{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "deb79d20",
   "metadata": {},
   "source": [
    "# Hyperspectral Tutorial\n",
    "\n",
    "Example analysis of a hyperspectral image. This includes white reference and dark reference normalization but please note that that sample image (corn kernel) in this case has already been normalized to the white and dark references before the original image was cropped. We are just showing these steps for the sake of the tutorial. \n",
    "\n",
    "Updated September 2025"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cf926d5-85b0-47c8-ab40-39f3a82c26a6",
   "metadata": {
    "id": "604065b5"
   },
   "source": [
    "# Section 1: Importing Image and Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "968a812b-b9e4-4aa9-bfbb-877f641d6f56",
   "metadata": {
    "id": "62ef791b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4.10.dev8+g83b15e008.d20250820'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the notebook display method\n",
    "# If widget is not working, then change to inline\n",
    "%matplotlib widget\n",
    "\n",
    "# Import libraries\n",
    "from plantcv import plantcv as pcv\n",
    "from plantcv.parallel import WorkflowInputs\n",
    "\n",
    "# Print out the version of PlantCV being used by the Jupyter kernel\n",
    "pcv.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63cbb587-b218-4f00-ab7c-b1aa570aa269",
   "metadata": {
    "id": "4468af74"
   },
   "source": [
    "## Input/Output variables\n",
    "\n",
    "The options class mimics the workflow command-line argument parser that is used for workflow parallelization. Using it while developing a workflow in Jupyter makes it easier to convert the workflow to a script later. Remember, always keep your raw images separate from your newly processed images!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1e7e9b03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Input/output options\n",
    "args = WorkflowInputs(\n",
    "    images=[\"./img/4-22-22_right_same_B73_top_00.raw\"],\n",
    "    names=\"image1\",\n",
    "    result=\"hyperspectral_result\",\n",
    "    outdir=\".\",\n",
    "    writeimg=True,\n",
    "    debug=\"plot\"\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "223254b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set debug to the global parameter \n",
    "pcv.params.debug = args.debug\n",
    "# Change display settings\n",
    "pcv.params.dpi = 100\n",
    "# Increase text size and thickness to make labels clearer\n",
    "# (size may need to be altered based on original image size)\n",
    "pcv.params.text_size = 0.5\n",
    "pcv.params.text_thickness = 1\n",
    "pcv.params.line_thickness = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "228039bf-3874-4f31-a0e6-20aca41adfe7",
   "metadata": {
    "id": "343a0816"
   },
   "source": [
    "## Read the input image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08d26d30-bdc3-4a99-9e92-b4d9c72606ce",
   "metadata": {
    "id": "Q4Na59TNNg1x"
   },
   "source": [
    "### Reading images into your environment using *pcv.readimage()*\n",
    "Inputs:\n",
    "   * filename = Image file to be read in\n",
    "   * mode     = How the image will be read into the notebook; either 'native' (default), 'rgb', 'gray', 'csv', or 'envi'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "964f1012",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "673a0340b60f477189d70bd0d852a7fb",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# read hyperspectral image. With the ENVI format the input file should be a .raw. \n",
    "#If you were using a differen format the filename should be a .hdr file that includes information for shaping data (see readimage instructions)\n",
    "spectral_array = pcv.readimage(filename=args.image1, mode='envi')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec2e4613-f229-408b-bf39-b7813536fa54",
   "metadata": {},
   "source": [
    "### Image calibration \n",
    "\n",
    "Read in dark and white reference files, please note that the one of the dimensions (likely width needs to match your raw data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a41f3f53-323a-4a1d-a02b-32dae0b70018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2e596c63c6bf4696a9652dcffdd78720",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "af399eeaed064c8c96394836463944c4",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "white_img = \"./img/white_reference_crop.raw\"\n",
    "dark_img = \"./img/dark_reference_crop.raw\"\n",
    "\n",
    "white_ref = pcv.readimage(filename= white_img, mode='envi')\n",
    "dark_ref= pcv.readimage(filename= dark_img, mode='envi')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10eed0ee-e4f5-4e23-b729-d645fa046287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4d4296a26af545f1b4808d6fbe611db8",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calibrate data to white and dark references\n",
    "\n",
    "calibrated_data = pcv.hyperspectral.calibrate(raw_data=spectral_array, white_reference=white_ref, dark_reference=dark_ref)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7809a8ef-3069-4041-be8c-25afceaba49f",
   "metadata": {},
   "source": [
    "## Segment seed "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9ec1bc07-1152-49c9-a087-721505d1e5c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6deeb5cb53e243d2b24ebfc945b7d8d9",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Next step is to segment the target object from the background pixels\n",
    "#This step is just to aid in picking a changel with good constrast between the target object and background\n",
    "\n",
    "colorspace_img = pcv.visualize.colorspaces(rgb_img=calibrated_data.pseudo_rgb, original_img=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ef6d8e2-6923-44a0-abc6-a4124cde05f9",
   "metadata": {},
   "source": [
    "In this example the l channel from LAB colorspace looks like it has good contrast.\n",
