semvec/pypi-packages huggingface dataset:
hienoi 0.2.0
2D particle playground
License: MIT
Keywords: 2D particles OpenGL nani NumPy

Hienoi
======

.. image:: https://img.shields.io/pypi/v/hienoi.svg
   :target: https://pypi.python.org/pypi/hienoi
   :alt: PyPI latest version

.. image:: https://img.shields.io/pypi/l/hienoi.svg
   :target: https://pypi.python.org/pypi/hienoi
   :alt: License


Hienoi is a framework offering an intuitive visual programming experience based
around simple 2D particle physics.


Features
--------

* 2D particle simulation.
* GUI with OpenGL viewport.
* user-friendly API.
* support for per-particle user attributes.
* fast when directly operating particles through NumPy.
* compatible with both Python 2 and Python 3.


Usage
-----

.. code-block:: python

   >>> import hienoi.application
   >>> from hienoi import Vector2f
   >>> def initialize_particle_simulation(sim):
   ...     sim.add_particle(position=Vector2f(25.0, 0.0))
   >>> def update_particle_simulation(sim):
   ...     particle = sim.particles[0]
   ...     particle.force -= particle.position
   >>> hienoi.application.run(
   ...     particle_simulation={
   ...         'initialize_callback': initialize_particle_simulation,
   ...         'postsolve_callback': update_particle_simulation,
   ...     })


More commented examples can be found in the ``demos`` folder.


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

.. code-block:: bash

   $ pip install hienoi


Demos
-----

intro
^^^^^

.. image:: img/intro.gif
   :alt: intro


equilibrium
^^^^^^^^^^^

.. image:: img/equilibrium.gif
   :alt: equilibrium


trail
^^^^^

.. image:: img/trail.gif
   :alt: trail


orbit
^^^^^

.. image:: img/orbit.gif
   :alt: orbit


Hotkeys
-------

+-----------------------+--------------------------------------------+
|  ``space`` + ``LMB``  |  move view                                 |
+-----------------------+--------------------------------------------+
|  ``space`` + ``RMB``  |  zoom view                                 |
+-----------------------+--------------------------------------------+
|  ``d``                |  change the display mode of the particles  |
+-----------------------+--------------------------------------------+
|  ``f``                |  fit the view to the scene                 |
+-----------------------+--------------------------------------------+
|  ``g``                |  show/hide the grid                        |
+-----------------------+--------------------------------------------+
|  ``r``                |  reset the view                            |
+-----------------------+--------------------------------------------+


Author
------

Christopher Crouzet
<`christophercrouzet.com <https://christophercrouzet.com>`_>
OpenATS 0.3.dev0
Open AI Training Station for Keras
License: MIT

OpenATS
=======

Open AI Training Station

TODO:
-----

※開発後削除

-  Trainer for Keras\_cnn\_audio.Trainer
-  Manager for Keras\_cnn\_audio.manage.py

Install
-------

https://pypi.python.org/pypi/OpenATS

::

    pip install OpenATS

ルール
------

-  r1. 1 Stationに 1 Dataset
-  r2. 1 Stationに n Model
-  r3. 1 Stationに n Setting

つまり、1StationにつきNxNパターンの学習を実施する.

Usage
-----

-  Init 初期ディレクトリツリーを作成する ``openATS init``
-  Create Dataset データセットを作り直す ``openATS makeDataset``
-  Run あるモデルと設定ファイルの組みで学習を開始する
   ``openATS run -m model001.py -s 01-setting.conf``
-  Run All モデル数x設定ファイルパターンの学習を開始する
   ``openATS runAll``
-  Test 少ないサンプルとエポック数で、正常に学習可能かテストする
   ``openATS run --test -m model001.py -s 01-settings.conf  openATS runALL --test``

Feature
-------

-  **テストモード** : epoch1, datasize100,
   batch\_size10,　計算ができるか実行する機能.
-  **モデル保存** :
-  (1)**BestAcc-Model** : 最大Acc時の重みを保存.
-  (2)**LastAcc-Model** : 最終Accを保存

