PEP-8 compliance, typo fixes.

2014-11-28 09:16:55 -08:00
1 parent a9537504c6
commit 545a4650a1
@@ -1,7 +1,7 @@
 {
 "metadata": {
  "name": "",
-  "signature": "sha256:b8b47f06752bcd373881f57282ed37ec8aee5434acd6a5476d94e6454106c397"
+  "signature": "sha256:59d697bb03f25b3d85c02ebd74747fc150ea9b244db398c0be39d839a761ca74"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
@@ -261,7 +261,7 @@
       "output_type": "pyout",
       "prompt_number": 1,
       "text": [
-        "<IPython.core.display.HTML at 0x7f85acbb8320>"
+        "<IPython.core.display.HTML at 0x7fc76a0444e0>"
       ]
      }
     ],
@@ -356,7 +356,7 @@
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       "text": [
-        "<matplotlib.figure.Figure at 0x7f859071e7b8>"
+        "<matplotlib.figure.Figure at 0x7fc751b90a90>"
       ]
      }
     ],
@@ -392,7 +392,7 @@
       "output_type": "display_data",
       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7f8590477780>"
+        "<matplotlib.figure.Figure at 0x7fc7518df2b0>"
       ]
      }
     ],
@@ -428,7 +428,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f85904062e8>"
+        "<matplotlib.figure.Figure at 0x7fc751596160>"
       ]
      }
     ],
@@ -467,7 +467,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f85acbbdba8>"
+        "<matplotlib.figure.Figure at 0x7fc7518c8da0>"
       ]
      }
     ],
@@ -494,7 +494,7 @@
       "output_type": "display_data",
       "png": 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       "text": [
-        "<matplotlib.figure.Figure at 0x7f85902e8be0>"
+        "<matplotlib.figure.Figure at 0x7fc751770c88>"
       ]
      }
     ],
@@ -529,7 +529,7 @@
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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f85903ea898>"
+        "<matplotlib.figure.Figure at 0x7fc751841208>"
       ]
      }
     ],
@@ -597,7 +597,7 @@
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAtwAAAGJCAYAAAC5AuSwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f85901085c0>"
+        "<matplotlib.figure.Figure at 0x7fc751547748>"
       ]
      }
     ],
@@ -626,7 +626,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f85900efa58>"
+        "<matplotlib.figure.Figure at 0x7fc751574b38>"
       ]
      }
     ],
@@ -682,7 +682,7 @@
      "n = len(weights)\n",
      "plt.xlim([1, n])\n",
      "    \n",
-      "plt.plot (range(1, n+1), estimates, '--', label='filter')\n",
+      "plt.plot(range(1, n+1), estimates, '--', label='filter')\n",
      "plt.plot(range(1, n+1), weights, label='measurements')\n",
      "plt.plot([1 ,n], [160,160+n], label='actual')\n",
      "plt.legend(loc=2)\n",
@@ -698,7 +698,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f85901f1a90>"
+        "<matplotlib.figure.Figure at 0x7fc7518e43c8>"
       ]
      }
     ],
@@ -763,7 +763,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f85901bba20>"
+        "<matplotlib.figure.Figure at 0x7fc751679198>"
       ]
      }
     ],
@@ -855,8 +855,8 @@
      "        \n",
      "    return results\n",
      "\n",
-      "def plot_g_h_results (measurements, filtered_data, title=''):    \n",
-      "    plt.plot (filtered_data, '--',label='filter')\n",
+      "def plot_g_h_results(measurements, filtered_data, title=''):    \n",
+      "    plt.plot(filtered_data, '--',label='filter')\n",
      "    plt.plot(measurements,label='measurement')\n",
      "    plt.legend(loc=4)\n",
      "    plt.title(title)\n",
@@ -865,8 +865,8 @@
      "\n",
      "plt.xlim([0,10])\n",
      "plt.plot([0,9],[160,170],label='actual')\n",
