{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/vanderlei/Documents/fatiando/fatiando/vis/mpl.py:70: UserWarning: This module will be removed in v0.6. We recommend the use of matplotlib.pyplot module directly. Some of the fatiando specific functions will remain.\n",
      "  \"specific functions will remain.\")\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import functions as fc\n",
    "import fourier_continuation as fc_c\n",
    "from timeit import default_timer as time\n",
    "from fatiando.gravmag import polyprism, sphere\n",
    "from fatiando import mesher, gridder,utils\n",
    "from fatiando.constants import G, SI2MGAL\n",
    "from scipy.sparse import diags\n",
    "from matplotlib import pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "from scipy.interpolate import griddata\n",
    "from scipy import interpolate\n",
    "from fatiando.vis import mpl\n",
    "import cPickle as pickle\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Open data and configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('synthetic_gz.pickle') as r:\n",
    "    synthetic_gz = pickle.load(r)\n",
    "    \n",
    "xi = synthetic_gz['x']\n",
    "yi = synthetic_gz['y']\n",
    "zi = synthetic_gz['z']\n",
    "zi_up = synthetic_gz['z_up']\n",
    "zi_down = synthetic_gz['z_down']\n",
    "dobs = synthetic_gz['gz_med']\n",
    "dobs_up = synthetic_gz['gz_up']\n",
    "dobs_down = synthetic_gz['gz_down']\n",
    "\n",
    "shape = (100, 100)\n",
    "area = [-5000, 5000, -4000, 4000]\n",
    "R = 1000\n",
    "xc, yc = -3000, 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Equivalent Layer Depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Equivalent Layer depth\n",
    "zj = np.ones_like(zi)*300"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fast Eq. Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.3862431049 seconds\n"
     ]
    }
   ],
   "source": [
    "# Predicted data\n",
    "itmax = 40\n",
    "s = time()\n",
    "rho, gzp = fc.fast_eq(xi,yi,zi,zj,shape,dobs,itmax)\n",
    "e = time()\n",
    "tcpu = e - s\n",
    "print tcpu, 'seconds'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fast Eq. Layer BCCB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0598001480103 seconds\n"
     ]
    }
   ],
   "source": [
    "# Predicted data\n",
    "itmax = 40\n",
    "s = time()\n",
    "rho_c, gzp_bccb = fc.fast_eq_bccb(xi,yi,zi,zj,shape,dobs,itmax)\n",
    "e = time()\n",
    "tcpu = e - s\n",
    "print tcpu, 'seconds'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upward Continuation and Downward Continuation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00499701499939 seconds\n",
      "8.85737490654 seconds\n",
      "0.00388503074646 seconds\n",
      "8.63595795631 seconds\n"
     ]
    }
   ],
   "source": [
    "N = shape[0]*shape[1]\n",
    "\n",
    "#up BCCB\n",
    "s = time()\n",
    "#zi_up = np.ones_like(zi)*-300\n",
    "BTTB_up = fc.bttb(xi,yi,zi_up,zj)\n",
    "cev_up = fc.bccb(shape,N,BTTB_up)\n",
    "gzp_bccb_up = fc.fast_forward_bccb(shape,N,rho_c,cev_up)\n",
