def test_vega_choropleth_map():
vega = vega_choroplethmap(1900, 1410,
[-73.994092, 40.753893, -73.977588, 40.759642],
"blue_to_red", [2.5, 5], 1.0,
"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] == -73.994092
assert vega_dict["marks"][0]["encode"]["enter"]["bounding_box"]["value"][
1] == 40.753893
assert vega_dict["marks"][0]["encode"]["enter"]["bounding_box"]["value"][
2] == -73.977588
assert vega_dict["marks"][0]["encode"]["enter"]["bounding_box"]["value"][
3] == 40.759642
assert vega_dict["marks"][0]["encode"]["enter"]["color_style"][
"value"] == "blue_to_red"
assert len(vega_dict["marks"][0]["encode"]["enter"]["ruler"]["value"]) == 2
assert vega_dict["marks"][0]["encode"]["enter"]["ruler"]["value"][0] == 2.5
assert vega_dict["marks"][0]["encode"]["enter"]["ruler"]["value"][1] == 5
assert vega_dict["marks"][0]["encode"]["enter"]["opacity"]["value"] == 1.0
assert vega_dict["marks"][0]["encode"]["enter"]["coordinate_system"][
"value"] == "EPSG:3857"
vega = vega_choroplethmap(1900, 1410,
[-73.994092, 40.753893, -73.977588, 40.759642],
"blue_to_red", [2.5, 5], 1.0).build()
vega_dict = json.loads(vega)
assert vega_dict["marks"][0]["encode"]["enter"]["coordinate_system"][
"value"] == "EPSG:4326"
def test_choropleth_map():
wkt_data = []
count_data = []
wkt_data.append("POLYGON (("
"-73.97324 40.73747, "
"-73.96524 40.74507, "
"-73.96118 40.75890, "
"-73.95556 40.77654, "
"-73.97324 40.73747))")
count_data.append(5.0)
arr_wkt = pandas.Series(wkt_data)
arr_wkb = arctern.ST_GeomFromText(arr_wkt)
arr_count = pandas.Series(count_data)
vega = vega_choroplethmap(
1024,
896,
bounding_box=[-73.998427, 40.730309, -73.954348, 40.780816],
color_gradient=["#0000FF", "#FF0000"],
color_bound=[2.5, 5],
opacity=1.0,
coordinate_system='EPSG:4326')
choropleth_map1 = arctern.choropleth_map_layer(vega, arr_wkb, arr_count)
save_png(choropleth_map1, "/tmp/test_choropleth_map1.png")
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 test_choropleth_map():
wkt_data = []
count_data = []
wkt_data.append("POLYGON (("
"200 200, "
"200 300, "
"300 300, "
"300 200, "
"200 200))")
count_data.append(5.0)
arr_wkt = pandas.Series(wkt_data)
arr_wkb = arctern.wkt2wkb(arr_wkt)
arr_count = pandas.Series(count_data)
vega = 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')
choropleth_map1 = arctern.choropleth_map(vega, arr_wkb, arr_count)
save_png(choropleth_map1, "/tmp/test_choropleth_map1.png")
def test_vega_choropleth_map():
vega = vega_choroplethmap(1900, 1410,
[-73.994092, 40.753893, -73.977588, 40.759642],
["#0000FF", "#FF0000"], [2.5, 5], 1.0,
"EPSG:3857", "max").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] == -73.994092
assert vega_dict["marks"][0]["encode"]["enter"]["bounding_box"]["value"][
1] == 40.753893
assert vega_dict["marks"][0]["encode"]["enter"]["bounding_box"]["value"][
2] == -73.977588
assert vega_dict["marks"][0]["encode"]["enter"]["bounding_box"]["value"][
3] == 40.759642
assert vega_dict["marks"][0]["encode"]["enter"]["color_gradient"]["value"][
0] == "#0000FF"
assert vega_dict["marks"][0]["encode"]["enter"]["color_gradient"]["value"][
1] == "#FF0000"
assert len(
vega_dict["marks"][0]["encode"]["enter"]["color_bound"]["value"]) == 2
assert vega_dict["marks"][0]["encode"]["enter"]["color_bound"]["value"][
0] == 2.5
assert vega_dict["marks"][0]["encode"]["enter"]["color_bound"]["value"][
1] == 5
assert vega_dict["marks"][0]["encode"]["enter"]["opacity"]["value"] == 1.0
