{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a3cf5a64",
   "metadata": {},
   "source": [
    "# Idealized Case 2: Two crossing blobs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba39498c",
   "metadata": {},
   "source": [
    "This tutorial explores the different methods of the linking process using an example of two crossing blobs. The following chapters will be covered:\n",
    "\n",
    "1. [Data generation](#1.-Data-generation)\n",
    "2. [Feature detection](#2.-Feature-detection)\n",
    "3. [Influence of tracking method](#3.-Influence-of-the-tracking-method)\n",
    "4. [Analysis](#4.-Analysis)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78c4536f",
   "metadata": {},
   "source": [
    "## 1. Data generation\n",
    "\n",
    "We start by importing the usual libraries and adjusting some settings:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "19d7880d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-18T14:32:29.398301Z",
     "start_time": "2025-12-18T14:32:28.556453Z"
    },
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:21.684316Z",
     "iopub.status.busy": "2026-02-02T20:12:21.684057Z",
     "iopub.status.idle": "2026-02-02T20:12:23.733718Z",
     "shell.execute_reply": "2026-02-02T20:12:23.733221Z"
    }
   },
   "outputs": [],
   "source": [
    "import tobac\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "import xarray as xr\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_context(\"talk\")\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4ea8ea1",
   "metadata": {},
   "source": [
    "We will need to generate our own dataset for this tutorial. For this reason we define some bounds for our system:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f9945358",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-18T14:32:29.403959Z",
     "start_time": "2025-12-18T14:32:29.402031Z"
    },
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:23.735646Z",
     "iopub.status.busy": "2026-02-02T20:12:23.735388Z",
     "iopub.status.idle": "2026-02-02T20:12:23.737312Z",
     "shell.execute_reply": "2026-02-02T20:12:23.736964Z"
    }
   },
   "outputs": [],
   "source": [
    "x_min, y_min, x_max, y_max = 0, 0, 1e5, 1e5\n",
    "t_min, t_max = 0, 10000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf88e80e-eb8f-4864-886b-1a78062a3963",
   "metadata": {},
   "source": [
    "We use these to create a mesh:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4d5bfe03-c00c-4eb2-9c35-d6fafa837dc6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-18T14:32:29.411850Z",
     "start_time": "2025-12-18T14:32:29.408217Z"
    },
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:23.738647Z",
     "iopub.status.busy": "2026-02-02T20:12:23.738539Z",
     "iopub.status.idle": "2026-02-02T20:12:23.742220Z",
     "shell.execute_reply": "2026-02-02T20:12:23.741782Z"
    }
   },
   "outputs": [],
   "source": [
    "def create_mesh(x_min, y_min, x_max, y_max, t_min, t_max, N_x=200, N_y=200, dt=520):\n",
    "    x = np.linspace(x_min, x_max, N_x)\n",
    "    y = np.linspace(y_min, y_max, N_y)\n",
    "    t = np.arange(t_min, t_max, dt)\n",
    "    mesh = np.meshgrid(t, y, x, indexing=\"ij\")\n",
    "\n",
    "    return mesh\n",
    "\n",
    "\n",
    "mesh = create_mesh(x_min, y_min, x_max, y_max, t_min, t_max)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f5e78ef",
   "metadata": {},
   "source": [
    "Additionally, we need to set velocities for our blobs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e322c61d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-18T14:32:29.415812Z",
     "start_time": "2025-12-18T14:32:29.414237Z"
    },
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:23.743335Z",
     "iopub.status.busy": "2026-02-02T20:12:23.743258Z",
     "iopub.status.idle": "2026-02-02T20:12:23.744831Z",
     "shell.execute_reply": "2026-02-02T20:12:23.744437Z"
    }
   },
   "outputs": [],
   "source": [
    "v_x = 10\n",
    "v_y = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d563c5eb",
   "metadata": {},
   "source": [
    "The dataset is created by using two functions. The first creates a wandering Gaussian blob as `numpy`-Array on our grid and the second transforms it into an `xarray`-DataArray with an arbitrary `datetime`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "19b360bc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-18T14:32:29.423642Z",
     "start_time": "2025-12-18T14:32:29.418798Z"
    },
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:23.745923Z",
     "iopub.status.busy": "2026-02-02T20:12:23.745837Z",
     "iopub.status.idle": "2026-02-02T20:12:23.748847Z",
     "shell.execute_reply": "2026-02-02T20:12:23.748436Z"
    }
   },
   "outputs": [],
   "source": [
    "def create_wandering_blob(mesh, x_0, y_0, v_x, v_y, t_create, t_vanish, sigma=1e7):\n",
    "    tt, yy, xx = mesh\n",
    "    exponent = (xx - x_0 - v_x * (tt - t_create)) ** 2 + (\n",
    "        yy - y_0 - v_y * (tt - t_create)\n",
    "    ) ** 2\n",
    "    blob = np.exp(-exponent / sigma)\n",
    "    blob = np.where(np.logical_and(tt >= t_create, tt <= t_vanish), blob, 0)\n",
    "\n",
    "    return blob\n",
    "\n",
    "\n",
    "def create_xarray(array, mesh, starting_time=\"2022-04-01T00:00\"):\n",
    "    tt, yy, xx = mesh\n",
    "    t = np.unique(tt)\n",
    "    y = np.unique(yy)\n",
    "    x = np.unique(xx)\n",
    "\n",
    "    N_t = len(t)\n",
    "    dt = np.diff(t)[0]\n",
    "\n",
    "    t_0 = np.datetime64(starting_time)\n",
    "    t_delta = np.timedelta64(dt, \"s\")\n",
    "\n",
    "    time = np.array([t_0 + i * t_delta for i in range(len(array))])\n",
    "\n",
    "    dims = (\"time\", \"x\", \"y\")\n",
    "    coords = {\"time\": time, \"x\": x, \"y\": y}\n",
    "    attributes = {\"units\": (\"m s-1\")}\n",
    "\n",
    "    data = xr.DataArray(data=array, dims=dims, coords=coords, attrs=attributes)\n",
    "    # data = data.projection_x_coordinate.assign_attrs({\"units\": (\"m\")})\n",
    "    # data = data.projection_y_coordinate.assign_attrs({\"units\": (\"m\")})\n",
    "    data = data.assign_coords(latitude=(\"x\", x / x.max() * 90))\n",
    "    data = data.assign_coords(longitude=(\"y\", y / y.max() * 90))\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a6af9d7",
   "metadata": {},
   "source": [
    "We use the first function to create two blobs whose paths will cross. To keep them detectable as seperate features in the dataset we don't want to just add them together. Instead we are going to use the highest value of each pixel by applying boolean masking and the resulting field is transformed into the `xarray` format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e73d775b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-18T14:32:29.529848Z",
     "start_time": "2025-12-18T14:32:29.425856Z"
    },
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:23.750136Z",
     "iopub.status.busy": "2026-02-02T20:12:23.750055Z",
     "iopub.status.idle": "2026-02-02T20:12:23.835998Z",
     "shell.execute_reply": "2026-02-02T20:12:23.835548Z"
    }
   },
   "outputs": [],
   "source": [
    "blob_1 = create_wandering_blob(mesh, x_min, y_min, v_x, v_y, t_min, t_max)\n",
    "blob_2 = create_wandering_blob(mesh, x_max, y_min, -v_x, v_y, t_min, t_max)\n",
    "blob_mask = blob_1 > blob_2\n",
    "blob = np.where(blob_mask, blob_1, blob_2)\n",
    "\n",
    "data = create_xarray(blob, mesh)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f11c0ce6",
   "metadata": {},
   "source": [
