def setNextPositionPerlin(self, *args, **kwargs):
self.updateFunction = self.updatePerlin
if('slow' in args):
self.currentSpeedLimit = StewartPlatform.SERVO_SPEED_LIMIT/2
else:
self.currentSpeedLimit = StewartPlatform.SERVO_SPEED_LIMIT*2
t = (time()-StewartPlatform.INIT_TIME) * StewartPlatform.PERLIN_TIME_SCALE
(x,y,z) = self.currentPosition.getTranslationAsList()
# direction
u = snoise4(x*StewartPlatform.PERLIN_POSITION_SCALE + 1*StewartPlatform.PERLIN_PHASE,
y*StewartPlatform.PERLIN_POSITION_SCALE + 1*StewartPlatform.PERLIN_PHASE,
z*StewartPlatform.PERLIN_POSITION_SCALE + 1*StewartPlatform.PERLIN_PHASE,
t + 1*StewartPlatform.PERLIN_PHASE)
v = snoise4(x*StewartPlatform.PERLIN_POSITION_SCALE + 2*StewartPlatform.PERLIN_PHASE,
y*StewartPlatform.PERLIN_POSITION_SCALE + 2*StewartPlatform.PERLIN_PHASE,
z*StewartPlatform.PERLIN_POSITION_SCALE + 2*StewartPlatform.PERLIN_PHASE,
t + 2*StewartPlatform.PERLIN_PHASE)
w = snoise4(x*StewartPlatform.PERLIN_POSITION_SCALE + 3*StewartPlatform.PERLIN_PHASE,
y*StewartPlatform.PERLIN_POSITION_SCALE + 3*StewartPlatform.PERLIN_PHASE,
z*StewartPlatform.PERLIN_POSITION_SCALE + 3*StewartPlatform.PERLIN_PHASE,
t + 3*StewartPlatform.PERLIN_PHASE)
#magnitude
thisSpeedScale = StewartPlatform.PERLIN_SPEED_SCALE/3 if ('slow' in args) else StewartPlatform.PERLIN_SPEED_SCALE
speed = min(StewartPlatform.PERLIN_MAX_SPEED, max(StewartPlatform.PERLIN_MIN_SPEED, thisSpeedScale*(snoise4(u,v,w,t)*0.5+0.5)))
# result
deltaDistances = (
u*speed,
v*speed,
w*speed)
deltaAngles = (
snoise2(v,t)*StewartPlatform.PERLIN_ANGLE_SCALE,
snoise2(w,t)*StewartPlatform.PERLIN_ANGLE_SCALE,
snoise2(u,t)*StewartPlatform.PERLIN_ANGLE_SCALE)
# pick new valid position
translateArg = kwargs.get('translate', '')
rotateArg = kwargs.get('rotate', '')
done = False
while not done:
translation = Vector3(
deltaDistances[0] if 'x' in translateArg else 0,
deltaDistances[1] if 'y' in translateArg else 0,
deltaDistances[2] if 'z' in translateArg else 0) + self.currentPosition.translation
translation.constrain(-StewartPlatform.PERLIN_DISTANCE_LIMIT, StewartPlatform.PERLIN_DISTANCE_LIMIT)
rotation = Vector3(
deltaAngles[0] if 'x' in rotateArg else 0,
deltaAngles[1] if 'y' in rotateArg else 0,
deltaAngles[2] if 'z' in rotateArg else 0) + self.currentPosition.rotation
rotation.constrain(-StewartPlatform.PERLIN_ANGLE_LIMIT, StewartPlatform.PERLIN_ANGLE_LIMIT)
done = self.setTargetAnglesSuccessfully(translation, rotation)
deltaDistances = map(lambda x:0.9*x, deltaDistances)
deltaAngles = map(lambda x:0.9*x, deltaAngles)
def precipitation(seed, width, height):
""""Precipitation is a value in [-1,1]"""
border = width / 4
random.seed(seed * 13)
base = random.randint(0, 4096)
temp = [[0 for x in xrange(width)] for y in xrange(height)]
from noise import snoise2
octaves = 6
freq = 64.0 * octaves
for y in range(0, height):
yscaled = float(y) / height
latitude_factor = 1.0 - (abs(yscaled - 0.5) * 2)
for x in range(0, width):
n = snoise2(x / freq, y / freq, octaves, base=base)
#Added to allow noise pattern to wrap around right and left.
if x < border:
n = (snoise2(x / freq, y / freq, octaves, base=base) * x /
border) + (snoise2(
(x + width) / freq, y / freq, octaves, base=base) *
(border - x) / border)
t = (latitude_factor + n * 4) / 5.0
temp[y][x] = t
return temp
def __next__(self):
num = len(self.path) # number of segments in spline
# randomly permute path positions
# generate a random number from (-1,1) for each spline section
r = np.zeros(num)
for i in range(num):
r[i] = snoise2(i, self.i/self.noiseDetail)
# dampen random values
r *= self.noiseScale
# permute our noise value by applying random values
# remember they are centered around 0 so noise will not drift
self.noise[:] += r
# pick a new random set or angles to drive towards
a = np.zeros(num)
for i in range(num):
a[i] = 2.0*pi*snoise2(i, self.i/self.noiseDetail)
rnd = np.column_stack((np.cos(a), np.sin(a)))
# using noise, move current points towards random points a little
self.path += rnd * np.reshape(self.noise, (num, 1))
# always force last item to equal first so there isn't disconnection in spline
self.path[num-1] = self.path[0]
# output an interpolate spline path from the points
self.interpolatedPath = self.randomInterpolation(self.path, self.density)
self.i += 1
return self.interpolatedPath
def create_noise_map(octaves):
biome = open("biome.pgm", 'wt')
foliage = open("foliage.pgm", 'wt')
width = 500
height = 500
biomefreq = 16.0 * octaves[0]
biome.write('P2\n')
biome.write('%s %s\n' % (width, height))
biome.write('255\n')
foliagefreq = 20.0 * octaves[1]
foliage.write('P2\n')
foliage.write('%s %s\n' % (width, height))
foliage.write('255\n')
for y in range(width):
for x in range(height):
biome.write("%s\n" % int(
snoise2(x / biomefreq, y / biomefreq, octaves[0]) * 127.0 +
128.0))
for y in range(width):
for x in range(height):
foliage.write("%s\n" % int(
snoise2(x / foliagefreq, y / foliagefreq, octaves[1]) * 127.0 +
128.0))
biome.close()
foliage.close()
def _calculate(seed, width, height):
"""Precipitation is a value in [-1,1]"""
border = width / 4
random.seed(seed * 13)
base = random.randint(0, 4096)
precipitations = numpy.zeros((height, width), dtype=float)
octaves = 6
freq = 64.0 * octaves
for y in range(height): #TODO: numpy
y_scaled = float(y) / height
latitude_factor = 1.0 - (abs(y_scaled - 0.5) * 2)
for x in range(width):
n = snoise2(x / freq, y / freq, octaves, base=base)
