Chapter 2: minor code formatting

Author: sash-ko

Chapter2_MorePyMC/MorePyMC.ipynb

@@ -797,7 +797,6 @@
       "             label = \"$N$: %d, $p$: %.1f\"%(N,p), \n",
       "             linewidth=3)\n",
       "    \n",
-      "\n",
       "plt.legend(loc=\"upper left\")\n",
       "plt.xlim(0, 10.5)\n",
       "plt.xlabel(\"$k$\")\n",
@@ -1341,6 +1340,7 @@
       "\n",
       "def logistic( x, beta):\n",
       "    return 1.0/( 1.0 + np.exp( beta*x) )\n",
+      "\n",
       "x = np.linspace( -4, 4, 100 )\n",
       "plt.plot(x, logistic( x, 1), label = r\"$\\beta = 1$\")\n",
       "plt.plot(x, logistic( x, 3), label = r\"$\\beta = 3$\")\n",
@@ -1430,6 +1430,7 @@
      "collapsed": false,
      "input": [
       "import scipy.stats as stats\n",
+      "\n",
       "nor = stats.norm\n",
       "x = np.linspace( -8, 7, 150 )\n",
       "mu = (-2, 0, 3)\n",
@@ -1442,8 +1443,6 @@
       "        label =\"$\\mu = %d,\\;\\\\tau = %.1f$\"%(_mu, _tau), color = _color )\n",
       "    plt.fill_between( x, nor.pdf( x, _mu, scale =1./_tau ), color = _color, \\\n",
       "         alpha = .33)\n",
-      "    \n",
-      "\n",
       "\n",
       "plt.legend(loc = \"upper right\")\n",
       "plt.xlabel(\"$x$\")\n",
@@ -1634,6 +1633,7 @@
      "collapsed": false,
      "input": [
       "figsize( 12.5, 4)\n",
+      "\n",
       "plt.plot( t, mean_prob_t, lw = 3, label = \"average posterior \\nprobability \\\n",
       "of defect\")\n",
       "plt.plot( t, p_t[0, :], ls=\"--\",label=\"realization from posterior\" )\n",
@@ -1671,6 +1671,7 @@
      "collapsed": false,
      "input": [
       "from scipy.stats.mstats import mquantiles\n",
+      "\n",
       "# vectorized bottom and top 5% quantiles for \"confidence interval\"\n",
       "qs = mquantiles(p_t,[0.05,0.95],axis=0)\n",
       "plt.fill_between(t[:,0],*qs,alpha = 0.7,\n",
@@ -1723,6 +1724,7 @@
      "collapsed": false,
      "input": [
       "figsize(12.5, 2.5)\n",
+      "\n",
       "prob_31 = logistic( 31, beta_samples, alpha_samples )\n",
       "\n",
       "plt.xlim( 0.995, 1)\n",
@@ -1768,7 +1770,6 @@
       "simulated = mc.Bernoulli( \"bernoulli_sim\", p)\n",
       "N = 10000\n",
       "\n",
-      "\n",
       "mcmc = mc.MCMC( [simulated, alpha, beta, observed ] )\n",
       "mcmc.sample( N )"
      ],
@@ -1798,6 +1799,7 @@
      "collapsed": false,
      "input": [
       "figsize(12.5, 5)\n",
+      "\n",
       "simulations = mcmc.trace(\"bernoulli_sim\")[:]\n",
       "print simulations.shape\n",
       "\n",
@@ -1962,9 +1964,10 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "figsize( 11., 1.5 )\n",
       "from separation_plot import separation_plot\n",
       "\n",
+      "figsize( 11., 1.5 )\n",
+      "\n",
       "separation_plot(posterior_probability, D )"
      ],
      "language": "python",
@@ -2071,6 +2074,7 @@
      "input": [
       "#type your code here.\n",
       "figsize(12.5, 4 )\n",
+      "\n",
       "plt.scatter( alpha_samples, beta_samples, alpha = 0.1 )\n",
       "plt.title( \"Why does the plot look like this?\" )\n",
       "plt.xlabel( r\"$\\alpha$\")\n",