def main():
""" Extracts latitude and longitude from the JSON feed, converts the list of dictionaries to a dataframe and then plot the coordinates via GeoPlotLib """
url = "https://earthquake.usgs.gov/earthquakes/feed/v1.0/summary/1.0_month.geojson"
json_obj = get_json(url)
entire_report_dictionary = json_loads_to_dictionary(json_obj)
number_of_records, id_lon_lat = extract_records(entire_report_dictionary)[
0], extract_records(entire_report_dictionary)[1]
print("writing to file\n")
with open('id_coordinates.csv', 'wt') as textiowrapper:
textiowrapper.writelines('name,lon,lat\n')
for i in range(number_of_records):
x = 0
id_num = str(id_lon_lat[i][x])
lon = str(id_lon_lat[i][x + 1])
lat = str(id_lon_lat[i][x + 2])
s = id_num + ',' + lon + ',' + lat
textiowrapper.writelines(s + '\n')
del x
textiowrapper.close()
print("done writing to file\n")
print("mapping the data\n")
data = read_csv('id_coordinates.csv')
geoplotlib.dot(data)
geoplotlib.show()
def getHistogramOfMatches(self):
#self.updateLocationDataFile()
data = read_csv(self.location_data)
print(data)
geoplotlib.hist(data, colorscale='sqrt', binsize=8)
geoplotlib.show()
def getDelaunayTriangulation(self):
#self.updateLocationDataFile()
data = read_csv(self.location_data)
print(data)
geoplotlib.delaunay(data, cmap='hot_r')
geoplotlib.show()
def getHeatmapOfMatches(self):
#self.updateLocationDataFile()
data = read_csv(self.location_data)
print(data)
geoplotlib.kde(data, bw=[5, 5])
geoplotlib.show()
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 test_graph():
import geoplotlib
from geoplotlib.utils import read_csv
movement_data = read_csv('./data/graph_movement.csv')
geoplotlib.graph(movement_data,
src_lat='SourceLat',
src_lon='SourceLon',
dest_lat='TargetLat',
dest_lon='TargetLon',
alpha=80,
linewidth=2,
color='inferno',
color_by='Weight',
levels=10)
geoplotlib.show()
def __init__(self):
self.data = read_csv('data/taxi.csv')
self.data = self.data.where(self.data['taxi_id'] == list(set(self.data['taxi_id']))[2])
self.t = self.data['timestamp'].min()
self.painter = BatchPainter()
#convert list with [phi, lambda, h] to [name, lat, lon]
Locations.append([Sname, deg(series[0].pos[0]), deg(series[0].pos[1])])
#path to SLOCs.csv
os.chdir(os.path.dirname(os.path.realpath(__file__)))
#empty SLOCs.csv
f = open("SLOCs.csv", "w+")
f.close()
#write csv to be read by geoplotlib
with open('SLOCs.csv', mode='w', newline='') as SLOCs:
SLOCs = csv.writer(SLOCs,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
SLOCs.writerow(['name', 'lat', 'lon'])
for station in Locations:
SLOCs.writerow([station[0], station[1], station[2]])
#plot stations
Plotdata = read_csv('SLOCs.csv')
gp.set_bbox(BoundingBox(north=9, west=110, south=1, east=95))
gp.dot(Plotdata, color='red', point_size=2)
gp.labels(Plotdata, 'name', color='black', font_size=8, anchor_x='center')
gp.tiles_provider('positron')
gp.show()
'https://maps-for-free.com/layer/relief/z{Z}/row{Y}/{Z}_{X}-{Y}.jpg'
#ToDo: change tile to map from the web (url above)
"""
Example of voronoi layer with filled area
"""
import geoplotlib
from geoplotlib.utils import read_csv, BoundingBox
data = read_csv('data/bus.csv')
geoplotlib.voronoi(data,
cmap='Blues_r',
max_area=1e5,
alpha=255,
f_tooltip=lambda d: d['name'])
geoplotlib.set_bbox(BoundingBox.DK)
geoplotlib.show()
import sys
import numpy
import geoplotlib
from geoplotlib.utils import read_csv, BoundingBox
from geoplotlib.colors import ColorMap
data = read_csv('data/out_FL.csv')
#TODO: apply filtering here rather than when making the csv
bb = BoundingBox.from_points(lons=data['lon'], lats=data['lat'])
#TODO: utilize layers
#mark the station locations
geoplotlib.dot(data, color=[0, 0, 0, 255])
#TODO: show based on zoom level
#geoplotlib.labels(data,'Station Name (LEA)', color=[150,150,190,255], font_size=9, anchor_x='center')
#geoplotlib.voronoi(data, cmap='Blues_r', max_area=8e3, alpha=200, f_tooltip=lambda d:d['Station Name (LEA)'] )
geoplotlib.kde(data, cmap='Blues_r', bw=10, cut_below=1e-4, scaling='lin')
#geoplotlib.delaunay(data,cmap='hot_r')
##post
geoplotlib.set_bbox(bb)
geoplotlib.set_smoothing(True)
geoplotlib.show()
"""
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, 5], cut_below=1e-6)
# lowering clip_above changes the max value in the color scale
#geoplotlib.kde(data, bw=[5,5], cut_below=1e-6, clip_above=1)
# different bandwidths
#geoplotlib.kde(data, bw=[20,20], cmap='coolwarm', cut_below=1e-6)
#geoplotlib.kde(data, bw=[2,2], cmap='coolwarm', cut_below=1e-6)
# linear colorscale
#geoplotlib.kde(data, bw=[5,5], cmap='jet', cut_below=1e-6, scaling='lin')
geoplotlib.set_bbox(BoundingBox.DK)
geoplotlib.show()
# Simple use of Geoplotlib
# Python v 3.6
# 5/15/2018
import geoplotlib
from geoplotlib.utils import read_csv
data = read_csv('data/mtb_ride_data.csv')
geoplotlib.dot(data)
geoplotlib.show()
f = open("VLOCs.csv", "w+")
f.close()
#write csv to be read by geoplotlib
with open('VLOCs.csv', mode='w', newline='') as VLOCs:
VLOCs = csv.writer(VLOCs, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
VLOCs.writerow(['name', 'S_lat', 'S_lon', 'D_lat', 'D_lon'])
for station in Locations:
VLOCs.writerow([station[0], station[1], station[2], station[3], station[4]])
#empty SLOCs.csv
f = open("SLOCs.csv", "w+")
f.close()
#write csv to be read by geoplotlib
with open('SLOCs.csv', mode='w', newline='') as SLOCs:
SLOCs = csv.writer(SLOCs, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
SLOCs.writerow(['name', 'lat', 'lon'])
for station in Locations:
SLOCs.writerow([station[0], station[1], station[2]])
#plot stations
Plotdata1 = read_csv('VLOCs.csv')
Plotdata2 = read_csv('SLOCs.csv')
gp.set_bbox(BoundingBox(north=9, west=110, south=1, east=95))
gp.graph(Plotdata1, 'S_lat', 'S_lon', 'D_lat', 'D_lon', linewidth=2, color='Blues')
gp.dot(Plotdata2, color='blue', point_size=3)
gp.labels(Plotdata2, 'name', color='black', font_size=8, anchor_x='center')
gp.tiles_provider('positron')
gp.show()
"""
Example of delaunay triangulation
"""
from geoplotlib.layers import DelaunayLayer
import geoplotlib
from geoplotlib.utils import read_csv, BoundingBox
data = read_csv('data/test_map2.csv')
geoplotlib.delaunay(data, cmap='hot_r')
geoplotlib.set_bbox(BoundingBox.DK)
geoplotlib.set_smoothing(True)
geoplotlib.show()
def __init__(self):
self.data = read_csv('data/taxi.csv')
self.cmap = colorbrewer(self.data['taxi_id'], alpha=220)
self.t = self.data['timestamp'].min()
self.painter = BatchPainter()
# The p-value in this case is 0.7716, which is Bigger than the standard thresholds of 0.05 or 0.01, so we accept the null hypothesis and we can say there isn't any statistically significant difference between the daily average per day of the week and the general daily average.
# # Geo-Plot : All crimes - 2017
# In[116]:
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]:
def __init__(self):
self.data = read_csv('SLOCs.csv')
self.cmap = colorbrewer(self.data['name'], alpha=220)
self.t = self.data['timestamp'].min()
self.painter = BatchPainter()
"""
Example of spatial graph
"""
import geoplotlib
from geoplotlib.utils import read_csv
data = read_csv('./data/flights.csv')
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()
def __init__(self):
self.data = read_csv('data/taxi.csv')
self.cmap = colorbrewer(self.data['taxi_id'], alpha=220)
self.t = self.data['timestamp'].min()
self.painter = BatchPainter()
"""
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='PuBuGn', 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.KBH)
geoplotlib.show()
)
schools = df.School
data = [go.Bar(x=df.School, y=df.Gap)]
py.iplot(data, filename='jupyter-basic_bar')
# # geoplotlib
# In[ ]:
import geoplotlib
from geoplotlib.utils import read_csv
data = read_csv('bus.csv')
geoplotlib.dot(data)
geoplotlib.show()
# # Direct plotting
# In[116]:
import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(200, 6),
index=pd.date_range('1/9/2009', periods=200),
columns=list('ABCDEF'))
df.plot(figsize=(20, 10)).legend(bbox_to_anchor=(1, 1))
from math import degrees
from geoplotlib.colors import colorbrewer
from geoplotlib.utils import epoch_to_str, BoundingBox, read_csv
if __name__ == "__main__":
path = sys.argv[1]
