diff --git a/.gitignore b/.gitignore index 44421968..2f593c69 100644 --- a/.gitignore +++ b/.gitignore @@ -158,3 +158,6 @@ dmypy.json # Cython debug symbols cython_debug/ + +# Precommit hooks: ruff cache +.ruff_cache diff --git a/Makefile b/Makefile index ec666620..c6efb7d0 100644 --- a/Makefile +++ b/Makefile @@ -27,6 +27,7 @@ rm-dev-db: @docker-compose -f docker-env/docker-compose-db.yaml rm rm-dev-env: + @docker stop bloom-test @docker rm bloom-test init-production: diff --git a/Trawlwatcher.py b/Trawlwatcher.py new file mode 100644 index 00000000..4bc7ea19 --- /dev/null +++ b/Trawlwatcher.py @@ -0,0 +1,44 @@ +from pathlib import Path + +import streamlit as st +from dotenv import load_dotenv + +st.set_page_config( + page_title="Bloom Trawlwatcher Demo app", + page_icon="🐟", + layout="wide", +) + +load_dotenv() + + +def local_css(file_name: str) -> None: + with Path.open(file_name) as f: + st.markdown(f"", unsafe_allow_html=True) + return + + +local_css(Path("styles.css")) + +st.write("![](https://upload.wikimedia.org/wikipedia/fr/e/e8/Logo_BLOOM.jpg)") +st.write("## Welcome to Bloom Trawlwatcher Demo app! 🐟") + +st.write( + """ +Trawlwatcher is a specialized application that processes and visualizes global AIS +(Automatic Identification System) vessel data. +It offers a platform to track real-time and historical vessel movements. +With a dedicated algorithm, Trawlwatcher can recognize distinct fishing +behaviors based on patterns in vessel movement, +providing invaluable information for effective fisheries management, maritime security, +and marine conservation initiatives. +Trawlwatcher`s interactive map interface simplifies +the process of visualizing vessel trajectories, +converting intricate AIS data into easy-to-understand visual representations. +Significantly, the application includes an overlay +of Marine Protected Areas (MPAs) on its maps, +enabling users to keep track of these vital regions +and increase awareness about potential disruptions and damaging activities. + +""", +) diff --git a/alembic/versions/1fd83d22bd1e_create_alert_table.py b/alembic/versions/1fd83d22bd1e_create_alert_table.py index 2d325ffd..f90740c6 100644 --- a/alembic/versions/1fd83d22bd1e_create_alert_table.py +++ b/alembic/versions/1fd83d22bd1e_create_alert_table.py @@ -19,7 +19,6 @@ def upgrade() -> None: - op.create_table( "alert", sa.Column( diff --git a/alembic/versions/e52b9542531c_create_marine_traffic_positions_and_.py b/alembic/versions/e52b9542531c_create_marine_traffic_positions_and_.py index c628f898..11e7085f 100644 --- a/alembic/versions/e52b9542531c_create_marine_traffic_positions_and_.py +++ b/alembic/versions/e52b9542531c_create_marine_traffic_positions_and_.py @@ -22,7 +22,6 @@ def upgrade() -> None: - op.create_table( "marine_traffic_vessel_positions", sa.Column( diff --git a/bloom/config.py b/bloom/config.py index 273182e9..aabd8e1a 100644 --- a/bloom/config.py +++ b/bloom/config.py @@ -4,7 +4,6 @@ class Settings(BaseSettings): - postgres_user = os.environ.get("POSTGRES_USER") postgres_password = os.environ.get("POSTGRES_PASSWORD") postgres_hostname = os.environ.get("POSTGRES_HOSTNAME") diff --git a/bloom/domain/vessels/vessel_trajectory.py b/bloom/domain/vessels/vessel_trajectory.py new file mode 100644 index 00000000..054bd8a8 --- /dev/null +++ b/bloom/domain/vessels/vessel_trajectory.py @@ -0,0 +1,234 @@ +import random +from datetime import datetime + +import folium +import geopandas as gpd +import numpy as np +import pandas as pd +from shapely.geometry import MultiPoint + +from bloom.domain.zones.mpa import ( + add_closest_marine_protected_areas, + get_closest_marine_protected_areas, +) + +from .visualize_trajectory import visualize + +ANGLE = 180 + + +def calculate_bearing(lat1: float, lon1: float, lat2: float, lon2: float) -> float: + # Calculate the bearing between two points + lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2]) + dlon = lon2 - lon1 + x = np.cos(lat1) * np.sin(lat2) - np.sin(lat1) * np.cos(lat2) * np.cos(dlon) + y = np.sin(dlon) * np.cos(lat2) + initial_bearing = np.arctan2(y, x) + initial_bearing = np.degrees(initial_bearing) + return (initial_bearing + 360) % 360 + + +def normalize_bearing(angle: float) -> float: + """ + Normalize a bearing difference to be within the range of -180 to 180. + """ + while angle <= -ANGLE: + angle += 360 + while angle > ANGLE: + angle -= 360 + return angle + + +class VesselTrajectory: + def __init__(self, metadata: pd.DataFrame, positions: gpd.GeoDataFrame) -> None: + self.metadata = metadata + self.positions = positions + + self.chunk_data(24) + + # Warning in CRS 4326 - the order is longitude,latitude + self.positions["lat"] = self.positions["geometry"].map(lambda pos: pos.y) + self.positions["lon"] = self.positions["geometry"].map(lambda pos: pos.x) + self.positions = self.compute_angle(self.positions) + self.positions = self.compute_change_direction_flag(self.positions, 50) + self.positions = self.compute_change_direction_flag(self.positions, 150) + self.positions = self.compute_rolling_deviation(self.positions, "3H") + + # def __init__(self, data: pd.DataFrame,vessel_id = None,crs = "EPSG:3857"): + + # # Convert the dataset to a GeoDataFrame + + def __repr__(self) -> str: + return f"""Vessel(n_points={len(self.positions)}, + n_voyages={self.n_voyages},n_chunks={self.n_chunks})""" + + @property + def n_voyages(self) -> int: + return self.positions["voyage_id"].nunique() + + @property + def n_chunks(self) -> int: + return self.positions["chunk_id"].nunique() + + @property + def centroid(self) -> tuple[float, float]: + # Make sure that your GeoDataFrame is named gdf and has a column 'geometry' + all_points = MultiPoint(self.positions["geometry"].unary_union) + centroid = all_points.centroid + + # Warning in CRS 4326 - the order is longitude,latitude + return (centroid.y, centroid.x) + + @property + def mpas(self) -> []: + if hasattr(self, "_mpas"): + return self._mpas + return None + + def get_closest_marine_protected_areas(self, radius: int = 100) -> None: + self._mpas, self._mpas_gdf = get_closest_marine_protected_areas( + self.centroid, + radius, + ) + + def visualize_trajectory( + self, + color_by_speed: bool = False, + marker_by_fishing: bool = False, + show_mpas: bool = True, + show_iucn: bool = True, + **kwargs: any, + ) -> folium.Map: + m = visualize(self.positions, color_by_speed, marker_by_fishing, **kwargs) + if self.mpas is not None and show_mpas: + add_closest_marine_protected_areas(self.mpas, m, show_iucn=show_iucn) + return m + + def query( + self, + query_str: str = None, + chunk_id: str = None, + voyage_id: str = None, + ) -> "VesselTrajectory": + if query_str is not None: + pass + elif chunk_id is not None: + query_str = f"chunk_id=={chunk_id}" + elif voyage_id is not None: + query_str = f"voyage_id=={voyage_id}" + + filtered_data = self.positions.query(query_str) + assert len(filtered_data) > 0 + filtered_vessel = VesselTrajectory(self.metadata, filtered_data.copy()) + + if hasattr(self, "_mpas"): + filtered_vessel._mpas = self._mpas + + filtered_vessel.positions.index = filtered_data.index + + return filtered_vessel + + def filter_by_date( + self, + start_date: datetime, + end_date: datetime, + ) -> "VesselTrajectory": + # Filter the data by the specified date range + filtered_data = self.positions[ + (self.positions["timestamp"] >= start_date) + & (self.positions["timestamp"] <= end_date) + ] + # Create a new Vessel object with the filtered data + return VesselTrajectory(self.metadata, filtered_data.copy()) + + def chunk_data(self, max_duration_hours: int) -> None: + # Calculate the duration of each chunk in seconds + max_duration_seconds = max_duration_hours * 3600 + + # Calculate the time deltas between successive rows + time_deltas = self.positions["timestamp"].diff(1).fillna(pd.Timedelta(0)) + + # Calculate the cumulative time deltas + cum_time_deltas = time_deltas.cumsum() + + # Calculate the chunk IDs based on the cumulative + # time deltas and