SAlib_example/SensitivityAnalysis.ipynb

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    "# Using Sensitivity Analysis on Discovery Cluster\n",
    "using Array Jobs to Interrogate Models\n",
    "\n",
    "Original Sensitivity Analysis notebook: Will Usher, UCL Energy Institute, 10th December 2015 <br/>\n",
    "Updates to demonstrate running array jobs on a cluster: Richard West, 2016\n",
    "\n",
    "In this version, most of the background and detail have been removed. Please refer to the original at https://github.com/SALib/SATut if you are not familiar with the system."
   ]
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   "source": [
    "from ipywidgets import widgets, interact\n",
    "from IPython.display import display\n",
    "%matplotlib inline\n",
    "import seaborn as sbn\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from IPython.core.pylabtools import figsize\n",
    "figsize(12, 10)\n",
    "sbn.set_context(\"talk\", font_scale=1)\n",
    "\n",
    "# The model used for this seminar is contained in the file model.py\n",
    "from model import cost_of_vehicle_to_grid, compute_profit, annualized_capital_cost, battery_lifetime, max_vehicle_power"
   ]
  },
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   "source": [
    "# Uncomment and execute the following line to see the contents of the `model.py` file\n",
    "# %load model.py"
   ]
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  {
   "cell_type": "markdown",
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     "slide_type": "slide"
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   "source": [
    "## Using SALib to run a Sensitivity Analysis\n",
    "\n",
    "As we saw earlier, SALib is a **free** **open-source** **Python** library which you can install by running the command\n",
    "\n",
    "```python\n",
    "pip install SALib\n",
    "```\n",
    "\n",
    "[Documentation](http://salib.readthedocs.org/) is available online.\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### Import the package"
   ]
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   "source": [
    "from SALib.sample import morris as ms\n",
    "from SALib.analyze import morris as ma\n",
    "from SALib.plotting import morris as mp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
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   "source": [
    "### Define a problem file\n",
    "\n",
    "In the code below, a problem file is used to define the variables we wish to explore.\n",
    "\n",
    "In the script we run on Discovery, it's important that the names here match the parameter names to the function that we're evaluating, i.e. `max_vehicle_power()` in the `model.py` file."
   ]
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   "source": [
    "morris_problem = {\n",
    "    # There are six variables\n",
    "    'num_vars': 7,\n",
    "    # These are their names\n",
    "    'names': ['connector_power', 'stored_energy', 'distance_driven', 'range_buffer', 'driving_efficiency', 'inverter_efficiency', 'dispatch_time'],\n",
    "    # These are their plausible ranges over which we'll move the variables\n",
    "    'bounds': [[2.3, 22], # connection_power (kW)\n",
    "               [50, 100], # battery size (kWh)\n",
    "               [0, 80], # distance driven (km)\n",
    "               [0, 80], # range buffer (km)\n",
    "               [4,5.5], # driving efficiency (kWh/km)\n",
    "               [0.87,0.97], # inverter efficienct (%)\n",
    "               [0.5, 24] # dispatch time - hours of the day in which the energy is dispatched\n",
    "              ],\n",
    "    # I don't want to group any of these variables together\n",
    "    'groups': None\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "subslide"
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   },
   "source": [
    "### Generate a Sample\n",
    "\n",
    "We then generate a sample using the `morris.sample()` procedure from the SALib package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sample array is  (8000, 7)\n",
      "Here are the first 10 rows:\n",
      "    15.433    100.000     53.333     80.000      4.000      0.870     24.000\n",
      "    15.433    100.000      0.000     80.000      4.000      0.870     24.000\n",
      "     2.300    100.000      0.000     80.000      4.000      0.870     24.000\n",
