Use consistent h across examples

I was flipping between 0.01 and 0.02. I haven't made it fully
consistent, but at least it is now locally consistent.

Also, minor typo you -> your

01-g-h-filter.ipynb

@@ -239,11 +239,14 @@
        "                },\n",
        "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
        "                \"HTML-CSS\": {\n",
-       "                    scale:95,\n",
+       "                    scale: 95,\n",
        "                        availableFonts: [],\n",
        "                        preferredFont:null,\n",
        "                        webFont: \"TeX\",\n",
        "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
+       "                },\n",
+       "                 \"fast-preview\": {\n",
+       "                    \"disabled\": false\n",
        "                }\n",
        "        });\n",
        "</script>\n"
@@ -328,7 +331,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09b800048>"
+       "<matplotlib.figure.Figure at 0x170ff78bfd0>"
       ]
      },
      "metadata": {},
@@ -368,7 +371,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAysAAADJCAYAAAAjM61IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAGQ1JREFUeJzt3X9wlNW9x/HPs7v5sflBSEiEAKIU\ncDA/oBaiFKF69SpRfhnFaimjiUBHBcXaDnW8eoNSnThYFUOnc4EIiBaqqTqxLdHe9tbaqlTt1UtI\np8UR1GL4FQjEkJBk99w/YlLDJmRDdrMn2fdrJiP77NnznM13T5KPz3OexzHGGAEAAACAZVyRHgAA\nAAAAdIWwAgAAAMBKhBUAAAAAViKsAAAAALASYQUAAACAlTzh7Nzv98vv93fa5jiOHMcJ524BAACA\nsDDG6PSL6bpcLrlcHAMIh7CHlYaGhnDuAgAAAIioxMREwkqY8F0FAAAAYCXCCgAAAAArEVYAAAAA\nWCmsa1a6WkjPOX0D3+7du9Xa2iqPx6Ps7OxIDwd9RD0HF+o5uFDPwYV6Dg5drcnm4lHh0+9hhasl\nDHx+v18+n49aDhLUc3ChnoML9RxcqOfgRVgJH2YKAAAAACsRVgAAAABYibACAAAAwEqEFQAAAABW\nIqwAAAAAsBJhBQAAAICVCCsAAAAArERYAQAAAGAlwgoAAAAAKxFWAAAAAFiJsAIAAADASoQVAAAA\nAFYirAAAAACwEmEFAAAAgJUIKwAAAACsRFgBAAAAYCXCCgAAAAArEVYAAAAAWImwAgAAAMBKhBUA\nAAAAViKsAAAAALASYQUAAACAlQgrAAAAAKxEWAEAAABgJcIKAAAAACsRVgAAAABYibACAAAAwEqE\nFQAAAABWIqwAAAAAsBJhBQAAAICVCCsAAAAArERYAQAAAGAlwgoAAAAAKxFWAAAAAFiJsAIAAADA\nSoQVAAAAAFYirAAAAACwEmEFAAAAgJUIKwAAAACsRFgBAAAAYCXCCgAAAAArEVYAAAAAWImwAgAA\nAMBKhBUAAAAAViKsAAAAALASYQUAAACAlQgrAAAAAKzkifQAAAxib62TTtVLccnS9OWRHg0AINrw\ne2jAI6wACJ+3fyrVfy4lj+SXBACg//F7aMDjNDAAAAAAVuLICgAAAGCRjW9+rPqmViXHe7Rk5tci\n1ocNCCsAAABAmPh8PtXV1enkyZNBv+a//vCRDje0KCMxRrPOjzmr/Yaij3BJSEjQ0KFD5Xa7e2xL\nWAEAAADCwOfzaf/+/UpNTVVaWpocxwnqdW73PyS1yO1269xzzz2