{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Granular phase function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "\n",
    "This tutorial showcases a more practical application and is a great introduction to write efficient integrator-like rendering code with the Mitsuba library.\n",
    "\n",
    "In volumetric rendering, the *phase function* describes the angular distribution of light scattering when interacting with a particle in the medium. Have you ever wondered how a participating medium would look like if every particle had the shape of a bunny, or was made of frosty glass? ❄️🐰 For this you would need to know the phase function of such a medium, and this is what we are going to compute in this tutorial.\n",
    "\n",
    "The following code is inspired from this [paper][1], where the authors manage to efficiently render granular materials, like sand, snow or sugar.\n",
    "\n",
    "At a very high-level, here are the key stages of the algorithm we are going to use in this tutorial:\n",
    "\n",
    "- Represent a single particle with a shape and a BSDF\n",
    "- Generate rays coming from random directions around the particle object\n",
    "- Compute bounces of the light path inside the particle until they escape\n",
    "- Once escaped, record the out-going direction in the local frame of the original direction into a histogram\n",
    "\n",
    "<div class=\"admonition important alert alert-block alert-success\">\n",
    "\n",
    "🚀 **You will learn how to:**\n",
    "\n",
    "<ul>\n",
    "  <li>Work with cartesian coordinate systems and coordinate frame objects</li>\n",
    "  <li>Use a sampler to generate random numbers</li>\n",
    "  <li>Write a custom integrator-like script</li>\n",
    "  <li>Use scatter reduction drjit routine to accumulate values into a histogram</li>\n",
    "</ul>\n",
    "\n",
    "</div>\n",
    "\n",
    "[1]: https://cs.dartmouth.edu/wjarosz/publications/meng15granular.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "As always, we start by importing the libraries and setting the variant."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import drjit as dr\n",
    "import mitsuba as mi\n",
    "\n",
    "mi.set_variant('llvm_ad_rgb')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initializating the scene \n",
    "\n",
    "To keep this tutorial simple, we are only going to compute the phase function of a dielectric sphere. Although only minor changes would be necessary to allow the use of other shapes and materials in this script (e.g., a frosty glass bunny ❄️🐰).\n",
    "\n",
    "On top of various mesh loaders (e.g., `ply`, `obj`, ...), Mitsuba supports several analytical shapes (e.g., `Sphere`, `Rectangle`, ...), that can be very handy when writing prototype applications.\n",
    "\n",
    "As done previously in other tutorials, we are going to use the [<code>load_dict()</code>][1] routine to instanciate a scene containing a sphere and a dielectric BSDF.\n",
    "\n",
    "[1]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.load_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "scene = mi.load_dict({\n",
    "    'type' : 'scene',\n",
    "    'grain' : {\n",
    "        'type' : 'sphere',\n",
    "    }\n",
    "})\n",
    "\n",
    "bsdf = mi.load_dict({\n",
    "    'type' : 'dielectric',\n",
    "    'int_ior' : 'water', # We can also use float values\n",
    "    'ext_ior' : 'air',\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performing Monte-Carlo integration requires the ability to generate random numbers. Mitsuba comes with a set of [<code>Sampler</code>][1] classes that can be used to do exactly that. For simplicity, in this application, we are going to use the most basic sampler, [<code>independent</code>][2], which we can instanciate with the [<code>load_dict()</code>][3] routine.\n",
    "\n",
    "In order to write vectorized code, we need to choose a *wavefront* size, corresponding to the number of light paths we are going to compute simultaneously. The `sampler` instance needs to be aware of the wavefront size so to produce random arrays of the right size. This can be done using the [<code>Sampler.seed()</code>][4] method at the same time as choosing a seed for our random number generator (here `0`).\n",
    "\n",
    "[1]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.Sampler\n",
    "[2]: https://mitsuba.readthedocs.io/en/latest/src/generated/plugins_samplers.html#independent-sampler-independent\n",
    "[3]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.load_dict\n",
    "[4]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.Sampler.seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampler = mi.load_dict({'type' : 'independent'})\n",
