semvec/pypi-packages huggingface dataset:
sensortransformer 0.1.6
Transformer Network for Time-Series and Wearable Sensor Data
License: MIT
Keywords: Transformer,Attention-Mechanism,Neural Network,Time-Series,Sensors

## Sensor Transformer (SeT)
Adaptation of <a href="https://arxiv.org/pdf/2010.11929.pdf">Vision Transformer (ViT)</a> for Time-Series and Sensor Data in Tensorflow. 

#### Usage

```python
import argparse
import tensorflow as tf
import sensortransformer import set_network

parser = argparse.ArgumentParser()
parser.add_argument("--signal-length", type=int)
parser.add_argument("--segment-size", type=int)
parser.add_argument("--num_channels", type=int)
parser.add_argument("--num_classes", type=int)
args = parser.parse_args()

"""
TF-Data objects. Instances must be of shape 
  x = (batch, signal_length, num_channels), y = (batch, num_classes)
"""
ds_train, ds_test = ...

model = set_network.SensorTransformer(
        signal_length=args.signal_length,
        segment_size=args.segment_size,
        channels=args.num_channels,
        num_classes=args.num_classes,
        num_layers=4,
        d_model=64,
        num_heads=4,
        mlp_dim=64,
        dropout=0.1,
)
model.compile(
    loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
      optimizer=tf.keras.optimizers.Adam(),
      metrics=[tf.keras.metrics.CategoricalAccuracy()],
)
model.fit(ds_train, epochs=50, verbose=1)
model.evaluate(ds_test)
```
sensor-transformer 0.1.4
Transformer Network for Time-Series and Wearable Sensor Data
License: MIT
Keywords: Transformer,Attention-Mechanism,Neural Network,Time-Series,Sensors

## Sensor Transformer (SeT)
Adaptation of <a href="https://arxiv.org/pdf/2010.11929.pdf">Vision Transformer (ViT)</a> for Time-Series and Sensor Data in Tensorflow. 

#### Usage

```python
import argparse
import tensorflow as tf
import transformer

parser = argparse.ArgumentParser()
parser.add_argument("--signal-length", type=int)
parser.add_argument("--segment-size", type=int)
parser.add_argument("--num_channels", type=int)
parser.add_argument("--num_classes", type=int)
args = parser.parse_args()

"""
TF-Data objects. Instances must be of shape 
  x = (batch, signal_length, num_channels), y = (batch, num_classes)
"""
ds_train, ds_test = ...

model = transformer.SensorTransformer(
        signal_length=args.signal_length,
        segment_size=args.segment_size,
        channels=args.num_channels,
        num_classes=args.num_classes,
        num_layers=4,
        d_model=64,
        num_heads=4,
        mlp_dim=64,
        dropout=0.1,
)
model.compile(
    loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),
      optimizer=tf.keras.optimizers.Adam(),
      metrics=[tf.keras.metrics.CategoricalAccuracy()],
)
model.fit(ds_train, epochs=50, verbose=1)
model.evaluate(ds_test)
```
librusapi 0.0.5
Scraper for Librus Synergia
License: MIT
Keywords: librus,scraper,api

.. contents::

librusapi - A Librus Synergia scraper
-------------------------------------

Goal of this project is to free students from the limitations of Librus Synergia software
mainly: giving the user **fresh** information unlike their mobile app that can only
refresh every **3h** or so.

Install
-------

.. code:: sh

    pip install librusapi

Tech used
---------

- written in ``python`` developed with ``3.9`` in mind

- ``requests`` library for fetching data, mainly html

- ``BeautifulSoup4`` library for parsing the data

Development specific
~~~~~~~~~~~~~~~~~~~~

- ``mypy`` for type checking

- ``pytest`` for running tests

- ``pdoc`` for creating html documentation

Quick start
-----------

Example usage of the API

Getting the timetable
~~~~~~~~~~~~~~~~~~~~~

.. code:: python

    from librusapi.token import get_token
    from librusapi import timetable

    token = get_token('username', 'password')
    lesson_units = timetable.lesson_units(token)

    for lu in lesson_units:
        print(lu)

Listing messages
~~~~~~~~~~~~~~~~

.. code:: python

    from librusapi.token import get_token
    from librusapi import messages

    token = get_token('username', 'password')
    messages = messages.list_messages(token)

    for msg in messages:
        print(msg)

Documentation
-------------

Docs can be found `here <https://ravensiris.github.io/librusapi/>`_

Working on the project
----------------------

.. code:: sh

    git clone https://github.com/ravensiris/librusapi
    cd librusapi
    python -m venv venv
    # This may be different depending on your operating system and shell
    source ./venv/bin/activate
    pip install -r requirements.txt
    # Installing as an editable library
    pip install -e .

Now you can import ``librusapi`` in your project

Generating docs
~~~~~~~~~~~~~~~

.. code:: sh

    pdoc --html librusapi

Status
------

Currenty you can:

- Authenticate and get a token

- List all lessons in a timetable

- List all messages ( can't read them yet )

Copyright 2021 Maksymilian Jodłowski

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
mkdocs-ponylang 0.2.8
Ponylang Material theme for MkDocs
License: MIT
Keywords: mkdocs,documentation,theme,ponylang

# Ponylang Mkdocs Theme

A customized theme based on https://github.com/squidfunk/mkdocs-material for
MkDocs of the Ponylang Standard Library documentation.

## Usage

Install the theme from PyPI:

```sh
pip install mkdocs-ponylang
```

In your `mkdocs.yml` file, change the theme:

```yaml
theme:
  name: ponylang
```

### Customization

#### Table of contents

```yaml
# Enable permalinks
markdown_extensions:
- markdown.extensions.toc:
    permalink: true

# Set maximum depth
theme:
  max_toc_depth: 2
```

#### Palette

```yaml
theme:
  palette:
    primary: brown
    accent: amber
```

You can choose from primary and accent colors listed [here](https://squidfunk.github.io/mkdocs-material/getting-started/#color-palette).

#### Icons

```yaml
theme:
  favicon: asserts/image/logo.png
  logo:
     icon: cloud
```

Logos can be images or Material icons (more info [here](https://squidfunk.github.io/mkdocs-material/getting-started/#logo)).

#### Source repository

```yaml
repo_name: ponylang/ponyc
repo_url: https://github.com/ponylang/ponyc
```

#### Other customizations

Extra customization options are available in the [Material theme documentation](https://squidfunk.github.io/mkdocs-material/getting-started/).

## Development

### Requirements

* Python 3
* npm
* libpng-dev

### Installation

This will set up all of the necessary packages to install the theme locally.

```sh
git clone https://github.com/ponylang/mkdocs-theme
cd mkdocs-theme
python -m venv venv
source venv/bin/activate
pip install -e .
npm install
```

### Build

Compile files from `src/` with Webpack:

```sh
npm run build
```

### Distribution

Upload to PyPI with Twine by calling the following Makefile targets:

```sh
make clean
make upload-to-pypi
```