GitHub #241 - remove deprecation warning

Newest version of Matplotlib has deprecated using strings
as booleans.

03-Gaussians.ipynb

@@ -16,7 +16,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -255,7 +255,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -373,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 37,
    "metadata": {
     "scrolled": true
    },
@@ -405,7 +405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -462,7 +462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -480,7 +480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -531,7 +531,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -570,7 +570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -600,16 +600,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 43,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2c2130495c0>"
+       "<Figure size 576x216 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -635,14 +635,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjEAAADeCAYAAADM4lGKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzt3XlUFHe6N/BvB1m7m9VhC4uK4tKJjAbjmI2b5B3HxIWIUYMgGp3ELYk34z0xi4oTTDTemGQy7s47Y+IScy7m6iQGnUhGxLyZKEZQcCMKjIbYKiCbDTTwe/9AWppuoIDeCr6fc/rY9dSvqp6q+lX1Y3VRrRBCCBARERHJzD32ToCIiIioK1jEEBERkSyxiCEiIiJZYhFDREREstTH3gl0VXl5ub1TICIiIgvz8vKS3JZXYoiIiEiWWMQQERGRLMn266SWOnPpiSwvKysLABAdHW3nTIj7wnFwXzgW7g/HYW5fdPUWEV6JISIiIlliEUNERESyxCKGiIiIZIlFDBEREckSixgiIiKSJRYxRES9kEKhgEKhsHcaRN3CIoaIiGSBhRe1xiKGiIiIZIlFDBEREckSixgiIiKSJRYxRNRtvFeh67jtiLqORQwRERHJEosYIiIikiWbFDG1tbWYO3cuwsPDoVarMWLECKSlpbXZ/sMPP0RgYCC8vLwwZ84c1NbW2iJNIiIikhGbFDH19fUIDQ1FRkYGysvLkZKSgmnTpqGwsNCk7aFDh7BmzRqkp6ejsLAQly9fRnJysi3SJJnhvQRE1BPwXNZ1CiGEsMeChw8fjuTkZEyZMsUoPmPGDPTr1w/vvvsuACA9PR0JCQm4du2aUbvy8nLD+/z8fOsn3IOMGjUKAHDixAk7Z9I9PWU9egLui66z17aT4z6TY85S9NT16oxBgwYZ3nt5eUmezi73xGi1Wly8eBEajcZkXF5eHqKiogzDUVFR0Gq1KCkpsWWKZo0aNcrQ2YiISD54/u6ZbH4lRq/X46mnnkJERAS2bNliMj4iIgIbNmzAuHHjDO1dXFxQUFCAfv36Gdq1vBLTmaqtO5ov99np4pXFWHo9srKyAADR0dEWmZ9UPWV/WBL3heOQui/ste3kuM+6k7Mjr68j52YN5o6Nrn6m2/RKTGNjI2bOnAkXFxesX7/ebBuVSoWKigrDcPN7tVptkxyJLIHfcRMRWZ/NihghBObOnQutVou9e/fC2dnZbDuNRoOcnBzDcE5ODgICAuDn52erVIl6JRZePRf3rX3YYrv39n3bx1YLWrBgAc6dO4fDhw/D3d29zXZJSUmYPXs2EhISEBQUhFWrVmH27Nntznv6lu8N7ycMD8LMMf2gq2vA7L8dN2n77AMhmBoditLqOizYedJkfOJvwjExKhjFt3R49fNso3EB8atRcfx/AQCXblThzS/OmEz/8hOD8MigvsgrLsfbX541Gf/auMF4INwXJ4tKsfbgBZPxKyYOgybYC8fyb+LP35resPxu3P2I+JUKh89qsS3zssn4D6f/GsHe7vgypxg7/1VkMn5T4gMAAOV9Txptt2bbn38Q7i5O2PF9Ib46/YvJ+M/njQEAbD16CennrgMAKisrAQC/On0cn8x5EADwcXo+vvvpptG0Ph4u2DyzafnvHTyPH4vKjMYHebnho+dGAAD++GUezhZXGI0f8CslVscNBwC88cVpBMSvBnB3/w8L9kTyxKb7rP5zzyn8Ul5jNP3IcB8sHTcEADB/x0mU3a4zGv/wwL545cmmm8tm/fU4avQNRuOfHOqPFx+LMFpmSy37XuvcgO71PQB44dEB+D/DAtrte24ACm7p8d9m8uuo7zn794f+ekGX+l5A/Grc/GodALTb93yVLvifrCtIPXnVZHxX+l4zN2cnm/a9yzeqjcab63vNx4X65Pft9r2A+NWoKbr7H7fu9j1z5z3lfU+iOjfdqO+17KOW6Hu2OO+5RzwIzwcnm2wDe5z3mrXX9wLiV6NBd7cvtdX3mnWl7/k8+QLK0rcBsP95T+pnbstjo7nvdZVNipiioiJs2bIFrq6uCAwMNMS3bNmCRx99FMOGDcPZs2cRFhaGcePG4bXXXsPjjz8OnU6HKVOm4I9//GMHS2jZ6S4D+AcABQBzV28uAbiNpotQvmbG/wRAB8AJgE8by9uKpk3nbWbcRQC1AJwBmPte7wKAOgAuADzNjD8PQA/AFYC5