diff options
author | Andrew Hale <TrumanBlending@gmail.com> | 2018-08-22 16:22:48 +0300 |
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committer | Andrew Hale <TrumanBlending@gmail.com> | 2018-08-22 16:34:10 +0300 |
commit | dade2b7548713d71a9d907c4aafab17e3c2d5fc6 (patch) | |
tree | 6c96746d2a17c11801d52729f8e7b5da34651b39 /source/blender | |
parent | 644fe7c90cbaae80a05f7995ee65a9a65f9fc074 (diff) |
Python: Cleanup Noise Module
Implements the changes detailed in T56281
Reviewers: campbellbarton
Reviewed By: campbellbarton
Differential Revision: https://developer.blender.org/D3590
Diffstat (limited to 'source/blender')
-rw-r--r-- | source/blender/python/mathutils/mathutils_noise.c | 489 |
1 files changed, 285 insertions, 204 deletions
diff --git a/source/blender/python/mathutils/mathutils_noise.c b/source/blender/python/mathutils/mathutils_noise.c index 839d1ffc588..9a545c126c0 100644 --- a/source/blender/python/mathutils/mathutils_noise.c +++ b/source/blender/python/mathutils/mathutils_noise.c @@ -40,18 +40,11 @@ #include "DNA_texture_types.h" +#include "../generic/py_capi_utils.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 */ @@ -198,6 +191,48 @@ static float frand(void) /* 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) { @@ -219,7 +254,7 @@ static void noise_vector(float x, float y, float z, int nb, float v[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 ampscale, float freqscale) { float amp, out, t; int i; @@ -243,7 +278,7 @@ static float turb(float x, float y, float z, int oct, int hard, int nb, /* 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 ampscale, float freqscale, float v[3]) { float amp, t[3]; int i; @@ -283,7 +318,7 @@ PyDoc_STRVAR(M_Noise_doc, PyDoc_STRVAR(M_Noise_random_doc, ".. function:: random()\n" "\n" -" Returns a random number in the range [0, 1].\n" +" Returns a random number in the range [0, 1).\n" "\n" " :return: The random number.\n" " :rtype: float\n" @@ -298,18 +333,19 @@ PyDoc_STRVAR(M_Noise_random_unit_vector_doc, "\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" +" :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) +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_ParseTuple(args, "|i:random_vector", &size)) + if (!PyArg_ParseTupleAndKeywords(args, kw, "|$i:random_unit_vector", (char **)kwlist, &size)) return NULL; if (size > 4 || size < 2) { @@ -317,52 +353,53 @@ static PyObject *M_Noise_random_unit_vector(PyObject *UNUSED(self), PyObject *ar return NULL; } - while (norm == 0.0f || norm >= 1.0f) { + while (norm == 0.0f || norm > 1.0f) { rand_vn(vec, size); norm = normalize_vn(vec, size); } return Vector_CreatePyObject(vec, size, NULL); } -/* This is dumb, most people will want a unit vector anyway, since this doesn't have uniform distribution over a sphere*/ -#if 0 + PyDoc_STRVAR(M_Noise_random_vector_doc, ".. function:: random_vector(size=3)\n" "\n" -" Returns a vector with random entries in the range [0, 1).\n" +" Returns a vector with random entries in the range (-1, 1).\n" "\n" " :arg size: The size of the vector to be produced.\n" -" :type size: Int\n" +" :type size: int\n" " :return: The random vector.\n" " :rtype: :class:`mathutils.Vector`\n" ); -static PyObject *M_Noise_random_vector(PyObject *UNUSED(self), PyObject *args) +static PyObject *M_Noise_random_vector(PyObject *UNUSED(self), PyObject *args, PyObject *kw) { - float vec[4] = {0.0f, 0.0f, 0.0f, 0.0f}; + static const char *kwlist[] = {"size", NULL}; + float *vec = NULL; int size = 3; - if (!PyArg_ParseTuple(args, "|i:random_vector", &size)) + if (!PyArg_ParseTupleAndKeywords(args, kw, "|$i:random_vector", (char **)kwlist, &size)) return NULL; - if (size > 4 || size < 2) { + if (size < 2) { PyErr_SetString(PyExc_ValueError, "Vector(): invalid size"); return NULL; } + vec = PyMem_New(float, size); + rand_vn(vec, size); - return Vector_CreatePyObject(vec, size, NULL); + return Vector_CreatePyObject_alloc(vec, size, NULL); } -#endif 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" +" 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" +" :type seed: int\n" ); static PyObject *M_Noise_seed_set(PyObject *UNUSED(self), PyObject *args) { @@ -374,139 +411,176 @@ static PyObject *M_Noise_seed_set(PyObject *UNUSED(self), PyObject *args) } PyDoc_STRVAR(M_Noise_noise_doc, -".. function:: noise(position, noise_basis=noise.types.STDPERLIN)\n" +".. 