diff --git a/disk_live_wire_test.py b/disk_live_wire_test.py
deleted file mode 100644
index ee162cb7f1d65273c90970bda4c684e8d723c96a..0000000000000000000000000000000000000000
--- a/disk_live_wire_test.py
+++ /dev/null
@@ -1,197 +0,0 @@
-import time
-import cv2
-import numpy as np
-import matplotlib.pyplot as plt
-from skimage import exposure
-from skimage.filters import gaussian
-from skimage.feature import canny
-from skimage.graph import route_through_array
-from scipy.signal import convolve2d
-
-#### Helper functions ####
-
-def load_image(path, type):
-    """
-    Load an image in either gray or color mode (then convert color to gray).
-    """
-    if type == 'gray':
-        img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
-        if img is None:
-            raise FileNotFoundError(f"Could not read {path}")
-    elif type == 'color':
-        img = cv2.imread(path, cv2.IMREAD_COLOR)
-        if img is None:
-            raise FileNotFoundError(f"Could not read {path}")
-        else:
-            img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-    else:
-        raise ValueError("type must be 'gray' or 'color'")
-    return img
-
-def downscale(img, points, scale_percent):
-    """
-    Downsample `img` to `scale_percent` size and scale the given points accordingly.
-    Returns (downsampled_img, (scaled_seed, scaled_target)).
-    """
-    if scale_percent == 100:
-        return img, (tuple(points[0]), tuple(points[1]))
-    else:
-        # Compute new dimensions
-        width = int(img.shape[1] * scale_percent / 100)
-        height = int(img.shape[0] * scale_percent / 100)
-        new_dimensions = (width, height)
-
-        # Downsample
-        downsampled_img = cv2.resize(img, new_dimensions, interpolation=cv2.INTER_AREA)
-
-        # Scaling factors
-        scale_x = width / img.shape[1]
-        scale_y = height / img.shape[0]
-
-        # Scale the points (x, y)
-        seed_xy = tuple(points[0])
-        target_xy = tuple(points[1])
-        scaled_seed_xy = (int(seed_xy[0] * scale_x), int(seed_xy[1] * scale_y))
-        scaled_target_xy = (int(target_xy[0] * scale_x), int(target_xy[1] * scale_y))
-
-        return downsampled_img, (scaled_seed_xy, scaled_target_xy)
-
-def compute_cost(image, sigma=3.0, disk_size=15):
-    """
-    Smooth the image, run Canny edge detection, then invert the edge map into a cost image.
-    """
-
-    # Apply histogram equalization
-    image_contrasted = exposure.equalize_adapthist(image, clip_limit=0.01)
-
-    # Apply smoothing
-    smoothed_img = gaussian(image_contrasted, sigma=sigma)
-
-    # Apply Canny edge detection
-    canny_img = canny(smoothed_img)
-
-    # Do disk thing
-    binary_img = canny_img
-    k_size = 17
-    kernel = circle_edge_kernel(k_size=disk_size)
-    convolved = convolve2d(binary_img, kernel, mode='same', boundary='fill')
-
-
-    # Create cost image
-    cost_img = (convolved.max() - convolved)**4  # Invert edges: higher cost where edges are stronger
-
-    return cost_img, canny_img
-
-def backtrack_pixels_on_image(img_color, path_coords, bgr_color=(0, 0, 255)):
-    """
-    Color the path on the (already converted BGR) image in the specified color.
-    `path_coords` should be a list of (row, col) or (y, x).
-    """
-    for (row, col) in path_coords:
-        img_color[row, col] = bgr_color
-    return img_color
-
-
-
-def circle_edge_kernel(k_size=5, radius=None):
-    """
-    Create a k_size x k_size array whose values increase linearly
-    from 0 at the center to 1 at the circle boundary (radius).
-
-    Parameters
-    ----------
-    k_size : int
-        The size (width and height) of the kernel array.
-    radius : float, optional
-        The circle's radius. By default, set to (k_size-1)/2.
-
-    Returns
-    -------
-    kernel : 2D numpy array of shape (k_size, k_size)
-        The circle-edge-weighted kernel.
-    """
-    if radius is None:
-        # By default, let the radius be half the kernel size
-        radius = (k_size - 1) / 2
-
-    # Create an empty kernel
-    kernel = np.zeros((k_size, k_size), dtype=float)
-
-    # Coordinates of the center
-    center = radius  # same as (k_size-1)/2 if radius is default
-
-    # Fill the kernel
-    for y in range(k_size):
-        for x in range(k_size):
-            dist = np.sqrt((x - center)**2 + (y - center)**2)
-            if dist <= radius:
-                # Weight = distance / radius => 0 at center, 1 at boundary
-                kernel[y, x] = dist / radius
-
-    return kernel
-
-
-
-
-
-
-#### Main Script ####
-def main():
-    # Define input parameters
-    image_path = 'agamodon_slice.png'
-    image_type = 'gray'        # 'gray' or 'color'
-    downscale_factor = 100     # % of original size
-    points_path = 'agamodonPoints.npy'
-
-    # Load image
-    image = load_image(image_path, image_type)
-
-    # Load seed and target points
-    points = np.int0(np.round(np.load(points_path)))  # shape: (2, 2), i.e. [[x_seed, y_seed], [x_target, y_target]]
-
-    # Downscale image and points
-    scaled_image, scaled_points = downscale(image, points, downscale_factor)
-    seed, target = scaled_points  # Each is (x, y)
-
-    # Convert to row,col for scikit-image (which uses (row, col) = (y, x))
-    seed_rc = (seed[1], seed[0])
-    target_rc = (target[1], target[0])
-
-    # Compute cost image
-    cost_image, canny_img = compute_cost(scaled_image, disk_size=17)
-
-
-    # Find path using route_through_array
-    # route_through_array expects: route_through_array(image, start, end, fully_connected=True/False)
-    start_time = time.time()
-    path_rc, cost = route_through_array(
-        cost_image, 
-        start=seed_rc, 
-        end=target_rc, 
-        fully_connected=True
-    )
-    end_time = time.time()
-
-    print(f"Elapsed time for pathfinding: {end_time - start_time:.3f} seconds")
-
-    # Convert single-channel image to BGR for coloring
-    color_img = cv2.cvtColor(scaled_image, cv2.COLOR_GRAY2BGR)
-
-    # Draw path. `path_rc` is a list of (row, col).
-    # If you want to mark it in red, do (0,0,255) because OpenCV uses BGR format.
-    color_img = backtrack_pixels_on_image(color_img, path_rc, bgr_color=(0, 0, 255))
-
-    # Display results
-    plt.figure(figsize=(20, 8))
-    plt.subplot(1, 2, 1)
-    plt.title("Cost image")
-    plt.imshow(cost_image, cmap='gray')
-
-    plt.subplot(1, 2, 2)
-    plt.title("Path from Seed to Target")
-    # Convert BGR->RGB for pyplot
-    plt.imshow(color_img[..., ::-1])
-    plt.show()
-
-if __name__ == "__main__":
-    main()
\ No newline at end of file