Switch to GFW infra API from csv

SkyTruth · Nov 25, 2024 · 4395334 · 4395334
1 parent f74dba6
commit 4395334
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Showing 2 changed files with 8 additions and 13 deletions.
diff --git a/cerulean_cloud/cloud_function_ais_analysis/utils/analyzer.py b/cerulean_cloud/cloud_function_ais_analysis/utils/analyzer.py
@@ -124,10 +124,10 @@ def __init__(self, s1_scene, **kwargs):
 
         if self.infra_gdf is None:
             self.infra_api_token = os.getenv("INFRA_API_TOKEN")
-            self.infra_gdf = self.load_infrastructure_data()
+            self.infra_gdf = self.load_infrastructure_data_api()
         self.coincidence_scores = np.zeros(len(self.infra_gdf))
 
-    def load_infrastructure_data(self, only_oil=True):
+    def load_infrastructure_data_csv(self, only_oil=True):
         """
         Loads infrastructure data from a CSV file.
         """

diff --git a/notebooks/ASA_test_environment.py b/notebooks/ASA_test_environment.py
@@ -243,19 +243,13 @@ def plot_coincidence(
     s1_scene = get_s1_scene(slick_gdf.s1_scene_id.iloc[0])
 
     source_types = []
-    # source_types += [1]  # ais
+    source_types += [1]  # ais
     source_types += [2]  # infra
     if not (  # If the last analyzer is for the same scene, reuse it
         analyzers
         and next(iter(analyzers.items()))[1].s1_scene.scene_id == s1_scene.scene_id
     ):
-        analyzers = {
-            s_type: ASA_MAPPING[s_type](
-                s1_scene,
-                gfw_infra_filepath="/Users/jonathanraphael/git/cerulean-cloud/cerulean_cloud/cloud_function_ais_analysis/SAR Fixed Infrastructure 202407 DENOISED UNIQUE.csv",
-            )
-            for s_type in source_types
-        }
+        analyzers = {s_type: ASA_MAPPING[s_type](s1_scene) for s_type in source_types}
 
     ranked_sources = pd.DataFrame(columns=["type", "st_name", "collated_score"])
     for s_type, analyzer in analyzers.items():
@@ -275,12 +269,13 @@ def plot_coincidence(
             ]
         )
 
-    if 2 in analyzers:
+    if 2 in analyzers.keys():
         plot_coincidence(analyzers[2], slick_id)
 
-    print(ranked_sources[["type", "st_name", "collated_score"]].head())
+    print(
+        ranked_sources[["type", "ext_id", "coincidence_score", "collated_score"]].head()
+    )
 
-    print(ranked_sources.head())
 # print(accumulated_sources)
 # %%
 fake_infra_gdf = generate_infrastructure_points(slick_gdf, 50000)