      "     2.300    100.000      0.000     26.667      4.000      0.870     24.000\n",
      "     2.300     66.667      0.000     26.667      4.000      0.870     24.000\n",
      "     2.300     66.667      0.000     26.667      4.000      0.937     24.000\n",
      "     2.300     66.667      0.000     26.667      4.000      0.937      8.333\n",
      "     2.300     66.667      0.000     26.667      5.000      0.937      8.333\n",
      "    15.433     50.000      0.000     80.000      4.500      0.937     24.000\n",
      "    15.433     50.000      0.000     26.667      4.500      0.937     24.000\n"
     ]
    }
   ],
   "source": [
    "number_of_trajectories = 1000\n",
    "sample = ms.sample(morris_problem, number_of_trajectories, num_levels=4, grid_jump=2)\n",
    "print(\"The sample array is \",sample.shape)\n",
    "print(\"Here are the first 10 rows:\")\n",
    "for j in range(10):\n",
    "    print(' '.join(['{:10.3f}'.format(i) for i in sample[j]]))\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're going to save the parameters to a file, so we can run the jobs separately not in this notebook.\n",
    "Because the parameters in the sensitivity analysis problem may not be (in fact *are not*!) in the same order as those expected by the function (this took me a while to notice) we save the column orders into the parameters file as a header."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "header = ' '.join(morris_problem['names'])\n",
    "np.savetxt(\"parameter_values.txt\", sample, header=header)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stop!  \n",
    "## Now (pretend) we need to run the simulations on Discovery\n",
    "\n",
    "To run this on Discovery, you will need to copy the `parameter_values.txt` file on to Discovery, along with a Python script file (eg. `script.py`) that looks like the cell a few lines below.  Use an SCP or SFTP program, as described earlier in the tutorial.\n",
    "\n",
    "This is how many simulations we will need to run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...but the Slurm on Discovery is configured with a maximum job array size of 1001. (Run `scontrol show config | grep MaxArraySize` on Discovery to check). So we will run 1000 jobs, each of which runs 8 simulations.  (In real life, split into as few jobs as reasonable, to avoid clogging Slurm with thousands of needless jobs. i.e. 100 jobs each of 80 simulations would be better, or even 10 jobs of 800).\n",
    "\n",
    "First, some tips and tricks that will help you write your own `script.py` (or understand mine). Be sure you understand each of these cells in isolation before putting them together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# connector_power stored_energy distance_driven range_buffer driving_efficiency inverter_efficiency dispatch_time\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['connector_power',\n",
       " 'stored_energy',\n",
       " 'distance_driven',\n",
       " 'range_buffer',\n",
       " 'driving_efficiency',\n",
       " 'inverter_efficiency',\n",
       " 'dispatch_time']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "header = open(\"parameter_values.txt\").readline() # open the file and read the first line\n",
    "print(header)\n",
    "header = header.strip('#\\n ') # remove the '#', space, and newline from the start and end\n",
    "column_names = header.split() # split on whitespace, to get a list\n",
    "column_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  15.43333333,  100.        ,   53.33333333,   80.        ,\n",
       "          4.        ,    0.87      ,   24.        ])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "big_parameter_list = np.loadtxt(\"parameter_values.txt\")\n",
    "parameters = big_parameter_list[0]\n",
    "parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'connector_power': 2.2999999999999998,\n",
       " 'dispatch_time': 16.166666666666664,\n",
       " 'distance_driven': 53.333333333333329,\n",
       " 'driving_efficiency': 4.0,\n",
       " 'inverter_efficiency': 0.93666666666666665,\n",
       " 'range_buffer': 0.0,\n",
       " 'stored_energy': 83.333333333333329}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arguments_dictionary = { key:value for key, value in zip(column_names, parameters)}\n",
    "arguments_dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.2999999999999998"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_vehicle_power(**arguments_dictionary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See [here](https://docs.python.org/3/tutorial/controlflow.html#unpacking-argument-lists)\n",
    "for how this unpacking of argument dictionaries works (the meaning of the `**`)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*The following cell contains the script.py. To update it uncomment the first `%load script.py` line and execute it - that will load in the `script.py` file which lives alongside this notebook and is where you should make changes if you are editing this tutorial.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load script.py\n",
    "# This is a script that you should run on Discovery,\n",
    "# as part of a Slurm Array job, with 1000 jobs.\n",
    "import numpy as np\n",
    "import os\n",
    "from model import max_vehicle_power\n",
    "big_parameter_list = np.loadtxt(\"parameter_values.txt\")\n",
    "header = open(\"parameter_values.txt\").readline()\n",
    "header = header.strip('#\\n ') # remove the '#', space, and newline from the start and end\n",
    "column_names = header.split()\n",
    "\n",
    "job_number = int(os.getenv('SLURM_ARRAY_TASK_ID', default='0'))\n",
    "assert 0<=job_number<1000, \"Job number should run from 0 to 999\"\n",
    "for i in range(8):\n",
    "    parameter_number = (8 * job_number) + i\n",
    "    parameters = big_parameter_list[parameter_number]\n",
    "    arguments_dictionary = { key:value for key, value in zip(column_names, parameters)}\n",
    "    result = max_vehicle_power(**arguments_dictionary)\n",
    "    \"\"\"\n",
    "    Because we don't know what order the jobs will complete in,\n",
    "    the results may be written out of order!\n",
    "    To deal with this, we will write the job number in the results file\n",
    "    as well as the result:\n",
    "    \"\"\"\n",
    "    with open(\"results.txt\", 'a') as result_file: # 'a' is append mode, and will add to the file.\n",
    "        result_file.write('{} {}\\n'.format(parameter_number, result)) # the '\\n' is a new line\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then create a `submit.sh` script to run it as an Array job, to fill the `results.txt` file with results.\n",
    "But realize that your Python script above expects the job number to start at zero, so you'll probably want something like\n",
    "```\n",
    "#SBATCH --array=0-999%40\n",
    "```\n",
    "in your submit file. eg. your `submit.sh` may look something like this:\n",
    "\n",
    "```\n",
    "#!/bin/sh\n",
    "#SBATCH -n 1\n",
    "#SBATCH -N 1\n",
    "#SBATCH --job-name=SA\n",
    "#SBATCH --array=0-999\n",
    "#SBATCH -p ser-par-10g\n",
    "python3 script.py\n",
    "```\n",
    "\n",
    "Once your jobs have all finished, copy the `results.txt` back to your computer and put it alongside this Notebook. Hopefully we can import it like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "np.loadtxt(\"results.txt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In an ideal world, that would have worked. But computers aren't ideal, and it probably didn't. Open the `results.txt` file in a text editor and take a look at it carefully. My results file starts out looking OK:\n",
    "\n",
    "```\n",
    "0 2.416666666666667\n",
    "1 2.9\n",
    "2 2.3\n",
    "3 2.3\n",
    "4 2.175\n",
    "8 1.2575617283950618\n",
    "5 2.3\n",
    "9 1.7201131687242797\n",
    "```\n",
    "\n",
    "but there are some blank lines:\n",
    "\n",
    "```\n",
    "87 5.127272727272728\n",
    "64 2.509259259259259\n",
    "65 3.763888888888889\n",
    "\n",
    "73 15.433333333333334\n",
    "74 1.5426804123711344\n",
    "68 3.257828282828283\n",
    "```\n",
    "\n",
    "and occasionally really weird things:\n",
    "\n",
    "```\n",
    "70 9.382545454545456\n",
    "77 2.3\n",
    "78 2.3\n",
    "088888888889\n",
    "79 2.3\n",
    "56 4.697333333333334\n",
    "```\n",
    "\n",
    "The problem is (I think) that the different compute nodes running different processes all trying to write to the same file, which is stored on a networked file system on yet another computer, are conflicting, instead of waiting their turn to write to the file. This is notoriously difficult to manage, as there are all sorts of levels of cacheing involved. \n",
