Updated for Python 3.6

01-g-h-filter.ipynb

@@ -22,7 +22,6 @@
    },
    "outputs": [],
    "source": [
-    "from __future__ import division, print_function\n",
     "%matplotlib inline"
    ]
   },
@@ -38,7 +37,7 @@
        "        <style>\n",
        "        .output_wrapper, .output {\n",
        "            height:auto !important;\n",
-       "            max-height:100000px; \n",
+       "            max-height:100000px;\n",
        "        }\n",
        "        .output_scroll {\n",
        "            box-shadow:none !important;\n",
@@ -245,7 +244,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 165.0190\n"
+      "Average of measurements is 164.9683\n"
      ]
     }
    ],
@@ -253,7 +252,7 @@
     "import numpy as np\n",
     "measurements = np.random.uniform(160, 170, size=10000)\n",
     "mean = measurements.mean()\n",
-    "print('Average of measurements is {:.4f}'.format(mean))"
+    "print(f'Average of measurements is {mean:.4f}')"
    ]
   },
   {
@@ -276,13 +275,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 164.9996\n"
+      "Average of measurements is 164.9450\n"
      ]
     }
    ],
    "source": [
     "mean = np.random.normal(165, 5, size=10000).mean()\n",
-    "print('Average of measurements is {:.4f}'.format(mean))"
+    "print(f'Average of measurements is {mean:.4f}')"
    ]
   },
   {
@@ -1090,7 +1089,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -1127,7 +1126,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -1181,7 +1180,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -1244,7 +1243,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -1564,7 +1563,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3ba13a32dbf34fc6a4be62e13868abb3",
+       "model_id": "696326f270d74e9b96e1e2ad77c7250d",
        "version_major": 2,
        "version_minor": 0
       },