def test_to_spatial_rdd_df_and_geom_field_name(self):
spatial_df = self._create_spatial_point_table()
spatial_rdd = Adapter.toSpatialRdd(spatial_df, "geom")
spatial_rdd = Adapter.toSpatialRdd(spatial_df, "s")
spatial_rdd.analyze()
assert spatial_rdd.approximateTotalCount == 121960
assert spatial_rdd.boundaryEnvelope == Envelope(
-179.147236, 179.475569, -14.548699, 71.35513400000001)
def test_read_shapefile_to_dataframe(self):
spatial_rdd = ShapefileReader.readToGeometryRDD(
self.spark.sparkContext, shape_file_input_location)
spatial_rdd.analyze()
logging.info(spatial_rdd.fieldNames)
df = Adapter.toDf(spatial_rdd, self.spark)
df.show()
def test_read_csv_point_into_spatial_rdd(self):
df = self.spark.read.\
format("csv").\
option("delimiter", "\t").\
option("header", "false").\
load(area_lm_point_input_location)
df.show()
df.createOrReplaceTempView("inputtable")
spatial_df = self.spark.sql(
"select ST_PointFromText(inputtable._c0,\",\") as arealandmark from inputtable"
)
spatial_df.show()
spatial_df.printSchema()
spatial_rdd = Adapter.toSpatialRdd(spatial_df, "arealandmark")
spatial_rdd.analyze()
Adapter.toDf(spatial_rdd, self.spark).show()
def test_read_mixed_wkt_geometries_into_spatial_rdd_with_unique_id(self):
df = self.spark.read.format("csv").\
option("delimiter", "\t").\
option("header", "false").\
load(mixed_wkt_geometry_input_location)
df.show()
df.createOrReplaceTempView("inputtable")
spatial_df = self.spark.sql(
"select ST_GeomFromWKT(inputtable._c0) as usacounty, inputtable._c3, inputtable._c5 from inputtable"
)
spatial_df.show()
spatial_df.printSchema()
spatial_rdd = Adapter.toSpatialRdd(spatial_df, "usacounty")
spatial_rdd.analyze()
assert (Adapter.toDf(spatial_rdd, self.spark).columns.__len__() == 3)
Adapter.toDf(spatial_rdd, self.spark).show()
def test_distance_join_result_to_dataframe(self):
point_csv_df = self.spark.\
read.\
format("csv").\
option("delimiter", ",").\
option("header", "false").load(
area_lm_point_input_location
)
point_csv_df.createOrReplaceTempView("pointtable")
point_df = self.spark.sql(
"select ST_Point(cast(pointtable._c0 as Decimal(24,20)),cast(pointtable._c1 as Decimal(24,20))) as arealandmark from pointtable"
)
point_rdd = Adapter.toSpatialRdd(point_df, "arealandmark")
point_rdd.analyze()
polygon_wkt_df = self.spark.read.\
format("csv").\
option("delimiter", "\t").\
option("header", "false").load(
mixed_wkt_geometry_input_location
)
polygon_wkt_df.createOrReplaceTempView("polygontable")
polygon_df = self.spark.\
sql("select ST_GeomFromWKT(polygontable._c0) as usacounty from polygontable")
polygon_rdd = Adapter.toSpatialRdd(polygon_df, "usacounty")
polygon_rdd.analyze()
circle_rdd = CircleRDD(polygon_rdd, 0.2)
point_rdd.spatialPartitioning(GridType.QUADTREE)
circle_rdd.spatialPartitioning(point_rdd.getPartitioner())
point_rdd.buildIndex(IndexType.QUADTREE, True)
join_result_pair_rdd = JoinQuery.\
DistanceJoinQueryFlat(point_rdd, circle_rdd, True, True)
join_result_df = Adapter.toDf(join_result_pair_rdd, self.spark)
join_result_df.printSchema()
join_result_df.show()
def test_read_csv_point_into_spatial_rdd_by_passing_coordinates(self):
df = self.spark.read.format("csv").\
option("delimiter", ",").\
option("header", "false").\
