Getting started
===============


## Dependency on WebOOB

**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.

If you `pip install -r requirements.txt` it will install the latest
development version of [WebOOB](https://git.weboob.org/weboob/devel/) and the
[WebOOB modules](https://git.weboob.org/weboob/modules/), which should be the
best version available out there. You should update these packages regularly,
as they evolve quickly.

WebOOB is made of two parts: a core and modules (which is the actual code
fetching data from websites). Modules tend to break often and are then updated
often, you should keep them up to date. This can be done by installing the
`weboob-modules` package listed in the `requirements.txt` and using the
default configuration.

This is a safe default configuration. However, a better option is usually to
clone [WebOOB git repo](https://git.weboob.org/weboob/devel/) somewhere, on
your disk, to point `modules_path` configuration option to
`path_to_weboob_git/modules` (see the configuration section below) and to run
a `git pull; python setup.py install` in the WebOOB git repo often.


## TL;DR

An alternative method is available using Docker. See [2.docker.md](2.docker.md).

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. You can now run `python -m flatisfy import --config config.json` to fetch
   available flats, filter them and import everything in a SQLite database,
   usable with the web visualization.
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.

_Note_: `Flatisfy` requires an up-to-date Node version. You can find
instructions on the [NodeJS website](https://nodejs.org/en/) to install latest
LTS version.

_Note_: Alternatively, you can `python -m flatisfy fetch --config config.json`
to fetch available flats, filter them and output them as a filtered JSON list
(the web visualization will not be able to display them). This is mainly
useful if you plan in integrating Flatisfy in your own pipeline.



## Available commands

The available commands are:

* `init-config` to generate an empty configuration file, either on the `stdin`
  or in the specified file.
* `build-data` to rebuild OpenData datasets.
* `fetch` to load and filter housings posts and output a JSON dump.
* `filter` to filter again the flats in the database (and update their status)
  according to changes in config. It can also filter a previously fetched list
  of housings posts, provided as a JSON dump (with a `--input` argument).
* `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.

_Note:_ Fetching flats can be quite long and take up to a few minutes. This
should be better optimized. To get a verbose output and have an hint about the
progress, use the `-v` argument. It can remain stuck at "Loading flats for
constraint XXX...", which simply means it is fetching flats (using WebOOB
under the hood) and this step can be super long if there are lots of flats to
fetch. If this happens to you, you can set `max_entries` in your config to
limit the number of flats to fetch.


### Common arguments

You can pass some command-line arguments to Flatisfy commands, common to all the available commands. These are

* `--help`/`-h` to get some help message about the current command.
* `--data-dir DIR` to overload the `data_directory` value from config.
* `--config CONFIG` to use the config file located at `CONFIG`.
* `--passes [0, 1, 2, 3]` to overload the `passes` value from config.
* `--max-entries N` to overload the `max_entries` value from config.
* `-v` to enable verbose output.
* `-vv` to enable debug output.
* `--constraints` to specify a list of constraints to use (e.g. to restrict
  import to a subset of available constraints from the config). This list
  should be passed as a comma-separated list.


## 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 for `PUBLIC_TRANSPORT` mode.
* `mapbox_api_key` is an API token for [Mapbox](http://mapbox.com/)
  which is required to compute travel times for `WALK`, `BIKE` and `CAR`
  modes.
* `modules_path` is the path to the WebOOB modules. It can be `null` if you
  want WebOOB to use the locally installed [WebOOB
  modules](https://git.weboob.org/weboob/modules), which you should install
  yourself. This is the default value. If it is a string, it should be an
  absolute path to the folder containing WebOOB  modules.
* `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).
* `backends` is a list of WebOOB backends to enable. It defaults to any
  available and supported WebOOB backend.
* `store_personal_data` is a boolean indicated whether or not Flatisfy should
  fetch personal data from housing posts and store them in database. Such
  personal data include contact phone number for instance. By default,
  Flatisfy does not store such personal data.
* `max_distance_housing_station` is the maximum distance (in meters) between
  an housing and a public transport station found for this housing (default is
  `1500`). This is useful to avoid false-positive.
* `duplicate_threshold` is the minimum score in the deep duplicate detection
  step to consider two flats as being duplicates (defaults to `15`).
* `serve_images_locally` lets you download all the images from the housings
  websites when importing the posts. Then, all your Flatisfy works standalone,
  serving the local copy of the images instead of fetching the images from the
  remote websites every time you look through the fetched housing posts.

_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), `SHARING` (for a shared housing), `FURNISHED_RENT` (for a furnished
  rent), `VIAGER` (for a viager, lifetime sale).
* `house_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` (as strings) 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],
        "mode": A transport mode,
        "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. `mode` should be either `PUBLIC_TRANSPORT`, `WALK`, `BIKE` or `CAR`.
  Beware that `time` constraints are in **seconds**. You should take
  some margin as the travel time computation is done with found nearby public
  transport stations, which is only a rough estimate of the flat position. For
  `PUBLIC_TRANSPORT` the travel time is computed assuming a route the next
  Monday at 8am.
 * `minimum_nb_photos` lets you filter out posts with less than this number of
   photos.
 * `description_should_contain` lets you specify a list of terms that should
   be present in the posts descriptions. Typically, if you expect "parking" to
   be in all the posts Flatisfy fetches for you, you can set
   `description_should_contain: ["parking"]`.


You can think of constraints as "a set of criterias to filter out flats". You
can specify as many constraints as you want, in the configuration file,
provided that you name each of them uniquely.


## 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).
