Using real calculated risk scores for each department to be displayed on the map (#34)

* added a graphs.py file that will contain several indicators graphs objects to be used in the homepage.py file ; changed the layout of the homepage in order to better fit our needs

* flake8 style corrections

* blank line at the beginning of graphs.py

* styling errors

* styling errors

* Add files via upload

* using actual risk data to get a colormap with risk

* risk json import

* minor changes

* line too long

* deleted commented line

Author: nrslt

app/assets/up_arrow_png.png

@@ -8,3 +8,4 @@
 API_URL: str = os.getenv('API_URL')
 API_LOGIN: str = os.getenv('API_LOGIN')
 API_PWD: str = os.getenv('API_PWD')
+PYRORISK_FALLBACK: str = "https://github.com/pyronear/pyro-risks/releases/download/v0.1.0-data/pyrorisk_20200901.json"

app/config.py

@@ -10,9 +10,11 @@
 # NumPy to generate the random scores from 0 to 1 that we are using so far, as well as the classes
 import numpy as np
 
-# Useful imports to open and read the GeoJSON file
+# Useful imports to open and read the GeoJSON file, and to get risk data from the API
 from pathlib import Path
 import json
+import requests
+import config as cfg
 
 # Various modules provided by Dash to build the page layout
 import dash_core_components as dcc
@@ -34,10 +36,19 @@
 with open(Path(__file__).parent.joinpath('data', 'departements.geojson'), 'rb') as response:
     departments = json.load(response)
 
+# We fetch the deparment risk score json and store it in the risk_json variable
+# When everything is validated, we'll request the data directly from the API
+risk_json = requests.get(cfg.PYRORISK_FALLBACK).json()
 
-# We add to each department in the geojson a new property called "score" that corresponds to the random risk level
+# We add to each department in the geojson a new property called "score" that corresponds to the risk level
 for department in departments['features']:
-    department['properties']['score'] = np.random.rand()
+    dpt_name = department['properties']['nom']
+    geocode_list = [dpt['geocode'] for dpt in risk_json]
+    if dpt_name in geocode_list:
+        risk_json_index = geocode_list.index(dpt_name)
+        department['properties']['score'] = risk_json[risk_json_index]['score']
+    else:
+        department['properties']['score'] = 0
 
 
 # Preparing the choropleth map, fetching the departments GeoJSON and building the related map attribute