From b42feff554bbfa53bfd160da6fefef459fc07f02 Mon Sep 17 00:00:00 2001 From: Campbell Barton Date: Tue, 31 Jan 2012 04:58:06 +0000 Subject: previous move lost history on this file, restoring next commit. --- source/blender/python/mathutils/mathutils_noise.c | 911 ---------------------- 1 file changed, 911 deletions(-) delete mode 100644 source/blender/python/mathutils/mathutils_noise.c (limited to 'source') diff --git a/source/blender/python/mathutils/mathutils_noise.c b/source/blender/python/mathutils/mathutils_noise.c deleted file mode 100644 index fceff965fcb..00000000000 --- a/source/blender/python/mathutils/mathutils_noise.c +++ /dev/null @@ -1,911 +0,0 @@ -/* - * ***** 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 - -#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`s\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; -} -- cgit v1.2.3