electric_field_sim.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch_cluster import radius_graph\n",
    "from torch_scatter import scatter\n",
    "from e3nn import o3, nn\n",
    "from e3nn.io import SphericalTensor\n",
    "import plotly.graph_objects as go\n",
    "import matplotlib.pyplot as plt\n",
    "from models.electric_field_model.gate_points_networks_relaxed import SimpleNetwork,NetworkForAGraphWithAttributes\n",
    "from models.electric_field_model.relaxed_points_conv import RelaxedConvolution\n",
    "from torch_geometric.data import Data, DataLoader\n",
    "from utils import train, plot_output, plot_weights, setup_geom, sim_E_B_field\n",
    "from mpl_toolkits.mplot3d.art3d import Poly3DCollection\n",
    "import random\n",
    "import numpy as np\n",
    "torch.set_default_dtype(torch.float64)\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")#torch.device(\"cpu\")#torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constants\n",
    "q = 1.6e-19  # Charge of the particle (e.g., electron)\n",
    "m = 9.1e-31  # Mass of the particle (e.g., electron)\n",
    "dt = 0.1   # Time step for simulation\n",
    "steps = 1000 # Number of simulation steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "trajectories, velocities, accels = sim_E_B_field(np.array([1.0,0.0,0.0]),np.array([0,1.0,0]),steps,dt,q,m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(10, 15, 'B')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# Plot the trajectories\n",
    "colors = ['r', 'b', 'green']\n",
    "ind = 0\n",
    "for particle_traj in trajectories[0:1]:\n",
    "    ax.plot(particle_traj[:800, 0], particle_traj[:800, 1], particle_traj[:800, 2], color='black')\n",
    "    ind += 1\n",
    "\n",
    "# Set axis labels\n",
    "ax.set_xlabel('X Position (m)')\n",
    "ax.set_ylabel('Y Position (m)')\n",
    "ax.set_zlabel('Z Position (m)')\n",
    "\n",
    "# Remove tick marks\n",
    "#ax.set_xticks([])\n",
    "#ax.set_yticks([])\n",
    "#ax.set_zticks([])\n",
    "\n",
    "# Remove grid\n",
    "ax.grid(False)\n",
    "\n",
    "# Set the title\n",
    "ax.set_title('Charged Particle Trajectory in Electric and Magnetic Field',fontsize=14)\n",
    "\n",
    "# Add arrows for electric and magnetic fields to the side of the plot\n",
    "# Electric field in the x direction\n",
    "ax.quiver(10, 0, 0, 11, 0, 0, color='red', arrow_length_ratio=0.5)\n",
    "ax.text(25, -2, 0, 'E', color='red', fontsize=12)\n",
    "\n",
    "# Magnetic field in the y direction\n",
    "ax.quiver(10, 0, 0, 0, 11, 0, color='blue', arrow_length_ratio=0.5)\n",
    "ax.text(10, 15, 0, 'B', color='blue', fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "trajectories = np.asarray(trajectories)\n",
    "velocities = np.asarray(velocities)\n",
    "accels = np.asarray(accels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "L_max = 5\n",
    "irreps_input = o3.Irreps(None)\n",
    "irreps_output = o3.Irreps(\"1x1o\")\n",
    "irreps_node_attr = o3.Irreps(\"1x0e+1x1o\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "irreps_edge = o3.Irreps.spherical_harmonics(L_max)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = NetworkForAGraphWithAttributes(irreps_input, irreps_node_attr, irreps_output,\n",
    "max_radius = 1.8, num_neighbors = 4, num_nodes = 100,layers = 1,relaxed=True,pool_nodes=False).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.Adam(model.parameters(),5e-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NetworkForAGraphWithAttributes(\n",
       "  (mp): MessagePassing(\n",
       "    (layers): ModuleList(\n",
       "      (0): Compose(\n",
       "        (first): RelaxedConvolution(\n",
       "          (sc): FullyConnectedTensorProduct(1x0e+1x0o+1x1o+1x1e+1x2e+1x2o x 1x0e+1x1o -> 50x0o+250x0e+50x1o+50x1e+50x2o+50x2e | 1300 paths | 1300 weights)\n",
       "          (lin1): FullyConnectedTensorProduct(1x0e+1x0o+1x1o+1x1e+1x2e+1x2o x 1x0e+1x1o -> 1x0e+1x0o+1x1o+1x1e+1x2e+1x2o | 18 paths | 18 weights)\n",
       "          (relaxed_edge_tp): FullyConnectedTensorProduct(1x0e+1x0o+1x1o+1x1e x 1x0e+1x1o+1x2e -> 1x0e+1x0o+1x1o+1x1e+1x2e+1x2o | 18 paths | 18 weights)\n",
       "          (in_relaxed_tp): TensorProduct(1x0e+1x0o+1x1o+1x1e+1x2e+1x2o x 1x0e+1x0o+1x1o+1x1e+1x2e+1x2o -> 6x0o+6x0e+12x1o+12x1e+12x2o+12x2e | 60 paths | 60 weights)\n",
       "          (fc_relaxed): FullyConnectedNet[1, 100, 60]\n",
       "          (lin2): FullyConnectedTensorProduct(6x0o+6x0e+12x1o+12x1e+12x2o+12x2e x 1x0e+1x1o -> 50x0o+250x0e+50x1o+50x1e+50x2o+50x2e | 13200 paths | 13200 weights)\n",
