semvec/pypi-packages huggingface dataset:
environ-loader 0.0.2
Environ Loader
License: MIT
Requires Python: >=3.7,<4.0

# Environ Loader

A module for loading environment variables from various sources, like Windows .bat files and Bash .sh files.

Sometimes when writing a Python script, you want to load environment variables are defined in a text file. For example, when running Conan with the virtualenv generator,
Conan will generate shell files for Windows and Linux that contain variables like PATH that are configured to use the library/tool. So, if you are installing
a tool that the script uses with Conan, then you will have to load the environment variables before calling the tool. This is actually the original motivation
for the module.

It is not very sophisticated yet, only simple variable declarations are supported right now (for examples, Bash's $VAR style variable expansion is not supported, only ${VAR}).

## Features

- TODO

## Requirements

- TODO

## Installation

You can install _Environ Loader_ via [pip] from [PyPI]:

```console
$ pip install environ-loader
```

## License

Distributed under the terms of the [MIT license][license],
_Environ Loader_ is free and open source software.
astrocalculator 0.1.4
AstroCalculator, a Calculator for Astronomers and Physicists
License: MIT

# AstroCalculator, a Calculator for Astronomers and Physicists

## About

AstroCalculator is a calculator for astronomers and physicists written in Python.  
Author: Chong-Chong He (che1234@umd.edu)

## Installation

You can install astrocalculator from [PyPI](https://pypi.org/project/realpython-reader/):

```sh
pip install astrocalculator
```

`astrocalculator` is supported on Python 3.6 and above.

## How to use

Start the program with `calc`. You will see a command line interface where you can type in your inputs. A input can be 1) a single variable or constant like `k_B`, 2) an expression like `m_e c^2`, or 3) a list of variable assignments followed by a final expression to evaluate, e.g. `M = 1.4 M_sun, R = 10 km, sqrt(2 G M / R)`. 

Another way is to use it as a Python module in your script or interactively in iPython. Put the following in your code:

```python
from calc import *
```

Now you can use all the physical constants defined in `astrocalculator` as listed in [here](https://github.com/chongchonghe/acap/docs/constants.md). 

### Example inputs and outputs

```
Input[1]: m_p

Parsed input = m_p
Result (SI)  = 
  Name   = Proton mass
  Value  = 1.67262192369e-27
  Uncertainty  = 5.1e-37
  Unit  = kg
  Reference = CODATA 2018
Result (cgs) = 1.6726e-24 g

Input[2]: m_e c^2

Parsed input = c**2*m_e
Result (SI)  = 8.1871e-14 m N
Result (cgs) = 8.1871e-07 erg

Input[3]: in MeV

0.51100 MeV

Input[4]: M = 1.4 M_sun, R = 10 km, sqrt(2 G M / R)

Parsed input = sqrt(2*G*M*1/R)
Result (SI)  = 1.9277e+08 m / s
Result (cgs) = 1.9277e+10 cm / s

Input[5]: in km/s

1.9277e+05 km / s
```

## Todos

- [ ] parse keyword 'in' at the last line and automatically change User Unit.
- [ ] Add latex preview

## References

- https://docs.astropy.org/en/stable/units/
- https://docs.astropy.org/en/stable/constants/
- https://www.lidavidm.me/blog/posts/2013-09-15-implicit-parsing-in-sympy.html
- https://stackoverflow.com/questions/62507535/python-suppress-expansion-of-exponential-notation-in-parse-expr-sympy
beanmachine 0.2.0
Probabilistic Programming Language for Bayesian Inference
License: MIT
Requires Python: >=3.7
Keywords: Probabilistic Programming Language,Bayesian Inference,Statistical Modeling,MCMC,Variational Inference,PyTorch

# Bean Machine
<div align="center">
  <a href="http://beanmachine.org"> <img width="220px" height="220px" src="https://beanmachine.org/img/beanmachine.svg"></a>
</div>

[![Lint](https://github.com/facebookresearch/beanmachine/actions/workflows/lint.yml/badge.svg)](https://github.com/facebookresearch/beanmachine/actions/workflows/lint.yml)
[![Tests](https://github.com/facebookresearch/beanmachine/actions/workflows/test.yml/badge.svg)](https://github.com/facebookresearch/beanmachine/actions/workflows/test.yml)
[![PyPI](https://img.shields.io/pypi/v/beanmachine)](https://pypi.org/project/beanmachine)


## Overview

Bean Machine is a probabilistic programming language for inference over statistical models written in the Python language using a declarative syntax. Bean Machine is built on top of PyTorch and Bean Machine Graph, a custom C++ backend.
Check out our [tutorials](https://beanmachine.org/docs/overview/tutorials/Coin_flipping/CoinFlipping/) and [Quick Start](https://beanmachine.org/docs/overview/quick_start/) to get started!

## Installation
Bean Machine supports Python 3.7-3.10 and PyTorch 1.12.

### Install the Latest Release with Pip

```bash
python -m pip install beanmachine
```

### Install from Source

To download the latest Bean Machine source code from GitHub:

```bash
git clone https://github.com/facebookresearch/beanmachine.git
cd beanmachine
```

Then, you can choose from any of the following installation options.

#### Anaconda

We recommend using [conda](https://docs.conda.io/en/latest/) to manage the virtual environment and install the necessary build dependencies.

```bash
conda create -n {env name} python=3.8; conda activate {env name}
conda install -c conda-forge boost-cpp eigen=3.4.0
python -m pip install .
```

#### Docker

```bash
docker build -t beanmachine .
docker run -it beanmachine:latest bash
```

#### Validate Installation

If you would like to run the builtin unit tests:

```bash
python -m pip install "beanmachine[test]"
pytest src
```

## License
Bean Machine is MIT licensed, as found in the [LICENSE](LICENSE) file.