semvec/pypi-packages huggingface dataset:
sphinx-cache 0.0.1
Sphinx extension for storing and restoring `.doctree` cache.
License: MIT
Requires Python: >=3.8.0,<4.0

Sphinx-Cache Docs
=================

Sphinx-Cache is a `Sphinx <https://www.sphinx-doc.org>`_ extension for storing and restoring the `.doctrees` cache.

.. _install:

Installation
------------
We recommend using the latest version of Python. Sphinx-Cache supports Python 3.6 and newer versions.

**Using pip**

.. code-block:: bash

   $ pip install sphinx-cache

**From source**

.. code-block:: bash

   $ git clone https://github.com/useblocks/sphinx-cache
   $ cd sphinx-cache
   $ pip install .

.. _config:

Configuration
-------------

All configurations take place in your project's ``conf.py`` file.

Activation
**********

Add **sphinx_cache** to the extensions list.

.. code-block:: python

   extensions = ["sphinx_cache",]

Options
*******

All configuration options start with the prefix ``cache_`` for **Sphinx-Cache**.

.. _cache_store_path:

cache_store_path
++++++++++++++++

The ``cache_store_path`` option specifies the directory path where you want to save the doctree cache files.

.. note::

   The path should be specified as a relative path relative to the directory of the ``conf.py`` file.
    For example: :code:`cache_store_path = '.cache/'`.

Default: ``.cache/``

.. _cache_doctree_path:

cache_doctree_path
++++++++++++++++++

The ``cache_doctree_path`` option specifies the directory path of the doctree folder.

.. note::

   * The path should be specified as a relative path relative to the directory of the ``conf.py`` file.
     For example: ``cache_doctree_path = '_build/.doctrees'``.
   * We use the value provided for the ``cache_doctree_path`` as directory for storing pickled doctrees.
     If you don't specify the value for the ``cache_doctree_path`` in the ``conf.py`` file, we use the default
     value for the ``cache_doctree_path`` (i.e. ``_build/.doctrees``).

Default: ``_build/.doctrees``
regli 2023.829.0
REgular Grid Linear Interpolator.
License: MIT

## regli [![DOI](https://zenodo.org/badge/153591296.svg)](https://zenodo.org/badge/latestdoi/153591296)
REgular Grid Linear Interpolator, capable to deal with spectral library or similar model data.

This package implements the basic *bilinear interpolation* in multi-dimension.
However, as commented in *Numerical Recipes*: 

Bilinear interpolation is frequently “close enough for government work.”
As the interpolating point wanders from grid square to grid square,
the interpolated func- tion value changes continuously.
However, the gradient of the interpolated function changes
discontinuously at the boundaries of each grid square.

As a consequence, the interpolation model can not, in general, 
be used as a forward model and incorporated with 
Gradient-based optimization algorithms to estimate parameters.

## author
Bo Zhang, [bozhang@nao.cas.cn](mailto:bozhang@nao.cas.cn)

## home page
- [https://github.com/hypergravity/regli](https://github.com/hypergravity/regli)
- [https://pypi.org/project/regli/](https://pypi.org/project/regli/)

## install
- for the latest **stable** version: `pip install -U regli`
- for the latest **github** version: `pip install -U git+git://github.com/hypergravity/regli`
- for the Zenodo version: [https://zenodo.org/record/3461514](https://zenodo.org/record/3461514)

## test


```python
from regli import test
test()
```
output:
```
regli.interp3 x 10000: 0.5675415992736816 sec
regli.interpn x 10000: 2.5326197147369385 sec
rgi x 10000: 5.4028871059417725 sec
```

