def test_vega_pointmap():
vega = vega_pointmap(1900, 1410,
[-73.998427, 40.730309, -73.954348, 40.780816], 3,
"#2DEF4A", 0.5, "EPSG:3857").build()
vega_dict = json.loads(vega)
assert vega_dict["width"] == 1900
assert vega_dict["height"] == 1410
assert vega_dict["marks"][0]["encode"]["enter"]["bounding_box"]["value"][
0] == -73.998427
assert vega_dict["marks"][0]["encode"]["enter"]["bounding_box"]["value"][
1] == 40.730309
assert vega_dict["marks"][0]["encode"]["enter"]["bounding_box"]["value"][
2] == -73.954348
assert vega_dict["marks"][0]["encode"]["enter"]["bounding_box"]["value"][
3] == 40.780816
assert vega_dict["marks"][0]["encode"]["enter"]["shape"][
"value"] == "circle"
assert vega_dict["marks"][0]["encode"]["enter"]["point_color"][
"value"] == "#2DEF4A"
assert vega_dict["marks"][0]["encode"]["enter"]["point_size"]["value"] == 3
assert vega_dict["marks"][0]["encode"]["enter"]["opacity"]["value"] == 0.5
assert vega_dict["marks"][0]["encode"]["enter"]["coordinate_system"][
"value"] == "EPSG:3857"
vega = vega_pointmap(1900, 1410,
[-73.998427, 40.730309, -73.954348, 40.780816], 3,
"#2DEF4A", 0.5).build()
vega_dict = json.loads(vega)
assert vega_dict["marks"][0]["encode"]["enter"]["coordinate_system"][
"value"] == "EPSG:4326"
def test_point_map():
x_data = []
y_data = []
x_data.append(-73.96524)
x_data.append(-73.96118)
x_data.append(-73.97324)
x_data.append(-73.98456)
y_data.append(40.73747)
y_data.append(40.74507)
y_data.append(40.75890)
y_data.append(40.77654)
arr_x = pandas.Series(x_data)
arr_y = pandas.Series(y_data)
points = arctern.ST_Point(arr_x, arr_y)
vega = vega_pointmap(
1024,
896,
bounding_box=[-73.998427, 40.730309, -73.954348, 40.780816],
point_size=10,
point_color="#0000FF",
opacity=1.0,
coordinate_system="EPSG:4326")
curve_z1 = arctern.point_map_layer(vega, points)
save_png(curve_z1, "/tmp/test_curve_z1.png")
def test_point_map():
x_data = []
y_data = []
# y = 150
for i in range(100, 200):
x_data.append(i)
y_data.append(150)
# y = x - 50
for i in range(100, 200):
x_data.append(i)
y_data.append(i - 50)
# y = 50
for i in range(100, 200):
x_data.append(i)
y_data.append(50)
arr_x = pandas.Series(x_data)
arr_y = pandas.Series(y_data)
vega_circle2d = vega_pointmap(
1024, 896, [-73.998427, 40.730309, -73.954348, 40.780816], 3,
"#2DEF4A", 0.5, "EPSG:43")
vega_json = vega_circle2d.build()
curve_z1 = arctern.point_map(arr_x, arr_y, vega_json.encode('utf-8'))
save_png(curve_z1, "/tmp/test_curve_z1.png")
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 test_point_map():
x_data = []
y_data = []
# y = 150
for i in range(100, 200):
x_data.append(i)
y_data.append(150)
# y = x - 50
for i in range(100, 200):
x_data.append(i)
y_data.append(i - 50)
# y = 50
for i in range(100, 200):
x_data.append(i)
y_data.append(50)
arr_x = pandas.Series(x_data)
arr_y = pandas.Series(y_data)
points = arctern.ST_Point(arr_x, arr_y)
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")
curve_z1 = arctern.point_map(vega, points)
save_png(curve_z1, "/tmp/test_curve_z1.png")
def test_point_map():
x_data = []
y_data = []
# y = 150
for i in range(100, 200):
x_data.append(i)
y_data.append(150)
# y = x - 50
for i in range(100, 200):
x_data.append(i)
y_data.append(i - 50)
# y = 50
for i in range(100, 200):
x_data.append(i)
y_data.append(50)
arr_x = pandas.Series(x_data)
arr_y = pandas.Series(y_data)
points = arctern.ST_Point(arr_x, arr_y)
vega = vega_pointmap(1024, 896, [-73.998427, 40.730309, -73.954348, 40.780816], 3, "#2DEF4A", 0.5, "EPSG:4326")
curve_z1 = arctern.point_map(vega, points)
save_png(curve_z1, "/tmp/test_curve_z1.png")
def plot_pointmap(ax,
points,
bounding_box,
coordinate_system='EPSG:4326',
point_size=3,
point_color='red',
point_opacity=1.0,
**extra_contextily_params):
"""
:type ax: AxesSubplot
:param ax: Matplotlib axes object on which to add the basemap.
