{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Global sensitivity analysis\n",
    "\n",
    "Global sensitivity analysis is a Monte Carlo based method to rank the importance of parameters in a given modelling problem. As opposed to local senstivity analysis, it does not require the construction of the Jacobian, making it a flexible tool to evaluate complex problems.\n",
    "\n",
    "Global sensitivty analysis is available in mainly uncertainty quantificaiton packages, as well as some flow and transport programs (e.g. iTOUGH2). GSA is also very popular in catchment modelling and civil engineering/risk analysis problems.\n",
    "\n",
    "Some GSA work in hydrogeophysics (mainly by Berkeley Lab):\n",
    "\n",
    "* coupled hydrological-thermal-geophysical inversion ([Tran et al 2017](https://www.hydrol-earth-syst-sci.net/20/3477/2016/hess-20-3477-2016.pdf))\n",
    "> Nicely show how to simplify (i.e. reduce the number of parameters) for a very complex, highly coupled problem \n",
    "* making sense of global senstivity analysis ([Wainwright et al 2014](https://doi.org/10.1016/j.cageo.2013.06.006))\n",
    "> Very good review article\n",
    "* Sensitivity analysis of environmental models ([Pianosi et al 2014](https://doi.org/10.1016/j.envsoft.2016.02.008))\n",
    "> A systematic review, includes GLUE and RSA\n",
    "* hydrogeology of a nuclear site in the Paris Basin ([Deman et al 2016](https://doi.org/10.1016/j.ress.2015.11.005))\n",
    "> A different GSA method was used instead here to look at the low probability breakthrough events\n",
    "* Global Sensitivity and Data-Worth Analyses in iTOUGH2 User's Guide ([Wainwright et al 2016](http://eesatough.lbl.gov/assets/files/02/documentation/iTOUGH2-Sensitivity_Analysis.pdf))\n",
    "> An useful manual if you want to learn about the details of setting up a probllem.\n",
    "\n",
    "In this tutorial, we will see how to link the **ResIPy** API and **SALib** for senstivity analysis. Two key elements of SA are (i) forward modelling (Monte Carlo runs) and (ii) specifying the parameter ranges. This notebook will showcase of the use of the *Method of Morris*, which is known for its relatively small computational cost. This tutorial is modified from the one posted on https://github.com/SALib/SATut to demonstrate its coupling with **ResIPy**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Morris sensitivity method\n",
    ">The Morris one-at-a-time (OAT) method (Morris, 1991) can be considered as an extension of the local sensitivity method. Each parameter range is scaled to the unit interval [0, 1] and partitioned into $(p−1)$ equally-sized intervals. The reference value of each parameter is selected randomly from the set ${0, 1/(p−1), 2/(p−1), …, 1−Δ}$. The fixed increment $Δ=p/{2(p−1)}$ is added to each parameter in random order to compute the elementary effect ($EE$) of $x_i$\n",
    "\n",
    "\n",
    "$$EE_i=\\frac{1}{\\tau_y}\\frac{f(x_1*,...,x_i*+\\Delta,...,x_k*)-f(x_1*,...,x_k*)}{\\Delta}$$\n",
    "\n",
    "> where ${x_i⁎}$ is the randomly selected parameter set, and τy is the output-scaling factor. To compute EEi for k parameters, we need (k+1) simulations (called one “path”) in the same way as that of the local sensitivity method. By having multiple paths, we have an ensemble of EEs for each parameter. The total number of simulations is r(k+1), where r is the number of paths.\n",
    "\n",
    "We compute three statistics: the mean $EE$, standard deviation (STD) of $EE$, and mean of absolute $EE$. \n",
    "* **mean EE ($\\mu$)** represents the average effect of each parameter over the parameter space, the mean EE can be regarded as a global sensitivity measure.\n",
    "* **mean |EE| ($\\mu*$)** is used to identify the non-influential factors, \n",
    "* **STD of EE ($\\sigma$)** is used to identify nonlinear and/or interaction effects. (The standard error of mean (SEM) of EE, defined as $SEM=STD/r^{0.5}$, is used to calculate the confidence interval of mean EE (Morris, 1991))\n",
    "\n",
    "This cell is copied from ([Wainwright et al 2014](https://www.sciencedirect.com/science/article/pii/S0098300413001702))\n",
    "\n",
    "## Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "import os\n",
    "import sys\n",
    "sys.path.append('../src') # add here the relative path of the API folder\n",
    "from resipy import Project\n",
    "import numpy as np # numpy for electrode generation\n",
    "import pandas as pd\n",
    "from IPython.utils import io  # suppress R2 outputs during MC runs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The SALib package\n",
