Minimal example for working with measurement wheel 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(os.path.split(filename)[0], 'example_data', 'Optipush_example.data')
mw_data = wl.com.load(filename)
print("Measurement wheel data is stored in a: ", type(mw_data))

================================================================================
Initializing loading for C:\Users\rick_\Development\worklab2\example_data\Optipush_example.data ...
File identified as Optipush datafile. Attempting to load ...
Data loaded!
================================================================================

Measurement wheel data is stored in a:  <class 'pandas.core.frame.DataFrame'>

The DataFrame contains all the information from the file, but nothing extra:

mw_data.columns

Index(['time', 'fx', 'fy', 'fz', 'mx', 'my', 'torque', 'angle'], dtype='object')

Before getting more infos it would be a good idea to apply some filtering:

mw_data = wl.kin.filter_mw(mw_data)

Now let’s get more infos:

mw_data = wl.kin.process_mw(mw_data)
mw_data.head()

	time	fx	fy	fz	mx	my	torque	angle	aspeed	speed	dist	acc	ftot	uforce	force	power	work
0	0.000	2.838031	1.753805	-4.058034	0.004851	0.005135	-0.054472	3.673093	3.352298	1.039212	0.000000	2.535617	5.253370	-0.198079	-0.175715	-0.182605	-0.000913
1	0.005	2.401395	1.666210	-4.385999	-0.002135	0.004741	-0.058072	3.689854	3.393195	1.051890	0.005228	3.594796	5.270667	-0.211171	-0.187329	-0.197050	-0.000985
2	0.010	1.963322	1.620943	-4.642096	-0.006774	0.002821	-0.060339	3.707025	3.468259	1.075160	0.010545	4.264677	5.294445	-0.219414	-0.194642	-0.209271	-0.001046
3	0.015	1.527743	1.645536	-4.771329	-0.007243	-0.001333	-0.060383	3.724537	3.530765	1.094537	0.015970	3.537893	5.273269	-0.219573	-0.194782	-0.213197	-0.001066
4	0.020	1.104789	1.748478	-4.743367	-0.002454	-0.007348	-0.058016	3.742333	3.582384	1.110539	0.021482	2.901078	5.174675	-0.210967	-0.187148	-0.207835	-0.001039

That’s more like it!

mw_data.plot("time", "torque");

Let’s see if we can find some pushes in these data:

pushes = wl.kin.push_by_push_mw(mw_data)
print(f"There are {len(pushes)} complete pushes in the data")
pushes.head()

There are 29 complete pushes in the data

	stop	start	peak	tstart	tstop	tpeak	cangle	ptime	meanpower	maxpower	meantorque	maxtorque	meanforce	maxforce	work	feff	slope	ctime	reltime
0	195	97	176	0.485	0.975	0.88	1.676823	0.490	36.376315	94.426828	10.559981	24.357933	38.399932	88.574302	18.006276	55.197071	61.665653	2.695	18.181818
1	735	636	718	3.180	3.675	3.59	1.630329	0.495	36.170961	86.937413	10.449778	22.283936	37.999192	81.032495	18.085480	52.638096	54.351063	3.095	15.993538
2	1349	1255	1330	6.275	6.745	6.65	1.601879	0.470	45.783597	102.833058	12.917944	25.623400	46.974343	93.175999	21.747209	63.410359	68.329066	3.130	15.015974
3	1994	1881	1972	9.405	9.970	9.86	1.781313	0.565	33.413155	86.508159	10.060917	22.747502	36.585153	82.718191	19.045498	56.930325	49.994511	3.190	17.711599
4	2629	2519	2612	12.595	13.145	13.06	1.897524	0.550	43.382667	118.642142	12.244082	29.976050	44.523935	109.003817	24.077380	61.715078	64.464623	3.590	15.320334

You can achieve the exact same thing using the auto_process function. Which chains the above operations into one monolithic function and returns the data and the pushes, as such:

mw_data, pushes = wl.kin.auto_process(mw_data, *args, **kwargs)

Now make a christmas tree!

wl.plots.plot_pushes(mw_data, pushes);

Magnificent! 😍