def draw_world_weighted_point_map(spark):
df = spark.read.format("csv").option("header", True).option(
"delimiter", ","
).schema(
"continent string, country string, locationId string, longitude double, latitude double,"
"currentConfirmedCount int, confirmedCount int, suspectedCount int, curedCount int, deadCount int, "
"updateTime timestamp").load(country_csv).cache()
df.createOrReplaceTempView("COVID_country")
register_funcs(spark)
# 1
res1 = spark.sql(
"select ST_Point(longitude, latitude) as point from COVID_country ")
res1.createOrReplaceTempView("res1")
res1 = spark.sql("select * from res1 where point != 'POINT (nan nan)' ")
res1.show(20, False)
vega1 = vega_weighted_pointmap(
3000, 2000, [-289.095983, -73.863121, 289.095983, 73.863121],
"#EEEEEE", [2, 60], [6], 1.0, "EPSG:4326")
res_png1 = weighted_pointmap(res1, vega1)
save_png(res_png1, './COVID_country_weighted_point_map1.png')
spark.catalog.dropGlobalTempView("COVID_country")
def draw_heat_map(spark):
df = spark.read.format("csv").option("header", True).option(
"delimiter", ","
).schema(
"VendorID string, tpep_pickup_datetime timestamp, tpep_dropoff_datetime timestamp, passenger_count long, trip_distance double, pickup_longitude double, pickup_latitude double, dropoff_longitude double, dropoff_latitude double, fare_amount double, tip_amount double, total_amount double, buildingid_pickup long, buildingid_dropoff long, buildingtext_pickup string, buildingtext_dropoff string"
).load("file:///tmp/0_5M_nyc_taxi_and_building.csv").cache()
df.show(20, False)
df.createOrReplaceTempView("nyc_taxi")
# df.createOrReplaceGlobalTempView("nyc_taxi")
res = spark.sql(
"select pickup_latitude as x, pickup_longitude as y, passenger_count as w from nyc_taxi"
)
res.printSchema()
res.createOrReplaceTempView("pickup")
register_funcs(spark)
res = spark.sql(
"select ST_Transform(ST_Point(x, y), 'EPSG:4326','EPSG:3857' ) as pickup_point, w from pickup"
)
res.show(20, False)
res.createOrReplaceTempView("project")
res = spark.sql(
"select Projection(pickup_point, 'POINT (4534000 -12510000)', 'POINT (4538000 -12513000)', 1024, 896) as point, w from project"
)
res.show(20, False)
vega_heat_map = VegaHeatMap(300, 200, 10.0)
vega = vega_heat_map.build()
res = heatmap(res, vega)
save_png(res, '/tmp/heatmap.png')
spark.sql("show tables").show()
spark.catalog.dropGlobalTempView("nyc_taxi")
def draw_heat_map(spark):
start_time = time.time()
df = spark.read.format("csv").option("header", True).option(
"delimiter", ","
).schema(
"VendorID string, tpep_pickup_datetime timestamp, tpep_dropoff_datetime timestamp, passenger_count long, trip_distance double, pickup_longitude double, pickup_latitude double, dropoff_longitude double, dropoff_latitude double, fare_amount double, tip_amount double, total_amount double, buildingid_pickup long, buildingid_dropoff long, buildingtext_pickup string, buildingtext_dropoff string"
).load("file:///tmp/0_5M_nyc_taxi_and_building.csv").cache()
df.createOrReplaceTempView("nyc_taxi")
register_funcs(spark)
res = spark.sql(
"select ST_Point(pickup_longitude, pickup_latitude) as point, passenger_count as w from nyc_taxi where ST_Within(ST_Point(pickup_longitude, pickup_latitude), ST_GeomFromText('POLYGON ((-73.998427 40.730309, -73.954348 40.730309, -73.954348 40.780816 ,-73.998427 40.780816, -73.998427 40.730309))'))"
)
res.show()
vega = vega_heatmap(1024, 896,
[-73.998427, 40.730309, -73.954348, 40.780816], 10.0,
'EPSG:4326')
res = heatmap(vega, res)
save_png(res, '/tmp/heatmap.png')
spark.sql("show tables").show()
spark.catalog.dropGlobalTempView("nyc_taxi")
print("--- %s seconds ---" % (time.time() - start_time))
def draw_point_map(spark):
# file 0_5M_nyc_build.csv is generated from New York taxi data and taxi zone shapefile. Data is available at the following URL: https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page
df = spark.read.format("csv").option("header", True).option(
"delimiter", ","
).schema(
"VendorID string, tpep_pickup_datetime timestamp, tpep_dropoff_datetime timestamp, passenger_count long, trip_distance double, pickup_longitude double, pickup_latitude double, dropoff_longitude double, dropoff_latitude double, fare_amount double, tip_amount double, total_amount double, buildingid_pickup long, buildingid_dropoff long, buildingtext_pickup string, buildingtext_dropoff string"
).load(data_path).cache()
df.show(20, False)
df.createOrReplaceTempView("nyc_taxi")
# df.createOrReplaceGlobalTempView("nyc_taxi")
res = spark.sql(
"select pickup_latitude as x, pickup_longitude as y from nyc_taxi")
res.printSchema()
res.createOrReplaceTempView("pickup")
register_funcs(spark)
res = spark.sql(
"select ST_Transform(ST_Point(x, y), 'EPSG:4326','EPSG:3857' ) as pickup_point from pickup"
)
res.show(20, False)
res.createOrReplaceTempView("project")
res = spark.sql(
"select Projection(pickup_point, 'POINT (4534000 -12510000)', 'POINT (4538000 -12513000)', 1024, 896) as point from project"
)
res.show(20, False)
vega_point_map = VegaCircle2d(1900, 1410, 3, "#2DEF4A", 0.5)
vega = vega_point_map.build()
res = pointmap(res, vega)
save_png(res, '/tmp/pointmap.png')
spark.sql("show tables").show()
spark.catalog.dropGlobalTempView("nyc_taxi")
def draw_point_map(spark):
# file 0_5M_nyc_taxi_and_building.csv could be obtained from arctern-turoial warehouse under zilliztech account. The link on github is https://github.com/zilliztech/arctern-tutorial
df = spark.read.format("csv").option("header", True).option(
"delimiter", ","
).schema(
"VendorID string, tpep_pickup_datetime timestamp, tpep_dropoff_datetime timestamp, passenger_count long, trip_distance double, pickup_longitude double, pickup_latitude double, dropoff_longitude double, dropoff_latitude double, fare_amount double, tip_amount double, total_amount double, buildingid_pickup long, buildingid_dropoff long, buildingtext_pickup string, buildingtext_dropoff string"
).load("file:///tmp/0_5M_nyc_taxi_and_building.csv").cache()
df.show(20, False)
df.createOrReplaceTempView("nyc_taxi")
# df.createOrReplaceGlobalTempView("nyc_taxi")
res = spark.sql(
"select pickup_latitude as x, pickup_longitude as y from nyc_taxi")
res.printSchema()
res.createOrReplaceTempView("pickup")
register_funcs(spark)
res = spark.sql(
"select ST_Transform(ST_Point(x, y), 'EPSG:4326','EPSG:3857' ) as pickup_point from pickup"
)
res.show(20, False)
res.createOrReplaceTempView("project")
res = spark.sql(
"select Projection(pickup_point, 'POINT (4534000 -12510000)', 'POINT (4538000 -12513000)', 1024, 896) as point from project"
)
res.show(20, False)
vega_point_map = VegaCircle2d(1900, 1410, 3, "#2DEF4A", 0.5)
vega = vega_point_map.build()
res = pointmap(res, vega)
save_png(res, '/tmp/pointmap.png')
spark.sql("show tables").show()
spark.catalog.dropGlobalTempView("nyc_taxi")
def draw_point_map(spark):
start_time = time.time()
# file 0_5M_nyc_taxi_and_building.csv could be obtained from arctern-turoial warehouse under zilliztech account. The link on github is https://github.com/zilliztech/arctern-tutorial
df = spark.read.format("csv").option("header", True).option(
"delimiter", ","
).schema(
"VendorID string, tpep_pickup_datetime timestamp, tpep_dropoff_datetime timestamp, passenger_count long, trip_distance double, pickup_longitude double, pickup_latitude double, dropoff_longitude double, dropoff_latitude double, fare_amount double, tip_amount double, total_amount double, buildingid_pickup long, buildingid_dropoff long, buildingtext_pickup string, buildingtext_dropoff string"
).load("file:///tmp/0_5M_nyc_taxi_and_building.csv").cache()
df.createOrReplaceTempView("nyc_taxi")
register_funcs(spark)
res = spark.sql(
"select ST_Point(pickup_longitude, pickup_latitude) as point from nyc_taxi where ST_Within(ST_Point(pickup_longitude, pickup_latitude), ST_GeomFromText('POLYGON ((-73.998427 40.730309, -73.954348 40.730309, -73.954348 40.780816 ,-73.998427 40.780816, -73.998427 40.730309))'))"
)
vega = vega_pointmap(
1024,
896,
bounding_box=[-73.998427, 40.730309, -73.954348, 40.780816],
point_size=3,
point_color="#2DEF4A",
opacity=0.5,
coordinate_system="EPSG:4326")
res = pointmap(vega, res)
save_png(res, '/tmp/pointmap.png')
spark.sql("show tables").show()
spark.catalog.dropGlobalTempView("nyc_taxi")
print("--- %s seconds ---" % (time.time() - start_time))
def _create_session(self):
"""
clone new session
"""
session = self.session.newSession()
register_funcs(session)
return session
def draw_choropleth_map(spark):
start_time = time.time()
df = spark.read.format("csv").option("header", True).option(
"delimiter", ","
).schema(
"VendorID string, tpep_pickup_datetime timestamp, tpep_dropoff_datetime timestamp, passenger_count long, trip_distance double, pickup_longitude double, pickup_latitude double, dropoff_longitude double, dropoff_latitude double, fare_amount double, tip_amount double, total_amount double, buildingid_pickup long, buildingid_dropoff long, buildingtext_pickup string, buildingtext_dropoff string"
).load("file:///tmp/0_5M_nyc_taxi_and_building.csv").cache()
