Altered look of residual charts.

User ulenka on gitter suggest changing the color for the residual line. I found my changes garish, so I switched to a thicker black line and changed the grids to dotted.
2015-07-24 20:34:36 -07:00
1 parent 056ada57af
commit 0d836c7e03
@@ -329,7 +329,7 @@
     "data": {
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      "text/plain": [
-       "<matplotlib.figure.Figure at 0x5988be0>"
+       "<matplotlib.figure.Figure at 0x51d69b0>"
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     },
     "metadata": {},
@@ -365,7 +365,7 @@
     "data": {
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      "text/plain": [
-       "<matplotlib.figure.Figure at 0x5988ba8>"
+       "<matplotlib.figure.Figure at 0x51d6978>"
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     "metadata": {},
@@ -397,7 +397,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8062da0>"
+       "<matplotlib.figure.Figure at 0x7887358>"
      ]
     },
     "metadata": {},
@@ -433,7 +433,7 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "Average of measurements is 165.0310\n"
+      "Average of measurements is 165.0330\n"
     ]
    }
   ],
@@ -464,7 +464,7 @@
     "name": "stdout",
     "output_type": "stream",
     "text": [
-      "Average of measurements is 164.8983\n"
+      "Average of measurements is 164.9686\n"
     ]
    }
   ],
@@ -497,7 +497,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x5988240>"
+       "<matplotlib.figure.Figure at 0x51d65c0>"
      ]
     },
     "metadata": {},
@@ -535,7 +535,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x812f160>"
+       "<matplotlib.figure.Figure at 0x51d6198>"
      ]
     },
     "metadata": {},
@@ -564,7 +564,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x82037b8>"
+       "<matplotlib.figure.Figure at 0x7a27c18>"
      ]
     },
     "metadata": {},
@@ -595,7 +595,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8281400>"
+       "<matplotlib.figure.Figure at 0x7ab7b38>"
      ]
     },
     "metadata": {},
@@ -627,7 +627,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA48AAAEtCAYAAACoIlXDAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x81a8cf8>"
+       "<matplotlib.figure.Figure at 0x7a27160>"
      ]
     },
     "metadata": {},
@@ -660,9 +660,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x78c99e8>"
+       "<matplotlib.figure.Figure at 0x71094e0>"
      ]
     },
     "metadata": {},
@@ -689,9 +689,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x80f6d68>"
+       "<matplotlib.figure.Figure at 0x7a5ef98>"
      ]
     },
     "metadata": {},
@@ -726,14 +726,15 @@
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "scrolled": true
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8042630>"
+       "<matplotlib.figure.Figure at 0x795add8>"
      ]
     },
     "metadata": {},
@@ -794,7 +795,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8535668>"
+       "<matplotlib.figure.Figure at 0x7d6f4e0>"
      ]
     },
     "metadata": {},
@@ -871,7 +872,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6sAAAEmCAYAAACEUwaqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x85565c0>"
+       "<matplotlib.figure.Figure at 0x7d787f0>"
      ]
     },
     "metadata": {},
@@ -911,9 +912,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8061320>"
+       "<matplotlib.figure.Figure at 0x7a5e9b0>"
      ]
     },
     "metadata": {},
@@ -1016,7 +1017,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x857ca20>"
+       "<matplotlib.figure.Figure at 0x7a8d6a0>"
      ]
     },
     "metadata": {},
@@ -1423,9 +1424,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5kAAAEPCAYAAADI9gBRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5kAAAEPCAYAAADI9gBRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x81771d0>"
+       "<matplotlib.figure.Figure at 0x78b65f8>"
      ]
     },
     "metadata": {},
@@ -1519,9 +1520,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5EAAAERCAYAAADi/SNOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8178898>"
+       "<matplotlib.figure.Figure at 0x78aa438>"
      ]
     },
     "metadata": {},
@@ -1557,7 +1558,7 @@
    {
     "data": {
      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x96dc358>]"
+       "[<matplotlib.lines.Line2D at 0x92e2dd8>]"
      ]
     },
     "execution_count": 31,
@@ -1566,9 +1567,9 @@
    },
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuYAAAEWCAYAAAAq41LXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8204c88>"
+       "<matplotlib.figure.Figure at 0x7886710>"
      ]
     },
     "metadata": {},
@@ -1620,9 +1621,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5kAAAEPCAYAAADI9gBRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5kAAAEPCAYAAADI9gBRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x958e198>"
+       "<matplotlib.figure.Figure at 0x8dab550>"
      ]
     },
     "metadata": {},
@@ -1683,9 +1684,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6UAAAEPCAYAAAC6OWLnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA6UAAAEPCAYAAAC6OWLnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x97200b8>"
+       "<matplotlib.figure.Figure at 0x9321400>"
      ]
     },
     "metadata": {},
@@ -1748,9 +1749,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5oAAAEPCAYAAAAjwbtSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5oAAAEPCAYAAAAjwbtSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x977e358>"
+       "<matplotlib.figure.Figure at 0x938a7b8>"
