| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, |
| # software distributed under the License is distributed on an |
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY |
| # KIND, either express or implied. See the License for the |
| # specific language governing permissions and limitations |
| # under the License. |
| |
| import logging |
| |
| import pyspark |
| import pytest |
| from pyspark.sql import DataFrame |
| from pyspark.sql.functions import expr |
| from pyspark.sql.functions import col |
| |
| from sedona import version |
| from sedona.core.SpatialRDD import PolygonRDD, CircleRDD |
| from sedona.core.enums import FileDataSplitter, GridType, IndexType |
| from sedona.core.formatMapper.shapefileParser.shape_file_reader import ShapefileReader |
| from sedona.core.geom.envelope import Envelope |
| from sedona.core.jvm.config import is_greater_or_equal_version |
| from sedona.core.spatialOperator import JoinQuery |
| from sedona.utils.adapter import Adapter |
| from tests import geojson_input_location, shape_file_with_missing_trailing_input_location, \ |
| geojson_id_input_location |
| from tests import shape_file_input_location, area_lm_point_input_location |
| from tests import mixed_wkt_geometry_input_location |
| from tests.test_base import TestBase |
| |
| |
| class TestAdapter(TestBase): |
| |
| 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_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() |
| |
| def test_read_csv_point_into_spatial_rdd_with_unique_id_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() |
| |
| def test_read_mixed_wkt_geometries_into_spatial_rdd(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 from inputtable") |
| spatial_df.show() |
| spatial_df.printSchema() |
| spatial_rdd = Adapter.toSpatialRdd(spatial_df, "usacounty") |
| spatial_rdd.analyze() |
| Adapter.toDf(spatial_rdd, self.spark).show() |
| assert (Adapter.toDf(spatial_rdd, self.spark).columns.__len__() == 1) |
| 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_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_shapefile_with_missing_to_dataframe(self): |
| spatial_rdd = ShapefileReader.\ |
| readToGeometryRDD(self.spark.sparkContext, shape_file_with_missing_trailing_input_location) |
| |
| spatial_rdd.analyze() |
| logging.info(spatial_rdd.fieldNames) |
| |
| df = Adapter.toDf(spatial_rdd, self.spark) |
| df.show() |
| |
| def test_geojson_to_dataframe(self): |
| spatial_rdd = PolygonRDD( |
| self.spark.sparkContext, geojson_input_location, FileDataSplitter.GEOJSON, True |
| ) |
| |
| spatial_rdd.analyze() |
| Adapter.toDf(spatial_rdd, self.spark).show() |
| df = Adapter.toDf(spatial_rdd, self.spark) |
| |
| assert (df.columns[1] == "STATEFP") |
| |
| def test_convert_spatial_join_result_to_dataframe(self): |
| 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() |
| |
| 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() |
| |
| point_rdd.spatialPartitioning(GridType.QUADTREE) |
| polygon_rdd.spatialPartitioning(point_rdd.getPartitioner()) |
| |
| point_rdd.buildIndex(IndexType.QUADTREE, True) |
| |
| join_result_point_rdd = JoinQuery.\ |
| SpatialJoinQueryFlat(point_rdd, polygon_rdd, True, True) |
| |
| join_result_df = Adapter.toDf(join_result_point_rdd, self.spark) |
| join_result_df.show() |
| |
| join_result_df2 = Adapter.toDf(join_result_point_rdd, ["abc", "def"], list(), self.spark) |
| join_result_df2.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_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 _create_spatial_point_table(self) -> DataFrame: |
| df = self.spark.read.\ |
| format("csv").\ |
| option("delimiter", "\t").\ |
| option("header", "false").\ |
| load(area_lm_point_input_location) |
| |
| df.createOrReplaceTempView("inputtable") |
| |
| spatial_df = self.spark.sql("select ST_PointFromText(inputtable._c0,\",\") as geom from inputtable") |
| |
| return spatial_df |
| |
| 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_to_spatial_rdd_df_with_non_geom_fields(self): |
| spatial_df = self._create_spatial_point_table() |
| spatial_df = spatial_df.withColumn("i", expr("10")).withColumn("s", expr("'20'")) |
| spatial_rdd = Adapter.toSpatialRdd(spatial_df, "geom") |
| assert spatial_rdd.fieldNames == ['i', 's'] |
| spatial_rdd.analyze() |
| assert spatial_rdd.approximateTotalCount == 121960 |
| assert spatial_rdd.boundaryEnvelope == Envelope(-179.147236, 179.475569, -14.548699, 71.35513400000001) |
| |
| def test_to_spatial_rdd_df_with_custom_user_data_field_names(self): |
| spatial_df = self._create_spatial_point_table() |
| spatial_df = spatial_df.withColumn("i", expr("10")).withColumn("s", expr("'20'")) |
| spatial_rdd = Adapter.toSpatialRdd(spatial_df, "geom", ["i2", "s2"]) |
| assert spatial_rdd.fieldNames == ['i2', 's2'] |
| spatial_rdd.analyze() |
| assert spatial_rdd.approximateTotalCount == 121960 |
| assert spatial_rdd.boundaryEnvelope == Envelope(-179.147236, 179.475569, -14.548699, 71.35513400000001) |
| |
| def test_to_spatial_rdd_df(self): |
| spatial_df = self._create_spatial_point_table() |
| |
| spatial_rdd = Adapter.toSpatialRdd(spatial_df, "geometry") |
| |
| spatial_rdd.analyze() |
| |
| assert spatial_rdd.approximateTotalCount == 121960 |
| assert spatial_rdd.boundaryEnvelope == Envelope(-179.147236, 179.475569, -14.548699, 71.35513400000001) |
| |
| @pytest.mark.skipif(is_greater_or_equal_version(version, "1.0.0"), reason="Deprecated in Sedona") |
| 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() |
| |
| 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 |
