Edits: languages.pymd & reference.pymd

Author: metasim

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

@@ -1,6 +1,6 @@
 # API Languages
 
-One of the great powers of RasterFrames, afforded by Spark SQL, is the ability to express computation in multiple programming languages. This tutorial is centered around Python because that's the most common language used in data science and GIS analytics. However, Scala (the implementation language of RasterFrames) and SQL are also fully supported. Examples in Python can be mechanically translated into the other two languages without much difficulty once the naming conventions are understood. In the sections below we will show the same example program in each language. We will compute the average NDVI per month for a single _tile_ in Tanzania.
+One of the great powers of RasterFrames, afforded by Spark SQL, is the ability to express computation in multiple programming languages. This documentation focuses on Python because it is the most commonly used language in data science and GIS analytics. However, Scala (the implementation language of RasterFrames) and SQL are also fully supported. Examples in Python can be mechanically translated into the other two languages without much difficulty once the naming conventions are understood. In the sections below we will show the same example program in each language. We will compute the average NDVI per month for a single _tile_ in Tanzania.
 
 ```python, imports, echo=False
 from pyspark.sql.functions import *
@@ -170,4 +170,6 @@ val result = red_nir_tiles_monthly_2017
   .agg(rf_agg_stats(rf_normalized_difference($"nir", $"red")) as "ndvi_stats")
   .orderBy("month")
   .select("month", "ndvi_stats.*")
+  
+result.show()  
 ```

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

@@ -1,6 +1,6 @@
 # Function Reference
 
-RasterFrames provides SQL and Python bindings to many UDFs using the `Tile` column type. In Spark SQL, the functions are already registered in the SQL engine; they are usually prefixed with `rf_`. In Python, they are available in the `pyrasterframes.rasterfunctions` module.
+RasterFrames provides a rich set of columnar function for processing geospatial raster data. In Spark SQL, the functions are already registered in the SQL engine; they are usually prefixed with `rf_`. In Python, they are available in the `pyrasterframes.rasterfunctions` module.
 
 The convention in this document will be to define the function signature as below, with its return type, the function name, and named arguments with their types.
 
@@ -30,7 +30,7 @@ become available for use with DataFrames. You can view all of the available func
 
 ## Vector Operations
 
-Various LocationTech GeoMesa user-defined functions (UDFs) to deal with `geomtery` type columns are provided in the SQL engine and within the `pyrasterframes.rasterfunctions` Python module. These are documented in the [LocationTech GeoMesa Spark SQL documentation](https://www.geomesa.org/documentation/user/spark/sparksql_functions.html#). These functions are all prefixed with `st_`.
+Various LocationTech GeoMesa user-defined functions (UDFs) dealing with `geomtery` type columns are provided in the SQL engine and within the `pyrasterframes.rasterfunctions` Python module. These are documented in the [LocationTech GeoMesa Spark SQL documentation](https://www.geomesa.org/documentation/user/spark/sparksql_functions.html#). These functions are all prefixed with `st_`.
 
 RasterFrames provides some additional functions for vector geometry operations.
 
@@ -39,7 +39,7 @@ RasterFrames provides some additional functions for vector geometry operations.
     Geometry st_reproject(Geometry geom, String origin_crs, String destination_crs)
 
 
-Reproject the vector `geom` from `origin_crs` to `destination_crs`. Both `_crs` arguments are either [proj4](https://proj4.org/usage/quickstart.html) strings, [EPSG codes](https://www.epsg-registry.org/) codes or [OGC WKT](https://www.opengeospatial.org/standards/wkt-crs) for coordinate reference systems.
+Reproject the vector `geom` from `origin_crs` to `destination_crs`. Both `_crs` arguments are either [proj4](https://proj4.org/usage/quickstart.html) strings, [EPSG codes](https://www.epsg-registry.org/) or [OGC WKT](https://www.opengeospatial.org/standards/wkt-crs) for coordinate reference systems.
 
 
 ### st_extent
@@ -76,7 +76,7 @@ Get the cell type of the `tile`. The cell type can be changed with @ref:[rf_conv
 
     Tile rf_tile(ProjectedRasterTile proj_raster)
 
-Get the fully realized (non-lazy) `tile` from the `ProjectedRasterTile` or `RasterSource` type tile column.
+Get the fully realized (non-lazy) `tile` from a `ProjectedRasterTile` struct column.
 