the maximum duration + chunk_ids = (cum_time_deltas.dt.total_seconds() // max_duration_seconds).astype( + int, + ) + + # Assign consecutive chunk IDs based on the order + # in which they appear in the data + rank = chunk_ids.rank(method="dense").astype(int) + consecutive_chunk_ids = rank - rank.min() + + # Add the consecutive chunk IDs to the dataframe + self.positions["chunk_id"] = consecutive_chunk_ids + + def sample(self) -> "VesselTrajectory": + chunk_id = random.choice(list(self.positions["chunk_id"].unique())) + return self.query(f"chunk_id=={chunk_id}") + + def is_in_mpas( + self, + mpas_gdf: gpd.GeoDataFrame = None, + how: str = "inner", + ) -> gpd.GeoDataFrame: + if mpas_gdf is None: + mpas_gdf = self._mpas_gdf + + return self.positions.sjoin( + mpas_gdf, + how=how, + predicate="intersects", + ).drop_duplicates(subset="timestamp") + + def compute_angle(self, gdf: gpd.GeoDataFrame) -> gpd.GeoDataFrame: + # Now, compute the bearings + gdf["bearing"] = calculate_bearing( + gdf["lat"].shift(), + gdf["lon"].shift(), + gdf["lat"], + gdf["lon"], + ) + + # Compute the change in bearings + gdf["angle"] = gdf["bearing"].diff() + gdf["angle"] = gdf["angle"].map(normalize_bearing) + gdf["angle_abs"] = gdf["angle"].abs() + return gdf + + def compute_change_direction_flag( + self, + gdf: gpd.GeoDataFrame, + threshold: str = 30, + ) -> gpd.GeoDataFrame: + if "angle" not in gdf.columns: + gdf = self.compute_angle(gdf) + gdf[f"flag_change_{threshold}"] = (gdf["angle"].abs() > threshold).astype(int) + return gdf + + def compute_rolling_deviation( + self, + gdf: gpd.GeoDataFrame, + period: str = "3H", + ) -> gpd.GeoDataFrame: + # Ensure your 'timestamp' column is of datetime type + gdf["timestamp"] = pd.to_datetime(gdf["timestamp"]) + + # Set timestamp column as index + gdf = gdf.set_index("timestamp") + + # Calculate the rolling standard deviation for 'bearing_change' + gdf[f"rolling_angle_{period}"] = gdf["angle"].rolling(period).std() + + return gdf.reset_index() # Reset index if necessary + + +# # For the second flag, we can use a rolling window +# to check for consistent increases or decreases in latitude + +# # Create the second flag if either condition is met + +# # You may want to do the same for the 'lon' variable, +# depending on the trajectories you're dealing with diff --git a/bloom/domain/vessels/visualize_trajectory.py b/bloom/domain/vessels/visualize_trajectory.py new file mode 100644 index 00000000..bca9eb63 --- /dev/null +++ b/bloom/domain/vessels/visualize_trajectory.py @@ -0,0 +1,127 @@ +from typing import Any + +import folium +import geopandas as gpd +import matplotlib.colors as mcolors +import matplotlib.pyplot as plt +import pandas as pd +from folium.plugins import AntPath + + +def visualize( + data: gpd.GeoDataFrame, + marker_by_fishing: bool = False, + with_animation: bool = False, + min_speed: float = 2.0, + max_speed: float = None, + weight: float = 5, + minutes_ais: int = 120, + cmap: Any = plt.cm.Spectral_r, +) -> folium.Map: + # Create a map centered on the first latitude/longitude position + map_center = [data.iloc[0]["lat"], data.iloc[0]["lon"]] + m = folium.Map(location=map_center, zoom_start=6) + + # Add markers for the first and last points + folium.Marker( + location=[data.iloc[0]["lat"], data.iloc[0]["lon"]], + icon=folium.Icon(icon="play", color="blue", prefix="fa"), + ).add_to(m) + folium.Marker( + location=[data.iloc[-1]["lat"], data.iloc[-1]["lon"]], + icon=folium.Icon(icon="stop", color="red"), + ).add_to(m) + + # Add markers for each data point only when the boat is fishing + if marker_by_fishing: + for _i, row in data.iterrows(): + if row["is_fishing"] > 0: + folium.Marker( + location=[row["lat"], row["lon"]], + icon=folium.Icon(icon="fish", color="blue", prefix="fa"), + ).add_to(m) + + # Define a function to color the polyline based on speed + def speed_color(speed: float, cmap: Any = cmap) -> str: + rgba = cmap(speed) + return mcolors.rgb2hex(rgba) + + def antpath_delay( + speed: float, + min_delay: int = 1000, + max_delay: int = 20000, + ) -> float: + return min_delay + (1 - speed) * (max_delay - min_delay) + + if max_speed is None: + max_speed = data["speed"].max() + + speeds = data["speed"].clip(min_speed, max_speed) + normalized_speeds = (speeds - min_speed) / (max_speed - min_speed) + colors = normalized_speeds.map(speed_color).tolist() + delays = normalized_speeds.map(antpath_delay).tolist() + + stop_condition = ( + (data["timestamp"] - data["last_position_time"]) + >= pd.Timedelta(minutes=minutes_ais) + ).tolist() + + if not with_animation: + # Add lines connecting each latitude/longitude point with a color based on speed + for i in range(0, len(data) - 1): + color = "#6a6b6c" if stop_condition[i] else colors[i] + + loc = [ + (data.iloc[i]["lat"], data.iloc[i]["lon"]), + (data.iloc[i + 1]["lat"], data.iloc[i + 1]["lon"]), + ] + folium.PolyLine( + locations=loc, + color=color, + weight=weight, + opacity=1, + ).add_to(m) + + else: + # Create a list of locations and corresponding colors based on speed + for i in range(len(data) - 1): + loc = [ + (data.iloc[i]["lat"], data.iloc[i]["lon"]), + (data.iloc[i + 1]["lat"], data.iloc[i + 1]["lon"]), + ] + + AntPath( + locations=loc, + color=colors[i], + weight=weight, + opacity=0.4, + dash_array=[1, 10], + delay=delays[i], + pulse_color=colors[i], + ).add_to(m) + + return m + + +# def visualize(data, color_by_speed: bool = False, marker_by_fishing: bool = False): +# # Create a map centered on the first latitude/longitude position + +# # Add lines connecting each latitude/longitude point + +# # Set the color of the polyline based on speed if requested +# if color_by_speed: +# for color, s_min, s_max in speed_ranges: +# folium.PolyLine( +# ).add_to(m) +# folium.PolyLine( +# ).add_to(m) + +# # Add arrows to indicate the sense of the trajectory +# for i, row in data.iterrows(): +# if i == 0: + +# # Add markers for each fishing point if requested +# if marker_by_fishing: +# for i, row in fishing_points.iterrows(): +# folium.Marker( +# ).add_to(m) diff --git a/bloom/domain/zones/mpa.py b/bloom/domain/zones/mpa.py new file mode 100644 index 00000000..df6323a9 --- /dev/null +++ b/bloom/domain/zones/mpa.py @@ -0,0 +1,77 @@ +import folium +import geopandas as gpd +from geoalchemy2.elements import WKTElement +from geopy import distance +from shapely import wkb +from shapely.geometry import Point, Polygon +from sqlalchemy import func + +from bloom.config import settings +from bloom.infra.database.database_manager import Database +from bloom.infra.database.sql_model import MPA + + +def get_closest_marine_protected_areas( + coord: tuple[float, float] = (58.373683, -8.080092), + radius: int = 100, +) -> (list[MPA], gpd.GeoDataFrame): + ##### CREATE CIRCLE + db = Database(settings.db_url) + + # Create a Point object + Point(coord[1], coord[0]) + + # Calculate the coordinates of the circle's boundary points + boundary_points = [] + for bearing in range(0, 360, 60): + destination = distance.distance(kilometers=radius).destination(coord, bearing) + boundary_points.append((destination.longitude, destination.latitude)) + + # Create a Polygon from the boundary points + circle = Polygon(boundary_points) + + # Assuming circle is a Shapely Polygon object with coordinates in WGS84 (SRID 4326) + circle_srid = 4326 + + # Convert the Polygon object to a WKT string + wkt_circle = WKTElement(circle.wkt, srid=circle_srid) + + with db.session() as session: + mpas = ( + session.query(MPA) + .filter(func.ST_Intersects(MPA.geometry, wkt_circle)) + .all() + ) + if mpas: + print(f"Circle overlaps with the following polygons: {len(mpas)}") + session.close() + + gdf = convert_list_of_mpas_to_gdf(mpas) + + return mpas, gdf + + +def add_closest_marine_protected_areas( + mpas: list[MPA], + m: folium.Map, + show_iucn: bool = True, +) -> None: + for mpa in mpas: + mpa.add_to_map(m, show_iucn=show_iucn) + + +def convert_list_of_mpas_to_gdf(mpas: list[MPA]) -> gpd.GeoDataFrame: + keys = [ + "name", + "iucn_category", + "color", + "protected_area_category", + "gov_type", + "type", + "index", + ] + mpas_metadata = [{key: getattr(mpa, key) for key in keys} for mpa in mpas] + mpas_geometry = [wkb.loads(bytes(mpa.geometry.data)) for mpa in mpas] + + gdf = gpd.GeoDataFrame(mpas_metadata, geometry=mpas_geometry, crs="EPSG:4326") + return gdf diff --git a/bloom/infra/database/database_manager.py b/bloom/infra/database/database_manager.py index b99a948d..d3f354ca 100644 --- a/bloom/infra/database/database_manager.py +++ b/bloom/infra/database/database_manager.py @@ -13,7 +13,6 @@ class Database: def __init__(self, db_url: str) -> None: - print("INIT DB") self._engine = create_engine(db_url, echo=True) self._session_factory = orm.scoped_session( orm.sessionmaker( diff --git a/bloom/infra/database/sql_model.py b/bloom/infra/database/sql_model.py index 2a250b02..52878daf 100644 --- a/bloom/infra/database/sql_model.py +++ b/bloom/infra/database/sql_model.py @@ -1,7 +1,20 @@ import uuid +from typing import Any +import folium from geoalchemy2 import Geometry -from sqlalchemy import UUID, Boolean, Column, DateTime, Float, Integer, String +from shapely import wkb +from sqlalchemy import ( + UUID, + BigInteger, + Boolean, + Column, + DateTime, + Float, + Integer, + String, + Text, +) from bloom.infra.database.database_manager import Base @@ -84,6 +97,67 @@ class Alert(Base): vessel_id = Column("vessel_id", Integer) +IUCN_CATEGORIES = { + "Ia": {"name": "Strict Nature Reserve", "color": "#FF0000"}, # Red + "Ib": {"name": "Wilderness Area", "color": "#FF3300"}, # + "II": {"name": "National Park", "color": "#FF6600"}, # + "III": {"name": "Natural Monument or Feature", "color": "#FF9900"}, # + "IV": {"name": "Habitat/Species Management Area", "color": "#FFCC00"}, # + "V": {"name": "Protected Landscape/Seascape", "color": "#FFFF00"}, # + "VI": { + "name": "Protected area with sustainable use of natural resources", + "color": "#FFFF66", + }, # Yellow +} + + +class MPA(Base): + __tablename__ = "mpa" + geometry = Column("geometry", Geometry("POLYGON")) + gov_type = Column("GOV_TYPE", Text) + iucn_category = Column("IUCN_CAT", Text) + name = Column("NAME", Text) + type = Column("DESIG_TYPE", Text) + index = Column("index", BigInteger, primary_key=True, index=True) + + def __repr__(self) -> str: + return ( + f"MarineProtectedArea(name={self.name}," + f"type={self.type},iucn_category={self.iucn_category})" + ) + + def get_polygon(self) -> Any: + shapely_polygon = wkb.loads(bytes(self.geometry.data)) + return shapely_polygon.__geo_interface__ + + @property + def protected_area_category(self) -> str: + return IUCN_CATEGORIES.get(self.iucn_category, {"name": "Unknown"})["name"] + + @property + def color(self) -> str: + return IUCN_CATEGORIES.get(self.iucn_category, {"color": "#808080"})["color"] + + def add_to_map(self, m: folium.Map, show_iucn: bool = True) -> None: + polygon = self.get_polygon() + color = self.color + + if show_iucn: + folium.GeoJson( + polygon, + style_function=lambda _, color=color: { + "fillColor": color, + "color": color, + }, + tooltip=f"Protected area : {self.name}, " + f"IUCN category :{self.iucn_category} " + f"{self.protected_area_category}", + ).add_to(m) + + else: + folium.GeoJson(polygon).add_to(m) + + # class DistanceShore(Base): diff --git a/bloom/infra/http/spire_api_utils.py b/bloom/infra/http/spire_api_utils.py index cdc96893..94ba906b 100644 --- a/bloom/infra/http/spire_api_utils.py +++ b/bloom/infra/http/spire_api_utils.py @@ -24,7 +24,6 @@ def get_pageinfo_elements(self, response: dict[str, Any]) -> tuple[str, bool]: return endcursor, hasnextpage def _should_stop_paging(self, endcursor: str, hasnextpage: bool) -> bool: - if endcursor and hasnextpage: return False if not hasnextpage or endcursor is None: diff --git a/bloom/infra/repositories/repository_alert.py b/bloom/infra/repositories/repository_alert.py index 409fe7e1..24aaac47 100644 --- a/bloom/infra/repositories/repository_alert.py +++ b/bloom/infra/repositories/repository_alert.py @@ -17,7 +17,6 @@ def __init__( def save_alerts(self, timestamp: datetime) -> None: with self.session_factory() as session: - sql = text( f""" INSERT INTO alert(timestamp,vessel_id,cross_mpa,mpa_ids) diff --git a/bloom/infra/repositories/repository_vessel.py b/bloom/infra/repositories/repository_vessel.py index e30e88ed..f43006ce 100644 --- a/bloom/infra/repositories/repository_vessel.py +++ b/bloom/infra/repositories/repository_vessel.py @@ -1,14 +1,18 @@ from contextlib import AbstractContextManager from datetime import datetime from pathlib import Path +from typing import Any +import geopandas as gpd import pandas as pd from dependency_injector.providers import Callable from geoalchemy2.shape import from_shape -from shapely import Point +from shapely import Point, wkb +from sqlalchemy.orm import Session from bloom.config import settings from bloom.domain.vessel import Vessel, VesselPositionMarineTraffic +from bloom.domain.vessels.vessel_trajectory import VesselTrajectory from bloom.infra.database import sql_model from bloom.logger import logger @@ -50,7 +54,7 @@ def save_marine_traffic_vessels_positions( self, vessels_positions_list: list[VesselPositionMarineTraffic], timestamp: datetime, - # refactor: according to me, domain class is useless here if we have two tables + # according to me, domain class is useless here if we have two tables ) -> None: with self.session_factory() as session: sql_vessel_position_objects = [ @@ -195,3 +199,74 @@ def map_schema_marine_traffic_to_sql_vessel_position( speed=vessel_position.speed, navigation_status=vessel_position.navigation_status, ) + + def get_all_positions( + self, + mmsi: str, + session: Session, + ) -> list[sql_model.VesselPositionSpire]: + positions = ( + session.query(sql_model.VesselPositionSpire) + .filter(sql_model.VesselPositionSpire.mmsi == mmsi) + .all() + ) + if positions: + return positions + else: + return [] + + def get_vessel_trajectory( + self, + mmsi: str, + as_trajectory: bool = True, + ) -> Point | None: #Point ? + def convert_wkb_to_point(x: Any) -> Point | None: + try: + point = wkb.loads(bytes(x.data)) + return point # noqa: TRY300 + except: # noqa: E722 + return None + + with self.session_factory() as session: + vessel = session.query(sql_model.Vessel).filter_by(mmsi=mmsi).first() + positions = ( + self.get_all_positions(mmsi, session) if vessel is not None else [] + ) + + df = ( + pd.DataFrame([p.__dict__ for p in positions]) + .drop(columns=["_sa_instance_state"]) + .sort_values("timestamp") + .drop_duplicates(subset=["mmsi", "timestamp"]) + .reset_index(drop=True) + ) + + df["geometry"] = df["position"].map(convert_wkb_to_point) + + # With CRS 4326, the coordinates are reversed + # Temporary fix + #df.loc[df["timestamp"] <= "2023-06-28 14:44:00", "geometry"] = df[ + # "geometry" + #].apply(lambda point: Point(point.y, point.x)) + + df["is_fishing"] = df["navigation_status"] == "ENGAGED_IN_FISHING" + + # Create a boolean Series where True represents a change from 'MOORED' to something else + condition = (df["navigation_status"].shift() == "MOORED") & ( + df["navigation_status"] != "MOORED" + ) + + # Use cumsum to generate the 'voyage_id'. + # The cumsum operation works because 'True' is treated as 1 and 'False' as 0. + df["voyage_id"] = condition.cumsum() + + positions = gpd.GeoDataFrame(df, geometry="geometry", crs="EPSG:4326") + metadata = { + k: v for k, v in vessel.