semvec/pypi-packages huggingface dataset:
ddd-api-gateway 0.0.7
DDD base framework for python

DDD Api Gateway Framework
=========================

|Build Status| |Pypi Status| |Coveralls Status|

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

From source code:

::

    python setup.py install

From pypi:

::

    pip install ddd-api-gateway

Usage
-----

::

    from ddd_base.value_object import ValueObject


    class TheValueObject(ValueObject):

        def __init__(self, name):
            super(TheValueObject, self).__init__()
            self.name = name

        def __eq__(self, other):
            if not isinstance(other, ValueObject):
                return NotImplemented

            return self.name == other.name


    def test_value_object_compare():
        a_value_object = TheValueObject("name")
        b_value_object = TheValueObject("name")

        assert a_value_object.same_as(b_value_object)



License
-------

This software is licensed under the `MIT license <http://en.wikipedia.org/wiki/MIT_License>`_

See `License file <https://github.com/sunwei/ddd-base/blob/master/LICENSE>`_

.. |Build Status| image:: https://travis-ci.com/sunwei/ddd-api-gateway.svg?branch=master
   :target: https://travis-ci.com/sunwei/ddd-api-gateway
.. |Pypi Status| image:: https://badge.fury.io/py/ddd-api-gateway.svg
   :target: https://badge.fury.io/py/ddd-api-gateway
.. |Coveralls Status| image:: https://coveralls.io/repos/github/sunwei/ddd-api-gateway/badge.svg?branch=master
   :target: https://coveralls.io/github/sunwei/ddd-api-gateway?branch=master

Changelog
=========

0.0.7
-----

- Route specification need ssl

0.0.6
-----

- Add endpoint hostname property

0.0.5
-----

- Expose exceptions message

0.0.4
-----

- Expose exceptions

0.0.3
-----

- Add entity repository

0.0.2
-----

- Adjust for travis test and cover all percentage

0.0.1
-----

- Initial public release
appel-geocode 1.1.0
A faster spatial join/reverse geocoding algorithm
Requires Python: >=3.6

# Project Title

APPEL: A faster spatial join/reverse geocoding threaded algorithm

## Getting Started


Spatial joins are like common relational database joins, just for geographic data. 
In general, we have a set of coordinates and we want to know which point is in which polygon, this operation is described as reverse geocoding.
The algorithm relies on standard Point-In-Polygon operations but tends to minimize then.

APPEL proposes a new way to spatial joins that promises to be faster than than trivial brute force and R-Tree implementation of PostGIS and GeoPandas.
For a million points it takes about 14 seconds to finish locating, while PostGIS takes about 7 minutes and GeoPandas takes 1 minutes and 7 seconds. 
All on the same machine.

To do so, the polygons to be searched are organized through a tree. The tree levels are predefined based on states, mesoregions, microregions and municipalities.
In addition, it is considered that the geographic points provided by users are more likely to be located in more populated regions.
Therefore, these areas are sorted to be the first to be found at each level of the tree. 

Currently, the systems works only for Brazil territory and locates the cities of each point.
But it's principle is extensible for any geographical region.
You'll need just a shapefile (or equivalent) with each region and subregions population to build the data structure.


These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

### Prerequisites

```
Python 3.6
GeoPandas
Numpy
Shapely
```

### Installing

A step by step series of examples that tell you how to get a development env running
Just use pip.

```
pip install appel
```
By default, it comes with a tree for Brazil's regions.

To use, you must input the longitude and latitudes in the query function:
```
from numpy import array

from appel.searchtree import SearchTree

search = SearchTree()

longitudes = array([2.748047, -20.890625], dtype='float32')
latitudes = array([-63.03125, -49.53125], dtype='float32')
results = search.query(longitudes, latitudes)
print(results)

```

It will return a dataframe with latitudes longitudes and the city id.

## Running the tests

Just run methods on the classes of test package.

## Built With

* [GeoPandas](http://geopandas.org/) - Essential for reading shapely files and build the search data structure.
* [Shapely](https://github.com/Toblerity/Shapely) - vectorized contains function is the core of the search algorithm.

## Contributing

Currently I don't have a fixed system. Use issues for critics, help or any question in general.

## Authors

* **Gabriel Coimbra** - [gcoimbra](https://github.com/gcoimbra/)

## License

This project is licensed under the GNU Affero General Public License - see the [LICENSE.md](LICENSE.md) file for details

## Acknowledgments
Thanks [PurpleBooth](https://gist.github.com/PurpleBooth) for this [README.md](https://gist.github.com/PurpleBooth/109311bb0361f32d87a2) template.
workdown 0.0.4
Write Markdown and have it published and hosted on Cloudflare Workers
Requires Python: >=3.6

# Workdown

A system to write Markdown and have it published and hosted via [Cloudflare Workers](https://workers.cloudflare.com/)

## Pre-requisites
* Python 3
* Install [`wrangler`](https://github.com/cloudflare/wrangler)

## Usage
1. `pip3 install workdown`
2. Run `wrangler generate site https://github.com/eldridgea/workdown-example-site`. This will generate a folder called `site` containing the folder structure `workdown` requires to work. 
3. Configure wrangler.toml with your Cloudflare details, make sure your route ends in a wildcard e.g. `route = "https://example.com/*".`
4. Create a KV Namespace and bind in to `pages` in your `wrangler.toml` file.
5. Make a markdown file ending in `.md` in the `content/` directory If you want it to be your main page, name it `index.md`.
6. From the `site` folder (or whatever you named it) run `workdown`
9. Done!

## CSS
CSS should go in the `assets/` directory. They will be available as their full filename under `css`. For example if you make `main.css`, it will be `example.com/css/main.css`

## Partials
Here is where you can customize the header and footer of pages in HTML. Currently all partials apply to all pages generated. Good things to do here would be if you have a static CSS file in `css/` or would like to include a a fonts CDN, add it into the `<head>` in the `partials/header.html` so it will be usable on your pages. 

## Notes
The path for content will be the name of the markdown file with no extension. For example if you make `contact.md`, it will be `example.com/contact`.

## Install directly from Github
`pip3 install git+https://github.com/eldridgea/workdown`

## Manual Install
1. `git clone https://github.com/eldridgea/workdown`
2. `cd workdown`
3. `python3 setup.py install`