doc/08-Discover-and-run-ImageJ-commands.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is part of the PyImageJ [Tutorial Series](./notebooks.rst), and assumes familiarity with the ImageJ API. Dedicated tutorials for ImageJ can be found [here](https://imagej.net/tutorials/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8 Discover ImageJ commands with the Recorder\n",
    "\n",
    "The original ImageJ contains many useful commands that can be hard to use in PyImageJ without some prior knowledge on the their parameters. This notebook demonstrates how to use ImageJ's `Recorder` feature to record ImageJ commands in a supported language (`IJ Macro`, `BeanScript`, `Java` and `JavaScript`). \n",
    "\n",
    "## 8.1 Example 1: Apply \"Find Maxima...\" command to an image\n",
    "\n",
    "In this example we will use \"Find Maxima\" on a test image. Once the image is loaded, open the `Recorder` (see image below) and begin running commands to capture the code lines.\n",
    "\n",
    "![Open macro recorder](doc-images/imagej_ui_macro_recorder.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the `Recorder` window is open and listening for commands, set the recorder language to either `Java` or `JavaScript` before running the \"Find Maxima...\" command. Next run the \"Find Maxima...\" command (**Process** > **Find Maxima...**) to capture the code lines.\n",
    "\n",
    "![Macro recorder](doc-images/macro_recorder_find_maxima.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Running \"Find Maxima...\" (set to `prominence=1000` and `Point Selection`) on `test_still.tif` results in 21 detections which are then overlayed over the input image.:\n",
    "\n",
    "![Result](doc-images/test_still_find_maxima.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the `Java` code for the \"Find Maxima...\" command we can replicate this workflow in PyImageJ. The \"Find Maxima...\" command will overlay detections ontop of a displayed input image, therefore we will need to initialize ImageJ in `interactive` mode. Please note that MacOS users will have to change the mode to `gui` due to architecture limitations. For more information please visit the [initialization](Initialization.md) documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ImageJ2 version: 2.9.0/1.53t\n"
     ]
    }
   ],
   "source": [
    "import imagej\n",
    "\n",
    "# initialize ImageJ2\n",
    "ij = imagej.init(mode='headless')\n",
    "print(f\"ImageJ2 version: {ij.getVersion()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the \"Find Maxima...\" and other original ImageJ commands work with the `ImagePlus` object type (instead of the newer ImageJ2/ImgLib2 `Dataset` and `ImgPlus` object types) we need to first convert the `Dataset` returned from `ij.io().open()` to an `ImagePlus`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** There is a currently a bug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Operating in headless mode - the original ImageJ will have limited functionality.\n",
      "Operating in headless mode - the WindowManager class will not be fully functional.\n"
     ]
    }
   ],
   "source": [
    "# HACK: # HACK: Work around ImagePlus#show() failure if no ImagePlus objects are already registered.\n",
    "if ij.WindowManager.getIDList() is None:\n",
    "    ij.py.run_macro('newImage(\"dummy\", \"8-bit\", 1, 1, 1);')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# open test image and convert from Dataset to ImagePlus\n",
    "dataset = ij.io().open('sample-data/test_still.tif')\n",
    "imp = ij.py.to_imageplus(dataset)\n",
    "\n",
    "# show the image\n",
    "ij.py.show(imp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, show the image with ImageJ and run `Find Maxima...` using the same `Java` syntax generated from the recorder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[java.lang.Enum.toString] [INFO] null = img[\"test_still.tif\" (-3), 16-bit, 300x300x1x1x1]\n",
      "Operating in headless mode - the IJ class will not be fully functional.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] null = img[\"test_still.tif\" (-3), 16-bit, 300x300x1x1x1]\n"
     ]
    }
   ],
   "source": [
    "# show image and then find maxima\n",
    "imp.getProcessor().resetMinAndMax()\n",