    "e = time()\n",
    "tcpu = e - s\n",
    "print tcpu, 'seconds'\n",
    "\n",
    "s = time()\n",
    "A = fc.sensibility_matrix(xi,yi,zi_up,zj,N)\n",
    "gzp_up = A.dot(rho)\n",
    "e = time()\n",
    "tcpu = e - s\n",
    "print tcpu, 'seconds'\n",
    "\n",
    "\n",
    "#down BCCB\n",
    "s = time()\n",
    "#zi_down = np.ones_like(zi)*-50\n",
    "BTTB_down = fc.bttb(xi,yi,zi_down,zj)\n",
    "cev_down = fc.bccb(shape,N,BTTB_down)\n",
    "gzp_bccb_down = fc.fast_forward_bccb(shape,N,rho_c,cev_down)\n",
    "e = time()\n",
    "tcpu = e - s\n",
    "print tcpu, 'seconds'\n",
    "\n",
    "s = time()\n",
    "A = fc.sensibility_matrix(xi,yi,zi_down,zj,N)\n",
    "gzp_down = A.dot(rho)\n",
    "e = time()\n",
    "tcpu = e - s\n",
    "print tcpu, 'seconds'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Upward plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "delta_gz_bccb_up = dobs_up-gzp_bccb_up\n",
    "delta_gz_up = dobs_up-gzp_up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Projection_model\n",
    "phi = np.linspace(0, 2.*np.pi, 36)  #36 points\n",
    "x = xc + R*np.cos(phi)\n",
    "y = yc + R*np.sin(phi)\n",
    "\n",
    "x_p = [-3000., -3500, 0, 500, -3000.]\n",
    "y_p = [-500., 0, 4500, 4000, -500.]\n",
    "\n",
    "x_p2 = [-3000, -2500, 3500, 3000, -3000.]\n",
    "y_p2 = [4000, 4500, 0, -500, 4000]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "plt.figure(figsize=(6,16))\n",
    "\n",
    "plt.subplot(311)\n",
    "plt.title('(a)', y=0.91, x=-0.13, fontsize=14)\n",
    "plt.tricontourf(yi,xi,dobs_up,22,cmap='jet',\n",
    "                vmin=synthetic_gz['gz_min'],vmax=synthetic_gz['gz_max'])\n",
    "plt.plot(x_p,y_p,color=\"k\", linewidth=3)\n",
    "plt.plot(x_p2,y_p2,color=\"k\", linewidth=3)\n",
    "plt.plot(y, x, color=\"k\", linewidth=3)\n",
    "cb = plt.colorbar(shrink=1)\n",
    "#plt.axis('scaled')\n",
    "cb.set_label('$Gz$ ( $mGal$ )', rotation=90, fontsize=14)\n",
    "plt.xlim(np.min(yi),np.max(yi))\n",
    "plt.ylim(np.min(xi),np.max(xi))\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "#plt.xlabel('Easting coordinate y (km)', fontsize=14)\n",
    "plt.ylabel('Northing coordinate x (m)', fontsize=14)\n",
    "mpl.m2km()\n",
    "\n",
    "#delta_gz_up = dobs_up-gzp_up\n",
    "plt.subplot(312)\n",
    "plt.title('(b)', y=0.91, x=-0.13, fontsize=14)\n",
    "plt.tricontourf(yi,xi,delta_gz_up,22,cmap='jet')\n",
    "plt.plot(x_p,y_p,color=\"k\", linewidth=3)\n",
    "plt.plot(x_p2,y_p2,color=\"k\", linewidth=3)\n",
    "plt.plot(y, x, color=\"k\", linewidth=3)\n",
    "cb = plt.colorbar(shrink=1)\n",
    "#plt.axis('scaled')\n",
    "cb.set_label('$Gz$ ( $mGal$ )', rotation=90, fontsize=14)\n",
    "plt.xlim(np.min(yi),np.max(yi))\n",
    "plt.ylim(np.min(xi),np.max(xi))\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "#plt.xlabel('Easting coordinate y (km)', fontsize=14)\n",
    "plt.ylabel('Northing coordinate x (m)', fontsize=14)\n",
    "mpl.m2km()\n",
    "\n",
    "#delta_gz_bccb_up = dobs_up-gzp_bccb_up\n",
