autopep8 --ignore=E501 fixes. Marginal improvement.

eli-b · eli-b · commit 6ed5b04c2a55 · 2014-02-08T20:19:25.000+02:00
diff --git a/Chapter3_MCMC/IntroMCMC.ipynb b/Chapter3_MCMC/IntroMCMC.ipynb
@@ -64,7 +64,7 @@
       "ax = fig.add_subplot(122, projection='3d')\n",
       "ax.plot_surface(X, Y, M, cmap=cm.jet, vmax=1, vmin=-.15)\n",
       "ax.view_init(azim=390)\n",
-      "plt.title(\"Uniform prior landscape; alternate view\")"
+      "plt.title(\"Uniform prior landscape; alternate view\")\n"
      ],
      "language": "python",
      "metadata": {},
@@ -122,7 +122,7 @@
       "ax = fig.add_subplot(122, projection='3d')\n",
       "ax.plot_surface(X, Y, M, cmap=cm.jet)\n",
       "ax.view_init(azim=390)\n",
-      "plt.title(\"$Exp(3), Exp(10)$ prior landscape; \\nalternate view\")"
+      "plt.title(\"$Exp(3), Exp(10)$ prior landscape; \\nalternate view\")\n"
      ],
      "language": "python",
      "metadata": {},
@@ -158,29 +158,29 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "### create the observed data\n",
+      "# create the observed data\n",
       "\n",
-      "#sample size of data we observe, trying varying this (keep it less than 100 ;)\n",
+      "# sample size of data we observe, trying varying this (keep it less than 100 ;)\n",
       "N = 1\n",
       "\n",
-      "#the true parameters, but of course we do not see these values...\n",
+      "# the true parameters, but of course we do not see these values...\n",
       "lambda_1_true = 1\n",
       "lambda_2_true = 3\n",
       "\n",
       "#...we see the data generated, dependent on the above two values.\n",
       "data = np.concatenate([\n",
       "    stats.poisson.rvs(lambda_1_true, size=(N, 1)),\n",
       "    stats.poisson.rvs(lambda_2_true, size=(N, 1))\n",
-      "    ],  axis=1)\n",
+      "], axis=1)\n",
       "print \"observed (2-dimensional,sample size = %d):\" % N, data\n",
       "\n",
-      "#plotting details.\n",
+      "# plotting details.\n",
       "x = y = np.linspace(.01, 5, 100)\n",
       "likelihood_x = np.array([stats.poisson.pmf(data[:, 0], _x)\n",
       "                        for _x in x]).prod(axis=1)\n",
       "likelihood_y = np.array([stats.poisson.pmf(data[:, 1], _y)\n",
       "                        for _y in y]).prod(axis=1)\n",
-      "L = np.dot(likelihood_x[:, None], likelihood_y[None, :])"
+      "L = np.dot(likelihood_x[:, None], likelihood_y[None, :])\n"
      ],
      "language": "python",
      "metadata": {},
@@ -200,7 +200,7 @@
      "collapsed": false,
      "input": [
       "figsize(12.5, 12)\n",
-      "#matplotlib heavy lifting below, beware!\n",
+      "# matplotlib heavy lifting below, beware!\n",
       "subplot(221)\n",
       "uni_x = stats.uniform.pdf(x, loc=0, scale=5)\n",
       "uni_y = stats.uniform.pdf(x, loc=0, scale=5)\n",
@@ -244,7 +244,7 @@
       "plt.title(\"Landscape warped by %d data observation;\\n Exponential priors on \\\n",
       "$p_1, p_2$.\" % N)\n",
       "plt.xlim(0, 5)\n",
-      "plt.ylim(0, 5)"
+      "plt.ylim(0, 5)\n"
      ],
      "language": "python",
      "metadata": {},
@@ -334,12 +334,12 @@
      "collapsed": false,
      "input": [
       "figsize(12.5, 4)\n",
-      "data = np.loadtxt(\"data/mixture_data.csv\",  delimiter=\",\")\n",
+      "data = np.loadtxt(\"data/mixture_data.csv\", delimiter=\",\")\n",
       "\n",
-      "hist(data, bins=20,  color=\"k\", histtype=\"stepfilled\", alpha=0.8)\n",
+      "hist(data, bins=20, color=\"k\", histtype=\"stepfilled\", alpha=0.8)\n",
       "plt.title(\"Histogram of the dataset\")\n",
       "plt.ylim([0, None])\n",
-      "print data[:10], \"...\""
+      "print data[:10], \"...\"\n"
      ],
      "language": "python",
      "metadata": {},
@@ -387,9 +387,9 @@
       "\n",
       "p = pm.Uniform(\"p\", 0, 1)\n",
       "\n",
-      "assignment = pm.Categorical(\"assignment\", [p, 1-p], size=data.shape[0])\n",
+      "assignment = pm.Categorical(\"assignment\", [p, 1 - p], size=data.shape[0])\n",
       "print \"prior assignment, with p = %.2f:\" % p.value\n",
-      "print assignment.value[:10], \"...\""
+      "print assignment.value[:10], \"...\"\n"
      ],
      "language": "python",
      "metadata": {},
@@ -426,7 +426,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "taus = 1.0/pm.Uniform(\"stds\", 0, 100, size=2) ** 2\n",
+      "taus = 1.0 / pm.Uniform(\"stds\", 0, 100, size=2) ** 2\n",
       "centers = pm.Normal(\"centers\", [120, 190], [0.01, 0.01], size=2)\n",
       "\n",
       "\"\"\"\n",
@@ -447,7 +447,7 @@
       "\n",
       "print \"Random assignments: \", assignment.value[:4], \"...\"\n",
       "print \"Assigned center: \", center_i.value[:4], \"...\"\n",
-      "print \"Assigned precision: \", tau_i.value[:4], \"...\""
+      "print \"Assigned precision: \", tau_i.value[:4], \"...\"\n"
      ],
      "language": "python",
      "metadata": {},
@@ -468,11 +468,11 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "#and to combine it with the observations:\n",
+      "# and to combine it with the observations:\n",
       "observations = pm.Normal(\"obs\", center_i, tau_i, value=data, observed=True)\n",
       "\n",
-      "#below we create a model class\n",
-      "model = pm.Model([p, assignment, taus, centers])"
+      "# below we create a model class\n",
+      "model = pm.Model([p, assignment, taus, centers])\n"
      ],
      "language": "python",
      "metadata": {},
@@ -495,7 +495,7 @@
      "collapsed": false,
      "input": [
       "mcmc = pm.MCMC(model)\n",
-      "mcmc.sample(50000)"
+      "mcmc.sample(50000)\n"
      ],
      "language": "python",
      "metadata": {},
@@ -527,9 +527,9 @@
       "lw = 1\n",
       "center_trace = mcmc.trace(\"centers\")[:]\n",
       "\n",
-      "#for pretty colors later in the book.\n",
+      "# for pretty colors later in the book.\n",
       "colors = [\"#348ABD\", \"#A60628\"] if center_trace[-1, 0] > center_trace[-1, 1] \\\n",
-      "                                else [\"#A60628\", \"#348ABD\"]\n",
+      "    else [\"#A60628\", \"#348ABD\"]\n",
       "\n",
       "plot(center_trace[:, 0], label=\"trace of center 0\", c=colors[0], lw=lw)\n",
       "plot(center_trace[:, 1], label=\"trace of center 1\", c=colors[1], lw=lw)\n",
@@ -551,7 +551,7 @@
       "     color=\"#467821\", lw=lw)\n",
       "plt.xlabel(\"Steps\")\n",
       "plt.ylim(0, 1)\n",
-      "plt.legend()"
+      "plt.legend()\n"
      ],
      "language": "python",
      "metadata": {},
@@ -583,7 +583,7 @@
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "mcmc.sample(100000)"
+      "mcmc.sample(100000)\n"
      ],
      "language": "python",
      "metadata": {},
@@ -621,13 +621,13 @@
       "     lw=lw, alpha=0.4, c=colors[0])\n",
       "\n",
       "x = np.arange(50000, 150000)\n",
-      "plot(x, center_trace[:, 0], label=\"new trace of center 0\",  lw=lw, c=\"#348ABD\")\n",
-      "plot(x, center_trace[:, 1], label=\"new trace of center 1\",  lw=lw, c=\"#A60628\")\n",
+      "plot(x, center_trace[:, 0], label=\"new trace of center 0\", lw=lw, c=\"#348ABD\")\n",
+      "plot(x, center_trace[:, 1], label=\"new trace of center 1\", lw=lw, c=\"#A60628\")\n",
       "\n",
       "plt.title(\"Traces of unknown center parameters\")\n",
       "leg = plt.legend(loc=\"upper right\")\n",
       "leg.get_frame().set_alpha(0.8)\n",
-      "plt.xlabel(\"Steps\")"
+      "plt.xlabel(\"Steps\")\n"
      ],
      "language": "python",
      "metadata": {},
@@ -668,9 +668,9 @@
       "    plt.title(\"Posterior of standard deviation of cluster %d\" % i)\n",
       "    plt.hist(std_trace[:, i], color=colors[i], bins=30,\n",
       "             histtype=\"stepfilled\")\n",
-      "    #plt.autoscale(tight=True)\n",
+      "    # plt.autoscale(tight=True)\n",
       "\n",
-      "plt.tight_layout()"
+      "plt.tight_layout()\n"
      ],
      "language": "python",
      "metadata": {},
@@ -703,7 +703,7 @@
       "       [\"%.2f\" % s for s in np.sort(data)[::40]])\n",
       "plt.ylabel(\"posterior sample\")\n",
       "plt.xlabel(\"value of $i$th data point\")\n",
-      "plt.title(\"Posterior labels of data points\")"
+      "plt.title(\"Posterior labels of data points\")\n"
      ],
      "language": "python",
