def suppress_nonmaxima(self, r=7):
"""
Removes from this cluster all points within *r* pixels of a point with
greater intensity. In the case of a tie, only the ``last-encountered''
tied point is preserved.
"""
local_maxima = []
for p in self.points:
z_p = get_z(p)
neighbor_zs = [ get_z(q)
for q in self.points
if q != p and sqdist(p, q) < r**2 ]
if neighbor_zs == list():
local_maxima.append(p)
else:
z_max = max(neighbor_zs)
if z_p > z_max:
local_maxima.append(p)
elif z_p == z_max:
# eliminate tie
set_z(p, 0)
self.points = local_maxima
def suppress_nonmaxima(self, r=7):
"""
Removes from this cluster all points within *r* pixels of a point with
greater intensity. In the case of a tie, only the ``last-encountered''
tied point is preserved.
"""
local_maxima = []
for p in self.points:
z_p = get_z(p)
is_max = True
tie = False
for q in [ other for other in self.points if other != p ]:
z_q = get_z(q)
if sqdist(p, q) < r**2:
if z_q > z_p:
is_max = False
elif z_q == z_p:
is_max = False
tie = True
if is_max:
# count it
local_maxima.append(p)
elif tie:
# eliminate tie
set_z(p, 0)
self.points = local_maxima
def remove_geographic_outliers(self):
avg_nn = 0
for p in self.points:
avg_nn += sqrt(min([ sqdist(p, q)
for q in self.points
if q != p ]))
avg_nn /= len(self.points)
inliers = []
for p in self.points:
min_neighbor = sqrt(min([ sqdist(p, q)
for q in self.points
if q != p ]))
if 0.5*avg_nn < min_neighbor and min_neighbor < 2*avg_nn:
inliers.append(p)
self.points = inliers
def d_center(self):
"""
Returns the most recent change in center coordinates as the squared distance
between current and previous centers or *None* if no previous center exists.
"""
if self.prevcenter == None:
return None
else:
return sqdist(self.prevcenter, self.center)
def _calc_corners(self):
"""
Locates four corners to approximate the convex hull of *self.points* and
saves them as *self.corners*.
"""
corners = None
max_area = 0
origin = Point(0, 0)
preimage_corners = [ Point(0, 0),
Point(1, 0),
Point(1, 1),
Point(0, 1) ]
for P in self._all_quads():
area = A_2x(P)
if area > max_area:
if sqdist(P[0], origin) == min([ sqdist(p, origin) for p in P ]):
max_area = area
corners = P
self.corners = corners
def sqdist_to(self, point):
"""
Returns the squared distance of a point from the center of this cluster.
"""
return sqdist(self.center, point)
