examples: Update bs_ivbp

Author: George Bisbas

examples/finance/bs_ivbp.ipynb

@@ -37,13 +37,12 @@
    ],
    "source": [
     "from devito import (Eq, Grid, TimeFunction, Operator, solve, Constant, \n",
-    "                    SpaceDimension, configuration, SubDomain, centered)\n",
+    "                    SpaceDimension, configuration, centered)\n",
     "\n",
     "from mpl_toolkits.mplot3d import Axes3D\n",
     "from mpl_toolkits.mplot3d.axis3d import Axis\n",
     "import matplotlib.pyplot as plt\n",
     "import matplotlib as mpl\n",
-    "from matplotlib import cm\n",
     "\n",
     "from sympy.stats import Normal, cdf\n",
     "import numpy as np\n",
@@ -242,8 +241,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Operator `Kernel` run in 0.01 s\n",
-      "Operator `Kernel` run in 0.01 s\n"
+      "Operator `Kernel` ran in 0.01 s\n",
+      "Operator `Kernel` ran in 0.01 s\n"
      ]
     },
     {
@@ -263,7 +262,7 @@
     "\n",
     "# Run our operators\n",
     "startDevito = timer.time()\n",
-    "    \n",
+    "\n",
     "# Apply operator\n",
     "op.apply(dt=dt0)\n",
     "\n",
@@ -293,7 +292,7 @@
     "#NBVAL_IGNORE_OUTPUT\n",
     "\n",
     "# Get an appropriate ylimit\n",
-    "slice_smax = v.data[:,int(smax-smin-padding)]\n",
+    "slice_smax = v.data[:, int(smax-smin-padding)]\n",
     "ymax = max(slice_smax) + 2\n",
     "\n",
     "# Plot\n",
@@ -477,8 +476,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "devito pde    timesteps:         2000,     0.205647s runtime\n",
-      "call_value_bs timesteps:            5,     3.813932s runtime\n"
+      "devito pde    timesteps:         2000,     0.019737s runtime\n",
+      "call_value_bs timesteps:            5,     2.596800s runtime\n"
      ]
     },
     {
@@ -501,10 +500,10 @@
     "# https://aaronschlegel.me/black-scholes-formula-python.html\n",
     "def call_value_bs(S, K, T, r, sigma):\n",
     "    N = Normal('x', 0.0, 1.0)\n",
-    "    \n",
+    "\n",
     "    d1 = (np.log(S / K) + (r + 0.5 * sigma ** 2) * T) / (sigma * np.sqrt(T))\n",
     "    d2 = (np.log(S / K) + (r - 0.5 * sigma ** 2) * T) / (sigma * np.sqrt(T))\n",
-    "    \n",
+    "\n",
     "    call = (S * cdf(N)(d1) - K * np.exp(-r * T) * cdf(N)(d2))\n",
     "    return call\n",
     "\n",
@@ -563,7 +562,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f5495584640>]"
+       "[<matplotlib.lines.Line2D at 0x7fe2669fda50>]"
       ]
      },
      "execution_count": 9,
@@ -587,8 +586,8 @@
     "#NBVAL_IGNORE_OUTPUT\n",
     "\n",
     "# Plot the l2 norm of the formula and our solution over time\n",
-    "t_range = np.linspace(dt0,1.0,50)\n",
-    "x_range     = range(padding, smax-smin-padding*2, 1)\n",
+    "t_range = np.linspace(dt0, 1.0, 50)\n",
+    "x_range = range(padding, smax-smin-padding*2, 1)\n",
     "vals = []\n",
     "\n",
     "for t in t_range:\n",
@@ -600,7 +599,7 @@
     "\n",
     "    rms = np.sqrt(np.float64(l2 / len(x_range)))\n",
     "    vals.append(rms)\n",
-    "    \n",
+    "\n",
     "plt.figure(figsize=(12,10))\n",
     "plt.plot(t_range, np.array(vals))"
    ]
@@ -613,7 +612,7 @@
     {
      "data": {
       "text/plain": [
-       "0.00581731890853893"
+       "np.float64(0.005885208362743295)"
       ]
      },
      "execution_count": 10,