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blender-archive/source/blender/python/mathutils/mathutils_noise.c

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/*
* ***** BEGIN GPL LICENSE BLOCK *****
*
* This program is free software; you can redistribute it and/or
* modify it under the terms of the GNU General Public License
* as published by the Free Software Foundation; either version 2
* of the License, or (at your option) any later version.
*
* This program is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with this program; if not, write to the Free Software Foundation,
* Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
*
* The Original Code is Copyright (C) 2001-2002 by NaN Holding BV.
* All rights reserved.
*
* This is a new part of Blender.
*
* Contributor(s): eeshlo, Campbell Barton
*
* ***** END GPL LICENSE BLOCK *****
*/
/** \file blender/python/mathutils/mathutils_noise.c
* \ingroup mathutils
*
* This file defines the 'noise' module, a general purpose module to access
* blenders noise functions.
*/
/************************/
/* Blender Noise Module */
/************************/
#include <Python.h>
#include "structseq.h"
#include "BLI_blenlib.h"
#include "BLI_math.h"
#include "BLI_utildefines.h"
#include "MEM_guardedalloc.h"
#include "DNA_texture_types.h"
#include "mathutils.h"
#include "mathutils_noise.h"
/* 2.6 update
* Moved to submodule of mathutils.
* All vector functions now return mathutils.Vector
* Updated docs to be compatible with autodocs generation.
* Updated vector functions to use nD array functions.
* noise.vl_vector --> noise.variable_lacunarity
* noise.vector --> noise.noise_vector
*/
/*-----------------------------------------*/
/* 'mersenne twister' random number generator */
/*
A C-program for MT19937, with initialization improved 2002/2/10.
Coded by Takuji Nishimura and Makoto Matsumoto.
This is a faster version by taking Shawn Cokus's optimization,
Matthe Bellew's simplification, Isaku Wada's real version.
Before using, initialize the state by using init_genrand(seed)
or init_by_array(init_key, key_length).
Copyright (C) 1997 - 2002, Makoto Matsumoto and Takuji Nishimura,
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
3. The names of its contributors may not be used to endorse or promote
products derived from this software without specific prior written
permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Any feedback is very welcome.
http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/emt.html
email: m-mat @ math.sci.hiroshima-u.ac.jp (remove space)
*/
/* Period parameters */
#define N 624
#define M 397
#define MATRIX_A 0x9908b0dfUL /* constant vector a */
#define UMASK 0x80000000UL /* most significant w-r bits */
#define LMASK 0x7fffffffUL /* least significant r bits */
#define MIXBITS(u,v) (((u) & UMASK) | ((v) & LMASK))
#define TWIST(u,v) ((MIXBITS(u,v) >> 1) ^ ((v)&1UL ? MATRIX_A : 0UL))
static unsigned long state[N]; /* the array for the state vector */
static int left = 1;
static int initf = 0;
static unsigned long *next;
/* initializes state[N] with a seed */
static void init_genrand(unsigned long s)
{
int j;
state[0] = s & 0xffffffffUL;
for (j = 1; j < N; j++) {
state[j] =
(1812433253UL *
(state[j - 1] ^ (state[j - 1] >> 30)) + j);
/* See Knuth TAOCP Vol2. 3rd Ed. P.106 for multiplier. */
/* In the previous versions, MSBs of the seed affect */
/* only MSBs of the array state[]. */
/* 2002/01/09 modified by Makoto Matsumoto */
state[j] &= 0xffffffffUL; /* for >32 bit machines */
}
left = 1;
initf = 1;
}
static void next_state(void)
{
unsigned long *p = state;
int j;
/* if init_genrand() has not been called, */
/* a default initial seed is used */
if (initf == 0)
init_genrand(5489UL);
left = N;
next = state;
for (j = N - M + 1; --j; p++)
*p = p[M] ^ TWIST(p[0], p[1]);
for (j = M; --j; p++)
*p = p[M - N] ^ TWIST(p[0], p[1]);
*p = p[M - N] ^ TWIST(p[0], state[0]);
}
/*------------------------------------------------------------*/
static void setRndSeed(int seed)
{
if (seed == 0)
init_genrand(time(NULL));
else
init_genrand(seed);
}
/* float number in range [0, 1) using the mersenne twister rng */
static float frand(void)
{
unsigned long y;
if (--left == 0)
next_state();
y = *next++;
/* Tempering */
y ^= (y >> 11);
y ^= (y << 7) & 0x9d2c5680UL;
y ^= (y << 15) & 0xefc60000UL;
y ^= (y >> 18);
return (float) y / 4294967296.f;
}
/*------------------------------------------------------------*/
/* Utility Functions */
/*------------------------------------------------------------*/
/* Fills an array of length size with random numbers in the range (-1, 1)*/
static void rand_vn(float *array_tar, const int size)
{
float *array_pt = array_tar + (size-1);
int i = size;
while (i--) { *(array_pt--) = 2.0f * frand() - 1.0f; }
}
/* Fills an array of length 3 with noise values */
static void noise_vector(float x, float y, float z, int nb, float v[3])
{
/* Simply evaluate noise at 3 different positions */
v[0] = (float)(2.0f * BLI_gNoise(1.f, x + 9.321f, y - 1.531f, z - 7.951f, 0, nb) - 1.0f);
v[1] = (float)(2.0f * BLI_gNoise(1.f, x, y, z, 0, nb) - 1.0f);
v[2] = (float)(2.0f * BLI_gNoise(1.f, x + 6.327f, y + 0.1671f, z - 2.672f, 0, nb) - 1.0f);
}
/* Returns a turbulence value for a given position (x, y, z) */
static float turb(float x, float y, float z, int oct, int hard, int nb,
float ampscale, float freqscale)
{
float amp, out, t;
int i;
amp = 1.f;
out = (float)(2.0f * BLI_gNoise(1.f, x, y, z, 0, nb) - 1.0f);
if (hard)
out = fabsf(out);
for (i = 1; i < oct; i++) {
amp *= ampscale;
x *= freqscale;
y *= freqscale;
z *= freqscale;
t = (float)(amp * (2.0f * BLI_gNoise(1.f, x, y, z, 0, nb) - 1.0f));
if (hard)
t = fabsf(t);
out += t;
}
return out;
}
/* Fills an array of length 3 with the turbulence vector for a given
position (x, y, z) */
static void vTurb(float x, float y, float z, int oct, int hard, int nb,
float ampscale, float freqscale, float v[3])
{
float amp, t[3];
int i;
amp = 1.f;
noise_vector(x, y, z, nb, v);
if (hard) {
v[0] = fabsf(v[0]);
v[1] = fabsf(v[1]);
v[2] = fabsf(v[2]);
}
for (i = 1; i < oct; i++) {
amp *= ampscale;
x *= freqscale;
y *= freqscale;
z *= freqscale;
noise_vector(x, y, z, nb, t);
if (hard) {
t[0] = fabsf(t[0]);
t[1] = fabsf(t[1]);
t[2] = fabsf(t[2]);
}
v[0] += amp * t[0];
v[1] += amp * t[1];
v[2] += amp * t[2];
}
}
/*-------------------------DOC STRINGS ---------------------------*/
PyDoc_STRVAR(M_Noise_doc,
"The Blender noise module"
);
/*------------------------------------------------------------*/
/* Python Functions */
/*------------------------------------------------------------*/
PyDoc_STRVAR(M_Noise_random_doc,
".. function:: random()\n"
"\n"
" Returns a random number in the range [0, 1].\n"
"\n"
" :return: The random number.\n"
" :rtype: float\n"
);
static PyObject *M_Noise_random(PyObject *UNUSED(self))
{
return PyFloat_FromDouble(frand());
}
PyDoc_STRVAR(M_Noise_random_unit_vector_doc,
".. function:: random_unit_vector(size=3)\n"
"\n"
" Returns a unit vector with random entries.\n"
"\n"
" :arg size: The size of the vector to be produced.\n"
" :type size: Int\n"
" :return: The random unit vector.\n"
" :rtype: :class:`mathutils.Vector`\n"
);
static PyObject *M_Noise_random_unit_vector(PyObject *UNUSED(self), PyObject *args)
{
float vec[4] = {0.0f, 0.0f, 0.0f, 0.0f};
float norm = 2.0f;