    "sampler.seed(0, wavefront_size=int(1e7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating primary rays\n",
    "\n",
    "In the following cell, we are going to generate our primary rays, coming from all directions towards the center of the scene. For this, we use the sampler instance to generate random ray directions and offsets for the ray origins. The `Sampler` class only implements methods for producing 1D/2D uniformly distributed points in the unit interval/square. Fortunately, Mitsuba provides a whole set of warping techniques that map from the unit square to other domains such as spheres, hemispheres, etc. Here we leverage the `warp.square_to_uniform_sphere()` routine to generate a set of random ray directions from the 2D unit points given using [<code>Sampler.next_2d()</code>][1].\n",
    "\n",
    "We then construct a coordinate frame [<code>Frame3f</code>][2] around the sampled ray directions which we will use to convert between local and world coordinate spaces.\n",
    "\n",
    "We sample ray origin positions by first computing 2D offsets in $[-1, 1]^2$ using the sampler instance. We can then easily compute 3D positions in local coordinates, where we offset the z-component by `-1` as the z-axis represents the forward ray direction. Using the coordinate frame object, we convert the ray origin positions from local to world coordinate space.\n",
    "\n",
    "The [<code>Scene</code>][3] class exposes a [<code>bbox()</code>][4] method that can be used to retrieve the bounding box of the entire scene. We then convert it to a bounding sphere and use it to move the ray origin accordingly so to make sure our set of rays covers the entire scene domain.\n",
    "\n",
    "Finally, we create primary rays of type [<code>Ray3f</code>][5], setting the desired wavelength for our simulation.\n",
    "\n",
    "[1]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.Sampler.next_2d\n",
    "[2]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.Frame3f\n",
    "[3]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.Scene\n",
    "[4]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.Scene.bbox\n",
    "[5]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.Ray3f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample ray directions\n",
    "d = mi.warp.square_to_uniform_sphere(sampler.next_2d())\n",
    "\n",
    "# Construct coordinate frame object\n",
    "frame = mi.Frame3f(d)\n",
    "\n",
    "# Sample ray origins\n",
    "xy_local = 2.0 * sampler.next_2d() - 1.0\n",
    "local_o = mi.Vector3f(xy_local.x, xy_local.y, -1.0)\n",
    "world_o = frame.to_world(local_o)\n",
    "\n",
    "# Move ray origin according to scene bounding sphere\n",
    "bsphere = scene.bbox().bounding_sphere()\n",
    "o = world_o * bsphere.radius + bsphere.center\n",
    "\n",
    "# Construct rays\n",
    "rays = mi.Ray3f(o, d)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Intra-grain transport\n",
    "\n",
    "Let's now use those rays to construct light paths bouncing many times inside of the particle object.\n",
    "\n",
    "For this, we perform a first ray intersection query with the scene using [<code>Scene.ray_intersect()</code>][1] which will return a [<code>SurfaceInteraction3f</code>][2] object, containing the surface interaction information. We use [<code>is_valid()</code>][2] to find the rays that actually interact with the object.\n",
    "\n",
    "After initializing the `throughput` and `active` variables, we perform a symbolic loop to compute the different bounces of the light paths. At every iteration of the loop. we compute the following:\n",
    "\n",
    "- Sample new directions from the BSDF using the sampler instance and the current surface interaction\n",
    "- Update the throughput and rays for the next bounce using the [<code>SurfaceInteraction3f.spawn_ray()</code>][3] method\n",
    "- Trace the new set of rays to find the next intersection with the object\n",
    "- Evaluate the kernels to make sure the JIT compiler doesn't accumulate instructions of all loop iterations\n",
    "\n",
    "The use of [<code>mi.Loop</code>][4] prevents the JIT compiler to unroll the loop and produce a long kernel, making the kernel length independent of the number of bounces.\n",
    "\n",
    "[1]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.Scene.ray_intersect\n",
    "[2]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.SurfaceInteraction3f\n",
    "[3]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.SurfaceInteraction3f.spawn_ray\n",
    "[4]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find first ray intersection with the object\n",
    "si = scene.ray_intersect(rays)\n",
    "valid = si.is_valid()\n",
    "\n",
    "# Maximum number of bounces\n",
