semvec/pypi-packages huggingface dataset:
ssler 0.0.2
Easy SSL for your localhost server
Keywords: ssl,python,localhost

# SSLER


Easily test with SSL in your localhost.

*Might be a bit slow for now*


```
pip install ssler
```

### Usage

- Run your server as normal
- Call `ssler` with your localhost server address
- Now use the `ssler` address instead of your server address

```bash
ssler
Easily redirect your localhost server though an a server with self signed certificate

usage: ssler '<domain>' '<ssler_port(optional)>'
examples:
    - ssler 'localhost:8080'
    - ssler 'http://33.22.22.88'
    - ssler 'localhost:8080' 8888
```
django-sluggable 0.7.0
Slug your multiple models and manage redirections

django-sluggable
================

.. image:: https://secure.travis-ci.org/thoas/django-sluggable.png?branch=master
    :alt: Build Status
    :target: http://travis-ci.org/thoas/django-sluggable


django-sluggable is a library to manage your slugs and redirect old slugs
to a new one. With this library, you will have the plain history of your operations.

Installation
------------

To install it, simply ::

    pip install django-sluggable

Documentation
-------------

For documentation, usage, and examples, see:
http://django-sluggable.readthedocs.org

Inspiration
-----------

Some code are from django-autoslug_ and old codebase of Ulule by twidi_.

A big thanks to them.

.. _django-autoslug: https://pypi.python.org/pypi/django-autoslug
.. _twidi: https://github.com/twidi
hmmviz 0.0.7
A package for visualizing state transition graphs from hidden Markov models or other models.
Requires Python: >=3.6

# hmmviz
A package for visualizing state transition graphs from hidden Markov models or other models.

This package is meant to serve as an out-of-the-box means of plotting small graphs (less than 10 nodes) in a customizable way.

## Installation
Developed with Python 3.6

    pip install hmmviz

## Usage
Each graph object takes a pandas DataFrame with states indices and columns and transition probabilities (from index to column). However, these can be instantiated using different data structures such as numpy arrays, networkx graphs, etc. (**help needed**) via the appropriate class methods.
as values.

### Plotting a Transition Matrix from a Markov Process
```python
from hmmviz import TransGraph
import pandas as pd
import matplotlib.pyplot as plt

sequence = ['sunny', 'sunny', 'rainy', 'rainy', 'sunny', 'sunny', 'sunny', 'rainy']

T = pd.crosstab(
    pd.Series(sequence[:-1], name='Today'),
    pd.Series(sequence[1:], name='Tomorrow'),
    normalize=0
)
# Tomorrow  rainy  sunny
# Today                 
# rainy       0.5    0.5
# sunny       0.4    0.6

graph = TransGraph(T)

# looks best on square figures/axes
fig = plt.figure(figsize=(6, 6))

graph.draw()

plt.show()
```

This will make an all black graph with labels on the nodes but none on the edges.

<img src="https://raw.githubusercontent.com/benrussell80/hmmviz/master/images/basic_graph.png" width="480">

If we want to make the graph more colorful and informative we can pass some parameters into the graph's draw method.

```python
# same T as before
graph = TransGraph(T)

fig = plt.figure(figsize=(6, 6))

nodelabels = {'sunny':  '☁☀', 'rainy': '��☂'}
colors = {'sunny': 'orange', 'rainy': 'blue'}

graph.draw(
    nodelabels=nodelabels, nodecolors=colors, edgecolors=colors, edgelabels=True,
    nodefontsize=16,
)

plt.show()
```

<img src="https://raw.githubusercontent.com/benrussell80/hmmviz/master/images/colorful_graph.png" width="480">

hmmviz really shines for graphs with 4 or 5 nodes. For larger graphs consider using a package like nxviz.

```python
import numpy as np

arr = np.ones((4, 4)) * 0.25

labels = ['s0', 's1', 's2', 's3']

graph = TransGraph.from_array(arr, labels)

colors = {f's{i}': f'C{i}' for i in range(4)}

plt.figure(figsize=(6, 6))

graph.draw(edgecolors=colors, nodecolors=colors, nodelabels=False, edgewidths=2)

plt.show()
```

<img src="https://raw.githubusercontent.com/benrussell80/hmmviz/master/images/four_node_graph.png" width="480">