    "Let's check if we achived what we wanted by plotting the result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "53893ecb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-18T14:32:30.883266Z",
     "start_time": "2025-12-18T14:32:29.535146Z"
    },
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:23.837626Z",
     "iopub.status.busy": "2026-02-02T20:12:23.837536Z",
     "iopub.status.idle": "2026-02-02T20:12:25.149165Z",
     "shell.execute_reply": "2026-02-02T20:12:25.148645Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x318a317f0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2600x2000 with 21 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot(\n",
    "    cmap=\"viridis\",\n",
    "    col=\"time\",\n",
    "    col_wrap=5,\n",
    "    x=\"x\",\n",
    "    y=\"y\",\n",
    "    size=5,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c381bcc",
   "metadata": {},
   "source": [
    "Looks good! We see two features crossing each other, and they are clearly separable in every frame."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8cd3596",
   "metadata": {},
   "source": [
    "## 2. Feature detection\n",
    "\n",
    "Before we can perform the tracking we need to detect the features with the usual function. The grid spacing is deduced from the generated field. We still need to find a reasonable threshold value. Let's try a really high one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "440f2acb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-18T14:32:31.120430Z",
     "start_time": "2025-12-18T14:32:30.930570Z"
    },
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:25.151081Z",
     "iopub.status.busy": "2026-02-02T20:12:25.150986Z",
     "iopub.status.idle": "2026-02-02T20:12:25.328294Z",
     "shell.execute_reply": "2026-02-02T20:12:25.327826Z"
    }
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "spacing = np.diff(np.unique(mesh[1]))[0]\n",
    "\n",
    "dxy, dt = tobac.get_spacings(data, grid_spacing=spacing)\n",
    "features = tobac.feature_detection_multithreshold(data, dxy, threshold=0.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "829147c5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-18T14:32:31.175136Z",
     "start_time": "2025-12-18T14:32:31.124846Z"
    },
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:25.330071Z",
     "iopub.status.busy": "2026-02-02T20:12:25.329899Z",
     "iopub.status.idle": "2026-02-02T20:12:25.379848Z",
     "shell.execute_reply": "2026-02-02T20:12:25.379341Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='hdim_1', ylabel='hdim_2'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zxufxLpcru9cLAAAQWuHr4MGDMmzYMLlz546UK1dOhg4dKm+88YY0a9bMhKqNGzdK69atzX4AAAC4W7hk0SeffCI3b96URo0ayYYNG6RkyZLubX/5y1/k5ZdfllOnTpkaMG2arFu3blbfAgAAIGhlueZLA1VYWJhMnTr1ruClfvOb38jWrVulVq1acvbsWenQoYP89NNP9/J6AQAAQit8HT9+XPLnzy9t27b1uL1OnTqm6bFevXpy7tw56dixIwEMAAAgu+ErOTlZihUrJuHh3lssy5cvL+vXr5f69eu7A9j+/fuz+lYAAABBJ8vhq0yZMu47HDPab926dXfVgBHAAABAqMty+GrYsKFZbtq0KcN9NYBpDZgGMO0DpgEso9AGAAAQzLIcvh555BEzpMTChQsztb8GMO2k7wSw69evZ+c6AQAAQjN8devWzSwXLVpkmhMzQ8cDc/qAAQAAhLIsj/Ol43vNnz9fUlJS5Nq1a6ZzfWY4TZAzZswwA7QCAACEoiyHL9WnT59svVnp0qUlNjY2W8cCAACE9MTa91rLli3N4KwAAADBLFs1X7nh5MmTcuHChby+DAAAgNCo+QIAAAgFhC8AAACLCF8AAAAWEb4AAAAsInwBAABYRPgCAACwiPAFAABgEeELAADAIsIXAACARYQvAACAUAxfLpcrry8BAAAgdOZ2jIuLk9u3b+f1ZQAAAIRG+KpcuXJeXwIAAEBgNDtqk+HcuXOlc+fOUr58eSlUqJDkz5/f6yM83G8yHwAAgFU5TkEJCQny+OOPy+bNm+m3BQAAkNvha9y4cbJp0yZTo9WnTx/p0qWLlCtXLldqt86dOydr1qyR7du3mz5iu3btkqSkJImOjjavfUlJSZGpU6fK/Pnz5dChQ6Z27sEHH5RXXnlFnn76aZ/HHjlyRCZMmCD/+Mc/5OLFi1K2bFlTyzdmzBipUaPGPf6UAAAgmOU4IS1evFjCwsJk2rRpMnToUMlNCxculNdffz3Lx924cUMee+wxd0hs0KCBXL9+XdatW2ceo0aNko8++sjjsVu2bDFBS2v4IiMjpVGjRnL48GHTzKqfXcNgy5Yt78GnAwAAoSDHfb4uXLhgarkGDhwoua1EiRLSqVMnefPNN2XJkiXywQcfZOo4DVcavLSW6scff5Tdu3eb2q8VK1aYGrCJEyfKN998k+64xMRE6dmzpwle/fv3lzNnzpgatrNnz0q/fv3k2rVrZrvWvgEAAFgJXxUqVJAiRYpIwYIFJbdpAFq9erV8+OGHJvToe2fk/PnzMnPmTPP8iy++kLp167q3de/eXUaOHOluPk1r9uzZJmjVrl1bZsyYIYULFzbrdannrFWrlpw6dUo+//zze/gpAQBAMMtx+NI+Xr/88ov89NNP4o9WrlwpN2/eNAGqQ4cO6bYPHjzYLHfu3GmaE1PTZkXVt2/fdOFSX2vtl1q0aFEufgIAABBMchy+3nrrLbnvvvtk2LBhplO7v9m6datZtm3b1uP2SpUquTvNO/sqHfDV6cTv7Vhnvd4AwACxAADASof7qlWryqpVq+SZZ54xdx2OGDFCmjdvLsWLF8/wOBsOHDhgllrz5Y02Hx49elT279/vXnfs2DFTY+brWD1OJScny/Hjx6VmzZpe32PWrFmmGTMz9u3bl6n9AABA4Lkn40FoP6pu3brJJ598YvplZUTvjrx165bYEB8fb5ZRUVFe93G2XblyJd1xvo5NvT71sZ5o3zFt2gQAAKEtx+Hr0qVL0r59e3dtTWYmyLY5ibYOM6F83RCgdzyq1HctOsf5OtY5Lu2xnujNAc2aNcvUNWtZcgclAADBKcfh691335V///vf5o5HbXLMzUFWs8O5Q9FpQvREmw1VREREuuOcY1O/Tntc2mM90Y79Tuf+jGjzLbVkAAAEpxwnJB0fS5sRddBR7fflb3Rg1LTNiGk525x90z7X7RUrVvR6XNr9AQAAcnWQVW2W03G3/FGdOnXMUgdV9cYZYsLZV1WvXt3d3OjtWOc4bX6sVq3aPb1uAAAQnHIcvrRGqECBAmbaHn/UunVrs9QR7j05ffq0udMx9b5Km021+U9t3LjR47HO+hYtWvjt5wcAAEEWvnSUeJ0nMaOJrfNKjx49TDg8ePCgmcfR0xAQqmnTpumGlOjVq5dZzps3L90YZtoP7MsvvzTPe/funYufAAAABJMch68xY8aY2q8hQ4bIzz//LP5GO/87Hd0HDBhw11he2l9t0qRJ5vnYsWPTHavHlS9f3jQ76udz7oDUpb7WZkf97DbmtQQAAMEhxx3u9+7daya4Hj58uNSvX18GDRokLVu2zHCQ1UceeSTL73Xy5ElTQ5X2bkOdKLt06dLu9TpfozNno9KAtWPHDtmyZYs0aNBAGjZsaCbLdvps6V2aWkOWVtGiRc0E3noHp95QsHz5cjOQ6pEjR8y4XsWKFZOlS5eaOz0BAACshC8d40vvdlRXr16VCRMm5NogqzqFz+XLl9Ot13OlXp+YmHjXdh0GYv369TJ16lSZP3++GfVeO9O3a9dOXn31VZ83C8TExJhwN378eDOp9549e6RMmTImrMXGxvoc1R4AACCtezIYV1YHTc3uIKt6B2J2j9WwlbZGLLN0GiHt9wUAAJDn4evOnTs5vggAAIBQkeMO9wAAAMg8whcAAIBFhC8AAAB/7fP15z//2SxLlizpHprBWZdVL7zwQraOAwAACJnw1bdvXzNMRN26dd3hy1mXFbo/4QsAAISiLIWvqlWrmuCko7qnXQcAAIB7HL6OHTuWqXUAAADwjA73AAAAFhG+AAAALCJ8AQAA+Gufr/fee++evfE777xzz84FAAAQlOFr3LhxOb6zUSfG1nMQvgAAQCjKUvh65JFHvIavH374Qa5evWqeV6pUSSpXrmyenz59Wk6dOmWelypVSpo0aZLzqwYAAAiF8LV+/XqP69966y355z//Kc8995ypHbv//vvv2n7o0CF599135euvv5Y2bdrIBx98kLOrBgAACIXw5cnSpUtl0qRJMnToUPnkk0887lO7dm356quvzLREEydOlObNm8vTTz+d07cGAAAIvbsdNXBpU6TWeGXE2cdbSAMAAAh2OQ5fe/bsMTVapUuXznBf3Uf7fe3evTunbwsAABCa4Ss5OVl++eUXSUhIyHBf3Uf31WMAAABCUY7DV926deXOnTuZakrUfW7fvm2OAQAACEU5Dl/9+vUzY3eNGTPG3NHoqQbs+vXrZoBW3Uf7h+kxAAAAoSjHdzvqXY7//d//Lf/4xz9MwPr444/N3Yw61pcGLR3jKy4uTpKSkkxIe+yxx8wxAAAAoSjH4StfvnyycuVKefPNN02zYmJiomzYsME9GKsGLvNG4eEmdOlQE3oMAABAKMpx+FIFCxaUKVOmyH/8x3/IkiVLTE3XhQsXzLayZcuamrCePXtKxYoV78XbAQAAhHb4clSoUEFeffXVe3lKAACAoEL7HwAAQCDWfB0+fFgWLVpkBl2Nj4+XlJQUr/tqf7DvvvvuXr01AABAaIUvHWJiwoQJZrwvp4O9L05nfAAAgFCT4/D19ddfm/CltEN9ly5dzFLvbgQAAMDdcpyQPv30U7Ps3r27aXbUOx8BAACQSx3u9+7da5oRP/vsM4IXAABAbocvDV4lSpRgDC8AAAAb4euBBx4wo9onJyfn9FQAAABBL8fha+DAgWZYicWLF9+bKwIAAAhiOQ5fgwYNMp3thw0bZuZ0BAAAwD262/G9997zuL5JkyayceNG6dChg8TExEirVq2kePHiPs/1zjvvZOWtAQAAQi98jRs3zucAqTrA6qZNm2Tz5s0ZnovwBQAAQlGWwtcjjzzC6PQAAAC2wtf69etz8l4AAAAhL8cd7gEAAJB5hC8AAACLCF8AAAAWEb4AAAAsInwBAABYRPgCAACwiPAFAABgEeELAADAIsIXAACARYQvAAAAiwhfAAAAFhG+AAAALCJ8AQAAWET4AgAAsIjwBQAAYBHhCwAAwCLCFwAAgEWELwAAAIsIXwAAABYRvgAAACwifAEAAFhE+AIAALCI8AUAAGAR4QsAAMAiwhcAAIBFhC8AAACLCF8AAAAWhUz4GjdunISFhfl8zJw50+OxKSkp8vHHH0uTJk2kaNGiEhUVJR07dpRly5ZZ/xwAACCwhUuIKVu2rNx///0et1WoUCHduhs3bshjjz0mmzZtkvz580uDBg3k+vXrsm7dOvMYNWqUfPTRRxauHAAABIOQC19du3aVefPmZXp/DVcavGrUqCF/+9vfpG7dumb9ypUr5ZlnnpGJEydKTEyMdOvWLRevGgAABIuQaXbMjvPnz7ubIr/44gt38FLdu3eXkSNHups0AQAAMoPw5YPWbt28eVNq164tHTp0SLd98ODBZrlz5045fPhwpgocAACEtpBrdty9e7f06dNHzp07J8WLF5fGjRvLs88+a/pypbV161azbNu2rcdzVapUyTRHHj161Oxbq1atXL9+AAAQ2EIufP3www/mkbp26/3335fhw4fL5MmTTad6x4EDB8xSa7680cCl4Wv//v25fOUAACAYhEz4Kl++vOmj9fTTT5vApLVeGq4+++wz069r6tSpUrBgQdOB3hEfH2+WOrSEN862K1eu+Hz/WbNmyezZszN1rfv27cvkpwIAAIEmZMLXkCFD0q1r1KiRzJgxwzQd6l2NU6ZMkZdfflmqV6/uHmZCaSjzplChQmaZlJTk8/3Pnj1r+oYBAIDQFjLhy5cRI0bItGnT5MyZM/LNN9/Iq6++atYXLlzYLLXTvTfJyclmGRER4fM9dAyxZs2aZbrmK6MwBwAAAhPhS8T082rVqpUsX77c3c9LRUZG3tX86ImzzdnXG70z0rk7MiPR0dHUkgEAEKQYauL/c5oWb9265S6cOnXqmOWhQ4e8FqAzxISzLwAAgC+Er/9v7969Zlm5cmV34bRu3dosdYR7T06fPm3udEy9LwAAgC+ELxFZtWqV/Pjjj6ZAOnfu7C6cHj16SIECBeTgwYNmHkdPdzCqpk2b+