    "SALib is a **free** **open-source** **Python** library\n",
    "\n",
    "\n",
    "\n",
    "If you use Python, you can install it by running the command\n",
    "\n",
    "`pip install SALib`\n",
    "\n",
    "[Documentation](http://salib.readthedocs.org/) is available online and you can also view the code on [Github](http://salib.github.io/SALib/).\n",
    "\n",
    "The library includes:\n",
    "* Sobol Sensitivity Analysis ([Sobol 2001](http://www.sciencedirect.com/science/article/pii/S0378475400002706), [Saltelli 2002](http://www.sciencedirect.com/science/article/pii/S0010465502002801), [Saltelli et al. 2010](http://www.sciencedirect.com/science/article/pii/S0010465509003087))\n",
    "* Method of Morris, including groups and optimal trajectories ([Morris 1991](http://www.tandfonline.com/doi/abs/10.1080/00401706.1991.10484804), [Campolongo et al. 2007](http://www.sciencedirect.com/science/article/pii/S1364815206002805))\n",
    "* Fourier Amplitude Sensitivity Test (FAST) ([Cukier et al. 1973](http://scitation.aip.org/content/aip/journal/jcp/59/8/10.1063/1.1680571), [Saltelli et al. 1999](http://amstat.tandfonline.com/doi/abs/10.1080/00401706.1999.10485594))\n",
    "* Delta Moment-Independent Measure ([Borgonovo 2007](http://www.sciencedirect.com/science/article/pii/S0951832006000883), [Plischke et al. 2013](http://www.sciencedirect.com/science/article/pii/S0377221712008995))\n",
    "* Derivative-based Global Sensitivity Measure (DGSM) ([Sobol and Kucherenko 2009](http://www.sciencedirect.com/science/article/pii/S0378475409000354))\n",
    "* Fractional Factorial Sensitivity Analysis ([Saltelli et al. 2008](http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470059974.html))\n",
    "\n",
    "[SALib Tutorial](https://github.com/SALib/SATut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the packages\n",
    "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": {},
   "source": [
    "### Create ERT forward problem with **ResIPy**\n",
    "\n",
    "In the code below, created a `Project` forward problem to be analyzed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Working directory is: /media/jkl/data/phd/resipy/src/resipy\n",
      "clearing dirname\n",
      "Creating triangular mesh...done (1887 elements)\n",
      "165 quadrupoles generated.\n",
      "Writing .in file and mesh.dat... done\n",
      "Writing protocol.dat... done\n",
      "Running forward model... \n",
      "\n",
      " >> R  2    R e s i s t i v i t y   I n v e r s i o n   v4.10 <<\n",
      "\n",
      " >> D a t e : 03 - 12 - 2023\n",
      " >> My beautiful survey                                                             \n",
      " >> F o r w a r d   S o l u t i o n   S e l e c t e d <<\n",
      " >> Determining storage needed for finite element conductance matrix\n",
      " >> Generating index array for finite element conductance matrix\n",
      " >> Reading start resistivity from resistivity.dat               \n",
      "\n",
      " Measurements read:   165     Measurements rejected:     0\n",
      "\n",
      " >> Total Memory required is:          0.000 Gb\n",
      "0/165 reciprocal measurements found.\n",
      "Forward modelling done."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n",
      "/media/jkl/data/phd/resipy/pyenv/lib/python3.10/site-packages/numpy/lib/function_base.py:2458: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
      "  res = asanyarray(outputs, dtype=otypes[0])\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = Project(typ='R2')\n",
    "\n",
    "elec = np.zeros((24,3))\n",
    "elec[:,0] = np.arange(0, 24*0.5, 0.5) # with 0.5 m spacing and 24 electrodes\n",
    "k.setElec(elec)\n",
    "#print(k.elec)\n",
    "\n",
    "# defining electrode array\n",
    "x = np.zeros((24, 3))\n",
    "x[:,0] = np.arange(0, 24*0.5, 0.5)\n",
    "k.setElec(elec)\n",
    "\n",
    "# creating mesh\n",
    "k.createMesh(res0=20)\n",
    "\n",
    "# add region\n",
    "k.addRegion(np.array([[2,-0.3],[2,-2],[3,-2],[3,-0.3],[2,-0.3]]), 50)\n",
    "k.addRegion(np.array([[5,-2],[5,-3.5],[8,-3.5],[8,-2],[5,-2]]), 500)\n",
    "\n",
    "# define sequence\n",
    "k.createSequence([('dpdp1', 1, 10)])\n",
    "\n",
    "# forward modelling\n",
    "k.forward(noise=0.025)\n",
    "\n",
    "# read results\n",
    "fwd_dir = os.path.relpath('../src/resipy/invdir/fwd')\n",
    "\n",
    "obs_data = np.loadtxt(os.path.join(fwd_dir, 'R2_forward.dat'),skiprows =1)\n",
    "obs_data = obs_data[:,6]\n",
    "\n",
    "# plot\n",
