semvec/pypi-packages huggingface dataset:
quarto-prerender 0.1.0
Render Quarto .qmd files to Markdown with figure management
Requires Python: >=3.10
Keywords: jupyter, markdown, quarto, static-site-generator

# quarto-prerender

Render Quarto `.qmd` files to Markdown, designed to work as a pre-processor for static site generators.

```
.qmd → .md + figures
```

## Installation

```bash
# Requires Quarto CLI
brew install quarto  # macOS

# Install the tool
uv sync
```

## Python Environment for Notebooks

Quarto needs a Python environment with Jupyter and your notebook dependencies (numpy, matplotlib, etc.). Create a separate data science venv:

```bash
# Create the environment
uv venv ~/envs/datascience --python 3.12

# Install packages
uv pip install --python ~/envs/datascience/bin/python \
  jupyter numpy matplotlib pandas

# Use it with quarto-prerender
quarto-prerender pages/ --python ~/envs/datascience/bin/python
```

Add more packages as needed for your notebooks.

## Usage

```bash
# Render all .qmd files in a directory
quarto-prerender pages/

# With options
quarto-prerender pages/ \
  --cache .quarto_cache \
  --assets pages/assets/quarto \
  --force

# Watch mode
quarto-prerender pages/ --watch

# Verbose output
quarto-prerender pages/ -v
```

## How It Works

1. Finds all `.qmd` files in the target directory
2. Runs `quarto render` with GFM output format
3. Moves generated figures to `assets/quarto/{path}/`
4. Fixes figure paths in the generated `.md`
5. Preserves original frontmatter (public, published, etc.)

## Directory Structure

```
pages/
├── analysis/
│   ├── report.qmd          # Source
│   └── report.md           # Generated
└── assets/
    └── quarto/             # Generated figures
        └── analysis/
            └── report_files/
                └── figure-1.png
```

## Obsidian Integration

Use the [qmd-as-md-obsidian](https://github.com/danieltomasz/qmd-as-md-obsidian) plugin to edit `.qmd` files in Obsidian.

## Example .qmd File

```yaml
---
title: "Analysis Report"
public: true
date: 2025-01-15
tags: [ml, analysis]
execute:
  freeze: auto
jupyter: python3
---

# Model Performance

```{python}
#| label: fig-loss
#| fig-cap: "Training loss over epochs"
#| out-width: "600px"

import matplotlib.pyplot as plt
import numpy as np

epochs = np.arange(100)
loss = np.exp(-epochs/20) + 0.1 * np.random.randn(100)

plt.figure(figsize=(10, 6))
plt.plot(epochs, loss)
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.show()
```
```

## Figure Sizing

Figures are responsive by default (no fixed width). To set a specific width for a figure, use Quarto's `out-width` option:

```python
#| out-width: "600px"
```

For responsive images on mobile, ensure your site's CSS includes `img { max-width: 100%; }`.

## Gitignore

Add to your content repository's `.gitignore`:

```gitignore
# Quarto cache
.quarto_cache/

# Generated figures
assets/quarto/
```
nerdd-module 0.3.52
Base package to create NERDD modules
Keywords: science, research, development, nerdd

# NERDD Module

This package provides the basis to implement molecular prediction modules in the
NERDD ecosystem.

## Installation

``` bash
pip install -U nerdd-module
```


## Contribute

1. Fork and clone the code
2. Install test dependencies with `pip install -e .[test,dev,csp]` 
3. Install pre-commit hooks `pre-commit install`
4. Run tests via `pytest` or `pytest-watch` (short: `ptw`)
5. Build docs via `pip install -e .[docs]`  and `mkdocs serve`
gptdb-sandbox 0.7.3
A secure sandbox execution environment for GPT-DB Agent
Requires Python: >=3.10

# GPT-DB Sandbox

背景：AI Agent 逐步成为使用 AI 解决真实环境中各类问题的有力工具，然而真实环境的任务隔离性和安全性是企业落地中必然要考虑的问题。 GPT-DB Agent 目前不支持统一、可扩展的安全沙箱环境。 

#### 预期目标：

 为 GPT-DB Agent 实现一个安全的沙箱执行环境（支持 Agent、工具的运行和多语言代码的执行）。 分三个部分：

1. 基于 GPT-DB Agent + Docker 容器实现安全的代码执行环境，支持 Python、Shell、Node.js 等代码的执行，改造 GPT-DB 现有的代码执行智能体。 
2. 支持有状态的沙箱环境，多次代码执行可以在相同的环境中，并且上次环境的变更能影响下次的执行（例如第一次执行安装 pypi 依赖，第二次执行安装后的依赖能正常使用） 
3. 插件化的安全沙箱环境实现，设计统一的沙箱环境接口，支持 Docker、Podman、本地进程（基Cgroup/Namespace/WebAssembly等）等沙箱环境的实现。

#### 产出要求：

1. 项目设计文档（含架构图、原理图、实现细节等）
2. 实现安全沙箱环境的核心模块（统一沙箱环境接口，Docker 实现和本地进程的实现）
3. 提供完整的使用教程文档说明
4. 基于沙箱环境，开发一个支持 Python 等代码执行的 Agent 案例
pysmi 1.6.3
A pure-Python implementation of SNMP/SMI MIB parsing and conversion library.
Requires Python: >=3.9

# SNMP MIB Compiler

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PySMI is a pure-Python implementation of
[SNMP SMI](https://en.wikipedia.org/wiki/Management_information_base) MIB parser.
This tool is designed to turn ASN.1 MIBs into various formats. As of this moment,
JSON and [PySNMP](https://github.com/lextudio/pysnmp) modules can be generated
from ASN.1 MIBs.

## Features

* Understands SMIv1, SMIv2 and de-facto SMI dialects
* Turns MIBs into PySNMP classes and JSON documents
* Maintains an index of MIB objects over many MIB modules
* Automatically pulls ASN.1 MIBs from local directories, ZIP archives,
  and HTTP servers
* 100% Python, works with Python 3.9+

PySMI documentation can be found at [PySMI site](https://www.pysnmp.com/pysmi).

## How to get PySMI

The pysmi package is distributed under terms and conditions of 2-clause
BSD [license](https://www.pysnmp.com/pysmi/license.html). Source code is freely
available as a GitHub [repo](https://github.com/lextudio/pysmi).

You could `pip install pysmi` or download it from [PyPI](https://pypi.org/project/pysmi/).

If something does not work as expected,
[open an issue](https://github.com/lextudio/pysnmp/issues) at GitHub.

Copyright (c) 2015-2020, [Ilya Etingof](mailto:etingof@gmail.com).
Copyright (c) 2022-2026, [LeXtudio Inc.](mailto:support@lextudio.com).
All rights reserved.