-      "data = g_h_filter (data=weights, x0=160, dx=1, g=6./10, h = 2./3, dt=1.)\n",
-      "plot_g_h_results (weights, data)"
+      "data = g_h_filter(data=weights, x0=160, dx=1, g=6./10, h = 2./3, dt=1.)\n",
+      "plot_g_h_results(weights, data)"
     ],
     "language": "python",
     "metadata": {},
@@ -876,7 +876,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f85900fc9e8>"
+        "<matplotlib.figure.Figure at 0x7fc75183cb38>"
       ]
      }
     ],
@@ -940,12 +940,12 @@
     "collapsed": false,
     "input": [
      "import numpy.random as random\n",
-      "def gen_data (x0, dx, count, noise_factor):\n",
+      "def gen_data(x0, dx, count, noise_factor):\n",
      "    return [x0 + dx*i + random.randn()*noise_factor for i in range (count)]\n",
      "\n",
-      "measurements = gen_data (0, 1, 30, 1)\n",
-      "data = g_h_filter (data=measurements, x0=0, dx=1, dt=1, g=.2, h=0.02)\n",
-      "plot_g_h_results (measurements, data)"
+      "measurements = gen_data(0, 1, 30, 1)\n",
+      "data = g_h_filter(data=measurements, x0=0, dx=1, dt=1, g=.2, h=0.02)\n",
+      "plot_g_h_results(measurements, data)"
     ],
     "language": "python",
     "metadata": {},
@@ -953,9 +953,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f8590129ef0>"
+        "<matplotlib.figure.Figure at 0x7fc7518fd3c8>"
       ]
      }
     ],
@@ -1000,8 +1000,8 @@
     "collapsed": false,
     "input": [
      "zs = gen_data (x0=5, dx=2, count=100, noise_factor=10)\n",
-      "data = g_h_filter (data=zs, x0=100., dx=2., dt=1.,g=0.2, h=0.01)\n",
-      "plot_g_h_results  (measurements=zs, filtered_data=data)"
+      "data = g_h_filter(data=zs, x0=100., dx=2., dt=1.,g=0.2, h=0.01)\n",
+      "plot_g_h_results(measurements=zs, filtered_data=data)"
     ],
     "language": "python",
     "metadata": {},
@@ -1009,9 +1009,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f859027e2b0>"
+        "<matplotlib.figure.Figure at 0x7fc7516167f0>"
       ]
      }
     ],
@@ -1063,8 +1063,8 @@
     "collapsed": false,
     "input": [
      "zs = gen_data (x0=5, dx=2, count=100, noise_factor=100)\n",
-      "data = g_h_filter (data=zs, x0=5., dx=2., g=0.2, h=0.02)\n",
-      "plot_g_h_results  (measurements=zs, filtered_data=data)"
+      "data = g_h_filter(data=zs, x0=5., dx=2., g=0.2, h=0.02)\n",
+      "plot_g_h_results(measurements=zs, filtered_data=data)"
     ],
     "language": "python",
     "metadata": {},
@@ -1072,9 +1072,9 @@
      {
       "metadata": {},
       "output_type": "display_data",
-       "png": 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truncated
+       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f85902e5da0>"
+        "<matplotlib.figure.Figure at 0x7fc75178d9e8>"
       ]
      }
     ],
@@ -1148,7 +1148,7 @@
       "output_type": "display_data",
       "png": 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truncated
       "text": [
-        "<matplotlib.figure.Figure at 0x7f859034b128>"
+        "<matplotlib.figure.Figure at 0x7fc7518411d0>"
       ]
      }
     ],
@@ -1213,17 +1213,33 @@
      "plot_g_h_results (zs, data, 'g = 0.5')\n",
      "\n",
      "data = g_h_filter(data=zs, x0=0., dx=5., dt=1.,g=0.9, h=0.01)\n",
-      "plot_g_h_results  zs, data, 'g = 0.9')"
+      "plot_g_h_results(zs, data, 'g = 0.9')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
-       "ename": "SyntaxError",
-       "evalue": "invalid syntax (<ipython-input-22-786b45432a0a>, line 10)",
-       "output_type": "pyerr",
-       "traceback": [