    "ij.ui().show(imp)\n",
    "ij.IJ.run(imp, \"Find Maxima...\", \"prominence=1000 output=[Point Selection]\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.2 Example 2: Extract a slice and run \"Analyze Particles...\"\n",
    "\n",
    "Let's try a more complicated example next. This Java code was generated with the ImageJ Recorder while analyzing some data.\n",
    "\n",
    "```java\n",
    "imp = IJ.openImage(\"sample-data/test_timeseries.tif\");\n",
    "imp2 = new Duplicator().run(imp, 3, 3, 1, 1, 14, 14);\n",
    "IJ.run(imp, \"Enhance Contrast\", \"saturated=0.35\");\n",
    "IJ.setAutoThreshold(imp, \"Moments dark\");\n",
    "IJ.run(imp, \"Analyze Particles...\", \"  show=Overlay display clear\");\n",
    "```\n",
    "\n",
    "The Java code takes the `test_timeseries.tif` sample data (4D: [X, Y, Channel, Time]) and performs the following operations:\n",
    "\n",
    "1. Open the test data.\n",
    "2. Duplicate channel 3, frame 14 (extracts a single still from the timeseries).\n",
    "3. Enhance the contrast of the image.\n",
    "4. Threshold with \"Moments dark\".\n",
    "5. Analyze particles and display results via overlay.\n",
    "\n",
    "**Results:**\n",
    "\n",
    "![Analyze Particles](doc-images/test_still_analyze_particles.png)\n",
    "\n",
    "Note that in this example we will use the orginal ImageJ's image opener (`IJ.openImage()`) instead of ImageJ2's (`ij.io().open()`). The original ImageJ's opener is more limited than ImageJ2's however it will return an `ImagePlus` image object instead of a `Dataset`, thus no conversion step is needed like in the previous example.\n",
    "\n",
    "Just like in the _Example 1_ the `Java` code generated from the Recorder can be typically used with little to no modification for language syntax (take note of the different syntax needed to use the `Duplicator`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] null = img[\"DUP_test_timeseries.tif\" (-6), 16-bit, 250x250x1x1x1]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[java.lang.Enum.toString] [INFO] null = img[\"DUP_test_timeseries.tif\" (-6), 16-bit, 250x250x1x1x1]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \tArea\tMean\tMin\tMax\n",
      "1\t0.211\t1293\t1141\t1445\n",
      "2\t0.739\t1216.143\t1085\t1473\n",
      "3\t0.106\t1333.000\t1333\t1333\n",
      "4\t0.528\t1193.400\t1099\t1295\n",
      "5\t0.317\t1241.667\t1087\t1384\n",
      "6\t180.091\t1513.540\t1083\t4462\n",
      "7\t0.106\t1104.000\t1104\t1104\n",
      "8\t3.803\t1478.194\t1085\t2510\n",
      "9\t0.211\t1268.000\t1161\t1375\n",
      "10\t0.211\t1171.000\t1108\t1234\n",
      "11\t0.106\t1257.000\t1257\t1257\n",
      "12\t0.423\t1371.250\t1186\t1686\n",
      "13\t0.317\t1140.667\t1109\t1157\n",
      "14\t0.423\t1187.500\t1097\t1241\n",
      "15\t0.528\t1182.800\t1132\t1312\n",
      "16\t0.423\t1302.500\t1117\t1634\n",
      "17\t0.739\t1215.286\t1102\t1352\n",
      "18\t0.423\t1234.500\t1192\t1276\n",
      "19\t1.479\t1192.214\t1086\t1398\n",
      "20\t0.845\t1422.125\t1150\t1897\n",
      "21\t0.211\t1116.500\t1113\t1120\n",
      "22\t0.211\t1141.500\t1128\t1155\n",
      "23\t0.106\t1168.000\t1168\t1168\n",
      "24\t0.106\t1163.000\t1163\t1163\n",
      "25\t0.211\t1126.000\t1102\t1150\n",
      "26\t0.423\t1222.750\t1086\t1383\n",
      "27\t22.498\t1762.784\t1084\t5446\n",
      "28\t1.162\t1330.364\t1092\t1829\n",
      "29\t0.634\t1167.667\t1110\t1227\n",
      "30\t0.739\t1328.571\t1137\t1482\n",
      "31\t1.162\t1216.818\t1089\t1455\n",
      "32\t0.106\t1124.000\t1124\t1124\n",
      "33\t0.317\t1166.333\t1101\t1208\n",
      "34\t0.106\t1092.000\t1092\t1092\n",
      "35\t0.106\t1136.000\t1136\t1136\n",
      "36\t0.211\t1106.000\t1092\t1120\n",
      "37\t3.063\t1362.310\t1083\t2069\n",
      "38\t0.106\t1088.000\t1088\t1088\n",
      "39\t0.211\t1150.500\t1104\t1197\n",
      "40\t0.211\t1123.500\t1110\t1137\n",
      "41\t1.901\t1434.944\t1103\t2129\n",
      "42\t0.211\t1128.500\t1100\t1157\n",
      "43\t8.344\t1414.139\t1091\t2321\n",
      "44\t0.951\t1351.111\t1134\t1652\n",
      "45\t0.951\t1435.444\t1088\t1798\n",
      "46\t0.528\t1161.400\t1106\t1248\n",
      "47\t0.528\t1120.200\t1102\t1144\n",
      "48\t0.528\t1277.400\t1110\t1547\n",
      "49\t0.951\t1182.667\t1088\t1279\n",
