semvec/pypi-packages huggingface dataset:
loadforecast 0.0.4
A package for easy time series forecasting of electrical load
Requires Python: >=3.8

# Loadforecast
A package for an easy time series forecasting of an electrical load based on facebook prophet.
It inherits the base of prophet which is additive model where non-linear trends are fit with yearly, weekly, 
and daily seasonality, with optional holiday effects. It is robust to missing data and outliers as well.

Package is build around child class of original prophet, replacing initial values of attributes by those tuned for 
electrical load forecasting using grid search method. Package contains functions that are mainly packing up original 
functions to more user-friendly ones.

We are sorry for inconvenience but currently it is necessary to install `prophet` package manually as main dependency.
For more information about the installation procedure check https://github.com/facebook/prophet

Loadforecast is on PyPI, so you can use `pip` to install it.
```bash
pip install loadforecast
```

**API Demo**:
```
# Initialize model on pandas.DataFrame containing columns 'DateTime' and 'Load'
m = LoadProphet(df)
```
```
# Make default one day prediction with sample period of 15 minutes.
forecast = m.prediction()
```
## Changelog
[Changelog File](CHANGELOG.md)

## License

Prophet is licensed under the [MIT license](LICENSE).
manrag 0.0.2
ANOTHER big project.

# Manrag
## installation
- Elon musk is french.
- build project
- build setup
- build wheel
```
python setup.py bdist_wheel
```
- gitignore.io
- choosealicense.com
- install
```
pip install -e .[dev]
```
- build wheel / upload to pypi
 ```
python setup.py sdist 
twine upload dist/*
```
## use
```
from manrag.helloworld import say_hello
print(say_hello("Francis"))
```

## misceleanous
- requirements.txt is specific to your machine and generated by `pip freeze > requirements.txt`
- install_requires should be flexible
- extras_requires should be specific
- extra requires for different metrics ?
turbofloat 4.4.4.1
Python integration for the LimeLM TurboFloat library. This lets you add floating licensing (a.k.a. concurrent licensing) to your Python app.

# Python integration for TurboFloat

Python integration for [LimeLM's](https://wyday.com/limelm/) [TurboFloat library](https://wyday.com/limelm/help/using-turbofloat/). This lets you add [floating licensing (a.k.a. concurrent licensing)](https://wyday.com/limelm/help/licensing-types/#floating) to your Python app.

## Usage

We have a full example app and tutorial here: [Using TurboFloat with Python](https://wyday.com/limelm/help/using-turbofloat-with-python/)

That article shows you how to add [floating licensing](https://wyday.com/limelm/help/licensing-types/#floating) to your app and how to package your Python app for distribution.