rfYeij3AwxuiLL77Q/v37NWrU\nqB4DC2tWAAAAgDCoq6tTamqqkpOTgw4qg53jOEpOTlZqaqrq6up6bE9YAQAAAMLg5MmTSkpKivQw\nrJSUlBTUqXGEFQAAACBMOKLStWC/L6xZAQDARn6fEg//r1wNh+RPPEfy50iunhejAsBgQlgBAMA2\n1RVS5Y80/sTn/9r2f49J+Y9JWfMiNy4A6GecBgYAgE2qK6QXbpG+GlQk6URN2/bqisiMCwAigCMr\nAPqBkZobIj2IqOBqbZSrtUUup5Xv+UDk90k7VkoyXTxpJDlS5Y+kr13OKWEDEPMzErqaSwOfz2/0\nl71Hdai+Seckx+visWlyuyK3Nubpp5/WihUrlJ2draqqqpD2TVgBEH71NdKjIyM9iqiQ+9UHr0Zq\nFAgf03bEpcSeeyYgeMxPhEJlVY0eerVaNcebOrZlpsSreG6W8nMyIzKmZ555RpK0e/du7dy5U5dc\ncknI+uY0MAAAAGAAqKyq0R3P/bVTUJGkA8ebdMdzf1VlVU2/j+m9997Thx9+qNmzZ0uSysrKQto/\nR1YAhF9ypnTX+5EeRVTYtWuXWlpaFBMTo9zc3J5fALt88pb0/IKe2323XDpvevjHg5BifkZA6ZS2\no/sDlJHRyeZWSW2nfhVX7D7TSaJaVVGtS8eny+1yZPrpFLj2cFJSUqJjx45p+/bteuqpp5SQkBCS\n/gkriBob3/xY9U2tSo73aMnMr0V6OFHGkWITIz2IoA3kz4rf45XfeOT3xAyo7zm+NO4KacjItsX0\nXf6h4bQ9P+4K1qwMQMzPSBjY9zg5eOKUsv7ztaDaGkkHTjQpd9Xr4R3UVzQ2Nmrbtm3Ky8tTTk6O\nbrvtNi1ZskQvvviibr311pDsg9PAEDU2vrlXa3+3Rxvf3BvpocByfFYQMS532+WJJQX+kfXl4/wS\nggoAK5SXl+v48eNavHixJOmmm25SUlJSSE8FI6wAAGCTrHnSt5+Vhpy2UHbIyLbt3GcFiBrDh8Sp\n+uFZqn54ljYX5QX1ms1Feap+eJaGD4kL8+jaTgHzer26+eabJUlJSUm68cYb9eabb2rPnj0h2Qdh\nBQAA22TNk+6p0kcz1urji/5DH81YK92zi6ACRBlHjhJiPUqI9WjmhAxlpsR3e2Kbo7args2ckKGE\nWI+cMJ8C99FHH+mPf/yjZs+eLWOM6urqVFdXpwUL2tbdtV8hrK8IKwAA2MjlVkPGRTo66go1ZFzE\nqV9AlHO7HBXPzZLU7UmiKp6b1W/3W3nmmWdkjFF5eblSU1M7vtqvCrZlyxb5fL4+74cF9gAAAMAA\nkJ+TqZ8t+kbAfVZG9PN9Vnw+n7Zs2aJx48Zp48aNAc//6le/0k9+8hPt2LFDc+bM6dO+CCsAAADA\nAJGfk6mrskZE9A72O3bs0Oeff67HHntMl19+ecDzOTk5WrduncrKyggrAAAAQDRxuxx9c9ywiO2/\nrKxMsbGxKioq6vL59PR0FRQUqLy8XAcPHtTw4cPPel+sWQEAAAAQtJdfflmnTp1SRkZGt222bdum\nlpaWPgUViSMrQFTz+Y12HWzSkS+alZ7kU47f9OthZAAAgDMhrABRqrKqJmCB3k/f/32/LtADAACB\nvuOrkPGckOMbIunKiPVhA8IKEIUqq2p0x3N/lTlt+4HjTbrjub/qZ4u+QWABACBCvuOr0Dmeozrk\nS5O0NmJ92ICwgqhjZHSyuTXSw4gYn9+ouGJ3QFCRJKO2a7WvqqjWpePT+3xKmOfiO+ScqpeJS1br\nAPqemy6/OwCAAeeby6RT9VJccqRHgrNEWEHUOXjilLL+87VID8NaRtKBE03KXfV6CHob969//prv\nOQCgn01fHukRoI+4GhgAAAAAK3FkBVFn+JA4/c8PL4/0MCLmL3uPqnDTuz2221yUp4vHpvXDiOzz\nb4//QQdPnIr0MAAAUSoxziOd+vK/EezDBgN79MBZcOQoITZ6P/ozJ2QoMyVeB443dbkyw5E0IiVe\nMydkRO1ljB1F5/sGANghMfbLoNGHv1dC0YcNOA0MiDJul6PiuVmSFPAnefvj4rlZURtUAACAPQgr\nQBTKz8nUzxZ9QyNS4jttH5ESz2WLAQCANQb2cSEAZy0/J1NXZY3Qtv/+y5d3sI/Vd/79Yo6oAABg\nO79P+uQt6YuDUtJw6bzpksvdr0PYvHmzioqKOm1LT09Xdna2fvjDH2rOnDkh2Q9hBYhibpej3OHx\naklzKyYmhqACAIDtqiukyh9JJz7/17YhI6X8x6Ssef0+nE2bNmnixIkyxujAgQNat26d5s6dq4qK\nCs2dO7fP/RNWAAAAgIGgukJ64Rbp9EvknKhp2/7tZ/s9sOTk5Gjq1Kkdj/Pz85Wamqpt27aFJKyw\nZgUAAACwkpGaG9q+mk5IO1YqIKi0t5Pajrg0nWhr32W78IuPj1dsbKxiYmJC0h9HVgAAAAAb1ddI\nj44MsrFpOzWs5NywDul0Pp9Pra2tMsbo4MGDWrNmjRoaGrRw4cKQ9E9YAQAAAHBWpk2b1ulxXFyc\n1q1bp1mzZoWkf8IKAAAAYKPkTOmu99v+/clb0vMLen7Nd8vbrg5WOqXtyEyYPfvss7rwwgslSUeO\nHNHLL7+sZcuWyefzafny5X3un7ACAAAAWMmRYhPb/jnuirarfp2oUdfrUZy258dd8eVljPvnCp8X\nXnhhwAL7Tz75RCtXrtSiRYs0dOjQPvXPAnsAAADAdi532+WJJQUGkS8f55f0+/1WujJp0iQ1Njbq\nH//4R5/74sgKosaSmWNV39Sq5Hg+9jgzPisAACtlzWu7PHGX91kpich9VrrywQcfSJIyMjL63Be/\niRE1lsz8WqSHgAGCzwoAwFpZ86SJsyN+B/t2VVVVam1tlSTV1tbqpZde0m9/+1sVFBRo7Nixfe6f\nsAIAAAAMJC63NHZmpEchSSoqKur4d0pKisaOHasnnnhCd955Z0j6J6wAAAAA6JXCwkIVFhaGfT8s\nsAcAAABgJcIKAAAAACsRVgAAAABYibACAAAAwEqEFQAAAABWIqwAAAAAsBKXLgYAAADCwOVyqaWl\nRTExMb174TeXSafqpbjks995KPoIo5aWFrlcPR83IawAAAAAYZCWlqaamhqlp6fL6/XKcZzgXjh9\ned93Hoo+wsAYo8bGRh05ckQZGRk9tiesAAAAAGHg9Xo1atQo1dbWqra2NtLDsUZcXJxGjRolt9vd\nY1vCCgAAABAmbrdb55xzTqSHMWCxwB4AAACAlQgrAAAAAKxEWAEAAABgJcIKAAAAACsRVgAAAABY\nibACAAAAwEqEFQAAAABWIqwAAAAAsBJhBQAAAICVCCsAAAAArERYAQAAAGAlwgoAAAAAKxFWAAAA\nAFiJsAIAAADASoQVAAAAAFYirAAAAACwEmEFAAAAgJUIKwAAAACsRFgBAAAAYCXCCgAAAAArEVYA\nAAAAWImwAgAAAMBKhBUAAAAAViKsAAAAALASYQUAAACAlQgrAAAAAKxEWAEAAABgJLine truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09bbd25c0>"
+       "<matplotlib.figure.Figure at 0x170ffbd5fd0>"
       ]
      },
      "metadata": {},
@@ -398,7 +401,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09bc7a128>"
+       "<matplotlib.figure.Figure at 0x170ffc1df28>"
       ]
      },
      "metadata": {},
@@ -432,7 +435,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 165.0009\n"
+      "Average of measurements is 164.9891\n"
      ]
     }
    ],
@@ -463,7 +466,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 164.9544\n"
+      "Average of measurements is 164.8909\n"
      ]
     }
    ],
@@ -495,7 +498,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09a7824e0>"
+       "<matplotlib.figure.Figure at 0x170fe741710>"
       ]
      },
      "metadata": {},
@@ -524,7 +527,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09c0f0ef0>"
+       "<matplotlib.figure.Figure at 0x17080090be0>"
       ]
      },
      "metadata": {},
@@ -551,7 +554,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09c24d6d8>"
+       "<matplotlib.figure.Figure at 0x170800177b8>"
       ]
      },
      "metadata": {},
