{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GeMpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"../\")\n",
    "import gempy as gp\n",
    "\n",
    "%matplotlib inline\n",
    "import pymc\n",
    "import numpy as np\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "geo_data = gp.read_pickle(\"../Tutorial/BasicFault.pickle\")\n",
    "geo_data.n_faults = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>formation</th>\n",
       "      <th>series</th>\n",
       "      <th>order_series</th>\n",
       "      <th>isFault</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>800.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>-1600.0</td>\n",
       "      <td>MainFault</td>\n",
       "      <td>fault</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1200.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>-400.0</td>\n",
       "      <td>MainFault</td>\n",
       "      <td>fault</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1100.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>-700.0</td>\n",
       "      <td>MainFault</td>\n",
       "      <td>fault</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>900.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>-1300.0</td>\n",
       "      <td>MainFault</td>\n",
       "      <td>fault</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>MainFault</td>\n",
       "      <td>fault</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        X       Y       Z  formation series  order_series  isFault\n",
       "0   800.0  1000.0 -1600.0  MainFault  fault             1     True\n",
       "1  1200.0  1000.0  -400.0  MainFault  fault             1     True\n",
       "2  1100.0  1000.0  -700.0  MainFault  fault             1     True\n",
       "3   900.0  1000.0 -1300.0  MainFault  fault             1     True\n",
       "4  1000.0  1000.0 -1000.0  MainFault  fault             1     True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_data.interfaces.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<gempy.Visualization.PlotData at 0x7f5ab768a6d8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAb4AAAFgCAYAAADEuRy2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGHxERCQqDD4iIhIVBh8REYnK/wPfZo1Xx/7UOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5ab7e5c470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "geo_data.geo_data_type = 'whatever'\n",
    "gp.plot_data(geo_data, direction='y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# stdev for x,y,z interface coordinates\n",
    "geo_data.interfaces['X_std'] = None\n",
    "geo_data.interfaces['Y_std'] = None\n",
    "geo_data.interfaces['Z_std'] = 100\n",
    "geo_data.interfaces['dist_type'] = \"Normal\" # should be initially None\n",
    "geo_data.interfaces['X_dist'] = None\n",
    "geo_data.interfaces['Y_dist'] = None\n",
    "geo_data.interfaces['Z_dist'] = None\n",
    "\n",
    "#geo_data.foliations['X_std'] = None\n",
    "#geo_data.foliations['Y_std'] = 0\n",
    "#geo_data.foliations['Z_std'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "geo_data.interfaces[\"Z_std\"] = 500"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>formation</th>\n",
       "      <th>series</th>\n",
       "      <th>order_series</th>\n",
       "      <th>isFault</th>\n",
       "      <th>X_std</th>\n",
       "      <th>Y_std</th>\n",
       "      <th>Z_std</th>\n",
       "      <th>dist_type</th>\n",
       "      <th>X_dist</th>\n",
       "      <th>Y_dist</th>\n",
       "      <th>Z_dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>800.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>-1600.0</td>\n",
       "      <td>MainFault</td>\n",
       "      <td>fault</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>500</td>\n",
       "      <td>Normal</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1200.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>-400.0</td>\n",
       "      <td>MainFault</td>\n",
       "      <td>fault</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>500</td>\n",
       "      <td>Normal</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1100.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>-700.0</td>\n",
       "      <td>MainFault</td>\n",
       "      <td>fault</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>500</td>\n",
       "      <td>Normal</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>900.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>-1300.0</td>\n",
       "      <td>MainFault</td>\n",
       "      <td>fault</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>500</td>\n",
       "      <td>Normal</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>-1000.0</td>\n",
       "      <td>MainFault</td>\n",
       "      <td>fault</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>500</td>\n",
       "      <td>Normal</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        X       Y       Z  formation series  order_series  isFault X_std  \\\n",
       "0   800.0  1000.0 -1600.0  MainFault  fault             1     True  None   \n",
       "1  1200.0  1000.0  -400.0  MainFault  fault             1     True  None   \n",
       "2  1100.0  1000.0  -700.0  MainFault  fault             1     True  None   \n",
       "3   900.0  1000.0 -1300.0  MainFault  fault             1     True  None   \n",
       "4  1000.0  1000.0 -1000.0  MainFault  fault             1     True  None   \n",
       "\n",
       "  Y_std  Z_std dist_type X_dist Y_dist Z_dist  \n",
       "0  None    500    Normal   None   None   None  \n",
       "1  None    500    Normal   None   None   None  \n",
       "2  None    500    Normal   None   None   None  \n",
       "3  None    500    Normal   None   None   None  \n",
       "4  None    500    Normal   None   None   None  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_data.interfaces.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float32\n",
      "[2, 2]\n"
     ]
    }
   ],
   "source": [
    "interp_data = gp.InterpolatorInput(geo_data, \n",
    "                                   u_grade = [3, 3],\n",
    "                                   compile_theano=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 3]\n"
     ]
    }
   ],
   "source": [
    "sol = gp.compute_model(interp_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "adding dataframe columns for uncertainty analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create pymc.Distribution objects for each stochastic value and put into df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Z_rest = pymc.Normal('Z_unc_rest',  interp_data.interpolator.pandas_rest_layer_points['Z'].as_matrix().astype('float32'),\n",
    "           1./interp_data.interpolator.pandas_rest_layer_points['Z_std'].as_matrix().astype('float32'))\n",
    "\n",
    "Z_ref = pymc.Normal('Z_unc_ref',  interp_data.interpolator.pandas_ref_layer_points['Z'].as_matrix().astype('float32'),\n",
    "           1./interp_data.interpolator.pandas_ref_layer_points['Z_std'].as_matrix().astype('float32'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.46208002, -0.16228111,  0.15947797,  0.27908407, -0.06837028,\n",
       "         0.11290613,  1.56334182,  1.21185192,  0.67252936,  0.33573741,\n",
       "         0.24979466,  0.20912485,  0.46076347,  0.64697701,  0.7478793 ,\n",
       "         0.25877077,  0.31715363,  0.83621002,  0.38905729,  0.76213303,\n",
       "         0.91305479,  0.58656948,  1.35064574,  0.62597685,  0.97317111,\n",
       "         0.2814209 ,  0.58052269,  0.34527976,  0.44034878,  0.5655428 ,\n",
       "        -0.11403995,  0.4879321 ,  0.38160715,  0.54497289]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z_rest.get_stoch_value()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create pymc.Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 3]\n",
      "[ 0.  0.  0. ...,  0.  0.  0.]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "i = interp_data.get_input_data()\n",
    "\n",
    "@pymc.deterministic\n",
    "def gempy_model(value=0, input_ =  i, Z_rest_m = Z_rest, Z_ref_m = Z_ref):\n",
    "          #xp = X_priors, yp = Y_priors, zp = Z_priors, ref = data_ref):\n",
    "    \n",
    "\n",
    "    Z_ref_m_rep = np.apply_over_axes(lambda x,y:\n",
    "                    np.repeat(x, interp_data.interpolator.len_interfaces - 1),\n",
    "                    Z_ref_m, axes = [0])\n",
    "   \n",
    "    input_[4][:, 2] = Z_ref_m_rep\n",
    "    input_[5][:, 2] = Z_rest_m\n",
    "    \n",
    "    sol, pot = interp_data.th_fn(*input_)\n",
    "    print(sol[0,0,:])\n",
    "    # return solution\n",
    "    return sol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# set number of iterations\n",
    "iterations = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# set model\n",
    "pymc_model = pymc.Model(Z_ref, Z_rest, gempy_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MCFS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2.  2.  2. ...,  2.  2.  2.]\n",
      " [-------          20%                  ] 2 of 10 complete in 2.3 sec[ 0.  0.  0. ...,  2.  2.  2.]\n",
      " [-----------      30%                  ] 3 of 10 complete in 4.6 sec[ 2.  2.  2. ...,  4.  4.  4.]\n",
      " [---------------  40%                  ] 4 of 10 complete in 6.8 sec[ 2.  2.  2. ...,  5.  5.  5.]\n",
      " [-----------------50%                  ] 5 of 10 complete in 9.0 sec"
     ]
    }
   ],
   "source": [
    "MCFS_RUN = pymc.MCMC(pymc_model)\n",
    "MCFS_RUN.sample(iter=iterations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def p(i):\n",
    "    ax.imshow(MCFS_RUN.trace(\"gempy_model\", chain=-1)[i][0, 0, :].reshape(50,50,50)[:,24,:].T, origin=\"lower\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'interact' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-373a0f7b5c39>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0minteract\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwidgets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIntSlider\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'interact' is not defined"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAEMCAYAAAAxoErWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc37907f588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "interact(p, i=widgets.IntSlider(min=0, max=9, step=1, value=0));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fc37822bc18>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQwAAAEKCAYAAADn1WuOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAgOAGYEBwIzAAGD2f0bHiqYcswzOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc39507f6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(MCFS_RUN.trace(\"gempy_model\", chain=-1)[3][0, 0, :].reshape(50,50,50)[:,24,:].T, origin=\"lower\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.1"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "colors": {
    "hover_highlight": "#DAA520",
    "running_highlight": "#FF0000",
    "selected_highlight": "#FFD700"
   },
   "moveMenuLeft": true,
   "nav_menu": {
    "height": "99px",
    "width": "252px"
   },
   "navigate_menu": true,
   "number_sections": true,
   "sideBar": true,
   "threshold": 4,
   "toc_cell": false,
   "toc_section_display": "block",
   "toc_window_display": false,
   "widenNotebook": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}