semvec/pypi-packages huggingface dataset:
jaxace 0.4.1
JAX implementation of AbstractCosmologicalEmulators.jl interface with artifact support
Requires Python: <4.0,>=3.10

# jaxace

[![Tests](https://github.com/CosmologicalEmulators/jaxace/actions/workflows/tests.yml/badge.svg)](https://github.com/CosmologicalEmulators/jaxace/actions/workflows/tests.yml)
[![Documentation](https://img.shields.io/badge/docs-stable-blue)](https://cosmologicalemulators.github.io/jaxace/stable/)
[![Documentation](https://img.shields.io/badge/docs-dev-blue)](https://cosmologicalemulators.github.io/jaxace/dev/)
[![codecov](https://codecov.io/gh/CosmologicalEmulators/jaxace/graph/badge.svg?token=8DGPCJR8KX)](https://codecov.io/gh/CosmologicalEmulators/jaxace)

JAX/Flax implementation of cosmological emulators with automatic JIT compilation.

## Installation

```bash
pip install -e .
```

## Usage

```python
import jaxace
import jax.numpy as jnp

# Cosmology
cosmo = jaxace.w0waCDMCosmology(
    ln10As=3.044, ns=0.9649, h=0.6736,
    omega_b=0.02237, omega_c=0.1200,
    m_nu=0.06, w0=-1.0, wa=0.0
)

# Background functions
z = jnp.array([0.0, 0.5, 1.0])
growth = jaxace.D_z_from_cosmo(z, cosmo)
distance = jaxace.r_z_from_cosmo(z, cosmo)

# Neural network emulator
emulator = jaxace.init_emulator(nn_dict, weights, jaxace.FlaxEmulator)
output = emulator(input_data)  # Auto-JIT + batch detection
```

## Features

- Background cosmology (growth, distances, Hubble)
- Neural network emulators with auto-JIT
- Massive neutrinos and dark energy support
- Full JAX integration (grad, vmap, jit)
gym_info 0.1.0
Information-theoretic metrics for Gymnasium environments
Requires Python: >=3.10

# gym_info

Information-theoretic diagnostics for Gymnasium environments.

`gym_info` helps you inspect **state and action entropies** in RL environments with minimal changes to your existing Gymnasium code. You wrap an environment once, run your usual interaction loop, and then query global or per-episode entropy metrics, tables, and simple reports.

---

## Installation

```bash
pip install gym_info
# or, with uv:
# uv add gym_info
```

Requirements:
- Python: $\geq$ 3.10
- Gymnasium: $\geq$ 1.2.2

## Quick Start

```bash
import gymnasium as gym
import gym_info as gi

# 1. Create and wrap the environment
env = gym.make("CartPole-v1")
env = gi.attach(env, preset="classic_control", run_id="demo-run")

# 2. Run your usual interaction loop
obs, info = env.reset(seed=0)
for _ in range(200):
    action = env.action_space.sample()
    obs, reward, terminated, truncated, info = env.step(action)
    if terminated or truncated:
        obs, info = env.reset()

# 3. Global entropies for the whole run (in bits)
metrics = gi.entropies(env)
print(metrics.H_S, metrics.H_A, metrics.H_A_given_S)

# 4. High-level summary
summ = gi.summary(env)
gi.print_table(summ)         # text table
gi.plot_entropies(summ)      # matplotlib figure

```

## Per-episode Analysis

You can inspect entropies for each completed episode separately.

```bash
from gym_info import entropies_per_episode, episode_entropy_series, episode_entropy_dataframe

episode_metrics = entropies_per_episode(env)
for i, m in enumerate(episode_metrics):
    print(f"Episode {i}: H(S)={m.H_S:.3f}, H(A)={m.H_A:.3f}, H(A|S)={m.H_A_given_S:.3f}")

series = episode_entropy_series(env)
df = episode_entropy_dataframe(env)
```

## Discretization

gym_info works by discretizing continuous observations and actions into bins and then computing histogram-based entropies.

- A preset encapsulates reasonable default binning strategies for a family of environments.

- Currently, the main preset is:

  - preset="classic_control": designed for standard classic control environments such as CartPole-v1 and MountainCar-v0.

You can override discretization globally by passing obs_bins and action_bins to attach:

```bash
env = gi.attach(
    env,
    preset="classic_control",
    run_id="demo-run",
    obs_bins=20,    # here
    action_bins=5,  # here
)
```
twtr 0.1.4
Minimal CLI to tweet via Twitter API.
Requires Python: >=3.8

# twtr

## Setup

1. Make a free account and project at [developer.x.com](https://developer.x.com) and get the following keys:

- Bearer Token
- Consumer Key
- Consumer Secret
- Access Token
- Access Token Secret

![](https://github.com/kiankyars/twtr/blob/main/keys.png?raw=true)

2. Add these lines to your `~/.zshrc` or `~/.bashrc`:
> December 2025 update: this is honestly bad practice, so I recommend putting your environment variables in a separate .env file and then exporting that. Nevertheless, you need to somehow expose these keys to the environment.
```sh
export TWEEPY_BEARER_TOKEN="<your_bearer_token>"
export TWEEPY_CONSUMER_KEY="<your_consumer_key>"
export TWEEPY_CONSUMER_SECRET="<your_consumer_secret>"
export TWEEPY_ACCESS_TOKEN="<your_access_token>"
export TWEEPY_ACCESS_TOKEN_SECRET="<your_access_token_secret>"
```
Then reload your config:
```sh
source ~/.zshrc
```
or
```sh
source ~/.bashrc
```

