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

/*
 * ***** 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.
 *
 * 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 "BLI_math.h"
#include "BLI_noise.h"
#include "BLI_utildefines.h"

#include "DNA_texture_types.h"

#include "../generic/py_capi_utils.h"

#include "mathutils.h"
#include "mathutils_noise.h"

/*-----------------------------------------*/
/* '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;
static float state_offset_vector[3 * 3];

/* 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;

	/* update vector offset */
	{
		const unsigned long *state_offset = &state[N - ARRAY_SIZE(state_offset_vector)];
		const float range = 32;  /* range in both pos/neg direction */
		for (j = 0; j < ARRAY_SIZE(state_offset_vector); j++, state_offset++) {
			/* overflow is fine here */
			state_offset_vector[j] = (float)(int)(*state_offset) * (1.0f / (INT_MAX / range));
		}
	}
}

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 */
/*------------------------------------------------------------*/

#define BPY_NOISE_BASIS_ENUM_DOC \
"   :arg noise_basis: Enumerator in ['BLENDER', 'PERLIN_ORIGINAL', 'PERLIN_NEW', 'VORONOI_F1', 'VORONOI_F2', " \
									"'VORONOI_F3', 'VORONOI_F4', 'VORONOI_F2F1', 'VORONOI_CRACKLE', " \
									"'CELLNOISE'].\n" \
"   :type noise_basis: string\n" \

#define BPY_NOISE_METRIC_ENUM_DOC \
"   :arg distance_metric: Enumerator in ['DISTANCE', 'DISTANCE_SQUARED', 'MANHATTAN', 'CHEBYCHEV', " \
										"'MINKOVSKY', 'MINKOVSKY_HALF', 'MINKOVSKY_FOUR'].\n" \
"   :type distance_metric: string\n" \

/* Noise basis enum */
#define DEFAULT_NOISE_TYPE TEX_STDPERLIN

static PyC_FlagSet bpy_noise_types[] = {
	{TEX_BLENDER,         "BLENDER"},
	{TEX_STDPERLIN,       "PERLIN_ORIGINAL"},
	{TEX_NEWPERLIN,       "PERLIN_NEW"},
	{TEX_VORONOI_F1,      "VORONOI_F1"},
	{TEX_VORONOI_F2,      "VORONOI_F2"},
	{TEX_VORONOI_F3,      "VORONOI_F3"},
	{TEX_VORONOI_F4,      "VORONOI_F4"},
	{TEX_VORONOI_F2F1,    "VORONOI_F2F1"},
	{TEX_VORONOI_CRACKLE, "VORONOI_CRACKLE"},
	{TEX_CELLNOISE,       "CELLNOISE"},
	{0, NULL}
};

/* Metric basis enum */
#define DEFAULT_METRIC_TYPE TEX_DISTANCE

static PyC_FlagSet bpy_noise_metrics[] = {
	{TEX_DISTANCE,         "DISTANCE"},
	{TEX_DISTANCE_SQUARED, "DISTANCE_SQUARED"},
	{TEX_MANHATTAN,        "MANHATTAN"},
	{TEX_CHEBYCHEV,        "CHEBYCHEV"},
	{TEX_MINKOVSKY,        "MINKOVSKY"},
	{TEX_MINKOVSKY_HALF,   "MINKOVSKY_HALF"},
	{TEX_MINKOVSKY_FOUR,   "MINKOVSKY_FOUR"},
	{0, NULL}
};

/* 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 */
	const float *ofs = state_offset_vector;
	for (int j = 0; j < 3; j++) {
		v[j] = (2.0f * BLI_gNoise(1.0f, x + ofs[0], y + ofs[1], z + ofs[2], 0, nb) - 1.0f);
		ofs += 3;
	}
}

/* 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, in the range [2, 4].\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, PyObject *kw)
{
	static const char *kwlist[] = {"size", NULL};
	float vec[4] = {0.0f, 0.0f, 0.0f, 0.0f};
	float norm = 2.0f;
	int size = 3;

	if (!PyArg_ParseTupleAndKeywords(
	            args, kw, "|$i:random_unit_vector", (char **)kwlist,
	            &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, NULL);
}

