{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "abf6b106",
   "metadata": {},
   "source": [
    "# Methods and Parameters for Feature Detection: Part 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "388df18a",
   "metadata": {},
   "source": [
    "In this notebook, we will take a detailed look at tobac's feature detection and examine some of its parameters. We concentrate on:\n",
    "\n",
    "- [Minima and Maxima and Multiple Thresholds for Feature Identification](#Minima-and-Maxima-and-Multiple-Thresholds-for-Feature-Identification)\n",
    "- [Feature Position](#Feature-Position)\n",
    "- [Sigma Parameter for Smoothing of Noisy Data](#Sigma-Parameter-for-Smoothing-of-Noisy-Data)\n",
    "- [Band Pass Filter for Input Fields](#Band-Pass-Filter-for-Input-Fields-via-Parameter-wavelength_filtering)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7451e92f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:41.743212Z",
     "iopub.status.busy": "2026-02-02T20:12:41.742982Z",
     "iopub.status.idle": "2026-02-02T20:12:43.038033Z",
     "shell.execute_reply": "2026-02-02T20:12:43.037517Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_context(\"talk\")\n",
    "\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ac0fa647",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:43.039834Z",
     "iopub.status.busy": "2026-02-02T20:12:43.039667Z",
     "iopub.status.idle": "2026-02-02T20:12:43.778933Z",
     "shell.execute_reply": "2026-02-02T20:12:43.778474Z"
    }
   },
   "outputs": [],
   "source": [
    "import tobac\n",
    "import tobac.testing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83ac9179",
   "metadata": {},
   "source": [
    "## Minima and Maxima and Multiple Thresholds for Feature Identification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15f161c3",
   "metadata": {},
   "source": [
    "Feature identification search for local maxima in the data.\n",
    "\n",
    "When working different inputs it is sometimes necessary to switch the feature detection from finding maxima to minima. Furthermore, for more complex datasets containing multiple features differing in magnitude, a categorization according to this magnitude is desirable. Both will be demonstrated with the `make_sample_data_2D_3blobs()` function, which creates such a dataset. For the search for minima we will simply turn the dataset negative:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7647af2f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:43.780993Z",
     "iopub.status.busy": "2026-02-02T20:12:43.780782Z",
     "iopub.status.idle": "2026-02-02T20:12:43.847612Z",
     "shell.execute_reply": "2026-02-02T20:12:43.847050Z"
    }
   },
   "outputs": [],
   "source": [
    "data = tobac.testing.make_sample_data_2D_3blobs(data_type=\"xarray\")\n",
    "neg_data = -data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c80b7e3",
   "metadata": {},
   "source": [
    "Let us have a look at frame number 50:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0935edb1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:43.849002Z",
     "iopub.status.busy": "2026-02-02T20:12:43.848919Z",
     "iopub.status.idle": "2026-02-02T20:12:44.019041Z",
     "shell.execute_reply": "2026-02-02T20:12:44.018658Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 50\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(14, 10))\n",
    "\n",
    "im1 = ax1.imshow(data.data[50], cmap=\"Greys\")\n",
    "ax1.set_title(\"data\")\n",
    "cbar = plt.colorbar(im1, ax=ax1)\n",
    "\n",
    "im2 = ax2.imshow(neg_data.data[50], cmap=\"Greys\")\n",
    "ax2.set_title(\" negative data\")\n",
    "cbar = plt.colorbar(im2, ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7aed687",
   "metadata": {},
   "source": [
    "As you can see the data has 3 maxima/minima with different extremal values. To capture these, we use list comprehensions to obtain multiple thresholds in the range of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b6c131c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:44.020697Z",
     "iopub.status.busy": "2026-02-02T20:12:44.020594Z",
     "iopub.status.idle": "2026-02-02T20:12:44.022358Z",
     "shell.execute_reply": "2026-02-02T20:12:44.021926Z"
    }
   },
   "outputs": [],
   "source": [
    "thresholds = [i for i in range(9, 18)]\n",
    "neg_thresholds = [-i for i in range(9, 18)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3162981a",
   "metadata": {},
   "source": [
    "These can now be passed as arguments to `feature_detection_multithreshold()`. With the `target`-keyword we can set a flag whether to search for minima or maxima. The standard is `\"maxima\"`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fd4be7e7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:44.023619Z",
     "iopub.status.busy": "2026-02-02T20:12:44.023544Z",
     "iopub.status.idle": "2026-02-02T20:12:44.844815Z",
     "shell.execute_reply": "2026-02-02T20:12:44.844348Z"
    }
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "dxy, dt = tobac.utils.get_spacings(data)\n",
    "\n",
    "features = tobac.feature_detection_multithreshold(\n",
    "    data, dxy, thresholds, target=\"maximum\"\n",
    ")\n",
    "features_inv = tobac.feature_detection_multithreshold(\n",
    "    neg_data, dxy, neg_thresholds, target=\"minimum\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27e156b2",
   "metadata": {},
   "source": [
    "Let's scatter the detected features onto frame 50  of the dataset and create colorbars for the threshold values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "16ddb295",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:44.846524Z",
     "iopub.status.busy": "2026-02-02T20:12:44.846366Z",
     "iopub.status.idle": "2026-02-02T20:12:45.008322Z",
     "shell.execute_reply": "2026-02-02T20:12:45.007806Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'threshold')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask_1 = features[\"frame\"] == n\n",
    "mask_2 = features_inv[\"frame\"] == n\n",
    "\n",
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(14, 10))\n",
    "\n",
    "ax1.imshow(data.data[50], cmap=\"Greys\")\n",
    "im1 = ax1.scatter(\n",
    "    features.where(mask_1)[\"hdim_2\"],\n",
    "    features.where(mask_1)[\"hdim_1\"],\n",
    "    c=features.where(mask_1)[\"threshold_value\"],\n",
    "    cmap=\"tab10\",\n",
    ")\n",
    "cbar = plt.colorbar(im1, ax=ax1)\n",
    "cbar.ax.set_ylabel(\"threshold\")\n",
    "\n",
    "ax2.imshow(neg_data.data[50], cmap=\"Greys\")\n",
    "im2 = ax2.scatter(\n",
    "    features_inv.where(mask_2)[\"hdim_2\"],\n",
    "    features_inv.where(mask_2)[\"hdim_1\"],\n",
    "    c=features_inv.where(mask_2)[\"threshold_value\"],\n",
    "    cmap=\"tab10\",\n",
    ")\n",
    "cbar = plt.colorbar(im2, ax=ax2)\n",
    "cbar.ax.set_ylabel(\"threshold\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79fe9250",
   "metadata": {},
   "source": [
    "The three features were found in both data sets, and the color bars indicate which threshold they belong to. When using multiple thresholds, note that the order of the list is important. Each feature is assigned the threshold value that was reached last. Therefore, it makes sense to start with the lowest value in case of maxima."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eaa26b3",
   "metadata": {},
   "source": [
    "## Feature Position"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9e4d6ea",
   "metadata": {},
   "source": [
    "To explore the influence of the `position_threshold` flag we need a radially asymmetric feature. Let's create a simple one by adding two 2d-gaussians and add an extra dimension for the time, which is required for working with tobac:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2f461b2b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:45.009921Z",
