Merge branch 'feature/courtney-edits' of github.com:s22s/rasterframes into feature/courtney-edits

Author: vpipkt

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

@@ -146,7 +146,7 @@ In the initial examples on this page, we used @ref:[`rf_tile`](reference.md#rf-t
 
 ## Multiband Rasters
 
-A multiband raster represents a three dimensional numeric array. The first two dimensions are spatial, and the third dimension is typically designated as different bands. The bands may represent intensity of different wavelengths of light (or other electromagnetic radiation). The different bands may represent other phenomena such as measurement time, quality indications, or additional measurements.
+A multiband raster represents a three dimensional numeric array. The first two dimensions are spatial, and the third dimension is typically designated as different bands. The bands could represent intensity of different wavelengths of light (or other electromagnetic radiation), or they could represent other phenomena such as measurement time, quality indications, or additional measurements.
 
 When reading a multiband raster or a _Catalog_ describing multiband rasters, you will need to know ahead of time which bands you want to read. You will specify the bands to read, indexed from zero, passing a list of integers into the `band_indexes` parameter of the `raster` reader.
 
@@ -182,4 +182,4 @@ mb2.printSchema()
 
 [CRS]: concepts.md#coordinate-reference-system--crs
 [Extent]: concepts.md#extent
-[Tile]: concepts.md#tile
+[Tile]: concepts.md#tile

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

@@ -1,8 +1,8 @@
 # Writing Raster Data
 
-RasterFrames is oriented toward large scale analyses of spatial data. The primary output for most use cases may be a @ref:[statistical summary](aggregation.md), a @ref:[machine learning model](machine-learning.md), or some other result that is generally much smaller than the input data set. 
+RasterFrames is oriented toward large scale analyses of spatial data. The primary output of these analyses could be a @ref:[statistical summary](aggregation.md), a @ref:[machine learning model](machine-learning.md), or some other result that is generally much smaller than the input data set.
 
-However there are times in any analysis where writing a representative sample of the work in progress provides invaluable feedback on the process and results.
+However, there are times in any analysis where writing a representative sample of the work in progress provides invaluable feedback on the current state of the process and results.
 
 ```python imports, echo=False
 import pyrasterframes
@@ -13,12 +13,12 @@ spark = pyrasterframes.get_spark_session()
 
 ## Tile Samples
 
-When collecting a _tile_ (see discussion of the RasterFrame @ref:[schema](raster-read.md#single-raster) for orientation to the concept) to the Python Spark driver, we have some convenience methods to quickly visualize the _tile_. 
+When collecting a _tile_ (see discussion of the RasterFrame @ref:[schema](raster-read.md#single-raster) for orientation to the concept) to the Python Spark driver, we have some convenience methods to quickly visualize the _tile_.
 
-In an IPython or Jupyter interpreter a `Tile` object will be displayed as an image with limited metadata.
+In an IPython or Jupyter interpreter, a `Tile` object will be displayed as an image with limited metadata.
 
 ```python tile_sample
-def scene(band): 
+def scene(band):
     return 'https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/' \
              'MCD43A4.A2019059.h11v08.006.2019072203257_B{}.TIF'.format(band)
 raster_url = scene('02')
@@ -33,7 +33,7 @@ display(tile) # IPython.display function
 
 ## DataFrame Samples
 
-Within an IPython or Jupyter interpreter a Pandas DataFrame containing a column of _tiles_ will be rendered as the samples discussed above. Simply import the `rf_ipython` submodule to enable enhanced HTML rendering of a Pandas DataFrame.
+Within an IPython or Jupyter interpreter, a Pandas DataFrame containing a column of _tiles_ will be rendered as the samples discussed above. Simply import the `rf_ipython` submodule to enable enhanced HTML rendering of a Pandas DataFrame.
 
 In the example below, notice the result is limited to a small subset. For more discussion about why this is important, see the @ref:[Pandas and NumPy discussion](numpy-pandas.md).
 
@@ -48,7 +48,7 @@ pandas_df = spark.read.raster(raster_url, tile_dimensions=(64, 64)) \
 pandas_df.dtypes
 ```
 
-Viewing the DataFrame in Jupyter looks like this. 
+Viewing the DataFrame in Jupyter looks like this.
 
 ```python show_pandas, evaluate=False
 pandas_df
@@ -59,16 +59,16 @@ pandas_df
 
 ## GeoTIFFs
 
-GeoTIFF is one of the most common file formats for spatial data, providing flexibility in data encoding, representation, and storage. RasterFrames provides a specialized Spark DataFrame writer for rendering a RasterFrame to a GeoTiff.
+GeoTIFF is one of the most common file formats for spatial data, providing flexibility in data encoding, representation, and storage. RasterFrames provides a specialized Spark DataFrame writer for rendering a RasterFrame to a GeoTIFF.
 
 One downside to GeoTIFF is that it is not a big data native format. To create a GeoTIFF all the data to be encoded has to be in the memory of one compute node (in Spark parlance, this is a "collect"), limiting it's maximum size substantially compared to that of a full cluster environment. When rendering GeoTIFFs in RasterFrames, you either need to specify the dimensions of the output raster, or be aware of how big the collected data will end up being.
 
-Fortunately, we can use the cluster computing capability to downlample the data (using nearest-neighbor) into a more manageble size. For sake of example, let's render a simple RGB overview image of our scene as a small raster, reprojecting it to latitude and longitude coordinates on the [WGS84](https://en.wikipedia.org/wiki/World_Geodetic_System) reference ellipsoid (aka [EPSG:4326](https://spatialreference.org/ref/epsg/4326/)):
+Fortunately, we can use the cluster computing capability to downsample the data (using nearest-neighbor) into a more manageable size. For sake of example, let's render a simple RGB overview image of our scene as a small raster, reprojecting it to latitude and longitude coordinates on the [WGS84](https://en.wikipedia.org/wiki/World_Geodetic_System) reference ellipsoid (aka [EPSG:4326](https://spatialreference.org/ref/epsg/4326/)):
 
 ```python write_geotiff
 import os.path
 from docs import docs_dir
-cat = """  
+cat = """
 red,green,blue
 {},{},{}
 """.format(scene('01'), scene('04'), scene('03'))
@@ -83,7 +83,7 @@ View it with `rasterio` to check the results:
 import rasterio
 import numpy as np
 from rasterio.plot import show, show_hist
- 
+
 with rasterio.open(outfile) as src:
     cells = np.clip(src.read(), 0, 1800).astype('float32')
     show(cells)
@@ -100,12 +100,12 @@ with rasterio.open(outfile) as src:
 
 ## Parquet
 
-You can write the Spark DataFrame to an [Apache Parquet][Parquet] "file". This format is designed to work across different projects in the Hadoop ecosystem. It also provides a variety of optimizations for query against data written in the format. 
+You can write the Spark DataFrame to an [Apache Parquet][Parquet] "file". This format is designed to work across different projects in the Hadoop ecosystem. It also provides a variety of optimizations for query against data written in the format.
 
 ```python write_parquet, evaluate=False
 spark_df.withColumn('exp', rf_expm1('proj_raster')) \
     .write.mode('append').parquet('hdfs:///rf-user/sample.pq')
 ```
 
 [GeoTrellis]: https://geotrellis.readthedocs.io/en/latest/
-[Parquet]: https://spark.apache.org/docs/latest/sql-data-sources-parquet.html
+[Parquet]: https://spark.apache.org/docs/latest/sql-data-sources-parquet.html