def oldmain(): from gprf.seismic.seismic_util import load_events from sigvisa.treegp.util import mkdir_p mkdir_p("scraped_events") s = load_events(basedir="/home/dmoore/mkar_stuff") outfile = open("fakescraped.txt", 'w') for i, (ev, (w, srate1)) in enumerate(s): try: #lon, lat, smaj, smin, strike, depth, depth_err = scrape_isc(ev) lon, lat, smaj, smin, strike, depth, depth_err = fakescrape(ev) except Exception as e: print e lon, lat, smaj, smin, strike, depth, depth_err = ev.lon, ev.lat, 20.0, 20.0, 0, ev.depth, 0.05 * ev.depth + 1.0 st = "%d, %d, %.4f, %.4f, %.1f, %.1f, %d, %.1f, %.1f" % ( i, ev.evid, lon, lat, smaj, smin, strike, depth, depth_err) print st outfile.write(st + "\n") outfile.flush()
print c if c > best_c: best_c = c best_widxs = widxs if max_s is not None: t1 = time.time() if t1-t0 > max_s: break return best_c, best_widxs fd = load_seismic_locations() lls = fd[:, [COL_LON, COL_LAT]] s = load_events(sta="mkar", basedir="/home/dmoore/mkar_stuff") from sklearn.cluster import KMeans np.random.seed(0) n_clusters = 4000 km = KMeans(n_clusters=n_clusters, init='k-means++', n_init=2, max_iter=300, tol=0.0001, precompute_distances='auto', verbose=1, random_state=None, copy_x=True, n_jobs=1) r = km.fit(lls) clusters = [] for i in range(n_clusters): idx = km.labels_==i lli =lls[idx,:] clusters.append(fd[idx, :]) if len(lli) > 100: print "cluster", i, "size", len(lli)
print c if c > best_c: best_c = c best_widxs = widxs if max_s is not None: t1 = time.time() if t1 - t0 > max_s: break return best_c, best_widxs fd = load_seismic_locations() lls = fd[:, [COL_LON, COL_LAT]] s = load_events(sta="mkar", basedir="/home/dmoore/mkar_stuff") from sklearn.cluster import KMeans np.random.seed(0) n_clusters = 4000 km = KMeans(n_clusters=n_clusters, init='k-means++', n_init=2, max_iter=300, tol=0.0001, precompute_distances='auto', verbose=1, random_state=None, copy_x=True, n_jobs=1) r = km.fit(lls)