Remove unneeded catalog param keywords from docs

Signed-off-by: Jason T. Brown <[email protected]>

Author: vpipkt

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

@@ -42,7 +42,7 @@ red_nir_monthly_2017.printSchema()
 
 ```python, step_3_python
 red_nir_tiles_monthly_2017 = spark.read.raster(
-    catalog=red_nir_monthly_2017,
+    red_nir_monthly_2017,
     catalog_col_names=['red', 'nir'],
     tile_dimensions=(256, 256)
 )

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

@@ -40,7 +40,7 @@ catalog_df = spark.createDataFrame([
     Row(red=uri_pattern.format(4), nir=uri_pattern.format(8))
 ])
 df = spark.read.raster(
-    catalog=catalog_df,
+    catalog_df,
     catalog_col_names=['red', 'nir']
 )
 df.printSchema()

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

@@ -90,7 +90,7 @@ from pyspark.sql import Row
 blue_uri = 'https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/B02.tif'
 scl_uri = 'https://s22s-test-geotiffs.s3.amazonaws.com/luray_snp/SCL.tif'
 cat = spark.createDataFrame([Row(blue=blue_uri, scl=scl_uri),])
-unmasked = spark.read.raster(catalog=cat, catalog_col_names=['blue', 'scl'])
+unmasked = spark.read.raster(cat, catalog_col_names=['blue', 'scl'])
 unmasked.printSchema()
 ```
 

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

@@ -51,7 +51,7 @@ cat = spark.read.format('aws-pds-modis-catalog').load() \
             (col('acquisition_date') < lit('2018-02-22'))
         )
 
-spark_df = spark.read.raster(catalog=cat, catalog_col_names=['B01']) \
+spark_df = spark.read.raster(cat, catalog_col_names=['B01']) \
                 .select(
                     'acquisition_date',
                     'granule_id',

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

@@ -101,8 +101,6 @@ modis_catalog = spark.read \
     .withColumn('red' , F.concat('base_url', F.lit("_B01.TIF"))) \
     .withColumn('nir' , F.concat('base_url', F.lit("_B02.TIF")))
 
-modis_catalog.printSchema()
-
 print("Available scenes: ", modis_catalog.count())
 ```
 
@@ -124,10 +122,7 @@ equator.select('date', 'gid')
 Now that we have prepared our catalog, we simply pass the DataFrame or CSV string to the `raster` DataSource to load the imagery. The `catalog_col_names` parameter gives the columns that contain the URI's to be read.
 
 ```python, read_catalog
-rf = spark.read.raster(
-    catalog=equator,
-    catalog_col_names=['red', 'nir']
-)
+rf = spark.read.raster(equator, catalog_col_names=['red', 'nir'])
 rf.printSchema()
 ```
 
@@ -179,7 +174,7 @@ mb.printSchema()
 
 If a band is passed into `band_indexes` that exceeds the number of bands in the raster, a projected raster column will still be generated in the schema but the column will be full of `null` values.
 
-You can also pass a `catalog` and `band_indexes` together into the `raster` reader. This will create a projected raster column for the combination of all items passed into `catalog_col_names` and `band_indexes`. Again if a band in `band_indexes` exceeds the number of bands in a raster, it will have a `null` value for the corresponding column.
+You can also pass a _catalog_ and `band_indexes` together into the `raster` reader. This will create a projected raster column for the combination of all items in `catalog_col_names` and `band_indexes`. Again if a band in `band_indexes` exceeds the number of bands in a raster, it will have a `null` value for the corresponding column.
 
 Here is a trivial example with a _catalog_ over multiband rasters. We specify two columns containing URIs and two bands, resulting in four projected raster columns.
 
@@ -191,7 +186,7 @@ mb_cat = pd.DataFrame([
     },
 ])
 mb2 = spark.read.raster(
-    catalog=spark.createDataFrame(mb_cat),
+    spark.createDataFrame(mb_cat),
     catalog_col_names=['foo', 'bar'],
     band_indexes=[0, 1],
     tile_dimensions=(64,64)

pyrasterframes/src/main/python/docs/supervised-learning.pymd

@@ -33,10 +33,8 @@ catalog_df = pd.DataFrame([
     {b: uri_base.format(b) for b in cols}
 ])
 
-df = spark.read.raster(catalog=catalog_df,
-					   catalog_col_names=cols,
-					   tile_dimensions=(128, 128)
-					   ).repartition(100)
+df = spark.read.raster(catalog_df, catalog_col_names=cols, tile_dimensions=(128, 128)) \
+					  .repartition(100)
 
 df = df.select(
     rf_crs(df.B01).alias('crs'),

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

@@ -97,7 +97,7 @@ We then [reproject](https://gis.stackexchange.com/questions/247770/understanding
 ```python read_catalog
 raster_cols = ['B01', 'B02',] # red and near-infrared respectively
 park_rf = spark.read.raster(
-        catalog=park_cat.select(['acquisition_date', 'granule_id', 'geo_simp'] + raster_cols),
+        park_cat.select(['acquisition_date', 'granule_id', 'geo_simp'] + raster_cols),
         catalog_col_names=raster_cols) \
     .withColumn('park_native', st_reproject('geo_simp', lit('EPSG:4326'), rf_crs('B01'))) \
     .filter(st_intersects('park_native', rf_geometry('B01'))) 

pyrasterframes/src/main/python/docs/unsupervised-learning.pymd

@@ -37,7 +37,7 @@ filenamePattern = "L8-B{}-Elkton-VA.tiff"
 catalog_df = pd.DataFrame([
     {'b' + str(b): os.path.join(resource_dir_uri(), filenamePattern.format(b)) for b in range(1, 8)}
 ])
-df = spark.read.raster(catalog=catalog_df, catalog_col_names=catalog_df.columns)
+df = spark.read.raster(catalog_df, catalog_col_names=catalog_df.columns)
 df = df.select(
     rf_crs(df.b1).alias('crs'),
     rf_extent(df.b1).alias('extent'),