def show_google_map(paths, API_key, region):
lines = []
for f in pbar()(paths.fragments):
flines = []
for l in f:
line_coords = np.r_[list(l.coords.xy)].T
for i in range(len(line_coords) - 1):
flines.append(
gmaps.Line(start=tuple(line_coords[i][::-1]),
end=tuple(line_coords[i + 1][::-1])))
lines.append(flines)
lines = flatten(lines)
print "found", len(lines), "line segments"
markers = []
for o, f in pbar()(zip(flatten(paths.resampled_orientations),
flatten(paths.resampled_fragments))):
coords = np.r_[list(f.xy)].T
markers.append([
gmaps.Marker((coords[i][1], coords[i][0]),
info_box_content=str(o[i]))
for i in range(len(coords))
])
markers = flatten(markers)
print "found", len(markers), "sampling locations"
gmaps.configure(api_key=API_key)
gmap_b = gmaps.Polygon([(i[1], i[0]) for i in region])
fig = gmaps.figure(center=tuple(region.mean(axis=0)[::-1]), zoom_level=16)
fig.add_layer(gmaps.drawing_layer(features=[gmap_b] + lines + markers))
return fig
def plot_route_map(self, freq):
station_pairs = [sorted(i) for i in (self.route_dis.index)]
bs = self.bikesystem
fig = gmaps.figure(center=tuple(bs.locate(bs.mp.index[0])),
zoom_level=11)
layer = gmaps.drawing_layer(features=[
gmaps.Line(start=tuple(bs.locate(i)), end=tuple(bs.locate(j)))
for i, j in self.highfreq_routes(freq)
])
fig.add_layer(layer)
return fig
def predictSingle(self, imgFile, dataDir, ployGrid=None):
'''
Predicts softmax ouput by trained model for single image and plots it
imgFile has to look like:
# eg: <gridNo>+<lat,long>+<imageNo_date>.jpg
# eg: 60+48.4271513,-110.5611851+0_2009-06.jpg
'''
xx,yy = self.readData([imgFile], dataDir)
yp = self.model.predict(xx)[0]
yn = list(map(lambda x:x/max(yp), yp))
dist, start, end = self.gridDist(ployGrid[np.argmax(yy[0])],ployGrid[np.argmax(yp)])
if ployGrid:
mx = max(yn)
mn = min(yn)
plt.plot([start[1],end[1]], [start[0],end[0]], color='black',
label="Distance: {} miles".format(round(dist,3)))
for k,i in ployGrid.items():
if k==np.argmax(yy[0]):
plt.plot(i[:,1],i[:,0],color='blue',label="Actual Grid", alpha=1)
else:
plt.plot(i[:,1],i[:,0],color='black', alpha=0.7)
plt.fill(i[:,1],i[:,0],color='red', alpha=yn[k])
plt.legend(loc="lower left")
plt.show()
gPoly = []
gLine = gmaps.Line(
start=start,
end=end,
stroke_color = 'blue'
)
for grid, polygon in ployGrid.items():
gPoly.append(gmaps.Polygon(
list(polygon),
stroke_color='black',
fill_color='red',
fill_opacity=float(yn[grid])
))
fig = gmaps.figure(center=(39.50,-98.35), zoom_level=4)
fig.add_layer(gmaps.drawing_layer(features=gPoly))
fig.add_layer(gmaps.drawing_layer(features=[gLine]))
fig.add_layer(gmaps.symbol_layer([start], scale=3,
fill_color='green',stroke_color='green', info_box_content='Expected'))
fig.add_layer(gmaps.symbol_layer([end], scale=3,
fill_color='yellow', stroke_color='yellow',
info_box_content='Predicted: {}'.format(dist)))
embed_minimal_html('gmap.html', views=fig)
webbrowser.open('gmap.html',new=1)
return dist
def map_plot(idx, d):
line_coords = list(
map(lambda i: polylines[i[0]][i[1]], list(zip(idx[:-1], idx[1:]))))
# Features to draw on the map
lines = []
markers = []
for points in line_coords:
for p1, p2 in zip(points[:-1], points[1:]):
lines.append(gmaps.Line(start=p1, end=p2, stroke_weight=3.0))
for i, p in enumerate(map(lambda x: candidate_stops[x], idx)):
markers.append(gmaps.Marker(p))
d.features = lines + markers
def route(start, end, fig, color, stroke_weight_line, mode='walking'):
directions_result = google_maps.directions(start, end, mode=mode)
polyline_directions = polyline.decode(
directions_result[0]['overview_polyline']['points'])
polyline_result = [
(a, b) for a, b in zip(polyline_directions, polyline_directions[1:])
]
for i in range(len(polyline_result)):
line_drawing = gmaps.Line(start=polyline_result[i][0],
end=polyline_result[i][1],
stroke_weight=stroke_weight_line,
stroke_color=color,
stroke_opacity=1.0)
drawing = gmaps.drawing_layer(features=[line_drawing],
show_controls=False)
fig.add_layer(drawing)
def predictSingle(self, imgFile, dataDir, ployGrid):
'''
Predicts softmax ouput by trained model for single image and plots it
mgFiles: String that contains test location image triplet folder name. String has to look like:
# <gridNo>+<lat,long>
# 60+48.4271513,-110.5611851
dataDir: Directory that stores combined image files eg: "/dataCombinedSamples/"
polyGrid: List of polygons that contain make up the USA split into grids.