df.createOrReplaceTempView("nyc_taxi")
register_funcs(spark)
res = spark.sql(
"select ST_GeomFromText(buildingtext_dropoff) as polygon, passenger_count as w from nyc_taxi where (buildingtext_dropoff!='')"
)
vega1 = vega_choroplethmap(
1900,
1410,
bounding_box=[-73.994092, 40.753893, -73.977588, 40.759642],
color_gradient=["#0000FF", "#FF0000"],
color_bound=[2.5, 5],
opacity=1.0,
coordinate_system='EPSG:4326')
res1 = choroplethmap(vega1, res)
save_png(res1, '/tmp/choroplethmap1.png')
spark.sql("show tables").show()
spark.catalog.dropGlobalTempView("nyc_taxi")
print("--- %s seconds ---" % (time.time() - start_time))
def __init__(self, db_config):
envs = db_config['spark'].get('envs', None)
if envs: # for spark on yarn
self._setup_driver_envs(envs)
import uuid
self._db_id = str(uuid.uuid1()).replace('-', '')
self._db_name = db_config['db_name']
self._db_type = 'spark'
self._table_list = []
print("init spark begin")
import socket
localhost_ip = socket.gethostbyname(socket.gethostname())
_t = SparkSession.builder \
.appName(db_config['spark']['app_name']) \
.master(db_config['spark']['master-addr']) \
.config('spark.driver.host', localhost_ip) \
.config("spark.sql.execution.arrow.pyspark.enabled", "true")
configs = db_config['spark'].get('configs', None)
if configs:
for key in configs:
_v = configs.get(key)
if _v:
print("spark config: {} = {}".format(key, _v))
_t = _t.config(key, _v)
self.session = _t.getOrCreate()
print("init spark done")
register_funcs(self.session)
def run(sql):
"""
submit sql to spark
"""
session = INSTANCE.create_session()
register_funcs(session)
return session.sql(sql)
def draw_world_include_province_weighted_point_map(spark):
# 1
df = spark.read.format("csv").option("header", True).option(
"delimiter", ","
).schema(
"Province string, Country string, Longitude double, Latitude double, ConfirmedCount int,"
"DeadCount int, CuredCount int, LastUpdateTime string").load(
country_with_province_csv).cache()
df.createOrReplaceTempView("COVID_country_province")
register_funcs(spark)
res2 = spark.sql(
"select ST_Point(Longitude, Latitude) as point, ConfirmedCount as s from COVID_country_province "
"where LastUpdateTime like '%03-29%'")
res2.createOrReplaceTempView("res2")
res2 = spark.sql("select * from res2 where point != 'POINT (nan nan)' ")
vega2 = vega_weighted_pointmap(
3000, 2000, [-289.095983, -73.863121, 289.095983, 73.863121],
"#F0356D", [2, 60], [6, 60], 1.0, "EPSG:4326")
res_png2 = weighted_pointmap(res2, vega2)
save_png(res_png2, './COVID_country_weighted_point_map2.png')
spark.catalog.dropGlobalTempView("COVID_country_province")
def run_curve_z(spark):
curve_z_df = spark.read.json("/tmp/z_curve.json").cache()
curve_z_df.createOrReplaceTempView("curve_z")
register_funcs(spark)
hex_data = spark.sql("select my_plot(x, y) from curve_z").collect()[0][0]
str_hex_data = str(hex_data)
import binascii
binary_string = binascii.unhexlify(str_hex_data)
with open('/tmp/hex_curve_z.png', 'wb') as png:
png.write(binary_string)
def draw_weighted_point_map(spark):
start_time = time.time()
df = spark.read.format("csv").option("header", True).option(
"delimiter", ","
).schema(
"VendorID string, tpep_pickup_datetime timestamp, tpep_dropoff_datetime timestamp, passenger_count long, trip_distance double, pickup_longitude double, pickup_latitude double, dropoff_longitude double, dropoff_latitude double, fare_amount double, tip_amount double, total_amount double, buildingid_pickup long, buildingid_dropoff long, buildingtext_pickup string, buildingtext_dropoff string"
).load("file:///tmp/0_5M_nyc_taxi_and_building.csv").cache()
df.createOrReplaceTempView("nyc_taxi")
register_funcs(spark)
# single color and single stroke width
res1 = spark.sql(
"select ST_Point(pickup_longitude, pickup_latitude) as point from nyc_taxi where ST_Within(ST_Point(pickup_longitude, pickup_latitude), ST_GeomFromText('POLYGON ((-73.998427 40.730309, -73.954348 40.730309, -73.954348 40.780816 ,-73.998427 40.780816, -73.998427 40.730309))'))"
)
vega1 = vega_weighted_pointmap(
1024, 896, [-73.998427, 40.730309, -73.954348, 40.780816], ["#87CEEB"],
[0, 2], [5], 1.0, "EPSG:4326")
res1 = weighted_pointmap(vega1, res1)
save_png(res1, '/tmp/weighted_pointmap_0_0.png')
# multiple color and single stroke width
res2 = spark.sql(
"select ST_Point(pickup_longitude, pickup_latitude) as point, tip_amount as c from nyc_taxi where ST_Within(ST_Point(pickup_longitude, pickup_latitude), ST_GeomFromText('POLYGON ((-73.998427 40.730309, -73.954348 40.730309, -73.954348 40.780816 ,-73.998427 40.780816, -73.998427 40.730309))'))"
