{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Advanced Examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Making a Monte Carlo parameter study\n", "In this example, we will make a Monte Carlo study. This is a case where the python library shows it's advantage. \n", "\n", "We will make a Monte Carlo study on the position of patella tendon insertion and origin in the simplified knee model used in the first tutorial. Thus, we need some macros that change two `sRel` variables in the model. In this case, we choose the values from a truncated normal distribution, but any statistical distribution could have been used. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from scipy.stats import distributions\n", "# Truncated normal between +/- 2SD.\n", "patella_tendon_insertion = distributions.truncnorm(-2,2,[0.02, 0.12, 0], [0.01,0.01,0.01]) \n", "patella_tendon_origin = distributions.truncnorm(-2,2,[0.0,-0.03, 0], [0.01,0.01,0.01]) " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[classoperation Main.MyModel.Tibia.Patella2.sRel \"Set Value\" --value=\"{0.02,0.12,2.78291642467e-18}\",\n", " classoperation Main.MyModel.Patella.Lig.sRel \"Set Value\" --value=\"{2.78291642467e-18,-0.03,2.78291642467e-18}\"]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from anypytools import macro_commands as mc\n", "\n", "macro = [\n", " mc.SetValue_random('Main.MyModel.Tibia.Patella2.sRel', patella_tendon_insertion),\n", " mc.SetValue_random('Main.MyModel.Patella.Lig.sRel', patella_tendon_origin)\n", "]\n", "\n", "macro " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The default representation of the macro is 50% percentile values from the distribution (i.e. the mean value). To generate something random we need the `AnyMacro` helper class. \n", "\n", "The `AnyMacro` helper class can generate multiple macros from a single macro." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from anypytools import AnyMacro\n", "\n", "mg = AnyMacro(macro)\n", "monte_carlo_macros = mg.create_macros_MonteCarlo(100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first generated macro just has the default values." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['classoperation Main.MyModel.Tibia.Patella2.sRel \"Set Value\" --value=\"{0.02,0.12,2.78291642467e-18}\"',\n", " 'classoperation Main.MyModel.Patella.Lig.sRel \"Set Value\" --value=\"{2.78291642467e-18,-0.03,2.78291642467e-18}\"']" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "monte_carlo_macros[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next two macros have random offsets. " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[['classoperation Main.MyModel.Tibia.Patella2.sRel \"Set Value\" --value=\"{0.0144267872987,0.109982579448,-0.01776142425}\"',\n", " 'classoperation Main.MyModel.Patella.Lig.sRel \"Set Value\" --value=\"{0.00182301737147,-0.0385242952422,0.0183692935384}\"'],\n", " ['classoperation Main.MyModel.Tibia.Patella2.sRel \"Set Value\" --value=\"{0.0242051783405,0.103809162986,0.00739209161022}\"',\n", " 'classoperation Main.MyModel.Patella.Lig.sRel \"Set Value\" --value=\"{-0.00636881243957,-0.0180115562062,0.0118074789971}\"'],\n", " ['classoperation Main.MyModel.Tibia.Patella2.sRel \"Set Value\" --value=\"{0.0249230579594,0.115461116587,-0.00698883214514}\"',\n", " 'classoperation Main.MyModel.Patella.Lig.sRel \"Set Value\" --value=\"{0.00483548485266,-0.014494203176,0.00744105538977}\"']]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "monte_carlo_macros[1:4]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let us expand the macro to also load and run the model. " ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "macro = [\n", " mc.Load('Knee.any'),\n", " mc.SetValue_random('Main.MyModel.Tibia.Patella2.sRel', patella_tendon_insertion ) ,\n", " mc.SetValue_random('Main.MyModel.Patella.Lig.sRel', patella_tendon_origin ) ,\n", " mc.RunOperation('Main.MyStudy.InverseDynamics'),\n", " mc.Export('Main.MyStudy.Output.Abscissa.t', \"time\"),\n", " mc.Export('Main.MyStudy.Output.MaxMuscleActivity', \"MaxMuscleActivity\")\n", "]\n", "mg = AnyMacro(macro, seed=1)\n", "monte_carlo_macros = mg.create_macros_MonteCarlo(100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Running the Monte Carlo macro" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The macro is passed to the AnyPyProcess object which excutes the macros" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "1cb74f95431d49bcab3f5a4bc0ddcf49", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Completed: \u001b[1;36m100\u001b[0m\n" ] }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from anypytools import AnyPyProcess \n",
