Fixed regression and made consistent use of create_rf_spark_session

Author: metasim

docs/src/main/paradox/release-notes.md

@@ -5,6 +5,7 @@
 ### 0.8.3
 
 * Updated `rf_crs` to accept string columns containing CRS specifications. ([#366](https://github.com/locationtech/rasterframes/issues/366))
+* _Breaking_ (potentially): removed `pyrasterframes.create_spark_session` in lieu of `pyrasterframes.utils.create_rf_spark_session`
 
 ### 0.8.2
 

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

@@ -2,10 +2,11 @@
 
 ```python, setup, echo=False
 import pyrasterframes
+from pyrasterframes.utils import create_rf_spark_session
 import pyrasterframes.rf_ipython
 from pyrasterframes.rasterfunctions import rf_crs, rf_extent, rf_tile, rf_data_cells
 from pyspark.sql.functions import col, lit
-spark = pyrasterframes.get_spark_session()
+spark = create_rf_spark_session()
 
 # Note that this is the same URI as in the getting started page...
 df = spark.read.raster('https://modis-pds.s3.amazonaws.com/MCD43A4.006/11/08/2019059/MCD43A4.A2019059.h11v08.006.2019072203257_B02.TIF')

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

@@ -29,7 +29,8 @@ from pyrasterframes import rf_ipython
 
 ```python, version
 import pyrasterframes
-spark = pyrasterframes.get_spark_session()
+from pyrasterframes.utils import create_rf_spark_session
+spark = create_rf_spark_session()
 ```
 
 Then, you can read a raster and work with it in a Spark DataFrame.

pyrasterframes/src/main/python/docs/nodata-handling.pymd

@@ -12,14 +12,15 @@ To understand how NoData is handled in RasterFrames, we first need to understand
 
 ```python setup, echo=False
 import pyrasterframes
+from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes.rasterfunctions import *
 import pyrasterframes.rf_ipython
 from IPython.display import display
 import pandas as pd
 import numpy as np
 from pyrasterframes.rf_types import Tile
 
-spark = pyrasterframes.get_spark_session()
+spark = create_rf_spark_session()
 ```
 
 The `CellType` class from the `rf_types` submodule allows us to create a representation of any valid cell type. There are convenience methods to create instances for a variety of basic types.

pyrasterframes/src/main/python/docs/numpy-pandas.pymd

@@ -34,9 +34,10 @@ As discussed in the @ref:[raster writing chapter](raster-write.md#dataframe-samp
 
 ```python, spark_session,  echo=False
 import pyrasterframes
+from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes.rasterfunctions import *
 from IPython.display import display
-spark = pyrasterframes.get_spark_session()
+spark = create_rf_spark_session()
 
 ```
 

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

@@ -6,11 +6,12 @@ However, there are times in any analysis where writing a representative sample o
 
 ```python imports, echo=False
 import pyrasterframes
+from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes.rasterfunctions import *
 from IPython.display import display
 import os.path
 
-spark = pyrasterframes.get_spark_session()
+spark = create_rf_spark_session()
 ```
 
 ## Tile Samples

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

@@ -12,11 +12,12 @@ For the Scala documentation on these functions, see @scaladoc[`RasterFunctions`]
 
 ```python imports, echo=False
 import pyrasterframes
+from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes.rasterfunctions import *
 from IPython.display import display
 import os.path
 
-spark = pyrasterframes.get_spark_session()
+spark = create_rf_spark_session()
 ```
 
 ## List of Available SQL and Python Functions

pyrasterframes/src/main/python/docs/time-series.pymd

@@ -6,14 +6,15 @@
 from IPython.display import display
 
 import pyrasterframes
+from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes.rasterfunctions import *
 import pyrasterframes.rf_ipython
 
 from pyspark.sql.functions import udf, lit
 from geomesa_pyspark.types import MultiPolygonUDT
 
 # This job is more memory bound, so reduce the concurrent tasks.
-spark = pyrasterframes.get_spark_session("local[4]")
+spark = create_rf_spark_session("local[4]")
 ```
 
 In this example, we will show how the flexibility of the DataFrame concept for raster data allows a simple and intuitive way to extract a time series from Earth observation data. We will continue our example from the @ref:[Zonal Map Algebra page](zonal-algebra.md).

pyrasterframes/src/main/python/docs/vector-data.pymd

@@ -10,10 +10,11 @@ RasterFrames provides a variety of ways to work with spatial vector data (points
 
 ```python, setup, echo=False
 import pyrasterframes
+from pyrasterframes.utils import create_rf_spark_session
 import pyrasterframes.rf_ipython
 import geopandas
 import folium
-spark = pyrasterframes.get_spark_session('local[2]')
+spark = create_rf_spark_session('local[2]')
 ```
 
 ## GeoJSON DataSource

pyrasterframes/src/main/python/docs/zonal-algebra.pymd

@@ -4,14 +4,15 @@
 from IPython.display import display
 
 import pyrasterframes
+from pyrasterframes.utils import create_rf_spark_session
 from pyrasterframes.rasterfunctions import *
 import pyrasterframes.rf_ipython
 
 from pyspark.sql.functions import udf, lit
 from geomesa_pyspark.types import MultiPolygonUDT
 
 # This seemed to work for time-series!
-spark = pyrasterframes.get_spark_session("local[4]")
+spark = create_rf_spark_session("local[4]")
 ```
 
 ## Definition