)
vega2 = vega_weighted_pointmap(
1024, 896, [-73.998427, 40.730309, -73.954348, 40.780816],
["#0000FF", "#FF0000"], [0, 2], [5], 1.0, "EPSG:4326")
res2 = weighted_pointmap(vega2, res2)
save_png(res2, '/tmp/weighted_pointmap_1_0.png')
# single color and multiple stroke width
res3 = spark.sql(
"select ST_Point(pickup_longitude, pickup_latitude) as point, fare_amount as s from nyc_taxi where ST_Within(ST_Point(pickup_longitude, pickup_latitude), ST_GeomFromText('POLYGON ((-73.998427 40.730309, -73.954348 40.730309, -73.954348 40.780816 ,-73.998427 40.780816, -73.998427 40.730309))'))"
)
vega3 = vega_weighted_pointmap(
1024, 896, [-73.998427, 40.730309, -73.954348, 40.780816], ["#87CEEB"],
[0, 2], [0, 10], 1.0, "EPSG:4326")
res3 = weighted_pointmap(vega3, res3)
save_png(res3, '/tmp/weighted_pointmap_0_1.png')
# multiple color and multiple stroke width
res4 = spark.sql(
"select ST_Point(pickup_longitude, pickup_latitude) as point, tip_amount as c, fare_amount as s from nyc_taxi where ST_Within(ST_Point(pickup_longitude, pickup_latitude), ST_GeomFromText('POLYGON ((-73.998427 40.730309, -73.954348 40.730309, -73.954348 40.780816 ,-73.998427 40.780816, -73.998427 40.730309))'))"
)
vega4 = vega_weighted_pointmap(
1024, 896, [-73.998427, 40.730309, -73.954348, 40.780816],
["#0000FF", "#FF0000"], [0, 2], [0, 10], 1.0, "EPSG:4326")
res4 = weighted_pointmap(vega4, res4)
save_png(res4, '/tmp/weighted_pointmap_1_1.png')
spark.sql("show tables").show()
spark.catalog.dropGlobalTempView("nyc_taxi")
print("--- %s seconds ---" % (time.time() - start_time))
def __init__(self):
self.session = SparkSession.builder \
.appName("Arctern") \
.master(config.INSTANCE.get("spark", "master-addr")) \
.config("spark.executorEnv.PYSPARK_PYTHON",
config.INSTANCE.get("spark", "executor-python")
) \
.config("spark.sql.execution.arrow.pyspark.enabled", "true") \
.config("spark.databricks.session.share", "false") \
.getOrCreate()
register_funcs(self.session)
def draw_weighted_point_map(spark):
df = spark.read.format("csv").option("header", True).option(
"delimiter", ","
).schema(
"VendorID string, tpep_pickup_datetime timestamp, tpep_dropoff_datetime timestamp, passenger_count long, trip_distance double, pickup_longitude double, pickup_latitude double, dropoff_longitude double, dropoff_latitude double, fare_amount double, tip_amount double, total_amount double, buildingid_pickup long, buildingid_dropoff long, buildingtext_pickup string, buildingtext_dropoff string"
).load(data_path).cache()
df.show(20, False)
df.createOrReplaceTempView("nyc_taxi")
register_funcs(spark)
# single color and single stroke width
res1 = spark.sql(
"select ST_Point(pickup_longitude, pickup_latitude) as point from nyc_taxi where ST_Within(ST_Point(pickup_longitude, pickup_latitude), 'POLYGON ((-73.998427 40.730309, -73.954348 40.730309, -73.954348 40.780816 ,-73.998427 40.780816, -73.998427 40.730309))')"
)
vega1 = vega_weighted_pointmap(
1024, 896, [-73.998427, 40.730309, -73.954348, 40.780816], "#87CEEB",
[0, 2], [5], 1.0, "EPSG:4326")
res1 = weighted_pointmap(res1, vega1)
save_png(res1, '/tmp/weighted_pointmap_0_0.png')
# multiple color and single stroke width
res2 = spark.sql(
"select ST_Point(pickup_longitude, pickup_latitude) as point, tip_amount as c from nyc_taxi where ST_Within(ST_Point(pickup_longitude, pickup_latitude), 'POLYGON ((-73.998427 40.730309, -73.954348 40.730309, -73.954348 40.780816 ,-73.998427 40.780816, -73.998427 40.730309))')"
)
vega2 = vega_weighted_pointmap(
1024, 896, [-73.998427, 40.730309, -73.954348, 40.780816],
"blue_to_red", [0, 2], [5], 1.0, "EPSG:4326")
res2 = weighted_pointmap(res2, vega2)
save_png(res2, '/tmp/weighted_pointmap_1_0.png')
# single color and multiple stroke width
res3 = spark.sql(
"select ST_Point(pickup_longitude, pickup_latitude) as point, fare_amount as s from nyc_taxi where ST_Within(ST_Point(pickup_longitude, pickup_latitude), 'POLYGON ((-73.998427 40.730309, -73.954348 40.730309, -73.954348 40.780816 ,-73.998427 40.780816, -73.998427 40.730309))')"
)
vega3 = vega_weighted_pointmap(
1024, 896, [-73.998427, 40.730309, -73.954348, 40.780816], "#87CEEB",
[0, 2], [0, 10], 1.0, "EPSG:4326")
res3 = weighted_pointmap(res3, vega3)
save_png(res3, '/tmp/weighted_pointmap_0_1.png')
# multiple color and multiple stroke width
res4 = spark.sql(
"select ST_Point(pickup_longitude, pickup_latitude) as point, tip_amount as c, fare_amount as s from nyc_taxi where ST_Within(ST_Point(pickup_longitude, pickup_latitude), 'POLYGON ((-73.998427 40.730309, -73.954348 40.730309, -73.954348 40.780816 ,-73.998427 40.780816, -73.998427 40.730309))')"
