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{
"cells": [
{
"cell_type": "code",
"source": [
"!git clone https://github.com/cpwan/RLOR\n",
"%cd RLOR"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5a69iB04JzY2",
"outputId": "13a3a63e-34bf-4d8b-a853-9d2597cd03d5"
},
"id": "5a69iB04JzY2",
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Cloning into 'RLOR'...\n",
"remote: Enumerating objects: 52, done.\u001b[K\n",
"remote: Counting objects: 100% (52/52), done.\u001b[K\n",
"remote: Compressing objects: 100% (35/35), done.\u001b[K\n",
"remote: Total 52 (delta 12), reused 52 (delta 12), pack-reused 0\u001b[K\n",
"Unpacking objects: 100% (52/52), 5.19 MiB | 7.89 MiB/s, done.\n",
"/content/RLOR\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "dbe3c5ed",
"metadata": {
"id": "dbe3c5ed"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import torch\n",
"import gym\n",
"from models.attention_model_wrapper import Agent"
]
},
{
"cell_type": "markdown",
"id": "985bf6e6",
"metadata": {
"id": "985bf6e6"
},
"source": [
"# Define our agent"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "953a7fde",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "953a7fde",
"outputId": "06f10aaf-57ca-4870-d22b-f71a14ea4ec4"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<All keys matched successfully>"
]
},
"metadata": {},
"execution_count": 3
}
],
"source": [
"device = 'cuda'\n",
"ckpt_path = './runs/tsp-v0__ppo_or__1__1678160003/ckpt/12000.pt'\n",
"agent = Agent(device=device, name='tsp').to(device)\n",
"agent.load_state_dict(torch.load(ckpt_path))"
]
},
{
"cell_type": "markdown",
"id": "2cbaa255",
"metadata": {
"id": "2cbaa255"
},
"source": [
"# Define our environment"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c2bd466f",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "c2bd466f",
"outputId": "92450c8d-f5db-444d-f465-da4a98667799"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.9/dist-packages/gym/utils/passive_env_checker.py:31: UserWarning: \u001b[33mWARN: A Box observation space has an unconventional shape (neither an image, nor a 1D vector). We recommend flattening the observation to have only a 1D vector or use a custom policy to properly process the data. Actual observation shape: (50, 2)\u001b[0m\n",
" logger.warn(\n",
"/usr/local/lib/python3.9/dist-packages/gym/core.py:317: DeprecationWarning: \u001b[33mWARN: Initializing wrapper in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001b[0m\n",
" deprecation(\n",
"/usr/local/lib/python3.9/dist-packages/gym/wrappers/step_api_compatibility.py:39: DeprecationWarning: \u001b[33mWARN: Initializing environment in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001b[0m\n",
" deprecation(\n",
"/usr/local/lib/python3.9/dist-packages/gym/vector/vector_env.py:56: DeprecationWarning: \u001b[33mWARN: Initializing vector env in old step API which returns one bool array instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001b[0m\n",
" deprecation(\n"
]
}
],
"source": [
"from wrappers.syncVectorEnvPomo import SyncVectorEnv\n",
"from wrappers.recordWrapper import RecordEpisodeStatistics\n",
"\n",