# Added to allow noise pattern to wrap around right and left.
if x < border:
n = (
snoise2(x / freq, y / freq, octaves, base=base) * x /
border) + (snoise2(
(x + width) / freq, y / freq, octaves, base=base) *
(border - x) / border)
precipitation = (latitude_factor + n * 4) / 5.0
precipitations[y, x] = precipitation
return precipitations
def temperature(seed, elevation, mountain_level):
width = len(elevation[0])
height = len(elevation)
random.seed(seed * 7)
base = random.randint(0, 4096)
temp = [[0 for x in xrange(width)] for y in xrange(height)]
from noise import snoise2
border = width / 4
octaves = 6
freq = 16.0 * octaves
for y in range(0, height):
yscaled = float(y) / height
latitude_factor = 1.0 - (abs(yscaled - 0.5) * 2)
for x in range(0, width):
n = snoise2(x / freq, y / freq, octaves, base=base)
#Added to allow noise pattern to wrap around right and left.
if x <= border:
n = (snoise2(x / freq, y / freq, octaves, base=base) * x / border) + (snoise2((x+width) / freq, y / freq, octaves, base=base) * (border-x)/border)
t = (latitude_factor * 3 + n * 2) / 5.0
if elevation[y][x] > mountain_level:
if elevation[y][x] > (mountain_level + 29):
altitude_factor = 0.033
else:
altitude_factor = 1.00 - (float(elevation[y][x] - mountain_level) / 30)
t *= altitude_factor
temp[y][x] = t
return temp
def setNextPositionPerlin(self, *args, **kwargs):
self.updateFunction = self.updatePerlin
if('slow' in args):
self.currentSpeedLimit = StewartPlatform.SERVO_SPEED_LIMIT/2
else:
self.currentSpeedLimit = StewartPlatform.SERVO_SPEED_LIMIT*2
t = (time()-StewartPlatform.INIT_TIME) * StewartPlatform.PERLIN_TIME_SCALE
(x,y,z) = self.currentPosition.getTranslationAsList()
# direction
u = snoise4(x*StewartPlatform.PERLIN_POSITION_SCALE + 1*StewartPlatform.PERLIN_PHASE,
y*StewartPlatform.PERLIN_POSITION_SCALE + 1*StewartPlatform.PERLIN_PHASE,
z*StewartPlatform.PERLIN_POSITION_SCALE + 1*StewartPlatform.PERLIN_PHASE,
t + 1*StewartPlatform.PERLIN_PHASE)
v = snoise4(x*StewartPlatform.PERLIN_POSITION_SCALE + 2*StewartPlatform.PERLIN_PHASE,
y*StewartPlatform.PERLIN_POSITION_SCALE + 2*StewartPlatform.PERLIN_PHASE,
z*StewartPlatform.PERLIN_POSITION_SCALE + 2*StewartPlatform.PERLIN_PHASE,
t + 2*StewartPlatform.PERLIN_PHASE)
w = snoise4(x*StewartPlatform.PERLIN_POSITION_SCALE + 3*StewartPlatform.PERLIN_PHASE,
y*StewartPlatform.PERLIN_POSITION_SCALE + 3*StewartPlatform.PERLIN_PHASE,
z*StewartPlatform.PERLIN_POSITION_SCALE + 3*StewartPlatform.PERLIN_PHASE,
t + 3*StewartPlatform.PERLIN_PHASE)
#magnitude
thisSpeedScale = StewartPlatform.PERLIN_SPEED_SCALE/3 if ('slow' in args) else StewartPlatform.PERLIN_SPEED_SCALE
speed = min(StewartPlatform.PERLIN_MAX_SPEED, max(StewartPlatform.PERLIN_MIN_SPEED, thisSpeedScale*(snoise4(u,v,w,t)*0.5+0.5)))
# result
deltaDistances = (
u*speed,
v*speed,
w*speed)
deltaAngles = (
snoise2(v,t)*StewartPlatform.PERLIN_ANGLE_SCALE,
snoise2(w,t)*StewartPlatform.PERLIN_ANGLE_SCALE,
snoise2(u,t)*StewartPlatform.PERLIN_ANGLE_SCALE)
# pick new valid position
translateArg = kwargs.get('translate', '')
rotateArg = kwargs.get('rotate', '')
done = False
while not done:
translation = Vector3(
deltaDistances[0] if 'x' in translateArg else 0,
deltaDistances[1] if 'y' in translateArg else 0,
deltaDistances[2] if 'z' in translateArg else 0) + self.currentPosition.translation
translation.constrain(-StewartPlatform.PERLIN_DISTANCE_LIMIT, StewartPlatform.PERLIN_DISTANCE_LIMIT)
rotation = Vector3(
deltaAngles[0] if 'x' in rotateArg else 0,
deltaAngles[1] if 'y' in rotateArg else 0,
deltaAngles[2] if 'z' in rotateArg else 0) + self.currentPosition.rotation
rotation.constrain(-StewartPlatform.PERLIN_ANGLE_LIMIT, StewartPlatform.PERLIN_ANGLE_LIMIT)
done = self.setTargetAnglesSuccessfully(translation, rotation)
deltaDistances = map(lambda x:0.9*x, deltaDistances)
deltaAngles = map(lambda x:0.9*x, deltaAngles)
