Diffusion des préavis auto [pipeline] - flaguer isBeingSent un préavis hors scope uniquement si des unités le ciblent

datascience/src/pipeline/flows/distribute_pnos.py

@@ -63,6 +63,10 @@
 )
 from src.pipeline.shared_tasks.dates import get_utcnow, make_timedelta
 from src.pipeline.shared_tasks.infrastructure import execute_statement
+from src.pipeline.shared_tasks.pnos import (
+    extract_pno_units_ports_and_segments_subscriptions,
+    extract_pno_units_targeting_vessels,
+)
 
 
 @task(checkpoint=False)
@@ -110,22 +114,6 @@ def extract_pnos_to_generate(
     return (pnos, generation_needed)
 
 
-@task(checkpoint=False)
-def extract_pno_units_targeting_vessels() -> pd.DataFrame:
-    return extract(
-        db_name="monitorfish_remote",
-        query_filepath="monitorfish/pno_units_targeting_vessels.sql",
-    )
-
-
-@task(checkpoint=False)
-def extract_pno_units_ports_and_segments_subscriptions() -> pd.DataFrame:
-    return extract(
-        db_name="monitorfish_remote",
-        query_filepath="monitorfish/pno_units_ports_and_segments_subscriptions.sql",
-    )
-
-
 @task(checkpoint=False)
 def fetch_control_units_contacts() -> pd.DataFrame:
     r = requests.get(MONITORENV_API_ENDPOINT + "control_units")

datascience/src/pipeline/flows/enrich_logbook.py

@@ -21,6 +21,10 @@
 from src.pipeline.processing import prepare_df_for_loading
 from src.pipeline.shared_tasks.control_flow import check_flow_not_running
 from src.pipeline.shared_tasks.dates import get_utcnow, make_periods
+from src.pipeline.shared_tasks.pnos import (
+    extract_pno_units_ports_and_segments_subscriptions,
+    extract_pno_units_targeting_vessels,
+)
 from src.pipeline.shared_tasks.segments import extract_all_segments
 from src.pipeline.utils import psql_insert_copy
 
@@ -266,12 +270,15 @@ def compute_pno_types(
           - logbook_reports_pno_id `int` `1`
           - cfr `str` `FRA000000000`
           - `predicted_arrival_datetime_utc` `datetime`
+          - year `int` `2023`
+          - species `str` `'COD'`
           - trip_gears `List[dict]`
             `[{"gear": "xxx", "mesh": yyy, "dimensions": "zzz}, {...}]`
-          - species `str` `'COD'`
           - fao_area `str` `'27.7.d'`
-          - flag_state `str` `'FRA'`
           - weight `float` `150.5`
+          - flag_state `str` `'FRA'`
+          - locode `str` `CCXXX`
+          - facade `str` `NAMO`
 
         pno_types (pd.DataFrame): DataFrame of pno_types definitions. 1 line = 1 rule.
           Must have columns :
@@ -293,6 +300,7 @@ def compute_pno_types(
 
             - logbook_reports_pno_id `int` `1`
             - cfr `str` `FRA000000000`
+            - locode `str` `CCXXX`
             - `predicted_arrival_datetime_utc` `datetime`
             - trip_gears `List[dict]`
               `[{"gear": "xxx", "mesh": yyy, "dimensions": "zzz}, {...}]`
@@ -320,6 +328,7 @@ def compute_pno_types(
         [
             "logbook_reports_pno_id",
             "cfr",
+            "locode",
             "flag_state",
             "predicted_arrival_datetime_utc",
         ]
@@ -451,7 +460,10 @@ def compute_pno_risk_factors(
 
 
 def flag_pnos_to_verify_and_send(
-    pnos: pd.DataFrame, predicted_arrival_threshold: datetime
+    pnos: pd.DataFrame,
+    pno_units_targeting_vessels: pd.DataFrame,
+    pno_units_ports_and_segments_subscriptions: pd.DataFrame,
+    predicted_arrival_threshold: datetime,
 ):
     pnos = pnos.copy(deep=True)
 
@@ -462,8 +474,76 @@ def flag_pnos_to_verify_and_send(
     pnos["is_verified"] = False
     pnos["is_sent"] = False
 