assert vega_dict["marks"][0]["encode"]["enter"]["coordinate_system"][
"value"] == "EPSG:3857"
assert vega_dict["marks"][0]["encode"]["enter"]["aggregation_type"][
"value"] == "max"
vega = vega_choroplethmap(1900, 1410,
[-73.994092, 40.753893, -73.977588, 40.759642],
["#0000FF", "#FF0000"]).build()
vega_dict = json.loads(vega)
assert len(
vega_dict["marks"][0]["encode"]["enter"]["color_bound"]["value"]) == 2
assert vega_dict["marks"][0]["encode"]["enter"]["color_bound"]["value"][
0] == 0
assert vega_dict["marks"][0]["encode"]["enter"]["color_bound"]["value"][
1] == 0
assert vega_dict["marks"][0]["encode"]["enter"]["opacity"]["value"] == 1.0
assert vega_dict["marks"][0]["encode"]["enter"]["coordinate_system"][
"value"] == "EPSG:3857"
assert vega_dict["marks"][0]["encode"]["enter"]["aggregation_type"][
"value"] == "sum"
def choroplethmap(ax,
region_boundaries,
weights,
bounding_box,
color_gradient,
color_bound=None,
opacity=1.0,
coordinate_system='EPSG:3857',
aggregation_type='max',
**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 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 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_choroplethmap(w,
h,
bounding_box=bounding_box,
color_gradient=color_gradient,
color_bound=color_bound,
opacity=opacity,
aggregation_type=aggregation_type,
coordinate_system=coordinate_system)
hexstr = arctern.choropleth_map_layer(vega, region_boundaries, weights)
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 test_choropleth_map():
wkt_data = []
count_data = []
wkt_data.append("POLYGON (("
"200 200, "
"200 300, "
"300 300, "
"300 200, "
"200 200))")
count_data.append(5.0)
arr_wkt = pandas.Series(wkt_data)
arr_wkb = arctern.wkt2wkb(arr_wkt)
arr_count = pandas.Series(count_data)
vega = vega_choroplethmap(1900, 1410, [-73.994092, 40.753893, -73.977588, 40.759642], ["#0000FF", "#FF0000"], [2.5, 5], 1.0, 'EPSG:4326')
choropleth_map1 = arctern.choropleth_map(vega, arr_wkb, arr_count)
save_png(choropleth_map1, "/tmp/test_choropleth_map1.png")
def draw_choropleth_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.createOrReplaceTempView("nyc_taxi")
res = spark.sql(
"select buildingtext_dropoff as wkt, passenger_count as w from nyc_taxi"
)
vega = vega_choroplethmap(1900, 1410,
[-73.994092, 40.753893, -73.977588, 40.759642],
"blue_to_red", [2.5, 5], 1.0, 'EPSG:4326')
res = choroplethmap(res, vega)
save_png(res, '/tmp/choroplethmap.png')
spark.sql("show tables").show()
spark.catalog.dropGlobalTempView("nyc_taxi")
def test_choropleth_map():
wkt_data = []
count_data = []
wkt_data.append("POLYGON (("
"200 200, "
"200 300, "
"300 300, "
"300 200, "
"200 200))")
count_data.append(5.0)
arr_wkt = pandas.Series(wkt_data)
arr_count = pandas.Series(count_data)
vega_choropleth_map = vega_choroplethmap(
1900, 1410, [-73.994092, 40.753893, -73.977588, 40.759642],
"blue_to_red", [2.5, 5], 1.0, 'EPSG:4326')
vega_json = vega_choropleth_map.build()
arr_wkb = arctern.wkt2wkb(arr_wkt)
choropleth_map1 = arctern.choropleth_map(arr_wkb, arr_count,
vega_json.encode('utf-8'))
save_png(choropleth_map1, "/tmp/test_choropleth_map1.png")
def choroplethmap(ax,
region_boundaries,
weights,
bounding_box,
color_gradient,
color_bound=None,
opacity=1.0,
coordinate_system='EPSG:3857',
aggregation_type='max',
**extra_contextily_params):
"""
Render region boundaries in matplotlib
:type ax: AxesSubplot
:param ax: Matplotlib axes object on which to add the basemap.