      ]
     },
     "metadata": {},
@@ -1828,7 +1829,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5kAAAGNCAYAAACbjrWCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x97500b8>"
+       "<matplotlib.figure.Figure at 0x93d45c0>"
      ]
     },
     "metadata": {},
@@ -1867,9 +1868,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x821d710>"
+       "<matplotlib.figure.Figure at 0x937e320>"
      ]
     },
     "metadata": {},
@@ -1912,9 +1913,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x80234e0>"
+       "<matplotlib.figure.Figure at 0x935f390>"
      ]
     },
     "metadata": {},
@@ -1959,9 +1960,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x792b828>"
+       "<matplotlib.figure.Figure at 0x7d29780>"
      ]
     },
     "metadata": {},
@@ -2001,16 +2002,16 @@
  },
  {
   "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9748c50>"
+       "<matplotlib.figure.Figure at 0x79746a0>"
      ]
     },
     "metadata": {},
@@ -2055,9 +2056,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9785898>"
+       "<matplotlib.figure.Figure at 0x9306208>"
      ]
     },
     "metadata": {},
@@ -2091,9 +2092,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x80bb9b0>"
+       "<matplotlib.figure.Figure at 0x7d5cb38>"
      ]
     },
     "metadata": {},
@@ -2159,7 +2160,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8126048>"
+       "<matplotlib.figure.Figure at 0x93daf28>"
      ]
     },
     "metadata": {},
@@ -2208,9 +2209,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAEjCAYAAABaRN5lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x9728860>"
+       "<matplotlib.figure.Figure at 0x93cea20>"
      ]
     },
     "metadata": {},
@@ -2239,9 +2240,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5EAAAEjCAYAAACrT4XoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5EAAAEjCAYAAACrT4XoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x95e4c88>"
+       "<matplotlib.figure.Figure at 0x7b4c1d0>"
      ]
     },
     "metadata": {},
@@ -2272,9 +2273,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAEjCAYAAABaRN5lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAEjCAYAAABaRN5lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XdYFNfXwPHvLB0FVBQUKzasUewaK6KiJhqNPfZEoz8r\nJrEktldjSdSoMYmaGBtGY4ldgxUbYMNYUOy9YBdsSNl5/9iwQFRYYJcFPJ/n2cd7h5k7Z9ZB9+y9\nc6+iqqqKEEIIIYQQQghhIhpzByCEEEIIIYQQInuTxFMIIYQQQgghhElJ4imEEEIIIYQQwqQk8RRC\nCCGEEEIIYVKSeAohhBBCCCGEMClJPIUQQgghhBBCmJQknkIIIdJEo9Hg7u5u7jCEEEIIkQVI4imE\nECayZcsWGjVqhJOTEw4ODtSqVYulS5emqa3g4GBatmxJnjx5sLe3p1KlSsyePRutVvvavjdv3mTS\npEm0b9+ekiVLotFo0Gg0nD9/Pr2X9BpFUYzepiHCwsLo0KEDLi4u2NnZUaZMGcaPH09UVFSq2lmz\nZg2DBg2iXr16ODo6otFo6Ny5s4miFkIIId5dluYOQAghsqOff/6ZQYMGkTdvXrp37461tTWrV6+m\nZ8+enDp1