def apply_decluster_smaller(self):
"""
apply window method to a smaller catalog and write mainshocks on file
"""
# get instances of classes we'll need
catalog = Catalog()
window_var = WindowVar()
# obtain a smaller catalog, so we can run this function faster
catalog.get_smaller_catalog(300)
# from the catalog we want, get earthquakes array on memory
earthquake_array = catalog.get_earthquake_array()
# decluster array, separating mainshocks and aftershocks
declustered_array = window_var.decluster(earthquake_array)
# save declustered array using pickle
file_aux1 = open('data_persistence/declustered_array_window_var', 'wb')
pickle.dump(declustered_array, file_aux1)
# open declustered array using another name
file_aux2 = open('data_persistence/declustered_array_window_var', 'rb')
new_declustered_array = pickle.load(file_aux2)
# recover declustered array using pickle
# record the mainshocks on a catalog
catalog.record_mainshocks(new_declustered_array, file_write='../results/window_var_method/mainshocks.txt',
file_read='../catalogs/reduced_jma.txt')
def apply_decluster_smaller(self):
"""
apply window method to a smaller catalog and write mainshocks on file
"""
# get instances of classes we'll need
catalog = Catalog()
kmeans = KMeans()
# obtain a smaller catalog, so we can run this function faster
catalog.get_smaller_catalog(300)
# from the catalog we want, get earthquakes array on memory
earthquake_array = catalog.get_earthquake_array()
# decluster array, separating mainshocks and aftershocks
declustered_array = kmeans.do_kmeans(earthquake_array, 25)
# record the mainshocks on a catalog
catalog.record_mainshocks(declustered_array, file_write='../results/mainshocks.txt', file_read='../catalogs/reduced_jma.txt')
def compare_window_kmeans(num_entries):
"""
receives a number of entries
the function applies to a catalog with that number of entries
the window method and the kmeans to decluster the catalog
the function returns a comparation, showing how much there is
of a difference between the two
Complexity: O(n^2)
"""
# obtain a smaller catalog
catalog = Catalog()
catalog.get_smaller_catalog(num_entries)
# get earthquake array of that catalog
quakes = catalog.get_earthquake_array()
# get declustered array of that catalog, according to the window method
window = Window()
window_quakes = window.decluster(quakes)
# get the number of mainshocks of that catalog, according to the kmeans clustering method
num_mainshocks = 0
for i in range(len(window_quakes)):
if window_quakes[i].is_aftershock == False:
num_mainshocks += 1
print(num_mainshocks)
# apply declustering using the kmeans method to the catalog
kmeans = KMeans()
kmeans_quakes = kmeans.do_kmeans(quakes, num_mainshocks)
# show what are the differences between both methods
for i in range(len(quakes)):
if window_quakes[i].is_aftershock != kmeans_quakes[i].is_aftershock:
print("found a difference!")
# obtain declustered catalog for Tohoku
bounds = catalog.get_tohoku_bounds()
catalog.select_geographically(bounds, '../results/mainshocks.txt', '../results/mainshocks_tohoku.txt')
#### TESTS AND SIMULATIONS - just comment them out ####
# simulation to see how many earthquakes there are in a given range of magnitudes - TESTED
#sim = CatalogSimulation()
#sim.simple_report()
# see if it's possible to represent all Earthquakes on the catalog in memory - TESTED
#catalog = Catalog()
#catalog.get_earthquake_array()
# obtain a smaller catalog, will be good for preliminar tests - TESTED
catalog = Catalog()
catalog.get_smaller_catalog(40)
# see if we can obtain a catalog which selects the regions geographically - TESTED
#catalog = Catalog()
#bounds = [120.0, 130.0, 30.0, 40.0]
#catalog.select_geographically(bounds, '../results/mainshocks.txt', '../results/stricted_mainshocks.txt')
# get declustered catalogs for Yuri - TESTED
#catalog = Catalog()
#sim = CatalogSimulation()
#sim.help_yuri(catalog)
# obtain a catalog of only mainshocks - NOT TESTED
#catalog = Catalog()
#catalog.get_mainshocks()
