some minor updates to text

Author: kadrlica

08a_Interactive_Image_Visualization.ipynb

@@ -28,7 +28,7 @@
    "source": [
     "### Learning Objectives\n",
     "\n",
-    "This tutorial, together with tutorial `08b_Interactive_Catalog_Visualization` introduces three open-source Python libraries that enable powerful interactive visualization of images and catalogs. \n",
+    "This tutorial, together with tutorial `08b_Interactive_Catalog_Visualization`, introduces three open-source Python libraries that enable powerful interactive visualization of images and catalogs. \n",
     " 1. [**HoloViews**](http://holoviews.org): Produce high-quality interactive visualizations easily by annotating plots and images rather than using direct calls to a plotting library\n",
     " 2. [**Bokeh**](https://bokeh.org): A powerful data visualization library that provides interactive tools including brushing and linking between multiple plots. `Holoviews` + `Bokeh`\n",
     " 3. [**Datashader**](https://datashader.org): Accurately render very large datasets quickly and flexibly.\n",
@@ -132,14 +132,14 @@
    "source": [
     "### 1. Data preparation\n",
     "\n",
-    "The basis for any data visualization is the underlying data. In this tutorial we will work with both images. We will retrieve a `calexp` and a `coadd` image via the Butler. For more details about using the Butler, please refer to tutorial `04_Intro_to_Butler`.   For examples of working with tabular data, see notebook `08b_Interactive_Catalog_Visualization`. "
+    "The basis for any data visualization is the underlying data. In this tutorial we will work with images.  For DP0.1, images can only be accessed via the `Butler` (<a href=\"https://pipelines.lsst.io/modules/lsst.daf.butler/index.html\">documentation</a>), an LSST Science Pipelines software package that allows you to fetch the LSST data you want without having to know its location or format. For more details about using the Butler, please refer to tutorial `04_Intro_to_Butler`. For visualization examples with tabular data, see `08b_Interactive_Catalog_Visualization`. "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For DP0.1, images can only be accessed via the `butler` (<a href=\"https://pipelines.lsst.io/modules/lsst.daf.butler/index.html\">documentation</a>), an LSST Science Pipelines software package that allows you to fetch the LSST data you want without you having to know its location or format. For more details on how to use the Butler, see tutorial `04_Intro_to_Butler`. We will retrieve a calexp and a deep r-band coadd image from a dataset, specifying a tract and patch to work with. "
+    "We will start by using the butler to retrieve a calexp image (specifying a visit, detector, and band) and a deep coadd image (specifying a tract, patch, and band)."
    ]
   },
   {
@@ -204,7 +204,7 @@
     "\n",
     "In the tutorial `03_Image_Display_and_Manipulation` we saw how to use the `lsst.afw.display` library to visualize exposure images and in tutorial `03b_Image_Display_with_Firefly` we saw how to do the same using Firefly. In this example we demonstrate image visualization at the pixel level with Holoviews.\n",
     "\n",
-    "We will use the holoviews Image Element to visualise a calexp. We will then overlay a Holoviews DynamicMap on the image to compute and display elements dynamically, allowing exploration of large datasets. DynamicMaps generate elements on the fly allowing exploration of parameters with arbitrary resolution. DynamicMaps are lazy in the sense they only compute as much data as the user wishes to explore. An Overlay is a collection of HoloViews objects that are displayed simultanously, e.g a Curve superimposed on a Scatter plot of data. You can build a Overlay between any two HoloViews objects, which can have different types using the * operator. "
+    "We will use the holoviews Image Element to visualize a calexp. We will then overlay a Holoviews DynamicMap on the image to compute and display elements dynamically, allowing exploration of large datasets. DynamicMaps generate elements on the fly allowing exploration of parameters with arbitrary resolution. DynamicMaps are lazy in the sense they only compute as much data as the user wishes to explore. An Overlay is a collection of HoloViews objects that are displayed simultanously, e.g a Curve superimposed on a Scatter plot of data. You can build a Overlay between any two HoloViews objects, which can have different types using the * operator. "
    ]
   },
   {
@@ -229,7 +229,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "LSST’s image classes (Image, Mask, MaskedImage, and Exposure) use a pixel indexing convention that is different from both the convention used by `numpy.ndarray` objects and the convention used in FITS images.  LSST typically labels the center of the lower left pixel of an image (0, 0). Consequently, we need to flip the image data array."
+    "LSST’s image classes (Image, Mask, MaskedImage, and Exposure) use a pixel indexing convention that is different from both the convention used by `numpy.ndarray` objects and the convention used in FITS images (as documented [here](https://pipelines.lsst.io/modules/lsst.afw.image/indexing-conventions.html)).  LSST typically labels the center of the lower left pixel of an image (0, 0). Consequently, we need to flip the image data array."
    ]
   },
   {