imnTphnc1oYNG/j444+xt7enY8eO5MmTh40bN+Lr60tgYCCrVq1Ksv/Ro0cZM2aMvkcy\nV65cREREGPsSzebw4cN4eXkRGxtLu3btKFy4MLt27WLChAns2rWLXbt2YW1tbVBb3377LSdPnsTB\nwYFChQpx9uxZsyXTQgghRHamqKqqmjsIIYTITq5evUqZMmVwcHAgJCSEIkWKAPDkyROqV6/OpUuX\nCAoKolatWim2FRkZScmSJXn69CmBgYFUqVIFgFevXuHl5UVwcDArVqygY8eO+mNu3brF1atXqVSp\nEjlz5qRhw4bs27ePs2fPUrp0aaNdp0ajoVixYly+fNlobaYkLi6OihUrcu7cOTZs2MAHH3wAgKqq\ndOjQgb/++ospU6YwYsQIg9rbs2cPhQsXpkSJEuzdu5dGjRrRqVMnli9fbsrLEEIIId45MtRWCJGt\nqarK7NmzKVeuHHZ2dhQqVIhBgwYRERFBsWLFTPKM4sKFC4mOjmbgwIH6pBMgV65cfP311wDMmzfP\noLbWrFnDgwcP6NSpkz7pBLCxseHbb78FYO7cuUmOKViwIO+//z45c+ZM76UY5MWLF3z11VcUKVIE\nW1tbSpUqxffff2+Sc+3du5ezZ89Sv359fdIJuiG/8ec09L0FaNiwISVKlAB094oQQgghTEOG2goh\nsrUBAwYwb9483Nzc6Nu3L9bW1mzatInDhw8TGxtr8JDM1Ni9ezcAPj4+r/2sefPmAAQEBKS7rfr1\n62NnZ0dwcDAxMTFYWVmlNeQ0i46OpmnTpty5c4eWLVtiaWnJunXrGDlyJFFRUYwdO9ao50vu/XB3\nd6dUqVJcvHiRy5cvU7x4caOeWwghhBBpJ4mnECLb2r9/P/PmzaNUqVIcPnwYJycnACZPnoy3tze3\nb9+mWLFiSY6JiIhg5syZqTpPo0aNaNCggb5+7tw5FEV547DW/PnzY29vz82bN4mKisLW1jbZts+d\nOwfwxrYsLCxwd3cnLCyMy5cv4+Hhkaq4jeH27dtUrlyZXbt2YWNjA8DYsWMpXbo0M2fO5Ouvv8bS\nMuG/mlmzZvHkyROD23d3d6dHjx76enLvB0CpUqW4cOECFy5ckMRTCCGEyEQk8RRCZFtLliwBYNSo\nUfqkE8DKyoopU6ZQt27d1455/PgxEyZMMPgciqKg0WiSJJ7xE/kkPmdiTk5OvHz5koiIiBQTz4iI\nCBRFSbYtVVVTlcwZk6Io/Pjjj/qkEyBfvny0atUKPz8/zp8/T7ly5fQ/mz17NteuXTO4/YYNGyZJ\nPA15bwGzvR9CCCGEeDNJPIUQ2dY///wD8MYEs2bNmlhYWLy2vVixYm9cokS8mZOT0xt7FgsXLgzo\nEvnErly5kiFxCSGEECJzkcmFhBDZVnxvoaur62s/s7CwwNnZ2STnje91e9sSJin12v23LVVVU2wr\nV65caQk13d523vjhtXFxcUY9n6HvrbneDyGEEEK8mfR4CiGyLUdHRwDCw8NxcHBI8rO4uDgePnyI\nvb19ku3GeMbTw8ODoKAgzp0799qSKXfu3OHFixcULlw4xWG28W2FhIRw7tw5PD09k/wsNjaWK1eu\nYGVllWWeZ0zvM55lypQBEp71/K8LFy689flaIYQQQpiPJJ5CiGyrSpUqHD9+nAMHDlCqVKkkPzt4\n8OAbe+Pin/FUFMWg5TXe9Ixn48aNCQoKwt/f/7XE8++//wbAy8vLoGto3Lgxy5cvx9/fn06dOiX5\n2b59+3j58iUNGjQwy4y2aZHeZzy9vLyYNGkS/v7+jBw5Msm+ly9f5sKFCyZbJkcIIYQQaSdDbYUQ\n2Vb37t0BmDJlSpJetujoaP16mv8V/4xnXFwcWq02xVdcXNxrS4b06tULGxsbfvrppyRJ1uPHj5k8\neTKKotCvX78kxzx8+JCzZ88SHh6eZHu7du3Imzcvf/75JyEhIfrtUVFRjB49GoD+/fsb9H4kl0gv\nXrwYjUZDo0aNDGorra5cuWLQ+xr/il8+JV6DBg0oW7Ys+/btY9OmTfrtWq2WESNGALz23r58+ZKz\nZ89y+fJlk16bEEIIId5OejyFENlW/fr16du3L7/++isVKlSgbdu22NjYsHHjRnLnzo2bmxsajfG/\nfytWrBjTp09n0KBBVKtWjY4dO2JlZcWaNWu4desWX375JTVr1kxyzJw5c5gwYQI9evRg0aJF+u0O\nDg4sWLCAjz/+mIYNG9KpUydy587Nxo0bOX/+PO3bt6dDhw6vxdCzZ099+ezZs4Budt/44cdt2rSh\ndevW+n3iJ1QyxbqmxqTRaFi0aBFeXl60a9eOdu3aUbhwYXbt2kVISAh169bF19c3yTGHDh3Cy8uL\nokWLvja50fr161m/fj2APuk/ePCg/v1TFCXJ34cQQggh0kYSTyFEtjZ37lzKlCnD/Pnz+fXXX8mb\nNy9t2rRh0qRJFCxYEBcXF5Ocd8CAAbi7u/P999+zdOlSVFWlXLlyTJ48mW7dur22v6Io+td/tWrV\niv379zNx4kT++usvoqKiKFWqFDNnzmTw4MFvPP/SpUv1w4Xj292wYYO+Xrx48SSJ58mTJ4GEXuL0\netu1GEONGjU4cuQI48ePZ8eOHURGRlKsWDHGjRvHyJEjXxt2HB/Hm+I5ceKE/r2K3+fatWv4+fnp\n3ytJPIUQQoj0U1RDHmISQohs5sKFC3