Upload Sim_Setup_Fcns.py

Sim_Setup_Fcns.py

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+from PIL import Image
+
+def load_and_crop_image(path="Carson_map.png", crop_box=(15, 15, 1000, 950)):
+    img = Image.open(path).convert("RGB")
+    cropped_img = img.crop(crop_box)
+    return cropped_img
+
+
+from sklearn.cluster import KMeans
+import numpy as np
+
+def cluster_image(cropped_img, n_clusters=6):
+    img_array = np.array(cropped_img)
+    pixels = img_array.reshape(-1, 3)
+    kmeans = KMeans(n_clusters=n_clusters, random_state=42).fit(pixels)
+    labels = kmeans.labels_.reshape(img_array.shape[:2])
+    return labels
+
+
+from collections import Counter
+import numpy as np
+
+def build_parcel_map(clustered_img, grid_size=25):
+    height, width = clustered_img.shape
+    n_rows = height // grid_size
+    n_cols = width // grid_size
+    parcel_map = np.zeros((n_rows, n_cols), dtype=int)
+
+    for i in range(n_rows):
+        for j in range(n_cols):
+            patch = clustered_img[i*grid_size:(i+1)*grid_size, j*grid_size:(j+1)*grid_size].flatten()
+            dominant = Counter(patch).most_common(1)[0][0]
+            parcel_map[i, j] = dominant
+
+    return parcel_map, n_rows, n_cols
+
+
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+from matplotlib.colors import ListedColormap
+
+def plot_parcel_map(parcel_map, cluster_labels, land_colors, title="25×25 Land Parcels by Land Type"):
+    cmap = ListedColormap(land_colors)
+    plt.figure(figsize=(10, 8))
+    plt.imshow(parcel_map, cmap=cmap, origin='upper')
+    legend_patches = [mpatches.Patch(color=land_colors[i], label=cluster_labels[i]) for i in cluster_labels]
+    plt.legend(handles=legend_patches, bbox_to_anchor=(1.05, 1), loc='upper left', title="Land Type")
+    plt.title(title)
+    plt.axis('off')
+    plt.tight_layout()
+    plt.show()
+
+def plot_parcel_map_to_file(parcel_map, cluster_labels, land_colors, save_path="clustered_map.png", title="25×25 Land Parcels by Land Type"):
+    cmap = ListedColormap(land_colors)
+    fig, ax = plt.subplots(figsize=(10, 8))
+    cax = ax.imshow(parcel_map, cmap=cmap, origin='upper')
+    legend_patches = [mpatches.Patch(color=land_colors[i], label=cluster_labels[i]) for i in cluster_labels]
+    ax.legend(handles=legend_patches, bbox_to_anchor=(1.05, 1), loc='upper left', title="Land Type")
+    ax.set_title(title)
+    ax.axis('off')
+    plt.tight_layout()
+    plt.savefig(save_path)
+    plt.close(fig)
+
+
+def get_cluster_labels():
+    return {
+        0: 'Pasture/Desert',
+        1: 'Productive Grass',
+        2: 'Pasture/Desert',
+        3: 'Riparian Sensitive Zone',
+        4: 'Rocky Area',
+        5: 'Water'
+    }
+
+
+def get_land_colors():
+    return ['#dfb867', '#a0ca76', '#dfb867', '#5b8558', '#888888', '#3a75a8']
+
+
+
+
+