notebooks/introductory_dataflows.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "ef0d4a65-1e86-4570-bd56-0e683df3cc72"
    }
   },
   "source": [
    "# Hello world\n",
    "\n",
    "## Quickest introduction to Nipype workflows "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbpresent": {
     "id": "f834221c-3c73-47ce-b36e-ba3f17bd3d60"
    }
   },
   "source": [
    "![Nipype architecture](https://raw.github.com/satra/intro2nipype/master/images/arch.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [Existing documentation](http://nipype.readthedocs.io/en/latest/)\n",
    "\n",
    "- [Comprehensive tutorials](https://miykael.github.io/nipype_tutorial/)\n",
    "\n",
    "- [Visualizing the evolution of Nipype](https://www.youtube.com/watch?v=cofpD1lhmKU)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import a few things from nipype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "bb1cfcc5-5cbf-4097-b8a9-fe4d74ce6bcd"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from nipype import Workflow, Node, Function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating Workflow with one Node that adds two numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "nbpresent": {
     "id": "d0adfd78-01e1-4623-983a-bbf53a9bb858"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "170829-12:57:38,977 workflow INFO:\n",
      "\t Workflow hello settings: ['check', 'execution', 'logging']\n",
      "170829-12:57:39,26 workflow INFO:\n",
      "\t Running serially.\n",
      "170829-12:57:39,29 workflow INFO:\n",
      "\t Executing node a_plus_b in dir: /opt/tutorial/notebooks/hello/a_plus_b\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\n",
       "sum = 4"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def sum(a, b):\n",
    "    return a + b\n",
    "\n",
    "wf = Workflow('hello')\n",
    "\n",
    "adder = Node(Function(input_names=['a', 'b'],\n",
    "                      output_names=['sum'],\n",
    "                      function=sum), \n",
    "             name='a_plus_b')\n",
    "\n",
    "adder.inputs.a = 1\n",
    "adder.inputs.b = 3\n",
    "\n",
    "wf.add_nodes([adder])\n",
    "\n",
    "wf.base_dir = os.getcwd()\n",
    "\n",
    "eg = wf.run()\n",
    "\n",
    "eg.nodes()[0].result.outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a second node and connecting to the ``hello`` Workflow "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "170829-12:57:40,270 workflow INFO:\n",
      "\t Workflow hello settings: ['check', 'execution', 'logging']\n",
      "170829-12:57:40,330 workflow INFO:\n",
      "\t Running serially.\n",
      "170829-12:57:40,332 workflow INFO:\n",
      "\t Executing node a_plus_b in dir: /opt/tutorial/notebooks/hello/a_plus_b\n",
      "170829-12:57:40,415 workflow INFO:\n",
      "\t Executing node concat_a_b in dir: /opt/tutorial/notebooks/hello/concat_a_b\n",
      "[hello.a_plus_b, hello.concat_a_b]\n"
     ]
    }
   ],
   "source": [
    "def concat(a, b):\n",
    "    return [a, b]\n",
    "\n",
    "\n",
    "concater = Node(Function(input_names=['a', 'b'],\n",
    "                         output_names=['some_list'],\n",
    "                         function=concat), \n",
    "                name='concat_a_b')\n",
    "\n",
    "wf.connect(adder, 'sum', concater, 'a')\n",
    "concater.inputs.b = 3\n",
    "\n",
    "eg = wf.run()\n",
    "print(eg.nodes())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we can check results of our Workflow, we should see a list:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "some_list = [4, 3]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eg.nodes()[-1].result.outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will try to add additional Node that adds one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "170829-12:57:42,994 workflow INFO:\n",
      "\t Workflow hello settings: ['check', 'execution', 'logging']\n",
      "170829-12:57:43,42 workflow INFO:\n",
      "\t Running serially.\n",
      "170829-12:57:43,44 workflow INFO:\n",
      "\t Executing node a_plus_b in dir: /opt/tutorial/notebooks/hello/a_plus_b\n",
      "170829-12:57:43,66 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:57:43,79 workflow INFO:\n",
      "\t Executing node concat_a_b in dir: /opt/tutorial/notebooks/hello/concat_a_b\n",
      "170829-12:57:43,150 workflow INFO:\n",
      "\t Executing node add_1 in dir: /opt/tutorial/notebooks/hello/add_1\n",
      "PLUS ONE, a = [4, 3]\n",
      "170829-12:57:43,196 workflow ERROR:\n",
      "\t ['Node add_1 failed to run on host db3db9bfe867.']\n",
      "170829-12:57:43,200 workflow INFO:\n",
      "\t Saving crash info to /opt/tutorial/notebooks/crash-20170829-125743-neuro-add_1-8b4dad6e-0d61-4d9d-8870-45eea5011a06.pklz\n",
      "170829-12:57:43,202 workflow INFO:\n",
      "\t Traceback (most recent call last):\n",
      "  File \"/opt/conda/envs/neuro/lib/python3.6/site-packages/nipype/pipeline/plugins/linear.py\", line 43, in run\n",
      "    node.run(updatehash=updatehash)\n",
      "  File \"/opt/conda/envs/neuro/lib/python3.6/site-packages/nipype/pipeline/engine/nodes.py\", line 372, in run\n",
      "    self._run_interface()\n",
      "  File \"/opt/conda/envs/neuro/lib/python3.6/site-packages/nipype/pipeline/engine/nodes.py\", line 482, in _run_interface\n",
      "    self._result = self._run_command(execute)\n",
      "  File \"/opt/conda/envs/neuro/lib/python3.6/site-packages/nipype/pipeline/engine/nodes.py\", line 613, in _run_command\n",
      "    result = self._interface.run()\n",
      "  File \"/opt/conda/envs/neuro/lib/python3.6/site-packages/nipype/interfaces/base.py\", line 1083, in run\n",
      "    runtime = self._run_wrapper(runtime)\n",
      "  File \"/opt/conda/envs/neuro/lib/python3.6/site-packages/nipype/interfaces/base.py\", line 1031, in _run_wrapper\n",
      "    runtime = self._run_interface(runtime)\n",
      "  File \"/opt/conda/envs/neuro/lib/python3.6/site-packages/nipype/interfaces/utility/wrappers.py\", line 192, in _run_interface\n",
      "    out = function_handle(**args)\n",
      "  File \"<string>\", line 3, in plus_one\n",
      "TypeError: can only concatenate list (not \"int\") to list\n",
      "Interface Function failed to run. \n",
      "\n",
      "170829-12:57:43,207 workflow INFO:\n",
      "\t ***********************************\n",
      "170829-12:57:43,209 workflow ERROR:\n",
      "\t could not run node: hello.add_1\n",
      "170829-12:57:43,211 workflow INFO:\n",
      "\t crashfile: /opt/tutorial/notebooks/crash-20170829-125743-neuro-add_1-8b4dad6e-0d61-4d9d-8870-45eea5011a06.pklz\n",
      "170829-12:57:43,212 workflow INFO:\n",
      "\t ***********************************\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "Workflow did not execute cleanly. Check log for details",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-673369f26ab3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mwf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconcater\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'some_list'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mplusone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'a'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0meg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnodes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/neuro/lib/python3.6/site-packages/nipype/pipeline/engine/workflows.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, plugin, plugin_args, updatehash)\u001b[0m\n\u001b[1;32m    588\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mstr2bool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'execution'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'create_report'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    589\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_write_report_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexecgraph\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 590\u001b[0;31m         \u001b[0mrunner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexecgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mupdatehash\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mupdatehash\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    591\u001b[0m         \u001b[0mdatestr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdatetime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutcnow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrftime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'%Y%m%dT%H%M%S'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    592\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mstr2bool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'execution'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'write_provenance'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/envs/neuro/lib/python3.6/site-packages/nipype/pipeline/plugins/linear.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, graph, config, updatehash)\u001b[0m\n\u001b[1;32m     59\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_callback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_status_callback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'exception'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0mreport_nodes_not_run\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnotrun\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/conda/envs/neuro/lib/python3.6/site-packages/nipype/pipeline/plugins/base.py\u001b[0m in \u001b[0;36mreport_nodes_not_run\u001b[0;34m(notrun)\u001b[0m\n\u001b[1;32m     99\u001b[0m                 \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m         \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minfo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"***********************************\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m         raise RuntimeError(('Workflow did not execute cleanly. '\n\u001b[0m\u001b[1;32m    102\u001b[0m                             'Check log for details'))\n\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Workflow did not execute cleanly. Check log for details"
     ]
    }
   ],
   "source": [
    "def plus_one(a):\n",
    "    print(\"PLUS ONE, a = {}\".format(a))\n",
    "    return a + 1\n",