      "50\t0.211\t1184.000\t1110\t1258\n",
      "51\t3.274\t1360.774\t1098\t1866\n",
      "52\t0.423\t1267.250\t1116\t1458\n",
      "53\t3.908\t1232.703\t1083\t1597\n",
      "54\t11.302\t1550.327\t1096\t2918\n",
      "55\t0.211\t1088.500\t1087\t1090\n",
      "56\t0.106\t1138.000\t1138\t1138\n",
      "57\t1.901\t1470.667\t1102\t2386\n",
      "58\t0.106\t1084.000\t1084\t1084\n",
      "59\t1.373\t1266.846\t1145\t1573\n",
      "60\t2.113\t1330.600\t1131\t1581\n",
      "61\t2.958\t1209.321\t1083\t1670\n",
      "62\t1.056\t1364.300\t1106\t1748\n",
      "63\t0.423\t1334.500\t1289\t1374\n",
      "64\t0.106\t1141.000\t1141\t1141\n",
      "65\t1.162\t1330.818\t1083\t1694\n",
      "66\t0.423\t1250.500\t1131\t1375\n",
      "67\t0.423\t1182.250\t1102\t1337\n",
      "68\t0.211\t1120.500\t1120\t1121\n",
      "69\t0.106\t1097.000\t1097\t1097\n",
      "70\t25.561\t1346.455\t1084\t2808\n",
      "71\t1.162\t1133.455\t1084\t1221\n",
      "72\t6.338\t1389.550\t1113\t2426\n",
      "73\t0.634\t1148.333\t1089\t1231\n",
      "74\t1.796\t1360.118\t1084\t1974\n",
      "75\t0.951\t1351.000\t1095\t1746\n",
      "76\t0.106\t1113.000\t1113\t1113\n",
      "77\t0.634\t1129.167\t1089\t1212\n",
      "78\t0.106\t1108.000\t1108\t1108\n",
      "79\t0.106\t1090.000\t1090\t1090\n",
      "80\t0.106\t1105.000\t1105\t1105\n",
      "81\t0.106\t1138.000\t1138\t1138\n",
      "82\t1.584\t1573.533\t1103\t2296\n",
      "83\t0.106\t1108.000\t1108\t1108\n",
      "84\t0.211\t1103.500\t1085\t1122\n",
      "85\t0.106\t1092.000\t1092\t1092\n",
      "86\t0.211\t1103.000\t1084\t1122\n",
      "87\t2.852\t1268.963\t1091\t1598\n",
      "88\t0.423\t1100.750\t1083\t1122\n",
      "89\t0.106\t1110.000\t1110\t1110\n",
      "90\t0.317\t1136.333\t1102\t1188\n",
      "91\t0.106\t1126.000\t1126\t1126\n",
      "92\t0.106\t1116.000\t1116\t1116\n",
      "93\t0.317\t1141.667\t1084\t1233\n",
      "94\t0.106\t1083.000\t1083\t1083\n",
      "95\t1.479\t1183.571\t1085\t1314\n",
      "96\t0.211\t1138.000\t1135\t1141\n",
      "97\t2.429\t1494.130\t1084\t2113\n",
      "98\t0.211\t1125.000\t1102\t1148\n",
      "99\t0.211\t1098.500\t1085\t1112\n",
      "100\t0.423\t1157.250\t1100\t1208\n",
      "101\t0.739\t1273.714\t1125\t1414\n",
      "102\t0.634\t1142.833\t1096\t1169\n",
      "103\t0.211\t1127.500\t1099\t1156\n",
      "104\t0.739\t1239.714\t1095\t1509\n",
      "105\t0.106\t1091.000\t1091\t1091\n",
      "106\t0.845\t1167.000\t1084\t1255\n",
      "107\t0.211\t1085.000\t1085\t1085\n",
      "108\t1.479\t1309.357\t1097\t1704\n",
      "109\t0.528\t1316.600\t1211\t1587\n",
      "110\t0.951\t1314.778\t1133\t1693\n",
      "111\t0.423\t1148.250\t1097\t1244\n",
      "112\t0.423\t1146.500\t1103\t1165\n",
      "113\t1.373\t1357.538\t1092\t1626\n",
      "114\t0.106\t1156.000\t1156\t1156\n",
      "115\t10.563\t1473.170\t1084\t2716\n",
      "116\t0.211\t1161.000\t1156\t1166\n",
      "117\t0.106\t1095.000\t1095\t1095\n",
      "118\t0.106\t1101.000\t1101\t1101\n",
      "119\t5.176\t1373.796\t1089\t2209\n",
      "120\t0.317\t1266.333\t1203\t1303\n",
      "121\t0.528\t1288.200\t1110\t1490\n",
      "122\t2.113\t1429.950\t1098\t2307\n",
      "123\t1.584\t1249.067\t1090\t1491\n",
      "124\t2.007\t1558.421\t1099\t2453\n",
      "125\t0.106\t1112.000\t1112\t1112\n",
      "126\t1.056\t1243.600\t1085\t1465\n",
      "127\t2.218\t1518.762\t1094\t2666\n",
      "128\t0.211\t1172.000\t1159\t1185\n",
      "129\t0.634\t1275.167\t1099\t1602\n",
      "130\t0.106\t1131.000\t1131\t1131\n",
      "131\t0.106\t1277.000\t1277\t1277\n",
      "132\t0.106\t1119.000\t1119\t1119\n",
      "133\t0.317\t1191.000\t1130\t1236\n",
      "134\t0.106\t1163.000\t1163\t1163\n",
      "135\t0.211\t1141.500\t1113\t1170\n",