r9DOAagH4AZAZWb8WQANANwBKM2M/7TFe9ODFfgrAHFnWnPF5tY7/6ru5AA4OzcfNJUAmk+A6jvr0FIjgOYPb080bYOWGgA0353vhaZt2FI9gH/ded9y2zevx78BfHfnvQ+a9mFLRQCO3nnvC9MLkYUA/nnnvR+a+lBLBQDS77zvC1Mt+17r3IDu9718ADVor+/17XsBZVACGGpm/N2+l5HR9Nd+MTGRZtodQHPfy8i42KJdR30PaOof7fW9RgAed16tdb7v3SVg277X+tRp2vfuHhdV6LjvAXfXr7t9r72r1p/CtO/9Akv0Pduc91rm3JLtz3t3ddT3Wk7fVt9rlovO972Wy7D3eU/aZ67xsdHc96abmbZjdvsT6+4yvgnoc5ssU6GYBwAQwvSGZDnpznqYm7b5eT8tb7y2BUfeH/bKTeq+MJef1JjU+fV23dkXltadfWuLXBx92q6yxfEjx2PP3LFRXn63iHHYG3uJbE2hmGc4yO3NkXIhIuoJWMQQERF1Av9D4jhYxIAdkoioLTw/kiNjEWMj9joR8AREZH08zqgnc+T+zSLGChx5h8uVo2xTS+fhKOtlT9wGlsXt2TNwP0rDIoaoDY50EnGkXHoqc9uY253YBxwbixiyCh74RERkbSxiiMgqWMgSmbLFcdGbjj0WMdSj9KaDl+SH/ZPIsnpdEcMbM81zpPVwpFzsobevP0nnSH3FkXKh3sNmPwApN3J8lDMRUXfI8acIHIWjr4O9ft7CnP79V1ssl153JcaR8H8uRJbH44qo9+hRV2IcvRK2JEdaV0tW1bbgSNuO5If9xza4nUkKXokhIiIiWWIRQzbDy/xkL+x7RKZ6wnHBIoYI8jyYpeYsx3Xr7bjPiKRhEdOD8MRHRNRzz4U9db26g0UMERERdYqjFFQsYqhNjtJJidrCPkrUu/WIP7GevqUvACAgfjWqz2cCAHR1CvSfXQIAiImJNLRV3vckqnPTUVp9Dxbs9DVM1zyfxN9UY2KUDk7qvug7YYlh3s3tKo7/LwDg0o0+ePMLb6NpAeDlJyoBAM7+/U2mBYCTRS54ILwOJ4tcTKYFgBUTy6EJ1uNYviv+/K3aZF3fjbuFiF/Vwz3iQXg+ONlkGTe/WgcA+DLHHTv/pTRZxqbEUsN2MJefrk4BdxeBHd+bTtvS1qMqpJ9zM5p21l/98Mmcpm3u9dBzcAuPMpq27zNv4Oa+prbvHfTEj0UuyMi4CKBpHwV5NeCj58oAAD5PvmCSn770Z8PwG2a2/bBgvWH8f+7xwS/lTkZtRobXYem4CkMuredfU5RjGJ71Vz+T+T85tMYw3tw2mTBch5ljqqGrU5jddrboeyHOQP5NJZYe6nzfc/bvD/31AqO+17Jdc987fNYN2zJVRusute/5KhvxP1keSD3pYZJfy7731Wl3k+37+bybACzX91oK8mowvP/jl144W+xsNP83vvDG6rhbAADf370EZ997jebv8+QLKEvfBuBu36upadpGbm5uFul7Lz5WZRRrmd+O75U27XuttXXea/bauAqL9D1z5z0A+HB6GYK9Gzp93mu2/fmSNvteQPxqaD97A8Ddvtdy/m7Owqjvtd52DboKw/B7Bz1NcrNG3+vOea9GrzBa/7bOe1L73rMP3MbU6Nsd9r2u6hFFDBB059+Ld/59EUADgP9uNb6lJAAnW00XBCAcQDCAt8xM23L+VQDOtJoWAB4zk1fLaScB8AVQCmCrmXZPAvACcBNAvpm8xwJQAfi/HeRXDKDITH4TALzQzrRzADgBKASwx0y7ZqMBXG81bSiAKXfe/7OdaV8EcB5AWav83ACMADCvRczcugHAaQCXW7XzbNF+IICaVvP3ATBE4vyP4+72b27n36K9ufUKAtAP9ux7N29qcOuWHsak9r1m43F33Vsuo7nvaXF325vLr72+5wLgCoCrZqZt2fd+MZNX3J1/L8Eyfa8ltxbv7wPQ/MHTPP8hAIbfef9yB/M/BaAGen3TB7ubmxqW6HsKxUAAwLTN/8/MtA/Dtn2vtbbOe83+A5bpe+bOewDwFAB3dP681ywWbfe9luvf3Pdazt8JTX1Pyr49f2cZLdtZvu9177zXAGNtnfek9b3nn1+OabnpKKmqRft9r9zMOnVMIYQQXZrSzsrL766wl5cXAEChaKogm1ep9bAcYuY4Un5yi/W2fZaVlQUAiI6OttoyzOkJy5DjvnD085TUdvbqK1I40nZ3pH1r6Zi5z3QpeE8MERERyVIP+Tqp5+jq/xaIAGDUqFEArNt/2EdJKvYVsjZeiSHqJoVCYThZExGR7bCIISIiAv9DIkcsYojI4fDDxLE50v7pai6OtA7UdTYrYtavX4/o6Gi4urpi9uzZbbbbvn07nJycoFKpDK8jR47YKk0iIiKSCZvd2BscHIxly5bh0KFD0Onaf7DNmDFjcOzYMRtlRkS9DW84JeoZbFbExMU1PagqKysLV69e7aB15zQ/j0HqMGO9L+YoeTDmWDFHyYMx+8UcJQ9Hi5ljzeUOGjRIUg6tOeQ9MadOnULfvn0RGRmJlJQU1NfX2zslIiKiHm/UqFGGRzXIgc2f2Lts2TJcvXoV27dvNzv+8uXLUCgUCA8PR15eHqZPn46ZM2fijTfeMGrXU5/Yy5j8ntjLmPxiPeXpyYw5dh9gzEGe2KvX65Gbm4tjx44hNzcXen3r32axnAEDBqB///645557cP/992PFihVITU212vKIiIhIntq9J+bAgQPYvHkLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2c214170550>"
+       "<Figure size 576x216 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -652,8 +652,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "mean = 1.776\n",
-      "std  = 0.147\n"
+      "mean = 1.783\n",
+      "std  = 0.143\n"
      ]
     }
    ],
