{
    "cells": [
        {
            "cell_type": "markdown",
            "id": "faad71d7",
            "metadata": {},
            "source": [
                "# Nuclear Fusion Tokamak\n",
                "![](./nuclearfusion_banner.png)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "9abd82ad",
            "metadata": {},
            "source": [
                "### Connect to BioExplorer backend"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "ecf1c452",
            "metadata": {},
            "outputs": [],
            "source": [
                "from bioexplorer import BioExplorer, Vector3\n",
                "\n",
                "be = BioExplorer('localhost:5000')\n",
                "core = be.core_api()\n",
                "be.reset_scene()\n",
                "print('BioExplorer version ' + be.version())"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "0ba057d7",
            "metadata": {},
            "source": [
                "### Generate random data set:\n",
                "Vectors composed of a position and a direction. The length of the vector is the value of the voltage"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "ce421549",
            "metadata": {},
            "outputs": [],
            "source": [
                "import math\n",
                "import random\n",
                "import numpy as np\n",
                "import time\n",
                "\n",
                "''' Settings '''\n",
                "nb_particles = 2000\n",
                "use_randomness = True\n",
                "\n",
                "def generate_random_point_in_torus(main_radius, tube_radius):\n",
                "    theta = 2 * math.pi * random.random()\n",
                "    phi = 2 * math.pi * random.random()\n",
                "    x = (main_radius + tube_radius * math.cos(phi)) * math.cos(theta)\n",
                "    y = tube_radius * math.sin(phi)\n",
                "    z = (main_radius + tube_radius * math.cos(phi)) * math.sin(theta)\n",
                "    return x, y, z\n",
                "\n",
                "origins = list()\n",
                "targets = list()\n",
                "origins_radii = list()\n",
                "targets_radii = list()\n",
                "\n",
                "seed = int(time.time())\n",
                "random.seed(seed)\n",
                "for i in range(nb_particles):\n",
                "    x = 0\n",
                "    y = 0\n",
                "    z = 0\n",
                "    x, y, z = generate_random_point_in_torus(10.0, 2.0)\n",
                "    origin = Vector3(x, y ,z)\n",
                "    \n",
                "\n",
                "    length = 0.2\n",
                "    cp = np.cross([x, y, z], [0, 1, 0])\n",
                "    if use_randomness:\n",
                "        cp[0] += (random.random() - 0.5)\n",
                "        cp[1] += (random.random() - 0.5)\n",
                "        cp[2] += (random.random() - 0.5)\n",
                "        length = random.random() * 0.2\n",
                "\n",
                "    target = Vector3(\n",
                "        origin.x + cp[0] * length,\n",
                "        origin.y + cp[1] * length,\n",
                "        origin.z + cp[2] * length\n",
                "    )\n",
                "    origins.append(origin)\n",
                "    targets.append(target)\n",
                "    origins_radii.append(0.1)\n",
                "    targets_radii.append(0.0)\n",
                "\n",
                "status = be.add_cones(\n",
                "    name='Particles',\n",
                "    origins=origins, origins_radii=origins_radii,\n",
                "    targets=targets, targets_radii=targets_radii,\n",
                "    color=Vector3(1.0, 1.0, 1.0), opacity=1.0)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "5a21827f",
            "metadata": {},
            "source": [
                "## Generate new model containing the field"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "a9f39aa3",
            "metadata": {},
            "outputs": [],
            "source": [
                "be.build_fields(\n",
                "    voxel_size=0.25, density=1.0,\n",
                "    data_type=be.FIELD_DATA_TYPE_VECTOR\n",
                ")\n",
                "\n",
                "status = core.set_renderer(current='advanced')"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "b751ad42",
            "metadata": {},
            "source": [
                "### Volume settings"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "9da4c165",
            "metadata": {},
            "outputs": [],
            "source": [
                "status = core.set_field_parameters(\n",
                "    distance=1, cutoff=200, epsilon=0.001,\n",
                "    gradient_shading=False, sampling_rate=0.25)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "f7c0c1a2",
            "metadata": {},
            "source": [
                "### Attach transfer function to newly created model"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "2034dc01",
            "metadata": {
                "scrolled": false
            },
            "outputs": [],
            "source": [
                "from bioexplorer import TransferFunction\n",
                "model_ids = be.get_model_ids()['ids']\n",
                "model_id = model_ids[len(model_ids)-1]\n",
                "tf = TransferFunction(\n",
                "    bioexplorer=be, model_id=model_id,\n",
                "    filename='fusion.1dt',\n",
                "    value_range=[0.1, 5.0])"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "aa04cb63",
            "metadata": {},
            "source": [
                "## Fusion reactor Tokamak 3D model\n",
                "Can be downloaded from: https://blendermarket.com/products/fusion-reactor-tokamak"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "b69e7773",
            "metadata": {},
            "outputs": [],
            "source": [
                "import os\n",
                "transformation={\n",
                "    'rotation': [0.0, -0.25038000405444144, 0.0, 0.9681476403781076],\n",
                "    'rotation_center': [0.0, 0.0, 0.0],\n",
                "    'scale': [12.0, 12.0, 12.0],\n",
                "    'translation': [0.0, -1.0, 0.0]\n",
                "}\n",
                "model_filename = 'Fusion_reactor_by_www_up3d_de_without_plasma.obj'\n",
                "core.add_model(name='Fusion Reactor Tokamak', path=model_filename)\n",
                "model_ids = be.get_model_ids()['ids']\n",
                "status = core.update_model(id=model_ids[len(model_ids)-1], transformation=transformation)"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "805cbbeb",
            "metadata": {},
            "source": [
                "## Rendering settings"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "8b48082a",
            "metadata": {},
            "outputs": [],
            "source": [
                "status = be.reset_camera()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "09f3a8ca",
            "metadata": {},
            "outputs": [],
            "source": [
                "status = core.set_renderer(\n",
                "    background_color=[0.25, 0.25, 0.25],\n",
                "    current='advanced', \n",
                "    samples_per_pixel=1,\n",
                "    subsampling=4,\n",
                "    max_accum_frames=1024)\n",
                "params = core.AdvancedRendererParams()\n",
                "params.gi_samples = 0\n",
                "params.gi_strength = 0.0\n",
                "params.gi_ray_length = 150.0\n",
                "params.shadow_intensity = 0.5\n",
                "params.soft_shadow_strength = 0.1\n",
                "params.max_ray_depth = 3\n",
                "params.show_background = True\n",
                "params.fog_start = 100.0\n",
                "params.fog_thickness = 20.0\n",
                "status = core.set_renderer_params(params)\n",
                "status = core.set_renderer()"
            ]
        },
        {
            "cell_type": "markdown",
            "id": "8713d0c5",
            "metadata": {},
            "source": [
                "## Widgets to modify materials"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "id": "afcf63a4",
            "metadata": {},
            "outputs": [],
            "source": [
                "from bioexplorer import Widgets\n",
                "w = Widgets(be)\n",
                "w.display_model_material()"
            ]
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3 (ipykernel)",
            "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": "3.10.6"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 5
}