Working with ergometer data#

Import the worklab module:

import os
import worklab as wl

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

filename = os.getcwd()
filename = os.path.join(filename, '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\Janssen\PycharmProjects\worklab\docs\chapters\examples\example_data\Esseda_example_LEM.xls ...
File identified as Esseda datafile. Attempting to load ...
Data loaded!
================================================================================

Ergometer data is stored in a:  <class 'dict'>

ergo_data.keys()

dict_keys(['left', 'right'])

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

ergo_data["left"].head()

	time	force
0	0.01	0.788487
1	0.02	0.780378
2	0.03	0.797562
3	0.04	0.792799
4	0.05	0.838543

Processing is identical however:

filter
process
push-by-push

ergo_data = wl.kin.filter_ergo(ergo_data)
ergo_data = wl.kin.process_ergo(ergo_data)
ergo_data["left"].head()

	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:

ergo_data["left"].plot("time", "torque");

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

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:

pushes = wl.kin.push_by_push_ergo(ergo_data)
print(type(pushes))
print(pushes.keys())
pushes["left"].head()

<class 'dict'>
dict_keys(['left', 'right'])

	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.

wl.plots.plot_ergometer_pushes(ergo_data, pushes);

Very festive! 🎄