    "Alternatively instead of using the pseudo_rgb image you could calculate an index from the hyperspectral values by using the 'pcv.spectral_index' function and finding an index that has good seperation between object and background then thresholding like this pipeline does\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "16bbc962-bdf3-4f2f-a59a-84c4a0621c0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "98a789a7516d41d3ad53317fae283353",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
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       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Store the \"l\" channel to a variable\n",
    "l = pcv.rgb2gray_lab(rgb_img=calibrated_data.pseudo_rgb, channel='l')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "873e50f2-7271-4e63-944c-04ba6b6372b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  #altair-viz-d94063ead6f240c991bc3ba31408c3d5.vega-embed {\n",
       "    width: 100%;\n",
       "    display: flex;\n",
       "  }\n",
       "\n",
       "  #altair-viz-d94063ead6f240c991bc3ba31408c3d5.vega-embed details,\n",
       "  #altair-viz-d94063ead6f240c991bc3ba31408c3d5.vega-embed details summary {\n",
       "    position: relative;\n",
       "  }\n",
       "</style>\n",
       "<div id=\"altair-viz-d94063ead6f240c991bc3ba31408c3d5\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-d94063ead6f240c991bc3ba31408c3d5\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-d94063ead6f240c991bc3ba31408c3d5\");\n",
       "    }\n",
       "\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      let deps = [\"vega-embed\"];\n",
       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-ec57c8376faeae3312e2285af8e2148d\"}, \"mark\": {\"type\": \"line\", \"point\": true}, \"encoding\": {\"color\": {\"field\": \"color channel\", \"type\": \"nominal\"}, \"tooltip\": [{\"field\": \"pixel intensity\", \"type\": \"quantitative\"}, {\"field\": \"proportion of pixels (%)\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"pixel intensity\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"proportion of pixels (%)\", \"type\": \"quantitative\"}}, \"params\": [{\"name\": \"param_1\", \"select\": {\"type\": \"interval\", \"encodings\": [\"x\", \"y\"]}, \"bind\": \"scales\"}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.20.1.json\", \"datasets\": {\"data-ec57c8376faeae3312e2285af8e2148d\": [{\"pixel intensity\": 12.65, \"proportion of pixels (%)\": 12.378094523630907, \"hist_count\": 165, \"color channel\": \"0\"}, {\"pixel intensity\": 37.95, \"proportion of pixels (%)\": 14.403600900225054, \"hist_count\": 192, \"color channel\": \"0\"}, {\"pixel intensity\": 63.25, \"proportion of pixels (%)\": 7.351837959489872, \"hist_count\": 98, \"color channel\": \"0\"}, {\"pixel intensity\": 88.55000000000001, \"proportion of pixels (%)\": 5.776444111027757, \"hist_count\": 77, \"color channel\": \"0\"}, {\"pixel intensity\": 113.85, \"proportion of pixels (%)\": 6.151537884471118, \"hist_count\": 82, \"color channel\": \"0\"}, {\"pixel intensity\": 139.15, \"proportion of pixels (%)\": 6.601650412603151, \"hist_count\": 88, \"color channel\": \"0\"}, {\"pixel intensity\": 164.45, \"proportion of pixels (%)\": 8.177044261065266, \"hist_count\": 109, \"color channel\": \"0\"}, {\"pixel intensity\": 189.75, \"proportion of pixels (%)\": 10.427606901725431, \"hist_count\": 139, \"color channel\": \"0\"}, {\"pixel intensity\": 215.05, \"proportion of pixels (%)\": 19.80495123780945, \"hist_count\": 264, \"color channel\": \"0\"}, {\"pixel intensity\": 240.35000000000002, \"proportion of pixels (%)\": 8.927231807951989, \"hist_count\": 119, \"color channel\": \"0\"}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot histogram of data to more easily select a threshold. \n",
    "# Please note this step would not be included in a workflow when run in parallel\n",
    "\n",
    "hist_figure1= pcv.visualize.histogram(l, bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b6088eb2-f385-4ca8-9e1f-49636c6290e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0b60d2f42bb64816891fd7f83d4325cd",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='data:image/png;base64,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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select a threshold value that isolates your target object well. If necessary do additional steps to clean up the mask.\n",
    "\n",
    "corn_thresh = pcv.threshold.binary(gray_img=l, threshold=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b324f4f-c32b-4e81-b30c-50623061d642",
   "metadata": {},
   "source": [
    "## Spectral analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2bd4aafa-3228-4c83-8265-d4d354a8e81d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  #altair-viz-e6dc74e3a00e4293a1ba1305ccb655d4.vega-embed {\n",
       "    width: 100%;\n",
       "    display: flex;\n",
       "  }\n",
       "\n",
       "  #altair-viz-e6dc74e3a00e4293a1ba1305ccb655d4.vega-embed details,\n",
       "  #altair-viz-e6dc74e3a00e4293a1ba1305ccb655d4.vega-embed details summary {\n",
       "    position: relative;\n",
       "  }\n",
       "</style>\n",
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\"value\": 0.5544657157911014, \"label\": 1032.178}, {\"sample\": \"kernel_1\", \"value\": 0.5592109301447306, \"label\": 1033.426}, {\"sample\": \"kernel_1\", \"value\": 0.5499706330213309, \"label\": 1034.674}, {\"sample\": \"kernel_1\", \"value\": 0.550773568110507, \"label\": 1035.922}, {\"sample\": \"kernel_1\", \"value\": 0.5522316486664396, \"label\": 1037.171}, {\"sample\": \"kernel_1\", \"value\": 0.551566820761593, \"label\": 1038.42}, {\"sample\": \"kernel_1\", \"value\": 0.5498056564774716, \"label\": 1039.669}, {\"sample\": \"kernel_1\", \"value\": 0.55421249179133, \"label\": 1040.919}, {\"sample\": \"kernel_1\", \"value\": 0.5533642096501472, \"label\": 1042.169}, {\"sample\": \"kernel_1\", \"value\": 0.5515601728323729, \"label\": 1043.419}, {\"sample\": \"kernel_1\", \"value\": 0.5449825482644058, \"label\": 1044.669}, {\"sample\": \"kernel_1\", \"value\": 0.546954461031567, \"label\": 1045.919}, {\"sample\": \"kernel_1\", \"value\": 0.5475446130442241, \"label\": 1047.17}, {\"sample\": \"kernel_1\", \"value\": 0.5427576184105017, \"label\": 1048.421}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.FacetChart(...)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Look at a histogram of the hyperspectral data, if you had more than one object you would need a matrix with \n",
