def visualize(recs):
'''
Takes the direct output from calculation, maps the 3 recommendations
based on their cuisine locations
'''
if recs != []:
rv = []
for idx, tup in enumerate(recs):
lat, lon = tup[1]['coordinates']
rv.append(["#" + str(idx + 1) + ". " + tup[1]['name'] + \
" (" + tup[1]['cuisine'] + ")", lat, lon])
header = ['name', 'lat', 'lon']
with open("form/static_files/img/visualization.csv", "w") as csvfile:
writer = csv.writer(csvfile)
writer.writerow([h for h in header])
writer.writerows(rv)
data = geoplotlib.utils.read_csv(
"form/static_files/img/visualization.csv")
geoplotlib.dot(data)
geoplotlib.labels(data, 'name', color=[0, 0, 255, 255], font_size=10, \
anchor_x='center')
geoplotlib.savefig("form/static_files/img/visualization")
def geo_dot(file): # file must have at top: name,lat,lon
"""
Renders a geo dot graph
:param file: path to file
:return: saves image to temp/map.png
"""
data = read_csv(file)
geoplotlib.dot(data, point_size=3)
# geoplotlib.show()
geoplotlib.savefig('temp/map')
def geo_spatial(file):
"""
Renders a geo spatial graph
:param file: path to file
:return: saves image to temp/spatial.png
"""
data = read_csv(file)
geoplotlib.graph(data, src_lat='lat_departure', src_lon='lon_departure', dest_lat='lat_arrival',
dest_lon='lon_arrival', color='hot_r', alpha=16, linewidth=2)
# geoplotlib.show()
geoplotlib.savefig('temp/spatial')
def makeMap(data):
'''
1. use Kmeans cluster lat and lon of each rsvp event
2. plot them on a world map
3. take a screenshot of the map and save
'''
geoplotlib.add_layer(KMeansLayer(data))
geoplotlib.set_smoothing(True)
geoplotlib.set_bbox(geoplotlib.utils.BoundingBox.WORLD)
geoplotlib.savefig('clustermap')
return None
def displayChargers(chargers, color="red", img_name="charging_points"):
""" Puts the charging points in a map. Saves image to disk. """
lats, lons, powers = [], [], []
for center in chargers:
for charger in center:
lats.append(charger["coords"][0])
lons.append(charger["coords"][1])
powers.append(charger["power"])
thedata = pd.DataFrame({"lat": lats, "lon": lons, "powers": powers})
geoplotlib.dot(thedata, color=color)
geoplotlib.savefig(img_name)
return True
def _draw_hot(day, hour, minute):
"""
geoplotlib热力图
:param day:
:param hour, minute:
:param tp:
:return:
"""
data = da.loc_state(day, hour, minute)
bbox1 = BoundingBox(north=32, south=30.7, west=122.2, east=120.8)
gp.set_bbox(bbox1)
# gp.shapefiles('F:\\road_datas\\ShangHai\\boundary', shape_type='empty')
gp.kde(data, bw=[0.5, 0.5], cmap='jet', scaling='wjk')
gp.savefig('001')
gp.show()
def saveGeoData(dataFrame, feature, label, tag):
plot_inc_d = {}
for inc in dataFrame[feature].unique():
accidents = dataFrame.loc[dataFrame[feature].isin([inc])]
plot_inc = {}
plot_inc = accidents[['LATITUDE', 'LONGITUDE']]
plot_inc.columns = ['lat', 'lon']
for col in plot_inc.columns:
plot_inc_d[col] = plot_inc[col].tolist()
# Plotting the data w. geoplotlib
print label + ":", inc
print "Samples:", len(accidents)
gpl.kde(plot_inc_d, bw=1, cut_below=2e-4)
gpl.set_bbox(
BoundingBox(north=40.93, west=-73.85, south=40.53, east=-73.83))
gpl.savefig('geo' + str(tag) + "_" + str(inc))
plot_inc_d = {}
def drawmap(self):
"""
Multiple examples of kernel density estimation visualization
"""
data = self.get_dao_object()
# geoplotlib.kde(data, bw=5, cut_below=1e-4)
# lowering clip_above changes the max value in the color scale
# geoplotlib.kde(data, bw=5, cut_below=1e-4, clip_above=.1)
# different bandwidths
geoplotlib.kde(data, bw=20, cmap=self.cmap, cut_below=1e-4)
# geoplotlib.kde(data, bw=2, cmap='PuBuGn', cut_below=1e-4)
# linear colorscale
# geoplotlib.kde(data, bw=5, cmap='jet', cut_below=1e-4, scaling='lin')
geoplotlib.set_bbox(BoundingBox.from_nominatim('CHINA'))
geoplotlib.savefig(self.ouput_filename)
def plottrack(self, tailnum, airlinecode):
frame = self.flightdata
frame = frame[[
'TAIL_NUM', 'OP_UNIQUE_CARRIER', 'FL_DATE', 'ORIGIN_AIRPORT_ID',
'ORIGIN', 'CRS_DEP_TIME', 'DEST_AIRPORT_ID', 'DEST', 'CRS_ARR_TIME'
]]
frame = frame.sort_values(['FL_DATE', 'CRS_DEP_TIME'],
ascending=True).groupby(
['TAIL_NUM', 'OP_UNIQUE_CARRIER'])
alldata = frame.get_group(
