Compute a record power profileΒΆ
This example illustrates the usage of skcycling.Rider to compute
easily record power-profile,
print(__doc__)
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# License: BSD 3 clause
We will use the skcycling.Rider class to compute power-profile for
the toy data sets.
Out:
The computed activities are:
2014-05-07 12:26:22 2014-05-11 09:39:38 \
cadence 00:00:01 78.000000 100.000000
00:00:02 64.000000 89.000000
00:00:03 62.666667 68.333333
00:00:04 62.500000 59.500000
00:00:05 64.400000 63.200000
00:00:06 64.500000 66.500000
00:00:07 64.571429 69.285714
00:00:08 64.625000 71.875000
00:00:09 64.222222 67.888889
00:00:10 62.000000 76.800000
00:00:11 61.909091 73.090909
00:00:12 62.083333 75.333333
00:00:13 61.846154 70.769231
00:00:14 64.928571 73.142857
00:00:15 60.466667 75.400000
00:00:16 65.437500 82.562500
00:00:17 66.000000 78.647059
00:00:18 65.888889 79.666667
00:00:19 65.842105 80.473684
00:00:20 65.550000 81.250000
00:00:21 66.000000 81.952381
00:00:22 66.136364 82.727273
00:00:23 66.391304 83.391304
00:00:24 66.625000 84.000000
00:00:25 66.960000 84.560000
00:00:26 67.307692 85.153846
00:00:27 67.666667 85.740741
00:00:28 67.964286 86.214286
00:00:29 68.103448 86.689655
00:00:30 68.233333 87.133333
... ... ...
speed 01:51:14 NaN NaN
01:51:15 NaN NaN
01:51:16 NaN NaN
01:51:17 NaN NaN
01:51:18 NaN NaN
01:51:19 NaN NaN
01:51:20 NaN NaN
01:51:21 NaN NaN
01:51:22 NaN NaN
01:51:23 NaN NaN
01:51:24 NaN NaN
01:51:25 NaN NaN
01:51:26 NaN NaN
01:51:27 NaN NaN
01:51:28 NaN NaN
01:51:29 NaN NaN
01:51:30 NaN NaN
01:51:31 NaN NaN
01:51:32 NaN NaN
01:51:33 NaN NaN
01:51:34 NaN NaN
01:51:35 NaN NaN
01:51:36 NaN NaN
01:51:37 NaN NaN
01:51:38 NaN NaN
01:51:39 NaN NaN
01:51:40 NaN NaN
01:51:41 NaN NaN
01:51:42 NaN NaN
01:51:43 NaN NaN
2014-07-26 16:50:56
cadence 00:00:01 60.000000
00:00:02 58.000000
00:00:03 56.333333
00:00:04 59.250000
00:00:05 61.000000
00:00:06 62.333333
00:00:07 63.571429
00:00:08 63.750000
00:00:09 63.444444
00:00:10 63.000000
00:00:11 62.363636
00:00:12 61.916667
00:00:13 62.076923
00:00:14 62.642857
00:00:15 63.400000
00:00:16 62.625000
00:00:17 61.823529
00:00:18 61.109223
00:00:19 60.468319
00:00:20 59.889806
00:00:21 59.364771
00:00:22 58.885922
00:00:23 58.447235
00:00:24 58.043689
00:00:25 57.671068
00:00:26 57.325803
00:00:27 57.004854
00:00:28 56.705617
00:00:29 56.425845
00:00:30 68.500000
... ...
speed 01:51:14 5.478008
01:51:15 5.478270
01:51:16 5.478586
01:51:17 5.478993
01:51:18 5.479495
01:51:19 5.480122
01:51:20 5.480738
01:51:21 5.481302
01:51:22 5.481879
01:51:23 5.482435
01:51:24 5.482988
01:51:25 5.483579
01:51:26 5.484132
01:51:27 5.484607
01:51:28 5.485022
01:51:29 5.485448
01:51:30 5.485894
01:51:31 5.486276
01:51:32 5.486645
01:51:33 5.486973
01:51:34 5.487264
01:51:35 5.487511
01:51:36 5.487729
01:51:37 5.487925
01:51:38 5.488108
01:51:39 5.488282
01:51:40 5.488451
01:51:41 5.488631
01:51:42 5.488807
01:51:43 5.488291
[40218 rows x 3 columns]
The different power-profile for the activities can be plotted as follow
import matplotlib.pyplot as plt
rider.power_profile_.loc['power'].plot()
plt.xlabel('Time')
plt.ylabel('Power (W)')
Once that the power-profile for each activity are computed, we can compute the record power-profile for the rider and plot it.
rider.record_power_profile()['power'].plot(alpha=0.5,
style='--',
legend=True)
plt.xlabel('Time')
plt.ylabel('Power (W)')
plt.show()
Total running time of the script: ( 0 minutes 26.851 seconds)