    "# each object labeled with a different value.\n",
    "\n",
    "spectral_hist  = pcv.analyze.spectral_reflectance(hsi=calibrated_data, labeled_mask=corn_thresh, n_labels=1, label=\"kernel\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d052b78-3032-482d-9407-1e2b614f17a4",
   "metadata": {},
   "source": [
    "## Calculate spectral indicies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8b409dd2-f9c5-41d1-a80d-5df3e735d98a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6c830299c8dd4b8e8a1df253098f95d5",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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UTuyQGwfmxCW50UpudJzDjQF06t39ceqd6D3Ji1J0oqzc+oKCAqu3Ex3nrts5J26cmrsWJ2LQjdNL8jgnTZoUuzbJc+L+98G5f5KMAUwyvvDjjz+OXZtk/ONYwRNAAACAwOTsALh9+3bdddddqqysVF5enl566aVhX4+iSOvXr1dlZaXy8/O1bNky7d+/PzuLBQAAGEU5OwD29PRowYIF2rx583m//uSTT2rjxo3avHmzdu3apfLyct1xxx3q6uoa5ZUCAACMrpz9GcDa2lrV1tae92tRFGnTpk167LHHtGLFCknSM888o7KyMj333HP6/ve/P5pLBQAAGFU5+wTwYhobG9XS0qKamprM51KplG655Rbt2LEjiysDAABIXs4+AbyYlpYWSVJZWdmwz5eVlenQoUMX/Hv9/f3DfpOqs7MzmQUCAAAkKMgngEPOfmuBKIou+nYDdXV1SqfTmY9Zs2YlvUQAAIARF+QAOPQ+dUNPAoe0trae81Tws9atW6eOjo7MR1NTU6LrBAAASEKQA2B1dbXKy8tVX1+f+dzAwIAaGhq0dOnSC/69VCqlKVOmDPsAAAAYa3L2ZwC7u7v10UcfZf7c2NiovXv3qqSkRLNnz9aaNWu0YcMGzZkzR3PmzNGGDRtUUFCg++67L4urBgAASF7ODoC7d+/Wrbfemvnz2rVrJUkrV67Uz3/+cz3yyCM6efKkHnzwQZ04cUKLFi3SG2+8YcWpAQAAjEV5URRF2V7EWNXZ2al0Oq1vfOMbsfMNjx49mth6brjhhti1bl7vVVddFbvW/e1o5823Dx8+bPV2sjid3GDJW7ertbU1dm1JSYnV290fJzPTyTKVvMxW96XKzXh1ONeVkx0r/fq7F3HFzSAf4pxvt/fAwIBV39fXl9haHG5er8PZSynZDGPn3nRzco8cORK71jlGybt/rr/++ti1g4OD2rZtmzo6OoL9ca4gfwYQAAAgZAyAAAAAgWEABAAACAwDIAAAQGAYAAEAAALDAAgAABAYBkAAAIDAMAACAAAEhgEQAAAgMAyAAAAAgWEABAAACExyAYsB6ejoiJ1VOXXq1Nh9nWxfSVq4cGHsWjf/8sMPP4xd29PTY/V2smmd/FDJy8t0s33b29tj17o5qU7+pZs32t/fb9U757ygoMDq7XDzOnt7e2PXurmqbp6pY/LkyYn1dq6VdDpt9XbPiXOc7uuVk3vr3j/OuouKiqze7uubw1l3R0eH1du5748dO2b1zs/Pj107Y8aM2LWnTp2y1pGLeAIIAAAQGAZAAACAwDAAAgAABIYBEAAAIDAMgAAAAIFhAAQAAAgMAyAAAEBgGAABAAACwwAIAAAQGAZAAACAwBAFNwLy8/M1ceLEWLVXXHFF7L5f+cpXrHVUVFTErj1y5IjV+/jx47Fr3Sg4J7bJjWvLy8tLrPfp06dj1zrHKHnnMG4M4RA3asyJsXPOt+RFfLn7E0VR7Fp33c7euzFzzvl249eciEE3MtCNO3S4++PUJxnXlkqlrN7ONevuvfu67HDWUlhYaPV2ItsuueSS2LVJXq9jBU8AAQAAAsMACAAAEBgGQAAAgMAwAAIAAASGARAAACAwDIAAAACBYQAEAAAIDAMgAABAYBgAAQAAAsMACAAAEBgGQAAAgMCQBTwCxo8fHzvrderUqbH7lpSUWOu47LLLYtceOnTI6n3y5MnYtU5OquRla7qZoCdOnIhd62ZrOlmpbhawkx/rnhMnW1OSCgoKYtc6WaaSYmdoS/7+OOfc7e3U9/b2Wr2ddbv3mnNdpdNpq7eT6yx5ObnuNe68prjXrJu97XDuTXcdTvatc/4kqbu7O3ate69dfvnlsWud13v3dTAX8QQQAAAgMAyAAAAAgWEABAAACAwDIAAAQGAYAAEAAALDAAgAABAYBkAAAIDAMAACAAAEhgEQAAAgMAyAAAAAgSEKbgTk5+fHjrS67rrrYvddtGiRtQ4nGsiJdpOk9vb22LVO5JDkRaq5vZ14KjeiyDnfTuSZ5EV8uTFzzvlOei3OeXF7OxFfbiyUcx2615XDjQNz6t0IOydmTvKuQ/c4nXOeZLSbu/dOpJr7muJcs27EoHP/uJGBTiShs5fu9ZqLeAIIAAAQmJwdALdv36677rpLlZWVysvL00svvTTs66tWrVJeXt6wj8WLF2dnsQAAAKMoZwfAnp4eLViwQJs3b75gzZ133qnm5ubMx6uvvjqKKwQAAMiOnP0ZwNraWtXW1l60JpVKqby8fJRWBAAA8OWQs08A49i2bZtKS0s1d+5c3X///Wptbb1ofX9/vzo7O4d9AAAAjDXBDoC1tbX653/+Z7355pv6q7/6K+3atUvLly+/6G+m1dXVKZ1OZz5mzZo1iisGAAAYGTn7LeDPc++992b+ef78+Vq4cKGqqqr0yiuvaMWKFef9O+vWrdPatWszf+7s7GQIBAAAY06wA+DZKioqVFVVpQMHDlywJpVKKZVKjeKqAAAARl6w3wI+W1tbm5qamlRRUZHtpQAAACQqZ58Adnd366OPPsr8ubGxUXv37lVJSYlKSkq0fv16fetb31JFRYU+/vhjPfroo5o+fbq++c1vZnHVAAAAycvZAXD37t269dZbM38e+tm9lStXasuWLdq3b5+effZZtbe3q6KiQrfeequef/55FRcXZ2vJAAAAoyJnB8Bly5ZdNAv09ddfH7F/V3d3d+xcRiczsaioyFqHk63pvoWNkwva1dVl9XZ+rjIvL8/q7eRlujnDkydPjl3b0dFh9Xb09fVZ9e45dLI7nfxdydsfN7vTyTNNMlPXzRl2uLmqzjl0s2aTvK5chYWFsWt7enqs3klmrjvn0D1/zv3gZBJL3mthfn6+1dvJAHeyl5PM6B4rOAMAAACBYQAEAAAIDAMgAABAYBgAAQAAAsMACAAAEBgGQAAAgMAwAAIAAASGARAAACAwDIAAAACBYQAEAAAITM5GwY2m6upqTZo0KVbttddeG7uvE4EjSUeOHEmkVvLixtxYLScqK+55HuLE4zlRSe5a3DgwJxLKPd9uhFSSsWdO/J67907Uk3tOnPqpU6davT/99FOr3uGs2z3fblRfktw4SodznElGDLpxbU69u/dO5J17P0ybNi12rRO5mWRE41jBE0AAAIDAMAACAAAEhgEQAAAgMAyAAAAAgWEABAAACAwDIAAAQGAYAAEAAALDAAgAABAYBkAAAIDAMAACAAAEhgEQAAAgMGQBj4B58+YpPz8/Vm3cOknat2+ftY7W1tbYtW1tbVZvJ7M1yUxQNx85yezTnp6e2LWFhYVWbyfDePLkyVZvJ7dTkqIoil1bVFRk9XayT911O/vpHKPLzaV1rnEn71jyjtPJ/5a81wjJv26TkkqlrHonM9zNF+/t7Y1d6+69ky/uvLZJ3l46/w2UpNLSUqs+Lvd6zUU8AQQAAAgMAyAAAEBgGAABAAACwwAIAAAQGAZAAACAwDAAAgAABIYBEAAAIDAMgAAAAIFhAAQAAAgMAyAAAEBgiIIbAdXV1bGjvpzYoQ8//NBax9GjR2PXupFqJSUlsWudGDPJix06deqU1dvhnhOn3j0nToSUe07ceCon5snt7cRTudFXznlxIukkL1LNPSdORJW7buf1x43Hc6PdnJhGlxMD6O6PE5HX3d1t9XbiDt3ITSdmzo3Hc+LdiouLrd7OcSZ5fecingACAAAEhgEQAAAgMAyAAAAAgWEABAAACAwDIAAAQGAYAAEAAALDAAgAABAYBkAAAIDAMAACAAAEhgEQAAAgMAyAAAAAgSELeASUlpaqqKgoVu2JEydi921ra7PWcfDgwdi1bn6sU+9mLDp5pm4erJPX62ZrOhmibt6ow91LNz/W4e5PknmcTj6pk0ksefvpZkw7Obbuup0cW5e7l85xuut2Mnjd+8G535J8vXJzhh1urvPMmTNj186YMcPq7eQjd3V1xa5NMld+rOAJIAAAQGBydgCsq6vTjTfeqOLiYpWWluqee+7RBx98MKwmiiKtX79elZWVys/P17Jly7R///4srRgAAGB05OwA2NDQoNWrV2vnzp2qr6/X4OCgampq1NPTk6l58skntXHjRm3evFm7du1SeXm57rjjDusxMgAAwFiTsz8D+Nprrw3789NPP63S0lLt2bNHN998s6Io0qZNm/TYY49pxYoVkqRnnnlGZWVleu655/T9738/G8sGAABIXM4+ATxbR0eHJKmkpESS1NjYqJaWFtXU1GRqUqmUbrnlFu3YseO8Pfr7+9XZ2TnsAwAAYKwJYgCMokhr167VTTfdpPnz50uSWlpaJEllZWXDasvKyjJfO1tdXZ3S6XTmY9asWckuHAAAIAFBDIAPPfSQ3n33Xf3Lv/zLOV87+20Uoii64FsrrFu3Th0dHZmPpqamRNYLAACQpJz9GcAhDz/8sF5++WVt37592HsVlZeXS/r1k8CKiorM51tbW895KjgklUoplUolu2AAAICE5ewTwCiK9NBDD+mFF17Qm2++qerq6mFfr66uVnl5uerr6zOfGxgYUENDg5YuXTraywUAABg1OfsEcPXq1Xruuef0b//2byouLs78XF86nVZ+fr7y8vK0Zs0abdiwQXPmzNGcOXO0YcMGFRQU6L777svy6gEAAJKTswPgli1bJEnLli0b9vmnn35aq1atkiQ98sgjOnnypB588EGdOHFCixYt0htvvKHi4mLr3zV9+vTYf8eJtent7bXW4cQlub/BPHHixNi1bjzV4OBg7Fo3rs3pHTfOb4hzvt0oK2fdbpRVQUGBVe9EJg0MDFi9negrN07PWYtzfUvJxuk5MVxJnm/nGpS8aDfJuyfcOD2ntxup5uy9GzfmnEP3GnRel929/OyPUX0e9/Vn6tSpsWv/93//N3ate33nopwdAOPkUubl5Wn9+vVav3598gsCAAD4ksjZnwEEAADA+TEAAgAABIYBEAAAIDAMgAAAAIFhAAQAAAgMAyAAAEBgGAABAAACwwAIAAAQGAZAAACAwDAAAgAABCZno+BGU2lpqaZMmRKrtrm5OXZfJzfY7R0nKu+zksw+dfIyk8wZds+3kx/r5timUqnYtYWFhVZvNz/WXbvD2U8nI9c1bpz3/8JOfX9/v9X7+PHjsWvz8/Ot3s416/Z2M3Xd8+Jw7ns3Z9h5nXDvnZ6enti17rqdfN9Zs2ZZvZ1zkk6nrd6NjY2xaz/99NPYtUm+ro0VPAEEAAAIDAMgAABAYBgAAQAAAsMACAAAEBgGQAAAgMAwAAIAAASGARAAACAwDIAAAACBYQAEAAAIDAMgAABAYBgAAQAAAkMW8Ajo6uqKnWnqZCZ2dXVZ63ByPp3MScnLDnYzRJ0s287OTqu3k6nr5gz39vbGrk0yx9bNBHWzg/v6+mLXuvmaTsa0sw7Jyxw9c+aM1dvJU3buS0kqKiqKXevku0reNeueE3fvnbxeN4s8yexyZ+/b29ut3nEz5SUvQ12SZs6cGbu2oKDA6u2s++jRo4n1vvHGG2PXDgwM6P3337fWkmt4AggAABAYBkAAAIDAMAACAAAEhgEQAAAgMAyAAAAAgWEABAAACAwDIAAAQGAYAAEAAALDAAgAABAYBkAAAIDAEAU3Avr6+mLHPTlxY05s09A6kjJt2rTE1uFEX7m6u7tj17pRVk4MlxMBKHkxWU5Mn+RH3jmxWk78muRFZbnXibOfzvl2JXldHT9+3OrtRBK6MXPOvSZJ48bFf/7g7o8Tj+jGS/b398euLSkpsXo797J7H1999dWxa534NUk6cOBA7FonnlPyzklbW1vsWjdKLxfxBBAAACAwDIAAAACBYQAEAAAIDAMgAABAYBgAAQAAAsMACAAAEBgGQAAAgMAwAAIAAASGARAAACAwDIAAAACBYQAEAAAIDFnAI+Do0aPq6emJVevkTjoZrC43B9HJv3TyRiVpYGAgdq2bf+nkmbqZrU7msXtOOjo6YtfGzaEe4l5XTg5r3PtgiLOfTr6r5F2zbu8kc0Sddbt5vc7+uBnTbsark49dUFBg9e7q6opde+bMGau3k+/r3mvOui+77DKrt3NdHTlyxOrt3A/O670kXXLJJbFr3fs4dDn7BLCurk433nijiouLVVpaqnvuuUcffPDBsJpVq1YpLy9v2MfixYuztGIAAIDRkbMDYENDg1avXq2dO3eqvr5eg4ODqqmpOef/gO+88041NzdnPl599dUsrRgAAGB05Oy3gF977bVhf3766adVWlqqPXv26Oabb858PpVKqby8fLSXBwAAkDU5+wTwbEM/U3X2z29s27ZNpaWlmjt3ru6//361trZmY3kAAACjJmefAH5WFEVau3atbrrpJs2fPz/z+draWn37299WVVWVGhsb9ZOf/ETLly/Xnj17zvsDzf39/cN+kNb5hQ4AAIAviyAGwIceekjvvvuu3n777WGfv/feezP/PH/+fC1cuFBVVVV65ZVXtGLFinP61NXV6U//9E8TXy8AAECScv5bwA8//LBefvllvfXWW5o5c+ZFaysqKlRVVaUDBw6c9+vr1q1TR0dH5qOpqSmJJQMAACQqZ58ARlGkhx9+WC+++KK2bdum6urqz/07bW1tampqUkVFxXm/nkql7Pe6AgAA+LLJ2SeAq1ev1j/90z/pueeeU3FxsVpaWtTS0pJ589Hu7m79+Mc/1n/+53/q448/1rZt23TXXXdp+vTp+uY3v5nl1QMAACQnZ58AbtmyRZK0bNmyYZ9/+umntWrVKo0fP1779u3Ts88+q/b2dlVUVOjWW2/V888/r+Li4iysGAAAYHTk7AD4eTFG+fn5ev3110fk39XX1xc7gubjjz+O3deNzHHixtyYp/z8/Ni1Sa7bjW1y1jJunPdA3Inh6u7utno758SNP3Ki3SQvssv9EQnnnDvRe5J3nG5klxMb6N5rzrXiRgw6cWDu/dDW1mbVO/3d42xvb49d616zRUVFsWtLS0ut3s5aLr/8cqv3u+++G7vWuU4k77XQjVF07k1nHe71nYs4AwAAAIFhAAQAAAgMAyAAAEBgGAABAAACwwAIAAAQGAZAAACAwDAAAgAABIYBEAAAIDAMgAAAAIFhAAQAAAgMAyAAAEBgcjYLeDT98pe/jJ1V6WTZuvmXLS0tsWudPEvJy290s4AdU6ZMseqdHFt33Xl5ebFrCwsLrd4dHR2xa90sUzeb1snidHOGZ8yYEbvWzQJ2rlm3t5O/7F5XTp5pkhnGrqlTp1r1zutVc3Oz1bu6ujp27SeffGL1do6zt7fX6u3c904+u+S9BqXTaau3sz/ua/jhw4dj1zp7475W5SKeAAIAAASGARAAACAwDIAAAACBYQAEAAAIDAMgAABAYBgAAQAAAsMACAAAEBgGQAAAgMAwAAIAAASGARAAACAwRMGNgPfeey92fNPVV18du++HH35oraOkpCR27bhx3uzvRHY5UVaSdOLEidi1bpSVEwU3ceJEq7cT8eWeEyeqr7u72+rtRiA5a3Hi1yQvgs3de6e+v7/f6u2cc/d8OxGD7vl2zolzX/4mnPvNiXaTvP25/PLLrd5dXV2xa92oPuecONeJJE2fPj127aFDh6zezrqbmpqs3k4kamtra+zaJGMRxwqeAAIAAASGARAAACAwDIAAAACBYQAEAAAIDAMgAABAYBgAAQAAAsMACAAAEBgGQAAAgMAwAAIAAASGARAAACAwDIAAAACBIQt4BHR1dcXOQvz5z39u9XUsX748dm1FRYXVO51Ox651MyodTv6u5GUYR1Fk9e7o6Ihd62a2OtmahYWFVu+enh6rvrOzM3atm3nsnEM3U/eSSy6JXevmk06dOjV2bW9vr9XbuX/cvSwuLo5dW1paavV213LFFVfErnXve+f1yuVkY7uZ6869dubMGat3ktnYzr351a9+1ep99OjR2LWpVCp27eDgoPbv32+tJdfwBBAAACAwDIAAAACBYQAEAAAIDAMgAABAYBgAAQAAAsMACAAAEBgGQAAAgMAwAAIAAASGARAAACAwDIAAAACBIQpuBLS2tmrChHin0on4cqN+Dh8+HLv27bfftnrfeOONsWvd+CMnWsmJM5KksrKy2LXd3d1W776+vti1zt5I3jmcMmWK1duNJnPi3VpbW63eTtyYGwXnnEM3dtGJynKjEZ1ov8mTJ1u958+fH7vWjXZrb2+36p3Ys7ivr0OcqEv3unJeg9x4SYe7bidOz40BdF4LP/roI6u3s/fONev+tyQX8QQQAAAgMDk7AG7ZskXXXXedpkyZoilTpmjJkiX6xS9+kfl6FEVav369KisrlZ+fr2XLlgUfDA0AAMKQswPgzJkz9cQTT2j37t3avXu3li9frrvvvjsz5D355JPauHGjNm/erF27dqm8vFx33HGH/a0gAACAsSZnB8C77rpL3/jGNzR37lzNnTtXf/EXf6GioiLt3LlTURRp06ZNeuyxx7RixQrNnz9fzzzzjHp7e/Xcc89le+kAAACJytkB8LNOnz6trVu3qqenR0uWLFFjY6NaWlpUU1OTqUmlUrrlllu0Y8eOC/bp7+9XZ2fnsA8AAICxJqcHwH379qmoqEipVEoPPPCAXnzxRV1zzTVqaWmRdO5viJaVlWW+dj51dXVKp9OZj1mzZiW6fgAAgCTk9AB41VVXae/evdq5c6d+8IMfaOXKlXrvvfcyXz/77RmiKLroWzasW7dOHR0dmY+mpqbE1g4AAJCUnH4fwEmTJunKK6+UJC1cuFC7du3ST3/6U/3xH/+xJKmlpWXY+0W1trZe9H3jUqmUUqlUsosGAABIWE4/ATxbFEXq7+9XdXW1ysvLVV9fn/nawMCAGhoatHTp0iyuEAAAIHk5+wTw0UcfVW1trWbNmqWuri5t3bpV27Zt02uvvaa8vDytWbNGGzZs0Jw5czRnzhxt2LBBBQUFuu+++7K9dAAAgETl7AB49OhRfe9731Nzc7PS6bSuu+46vfbaa7rjjjskSY888ohOnjypBx98UCdOnNCiRYv0xhtvqLi4OPa/Yyjmx4nkceJn3Cg4Zx1u71OnTsWudaPgnIgiN77HOU5nHVJy+y55EVJJRlm59e515fR21+2cFzeyyzlONwrO6Z3kfexeV0m+XrmSPM4kI8SctTjHKHnXods7yddCh3MfD60jybi+L7u8KOSj/4IOHz7MbwIDADBGNTU1aebMmdleRlYwAH4BZ86c0ZEjR1RcXDzs/646Ozs1a9YsNTU1acqUKVlcYXJCOEaJ48wlIRyjxHHmkhCOUcrOcUZRpK6uLlVWVtrftcoVOfst4NEwbty4i/6fw1AOcS4L4RgljjOXhHCMEseZS0I4Rmn0jzOdTo/av+vLKMyxFwAAIGAMgAAAAIFhAExAKpXS448/ntNvGh3CMUocZy4J4RgljjOXhHCMUjjH+WXDL4EAAAAEhieAAAAAgWEABAAACAwDIAAAQGAYAAEAAALDADjCnnrqKVVXV2vy5Mm64YYb9B//8R/ZXtKIWr9+vfLy8oZ9lJeXZ3tZX9j27dt11113qbKyUnl5eXrppZeGfT2KIq1fv16VlZXKz8/XsmXLtH///uws9jf0ece4atWqc/Z28eLF2VnsF1BXV6cbb7xRxcXFKi0t1T333KMPPvhgWM1Y3884x5gL+7llyxZdd911mTcIXrJkiX7xi19kvj7W93HI5x1nLuzl2erq6pSXl6c1a9ZkPpcr+zlWMACOoOeff15r1qzRY489pnfeeUdf//rXVVtbq08++STbSxtR8+bNU3Nzc+Zj37592V7SF9bT06MFCxZo8+bN5/36k08+qY0bN2rz5s3atWuXysvLdccdd6irq2uUV/qb+7xjlKQ777xz2N6++uqro7jCkdHQ0KDVq1dr586dqq+v1+DgoGpqatTT05OpGev7GecYpbG/nzNnztQTTzyh3bt3a/fu3Vq+fLnuvvvuzFAw1vdxyOcdpzT29/Kzdu3apZ/97Ge67rrrhn0+V/ZzzIgwYn77t387euCBB4Z97uqrr47+5E/+JEsrGnmPP/54tGDBgmwvI1GSohdffDHz5zNnzkTl5eXRE088kflcX19flE6no7/927/Nwgq/uLOPMYqiaOXKldHdd9+dlfUkqbW1NZIUNTQ0RFGUm/t59jFGUe7u59SpU6N/+Id/yMl9/Kyh44yi3NrLrq6uaM6cOVF9fX10yy23RD/84Q+jKMrN+/LLjieAI2RgYEB79uxRTU3NsM/X1NRox44dWVpVMg4cOKDKykpVV1frO9/5jg4ePJjtJSWqsbFRLS0tw/Y2lUrplltuybm93bZtm0pLSzV37lzdf//9am1tzfaSvrCOjg5JUklJiaTc3M+zj3FILu3n6dOntXXrVvX09GjJkiU5uY/Succ5JFf2cvXq1fqd3/kd3X777cM+n6v7+WU2IdsLyBXHjh3T6dOnVVZWNuzzZWVlamlpydKqRt6iRYv07LPPau7cuTp69Kj+/M//XEuXLtX+/fs1bdq0bC8vEUP7d769PXToUDaWlIja2lp9+9vfVlVVlRobG/WTn/xEy5cv1549e8bsO/RHUaS1a9fqpptu0vz58yXl3n6e7xil3NnPffv2acmSJerr61NRUZFefPFFXXPNNZmhIFf28ULHKeXOXm7dulX//d//rV27dp3ztVy7L8cCBsARlpeXN+zPURSd87mxrLa2NvPP1157rZYsWaIrrrhCzzzzjNauXZvFlSUv1/f23nvvzfzz/PnztXDhQlVVVemVV17RihUrsriy39xDDz2kd999V2+//fY5X8uV/bzQMebKfl511VXau3ev2tvb9a//+q9auXKlGhoaMl/PlX280HFec801ObGXTU1N+uEPf6g33nhDkydPvmBdruznWMC3gEfI9OnTNX78+HOe9rW2tp7zfzS5pLCwUNdee60OHDiQ7aUkZui3nEPb24qKClVVVY3ZvX344Yf18ssv66233tLMmTMzn8+l/bzQMZ7PWN3PSZMm6corr9TChQtVV1enBQsW6Kc//WlO7aN04eM8