-    pnos["is_being_sent"] = (~pnos.is_in_verification_scope) * (
-        pnos.predicted_arrival_datetime_utc >= predicted_arrival_threshold
+    segment_subscriptions = (
+        pno_units_ports_and_segments_subscriptions.explode("unit_subscribed_segments")[
+            ["port_locode", "unit_subscribed_segments"]
+        ]
+        .dropna()
+        .reset_index(drop=True)
+        .assign(is_target_segment=True)
+    )
+
+    target_ports = set(
+        pno_units_ports_and_segments_subscriptions.loc[
+            pno_units_ports_and_segments_subscriptions.receive_all_pnos_from_port,
+            "port_locode",
+        ].tolist()
+    )
+
+    target_vessels = set(pno_units_targeting_vessels["cfr"].dropna().tolist())
+
+    pnos_with_segments = (
+        pnos[["logbook_reports_pno_id", "locode", "trip_segments"]]
+        .explode("trip_segments")
+        .assign(
+            trip_segment=lambda x: x.trip_segments.map(
+                lambda d: d.get("segment"), na_action="ignore"
+            )
+        )
+        .drop(columns=["trip_segments"])
+        .reset_index(drop=True)
+        .dropna()
+    )
+
+    if len(pnos_with_segments) > 0 and len(segment_subscriptions) > 0:
+        segment_matches = pd.merge(
+            pnos_with_segments,
+            segment_subscriptions,
+            how="left",
+            left_on=["locode", "trip_segment"],
+            right_on=["port_locode", "unit_subscribed_segments"],
+        )
+        logbook_reports_targeting_segment_ids = set(
+            segment_matches.loc[
+                segment_matches.is_target_segment.fillna(False),
+                "logbook_reports_pno_id",
+            ]
+        )
+    else:
+        logbook_reports_targeting_segment_ids = set()
+
+    if len(target_ports) > 0:
+        logbook_reports_targeting_port_ids = set(
+            pnos.loc[pnos.locode.isin(target_ports), "logbook_reports_pno_id"].tolist()
+        )
+    else:
+        logbook_reports_targeting_port_ids = set()
+
+    if len(target_vessels) > 0:
+        logbook_reports_targeting_vessel_ids = set(
+            pnos.loc[pnos.cfr.isin(target_vessels), "logbook_reports_pno_id"].tolist()
+        )
+    else:
+        logbook_reports_targeting_vessel_ids = set()
+
+    logbook_reports_to_sent_ids = logbook_reports_targeting_segment_ids.union(
+        logbook_reports_targeting_port_ids
+    ).union(logbook_reports_targeting_vessel_ids)
+
+    pnos["is_being_sent"] = (
+        (~pnos.is_in_verification_scope)
+        * (pnos.predicted_arrival_datetime_utc >= predicted_arrival_threshold)
+        * pnos.logbook_reports_pno_id.isin(logbook_reports_to_sent_ids)
     )
 
     return pnos
@@ -574,6 +654,8 @@ def extract_enrich_load_logbook(
     pno_types: pd.DataFrame,
     control_anteriority: pd.DataFrame,
     all_control_priorities: pd.DataFrame,
+    pno_units_targeting_vessels: pd.DataFrame,
+    pno_units_ports_and_segments_subscriptions: pd.DataFrame,
     utcnow: datetime,
 ):
     """Extract pnos for the given `Period`, enrich and update the `logbook` table.
@@ -622,7 +704,10 @@ def extract_enrich_load_logbook(
 
     logger.info("Flagging PNOs to verify_and_distribute...")
     pnos = flag_pnos_to_verify_and_send(
-        pnos=pnos_with_risk_factors, predicted_arrival_threshold=utcnow
+        pnos=pnos_with_risk_factors,
+        pno_units_targeting_vessels=pno_units_targeting_vessels,
+        pno_units_ports_and_segments_subscriptions=pno_units_ports_and_segments_subscriptions,
+        predicted_arrival_threshold=utcnow,
     )
 
     logger.info("Loading")
@@ -649,6 +734,10 @@ def extract_enrich_load_logbook(
         all_control_priorities = extract_all_control_priorities()
         pno_types = extract_pno_types()
         control_anteriority = extract_control_anteriority()
+        pno_units_targeting_vessels = extract_pno_units_targeting_vessels()
+        pno_units_ports_and_segments_subscriptions = (
+            extract_pno_units_ports_and_segments_subscriptions()
+        )
 
         with case(recompute_all, True):
             reset = reset_pnos.map(periods)
@@ -658,6 +747,10 @@ def extract_enrich_load_logbook(
                 pno_types=unmapped(pno_types),
                 control_anteriority=unmapped(control_anteriority),
                 all_control_priorities=unmapped(all_control_priorities),
+                pno_units_targeting_vessels=unmapped(pno_units_targeting_vessels),
+                pno_units_ports_and_segments_subscriptions=unmapped(
+                    pno_units_ports_and_segments_subscriptions
+                ),
                 utcnow=unmapped(utcnow),
                 upstream_tasks=[reset],
             )
@@ -669,6 +762,10 @@ def extract_enrich_load_logbook(
                 pno_types=unmapped(pno_types),
                 control_anteriority=unmapped(control_anteriority),
                 all_control_priorities=unmapped(all_control_priorities),
+                pno_units_targeting_vessels=unmapped(pno_units_targeting_vessels),
+                pno_units_ports_and_segments_subscriptions=unmapped(
+                    pno_units_ports_and_segments_subscriptions
+                ),
                 utcnow=unmapped(utcnow),
             )
 

datascience/src/pipeline/queries/monitorfish/pno_species_and_gears.sql

@@ -21,6 +21,7 @@ pno_species AS (
         flag_state,
         trip_number,
         report_datetime_utc,
+        p.locode,
         p.facade,
         (r.value->>'tripStartDate')::TIMESTAMPTZ AS trip_start_date,
         (r.value->>'predictedArrivalDatetimeUtc')::TIMESTAMPTZ AS predicted_arrival_datetime_utc,
@@ -103,6 +104,7 @@ SELECT
     s.fao_area,
     s.weight,
     s.flag_state,
+    s.locode,
     s.facade
 FROM pno_species s
 LEFT JOIN far_gears fg

