A toolkit for non-programming rungling of motion data measured by a 3D motion analyzer
Project description
humo
A toolkit for non-programmer to wrangle the motion data measured by 3D motion analyzer
Description
A toolkit for non-programmer to wrangle the motion data measured by 3D motion analyzer
DEMO:
Features
- Intuitive operability
- Seamlessly from data shaping to analysis
For more information, see humo -HP.
Requirement
- numpy
- scipy
- pandas
- matplotlib
Usage
quick start
import humo
# loading data and making humo object
data = humo.dataIO.load_data() # open pkl file
cfg = humo.dataIO.load_cfg() # open cfg.json
obj = humo.motion.CoreMain(data, cfg) # make humo object
# get right shoulder angle
Rsho = obj.getJointAngle("Rshoulder")
# get multi data
shoulder = obj.getJointAngle(["Rshoulder","Lshoulder"])
shoulder.Lshoulder # getting Lshoulder angle
shoulder.Lshoulder.x # getting Lshoulder x axis angle
Installation
pip install humo
Anything Else
The pickle data needed to create a humo object is created from a csv file. By converting it to a pickle file, very fast data loading can be achieved.
import humo
# Procedure to convert csv file to pkl file
c2p = humo.Preprocess.convert2pkl() # opening csv file
c2p.datacleansing() # cleansing csv data
c2p.convert2pkl() # save csv file as pkl file
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