"env_id = 'tsp-v0'\n",
"env_entry_point = 'envs.tsp_vector_env:TSPVectorEnv'\n",
"seed = 0\n",
"\n",
"gym.envs.register(\n",
" id=env_id,\n",
" entry_point=env_entry_point,\n",
")\n",
"\n",
"def make_env(env_id, seed, cfg={}):\n",
" def thunk():\n",
" env = gym.make(env_id, **cfg)\n",
" env = RecordEpisodeStatistics(env)\n",
" env.seed(seed)\n",
" env.action_space.seed(seed)\n",
" env.observation_space.seed(seed)\n",
" return env\n",
" return thunk\n",
"\n",
"envs = SyncVectorEnv([make_env(env_id, seed, dict(n_traj=1))])"
]
},
{
"cell_type": "markdown",
"id": "c363d489",
"metadata": {
"id": "c363d489"
},
"source": [
"# Inference"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "bbee9e3c",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bbee9e3c",
"outputId": "11750d60-eb7c-4d9c-8b40-3a8a92021ffe"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.9/dist-packages/gym/utils/passive_env_checker.py:174: UserWarning: \u001b[33mWARN: Future gym versions will require that `Env.reset` can be passed a `seed` instead of using `Env.seed` for resetting the environment random number generator.\u001b[0m\n",
" logger.warn(\n",
"/usr/local/lib/python3.9/dist-packages/gym/utils/passive_env_checker.py:190: UserWarning: \u001b[33mWARN: Future gym versions will require that `Env.reset` can be passed `return_info` to return information from the environment resetting.\u001b[0m\n",
" logger.warn(\n",
"/usr/local/lib/python3.9/dist-packages/gym/utils/passive_env_checker.py:195: UserWarning: \u001b[33mWARN: Future gym versions will require that `Env.reset` can be passed `options` to allow the environment initialisation to be passed additional information.\u001b[0m\n",
" logger.warn(\n",
"/usr/local/lib/python3.9/dist-packages/gym/utils/passive_env_checker.py:165: UserWarning: \u001b[33mWARN: The obs returned by the `reset()` method is not within the observation space.\u001b[0m\n",
" logger.warn(f\"{pre} is not within the observation space.\")\n",
"/usr/local/lib/python3.9/dist-packages/gym/utils/passive_env_checker.py:141: UserWarning: \u001b[33mWARN: The obs returned by the `reset()` method was expecting numpy array dtype to be float32, actual type: float64\u001b[0m\n",
" logger.warn(\n",
"/usr/local/lib/python3.9/dist-packages/gym/utils/passive_env_checker.py:227: DeprecationWarning: \u001b[33mWARN: Core environment is written in old step API which returns one bool instead of two. It is recommended to rewrite the environment with new step API. \u001b[0m\n",
" logger.deprecation(\n",
"/usr/local/lib/python3.9/dist-packages/gym/utils/passive_env_checker.py:234: UserWarning: \u001b[33mWARN: Expects `done` signal to be a boolean, actual type: <class 'numpy.ndarray'>\u001b[0m\n",
" logger.warn(\n",
"/usr/local/lib/python3.9/dist-packages/gym/utils/passive_env_checker.py:141: UserWarning: \u001b[33mWARN: The obs returned by the `step()` method was expecting numpy array dtype to be float32, actual type: float64\u001b[0m\n",
" logger.warn(\n",
"/usr/local/lib/python3.9/dist-packages/gym/utils/passive_env_checker.py:165: UserWarning: \u001b[33mWARN: The obs returned by the `step()` method is not within the observation space.\u001b[0m\n",