    "\n",
    "plusone = Node(Function(input_names=['a'],\n",
    "                        output_names=['out'],\n",
    "                        function=plus_one), \n",
    "               name='add_1')\n",
    "\n",
    "wf.connect(concater, 'some_list', plusone, 'a')\n",
    "\n",
    "eg = wf.run()\n",
    "print(eg.nodes())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This time the workflow didn't execute cleanly and we got an error. We can use ``nipypecli`` to read the crashfile (note, that if you have multiple crashfiles in the directory you'll have to provide a full name):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "\r\n",
      "File: /opt/tutorial/notebooks/crash-20170829-125743-neuro-add_1-8b4dad6e-0d61-4d9d-8870-45eea5011a06.pklz\r\n",
      "Node: hello.add_1\r\n",
      "Working directory: /opt/tutorial/notebooks/hello/add_1\r\n",
      "\r\n",
      "\r\n",
      "Node inputs:\r\n",
      "\r\n",
      "a = [4, 3]\r\n",
      "function_str = def plus_one(a):\r\n",
      "    print(\"PLUS ONE, a = {}\".format(a))\r\n",
      "    return a + 1\r\n",
      "\r\n",
      "ignore_exception = False\r\n",
      "\r\n",
      "\r\n",
      "\r\n",
      "Traceback: \r\n",
      "Traceback (most recent call last):\r\n",
      "  File \"/opt/conda/envs/neuro/lib/python3.6/site-packages/nipype/pipeline/plugins/linear.py\", line 43, in run\r\n",
      "    node.run(updatehash=updatehash)\r\n",
      "  File \"/opt/conda/envs/neuro/lib/python3.6/site-packages/nipype/pipeline/engine/nodes.py\", line 372, in run\r\n",
      "    self._run_interface()\r\n",
      "  File \"/opt/conda/envs/neuro/lib/python3.6/site-packages/nipype/pipeline/engine/nodes.py\", line 482, in _run_interface\r\n",
      "    self._result = self._run_command(execute)\r\n",
      "  File \"/opt/conda/envs/neuro/lib/python3.6/site-packages/nipype/pipeline/engine/nodes.py\", line 613, in _run_command\r\n",
      "    result = self._interface.run()\r\n",
      "  File \"/opt/conda/envs/neuro/lib/python3.6/site-packages/nipype/interfaces/base.py\", line 1083, in run\r\n",
      "    runtime = self._run_wrapper(runtime)\r\n",
      "  File \"/opt/conda/envs/neuro/lib/python3.6/site-packages/nipype/interfaces/base.py\", line 1031, in _run_wrapper\r\n",
      "    runtime = self._run_interface(runtime)\r\n",
      "  File \"/opt/conda/envs/neuro/lib/python3.6/site-packages/nipype/interfaces/utility/wrappers.py\", line 192, in _run_interface\r\n",
      "    out = function_handle(**args)\r\n",
      "  File \"<string>\", line 3, in plus_one\r\n",
      "TypeError: can only concatenate list (not \"int\") to list\r\n",
      "Interface Function failed to run. \r\n",
      "\r\n",
      "\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!LC_ALL= nipypecli crash crash*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It clearly shows the problematic Node and the its input. We tried to add an integer to a list, this operation is not allowed in Python. \n",
    "\n",
    "Let's try using MapNode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "170829-12:57:54,95 workflow INFO:\n",
      "\t Workflow hello_mapnode settings: ['check', 'execution', 'logging']\n",
      "170829-12:57:54,172 workflow INFO:\n",
      "\t Running serially.\n",
      "170829-12:57:54,176 workflow INFO:\n",
      "\t Executing node a_plus_b in dir: /opt/tutorial/notebooks/hello_mapnode/a_plus_b\n",
      "170829-12:57:54,190 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:57:54,202 workflow INFO:\n",
      "\t Executing node concat_a_b in dir: /opt/tutorial/notebooks/hello/concat_a_b\n",
      "170829-12:57:54,218 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:57:54,231 workflow INFO:\n",
      "\t Executing node add_1 in dir: /opt/tutorial/notebooks/hello_mapnode/add_1\n",
      "[hello_mapnode.a_plus_b, hello.concat_a_b, hello_mapnode.add_1]\n"
     ]
    }
   ],
   "source": [
    "from nipype import MapNode\n",
    "\n",
    "plusone = MapNode(Function(input_names=['a'],\n",
    "                           output_names=['out'],\n",
    "                           function=plus_one), \n",
    "                  iterfield=['a'],\n",
    "                  name='add_1')\n",
    "\n",
    "wf = Workflow('hello_mapnode')\n",
    "\n",
    "adder = Node(Function(input_names=['a', 'b'],\n",
    "                      output_names=['sum'],\n",
    "                      function=sum), \n",
    "             name='a_plus_b')\n",
    "\n",
    "adder.inputs.a = 1\n",
    "adder.inputs.b = 3\n",
    "wf.connect(adder, 'sum', concater, 'a')\n",
    "concater.inputs.b = 3\n",
    "\n",
    "wf.connect(concater, 'some_list', plusone, 'a')\n",
    "\n",
    "wf.base_dir = os.getcwd()\n",
    "\n",
    "eg = wf.run()\n",
    "print(eg.nodes())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the workflow finished without problems, let's see the results from ``hello.add_1``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bunch(out=[5, 4])\n"
     ]
    }
   ],
   "source": [
    "print(eg.nodes()[2].result.outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now we will run example with ``iterables``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "170829-12:57:56,972 workflow INFO:\n",
      "\t Workflow hello_mapnode settings: ['check', 'execution', 'logging']\n",
      "170829-12:57:57,24 workflow INFO:\n",
      "\t Running serially.\n",
      "170829-12:57:57,31 workflow INFO:\n",
      "\t Executing node a_plus_b.aI.a1 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_2/a_plus_b\n",
      "170829-12:57:57,101 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:57:57,118 workflow INFO:\n",
      "\t Executing node concat_a_b.a1 in dir: /opt/tutorial/notebooks/hello/_a_2/concat_a_b\n",
      "170829-12:57:57,135 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:57:57,153 workflow INFO:\n",
      "\t Executing node add_1.a1 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_2/add_1\n",
      "170829-12:57:57,175 workflow INFO:\n",
      "\t Executing node a_plus_b.aI.a0 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_1/a_plus_b\n",
      "170829-12:57:57,190 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:57:57,202 workflow INFO:\n",
      "\t Executing node concat_a_b.a0 in dir: /opt/tutorial/notebooks/hello/_a_1/concat_a_b\n",
      "170829-12:57:57,251 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:57:57,263 workflow INFO:\n",
      "\t Executing node add_1.a0 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_1/add_1\n",
      "[hello_mapnode.a_plus_b.aI.a0, hello.concat_a_b.a0, hello_mapnode.add_1.a0, hello_mapnode.a_plus_b.aI.a1, hello.concat_a_b.a1, hello_mapnode.add_1.a1]\n"
     ]
    }
   ],
   "source": [
    "adder.iterables = ('a', [1, 2])\n",
    "adder.inputs.b = 2\n",
    "\n",
    "eg = wf.run()\n",
    "print(eg.nodes())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have 6 nodes, we can check results for `` hello.add_1.a1``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bunch(out=[5, 4])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eg.nodes()[5].result.outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "170829-12:58:00,631 workflow INFO:\n",
      "\t Generated workflow graph: /opt/tutorial/notebooks/hello_mapnode/graph.dot.png (graph2use=exec, simple_form=True).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/opt/tutorial/notebooks/hello_mapnode/graph.dot.png'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wf.write_graph(graph2use='exec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot a general structure of the workflow:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WnTp1ahIrG723336bBAIBPf300y6bhkvfDXl5eSSXyykpKYmuXbvmykm5HQsX+9555x2X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\nBAsXhmFcQgDgkLuLYBjm7vP/AalL9mFZhFUyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"hello_mapnode/graph.dot.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And more detailed structure with all nodes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nTmnjxo00EgAAMIBGAhAA3n33Xe3atUuPPvqo6SgA4BJffvmlzp07p+7du5uO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\naLfh4nkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"hello_mapnode/graph_detailed.dot.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will introduce another iterables, for the concater Node:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "170829-12:58:19,594 workflow INFO:\n",
      "\t Workflow hello_mapnode settings: ['check', 'execution', 'logging']\n",
      "170829-12:58:19,643 workflow INFO:\n",
      "\t Running serially.\n",
      "170829-12:58:19,645 workflow INFO:\n",
      "\t Executing node a_plus_b.bI.b1 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_2/a_plus_b\n",
      "170829-12:58:19,658 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:58:19,669 workflow INFO:\n",
      "\t Executing node concat_a_b.aI.a0.b1 in dir: /opt/tutorial/notebooks/hello/_a_2/_b_3/concat_a_b\n",
      "170829-12:58:19,684 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:58:19,700 workflow INFO:\n",
      "\t Executing node add_1.a0.b1 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_2/_b_3/add_1\n",
      "170829-12:58:19,740 workflow INFO:\n",
      "\t Executing node _add_10 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_2/_b_3/add_1/mapflow/_add_10\n",
      "PLUS ONE, a = 4\n",
      "170829-12:58:19,847 workflow INFO:\n",
      "\t Executing node _add_11 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_2/_b_3/add_1/mapflow/_add_11\n",
      "PLUS ONE, a = 3\n",
      "170829-12:58:19,956 workflow INFO:\n",
      "\t Executing node concat_a_b.aI.a1.b1 in dir: /opt/tutorial/notebooks/hello/_a_2/_b_4/concat_a_b\n",
      "170829-12:58:19,975 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:58:19,990 workflow INFO:\n",
      "\t Executing node add_1.a1.b1 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_2/_b_4/add_1\n",
      "170829-12:58:20,28 workflow INFO:\n",
      "\t Executing node _add_10 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_2/_b_4/add_1/mapflow/_add_10\n",
      "PLUS ONE, a = 4\n",
      "170829-12:58:20,121 workflow INFO:\n",
      "\t Executing node _add_11 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_2/_b_4/add_1/mapflow/_add_11\n",