      "136\t0.106\t1119.000\t1119\t1119\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[java.lang.Enum.toString]  \tArea\tMean\tMin\tMax[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 1\t0.211\t1293\t1141\t1445[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 2\t0.739\t1216.143\t1085\t1473[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 3\t0.106\t1333.000\t1333\t1333[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 4\t0.528\t1193.400\t1099\t1295[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 5\t0.317\t1241.667\t1087\t1384[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 6\t180.091\t1513.540\t1083\t4462[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 7\t0.106\t1104.000\t1104\t1104[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 8\t3.803\t1478.194\t1085\t2510[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 9\t0.211\t1268.000\t1161\t1375[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 10\t0.211\t1171.000\t1108\t1234[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 11\t0.106\t1257.000\t1257\t1257[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 12\t0.423\t1371.250\t1186\t1686[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 13\t0.317\t1140.667\t1109\t1157[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 14\t0.423\t1187.500\t1097\t1241[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 15\t0.528\t1182.800\t1132\t1312[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 16\t0.423\t1302.500\t1117\t1634[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 17\t0.739\t1215.286\t1102\t1352[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 18\t0.423\t1234.500\t1192\t1276[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 19\t1.479\t1192.214\t1086\t1398[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 20\t0.845\t1422.125\t1150\t1897[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 21\t0.211\t1116.500\t1113\t1120[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 22\t0.211\t1141.500\t1128\t1155[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 23\t0.106\t1168.000\t1168\t1168[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 24\t0.106\t1163.000\t1163\t1163[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 25\t0.211\t1126.000\t1102\t1150[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 26\t0.423\t1222.750\t1086\t1383[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 27\t22.498\t1762.784\t1084\t5446[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 28\t1.162\t1330.364\t1092\t1829[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 29\t0.634\t1167.667\t1110\t1227[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 30\t0.739\t1328.571\t1137\t1482[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 31\t1.162\t1216.818\t1089\t1455[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 32\t0.106\t1124.000\t1124\t1124[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 33\t0.317\t1166.333\t1101\t1208[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 34\t0.106\t1092.000\t1092\t1092[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 35\t0.106\t1136.000\t1136\t1136[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 36\t0.211\t1106.000\t1092\t1120[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 37\t3.063\t1362.310\t1083\t2069[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 38\t0.106\t1088.000\t1088\t1088[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 39\t0.211\t1150.500\t1104\t1197[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 40\t0.211\t1123.500\t1110\t1137[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 41\t1.901\t1434.944\t1103\t2129[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 42\t0.211\t1128.500\t1100\t1157[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 43\t8.344\t1414.139\t1091\t2321[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 44\t0.951\t1351.111\t1134\t1652[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 45\t0.951\t1435.444\t1088\t1798[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 46\t0.528\t1161.400\t1106\t1248[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 47\t0.528\t1120.200\t1102\t1144[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 48\t0.528\t1277.400\t1110\t1547[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 49\t0.951\t1182.667\t1088\t1279[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 50\t0.211\t1184.000\t1110\t1258[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 51\t3.274\t1360.774\t1098\t1866[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 52\t0.423\t1267.250\t1116\t1458[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 53\t3.908\t1232.703\t1083\t1597[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 54\t11.302\t1550.327\t1096\t2918[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 