    "\n",
    "app = AnyPyProcess()\n",
    "monte_carlo_results = app.start_macro( monte_carlo_macros )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output object (`monte_carlo_result`) is a list-like object where each element is a dictionary with the output of the corresponding simulation.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length : 100\n",
      "Data keys in first element:  ['time', 'MaxMuscleActivity', 'task_macro_hash', 'task_id', 'task_work_dir', 'task_name', 'task_processtime', 'task_macro', 'task_logfile']\n"
     ]
    }
   ],
   "source": [
    "print('Length :', len(monte_carlo_results) )\n",
    "print('Data keys in first element: ', list(monte_carlo_results[0].keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Filtering results with Errors\n",
    "If any model errors occured in some of the simulations, then the output may be missing. That can be a problem for futher processing. So we may want to remove those simulations. \n",
    "\n",
    "That is easily done. Results from simuations with errors will contain a  an 'ERROR' key. We can use that to filter the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "monte_carlo_results[:] = [\n",
    "    output \n",
    "    for output in monte_carlo_results \n",
    "    if 'ERROR' not in output\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting data from the resutls\n",
    "The object looks and behaves like a list, but it can do more things than a standard Python list. If we use it as a dictionary and pass the variable names, it will return that variable concatenated over all runs. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.00890538, 0.00927552, 0.00986515, ..., 0.00986515, 0.00927552,\n",
       "        0.00890538],\n",
       "       [0.00761831, 0.00793615, 0.00844398, ..., 0.00844398, 0.00793614,\n",
       "        0.00761831],\n",
       "       [0.00965061, 0.01004897, 0.0106812 , ..., 0.0106812 , 0.01004897,\n",
       "        0.00965062],\n",
       "       ...,\n",
       "       [0.01007754, 0.0104962 , 0.01116247, ..., 0.01116247, 0.01049619,\n",
       "        0.01007754],\n",
       "       [0.01026166, 0.01068757, 0.01136452, ..., 0.01136452, 0.01068757,\n",
       "        0.01026167],\n",
       "       [0.00981378, 0.01021894, 0.01086207, ..., 0.01086207, 0.01021893,\n",
       "        0.00981379]], shape=(100, 100))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monte_carlo_results['MaxMuscleActivity']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting the results\n",
    "Finally we can plot the result of the Monte Carlo study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "\n", "time = monte_carlo_results['time'][0]\n", "max_muscle_activity = monte_carlo_results[\"MaxMuscleActivity\"]\n", "plt.fill_between(time, max_muscle_activity.min(0), max_muscle_activity.max(0),alpha=0.4 )\n", "for trace in max_muscle_activity:\n", " plt.plot(time, trace,'b', lw=0.2 )\n", "# Plot result with the mean of the inputs ( stored in the first run)\n", "plt.plot(time, max_muscle_activity[0],'r--', lw = 2, ) \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Latin Hypercube sampling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Monte Carlo studies are not very efficient when investigating the effect of many parameters. It quickly becomes necessary to run the model thousands of times. Not very convenient if the AnyBody model takes a long time to run. \n", "\n", "Another approach is to use Latin Hypercube sampling. From Wikipedia\n", "> Latin hypercube sampling (LHS) is a statistical method for generating a sample of plausible collections of parameter values from a multidimensional distribution. The sampling method is often used to construct computer experiments.\n", "\n", "Using LHS we can generate a sample that better spans the whole multidimensional space. Thus, fever model evaluations are necessary. See [pyDOE](http://pythonhosted.org/pyDOE/randomized.html) for examples (and explanation of the `criterion`/`iterations` parameters which can be parsed to `create_macros_LHS()`).\n", "\n", "To following uses LHS to do the same as in the previous example:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "D:\\repos\\AnyPyTools\\.pixi\\envs\\jupyter\\Lib\\site-packages\\pyDOE\\doe_factorial.py:192: SyntaxWarning: \"\\-\" is an invalid escape sequence. Such sequences will not work in the future. Did you mean \"\\\\-\"? A raw string is also an option.