def make_branches(self, world):
"""
Generate the branches and enter them in world.
"""
height = self.height
topy = self.pos[1] + int(self.trunkheight + 0.5)
# endrad is the base radius of the branches at the trunk
endrad = max(self.trunkradius * (1 - self.trunkheight / height), 1)
for coord in self.foliage_cords:
distance = dist((coord[0], coord[2]), (self.pos[0], self.pos[2]))
ydist = coord[1] - self.pos[1]
# value is a magic number that weights the probability
# of generating branches properly so that
# you get enough on small trees, but not too many
# on larger trees.
# Very difficult to get right... do not touch!
value = (self.branchdensity * 220 * height) / ((ydist + distance) ** 3)
if value < random():
continue
posy = coord[1]
slope = self.branchslope + (0.5 - random()) * .16
if coord[1] - distance * slope > topy:
# Another random rejection, for branches between
# the top of the trunk and the crown of the tree
threshhold = 1 / height
if random() < threshhold:
continue
branchy = topy
basesize = endrad
else:
branchy = posy - distance * slope
basesize = (endrad + (self.trunkradius - endrad) *
(topy - branchy) / self.trunkheight)
startsize = (basesize * (1 + random()) * PHI *
(distance / height) ** PHI)
if startsize < 1.0:
startsize = 1.0
rndr = sqrt(random()) * basesize * PHI
rndang = random() * 2 * pi
rndx = int(rndr * sin(rndang) + 0.5)
rndz = int(rndr * cos(rndang) + 0.5)
startcoord = [self.pos[0] + rndx,
int(branchy),
self.pos[2] + rndz]
endsize = 1.0
self.taperedcylinder(startcoord, coord, startsize, endsize, world,
blocks["log"].slot)
def make_branches(self, world):
"""
Generate the branches and enter them in world.
"""
height = self.height
topy = self.pos[1] + int(self.trunkheight + 0.5)
# endrad is the base radius of the branches at the trunk
endrad = max(self.trunkradius * (1 - self.trunkheight / height), 1)
for coord in self.foliage_cords:
distance = dist((coord[0], coord[2]), (self.pos[0], self.pos[2]))
ydist = coord[1] - self.pos[1]
# value is a magic number that weights the probability
# of generating branches properly so that
# you get enough on small trees, but not too many
# on larger trees.
# Very difficult to get right... do not touch!
value = (self.branchdensity * 220 * height) / (
(ydist + distance)**3)
if value < random():
continue
posy = coord[1]
slope = self.branchslope + (0.5 - random()) * .16
if coord[1] - distance * slope > topy:
# Another random rejection, for branches between
# the top of the trunk and the crown of the tree
threshhold = 1 / height
if random() < threshhold:
continue
branchy = topy
basesize = endrad
else:
branchy = posy - distance * slope
basesize = (endrad + (self.trunkradius - endrad) *
(topy - branchy) / self.trunkheight)
startsize = (basesize * (1 + random()) * PHI *
(distance / height)**PHI)
if startsize < 1.0:
startsize = 1.0
rndr = sqrt(random()) * basesize * PHI
rndang = random() * 2 * pi
rndx = int(rndr * sin(rndang) + 0.5)
rndz = int(rndr * cos(rndang) + 0.5)
startcoord = [self.pos[0] + rndx, int(branchy), self.pos[2] + rndz]
endsize = 1.0
self.taperedcylinder(startcoord, coord, startsize, endsize, world,
blocks["log"].slot)
def shapefunc(self, y):
twigs = ProceduralTree.shapefunc(self, y)
if twigs is not None:
return twigs
if y < self.height * (.282 + .1 * sqrt(random())):
return None
radius = self.height / 2
adj = self.height / 2 - y
if adj == 0:
distance = radius
elif abs(adj) >= radius:
distance = 0
else:
distance = dist((0, 0), (radius, adj))
distance *= PHI
return distance
def shapefunc(self, y):
twigs = ProceduralTree.shapefunc(self, y)
if twigs is not None:
return twigs
if y < self.height * (.282 + .1 * sqrt(random())):
return None
radius = self.height / 2
adj = self.height / 2 - y
if adj == 0:
distance = radius
elif abs(adj) >= radius:
distance = 0
else:
distance = dist((0, 0), (radius, adj))
distance *= PHI
return distance
def make_roots(self, rootbases, world):
"""generate the roots and enter them in world.
