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_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):
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 db_query():
"""
/db/query handler
"""
log.INSTANCE.info('POST /db/query: {}'.format(request.json))
if not utils.check_json(request.json, 'id') \
or not utils.check_json(request.json, 'query') \
or not utils.check_json(request.json['query'], 'type') \
or not utils.check_json(request.json['query'], 'sql'):
return jsonify(status='error', code=-1, message='query format error')
query_sql = request.json['query']['sql']
query_type = request.json['query']['type']
content = {}
content['sql'] = query_sql
content['err'] = False
db_instance = db.CENTER.get(str(request.json['id']), None)
if db_instance is None:
return jsonify(status="error",
code=-1,
message='there is no database whose id equal to ' +
str(request.json['id']))
if query_type == 'sql':
res = db_instance.run_for_json(query_sql)
data = []
for row in res:
obj = json.loads(row)
data.append(obj)
content['result'] = data
else:
if not utils.check_json(request.json['query'], 'params'):
return jsonify(status='error',
code=-1,
message='query format error')
query_params = request.json['query']['params']
res = db_instance.run(query_sql)
if query_type == 'point':
vega = vega_pointmap(int(query_params['width']),
int(query_params['height']),
query_params['point']['bounding_box'],
int(query_params['point']['point_size']),
query_params['point']['point_color'],
float(query_params['point']['opacity']),
query_params['point']['coordinate_system'])
data = pointmap(vega, res)
content['result'] = data
elif query_type == 'heat':
vega = vega_heatmap(int(query_params['width']),
int(query_params['height']),
query_params['heat']['bounding_box'],
float(query_params['heat']['map_zoom_level']),
query_params['heat']['coordinate_system'],
query_params['heat']['aggregation_type'])
data = heatmap(vega, res)
content['result'] = data
elif query_type == 'choropleth':
vega = vega_choroplethmap(
int(query_params['width']), int(query_params['height']),
query_params['choropleth']['bounding_box'],
query_params['choropleth']['color_gradient'],
query_params['choropleth']['color_bound'],
float(query_params['choropleth']['opacity']),
query_params['choropleth']['coordinate_system'],
query_params['choropleth']['aggregation_type'])
data = choroplethmap(vega, res)
content['result'] = data
elif query_type == 'weighted':
vega = vega_weighted_pointmap(
int(query_params['width']), int(query_params['height']),
query_params['weighted']['bounding_box'],
query_params['weighted']['color_gradient'],
query_params['weighted']['color_bound'],
query_params['weighted']['size_bound'],
float(query_params['weighted']['opacity']),
query_params['weighted']['coordinate_system'])
data = weighted_pointmap(vega, res)
content['result'] = data
elif query_type == 'icon':
vega = vega_icon(int(query_params['width']),
int(query_params['height']),
query_params['icon']['bounding_box'],
query_params['icon']['icon_path'],
query_params['icon']['coordinate_system'])
data = icon_viz(vega, res)
content['result'] = data
else:
return jsonify(status="error",
code=-1,
message='{} not support'.format(query_type))
return jsonify(status="success", code=200, data=content)
def db_query():
"""
/db/query handler
"""
if not utils.check_json(request.json, 'id') \
or not utils.check_json(request.json, 'query') \
or not utils.check_json(request.json['query'], 'type') \
or not utils.check_json(request.json['query'], 'sql'):
return jsonify(status='error', code=-1, message='query format error')
query_sql = request.json['query']['sql']
query_type = request.json['query']['type']
content = {}
content['sql'] = query_sql
content['err'] = False
if query_type == 'sql':
res = spark.Spark.run_for_json(query_sql)
data = []
for row in res:
obj = json.loads(row)
data.append(obj)
content['result'] = data
else:
if not utils.check_json(request.json['query'], 'params'):
return jsonify(status='error',
code=-1,
message='query format error')
query_params = request.json['query']['params']
res = spark.Spark.run(query_sql)
if query_type == 'point':
vega = vega_pointmap(int(query_params['width']),
int(query_params['height']),
query_params['point']['bounding_box'],
int(query_params['point']['stroke_width']),
query_params['point']['stroke'],
float(query_params['point']['opacity']),
query_params['point']['coordinate'])
data = pointmap(res, vega)
content['result'] = data
elif query_type == 'heat':
vega = vega_heatmap(int(query_params['width']),
int(query_params['height']),
float(query_params['heat']['map_scale']),
query_params['heat']['bounding_box'],
query_params['heat']['coordinate'])
data = heatmap(res, vega)
content['result'] = data
elif query_type == 'choropleth':
vega = vega_choroplethmap(
int(query_params['width']), int(query_params['height']),
query_params['choropleth']['bounding_box'],
query_params['choropleth']['color_style'],
query_params['choropleth']['rule'],
float(query_params['choropleth']['opacity']),
query_params['choropleth']['coordinate'])
data = choroplethmap(res, vega)
content['result'] = data
else:
return jsonify(status="error",
code=-1,
message='{} not support'.format(query_type))
return jsonify(status="success", code=200, data=content)
size_bound=[3, 15],
opacity=1.0,
coordinate_system="EPSG:4326")
res = weighted_pointmap(vega, pickup_df)
save_png(res, "/tmp/arctern_weighted_pointmap.png")
# 在指定地理区域（经度范围：-73.991504 至 -73.945155；纬度范围：40.770759 至 40.783434）中随机选取 200 个坐标点，并将 fare_amount 作为热力值。
pickup_sql = f"select st_point(pickup_longitude, pickup_latitude) as point, fare_amount as weight from nyc_taxi where (pickup_longitude between {pos1[0]} and {pos2[0]}) and (pickup_latitude between {pos1[1]} and {pos2[1]}) limit {limit_num}"
pickup_df = spark.sql(pickup_sql)
# 根据查询结果绘制热力图图层。
vega = vega_heatmap(1024,
384,
bounding_box=[pos1[0], pos1[1], pos2[0], pos2[1]],
map_zoom_level=13.0,
coordinate_system="EPSG:4326")
res = heatmap(vega, pickup_df)
save_png(res, "/tmp/arctern_heatmap.png")
# 在指定地理区域（经度范围：-73.991504 至 -73.945155；纬度范围：40.770759 至 40.783434）中随机选取 200 个坐标点，并将 fare_amount 作为颜色权重。
pickup_sql = f"select ST_GeomFromText(buildingtext_pickup) as buildings, fare_amount as color_weight from nyc_taxi where (pickup_longitude between {pos1[0]} and {pos2[0]}) and (pickup_latitude between {pos1[1]} and {pos2[1]}) and (buildingtext_pickup!='') limit {limit_num}"
pickup_df = spark.sql(pickup_sql)
# 根据查询结果绘制轮廓图图层。轮廓的填充颜色根据 color_weight 在 "#115f9a" ~ "#d0f400" 之间变化。
vega = vega_choroplethmap(1024,
384,
bounding_box=[pos1[0], pos1[1], pos2[0], pos2[1]],
color_gradient=["#115f9a", "#d0f400"],
color_bound=[2.5, 5],
opacity=1.0,
coordinate_system="EPSG:4326")
res = choroplethmap(vega, pickup_df)
save_png(res, "/tmp/arctern_choroplethmap.png")
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)