int size = 3;
if (!PyArg_ParseTuple(args, "|i:random_vector", &size))
return NULL;
if (size > 4 || size < 2) {
PyErr_SetString(PyExc_ValueError, "Vector(): invalid size");
return NULL;
}
while (norm == 0.0f || norm >= 1.0f) {
rand_vn(vec, size);
norm = normalize_vn(vec, size);
}
return Vector_CreatePyObject(vec, size, Py_NEW, NULL);
}
/* This is dumb, most people will want a unit vector anyway, since this doesn't have uniform distribution over a sphere*/
/*
PyDoc_STRVAR(M_Noise_random_vector_doc,
".. function:: random_vector(size=3)\n"
"\n"
" Returns a vector with random entries in the range [0, 1).\n"
"\n"
" :arg size: The size of the vector to be produced.\n"
" :type size: Int\n"
" :return: The random vector.\n"
" :rtype: :class:`mathutils.Vector`\n"
);
static PyObject *M_Noise_random_vector(PyObject *UNUSED(self), PyObject *args)
{
float vec[4]= {0.0f, 0.0f, 0.0f, 0.0f};
int size= 3;
if (!PyArg_ParseTuple(args, "|i:random_vector", &size))
return NULL;
if (size > 4 || size < 2) {
PyErr_SetString(PyExc_ValueError, "Vector(): invalid size");
return NULL;
}
rand_vn(vec, size);
return Vector_CreatePyObject(vec, size, Py_NEW, NULL);
}
*/
PyDoc_STRVAR(M_Noise_seed_set_doc,
".. function:: seed_set(seed)\n"
"\n"
" Sets the random seed used for random_unit_vector, random_vector and random.\n"
"\n"
" :arg seed: Seed used for the random generator.\n"
" :type seed: Int\n"
);
static PyObject *M_Noise_seed_set(PyObject *UNUSED(self), PyObject *args)
{
int s;
if (!PyArg_ParseTuple(args, "i:seed_set", &s))
return NULL;
setRndSeed(s);
Py_RETURN_NONE;
}
PyDoc_STRVAR(M_Noise_noise_doc,
".. function:: noise(position, noise_basis=noise.types.STDPERLIN)\n"
"\n"
" Returns noise value from the noise basis at the position specified.\n"
"\n"
" :arg position: The position to evaluate the selected noise function at.\n"
" :type position: :class:`mathutils.Vector`\n"
" :arg noise_basis: The type of noise to be evaluated.\n"
" :type noise_basis: Value in noise.types or int\n"
" :return: The noise value.\n"
" :rtype: float\n"
);
static PyObject *M_Noise_noise(PyObject *UNUSED(self), PyObject *args)
{
PyObject *value;
float vec[3];
int nb = 1;
if (!PyArg_ParseTuple(args, "O|i:noise", &value, &nb))
return NULL;
if (mathutils_array_parse(vec, 3, 3, value, "noise: invalid 'position' arg") == -1)
return NULL;
return PyFloat_FromDouble((2.0f * BLI_gNoise(1.0f, vec[0], vec[1], vec[2], 0, nb) - 1.0f));
}
PyDoc_STRVAR(M_Noise_noise_vector_doc,
".. function:: noise_vector(position, noise_basis=noise.types.STDPERLIN)\n"
"\n"
" Returns the noise vector from the noise basis at the specified position.\n"
"\n"
" :arg position: The position to evaluate the selected noise function at.\n"
" :type position: :class:`mathutils.Vector`\n"
" :arg noise_basis: The type of noise to be evaluated.\n"
" :type noise_basis: Value in noise.types or int\n"
" :return: The noise vector.\n"
" :rtype: :class:`mathutils.Vector`\n"
);
static PyObject *M_Noise_noise_vector(PyObject *UNUSED(self), PyObject *args)
{
PyObject *value;
float vec[3], r_vec[3];
int nb = 1;
if (!PyArg_ParseTuple(args, "O|i:noise_vector", &value, &nb))
return NULL;
if (mathutils_array_parse(vec, 3, 3, value, "noise_vector: invalid 'position' arg") == -1)
return NULL;
noise_vector(vec[0], vec[1], vec[2], nb, r_vec);
return Vector_CreatePyObject(r_vec, 3, Py_NEW, NULL);
}
PyDoc_STRVAR(M_Noise_turbulence_doc,
".. function:: turbulence(position, octaves, hard, noise_basis=noise.types.STDPERLIN, amplitude_scale=0.5, frequency_scale=2.0)\n"
"\n"
" Returns the turbulence value from the noise basis at the specified position.\n"
"\n"
" :arg position: The position to evaluate the selected noise function at.\n"
