examples/003 Lights.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "logging.getLogger('trimesh').disabled = True\n",
    "logging.getLogger('shapely.geos').disabled = True\n",
    "logging.getLogger('matplotlib').setLevel(logging.CRITICAL)\n",
    "import functools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from pvtrace import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Light sources\n",
    "\n",
    "Light sources are **generators** of rays. They are useful to sample spatial, angular or spectral properties of rays automatically."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a default light object (with no arguments) emits rays as:\n",
    " * vertically, direction = (0, 0, 1)\n",
    " * from origin, position = (0, 0, 0)\n",
    " * wavelength = 555nm\n",
    " \n",
    "this is somewhat similar to a laser."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Ray(pos=(0.00, 0.00, 0.00), dir=(0.00, 0.00, 1.00), nm=555.00, alive=True)]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "light = Light()\n",
    "list(light.emit(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the `emit` method is a Python generator so to get out the value it needs to be iterated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sampling directions\n",
    "\n",
    "Supply a `phase_function` to sample angular ranges,\n",
    "\n",
    "```python\n",
    "Light(direction=isotropic)\n",
    "Light(direction=lambertian)\n",
    "```\n",
    "\n",
    "Some phase functions takes arguments, such as `cone(half_angle)`,\n",
    "\n",
    "```python\n",
    "Light(direction=functools.partial(cone, np.pi/8))\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You can open the visualizer by visiting the following URL:\n",
      "http://127.0.0.1:7017/static/\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div style=\"height: 400px; width: 600px; overflow-x: auto; overflow-y: hidden; resize: both\">\n",
       "<iframe src=\"http://127.0.0.1:7017/static/\" style=\"width: 100%; height: 100%; border: none\"></iframe>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "world = Node(\n",
    "    name=\"world (air)\",\n",
    "    geometry=Sphere(\n",
    "        radius=10.0,\n",
    "        material=Material(refractive_index=1.0)\n",
    "    )\n",
    ")\n",
    "scene = Scene(world)\n",
    "light = Light(\n",
    "    direction=functools.partial(cone, np.pi/4)\n",
    ")\n",
    "vis = MeshcatRenderer()\n",
    "vis.render(scene)\n",
    "for ray in light.emit(100):\n",
    "    steps = photon_tracer.follow(scene, ray)\n",
    "    path, decisions = zip(*steps)\n",
    "    vis.add_ray_path(path)\n",
    "vis.vis.jupyter_cell()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Samping positions\n",
    "\n",
    "A spatial mask defines allowed locations from which rays can be generated.\n",
    "\n",
    "```python\n",
    "Light(position=functools.partial(rectangular_mask, 1, 1))  # half side-length (x, y)\n",
    "Light(position=functools.partial(cube_mask, 1, 1, 1))  # half side-length (x, y, z)\n",
    "Light(position=functools.partial(circular_mask, 1))  # radius of the circle\n",
    "```\n",
    "\n",
    "`functools.partial` is used because these take arguments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You can open the visualizer by visiting the following URL:\n",
      "http://127.0.0.1:7022/static/\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<div style=\"height: 400px; width: 600px; overflow-x: auto; overflow-y: hidden; resize: both\">\n",
       "<iframe src=\"http://127.0.0.1:7022/static/\" style=\"width: 100%; height: 100%; border: none\"></iframe>\n",
       "</div>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Square emitter\n",
    "light = Light(position=functools.partial(square_mask, 1, 0.3))\n",
    "vis = MeshcatRenderer()\n",
    "vis.render(scene)\n",
    "for ray in light.emit(100):\n",
    "    steps = photon_tracer.follow(scene, ray)\n",
    "    path, decisions = zip(*steps)\n",
    "    vis.add_ray_path(path)\n",
    "vis.vis.jupyter_cell()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Any combination of position and divergence\n",
    "\n",
    "The power of the delegate approach is that, the angular and position delegates are independent, meaning that any combination is allowed and trivial to configure. In the example below we have used a cone divergence with a square mask\n",
    "\n",
    "```python\n",
    "Light(\n",
    "    position=position=functools.partial(square_mask, 1, 0.3),\n",
    "    direction=lambertian\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sampling a spectrum\n",
    "\n",
    "To sample wavelength from a spectrum use a `Distribution` object.\n",
    "\n",
    "Let's make an emission spectrum based on a Gaussian centred at 600nm. If you have experimental data you could import it as x, y column and use that instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def make_emission_spectrum(wavelengths):\n",
    "    return np.exp(-((wavelengths-600.0)/50.0)**2)\n",
    "x = np.linspace(400, 800)\n",
    "y = np.exp(-((x-600.0)/50.0)**2)\n",
    "plt.plot(x, y)\n",
    "plt.xlabel('Wavelength (nm)')\n",
    "plt.grid(linestyle='dotted')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist = Distribution(x, y)\n",
    "dist.sample(np.random.uniform())\n",
    "light = Light(\n",
    "    wavelength=lambda: dist.sample(np.random.uniform())\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Emit 10000 rays and plot a histogram of the distribution of wavelengths against the input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist([x.wavelength for x in list(light.emit(10000))], bins=20, density=True, histtype='step', label='sample')\n",
    "plt.plot(x, y/np.trapz(y, x), label='distribution')\n",
    "plt.legend()\n",
    "plt.xlabel(\"Wavelength (nm)\")\n",
    "plt.grid(linestyle='dotted')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}