From 71ec019f50fa9880ce8e7e73d5bfb196d4406636 Mon Sep 17 00:00:00 2001 From: anjalisadanandanp Date: Sat, 18 Jan 2025 15:40:09 +0530 Subject: [PATCH] add ranger placement optimisation code --- .../optimise_ranger_locations.py | 576 ++++++++++++++++++ .../optimization_loss.png | Bin 0 -> 158280 bytes ...y__numrangers3_rangervisibility500m_v1.png | Bin 0 -> 532053 bytes ...s__numrangers3_rangervisibility500m_v1.png | Bin 0 -> 733636 bytes ...s__numrangers3_rangervisibility500m_v2.png | Bin 0 -> 532071 bytes 5 files changed, 576 insertions(+) create mode 100644 trajectory_analysis/assign_payoff_rangers/optimise_ranger_locations.py create mode 100644 trajectory_analysis/assign_payoff_rangers/optimization_loss.png create mode 100644 trajectory_analysis/assign_payoff_rangers/trajectories_until_ranger_proximity__numrangers3_rangervisibility500m_v1.png create mode 100644 trajectory_analysis/assign_payoff_rangers/trajectories_with_ranger_locations__numrangers3_rangervisibility500m_v1.png create mode 100644 trajectory_analysis/assign_payoff_rangers/trajectories_with_ranger_locations__numrangers3_rangervisibility500m_v2.png diff --git a/trajectory_analysis/assign_payoff_rangers/optimise_ranger_locations.py b/trajectory_analysis/assign_payoff_rangers/optimise_ranger_locations.py new file mode 100644 index 0000000..352d7f7 --- /dev/null +++ b/trajectory_analysis/assign_payoff_rangers/optimise_ranger_locations.py @@ -0,0 +1,576 @@ +import os + +import sys +sys.path.append(os.getcwd()) + +import pandas as pd +from shapely.geometry import Point +import matplotlib.pyplot as plt +from geopandas import GeoDataFrame +from datetime import datetime +import yaml +from osgeo import gdal +import numpy as np +from pyproj import Proj, transform +from mpl_toolkits.basemap import Basemap +import matplotlib.colors as mcolors +import matplotlib.cm as cm +from matplotlib import colors +from scipy.optimize import minimize +from sklearn.cluster import KMeans + +import warnings +warnings.filterwarnings('ignore') + +os.chdir("/home2/anjali/GitHub/abm-elephant-project") + +class optimise_ranger_locations(): + + def __init__(self, num_best_trajs, num_rangers, ranger_visibility_radius, expt_folder): + self.num_best_trajs = num_best_trajs + self.num_rangers = num_rangers + self.ranger_visibility_radius = ranger_visibility_radius + self.expt_folder = expt_folder + self.loss_history = [] + + self.get_best_trajectories() + + def get_sorted_trajectories(self): + + folder = os.path.join(os.getcwd(), "trajectory_analysis/outputs", self.expt_folder) + self.sorted_trajs_df = pd.read_csv(os.path.join(folder, "ordered_experiments.csv")) + + return + + def get_best_trajectories(self): + + self.get_sorted_trajectories() + + self.best_trajs = [] + self.max_simulation_length = 0 + + for traj in range(self.num_best_trajs): + path = self.sorted_trajs_df["file_path"].iloc[traj] + data = pd.read_csv(os.path.join(str(path))) + self.best_trajs.append(data) + + if len(data) > self.max_simulation_length: + self.max_simulation_length = len(data) + + return + + def plot_trajectories_with_ranger_location(self): + + fig, ax = plt.subplots(figsize = (10,10)) + ax.yaxis.set_inverted(True) + + ds = gdal.Open(os.path.join("mesageo_elephant_project/elephant_project/experiment_setup_files/environment_seethathode/Raster_Files_Seethathode_Derived/area_1100sqKm/reso_30x30/LULC.tif")) + data_LULC = ds.ReadAsArray() + data_LULC = np.flip(data_LULC, axis=0) + + data_value_map = {1:1, 2:3, 3:4, 4:5, 5:6, 6:9, 7:10, 8:14, 9:15} + + for i in