n7G4l3v27FFra6tuuOEGTZgwQRMmTFBDQ4P++q//WhMmTMjsWa7s51jAADhCJk2apBtuuEH19fXDPl9fX6+lS5dmaVXJ6+/v1/vvv6+KiopsLyUx1dXVKi8vH7a3AwMDamhoyOm9bWtrU1NT05jb2yiK9NBDD+mFF17Qm2++qerq6mFfz4X9/LxjPJ+xup9ni6JI/f39ObGPFzN0nOczFvfytttu0759+7R3797Mx8KFC/X7v//72rt3ry6//PKc3s8vpSz98klO2rp1azRx4sToH//xH6P33nsvWrNmTVRYWBh9/PHH2V7aiPnRj34Ubdu2LTp48GC0c+fO6Hd/93ej4uLiMX+MXV1d0TvvvBO98847kaRo48aN0TvvvBMdOnQoiqIoeuKJJ6J0Oh298MIL0b59+6Lvfve7UUVFRdTZ2Znllcd3sWPs6uqKfvSjH0U7duyIGhsbo7feeitasmRJdOmll46pY4yiKPrBD34QpdPpaNu2bVFzc3Pmo7e3N1Mz1vfz844xV/Zz3bp10fbt26PGxsbo3XffjR599NFo3Lhx0RtvvBFF0djfxyEXO85c2cvz+exvAUdR7uznWMEAOML+5m/+JqqqqoomTZoUXX/99cPeliEX3HvvvVFFRUU0ceLEqLKyMlqxYkW0f//+bC/rC3vrrbciSed8rFy5MoqiX79FweOPPx6Vl5dHqVQquvnmm6N9+/Zld9Gmix1jb29vVFNTE82YMSOaOHFiNHv27GjlypXRJ598ku1l2853jJKip59+OlMz1vfz844xV/bzD/7gDzKvpzNmzIhuu+22zPAXRWN/H4dc7DhzZS/P5+wBMFf2c6zIi6IoGr3njQAAAMg2fgYQAAAgMAyAAAAAgWEABAAACAwDIAAAQGAYAAEAAALDAAgAABAYBkAAAIDAMAACAAAEhgEQAAAgMAyAAAAAgWEABAAACAwDIAAAQGAYAAEAAALDAAgAABAYBkAAAIDAMAACAAAEhgEQAAAgMAyAAAAAgWEABAAACAwDIAAAQGAYAAEAAALDAAgAABAYBkAAAIDAMAACAAAEhgEQAAAgMAyAAAAAgWEABAAACAwDIAAAQGAYAAEAAALDAAgAABAYBkAAAIDAMAACAAAEhgEQAAAgMAyAAAAAgWEABAAACAwDIAAAQGAYAAEAAALz/wAStUiID1BGjgAAAABJRU5ErkJggg==",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate an index (many are available see plantcv.spectral_index)\n",
    "# Or you can calculate your own manually by using plantcv.hyperspectral.extract_wavelength\n",
    "ndvi_index  = pcv.spectral_index.ndvi(hsi=calibrated_data, distance=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7190c1b4-6fd9-4956-aa6a-f6250d7560f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "40528859f43e493b9eb197a933513558",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the Carotenoid Index from the datacube \n",
    "\n",
    "index_array_car = pcv.spectral_index.psri(hsi=calibrated_data, distance=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "745ded87-0b48-49f5-9e2c-d4480478c6f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  #altair-viz-982647730e4842868edb71ab62d6ad43.vega-embed {\n",
       "    width: 100%;\n",
       "    display: flex;\n",
       "  }\n",
       "\n",
       "  #altair-viz-982647730e4842868edb71ab62d6ad43.vega-embed details,\n",
       "  #altair-viz-982647730e4842868edb71ab62d6ad43.vega-embed details summary {\n",
       "    position: relative;\n",
       "  }\n",
       "</style>\n",
       "<div id=\"altair-viz-982647730e4842868edb71ab62d6ad43\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-982647730e4842868edb71ab62d6ad43\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-982647730e4842868edb71ab62d6ad43\");\n",
       "    }\n",
       "\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      let deps = [\"vega-embed\"];\n",
       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300, \"stroke\": null}, \"axis\": {\"grid\": false}, \"facet\": {\"columns\": 1, \"spacing\": 0}, \"title\": {\"anchor\": \"end\"}}, \"data\": {\"name\": \"data-4b1b259f6a2cb63589442e604ac45339\"}, \"facet\": {\"field\": \"sample\", \"header\": {\"labelAngle\": 0, \"labelAlign\": \"left\", \"labelOrient\": \"left\"}, \"title\": null, \"type\": \"nominal\"}, \"spec\": {\"mark\": {\"type\": \"area\", \"fillOpacity\": 0.8, \"interpolate\": \"monotone\", \"stroke\": \"lightgray\", \"strokeWidth\": 0.5}, \"encoding\": {\"x\": {\"field\": \"label\", \"title\": \"Bin labels\", \"type\": \"quantitative\"}, \"y\": {\"axis\": null, \"field\": \"value\", \"scale\": {\"range\": [10, -100]}, \"type\": \"quantitative\"}}, \"height\": 10, \"width\": 500}, \"bounds\": \"flush\", \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.20.1.json\", \"datasets\": {\"data-4b1b259f6a2cb63589442e604ac45339\": 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0.9299999999999999}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 0.95}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 0.97}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 0.99}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.FacetChart(...)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph the histogram of the spectral index\n",
    "\n",
    "ndvi_output=pcv.analyze.spectral_index(index_img=ndvi_index, labeled_mask=corn_thresh,\n",
    "                           min_bin=-1, max_bin=1, label=\"kernel\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3bc307da-72cd-4787-8df6-b58f84a66bde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3da688335154474f9778ddc7ba9f126a",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pseudocolor the image as a visualization\n",
    "\n",
    "ndvi_img = pcv.visualize.pseudocolor(gray_img = ndvi_index.array_data, mask=corn_thresh, cmap='jet', \n",
    "                                           min_value=-0.25, max_value=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f75e6bc2-7329-4f27-beed-d159350f67c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cda56b93b5be49af8873cc58fb872999",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
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       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate an index (many are available see plantcv.spectral_index)\n",
    "# Or you can calculate your own manually by using plantcv.hyperspectral.extract_wavelength\n",
    "ari_index  = pcv.spectral_index.ari(hsi=calibrated_data, distance=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e194b941-bf6b-43b2-9e32-85573f85947d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "  #altair-viz-55aa9b522392460b9c49fc14067b25de.vega-embed {\n",
       "    width: 100%;\n",
       "    display: flex;\n",
       "  }\n",
       "\n",
       "  #altair-viz-55aa9b522392460b9c49fc14067b25de.vega-embed details,\n",
       "  #altair-viz-55aa9b522392460b9c49fc14067b25de.vega-embed details summary {\n",
       "    position: relative;\n",
       "  }\n",
       "</style>\n",
       "<div id=\"altair-viz-55aa9b522392460b9c49fc14067b25de\"></div>\n",
       "<script type=\"text/javascript\">\n",
       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