__dict__.items() if k != "_sa_instance_state" + } + + if as_trajectory: + trajectory = VesselTrajectory(metadata, positions) + return trajectory + else: + return metadata, positions diff --git a/bloom/usecase/GenerateAlerts.py b/bloom/usecase/GenerateAlerts.py index 57d2dd4c..77e2eb42 100644 --- a/bloom/usecase/GenerateAlerts.py +++ b/bloom/usecase/GenerateAlerts.py @@ -20,11 +20,9 @@ def __init__( self.raster_repository: RepositoryAlert = raster_repository def get_distance_shore(self, point: Point) -> None: - return self.raster_repository.select_distance_shore(point) def generate_alerts(self, timestamp: datetime) -> None: - self.alert_repository.save_alerts(timestamp) list_alert = self.alert_repository.load_alert(timestamp) @@ -38,7 +36,6 @@ def send_slack_alert( alert: Alert, type_name: str = "Vessel in a Protected Area", ) -> int: - slack_url = os.environ.get("SLACK_URL") webhook = WebhookClient(slack_url) blocks = ( diff --git a/bloom/usecase/GetVesselsFromSpire.py b/bloom/usecase/GetVesselsFromSpire.py index 494a00dd..8f512d1d 100644 --- a/bloom/usecase/GetVesselsFromSpire.py +++ b/bloom/usecase/GetVesselsFromSpire.py @@ -17,7 +17,6 @@ def __init__( self, vessel_repository: RepositoryVessel, ) -> None: - self.vessel_repository: RepositoryVessel = vessel_repository spire_token = os.environ.get("SPIRE_TOKEN") diff --git a/bloom/usecase/ScrapVesselsFromMarineTraffic.py b/bloom/usecase/ScrapVesselsFromMarineTraffic.py index 6bfff6c8..3360d7a8 100644 --- a/bloom/usecase/ScrapVesselsFromMarineTraffic.py +++ b/bloom/usecase/ScrapVesselsFromMarineTraffic.py @@ -1,3 +1,4 @@ +from collections.abc import Generator from datetime import datetime from bloom.domain.vessel import Vessel, VesselPositionMarineTraffic @@ -34,7 +35,8 @@ def scrap_vessels(self, timestamp: datetime) -> None: timestamp, ) - def batch(self, iterable, n=1): # noqa: ANN201 ANN001 + # Fixed typing for precommit hook (ruff) + def batch(self, iterable: list[Vessel], n: int = 1) -> Generator[list[Vessel]]: length = len(iterable) for ndx in range(0, length, n): yield iterable[ndx : min(ndx + n, length)] diff --git a/pages/1_Vessel_Exploration.py b/pages/1_Vessel_Exploration.py new file mode 100644 index 00000000..a266091b --- /dev/null +++ b/pages/1_Vessel_Exploration.py @@ -0,0 +1,152 @@ +import pandas as pd +import streamlit as st +from dotenv import load_dotenv +from streamlit_folium import st_folium + +from bloom.config import settings +from bloom.infra.database.database_manager import Database +from bloom.infra.repositories.repository_vessel import RepositoryVessel + +st.set_page_config(page_title="Vessel Exploration", page_icon="βš“", layout="wide") + + +def local_css(file_name): + with open(file_name) as f: + st.markdown(f"", unsafe_allow_html=True) + + +local_css("styles.css") + +db = Database(settings.db_url) +rep = RepositoryVessel(db.session) + + +# @st.cache_data # πŸ‘ˆ Add the caching decorator +def load_trajectory(mmsi, mpa): + vessel = rep.get_vessel_trajectory(mmsi) + if mpa: + vessel.get_closest_marine_protected_areas() + return vessel + + +# @st.cache_data # πŸ‘ˆ Add the caching decorator +def load_trawlers(): + trawlers = pd.read_csv("data/chalutiers_pelagiques.csv", sep=";") + return trawlers + + +# ------------------------------------------------------------------------------- +# Vessel selection + +trawlers = load_trawlers() +options_names = [None, *list(trawlers["ship_name"].unique())] +options = [ + None, + 246014000, + 224068000, + 277510000, + 205775000, + 224048290, + 228110000, + 263576000, + 224148000, + 238561110, + 237034000, +] +other_mmsis = [ + int(x) + for x in list(trawlers["mmsi"].unique()) + if not pd.isnull(x) and int(x) not in options[1:] +] +options = options + other_mmsis + +if "vessel_mmsi" not in st.session_state: + st.session_state.vessel_mmsi = None + + +def select_vessel(mmsi): + st.session_state.vessel_mmsi = mmsi + + +with st.sidebar.form("Vessel selection"): + mmsi = st.selectbox("Select from vessel MMSI", options) + load_mpa = st.checkbox("Load MPA") + submitted = st.form_submit_button("Load", on_click=select_vessel, args=(mmsi,)) + +# ------------------------------------------------------------------------------- +# Vessel exploration main page + +if st.session_state.vessel_mmsi is None: + st.write("Select a vessel from the dropdown list") + +else: + mmsi = st.session_state.vessel_mmsi + v = load_trajectory(mmsi, load_mpa) + + # if "mpas" not in st.session_state: + # if hasattr(v,"_mpas"): + + st.sidebar.write(f"### Vessel *{v.metadata['ship_name']}*") + st.sidebar.write(v.metadata) + + col1, col2, col3 = st.columns(3) + + st.session_state.range = (0, len(v.positions)) + + def change_range(voyage_id): + vf = v.positions.query(f"voyage_id=={voyage_id}") + min_value = int(vf.index.min()) + max_value = int(vf.index.max()) + st.session_state.range = (min_value, max_value) + + with col1: + if "voyage_id" not in st.session_state: + st.session_state.voyage_id = 0 + change_range(0) + + voyage_id = st.slider("Voyage id", min_value=0, max_value=v.n_voyages) + if voyage_id != st.session_state.voyage_id: + change_range(voyage_id) + + vi = v.query(voyage_id=voyage_id) + vi.positions = v.positions.copy() + + with col2: + min_value, max_value = st.slider( + "Chunk id", + min_value=0, + max_value=len(v.positions), + value=st.session_state.range, + step=5, + ) + vi.positions = vi.positions.loc[min_value:max_value] + + with col3: + show_markers_fishing = st.checkbox("See fishing marker") + show_mpas = st.checkbox("See marine protected areas") + + col1, col2, col3, col4 = st.columns(4) + col1.write(f"From: {str(vi.positions['timestamp'].min())[:19]}") + col1.write(f"To: {str(vi.positions['timestamp'].max())[:19]}") + col2.metric("Records", len(vi.positions)) + col3.metric( + "Duration", + str(vi.positions["timestamp"].max() - vi.positions["timestamp"].min()).split( + ".", + )[0][:-3], + ) + col4.metric("Average speed", round(vi.positions["speed"].mean(), 2)) + + if show_mpas: + vi.get_closest_marine_protected_areas() + + folium_map = vi.visualize_trajectory( + marker_by_fishing=show_markers_fishing, + show_mpas=show_mpas, + show_iucn=True, + ) + + # call to render Folium map in Streamlit + st_folium(folium_map, width=2000, returned_objects=[]) + + # if reload_mpas: diff --git a/poetry.lock b/poetry.lock index e8f9d20d..3b7bdfaf 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+[package.extras] +docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"] +testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] + [metadata] lock-version = "2.0" python-versions = "^3.10" -content-hash = "972cf463b82b4daea01de3b653b1ff62110b70e7b7be2f124ecfdfa37e353153" +content-hash = "f08a9b1a827467964de915c57bf166bff6f87e3b2e0a43494fb9fbce7fc69b95" diff --git a/pyproject.toml b/pyproject.toml index c2018ff5..0e862264 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -29,6 +29,13 @@ requests = "2.31.0" gql = "^3.4.0" requests-toolbelt = "^0.10.1" slack-sdk = "^3.21.3" +folium = "^0.14.0" +matplotlib = "^3.8.0" +geopy = "^2.4.0" +streamlit = "^1.27.2" +streamlit-folium = "^0.15.0" +python-dotenv + [tool.poetry.group.dev.dependencies] pre-commit = "^3.0.3" @@ -67,7 +74,7 @@ select = [ "W", "YTT", ] -ignore = ["A003", "ANN101", "ANN401", "N805", "N818"] +ignore = ["A003", "ANN101", "ANN401", "N805", "N818", "PLR0913", "RET504", "RET505"] fixable = [ "A", @@ -118,10 +125,11 @@ exclude = [ "build", "dist", "venv", + "pages", "tests" ] -line-length = 88 +line-length = 96 dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$" diff --git a/styles.css b/styles.css new file mode 100644 index 00000000..e7c222dd --- /dev/null +++ b/styles.css @@ -0,0 +1,5 @@ +@import url('https://fonts.googleapis.com/css2?family=Poppins&display=swap'); + +body,h1,h2,h3,h4,h5,p { + font-family: 'Poppins', sans-serif !important; +} diff --git a/tests/test_alert.py b/tests/test_alert.py index 1422ab4a..911aba1c 100644 --- a/tests/test_alert.py +++ b/tests/test_alert.py @@ -13,7 +13,6 @@ def test_launch_alert(): - use_cases = UseCases() alert_usecase = use_cases.generate_alert_usecase() status_code = alert_usecase.send_slack_alert( diff --git a/tests/test_marine_traffic_scrapper.py b/tests/test_marine_traffic_scrapper.py index c5918460..b4012ff9 100644 --- a/tests/test_marine_traffic_scrapper.py +++ b/tests/test_marine_traffic_scrapper.py @@ -56,7 +56,6 @@ def test_driver_tabs_opening(): - marine_traffic_scrapper = MarineTrafficVesselScraper() vessel_url = f"{marine_traffic_scrapper.base_url}{test_vessels[0].IMO}" with Driver() as driver: