Issue #343

Measurements were being plotted off by one (day 0 instead of the
correct day 1).

01-g-h-filter.ipynb

@@ -245,7 +245,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 164.9859\n"
+      "Average of measurements is 165.0190\n"
      ]
     }
    ],
@@ -276,7 +276,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Average of measurements is 164.9867\n"
+      "Average of measurements is 164.9996\n"
      ]
     }
    ],
@@ -663,7 +663,7 @@
     },
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -701,7 +701,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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truncated
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truncated
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -746,7 +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>"
       ]
@@ -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
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -1244,7 +1244,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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truncated
       "text/plain": [
        "<Figure size 1000x400 with 1 Axes>"
       ]
@@ -1564,7 +1564,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "608b894ba39a450d8e743a59aea82d34",
+       "model_id": "3ba13a32dbf34fc6a4be62e13868abb3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -2167,7 +2167,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   },
   "nbdime-conflicts": {
    "local_diff": [

kf_book/gh_internal.py

@@ -48,7 +48,7 @@ def plot_hypothesis2():
         plt.errorbar(range(1, 11), [169, 170, 169,171, 170, 171, 169, 170, 169, 170],
                      xerr=0, yerr=6, fmt='bo', capthick=2, capsize=10)
         plt.plot([1, 10], [169, 170.5], color='g', ls='--')
-        
+
         plt.xlim(0, 11)
         plt.ylim(150, 185)
         plt.xlabel('day')
@@ -89,7 +89,7 @@ def plot_hypothesis4():
         book_plots.show_legend()
         plt.grid(False)
 
-    
+
 def plot_hypothesis5():
     weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
            169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
@@ -108,8 +108,8 @@ def plot_hypothesis5():
         plt.ylabel('weight (lbs)')
         book_plots.show_legend()
         plt.grid(False)
-        
-    
+
+
 def plot_estimate_chart_1():
     with figsize(y=2.5):
         plt.figure()
@@ -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(weights, color='k', lines=False)
+    book_plots.plot_measurements(range(1, len(weights)+1), 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')