@@ -689,7 +689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -722,7 +722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -759,14 +759,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2c21442bc50>"
+       "<Figure size 576x216 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -780,7 +780,7 @@
     "    plt.plot([i ,i], [mean, X[i]], color='k')\n",
     "plt.axhline(mean)\n",
     "plt.xlim(-1, len(X))\n",
-    "plt.tick_params(axis='x', labelbottom='off')"
+    "plt.tick_params(axis='x', labelbottom=False)"
    ]
   },
   {
@@ -800,7 +800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -838,14 +838,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2c214427a58>"
+       "<Figure size 576x216 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -877,14 +877,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2c213f2e6d8>"
+       "<Figure size 576x216 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -915,14 +915,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2c214106748>"
+       "<Figure size 576x216 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -979,16 +979,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 52,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2c2142b5da0>"
+       "<Figure size 576x216 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1030,7 +1030,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -1076,7 +1076,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -1102,16 +1102,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 55,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2c2143c44e0>"
+       "<Figure size 576x216 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1157,14 +1157,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2c214558e48>"
+       "<Figure size 576x216 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1187,33 +1187,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9fd4281843f941b8a16ca99c17362120",
+       "model_id": "eeeb2de72db747209765963d4fd91912",
        "version_major": 2,
        "version_minor": 0
       },
-      "text/html": [
-       "<p>Failed to display Jupyter Widget of type <code>interactive</code>.</p>\n",
-       "<p>\n",
-       "  If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
-       "  that the widgets JavaScript is still loading. If this message persists, it\n",
-       "  likely means that the widgets JavaScript library is either not installed or\n",
-       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
-       "  Widgets Documentation</a> for setup instructions.\n",
-       "</p>\n",
-       "<p>\n",
-       "  If you're reading this message in another frontend (for example, a static\n",
-       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
-       "  it may mean that your frontend doesn't currently support widgets.\n",
-       "</p>\n"
-      ],
       "text/plain": [
-       "interactive(children=(FloatSlider(value=5.0, description='mu', max=10.0), FloatSlider(value=0.6000000000000001, description='variance', max=1.0, min=0.2), Output()), _dom_classes=('widget-interact',))"
