semvec/pypi-packages huggingface dataset:
spoox 0.3.2
SPOOX – SPlit lOOp eXpand – Terminal Assistant
Requires Python: >=3.10

## SPOOX

<br>
  
**SPOOX – SPlit lOOp eXpand**

A terminal-integrated, LLM-powered multi-agent system (MAS) designed to assist developers directly within their terminal.
_Spoox CLI_ provides intelligent assistance for OS tasks, server management workflows, and software engineering challenges.
The architectures of these agent systems are based on the _spoox_ MAS design framework,
a generic architectural framework for multi-agent topology and communication design. 

Several differently scaled terminal MAS variants have been developed and are accessible through a terminal CLI: _spoox-s_, _spoox-m_, and _spoox-l_.
The spoox-m variant achieved first place on the [Terminal Bench leaderboard](https://www.tbench.ai/leaderboard/terminal-bench/2.0?models=GPT-5-Mini) 
for the gpt-5-mini model and is therefore used as the default configuration for the _spoox CLI_.


More information and code are available on the [Spoox GitHub repository](https://github.com/plaume8/spoox).

<br>

Authors: 
[Linus Sander](mailto:linus.sander@tum.de),
[Fengjunjie Pan](mailto:f.pan@tum.de),
[Alois Knoll](mailto:k@tum.de)
pymedm 2.2.8
Penalized Maximum-Entropy Dasymetric Modeling (P-MEDM) in Python.
Requires Python: >=3.11

# PyMEDM: Penalized Maximum-Entropy Dasymetric Modeling (P-MEDM) in Python

![tag](https://img.shields.io/github/v/release/likeness-pop/pymedm?include_prereleases&sort=semver)
[![PyPI version](https://badge.fury.io/py/pymedm.svg)](https://badge.fury.io/py/pymedm)
[![Conda Version](https://img.shields.io/conda/vn/conda-forge/pymedm.svg)](https://anaconda.org/conda-forge/pymedm)

[![Continuous Integration](https://github.com/likeness-pop/pymedm/actions/workflows/continuous_integration.yml/badge.svg)](https://github.com/likeness-pop/pymedm/actions/workflows/continuous_integration.yml)
[![codecov](https://codecov.io/gh/likeness-pop/pymedm/branch/develop/graph/badge.svg)](https://codecov.io/gh/likeness-pop/pymedm)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

This is a GPU-ready Python port of [PMEDMrcpp](https://bitbucket.org/jovtc/pmedmrcpp/src/master) via `jax` and `jaxopt`. Support for usage on Windows is not guaranteed.

## Installation

### Conda-forge (recommended)

The `pymedm` feedstock is available via the [conda-forge channel](https://github.com/conda-forge/pymedm-feedstock).

```
$ conda install --channel conda-forge pymedm
```

### PyPI

`pymedm` is available on the [Python Package Index](https://pypi.org/project/pymedm/).

```
$ pip install pymedm
```

### Source

#### Directly via GitHub + `pip`

```
$ pip install git+https://github.com/likeness-pop/pymedm.git@develop
```

#### Download + `pip`

Download the source distribution (``.tar.gz``) and decompress where desired. From that location:

```
$ pip install .
```

## Usage

* See usage examples in [`./notebooks/`](https://github.com/likeness-pop/pymedm/tree/main/notebooks)

## Development

1. Clone the repository to the desired location.
2. Install in editable mode
   * Navigate to where the repo was cloned locally.
   * Within that directory:
      ```
      $ pip install -e .
      ```
3. Open an Issue for discussion
4. In a branch off `develop`, implement update/bug fix/etc.
5. Create a minimal Pull Request with clear description linked back to the associated issue from (3.)
6. Wait for review from maintainers
7. Adjust as directed
8. Once merged, fetch down `origin/develop` and merge into the local `develop`
9. Delete the branch created in (4.)
10. Start over at (2.)

## References

1. **Leyk, S., Nagle, N. N., & Buttenfield, B. P.** (2013). Maximum entropy dasymetric modeling for demographic small area estimation. Geographical Analysis, 45(3), 285-306.
2. **Nagle, N. N., Buttenfield, B. P., Leyk, S., & Spielman, S.** (2014). Dasymetric modeling and uncertainty. Annals of the Association of American Geographers, 104(1), 80-95.
pytensor-distributions 0.1.2
PyTensor powered distributions.
Requires Python: >=3.11

# PyTensor-distributions

PyTensor powered distributions

## Overview

PyTensor-distributions provides a collection of core probability expressions implemented in PyTensor. 

