semvec/pypi-packages huggingface dataset:
pipelayer 0.7.0
A lightweight pipeline framework
Requires Python: >=3.7

# PipeLayer

[![LATEST](https://img.shields.io/github/v/release/greater-than/pipelayer?style=for-the-badge&logo=PyPi&logoColor=white)](https://pypi.org/project/pipelayer/)![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pipelayer?style=for-the-badge&logo=Python&logoColor=white)[![PyPI - License](https://img.shields.io/pypi/l/pipelayer?style=for-the-badge)](LICENSE)


PipeLayer is a event-driven pipeline framework. Define a series of steps, and chain them together to create modular applications.
<br>

### Table of Contents

* [Installation](#install)
* [Getting Started](#get-started)
* [The Framework](http://greaterthan.solutions/pipelayer/framework)<br>
  *Complete documentation can be found here: [greaterthan.solutions/pipelayer](http://greaterthan.solutions/pipelayer)*
<br><br>


<div id="install"></div>

## Installation

From the command line:
```sh
pip install pipelayer
```


<div id="get-started"></div>

## Getting Started

### Step 1: Create The Filters

`hello_world_filters.py`
```python
from pipelayer import Filter


class HelloFilter(Filter):
    def run(self, data, context):
        return "Hello"


class WorldFilter(Filter):
    def run(self, data, context):
        return f"{data}, World!"
```

`functions.py`
```python
def create_message(data, context):
    return {"message": data}
```

### Step 2: Create The Pipeline
Create a module to run the pipeline:

`app.py`
```python
import json
from pipelayer import Pipeline
from pipelayer.util import render_manifest

from functions import create_message
from hello_world_filters import HelloFilter, WorldFilter


if __name__ == "__main__":
    hello_world_pipeline = Pipeline(
        [
            HelloFilter,
            WorldFilter,
            create_message,
            lambda data, context: json.dumps(data),
        ]
    )

    output = hello_world_pipeline.run(None)

    # output = '{"message": "Hello, World!"}'

    print("\nPipeline Output:")
    print(output)
    print("\nManifest:")
    print(render_manifest(hello_world_pipeline.manifest))

```

### Step 3: Run the Pipeline
from the command line:
```sh
run app.py
```
cgmodels 0.8.0
Models used at clinical genomics
Requires Python: >=3.6,<4.0

# cgmodels

![Tests][tests-badge] [![codecov][codecov-badge]][codecov-url][![CodeFactor][codefactor-badge]][codefactor-url][![Code style: black][black-badge]][black-url]

Library that work as an interface between services at Clinical Genomics. 
In most cases where multiple services needs access to a common API, those models should be defined here.

## Usage

Currently **cgmodels** support contracts for the following applications:

- crunchy
- demultiplex

## Installation

### Pypi

```
pip install cgmodels
```

### Github

Install [poetry][poetry]

```
git clone https://github.com/Clinical-Genomics/cgmodels
poetry install 
```


[black-badge]: https://img.shields.io/badge/code%20style-black-000000.svg
[black-url]: https://github.com/psf/black 
[codefactor-badge]: https://www.codefactor.io/repository/github/clinical-genomics/cgmodels/badge
[codefactor-url]: https://www.codefactor.io/repository/github/clinical-genomics/cgmodels
[tests-badge]: https://github.com/Clinical-Genomics/cgmodels/workflows/Tests/badge.svg
[codecov-badge]: https://codecov.io/gh/Clinical-Genomics/cgmodels/branch/main/graph/badge.svg?token=MA62EOQTX7
[codecov-url]: https://codecov.io/gh/Clinical-Genomics/cgmodels
[poetry]: https://python-poetry.org/docs/#installation
vimstrument 0.0.1
"todo manager with VIM integration"
Requires Python: >=3.9

added to PYTHONPATH under the hood. For each directory in vim’s runtimepath, vim adds the subdirectory python3 (and also pythonx) to the python module search path. 
As a result, we could omit the explicit path addition to the python.vim (aka convert_fstring.vim) file if we organized the plugin like
cds4-computation 0.1
Several calculations of two numbers

## My Calculator

This package computes the various computations of Maths like :
* Greatest Common Divisor
* Lowest Common Multiple
* Reducing Fraction

## Installations
__Through pypi package__
``` Shell
$ pip install my-calculator
```


## Usage
After the package is installed, several operation can be performed like:

__Greatest Common Divisor__
``` Python
from my_calculator import Calculator
Calculator.gcd(8, 16, 24)
# Output : 8
Calculator.gcd(10, 12)
# Output : 2

```

__Lowest Common Multiple__
``` Python
from my_calculator import Calculator
Calculator.lcm(10, 20, 30)
# Output : 60
Calculator.lcm(10,5)
# Output : 10
```

__Fraction reducer__
``` Python
from my_calculator import Calculator
Calculator.reduce_fraction(10,12)
# Output : (5,6)
```

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

### TO DO:
1. Other Mathematical functions addition
Neuromodcell 0.2.0
Code for optimising for neuromodulation fo single cell NEURON models
Requires Python: >=3.6



# Neuromodcell

[![Join the chat at https://gitter.im/Neuro_modulation/community](https://badges.gitter.im/Neuro_modulation/community.svg)](https://gitter.im/Neuro_modulation/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)

[![codecov](https://codecov.io/gh/jofrony/Neuromodulation/branch/main/graph/badge.svg?token=LKUJ5SC457)](https://codecov.io/gh/jofrony/Neuromodulation)


Optimizing neuromodulation in multicompartmental neuron model.

### Tutorial for setting up neuromodulation for single cell models

See examples/ for Jupyter Notebook on dSPN optimization and analysis


# Install

To use Neuromodcell, you first have to install [NEURON](https://www.neuron.yale.edu/neuron/download) on your machine , then install Neuromodcell via

```
pip install neuromodcell

```

# Models

The multicompartmental models should have morphology file (SWC), mechanisms.json (JSON) and parameters.json (JSON) files. See examples/models/dspn for examples of dSPN multicompartmental models. 

# Support

We provide support via gitter chat or github issues page

# Requirements
<ul>
<li>neuron</li>
<li>elephant</li>
<li>bluepyopt</li>
<li>deepdif</li>
<li>matplotlib</li>
</ul>