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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Version 1 Simulation\n",
"\n",
"The first version of this series is a basic control model. Given an elevation profile $H(x)$ and a time target, minimize energy usage.\n",
"We assume the time target is constant, since we are racing at a given overall pace. In other words, we already know the average speed $E(V) = dist/time$"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import jax.numpy as jnp\n",
"from jax import jit, vmap, lax\n",
"from jax import random\n",
"import matplotlib.pyplot as plt\n",
"\n",
"@jit\n",
"def _cov_math(t, s, H):\n",
" return 0.5 * (jnp.abs(t) ** 2 * H + jnp.abs(s) ** 2 * H - jnp.abs(t - s) ** 2 * H)\n",
"\n",
"\n",
"def _fbm_covariance(n, H) -> jnp.ndarray:\n",
" tidx = jnp.arange(1, n + 1)\n",
" t, s = jnp.meshgrid(tidx, tidx)\n",
"\n",
" # fBm covariance equation from wikipedia\n",
" cov = 0.5 * (jnp.abs(t) ** 2 * H + jnp.abs(s) ** 2 * H - jnp.abs(t - s) ** 2 * H)\n",
" return cov\n",
"\n",
"# generate terrain using fractional brownian motion\n",
"def gen_elevation_profile(rngkey: random.PRNGKey, n_steps: int, H: float):\n",
" t = jnp.linspace(0,1,n_steps)\n",
" cov = _fbm_covariance(n_steps, H)\n",
" # using the \"method 1\" (cholesky decomposition)\n",
" sigma = jnp.linalg.cholesky(cov)\n",
" # create a vector of n_steps gaussian normal values\n",
" v = random.normal(rngkey, shape=(n_steps))\n",
" # convert these to fbm lines\n",
"\n",
" fbm_samples = sigma * v\n",
"\n",
" return t, fbm_samples\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[nan -0. -0. ... -0. -0. -0.]\n",
" [nan nan -0. ... -0. -0. -0.]\n",
" [nan nan nan ... -0. -0. -0.]\n",
" ...\n",
" [nan nan nan ... nan -0. -0.]\n",
" [nan nan nan ... nan nan -0.]\n",
" [nan nan nan ... nan nan nan]]\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x600 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"key = random.PRNGKey(0)\n",
"steps = 1000\n",
"samples = 5\n",
"\n",
"H = 0.6\n",
"\n",
"t, fbm = gen_elevation_profile(key, steps, H)\n",
"plt.figure(figsize=(12,6))\n",
"print(fbm)\n",
"for i in range(fbm.shape[0]):\n",
" plt.plot(t, fbm[i], label=f\"Sample {i}\")"
]
},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
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"[<matplotlib.lines.Line2D at 0x7101983fd730>]"
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]
},
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"execution_count": 3,
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"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": [
"steps = 100\n",
"key = random.key(25)\n",
"\n",
"def uniform_window(n):\n",
" return jnp.ones(n)/n\n",
"\n",
"def generate_basic_terrain(key, steps=100, yscale=1.0, xscale=10.0, window=uniform_window, window_size=5):\n",
