Fixed alignment of measurements #300

Measurements should start at day 0, not day 1.

01-g-h-filter.ipynb

@@ -245,7 +245,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 164.9911\n"
+      "Average of measurements is 164.9861\n"
      ]
     }
    ],
@@ -276,7 +276,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 164.9379\n"
+      "Average of measurements is 165.1045\n"
      ]
     }
    ],
@@ -642,7 +642,28 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "text/plain": [
+       "[158.0,\n",
+       " 164.2,\n",
+       " 160.3,\n",
+       " 159.9,\n",
+       " 162.1,\n",
+       " 164.6,\n",
+       " 169.6,\n",
+       " 167.4,\n",
+       " 166.4,\n",
+       " 171.0,\n",
+       " 171.2,\n",
+       " 172.6]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -654,7 +675,8 @@
    "source": [
     "# plot results\n",
     "book_plots.set_figsize(10)\n",
-    "gh.plot_gh_results(weights, estimates, predictions, [160, 172])"
+    "gh.plot_gh_results(weights, estimates, predictions, [160, 172])\n",
+    "weights"
    ]
   },
   {
@@ -679,7 +701,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -724,7 +746,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -955,6 +977,15 @@
     "scrolled": false
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[158.0, 164.2, 160.3, 159.9, 162.1, 164.6, 169.6, 167.4, 166.4, 171.0, 171.2, 172.6]\n",
+      "[159.2   161.8   162.1   160.78  160.985 163.311 168.1   169.696\n",
+      " 168.204 169.164 170.892 172.629]\n"
+     ]
+    },
     {
      "data": {
       "image/png": "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 truncated
@@ -986,7 +1017,9 @@
     "\n",
     "book_plots.plot_track([0, 11], [160, 172], label='Actual weight')\n",
     "data = g_h_filter(data=weights, x0=160., dx=1., g=6./10, h=2./3, dt=1.)\n",
-    "plot_g_h_results(weights, data)"
+    "plot_g_h_results(weights, data)\n",
+    "print(weights)\n",
+    "print(data)"
    ]
   },
   {
@@ -1057,7 +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>"
       ]
@@ -1094,7 +1127,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5cAAAFrCAYAAABFWn2uAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAgAElEQVR4nOy9eZwdVZn//7n3dmdPJyFkJSQNISSSYPyi4q6M46gjzKg/Rmd0dBwc1HFgRr9+dYZBFGQTF0BB2UVZjCDIHhII2UP2rTvdSS/pfd/323ev3x91T91TVaeqTm136/Oel9Ph1lmeOvWc52zPOScgSZIEgUAgEAgEAoFAIBAIXBDMtQACgUAgEAgEAoFAICh8xOBSIBAIBAKBQCAQCASuEYNLgUAgEAgEAoFAIBC4RgwuBQKBQCAQCAQCgUDgGjG4FAgEAoFAIBAIBAKBa0pyLYCWVCqFVCql+i0QCCAQCORIIoFAIBAIBAKBQCCYWkiSBO3FIsFgEMGg8fpkXg4uJyYmci2GQCAQCAQCgUAgEAgoZs+ebTq4FG6xAoFAIBAIBAKBQCBwjRhcCgQCgUAgEAgEAoHANWJwKRAIBAKBQCAQCAQC1+TdnkvWwT1Wvr0CgUAgEAgEAoFAIPAO1lk4VoesFsTg0upUIoFAIBAIBAKBQCAQ+IvV4FKM2AQCgUAgEAgEAoFA4Jq8W7ksFOrq6pBIJFBSUoKLL7441+IIcozQBwGN0AcBjdAHAUHogoBG6IOAplj0QQwuHTI5OYl4PI7S0tJciyLIA4Q+CGiEPghohD4ICEIXBDRCHwQ0xaIPwi3WIYFAQPmfQCD0QUAj9EFAI/RBQBC6IKAR+iCgKRZ9CEiSJOVaCJpUKoWxsTHVb3PnzhUH+ggEAoFAIBAIBAJBlnAyLhMjNoFAIBAIBAKBQCAQuEYMLgUCgUAgEAgEAoFA4BoxuBQIBAKBQCAQCAQCgWvEabEO6evrQzKZRCgUwqJFi3ItjiDHCH0Q0Ah9ENAIfRAQhC4IaIQ+CGiKRR/E4NIhnZ2dynHBhawAAm8Q+iCgEfogoBH6ICAIXRDQCH0Q0BSLPgi3WIFAIOBkOBzDJ+/djZ7RSK5FEQgEAoFAIMg7xMqlQ1atWoVUKiWuSBEAEPowVdhb34+6nnFsruzC1z98gWE4oQ8CGqEPAoLQBQGN0AcBTbHogxhcOmT+/Pm5FkGQRwh9mFpYXQ4s9EFAI/RBQBC6IKAR+iCgKRZ9KOyhsUAgEAgEAoFAIBAI8gIxuBQIBAKBQCAQCAQCgWuEW6xDEokEJElCIBBASYkoxqmO0IepQSDAF07og4BG6IOAIHRBQCP0QUBTLPpQuJLnmOrqauW44I0bN+ZaHEGOEfogoBH6IKAR+iAgCF0Q0Ah9ENAUiz4It1iBQCCwiSRZHekjEAgEAoFAMPUQK5cOKSsrQyKRKOhla4F3CH2YGgTA5xcr9EFAI/RBQBC6IKAR+iCgKRZ9KGzpc8gFFxjfcSeYegh9ENAIfRDQCH0QEIQuCGhofRgOxzB3RilCQc7N/YKio1jsg3CLFQgEAoFAIBBknT8fbcP6H2/NtRh5wbtu3Ya7tpzJtRgCgWvE4FIgENimsn0Yo5F4rsUQCAQCQQHz67fqMRFL5lqMvGFvfX+uRRAIXCMGlwKBQCGaSKJtMGwZ7u9/8za+9eSxLEgkEAgEAoFAICgUxJ5LhzQ2Niqbbi+88MJciyPIMcWiD//7l1N44UQHmu+60jLs2b7xLEhUmBSLPgi8QeiDgCB0QUBD64NAUCz2ISsrlydPnsSVV16JlStXYubMmTjnnHPwgQ98AE8//XQ2sveFsbExjI6OYmxsLNeiCPKAYtGHIy2DuRYhrwmkz1mwuomkWPShGJEkCf/zfCUaszg5IvRBQBC6IKAR+iCgKRZ9yMpUyfDwMM4//3x86UtfwnnnnYeJiQn88Y9/xFe/+lU0NzfjpptuyoYYAoFAIJjijE4m8OzRNnSPRvDE1y/PtTgCgUAgEBQVWRlcXnHFFbjiiitUv1111VVoamrCI488UpCDyw0bNuRaBEEeUSz6YLUiJ+CjWPShmMmmqgt9EBCELgho1PrQnjM5BPlBsdiHnDp5n3vuuejt7c2lCI4JhUK5FkGQRwh9ENAIfchjcnCFnNAHAUHogoBG6IOAplj0IauDy1QqhVQqhaGhITz33HN444038Jvf/MYyXnV1NVatWoWysjLlt2g0ipqaGgDAggULsHLlSlWc+vp6hMPyqZcbN25UPevv70dHRwcAYOXKlViwYIHyLJlMoqqqCgAwd+5c3YbapqYmjI6OAgDWr1+v2oQ9PDyMlpYWAMDy5cuxaNEiVdzKykpIkoSZM2fi4osvVj1ra2vD4KC8323t2rWYMWOG8mx8fBwNDQ0AgMWLF2PZsmWquKdPn0Y8HkdpaSkuueQS1bOuri5lAL969WrMmTNHeRaJRFBbWwsAOOecc3D++eer4tbV1WFychKBQADvfOc7Vc/6+vrQ2dkJAFi1ahXmz5+vPEskEqiurgYAlJWV6S6FbWxsVPzJN2zYoKpMQ0NDaG1tBQCcd955OPfcc1VxKyoqAACzZs3CmjVrVM9aW1sxNDQEAFi3bh2mT5+uPBsdHUVTUxMAYMmSJVi6dKkqbnV1NRKJBKZPn45169apnnV2dqKvrw8AcNFFF2H27NnKs3A4jPr6egDAwoULsWLFClXc2tpaRCIRhEIh3YxUb28vurq6AADl5eWYN2+e8iwWi+HMGfm+q3nz5qG8vFwVt6GhAePj8p6xSy+9FMFgZvv0wMAA2tvlGdAVK1Zg4cKFyrNUKoVTp04BAObMmYPVq1er0o3HEwDkcn7HO96BadOmKc9GRkbQ3NwMI6qqqpBMJjFjxgysXbtW9ay9vR0DAwMAgDVr1mDWrFnKs4mJCZw9exYAsGjRIixfvlwVt6amBtFoFCUlJVi/fr3qWXd3N3p6egDIlw9nw0YAsk5UVIwJG1GANqKtR9bDVCqleiZsBJRyMbMRzc3NGBkZAQBTG7Fs2TIsXrxYFXeq2AjRj8hgZSPi8Zjy3vliI3LZj4jH9Vd8CRshI2yETK5shB2yOrj8j//4Dzz88MMAgGnTpuG+++7Dt771Lct4iUQCksZfT5IkpRImEglmHFYlBeSKQZ5pOxgAuNPVykSnm0zq722Kx+OQJAmlpaW6Z8lk0lW6Ru9Kp6t9V7oMWemSdw0E9FP9vOnmy7exetd4PI5EIqEyrgSzb8OTbjweZz4zS5fEtUqXBW8ZsuJLaWdB5jPqXVnEYjGkUinmzJsXZcgqI950vdJDUhMS6XyFjSg8G5EQNsKVjaBl0mL1rlPBRtAy8aQ71W0EkTOfbEQu+xEshI3QpytsRPZthB2yOri88cYbce