# See the License for the specific language governing permissions and
# limitations under the License.
-# Provides the noise generators `PerlinNoise` and `InterpolatedNoise`
-module noise
+# Noise generators `PerlinNoise` and `InterpolatedNoise`
+module noise is serialize
+
+import serialization
# 2D noise generator
abstract class Noise
# Seed to the random number generator `gradient_vector`
#
# By default, `seed` has a random value created with `Int::rand`.
- var seed: Int = 19559.rand is lazy, writable
+ var seed: Int = 19511359.rand is lazy, writable
end
# 2D Perlin noise generator using layered `InterpolatedNoise`
# Used to get seeds for layers from the previous layers or `seed`.
protected fun pseudo_random(value: Int): Int
do
- return value + 2935391 % 954847
+ return (value * 3537391).mask % 1291377
end
end
var ix1 = sx.lerp(n0, n1)
var val = sy.lerp(ix0, ix1)
- # Return value in [min...max] from val in [-0.5...0.5]
+ # Return value in [min...max] from val in [-1.0...1.0]
+ val /= 2.0
val += 0.5
return val.lerp(min, max)
end
#
# These magic prime numbers were determined good enough by
# non-emperical experimentation. They may need to be changed/improved.
- var i = 17957*seed + 45127*x + 22613*y
- var mod = 19031
+ var seed = 817721 + self.seed
+ var i = seed * (x+seed) * 25111217 * (y+seed) * 72233613
+ var mod = 137121
+ var angle = (i.mask.abs%mod).to_f*2.0*pi/mod.to_f
+
+ # Debug code to evaluate the efficiency of the random angle generator
+ # The average of the produced angles should be at pi
+ #
+ #var sum = once new Container[Float](0.0)
+ #var count = once new Container[Float](0.0)
+ #sum.item += angle
+ #count.item += 1.0
+ #if count.item.to_i % 1000 == 0 then print "avg:{sum.item/count.item}/{count.item} i:{i} a:{angle} ({x}, {y}: {seed})"
- var angle = (i%mod).to_f*2.0*pi/mod.to_f
if w == 0 then return angle.cos
return angle.sin
end
return dx*gradient_vector(ix, iy, 0) + dy*gradient_vector(ix, iy, 1)
end
end
+
+redef universal Int
+ # The value of the least-significant 30 bits of `self`
+ #
+ # This mask is used as compatibility with 32 bits architecture.
+ # The missing 2 bits are used to tag integers by the Nit system.
+ private fun mask: Int
+ do
+ return self & 0x3FFF_FFFF
+ end
+end
+
+redef universal Float
+ # Smoothened `self`, used by `ImprovedNoise`
+ private fun fade: Float do return self*self*self*(self*(self*6.0-15.0)+10.0)
+end
+
+# Direct translation of Ken Perlin's improved noise Java implementation
+#
+# This implementation differs from `PerlinNoise` on two main points.
+# This noise is calculated for a 3D point, vs 2D in `PerlinNoise`.
+# `PerlinNoise` is based off a customizable seed, while this noise has a static data source.
+class ImprovedNoise
+
+ # Permutations
+ private var p: Array[Int] = [151,160,137,91,90,15,
+ 131,13,201,95,96,53,194,233,7,225,140,36,103,30,69,142,8,99,37,240,21,10,23,
+ 190, 6,148,247,120,234,75,0,26,197,62,94,252,219,203,117,35,11,32,57,177,33,
+ 88,237,149,56,87,174,20,125,136,171,168, 68,175,74,165,71,134,139,48,27,166,
+ 77,146,158,231,83,111,229,122,60,211,133,230,220,105,92,41,55,46,245,40,244,
+ 102,143,54, 65,25,63,161, 1,216,80,73,209,76,132,187,208, 89,18,169,200,196,
+ 135,130,116,188,159,86,164,100,109,198,173,186, 3,64,52,217,226,250,124,123,
+ 5,202,38,147,118,126,255,82,85,212,207,206,59,227,47,16,58,17,182,189,28,42,
+ 223,183,170,213,119,248,152, 2,44,154,163, 70,221,153,101,155,167, 43,172,9,
+ 129,22,39,253, 19,98,108,110,79,113,224,232,178,185, 112,104,218,246,97,228,
+ 251,34,242,193,238,210,144,12,191,179,162,241, 81,51,145,235,249,14,239,107,
+ 49,192,214, 31,181,199,106,157,184, 84,204,176,115,121,50,45,127, 4,150,254,
+ 138,236,205,93,222,114,67,29,24,72,243,141,128,195,78,66,215,61,156,180] * 2
+
+ # Noise value in [-1..1] at 3D coordinates `x, y, z`
+ fun noise(x, y, z: Float): Float
+ do
+ var xx = x.floor.to_i & 255
+ var yy = y.floor.to_i & 255
+ var zz = z.floor.to_i & 255
+
+ x -= x.floor
+ y -= y.floor
+ z -= z.floor
+
+ var u = x.fade
+ var v = y.fade
+ var w = z.fade
+
+ var a = p[xx ] + yy
+ var aa = p[a ] + zz
+ var ab = p[a+1 ] + zz
+ var b = p[xx+1] + yy
+ var ba = p[b ] + zz
+ var bb = p[b+1 ] + zz
+
+ return w.lerp(v.lerp(u.lerp(grad(p[aa ], x, y, z ),
+ grad(p[ba ], x-1.0, y, z )),
+ u.lerp(grad(p[ab ], x, y-1.0, z ),
+ grad(p[bb ], x-1.0, y-1.0, z ))),
+ v.lerp(u.lerp(grad(p[aa+1], x, y, z-1.0),
+ grad(p[ba+1], x-1.0, y, z-1.0)),
+ u.lerp(grad(p[ab+1], x, y-1.0, z-1.0),
+ grad(p[bb+1], x-1.0, y-1.0, z-1.0))))
+ end
+
+ # Value at a corner of the grid
+ private fun grad(hash: Int, x, y, z: Float): Float
+ do
+ var h = hash & 15
+ var u = if h < 8 then x else y
+ var v = if h < 4 then y else if h == 12 or h == 14 then x else z
+ return (if h.is_even then u else -u) + (if h & 2 == 0 then v else -v)
+ end
+end
nitlanguage / nit.git / blobdiff