semvec/pypi-packages huggingface dataset:
lhub_integ 1.0.14
The Logichub Integration SDK
License: MIT
Requires Python: <4.0,>=3.6
Keywords: security, automation, logichub

# lhub_integ
Python package to shim basic scripts to work with integration machinery.
This package requires Python 3.6:
```
# Optional: install Python 3.6 with pyenvv
brew install pyenv
pyenv install 3.6.6
pyenv init # Follow the instructions
pyenv local 3.6.6
python --version
```

```
pip install lhub_integ
```

## Usage (as an integration writer)
To write a Python script that is convertible into an integration:

1. Create a directory that will contain your integration
2. Install lhub_integ as a local package:
```pip install lhub_integ```

Python scripts must provide an entrypoint function with some number of arguments. These arguments will correspond to columns
in the input data. The function should return a Python dictionary that can be serialized to JSON

```python
def process(url, num_bytes: int):
  return {'output': url + 'hello'}
```

### Specifying Dependencies
You must create a `requirements.txt` file specifying your dependencies. To create a dependency bundle run:
```
$ bundle-requirements
```
This script is provided when you install lhub_integ.

### Publishing
You will need the PyPi username and password that are in 1Password (search for PyPi)

1. Bump the version in pyproject.toml

2. `poetry publish --build`
in2lambda 1.0.0
Converts content ready for import into Lambda Feedback
License: MIT
Requires Python: <4.0,>=3.10

# in2lambda

[![GitHub Workflow Status (with event)](https://img.shields.io/github/actions/workflow/status/lambda-feedback/in2lambda/test.yml?style=flat-square&logo=github&label=tests)](https://github.com/lambda-feedback/in2lambda/actions/workflows/test.yml)
[![GitHub Workflow Status (with event)](https://img.shields.io/github/actions/workflow/status/lambda-feedback/in2lambda/deploy-docs.yml?style=flat-square&logo=googledocs&logoColor=white&label=docs)](https://lambda-feedback.github.io/in2lambda/)

in2lambda is a Python command line tool and library that automagically uploads questions to [Lambda Feedback](https://lambdafeedback.com/).

Find out more in the [documentation](https://lambda-feedback.github.io/in2lambda/).

```
$ pip install in2lambda
```
netpyne 1.1.1
A Python package to develop, simulate and analyse biological neuronal networks in NEURON.
License: MIT
Keywords: neuron, network, developing, framework, biological, simulation

# NetPyNE (Python package)
## Description
NetPyNE is a Python package to facilitate the development, simulation, parallelization, analysis, and optimization of biological neuronal networks using the NEURON simulator.

For more details, installation instructions, documentation, tutorials, forums, videos and more, please visit: www.netpyne.org

This package is developed and maintained by Dura-Bernal lab (http://dura-bernal.org) and the Neurosim lab (www.neurosimlab.org).

[![Build](https://github.com/Neurosim-lab/netpyne/actions/workflows/tests.yml/badge.svg)](https://github.com/Neurosim-lab/netpyne/actions/workflows/tests.yml)

## Acknowledgements
This work was funded by grants from the NIH, NSF, NYS SCIRB, UK Welcome Trust and Australian Research Council. We are thankful to all the contributors that have collaborated in the development of this open source tool via GitHub.

This project was supported by:
- HBP Brain Simulation Platform funded from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2).
- EBRAINS research infrastructure, funded from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3).

This project has received a Voucher "Integration of NetPyNE into EBRAINS Platform (EBRnetpyne)" from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement:
- No. 785907 (Human Brain Project SGA2).
- No. 945539 (Human Brain Project SGA3).