PyDoc_STRVAR(M_Noise_random_vector_doc,
".. function:: random_vector(size=3)\n"
"\n"
"   Returns a vector with random entries in the range (-1, 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, PyObject *kw)
{
	static const char *kwlist[] = {"size", NULL};
	float *vec = NULL;
	int size = 3;

	if (!PyArg_ParseTupleAndKeywords(
	            args, kw, "|$i:random_vector", (char **)kwlist,
	            &size))
	{
		return NULL;
	}

	if (size < 2) {
		PyErr_SetString(PyExc_ValueError, "Vector(): invalid size");
		return NULL;
	}

	vec = PyMem_New(float, size);

	rand_vn(vec, size);

	return Vector_CreatePyObject_alloc(vec, size, NULL);
}

PyDoc_STRVAR(M_Noise_seed_set_doc,
".. function:: seed_set(seed)\n"
"\n"
"   Sets the random seed used for random_unit_vector, and random.\n"
"\n"
"   :arg seed: Seed used for the random generator.\n"
"      When seed is zero, the current time will be used instead.\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='PERLIN_ORIGINAL')\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.\n"
"   :type position: :class:`mathutils.Vector`\n"
BPY_NOISE_BASIS_ENUM_DOC
"   :return: The noise value.\n"
"   :rtype: float\n"
);
static PyObject *M_Noise_noise(PyObject *UNUSED(self), PyObject *args, PyObject *kw)
{
	static const char *kwlist[] = {"", "noise_basis", NULL};
	PyObject *value;
	float vec[3];
	const char *noise_basis_str = NULL;
	int noise_basis_enum = DEFAULT_NOISE_TYPE;

	if (!PyArg_ParseTupleAndKeywords(
	            args, kw, "O|$s:noise", (char **)kwlist,
	            &value, &noise_basis_str))
	{
		return NULL;
	}

	if (!noise_basis_str) {
		/* pass through */
	}
	else if (PyC_FlagSet_ValueFromID(
	                 bpy_noise_types, noise_basis_str, &noise_basis_enum, "noise") == -1)
	{
		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, noise_basis_enum) - 1.0f));
}

PyDoc_STRVAR(M_Noise_noise_vector_doc,
".. function:: noise_vector(position, noise_basis='PERLIN_ORIGINAL')\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.\n"
"   :type position: :class:`mathutils.Vector`\n"
BPY_NOISE_BASIS_ENUM_DOC
"   :return: The noise vector.\n"
"   :rtype: :class:`mathutils.Vector`\n"
);
static PyObject *M_Noise_noise_vector(PyObject *UNUSED(self), PyObject *args, PyObject *kw)
{
	static const char *kwlist[] = {"", "noise_basis", NULL};
	PyObject *value;
	float vec[3], r_vec[3];
	const char *noise_basis_str = NULL;
	int noise_basis_enum = DEFAULT_NOISE_TYPE;

	if (!PyArg_ParseTupleAndKeywords(
	            args, kw, "O|$s:noise_vector", (char **)kwlist,
	            &value, &noise_basis_str))
	{
		return NULL;
	}

	if (!noise_basis_str) {
		/* pass through */
	}
	else if (PyC_FlagSet_ValueFromID(
	                 bpy_noise_types, noise_basis_str, &noise_basis_enum, "noise_vector") == -1)
	{
		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], noise_basis_enum, r_vec);

