semvec/pypi-packages huggingface dataset:
mlx.unity2junit 0.0.9
Python script for converting Unity test framework log file to xUnit/JUnit XML
Requires Python: >=3.9
Keywords: xUnit, JUnit, XML, Unity test framework, testing

.. image:: https://img.shields.io/badge/License-Apache%202.0-blue.svg
    :target: https://opensource.org/licenses/Apache-2.0
    :alt: Apache 2.0 License

.. image:: https://badge.fury.io/py/mlx.unity2junit.svg
    :target: https://badge.fury.io/py/mlx.unity2junit
    :alt: PyPI packaged release

.. image:: https://github.com/melexis/unity2junit/actions/workflows/python-package.yml/badge.svg?branch=main
    :target: https://github.com/melexis/unity2junit/actions/workflows/python-package.yml
    :alt: Build status

.. image:: https://img.shields.io/badge/Documentation-published-brightgreen.svg
    :target: https://melexis.github.io/unity2junit/
    :alt: Documentation

.. image:: https://codecov.io/gh/melexis/unity2junit/coverage.svg
    :target: https://codecov.io/gh/melexis/unity2junit
    :alt: Code Coverage

.. image:: https://img.shields.io/badge/contributions-welcome-brightgreen.svg
    :target: https://github.com/melexis/unity2junit/issues
    :alt: Contributions welcome

=========================
Documentation unity2junit
=========================

This script can convert a Unity test framework log file to JUnit/xUnit (.xml) file.

.. contents:: `Contents`
    :depth: 2
    :local:

----
Goal
----

This script allows you convert Unity test framework log file to JUnit format.

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

.. code-block:: console

    pip3 install mlx.unity2junit

-----
Usage
-----

.. code-block:: console

    unity2junit


--------
Behavior
--------
pitchoune 0.60.5
Load & save data & chat with LLMs
Requires Python: >=3.12.2

# Pitchoune

A Python library on top of Polars to load & save (normal or streaming mode) data from files.
Can also chat with LLM in local with Ollama or with OpenAI API.

# Features

- Load / save dataframe :
    - Handle files types : xlsx, csv, tsv, jsonl
    - Possibility to register your own file types (see custom_io_example)

- Read / write stream :
    - Handle files types : jsonl (soon csv/tsv)
    - Call the decorated function for each row of the streamed file
    - Inject each column as a parameter of the function
    - Provide as parameters the current_row and total_row
    - Can handle recovery (in case of error or process stopping) with the parameter "recover_from".
      Just set it to the output file path and it will use the right row for the read_stream function.
    - Multiple outputs (see multiple_outputs_example.py)

- Current workdir for filepaths : set PITCHOUNE_WORKDIR env variable

- <HOME> token replacement in filepaths

- conf: token replacement in filepaths (read value from key in .conf file)

- handle .conf file in PITCHOUNE_WORKDIR

- Save to a file with human check option enabled :
    - Open the file with the default editor
    - Then wait for the user to review it & save it

- Use chat :
    - Ollama chat (see chat_example)
    - OpenAI chat (you must then set OPENAI_API_KEY env variable)

# Todo

- Testing
deephaven-plugin-plotly-express 0.18.3
Deephaven Chart Plugin
Keywords: deephaven, plugin, graph

# Deephaven Plugin for Charts

Custom implementation built on top of plotly express to make it compatible with deephaven tables.

## Build

To create your build / development environment (skip the first two lines if you already have a venv):

```sh
python -m venv .venv
source .venv/bin/activate
pip install --upgrade pip setuptools
pip install build deephaven-plugin plotly
```

To build:

```sh
python -m build --wheel
```

The wheel is stored in `dist/`.

To test within [deephaven-core](https://github.com/deephaven/deephaven-core), note where this wheel is stored (using `pwd`, for example).
Then, follow the directions in the top-level README.md to install the wheel into your Deephaven environment.

To unit test, run the following command from the root of the repo:

```sh
tox -e py
```

## Usage

Once you have the plugin installed and the server started, the recommended way to import the package mirrors plotly express:

```python
import deephaven.plot.express as dx
```

Then, you can create a table (or use an existing one) and start plotting

```python
from deephaven.column import int_col, string_col
import deephaven.plot.express as dx
from deephaven import new_table

source = new_table(
    [
        string_col("Categories", ["A", "B", "C"]),
        int_col("Values", [1, 3, 5]),
    ]
)

fig = dx.bar(table=source, x="Categories", y="Values")
```

## Docs
Docs can be built locally

Install the necessary dependencies:
```shell
pip install -r ../../sphinx_ext/sphinx-requirements.txt
pip install dist/deephaven_plugin_plotly_express-*.whl
```
then run the docs make script:
```shell
python make_docs.py
```

The files will be built into `docs/build/markdown`.
Note that these built files should not be committed to the repository.
meson-python 0.19.0
Meson Python build backend (PEP 517)
Requires Python: >=3.9
Keywords: meson, build, backend, pep517, package

.. SPDX-FileCopyrightText: 2021 The meson-python developers

.. SPDX-License-Identifier: MIT

meson-python
============

``meson-python`` is a Python build back-end built on top of the Meson__
build system. It enables using Meson for the configuration and build
steps of Python packages. Meson is an open source build system meant
to be both extremely fast, and, even more importantly, as user
friendly as possible. ``meson-python`` is best suited for building
Python packages containing extension modules implemented in languages
such as C, C++, Cython, Fortran, Pythran, or Rust. Consult the
documentation__ for more details.

Questions regarding the use of ``meson-python`` can be directed to the
discussions__ space on GitHub. Bug reports and feature requests can be
filed as GitHub issues__.

__ https://mesonbuild.com/
__ https://mesonbuild.com/meson-python/
__ https://github.com/mesonbuild/meson-python/discussions/
__ https://github.com/mesonbuild/meson-python/issues/