## doc
```python
# import Regli
from regli import Regli
import numpy as np

# construct grid coordinates
x1 = np.linspace(-1, 1, 30)     
x2 = np.linspace(-1, 1, 30)
x3 = np.linspace(-1, 1, 30)

# initiate regli using coordinates
regli = Regli(x1, x2, x3)

# an arbitrary function of coordinates (for demo)
f = lambda _x1, _x2, _x3: _x1 + _x2 + _x3

# regli.flats stores flattened coordinates of ND grid
flats = regli.flats
# evaluate your function on flats
values = np.array([f(*_) for _ in flats]).reshape(-1, 1)
# set values for regli
regli.set_values(values)        

regli(pos)                      # use any of the 3 ways to interpolate
regli.interpn(pos)              # method 1 is equivalent to 2
regli.interp3(pos)              # this is accelerated for 3D

```

## How to cite the code
BibTex:
```
@misc{https://doi.org/10.5281/zenodo.3461514,
  doi = {10.5281/zenodo.3461514},
  url = {https://zenodo.org/record/3461514},
  author = {Zhang, Bo},
  title = {hypergravity/regli: The Zenodo version},
  publisher = {Zenodo},
  year = {2019}
}
```
Please go to [https://search.datacite.org/works/10.5281/zenodo.3461514](https://search.datacite.org/works/10.5281/zenodo.3461514) for other formats.
networkh3 0.0.5
A package to return H3 hexagons based on an OSMnx network
License: MIT
Keywords: OSMnx,H3,Routing,Network analysis

<h1 align="left">networkh3</h1>
<p>
  <img alt="Version" src="https://img.shields.io/badge/version-0.0.5-blue.svg?cacheSeconds=2592000" />
  <a href="#" target="_blank">
    <img alt="License: MIT License" src="https://img.shields.io/badge/License-MIT License-yellow.svg" />
  </a>
</p>

<p>A package to return clipped H3 hexagons from the extent of an OSMnx network. Useful for anyone looking to use the H3 spatial indexing system in route analyses or spatial analyses, and for saving time sourcing H3 hexagons.</p>

## Examples
<p>
<img src="https://raw.githubusercontent.com/callumscoby/networkh3/main/images/london_example.png" height="258px"/>
<img src="https://raw.githubusercontent.com/callumscoby/networkh3/main/images/washington_example.png" height="258px" />


<p>(L-R): London, Washington DC</p></p>

<p>Example workflows are available in the <a href="https://github.com/callumscoby/networkh3">Github</a> repository.</p>

## Install

Install the latest version from PyPI:

```sh
pip install networkh3
```

## Import

```sh
from networkh3 import NETWORKH3
```

## Usage

NETWORKH3 requires three parameters: the area of interest, the type of OSMNx network, and the resolution of the returned H3 hexagons:

```sh
from networkh3 import NETWORKH3

NETWORKH3.get_h3('Leeds, United Kingdom', 'drive', 9)
```

Optional style keywords can also be specified:

```sh
from networkh3 import NETWORKH3
import contextily as cx

NETWORKH3.get_h3('Leeds, United Kingdom', 'drive', 9, 
                network_kwargs={
                  'node_size': 1, 
                  'node_color': 'black',
                  'edge_color': 'red',
                  'edge_linewidth': 0.2}, 
                h3_kwargs={
                  'facecolor': 'white', 
                  'alpha': 0.6}, 
                basemap_kwargs={
                  'source': cx.providers.Stamen.TonerLite}
                  )
```

The network and clipped H3 hexagons can then be used in analysis:

```sh
# Calling the network
NETWORKH3.network

# Calling the clipped H3 hexagons
NETWORKH3.h3
```

## Issues and support

<p>See the <a href="https://github.com/callumscoby/networkh3">Github</a> repository to report an issue or contribute to this project.</p>


## Contact

* Website: <a href="callumscoby.com">callumscoby.com</a>
* Twitter: [@ScobyCallum](https://twitter.com/ScobyCallum)
* Github: [@callumscoby](https://github.com/callumscoby)
* LinkedIn: [@callumscoby](https://linkedin.com/in/callumscoby)

## Acknowledgements
* <p>⌇ <a href="https://github.com/gboeing/osmnx">OSMnx</a></p>

* <p>🗺 <a href="https://github.com/uber/h3">H3</a></p>