	return Vector_CreatePyObject(r_vec, 3, NULL);
}

PyDoc_STRVAR(M_Noise_turbulence_doc,
".. function:: turbulence(position, octaves, hard, noise_basis='PERLIN_ORIGINAL', 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.\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"
BPY_NOISE_BASIS_ENUM_DOC
"   :arg amplitude_scale: The amplitude scaling factor.\n"
"   :type amplitude_scale: float\n"
"   :arg frequency_scale: The frequency scaling factor\n"
"   :type frequency_scale: float\n"
"   :return: The turbulence value.\n"
"   :rtype: float\n"
);
static PyObject *M_Noise_turbulence(PyObject *UNUSED(self), PyObject *args, PyObject *kw)
{
	static const char *kwlist[] = {"", "", "", "noise_basis", "amplitude_scale", "frequency_scale", NULL};
	PyObject *value;
	float vec[3];
	const char *noise_basis_str = NULL;
	int oct, hd, noise_basis_enum = DEFAULT_NOISE_TYPE;
	float as = 0.5f, fs = 2.0f;

	if (!PyArg_ParseTupleAndKeywords(
	            args, kw, "Oii|$sff:turbulence", (char **)kwlist,
	            &value, &oct, &hd, &noise_basis_str, &as, &fs))
	{
		return NULL;
	}

	if (!noise_basis_str) {
		/* pass through */
	}
	else if (PyC_FlagSet_ValueFromID(
	                 bpy_noise_types, noise_basis_str, &noise_basis_enum, "turbulence") == -1)
	{
		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, noise_basis_enum, as, fs));
}

PyDoc_STRVAR(M_Noise_turbulence_vector_doc,
".. function:: turbulence_vector(position, octaves, hard, noise_basis='PERLIN_ORIGINAL', 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.\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"
BPY_NOISE_BASIS_ENUM_DOC
"   :arg amplitude_scale: The amplitude scaling factor.\n"
"   :type amplitude_scale: float\n"
"   :arg frequency_scale: The frequency scaling factor\n"
"   :type frequency_scale: float\n"
"   :return: The turbulence vector.\n"
"   :rtype: :class:`mathutils.Vector`\n"
);
static PyObject *M_Noise_turbulence_vector(PyObject *UNUSED(self), PyObject *args, PyObject *kw)
{
	static const char *kwlist[] = {"", "", "", "noise_basis", "amplitude_scale", "frequency_scale", NULL};
	PyObject *value;
	float vec[3], r_vec[3];
	const char *noise_basis_str = NULL;
	int oct, hd, noise_basis_enum = DEFAULT_NOISE_TYPE;
	float as = 0.5f, fs = 2.0f;

	if (!PyArg_ParseTupleAndKeywords(
	            args, kw, "Oii|$sff:turbulence_vector", (char **)kwlist,
	            &value, &oct, &hd, &noise_basis_str, &as, &fs))
	{
		return NULL;
	}

	if (!noise_basis_str) {
		/* pass through */
	}
	else if (PyC_FlagSet_ValueFromID(
	                 bpy_noise_types, noise_basis_str, &noise_basis_enum, "turbulence_vector") == -1)
	{
		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, noise_basis_enum, as, fs, r_vec);

	return Vector_CreatePyObject(r_vec, 3, NULL);
}

/* F. Kenton Musgrave's fractal functions */
PyDoc_STRVAR(M_Noise_fractal_doc,
".. function:: fractal(position, H, lacunarity, octaves, noise_basis='PERLIN_ORIGINAL')\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.\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"
BPY_NOISE_BASIS_ENUM_DOC
"   :return: The fractal Brownian motion noise value.\n"
"   :rtype: float\n"
);
static PyObject *M_Noise_fractal(PyObject *UNUSED(self), PyObject *args, PyObject *kw)
{
	static const char *kwlist[] = {"", "", "", "", "noise_basis", NULL};
	PyObject *value;
	float vec[3];
	const char *noise_basis_str = NULL;
	float H, lac, oct;
	int noise_basis_enum = DEFAULT_NOISE_TYPE;

	if (!PyArg_ParseTupleAndKeywords(
	            args, kw, "Offf|$s:fractal", (char **)kwlist,
	            &value, &H, &lac, &oct, &noise_basis_str))
	{
		return NULL;
	}

	if (!noise_basis_str) {
		/* pass through */
	}
	else if (PyC_FlagSet_ValueFromID(
	                 bpy_noise_types, noise_basis_str, &noise_basis_enum, "fractal") == -1)
	{
		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, noise_basis_enum));
}

