semvec/pypi-packages huggingface dataset:
xyzpy 1.2.1
Easily generate large parameter space data
License: MIT
Requires Python: >=3.5

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.. image:: https://codecov.io/gh/jcmgray/xyzpy/branch/master/graph/badge.svg
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  :alt: Code Coverage
.. image:: https://img.shields.io/lgtm/grade/python/g/jcmgray/xyzpy.svg
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.. image:: https://readthedocs.org/projects/xyzpy/badge/?version=latest
  :target: http://xyzpy.readthedocs.io/en/latest/?badge=latest
  :alt: Documentation Status

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`xyzpy <https://github.com/jcmgray/xyzpy>`__ is python library for efficiently
generating, manipulating and plotting data with a lot of dimensions, of the
type that often occurs in numerical simulations. It stands wholly atop the
labelled N-dimensional array library `xarray <http://xarray.pydata.org/en/stable/>`__.
The project's documentation is hosted on `readthedocs <http://xyzpy.readthedocs.io/>`__.

The aim is to take the pain and errors out of generating and exploring data
with a high number of possible parameters. This means:

- you don't have to write super nested for loops
- you don't have to remember which arrays/dimensions belong to which variables/parameters
- you don't have to parallelize over or distribute runs yourself
- you don't have to worry about loading, saving and merging disjoint data
- you don't have to guess when a set of runs is going to finish
- you don't have to write batch submission scripts or leave the notebook to use SGE, PBS or SLURM

As well as the ability to automatically parallelize over runs, ``xyzpy``
provides the ``Crop`` object that allows runs and results to be written to disk,
these can then be run by any process with access to the files - e.g. a batch system
such as SGE, PBS or SLURM - or just serve as a convenient persistent progress mechanism.

Once your data has been aggregated into a ``xarray.Dataset`` or ``pandas.DataFrame``
there exists many powerful visualization tools such as
`seaborn <https://seaborn.pydata.org/>`_, `altair <https://altair-viz.github.io/>`_, and
`holoviews <https://holoviews.org/#>`_ / `hvplot <https://hvplot.holoviz.org/>`_.
To these ``xyzpy`` adds also a simple 'oneliner' interface for interactively plotting the data
using `bokeh <https://bokeh.pydata.org/en/latest/>`__, or for static, publication ready figures
using `matplotlib <https://matplotlib.org/>`__, whilst being able to see the dependence on
up to 4 dimensions at once.

.. image:: docs/ex_simple.png

Please see the `docs <http://xyzpy.readthedocs.io/>`__ for more information.
webio-jupyter-extension 0.1.0
A JupyterLab extension.
License: MIT
Requires Python: >=3.6
Keywords: Jupyter,JupyterLab,JupyterLab3

# webio_jupyter_extension

## Requirements

* JupyterLab >= 3.0

## Install

To install the extension, execute:

```bash
pip install webio_jupyter_extension
```

## Uninstall

To remove the extension, execute:

```bash
pip uninstall webio_jupyter_extension
```


## Contributing

## How installing the server/labextension components works

tl;dr: We define some `data_files` in the `setup.py` that places the relavent server and labextension files in the right place. This means it's also automatically cleaned up when uninstalled.

### Differences from typical Jupyter
Because *reasons*, we actually differ from the normal Jupyter labextension cookiecutter.
Namely, we actually have a pre-build process before we pass it off to the rest of the
Jupyterlab installation process. The reason we do this is that we need some special
webpack configuration to deal with SystemJS (which we use to load custom JS/CSS resources
at runtime) and that custom webpack configuration is hard to do within the Jupyter
labextension tooling.

### Development install

Note: You will need NodeJS to build the extension package.

```bash
# Clone the repo to your local environment
# Change directory to the webio_jupyter_extension directory
# Install package in development mode
pip install -e .

# Link your development version of the extension with JupyterLab
jupyter labextension develop . --overwrite

# Server extension must be manually installed in develop mode
jupyter server extension enable webio_jupyter_extension.serverextension

# Rebuild extension Typescript source after making changes
yarn build
```

By default, the `yarn build` command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:

```bash
jupyter lab build --minimize=False
```

### Development uninstall

```bash
jupyter server extension disable webio_jupyter_extension.serverextension
pip uninstall webio_jupyter_extension
```

In development mode, you will also need to remove the symlink created by `jupyter labextension develop`
command. To find its location, you can run `jupyter labextension list` to figure out where the `labextensions`
folder is located. Then you can remove the symlink named `webio-jupyter-labextension` within that folder.