def plot_location_node(locs, labels):
mymap = gmp.from_geocode("New York")
clist = color_dicts.html_color_codes.keys()
for cur_label in range(np.max(labels)):
print("cur_label", cur_label)
path = [[], []]
color = ''
for i in range(len(locs)):
loc = locs[i]
cluster_label = labels[i]
assert (loc != 'not applicable')
if cluster_label == cur_label:
# print("Color", clist[cluster_label])
color = clist[cluster_label]
path[0].append(float(loc.split(' ')[0]))
path[1].append(-float(loc.split(' ')[2]))
edge = [tuple(path[0]), tuple(path[1])]
# mymap.heatmap(edge[0], edge[1], threshold=5, radius=40)
mymap.scatter(edge[0], edge[1], c=color, s=200, marker=False, alpha=1)
mymap.draw("cluster_map.html")
def __init__(self, location, file_path):
self._file_path = file_path
self._gmap = GoogleMapPlotter.from_geocode(location)
self._marker_list = []
self._jpg_paths = []
self._per_dir_jpg_paths = {}
longitude = float(DB[tweet]['place'][u'bounding_box'][u'coordinates'][0][0][0])
latitude = float(DB[tweet]['place'][u'bounding_box'][u'coordinates'][0][0][1])
if (category == 'PO'):
po_lon.append(longitude)
po_lat.append(latitude)
elif category == 'PL':
pl_lon.append(longitude)
pl_lat.append(latitude)
po_scatter_path = (po_lat,po_lon)
pl_scatter_path = (pl_lat,pl_lon)
if (city is 'US'):
mymap = GoogleMapPlotter(39.908213, -99.675441, 4)
else:
mymap = GoogleMapPlotter.from_geocode(city, 12)
lat, lng = mymap.geocode(city)
for group in ultralist:
drawGroupBox(group,mymap)
mymap.scatter(po_scatter_path[0], po_scatter_path[1], c='tomato', marker=True)
mymap.scatter(pl_scatter_path[0], pl_scatter_path[1], c='lemonchiffon', marker=True)
mymap.draw('./mymap.html')
webbrowser.open(tweetMap)
if (searchType == 'real'):
startDate = raw_input('Enter todays date: ')
while True:
for tweet in DB:
for c in cameras:
count, density = gen.generate_features(c)
traffic_stat = clf.predict([count, density])
if traffic_stat == 2:
print(c, ' : High traffic')
elif traffic_stat == 1:
print(c, ' : Medium traffic')
else:
print(c, ' : Low traffic')
traffic.append(traffic_stat)
count = [traffic.count(0), traffic.count(1), traffic.count(2)]
status = count.index(max(count))
color = ['green', 'yellow', 'red']
gmap = gmp.from_geocode(
"Veermata Jijabai Technological Institute, Mumbai, Maharashtra")
gmap.scatter([19.018892, 19.024714], [72.855786, 72.856964],
color[status],
size=40,
marker=False)
gmap.scatter(
[19.019693, 19.020433, 19.021336, 19.022432, 19.024055, 19.023030],
[72.856173, 72.856409, 72.856570, 72.856763, 72.857053, 72.856914],
color[status],
size=10,
marker=False)
##this will generate html file of google map
gmap.draw("map.html")
def ua_map(datax, datay, sales):
ua_map = GoogleMapPlotter.from_geocode("Ukraine", 7)
for (x, y, s) in zip(datax, datay, sales):
ua_map.scatter([x], [y], c='#%02x%02x%02x' % convert_to_rgb(s+1, maxval=10), size=1000+10*s, marker=False, alpha=0.5)
#ua_map.heatmap(datax, datay, radius=40)
ua_map.draw("mymap.html")
xy = X[class_member_mask & ~core_samples_mask]
plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=col,
markeredgecolor='k', markersize=2)
plt.title('Estimated number of clusters: %d, %d' % (n_clusters_, len(labels[labels == -1])))
plt.show()
print('Singled: %d, max: %d' % (data.groupby(by='cluster').count().query('Customer_Code == 1').count()[0], data.groupby(by='cluster').count().max()[0]))
return data, last_index
#Кластеризуем дважды, при этом запоминаем номер последнего кластера, чтобы кластера не пересекались
kiev_loc, index = cluster(kiev_loc)
kiev_loc_singles = kiev_loc[kiev_loc.cluster.isin(kiev_loc.groupby(by='cluster').count().query('Customer_Code == 1').reset_index()['cluster'])]
kiev_loc_singles, index = cluster(kiev_loc_singles, e=0.00441, cluster_last_index=index)
kiev_loc.update(kiev_loc_singles)
kiev_loc.cluster = kiev_loc.cluster.astype(int)
print('Result: Singled: %d, max: %d' % (kiev_loc.groupby(by='cluster').count().query('Customer_Code == 1').count()[0], kiev_loc.groupby(by='cluster').count().max()[0]))
#points are near each to other?
kiev_loc.groupby(by='cluster').agg(np.std).plot()
ua_map = GoogleMapPlotter.from_geocode("Ukraine, Kiev", 12)
for (code, x, y, cluster) in kiev_loc.as_matrix():
ua_map.scatter([y], [x], c='#%02x%02x%02x' % convert_to_rgb(cluster, minval=kiev_loc.cluster.min(), maxval=kiev_loc.cluster.max()), size=50, marker=False, alpha=0.5)
ua_map.draw("clustered.html")
#50.41916499 30.51916459