{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second Moment About the Mean"
   ]
  },
  {
   "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": "f99f5d1f-8451-4975-84ca-73d7e4e9433c",
       "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 [Second Moment About the Mean](https://en.wikipedia.org/wiki/Central_moment#Univariate_moments) of any [`Iterable`](https://docs.oracle.com/javase/8/docs/api/java/lang/Iterable.html), using the [`mean`](mean.ipynb) and [`size`](size.ipynb) `Op`s. The Second Moment About the Mean is equal to the [Variance](https://en.wikipedia.org/wiki/Variance) of the distribution."
   ]
  },
  {
   "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.cos(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 `moment2AboutMean()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4002.4342529744367"
      ]
     },
     "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().moment2AboutMean(output, sinusoid32)\n",
    "\n",
    "output"
   ]
  }
 ],
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   "display_name": "Groovy",
   "language": "groovy",
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   "name": "Groovy",
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   "version": "2.4.3"
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  "toc": {
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  }
 },
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