h4eNC5c2f++OMPc4djdpUrVyY6OprTp0+bLGEUQgghxLtL\nejyFENnavXv3yJs3b5IhtS9evGDo0KGAbsjpu+7Ro0ecOnWK5cuXS9IphBBCCJOQHk8hRLY2evRo\n/Pz8aNSoEfnz5yc8PJxdu3Zx69YtWrRowebNm80dohBCCCFEtic9nkKIbM3b25t//vmH7du38+jR\nI6ysrChdujRDhw7V93oKIYQQQgjTytQ9nhEREeYOQQghhBBCiDRzcnIydwhCZAqyjqcQQgghhBBC\nCJOSxFMIIYQQQgghhEllmWc8ZZiCeJujR48CUK1aNTNHIjIzuU+EIeQ+EYaQ+0SkRB4XE+J10uMp\nhBBCCCGEEMKkJPEUQgghhBBCCGFSkngKIYQQQgghhDApSTyFEEIIIYQQQpiUJJ5CCCGEEEIIIUxK\nEk8hhBBCCCGEECaVZZZTEUIIIYR418TEqly+BTfu6V43E/158x645YXfv4aC+RRzhyqEEMmSxFMI\nIYQQIhO6eFPFa5AuwXyb0MvQdhTs+0XFxlqSTyFE5iVDbYUQQgghMpmXr1Taf5N80hnvSBgMnW36\nmIQQIj2kx1MIIYQQIpMZ+AOcuKgrW1lCrfJQ2AUKuer+LOwC/1yA//tdt8/89VC7gkr35tLrKYTI\nnCTxFEIIIYTIRBZuVlm0OaH+oy98/tHrCeWHdVXOXoWVu3T1ft9DpZIqlUpJ8imEyHySHWo7ZcoU\nqlevjpOTEy4uLrRq1YrTp08n2adnz55oNJokrzp16iR70j179rx2jEaj4fz58+m/IiGEEEKILOr4\neZWBMxLqXZtB39Zv3ldRFH4bCWWL6epR0dDuG3jyVDV5nEIIkVrJJp579+5l4MCBBAcHs3v3biwt\nLfH29ubx48f6fRRFoUmTJoSHh+tfW7duNejkZ86cSXJcyZIl03c1QgghhBBmEBYWxieffEK9evX4\n9NNPuXHjRqrbePJUpf1oXQIJUN4d5n6l+6z1NjntFf6aDDntdPVLt6DHRNBqJfkUQmQuyQ619ff3\nT1L38/PDycmJoKAgWrZsCYCqqlhbW+Pi4pLqk+fLlw9nZ+dUHyeEEEIIkVmEhYVRs2ZNnj59CsCB\nAwfYunUrISEhuLm5GdSGqqr0nqxLHEGXSK6ZBDnsUh42W6aowsJvVDqM1tU3BcJUP/i6R5ouRwgh\nTCJVs9pGRkai1WrJnTu3fpuiKBw4cABXV1c8PDzo27cv9+/fN6i9atWq4ebmhre3N3v27ElV4EII\nIYQQmcG3337L06dP8fHxwd/fn5o1axIeHs6sWbMMbmPGCli/L6G+YBR4FDX8Wc12jRR8OyXUxy6A\nHYel11MIkXmkKvEcMmQInp6e1K5dW7/Nx8cHPz8/du/ezYwZMzh8+DBeXl5ER0e/tR03NzfmzZvH\n2rVrWbt2LR4eHjRu3JgDBw6k/UqEEEIIIczg2rVrAPj6+tKsWTM+//zzJNtTsv+4yqh5CfVB7aFD\n49RPEDS1P9SvrCtrtdBlPNx5IMmnECJzUFRVNehfpGHDhrFq1SoOHDhAsWLF3rrfnLine truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x95e4cf8>"
+       "<matplotlib.figure.Figure at 0x7ad71d0>"
      ]
     },
     "metadata": {},
@@ -2322,9 +2323,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAEjCAYAAABaRN5lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAEjCAYAAABaRN5lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x827f048>"
+       "<matplotlib.figure.Figure at 0x7ae5f60>"
      ]
     },
     "metadata": {},
@@ -2353,9 +2354,9 @@
   "outputs": [
    {
     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA54AAAEjCAYAAABaRN5lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XlcVPX+x/HXALKIiqiIIiqoLC6ZG7jkUm65tOfS8sus\n22JpuZRlyzXtVla2aFbarmHe1PKqt8yuKWKGG5a5IIKKCy5pLrigoHB+f3wFHRUYFByW9/PxmMdh\nvnPOmc8wI87nfL/fz9dmWZaFiIiIiIiISBFxcXYAIiIiIiIiUrop8RQREREREZEipcRTRERERERE\nipQSTxERERERESlSSjxFRERERESkSCnxFBERERERkSKlxFNERABwcXEhODjY2WGIiIhIKaTEU0TE\nQT/++CM33XQTPj4+VKxYkTZt2vD1119f0blWrFhB7969qVKlCuXLl+f6669n4sSJZGVlXbJvSkoK\nr7/+On379qVBgwa4uLjg4uJCYmLi1b6kS9hstkI/pyM2b95Mv379qF69Ol5eXoSHhzNmzBhOnz5d\n4HOlpKTwj3/8g4CAADw9PQkODmb48OEcPXo012OK6/shIiJSWtgsy7KcHYSISHH30Ucf8dRTT1Gt\nWjX69