    "plt.subplot(313)\n",
    "plt.title('(c)', y=0.91, x=-0.13, fontsize=14)\n",
    "plt.tricontourf(yi,xi,delta_gz_bccb_up,22,cmap='jet')\n",
    "plt.plot(x_p,y_p,color=\"k\", linewidth=3)\n",
    "plt.plot(x_p2,y_p2,color=\"k\", linewidth=3)\n",
    "plt.plot(y, x, color=\"k\", linewidth=3)\n",
    "cb = plt.colorbar(shrink=1)\n",
    "#plt.axis('scaled')\n",
    "cb.set_label('$Gz$ ( $mGal$ )', rotation=90, fontsize=14)\n",
    "plt.xlim(np.min(yi),np.max(yi))\n",
    "plt.ylim(np.min(xi),np.max(xi))\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.xlabel('Easting coordinate y (km)', fontsize=14)\n",
    "plt.ylabel('Northing coordinate x (m)', fontsize=14)\n",
    "mpl.m2km()\n",
    "plt.tight_layout(True)\n",
    "#plt.savefig('../manuscript/Fig/upward_med.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0031299438371729044\n",
      "0.03444145923923826\n",
      "0.0031299438371728884\n",
      "0.03444145923923814\n"
     ]
    }
   ],
   "source": [
    "mean = np.mean(delta_gz_up)\n",
    "print mean\n",
    "std = np.std(delta_gz_up)\n",
    "print std\n",
    "mean = np.mean(delta_gz_bccb_up)\n",
    "print mean\n",
    "std = np.std(delta_gz_bccb_up)\n",
    "print std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downward plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "delta_gz_down = dobs_down-gzp_down\n",
    "delta_gz_bccb_down = dobs_down-gzp_bccb_down"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "plt.figure(figsize=(6,16))\n",
    "\n",
    "plt.subplot(311)\n",
    "plt.title('(a)', y=0.91, x=-0.13, fontsize=14)\n",
    "plt.tricontourf(yi,xi,dobs_down,22,cmap='jet',\n",
    "                vmin=synthetic_gz['gz_min'],vmax=synthetic_gz['gz_max'])\n",
    "plt.plot(x_p,y_p,color=\"k\", linewidth=3)\n",
    "plt.plot(x_p2,y_p2,color=\"k\", linewidth=3)\n",
    "plt.plot(y, x, color=\"k\", linewidth=3)\n",
    "cb = plt.colorbar(shrink=1)\n",
    "#plt.axis('scaled')\n",
    "cb.set_label('$Gz$ ( $mGal$ )', rotation=90, fontsize=14)\n",
    "plt.xlim(np.min(yi),np.max(yi))\n",
    "plt.ylim(np.min(xi),np.max(xi))\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "#plt.xlabel('Easting coordinate y (km)', fontsize=14)\n",
    "plt.ylabel('Northing coordinate x (m)', fontsize=14)\n",
    "mpl.m2km()\n",
    "\n",
    "#delta_gz_down = dobs_down-gzp_down\n",
    "plt.subplot(312)\n",
    "plt.title('(b)', y=0.91, x=-0.13, fontsize=14)\n",
    "plt.tricontourf(yi,xi,delta_gz_down,22,cmap='jet')\n",
    "plt.plot(x_p,y_p,color=\"k\", linewidth=3)\n",
    "plt.plot(x_p2,y_p2,color=\"k\", linewidth=3)\n",
    "plt.plot(y, x, color=\"k\", linewidth=3)\n",
    "cb = plt.colorbar(shrink=1)\n",
    "#plt.axis('scaled')\n",
    "cb.set_label('$Gz$ ( $mGal$ )', rotation=90, fontsize=14)\n",
    "plt.xlim(np.min(yi),np.max(yi))\n",
    "plt.ylim(np.min(xi),np.max(xi))\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "#plt.xlabel('Easting coordinate y (km)', fontsize=14)\n",
    "plt.ylabel('Northing coordinate x (m)', fontsize=14)\n",
    "mpl.m2km()\n",