      "PLUS ONE, a = 4\n",
      "170829-12:58:20,233 workflow INFO:\n",
      "\t Executing node a_plus_b.bI.b0 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_1/a_plus_b\n",
      "170829-12:58:20,246 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:58:20,255 workflow INFO:\n",
      "\t Executing node concat_a_b.aI.a0.b0 in dir: /opt/tutorial/notebooks/hello/_a_1/_b_3/concat_a_b\n",
      "170829-12:58:20,272 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:58:20,284 workflow INFO:\n",
      "\t Executing node add_1.a0.b0 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_1/_b_3/add_1\n",
      "170829-12:58:20,317 workflow INFO:\n",
      "\t Executing node _add_10 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_1/_b_3/add_1/mapflow/_add_10\n",
      "PLUS ONE, a = 3\n",
      "170829-12:58:20,402 workflow INFO:\n",
      "\t Executing node _add_11 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_1/_b_3/add_1/mapflow/_add_11\n",
      "PLUS ONE, a = 3\n",
      "170829-12:58:20,526 workflow INFO:\n",
      "\t Executing node concat_a_b.aI.a1.b0 in dir: /opt/tutorial/notebooks/hello/_a_1/_b_4/concat_a_b\n",
      "170829-12:58:20,544 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:58:20,556 workflow INFO:\n",
      "\t Executing node add_1.a1.b0 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_1/_b_4/add_1\n",
      "170829-12:58:20,593 workflow INFO:\n",
      "\t Executing node _add_10 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_1/_b_4/add_1/mapflow/_add_10\n",
      "PLUS ONE, a = 3\n",
      "170829-12:58:20,681 workflow INFO:\n",
      "\t Executing node _add_11 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_1/_b_4/add_1/mapflow/_add_11\n",
      "PLUS ONE, a = 4\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[hello_mapnode.a_plus_b.bI.b0,\n",
       " hello.concat_a_b.aI.a0.b0,\n",
       " hello_mapnode.add_1.a0.b0,\n",
       " hello.concat_a_b.aI.a1.b0,\n",
       " hello_mapnode.add_1.a1.b0,\n",
       " hello_mapnode.a_plus_b.bI.b1,\n",
       " hello.concat_a_b.aI.a0.b1,\n",
       " hello_mapnode.add_1.a0.b1,\n",
       " hello.concat_a_b.aI.a1.b1,\n",
       " hello_mapnode.add_1.a1.b1]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "concater.iterables = ('b', [3, 4])\n",
    "eg = wf.run()\n",
    "eg.nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "170829-12:58:24,672 workflow INFO:\n",
      "\t Generated workflow graph: /opt/tutorial/notebooks/hello_mapnode/graph.dot.png (graph2use=exec, simple_form=True).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/opt/tutorial/notebooks/hello_mapnode/graph.dot.png'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wf.write_graph(graph2use='exec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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AAAA2i8YXYILExEQNGzZMNWvW1Nq1azVz5kxt2rRJfn5+ZkdDEVeq\nVCktXbpUgYGBatWqldatW2d2JAAAgLuyYcMGtWzZUi1btlSZMmW0bds2TZ06VWXLljU7Goq4unXr\nav369Tp69KgCAwN18uRJsyMBAADYJIthGIbZIZA30tLSrnlcunTppuMyMzNVvHhxubq6ysnJSSVK\nlJCbm5tKlSql4sWLF3DqomXv3r36v//7P82fP1/ly5fXmDFjNGDAADk6OpodDbhGRkaGevbsqZUr\nV2rZsmVq2bKl2ZEAADBNVlaW0tLSdObMGZ05c0ZpaWnKysq6YVxmZqbs7e1VokQJlSxZUqVLl1aJ\nEiVUqlQplS5dWhaLxYT0RUNWVpZ+/PFHffDBB9q0aZMCAgL0zjvvKCAgwOxowA2OHDmiwMBAubm5\nac2aNXJ3dzc7EgAAgE2h8WUlMjMztX//fh08eFCHDx/W4cOHdeTIER0+fFjHjh1TampqnuzHxcVF\n5cuXV/ny5VWuXDmVK1dO7u7uqlSpkry8vOTt7S0fHx95enrSrMmlc+fOKTw8XLNmzdKmTZtUu3Zt\nvfLKK+rduzefIQq1zMxM9enTR//973+1dOlStWrVyuxIAADki3Pnzum33367oc4+cuSIkpKSdP78\n+fveR7FixXJq66v/9fLykqenp3x8fOTt7S0vLy95eHjkwVEVDQcOHNDs2bM1Z84c/fnnn+rQoYNe\ne+01NW3a1OxowG0dPnxYgYGBcnV1VVRUlMqXL292JAAAAJtB46sQysrK0v79+7Vt2zZt375d27Zt\n086dO3XhwgVJkoeHh6pWraoqVaqoSpUq8vHxkbu7u9zc3OTm5iZXV9ecK7huJTMzU+fOndP58+d1\n6dIlpaWl6fz58zp58qROnDih1NRUpaam5rxOTExUcnLyNd9s9fDwUJUqVXKyVK1aNed5pUqVinRT\nxzAMbdiwQbNmzdKiRYuUlZWlrl27KiwsTIGBgSpWjLuMwjpkZWWpX79+Wrx4sZYvX64nn3zS7EgA\nANyX8+fPa+fOndfU2vv371d2drYcHBxUqVKlnDq7SpUq8vb2zqmxy5Qpk1Nz29vb33IfFy9e1KVL\nl3T69GldvnxZFy5c0MmTJ3Pq6yv/pqSkKCkpSceOHdOpU6dy1nd0dJSPj881NfbVNbenp2dBfFSF\n1vnz57Vo0SLNnDlT0dHR8vLyUmhoqMLCwlS9enWz4wG5Fh8fr8DAQDk5OWndunXcjhMAACCP0Pgq\nJE6dOqXIyEj9+OOPWrVqlU6dOqXixYurfv368vPzk5+fnxo2bKhatWqpRIkSpmTMysrS8ePHlZiY\nqGPHjikhIeGGb8WePXtWkmRnZydvb+8bTtavPLy8vGyu+ZOamqo1a9Zo1apVioyMVHJysvz8/BQW\nFqZnnnlGpUuXNjsicE+ysrLUt29fLV26VCtXruSWQQAAq7N//379+OOPWrFihaKjo5WRkaFy5crl\n1Nl+fn56+OGH5evrKzs7O1MyXrx4UYmJiUpKSlJ8fLwSEhJyauzDhw8rPj5ef/31lySpRIkSNzTD\nrn5ui38837dvn1atWqVVq1Zpw4YNys7OVseOHRUWFqbWrVub9nMD7ldCQoIef/xxubm5ae3atSpT\npozZkQAAAKwejS8THTlyROHh4Vq+fLk2bdqkYsWKqXnz5mrbtq2efPJJ1a1bVw4ODmbHvCsnT568\nphF2dWPsyJEjOfOOOTo6qnLlyjc9Ya9atapV3Obh2LFj+uWXX7R161ZFRkZq27ZtsrOzU9OmTdWm\nTRu1b99ederUMTsmkCeysrL0zDPPaNWqVYqKilKjRo3MjgQAwC0ZhqHo6GgtWbJEP/74ow4cOKBy\n5cqpdevWatu2rZo2baqqVauaHfOuZGdn688//7ymxr76cezYMWVnZ0uS3NzcblpjX3nc7s4QhUFW\nVpZ+//137dixQ9HR0YqMjNTRo0dVtmxZtWrVSm3atFGHDh1Urlw5s6MCeeLgwYN6/PHHValSJUVG\nRsrFxcXsSAAAAFaNxlcBy8jI0A8//KCvvvoqZxLbtm3bqm3btgoKCpKbm5vZEfONYRhKSkq65cl6\nYmJizq0UnZycVLVqVVWuXPmaucW8vLzk6+urChUqyN3dPd+vGjMMQ8ePH9fRo0cVHx+vXbt2aceO\nHfrll1+UnJwsSapevbpatGihNm3aqEWLFpykwGb99ddfCg4O1saNG/XTTz+pYcOGZkcCAOAaKSkp\nmjNnjqZPn679+/erbt26ateundq1ayd/f3+bviror7/+0tGjR29aZx85ckQpKSk5YytWrJhzG0cf\nH59r6mxPT095eHgUSE17+fJlJSYmKj4+XgcOHNDOnTv1yy+/aPfu3bpw4YIcHR3VsGFDtW7dWm3a\ntFGjRo1s+meIou2PP/5QQECAqlevrlWrVsnJycnsSAAAAFaLxlcBOXjwoKZPn67Zs2crJSVFrVu3\n1nPPPaf27dvfdn6AoiQjI+OG2ycmJCRcc2vFqycWL1asmNzd3W94lCxZUiVLllTp0qVVsmRJlShR\nQm5ubjdtkp09e1bnzp1Tenq60tPTdfr0aZ07d05JSUlKSEhQQkJCzi1l7OzsVLNmTTVs2FANGjTI\n+ZdbGKIouXjxojp06KBff/1V69evV+3atc2OBAAo4gzD0Nq1a/XVV19p6dKlKlWqlHr37q3nnntO\n9erVMzteoZGenn7Tq8T+/PNPJSQk6Pjx48rIyMgZX7x4cbm7u6t8+fKqWLGi3N3d5eTkJFdXV5Us\nWVJOTk5yc3OTo6PjTZtkWVlZOnv2rNLS0q6pt1NTU3Xs2DHFx8fr+PHjunI66uTkpPr1619TZ9ep\nU8fq7oAB3I/du3crMDBQjRo10rJly4r0vNkAAAD3g8ZXPtu9e7fee+89RUREyMvLS/3799ezzz6r\nSpUqmR3NKqWlpSkxMVEpKSlKTk5WamqqUlJSlJqamjNJ+MWLF3Xx4kWdOXNGFy5c0IULF3LmHrue\ni4uLXFxc5OzsLBcXF5UpU0bOzs7y8PCQr6+vfH19VaVKFfn6+srb25sTb0B/TygfFBSkxMRExcTE\nyMfHx+xIAIAiKDs7W4sWLdJ7772nXbt2qVmzZnruuefUrVs3lSxZ0ux4VufKnQ6SkpJy6uwrtfaJ\nEyd08uRJpaen6+zZszk19pkzZ3Jq7+sVK1ZMbm5ucnV1vabmLleunDw8PHJq7EqVKsnX11cVK1Y0\n4aiBwmf79u0KDAxU27ZtNX/+fJubGxsAAKAg0PjKJ/v379fo0aO1dOlS1a1bV2+88YaCg4O5NQcA\nm5CWlqbHH39cFy5c0MaNG1WhQgWzIwEAipAlS5ZozJgx+t///qeQkBCNHj1a9evXNzsWAOSJn3/+\nWU899ZTCwsI0ZcoUs+MAAABYHb46lMdSUlL0wgsvqG7duvrjjz+0dOlS7dy5U926daPpBcBmuLm5\nacWKFcrIyFCHDh2Unp5udiQAQBGwZcsWNW/eXMHBwXr44Ye1d+9eLViwgKYXAJvyxBNPaMGCBfr6\n66/1zjvvmB0HAADA6tD4yiPZ2dn6+uuvVbt2bS1dulRTp07Vzp071bFjR1ksFrPjAUCe8/Ly0po1\na3T06FF16dJFly9fNjsSAMBGnTp1SoMGDVLTpk1lZ2enLVu26LvvvlOtWrXMjgYA+aJTp0764osv\nNHbsWH366admxwEAALAq9mYHsAW///67+vfvr7i4OA0dOlTjxo2Ts7Oz2bEAIN9Vr15dP/zwgwID\nAzVgwADNnTuXZj8AIE999913GjZsmOzt7TVv3jz17NnT7EgAUCAGDhyo5ORkjRw5Uj4+PuratavZ\nkQAAAKwCV3zdh+zsbH3yySdq2LChMjMztX37dn300Uc0vQAUKY0aNdLixYsVHh6usWPHmh0HAGAj\nUlNT1a1bN/Xq1UshISHat28fTS8ARc6///1vDRo0SL1799bmzZvNjgMAAGAVLIZhGGaHsEbJycnq\n1auXNmzYoDfffFNjxoyRvT0X0AEoumbOnKlnn31WU6dO1fPPP292HACAFVu7dq169eole3t7zZw5\nU61atTI7EgCYJisrS8HBwYqJidGmTZtUo0YNsyMBAAAUajS+7sH69evVs2dPlSpVSgsWLJCfn5/Z\nkQCgUPj3v/+t999/X8uXL+ePlACAu5adna333ntPb7/9trp06aKvv/5apUuXNjsWAJjuwoULCgwM\nVGpqqmJjY1W+fHmzIwEAABRaNL7u0qRJk/Tqq6+qY8eOmjlzptzc3MyOBACFhmEYCg0N1bJlyxQd\nHa169eqZHQkAYCXOnj2rnj17KioqSh9++KGGDh1qdiQAKFSSkpLk7++vSpUqac2aNSpevLjZkQAA\nAAolGl+5lJGRoRdffFHTp0/XxIkTNWrUKFksFrNjAUChc/nyZbVu3VqHDx/W5s2b5enpaXYkAEAh\nd+TIEXXo0EGpqalaunSpGjdubHYkACiUfvvtNzVr1kzt2rXTvHnz+LsEAADATRQzO4A1OHv2rNq2\nbatvv/1W33//vV555RWKSwC4heLFi2vJkiUqVaqUOnfurIsXL5odCQBQiMXFxalx48ays7PT1q1b\naXoBwG089NBDWrx4sSIiIvTuu++aHQcAAKBQovF1BykpKQoMDNRvv/2mDRs2qFOnTmZHAoBCr0yZ\nMlqxYoUOHTqk0NBQcXExAOBmfvrpJ7Vo0UKPPPKINm7cKF9fX7MjAUCh16JFC02bNk1vvfWWwsPD\nzY4DAABQ6ND4uo2EhAQFBATo9OnTio6OVoMGDcyOBABWo2rVqvruu++0ZMkSTZgwwew4AIBCZsmS\nJWrXrp06dOigZcuWydnZ2exIAGA1+vfvr8GDByssLEzbtm0zOw4AAEChwhxft5CYmKjHH39cTk5O\nioyMZI4aALhHn332mYYNG6YFCxaoW7duZscBABQCixcvVo8ePTRo0CB9+umnKlaM7+MBwN3KzMxU\nmzZttG/fPsXFxcnLy8vsSAAAAIUCja+bSE5O1pNPPinDMLRu3Tp5eHiYHQkArNrgwYM1b948xcTE\nqF69embHAQCYaOXKlerSpYv69u2rL7/8krlzAeA+nDp1Sk2aNJG7u7vWrVun4sWLmx0JAADAdDS+\nrpOamqqAgABJ0rp161SxYkWTEwGA9cvIyFBQUJAOHz6sbdu2yd3d3exIAAATrFq1Sp06dVL//v01\nZcoUml4AkAf27dsnf39/denSRbNmzTI7DgAAgOm4p8hVzp8/r/bt2+vSpUuKioqi6QUAecTBwUGL\nFi2SxWJRjx49lJmZaXYkAEABi4uLU0hIiHr27EnTCwDy0AMPPKBvv/1Wc+fO1RdffGF2HAAAANPR\n+PpHVlaWevXqpYMHD2rlypXcGxsA8li5cuW0dOlSxcbGavTo0WbHAQAUoIMHD6pDhw4KCAjQ9OnT\naXoBQB5r27at3n77bY0YMUIbNmwwOw4AAICpuNXhPwYNGqT58+dr7dq1evTRR82OAwA2a/78+erT\np4++++47de/e3ew4AIB8dvLkSTVu3Fhly5bVunXr5OTkZHYkALBJhmEoJCREGzdu1Pbt2+Xt7W12\nJAAAAFPQ+JL0xRdfaOjQofr+++/VqVMns+MAgM0bOnSopk+frpiYGDVo0MDsOACAfJKZmanWrVvr\nwIEDiouLU4UKFcyOBAA27dy5c2rSpIlcXV31888/q3jx4mZHAgAAKHBFvvEVHR2tli1bauzYsXrj\njTfMjgMARUJmZqZatmypo0ePatu2bSpXrpzZkQAA+eCll17SjBkztGHDBvn5+ZkdBwCKhP3796tx\n48bq2bOnpk6danYcAACAAlekG18JCQmqW7euHnzwQQ0fPpy5BgAgDzRq1EhVqlS547jk5GQ98sgj\natq0qRYuXJirbR85ckRxcXH3mRAAcIWHh4eaN2+eL9ueM2eOwsLCNHz4cPn7++fLPgCgKLGzs1Pb\ntm1VokSJO479/vvv9fTTTys8PFwhISG52n50dLSOHz9+vzEBAP/I7d9HAOS9Itv4yszM1BNPPKFN\nmzapiH4EAJAvevTooe+++y5XY1evXq3WrVtr9erVatWq1R3H9+zZUwsWLLjfiACAf9jb2ysjIyPP\nt5uUlKSaNWsqPT09z7cNAEXZ4sWL1bVr11yNfe6557Rq1Srt27dPzs7Odxzv4OCgzMzM+40IAPjH\n3fx9BEDesjc7gFn+/e9/a/v27TIMQ+Hh4erWrZvZke7KwoUL1b17d5p2sDlX/lvM7RVAKFy6deum\nrKysXI8PCgpSly5d9Oqrr2rHjh13HJ+VlaWQkBCr/P2wWCxW+f83wO1Qj1i3Kz+//DBy5Eg5OTkp\nPT3dKn8/qEdgq6hHrJvFYrmrxtSECRO0ePFiTZ48OVdTO2RmZlrl7wf1CGwV9Yh1u9u/jwDIW8XM\nDmCGtWvX6oMPPtDnn39udhQAKPJGjRqlnTt35qrxBQAo/Pbs2aOFCxeqV69eZkcBgCLN3d1doaGh\nmj17Nk0hAABQpBS5xtfJkyf1zDPPqEePHnr22WfNjgMARZ6/v79q1Kihb775xuwoAIA8MHbsWNWv\nX1+PPvqo2VEAoMjr27evDhw4oNjYWLOjAAAAFJgi1/h66aWXZGdnx9VeAFBIWCwWdenSRatXrzY7\nCgDgPh06dEjLli3Tm2++KYvFYnYcACjyGjRooKpVq1JrAwCAIqVINb5++OEHLViwQNOnT1eZMmXM\njgMA+Mdjjz2mvXv36tSpU2ZHAQDch2nTpsnb21sdO3Y0OwoA4B+PPfaY/h97dx4eVXnw//8zIYQt\nAQJhC2ErImDZ1ZIUoS6IVYqKCNQqIqiAaBVql8flsvL0V5enj+KKVgu44KOAW0UUEWhlEpKwr4IK\nAdkCBEjCloSQ3L8/+E5KSEgmk5m558y8X9c110VmOeeTmfCZ+8w955wVK1bYjgEAABA0ETPxlZeX\np/vuu0933XWXrr/+ettxAADnSElJkSRlZGRYTgIA8FVRUZFmzZqle++9V9HR0bbjAAD+n5///OfK\nyMhQaWmp7SgAAABBETETX7/73e9kjNHzzz9vOwpChMvlKrvURGlpqd566y0lJSVxCB/ATxISEpSY\nmKitW7fajoIQRWcDoW/RokXKzc3VuHHjbEdBCPC1t7/++mtdddVVaty4sRo3bqyrr75aS5YsCVBK\nIDL06tVLx48f1969e21HQYiiswEA4SYiJr5WrVqlt99+Wy+++KKaNm1qOw5ChDGmxo9ZvHix+vbt\nq1mzZmnfvn0BSAVErosuukg7duywHQMhis4GQt/777+vQYMGKSkpk3AkBgAAIABJREFUyXYUhABf\nevvtt9/WkCFD1LNnT2VlZSkrK0s9evTQkCFDNGfOnACkBCLDRRddJEnavn275SQIVXQ2ACDchP3E\nV2lpqR544AFdddVVuvXWW23HgcM9+OCDmjZtmpYvX247ChB2LrroIv3www+2YyCM0NlA8Jw8eVKf\nf/65fv3rX9uOAofKzs7W/fffr5SUFL344otKSEhQQkKCXnzxRfXv31+TJ0/WwYMHbccEHKlly5Zq\n0qQJE1/wGzobABDqwn7i6+9//7vWrl2r6dOn246CMLB582bdfPPNtmMAYaldu3YcfgV+RWcDwbN4\n8WIVFhbqlltusR0FDjVz5kydPHlS48ePL3eoLZfLpfHjx+v48eOaNWuWxYSAsyUlJTHWht/Q2QCA\nUBfWE19HjhzR448/rilTpqhnz5624yAMcKJ2IHASEhJ05MgR2zEQRuhsIHgWLFig5ORktWjRwnYU\nOJTnnDD9+/evcJvnusWLFwc1ExBOWrRowVgbfkNnAwBCXVhPfD3zzDOKjo7WE088YTsKQtzu3bs1\nfPhwNWnSRLGxsRo6dKi2bt1qOxYQURISEnT06FGVlpbajoIQR2cDoaW0tFRffvmlhg4dajsKQpQ3\nve35uV27dhUe3759e0nStm3bAh8WCFMJCQk6fPiw7RhwADobABAOwnbia9++fXr11Vf1+OOPKy4u\nznYchLgJEyZo6tSp2rt3r/75z39q7dq1GjBggHbt2mU7GhAxmjdvrpKSEuXn59uOghBHZwOhZe3a\ntTpw4IBuuOEG21EQorzp7by8PElSbGxshcd7rsvNzQ1KXiAcNW/enD2+4BU6GwAQDsJ24uvPf/6z\nWrVqpQkTJvh1uS6Xq9JLZbcnJSUpJyfH6+XAnkmTJmnQoEGKi4vTNddco2eeeUa5ubl68sknbUdD\niFm4cKFuuukmtW7dWjExMWrdurWGDRumTz/9tMJ9q+uL6u5Xk0s4aNSokSTp1KlTfl0uvR1+6Gx4\ni84OjqVLl6ply5bq1atXrZdFZ4cnehveoLMDq2HDhoyz4RU6G96itwGEsrCc+Pr+++/19ttv67//\n+79Vr149vy7bGCNjjFc/79u3T7fddptKSkqqXM75y0DwDRw4sNzPgwcPlsQxqfEfxcXFuuOOO3T7\n7bfr6quv1qpVq3TixAmtWrVK11xzjcaOHasRI0aooKCg7DHV9UVl11f27wstJ9y6o0GDBpL8P/FF\nb4cfOhvVobOD65tvvtGVV17plw8a6Ozw5E1vN23aVJJ04sSJCo/3XBcfHx+oiLCIzg6OBg0alHsO\n/YHODk90NqpDbwNwgrCc+HryySfVrVs3/eY3v7Gao3Xr1lq6dCnnGHOA5s2bl/s5ISFBki74jTRE\nnt/+9reaN2+elixZooceekjt2rVTTEyM2rVrpylTpmjx4sX67LPP/L6XaSTxTHz5e4O8JuhtZ6Cz\nUR06O3jOnDmjtLQ0XXnllUFfN53tHN70dvfu3SVJe/bsqfD43bt3S5K6desWqIiwiM4OjgYNGvj9\nC2Y1QWc7B52N6tDbAJwg7Ca+srKyNH/+fD322GOqU6eO1Sxz585VdHS0nn76aX3++edWs6Bq559T\nyHPS3xYtWtiIgxCTmZmpv//977rrrrt02WWXVXqf/v37684779ScOXPkdrtrvc6afFMpXL7V5NlD\nt7Cw0FoGetsZ6GxUhc4Org0bNujYsWP6xS9+EfR109nO4U1ve/YoyMzMrPD4lStXSpKGDBkSqIiw\nhM4Onvr166uoqMja+uls56CzURV6G4BThN3E1zPPPKP27dvr1ltvtR1FgwYN0lNPPSVjjMaMGaOd\nO3fajoQLSE9PL/fzkiVLJDFQw1mvv/66JFXbKyNHjpQkvfnmmwHPFI48A1Sbx+Smt52BzkZV6Ozg\nWrVqlRo3bmzlW910tnN409vjx49Xo0aNNHv27AqPnz17tmJjYzVu3LjABkXQ0dnBU1payjgbXqGz\nURV6G4BThNXE14EDB/Tuu+/qv/7rvxQdHW07jiTpD3/4g4YPH668vDyNGDHC6p4MuLCnn35aK1as\n0IkTJ7Rs2TI98sgjio+P5+StkKSybyj17Nmzyvv16tVLkpSWlhbwTOHIM/EVFWX3rYneDn10NqpC\nZwfX2rVr1bdvX2vdTWc7gze9nZiYqFdeeUUrVqzQlClTdPjwYR0+fFgPPfSQ0tPTNWPGDLVu3dre\nL4GAoLODxxhjdeJLorOdgs5GVehtAE4RVhNf//u//6tmzZrpzjvvtB2lnNmzZ+uiiy7SunXr9MAD\nD9iOg//n3EH/a6+9pmnTpqlNmza68cYb1adPH6Wlpaljx46VPu7cx57/M8LP/v37JVU81vn5PLdn\nZ2cHPFM4Ki0tlWR3jy8Pejv00NnwFp0dXGvXrlW/fv2sZqCzQ5MvvX3XXXfpq6++0vr169WpUyd1\n6tRJGzdu1OLFizVmzJgg/wYIBjo7eEpLS61/wUyis0MVnQ1v0dsAnCI0dovyg5MnT+of//iHHn30\n0bLzxISKJk2a6KOPPlJycrJmzpypAQMGsMt3CDj/uL9fffWVT48DPDwbC3yo7ptQ2eNLordDEZ0N\nf6Oza+/06dPavHmzpkyZYjUHnR2afO3tIUOGcOhaVEBn114o7PEl0dmhis6Gv9HbAGyz/+min8yZ\nM0eFhYUhO2Dq1auXXnvtNUnS/fffr/Xr11tOBMBbbdq0kSQdPXq0yvt5TvqbmJhY7nrPRE5JSckF\nH1tSUhISEz42hdIeXxK9DTgVnR08W7ZsUVFRkS699FLbUehswKHo7OAJlT2+JDobcDJ6G4BThE2L\nvPHGG7rtttvUokUL21EuaOzYsZowYYIKCgp06623Ki8vz3YkAF4YOHCgJGnjxo1V3s9z+6BBg8pd\nHxcXJ0nKz8+/4GNzc3PVuHHj2sR0vFDa48uD3gach84OnjVr1qhhw4a6+OKLbUeRRGcDTkRnB0+o\n7PHlQWcDzkRvA3CK0Pl0sRZSU1O1du1aTZ482XaUar300ku69NJLtWPHDo0dO9Z2HABemDRpkiTp\no48+qvJ+8+fPL3d/j65du0qSNm/efMHHbt68OWQ+OLQl1Pb48qC3AWehs4Nn3bp16tu3r+rUqWM7\nShk6G3AWOjt4QmmPLw86G3AeehuAU4TWqMdHf//733XppZfq8ssvtx2lWvXq1dOHH36o+Ph4ffbZ\nZ7bjAPBCcnKyJk6cqNmzZ2v16tWV3iczM1PvvPOOJk6cWKGLhg0bJunsiZwvZObMmRo6dKj/QjtQ\nKO7xJdHbgNPQ2cHjmfgKJXQ24Cx0dvCE2h5fEp0NOBG9DcApQuvTRR+cPHlSn376qe6++27bUbzW\nsWNHzZkzJ+QGnQAu7OWXX9bIkSN17bXX6qWXXtLevXtVXFysvXv36sUXX9R1112n0aNH6+WXX67w\n2IceekiXXHKJ3nrrLd1///3avHmzioqKVFRUpE2bNum+++7TqlWrNGXKFAu/WegoLCyUdHYDONTQ\n24Cz0NnB8d1336l79+62Y1RAZwPOQmcHR1FRkerXr287RgV0NuA89DYAJ3D8xNcnn3yioqIijRw5\nMijrc7lc5QZkVf18/m3nuuGGG/TYY48FNiwAv6lbt67ee+89zZkzR0uWLNGll16qRo0aqV+/fvr6\n6681Z84czZkzR3Xr1q3w2Li4OKWnp2vatGlauXKlBgwYoEaNGqlFixYaO3asWrRooczMzAsew7q6\n3gkXnokvf2+Q09tA5KGzAy8/P19Hjx7VT37yE78ul84GIg+dHRyFhYWMswH4Bb0NwAmibQeorffe\ne0+//OUvlZCQEJT1eQ7F5evt5/rLX/6iv/zlL7WNBCCIhg4d6tMu940bN9YTTzyhJ554osaPrUmv\nOFmgJr7obSBy0dmBs2PHDkny+8QXnQ1ELjo7sAIx8UVnA5GN3gYQyhy9x1dOTo6WLFmi22+/3XYU\nAEAtBWriCwDgf1lZWYqKilKHDh1sRwEAeCEQE18AAAChytF7fH300UeqX79+2YkRI9GoUaNsRwD8\nKj09XSkpKbZjwIJImPiaPn26PvzwQ9sxAL/Zs2eP7QiwJCsrS0lJSSF5XkZ/SU9PZ6wNIGxEwsQX\nnY1ww+cjAOA7R+/x9fnnn+vaa69Vw4YNbUcBANRSYWGhoqOjVadOHdtRAADVyMrKUufOnW3HAAB4\nqbCwMKy/rAAAAHAux+7xVVBQoH/961966aWXbEexat68ebYjAH7Ft/QiVyR8C3Xq1Kn8jSOszJs3\nT6NHj7YdAxZkZWX5/fxeoSYlJYWxNsKOy+WyHQGWFBYWKj4+3naMgKKzEW7YdgQA3zl2j69ly5ap\noKBAv/zlL21HAQD4QSRMfAFAuMjKylKnTp1sxwAAeImxNgAAiCSOnfj64osv1LdvX7Vt29Z2FACA\nHxQVFbExDgAOUFpaqt27d6tjx462owAAvMTEFwAAiCSOnfhatmyZhgwZYjsGAMBP2BgHAGc4cuSI\niouL1aZNG9tRAABe4ktmAAAgkjhy4uvw4cP67rvvdMUVV9iOAgDwEzbGAcAZcnJyJEktW7a0nAQA\n4K3CwkLVq1fPdgwAAICgiLYdwBcrVqyQdPaE00CgnXsCaGOMxSRAeGOPL/gDnQ0E3qFDhyQx8QX/\noLeB4GCsDX+gswEATuHIPb7S0tLUvXt3NWvWzHYUhJmBAwdq4MCB5a6rajBX2f0B+IaNcdQUnQ3Y\ncfjwYUVFRal58+a2o8Bh6G3AHsbaqCk6GwDgZI7c4yszM5O9veATz7eTLjRYKy0trdHyLnT/6tYD\noCI2xnE+OhsITXl5eYqLi1OdOnVsR0GIobeB0MVYG+ejswEA4cyRE19btmzRLbfcYjsGwlBaWlpA\n7w/gwtgYR03R2YAdx48fV2xsrO0YcCB6G7CHsTZqis4GADiZ4w51ePToUR0+fFhdu3a1HQUA4Eds\njAOAMxw/flxxcXG2YwAAaqCoqIixNgAAiBiOm/jatm2bJKlbt26Wk8A2l8tVdvH2+vPvc88991T7\nOF/Wf6H1nPsYz+WDDz4ou3/Hjh1rlAEIJ0VFRYqJibEdAwFCZwPh48SJE+zxFQHobSB8nD59WqWl\npapXr57tKAgQOhsAgPIcd6jD7777Tg0aNFC7du1sR4FlxphKB0DeXF/ZsaUv9LjarP/89RhjtHTp\nUg0ePFht2rTRrl27yn3Q//jjj+uf//ynFixY4HUOIFyUlpZyvpgwRmcD4YM9dCMDvQ2ED8+5l6Ki\nHPfdZ3iJzgYAoDzHjXr27t2r9u3bM2CDY11zzTXq3bu3srOzy32bSZJeeuklPfTQQ5aSAQDOR2cD\nFZWUlPBFBYQsehsAnIPOBgAEiuNmj44ePapmzZr5dZmjR4+udBfrUL6MHj3ar88Bgmvq1KmSpOnT\np5ddt2zZMpWWlmrw4MG2YoWM+fPnW/8/xsW3y/z58/n7uMAFzkVnV8/2/y8uvl18HU+eOXNG0dHe\nHzjC9u/pyyUY72cIHHq7ak7c/uVy9sLfR+UXPh9xNjq7ak7d/uXCeBKwzXGHOgzExNfUqVOVkpLi\n12UGWnp6erlBAZzltttu0yOPPKL169dr2bJluvrqq/Xiiy/ybab/JyUlpWzwC2epTS9VdoiNyjj1\n72PUqFG2I8BHdHb15s2bZzsCfODreLKme3w58e+Dcbaz0dtVc+L2L86q7XjS5ap+8syJfx98PuJs\ndHbVnLr9C8aTgG2Om/jKzc31+8RXcnKyRo4c6ddlBpq3HxCHO5fLJWOMiouLVbduXUlSfn6+5VTV\ni4mJ0QMPPKDHHntMzz//vDp27Kj09PQKu/ZHqqSkJMf9n8RZtf1Gkzcb4/x9OBedHb74P+lMvo4n\nPf+XveXEvw++oXsWvR2enLj9i9qpSWc78e+Dz0fOorPDE9u/zsV4ErDLcYc6PHXqlBo2bGg7BkJE\n69atJUnZ2dll161bt+6C9/f87RQXF+vUqVNKSEgISC5v1jNp0iQ1bNhQX3zxhR588EHdc889atCg\nQUDyAEAooLOB8FC/fn0VFhbajoEgoLcBwDnobAAA/sNxE1/16tVTUVGR7RgIEddee60k6W9/+5vy\n8/O1bds2/eMf/7jg/Xv16iVJWrlypRYsWBCwQzh4s55mzZpp7NixMsboq6++0uTJkwOSBQBCBZ0N\nhIcGDRow8RUh6G0AcA46GwCA/3DcxBffMMW5nnvuOf3mN7/R3Llz1bZtW/3xj3/U008/XXb7+YdN\ne/nll9W7d28NGTJEL7zwgp577rlK71ubf1e3nnNNnTpVUVFRuvXWW5WUlOTtrw2ELQ5TEt7obCA8\nMPEVOehtIDx4czhxOB+dDQDAfzjuHF8NGjTQiRMnbMdAiEhISNB7771X4foLfXh+2WWXaf369ZXe\ndqHH1PT66tZzrs6dO6tVq1actBXQ2X4vKCiwHQMBRGcD4SEuLk7Hjh2zHQNBQG8D4aFevXqKiopi\nrB3m6GwAAP7DcXt8NW3aVEePHrUdA/CLhQsXql27dkpOTrYdBbAuNjZWJ0+etB0DuCA6GzirRYsW\nOnTokO0YQLXobeAsl8ulhg0bMtZGSKOzAQD+5LiJry5duui7776zHQPwmcvlUkZGhnJzczVt2jQ9\n9thjtiMBIaFRo0ZsjCPk0NlARS1btlRBQYGOHz9uOwpQAb0NVI6xNkIRnQ0ACBTHTXx1795dhw4d\n0pEjR2xHAXyWkpKiLl266Fe/+pVuvPFG23GAkNCkSRPl5+eX/ZyXl6dPP/3UYiLgLDobKK9Vq1aS\nVG6vr7y8PJWUlNiKBJRDbwMVnT/W3rRpk9asWWMxEXAWnQ0ACATHneOre/fukqStW7fqiiuusJwG\nqLmqjn0NRLKWLVvq4MGDkqRPPvlEEydOVE5Ojr7//nt16dLFcjpEKjobqKhjx46SpKysLHXu3Fnv\nvfeeJkyYoD//+c/64x//aDccIh69DVSuVatWOnjwoAoKCjRt2jT97W9/U7du3bRlyxbb0RDB6GwA\nQKA4bo+vpKQkJSUlacmSJbajAAD8qHXr1srJydFNN92kW265RUeOHFGdOnXkdrttRwMAnKNZs2Zq\n3ry5Nm3apPHjx+uOO+7QqVOnNGfOHNvRAAAX0Lp1a23cuFHdu3fXc889p9LSUm3durXcXmAAAADh\nwnETXy6XS8OHD9cnn3xiOwoAwE+MMdq6datcLpe+/PJLSVJpaalcLhcTXwAQgtq3b6//7//7//Tu\nu++WXbd582bt2bPHYioAQGXy8vK0f/9+LV++XPv27dOZM2cknR2Dr1ixwnI6AAAA/3PcxJck3XLL\nLdq4caO+//5721EAALW0Y8cOXXXVVZo2bZpKSkpUXFxcdtuZM2e0bNkyi+kAAOd75513tGXLFh0/\nfrzsw1NJqlOnjv75z39aTAYAON+CBQvUrVs3rVy5UsaYcr0dExOj1NRUi+kAAAACw5ETXwMHDlSb\nNm00a9asctfn5eVxfGoAcAhjjJ555hldcsklWrFihYwxlR7jfffu3crOzraQEABwrhMnTuiOO+7Q\n2LFjVVxcXO7DU+nsnrofffSRpXQAgHPl5ORo2LBhuvHGG5WTk1Puy2Uep0+f5ktmAAAgLDly4qtO\nnTqaMmWKXnvtNeXk5JRd/9hjj6l379763//9X06QCSBsuVyusouTnThxQk899ZSKi4sr3RD3cLlc\nfBMVgGOFS2fv3r1bvXv31rx58yRVfjL60tJSud1u5ebmBjseAPhFuHS2JC1fvlyff/65pLP9fCFr\n1qxRUVFRsGIBgF+FU28D8K9o2wF8NXnyZL388sv605/+pFmzZqmkpEQffPCBSkpK9Mc//lGpqal6\n55131LhxY9tRA4piRzgaOXKk7QghzRhzwf/7AwcOlCRHnBcrLi5Oa9as0bBhw7Rjx44Kew541K1b\nV6mpqWHxdzF69GiNHj3adgwAQRQunZ2fn68jR45Uez9jjBYuXKg77rgjCKkCa/78+Yy1gQgTLp0t\nSSNGjNB7772nu+++W2fOnLngWLu4uFirVq3SFVdcEeSE/kdnIxyFw3ZwIIVTbwPwL8dOfMXGxuql\nl17SiBEjdMMNN6hZs2Y6evSopLOl98UXX6h379767LPP1LNnT8tpA8fzrVsgXEyfPt12BEer6tuc\n3vAMGIO112yXLl20evVqjR8/Xh9++GGl6z19+rSWLl0alDyBNnXqVKWkpNiOAfhNeno6vV0LTurs\nnj176rvvvtO9996rBQsWyOVyVbpel8uljz/+OCwmvlJSUjR16lTbMQC/GjVqlO0IjuWkzvb4zW9+\nox49emjYsGHKzs6u9CgLMTExcrvdYTHxxecjCDeMs2vHib0NwH8cO/ElScOHD9fkyZM1btw4DR48\nWDExMTp9+rSks99a2rt3ry699FK9/vrrGj9+vOW0gcE3PxBu5s+fbzuCo6WlpdmOUGOxsbGaN2+e\n3njjDU2ePFmSVFJSUu4+W7duVX5+vo14fpWcnExvI6ywEVg7TuvsVq1a6bPPPtP8+fN1zz33qKCg\noMKHqCUlJVq0aJEKCwstpfSfpKQkOhtAGad1tkevXr20adMmjRkzRp9//nmFD4KLi4v1zTff6JFH\nHrGU0H/obIQbPh+pHaf2NgD/cOQ5vs41ffp0XX311Vq0aFHZpJfHmTNnVFxcrLvvvlsTJkyocDsA\nIHRMmDBB//rXvxQfH6+6deuWu620tFTp6emWkgEAzjVy5Eht27ZN1157baWHlikoKAibPXUBIBw0\nbtxYn376qZ566ilFRUUpKuo/HwUZY5Samlrhi2cAAABO5viJr7p162ry5MnVfqt09uzZuvzyy7Vr\n166A5lmyZIluvPFGxcfHq379+urXr58++OCDgK4TiHT5+fmaOnWqfvKTn6h+/fpq3ry5fv7zn+v3\nv/+9Vq5cWe6+Bw4c0MSJE5WUlKSYmBglJSVp0qRJOnjwYLn7nXuC1P3792vEiBGKi4tT8+bNNXbs\nWOXn52vXrl268cYb1bhxY7Vu3Vp33XWX8vLyKuQ7dOiQ7rvvvrJ1tm3bVhMmTNCBAwf8+jxc6KSu\n3j4/5z7Os5x77rnHrxmrM3DgQG3YsEF9+/ZVnTp1yq6PiYlRamqq39dHZwPBR2dXzOzL82O7s9u0\naaOFCxfqrbfeUoMGDcp9YaFu3br69NNPA7JeehsILjq7YmZfnh/bne1Z75/+9CctXLhQsbGx5Xr7\n5MmT2rx5s9/XSWcDwUdvV8zsy/MTCr0NoJZMGJg4caKJiYkxkqq81K1b1zRp0sQsWrSo7LGSzNy5\nc/2WRZK5+eabTU5Ojvnxxx/NtddeaySVW6c/zJ0714TJyweUM3LkSDNy5MgaPeamm24ykswLL7xg\nTpw4YYqKisy2bdvM8OHDy/0/yc7ONu3atTOJiYlm6dKl5tixY2bJkiWmdevWpkOHDubAgQPlluvp\njjvuuMN8++23Ji8vz9x///1Gkhk6dKgZPnx42fX33XefkWTuvffecss4cOCA6dChg2nVqpX56quv\nzPHjx83y5ctNhw4dTKdOnUxubq5Pz5MnmzfXe/v8VLVcb/ny+lWmuLjY/OlPfzKSjMvlMpJMSkqK\n35bvEazO9qzLn+83QCjwZTxCZ1d9fTA721/jyZ07d5qBAweaqKioskzx8fHm/fff9/t4NVi97e/3\nGyBU1HQ8QmdXfX0wO9uzDH+MJ3fv3m369u1roqOjjSQTHR1tXn75ZT4fAUIMn494J1R7m/EkYJfj\nRwZnzpwx8fHx1U56eS5RUVHG5XKZP/3pT6akpCQgA7udO3eW/bx161YjyQwcONBv6zCGgR3Cly8D\ng8aNGxtJZv78+eWu37dvX7n/J/fee6+RZN59991y93vrrbeMJDNx4sRy13t649///neFZZ5//Z49\ne4wk07Zt23LLmDhxopFkZs6cWe76jz/+2Egyjz76aI1+1/OzeXO9t89PVcv1lr8Hdu+8846pV6+e\ncblcJiYmxowYMcLvE1