semvec/pypi-packages huggingface dataset:
kappagate 0.1.3
Predict valid-clone-rates in Golden Gate DNA assemblies
License: MIT
Keywords: DNA assembly synthetic biology golden gate

Kappagate
=========

kappagate is a Python library built on top ``kappy`` to use the Kappa language (for simulation of biological complexation processes) in a pythonic way, using Python objects to define agents and rules, and with some methods
for plotting complexes and time series.

Infos
-----

**PIP installation:**

.. code:: bash

  pip install kappagate

**Github Page**

`<https://github.com/Edinburgh-Genome-Foundry/kappagate>`_

**License:** MIT, Copyright Edinburgh Genome Foundry

More biology software
---------------------

.. image:: https://raw.githubusercontent.com/Edinburgh-Genome-Foundry/Edinburgh-Genome-Foundry.github.io/master/static/imgs/logos/egf-codon-horizontal.png
  :target: https://edinburgh-genome-foundry.github.io/

kappagate is part of the `EGF Codons <https://edinburgh-genome-foundry.github.io/>`_ synthetic biology software suite for DNA design, manufacturing and validation.
filterv 0.0.3
Visualizing what the filter values do to an image made easy! Just a few steps and you're good to go!
License: MIT
Keywords: filter,visual,machine learning,CNN,Convolutional,images,plot

# filterV


[![Build Status](https://travis-ci.org/joemccann/dillinger.svg?branch=master)](https://travis-ci.org/joemccann/dillinger)

*filterV is a short program that enables the user to visualize how any image would look after applying the Convolutional filters.*


### Installation

filterV requires python3 to run.

Enter the following command in your command-line (windows):
```sh
pip install filterv
```

### Using filterV

It's super easy to use...!

    import filterv.filter as f             # import the library
    F = f.Filter()                         # create a Class Instance

    input_img  = F.load_img(path_to_image) # load the image
    F.parameters()                         # Initialize the parameters
    output_img = F.convert()               # Filter the Image
    
    F.plot(input_img, output_img)          # Plot the outputs
    
License
--------

MIT


**Free Software, Hell Yeah!**