++Pu7s7s2fPJiUlhWeeeYbx48c7fK558+Zx9913U758efr370+VKlWYP38+W7ZsoU+fPsya\nNctu/7lz53LXXXfl9EgeOnSI1NRUNm/eTGhoaKG9RhcXF4KCgti+fXuhndMRq1evpnPnzpw9e5Y+\nffpQu3ZtFi9eTFxcHDfccAOLFy/G3d3doXNt376ddu3aceDAAe644w7Cw8NZtWoV0dHRhIWF8dtv\nv1GlShW7Y4rr+yEiIlKqWCIikqfk5GTLw8PDqlatmrVz586c9iNHjlgNGjSwbDabtWLFCofOlZqa\navn5+Vmenp7W2rVrc9pPnz5ttWvXzrLZbNa3335rd0xKSoq1fPly6/jx45ZlWVanTp0sm81mbdmy\npRBe3Xk2m80KDg4u1HPm5+zZs1bDhg0tFxcX67///W9Oe1ZWltWnTx/LZrNZb775psPn6969u2Wz\n2awPP/zQrn3EiBGWzWazBg0aZNdenN8PERGR0kRDbUWkRLEsi4kTJ9KoUSO8vLwIDAzkqaeeIjU1\nlaCgoCKZo/jll1+SkZHBkCFDqFOnTk575cqVefHFFwGYMmWKQ+f67rvv+Pvvv7nnnnto0aJFTruH\nhwevvfYaAJMnT7Y7platWtxwww1UqFDhal+KQ9LS0hg5ciR16tTB09OTkJAQ3n777SJ5rpiYGBIS\nEujYsSO33HJLTrvNZst5Tkd/t9u2bWPRokUEBwczePBgu8fGjh1L+fLlmT59OmlpaTntJeH9EBER\nKQ2UeIpIiTJ48GCGDx/OsWPHeOyxx7j33ntZtGgR3bt35+zZs0UyR3HJkiUA9OjR45LHevbsCUB0\ndPRVn6tjx454eXmxYsUKzpw5c6XhXpWMjAy6d+/OnDlz6N27N48++iinTp1i1KhRvPrqq4X+fHn9\nPoKDgwkJCWHXrl0ODf/Nfg+6d+9+yWMVKlTghhtu4OTJk6xcudKh5y8O74eIiEhp4ebsAEREHPXr\nr78yZcoUQkJCWL16NT4+PgC88cYbdO3alb179xIUFGR3TGpqKu+//36Bnuemm26iU6dOOfe3bNmC\nzWa77Py9GjVqUL58eVJSUjh9+jSenp55nnvLli0Alz2Xq6srwcHBbN68me3btxMWFlaguAvD3r17\nadasGYsXL8bDwwOA0aNHExoayvvvv8+LL76Im9v5/zomTJiQZ9GeiwUHB/Pggw/m3M/r9wEQEhJC\nUlISSUlJ1KtXL89zO3KuRYsWkZSUROfOnfM9pji8HyIiIqWFEk8RKTGmTZsGwAsvvJCTdAKUK1eO\ncePG0b59+0uOOXLkSIF66mw2Gy4uLnaJZ2pqKoDdc17Ix8eHU6dOkZqamm/imZqais1my/NclmUV\nKJkrTDabjQ8++CAn6QTw8/PjtttuIyoqisTERBo1apTz2MSJE9m5c6fD57/xxhvtEk9HfreAQ7+P\nKzlXcX8/RERESgslniJSYvzxxx8Al00wW7dujaur6yXtQUFBl10SQy7Px8fnsj2LtWvXBkwif6Hk\n5ORrEpeIiIiUbJrjKSIlRnbvlL+//yWPubq6UrVq1SJ53uzesOwetcvFdeF++Z3Lsqx8z1W5cuUr\nCfWq5fa82cNrMzMzC/X5HP3dOvL7uJJzFff3Q0REpLRQj6eIlBiVKlUCYP/+/VSsWNHusczMTA4d\nOkT58uXt2gtjjmdYWBixsbFs2bKFNm3a2O27b98+0tLSqF27dr7DbLPPtXbtWrZs2ULz5s3tHjt7\n9izJycmUK1cu3/mMxcXVzvEMDw8Hzs+1vFhSUlKu82sv5si5wH4+Z2l7P0RERIorJZ4iUmK0aNGC\ndevWsXz5ckJCQuweW7ly5WV747LneNpsNizLyvc5LjfHs0uXLsTGxrJw4cJLEs+ffvoJIKdYTX66\ndOnCjBkzWLhwIffcc4/dY8uWLePUqVN06tSJcuXKOXQ+Z7vaOZ6dO3fm9ddfZ+HChYwaNcpu3+3b\nt5OUlOTwMjk33XQTAIsWLcKyLLsKx8ePH+e3337D29vb7j0sbe+HiIhIcaWhtiJSYgwYMACAcePG\n2fWyZWRk5KynebHsOZ6ZmZlkZWXle8vMzGT06NF253jooYfw8PDgww8/tEuyjhw5whtvvIHNZmPQ\noEF2xxw6dIiEhAT2799v196nTx+qVavGt99+y9q1a3PaT58+zcsvvwzAE0884dDvI69EeurUqbi4\nuOQkY0UlOTnZod9r9i17+ZJsnTp1omHDhixbtoz//ve/Oe1ZWVk8//zzAJf8bk+dOkVCQsIlS6zU\nq1eP7t27k5yczEcffWT32CuvvEJaWhoPPPAAXl5eOe3X6v0QEREp69TjKSIlRseOHXnsscf49NNP\nadKkCXfddRceHh7Mnz8fX19fAgICcHEp/OtpQUFBvPPOOzz11FO0atWK/v37U65cOb777jv27NnD\ns88+S+vWre2OmTRpEq+++ioPPvggX331VU57xYoV+fzzz7n77ru58cYbueeee/D19WX+/PkkJibS\nt29f+vXrd0kMAwcOzPk5ISEBMNV9s4cf33nnndx+++05+2QXVHJ3dy+030NRcHFx4auvvqJz5870\n6dOHPn36ULt2bRYvXszatWtp3749w4cPtztm1apVdO7cmbp1615S3Ojjjz+mXbt2PP300yxevJjw\n8HBWrVrF0qVLCQsL4/XXX7fb/1q