    "max_bounces = 10\n",
    "\n",
    "# Loop state variables\n",
    "throughput = mi.Spectrum(1.0)\n",
    "active = mi.Bool(valid)\n",
    "i = mi.UInt32(0)\n",
    "\n",
    "loop = mi.Loop(name=\"\", state=lambda:(i, sampler, rays, si, throughput, active))\n",
    "\n",
    "while loop(active & (i < max_bounces)):\n",
    "    # Sample new direction\n",
    "    ctx = mi.BSDFContext()\n",
    "    bs, bsdf_val = bsdf.sample(ctx, si, sampler.next_1d(), sampler.next_2d(), active)\n",
    "\n",
    "    # Update throughput and rays for next bounce\n",
    "    throughput[active] *= bsdf_val\n",
    "    rays[active] = si.spawn_ray(si.to_world(bs.wo))\n",
    "\n",
    "    # Find next intersection\n",
    "    si = scene.ray_intersect(rays, active)\n",
    "    active &= si.is_valid()\n",
    "    \n",
    "    # Increase loop iteration counter\n",
    "    i += 1\n",
    "    \n",
    "# We don't care about a specific color for this tutorial\n",
    "throughput = mi.luminance(throughput)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calculating the histogram\n",
    "\n",
    "We are only interested in the light paths that have escaped the object, hence we first need to update the `valid` array mask.\n",
    "\n",
    "The escaping directions are given by the final ray direction, which we convert to local coordinates using the previously built `frame` object. It is then straighforward to compute the `theta` angle in spherical coordinates and the corresponding bin index in the histrogram.\n",
    "\n",
    "Here it is important to account for the distortion introduced in the projection to spherical coordinates, hence we divide the throughput variable with the projection jacobian.\n",
    "\n",
    "The histogram array is initialized using `dr.zero` and we accumulate the computed values into their corresponding bins using the `dr.scatter_reduce(dr.ReduceOp.Add, ...)` routine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only account for rays that have escaped\n",
    "valid &= ~active\n",
    "\n",
    "# Resolution of the histogram\n",
    "histogram_size = 512\n",
    "\n",
    "# Convert escaping directions into histogram bin indices\n",
    "cos_theta = mi.Frame3f.cos_theta(frame.to_local(rays.d))\n",
    "theta = dr.acos(cos_theta)\n",
    "theta_idx = mi.UInt32(theta / dr.pi * histogram_size)\n",
    "\n",
    "# Account for projection jacobian\n",
    "throughput *= 1.0 / dr.sqrt(1 - cos_theta**2)\n",
    "\n",
    "# Accumulate values into the histogram\n",
    "histogram = dr.zeros(mi.Float, histogram_size)\n",
    "dr.scatter_reduce(dr.ReduceOp.Add, histogram, throughput, theta_idx, valid)\n",
    "\n",
    "# Execute the kernel by evaluating the histogram\n",
    "dr.eval(histogram)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the histogram\n",
    "\n",
    "Let's now take a look at the resulting angular distribution! We plot it in log scale on a regular plot as well as a polar plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "nbsphinx-thumbnail": {},
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "x = dr.linspace(mi.Float, 0, dr.pi, len(histogram), endpoint=True)\n",
    "histogram_log = dr.log(histogram)\n",
    "\n",
    "fig = plt.figure(figsize = (10, 5))\n",
    "\n",
    "ax = fig.add_subplot(121, title='Angular distribution') \n",
    "ax.plot(dr.cos(x), histogram_log, color='C0')\n",
    "ax.set_xlabel(r'$\\cos(\\theta)$', size=15)\n",
    "ax.set_ylabel(r'$p(\\cos(\\theta))$', size=15)\n",
    "\n",
    "ax = fig.add_subplot(122, polar=True, title='polar plot') \n",
    "ax.plot( x, histogram_log, color='C0')\n",
    "ax.plot(-x, histogram_log, color='C0', label='test');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## See also\n",
    "\n",
    "- [The <code>independent</code> sampler][1]\n",
    "- [<code>mitsuba.warp.square_to_uniform_sphere()</code>][2]\n",
    "- [<code>mitsuba.Ray3f</code>][3]\n",
    "\n",
    "[1]: https://mitsuba.readthedocs.io/en/latest/src/generated/plugins_samplers.html#independent-sampler-independent\n",
    "[2]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.warp.sqaure_to_uniform_sphere\n",
    "[3]: https://mitsuba.readthedocs.io/en/latest/src/api_reference.html#mitsuba.Ray3f"
   ]
  }
 ],
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   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
   "version": "3.9.12"
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  "metadata": {
   "interpreter": {
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   }
  },
  "mimetype": "text/x-python",
  "name": "python",
  "npconvert_exporter": "python",
  "pygments_lexer": "ipython3",
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 },
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}