[DOCS] Run Python black on Markdown code blocks

README.md

@@ -93,25 +93,43 @@ This example loads NYC taxi trip records and taxi zone information stored as .CS
 #### Load NYC taxi trips and taxi zones data from CSV Files Stored on AWS S3
 
 ```python
-taxidf = sedona.read.format('csv').option("header","true").option("delimiter", ",").load("s3a://your-directory/data/nyc-taxi-data.csv")
-taxidf = taxidf.selectExpr('ST_Point(CAST(Start_Lon AS Decimal(24,20)), CAST(Start_Lat AS Decimal(24,20))) AS pickup', 'Trip_Pickup_DateTime', 'Payment_Type', 'Fare_Amt')
+taxidf = (
+    sedona.read.format("csv")
+    .option("header", "true")
+    .option("delimiter", ",")
+    .load("s3a://your-directory/data/nyc-taxi-data.csv")
+)
+taxidf = taxidf.selectExpr(
+    "ST_Point(CAST(Start_Lon AS Decimal(24,20)), CAST(Start_Lat AS Decimal(24,20))) AS pickup",
+    "Trip_Pickup_DateTime",
+    "Payment_Type",
+    "Fare_Amt",
+)
 ```
 
 ```python
-zoneDf = sedona.read.format('csv').option("delimiter", ",").load("s3a://your-directory/data/TIGER2018_ZCTA5.csv")
-zoneDf = zoneDf.selectExpr('ST_GeomFromWKT(_c0) as zone', '_c1 as zipcode')
+zoneDf = (
+    sedona.read.format("csv")
+    .option("delimiter", ",")
+    .load("s3a://your-directory/data/TIGER2018_ZCTA5.csv")
+)
+zoneDf = zoneDf.selectExpr("ST_GeomFromWKT(_c0) as zone", "_c1 as zipcode")
 ```
 
 #### Spatial SQL query to only return Taxi trips in Manhattan
 
 ```python
-taxidf_mhtn = taxidf.where('ST_Contains(ST_PolygonFromEnvelope(-74.01,40.73,-73.93,40.79), pickup)')
+taxidf_mhtn = taxidf.where(
+    "ST_Contains(ST_PolygonFromEnvelope(-74.01,40.73,-73.93,40.79), pickup)"
+)
 ```
 
 #### Spatial Join between Taxi Dataframe and Zone Dataframe to Find taxis in each zone
 
 ```python
-taxiVsZone = sedona.sql('SELECT zone, zipcode, pickup, Fare_Amt FROM zoneDf, taxiDf WHERE ST_Contains(zone, pickup)')
+taxiVsZone = sedona.sql(
+    "SELECT zone, zipcode, pickup, Fare_Amt FROM zoneDf, taxiDf WHERE ST_Contains(zone, pickup)"
+)
 ```
 
 #### Show a map of the loaded Spatial Dataframes using GeoPandas
@@ -120,14 +138,14 @@ taxiVsZone = sedona.sql('SELECT zone, zipcode, pickup, Fare_Amt FROM zoneDf, tax
 zoneGpd = gpd.GeoDataFrame(zoneDf.toPandas(), geometry="zone")
 taxiGpd = gpd.GeoDataFrame(taxidf.toPandas(), geometry="pickup")
 
-zone = zoneGpd.plot(color='yellow', edgecolor='black', zorder=1)
-zone.set_xlabel('Longitude (degrees)')
-zone.set_ylabel('Latitude (degrees)')
+zone = zoneGpd.plot(color="yellow", edgecolor="black", zorder=1)
+zone.set_xlabel("Longitude (degrees)")
+zone.set_ylabel("Latitude (degrees)")
 
 zone.set_xlim(-74.1, -73.8)
 zone.set_ylim(40.65, 40.9)
 
-taxi = taxiGpd.plot(ax=zone, alpha=0.01, color='red', zorder=3)
+taxi = taxiGpd.plot(ax=zone, alpha=0.01, color="red", zorder=3)
 ```
 
 ## Docker image

docs/api/sql/Raster-visualizer.md

@@ -78,9 +78,14 @@ Example:
 
 ```python
 from sedona.raster_utils.SedonaUtils import SedonaUtils
+
 # Or from sedona.spark import *
 
-df = sedona.read.format('binaryFile').load(DATA_DIR + 'raster.tiff').selectExpr("RS_FromGeoTiff(content) as raster")
+df = (
+    sedona.read.format("binaryFile")
+    .load(DATA_DIR + "raster.tiff")
+    .selectExpr("RS_FromGeoTiff(content) as raster")
+)
 htmlDF = df.selectExpr("RS_AsImage(raster, 500) as raster_image")
 SedonaUtils.display_image(htmlDF)
 ```

docs/api/sql/Spider.md

@@ -24,9 +24,18 @@ Sedona offers a spatial data generator called Spider. It is a data source that g
 Once you have your [`SedonaContext` object created](../Overview#quick-start), you can create a DataFrame with the `spider` data source.
 