def gradients_perlin(writer, period, speed): """ Creates random Perlin-noise gradients emanating from centre. :param writer: A writer to use :type writer: :class:`skyscreen_core.memmap_interface.NPMMAPScreenWriter` """ period = float(period) speed = int(speed) with writer as writer_buf: screen = np.zeros((Screen.screen_vane_count, Screen.screen_max_magnitude, 3)) writer_buf_reshaped = writer_buf.reshape((Screen.screen_vane_count, Screen.screen_max_magnitude, 3)) count = 0 while True: for i in xrange(speed): r = noise.snoise2(1, count/period) * 128 + 128 g = noise.snoise2(2, count/period) * 128 + 128 b = noise.snoise2(3, count/period) * 128 + 128 colour = (r, g, b) screen[:, :-1, :] = screen[:,1:,:] screen[:, -1, :] = colour count += 1 writer_buf_reshaped[:,:,:] = screen writer.frame_ready()
def _calculate(seed, width, height):
"""Precipitation is a value in [-1,1]"""
border = width / 4
random.seed(seed * 13)
base = random.randint(0, 4096)
precipitations = numpy.zeros((height, width), dtype=float)
octaves = 6
freq = 64.0 * octaves
for y in range(height):#TODO: numpy
y_scaled = float(y) / height
latitude_factor = 1.0 - (abs(y_scaled - 0.5) * 2)
for x in range(width):
n = snoise2(x / freq, y / freq, octaves, base=base)
# Added to allow noise pattern to wrap around right and left.
if x < border:
n = (snoise2(x / freq, y / freq, octaves,
base=base) * x / border) + (
snoise2((x + width) / freq, y / freq, octaves,
base=base) * (border - x) / border)
precipitation = (latitude_factor + n * 4) / 5.0
precipitations[y, x] = precipitation
return precipitations
def land_gen(self):
"""
Uses simplex noise to create a random map of values that roughly resemble real life elevation values
Uses notes from https://www.redblobgames.com/maps/terrain-from-noise/
"""
self.sea_value = 0
elevation_seed = np.random.randint(1, 1000)
moisture_seed = np.random.randint(1, 1000)
print("Elevation seed: ", elevation_seed, "\nMoisture seed: ",
moisture_seed)
for x in range(self.size[0]):
for y in range(self.size[1]):
elevation = snoise2(x / self.size[0],
y / self.size[1],
octaves=8,
base=elevation_seed) / 2 + 0.5
moisture = snoise2(x / self.size[0],
y / self.size[1],
octaves=24,
base=moisture_seed) / 2 + 0.5
sea_level = 0.4
if elevation < sea_level:
self.sea_value += 1
self.elevation[x][y] = elevation
self.moisture[x][y] = moisture
def precipitation(seed, width, height):
""""Precipitation is a value in [-1,1]"""
border = width / 4
random.seed(seed * 13)
base = random.randint(0, 4096)
temp = [[0 for x in xrange(width)] for y in xrange(height)]
from noise import snoise2
octaves = 6
freq = 64.0 * octaves
for y in range(0, height):
yscaled = float(y) / height
latitude_factor = 1.0 - (abs(yscaled - 0.5) * 2)
for x in range(0, width):
n = snoise2(x / freq, y / freq, octaves, base=base)
#Added to allow noise pattern to wrap around right and left.
if x < border:
n = (snoise2(x / freq, y / freq, octaves, base=base) * x / border) + (snoise2((x+width) / freq, y / freq, octaves, base=base) * (border-x)/border)
t = (latitude_factor + n * 4) / 5.0
temp[y][x] = t
return temp
def random_pos(pos_x, pos_y):
return [
pos_x * cell_size +
noise.snoise2(pos_x * 124.324 - 519.6534, pos_y * 312.1253 + 812.1241)
* cell_size * 0.5, pos_y * cell_size +
noise.snoise2(pos_y * 224.324 - 219.6534,
pos_x * 414.1253 + 912.1241) * cell_size * 0.5
]
def fault_level(x, y, seed):
FL = 1.0 - abs(snoise2(x * FAULT_SCALE_F, y * FAULT_SCALE_F, FAULT_OCTAVES,
base=seed + 10, repeatx=FAULT_SCALE))
thold = max(0.0, (FL - FAULT_THRESHOLD) / (1.0 - FAULT_THRESHOLD))
FL *= abs(snoise2(x * ERODE_SCALE_F, y * ERODE_SCALE_F, FAULT_EROSION_OCTAVES, 0.85,
base=seed, repeatx=FAULT_EROSION_SCALE))
FL *= math.log10(thold * 9.0 + 1.0)
return FL
def random_color(pos_x, pos_y):
return [
int(128 + noise.snoise2(pos_x * 124.324 - 519.6534, pos_y * 312.1253 +
812.1241) * 128),
int(128 + noise.snoise2(pos_y * 224.324 - 219.6534, pos_x * 414.1253 +
912.1241) * 128),
int(128 + noise.snoise2(pos_x * 274.324 - 119.6534, pos_y * 114.1253 +
512.1241) * 128)
]
def generateElevations(self):
elevation = [0] * self.width
base = self.height / 4
j = self.seed / Constants.SEED_MAX
for i in range(self.width):
elevation[i] += snoise2(i / self.width, j, 4)
elevation[i] += snoise2(i / self.width, j, 16)
elevation[i] *= base
return elevation
def noise_tile(x, y, seed):
"""Computes a (simplex) noise value for a given coordinate and seed"""
nx = float(x) / size - 0.5
ny = float(y) / size - 0.5
e = snoise2(nx, ny, octaves=8, base=seed)
e += 0.5 * snoise2(2*nx, 2*ny, octaves=8, base=seed)
e += 0.25 * snoise2(4*nx, 4*ny, octaves=8, base=seed)
