flatisfy/doc/0.getting_started.md

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2017-04-25 15:58:06 +02:00
Getting started
===============
2017-04-27 11:41:51 +02:00
**Important**: Flatisfy relies on [Weboob](http://weboob.org/) to fetch
housing posts from housing websites. Then, you should install the [`devel`
branch](https://git.weboob.org/weboob/devel/) and update it regularly,
especially if Flatisfy suddenly stops fetching housing posts.
**Note**: For the moment, it requires [this MR on
Weboob](https://git.weboob.org/weboob/devel/merge_requests/31) which has not
yet been merged.
2017-04-25 15:58:06 +02:00
## TL;DR
1. Clone the repository.
2. Install required Python modules: `pip install -r requirements.txt`.
3. Init a configuration file: `python -m flatisfy init-config > config.json`.
Edit it according to your needs (see below).
4. Build the required data files:
`python -m flatisfy build-data --config config.json`.
5. Use it to `fetch` (and output a filtered JSON list of flats) or `import`
(into an SQLite database, for the web visualization) a list of flats
matching your criteria.
6. Install JS libraries and build the webapp:
`npm install && npm run build:dev` (use `build:prod` in production).
7. Use `python -m flatisfy serve --config config.json` to serve the web app.
## Available commands
The available commands are:
* `init-config` to generate an empty configuration file, either on the `stdin`
or in the specified file.
* `fetch` to load and filter housings posts and output a JSON dump.
* `filter` to filter a previously fetched list of housings posts, provided as
a JSON dump.
* `import` to import and filter housing posts into the database.
* `serve` to serve the built-in webapp with the development server. Do not use
in production.
## Configuration
List of configuration options:
* `data_directory` is the directory in which you want data files to be stored.
`null` is the default value and means default `XDG` location (typically
`~/.local/share/flatisfy/`)
* `max_entries` is the maximum number of entries to fetch.
* `passes` is the number of passes to run on the data. First pass is a basic
filtering and using only the informations from the housings list page.
Second pass loads any possible information about the filtered flats and does
better filtering.
* `database` is an SQLAlchemy URI to a database file. Defaults to `null` which
means that it will store the database in the default location, in
`data_directory`.
* `navitia_api_key` is an API token for [Navitia](https://www.navitia.io/)
which is required to compute travel times.
* `modules_path` is the path to the Weboob modules. It can be `None` if you
want Weboob to use the locally pip-installed modules (default value).
* `port` is the port on which the development webserver should be
listening (default to `8080`).
* `host` is the host on which the development webserver should be listening
(default to `127.0.0.1`).
* `webserver` is a server to use instead of the default Bottle built-in
webserver, see [Bottle deployment
doc](http://bottlepy.org/docs/dev/deployment.html).
_Note:_ In production, you can either use the `serve` command with a reliable
webserver instead of the default Bottle webserver (specifying a `webserver`
value) or use the `wsgi.py` script at the root of the repository to use WSGI.
### Constraints
You should specify some constraints to filter the resulting housings list,
under the `constraints` key. The available constraints are:
* `type` is the type of housing you want, either `RENT` (to rent), `SALE` (to
buy) or `SHARING` (for a shared housing).
* `housing_types` is a list of house types you are looking for. Values can be
`APART` (flat), `HOUSE`, `PARKING`, `LAND`, `OTHER` (everything else) or
`UNKNOWN` (anything which was not matched with one of the previous
categories).
* `area` (in m²), `bedrooms`, `cost` (in currency unit), `rooms`: this is a
tuple of `(min, max)` values, defining an interval in which the value should
lie. A `null` value means that any value is within this bound.
* `postal_codes` is a list of postal codes. You should include any postal code
you want, and especially the postal codes close to the precise location you
want.
* `time_to` is a dictionary of places to compute travel time to them.
Typically,
```
"time_to": {
"foobar": {
"gps": [LAT, LNG],
"time": [min, max]
}
}
```
means that the housings must be between the `min` and `max` bounds (possibly
`null`) from the place identified by the GPS coordinates `LAT` and `LNG`
(latitude and longitude), and we call this place `foobar` in human-readable
form. Beware that `time` constraints are in **seconds**.
## Building the web assets
If you want to build the web assets, you can use `npm run build:dev`
(respectively `npm run watch:dev` to build continuously and monitor changes in
source files). You can use `npm run build:prod` (`npm run watch:prod`) to do
the same in production mode (with minification etc).