     "iopub.status.busy": "2026-02-02T20:12:45.009820Z",
     "iopub.status.idle": "2026-02-02T20:12:45.083869Z",
     "shell.execute_reply": "2026-02-02T20:12:45.083526Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x305192710>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6wq9+UYwTN5st+0fPWl29jHI/rPsuVjLKPLF65yivCWomRqVspCwjxhFXHu9K5t6pMPGV6UFEBQAAAEDqpPGHKQAAACBVIga3J46ICgAAAIDUoaECAAAAIHXo+gUAAAC0worpgMH0RiodERUAAAAAqUNEBQAAACgiSnplehBRAQAAAJA+NA0BAAAApA5dvwAAAICiK9MnO7jdJlpaOhFRAQAAAJA6RFQAAACAVpkOGExvpNJVTkPFHetMaSeYqcrot6/MY7P6t97W6PJE1fr9iGp0efK1yvR1+g95vk73Qc11M2UvI6zTbT+s1aXflUcX9I2y+iCxNo+tsum7zsapUl5XqSCn3wltnqheeQ5Wx7hM7dSVEec72QYmdd/D2qutsfqTymrzhJG6DHW9YuyHOk9ky364te+tifTvrTW6k90EujKs8n36ohBdeqvfbzGm/OcU0AaV01ABAAAAYmCMSsdgjAoAAACA1KGhAgAAACB16PoFAAAAFMHK9MkjogIAAAAgdYioAAAAAK2w1kiY8PTE1jI9MREVAAAAAKlDQwUAAABA6tD1CwAAACgiYqX4xBFRAQAAANA1GiofffSRPPLII3LVVVfJ2LFjpVu3bmKMkaOOOqpo3lwuJ3feeaeMGDFCunfvLr169ZIJEybI3Llz41QFAAAAKP/K9DZI9GY5pvG6fj3++ONy9dVXq/PV19fLt771LVm0aJFkMhkZPny4bN++XRYuXOhv1157rfz85z8v02ExIplMaUlLTdeWPFX6MmxWlyeq1peRr9XlCWt1bd18rX4Gi1w3XZ68Mv2uPNr0ustHWKe/3IR1kS5DdaT/VCjzZKr0ZQSmvJfaKMasKFFed96GjfrfdLR5wmpd+qqd+v22gSlrek99vGP8XmZTlt6JdJmCKMbXr9WVYZTpfZ4wVKW3GeXxs/prSKzvZC3texXp9tsE+v22ynMqESbGNSHGeQi0KaKyzz77yEknnSTXXXedPPnkk3LbbbeVlM81RFwjZdCgQfKHP/xBVqxYIe+88448/fTTUlNTI7fffrvMmzcvTpUAAACAMjH+h7Ekb8KYmHgNlUsuuURefPFF+V//63/JOeecI/369SuaZ9OmTTJjxgz/+IEHHpChQ4c2vXbmmWfKlClT/OObbropTpUAAAAAdCGJDaZ/5plnpLGxUQ499FA54YQT9np90qRJ/n7ZsmWydu3apKoFAAAAoJIbKosXL/b348ePb/b1Aw880HcJ2z0tAAAAkIrB9BIkerMdvdOV1FBZvXq1v3cRlZYMHjzY369atSqpagEAAACo5AUfN2/e7O/ddMQtKby2ZcuWVrc1c+ZMmTVrVknlrly5UlVPAAAAoD1mmUQnaai4qYmd6urqFtO4mb+cnTt3trqtjRs3+rEsAAAAALqmxBoqtbW1/t4NqG9JQ0ODv6+rq2t1W26WsVGjRpUcUSnW8AEAAABQoQ2Vnj177tEFrDmF1wppW+JmCCvMElbM6NGjib4AAAAgNuvWUUluaHdTmZUusXd8yJAh/t4t8NiSwrTEhbQAAAAAKlNiDZVjjjnG37uV6Zvz4YcfynvvvbdHWgAAACANQmsSvSHBhspZZ50l2WxW1qxZIwsXLmx2Ji9n5MiRrU5hDAAAAKDrS2yMyn777efHldx3331y6aWXynPPPSdDhw71r82bN0/uuOMO//jGG28sWx2MKbF1mtG332xVRpc+q0sfJ09UrS8jqtbte1ija/Hna/W/EOTrlGV0UxchuR66ZZXy3XXpbV2orJFI0C2vSl9do0vv82R1eWqU6Z1MEEk5hZH+85rL6z4bDTn9pbKxIatKHyo/rznlNcex2mtbEOcXPW0e/ZJmRvlLo1GeIyaKUSflORWrjFB53Yn0nz0T6s51Y5V1UqaP817ZUr/rdy9DmcdqLzv6rwAxys+fjXEtFFve63NX4k7dpKcntqz4GK+h8sEHH/jIx5dn61qxYoX06dOn6fkpU6b4W4FrjCxdulR+97vfyfDhw+WII46Qbdu2NY1Nueaaa3zkBQAAAEBli9VQCcNQPv30072ez+fzezy/Y8eOPV530w6/8sorMm3aNHnkkUf8avVuXZXjjjtOrrzySjnnnHPiVAcAAABAFxOroTJw4MBY4VvHNUy+HGkBAAAA0ixS9/lDW/GOAwAAABXgo48+8r2arrrqKhk7dqx069bNj9E66qijYm3PBS5++9vfynXXXSfjxo2T3r17+8mz+vbtKyeffLI8+uijsYMbiQ6mBwAAADojt/himPACjLYM5T3++ONy9dVXt9v2FixYICeddFLT/w855BAZNGiQX3LkxRdf9LfHHntMnnrqKampqVFvn4gKAAAAUAH22Wcf37BwEZAnn3xSbrvttjZtz0VLXMPk3nvvlU2bNvkJspYsWeLHrP/617/2jZNnn3029qy+RFQAAACACnDJJZf4W8FDDz3Upu2NGTNGVq1a5bt7fdmFF17oZwr+x3/8R5k9e7ZvFAWBLkZCRAUAAAAowq2jkuSts0RommukFJx22mn+fvPmzfLJJ5+ot09DBQAAAEC7q6+v32OZEi26fgEAAABFMD2xnhtI74wYMcJHX7RoqAAAAAAptXLlShk9enTJ6S+//HKZNGmSdLRly5bJjBkz/GM3eD+OymmouK5+mUxpaZUDfbyqErf9BVulLyNS5omq9WWENbo+kXll+rBO3+cyVEYK893083Xnu+vy2O55Vfps95yyRi5E2qhK/5XaBnUZ+9T8Z0i2FLUZ/X5UZ0Ipp3ykP8935Kt16XO69M5f6nXTMG6v0qVvzLTcJ7gl+UB7yY9xLYx0n3GjTO/zhOVNH+RjXJ/zuu8AE+qvUyYf6dJHMb7iQ10ZEkXl3b6T0b1XcXr2a9d5MMr9tibGZ8nGeK+0tPWKUydT4hGJv9RGIlz1osSnJ95l586d/o/+Um3cuFE6mpsB7Oyzz5ZcLufvzzvvvFjbqZyGCgAAANDJ1NXVybBhw0pO369fP+lIn332mR9Ev379eh8JasvMYjRUAAAAgJQaNmyYLF26VDqDbdu2yamnnirLly+X4cOHywsvvBBrbEoBDRUAAACgVUbCxKcMNtKZ7NixQ04//XRZvHixDBkyRF566SXp3bt3m7bJ9MQAAAAA2jQN8VlnnSWvvvqqDBw4UF5++WXZf//9pa2IqAAAAACtcPMtJD09sU35BAMFbsD8Oeec4yMo/fv3lwULFvj79kBEBQAAAECLxo0b5yMl06ZN2+P5MAzlggsukPnz5/sIimukDBo0SNoLERUAAACgAnzwwQcycuTIpv83NOxa2mDFihXSp0+fpuenTJnibwUbNmyQ999/X7Zu3brH9p544gmZM2eOf1xbWyvf/e53Wyx7+vTpe5RdChoqAAAAQBFR4oPp25+LgHz66ad7PZ/P5/d43g2ML0WhoeOsW7fO31qbtliLhgoAAABQAQYOHKhe4NRpqQFy8cUX+1u50FABAAAAUroyfSVjMD0AAACA1KmgiIoRCUprl9mMvv1mS9x2m8rI6vJEWX3LP1TmCWukrOl9nlpl+jr9bxC2LlSlz3bPqdJ/pcdOZY1EenXT5eldu11fRnVpfVALumf+sy9qqWqCvJRTzmbUebbndSfi1lyduozN2W669FW6c/CzQF+nemX6fBTjKyLSXadMpL9OBXldHu0pmFdu3zGhbr+DfIzvgLzyXM9H6jKkSleGiaKypnesVebRpncy+utI2eeZ1X6WghjvbcRv9op3uAPGqBipdERUAAAAAKQODRUAAAAAqVNBXb8AAACAeJJemR5EVAAAAACkEBEVAAAAoNj0xAkPpreJlpZOxLAAAAAApA4NFQAAAACpQ9cvAAAAoIikV6YHERUAAAAAKUREBQAAAGiFtcmvTG8TLi+NGKMCAAAAIHWIqAAAAABFJB1RQSU1VNy5FZR4ggUxAk0Z3clrq/RlRFldniir/0Bp82jTh9XKCrk8tbqZxMO6SF1G0C2vSl9X16hK36vbTmWNRPar+1yVvl/tZ+oy+mS3qdJ/NbNDXUZtkFOlD0R3/Oqt/qTaFtaq0m/OdleXUZfR7XdVoD9vtaJI93ltDPXXkFCZJ8jpy8jrPq5ilOnj1CmjzBPmM+oyTF53LQzy+nPKRMo/C0JlGZkY53mkzGP1q0+YSJfHGt3xNsr0vgztnwmhuggxpf5d9AUbxfj7yJb/2oaui65fAAAAAFKnciIqAAAAQEx0/UoeERUAAAAAqUNEBQAAAGiFG8WU+PTEAiIqAAAAAFKHhgoAAACA1KHrFwAAAFBE5Ne6QJKIqAAAAABIHSIqAAAAQCusmA4YTG+k0hFRAQAAAJA6NFQAAAAApE5ldf0yJYbQMvr2mw10eWygD+dFGV2eKKMuQiLlGRFly5t+Vx7lTOLVkbqM6pq8Kv1XahtU6XvXblfWSKRf7Weq9AfWbFGXsX+Vroy/yuxQl9Et0L1XWjmrP9E/j+pU6Xtk9lGXkTWhlFM+0l+ncqEuz2fK9E6Y1+UJG/XXwkCZJ6jRpQ8blRVyeXK6/Q5y+hUSoqyuDKNM71jtMa/Sff5MpL8+m1D3WbJBjC+/jPJ42DKnd5T7LSbGb89WfzzUSq5Xyrs52Q5Ymd4mW1waEVEBAAAAkDqVFVEBAAAAYkg8ogIiKgAAAADSh4gKAAAAUGS4SPLTE4MxKgAAAABSh4YKAAAAgNSh6xcAAABQhGUwfeKIqAAAAABIHSIqAAAAQKuMRIkvSmmk0hFRAQAAAJA6NFQAAAAApA5dvwAAAIBWsI5Kx6ishoox7ZtudxlT3vQx4l82Rhk2o0sfKc+gKKtfvkibx1RH6jKqs3lV+n1q6lXpe1XvUNZIpE92myr9/lWfqcvQ5umV0e9Hd5Mva5i33uoDw58HuuNXa3JSbjnlh68hUn5YRWRnPqtKX9+oS+/sqNHVK6rRH7+oRpm+UXctDKt124+TJ8jp9zvI6a6FQU5/jthQd/00eeX1Nh+KWpXui8ZY/feMjXTniMnojp+N9N9LRvm3SIxLoUiky2QC/X7YiGULEV9lNVQAAAAALdsB0xPbZItLI8aoAAAAAEgdIioAAABAERELPiaOiAoAAACA1KGhAgAAACB16PoFAAAAFBnXnvRgeptoaelERAUAAABA6hBRAQAAAFplOmAwvZFKR0QFAAAAQOrQUAEAAACQOnT9AgAAAIqwjG5PXAU1VIyIKbGvX6np2pDHxihDm8fGiJdFGV16W+b0Pk+V7sqQqYrUZdRk86r0tZmcKn33TIOyRiJfzexQpf8rZXqnlzJPr0D3Pjndje5EDJTnec7qj3dtpD8eWvU2q0q/I6pWpf9LvlZZI5Gt1d1U6bfV1KjLaKjR7XdYo78ohNW6PBldlSSq1l+fQ2UZ2jo5UZWuXlFW/yVgcro8tkpZRpX+eJsoKu8XmRNqy9B9L5mMvk7av4lNGMYog7+8kW4V1FABAAAA9FyTLkp4cLtNtLR0YowKAAAAgNQhogIAAAC0xia/4KMQUiGiAgAAACB96PoFAAAAIHXo+gUAAAAUkfzK9CCiAgAAACB1iKgAAAAARbDgY/KIqAAAAAAV4KOPPpJHHnlErrrqKhk7dqx069ZNjDFy1FFHtXnbCxculDPOOEP69u0rdXV1cvjhh8vUqVNl+/btsbdJRAUAAACoAI8//rhcffXV7b7d6dOn+8aPtVb69+8vBx10kLz99tty6623ylNPPSWLFi2SXr16qbdLRAUAAAAosqSJW0cl0Zu0v3322UdOOukkue666+TJJ5+U2267rc3bXLp0qUyePNk/njlzpqxfv16WLVsm7777rowePVpWrlwpl112WaxtE1EBAAAAKsAll1zibwUPPfRQm7d5yy23SBRF8p3vfEcuv/zypucPOOAAeeyxx3wXsLlz58pbb70lX//611XbpqHSUUz588SaRc+kLH2MPIHR/waRCSJV+upMqEpfE+SVNRKpDXKq9N2CBnUZ3Y2uXt2NPgjbI6hRpQ+UBzxvdMdil0ZV6pwyvbMj2KFK/1cZXfp9quqVNRLpUaU7R+qyunPQ2ZbVHY8wq/vsOTarOw+jat05FWWVFYqRJ4rx7avej0b9BTfIZlTpbag7fiYf43hHyjKUdfJ5MrpzylrltdDq6yTqMmL8/q7NE+m/A0yp369xLuWJ2hXlSLrMtNu2bZs8//zz/vHujZSCww47TCZMmCAvvfSSzJkzR91QoesXAAAAALXly5dLQ0OD1NTUyJgxY5pNM378eH+/ePFi9faJqAAAAACtsR2w4KOV1Fu9erW/P/jggyWbbT60PHjwYH+/atUq9fZpqAAAAAAptXLlSj8ovVSuC9akSZMkCZs3b/b3rc3oVXhty5Yt6u3TUAEAAABSaufOnX4WrVJt3LhRklJfv2u8ZHV1dYtpXLewwn5o0VABAAAAUroyfV1dnQwbNqzk9P369ZOk1NbW+vvGxpYnnXFjWAr7oUVDBQAAAEipYcOG+bVK0qhnz557dAFrTuG1QloNGioAAABACQs+JslK+g0ZMsTfu0Uec7lcswPq165du0daDaYnBgAAAKA2atQoPz7Fde964403mk3z2muv+ftjjz1WvX0aKgAAAADUevToIaeccop/PGvWrL1eX7NmjSxYsMA/Pvfcc9Xbp6ECAAAAFOG6fiV5S5Nx48bJwIEDZdq0aXu9NnXqVDHGyMMPP+wbK/aLWQfc7GPnn3++RFEkEydOlBEjRqjLpaECAAAAVIAPPvhA+vTp03S78sor/fMrVqzY4/k77rhjj3wbNmyQ999/X7Zu3brXNo8++mi55557/GO3fsuAAQN8l7BBgwb5SQCGDh0qs2fPjlVfBtMDKRVIlEAZyvRG/wtPILo8GROUfbRhrcmo0mclH6OMXFnTdwtangqyJXUZXRk1Vfr9rqoKVelNVn+eR1lb5vT681ybJ2x+AedWZap0ZURZ/W+RUV73Xpmcrgxbpa+TCXWfV6mKce2MovLOUxtjXlsT6fLYGNdn9yu4qow4P2/rLgmp1hkGtxcThqF8+umnez2fz+f3eH7Hjh2iMXnyZDnyyCPl7rvvltdff10+/vhj32Bx3b2uv/5630UsDhoqAAAAQAUYOHBgU9csjXXr1hVNc+KJJ/pbe6KhAgAAABSRtnEjlYAxKgAAAABSh4YKAAAAgNSh6xcAAABQdGn6hN8im3B5KUREBQAAAEDqEFEBAAAAWtURizAaqXREVAAAAACkDg0VAAAAAKlD1y8AAACg2Fj6hAe322SL6xoRFbea5W9/+1u57rrrZNy4cdK7d2/JZrPSt29fOfnkk+XRRx9tdcXLXC4nd955p4wYMUK6d+8uvXr1kgkTJsjcuXPbui8AAAAAKjWismDBAjnppJOa/n/IIYfIoEGD5L333pMXX3zR3x577DF56qmnpKamZo+89fX18q1vfUsWLVokmUxGhg8fLtu3b5eFCxf627XXXis///nPpSLY8ucxMcowkTKDTd9+RzEGu4WRrs2eV6bP2YyyRiL1tjqBMrT7oT1BRPImLO/xjnFShSn8nSpQfvgy6g+rSFWgOxZZZfpdZejqZTL6YxFV6fJEyo+GNr3PU1Xe9Lvy6K5tVpne58koy8jq3iwb6s9bk9flsRl9r3YT6PIYo3yflOm