def getMajor(curr_Mc,**kwargs):
"""
:param curr_Mc: low-cut magnitude
:return:
- aCluster: list of clusters
each cluster is a catalog contains a major shock and its offsprings
'sel_p','sel_c' is used to select related NND data in aNND
- aNND: nearest neighbor distance
'aEqID_p': parent's ID of the pair
'aEqID_c': child's ID of the pair
"""
import numpy as np
import os
# ------------------------------my modules--------------------------------------
import sys
sys.path.append('/auto/home/lhuang/PycharmProjects/clustering-analysis-master')
import src.data_utils as data_utils
eqCat = EqCat() # original catalog
eqCatMc = EqCat() # this catalog will be modified with each Mc iteration
# =================================1==============================================
# dir, file, params
# ================================================================================
data_dir = './data' # Todo: .. or .
file_in = 'hs_1981_2018_all.mat'
eqCat.loadMatBin(os.path.join(data_dir, file_in))
eqCat.toCart_coordinates()
eqCatMc.copy(eqCat)
if 'min' in kwargs.keys():
min = kwargs['min']
else:
min = 6.0
if 'max' in kwargs.keys():
max = kwargs['max']
else:
max = None
eqCatMc.selectEvents(min,max,'Mag')
# load nearest neighbor distances
NND_file = './data/%s_NND_Mc_%.1f_HD.mat' % (file_in.split('.')[0], curr_Mc)# Todo: .. or .
dNND = data_utils.loadmat(NND_file) # , struct_as_record=True)
aCluster = np.array([])
for i in list(range(eqCatMc.size())):
cat = EqCat()
cat.copy(eqCat)
sel_c = dNND['aEqID_p'] == eqCatMc.data['N'][i]
sel_p = dNND['aEqID_c'] == eqCatMc.data['N'][i]
sel = np.logical_or(sel_p,sel_c)
cat.selEventsFromID(dNND['aEqID_c'][sel],repeats=True)
cat.data['sel_p'] = sel_p
cat.data['sel_c'] = sel_c
aCluster=np.append(aCluster,cat)
print("major earthquake:%.1f"%cat.data['Time'][0],"mag:",cat.data['Mag'][0])
print("Total Ms: %d" % aCluster.shape[0])
return aCluster,dNND
#eqCat.selectEvents( tmin, tmax, 'Time')
print( 'no. of events after initial selection', eqCat.size())
iMc = 0
for f_Mc in dPar['a_Mc']:
eta_0_file = '%s/%s_Mc_%.1f_eta_0.txt'%(data_dir, file_in, f_Mc)
# load eta_0 value
if os.path.isfile( eta_0_file):
print( 'load eta_0 from file'),
f_eta_0 = np.loadtxt( eta_0_file, dtype = float)
print( f_eta_0)
else:
print( 'could not find eta_0 file', eta_0_file, 'use value from dPar', dPar['eta_0'])
f_eta_0 = dPar['eta_0']
# cut below current completeness
eqCatMc.copy( eqCat)
eqCatMc.selectEvents( f_Mc, None, 'Mag')
print( 'current catalog size: ',eqCatMc.size())
# load nearest neighbor distances
NND_file = '%s_NND_Mc_%.1f.mat'%(os.path.basename( file_in).split('.')[0], f_Mc)
dNND = dataIO.loadmat( os.path.join( data_dir, NND_file))
print dNND.keys()
dNND['aNND'] = np.log10( dNND['aNND'])
#==================================3=============================================
# "declustering" step
#================================================================================
#catChild, catPar = create_parent_child_cat( projCat, dNND)
catChild.copy( eqCat)
catParent.copy( eqCat)
catChild.selEventsFromID( dNND['aEqID_c'], eqCatMc, repeats = True)
catParent.selEventsFromID( dNND['aEqID_p'], eqCatMc, repeats = True)
# load data, select events
#================================================================================
eqCat.loadMatBin(os.path.join(data_dir, file_in))
print('total no. of events', eqCat.size())
eqCat.selectEvents(dPar['a_Mc'][0], None, 'Mag')
#eqCat.selectEvents( tmin, tmax, 'Time')
print('no. of events after initial selection', eqCat.size())
iMc = 0
for f_Mc in dPar['a_Mc']:
# load file with IDs of events within family
clust_file = file_in.replace('all.mat', 'Mc_%.1f_clusters.mat' % (f_Mc))
dClust = data_utils.loadmat(os.path.join(data_dir, clust_file), )
# cut below current completeness
eqCatMc.copy(eqCat)
eqCatMc.selectEvents(f_Mc, None, 'Mag')
n_aboveMc = eqCatMc.size()
print('current catalog size: ', eqCatMc.size())
#=================================1==========================================================================
# singles are counted as MS with 0 AS
#============================================================================================================
print 'total number of clusters', len(
dClust.keys()), 'no. of BG events', dClust['0'].shape[0]
a_ID_single = dClust['0']
# IDs of BG events
a_iSel = np.zeros(eqCatMc.size(), dtype=int)
a_mag_single = np.zeros(len(a_ID_single))
a_N_AS_single = np.zeros(len(a_ID_single))
}
#================================================================================
# load data, event selection
#================================================================================
eqCat.loadMatBin( os.path.join( dir_in, file_in))
print( 'total no. of events', eqCat.size())
eqCat.selectEvents( dPar['aMc'][0], None, 'Mag')
#eqCat.selectEvents( tmin, tmax, 'Time')
print( 'no. of events after initial selection', eqCat.size())