## Usage

Install from PyPI with uv:

```sh
uv tool install twtr
```

Then tweet from the CLI:

```sh
twtr "your tweet here"
```

## Development

Set up the development environment:

```sh
uv sync
```

Run the package locally for testing:

```sh
# Using the entry point script
uv run twtr "your tweet here"

# Or as a Python module
uv run python -m twtr.cli "your tweet here"
```

## Structure
```
.
├── LICENSE
├── README.md
├── keys.png
├── pyproject.toml
├── twtr
│   ├── __init__.py      # Public API (expose main functions/classes)
│   └── cli.py           # CLI entry point (separate from core logic)
└── uv.lock
```
sphinx-markdown-builder-reloaded 0.6.9
A Sphinx extension to add markdown generation support.
Requires Python: >=3.7
Keywords: sphinx, sphinx-extension, markdown, docs, documentation, builder

# sphinx-markdown-builder

[![Coverage Status](https://coveralls.io/repos/github/liran-funaro/sphinx-markdown-builder/badge.svg?branch=main&cache-bust=1)](https://coveralls.io/github/liran-funaro/sphinx-markdown-builder?branch=main)

A Sphinx extension to add markdown generation support.

## Install

```sh
pip3 install sphinx-markdown-builder==0.6.8
```

## Usage

Add the extension to your `conf.py` file:
```python
extensions = [
    ...,
    "sphinx_markdown_builder",
    ...,
]
```

Build markdown files with `sphinx-build` command
```sh
sphinx-build -M markdown ./docs ./build
```

## Configurations

You can add the following configurations to your `conf.py` file:

* `markdown_anchor_sections`/`markdown_anchor_signatures`: If set to `True`, 
  then anchors will be added before each section/function/class signature. 
  This allows references to a specific anchor in the document.
* `markdown_docinfo`: Adds metadata to the top of each document containing author, copyright, and version.
* `markdown_http_base`: If set, all references will link to this prefix address
* `markdown_uri_doc_suffix`: If set, all references will link to documents with this suffix.
* `markdown_file_suffix`: Sets the file extension for generated markdown files (default: `.md`).
* `markdown_bullet`: Sets the bullet marker.
* `markdown_flavor`: If set to `github`, output will suit GitHub's flavor of Markdown.

For example, if your `conf.py` file have the following configuration:

```python
markdown_http_base = "https://your-domain.com/docs"
markdown_uri_doc_suffix = ".html"
markdown_bullet = "*"
```

Then a reference to `your-doc-name#your-header` will be substituted with `https://your-domain.com/docs/your-doc-name.html#your-header`. 


## Contributing

See the [code contribution guidelines](CONTRIBUTING.md) for more information.

## Credits
This project forked from [clayrisser/sphinx-markdown-builder], which was developed by [Clay Risser] under the [MIT] license.

The original implementation was based on [doctree2md] by [Matthew Brett] under the [BSD-2] license.

## License

[MIT]

[clayrisser/sphinx-markdown-builder]: https://github.com/clayrisser/sphinx-markdown-builder
[Clay Risser]: https://github.com/clayrisser
[doctree2md]: https://github.com/matthew-brett/nb2plots/blob/master/nb2plots/doctree2md.py
[Matthew Brett]: https://github.com/matthew-brett
[MIT]: LICENSE
[BSD-2]: https://github.com/matthew-brett/nb2plots/blob/main/LICENSE
lpibydevcoder 0.2.0
Гибридный язык программирования: Python + C++ + Rust стиль
Requires Python: >=3.6

LpibyDevCooder
Автор
DevCooder
GitHub - [text](https://github.com/ZeroMurder)

Описание
LpibyDevCooder — минималистичный интерпретируемый язык программирования для обучения и экспериментов. Полностью реализован стек: лексер → парсер → интерпретатор.

🎯 Основные возможности
текст
✅ Арифметика: +, -, *, /
✅ Переменные: x := 10
✅ Ввод/вывод: pr(), ind()
✅ 30+ встроенных функций
✅ Типы: числа, строки, списки, словари
✅ REPL с историей команд
✅ CLI: lpi file.lpi
✅ Обработка ошибок
🏗️ Компоненты
текст
lexer.py     # Токенизатор (30+ токенов)
parser.py    # Парсер AST
interpreter.py # Интерпретатор
__init__.py  # REPL + CLI
setup.py     # pip install -e .
🚀 Примеры кода
текст
# Арифметика
pr(3 + 5 * 2)    # 13

# Переменные
x := 10; y := 20
pr(x + y)        # 30

# Списки
lst := list(3,1,2)
pr(sort(lst))    # [1,2,3]

# Массивы
vec := vec(5)    # [0,0,0,0,0]
arr := arr(3,99) # [99,99,99]

# Функции
pr(mx(1,5,3))    # 5
pr(bit(42))      # 101010
📋 Быстрый старт
Установка
удар
pip install lpibydevcoder
Запуск
удар
# REPL
lpi

# Файл
lpi script.lpi

# В Python
from lpibydevcoder import lpi_eval
lpi_eval('pr("Hello!")')
REPL пример
текст
lpi> pr(42 + 10)
52
lpi> x := 5
lpi> pr(x * 2)
10
lpi> help()
📚 Встроенные функции
Вывод/ввод	Конвертация	Математика	Списки
pr()	st()	square()	list()
ind()	in()	ab()	sort()
fl()	mx()	rev()
rn()	mn()	range()
Строки	Битовые	Массивы	Прочее
sr()	bit()	vec()	len()
len()		arr()	help()
nosq()
🎮 Цели проекта
Обучение компиляторам

Быстрые скрипты

Расширяемость

Производительность

LpibyDevCooder — идеальная платформа для изучения языковых технологий и создания скриптов!​