9QJlHeT3flUf5+Qv11wQxJdbL7W76Ls17YGX6ThJRcQ2Te++9VzZt2iRr166VJUuWyKeffiq//vWvfePk2WeflRtvvHGvvK4h4hopLv8f/vAHWbFihbzzzjvy9NNP+3y33367zJs3r732DQAAAEClNFTGjBkjq1atkh//+Mey77777vHahRdeKD/72c/849mzZ0sU/eevIK5RM2PGDP/4gQcekKFDhza9duaZZ8qUKVP845tuuin+3gAAAADl4HpsJHmDvqGyzz77+DEpLTnttNP8/ebNm+WTTz5pev6ZZ56RxsZGOfTQQ+WEE07YK9+kSZP8/bJly3yUBgAAAEDlavfpid04lIK6urqmx4sXL/b348ePbzbfgQce6LuE7Z4WAAAAQGVq94aKG0jvuFm9XPSlYPXq1f7eRVRaMnjwYH/vupYBAAAAqWB3zT2Q5E1SPrlAp1tHxXXbKoxDcdMX7851BXPcdMQtKby2ZcuWVsuZOXOmzJo1q6Q6rVy5sqR0AAAAALpgQ8UNlj/77LP9Oinu/rzzzmu2S1h1dctTrhamM965c2erZW3cuNE3igAAAIBEEOHonA2Vzz77zA+iX79+vYwePVoeeuihvdLU1tb6ezegviUNDQ17jW1pTr9+/WTUqFElR1SKNXwAAAAAdLGGyrZt2+TUU0+V5cuX+wUcX3jhhT3GphT07Nlzjy5gzSm8VkjbEjdDWGGWsGJcw4noCwAAAFBBg+l37Nghp59+up+la8iQIfLSSy9J7969m03rXnfcAo8tKUxLXEgLAAAApGVl+iRvaENDxY05Oeuss+TVV1+VgQMHyssvvyz7779/i+mPOeYYf+9Wpm/Ohx9+KO+9994eaQEAAABUplgNFTdg/pxzzvERlP79+8uCBQv8fWtco8YtFLlmzRpZuHBhszN5OSNHjmx1CmMAAAAgcTbhG/RjVMIwlAsuuEDmz5/vIyiukVJYqLE1++23nx9Xct9998mll14qzz33nAwdOtS/Nm/ePLnjjjv84xtvvLFMh6UwKXUpSWOcHco8JkYZ2jwmUhehzmPC8qb3efK68GeU17e/c/mMKv2OfMuz1zVne37XjHYa28JdE1CU6vOo9Ukoms0T/OcCraWojXZNeKHTqCvD6I5FGONq3mB1J3pO9CH4RtHtR2TbfVmrvWSM7r0KlOl9nkD33pogxvVW+VbZTHnT+zxV5U3vRMo8UVZ/3kZZ3Zsb5HXH22b057mt0uUxYYwDWKX88ouU6ZP4uyLSl2GN7hwxyvS+jJIPH12dsDf1pfKJJ56QOXPmNM3k9d3vfrfFtNOnT/cRkgLXGFm6dKn87ne/8wPvjzjiCD8YvzA25ZprrvGRFwAAAACVTd1QKUwh7Kxbt87fWpu2eHdu2uFXXnlFpk2bJo888ohfrd6tq3LcccfJlVde6buTAQAAAGnDAPdO0FC5+OKL/S0u1zCZMmWKvwEAAABAWVemBwAAALqkjhjgbhMuL4XKP3oTAAAAAJSIqAAAAABFZyVLemYyI5WOiAoAAACA1KGhAgAAACB16PoFAAAAFMPg9sQRUQEAAACQOkRUAAAAgGKIqCSushoqtsQzLIz02w5tedO7uR9Cbfo4ZehmmAjyuu0HOf0MFto8YaM+UNiQ030UduSqVem35uqUNRLZnO2uSt8js4+6jFqTk3LLSaMqfVaUJ1UMOeVMKlsj3fF2dkQ1qvTblenro6yyRiK5KKNKH9nyzzgTq4TAljW9zehrZXVvrUQxylAePokyNoH90F1vTZX++mwzukqZjP473Gr3I1CmNzHOKW2eIManyQbl+Ttqd6HyjxdgN3T9AgAAAJA6lRVRAQAAAOJIIMqMPRFRAQAAAJA6RFQAAACAVtiYQ3TawiZbXCoRUQEAAACQOkRUAAAAgKIhlYTfIptweSlERAUAAABA6tBQAQAAAJA6dP0CAAAAimF64sQRUQEAAACQOkRUAAAAgFa4pR5NwoPbTbLFpRIRFQAAAACpU1kRlVJX6omxoo+JImX6GGXkdWUEeX07NMgr0+fKm35XHt1vCmGjfr8bG7Kq9H+pr1Gl35ztpqyRSF1G92ZlTSjlVm9175OzI9ihSl9rYpwkSo2SUaXfEemOt/Np2EOV/vOoVpV+R1StrJFIQ6Tb7zDSf5ZsCvtwW+Vu2EB/fbaBbr+t7lB4kfIbO6rSHwttnkCZXvs+eVW6A2hz+vPWBMo8GWV65WfPC3Xf+WJi7LcyRBAnoGBM+q4J6Dwqq6ECAAAAxMG6Jomj6xcAAACA1CGiAgAAABSTwq6tXR0RFQAAAACpQ0QFAAAAKDY+JekxKjbh8lKIiAoAAACA1KGhAgAAACB16PoFAAAAFENXrMQRUQEAAACQOkRUAAAAgGKIqCSOiAoAAACA1KmqqFZwGJWWNiox3e5CXTPb5PVlBMoygpy+6a/Nk2nUbT/TqF8sKarX5Qmr9e3vsDqjSr+9qkaVfnNVqKyRSFUQ4zxUylndfu+IqtVl/FVmhyp9rcmp0gdG/z5FVneObI90x9v5PKpVpd+S767bfl63fWdHXnf8GkL9V0QYBZ3/B8qg/OvAKT96seqlPM135cnodiRSpg+q9JWyyu9LE6MMCZV5AmV6o//uMxnlZymKsRih9qspxn4gnRYuXCh33323vP7667Jt2zYZMGCA/O3f/q1cd9110r277vvI2bx5s9/es88+K++88440NjZK37595ZhjjpEf/ehHcsIJJ8SqJxEVAAAAoJRfJJK8lcn06dPlxBNP9I2K2tpaGTZsmKxbt05uvfVWOfroo32jQ2PNmjVy5JFHym233Sa///3vZb/99pMjjjjCN4Dmzp0rEyZM8NuOg4YKAAAAUAGWLl0qkydP9o9nzpwp69evl2XLlsm7774ro0ePlpUrV8pll12m2uYVV1whf/rTn+Swww6Tt956S9auXeu3+cknn8jUqVN9mp/97GeyYsUKdX1pqAAAAABFGJvsrRxuueUWiaJILrzwQrn88svFfNGd74ADDpDHHntMgiDwURDX4CjF559/7ruROXfddZcMHz686bXq6mq5+eab5b/8l/8i1lp57rnn1PWloQIAAAB0cdu2bZPnn3/eP3aNlC9zERHXTcuZM2dOSdtsaGjwjRDnkEMOaTZN4flcTjcG1aGhAgAAALTGdtCtHS1fvtw3LGpqamTMmDHNphk/fry/X7x4cUnb7NOnjxx00EH+8b//+7/v9Xp9fb0sWbLEP/7GN76hrjMNFQAAAKCLW716tb8/+OCDJZvNNptm8ODB/n7VqlUlb/eOO+7wXcimTJkis2fPlo8++kh27Njhx8N8+9vf9uNgzjnnHDn55JPVda6c6YkBAACATmblypV+oHupXLeuSZMm7fV8YTavXr16tZi38NqWLVtKLu+8886Tr3zlK/I//+f/3KtLmYu43HffffL9739f4qChAgAAAKTUzp07/Sxapdq4cWOzz7tuWIVB7i1x3cIKZWq4mb5cQ8hFVlxXsJ49e/rn/vznP8usWbNk1KhRcuyxx4oWDRUAAAAgperq6vxaJ6Xq169fs8+7NVMctxhjS9wYlkKZpfrhD38o//zP/ywjRozwUxC7NVUKg+fdIpDXX3+9H6TvxrC4BosGDRUAAACgiHJNGVzMsGHD/HiPtnJRDqe1BR0LrxXSFuOmMb7//vulqqpKnnrqqaYxLo4bB+NWuv/jH/8ov/rVr+SnP/2pzJ8/X1VnBtMDAAAAXdyQIUP8vRvc3tJUwa671u5pi1m0aJGfnthNbbx7I2V3//W//ld//8Ybb6jrXEERFSvyxTzPxZgw0m8+DFXJTZhRF2FyunoFeX3TP9OoyxM2Gt32d3WP1JXRclfKZlXt1NXJyVXpjkdjpvnZMlryWVB6CDWufKT/3aEh0u33X/K7wsYa+1TpDnq3oOWQdHMyJsbnVak+0h1vZ0ekO3E/V763Wxv159S23K6+x6VqiHGdCpXnoY30n9f2nrKzPVjtx8+Uvwyb0RcSZXRvbkZbp0BfJ22eOPttgqCsdTLaN8oJlfvxxcJ9KtrjoT7RXZ4SzymTzs92VzJq1Cg/PsV173KNhrFjx+6V5rXXXvP3pY4ncQs+OoWFI5tTWGelMEZGg4gKAAAA0CojYhO+SYzGZyt69Oghp5xyin/sBrh/2Zo1a2TBggX+8bnnnlvSNguRFzf18bvvvttsmsIik0OHDlXXmYYKAAAAUAGmTp3qox8PP/ywb6wUoh1uprDzzz9foiiSiRMn+oHxuxs3bpwMHDhQpk2btsfzruGz3377ST6f942bP/zhD02vue5ld955pzz00EP+/xdddJG6vjRUAAAAgGI68ar0BUcffbTcc889/rFba2XAgAG+S9igQYP8gH0X9XCLNn7Zhg0b5P3335etW7fu8Xy3bt3kX/7lX3y0xq1872b8cg2akSNHSu/evf0ikI5b+NHNDqZFQwUAAACoEJMnT5YXX3xRTjvtNNm+fbu8/fbbvsFyww03yJIlS/wijRpu6uH/9//+n9/u1772Nfnkk0/8/90Ux6eeeqo89thjfkawTEY/7rGCBtMDAAAAMXWhwf4nnniiv5Vq3bp1rb7uGjq/+MUvpL0RUQEAAACQOjRUAAAAAKQOXb8AAACAlK5MX8mIqAAAAABIHSIqAAAAQGvKOGVwq2VWOCIqAAAAAFKHhgoAAACA1Kmcrl8ufBaGpaWN9AvSSL7EbX/B5HTpnSCrq1fQGKnLyFQZVfqqrC4uGWWVFXJl7NTVyQa69D5PRtdmzwe6j0696EWRbj9yof53h5153QHZWt1NXUaPqgZV+rpMTpW+KtB/ljLKEZG5GNeEBmWeHflqVfptuRpljUS2Nery1DfqP7C5nG6/baj/vIo1nb77hHYXfJ4giTLKe73V7kOcMsTE+A5Q5jFBUPY6ibqMGG+uOo/+74ouJYXXkq6OiAoAAACA1KmciAoAAAAQE9MTJ4+ICgAAAIDUIaICAAAAlGPgF9qEiAoAAACA1KGhAgAAACB16PoFAAAAFMP0xIkjogIAAAAgdYioAAAAAEWmJk56emJDBIeICgAAAID0oesXAAAAgNSpoK5fVmwYlZTSlJiuTXnyMcrIhar0mQZ9O9RW6eYIjxp06asyoq+TcjdsEGOec3UeXaXykf6j1hjq6vRZqD/e9Y1ZVfptNTXqMuqyOVX6mqq8Kn020H0unEAZT49izJ0fRrrj0RDqzpGGMFP2461N74R5Xb1sXn/eGuVnw2gvt0l0t4hxmbJGl8kGtuz1ssrT0GZi7Lh6v/VlmKC8dVKnj5Mnge8+E2M/tOdtqtEVK3FEVAAAAACkTgVFVAAAAIB4GNyePCIqAAAAAFKHhgoAAACA1KHrFwAAAFAMg+kTR0QFAAAAQOoQUQEAAACKIaKSOCIqAAAAAFKHiAoAAADQGtsB0xPbhMtLISIqAAAAAFKHhgoAAACA1KGhAgAAACB1KmeMiuvnF4Wlpc3n9dvPKNt8OX0ZJtCVESjTO5kqo0ofZXTpbYw62UBXRrxOpMoyIm16/X6Hoa6MMK8vY0dNRpW+oSarLmNbtsTP3ReqqpTpg0hZI/fZ0OfRslZ5/JTniDa9k8vpjneY16X3eRq118IYv5flde+tUX6WTIzTQ33ZSWnfc+VpK9Zor8/K9I72FNF+Z8Spl/p7KUadAFRQQwUAAACIK6U/MHRldP0CAAAAkDo0VAAAAACkDl2/AAAAgCISX0cFRFQAAAAApA8RFQAAAKAYIiqJY4wKAAAAgNQhogIAAAAUQ0QlcURUAAAAAKQODRUAAAAAqUPXLwAAAKA1tgOmJ7YJl5dCldVQiUo74jaM1Js2+VCXPogRzAryuvQZoy9CmacqUJahr1KMPEHZrwYmMmVN7wQ5XZ6wUV9GVKN7r8KajLqMMKv7PBlt+oz+Sm4C5fGW8tPuhY1xTtlQl8fmY3yWcro8Rnmex7kUGuUlPc7nVXsA4/zBY2xl/tViTQKfwCTKKHOdTIx9qMwzCp1JZTVUAAAAgDho2SWOMSoAAAAAUoeGCgAAAIDUoesXAAAA0Ao3AijpwfQm2eJSiYgKAAAAgNQhogIAAAAUw2D6xBFRAQAAAJA6RFQAAACAYoioJI6ICgAAAIDUoaECAAAAoGs0VObNmyc//OEP5ZhjjpH+/ftLbW2t9OjRQ4444giZPHmyvP/++y3mzeVycuedd8qIESOke/fu0qtXL5kwYYLMnTu3LfsBAAAAlIfdNT1xkjehq1m8hsrdd98t//zP/yzLli2TTCYjRx55pPTt21dWrlwp9957r3zta1+Tf/u3f9srX319vW+UTJkyRf7whz/IoYce6hsqCxculHPOOUeuu+669jiVAAAAAFTiYPpLLrlEpk6dKuPGjZOampqm59euXetfe/XVV+WCCy6QdevW+ahJwbXXXiuLFi2SQYMGyXPPPSdDhw71zz/zzDPyd3/3d3L77bfL2LFj5W/+5m+k3VkrEoalpQ30S+zYvC6PMfoy1Hky+naoCXR5AmWdEpm9IcYvEMYqj1+Jp1JBoDw/nHxeWUajvozoPz++JQmrM+oybFZ3TkVZ3QGMqmIccO1HI0jhz1pxqqQ+z2MsN6Y81wPlee6YnHI/lGVo0zuB8pogUfmPeZzF6bR5jPtuReWI8feRaD8bacbp3jkiKt/5znfkxBNP3KOR4gwePFieeOIJ//jPf/6zb7AUbNq0SWbMmOEfP/DAA02NFOfMM8/0URbnpptuircnAAAAALqMdh9Mv99++/nuXM6OHTuanndRk8bGRt/d64QTTtgr36RJk/y9607mIjMAAAAAKle7N1TcOJXNmzdLEAQycuTIpucXL17s78ePH99svgMPPNB3Cds9LQAAAJAKNuFbGbnx4WeccYYfY15XVyeHH364H9axffv2Nm13/vz58u1vf1sOOOAA3/PKBTDcsI6f/vSnktf2aW+vhoq1Vj7++GM/c5frxuX85Cc/kUMOOaQpzerVq/29i6i0xHUdc1atWtUe1QIAAACwm+nTp/shHM8++6yfuXfYsGF+XPmtt94qRx99tA84aLlGyIUXXiinn366/N//+3/9ZFtuhl83K/CSJUvkn/7pn/ykWlptGtv8yCOP+ErtzrXIHn30Ufn7v//7PZ4v7HShW1hzCq9t2bKl1XJnzpwps2bNKjnCAwAAALRFnAkq0mbp0qV+KZHC39OXXXaZn6zpT3/6kw82uNfdc0899ZRqu9///vd9u8A1TmbPnu0bPAVuKMhLL72019j2sjdU9t13Xx/OiaJINmzYIB9++KGPnLiGyl//9V/7NVYKCq2o6urqFrdX2IGdO3e2Wu7GjRv9WBYAAAAApbnlllv83+1uYqzLL7+86XnXVeuxxx7zAQfXQ+qtt96Sr3/96yV3I/vf//t/+20sWLBgr6BEt27dmnpcJdpQOfnkk/2t4N1335VrrrlGfvOb3/jFIN1aKV/96lf9ay605LgB9S1paGjw966vXGv69esno0aNKjmiUqzhAwAAALSqk0dUtm3bJs8//7x/vHsjpeCwww7z6x266MecOXNKbqjcc889/v5//I//0WrPqTjadVkLNyblySef9GEf10i577775B//8R/9az179vT3rfV7K7xWSNsSN0NYYZawYkaPHk30BQAAABVt+fLlPijgejCNGTOm2TRu0ivXUCl1YivXY+qFF17wj8866yz5j//4D3nwwQdlzZo1PvBw1FFH+TUWd+9l1aGzfrnBM6eddpp/7AbPFAwZMsTfv/POOy3mLUxLXEgLAAAAoO0KE1sdfPDBks1m22ViqxUrVkgul/MLvLtghetRdf/99/vGzrx58+TGG2/0f9e7CE0cZVko3FXYcX3gClzFXQvLrUzfHDe+5b333mtKCwAAAKSC7YDB9PY/hzG4HkKlct26mut