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 at.\n" +" :arg position: The position to evaluate the selected noise function.\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" +BPY_NOISE_BASIS_ENUM_DOC " :return: The noise value.\n" " :rtype: float\n" ); -static PyObject *M_Noise_noise(PyObject *UNUSED(self), PyObject *args) +static PyObject *M_Noise_noise(PyObject *UNUSED(self), PyObject *args, PyObject *kw) { + static const char *kwlist[] = {"", "noise_basis", NULL}; PyObject *value; float vec[3]; - int nb = 1; - if (!PyArg_ParseTuple(args, "O|i:noise", &value, &nb)) + const char *noise_basis_str = NULL; + int noise_basis_enum = DEFAULT_NOISE_TYPE; + + if (!PyArg_ParseTupleAndKeywords(args, kw, "O|$s:noise", (char **)kwlist, &value, &noise_basis_str)) + return NULL; + + if (!noise_basis_str) { + /* pass through */ + } + else if (PyC_FlagSet_ValueFromID(bpy_noise_types, noise_basis_str, &noise_basis_enum, "noise") == -1) { return NULL; + } if (mathutils_array_parse(vec, 3, 3, value, "noise: invalid 'position' arg") == -1) return NULL; - return PyFloat_FromDouble((2.0f * BLI_gNoise(1.0f, vec[0], vec[1], vec[2], 0, nb) - 1.0f)); + return PyFloat_FromDouble((2.0f * BLI_gNoise(1.0f, vec[0], vec[1], vec[2], 0, noise_basis_enum) - 1.0f)); } PyDoc_STRVAR(M_Noise_noise_vector_doc, -".. function:: noise_vector(position, noise_basis=noise.types.STDPERLIN)\n" +".. function:: noise_vector(position, noise_basis='PERLIN_ORIGINAL')\n" "\n" " Returns the noise vector from the noise basis at the specified position.\n" "\n" -" :arg position: The position to evaluate the selected noise function at.\n" +" :arg position: The position to evaluate the selected noise function.\n" " :type position: :class:`mathutils.Vector`\n" -" :arg noise_basis: The type of noise to be evaluated.\n" -" :type noise_basis: Value in noise.types or int\n" +BPY_NOISE_BASIS_ENUM_DOC " :return: The noise vector.\n" " :rtype: :class:`mathutils.Vector`\n" ); -static PyObject *M_Noise_noise_vector(PyObject *UNUSED(self), PyObject *args) +static PyObject *M_Noise_noise_vector(PyObject *UNUSED(self), PyObject *args, PyObject *kw) { + static const char *kwlist[] = {"", "noise_basis", NULL}; PyObject *value; float vec[3], r_vec[3]; - int nb = 1; + const char *noise_basis_str = NULL; + int noise_basis_enum = DEFAULT_NOISE_TYPE; - if (!PyArg_ParseTuple(args, "O|i:noise_vector", &value, &nb)) + if (!PyArg_ParseTupleAndKeywords(args, kw, "O|$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], nb, r_vec); + 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=noise.types.STDPERLIN, amplitude_scale=0.5, frequency_scale=2.0)\n" +".. 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 at.\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" -" :arg noise_basis: The type of noise to be evaluated.\n" -" :type noise_basis: Value in mathutils.noise.types or int\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: Value in noise.types or int\n" +" :type frequency_scale: float\n" " :return: The turbulence value.\n" " :rtype: float\n" ); -static PyObject *M_Noise_turbulence(PyObject *UNUSED(self), PyObject *args) +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]; - int oct, hd, nb = 1; + const char *noise_basis_str = NULL; + int oct, hd, noise_basis_enum = DEFAULT_NOISE_TYPE; float as = 0.5f, fs = 2.0f; - if (!PyArg_ParseTuple(args, "Oii|iff:turbulence", &value, &oct, &hd, &nb, &as, &fs)) + 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, nb, as, fs)); + 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=noise.types.STDPERLIN, amplitude_scale=0.5, frequency_scale=2.0)\n" +".. 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 at.\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" -" :arg noise_basis: The type of noise to be evaluated.