" key, split = random.split(key)\n",
" v = random.normal(split, shape=(steps))\n",
" y = jnp.cumsum(v) * yscale\n",
" # smooth with a windowing function\n",
" y_smooth = jnp.convolve(y, window(window_size), mode='same')\n",
" # compute the x-values\n",
" x = jnp.arange(steps) * xscale\n",
" return x,y_smooth\n",
"\n",
"\n",
"x,y = generate_basic_terrain(key)\n",
" \n",
"slope = jnp.atan(jnp.diff(y, prepend=0) / 10.0) * 180 / jnp.pi\n",
"plt.plot(x,y)\n",
"plt.plot(x, slope)"
]
},
{
"cell_type": "code",
get things going kinda
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"execution_count": 4,
stashing
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"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
get things going kinda
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"[<matplotlib.lines.Line2D at 0x7101ac7f1070>]"
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]
},
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"execution_count": 4,
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"metadata": {},
"output_type": "execute_result"
},
{
"data": {
get things going kinda
2024-12-13 23:31:10 +00:00
"image/png": "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
stashing
2024-12-12 01:59:37 +00:00
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# we can compute the slope at any point along the terrain\n",
"plt.plot(x, slope)"
]
},
get things going kinda
2024-12-13 23:31:10 +00:00
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"%run ../src/solarcarsim/physsim.py"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CarParams(mass=800, frontal_area=1.3, drag_coeff=0.18, rolling_coeff=0.002, moter_eff=0.93, wheel_radius=0.23, max_speed=30.0, solar_area=5.0, solar_eff=0.2, n_motors=2, motor=MotorParams(kv=8.43, kt=1.1, resistance=100.0, friction_coeff=0.001, iron_coeff=0.001), battery=BatteryParams(shape=(36, 19), resistance=0.0126, initial_energy=66600.0))\n"
]
}
],
"source": [
"from functools import partial\n",
"import jax\n",
"p = CarParams()\n",
"print(p)\n",
"\n",
"\n",
"def control_fn(time):\n",
" # for the first minute, go at 15 m/s\n",
" return 10 + time * 10/60\n",
"\n",
"def wrapper(curr_state, _):\n",
" vel = control_fn(curr_state[1])\n",
" next_state = forward(curr_state, 0.1, vel, p)\n",
" return next_state, next_state\n",
"\n",
"state_init = jnp.array([0.0, 0.0, 45.5e6])\n",
