updates

sensitivity_introduction.ipynb

@@ -9,7 +9,7 @@
     "<!-- dom:AUTHOR: Leif Rune Hellevik at Department of Structural Engineering, NTNU -->\n",
     "<!-- Author: --> **Leif Rune Hellevik**, Department of Structural Engineering, NTNU\n",
     "\n",
-    "Date: **Jan 16, 2017**"
+    "Date: **Jan 17, 2017**"
    ]
   },
   {
@@ -27,7 +27,7 @@
     "%autoreload 2\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "import chaospy as pc\n",
+    "import chaospy as cp\n",
     "from numpy import linalg as LA"
    ]
   },
@@ -315,7 +315,7 @@
     "Z=np.zeros(shape=(r,N))\n",
     "pdf=[]\n",
     "for i, z in enumerate(zm):\n",
-    "    pdf.append(pc.Normal(z[0],z[1]))\n",
+    "    pdf.append(cp.Normal(z[0],z[1]))\n",
     "    Z[i,:]=pdf[i].sample(N)\n",
     "\n",
     "Y = linear_model(w,Z,N)\n",
@@ -558,7 +558,7 @@
    "outputs": [],
    "source": [
     "#  Sensitivity indices based on sampled values\n",
-    "print 'std(Y)=',pc.std(Y, 0), '  rel.err=', (pc.std(Y, 0)-s_y)/s_y\n",
+    "print 'std(Y)=',np.std(Y, 0), '  rel.err=', (np.std(Y, 0)-s_y)/s_y\n",
     "print np.mean(Y,0), np.sqrt(np.var(Y,0))"
    ]
   },
@@ -902,15 +902,15 @@
    "source": [
     "# Polychaos computations\n",
     "Npc=80\n",
-    "jpdf = pc.J(pc.Normal(zm[0,0],zm[0,1]),pc.Normal(zm[1,0],zm[1,1]),pc.Normal(zm[2,0],zm[2,1]),pc.Normal(zm[3,0],zm[3,1]))\n",
+    "jpdf = cp.J(cp.Normal(zm[0,0],zm[0,1]),cp.Normal(zm[1,0],zm[1,1]),cp.Normal(zm[2,0],zm[2,1]),cp.Normal(zm[3,0],zm[3,1]))\n",
     "Zpc=jpdf.sample(Npc)\n",
     "Ypc=linear_model(w, Zpc, Npc)\n",
     "Npol=4\n",
-    "poly = pc.orth_chol(Npol, jpdf)\n",
-    "approx = pc.fit_regression(poly, Zpc, Ypc, rule=\"T\")\n",
+    "poly = cp.orth_chol(Npol, jpdf)\n",
+    "approx = cp.fit_regression(poly, Zpc, Ypc, rule=\"T\")\n",
     "\n",
-    "print 'Expected value from pc=', pc.E(approx,jpdf), '   std=',pc.Std(approx,jpdf)\n",
-    "s_pc= pc.Sens_m(approx,jpdf)\n",
+    "print 'Expected value from cp=', cp.E(approx,jpdf), '   std=',cp.Std(approx,jpdf)\n",
+    "s_pc= cp.Sens_m(approx,jpdf)\n",
     "print 'Sensitivity indices from polychaos', s_pc\n",
     "print 'rel err', (s_pc-s**2)/s**2"
    ]
@@ -931,9 +931,9 @@
     "    Zpc=jpdf.sample(Npc)\n",
     "    Ypc=linear_model(w, Zpc, Npc)\n",
     "    Npol=4\n",
-    "    poly = pc.orth_chol(Npol, jpdf)\n",
-    "    approx = pc.fit_regression(poly, Zpc, Ypc, rule=\"T\")\n",
-    "    s_pc= pc.Sens_m(approx,jpdf)\n",
+    "    poly = cp.orth_chol(Npol, jpdf)\n",
+    "    approx = cp.fit_regression(poly, Zpc, Ypc, rule=\"T\")\n",
+    "    s_pc= cp.Sens_m(approx,jpdf)\n",
     "    error.append(LA.norm((s_pc-s**2)/s**2))\n",
     " \n",
     "plt.figure()\n",