       "  (function(spec, embedOpt){\n",
       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-55aa9b522392460b9c49fc14067b25de\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-55aa9b522392460b9c49fc14067b25de\");\n",
       "    }\n",
       "\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm/vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm/vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm/vega-lite@5.20.1?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm/vega-embed@6?noext\",\n",
       "    };\n",
       "\n",
       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      let deps = [\"vega-embed\"];\n",
       "      require(deps, displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"5.20.1\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 300, \"continuousHeight\": 300, \"stroke\": null}, \"axis\": {\"grid\": false}, \"facet\": {\"columns\": 1, \"spacing\": 0}, \"title\": {\"anchor\": \"end\"}}, \"data\": {\"name\": \"data-b7169b1d396acd6a575f30affa9bf686\"}, \"facet\": {\"field\": \"sample\", \"header\": {\"labelAngle\": 0, \"labelAlign\": \"left\", \"labelOrient\": \"left\"}, \"title\": null, \"type\": \"nominal\"}, \"spec\": {\"mark\": {\"type\": \"area\", \"fillOpacity\": 0.8, \"interpolate\": \"monotone\", \"stroke\": \"lightgray\", \"strokeWidth\": 0.5}, \"encoding\": {\"x\": {\"field\": \"label\", \"title\": \"Bin labels\", \"type\": \"quantitative\"}, \"y\": {\"axis\": null, \"field\": \"value\", \"scale\": {\"range\": [10, -100]}, \"type\": \"quantitative\"}}, \"height\": 10, \"width\": 500}, \"bounds\": \"flush\", \"$schema\": \"https://vega.github.io/schema/vega-lite/v5.20.1.json\", \"datasets\": {\"data-b7169b1d396acd6a575f30affa9bf686\": [{\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": -4.95}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": -4.85}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": -4.75}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": -4.65}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": -4.55}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": -4.45}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": -4.35}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": -4.25}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": -4.15}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": -4.05}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": -3.95}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": -3.8499999999999996}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": -3.75}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": -3.65}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": -3.55}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 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{\"sample\": \"kernel_1\", \"value\": 17.786069651741293, \"label\": 0.6500000000000004}, {\"sample\": \"kernel_1\", \"value\": 13.432835820895523, \"label\": 0.7500000000000004}, {\"sample\": \"kernel_1\", \"value\": 8.955223880597014, \"label\": 0.8500000000000005}, {\"sample\": \"kernel_1\", \"value\": 6.965174129353234, \"label\": 0.9500000000000002}, {\"sample\": \"kernel_1\", \"value\": 5.970149253731343, \"label\": 1.0500000000000003}, {\"sample\": \"kernel_1\", \"value\": 2.736318407960199, \"label\": 1.1500000000000004}, {\"sample\": \"kernel_1\", \"value\": 3.109452736318408, \"label\": 1.2500000000000004}, {\"sample\": \"kernel_1\", \"value\": 1.7412935323383085, \"label\": 1.3500000000000005}, {\"sample\": \"kernel_1\", \"value\": 1.4925373134328357, \"label\": 1.4500000000000002}, {\"sample\": \"kernel_1\", \"value\": 1.2437810945273633, \"label\": 1.5500000000000003}, {\"sample\": \"kernel_1\", \"value\": 0.8706467661691543, \"label\": 1.6500000000000004}, {\"sample\": 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\"value\": 0.4975124378109453, \"label\": 2.8500000000000005}, {\"sample\": \"kernel_1\", \"value\": 0.12437810945273632, \"label\": 2.95}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 3.05}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 3.1500000000000004}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 3.250000000000001}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 3.3500000000000005}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 3.45}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 3.55}, {\"sample\": \"kernel_1\", \"value\": 0.12437810945273632, \"label\": 3.6500000000000004}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 3.750000000000001}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 3.8500000000000005}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 3.95}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 4.05}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 4.15}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 4.250000000000001}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 4.3500000000000005}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 4.45}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 4.550000000000001}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 4.650000000000001}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 4.750000000000001}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 4.8500000000000005}, {\"sample\": \"kernel_1\", \"value\": 0.0, \"label\": 4.95}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.FacetChart(...)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Graph the histogram of the spectral index\n",
    "\n",
    "ari_output=pcv.analyze.spectral_index(index_img=ari_index, labeled_mask=corn_thresh,\n",