" logger.warn(f\"{pre} is not within the observation space.\")\n",
"/usr/local/lib/python3.9/dist-packages/gym/utils/passive_env_checker.py:260: UserWarning: \u001b[33mWARN: The reward returned by `step()` must be a float, int, np.integer or np.floating, actual type: <class 'numpy.ndarray'>\u001b[0m\n",
" logger.warn(\n"
]
}
],
"source": [
"num_steps = 51\n",
"trajectories = []\n",
"agent.eval()\n",
"obs = envs.reset()\n",
"for step in range(0, num_steps):\n",
" # ALGO LOGIC: action logic\n",
" with torch.no_grad():\n",
" action, logits = agent(obs)\n",
" obs, reward, done, info = envs.step(action.cpu().numpy())\n",
" trajectories.append(action.cpu().numpy())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "f0fbf6fd",
"metadata": {
"id": "f0fbf6fd"
},
"outputs": [],
"source": [
"nodes_coordinates = obs['observations'][0]\n",
"final_return = info[0]['episode']['r']\n",
"resulting_traj = np.array(trajectories)[:,0,0]"
]
},
{
"cell_type": "markdown",
"source": [
"## Results"
],
"metadata": {
"id": "5n9rBoH5Q8gn"
},
"id": "5n9rBoH5Q8gn"
},
{
"cell_type": "code",
"execution_count": 7,
"id": "dff29ef4",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "dff29ef4",
"outputId": "dcffcda5-5728-464c-ee3e-0fcc702443ff"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"A route of length [-5.908508]\n",
"The route is:\n",
" [26 34 33 49 37 21 48 43 31 28 42 29 47 39 38 23 27 30 7 32 24 40 20 14\n",
" 25 1 18 22 0 11 2 16 45 15 46 12 17 41 8 13 3 6 44 9 10 19 36 5\n",
" 35 4 26]\n"
]
}
],
"source": [
"print(f'A route of length {final_return}')\n",
"print('The route is:\\n', resulting_traj)"
]
},
{
"cell_type": "markdown",
"id": "b009802e",
"metadata": {
"id": "b009802e"
},
"source": [
"## Display it in a 2d-grid\n",
"- Darker color means later steps in the route."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "dc681a06",
"metadata": {
"tags": [
"\"hide-cell\""
],
"cellView": "form",
"id": "dc681a06"
},
"outputs": [],
"source": [
"#@title Helper function for plotting\n",
"# colorline taken from https://nbviewer.org/github/dpsanders/matplotlib-examples/blob/master/colorline.ipynb\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.collections import LineCollection\n",
"from matplotlib.colors import ListedColormap, BoundaryNorm\n",
"\n",
"def make_segments(x, y):\n",
" '''\n",
" Create list of line segments from x and y coordinates, in the correct format for LineCollection:\n",
" an array of the form numlines x (points per line) x 2 (x and y) array\n",
" '''\n",
"\n",
" points = np.array([x, y]).T.reshape(-1, 1, 2)\n",
" segments = np.concatenate([points[:-1], points[1:]], axis=1)\n",
" \n",
" return segments\n",
"\n",
"def colorline(x, y, z=None, cmap=plt.get_cmap('copper'), norm=plt.Normalize(0.0, 1.0), linewidth=1, alpha=1.0):\n",
" '''\n",
" Plot a colored line with coordinates x and y\n",
" Optionally specify colors in the array z\n",
" Optionally specify a colormap, a norm function and a line width\n",
" '''\n",
" \n",
" # Default colors equally spaced on [0,1]:\n",
" if z is None:\n",
" z = np.linspace(0.3, 1.0, len(x))\n",