hyOAgAAIKTClwYr7W+lA6ymdufOHVmwYIEZdFU98cQT0qJFC/f2cuXKuftpDRgw4K6xvLRj/qRJk8zzsWPHWvokAAAg0IVEh/uUlBQzxpY+dFyuatWqSXh4uOnL5YzPpaPYz58/P92xGrB27NghW7ZsMaPgN2zYUBISEtx9vfROSa0hAwAAyIyQqPnScbsmTJhgarZKlSplQpeOcq+d7Lt27SpfffWVaVbUbWnpEBLr1683g6/Wr1/f3A156dIladeunSxZssSMig8AAJBZIVHzpaHq7bffzvbxGtJ0dHx9AAAA5ERI1HwBAAD4C8IXAACARYQvAAAAiwhfAAAAFhG+AAAALCJ8AQAAWET4AgAAsIjwBQAAYBHhCwAAwCLCFwAAgEWELwAAAIsIXwAAABYRvgAAACwifAEAAFhE+AIAALCI8AUAAGAR4QsAAMAiwhcAAIBFhC8AAACLCF8AAAAWEb4AAAAsInwBAABYRPgCAACwiPAFAABgEeELAADAIsIXAACARYQvAAAAiwhfAAAAFhG+AAAALCJ8AQAAWET4AgAAsIjwBQAAYBHhCwAAwCLCFwAAgEWELwAAAIsIXwAAABYRvgAAACwifAEAAFhE+AIAALCI8AUAAGAR4QsAAMAiwhcAAIBFhC8AAACLCF8AAAAWEb4AAAAsInwBAABYRPgCAACwiPAFAABgEeELAADAIsIXAACARYQvAAAAiwhfAAAAFhG+AAAALCJ8AQAAWET4AgAAsIjwBQAAYBHhCwAAwCLCFwAAgEWELwAAAIsIXwAAABYRvgAAACwifAEAAFhE+AIAALCI8AUAAGAR4QsAAMAiwhcAAIBFhC8AAACLCF+ZtG7dOnnyySelTJkyEhERIQ888IDExsbK9evXc/d/CAAABBXCVyZMnz5dHn30UVm1apUULlxY6tWrJ8eOHZMJEyZIixYtJD4+Pvf/pwAAQFAgfGVgx44d8tprr5nns2bNkhMnTsjOnTvlyJEjEh0dLfv27ZNBgwbZ+L8CAABBIDyvL8DfjR8/Xu7cuSMvvPCCvPTSS+71FStWlAULFpjmx2XLlsmePXukcePGeXqtAPzbyfhEWRx3Uk5dSZLKkRHSu3kVqRJVJK8vC4BlhC8fEhIS5H/+53/M89TBy3H//fdLx44dZc2aNbJ48WLCFwCv/rrrtPx+8W65dcflXvfZ+sMyuXcTeappJUoOCCE0O/qwa9cuSU5OlkKFCknLli097tO2bVuz3Lp1a+78DwEIihqvtMFL6Wtdr9sBhA5qvnw4cOCAWVatWlUKFCjgcZ9atWqZ5f79+30WtPYXmz17dqb+U7QfGYDgoU2NaYOXQ9fr9jc617V+XQDyBuHLB+cuxqioKK/7ONuuXLnis6DPnj1rOuoDCD3axysn2wEEF8KXDzdu3DDLggULet1HmyRVUpLvH54VKlSQZs2aZbrmK6PzAQgc2rk+J9sBBBfClw86ppe6efOm1320T5jSgVd9GTx4sHlkhg5hQS0ZEDz0rkbtXO+p6TE8X5jZDiB00OHeh8jISLP0NYiqs83ZFwDS0uEk9K5GDVqp6Wtdz3ATQGih5suHOnXqmKUOrJqSkuKx0/3hw4fv2hcAPNHhJKKrRTLOFwDCly/aR0v7e2nT4rZt2yQmJibdPhs3bjTLNm3a8OUEwCet4eKuRgA0O/pQrFgx6dKli3nuaZiIgwcPytq1a83zXr168dUEAAAyRPjKQGxsrISFhclXX31lApjL5XIPHfHcc8+ZqYeeeuopadKkScalDQAAQh7hKwMtWrSQKVOmmOd6t2K1atVMc2SNGjXMpNt169aVOXPmhPwXEgAAyBzCVya89tprsnr1aunatatcv35d/v3vf5sQNnr0aImLi5PSpUtnsrgBAECo427HTHr00UfNAwAAICeo+QIAALCI8AUAAGAR4QsAAMAiwhcAAIBFhC8AAACLwlzOqKHwG1FRUXLlyhWJiIiQevXq5fXlAACATNi3b58kJSVJZGSkxMfHe92P8OWHihQpYv7zAABA4NHKk8TERK/bGefLD5UtW1YuXLgghQsXNiPphzLnrwhqASnrYMHXNOUcTPh6vtvRo0flxo0b5ve4L4QvP3Ts2LG8vgS/ER0dLTt37jTNrzqdEyjrQMfXNOUcTPh6zh463AMAAFhE+AIAALCI8AUAAGAR4QsAAMAiwhcAAIBFhC8AAACLCF8AAAAWEb4AAAAsInwBAABYRPgCAACwiOmF4NdeeuklOXv2rFSoUCGvLyXoUdaUczDh65ly9mdhLpfLldcXAQAAECpodgQAALCI8AUAAGAR4QsAAMAiwhcAAIBFhC9Yd+TIEenfv79UrlxZChUqJFWqVJEBAwbI0aNH/eK8n376qYSFhZlH+/btJVD5Sznfvn1b1qxZI6+99pq0bNlSSpUqJQULFjR3sPbo0UNWrVol/mzdunXy5JNPSpkyZSQiIkIeeOABiY2NlevXr1s/Z0pKinz88cfSpEkTKVq0qERFRUnHjh1l2bJlEuj8oZwTEhJkwYIF0rdvX6lfv74UKVJEChcuLLVq1TJf43v37pVA5w/l7E3v3r3dP3vHjRsnQU3vdgRs+f77713FihXTO2xdkZGRrujoaFepUqXM6+LFi7v+9a9/5el5T5w4YfbX4/TRrl07VyDyp3L+/PPP3eWZL18+V506dVzNmjVzlShRwr3+pZdect25c8flb/74xz+6wsLCzDVWrlzZ1bRpU1ehQoXM63r16rkuX75s7ZxJSUmuhx9+2OyXP39+V+PGjV21atVyl+GoUaNcgcpfyvl3v/uduzwjIiJMGTds2NB9XIECBVxz5851BSp/KWdPli9f7i57fYwdO9YVzAhfsOb69euuChUqmG+s/v37m18mSpf9+vVzf/MmJibm2Xkff/xx84utW7duARu+/K2c58yZY36JaQj7+eef3etTUlJckydPdv/gnjFjhsufxMXFmbCo1zdr1ix3ODx9+rQJnXrNTz/9tLVzDhs2zGyvUaOG66effnKvX7FihfuX3cqVK12Bxp/KWcPXE0884Vq1apUrOTnZvT4+Pt7Vp08fd/D93//9X1eg8adyTkt/LlSsWNFVpUoV93GEL+Ae+c///E/zTVW7du27frApfe38Fa9/SeXFeefPn2/2e/311803fqCGL38rZ/3L11et1qBBg8xxTZo0cfmTHj16mOt64YUX0m07cOCA+aWj23fv3p3r5zx37pyrYMGCZtvatWvTHRsbG2u2aY1ioPGncr506ZLXc968edPVoEEDc9zw4cNdgcafyjmtgQMHmv30Dwn9mUv4Au6hhx56yHxTTZgwweN2Xa/btWnF9nkvXLjgKl26tKtq1aquhISEgA5f/lzOnixbtswcV7hwYZe/uHbtmrs2adOmTR736dSpk9k+ZsyYXD/n7Nmz3cHXk1OnTrmbaw4dOuQKFP5Wzhlxah9/9atfuQKJP5fz2rVrTc2ZU0MWKuGLDvewQjtdx8XFmedt27b1uI+zfvv27WZ/m+cdPny4XLp0yXS2147Mgcrfy9mTGzdumKV2bvYXu3btkuTkZHNDgd4k4Ovzbt26NdfP6bz2VvaVKlWSGjVqZOl6/IG/lXMgfq0GcjknJSXJoEGDpHjx4jJ9+nQJJYQvWHHs2DG5efOmeV67dm2P++gdRUq/oY8fP27tvHq3nd7h1KtXL3PHTiDz53L2RsveV7DICwcOHDDLqlWrSoECBXx+3v379+f6OZ1jvZV9dq7HH/hbOfuiQWHFihV+97UayOUcGxsrhw8flg8++EAqVqwooYTwBSvi4+Pdz/X2eE9Sr79y5YqV8167dk2GDBkiJUuWlD/+8Y8S6Py1nL355ptvzENvLR85cqT4C+fzevusqbdltQyzc87cuB5/4G/l7MuYMWPk/PnzZjgFHWolkPhjOcfFxcnUqVOlVatW8vLLL0uoCc/rC0BocKrrlY7x5IlWX6f+K9PGefUX/qlTp+Szzz4z404FOn8tZ09++uknef75581zHQPsoYceEn/hfF5vnzX1581qGWbnnLlxPf7A38rZm4ULF8qUKVPM8zlz5kiJEiUkkPhbOaekpJhx0/SPrtmzZ0u+fKFXDxR6nxhZpjVDzsB3WXmkHqBUByp0OM1XaWlzlUMH6suMnJx348aNMmvWLGnTpo35jHktWMvZk5MnT0qXLl3k6tWr8vjjj8vEiRPFnzif19tnTf15s1qG2TlnblyPP/C3cvZk9erV8uKLL5rn77//vhkYOND4WzlPnDhR9uzZIyNGjJDGjRtLKKLmCxnSJrly5cpluaRSV0dHRkbeVV3tqX0/ddNW6v19ye55b926JQMHDpTw8HDzl5eGmLwWjOXsyblz5+TRRx+VEydOmOC4dOlSr31G8opz/ak/U1rOtqyWYXbOmRvX4w/8rZzT2rBhgzz11FMmYLz55psyevRoCUT+VM6HDh2SCRMmSM2aNWXs2LESqghfyJD+lZLTmonq1aub6mn9IabffJ5+eWvHS6equlq1arl6Xp1GRDuMavjq1KlTumN0u/r++++lfPny7rv4dCqd3BKM5ZzWxYsXzXQ4Bw8eNM2M2t8rda2av6hTp45ZakDUJhJP4dD5vM6+uXlOfb1582ZT9t5k9Xr8gb+Vc2pbtmyRJ554QhITE2XYsGHy4YcfSqDyp3LWKZqSk5PNH2HOHbqeAtvkyZNl5syZ5meu/uwNNjQ7wgoNOdHR0e7mPk+c9S1atJD8+fNbOa/WgGkn2rQPZ04y/aHirMvK8Al5xV/L2fmhqkF337595lzffvutFCtWTPxRs2bNTNjUXxLbtm3z+Xm12Tq3z9m6dWuz3LRpk8fjTp8+7Z5b09k3EPhbOTt27NghXbt2NX+E6VAI2jE8kPljOScmJnr82as/c5X+DNbX+gdbUMrrgcYQOv7whz+4B4rU0aK9jZA+bdq0PD9vIA+y6o/lfPXqVfe0ITrVUHbmkLPNmWIqo9G7f/jhh1w/p45wr/MKZjTCvc6rF2j8qZzVnj17XFFRUWb7888/77p9+7YrGPhbOXsTKoOsEr5gjY4cX758eZ9zA+r8XjqHYFojRoxwVatWzfWb3/zmnp43GMOXv5WzvnYmhNaJds+fP+8KBNu2bTMjb6edt+7MmTPuIPnUU0+lOy4mJsaUoU7HdK/OqV555RWPczvqfI7OSON//etfXYHGn8pZA0O5cuXM9meeecZ169YtV7Dwp3L2hfAF5AKdhqJo0aLmmzIyMtJ8g+pSXxcrVsy1ZcsWj8e9+OKLPsNQds8bjOHL38r5gw8+cE9988ADD5gf5t4eZ8+edfkT/YXjTPytk/5qzZITdOrWreu6ePFiumP0F5Wvv9yzc06lE5a3adPGPbmzzoXp1DbqQ4NzoPKXcu7cubO7PFu1auX167RXr16uQOQv5ewL4QvIJTr3nP6S11oSbUrRZd++fV2HDx/2ekxGoSC75w3W8OVP5eyUZWYeR48edfmbNWvWuLp27WqaovSXSp06dVyjR482c9t5ktEvq+ycM3Xz7sSJE12NGjVyRUREuEqWLGn+r5YsWeIKdP5Qzs4v/owe+t6Byh/K2ZdQCV9h+k9e9zsDAAAIFdztCAAAYBHhCwAAwCLCFwAAgEWELwAAAIsIXwAAABYRvgAAACwifAEAAFhE+AIAALCI8AUAAGAR4QsAAMAiwheAgNe+fXsJCwuTcePGWT9/9erVzbZ58+blynsDCD7heX0BAAB7jh07Jps2bZIdO3bIzp07ZdeuXXLt2jWz7ejRoyZMAshdhC8AyIFatWpJ4cKFpWTJkgFRjlp796c//SmvLwMIaYQvAMiB7777LqDKL1++fCYwNmvWTKKjo8Xlcslbb72V15cFhBTCFwCEkDlz5kj+/Pndr9evX5+n1wOEIjrcAwgqWpOjAaNVq1ZSokQJKV68uLRp00bmz5/v9Zjbt2/LJ598YmqDihYtKlFRUaaT/ZIlSzJ8P18d7nW9PjTgXL58Wd544w1T6xQRESHVqlWTV155RS5evOje//jx4/Lyyy9LjRo1TFNm1apVZcSIEe4+WfdC6uAFIG9Q8wUgaGiI+vWvfy0rVqyQ8PBwKVKkiAkuW7duNY+DBw/Ku+++e9cxycnJ0qNHD/n73//ubpYrWLCgbNiwQf75z3/KqFGjcnxdJ06ckOeff15OnTplwt2dO3fMuk8//VTWrl0r33//vbm2xx9/XC5dumRCo36WkydPypQpU+Rf//qXuRaCExAcqPkCEDQ0zGgtk9ZC/fLLL3L16lUTYLp162a2T5gwwYSc1LS/kwYvraHS7VeuXDGPc+fOmVqoiRMnyg8//JCj6xo+fLiULl3aBMCEhATzWLBggQmH+/btk9jYWOndu7c0btxY9u7da65bQ+P06dNN4Nq8ebN8+eWXOboGAP6D8AUgaGhoWr58ubz44oumaU9VrlxZFi9eLBUrVjQ1TosWLXLvf+bMGRNw1JgxY+Ttt982tU6qbNmy8tlnn8lzzz1nwlBOFCpUSNasWWOaQlWBAgXk2WefNU2KSps8ixUrJt9++600aNDArNNmR22W7NOnj3m9cOHCHF0DAP9B+AIQNGJiYqRDhw4ew0+XLl3M8z179rjXa5+uW7dumaD2+9//3uM578XArYMGDZL77rsv3XrnmpT2B9Pr9LZP6usGENgIXwCChlOz5InWfKn4+Hj3uri4OLNs3ry5u8YrrTp16kilSpVydF0tW7b0uL5cuXLu5y1atPC5j9bqAQgOhC8AQUPvbPRGO+CrlJQU97oLFy6YZUbhSpsuc+O6nGvKzD5aQwcgOBC+AIQ87WwPALYQvgCELO1Ur3QICF9Onz5t6YoAhALCF4CQpX29nL5f3gYy1aEpMgpnAJAVhC8AIatnz56mT1VSUpL84Q9/8LjPe++9Z/26AAQ3wheAkKUd7YcOHWqejx8/Xj788EN3DZhO+6PjbOm0RCVLlpRgoTcc6Cj6ziP1GGZ6R2XqbalvTgBw7xC+AIQ0HcG+U6dOZgDW0aNHS2RkpJnbUYd40BHzdXqhBx98UIKFjpZfpkwZ9+Opp55yb9O5LVNv030B3HuELwAhTUeS/9vf/ibTpk0zIUvnddTJudu2bWtGw//oo4/y+hIBBJkwl/6UAQAAgBXUfAEAAFhE+AIAALCI8AUAAGDR/00sBgDweydPnvQ6Cbc3VapUke3bt+faNQHIGsIXAASQ27dvy/nz57N8RycA/8HdjgAAABbR5wsAAMAiwhcAAIBFhC8AAACLCF8AAAAWEb4AAAAsInwBAABYRPgCAACwiPAFAAAg9vw/1EKiV79nIz4AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "features.plot.scatter(x=\"hdim_1\", y=\"hdim_2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb5b54e0",
   "metadata": {},
   "source": [
