Leisures

mobility/choice_models/destination_sequence_sampler.py

@@ -409,12 +409,12 @@ def spatialize_other_motives(self, chains, dest_prob, costs, alpha, seed):
 
         logging.info("Spatializing other motives...")
 
-        chains_step = ( 
+        chains_step = (
             chains
             .filter(pl.col("seq_step_index") == 1)
-            .with_columns(pl.col("home_zone_id").alias("from"))
+            .with_columns(pl.col("home_zone_id").cast(pl.Int32).alias("from"))
         )
-
+                
         seq_step_index = 1
         spatialized_chains = []
 
@@ -532,7 +532,7 @@ def spatialize_trip_chains_step(self, seq_step_index, chains_step, dest_prob, co
             chains_step
             .filter(pl.col("is_anchor"))
             .with_columns(
-                to=pl.col("anchor_to")
+                to=pl.col("anchor_to").cast(pl.Int32)
             )
             .select(["demand_group_id", "home_zone_id", "motive_seq_id", "motive", "anchor_to", "from", "to"])
         )

mobility/motives/leisure.py

@@ -1,25 +1,24 @@
 import pandas as pd
 import polars as pl
-
+import geopandas as gpd
 from typing import List
+import numpy as np
+import os
 
 from mobility.motives.motive import Motive
-    
+from mobility.parsers.leisure_facilities_distribution import LeisureFacilitiesDistribution
 
 class LeisureMotive(Motive):
 
     def __init__(
-        self,
-        value_of_time: float = 10.0,
-        saturation_fun_ref_level: float = 1.5,
-        saturation_fun_beta: float = 4.0,
-        survey_ids: List[str] = ["7.71", "7.72", "7.73", "7.74", "7.75", "7.76", "7.77", "7.78"],
-        radiation_lambda: float = 0.99986,
-        opportunities: pd.DataFrame = None
-    ):
-
-        if opportunities is None:
-            raise ValueError("No built in leisure opportunities data for now, please provide an opportunities dataframe when creating instantiating the LeisureMotive class (or don't use it at all and let the OtherMotive model handle this motive).")
+            self,
+            value_of_time: float = 10.0,
+            saturation_fun_ref_level: float = 1.5,
+            saturation_fun_beta: float = 4.0,
+            survey_ids: List[str] = ["7.71", "7.72", "7.73", "7.74", "7.75", "7.76", "7.77", "7.78"],
+            radiation_lambda: float = 0.99986,
+            opportunities: pd.DataFrame = None
+        ):
 
         super().__init__(
             name="leisure",
@@ -34,28 +33,104 @@ def __init__(
 
     def get_opportunities(self, transport_zones):
 
-        transport_zones = transport_zones.get().drop("geometry", axis=1)
-        transport_zones["country"] = transport_zones["local_admin_unit_id"].str[0:2]
-
-        tz_lau_ids = transport_zones["local_admin_unit_id"].unique().tolist()
+        if self.opportunities is not None:
 
-        opportunities = self.opportunities.loc[tz_lau_ids, "n_opp"].reset_index()
+            opportunities = self.opportunities
 
-        opportunities = pd.merge(
-            transport_zones[["transport_zone_id", "local_admin_unit_id", "country", "weight"]],
-            opportunities[["local_admin_unit_id", "n_opp"]],
-            on="local_admin_unit_id"
-        )
-
-        opportunities["n_opp"] = opportunities["weight"]*opportunities["n_opp"]
+        else:
 
-        opportunities = ( 
-            opportunities[["transport_zone_id", "n_opp"]]
-            .rename({"transport_zone_id": "to"}, axis=1)
-        )
+            transport_zones = transport_zones.get()
+
+            opportunities = LeisureFacilitiesDistribution().get()
+
+            opportunities = gpd.sjoin(
+                opportunities,
+                transport_zones,
+                how="left",  
+                predicate="within"   
+            ).drop(columns=["index_right"])
+            opportunities = opportunities.dropna(subset=["transport_zone_id"])
+
+            opportunities["country"] = opportunities["local_admin_unit_id"].str[0:2]
+
+            opportunities = (
+                opportunities.groupby(["transport_zone_id", "local_admin_unit_id", "country", "weight"], dropna=False)["freq_score"]
+                  .sum()
+                  .reset_index()
+            )
+
+            opportunities["n_opp"] = opportunities["weight"]*opportunities["freq_score"]
 
+            opportunities = ( 
+                opportunities[["transport_zone_id", "n_opp"]]
+                .rename({"transport_zone_id": "to"}, axis=1)
+            )
+            opportunities["to"] = opportunities["to"].astype("Int64")
+
+            if os.environ.get("MOBILITY_DEBUG") == "1":
+                self.plot_opportunities_map(
+                    transport_zones, 
+                    opportunities, 
+                    use_log = False
+                    )
+
         opportunities = pl.from_pandas(opportunities)
         opportunities = self.enforce_opportunities_schema(opportunities)
-    
+
         return opportunities
-
+
+
+    def plot_opportunities_map(
+            self,
+            transport_zones: gpd.GeoDataFrame,
+            opportunities: pd.DataFrame,
+            zone_id_col: str = "transport_zone_id",
+            opp_zone_col: str = "to",
+            value_col: str = "n_opp",
+            use_log: bool = False            
+        ):
+
+            if not isinstance(transport_zones, gpd.GeoDataFrame):
+                tz = gpd.GeoDataFrame(transport_zones, geometry="geometry", crs="EPSG:4326")
+            else:
+                tz = transport_zones
+
+            if pl is not None and isinstance(opportunities, pl.DataFrame):
+                opp = opportunities.to_pandas()
+            else:
+                opp = opportunities.copy()
+
+            m = tz.merge(
+                opp.rename(columns={opp_zone_col: zone_id_col}),
+                on=zone_id_col,
+                how="left"
+            )
+
+            m[value_col] = m[value_col].fillna(0)
+            m = m[m["geometry"].notna()]
+            m = m[~m.geometry.is_empty]
+
+            if not m.geometry.is_valid.all():
+                m["geometry"] = m.buffer(0)
+                m = m[m["geometry"].notna()]
+                m = m[~m.geometry.is_empty]
+
+            if use_log:
+                log_col = f"log_{value_col}"
+                m[log_col] = np.log1p(m[value_col])
+                col_to_plot = log_col
+            else:
+                col_to_plot = value_col
+
+            ax = m.plot(
+                column=col_to_plot,
+                legend=True,
+                cmap="plasma",
+                linewidth=0.1,
+                edgecolor="white",
+                aspect=1
+            )
+            ax.set_axis_off()
+
+            return ax
+

mobility/motives/leisure_old.py

@@ -0,0 +1,61 @@
+import pandas as pd
+import polars as pl
+
+from typing import List
+
+from mobility.motives.motive import Motive
+
+
+class LeisureMotive(Motive):
+
+    def __init__(
+        self,
+        value_of_time: float = 10.0,
+        saturation_fun_ref_level: float = 1.5,
+        saturation_fun_beta: float = 4.0,
+        survey_ids: List[str] = ["7.71", "7.72", "7.73", "7.74", "7.75", "7.76", "7.77", "7.78"],
+        radiation_lambda: float = 0.99986,
+        opportunities: pd.DataFrame = None
+    ):
+
+        if opportunities is None:
+            raise ValueError("No built in leisure opportunities data for now, please provide an opportunities dataframe when creating instantiating the LeisureMotive class (or don't use it at all and let the OtherMotive model handle this motive).")
+
+        super().__init__(
+            name="leisure",
+            value_of_time=value_of_time,
+            survey_ids=survey_ids,
+            radiation_lambda=radiation_lambda,
+            opportunities=opportunities,
+            saturation_fun_ref_level=saturation_fun_ref_level,
+            saturation_fun_beta=saturation_fun_beta
+        )
+
+
+    def get_opportunities(self, transport_zones):
+
+        transport_zones = transport_zones.get().drop("geometry", axis=1)
+        transport_zones["country"] = transport_zones["local_admin_unit_id"].str[0:2]
+
+        tz_lau_ids = transport_zones["local_admin_unit_id"].unique().tolist()
+
+        opportunities = self.opportunities.loc[tz_lau_ids, "n_opp"].reset_index()
+
+        opportunities = pd.merge(
+            transport_zones[["transport_zone_id", "local_admin_unit_id", "country", "weight"]],
+            opportunities[["local_admin_unit_id", "n_opp"]],
+            on="local_admin_unit_id"
+        )
+
+        opportunities["n_opp"] = opportunities["weight"]*opportunities["n_opp"]
+
+        opportunities = ( 
+            opportunities[["transport_zone_id", "n_opp"]]
+            .rename({"transport_zone_id": "to"}, axis=1)
+        )
+
+        opportunities = pl.from_pandas(opportunities)
+        opportunities = self.enforce_opportunities_schema(opportunities)
+
+        return opportunities
+