:type region_boundaries: GeoSeries
:param region_boundaries: Sequence of polygons, as region boundaries to plot.
:type weights: Series(dtype: float|int64)
:param weights: Color weights for polygons
:type bounding_box: list
:param bounding_box: Specify the bounding rectangle [west, south, east, north].
:type color_gradient: list
:param color_gradient: Specify range of color gradient.
Either use ["hex_color"] to specify a same color for all polygons,
or ["hex_color1", "hex_color2"] to specify a color gradient ranging from "hex_color1" to "hex_color2"
:type color_bound: list
:param color_bound: Specify weight range [w1, w2] binding to color_gradient.
Needed only when color_gradient has two value ["color1", "color2"].
Bind w1 to "color1", and w2 to "color2".
When weight < w1 or weight > w2, truncate to w1/w2 accordingly.
Default as None
:type opacity: float
:param opacity: Opacity of polygons, 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 aggregation_type: str
:param aggregation_type: Aggregation type of data processing. Default as 'max'
: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_data.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})
>>> input = df[pd.notna(df['region_boundaries'])].groupby(['region_boundaries']).mean().reset_index()
>>> polygon = arctern.GeoSeries(input['region_boundaries'])
>>> # plot choroplethmap
>>> fig, ax = plt.subplots(figsize=(10, 6), dpi=200)
>>> arctern.plot.choroplethmap(ax, polygon, input['color_weights'], bounding_box=[-74.01124953254566,40.73413446570038,-73.96238859103838,40.766161712662296], color_gradient=["#115f9a","#d0f400"], color_bound=[5,18], opacity=1.0, coordinate_system='EPSG:4326', aggregation_type="mean")
>>> 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_choroplethmap(w,
h,
bounding_box=bounding_box,
color_gradient=color_gradient,
color_bound=color_bound,
opacity=opacity,
aggregation_type=aggregation_type,
coordinate_system=coordinate_system)
hexstr = arctern.choropleth_map_layer(vega, region_boundaries, weights)
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)
save_png(png, "/tmp/arctern_weighted_pointmap_pandas.png")
vega = vega_heatmap(1024,
384,
bounding_box=[pos1[0], pos1[1], pos2[0], pos2[1]],
map_zoom_level=13.0,
coordinate_system="EPSG:4326")
png = heat_map_layer(
vega, ST_Point(pickup_df.pickup_longitude, pickup_df.pickup_latitude),
df.head(limit_num).fare_amount)
save_png(png, "/tmp/arctern_heatmap_pandas.png")
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")
png = choropleth_map_layer(vega,
ST_GeomFromText(pickup_df.buildingtext_pickup),
df.head(limit_num).fare_amount)
save_png(png, "/tmp/arctern_choroplethmap_pandas.png")
vega = vega_icon(1024,
384,
bounding_box=[pos1[0], pos1[1], pos2[0], pos2[1]],
icon_path='/path/to/arctern-color.png',
coordinate_system="EPSG:4326")
png = icon_viz_layer(
vega,
def choroplethmap(ax,
region_boundaries,
weights,
bounding_box,
color_gradient,
color_bound=None,
opacity=1.0,
coordinate_system='EPSG:3857',
aggregation_type='max',
**extra_contextily_params):
"""
Plots a choropleth map in matplotlib.
