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. Use `python -m flatisfy serve --config config.json` to serve the web app.
## 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 **per Weboob
backend** (that is per housing website).
*`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.
*`queries` is a list of queries defined in `flatboob` that should be fetched.
*`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.
### Constraints
You can specify constraints, under the `constraints` key. The available
constraints are:
*`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 allowed postal codes. You should include any
postal code you want, and especially the postal codes close to the precise
location you want. You MUST provide some postal codes.
*`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**.
## 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 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 stations with their positions in Paris and nearby areas.
Both datasets are licensed under the Open Data Commons Open Database License
