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)