" :type position: :class:`mathutils.Vector`\n"
" :arg octaves: The number of different noise frequencies used.\n"
" :type octaves: int\n"
" :arg hard: Specifies whether returned turbulence is hard (sharp transitions) or soft (smooth transitions).\n"
" :type hard: :boolean\n"
" :arg noise_basis: The type of noise to be evaluated.\n"
" :type noise_basis: Value in mathutils.noise.types or int\n"
" :arg amplitude_scale: The amplitude scaling factor.\n"
" :type amplitude_scale: float\n"
" :arg frequency_scale: The frequency scaling factor\n"
" :type frequency_scale: Value in noise.types or int\n"
" :return: The turbulence value.\n"
" :rtype: float\n"
);
static PyObject *M_Noise_turbulence(PyObject *UNUSED(self), PyObject *args)
{
PyObject *value;
float vec[3];
int oct, hd, nb = 1;
float as = 0.5f, fs = 2.0f;
if (!PyArg_ParseTuple(args, "Oii|iff:turbulence", &value, &oct, &hd, &nb, &as, &fs))
return NULL;
if (mathutils_array_parse(vec, 3, 3, value, "turbulence: invalid 'position' arg") == -1)
return NULL;
return PyFloat_FromDouble(turb(vec[0], vec[1], vec[2], oct, hd, nb, as, fs));
}
PyDoc_STRVAR(M_Noise_turbulence_vector_doc,
".. function:: turbulence_vector(position, octaves, hard, noise_basis=noise.types.STDPERLIN, amplitude_scale=0.5, frequency_scale=2.0)\n"
"\n"
" Returns the turbulence vector from the noise basis at the specified position.\n"
"\n"
" :arg position: The position to evaluate the selected noise function at.\n"
" :type position: :class:`mathutils.Vector`\n"
" :arg octaves: The number of different noise frequencies used.\n"
" :type octaves: int\n"
" :arg hard: Specifies whether returned turbulence is hard (sharp transitions) or soft (smooth transitions).\n"
" :type hard: :boolean\n"
" :arg noise_basis: The type of noise to be evaluated.\n"
" :type noise_basis: Value in mathutils.noise.types or int\n"
" :arg amplitude_scale: The amplitude scaling factor.\n"
" :type amplitude_scale: float\n"
" :arg frequency_scale: The frequency scaling factor\n"
" :type frequency_scale: Value in noise.types or int\n"
" :return: The turbulence vector.\n"
" :rtype: :class:`mathutils.Vector`\n"
);
static PyObject *M_Noise_turbulence_vector(PyObject *UNUSED(self), PyObject *args)
{
PyObject *value;
float vec[3], r_vec[3];
int oct, hd, nb = 1;
float as =0.5f, fs = 2.0f;
if (!PyArg_ParseTuple(args, "Oii|iff:turbulence_vector", &value, &oct, &hd, &nb, &as, &fs))
return NULL;
if (mathutils_array_parse(vec, 3, 3, value, "turbulence_vector: invalid 'position' arg") == -1)
return NULL;
vTurb(vec[0], vec[1], vec[2], oct, hd, nb, as, fs, r_vec);
return Vector_CreatePyObject(r_vec, 3, Py_NEW, NULL);
}
/* F. Kenton Musgrave's fractal functions */
PyDoc_STRVAR(M_Noise_fractal_doc,
".. function:: fractal(position, H, lacunarity, octaves, noise_basis=noise.types.STDPERLIN)\n"
"\n"
" Returns the fractal Brownian motion (fBm) noise value from the noise basis at the specified position.\n"
"\n"
" :arg position: The position to evaluate the selected noise function at.\n"
" :type position: :class:`mathutils.Vector`\n"
" :arg H: The fractal increment factor.\n"
" :type H: float\n"
" :arg lacunarity: The gap between successive frequencies.\n"
" :type lacunarity: float\n"
" :arg octaves: The number of different noise frequencies used.\n"
" :type octaves: int\n"
" :arg noise_basis: The type of noise to be evaluated.\n"
" :type noise_basis: Value in noise.types or int\n"
" :return: The fractal Brownian motion noise value.\n"
" :rtype: float\n"
);
static PyObject *M_Noise_fractal(PyObject *UNUSED(self), PyObject *args)
{
PyObject *value;
float vec[3];
float H, lac, oct;
int nb = 1;