      "metadata": {},
@@ -728,13 +728,13 @@
      "input": [
       "cmap = mpl.colors.LinearSegmentedColormap.from_list(\"BMH\", colors)\n",
       "assign_trace = mcmc.trace(\"assignment\")[:]\n",
-      "scatter(data, 1-assign_trace.mean(axis=0), cmap=cmap,\n",
+      "scatter(data, 1 - assign_trace.mean(axis=0), cmap=cmap,\n",
       "        c=assign_trace.mean(axis=0), s=50)\n",
       "plt.ylim(-0.05, 1.05)\n",
       "plt.xlim(35, 300)\n",
       "plt.title(\"Probability of data point belonging to cluster 0\")\n",
       "plt.ylabel(\"probability\")\n",
-      "plt.xlabel(\"value of data point\")"
+      "plt.xlabel(\"value of data point\")\n"
      ],
      "language": "python",
      "metadata": {},
@@ -778,7 +778,7 @@
       "plt.fill_between(x, y, color=colors[0], alpha=0.3)\n",
       "\n",
       "plt.legend(loc=\"upper left\")\n",
-      "plt.title(\"Visualizing Clusters using posterior-mean parameters\")"
+      "plt.title(\"Visualizing Clusters using posterior-mean parameters\")\n"
      ],
      "language": "python",
      "metadata": {},
@@ -817,7 +817,7 @@
       "\n",
       "plt.plot(ex_mcmc.trace(\"x\")[:])\n",
       "plt.plot(ex_mcmc.trace(\"y\")[:])\n",
-      "plt.title(\"Displaying (extreme) case of dependence between unknowns\")"
+      "plt.title(\"Displaying (extreme) case of dependence between unknowns\")\n"
      ],
      "language": "python",
      "metadata": {},
@@ -891,10 +891,10 @@
       "p_trace = mcmc.trace(\"p\")[:]\n",
       "x = 175\n",
       "\n",
-      "v = p_trace*norm_pdf(x, loc=center_trace[:, 0], scale=std_trace[:, 0]) > \\\n",
-      "    (1-p_trace)*norm_pdf(x, loc=center_trace[:, 1], scale=std_trace[:, 1])\n",
+      "v = p_trace * norm_pdf(x, loc=center_trace[:, 0], scale=std_trace[:, 0]) > \\\n",
+      "    (1 - p_trace) * norm_pdf(x, loc=center_trace[:, 1], scale=std_trace[:, 1])\n",
       "\n",
-      "print \"Probability of belonging to cluster 1:\", v.mean()"
+      "print \"Probability of belonging to cluster 1:\", v.mean()\n"
      ],
      "language": "python",
      "metadata": {},
@@ -988,7 +988,7 @@
       "plt.plot(y_t, label=\"$y_t$\", lw=3)\n",
       "plt.plot(x_t, label=\"$x_t$\", lw=3)\n",
       "plt.xlabel(\"time, $t$\")\n",
-      "plt.legend()"
+      "plt.legend()\n"
      ],
      "language": "python",
      "metadata": {},
@@ -1014,7 +1014,7 @@
      "collapsed": false,
      "input": [
       "def autocorr(x):\n",
-      "    #from http://tinyurl.com/afz57c4\n",
+      "    # from http://tinyurl.com/afz57c4\n",
       "    result = np.correlate(x, x, mode='full')\n",
       "    result = result / np.max(result)\n",
       "    return result[result.size / 2:]\n",
@@ -1030,7 +1030,7 @@
       "plt.legend(title=\"Autocorrelation\")\n",
       "plt.ylabel(\"measured correlation \\nbetween $y_t$ and $y_{t-k}$.\")\n",
       "plt.xlabel(\"k (lag)\")\n",
-      "plt.title(\"Autocorrelation plot of $y_t$ and $x_t$ for differing $k$ lags.\")"
+      "plt.title(\"Autocorrelation plot of $y_t$ and $x_t$ for differing $k$ lags.\")\n"
      ],
      "language": "python",
      "metadata": {},
@@ -1087,7 +1087,7 @@
       "plt.ylabel(\"measured correlation \\nbetween $y_t$ and $y_{t-k}$.\")\n",
       "plt.xlabel(\"k (lag)\")\n",
       "plt.title(\"Autocorrelation of $y_t$ (no thinning vs. thinning) \\\n",
-      "at differing $k$ lags.\")"
+      "at differing $k$ lags.\")\n"
      ],
      "language": "python",
      "metadata": {},
@@ -1130,7 +1130,7 @@
       "from pymc.Matplot import plot as mcplot\n",
       "\n",
       "mcmc.sample(25000, 0, 10)\n",
-      "mcplot(mcmc.trace(\"centers\", 2), common_scale=False)"
+      "mcplot(mcmc.trace(\"centers\", 2), common_scale=False)\n"
      ],
      "language": "python",
      "metadata": {},
@@ -1249,7 +1249,7 @@
       "def css_styling():\n",
       "    styles = open(\"../styles/custom.css\", \"r\").read()\n",
       "    return HTML(styles)\n",
-      "css_styling()"
+      "css_styling()\n"
      ],
      "language": "python",
      "metadata": {},
@@ -1338,4 +1338,4 @@
    "metadata": {}
   }
  ]
-}
+}
