def determine_pareto_curve()

in src/hull_to_pareto.py [0:0]

• 19 lines of code
• 8 McCabe index (conditional complexity)

def determine_pareto_curve(ccw_points):
    """
    Converts a convex hull given by scipy into the points along a pareto curve where the south west quadrant is
    considered to be more optimal. This gives us a way to efficiently trace pareto curve described by linear
    combininations of any mixtures we find.
    """
    # Determine the point with the lowest x value, break ties by lower y

    ccw_points = ccw_points.tolist()  # Convrt to python list
    min_index = ccw_points.index(min(ccw_points))  # Get index of min tuple
    ccw_points = ccw_points[min_index:] + (ccw_points[:min_index])  # concatenate the lists

    # Now we have the points in CCW order, starting with the left most point on the pareto curve.
    # We simply iterate over until the x values decreses, and keep all points before that.

    curr_index = 0
    broken = False
    last_x = ccw_points[0][0]
    last_y = ccw_points[0][1]
    for x, y in ccw_points:
        if x < last_x or y > last_y:  # if at any point we start going back in x or up in y we terminate
            stop_index = curr_index  # exclusive stopping index
            broken = True
            break

        last_x = x
        last_y = y
        curr_index += 1

    # If we never decrease x, we don't want to eliminate any points
    if not broken:
        stop_index = len(ccw_points)

    # Now, we just keep the pareto part from left to right
    return np.array(ccw_points[:stop_index])