return e
def at_point(self, point: Tuple[float, float]) -> float:
x = point[0] + self.seed[0]
y = point[1] + self.seed[1]
value1 = (snoise2(x / 10000, y / 10000) + 1) / 2
value2 = (snoise2((x / 20000) + 500, (y / 20000) + 500) + 1) / 2
value3 = (snoise2((x / 20000) + 1000, (y / 20000) + 1000) + 1) / 2
return math.pow(((value1 * value2) + value3), 2)
def f(x: float, y: float) -> float:
scale = 15
fxy = (
0 + scale / 1 * noise.snoise2(x / 100 * 1 + 0, y / 100.0 * 1 + 0) +
scale / 4 * noise.snoise2(x / 100 * 2 + 100, y / 100.0 * 2 + 100)
# + scale / 16 * noise.snoise2(x / 100 * 4, y / 100.0 * 4)
)
return fxy
def sumNoise(point, frequency, octaves=2, lacunarity=2.0, persistence=0.5):
sum = (snoise2(point[0] / frequency, point[1] / frequency) / 2.0) + 0.5
amplitude = 1.0
ran = 1.0
for o in range(1, octaves):
frequency /= lacunarity
amplitude *= persistence
ran += amplitude
sum += (snoise2(point[0] / frequency, point[1] / frequency) * 0.5 + 0.5) * amplitude
return sum / ran
def sumInflectedNoise(point, frequency, octaves=2, lacunarity=2.0, persistence=0.5):
sum = abs(snoise2(point[0] / frequency, point[1] / frequency))
amplitude = 1.0
ran = 1.0
for o in range(1, octaves):
frequency /= lacunarity
amplitude *= persistence
ran += amplitude
sum += abs(snoise2(point[0] / frequency, point[1] / frequency)) * amplitude
return sum / ran
def make_world(self, map_width, map_height, player, entities, max_monsters_per_spawn, kolors, current_roster, current_mm):
#elevation = randint(0, 3) #low, level, moderate, high
#moisture = randint(0, 2) #dry, normal, water
#temperature = randing(0, 2) #cold, normal, hot
#vegetation = randint(0, 2) #none, grass, woods
scale = 100.0
octaves = 6
persistence = 0.5
lacunarity = 2.0
seed_h = randint(0, 131072)
seed_v = randint(0, 131072)
water_threshold = -0.25
shallows_threshold = -0.2
sand_threshold = -0.1775
plains_threshold = 0.1
hills_threshold = 0.3
mountain_threshold = 0.425
vegetation_threshold = -0.05
world_height = [[0
for y in range(0, map_height)]
for x in range(0, map_width)]
world_vegetation = [[0
for y in range(0, map_height)]
for x in range(0, map_width)]
for y in range(0, map_height):
for x in range(0, map_width):
#Height map
world_height[x][y] = noise.snoise2(x/scale, y/scale, octaves=octaves, persistence=persistence, lacunarity=lacunarity, repeatx=map_width, repeaty=map_height, base=seed_h)
if world_height[x][y] < water_threshold:
self.tiles[x][y].terrain = 0 #Water
elif world_height[x][y] < shallows_threshold:
self.tiles[x][y].terrain = 1 #Shallows
elif world_height[x][y] < sand_threshold:
self.tiles[x][y].terrain = 2 #Sand
elif world_height[x][y] < plains_threshold:
self.tiles[x][y].terrain = 3 #Plains
elif world_height[x][y] < hills_threshold:
self.tiles[x][y].terrain = 4 #Hills
elif world_height[x][y] < mountain_threshold:
self.tiles[x][y].terrain = 5 #Mountains
else:
self.tiles[x][y].terrain = 6 #Snow
#Vegetation map
world_vegetation[x][y] = noise.snoise2(x/scale, y/scale, octaves=octaves, persistence=persistence, lacunarity=lacunarity, repeatx=map_width, repeaty=map_height, base=seed_v)
if world_vegetation[x][y] < vegetation_threshold:
self.tiles[x][y].vegetation = 1 #Yes
else:
self.tiles[x][y].vegetation = 0 #None
#Tile settings
self.tiles[x][y].blocked = False
self.tiles[x][y].block_sight = False
def snoise2(x, y=None, octaves=1, gain=0.5, lacunarity=2, amp=1, repeat=1):
if y is not None:
return amp * fit_11_to_01(
noise.snoise2(x * repeat, y * repeat, octaves, gain, lacunarity))
else:
r = np.empty((len(x), ), dtype=np.float64)
for i, d in enumerate(x):
r[i] = amp * fit_11_to_01(
noise.snoise2(d[0] * repeat, d[1] * repeat, octaves, gain,
lacunarity))
return r
def map_selection_lasso(self):
url = self.locust.host + '/map/selection/lasso'
# for lasso we will extrude a circle from a point placed upon the usa
point = (util.rng_usa_lat(), util.rng_usa_lng())
# size of shape
latsize = random.uniform(1, 5)
lngsize = random.uniform(1, 10)
# how many vertices
detail = 3
# spacing on ellipsoid between each vertex
spacing = (math.pi * 2) / detail
# mag or magnitude refers to the amount our noise value should apply
# should it be strong (large hills) or weak (small bumps)
mag = random.uniform(1, 7)
