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
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)
print 'tot. ev', eqCatMc.size(), 'parents', np.unique( catParent.data['N']).shape[0], 'children', np.unique( catChild.data['N']).shape[0]
#==================================4=============================================
# spanning tree
#================================================================================
plt.figure( 1)
plot_file = 'plots/k_r_distr_hist.png' #TODO:'plots/k_r_distr_rate_smoothed.png','plots/k_r_distr_rate.png'
dPar = {'Mc':2.5,
'minMag':4.0, # minimum magnitude of mainshocks
'maxMag':8.0, # maximum magnitude of mainshocks
'k': 2,
'HD_binsize':0.1,
'lambda_binsize':0.08,
# fitting coefficient
'n':-1.35, 'c':1.0,
'q':0.35, 'd':1.2,'gamma':0.6,
}
# =================================1==============================================
# load catalog and select
# ================================================================================
dCluster = data_utils.loadmat(input_file)
a_MS_mag = dCluster['a_MS_mag']
a_AS_dist = dCluster['a_AS_dist']
### use different colors to discriminate different sets
index = np.arange(0,len(a_MS_mag),1)
norm=co.Normalize(vmin=0,vmax=len(a_MS_mag)-1)
cmap=plt.cm.RdYlGn
pointcolors = plt.cm.ScalarMappable(norm, cmap)
cols = pointcolors.to_rgba(index)
plt.figure()
ax = plt.subplot()
#index=[6] # for test, only plot one set
for i in index:
}
#=================================2==============================================
# 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
# fractal dimension and b for eq. (1)
'D': 1.6, #1.6 TODO: - these values should be constrained independently
'b': 1.0,
#=================plotting==============
'eta_binsize': .2,
'nnd_binsize': .2,
'hd_binsize': .2,
'xmin': 0,
'xmax': 1.0,
'eta_0': -5.0,
}
#=================================1==============================================
# load data, select events
#================================================================================
asSets = data_utils.loadmat('data/stack_events_2.5_7_atep1.mat')
a_MS_mag = asSets['a_MS_mag']
a_AS_dist = asSets['a_AS_dist']
i = 0
for f_MSmag, dist in zip(a_MS_mag, a_AS_dist):
# ==================================3=============================================
# plot distance decay
# ================================================================================
name = '<m>=%.2f' % f_MSmag
fig1 = plt.figure(1)
ax1 = plt.subplot(111)
ax1.set_title(name)
for k in [20, 50, 100, 200]:
eqCat.selectEvents(dPar['aMc'][0], None, 'Mag')
#eqCat.selector( tmin, tmax, 'Time')
print( 'no. of events after initial selection', eqCat.size())
# cut below current completeness
#################################################
curr_Mc=dPar['aMc'][0]
k=dPar['k'][2]
# cut below current completeness
eqCatMc.copy(eqCat)
eqCatMc.selectEvents(curr_Mc, None, 'Mag')
print('current catalog size: ', eqCatMc.size())
# load nearest neighbor distances
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)
# ================================================================================
# all event pairs
# ================================================================================
catChild.copy(eqCatMc)
catParent.copy(eqCatMc)
# catChild, catPar = create_parent_child_cat( projCat, dNND)
catChild.selEventsFromID(dNND['aEqID_c'], repeats=True)
catParent.selEventsFromID(dNND['aEqID_p'], repeats=True)
print('before::: size of parent catalog', catParent.size(), 'size of offspring cat', catChild.size())
# ================================================================================
# bigger parent event pairs
# ================================================================================
# select event pairs with parent event larger than M_pt
eqcat = EqCat()
mins = np.arange(3.5,7.0,0.5) # TODO: min 2.6, 3.0, 3.5
maxs = mins + 0.5
a_MS_mag = [] # average mainshock magnitude of each set
a_AS_dist = [] # aftershock haversine distance distribution of each set
# load earthquake set, mainshocks [min,max]
for min,max in zip(mins,maxs):
aCluster, dNND = getMajor(dPar['Mc'], min=min, max=max)
aMag = np.zeros(len(aCluster))
aDist = np.array([])
sel = aMag >= 0
for i,cluster in enumerate(aCluster):
aMag[i] = cluster.data['Mag'][0]
sel_c = cluster.data['sel_c'] # offspring
aNND = dNND['aNND'][sel_c]
aHD = dNND['aHD'][sel_c]
sel_cl = np.log10(aNND) <= -5
aHD = aHD[sel_cl]
aDist = np.concatenate((aDist,aHD),axis=0)
a_MS_mag.append(np.mean(aMag))
a_AS_dist.append(aDist)
scipy.io.savemat('data/stack_events.mat',{'a_MS_mag':a_MS_mag,'a_AS_dist':a_AS_dist},do_compression = True)
dCluster = data_utils.loadmat('data/stack_events.mat')
print(dCluster['a_MS_mag'])
print(dCluster['a_AS_dist'])