{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No Outputs"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#!groovy\n",
    "// @ImageJ ij\n",
    "// @NotebookService nb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "geometricMean = 106.86402515788897\n",
      "harmonicMean = 60.81451530914875\n",
      "kurtosis = 2.250389182944452\n",
      "max = 252.9996057999923\n",
      "mean = 130.35596075261444\n",
      "median = 129.34019425677636\n",
      "min = 1.293813403205192\n",
      "moment1AboutMean = -2.877214910768089E-13\n",
      "moment2AboutMean = 3982.1563522625042\n",
      "moment3AboutMean = -10221.960619118927\n",
      "moment4AboutMean = 3.569046079615442E7\n",
      "size = 15000.0\n",
      "skewness = -0.04067366532821499\n",
      "stdDev = 63.106432691542885\n",
      "sum = 1955339.4112892165\n",
      "sumOfInverses = 246.65164103911627\n",
      "sumOfLogs = 70073.35850104403\n",
      "sumOfSquares = 3.146224928399938E8\n",
      "variance = 3982.4218470523483\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "No Outputs"
      ]
     },
     "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",
    "println(\"geometricMean = \" + ij.op().stats().geometricMean(sinusoid32))\n",
    "println(\"harmonicMean = \" + ij.op().stats().harmonicMean(sinusoid32))\n",
    "println(\"kurtosis = \" + ij.op().stats().kurtosis(sinusoid32))\n",
    "println(\"max = \" + ij.op().stats().max(sinusoid32))\n",
    "println(\"mean = \" + ij.op().stats().mean(sinusoid32))\n",
    "println(\"median = \" + ij.op().stats().median(sinusoid32))\n",
    "println(\"min = \" + ij.op().stats().min(sinusoid32))\n",
    "println(\"moment1AboutMean = \" + ij.op().stats().moment1AboutMean(sinusoid32))\n",
    "println(\"moment2AboutMean = \" + ij.op().stats().moment2AboutMean(sinusoid32))\n",
    "println(\"moment3AboutMean = \" + ij.op().stats().moment3AboutMean(sinusoid32))\n",
    "println(\"moment4AboutMean = \" + ij.op().stats().moment4AboutMean(sinusoid32))\n",
    "println(\"size = \" + ij.op().stats().size(sinusoid32))\n",
    "println(\"skewness = \" + ij.op().stats().skewness(sinusoid32))\n",
    "println(\"stdDev = \" + ij.op().stats().stdDev(sinusoid32))\n",
    "println(\"sum = \" + ij.op().stats().sum(sinusoid32))\n",
    "println(\"sumOfInverses = \" + ij.op().stats().sumOfInverses(sinusoid32))\n",
    "println(\"sumOfLogs = \" + ij.op().stats().sumOfLogs(sinusoid32))\n",
    "println(\"sumOfSquares = \" + ij.op().stats().sumOfSquares(sinusoid32))\n",
    "println(\"variance = \" + ij.op().stats().variance(sinusoid32))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image1 range = (0.0, 254.0)\n",
      "image2 range = (0.020272091031074524, 255.97271728515625)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "No Outputs"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "// Prepare a couple of equally sized images.\n",
    "import net.imglib2.type.numeric.real.FloatType\n",
    "image1 = ij.op().run(\"create.img\", [160, 96], new FloatType())\n",
    "image2 = ij.op().run(\"copy.rai\", image1)\n",
    "\n",
    "// Gradient toward bottom right.\n",
    "ij.op().image().equation(image1, \"p[0] + p[1]\")\n",
    "minMax1 = ij.op().stats().minMax(image1)\n",
    "println(\"image1 range = (\" + minMax1.getA() + \", \" + minMax1.getB() + \")\")\n",
    "\n",
    "// Sinusoid.\n",
    "ij.op().image().equation(image2, \"64 * (Math.sin(0.1 * p[0]) + Math.cos(0.1 * p[1])) + 128\")\n",
    "minMax2 = ij.op().stats().minMax(image2)\n",
    "println(\"image2 range = (\" + minMax2.getA() + \", \" + minMax2.getB() + \")\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb.display(image1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nb.display(image2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Available operations:\n",
       "\t(Object out) =\n",
       "\tnet.imagej.ops.eval.DefaultEval(\n",
       "\t\tString in,\n",
       "\t\tMap vars?)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ij.op().help(\"eval\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Scijava",
   "language": "groovy",
   "name": "scijava"
  },
  "language_info": {
   "codemirror_mode": "groovy",
   "file_extension": "",
   "mimetype": "",
   "name": "scijava",
   "nbconverter_exporter": "",
   "pygments_lexer": "groovy",
   "version": "1.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}