semvec/pypi-packages huggingface dataset:
jmstate 0.13.4
Joint modeling with automatic differentiation
Requires Python: >=3.10

# 📦 jmstate

**jmstate** is a Python package for **multi-state nonlinear joint modeling**.  
It leverages **PyTorch** for automatic differentiation and vectorized computation, making it efficient and scalable. The package provides a flexible framework where you can use **neural networks as regression and link functions**, while still offering simpler built-in options like parametric baseline hazards.

With **jmstate**, you can model longitudinal data jointly with multi-state transitions (e.g. health progression), capture nonlinear effects, and perform inference in complex real-world settings.

---

## ✨ Features

- **Multi-State Joint Modeling**  
  Supports subjects moving through multiple states with transition intensities that depend on longitudinal trajectories and covariates.

- **Nonlinear Flexibility**  
  Use neural networks (or any PyTorch model) as regression or link functions.

- **Built-in Tools**  
  Includes default baseline hazards, regression, link functions, and analysis utilities.

- **Automatic Differentiation**  
  Powered by PyTorch for efficient gradient computation and vectorization.

---

## 🚀 Installation

```bash
pip install jmstate
```

---

## 📖 Learn More

For tutorials, API reference, visit the official site:  
👉 [jmstate's documentation](https://felixlaplante0.github.io/jmstate/)
dagit 1.12.15
Web UI for dagster.
Requires Python: <3.15,>=3.10

=================
Dagster UI
=================

Usage
~~~~~

Eg in dagster_examples

.. code-block:: sh

  dagit -p 3333

Running dev ui:

.. code-block:: sh

  NEXT_PUBLIC_BACKEND_ORIGIN="http://localhost:3333" yarn start
webu 0.7.3
Web Utils for browsing and scraping
Requires Python: >=3.6

# WebU

Web Utils for browsing and scraping.

![](https://img.shields.io/pypi/v/webu?label=webu&color=blue&cacheSeconds=60)

## Install

```sh
pip install webu --upgrade
```

## Usage

Run example:

```sh
python example.py
```

See: [example.py](https://github.com/Hansimov/webu/blob/main/example.py)
pyoptix-contrib 0.1.1
Community-maintained Python bindings for NVIDIA OptiX (fork of otk-pyoptix with OptiX 9.1 support)
Requires Python: >=3.8
Keywords: optix, nvidia, ray-tracing, gpu, cuda, pybind11

# pyoptix-contrib

Community-maintained Python bindings for [NVIDIA OptiX](https://developer.nvidia.com/optix), forked from [NVIDIA/otk-pyoptix](https://github.com/NVIDIA/otk-pyoptix).

This fork adds support for OptiX 9.1 features including cluster acceleration structures and cooperative vectors.

## Requirements

- [OptiX SDK](https://developer.nvidia.com/designworks/optix/download) 7.6 or newer (9.1+ for cluster accel / coop vec features)
- [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads) 12.6 or newer
- [CMake](https://cmake.org/)
- A C++17 compiler

## Installation

```bash
export OptiX_INSTALL_DIR=/path/to/OptiX-SDK
pip install pyoptix-contrib
```

On Windows (PowerShell):

```powershell
$env:OptiX_INSTALL_DIR = 'C:\ProgramData\NVIDIA Corporation\OptiX SDK 9.1.0'
pip install pyoptix-contrib
```

The package builds from source via CMake, so the OptiX SDK must be available at install time.

For additional CMake arguments, use the `PYOPTIX_CMAKE_ARGS` environment variable.

## Usage

```python
import optix

# Create a device context
ctx = optix.deviceContextCreate(cuda_context, optix.DeviceContextOptions())

# Query device properties
rtcore_version = optix.deviceContextGetProperty(
    ctx, optix.DeviceProperty.DEVICE_PROPERTY_RTCORE_VERSION
)
```

See the [examples](https://github.com/brendancol/otk-pyoptix/tree/master/examples) directory for complete samples including triangle rendering, curves, denoising, and motion blur.

## What's New in This Fork

- **OptiX 9.1 support**: Conditional compilation via `IF_OPTIX91` macro
- **Cluster acceleration structures**: Enums, structs, and host functions (`clusterAccelComputeMemoryUsage`, `clusterAccelBuild`)
- **Cooperative vectors**: Element types, matrix layouts, and description structs
- **New primitive types**: ROCAPS curve variants and associated flags
- **`allowClusteredGeometry`** pipeline compile option
- **New device properties**: `COOP_VEC`, `CLUSTER_ACCEL`, max cluster vertices/triangles/SBT index/clusters-per-GAS

## Windows: CUDA DLL Loading (Python 3.8+)

Python 3.8+ on Windows no longer uses `PATH` to find DLLs. PyOptiX will auto-detect CUDA from the `CUDA_PATH` environment variable. If auto-detection fails, set `CUDA_BIN_DIR`:

```powershell
$env:CUDA_BIN_DIR = 'C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.9\bin'
```

## License

BSD 3-Clause. See [LICENSE.txt](https://github.com/brendancol/otk-pyoptix/blob/master/LICENSE.txt).

## Acknowledgments

Original work by Keith Morley and NVIDIA Corporation ([NVIDIA/otk-pyoptix](https://github.com/NVIDIA/otk-pyoptix)).
cbspy 0.0.1
A repository for interacting with and manipulating data from CBS Statline.
Requires Python: <4.0,>=3.10
Keywords: python

# cbspy

[![Release](https://img.shields.io/github/v/release/thomaspinder/cbspy)](https://img.shields.io/github/v/release/thomaspinder/cbspy)
[![Build status](https://img.shields.io/github/actions/workflow/status/thomaspinder/cbspy/main.yml?branch=main)](https://github.com/thomaspinder/cbspy/actions/workflows/main.yml?query=branch%3Amain)
[![codecov](https://codecov.io/gh/thomaspinder/cbspy/branch/main/graph/badge.svg)](https://codecov.io/gh/thomaspinder/cbspy)
[![License](https://img.shields.io/github/license/thomaspinder/cbspy)](https://img.shields.io/github/license/thomaspinder/cbspy)

A modern Python client for [CBS Statline](https://opendata.cbs.nl) open data that returns Polars DataFrames with human-readable column names.

- **Github repository**: <https://github.com/thomaspinder/cbspy/>
- **Documentation**: <https://thomaspinder.github.io/cbspy/>

## Installation

```bash
pip install cbspy
```

## Quick Start

```python
import cbspy

client = cbspy.Client()

# Discover available tables
tables = client.list_tables(language="en")
print(tables.head())
# shape: (5, 7)
# id, title, description, period, frequency, record_count, modified

# Inspect a table's structure
meta = client.get_metadata("37296eng")
for col in meta.properties:
    print(f"{col.id}: {col.display_name} ({col.unit})")

# Fetch data with human-readable column names
df = client.get_data("37296eng")
print(df.head())
# Columns like "Total population", "Males", "Females" instead of
# "TotalPopulation_1", "Males_2", "Females_3"

# Filter by time period
df = client.get_data("37296eng", periods=["2022JJ00", "2023JJ00"])
```

## Development

```bash
make install    # Create venv and install pre-commit hooks
make test       # Run tests with coverage
make check      # Run linting and type checking
```

---

Repository initiated with [fpgmaas/cookiecutter-uv](https://github.com/fpgmaas/cookiecutter-uv).
freq-pick 0.1.0
Add your description here
Requires Python: >=3.13

# freq-pick

Interactive frequency picker for a single spectrum. The API accepts a precomputed
frequency axis and magnitude array, launches a matplotlib UI, and returns a
deterministic list of selected bin indices and frequencies. Optional PNG/JSON
artifacts capture the selection.

## Install

```bash
uv sync
```

## API (primary)

```python
from pathlib import Path
import numpy as np

from freq_pick.core import Spectrum
from freq_pick.core import pick_freqs_matplotlib

f_hz = np.linspace(0.0, 200.0, 1001)
mag = np.sin(f_hz / 10.0) ** 2

spectrum = Spectrum(f_hz=f_hz, mag=mag, display_domain="linear")
selection = pick_freqs_matplotlib(
    spectrum,
    user_snap_hz=0.5,
    xlim=(0.0, 200.0),
    title="Demo",
    title_append="[1/5]",
    artifact_dir=Path("artifacts"),
    artifact_stem="demo",
)

print(selection.selected_idx)
print(selection.selected_hz)
```

### Controls

- `shift + drag` rectangle: select max magnitude in the rectangle
- `q`: commit and quit
- `Esc`: cancel
- `c`: clear selection
- `x`: delete nearest selected peak to cursor
- `l`: toggle y scale (linear/dB)
- `h`: toggle help overlay

## CLI

```bash
uv run freq-pick \
  --in spectrum.npz \
  --out artifacts \
  --stem run1 \
  --snap-hz 0.5 \
  --domain dB \
  --xlim 0 200 \
  --title "Spec A" \
  --title-append "[3/10]"
```

Input `.npz` must include `f_hz` and `mag` arrays.

## Artifacts

When `artifact_dir` and `artifact_stem` are provided, the picker writes:

- `{stem}_pick.png`
- `{stem}_pick.json`

JSON keys:

- `schema_version`
- `selected_hz`
- `selected_idx`
- `settings`
- `spectrum_meta`
- `display_domain`

## Development