51J4TW+0+btxxkRq3ePu4cePk3nvvlSOOOELef/9937PKNVL++3//777B4npddWhDxY1B+dd//Vf/ePd1VFw46Ec/+pEPBblBN19e9NHNPFDI09oUxgAAAECl2Llzp2oYQ6Hx8GXtObHV7uNeCtMT9+nTx6+j8pWvfKVpzMvjjz/u//Z/8803/RTFTzzxhJS1oeK6cz399NN+tgBXgS+HlK688krfhcvNm+ymNytwC7641p0bt3LppZfKc889J0OHDvWvudDQHXfc4R+7EFE5WPcvDEtKawKjL8CY8qaPkyfGfrR7X8B2oD5JY/ziYSLdnpvSTqUmgX6NIzHKPEGN/nhHjbo8meYjxa2XUa0rI8rqDmCUUVbInSLaPIH+pLJp/DApd8P8Z1C89DyhKX8Z2s9GzpT18x0nTxCjDG2eOO+t+vqpTq//LJkYeYCy6KBTsa6uzq91Uio36VRz2nNiqy9vsxDJKTRSCtwC8FdffbVcdNFFfiyL623lnivb34Cu5eQWhHE3t5rlQQcd5Pu5udbb+vXrm8JGLszjVpvfnWuMuPmZf/e738nw4cN9WMhtrzA2xc0Y5iIvAAAAAMQ3Utzfz23VnhNbfXmbTkuNqcLzf/nLX/z2XeSlbA0V17fsl7/8pbzyyivy+9//3g+Odwu5uGmIXb+0U0891UdOmquEa525fNOmTfOLwrgIjAs/HXfccT4Sc84552irAwAAAKCIwmRVLrDgxpM3N6BeO7GVW3eluejK7nZ/Piyxd1PshoprOblGhbvF4RombrCNuwEAAACdQifvhThq1Cj/d7jr3vXGG2/4Rdu/7LXXXvP3xx57bEnbdL2nBgwY4AfOFxo5X1Z43o1/6d27t6rOaexFDQAAAKAdufHjp5xyin88a9asvV53g97dyvLOueeeW/J2/9t/+2/+/le/+tUeM/4W/J//83/8vetBVVWli5HQUAEAAACKMAnfymHq1KlijJGHH37YN1bsF5NVuLHm559/vm9oTJw4ca9phN3wjoEDB/rhG1/2k5/8xA8BcdMou4HzhcH6bttuqmI3aZYr8/rrr1fXl4YKAAAAUAGOPvpoueeee/xjN6bcddtyXcIGDRrkB+y7GXlnz569V74NGzb47l1bt27d6zU3uZZb7NGNRXfj2Pfff3/5xje+IQcccIBMnjzZN1LchFrHH3+8ur40VAAAAIBibMK3MnGNhxdffFFOO+002b59u7z99tu+wXLDDTf4ZUg0s3IVnHTSSX6V+osvvtivUr98+XK/tsqZZ57p1090UZfUrEwPAAAAIJ1OPPFEfyvVunXriqZx6ys++OCD0p6IqAAAAABIHSIqAAAAQCv8APeEpyc2yRaXSkRUAAAAAKROZUVUvpiCrWiycO85oIsxRpfHmjhl5HXpA31b3KawpWtKPG5NYvziYSJdpiCv2/N8Xn8sgpwuT7hrNkBdnmpd+qhavx9RVpteV0aUETWrzGMzMT5Lge38PxvF+ixp0+vfW+WlUIxuIWQJcrr08eqkf3O1+6G9rsXJoz7ecX6R1n5dxtjvUv8+SJSyToVpZssqznvbVZR5gHuLZVa4NH41AgAAAKhwNFQAAAAApE5ldf0CAAAA4qArVuKIqAAAAABIHSIqAAAAQBFJT08MIioAAAAAUoiICgAAAFAMEZXEMUYFAAAAQOrQUAEAAACQOnT9AgAAAIpgMH3yiKgAAAAASB0iKgAAAECxgfRJD6a3CZeXQpXTUHEHO4pKS2uMfvthKGk7X2PshTqPTSCEZ62ylEj/yTb5jK4IZXoT6vc8k9MdjTCnLyOsVqbPqouQSJknyur2O4pxFbPKPFZ3uHflCXT7YZUfPht0ka4RMb6IA+Xl1mjT50UtyCnTxyhDux9xytC/t7as6T3ld0Cs7jnl/p7Rbh+Al9KvOgAAAACVrHIiKgAAAEBMDKZPHhEVAAAAAKlDRAUAAAAohqFGiSOiAgAAACB1aKgAAAAASB26fgEAAADF0PUrcURUAAAAAKQOERUAAACgCKYnTh4RFQAAAACpQ0QFAAAAKDY+JekxKjbh8lKoohoq1pZ2xE0Y6retTG8knee3Kfd+RDFqFUaq5EGkP62Nsl4m1KUP8vrgZZjP6MrI6d/bIKerVyarLkK0hyNUlhHjcItV5oky+k+szZQ3fZyLiC37B1x/4YnVlUJ3SZBAeUnXfr59GXll+py6CMkoP+Nx9kObxyiPRaw6ab83ImWl4tRLW0aJf3+0KU+c79c4ebTi7DvwBbp+AQAAAEidioqoAAAAABIjwGwSjg6ZREtLJyIqAAAAAFKHiAoAAABQDMNtEkdEBQAAAEDq0FABAAAAkDp0/QIAAACKYGX65BFRAQAAAJA6RFQAAACAYhhMnzgiKgAAAABSh4gKAAAA0BrbAWNUbMLlpRARFQAAAACpU0ERFSsSldY0tTGabyYMtbXRlyHlp62Xscoc2ayyhBhlaNP746fLY/KRKr3NZ5Q1cmXo6hRl9SdukFOWUaU/C6NqXZ6MsoxYdVJe+SL94VOXof3ZKM51SpvHmvJfddSfb0eZJdBdnsUo08fJk1F+9pwgn0AZyjxBLirrtTbO9TZOGervjQS+lyTS7bdYZfoYbJz9ANqgghoqAAAAQEy00xJH1y8AAAAAqUNEBQAAACiCBR+TR0QFAAAAQOrQUAEAAACQOnT9AgAAAIphMH3iiKgAAAAASB0iKgAAAEARDKZPHhEVAAAAAKlDRAUAAABojbW7bkmyDIohogIAAAAgdSonouIapTYqLW2kb79ZZRYThvoylOmNlJ9V7rhJ4BcFE+MXCBtFujIi5Ucnr9u+EyjzmKz+vA1yGVX6KEYZUaMpaxm2Sn9WRVllnTL6cypS1kt7DbEZ/X5bZRYb2C7RT9woP34m0u93kFeWEerLyOR0eQJlep8nb8t7nYpxLTShMk+MMiQflrdO2vRJ/Zpe6t9FBcrvSqCtKqehAgAAAMRgOmAwvUm2uFSi6xcAAACA1CGiAgAAABSTzt6wXRoRFQAAAACpQ0MFAAAAQOrQ9QsAAABo5xkE0XZEVAAAAIAKsnDhQjnjjDOkb9++UldXJ4cffrhMnTpVtm/f3i7bnz9/vhhj/G3gwIGxt0NDBQAAACi6Hl8H3Mpg+vTpcuKJJ8qzzz4rtbW1MmzYMFm3bp3ceuutcvTRR8vmzZvbtP3PP/9crrjiinapKw0VAAAAoAIsXbpUJk+e7B/PnDlT1q9fL8uWLZN3331XRo8eLStXrpTLLrusTWVce+218sEHH8jEiRPbXF8aKgAAAEARbsHHJG/lcMstt0gURXLhhRfK5Zdf7rtmOQcccIA89thjEgSBzJ07V956661Y21+0aJHMmDFDzj77bDnrrLPaXF8aKgAAAEAXt23bNnn++ef9Y9dI+bLDDjtMJkyY4B/PmTNHvf36+nr53ve+Jz169PDdy9oDDRUAAACgi1u+fLk0NDRITU2NjBkzptk048eP9/eLFy9Wb//mm2+WVatWyW233SYHHnigtIeKmp7YRqXF0UwQY/65SNfmszGaiCYMdWXoixCxulxGuSNWuf0vMpX1fdqVR/lRCJXnSFUmxjSIujrZUH9SWeV+mJy+jCCr2/corzveNrMrbK0qI6s8b/WHT6IqU9b9iDL6z5INlO+V/q0VGyOPlrpLhDK9KfG7YneB8rJjwhhl5HR5AuVnyedp1F4TdOmDvP771SjzxPkOkCgqb/oY333a67P6e8mJca7ry4i6zqrvcf6GSZHVq1f7+4MPPliy2WyzaQYPHuzvXYND480335Q777zTN4B+8IMfSHupqIYKAAAA0JmsXLnSD3QvlevWNWnSpL2eL8zm1atXrxbzFl7bsmVLyeWFYSiXXnqpfzxr1iw/zqW90FABAAAAiijXAPdidu7c6WfmKtXGjRtbHEPiVFdXt5jXdQsrlFmqu+66y9dvypQpMmLECGlPNFQAAACAlKqrq/NrnZSqX79+zT7v1kxxGhsbW8zrxrAUyizFmjVr5KabbpJBgwbJjTfeKO2NhgoAAACQUsOGDfPrn7RVz549/X1rCzoWXiukLcYt7OgiNffff79069ZN2hsNFQAAAKCYzj2WXoYMGeLv3SKPuVyu2QH1a9eu3SNtMa4B5dZiueiii/Z6rdB9zC3+uP/++/vHbo2Wb37zmyXXmYYKAAAA0MWNGjXKj09x3bveeOMNGTt27F5pXnvtNX9/7LHHqmZ03bRpU4uvuwUmC6+31u2sOayjAgAAALT613gHrExv2/eQuIUYTznllKbZuZobb7JgwQL/+Nxzzy1pm1u3bvUNleZuDz74oE8zYMCApueOP/54VZ1pqAAAAAAVYOrUqb6r1sMPP+wbK4X17dxMYeeff76PfkycOHGv2bvGjRsnAwcOlGnTpiVaXxoqAAAAQDHuj/okb2Vw9NFHyz333OMfu7VWXLTDdQlzs3a58SZDhw6V2bNn75Vvw4YN8v777/sISpJoqAAAAAAVYvLkyfLiiy/KaaedJtu3b5e3337bN1huuOEGWbJkifTp00fSgsH0AAAAQAU58cQT/a1U69atU5dx8cUX+1tb0FBpho304TYTRLoMkT6YZZVZTBiqy9CGGrXvlInx3ooyj83o31ujDbFGuuNtlOm9UJmnKqMuwuR1ZdiqGOetcj9MTleGzer3O1DudxTjnAqqjLIMXfoYVRIb6Mqw+rdWrNGVEYf686pMbmJ8XE1oy15GkIvKep47RluGMr3JhWW/Tqmvnf74lbmMWN/HUXnTfzFbkzJD+ctIKdMBK9ObZItLJbp+AQAAAEgdIioAAABAMV0jONSpEFEBAAAAkDo0VAAAAACkDl2/AAAAgCKSHkwPIioAAAAAUoiICgAAANAaF02Js8RCW9hki0sjxqgAAAAASB0iKgAAAEAxRDgSR0QFAAAAQOrQUAEAAACQOhXU9cuK2Kh8W490bT4TxKiLsgwbJ0ZpdXmMdvNGm0NfRpzjbJX7LaGuDBPFON6ZBMrIh7r0VRl1ESavq5etUp7nymPh82SUn1dlnXwZge7MDbT7rdz+rjzK9Bl9GRLjM1522utajEunCW1Z08cqQ/nZcwJlHpMLy5rey+V1ZWiva442TxiW/Tql/Z6JNdBb+70R53umC2F64uQRUQEAAACQOhUUUQEAAABi0va+QJsRUQEAAACQOjRUAAAAAKQOXb8AAACA1tgOGExvEy4vhYioAAAAAEgdIioAAABAMUQ4EkdEBQAAAEDq0FABAAAAkDp0/QIAAABaZcUkvo6KlUrXLhGV+fPnizHG3wYOHNhiulwuJ3feeaeMGDFCunfvLr169ZIJEybI3Llz26MaAAAAALqINkdUPv/8c7niiiuKpquvr5dvfetbsmjRIslkMjJ8+HDZvn27LFy40N+uvfZa+fnPf97W6gAAAADtL+JN7XQNFdfA+OCDD2TixInym9/8ptV0rpEyaNAgee6552To0KH++WeeeUb+7u/+Tm6//XYZO3as/M3f/I10SlZ39tpIH8wyQQKfEGW9rISq9C7qpmW1odZMRl2GibRl6NJb5fnhRbo8JtQdC69Kdwkwyjo5Vr0fuuNn8jHqVKU8z2OcU6ItQ7kfNojxWdLmifF51cbpbZwylNTdNeJcapVlqK85Mc51E0blL0P7+cvldel9GcprW5xrofI6pb2uSRSW/++KON2SkujKVPK5TjcntHPXL9fwmDFjhpx99tly1llntZhu06ZNPp3zwAMPNDVSnDPPPFOmTJniH990001tqQ4AAADQ7txij+5Hj2RvUvFiN1RcV67vfe970qNHD5k+fXqraV3UpLGxUQ499FA54YQT9np90qRJ/n7ZsmWydu3aij8oAAAAQKWL3VC5+eabZdWqVXLbbbfJgQce2GraxYsX+/vx48c3+7rL77qE7Z4WAAAAQOWK1VB58803/exdY8aMkR/84AdF069evdrfu4hKSwYPHuzvXeMHAAAASBWb8A36wfRhGMqll17qH8+aNUuCoHhbZ/Pmzf7eTUfcksJrW7ZsKbq9mTNn+rJLsXLlypLSAQAAAOjEDZW77rrLjyVxA+DdeiiljmdxqqurW0xTU1Pj73fu3Fl0exs3bvR1AAAAABKR+IKPUDVU1qxZ42fmcuNJbrzxxpLz1dbW+ns3oL4lDQ0N/r6urq7o9vr16yejRo0qOaJSSuMHAAAAQCdtqLiFHV105P7775du3bqVnK9nz557dAFrTuG1QtrWuFnCCjOFFTN69GiiLwAAAEBXbqgsXbrUL9h30UUX7fVaIWrhFn/cf//9/eO5c+fKN7/5TRkyZIj8+7//u7zzzjstbrswLbFLCwAAAKQJ65p0gjEqbuVTt4BjS6Ioanq90NXrmGOOkQcffNAvENmcDz/8UN57772mtAAAAAAqm2p64q1bt/qGSnM31xBxBgwY0PTc8ccf759zq9Zns1k/xmXhwoXNzuLljBw5stUpjAEAAIAOG0yf5A36iEoc++23nx9Tct999/mpjZ977jkZOnSof23evHlyxx13+MeaAfqxlHrQjZGys1GMLLHX5yyZCZT1UtbJxtgFE+nfKy2rPOYmgeOtvUjZIKMuwmgvhFGMMkLlvlfp0tuM/qQyoW4/TCbG5zWnq5epUn6WMjGuU8rz3AYxyoiTJ20iW/4uITGuayZUFpKPU0aoy6D8fJu8cvu+DGWeGGXYfL68dYpxTqnzxPmuVOZxP0IDXa6h4rjGiBvj8rvf/U6GDx8uRxxxhGzbtq1pbMo111zjIy8AAABAqlj3w2nyZVa68v9E/wU37fArr7wit99+u3zta1/zq9X/+c9/luOOO06efPJJvz4LAAAAALRrROXiiy/2t9a4BR/dQpHuBgAAAAAd3vULAAAA6LQYo9N1u34BAAAAQKmIqAAAAADFMLg9cURUAAAAAKQODRUAAAAAqUPXLwAAAKBVVr9IcptZqXREVAAAAACkDhEVAAAAoFhwI+mIik22uDQiogIAAAAgdYioNCdOi9kYKTsb6dIbfTvURrp9N4GyTqGoWe1+xDh+Rnn8rLaMTEZfJ+WxkIx+v22kPG/DSL8fGeXxi3RlmCDG7y1VujKsdh/i1CsMyr7fVnmex7iE6K+FSVw7k/gOUOYxYfnLkHyMC67286e9JiRQJ5vP68sIlfXS7rd2+24/tHlinLfq7zLt95IvJOo60QP9VyDaiIgKAAAAgNShoQIAAAAgdej6BQAAABSR/PTEIKICAAAAIHWIqAAAAADFEFFJHBEVAAAAAKlDQwUAAABA6tD1CwAAAGgNK9N3CCIqAAAAAFKHiAoAAABQDCvTJ46ICgAAAFBBFi5cKGeccYb07dtX6urq5PDDD5epU6fK9u3bVdux1spvf/tbue6662TcuHHSu3dvyWazfrsnn3yyPProoz5NXERU2ov2IBgjZWfL3/S3UfnbuiZQ7keMOlllFhMl8N5qz5EYFwKTUe54pC/Dat9c5X6YOJ8l5fEzQYzzXPveKsuwgX6/1fsR572NUa8uQfvZiHMN0X42whhlaOulLSMMY3zPlL+Mcu+HjVMnm8A5pT1vE/i7Iq2M2MQXfDR+YEz7mz59ulx11VW+AdG/f3856KCD5O2335Zbb71VnnrqKVm0aJH06tWrpG0tWLBATjrppKb/H3LIITJo0CB577335MUXX/S3xx57zG+3pqZGXVciKgAAAEAFWLp0qUyePNk/njlzpqxfv16WLVsm7777rowePVpWrlwpl112Wcnbc40d1zC59957ZdOmTbJ27VpZsmSJfPrpp/LrX//aN06effZZufHGG2PVl4YKAAAAUAFuueUWiaJILrzwQrn88subeiUccMABPvIRBIHMnTtX3nrrrZK2N2bMGFm1apX8+Mc/ln333XeP11wZP/vZz/zj2bNn+3K1