The goal of this package is to work as a unified, well tested and performant source for core probability expressions. This reduces redundancy across packages and allows other libraries to focus on providing specialized features and APIs on top of these core expressions.

The core expressions implemented in PyTensor-distributions are:

- `pdf`: the probability density function of a distribution. (We use pdf for both discrete and continuous distributions)
- `cdf`: the cumulative distribution function of a distribution.
- `ppf`: the percent point function (inverse of cdf) of a distribution.
- `sf`: the survival function (1 - cdf) of a distribution.
- `isf`: the inverse survival function (inverse of sf) of a distribution.
- `logpdf`: the log-probability of a distribution. (We use pdf for both discrete and continuous distributions)
- `logcdf`: the log-cumulative distribution function of a distribution.
- `logsf`: the log-survival function (1 - cdf) of a distribution
- `mean`: the mean of a distribution.
- `mode`: the mode of a distribution.
- `median`: the median of a distribution.
- `var`: the variance of a distribution.
- `std`: the standard deviation of a distribution.
- `skewness`: the skewness of a distribution.
- `kurtosis`: the kurtosis of a distribution.
- `entropy`: the entropy of a distribution.
- `rvs`: the random variates of a distribution.

Some distributions may not have all of these expressions implemented.

The package follows a very minimal design, with one file per distribution and some utility files for common functions and tests. The core expressions are implemented as a collection of functions, no classes are used. This is on purpose, to keep the package as simple as possible and let other packages implement more structured APIs on top of these core expressions.

PyTensor-distributions is still in early development, and we are still adding more distributions, tests, and working on improving robustness.


## Contributions

PyTensor-distributions is a community project and welcomes contributions.


## Code of Conduct

We follow the PyMC [Code of Conduct](hhttps://github.com/pymc-devs/pymc/blob/main/CODE_OF_CONDUCT.md)

## Donations

PyTensor-distributions, like other PyMC projects, is a non-profit project under the NumFOCUS umbrella. If you want to finnancially support PyTensor-distributions or other PyMC projects financially, you can donate [here](https://numfocus.org/donate-to-pymc).

## Sponsors
[![NumFOCUS](https://www.numfocus.org/wp-content/uploads/2017/07/NumFocus_LRG.png)](https://numfocus.org)
ourskyai-sda-api 1.3.9903
OurSky SDA
Keywords: OpenAPI, OpenAPI-Generator, OurSky SDA

    The basic flow for a new organization is as follows: 1. View the available satellite targets with the [satellite targets](#tag/satellite-targets/GET/v1/satellite-targets) endpoint. Copy the id of the target you want to observe. 2. Create an organization target with the [organization target](#tag/organization-targets/GET/v1/organization-targets) endpoint. Use the id copied from above. 3. Create a webhook with the [webhook](#tag/webhooks/POST/v1/communications/webhook) endpoint to receive OSRs automatically (preferred) or use the [OSRs](#tag/observation-sequence-results/GET/v1/observation-sequence-results) endpoint to poll for OSRs.  Check out our [examples](https://github.com/ourskyai/oursky-examples) repository to see usage in each language.
hito_tools 26.1
Modules for interacting with Hito and NSIP
Requires Python: >3.11

# hito_tools module

Ce repository contient le module Pyhton `hito_tools` utilisé par les autres outils Python autour de Hito (OSITAH, hito2lists...).


## Installation

### Environnement Python

L'installation de [hito_tools](https://pypi.org/project/hito-tools) nécessite un environnement Python avec une version >= 3.8.
Il est conseillé d'utiliser un environnement
virtuel pour chaque groupe d'applications et de déployer le module `hito_tools` dans cet environnemnt. Il est recommandé d'utiliser
une distribution de Python totalement indépendante du système d'exploitation comme [pyenv](https://github.com/pyenv/pyenv),
[poetry](https://python-poetry.org) ou [Anaconda](https://www.anaconda.com/products/individual). Pour la création d'un
environnement virtuel avec Conda, voir la 
[documentation spécifique](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-with-commands).

Les modules Python requis par ce module sont :
* pandas (conda-forge)
* requests (conda-forge)

Avec `conda`, il faut utiliser l'option `-c conda-forge` lors de la commande `conda install`. 


### Installation du module hito_tools

L'installation se fait avec la commande `pip` de l'environnement Python utilisé :

```bash
pip install hito_tools
```