Minimal example for working with ergometer data

Import the worklab module:

[1]:
import os
import worklab as wl

Import the data with com.load() or the device specific load function:

[2]:
filename = os.getcwd()
filename = os.path.join(os.path.split(filename)[0], 'example_data', 'Esseda_example_LEM.xls')
ergo_data = wl.com.load(filename)
print("Ergometer data is stored in a: ", type(ergo_data))

================================================================================
Initializing loading for C:\Users\rick_\Development\worklab2\example_data\Esseda_example_LEM.xls ...
File identified as Esseda datafile. Attempting to load ...
Data loaded!
================================================================================

Ergometer data is stored in a:  <class 'dict'>
[3]:
ergo_data.keys()
[3]:
dict_keys(['left', 'right'])

The ergometer data is a little more simple than measurement wheel data, (but you do have two modules):

[4]:
ergo_data["left"].head()
[4]:
time force speed
0 0.01 0.788487 0.0
1 0.02 0.780378 0.0
2 0.03 0.797562 0.0
3 0.04 0.792799 0.0
4 0.05 0.838543 0.0

Processing is identical however:

  • filter

  • process

  • push-by-push

[5]:
ergo_data = wl.kin.filter_ergo(ergo_data)
ergo_data = wl.kin.process_ergo(ergo_data)
ergo_data["left"].head()
[5]:
time force speed torque acc power dist work uforce aspeed angle
0 0.01 0.788493 -1.163828e-14 0.244433 -2.799567e-13 -9.176693e-15 0.000000e+00 -9.176693e-17 0.888846 -3.754282e-14 0.000000e+00
1 0.02 0.793392 -1.443784e-14 0.245951 -2.414086e-13 -1.145486e-14 -1.303806e-16 -1.145486e-16 0.894369 -4.657369e-14 -4.205825e-16
2 0.03 0.802370 -1.646645e-14 0.248735 -1.179231e-13 -1.321219e-14 -2.849020e-16 -1.321219e-16 0.904490 -5.311757e-14 -9.190388e-16
3 0.04 0.812440 -1.679630e-14 0.251857 1.148631e-13 -1.364600e-14 -4.512158e-16 -1.364600e-16 0.915842 -5.418163e-14 -1.455535e-15
4 0.05 0.811675 -1.416918e-14 0.251619 4.896777e-13 -1.150078e-14 -6.060432e-16 -1.150078e-16 0.914979 -4.570705e-14 -1.954978e-15

Now you have almost all parameters that you will ever need:

[6]:
ergo_data["left"].plot("time", "torque");
../_images/examples_ex_kinetics_ergo_11_0.png

Let’s do a christmas tree for a smaller section of the data:

[7]:
ergo_data["left"] = ergo_data["left"].iloc[:3000, :]
ergo_data["right"] = ergo_data["right"].iloc[:3000, :]

Get the pushes with the push by push function:

[8]:
pushes = wl.kin.push_by_push_ergo(ergo_data)
print(type(pushes))
print(pushes.keys())
pushes["left"].head()
<class 'dict'>
dict_keys(['left', 'right'])
[8]:
stop start peak tstart tstop tpeak cangle ptime meanpower maxpower meantorque maxtorque meanforce maxforce work slope ctime reltime
0 351 276 311 2.77 3.52 3.12 1.180683 0.75 34.260157 77.243329 20.808663 33.636038 75.667865 122.312864 26.037719 96.102965 1.15 65.217391
1 419 391 409 3.92 4.20 4.10 1.122666 0.28 67.705997 142.440912 16.666537 34.028533 60.605589 123.740121 19.634739 189.047406 0.77 36.363636
2 474 468 472 4.69 4.75 4.73 0.272526 0.06 15.354861 27.136431 3.381357 5.975240 12.295844 21.728147 1.074840 149.381011 0.12 50.000000
3 493 480 487 4.81 4.94 4.88 0.599194 0.13 44.567863 79.093543 9.648388 17.036661 35.085049 61.951494 6.239501 243.380868 0.60 21.666667
4 547 540 543 5.41 5.48 5.44 0.319896 0.07 31.561252 54.087009 6.901818 11.812804 25.097520 42.955650 2.524900 393.760127 0.12 58.333333

This is all very similar to the measurement wheel, except you don’t have access to 3D forces. Similar to the measurement wheel data, all of the above can be achieved with the auto_process function.

[9]:
wl.plots.plot_ergometer_pushes(ergo_data, pushes);
../_images/examples_ex_kinetics_ergo_17_0.png

Very festive! 🎄