:type points: Series(dtype: object)
:param points: Points in WKB form
:type bounding_box: (float, float, float, float)
:param bounding_box: The bounding rectangle, as a [left, upper, right, lower]-tuple.
value should be of :coordinate_system:
:type coordinate_system: str
:param coordinate_system: either 'EPSG:4326' or 'EPSG:3857'
:type point_szie: int
:param point_size: size of point
:type point_color: str
:param point_color: specify color, using matplotlib.colors
:type opacity: float
:param opacity: opacity of point
:type extra_contextily_params: dict
:param extra_contextily_params: extra parameters for contextily.add_basemap.
See https://contextily.readthedocs.io/en/latest/reference.html
"""
from matplotlib import colors, pyplot as plt
import contextily as cx
bbox = _transform_bbox(bounding_box, coordinate_system, 'epsg:3857')
color_hex = colors.to_hex(point_color)
w, h = _get_recom_size(bbox[2] - bbox[0], bbox[3] - bbox[1])
vega = vega_pointmap(w,
h,
bounding_box=bounding_box,
point_size=point_size,
point_color=color_hex,
opacity=point_opacity,
coordinate_system=coordinate_system)
hexstr = arctern.point_map_layer(vega, points)
f = io.BytesIO(base64.b64decode(hexstr))
img = plt.imread(f)
ax.set(xlim=(bbox[0], bbox[2]), ylim=(bbox[1], bbox[3]))
cx.add_basemap(ax, **extra_contextily_params)
ax.imshow(img,
alpha=img[:, :, 3],
extent=(bbox[0], bbox[2], bbox[1], bbox[3]))
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 pointmap(ax,
points,
bounding_box,
point_size=3,
point_color='#115f9a',
opacity=1.0,
coordinate_system='EPSG:3857',
**extra_contextily_params):
"""
Plot pointmap in Matplotlib
:type ax: AxesSubplot
:param ax: Matplotlib axes object on which to add the basemap.
:type points: GeoSeries
:param points: Sequence of Points
:type bounding_box: list
:param bounding_box: Specify the bounding rectangle [west, south, east, north].
:type point_size: int
:param point_size: Diameter of point, default as 3
:type point_color: str
:param point_color: Specify point color in Hex Color Code, default as "#115f9a"
:type opacity: float
:param opacity: Opacity of point, ranged from 0.0 to 1.0, default as 1.0
:type coordinate_system: str
:param coordinate_system: Coordinate Reference System of the geometry objects.
Must be SRID formed, e.g. 'EPSG:4326' or 'EPSG:3857'
Default as 'EPSG:3857'
:type extra_contextily_params: dict
:param extra_contextily_params: Extra parameters will be passed to contextily.add_basemap.
See https://contextily.readthedocs.io/en/latest/reference.html for details
:example:
>>> import pandas as pd
>>> import numpy as np
>>> import arctern
>>> import matplotlib.pyplot as plt
>>> # read from test.csv
>>> # Download link: https://raw.githubusercontent.com/arctern-io/arctern-resources/benchmarks/benchmarks/dataset/layer_rendering_test_data/test_data.csv
>>> df = pd.read_csv("/path/to/test_data.csv", dtype={'longitude':np.float64, 'latitude':np.float64, 'color_weights':np.float64, 'size_weights':np.float64, 'region_boundaries':np.object})
>>> points = arctern.GeoSeries.point(df['longitude'], df['latitude'])
>>> # plot pointmap
>>> fig, ax = plt.subplots(figsize=(10, 6), dpi=200)
>>> arctern.plot.pointmap(ax, points, [-74.01398981737215,40.71353244267465,-73.96979949831308,40.74480271529791], point_size=10, point_color='#115f9a',coordinate_system="EPSG:4326")
>>> plt.show()
"""
from matplotlib import pyplot as plt
import contextily as cx
bbox = _transform_bbox(bounding_box, coordinate_system, 'epsg:3857')
w, h = _get_recom_size(bbox[2] - bbox[0], bbox[3] - bbox[1])
vega = vega_pointmap(w,
h,
bounding_box=bounding_box,
point_size=point_size,
point_color=point_color,
opacity=opacity,
coordinate_system=coordinate_system)
hexstr = arctern.point_map_layer(vega, points)