" \n",
" # Special case if a single number:\n",
" if not hasattr(z, \"__iter__\"): # to check for numerical input -- this is a hack\n",
" z = np.array([z])\n",
" \n",
" z = np.asarray(z)\n",
" \n",
" segments = make_segments(x, y)\n",
" lc = LineCollection(segments, array=z, cmap=cmap, norm=norm, linewidth=linewidth, alpha=alpha)\n",
" \n",
" ax = plt.gca()\n",
" ax.add_collection(lc)\n",
" \n",
" return lc\n",
"\n",
"def plot(coords):\n",
" x,y = coords.T\n",
" lc = colorline(x,y,cmap='Reds')\n",
" plt.axis('square')\n",
" return lc"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "bb0548fb",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
},
"id": "bb0548fb",
"outputId": "3e967b86-32e9-4be3-e5b7-c8a457aa12a7"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.LineCollection at 0x7f15aabac8b0>"
]
},
"metadata": {},
"execution_count": 9
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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dfy8BDTjUwRQQQOL993pN7V++fBlhgwadN/psUEoKSTM/A4uZnCmTqNrl3q2n/kr+ug2EtWpFhIviAfqc+cHR9C9wQ7+/cv9+9j3xJBv7D6BszTo6/+dFus//mtjJEzE18pDHlldNojovj9IVKw1W23Ac5h/iVFpTaCgJf3+G6Nt+w8GbbqB01qe6G+Bi9syb75JR/pP4pPlbDhlE6bbtVBUVG563UoqSH1eSedM0to25AgkIpOe3C0h94/8I75/e5LlWsVhoff+95D33gkfNo6qrqVi1itCBAxv0vogrJ5H03occe3cGBQ/9QXcDXIgrlvTWxSfNbw4OJnboYAoXGrfF0V5ZScFHn7B1+Gj2PvwoUcOG0mftKto//ihBSadvYmwaLSddSU1BAWU//Ghovg3hxMaNBHbogKVlw8+iC+zs6AZIYCA5V0+ialemCxT6N6UHcqjIzychva/L7uGT5geIN2jKrzo/n5xnn2dj+gCK53xNuz89Qq+li0n49Y2Yw1wTaVUsFhIfuM+jtb+jyd/4uWNTSAjxf3uamDvu4uBNN1Ly6UzdDTCQvfMXkjxqBCYXHkDru+YfeRnFP6zAWt64RSjWsjKy77mPzYOHU1NcTNrsmXT96H2iLhvmluOXYq4cj/XoMUqXLnf5veqjfNkyQgc1/eSj8PETSPrgI0ref5f8Pz6IraTEAHUaVzf5wYfNHxAVRVS/PhR9v6RR78/590sos4k+q5bT8Z//ICQ1xViB50HMZhIfvI+8591f+1sLCqg5mEtIr96G5BfYsRNJMz/DEp9A3vgxFP/5MSo3rtctgUZSU17OoZWraDd8mEvv47PmB0gYN6bRTf+y9RuJmzQRS3S0waqcJ/qKcdjKyij9bolb71v+w3JCLxmIGBg33hQcTOwfHqL1p59jaZ9M8Z//RN4VGZS88RrWw4cNu48/kLNkGfF9+xAUGXH+xE3Ap80fnzGKwkWLsdfUNOh9SinKd/5EaNcuLlLmHCdr/4Nurv3rW9JrFJb4BCJvuY3EL+cR+/dnseblcWjyePJ/M43yb+aiDAxD1VxxR5MffNz8IYmJhCa352gDV8xVHzqEKSCAwHjnQ4m5iujLx6EqKyn51j3RiZXNRsWKHwm71LUnHYsIQb160/LPT9Fm8TJajL+S47NnkTt8MMV//TNVW7fobkE9KKVctovvdHza/ACxw4ZQsPi7Br3HG2r9k4jJROKD95P3/L/dYobKrVsxx8UR0Lq1y+91ElNwMGHjriDhjRm0/vRzzPEJFD30IIcmXk7JjDexFRka9MmnKdy0hYDwFkR37uTye/m8+Su276BFu7bnT1j3PTt2EuYl5geIGpuBstkoMXDdwtkoX768SVN8TcXSOpGo39xF4tyFxDz+F2r27CZv/FgK7rmD8kULUDXeHfXI1eydv5COYzLcci+fNn/Rwm+pyN5Nmxuua9D7ynf+5NRGHHdxsvY/6Ibav3z5Upf19xuCiBDcL53Yv/6DNt8uIXTkaMr+9wG5wwdz5Jm/Ub3T+2I2uIM98+aTnDHSLffyWfPbTpwg89HHueCZv2NqYDhjb2r2nyQqYxSCcMyFG5Zsx45RnZVFSL96T2z2GKbQMFpMmEirGe/T6sOZmFqEU3Dv3eRdNYHSD97FduSIpyW6DWkRDm46vddnzb/vpVcI79mDlsOc25hyEntNDZV79hCamuoiZY1DREj8w/2OeX+73SX3KF/xIyH9L2xwYelOAtq2I+qee2kzfzHRv3+Y6m1bOXj5KAruu4eK7xejGjiz42t0GjOKTW/McMu9fNL8Fbv3kDvjXVL/9mSD33siezdBbdpgDg0xXlgTiRw5AgkMpHjmLJfk78opPqMRk4mQiwcQ+8zzJC34npABl3DkqSeo3rnd09JcSo9fXc+ehd9SlnfI5ffyOfMrpch85HHa//ZughMTG/z+Ci9s8p9EROjw8gscfv558l/+D8pmMyxvpVST1/N7CgkKonrFckJ69ybwHIFHmgPBUZF0vXoSm956x+X38jnzF3w9j8q8PNr95tZGvb98509eNdJ/OiGpqVzw9RyOr1jB7qnXUp1nzJmnVZmZmIKDCWzf3pD83IWqqqL4wd9CQAAx/3wRCWhcHAVfou8dt7L5rXcNPZevPnzK/Nbj5ex6/Em6PPs0poCARuVRvvMnQr1opL8+Alq1ouOHHxA+dChZl4/n2Nx5Tc6zfNlSwgY3bHzE06jKSorvvwtTaBgx//gX0sj/ua8R27ULcd3TyJz9pUvv41Pm3/vCi0RfcjHRAwc0Oo+Knd41x382xGwm4e676PD2mxx65lly/vgwtiaE0a7wsSa//cQJiu+9A1NUNNF//6ffGP8k/e76Deteed2lU78+Y/7jmbvI+9/HpPz58UbnYS0tpebIUYJ9qOkb2rs3qd/MRdVUkzX2ciq2bmtUPnaThUAvm+E4G/aKcop/ezvmhFZE//VZQzcg+QqdMkZSeewYeS48ns4nzK+UIvOhR+nw4P0EJcQ36L12q5Wyrds4+PY77LznPrft1zcSc4sWtPv3CyTcdy97bvwVBdPfaPB0oDkujpKvv3aRQuOwlx+n+J7bsbRtT9STT9cbBt0fEJOJvr+Zxvr/TnfdPTy1uSI9PV2tW+dcqXZo1mz2v/oaFy6ah+k8tUDNkSOUrN9A6dp1lKxbT9mmLQQltiYyvR8R6f2IzRhNYAMi7nobVQdyOHDvvZhCw2j3wr8IcLIwPLFlCwfuuovUpUu91lD2sjKK77kNS8oFRD36Z58rpI2m8lgJr3XtzbT1KwhPbNxeDBFZr5Sqd1WX17enrKWlZD35N3rOmH6G8ZXNRnnmLkrXradk3XpK1q6jOr+AiD69ieifTru77ySibx8CPLhn32iC2rWl86czyX/pZXaNHUfbZ58hYsTw874vpGdPLLGxlH3/PREjRrhBacOwl5VSdNetBHbrTuRDjxt+KKUvEhwVSdo1V7HprXcY9Pgjhufv9TV/5qNPYKuoIO3F56kpKaF0/UaH2deuo2zjJgJiW56q1SP7pxPW5QKvrdmM5vjqNRy4734iR46k9aMPYwoOPmf6o7NnU/LFFyS/956bFDqHveQYRXfeQmCfdCJ//4g2fh2Kfsrk44wJ3JG5GUsjVmaeq+b3avMfW7eejVOuIz5jFOXbtlOZk0t4r55E9O/nMHy/fj7dhDcC67ESch95lKrdu2n/n5cJvuDsg3r2qioyBw6k48yZBHXs6EaVZ8d29CjFd95C0EUDiLjvD9r49fDJFZPpdu0Uul93TYPfey7zo5TyyE+/fv3U+ajML1Abrp6qct58W5Vu3qJsNTXnfY8/YrfbVfEnn6itvfqownffU3a7/axpDz37rMr7y1/cqO7clM14Qx175q/n1OzvZM2dr9655LJGfUbAOnUWDzo1oiIiGSKSKSLZIvLwWdJMEZEdIrJdRP7X4CKqHoLi4+gz8yOSpt1MeM8e5x3s81dEhJgpU0iZPYsjH3/Cvltvw3qWnXAx113Hsc8/95qjt2vWrCB40BBd458DV037ndf8ImIGXgXGAGnAtSKSdlqaFOARYKBSqhtwn6EqNU4R1LEjnb+YTVDHTuzKGFvvoSCBSUmE9u/PsS++cL/AerDlHcTcpmHBWPwNW3U1Cen92Dnrc0PzdabmvxDIVkrtUUpVAx8DE05LcxvwqlLqKIBSqsBQlRqnMQUGkvjYI7R9/jkO3P8AeU//A3v1L6PjtPzVryh+7z2Px9BTNhu2gsOYWzd8g5Y/cGT3HhY9/Dgvde5BxZGj9Lz5RkPzd8b8bYCcOs9za1+rSyqQKiI/isgqEak3DpGI3C4i60RkXWGhjtvmSsIHD+KC+fOoysome+JkqvbsOXUtbOBAVE0NFWvXelAh2AsOY4qOQRp56GlzxG618tOXX/PhuEnMGJqBmM3csmwR1331KfEG70kxqhNtAVKAoUASsExEeiiljtVNpJSaDkwHx2i/QffWnAVLy5Ykv/0mxe+9T9akq0h89GGir74aEaHlDTdw5P33CbvwQo/ps+UdxJxo7DmIvkpZ3iE2znifjW+/R2S7tvS7/RaumTS+UdN7zuKM+Q8CdTtlSbWv1SUXWK2UqgH2isguHIWBZ6sWDSJC7K9/RdjFF3Hgnt9RumQpbf/xNFGTJ5P/4ovUFBQQEN+wJdNGYTuYiznRNWfP+wJKKfZ9v4x1b7zNviXL6XbVRKZ+8QkJPbq55f7ONPvXAiki0kFEAoGpwJzT0nyBo9ZHRGJxdAP2oPEaQi64gJSvvsQSG0vmmLGc+CmTqCuu4OhHH3lMk7/W/CeOHmPVy//lvz0vYuEfH6PDsCH8btdmxv7nX24zPjhR8yulrCJyD7AAMANvK6W2i8hTOOYQ59ReGyUiOwAb8AelVLErhWsajik4mKSn/kLp4MHsv/NOIkePpvjDD4m76y6PbJm1Hcwh8KJL3H5fT5G3bgPrXn+LzK/m0TljFONff5mkARd5bJrTqT6/UmoeMO+0156o81gBD9T+aLyciBHDSZ03jwP3P4C1vIIjH39MyxuNHUl2BkfN37yb/dXl5WyfOZv1b8zgxJGj9LvtZoY//RfC4mI9Lc37N/ZoXENAQjwdP3iPQ8/+k5A+fTyiwZaX22yb/YU/ZbLhjRls/ehT2l5yMUOffJROIy7zqp2K2vx+jJhMJD5S74JNl6MqK7GXlmCK88xgoyuwVVfz05y5rJ8+g+JdWfS+6QZuW7WUyHbe2brR5td4BNvhPMwJrZrFDsySA7lseOtdNr37AS1TU+h3+810GT8Os5evX9Dm13gEX5/mU3Y7uxctZt30GeSuXE2Pa6/mhvlfENflAk9Lcxptfo1HsObm+GR/v7ywiM3v/Y/1b8wgJCaafrfdzKT33iAwLMzT0hqMNr/GI9h3biXgwsZHYXYnSilyV65m3fQZZM9fyAXjxzH5g7dITO/raWlNQptf43aU1UrNyuWE3nWfp6Wck6qyMrZ+9Cnrp7+Ntaqa9NtvJuPfzxISHeVpaYagza9xO9YtGzEltsEc38rTUurl0JatbJg+gx2ffUHy0EGMeu5pkocOanYxB7T5NW6nevn3BF46zNMyzsoP//oPmd8soseUifT91XUk9e/X7IwPPhK3X9N8UEo5zD/Ye81/1Tuvc8vCOYS2bMln0+7muU7dmfvgI+z7YQV2Aw9P9TS65te4Fdve3WCzYe7svVNiIkJi7x4k9u7ByL88RsGOn9g2ew5f3ftHjhcWkTZhHN0nTSB50CWYfTi0nFdH79U0P068+wb24kLCHnjU01IaRVHWbrZ/Podts+dw7EAOaePH0W3iFXQcNhiLFy7q8dnQ3ZrmR8mt1xFy+28J9JFpvnNxdN8Btn8+h+2fz6FwVzZdxo6m26TxdB4xjIDznKHgLrT5NV6BvbiIY9eOJ3ru0mZ36m5J7kF2fDmX7Z/P4dDmraRmjKTbxPGkZowgMDTUY7q0+TVeQeWXs6hZv5rwp57ztBSXcjy/gB1z5rJt9hxy166n8/BhdJ80ngvGjiIoPNytWrT5NV5B6e/vJmjUOIJGjfW0FLdRUXyEHV/NY/vnc9j/wyo6DLmU7hPH0+WKMYRERbr8/tr8Go+jTlRw9PJhRH2xCFN4hKfleIQTx0rInDufbbPnsGfpctoNuJDukybQ9YqxhLno2DmfPqVX0zyoWbMSS1oPvzU+QEhUJL2vv4be119DVVkZmd8sYvvnXzHvD4+RlN6XbhOvIG3C5YS3SnCLHm1+jVuoXv49AYOGelqG1xAUHk7PKZPoOWUS1RUVZC38ju2zv2Th43+lVY9udJs4nm5XXk5kkut2Pupmv8blKJuNo1cMI/KtjzC39r1tvO7EWlVF9rffs/3zr9j59TfEpnSm28Qr6DZxPDEd2jc4P93s13gU67bNmFrGauM7gSUoiC7jMugyLgNbTQ17lixn2+w5vHrxMEb9/QkuuvUm4+5lWE4azVmoXv49gYMu87QMn8McEEDKyMtIGXkZMamdyduy3dD89cYejcupXr6EQN3fbxLF2XuIT+1saJ7a/BqXYtu/F1VRjrmL+06iaY4U7comLjXF0Dy1+TUuxdHkH9os98O7k8Ks3cSmdjI0T21+jUtxmN979+77AtXl5VQUHyGqXdvzJ24AesDPx1BlpVh3bEFEkMQkTK0SEYv3bZJRVivV8+egqioJ6HeRp+X4NEVZu2nZqQMmg0/70eb3EVR1NTVfzaT6k3cx9R+IKsjHnpeDKipAYuMxJSY5flq3xdSmbe3ztkhIiHt1WmuoXvA1Ve9Nx5TQmhaPPIl44T53X6JgVzZxBg/2gTa/16PsdqxLF1I147+YkjsR8s/XMCf/3PdT1hpU/iHsB3Ow5+Viz8uhZttGx/PDeUhoWJ3CoLZAqH0uEVGG9cVVTQ3V8+dQ9f4bmFonEfrIX7H0rndtiaaBFO3KJjZFm9+vsG5eR9WbLwMQ/OATWHqdaSaxBCBt2mFq0+6Ma8puRxUXYc87UFsw5FKzcumpggKlMLVO+mXh0Kado2CIjXfqUElVU0P1vM+p/OAtzG3bE/qnp7H09O149t5G4a5sUkYMNTxfbX4vxLZvN1Vvv4J9/x6Cbr4Ly+CRjTrdVUwmJC7ecRhmPQWHKi2pLQgchYFt22ZqFn7t6E6UlWFqlYgp8eeCQU4WDq0SQSmq5852mD65E2F/fhZL994G/PWa0ynalc0ld95qeL5OmV9EMoCXADPwplLqmbOkmwzMAvorpfTC/UZg3bKByr89RODUmwn407Mu7S9LRCTmiEjMXbufcU2dOIH9UO6proRt/x7sK5dhz8tFFeVj6toTAgIIe+p5LN16uUyjv6OUonBXNrGe6POLiBl4FRgJ5AJrRWSOUmrHaenCgXuB1Yar9COsP3xLwPXTCJww1aM6JCQEc8cUzB3PXFiirDWoykpMLdwblcYfOV5QiCnAQljLGMPzdqYteSGQrZTao5SqBj4GJtST7q/As0Clgfr8jxMVXr/nXSwB2vhuojAzizgXDPaBc+ZvA+TUeZ5b+9opRKQv0FYpNddAbX6JOlGBhHgu4KPGuyjM2k3cBZ4z/zkRERPwAvCgE2lvF5F1IrKusLCwqbdunpyoAG1+TS2umuYD58x/EKi7rjCp9rWThAPdgSUisg+4GJgjImcMLyulpiul0pVS6XFxcY1X3YzRNb+mLoW7sjxa868FUkSkg4gEAlOBOScvKqVKlFKxSqlkpVQysAoYr0f7G4c6UY6EhHlahsZLKN6zj9jOxm7oOcl5za+UsgL3AAuAncBMpdR2EXlKRMa7RJU/o5v9mjqUn6jE7KLTf5ya51dKzQPmnfbaE2dJO7TpsvwX3ezX1CWsZQzlRcWQYnztr1f4NQBVWYH1x8VQXg7BwUhgsON3UEjt72AICobgkNrHIWCxOL1+XikFlSfAzZtxNN5LeHwcZfkFLslbm78B1Hz8BvbDeZgS2kBVJfaqE1BZiaqqhKpKVO1zqk6gqqqg6gTYVZ2CIQSCgpFgRyEhtc9PXlfmADBbELP+t2gchMfHUVbgmpkx/S1zElVehm3ZQoKfm4G0jHf+fVarozCoLRQchUQlVJ6o/f1zwSFVlQT/7SUX/hUaX8NR82vzexTr4q8x97m4QcYHEIsFLOFImF4Rp2k44QnxFGbtdkneOoyXEyirFdv8WVjGTfG0FI2f0SI+ltIC1/T5tfmdwLZ6CZLQBlPHCzwtReNnhCfEc7ygyCV5a/OfB6UU1q8/wXL5NZ6WovFDXDnar81/Huw7N8OJckx9BnhaisYPCU+Id9lovzb/ebDOnYll7JRGRdLRaJpKaEw0laVl2GpqDM9bf6PPgf1QDvbMrZgHj/a0FI2fYjKZaBHbkuOFxvf7tfnPgfWbWVhGjEeC9Yo7jedoER9HqQv6/dr8Z0EdL8X2wyIsoyZ6WorGzwlPiOO4C/r92vxnwfrtHMz9BiIxsZ6WovFzXLXKT5u/HpS1Buv82VjG6ek9jecJj3fNiL82fz3YVnyHqU17TMmuiaCi0TSEFvGxLpnr1+Y/DaWUY3rvcr2UV+MdRCTE69F+d2DfvhGqqzD10ifLarwDPdrvJqxzZ2IZpxf1aLwHPdrvBux5B7Bn79CLejRehR7tdwPWeZ9iGTkBCQzytBSN5hQt4uM4XljkCPNmINr8tajSY9hWLNaLejReR0BQEIGhoVQcPWpovjqSTy3WRV9ivnAwEmX8gYj+hrJaIf8AqqTYMXYSHQ/R8bpF1QQc/f4iwmKM+35q8wOqphrrws8JeuwFT0vxOZTdDsWHUAf3onL3oA7ugYJciElAkrugyo7CkXw4VgTBoY7XY+IhOs7xODoeYuKRYB2u/FycHPFP6JJqWJ7a/IDtx8WY2nXC1K6jp6V4NUopKD3ys8kP7kHl7YPQcCSpI5LYAVOPi6B18hm1vFJ2KD0GR/JRRwvgSAFqx1rUEcdjLAGOQiDG0UpwFAxxEBPvyN/J8OfNFVeM+Pu9+R2Lej4h4Pq7PC3F61AVx382+cG9jsdKIW06Ikkd4dJxmBI7OBWcVMQEkTEQGYN06PrL+ygF5aWOAuFogaOlkLUZ+9ECKM6HvkMwj/TvRVfhccaP+Pu9+e1b14PdjqlXf09L8SjKakXl7YHaWl0d3APlpUhiB2jTEel9KaZxN0JkS8NrYRGBFpHQIhJpl/JLXYcPYJ/7rqH380VcEdHH781vnfuJY1GPHzYrld2O2rsDtXklavsa6JiGRLZEOvfANHQCtGzt+cVOZgvYbJ7V4AW0iI/l4Kathubp1+a35+7DvjeLwAf/5mkpbkMpBTnZ2DevQG1dBeFRmHpdgumepx19bG/DbAGb8SGsfI2IhHgyDV7f79fmt86diWXUlc1+CkopBYcPOAy/ZSVYAjD1HIDptseRuERPyzs3FgvYrJ5W4XFcsb7fb82vSo5iW72E4Bf/52kpLkMVHUZtXoF9ywqorkJ6DsB8w/2O0Xhf6eZYAsCqa35H/H7d5zcE68IvMF88DImI8rQUQ1ElxagtK7FvXgElR5AeF2GedDu07ez5/ntjMOuaHyA8PpYygw/v8Evzq+oqrIu+IOiJ5nEopjpeitq22mH4/BykW39MGdciHdIQs9nT8pqG2QJWbf7giAhs1dVUnzhBoEFHuDtlfhHJAF4CzMCbSqlnTrv+AHArYAUKgVuUUvsNUegCbD8swtQhFVNSsqelNBpVWYHavha1eQXqQBZyQW9Mg8Yhqb0QS4Cn5RmH2QJ2G0rZHWsF/BQRITzesdAnpn07Q/I8r/lFxAy8CowEcoG1IjJHKbWjTrKNQLpSqkJE7gT+CXhlALyTkXoCfv07T0tpMKq6CpW50WH47G1IxzSk3xBMN9yPBAZ7Wp5LEJGfp/ss/mt+OBnOy43mBy4EspVSewBE5GNgAnDK/Eqp7+ukXwXcYIg6F2DfvAZMZkw9+nlailMoqxWVtQW1ZQXqp41IUiek1wBMk29HQlp4Wp57ONn0b04tmkZg9EIfZ8zfBsip8zwXOFeMq2nAN/VdEJHbgdsB2rUzpvRqKNa5n2C53LsX9Zyx+CaujWNqbuwNSHiUp+W5n1Nz/f59eIrRh3YaOuAnIjcA6cCQ+q4rpaYD0wHS09ONjUzgBPYDu7Hn7CXwkuHuvvV58bnFN+7EEqAH/ag1v5tr/oNA2zrPk2pf+wUiMgJ4DBiilKoyRp6xWOd+imXURCQg0NNSAB9ffONO9Co/wLHQ5+iBXMPyc8b8a4EUEemAw/RTgevqJhCRPsDrQIZSyjWHiTcRdawY29plXrOox36iHPv0v0JluW8uvnEnepUf4Ojz56zfaFh+5zW/UsoqIvcAC3BM9b2tlNouIk8B65RSc4DngBbAp7Vf3gNKqfGGqTQA68IvMF8y3HsW9ezZCUHBmH/7tG8uvnEneq4fqF3o4+4+v1JqHjDvtNeeqPN4hGGKXIBjUc+XBP3lFU9LOYVERENlhTa+M+hVfsDJ0X7jVvn5xTfPtnQ+ppQ0TImemWGol8gYKDniaRW+gSVAmx/jR/ubvfmV3e4IyW3woZtNDqPcIhIqyx3BLjXnxmzRm3uAsNiWVBw9ht2g+AbN3vz2TashMAhTWm/j8szcQs3vr8eeu7fReYjJBC2ioMzYcMzNEl3zA2C2WAiJiuR4UbEh+TV78zsW9Vxj2Ci6PWsb1leeQiKjoexY0zKLiNZNfycQswWla36g9tBOg+b6m7X57fuysB88gHnAMGPy270D63/+guX2h5DYBKg60aT8JDIGVapr/vOiB/xOYeRx3c3a/Na5M7FkTDJkl5t9bybWl/6MZdrvMfXoD0HBUFnZtEwjYqBU1/znRc/zn8LIEf9ma351pAjb+hVYRjR9uYF9fzbWF/+E5eb7Tx3dLcEhqKbW/BHRKN3sPyfqRDnqeIme56/FEcLbmJq/2QbzsC6YjfnSkUiLiCblY8/Zi/WFR7Hc+DtMfS75+UJQcJOb/UTGwOGc86fzM9SJ46jsLajMjZC3F9p3QfoO9rQsryA8wbj1/c3S/KryBNbFXxH0t/9rWj4H92P918OYr7sLU/qgX14Mj0btWI894ORJM404XSYiBnSfH6g1fNYW1K4NkLcPkrsi3Qcg429t9gFWG0KL+DgKsxs/y1SXZml+29L5mLr0xNQqqdF5qEMHqHn+IcxTbsN80dAzrptGTkJZTKjiw5C1FfvJY6dsNoiJO3XUlMTEO56fPIYqMhoxOUJrSUQ0dj/u89dv+EuQ8bdpw5+F8IR4jhfqmr9eHIt6ZhJwx8ONz+NwLjXPPYR58s2YL6l/5bKYTMjwSWe+90QFHC2oPYOu0PH74N7awqEQKkohuQuYTEh4JJQYM2frK6iK46jszY4m/aF9SHJXTD0uAW14pzBylV+zM799wwoIDcfUpWej3q8KDjlq/Ak3Yr50dIPfLyGhEJKMJCbXn39NDepoEZQWw9EiZOiERun0Jeo1fM+BMEEbvqE4+vzGjPY3O/Nb585scKQeVXEc+87N2LdvQOXnYh47FfOQsS7RJwEBSHxriG/tkvy9BVVRhsrajNq1EQ4f+NnwV97uNfEUfJGTNb9SqskL15qV+e17MlH5efX20euiaqqxZ+/Avm099h0bUbl7MXVOw9StH6bLrsDsw1F9PUm9hu91KVzZTRu+iShlB5uVABOYAyxUlpUREtG0maxmZX7r159gHjMZsfzyz1J2O+rAbuzb12PfvgF71g4ksR2m7v2wXD0NU+duSKD+cjaGU4bP3Aj5Jw0/CK5Ma3aGVzVVoBRYAn+xFVvZbY6NRzar4+e0x8pmdUQistb+Pj2NrcaxwetUunrystvAHAAWC1P/dBdmS9Ot22zMby/Kx7ZpNcHT7nc8L8jDvm2Dw/A7NiIRUZjS+mC+bDwBdz+BhPlJ5FsXYS8+hFr0CRQeRDqkYeo9CDo0P8PXxf7tx3D8qMOggcFgMteuPFSnjIk5wLEc2WxxbEgyBzgqI3Oda5YAx/tPpbFgsgSclua09CbzqWZ+vyuM+XuajfmtX32MKbUb1o9fx75tA8