Parameters
----------
ax : matplotlib.axes.Axes
Axes where geometries will be plotted.
region_boundaries : GeoSeries
Sequence of polygons, as region boundaries to plot.
weights : Series
Color weights for polygons
bounding_box : list
Bounding box of the map. For example, [west, south, east, north].
color_gradient : list
Range of color gradient.
Either use ["hex_color"] to specify a same color for all geometries, or ["hex_color1", "hex_color2"] to specify a color gradient ranging from "hex_color1" to "hex_color2".
color_bound : list, optional
Weight range [w1, w2] of ``color_gradient``.
Needed only when ``color_gradient`` has two values ["color1", "color2"]. Binds w1 to "color1", and w2 to "color2". When weight < w1 or weight > w2, the weight will be truncated to w1 or w2 accordingly.
opacity : float, optional
Opacity of polygons, 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.
aggregation_type : str, optional
Aggregation type, by default 'max'.
**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_data.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})
>>> input = df[pd.notna(df['region_boundaries'])].groupby(['region_boundaries']).mean().reset_index()
>>> polygon = arctern.GeoSeries(input['region_boundaries'])
>>> # plot choroplethmap
>>> fig, ax = plt.subplots(figsize=(10, 6), dpi=200)
>>> arctern.plot.choroplethmap(ax, polygon, input['color_weights'], bounding_box=[-74.01124953254566,40.73413446570038,-73.96238859103838,40.766161712662296], color_gradient=["#115f9a","#d0f400"], color_bound=[5,18], opacity=1.0, coordinate_system='EPSG:4326', aggregation_type="mean")
>>> 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_choroplethmap(w,
h,
bounding_box=bounding_box,
color_gradient=color_gradient,
color_bound=color_bound,
opacity=opacity,
aggregation_type=aggregation_type,
coordinate_system=coordinate_system)
hexstr = arctern.choropleth_map_layer(vega, region_boundaries, weights)
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_choropleth_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")
res = spark.sql("select buildingtext_dropoff as wkt, passenger_count as w from nyc_taxi")
# 1-9 test color_style
# 1 blue_to_red
vega_1 = vega_choroplethmap(1900, 1410, [-73.994092, 40.753893, -73.977588, 40.759642], "blue_to_red",
[2.5, 5], 1.0, 'EPSG:4326')
baseline1 = choroplethmap(res, vega_1)
choropleth_map1_1 = choroplethmap(res, vega_1)
choropleth_map1_2 = choroplethmap(res, vega_1)
baseline_png1 = png_path + "choropleth_map_nyc_1.png"
save_png(baseline1, baseline_png1)
save_png(choropleth_map1_1, png_path + "test_choropleth_map_nyc_1-1.png")
save_png(choropleth_map1_2, png_path + "test_choropleth_map_nyc_1-2.png")
# 2 green_yellow_red
vega_2 = vega_choroplethmap(1900, 1410, [-73.994092, 40.753893, -73.977588, 40.759642], "green_yellow_red",
[2.5, 5], 1.0, 'EPSG:4326')
baseline2 = choroplethmap(res, vega_2)
choropleth_map2_1 = choroplethmap(res, vega_2)
choropleth_map2_2 = choroplethmap(res, vega_2)
baseline_png2 = png_path + "choropleth_map_nyc_2.png"
save_png(baseline2, baseline_png2)
save_png(choropleth_map2_1, png_path + "test_choropleth_map_nyc_2-1.png")