range(1,10): + data_LULC[data_LULC == data_value_map[i]] = i + + row_size, col_size = data_LULC.shape + xmin, xres, xskew, ymax, yskew, yres = ds.GetGeoTransform() + + outProj, inProj = Proj(init='epsg:4326'),Proj(init='epsg:3857') + LON_MIN,LAT_MIN = transform(inProj, outProj, xmin, ymax + yres*col_size) + LON_MAX,LAT_MAX = transform(inProj, outProj, xmin + xres*row_size, ymax) + + map = Basemap(llcrnrlon=LON_MIN,llcrnrlat=LAT_MIN,urcrnrlon=LON_MAX,urcrnrlat=LAT_MAX, epsg=4326, resolution='l') + + levels = [0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5] + clrs = ["greenyellow","mediumpurple","turquoise", "plum", "black", "blue", "yellow", "mediumseagreen", "forestgreen"] + cmap, norm = colors.from_levels_and_colors(levels, clrs) + + map.imshow(data_LULC, cmap = cmap, norm=norm, extent=[LON_MIN, LON_MAX, LAT_MIN, LAT_MAX], alpha = 0.5) + + map.drawmeridians([LON_MIN,(LON_MIN+LON_MAX)/2-(LON_MAX-LON_MIN)*1/4,(LON_MIN+LON_MAX)/2,(LON_MIN+LON_MAX)/2+(LON_MAX-LON_MIN)*1/4,LON_MAX], labels=[0,1,0,1],) + map.drawparallels([LAT_MIN,(LAT_MIN+LAT_MAX)/2-(LAT_MAX-LAT_MIN)*1/4,(LAT_MIN+LAT_MAX)/2,(LAT_MIN+LAT_MAX)/2+(LAT_MAX-LAT_MIN)*1/4,LAT_MAX], labels=[1,0,1,0]) + + cbar = plt.colorbar(ticks=[1,2,3,4,5,6,7,8,9],fraction=0.046, pad=0.04) + cbar.ax.set_yticks(ticks=[1,2,3,4,5,6,7,8,9]) + cbar.ax.set_yticklabels(["Deciduous Broadleaf Forest","Built-up Land","Mixed Forest","Shrubland","Barren Land","Water Bodies","Plantations","Grassland","Broadleaf evergreen forest"]) + + for data in self.best_trajs: + + outProj, inProj = Proj(init='epsg:4326'),Proj(init='epsg:3857') + longitude, latitude = transform(inProj, outProj, data["longitude"], data["latitude"]) + x_new, y_new = map(longitude,latitude) + C = np.arange(len(x_new)) + nz = mcolors.Normalize() + nz.autoscale(C) + + ax.quiver(x_new[:-1], y_new[:-1], + x_new[1:]-x_new[:-1], y_new[1:]-y_new[:-1], + scale_units='xy', angles='xy', + scale=1, zorder=1, color = cm.jet(nz(C)), + width=0.0025, + alpha=0.5) + + ax.scatter(x_new[0], y_new[0], 25, marker='o', color='black', zorder=2) + ax.scatter(x_new[-1], y_new[-1], 25, marker='^', color='black', zorder=2) + + for ranger in self.ranger_location: + longitude, latitude = transform(inProj, outProj, ranger[0], ranger[1]) + x_new, y_new = map(longitude,latitude) + + ax.scatter(x_new, y_new, 25, marker='x', color='white', zorder=2) + + radius = self.ranger_visibility_radius/(111*1000) + circle = plt.Circle((x_new, y_new), radius, facecolor='purple', fill=True, alpha=0.5, edgecolor='black', linewidth=1) + ax.add_artist(circle) + + plt.savefig("trajectory_analysis/assign_payoff_rangers/trajectories_with_ranger_locations__numrangers" + str(self.num_rangers) + "_rangervisibility" + str(self.ranger_visibility_radius) + "m_v1.png", dpi=300, bbox_inches='tight') + + def filter_trajectories_in_ranger_radius(self): + """Filter trajectories that intersect with ranger visibility circles.""" + + intersecting_trajs = [] + non_intersecting_trajs = [] + + for data in self.best_trajs: + trajectory_intersects = False + longitude, latitude = data["longitude"], data["latitude"] + + + for ranger in self.ranger_location: + ranger_lon, ranger_lat = ranger[0], ranger[1] + + for lon, lat in zip(longitude, latitude): + distance = np.sqrt((lon - ranger_lon)**2 + (lat - ranger_lat)**2) + + if distance <= self.ranger_visibility_radius: + trajectory_intersects = True + break + + if trajectory_intersects: + break + + if trajectory_intersects: + intersecting_trajs.append(data) + else: + non_intersecting_trajs.append(data) + + return