\n" -" :type noise_basis: Value in mathutils.noise.types or int\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: Value in noise.types or int\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) +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]; - int oct, hd, nb = 1; + const char *noise_basis_str = NULL; + int oct, hd, noise_basis_enum = DEFAULT_NOISE_TYPE; float as = 0.5f, fs = 2.0f; - if (!PyArg_ParseTuple(args, "Oii|iff:turbulence_vector", &value, &oct, &hd, &nb, &as, &fs)) + + 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, nb, as, fs, r_vec); + 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=noise.types.STDPERLIN)\n" +".. 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 at.\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" @@ -514,33 +588,42 @@ PyDoc_STRVAR(M_Noise_fractal_doc, " :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" +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) +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 nb = 1; + int noise_basis_enum = DEFAULT_NOISE_TYPE; - if (!PyArg_ParseTuple(args, "Offf|i:fractal", &value, &H, &lac, &oct, &nb)) + if (!PyArg_ParseTupleAndKeywords(args, kw, "Offf|$s:fractal", (char **)kwlist, + &value, &H, &lac, &oct, &noise_basis_str)) return NULL; + if (!noise_basis_str) { + /* pass through */ + } + else if (PyC_FlagSet_ValueFromID(bpy_noise_types, noise_basis_str, &noise_basis_enum, "fractal") == -1) { + return NULL; + } + if (mathutils_array_parse(vec, 3, 3, value, "fractal: invalid 'position' arg") == -1) return NULL; - return PyFloat_FromDouble(mg_fBm(vec[0], vec[1], vec[2], H, lac, oct, nb)); + return PyFloat_FromDouble(mg_fBm(vec[0], vec[1], vec[2], H, lac, oct, noise_basis_enum)); } PyDoc_STRVAR(M_Noise_multi_fractal_doc, -".. function:: multi_fractal(position, H, lacunarity, octaves, noise_basis=noise.types.STDPERLIN)\n" +".. function:: multi_fractal(position, H, lacunarity, octaves, noise_basis='PERLIN_ORIGINAL')\n" "\n" " Returns multifractal noise value from the noise basis at the specified position.\n" "\n" -" :arg position: The position to evaluate the selected noise function at.\n" +" :arg position: The position to evaluate the selected noise function.\n" " :type position: :class:`mathutils.Vector`\n" " :arg H: The fractal increment factor.\n" " :type H: float\n" @@ -548,65 +631,95 @@ PyDoc_STRVAR(M_Noise_multi_fractal_doc, " :type lacunarity: float\n" " :arg octaves: The number of different noise frequencies used.\n" " :type octaves: int\n" -" :arg noise_basis: The type of noise to be evaluated.\n" -" :type noise_basis: Value in noise.types or int\n" +BPY_NOISE_BASIS_ENUM_DOC " :return: The multifractal noise value.\n" " :rtype: float\n" ); -static PyObject *M_Noise_multi_fractal(PyObject *UNUSED(self), PyObject *args) +static PyObject *M_Noise_multi_fractal(PyObject *UNUSED(self), PyObject *args, PyObject *kw) { + static const char *kwlist[] = {"", "", "", "", "noise_basis", NULL}; PyObject *value; float vec[3]; + const char *noise_basis_str = NULL; float H, lac, oct; - int nb = 1; + int noise_basis_enum = DEFAULT_NOISE_TYPE; + + if (!PyArg_ParseTupleAndKeywords(args, kw, "Offf|$s:multi_fractal", (char **)kwlist, + &value, &H, &lac, &oct, &noise_basis_str)) + return NULL; - if (!PyArg_ParseTuple(args, "Offf|i:multi_fractal", &value, &H, &lac, &oct, &nb)) + 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, nb)); + 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=noise.types.STDPERLIN, noise_type2=noise.types.STDPERLIN)\n" +".. 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 at.\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: 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" +" :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) +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 nt1 = 1, nt2 = 1; + int noise_type1_enum = DEFAULT_NOISE_TYPE, noise_type2_enum = DEFAULT_NOISE_TYPE; - if (!PyArg_ParseTuple(args, "Of|ii:variable_lacunarity", &value, &d, &nt1, &nt2)) + 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, nt1, nt2)); + 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=noise.types.STDPERLIN)\n" +".. 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 at.\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" @@ -616,33 +729,42 @@ PyDoc_STRVAR(M_Noise_hetero_terrain_doc, " :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" +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) +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 nb = 1; + 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 (!PyArg_ParseTuple(args, "Offff|i:hetero_terrain", &value, &H, &lac, &oct, &ofs, &nb)) + 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, nb)); + 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=noise.types.STDPERLIN)\n" +".. 