)
vega4 = vega_weighted_pointmap(
1024, 896, [-73.998427, 40.730309, -73.954348, 40.780816],
"blue_to_red", [0, 2], [0, 10], 1.0, "EPSG:4326")
res4 = weighted_pointmap(res4, vega4)
save_png(res4, '/tmp/weighted_pointmap_1_1.png')
spark.sql("show tables").show()
spark.catalog.dropGlobalTempView("nyc_taxi")
def run_test_plot(spark):
register_funcs(spark)
raw_data = []
raw_data.extend([(0, 'polygon((0 0,0 1,1 1,1 0,0 0))')])
raw_data.extend([(1, 'linestring(0 0,0 1,1 1,1 0,0 0)')])
raw_data.extend([(2, 'point(2 2)')])
wkt_collect = "GEOMETRYCOLLECTION(" \
"MULTIPOLYGON (((0 0,0 1,1 1,1 0,0 0)),((1 1,1 2,2 2,2 1,1 1)))," \
"POLYGON((3 3,3 4,4 4,4 3,3 3))," \
"LINESTRING(0 8,5 5,8 0)," \
"POINT(4 7)," \
"MULTILINESTRING ((1 1,1 2),(2 4,1 9,1 8))," \
"MULTIPOINT (6 8,5 7)" \
")"
raw_data.extend([(3, wkt_collect)])
raw_schema = StructType([
StructField('idx', LongType(), False),
StructField('geo', StringType(), False)
])
df = spark.createDataFrame(data=raw_data, schema=raw_schema)
df.createOrReplaceTempView("geoms")
df2 = spark.sql("select st_geomfromtext(geo) from geoms")
# run baseline
fig1, ax1 = plt.subplots()
plot(ax1, df2)
ax1.grid()
baseline_png1 = png_path + "plot_test_1.png"
fig1.savefig(baseline_png1)
# run plot test
fig2, ax2 = plt.subplots()
plot(ax2, df2)
ax2.grid()
plot_test1 = png_path + "test_plot_test_1.png"
fig2.savefig(plot_test1)
spark.catalog.dropGlobalTempView("nyc_taxi")
assert run_diff_png(baseline_png1, plot_test1)
def draw_heat_map(spark):
df = spark.read.format("csv").option("header", True).option(
"delimiter", ","
).schema(
"VendorID string, tpep_pickup_datetime timestamp, tpep_dropoff_datetime timestamp, passenger_count long, trip_distance double, pickup_longitude double, pickup_latitude double, dropoff_longitude double, dropoff_latitude double, fare_amount double, tip_amount double, total_amount double, buildingid_pickup long, buildingid_dropoff long, buildingtext_pickup string, buildingtext_dropoff string"
).load(data_path).cache()
df.show(20, False)
df.createOrReplaceTempView("nyc_taxi")
register_funcs(spark)
res = spark.sql(
"select ST_Point(pickup_longitude, pickup_latitude) as point, passenger_count as w from nyc_taxi where ST_Within(ST_Point(pickup_longitude, pickup_latitude), 'POLYGON ((-73.998427 40.730309, -73.954348 40.730309, -73.954348 40.780816 ,-73.998427 40.780816, -73.998427 40.730309))')"
)
vega = vega_heatmap(1024, 896, 10.0,
[-73.998427, 40.730309, -73.954348, 40.780816],
'EPSG:4326')
res = heatmap(res, vega)
save_png(res, '/tmp/heatmap.png')
spark.sql("show tables").show()
spark.catalog.dropGlobalTempView("nyc_taxi")
def draw_point_map(spark):
# file 0_5M_nyc_build.csv is generated from New York taxi data and taxi zone shapefile. Data is available at the following URL: https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page
df = spark.read.format("csv").option("header", True).option(
"delimiter", ","
).schema(
"VendorID string, tpep_pickup_datetime timestamp, tpep_dropoff_datetime timestamp, passenger_count long, trip_distance double, pickup_longitude double, pickup_latitude double, dropoff_longitude double, dropoff_latitude double, fare_amount double, tip_amount double, total_amount double, buildingid_pickup long, buildingid_dropoff long, buildingtext_pickup string, buildingtext_dropoff string"
).load(data_path).cache()
df.show(20, False)
df.createOrReplaceTempView("nyc_taxi")
register_funcs(spark)
res = spark.sql(
"select ST_Point(pickup_longitude, pickup_latitude) as point from nyc_taxi where ST_Within(ST_Point(pickup_longitude, pickup_latitude), 'POLYGON ((-73.998427 40.730309, -73.954348 40.730309, -73.954348 40.780816 ,-73.998427 40.780816, -73.998427 40.730309))')"
)
vega = vega_pointmap(1024, 896,
[-73.998427, 40.730309, -73.954348, 40.780816], 3,
"#2DEF4A", 0.5, "EPSG:4326")
res = pointmap(res, vega)
save_png(res, '/tmp/pointmap.png')
spark.sql("show tables").show()
spark.catalog.dropGlobalTempView("nyc_taxi")
def run_test_heat_map(spark):
df = spark.read.format("csv").option("header", True).option("delimiter", ",").schema(
"VendorID string, tpep_pickup_datetime timestamp, tpep_dropoff_datetime timestamp, passenger_count long, "
"trip_distance double, pickup_longitude double, pickup_latitude double, dropoff_longitude double, "
"dropoff_latitude double, fare_amount double, tip_amount double, total_amount double, buildingid_pickup long, "
