def test_vega_icon():
vega = vega_icon(1900, 1410,
[-73.998427, 40.730309, -73.954348, 40.780816],
"icon_path", "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"]["icon_path"][
"value"] == "icon_path"
assert vega_dict["marks"][0]["encode"]["enter"]["coordinate_system"][
"value"] == "EPSG:3857"
vega = vega_icon(1900, 1410,
[-73.998427, 40.730309, -73.954348, 40.780816],
"icon_path").build()
vega_dict = json.loads(vega)
assert vega_dict["marks"][0]["encode"]["enter"]["coordinate_system"][
"value"] == "EPSG:3857"
def test_icon_viz():
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)
import os
dir_path = os.path.dirname(os.path.realpath(__file__))
png_path = dir_path + "/../images/taxi.png"
vega = vega_icon(
1024,
896,
bounding_box=[-73.998427, 40.730309, -73.954348, 40.780816],
icon_path=png_path,
coordinate_system="EPSG:4326")
icon_buf = arctern.icon_viz_layer(vega, points)
save_png(icon_buf, "/tmp/test_icon_viz.png")
def iconviz(ax,
points,
bounding_box,
icon_path,
coordinate_system='EPSG:3857',
**extra_contextily_params):
"""
Plots an icon 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].
icon_path : str
Absolute path to icon file.
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_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}, nrows=10)
>>> points = arctern.GeoSeries.point(df['longitude'], df['latitude'])
>>> # plot icon visualization
>>> # Download icon-viz.png : https://raw.githubusercontent.com/arctern-io/arctern-docs/master/img/icon/icon-viz.png
>>> fig, ax = plt.subplots(figsize=(10, 6), dpi=200)
>>> arctern.plot.iconviz(ax, points, bounding_box=[-74.01424568752932, 40.72759334104623, -73.96056823889673, 40.76721122683304], icon_path='/path/to/icon-viz.png', 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_icon(w,
h,
bounding_box=bounding_box,
icon_path=icon_path,
coordinate_system=coordinate_system)
hexstr = arctern.icon_viz_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 draw_icon_viz(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 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))'))")
icon_path = "/tmp/taxi.png"
vega = vega_icon(1024, 896, [-73.998427, 40.730309, -73.954348, 40.780816], icon_path, "EPSG:4326")
res = icon_viz(vega, res)
save_png(res, '/tmp/icon_viz.png')
spark.sql("show tables").show()
spark.catalog.dropGlobalTempView("nyc_taxi")
print("--- %s seconds ---" % (time.time() - start_time))
def test_icon_viz():
x_data = []
y_data = []
for i in range(5):
x_data.append(i * 100)
y_data.append(i * 100)
arr_x = pandas.Series(x_data)
arr_y = pandas.Series(y_data)
points = arctern.ST_Point(arr_x, arr_y)
import os
dir_path = os.path.dirname(os.path.realpath(__file__))
png_path = dir_path + "/../images/taxi.png"
vega = vega_icon(800, 600, [-73.998427, 40.730309, -73.954348, 40.780816],
png_path, "EPSG:43")
icon_buf = arctern.icon_viz(vega, points)
save_png(icon_buf, "/tmp/test_icon_viz.png")
def iconviz(ax,
points,
bounding_box,
icon_path,
coordinate_system='EPSG:3857',
**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_icon(w,
h,
bounding_box=bounding_box,
icon_path=icon_path,
coordinate_system=coordinate_system)
hexstr = arctern.icon_viz_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 iconviz(ax,
points,
bounding_box,
icon_path,
coordinate_system='EPSG:3857',
**extra_contextily_params):
"""
Plot points as icons on map 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 icon_path: str
:param icon_path: Absolute path to icon file
: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_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}, nrows=10)
>>> points = arctern.GeoSeries.point(df['longitude'], df['latitude'])
>>> # plot icon visualization
>>> # Download icon-viz.png : https://raw.githubusercontent.com/arctern-io/arctern-docs/master/img/icon/icon-viz.png
>>> fig, ax = plt.subplots(figsize=(10, 6), dpi=200)
>>> arctern.plot.iconviz(ax, points, bounding_box=[-74.01424568752932, 40.72759334104623, -73.96056823889673, 40.76721122683304], icon_path='/path/to/icon-viz.png', 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_icon(w,
h,
bounding_box=bounding_box,
icon_path=icon_path,
coordinate_system=coordinate_system)
hexstr = arctern.icon_viz_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)
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,
ST_Point(
pickup_df.head(25).pickup_longitude,
pickup_df.head(25).pickup_latitude))
save_png(png, "/tmp/arctern_iconviz_pandas.png")
vega = vega_fishnetmap(1024,
384,
bounding_box=[pos1[0], pos1[1], pos2[0], pos2[1]],
cell_size=8,
cell_spacing=1,
opacity=1.0,