aKgAAAAAxbiuX0ne2tm2bdvk+eef949dI+XLDjvsMJkwYYJ/PGfOnJK2uc8++/gxKS057bTT/P3mzZvlk08+UdeZhgoAAADQxS1fvlwaGhp8dywXCWnO+PHj/f3ixYvbpcz6+vqmx27QvhaD6QEAAIAuvuDj6tWr/f3BBx/cYhRk8ODB/t5152oPrjuZM2LECB990aKhAgAAAKTUypUr/UD3UrluXZMmTdrredf9ymltRq/Ca1u2bJG2coP0Z8yY4R+76YvjoKECAAAApNTOnTv9H/2l2rhxY6vdsKqrq1vMW5hC2JXZFm4GsLPPPltyuZy/P++882Jth4YKAAAAUEzSXb92G9sxbNgwKVW/fv2afb62ttbfNzY2tpjXjWEplBnXZ5995gfRu6mPXSTooYceir0tGioAAABASg0bNsyvf9JWPXv23KMLWHMKrxXSxplZ7NRTT/UD94cPHy4vvPBCrLEpBTRUAAAAgGL0y4CkypAhQ/y9i3S4LlnNDah3CzbunlZjx44dcvrpp/sZw1z+l156SXr37t2mOjM9MQAAANDFjRo1yo9Pcd273njjjWbTvPbaa/7+2GOPVW3bjX8566yz5NVXX5WBAwfKyy+/LPvvv3+b60xEpSv3c/xitdGyssqfF4y+bWwj3Xtlghg/eYS65Fa7HzGOd2G12HKWYZWrxJpMpvzniHI/bJzzXFmG+lg4kfK9UpZhMjF+Z9LuR5z9TuK6k0baz1+c7wBtnjBKoAzdxdPGqVOkvEArvzPi7Yf2SyNGnZTXZ9tB4yfa+zs8zUxK3+NS9ejRQ0455RSZN2+ezJo1S8aOHbvH62vWrJEFCxb4x+eee27J23XRmXPOOcdHUPr37++34e7bAxEVAAAAoAJMnTrV//j28MMP+8ZKoYHrZgo7//zzJYoimThxol/3ZHfjxo3zkZJp06bt8XwYhnLBBRfI/PnzfQTFNVIGDRrUbvUlogIAAABUgKOPPlruuece+Yd/+Ae/1sqtt94qffr0kbffftt3CRs6dKjMnj17r3wbNmyQ999/X7Zu3brH80888YTMmTOnaVax7373uy2WPX36dBk5cqSqvjRUAAAAgNa4yEPiK9Pbsmx28uTJcuSRR8rdd98tr7/+unz88ccyYMAA393r+uuv913ESlWYzthZt26dv7U2bbEWDRUAAACggpx44on+VqqWGiAXX3yxv5ULDRUAAACgmC40MUBnwWB6AAAAAKlDQwUAAABA6tD1CwAAACimk6+j0hkRUQEAAACQOkRUAAAAgGKIqCSOiAoAAACA1CGiAgAAALTGDU9JfMFHqXg0VLqyOB8oY6SsbKTPY3SBPxtjnnMTmPLuR6QPXlptljBUl2GUxzvWNVO5I0Z7/LTHLsZnw8b5XIS6c8RklAc8jFGnIEjX9SCpMrSS+GMkThlRVPYyrPK8VV8LtduPU0aM7wCrvX5q31vtsfNFaMuwZX9v43y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      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-2, 2)\n",
    "y = np.linspace(-2, 2)\n",
    "xx, yy = np.meshgrid(x, y)\n",
    "\n",
    "exp1 = 1.5 * np.exp(-((xx + 0.5) ** 2 + (yy + 0.5) ** 2))\n",
    "exp2 = 0.5 * np.exp(-((0.5 - xx) ** 2 + (0.5 - yy) ** 2))\n",
    "\n",
    "asymmetric_data = np.expand_dims(exp1 + exp2, axis=0)\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.imshow(asymmetric_data[0])\n",
    "plt.colorbar(shrink=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aff47fbd",
   "metadata": {},
   "source": [
    "To feed this data into the feature detection we need to convert it into an `xarray.DataArray`. Before we do that we select an arbitrary time and date for the single frame of our synthetic field:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e2852337",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:45.085454Z",
     "iopub.status.busy": "2026-02-02T20:12:45.085347Z",
     "iopub.status.idle": "2026-02-02T20:12:45.092054Z",
     "shell.execute_reply": "2026-02-02T20:12:45.091650Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "  --xr-font-color0: var(\n",
       "    --jp-content-font-color0,\n",
       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
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       "  --xr-font-color2: var(\n",
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       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
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       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
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       "    --jp-border-color2,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
       "  );\n",
       "  --xr-disabled-color: var(\n",
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       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
       "  );\n",
       "  --xr-background-color: var(\n",
       "    --jp-layout-color0,\n",
       "    var(--pst-color-on-background, white)\n",
       "  );\n",
       "  --xr-background-color-row-even: var(\n",
       "    --jp-layout-color1,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
       "  );\n",
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       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
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       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
       "  );\n",
       "  --xr-border-color: var(\n",
       "    --jp-border-color2,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
       "  );\n",
       "  --xr-disabled-color: var(\n",
       "    --jp-layout-color3,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
       "  );\n",
       "  --xr-background-color: var(\n",
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       "    var(--pst-color-on-background, #111111)\n",
       "  );\n",
       "  --xr-background-color-row-even: var(\n",
       "    --jp-layout-color1,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
       "  );\n",
       "  --xr-background-color-row-odd: var(\n",
       "    --jp-layout-color2,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
       "  );\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
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       "  min-width: 300px;\n",
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       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-obj-name,\n",
       ".xr-group-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-group-name::before {\n",
       "  content: \"📁\";\n",
       "  padding-right: 0.3em;\n",
       "}\n",
       "\n",
       ".xr-group-name,\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
       "  margin-block-start: 0;\n",
       "  margin-block-end: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "  border: 2px solid transparent !important;\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0) !important;\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: \"►\";\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: \"▼\";\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-top: 4px;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-group-box {\n",
       "  display: inline-grid;\n",
       "  grid-template-columns: 0px 20px auto;\n",
       "  width: 100%;\n",
       "}\n",
       "\n",
       ".xr-group-box-vline {\n",
       "  grid-column-start: 1;\n",
       "  border-right: 0.2em solid;\n",
       "  border-color: var(--xr-border-color);\n",
       "  width: 0px;\n",
       "}\n",
       "\n",
       ".xr-group-box-hline {\n",
       "  grid-column-start: 2;\n",
       "  grid-row-start: 1;\n",
       "  height: 1em;\n",
       "  width: 20px;\n",
       "  border-bottom: 0.2em solid;\n",
       "  border-color: var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-group-box-contents {\n",
       "  grid-column-start: 3;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  border-color: var(--xr-background-color-row-odd);\n",
       "  margin-bottom: 0;\n",
       "  padding-top: 2px;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "  border-color: var(--xr-background-color-row-even);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  border-top: 2px dotted var(--xr-background-color);\n",
       "  padding-bottom: 20px !important;\n",
       "  padding-top: 10px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in + label,\n",
       ".xr-var-data-in + label,\n",
       ".xr-index-data-in + label {\n",
       "  padding: 0 1px;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-data > pre,\n",
       ".xr-index-data > pre,\n",
       ".xr-var-data > table > tbody > tr {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
       "  color: var(--xr-font-color0);\n",
       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
       "  stroke-width: 0.8px;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (time: 1, y: 50, x: 50)&gt; Size: 20kB\n",
       "array([[[0.01666536, 0.02114886, 0.02648334, ..., 0.00083392,\n",
       "         0.00058509, 0.00040694],\n",
       "        [0.02114886, 0.02683866, 0.03360844, ..., 0.00109464,\n",
       "         0.00077154, 0.0005393 ],\n",
       "        [0.02648334, 0.03360844, 0.04208603, ..., 0.00142435,\n",
       "         0.00100896, 0.00070907],\n",
       "        ...,\n",
       "        [0.00083392, 0.00109464, 0.00142435, ..., 0.01405279,\n",