    "\n",
    "What we'll try next is to split the 8000 jobs into 100 jobs each running 80 parameter sets, and have each job print the results into its standard output file, which Slurm will collect into 100 separate files. Then we'll concatenate the 100 output files when the jobs are all done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%load script2.py\n",
    "# This is a script that you should run on Discovery,\n",
    "# as part of a Slurm Array job, with 100 jobs.\n",
    "import numpy as np\n",
    "import os\n",
    "from model import max_vehicle_power\n",
    "big_parameter_list = np.loadtxt(\"parameter_values.txt\")\n",
    "header = open(\"parameter_values.txt\").readline()\n",
    "header = header.strip('#\\n ') # remove the '#', space, and newline from the start and end\n",
    "column_names = header.split()\n",
    "\n",
    "job_number = int(os.getenv('SLURM_ARRAY_TASK_ID', default='0'))\n",
    "assert 0<=job_number<100, \"Job number should run from 0 to 99\"\n",
    "for i in range(80):\n",
    "    parameter_number = (80 * job_number) + i\n",
    "    parameters = big_parameter_list[parameter_number]\n",
    "    arguments_dictionary = { key:value for key, value in zip(column_names, parameters)}\n",
    "    result = max_vehicle_power(**arguments_dictionary)\n",
    "    print('{} {}'.format(parameter_number, result))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you run a batch job like this:\n",
    "\n",
    "```\n",
    "#!/bin/sh\n",
    "#SBATCH -n 1\n",
    "#SBATCH -N 1\n",
    "#SBATCH --job-name=SA\n",
    "#SBATCH --array=0-99\n",
    "#SBATCH -p ser-par-10g\n",
    "#SBATCH -e error_%a.log\n",
    "#SBATCH -o output_%a.log\n",
    "stdbuf -o0 -e0 python3 -u script2.py\n",
    "```\n",
    "\n",
    "Then you have run it on Discovery you should have 100 files named `output_0.log` etc. (and 100 named `error_0.log` which are hopefully empty).  Concatenate them all the error files to the screen to check they're empty, then concatenate all the output files into a `results.txt` file:\n",
    "```\n",
    "$ cat error*.log\n",
    "$ cat output*.log > results.txt\n",
    "```\n",
    "then copy the results.txt file back to your computer and put it alongside this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing and analyzing the results \n",
    "Then come back here to load the results and continue the sensitivy analysis.\n",
    "Because our results file may not be in order, but contains the job number at the start of each line, we need to do a little manipulation to get the `output` array as needed by SALib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.          2.41666667]\n",
      " [ 1.          2.9       ]\n",
      " [ 2.          2.3       ]\n",
      " [ 3.          2.3       ]\n",
      " [ 4.          2.175     ]]\n",
      "[ 2.41666667  2.9         2.3         2.3         2.175     ]\n"
     ]
    }
   ],
   "source": [
    "results_array = np.loadtxt(\"results.txt\")\n",
    "print(results_array[:5,])\n",
    "results_dict = dict()\n",
    "for number, value in results_array:\n",
    "    i = int(number)\n",
    "    assert i not in results_dict, \"Duplicate entry! {}\".format(i)\n",
    "    results_dict[i] = value\n",
    "output = np.array([results_dict[i] for i in range(len(results_dict))])\n",
    "print(output[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will re-load the sample array from the input file, in case the one in memory changed.\n",
    " Ensure the `parameter_values.txt` is the same file you had on Discovery \n",
    " when you generated the results.txt.\n",
    "Because the sample is based on random numbers, it changes every time you run the notebook cell above  to generate it, and your results will be completely wrong if you use the wrong sample to analyze the output!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  15.43333333,  100.        ,   53.33333333, ...,    4.        ,\n",
       "           0.87      ,   24.        ],\n",
       "       [  15.43333333,  100.        ,    0.        , ...,    4.        ,\n",
       "           0.87      ,   24.        ],\n",
       "       [   2.3       ,  100.        ,    0.        , ...,    4.        ,\n",
       "           0.87      ,   24.        ],\n",
       "       ..., \n",
       "       [  15.43333333,  100.        ,   80.        , ...,    5.        ,\n",
       "           0.97      ,    0.5       ],\n",
       "       [  15.43333333,  100.        ,   80.        , ...,    5.        ,\n",
       "           0.97      ,   16.16666667],\n",
       "       [  15.43333333,  100.        ,   80.        , ...,    4.        ,\n",
       "           0.97      ,   16.16666667]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = np.loadtxt(\"parameter_values.txt\")\n",
    "sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Factor Prioritisation\n",
    "\n",
    "We'll run a sensitivity analysis to see which is the most influential parameter.\n",
    "\n",
    "The results parameters are called **mu**, **sigma** and **mu_star**.\n",
    "\n",
    "* **Mu** is the mean effect caused by the input parameter being moved over its range.\n",
    "* **Sigma** is the standard deviation of the mean effect.\n",
    "* **Mu_star** is the mean absolute effect."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name                      mu mu_star   sigma\n",
      "connector_power         5.86    5.86    8.16\n",
      "stored_energy           1.93    1.93    1.90\n",
      "distance_driven      -  0.68    0.68    0.69\n",
      "range_buffer         -  0.68    0.68    0.68\n",
      "driving_efficiency      0.23    0.23    0.29\n",
      "inverter_efficiency     0.23    0.23    0.27\n",
      "dispatch_time        -  9.11    9.11    9.14\n"
     ]
    }
   ],
   "source": [
    "Si = ma.analyze(morris_problem, sample, output, print_to_console=False)\n",
    "print(\"{:20s} {:>7s} {:>7s} {:>7s}\".format(\"Name\", \"mu\", \"mu_star\", \"sigma\"))\n",
    "for name, s1, st, mean in zip(morris_problem['names'], Si['mu'], Si['mu_star'], Si['sigma']):\n",
    "    print(\"{:20s} {:=7.2f} {:=7.2f} {:=7.2f}\".format(name, s1, st, mean))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We can plot the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11782af98>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f6ptv5sxZmD17dp6Pl0r5DzdpVmhoCHr06JPt+Js3r9G0aXMYGRkXYVRElBNV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117a6fcf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1,2)\n",
    "mp.horizontal_bar_plot(ax1, Si, param_dict={})\n",
    "mp.covariance_plot(ax2, Si, {})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Sanity check\n",
    "Because this is such a trivially fast problem, we can do all 8000 simulations on laptop in a fraction of a second, so let's re-do it here in the notebook to compare with our results from above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name                      mu mu_star   sigma\n",
      "connector_power         5.86    5.86    8.16\n",
      "stored_energy           1.93    1.93    1.90\n",
      "distance_driven      -  0.68    0.68    0.69\n",
      "range_buffer         -  0.68    0.68    0.68\n",
      "driving_efficiency      0.23    0.23    0.29\n",
      "inverter_efficiency     0.23    0.23    0.27\n",
      "dispatch_time        -  9.11    9.11    9.14\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x11a544ac8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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v9c45c+YszJ49O1/HSqX8h5s0KzQ0BD169M5x/M2b12jatDmMjIyLMCoiyo2q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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119a9c908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def monte_carlo_large(data):\n",
    "    y = max_vehicle_power(data[0], data[1], data[2], data[3], data[6], data[4], data[5])\n",
    "    return y\n",
    "# Run the sample through the monte carlo procedure of the power model\n",
    "output = monte_carlo_large(sample.T)\n",
    "Si = ma.analyze(morris_problem, sample, output, print_to_console=False)\n",
    "print(\"{:20s} {:>7s} {:>7s} {:>7s}\".format(\"Name\", \"mu\", \"mu_star\", \"sigma\"))\n",
    "for name, s1, st, mean in zip(morris_problem['names'], Si['mu'], Si['mu_star'], Si['sigma']):\n",
    "    print(\"{:20s} {:=7.2f} {:=7.2f} {:=7.2f}\".format(name, s1, st, mean))\n",
    "fig, (ax1, ax2) = plt.subplots(1,2)\n",
    "mp.horizontal_bar_plot(ax1, Si, param_dict={})\n",
    "mp.covariance_plot(ax2, Si, {})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:py3]",
   "language": "python",
   "name": "conda-env-py3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}