"_, out = jax.lax.scan(wrapper, state_init, None, length=1000)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7101ac1667b0>]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAlkAAAGsCAYAAADuT7JwAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAAB7QUlEQVR4nO3deVwU5R8H8M/uAssNci6ngiiIgLeIt2kqmXl1aHibpqFpVprdZqVph5rmlWeeWV5ZWd54ICKIoCgKIpccAnLLArvz+4Pc4qcmGMvA8nm/XvPSnXl29zujMh9nnnkeiSAIAoiIiIioVknFLoCIiIhIFzFkEREREWkBQxYRERGRFjBkEREREWkBQxYRERGRFjBkEREREWkBQxYRERGRFjBkEREREWkBQxYRERGRFjBkEREREWkBQxYRERFVW0hICAYPHgxHR0dIJBLs27evRu//+OOPIZFIHlhMTEy0U7CIGLKIiIio2oqLi9GmTRusXLnyid7/1ltvIT09vcri7e2NF154oZYrFR9DFhEREVVbYGAgPv30UwwbNuyh25VKJd566y04OTnBxMQE/v7+OHHihGa7qakpFAqFZsnMzERsbCwmTZpUR3tQdxiyiIiIqNZMnz4doaGh2LlzJ6Kjo/HCCy9g4MCBuHHjxkPbf//992jZsiV69OhRx5VqH0MWERER1Yrk5GRs3LgRu3fvRo8ePdC8eXO89dZb6N69OzZu3PhA+9LSUmzbtk0nr2IBgJ7YBRAREZFuiImJgUqlQsuWLausVyqVsLa2fqD93r17UVhYiHHjxtVViXWKIYuIiIhqRVFREWQyGSIiIiCTyapsMzU1faD9999/j2effRb29vZ1VWKdYsgiIiKiWtGuXTuoVCpkZWU9to9VYmIijh8/jgMHDtRRdXWPIYuIiIiqraioCPHx8ZrXiYmJiIqKgpWVFVq2bImgoCCMHTsWX331Fdq1a4c7d+7g6NGj8PPzw6BBgzTv27BhAxwcHBAYGCjGbtQJiSAIgthFEBERUcNw4sQJ9OnT54H148aNw6ZNm1BeXo5PP/0UW7ZsQVpaGmxsbNClSxfMnz8fvr6+AAC1Wo2mTZti7Nix+Oyzz+p6F+oMQxYRERGRFnAIByIiIiItYMgiIiIi0gKd7fheUVGBixcvwt7eHlIpsyQREVFDoFarkZmZiXbt2kFPr2HHlIZd/b+4ePEiOnfuLHYZRERE9ATOnz+PTp06iV3Gf6KzIev+wGbnz5+Hg4ODyNUQERFRdaSnp6Nz5846MUCpzoas+7cIHRwc4OzsLHI1REREVBO60NWn4e8BERERUT3EkEVERESkBQxZRERERFrAkEVERESkBQxZRERERFrAkEVERESkBQxZRERERFrAkEVERESkBQxZRERERFrAkEVEREQ6YdGiRZBIJJg1a9Yj22zatAkSiaTKYmhoWKXN+PHjH2gzcODAGtejs9PqEBERUeMRHh6ONWvWwM/P77Ftzc3NERcXp3ktkUgeaDNw4EBs3LhR81oul9e4JoYsIiIiatCKiooQFBSEdevW4dNPP31se4lEAoVC8a9t5HL5Y9s8Dm8X1tDPEamY/8sVbD57CyfispCUU4wKlVrssoiIiOpMaEIOJm4KR5GyQmvfUVhYiIKCAs2iVCof2TY4OBiDBg1Cv379qvXZRUVFaNq0KVxcXDBkyBBcuXLlgTYnTpyAnZ0dPD09MW3aNOTk5NR4H3glq4aOXcvCrzHpVdbpSSVwbmKEptYmaGZtXPmrTeWvLk2MYaDHLEtERLohJjUfk7dcQJGyAt8dj8ecgV5a+R5vb+8qrz/66CN8/PHHD7TbuXMnIiMjER4eXq3P9fT0xIYNG+Dn54f8/Hx8+eWX6Nq1K65cuQJnZ2cAlbcKhw8fDjc3NyQkJODdd99FYGAgQkNDIZPJqr0PDFk1NLiNA5yaGOFWdjGSckpwK6cYygo1buWU4FZOCU7+X3upBHC0NEKzv4JXM2sTNLc1RXNbUzg1MYJM+uB9YCIiovoo4U4Rxm08jyJlBbq4W+H1vi209l2xsbFwcnLSvH5Yn6iUlBTMnDkThw8ffqDz+qMEBAQgICBA87pr165o1aoV1qxZgwULFgAARo4cqdnu6+sLPz8/NG/eHCdOnEDfvn2rvQ8MWTU00McBA30cNK/VagFZhUrcyilGUk4xbuWUVP6aXRnASspUSL17D6l37+F0fNXPMtCTws3aBM3t/g5ezW1N4W5rAhM5/2iIiKj+uJ13D2O+D0NucRl8nSywbmxHGOpX/6pOTZmZmcHc3Pxf20RERCArKwvt27fXrFOpVAgJCcGKFSugVCofe+VJX18f7dq1Q3x8/CPbuLu7w8bGBvHx8QxZdUkqlUBhYQiFhSG6uFtX2SYIArKLypCUU4zEv658JWYXI+FOEW5mF6OsQo24zELEZRY+8LkOFoZ/hS4TNLerDF8t7ExhayZ/6FMQRERE2pJTpMSY9WG4nV8Kd1sTbJrQCWaG+mKXhb59+yImJqbKugkTJsDLywtz586t1q09lUqFmJgYPPPMM49sk5qaipycHDg4ODyyzcMwZGmRRCKBrZkctmZydGxmVWWbSi3gdt49xN8pQkJWERLu/BW+7hQhu6gM6fmlSM8vxen47CrvszTWR0t7M3jam6Gl4q9f7U1haWxQl7tGRESNREFpOcZtPI+EO8VwsDDED5P8YW1a8+EMtMHMzAw+Pj5V1pmYmMDa2lqzfuzYsXBycsLChQsBAJ988gm6dOkCDw8P5OXlYcmSJUhKSsIrr7wCoLJT/Pz58zFixAgoFAokJCRgzpw58PDwwIABA2pUH0OWSGRSCVysjOFiZYw+nnZVtuWXlCMhu2r4Ssgqwq2cYuSVlON8Yi7OJ+ZWeY+9ufyB8NXC3hTGBvwjJiKiJ3OvTIVXNl3A5bQCWJsYYOsr/nCyNBK7rBpJTk6GVPr3A2h3797F5MmTkZGRgSZNmqBDhw44e/aspqO9TCZDdHQ0Nm/ejLy8PDg6OqJ///5YsGBBjcfKkgiCINTq3tQTqampcHFxQUpKiuZpgYautFyFhDtFuJ5ZiLiM+78WIi3v3iPf42plDG8Hc3g7mmt+dbAw5C1HIiL6V2UVakz54QJOxN2BmaEedkzuAh8nC61/ry6dv3mZowEx1JehtaMFWjtW/UteWFqOG1lFuJ5R2b/rfgjLLlIiObcEybklOHQlQ9O+ibF+ldDV2tEC7jYm0JNxqAkiIqrs0vLGj1E4EXcHhvpSbBzfqU4Clq5hyNIBZob6aO/aBO1dm1RZn1OkRFxGIWLTCxB7uwCx6QW4kVWEuyXlOBOfgzPxfw+sZqAnhZfCDN4O5mjtaA5fZ0t4Kcy0+uQIERHVP4Ig4P19Mfg1Oh36MglWj+7wQL9iqh6GLB1mbSpHVw85unrYaNaVlqtwI7MIsen5muAVe7sAxWUqRKfmIzo1X9NWXyaBp8IMvk6WaONsAT9nS7SwN4U+r3gREekkQRCw8Pdr2HE+BVIJsPSlduj9f/2GqfoYshoZQ30ZfJ0t4Ov892VftVpAyt0SXLldGbgu364MW7nFZbicVoDLaQXYcb6yrVxPitaO5vBztoTfX8HL3cYEUg6qSkTU4H13IgFrQ24CABYN98Mgv5oNWUBVMWQRpFIJmlqboKm1CZ7xrfwHJQgC0vLuaa5uRafmISY1H4XKCkQm5yEyOU/zflO5Htq4WFTesmzaBO1dmsDCWPzxU4iIqPq2hN7Ckj/iAADvD2qFFzu5iFxRw1fjkBUSEoIlS5YgIiIC6enp2Lt3L4YOHarZ/qin1hYvXoy3334bANCsWTMkJSVV2b5w4UK88847mtfR0dEIDg5GeHg4bG1tMWPGDMyZM6em5dITkkgkcG5iDOcmxprgpVYLuJVTjJi0fFxKqQxel2/no0hZ8UAfr+a2JprQ1aFpE3jYmvJqFxFRPbX3Yio+3F85SfLrT3nglR7uIlekG2ocsoqLi9GmTRtMnDgRw4cPf2B7enrVyZN///13TJo0CSNGjKiy/pNPPsHkyZM1r83MzDS/LygoQP/+/dGvXz+
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax1 = plt.subplots()\n",
"ax2 = ax1.twinx()\n",
"x = out[:,1]\n",
"ax1.plot(x, out[:,0], label=\"position\")\n",
"ax2.plot(x, out[:,2], label=\"energy\")\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"x = jnp.linspace(0,30, 1000)\n",
"dragf = drag_force(x, 1.3, 0.18, 1.184)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7101a4010b30>]"
]
},
"execution_count": 9,
"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": [
"plt.plot(x, dragf)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"%run ../src/solarcarsim/noise.py"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"key = random.key(123)\n",
"fractal_tex = generate_noise_texture(key, 256, 256, \"fractal\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7101a4373b30>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAa4AAAGiCAYAAAC/NyLhAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAADqIElEQVR4nO29fcwtV3Ue/sx5wTfm49o1xr52AsSQhI/wEQrEuUpC02JhG0pDcaVArBQiBAq1o4ITQh0RCGlVtzRqoqQkqFIFiQT5QAqgoIRfHRNMaYwTnCACJBZGtIbiaxKQfcEEY79n//6Y2XvWXnuttdeemfO+99x71quj98z+njnn7GeeZ629pwshBOxsZzvb2c52tiW2OuwB7GxnO9vZznbWYjvg2tnOdraznW2V7YBrZzvb2c52tlW2A66d7WxnO9vZVtkOuHa2s53tbGdbZTvg2tnOdraznW2V7YBrZzvb2c52tlW2A66d7WxnO9vZVtkOuHa2s53tbGdbZTvg2tnOdraznW2VHRpwve1tb8N3fud34tu+7dtw6aWX4s///M8Payg729nOdrazLbJDAa7f+73fw3XXXYc3v/nN+Mu//Es84xnPwOWXX44vf/nLhzGcne1sZzvb2RZZdxib7F566aV4znOeg//23/4bAGC9XuMxj3kMfvqnfxr/7t/9u4Mezs52trOd7WyL7CEH3eG3vvUt3Hbbbbj++utT2mq1wmWXXYZbbrlFrHP//ffj/vvvT8fr9Rpf/epX8ahHPQpd1218zDvb2c52trNlLYSAr33ta7j44ouxWrWJfwcOXH//93+P/f19XHjhhVn6hRdeiL/9278V69xwww14y1vechDD29nOdraznR2gfeELX8B3fMd3NNU5cOCaYtdffz2uu+66dHzvvffisY99LP77/3oKzn7EXkrfDzZq7yNnZ+vBxbce0vdDl9L3wwr7WGEdOuxjhRBW2EeH9dDHA2GV+lsP6Wt02A9jHQBYhw5r0m7Wf/CxxVWnq7lSG2tyHdYkfZ+UXYe2sawh9IO8vfE8u/y8w3h91qFDCBj+d3hwqBfCWCcAWK/78n06EEiZeDViWhS7w1Auvk9Gzi2lZ2nluZn6uXKt8j7p265Ml8bE25bai/lKX9b4AADSd6nT3od6GatdXsT6iil9pU+7I/WHsumwC2Nah+x9l70n5cl/1iUA+zc3no9chqavWJ8rhKz/LpULqc+OvF91ASvyAedt03R7/LSNVbfOykh99eNZj8cIKQ0A9rqQZjPa3orMOLx9ANjDWPb++x7A257//+GRj3wkWu3Agev888/H3t4e7r777iz97rvvxrFjx8Q6R44cwZEjR4r0hz+iw8MeST+x8ovEwQLIAS6C2XoAqj6/E8FrPYAXADwQ9tLkv09Bi4BX3/4qm8Bj2lJWB64x3wIura2iDPuJJ7API1DHcutAX6siLYQOK5K2v16NwEWAan/dJZDqhvwRoDoGWDlw8eMOKEArHtFvT3aWjusSAq/D8yXAqYDXFOBy3gwBKMGmFcCkY6t9nl20VfaTJup0PJbVgKvrQgZSHcuj7dIhSGClgZOVv+rKfAmUOuSTOy2b0hHEdrQxewCLlrNAa4+MoQStkNpcdf1vvwelPROwaP7e0N4Ud8+BRxWeddZZeNaznoWbbroppa3Xa9x00004fvx4W1vdPs7q9rHXrdXXQ7sHscI6e2VlELCH4YPC8BruHPaGtFUXhv99eQCpDrcVAva6dfah8Q8y9nOqmecuc8Vm5PTlZndxUpu87tJWA63+gALe8B4MZ0KXv8w+Wfs10wChVpbXWcK1y8+PXojsfZcDJgfN4gKyesq1LK6dwDZFwF/INNCKYNcpaVL+qhtftDxt2wtaqU0GWtQouInHCC7QSq/JoDXOaRngCnNdzI9z7xw7FKnwuuuuw8tf/nI8+9nPxvd///fjV3/1V3HffffhJ3/yJ5va6QaE3zO+1fvoyos0FF9jhb1ujf2w6tvo1ok57HUBCOv+GxYAdCtyvAK69fgfYxplc6uhjX2ssOoC1qFL/4H+Q12SfVm233InXrH4xY3sa68LYvv0nIH+2tIf1xrIJquYx5nfqgP2lY9YAw1J/qOglZKEfMs2FYPbdaTtLrQxp7kW+4qfTQAyGtqRcrQMkM/+/NpIp8D7wsBWpT6k6qQsHVqLcSCgwGKVy+v42pfa1gBTYirSOKU6WTnw87PlO60t6SZVAy2pHZrOWZbUfosdCnD92I/9GP7u7/4Ob3rTm3DixAl83/d9Hz74wQ8WARs1i4wqmuTjiilUMtzr9ntAwwA06UcTAWk4zsCKH68G2AwJsFbDew5iEaA08AKWkw81mVArM8dWCFkfq46AU9D7523EYha4ph9+zTcVlONG0JoFUEuCW/ruLdyuZBRUKDBl76kOFmywMvNyAEtSbuwjtt0Nci6faAnAJbnXABpNjRKBpQA2tVmVDfF8yrTisebTAnK2pQFVTc2QACvrgzGtWGev0xlYLMOZFm2DMqxoSwFWtEMLzrj22mtx7bXXzmrjod0aewyQovFAjARSyYb3gxyYACxjUcA+9nqHY4cMiNZYpYm673sEr31gBLHBx2WBV2yzH7cfVLzBHZu0CF6RddFrQkGptwjqYwoFq44wjVUX0F+9vMyKsLuuCzKzGkwDLQ2wDgWsyDnPZl1WeW18NVZk1eMgpvVD2VuRVgJYBl7ZuDAAWZ11ZSwudqmwHqmMBFY2A9PZmgRaVjlNIvSCFndh1FgdBy2aXgKZDlpehhXTJFeL17YiqlCz6HcSTWIVtGz6fFajnJjubMcgDCqD7WENdKsBeIhUSMBuP4zyY+qBgZSVPkc+nBqUMeYdDhBS5ha/8DVp0wItmYnp0mGfz/PM7rfLrHOhbKrIq0iItJ4EYlYfUn0JGCzWNdM429JAS+tXk/Q8oKX5tXgbNXlwDmhF/1Wsp/m1pLYlpsXfJ4BCmSaNvcW2HLgCzkLPsgqG1QkMhk7ahFVp4JVkQ0Ey1Hwue90a67A3tDVKhpK/C9BBrWZLgkytLf7DKnxQAuuKTJO3s2bXLQKQdR0oEwN8qpkU9g5aT43oqzQ8wQrQbJX9uFwYr4frQngHyfrL8hQJUatXnK/Axgwfmod1qacR+sZbQU4qbzGelrISaPFynG1p5gWtmryoBm4ofi0afKUxLc6yNgFY0bYauB7S7ae7I8ojKKvYwz5hT4TNhFEqROjrr0HBCgV4xUm3lwo7Vqb/vx7kQsp4KAPjkuEUO+gQeG5SEAUHr1SOTDQ5Ux3TIjBReXBNjvs8YI2SZWXymmKFRCiBFp8fJ1wXPnlNa2OmXJgNQHlvDgAlOKU2JMok1KNpvJ7G3izmpYEVL1sJ7PAYX3fVp9VBTSonSYNev1aW7jinKaClRwvmEqEXtExZUACxOZGFWw1cMZS9sCKKMIO1oQzJS/6pYdok4LUfxgXO1NfV+1qQJuLY44qCVMqrMxovgNRAq8VOCR/ZcO4c7HkZ6tdaKuJOAq0pYNNcdwOsbjHjgALooAGUbMqSA2O9GnhB9lFRuVDKt6ylvAVaXtlQ6kqLXpxiGmNSywugFU3za0n1pWONBVqgRX1lU2yrgWvVBTy0W9cn4My3NcYZ7g8BFlIoe7QYNUhZ1xhCPzK42M4oOa4K1hXls6l+rNYFwnN2yuBltfBZrR0apEHL9+0hBW5krI34t2gQy/4wYa3QDTcLQ7+dHZzRakVbnt/VQWN/q8yIsnyn+QY1EJoCYFZ9DbzkQYFGGEp1wlCuBgiSn0za5UIzj3TIm+CsSWujlP3qjE6yWiBGXjZnSAU4cdmQrdWi7GuP7LIRj2kbGRubuYYL2HLgioyL3h3soxvXGaWgijD6wMi6qyQjcllQkQwjK1tjlQI1ou9rP+yV7ItEGEpsYo5cGG2KRDjVp1b74dTkwtGvRyIPI5gBaRIsfFrkmAdl9BNBP5sHpUxmKcIwHktlzNOslzUn4oa2eZtaXSeYaYAl5Qfqz5L6sGRECwAt8KqB2AFbDWyoaYClteMJ7Gg1jQWltArb6vPkKEIKUoAOWhpg5YEdIfs
