semvec/pypi-packages huggingface dataset:
welut 1.0.4
Welut is a ebook converter
License: MIT
Keywords: welut


    **Welut** is a ebook converter


Requirements
=============

* **ebook-convert:** ``$ sudo apt install calibre`` - *convert epub & mobi to pdf.*
* **pdftocairo:** ``$ sudo apt install -y poppler-utils`` - *convert pdf to images*


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

Welut is available directly from `PyPI`_:

1. Installing the package.

::

    $ pip install welut


2. Don't forget to add ``'welut'`` to your ``'INSTALLED_APPS'``.

::

    # settings.py
    INSTALLED_APPS = [
        ....
        'welut',
    ]


3. Doing makemigrations & migrate

::

    ./manage.py makemigrations welut
    ./manage.py migrate welut



Configuration (``settings.py``)
---------------------------------------

::

    WELUT_EXTENSIONS = ['.pdf', '.epub', '.mobi']         # support file extensions
    WELUT_REMOVED_EXTENSIONS = ['.pdf', '.epub', '.mobi'] # file to remove after uploaded
    WELUT_IMAGES_EXTENSION = '.png'                       # format images extension


Usage
------------------------------

You can using ``ForeignKey`` or ``OneToOneField``.

::

    from django.db import models
    from welut.models import EbookConverter


    class EBook(models.Model):
        title = models.CharField(max_length=200)
        ebook_file = models.ForeignKey(EbookConverter, related_name='ebook_file')
        created = models.DateTimeField(auto_now_add=True)
        modified = models.DateTimeField(auto_now=True)

        def __str__(self):
            return self.title

        def get_files(self):
            """ return list images of ebook per-page """
            return self.ebook_file.get_files()



**Templates**

::

    {% for img_url in object.get_files %}
      <img src="{{ MEDIA_URL }}{{ img_url }}">
    {% endfor %}



Usage Preview
------------------------------

.. image:: https://raw.githubusercontent.com/agusmakmun/welut/master/welut_demo/media/welut-demo.png



.. _PyPI: https://pypi.python.org/pypi/welut
blockchain-proofs 0.2.1
Implements chainpoint v2 proof of existence approach
License: MIT
Keywords: blockchain proof receipt chainpoint validation

=================
blockchain-proofs
=================
Implementation for generating and validating blockchain proofs/receipts (for proof of existence). Currently it offers support only for Chainpoint v2 (<http://www.chainpoint.org/>) but it can be easily extended.

Note that it also validates that the merkleRoot is properly stored in the blockchain (testnet or mainnet).

In the future the library may incorporate other approaches like OpenTimestamps (<https://github.com/opentimestamps>).

Installation
------------
``$ pip install blockchain-proofs``

Example usage
-------------

``$ python``

>>> from blockchain_proofs import ChainPointV2
>>> leafs = ['a', 'b', 'c']
>>> cp = ChainPointV2()
>>> cp.add_leaf(leafs, True)
>>> cp.make_tree()
>>> cp.get_leaf_count()
3
>>> cp.get_merkle_root()
'7075152d03a5cd92104887b476862778ec0c87be5c2fa1c0a90f87c49fad6eff'
>>> cp.get_receipt(0, "e8b4177a30c8af5dd5f33adbc530e8970b593af1001b322c289e60b4ce395e13")
{'type': 'ChainpointSHA256v2', 'targetHash': 'ca978112ca1bbdcafac231b39a23dc4da786eff8147c4e72b9807785afee48bb', 'anchors': [{'type': 'BTCOpReturn', 'sourceId': 'e8b4177a30c8af5dd5f33adbc530e8970b593af1001b322c289e60b4ce395e13'}], 'proof': [{'right': '3e23e8160039594a33894f6564e1b1348bbd7a0088d42c4acb73eeaed59c009d'}, {'right': '2e7d2c03a9507ae265ecf5b5356885a53393a2029d241394997265a1a25aefc6'}], '@context': 'https://w3id.org/chainpoint/v2', 'merkleRoot': '7075152d03a5cd92104887b476862778ec0c87be5c2fa1c0a90f87c49fad6eff'}
apn 2.0.7
Accessor Parcel Number format lookup
License: MIT
Keywords: apn real estate format

apn
====================================================

Validate or lookup Assessor's Parcel Number (APN) formats for any given state/county in the United States.
----------------------------------------------------------------------------------------------------------
Lookup or validate an APN format for
a given county and/or state.