pag6lbX8y9Lybg+rsc8/Aa48jPgeNHkVuewBQW7mk17qH8GKZx0yCkBaqmGhHAbDk1detrNBvzi8XiONWlbUcCMq5CEtvreHguRLr2h0P7UAs/RE24zWcN0CCCwxzRl0LDm8UsRbMxf8CNd3tagl8hIjBsMurLN1BLZsOwq5p/YRsUCpXlnlZhGM12Y4/G9YjJjGnsTai8PaiNSz0tx+VIcBiqssLTMgxDm1/TJCQoGNP421Hrv0ft3uppOa4lWNf8Gs0vkIhoTOOnYf/2Y1R+M96lWNvnby5o82sMQRLaYRp+Dfav3kS5MS6hstagsrdg37EW1dSwauejmdX8zWbAT+N5pHNPpKQY+5fTMV19LxLkmmPDlbJD3l5U5gbUnq0Q2wZatkKtmY906on0GYq0iDT8vhIchr0Z1fza/BpDkb5D4Vgh9nnvYDJ4ClAdyXcYftcGCApGUvthmvoA0iLKcT19OGrzcuyzXkY693IUAmFNWwL7C3TNr9GcnV9OAX4OwyY3aQpQlZeisjY5DF9RhqT0wTTuZiT2zINMJaQFcvEYVM9LHYXApy8hKX2QPkOQUAMWIjWzPr82v8ZwTk4B2me+BBuXOloDDUDVVKP2bHUYPj/HsXx4wFho09mp480kNBwZMNZRCGxahn3mi5DaF+k5EFNtK6FR1Nb8Ruyo8wa0+TUuQYKCMU24DfsnL6EiWyKdepwzvbLbIDfb0azft8NxYvEF6UjGrxu9X0DCIpCBl6N6D0L9tA71xWvY4pIwpfaBdhcg5oZ9/SUgEEQcm22awR4GbX6Ny5CIGExXTMP+5euYWkQhCW1/cV0pBUV5qMz1qKxN0CIKSe2LaeAVSKhxG68kLBLpNxzVaxBq307sP62FlXORjj2Q1L5ITILzmQWHOfr92vwazbmRVj9PAZquuQ8Jj0aVHUXt2ojK3ADWauSCvpiuvAOJjnetFksg0rkXdO6FKj2CytqEfdGHENICSe2DdOiOBIWcO5OTg37h0S7V6g60+TUuxzEFWIR99msQEQXFh5HOPTENnQyt2yPi/uUmEhGD9LsM1Wco5O1G7dqIfd23SNtUJLUvtDqLrmY06KfNr3EL0ncYxLZBrNWOKL0N7G+7CjGZICkFSUpBVZajdm/FvuobsNYgKb2Rzr1/sWZAgkNRleX4/nCfk+YXkQzgJcAMvKmUeua060HAe0A/oBi4Rim1z1ipGl9GRJD2F3haxjmR4DCk28WotIugOM/RGvjyNYhtc2qQ0K9qfhExA68CI4FcYK2IzFFK7aiTbBpwVCnVWUSmAs8C17hCsEbjakTE0UqJbYO6cBRq/0+nBgmJiofo5hEYxpma/0IgWym1B0BEPgYmAHXNPwF4svbxLOAVERGllDJQq0bjdsQSiHTqCZ16okqPYN+5Bknp42lZhuDMSEsboO5Wrdza1+pNo5SyAiVAy9MzEpHbRWSdiKwrNOjUEY3GXUhEDOaLMjA1ZGrQi3HrMKtSarpSKl0plR4X1zyaThqNr+KM+Q8CdVdnJNW+Vm8aEbEAkTgG/jQajZfijPnXAiki0kFEAoGpwJzT0swBfl37+CrgO93f12i8m/MO+CmlrCJyD7AAx1Tf20qp7SLyFLBOKTUHeAt4X0SygSM4CgiNRuPFODXPr5SaB8w77bUn6jyuBK42VppGo3ElOoyXRuOnaPNrNH6KNr9G46eIpwblRaQQ2F/PpVigyM1yGorW2HS8XR80D43tlVL1LqrxmPnPhoisU0qle1rHudAam46364Pmr1E3+zUaP0WbX6PxU7zR/NM9LcAJtMam4+36oJlr9Lo+v0ajcQ/eWPNrNBo3oM2v0fgpHjO/iGSISKaIZIvIw/VcDxKRT2qvrxaRZC/U+ICI7BCRLSKyWETae5O+Oukmi4gSEbdPWzmjUUSm1H6O20Xkf96mUUTaicj3IrKx9n891s363haRAhHZdpbrIiIv1+rfIiJ9ncpYKeX2Hxy7A3cDHYFAYDOQdlqau4DXah9PBT7xQo3DgNDax3e6U6Mz+mrThQPLgFVAuhd+hinARiC69nm8F2qcDtxZ+zgN2OdmjYOBvsC2s1wfC3wDCHAxsNqZfD1V85+KC6iUqgZOxgWsywTg3drHs4Dh4t4D0s6rUSn1vVLqZCjXVTgCnXiNvlr+iiOgaqUbtZ3EGY23Aa8qpY4CKKUKvFCjAk4e9xsJ5LlRH0qpZTi2yp+NCcB7ysEqIEpEWp8vX0+Z37C4gC7EGY11mYaj9HUX59VX2/xrq5Sa60ZddXHmM0wFUkXkRxFZVRsm3p04o/FJ4AYRycWxtf237pHmNA39rgL60A5DEJEbgHRgiKe1nEQcx828ANzkYSnnw4Kj6T8UR8tpmYj0UEod86So07gWeEcp9S8RGYAjcE13pZTd08Kagqdqfl+IC+iMRkRkBPAYMF4pVeUmbXB+feFAd2CJiOzD0Rec4+ZBP2c+w1xgjlKqRim1F9iFozBwF85onAbMBFBKrQSCcWyo8Rac+q6egTsHLuoMUFiAPUAHfh5k6XZamrv55YDfTC/U2AfHYFGKN36Gp6VfgvsH/Jz5DDOAd2sfx+Jovrb0Mo3fADfVPu6Ko88vbv4skzn7gN84fjngt8apPN35B5wmeCyOUn438Fjta0/hqEHBUbp+CmQDa4COXqjxWyAf2FT7M8eb9J2W1u3md/IzFBzdkx3AVmCqF2pMA36sLRg2AaPcrO8j4BBQg6OlNA24A7ijzmf4aq3+rc7+n/XyXo3GT9Er/DQaP0WbX6PxU7T5NRo/RZtfo/FTtPk1Gj9Fm1+j8VO0+TUaP+X/Abem6TLSC0KwAAAAAElFTkSuQmCC\n"
},
"metadata": {
"needs_background": "light"
}
}
],
"source": [
"plot(nodes_coordinates[resulting_traj])"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
},
"colab": {
"provenance": []
},
"accelerator": "GPU",
"gpuClass": "standard"
},
"nbformat": 4,
"nbformat_minor": 5
} |