55\t0.211\t1088.500\t1087\t1090[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 56\t0.106\t1138.000\t1138\t1138[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 57\t1.901\t1470.667\t1102\t2386[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 58\t0.106\t1084.000\t1084\t1084[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 59\t1.373\t1266.846\t1145\t1573[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 60\t2.113\t1330.600\t1131\t1581[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 61\t2.958\t1209.321\t1083\t1670[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 62\t1.056\t1364.300\t1106\t1748[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 63\t0.423\t1334.500\t1289\t1374[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 64\t0.106\t1141.000\t1141\t1141[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 65\t1.162\t1330.818\t1083\t1694[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 66\t0.423\t1250.500\t1131\t1375[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 67\t0.423\t1182.250\t1102\t1337[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 68\t0.211\t1120.500\t1120\t1121[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 69\t0.106\t1097.000\t1097\t1097[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 70\t25.561\t1346.455\t1084\t2808[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 71\t1.162\t1133.455\t1084\t1221[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 72\t6.338\t1389.550\t1113\t2426[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 73\t0.634\t1148.333\t1089\t1231[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 74\t1.796\t1360.118\t1084\t1974[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 75\t0.951\t1351.000\t1095\t1746[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 76\t0.106\t1113.000\t1113\t1113[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 77\t0.634\t1129.167\t1089\t1212[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 78\t0.106\t1108.000\t1108\t1108[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 79\t0.106\t1090.000\t1090\t1090[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 80\t0.106\t1105.000\t1105\t1105[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 81\t0.106\t1138.000\t1138\t1138[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 82\t1.584\t1573.533\t1103\t2296[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 83\t0.106\t1108.000\t1108\t1108[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 84\t0.211\t1103.500\t1085\t1122[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 85\t0.106\t1092.000\t1092\t1092[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 86\t0.211\t1103.000\t1084\t1122[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 87\t2.852\t1268.963\t1091\t1598[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 88\t0.423\t1100.750\t1083\t1122[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 89\t0.106\t1110.000\t1110\t1110[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 90\t0.317\t1136.333\t1102\t1188[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 91\t0.106\t1126.000\t1126\t1126[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 92\t0.106\t1116.000\t1116\t1116[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 93\t0.317\t1141.667\t1084\t1233[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 94\t0.106\t1083.000\t1083\t1083[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 95\t1.479\t1183.571\t1085\t1314[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 96\t0.211\t1138.000\t1135\t1141[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 97\t2.429\t1494.130\t1084\t2113[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 98\t0.211\t1125.000\t1102\t1148[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 99\t0.211\t1098.500\t1085\t1112[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 100\t0.423\t1157.250\t1100\t1208[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 101\t0.739\t1273.714\t1125\t1414[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 102\t0.634\t1142.833\t1096\t1169[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 103\t0.211\t1127.500\t1099\t1156[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 104\t0.739\t1239.714\t1095\t1509[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 