semvec/pypi-packages huggingface dataset:
mwiszenko-med 1.0.1
Awesome med created by mwiszenko

# med

[![codecov](https://codecov.io/gh/mwiszenko/MED/branch/main/graph/badge.svg?token=GD9WUZk0lo)](https://codecov.io/gh/mwiszenko/MED)
[![CI](https://github.com/mwiszenko/MED/actions/workflows/main.yml/badge.svg)](https://github.com/mwiszenko/MED/actions/workflows/main.yml)
[![PyPI](https://img.shields.io/pypi/v/mwiszenko-med)](https://pypi.python.org/pypi/mwiszenko-med/)

Awesome med created by mwiszenko

## Install it from PyPI

```bash
pip install med
```

## Usage

```py
from med import BaseClass
from med import base_function

BaseClass().base_method()
base_function()
```

```bash
$ python -m med
#or
$ med
```

## Development

Read the [CONTRIBUTING.md](CONTRIBUTING.md) file.
platinumtools 0.4.5
DDAPP Modules
Keywords: python,utilities


# DDAP Modules

## Introduction

Here some useful modules to be used when for the development of DDAP

- So they can be plugged into ddanalytics applications and so forth.

## Features

- [x] dda_constants
- [x] etl_functions

### Classes

- [ ] guardian
  - [ ] BasicTokenGuardian
- [ ] 



## Updated Instructions
trajectory-manifold 0.0.2
Statistically Rigorous ODE Forecasting
Requires Python: >=3.9

# Trajectory Manifold

[![Documentation](https://img.shields.io/badge/Documentation-Main-darkgreen)](https://www.helmuthnaumer.com/trajectory_manifold/)

This repository contains code around statistical estimation on the trajectory manifold.

It is intended to accompany a paper currently under review.

The directory `figures` contains code to reproduce the figures in the work, while the directory `examples` contains the quick start example from the documentation.