semvec/pypi-packages huggingface dataset:
dcoraid 1.1.0
GUI/CLI for managing data on DCOR
Requires Python: <4,>=3.10
Keywords: RT-DC, DC, deformability, cytometry

|DCOR-Aid|
==========

|PyPI Version| |Build Status| |Coverage Status|


DCOR-Aid is a GUI for managing data on DCOR (https://dc.readthedocs.io).


Installation
------------
Installers for Windows and macOS are available at the
`release page <https://github.com/DCOR-dev/DCOR-Aid/releases>`__.

If you have Python 3 installed, you can install DCOR-Aid with::

    pip install dcoraid[GUI]

After installation with pip, you can start DCOR-Aid with::

    python -m dcoraid

Testing
-------
By default, testing is done with https://dcor-dev.mpl.mpg.de and the user
"dcoraid". The API key must either be present in the environment variable
``DCOR_API_KEY`` or in the file ``tests/api_key``.

::

    pip install -e .[GUI]
    pip install pytest pytest-qt
    pytest tests


.. |DCOR-Aid| image:: https://raw.github.com/DCOR-dev/DCOR-Aid/main/dcoraid/img/splash.png
.. |PyPI Version| image:: https://img.shields.io/pypi/v/dcoraid.svg
   :target: https://pypi.python.org/pypi/DCOR-Aid
.. |Build Status| image:: https://img.shields.io/github/actions/workflow/status/DCOR-dev/DCOR-Aid/check.yml
   :target: https://github.com/DCOR-dev/DCOR-Aid/actions?query=workflow%3AChecks
.. |Coverage Status| image:: https://img.shields.io/codecov/c/github/DCOR-dev/DCOR-Aid/main.svg
   :target: https://codecov.io/gh/DCOR-dev/DCOR-Aid
spseeker 0.0.1
InsideOpt Seeker Distribution for SoPlenty on Linux
Requires Python: >=3.12.0
Keywords: insideopt, seeker, optimization

================
insideopt-seeker
================

InsideOpt Seeker for SoPlenty on Linux Distribution

Credits
-------

This package was created with Cookiecutter_.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage
amulet-map-editor 0.10.50b3
A new Minecraft world editor and converter that supports all versions since Java 1.12 and Bedrock 1.7.
Requires Python: >=3.11

# Amulet Map Editor

![Build](../../workflows/Build/badge.svg)
![Unittests](../../workflows/Unittests/badge.svg?event=push)
![Stylecheck](../../workflows/Stylecheck/badge.svg?event=push)
[![Documentation Status](https://readthedocs.org/projects/amulet-map-editor/badge)](https://amulet-map-editor.readthedocs.io)

A new Minecraft world editor and converter that supports all versions since Java 1.12 and Bedrock 1.7.

![cover](resource/img/cover.jpg)

## Installing (Currently Windows only)

Purchase and download the installer for your operating system from [amuletmc.com](https://www.amuletmc.com).
Run the installer and follow the instructions.

## Running from Source

**If you are running a compiled build you do NOT need to do this.**

See instructions on [amuletmc.com](https://www.amuletmc.com/installing-from-source)

## Running with Docker (Linux)
The Docker image runs on any Linux distro with Docker support.
To run the Docker image, clone this repository and run `rundocker.sh`.
Compatibility with wayland is done through xwayland for x11 support.

## Legacy builds

Old versions (prior to 0.10.45) can be found on our [releases page](https://github.com/Amulet-Team/Amulet-Map-Editor/releases).

Extract the contained folder to a location on your computer and run `amulet_app.exe`.

## Contributing

For information about contributing to this project, please read the [contribution](contributing.md) file.
bagpipes 1.3.5
Galaxy spectral fitting

**Bayesian Analysis of Galaxies for Physical Inference and Parameter EStimation**

Bagpipes is a state of the art code for generating realistic model galaxy spectra and fitting these to spectroscopic and photometric observations. For further information please see the Bagpipes documentation at `bagpipes.readthedocs.io <http://bagpipes.readthedocs.io>`_.

**Installation**

Bagpipes can be installed with pip:

.. code::

    pip install bagpipes

Please note you cannot run the code just by cloning the repository as the large grids of models aren't included.

**Sampling algorithms within Bagpipes**

The default sampler (and historically the only option) for fitting models to data is the `MultiNest <https://github.com/JohannesBuchner/MultiNest>`_ code, however this requires separate installation, which can be challenging on some systems. Bagpipes is now also compatible with the pure Python `nautilus <https://github.com/johannesulf/nautilus>`_ nested sampling algorithm, which is now installed by default along with Bagpipes, and will be automatically used for fitting if MultiNest is not installed. Even if you are used to using Bagpipes with Multinest, you may wish to try out Nautlus, as this may yield faster and/or more accurate results in some circumstances. For more information please see the `bagpipes documentation <http://bagpipes.readthedocs.io>`_.

**Published papers and citing the code**

Bagpipes is described primarily in Section 3 of `Carnall et al. (2018) <https://arxiv.org/abs/1712.04452>`_, with further development specific to spectroscopic fitting described in Section 4 of `Carnall et al. (2019b) <https://arxiv.org/abs/1903.11082>`_. These papers are the best place to start if you want to understand how the code works.

If you make use of Bagpipes, please include a citation to `Carnall et al. (2018) <https://arxiv.org/abs/1712.04452>`_ in any publications. You may also consider citing `Carnall et al. (2019b) <https://arxiv.org/abs/1903.11082>`_, particularly if you are fitting spectroscopy.

Please note development of the code has been ongoing since these works were published, so certain parts of the code are no longer as described. Please inquire if in doubt.


.. image:: docs/images/sfh_from_spec.png
fluxopt 0.0.1rc0
Energy system optimization with linopy — progressive modeling, from simple to complex.
Requires Python: >=3.12
Keywords: energy systems, linear programming, linopy, optimization

# fluxopt

Energy system optimization with [linopy](https://github.com/PyPSA/linopy) — detailed dispatch, scaled to multi period planning.

[![PyPI](https://img.shields.io/pypi/v/fluxopt)](https://pypi.org/project/fluxopt/)
[![Downloads](https://img.shields.io/pypi/dm/fluxopt)](https://pypi.org/project/fluxopt/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python 3.12+](https://img.shields.io/badge/python-3.12+-blue.svg)](https://www.python.org/downloads/)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

> **Early development** — the API may change between releases.
> Planned features and progress are tracked in [Issues](https://github.com/FBumann/fluxopt/issues).

## Installation

```bash
pip install fluxopt
```

Includes the [HiGHS](https://highs.dev/) solver out of the box.

## Quick Start

```python
from datetime import datetime, timedelta

import fluxopt as fx

timesteps = [datetime(2024, 1, 1) + timedelta(hours=i) for i in range(4)]

result = fx.optimize(
    timesteps=timesteps,
    buses=[fx.Bus('electricity')],
    effects=[fx.Effect('cost', is_objective=True)],
    ports=[
        fx.Port('grid', imports=[
            fx.Flow(bus='electricity', size=200, effects_per_flow_hour={'cost': 0.04}),
        ]),
        fx.Port('demand', exports=[
            fx.Flow(bus='electricity', size=100, fixed_relative_profile=[0.5, 0.8, 1.0, 0.6]),
        ]),
    ],
)
```

## Development

Requires [uv](https://docs.astral.sh/uv/) and Python >= 3.12.

```bash
uv sync --group dev      # Install deps
uv run pytest -v         # Run tests
uv run ruff check .      # Lint
uv run ruff format .     # Format
```

## License

MIT