intersecting_trajs, non_intersecting_trajs + + def plot_filtered_trajectories(self, intersecting_trajs, non_intersecting_trajs): + + fig, ax = plt.subplots(figsize=(10,10)) + ax.yaxis.set_inverted(True) + + ds = gdal.Open(os.path.join("mesageo_elephant_project/elephant_project/experiment_setup_files/environment_seethathode/Raster_Files_Seethathode_Derived/area_1100sqKm/reso_30x30/LULC.tif")) + data_LULC = ds.ReadAsArray() + data_LULC = np.flip(data_LULC, axis=0) + + data_value_map = {1:1, 2:3, 3:4, 4:5, 5:6, 6:9, 7:10, 8:14, 9:15} + + for i in range(1,10): + data_LULC[data_LULC == data_value_map[i]] = i + + row_size, col_size = data_LULC.shape + xmin, xres, xskew, ymax, yskew, yres = ds.GetGeoTransform() + + outProj, inProj = Proj(init='epsg:4326'),Proj(init='epsg:3857') + LON_MIN,LAT_MIN = transform(inProj, outProj, xmin, ymax + yres*col_size) + LON_MAX,LAT_MAX = transform(inProj, outProj, xmin + xres*row_size, ymax) + + map = Basemap(llcrnrlon=LON_MIN,llcrnrlat=LAT_MIN,urcrnrlon=LON_MAX,urcrnrlat=LAT_MAX, epsg=4326, resolution='l') + + levels = [0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5] + clrs = ["greenyellow","mediumpurple","turquoise", "plum", "black", "blue", "yellow", "mediumseagreen", "forestgreen"] + cmap, norm = colors.from_levels_and_colors(levels, clrs) + + map.imshow(data_LULC, cmap = cmap, norm=norm, extent=[LON_MIN, LON_MAX, LAT_MIN, LAT_MAX], alpha = 0.5) + + map.drawmeridians([LON_MIN,(LON_MIN+LON_MAX)/2-(LON_MAX-LON_MIN)*1/4,(LON_MIN+LON_MAX)/2,(LON_MIN+LON_MAX)/2+(LON_MAX-LON_MIN)*1/4,LON_MAX], labels=[0,1,0,1],) + map.drawparallels([LAT_MIN,(LAT_MIN+LAT_MAX)/2-(LAT_MAX-LAT_MIN)*1/4,(LAT_MIN+LAT_MAX)/2,(LAT_MIN+LAT_MAX)/2+(LAT_MAX-LAT_MIN)*1/4,LAT_MAX], labels=[1,0,1,0]) + + cbar = plt.colorbar(ticks=[1,2,3,4,5,6,7,8,9],fraction=0.046, pad=0.04) + cbar.ax.set_yticks(ticks=[1,2,3,4,5,6,7,8,9]) + cbar.ax.set_yticklabels(["Deciduous Broadleaf Forest","Built-up Land","Mixed Forest","Shrubland","Barren Land","Water Bodies","Plantations","Grassland","Broadleaf evergreen forest"]) + + for data in intersecting_trajs: + longitude, latitude = transform(inProj, outProj, data["longitude"], data["latitude"]) + x_new, y_new = map(longitude, latitude) + + ax.quiver(x_new[:-1], y_new[:-1], + x_new[1:]-x_new[:-1], y_new[1:]-y_new[:-1], + scale_units='xy', angles='xy', + scale=1, zorder=1, color='red', width=0.0025) + + ax.scatter(x_new[0], y_new[0], 25, marker='o', color='black') + ax.scatter(x_new[-1], y_new[-1], 25, marker='^', color='black') + + for data in non_intersecting_trajs: + longitude, latitude = transform(inProj, outProj, data["longitude"], data["latitude"]) + x_new, y_new = map(longitude, latitude) + + ax.quiver(x_new[:-1], y_new[:-1], + x_new[1:]-x_new[:-1], y_new[1:]-y_new[:-1], + scale_units='xy', angles='xy', + scale=1, zorder=1, color='green', width=0.0025) + + ax.scatter(x_new[0], y_new[0], 25, marker='o', color='black') + ax.scatter(x_new[-1], y_new[-1], 25, marker='^', color='black') + + for ranger in self.ranger_location: + longitude, latitude = transform(inProj, outProj, ranger[0], ranger[1]) + x_new, y_new = map(longitude,latitude) + ax.scatter(x_new, y_new, 25, marker='x', color='white') + + radius = self.ranger_visibility_radius/(111*1000) + circle = plt.Circle((x_new, y_new), radius, facecolor='purple', fill=True, alpha=0.5, edgecolor='black', linewidth=1) + ax.add_artist(circle) + + plt.savefig("trajectory_analysis/assign_payoff_rangers/trajectories_with_ranger_locations__numrangers" + str(self.num_rangers) + "_rangervisibility" + str(self.ranger_visibility_radius) + "m_v2.png", dpi=300, bbox_inches='tight') + + def