def setUp(self):
"""
Read the sample catalogue
"""
flnme = 'afteran_test_catalogue.csv'
filename = os.path.join(self.BASE_DATA_PATH, flnme)
parser = CsvCatalogueParser(filename)
self.cat = parser.read_file()
self.dec = Afteran()
class AfteranTestCase(unittest.TestCase):
"""
Unit tests for the Afteran declustering algorithm class.
"""
BASE_DATA_PATH = os.path.join(os.path.dirname(__file__), 'data')
def setUp(self):
"""
Read the sample catalogue
"""
flnme = 'afteran_test_catalogue.csv'
filename = os.path.join(self.BASE_DATA_PATH, flnme)
parser = CsvCatalogueParser(filename)
self.cat = parser.read_file()
self.dec = Afteran()
def test_dec_afteran(self):
"""
Testing the Afteran algorithm
"""
config = {
'time_distance_window' : GardnerKnopoffWindow(),
'time_window' : 60.,
}
# Instantiate the declusterer and process the sample catalogue
#self.dec = Afteran()
print dir(self.dec)
vcl, flagvector = self.dec.decluster(self.cat, config)
print 'vcl:',vcl
print 'flagvector:',flagvector, self.cat.data['flag']
self.assertTrue(np.allclose(flagvector,self.cat.data['flag']))
def test_find_aftershocks(self):
'''
Tests the find aftershocks function
'''
# Test when aftershocks are in array
year_dec = np.array([0.10, 0.20, 0.5, 0.60, 0.80, 1.2])
vsel = np.array([3, 4, 5])
time_window = 0.25
expected_result = (np.array([False, False, False, True, True, False]),
True)
model_result = self.dec._find_aftershocks(vsel, year_dec, 0.25, 2, 6)
self.assertTrue(np.all(expected_result[0] == model_result[0]))
self.assertTrue(model_result[1])
# Test when no aftershocks are found - reduce window to < 0.1
expected_result = (
np.array([False, False, False, False, False, False]), False)
model_result = self.dec._find_aftershocks(vsel, year_dec, 0.09, 2, 6)
self.assertTrue(np.all(expected_result[0] == model_result[0]))
self.assertFalse(model_result[1])
def test_find_foreshocks(self):
'''
Tests the find_foreshocks function
'''
# Test when aftershocks are in array
year_dec = np.array([0.10, 0.40, 0.5, 0.60, 0.80, 1.2])
vsel = np.array([0, 1])
time_window = 0.25
expected_result = (
np.array([False, True, False, False, False, False]), True)
model_result = self.dec._find_foreshocks(vsel, year_dec, 0.25, 2, 6)
self.assertTrue(np.all(expected_result[0] == model_result[0]))
self.assertTrue(model_result[1])
# Test when no aftershocks are found - reduce window to < 0.1
expected_result = (
np.array([False, False, False, False, False, False]), False)
model_result = self.dec._find_foreshocks(vsel, year_dec, 0.09, 2, 6)
self.assertTrue(np.all(expected_result[0] == model_result[0]))
self.assertFalse(model_result[1])
def decluster_catalogue(catalogue, config):
###
### Catalogue cache or read/cache
###
# Set up the declustering algorithm
# Step 1 - set-up the tool
if config['decluster_method'] == 'afteran':
decluster_method = Afteran()
elif config['decluster_method'] == 'gardner_knopoff':
decluster_method = GardnerKnopoffType1()
else:
print "invalid decluster_method configuration: use [afteran|gardner_knopoff]"
return None
print 'Running declustering ...'
cluster_vector, flag_vector = decluster_method.decluster(catalogue, config)
print 'done!'
print '%s clusters found' % np.max(cluster_vector)
print '%s Non-poissionian events identified' % np.sum(flag_vector != 0)
if config['plot']:
###
### Map Config
###
map_dpi = 90
add_geology = True
add_sourcemodel = True
savefig=False
#map_title = 'Brazilian Seismic Zones'
map_title = 'Clusters'
#map_title = 'ISC-GEM Catalogue'
#map_title = 'South-American Lithology'
# Configure the limits of the map and the coastline resolution
map_config = {'min_lon': -80.0, 'max_lon': -30.0, 'min_lat': -37.0, 'max_lat': 14.0, 'resolution':'l'}
#map_config = {'min_lon': -72.0, 'max_lon': -68.0, 'min_lat': -22.0, 'max_lat': -18.0, 'resolution':'l'}
#map_config = {'min_lon': -95.0, 'max_lon': -25.0, 'min_lat': -65.0, 'max_lat': 25.0, 'resolution':'l'}
basemap = HMTKBaseMap(map_config, map_title, dpi=map_dpi)
#basemap.add_catalogue(catalogue, linewidth=0.2, alpha=0.1, overlay=True)
idx = cluster_vector != 0
x = catalogue.data['longitude'][idx]
y = catalogue.data['latitude'][idx]
c = cluster_vector[idx]
basemap.add_colour_scaled_points(x, y, c,
overlay=True,
shape='s', alpha=0.5, size=100,
linewidth=0.5, facecolor='none',
cmap=plt.cm.get_cmap('Paired'),
)
plt.show()
if config['figname']:
basemap.savemap(config['figname'])
print 'Original catalogue had %s events' % catalogue.get_number_events()
catalogue.select_catalogue_events(flag_vector == 0)
print 'Purged catalogue now contains %s events' % catalogue.get_number_events()
if config['filename']:
writer = CsvCatalogueWriter(config['filename'])
writer.write_file(catalogue)
return catalogue
declust_config = {
'time_distance_window': GardnerKnopoffWindow(),
'fs_time_prop': 1.0
}
print declust_config
print 'Running declustering ...'
vcl, flag_vector = gardner_knopoff.decluster(catalogue, declust_config)
print 'done!'
print '%s clusters found' % np.max(vcl)
print '%s Non-poissionian events identified' % np.sum(flag_vector != 0)
# In[ ]:
# Setup the algorithm
afteran = Afteran()
declust_config = {
'time_distance_window': GardnerKnopoffWindow(),
'time_window': 60.
}
print 'Running Afteran ...'
# Run Afteran
vcl2, flag_vector2 = afteran.decluster(catalogue, declust_config)
print 'done!'
print '%s clusters found' % np.max(vcl2)
print '%s Non-poissionian events identified' % np.sum(flag_vector2 != 0)
# In[ ]:
catalogue.select_catalogue_events(flag_vector == 0)
print 'Purged catalogue now contains %s events' % catalogue.get_number_events()
print 'Running declustering ...'
vcl, flag_vector = gardner_knopoff.decluster(catalogue, declust_config)
print 'done!'
print '%s clusters found' % np.max(vcl)
print '%s Non-poissionian events identified' % np.sum(flag_vector != 0)
# In[ ]:
# Setup the algorithm
afteran = Afteran()
declust_config = {'time_distance_window': GardnerKnopoffWindow(),
'time_window': 60.}
print 'Running Afteran ...'
# Run Afteran
vcl2, flag_vector2 = afteran.decluster(catalogue, declust_config)
print 'done!'
print '%s clusters found' % np.max(vcl2)
print '%s Non-poissionian events identified' % np.sum(flag_vector2 != 0)
# In[ ]:
catalogue.select_catalogue_events(flag_vector == 0)
print 'Purged catalogue now contains %s events' % catalogue.get_number_events()