       "          (lin3): FullyConnectedTensorProduct(6x0o+6x0e+12x1o+12x1e+12x2o+12x2e x 1x0e+1x1o -> 1x0e | 18 paths | 18 weights)\n",
       "        )\n",
       "        (second): Gate (50x0o+250x0e+50x1o+50x1e+50x2o+50x2e -> 50x0o+50x0e+50x1o+50x1e+50x2o+50x2e)\n",
       "      )\n",
       "      (1): RelaxedConvolution(\n",
       "        (sc): FullyConnectedTensorProduct(50x0o+50x0e+50x1o+50x1e+50x2o+50x2e x 1x0e+1x1o -> 1x1o | 200 paths | 200 weights)\n",
       "        (lin1): FullyConnectedTensorProduct(50x0o+50x0e+50x1o+50x1e+50x2o+50x2e x 1x0e+1x1o -> 50x0o+50x0e+50x1o+50x1e+50x2o+50x2e | 45000 paths | 45000 weights)\n",
       "        (relaxed_edge_tp): FullyConnectedTensorProduct(1x0e+1x0o+1x1o+1x1e x 1x0e+1x1o+1x2e -> 50x0o+50x0e+50x1o+50x1e+50x2o+50x2e | 900 paths | 900 weights)\n",
       "        (in_relaxed_tp): TensorProduct(50x0o+50x0e+50x1o+50x1e+50x2o+50x2e x 50x0o+50x0e+50x1o+50x1e+50x2o+50x2e -> 300x0e+600x1o | 45000 paths | 45000 weights)\n",
       "        (fc_relaxed): FullyConnectedNet[1, 100, 45000]\n",
       "        (lin2): FullyConnectedTensorProduct(300x0e+600x1o x 1x0e+1x1o -> 1x1o | 900 paths | 900 weights)\n",
       "        (lin3): FullyConnectedTensorProduct(300x0e+600x1o x 1x0e+1x1o -> 1x0e | 900 paths | 900 weights)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_elems = 10\n",
    "data_list = []\n",
    "num = 500\n",
    "for i in range(num):\n",
    "    curr_data = Data(\n",
    "    y = torch.DoubleTensor(accels[0,i,:]).reshape(1,3).to(device),\n",
    "    node_attr = torch.cat((torch.ones(1, dtype=torch.float64), torch.DoubleTensor(velocities[0,i, :])), dim=0).reshape(1,4),\n",
    "    pos = torch.DoubleTensor([0,0,0]).reshape(1,3)\n",
    "    ).to(device)\n",
    "    data_list.append(curr_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 50\n",
    "loader = DataLoader(data_list, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reg_norm_loss(relaxed_weights):\n",
    "    return torch.norm(relaxed_weights[1])+torch.norm(relaxed_weights[2:5])+torch.norm(relaxed_weights[5:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.98)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0\n",
      "0.07433428446157356\n",
      "Epoch: 10\n",
      "0.07286473011011337\n",
      "Epoch: 20\n",
      "0.07143330441258965\n",
      "Epoch: 30\n",
      "0.070371915713236\n",
      "Epoch: 40\n",
      "0.06978362451452261\n",
      "Epoch: 50\n",
      "0.06944223522480174\n",
      "Epoch: 60\n",
      "0.0691374918301535\n",
      "Epoch: 70\n",
      "0.06879465229404576\n",
      "Epoch: 80\n",
      "0.06825917530994932\n",
      "Epoch: 90\n",
      "0.0667849385451797\n",
      "Epoch: 100\n",
      "0.059298845415281506\n",
      "Epoch: 110\n",
      "0.050148978541045386\n",
      "Epoch: 120\n",
      "0.039409279678554426\n",
      "Epoch: 130\n",
      "0.02924440565698823\n",
      "Epoch: 140\n",
      "0.016549698868456014\n",
      "Epoch: 150\n",
      "0.01114948879048603\n",
      "Epoch: 160\n",
      "0.009454298751839705\n",
      "Epoch: 170\n",
      "0.010706137289821747\n",
      "Epoch: 180\n",
      "0.007901341967350407\n",
      "Epoch: 190\n",
      "0.007559677887971011\n",
      "Epoch: 200\n",
      "0.006972967481477509\n",
      "Epoch: 210\n",
      "0.006639554785827063\n",
      "Epoch: 220\n",
      "0.0064765998964752824\n",
      "Epoch: 230\n",
      "0.006401344614981916\n",
      "Epoch: 240\n",
      "0.006116926329876037\n",
      "Epoch: 250\n",
      "0.00586126916691753\n",
      "Epoch: 260\n",
      "0.0056247076149177995\n",
      "Epoch: 270\n",
      "0.005386414103800525\n",
      "Epoch: 280\n",
      "0.0050832409010750515\n",
      "Epoch: 290\n",
      "0.004959906716323406\n",
      "Epoch: 300\n",
      "0.0046815555215970256\n",
      "Epoch: 310\n",
      "0.004559875147201942\n",
      "Epoch: 320\n",
      "0.004533794929872715\n",
      "Epoch: 330\n",
      "0.004305090460074214\n",
      "Epoch: 340\n",
      "0.004203644205880034\n",
      "Epoch: 350\n",
      "0.004105938183537222\n",
      "Epoch: 360\n",
      "0.004048548190443729\n",
      "Epoch: 370\n",
      "0.003993096273074656\n",
      "Epoch: 380\n",
      "0.003972197916903422\n",
      "Epoch: 390\n",
      "0.003964233749246118\n",
      "Epoch: 400\n",
      "0.0037530169168716212\n",
      "Epoch: 410\n",
      "0.0037230201039092003\n",
      "Epoch: 420\n",
      "0.003600053401196658\n",
      "Epoch: 430\n",
      "0.003576312345262831\n",
      "Epoch: 440\n",
      "0.003430257424470354\n",
      "Epoch: 450\n",
      "0.003441759999354209\n",
      "Epoch: 460\n",