# mult refers to how much our positions should be taken into account.
# a low value will result in a smooth surface, a large value equates to a very varying surface
mult = random.uniform(0.25, 0.5)
vertices = []
for i in range(0, detail):
angle = spacing * i
ax = math.cos(angle)
ay = math.sin(angle)
axm = ax * mult
aym = ay * mult
lat = point[0] + (ax * latsize * ((1.1 + noise.snoise2(axm, aym)) * mag))
lng = point[1] + (ay * lngsize * ((1.1 + noise.snoise2(axm, aym)) * mag))
vertices.append(str(lat) + "," + str(lng))
params = {
'filter[rating]': '0',
'filter[is_approved]': 'true',
'filter[type]': util.rng_event(),
'vertices[]': vertices
}
self.response = self.client.request(
method='GET',
url=url,
headers=shared.get_headers(),
params=params,
)
def layeredSimplex(self, x, y, seed):
period = 0.5
adjustedX = (x / 2) * period
adjustedY = (y / 2) * period
map1 = snoise2(adjustedX, adjustedY, 1, base=seed)
map2 = snoise2(adjustedX, adjustedY, 2, base=seed)
map3 = snoise2(adjustedX, adjustedY, 3, base=seed)
merged = (map1 * 3 + map2 * 2 + map3 * 1) / 6
return merged
def gethighlow():
l = h = (noise.snoise2(((0 + shiftx) / freq),
((0 + shifty) / freq), octave) + 1) / 2
for x in range(imgwidth):
for y in range(imgheight):
i = (noise.snoise2(((x + shiftx) / freq),
((y + shifty) / freq), octave) + 1) / 2
if i > h:
h = i
if i < l:
l = i
h = (h - l)
return h, l
def main(seed, mx, my, wx, wy, flatness=0.4):
MAP_ROUND = 4
mx, my = range(mx * MAP_ROUND), range(my * MAP_ROUND)
octaves = 32
freq = octaves * 8
l = [[0 for i in my] for i in my]
for x in mx:
for y in my:
l[x][y] = snoise2(x / freq + wx * len(mx),
y / freq + wy * len(my),
octaves,
base=0)
fl = []
for x in range(len(l) // MAP_ROUND):
fl.append([])
for y in range(len(l[0]) // MAP_ROUND):
avg = 0
c = 0
for i in range(MAP_ROUND):
for j in range(MAP_ROUND):
c += 1
avg += l[MAP_ROUND * x + i][MAP_ROUND * y + j]
fl[x].append(avg / c)
return l
def create_noise_map(size, freq, octaves):
nmap = []
freq *= octaves
for x in range(size):
for y in range(size):
nmap.append(snoise2(x / freq, y / freq, octaves))
return nmap
def createNoiseList(width, height, inBytes=False):
octaves = 4
freq = 8.0 * octaves
# minZ= 0
# maxZ = 0
# cumulativeZ = 0
# count = 0
texels = [] #[0 for n in range(width*height)]
for y in range(height):
row = []
for x in range(width):
#z = int(snoise2(x / freq, y / freq, octaves) * 127.0 + 128.0)
# Noise between 0 and 1
z = snoise2(x / freq, y / freq, octaves)
if inBytes:
z = struct.pak('<B', z & 0xFFFF)
# if z < minZ:
# minZ = z
# if z > maxZ:
# maxZ = z
# cumulativeZ += z
# count += 1
row.append(z)
texels.append(row)
# print("minZ: " + str(minZ))
# print("maxZ: " + str(maxZ))
# print("Average Z: " + (str(cumulativeZ/count)))
return texels
def create_trees(pmap, spawn_rate, x_offset, y_offset):
def mergeable(x, y):
if (x, y - 1) not in pmap.ground_layer.keys() and (x + 1, y - 1) not in pmap.ground_layer.keys():
if pmap.get_tile("ground_layer", x, y) == ("na", 2, 2) and pmap.get_tile("ground_layer", x + 1, y) == ("na", 2, 2):
if pmap.get_tile("secondary_ground", x, y - 1) == ("na", 2, 1) and pmap.get_tile("secondary_ground", x + 1, y - 1) == ("na", 2, 1):
return True
return False
octaves = 2
freq = 40
for y in range(pmap.height):
for x in range(pmap.width):
if pmap.tile_heights.get((x, y), -1) <= pmap.highest_path:
if (x, y) not in pmap.ground_layer.keys() and (x, y) not in pmap.secondary_ground.keys() and (x, y - 1) not in pmap.secondary_ground.keys() and (x, y) not in pmap.buildings.keys() and (x, y) not in pmap.decoration_layer.keys() and (x, y - 1) not in pmap.decoration_layer.keys():
if abs(snoise2((x + x_offset) / freq, (y + y_offset) / freq, octaves)) * 2 > 1 - (spawn_rate / 100) and random.random() > 0.5:
pmap.decoration_layer[(x, y - 2)] = ("na", 2, 0)
pmap.secondary_ground[(x, y - 1)] = ("na", 2, 1)
pmap.ground_layer[(x, y)] = ("na", 2, 2)
for y in range(pmap.height):
for x in range(pmap.width):
if mergeable(x, y):
pmap.decoration_layer[(x, y - 2)] = ("na", 1, 5)
pmap.decoration_layer[(x + 1, y - 2)] = ("na", 2, 5)
pmap.secondary_ground[(x, y - 1)] = ("na", 1, 6)
pmap.secondary_ground[(x + 1, y - 1)] = ("na", 2, 6)
pmap.ground_layer[(x, y)] = ("na", 1, 7)
pmap.ground_layer[(x + 1, y)] = ("na", 2, 7)
def create_heightmap(self, _freq_multiplier, _octaves):
freq = self.size_x * _freq_multiplier
offset = round((random() + random()) * 100000)
# _min, _max = 1000, 0
for k in range(self.size_y):
for i in range(self.size_x):
self.height[i, k] = round((1 + snoise2(
(i + offset) / freq,