calculate_landuse_time(self, trajectory): + """Calculate time steps spent in each landuse type.""" + + ds = gdal.Open(os.path.join("mesageo_elephant_project/elephant_project/experiment_setup_files/environment_seethathode/Raster_Files_Seethathode_Derived/area_1100sqKm/reso_30x30/LULC.tif")) + data_LULC = ds.ReadAsArray() + data_LULC = np.flip(data_LULC, axis=0) + + data_value_map = {1:1, 2:3, 3:4, 4:5, 5:6, 6:9, 7:10, 8:14, 9:15} + + for i in range(1,10): + data_LULC[data_LULC == data_value_map[i]] = i + + row_size, col_size = data_LULC.shape + xmin, xres, xskew, ymax, yskew, yres = ds.GetGeoTransform() + + landuse_times = { + 1: 0, # Deciduous Broadleaf Forest + 2: 0, # Built-up Land + 3: 0, # Mixed Forest + 4: 0, # Shrubland + 5: 0, # Barren Land + 6: 0, # Water Bodies + 7: 0, # Plantations + 8: 0, # Grassland + 9: 0 # Broadleaf evergreen forest + } + + for lon, lat in zip(trajectory["longitude"], trajectory["latitude"]): + # Convert coordinates to pixel indices + px = int((lon - xmin) / xres) + py = int((lat - ymax) / yres) + + # Get landuse type at location + landuse = data_LULC[py, px] + landuse_times[landuse] += 1 + + total_time = sum(landuse_times.values()) + assert total_time == len(trajectory), "Total time steps don't match trajectory length" + + return landuse_times + + def calculate_daily_landuse_time(self, trajectory, steps_per_day=288): + """Calculate landuse time for each day.""" + + days = len(trajectory["longitude"]) // steps_per_day + daily_landuse = [] + + for day in range(days): + start_idx = day * steps_per_day + end_idx = start_idx + steps_per_day + + day_trajectory = trajectory.iloc[start_idx:end_idx] + + landuse_times = self.calculate_landuse_time(day_trajectory) + daily_landuse.append(landuse_times) + + return daily_landuse + + def is_df_in_list(self, target_df, df_list): + return any(target_df.equals(df) for df in df_list) + + def find_first_visible_entry(self, trajectory): + + first_visible_entries = None + + for idx, row in trajectory.iterrows(): + for ranger_location in self.ranger_location: + distance = ((row['longitude'] - ranger_location[0])**2 + + (row['latitude'] - ranger_location[1])**2)**0.5 + + if distance <= self.ranger_visibility_radius: + first_visible_entries = idx + return first_visible_entries + else: + first_visible_entries = None + + return first_visible_entries + + def plot_trajectories_untill_ranger_intervention(self): + + fig, ax = plt.subplots(figsize = (10,10)) + ax.yaxis.set_inverted(True) + + ds = gdal.Open(os.path.join("mesageo_elephant_project/elephant_project/experiment_setup_files/environment_seethathode/Raster_Files_Seethathode_Derived/area_1100sqKm/reso_30x30/LULC.tif")) + data_LULC = ds.ReadAsArray() + data_LULC = np.flip(data_LULC, axis=0) + + data_value_map = {1:1, 2:3, 3:4, 4:5, 5:6, 6:9, 7:10, 8:14, 9:15} + + for i in range(1,10): + data_LULC[data_LULC == data_value_map[i]] = i + + row_size, col_size = data_LULC.shape + xmin, xres, xskew, ymax, yskew, yres = ds.GetGeoTransform() + + outProj, inProj = Proj(init='epsg:4326'),Proj(init='epsg:3857') + LON_MIN,LAT_MIN = transform(inProj, outProj, xmin, ymax + yres*col_size) + LON_MAX,LAT_MAX = transform(inProj, outProj, xmin + xres*row_size, ymax) + + map = Basemap(llcrnrlon=LON_MIN,llcrnrlat=LAT_MIN,urcrnrlon=LON_MAX,urcrnrlat=LAT_MAX, epsg=4326, resolution='l') + + levels = [0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5] + clrs = ["greenyellow","mediumpurple","turquoise", "plum", "black", "blue", "yellow", "mediumseagreen", "forestgreen"] + cmap, norm = colors.from_levels_and_colors(levels, clrs) + + map.imshow(data_LULC, cmap = cmap, norm=norm, extent=[LON_MIN, LON_MAX, LAT_MIN, LAT_MAX], alpha = 0.5) + + map.drawmeridians([LON_MIN,(LON_MIN+LON_MAX)/2-(LON_MAX-LON_MIN)*1/4,(LON_MIN+LON_MAX)/2,(LON_MIN+LON_MAX)/2+(LON_MAX-LON_MIN)*1/4,LON_MAX], labels=[0,1,0,1],) + map.drawparallels([LAT_MIN,(LAT_MIN+LAT_MAX)/2-(LAT_MAX-LAT_MIN)*1/4,(LAT_MIN+LAT_MAX)/2,(LAT_MIN+LAT_MAX)/2+(LAT_MAX-LAT_MIN)*1/4,LAT_MAX], labels=[1,0,1,0]) + + cbar = plt.colorbar(ticks=[1,2,3,4,5,6,7,8,9],fraction=0.046, pad=0.04) + cbar.ax.set_yticks(ticks=[1,2,3,4,5,6,7,8,9]) + cbar.ax.set_yticklabels(["Deciduous Broadleaf Forest","Built-up Land","Mixed Forest","Shrubland","Barren Land","Water Bodies","Plantations","Grassland","Broadleaf evergreen forest"]) + + for i, traj in enumerate(self.best_trajs): + + first_visible_entry = self.find_first_visible_entry(traj) + + if first_visible_entry is not None: + data = traj.iloc[:first_visible_entry + 1] + + else: + data = traj + + outProj, inProj = Proj(init='epsg:4326'),Proj(init='epsg:3857') + longitude, latitude = transform(inProj, outProj, data["longitude"], data["latitude"]) + x_new, y_new = map(longitude,latitude) + + ax.plot(x_new, y_new, + 'b-', alpha=0.4, label='Trajectory') + + ax.scatter(x_new[0], y_new[0], 25, marker='o', color='black', zorder=2) + ax.scatter(x_new[-1], y_new[-1], 25, marker='^', color='black', zorder=2) + + for ranger in self.ranger_location: + longitude, latitude = transform(inProj, outProj, ranger[0], ranger[1]) + x_new, y_new = map(longitude,latitude) + + ax.scatter(x_new, y_new, 25, marker='x', color='white', zorder=2) + + radius = self.ranger_visibility_radius/(111*1000) + circle = plt.Circle((x_new, y_new), radius, facecolor='purple', fill=True, alpha=0.5, edgecolor='black', linewidth=1) + ax.add_artist(circle) + + plt.savefig("trajectory_analysis/assign_payoff_rangers/trajectories_until_ranger_proximity__numrangers" + str(self.num_rangers) + "_rangervisibility" + str(self.ranger_visibility_radius) + "m_v1.png", dpi=300, bbox_inches='tight') + + return + + def cost_function_v1(self, x): + + ranger_positions = x.reshape(-1, 2) + + uncovered_trajectories = 0 + for traj in self.best_trajs: + traj_covered = False + for pos in ranger_positions: + for lon, lat in zip(traj["longitude"], traj["latitude"]): + distance = np.sqrt((lon - pos[0])**2 + (lat - pos[1])**2) + if distance <= self.ranger_visibility_radius: + traj_covered = True + break + if traj_covered: + break + if not traj_covered: + uncovered_trajectories += 1 + + return uncovered_trajectories + + def cost_function_v2(self, x): + + ranger_positions = x.reshape(-1, 2) + + uncovered_trajectories = 0 + for traj in self.best_trajs: + traj_covered = False + for pos in ranger_positions: + for lon, lat in zip(traj["longitude"], traj["latitude"]): + distance = np.sqrt((lon - pos[0])**2 + (lat - pos[1])**2) + if distance <= self.ranger_visibility_radius: + traj_covered = True + break + if traj_covered: + break + if not traj_covered: + uncovered_trajectories += 1 + + forest_penalty = 0 + ds = gdal.Open(os.path.join("mesageo_elephant_project/elephant_project/experiment_setup_files/environment_seethathode/Raster_Files_Seethathode_Derived/area_1100sqKm/reso_30x30/LULC.tif")) + data_LULC = ds.ReadAsArray() + + data_value_map = {1:1, 2:3, 3:4, 4:5, 5:6, 6:9, 7:10, 8:14, 9:15} + for i in range(1,10): + data_LULC[data_LULC == data_value_map[i]] = i + + row_size, col_size = data_LULC.shape + xmin, xres, xskew, ymax, yskew, yres = ds.GetGeoTransform() + + for pos in ranger_positions: + px = int((pos[0] - xmin) / xres) + py = int((pos[1] - ymax) / yres) + + landuse = data_LULC[py, px] + if landuse != 7: + forest_penalty += self.num_best_trajs + + self.loss_history.append(uncovered_trajectories + forest_penalty) + + return uncovered_trajectories + forest_penalty + + def optimize(self): + + initial_positions = [] + + all_lons = np.concatenate([traj["longitude"].values for traj in self.best_trajs]) + all_lats = np.concatenate([traj["latitude"].values for traj in self.best_trajs]) + # points = np.column_stack((all_lons, all_lats)) + # kmeans = KMeans(n_clusters=self.num_rangers, random_state=42) + # kmeans.fit(points) + # initial_positions = kmeans.cluster_centers_.flatten() + + ds = gdal.Open(os.path.join("mesageo_elephant_project/elephant_project/experiment_setup_files/environment_seethathode/Raster_Files_Seethathode_Derived/area_1100sqKm/reso_30x30/LULC.tif")) + data_LULC = ds.ReadAsArray() + data_value_map = {1:1, 2:3, 3:4, 4:5, 5:6, 6:9, 7:10, 8:14, 9:15} + for i in range(1,10): + data_LULC[data_LULC == data_value_map[i]] = i + xmin, xres, xskew, ymax, yskew, yres = ds.GetGeoTransform() + plantation_indices = np.where(data_LULC == 7) + plantation_points = [] + for py, px in zip(*plantation_indices): + lon = xmin + px * xres + lat = ymax + py * yres + plantation_points.append([lon, lat]) + plantation_points = np.array(plantation_points) + + lon_min = min(all_lons) + lon_max = max(all_lons) + lat_min = min(all_lats) + lat_max = max(all_lats) + + plantation_points = plantation_points[(plantation_points[:,0] >= lon_min) & (plantation_points[:,0] <= lon_max) & (plantation_points[:,1] >= lat_min) & (plantation_points[:,1] <= lat_max)] + indices = np.random.choice(len(plantation_points), self.num_rangers, replace=False) + plantation_points = plantation_points[indices] + initial_positions = plantation_points.flatten() + + bounds = [(lon_min, lon_max), (lat_min, lat_max)] * self.num_rangers + + result = minimize( + self.cost_function_v2, + initial_positions, + method='Nelder-Mead', + bounds=bounds, + options={'maxiter': 1000} + ) + + self.ranger_location = result.x.reshape(-1, 2) + + fig, ax = plt.subplots(figsize=(6.8, 4.2)) + ax.plot(self.loss_history, 'bo-', markersize=4, linewidth=1, alpha=0.7, + label='Loss per iteration') + + ax.set_xlabel('Iteration Number', fontsize=12) + ax.set_ylabel('Loss Value', fontsize=12) + + min_loss_idx = np.argmin(self.loss_history) + ax.annotate(f'Minimum Loss: {min(self.loss_history):.2f}', + xy=(min_loss_idx, self.loss_history[min_loss_idx]), + xytext=(10, 10), textcoords='offset points', + bbox=dict(boxstyle='round,pad=0.5', fc='yellow', alpha=0.5), + arrowprops=dict(arrowstyle='->')) + + ax.grid(True, linestyle='--', alpha=0.7) + ax.legend(loc='upper right') + + plt.savefig('trajectory_analysis/assign_payoff_rangers/optimization_loss.png', dpi=300, bbox_inches='tight') + plt.close() + + return + + +model_params = { + "year": 2010, + "month": "Jul", + "num_bull_elephants": 1, + "area_size": 1100, + "spatial_resolution": 30, + "max_food_val_cropland": 100, + "max_food_val_forest": 10, + "prob_food_forest": 0.10, + "prob_food_cropland": 0.10, + "prob_water_sources": 0.00, + "thermoregulation_threshold": 28, + "num_days_agent_survives_in_deprivation": 10, + "knowledge_from_fringe": 1500, + "prob_crop_damage": 0.05, + "prob_infrastructure_damage": 