semvec/pypi-packages huggingface dataset:
tet 0.4.2
Unearthly intelligent batteries-included application framework built on Pyramid
Keywords: web wsgi bfg pylons pyramid

![tet](https://raw.githubusercontent.com/tetframework/tet/master/docs/_static/tet.png)

[![PyPI version](https://img.shields.io/pypi/v/tet.svg)](https://pypi.org/project/tet/)
[![Python versions](https://img.shields.io/pypi/pyversions/tet.svg)](https://pypi.org/project/tet/)
[![License](https://img.shields.io/pypi/l/tet.svg)](https://pypi.org/project/tet/)
[![Documentation Status](https://readthedocs.org/projects/tet/badge/?version=latest)](https://tet.readthedocs.io/en/latest/?badge=latest)
[![GitHub stars](https://img.shields.io/github/stars/tetframework/tet.svg?style=social)](https://github.com/tetframework/tet)

Unearthly intelligent batteries-included application framework built on Pyramid.

## How to install

```
pip install tet
```


# Changes


2025-01-28  Antti Haapala  <antti.haapala@anttipatterns.com>

    * Add Python 3.12, 3.13, 3.14 support. Drop Python 3.6-3.8 support.
    * Add Sphinx documentation and ReadTheDocs integration.

2021-03-19  Antti Haapala  <antti.haapala@anttipatterns.com>

    * The tet.di request scoped services are now truly instantiated per request!

2016-08-19  Antti Haapala  <antti.haapala@anttipatterns.com>

    * SQLAlchemy root factory now gives NotFound on DataError; made into a implicit-namespace package;
      fixed backports.typing to greater than or equal to 1.1.

2013-09-07  Antti Haapala  <antti.haapala@anttipatterns.com>

    * renamed the package to `tet`
teamtomo 0.0.1
Modular Python packages for cryo-EM and cryo-ET
Requires Python: >=3.11

# TeamTomo

TeamTomo is a set of modular Python package for cryo-EM and cryo-ET for the modern scientific computing environment.
This unified repository contains most of the core TeamTomo data processing functionality under a single umbrella for better maintainability and cross-package development.

## List of Packages

TODO

## Getting Started

### Installation for Developers

The development workspace depends on the [uv](https://github.com/astral-sh/uv) tool for Python environment/package management.

First, clone and navigate into the root of the repository:

```bash
git clone https://github.com/teamtomo/teamtomo.git
cd teamtomo
```

Then, create and activate a new virtual environment:

```bash
uv venv
source .venv/bin/activate
```

And finally sync with the repository to install packages

```bash
uv sync --all-extras --all-packages
```

Note that the `--all-extras` and `--all-packages` flags install the development and testing requirements for all sub-packages. More granular install options are possible, if your system requires it.
generate-fastapi-typed-routes 0.3.2
Generate typed url_path_for functions for FastAPI applications.
Requires Python: >=3.11
Keywords: fastapi, typing, routes, codegen

# Type-Safe Route URL Generator for FastAPI

Stop guessing route names. This tool analyzes your FastAPI application and generates a typed wrapper for `url_path_for`, giving you instant autocompletion and catching typos before they hit production.

## Installation

```bash
uv add generate-fastapi-typed-routes --dev
uvx generate-fastapi-typed-routes@latest
```

## Usage

Point the tool at your FastAPI app and tell it where to save the generated code:

```bash
uvx generate-fastapi-typed-routes@latest --app-module myapp.main:app --output myapp/routes.py
```

Now, instead of using the raw `app.url_path_for`, import your generated function:

```python
from myapp.routes import app_url_path_for

# Complete with IDE autocompletion and type checking!
url = app_url_path_for("get_user_profile", user_id=123)
```

### CLI Arguments

*   `--app-module`: The import path to your FastAPI app instance (e.g., `src.main:app`). You can pass this multiple times to generate helpers for multiple apps in one file.
*   `--output`: The file path where the generated Python code will be saved.
*   `--prefix`: (Optional) Custom prefix for the generated function. Defaults to the app variable name (e.g., `app` becomes `app_url_path_for`). Use this if you have multiple apps to keep things distinct.
*   `--directory` / `-d`: (Optional) The directory containing the application module (default: current directory). Use this if your app is not in the current working directory.

## Features

*   **Zero Runtime Overhead:** The generated code is just type hints and a simple wrapper.
*   **IDE Autocompletion:** Never type a route name manually again. Your editor will list every available route name defined in your app.
*   **Refactoring Safe:** Change a route name in your app, and your type checker (mypy, pyright) will flag every place usage that needs updating.
*   **Multi-App Support:** Easily manage routes for projects with multiple FastAPI instances.

# [MIT License](LICENSE.md)
gest-api 0.1
A standard for generating test points and exchanging variables, objectives, and constraints in optimization problems
Requires Python: >=3.10
Keywords: optimization, workflows, generators

# Overview

This repository is an effort to standardize the interface of the **generators** in optimization libraries such as:

- [`Xopt`](https://github.com/ChristopherMayes/Xopt)
- [`optimas`](https://github.com/optimas-org/optimas)
- [`libEnsemble`](https://github.com/Libensemble/libensemble)
- [`rsopt`](https://github.com/radiasoft/rsopt)

**The objective of this effort is for these different libraries to be able to use each other's generators with little effort.**

*Examples:*
> Using `libEnsemble` generators in `Optimas`:
> APOSMM [NLopt](https://github.com/optimas-org/optimas/blob/f7f5e656f4b98e64a0c2849b6f73aabd49af7682/examples/libe_aposmm_nlopt/run_example.py) - [IBCDFO](https://github.com/optimas-org/optimas/blob/f7f5e656f4b98e64a0c2849b6f73aabd49af7682/examples/libe_aposmm_ibcdfo/run_example.py)
>
> Using `Xopt` generators in `Optimas`:
> [Multiple Examples](https://github.com/optimas-org/optimas/blob/f49bd63d329a8aca2a3444a6a93dbda548f71cfa/tests/test_xopt_generators.py)
>
> Using `Xopt` generators in `libEnsemble`:
> [ExpectedImprovement](https://github.com/Libensemble/libensemble/blob/589d8fb8b0019469ad6a2784e6bcd5e2c2cac8ee/libensemble/tests/regression_tests/test_xopt_EI.py) - [(with Xopt-style sim)](https://github.com/Libensemble/libensemble/blob/589d8fb8b0019469ad6a2784e6bcd5e2c2cac8ee/libensemble/tests/regression_tests/test_xopt_EI_xopt_sim.py)
>
> Using `Optimas` generators in `libEnsemble`:
> [Multi-Fidelity Ax Generator](https://github.com/Libensemble/libensemble/blob/e7fef19c19343c5eceaa4353985aa963ebada46c/libensemble/tests/regression_tests/test_optimas_ax_mf.py)
>
> Using `libEnsemble` generators in `Xopt`:
> [APOSMM Generator](https://github.com/xopt-org/Xopt/blob/cfb8d704637aee31e836357cf47ac0acb82bdc71/xopt/tests/generators/external/test_aposmm.py)

# Definitions

- **Generator:**

  A generator is an object that recommends points to be evaluated in an optimization. It can also receive data (evaluations from past or ongoing optimization), which helps it make more informed recommendations.

  *Note:* The generator does **not** orchestrate the overall optimization (e.g. dispatch evaluations, etc.). As such, it is distinct from `libEnsemble`'s `gen_f` function, and is not itself "workflow" software.

- **Variables, Objectives, Constraints (VOCS):**

  A `VOCS` is an object that specifies the names and types of components of the optimization problem that will be used by the generator. Each generator will validate that it can handle the specified set of variables, objectives, constraints, etc.

# Documentation

For complete API documentation, examples, and detailed specifications, see the official documentation:

**https://generator-standard.readthedocs.io**

# Installation

The Python abstract classes that define the standard can be installed with:

```
pip install gest-api
```
python3-cyberfusion-common 2.12.2
Common utilities.

# python3-cyberfusion-common

Common utilities.

These utilities are mostly used by Cyberfusion libraries, but are also useful standalone.

# Install

## PyPI

Run the following command to install the package from PyPI:

    pip3 install python3-cyberfusion-common

## Debian

Run the following commands to build a Debian package:

    mk-build-deps -i -t 'apt -o Debug::pkgProblemResolver=yes --no-install-recommends -y'
    dpkg-buildpackage -us -uc

# Configure

No configuration is supported.

# Usage

See code.
PyPwnCollege 0.1.8
A wrapper for the pwn.college API. This allows automating interactions with pwn.college and also includes a command-line-interface(CLI) to list dojos/modules, submit flags, start challenges, run commands in challenge containers and more.
Requires Python: <4.0,>=3.8
Keywords: pwn.college, pwncollege, automation

# PyPwnCollege

[![Run Tests](https://github.com/zeeshan12340/pypwncollege/actions/workflows/tests.yml/badge.svg?branch=main)](https://github.com/zeeshan12340/pypwncollege/actions/workflows/tests.yml)
[![codecov](https://codecov.io/gh/zeeshan12340/pypwncollege/branch/main/graph/badge.svg)](https://codecov.io/gh/zeeshan12340/pypwncollege)
[![Documentation Status](https://readthedocs.org/projects/pypwncollege/badge/?version=latest)](https://pypwncollege.readthedocs.io/en/latest/?badge=latest)
[![PyPI version](https://badge.fury.io/py/PyPwnCollege.svg)](https://badge.fury.io/py/PyPwnCollege)
[![PyPI Statistics](https://img.shields.io/pypi/dm/pypwncollege.svg)](https://pypistats.org/packages/pypwncollege)

PyPwnCollege is an unofficial Python library to interact with the pwn.college API and website.

## Install

```bash
pip install pypwncollege
```

## Demo

> Note: Username/Password is not stored on the system, only the session cookie "app_cookie" is stored in ~/.pwncli.json. Re-login might be needed if that cookie expires.

```py
from pwncollege import PWNClient
# Create an API connection
client = PWNClient(email="user@example.com", password="S3cr3tP455w0rd!")
# Print the User associated with the client
print(client.user)
```

## CLI

The library also comes with a CLI to interact with the API which is a work in progress. It is included in the published pip package and should be available in shell.