    "k.showMesh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Option to view resistivity fields with pyvista \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1024x768>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table style='width: 100%;'><tr><th>Header</th><th>Data Arrays</th></tr><tr><td>\n",
       "<table style='width: 100%;'>\n",
       "<tr><th>UnstructuredGrid</th><th>Information</th></tr>\n",
       "<tr><td>N Cells</td><td>893</td></tr>\n",
       "<tr><td>N Points</td><td>496</td></tr>\n",
       "<tr><td>X Bounds</td><td>-1.400e-01, 1.168e+01</td></tr>\n",
       "<tr><td>Y Bounds</td><td>-3.830e+00, 0.000e+00</td></tr>\n",
       "<tr><td>Z Bounds</td><td>0.000e+00, 0.000e+00</td></tr>\n",
       "<tr><td>N Arrays</td><td>2</td></tr>\n",
       "</table>\n",
       "\n",
       "</td><td>\n",
       "<table style='width: 100%;'>\n",
       "<tr><th>Name</th><th>Field</th><th>Type</th><th>N Comp</th><th>Min</th><th>Max</th></tr>\n",
       "<tr><td>Resistivity(ohm.m)</td><td>Cells</td><td>float64</td><td>1</td><td>2.000e+01</td><td>5.000e+02</td></tr>\n",
       "<tr><td><b>Resistivity(log10)</b></td><td>Cells</td><td>float64</td><td>1</td><td>1.301e+00</td><td>2.699e+00</td></tr>\n",
       "</table>\n",
       "\n",
       "</td></tr> </table>"
      ],
      "text/plain": [
       "UnstructuredGrid (0x7fdba5c236a0)\n",
       "  N Cells:    893\n",
       "  N Points:   496\n",
       "  X Bounds:   -1.400e-01, 1.168e+01\n",
       "  Y Bounds:   -3.830e+00, 0.000e+00\n",
       "  Z Bounds:   0.000e+00, 0.000e+00\n",
       "  N Arrays:   2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pyvista as pv\n",
    "mesh = pv.read(os.path.join(fwd_dir, 'forward_model.vtk'))\n",
    "mesh.plot(scalars='Resistivity(log10)',notebook=True)\n",
    "mesh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=1024x768>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pyvista as pv\n",
    "\n",
    "plotter = pv.Plotter()\n",
    "mesh = pv.read(os.path.join(fwd_dir, 'forward_model.vtk'))\n",
    "\n",
    "plotter.add_mesh(mesh,cmap=\"viridis\" ,stitle=\"Resistivity(log10)\") #scalars='Resistivity(log10)'\n",
    "#plotter.show_axes()\n",
    "\n",
    "_ = plotter.show_bounds(grid='front', location='outer', all_edges=True, xlabel=\"X [m]\",ylabel=\"Y [m]\")\n",
    "plotter.add_text('pyvista example')\n",
    "#plotter.update_scalar_bar_range([-2000,2000], name=\"Resistivity(log10)\")\n",
    "\n",
    "#plotter.add_mesh(mesh, cmap=\"bone\", opacity=\"linear\", stitle=\"Linear Opacity\")\n",
    "plotter.view_xy()\n",
    "plotter.show()\n",
    "#plotter.show(screenshot='test.png')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define a problem file\n",
    "\n",
    "In the code below, a problem file is used to define the parameters and their ranges we wish to explore, which corresponds to the following table:\n",
    "\n",
    "|         Parameter        |  Range   |  Description   |\n",
    "|--------------------------|----------|----------|\n",
    "|   rho0 [ohm m]  | 10^[0.5,3.5] | background |\n",
    "|   rho1 [ohm m]  | 10^[0.5,3.5] |inclusion A|\n",
    "|   rho2 [ohm m]  | 10^[0.5,3.5] |inclusion B|\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "morris_problem = {\n",
    "    'num_vars': 3,\n",
    "    # These are their names\n",
    "    'names': ['rho1', 'rho2', 'rho3'], # can add z1 z2 etc.\n",
    "    # Plausible ranges over which we'll move the variables\n",
    "    'bounds': [[0.5,3.5], # log10 of rho (ohm m)\n",
    "               [0.5,3.5], \n",
    "               [0.5,3.5]#, \n",
    "   #            [-3,-1], \n",
    "   #            [-7,-4], \n",
    "              ],\n",
    "    # I don't want to group any of these variables together\n",
    "    'groups': None\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.16666667 1.5        1.83333333]\n"
     ]
    }
   ],
   "source": [
    "number_of_trajectories = 20\n",
    "sample = ms.sample(morris_problem, number_of_trajectories, num_levels=10)\n",
    "len(sample)\n",
    "print(sample[79,:])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the sample through the monte carlo procedure in R2\n",
    "Great! You have defined your problem and have created a series of input files for forward runs. Now you need to run R2 for each of them to obtain their ERT responses.\n",
    "\n",