(k + offset) / freq, octaves=_octaves)) *
(self.stepping / 2))
self.colormap[i, k] = Tools.get_color(self.height[i, k],
self.stepping,
self.waterlvl,
self.grasslvl)
# _min = self.height[i, k] if self.height[i, k] < _min else _min
# _max = self.height[i, k] if self.height[i, k] > _max else _max
print('-' * 24)
print('Distribution of heights:')
_terrain = ['Water', 'Forest', 'Mountain']
_prop_heights = Tools.get_prop_heights()
for i in _prop_heights:
print(
_terrain[i].zfill(10).replace('0', ' '), '->',
round((_prop_heights[i] * 100) / (self.size_x * self.size_y),
2), '%')
print('-' * 24)
def test_simplex_2d_range(self):
from noise import snoise2
for i in range(-10000, 10000):
x = i * 0.49
y = -i * 0.67
n = snoise2(x, y)
self.assertTrue(-1.0 <= n <= 1.0, (x, y, n))
def update(self, canvas):
# count the number of neighbours
canvas.clear()
for x in range(0, canvas.width):
for y in range(0, canvas.height):
noise = int(
snoise2((x + self.xoff) / self.freq, y /
self.freq, self.octaves) * 127.0 + 128.0)
if (noise > 200):
canvas.draw_pixel(x, y, Color(1, 41, 95))
elif (noise > 170):
canvas.draw_pixel(x, y, Color(67, 127, 151))
elif (noise > 140):
canvas.draw_pixel(x, y, Color(132, 147, 36))
elif (noise > 110):
canvas.draw_pixel(x, y, Color(255, 179, 15))
elif (noise > 80):
canvas.draw_pixel(x, y, Color(253, 21, 27))
elif (noise > 50):
canvas.draw_pixel(x, y, Color(181, 189, 104))
elif (noise > 20):
canvas.draw_pixel(x, y, Color(245, 151, 78))
elif (noise > 0):
canvas.draw_pixel(x, y, Color(241, 85, 65))
self.xoff += 1
def make_layer(type_id, width, height, threshold=0.3, jitter=0.5, scale=16, offset=None):
layer = ''
if offset is None:
offset = random.random() * random.randint(width, width * 100)
else:
random.seed(offset)
for y in range(0, height):
for x in range(0, width):
weight1 = noise.snoise2(
x / scale,
y / scale,
octaves=1,
persistence=0.01,
base=offset,
)
weight2 = random.random() * 2 - 1
weight = (weight1 * (2-jitter) + weight2 * jitter) / 2
if callable(threshold):
t = threshold(x, y)
else:
t = default_threshold(x, y, threshold)
if weight > t:
layer += type_id
else:
layer += ' '
layer += '\n'
return layer
def fill_map(cls, sim_map, octaves, freq, persistence, nb_clusters, seed):
"""Met à jour l'état de la grille.
Calcule le bruit de Perlin pour chaque position x et y dans la
grille et selon l'unité de temps actuelle. L'unité de temps est
un entier itérant à l'infini dans un intervalle arbitraire (ici
0..W).
Une fois le bruit calculé pour une position, teste si la valeur
de cette position a changé et si c'est le cas, met à jour les
pixels de l'image correspondants à cette position.
"""
for index, _ in ndenumerate(sim_map.grid):
x, y = index
i = cls.normalize(float(x), float(sim_map.width))
j = cls.normalize(float(y), float(sim_map.height))
noise = snoise2(i / freq,
j / freq,
persistence=persistence,
octaves=octaves,
base=seed)
id_cluster = floor(cls.normalize(noise, -1, +1) * nb_clusters)
sim_map.grid[x][y] = int(id_cluster)
def generate_noise(x, y=0, z=None, w=None, scale=1, offset_x=0.0, offset_y=0.0,
octaves=1, persistence=0.5, lacunarity=2.0):
"""Generate simplex noise.
:param x: The x coordinate of the noise value
:param y: The y coordinate of the noise value
:param z: The z coordinate of the noise value
:param w: A fourth dimensional coordinate
:param scale: The scale of the base plane
:param float offset_x: How much to offset `x` by on the base plane
:param float offset_y: How much to offset `y` by on the base plane
:param int octaves: The number of passes to make calculating noise
:param float persistence: The amplitude multiplier per octave
:param float lacunarity: The frequency multiplier per octave
"""
x = (x + offset_x) / scale
y = (y + offset_y) / scale
if z is not None:
z /= scale
if w is not None:
w /= scale
if z is None and w is None:
return noise.snoise2(x, y, octaves=octaves,
lacunarity=lacunarity,
persistence=persistence)
elif w is None:
return noise.snoise3(x, y, z, octaves=octaves,
lacunarity=lacunarity,
persistence=persistence)
else:
return noise.snoise4(x, y, z, w, octaves=octaves,
lacunarity=lacunarity,
persistence=persistence)
def generate_heightmap(mapsize, method):
heightmap = np.zeros((mapsize, mapsize))
if (method == "perlin"):
offset = randint(0, (mapsize - 1))
print("Generating Perlin Terrain...")
for x in range(mapsize):
for y in range(mapsize):
heightmap[x][y] = noise.snoise2(x / (mapsize - 1) + offset,
y / (mapsize - 1) + offset,
octaves=4) * 128 + 128
if (method == "worley"):
print("Generating Worley Terrain...")