f = io.BytesIO(base64.b64decode(hexstr))
img = plt.imread(f)
ax.set(xlim=(bbox[0], bbox[2]), ylim=(bbox[1], bbox[3]))
cx.add_basemap(ax, **extra_contextily_params)
ax.imshow(img,
alpha=img[:, :, 3],
extent=(bbox[0], bbox[2], bbox[1], bbox[3]))
ax.axis('off')
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)
ST_AsText(ST_Point(pickup_df.pickup_longitude,
pickup_df.pickup_latitude)).head()
ST_AsText(
ST_Transform(
ST_Point(pickup_df.pickup_longitude, pickup_df.pickup_latitude),
'epsg:4326', 'epsg:3857')).head()
from arctern.util import save_png
from arctern.util.vega import vega_pointmap, vega_weighted_pointmap, vega_heatmap, vega_choroplethmap, vega_icon, vega_fishnetmap
vega = vega_pointmap(1024,
384,
bounding_box=[pos1[0], pos1[1], pos2[0], pos2[1]],
point_size=10,
point_color="#2DEF4A",
opacity=1,
coordinate_system="EPSG:4326")
png = point_map_layer(
vega, ST_Point(pickup_df.pickup_longitude, pickup_df.pickup_latitude))
save_png(png, '/tmp/arctern_pointmap_pandas.png')
vega = vega_weighted_pointmap(
1024,
384,
bounding_box=[pos1[0], pos1[1], pos2[0], pos2[1]],
color_gradient=["#115f9a", "#d0f400"],
color_bound=[1, 50],
size_bound=[3, 15],
opacity=1.0,
def pointmap(ax,
points,
bounding_box,
point_size=3,
point_color='#115f9a',
opacity=1.0,
coordinate_system='EPSG:3857',
**extra_contextily_params):
"""
Plots a point map in Matplotlib.
Parameters
----------
ax : matplotlib.axes.Axes
Axes where geometries will be plotted.
points : GeoSeries
Sequence of points.
bounding_box : list
Bounding box of the map. For example, [west, south, east, north].
point_size : int, optional
Diameter of points, by default 3.
point_color : str, optional
Point color in Hex Color Code, by default '#115f9a'.
opacity : float, optional
Opacity of points, ranged from 0.0 to 1.0, by default 1.0.
coordinate_system : str, optional
The Coordinate Reference System (CRS) set to all geometries, by default 'EPSG:3857'.
Only supports SRID as a WKT representation of CRS by now.
**extra_contextily_params: dict
Extra parameters passed to `contextily.add_basemap. <https://contextily.readthedocs.io/en/latest/reference.html>`_
Examples
-------
>>> import pandas as pd
>>> import numpy as np
>>> import arctern
>>> import matplotlib.pyplot as plt
>>> # read from test.csv
>>> # Download link: https://raw.githubusercontent.com/arctern-io/arctern-resources/benchmarks/benchmarks/dataset/layer_rendering_test_data/test_data.csv
>>> df = pd.read_csv("/path/to/test_data.csv", dtype={'longitude':np.float64, 'latitude':np.float64, 'color_weights':np.float64, 'size_weights':np.float64, 'region_boundaries':np.object})
>>> points = arctern.GeoSeries.point(df['longitude'], df['latitude'])
>>> # plot pointmap
>>> fig, ax = plt.subplots(figsize=(10, 6), dpi=200)
>>> arctern.plot.pointmap(ax, points, [-74.01398981737215,40.71353244267465,-73.96979949831308,40.74480271529791], point_size=10, point_color='#115f9a',coordinate_system="EPSG:4326")
>>> plt.show()
"""
from matplotlib import pyplot as plt
import contextily as cx
bbox = _transform_bbox(bounding_box, coordinate_system, 'epsg:3857')
w, h = _get_recom_size(bbox[2] - bbox[0], bbox[3] - bbox[1])
vega = vega_pointmap(w,
h,
bounding_box=bounding_box,
point_size=point_size,
point_color=point_color,
opacity=opacity,
coordinate_system=coordinate_system)
hexstr = arctern.point_map_layer(vega, points)
f = io.BytesIO(base64.b64decode(hexstr))
img = plt.imread(f)
ax.set(xlim=(bbox[0], bbox[2]), ylim=(bbox[1], bbox[3]))
cx.add_basemap(ax, **extra_contextily_params)
ax.imshow(img,
alpha=img[:, :, 3],
extent=(bbox[0], bbox[2], bbox[1], bbox[3]))
ax.axis('off')
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)
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")