      "0.0036229654281756543\n",
      "Epoch: 470\n",
      "0.0036610127594524145\n",
      "Epoch: 480\n",
      "0.0031843011857575613\n",
      "Epoch: 490\n",
      "0.003045797415051499\n",
      "Epoch: 500\n",
      "0.002999782234748813\n",
      "Epoch: 510\n",
      "0.0028650347573532836\n",
      "Epoch: 520\n",
      "0.002780788436412825\n",
      "Epoch: 530\n",
      "0.002681404770788759\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[62], line 11\u001b[0m\n\u001b[1;32m      8\u001b[0m total_loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m data \u001b[38;5;129;01min\u001b[39;00m loader:\n\u001b[1;32m     10\u001b[0m     \u001b[38;5;66;03m# Forward pass\u001b[39;00m\n\u001b[0;32m---> 11\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     12\u001b[0m     regularization_loss \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor(\u001b[38;5;241m0.0\u001b[39m)\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m     13\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m name, module \u001b[38;5;129;01min\u001b[39;00m model\u001b[38;5;241m.\u001b[39mnamed_modules():  \u001b[38;5;66;03m# Accessing the Sequential part\u001b[39;00m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/symm_breaking/lib/python3.9/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m~/Desktop/MIT_Lab/RelaxedE3NN/electric_field_model/gate_points_networks_relaxed.py:184\u001b[0m, in \u001b[0;36mNetworkForAGraphWithAttributes.forward\u001b[0;34m(***failed resolving arguments***)\u001b[0m\n\u001b[1;32m    182\u001b[0m edge_attr \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39meye(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mirreps_edge_attr\u001b[38;5;241m.\u001b[39mdim, dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat64)\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m    183\u001b[0m edge_length_embedding \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mtensor([[\u001b[38;5;241m1.0\u001b[39m]], dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mfloat64)\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m--> 184\u001b[0m node_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnode_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnode_attr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge_src\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge_dst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge_attr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge_length_embedding\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    186\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpool_nodes:\n\u001b[1;32m    187\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m scatter(node_outputs, batch, dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\u001b[38;5;241m.\u001b[39mdiv(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_nodes\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m0.5\u001b[39m)\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/symm_breaking/lib/python3.9/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m~/Desktop/MIT_Lab/RelaxedE3NN/electric_field_model/gate_points_message_passing_relaxed.py:138\u001b[0m, in \u001b[0;36mMessagePassing.forward\u001b[0;34m(self, node_features, node_attr, edge_src, edge_dst, edge_attr, edge_scalars)\u001b[0m\n\u001b[1;32m    136\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, node_features, node_attr, edge_src, edge_dst, edge_attr, edge_scalars) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m torch\u001b[38;5;241m.\u001b[39mTensor:\n\u001b[1;32m    137\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m lay \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlayers:\n\u001b[0;32m--> 138\u001b[0m         node_features \u001b[38;5;241m=\u001b[39m \u001b[43mlay\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnode_features\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnode_attr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge_src\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge_dst\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge_attr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43medge_scalars\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    140\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m node_features\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/symm_breaking/lib/python3.9/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m~/Desktop/MIT_Lab/RelaxedE3NN/electric_field_model/gate_points_message_passing_relaxed.py:30\u001b[0m, in \u001b[0;36mCompose.forward\u001b[0;34m(self, *input)\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;28minput\u001b[39m):\n\u001b[0;32m---> 