semvec/pypi-packages huggingface dataset:
parallel-codex 1.0.2
Python toolkit for orchestrating Parallel Codex agents across isolated worktrees.
License: MIT
Requires Python: >=3.11
Keywords: agents, automation, codex, git, worktree

# parallel-codex

**Primary implementation** of Parallel Codex - Python toolkit for orchestrating Parallel Codex agents working in isolated Git worktrees.

**Current Status:** This package currently provides a **Textual TUI** for managing parallel agent sessions.

## Install

```bash
uv tool install parallel-codex
# or: pip install parallel-codex
```

This installs the `parallel-codex` CLI.

Prerequisites:
- The `codex` command must be available on your PATH.
- Codex CLI must be logged in: run `echo $OPENAI_API_KEY | codex login --with-api-key` once before using the TUI.

## TUI Usage

The TUI is the main entry point. It allows you to manage multiple Codex sessions, where each session operates on its own git worktree.

```bash
# Run the TUI
parallel-codex tui
```

### Development Mode

To see internal logs and debug information (MCP activity, stdout/stderr) inside the application:

```bash
parallel-codex tui --dev-log-panel
```

### TUI Features

- **Multiple Sessions:** Create and switch between up to three visible sessions (`Ctrl+N`, `Ctrl+Tab`).
- **Isolated Worktrees:** Each session creates a git worktree under `./.agents/<session-name>` and checks out a branch `pcx/<session-name>`.
- **Codex Integration:** Chats are sent to the `codex` MCP server, with file edits applied to the specific session's worktree.

## Development

From `packages/python-package`:

```bash
# Install dependencies
uv sync

# Run TUI from source
uv run src/main.py tui --dev-log-panel
```

Or from the repo root:

```bash
uv run --project packages/python-package python -m parallel_codex.cli tui --dev-log-panel
```

To configure the repository root used by the TUI when running from source:

```bash
export PARALLEL_CODEX_REPO_ROOT=/path/to/your/repo
```
pygformula 1.2.0
A python implementation of the parametric g-formula
License: MIT
Requires Python: >=3.8
Keywords: causal inference, g-formula, observational studies, longitudinal data

# pygformula: a python implementation of the parametric g-formula

[![PyPI version](https://badge.fury.io/py/pygformula.svg)](https://pypi.org/project/pygformula)
[![Documentation Status](https://readthedocs.org/projects/pygformula/badge/?version=latest)](https://pygformula.readthedocs.io)
[![Downloads](https://static.pepy.tech/badge/pygformula)](https://pepy.tech/project/pygformula)

**Authors: Jing Li, Sophia Rein, Sean McGrath, Roger Logan, Ryan O’Dea, Miguel Hernán**


## Overview
The pygformula package implements the non-iterative conditional expectation (NICE) estimator of the g-formula algorithm
(Robins, 1986). The g-formula can estimate an outcome’s counterfactual mean or risk under hypothetical treatment strategies
(interventions) when there is sufficient information on time-varying treatments and confounders.


### Features

* Treatments: discrete or continuous time-varying treatments.
* Outcomes: failure time outcomes or continuous/binary end of follow-up outcomes.
* Interventions: interventions on a single treatment or joint interventions on multiple treatments.
* Random measurement/visit process.
* Incorporation of a priori knowledge of the data structure.
* Censoring events.
* Competing events.


## Requirements

The package requires python 3.8+ and these necessary dependencies:

- cmprsk
- joblib
- lifelines
- matplotlib
- numpy
- pandas
- prettytable
- pytruncreg
- scipy
- seaborn
- statsmodels
- tqdm


## Documentation

The online documentation is available at [pygformula documentation](https://pygformula.readthedocs.io).

## Issues

If you have any issues, please open an [issue](https://github.com/CausalInference/pygformula/issues) on github, we will 
regularly check the questions. For any additional questions or comments, please email jing_li@hsph.harvard.edu.