PyDoc_STRVAR(M_Noise_multi_fractal_doc,
".. function:: multi_fractal(position, H, lacunarity, octaves, noise_basis='PERLIN_ORIGINAL')\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.\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"
BPY_NOISE_BASIS_ENUM_DOC
"   :return: The multifractal noise value.\n"
"   :rtype: float\n"
);
static PyObject *M_Noise_multi_fractal(PyObject *UNUSED(self), PyObject *args, PyObject *kw)
{
	static const char *kwlist[] = {"", "", "", "", "noise_basis", NULL};
	PyObject *value;
	float vec[3];
	const char *noise_basis_str = NULL;
	float H, lac, oct;
	int noise_basis_enum = DEFAULT_NOISE_TYPE;

	if (!PyArg_ParseTupleAndKeywords(
	            args, kw, "Offf|$s:multi_fractal", (char **)kwlist,
	            &value, &H, &lac, &oct, &noise_basis_str))
	{
		return NULL;
	}

	if (!noise_basis_str) {
		/* pass through */
	}
	else if (PyC_FlagSet_ValueFromID(
	                 bpy_noise_types, noise_basis_str, &noise_basis_enum, "multi_fractal") == -1)
	{
		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, noise_basis_enum));
}

PyDoc_STRVAR(M_Noise_variable_lacunarity_doc,
".. function:: variable_lacunarity(position, distortion, noise_type1='PERLIN_ORIGINAL', noise_type2='PERLIN_ORIGINAL')\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.\n"
"   :type position: :class:`mathutils.Vector`\n"
"   :arg distortion: The amount of distortion.\n"
"   :type distortion: float\n"
"   :arg noise_type1: Enumerator in ['BLENDER', 'PERLIN_ORIGINAL', 'PERLIN_NEW', 'VORONOI_F1', 'VORONOI_F2', " \
									"'VORONOI_F3', 'VORONOI_F4', 'VORONOI_F2F1', 'VORONOI_CRACKLE', " \
									"'CELLNOISE'].\n"
"   :type noise_type1: string\n"
"   :arg noise_type2: Enumerator in ['BLENDER', 'PERLIN_ORIGINAL', 'PERLIN_NEW', 'VORONOI_F1', 'VORONOI_F2', " \
									"'VORONOI_F3', 'VORONOI_F4', 'VORONOI_F2F1', 'VORONOI_CRACKLE', " \
									"'CELLNOISE'].\n"
"   :type noise_type2: string\n"
"   :return: The variable lacunarity noise value.\n"
"   :rtype: float\n"
);
static PyObject *M_Noise_variable_lacunarity(PyObject *UNUSED(self), PyObject *args, PyObject *kw)
{
	static const char *kwlist[] = {"", "", "noise_type1", "noise_type2", NULL};
	PyObject *value;
	float vec[3];
	const char *noise_type1_str = NULL, *noise_type2_str = NULL;
	float d;
	int noise_type1_enum = DEFAULT_NOISE_TYPE, noise_type2_enum = DEFAULT_NOISE_TYPE;

	if (!PyArg_ParseTupleAndKeywords(
	            args, kw, "Of|$ss:variable_lacunarity", (char **)kwlist,
	            &value, &d, &noise_type1_str, &noise_type2_str))
	{
		return NULL;
	}

	if (!noise_type1_str) {
		/* pass through */
	}
	else if (PyC_FlagSet_ValueFromID(
	                 bpy_noise_types, noise_type1_str, &noise_type1_enum, "variable_lacunarity") == -1)
	{
		return NULL;
	}

	if (!noise_type2_str) {
		/* pass through */
	}
	else if (PyC_FlagSet_ValueFromID(
	                 bpy_noise_types, noise_type2_str, &noise_type2_enum, "variable_lacunarity") == -1)
	{
		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, noise_type1_enum, noise_type2_enum));
}