datascience/src/pipeline/shared_tasks/pnos.py

@@ -0,0 +1,20 @@
+import pandas as pd
+from prefect import task
+
+from src.pipeline.generic_tasks import extract
+
+
+@task(checkpoint=False)
+def extract_pno_units_targeting_vessels() -> pd.DataFrame:
+    return extract(
+        db_name="monitorfish_remote",
+        query_filepath="monitorfish/pno_units_targeting_vessels.sql",
+    )
+
+
+@task(checkpoint=False)
+def extract_pno_units_ports_and_segments_subscriptions() -> pd.DataFrame:
+    return extract(
+        db_name="monitorfish_remote",
+        query_filepath="monitorfish/pno_units_ports_and_segments_subscriptions.sql",
+    )

datascience/tests/test_pipeline/conftest.py

@@ -0,0 +1,49 @@
+import pandas as pd
+import pytest
+
+
+@pytest.fixture
+def pno_units_targeting_vessels():
+    return pd.DataFrame(
+        {
+            "vessel_id": [2, 4, 7],
+            "cfr": ["ABC000542519", None, "___TARGET___"],
+            "control_unit_ids_targeting_vessel": [[4], [1, 2], [4]],
+        }
+    )
+
+
+@pytest.fixture
+def pno_units_ports_and_segments_subscriptions():
+    return pd.DataFrame(
+        {
+            "port_locode": [
+                "FRCQF",
+                "FRDKK",
+                "FRDPE",
+                "FRLEH",
+                "FRLEH",
+                "FRZJZ",
+                "FRZJZ",
+            ],
+            "control_unit_id": [1, 2, 4, 2, 3, 2, 3],
+            "receive_all_pnos_from_port": [
+                False,
+                False,
+                True,
+                False,
+                False,
+                False,
+                False,
+            ],
+            "unit_subscribed_segments": [
+                ["SWW01/02/03"],
+                [],
+                [],
+                [],
+                ["SWW01/02/03", "NWW01"],
+                [],
+                ["SWW01/02/03", "NWW01"],
+            ],
+        }
+    )

datascience/tests/test_pipeline/test_flows/test_distribute_pnos.py

@@ -33,8 +33,6 @@
     create_email,
     create_sms,
     extract_fishing_gear_names,
-    extract_pno_units_ports_and_segments_subscriptions,
-    extract_pno_units_targeting_vessels,
     extract_pnos_to_generate,
     extract_species_names,
     fetch_control_units_contacts,
@@ -1017,53 +1015,6 @@ def pno_pdf_document_to_distribute_verified_assigned(
     )
 
 
-@pytest.fixture
-def pno_units_targeting_vessels():
-    return pd.DataFrame(
-        {
-            "vessel_id": [2, 4, 7],
-            "cfr": ["ABC000542519", None, "___TARGET___"],
-            "control_unit_ids_targeting_vessel": [[4], [1, 2], [4]],
-        }
-    )
-
-
-@pytest.fixture
-def pno_units_ports_and_segments_subscriptions():
-    return pd.DataFrame(
-        {
-            "port_locode": [
-                "FRCQF",
-                "FRDKK",
-                "FRDPE",
-                "FRLEH",
-                "FRLEH",
-                "FRZJZ",
-                "FRZJZ",
-            ],
-            "control_unit_id": [1, 2, 4, 2, 3, 2, 3],
-            "receive_all_pnos_from_port": [
-                False,
-                False,
-                True,
-                False,
-                False,
-                False,
-                False,
-            ],
-            "unit_subscribed_segments": [
-                ["SWW01/02/03"],
-                [],
-                [],
-                [],
-                ["SWW01/02/03", "NWW01"],
-                [],
-                ["SWW01/02/03", "NWW01"],
-            ],
-        }
-    )
-
-
 @pytest.fixture
 def monitorenv_control_units_api_response() -> list:
     return [
@@ -1530,20 +1481,6 @@ def test_extract_species_names(reset_test_data, species_names):
     assert res == species_names
 
 
-def test_extract_pno_units_targeting_vessels(
-    reset_test_data, pno_units_targeting_vessels
-):
-    res = extract_pno_units_targeting_vessels.run()
-    pd.testing.assert_frame_equal(res, pno_units_targeting_vessels)
-
-
-def test_extract_pno_units_ports_and_segments_subscriptions(
-    reset_test_data, pno_units_ports_and_segments_subscriptions
-):
-    res = extract_pno_units_ports_and_segments_subscriptions.run()
-    pd.testing.assert_frame_equal(res, pno_units_ports_and_segments_subscriptions)
-
-
 def test_extract_fishing_gear_names(reset_test_data, fishing_gear_names):
     res = extract_fishing_gear_names.run()
     assert res == fishing_gear_names