Improved problem statement #262

01-g-h-filter.ipynb

@@ -245,7 +245,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 164.9861\n"
+      "Average of measurements is 164.9859\n"
      ]
     }
    ],
@@ -276,7 +276,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 165.1045\n"
+      "Average of measurements is 164.9867\n"
      ]
     }
    ],
@@ -1090,7 +1090,7 @@
    "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 +1127,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -1181,7 +1181,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -1244,7 +1244,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -1279,7 +1279,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Write a new data generation function that adds in a constant acceleration factor to each data point. In other words, increment dx as you compute each data point so that the velocity (dx) is ever increasing. Set the noise to 0, $g=0.2$ and $h=0.02$ and plot the results. Explain what you see."
+    "Write a new data generation function that adds in a constant acceleration factor to each data point. In other words, increment dx as you compute each data point so that the velocity (dx) is ever increasing. Set the noise to 0, $g=0.2$ and $h=0.02$ and plot the results using `plot_g_h_results` or your own routine. Play around with different accererations and times steps. Explain what you see."
    ]
   },
   {
@@ -1309,7 +1309,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -1322,15 +1322,13 @@
     "def gen_data(x0, dx, count, noise_factor, accel=0.):\n",
     "    zs = []\n",
     "    for i in range(count):\n",
-    "        #zs.append(x0 + dx*i + randn()*noise_factor)\n",
     "        zs.append(x0 + accel * (i**2) / 2 + dx*i + randn()*noise_factor)\n",
     "        dx += accel\n",
     "    return zs\n",
     "   \n",
     "predictions = []\n",
-    "zs = gen_data(x0=10., dx=0., count=20, noise_factor=0, accel=2.)\n",
+    "zs = gen_data(x0=10., dx=0., count=20, noise_factor=0, accel=9.)\n",
     "data = g_h_filter(data=zs, x0=10., dx=0., g=0.2, h=0.02)\n",
-    "plt.xlim([0, 20])\n",
     "plot_g_h_results(measurements=zs, filtered_data=data)"
    ]
   },
@@ -1566,7 +1564,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "237784265f9c4f5aa5442b1875175916",
+       "model_id": "608b894ba39a450d8e743a59aea82d34",
        "version_major": 2,
        "version_minor": 0
       },