Removed #reset from magic

I was using comment to detect which %matplotlib inline to comment out to generate the PDF but it generates a UsageError. I need to make my script smarter instead. The problem is that I don't want to remove the first inline definition
2017-01-18 10:59:43 -08:00
1 parent f0e78c7037
commit 91e9e08377
@@ -377,7 +377,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x15b57b83390>"
+       "<matplotlib.figure.Figure at 0x21541ec1390>"
      ]
     },
     "metadata": {},
@@ -5141,18 +5141,6 @@
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
-  },
-  "widgets": {
-   "state": {
-    "93f4411da7ea42738e12c0ac83448a20": {
-     "views": [
-      {
-       "cell_index": 121
-      }
-     ]
-    }
-   },
-   "version": "1.2.0"
  }
 },
 "nbformat": 4,
@@ -399,7 +399,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x195c93946a0>"
+       "<matplotlib.figure.Figure at 0x20e6c4c86d8>"
      ]
     },
     "metadata": {},
@@ -530,7 +530,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x195c97d27b8>"
+       "<matplotlib.figure.Figure at 0x20e6c95ae10>"
      ]
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     "metadata": {},
@@ -664,7 +664,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x195c9e67588>"
+       "<matplotlib.figure.Figure at 0x20e6cf85748>"
      ]
     },
     "metadata": {},
@@ -806,7 +806,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x195c9465588>"
+       "<matplotlib.figure.Figure at 0x20e6c99f2b0>"
      ]
     },
     "metadata": {},
@@ -1638,7 +1638,7 @@
    "    time.sleep(0.05)\n",
    "    \n",
    "# reset to noninteractive plot settings\n",
-    "%matplotlib inline #reset\n",
+    "%matplotlib inline\n",
    "set_figsize(y=2);"
   ]
  },
@@ -1702,7 +1702,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x195c9e53c50>"
+       "<matplotlib.figure.Figure at 0x20e6cf737f0>"
      ]
     },
     "metadata": {},
@@ -1754,7 +1754,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x195ca51bb00>"
+       "<matplotlib.figure.Figure at 0x20e6d6393c8>"
      ]
     },
     "metadata": {},
@@ -2584,7 +2584,7 @@
    "print('Final Belief:', belief)\n",
    "\n",
    "# reset to noninteractive plot settings\n",
-    "%matplotlib inline #reset\n",
+    "%matplotlib inline\n",
    "set_figsize(y=2)"
   ]
  },
@@ -2674,7 +2674,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAz4AAADYCAYAAAAqPjbaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x195ca66ec50>"
+       "<matplotlib.figure.Figure at 0x20e6c8a8e10>"
      ]
     },
     "metadata": {},
@@ -2712,7 +2712,7 @@
     "data": {
      "image/png": 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      "text/plain": [
-       "<matplotlib.figure.Figure at 0x195c9484470>"
+       "<matplotlib.figure.Figure at 0x20e6e7f0278>"
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     "metadata": {},
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x195ca5876a0>"
+       "<matplotlib.figure.Figure at 0x20e6d6fce48>"
      ]
     },
     "metadata": {},
@@ -2805,7 +2805,7 @@
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      "text/plain": [
-       "<matplotlib.figure.Figure at 0x195ca5fbe80>"
+       "<matplotlib.figure.Figure at 0x20e6d6daa58>"
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@@ -2836,7 +2836,7 @@
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x195cb73e4e0>"
+       "<matplotlib.figure.Figure at 0x20e6e859400>"
      ]
     },
     "metadata": {},
@@ -2867,7 +2867,7 @@
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-       "<matplotlib.figure.Figure at 0x195c9f83ac8>"
+       "<matplotlib.figure.Figure at 0x20e6d09cac8>"
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      "text/plain": [
-       "<matplotlib.figure.Figure at 0x195cb828b38>"
+       "<matplotlib.figure.Figure at 0x20e6e942dd8>"
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     "metadata": {},
@@ -2943,7 +2943,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x195ca6446d8>"
+       "<matplotlib.figure.Figure at 0x20e6c8d3da0>"
      ]
     },
     "metadata": {},
@@ -3805,7 +3805,7 @@
    "        fig.canvas.draw()\n",
    "        time.sleep(sleep)\n",
    "    plt.show()\n",
-    "    %matplotlib inline #reset\n",
+    "    %matplotlib inline\n",
    "    set_figsize(y=2)\n",
    "    print('Final posterior:', pos)\n",
    "     \n",
@@ -5449,7 +5449,7 @@
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3kAAAIbCAYAAABWu1cwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x195c9f764e0>"
+       "<matplotlib.figure.Figure at 0x20e6d72fcc0>"
      ]
     },
     "metadata": {},
@@ -5652,7 +5652,7 @@
     "data": {
      "image/png": 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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x195c98748d0>"
+       "<matplotlib.figure.Figure at 0x20e6c8e1978>"
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     },
     "metadata": {},
@@ -5703,7 +5703,7 @@
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x195c9fa3d30>"
+       "<matplotlib.figure.Figure at 0x20e6e8619b0>"
      ]
     },
     "metadata": {},
@@ -5737,7 +5737,7 @@
     "data": {
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truncated
      "text/plain": [
-       "<matplotlib.figure.Figure at 0x195c9ea7710>"
+       "<matplotlib.figure.Figure at 0x20e6e894ac8>"
      ]
     },
     "metadata": {},