semvec/pypi-packages huggingface dataset:
lazygen 0.10
Competitive programming solution table generator
Requires Python: >=3.7
Keywords: programming competitive readmes

# lazy
[![Build Status](https://travis-ci.com/terror/lazy.svg?branch=master)](https://travis-ci.com/terror/lazy)  
Programming problem solution README table generator

# Installation
You can simply use pip to install lazy:  
```bash
$ pip install lazygen
```

# Usage
Run lazy as a command
```bash
$ lazy
```  
This will prompt you for your github repository link and absolute path to the repository.

## Example
```bash
Github link to repository: https://github.com/lazy/example
Local path to repository: /Absolute/path/to/repository
```

This will then look for a `config.json` file that looks something like this

```JSON
{
"Ignore": ["CSES"],
"Paths": ["Kattis", "Dmoj", "Leetcode"]
}
```

## File Structure
`lazy` as of now, requires a specific file structure in order to work.

```
Github/lazy/example/
├── SITE_NAME
│   └── PROBLEM_ID
│       └── solution.cpp
├── SITE_NAME
│   ├── PROBLEM_ID
│   │   └── solution.cpp
│   └── PROBLEM_ID
│       └── solution.py
├── SITE_NAME
│   ├── PROBLEM_ID
│   │   ├── solution.cpp
│   │   └── solution.py
│   └── PROBLEM_ID
│       └── solution.py
├── README.md
└── config.json
```

Where PROBLEM_ID is the id for the solved problem and SITE_NAME is the problem website name.  
For instance, https://open.kattis.com/problems/opensource -> Kattis/opensource/solution

## Result

Potential sample result for a folder in the sample file structure above.

### Kattis
| Problem | Languages |
| ------- | --------- |
 [Gerrymandering](https://open.kattis.com/problems/gerrymandering) | [Python](https://github.com/terror/lazy/blob/master/example/Kattis/gerrymandering/solution.py)
 [Fenwick](https://open.kattis.com/problems/fenwick) | [C++](https://github.com/terror/lazy/blob/master/example/Kattis/fenwick/solution.cpp), [Python](https://github.com/terror/lazy/blob/master/example/Kattis/fenwick/solution.py)


The current sites being supported can be found [here](https://github.com/terror/lazy/blob/master/constants/sites.py). A full sample repository can be found [here](https://github.com/terror/lazy/tree/master/example) 

# Development
Fork the repository
```bash
$ git clone <fork>
```
```
$ pip3 install -r requirements.txt
```
```
$ python3 lazy
```

# License
MIT
fos 1.0.0
Deeplearning framework for PyTorch

.. image:: https://travis-ci.com/neurallayer/fos.svg?branch=master
    :target: https://travis-ci.com/neurallayer/fos


Introduction
============
**FOS** is a Python framework that makes it easy to develop neural network models 
in PyTorch. Some of its main features include:

* Less boilerplate code required, see also the example below.
* Lightweight and no magic under the hood that might get in the way.
* You can extend Fos using common OO patterns.
* Get the insights you need into the performance of the model.


Installation
============
You can install FOS using pip::

    pip install fos
    
Or alternatively from the source::

    python setup.py install
    
Fos requires Python 3.6 or higher.


Usage
=====
Training a model, requires just a few lines of code. First create the model, 
optimizer and loss function that you want to use, using normal PyTorch code::

   model = resnet18()
   optim = Adam(model.parameters())
   loss = F.binary_cross_entropy_with_logits

Then create the FOS workout that will take care of the training and output::

   workout = Workout(net, loss, optim)
  
And we are ready to start the training::

   workout.fit(train_data, valid_data, epochs=5)


Examples
========
You can find several example Jupyter notebooks `here <https://github.com/neurallayer/fos/tree/master/examples>`_ 

You can also run them on Google Colab directly:

* `Basic https://colab.research.google.com/github/neurallayer/fos/blob/master/examples/basic_fos.ipynb`
* `MINST https://colab.research.google.com/github/neurallayer/fos/blob/master/examples/mnist_fos.ipynb`
* `Inputs https://colab.research.google.com/github/neurallayer/fos/blob/master/examples/inputs_fos.ipynb`
* `Tensorboard https://colab.research.google.com/github/neurallayer/fos/blob/master/examples/tensorboard_fos.ipynb`



Contribution
============
If you want to help out, we appreciate all contributions. 
Please see the contribution guidelines for more information.

As always, PRs are welcome :)=
animalcrossing 0.0.1
Animal crossing for computer vision
Requires Python: >=3.6

# Animal Crossing library
I don't have a Nintendo Switch so this is the next best thing.

Animal crossing villagers for computer vision projects.

And basic utilities for computer vision.

![animalcrossing](./random/villager.jpg)
[Image source animal crossing world](https://animalcrossingworld.com/2020/02/250-high-resolution-animal-crossing-new-horizons-villager-special-character-renders/)

[Nintendo Germany, NintenDaan](https://twitter.com/NintenDaan)
## Quickstart

    import animalcrossing as ac
    
    ac.random_villager()
    
## Utilities
download_images method to pull images from the internet with the pandas dataframe

The jupyter_display method displays images in jupyter notebook

    import pandas as pd
    urls = ['https://i.ytimg.com/vi/XOXckrf_RoQ/maxresdefault.jpg', 
            'https://i.insider.com/56688a26dd0895106a8b478b?width=1762.jpg']
    df = pd.DataFrame(urls, columns=['Name'])
    
    # export to csv, make sure index is false
    df.to_csv('walle.csv', index=False)
    ac.download_images('walle.csv')

    ac.jupyter_display('output/0.jpg')
    

## Get all images
Grabs about 250 images but will take a long time to scrape.

    ac.animal_crossing_images()

## License
The technology is registered under MIT but please respect nintendo's trademark

ANIMAL CROSSING is a trademark of Nintendo of America Inc.