semvec/pypi-packages huggingface dataset:
hidapi 0.15.0
A Cython interface to the hidapi from https://github.com/libusb/hidapi

cython-hidapi
=============

.. contents::

Description
-----------

A Cython interface to `HIDAPI <https://github.com/libusb/hidapi>`_ library.

This has been tested with:

* `TREZOR <https://trezor.io/>`_ Hardware Wallet
* the PIC18F4550 on the development board from CCS with their example program
* the Fine Offset WH3081 Weather Station

It works on Linux, Windows and macOS.

Software Dependencies
---------------------

* `Python <http://python.org>`_
* `Cython <http://cython.org>`_
* hidraw or libusb+libudev on Linux

License
-------

cython-hidapi may be used by one of three licenses as outlined in LICENSE.txt

Install
-------

::

    $ sudo apt-get install python-dev libusb-1.0-0-dev libudev-dev
    $ sudo pip install --upgrade setuptools
    $ sudo pip install hidapi

For other download options visit the `PyPi page <https://pypi.python.org/pypi/hidapi/>`_.

Build from source
-----------------

1. Download cython-hidapi archive::

    $ git clone --recursive https://github.com/trezor/cython-hidapi.git
    $ cd cython-hidapi

2. Build cython-hidapi extension module::

    $ python setup.py build

   On Linux, the hidraw API is now used by default, and the libusb API is not
   included. That is, --without-libusb is now the default behavior.
   To force the use of the libusb API instead, use the --with-libusb option::

    $ python setup.py build --with-libusb

   Or you can also use the environment variable::

    $ export HIDAPI_WITH_LIBUSB=1
    $ python setup.py build

3. Install cython-hidapi module into your Python distribution::

    $ sudo python setup.py install

   **Alternatively**, you can run pip directly, which will call the necessary build and install commands::

    $ pip install -e .

4. Test install::

    $ python
    >>> import hid
    >>>

5. Try example script::

    $ python try.py

Udev rules
----------

For correct functionality under Linux, you need to create a rule file similar
to `this one <https://raw.githubusercontent.com/trezor/trezor-common/master/udev/51-trezor.rules>`_
in your udev rules directory.

Also you might need to call ``udevadm control --reload-rules`` to reload the rules.

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

Documentation can be built using Sphinx::

   $ cd docs && make html

The HTML output will be in ``docs/_build/html``.

The documentation is extracted from the code using ``autodoc``.

Note that the build output is used. So rebuilt the package before generating documentation.

Built documentation is also deployed to: `https://trezor.github.io/cython-hidapi/ <https://trezor.github.io/cython-hidapi/>`_.
libaudit 4.0.0
Общая библиотека логирования изменений данных в БД

# Библиотека логирования изменений данных в БД
## Подключение

requirements:

    libaudit>=2.0.0,<3.0

settings:

    INSTALLED_APPS = [
        ...
        'libaudit',
        ...
    ]

    MIDDLEWARE = [
        ...
        'django.contrib.auth.middleware.AuthenticationMiddleware',
        'libaudit.middleware.AuditLogMiddleware',
        ...
    ]

    LIBAUDIT = {
        'HANDLER': 'libaudit.handlers.database_table',  # или иной обработчик
        'CONTEXT_RESOLVER_CLS': 'my_app.audit.MyAuditContextResolver',
        'EXCLUDED_TABLES': (),  # опционально
        'SETUP_AUDIT_CONTEXT_FOR_READONLY_REQUESTS': False,  # опционально
	}

Реализовано два обработчика: [хранение в таблице](src/libaudit/handlers/database_table/README.md) и [отправка в стандартный вывод СУБД](src/libaudit/handlers/stdout/README.md).

Дополнения: [contrib](src/libaudit/contrib/README.md)

## Запуск тестов
    $ tox
vdma-lif 1.0.0.7
JSON parsers and models for the VDMA - LIF (Layout Interchange Format), which is used for defining track layouts and exchanging information between the integrator of driverless transport vehicles and a third-party master control system.
Requires Python: >=3.7
Keywords: json, vdma, lif, vda5050

# VDMA-LIF JSON Parsers

JSON parsers and models for the VDMA - LIF (Layout Interchange Format), which is used for defining track layouts and exchanging information between the integrator of driverless transport vehicles and a third-party master control system.

The models are generated from a json schema which can be found in the [vdma-lif repository](https://github.com/continua-systems/vdma-lif.git).

## Install

```bash
pip install vmda_lif
```

## Usage

### Read layouts from file
```python
from vdma_lif.parser import LIFParser
layout_collection = LIFParser.from_file("example.lif.json")
```

### Read layouts from string
```python
from vdma_lif.parser import LIFParser
layout_collection = LIFParser.from_json(layout_collection_str)
```

### Convert into json
```
layout_collection_str = LIFParser.to_json(layout_collection)
```

### Write layouts to file:
```python
LIFParser.to_file(layout_collection, "example.lif.json")
```

## License

This project is licensed under the **MIT License**.

## Maintainers

This repository is maintained by Continua Systems GmbH. For any inquiries, please contact us at:

* Website: https://continua.systems
* Email: info@continua.systems
teklia-worker-configuration 0.2.2
Scripts for Arkindex worker configuration
Requires Python: >=3.10
Keywords: python

# Arkindex worker configuration

Scripts for Arkindex worker configuration

### Development

For development and tests purpose it may be useful to install the project as a editable package with pip.