9HyIiImWelYc33njDatWqlVWpUiXLz8/PuvXWW62NGzdest+W\nLVusO++806pcubJVvnx5q0WLFtbmzZtzPW90dLRls9kuuW3ZsiWvcERErKysLOv999+3wsPDLQ8P\nD6tWrVrWkCFDrNTUVKtChQpW8+bNi+y5f/zxR6tTp05WxYoVrQoVKliRkZHW119/fdl9x4wZY7m4\nuFgPPfTQZR9fsWKF1atXL8vX19fy8vKymjZtak2YMMHKysq67P42m81ycXHJ2Wbfsu+PHTvWbv+h\nQ4daNpvNmj59usOvz2azWcHBwXm+npiYGIfPVxDx8fFWv379LD8/P8vDw8MKCwuzxowZY50+ffqS\nfZcuXZpnrCkpKdbDDz9s1axZ03J3d7eCgoKs4cOHW0ePHs31+Yv6/RARESnrbJaV+9igHj16cO+9\n9xIREUFWVhajR49mxYoVxMfH4+vrC5hhVpGRkQwcOJD777+fypUrk5CQQJMmTQgMDLzseZcuXUrn\nzp2Jj4+nSpUqOe3VqlUrkt4KESn9kpKSCAsL49577+Wbb75xdjhO16xZMzIyMti0aZPdXEcRERER\nZ8hzqO3ChQvt7kdFReHj40NsbCy9e/cG4KWXXqJHjx6MHz8+Z7+goCCHntzPz6/Ilj8QkdLpwIED\nl1ykSktLY9iwYYAZ4ljWHT58mA0bNjBjxgwlnSIiIlIsFKh78dixY2RlZeX0dmZlZfHDDz/QsGFD\nevToQfXq1YmMjGTWrFkOna9Vq1YEBATQtWtXli5dWuDgRaTs+eCDDwgODmbgwIGMGjWKgQMHEhYW\nxk8//USvXr3o06ePs0N0uipVqpCZmUn//v2dHYqIiIgIAHkOtb1Yv3792LZtG3FxcdhsNvbv309A\nQADly5fntddeo3PnzixevJjnnnuOefPm0atXr8ueJzExkaVLlxIREUF6ejpRUVFMmTKFmJgY2rdv\nX2gvTkRKn6VLlzJ+/Hj++OMPDh8+TLly5QgNDeW+++5j2LBhuLq6OjtEEREREbmIw4nniBEjmDVr\nFsuXL88ZSrt3714CAwO57777mD59es6+999/P0eOHGHBggUOB9K7d2/c3NyYN29eTltqaqrDx4uI\niIiIFDc+Pj7ODkGkWHBoqO3w4cOZOXMmS5YssZu/Wa1aNdzc3GjUqJHd/uHh4ezatatAgURGRpKU\nlFSgY0RERERERKT4y3cdz6FDhzJ79myio6MJDQ21e8zd3Z2IiIicNcyyJSYmOlxgKNu6desICAgo\n0DEiIiIiIiJS/OWZeA4ePJjp06czd+5cfHx82L9/P2AW3Pb29gbgueeeo1+/fnTo0IGbbrqJ6Oho\nZs6caTdkdsCAAdhsNqZNmwbAhAkTCA4OplGjRmRkZDB9+nTmzZvHnDlzco1FwxQkN3FxcYApViWS\nG31OxBH6nIgj9DmR/Gi6mMil8kw8J0+ejM1mo0uXLnbtY8aMYfTo0QDcfvvtfPrpp7zxxhsMHTqU\n0NBQoqKi6NmzZ87+u3fvtivpf+bMGUaOHElKSgpeXl40adKEBQsW0KNHj8J8bSIiILine truncated
+      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x8156588>"
+       "<matplotlib.figure.Figure at 0x795a5c0>"
      ]
     },
     "metadata": {},
@@ -2622,7 +2623,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.4.1"
+   "version": "3.4.3"
  }
 },
 "nbformat": 4,
@@ -80,6 +80,8 @@ def plot_estimate_chart_1():
    plt.scatter ([1], [159], c='r')
    plt.xlabel('day')
    plt.ylabel('weight (lbs)')
+    ax.xaxis.grid(True, which="major", linestyle='dotted')
+    ax.yaxis.grid(True, which="major", linestyle='dotted')
    plt.show()


@@ -95,7 +97,9 @@ def plot_estimate_chart_2():
    plt.text (1.0, 164.4, "measurement ($z$)",ha='center',va='bottom',fontsize=18,color='blue')
    plt.text (0, 157.8, "estimate ($\hat{x}_{t-1}$)", ha='center', va='top',fontsize=18)
    plt.xlabel('day')
-    plt.ylabel('weight (lbs)')    
+    plt.ylabel('weight (lbs)')
+    ax.xaxis.grid(True, which="major", linestyle='dotted')
+    ax.yaxis.grid(True, which="major", linestyle='dotted')
    plt.show()

 def plot_estimate_chart_3():
@@ -106,7 +110,7 @@ def plot_estimate_chart_3():

    ax.annotate('', xy=[1,159], xytext=[1,164.2],