if (!PyArg_ParseTuple(args, "Offf|i:fractal", &value, &H, &lac, &oct, &nb))
return NULL;
if (mathutils_array_parse(vec, 3, 3, value, "fractal: invalid 'position' arg") == -1)
return NULL;
return PyFloat_FromDouble(mg_fBm(vec[0], vec[1], vec[2], H, lac, oct, nb));
}
PyDoc_STRVAR(M_Noise_multi_fractal_doc,
".. function:: multi_fractal(position, H, lacunarity, octaves, noise_basis=noise.types.STDPERLIN)\n"
"\n"
" Returns multifractal noise value from the noise basis at the specified position.\n"
"\n"
" :arg position: The position to evaluate the selected noise function at.\n"
" :type position: :class:`mathutils.Vector`\n"
" :arg H: The fractal increment factor.\n"
" :type H: float\n"
" :arg lacunarity: The gap between successive frequencies.\n"
" :type lacunarity: float\n"
" :arg octaves: The number of different noise frequencies used.\n"
" :type octaves: int\n"
" :arg noise_basis: The type of noise to be evaluated.\n"
" :type noise_basis: Value in noise.types or int\n"
" :return: The multifractal noise value.\n"
" :rtype: float\n"
);
static PyObject *M_Noise_multi_fractal(PyObject *UNUSED(self), PyObject *args)
{
PyObject *value;
float vec[3];
float H, lac, oct;
int nb = 1;
if (!PyArg_ParseTuple(args, "Offf|i:multi_fractal", &value, &H, &lac, &oct, &nb))
return NULL;
if (mathutils_array_parse(vec, 3, 3, value, "multi_fractal: invalid 'position' arg") == -1)
return NULL;
return PyFloat_FromDouble(mg_MultiFractal(vec[0], vec[1], vec[2], H, lac, oct, nb));
}
PyDoc_STRVAR(M_Noise_variable_lacunarity_doc,
".. function:: variable_lacunarity(position, distortion, noise_type1=noise.types.STDPERLIN, noise_type2=noise.types.STDPERLIN)\n"
"\n"
" Returns variable lacunarity noise value, a distorted variety of noise, from noise type 1 distorted by noise type 2 at the specified position.\n"
"\n"
" :arg position: The position to evaluate the selected noise function at.\n"
" :type position: :class:`mathutils.Vector`\n"
" :arg distortion: The amount of distortion.\n"
" :type distortion: float\n"
" :arg noise_type1: The type of noise to be distorted.\n"
" :type noise_type1: Value in noise.types or int\n"
" :arg noise_type2: The type of noise used to distort noise_type1.\n"
" :type noise_type2: Value in noise.types or int\n"
" :return: The variable lacunarity noise value.\n"
" :rtype: float\n"
);
static PyObject *M_Noise_variable_lacunarity(PyObject *UNUSED(self), PyObject *args)
{
PyObject *value;
float vec[3];
float d;
int nt1 = 1, nt2 = 1;
if (!PyArg_ParseTuple(args, "Of|ii:variable_lacunarity", &value, &d, &nt1, &nt2))
return NULL;
if (mathutils_array_parse(vec, 3, 3, value, "variable_lacunarity: invalid 'position' arg") == -1)
return NULL;
return PyFloat_FromDouble(mg_VLNoise(vec[0], vec[1], vec[2], d, nt1, nt2));
}
PyDoc_STRVAR(M_Noise_hetero_terrain_doc,
".. function:: hetero_terrain(position, H, lacunarity, octaves, offset, noise_basis=noise.types.STDPERLIN)\n"
"\n"
" Returns the heterogeneous terrain value from the noise basis at the specified position.\n"
"\n"
" :arg position: The position to evaluate the selected noise function at.\n"
" :type position: :class:`mathutils.Vector`\n"
" :arg H: The fractal dimension of the roughest areas.\n"
" :type H: float\n"
" :arg lacunarity: The gap between successive frequencies.\n"
" :type lacunarity: float\n"
" :arg octaves: The number of different noise frequencies used.\n"
" :type octaves: int\n"
" :arg offset: The height of the terrain above 'sea level'.\n"
" :type offset: float\n"
" :arg noise_basis: The type of noise to be evaluated.\n"
" :type noise_basis: Value in noise.types or int\n"
" :return: The heterogeneous terrain value.\n"
" :rtype: float\n"
);
static PyObject *M_Noise_hetero_terrain(PyObject *UNUSED(self), PyObject *args)