* Use a virtualenv (e.g. with virtualenvwrapper `mkvirtualenv -a . worker_configuration`)
* Install worker-configuration as a package (e.g. `pip install -e .`)

### Linter

Code syntax is analyzed before submitting the code.\
To run the linter tools suite you may use pre-commit.

```shell
pip install pre-commit
pre-commit run -a
```

### Run tests

Tests are executed with `tox` using [pytest](https://pytest.org).

```shell
pip install tox
tox
```

To recreate tox virtual environment (e.g. a dependencies update), you may run `tox -r`.

Run a single test module: `tox -- <test_path>`
Run a single test: `tox -- <test_path>::<test_function>`
ezomero 3.2.2
A suite of convenience functions for working with OMERO.
Requires Python: >=3.9

![](https://raw.githubusercontent.com/TheJacksonLaboratory/ezomero/main/coverage.svg) [![badge-doi](https://img.shields.io/badge/doi-10.1101%2F2023.06.29.546930-purple)](https://doi.org/10.1101/2023.06.29.546930) 

# ezomero
A module with convenience functions for writing Python code that interacts with OMERO.


# Installation

ezomero's dependencies are easily pip-installable from PyPI, except for `zeroc-ice==3.6.5`. For those, we recommend pip-installing using one of the [wheels](https://github.com/glencoesoftware/zeroc-ice-py-linux-x86_64/releases) provided by Glencoe Software (use the one compatible  with your OS/Python version - link provided is for Linux wheels, for more information see [this Glencoe Software blog post](https://www.glencoesoftware.com/blog/2023/12/08/ice-binaries-for-omero.html)).

In general, we **strongly** recommend starting from a clean virtual environment, `pip install`ing `zeroc-ice` from a Glencoe wheel, and only then doing `pip install ezomero`. 

If you want to use `get_table` and `post_table` to/from Pandas dataframes, you need to install `ezomero[tables]` - that install an optional `pandas` dependency. Installing ezomero without this will default `get_table` and `post_table` to use lists of row lists as their default.

# Usage

In general, you will need to create a `BlitzGateway` object using `ezomero.connect()`, then pass the `conn` object to most of these helper functions along with function-specific parameters.


# Documentation

Documentation is available at [https://erickmartins.github.io/ezomero](https://erickmartins.github.io/ezomero)

# Development

You will need Docker installed and running to run the tests.

Setup your "omero" python environment with a local ezomero and pytest:
```
> conda activate omero  # Activate your omero environment with conda or pip
(omero) > cd /your_local_clone/ezomero
(omero) > pip install -e .
(omero) > pip install pytest
```

To run the tests, startup the test OMERO server with Docker and run pytest
```
> cd /your_local_clone/ezomero
> docker-compose -f tests/docker-compose.yml up -d
> conda activate omero
(omero) > python -m pytest .\tests
```
eval-ab-3d-mot 2.2.1
Evaluation for the Pure AB-3D-MOT.
Requires Python: >=3.8
Keywords: ClavIA, ClearMOT, Evaluation of multi-object tracking, MOTA, baseline for nuScenes

# Evaluation of a base of 3D multiple-object tracking (AB3DMOT) 

Evaluation part of the AB3DMOT by Xinshuo Weng (https://github.com/xinshuoweng/AB3DMOT)
The purpose of the package is to enable calculation of the detection+tracking quality
metrics for 3D tracking with KITTI data set.

Apart from the refactored evaluation part of the AB3DMOT, a binary classifier 
of the association outcomes is included. See the section 
[Run the pure AB-3D-MOT tracker and assess the association quality using ClavIA](#run-the-pure-ab-3d-mot-tracker-and-assess-the-association-quality-using-clavia) 

## Installation

Should be as easy as `pip install eval-ab-3d-mot`, but if you downloaded the repo,
then `uv sync` standing in the root folder.

## Download the detections & annotations

Should be as easy as

```
git clone https://github.com/kovalp/eval-ab-3d-mot.git
```

The detections (R-CNN) and annotations (training subset of KITTI)
are now in the folder `eval-ab-3d-mot/assets`.

## Command-line scripts

The command-line scripts are equipped with `--help` option which should be 
sufficient to guess their usage.

### Run the pure AB-3D-MOT tracker

```
batch-run-ab-3d-mot assets/detections/kitti/point-r-cnn-training/car/*.txt
```

Apart from the detections, the `pure-ab-3d-mot` tracker could be fed with KITTI annotations.

```
batch-run-ab-3d-mot-annotations assets/annotations/kitti/training/*.txt
```

By default, the car category is selected.

In both cases, consuming detections or annotations, the output is stored into text files.
The output of the tracker could be evaluated with ClearMOT metric.


### Evaluate the output of the pure AB-3D-MOT tracker using ClearMOT metric 

```
batch-eval-ab-3d-mot assets/annotations/kitti/training/*.txt
```

### Run the pure AB-3D-MOT tracker and evaluate the association quality using ClavIA

```
run-ab-3d-mot-with-clavia assets/annotations/kitti/training/*.txt
```

The script runs the tracker feeding it with (KITTI) annotations.
The result of the tracking is analysed with respect to the association accuracy.
The script allows to select the category of the objects to track (option 
`--category-obj` or `-c` for short).

Apart from the object category, it is possible to choose another category for
tracker *parameters*. Normally, the object category should be the same as
parameters category. By choosing a different parameter category, one could see
the effect of choosing different tracker parameters on the same detections.
The parameter category can be defined via the option `--category-prm` or `-p` for short.
If the option is absent, the parameter category will be the same as object category.

Note that some of the tracker parameters (`algorithm`, `metric`, `threshold` and `max-age`)
are possible to set via command-line options. These parameters affect the association.