105\t0.106\t1091.000\t1091\t1091[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 106\t0.845\t1167.000\t1084\t1255[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 107\t0.211\t1085.000\t1085\t1085[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 108\t1.479\t1309.357\t1097\t1704[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 109\t0.528\t1316.600\t1211\t1587[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 110\t0.951\t1314.778\t1133\t1693[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 111\t0.423\t1148.250\t1097\t1244[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 112\t0.423\t1146.500\t1103\t1165[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 113\t1.373\t1357.538\t1092\t1626[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 114\t0.106\t1156.000\t1156\t1156[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 115\t10.563\t1473.170\t1084\t2716[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 116\t0.211\t1161.000\t1156\t1166[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 117\t0.106\t1095.000\t1095\t1095[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 118\t0.106\t1101.000\t1101\t1101[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 119\t5.176\t1373.796\t1089\t2209[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 120\t0.317\t1266.333\t1203\t1303[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 121\t0.528\t1288.200\t1110\t1490[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 122\t2.113\t1429.950\t1098\t2307[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 123\t1.584\t1249.067\t1090\t1491[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 124\t2.007\t1558.421\t1099\t2453[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 125\t0.106\t1112.000\t1112\t1112[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 126\t1.056\t1243.600\t1085\t1465[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 127\t2.218\t1518.762\t1094\t2666[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 128\t0.211\t1172.000\t1159\t1185[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 129\t0.634\t1275.167\t1099\t1602[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 130\t0.106\t1131.000\t1131\t1131[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 131\t0.106\t1277.000\t1277\t1277[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 132\t0.106\t1119.000\t1119\t1119[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 133\t0.317\t1191.000\t1130\t1236[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 134\t0.106\t1163.000\t1163\t1163[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 135\t0.211\t1141.500\t1113\t1170[java.lang.Enum.toString] \n",
      "[java.lang.Enum.toString] 136\t0.106\t1119.000\t1119\t1119[java.lang.Enum.toString] \n"
     ]
    }
   ],
   "source": [
    "from scyjava import jimport\n",
    "\n",
    "# get ImageJ's duplicator\n",
    "Duplicator = jimport('ij.plugin.Duplicator')\n",
    "\n",
    "# run ImageJ commands\n",
    "imp_timeseries = ij.IJ.openImage(\"sample-data/test_timeseries.tif\")\n",
    "imp_extract = Duplicator().run(imp_timeseries, 3, 3, 1, 1, 14, 14) # visit the Javadoc for more info https://imagej.nih.gov/ij/developer/api/ij/ij/plugin/Duplicator.html\n",
    "ij.IJ.run(imp_extract, \"Enhance Contrast\", \"saturated=0.35\")\n",
    "ij.ui().show(imp_extract)\n",
    "ij.IJ.setAutoThreshold(imp_extract, \"Moments dark\")\n",
    "ij.IJ.run(imp_extract, \"Analyze Particles...\", \" show=Overlay display clear\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ImageJ macro language\n",
    "\n",
    "It is possible to record workflows in the original ImageJ macro language and run those macros in PyImageJ with `ij.py.run_macro()` (see the[07-Running-Macros-Scripts-and-Plugins](07-Running-Macros-Scripts-and-Plugins.ipynb) notebook for more information). While this is functional, we do not recommend using the macro language as it is more fragile and less powerful due to the orignal ImageJ internal operations. For example, macros are single threaded only and do not take advantage of multi-threaded processing. For more information on ImageJ macro limitations please visit the [macro](https://imagej.net/scripting/macro#overcoming-limitations) wiki page."
   ]
  }
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