    "\n",
    "#delta_gz_bccb_down = dobs_down-gzp_bccb_down\n",
    "plt.subplot(313)\n",
    "plt.title('(c)', y=0.91, x=-0.13, fontsize=14)\n",
    "plt.tricontourf(yi,xi,delta_gz_bccb_down,22,cmap='jet')\n",
    "plt.plot(x_p,y_p,color=\"k\", linewidth=3)\n",
    "plt.plot(x_p2,y_p2,color=\"k\", linewidth=3)\n",
    "plt.plot(y, x, color=\"k\", linewidth=3)\n",
    "cb = plt.colorbar(shrink=1)\n",
    "#plt.axis('scaled')\n",
    "cb.set_label('$Gz$ ( $mGal$ )', rotation=90, fontsize=14)\n",
    "plt.xlim(np.min(yi),np.max(yi))\n",
    "plt.ylim(np.min(xi),np.max(xi))\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "plt.xlabel('Easting coordinate y (km)', fontsize=14)\n",
    "plt.ylabel('Northing coordinate x (m)', fontsize=14)\n",
    "mpl.m2km()\n",
    "plt.tight_layout(True)\n",
    "#plt.savefig('../manuscript/Fig/downward_med.png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.001007217244496282\n",
      "0.03831202233877217\n",
      "-0.0010072172444962715\n",
      "0.038312022338772185\n"
     ]
    }
   ],
   "source": [
    "mean = np.mean(delta_gz_down)\n",
    "print mean\n",
    "std = np.std(delta_gz_down)\n",
    "print std\n",
    "mean = np.mean(delta_gz_bccb_down)\n",
    "print mean\n",
    "std = np.std(delta_gz_bccb_down)\n",
    "print std"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison Upward - BCCB vs. Fast vs. Fourier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Up Fourier\n",
    "gzp_fourier_up = fc_c.upcontinue(xi, yi, dobs, shape, 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.9005183543218769 1.9026002023084692\n"
     ]
    }
   ],
   "source": [
    "# define the scale for residuals\n",
    "delta_gz_fourier_up = dobs_up-np.ravel(gzp_fourier_up)\n",
    "scale_max = np.max([delta_gz_bccb_up, delta_gz_up, delta_gz_fourier_up])\n",
    "scale_min = np.min([delta_gz_bccb_up, delta_gz_up, delta_gz_fourier_up])\n",
    "\n",
    "print scale_min, scale_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "colorbar_ranges = np.linspace(-2, 2, 21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2. , -1.8, -1.6, -1.4, -1.2, -1. , -0.8, -0.6, -0.4, -0.2,  0. ,\n",
       "        0.2,  0.4,  0.6,  0.8,  1. ,  1.2,  1.4,  1.6,  1.8,  2. ])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colorbar_ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 311.76x280.584 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "font_title = 8\n",
    "font_ticks = 5\n",
    "font_labels = 6\n",
    "\n",
    "\n",
    "height=9.\n",
    "width = 10.\n",
    "height_per_width = height/width\n",
    "#plt.figure(figsize=(10,9))\n",
    "plt.figure(figsize=(4.33,4.33*height_per_width))\n",
    "\n",
    "plt.subplot(221)\n",
    "plt.title('(a)', y=0.95, x=-0.18, fontsize=font_title)\n",
    "# plt.tricontourf(yi,xi,dobs_up,22,cmap='jet',\n",
    "#                 vmin=synthetic_gz['gz_min'],vmax=synthetic_gz['gz_max'])\n",
    "plt.contourf(yi.reshape(shape),xi.reshape(shape),dobs_up.reshape(shape),\n",
    "             22,cmap='jet',vmin=synthetic_gz['gz_min'],vmax=synthetic_gz['gz_max'])\n",
    "plt.plot(x_p,y_p,color=\"k\", linewidth=1)\n",
    "plt.plot(x_p2,y_p2,color=\"k\", linewidth=1)\n",
    "plt.plot(y, x, color=\"k\", linewidth=1)\n",
    "cb = plt.colorbar(shrink=1)\n",
    "#plt.axis('scaled')\n",
    "cb.set_label('Gravity data (mGal)', rotation=90, fontsize=font_labels)\n",
    "cb.ax.tick_params(labelsize=font_ticks)\n",
    "\n",
    "plt.xlim(np.min(yi),np.max(yi))\n",
    "plt.ylim(np.min(xi),np.max(xi))\n",
    "plt.xticks(fontsize=font_ticks)\n",
    "plt.yticks(fontsize=font_ticks)\n",
    "#plt.xlabel('Easting coordinate y (km)', fontsize=14)\n",
    "plt.ylabel('Northing coordinate x (m)', fontsize=font_labels)\n",
    "mpl.m2km()\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.title('(b)', y=0.95, x=-0.18, fontsize=font_title)\n",
    "#plt.tricontourf(yi,xi,delta_gz_bccb_up,22,cmap='jet', vmin = scale_min, vmax = scale_max)\n",
    "plt.contourf(yi.reshape(shape),xi.reshape(shape),delta_gz_bccb_up.reshape(shape),\n",
    "             22,cmap='jet',vmin=scale_min,vmax=scale_max)\n",
    "plt.plot(x_p,y_p,color=\"k\", linewidth=1)\n",
    "plt.plot(x_p2,y_p2,color=\"k\", linewidth=1)\n",
    "plt.plot(y, x, color=\"k\", linewidth=1)\n",
    "\n",
    "#define colorbar\n",
    "cbar = plt.cm.ScalarMappable(cmap=cm.jet)\n",
    "cbar.set_array(delta_gz_bccb_up)\n",
    "cbar.set_clim(scale_min, scale_max)\n",
    "cb = plt.colorbar(cbar, shrink=1, boundaries=colorbar_ranges)\n",
    "cb.set_label('Residuals (mGal)', rotation=90, fontsize=font_labels)\n",
    "cb.ax.tick_params(labelsize=font_ticks)\n",
    "\n",
    "plt.xlim(np.min(yi),np.max(yi))\n",
    "plt.ylim(np.min(xi),np.max(xi))\n",
    "plt.xticks(fontsize=font_ticks)\n",
    "plt.yticks(fontsize=font_ticks)\n",
    "#plt.xlabel('Easting coordinate y (km)', fontsize=14)\n",
    "#plt.ylabel('Northing coordinate x (m)', fontsize=14)\n",
    "mpl.m2km()\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.title('(c)', y=0.95, x=-0.18, fontsize=font_title)\n",
    "#plt.tricontourf(yi,xi,delta_gz_up,22,cmap='jet', vmin = scale_min, vmax = scale_max)\n",
    "plt.contourf(yi.reshape(shape),xi.reshape(shape),delta_gz_up.reshape(shape),\n",
    "             22,cmap='jet',vmin=scale_min,vmax=scale_max)\n",
    "plt.plot(x_p,y_p,color=\"k\", linewidth=1)\n",
    "plt.plot(x_p2,y_p2,color=\"k\", linewidth=1)\n",
    "plt.plot(y, x, color=\"k\", linewidth=1)\n",
    "\n",
    "#define colorbar\n",
    "cbar = plt.cm.ScalarMappable(cmap=cm.jet)\n",
    "cbar.set_array(delta_gz_up)\n",
    "cbar.set_clim(scale_min, scale_max)\n",
    "cb = plt.colorbar(cbar, shrink=1, boundaries=colorbar_ranges)\n",
    "cb.set_label('Residuals (mGal)', rotation=90, fontsize=font_labels)\n",
    "cb.ax.tick_params(labelsize=font_ticks)\n",
    "\n",
    "plt.xlim(np.min(yi),np.max(yi))\n",
    "plt.ylim(np.min(xi),np.max(xi))\n",
    "plt.xticks(fontsize=font_ticks)\n",
    "plt.yticks(fontsize=font_ticks)\n",
    "plt.xlabel('Easting coordinate y (km)', fontsize=font_labels)\n",
    "plt.ylabel('Northing coordinate x (m)', fontsize=font_labels)\n",
    "mpl.m2km()\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.title('(d)', y=0.95, x=-0.18, fontsize=font_title)\n",
    "#plt.tricontourf(yi,xi,delta_gz_fourier_up,22,cmap='jet', vmin = scale_min, vmax = scale_max)\n",
    "plt.contourf(yi.reshape(shape),xi.reshape(shape),delta_gz_fourier_up.reshape(shape),\n",
    "             22,cmap='jet',vmin=scale_min,vmax=scale_max)\n",
    "plt.plot(x_p,y_p,color=\"k\", linewidth=1)\n",
    "plt.plot(x_p2,y_p2,color=\"k\", linewidth=1)\n",
    "plt.plot(y, x, color=\"k\", linewidth=1)\n",
    "\n",
    "#define colorbar\n",
    "cbar = plt.cm.ScalarMappable(cmap=cm.jet)\n",
    "cbar.set_array(delta_gz_fourier_up)\n",
    "cbar.set_clim(scale_min, scale_max)\n",
    "cb = plt.colorbar(cbar, shrink=1, boundaries=colorbar_ranges)\n",
    "cb.set_label('Residuals (mGal)', rotation=90, fontsize=font_labels)\n",
    "cb.ax.tick_params(labelsize=font_ticks)\n",
    "\n",
    "plt.xlim(np.min(yi),np.max(yi))\n",
    "plt.ylim(np.min(xi),np.max(xi))\n",
    "plt.xticks(fontsize=font_ticks)\n",
    "plt.yticks(fontsize=font_ticks)\n",
    "plt.xlabel('Easting coordinate y (km)', fontsize=font_labels)\n",
    "#plt.ylabel('Northing coordinate x (m)', fontsize=4)\n",
    "mpl.m2km()\n",
    "\n",
    "plt.tight_layout(True)\n",
    "#plt.savefig('../manuscript/Fig/upward_fourier_med.png', dpi=300)\n",
    "plt.savefig('../manuscript/Fig/Figure7.png', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.03444145923923814\n",
      "0.03444145923923826\n",
      "-0.029923731936085017\n",
      "0.26232817198362085\n"
     ]
    }
   ],
   "source": [
    "print np.std(delta_gz_bccb_up)\n",
    "print np.std(delta_gz_up)\n",
    "print np.mean(delta_gz_fourier_up)\n",
    "print np.std(delta_gz_fourier_up)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison Downward - BCCB vs. Fast vs. Fourier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "fourier_continuation.py:57: UserWarning: Using 'height' <= 0 means downward continuation, which is known to be unstable.\n",
      "  \"which is known to be unstable.\")\n"
     ]
    }
   ],
   "source": [
    "gzp_fourier_down = fc_c.upcontinue(xi, yi, dobs, shape, -50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-4.117539688908588 4.121771949535535\n"
     ]
    }
   ],
   "source": [
    "# define the scale for residuals\n",
    "delta_gz_fourier_down = dobs_down-np.ravel(gzp_fourier_down)\n",
    "scale_max = np.max([delta_gz_bccb_down, delta_gz_down, delta_gz_fourier_down])\n",
    "scale_min = np.min([delta_gz_bccb_down, delta_gz_down, delta_gz_fourier_down])\n",
    "\n",
    "print scale_min, scale_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "colorbar_ranges = np.arange(-4.4, 4.41, 0.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-4.40000000e+00, -4.00000000e+00, -3.60000000e+00, -3.20000000e+00,\n",
       "       -2.80000000e+00, -2.40000000e+00, -2.00000000e+00, -1.60000000e+00,\n",
       "       -1.20000000e+00, -8.00000000e-01, -4.00000000e-01,  3.55271368e-15,\n",
       "        4.00000000e-01,  8.00000000e-01,  1.20000000e+00,  1.60000000e+00,\n",
       "        2.00000000e+00,  2.40000000e+00,  2.80000000e+00,  3.20000000e+00,\n",
       "        3.60000000e+00,  4.00000000e+00,  4.40000000e+00])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colorbar_ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 311.76x280.584 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "font_title = 8\n",
    "font_ticks = 5\n",
    "font_labels = 6\n",
    "\n",
    "height=9.\n",
    "width = 10.\n",
    "height_per_width = height/width\n",
    "#plt.figure(figsize=(10,9))\n",
    "plt.figure(figsize=(4.33,4.33*height_per_width))\n",
    "\n",
    "plt.subplot(221)\n",
    "plt.title('(a)', y=0.95, x=-0.18, fontsize=font_title)\n",
    "#plt.tricontourf(yi,xi,dobs_down,22,cmap='jet',\n",
    "#                vmin=synthetic_gz['gz_min'],vmax=synthetic_gz['gz_max'])\n",
    "plt.contourf(yi.reshape(shape),xi.reshape(shape),dobs_down.reshape(shape),\n",
    "             22,cmap='jet',vmin=synthetic_gz['gz_min'],vmax=synthetic_gz['gz_max'])\n",
    "plt.plot(x_p,y_p,color=\"k\", linewidth=1)\n",
    "plt.plot(x_p2,y_p2,color=\"k\", linewidth=1)\n",
    "plt.plot(y, x, color=\"k\", linewidth=1)\n",
    "cb = plt.colorbar(shrink=1)\n",
    "#plt.axis('scaled')\n",
    "cb.set_label('Gravity data (mGal)', rotation=90, fontsize=font_labels)\n",
    "cb.ax.tick_params(labelsize=font_ticks)\n",
    "\n",
    "plt.xlim(np.min(yi),np.max(yi))\n",
    "plt.ylim(np.min(xi),np.max(xi))\n",
    "plt.xticks(fontsize=font_ticks)\n",
    "plt.yticks(fontsize=font_ticks)\n",
    "#plt.xlabel('Easting coordinate y (km)', fontsize=14)\n",
    "plt.ylabel('Northing coordinate x (m)', fontsize=font_labels)\n",
    "mpl.m2km()\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.title('(b)', y=0.95, x=-0.18, fontsize=font_title)\n",
    "#plt.tricontourf(yi,xi,delta_gz_bccb_down,22,cmap='jet', vmin = scale_min, vmax = scale_max)\n",
    "plt.contourf(yi.reshape(shape),xi.reshape(shape),delta_gz_bccb_down.reshape(shape),\n",
    "             22,cmap='jet',vmin=scale_min,vmax=scale_max)\n",
    "plt.plot(x_p,y_p,color=\"k\", linewidth=1)\n",
    "plt.plot(x_p2,y_p2,color=\"k\", linewidth=1)\n",
    "plt.plot(y, x, color=\"k\", linewidth=1)\n",
    "\n",
    "#define colorbar\n",
    "cbar = plt.cm.ScalarMappable(cmap=cm.jet)\n",
    "cbar.set_array(delta_gz_bccb_down)\n",
    "cbar.set_clim(scale_min, scale_max)\n",
    "cb = plt.colorbar(cbar, shrink=1, boundaries=colorbar_ranges)\n",
    "cb.set_label('Residuals (mGal)', rotation=90, fontsize=font_labels)\n",
    "cb.ax.tick_params(labelsize=font_ticks)\n",
    "\n",
    "plt.xlim(np.min(yi),np.max(yi))\n",
    "plt.ylim(np.min(xi),np.max(xi))\n",
    "plt.xticks(fontsize=font_ticks)\n",
    "plt.yticks(fontsize=font_ticks)\n",
    "#plt.xlabel('Easting coordinate y (km)', fontsize=14)\n",
    "#plt.ylabel('Northing coordinate x (m)', fontsize=14)\n",