2116K3txevvvoqrr/+ekxMTOD73/++abySkhKdYQoEAkrBsY5wLikpYRYsAASDQeUZywjwpEtWYEtLS5XZCDpdVsUoLS2FJEnMdEOhkBJX+6486dJ/jdLVvitdhqx0ybuyGgXedHPxbYgMRjIZlSEdhsbs25A4qVQKsVgMQ0NDqtmp0tJSJJNJZp5m6dLvaiSv2zJkxQ+kh0/MZwblQ5g2bRqSyaTjMiThWPKmUinb9cZPPST5GqVL3mNkZEQ1ay5shD7dnNkIzTM/bQRJj9UJKTQbQcukxaoMp5KNMEp3aGhI+d7BYNBRXS4mG0F+z0sbkYV+xNDQECKRCFNOOl1hI6aGjRgaGlK8PN3UZT9shB0CEmsYnyW+/e1v47HHHkNnZ6eyrJtKpXRH8M6dO9e04uWCiooKxOOyC4l2KVww9SgWffjQXTvQMTxpec9l+Q2bsWjudBz54SeyJFl+sOVUF779x+O48TPr8M2PrjYMVyz6UIyMRuJ45y1v4iNrzsVT//a+rOQp9EFAcKMLw+EY5s+aZh2wgOBtc4oVog8lJSX43DMdWLd0LrZ+96O5Fquo6B2LYOHs6QgFc7Dh3ib52FY4GZfldMR2+eWXI5FIoLGxMZdiCAQCAReMiWGBQCDwnZ21vXjXrdtQ213Y999pETZVRhzU7g+plITL79iOu9+szbUoU4qcnha7c+dOBINB3UbWQuC8885DKpXKuxVVQW4oFn3IoSNDUVEs+iDwBqEPAoJTXajukA9IaRsMY+3SuX6IJsgBRB8kBAB0KL8/vq8Je+r78IdrxF28bkil+zRHmgd9z2trVTf+/eljaLjzM45XSYulrcjK4PKb3/wmysrKcPnll2PJkiXo7+/Hc889h2effRY/+MEPdCcdFQLa08cEUxuhDwIAuGPzafz+7WacvfMzuRZFYEAuFkqEfRAQ3OqCmP4rLog+JFOZL9vUP4FbXzudK5GKkkAWLP+TB5oBAPFkCqGgsz2LxdJWZGVw+YEPfAC///3v8cQTT2B4eBhz5szBxo0b8dRTT+ErX/lKNkQQCAQciI4LH0YLvI/ubcquIAKBQCAoeGivoZ++fiaHkhQXok+TG7IyuLzmmmtwzTXXZCMrQRboHolg6bwZ1gEFAhMkScJf37Mbt312Az50UXHM1gkKB+EBLhAIBMWNYuez4LIi2pQMhe3UK8g6VR0jeP9Pt2NnTW+uRRH4QDaNYzIlobFvAvdsq8tepoIpD+u4freMReJIpUTPQuAffuhtPkBea6rv95/ab+8fkijZnJDTA30KmXw8LtgvKtuHcd2m43jrex9D22AYAFDbM4a/Wrc4x5LlD8WiD8IQWyH3hKxKqVj0oRjxoxN76S1v4vq/ugjf/9Ra5nOhDwJCrnXhdOcozp0zDYvLhPdRPkD0IRDKdMeLdB4hJxTanEWu7YNXiJVLgSWP7m1C2+Ak+saiuRZFkEcUmtEWCGi8nkjZVSe8OQT5z2fu24tP3LM712LomOrtCf3+b1T35E6QIkWM17OLWLl0yKxZs5BIJFBSMnWKcKobfzOKRR+Line truncated
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -1148,7 +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>"
       ]
@@ -1211,7 +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>"
       ]
@@ -1533,7 +1566,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "166a1f8132834126b828d5b075b2263c",
+       "model_id": "237784265f9c4f5aa5442b1875175916",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2068,7 +2101,7 @@
    "cell_type": "code",
    "execution_count": 52,
    "metadata": {
-    "scrolled": false
+    "scrolled": true
    },
    "outputs": [
     {

kf_book/gh_internal.py

@@ -179,7 +179,7 @@ def plot_gh_results(weights, estimates, predictions, actual, time_step=0):
     else:
         rng = range(n, n+1)
     xs = range(n+1)
-    book_plots.plot_measurements(xs[1:], weights, color='k', lines=False)
+    book_plots.plot_measurements(weights, color='k', lines=False)
     book_plots.plot_filter(xs, estimates, marker='o', label='Estimates')
     book_plots.plot_track(xs[1:], predictions, c='r', marker='v', label='Predictions')
     plt.plot([xs[0], xs[-1]], actual, c='k', lw=1, label='Actual')