Suppressed spurious deprecation

Matplotlib emits deprecation when you copy rcParams, which is
foolish, so I suppressed it.

12-Particle-Filters.ipynb

@@ -111,7 +111,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": "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 truncated
+      "image/png": "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 truncated
       "text/plain": [
        "<Figure size 900x400 with 2 Axes>"
       ]
@@ -354,27 +354,6 @@
    "execution_count": 7,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Anaconda3\\lib\\_collections_abc.py:841: MatplotlibDeprecationWarning: \n",
-      "The examples.directory rcparam was deprecated in Matplotlib 3.0 and will be removed in 3.2. In the future, examples will be found relative to the 'datapath' directory.\n",
-      "  self[key] = other[key]\n",
-      "C:\\Anaconda3\\lib\\_collections_abc.py:841: MatplotlibDeprecationWarning: \n",
-      "The savefig.frameon rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
-      "  self[key] = other[key]\n",
-      "C:\\Anaconda3\\lib\\_collections_abc.py:841: MatplotlibDeprecationWarning: \n",
-      "The text.latex.unicode rcparam was deprecated in Matplotlib 3.0 and will be removed in 3.2.\n",
-      "  self[key] = other[key]\n",
-      "C:\\Anaconda3\\lib\\_collections_abc.py:841: MatplotlibDeprecationWarning: \n",
-      "The verbose.fileo rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
-      "  self[key] = other[key]\n",
-      "C:\\Anaconda3\\lib\\_collections_abc.py:841: MatplotlibDeprecationWarning: \n",
-      "The verbose.level rcparam was deprecated in Matplotlib 3.1 and will be removed in 3.3.\n",
-      "  self[key] = other[key]\n"
-     ]
-    },
     {
      "data": {
       "image/png": 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truncated

kf_book/pf_internal.py

@@ -17,6 +17,8 @@ from __future__ import (absolute_import, division, print_function,
                         unicode_literals)
 
 
+import warnings
+
 from filterpy.monte_carlo import stratified_resample, residual_resample
 import matplotlib as mpl
 import matplotlib.pyplot as plt
@@ -112,13 +114,17 @@ def plot_random_pd():
           .5*norm(x, .5, .08) +
            np.sqrt(norm(x, 0.8, 0.06)) +0.1 * (1 - sigmoid(x, 0.45, 0.15)))
 
-    # hack because of bug `with plt.xkcd()` doesn't return context correctly
-    saved_state = mpl.rcParams.copy()
-    plt.xkcd()
-    plt.axes(xticks=[], yticks=[], frameon=False)
-    plt.plot(x, y2)
-    plt.ylim([0, max(y2)+.1])
-    mpl.rcParams.update(saved_state)
+    with warnings.catch_warnings():
+        # matplotlib warns about deprecated rcParams when you copy. Oye.
+        warnings.simplefilter("ignore", mpl.MatplotlibDeprecationWarning)
+
+        # hack because of bug `with plt.xkcd()` doesn't return context correctly
+        saved_state = mpl.rcParams.copy()
+        plt.xkcd()
+        plt.axes(xticks=[], yticks=[], frameon=False)
+        plt.plot(x, y2)
+        plt.ylim([0, max(y2)+.1])
+        mpl.rcParams.update(saved_state)
 
 
 def plot_monte_carlo_ukf():