save_png(choropleth_map2_2, png_path + "test_choropleth_map_nyc_2-2.png")
# 3 blue_white_red
vega_3 = vega_choroplethmap(1900, 1410, [-73.994092, 40.753893, -73.977588, 40.759642], "blue_white_red",
[2.5, 5], 1.0, 'EPSG:4326')
baseline3 = choroplethmap(res, vega_3)
choropleth_map3_1 = choroplethmap(res, vega_3)
choropleth_map3_2 = choroplethmap(res, vega_3)
baseline_png3 = png_path + "choropleth_map_nyc_3.png"
save_png(baseline3, baseline_png3)
save_png(choropleth_map3_1, png_path + "test_choropleth_map_nyc_3-1.png")
save_png(choropleth_map3_2, png_path + "test_choropleth_map_nyc_3-2.png")
# 4 skyblue_to_white
vega_4 = vega_choroplethmap(1900, 1410, [-73.994092, 40.753893, -73.977588, 40.759642], "skyblue_to_white",
[2.5, 5], 1.0, 'EPSG:4326')
baseline4 = choroplethmap(res, vega_4)
choropleth_map4_1 = choroplethmap(res, vega_4)
choropleth_map4_2 = choroplethmap(res, vega_4)
baseline_png4 = png_path + "choropleth_map_nyc_4.png"
save_png(baseline4, baseline_png4)
save_png(choropleth_map4_1, png_path + "test_choropleth_map_nyc_4-1.png")
save_png(choropleth_map4_2, png_path + "test_choropleth_map_nyc_4-2.png")
# 5 purple_to_yellow
vega_5 = vega_choroplethmap(1900, 1410, [-73.994092, 40.753893, -73.977588, 40.759642], "purple_to_yellow",
[2.5, 5], 1.0, 'EPSG:4326')
baseline5 = choroplethmap(res, vega_5)
choropleth_map5_1 = choroplethmap(res, vega_5)
choropleth_map5_2 = choroplethmap(res, vega_5)
baseline_png5 = png_path + "choropleth_map_nyc_5.png"
save_png(baseline5, baseline_png5)
save_png(choropleth_map5_1, png_path + "test_choropleth_map_nyc_5-1.png")
save_png(choropleth_map5_2, png_path + "test_choropleth_map_nyc_5-2.png")
# 6 red_transparency
vega_6 = vega_choroplethmap(1900, 1410, [-73.994092, 40.753893, -73.977588, 40.759642], "red_transparency",
[2.5, 5], 1.0, 'EPSG:4326')
baseline6 = choroplethmap(res, vega_6)
choropleth_map6_1 = choroplethmap(res, vega_6)
choropleth_map6_2 = choroplethmap(res, vega_6)
baseline_png6 = png_path + "choropleth_map_nyc_6.png"
save_png(baseline6, baseline_png6)
save_png(choropleth_map6_1, png_path + "test_choropleth_map_nyc_6-1.png")
save_png(choropleth_map6_2, png_path + "test_choropleth_map_nyc_6-2.png")
# 7 blue_transparency
vega_7 = vega_choroplethmap(1900, 1410, [-73.994092, 40.753893, -73.977588, 40.759642], "blue_transparency",
[2.5, 5], 1.0, 'EPSG:4326')
baseline7 = choroplethmap(res, vega_7)
choropleth_map7_1 = choroplethmap(res, vega_7)
choropleth_map7_2 = choroplethmap(res, vega_7)
baseline_png7 = png_path + "choropleth_map_nyc_7.png"
save_png(baseline7, baseline_png7)
save_png(choropleth_map7_1, png_path + "test_choropleth_map_nyc_7-1.png")
save_png(choropleth_map7_2, png_path + "test_choropleth_map_nyc_7-2.png")
# 8 blue_green_yellow
vega_8 = vega_choroplethmap(1900, 1410, [-73.994092, 40.753893, -73.977588, 40.759642], "blue_green_yellow",
[2.5, 5], 1.0, 'EPSG:4326')
baseline8 = choroplethmap(res, vega_8)
choropleth_map8_1 = choroplethmap(res, vega_8)
choropleth_map8_2 = choroplethmap(res, vega_8)
baseline_png8 = png_path + "choropleth_map_nyc_8.png"
save_png(baseline8, baseline_png8)