/B6GzPupj4QrjxZTxCZ1d9fTA725/jyZKSEvPCCy+YmJiYsgmw//7v/w7I\nxFcwepsPKhCuajoeobOrvj6Yne1Zhr/Gk6dOnTJ33nln2ZfMRo4cyecjQIjh8xHvhGpvM54E7HL8\noQ6/+eYb5ebmen3/0tJSGWP07LPPavjw4X7PY4xRx44dy37u0qWLJOnbb7/1+7rgHBfaxTpY6wz0\n+r/++mtdddVVaty4sRo3bqyrr75aS5YsCci6KjNixAhJZ8850r59e91zzz2aN2+eEhISZIwpu9/n\nn38uSbr66qvLPX7w4MHlbj9fv379yv7dunXrSq9PTEyUJO3fv7/cYxcsWCBJuv7668tdP2jQoHK3\nB5K3z48/vfrqq3rsscf05ptvas+ePT4vZ8yYMcrMzFS7du10+vRpHT161I8p6WxULtw7u7S0VG+9\n9ZaSkpKC+jt60NlVs9HZGRkZmjJlil544QWtWLHCp/VERUXpoYce0qZNm3TppZdKklavXu3vqPQ2\nKhXuvW1zrE1nV81GZ0vSvffeq7/+9a/6+OOPderUKZ+W0aBBA7399tuaMWOGoqOj9c033/g5JZ2N\nyoV7ZzPWprfP9/zzz2vcuHGaOHGiHn/8cb366qtatGiRsrOzA7I+AGc5fuKroKBA7du3V9u2bdW2\nbVv99Kc/VY8ePcoul112mX7xi1+Uu9xyyy0aOXKkOnfu7NcseXl5evTRR9W9e3fFxcXJ5XIpOjpa\nknTkyBG/rgvOEsiNnqrWee56z//ZX95++20NGTJEPXv2VFZWlrKystSjRw8NGTJEc+bM8fv6KjNr\n1ix99NFHGjFihE6cOKGZM2dq9OjR6tKli9avX192v5ycHElSQkJCucd7fj506FCly4+Liyv797kn\ngq7s+vOfY88yExMTyw2wPevcsWNHzX5ZH3j7/PjT8ePH9cWB6ZZ9AAAgAElEQVQXX+jhhx9Wx44d\nNXbsWB07dsynZfXu3Vvr16/XlClT1LBhQ79lpLNxIeHc2YsXL1bfvn01a9Ys7du3z+/L9wadXTUb\nnV1aWqrVq1frf/7nfzRgwAD169fP50mriy++WOnp6Zo+fTpjbQRNOPe27bE2nV01G50tST/++KPe\nffddjR49WklJSZoxY4bPy5o0aZJSU1M1fvx4Pyaks3Fh4dzZjLXLX09v/8ehQ4eUlZWlBQsW6Mkn\nn9T111+vxMREJSUl6d5779XChQtVUlISsPUDESmQu5M5gfy4K7/neNV//vOfzZEjR8qtw99PNbvy\nVy4Qz7W/2MoWyPXu37/fNGrUyKSkpJjS0tKy60tLS01ycrKJi4urcFzo6tR2V/CSkhKzfPlyc911\n1xlJpk+fPmW3JSYmGklm37595R6zd+/eSnfDv9BzV5Pr27ZtaySZo0eP+vw7Vaam2Tyqen68eXx1\nzn/9ioqKzPvvv28SExNN7969zd69e31edmXLr41gdrZnuRzqsDw6O/jr7dq1q/nkk0/8tq7ajkfo\nbLudXdnrt3btWjN48GDTqFEj89lnn/m87AstvzaC2dscmqZy9HZw1xuIsXZtxiN0tt3O9izj3Nfv\n0KFD5pFHHjF16tQxDzzwgDlz5oxfl18bfD5iH50d/PX6e6zN5yPeCdXerur1O3r0qFm6dKn5y1/+\nYvr372+ioqJMx44dzSuvvGKKi4t9XieA/3D8Hl+hJC0tTZL08MMPq1mzZpKkoqIim5GAgJo5c6ZO\nnjyp8ePHl9uF3+Vyafz48Tp+/LhmzZoV8Bwul0t79+6VdPabRQMHDtTcuXMlSVu3bi2737BhwyRJ\nS5cuLfd4z6FiPLf708033yxJ+ve//13hNrfbrZSUFL+v83zePj+SyvaoKi4u1qlTpyp8+6umYmJi\n9Otf/1oZGRkqKSnRsGHDVFxcXKtl+gudjUi0efPmsl6yhc6ums3OlqS+fftq0aJFGjNmjG699VZt\n3Lix1sv0F3obkSYUxtp0dtVsd3aLFi301FNP6aOPPtLMmTP117/+tdbL9Bc6G5GIsXbV6O3/iI+P\n19VXX63HH39cGRkZ2rZtm2666SY9/PDD6tOnjzZs2OC3dQGRiokvPxo4cKAk6emnn1ZeXp6OHj2q\nRx991HIqIHA8A6L+/ftXuM1z3eLFi4OS5Z577tGWLVtUVFSkgwcP6tl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d0dHRat++ve0o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QFy5/HqbhzqyYSqLtuxLEuffvqpBgwYoJtuuklHjx5VVFRUxfN/+MMftGbNGq1bt87tHP506tQp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\nAAAAAACA+vo/6Yhhg/2dnZwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"hello_mapnode/graph_detailed.dot.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will introduce JoinNode that allows us to merge results together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "170829-12:58:34,263 workflow INFO:\n",
      "\t Workflow hello_mapnode settings: ['check', 'execution', 'logging']\n",
      "170829-12:58:34,311 workflow INFO:\n",
      "\t Running serially.\n",
      "170829-12:58:34,313 workflow INFO:\n",
      "\t Executing node a_plus_b.bI.b1 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_2/a_plus_b\n",
      "170829-12:58:34,328 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:58:34,338 workflow INFO:\n",
      "\t Executing node concat_a_b.aI.a0.b1 in dir: /opt/tutorial/notebooks/hello/_a_2/_b_3/concat_a_b\n",
      "170829-12:58:34,350 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:58:34,363 workflow INFO:\n",
      "\t Executing node add_1.a0.b1 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_2/_b_3/add_1\n",
      "170829-12:58:34,393 workflow INFO:\n",
      "\t Executing node _add_10 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_2/_b_3/add_1/mapflow/_add_10\n",
      "PLUS ONE, a = 4\n",
      "170829-12:58:34,470 workflow INFO:\n",
      "\t Executing node _add_11 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_2/_b_3/add_1/mapflow/_add_11\n",
      "PLUS ONE, a = 3\n",
      "170829-12:58:34,573 workflow INFO:\n",
      "\t Executing node concat_a_b.aI.a1.b1 in dir: /opt/tutorial/notebooks/hello/_a_2/_b_4/concat_a_b\n",
      "170829-12:58:34,588 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:58:34,600 workflow INFO:\n",
      "\t Executing node add_1.a1.b1 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_2/_b_4/add_1\n",
      "170829-12:58:34,632 workflow INFO:\n",
      "\t Executing node _add_10 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_2/_b_4/add_1/mapflow/_add_10\n",
      "PLUS ONE, a = 4\n",
      "170829-12:58:34,724 workflow INFO:\n",
      "\t Executing node _add_11 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_2/_b_4/add_1/mapflow/_add_11\n",
      "PLUS ONE, a = 4\n",
      "170829-12:58:34,821 workflow INFO:\n",
      "\t Executing node a_plus_b.bI.b0 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_1/a_plus_b\n",
      "170829-12:58:34,834 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:58:34,850 workflow INFO:\n",
      "\t Executing node concat_a_b.aI.a0.b0 in dir: /opt/tutorial/notebooks/hello/_a_1/_b_3/concat_a_b\n",
      "170829-12:58:34,865 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:58:34,880 workflow INFO:\n",
      "\t Executing node add_1.a0.b0 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_1/_b_3/add_1\n",
      "170829-12:58:34,912 workflow INFO:\n",
      "\t Executing node _add_10 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_1/_b_3/add_1/mapflow/_add_10\n",
      "PLUS ONE, a = 3\n",
      "170829-12:58:35,5 workflow INFO:\n",
      "\t Executing node _add_11 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_1/_b_3/add_1/mapflow/_add_11\n",
      "PLUS ONE, a = 3\n",
      "170829-12:58:35,107 workflow INFO:\n",
      "\t Executing node concat_a_b.aI.a1.b0 in dir: /opt/tutorial/notebooks/hello/_a_1/_b_4/concat_a_b\n",
      "170829-12:58:35,121 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:58:35,134 workflow INFO:\n",
      "\t Executing node add_1.a1.b0 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_1/_b_4/add_1\n",
      "170829-12:58:35,164 workflow INFO:\n",
      "\t Executing node _add_10 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_1/_b_4/add_1/mapflow/_add_10\n",
      "PLUS ONE, a = 3\n",
      "170829-12:58:35,240 workflow INFO:\n",
      "\t Executing node _add_11 in dir: /opt/tutorial/notebooks/hello_mapnode/_a_1/_b_4/add_1/mapflow/_add_11\n",
      "PLUS ONE, a = 4\n",
      "170829-12:58:35,345 workflow INFO:\n",
      "\t Executing node join_scale_data.a1 in dir: /opt/tutorial/notebooks/hello_mapnode/_b_4/join_scale_data\n",
      "170829-12:58:35,359 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:58:35,377 workflow INFO:\n",
      "\t Executing node join_scale_data.a0 in dir: /opt/tutorial/notebooks/hello_mapnode/_b_3/join_scale_data\n",
      "170829-12:58:35,389 workflow INFO:\n",
      "\t Collecting precomputed outputs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[hello_mapnode.join_scale_data.a0,\n",
       " hello_mapnode.join_scale_data.a1,\n",
       " hello_mapnode.a_plus_b.bI.b0,\n",
       " hello.concat_a_b.aI.a0.b0,\n",
       " hello_mapnode.add_1.a0.b0,\n",
       " hello.concat_a_b.aI.a1.b0,\n",
       " hello_mapnode.add_1.a1.b0,\n",
       " hello_mapnode.a_plus_b.bI.b1,\n",
       " hello.concat_a_b.aI.a0.b1,\n",
       " hello_mapnode.add_1.a0.b1,\n",
       " hello.concat_a_b.aI.a1.b1,\n",
       " hello_mapnode.add_1.a1.b1]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def merge_and_scale_data(data2):\n",
    "    import numpy as np\n",
    "    return (np.array(data2) * 1000).tolist()\n",
    "\n",
    "\n",
    "from nipype import JoinNode\n",
    "joiner = JoinNode(Function(input_names=['data2'],\n",
    "                          output_names=['data_scaled'],\n",
    "                          function=merge_and_scale_data),\n",
    "                 name='join_scale_data',\n",
    "                 joinsource=adder,\n",
    "                 joinfield=['data2'])\n",
    "\n",
    "wf.connect(plusone, 'out', joiner, 'data2')\n",
    "\n",
    "eg = wf.run()\n",
    "eg.nodes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the output of ``hello.join_scale_data.a0`` node:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "data_scaled = [[4000, 4000], [5000, 4000]]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eg.nodes()[0].result.outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "170829-12:58:40,477 workflow INFO:\n",
      "\t Generated workflow graph: /opt/tutorial/notebooks/hello_mapnode/graph.dot.png (graph2use=exec, simple_form=True).\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/opt/tutorial/notebooks/hello_mapnode/graph.dot.png'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wf.write_graph(graph2use='exec')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SyWQ2p+NOWlpaKDg4uNtzqnszjUREOTk5FB8fT1KplJKSkujKlSvd1tzxdb3tb288HRUV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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"hello_mapnode/graph.dot.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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86sdrFfn3gBrxG2xIBVJSW1u7fft2f39/XV1d2lnkAI/HGzNmzMmTJ1NTU/X1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\nT5w40eSSZWVln3zyib+/v5WVlYmJiUAg8PX1Xbdun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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(\"hello_mapnode/graph_detailed.dot.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "nbpresent": {
     "id": "6361e837-5e6f-4df9-aff6-d20c5909af56"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "170829-12:58:53,520 workflow INFO:\n",
      "\t Workflow hello_mapnode settings: ['check', 'execution', 'logging']\n",
      "170829-12:58:53,570 workflow INFO:\n",
      "\t Running in parallel.\n",
      "170829-12:58:53,577 workflow INFO:\n",
      "\t Executing: a_plus_b.bI.b1 ID: 0\n",
      "170829-12:58:53,588 workflow INFO:\n",
      "\t [Job finished] jobname: a_plus_b.bI.b1 jobid: 0\n",
      "170829-12:58:53,592 workflow INFO:\n",
      "\t Executing: a_plus_b.bI.b0 ID: 5\n",
      "170829-12:58:53,601 workflow INFO:\n",
      "\t [Job finished] jobname: a_plus_b.bI.b0 jobid: 5\n",
      "170829-12:58:53,606 workflow INFO:\n",
      "\t Executing: concat_a_b.aI.a0.b1 ID: 1\n",