    "As you can see almost no features are detected. This means our threshold is too high and neglects many datapoints. Therefore it is a good idea to try a low threshold value:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f9537137",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-18T14:32:31.219657Z",
     "start_time": "2025-12-18T14:32:31.182432Z"
    },
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:25.381351Z",
     "iopub.status.busy": "2026-02-02T20:12:25.381236Z",
     "iopub.status.idle": "2026-02-02T20:12:25.420265Z",
     "shell.execute_reply": "2026-02-02T20:12:25.419867Z"
    }
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "features = tobac.feature_detection_multithreshold(data, dxy, threshold=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "75c4de62",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-18T14:32:31.271021Z",
     "start_time": "2025-12-18T14:32:31.223332Z"
    },
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:25.421895Z",
     "iopub.status.busy": "2026-02-02T20:12:25.421802Z",
     "iopub.status.idle": "2026-02-02T20:12:25.467019Z",
     "shell.execute_reply": "2026-02-02T20:12:25.466655Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='hdim_1', ylabel='hdim_2'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "features.plot.scatter(x=\"hdim_1\", y=\"hdim_2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b47d4aae",
   "metadata": {},
   "source": [
    "Here the paths of the blobs are clearly visible, but there is an area in the middle where both merge into one feature. This should be avoided. Therefore we try another value for the threshold somewhere in the middle of the available range:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6d3b79f8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-18T14:32:31.315762Z",
     "start_time": "2025-12-18T14:32:31.278788Z"
    },
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:25.468436Z",
     "iopub.status.busy": "2026-02-02T20:12:25.468344Z",
     "iopub.status.idle": "2026-02-02T20:12:25.506796Z",
     "shell.execute_reply": "2026-02-02T20:12:25.506324Z"
    }
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "features = tobac.feature_detection_multithreshold(data, dxy, threshold=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b889231b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-18T14:32:31.365875Z",
     "start_time": "2025-12-18T14:32:31.319144Z"
    },
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:25.508392Z",
     "iopub.status.busy": "2026-02-02T20:12:25.508298Z",
     "iopub.status.idle": "2026-02-02T20:12:25.551374Z",
     "shell.execute_reply": "2026-02-02T20:12:25.550948Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='hdim_1', ylabel='hdim_2'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "features.plot.scatter(x=\"hdim_1\", y=\"hdim_2\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edb7398d",
   "metadata": {},
   "source": [
    "This is the picture we wanted to see. This means we can continue working with this set of features."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ba1dec0",
   "metadata": {},
   "source": [
    "## 3. Influence of the tracking method"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5909b787",
   "metadata": {},
   "source": [
    "Now the tracking can be performed. We will create two outputs, one with `method = 'random'`, and the other one with `method = 'predict'`. Since we know what the velocities of our features are beforehand, we can select a reasonable value for `v_max`. Normally this would need to be finetuned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aadaf159",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:25.552859Z",
     "iopub.status.busy": "2026-02-02T20:12:25.552752Z",
     "iopub.status.idle": "2026-02-02T20:12:25.611110Z",
     "shell.execute_reply": "2026-02-02T20:12:25.610714Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frame 19: 2 trajectories present.\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "v_max = 20\n",
    "\n",
    "track_1 = tobac.linking_trackpy(\n",
    "    features, data, dt, dxy, v_max=v_max, method_linking=\"random\"\n",
    ")\n",
    "\n",
    "track_2 = tobac.linking_trackpy(\n",
    "    features, data, dt, dxy, v_max=v_max, method_linking=\"predict\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7bc02685-073f-4f32-be76-7e482d4f85f4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-18T14:32:31.455303Z",
     "start_time": "2025-12-18T14:32:31.444214Z"
    },
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:25.612716Z",
     "iopub.status.busy": "2026-02-02T20:12:25.612637Z",
     "iopub.status.idle": "2026-02-02T20:12:25.623283Z",
     "shell.execute_reply": "2026-02-02T20:12:25.622785Z"
    }
   },
   "outputs": [
    {
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       "      <td>2022-04-01 00:08:40</td>\n",
       "      <td>2022-04-01 00:08:40</td>\n",
       "      <td>5186.647523</td>\n",
       "      <td>94813.352477</td>\n",
       "      <td>4.667983</td>\n",
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       "      <td>2</td>\n",
       "      <td>0 days 00:08:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>20.678571</td>\n",
       "      <td>20.678571</td>\n",
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       "      <td>5</td>\n",
       "      <td>2022-04-01 00:17:20</td>\n",
       "      <td>2022-04-01 00:17:20</td>\n",
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       "      <td>10391.241924</td>\n",
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       "      <td>0 days 00:17:20</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
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       "      <td>20.678571</td>\n",
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       "      <td>28</td>\n",
       "      <td>0.8</td>\n",
       "      <td>6</td>\n",
       "      <td>2022-04-01 00:17:20</td>\n",
       "      <td>2022-04-01 00:17:20</td>\n",
       "      <td>10391.241924</td>\n",
       "      <td>89608.758076</td>\n",
       "      <td>9.352118</td>\n",
       "      <td>80.647882</td>\n",
       "      <td>2</td>\n",
       "      <td>0 days 00:17:20</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
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       "      <td>0.8</td>\n",
       "      <td>7</td>\n",
       "      <td>2022-04-01 00:26:00</td>\n",
       "      <td>2022-04-01 00:26:00</td>\n",
       "      <td>15577.889447</td>\n",
       "      <td>15577.889447</td>\n",
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       "      <td>1</td>\n",
       "      <td>0 days 00:26:00</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>25</td>\n",
       "      <td>0.8</td>\n",
       "      <td>8</td>\n",
       "      <td>2022-04-01 00:26:00</td>\n",
       "      <td>2022-04-01 00:26:00</td>\n",
       "      <td>15577.889447</td>\n",
       "      <td>84422.110553</td>\n",
       "      <td>14.020101</td>\n",
       "      <td>75.979899</td>\n",
       "      <td>2</td>\n",
       "      <td>0 days 00:26:00</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>41.321429</td>\n",
       "      <td>157.678571</td>\n",
       "      <td>28</td>\n",
       "      <td>0.8</td>\n",
       "      <td>10</td>\n",
       "      <td>2022-04-01 00:34:40</td>\n",
       "      <td>2022-04-01 00:34:40</td>\n",
       "      <td>20764.536971</td>\n",
       "      <td>79235.463029</td>\n",
       "      <td>18.688083</td>\n",
       "      <td>71.311917</td>\n",
       "      <td>2</td>\n",
       "      <td>0 days 00:34:40</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>41.321429</td>\n",
