semvec/pypi-packages huggingface dataset:
epw 1.2.dev2
EnergyPlus wrapper for Python
Keywords: epw

=================
EnergyPlusWrapper
=================

Copyright (c) 2019 Jérémie DECOCK (www.jdhp.org)

* Web site: http://www.jdhp.org/software_en.html#epw
* Online documentation: https://jdhp.gitlab.io/epw
* Examples: https://jdhp.gitlab.io/epw/gallery/

* Notebooks: https://gitlab.com/jdhp/eplus-python-wrapper-notebooks
* Source code: https://gitlab.com/jdhp/eplus-python-wrapper
* Issue tracker: https://gitlab.com/jdhp/eplus-python-wrapper/issues
* EnergyPlusWrapper on PyPI: https://pypi.org/project/epw
* EnergyPlusWrapper on Anaconda Cloud: https://anaconda.org/jdhp/epw


Description
===========

EnergyPlus wrapper for Python

Note:

    This project is still in beta stage, so the API is not finalized yet.


Dependencies
============

*  Python >= 3.0

.. _install:

Installation
============

Gnu/Linux
---------

You can install, upgrade, uninstall EnergyPlusWrapper with these commands (in a
terminal)::

    pip install --pre epw
    pip install --upgrade epw
    pip uninstall epw

Or, if you have downloaded the EnergyPlusWrapper source code::

    python3 setup.py install

.. There's also a package for Debian/Ubuntu::
.. 
..     sudo apt-get install epw

Windows
-------

.. Note:
.. 
..     The following installation procedure has been tested to work with Python
..     3.4 under Windows 7.
..     It should also work with recent Windows systems.

You can install, upgrade, uninstall EnergyPlusWrapper with these commands (in a
`command prompt`_)::

    py -m pip install --pre epw
    py -m pip install --upgrade epw
    py -m pip uninstall epw

Or, if you have downloaded the EnergyPlusWrapper source code::

    py setup.py install

MacOSX
-------

.. Note:
.. 
..     The following installation procedure has been tested to work with Python
..     3.5 under MacOSX 10.9 (*Mavericks*).
..     It should also work with recent MacOSX systems.

You can install, upgrade, uninstall EnergyPlusWrapper with these commands (in a
terminal)::

    pip install --pre epw
    pip install --upgrade epw
    pip uninstall epw

Or, if you have downloaded the EnergyPlusWrapper source code::

    python3 setup.py install


Documentation
=============

* Online documentation: https://jdhp.gitlab.io/epw
* API documentation: https://jdhp.gitlab.io/epw/api.html


Example usage
=============

::

    import epw
    import matplotlib.pyplot as plt

    sub_dict = {("Material", "BETON 20CM", "Conductivity"): 9.99}

    df = epw.core.sub_run(epw.data.idf_cube_path(),
                          weather_file_path=epw.data.weather_san_francisco_tmy_path(),
                          sub_dict=sub_dict)

    df.plot()
    plt.show()


Bug reports
===========

To search for bugs or report them, please use the EnergyPlusWrapper Bug Tracker at:

    https://gitlab.com/jdhp/eplus-python-wrapper/issues


License
=======

This project is provided under the terms and conditions of the `MIT License`_.


.. _MIT License: http://opensource.org/licenses/MIT
.. _command prompt: https://en.wikipedia.org/wiki/Cmd.exe
podsearchler 0.3.4
Let's find some podcasts!
Requires Python: >=3.7

# Podcast searcher

> Search any podcast in iTunes library

[![PyPI Version][pypi-image]][pypi-url]
[![Build Status][build-image]][build-url]
[![Code Coverage][coverage-image]][coverage-url]
[![Code Quality][quality-image]][quality-url]

`podsearchler` finds podcasts via [iTunes Search API](https://developer.apple.com/library/archive/documentation/AudioVideo/Conceptual/iTuneSearchAPI/index.html).