    "                           min_bin=-5, max_bin=5, label=\"kernel\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3b15ffe1-60a5-44d3-a55f-8cca78a577b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "18785ce707a4408ab4844d0eb6ab6757",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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VDSbrIiL2xqp07e5Yl65ttZ5I1R07lmsZ17VWcAJgZQ4BAFBP7gNYmQAIANSTGcDKLAIBAGgYGRgAqKdWVEsyTgELgABATTkFXJlDAADUk0UglbkGEACgYcwAAgD15BRwZQ4BAFBPAmBlDsEc2L17d/T3989Y19ubO9xTU1Ppbe/atStdu2BB7oz/2Fj+LvRvfOMb07XDw8Pp2rVr16bqDh06lB7z7rvvTtdmx735nN9Ljxn/IV+avbn/9lVnpYc8WtCJo6RrR1ZJJ4qS7fcnuzaUdPeYiJk/z6W1JR0zxgq6hmS7S7SinR6zW11D2slj0Jvs7hIRMVHwuma7xpQo2dfse7CkE0jJ898cT6VrFy5cmKo7ceJEqm56ejq97SILotr1fIUXwG3bti2+9rWvxaOPPhqDg4Px1re+NX7nd34nXv/611fY+OnBNYAAQD31zuKnwD/+4z/G9ddfH/fdd1/ccccd0W6346qrrkq3wjsdmQEEAHgNf/d3f3fSf3/5y1+OVatWxQMPPBBvf/vb52mvZkcABADqaZ6uATx8+HBERCxfvnx2A80jARAAqKdZ3gdwdHT0pF8PDAzEwMBrX1c5PT0dn/rUp+KKK66ILVu2VNj46cE1gABAPc3yGsANGzbEyMjISz/btm2bcZM33HBD/OAHP4g//dM/nfOncyqZAQQA6mmWvYB37tx50h0qZpr9+8QnPhF//dd/Hd/5zndi/fr1FTZ8+hAAAYBGGh4eTt2ibHp6Oj7xiU/EX/zFX8Sdd94ZmzZtOgV7110CIABQT6doEcj1118ff/InfxJ/9Vd/FUNDQ7Fnz56IiBgZGYnBwcEKOzD/XAMIANRTaxY/BW655ZY4fPhwXHnllbF27dqXfr761a/O1TM55cwAAgD1dIpmALvWyWQeCYBzYGxsLCYnJ2es63RyLYNarfw/TbJteEpqS1rRrVmzJl07NJRv7/Xss8+m6iYmJtJj3vxTBW3bLk3WFdwC6sCqXGuliIhnInd9SUl7sUOxNF3bjVZUJW24Sp5XSdu2rG7ta1a2vV1Evr1at5Qcq2xtyTHtRnu3klZ8Ja9V9nkdL9j+0jiUri35DnjXQ/8xVfffz/lSqq7k7wqnhgAIANTTLFcBN5kACADU0yxvBN1kAiAAUE/z1AruTOAQAAD1JABW5jYwAAANIwMDAPVkBrAyhwAAqKXpBRHTFRZ0TDv/KQACAPXU6X3hp8rjmk4GBgBoGBkYAKglM4DVOQSn0ObNm1N1Dz/8cHrMjRs3pmvb7Xaqrq+vLz1mSX/E7du3p2tvfleybdu56SFjcm2+9viS3DE42FqaHnN/rEzXHolc27zjMZges6RlWEkrrIFkK6yS7Q9EvsXfeLIVXG8XWoaV6FbLtm48r06N/jRk338REePJtoUlSo7VomSLxZL2dssKWsGVPP9sO8je3tzz71YruHarJ9qtngqPm46IM6+/b4n6fMoBAP6dTm9vdHrLA2CndzoiJud+h2pEAAQAaqnTakWnwgxgpyUAWgQCANAwZgABgFqailZ0onwGcKrh1/9FCIAAQE21oxXtCgGwLQAKgABAPXWiFZ0KV7N1ojurkutEAAQAaql6ACyfNTzTWAQCANAwZgABgFoyA1idADgHzjrrrBgYmPkO6/fee29qvCuvvDK97Yceeihd+/rXvz5Vt2bNmvSYjz/+eLr2C2+9JV0bF+bKjq3Lf/CfGdiYrs124ijpmDFW0LUj212gFbnuLhERE13oglCipGNFyXHNHoNudeIo6drQDf3Jrikl77+S91U3dLr0WpV0DemG7Hsw+5pG5L+rIsreq2+IXEeqLx45kqrrVicQAbA6ARAAqKVOtKItAFbiGkAAgIYxAwgA1FInet0GpiIBEACopU4sqHTN6PxeuXt6EAABgFp6YRGIAFiFAAgA1NILreDKA+D8rnM/PVgEAgDQMGYAAYBamoreSqeAp9wGRgAEAOrJNYDVCYAAQC0JgNUJgHNg5cqVsXDhwhnrBgdzrZgefPDB9LZXrFiRrh0eHk7V7d27Nz3mF/6vgvZui/Ol29edlarbGRvSY2bbq0VEHIklcz7mRPSna7OtoErau5V8SZa0jMq2bSsZs6QVWbbFXMmF4t2q7YZsi7eBgvZiJa34SmRbsZV8Vkpk34Pdau+XfVYlLevGC74DSsY9FEtTdQsWWEpQVwIgAFBL1e8DON2FvakXARAAqKXqt4ERAAVAAKCWXmgFVx5lXAMoAAIANTVVcRHIlBlAN4IGAGgaM4AAQC1Vvw2MGUABEACopXYsqLgIZKoLe1MvAiAAUEvVF4GYARQAAYBaqn4K2AygRSAAAA1jBnAOPPbYY9HfP3OTnwMHDqTGO378eHrbr3vd69K1U1O5f/Fsm/yv6TGfOH99unZ/5NvWZdsblfzL70gMdaW2G+a7vViJbNu6brR3i8i/V0raYHXj+M9328BOwTEtOVbzraRt26IYm9ftDya3f7ygFd/KeD5dW+Jf46Jk5R1d2X6WGcDqBEAAoJY6FTuBCIACIABQUxaBVOcaQACAhjEDCADUUicWVLwGUDdgARAAqKXqi0Dqs9CuWwRAAKCWBMDqBEAAoJaqrwIWAC0CAQBoGDOAAEAtVb8NjPsACoAAQC25BrA6AXAOHD16NPr6+masW7duXWq87du3p7fd29uFl/DafOmhWNqV2mx7r7EYTI85kRwzoqy9Uzdk97VkP0tasZXoxhdpSSu4brT3yr7/IvLHteT4D3ahbV7Je6XkNe3GuEvjUHrM8YK2eVmDkW/HWSLb4m1F7E+PWXL921NxXrp2YzyTqlu2bFmqrtPpxHPPPZfeflb128C4As4RAABI+MIXvhCbNm2KhQsXxtatW+Ouu+6a712qTAAEAGqp/eNVwFV+Sn31q1+NT37yk/Ebv/Eb8b3vfS/e9ra3xTXXXBM7duzowjPrPgEQAKilFxeBVPkpdfPNN8eHP/zh+MhHPhIXXnhhfP7zn48NGzbELbfc0oVn1n2uAQQAammq4iKQqR8/ZnR09KTfDwwMxMDAy68BnpiYiAceeCA+/elPn/T7q666Ku65557i7Z8OzAACALX04irgKj8RERs2bIiRkZGXfrZt2/aK23n++eej0+nE6tWrT/r96tWrY8+ePV1/nt1gBhAAaKSdO3fG8PDwS//9SrN//15PT89J/z09Pf2y39WFAAgA1NJsbwMzPDx8UgB8NStXroxWq/Wy2b59+/a9bFawLpwCBgBq6VStAu7v74+tW7fGHXfccdLv77jjjnjrW986l0/plDEDCADUUvVWcOWP+dSnPhUf/OAH49JLL43LL788br311tixY0d87GMfKx7rdCAAzoELLrhgxusGIiIeeeSR1Hj9/XN/Z/uIiP/yx/93qm7yE/kxx5J3tu9W7Xx3LJhvEwVdELrzrsrrVneV7GtV5b5fc6nkD85AjKdrs11LSsbsj4l0bTc+gyVjDsXRdG3281JyrLrxvi7pGpPtLhJRdlz/22UPpuqy3T2mpurfe/d973tf7N+/P377t387du/eHVu2bImvf/3rcc4558z3rlUiAAIAtTTb28CU+vjHPx4f//jHKz32dCMAAgC11KkYAOtylqebBEAAoJba0YoFFcLcfF8ScjoQAAGAWnphBrDKIhAB0G1gAAAaxgwgAFBLrgGsTgAEAGpJAKxOAAQAaulU3wbmTCIAAgC11I5W9FgFXIlFIAAADWMGcA7cc8890ds786EcHR1Njbdq1ar0tpctW5aujXfmyp4ZWZ8ecn+sSNceiaF0bVbJ1H+drvnI7mu32qtNJNuLvSDbNqs7zeiybbN6u3SsskrefyWzEyVtw7LGYjBdO1DQNm4wxlJ1Jbf1KGsbd2RO60r1Jz8rS+NQesySz+rNlz+frh0by71W5513Xqqu3W7HAw88kN5+VidascBtYCoRAAGAWupUvBG0ACgAAgA1JQBW5xpAAICGMQMIANSSVcDVCYAAQC1NRW+lXsBT4o8jAADUU6fiDKBrAAVAAKCmOrGgYgC0BMIRAABoGDOAAEAtvbCYwyKQKgRAAKCWOtEbPZU6gYg/jsAcuOSSS2JgYOZ2PE8++WRqvPPPPz+97d9q/3a6Nv6nXNneyLeiK7mQdqKgFVi2xVnJ9rvRNq1kzG5cdDzfxz8i3wpsvtv2lbSCK/njMJBs79WbbIMWUTY7kT3+Y7EoPeaign0t+wzkjmtJe7vs8S9RcvxL2sYdT74Gu2NdesxdBbVHj+5M12Zbwb3pTW9K1U1OTnalFdxUtCp9X0yZAXQNIABA05gBBABqqVPxGkC3gREAAYCaEgCrEwABgFpqx4KYdh/ASgRAAKCWXlhYZBVwFSIwAEDDiMAAQC25BrA6ARAAqKWpigHQfQAFQACgptrRigUCYCUC4BwYGxuLdnvmO9evWpXrsNHbW/CybMmXZm8YPxEzdzV50XhBbX+yY8EL+5DrWtGtfo6DcTxVV9JdY7ygtqRrRVZJx4b57nAy30o6UWSVdOLoL+hukf1clXz+SpQcq/l+X2eP68rYnx6z5HVdkRy35LW64dL834vJycl07TnnnJOqW79+fapufHzuO7ZEvPD9M10hygiAFoEAADSOGUAAoJZemAF0CrgKARAAqCUBsDoBEACopc5UK6anKgTACo8507gGEACgYcwAAgC11Gm3YqpdPps3XeExZxoBEACopU67N3ra5VFmusJjzjSOAABQS532guipNAPoCjgBEACopU67VTEAOgUsAgMANIwZwDkwMZFr27N8+fJU3bJly/IbX50vPfaWXN4vaW82UNCy6lAsTddmWyGNx1B6zG60oSoxUNDeKdterawVXv51zbbCi8i34ipphVfS3iv7fi1pWdbpwlfjUBwp2H7+dc3WlrUCzB+rkvd1yT7kt5//Dsp+Xko+V2Xvq9y4//ldr0+POT19MF171llnpWvPPvvsOR3zxIkT6W2XaLdb0TNpBrAKARAAqKXpTm9MdypEmSqPOcM4AgBAPbVbL/xUeVzDuQYQAKBhzAACAPVkBrAyARAAqKdOT0S7p9rjGk4ABADqqf3jnyqPazgBEACoJwGwMotAAAAaxgwgAFBPZgArEwABgHpqR8Rkxcc1nAA4Bw4fPhx9fX0z1m3cuDE13sGD+dY+sS5fOjawKFcXubqIfMu2iIglBa2wJmIgVVfShqlESSuurJI2WNntl7QsG4qjc779iHyLt261DOvGsSp5/t14D5Y8//6CVmjzLXusSt4rJd9X2bZx3Wob+ZFrfjpV127nWzGWtHe76KKL0rVHjuS+r8fGxlJ13WoFF50f/1R5XMO5BhAAoGEEQACgntqz+OmCZ555Jj784Q/Hpk2bYnBwMDZv3hyf+cxnYmIiP6t9qjgFDADU02m2COTRRx+Nqamp+OIXvxjnnXdePPLII3HdddfFsWPH4qabburORisSAAGAejrNAuDVV18dV1999Uv/fe6558Zjjz0Wt9xyiwAIADAnOlEtzJ3CRSCHDx+O5cuXn7oNJgmAAEAjjY6OnvTfAwMDMTCQuwtFxlNPPRW///u/H5/73OfmbMy5YhEIAFBPs1wEsmHDhhgZGXnpZ9u2ba+4mRtvvDF6enpe8+e73/3uSY/ZtWtXXH311fHe9743PvKRj8z9c58lM4AAQD3N8hrAnTt3xvDw8Eu/frXZvxtuuCHe//73v+aQ//5ev7t27Yp3vOMdcfnll8ett95aYQe7TwAEAOppMqp1AvnxY4aHh08KgK9m5cqVsXLlytTQzz77bLzjHe+IrVu3xpe//OVYsOD0PNkqAAIA9XSadQLZtWtXXHnllXH22WfHTTfdFM8999xL/9+aNWu6s9GKBMA5cPDgwejtnflQTk9Pp8ZbtmxZfuMFC4v2x4pUXUkbrJLasvZKuZZNYzFYMObc60bLtIiIRZFvBZV1vOBYlbTtyyp5/kMF2x9Mnv8paQVX0l4t27ZsMHItsyLyrRAj8p+rkraNJe3tSj7X2X0o2X7Ja5Vtsbfi2IH0mO99/7vTtQMDue+Lnp6e9JglrUN37dqVrr3hhhtSdbfddluqbny8Pi0LZ+P222+PJ598Mp588slYv379Sf9fNgOcKqfnvCQAwExevA1M6U+XZgCvvfbamJ6