       "      <td>41.321429</td>\n",
       "      <td>28</td>\n",
       "      <td>0.8</td>\n",
       "      <td>9</td>\n",
       "      <td>2022-04-01 00:34:40</td>\n",
       "      <td>2022-04-01 00:34:40</td>\n",
       "      <td>20764.536971</td>\n",
       "      <td>20764.536971</td>\n",
       "      <td>18.688083</td>\n",
       "      <td>18.688083</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days 00:34:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>51.678571</td>\n",
       "      <td>51.678571</td>\n",
       "      <td>28</td>\n",
       "      <td>0.8</td>\n",
       "      <td>11</td>\n",
       "      <td>2022-04-01 00:43:20</td>\n",
       "      <td>2022-04-01 00:43:20</td>\n",
       "      <td>25969.131371</td>\n",
       "      <td>25969.131371</td>\n",
       "      <td>23.372218</td>\n",
       "      <td>23.372218</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days 00:43:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>51.678571</td>\n",
       "      <td>147.321429</td>\n",
       "      <td>28</td>\n",
       "      <td>0.8</td>\n",
       "      <td>12</td>\n",
       "      <td>2022-04-01 00:43:20</td>\n",
       "      <td>2022-04-01 00:43:20</td>\n",
       "      <td>25969.131371</td>\n",
       "      <td>74030.868629</td>\n",
       "      <td>23.372218</td>\n",
       "      <td>66.627782</td>\n",
       "      <td>2</td>\n",
       "      <td>0 days 00:43:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>62.192308</td>\n",
       "      <td>62.192308</td>\n",
       "      <td>26</td>\n",
       "      <td>0.8</td>\n",
       "      <td>13</td>\n",
       "      <td>2022-04-01 00:52:00</td>\n",
       "      <td>2022-04-01 00:52:00</td>\n",
       "      <td>31252.415926</td>\n",
       "      <td>31252.415926</td>\n",
       "      <td>28.127174</td>\n",
       "      <td>28.127174</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days 00:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>62.192308</td>\n",
       "      <td>136.807692</td>\n",
       "      <td>26</td>\n",
       "      <td>0.8</td>\n",
       "      <td>14</td>\n",
       "      <td>2022-04-01 00:52:00</td>\n",
       "      <td>2022-04-01 00:52:00</td>\n",
       "      <td>31252.415926</td>\n",
       "      <td>68747.584074</td>\n",
       "      <td>28.127174</td>\n",
       "      <td>61.872826</td>\n",
       "      <td>2</td>\n",
       "      <td>0 days 00:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>72.321429</td>\n",
       "      <td>72.321429</td>\n",
       "      <td>28</td>\n",
       "      <td>0.8</td>\n",
       "      <td>15</td>\n",
       "      <td>2022-04-01 01:00:40</td>\n",
       "      <td>2022-04-01 01:00:40</td>\n",
       "      <td>36342.426418</td>\n",
       "      <td>36342.426418</td>\n",
       "      <td>32.708184</td>\n",
       "      <td>32.708184</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days 01:00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>72.321429</td>\n",
       "      <td>126.678571</td>\n",
       "      <td>28</td>\n",
       "      <td>0.8</td>\n",
       "      <td>16</td>\n",
       "      <td>2022-04-01 01:00:40</td>\n",
       "      <td>2022-04-01 01:00:40</td>\n",
       "      <td>36342.426418</td>\n",
       "      <td>63657.573582</td>\n",
       "      <td>32.708184</td>\n",
       "      <td>57.291816</td>\n",
       "      <td>2</td>\n",
       "      <td>0 days 01:00:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>82.678571</td>\n",
       "      <td>82.678571</td>\n",
       "      <td>28</td>\n",
       "      <td>0.8</td>\n",
       "      <td>17</td>\n",
       "      <td>2022-04-01 01:09:20</td>\n",
       "      <td>2022-04-01 01:09:20</td>\n",
       "      <td>41547.020818</td>\n",
       "      <td>41547.020818</td>\n",
       "      <td>37.392319</td>\n",
       "      <td>37.392319</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days 01:09:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>82.678571</td>\n",
       "      <td>116.321429</td>\n",
       "      <td>28</td>\n",
       "      <td>0.8</td>\n",
       "      <td>18</td>\n",
       "      <td>2022-04-01 01:09:20</td>\n",
       "      <td>2022-04-01 01:09:20</td>\n",
       "      <td>41547.020818</td>\n",
       "      <td>58452.979182</td>\n",
       "      <td>37.392319</td>\n",
       "      <td>52.607681</td>\n",
       "      <td>2</td>\n",
       "      <td>0 days 01:09:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>93.192308</td>\n",
       "      <td>105.807692</td>\n",
       "      <td>26</td>\n",
       "      <td>0.8</td>\n",
       "      <td>20</td>\n",
       "      <td>2022-04-01 01:18:00</td>\n",
       "      <td>2022-04-01 01:18:00</td>\n",
       "      <td>46830.305373</td>\n",
       "      <td>53169.694627</td>\n",
       "      <td>42.147275</td>\n",
       "      <td>47.852725</td>\n",
       "      <td>2</td>\n",
       "      <td>0 days 01:18:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>93.192308</td>\n",
       "      <td>93.192308</td>\n",
       "      <td>26</td>\n",
       "      <td>0.8</td>\n",
       "      <td>19</td>\n",
       "      <td>2022-04-01 01:18:00</td>\n",
       "      <td>2022-04-01 01:18:00</td>\n",
       "      <td>46830.305373</td>\n",
       "      <td>46830.305373</td>\n",
       "      <td>42.147275</td>\n",
       "      <td>42.147275</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days 01:18:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>103.321429</td>\n",
       "      <td>95.678571</td>\n",
       "      <td>28</td>\n",
       "      <td>0.8</td>\n",
       "      <td>21</td>\n",
       "      <td>2022-04-01 01:26:40</td>\n",
       "      <td>2022-04-01 01:26:40</td>\n",
       "      <td>51920.315865</td>\n",
       "      <td>48079.684135</td>\n",
       "      <td>46.728284</td>\n",
       "      <td>43.271716</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days 01:26:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>103.321429</td>\n",
       "      <td>103.321429</td>\n",
       "      <td>28</td>\n",
       "      <td>0.8</td>\n",
       "      <td>22</td>\n",
       "      <td>2022-04-01 01:26:40</td>\n",
       "      <td>2022-04-01 01:26:40</td>\n",
       "      <td>51920.315865</td>\n",
       "      <td>51920.315865</td>\n",
       "      <td>46.728284</td>\n",
       "      <td>46.728284</td>\n",
       "      <td>2</td>\n",
       "      <td>0 days 01:26:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>113.807692</td>\n",
       "      <td>85.192308</td>\n",
       "      <td>26</td>\n",
       "      <td>0.8</td>\n",
       "      <td>23</td>\n",
       "      <td>2022-04-01 01:35:20</td>\n",
       "      <td>2022-04-01 01:35:20</td>\n",
       "      <td>57189.795129</td>\n",
       "      <td>42810.204871</td>\n",
       "      <td>51.470816</td>\n",
       "      <td>38.529184</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days 01:35:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>113.807692</td>\n",
       "      <td>113.807692</td>\n",
       "      <td>26</td>\n",
       "      <td>0.8</td>\n",
       "      <td>24</td>\n",
       "      <td>2022-04-01 01:35:20</td>\n",
       "      <td>2022-04-01 01:35:20</td>\n",
       "      <td>57189.795129</td>\n",
       "      <td>57189.795129</td>\n",
       "      <td>51.470816</td>\n",
       "      <td>51.470816</td>\n",
       "      <td>2</td>\n",
       "      <td>0 days 01:35:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>124.192308</td>\n",
       "      <td>74.807692</td>\n",
