doc/feature_detection/notebooks/multiple_thresholds_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How multiple thresholds changes the features detected"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-15T16:14:27.894065Z",
     "iopub.status.busy": "2023-08-15T16:14:27.893581Z",
     "iopub.status.idle": "2023-08-15T16:14:38.976596Z",
     "shell.execute_reply": "2023-08-15T16:14:38.975681Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tobac\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generate Feature Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we will generate some simple feature data where the features that we want to detect are *higher* values than the surrounding (0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-15T16:14:38.981371Z",
     "iopub.status.busy": "2023-08-15T16:14:38.980828Z",
     "iopub.status.idle": "2023-08-15T16:14:39.374743Z",
     "shell.execute_reply": "2023-08-15T16:14:39.373872Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dimensions here are time, y, x.\n",
    "input_field_arr = np.zeros((1,100,200))\n",
    "input_field_arr[0, 15:85, 10:185]=50\n",
    "input_field_arr[0, 20:80, 20:80]=100\n",
    "input_field_arr[0, 40:60, 125:170] = 100\n",
    "input_field_arr[0, 30:40, 30:40]=200\n",
    "input_field_arr[0, 50:75, 50:75]=200\n",
    "input_field_arr[0, 55:70, 55:70]=300\n",
    "\n",
    "plt.pcolormesh(input_field_arr[0])\n",
    "plt.colorbar()\n",
    "plt.title(\"Base data\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now need to generate an Iris DataCube out of this dataset to run tobac feature detection. \n",
    "One can use xarray to generate a DataArray and then convert it to Iris, as done here. \n",
    "Version 2.0 of tobac (currently in development) will allow the use of xarray directly with tobac. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-15T16:14:39.462543Z",
     "iopub.status.busy": "2023-08-15T16:14:39.462138Z",
     "iopub.status.idle": "2023-08-15T16:14:41.054125Z",
     "shell.execute_reply": "2023-08-15T16:14:41.053049Z"
    }
   },
   "outputs": [],
   "source": [
    "input_field_iris = xr.DataArray(input_field_arr, dims=['time', 'Y', 'X'], coords={'time': [np.datetime64('2019-01-01T00:00:00')]}).to_iris()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Single Threshold\n",
    "Let's say that you are looking to detect any features above value 50 and don't need to separate out individual cells within the larger feature. For example, if you're interested in tracking a single mesoscale convective system, you may not care about the paths of individual convective cells within the feature. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-15T16:14:41.058529Z",
     "iopub.status.busy": "2023-08-15T16:14:41.058272Z",
     "iopub.status.idle": "2023-08-15T16:14:41.603331Z",
     "shell.execute_reply": "2023-08-15T16:14:41.601089Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thresholds = [50,]\n",
    "# Using 'center' here outputs the feature location as the arithmetic center of the detected feature\n",
    "single_threshold_features = tobac.feature_detection_multithreshold(field_in = input_field_iris, dxy = 1000, threshold=thresholds, target='maximum', position_threshold='center')\n",
    "plt.pcolormesh(input_field_arr[0])\n",
    "plt.colorbar()\n",
    "# Plot all features detected\n",
    "plt.scatter(x=single_threshold_features['hdim_2'].values, y=single_threshold_features['hdim_1'].values, color='r', label=\"Detected Features\")\n",
    "plt.legend()\n",
    "plt.title(\"Single Threshold of 50\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's try a single threshold of 150, which will give us two features on the left side of the image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-15T16:14:41.615868Z",
     "iopub.status.busy": "2023-08-15T16:14:41.614704Z",
     "iopub.status.idle": "2023-08-15T16:14:41.958739Z",
     "shell.execute_reply": "2023-08-15T16:14:41.958141Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thresholds = [150,]\n",
    "# Using 'center' here outputs the feature location as the arithmetic center of the detected feature\n",
    "single_threshold_features = tobac.feature_detection_multithreshold(field_in = input_field_iris, dxy = 1000, threshold=thresholds, target='maximum', position_threshold='center')\n",
