semvec/pypi-packages huggingface dataset:
cyclotron-aiokafka 0.7.0
AioKafka driver for cyclotron
License: MIT

====================================
AsyncIO Kafka support for Cyclotron
====================================

.. image:: https://badge.fury.io/py/cyclotron-aiokafka.svg
    :target: https://badge.fury.io/py/cyclotron-aiokafka

.. image:: https://github.com/mainro/cyclotron-aiokafka/workflows/Python%20package/badge.svg
    :target: https://github.com/mainro/cyclotron-aiokafka/actions?query=workflow%3A%22Python+package%22
    :alt: Github WorkFlows

This is a `Cyclotron <https://github.com/MainRo/cyclotron-py>`_ driver for Kafka
producer and consumers. It is based on `AIOKafka
<https://github.com/aio-libs/aiokafka>`_, and designed specifically for
`makinage <https://github.com/maki-nage/makinage>`_. No doc for now as the code
is still experimental.
tadgrad 0.1.1
Machine Learning from scratch
License: MIT
Keywords: python

# tadgrad

Machine Learning from scratch


## Install

From PyPi
```sh
pip install tadgrad
```

From GitHub
```sh
pip install git+https://github.com/cospectrum/tadgrad.git
```


## Usage

```python
from tadgrad import Network, Layer, LinLayer
from tadgrad.activations import relu
from tadgrad.losses import MSE
from tadgrad.optim import GD


nn = Network(loss=MSE)
nn.append(LinLayer(2, 3))
nn.append(Layer(relu))
nn.append(LinLayer(3, 2))
nn.optim = GD(nn.layers, lr=3e-4)

X = [[2, 1], [3, 4], [5, 6]]
labels = [[4, 2], [-2, 3], [2, 1]]
nn.fit(X, labels)

prediction: list = nn.predict([2, 2])
```
ass-tag-parser 2.4.1
Parse ASS subtitle format tags markup.
License: MIT
Requires Python: >=3.9

ass_tag_parser
==============

[![Build](https://github.com/bubblesub/ass_tag_parser/actions/workflows/build.yml/badge.svg)](https://github.com/bubblesub/ass_tag_parser/actions/workflows/build.yml)

A Python library for serialization and deserialization of ASS subtitle file
format tags markup.

Not to confuse with parsing `.ass` files that can be manipulated with
[`ass_parser`](https://github.com/bubblesub/ass_parser).


**Example**:

```python3
from ass_tag_parser import parse_ass


result = parse_ass(
    r"{\an5\pos(175,460)\fnUtopia with Oldstyle figures\fs90\bord0\blur3"
    r"\1c&H131313&\t(0,1000,2,\1c&H131340&)\t(1000,2000,\1c&H1015B2&"
    r"\blur1.4)}Attack No. 1{NOTE:アタックNo.1}"
)
print(result)
print(result[2].meta)
```

**Result**:

```python3 console
[
    AssTagListOpening(),
    AssTagAlignment(alignment=5, legacy=False),
    AssTagPosition(x=175.0, y=460.0),
    AssTagFontName(name="Utopia with Oldstyle figures"),
    AssTagFontSize(size=90.0),
    AssTagBorder(size=0.0),
    AssTagBlurEdgesGauss(weight=3.0),
    AssTagColor(red=19, green=19, blue=19, target=1, short=False),
    AssTagAnimation(
        tags=[AssTagColor(red=64, green=19, blue=19, target=1, short=False)],
        time1=0.0,
        time2=1000.0,
        acceleration=2.0,
    ),
    AssTagAnimation(
        tags=[
            AssTagColor(red=178, green=21, blue=16, target=1, short=False),
            AssTagBlurEdgesGauss(weight=1.4),
        ],
        time1=1000.0,
        time2=2000.0,
        acceleration=None,
    ),
    AssTagListEnding(),
    AssText(text="Attack No. 1"),
    AssTagListOpening(),
    AssTagComment(text="NOTE:アタックNo.1"),
    AssTagListEnding(),
]
Meta(start=5, end=18, text="\\pos(175,460)")
```

Starting from version 2.2, drawing commands are parsed automatically.

---

### Serializing the tree back

ASS tree: `compose_ass`. Note that you don't need to supply `AssTagListOpening`
nor `AssTagListEnding` tags in the input item list – this function inserts them
automatically.

Draw commands: `compose_draw_commands`.

# Contributing

```sh
# Clone the repository:
git clone https://github.com/bubblesub/ass_tag_parser.git
cd ass_tag_parser

# Install to a local venv:
poetry install

# Install pre-commit hooks:
poetry run pre-commit install

# Enter the venv:
poetry shell
```

This project uses [poetry](https://python-poetry.org/) for packaging,
install instructions at [poetry#installation](https://python-poetry.org/docs/#installation)
gym-stopping 0.0.2
A OpenAI Gym Env for continuous control
License: MIT

# gym-stopping

Brief description:
- control space: discrete {0,1}, continue or stop
- state space: continuous (0, \infty), [0,1], stock price and time to maturity
- default parameters: spot = 90.0, strike = 100.0, drift = 0.02, sigma = 0.20, maturity = 1.0, periods = 365   


To install:
- pip install gym-stopping
- import gym_stopping

To load:
- env = gym.make("stopping-v0")

To update version after changes:
- change version to, e.g., 1.0.7 from setup.py file
- git clone https://github.com/claudia-viaro/gym-stopping.git
- cd gym-update
- python setup.py sdist bdist_wheel
- twine check dist/*
- twine upload --repository-url https://upload.pypi.org/legacy/ dist/*