notebooks/3-Advanced-Topics/3-Using-Special-Ops.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Special Ops"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook dives into some technical details of the ImageJ Ops library.\n",
    "\n",
    "It is recommended that you first read and understand the <a href=\"../1-Using-ImageJ/2-ImageJ-Ops.ipynb\">ImageJ Ops notebook</a> before tackling this one."
   ]
  },
  {
   "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": "2e223d23-b953-4e6f-8db7-3c5a0b5687ec",
       "version_major": 2,
       "version_minor": 0
      },
      "method": "display_data"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "2.0.0-rc-71"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%classpath config resolver scijava.public \"https://maven.scijava.org/content/groups/public\"\n",
    "%classpath add mvn net.imagej imagej 2.0.0-rc-71\n",
    "ij = new net.imagej.ImageJ()\n",
    "ij.getVersion()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial covers the so-called `SPECIAL` ops. Every special op produces one primary output from some fixed number of primary inputs. The number of such inputs for a given op is known as its `ARITY`. The framework provides interfaces for three arities:\n",
    "* 1 input = UNARY e.g.: eight = math.sqrt(64)\n",
    "* 2 inputs = BINARY e.g.: eight = math.add(3, 5)\n",
    "* 0 inputs = NULLARY e.g.: zero = math.zero()\n",
    "Additional arities (e.g., ternary) are feasible, but not implemented.\n",
    "\n",
    "The `SPECIAL` ops can be classified into three main categories: `COMPUTER`, `FUNCTION` and `INPLACE`:\n",
    "* `COMPUTER`: An op which will write its result to pre-allocated output, i.e. an empty (zero) image\n",
    "* `FUNCTION`: An op which will allocate on its own and then return it\n",
    "* `INPLACE`: An op which will write its output to one of the input arguments, hence mutating one of the inputs\n",
    "\n",
    "Special ops may also have any number of additional secondary inputs, which do not affect the arity of the op. More on that below.\n",
    "\n",
    "Let's try some example op calls, to illustrate the difference between these various kinds of special ops.\n",
    "\n",
    "First, we will create some test images to work with. In order to do that, we define and use a helper function, which creates an image of specified width and height using the `create.img` op, and then populates the contents of that image using the `image.equation` op, which sets the value of each sample according to a specified formula. In this case, we create four images depicting different patterns based on the location of a sample, which may be accessed via the variable `p[]` inside the formula."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created image: Sinusoid of size [150,150]\n",
      "Created image: Gradient of size [150,150]\n",
      "Created image: Circle of size [150,150]\n",
      "Created image: Diamond of size [150,150]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class =\"scijava\"><thead><tr><th>sinusoid</th><th>gradient</th><th>spot</th><th>cross</th></tr></thead><tbody><tr><td><img src=\"data:image/png;charset=utf-8;base64,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\" /></td><td><img src=\"data:image/png;charset=utf-8;base64,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\" /></td><td><img src=\"data:image/png;charset=utf-8;base64,iVBORw0KGgoAAAANSUhEUgAAAJYAAACWCAYAAAA8AXHiAAAlpUlEQVR42u2ddYws19XEW+FEIYcZHGZmZmZmZmYGh5mZ2Q4zo5mZmZktW5Yty/19Z6Qa1dbWudC7m/zTkY6ct9Pdb3f696rq1p2dGcb//9+FF144XnDBBeP5558/nnfeeeO55547nnPOOePZZ589nnXWWeOZZ545nn766eOpp546nnLKKeNJJ500nnjiiePxxx8/HnfcceMxxxwzHn300eORRx45HnHEEePhhx8+HnrooeMhhxwyHnTQQeOBBx44HnDAAeN+++037rvvvuM+++wz7r333uOee+457rHHHuPuu+8+7rbbbuOuu+467rLLLuNOO+007rjjjuMOO+wwbr/99uN22203brvttuM222wzbr311uNWW221mC233HL8z3/+M/773/8e//Wvfy3mn//85/iPf/xj/Pvf/76Yv/3tb4v561//upi//OUvi/nzn/+8mD/96U8r5o9//OPk0Wvh78Dfie8B3xO+x/h+4/vGzxA/T/xc8fPhZ42fO37+eB7i+YjnJZ6feJ7i+YrnLZ6/eB7j+YznNZ7feJ7j+Y7nPZ7/uA9xP+K+xP2J+xT3K+5b3L+4j3E/477G/Y37HPc77nvc/+AgeAgugo/gJHgJboKf4Ch4GmpQnXHGGSlUxx577OKbOOqooxbfVHyDhx122OKbPfjggxfffPwg+++//+KHAlB77bXX4odWoHbeeecqUK0wMUgKEUD4wx/+sJzf//73q+Z3v/tdddx5fF2FjmFj0FohKwEWz58CFs9zPN8ALO5D3I+4L3F/4j7F/Yr7Fvcv7mPcz7ivcX8zuIKLElxDK1Qnn3zy4uInnHBCEar4VxDfrFMpABX/mhSo+FfXA1QGEysSg6QAMRy//e1vV8xvfvOb7tFrOPgYNgaNFS2DrBWweB4VMKhXPP9OveJ+xX0rwRX3Pe5/cNAC19ALVUhk/GUhmfGXZ9bnVIptj4GKf21TgGKYWJVYkRQkhefXv/71cn71q1+tml/+8pfpuOP5egqdA40VTdUMkPUCBnsEYGyPTr0ya4z7G/c57nfc9x64hgyq0047bQlVSKFChTyVWV9NpTRH1YBimBQop0oMkkLkoPnFL36xnJ///Ofdw+c7+BxsDJqqmQLGkNUAK+Uvp16ZNXLuYriCB8AVnDi4hh6oNKSXrM9lqZLtOaBK6uSUSRWJQVKAGIqf/exny/npT39qZ4sttlhOdgxfx0HHsDForGiZkmUq5gAr2aPLXiVrdKG+Ba5hClSlPOVWfE6l2PZqCpWpUwYTKxKD5OABLJtvvvmK+clPftI8eq4DkIFj0FjRSpCVAHMKxvbo1MutHGu5qweuIctULVBleUqtz6kU257LUK1AZTCxGmUQAYwf//jHy/nRj340efg6Cp3CxqpWgqwFMJfB2B6deqk1lnJXK1ycuYYWqHjl50K65im1vppKaSjvBYphYlVikBQiwPDDH/5wOT/4wQ9WzPe///3q6Dl8PQWOYWPQVM3YLlsB05BfU6/MGjl3uVDPK8YSXAOXnz1QuZCeWV9NpdT2SkC5zMTKpIrEIClADMj3vve95Xz3u9/tHj7fgcewMWisaKxkLpOVACvZo1OvzBqzUN8L15BVCmp/GVRaJTjr4xWfUym2vRagHEysTKpICpLC853vfGc53/72t1fNt771rVXjjuPrKHQONFa0TMmcimWAZfYI9XIrR7ZGrSRqcKktchUxcKO+FqiyPKXW51SKbc9ZXgmoEkwOJAUIoHzzm99cMd/4xjeaR89V+BQ2Ba0EWQaYs0i2R6deao2l3LUWuIKnQaHinqoGlQvpmqfU+moqpRmqFagMJgZJIQIYX//615fzta99bfLwdRQ6ho1ByyBrAUwzWE291Bpd7nKhvgUuLVEH3fvLVn8lqFxIz6yvplIaynuAYphYkRgkBeirX/3qcr7yla+smC9/+cvV0XP4egqcgsaKxpC1AqYhv6ZemTVmob4Gl64WeW9xcFBx+dkDlYZ0Z301lWLbqwHl1IlhYkVSkBSeL33pS8v54he/2D18vkLnQGNFYyVTFasBltmjUy9njS7U98KlDX3wNOiGskLFlUIrVC5PuRWfUym2vRJQGUysTAqSQgQovvCFLyzn85///Ir53Oc+l44ey9dR4Bg2Bi1TMoUsA4zt0amXWzlmuasHLu25sLeIjeuBawW3TZMF9RaoNE+p9dVUqgeoEkwMkgLEoHz2s59dzmc+85nm4fMceAwbg1aCrBWwmnqpNbrc1QqXBnq3/QO4BtdVKVTcUzmoeOXnQjrylFpfTaU0Q7UAlcGkICk8n/70p5fzqU99ys4nP/nJ9DE+X6FzoGWQ1QDTDFZTL7VGzl0u1POKMYNLS1RsXHPHNegK0O39uUrBKRVDpSHdWV9NpTiU9wDlYGKQFKAAJuYTn/jEivn4xz9eHT0H11LgFDQHWStgHPJr6uWs0YV6hUuVS6sIt7fIcA3ZClCh4vKzByqXp3TF51RKba8XKIaJQVKIAMjHPvax5Xz0ox/tHj5foWPYGDSGrAewzB6derE1au6aApc29IBLV4qDWwHqhrJCxZVCK1Sap+KH5hWfqpTangOqBBOAAkwMkgL0kY98ZDkf/vCHV8yHPvShdPRYvo4Cp6ABMqdkDJkDLLNHqBevHBUu5K4euLTnwt6iviqC4RqyXOWgQqOeBXWFikO65im1PmQpqJTanipUSZ0ymBQkheeDH/zgcj7wgQ80D5+n0DnQSpA5wFTB1B6hXsheao0ud3God3BpoHfbP26lCEscSlBxWFeouKfqgQp5KrM+p1JqeS1AOZgcRIBjs802W8773//+VfO+971vOe5xPl+BY9gyyGqAqUU69cqskXNXL1xaomLjulRDBE9DKVcpVFgBKlS8+muFKrM+p1KaoVqBcjAxRArNe9/73hXznve8pzp6jsLHsGWQtQCmGcypV8kae+DSKgLbP7y36FaKbImDg8rlKgcVl58uU/VA5bJUZns9QDmYGCQA8u53v3s573rXu7qHz2foGDQHWStgmT267DUFLs1crqHXGoItUeEaarkq66oUKq4UHFQc0rM8pdYHlWLby4BqgcmBBDDe+c53LuYd73jHinn729+ejh6LayhsCloNsgwwtkcN9w4uzV28Yszg0p4Le4tZx5XlraHFAnUF6LZppkCFPJVZH1SKbc8plAMqg8lBBFDe9ra3Leetb31r8/B5Ch3DVoJMAXMKxvao2UutkXPXFLh0+8etFEuWOLRC5cK6QsU9lbO/DKrM+pxKqeXVgHIwMUiA4y1vecty3vzmN6+YN73pTatGj+HzGTgGzUFWAkwt0qlXZo01uNQWXYmKjeueMA+4hhYLLEGFsL4eUKn1cZaCSmmGagEKMLEaMUQMzBvf+MblvOENb6gOH6/gMWisaICsBphmMKiXy15sjWuFS/cWHVw1SxymWiCvAF2j3gpVlqfiSeMs5WyvByjAxCqkAL3+9a9fzOte97ruwbkKHKsbQ9YKmLNHzl4ZXMhdLXC5ht6tFHsscYBaTYFKc5WDCqu/GlSap9T6oFJsew6oFpgYJIDx2te+djGvec1rVsyrX/3qdPRYXINhY9BqkDnA2B413DNcmrtqcOlqEXC1rBRrcAVPw1QLLEGFRn0KVJn1OZVShXJAZTAxSADlVa961XJe+cpXNg+fx9AxaBlkCpgqmFOvzBqnwKUNvYNriiUO62WBvAJkqLin6oFKrc+plFpeBhTDxEqkEL3iFa9YzMtf/vIV87KXvWzV6DE4V2FjZVPIHGBqkU69nDX2wKU9F+Aq5a1e1RoytZoKFecqBxUHdYXK5SlYn1Mptr0WoAATg6TwvPSlL13OS17ykurw8QqdgsZKVgIss0eoF1uj5q4MLg30gKtUQ7TC5VRrYLVaqwVmUKH8bIVK85RCBZVi22sFCjAxSIDkxS9+8XJe9KIXrZgXvvCFy9HH+DwGjkEDZC2AsT1CvUpwIXe1wOUa+izMT7FEqNYAtVpvC0SuclBh9VeDCnlKrc+plAOqBhNDxOC84AUvWM7zn//8dPg4BY9BK0HmAHPqpdbIuasGl1YRgCvLW2u1xOBpCLpaAvtaoEKuQvmZZaoMKrU+p1KqUApUBpMC9LznPW8xz33uc5sH5yhwJcgYMFUwp15qjTW4NHOhRNXtnylwtQT5oRbY12KBHNYZKu6peqCC9TmVYsvLgGKYGCQA8pznPGcxz372s1fMs571rFWjx+Bcho1BY8gcYGyRTr3YGnvg0p6L4SrlrRZLLKnWkKnVFAtsyVUOKg7qCpXmqXiSnUqx7dWAAkwMEoB55jOfuZxnPOMZ1eHjGToGDZCVAMvsEeqlcCF3Obh0tQi4WvLWFEt0qjU4tWoJ7L25SqFC+dkKFfKUQuVUqgUowKQQPf3pT1/M0572tBXz1Kc+dTn6GM5R2BiyGmBOvRxcyF0tcGmJWoKrZIktQV5Va+hVq7VYIId1hQqrvxpUan1OpRSoEkwMEcB5ylOespwnP/nJ6fBxCh1AyyBTwJx6qTW2wKVVBOAq5a1WS+xRrcFlq0ytegJ7a1hH+ZllqgwqWJ9TKVUoBsrBxAA96UlPWswTn/jE5sE5DFwGGQBTBXPqxdZYgkszF0rU1jDfE+Qz1dKsNbSqVS2wt1pgBhV6qhJUmqfiyXcqxZbngGKYGKQnPOEJi3n84x+/nMc97nHp8HE4V0FjyBQwtkinXgpX/PwluLTncnC1WGItyLeo1rBWtVqrBWKbRqHioK5QIU8pVFAptr0SUIBJIXrsYx+7mMc85jHVwbEKG0OWAcb2CPVycCF3Obh0tQi4srzVY4lrUa1Be6tetWoJ7C1QIVehp8rsj6FS63MqVQMKIAGURz/60Yt51KMetZxHPvKRq4YfxzkMGyArAebUS63RwaW2iJ5LG/oSXC1Bvle1uNcaXMu+HmrVaoEc1hUqrP5qUGmWYpVSoDKYGKBHPOIRy3n4wx+eDh/HwGWQKWBOvdgaa3BpFcFw1fIWW+J6qJa28Quwai17r1qtxQLRqCtUnKniydY8FTfFqRQrFAPlYAIwD3vYwxbz0Ic+dMU85CEPWfU1HMuwOcgAGCuYUy+FK35OhksDPUrULG+1WmKvatXa+CFkS/cEW3qrTK1aAnsrVOipFCpWKuQphopVii1PgQJMDFHAE/PgBz94MQ960IPSwTE4h2FjyBgwtkhVL4YLucspF6oI7rlqcLUE+VbVcr2W7iEOWWjXlr11JVhSq1YLRFhXqBDUHVRqfU6lMqAAEoB54AMfuJgHPOAB1cGxDBsgc4A59VJrzODS1SLgKuUttcRe1XIrRG3jXYgf2AY5tOueoOuterLVVAtE+alQsf0xVJqloFI1oADS/e9//8Xc7373W85973vfVcOP4xyAVgNM1YutUeHSzIWey+WtVkvsyVqu19I9RA7xsMOhJ7Rry+5Wgk6taoG9FSpUCiWoYH2qUg4oBxPAuc997rOYe9/73ungGIZNIXOAqXqxNWZwaRVRg6sW5J1quRWitvGtIX5QG9TNZg7t2rK73qpHrVotEGFdoeJMpVAhS0GlVKEAFGBikO51r3st5p73vOdy7nGPeyyHv45jFTRAFn+PKhjUC9krg0sDPeDK8pazxB7Vcr2WtvEc4nVzmu1wqNlgLbRrb9WaraZaIHqqKVDB8hQowMQQ3f3ud1/M3e52t3RwDMPGkDFgsMgpcKHnmmqJrVlLe62eEK92OGSrQbbBUmjXlt2tBHvVqgcqBHUHFaxPVcoBBZgAzV3vetfxLne5S3XiOAYNkClgql6wRgeXBvoWuHpVy60QS3uIGuIzO8TqcCitBjMbrFUM2lu5bJWpVYsFRv6I/qcGlWYpqFQGFGC6853vvJg73elOi7njHe+4avAYjmXIHGBQL81eNbji58zyllpiploua2mvVaseWuyQV4eDK0Wz1SDbYCm0t/RWTq2mWiBWfxlUsD6olAIFmBikO9zhDou5/e1vnw6OUdDiegoY1Iut0cGF1eJUS3Sq1dJrlUJ8ZofZFs8CLN4b5FI0Ww222qC27C0rwZJatUKFTOWggkqxQgEowARobne72423ve1tl3Ob29xmOfz1OI5BA2RxXVYwqJeDSzNXDa5e1dIVorbxU+yQV4dcliJnDVozZKVothosbTZzaNfeqjVbtVogKoVWqKBSDBRgAkC3vvWtF3OrW91q1eAxhg2QATBVrxa40HO1WGJr1tJeqxTiMzvk1WFWlnLtMLiagfcGuRTNVoOtNqgtu64EnVq1WCBDhdUfMpVChRwFlVKgANItb3nLxdziFrdIB8cANAUM6oX85eBCoOfVYvw8LZboVEtXiNrGT7FDXh1yWZrtHS7BcvmKa4asFJ1igxraubdy2SpTqyxX1aCC9UGlHFABzc1vfvPxZje72WJuetObrho8FscBMgUM6sXWWIPL5a1MtVzW4l6rFOJb7TArS13twDlrmJKvsi2cKTaovVVLtmqxQKz+MqigUgoUg3STm9xkvPGNb5xOPM6gKWBQrwwurBZbLLEla2mvNcUOsy2e3pw1ZP3V1Hw1xQY1tPNKsFetHFTIVA4qViiG6UY3utFiNt1008Xc8IY3XA6+hmMYMlYwB5dmLoarV7V4hVgK8a12uNacxX3W0Npf9eYrLkWz1WBmg9pbabbK1EotMJb6LVBBpQAUQLrBDW6wmOtf//qrBo8BNACm6lWCK74/tcRMtTRraa/VYoe8OszK0p6cVeqzhpbgnvVXtXyVbeFk3VUptPNK0KlVzQIR1Bkq5CioFAMV8Fzvetcbr3vd6y7nOte5zoo/x+OADIBBvZC/GC4E+polOtXiFWIpxGedVrbFU8tZtT4rC/BDrRh1wT3bdHZte1aKZqvBzAZLK8GSWmVQQakCAqiUAhUgXfva1x6vda1rrZr4OkBjwKBegEuVi+FqVS1dIbbYIa8Os7LUtfDZpnQpwLuidChtPE8J7vwSGbc3yKUov4oh665Kob2WrdQCY7UWwTqDioEKeK55zWsu5hrXuMaqwWNxHAOWwRV/b/z9aom1rFUK8Vmnxa964LLU7R26l9JMCfC6IT1kK0L32vYpwb0nX2WrwcwGdSXYqlYI6rA/hgoKFfBc/epXH692tast5qpXvepy8LV4PI6DgilcyFwlS1TV0hViix3y6nBKzpoS4N2GNK8MV4DlXtHgVoTuZTIuuGf9VU++wmows0HtrThbZWpVggoKBZiucpWrjFe+8pVXTXwdkEHBSnBlqsVZS3utzA6xOuzJWbU+ywV4fhlNaWXoXukwlKoG94qG2orQFaMuuHN/xW17LV/xajCzQV4JqlqxBUbgjmzEUEGlANSVrnSlcZNNNlnMFa94xeX/j68DMKgXwxXXjeuzJapq8Qoxs0NeHdZyFrfwrs9yAd69+K+2MnSvdEjB6q0a+JcmSitCV4zWgju37S5fZavBzAZraoVMBaUKWABUwHSFK1xhvPzlL7+c+DMgi+MAF2wxrldTrcwOs9Why1muha8FeC5KSytD90sWrZVDESy3R9haNfDrr0orQi5GS8GdN5y5ZkC+4tUgVwxsg5ytVK0yqADUZS972VUDwDK4VLU4a7EdcvXAq0PkLK4deGO6FOBdUepWhu71Wa2Vg9szXAWWK0dLHZbbI2ytGlzj7laEvI1TCu4uX/FqMLNBVitYYGSmsDdAdbnLXW4B0WUuc5nx0pe+9HLiz/H1eBxwxXlxPiyRVSuzQ7c6dLWDC/C8vVNaGboGvrVycHuGpS6LS9IVYLWWo6UOy+0Ruq0cVzVw464rQt7GccG9lq9QMbANcrZitQqbg1IFRJe61KXGS17yksuJPwOuOC6OZ9XirMV2iOqhlrNcgHfbOwoWN/CucnAvV3Z7hq7L6i1Jm8Fyr2roBct1WG4rx1UNpRWhC+5cM7h85WyQ1YqhusQlLjFe/OIXHy92sYst/ht/ZrhYtZwdupzFtYML8KWVYalycFs7pS6rFywuSbvBci9H7gWrtRwtdVhu47kEFu8NloK7y1dsg1CrsL1QqIDpohe96HiRi1xk8d/4c3w9HodqsR26nFUK8Lx32AMWVw6lLqu1JJ0KFrfv6wqWe7nMRoPFr72aChavBgEW2yDACqUKqIZhWPw3/qxgxXkKVlx/rWBx5fDfAovb9/8pWLNizYo1Z6w5Y/3vMta8KpxXheu6Kpx7rLnHWtcea27e5+Z9Q5r3ea9w3ivcsL3C+dUN86sb1vXVDfPrsebXY23I67HmV5DOryDdsFeQzq95n1/zviGveZ9/S2f+LZ11/y2d+fcK598r3JDfK5x/E3r+TegN+U3o+b0b5vdu2JD3bpjfbWZ+t5kNebeZ+f2x5vfH2pD3x5rf0W9+R78NeUe/+T1I5/cg3ZD3IJ3fNXl+1+QNedfk+X3e5/d535D3eZ8/mWL+ZIoN+WSK+bN05s/S2ZDP0pk//Wv+9K8N+fSv+fMK588r3JDPK5w/YXX+hNUN+YTV+TOh58+E3pDPhJ4/xX7+FPsN+RT7LbfccmQ71NVha4jPqgddIWa9llOtLG9lcOlqka2R1SsDDJABNIbNDY7BObiGA0pVCtanq78MqixXtaqV9laqVq5iaAntuhoMjhZgbbXVViPboZalunfoXgvfo1pTLdGFeWeLgCv6H7ZGVS8GjCEDaAwbQ6dfw7E4FzApUKpSsL74Pl1QB1QurE+1wF610te2Z3uDKEVhg8HTAizYYWuIr6lWKWvVgjxbostbmXIBLlQRao2avQBYBhkGwLjh4xxMDBRnKbU+VAouUzFULle1WmBJrVy2ytSqFtqDo+Bp2HrrrcewQ6gW7x26LZ5W1SptTjvVKuWtFriwWkTP5dSLAWOLVMgAGgbQ8PDjOMfBxAoFoFSl0FNh9dcCVSlXtagVW2CvWukWDmwQahU8Ddtss83IqqUhfi2qtRZLdGHewaVVBOcuVa8SYIAMoDFspcGxOBfXKgGlKoU8pZWCg8qF9bVY4HqoFYf24Ch4GrbddtuRVYtDfKtqZSvEliAPS8zyVgaXWy1q7lL1ygBjyBg0hs0NH4dzAVMJKFUpzlNu9ZdBleWqVgvM1MqtBGtqFdxArYKnYbvtthvXqlrcxmd7iFCtlrxVgssFevRcao2avRQwhUxBax0GSWFyQHGWUutDT+WCegmqllyVqZVaoPZWvWoVPA3bb7/92KpapRXiWi3RhfkaXKgiUKI69eLsxYBlkAE0DGBxw8fhXAcTA6VZSlUK5ScqhRpULqyv1QJbV4KZWgVPww477DCqanH1oKqV9VpOtUqWmOWtFri05+LcperFgLFFOsgAGsOm0DE8DBFAKsHECgWgVKWQp7SnaoEqy1WtFpipleutWK2CF1ar4GnYcccdx17VagnyPXmrFS5XRWjuUvWqAcaQMWgMW2n4eFwDMLUApSrFecpVCq1Q9eSqlsDeo1bB07DTTjuNTrXQa5VUa6olZmE+g8utFlGiqjWqepUAY8gUNB4Aw6PHMEgMUw0oVSm2PpSfbvWXQZWF9bVYYKZW3FuxWgVPwy677DJmqlVq43uCfClv9cKFngsNvVMvzl4OMIVMQWPYWgbnMEgKUwYUZylVKTTq6Kl6oSrlqp7AXmvZVa2Cp2HXXXcdd95556Jq9VpiLW+5MF+DC1UESlTOXapezh4BWAkyDMBg4NzwcXx+BpMCpbanKoU8hfITlUINKhfWe3NViwVmahU8DbvtttsYqhWWWFOtqZaY5a0WuLTncrlL1UsBg0WWIGPQABuPgsPD5+FaJZhUoQCUqpTmKe2pWqBqyVW9FlhSq+AoeBp23333EarFljhVtWp5qxUuV0UwXGyNql4tgDFkChoGkJSGj2eQGKZWoFSl2PoyqDio16Cq5aqpasUWGBwFT8Mee+wxsmqttyVmYT6Dy60WUaJi+0etUbNXDTCGTEFj2BQ6Hj0G5zJIDFMLUJyl1PqwTYPy063+MqiysL7eFgi1Cp6GPffcc8xUa6olZnmrFy70XGjoOXepenH2ygBTyFjJGDSGrWX4PFwLIClMGVCapVSlkKfQqKOn6oUqy1VrtUBWq+Bp2HvvvceAa4pqteStKXBpiepyl6qXs0cGrASZgsYDUNzosQxSCSYHlNqeqpTmKS0/p0DVkqumqFXwNOyzzz7jXnvtNa6XJWZhvgUu13O53OXUSwFjiyxBpqAxbD2DcxmkFphUoQCUqpTLU66naoEqC+vrYYHBUfA07LvvviNUa4ollvJWK1yuigBcmTVq9moFjCFju8QACAauNHw8XwcgMUw9QHGWyqwvg0qDegmqUq6aYoHBUfA07LfffmOLarXkrfWACyUqtn84d7E1avZqAYwhYyVj0FjVWN109Bg+H9cESAxTC1CapdT6kKewTYPycz2gaslVNbUKnoYDDjhgbIXLWWIW5ks1hIMLPRcaepe7uJJgwNgeM8AyyBQ0VrXW4fMYpAymGlBse1ApVAkuT6FRR09Vg0pXgFlYn2KBwVHwNBx44IHj/vvvP7ZYYilvTYVLS9RS7lL1YsDYHhmwEmQAzcGm0LnRYxkigFSCyQHF4ZyzFKtUlqe0/JwKVSlX1SwwOAqehoMOOmhU1WrNWy0rxcwWXc/FcDlrdNnLKVgLZBloDFvP8PkKUgtMTqFclsqszzXqbvVXgsqF9dZcxWoVPA2HHHLI6OBylpiFebdSnAIX9hYza9Ts1QNYBpmCxgNASqPnMEgZTD1AaZbKrA97f1OgcivALKzXLDA4Cp6GQw89dDz44INHZ4m9Yb4XLpSo2P7h3OWsUbNXK2AZZACNYWPgeAAMQ8PD5+Oa+DscTK1AaZbKrA95Cts0KD97oeoN62qBwVHwNBx++OFjCS62RBfmp8ClDX0pd6l6ZfaYAZZBBtAcbAxcy/B5DBFAymBqAYptz6lUlqe0UZ8ClQvrpVwFqIKn4YgjjhgPO+yw0VliFubdSjGDizOXK1E51DtrdNlLw70DrAaZA41hU+jc6LF8HQapBlMGFIdzl6VK1qfbNKgUskzFULkVYBbW1QKDo+BpOPLII8dQrQwu5C0X5nvhcg09rxidNbrspYBpBnMqxpApaAwbA9czfD6uySAxTCV1UstjoDRLZdaHlZ9r1HuhcmE9y1WAKngajj766BFwsSVmYX4KXK5Exd4i5y62RlYvzV49gGVKBtAcbApdafQchkhBUph6gdIsxSrF1oc8hb0/V35OgSoL62yBwVHwNBxzzDHjUUcdZeFC3nIrxVLH5VaLgAvbPy53ZeqV2WMNsAwyBY1hY+B0GBgdPp+vC5AymHqAYtsrqZTmKWzTuJ7KQaW1glsBulwFqIKn4bjjjhsBF+ctF+ZLcGWBXht6DfVsjSX1cvZYAqwEmQMNsGEYjtrweXw9BqkEUwtQanslldI8pXt/rlLIlEqhcmEdFhj8BEfB03D88cePxx57rIUrfLNWQ9Tgcts/LnfV1KsVsBpkChrDxsA58HT0WL4Ors0g1WDqAaqkUi5PuW2aHqjcCpBzFUMVPA0nnnjiCLiQt9xKsRcu19Bj45pDPVtjSb0ywGoqxpApaA42Bq5n+HyGSEFimGrqVAKqpFJsfQjp2FB2jXovVBrWg5fgJvgJjoKn4aSTThodXG6l2AKXK1Gxt+hCPVujqhevHEuAcchnwDIlc6AxbApdafQcvh6DlCmTA4ozVAYUr/hUpdj6NKRj78+Vn1OgQq5iqIKn4ZRTThkDrhNOOGGMvKVwRShrgcv1XNj+yUJ9Tb145dgDmELGSqagMWwM3JTh67AiASRWJqdOrUDpiq+kUllIxzaN66laoOKwDqiCn+AoeBpOPfXUUeEKn2yFy1URureY5a6aejl7rAFWgkxBc7ApdC2j5zJEClIJplag1PZKKpXlKd37c5VCK1TBC0MVPA2nn376GHCdfPLJa4bLbf8wXM4aS+rFgLE9OsBqkGWgMWwMHA9A4XHH8XX4+gpSDaYSUFwhAKiSSjnry6Di8nMqVMFR8DSceeaZI8MV/shwacfl4HINPTauXahna1T10pVjCbCSijFkbJcONMCGYShah8/n6zJIbHMMU0mdWoDiFZ+qFFufhnRsKLtGvQYVVoCAKrgBVMHTcNZZZ41nnHHGJLhciYq9xSzUqzWqevHKsQSYhnwGLFMyBY1hY+AceNnoOXw9ViSAlClTpk7O8hgoXfGpSrH1ZSFdX1PlKoUeqIKn4eyzzx5b4MpWi4BLN65LuaukXs4eWwFTyFjJFDQHGwM3Zfg6DJGCxMqkMPUCpbZXUqksT+mGsuupMvtzUAVPwznnnDMyXKeddloXXG5vkeFy1lhSr8wea4CVIHNqlsGm0LWMnqsQOVUqwdQKlLO9kko563OvUtBGvQWq4AZQBU/DueeeO06By23/AC4O9c4aVb24lmDA2B4zwGqQuUzGisbAKXQOQAXGwcNqpCCxzdVgqgHFFQKA4hpBVcpZH0J6BhWXn61QBU/DeeedN/bA5Rp6bFxnoV6tUdVLV441BSupGEPGdqmgMWwMnELXOnw+X5cVCSCxzTFMJXWqKZRb8alKsfVlIR0byq5Rb4UqeBrOP//8MYMrqyIAF/YWs1DP1sirRlUvXjnWACupWKZkCpqDjYHjYUh03PF8PYZIQcqUqaRONaB0xacqxVVClqcQ0rH358pPl6kYquBpuOCCC8ZeuHTjupa7auqV2WMrYAoZK5lTMwaNYVPoekavwddnkFiVWJkUpilAqe2VVKqWp3hDOe53D1TB03DhhReOrXChoee9RQeXs0ZVL85eDBjbYwtgJcicmqmiKXA8DEc27jy+LiuSU6USTD1AcYUAoDRLsUo568ugivuNRr0FquDp/wDrHMC3JUNMjQAAAABJRU5ErkJggg==\" /></td><td><img src=\"data:image/png;charset=utf-8;base64,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\" /></td></tr></tbody></table>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import net.imglib2.type.numeric.real.DoubleType\n",
    "\n",
    "// Helper function to create DoubleType images from a formula\n",
    "imageFromFormula = { name, width, height, formula ->      \n",
    "    img = ij.op().run(\"create.img\", [width, height], new DoubleType())\n",
    "    ij.op().run(\"image.equation\", img, formula)\n",
    "    \n",
    "    println(\"Created image: \" + name + \" of size [\" + width + \",\" + height + \"]\")\n",
    "    \n",
    "    return img\n",
    "}\n",
    "\n",
    "sinusoid = imageFromFormula(\"Sinusoid\", 150, 150, \"10 * (Math.cos(0.3*p[0]) + Math.sin(0.5*p[1]))\")\n",
    "gradient = imageFromFormula(\"Gradient\", 150, 150, \"p[0] + p[1]\")\n",
    "spot     = imageFromFormula(\"Circle\",   150, 150, \"Math.sqrt((p[0]-75)*(p[0]-75) + (p[1]-75)*(p[1]-75))\")\n",
    "cross    = imageFromFormula(\"Diamond\",  150, 150, \"Math.abs(p[0]-75) + Math.abs(p[1]-75)\")\n",
    "\n",
    "ij.notebook().display([[\"sinusoid\": sinusoid, \"gradient\": gradient, \"spot\": spot, \"cross\": cross]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## BINARY OPS\n",
    "First we will look at binary ops. A `BINARY` op is one with two primary inputs, which produces an output. Suppose we want to add two images together. There are several versions of the `math.add` op. In the following example we will try the functional version:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;charset=utf-8;base64,iVBORw0KGgoAAAANSUhEUgAAAJYAAACWCAYAAAA8AXHiAAAzUklEQVR42u2dc/AtSfP057Ft27Zt27Zt27Zt27Zt27af+UXOG58TubndPT0Hd3efd29ExffMuWf+y6jKzs6qGg5+8IOPhzjEIabQ54Me9KDjQQ5ykPFABzrQeMADHnA8wAEOMA7DMIU+63v9v357qEMdajzsYQ87HuEIRxiPfOQjj0c72tHGYxzjGOMxj3nMVej56Ec/+njUox51+s2RjnSk8YhHPOL0jv7qWd/r//XbYx/72OPxj3/88UQnOtF40pOedDz5yU8+nvKUpxxPdapTjac+9amnv3o+xSlOMZ7sZCebfnPiE594+j2hZ97V70972tOOZzjDGcYzn/nM49nOdrbxnOc853iuc51rPM95zrOKc5/73NP35zjHOabfnPWsZx3PcpazTO8o9Fnf6//12/Od73zjhS50ofGiF73oeIlLXGK81KUuNV7mMpcZL3vZy65Cz/r+kpe85Hjxi198vNjFLjb9/iIXucj0V8/6Xr/R769whSuMV77ylcerXe1q4zWvec3xOte5znjd6153vN71rjde//rXn/7qWd9f61rXGq9xjWuMV7/61affE3rWu9e+9rWn39/whjccb3rTm463uMUtxlvf+tbjbW972/H2t7/9eIc73GEKfb7d7W43fa//v9WtbjX99uY3v/kqbnnLW07f6zd65853vvN4t7vdbbznPe853uc+9xnvd7/7jQ94wAPGBz7wgasYDnOYw0zg0F+FwHLIQx5yAs7BDnawCUSEQKfv9P+HPvShx8Mf/vATMBwUxznOccbjHe94qzjucY87fXesYx1rBTIBUO8oAKP+X789wQlOMAEDUJzmNKcZT3e6042nP/3pV6FngQWgCWT6PaFngY93z3SmM01AARTnPe95x/Of//zjBS5wgfGCF7zg9FfPAkuCjNAz7+r3F77whSdgAIrLX/7y4xWveMXxSle60ir0rO8vd7nLTSC79KUvvQKaAjDy7lWvetUJLIDiBje4wXijG91ovPGNb7wKPet7AQ2Q6feEnnlXv7/ZzW42AQNA3elOd5qAcZe73GW8613vOv3Vs74XaADZbW5zm1X4u/r9Pe5xj/He9773BCiB6MEPfvD40Ic+dHzYwx42hT4PZA79FVAOd7jDTUATcAQgsplCz2QpQCVgKDMBKAHjhCc84Sp76LMykP5PvxH4BCIymj7zrn57kpOcZG+ZRsAgcyj0fMYznnECmX4jAAEy/dWzvtf/AyoBQ6ARgAQmAYPMob96VgYCZAKQZzTAqHf1+1KmucpVrrLKHAKJnvW9QKPfCGCe0fSs7wVCQCVgCDQC0E1ucpMJGJ499KzvBRplI0BGVtOzvtdv9PtSprn73e8+gYPQs74XaASeO97xjquMptCz3hUQ9Xtlqfvf//7jgx70oAlED3/4w8dHPvKR46Me9agp9HmgFOmvAoCRxQQkQmDTd/p/gdEzFcAQmAQOQtlH3wtwnsH0jmc4MpXKm7KNg0plSOAgKFGAiwxGkOF418sXoPJS5CVK4NJvyGCEA1K/V8ZRtnFgqAwJHISeARgZTL9XhlLku16+AJXKkjIOoWd9r/JGBgNg+uuA1O+VcZRtHBgqYfe6172mrKO/etb3gIsMRvCuAKjfk6ke8pCHrED16Ec/enzMYx4zhT7PAktgcmB5tuoFlr7bFbD0mxawvAQCrOQ4mwJLWWkpsPSX0pklcA5YykQJLKIELM9WApDAUQOWAFQDVpbAJrCUeQgHFYDycgi3SsJOKVTJ8zJIKXRQUQoJBxelUBxJJU0A8VIIoLwU6jeUQgJulYSdUijwAC6iBCovh6VSKM6kciaAAC4n0g4qAEU51F+4VRJ2SqHAQykEUJmtKIUEvMxLoXiTypmyEeASmACUl0KBKEsh4HLC7qXwEY94xF5KoWIQpyK8/HFKFFkXaYe46/vSadB5lgBGJKAg8ASnSAeXTnSAC64kkJChElCcEgk9824NXAKIgEQ4oCDwOjnqHT9FetbTaRBwUdYEEsIJPKdE/Z7QM+/WwCUACSQCE9yqROAJPet7sh6nQcBF9hFIiASUfguJ119OkS1wibQrexEDJY4MlbLDgQ984EliUOizvq9JDQIIGYhwQHEaPMpRjjK9o9BnToaUVKQGgYSToZe6BBSyA6HnltRABqLEeXYCTGc/+9mn3xN65mQIuJAaBBJOhgIJ4YDiNKjfK9vpLyfDmtTA6c5LnQNKpVOyg35