Search parameters:

-   _query_ - search string (name, author etc)
-   _country_ - ISO alpha-2 country code (us, de, fr etc), default: us
-   _limit_ - maximum number or search results, default: 5

Returned attributes for each podcast:

-   _id_ - iTunes ID (e.g., `979020229`)
-   _name_ - podcast name (`Talk Python To Me`)
-   _author_ - author name (`Michael Kennedy (@mkennedy)`)
-   _url_ - Apple Podcasts URL (`https://podcasts.apple.com/us/podcast/id979020229`)
-   _feed_ - podcast RSS URL (`https://talkpython.fm/episodes/rss`)
-   _category_ - main category (`Technology`)
-   _image_ - 600x600px image URL (`https://is4-ssl.mzstatic.com/image/.../600x600bb.jpg`)
-   _country_ - ISO alpha-3 country code (`USA`)
-   _episode_count_ - episode count this year (`26`)

## Installation

```sh
pip install podsearchler
```

## Usage

Search podcasts by query:

```python
>>> import podsearchler
>>> podcasts = podsearchler.search("python", country="us", limit=10)
>>> podcasts[0].name
'Talk Python To Me'
>>> podcasts[0].author
'Michael Kennedy (@mkennedy)'
>>> podcasts[0].url
'https://podcasts.apple.com/us/podcast/id979020229'
```

Retrieve podcast by iTunes ID:

```python
>>> import podsearchler
>>> podcast = podsearchler.get(979020229)
>>> podcast.name
'Talk Python To Me'
```

## Development setup

```sh
$ python3 -m venv env
$ . env/bin/activate
$ make deps
$ tox
```

## Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Make sure to add or update tests as appropriate.

Use [Black](https://black.readthedocs.io/en/stable/) for code formatting and [Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0-beta.4/) for commit messages.

## [Changelog](CHANGELOG.md)

## License

[MIT](https://choosealicense.com/licenses/mit/)

<!-- Badges -->

[pypi-image]: https://img.shields.io/pypi/v/podsearchler
[pypi-url]: https://pypi.org/project/podsearchler/
[build-image]: https://github.com/nalgeon/podsearchler-py/actions/workflows/build.yml/badge.svg
[build-url]: https://github.com/nalgeon/podsearchler-py/actions/workflows/build.yml
[coverage-image]: https://codecov.io/gh/nalgeon/podsearchler-py/branch/main/graph/badge.svg
[coverage-url]: https://codecov.io/gh/nalgeon/podsearchler-py
[quality-image]: https://api.codeclimate.com/v1/badges/3130fa0ba3b7993fbf0a/maintainability
[quality-url]: https://codeclimate.com/github/nalgeon/podsearchler-py
grahamscandelaunay 0.1.0
Use Graham Scan to incrementally solve delaunay triangulation
Requires Python: >=3.6

# graham-scan-based-incremental-delaunay

Graham Scan-Based Incremental Delaunay Triangulation:
- Sort the input points by x-coordinate
- Select the leftmost, bottommost point as the pivot point
- Sort the other points by angle relative to the pivot point their slopes relative to the pivot
- Construct the base convex hull using the pivot point and the first two points sorted by angle
- Incrementally add the next sorted point and use Graham Scan to get the convex hull, saving any edges that were made
- Flip the current triangulation to Delaunay and repeat until done

TODO LIST:
- [x] Implement Halfedge data structure, with addleaf and addedge
  - [x] get two sorting algorithms loglinear time, one for x-coord and one for slope
  - [x] Use a stack to track points on the convex hull
  - [x] Use a queue to track edges that need to be flipped
  - [x] Save all the edges made during the convex hull process
  - [x] Check the queued edges if they are locally Delaunay
  - [x] Flip non-locally Delaunay edges
- [x] Work on visualization
- [ ] Write some tests
  - [ ] Test sorting for setting up the points
  - [ ] Test Convex Hull triangulation
  - [ ] Test Locally Delaunay check
  - [ ] Test edge flipping on a simple case

Due Date: final code and presentation on April 29th

Timeline for progress:
- 4/1
- 4/8
- 4/15
- 4/22
- 4/29 project due along with presentation

## Environment and package info
Please make sure you have installed the pyglet and numpy before running the code, Those two package can be installed using pip command
```pip install numpy``` and ```pip install pyglet```

This project has been uploaded to PyPi as a package. You can download the package by the following command:
```python3 -m pip install grahamscan-delaunay```
After install the package, you can start the project by
```python3```
and then ```>>> import grahamscan_delaunay```
## Visualization
Once you start the program, an empty window will pop up. You can add a point by click inside the window, or randomly generate one by pressing ```G```. 
Once you finish adding points, press ```S``` to start the Delaunay algorithm step-by-step visualization. 
To go to the next step, press the Space Bar. 
To reset to the start of the algorithm, press ```R```. To clear all points, press ```C```.
The red point indicates the most recently added point. 
The blue edge indicates that the edge will be checked if it is Locally Delaunay. 
A black point indicates that the point in in the triangulation while a gray point indicates that it has not yet been added to the triangulation.
When the algorithm is complete, the background will turn green. Press ```R``` or ```C``` to reset the visualization.