       "      <td>26</td>\n",
       "      <td>0.8</td>\n",
       "      <td>25</td>\n",
       "      <td>2022-04-01 01:44:00</td>\n",
       "      <td>2022-04-01 01:44:00</td>\n",
       "      <td>62408.194820</td>\n",
       "      <td>37591.805180</td>\n",
       "      <td>56.167375</td>\n",
       "      <td>33.832625</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days 01:44:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>124.192308</td>\n",
       "      <td>124.192308</td>\n",
       "      <td>26</td>\n",
       "      <td>0.8</td>\n",
       "      <td>26</td>\n",
       "      <td>2022-04-01 01:44:00</td>\n",
       "      <td>2022-04-01 01:44:00</td>\n",
       "      <td>62408.194820</td>\n",
       "      <td>62408.194820</td>\n",
       "      <td>56.167375</td>\n",
       "      <td>56.167375</td>\n",
       "      <td>2</td>\n",
       "      <td>0 days 01:44:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>134.678571</td>\n",
       "      <td>64.321429</td>\n",
       "      <td>28</td>\n",
       "      <td>0.8</td>\n",
       "      <td>27</td>\n",
       "      <td>2022-04-01 01:52:40</td>\n",
       "      <td>2022-04-01 01:52:40</td>\n",
       "      <td>67677.674085</td>\n",
       "      <td>32322.325915</td>\n",
       "      <td>60.909907</td>\n",
       "      <td>29.090093</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days 01:52:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>134.678571</td>\n",
       "      <td>134.678571</td>\n",
       "      <td>28</td>\n",
       "      <td>0.8</td>\n",
       "      <td>28</td>\n",
       "      <td>2022-04-01 01:52:40</td>\n",
       "      <td>2022-04-01 01:52:40</td>\n",
       "      <td>67677.674085</td>\n",
       "      <td>67677.674085</td>\n",
       "      <td>60.909907</td>\n",
       "      <td>60.909907</td>\n",
       "      <td>2</td>\n",
       "      <td>0 days 01:52:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>144.807692</td>\n",
       "      <td>144.807692</td>\n",
       "      <td>26</td>\n",
       "      <td>0.8</td>\n",
       "      <td>30</td>\n",
       "      <td>2022-04-01 02:01:20</td>\n",
       "      <td>2022-04-01 02:01:20</td>\n",
       "      <td>72767.684577</td>\n",
       "      <td>72767.684577</td>\n",
       "      <td>65.490916</td>\n",
       "      <td>65.490916</td>\n",
       "      <td>2</td>\n",
       "      <td>0 days 02:01:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>144.807692</td>\n",
       "      <td>54.192308</td>\n",
       "      <td>26</td>\n",
       "      <td>0.8</td>\n",
       "      <td>29</td>\n",
       "      <td>2022-04-01 02:01:20</td>\n",
       "      <td>2022-04-01 02:01:20</td>\n",
       "      <td>72767.684577</td>\n",
       "      <td>27232.315423</td>\n",
       "      <td>65.490916</td>\n",
       "      <td>24.509084</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days 02:01:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>155.321429</td>\n",
       "      <td>43.678571</td>\n",
       "      <td>28</td>\n",
       "      <td>0.8</td>\n",
       "      <td>31</td>\n",
       "      <td>2022-04-01 02:10:00</td>\n",
       "      <td>2022-04-01 02:10:00</td>\n",
       "      <td>78050.969131</td>\n",
       "      <td>21949.030869</td>\n",
       "      <td>70.245872</td>\n",
       "      <td>19.754128</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days 02:10:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>155.321429</td>\n",
       "      <td>155.321429</td>\n",
       "      <td>28</td>\n",
       "      <td>0.8</td>\n",
       "      <td>32</td>\n",
       "      <td>2022-04-01 02:10:00</td>\n",
       "      <td>2022-04-01 02:10:00</td>\n",
       "      <td>78050.969131</td>\n",
       "      <td>78050.969131</td>\n",
       "      <td>70.245872</td>\n",
       "      <td>70.245872</td>\n",
       "      <td>2</td>\n",
       "      <td>0 days 02:10:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>165.678571</td>\n",
       "      <td>33.321429</td>\n",
       "      <td>28</td>\n",
       "      <td>0.8</td>\n",
       "      <td>33</td>\n",
       "      <td>2022-04-01 02:18:40</td>\n",
       "      <td>2022-04-01 02:18:40</td>\n",
       "      <td>83255.563532</td>\n",
       "      <td>16744.436468</td>\n",
       "      <td>74.930007</td>\n",
       "      <td>15.069993</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days 02:18:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "      <td>165.678571</td>\n",
       "      <td>165.678571</td>\n",
       "      <td>28</td>\n",
       "      <td>0.8</td>\n",
       "      <td>34</td>\n",
       "      <td>2022-04-01 02:18:40</td>\n",
       "      <td>2022-04-01 02:18:40</td>\n",
       "      <td>83255.563532</td>\n",
       "      <td>83255.563532</td>\n",
       "      <td>74.930007</td>\n",
       "      <td>74.930007</td>\n",
       "      <td>2</td>\n",
       "      <td>0 days 02:18:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>175.916667</td>\n",
       "      <td>23.083333</td>\n",
       "      <td>24</td>\n",
       "      <td>0.8</td>\n",
       "      <td>35</td>\n",
       "      <td>2022-04-01 02:27:20</td>\n",
       "      <td>2022-04-01 02:27:20</td>\n",
       "      <td>88400.335008</td>\n",
       "      <td>11599.664992</td>\n",
       "      <td>79.560302</td>\n",
       "      <td>10.439698</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days 02:27:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>175.916667</td>\n",
       "      <td>175.916667</td>\n",
       "      <td>24</td>\n",
       "      <td>0.8</td>\n",
       "      <td>36</td>\n",
       "      <td>2022-04-01 02:27:20</td>\n",
       "      <td>2022-04-01 02:27:20</td>\n",
       "      <td>88400.335008</td>\n",
       "      <td>88400.335008</td>\n",
       "      <td>79.560302</td>\n",
       "      <td>79.560302</td>\n",
       "      <td>2</td>\n",
       "      <td>0 days 02:27:20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>186.321429</td>\n",
       "      <td>12.678571</td>\n",
       "      <td>28</td>\n",
       "      <td>0.8</td>\n",
       "      <td>37</td>\n",
       "      <td>2022-04-01 02:36:00</td>\n",
       "      <td>2022-04-01 02:36:00</td>\n",
       "      <td>93628.858579</td>\n",
       "      <td>6371.141421</td>\n",
       "      <td>84.265973</td>\n",
       "      <td>5.734027</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days 02:36:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>186.321429</td>\n",
       "      <td>186.321429</td>\n",
       "      <td>28</td>\n",
       "      <td>0.8</td>\n",
       "      <td>38</td>\n",
       "      <td>2022-04-01 02:36:00</td>\n",
       "      <td>2022-04-01 02:36:00</td>\n",
       "      <td>93628.858579</td>\n",
       "      <td>93628.858579</td>\n",
       "      <td>84.265973</td>\n",
       "      <td>84.265973</td>\n",
       "      <td>2</td>\n",
       "      <td>0 days 02:36:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>196.678571</td>\n",
       "      <td>2.321429</td>\n",
       "      <td>28</td>\n",
       "      <td>0.8</td>\n",
       "      <td>39</td>\n",
       "      <td>2022-04-01 02:44:40</td>\n",
       "      <td>2022-04-01 02:44:40</td>\n",
       "      <td>98833.452979</td>\n",
       "      <td>1166.547021</td>\n",
       "      <td>88.950108</td>\n",
       "      <td>1.049892</td>\n",
       "      <td>1</td>\n",
       "      <td>0 days 02:44:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>19</td>\n",
       "      <td>2</td>\n",
       "      <td>196.678571</td>\n",
