[US] Établir un rapport de tous les paramètres d'entrée #198

examples/quickstart-fr.py.py

@@ -38,3 +38,6 @@
 # You can also plot the flows, with labels for the cities that are bigger than their neighbours
 labels = pop_trips.get_prominent_cities()
 pop_trips.plot_od_flows(labels=labels)
+
+rapport = pop_trips.parameters_dict()
+print(rapport.T)

mobility/choice_models/population_trips.py

@@ -4,6 +4,7 @@
 import shutil
 import random
 import warnings
+import pandas as pd
 
 import geopandas as gpd
 import matplotlib.pyplot as plt
@@ -685,8 +686,9 @@ def plot_od_flows(self, mode="all", motive="all", period="weekdays", level_of_de
         # Put a legend for width on bottom right, title on the top
         x_min = float(biggest_flows[["x"]].min().iloc[0])
         y_min = float(biggest_flows[["y"]].min().iloc[0])
-        plt.plot([x_min, x_min+4000], [y_min, y_min], linewidth=2, color=color)
-        plt.text(x_min+6000, y_min-1000, "1 000", color=color)
+        plt.plot([x_min-6000, x_min-4000], [y_min, y_min], linewidth=2, color=color)
+        plt.text(x_min-2000, y_min-200, "1 000", color=color)
+        plt.text(x_min-6000, y_min-2000, f"hash: {self.inputs_hash}", fontsize=7, color=color)
         plt.title(f"{mode_name} flows between transport zones on {period}")
 
         # Draw all origin-destinations
@@ -717,7 +719,7 @@ def get_prominent_cities(self, n_cities=20, n_levels=3, distance_km=2):
         """
         Get the most prominent cities, ie the biggest cities that are not close to a bigger city.
 
-        Useful to label a map and reducing the number of overlaps without mising an important city.
+        Useful to label a map and reducing the number of overlaps without missing an important city.
 
         Parameters
         ----------
@@ -771,3 +773,38 @@ def get_prominent_cities(self, n_cities=20, n_levels=3, distance_km=2):
         geoflows = geoflows.merge(xy_coords, left_index=True, right_index=True)
 
         return geoflows
+
+    def parameters_dict(self) :
+        params_general = {
+            "inner_radius": self.population.transport_zones.inner_radius,
+            "local_admin_unit_id": self.population.transport_zones.study_area.local_admin_unit_id,
+            "level_of_detail" : self.population.transport_zones.level_of_detail,
+            "nb_local_admin_units": len(self.population.transport_zones.study_area.get()),
+            "osm_geofabrik_extract_date": self.population.transport_zones.osm_buildings.geofabrik_extract_date,
+            "population_sample_size": self.population.sample_size,
+            "survey_used": [s.survey_name for s in self.surveys],
+            "inputs_hash" : self.inputs_hash
+        }
+        params_modes = {
+            key: value
+            for i, m in enumerate(self.modes, start=1)
+            for key, value in [
+                (f"mode_{i}", m.name),
+                (f"mode_{i}_filter_max_time",m.travel_costs.routing_parameters.filter_max_time),
+                (f"mode_{i}_filter_max_speed",m.travel_costs.routing_parameters.filter_max_speed),
+                (f"mode_{i}_cost_constant",m.generalized_cost.parameters.cost_constant),
+                (f"mode_{i}_cost_of_distance",m.generalized_cost.parameters.cost_of_distance),
+                (f"mode_{i}_cost_of_time_intercept",m.generalized_cost.parameters.cost_of_time.intercept) #à voir ce qu'on veut connaitre
+            ]
+        }
+        params_motives = {
+            key: value
+            for i, m in enumerate(self.motives, start=1)
+            for key, value in [
+                (f"motive_{i}", m.name),
+                (f"motive_{i}_value_of_time",m.value_of_time),
+                (f"motive_{i}_value_of_time_v2",m.value_of_time_v2)
+            ]
+        }
+        params = params_general | params_modes | params_motives
+        return pd.DataFrame([params])