save_png(choropleth_map8_1, png_path + "test_choropleth_map_nyc_8-1.png")
save_png(choropleth_map8_2, png_path + "test_choropleth_map_nyc_8-2.png")
# 9 white_blue
vega_9 = vega_choroplethmap(1900, 1410, [-73.994092, 40.753893, -73.977588, 40.759642], "white_blue",
[2.5, 5], 1.0, 'EPSG:4326')
baseline9 = choroplethmap(res, vega_9)
choropleth_map9_1 = choroplethmap(res, vega_9)
choropleth_map9_2 = choroplethmap(res, vega_9)
baseline_png9 = png_path + "choropleth_map_nyc_9.png"
save_png(baseline9, baseline_png9)
save_png(choropleth_map9_1, png_path + "test_choropleth_map_nyc_9-1.png")
save_png(choropleth_map9_2, png_path + "test_choropleth_map_nyc_9-2.png")
# 10-12 test ruler
# 10 ruler: [1, 500]
vega_10 = vega_choroplethmap(1900, 1410, [-73.994092, 40.753893, -73.977588, 40.759642], "blue_to_red",
[1, 500], 1.0, 'EPSG:4326')
baseline10 = choroplethmap(res, vega_10)
choropleth_map10_1 = choroplethmap(res, vega_10)
choropleth_map10_2 = choroplethmap(res, vega_10)
baseline_png10 = png_path + "choropleth_map_nyc_10.png"
save_png(baseline10, baseline_png10)
save_png(choropleth_map10_1, png_path + "test_choropleth_map_nyc_10-1.png")
save_png(choropleth_map10_2, png_path + "test_choropleth_map_nyc_10-2.png")
# 11 ruler: [1, 10000]
vega_11 = vega_choroplethmap(1900, 1410, [-73.994092, 40.753893, -73.977588, 40.759642], "blue_to_red",
[1, 10000], 1.0, 'EPSG:4326')
baseline11 = choroplethmap(res, vega_11)
choropleth_map11_1 = choroplethmap(res, vega_11)
choropleth_map11_2 = choroplethmap(res, vega_11)
baseline_png11 = png_path + "choropleth_map_nyc_11.png"
save_png(baseline11, baseline_png11)
save_png(choropleth_map11_1, png_path + "test_choropleth_map_nyc_11-1.png")
save_png(choropleth_map11_2, png_path + "test_choropleth_map_nyc_11-2.png")
# 12 ruler: [0, 2.5]
vega_12 = vega_choroplethmap(1900, 1410, [-73.994092, 40.753893, -73.977588, 40.759642], "blue_to_red",
[0, 2.5], 1.0, 'EPSG:4326')
baseline12 = choroplethmap(res, vega_12)
choropleth_map12_1 = choroplethmap(res, vega_12)
choropleth_map12_2 = choroplethmap(res, vega_12)
baseline_png12 = png_path + "choropleth_map_nyc_12.png"
save_png(baseline12, baseline_png12)
save_png(choropleth_map12_1, png_path + "test_choropleth_map_nyc_12-1.png")
save_png(choropleth_map12_2, png_path + "test_choropleth_map_nyc_12-2.png")
# 13-15 test opacity
# 13 opacity: 0.0
vega_13 = vega_choroplethmap(1900, 1410, [-73.994092, 40.753893, -73.977588, 40.759642], "purple_to_yellow",
[2.5, 5], 0.0, 'EPSG:4326')
baseline13 = choroplethmap(res, vega_13)
choropleth_map13_1 = choroplethmap(res, vega_13)
choropleth_map13_2 = choroplethmap(res, vega_13)
baseline_png13 = png_path + "choropleth_map_nyc_13.png"
save_png(baseline13, baseline_png13)
save_png(choropleth_map13_1, png_path + "test_choropleth_map_nyc_13-1.png")
save_png(choropleth_map13_2, png_path + "test_choropleth_map_nyc_13-2.png")
# 14 opacity: 1.0
vega_14 = vega_choroplethmap(1900, 1410, [-73.994092, 40.753893, -73.977588, 40.759642], "purple_to_yellow",
[2.5, 5], 1.0, 'EPSG:4326')
baseline14 = choroplethmap(res, vega_14)
choropleth_map14_1 = choroplethmap(res, vega_14)