30\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfirst\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     31\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msecond(x)\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/symm_breaking/lib/python3.9/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m~/Desktop/MIT_Lab/RelaxedE3NN/electric_field_model/relaxed_points_conv.py:155\u001b[0m, in \u001b[0;36mRelaxedConvolution.forward\u001b[0;34m(self, node_input, node_attr, edge_src, edge_dst, edge_attr, edge_scalars)\u001b[0m\n\u001b[1;32m    153\u001b[0m     relaxed_weights \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrelaxed_edge_tp(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrelaxed_weights,edge_attr)\n\u001b[1;32m    154\u001b[0m     \u001b[38;5;66;03m# node size 1\u001b[39;00m\n\u001b[0;32m--> 155\u001b[0m     node_features \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43min_relaxed_tp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnode_features\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrelaxed_weights\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m)\u001b[49m \n\u001b[1;32m    156\u001b[0m     \u001b[38;5;66;03m#new_edge_features = self.in_relaxed_tp(node_features, relaxed_weights, weight)\u001b[39;00m\n\u001b[1;32m    157\u001b[0m     \u001b[38;5;66;03m#node_features = scatter(new_edge_features, edge_dst, dim=0, dim_size=node_input.shape[0]).div(self.num_neighbors**0.5)\u001b[39;00m\n\u001b[1;32m    158\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    159\u001b[0m     weight \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfc(edge_scalars)\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/symm_breaking/lib/python3.9/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/symm_breaking/lib/python3.9/site-packages/e3nn/o3/_tensor_product/_tensor_product.py:546\u001b[0m, in \u001b[0;36mTensorProduct.forward\u001b[0;34m(self, x, y, weight)\u001b[0m\n\u001b[1;32m    544\u001b[0m \u001b[38;5;66;03m# - PROFILER - with torch.autograd.profiler.record_function(self._profiling_str):\u001b[39;00m\n\u001b[1;32m    545\u001b[0m real_weight \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_weights(weight)\n\u001b[0;32m--> 546\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_compiled_main_left_right\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreal_weight\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/opt/miniconda3/envs/symm_breaking/lib/python3.9/site-packages/torch/nn/modules/module.py:1501\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1496\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1497\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1498\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1499\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1500\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "criterion = torch.nn.MSELoss()\n",
    "# Training loop\n",
    "num_epochs = 1500\n",
    "weight_decay = 1e-4\n",
    "losses = []\n",
    "best_loss = 1e10\n",
    "for epoch in range(num_epochs):\n",
    "    total_loss = 0\n",
    "    for data in loader:\n",
    "        # Forward pass\n",
    "        output = model(data)\n",
    "        regularization_loss = torch.tensor(0.0).to(device)\n",
    "        for name, module in model.named_modules():  # Accessing the Sequential part\n",
    "            if isinstance(module, RelaxedConvolution):\n",
    "                regularization_loss += reg_norm_loss(module.relaxed_weights)\n",
    "        regularization_loss *= weight_decay\n",
    "        loss = criterion(output, data.y)\n",
    "        loss += regularization_loss\n",
    "        total_loss += loss.item()\n",
    "\n",
    "    # Backward pass and optimization\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    #scheduler.step()\n",
    "    # Print or log the relaxed weights\n",
    "    if total_loss/len(data_list) < best_loss:\n",
    "        best_model = model.state_dict()\n",
    "    if epoch % 10 == 0:\n",
    "        print(\"Epoch:\", epoch)\n",
    "        print(total_loss/len(data_list))\n",
    "    losses.append(total_loss/len(data_list))\n",
    "for name, param in model.named_parameters():\n",
    "    if 'relaxed_weights' in name:\n",
    "        print(name, param.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "equiv_loss = losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 14})\n",
    "plt.figure(figsize=(8,5))\n",
    "plt.plot(equiv_loss, label = 'Relaxed Weights')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.title('Loss for Equiv and Relaxed Weight Model')\n",