       "         0.01121917, 0.00883872],\n",
       "        [0.00058509, 0.00077154, 0.00100896, ..., 0.01121917,\n",
       "         0.00895731, 0.00705704],\n",
       "        [0.00040694, 0.0005393 , 0.00070907, ..., 0.00883872,\n",
       "         0.00705704, 0.00556009]]], shape=(1, 50, 50))\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[s] 8B 2022-04-01\n",
       "  * y        (y) float64 400B -2.0 -1.918 -1.837 -1.755 ... 1.837 1.918 2.0\n",
       "  * x        (x) float64 400B -2.0 -1.918 -1.837 -1.755 ... 1.837 1.918 2.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-obj-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span class='xr-has-index'>y</span>: 50</li><li><span class='xr-has-index'>x</span>: 50</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e74c9af2-b5c5-4463-b1bf-ca4d372d7176' class='xr-array-in' type='checkbox' checked><label for='section-e74c9af2-b5c5-4463-b1bf-ca4d372d7176' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.01667 0.02115 0.02648 0.03272 ... 0.01092 0.008839 0.007057 0.00556</span></div><div class='xr-array-data'><pre>array([[[0.01666536, 0.02114886, 0.02648334, ..., 0.00083392,\n",
       "         0.00058509, 0.00040694],\n",
       "        [0.02114886, 0.02683866, 0.03360844, ..., 0.00109464,\n",
       "         0.00077154, 0.0005393 ],\n",
       "        [0.02648334, 0.03360844, 0.04208603, ..., 0.00142435,\n",
       "         0.00100896, 0.00070907],\n",
       "        ...,\n",
       "        [0.00083392, 0.00109464, 0.00142435, ..., 0.01405279,\n",
       "         0.01121917, 0.00883872],\n",
       "        [0.00058509, 0.00077154, 0.00100896, ..., 0.01121917,\n",
       "         0.00895731, 0.00705704],\n",
       "        [0.00040694, 0.0005393 , 0.00070907, ..., 0.00883872,\n",
       "         0.00705704, 0.00556009]]], shape=(1, 50, 50))</pre></div></div></li><li class='xr-section-item'><input id='section-0d4920f7-5941-4451-b6c6-6bfc9cd8277c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0d4920f7-5941-4451-b6c6-6bfc9cd8277c' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[s]</div><div class='xr-var-preview xr-preview'>2022-04-01</div><input id='attrs-4785ea39-fdae-469c-abc5-5951690b40a0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4785ea39-fdae-469c-abc5-5951690b40a0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-432a1d28-f26f-4172-af0b-1b569c34b102' class='xr-var-data-in' type='checkbox'><label for='data-432a1d28-f26f-4172-af0b-1b569c34b102' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-04-01T00:00:00&#x27;], dtype=&#x27;datetime64[s]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.0 -1.918 -1.837 ... 1.918 2.0</div><input id='attrs-0a0028a5-04b6-43b8-9c03-99fce926f18d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0a0028a5-04b6-43b8-9c03-99fce926f18d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3c59d8a-6c94-4b32-804b-28a23a99b65a' class='xr-var-data-in' type='checkbox'><label for='data-b3c59d8a-6c94-4b32-804b-28a23a99b65a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-2.      , -1.918367, -1.836735, -1.755102, -1.673469, -1.591837,\n",
       "       -1.510204, -1.428571, -1.346939, -1.265306, -1.183673, -1.102041,\n",
       "       -1.020408, -0.938776, -0.857143, -0.77551 , -0.693878, -0.612245,\n",
       "       -0.530612, -0.44898 , -0.367347, -0.285714, -0.204082, -0.122449,\n",
       "       -0.040816,  0.040816,  0.122449,  0.204082,  0.285714,  0.367347,\n",
       "        0.44898 ,  0.530612,  0.612245,  0.693878,  0.77551 ,  0.857143,\n",
       "        0.938776,  1.020408,  1.102041,  1.183673,  1.265306,  1.346939,\n",
       "        1.428571,  1.510204,  1.591837,  1.673469,  1.755102,  1.836735,\n",
       "        1.918367,  2.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.0 -1.918 -1.837 ... 1.918 2.0</div><input id='attrs-8f903746-8bfa-4737-a713-314586651039' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f903746-8bfa-4737-a713-314586651039' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71eaa38f-ae2d-4e6a-a5d9-aa0978384209' class='xr-var-data-in' type='checkbox'><label for='data-71eaa38f-ae2d-4e6a-a5d9-aa0978384209' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-2.      , -1.918367, -1.836735, -1.755102, -1.673469, -1.591837,\n",
       "       -1.510204, -1.428571, -1.346939, -1.265306, -1.183673, -1.102041,\n",
       "       -1.020408, -0.938776, -0.857143, -0.77551 , -0.693878, -0.612245,\n",
       "       -0.530612, -0.44898 , -0.367347, -0.285714, -0.204082, -0.122449,\n",
       "       -0.040816,  0.040816,  0.122449,  0.204082,  0.285714,  0.367347,\n",
       "        0.44898 ,  0.530612,  0.612245,  0.693878,  0.77551 ,  0.857143,\n",
       "        0.938776,  1.020408,  1.102041,  1.183673,  1.265306,  1.346939,\n",
       "        1.428571,  1.510204,  1.591837,  1.673469,  1.755102,  1.836735,\n",
       "        1.918367,  2.      ])</pre></div></li></ul></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (time: 1, y: 50, x: 50)> Size: 20kB\n",
       "array([[[0.01666536, 0.02114886, 0.02648334, ..., 0.00083392,\n",
       "         0.00058509, 0.00040694],\n",
       "        [0.02114886, 0.02683866, 0.03360844, ..., 0.00109464,\n",
       "         0.00077154, 0.0005393 ],\n",
       "        [0.02648334, 0.03360844, 0.04208603, ..., 0.00142435,\n",
       "         0.00100896, 0.00070907],\n",
       "        ...,\n",
       "        [0.00083392, 0.00109464, 0.00142435, ..., 0.01405279,\n",
       "         0.01121917, 0.00883872],\n",
       "        [0.00058509, 0.00077154, 0.00100896, ..., 0.01121917,\n",
       "         0.00895731, 0.00705704],\n",
       "        [0.00040694, 0.0005393 , 0.00070907, ..., 0.00883872,\n",
       "         0.00705704, 0.00556009]]], shape=(1, 50, 50))\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[s] 8B 2022-04-01\n",
       "  * y        (y) float64 400B -2.0 -1.918 -1.837 -1.755 ... 1.837 1.918 2.0\n",
       "  * x        (x) float64 400B -2.0 -1.918 -1.837 -1.755 ... 1.837 1.918 2.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date = np.datetime64(\n",
    "    \"2022-04-01T00:00\",\n",
    ")\n",
    "assym = xr.DataArray(\n",
    "    data=asymmetric_data, coords={\"time\": np.expand_dims(date, axis=0), \"y\": y, \"x\": x}\n",
    ")\n",
    "assym"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1263c19",
   "metadata": {},
   "source": [
    "Since we do not have a dt in this dataset, we can not use the `get_spacings()`-utility this time and need to calculate the `dxy` spacing manually:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "afa29aa4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:45.093383Z",
     "iopub.status.busy": "2026-02-02T20:12:45.093294Z",
     "iopub.status.idle": "2026-02-02T20:12:45.095160Z",
     "shell.execute_reply": "2026-02-02T20:12:45.094818Z"
    }
   },
   "outputs": [],
   "source": [
    "dxy = assym.diff(\"x\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9096da2f",
   "metadata": {},
   "source": [
    "Finally, we choose a threshold in the datarange and apply the feature detection with the four `position_threshold` flags\n",
    "- 'center' \n",
    "- 'extreme'\n",
    "- 'weighted_diff'\n",
    "- 'weighted_abs'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "04c5c17c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:45.096417Z",
     "iopub.status.busy": "2026-02-02T20:12:45.096334Z",
     "iopub.status.idle": "2026-02-02T20:12:45.115151Z",
     "shell.execute_reply": "2026-02-02T20:12:45.114824Z"
    }
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "threshold = 0.2\n",
    "features_center = tobac.feature_detection_multithreshold(\n",
    "    assym, dxy, threshold, position_threshold=\"center\"\n",
    ")\n",
    "features_extreme = tobac.feature_detection_multithreshold(\n",
    "    assym, dxy, threshold, position_threshold=\"extreme\"\n",