choropleth_map14_2 = choroplethmap(res, vega_14)
baseline_png14 = png_path + "choropleth_map_nyc_14.png"
save_png(baseline14, baseline_png14)
save_png(choropleth_map14_1, png_path + "test_choropleth_map_nyc_14-1.png")
save_png(choropleth_map14_2, png_path + "test_choropleth_map_nyc_14-2.png")
# 15 opacity: 0.5
vega_15 = vega_choroplethmap(1900, 1410, [-73.994092, 40.753893, -73.977588, 40.759642], "purple_to_yellow",
[2.5, 5], 0.5, 'EPSG:4326')
baseline15 = choroplethmap(res, vega_15)
choropleth_map15_1 = choroplethmap(res, vega_15)
choropleth_map15_2 = choroplethmap(res, vega_15)
baseline_png15 = png_path + "choropleth_map_nyc_15.png"
save_png(baseline15, baseline_png15)
save_png(choropleth_map15_1, png_path + "test_choropleth_map_nyc_15-1.png")
save_png(choropleth_map15_2, png_path + "test_choropleth_map_nyc_15-2.png")
# 16-18 test size
# 16 width: 256, height: 256
vega_16 = vega_choroplethmap(256, 256, [-73.994092, 40.753893, -73.977588, 40.759642], "purple_to_yellow",
[2.5, 5], 1.0, 'EPSG:4326')
baseline16 = choroplethmap(res, vega_16)
choropleth_map16_1 = choroplethmap(res, vega_16)
choropleth_map16_2 = choroplethmap(res, vega_16)
baseline_png16 = png_path + "choropleth_map_nyc_16.png"
save_png(baseline16, baseline_png16)
save_png(choropleth_map16_1, png_path + "test_choropleth_map_nyc_16-1.png")
save_png(choropleth_map16_2, png_path + "test_choropleth_map_nyc_16-2.png")
# 17 width: 200, height: 200
vega_17 = vega_choroplethmap(200, 200, [-73.994092, 40.753893, -73.977588, 40.759642], "purple_to_yellow",
[2.5, 5], 1.0, 'EPSG:4326')
baseline17 = choroplethmap(res, vega_17)
choropleth_map17_1 = choroplethmap(res, vega_17)
choropleth_map17_2 = choroplethmap(res, vega_17)
baseline_png17 = png_path + "choropleth_map_nyc_17.png"
save_png(baseline17, baseline_png17)
save_png(choropleth_map17_1, png_path + "test_choropleth_map_nyc_17-1.png")
save_png(choropleth_map17_2, png_path + "test_choropleth_map_nyc_17-2.png")
# 18 width: 500, height: 200
vega_18 = vega_choroplethmap(500, 200, [-73.994092, 40.753893, -73.977588, 40.759642], "purple_to_yellow",
[2.5, 5], 1.0, 'EPSG:4326')
baseline18 = choroplethmap(res, vega_18)
choropleth_map18_1 = choroplethmap(res, vega_18)
choropleth_map18_2 = choroplethmap(res, vega_18)
baseline_png18 = png_path + "choropleth_map_nyc_18.png"
save_png(baseline18, baseline_png18)
save_png(choropleth_map18_1, png_path + "test_choropleth_map_nyc_18-1.png")
save_png(choropleth_map18_2, png_path + "test_choropleth_map_nyc_18-2.png")
# 19 width: 10, height: 10
vega_19 = vega_choroplethmap(10, 10, [-73.994092, 40.753893, -73.977588, 40.759642], "purple_to_yellow",
[2.5, 5], 1.0, 'EPSG:4326')
baseline19 = choroplethmap(res, vega_19)
choropleth_map19_1 = choroplethmap(res, vega_19)
choropleth_map19_2 = choroplethmap(res, vega_19)
baseline_png19 = png_path + "choropleth_map_nyc_19.png"
save_png(baseline19, baseline_png19)
save_png(choropleth_map19_1, png_path + "test_choropleth_map_nyc_19-1.png")
save_png(choropleth_map19_2, png_path + "test_choropleth_map_nyc_19-2.png")
spark.catalog.dropGlobalTempView("nyc_taxi")
