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convexhull.py

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"""convexhull.py

Calculate the convex hull of a set of n 2D-points in O(n log n) time.  
Taken from Berg et al., Computational Geometry, Springer-Verlag, 1997.
Prints output as EPS file.

When run from the command line it generates a random set of points
inside a square of given length and finds the convex hull for those,
printing the result as an EPS file.

Usage:

    convexhull.py <numPoints> <squareLength> <outFile>

Dinu C. Gherman
"""


import sys, string, random


######################################################################
# Helpers
######################################################################

def _myDet(p, q, r):
    """Calc. determinant of a special matrix with three 2D points.

    The sign, "-" or "+", determines the side, right or left,
    respectivly, on which the point r lies, when measured against
    a directed vector from p to q.
    """

    # We use Sarrus' Rule to calculate the determinant.
    # (could also use the Numeric package...)
    sum1 = q[0]*r[1] + p[0]*q[1] + r[0]*p[1]
    sum2 = q[0]*p[1] + r[0]*q[1] + p[0]*r[1]

    return sum1 - sum2


def _isRightTurn((p, q, r)):
    "Do the vectors pq:qr form a right turn, or not?"

    assert p != q and q != r and p != r
            
    if _myDet(p, q, r) < 0:
        return 1
    else:
        return 0


def _isPointInPolygon(r, P):
    "Is point r inside a given polygon P?"

    # We assume the polygon is a list of points, listed clockwise!
    for i in xrange(len(P[:-1])):
        p, q = P[i], P[i+1]
        if not _isRightTurn((p, q, r)):
            return 0 # Out!        

    return 1 # It's within!


def _makeRandomData(numPoints=10, sqrLength=100, addCornerPoints=0):
    "Generate a list of random points within a square."
    
    # Fill a square with random points.
    min, max = 0, sqrLength
    P = []
    for i in xrange(numPoints):
        rand = random.randint
        x = rand(min+1, max-1)
        y = rand(min+1, max-1)
        P.append((x, y))

    # Add some "outmost" corner points.
    if addCornerPoints != 0:
        P = P + [(min, min), (max, max), (min, max), (max, min)]

    return P




######################################################################
# Public interface
######################################################################

def convex_hull(P):
    "Calculate the convex hull of a set of points."

    # Get a local list copy of the points and sort them lexically.
    points = map(None, P)
    points.sort()

    # Build upper half of the hull.
    upper = [points[0], points[1]]
    for p in points[2:]:
        upper.append(p)
        while len(upper) > 2 and not _isRightTurn(upper[-3:]):
            del upper[-2]

    # Build lower half of the hull.
    points.reverse()
    lower = [points[0], points[1]]
    for p in points[2:]:
        lower.append(p)
        while len(lower) > 2 and not _isRightTurn(lower[-3:]):
            del lower[-2]

    # Remove duplicates.
    del lower[0]
    del lower[-1]

    # Concatenate both halfs and return.
    return tuple(upper + lower)


######################################################################
# Test
######################################################################

def test():
    a = 200
    p = _makeRandomData(30, a, 0)
    c = convexHull(p)

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semanticmodel
Author(s): Julian ("Mac") Mason
autogenerated on Tue Mar 5 12:38:30 2013