 ```python
-df_random_points = sedona.read.format("spider").load(n=1000, distribution='uniform')
-df_random_boxes = sedona.read.format("spider").load(n=1000, distribution='gaussian', geometryType='box', maxWidth=0.05, maxHeight=0.05)
-df_random_polygons = sedona.read.format("spider").load(n=1000, distribution='bit', geometryType='polygon', minSegment=3, maxSegment=5, maxSize=0.1)
+df_random_points = sedona.read.format("spider").load(n=1000, distribution="uniform")
+df_random_boxes = sedona.read.format("spider").load(
+    n=1000, distribution="gaussian", geometryType="box", maxWidth=0.05, maxHeight=0.05
+)
+df_random_polygons = sedona.read.format("spider").load(
+    n=1000,
+    distribution="bit",
+    geometryType="polygon",
+    minSegment=3,
+    maxSegment=5,
+    maxSize=0.1,
+)
 ```
 
 Now we have three DataFrames with random spatial data. We can show the first three rows of the `df_random_points` DataFrame to verify the data is generated correctly.
@@ -57,22 +66,24 @@ import matplotlib.pyplot as plt
 import geopandas as gpd
 
 # Convert DataFrames to GeoDataFrames
-gdf_random_points = gpd.GeoDataFrame(df_random_points.toPandas(), geometry='geometry')
-gdf_random_boxes = gpd.GeoDataFrame(df_random_boxes.toPandas(), geometry='geometry')
-gdf_random_polygons = gpd.GeoDataFrame(df_random_polygons.toPandas(), geometry='geometry')
+gdf_random_points = gpd.GeoDataFrame(df_random_points.toPandas(), geometry="geometry")
+gdf_random_boxes = gpd.GeoDataFrame(df_random_boxes.toPandas(), geometry="geometry")
+gdf_random_polygons = gpd.GeoDataFrame(
+    df_random_polygons.toPandas(), geometry="geometry"
+)
 
 # Create a figure and a set of subplots
 fig, axes = plt.subplots(1, 3, figsize=(15, 5))
 
 # Plot each GeoDataFrame on a different subplot
-gdf_random_points.plot(ax=axes[0], color='blue', markersize=5)
-axes[0].set_title('Random Points')
+gdf_random_points.plot(ax=axes[0], color="blue", markersize=5)
+axes[0].set_title("Random Points")
 
-gdf_random_boxes.boundary.plot(ax=axes[1], color='red')
-axes[1].set_title('Random Boxes')
+gdf_random_boxes.boundary.plot(ax=axes[1], color="red")
+axes[1].set_title("Random Boxes")
 
-gdf_random_polygons.boundary.plot(ax=axes[2], color='green')
-axes[2].set_title('Random Polygons')
+gdf_random_polygons.boundary.plot(ax=axes[2], color="green")
+axes[2].set_title("Random Polygons")
 
 # Adjust the layout
 plt.tight_layout()
@@ -122,8 +133,11 @@ Example:
 
 ```python
 import geopandas as gpd
-df = sedona.read.format("spider").load(n=300, distribution='uniform', geometryType='box', maxWidth=0.05, maxHeight=0.05)
-gpd.GeoDataFrame(df.toPandas(), geometry='geometry').boundary.plot()
+
+df = sedona.read.format("spider").load(
+    n=300, distribution="uniform", geometryType="box", maxWidth=0.05, maxHeight=0.05
+)
+gpd.GeoDataFrame(df.toPandas(), geometry="geometry").boundary.plot()
 ```
 
 ![Uniform Distribution](../../image/spider/spider-uniform.png)
@@ -145,8 +159,11 @@ Example:
 
 ```python
 import geopandas as gpd
-df = sedona.read.format("spider").load(n=300, distribution='gaussian', geometryType='polygon', maxSize=0.05)
-gpd.GeoDataFrame(df.toPandas(), geometry='geometry').boundary.plot()
+
+df = sedona.read.format("spider").load(
+    n=300, distribution="gaussian", geometryType="polygon", maxSize=0.05
+)
+gpd.GeoDataFrame(df.toPandas(), geometry="geometry").boundary.plot()
 ```
 
 ![Gaussian Distribution](../../image/spider/spider-gaussian.png)
@@ -170,8 +187,11 @@ Example:
 
