semvec/pypi-packages huggingface dataset:
ml4pd 2022.11.9
ML4PD - an open-source libray for building Aspen-like process models via machine learning.
Requires Python: >=3.8

# ml4pd

Process design with Machine Learning.

## How to Install for Users

```
pip install ml4pd git+https://github.com/NREL/ml4pd_models.git@v1
```

## How to Set Up for Development

```   
git clone https://github.com/NREL/ml4pd.git
git clone https://github.com/NREL/ml4pd_models.git
mamba env create -f ml4pd/environment.yml
conda develop ml4pd
conda develop ml4pd_models
```

### Additional GitHub repositories for docs, tests and training
- `ml4pd_utils`: code base for generating & preparing data for training.
- `autoaspen`: database for data obtained by aspen & python.

### To debug docs
- Use `mkdocs serve` within `ml4pd` directory, then go to `localhost:8000`.
- `generate_site.py` gets docstrings (written in makrdown) from classes and put them in the right directory.
- To make changes to notebooks, add `ml4pd` to path with `sys.path.append()` or `conda develop`.

### Relationship with ml4pd_models
To minimize manual work, ml4pd dependends on a specific ml4pd_models github branch. When changes are made to
either ml4pd or ml4pd_models that will break compatibility, create new branch in ml4pd_models, and apply change
to workflow .yml files, this README and index.md in docs.
huedominance 0.1.2
Hue Range Dominance Index (HRDI) algorithm
Requires Python: >=3.5
Keywords: plant phenology,phenocam,vegetation

# huedominance

Provides **Hue Range Dominance Index (HRDI)** algorithm &mdash; a new vegetation index proposed for PhenoCam plant phenology image analysis.

For comparison with Green Chromatic Coordinate (GCC), a `gcc` function is available, too.

To install:

`pip3 install huedominance`

To use from command line (example):

```bash
python3 -m huedominance 100 170 green_leaves.jpg
```

To use in your Python code:

```python
from huedominance.hrdi import hrdi
from huedominance.gcc import gcc
from PIL import Image

image = Image.open('/path/to/your/image')

green_leaves_hrdi, hrdi_image = hrdi(image, 100, 170, make_illustration = True)
green_leaves_gcc, gcc_image = gcc(image, make_illustration = True)

hrdi_image.save('/path/to/hrdi/illustrative/signal/image', 'JPEG')
gcc_image.save('/path/to/gcc/illustrative/signal/image', 'JPEG')

red_leaves_hrdi, _ = hrdi(image, -30, 10)
```

Please refer to Python help on function `hrdi` for details about the algorithm.