P6Jl3S+DyDETJc0C57EAgU9TAxclQpZEY/NTnEoNA5DqW/tZ0LJVRBwinvdSx9Bt0LAKOlyXVwUUGIlxi0G+cy9V0LGU7lVIHSOpYAlVJx4LjlUpqScei3JGhABRcjtAz7wIulU5xMwcIZS51LAGppmNlSa3pWGhZSAwCknM5OF6CCyJ/3/vedwIXOpYy2KRjCSAEZY8s5eIoIbDp/9GzVDrJWgKPlzfXrchScDkCcJUOAkgPZC5CQNP3ykwOKr3jBwgOAspwnrUEHoFIYCI8UzmonN/VDgLKQAIP5c1BJdCQpeByhJ55Fz3Ls5YyEgKpg0rf6/9F+OFyhJ5LBwH4lsBDeSP0rO8FHgeV8zveRc9KgdTBpb//32QsAWv/jLUHMxb8yk+BCTD4VY1jCTACzxzHcoARqO+8m6BywVOEvaa6O8/SZ7KccyyBykm4wEGUVHd4lv6ivvOug6rFsVDdnWMRZLnkWIAKEi5wuF7lpL3GsXjXQVXiWK66O8DgWfpLlkuOBagEpr1xLMkLqbrnyZBActD/67cpkur0J2CgvtekBjIZ2Y13U8sSoHQCFD9SSSJcv3LV3e8UkRzgVl7CpK4LGKjvqO6lkyEBoPw6x0sYJ0JKEqq7Sw1kMcKvgshUXsJ0AkRyIEpSA5nM1Xe/zvESxolQGYdw/Qoi76USMOr0qN+nlqUTIZKDQp8Hzx4IpSmSEnoWoPwk6Hd8cBuyBhnERVKXIBSIo5mlHBRkDULPKZIiQXCfqO/9JOh3fHAbzx4IpQCNO0TCFXuXGRwU4jZkDThQSSQl9Mw1EOXLQQG3UdYg/JI4JQiXJlJmABQIo+JGhJ5TJNXvCVR3PwkqQwlQiKOPfexjx8c97nFT6PPgbgSXBNKJANFOJ0ISbbKGE+2SEwGSredeop36UoKs5UTIOz5KUI8TYR2iTRZBEnAnAiQbMNaIdupI6URwLkT0EG13IuizgKLvE2QEYEzFXUKoQPT4xz9+fOITnzg+6UlPWsWwqeaEoOluhHU1pwSVOwpKmhN8qEdzQtB0N0Kv5kR283d3pTkJHE6UlW3gREs0pxKoapqTnvW9AwyQ6a+4VAlUyk5PeMITxic/+cnjU57ylPGpT33qKgZKkt/hZfZyiwskndObcyLUbkoSRNvBBdEmyHJZ/iDagMrLkrsPyF6QbYg2JJ3Tm3Mi1G4vSw6utLcgZJbKn8Ah4PglcF4OZ/YiyHJ+enNOhFLuZQke5DIBZBtQlcqfwKHsJKItcEgtJ/TMaQ5w6fcOKt7lCkd3gspUyk4C1dOe9rQpnv70p09/p4zloIJcr3OC84y1jROcn8LyBCdgbeME56By39Q2TnCKnhNcyZC3J05wgErP+l7gcVARvEvGErDEq5SxVAKVsZSlANeUsfxCuMWzcD9QDktZC4BwKvPTWgqaBGSfd0tZC34lMNWuXtCd0JwcmJm1AIiARHgZdDLvLggHZmYt15zytJY8i9CzAzOzFgBR9vHTWknQJNCceDezFgARkIgsg+56gF85MD1rAS5lLgGMGPxC2AGV5j8FPqyWoIlSTiSgkCAIV8rhWnkiJANl2QNMSBAEVps5QdMvhEsKOTIE0gQEHq7l4ILA+4WwA8rdDgRWm5agiYPTnZ0OKCQIwk+T8DQHlyvlWfYAExKEmwJrrgYyl8qi+BYxQMpTbnBNCwEV/apmmXG5gWiZ/ZAbsNsALudbKouo3pmd0j5DlOSGBFdJbkj7DHwOGzN2G8DlfIurFC91Dih3O7gXq2WZIQNR5lwVdycp5Rd+p3f9ZOl8SwBxucGzU9pnCLSvGrhE5JW9dEIkBkqcSwsCj5v8JIpiS3ZxFMU9tSwvdTV7shv9EFbJeimO+rWK3+GhX3FAILDalA4CrmWpxBF6TnsyXM4PEJ71XBwFXF7qHFTpHnUHaekggJbl1yp+h5f2ZMKFVbKei6OAC7Ofe7LQrzh9+om0dBBwLUvgUvYiBudP+kzp2wWw8pAAuHqBRfQAq3bCBFjuIuXuzoHloNoEWOl755Dgh4ceYBEOLD957gpYgKsFLLLWXoBFKUSr8qyFk9RLIfeD2UgBMLgALpVCV93RzgBVNlKUSiHRUwo5XWYjBeXMCbkT9cxarVLoPGtpKQRUlMJspKCcUQoJPWfWmiuF8CzKmavvWQq9c4fwd/X7uVKoz1Xy7lc7hKvvgMrv+Jy8I4i6vFAj77xbus5xXYooqe5O4Evk3e/4auQd1d3JO0GGq13noEvh4kzy7lc7LfLud3yuS7mrM8m7E3gyXO06J8l7TXWHb0HeeRdQUQI5GULadUKcyPs2lHfPNJsq78o2vcr7ErdnZpp1lXcvf7tS3j3T7AnlvSQ59Cjvyk5V5X2fdiJ4U+o+5UTIS2Jveti3OBE8iyxxImS7VsmJIKDoe7/eEYciAKNnKVR3gUhq+zOf+czxWc961ioGlwf0OU9xPXzIFfMWH3IXgqvzdOLg0NwVH3LFnBK0lA95q9a6fMivflqNpZvwIW/VmuNDItv6HpChyBM0pWaWksouQD372c8en/vc547Pe97zVjF4a3o6ENzmQtcNnKjkRkDxRtD0q5a0uRBkuJbtlzJEuAMhsxjdNwAy+/8gynAiwh2Z6aeiy7nW/weoXND0q5a0uRAOSG8sdaJMGXIhs2RzIRJUWb7gRMo4hJ4BWGYx/XVAkqkA1XOe85wJSM9//vPHF7zgBeMLX/jC6e8A2QZcpcxFUDaxuZS6YOA3hFt9nWQ7sABlaeAGVpUEFlbfBJaXTd7NbOXAcpLtVl+ARfjJLwduuJ9coCLc6us9gQks3s1slcACXGSuLI+UTd51YAEOfFMo5QBL3yewiASWZyuABahWwHKngYNqiSnP29lLpjwHFeWQAFy8WxIyvV0KpdzLYa8pz8VIyDNRAhXlkCEf/m7qTTVTHqByUx7h3c5+ivN2difQXg5Te6qZ8lLITFNeDVQM9yBr8S53gyLrz3jGMyY+RRlU1iKGHpW8JWYKXJuq5D1i5j6lkveKmX7U36dU8nXFzBK/wvHACZJ3ARfEHXCJZyl7CWT6O7im5IDKLhvaupgOU2p6gIj7hXACihMhgRui1PTgbe2UOL/Hc/uM61w+qigtM94S75pSaSqMdy6XupYdXLS1+4WwAyq7bDgZ8m7NTuwlzm3DAAq3A+E+LrfMAC6IuECS2cntM2hc3n2TF89kLp0KBTCVRmJ/o9+M0c87lvc3+tWNfvix9mL029+avL81eevWZPQpn7yHg9TBhYM0M1aWQ9enPGMtcZB6xppzkNJk6gcGd5DWyuE2HKSesdJBSsaiBObp00+lJR9Wy0FKxnJZgyg5SFsZKx2kAlfNQVorh+kgnTLWOpNhMlvVJsPk/KqcIbrJZBg4VnbuwLM2mQzjANuTk2HIVq3JMD6/Kjt31p0MU1Ld/S7RAenZCo4lIDnH0ucBezAnw9LVTnqnkBzyOoc5BzkkLV0IfjKseadK0/f8VDg3JM3lirzO8VNhtmVlk+mcd2rd6Xt+KuTdvM7JU2HNkNeavkem4mTn3ilX3fV9ngoJf1e/9+scToWcCFenwqVOBLcglxpL3Ua8rhNBwHAdqteJ4OKov5uE223EmzgRBBTXodyJgEjq6vucjXhXTgSBxXUoxFGcCK6+AzJOjdwron/p95wE0bCkW0kYfdGLXjSFPg89RNvvEznFeZba1Ing04xLTgTnROs6EQSMOaKdpzfuEJ0T8W7e8XGK8+zhRNuveGpE24fH+kRiJ9m15gci3807Pj/FEXrW9wKaK/HcKULSPUtJtwJQL3nJS8aXvvSl48te9rJVDKVJefCh0qQ8SHaCyh0FzonWmZTHGEaIcu+kPDIbbgQfHuuZhhK0KR9iDCOcxvmQXxDnSY5wgq7fJx+iBPXwIR8llHwoL47RnQgkAgeYN0Y4QdfvVeoEKmUnAerlL3/5+MpXvnJ81ateNYU+D70WF0CVFhdlHJ9/Tjkieiwu/m5aXFzMzMvhBFfL4qKM4/PPl1hcSmJmWlxwFWwybANO5DYVxMylFpeS3lSzuOhZ33v2AmC0dWX5032gMpWyE6B69atfvYoBnlOa71lTyVHYU8hMlbw2O92V8ppKzimsppJDsFMl9xlVvJtCpqvkfjHs1y4OqpZKTglMYPl8z16VPK9gUiXPaxfKIlmrBCy/gkmVHFCRtQQsgQhQEd7trN8DrBe/+MUTsF7xilfsBVj6PKyz8cFJ+9zGh9LVS8/Gh8xa62x8cNLes/EhO5dz40NePGfWam18cN0pNz44aW9tfKhdvaCUezmsGfMAiIBEUAYFKif0hHMzspYuneFXgEuh7KW/g18It6blMbcdQbMELvgSBD4VcrfQEBD4vHh2cFHaiJKQiT8LUyCCZglc8CW/EHYhM8cQOYGvgcu7lp24l6blET66sQQu+BLHfs9QaaHx2e2l9izABV8SSAgHlBN3JAmfKAO4RN6xyyhzQeCJwUudA8rdDnONDxj1uErJxoeSJ4vADcG7CS6XG1y0LHmyvPGBdx1cSAZcpXipK41fhNNhX/Z3E1y1xgcA5W4Hwg2CDi4kg8xCnp3SPkOk3JDg4oTnpQ5Apa7l89u9nd7BRVl0uUExcFdX07BQ5L1jueR1d3B5qet1kJbEUQcIJc4zVAqlLQepyw41B2lqWHA598iXxNF0kPrFcAqlcDnneCWvOy5Qv6tb4iBNcbTmIPUM5UKpl18AlgcBZAd3kBIrYLlmlX2ADixv9XIvVhr9HFipwLvZz9ea1EoiwCIcWGml8cOD3w86sNzol5pVKvAtYJWyloOq5ntvGf0cWGQeAYQozViA07nFptax7NqUG/0Alp88iTT6JbAQSQHVZPSrDfDwMujW5Bzg4TPUXXl3a/LcAA+f3leaseC2FezJpazVGuDBRGKfwJcDPBJcZC3Kob+bF8c1azJZy63JrQEeTCT2CXyUw7kBHiVrcl4cw5dcp9JzZi0AVlLe0cO4ynG/OzGURg6hus+NHGoN5t/GyCEfG7TJyKHWmOttjBxyN8I6I4dKU2G2PXLI3Qg5cshVdyfuhHMz/T71LHEqJ+/6PDjfqe2o8dFE3liauwBTefeJLQkyV969h7DUsuWTWWqTiD2b+XC10i5AV96JkoZFJnPHJ+9myxZCp/OdkjGvpbzDq3zImSvvyA0lY55nNn83lXdE0lTe82qHTFZS3uFVAErKO1IDMfTsAexxIrSmuOzKiZCbtXqcCO4mWMeJQOnrdSIs2QO41IngCnw6EchSc04E3Aj63kEG0NwZ6llKEgNXOa95zWvG1772tePrXve6VQw9fMj9Vb2tWmnvzSYI50OtxlJ3WvbwoblWraV8yE9vrcZSb9Valw/lKMbkQ9mq5WQ7W7VKjaWlVi3atfS9QKNs5Io8Cj3vIooqQ0llF6De8IY3jG984xvHN73pTasYfPXHOgu3041QWriN7lRauL1kzPWStR9Lx1ynkLnpmOuakEkWc07UmouQY65ruhPhgCRTASrcCAiaCn3G6qL/F3j0WwGMSFApU+naBlAJSG95y1vGt771rasYavtkPHM50XaLTGuTuxPtHMXoM6lK+2RKfnJf9uhE2xVyb+lassl9HaJdcmjmPpm0+gIswj1XpcGxXKUk0c6THOBypbyUrXAkOLAcXJ659HvCPVcAS9lKJU+Z6s1vfvMEpre97W1TTMCqbcBKYx5Zi1Oc602bbsDyobNJuJM8zw3m9w1YqTdtugHLr2+ScPuyIsJPb56tCC+hrjfVBvM7qU55gPCOZf0+CTenOIGJAFQCD6CiJHrG8otncStlrNe//vVTxhK4lLWIYZcq+bpzFnpU8tachXVV8qVzFva0St47Z2GpSu6Acn5F+OAPJ+6UQ4HLOZY+Dz1LvL2lq9efvmSJd+tUmOtBlmyHX+pPLy3xzn7BHn/60iXepVNhbYl376mw15+egMLtkKdCb6fvOhXWjH5unyHW2arlsxXcPrNklpVfBO9ylhVzPV00JTadZeX2mXVnWZGh0j4Dn4PftWZZ+UkvjX5un/EDg+tfgMv5lsCFF0sgm4x+2apFO/wuWrXck+XiaG+rVsk9um6rVqkdPnsB3Z68T7dqldrht9WqVfNkEYCrdBAoWZMno9+u5yyUHKT7pTkL7iCtNZbuqTkLvQ7S1pyFBBbgovz1OEgFrJKDFBfpBCwfdV3jV6zMpQzWshVE3NfT+v6/7NzhPhFAeiOFgwqeRdTKIQHPag3n99OeC6LpeycckN5I4bZkTnpEiV9xj+it87VsBc9yncrLoXfuEA5ITpfuoYKIIyW4IJqZi0hQebYCVPAsYtinLS6lWVZ7wuLiS41oTy9tfdivW1xScihZXFJ1pzwSJeKO5CBAiayjvhMDmQORdM6J0NpP42Wox4nQEkhrC7d7nQgurpbKF/xmXSdCNkAsdSL46hHezfK1RCCtORGyAcIFUpwIvQIpl85c5zQFUnde9joRPEvliGt3IpBBSk2mc06EPMX1OhEQVL3VS5lmG06Eknblpzgn20ucCJ6ltuFEKGlXfopLJ4KelYEENL/iIQCUZyllKAFKQHrHO94xvutd75ri3e9+9/R36OVDZLbkQ5lp1uVDJXsLnGYbfCgzzRI+RGbj5Of2lnX4kMsCcxOJXf1ewofS3lLjQ8gD+l7/L+CRyQgBKu8HAdU73/nO8T3vec/43ve+d3zf+963igExs7Zs2wVNQFXTm+A1CJl+3bJEzPSL4zkx02UCwsXMmlK+DTHTL45TzEx13DOYL6Js6U01MVOASTGTKImZfnEMJ5JartBnAEYXs37r7fIuhHKFo3KnTKUMJVC9//3vHz/wgQ+sYiip5Jm1aip5a7bnpip5Do5dRyXvme25qUqeg2PXUcnX8U4tVcm9jKVK7sAia5WABa+aA5Y+D8gDpa0PXga9X9CzlpdCV8m9FKbrE1KPCyJLYdpdKIWEl0KfnOcKuZv6BEYvhQCEzOVKec5baJXCtLt4KfR7vORahM9nSH7lAHELSxryXCmvlcK0u6CQE14K4VpeCl0ETX6lUgi4KIP6vFPyvomNeI68w61KNuJNyXvPAI/9yfveybsAtiLvXurmRg5h8kv9yk1+Tsg9O6V9xkcO1VwNgIurlJ6RQ0gNGATTMrN05FBqXrWToXctLx05VLLMlEYOeXZKyYFIk1+CS8DJkUMOqNS2cEPwrltmABdSw9vf/vYJZPq7NwcpRr9dOEjdouxerF04SNPoV5pnlUa/bIV3F+k+7SD1q5Y97SAFYIscpPsmo18ta1HaNjH6ObBqRr/cBVgz+pWylhv9cm5V+rJaRj8H1qZGv1LW2pbRL4GF0U+gmox+rrwnuOasyaXN8NuyJvtYxqXW5Dnl3TPWNpR3V89ddc8yOGdN3kR5d2tyTXlPa/JS5T0bKZrKOxLCuoP5042wdDD/umOu5wbzt8Zc5xLtHHPtAFt3zHXvYP6lY66duHvnDuETZUrzrDjhMXYIaaHEswiyHCKrg0rZCQKvEyIxeFmqDUpLucEvnVtyQ9qG3eayRG7AFuxyQ17tuDHPm1Ln5AbC5YYcQ+RuhJbc4NNZ1pEblG165Qa3ubgxD7JfOxGiR6XckKdDMllJbpBImnKDq++T3FBzIriduOVEYKvWJk6Eljjqc6R6prjMbdVqORHgQes4EXyrVu8Ul9ZWrT3pRNAzQimaFkDDwMe7ZCkXRyWKfvCDHxw/9KEPrWIga5TItnMhP8H5ROLWCS63MiQfap3gfFhGq1UrT3Dwod4T3CatWqWJxDkPwYeZzZ3g0vOUJziySDaZQrZrrVp5x5cnOD3re4HGr3kIwIjizv2gFHaB6CMf+cj4sY99bPz4xz8+hT4PuU9mbu1H78Jtv78rAWzJwu3S2o+UCHzUdWnMdc8+GcYvOhfytR+9C7edC5Wm5RGlMddupPMLYELPCTAymU99qYHK9SZCzwCMLhv9nnCCTqYCVB/96EcnMH3yk5+c4lOf+tT0d6gRbZ+h7rM9e4l23uHlsqLWBqwS0S5twHKF3Mn2ukQ7lxUtIdqlDVi1GerrEm13HzjRdrKNUr6EaOuZ05wA5OCirYt3yVYqfx/+8Ien7PSJT3xiBapPf/rT09+ujIVCvmQwfwlULmp6xmoJmX5Pt0nGYjD/0ow1twGrpJLvKmM5sU5Rk5grgwJPb8ZSWezNWA6uKWM5xyppTi2OVfKVk322wbE2aYd3fpa+8m1yrHU1J+dYvLtLjgVPco5FlurlWICri2PVlni3JsP0bId3R2fpZDg3GSaV8nWWePduh8/JMC4/zE2G8czVWuLdmgzT2g6/zmSYmquhNhnGAeVuB++88Xb6BJdOhCqLAphCnwe/EK7tWyaQHRxc6R515bsmNcDnEFN5twYuMhBRanqg9CKiIjkArnSPck9H1DZrEd6JUwMXGSizU+pZhDtPSxvi/Z6OKEkNlF5fZ9IClxv9PEO5nuUHBneeAi7nW9KukBwUk9HPB6Wts8Q7Z6kvXeLta00oqbUl3kTPEu9W/597sXKJd4qkucTbS+o2lni39ixnq5bf4blI6jPbfa0JJdW1LLJPmvxKIilB72AeBMTNZJEBXMpexFCbvrcLYNWm7/UCa5Pt8Ams3j2AmwKrtAewtR3egVXaDl8z++0SWGStGrBQ3PcCLNepWr537hNpSnWDn4PKy6CT9bQnE6jvvFu643NPVG0qDOXQbcl5unRQcdrz/cdpTwZgrr7zbkoPcCU/9bnv3c1/hAPS9TAHBic9V91L9mQi303pAa7kOpWe0/dOOUR99+l96GFkK2zJlELF4FuzSqq7D/eobXzIicS58aFE4N3i4u8KVLWND25vKRH4uY0POZE4Z1nlidC3PpQWTPZsfOBE6BksLS6lTJUbH9w3VSLwhF8F6fd+ncO4oZxlVSLwZDH3yJcyFXqWSLukB2JwN4JfEqcTobZwO1vYW06E0sLtkvJeW7i9xInQmryXHAcy7U6E2sLtktTQciKk7MBJ0U+QrYEbJeUdgdSV93QipNRQE0jRsfS9yw4l5d0Je0l5l4ZFDO5EKHGhdCJ4R86unAh+itvUiZAbtXI80CZOBE5x3lXT2/jg44F4d1dOBE5xiJvpRNCzvhdoAJnfJbqvXb9XqVOGEqAEos985jPj5z73ufHzn//8FPo8rMOHXBpwPuTSgCvhpT7AOT6UulMvH6pJA9vgQ63GUgg2nGgJH3JpYBt8qNVYWuJDegZkNEP4lY+f/ACVlHZd3whEX/jCF8YvfelLU3z5y1+e/g61EYw0maaY2TuC0XfzLRUzS7bfHjHT51OtM4JxqZhZG8GYl8PeZLrJCMaWmOlbTmuNpQBDJUy8iNAz/YCewQjXqrgfVKb67Gc/uwLVV77ylfGrX/3qFPq8U2Ctq5LvD6z/AWBBoimHCap1pAEn0ruSBgCVl0JiiTSQix+zJb5HGvAB+06kS6DycrhPSwMCFKUQULVKIdc3IuylUqgyuCqFHPe5FC5NzCsNNMvN8D7QrKaSp+OhNcBjbqCZQJcT81zM9Oub1kCzXPyY8z1bA80ErjmVPCfm+RAPH2im3/YMNCvNWCip5MmzRMpzgIeA4gReAPJTIuHv6vecBgGXMpdIuwCm0OfBS13PbM/sWk6pwZc8Uu6WzvZMEl+a7QmgSrM9S13LDq5cfpSzPX3fcmu2Z5J4X+TdM9uztPxobranAyr7Ar0DpzUdxruWyVBpn9HvCZcpKKkOLk6GAphCn4fejppt6Fitjppt61hp9Esda0lHzSY6VqmjZh0da2lHzTZ1LPoGazqWe7FWOlbN6JdrcjH7lYx+uSE+2+F7jX5z7fA5t8qbTN39UDP65Yb4NPpl97K3xC9th0+jn4OqZfRLV4KDa4nRr9RYWjP6pXvU+ZzbbEoHAfQswEVsJWP5dcwmPYC586aWsdKa3JuxfEHk0h7AfHdXPYBMJN5WxlpiTSZjlazJpYylE2I1Y+WY6+z/mxtzjRuhNT+9NNqxNOY6y2DOT8+2rBzt2BpzjRvBCXzvmOsSx+odc43q3jvmGjcCBN5lBD8FJsAI98pnGYSEIyEQqbpzh5hu0yyD+N1F4J1jKYba9D0/Gbopr3f6npvy8mrHO3hKprwsYS1TnoPMu3nWmb631JSXJczV95y+5yfDdafvpX7lUoNnttb0PU6DPn3P9SukBi+VPgMLLYvrHE6EX/ziF1fquz4P6zgRnKzPORFK3vT9qhOh5pnalhNBmWbOieDkOv1UPU4EPOruRNAzmpZLEATiqJN1ZSgBCnH0a1/72hRf//rXp7+Dc6JSD6CT7XV7AClX2VEDJ9qkB7DmRFjaA5hOhH1DD2Cto4ZytUkPIFxInwUUfZ8gIwCjZynpVRJDBaJvfOMb47e+9a3x29/+9ioG50S71pxy1LXPW+/RnEr7ZDbRnHrWfuxbNCe4EKVqm5oTvYAJMIIOnBKovvnNb05A+u53vzt+73vfW8XQswHLiTZKeWlwLEo5Jal3A5ar7DkbwYk26ngq5BBtgiyXfnLIMqDq2YDl86n83ZyNgNrNFiwn2z7i2udSkeXS9kumclD5HV5J0CR4t9RYquyEUq6rGELP+t6zl34vgAEq3tXvxaFU/shUgOr73//+KoYlJ7glG7CWnOB6NmBtcoKb24DVOsHlVL3WTplNT3DKWK0TXGYslwgyY9WEzMxYZC3KooMKLsbpzzOWgCU+JWB95zvf2Qu4pozFRXAO43cyv6QdHr3J2+HJVNkOj62mtx0+Z6WnoEn4TNBWOzwTWtCdSp3L22iHd0ETnuVkv9YOD79KzYlM5ZoT0dMOTys8JZBMBaBwPBCuV2XWErgohwIYMXARzKVwab7n0h01fiFcUsi9c3ndHTUAyt0OxJIdNVn2cj1uz44aJ/B+IVxSyH1a3tIdNSWF3PsCkSZKVmI/1VHesuwJQO52IPxd/T7BBYEXwBT6PEDKIeglTxaB5EBLfClzkYEIB1SuLHE3RB4CkBu4SnFy7gNjsyfQewZ5NzMXo4Moc66Kp32G8BlYDi4sMz6QgygNjIXPwfH83cxcfk9HOKByZQmSg7fEAy6G/JOBKHN0LuPJcrcDwbsl96jAJSIvHxaSgz4PPiTNh8XmNL4l69pqS7x3MSSttsS7d13broaktZZ4uwtinSFpgMq5HNEakuZ6FABzT1bak33xkoujHATQsgCXspdCn2eBVZu+l42l+9bpe9lYuovpe4BjCbBy+l42lm5z+p44WWn6XglYfkhIYPlVziywvBSmPRlw1Uph8iwf4LGNUujlbL9WCp2oJ7iyFJYaKfbzpdBb01tjh7hPdCcDoOKOj9MdNhdvy0r/O4ECX7vOcV2KKKnuTuB97rqDijs+Tnfeml5S3QGZl87adY6T92zLyqsdwueuAyq/4+N0p4zjbVmlJlMC8l5rLPV5VkRJdYfAk+HyZOjXOToZQt4lP+jv0LNwexfKe+pYe1p57124vanyjo5VU95Tx1qivLd0rD2pvLuOtVLe/1edCK09gC0nQk5y2SedCKU9gFwSb9OJgE89xVKfK+qXzip1ylDSrgSiH/zgB+OPfvSj8cc//vEqhm3woZyW18uHagPNanyI6OFDrcbSFCjX4UOpO63Lh1qNpTnQbB0+lLpTiQ9BuAEZ1zwEnTiZpaSyC1A/+clPxp/97Gfjz3/+81UMvWOua4uKcCP4FqxcVDQ3gtHfzfLl+2SyRKXNpbWoCDeCb8HqHcHo+2R4t2dR0dIx1+5GABguaPpVS9pcCAdk9v8BKjgRoWd9T+uWd9voLxkuMxWgEpB+8YtfjL/85S9XMawz23PJ/PTSWrVN5qcvme3ZuwGrd59Ma/zi3GxPlwlawGrN9gRcZC4n2W7gy9kKvcBS5vLySHimA1hkq5/+9KcTsADUr371q/8HLN+CVQKVjwha15SXS4q2acqrbcD6XzblAapNTHkCE6a8BBWDPQh4mV88i6yLW4lPAS5lLWLwy+B1xUwHV69KnmKmuxp2JWY6uDjqLxUz3dWwDTGzNSVvG2KmwDUnZiagOCkS8KwauCiJAhgx+AhGB5S7HUqzPdMy47M9Nx3B6OBaMoLRl3h7O31rtucmIxgBl7fE5xJv37ecncv5bs9sTwdU9gXihvCWeAcXJN65E1wKQLnbgfB30zIjcKks/vCHP5wAptDnrRj9HFzrGv2yTatl9Mut8L1GvwRXr9Gv1lGzK6Ofg2tTox9tWjWjn2tZaZ9JJ0TJMlM1+u1Ja/KebIdv+d1L7fApkO6b2+H3tDUZtX6RNRl9CnCVDH+9PYDwpsxYuf/G1fdWD2BmLHeQkrHSQeqnUt5t9QAmn8r9N0t6AEsZKw1/7iLNd5f0AHoHc/YAZsaq9QCWMpbLGp613EGaGUualmesCVjrzE/3bJVlMOenlzjW3Pz00tih3vnptckw25if3rIl50lvyfx0z1bb4Fg1W3JpMkxNdS9xLM9WJY4Fz5o4Vmv6nk+MoWR6D2FpNgKalA9J83lWzrsomzkkzZduzw1JS97lfIx38zrHLS6bDEljNoJLDr1D0tziwrvZWIps0DMkzflYDkkTMAAUc6wYlOYnQ1fdBTLCr4JookAkFYiQHKS+K6ZTYcmJkOp7yYmQTs+0EW/qRMi5COs4EdLesm0nQhJuytBSJ0LaWzZ1ImSWSsLtNmKsxKm+6/eE61/6vWtYApJ0Kwmjv/71r6fQ56GXaEO2k2gzMc8nEq9LtHOCC2R5G0SbiXk+kXgJ0c53k2hzzN+UaDMxD7LsnGgJ0Ua76iHaetb3DjKEU8RU3iVLSbcCUL/97W/H3/3ud+Pvf//7VQzrTMpLULUm5ZX4UM+kvFxWtM6kvATV3KQ8+FBtUl7r9DY3Kc+tLjkpL0G1Dh/KnYE1e4v4kLINnEihZwcYnirCCbqDStlJgPrDH/4w/ulPf5riz3/+8/R3cJuL86C0uDio5pZu1ywu2b6FzSX3yTgnQu3edNhG2n69q4XSVAIX4XPXvbF0zuJSGrbhJDuNeGn7RcgkHFzevpUWl2wsrVlcINslcBFkOS9/ug/8zW9+M2WnP/7xjxOg/vKXv6xiSJXcr10cVETvoiIHlc/3bKnkrUVFzndKs9P9FLd0UZGf4koqufunehYV+cWwNycAKqKkkjuwULoTVH7t0lLJS0JmquQAC5lAmcmzlgPLL54TWGQrgUp/h96ND+70zItnlG7f+ED0bHzwdwUudyW4Sp6LH7NzOTc+5MUzSvfSjQ8llTyNeQDEL4SXbnwoZS30JgBV61wmsh0+7S4ARNmHyEzlrge3HLeylsqhAEYMiJqt4R1IET66seRNhy/5hbADyt0OPrs9u5bd9QlfgsB7hkoLDeGjGxNcZC5KG1FqheeEiCzhgmaCC77kF8IlCzEnREyBNW864HIC76XPibvLEVht/ETo4PKWeIVnqLTQEFhtauAS14LAEyu5oTYw1iUHvFitxgeXGjgJ5uKjObnBLcVzcoPbZ4i5xgffO7OrxofawFg4nc/3XNr4gCfL5Qai1fgAQMhAlLuaJyvlhhq4Um5QDJQ4yl1pm4Q7SL0jpzQ4FunBHaQplMLlUOkBV2lwrDtIidwKX3KQOjBTdgAgAMwzVAql6SDNg8CcgzQ1LLgcBwgHZsoOAMRLnc9WcC5Xc5C67AC4kBBSWnB7MguXOEA4MFN2AFyY/SY/ljsNar73BFapsdQzD9pUlj73Zc0By7MWpc2Blb53N/uV9gDWgEU4sNJK4xab0gkTcPh9nZe+9L0nsLKxtAdY7stysx+Hh9IJs2T0A1jpe4fTObA8ayWw3J48ASsHeJC1lgzwyAl8tQEenrVygIdbk3c1wCMn8K0zwMOtyT0DPMhaSwZ4+B1fa4CHZ60c4OHW5LkBHjVwEVwDlRopdEIEUCqHxFAbOeRl0cn7kpFDNdXdxw71jByCvPvYoVTdARkEfp2RQ6XRji3yXhs5lJpVlsV1Rw7VVHdA5h3OLf9Ujhxy1d2tykneHVQt8q7PVeU9h/W7IS95VUl5J3p21HgPYamx1MsRnKfUZNpS3mlh9z7Anh01OcSfd1N5RyR1vtO7oyYH8rtFxcuRSw4JMsI9V/p9ZhpE0h7l3aWGVN5V+gAUUgPq+6S8965razkRsvlh6bq29KfvyomQzQ/rOBHmprikE4GmhyVOBOdELSeCX+/UnAhJtktOBAFF3yfI3Bla068Eor/+9a/j3/72t/Hvf//7KoaegWZzrVolPuStWnN8yNu8kg95qxYZpMSHslUr+RBGOp9IvHSgmb+bfKjWqtUz0CxbtWh4gA+ROXr4ULZqJR+iVcv5kJ71vUADyFDkuf7hXS6dlaWksgtQ//znP8d//etf47///e9VDK2F2wCsNObaPVM55rp34XZrn8zcmOu5tR+Aam6fzNKF2+mZai3cdmXcuVBpzDWeqbkx1yUhkyxGduNdz1QODJUwZRwBRH+xuoiEexYj/F39XqDStQ2gEpD++9//jvzT5yH3ydSWFfk+mfSTl/bJ5B1eZi6f7dmzAat3tmdtA5Z3wdT2yZRme87tkykBa+lsz8xWtdmezn8ycxHumyqd3gCHQEXoOTMX4KJsukWGbKWSJ2D95z//2Quw9G9wl8E6G7By8EbvBixf9s0pDpXcCTcnsU02YJUGbwAuH8yfpzdKImWQUxwquRNuTnHuNCiBipLo/YIlbkUp84tgJ9UOKgZ8kLXyCsYJN6c4gcmzVakcEmhVJW71j3/8YyqDgIsYanMW9s0qeenqpVcld6W7VyV3nrVUJefqZV2VHBGzpJI7iV9XJUfQJEullQaeVQKXyiHggmcRg18Gr+tP7z0Vlvzppa7lPXEqJAO5tlQaPbTpqXDOn97yYjlvys7lXn/6pqdCToatUyEkfi+nwt5ZVi2jH2a9TWdZpWVmzui3ZJYVE2K8JX7JLKt8d1ezrJgQk0a/pbOsEEZ7ZlnpObUsSi8cr+RqEN8CXHsz+i1t1Wot8XZNapNWrZ4l3nPt8L1LvDdp1WKJ9zqtWuss8e61JmerlsonIqdbk73k6fs5azImQQ4CaU0WuPZiTZ5zkLq1pnfOQl4OZ1t875yFvARe10HqQzyWzllIB+mSOQs4SB1YLQepD/HY1EHaM2dhXQepgKVyKGC5g5QYkBLQqkqDPAh4FiS+1LIFzyJ8MkzOEPU9OamHASqIuOtUOWrIp8J4Rw/vZssWJB4pwQXRzFyEA9Kvc/BQQcRdTvBRQzlDlDtFn96Xd3x+AUyUVHe/S3RA+nWOA4OTnvOrLId+l+jvZiMFfnfUd4U+/x9G2OtBKbUo9wAAAABJRU5ErkJggg==\" />"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "//Pointwise add FUNCTION\n",
    "functionOutput = ij.op().run(\"math.add\", sinusoid, gradient)\n",
    "ij.notebook().display(functionOutput)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above invocation calls a `math.add` `FUNCTION`, which allocates and returns a newly created output image, without mutating any of the input data. This is great from a functional standpoint, but costs space and time. Now suppose we already have a properly dimensioned output image allocated and ready to go. In that case, it would be wasteful to call math.add as a function, only to then copy the data from the newly allocated result back into our already-existing image. Much better would be to tell `math.add` to use our existing output buffer directly. Fortunately, this approach is made possible by `COMPUTER` ops, which require you to specify a pre-allocated output buffer where the result will be stored. \n",
    "\n",
    "For example, imagine you want to do some analysis on a large set of images which would require to run e.g. a Gaussian smoothing as an intermediate operation. In this case, this intermediate step would not have to be store as it is not part of the end result. Thus, we can make use of the `COMPUTER` paradigm. We pre-allocate a single output buffer to store the results of the smoothing and continue with our analysis for the current image. For the next image, we simply overwrite the same buffer and continue. This approach is much faster compared to calculating the smoothing using a `FUNCTION` which would create a new output buffer for every intermediate step.\n",
    "\n",
    "In the following example, we will first pre-allocate an output image and then write the result of the addition to this image:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;charset=utf-8;base64,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\" />"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "// Perallocate output image\n",
    "computerOutput = ij.op().run(\"create.img\", sinusoid)\n",
    "// Pointwise add using a COMPUTER\n",
    "ij.op()run(\"math.add\", computerOutput, spot, gradient)\n",
    "ij.notebook().display(computerOutput)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above call, the same operation is performed, but the output is written into the provided \"result\" buffer. `COMPUTER` ops do have an important restriction: the output buffer __MUST NOT__ be the same reference as any of the inputs. Otherwise, the computer would end up mutating the input data as a consequence of writing to the output buffer. Hence, computers cannot be used to calculate results \"in place\" -- that's what `INPLACE` ops are for.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import net.imagej.ops.special.inplace.Inplaces;\n",
    "import net.imagej.ops.Ops\n",
    "\n",
    "// Create a copy of one of the example images which we want to mutate\n",
    "gradientCopy = ij.op().run(\"copy.rai\", gradient)\n",
    "// Create an INPLACE math.add Op\n",
    "inplaceOp = Inplaces.binary1(ij.op(), Ops.Math.Add.class, gradientCopy, sinusoid)\n",
    "\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The maximum pixel value is: 308.6507787063433\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;charset=utf-8;base64,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\" />"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inplaceOp.mutate(gradientCopy)\n",
    "max = ij.op()run(\"stats.max\", gradientCopy)\n",
    "println(\"The maximum pixel value is: \" + max)\n",
    "ij.notebook().display(gradientCopy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above invocation differs from earlier: the `Inplaces.binary1` method requests a `math.add` binary op which mutates its first argument inplace. A BinaryInplace1Op instance is returned, upon which we then call mutate(). In order to show that the content of the mutated image actually changes, we also calculate and print the maximum pixel value of the image using the `stats.max` op. If the above cell is executed more than once, one can see that the maximum of the image actually changes as values are added to the pixels. Note that we stored the `INPLACE` op in a variable. Such ops can be used in other ways too; see `REUSING SPECIAL OPS` below.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## UNARY and NULLARY OPS\n",
    "In addition to binary ops which have the `ARITY` 2, there are also `UNARY` (one primary input, one primary output) and `NULLARY` (no primary inputs, one primary output). Next, we are going to look at an example for both. First, we look at the `UNARY` `math.sqrt` op which simply calculates the square root of a number. Note, that this op is also a `COMPUTER`, meaning we first create an output container to write the result to."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "The sqrt of 64 is 8.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import net.imglib2.type.numeric.real.DoubleType\n",