    "For this example, each sample takes a few seconds to run on a PC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 13\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 14\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 16\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 17\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 18\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 19\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 21\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 22\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 23\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 24\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 25\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 26\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 27\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 28\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 29\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 30\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 31\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 32\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 33\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 34\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 35\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 36\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 37\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 38\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 39\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 40\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 41\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 42\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 43\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 44\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 45\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 46\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 47\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 48\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 49\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 51\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 52\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 53\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 54\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 55\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 56\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 57\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 58\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 59\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 60\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 61\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 62\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 63\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 64\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 65\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 66\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 67\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 68\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 69\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 70\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 71\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 72\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 73\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 74\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 75\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 76\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 77\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 78\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sample 79\n",
      "Running sample 80\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All ok\n"
     ]
    }
   ],
   "source": [
    "#%%capture\n",
    "simu_ensemble = np.zeros((len(obs_data),len(sample)))\n",
    "for ii in range(0, len(sample)):\n",
    "    with io.capture_output() as captured:          # suppress inline output from ResIPy\n",
    "        # creating mesh\n",
    "        k.createMesh(res0=10**sample[ii,0])   # need to use more effective method, no need to create mesh every time\n",
    "\n",
    "        # add region\n",
    "        k.addRegion(np.array([[2,-0.3],[2,-2],[3,-2],[3,-0.3],[2,-0.3]]), 10**sample[ii,1])\n",
    "        k.addRegion(np.array([[5,-2],[5,-3.5],[8,-3.5],[8,-2],[5,-2]]), 10**sample[ii,2])\n",
    "\n",
    "        # forward modelling\n",
    "        k.forward(noise=0.025, iplot = False)\n",
    "        out_data = np.loadtxt(os.path.join(fwd_dir, 'R2_forward.dat'),skiprows =1)\n",
    "        simu_ensemble[:,ii] = out_data[:,6]\n",
    "    print(\"Running sample\",ii+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Factor Prioritisation\n",
    "\n",
    "We'll run a sensitivity analysis of the power module 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.\n",
    "\n",
