def test_perlin_noise_wrap(self):
gen = perlin_noise.perlinNoiseGenerator()
gen.wrap_x = 3
gen.wrap_y = 3
self.assertTrue(gen.noise2d(1,0)==gen.noise2d(1,3))
self.assertAlmostEqual(gen.interpolate(1,0), gen.interpolate(1,3))
self.assertAlmostEqual(gen.interpolate(1.5,0), gen.interpolate(1.5,3))
def test_perlin_noise_wrap(self):
gen = perlin_noise.perlinNoiseGenerator()
gen.wrap_x = 3
gen.wrap_y = 3
self.assertTrue(gen.noise2d(1, 0) == gen.noise2d(1, 3))
self.assertAlmostEqual(gen.interpolate(1, 0), gen.interpolate(1, 3))
self.assertAlmostEqual(gen.interpolate(1.5, 0),
gen.interpolate(1.5, 3))
def buildPerlinNoiseGenerator(self, smoothness) -> perlin_noise.perlinNoiseGenerator:
"""
Creates a perlin noise generator fitting the requirements of this world map.
"""
grid_size_x = self.max_x/smoothness
grid_size_y = self.max_y/smoothness
if (grid_size_x < 1):
grid_size_x = 1
if (grid_size_y < 1):
grid_size_y = 1
gen = perlin_noise.perlinNoiseGenerator()
gen.wrap_x = grid_size_x
gen.wrap_y = grid_size_y
return gen
def buildPerlinNoiseGenerator(
self, smoothness) -> perlin_noise.perlinNoiseGenerator:
"""
Creates a perlin noise generator fitting the requirements of this world map.
"""
grid_size_x = self.max_x / smoothness
grid_size_y = self.max_y / smoothness
if (grid_size_x < 1):
grid_size_x = 1
if (grid_size_y < 1):
grid_size_y = 1
gen = perlin_noise.perlinNoiseGenerator()
gen.wrap_x = grid_size_x
gen.wrap_y = grid_size_y
return gen
def test_perlin_noise(self):
gen = perlin_noise.perlinNoiseGenerator()
a = gen.noise2d(2, 3)
b = gen.noise2d(2, 3)
c = gen.noise2d(3, 2)
self.assertTrue(a == b)
self.assertTrue(a != c)
gen2 = mockPerlinNoise()
self.assertTrue(gen2.noise2d(3, 3) != gen.noise2d(3, 3))
point = 1, 1.4
this_one = gen.interpolate(*point)
that_one = gen.interpolate(*point)
a_different_one = gen.interpolate(30, 8.3)
self.assertTrue(this_one == that_one)
self.assertTrue(this_one != a_different_one)
a_mid_point = 30.5, 30.5
x, y = a_mid_point
a = math.floor(x), math.floor(y)
b = math.floor(x), math.ceil(y)
c = math.ceil(x), math.floor(y)
d = math.ceil(x), math.ceil(y)
outer_points = a, b, c, d
noise = [gen.noise2d(x, y) for x, y in outer_points]
average = sum(noise) / float(len(noise))
calculated = gen.interpolate(30.5, 30.5)
#Algorithm requires that a point midway between all nearest vertices have a height equal to that
#of all points.
self.assertTrue(math.fabs(average - calculated) < 0.001)
rounded_point = 30, 30
#Checks that the noise generated for that point is the same as the interpolated value for that point.
self.assertTrue(
gen.interpolate(*rounded_point) == gen.noise2d(*rounded_point))
def test_perlin_noise(self):
gen = perlin_noise.perlinNoiseGenerator()
a = gen.noise2d(2,3)
b = gen.noise2d(2,3)
c = gen.noise2d(3,2)
self.assertTrue(a==b)
self.assertTrue(a!=c)
gen2 = mockPerlinNoise()
self.assertTrue(gen2.noise2d(3,3) != gen.noise2d(3,3))
point = 1,1.4
this_one = gen.interpolate(*point)
that_one = gen.interpolate(*point)
a_different_one = gen.interpolate(30,8.3)
self.assertTrue(this_one == that_one)
self.assertTrue(this_one != a_different_one)
a_mid_point = 30.5,30.5
x,y=a_mid_point
a = math.floor(x),math.floor(y)
b = math.floor(x),math.ceil(y)
c = math.ceil(x), math.floor(y)
d = math.ceil(x), math.ceil(y)
outer_points = a,b,c,d
noise = [gen.noise2d(x,y) for x,y in outer_points]
average = sum(noise)/float(len(noise))
calculated = gen.interpolate(30.5,30.5)
#Algorithm requires that a point midway between all nearest vertices have a height equal to that
#of all points.
self.assertTrue(math.fabs(average - calculated) < 0.001)
rounded_point = 30,30
#Checks that the noise generated for that point is the same as the interpolated value for that point.
self.assertTrue(gen.interpolate(*rounded_point) == gen.noise2d(*rounded_point))