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.imshow(fractal_tex)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"import pyvista as pv\n",
"import numpy as np\n",
"a = np.array(fractal_tex)\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/saji/Documents/Code/solarcarsim/.venv/lib/python3.12/site-packages/pyvista/plotting/texture.py:682: UserWarning: Expected `image` dtype to be ``np.uint8``. `image` has been copied and converted to np.uint8.\n",
" warnings.warn(\n"
]
}
],
"source": [
"tex = pv.numpy_to_texture(a)\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/saji/Documents/Code/solarcarsim/.venv/lib/python3.12/site-packages/pyvista/core/utilities/points.py:55: UserWarning: Points is not a float type. This can cause issues when transforming or applying filters. Casting to ``np.float32``. Disable this by passing ``force_float=False``.\n",
" warnings.warn(\n"
]
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.arange(0,256)\n",
"y = np.arange(0,256)\n",
"x, y = np.meshgrid(x, y)\n",
"fig, ax = plt.subplots(subplot_kw={\"projection\": \"3d\"})\n",
"ax.plot_surface(x,y,a)\n",
"grid=pv.StructuredGrid(x,y, a * 100)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7100e506cf80>]"
]
},
"execution_count": 16,
"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": [
"key = random.key(123)\n",
"key, subkey = random.split(key)\n",
"y = generate_elevation_profile(subkey, 100, scale=100)\n",
"plt.plot(y)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7100e4f72db0>"
]
},
"execution_count": 17,
"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": [
"key, subkey = random.split(key)\n",
"y = generate_wind_field(subkey, 100, 100)\n",
"plt.imshow(y)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Slope (rad)')"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 1200x600 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%run ../src/solarcarsim/physsim.py\n",
"from jax import random\n",
"wind, elevation, slope = make_environment(random.key(123))\n",
"fig, (ax1, ax2) = plt.subplots(1,2, figsize=(12,6))\n",
"fig.set_tight_layout('auto')\n",
"fig.suptitle(\"Generated Environment\")\n",
"\n",
"ax1.imshow(wind, aspect='auto')\n",
"ax1.set_ylabel(\"Time (sec)\")\n",
"ax1.set_xlabel(\"Distance (m)\")\n",
"ax_slope = ax2.twinx()\n",
"ax2.plot(elevation)\n",
"ax2.set_ylabel(\"Elevation (m)\")\n",
"ax2.set_xlabel(\"Distance (m)\")\n",
"ax_slope.plot(slope, color='r')\n",
