8.731,8.824,8.994,9.103,9.258,9.473,9.579,9.792,9.797,9.999,10.03,10,9.914,9.943,9.914,9.884,9.7813,9.676,9.502,9.440,9.245,9.272,9.091,8.858,8.761,8.397,8.225,8.007,7.862,7.725,7.367,7.149,6.938])

 diode4 = ([-60,-57,-54,-51,-48,-45,-42,-39,-36,-33,-30,-27,-24,-21,-18,-15,-12,-9,-6,-3,0,3,6,9,12,15,18,21,24,27,30,33,36,39,42,45,48,51,54,57,60],[0.01001,0.01001,0.01005,0.01017,0.01002,0.01003,0.009996,0.01015,0.01005,0.01021,0.01014,0.01016,0.01019,0.01014,0.01022,0.01021,0.01018,0.01015,0.01022,0.01030,0.01003,0.01015,0.01010,0.01022,0.01020,0.01021,0.01018,0.01006,0.01012,0.01006,0.01007,0.01012,0.01008,0.01003,0.01010,0.01012,0.01006,0.01009,0.01001,0.01004,0.009927])



 angle1 = np.array(PMT1[0])
 area1 = np.array(PMT1[1])
 p1 = np.array(diode1[1])
 plt.plot(angle1,area1*np.mean(p1)/p1)

 angle2 = np.array(PMT2[0])
 area2 = np.array(PMT2[1])
 p2 = np.array(diode2[1])
 plt.plot(angle2,area2*np.mean(p2)/p2)

 angle4 = np.array(PMT4[0])
 area4 = np.array(PMT4[1])
 p4 = np.array(diode4[1])
 plt.plot(angle4,area4*np.mean(p4)/p4)


 plt.show()

Image

4

1 回答 1

0

您可以在 NumPy 中进行必要的计算，然后绘制结果。

为了有效地做到这一点，我首先将所有面积值组合到一个二维数组中np.vstack。然后，您可以通过在该数组上调用适当的 NumPy 方法来计算各种测量系列之间的平均值等axis=0。

所有三个系列的角度值都相同，因此我们可以angle1始终使用。

“误差线”是一个模棱两可的术语，因此您必须决定误差线应该代表什么。在下面的代码中，我选择显示 +/- 一个样本标准差。在任何情况下，您都可以使用plt.fill_between将这些值的系列绘制为误差带。

assert (angle1 == angle2).all()
assert (angle1 == angle4).all()

area1_norm = area1 * np.mean(p1) / p1
area2_norm = area2 * np.mean(p2) / p2
area4_norm = area4 * np.mean(p4) / p4

areas = np.vstack([area1_norm, area2_norm, area4_norm])
means = areas.mean(axis=0)
stds = areas.std(axis=0, ddof=1)

plt.plot(angle1, means)
plt.fill_between(angle1, means - stds, means + stds, alpha=0.3)
plt.show()

结果图

于 2022-01-02T13:41:01.687 回答