"buildingid_dropoff long, buildingtext_pickup string, buildingtext_dropoff string").load(
file_path).cache()
df.createOrReplaceTempView("nyc_taxi")
register_funcs(spark)
res = spark.sql(
"select ST_Point(pickup_longitude, pickup_latitude) as point, passenger_count as w from nyc_taxi where ST_Within(ST_Point(pickup_longitude, pickup_latitude), 'POLYGON ((-73.998427 40.730309, -73.954348 40.730309, -73.954348 40.780816 ,-73.998427 40.780816, -73.998427 40.730309))')")
# 1 size:1024*896, map_scale: 10.0
vega_1 = vega_heatmap(1024, 896, 10.0, [-73.998427, 40.730309, -73.954348, 40.780816], 'EPSG:4326')
baseline1 = heatmap(res, vega_1)
heat_map1_1 = heatmap(res, vega_1)
heat_map1_2 = heatmap(res, vega_1)
baseline_png1 = png_path + "heat_map_nyc_1.png"
save_png(baseline1, baseline_png1)
save_png(heat_map1_1, png_path + "test_heat_map_nyc_1-1.png")
save_png(heat_map1_2, png_path + "test_heat_map_nyc_1-2.png")
# 2 map_scale: 0.0
vega_2 = vega_heatmap(1024, 896, 0.0, [-73.998427, 40.730309, -73.954348, 40.780816], 'EPSG:4326')
baseline2 = heatmap(res, vega_2)
heat_map2_1 = heatmap(res, vega_2)
heat_map2_2 = heatmap(res, vega_2)
baseline_png2 = png_path + "heat_map_nyc_2.png"
save_png(baseline2, baseline_png2)
save_png(heat_map2_1, png_path + "test_heat_map_nyc_2-1.png")
save_png(heat_map2_2, png_path + "test_heat_map_nyc_2-2.png")
# 3 map_scale: 12.0
vega_3 = vega_heatmap(1024, 896, 12.0, [-73.998427, 40.730309, -73.954348, 40.780816], 'EPSG:4326')
baseline3 = heatmap(res, vega_3)
heat_map3_1 = heatmap(res, vega_3)
heat_map3_2 = heatmap(res, vega_3)
baseline_png3 = png_path + "heat_map_nyc_3.png"
save_png(baseline3, baseline_png3)
save_png(heat_map3_1, png_path + "test_heat_map_nyc_3-1.png")
save_png(heat_map3_2, png_path + "test_heat_map_nyc_3-2.png")
# 4 map_scale: 5.5
vega_4 = vega_heatmap(1024, 896, 5.5, [-73.998427, 40.730309, -73.954348, 40.780816], 'EPSG:4326')
baseline4 = heatmap(res, vega_4)
heat_map4_1 = heatmap(res, vega_4)
heat_map4_2 = heatmap(res, vega_4)
baseline_png4 = png_path + "heat_map_nyc_4.png"
save_png(baseline4, baseline_png4)
save_png(heat_map4_1, png_path + "test_heat_map_nyc_4-1.png")
save_png(heat_map4_2, png_path + "test_heat_map_nyc_4-2.png")
# 5 size:200*200
vega_5 = vega_heatmap(200, 200, 10.0, [-73.998427, 40.730309, -73.954348, 40.780816], 'EPSG:4326')
baseline5 = heatmap(res, vega_5)
heat_map5_1 = heatmap(res, vega_5)
heat_map5_2 = heatmap(res, vega_5)
baseline_png5 = png_path + "heat_map_nyc_5.png"
save_png(baseline5, baseline_png5)
save_png(heat_map5_1, png_path + "test_heat_map_nyc_5-1.png")
save_png(heat_map5_2, png_path + "test_heat_map_nyc_5-2.png")
spark.catalog.dropGlobalTempView("nyc_taxi")
assert run_diff_png(baseline_png1, png_path + "test_heat_map_nyc_1-1.png", 0.1)
assert run_diff_png(baseline_png1, png_path + "test_heat_map_nyc_1-2.png", 0.1)
assert run_diff_png(baseline_png2, png_path + "test_heat_map_nyc_2-1.png", 0.1)
assert run_diff_png(baseline_png2, png_path + "test_heat_map_nyc_2-2.png", 0.1)
assert run_diff_png(baseline_png3, png_path + "test_heat_map_nyc_3-1.png", 0.15)
assert run_diff_png(baseline_png3, png_path + "test_heat_map_nyc_3-2.png", 0.15)
assert run_diff_png(baseline_png4, png_path + "test_heat_map_nyc_4-1.png", 0.1)
assert run_diff_png(baseline_png4, png_path + "test_heat_map_nyc_4-2.png", 0.1)
assert run_diff_png(baseline_png5, png_path + "test_heat_map_nyc_5-1.png", 0.2)
assert run_diff_png(baseline_png5, png_path + "test_heat_map_nyc_5-2.png", 0.2)
def run_st_intersection(spark):
test_df = spark.read.json("/tmp/intersection.json").cache()
test_df.createOrReplaceTempView("intersection")
register_funcs(spark)
spark.sql("select ST_Intersection_UDF(left, right) from intersection").show()
#df.show()
df.createOrReplaceTempView(table_name)
rs = spark.sql(sql).cache()
#rs.printSchema()
#rs.show()
save_result("results/%s" % table_name, rs)
if __name__ == "__main__":
url = 'local'
spark_session = SparkSession.builder.appName("Python zgis sample").master(url).getOrCreate()
spark_session.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
clear_result_dir('/tmp/results')
register_funcs(spark_session)
run_test_st_geomfromgeojson(spark_session)
run_test_st_geomfromgeojson2(spark_session)
run_test_st_curvetoline(spark_session)
run_test_st_point(spark_session)
run_test_envelope_aggr_1(spark_session)
run_test_envelope_aggr_curve(spark_session)
run_test_envelope_aggr_2(spark_session)
run_test_union_aggr_2(spark_session)
run_test_union_aggr_curve(spark_session)
run_test_st_isvalid_1(spark_session)