Basic usage:

```bash
pwncli --help
pwncli login -u test1337 -p test1337
pwncli get --dojos
pwncli challenge -d fundamentals -m program-misuse -c level-1 -f flag{test}
```

> Note: Not specifying the password on the command line will make pwncli request it like sudo does.

## Documentation

The documentation is available [here](https://pypwncollege.readthedocs.io/en/latest/).

## Current Features

- Logging into the API
- Fetching Dojos, Modules, Challenges
  - Starting a Challenge
  - Running an ssh command on a challenge
  - Submitting flags
- Getting User details
- Getting rankings in dojos and modules
- Getting information about belted users

## Contributing

If you find a bug or want to add a feature, feel free to open an issue or a pull request.
rachis 2026.1.0
None
Requires Python: <3.12,>=3.10

# Rachis Framework (Formerly Known as QIIME 2 Framework (Q2F))

![](https://github.com/qiime2/qiime2/workflows/ci-dev/badge.svg)

Source code repository for the QIIME 2™ Framework.

Are you looking for:
- the QIIME 2 homepage? That's [https://qiime2.org](https://qiime2.org).
- learning resources for microbiome marker gene (i.e., amplicon) analysis? See the [QIIME 2 *amplicon distribution* documentation](https://amplicon-docs.readthedocs.io).
- learning resources for microbiome metagenome analysis? See the [MOSHPIT documentation](https://moshpit.readthedocs.io).
- installation instructions, plugins, books, videos, workshops, or resources? See the [QIIME 2 Library](https://library.qiime2.org).
- general help? See the [QIIME 2 Forum](https://forum.qiime2.org)
- information about contributing? See the [Contributing Guide](https://github.com/qiime2/.github/blob/main/CONTRIBUTING.md).
- information about the developers? See the [Caporaso Lab website](https://caplab.bio).

## Citing QIIME 2

If you use QIIME 2 for any published research, please cite [Bolyen, Rideout, Dillon, Bokulich, et al. (2019)](https://doi.org/10.1038/s41587-019-0209-9).
py-buzz 8.0.0
"That's not flying, it's falling with style": Exceptions with extras
Requires Python: >=3.10

[![Latest Version](https://img.shields.io/pypi/v/py-buzz?label=pypi-version&logo=python&style=plastic)](https://pypi.org/project/py-buzz/)
[![Python Versions](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2Fdusktreader%2Fpy-buzz%2Fmain%2Fpyproject.toml&style=plastic&logo=python&label=python-versions)](https://www.python.org/)
[![Build Status](https://github.com/dusktreader/py-buzz/actions/workflows/main.yml/badge.svg)](https://github.com/dusktreader/py-buzz/actions/workflows/main.yml)
[![Documentation Status](https://github.com/dusktreader/py-buzz/actions/workflows/docs.yml/badge.svg)](https://dusktreader.github.io/py-buzz/)

![py-buzz-logo](https://github.com/dusktreader/py-buzz/blob/main/docs/source/images/buzz-logo-text.png)

**That's not flying, _it's falling with style_: Exceptions with extras**

![asciicast](https://github.com/dusktreader/py-buzz/blob/main/docs/source/images/py-buzz.gif)

py-buzz is fully equipped with a suite of exception tools that will save you
from writing the same code over and over again in your python projects. These
include:

* checking many conditions and reporting which ones failed (`check_expressions()`)
* catching exceptions wrapping them in clearer exception types with better error messages (`handle_errors()`)
* retrying operations with exponential backoff until they succeed (`retry()`)
* checking that values are defined and raising errors if not (`enforce_defined()`)
* checking that values are a certain type and raising errors if not (`ensure_type()`)
* checking conditions and raising errors on failure (`require_condition()`)

py-buzz also provides an exception class, Buzz, that can be used  as a base class
for custom exceptions within a project.

## Super-quick Start

* Only requires Python 3.10 or later
* Installed with pip (`$ pip install py-buzz`)
* Each feature demonstrated in an executable demo "extra"

## Documentation

The complete documentation can be found at the [py-buzz documentation page](https://dusktreader.github.io/py-buzz/)
pstorch 0.1.6
The goal of this project is to make pytorch easier to use.

# pstorch

Generate a ready-to-run PyTorch project in seconds.

## Install

```bash
pip install pstorch
```

## Python

```python
import pstorch

pstorch.generate(
    name="pytorch",
    input_dim=784,
    output_dim=10,
    hidden=[256, 128],
    metrics=["accuracy", "loss"],
    prod=False,
)
```

Then run the boilerplate:

```bash
python main.py
```

## CLI

```bash
pstorch generate pytorch --input-dim 784 --output-dim 10 --hidden 256,128 --metrics accuracy,loss
```

Defaults example:

```bash
pstorch generate
```

Named default example:

```bash
pstorch generate pytorch
```

Output:

```
src/
    __init__.py
    config.py
    models.py
    data.py
    train.py
    predict.py
main.py
requirements.txt
```