    "plt.legend()\n",
    "plt.savefig('losses.pdf')\n",
    "#plt.yscale('log')\n",
    "#plt.yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.load_state_dict(best_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mp.layers.0.first.relaxed_weights tensor([ 1.0436, -0.0688, -0.1574, -0.1746, -0.0540, -0.1504, -0.1287,  0.0877])\n",
      "mp.layers.1.relaxed_weights tensor([ 1.0522e+00, -1.0861e-01,  1.0278e-01, -1.9186e-03, -4.5899e-03,\n",
      "         8.8293e-04, -2.1986e-01, -1.3817e-02])\n"
     ]
    }
   ],
   "source": [
    "for name, param in model.named_parameters():\n",
    "    if 'relaxed_weights' in name:\n",
    "        print(name, param.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "irreps_relaxed_iter =o3.Irrep.iterator(1)\n",
    "irreps_relaxed = o3.Irreps(\"\")\n",
    "for irrep in irreps_relaxed_iter:\n",
    "    irreps_relaxed += irrep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1x0e+1x0o+1x1o+1x1e"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "irreps_relaxed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# look at the relaxed weights of the last layer\n",
    "relaxed_weights = best_model['mp.layers.1.relaxed_weights']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "E_field_pred = relaxed_weights[2:5]\n",
    "E_field_pred /= np.linalg.norm(E_field_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 0.9988, -0.0186, -0.0446])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "E_field_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "B_field_pred = relaxed_weights[5:]\n",
    "B_field_pred /= np.linalg.norm(B_field_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 0.0040, -0.9980, -0.0627])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_field_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "line": {
          "color": "black",
          "width": 12
         },
         "mode": "lines",
         "name": "Predicted E field",
         "opacity": 0.9,
         "type": "scatter3d",
         "x": [
          0,
          0.9988307511709015
         ],
         "y": [
          0,
          -0.018644802703335538
         ],
         "z": [
          0,
          -0.044603832206730996
         ]
        },
        {
         "line": {
          "color": "gray",
          "width": 12
         },
         "mode": "lines",
         "name": "Predicted B field",
         "opacity": 0.9,
         "type": "scatter3d",
         "x": [
          0,
          0.004007987326929031
         ],
         "y": [
          0,
          -0.998023069697976
         ],
         "z": [
          0,
          -0.0627207173764466
         ]
        },
        {
         "line": {
          "color": "red",
          "width": 12
         },
         "mode": "lines",
         "name": "E field",
         "opacity": 1,
         "type": "scatter3d",
         "x": [
          0,
          1
         ],
         "y": [
          0,
          0
         ],
         "z": [
          0,
          0
         ]
        },
        {
         "line": {
          "color": "blue",
          "width": 12
         },
         "mode": "lines",
         "name": "B field",
         "opacity": 1,
         "type": "scatter3d",
         "x": [
          0,
          0
         ],
         "y": [
          0,
          1
         ],
         "z": [
          0,
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         ]
        },
        {
         "anchor": "tail",
         "colorscale": [
          [
           0,
           "black"
          ],
          [
           1,
           "black"
          ]
         ],
         "name": "Predicted E field",
         "opacity": 0.9,
         "showscale": false,
         "type": "cone",
         "u": [
          0.9988307511709015
         ],
         "v": [
          -0.018644802703335538
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         "w": [
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         "x": [
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         "z": [
          -0.044603832206730996
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        },
        {
         "anchor": "tail",
         "colorscale": [
          [
           0,
           "gray"
          ],
          [
           1,
           "gray"
          ]
         ],
         "name": "Predicted B field",
         "opacity": 0.9,
         "showscale": false,
         "type": "cone",
         "u": [
          0.004007987326929031
         ],
         "v": [
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         "z": [