# project to equi-distant coordiante system for cartesian distances
eqCat.toCart_coordinates( projection = 'eqdc')#'eqdc')
for f_Mc in dPar['aMc']:
print( '-------------- current Mc:', f_Mc, '---------------------')
# select magnitude range
eqCatMc.copy( eqCat)
eqCatMc.selectEvents( f_Mc, None, 'Mag')
print( 'catalog size after MAG selection', eqCat.size())
# this dictionary is used in module: clustering
dConst = {'Mc' : f_Mc,
'b' : dPar['b'],
'D' : dPar['D']}
#=============================2===================================================
# randomize catalog
#=================================================================================
a_Eta_0 = np.zeros( dPar['nBoot'])
for i_Bs in range( dPar['nBoot']):
ranCat.copy( eqCatMc)
ranCat.data['X'] = np.random.uniform( eqCatMc.data['X'].min(), eqCatMc.data['X'].max(), size = eqCatMc.size())
def getLanders():
eqCat = EqCat() # original catalog
eqCatMc = EqCat() # this catalog will be modified with each Mc iteration
catLanders = EqCat()
catCoso = EqCat()
# =================================1==============================================
# dir, file, params
# ================================================================================
data_dir = '../data'
plot_dir = '../plots'
file_in = 'hs_1981_2011_all.mat'
dPar = {
'a_Mc': np.array([3.0, 4.0]), # np.array( [2.0, 2.5, 3.0, 3.5]),
# separate clustered and background
'eta_0': -5.0,
'testPlot': True,
'D':
1.6, # 1.6 TODO: - these values should be contrained independently
'b': 1.0,
}
curr_Mc = dPar['a_Mc'][0]
# =================================2==============================================
# load data, select events
# ================================================================================
eqCat.loadMatBin(os.path.join(data_dir, file_in))
print('total no. of events', eqCat.size())
eqCat.selectEvents(curr_Mc, None, 'Mag')
#eqCat.toCart_coordinates()
# eqCat.selector( tmin, tmax, 'Time')
print('no. of events after initial selection', eqCat.size())
catLanders.copy(eqCat)
catCoso.copy(eqCat)
catLanders.selectEvents(7.0, None, 'Mag')
catLanders.selectEvents(34, 35, 'Lat')
catLanders.selectEvents(-117, -116, 'Lon')
catLanders.selectEvents(1992, 1993, 'Time')
#print("===========Landers Info============\n",catLanders.data)
catCoso.selectEvents(catLanders.data['Time'][0],
catLanders.data['Time'][0] + 0.1, 'Time')
catCoso.selectEvents(-118.5, -117, 'Lon')
catCoso.selectEvents(35.5, 36.5, 'Lat')
#print("===========Coso Info===============\nLon\tLat\tMag\tTime\t")
#for lon,lat,mag,time in zip(catCoso.data['Lon'],catCoso.data['Lat'],catCoso.data['Mag'],catCoso.data['Time']):
# print("%.3f\t%.3f\t%.2f\t%.8f\t"%(lon,lat,mag,time))
aEta = np.array([])
aT = np.array([])
aR = np.array([])
catAll = EqCat()
catAll.merge(catLanders, catCoso)
catAll.toCart_coordinates()
#print(catAll.data)
for x, y, time in zip(catAll.data['X'][1:], catAll.data['Y'][1:],
catAll.data['Time'][1:]):
x = catAll.data['X'][0]
y = catAll.data['Y'][0] + 3
t = (time - catAll.data['Time'][0]) * 10**(-dPar['b'] *
catAll.data['Mag'][0] / 2)
print("distance:", ((x - catAll.data['X'][0])**2 +
(y - catAll.data['Y'][0])**2)**0.5)
r = ((x - catAll.data['X'][0])**2 + (y - catAll.data['Y'][0])**2)**(
dPar['D'] / 2) * 10**(-dPar['b'] * catAll.data['Mag'][0] / 2)
eta = r * t
aEta = np.append(aEta, np.log10(eta))
aT = np.append(aT, np.log10(t))
aR = np.append(aR, np.log10(r))
print("===========Neareast Neighbor Distance===============\nr\tt\teta\t")
for r, t, eta in zip(
aR, aT, aEta
): #(catCoso.data['Time']-catLanders.data['Time'][0])*365.25*24*3600
print("%f\t%f\t%f" % (r, t, eta))
return catAll, aT, aR, aEta
# NND_file = 'data/%s_NND_Mc_%.1f_HD.mat'%( file_in.split('.')[0], curr_Mc)
dNND = data_utils.loadmat(NND_file) # , struct_as_record=True)
# load Landers/Joshua Tree earthquake catalog
cat_file = 'data/cat_%s.mat' % name
eqCat.loadMatBin(cat_file)
# ================================================================================
# calculate interval time for each pair
# ================================================================================
adTime = eqCat.data['Time'][1:]-eqCat.data['Time'][0]
aNND = dNND['aNND']
sel_cluster = np.log10(dNND['aNND']) <= -5
aClTime = adTime[sel_cluster]
adNND = aNND[sel_cluster] + 5
cluster = EqCat()
cluster.copy(eqCat)
print('all clustering aftershocks:',len(aClTime))
if len(aClTime)<=200:
print("***************************************************")
print("%s, insufficient offsprings." % name)
else:
print("***************************************************")
print("****%s, sufficient offsprings." % name)
sel_time = aClTime <= 3/365.25
sel_time1 = aClTime > 3/365.25
aClTime = aClTime[sel_time]
aClTime *= 365.25*24 # --> hr
print('min time interval:',min(aClTime),'max time interval:' ,max(aClTime))
print('earthquakes within 72h:',len(aClTime))
# ================================================================================
# plot map