 ### rf_extent
 
@@ -96,7 +96,7 @@ Fetch CRS structure representing the coordinate reference system of a `Projected
 
     Struct rf_mk_crs(String crsText)   
 
-Construct a CRS structure from one of its string representations. Three froms are supported:
+Construct a CRS structure from one of its string representations. Three forms are supported:
 
 * [EPSG code](https://www.epsg-registry.org/): `EPSG:<integer>`
 * [Proj4 string](https://proj.org/): `+proj <proj4 parameters>`
@@ -256,7 +256,7 @@ The binary local map algebra functions have similar variations in the Python API
 
 The SQL API does not require the `rf_local_op_double` or `rf_local_op_int` forms (just `rf_local_op`).
 
-Local map algebra operations for more than two tiles are implemented to work across rows in the data frame. As such, they are @ref:[aggregate functions](reference.md#tile-local-aggregate-statistics).
+Local map algebra operations for more than two `tile`s are implemented to work across rows in the DataFrame. As such, they are @ref:[aggregate functions](reference.md#tile-local-aggregate-statistics).
 
 ### rf_local_add
 
@@ -546,43 +546,43 @@ Aggregates over all of the rows in DataFrame of `tile` and returns a count of ea
 
 Local statistics compute the element-wise statistics across a DataFrame or group of `tile`s, resulting in a `tile` that has the same dimension.
 
-When these functions encounter a NoData in a cell location, it will be ignored. 
+When these functions encounter NoData in a cell location, it will be ignored. 
 
 ### rf_agg_local_max
 
     Tile rf_agg_local_max(Tile tile)
 
-Compute the cell-local maximum operation over Tiles in a column.
+Compute the cell-local maximum operation over `tile`s in a column.
 
 ### rf_agg_local_min
 
     Tile rf_agg_local_min(Tile tile)
 
-Compute the cell-local minimum operation over Tiles in a column.
+Compute the cell-local minimum operation over `tile`s in a column.
 
 ### rf_agg_local_mean
 
     Tile rf_agg_local_mean(Tile tile)
 
-Compute the cell-local mean operation over Tiles in a column.
+Compute the cell-local mean operation over `tile`s in a column.
 
 ### rf_agg_local_data_cells
 
     Tile rf_agg_local_data_cells(Tile tile)
 
-Compute the cell-local count of data cells over Tiles in a column. Returned `tile` has a cell type of `int32`.
+Compute the cell-local count of data cells over `tile`s in a column. Returned `tile` has a cell type of `int32`.
 
 ### rf_agg_local_no_data_cells
 
     Tile rf_agg_local_no_data_cells(Tile tile)
 
-Compute the cell-local count of NoData cells over Tiles in a column. Returned `tile` has a cell type of `int32`.
+Compute the cell-local count of NoData cells over `tile`s in a column. Returned `tile` has a cell type of `int32`.
 
 ### rf_agg_local_stats
 
     Struct[Tile, Tile, Tile, Tile, Tile] rf_agg_local_stats(Tile tile)
 
-Compute cell-local aggregate count, minimum, maximum, mean, and variance for a column of Tiles. Returns a struct of five `tile`s.
+Compute cell-local aggregate count, minimum, maximum, mean, and variance for a column of `tile`s. Returns a struct of five `tile`s.
 
 
 ## Converting Tiles
@@ -599,7 +599,7 @@ Create a row for each cell in `tile` columns. Many `tile` columns can be passed
 
     Int, Int, Numeric* rf_explode_tiles_sample(Double sample_frac, Long seed, Tile* tile)
 
-Python only. As with @ref:[`rf_explode_tiles`](reference.md#rf-explode-tiles), but taking a randomly sampled subset of cells. Equivalent to the below, but this implementation is optimized for speed. Parameter `sample_frac` should be between 0.0 and 1.0.
+Python only. As with @ref:[`rf_explode_tiles`](reference.md#rf-explode-tiles), but taking a randomly sampled subset of cells. Equivalent to the `rf_explode-tiles`, but allows a random subset of the data to be selected. Parameter `sample_frac` should be between 0.0 and 1.0.
 
 ### rf_tile_to_array_int