                arrowprops=dict(arrowstyle='-',
-                                ec='k', lw=1, shrinkA=8, shrinkB=8))
+                                ec='k', lw=3, shrinkA=8, shrinkB=8))

    est_y = ((164.2-158)*.8 + 158)
    plt.scatter ([0,1], [158.0,est_y], c='k',s=128)
@@ -118,10 +122,12 @@ def plot_estimate_chart_3():
    plt.text (0.95, est_y, "new estimate ($\hat{x}_{t}$)", ha='right', va='center',fontsize=18)
    plt.xlabel('day')
    plt.ylabel('weight (lbs)')
+    ax.xaxis.grid(True, which="major", linestyle='dotted')
+    ax.yaxis.grid(True, which="major", linestyle='dotted')
    plt.show()

 def plot_hypothesis():
-    plt.errorbar([1, 2, 3], [170, 161, 169], 
+    plt.errorbar([1, 2, 3], [170, 161, 169],
                 xerr=0, yerr=10, fmt='bo', capthick=2, capsize=10)

    plt.plot([1, 3], [180, 160], color='g', ls='--')
@@ -133,18 +139,18 @@ def plot_hypothesis():
    plt.ylabel('lbs')
    plt.tight_layout()
    plt.show()
-    
+
 def plot_hypothesis2():
-    plt.errorbar(range(1, 11), [169, 170, 169,171, 170, 171, 169, 170, 169, 170], 
+    plt.errorbar(range(1, 11), [169, 170, 169,171, 170, 171, 169, 170, 169, 170],
                 xerr=0, yerr=6, fmt='bo', capthick=2, capsize=10)
    plt.plot([1, 10], [169, 170.5], color='g', ls='--')
    plt.xlim(0, 11); plt.ylim(150, 185)
    plt.xlabel('day')
    plt.ylabel('lbs')
    plt.show()
-    
-    
-def plot_hypothesis3():   
+
+
+def plot_hypothesis3():
    weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
           169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
    plt.errorbar(range(1, 13), weights,
@@ -155,7 +161,7 @@ def plot_hypothesis3():
    plt.ylabel('weight (lbs)')
    plt.show()

-    
+
 def plot_hypothesis4():
    weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
           169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
@@ -170,8 +176,8 @@ def plot_hypothesis4():
    book_plots.show_legend()
    plt.show()

-    
-def plot_hypothesis5():   
+
+def plot_hypothesis5():
    weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
           169.6, 167.4, 166.4, 171.0, 171.2, 172.6]

@@ -186,13 +192,13 @@ def plot_hypothesis5():
    book_plots.show_legend()
    plt.show()

-def plot_g_h_results(measurements, filtered_data, 
-                     title='', z_label='Measurements', **kwargs):  
+def plot_g_h_results(measurements, filtered_data,
+                     title='', z_label='Measurements', **kwargs):
    book_plots.plot_filter(filtered_data, **kwargs)
    book_plots.plot_measurements(measurements, label=z_label)
    book_plots.show_legend()
    plt.title(title)
    plt.gca().set_xlim(left=0,right=len(measurements))
-    
+
 if __name__ == '__main__':
    create_predict_update_chart()