load(area_lm_point_input_location)
df.show()
df.createOrReplaceTempView("inputtable")
spatial_df = self.spark.sql(
"select ST_Point(cast(inputtable._c0 as Decimal(24,20)),cast(inputtable._c1 as Decimal(24,20))) as arealandmark from inputtable"
)
spatial_df.show()
spatial_df.printSchema()
spatial_rdd = SpatialRDD(self.spark.sparkContext)
spatial_rdd.rawJvmSpatialRDD = Adapter.toRdd(spatial_df)
spatial_rdd.analyze()
assert (Adapter.toDf(spatial_rdd, self.spark).columns.__len__() == 1)
Adapter.toDf(spatial_rdd, self.spark).show()
def test_geojson_to_dataframe(self):
spatial_rdd = PolygonRDD(self.spark.sparkContext,
geojson_input_location,
FileDataSplitter.GEOJSON, True)
spatial_rdd.analyze()
df = Adapter.toDf(spatial_rdd, self.spark).\
withColumn("geometry", expr("ST_GeomFromWKT(geometry)"))
df.show()
assert (df.columns[1] == "STATEFP")
def test_load_id_column_data_check(self):
spatial_rdd = PolygonRDD(self.spark.sparkContext,
geojson_id_input_location,
FileDataSplitter.GEOJSON, True)
spatial_rdd.analyze()
df = Adapter.toDf(spatial_rdd, self.spark)
df.show()
try:
assert df.columns.__len__() == 3
except AssertionError:
assert df.columns.__len__() == 4
assert df.count() == 1
def test_to_rdd_from_dataframe(self):
spatial_df = self._create_spatial_point_table()
spatial_df.show()
jsrdd = Adapter.toRdd(spatial_df)
spatial_rdd = SpatialRDD(self.sc)
spatial_rdd.rawJvmSpatialRDD = jsrdd
spatial_rdd.analyze()
assert spatial_rdd.approximateTotalCount == 121960
assert spatial_rdd.boundaryEnvelope == Envelope(
-179.147236, 179.475569, -14.548699, 71.35513400000001)
def test_to_df_srdd_fn_spark(self):
spatial_rdd = PolygonRDD(self.spark.sparkContext,
geojson_input_location,
FileDataSplitter.GEOJSON, True)
spatial_rdd.analyze()
assert spatial_rdd.approximateTotalCount == 1001
spatial_columns = [
"state_id", "county_id", "tract_id", "bg_id", "fips", "fips_short",
"bg_nr", "type", "code1", "code2"
]
spatial_df = Adapter.toDf(spatial_rdd, spatial_columns, self.spark)
spatial_df.show()
assert spatial_df.columns == ["geometry", *spatial_columns]
assert spatial_df.count() == 1001
def test_to_spatial_rdd_df_geom_column_id(self):
df = self.spark.read.\
format("csv").\
option("delimiter", "\t").\
option("header", "false").\
load(mixed_wkt_geometry_input_location)
df_shorter = df.select(
col("_c0").alias("geom"),
col("_c6").alias("county_name"))
df_shorter.createOrReplaceTempView("county_data")
spatial_df = self.spark.sql(
"SELECT ST_GeomFromWKT(geom) as geom, county_name FROM county_data"
)
spatial_df.show()
spatial_rdd = Adapter.toSpatialRdd(spatial_df, ["geom", "county_name"])
spatial_rdd.analyze()
assert spatial_rdd.approximateTotalCount == 100
def get_bdy_rdd(spark, bdy):
# load boundaries from Postgres
sql = """SELECT {}, name as {}, st_astext(geom) as wkt_geom
FROM admin_bdys_202008.{}_analysis""".format(
bdy["id_field"], bdy["name_field"], bdy["name"])
bdy_df = get_dataframe_from_postgres(spark, sql)
# create geometries from WKT strings into new DataFrame
bdy_df2 = bdy_df\
.withColumn("geom", f.expr("st_geomFromWKT(wkt_geom)")) \
.drop("wkt_geom")
# create rdd
output_rdd = Adapter.toSpatialRdd(bdy_df2, "geom")
output_rdd.analyze()
bdy_df2.unpersist()
bdy_df.unpersist()
return output_rdd