{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Getting Started with AnyPyTools"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running a simple macro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from anypytools import AnyPyProcess \n",
    "app = AnyPyProcess()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we need to instruct the AnyBody Modelling System to load the and run the model. We do this using _AnyScript_ macro commands. These are short commands that can automate operations in the AnyBody Modeling System (AMS). Operation that are normally done by pointing and clicking in the AMS graphical user interface. \n",
    "\n",
    "You can read more on _AnyScript_ macros in the \"User Interface Features\" tutorial that accompanies the AnyBody Modeling System.\n",
    "\n",
    "Now we define an _AnyScript_ macro that we want to run on the model.\n",
    "\n",
    "```\n",
    "load \"Knee.any\"\n",
    "operation Main.MyStudy.Kinematics\n",
    "run\n",
    "```\n",
    "\n",
    "The macro will command AnyBody to load the model and run the Kinematics operation. \n",
    "\n",
    "The macro is executed by parsing it to the `start_macro()` method of the `AnyPyProcess` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "943591ec0d6347ba8f84f27274e632a0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed: \u001b[1;36m1\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "macrolist = [\n",
    "    'load \"Knee.any\"',\n",
    "    'operation Main.MyStudy.Kinematics',\n",
    "    'run',\n",
    "]\n",
    "\n",
    "app.start_macro(macrolist);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Running multiple macros"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is easy to run multiple macros by adding an extra set of macro commands to the macro list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8c4b1de8a1eb4a7280d7ccd5cb5fd921",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed: \u001b[1;36m2\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "macrolist = [\n",
    "    ['load \"Knee.any\"',\n",
    "     'operation Main.MyStudy.Kinematics',\n",
    "     'run'],\n",
    "    ['load \"Knee.any\"',\n",
    "     'operation Main.MyStudy.InverseDynamics',\n",
    "     'run'],\n",
    "]\n",
    "app.start_macro(macrolist);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parallel execution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that AnyPyProcess will run the anyscript macros in parallel. Modern computers have multiple cores, but a single AnyBody instance can only utilize a single core, leaving us with a great potential for speeding things up through parallelization.\n",
    "\n",
    "To test this, let us create ten macros in a for-loop. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "macrolist = []\n",
    "for i in range(40):\n",
    "    macro = [\n",
    "        'load \"Knee.any\"',              \n",
    "        'operation Main.MyStudy.InverseDynamics',\n",
    "        'run',\n",
    "    ]\n",
    "    macrolist.append(macro)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AnyPyProcess has a parameter 'num_processes' that controls the number of parallel processes. Let us try a small example to see the difference in speed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6574b17df7e24d43993e44f6d3015ef4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed: \u001b[1;36m40\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First sequentially\n",
    "app = AnyPyProcess(num_processes = 1)\n",
    "app.start_macro(macrolist);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4355dfddc8964a36a35f1678c763df34",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed: \u001b[1;36m40\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Then with parallization\n",
    "app = AnyPyProcess(num_processes = 4)\n",
    "app.start_macro(macrolist);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Note*: In general you should not use a `num_processes` than the number of cores in your computer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting data from the AnyBody Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following macro, we have added a new class operation to 'Dump' the result of the maximum muscle activity. The `start_macro` method will return all the dumped variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b777d9604f34a31b4fd5fe3537fba71",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed: \u001b[1;36m1\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "macrolist = [\n",
    "    'load \"Knee.any\"',\n",
    "    'operation Main.MyStudy.InverseDynamics',\n",
    "    'run',\n",
    "    'classoperation Main.MyStudy.Output.MaxMuscleActivity \"Dump\"',\n",
    "] \n",
    "\n",
    "results = app.start_macro(macrolist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can export more variables by adding more `classoperation`. But there is a better way of doing this, as we shall see in the next tutorials. \n",
    "\n",
    "Finally, to make a plot we import the `matplotlib` library, and enable inline figures."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_muscle_act = results[0]['Main.MyStudy.Output.MaxMuscleActivity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "plt.plot(max_muscle_act);"
   ]
  }
 ],
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   "language": "python",
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