PyDoc_STRVAR(M_Noise_hetero_terrain_doc,
".. function:: hetero_terrain(position, H, lacunarity, octaves, offset, noise_basis='PERLIN_ORIGINAL')\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.\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"
BPY_NOISE_BASIS_ENUM_DOC
"   :return: The heterogeneous terrain value.\n"
"   :rtype: float\n"
);
static PyObject *M_Noise_hetero_terrain(PyObject *UNUSED(self), PyObject *args, PyObject *kw)
{
	static const char *kwlist[] = {"", "", "", "", "", "noise_basis", NULL};
	PyObject *value;
	float vec[3];
	const char *noise_basis_str = NULL;
	float H, lac, oct, ofs;
	int noise_basis_enum = DEFAULT_NOISE_TYPE;

	if (!PyArg_ParseTupleAndKeywords(
	            args, kw, "Offff|$s:hetero_terrain", (char **)kwlist,
	            &value, &H, &lac, &oct, &ofs, &noise_basis_str))
	{
		return NULL;
	}

	if (!noise_basis_str) {
		/* pass through */
	}
	else if (PyC_FlagSet_ValueFromID(
	                 bpy_noise_types, noise_basis_str, &noise_basis_enum, "hetero_terrain") == -1)
	{
		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, noise_basis_enum));
}

PyDoc_STRVAR(M_Noise_hybrid_multi_fractal_doc,
".. function:: hybrid_multi_fractal(position, H, lacunarity, octaves, offset, gain, noise_basis='PERLIN_ORIGINAL')\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.\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"
BPY_NOISE_BASIS_ENUM_DOC
"   :return: The hybrid multifractal value.\n"
"   :rtype: float\n"
);
static PyObject *M_Noise_hybrid_multi_fractal(PyObject *UNUSED(self), PyObject *args, PyObject *kw)
{
	static const char *kwlist[] = {"", "", "", "", "", "", "noise_basis", NULL};
	PyObject *value;
	float vec[3];
	const char *noise_basis_str = NULL;
	float H, lac, oct, ofs, gn;
	int noise_basis_enum = DEFAULT_NOISE_TYPE;

	if (!PyArg_ParseTupleAndKeywords(
	            args, kw, "Offfff|$s:hybrid_multi_fractal", (char **)kwlist,
	            &value, &H, &lac, &oct, &ofs, &gn, &noise_basis_str))
	{
		return NULL;
	}

	if (!noise_basis_str) {
		/* pass through */
	}
	else if (PyC_FlagSet_ValueFromID(
	                 bpy_noise_types, noise_basis_str, &noise_basis_enum, "hybrid_multi_fractal") == -1)
	{
		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, noise_basis_enum));
}

PyDoc_STRVAR(M_Noise_ridged_multi_fractal_doc,
".. function:: ridged_multi_fractal(position, H, lacunarity, octaves, offset, gain, noise_basis='PERLIN_ORIGINAL')\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.\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"
BPY_NOISE_BASIS_ENUM_DOC
"   :return: The ridged multifractal value.\n"
"   :rtype: float\n"
);
static PyObject *M_Noise_ridged_multi_fractal(PyObject *UNUSED(self), PyObject *args, PyObject *kw)
{
	static const char *kwlist[] = {"", "", "", "", "", "", "noise_basis", NULL};
	PyObject *value;
	float vec[3];
	const char *noise_basis_str = NULL;
	float H, lac, oct, ofs, gn;
	int noise_basis_enum = DEFAULT_NOISE_TYPE;

	if (!PyArg_ParseTupleAndKeywords(
	            args, kw, "Offfff|$s:ridged_multi_fractal", (char **)kwlist,
	            &value, &H, &lac, &oct, &ofs, &gn, &noise_basis_str))
	{
		return NULL;
	}

	if (!noise_basis_str) {
		/* pass through */
	}
	else if (PyC_FlagSet_ValueFromID(
	                 bpy_noise_types, noise_basis_str, &noise_basis_enum, "ridged_multi_fractal") == -1)
	{
		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, noise_basis_enum));
}