Version1.x
~~~~~~~~~~

-  **Datasource** : 信号データのみ対応
-  **Dataset** : mspec、mfccに対応
-  **Model.py** : Modelファイルはpyファイルのみ対応
-  **Setting.conf** : .confのみ対応

Version2.x
~~~~~~~~~~

-  **Datasource** : 画像データの入力に対応
-  **Model.json** : Modelファイルのjsonインポートに対応
-  **Setting.conf** : json対応
-  **HTTP Access** : 指定したWebサーバーにログを送信する機能

dataset.conf
------------

.. code:: conf

    [Datasource]
    datasource_dirfullpath = /home/ubuntu/datasource/mp3/
    allow_subdirs = True
    datasource_ext = mp3

    [Dataset]
    max_duration_ms = 3000 # max duration. Zero padding if shorter.
    error_if_shorter = True # error_if_shorter_than_max_duration
    pipeline = "mspec" # "mfcc", "mspec"
    nfft = 4096
    nmel = 128

setting.conf
------------

::

    [model]
    optimizer = "adam" # Keras opt. sgd,rmsprop,Adagrad,Adadelta,Adam,Adamax,Nadam
    # loss = "categorical_crossentropy" # binary_crossentropy
    metrics = 'accutuary'

    [train]
    epoch = 2
    batch_size = 32
    sample_size = 500

基本ディレクトリ構成
--------------------

initコマンドでコマンドを実行したディレクトリに初期構成を展開

::

    $ openATS init
    Create default directory tree ..

    .gitignore
    Stationfile
    /dataset/
        default-dataset.conf 
        /train/
        /test/
    /models/
        001_cnn_4layer_a0.py
        002_cnn_4layer_a0.py
    /settings/
        default-setting.conf
        argmentation.conf
        non-argumentation.conf
    /results/
        /001_cnn_4layer_a0-_sample-settings/
            /tflogs/
            training.csv
            validate.csv
            classification_report.csv
            acc_vs_epoch.json
            loss_vs_epoch.jon
            model.png
            model-bestepoch-200.model
            model-lastepoch-1200.model
            acc_vs_epoch.png
            loss_vs_epoch.png

.gitignore
----------

::

    *.pyc
    __pycache__/
    dataset/

Resultディレクトリ
------------------

-  結果ファイルの格納方法
   解析中にはテンポラリディレクトリに結果ファイルを保存する。解析が完了後Resultディレクトリに移動
stringcase 1.2.0
String case converter.
License: MIT

stringcase
==========

Convert string cases between camel case, pascal case, snake case etc...

|build_status_badge| |coverage_badge| |pypi_version_badge|

Usage
-----

.. code:: python

    import stringcase
    stringcase.camelcase('foo_bar_baz') # => "fooBarBaz"
    stringcase.camelcase('FooBarBaz') # => "fooBarBaz"
    stringcase.capitalcase('foo_bar_baz') # => "Foo_bar_baz"
    stringcase.capitalcase('FooBarBaz') # => "FooBarBaz"
    stringcase.constcase('foo_bar_baz') # => "FOO_BAR_BAZ"
    stringcase.constcase('FooBarBaz') # => "_FOO_BAR_BAZ"
    stringcase.lowercase('foo_bar_baz') # => "foo_bar_baz"
    stringcase.lowercase('FooBarBaz') # => "foobarbaz"
    stringcase.pascalcase('foo_bar_baz') # => "FooBarBaz"
    stringcase.pascalcase('FooBarBaz') # => "FooBarBaz"
    stringcase.pathcase('foo_bar_baz') # => "foo/bar/baz"
    stringcase.pathcase('FooBarBaz') # => "/foo/bar/baz"
    stringcase.sentencecase('foo_bar_baz') # => "Foo bar baz"
    stringcase.sentencecase('FooBarBaz') # => "Foo bar baz"
    stringcase.snakecase('foo_bar_baz') # => "foo_bar_baz"
    stringcase.snakecase('FooBarBaz') # => "_foo_bar_baz"
    stringcase.spinalcase('foo_bar_baz') # => "foo-bar-baz"
    stringcase.spinalcase('FooBarBaz') # => "-foo-bar-baz"
    stringcase.titlecase('foo_bar_baz') # => "Foo Bar Baz"
    stringcase.titlecase('FooBarBaz') # => " Foo Bar Baz"
    stringcase.trimcase('foo_bar_baz') # => "foo_bar_baz"
    stringcase.trimcase('FooBarBaz') # => "FooBarBaz"
    stringcase.uppercase('foo_bar_baz') # => "FOO_BAR_BAZ"
    stringcase.uppercase('FooBarBaz') # => "FOOBARBAZ"
    stringcase.alphanumcase('_Foo., Bar') # =>'FooBar'
    stringcase.alphanumcase('Foo_123 Bar!') # =>'Foo123Bar'