.. image:: https://travis-ci.org/dogpackdesign/apn-formats.svg?branch=master
    :target: https://travis-ci.org/profile/dogpackdesign

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

::

    pip install apn

Usage
-----

::

    usage: apn.py [-h] [-s STATE] [-c COUNTY] [-l] [-v]

    Look up APN format by State and/or County

    optional arguments:
      -h, --help            show this help message and exit
      -s STATE, --state STATE
                            US state to lookup APN format
      -c COUNTY, --county COUNTY
                            by specific county
      -l, --list            list all APNs
      -v, --version         current version

More examples of how to use in command line or in code

::

    $ apn -s WA -c King
    > import apn
    > results = apn.lookup('WA','King)

Authors
-------

- **Eric Proulx** - *Owner* - `Website <http://www.ericproulx.com/>`__
- **Ken Harmon** - *Owner* - `Website <https://kenharmon.net/>`__

Notes
-----

- Works with Python2 and Python3
- apn uses pkl files to store APN, state data

Development
-----------

- Clone repo
- Run ``python -m apn.cli.apn`` for console
- Changes/improvements welcome!
- To add data, please edit the apn.db Sqlite db and then run ``python build.py``


News
====

2.0.7
------

-   WE'LL DO IT LIVE
-   Added Python3.6 support
coda_kids 0.2.4
Partial pygame wrapper and game programming framework.
License: MIT

coda_kids
=========

coda_kids is a partial wrapper package around pygame. It's purpose is to
make game development more accessible to a younger audiences
of students. It provides inline documentation, wrappers for linter 
unfriendly features, and provides utility functionality/classes such 
as game objects and constants for common data such as colors and input.

It is currently being designed for and tested with a series of
game programming exercises for an educational textbook titled Coda Kids.

The package is being designed and implemented with and for the
following toolset:

Python 3.5.2 with latest Pygame and Pylint.
Latest Visual Studio Code (1.11+ as of writting) with the Python extension installed.

Simple Example
--------------
::

    import coda_kids as coda

    WINDOW = coda.Vector2(800, 600)
    SCREEN = coda.start(WINDOW, "Hello World!")
    TEXT = coda.TextObject(coda.color.BLACK, 48, "Hello World!")
    TEXT.location = WINDOW / 2 # center of window
    TEXT.centered = True

    while True:
        for event in coda.event.listing():
            if coda.event.quit_game(event):
                coda.stop()
        coda.start_draw(SCREEN, coda.color.WHITE)
        TEXT.draw(SCREEN)
        coda.end_draw()


To Do
=====

1) Add a 2D animation class supportable by objects.
2) Runtime type checking for coda_kids class constructors and function arguments.
3) Provide additional example code and documentation.
4) Clean up object interface methods.
5) Refactor finite state machine tools.

Change Log
==========

0.2.4
-----

- Added opinional 2d sprite animation class for use with objects.
- Added update loop for game objects that applies velocity and updates animations.

0.2.3
-----

- Added an output console utility for in window output text. 
  It can be found in coda_kids.utilities.
- Added support functions and documentation for starting and ending a frame.
- Cleaned up and added documentation to the .rst file.

0.2.2
-----

- Updated git repo and setup/url information.
- Added action system to coda for python class members. [Feature in Alpha]
- Updated object class to properly scale objects with it's scale member.
- Moved standard library imports to coda_kids.utilities


0.2.1
-----

- Reformatted some of the library utilities.

