semvec/pypi-packages huggingface dataset:
heaptree 0.0.1
Heaptree | Secure Cloud Instances to Enhance Your AI at Scale
Requires Python: >=3.6

# Heaptree SDK

## Prerequisites

### Use a Heaptree Conda environment

1. Install Miniconda: https://www.anaconda.com/download/success
2. After installing, close and reopen your terminal, or run `source ~/.zshrc`
   - Confirm that Conda was successfully installed with `conda --version`
3. Create new conda environment with `conda create --name heaptree`
4. Activate the environment with `conda activate heaptree`

### Set API keys

1. Login to the Heaptree Bitwarden.
2. Set the Anthropic and Heaptree API keys:

```bash
# Set the Anthropic API key
> conda env config vars set ANTHROPIC_API_KEY=...

# Set the Heaptree API key
> conda env config vars set HEAPTREE_API_KEY=...
```

3. List the API keys to confirm they were successfully set: `conda env config vars list`
4. You're all set!

## Setup

`pip3 install requirements.txt`

## Testing

`python -m test.test_client`

## Demo

`python -m playground.chart`
dsgrn 1.8.0
DSGRN (Dynamic Signatures Generated by Regulatory Networks)

# DSGRN
Dynamic Signatures Generated by Regulatory Networks

[![Build Status](https://travis-ci.org/shaunharker/DSGRN.svg?branch=master)](https://travis-ci.org/shaunharker/DSGRN) [![Coverage Status](https://coveralls.io/repos/github/shaunharker/DSGRN/badge.svg?branch=master)](https://coveralls.io/github/shaunharker/DSGRN?branch=master)[![DOI](https://zenodo.org/badge/35697682.svg)](https://zenodo.org/badge/latestdoi/35697682)

[Documentation](http://dsgrn.readthedocs.io/en/latest/)

## Overview 

This project uses computational dynamics to produce
dynamical surveys of switching models of regulatory 
networks. The resulting data is stored into a database
which can then be queried for further research.


## Installation

To get the latest tagged version from the PyPI repository:

```bash
pip install dsgrn
```

To uninstall:

```bash
pip uninstall dsgrn
```

## Installing from source

Assuming you have a C++ compiler and the Python 3 dependencies installed on your system, you can install from source with the command:

	pip install --upgrade --force-reinstall --no-deps --no-cache-dir git+https://github.com/marciogameiro/DSGRN.git

Alternatively, you can clone the GitHub repository and install with:

	git clone https://github.com/marciogameiro/DSGRN.git
	cd DSGRN
	./install.sh

If you don't have all the dependencies installed or if the above fails, see [Install.md](Install.md) for some options on how to proceed. Installing DSGRN as above on a Mac usually fails with the C++ compiler and the Python provided by Apple. In that case you need to install new versions as described in the link above.

## Usage

To check if DSGRN is installed do

```python,test
import dsgrn

network = dsgrn.Network("X1 : (~X1)(X2)\n X2 : (X1)(~X2)")
dsgrn.DrawGraph(network)
```

This should plot the network

<img src="network.png" width="120">

See the [GettingStarted.ipynb](https://github.com/marciogameiro/DSGRN/blob/master/Tutorials/GettingStarted.ipynb) jupyter notebook in the [Tutorials](https://github.com/marciogameiro/DSGRN/blob/master/Tutorials/) folder for the basic usage of DSGRN.

## Examples and Documentation

See [Tutorials](https://github.com/marciogameiro/DSGRN/blob/master/Tutorials/) folder for examples.

Also see the [documentation](https://shaunharker.github.io/DSGRN/).