       "      <td>196.678571</td>\n",
       "      <td>28</td>\n",
       "      <td>0.8</td>\n",
       "      <td>40</td>\n",
       "      <td>2022-04-01 02:44:40</td>\n",
       "      <td>2022-04-01 02:44:40</td>\n",
       "      <td>98833.452979</td>\n",
       "      <td>98833.452979</td>\n",
       "      <td>88.950108</td>\n",
       "      <td>88.950108</td>\n",
       "      <td>2</td>\n",
       "      <td>0 days 02:44:40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    frame  idx      hdim_1      hdim_2  num  threshold_value  feature  \\\n",
       "0       0    1    1.000000    1.000000    9              0.8        1   \n",
       "1       0    2    1.000000  198.000000    9              0.8        2   \n",
       "2       1    1   10.321429   10.321429   28              0.8        3   \n",
       "3       1    2   10.321429  188.678571   28              0.8        4   \n",
       "4       2    1   20.678571   20.678571   28              0.8        5   \n",
       "5       2    2   20.678571  178.321429   28              0.8        6   \n",
       "6       3    1   31.000000   31.000000   25              0.8        7   \n",
       "7       3    2   31.000000  168.000000   25              0.8        8   \n",
       "9       4    2   41.321429  157.678571   28              0.8       10   \n",
       "8       4    1   41.321429   41.321429   28              0.8        9   \n",
       "10      5    1   51.678571   51.678571   28              0.8       11   \n",
       "11      5    2   51.678571  147.321429   28              0.8       12   \n",
       "12      6    1   62.192308   62.192308   26              0.8       13   \n",
       "13      6    2   62.192308  136.807692   26              0.8       14   \n",
       "14      7    1   72.321429   72.321429   28              0.8       15   \n",
       "15      7    2   72.321429  126.678571   28              0.8       16   \n",
       "16      8    1   82.678571   82.678571   28              0.8       17   \n",
       "17      8    2   82.678571  116.321429   28              0.8       18   \n",
       "19      9    2   93.192308  105.807692   26              0.8       20   \n",
       "18      9    1   93.192308   93.192308   26              0.8       19   \n",
       "20     10    1  103.321429   95.678571   28              0.8       21   \n",
       "21     10    2  103.321429  103.321429   28              0.8       22   \n",
       "22     11    1  113.807692   85.192308   26              0.8       23   \n",
       "23     11    2  113.807692  113.807692   26              0.8       24   \n",
       "24     12    1  124.192308   74.807692   26              0.8       25   \n",
       "25     12    2  124.192308  124.192308   26              0.8       26   \n",
       "26     13    1  134.678571   64.321429   28              0.8       27   \n",
       "27     13    2  134.678571  134.678571   28              0.8       28   \n",
       "29     14    2  144.807692  144.807692   26              0.8       30   \n",
       "28     14    1  144.807692   54.192308   26              0.8       29   \n",
       "30     15    1  155.321429   43.678571   28              0.8       31   \n",
       "31     15    2  155.321429  155.321429   28              0.8       32   \n",
       "32     16    1  165.678571   33.321429   28              0.8       33   \n",
       "33     16    2  165.678571  165.678571   28              0.8       34   \n",
       "34     17    1  175.916667   23.083333   24              0.8       35   \n",
       "35     17    2  175.916667  175.916667   24              0.8       36   \n",
       "36     18    1  186.321429   12.678571   28              0.8       37   \n",
       "37     18    2  186.321429  186.321429   28              0.8       38   \n",
       "38     19    1  196.678571    2.321429   28              0.8       39   \n",
       "39     19    2  196.678571  196.678571   28              0.8       40   \n",
       "\n",
       "                  time              timestr             x             y  \\\n",
       "0  2022-04-01 00:00:00  2022-04-01 00:00:00    502.512563    502.512563   \n",
       "1  2022-04-01 00:00:00  2022-04-01 00:00:00    502.512563  99497.487437   \n",
       "2  2022-04-01 00:08:40  2022-04-01 00:08:40   5186.647523   5186.647523   \n",
       "3  2022-04-01 00:08:40  2022-04-01 00:08:40   5186.647523  94813.352477   \n",
       "4  2022-04-01 00:17:20  2022-04-01 00:17:20  10391.241924  10391.241924   \n",
       "5  2022-04-01 00:17:20  2022-04-01 00:17:20  10391.241924  89608.758076   \n",
       "6  2022-04-01 00:26:00  2022-04-01 00:26:00  15577.889447  15577.889447   \n",
       "7  2022-04-01 00:26:00  2022-04-01 00:26:00  15577.889447  84422.110553   \n",
       "9  2022-04-01 00:34:40  2022-04-01 00:34:40  20764.536971  79235.463029   \n",
       "8  2022-04-01 00:34:40  2022-04-01 00:34:40  20764.536971  20764.536971   \n",
       "10 2022-04-01 00:43:20  2022-04-01 00:43:20  25969.131371  25969.131371   \n",
       "11 2022-04-01 00:43:20  2022-04-01 00:43:20  25969.131371  74030.868629   \n",
       "12 2022-04-01 00:52:00  2022-04-01 00:52:00  31252.415926  31252.415926   \n",
       "13 2022-04-01 00:52:00  2022-04-01 00:52:00  31252.415926  68747.584074   \n",
       "14 2022-04-01 01:00:40  2022-04-01 01:00:40  36342.426418  36342.426418   \n",
       "15 2022-04-01 01:00:40  2022-04-01 01:00:40  36342.426418  63657.573582   \n",
       "16 2022-04-01 01:09:20  2022-04-01 01:09:20  41547.020818  41547.020818   \n",
       "17 2022-04-01 01:09:20  2022-04-01 01:09:20  41547.020818  58452.979182   \n",
       "19 2022-04-01 01:18:00  2022-04-01 01:18:00  46830.305373  53169.694627   \n",
       "18 2022-04-01 01:18:00  2022-04-01 01:18:00  46830.305373  46830.305373   \n",
       "20 2022-04-01 01:26:40  2022-04-01 01:26:40  51920.315865  48079.684135   \n",
       "21 2022-04-01 01:26:40  2022-04-01 01:26:40  51920.315865  51920.315865   \n",
       "22 2022-04-01 01:35:20  2022-04-01 01:35:20  57189.795129  42810.204871   \n",
       "23 2022-04-01 01:35:20  2022-04-01 01:35:20  57189.795129  57189.795129   \n",
       "24 2022-04-01 01:44:00  2022-04-01 01:44:00  62408.194820  37591.805180   \n",
       "25 2022-04-01 01:44:00  2022-04-01 01:44:00  62408.194820  62408.194820   \n",
       "26 2022-04-01 01:52:40  2022-04-01 01:52:40  67677.674085  32322.325915   \n",
       "27 2022-04-01 01:52:40  2022-04-01 01:52:40  67677.674085  67677.674085   \n",
       "29 2022-04-01 02:01:20  2022-04-01 02:01:20  72767.684577  72767.684577   \n",
       "28 2022-04-01 02:01:20  2022-04-01 02:01:20  72767.684577  27232.315423   \n",
       "30 2022-04-01 02:10:00  2022-04-01 02:10:00  78050.969131  21949.030869   \n",
       "31 2022-04-01 02:10:00  2022-04-01 02:10:00  78050.969131  78050.969131   \n",
       "32 2022-04-01 02:18:40  2022-04-01 02:18:40  83255.563532  16744.436468   \n",
       "33 2022-04-01 02:18:40  2022-04-01 02:18:40  83255.563532  83255.563532   \n",
       "34 2022-04-01 02:27:20  2022-04-01 02:27:20  88400.335008  11599.664992   \n",
       "35 2022-04-01 02:27:20  2022-04-01 02:27:20  88400.335008  88400.335008   \n",
       "36 2022-04-01 02:36:00  2022-04-01 02:36:00  93628.858579   6371.141421   \n",
       "37 2022-04-01 02:36:00  2022-04-01 02:36:00  93628.858579  93628.858579   \n",
       "38 2022-04-01 02:44:40  2022-04-01 02:44:40  98833.452979   1166.547021   \n",
       "39 2022-04-01 02:44:40  2022-04-01 02:44:40  98833.452979  98833.452979   \n",
       "\n",
       "     latitude  longitude  cell       time_cell  \n",