\n", " A = [item for item in re.split('\\-?\\s?\\+?', gen) if item] # remove empty strings\n" ] } ], "source": [ "patella_tendon_insertion = distributions.norm([0.02, 0.12, 0], [0.01,0.01,0.01]) \n", "patella_tendon_origin = distributions.norm([0.0,-0.03, 0], [0.01,0.01,0.01]) \n", "\n", "macro = [ \n", " mc.Load('Knee.any'),\n", " mc.SetValue_random('Main.MyModel.Tibia.Patella2.sRel', patella_tendon_insertion ) ,\n", " mc.SetValue_random('Main.MyModel.Patella.Lig.sRel', patella_tendon_origin ) ,\n", " mc.RunOperation('Main.MyStudy.InverseDynamics'),\n", " mc.Export('Main.MyStudy.Output.Abscissa.t', 'time'),\n", " mc.Export('Main.MyStudy.Output.MaxMuscleActivity', 'MaxMuscleActivity')\n", "]\n", "mg = AnyMacro(macro, seed=1)\n", "LHS_macros = mg.create_macros_LHS(25)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5b2de447f3ec43a29cfed31968dbbdb3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Completed: \u001b[1;36m25\u001b[0m\n" ] }, { "data": { "text/html": [ "
\n"
      ],
      "text/plain": []
     },
     "metadata": {},
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    }
   ],
   "source": [
    "from anypytools import AnyPyProcess \n",
    "\n",
    "app = AnyPyProcess()\n",
    "lhs_results = app.start_macro(LHS_macros)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\" The key: 'Abscissa.t' is not present in all elements of the output.\"",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[14]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m      1\u001b[39m get_ipython().run_line_magic(\u001b[33m'\u001b[39m\u001b[33mmatplotlib\u001b[39m\u001b[33m'\u001b[39m, \u001b[33m'\u001b[39m\u001b[33minline\u001b[39m\u001b[33m'\u001b[39m)\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplt\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m time = \u001b[43mlhs_results\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mAbscissa.t\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m[\u001b[32m0\u001b[39m]\n\u001b[32m      5\u001b[39m max_muscle_act = lhs_results[\u001b[33m'\u001b[39m\u001b[33mMaxMuscleActivity\u001b[39m\u001b[33m'\u001b[39m]\n\u001b[32m      6\u001b[39m plt.fill_between(time, max_muscle_act.min(\u001b[32m0\u001b[39m), max_muscle_act.max(\u001b[32m0\u001b[39m), alpha=\u001b[32m0.4\u001b[39m)\n",
      "\u001b[36mFile \u001b[39m\u001b[32mD:\\repos\\AnyPyTools\\anypytools\\tools.py:400\u001b[39m, in \u001b[36mAnyPyProcessOutputList.__getitem__\u001b[39m\u001b[34m(self, item)\u001b[39m\n\u001b[32m    396\u001b[39m key_in_all_elements = \u001b[38;5;28mall\u001b[39m(\n\u001b[32m    397\u001b[39m     \u001b[38;5;28msuper\u001b[39m(AnyPyProcessOutput, e).\u001b[34m__contains__\u001b[39m(key) \u001b[38;5;28;01mfor\u001b[39;00m e \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.list\n\u001b[32m    398\u001b[39m )\n\u001b[32m    399\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m key_in_all_elements:\n\u001b[32m--> \u001b[39m\u001b[32m400\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\n\u001b[32m    401\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33m The key: \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m is not present in all elements of the output.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    402\u001b[39m     ) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m    403\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m    404\u001b[39m     data = np.array(\n\u001b[32m    405\u001b[39m         [\u001b[38;5;28msuper\u001b[39m(AnyPyProcessOutput, e).\u001b[34m__getitem__\u001b[39m(key) \u001b[38;5;28;01mfor\u001b[39;00m e \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m.list]\n\u001b[32m    406\u001b[39m     )\n",
      "\u001b[31mKeyError\u001b[39m: \" The key: 'Abscissa.t' is not present in all elements of the output.\""
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "time = lhs_results['Abscissa.t'][0]\n",
    "max_muscle_act = lhs_results['MaxMuscleActivity']\n",
    "plt.fill_between(time, max_muscle_act.min(0), max_muscle_act.max(0), alpha=0.4)\n",
    "for trace in max_muscle_act:\n",
    "    plt.plot(time, trace,'b', lw=0.2 )\n",
    "# Plot the mean value that was stored in the first results\n",
    "plt.plot(time, max_muscle_act.mean(0),'r--', lw = 2, ) \n"
   ]
  }
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