semvec/pypi-packages huggingface dataset:
py-filestore 1.1.0
A python package that implements a static dictionary as a file system.

# Py Filestore

This is a simple package meant to create a static data structure similar to a dictionary that exists as a group of files.

**If you have used any previous versions prior to 1.1.0, you should update as the last few updates changed how items were saved in the index and how things got hashed.**

#### Example usage
First, download it using pip:

    pip install py-filestore

Then:
```python
from filestore import Filestore
import requests

# You can pass your preferred encoding in:
# store = Filestore(encoding='ascii') # It defaults to utf-8
# You can also force overwriting on population with
# store = Filestore(overwrite=True)
store = Filestore()

# You can treat this just like a python dictionary!
# You can populate it in two ways:
store['a'] = (1,2,3) 
store.append(('b', "The alphabet is pretty cool"))
store[1] = [13.23, 321.0]
# You can store any data that is supported by python's own
# pickle package. 
store['res'] = requests.get("https://api.github.com/")
# And if you have data that is not supported, you can write
# and set your own serializer and deserializer for it.

print(store)
# {'a': '(1, 2, 3)', 'b': 'The alphabet is pretty cool', 'res': '<Response [200]>', 1: [13.23, 321.0]}

# You can get items out too!
alpha = store['a']
print(alpha)
# (1, 2, 3)
print(alpha[1])
# 2

# The dictionary is saved to file under the directory ./.store
# which means you can close the session and return for it later
# as long as the working directory is the same when the class is initialized.
# However, this leaves residue on the file system. We can clean that up too!
store.clean_up() # All the saved data is gone now.
```



#### How it works:
Creating an instance of the class will create a directory on the system that will hold all the given information. An index file is created to allow repopulation over different sessions. 

When data is added to the *filestore*, the class hashes the key with a naive, non-cryptographic hashing algorithm called [FNV-1a](https://en.wikipedia.org/wiki/Fowler%E2%80%93Noll%E2%80%93Vo_hash_function#FNV-1a_hash) in 32 bits. This hash becomes the name of the file. Collisions are now handled in the case that they occur!

 The actual data gets serialized with python's [pickle](https://docs.python.org/3/library/pickle.html) and then encoded into base64 before being written to the disk. If your data is not compatible with [pickle](https://docs.python.org/3/library/pickle.html), you can write and assign your own serializer/deserializer using `store.set_serializer(my_serializer_function)` and `store.set_deserializer(my_deserializer_function)` prior to inserting or removing data.

 #### Tests
 Currently, the testing.py file is not comprehensive and tests random strings of a specific length for collisions. It could use some work, but it does prove that [FNV-1a](https://en.wikipedia.org/wiki/Fowler%E2%80%93Noll%E2%80%93Vo_hash_function#FNV-1a_hash) has collisions if you look hard enough.
mmcv-win 0.2.10
Open MMLab Computer Vision Foundation
Keywords: computer vision

MMCV
====

.. image:: https://travis-ci.com/open-mmlab/mmcv.svg?branch=master
  :target: https://travis-ci.com/open-mmlab/mmcv

.. image:: https://codecov.io/gh/open-mmlab/mmcv/branch/master/graph/badge.svg
  :target: https://codecov.io/gh/open-mmlab/mmcv

.. image:: 	https://img.shields.io/github/license/open-mmlab/mmcv.svg
  :target: https://github.com/open-mmlab/mmcv/blob/master/LICENSE


Introduction
------------

MMCV is a foundational python library for computer vision research and supports many
research projects in MMLAB, such as `MMDetection <https://github.com/open-mmlab/mmdetection>`_
and `MMAction <https://github.com/open-mmlab/mmaction>`_.

It provides the following functionalities.

- Universal IO APIs
- Image processing
- Video processing
- Image and annotation visualization
- Useful utilities (progress bar, timer, ...)
- PyTorch runner with hooking mechanism
- Various CNN architectures

See the `documentation <http://mmcv.readthedocs.io/en/latest>`_ for more features and usage.


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

Try and start with

.. code::

    pip install mmcv


or install from source

.. code::

    git clone https://github.com/open-mmlab/mmcv.git
    cd mmcv
    pip install .  # (add "-e" if you want to develop or modify the codes)
stylish 0.4.0
Style transfer using deep neural network.
Keywords: tensorflow,style,transfer,CNN

#######
Stylish
#######

.. image:: https://img.shields.io/pypi/v/stylish.svg
        :target: https://pypi.python.org/pypi/stylish

.. image:: https://readthedocs.org/projects/stylish/badge/?version=stable
        :target: http://stylish.readthedocs.io/en/stable
        :alt: Documentation Status

Style transfer using Deep Neural Network.

**********
References
**********

This implementation is based off of a combination of:

- `A Neural Algorithm of Artistic Style <https://arxiv.org/abs/1508.06576>`_ by
  Gatys et al. (2015)
- `Perceptual Losses for Real-Time Style Transfer and Super-Resolution
  <https://arxiv.org/abs/1603.08155>`_ by Johnson et al. (2016)
- `Instance Normalization: The Missing Ingredient for Fast Stylization
  <https://arxiv.org/abs/1607.08022>`_ by Ulyanov et al. (2017)

The project borrow some logic from Logan Engstrom's `Fast Style Transfer
<https://github.com/lengstrom/fast-style-transfer>`_ and Justin Johnson's
`Fast Neural Style <https://github.com/jcjohnson/fast-neural-style>`_.

*************
Documentation
*************

Full documentation, including installation and setup guides, can be found at
http://stylish.readthedocs.io/en/stable/