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 at.\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" @@ -654,33 +776,42 @@ PyDoc_STRVAR(M_Noise_hybrid_multi_fractal_doc, " :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" +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) +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 nb = 1; + int noise_basis_enum = DEFAULT_NOISE_TYPE; - if (!PyArg_ParseTuple(args, "Offfff|i:hybrid_multi_fractal", &value, &H, &lac, &oct, &ofs, &gn, &nb)) + 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, nb)); + 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=noise.types.STDPERLIN)\n" +".. 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 at.\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" @@ -692,62 +823,79 @@ PyDoc_STRVAR(M_Noise_ridged_multi_fractal_doc, " :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" +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) +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 nb = 1; + int noise_basis_enum = DEFAULT_NOISE_TYPE; - if (!PyArg_ParseTuple(args, "Offfff|i:ridged_multi_fractal", &value, &H, &lac, &oct, &ofs, &gn, &nb)) + 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, nb)); + 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=noise.distance_metrics.DISTANCE, exponent=2.5)\n" +".. 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 at.\n" +" :arg position: The position to evaluate the selected noise function.\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" +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) +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 dtype = 0; + int metric_enum = DEFAULT_METRIC_TYPE; float me = 2.5f; /* default minkowski exponent */ int i; - if (!PyArg_ParseTuple(args, "O|if:voronoi", &value, &dtype, &me)) + 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, dtype); + 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); @@ -765,7 +913,7 @@ PyDoc_STRVAR(M_Noise_cell_doc, "\n" " Returns cell noise value at the specified position.\n" "\n" -" :arg position: The position to evaluate the selected noise function at.\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" @@ -789,7 +937,7 @@ PyDoc_STRVAR(M_Noise_cell_vector_doc, "\n" " Returns cell noise vector at the specified position.\n" "\n" -" :arg position: The position to evaluate the selected noise function at.\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" @@ -812,19 +960,19 @@ static PyObject *M_Noise_cell_vector(PyObject *UNUSED(self), PyObject *args) 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}, + {"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} @@ -846,76 +994,9 @@ static struct PyModuleDef M_Noise_module_def = { 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, "BLENDER", TEX_BLENDER); - PyModule_AddIntConstant(submodule, "STDPERLIN", TEX_STDPERLIN); - PyModule_AddIntConstant(submodule, "NEWPERLIN", TEX_NEWPERLIN); - PyModule_AddIntConstant(submodule, "VORONOI_F1", TEX_VORONOI_F1); - PyModule_AddIntConstant(submodule, "VORONOI_F2", TEX_VORONOI_F2); - PyModule_AddIntConstant(submodule, "VORONOI_F3", TEX_VORONOI_F3); - PyModule_AddIntConstant(submodule, "VORONOI_F4", TEX_VORONOI_F4); - PyModule_AddIntConstant(submodule, "VORONOI_F2F1", TEX_VORONOI_F2F1); - PyModule_AddIntConstant(submodule, "VORONOI_CRACKLE", TEX_VORONOI_CRACKLE); - PyModule_AddIntConstant(submodule, "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, "DISTANCE", TEX_DISTANCE); - PyModule_AddIntConstant(submodule, "DISTANCE_SQUARED", TEX_DISTANCE_SQUARED); - PyModule_AddIntConstant(submodule, "MANHATTAN", TEX_MANHATTAN); - PyModule_AddIntConstant(submodule, "CHEBYCHEV", TEX_CHEBYCHEV); - PyModule_AddIntConstant(submodule, "MINKOVSKY_HALF", TEX_MINKOVSKY_HALF); - PyModule_AddIntConstant(submodule, "MINKOVSKY_FOUR", TEX_MINKOVSKY_FOUR); - PyModule_AddIntConstant(submodule, "MINKOVSKY", TEX_MINKOVSKY); - return submodule; } |