    "mpl.m2km()\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.title('(c)', y=0.95, x=-0.18, fontsize=font_title)\n",
    "#plt.tricontourf(yi,xi,delta_gz_down,22,cmap='jet', vmin = scale_min, vmax = scale_max)\n",
    "plt.contourf(yi.reshape(shape),xi.reshape(shape),delta_gz_down.reshape(shape),\n",
    "             22,cmap='jet',vmin=scale_min,vmax=scale_max)\n",
    "plt.plot(x_p,y_p,color=\"k\", linewidth=1)\n",
    "plt.plot(x_p2,y_p2,color=\"k\", linewidth=1)\n",
    "plt.plot(y, x, color=\"k\", linewidth=1)\n",
    "\n",
    "#define colorbar\n",
    "cbar = plt.cm.ScalarMappable(cmap=cm.jet)\n",
    "cbar.set_array(delta_gz_down)\n",
    "cbar.set_clim(scale_min, scale_max)\n",
    "cb = plt.colorbar(cbar, shrink=1, boundaries=colorbar_ranges)\n",
    "cb.set_label('Residuals (mGal)', rotation=90, fontsize=font_labels)\n",
    "cb.ax.tick_params(labelsize=font_ticks)\n",
    "\n",
    "plt.xlim(np.min(yi),np.max(yi))\n",
    "plt.ylim(np.min(xi),np.max(xi))\n",
    "plt.xticks(fontsize=font_ticks)\n",
    "plt.yticks(fontsize=font_ticks)\n",
    "plt.xlabel('Easting coordinate y (km)', fontsize=font_labels)\n",
    "plt.ylabel('Northing coordinate x (m)', fontsize=font_labels)\n",
    "mpl.m2km()\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.title('(d)', y=0.95, x=-0.18, fontsize=font_title)\n",
    "#plt.tricontourf(yi,xi,delta_gz_fourier_down,22,cmap='jet', vmin = scale_min, vmax = scale_max)\n",
    "plt.contourf(yi.reshape(shape),xi.reshape(shape),delta_gz_fourier_down.reshape(shape),\n",
    "             22,cmap='jet',vmin=scale_min,vmax=scale_max)\n",
    "plt.plot(x_p,y_p,color=\"k\", linewidth=1)\n",
    "plt.plot(x_p2,y_p2,color=\"k\", linewidth=1)\n",
    "plt.plot(y, x, color=\"k\", linewidth=1)\n",
    "\n",
    "#define colorbar\n",
    "cbar = plt.cm.ScalarMappable(cmap=cm.jet)\n",
    "cbar.set_array(delta_gz_fourier_down)\n",
    "cbar.set_clim(scale_min, scale_max)\n",
    "cb = plt.colorbar(cbar, shrink=1, boundaries=colorbar_ranges)\n",
    "cb.set_label('Residuals (mGal)', rotation=90, fontsize=font_labels)\n",
    "cb.ax.tick_params(labelsize=font_ticks)\n",
    "\n",
    "plt.xlim(np.min(yi),np.max(yi))\n",
    "plt.ylim(np.min(xi),np.max(xi))\n",
    "plt.xticks(fontsize=font_ticks)\n",
    "plt.yticks(fontsize=font_ticks)\n",
    "plt.xlabel('Easting coordinate y (km)', fontsize=font_labels)\n",
    "#plt.ylabel('Northing coordinate x (m)', fontsize=14)\n",
    "mpl.m2km()\n",
    "\n",
    "plt.tight_layout(True)\n",
    "#plt.savefig('../manuscript/Fig/downward_fourier_med.png', dpi=300)\n",
    "plt.savefig('../manuscript/Fig/Figure8.png', dpi=1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.038312022338772185\n",
      "0.03831202233877217\n",
      "0.007934556980489862\n",
      "0.5816240951130157\n"
     ]
    }
   ],
   "source": [
    "print np.std(delta_gz_bccb_down)\n",
    "print np.std(delta_gz_down)\n",
    "print np.mean(delta_gz_fourier_down)\n",
    "print np.std(delta_gz_fourier_down)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.17"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}