run_test_st_isvalid_curve(spark_session)
run_test_st_intersection(spark_session)
run_test_st_intersection_curve(spark_session)
run_test_st_convexhull(spark_session)
def run_st_point(spark):
points_df = spark.read.json("/tmp/points.json").cache()
points_df.createOrReplaceTempView("points")
register_funcs(spark)
spark.sql("select ST_Point(x, y) from points").show()
def run_test_point_map(spark):
# file 0_5M_nyc_taxi_and_building.csv could be obtained from arctern-turoial warehouse under zilliztech account. The link on github is https://github.com/zilliztech/arctern-tutorial
# file 0_10000_nyc_taxi_and_building.csv is from file 0_5M_nyc_taxi_and_building.csv first 10000 lines
df = spark.read.format("csv").option("header", True).option("delimiter", ",").schema(
"VendorID string, tpep_pickup_datetime timestamp, tpep_dropoff_datetime timestamp, passenger_count long, "
"trip_distance double, pickup_longitude double, pickup_latitude double, dropoff_longitude double, "
"dropoff_latitude double, fare_amount double, tip_amount double, total_amount double, buildingid_pickup long, "
"buildingid_dropoff long, buildingtext_pickup string, buildingtext_dropoff string").load(
file_path).cache()
df.createOrReplaceTempView("nyc_taxi")
register_funcs(spark)
res = spark.sql(
"select ST_Point(pickup_longitude, pickup_latitude) as point from nyc_taxi where ST_Within(ST_Point(pickup_longitude, pickup_latitude), 'POLYGON ((-73.998427 40.730309, -73.954348 40.730309, -73.954348 40.780816 ,-73.998427 40.780816, -73.998427 40.730309))')")
# 1 size:1024*896, point_size: 3, opacity: 0.5, color: #2DEF4A(green)
vega_1 = vega_pointmap(1024, 896, [-73.998427, 40.730309, -73.954348, 40.780816], 3, "#2DEF4A", 0.5, "EPSG:4326")
baseline1 = pointmap(res, vega_1)
point_map1_1 = pointmap(res, vega_1)
point_map1_2 = pointmap(res, vega_1)
baseline_png1 = png_path + "point_map_nyc_1.png"
save_png(baseline1, baseline_png1)
save_png(point_map1_1, png_path + "test_point_map_nyc_1-1.png")
save_png(point_map1_2, png_path + "test_point_map_nyc_1-2.png")
# 2 #F50404(red)
vega_2 = vega_pointmap(1024, 896, [-73.998427, 40.730309, -73.954348, 40.780816], 5, "#F50404", 0.5, "EPSG:4326")
baseline2 = pointmap(res, vega_2)
point_map2_1 = pointmap(res, vega_2)
point_map2_2 = pointmap(res, vega_2)
baseline_png2 = png_path + "point_map_nyc_2.png"
save_png(baseline2, baseline_png2)
save_png(point_map2_1, png_path + "test_point_map_nyc_2-1.png")
save_png(point_map2_2, png_path + "test_point_map_nyc_2-2.png")
# 3 color: #1455EE(blue)
vega_3 = vega_pointmap(1024, 896, [-73.998427, 40.730309, -73.954348, 40.780816], 5, "#1455EE", 0.5, "EPSG:4326")
baseline3 = pointmap(res, vega_3)
point_map3_1 = pointmap(res, vega_3)
point_map3_2 = pointmap(res, vega_3)
baseline_png3 = png_path + "point_map_nyc_3.png"
save_png(baseline3, baseline_png3)
save_png(point_map3_1, png_path + "test_point_map_nyc_3-1.png")
save_png(point_map3_2, png_path + "test_point_map_nyc_3-2.png")
# 4 size:1024*896, point_size: 3, opacity: 1, color: #2DEF4A
vega_4 = vega_pointmap(1024, 896, [-73.998427, 40.730309, -73.954348, 40.780816], 3, "#2DEF4A", 1.0, "EPSG:4326")
baseline4 = pointmap(res, vega_4)
point_map4_1 = pointmap(res, vega_4)
point_map4_2 = pointmap(res, vega_4)
baseline_png4 = png_path + "point_map_nyc_4.png"
save_png(baseline4, baseline_png4)
save_png(point_map4_1, png_path + "test_point_map_nyc_4-1.png")
save_png(point_map4_2, png_path + "test_point_map_nyc_4-2.png")
# 5 size:1024*896, point_size: 3, opacity: 0, color: #2DEF4A
vega_5 = vega_pointmap(1024, 896, [-73.998427, 40.730309, -73.954348, 40.780816], 3, "#2DEF4A", 0.0, "EPSG:4326")
baseline5 = pointmap(res, vega_5)
point_map5_1 = pointmap(res, vega_5)
point_map5_2 = pointmap(res, vega_5)
baseline_png5 = png_path + "point_map_nyc_5.png"
save_png(baseline5, baseline_png5)
save_png(point_map5_1, png_path + "test_point_map_nyc_5-1.png")
save_png(point_map5_2, png_path + "test_point_map_nyc_5-2.png")
# 6 size:200*200, point_size: 3, opacity: 0.5, color: #2DEF4A
vega_6 = vega_pointmap(200, 200, [-73.998427, 40.730309, -73.954348, 40.780816], 3, "#2DEF4A", 0.5, "EPSG:4326")
baseline6 = pointmap(res, vega_6)
point_map6_1 = pointmap(res, vega_6)
point_map6_2 = pointmap(res, vega_6)
baseline_png6 = png_path + "point_map_nyc_6.png"
save_png(baseline6, baseline_png6)
save_png(point_map6_1, png_path + "test_point_map_nyc_6-1.png")
save_png(point_map6_2, png_path + "test_point_map_nyc_6-2.png")
spark.catalog.dropGlobalTempView("nyc_taxi")