0.2.0
-----
- Fixed some formatting issues with the setup.py file.
PDUUSSDConverter 0.0.2.post1
A utility to convert between pdu, gsm codes,     ussd and string formats
License: MIT
Keywords: PDU USSD GSM HEX

# PDU-USSDConverter
A Simple Utility to Convert Between PDU encoded text, USSD/GSM and String and
Vice-Versa.
This is works well with python3


## Installation
`pip install PDUUSSDConverter`


## Example

```
from PDUUSSDConverter import converter


ussd_code = converter.text_to_pdu(''*124#'')  # 'AA988C3602' (length=10)

pdu_to_text = converter.pdu_to_text('54747A0E6A97E7F3F0B90CBA87E7A0F1DB6D2FCBE9657208'))  # 'This message was converted!' (length=27)


```

Other Operations Available:

```
gsm_code_to_text(param: gsm_code) // Convert a gsm code into a unicode sequence
text_to_gsm_code(param: String)   // Converts text into gsm code sequence
```


## Credits

[Junior Polegato](https://github.com/JuniorPolegato/pdu_gsm_ussd)
s2sphere 0.2.5
Python implementation of the S2 Geometry Library
License: MIT

s2sphere
========

.. image:: https://badge.fury.io/py/s2sphere.svg
    :target: https://pypi.python.org/pypi/s2sphere/
.. image:: https://travis-ci.org/sidewalklabs/s2sphere.svg?branch=master
    :target: https://travis-ci.org/sidewalklabs/s2sphere
.. image:: https://coveralls.io/repos/github/sidewalklabs/s2sphere/badge.svg?branch=master
    :target: https://coveralls.io/github/sidewalklabs/s2sphere?branch=master

Python implementation of a part of the C++ `S2 geometry library <https://code.google.com/p/s2-geometry-library/>`_.

Links: `Documentation <http://s2sphere.readthedocs.io>`_,
`GitHub <https://github.com/sidewalklabs/s2sphere>`_,
`Issue Tracker <https://github.com/sidewalklabs/s2sphere/issues>`_

Install with:

.. code-block:: sh

    pip install s2sphere


Quick set up for development (see the `Developing section in the docs <http://s2sphere.readthedocs.io/en/latest/dev.html>`_ for more details):

.. code-block:: sh

    pip install .[tests]
    nosetests -vv
pytorch-sublstm 0.0.2
Differentiable Neural Computer, for Pytorch
License: MIT
Requires Python: >=3
Keywords: cortical microcircuit pytorch sublstm

# subtractive LSTM (subLSTM), for Pytorch

[![Build Status](https://travis-ci.org/ixaxaar/pytorch-sublstm.svg?branch=master)](https://travis-ci.org/ixaxaar/pytorch-sublstm) [![PyPI version](https://badge.fury.io/py/pytorch-sublstm.svg)](https://badge.fury.io/py/pytorch-sublstm)

This is an implementation of subLSTM described in the paper [Cortical microcircuits as gated-recurrent neural networks, Rui Ponte Costa et al.](https://arxiv.org/abs/1711.02448)

## Install

```bash
pip install pytorch-sublstm
```


## Usage

**Parameters**:

Following are the constructor parameters:

| Argument | Default | Description |
| --- | --- | --- |
| input_size | `None` | Size of the input vectors |
| hidden_size | `None` | Size of hidden units |
| num_layers | `1` | Number of layers in the network |
| bias | `True` | Bias |
| batch_first | `False` | Whether data is fed batch first |
| dropout | `0` | Dropout between layers in the network |
| bidirectional | `False` | If the network is bidirectional |


### Example usage:

#### nn Interface
```python
import torch
from torch.autograd import Variable
from subLSTM.nn import SubLSTM

hidden_size = 20
input_size = 10
seq_len = 5
batch_size = 7
hidden = None

input = Variable(torch.randn(batch_size, seq_len, input_size))

rnn = SubLSTM(input_size, hidden_size, num_layers=2, bias=True, batch_first=True)

# forward pass
output, hidden = rnn(input, hidden)
```

#### Cell Interface

```python
import torch
from torch.autograd import Variable
from subLSTM.nn import SubLSTMCell

hidden_size = 20
input_size = 10
seq_len = 5
batch_size = 7
hidden = None

hx = Variable(torch.randn(batch_size, hidden_size))
cx = Variable(torch.randn(batch_size, hidden_size))

input = Variable(torch.randn(batch_size, input_size))

cell = SubLSTMCell(input_size, hidden_size, bias=True)
(hx, cx) = cell(input, (hx, cx))
```

### Tasks:

A language modeling task is included [here](./tasks/word_language_model/).
Refer to its [README](./tasks/word_language_model/README.md) for more info.


### Attributions:

A lot of the code is recycled from [pytorch](https://pytorch.org)