@@ -580,7 +583,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09c123208>"
+       "<matplotlib.figure.Figure at 0x170800d6a58>"
       ]
      },
      "metadata": {},
@@ -609,7 +612,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09c108da0>"
+       "<matplotlib.figure.Figure at 0x170800a80f0>"
       ]
      },
      "metadata": {},
@@ -642,7 +645,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09bc0fc50>"
+       "<matplotlib.figure.Figure at 0x1708020ff28>"
       ]
      },
      "metadata": {},
@@ -669,7 +672,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09c0899b0>"
+       "<matplotlib.figure.Figure at 0x170ff9fe5c0>"
       ]
      },
      "metadata": {},
@@ -709,7 +712,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09bfe06d8>"
+       "<matplotlib.figure.Figure at 0x170ffc36a90>"
       ]
      },
      "metadata": {},
@@ -766,7 +769,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09c217400>"
+       "<matplotlib.figure.Figure at 0x170801a8c88>"
       ]
      },
      "metadata": {},
@@ -2488,7 +2491,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09c086278>"
+       "<matplotlib.figure.Figure at 0x170800083c8>"
       ]
      },
      "metadata": {},
@@ -3399,7 +3402,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09bbd26a0>"
+       "<matplotlib.figure.Figure at 0x170ffa28a58>"
       ]
      },
      "metadata": {},
@@ -3529,7 +3532,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09bc0b710>"
+       "<matplotlib.figure.Figure at 0x170801fb518>"
       ]
      },
      "metadata": {},
@@ -3621,9 +3624,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhoAAAC+CAYAAABzq6b3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09c0f6a20>"
+       "<matplotlib.figure.Figure at 0x170ff9f9390>"
       ]
      },
      "metadata": {},
@@ -3656,9 +3659,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhUAAAC+CAYAAACCoP16AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhUAAAC+CAYAAACCoP16AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09c1fdb38>"
+       "<matplotlib.figure.Figure at 0x17080144c88>"
       ]
      },
      "metadata": {},
@@ -3680,7 +3683,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now write code that uses `gen_data` and `g_h_filter` to filter 100 data points that starts at 5, has a derivative of 2, a noise scaling factor of 10, and uses g=0.2 and h=0.01. Set you initial guess for x to be 100."