assert run_diff_png(baseline_png1, png_path + "test_point_map_nyc_1-1.png")
assert run_diff_png(baseline_png1, png_path + "test_point_map_nyc_1-2.png")
assert run_diff_png(baseline_png2, png_path + "test_point_map_nyc_2-1.png")
assert run_diff_png(baseline_png2, png_path + "test_point_map_nyc_2-2.png")
assert run_diff_png(baseline_png3, png_path + "test_point_map_nyc_3-1.png")
assert run_diff_png(baseline_png3, png_path + "test_point_map_nyc_3-2.png")
assert run_diff_png(baseline_png4, png_path + "test_point_map_nyc_4-1.png")
assert run_diff_png(baseline_png4, png_path + "test_point_map_nyc_4-2.png")
assert run_diff_png(baseline_png5, png_path + "test_point_map_nyc_5-1.png")
assert run_diff_png(baseline_png5, png_path + "test_point_map_nyc_5-2.png")
assert run_diff_png(baseline_png6, png_path + "test_point_map_nyc_6-1.png")
assert run_diff_png(baseline_png6, png_path + "test_point_map_nyc_6-2.png")
args = parse.parse_args()
source_file = args.source_file[0]
output_file = args.output_file[0]
run_times = int(args.run_times[0])
version_commit = args.version[0]
user_module = importlib.import_module(
"test_case." + (source_file.split(".")[0]).replace("/", "."),
"test_case/" + source_file)
spark = SparkSession \
.builder \
.appName("Python Arrow-in-Spark example") \
.getOrCreate()
spark.conf.set("spark.sql.execution.arrow.pyspark.enabled", "true")
register_funcs(spark)
all_time_info = {
"version": version_commit.split("-")[0],
"commit_id": version_commit.split("-")[-1],
"func_name": user_module.func_name
}
data_df = spark.read.format("csv").option("header", False).option(
"delimiter",
"|").schema(user_module.schema).load(user_module.csv_path).cache()
data_df.createOrReplaceTempView(user_module.func_name)
if hasattr(user_module, "spark_test"):
for times in range(run_times):
time_info = {}
begin_time = time.time()
def draw_china_weighted_point_map(spark):
df = spark.read.format("csv").option("header", True).option(
"delimiter", ","
).schema(
"continent string, country string, province string, provinceLocationId string, "
"provinceCurrentConfirmedCount int , provinceConfirmedCount int, provinceSuspectedCount int,"
"provinceCuredCount int, provinceDeadCount int, cityName string, longitude double, latitude double,"
"cityLocationId string, cityCurrentConfirmedCount int, cityConfirmedCount int, citySuspectedCount int,"
"cityCuredCount int, cityDeadCount int, updateTime timestamp").load(
china_csv).cache()
spark.catalog.dropGlobalTempView("COVID_china")
df.createOrReplaceTempView("COVID_china")
register_funcs(spark)
# 1
res1 = spark.sql(
"select ST_Point(longitude, latitude) as point from COVID_china where ST_Within(ST_Point(longitude, latitude), 'POLYGON ((71.604264 17.258977, 137.319408 17.258977, 137.319408 53.808533, 71.604264 53.808533, 71.604264 17.258977))')"
)
res1.createOrReplaceTempView("res1")
res1 = spark.sql("select * from res1 where point != 'POINT (nan nan)' ")
vega1 = vega_weighted_pointmap(
1024, 896, [71.604264, 17.258977, 137.319408, 53.808533], "#EEEEEE",
[2, 60], [6], 1.0, "EPSG:4326")
res_png1 = weighted_pointmap(res1, vega1)
save_png(res_png1, './COVID_china_weighted_point_map1.png')
# 2
res2 = spark.sql(
"select ST_Point(longitude, latitude) as point, provinceConfirmedCount as c from COVID_china "
"where ST_Within(ST_Point(longitude, latitude), "
"'POLYGON ((71.604264 17.258977, 137.319408 17.258977, 137.319408 53.808533,"
" 71.604264 53.808533, 71.604264 17.258977))')")
res2.createOrReplaceTempView("res2")
res2 = spark.sql("select * from res2 where point != 'POINT (nan nan)' ")
vega2 = vega_weighted_pointmap(
1024, 896, [71.604264, 17.258977, 137.319408, 53.808533],
"blue_to_red", [2, 1000], [6], 1.0, "EPSG:4326")
res_png2 = weighted_pointmap(res2, vega2)
save_png(res_png2, './COVID_china_weighted_point_map2.png')
# 3
res3 = spark.sql(
"select ST_Point(longitude, latitude) as point, provinceConfirmedCount as c, "
"provinceConfirmedCount as s from COVID_china "
"where ST_Within(ST_Point(longitude, latitude), "
"'POLYGON ((71.604264 17.258977, 137.319408 17.258977, 137.319408 53.808533,"
" 71.604264 53.808533, 71.604264 17.258977))')")
res3.createOrReplaceTempView("res3")
res3 = spark.sql("select * from res3 where point != 'POINT (nan nan)' ")
vega3 = vega_weighted_pointmap(
3000, 2000, [71.604264, 17.258977, 137.319408, 53.808533],
"blue_to_red", [2, 1000], [5, 1000], 1.0, "EPSG:4326")
res_png3 = weighted_pointmap(res3, vega3)
save_png(res_png3, './COVID_china_weighted_point_map3.png')
spark.catalog.dropGlobalTempView("COVID_china")
