semvec/pypi-packages huggingface dataset:
StoryLines 0.12
Line plots with Python & TikZ
Requires Python: >=2.7

# Line plots with Python & TikZ

![StoryLines logo](https://raw.githubusercontent.com/janberges/StoryLines/master/logo/logo.svg)

* `plot` - figure object
* `calc` - mathematical helpers
* `color` - blendable colors and colormaps
* `convert` - inches to centimeters etc.
* `cut` - remove redundant or unwanted points
* `fatband` - plot weights along line
* `files` - typeset TeX, rasterize PDF
* `group` - chunk sequences of points
* `png` - save and load RGB(A) graphics
* `proj` - project 3D object onto plane

## Installation

Optionally using a virtual environment:

    python3 -m venv venv
    source venv/bin/activate
    python3 -m pip install --upgrade pip setuptools wheel

Either from PyPI:

    python3 -m pip install storylines

Or from GitHub:

    python3 -m pip install git+https://github.com/janberges/StoryLines

## Documentation

The documentation and example scripts along with output can be found at
<https://janberges.github.io/StoryLines>.

Note that `storylines.module.member` is equivalent to `storylines.member`.

## License

This program is free software under the terms of the BSD Zero Clause License.

Copyright (C) 2016-2023 Jan Berges
reframer 0.0.1
Create, run and manage your intelligent LLM agents on dataframes.
Requires Python: >=3.9,<4.0
Keywords: reframe,agents,dataframes,client

# <a href="https://reframe.is/"><img src="https://d3g1vr8yw3euzd.cloudfront.net/nnext-ultra-wide-tingle.png" alt="Aigent"></a>

## About


Aigent is an experiomental agent framework powered by GPT-4 that operates on dataframes.
Powered by the remarkable GPT-4, this program ingeniously links together thoughts from
Large Language Models (LLMs) to autonomously accomplish your desired objectives.
As a groundbreaking example of GPT-4 operating entirely independently, Auto-GPT pushes
the frontiers of AI's potential to new and awe-inspiring heights.

<a href="https://twitter.com/intent/follow?screen_name=nnextai"><img src="https://img.shields.io/badge/Follow-nnextai-blue.svg?style=flat&logo=twitter"></a>

[Installation](#installation) |  [Quick Start](#quick-start) | [Documentation](#documentation)

## Installation

By far the easiest way to install the Aigent server is to use docker.

```sql
wget https://raw.githubusercontent.com/nnextai/aigent/main/docker-compose.yaml
docker-compose up -d
```

https://discord.gg/KkFzHRTF5K

### Install aigent client
```shell
pip install aigent
```

## Aigent strives to be

* 🥽 Transparent - through logging, and metrics that create visibility into the inner operations.
* 🤸🏾 Flexible - AI Agents and tools are independent of each other, allowing you to create workflows easily.
* 🧩 Composable. Aigents are simply executable python functions and classes with a well defined interface. You can easily construct sophisticated agents from basic building blocks. These building blocks can be obtained from our ecosystem or you can develop custom ones that are specific to your organization.
* 🛹 Incrementally adoptable - By using existing technologies such as Docker, Kubernetes and Celery Aigent enables you to seamlessly adopt it with your organization. From simple ZeroShot agents to sophisticated multi-step AI agents each component can be integrated into your existing workflows.
* 🔨 Reusable - once a tool is running, it can be utilized by various agents, thereby reducing operational overhead, increasing throughput and making tools easy to reason about.
* 🏎️ Fast by taking advantage of data parallelism and prompt sequencing in a manner increases efficiency and reduces the overall number of expensive API calls made to LLM endpoints.
* 🏟️ Rich ecosystem that enables your to pick and choose which tools and agents to deploy. Through contributions from open source developers, we are making great progress to develop a robust ecosystem of tools so that you always have a tool for the job.

# Features
* 🌐 Internet access for searches and information gathering
* 📥 Long-term and short-term memory management 
* 🧠 GPT-4 & Anthropic instances for text generation 
* 🔗 Access to popular websites and platforms 
* 🗃️ File storage and summarization with GPT-3.5 
* 🔌 Extensibility with Plugins

## Documentation

More documentation is available here [https://reframe.is/aigent/docs](https://reframe.is/aigent/docs).:
mavis 3.1.2
A Structural Variant Post-Processing Package
Requires Python: >=3.7

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    <object type='image/svg+xml' data='images/acronym.svg'>
    	<img src='docs/images/acronym.svg' onerror='this.src="images/acronym.svg"'>
    </object><br>
</object>

![PyPi](https://img.shields.io/pypi/v/mavis.svg) ![build](https://github.com/bcgsc/mavis/workflows/build/badge.svg?branch=master) [![codecov](https://codecov.io/gh/bcgsc/mavis/branch/master/graph/badge.svg)](https://codecov.io/gh/bcgsc/mavis) ![ReadTheDocs](https://readthedocs.org/projects/pip/badge/)

## About

[MAVIS](http://mavis.bcgsc.ca) is python command-line tool for the post-processing of structural variant calls.
The general [MAVIS](http://mavis.bcgsc.ca) pipeline consists of six main stages

- convert
- [cluster](https://mavis.readthedocs.io/en/latest/package/mavis/cluster)
- [validate](https://mavis.readthedocs.io/en/latest/package/mavis/validate)
- [annotate](https://mavis.readthedocs.io/en/latest/package/mavis/annotate)
- [pairing](https://mavis.readthedocs.io/en/latest/package/mavis/pairing)
- [summary](https://mavis.readthedocs.io/en/latest/package/mavis/summary)

## Getting Help

All steps in the MAVIS pipeline are called following the main mavis entry point. The usage menu can be viewed
by running without any arguments, or by giving the -h/--help option

``` bash
mavis -h
```

Help sub-menus can be found by giving the pipeline step followed by no arguments or the -h options

``` bash
mavis cluster -h
```

Common problems and questions are addressed on the [wiki](https://github.com/bcgsc/mavis/wiki/Help-and-Frequently-Asked-Questions).
If you have a question or issue that is not answered there (or already a github issue) please submit
a github issue to our [github page](https://github.com/bcgsc/mavis/issues) or contact us by email at [mavis@bcgsc.ca](mailto:mavis@bcgsc.ca)

## Getting Started

The simplest way to use MAVIS is via Singularity. The MAVIS docker container used
by singularity will take care of installing the aligner as well.

```bash
pip install -U setuptools pip wheel
pip install mavis_config  # also installs snakemake
```

Now you will run mavis via Snakemake as follows

```bash
snakemake \
    -j <MAX JOBS> \
    --configfile <YOUR CONFIG> \
    --use-singularity \
    -s Snakefile
```

For other installation options which do not use docker/singularity see the comprehensive install
instructions in the [user manual](https://mavis.readthedocs.io/en/latest/install)

## Citation

If you use MAVIS as a part of your project please cite

[Reisle,C. et al. (2018) MAVIS: Merging, Annotation, Validation, and Illustration of Structural variants. Bioinformatics.](https://doi.org/10.1093/bioinformatics/bty621)