      "170829-12:58:53,622 workflow INFO:\n",
      "\t [Job finished] jobname: concat_a_b.aI.a0.b1 jobid: 1\n",
      "170829-12:58:53,627 workflow INFO:\n",
      "\t Executing: concat_a_b.aI.a1.b1 ID: 3\n",
      "170829-12:58:53,644 workflow INFO:\n",
      "\t [Job finished] jobname: concat_a_b.aI.a1.b1 jobid: 3\n",
      "170829-12:58:53,650 workflow INFO:\n",
      "\t Executing: add_1.a0.b1 ID: 2\n",
      "170829-12:58:53,666 workflow INFO:\n",
      "\t Adding 2 jobs for mapnode add_1.a0.b1\n",
      "170829-12:58:53,674 workflow INFO:\n",
      "\t Executing: add_1.a1.b1 ID: 4\n",
      "170829-12:58:53,687 workflow INFO:\n",
      "\t Adding 2 jobs for mapnode add_1.a1.b1\n",
      "170829-12:58:53,695 workflow INFO:\n",
      "\t Executing: concat_a_b.aI.a0.b0 ID: 6\n",
      "170829-12:58:53,710 workflow INFO:\n",
      "\t [Job finished] jobname: concat_a_b.aI.a0.b0 jobid: 6\n",
      "170829-12:58:53,713 workflow INFO:\n",
      "\t Executing: concat_a_b.aI.a1.b0 ID: 8\n",
      "170829-12:58:53,725 workflow INFO:\n",
      "\t [Job finished] jobname: concat_a_b.aI.a1.b0 jobid: 8\n",
      "170829-12:58:53,732 workflow INFO:\n",
      "\t Executing: add_1.a0.b0 ID: 7\n",
      "170829-12:58:53,747 workflow INFO:\n",
      "\t Adding 2 jobs for mapnode add_1.a0.b0\n",
      "170829-12:58:53,753 workflow INFO:\n",
      "\t Executing: add_1.a1.b0 ID: 9\n",
      "170829-12:58:53,767 workflow INFO:\n",
      "\t Adding 2 jobs for mapnode add_1.a1.b0\n",
      "170829-12:58:53,773 workflow INFO:\n",
      "\t Executing: _add_10 ID: 12\n",
      "170829-12:58:53,784 workflow INFO:\n",
      "\t [Job finished] jobname: _add_10 jobid: 12\n",
      "170829-12:58:53,787 workflow INFO:\n",
      "\t Executing: _add_11 ID: 13\n",
      "170829-12:58:53,799 workflow INFO:\n",
      "\t [Job finished] jobname: _add_11 jobid: 13\n",
      "170829-12:58:53,803 workflow INFO:\n",
      "\t Executing: add_1.a0.b1 ID: 2\n",
      "170829-12:58:53,812 workflow INFO:\n",
      "\t [Job finished] jobname: add_1.a0.b1 jobid: 2\n",
      "170829-12:58:53,815 workflow INFO:\n",
      "\t Executing: _add_10 ID: 14\n",
      "170829-12:58:53,828 workflow INFO:\n",
      "\t [Job finished] jobname: _add_10 jobid: 14\n",
      "170829-12:58:53,832 workflow INFO:\n",
      "\t Executing: _add_11 ID: 15\n",
      "170829-12:58:53,842 workflow INFO:\n",
      "\t [Job finished] jobname: _add_11 jobid: 15\n",
      "170829-12:58:53,844 workflow INFO:\n",
      "\t Executing: _add_10 ID: 16\n",
      "170829-12:58:53,854 workflow INFO:\n",
      "\t [Job finished] jobname: _add_10 jobid: 16\n",
      "170829-12:58:53,859 workflow INFO:\n",
      "\t Executing: add_1.a1.b1 ID: 4\n",
      "170829-12:58:53,869 workflow INFO:\n",
      "\t [Job finished] jobname: add_1.a1.b1 jobid: 4\n",
      "170829-12:58:53,872 workflow INFO:\n",
      "\t Executing: _add_11 ID: 17\n",
      "170829-12:58:53,882 workflow INFO:\n",
      "\t [Job finished] jobname: _add_11 jobid: 17\n",
      "170829-12:58:53,887 workflow INFO:\n",
      "\t Executing: add_1.a0.b0 ID: 7\n",
      "170829-12:58:53,899 workflow INFO:\n",
      "\t [Job finished] jobname: add_1.a0.b0 jobid: 7\n",
      "170829-12:58:53,903 workflow INFO:\n",
      "\t Executing: _add_10 ID: 18\n",
      "170829-12:58:53,912 workflow INFO:\n",
      "\t [Job finished] jobname: _add_10 jobid: 18\n",
      "170829-12:58:53,916 workflow INFO:\n",
      "\t Executing: join_scale_data.a0 ID: 11\n",
      "170829-12:58:53,937 workflow INFO:\n",
      "\t [Job finished] jobname: join_scale_data.a0 jobid: 11\n",
      "170829-12:58:53,942 workflow INFO:\n",
      "\t Executing: _add_11 ID: 19\n",
      "170829-12:58:53,953 workflow INFO:\n",
      "\t [Job finished] jobname: _add_11 jobid: 19\n",
      "170829-12:58:53,958 workflow INFO:\n",
      "\t Executing: add_1.a1.b0 ID: 9\n",
      "170829-12:58:53,966 workflow INFO:\n",
      "\t [Job finished] jobname: add_1.a1.b0 jobid: 9\n",
      "170829-12:58:53,971 workflow INFO:\n",
      "\t Executing: join_scale_data.a1 ID: 10\n",
      "170829-12:58:53,987 workflow INFO:\n",
      "\t [Job finished] jobname: join_scale_data.a1 jobid: 10\n",
      "CPU times: user 210 ms, sys: 80 ms, total: 290 ms\n",
      "Wall time: 487 ms\n"
     ]
    }
   ],
   "source": [
    "%time eg = wf.run(plugin='MultiProc', plugin_args={'n_procs': 2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "nbpresent": {
     "id": "055c7435-88f1-45db-9562-63d5f910cac3"
    }
   },
   "outputs": [],
   "source": [
    "wf.base_dir = os.path.join(os.getcwd(), 'alt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "170829-12:59:01,216 workflow INFO:\n",
      "\t Workflow hello_mapnode settings: ['check', 'execution', 'logging']\n",
      "170829-12:59:01,270 workflow INFO:\n",
      "\t Running in parallel.\n",
      "170829-12:59:01,280 workflow INFO:\n",
      "\t Executing: a_plus_b.bI.b1 ID: 0\n",
      "170829-12:59:01,297 workflow INFO:\n",
      "\t [Job finished] jobname: a_plus_b.bI.b1 jobid: 0\n",
      "170829-12:59:01,304 workflow INFO:\n",
      "\t Executing: a_plus_b.bI.b0 ID: 5\n",
      "170829-12:59:01,315 workflow INFO:\n",
      "\t [Job finished] jobname: a_plus_b.bI.b0 jobid: 5\n",
      "170829-12:59:01,320 workflow INFO:\n",
      "\t Executing: concat_a_b.aI.a0.b1 ID: 1\n",
      "170829-12:59:01,339 workflow INFO:\n",
      "\t [Job finished] jobname: concat_a_b.aI.a0.b1 jobid: 1\n",
      "170829-12:59:01,342 workflow INFO:\n",
      "\t Executing: concat_a_b.aI.a1.b1 ID: 3\n",
      "170829-12:59:01,360 workflow INFO:\n",
      "\t [Job finished] jobname: concat_a_b.aI.a1.b1 jobid: 3\n",
      "170829-12:59:01,366 workflow INFO:\n",
      "\t Executing: add_1.a0.b1 ID: 2\n",
      "170829-12:59:01,388 workflow INFO:\n",
      "\t Adding 2 jobs for mapnode add_1.a0.b1\n",
      "170829-12:59:01,396 workflow INFO:\n",
      "\t Executing: add_1.a1.b1 ID: 4\n",
      "170829-12:59:01,413 workflow INFO:\n",
      "\t Adding 2 jobs for mapnode add_1.a1.b1\n",
      "170829-12:59:01,422 workflow INFO:\n",
      "\t Executing: concat_a_b.aI.a0.b0 ID: 6\n",
      "170829-12:59:01,439 workflow INFO:\n",
      "\t [Job finished] jobname: concat_a_b.aI.a0.b0 jobid: 6\n",
      "170829-12:59:01,441 workflow INFO:\n",
      "\t Executing: concat_a_b.aI.a1.b0 ID: 8\n",
      "170829-12:59:01,456 workflow INFO:\n",
      "\t [Job finished] jobname: concat_a_b.aI.a1.b0 jobid: 8\n",
      "170829-12:59:01,462 workflow INFO:\n",
      "\t Executing: add_1.a0.b0 ID: 7\n",
      "170829-12:59:01,476 workflow INFO:\n",
      "\t Adding 2 jobs for mapnode add_1.a0.b0\n",
      "170829-12:59:01,484 workflow INFO:\n",
      "\t Executing: add_1.a1.b0 ID: 9\n",
      "170829-12:59:01,502 workflow INFO:\n",
      "\t Adding 2 jobs for mapnode add_1.a1.b0\n",
      "170829-12:59:01,510 workflow INFO:\n",
      "\t Executing: _add_10 ID: 12\n",
      "170829-12:59:01,522 workflow INFO:\n",
      "\t [Job finished] jobname: _add_10 jobid: 12\n",
      "170829-12:59:01,525 workflow INFO:\n",
      "\t Executing: _add_11 ID: 13\n",
      "170829-12:59:01,540 workflow INFO:\n",
      "\t [Job finished] jobname: _add_11 jobid: 13\n",
      "170829-12:59:01,545 workflow INFO:\n",
      "\t Executing: add_1.a0.b1 ID: 2\n",
      "170829-12:59:01,557 workflow INFO:\n",
      "\t [Job finished] jobname: add_1.a0.b1 jobid: 2\n",
      "170829-12:59:01,560 workflow INFO:\n",
      "\t Executing: _add_10 ID: 14\n",
      "170829-12:59:01,573 workflow INFO:\n",
      "\t [Job finished] jobname: _add_10 jobid: 14\n",
      "170829-12:59:01,580 workflow INFO:\n",
      "\t Executing: _add_11 ID: 15\n",
      "170829-12:59:01,591 workflow INFO:\n",
      "\t [Job finished] jobname: _add_11 jobid: 15\n",
      "170829-12:59:01,594 workflow INFO:\n",
      "\t Executing: _add_10 ID: 16\n",
      "170829-12:59:01,605 workflow INFO:\n",
      "\t [Job finished] jobname: _add_10 jobid: 16\n",
      "170829-12:59:01,611 workflow INFO:\n",
      "\t Executing: add_1.a1.b1 ID: 4\n",
      "170829-12:59:01,624 workflow INFO:\n",
      "\t [Job finished] jobname: add_1.a1.b1 jobid: 4\n",
      "170829-12:59:01,628 workflow INFO:\n",
      "\t Executing: _add_11 ID: 17\n",
      "170829-12:59:01,640 workflow INFO:\n",
      "\t [Job finished] jobname: _add_11 jobid: 17\n",
      "170829-12:59:01,646 workflow INFO:\n",
      "\t Executing: add_1.a0.b0 ID: 7\n",
      "170829-12:59:01,658 workflow INFO:\n",
      "\t [Job finished] jobname: add_1.a0.b0 jobid: 7\n",
      "170829-12:59:01,661 workflow INFO:\n",
      "\t Executing: _add_10 ID: 18\n",
      "170829-12:59:01,676 workflow INFO:\n",
      "\t [Job finished] jobname: _add_10 jobid: 18\n",
      "170829-12:59:01,681 workflow INFO:\n",
      "\t Executing: join_scale_data.a0 ID: 11\n",
      "170829-12:59:01,700 workflow INFO:\n",
      "\t [Job finished] jobname: join_scale_data.a0 jobid: 11\n",
      "170829-12:59:01,703 workflow INFO:\n",
      "\t Executing: _add_11 ID: 19\n",
      "170829-12:59:01,716 workflow INFO:\n",
      "\t [Job finished] jobname: _add_11 jobid: 19\n",
      "170829-12:59:01,721 workflow INFO:\n",
      "\t Executing: add_1.a1.b0 ID: 9\n",
      "170829-12:59:01,733 workflow INFO:\n",
      "\t [Job finished] jobname: add_1.a1.b0 jobid: 9\n",
      "170829-12:59:01,740 workflow INFO:\n",
      "\t Executing: join_scale_data.a1 ID: 10\n",
      "170829-12:59:01,758 workflow INFO:\n",
      "\t [Job finished] jobname: join_scale_data.a1 jobid: 10\n",
      "CPU times: user 250 ms, sys: 100 ms, total: 350 ms\n",
      "Wall time: 566 ms\n"
     ]
    }
   ],
   "source": [
    "%time eg = wf.run(plugin='MultiProc', plugin_args={'n_procs': 2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "170829-12:59:27,443 workflow INFO:\n",
      "\t Workflow hello_mapnode settings: ['check', 'execution', 'logging']\n",
      "170829-12:59:27,522 workflow INFO:\n",
      "\t Running in parallel.\n",
      "170829-12:59:27,533 workflow INFO:\n",
      "\t Executing: a_plus_b.bI.b1 ID: 0\n",
      "170829-12:59:27,547 workflow INFO:\n",
      "\t [Job finished] jobname: a_plus_b.bI.b1 jobid: 0\n",
      "170829-12:59:27,553 workflow INFO:\n",
      "\t Executing: a_plus_b.bI.b0 ID: 5\n",
      "170829-12:59:27,566 workflow INFO:\n",