assert run_diff_png(baseline_png1, png_path + "test_choropleth_map_nyc_1-1.png")
assert run_diff_png(baseline_png1, png_path + "test_choropleth_map_nyc_1-2.png")
assert run_diff_png(baseline_png2, png_path + "test_choropleth_map_nyc_2-1.png")
assert run_diff_png(baseline_png2, png_path + "test_choropleth_map_nyc_2-2.png")
assert run_diff_png(baseline_png3, png_path + "test_choropleth_map_nyc_3-1.png")
assert run_diff_png(baseline_png3, png_path + "test_choropleth_map_nyc_3-2.png")
assert run_diff_png(baseline_png4, png_path + "test_choropleth_map_nyc_4-1.png")
assert run_diff_png(baseline_png4, png_path + "test_choropleth_map_nyc_4-2.png")
assert run_diff_png(baseline_png5, png_path + "test_choropleth_map_nyc_5-1.png")
assert run_diff_png(baseline_png5, png_path + "test_choropleth_map_nyc_5-2.png")
assert run_diff_png(baseline_png6, png_path + "test_choropleth_map_nyc_6-1.png")
assert run_diff_png(baseline_png6, png_path + "test_choropleth_map_nyc_6-2.png")
assert run_diff_png(baseline_png7, png_path + "test_choropleth_map_nyc_7-1.png")
assert run_diff_png(baseline_png7, png_path + "test_choropleth_map_nyc_7-2.png")
assert run_diff_png(baseline_png8, png_path + "test_choropleth_map_nyc_8-1.png")
assert run_diff_png(baseline_png8, png_path + "test_choropleth_map_nyc_8-2.png")
assert run_diff_png(baseline_png9, png_path + "test_choropleth_map_nyc_9-1.png")
assert run_diff_png(baseline_png9, png_path + "test_choropleth_map_nyc_9-2.png")
assert run_diff_png(baseline_png10, png_path + "test_choropleth_map_nyc_10-1.png")
assert run_diff_png(baseline_png10, png_path + "test_choropleth_map_nyc_10-2.png")
assert run_diff_png(baseline_png11, png_path + "test_choropleth_map_nyc_11-1.png")
assert run_diff_png(baseline_png11, png_path + "test_choropleth_map_nyc_11-2.png")
assert run_diff_png(baseline_png12, png_path + "test_choropleth_map_nyc_12-1.png")
assert run_diff_png(baseline_png12, png_path + "test_choropleth_map_nyc_12-2.png")
assert run_diff_png(baseline_png13, png_path + "test_choropleth_map_nyc_13-1.png")
assert run_diff_png(baseline_png13, png_path + "test_choropleth_map_nyc_13-2.png")
assert run_diff_png(baseline_png14, png_path + "test_choropleth_map_nyc_14-1.png")
assert run_diff_png(baseline_png14, png_path + "test_choropleth_map_nyc_14-2.png")
assert run_diff_png(baseline_png15, png_path + "test_choropleth_map_nyc_15-1.png")
assert run_diff_png(baseline_png15, png_path + "test_choropleth_map_nyc_15-2.png")
assert run_diff_png(baseline_png16, png_path + "test_choropleth_map_nyc_16-1.png")
assert run_diff_png(baseline_png16, png_path + "test_choropleth_map_nyc_16-2.png")
assert run_diff_png(baseline_png17, png_path + "test_choropleth_map_nyc_17-1.png")
assert run_diff_png(baseline_png17, png_path + "test_choropleth_map_nyc_17-2.png")
assert run_diff_png(baseline_png18, png_path + "test_choropleth_map_nyc_18-1.png")
assert run_diff_png(baseline_png18, png_path + "test_choropleth_map_nyc_18-2.png")
assert run_diff_png(baseline_png19, png_path + "test_choropleth_map_nyc_19-1.png")
assert run_diff_png(baseline_png19, png_path + "test_choropleth_map_nyc_19-2.png")