NUM_PTS = 50
N = 0
wp_ys = np.zeros((NUM_PTS))
wp_xs = np.zeros((NUM_PTS))
for i in range(NUM_PTS):
wp_ys[i] = randint(0, (mapsize - 1))
wp_xs[i] = randint(0, (mapsize - 1))
for x in range(mapsize):
for y in range(mapsize):
distances = [
hypot(wp_xs[i] - x, wp_ys[i] - y) for i in range(NUM_PTS)
]
distances.sort()
heightmap[x][y] = (mapsize - 1) - (
2 * (mapsize - 1) * distances[N] / distances[-1] +
(mapsize - 1) / 1.25 * distances[N + 1] / distances[-1] +
(mapsize - 1) / 1.5 * distances[N + 2] / distances[-1] +
(mapsize - 1) / 1.75 * distances[N + 3] / distances[-1])
print("Complete!")
return heightmap
def TempList(width,
height,
xoff,
yoff,
scale=.012,
octaves=2,
persistence=.2,
lacunarity=4):
start_time = time.clock()
tlist = [[0 for i in range(width * 2)] for j in range(height)]
miny = 1
maxy = 0
for x in range(width * 2):
for y in range(height):
lat = 2.0 * abs((1.0 * y / height) - 0.5)
triglat = math.cos(math.asin(lat))
tlist[y][x] = triglat**2 * (snoise2(
(x + xoff) * scale, (y + yoff) * scale, octaves, persistence,
lacunarity, width * scale) + 1) / 2
#tlist[y][x] = triglat**5
if tlist[y][x] < miny: miny = tlist[y][x]
if tlist[y][x] > maxy: maxy = tlist[y][x]
#noise2(x, y, octaves=1, persistence=0.5, lacunarity=2.0, repeatx=1024, repeaty=1024, base=0.0)
print "TempList created,", time.clock() - start_time, "SECONDS"
#print "Miny", miny, "Maxy",maxy
return tlist
def create_noise_map(size, freq, octaves):
nmap = []
freq *= octaves
for x in range(size):
for y in range(size):
nmap.append(snoise2(x/freq, y/freq, octaves))
return nmap
def get_elevation(x, y, seed=0):
e = 0
e += DETAIL*snoise2(CONTINENT_SCALE*x, CONTINENT_SCALE*y,
octaves=PERLIN_OCTAVES,
persistence=PERLIN_PERSISTENCE, lacunarity=PERLIN_LACUNARITY,
base=seed, repeatx=MAP_WIDTH*CONTINENT_SCALE)
return e
def add_noise_to_elevation(world, seed):
octaves = 6
freq = 16.0 * octaves
for y in range(world.height):
for x in range(world.width):
n = snoise2(x / freq * 2, y / freq * 2, octaves, base=seed)
world.elevation['data'][y][x] += n
def add_noise_to_elevation(world, seed):
octaves = 8
freq = 16.0 * octaves
for y in range(world.height):
for x in range(world.width):
n = snoise2(x / freq * 2, y / freq * 2, octaves, base=seed)
world.layers['elevation'].data[y, x] += n
def image_layer_noise_old(width=256, height=256, white_min=0.5, white_range=None):
#Add Noise
#TODO: Make a function to generate different types of noise, some for blending colors, others for contrast, etc.
x_start = 0
y_start = 0
# freq = 32.0 * octaves
freq = width
white_min *= 255.0
if not white_range:
white_range = 1-white_min
white_range *= 255.0
octaves = 42
period = 128.0
image_data = []
for y in range(height):
for x in range(width):
# noisenum = noise.snoise2(x_start + (x / freq), y_start + (y / freq), octaves)
noisenum = noise.snoise2(x/period, y/period, octaves)
# noisenum = pnoise2(x * 16.0 / width, y * 16.0 / width, octaves=64, repeatx=64.0, repeaty=64.0)
image_data.append((255, 255, 255, int(noisenum * white_range + white_min)))
return image_data
def add_noise_to_elevation(world, seed):
octaves = 8
freq = 16.0 * octaves
for y in range(world.height):
for x in range(world.width):
n = snoise2(x / freq * 2, y / freq * 2, octaves, base=seed)
world.layers['elevation'].data[y, x] += n
def __getitem__(self, idx):
x, y, zoom = idx
return snoise2(
(self.xseed + (x)*zoom) / self.frequency,
(self.yseed + (y)*zoom) / self.frequency,
self.octaves
)
def get_height(self, x, y): if 0 < y < self.world.height or 0 < x < self.world.width: height = abs(noise.snoise2(self.seed + x / 64, self.seed + y / 64, 1)) height = int(height*255) return height else: return 0
def test_simplex_2d_range(self):
from noise import snoise2
for i in range(-10000, 10000):
x = i * 0.49
y = -i * 0.67
n = snoise2(x, y)
self.assertTrue(-1.0 <= n <= 1.0, (x, y, n))
def random_grass():
def choose_sne_type(excluded_sne):
sne_type = random.randint(0, self.NB_SNE)
while sne_type in excluded_sne:
sne_type = random.randint(0, self.NB_SNE)
# Turn 80 percent of the flowers into tall grass
if sne_type == 2 and random.random() < 0.8: sne_type = 0
# Turn 0.5 percent of the tall grass into tall grass with a hidden item
if sne_type == 0 and random.random() < 0.005: sne_type = "0_p"
return "sne_" + str(sne_type)
octaves = 1
freq = 7
sne_probability = snoise2((x + x_offset) / freq,
(y + y_offset) / freq, octaves) + 0.5
if sne_probability > (
self.tall_grass_coverage /
100) or "l_1" in self.decoration_layer.get(
(x, y), "") or "l_5" in self.decoration_layer.get(
(x, y), ""):
grass_type = random.randint(0, 3)
return "g_" + str(grass_type)
else:
return choose_sne_type(self.EXCLUDED_SNE)
def generate_simplex_row(x,
size,
scale=100.0,
octaves=6,
persistence=0.5,
base=0.0):
"""
generate_simplex_row - function of generating one row of simplex noise.
