Small changes to filter notebook

Author: Eli

docsrc/notebooks/filters.ipynb

@@ -6,6 +6,8 @@
    "source": [
     "# Averging, Filtering and Low Passing Data\n",
     "\n",
+    "This tutorial covers the filtering tools in VTools as well as some motivation as to when and why you might do that instead of something simpler like taking a daily average. Sometimes doing that creates a 100% error and sometimes it is trivial ... when?\n",
+    "\n",
     "VTools provides simple methods for period averaging, filtering and low passing data, particularly tidal data. Basic period averaging and daily averaging is easily covered by the regular Pandas API, but these methods can distort tidal data or yield unepected results with missing data. \n",
     "\n",
     "As an introduction to the topic let's start by making a single time series with three of the most common tidal constituents. This produces a series that is complex enough to be interesting and realistic, but it doesn't have any low frequencies in it, so a reasonable tidal filtration should look like a flat line at zero. "
@@ -149,7 +151,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x2db5041b040>"
+       "<matplotlib.legend.Legend at 0x157b7953160>"
       ]
      },
      "execution_count": 3,
@@ -292,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {