Metadata-Version: 2.1
Name: humo
Version: 1.5.0.15
Summary: A toolkit for non-programmer to wrangle the motion data measured by 3D motion analyzer
Home-page: https://sites.google.com/view/pythonforeveryone/python-for-evryone
Author: Akira Kobayashi
License: MIT
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Programming Language :: Python
Classifier: Intended Audience :: Developers
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown

# humo
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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
```python
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

 ```python
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.

```python
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
```

## Author

[akira6](kobayashi.akira.5312@gmail.com)

## License

[MIT](http://b4b4r07.mit-license.org)



