Fix spelling errors in satellite notebook.

notebooks/Satellite_Data/Working with Satellite Data.ipynb

@@ -88,7 +88,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "list(cat.datasets)[-5:]"
+    "cat.datasets[-5:]"
    ]
   },
   {
@@ -115,7 +115,7 @@
     "<div class=\"alert alert-success\">\n",
     "    <b>EXERCISE</b>:\n",
     "     <ul>\n",
-    "      <li>Using the link to the Hurricane Irma archive above, get data from September 10, 2017 at 15Z for the mid-level water vapor channel (9) in the Mesoscale-1 sector. You could search through the datasets list manually, but instead try the `cat.datasets.filter_time_nearest` functinoality!</li>\n",
+    "      <li>Using the link to the Hurricane Irma archive above, get data from September 10, 2017 at 15Z for the mid-level water vapor channel (9) in the Mesoscale-1 sector. You could search through the datasets list manually, but instead try the `cat.datasets.filter_time_nearest` functionality!</li>\n",
     "    </ul>\n",
     "</div>"
    ]
@@ -135,11 +135,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %load solutions/satellite_data.py\n",
-    "cat = TDSCatalog('http://thredds-test.unidata.ucar.edu/thredds/catalog/casestudies/irma/goes16/Mesoscale-1/Channel09/20170910/catalog.xml')\n",
-    "ds = cat.datasets.filter_time_nearest(datetime(2017, 9, 10, 0))\n",
-    "print(ds.name)\n",
-    "ds = ds.remote_access(service='OPENDAP')"
+    "# %load solutions/satellite_data.py"
    ]
   },
   {
@@ -175,7 +171,7 @@
    "source": [
     "Our goal is to plot the imagery, so we'll be using the `Sectorized_CMI` variable from the `.variables` dictionary.\n",
     "Rather than just giving back the raw array of data, this gives back a `Variable` object; from here not only\n",
-    "can we get the raw data values, but there is useful metadata as well. We can see just what additional information\n",
+    "can we get the raw data values, but there are useful metadata as well. We can see just what additional information\n",
     "is present by printing out the `Variable` object:"
    ]
   },
@@ -342,7 +338,7 @@
    "metadata": {},
    "source": [
     "Now that we know how to properly reference the imagery data, we can plot\n",
-    "the data. CartoPy's projections are designed to interface with matplotlib, so they can just be passed as the `projection` keyword argument when creating an `Axes` using the `add_subplot` method. Since the x and y coordinates, as well as the image data, are referenced in the lambert conformal projection, we can pass all of them directly to plotting methods (such as `imshow`) with no additional information. The `extent` keyword argument to `imshow` is used to specify the bounds of the image data being plotted. It is **especially important** to specify that the `origin` is at the the upper left of the image (standard practice in imagery). If your forget this, your image will be flipped. Try it!"
+    "the data. CartoPy's projections are designed to interface with matplotlib, so they can just be passed as the `projection` keyword argument when creating an `Axes` using the `add_subplot` method. Since the x and y coordinates, as well as the image data, are referenced in the lambert conformal projection, we can pass all of them directly to plotting methods (such as `imshow`) with no additional information. The `extent` keyword argument to `imshow` is used to specify the bounds of the image data being plotted. It is **especially important** to specify that the `origin` is at the upper left of the image (standard practice in imagery). If your forget this, your image will be flipped. Try it!"
    ]
   },
   {
@@ -370,7 +366,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This is a nice start, but it would be nice to have better geographic references for the image. For example, where are the states? Cartopy's `feature` module has support for adding geographic features to plots ,with many features are built in. The `BORDERS` built-in feature contains country borders. There is also support for creating \"custom\" features from the [Natural Earth](http://www.naturalearthdata.com/) set of free vector and raster map data (CartoPy will automatically download the necessary data and cache it locally). Here we create a feature for states/provinces."
+    "This is a nice start, but it would be nice to have better geographic references for the image. For example, where are the states? Cartopy's `feature` module has support for adding geographic features to plots, with many features are built in. The `BORDERS` built-in feature contains country borders. There is also support for creating \"custom\" features from the [Natural Earth](http://www.naturalearthdata.com/) set of free vector and raster map data (CartoPy will automatically download the necessary data and cache it locally). Here we create a feature for states/provinces."
    ]
   },
   {
@@ -552,8 +548,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Well, that's a good idea, but the text is invisible in some places! White text and black text have issues, but we can outline the text using matplotlib's [path effects](http://matplotlib.org/users/patheffects_guide.html). More details on path effects can be found in the [documentation](http://matplotlib.org/users/patheffects_guide.html) if you want to know more.\n",
-    "\n"
+    "Well, that's a good idea, but the text is invisible in some places! White text and black text have issues, but we can outline the text using matplotlib's [path effects](http://matplotlib.org/users/patheffects_guide.html). More details on path effects can be found in the [documentation](http://matplotlib.org/users/patheffects_guide.html) if you want to know more."
    ]
   },
   {
@@ -649,7 +644,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First we'll import the animation support from matplotlib. We also tell it that we want it to render the animations to HTML using the HTML5 video tag:"
+    "First, we'll import the animation support from matplotlib. We also tell it that we want it to render the animations to HTML using the HTML5 video tag:"
    ]
   },
   {
@@ -690,7 +685,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Then we loop over a bunch of the datasets. For each one we pull out the data and plot both the timestamp and the image. The `ArtistAnimation` class takes the `Figure` instance and a list as required arguments. The contents of this list is a collection of matplotlib artists for each frame of the animation. In the loop below, we populate this list with the `Text` instance created when adding the timestamp as well as the image that results from plotting the data."
+    "Then we loop over a bunch of the datasets. For each one we pull out the data and plot both the timestamp and the image. The `ArtistAnimation` class takes the `Figure` instance and a list as required arguments. The contents of this list are a collection of matplotlib artists for each frame of the animation. In the loop below, we populate this list with the `Text` instance created when adding the timestamp as well as the image that results from plotting the data."
    ]
   },
   {