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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Integral Sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added new repo: scijava.public\n"
     ]
    },
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     "metadata": {},
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    {
     "data": {
      "text/plain": [
       "net.imagej.ImageJ@3baf5137"
      ]
     },
     "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 sum of a subregion of an `Img` using a [Summed-area table](https://en.wikipedia.org/wiki/Summed-area_table), a procedure that . This notebook will differ from the flow of most of the other `stats` `Op`s since this `Op` is a little more complicated; let's call `help()` to see how it is called:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Available operations:\n",
       "\t(DoubleType out?) =\n",
       "\tnet.imagej.ops.stats.IntegralSum(\n",
       "\t\tDoubleType out?,\n",
       "\t\tRectangleNeighborhood in)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ij.op().help('integralSum')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see the `Op` takes a [`RectangleNeighborhood`] (INSERT LINK) as input. This is because the `Op` calculated the sum over a given subregion of the `Img`, something that a `RectangleNeighborhood` is designed to provide. Let's start by creating an `Img`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ramp = ij.op().run(\"create.img\", [150, 100])\n",
    "formula = \"p[0] + p[1]\"\n",
    "ij.op().image().equation(ramp, formula)\n",
    "\n",
    "ij.notebook().display(ramp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have an `Img` we can create a `RectangleNeighborhood` (using a `RectangleNeighborhoodFactory`) to pass through to the `Op`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "net.imglib2.algorithm.neighborhood.RectangleNeighborhood@13a113d5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import net.imglib2.algorithm.neighborhood.RectangleNeighborhood\n",
    "\n",
    "//our current position in the neighborhood. This is not really important in this use case \n",
    "//since all we care about is the region as a whole (since we are summing each pixel), \n",
    "//however in other applications it can be useful.\n",
    "position = [5, 5] as long[]\n",
    "\n",
    "//The top left corner of the neighborhood\n",
    "min = [0, 0] as long[]\n",
    "\n",
    "//The bottom right corner of the neighborhood\n",
    "max = [10, 10] as long[]\n",
    "\n",
    "//An Interval object that defines the span of the image. This is, again, \n",
    "//not really important for our use case, ut we can pass through our ramp image since\n",
    "//all Imgs are Intervals\n",
    "span = ramp\n",
    "\n",
    "//A RandomAccess that allows us to access all of the data values of our Img\n",
    "randomAccess = ramp.randomAccess()\n",
    "\n",
    "neighborhood = RectangleNeighborhood.factory().create(position, min, max, span, randomAccess)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our `RectangleNeighborhood` we can pass it through to `integralSum()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import net.imglib2.type.numeric.real.DoubleType\n",
    "\n",
    "output = new DoubleType()\n",
    "\n",
    "ij.op().stats().integralSum(output, neighborhood)\n",
    "\n",
    "output"
   ]
  }
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