0.01, + "percent_memory_elephant": 0.375, + "radius_food_search": 750, + "radius_water_search": 750, + "radius_forest_search": 1500, + "fitness_threshold": 0.4, + "terrain_radius": 750, + "slope_tolerance": 30, + "max_time_steps": 288*10, + "aggression_threshold_enter_cropland": 1.0, + "elephant_agent_visibility_radius": 500, + "plot_stepwise_target_selection": False, + "threshold_days_of_food_deprivation": 0, + "threshold_days_of_water_deprivation": 3, + "number_of_feasible_movement_directions": 3, + "track_in_mlflow": False, + "elephant_starting_location": "user_input", + "elephant_starting_latitude": 1049237, + "elephant_starting_longitude": 8570917, + "elephant_aggression_value": 0.8, + "elephant_crop_habituation": False + } + +experiment_name = "exploratory-search-ID-01" + +elephant_category = "solitary_bulls" +starting_location = "latitude-" + str(model_params["elephant_starting_latitude"]) + "-longitude-" + str(model_params["elephant_starting_longitude"]) +landscape_food_probability = "landscape-food-probability-forest-" + str(model_params["prob_food_forest"]) + "-cropland-" + str(model_params["prob_food_cropland"]) +water_holes_probability = "water-holes-within-landscape-" + str(model_params["prob_water_sources"]) +memory_matrix_type = "random-memory-matrix-model" +num_days_agent_survives_in_deprivation = "num_days_agent_survives_in_deprivation-" + str(model_params["num_days_agent_survives_in_deprivation"]) +maximum_food_in_a_forest_cell = "maximum-food-in-a-forest-cell-" + str(model_params["max_food_val_forest"]) +elephant_thermoregulation_threshold = "thermoregulation-threshold-temperature-" + str(model_params["thermoregulation_threshold"]) +threshold_food_derivation_days = "threshold_days_of_food_deprivation-" + str(model_params["threshold_days_of_food_deprivation"]) +threshold_water_derivation_days = "threshold_days_of_water_deprivation-" + str(model_params["threshold_days_of_water_deprivation"]) +slope_tolerance = "slope_tolerance-" + str(model_params["slope_tolerance"]) +num_days_agent_survives_in_deprivation = "num_days_agent_survives_in_deprivation-" + str(model_params["num_days_agent_survives_in_deprivation"]) +elephant_aggression_value = "elephant_aggression_value_" + str(model_params["elephant_aggression_value"]) + +output_folder = os.path.join(experiment_name, starting_location, elephant_category, landscape_food_probability, + water_holes_probability, memory_matrix_type, num_days_agent_survives_in_deprivation, maximum_food_in_a_forest_cell, + elephant_thermoregulation_threshold, threshold_food_derivation_days, threshold_water_derivation_days, + slope_tolerance, num_days_agent_survives_in_deprivation, elephant_aggression_value, + str(model_params["year"]), str(model_params["month"])) + +optimizer = optimise_ranger_locations(num_best_trajs = 50, num_rangers=3, ranger_visibility_radius=500, expt_folder = output_folder) +optimizer.optimize() +optimizer.plot_trajectories_with_ranger_location() +optimizer.plot_trajectories_untill_ranger_intervention() +intersecting_trajs, non_intersecting_trajs = optimizer.filter_trajectories_in_ranger_radius() +optimizer.plot_filtered_trajectories(intersecting_trajs, non_intersecting_trajs) + diff --git a/trajectory_analysis/assign_payoff_rangers/optimization_loss.png b/trajectory_analysis/assign_payoff_rangers/optimization_loss.png new file mode 100644 index 0000000000000000000000000000000000000000..45550124cc4010e6aaa142afcbfd4309c1bf1c51 GIT binary patch literal 158280 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