rootbases = [[x,z,base_radius], ...] and is the list of locations
the roots can originate from, and the size of that location.
"""
height = self.height
for coord in self.foliage_cords:
# First, set the threshhold for randomly selecting this
# coordinate for root creation.
distance = dist((coord[0], coord[2]), (self.pos[0], self.pos[2]))
ydist = coord[1] - self.pos[1]
value = ((self.branchdensity * 220 * height) /
((ydist + distance) ** 3))
# Randomly skip roots, based on the above threshold
if value < random():
continue
# initialize the internal variables from a selection of
# starting locations.
rootbase = choice(rootbases)
rootx = rootbase[0]
rootz = rootbase[1]
rootbaseradius = rootbase[2]
# Offset the root origin location by a random amount
# (radialy) from the starting location.
rndr = sqrt(random()) * rootbaseradius * PHI
rndang = random() * 2 * pi
rndx = int(rndr * sin(rndang) + 0.5)
rndz = int(rndr * cos(rndang) + 0.5)
rndy = int(random() * rootbaseradius * 0.5)
startcoord = [rootx + rndx, self.pos[1] + rndy, rootz + rndz]
# offset is the distance from the root base to the root tip.
offset = [startcoord[i] - coord[i] for i in xrange(3)]
# If this is a mangrove tree, make the roots longer.
offset = [int(val * IPHI - 1.5) for val in offset]
rootstartsize = (rootbaseradius * IPHI * abs(offset[1]) /
(height * IPHI))
rootstartsize = max(rootstartsize, 1.0)
def make_roots(self, rootbases, world):
"""generate the roots and enter them in world.
rootbases = [[x,z,base_radius], ...] and is the list of locations
the roots can originate from, and the size of that location.
"""
height = self.height
for coord in self.foliage_cords:
# First, set the threshhold for randomly selecting this
# coordinate for root creation.
distance = dist((coord[0], coord[2]), (self.pos[0], self.pos[2]))
ydist = coord[1] - self.pos[1]
value = ((self.branchdensity * 220 * height) /
((ydist + distance)**3))
# Randomly skip roots, based on the above threshold
if value < random():
continue
# initialize the internal variables from a selection of
# starting locations.
rootbase = choice(rootbases)
rootx = rootbase[0]
rootz = rootbase[1]
rootbaseradius = rootbase[2]
# Offset the root origin location by a random amount
# (radialy) from the starting location.
rndr = sqrt(random()) * rootbaseradius * PHI
rndang = random() * 2 * pi
rndx = int(rndr * sin(rndang) + 0.5)
rndz = int(rndr * cos(rndang) + 0.5)
rndy = int(random() * rootbaseradius * 0.5)
startcoord = [rootx + rndx, self.pos[1] + rndy, rootz + rndz]
# offset is the distance from the root base to the root tip.
offset = [startcoord[i] - coord[i] for i in xrange(3)]
# If this is a mangrove tree, make the roots longer.
offset = [int(val * IPHI - 1.5) for val in offset]
rootstartsize = (rootbaseradius * IPHI * abs(offset[1]) /
(height * IPHI))
rootstartsize = max(rootstartsize, 1.0)
def test_pythagorean_triple(self):
five = dist((0, 0), (3, 4))
self.assertAlmostEqual(five, 5)
def test_pythagorean_triple(self):
five = dist((0, 0), (3, 4))
self.assertAlmostEqual(five, 5)