(tailnum, airlinecode)).reset_index().drop(columns=['index'])
origin = alldata['ORIGIN']
dest = alldata['DEST']
mergedata_origin = pd.merge(origin,
self.mapdata,
left_on=['ORIGIN'],
right_on=['name'],
how='left')
mergedata_origin = mergedata_origin.rename(columns={
'lat': 'lat_ori',
'lon': 'lon_ori'
})
mergedata_origin = mergedata_origin
mergedata_dest = pd.merge(dest,
self.mapdata,
left_on=['DEST'],
right_on=['name'],
how='left')
mergedata_dest = mergedata_dest.rename(columns={
'lat': 'lat_dest',
'lon': 'lon_dest'
})
mergedata_all = pd.concat([mergedata_origin, mergedata_dest], axis=1)
geoplotlib.graph(mergedata_all,
src_lat='lat_ori',
src_lon='lon_ori',
dest_lat='lat_dest',
dest_lon='lon_dest',
color='Reds',
linewidth=2)
geoplotlib.savefig(tailnum)
import geoplotlib
from pandasql import sqldf
import pandas as pd
df = pd.read_csv('data.csv')
destco = pd.read_csv('destination_latlong.csv')
orgco = pd.read_csv('origin_latlong.csv')
df['FL_DATE'] = pd.to_datetime(df['FL_DATE'], format='%Y-%m-%d')
airline = df.set_index('FL_DATE')
q = """
SELECT A.ORIGIN_CITY_NAME,A.DEST_CITY_NAME,DC.lat As dlat,DC.lon as dlon,OC.lat As olat,OC.lon as olon FROM airline as A join destco as DC on A.DEST_CITY_NAME=DC.DEST_CITY_NAME join orgco as OC on A.ORIGIN_CITY_NAME=OC.ORIGIN_CITY_NAME WHERE DEP_DELAY=0 AND ARR_DELAY=0;
"""
print 'working..'
result = sqldf(q, locals())
geoplotlib.graph(result,
src_lat='olat',
src_lon='olon',
dest_lat='dlat',
dest_lon='dlon',
color='rainbow',
alpha=32,
linewidth=2)
geoplotlib.savefig('no delay')
#!/usr/bin/env python2
import geoplotlib
from geoplotlib.utils import read_csv, BoundingBox, DataAccessObject
data = read_csv('filtered_lonlat.csv')
# http://andreacuttone.com/geoplotlib/api.html#module-geoplotlib
geoplotlib.dot(data, color=[0,0,0], point_size=1.5)
geoplotlib.kde(data, bw=10, cmap='PuBuGn', cut_below=1e-4, clip_above=1e-2, alpha=180)
geoplotlib.graph(read_csv('group0.csvgraph.csv'), src_lat='flat', src_lon='flon',
dest_lat='tlat', dest_lon='tlon', color=[0,0,0], linewidth=2)
geoplotlib.graph(read_csv('group1.csvgraph.csv'), src_lat='flat', src_lon='flon',
dest_lat='tlat', dest_lon='tlon', color=[0,255,0], linewidth=2)
geoplotlib.graph(read_csv('group2.csvgraph.csv'), src_lat='flat', src_lon='flon',
dest_lat='tlat', dest_lon='tlon', color=[128,0,128], linewidth=2)
geoplotlib.kde(read_csv('chokepoints.csv'), bw=10, cmap='hot',
cut_below=1e-4, clip_above=1e-2, alpha=180)
bbox = BoundingBox(north=25.7188,west=-80.280,south=25.711,east=-80.280)
geoplotlib.set_bbox(bbox)
geoplotlib.set_window_size(1400, 1600)
#geoplotlib.set_window_size(700, 800)
geoplotlib.tiles_provider('toner')
geoplotlib.set_smoothing(True)
geoplotlib.savefig('output')
#geoplotlib.show()
data = crime_data_2017_DF.loc[crime_data_2017_DF['Resolved'] == 'Resolved',['Category','X','Y']]
data = data.rename(columns={"Category":"name",
"X":"lon",
"Y":"lat"})
data.to_csv("../data/GraphData_2017.csv", encoding="utf-8", index=False)
data.head()
data = read_csv('../data/GraphData_2017.csv')
# In[117]:
geoplotlib.dot(data)
geoplotlib.savefig('../plot/22.AllCrimes_2017')
#geoplotlib.show()
# # Map plot for Unresolved Arson : 2017 vs. 2016 (using Folium)
# In[118]:
crime_data_2016_unresolvedArson_DF = crime_data_2016_DF.loc[
(crime_data_2016_DF['Resolved'] == 'Unresolved') &
(crime_data_2016_DF['Category'] == 'ARSON'), :]
crime_data_2017_unresolvedArson_DF = crime_data_2017_DF.loc[
"""
Multiple examples of kernel density estimation visualization
"""
import geoplotlib
from geoplotlib.utils import read_csv, BoundingBox, DataAccessObject
data = read_csv('data/opencellid_dk.csv')
#geoplotlib.kde(data, bw=5, cut_below=1e-4)
# lowering clip_above changes the max value in the color scale
#geoplotlib.kde(data, bw=5, cut_below=1e-4, clip_above=.1)
# different bandwidths
geoplotlib.kde(data, bw=20, cmap='OrRd', cut_below=1e-4)
#geoplotlib.kde(data, bw=2, cmap='PuBuGn', cut_below=1e-4)
# linear colorscale
#geoplotlib.kde(data, bw=5, cmap='jet', cut_below=1e-4, scaling='lin')
geoplotlib.set_bbox(BoundingBox.from_nominatim('CHINA'))
geoplotlib.savefig('full.png')
geoplotlib.show()