    ")\n",
    "features_diff = tobac.feature_detection_multithreshold(\n",
    "    assym, dxy, threshold, position_threshold=\"weighted_diff\"\n",
    ")\n",
    "features_abs = tobac.feature_detection_multithreshold(\n",
    "    assym, dxy, threshold, position_threshold=\"weighted_abs\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1dfad57f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:45.116573Z",
     "iopub.status.busy": "2026-02-02T20:12:45.116486Z",
     "iopub.status.idle": "2026-02-02T20:12:45.205782Z",
     "shell.execute_reply": "2026-02-02T20:12:45.205353Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x177a4aa50>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 10))\n",
    "plt.imshow(assym[0])\n",
    "plt.scatter(\n",
    "    features_center[\"hdim_2\"],\n",
    "    features_center[\"hdim_1\"],\n",
    "    color=\"black\",\n",
    "    marker=\"x\",\n",
    "    label=\"center\",\n",
    ")\n",
    "plt.scatter(\n",
    "    features_extreme[\"hdim_2\"],\n",
    "    features_extreme[\"hdim_1\"],\n",
    "    color=\"red\",\n",
    "    marker=\"x\",\n",
    "    label=\"extreme\",\n",
    ")\n",
    "plt.scatter(\n",
    "    features_diff[\"hdim_2\"],\n",
    "    features_diff[\"hdim_1\"],\n",
    "    color=\"purple\",\n",
    "    marker=\"x\",\n",
    "    label=\"weighted_diff\",\n",
    ")\n",
    "plt.scatter(\n",
    "    features_abs[\"hdim_2\"],\n",
    "    features_abs[\"hdim_1\"],\n",
    "    color=\"green\",\n",
    "    marker=\"+\",\n",
    "    label=\"weighted_abs\",\n",
    ")\n",
    "plt.colorbar(shrink=0.8)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb7d23d4",
   "metadata": {},
   "source": [
    "As you can see this parameter specifies how the postion of the feature is defined. These are the descriptions given in the [code](https://github.com/tobac-project/tobac/blob/v2.0-dev/tobac/themes/tobac_v1/feature_detection.py):\n",
    "\n",
    "- extreme:       get position as max/min position inside the identified region\n",
    "- center :       get position as geometrical centre of identified region\n",
    "- weighted_diff: get position as centre of identified region, weighted by difference from the threshold\n",
    "- weighted_abs:  get position as centre of identified region, weighted by absolute values if the field"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "516bc511",
   "metadata": {},
   "source": [
    "## Sigma Parameter for Smoothing of Noisy Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fe7ac53",
   "metadata": {},
   "source": [
    "Before the features are searched a gausian filter is applied to the data in order to smooth it. So let's import the filter used by tobac for a demonstration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "87ab364b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:45.207258Z",
     "iopub.status.busy": "2026-02-02T20:12:45.207145Z",
     "iopub.status.idle": "2026-02-02T20:12:45.208846Z",
     "shell.execute_reply": "2026-02-02T20:12:45.208479Z"
    }
   },
   "outputs": [],
   "source": [
    "from scipy.ndimage import gaussian_filter"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54447a25",
   "metadata": {},
   "source": [
    "This filter works performing a convolution of a (in our case 2-dimensional) gaussian function\n",
    "\n",
    "$$\n",
    "h(x, y) = \\frac{1}{2 \\pi \\sigma^2} \\exp \\left( - \\frac{x^2+y^2}{2 \\sigma^2} \\right)\n",
    "$$\n",
    "\n",
    "with our data and with this parameter we set the value of $\\sigma$.\n",
    "\n",
    "The effect of this filter can best be demonstrated on very sharp edges in the input. Therefore we create an array from a boolean mask of another 2d-Gaussian, which has only values of 0 or 1:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "eb34e564",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:45.210126Z",
     "iopub.status.busy": "2026-02-02T20:12:45.210053Z",
     "iopub.status.idle": "2026-02-02T20:12:45.212028Z",
     "shell.execute_reply": "2026-02-02T20:12:45.211711Z"
    }
   },
   "outputs": [],
   "source": [
    "x = np.linspace(-2, 2)\n",
    "y = np.linspace(-2, 2)\n",
    "xx, yy = np.meshgrid(x, y)\n",
    "\n",
    "exp = np.exp(-(xx**2 + yy**2))\n",
    "\n",
    "gaussian_data = np.expand_dims(exp, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6251dba",
   "metadata": {},
   "source": [
    "and we add some random noise to it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "06155b48",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:45.213287Z",
     "iopub.status.busy": "2026-02-02T20:12:45.213207Z",
     "iopub.status.idle": "2026-02-02T20:12:45.274950Z",
     "shell.execute_reply": "2026-02-02T20:12:45.274565Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x305337890>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(54321)\n",
    "\n",
    "noise = 0.3 * np.random.randn(*gaussian_data.shape)\n",
    "data_sharp = np.array(gaussian_data > 0.5, dtype=\"float32\")\n",
    "\n",
    "data_sharp += noise\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.imshow(data_sharp[0])\n",
    "plt.colorbar(shrink=0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "affb1af0",
   "metadata": {},
   "source": [
    "If we apply this filter to the data with increasing sigmas, increasingly smoothed data will be the result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d140f1d0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:45.276383Z",
     "iopub.status.busy": "2026-02-02T20:12:45.276301Z",
     "iopub.status.idle": "2026-02-02T20:12:45.486901Z",
     "shell.execute_reply": "2026-02-02T20:12:45.486458Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "non_smooth_data = gaussian_filter(data_sharp, sigma=0)\n",
    "smooth_data = gaussian_filter(data_sharp, sigma=1)\n",
    "smoother_data = gaussian_filter(data_sharp, sigma=5)\n",
    "\n",
    "fig, axes = plt.subplots(ncols=3, figsize=(16, 10))\n",
    "\n",
    "im0 = axes[0].imshow(non_smooth_data[0], vmin=0, vmax=1)\n",
    "axes[0].set_title(r\"$\\sigma = 0$\")\n",
    "\n",
    "im1 = axes[1].imshow(smooth_data[0], vmin=0, vmax=1)\n",
    "axes[1].set_title(r\"$\\sigma = 1$\")\n",
    "\n",
    "im2 = axes[2].imshow(smoother_data[0], vmin=0, vmax=1)\n",
    "axes[2].set_title(r\"$\\sigma = 5$\")\n",
    "\n",
    "cbar = fig.colorbar(im1, ax=axes.tolist(), shrink=0.4)\n",
    "cbar.set_ticks(np.linspace(0, 1, 11))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9532885",
   "metadata": {},
   "source": [
    "This is what happens in the background, when the `feature_detection_multithreshold()` function is called. The default value of `sigma_threshold` is 0.5. The next step is trying to detect features of the dataset with these   `sigma_threshold` values. We first need an xarray DataArray again:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "185a55f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:45.488274Z",
     "iopub.status.busy": "2026-02-02T20:12:45.488176Z",
     "iopub.status.idle": "2026-02-02T20:12:45.490488Z",
     "shell.execute_reply": "2026-02-02T20:12:45.490124Z"
    }
   },
   "outputs": [],
   "source": [
    "date = np.datetime64(\"2022-04-01T00:00\")\n",
    "input_data = xr.DataArray(\n",
    "    data=data_sharp, coords={\"time\": np.expand_dims(date, axis=0), \"y\": y, \"x\": x}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec3ebfe",
   "metadata": {},
   "source": [
    "Now we set a threshold and detect the features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f4dfa1eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:45.491816Z",
     "iopub.status.busy": "2026-02-02T20:12:45.491719Z",
     "iopub.status.idle": "2026-02-02T20:12:45.505139Z",
     "shell.execute_reply": "2026-02-02T20:12:45.504717Z"
    }
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "threshold = 0.9\n",