+       "interactive(children=(FloatSlider(value=5.0, description='mu', max=10.0), FloatSlider(value=0.6000000000000001…"
       ]
      },
      "metadata": {},
@@ -1399,7 +1384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 58,
    "metadata": {
     "scrolled": true
    },
@@ -1429,7 +1414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -1458,16 +1443,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 1.77   3.378 -3.014  5.337  4.285  4.797  1.477\n",
-      "  1.351  5.739  4.075 -1.575  0.681  0.202 -0.747\n",
-      "  1.392]\n"
+      "[ 6.617  5.3    3.104  5.279  7.17   3.813 -0.221\n",
+      "  1.413 -0.555 -2.062  0.492 -1.039  5.578 -3.175\n",
+      "  0.609]\n"
      ]
     }
    ],
@@ -1485,7 +1470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -1510,7 +1495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -1548,14 +1533,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2c2144ed978>"
+       "<Figure size 576x216 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1583,7 +1568,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1601,14 +1586,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2c214332908>"
+       "<Figure size 576x216 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1631,7 +1616,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1650,14 +1635,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x2c214169b38>"
+       "<Figure size 576x216 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1693,16 +1678,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "DescribeResult(nobs=5000, minmax=(1.3813891957392315, 19.42199152705908), mean=10.007529817656724, variance=2.7772693671119986, skewness=-0.07073510230931251, kurtosis=1.4943039523916015)"
+       "DescribeResult(nobs=5000, minmax=(-3.3558882657362066, 24.02783608230256), mean=10.036498244806243, variance=2.96816439267424, skewness=-0.035679621633594914, kurtosis=3.8111268433571883)"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1721,16 +1706,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "DescribeResult(nobs=10, minmax=(-0.8175084513413164, 1.1865956580866834), mean=0.2940337215902996, variance=0.6230354047551284, skewness=-0.41279400723365806, kurtosis=-1.3123468690692666)\n",
+      "DescribeResult(nobs=10, minmax=(-0.04693943665506825, 0.6562007196788996), mean=0.34537366184107265, variance=0.0613118821936125, skewness=-0.21435079986652267, kurtosis=-1.2763449406970233)\n",
       "\n",
-      "DescribeResult(nobs=300000, minmax=(-4.7480566375065525, 4.550774022502299), mean=0.0005748027839955969, variance=1.0005892638782288, skewness=6.280909739979528e-05, kurtosis=0.006885785867941863)\n"
+      "DescribeResult(nobs=300000, minmax=(-4.742809555723424, 4.672447921701554), mean=0.002356925171581094, variance=0.9970954717330218, skewness=0.007232863756995517, kurtosis=-0.0030043672414206135)\n"
      ]
     }
    ],
@@ -1844,7 +1829,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.4"
+   "version": "3.6.5"
   },
   "widgets": {
    "application/vnd.jupyter.widget-state+json": {