    "\n",
    "// Create preallocated output\n",
    "number = new DoubleType();\n",
    "// Calculate the square root of 64\n",
    "ij.op().run(\"math.sqrt\", number, new DoubleType(64))\n",
    "\n",
    "\"The sqrt of \" + 64 + \" is \" + number"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Second, we look at the `NULLARY` `math.zero` op. This op simply sets a number to zero. Note, that the variable `number` still contains the square root of the calculation from the cell above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "number == 0.0? : true"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import net.imglib2.type.numeric.real.DoubleType\n",
    "\n",
    "ij.op().run(\"math.zero\", number)\n",
    "\n",
    "\"number == 0.0? : \" + number.equals(new DoubleType(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reusing Special Ops\n",
    "One of the most useful characteristics of special ops is the ability to look them up in a type-safe way, obtaining a reference to an op instance which can then be used however you need -- one time or many."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "net.imagej.ops.math.IIToIIOutputII$Add@3ce19b95"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import net.imagej.ops.special.computer.Computers\n",
    "import net.imagej.ops.Ops\n",
    "\n",
    "// Create output for the COMPUTER\n",
    "computerOutput = ij.op().run(\"create.img\", sinusoid)\n",
    "// Get an instance of the \"math.add\" op and store it for later\n",
    "addOp = Computers.binary(ij.op(), Ops.Math.Add.class, computerOutput, gradient, cross)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we use the `Computers.binary` utility method to require a `BINARY` `COMPUTER` `Op`, so that we can take advantage of the BinaryComputerOp API, which is type-safe -- whereas the Op interface's `run(\"op.name\", inputs..)` method is not.\n",
    "\n",
    "As usual, compile-time type safety is a two-edged sword: helpful to avoid coding errors, but sometimes rather verbose. As this notebook uses Groovy, which is a Scripting language, we can get around some of Java's verboseness. In java, the line from above creating the op would look like this:\n",
    "\n",
    "```java\n",
    "final BinaryComputerOp<IterableInterval<DoubleType>, IterableInterval<DoubleType>, IterableInterval<DoubleType>> addOp = Computers.binary(ops, Ops.Math.Add.class, computerOutput, gradient, cross);\n",
    "```\n",
    "However, the verbose way of doing it helps to understand what it going on underneath.\n",
    "\n",
    "In the cell below we can now just call the `run()` method to execute the op using the inputs specified above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;charset=utf-8;base64,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\" />"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "addOp.run()\n",
    "ij.notebook().display(computerOutput)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or we just run the op specifying different arguments by calling the `compute()` method. Note that for the `compute()` method the order of arguments will change. In this case the output to write to will be the last argument. Also note that the name of the method coincides with the type of op we are using. Here, we are using a `COMPUTER` op, hence the method is called `compute()`. In the case of an `INPLACE` op, the method will be called `mutate()` as one of the inputs is mutated inplace."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;charset=utf-8;base64,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\" />"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "// Call the op using different inputs\n",
    "addOp.compute(gradient, spot, computerOutput)\n",
    "ij.notebook().display(computerOutput)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An op may additionally have secondary inputs. These are usually algorithm parameters or an `ENUM` that specifies the behaviour of the op. One example would be the sigma value or the out-of-bounds strategy used for a Gaussian filter op, which would look like this (sigma = 2, out-of-bounds = Boundary.SINGLE):\n",
    "\n",
    "```java\n",
    "gaussSigma2ComputerOp = Computers.unary(ij.op(), Ops.Filter.Gauss.class, input, input, 2, new OutOfBoundsMirrorFactory(Boundary.SINGLE))\n",
    "```\n",
    "Note the difference between primary and secondary inputs: the latter are not given when calling the special op's type-safe API; hence, secondary inputs remain fixed as given during the original matching (when we requested the op). Thus, the `UNARY` `COMPUTER` we saved in the variable `gaussSigma2ComputerOp` will always perform a Gaussian filtering with sigma 2. Example usage could look like this:\n",
    "\n",
    "```java\n",
    "gaussSigma2ComputerOp.compute(image, smoothedImage)\n",
    "```\n",
    "\n",
    "The variable `smoothedImage` will now contain the smoothed image using a sigma value of 2 as defined above when we requested the op.\n",
    "\n",
    "Which inputs are considered \"primary\" is a decision left to the author of each op. But here are two rules of thumb:\n",
    "* The primary inputs and outputs should correspond to the main relevant data structure -- e.g., ops which operate on images should declare the image argument(s) as primary.\n",
    "* There is usually an anticipated main use case involving variation of specific input(s) -- those should be chosen as the main inputs, so that the op can be reused across different values of those inputs. Hence, the inputs that change when executing the op several times are the primary ones, i.e. in most cases the input/output images."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reuse an op to improve performance\n",
    "It is important to understand that each call into the Ops framework requires the system to search for the best-matching op amongst all those available. This is very useful for reasons of extensibility: the best op for the job is always selected, and anyone can extend the system with new ops optimized for particular scenarios. However, matching can have substantial performance implications:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ij.op().run() needs\t2812 ms for 200 iterations\n",
      "addOp.compute() needs \t14 ms for 200 iterations\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import net.imglib2.type.numeric.real.DoubleType\n",
    "import net.imglib2.IterableInterval\n",
    "\n",
    "slowStart = System.currentTimeMillis();\n",
    "200.times {\n",
    "    // Search for the op every time and then run it\n",
    "    ij.op().run(\"math.add\", computerOutput, gradient, spot)\n",
