notebooks/1-Using-ImageJ/Ops/stats/kurtosis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kurtosis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added new repo: scijava.public\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
      "method": "display_data"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4dda0ebf-8f18-412d-89ee-6156be8fdf7d",
       "version_major": 2,
       "version_minor": 0
      },
      "method": "display_data"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "net.imagej.ImageJ@465e45d7"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "//load ImageJ\n",
    "%classpath config resolver scijava.public https://maven.scijava.org/content/groups/public\n",
    "%classpath add mvn net.imagej imagej 2.0.0-rc-67\n",
    "\n",
    "//create ImageJ object\n",
    "ij = new net.imagej.ImageJ()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This `Op` calculates the [Kurtosis](https://en.wikipedia.org/wiki/Kurtosis) of any [`Iterable`](https://docs.oracle.com/javase/8/docs/api/java/lang/Iterable.html) via the [Standard Deviation](stdDev.ipynb) and [Moment 4 About the Mean](moment4AboutMean.ipynb) `Op`s. The value of kurtosis is related to the \"fatness\" of the tails of the distribution of a list of data; the larger the kurtosis, the more frequent and more extreme outliers are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sinusoid32 = ij.op().run(\"create.img\", [150, 100])\n",
    "formula = \"63 * (Math.cos(0.3*p[0]) + Math.sin(0.3*p[1])) + 127\"\n",
    "ij.op().image().equation(sinusoid32, formula)\n",
    "\n",
    "ij.notebook().display(sinusoid32)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All `Img`s are `Iterable`s, so we can just pass through the `Img` to `kurtosis()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.250389182944452"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import net.imglib2.type.numeric.real.DoubleType\n",
    "\n",
    "output = new DoubleType()\n",
    "\n",
    "ij.op().stats().kurtosis(output, sinusoid32)\n",
    "\n",
    "output"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Groovy",
   "language": "groovy",
   "name": "groovy"
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   "name": "Groovy",
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   "version": "2.4.3"
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  "toc": {
   "base_numbering": 1,
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   "toc_window_display": false
  }
 },
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