efsWf040ZQACgnk6zG0HXiQAIANSTAFiZU8AAAA1jBhAAqCczgJUJgABAPdWgF/DpSgAEAOrJDGBlAiAAUE+TEQW3Yz35cQ1nEQgAQMOYAZwDfX19qU4gWSVjja7oS9eOxaJUXfZu+RFl3R3mW8nzyhos6NhR0oljInlcS7ZfoqQTRfa4lnT3KJHtRFHSXSV7/CPyHT5KjmmJbnTXKNnXpbE3XXs0hlJ1S7rQiaXE+z/wnnTtvn2707Vr165N1WV6075o6dKl6dqBgfzr+od/+Iepuve8J3esxsbG4pZbbklvP+00awVXJwIgAFBPrgGsTAAEAOrJKuDKXAMIANAwZgABgHpqR7VVwE4BC4AAQE1NRrVzmW4DIwACADVlFXBlAiAAUE8WgVRmEQgAQMOYAQQA6qkd1aayLAIRAAGAmpqMiJ6Kj2s4AXAOvO51r4v+/plbR7VaubXqixblWraVyra3WhqH0mOOFbU3m/tWWN1oAxUR0al0X4HX1o1WdCUty0pasZXsa/ZYdeOYlihpWlj2/HNfo4uSLeMiItoFx6obx3VFPJ+uLflcr4j9qbrVBw6nx/xfPvQ/p2snJ3N/9UdG8t9r4+P5tnVPP/10qu69731veszDh/PH6qKLLkrXZv8OffOb30zVTUx057vaIpDqBEAAoJ6cAq7MIhAAgIYxAwgA1JPbwFQmAAIA9VR1MYdFIAIgAFBTnah2MZsZQNcAAgA0jRlAAKCe2lHtPoBWAQuAAEBNCYCVCYAAQD1VDXICoAAIANRUJ6rNAFoEIgDOhcOHD0dfX9+MdUNDQ3O+7eEn8mvZ116wK1W3L1ZX3Z3XNBRH07XHky3mxiLfNq+kbVq2FVhJe68S48n2WgORb0NV0t6s5LiW7ENWt45rVsmxaiWnEgYLntOhWJau7U+2+OsteE4ln9VF4/nn9b7/7ZpU3YkTJ9Jj9vfnvwPHxnL7mm0ZF1HWXu173/teqm716vx38BNPPJGuXbduXbr27LPPTtVl2qBGRExPT6e3zakhAAIA9eQUcGUCIABQTwJgZQIgAFBP7YiocnbZNYBuBA0A0DRmAAGAeqo6k2cGUAAEAGrKKeDKBEAAoJ4EwMoEQACgntoRMVXhcVUec4axCAQAoGHMAM6B5cuXp++GntFuF9yg6Ef50uMX5Lo7LI1D+UELlHRXyHaXaEcrPebxgu4WWdmOHaU6yedVsv1sx4rS2v6YSNWVdGJ5Plaka1fG/lRdfxc6lkREDCSff6fg6zbbCSciYmU8n6orea8MHct3AnnPL16Vrl2wIPcd0OnkvysWLMjPYyxcuDBVt3v37vSYW7ZsSde+/e1vT9X9wz/8Q3rMkZGRdO2SJUvStZ/73OdSdYsW5b5XS7qrFOlEtVPAZgAFQACgptpR7VymACgAAgA1JQBWJgACAPU0GQJgRRaBAAA0jBlAAKCepqLaIpAqjznDCIAAQD21I6KnwuMEQAEQAKgpAbAy1wACADSMGUAAoJ4mwwxgRQIgAFBPnRAAKxIA50C73U61I2q1cu29BgfzbaA+8Y0b0rU3/4c/SNV1Ct4VE4vz7b1KWlENxliqrjeWpscsaUU3lmwbl21ZV7r9ktr8mPn2bhMFr1X2GAzFkfSYJVbH3lRdSdvAZ2JTuva8eDJVt6RLzz/7ufov/3FjeszR0eXp2v7+fIuvY8eOpeqmpvI3aZuezv8lP+ecc1J12fZmEREHDx5M195wQ+77+oEHHkiPedFFF6Vrb7vttnTtRz/60VTdF7/4xVRd11rBRQhzFbkGEACgYQRAAICGEQABABpGAAQAmGPj4+NxySWXRE9PTzz00EPzvTsvIwACADU1OYuf7vrVX/3VWLduXde3U5UACAAwh77xjW/E7bffHjfddNN878qrchsYAKCm2j/+qfK4iNHR0ZN+OzAwEAMD+dtgvZK9e/fGddddF3/5l39ZdEuhU80MIABQU7M7Bbxhw4YYGRl56Wfbtm2z2pvp6em49tpr42Mf+1hceumlsxqr28wAAgA1NbsZwJ07d8bw8PBLv3212b8bb7wxfuu3fus1R7z//vvjnnvuidHR0fj1X//1Cvt0agmAAEBNtaPago4XAuDw8PBJAfDV3HDDDfH+97//NWs2btwYn/3sZ+O+++57WZC89NJL4wMf+ED80R/9UYV97Y6e6ZI+OpxkdHQ0RkZG4qMf/WjqmoFnn302Ne7WrVvT+7B3b64NVkTEvn37UnUnTpxIj9lu5//l1d+fbxv355/NtSx64oL16TH3xqp07b5YnapblGxZF9GdVmD7Y2W69kgMpWs3xtNVduc1HU+214uIWJVs71aipBXdzjg7Xfv2A/+cq3vPFekxS/T25v4df/To0fSYixcvTteWfAdlW7xdd9116TFHRkbStU8/nXtfr1qV/64o+b7+9re/narLvqYREU8+mWtFGBFxxRX59+AjjzySqsu+V8bHx+N3f/d34/Dhw6nANZMX//5GPBFR8N32PxyJiPPnbH9etGPHjpOuK9y1a1f83M/9XPz5n/95vPnNb4716/N/s7rNDCAAUFNVb+nSndvAnH32yf94XLJkSUREbN68+bQKfxECIABQW7O7BrDJBEAAoKZmdw1gt23cuDFO1yvt3AYGAKBhzAACADXlFHBVAiAAUFOn1yKQOhEAAYCaMgNYlQAIANTU6b0I5HRmEQgAQMOYAQQAasop4KoEwDlw5MiRGB8fn7HuwIEDqfF++MMfFm07K9syqNVqpcd85zvfma4dGsq36/nUrblWXDt27EiPmW1DFRHxha/9Q6ruobgkPWZJK7KsiZi5BWEVK2N/urYVnVTddVduTo/Z0/O6/PaT79eSVoQl7RBbrXel6sbGcp//iDipldRMsp/rRYvyrfiuueaadO26devStX19fam6Cy64ID3mu9/97nTtTL1cX1TyXfWjH/0oXfu1r30tVXfeeeelx+x0cp+/iIjt27ena5cvX56q+/73v5+qm5zs1qILi0CqEgABgJoyA1iVAAgA1JRFIFVZBAIA0DBmAAGAmnIKuCoBEACoKYtAqhIAAYCaEgCrcg0gAEDDmAEEAGrKNYBVCYAAQE25DUxVAuAceMtb3hKDg4Mz1h0+fDg13u7du9Pb/smf/Ml0bba7wapVq9Jjfvvb307X/tIv/VK6trc399bcs2dPeswFC/JXPHz8F3MdTo4ePZoec2Dg9enaY8eOpepKugC02/l9XbZsS7q2p6cnVdfpHEqP2W7nv5yz3XBKujuUfAYzXYAiItavX58e8+KLL07X/vEf/3Gq7ld+5VfSYy5cuDBdOzw8nK69//77U3V33313esxsd42IiN/8zd9M1f3TP/1TeszHHnssXXv11Ven6hYvXpwes6Qb0tKlS9O12eeV/btW8pkuYwawKgEQAKipyagWZSwCsQgEAKBhzAACADXlFHBVAiAAUFMWgVQlAAIANWUGsCrXAAIANIwZQACgpiYjolXxcc0mAAIANXUsqp3Ozd2/80wmAAIAtdLf3x9r1qyJPXt+r/IYa9asif7+/jncq3oRAGdheno6IiKOHz+eqp+czE05l9wxPduFoGTciYmJ9JglnSiynUgi8s8re0wjyjqBdOO1Ktl+dtyyTiD5fS05rtlOICXbL6nNHoOSMaempua8tuS1Kjn+2a4x3Xr9u/F9UfK9VrL9bOee7Hd6RNm+Zt8rrVb+lGbJ8y/5Ds6Om31fvVj34t/N2Vq4cGE8/fTTRc///6+/v7+o682Zpmd6rl6NBvq3f/u32LBhw3zvBgDUws6dO4vaItI9AuAsTE1Nxa5du2JoaCg9CwIATTM9PR1HjhyJdevWFZ0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      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ari_img = pcv.visualize.pseudocolor(gray_img = ari_index.array_data, mask=corn_thresh, cmap='jet', \n",
    "                                           min_value=-5, max_value=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4c72b159-f24b-4376-bc20-4ff44ee47a86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2c8690e0e8ba4bce86db91bff5cce60a",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": 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",
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       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img 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' width=640.0/>\n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "804.0\n"
     ]
    }
   ],
   "source": [
    "#Do shape analysis (the output image will look a little strange because the image is so small)\n",
    "\n",
    "shape_image = pcv.analyze.size(img=calibrated_data.pseudo_rgb, labeled_mask=corn_thresh, n_labels=1, label=\"kernel\")\n",
    "\n",
    "#print out results just to see (this wouldn't be included in a workflow run in parallel)\n",
    "area = pcv.outputs.observations['kernel_1']['area']['value']\n",
    "print(area)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de850fee-7f4f-40ba-8345-af6eae6ea288",
   "metadata": {},
   "source": [
    "## Save the results\n",
    "\n",
    "During `analyze` functions ([`pcv.analyze.size`](https://plantcv.readthedocs.io/en/latest/analyze_size/), [`pcv.analyze.spectral_index`](https://plantcv.readthedocs.io/en/latest/analyze_spectral_index/), and [`pcv.analyze.spectral_reflectance`](https://plantcv.readthedocs.io/en/latest/analyze_spectral_reflectance/))  measurements are stored in the background in the `outputs` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "99bd3d40-f1d8-4ce9-9a05-754a9a3f836d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save out the results\n",
    "\n",
    "pcv.outputs.save_results(filename=args.result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e14d2eb9-e1a4-42a6-a691-454610a94292",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}