    "}\n",
    "slowEnd = System.currentTimeMillis();\n",
    "println(\"ij.op().run() needs\\t\" + (slowEnd - slowStart) + \" ms for 200 iterations\")\n",
    "\n",
    "fastStart = System.currentTimeMillis();\n",
    "200.times {\n",
    "    // Execute the same op instance repeatedly\n",
    "    addOp.compute(gradient, spot, computerOutput)\n",
    "}\n",
    "fastEnd = System.currentTimeMillis();\n",
    "\n",
    "println(\"addOp.compute() needs \\t\" + (fastEnd - fastStart) + \" ms for 200 iterations\")\n",
    "\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As one can see, reusing the same matched op every time is much faster. However, it is the responsibility of the caller to ensure that subsequent arguments passed in are compatible with that same op."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ops Chaining\n",
    "Some ops accept other ops as arguments, forming chains or trees of ops. For example, the map op executes the given op over all elements of an iteration. In the following example, we want to take the pixelwise square root of an image using the `math.sqrt` op. Unfortunately, this op is only applicable for singe numbers compared to the `math.add` op which can already handle `IterableInterval` for convenience. However, we can make use of the `map` op which expects another op as input and executes it on every element of an iterable; in this case every pixel. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The maximum pixel value:\n",
      "* before pointwise square root is:\t\t19.999118601072674\n",
      "* after pointwise square root should be:\t4.472037410518015\n",
      "* after pointwise square root is:\t\tNaN\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import net.imagej.ops.special.computer.Computers\n",
    "import net.imglib2.type.numeric.real.DoubleType\n",
    "import net.imagej.ops.Ops\n",
    "\n",
    "// Create output for the COMPUTER\n",
    "mapOutput = ij.op().run(\"create.img\", sinusoid)\n",
    "\n",
    "imgMax = ij.op().run(\"stats.max\", sinusoid)\n",
    "println(\"The maximum pixel value:\")\n",
    "println(\"* before pointwise square root is:\\t\\t\" + imgMax)\n",
    "println(\"* after pointwise square root should be:\\t\" + ij.op().run(\"math.sqrt\", imgMax.get()))\n",
    "\n",
    "// Create and save a sqrt op operating on DoubleType\n",
    "sqrtOp = Computers.unary(ij.op(), Ops.Math.Sqrt.class, DoubleType.class, DoubleType.class)\n",
    "\n",
    "// 'Map' the op on the 'sinusoid' image to calculate the square root of every pixel.\n",
    "ij.op().run(\"map\", mapOutput, sinusoid, sqrtOp);\n",
    "\n",
    "println(\"* after pointwise square root is:\\t\\t\" + ij.op().stats().max(mapOutput))\n",
    "\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above example, we additionally calculate some image statistics, namely the maximum pixel value in order to verify that the `map` op is actually doing what we'd expect. First we print the maximum of the `sinusoid` image and the square root of that value. After the map operation, the new maximum of the map output should be the same as previously calculated one, which is indeed the case :)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we want to look at a slightly more advanced example of chaining. Here, we want to calculate the Difference of Gaussian of an image which is a classical image processing operation. If we look at the method signatures of the `dog` op, we see that there is a version with the following inputs:\n",
    "* RandomAccessibleInterval out? : the output to write to\n",
    "* RandomAccessibleInterval in : the input to calculate the dog on\n",
    "* UnaryComputerOp gauss1 : an op that calculates the first Gaussian filtered version of the image\n",
    "* UnaryComputerOp gauss2 : an op that calculates the second Gaussian filtered version of the image\n",
    "* UnaryFunctionOp outputCreator : an op to allocate output if the op is used as a FUNCTION\n",
    "* UnaryFunctionOp tmpCreator : an op to create a temporary storage needed by the algorithm in order to calculate the first Gaussian\n",
    "\n",
    "Therefore, we will create all of these ops and call the `dog` op using them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"data:image/png;charset=utf-8;base64,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\" />"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import net.imglib2.type.numeric.real.DoubleType\n",
    "import net.imagej.ops.special.computer.Computers\n",
    "import net.imagej.ops.Ops;\n",
    "import net.imglib2.RandomAccessibleInterval\n",
    "import java.util.Collections;\n",
    "import net.imagej.ops.special.function.Functions;\n",
    "import net.imglib2.outofbounds.OutOfBoundsMirrorFactory;\n",
    "import net.imglib2.outofbounds.OutOfBoundsMirrorFactory.Boundary;\n",
    "\n",
    "// For this example we will use a different image as this will illustrate the result of the `dog` more nicely\n",
    "input = ij.scifio().datasetIO().open(\"https://imagej.net/images/clown.png\")\n",
    "// Convert the input to float in order to get a corerct result\n",
    "input = ij.op().run(\"convert.float32\", input)\n",
    "\n",
    "Class raiClass = RandomAccessibleInterval.class\n",
    "// Create two Gaussian filter ops with different sigmas as secondary inputs. Furthermore, we need to specify an out-of-bounds strategy\n",
    "gaussSigma2Op = Computers.unary(ij.op(), Ops.Filter.Gauss.class, input, input, 2, new OutOfBoundsMirrorFactory(Boundary.SINGLE))\n",
    "gaussSigma8Op = Computers.unary(ij.op(), Ops.Filter.Gauss.class, input, input, 8, new OutOfBoundsMirrorFactory(Boundary.SINGLE))\n",
    "\n",
    "// Create the tmp and output creator ops\n",
    "outputCreatorFunc = Functions.unary(ij.op(), Ops.Create.Img.class, raiClass, input)\n",
    "tmpCreatorFunc = Functions.unary(ij.op(), Ops.Create.Img.class, raiClass, input)\n",
    "\n",
    "// Allocate output\n",
    "dogOutput = ij.op().run(\"create.img\", input)\n",
    "// Run the `dog` op\n",
    "ij.op().run(\"dog\", dogOutput, input, gaussSigma2Op, gaussSigma8Op, outputCreatorFunc, tmpCreatorFunc)\n",
    "\n",
    "ij.notebook().display(dogOutput)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Groovy",
   "language": "groovy",
   "name": "groovy"
  },
  "language_info": {
   "codemirror_mode": "groovy",
   "file_extension": ".groovy",
   "mimetype": "",
   "name": "Groovy",
   "nbconverter_exporter": "",
   "version": "2.4.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {
    "height": "12px",
    "width": "214px"
   },
   "number_sections": false,
   "sideBar": false,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": false,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}