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pnoise1.jl
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pnoise1.jl
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#=
This is a Julia implementation of pnoise1, which is described
in this repository (https://github.com/caseman/noise).
=#
"""
https://github.com/caseman/noise/blob/bb32991ab97e90882d0e46e578060717c5b90dc5/_noise.h#L29-L61
"""
const PERM = @SVector UInt8[
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, 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,
]
function grad1(hash::Integer, x::Float32)
g = (hash & 7) + 1.0f0
if !iszero(hash & 8)
g = -1.0f0
end
return g * x
end
lerp(t, a, b) = a + t * (b - a)
"""
pnoise1(x)
Perlin noise -- pure Julia implementation.
The results of this computation are consistent with
the [Python package's](https://github.com/caseman/noise) `noise.pnoise1` function.
"""
function pnoise1(x::Float32)
repeat = 1024
base = 0
i = floor(Int, x) % repeat
ii = (i + 1) % repeat
i = (i & 255) + base
ii = (ii & 255) + base
x -= floor(x)
fx = x * x * x * (x * (x * 6 - 15) + 10)
f32data = lerp(fx, grad1(PERM[begin+i], x), grad1(PERM[begin+ii], x - 1)) * 0.4f0
return Float64(f32data)
end
pnoise1(x::Real) = pnoise1(Float32(x))