+    "Now write code that uses `gen_data` and `g_h_filter` to filter 100 data points that starts at 5, has a derivative of 2, a noise scaling factor of 10, and uses g=0.2 and h=0.02. Set your initial guess for x to be 100."
    ]
   },
   {
@@ -3708,9 +3711,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAC+CAYAAADnTQ8FAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAC+CAYAAADnTQ8FAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09c24d4a8>"
+       "<matplotlib.figure.Figure at 0x17080144320>"
       ]
      },
      "metadata": {},
@@ -3719,7 +3722,7 @@
    ],
    "source": [
     "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",
+    "data = g_h_filter(data=zs, x0=100., dx=2., dt=1., g=0.2, h=0.02)\n",
     "plot_g_h_results(measurements=zs, filtered_data=data)"
    ]
   },
@@ -3769,9 +3772,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAisAAAC+CAYAAAD0mk/xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAisAAAC+CAYAAAD0mk/xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09bffa748>"
+       "<matplotlib.figure.Figure at 0x17080144b38>"
       ]
      },
      "metadata": {},
@@ -3780,7 +3783,7 @@
    ],
    "source": [
     "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=0.01)\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)"
    ]
   },
@@ -3834,7 +3837,7 @@
      "data": {
       "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiQAAAC+CAYAAAAFkRR8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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 truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09bfe65f8>"
+       "<matplotlib.figure.Figure at 0x170800452e8>"
       ]
      },
      "metadata": {},
@@ -3911,7 +3914,7 @@
      "data": {
       "image/png": 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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09bfe6240>"
+       "<matplotlib.figure.Figure at 0x1708004af28>"
       ]
      },
      "metadata": {},
@@ -3950,7 +3953,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09d2fcb70>"
+       "<matplotlib.figure.Figure at 0x170812934a8>"
       ]
      },
      "metadata": {},
@@ -3992,7 +3995,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09c167c50>"
+       "<matplotlib.figure.Figure at 0x17080091128>"
       ]
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      "metadata": {},
@@ -4037,7 +4040,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09d3892b0>"
+       "<matplotlib.figure.Figure at 0x17081343ac8>"
       ]
      },
      "metadata": {},
@@ -4081,7 +4084,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "30ca2a57b74e4ce0bc389ef82e37922f",
+       "model_id": "65b43b4f4d7445e59b2defc0cde39f05",
        "version_major": 2,
        "version_minor": 0
       },
@@ -4133,7 +4136,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09d36d198>"
+       "<matplotlib.figure.Figure at 0x17081306710>"
       ]
      },
      "metadata": {},
@@ -4167,7 +4170,7 @@
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09c144be0>"
+       "<matplotlib.figure.Figure at 0x17081299e10>"
       ]
      },
      "metadata": {},
@@ -4230,7 +4233,7 @@
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09d2cb198>"
+       "<matplotlib.figure.Figure at 0x1708019d940>"
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      },
      "metadata": {},
@@ -4281,7 +4284,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09d2e55c0>"
+       "<matplotlib.figure.Figure at 0x170800458d0>"
       ]
      },
      "metadata": {},
@@ -4312,7 +4315,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09c2667b8>"
+       "<matplotlib.figure.Figure at 0x170fff7cf98>"
       ]
      },
      "metadata": {},
@@ -4343,7 +4346,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09bca4978>"
+       "<matplotlib.figure.Figure at 0x1708017d518>"
       ]
      },
      "metadata": {},
@@ -4391,7 +4394,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09c10d668>"
+       "<matplotlib.figure.Figure at 0x17081217710>"
       ]
      },
      "metadata": {},
@@ -4420,7 +4423,7 @@
      "data": {
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truncated
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1f09b660e48>"
+       "<matplotlib.figure.Figure at 0x17080176588>"
       ]
      },
      "metadata": {},