It can be loaded from eg: "infoExtraction/usaPolyGrid.pkl"
'''
# read image triplets from single file
xx, yy = self.readData([imgFile], dataDir)
# predict single image triplet
yp = self.model.predict(xx)[0]
# normalize prediction for better visualization
yn = list(map(lambda x: x / max(yp), yp))
# evaluate distance for single point
dist, start, end = self.gridDist(ployGrid[np.argmax(yy[0])],
ployGrid[np.argmax(yp)])
mx = max(yn)
mn = min(yn)
# plot result using matplotlib
plt.plot([start[1], end[1]], [start[0], end[0]],
color='black',
label="Distance: {} miles".format(round(dist, 3)))
for k, i in ployGrid.items():
if k == np.argmax(yy[0]):
plt.plot(i[:, 1],
i[:, 0],
color='blue',
label="Actual Grid",
alpha=1)
else:
plt.plot(i[:, 1], i[:, 0], color='black', alpha=0.7)
plt.fill(i[:, 1], i[:, 0], color='red', alpha=yn[k])
plt.legend(loc="lower left")
plt.show()
# plot result using google maps API
gPoly = []
gLine = gmaps.Line(start=start, end=end, stroke_color='blue')
for grid, polygon in ployGrid.items():
gPoly.append(
gmaps.Polygon(list(polygon),
stroke_color='black',
fill_color='red',
fill_opacity=float(yn[grid])))
fig = gmaps.figure(center=(39.50, -98.35), zoom_level=4)
fig.add_layer(gmaps.drawing_layer(features=gPoly))
fig.add_layer(gmaps.drawing_layer(features=[gLine]))
fig.add_layer(
gmaps.symbol_layer([start],
scale=3,
fill_color='green',
stroke_color='green',
info_box_content='Expected'))
fig.add_layer(
gmaps.symbol_layer([end],
scale=3,
fill_color='yellow',
stroke_color='yellow',
info_box_content='Predicted: {}'.format(dist)))
# save and display html page containing google maps API
embed_minimal_html('gmap.html', views=fig)
webbrowser.open('gmap.html', new=1)
return dist
#%%
# Charting Stuff on a Map
with open("./secrets.json") as f:
data = f.read()
api_key = json.loads(data)['key']
gmaps.configure(api_key=api_key)
fig = gmaps.figure()
fig.add_layer(gmaps.heatmap_layer(ems[['Location Latitude', 'Location Longitude']], weights=ems['Total Patients']))
fig
#%%
fig = gmaps.figure()
drawing = gmaps.drawing_layer(features=[
gmaps.Line((46.23, 5.86), (46.44, 5.24), stroke_weight=3.0),
gmaps.Marker((46.88, 5.45), label='D'),
gmaps.Polygon(
[(46.72, 6.06), (46.48, 6.49), (46.79, 6.91)],
fill_color='red'
)
])
fig.add_layer(drawing)
fig
#%%
gmaps.__version__
#%%