      "\t [Job finished] jobname: a_plus_b.bI.b0 jobid: 5\n",
      "170829-12:59:27,577 workflow INFO:\n",
      "\t Executing: concat_a_b.aI.a0.b1 ID: 1\n",
      "170829-12:59:27,595 workflow INFO:\n",
      "\t [Job finished] jobname: concat_a_b.aI.a0.b1 jobid: 1\n",
      "170829-12:59:27,599 workflow INFO:\n",
      "\t Executing: concat_a_b.aI.a1.b1 ID: 3\n",
      "170829-12:59:27,625 workflow INFO:\n",
      "\t [Job finished] jobname: concat_a_b.aI.a1.b1 jobid: 3\n",
      "170829-12:59:27,631 workflow INFO:\n",
      "\t Executing: add_1.a0.b1 ID: 2\n",
      "170829-12:59:27,651 workflow INFO:\n",
      "\t Adding 2 jobs for mapnode add_1.a0.b1\n",
      "170829-12:59:27,659 workflow INFO:\n",
      "\t Executing: add_1.a1.b1 ID: 4\n",
      "170829-12:59:27,672 workflow INFO:\n",
      "\t Adding 2 jobs for mapnode add_1.a1.b1\n",
      "170829-12:59:27,682 workflow INFO:\n",
      "\t Executing: concat_a_b.aI.a0.b0 ID: 6\n",
      "170829-12:59:27,696 workflow INFO:\n",
      "\t [Job finished] jobname: concat_a_b.aI.a0.b0 jobid: 6\n",
      "170829-12:59:27,700 workflow INFO:\n",
      "\t Executing: concat_a_b.aI.a1.b0 ID: 8\n",
      "170829-12:59:27,717 workflow INFO:\n",
      "\t [Job finished] jobname: concat_a_b.aI.a1.b0 jobid: 8\n",
      "170829-12:59:27,726 workflow INFO:\n",
      "\t Executing: add_1.a0.b0 ID: 7\n",
      "170829-12:59:27,745 workflow INFO:\n",
      "\t Adding 2 jobs for mapnode add_1.a0.b0\n",
      "170829-12:59:27,760 workflow INFO:\n",
      "\t Executing: add_1.a1.b0 ID: 9\n",
      "170829-12:59:27,776 workflow INFO:\n",
      "\t Adding 2 jobs for mapnode add_1.a1.b0\n",
      "170829-12:59:27,785 workflow INFO:\n",
      "\t Executing: _add_10 ID: 12\n",
      "170829-12:59:27,796 workflow INFO:\n",
      "\t [Job finished] jobname: _add_10 jobid: 12\n",
      "170829-12:59:27,800 workflow INFO:\n",
      "\t Executing: _add_11 ID: 13\n",
      "170829-12:59:27,814 workflow INFO:\n",
      "\t [Job finished] jobname: _add_11 jobid: 13\n",
      "170829-12:59:27,820 workflow INFO:\n",
      "\t Executing: add_1.a0.b1 ID: 2\n",
      "170829-12:59:27,831 workflow INFO:\n",
      "\t [Job finished] jobname: add_1.a0.b1 jobid: 2\n",
      "170829-12:59:27,836 workflow INFO:\n",
      "\t Executing: _add_10 ID: 14\n",
      "170829-12:59:27,845 workflow INFO:\n",
      "\t [Job finished] jobname: _add_10 jobid: 14\n",
      "170829-12:59:27,852 workflow INFO:\n",
      "\t Executing: _add_11 ID: 15\n",
      "170829-12:59:27,866 workflow INFO:\n",
      "\t [Job finished] jobname: _add_11 jobid: 15\n",
      "170829-12:59:27,869 workflow INFO:\n",
      "\t Executing: _add_10 ID: 16\n",
      "170829-12:59:27,881 workflow INFO:\n",
      "\t [Job finished] jobname: _add_10 jobid: 16\n",
      "170829-12:59:27,886 workflow INFO:\n",
      "\t Executing: add_1.a1.b1 ID: 4\n",
      "170829-12:59:27,896 workflow INFO:\n",
      "\t [Job finished] jobname: add_1.a1.b1 jobid: 4\n",
      "170829-12:59:27,900 workflow INFO:\n",
      "\t Executing: _add_11 ID: 17\n",
      "170829-12:59:27,911 workflow INFO:\n",
      "\t [Job finished] jobname: _add_11 jobid: 17\n",
      "170829-12:59:27,917 workflow INFO:\n",
      "\t Executing: add_1.a0.b0 ID: 7\n",
      "170829-12:59:27,934 workflow INFO:\n",
      "\t [Job finished] jobname: add_1.a0.b0 jobid: 7\n",
      "170829-12:59:27,938 workflow INFO:\n",
      "\t Executing: _add_10 ID: 18\n",
      "170829-12:59:27,949 workflow INFO:\n",
      "\t [Job finished] jobname: _add_10 jobid: 18\n",
      "170829-12:59:27,957 workflow INFO:\n",
      "\t Executing: join_scale_data.a0 ID: 11\n",
      "170829-12:59:27,973 workflow INFO:\n",
      "\t [Job finished] jobname: join_scale_data.a0 jobid: 11\n",
      "170829-12:59:27,976 workflow INFO:\n",
      "\t Executing: _add_11 ID: 19\n",
      "170829-12:59:27,988 workflow INFO:\n",
      "\t [Job finished] jobname: _add_11 jobid: 19\n",
      "170829-12:59:27,994 workflow INFO:\n",
      "\t Executing: add_1.a1.b0 ID: 9\n",
      "170829-12:59:28,5 workflow INFO:\n",
      "\t [Job finished] jobname: add_1.a1.b0 jobid: 9\n",
      "170829-12:59:28,10 workflow INFO:\n",
      "\t Executing: join_scale_data.a1 ID: 10\n",
      "170829-12:59:28,32 workflow INFO:\n",
      "\t [Job finished] jobname: join_scale_data.a1 jobid: 10\n",
      "CPU times: user 270 ms, sys: 140 ms, total: 410 ms\n",
      "Wall time: 623 ms\n"
     ]
    }
   ],
   "source": [
    "%time eg = wf.run(plugin='MultiProc', plugin_args={'n_procs': 2})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "solution2": "hidden",
    "solution2_first": true
   },
   "source": [
    "### Exercise 1\n",
    "\n",
    "Create a workflow to calculate a sum of factorials of numbers from a range between $n_{min}$ and $n_{max}$, i.e.:\n",
    "\n",
    "$$\\sum _{k=n_{min}}^{n_{max}} k! = 0! + 1! +2! + 3! + \\cdots$$ \n",
    "\n",
    "if $n_{min}=0$ and $n_{max}=3$\n",
    "$$\\sum _{k=0}^{3} k! = 0! + 1! +2! + 3!  =  1 + 1 + 2 + 6 = 10$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "solution2": "hidden"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "170829-12:59:35,43 workflow INFO:\n",
      "\t Workflow ex1 settings: ['check', 'execution', 'logging']\n",
      "170829-12:59:35,85 workflow INFO:\n",
      "\t Running serially.\n",
      "170829-12:59:35,87 workflow INFO:\n",
      "\t Executing node range_list in dir: /opt/tutorial/notebooks/ex1/range_list\n",
      "170829-12:59:35,101 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:59:35,117 workflow INFO:\n",
      "\t Executing node factorial in dir: /opt/tutorial/notebooks/ex1/factorial\n",
      "170829-12:59:35,141 workflow INFO:\n",
      "\t Executing node summing in dir: /opt/tutorial/notebooks/ex1/summing\n",
      "170829-12:59:35,154 workflow INFO:\n",
      "\t Collecting precomputed outputs\n"
     ]
    }
   ],
   "source": [
    "from nipype import Workflow, Node, MapNode, Function\n",
    "import os\n",
    "\n",
    "def range_fun(n_min, n_max):\n",
    "    return list(range(n_min, n_max+1))\n",
    "\n",
    "def factorial(n):\n",
    "    # print(\"FACTORIAL, {}\".format(n))\n",
    "    import math\n",
    "    return math.factorial(n)\n",
    "\n",
    "def summing(terms):\n",
    "    return sum(terms)\n",
    "\n",
    "wf_ex1 = Workflow('ex1')\n",
    "wf_ex1.base_dir = os.getcwd()\n",
    "\n",
    "range_nd = Node(Function(input_names=['n_min', 'n_max'],\n",
    "                         output_names=['range_list'],\n",
    "                         function=range_fun), \n",
    "                name='range_list')\n",
    "\n",
    "factorial_nd = MapNode(Function(input_names=['n'],\n",
    "                                output_names=['fact_out'],\n",
    "                                function=factorial), \n",
    "                       iterfield=['n'],\n",
    "                       name='factorial')\n",
    "\n",
    "summing_nd = Node(Function(input_names=['terms'],\n",
    "                           output_names=['sum_out'],\n",
    "                           function=summing), \n",
    "                  name='summing')\n",
    "\n",
    "\n",
    "range_nd.inputs.n_min = 0\n",
    "range_nd.inputs.n_max = 3\n",
    "\n",
    "wf_ex1.add_nodes([range_nd])\n",
    "wf_ex1.connect(range_nd, 'range_list', factorial_nd, 'n')\n",
    "wf_ex1.connect(factorial_nd, 'fact_out', summing_nd, \"terms\")\n",
    "\n",
    "\n",
    "eg = wf_ex1.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "solution2": "hidden"
   },
   "source": [
    "let's print all nodes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "solution2": "hidden"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[ex1.range_list, ex1.factorial, ex1.summing]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eg.nodes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "solution2": "hidden"
   },
   "source": [
    "the final result should be 10:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "solution2": "hidden"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "sum_out = 10"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eg.nodes()[2].result.outputs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "solution2": "hidden"
   },
   "source": [
    "we can also check the results of two other nodes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "solution2": "hidden"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "range_list = [0, 1, 2, 3]\n",
      "\n",
      "Bunch(fact_out=[1, 1, 2, 6])\n"
     ]
    }
   ],
   "source": [
    "print(eg.nodes()[0].result.outputs)\n",
    "print(eg.nodes()[1].result.outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#write your code here\n",
    "\n",
    "# 1. write 3 functions: one that return a list of number from specific range, \n",
    "#    second that returns n! (you can use math.factorial) and third that sums the elements from a list\n",
    "\n",
    "# 2. create a workflow and define the working directory\n",
    "\n",
    "# 3. define 3 nodes using Node and MapNode and connect them within the workflow\n",
    "\n",
    "# 4. run the workflow and check the results\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "solution2": "hidden",
    "solution2_first": true
   },
   "source": [
    "### Exercise 2\n",
    "\n",
    "Create a workflow to calculate the following sum for chosen $n$ and five different values of $x$:  $0$, $\\frac{1}{2} \\pi$, $\\pi$, $\\frac{3}{2} \\pi$, and $ 2 \\pi$.\n",
    "\n",
    "$\\sum _{{k=0}}^{{n}}{\\frac  {(-1)^{k}}{(2k+1)!}}x^{{2k+1}}\\quad =x-{\\frac  {x^{3}}{3!}}+{\\frac  {x^{5}}{5!}}-\\cdots $\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "solution2": "hidden"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "170829-12:59:46,176 workflow INFO:\n",
      "\t Workflow ex2 settings: ['check', 'execution', 'logging']\n",
      "170829-12:59:46,230 workflow INFO:\n",
      "\t Running serially.\n",
      "170829-12:59:46,231 workflow INFO:\n",
      "\t Executing node range_list in dir: /opt/tutorial/notebooks/ex2/range_list\n",
      "170829-12:59:46,243 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:59:46,252 workflow INFO:\n",
      "\t Executing node term.aI.a0 in dir: /opt/tutorial/notebooks/ex2/_x_0/term\n",
      "170829-12:59:46,276 workflow INFO:\n",