    "features_sharp = tobac.feature_detection_multithreshold(\n",
    "    input_data, dxy, threshold, sigma_threshold=0\n",
    ")\n",
    "features_smooth = tobac.feature_detection_multithreshold(\n",
    "    input_data, dxy, threshold, sigma_threshold=1\n",
    ")\n",
    "features_smoother = tobac.feature_detection_multithreshold(\n",
    "    input_data, dxy, threshold, sigma_threshold=5\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad094d3d",
   "metadata": {},
   "source": [
    "Attempting to plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2ea0633c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-02-02T20:12:45.506532Z",
     "iopub.status.busy": "2026-02-02T20:12:45.506445Z",
     "iopub.status.idle": "2026-02-02T20:12:45.622557Z",
     "shell.execute_reply": "2026-02-02T20:12:45.622159Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: No Feature Detected!\n"
     ]
    },
    {
     "data": {
      "image/png": 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DArEfALyH2A8AHk7+OG6++WZ59tln5fe//72b/PnKV74iPp/PLeP4mc98RiKRiFxyySUyffr0gdwseITzy/mU8QVyzKRiKcpLk85se94UYzzScIqd/PB36r/MdxjrCYyylxU1kg/JU/SBXR2dWfrHOWKMUe3Tx6fevW3G+IzRGMmPpNP0NEVnpjF4tz0GpESS4mt3l3ec0d6hb1d7nr0zSRPtwchNRpbGb5wyX4ysTmfPuHRRKW9rkzdrqmRJXY37/4MRsR8AvIfYDwAeTv4cd9xx8j//8z/y7W9/2x29/7bbbpPCwkJZuXKl+zrYxIkT5Te/+c1AbhI85OwTR8nMo0okNztNUlOD4ovxFIsvO/7kh/GAj5nIiflIh7Fpvjz7l/+otT/WbsbKI1jT7ENmJjOsY2YdL7dPjPWY67eWF+95ceQewIuyca4/1i52H7OIRGVIZqeMzsySEekZski2yGBE7AcA7yH2A4CHkz+O66+/XqZNmyZ33nmnvPXWW1JZWSmjRo1yX/X693//d/cx0UPB3xGVzJ29H3XozLB/k2sepj8u0VqmD1btGPZ0UG2vOF7/7a9sfpu5rF2z09X2nLX2b8w1U40nP4xfckcssPelZkpyXKXtAgfy27ohd31Edk3VMwlJzfZ68tfoj1hUTw3K1JI8mTa9WErysiWYkSX+lFSJBmMkf7riTKQ4r/c06dvckaN38oXthQU69PMcTo3xtJA/Gte++LvsaymS5Is7YRMIReNKssR6isoUa/3GMbP2RaLR2O8FxnnMrG3rsqtLmpJaupcZkZRgu6T7muTUtKjsmtgkq7bXqX0CjUn7fX0dDsT+xIz9jmCTvm11Tw5T2/Nq7cz3LmPIvwON/Rp/u73+qmOS404WF76vP8aYXK0/KhhOsZeVt1bfts2X5Jt9ujL045++S7/HSau0r6W2IiP2x/hjRXuBvkOZO/VOdRPsey9rPbGeFLVib8Vx+v1k9gZ7/9uNf6/zV9oHwBfRl1c1U19WJMYfnoYsMu4jsuxjRuzvi3v+voj7xH0Lcd836ON+PPf9A578cZx55pnuFxJcYWH/zNMPjirKkdy0NElKy5BAWsY+MxnO64j6BIm7T7ztu6fFuV0xts1ef4xFWX3sLvbyDmRfDsABHbM4F9aPi9q/Pr6AJCWnSzQSkezMdpk0LNdM/gwGxH4A8B5iPwAcGQ5L8gcesWqVHClG52VJWnKS+JNj/MkVOEIFgimSlhqUsuKsw70pAAAAAAahASv1DhwuThGm9GCSJAUC4guQ78Tg4/MH3OvXTWAeWW9zAQAAABgESP6gXzSGQtLUGjro5TS1hNyv/hT4qKS7+ztzP79qBAyU7ks3EKNEPQAAAABo+C0C/SIcicpPHl94UAkgJ+lz54OvuMsCsCeSlgAAAAAOnGfegfGHo5JS07tcRMVxaeb8uWv09rJ5dmJix+l6Li1nrT5/W6k9/kxLWe/KZN2GPdBs9llzrV5WqPBtvUzJ9tP0SiiOYJPenlJn7//CF1bIa39bKVkZvSuc1I3X159W3XtZja2h3YmfXJ8k1+u/7CY3mquXqqP1/Qk4h9k5Nb49vjv72F1RSeFv06vhRFLtj4xV1SvQHn/Z+C7r0oxRoSpg5N2S2vRrqTPTLl9jVbWKVe0rYlxOPn31sQfPNvpEkmJUKDOqtwXarSpg9vqtcxNOtjfarARmdEmut/elK83f57Q7BfWcSjjBtqiIsq7id3v/f0VrVGIVyPEKYv+hj/1pRvWu2kmBuCtH9Wfst2LSkH/Y+5K1olJtbzqqwOyz5QK9qlf2Bn3+sD67q3q6UWmw095m6x6jYIV+v7Brtl1VNbUmGlc1RUfzED2YptZH4v63L6lVbw8VxSjQ0GXce23S/yFpK7A3wKrO2DTS7pO6171Mt0hQbx/1nF1tr/Yo/b4wrdr+0BD7+yLu90XcJ+5biPsy6ON+PPf9nkn+YGA4yZv6pt5ljOta9V8AQs084QMAAAAAwKHGa18AAAAAAAAJjOQPANPOHdvlhuu/Jqccd7RMGztMjhozRH591884YgAAAAAwiPDaF9CPAkod7oCRYo3uMe+ROMh1R0eHfOmqT8m2LZslKytbpkybLsGkJBkydNiAb8vLL/5VVq/4UI49+UQ57sQTB3z9AAAAADCYkfwB+kF2ZqqUFGcfcBnucCQi5bVN0nAQ1dL626K/L3QTPyWlQ+TpF1+VrOzsmAM+H0qvvPhXefrJP8lX/ULyBwAAAADixGtfQD8oKTrwxI/D6TskP+uIOhebNqx3v8+YdZyb+AEAAAAADE6eefKnK8Uv9RN6189OL7fLNnel6484NIyxS+R2FnSp7c3tep+OBrvUdsFSff275uaYfUbN18vI7ThFn78rKxyjbLh+abSW2sesdYie/Ch8X19PV6q9rNy1+v7nrLfrs287S0+eFH3YJUkzo2554aTWqPh8+y717Tfe1fK3992XQMDvfh0sJwGU2tAu4a6IhLP1WsBd6fEvtytV3zandLjJ75NQx+6nkFLSUnteUUuqb7ev/1y9VGE4JRp3uedIwNhWX4wywcarc9Z5TmqIsS95+vGP2h9Zc38Cbcb8bXZBxmhmap9S7+6yQmHJXt0gXZ19V1Y5O3evbY11gr2D2O/h2P++/m9ya6H9QU7P04Ns1tp6s08gpCfHm0bqx3LIIyvMZTWcPUltr54e/78x9eP1+rVhPVS7utL0c1P8t61mnx2XjVLbK8fq25ykV6B3deTEd0/mLs+IsW35+vqL528wl1V+xTh9WcVmF8ncpm9b7kr9WKa8v9lcVsfsiWp76esNZh9if1/EfeWYEPfNzxBxn7g/2ON+PPf9nkn+ANg/d//PHXLPz+/s+X/ndSvnyzFs6HB55a9v9Ez7+6KF8vgTj8r7H7wn9Y31kpObKzOPPV6+eO3X5egZM/ssu7qyUhY895y89vIC2bxhg1TtqpCkYJKMGTdezr3oYvnM578oySkf/2ayY9tWOX/28T3/f++dd7pf3Y6dM0ce+Ms89+cvfPoyefetN+W3j/1Zjp/Td1ygtxf/Q774mcvluBPmyEOP7+7TbeIxZe73l597XXaW75D7H/2NfLh8mdTX1cld9/1Wzjz3fHd6Z2enzPvDo/L8U3+RDWvXSFtrqxSXlsrJp58h137lOikdMrTPeqsqK+T/7vmVLPr7q1K+c4ebfMzNz5ey4WVy0uy58sV//rIEg3ZSGQAAAAAOFskf4FCYPFmkujr2PIWFIqtWHXHH3xnQecaxx7uJil07d0h+YaGMKhvjTivOK3S/RyIR+cEP/12emPe4+/95efkyfsIk2b5ti7z01+fklb+9ILf89x1y2ac/22vZTuLk1z/9qaSkpkphcbGMnzRJ6uvrZOUH78sH7y2ThS++IL957E+SlJLszp+SkiozjjtetmzaKLXV1VI6bKiUDvt4wOlxznHuR8+/OF9+/uufSWZmlowoK5Pk5I8TUXW1NfKvX/pn+XDZMvH5fFI6dKiUDBkiWzZulD/+7iF58Zmn5TcPPuYOjN1tV/lO+adLz5fqqkpJCgZl5KgySUtPl8pdu+Std96UxW//Q6684rMSDNpP9AEAAADAwSL5AxwKTuJnX8mfI9SlV35WLvunz8r//vwOueeuO2XuaWfIj+78pTst+NFrX/f85ldu4mfkiFFy2y23ywnHzXFf+4pGo/L4ww/KT/7zZvnh92+S6TNmyegpE3qWfeycE+X+x/8os+bMkeTA7gSPY9fOnfLfN/+HO7Dzw7+5T770jevcdidB9LunnpHvX/9NeeaJP8klV35G/vV73z1k+37X/94p117zdfmXG2/oeRqnPbT79bfvfeOrbuJnzimnys0/+akMH7X7VYfWlha5/Qffl788/pj823X/Is/+7XVJTt69bw/+3z1u4mfOSafIHb+8W/LyC3rWVb9huzz7wjMSTPr4OAAAAADAocCAzwDiUt9QL7/57T3uUzF33/V/buKnm/NEzGc+/yX3q7OjQx558P969Z15wgky+5RT+rzm5DxF85Nf/6/7dMwzH71idjjMPfFUuf7r3+m1fc5TSotefUUWv/G6jBozVu767YM9iR9HekaG3HLHnXLUlGmyfdtWeemF5/oMmv3Zq7/YK/HjKCwoki9cdY2kpfUeiwwAAAAA+htP/gCIy99ff0XaQm0y54STZPy4j5/q2ZMzRs4jv/2NvP3mx+MDdXOepHnpuWdlyT8Wu0/8tLW1uk8MOfx+v2xav15CbW2SehiSIpdedIXa/rfnn3W/f/LyKyQtre+AsM52n37WubJyxQfy9uI35BMXXeq2d48B9Le/Pisnn3ZGzxNBAAAAADCQvJP88fWt+pPSYJcbquo7Vq1r6CK74kR4pT5oa8livUpJxfF6JY6YFYra7PWXz9E7pVXqo3+3iT3y/ejHK9T2lom9n17YU+1kff3V0/T1DH3DrnZUOVP/JbkrNdPsEzQqiCTXdoq/KyoSFfF1fVztS5LsUdE7SvV2v1KhrbsSVn8IlaZIOBoVn1GMJ6nV7uszLo1O45AltcS4llN87mfG4czVXWGrKztZVm1e6/68ftN6ufKaj5Mle1bh6gjtfj2sYtdO8UU+Pj4b162Vr3/hc7J9yxZ7R0Sksa5e0lKV5I/Pfl6xe/8DXSKBPS6t8EeVhbq3z6kc5oyIH87qW/KmbOpRbnugo/exWbtyd3We5+bNkzcWLlTXX1Nd5X7fVVHeUxXss1+8Rp5+6kmZ/9Sf5fW/v+ImgI6ZeZwcN2eOjB+tJ87cfWjvvX4nOeZcwuG0gNRNy5XOcN/Y5d+72J99er2F2O/Z2J9So1fAbC2w93/bd/X7gvQX8s0+Be/rG9A8VN/m1T/SK3o5gnV6gCt6L7Lf8aLbjlP1ZZUutpdVP1Y/NhXnjjT7NI3Rl5dSY1TNtIvNiW/3W7Z9RNPsTjmb9PbaSfo9Wf1pu8ew02Rv1SvERf327XJHtn4PYFWmrD9rvLmsnA3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      "text/plain": [
       "<Figure size 1400x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=3, figsize=(14, 10))\n",
    "plot_kws = dict(\n",
    "    color=\"red\",\n",
    "    marker=\"s\",\n",
    "    s=100,\n",
    "    edgecolors=\"w\",\n",
    "    linewidth=3,\n",
    ")\n",
    "\n",
    "im0 = axes[0].imshow(input_data[0])\n",
    "axes[0].set_title(r\"$\\sigma = 0$\")\n",
    "axes[0].scatter(\n",
    "    features_sharp[\"hdim_2\"], features_sharp[\"hdim_1\"], label=\"features\", **plot_kws\n",
    ")\n",
    "axes[0].legend()\n",
    "\n",
    "im0 = axes[1].imshow(input_data[0])\n",
    "axes[1].set_title(r\"$\\sigma = 1$\")\n",
    "axes[1].scatter(features_smooth[\"hdim_2\"], features_smooth[\"hdim_1\"], **plot_kws)\n",
    "\n",
    "im0 = axes[2].imshow(input_data[0])\n",
    "axes[2].set_title(r\"$\\sigma = 5$\")\n",
    "try:\n",
    "    axes[2].scatter(\n",
    "        features_smoother[\"hdim_2\"], features_smoother[\"hdim_1\"], **plot_kws\n",
    "    )\n",
    "except:\n",
    "    print(\"WARNING: No Feature Detected!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f011044b",
   "metadata": {},
   "source": [
    "Noise may cause some false detections (left panel) that are significantly reduced when a suitable smoothing parameter is chosen (middle panel)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf5e5994",
   "metadata": {},
   "source": [
    "## Band-Pass Filter for Input Fields via Parameter `wavelength_filtering`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20156c89",
   "metadata": {},
   "source": [
    "This parameter can be understood best, when looking at real instead of snythethic data. An example of usage is given [here](../Example_vorticity_tracking_model/Example_vorticity_tracking_model.ipynb)"
   ]
  }
 ],
 "metadata": {
  "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
}