{
PyObject *value;
float vec[3];
float H, lac, oct, ofs;
int nb = 1;
if (!PyArg_ParseTuple(args, "Offff|i:hetero_terrain", &value, &H, &lac, &oct, &ofs, &nb))
return NULL;
if (mathutils_array_parse(vec, 3, 3, value, "hetero_terrain: invalid 'position' arg") == -1)
return NULL;
return PyFloat_FromDouble(mg_HeteroTerrain(vec[0], vec[1], vec[2], H, lac, oct, ofs, nb));
}
PyDoc_STRVAR(M_Noise_hybrid_multi_fractal_doc,
".. function:: hybrid_multi_fractal(position, H, lacunarity, octaves, offset, gain, noise_basis=noise.types.STDPERLIN)\n"
"\n"
" Returns hybrid multifractal value from the noise basis at the specified position.\n"
"\n"
" :arg position: The position to evaluate the selected noise function at.\n"
" :type position: :class:`mathutils.Vector`\n"
" :arg H: The fractal dimension of the roughest areas.\n"
" :type H: float\n"
" :arg lacunarity: The gap between successive frequencies.\n"
" :type lacunarity: float\n"
" :arg octaves: The number of different noise frequencies used.\n"
" :type octaves: int\n"
" :arg offset: The height of the terrain above 'sea level'.\n"
" :type offset: float\n"
" :arg gain: Scaling applied to the values.\n"
" :type gain: float\n"
" :arg noise_basis: The type of noise to be evaluated.\n"
" :type noise_basis: Value in noise.types or int\n"
" :return: The hybrid multifractal value.\n"
" :rtype: float\n"
);
static PyObject *M_Noise_hybrid_multi_fractal(PyObject *UNUSED(self), PyObject *args)
{
PyObject *value;
float vec[3];
float H, lac, oct, ofs, gn;
int nb = 1;
if (!PyArg_ParseTuple(args, "Offfff|i:hybrid_multi_fractal", &value, &H, &lac, &oct, &ofs, &gn, &nb))
return NULL;
if (mathutils_array_parse(vec, 3, 3, value, "hybrid_multi_fractal: invalid 'position' arg") == -1)
return NULL;
return PyFloat_FromDouble(mg_HybridMultiFractal(vec[0], vec[1], vec[2], H, lac, oct, ofs, gn, nb));
}
PyDoc_STRVAR(M_Noise_ridged_multi_fractal_doc,
".. function:: ridged_multi_fractal(position, H, lacunarity, octaves, offset, gain, noise_basis=noise.types.STDPERLIN)\n"
"\n"
" Returns ridged multifractal value from the noise basis at the specified position.\n"
"\n"
" :arg position: The position to evaluate the selected noise function at.\n"
" :type position: :class:`mathutils.Vector`\n"
" :arg H: The fractal dimension of the roughest areas.\n"
" :type H: float\n"
" :arg lacunarity: The gap between successive frequencies.\n"
" :type lacunarity: float\n"
" :arg octaves: The number of different noise frequencies used.\n"
" :type octaves: int\n"
" :arg offset: The height of the terrain above 'sea level'.\n"
" :type offset: float\n"
" :arg gain: Scaling applied to the values.\n"
" :type gain: float\n"
" :arg noise_basis: The type of noise to be evaluated.\n"
" :type noise_basis: Value in noise.types or int\n"
" :return: The ridged multifractal value.\n"
" :rtype: float\n"
);
static PyObject *M_Noise_ridged_multi_fractal(PyObject *UNUSED(self), PyObject *args)
{
PyObject *value;
float vec[3];
float H, lac, oct, ofs, gn;
int nb = 1;
if (!PyArg_ParseTuple(args, "Offfff|i:ridged_multi_fractal", &value, &H, &lac, &oct, &ofs, &gn, &nb))
return NULL;
if (mathutils_array_parse(vec, 3, 3, value, "ridged_multi_fractal: invalid 'position' arg") == -1)
return NULL;
return PyFloat_FromDouble(mg_RidgedMultiFractal(vec[0], vec[1], vec[2], H, lac, oct, ofs, gn, nb));
}
PyDoc_STRVAR(M_Noise_voronoi_doc,
".. function:: voronoi(position, distance_metric=noise.distance_metrics.DISTANCE, exponent=2.5)\n"
"\n"
" Returns a list of distances to the four closest features and their locations.\n"
"\n"
" :arg position: The position to evaluate the selected noise function at.\n"
