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