       "0    0.452261   0.452261     1 0 days 00:00:00  \n",
       "1    0.452261  89.547739     2 0 days 00:00:00  \n",
       "2    4.667983   4.667983     1 0 days 00:08:40  \n",
       "3    4.667983  85.332017     2 0 days 00:08:40  \n",
       "4    9.352118   9.352118     1 0 days 00:17:20  \n",
       "5    9.352118  80.647882     2 0 days 00:17:20  \n",
       "6   14.020101  14.020101     1 0 days 00:26:00  \n",
       "7   14.020101  75.979899     2 0 days 00:26:00  \n",
       "9   18.688083  71.311917     2 0 days 00:34:40  \n",
       "8   18.688083  18.688083     1 0 days 00:34:40  \n",
       "10  23.372218  23.372218     1 0 days 00:43:20  \n",
       "11  23.372218  66.627782     2 0 days 00:43:20  \n",
       "12  28.127174  28.127174     1 0 days 00:52:00  \n",
       "13  28.127174  61.872826     2 0 days 00:52:00  \n",
       "14  32.708184  32.708184     1 0 days 01:00:40  \n",
       "15  32.708184  57.291816     2 0 days 01:00:40  \n",
       "16  37.392319  37.392319     1 0 days 01:09:20  \n",
       "17  37.392319  52.607681     2 0 days 01:09:20  \n",
       "19  42.147275  47.852725     2 0 days 01:18:00  \n",
       "18  42.147275  42.147275     1 0 days 01:18:00  \n",
       "20  46.728284  43.271716     1 0 days 01:26:40  \n",
       "21  46.728284  46.728284     2 0 days 01:26:40  \n",
       "22  51.470816  38.529184     1 0 days 01:35:20  \n",
       "23  51.470816  51.470816     2 0 days 01:35:20  \n",
       "24  56.167375  33.832625     1 0 days 01:44:00  \n",
       "25  56.167375  56.167375     2 0 days 01:44:00  \n",
       "26  60.909907  29.090093     1 0 days 01:52:40  \n",
       "27  60.909907  60.909907     2 0 days 01:52:40  \n",
       "29  65.490916  65.490916     2 0 days 02:01:20  \n",
       "28  65.490916  24.509084     1 0 days 02:01:20  \n",
       "30  70.245872  19.754128     1 0 days 02:10:00  \n",
       "31  70.245872  70.245872     2 0 days 02:10:00  \n",
       "32  74.930007  15.069993     1 0 days 02:18:40  \n",
       "33  74.930007  74.930007     2 0 days 02:18:40  \n",
       "34  79.560302  10.439698     1 0 days 02:27:20  \n",
       "35  79.560302  79.560302     2 0 days 02:27:20  \n",
       "36  84.265973   5.734027     1 0 days 02:36:00  \n",
       "37  84.265973  84.265973     2 0 days 02:36:00  \n",
       "38  88.950108   1.049892     1 0 days 02:44:40  \n",
       "39  88.950108  88.950108     2 0 days 02:44:40  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "track_1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e826cbf0",
   "metadata": {},
   "source": [
    "Let's have a look at the resulting tracks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "74f65fe0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-18T14:32:31.636618Z",
     "start_time": "2025-12-18T14:32:31.480825Z"
    },
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:25.624868Z",
     "iopub.status.busy": "2026-02-02T20:12:25.624710Z",
     "iopub.status.idle": "2026-02-02T20:12:25.757346Z",
     "shell.execute_reply": "2026-02-02T20:12:25.756891Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'predict')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(20, 10))\n",
    "\n",
    "cell_color = [None, \"C0\", \"C1\", \"C2\", \"C3\"]\n",
    "for i, cell_track in track_1.groupby(\"cell\"):\n",
    "    cell_track.plot(\n",
    "        x=\"x\",\n",
    "        y=\"y\",\n",
    "        ax=ax1,\n",
    "        marker=\"o\",\n",
    "        color=cell_color[i],\n",
    "        label=\"cell {0}\".format(int(i)),\n",
    "    )\n",
    "ax1.legend()\n",
    "ax1.set_title(\"random\")\n",
    "\n",
    "for i, cell_track in track_2.groupby(\"cell\"):\n",
    "    cell_track.plot(\n",
    "        x=\"x\",\n",
    "        y=\"y\",\n",
    "        ax=ax2,\n",
    "        marker=\"o\",\n",
    "        color=cell_color[i],\n",
    "        label=\"cell {0}\".format(int(i)),\n",
    "    )\n",
    "ax2.legend()\n",
    "ax2.set_title(\"predict\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "081e16ac",
   "metadata": {},
   "source": [
    "As you can see, there is a clear difference. While in the first link output the feature positions in the top half of the graph are linked into one cell, in the second output the path of the cell follows the actual way of the Gaussian blobs we created. This is possible because `method = \"predict\"` uses an extraploation to infer the next position from the previous timeframes."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f2096e8",
   "metadata": {},
   "source": [
    "## 4. Analysis\n",
    "\n",
    "We know that the second option is \"correct\", because we created the data. But can we also decude this by analyzing our tracks?\n",
    "\n",
    "Let's calculate the values for the velocities:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1d26f341",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-18T14:32:31.688302Z",
     "start_time": "2025-12-18T14:32:31.660715Z"
    },
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:25.758714Z",
     "iopub.status.busy": "2026-02-02T20:12:25.758616Z",
     "iopub.status.idle": "2026-02-02T20:12:25.787692Z",
     "shell.execute_reply": "2026-02-02T20:12:25.787051Z"
    }
   },
   "outputs": [],
   "source": [
    "track_1 = tobac.analysis.calculate_velocity(track_1)\n",
    "track_2 = tobac.analysis.calculate_velocity(track_2)\n",
    "\n",
    "v1 = track_1.where(track_1[\"cell\"] == 1).dropna().v.values\n",
    "v2 = track_2.where(track_1[\"cell\"] == 1).dropna().v.values"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "623f3b51",
   "metadata": {},
   "source": [
    "Visualizing these can help us with our investigation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7573ae59",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-18T14:32:31.873312Z",
     "start_time": "2025-12-18T14:32:31.694616Z"
    },
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:25.790166Z",
     "iopub.status.busy": "2026-02-02T20:12:25.790040Z",
     "iopub.status.idle": "2026-02-02T20:12:25.949843Z",
     "shell.execute_reply": "2026-02-02T20:12:25.949352Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax) = plt.subplots(figsize=(14, 6))\n",
    "\n",
    "ax.set_title(\"Cell 1\")\n",
    "\n",
    "mask_1 = track_1[\"cell\"] == 1\n",
    "mask_2 = track_2[\"cell\"] == 1\n",
    "\n",
    "track_1.where(mask_1).dropna().plot(\n",
    "    x=\"time\",\n",
    "    y=\"v\",\n",
    "    ax=ax,\n",
    "    label='method = \"random\"',\n",
    "    marker=\"^\",\n",
    "    linestyle=\"\",\n",
    "    alpha=0.5,\n",
    ")\n",
    "track_2.where(mask_2).dropna().plot(\n",
    "    x=\"time\",\n",
    "    y=\"v\",\n",
    "    ax=ax,\n",
    "    label='method = \"predict\"',\n",
    "    marker=\"v\",\n",
    "    linestyle=\"\",\n",
    "    alpha=0.5,\n",
    ")\n",
    "\n",
    "ticks = ax.get_xticks()\n",
    "\n",
    "plt.hlines(\n",
    "    [np.sqrt(v_x**2 + v_y**2)],\n",
    "    ticks.min(),\n",
    "    ticks.max(),\n",
    "    color=\"black\",\n",
    "    label=\"expected velocity\",\n",
    "    linestyle=\"--\",\n",
    ")\n",
    "\n",
    "\n",
    "ax.set_ylabel(\"$v$ [m/s]\")\n",
    "plt.legend()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d3b7d21",
   "metadata": {},
   "source": [
    "The expected velocity is just added for reference. But also without looking at this, we can see that the values for `method = \"random\"` have an outlier, that deviates far from the other values. This is a clear sign, that this method is not suited well for this case."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}