Merge pull request #475 from jbouffard/docs/more-fixes

More Formatting Fixes for the Docs

docs/guides/core-concepts.rst

@@ -1,5 +1,5 @@
 
-.. code:: ipython3
+.. code:: python3
 
    import datetime
    import numpy as np
@@ -27,7 +27,7 @@ have the ``no_data_value`` of the raster.
 **Note**: All rasters in GeoPySpark are represented as having multiple
 bands, even if the original raster just contained one.
 
-.. code:: ipython3
+.. code:: python3
 
     arr = np.array([[[0, 0, 0, 0],
                      [1, 1, 1, 1],
@@ -36,7 +36,7 @@ bands, even if the original raster just contained one.
     # The resulting Tile will set -10 as the no_data_value for the raster
     gps.Tile.from_numpy_array(numpy_array=arr, no_data_value=-10)
 
-.. code:: ipython3
+.. code:: python3
 
     # The resulting Tile will have no no_data_value
     gps.Tile.from_numpy_array(numpy_array=arr)
@@ -53,7 +53,7 @@ box*.
 **Note**: The values within the ``Extent`` must be ``float``\ s and not
 ``double``\ s.
 
-.. code:: ipython3
+.. code:: python3
 
     extent = gps.Extent(0.0, 0.0, 10.0, 10.0)
     extent
@@ -65,13 +65,13 @@ ProjectedExtent
 in addition to its CRS. Either the EPSG code or a proj4 string can be
 used to indicate the CRS of the ``ProjectedExtent``.
 
-.. code:: ipython3
+.. code:: python3
 
     # Using an EPSG code
     
     gps.ProjectedExtent(extent=extent, epsg=3857)
 
-.. code:: ipython3
+.. code:: python3
 
     # Using a Proj4 String
     
@@ -86,7 +86,7 @@ the area on Earth the raster represents, its CRS, and the time the data
 was represents. This point of time, called ``instant``, is an instance
 of ``datetime.datetime``.
 
-.. code:: ipython3
+.. code:: python3
 
     time = datetime.datetime.now()
     gps.TemporalProjectedExtent(extent=extent, instant=time, epsg=3857)
@@ -100,7 +100,7 @@ detail how many columns and rows the grid itself has, respectively.
 While ``tileCols`` and ``tileRows`` tell how many columns and rows each
 individual raster has.
 
-.. code:: ipython3
+.. code:: python3
 
     # Describes a layer where there are four rasters in a 2x2 grid. Each raster has 256 cols and rows.
     
@@ -113,7 +113,7 @@ LayoutDefinition
 ``LayoutDefinition`` describes both how the rasters are orginized in a
 layer as well as the area covered by the grid.
 
-.. code:: ipython3
+.. code:: python3
 
     layout_definition = gps.LayoutDefinition(extent=extent, tileLayout=tile_layout)
     layout_definition
@@ -138,17 +138,17 @@ of the cells within the rasters.
 Rather, it is best used for layers where operations and analysis will be
 performed.**
 
-.. code:: ipython3
+.. code:: python3
 
     # Creates a LocalLayout where each tile within the grid will be 256x256 pixels.
     gps.LocalLayout()
 
-.. code:: ipython3
+.. code:: python3
 
     # Creates a LocalLayout where each tile within the grid will be 512x512 pixels.
     gps.LocalLayout(tile_size=512)
 
-.. code:: ipython3
+.. code:: python3
 
     # Creates a LocalLayout where each tile within the grid will be 256x512 pixels.
     gps.LocalLayout(tile_cols=256, tile_rows=512)
@@ -165,12 +165,12 @@ original raster.
 **Note**: This layout strategy **should be used when the resulting layer
 is to be dispalyed in a TMS server.**
 
-.. code:: ipython3
+.. code:: python3
 
     # Creates a GobalLayout instance with the default values
     gps.GlobalLayout()
 
-.. code:: ipython3
+.. code:: python3
 
     # Creates a GlobalLayout instance for a zoom of 12
     gps.GlobalLayout(zoom=12)
@@ -190,7 +190,7 @@ represented by a pair of coordinates, ``col`` and ``row``, respectively.
 As its name and attributes suggest, ``SpatialKey`` deals solely with
 spatial data.
 
-.. code:: ipython3
+.. code:: python3
 
     gps.SpatialKey(col=0, row=0)
 
@@ -205,7 +205,7 @@ as well as a z value that represents a point in time called,
 is also an instance of ``datetime.datetime``. Thus, ``SpaceTimeKey``\ s
 deal with spatial-temporal data.
 
-.. code:: ipython3
+.. code:: python3
 
     gps.SpaceTimeKey(col=0, row=0, instant=time)
 
@@ -218,7 +218,7 @@ either be a ``SpatialKey`` or a ``SpaceTimeKey`` depending on the type
 of data within the layer. The ``minKey`` is the left, uppermost cell in
 the grid and the ``maxKey`` is the right, bottommost cell.
 
-.. code:: ipython3
+.. code:: python3
 
     # Creating a Bounds from SpatialKeys
     
@@ -228,7 +228,7 @@ the grid and the ``maxKey`` is the right, bottommost cell.
     bounds = gps.Bounds(min_spatial_key, max_spatial_key)
     bounds
 
-.. code:: ipython3
+.. code:: python3
 
     # Creating a Bounds from SpaceTimeKeys
     
@@ -250,7 +250,7 @@ easier means. For ``RasterLayer``, one call the method,
 ``collect_metadata()`` and ``TiledRasterLayer`` has the attribute,
 ``layer_metadata``.
 
-.. code:: ipython3
+.. code:: python3
 
     # Creates Metadata for a layer with rasters that have a cell type of int16 with the previously defined
     # bounds, crs, extent, and layout definition.

docs/tutorials/ingesting-an-image.rst

@@ -20,7 +20,7 @@ contains elevation data on the east coast of Sri Lanka.
 methods shown in this tutorial. However, this could not be done in this
 instance due to permission requirements needed to access the file.
 
-.. code:: ipython3
+.. code:: python3
 
     !curl -o /tmp/cropped.tif https://s3.amazonaws.com/geopyspark-test/example-files/cropped.tif
 
@@ -40,7 +40,7 @@ The Code
 
 With our file downloaded we can begin the ingest.
 
-.. code:: ipython3
+.. code:: python3
 
     import geopyspark as gps
     
@@ -62,7 +62,7 @@ set a certain way in order for GeoPySpark to work. Thus,
 parameters without having to worry about setting the other, required
 fields.
 
-.. code:: ipython3
+.. code:: python3
 
     conf = gps.geopyspark_conf(master="local[*]", appName="ingest-example")
     pysc = SparkContext(conf=conf)
@@ -74,7 +74,7 @@ After the creation of ``pysc``, we can now read in the data. For this
 example, we will be reading in a single GeoTiff that contains spatial
 data. Hence, why we set the ``layer_type`` to ``LayerType.SPATIAL``.
 
-.. code:: ipython3
+.. code:: python3
 
     raster_layer = gps.geotiff.get(layer_type=gps.LayerType.SPATIAL, uri="file:///tmp/cropped.tif")
 
@@ -92,7 +92,7 @@ show it in on a map at some point. Therefore, we have to make sure that
 the tiles within the layer are in the right projection. We can do this
 by setting the ``target_crs`` parameter.
 
-.. code:: ipython3
+.. code:: python3
 
     tiled_raster_layer = raster_layer.tile_to_layout(gps.GlobalLayout(), target_crs=3857)
     tiled_raster_layer
@@ -104,12 +104,12 @@ Now it's time to pyramid! With our reprojected data, we will create an
 instance of ``Pyramid`` that contains 12 ``TiledRasterLayer``\ s. Each
 one having it's own ``zoom_level`` from 11 to 0.
 
