The size of the data samples is very large, making it difficult to visualise the data points using a matplotlib plot.
Sample Code:
import matplotlib.pyplot as plt
plt.plot(myList_timestamps, myList_fitnessValues)
plt.xlabel('Timestamps (seconds)')
#plt.xticks(range(1, 51)
#plt.xticks(range(1, 53, 5))
plt.ylabel('WATT - MSU Fitness Values')
plt.title('Evolutionary Optimization - Execution Time')
plt.show()
Output:
I have 9113 candidates solutions as data samples to plot against 9113 data samples as fitness values. How should I plot this large data using python to better visualize the data?
Data Sample:
myList_timestamps = [[0.06160092353820801,
0.07070684432983398,
0.0794517993927002,
0.08730483055114746,
0.09506797790527344,
0.10278487205505371,
0.11050796508789062,
0.11819696426391602,
0.12598776817321777,
0.13364410400390625,
0.1412339210510254,
0.14882898330688477,
0.15642499923706055,
0.16405892372131348,
0.171644926071167,
0.17924880981445312,
0.1868269443511963,
0.1943988800048828,
0.2020108699798584,
0.21060776710510254,
0.219498872756958,
0.22813701629638672,
0.23638296127319336,
0.24529194831848145,
0.25347185134887695,
0.26166296005249023,
0.2696189880371094,
0.2773740291595459,
0.2849307060241699,
0.2925240993499756,
0.30014586448669434,
0.3077728748321533,
0.31533288955688477,
0.32283592224121094,
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0.4655318260192871,
0.4730229377746582,
0.48052191734313965,
0.488048791885376,
0.49558186531066895,
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myList_fitnessValues = [1.177397872785327,
1.1838368070851042,
1.198426283830517,
1.1971495165606483,
1.1300637485336795,
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]
How should I plot this large data using python to better visualize the data?
CodePudding user response：
A simple and common method to get a better overview about this kind of data is to calculate the 