flatisfy/README.md

Flatisfy
========

Flatisfy is your new companion to ease your search of a new housing :)


**Note**: This software is under heavy development at the moment, and the
database schema could change at any time. Do not consider it as being
production ready. However, I am currently using it for my own housing search
and it is working fine :)

<img src="doc/img/home.png" width="45%"/> <img src="doc/img/home2.png" width="45%"/>

It uses [Weboob](http://weboob.org/) to get all the housing posts on most of
the websites offering housings posts, and then offers a bunch of pipelines to
filter and deduplicate the fetched housings.


It can be used as a command-line utility, but also exposes a web API and
visualisation, to browse through the results.


_Note_: It is targeted at French users (due to the currently supported
websites), and in particular at people living close to Paris, as I developped
it for my personal use, and am currently living in Paris :) Any feedback and
merge requests to better support other countries / cities are more than
welcome!

_Note_: In this repository and across the code, I am using the name "flat". I
use it as a placeholder for "housing" and consider both are interchangeable.
This code is not restricted to handling flats only!


## Getting started

See the [getting started guide](doc/0.getting_started.md).


## Documentation

See the [dedicated folder](doc/).


## Screenshots

### Home page

Display available flats on a map:
<img src="doc/img/home.png"/>

And list them with the details in a sortable table:
<img src="doc/img/home2.png"/>


### Details page

Uniform display of the flat information in a dedicated page, with the contact etc:

<img src="doc/img/details.png"/>


### Follow flats

Track interesting flats and store them with notes:

<img src="doc/img/followed.png"/>


## OpenData

I am using the following datasets, available under `flatisfy/data_files`,
which covers Paris. If you want to run the script using some other location,
you might have to change these files by matching datasets.

* [LaPoste Hexasmal](https://datanova.legroupe.laposte.fr/explore/dataset/laposte_hexasmal/?disjunctive.code_commune_insee&disjunctive.nom_de_la_commune&disjunctive.code_postal&disjunctive.libell_d_acheminement&disjunctive.ligne_5) for the list of cities and postal codes in France.
* [RATP (Paris) stations](https://data.ratp.fr/explore/dataset/positions-geographiques-des-stations-du-reseau-ratp/table/?disjunctive.stop_name&disjunctive.code_postal&disjunctive.departement) for the list of subway/tram/bus stations with their positions in Paris and nearby areas.
* [Tcl (Lyon) stations](https://download.data.grandlyon.com/wfs/rdata?SERVICE=WFS&VERSION=2.0.0&outputformat=GEOJSON&maxfeatures=4601&request=GetFeature&typename=tcl_sytral.tclarret&SRSNAME=urn:ogc:def:crs:EPSG::4326) for the list of subway/tram/bus stations with their positions in Paris and nearby areas.

Both datasets are licensed under the Open Data Commons Open Database License
(ODbL): https://opendatacommons.org/licenses/odbl/.


## License

The content of this repository is licensed under an MIT license, unless
explicitly mentionned otherwise.


## Contributing

See the `CONTRIBUTING.md` file for more infos.


## Thanks

* [Weboob](http://weboob.org/)
* The OpenData providers listed above!
* Navitia for their really cool public transportation API.
* A lots of Python modules, required for this script (see `requirements.txt`).
* [Kresus](https://framagit.org/bnjbvr/kresus) which gave me part of the
  original idea (at least proved me such software based on scraping can
  achieve a high quality level :)
* Current favicon is from [Wikipedia](https://commons.wikimedia.org/wiki/File:Home_Icon.svg)