PyDoc_STRVAR(M_Noise_voronoi_doc,
".. function:: voronoi(position, distance_metric='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.\n"
"   :type position: :class:`mathutils.Vector`\n"
BPY_NOISE_METRIC_ENUM_DOC
"   :arg exponent: The exponent for Minkowski 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 *kw)
{
	static const char *kwlist[] = {"", "distance_metric", "exponent", NULL};
	PyObject *value;
	PyObject *list;
	PyObject *ret;
	float vec[3];
	const char *metric_str = NULL;
	float da[4], pa[12];
	int metric_enum = DEFAULT_METRIC_TYPE;
	float me = 2.5f;  /* default minkowski exponent */

	int i;

	if (!PyArg_ParseTupleAndKeywords(
	            args, kw, "O|$sf:voronoi", (char **)kwlist,
	            &value, &metric_str, &me))
	{
		return NULL;
	}

	if (!metric_str) {
		/* pass through */
	}
	else if (PyC_FlagSet_ValueFromID(
	                 bpy_noise_metrics, metric_str, &metric_enum, "voronoi") == -1)
	{
		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, metric_enum);

	for (i = 0; i < 4; i++) {
		PyObject *v = Vector_CreatePyObject(pa + 3 * i, 3, NULL);
		PyList_SET_ITEM(list, i, v);
		Py_DECREF(v);
	}

	ret = Py_BuildValue("[[ffff]O]", da[0], da[1], da[2], da[3], list);
	Py_DECREF(list);
	return ret;
}

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.\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.\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(r_vec, 3, 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 | METH_KEYWORDS, M_Noise_random_unit_vector_doc},
	{"random_vector", (PyCFunction) M_Noise_random_vector, METH_VARARGS | METH_KEYWORDS, M_Noise_random_vector_doc},
	{"noise", (PyCFunction) M_Noise_noise, METH_VARARGS | METH_KEYWORDS, M_Noise_noise_doc},
	{"noise_vector", (PyCFunction) M_Noise_noise_vector, METH_VARARGS | METH_KEYWORDS, M_Noise_noise_vector_doc},
	{"turbulence", (PyCFunction) M_Noise_turbulence, METH_VARARGS | METH_KEYWORDS, M_Noise_turbulence_doc},
	{"turbulence_vector", (PyCFunction) M_Noise_turbulence_vector, METH_VARARGS | METH_KEYWORDS, M_Noise_turbulence_vector_doc},
	{"fractal", (PyCFunction) M_Noise_fractal, METH_VARARGS | METH_KEYWORDS, M_Noise_fractal_doc},
	{"multi_fractal", (PyCFunction) M_Noise_multi_fractal, METH_VARARGS | METH_KEYWORDS, M_Noise_multi_fractal_doc},
	{"variable_lacunarity", (PyCFunction) M_Noise_variable_lacunarity, METH_VARARGS | METH_KEYWORDS, M_Noise_variable_lacunarity_doc},
	{"hetero_terrain", (PyCFunction) M_Noise_hetero_terrain, METH_VARARGS | METH_KEYWORDS, M_Noise_hetero_terrain_doc},
	{"hybrid_multi_fractal", (PyCFunction) M_Noise_hybrid_multi_fractal, METH_VARARGS | METH_KEYWORDS, M_Noise_hybrid_multi_fractal_doc},
	{"ridged_multi_fractal", (PyCFunction) M_Noise_ridged_multi_fractal, METH_VARARGS | METH_KEYWORDS, M_Noise_ridged_multi_fractal_doc},
	{"voronoi", (PyCFunction) M_Noise_voronoi, METH_VARARGS | METH_KEYWORDS, 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);

	/* use current time as seed for random number generator by default */
	setRndSeed(0);

	return submodule;
}