Install
-------

::

    $ pip install stringcase

License
-------

This software is released under the `MIT License <https://github.com/okunishinishi/python-stringcase/blob/master/LICENSE>`__.


Author
------

-  `Taka Okunishi <http://okunishitaka.com>`__

.. |build_status_badge| image:: http://img.shields.io/travis/okunishinishi/python-stringcase.svg?style=flat
   :target: http://travis-ci.org/okunishinishi/python-stringcase
.. |coverage_badge| image:: http://img.shields.io/coveralls/apeman-repo/apeman-task-contrib-coz.svg?style=flat
   :target: https://coveralls.io/github/apeman-repo/apeman-task-contrib-coz
.. |pypi_version_badge| image:: https://img.shields.io/pypi/v/stringcase.svg
   :target: https://pypi.python.org/pypi/stringcase
dominos 0.0.4
Dominos Pizza API
License: MIT
Keywords: dominos

dominos |build|
===============

.. |build| image:: https://travis-ci.org/tomasbasham/dominos.svg?branch=master
    :target: https://travis-ci.org/tomasbasham/dominos

Dominos Pizza remains today a closed system, not officially offering a public API from which to order delicious baked pizzas. However with the invent of their multiple mobile applications and their own website having been reimplemented in AngularJS it was apparent these all used some undocumented public API.

This packages implements an abstract layer to the Dominos Pizza (UK) API, returning raw response objects from which JSON payloads can be read.

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

PyPi
~~~~

To install dominos, simply:

.. code:: bash

    $ pip install dominos

GitHub
~~~~~~

Installing the latest version from Github:

.. code:: bash

    $ git clone https://github.com/tomasbasham/dominos
    $ cd dominos
    $ python setup.py install

Usage
-----

To use this package you simply have to instantiate a Dominos object:

.. code:: python

    from dominos.api import Client
    api = Client()
    response = api.get_nearest_store('AB12 000')
    print(response.json())

This returns the nearest store to the supplied postcode. There are many other methods implemented in this package to return store menus, add items to a basket and checkout.

Contributing
------------

1. Fork it ( https://github.com/tomasbasham/dominos/fork )
2. Create your feature branch (`git checkout -b my-new-feature`)
3. Commit your changes (`git commit -am 'Add some feature'`)
4. Push to the branch (`git push origin my-new-feature`)
5. Create a new Pull Request
rpython 0.2.1
RPython
License: MIT
Keywords: development

RPython
=======

RPython is a translation and support framework for producing implementations of
dynamic languages, emphasizing a clean separation between language
specification and implementation aspects.

By separating concerns in this way, it can automatically generate a
Just-in-Time compiler for any dynamic language. It also allows a mix-and-match
approach to implementation decisions, including many that have historically
been outside of a user's control, such as target platform, memory and threading
models, garbage collection strategies, and optimizations applied, including
whether or not to have a JIT in the first place.

Links
-----

* `Documentation <http://rpython.readthedocs.org>`_
* `Bug tracker <https://bitbucket.org/pypy/pypy/issues>`_
* `PyPy project <http://pypy.org>`_
* Mailing list: pypy-dev@python.org

Warning
-------

This is an experimental release of a randomly chosen, untested version of
RPython. Packaging issues are likely, feedback is welcome.