-        "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-22-786b45432a0a>\"\u001b[1;36m, line \u001b[1;32m10\u001b[0m\n\u001b[1;33m    plot_g_h_results  zs, data, 'g = 0.9')\u001b[0m\n\u001b[1;37m                       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+        "<matplotlib.figure.Figure at 0x7fc751684438>"
+       ]
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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fc75179e6a0>"
+       ]
+      },
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+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAtUAAAGDCAYAAADkjOwcAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fc751b83b38>"
       ]
      }
     ],
@@ -1246,17 +1262,43 @@
      "    zs.append(14)\n",
      "\n",
      "data = g_h_filter(data=zs, x0=4., dx=1., dt=1.,g=0.1, h=0.01)\n",
-      "plot_g_h_results (zs, data, 'g = 0.1')\n",
+      "plot_g_h_results(zs, data, 'g = 0.1')\n",
      "\n",
      "data = g_h_filter(data=zs, x0=4., dx=1., dt=1.,g=0.5, h=0.01)\n",
-      "plot_g_h_results (zs, data, 'g = 0.5')\n",
+      "plot_g_h_results(zs, data, 'g = 0.5')\n",
      "\n",
      "data = g_h_filter(data=zs, x0=4., dx=1., dt=1.,g=0.9, h=0.01)\n",
-      "plot_g_h_results (zs, data, 'g = 0.9')"
+      "plot_g_h_results(zs, data, 'g = 0.9')"
     ],
     "language": "python",
     "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": "iVBORw0KGgoAAAANSUhEUgAAAsYAAAGDCAYAAAAoFdb3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fc75161f1d0>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fc75175b7b8>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fc751900eb8>"
+       ]
+      }
+     ],
+     "prompt_number": 23
    },
    {
     "cell_type": "markdown",
@@ -1274,14 +1316,32 @@
      "zs = [5,6,7,8,9,9,9,9,9,10,11,12,13,14,15,16,16,16,16,16,16,16,16,16,16,16]\n",
      "    \n",
      "data = g_h_filter(data=zs, x0=4., dx=1., dt=1.,g=.302, h=0.054)\n",
-      "plot_g_h_results (zs, data, 'g = 0.302, h = 0.054')\n",
+      "plot_g_h_results(zs, data, 'g = 0.302, h = 0.054')\n",
      "\n",
      "data = g_h_filter(data=zs, x0=4., dx=1., dt=1.,g=.546, h=0.205)\n",
-      "plot_g_h_results (zs, data, 'g = 0.546, h = 0.205')"
+      "plot_g_h_results(zs, data, 'g = 0.546, h = 0.205')"
     ],
     "language": "python",
     "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fc7517fb6d8>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fc75174fb70>"
+       ]
+      }
+     ],
+     "prompt_number": 24
    },
    {
     "cell_type": "heading",
@@ -1307,7 +1367,7 @@
      "zs = np.linspace(0,1,50)\n",
      "\n",
      "data = g_h_filter(data=zs, x0=0, dx=0., dt=1.,g=.2, h=0.05)\n",
-      "plot_g_h_results (zs, data, 'dx=0, h = 0.05')\n",
+      "plot_g_h_results(zs, data, 'dx=0, h = 0.05')\n",
      "\n",
      "data = g_h_filter(data=zs, x0=0, dx=2., dt=1.,g=.2, h=0.05)\n",
      "plt.ylim([-1,5])\n",
@@ -1319,7 +1379,33 @@
     ],
     "language": "python",
     "metadata": {},
-     "outputs": []
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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+       "text": [
+        "<matplotlib.figure.Figure at 0x7fc7504ae6d8>"
+       ]
+      },
+      {
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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fc7504436d8>"
+       ]
+      },
+      {
+       "metadata": {},
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truncated
+       "text": [
+        "<matplotlib.figure.Figure at 0x7fc751658588>"
+       ]
+      }
+     ],
+     "prompt_number": 26
    },
    {
     "cell_type": "heading",