```bash
uv run pytest
```
nia-mcp-server 1.0.94
Nia
Requires Python: >=3.8
Keywords: ai, codebase, mcp, nia, search

# NIA MCP Server

The NIA MCP Server enables AI assistants like Claude to search and understand your indexed codebases through the Model Context Protocol (MCP).

## Quick Start

### Automatic Setup (Recommended) ✨

Get your API key from [https://trynia.ai/api-keys](https://trynia.ai/api-keys) and run:

```bash
yay
pipx run nia-mcp-server setup YOUR_API_KEY
```
wagtail-enap-designsystem 1.2.1.251
Módulo de componentes utilizado nos portais ENAP, desenvolvido com Wagtail + CodeRedCMS

# 📦 Enap Design System - Módulo para Wagtail

Este é um módulo customizado para o **Wagtail**, criado para facilitar a implementação de layouts e componentes reutilizáveis no CMS.

### 🛫 Outros READMEs
 README.md, doc geral do projeto [README.md](README.md)
 README-use.md, doc do uso do módulo [README-use.md](README-use.md) [ATUAL]
 README-pypi.md, doc subir pacote pypi [README-pypi.md](README-pypi.md)


# ENAP Design System

O **ENAP Design System** é um módulo para o Wagtail, baseado no CodeRedCMS, que fornece componentes reutilizáveis e templates pré-configurados para facilitar a criação de sites institucionais.

## Instalação

Para instalar o pacote via PyPI, utilize:

```bash
pip install wagtail-enap-designsystem
```

### Requisitos

- **Wagtail 6.4+**
- **CodeRedCMS 4.1.1+**
- **Django 4+**

## Configuração

Após a instalação, adicione `enap_designsystem` ao seu `INSTALLED_APPS` no `settings.py`:

```python
INSTALLED_APPS = [
    "enap_designsystem",
    "coderedcms",  # Certifique-se de que o CodeRedCMS está instalado

    # ... outros módulos, como por exemplo: ...
    "wagtail.contrib.forms",
    "wagtail.contrib.redirects",
    "wagtail.embeds",
    "wagtail.sites",
    "wagtail.users",
    "wagtail.snippets",
    "wagtail.documents",
    "wagtail.images",
    "wagtail.search",
    "wagtail.admin",
    "wagtail",
    "taggit",
    "modelcluster",
    "django.contrib.admin",
    "django.contrib.auth",
    "django.contrib.contenttypes",
    "django.contrib.sessions",
    "django.contrib.messages",
    "django.contrib.staticfiles",
]
```

### Executando Migrações

Após a instalação e configuração, rode as migrações para garantir que todas as tabelas necessárias sejam criadas:

```bash
python manage.py migrate
```

## Uso

O `enap_designsystem` adiciona os seguintes recursos ao seu projeto:

- **ENAPLayout**: Página base herdando de `CoderedWebPage`, com suporte a anotações.
- **RootPage**: Página raiz configurada para permitir apenas subpáginas do tipo `ENAPLayout`.
- **Componentes Wagtail**: Blocos personalizados para layouts institucionais.
- **Templates Pré-preenchidos**: Modelos prontos para diferentes tipos de páginas.

### Criando uma Página com ENAPLayout

No painel administrativo do Wagtail, ao criar uma nova página, selecione **ENAPLayout** para utilizar os templates e funcionalidades do módulo.

## Cache

Se estiver utilizando `wagtailcache`, certifique-se de configurar corretamente o cache, pois a função `cache_clear` ainda não tem suporte completo:

```python
WAGTAIL_CACHE_BACKEND = "default"
```

## Desenvolvimento

(OPCIONAL dev)
**Se estiver contribuindo para o desenvolvimento do módulo**, clone o repositório e instale no modo `editable`:

```bash
git clone https://github.com/seu-org/enap_designsystem.git
cd enap_designsystem
pip install -e .
```

Para rodar o ambiente de desenvolvimento:

```bash
python manage.py runserver
```

## Contribuindo

Pull requests são bem-vindos! Para sugestões e melhorias, abra uma issue no repositório oficial.

---

🏛️ **Desenvolvido por ENAP**
radiens-core 0.0.0b1
Python client for Radiens neuroscience platform
Requires Python: >=3.12
Keywords: data-analysis, electrophysiology, neuroscience

# radiens-core

**Python client for the Radiens electrophysiology analytics platform.**

## Overview

This package provides a modern, type-safe Python interface to the Radiens platform for neuroscience research. It enables real-time data acquisition, offline analysis, and curation of electrophysiological recordings.

## Installation

```bash
pip install radiens-core
```

## Requirements

- Python 3.12+
- Radiens platform access
- Valid Radiens credentials

## Support

For technical support or questions about using this client:

- Contact: NeuroNexus Support
- Email: <support@neuronexus.com>

## License

See [LICENSE](LICENSE) file.