Final documentation tweaks.

Author: metasim

docs/src/main/paradox/_template/page.st

@@ -31,6 +31,8 @@
     .md-left { float: left; }
     .md-right { float: right; }
     .md-clear { clear: both; }
+    table { font-size: 80%; }
+    code { font-size: 0.75em !important; }
   </style>
 </head>
 
@@ -132,7 +134,7 @@
               <div class="row site-footer-content">
                 <div class="small-12 text-center large-9 column">
                   <div class="copyright">
-                    <span class="text">&copy; $page.properties.("date.year")$
+                    <span class="text">Copyright &copy; $page.properties.("date.year")$
                     <a href="http://www.astraea.earth/">Astraea, Inc.</a></span>
                   </div>
                 </div>

pyrasterframes/src/main/python/docs/getting-started.pymd

@@ -1,6 +1,14 @@
 # Getting Started
 
-There are @ref:[several ways](getting-started.md#other-options) to use RasterFrames, and @ref:[several languages](languages.md) with which you can use it. Let's start with the simplest: the Python shell.  To get started you will need:
+@@@ note
+
+If you are new to Earth-observing imagery, you might consider looking at the [Concepts](concepts.md) section first.
+
+@@@
+
+There are @ref:[several ways](getting-started.md#other-options) to use RasterFrames, and @ref:[several languages](languages.md) with which you can use it.  
+
+The simplest way to get started with RasterFrames is via the the Python shell.  To get started you will need:
 
 1. [Python](https://www.python.org/) installed. Version 3.6 or greater is recommended.
 1. [`pip`](https://pip.pypa.io/en/stable/installing/) installed. If you are using Python 3, `pip` may already be installed.

pyrasterframes/src/main/python/docs/index.md

@@ -39,8 +39,8 @@ The source code can be found on GitHub at [locationtech/rasterframes](https://gi
 
 @@@ index
 * [Overview](description.md)
-* [Concepts](concepts.md)
 * [Getting Started](getting-started.md)
+* [Concepts](concepts.md)
 * [Raster Data I/O](raster-io.md)
 * [Vector Data](vector-data.md)
 * [Raster Processing](raster-processing.md)

pyrasterframes/src/main/python/docs/languages.pymd

@@ -30,7 +30,12 @@ red_nir_monthly_2017 = modis \
         col('B01').alias('red'),
         col('B02').alias('nir')
     ) \
-    .where((year('acquisition_date') == 2017) & (dayofmonth('acquisition_date') == 15) & (col('granule_id') == 'h21v09'))
+    .where(
+        (year('acquisition_date') == 2017) & 
+        (dayofmonth('acquisition_date') == 15) & 
+        (col('granule_id') == 'h21v09')
+    )
+red_nir_monthly_2017.printSchema()    
 ```
 
 ### Step 3: Read tiles

pyrasterframes/src/main/python/docs/raster-catalogs.pymd

@@ -43,13 +43,19 @@ scene1_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018185/MCD43
 scene2_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018188/MCD43A4.A2018188.h04v09.006.2018198232008_B01.TIF"
 
 # a pandas DF
-one_d_cat = pd.DataFrame({'B01': [scene1_B01, scene2_B01]})
+one_d_cat_pd = pd.DataFrame({'B01': [scene1_B01, scene2_B01]})
 
 # equivalent spark DF
-one_d_cat = spark.createDataFrame([Row(B01=scene1_B01), Row(B01=scene2_B01)])
+one_d_cat_df = spark.createDataFrame([Row(B01=scene1_B01), Row(B01=scene2_B01)])
 
 # equivalent CSV string
-one_d_cat = '\n'.join(['B01', scene1_B01, scene2_B01])
+one_d_cat_csv = '\n'.join(['B01', scene1_B01, scene2_B01])
+```
+
+This is what it looks like in DataFrame form:
+
+```python, view_spark_cat_1
+one_d_cat_df.show(truncate=False)
 ```
 
 ### Two-D
@@ -63,19 +69,25 @@ scene2_B01 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018188/MCD43
 scene2_B02 = "https://modis-pds.s3.amazonaws.com/MCD43A4.006/04/09/2018188/MCD43A4.A2018188.h04v09.006.2018198232008_B02.TIF"
 
 # Pandas DF
-my_cat = pd.DataFrame([
+two_d_cat_pd = pd.DataFrame([
     {'B01': [scene1_B01], 'B02': [scene1_B02]},
     {'B01': [scene2_B01], 'B02': [scene2_B02]}
 ])     
 
 # or
-my_cat_df = spark.createDataFrame([
+two_d_cat_df = spark.createDataFrame([
     Row(B01=scene1_B01, B02=scene1_B02),
     Row(B01=scene2_B01, B02=scene2_B02)
 ])
 
 # As CSV string
-my_cat = '\n'.join(['B01,B02', scene1_B01 + "," + scene1_B02, scene2_B01 + "," + scene2_B02])
+tow_d_cat_csv = '\n'.join(['B01,B02', scene1_B01 + "," + scene1_B02, scene2_B01 + "," + scene2_B02])
+```
+
+This is what it looks like in DataFrame form:
+
+```python, view_spark_cat_1
+two_d_cat_df.show(truncate=False)
 ```
 
 ## Using External Catalogs

pyrasterframes/src/main/python/docs/raster-write.pymd

@@ -93,7 +93,7 @@ os.remove(outfile)
 
 [GeoTrellis][GeoTrellis] is one of the key libraries upon which RasterFrames is built. It provides a Scala language API for working with geospatial raster data.  GeoTrellis defines a [tile layer storage](https://geotrellis.readthedocs.io/en/latest/guide/tile-backends.html) format for persisting imagery mosaics. RasterFrames can write data from a `RasterFrameLayer` into a [GeoTrellis Layer](https://geotrellis.readthedocs.io/en/latest/guide/tile-backends.html). RasterFrames provides a `geotrellis` DataSource that supports both @ref:[reading](raster-read.md#geotrellis-layers) and @ref:[writing](raster-write.md#geotrellis-layers) GeoTrellis layers.
 
-> An example is forthcoming. In the mean time referencing the [`GeoTrellisDataSourceSpec` test code](https://github.com/locationtech/rasterframes/blob/develop/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala) may help
+> An example is forthcoming. In the mean time referencing the [`GeoTrellisDataSourceSpec` test code](https://github.com/locationtech/rasterframes/blob/develop/datasource/src/test/scala/org/locationtech/rasterframes/datasource/geotrellis/GeoTrellisDataSourceSpec.scala) may help.
 
 ## Parquet
 

pyrasterframes/src/main/python/pyrasterframes/rf_ipython.py

@@ -29,6 +29,8 @@ def tile_to_png(tile, fig_size=None):
     import io
     from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
     from matplotlib.figure import Figure
+    import matplotlib.colors as colors
+    from matplotlib import cm
 
     # Set up matplotlib objects
     nominal_size = 2  # approx full size for a 256x256 tile
@@ -39,7 +41,10 @@ def tile_to_png(tile, fig_size=None):
     canvas = FigureCanvas(fig)
     axis = fig.add_subplot(1, 1, 1)
 
-    axis.imshow(tile.cells)
+    data = tile.cells
+    norm = colors.LogNorm(data.min(), data.max())
+
+    axis.imshow(data, norm=norm, cmap=cm.get_cmap("RdBu"))
     axis.set_aspect('equal')
     axis.xaxis.set_ticks([])
     axis.yaxis.set_ticks([])