Expose travel distance as output.

/spend 22h

Expose travel distance as output.
/spend 22h
Antoine Goutenoir
1 parent 1ae3ccd1
Showing 3 changed files with 53 additions and 48 deletions Show diff stats
flaskr/controllers/main_controller.py
flaskr/laws/travel_emission_linear_fit.py
flaskr/templates/estimation.html
@@ -262,7 +262,8 @@ def compute():  # process the queue of estimation requests
  
         destinations_by_city_key = {}
  
-        cities_sum = {}
+        cities_sum_foot = {}
+        cities_sum_dist = {}
         for model in emission_models:
             cities_dict = {}
             for _destination in _destinations:
@@ -282,11 +283,16 @@ def compute():  # process the queue of estimation requests
                         'city': city_key,
                         'address': _destination.address,
                         'footprint': 0.0,
+                        'distance': 0.0,
                     }
-                cities_dict[city_key]['footprint'] += footprint
-                if city_key not in cities_sum:
-                    cities_sum[city_key] = 0.0
-                cities_sum[city_key] += footprint
+                cities_dict[city_key]['footprint'] += footprint['co2eq_kg']
+                cities_dict[city_key]['distance'] += footprint['distance']
+                if city_key not in cities_sum_foot:
+                    cities_sum_foot[city_key] = 0.0
+                cities_sum_foot[city_key] += footprint['co2eq_kg']
+                if city_key not in cities_sum_dist:
+                    cities_sum_dist[city_key] = 0.0
+                cities_sum_dist[city_key] += footprint['distance']
  
             cities = [cities_dict[k] for k in cities_dict.keys()]
             cities = sorted(cities, key=lambda c: c['footprint'])
@@ -297,17 +303,21 @@ def compute():  # process the queue of estimation requests
  
         _results['footprints'] = footprints
  
-        total = 0.0
+        total_foot = 0.0
+        total_dist = 0.0
  
         cities_mean_dict = {}
-        for city in cities_sum.keys():
-            city_mean = 1.0 * cities_sum[city] / len(emission_models)
+        for city in cities_sum_foot.keys():
+            city_mean_foot = 1.0 * cities_sum_foot[city] / len(emission_models)
+            city_mean_dist = 1.0 * cities_sum_dist[city] / len(emission_models)
             cities_mean_dict[city] = {
                 'address': destinations_by_city_key[city].address,
                 'city': city,
-                'footprint': city_mean,
+                'footprint': city_mean_foot,
+                'distance': city_mean_dist,
             }
-            total += city_mean
+            total_foot += city_mean_foot
+            total_dist += city_mean_dist
  
         cities_mean = [cities_mean_dict[k] for k in cities_mean_dict.keys()]
         cities_mean = sorted(cities_mean, key=lambda c: c['footprint'])
@@ -317,8 +327,10 @@ def compute():  # process the queue of estimation requests
         }
         _results['cities'] = cities_mean
  
-        _results['total'] = total  # DEPRECATED
-        _results['footprint'] = total
+        _results['total'] = total_foot  # DEPRECATED
+        _results['footprint'] = total_foot
+
+        _results['distance'] = total_dist
  
         return _results
  
@@ -433,25 +445,17 @@ def consult_estimation(public_id, extension):
  
         si = StringIO()
         cw = csv.writer(si, quoting=csv.QUOTE_ALL)
-        cw.writerow([u"city", u"address", u"co2 (g)"])
+        cw.writerow([u"city", u"address", u"co2 (kg)", u"distance (km)"])
  
         results = estimation.get_output_dict()
-        if 'mean_footprint' in results:
-            for city in results['mean_footprint']['cities']:
-                cw.writerow([
-                    city['city'].encode(OUT_ENCODING),
-                    city['address'].encode(OUT_ENCODING),
-                    city['footprint']
-                ])
-        elif 'cities' in results:
-            for city in results['cities']:
-                cw.writerow([
-                    city['city'].encode(OUT_ENCODING),
-                    city['address'].encode(OUT_ENCODING),
-                    city['total']
-                ])
-
-        # HTTP headers?
+        for city in results['cities']:
+            cw.writerow([
+                city['city'].encode(OUT_ENCODING),
+                city['address'].encode(OUT_ENCODING),
+                round(city['footprint'], 3),
+                round(city['distance'], 3),
+            ])
+
         # return si.getvalue().strip('\r\n')
         return Response(
             response=si.getvalue().strip('\r\n'),
@@ -2,6 +2,7 @@ import numpy as np
 from geopy.distance import great_circle
  
  
+# @abc
 class BaseEmissionModel():
     def __init__(self, config):  # Constructor
         self.name = config.name
@@ -29,9 +30,10 @@ class EmissionModel(BaseEmissionModel):
             prefer_train_under_distance=0,  # meters
     ):
         footprint = 0.0
+        distance = 0.0
  
         #############################################
-        # TODO: find closest airport(s) and pick one
+        # TODO: find closest airport(s) and pick one?
         # We're going to need caching here as well.
         from collections import namedtuple
         origin_airport = namedtuple('Position', [
@@ -55,34 +57,33 @@ class EmissionModel(BaseEmissionModel):
         # ... TODO
  
         # I.b Airplane travel footprint
-        footprint += self.compute_airplane_footprint(
+        great_circle_distance = self.get_distance_between(
             origin_latitude=origin_airport.latitude,
             origin_longitude=origin_airport.longitude,
             destination_latitude=destination_airport.latitude,
             destination_longitude=destination_airport.longitude,
         )
+        footprint += self.compute_airplane_footprint(
+            distance=great_circle_distance
+        )
+        distance += great_circle_distance
  
-        # II.a Double the footprint if it's a round-trip
+        # II.a Double it up since it's a round-trip
         footprint *= 2.0
+        distance *= 2.0
  
-        return footprint
+        return {
+            'distance': distance,
+            'co2eq_kg': footprint,
+        }
+        # return footprint
  
     def compute_airplane_footprint(
             self,
-            origin_latitude,
-            origin_longitude,
-            destination_latitude,
-            destination_longitude
+            distance
     ):
         config = self.config.plane_emission_linear_fit
  
-        great_circle_distance = self.get_distance_between(
-            origin_latitude, origin_longitude,
-            destination_latitude, destination_longitude
-        )
-
-        distance = great_circle_distance
-
         distance = config.connecting_flights_scale * distance
  
         footprint = self.compute_airplane_distance_footprint(distance, config)
@@ -111,7 +112,7 @@ class EmissionModel(BaseEmissionModel):
         """
         :param distance: in km
         :param config:
-        :return:
+        :return: float
         """
         footprint = distance
         for interval in config.intervals:
@@ -193,7 +193,7 @@ jQuery(document).ready(function($){
     var vizid = "#cities_footprints_d3viz";
     var csvUrl = "/estimation/{{ estimation.public_id }}.csv";
     var x_key = 'city';
-    var y_key = 'co2 (g)';
+    var y_key = 'co2 (kg)';
  
     // Set the dimensions and margins of the graph
     var margin = {top: 10, right: 30, bottom: 150, left: 150},
@@ -214,7 +214,7 @@ jQuery(document).ready(function($){
     d3.csv(csvUrl, function(data) {
  
         // Extrema
-        var data_y_max = d3.max(data, function(d) { return parseFloat(d['co2 (g)']); });
+        var data_y_max = d3.max(data, function(d) { return parseFloat(d[y_key]); });
         var axis_y_max = ceil_value_to_magnitude(data_y_max);
  
         // X axis
@@ -266,7 +266,7 @@ jQuery(document).ready(function($){
     var vizid = "#cities_footprints_d3viz_lollipop";
     var csvUrl = "/estimation/{{ estimation.public_id }}.csv";
     var y_key = 'city';
-    var x_key = 'co2 (g)';
+    var x_key = 'co2 (kg)';
  
     var margin = {top: 10, right: 30, bottom: 150, left: 150},
         height = Math.max(300, 100+16*plots_config['cities_count']) - margin.top - margin.bottom;