files = os.listdir(path)
stations = []
for file in files:
stations.append(parse_binary_llh(path + '/' + file)[0])
print(stations[-1].name)
file = open('tmp.csv', 'w')
file.write('name,lat,lon\n')
for stat in stations:
name, lon, lat = (stat.name, degrees(stat.pos[0]),
degrees(stat.pos[1]))
print(name, lon, lat)
file.write(','.join([name, str(lon), str(lat)]) + '\n')
file.close()
data = read_csv('tmp.csv')
print(type(data))
geoplotlib.delaunay(data, cmap="hot_r")
geoplotlib.labels(data,
'name',
color=[0, 0, 255, 255],
font_size=10,
anchor_x='center')
geoplotlib.show()
import geoplotlib
from geoplotlib.utils import read_csv
data = read_csv('test_map2.csv')
geoplotlib.dot(data)
geoplotlib.show()
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import geoplotlib
from geoplotlib.utils import read_csv, BoundingBox
import sys
args = sys.argv
if len(args) != 2:
sys.exit(f"usage: ./{args[0]} /path/to/.csv")
data = read_csv(args[1])
geoplotlib.kde(data, 5, cut_below=1e-4, show_colorbar=False)
geoplotlib.set_bbox(BoundingBox.USA)
geoplotlib.show()
"""
Example of voronoi layer with filled area
"""
import geoplotlib
from geoplotlib.utils import read_csv, BoundingBox
data = read_csv("data/bus.csv")
geoplotlib.voronoi(data, cmap="Blues_r", max_area=1e5, alpha=255, f_tooltip=lambda d: d["name"])
geoplotlib.set_bbox(BoundingBox.DK)
geoplotlib.show()
"""
Example of delaunay triangulation
"""
from geoplotlib.layers import DelaunayLayer
import geoplotlib
from geoplotlib.utils import read_csv, BoundingBox
data = read_csv('data/bus.csv')
geoplotlib.delaunay(data, cmap='hot_r')
geoplotlib.set_bbox(BoundingBox.DK)
geoplotlib.set_smoothing(True)
geoplotlib.show()
import geoplotlib
from geoplotlib.colors import colorbrewer
from geoplotlib.utils import epoch_to_str, BoundingBox, read_csv
data = read_csv('./data/metro.csv')
geoplotlib.dot(data, 'r')
geoplotlib.labels(data, 'name', color=[0,0,255,255], font_size=10, anchor_x='center')
geoplotlib.set_bbox(BoundingBox.KBH)
geoplotlib.show()
#!/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()
import geoplotlib
from geoplotlib.colors import colorbrewer
from geoplotlib.utils import epoch_to_str, BoundingBox, read_csv
data = read_csv('./data/metro.csv')
geoplotlib.dot(data, 'r')
geoplotlib.labels(data,
'name',
color=[0, 0, 255, 255],
font_size=10,
anchor_x='center')
geoplotlib.set_bbox(BoundingBox.KBH)
geoplotlib.show()
#Scipy example of curve fitting noisy data
np.random.seed(0)
x = np.linspace(-1, 1, 2000)
y = np.cos(x) + 0.3*np.random.rand(2000)
p = np.polynomial.Chebyshev.fit(x, y, 90)
t = np.linspace(-1, 1, 200)
plt.plot(x, y, 'r.')
plt.plot(t, p(t), 'k-', lw=3)
plt.show()
#geoplot example of mapping patterns according to flight paths
data = read_csv('flights.csv')
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()
# 3D plotting and visualization of signals using numpy
np.random.seed(1)
N = 70
#!/usr/bin/env python2
import geoplotlib
from geoplotlib.utils import read_csv, BoundingBox
data = read_csv('./network_density.csv')
geoplotlib.voronoi(data, line_color='b', line_width=1)
geoplotlib.set_bbox(BoundingBox.DK)
geoplotlib.show()
"""
Example of spatial graph
"""
import geoplotlib
from geoplotlib.utils import read_csv
data = read_csv('./data/flights.csv')
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()
"""
Example of rendering markers
"""
import geoplotlib
from geoplotlib.utils import read_csv
metro = read_csv('./data/metro.csv')
s_tog = read_csv('./data/s-tog.csv')
geoplotlib.markers(metro, 'data/m.png', f_tooltip=lambda r: r['name'])
geoplotlib.markers(s_tog, 'data/s-tog.png', f_tooltip=lambda r: r['name'])
geoplotlib.show()
#!/usr/bin/env python2
import geoplotlib
from geoplotlib.utils import read_csv
data = read_csv('ui_network_density.csv')
geoplotlib.dot(data)
geoplotlib.show()
import geoplotlib
from geoplotlib.colors import colorbrewer
from geoplotlib.utils import epoch_to_str, BoundingBox, read_csv
data = read_csv('agentTest.csv')
plot points
geoplotlib.dot(data, 'b')
# plot the heatmap
#geoplotlib.hist(data, colorscale='sqrt', binsize=4)
# geoplotlib.show()
