def fishnetmap(ax,
points,
weights,
bounding_box,
color_gradient=["#0000FF", "#FF0000"],
cell_size=4,
cell_spacing=1,
opacity=1.0,
coordinate_system='epsg:3857',
aggregation_type='sum',
**extra_contextily_params):
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_fishnetmap(w,
h,
bounding_box=bounding_box,
color_gradient=color_gradient,
cell_size=cell_size,
cell_spacing=cell_spacing,
opacity=opacity,
coordinate_system=coordinate_system,
aggregation_type=aggregation_type)
hexstr = arctern.fishnet_map_layer(vega, points, 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 draw_fishnet_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_fishnetmap(
1024,
896,
bounding_box=[-73.998427, 40.730309, -73.954348, 40.780816],
color_gradient=["#0000FF", "#FF0000"],
cell_size=4,
cell_spacing=1,
opacity=1.0,
coordinate_system='EPSG:4326')
res = fishnetmap(vega, res)
save_png(res, '/tmp/fishnetmap.png')
spark.sql("show tables").show()
spark.catalog.dropGlobalTempView("nyc_taxi")
print("--- %s seconds ---" % (time.time() - start_time))
def test_fishnet_map():
x_data = []
y_data = []
c_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)
c_data.append(10)
c_data.append(20)
c_data.append(30)
c_data.append(40)
arr_x = pandas.Series(x_data)
arr_y = pandas.Series(y_data)
arr_c = pandas.Series(c_data)
points = arctern.ST_Point(arr_x, arr_y)
vega = vega_fishnetmap(
1024,
896,
bounding_box=[-73.998427, 40.730309, -73.954348, 40.780816],
color_gradient=["#0000FF", "#FF0000"],
cell_size=4,
cell_spacing=1,
opacity=1.0,
coordinate_system='EPSG:4326')
heat_map1 = arctern.fishnet_map_layer(vega, points, arr_c)
save_png(heat_map1, "/tmp/test_fishnetmap.png")
def fishnetmap(ax,
points,
weights,
bounding_box,
color_gradient=["#0000FF", "#FF0000"],
cell_size=4,
cell_spacing=1,
opacity=1.0,
coordinate_system='epsg:3857',
aggregation_type='sum',
**extra_contextily_params):
"""
Plot fishnetmap 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 weights: Series(dtype: float|int64)
:param weights: Color weight of points
: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 points,
or ["hex_color1", "hex_color2"] to specify a color gradient ranging from "hex_color1" to "hex_color2"
Current only default value ["#0000FF", "#FF0000"] is supported
:type cell_size: int
:param cell_size: Side length of fishnet cells, default as 4
:type cell_spacing: int
:param cell_spacing: Margin between adjacent fishnet cells, default as 1
:type opacity: float
:param opacity: Opacity of fishnet, 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 'sum'
: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})
>>> points = arctern.GeoSeries.point(df['longitude'], df['latitude'])
>>> # render fishnet
>>> fig, ax = plt.subplots(figsize=(10, 6), dpi=200)
>>> arctern.plot.fishnetmap(ax, points=points, weights=df['color_weights'], bounding_box=[-74.01424568752932, 40.72759334104623, -73.96056823889673, 40.76721122683304], cell_size=8, cell_spacing=2, opacity=1.0, 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_fishnetmap(w,
h,
bounding_box=bounding_box,
color_gradient=color_gradient,
cell_size=cell_size,
cell_spacing=cell_spacing,
opacity=opacity,
coordinate_system=coordinate_system,
aggregation_type=aggregation_type)
hexstr = arctern.fishnet_map_layer(vega, points, 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')
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,
coordinate_system="EPSG:4326")
png = fishnet_map_layer(
vega, ST_Point(pickup_df.pickup_longitude, pickup_df.pickup_latitude),
df.head(limit_num).fare_amount)
save_png(png, "/tmp/arctern_fishnetmap_pandas.png")
def fishnetmap(ax,
points,
weights,
bounding_box,
color_gradient=["#0000FF", "#FF0000"],
cell_size=4,
cell_spacing=1,
opacity=1.0,
coordinate_system='epsg:3857',
aggregation_type='sum',
**extra_contextily_params):
"""
Plots a fishnet map in Matplotlib.
Parameters
----------
ax : matplotlib.axes.Axes
Axes where geometries will be plotted.
points : GeoSeries
Sequence of points.
weights : Series
Color weights of polygons.
bounding_box : list
Bounding box of the map. For example, [west, south, east, north].
color_gradient : list, optional
Range of color gradient, by default ["#0000FF", "#FF0000"].
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".
cell_size : int, optional
Side length of fishnet cells, by default 4.
cell_spacing : int, optional
Margin between adjacent fishnet cells, by default 1.
opacity : float, optional
Opacity of the fishnet, 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 'sum'.
**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})
>>> points = arctern.GeoSeries.point(df['longitude'], df['latitude'])
>>> # render fishnet
>>> fig, ax = plt.subplots(figsize=(10, 6), dpi=200)
>>> arctern.plot.fishnetmap(ax, points=points, weights=df['color_weights'], bounding_box=[-74.01424568752932, 40.72759334104623, -73.96056823889673, 40.76721122683304], cell_size=8, cell_spacing=2, opacity=1.0, 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_fishnetmap(w,
h,
bounding_box=bounding_box,
color_gradient=color_gradient,
cell_size=cell_size,
cell_spacing=cell_spacing,
opacity=opacity,
coordinate_system=coordinate_system,
aggregation_type=aggregation_type)
hexstr = arctern.fishnet_map_layer(vega, points, 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
elif query_type == 'fishnet':
vega = vega_fishnetmap(
int(query_params['width']), int(query_params['height']),
query_params['fishnet']['bounding_box'],
query_params['fishnet']['color_gradient'],
int(query_params['fishnet']['cell_size']),
int(query_params['fishnet']['cell_spacing']),
float(query_params['fishnet']['opacity']),
query_params['fishnet']['coordinate_system'],
query_params['fishnet']['aggregation_type'])
data = fishnetmap(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)