 ```python
 import geopandas as gpd
-df = sedona.read.format("spider").load(n=300, distribution='bit', geometryType='point', probability=0.2, digits=10)
-gpd.GeoDataFrame(df.toPandas(), geometry='geometry').plot(markersize=1)
+
+df = sedona.read.format("spider").load(
+    n=300, distribution="bit", geometryType="point", probability=0.2, digits=10
+)
+gpd.GeoDataFrame(df.toPandas(), geometry="geometry").plot(markersize=1)
 ```
 
 ![Bit Distribution](../../image/spider/spider-bit.png)
@@ -195,8 +215,11 @@ Example:
 
 ```python
 import geopandas as gpd
-df = sedona.read.format("spider").load(n=300, distribution='diagonal', geometryType='point', percentage=0.5, buffer=0.5)
-gpd.GeoDataFrame(df.toPandas(), geometry='geometry').plot(markersize=1)
+
+df = sedona.read.format("spider").load(
+    n=300, distribution="diagonal", geometryType="point", percentage=0.5, buffer=0.5
+)
+gpd.GeoDataFrame(df.toPandas(), geometry="geometry").plot(markersize=1)
 ```
 
 ![Diagonal Distribution](../../image/spider/spider-diagonal.png)
@@ -218,8 +241,11 @@ Example:
 
 ```python
 import geopandas as gpd
-df = sedona.read.format("spider").load(n=2000, distribution='sierpinski', geometryType='point')
-gpd.GeoDataFrame(df.toPandas(), geometry='geometry').plot(markersize=1)
+
+df = sedona.read.format("spider").load(
+    n=2000, distribution="sierpinski", geometryType="point"
+)
+gpd.GeoDataFrame(df.toPandas(), geometry="geometry").plot(markersize=1)
 ```
 
 ![Sierpinski Distribution](../../image/spider/spider-sierpinski.png)
@@ -237,8 +263,11 @@ Example:
 
 ```python
 import geopandas as gpd
-df = sedona.read.format("spider").load(n=300, distribution='parcel', dither=0.5, splitRange=0.5)
-gpd.GeoDataFrame(df.toPandas(), geometry='geometry').boundary.plot()
+
+df = sedona.read.format("spider").load(
+    n=300, distribution="parcel", dither=0.5, splitRange=0.5
+)
+gpd.GeoDataFrame(df.toPandas(), geometry="geometry").boundary.plot()
 ```
 
 ![Parcel Distribution](../../image/spider/spider-parcel.png)
@@ -274,8 +303,11 @@ Example:
 
 ```python
 import geopandas as gpd
-df_random_points = sedona.read.format("spider").load(n=1000, distribution='uniform', translateX=0.5, translateY=0.5, scaleX=2, scaleY=2)
-gpd.GeoDataFrame(df_random_points.toPandas(), geometry='geometry').plot(markersize=1)
+
+df_random_points = sedona.read.format("spider").load(
+    n=1000, distribution="uniform", translateX=0.5, translateY=0.5, scaleX=2, scaleY=2
+)
+gpd.GeoDataFrame(df_random_points.toPandas(), geometry="geometry").plot(markersize=1)
 ```
 
 The data is now in the region `[0.5, 2.5] x [0.5, 2.5]`.

docs/api/sql/Stac.md

@@ -34,15 +34,19 @@ df.show()
 You can load a STAC collection from a s3 collection file object:
 
 ```python
-df = sedona.read.format("stac").load("s3a://example.com/stac_bucket/stac_collection.json")
+df = sedona.read.format("stac").load(
+    "s3a://example.com/stac_bucket/stac_collection.json"
+)
 df.printSchema()
 df.show()
 ```
 
 You can also load a STAC collection from an HTTP/HTTPS endpoint:
 
 ```python
-df = sedona.read.format("stac").load("https://earth-search.aws.element84.com/v1/collections/sentinel-2-pre-c1-l2a")
+df = sedona.read.format("stac").load(
+    "https://earth-search.aws.element84.com/v1/collections/sentinel-2-pre-c1-l2a"
+)
 df.printSchema()
 df.show()
 ```
@@ -225,20 +229,15 @@ client = Client.open("https://planetarycomputer.microsoft.com/api/stac/v1")
 
