Merge branch 'redesing_system_add_sat_vis' into redesing_system

containers/cleanair/gpjax_models/gpjax_models/models/vis.py

@@ -8,7 +8,7 @@
 from stdata.vis.spacetime import SpaceTimeVisualise
 
 
-directory_path = "/clean-air/clean-air-infrastructure/containers/cleanair/gpjax_models/data/dgp_small_inducing_and_maxiter "
+directory_path = Path('/Users/oliverhamelijnck/Downloads/dataset/')
 
 # Create the directory if it doesn't exist
 if not os.path.exists(directory_path):
@@ -18,27 +18,27 @@
 def load_data(root):
     with open(
         str(
-            "/clean-air/clean-air-infrastructure/containers/cleanair/gpjax_models/data/dgp_small_inducing_and_maxiter/3/dataset/training_dataset.pkl"
+            directory_path / "training_dataset.pkl"
         ),
         "rb",
     ) as file:
         training_data = pd.read_pickle(file)
     with open(
         str(
-            "/clean-air/clean-air-infrastructure/containers/cleanair/gpjax_models/data/dgp_small_inducing_and_maxiter/3/dataset/test_dataset.pkl"
+            directory_path/ "test_dataset.pkl"
         ),
         "rb",
     ) as file:
         testing_data = pd.read_pickle(file)
     # Load raw data using pickle
     with open(
-        "/clean-air/clean-air-infrastructure/raw_data_3.pkl",
+        directory_path / "raw_data_3.pkl",
         "rb",
     ) as file:
         raw_data = pd.read_pickle(file)
 
     with open(
-        "/clean-air/clean-air-infrastructure/test_true_dataset.pkl",
+        directory_path / "test_true_dataset.pkl",
         "rb",
     ) as file:
         true_y = pd.read_pickle(file)
@@ -74,7 +74,7 @@ def load_data(root):
 
 def load_results(root):
     with open(
-        str(root / "mrdgp_production_results" / "predictions_mrdgp.pkl"), "rb"
+        str(root / "predictions_mrdgp_3.pkl"), "rb"
     ) as file:
         results = pd.read_pickle(file)
     return results
@@ -90,11 +90,11 @@ def fix_df_columns(df):
 
 
 if __name__ == "__main__":
-    data_root = Path("containers/cleanair/gpjax_models/data")
+    data_root = directory_path
 
     training_data, testing_data, raw_data, day_3_gt = load_data(data_root)
     day_3_gt = day_3_gt.reset_index(drop=True)
-    train_laqn_df = fix_df_columns_dropna(raw_data["train"]["laqn"]["df"])
+    train_laqn_df = fix_df_columns(raw_data["train"]["laqn"]["df"])
     test_laqn_df = fix_df_columns(raw_data["test"]["laqn"]["df"])
     true_val = fix_df_columns(day_3_gt)
     # test_laqn_true_values = true_y
@@ -120,20 +120,30 @@ def fix_df_columns(df):
     # )
     train_end = train_laqn_df["epoch"].max()
     laqn_df = pd.concat([train_laqn_df, test_laqn_df])
-    #'geom' is the column containing Shapely Point geometries
-    hexgrid_df["geom"] = gpd.points_from_xy(hexgrid_df["lon"], hexgrid_df["lat"])
-
-    # Buffer each Point geometry by 0.002
-    hexgrid_df["geom"] = hexgrid_df["geom"].apply(lambda point: point.buffer(0.002))
-
-    # Create a GeoDataFrame using the 'geom' column
-    hexgrid_gdf = gpd.GeoDataFrame(hexgrid_df, geometry="geom")
-    hexgrid_df["pred"] = results["predictions"]["hexgrid"]["mu"][0].T
-    # hexgrid_df["pred"] = hexgrid_df["traffic"]
-    hexgrid_df["var"] = np.squeeze(results["predictions"]["hexgrid"]["var"][0])
-    vis_obj = SpaceTimeVisualise(
-        laqn_df, hexgrid_df, geopandas_flag=True, test_start=train_end
-    )
+    if False:
+        #'geom' is the column containing Shapely Point geometries
+        hexgrid_df["geom"] = gpd.points_from_xy(hexgrid_df["lon"], hexgrid_df["lat"])
+
+        # Buffer each Point geometry by 0.002
+        hexgrid_df["geom"] = hexgrid_df["geom"].apply(lambda point: point.buffer(0.002))
+
+        # Create a GeoDataFrame using the 'geom' column
+        hexgrid_gdf = gpd.GeoDataFrame(hexgrid_df, geometry="geom")
+        hexgrid_df["pred"] = results["predictions"]["hexgrid"]["mu"][0].T
+        # hexgrid_df["pred"] = hexgrid_df["traffic"]
+        hexgrid_df["var"] = np.squeeze(results["predictions"]["hexgrid"]["var"][0])
+    else:
+        hexgrid_df = None
+
+    sat_df = fix_df_columns(raw_data["train"]['sat']['df'])
+    # TODO: NEED TO CHECK!! this should match is handling the satllite data
+    sat_df = sat_df[['lon', 'lat', 'NO2', 'epoch', 'box_id']].groupby(['epoch', 'box_id']).mean().reset_index()
+    # copy predictions 
+    sat_df['pred'] = results["predictions"]['sat']['mu'][0]
+    sat_df['var'] = results["predictions"]['sat']['var'][0]
+    sat_df['observed'] = sat_df['NO2']
+
+    vis_obj = SpaceTimeVisualise( laqn_df, hexgrid_df, sat_df=sat_df, geopandas_flag=True, test_start=train_end)
 
     # Show the visualization
     vis_obj.show()