:param x: number of row, just for purposes of generation 2d array
:param size: size of the main array, here it is size of the row
:param scale: for controlling speed of changes in the array
:param octaves: number of layers, each with more detail
:param persistence: influence of each octave on the main one
:param base: seed for the array generation (same seed - same output)
:return: list of simplex noise values, of given size
"""
noise_row = []
x_scale = x / scale
for y in range(size):
noise_row.append(
snoise2(x_scale,
y / scale,
octaves=octaves,
persistence=persistence,
base=base))
return noise_row
def generate_map(worldId=0,width=WIDTH,height=HEIGHT,xoffset=0.0,yoffset=0.0,zoom=1.0):
""" Return a simple matrix of simplex noise from 0-255."""
mapdata = []
random.seed(worldId)
zoom=zoom * 100.0
riversource=[]
for x in xrange(height):
row=[]
for y in xrange (width):
xparam=float((x+xoffset)/zoom)
yparam=float((y+yoffset)/zoom)
noisevalue=snoise2(xparam, yparam, NOISEOCTAVES, 0.52,2.0, height/zoom*2, width/zoom, float(worldId) )
#convert 1.0...-1.0 to 255...0
pixel=int((noisevalue+1)/2*PIXEL_DEPTH-1)
cell={'height': pixel, 'x':x, 'y':y }
if (pixel < SEALEVEL):
cell['type']='water'
else:
cell['type']='land'
if (random.randint(0,10000) <5):
cell['riverhead']=True
riversource.append(cell)
row.append( cell )
mapdata.append(row)
#pp = pprint.PrettyPrinter(indent=4)
#pp.pprint(riversource)
return mapdata
def getBarricades(self,
octaves=1,
persistence=0.5,
lacunarity=2.0,
sensitivity=0.65,
scale=0.05,
underworld=False):
barricades = {}
for y in range(0, self.height):
for x in range(0, self.width):
if noise.snoise2(x * scale * 3,
y * scale * 3,
persistence=persistence,
lacunarity=lacunarity,
octaves=octaves) > sensitivity:
if not underworld: # Generate for overworld
if not self.overworld[y][x] in ["#", "H"]:
barricades[f"{y}/{x}"] = Barricade(
y, x, f"{y}/{x}", 0
) # Assign id to each barricade based on initial coordinates
else: # Generate for underworld
if not self.underworld[y][x] in ["#", "H"]:
barricades[f"{y}/{x}"] = Barricade(
y, x, f"{y}/{x}", 1
) # Assign id to each barricade based on initial coordinates
return barricades
def add_noise_to_elevation(world, seed):
octaves = 8
freq = 16.0 * octaves
for y in range(world.height):
for x in range(world.width):
n = snoise2(x / freq * 2, y / freq * 2, octaves, base=seed)
world.elevation['data'][y][x] += n
def noiseat(x, y):
global dx
global dy
return 1 + noise.snoise2(
(x+dx)/10.0,
(y+dy)/800.0,
24,
1)
def get_noise(octaves=42, width=256, height=256):
freq = 16.0 * octaves
output = []
for y in range(height):
for x in range(width):
output.append(int(noise.snoise2(x / freq, y / freq, octaves) * 127.0 + 128.0))
return output
def test_simplex_2d_octaves_range(self):
from noise import snoise2
for i in range(-1000, 1000):
for o in range(10):
x = -i * 0.49
y = i * 0.67
n = snoise2(x, y, octaves=o + 1)
self.assertTrue(-1.0 <= n <= 1.0, (x, n))
def getElevationAtPoint(self, point):
# Scale position and world dimensions according to elevation scaling value
x, y, worldWidth, worldHeight = [e*self.elevationScale for e in point+self.dimensions]
noiseValue = noise.snoise2(x, y, octaves=10, repeatx=worldWidth, repeaty=worldHeight)
# noiseValue is [-1.0, +1.0], so let's rescale and quantize it over [0, 20]
elevation = int(round((noiseValue + 1) * 10))
return elevation
def elevnoise(elevation, seed):
width = len(elevation[0])
height = len(elevation)
octaves = 6
freq = 16.0 * octaves
for y in range(0, height):
for x in range(0, width):
n = int(snoise2(x / freq * 2, y / freq * 2, octaves, base=seed))
elevation[y][x] += n
def make_array(oct, scale, wlevel=False, lac=2.0):
array = np.zeros((size, size), np.float32)
val = random.randint(0, 256)
for y in xrange(size):
for x in xrange(size):
v = snoise2(x * scale, y * scale, oct, persistence=.5, lacunarity=lac, base = val)
if wlevel != False:
v = add_waterlevel(v, wlevel)
array[x, y] += v
return array
def generateNoise(resolution, octaves, frequency):
arr = list()
for x in range(resolution):
arr.append(list())
for y in range(resolution):
sample = snoise2(x / frequency, y / frequency, octaves) + 0.5
arr[x].append(sample)
return arr
def simplex_noise_1d(i, seed=0, octaves=1, freq_ratio=16.0) -> float:
"""
Create simplex noise between 0 and 1
:param i: index of the noise
:param seed: seed to use
:param octaves: higher number makes the noise smoother
:param freq_ratio: how often to sample as the ratio changes
:return: float between 0 and 1
"""
freq = octaves * freq_ratio
return (noise.snoise2(seed, i / freq, octaves) + 1) / 2
def generate(resolution, octaves, frequency):
img = Image.new('RGB', (resolution, resolution), "black")
pixels = img.load()
for x in range(img.size[0]):
for y in range(img.size[1]):
sample = int(snoise2(x / frequency, y / frequency, octaves) * 127 + 128)
color = (sample, sample, sample)
pixels[x, y] = color
return img