      "\t Executing node summing.a0 in dir: /opt/tutorial/notebooks/ex2/_x_0/summing\n",
      "170829-12:59:46,350 workflow INFO:\n",
      "\t Executing node term.aI.a1 in dir: /opt/tutorial/notebooks/ex2/_x_1.5707963267948966/term\n",
      "170829-12:59:46,371 workflow INFO:\n",
      "\t Executing node summing.a1 in dir: /opt/tutorial/notebooks/ex2/_x_1.5707963267948966/summing\n",
      "170829-12:59:46,436 workflow INFO:\n",
      "\t Executing node term.aI.a2 in dir: /opt/tutorial/notebooks/ex2/_x_3.141592653589793/term\n",
      "170829-12:59:46,456 workflow INFO:\n",
      "\t Executing node summing.a2 in dir: /opt/tutorial/notebooks/ex2/_x_3.141592653589793/summing\n",
      "170829-12:59:46,520 workflow INFO:\n",
      "\t Executing node term.aI.a3 in dir: /opt/tutorial/notebooks/ex2/_x_4.71238898038469/term\n",
      "170829-12:59:46,540 workflow INFO:\n",
      "\t Executing node summing.a3 in dir: /opt/tutorial/notebooks/ex2/_x_4.71238898038469/summing\n",
      "170829-12:59:46,607 workflow INFO:\n",
      "\t Executing node term.aI.a4 in dir: /opt/tutorial/notebooks/ex2/_x_6.283185307179586/term\n",
      "170829-12:59:46,627 workflow INFO:\n",
      "\t Executing node summing.a4 in dir: /opt/tutorial/notebooks/ex2/_x_6.283185307179586/summing\n"
     ]
    }
   ],
   "source": [
    "# we can reuse function from previous exercise, but they need some edits\n",
    "from nipype import Workflow, Node, MapNode, JoinNode, Function\n",
    "import os\n",
    "import math\n",
    "\n",
    "def range_fun(n_max):\n",
    "    return list(range(n_max+1))\n",
    "\n",
    "def term(k, x):\n",
    "    import math\n",
    "    fract = math.factorial(2 * k + 1)\n",
    "    polyn = x ** (2 * k + 1) \n",
    "    return (-1)**k * polyn / fract\n",
    "\n",
    "def summing(terms):\n",
    "    return sum(terms)\n",
    "\n",
    "wf_ex2 = Workflow('ex2')\n",
    "wf_ex2.base_dir = os.getcwd()\n",
    "\n",
    "range_nd = Node(Function(input_names=['n_max'],\n",
    "                         output_names=['range_list'],\n",
    "                         function=range_fun), \n",
    "                name='range_list')\n",
    "\n",
    "term_nd = MapNode(Function(input_names=['k', 'x'],\n",
    "                           output_names=['term_out'],\n",
    "                           function=term), \n",
    "                  iterfield=['k'],\n",
    "                  name='term')\n",
    "\n",
    "summing_nd = Node(Function(input_names=['terms'],\n",
    "                           output_names=['sum_out'],\n",
    "                           function=summing), \n",
    "                name='summing')\n",
    "\n",
    "\n",
    "range_nd.inputs.n_max = 15\n",
    "\n",
    "x_list = [0, 0.5 * math.pi, math.pi, 1.5 * math.pi, 2 * math.pi]\n",
    "\n",
    "term_nd.iterables = ('x', x_list)\n",
    "\n",
    "wf_ex2.add_nodes([range_nd])\n",
    "wf_ex2.connect(range_nd, 'range_list', term_nd, 'k')\n",
    "wf_ex2.connect(term_nd, 'term_out', summing_nd, \"terms\")\n",
    "\n",
    "\n",
    "eg = wf_ex2.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "solution2": "hidden"
   },
   "source": [
    "let's check all nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "solution2": "hidden"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[ex2.range_list,\n",
       " ex2.term.aI.a0,\n",
       " ex2.summing.a0,\n",
       " ex2.term.aI.a1,\n",
       " ex2.summing.a1,\n",
       " ex2.term.aI.a2,\n",
       " ex2.summing.a2,\n",
       " ex2.term.aI.a3,\n",
       " ex2.summing.a3,\n",
       " ex2.term.aI.a4,\n",
       " ex2.summing.a4]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eg.nodes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "solution2": "hidden"
   },
   "source": [
    "let's print all results of ``ex2.summing``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "solution2": "hidden"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "sum_out = 0.0\n",
      "\n",
      "\n",
      "sum_out = 1.0000000000000002\n",
      "\n",
      "\n",
      "sum_out = 3.328027611925569e-16\n",
      "\n",
      "\n",
      "sum_out = -1.0000000000000022\n",
      "\n",
      "\n",
      "sum_out = -2.4376365177316827e-11\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(eg.nodes()[2].result.outputs)\n",
    "print(eg.nodes()[4].result.outputs)\n",
    "print(eg.nodes()[6].result.outputs)\n",
    "print(eg.nodes()[8].result.outputs)\n",
    "print(eg.nodes()[10].result.outputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "solution2": "hidden"
   },
   "source": [
    "Great, we just implemented pretty good Sine function! Those number should be approximately 0, 1, 0, -1 and 0. If they are not, try to increase $n_max$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# write your solution here\n",
    "\n",
    "# 1. write 3 functions: one that return a list of number from a range between 0 and some n, \n",
    "#    second that returns a term for a specific k, and third that sums the elements from a list\n",
    "\n",
    "# 2. create a workflow and define the working directory\n",
    "\n",
    "# 3. define 3 nodes using Node and MapNode and connect them within the workflow\n",
    "\n",
    "# 4. use iterables for 4 values of x\n",
    "\n",
    "# 5. run the workflow and check the final results for every value of x\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "solution2": "hidden",
    "solution2_first": true
   },
   "source": [
    "### Exercise 2a\n",
    "\n",
    "Use JoinNode to combine results from Exercise 2 in one container, e.g. a dictionary, that takes value $x$ as a key and the result from ``summing`` Node as a value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "solution2": "hidden"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "170829-12:59:53,494 workflow INFO:\n",
      "\t Workflow ex2 settings: ['check', 'execution', 'logging']\n",
      "170829-12:59:53,548 workflow INFO:\n",
      "\t Running serially.\n",
      "170829-12:59:53,551 workflow INFO:\n",
      "\t Executing node range_list in dir: /opt/tutorial/notebooks/ex2/range_list\n",
      "170829-12:59:53,571 workflow INFO:\n",
      "\t Collecting precomputed outputs\n",
      "170829-12:59:53,582 workflow INFO:\n",
      "\t Executing node term.aI.a0 in dir: /opt/tutorial/notebooks/ex2/_x_0/term\n",
      "170829-12:59:53,610 workflow INFO:\n",
      "\t Executing node summing.a0 in dir: /opt/tutorial/notebooks/ex2/_x_0/summing\n",
      "170829-12:59:53,707 workflow INFO:\n",
      "\t Executing node term.aI.a1 in dir: /opt/tutorial/notebooks/ex2/_x_1.5707963267948966/term\n",
      "170829-12:59:53,733 workflow INFO:\n",
      "\t Executing node summing.a1 in dir: /opt/tutorial/notebooks/ex2/_x_1.5707963267948966/summing\n",
      "170829-12:59:53,806 workflow INFO:\n",
      "\t Executing node term.aI.a2 in dir: /opt/tutorial/notebooks/ex2/_x_3.141592653589793/term\n",
      "170829-12:59:53,830 workflow INFO:\n",
      "\t Executing node summing.a2 in dir: /opt/tutorial/notebooks/ex2/_x_3.141592653589793/summing\n",
      "170829-12:59:53,938 workflow INFO:\n",
      "\t Executing node term.aI.a3 in dir: /opt/tutorial/notebooks/ex2/_x_4.71238898038469/term\n",
      "170829-12:59:53,971 workflow INFO:\n",
      "\t Executing node summing.a3 in dir: /opt/tutorial/notebooks/ex2/_x_4.71238898038469/summing\n",
      "170829-12:59:54,67 workflow INFO:\n",
      "\t Executing node term.aI.a4 in dir: /opt/tutorial/notebooks/ex2/_x_6.283185307179586/term\n",
      "170829-12:59:54,88 workflow INFO:\n",
      "\t Executing node summing.a4 in dir: /opt/tutorial/notebooks/ex2/_x_6.283185307179586/summing\n",
      "170829-12:59:54,184 workflow INFO:\n",
      "\t Executing node merge in dir: /opt/tutorial/notebooks/ex2/merge\n",
      "170829-12:59:54,199 workflow INFO:\n",
      "\t Collecting precomputed outputs\n"
     ]
    }
   ],
   "source": [
    "def merge_results(results, x):\n",
    "    return dict(zip(x, results))\n",
    "\n",
    "join_nd = JoinNode(Function(input_names=['results', 'x'],\n",
    "                            output_names=['results_cont'],\n",
    "                            function=merge_results),\n",
    "                   name='merge',\n",
    "                   joinsource=term_nd, # this is the node that used iterables for x\n",
    "                   joinfield=['results'])\n",
    "\n",
    "# taking the list of arguments from the previous part \n",
    "join_nd.inputs.x = x_list\n",
    "\n",
    "# connecting a new node to the summing_nd\n",
    "wf_ex2.connect(summing_nd, \"sum_out\", join_nd, \"results\")\n",
    "\n",
    "eg = wf_ex2.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "solution2": "hidden"
   },
   "source": [
    "let's print all nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "solution2": "hidden"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[ex2.range_list,\n",
       " ex2.merge,\n",
       " ex2.term.aI.a0,\n",
       " ex2.summing.a0,\n",
       " ex2.term.aI.a1,\n",
       " ex2.summing.a1,\n",
       " ex2.term.aI.a2,\n",
       " ex2.summing.a2,\n",
       " ex2.term.aI.a3,\n",
       " ex2.summing.a3,\n",
       " ex2.term.aI.a4,\n",
       " ex2.summing.a4]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eg.nodes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "solution2": "hidden"
   },
   "source": [
    "and results from ``merge`` Node:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "solution2": "hidden"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\n",
       "results_cont = {0: 0.0, 1.5707963267948966: 1.0000000000000002, 3.141592653589793: 3.328027611925569e-16, 4.71238898038469: -1.0000000000000022, 6.283185307179586: -2.4376365177316827e-11}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eg.nodes()[1].result.outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# write your code here\n",
    "\n",
    "# 1. create an additional function that takes 2 list and combines them into one container, e.g. dictionary\n",
    "\n",
    "# 2. use JoinNode to define a new node that merge results from Exercise 2 and connect it to the workflow\n",
    "\n",
    "# 3. run the workflow and check the results of the merging node"
   ]
  }
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