semvec/pypi-packages huggingface dataset:
r7-surcom-sdk 0.10.12
Python SDK used to interact and develop with Rapid7 Surface Command
Requires Python: >=3.11
Keywords: rapid7, Rapid7, r7_surcom_sdk, r7-surcom-sdk, SurfaceCommand, Surface, Command, sdk

# Surface Command SDK

The `surcom-sdk` helps you build Surface Command data connectors for the Rapid7 platform.

## Installation

* Install the `surcom-sdk`:

  ```bash
  pip install r7-surcom-sdk
  ```

* Verify the installed version:

  ```bash
  surcom --version
  ```

* Install the SDK with debugging dependencies:
  
  ```bash
  pip install 'r7-surcom-sdk[debug]'
  ```

## Configuration

* Initialize the `surcom-sdk` configuration:

  ```bash
  surcom config init
  ```

## Documentation

* View detailed help for any SDK command by adding `--help`:
  
  ```bash
  surcom connector --help
  ```
ng2nlm 1.1.0
Convert a Norton Guide database into a source for NotebookLM
Requires Python: >=3.13
Keywords: obsidian, notebooklm, ai, llm, machine-learning, norton-guide, help, ms-dos, clipper, xbase, dbase

# ng2nlm - Turn a Norton Guide into a source for NotebookLM

## Introduction

`ng2nlm` is a simple command line tool designed to turn a [Norton
Guide](https://en.wikipedia.org/wiki/Norton_Guides) file into a single
[Markdown](https://en.wikipedia.org/wiki/Markdown) file that can be uploaded
to Google's [NotebookLM](https://notebooklm.google.com/) (or presumably used
with other LLM tools for similar purposes).

It's probably fair to say that it's "opinionated" in that I wrote this to
serve my specific purpose; but where possible I've attempted to make it
generic and configurable.

## Installing

`ng2nlm` is a Python application and [is distributed via
PyPI](https://pypi.org/project/ng2nlm/). It can be installed with tools such
as [pipx](https://pipx.pypa.io/stable/):

```sh
pipx install ng2nlm
```

or [`uv`](https://docs.astral.sh/uv/):

```sh
uv tool install ng2nlm
```

Also, if you do have uv installed, you can simply use
[`uvx`](https://docs.astral.sh/uv/guides/tools/):

```sh
uvx ng2nlm
```

to run `ng2nlm`.

## Using

See [the main documentation](https://ng2nlm.davep.dev/) for details on how
to use the tool.

## Getting help

If you need some help using `ng2nlm`, or have ideas for improvements, please
feel free to drop by [the
discussions](https://github.com/davep/ng2nlm/discussions) and ask or
suggest. If you believe you've found a bug please feel free to [raise an
issue](https://github.com/davep/ng2nlm/issues).

[//]: # (README.md ends here)
weave-haystack 3.1.0
An integration of Weights & Biases Weave tracer with Haystack
Requires Python: >=3.10

# weave-haystack

[![PyPI - Version](https://img.shields.io/pypi/v/weave-haystack.svg)](https://pypi.org/project/weave-haystack)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/weave-haystack)](https://pypi.org/project/weave-haystack)

- [Integration page](https://haystack.deepset.ai/integrations/weights-and-bias-tracer)
- [Changelog](https://github.com/deepset-ai/haystack-core-integrations/blob/main/integrations/weave/CHANGELOG.md)

---

## Contributing

Refer to the general [Contribution Guidelines](https://github.com/deepset-ai/haystack-core-integrations/blob/main/CONTRIBUTING.md).

To run integration tests locally, you need to export the `WANDB_API_KEY` environment variable.
swiftcl 0.1.2
Add your description here
Requires Python: >=3.11

# SwiftCl

[![MIT license](https://img.shields.io/badge/License-MIT-blue.svg)](https://lbesson.mit-license.org/)
[![image](https://img.shields.io/badge/arXiv-2412.08701-B31B1B.svg?logo=arxiv&style=flat)](https://arxiv.org/abs/2505.22718)
[![Docs](https://badgen.net/badge/icon/Documentation?icon=https://cdn.jsdelivr.net/npm/simple-icons@v13/icons/gitbook.svg&label)](https://cosmo-docs.phys.ethz.ch/SwiftCl/)
[![Source Code](https://badgen.net/badge/icon/Source%20Code?icon=gitlab&label)](https://cosmo-gitlab.phys.ethz.ch/cosmo_public/swiftcl/)

A pipeline for the computation of the angular power spectrum beyond the Limber approximation. We offer a wide range of probes such as galaxy clustering, including magnification bias, redshift-space distortions and primordial non-Gaussianity, weak lensing, including intrinsic alignment, CMB lensing and CMB integrated Sachs-Wolfe effect.

The code is presented in [Reymond et al. (2025)](https://arxiv.org/abs/2505.22718)


## Installation
The package can be installed via pip:
```
pip install swiftcl
```

## Usage
Notebooks showing the use of the code for the computation of the angular power spectrum for the different probes and showcasing a mock MCMC with emcee, HMC and Fisher matrix can be found in the notebooks folder.

## Contributions
Contributions are welcome ! If you'd like to contribute, please open an issue.