    "> The higher the mean absolute effect for a parameter, the more sensitive/important it is*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name                 mean(EE) mean(|EE|) std(EE)\n",
      "rho1                 29271.700 29271.700 25700.482\n",
      "rho2                 1133.239 1146.483 2255.792\n",
      "rho3                 413.524 428.156 766.059\n"
     ]
    }
   ],
   "source": [
    "# Define an objective function: here I use the error weighted rmse\n",
    "def obj_fun(sim,obs,noise):\n",
    "    y = np.divide(sim-obs,noise)   # weighted data misfit\n",
    "    y = np.sqrt(np.inner(y,y))\n",
    "    return y\n",
    "\n",
    "output = np.zeros((1,len(sample)))\n",
    "for ii in range(0, len(sample)):\n",
    "    output[0,ii] = obj_fun(simu_ensemble[:,ii],obs_data,0.025*obs_data)    # assume 2.5% noise in the data\n",
    "    \n",
    "# Store the results for plotting of the analysis\n",
    "Si = ma.analyze(morris_problem, sample, output, print_to_console=False)\n",
    "print(\"{:20s} {:>7s} {:>7s} {:>7s}\".format(\"Name\", \"mean(EE)\", \"mean(|EE|)\", \"std(EE)\"))\n",
    "for name, s1, st, mean in zip(morris_problem['names'], \n",
    "                              Si['mu'], \n",
    "                              Si['mu_star'], \n",
    "                              Si['sigma']):\n",
    "    print(\"{:20s} {:=7.3f} {:=7.3f} {:=7.3f}\".format(name, s1, st, mean))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a plot\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(Si['mu_star'],Si['sigma'])\n",
    "#ax.plot(Si['mu_star'],2*Si['sigma']/np.sqrt(number_of_trajectories),'--',alpha=0.5)\n",
    "#ax.plot(np.array([0,Si['mu_star'][0]]),2*np.array([0,Si['sigma'][0]/np.sqrt(number_of_trajectories)]),'--',alpha=0.5)\n",
    "\n",
    "plt.title('Distribution of Elementary effects')\n",
    "plt.xlabel('mean(|EE|)')\n",
    "plt.ylabel('std($EE$)')\n",
    "for i, txt in enumerate(Si['names']):\n",
    "    ax.annotate(txt, (Si['mu_star'][i], Si['sigma'][i]))\n",
    "    \n",
    "# higher mean |EE|, more important factor\n",
    "# line within the dashed envelope means nonlinear or interaction effects dominant"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "\n",
    "Sensitivity analysis helps you:\n",
    "\n",
    "* Think through your assumptions\n",
    "* Quantify uncertainty\n",
    "* Focus on the most influential uncertainties first\n",
    "\n",
    "Learn [Python](https://www.python.org)\n",
    "\n",
    "Similar packages to SALib for other languages/programmes:\n",
    "\n",
    "* [Matlab Toolbox **SAFE** for GSA](http://www.sciencedirect.com/science/article/pii/S1364815215001188)\n",
    "* ['sensitivity' package for R](https://cran.r-project.org/web/packages/sensitivity/index.html) (Michael Tso used it for GSA in his leak detection paper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# run this so that a navigation sidebar will bee generated when exporting this notebook as HTML\n",
    "#%%javascript\n",
    "#$('<div id=\"toc\"></div>').css({position: 'fixed', top: '120px', left: 0}).appendTo(document.body);\n",
    "#$.getScript('https://kmahelona.github.io/ipython_notebook_goodies/ipython_notebook_toc.js');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on method addRegion in module resipy.Project:\n",
      "\n",
      "addRegion(xz, res0=100, phase0=1, blocky=False, fixed=False, ax=None, iplot=False) method of resipy.Project.Project instance\n",
      "    Add region according to a polyline defined by `xz` and assign it\n",
      "    the starting resistivity `res0`.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    xz : array\n",
      "        Array with two columns for the x and y coordinates.\n",
      "    res0 : float, optional\n",
      "        Resistivity values of the defined area.\n",
      "    phase0 : float, optional\n",
      "        Read only if you choose the cR2 option. Phase value of the defined\n",
      "        area in mrad\n",
      "    blocky : bool, optional\n",
      "        If `True` the boundary of the region will be blocky if inversion\n",
      "        is block inversion.\n",
      "    fixed : bool, optional\n",
      "        If `True`, the inversion will keep the starting resistivity of this\n",
      "        region.\n",
      "    ax : matplotlib.axes.Axes\n",
      "        If not `None`, the region will be plotted against this axes.\n",
      "    iplot : bool, optional\n",
      "        If `True` , the updated mesh with the region will be plotted.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(k.addRegion)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
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}