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        },
        {
         "anchor": "tail",
         "colorscale": [
          [
           0,
           "red"
          ],
          [
           1,
           "red"
          ]
         ],
         "name": "E field",
         "opacity": 1,
         "showscale": false,
         "type": "cone",
         "u": [
          1
         ],
         "v": [
          0
         ],
         "w": [
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         ],
         "x": [
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         ],
         "y": [
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         ],
         "z": [
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         ]
        },
        {
         "anchor": "tail",
         "colorscale": [
          [
           0,
           "blue"
          ],
          [
           1,
           "blue"
          ]
         ],
         "name": "B field",
         "opacity": 1,
         "showscale": false,
         "type": "cone",
         "u": [
          0
         ],
         "v": [
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         ],
         "w": [
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         ],
         "x": [
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         ],
         "y": [
          1
         ],
         "z": [
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         ]
        }
       ],
       "layout": {
        "scene": {
         "aspectmode": "cube",
         "xaxis": {
          "range": [
           -1.5,
           1.5
          ],
          "showbackground": false,
          "showgrid": false,
          "title": {
           "text": "X"
          }
         },
         "yaxis": {
          "range": [
           -1.5,
           1.5
          ],
          "showbackground": false,
          "showgrid": false,
          "title": {
           "text": "Y"
          }
         },
         "zaxis": {
          "range": [
           -1.5,
           1.5
          ],
          "showbackground": false,
          "showgrid": false,
          "title": {
           "text": "Z"
          }
         }
        },
        "showlegend": true,
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
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   "source": [
    "import plotly.graph_objects as go\n",
    "import numpy as np\n",
    "\n",
    "v1 = E_field_pred\n",
    "v2 = B_field_pred\n",
    "v3 = np.array([1.0, 0, 0])  # x-axis\n",
    "v4 = np.array([0, 1.0, 0])  # y-axis\n",
    "\n",
    "# Define colors for the vectors\n",
    "colors = ['black', 'gray', 'red', 'blue']#,'green','yellow']\n",
    "dashes = ['dash', 'dash', 'solid', 'solid']  # Set dash styles\n",
    "names = ['Predicted E field', 'Predicted B field', 'E field', 'B field']\n",
    "opacities = [0.9,0.9,1.0,1.0]  # Set opacities\n",
    "# Create line traces for each vector\n",
    "lines = [\n",
    "    go.Scatter3d(\n",
    "        x=[0, v[0]],\n",
    "        y=[0, v[1]],\n",
    "        z=[0, v[2]],\n",
    "        mode='lines',\n",
    "        line=dict(color=c,width=12),\n",
    "        opacity = o,\n",
    "        name=n\n",
    "    )\n",
    "    for v, c, o, n in zip([v1,v2,v3,v4], colors, opacities, names)\n",
    "]\n",
    "\n",
    "# Create cone traces for each vector\n",
    "cones = [\n",
    "    go.Cone(\n",
    "        x=[v[0]],\n",
    "        y=[v[1]],\n",
    "        z=[v[2]],\n",
    "        u=[v[0]],\n",
    "        v=[v[1]],\n",
    "        w=[v[2]],\n",
    "        anchor=\"tail\",\n",
    "        colorscale=[[0, c], [1, c]],\n",
    "        showscale=False,\n",
    "        opacity = o,\n",
    "        name=n\n",
    "    )\n",
    "    for v, c, o, n in zip([v1,v2,v3,v4], colors, opacities, names)\n",
    "]\n",
    "\n",
    "# Combine lines and cones in the figure\n",
    "fig = go.Figure(data=lines + cones)\n",
    "\n",
    "# Set layout\n",
    "fig.update_layout(\n",
    "    scene=dict(\n",
    "        xaxis=dict(range=[-1.5, 1.5], title='X',showgrid = False,showbackground = False),\n",
    "        yaxis=dict(range=[-1.5, 1.5], title='Y', showgrid = False,showbackground = False),\n",
    "        zaxis=dict(range=[-1.5, 1.5], title='Z', showgrid = False, showbackground = False),\n",
    "        aspectmode=\"cube\"\n",
    "    ),\n",
    "    showlegend=True\n",
    ")\n",
    "\n",
    "# Show plot\n",
    "fig.show()\n",
    "fig.write_html('vectors.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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