" :type position: :class:`mathutils.Vector`\n"
" :arg distance_metric: Method of measuring distance.\n"
" :type distance_metric: Value in noise.distance_metrics or int\n"
" :arg exponent: The exponent for Minkovsky distance metric.\n"
" :type exponent: float\n"
" :return: A list of distances to the four closest features and their locations.\n"
" :rtype: list of four floats, list of four :class:`mathutils.Vector` types\n"
);
static PyObject *M_Noise_voronoi(PyObject *UNUSED(self), PyObject *args)
{
PyObject *value;
PyObject *list;
float vec[3];
float da[4], pa[12];
int dtype = 0;
float me = 2.5f; /* default minkovsky exponent */
int i;
if (!PyArg_ParseTuple(args, "O|if:voronoi", &value, &dtype, &me))
return NULL;
if (mathutils_array_parse(vec, 3, 3, value, "voronoi: invalid 'position' arg") == -1)
return NULL;
list = PyList_New(4);
voronoi(vec[0], vec[1], vec[2], da, pa, me, dtype);
for (i = 0; i < 4; i++) {
PyList_SET_ITEM(list, i, Vector_CreatePyObject(pa + 3 * i, 3, Py_NEW, NULL));
}
return Py_BuildValue("[[ffff]O]", da[0], da[1], da[2], da[3], list);
}
PyDoc_STRVAR(M_Noise_cell_doc,
".. function:: cell(position)\n"
"\n"
" Returns cell noise value at the specified position.\n"
"\n"
" :arg position: The position to evaluate the selected noise function at.\n"
" :type position: :class:`mathutils.Vector`\n"
" :return: The cell noise value.\n"
" :rtype: float\n"
);
static PyObject *M_Noise_cell(PyObject *UNUSED(self), PyObject *args)
{
PyObject *value;
float vec[3];
if (!PyArg_ParseTuple(args, "O:cell", &value))
return NULL;
if (mathutils_array_parse(vec, 3, 3, value, "cell: invalid 'position' arg") == -1)
return NULL;
return PyFloat_FromDouble(cellNoise(vec[0], vec[1], vec[2]));
}
PyDoc_STRVAR(M_Noise_cell_vector_doc,
".. function:: cell_vector(position)\n"
"\n"
" Returns cell noise vector at the specified position.\n"
"\n"
" :arg position: The position to evaluate the selected noise function at.\n"
" :type position: :class:`mathutils.Vector`\n"
" :return: The cell noise vector.\n"
" :rtype: :class:`mathutils.Vector`\n"
);
static PyObject *M_Noise_cell_vector(PyObject *UNUSED(self), PyObject *args)
{
PyObject *value;
float vec[3], r_vec[3];
if (!PyArg_ParseTuple(args, "O:cell_vector", &value))
return NULL;
if (mathutils_array_parse(vec, 3, 3, value, "cell_vector: invalid 'position' arg") == -1)
return NULL;
cellNoiseV(vec[0], vec[1], vec[2], r_vec);
return Vector_CreatePyObject(NULL, 3, Py_NEW, NULL);;
}
static PyMethodDef M_Noise_methods[] = {
{"seed_set", (PyCFunction) M_Noise_seed_set, METH_VARARGS, M_Noise_seed_set_doc},
{"random", (PyCFunction) M_Noise_random, METH_NOARGS, M_Noise_random_doc},
{"random_unit_vector", (PyCFunction) M_Noise_random_unit_vector, METH_VARARGS, M_Noise_random_unit_vector_doc},
/*{"random_vector", (PyCFunction) M_Noise_random_vector, METH_VARARGS, M_Noise_random_vector_doc},*/
{"noise", (PyCFunction) M_Noise_noise, METH_VARARGS, M_Noise_noise_doc},
{"noise_vector", (PyCFunction) M_Noise_noise_vector, METH_VARARGS, M_Noise_noise_vector_doc},
{"turbulence", (PyCFunction) M_Noise_turbulence, METH_VARARGS, M_Noise_turbulence_doc},
{"turbulence_vector", (PyCFunction) M_Noise_turbulence_vector, METH_VARARGS, M_Noise_turbulence_vector_doc},
{"fractal", (PyCFunction) M_Noise_fractal, METH_VARARGS, M_Noise_fractal_doc},
{"multi_fractal", (PyCFunction) M_Noise_multi_fractal, METH_VARARGS, M_Noise_multi_fractal_doc},
{"variable_lacunarity", (PyCFunction) M_Noise_variable_lacunarity, METH_VARARGS, M_Noise_variable_lacunarity_doc},
{"hetero_terrain", (PyCFunction) M_Noise_hetero_terrain, METH_VARARGS, M_Noise_hetero_terrain_doc},