    "plt.pcolormesh(input_field_arr[0])\n",
    "plt.colorbar()\n",
    "# Plot all features detected\n",
    "plt.scatter(x=single_threshold_features['hdim_2'].values, y=single_threshold_features['hdim_1'].values, color='r', label=\"Detected Features\")\n",
    "plt.legend()\n",
    "plt.title(\"Single Threshold of 150\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives us two detected features with minimum values >150. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multiple Thresholds\n",
    "Now let's say that you want to detect all three maxima within this feature. You may want to do this, if, for example, you were trying to detect overhshooting tops within a cirrus shield. You could pick a single threshold, but if you pick 100, you won't separate out the two features on the left. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-15T16:14:41.961925Z",
     "iopub.status.busy": "2023-08-15T16:14:41.961671Z",
     "iopub.status.idle": "2023-08-15T16:14:42.303716Z",
     "shell.execute_reply": "2023-08-15T16:14:42.302472Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thresholds = [100, ]\n",
    "# Using 'center' here outputs the feature location as the arithmetic center of the detected feature\n",
    "single_threshold_features = tobac.feature_detection_multithreshold(field_in = input_field_iris, dxy = 1000, threshold=thresholds, target='maximum', position_threshold='center')\n",
    "plt.pcolormesh(input_field_arr[0])\n",
    "plt.colorbar()\n",
    "\n",
    "# Plot all features detected\n",
    "plt.scatter(x=single_threshold_features['hdim_2'].values, y=single_threshold_features['hdim_1'].values, color='r', label=\"Detected Features\")\n",
    "plt.legend()\n",
    "plt.title(\"Single Threshold of 100\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the power of having multiple thresholds. We can set thresholds of 50, 100, 150, 200 and capture both:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-15T16:14:42.307032Z",
     "iopub.status.busy": "2023-08-15T16:14:42.306781Z",
     "iopub.status.idle": "2023-08-15T16:14:42.846033Z",
     "shell.execute_reply": "2023-08-15T16:14:42.844862Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thresholds = [50, 100, 150, 200]\n",
    "# Using 'center' here outputs the feature location as the arithmetic center of the detected feature\n",
    "single_threshold_features = tobac.feature_detection_multithreshold(field_in = input_field_iris, dxy = 1000, threshold=thresholds, target='maximum', position_threshold='center')\n",
    "plt.pcolormesh(input_field_arr[0])\n",
    "plt.colorbar()\n",
    "\n",
    "# Plot all features detected\n",
    "plt.scatter(x=single_threshold_features['hdim_2'].values, y=single_threshold_features['hdim_1'].values, color='r', label=\"Detected Features\")\n",
    "plt.legend()\n",
    "plt.title(\"Thresholds: [50, 100, 150, 200]\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-08-15T16:14:42.851702Z",
     "iopub.status.busy": "2023-08-15T16:14:42.851440Z",
     "iopub.status.idle": "2023-08-15T16:14:43.299851Z",
     "shell.execute_reply": "2023-08-15T16:14:43.298918Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thresholds = [50, 100, 150, 200, 250]\n",
    "# Using 'center' here outputs the feature location as the arithmetic center of the detected feature\n",
    "single_threshold_features = tobac.feature_detection_multithreshold(field_in = input_field_iris, dxy = 1000, threshold=thresholds, target='maximum', position_threshold='center')\n",
    "plt.pcolormesh(input_field_arr[0])\n",
    "plt.colorbar()\n",
    "\n",
    "# Plot all features detected\n",
    "for i, threshold in enumerate(thresholds):\n",
    "    thresholded_points = single_threshold_features[single_threshold_features['threshold_value'] == threshold]\n",
    "    plt.scatter(x=thresholded_points['hdim_2'].values, \n",
    "                y=thresholded_points['hdim_1'].values, \n",
    "                color='C'+str(i),\n",
    "                label=\"Threshold: \"+str(threshold))\n",
    "plt.legend()\n",
    "plt.title(\"Thresholds: [50, 100, 150, 200]\")\n",
    "plt.show()"
   ]
  }
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