 ```python
 items = client.search(
-    collection_id="aster-l1t",
-    datetime="2020",
-    return_dataframe=False
+    collection_id="aster-l1t", datetime="2020", return_dataframe=False
 )
 ```
 
 ### Search Items on a Collection Within a Month and Max Items
 
 ```python
 items = client.search(
-    collection_id="aster-l1t",
-    datetime="2020-05",
-    return_dataframe=False,
-    max_items=5
+    collection_id="aster-l1t", datetime="2020-05", return_dataframe=False, max_items=5
 )
 ```
 
@@ -250,35 +249,26 @@ items = client.search(
     ids=["AST_L1T_00312272006020322_20150518201805"],
     bbox=[-180.0, -90.0, 180.0, 90.0],
     datetime=["2006-01-01T00:00:00Z", "2007-01-01T00:00:00Z"],
-    return_dataframe=False
+    return_dataframe=False,
 )
 ```
 
 ### Search Multiple Items with Multiple Bounding Boxes
 
 ```python
-bbox_list = [
-    [-180.0, -90.0, 180.0, 90.0],
-    [-100.0, -50.0, 100.0, 50.0]
-]
-items = client.search(
-    collection_id="aster-l1t",
-    bbox=bbox_list,
-    return_dataframe=False
-)
+bbox_list = [[-180.0, -90.0, 180.0, 90.0], [-100.0, -50.0, 100.0, 50.0]]
+items = client.search(collection_id="aster-l1t", bbox=bbox_list, return_dataframe=False)
 ```
 
 ### Search Items and Get DataFrame as Return with Multiple Intervals
 
 ```python
 interval_list = [
     ["2020-01-01T00:00:00Z", "2020-06-01T00:00:00Z"],
-    ["2020-07-01T00:00:00Z", "2021-01-01T00:00:00Z"]
+    ["2020-07-01T00:00:00Z", "2021-01-01T00:00:00Z"],
 ]
 df = client.search(
-    collection_id="aster-l1t",
-    datetime=interval_list,
-    return_dataframe=True
+    collection_id="aster-l1t", datetime=interval_list, return_dataframe=True
 )
 df.show()
 ```
@@ -288,9 +278,7 @@ df.show()
 ```python
 # Save items in DataFrame to GeoParquet with both bounding boxes and intervals
 client.get_collection("aster-l1t").save_to_geoparquet(
-    output_path="/path/to/output",
-    bbox=bbox_list,
-    datetime="2020-05"
+    output_path="/path/to/output", bbox=bbox_list, datetime="2020-05"
 )
 ```
 

docs/setup/azure-synapse-analytics.md

@@ -75,14 +75,23 @@ Start your notebook with:
 ```python
 from sedona.spark import SedonaContext
 
-config = SedonaContext.builder() \
-    .config('spark.jars.packages',
-            'org.apache.sedona:sedona-spark-shaded-3.4_2.12-1.6.1,'
-            'org.datasyslab:geotools-wrapper-1.6.1-28.2') \
-    .config("spark.serializer","org.apache.spark.serializer.KryoSerializer") \
-    .config("spark.kryo.registrator", "org.apache.sedona.core.serde.SedonaKryoRegistrator") \
-    .config("spark.sql.extensions", "org.apache.sedona.viz.sql.SedonaVizExtensions,org.apache.sedona.sql.SedonaSqlExtensions") \
+config = (
+    SedonaContext.builder()
+    .config(
+        "spark.jars.packages",
+        "org.apache.sedona:sedona-spark-shaded-3.4_2.12-1.6.1,"
+        "org.datasyslab:geotools-wrapper-1.6.1-28.2",
+    )
+    .config("spark.serializer", "org.apache.spark.serializer.KryoSerializer")
+    .config(
+        "spark.kryo.registrator", "org.apache.sedona.core.serde.SedonaKryoRegistrator"
+    )
+    .config(
+        "spark.sql.extensions",
+        "org.apache.sedona.viz.sql.SedonaVizExtensions,org.apache.sedona.sql.SedonaSqlExtensions",
+    )
     .getOrCreate()
+)
 
 sedona = SedonaContext.create(config)
 ```

docs/setup/databricks.md

@@ -56,6 +56,7 @@ SedonaSQLRegistrator.registerAll(spark)
 
 ```python
 from sedona.register.geo_registrator import SedonaRegistrator
+
 SedonaRegistrator.registerAll(spark)
 ```