{"hybrid_multi_fractal", (PyCFunction) M_Noise_hybrid_multi_fractal, METH_VARARGS, M_Noise_hybrid_multi_fractal_doc},
{"ridged_multi_fractal", (PyCFunction) M_Noise_ridged_multi_fractal, METH_VARARGS, M_Noise_ridged_multi_fractal_doc},
{"voronoi", (PyCFunction) M_Noise_voronoi, METH_VARARGS, M_Noise_voronoi_doc},
{"cell", (PyCFunction) M_Noise_cell, METH_VARARGS, M_Noise_cell_doc},
{"cell_vector", (PyCFunction) M_Noise_cell_vector, METH_VARARGS, M_Noise_cell_vector_doc},
{NULL, NULL, 0, NULL}
};
static struct PyModuleDef M_Noise_module_def = {
PyModuleDef_HEAD_INIT,
"mathutils.noise", /* m_name */
M_Noise_doc, /* m_doc */
0, /* m_size */
M_Noise_methods, /* m_methods */
NULL, /* m_reload */
NULL, /* m_traverse */
NULL, /* m_clear */
NULL, /* m_free */
};
/*----------------------------MODULE INIT-------------------------*/
PyMODINIT_FUNC PyInit_mathutils_noise(void)
{
PyObject *submodule = PyModule_Create(&M_Noise_module_def);
PyObject *item_types, *item_metrics;
/* use current time as seed for random number generator by default */
setRndSeed(0);
PyModule_AddObject(submodule, "types", (item_types = PyInit_mathutils_noise_types()));
PyDict_SetItemString(PyThreadState_GET()->interp->modules, "noise.types", item_types);
Py_INCREF(item_types);
PyModule_AddObject(submodule, "distance_metrics", (item_metrics = PyInit_mathutils_noise_metrics()));
PyDict_SetItemString(PyThreadState_GET()->interp->modules, "noise.distance_metrics", item_metrics);
Py_INCREF(item_metrics);
return submodule;
}
/*----------------------------SUBMODULE INIT-------------------------*/
static struct PyModuleDef M_NoiseTypes_module_def = {
PyModuleDef_HEAD_INIT,
"mathutils.noise.types", /* m_name */
NULL, /* m_doc */
0, /* m_size */
NULL, /* m_methods */
NULL, /* m_reload */
NULL, /* m_traverse */
NULL, /* m_clear */
NULL, /* m_free */
};
PyMODINIT_FUNC PyInit_mathutils_noise_types(void)
{
PyObject *submodule = PyModule_Create(&M_NoiseTypes_module_def);
PyModule_AddIntConstant(submodule, (char *)"BLENDER", TEX_BLENDER);
PyModule_AddIntConstant(submodule, (char *)"STDPERLIN", TEX_STDPERLIN);
PyModule_AddIntConstant(submodule, (char *)"NEWPERLIN", TEX_NEWPERLIN);
PyModule_AddIntConstant(submodule, (char *)"VORONOI_F1", TEX_VORONOI_F1);
PyModule_AddIntConstant(submodule, (char *)"VORONOI_F2", TEX_VORONOI_F2);
PyModule_AddIntConstant(submodule, (char *)"VORONOI_F3", TEX_VORONOI_F3);
PyModule_AddIntConstant(submodule, (char *)"VORONOI_F4", TEX_VORONOI_F4);
PyModule_AddIntConstant(submodule, (char *)"VORONOI_F2F1", TEX_VORONOI_F2F1);
PyModule_AddIntConstant(submodule, (char *)"VORONOI_CRACKLE", TEX_VORONOI_CRACKLE);
PyModule_AddIntConstant(submodule, (char *)"CELLNOISE", TEX_CELLNOISE);
return submodule;
}
static struct PyModuleDef M_NoiseMetrics_module_def = {
PyModuleDef_HEAD_INIT,
"mathutils.noise.distance_metrics", /* m_name */
NULL, /* m_doc */
0, /* m_size */
NULL, /* m_methods */
NULL, /* m_reload */
NULL, /* m_traverse */
NULL, /* m_clear */
NULL, /* m_free */
};
PyMODINIT_FUNC PyInit_mathutils_noise_metrics(void)
{
PyObject *submodule = PyModule_Create(&M_NoiseMetrics_module_def);
PyModule_AddIntConstant(submodule, (char *)"DISTANCE", TEX_DISTANCE);
PyModule_AddIntConstant(submodule, (char *)"DISTANCE_SQUARED", TEX_DISTANCE_SQUARED);
PyModule_AddIntConstant(submodule, (char *)"MANHATTAN", TEX_MANHATTAN);
PyModule_AddIntConstant(submodule, (char *)"CHEBYCHEV", TEX_CHEBYCHEV);
PyModule_AddIntConstant(submodule, (char *)"MINKOVSKY_HALF", TEX_MINKOVSKY_HALF);
PyModule_AddIntConstant(submodule, (char *)"MINKOVSKY_FOUR", TEX_MINKOVSKY_FOUR);
PyModule_AddIntConstant(submodule, (char *)"MINKOVSKY", TEX_MINKOVSKY);
return submodule;
}
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