-.. code:: ipython3
+.. code:: python3
 
     pyramided_layer = tiled_raster_layer.pyramid()
     pyramided_layer.max_zoom
 
-.. code:: ipython3
+.. code:: python3
 
     pyramided_layer.levels
 
@@ -120,7 +120,7 @@ To save all of the ``TiledRasterLayer``\ s within ``pyramid_layer``, we
 just have to loop through values of ``pyramid_layer.level`` and write
 each layer locally.
 
-.. code:: ipython3
+.. code:: python3
 
     for tiled_layer in pyramided_layer.levels.values():
         gps.write(uri="file:///tmp/ingested-image", layer_name="ingested-image", tiled_raster_layer=tiled_layer)

docs/tutorials/reading-in-sentinel-data.rst

@@ -38,7 +38,7 @@ For more information on the way the data is stored on S3, please see
 this
 `link <http://sentinel-pds.s3-website.eu-central-1.amazonaws.com/>`__.
 
-.. code:: ipython3
+.. code:: python3
 
     !curl -o /tmp/B01.jp2 http://sentinel-s2-l1c.s3.amazonaws.com/tiles/32/T/NM/2017/1/4/0/B01.jp2
     !curl -o /tmp/B09.jp2 http://sentinel-s2-l1c.s3.amazonaws.com/tiles/32/T/NM/2017/1/4/0/B09.jp2
@@ -49,15 +49,15 @@ The Code
 
 Now that we have the files, we can begin to read them into GeoPySpark.
 
-.. code:: ipython3
+.. code:: python3
 
     import rasterio
     import geopyspark as gps
     import numpy as np
     
     from pyspark import SparkContext
 
-.. code:: ipython3
+.. code:: python3
 
     conf = gps.geopyspark_conf(master="local[*]", appName="sentinel-ingest-example")
     pysc = SparkContext(conf=conf)
@@ -70,7 +70,7 @@ Once they are read in, we will then combine the three seperate numpy
 arrays into one. This combined array represents a single, multiband
 raster.
 
-.. code:: ipython3
+.. code:: python3
 
     jp2s = ["/tmp/B01.jp2", "/tmp/B09.jp2", "/tmp/B10.jp2"]
     arrs = []
@@ -89,7 +89,7 @@ With our raster data in hand, we can how begin the creation of a Python
 ``RDD``. Please see the `core concepts <core-concepts.ipynb>`__ guide
 for more information on what the following instances represent.
 
-.. code:: ipython3
+.. code:: python3
 
     # Create an Extent instance from rasterio's bounds
     extent = gps.Extent(*f.bounds)
@@ -104,23 +104,23 @@ way rasterio formats the projection of the read in rasters is not
 compatible with how GeoPySpark expects the ``CRS`` to be in. Thus, we
 had to do a bit of extra work to get it into the correct state
 
-.. code:: ipython3
+.. code:: python3
 
     # Projection information from the rasterio file
     f.crs.to_dict()
 
-.. code:: ipython3
+.. code:: python3
 
     # The projection information formatted to work with GeoPySpark
     int(f.crs.to_dict()['init'][5:])
 
-.. code:: ipython3
+.. code:: python3
 
     # We can create a Tile instance from our multiband, raster array and the nodata value from rasterio
     tile = gps.Tile.from_numpy_array(numpy_array=data, no_data_value=f.nodata)
     tile
 
-.. code:: ipython3
+.. code:: python3
 
     # Now that we have our ProjectedExtent and Tile, we can create our RDD from them
     rdd = pysc.parallelize([(projected_extent, tile)])
@@ -132,7 +132,7 @@ Creating the Layer
 From the ``RDD``, we can now create a ``RasterLayer`` using the
 ``from_numpy_rdd`` method.
 
-.. code:: ipython3
+.. code:: python3
 
     # While there is a time component to the data, this was ignored for this tutorial and instead the focus is just
     # on the spatial information. Thus, we have a LayerType of SPATIAL.