"ax_slope.set_ylabel(\"Slope (rad)\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7100e4dcb050>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# testing the indexing into the wind array.\n",
"# given an array of shape (10,2)\n",
"# return an array of (10,100,100)\n",
"key = random.key(0)\n",
"@jit\n",
"def lookup(x):\n",
" return lax.dynamic_slice(wind, x, (100, 100))\n",
"vlookup = vmap(lookup)\n",
"res = vlookup(jnp.array([[10,20], [9999, 600]]))\n",
"\n",
"fig, (ax1, ax2) = plt.subplots(1,2)\n",
"ax1.imshow(res[0])\n",
"ax2.imshow(res[1])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"%run ../src/solarcarsim/gym.py\n",
"import gymnasium as gym\n",
"from gymnasium.wrappers.jax_to_numpy import JaxToNumpy\n",
"from gymnasium.wrappers.vector import JaxToNumpy as VJaxToNumpy"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/saji/Documents/Code/solarcarsim/.venv/lib/python3.12/site-packages/stable_baselines3/common/env_checker.py:271: UserWarning: Your observation wind has an unconventional shape (neither an image, nor a 1D vector). We recommend you to flatten the observation to have only a 1D vector or use a custom policy to properly process the data.\n",
" warnings.warn(\n",
"/home/saji/Documents/Code/solarcarsim/.venv/lib/python3.12/site-packages/gymnasium/utils/env_checker.py:384: UserWarning: \u001b[33mWARN: The environment (<JaxToNumpy<SolarRaceV1 instance>>) is different from the unwrapped version (<SolarRaceV1 instance>). This could effect the environment checker as the environment most likely has a wrapper applied to it. We recommend using the raw environment for `check_env` using `env.unwrapped`.\u001b[0m\n",
" logger.warn(\n",
"/home/saji/Documents/Code/solarcarsim/.venv/lib/python3.12/site-packages/gymnasium/utils/env_checker.py:434: UserWarning: \u001b[33mWARN: Not able to test alternative render modes due to the environment not having a spec. Try instantiating the environment through `gymnasium.make`\u001b[0m\n",
" logger.warn(\n"
]
}
],
"source": [
"env = SolarRaceV1()\n",
"wrapped_env = JaxToNumpy(env)\n",
"env.reset()\n",
"from stable_baselines3.common.env_checker import check_env\n",
"from gymnasium.utils.env_checker import check_env as gym_check_env\n",
"check_env(wrapped_env)\n",
"gym_check_env(wrapped_env)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using cuda device\n",
"Wrapping the env with a `Monitor` wrapper\n",
"Wrapping the env in a DummyVecEnv.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/saji/Documents/Code/solarcarsim/.venv/lib/python3.12/site-packages/stable_baselines3/common/buffers.py:605: UserWarning: This system does not have apparently enough memory to store the complete replay buffer 80.85GB > 30.69GB\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 601 |\n",
"| ep_rew_mean | 1.25e+12 |\n",
"| time/ | |\n",
"| episodes | 4 |\n",
"| fps | 77 |\n",
"| time_elapsed | 31 |\n",
"| total_timesteps | 2404 |\n",
"| train/ | |\n",
"| actor_loss | -2.13e+10 |\n",
"| critic_loss | 1.37e+15 |\n",
"| ent_coef | 1.92 |\n",
"| ent_coef_loss | -6.08 |\n",
"| learning_rate | 0.0003 |\n",
"| n_updates | 2303 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 601 |\n",
"| ep_rew_mean | 1.26e+12 |\n",
"| time/ | |\n",
"| episodes | 8 |\n",
"| fps | 75 |\n",
"| time_elapsed | 63 |\n",
"| total_timesteps | 4808 |\n",
"| train/ | |\n",
"| actor_loss | -4.31e+10 |\n",
"| critic_loss | 5e+15 |\n",
"| ent_coef | 3.95 |\n",
"| ent_coef_loss | -12.9 |\n",
"| learning_rate | 0.0003 |\n",
"| n_updates | 4707 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 601 |\n",
"| ep_rew_mean | 1.27e+12 |\n",
"| time/ | |\n",
"| episodes | 12 |\n",
"| fps | 75 |\n",
"| time_elapsed | 95 |\n",
"| total_timesteps | 7212 |\n",
"| train/ | |\n",
"| actor_loss | -5.97e+10 |\n",
"| critic_loss | 1.1e+16 |\n",
"| ent_coef | 8.13 |\n",
"| ent_coef_loss | -19.6 |\n",
"| learning_rate | 0.0003 |\n",
"| n_updates | 7111 |\n",
"----------------------------------\n",
"----------------------------------\n",
"| rollout/ | |\n",
"| ep_len_mean | 601 |\n",
"| ep_rew_mean | 1.27e+12 |\n",
"| time/ | |\n",
"| episodes | 16 |\n",
"| fps | 75 |\n",
"| time_elapsed | 126 |\n",
"| total_timesteps | 9616 |\n",
"| train/ | |\n",
"| actor_loss | -7.87e+10 |\n",
"| critic_loss | 1.94e+16 |\n",
"| ent_coef | 16.7 |\n",
"| ent_coef_loss | -26.4 |\n",
"| learning_rate | 0.0003 |\n",
"| n_updates | 9515 |\n",
"----------------------------------\n"
]
},
{
"data": {
"text/plain": [
"<stable_baselines3.sac.sac.SAC at 0x7100e4ca9730>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# import a model and try it out!\n",
"from stable_baselines3 import PPO\n",
"model = PPO(\"MultiInputPolicy\", wrapped_env, verbose=1)\n",
"model.learn(total_timesteps=1_000_000)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Array([[[-4.9999666, -5.031969 , -5.063982 , ..., -6.714849 ,\n",
" -6.71299 , -6.7111654],\n",
" [-4.9998884, -5.031891 , -5.0639033, ..., -6.705859 ,\n",
" -6.7038655, -6.701909 ],\n",
" [-4.9997377, -5.03174 , -5.0637527, ..., -6.6968226,\n",
" -6.694696 , -6.692607 ],\n",
" ...,\n",
" [-4.8104963, -4.840162 , -4.869858 , ..., -6.505874 ,\n",
" -6.499703 , -6.4934487],\n",
" [-4.8117733, -4.8413825, -4.871023 , ..., -6.511339 ,\n",
" -6.5052385, -6.499054 ],\n",
" [-4.812991 , -4.8425455, -4.8721304, ..., -6.5165534,\n",
" -6.510523 , -6.504408 ]]], dtype=float32)"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pos = jnp.array([0])\n",
"time = jnp.array([0])\n",
"x = jnp.stack([pos,time], axis=1)\n",
"vlookup(x)"
]
},
stashing
2024-12-12 01:59:37 +00:00
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Reference in a new issue Copy permalink