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
# 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)
ax = plt.subplot(111)
for iEv in xrange( catParent.size()):
print 'MS', int( catParent.data['N'][iEv]), catParent.data['Time'][iEv], eqCatMc.data['Time'][iEv]
if dNND['aNND'][iEv] < dPar['eta_0']:#triggered cluster
ax.plot( [catParent.data['Time'][iEv]], [catParent.data['Lat'][iEv]], 'ro', ms = 12, alpha = .2)
ax.plot( [catParent.data['Time'][iEv],catChild.data['Time'][iEv]],
[catParent.data['Lat'][iEv], catChild.data['Lat'][iEv]], 'k-', marker = 'o', ms = 4, mew =1, mfc = 'none')
else: # independent events
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
sel = catParent.data['Mag'] >= dPar['M_pt']
catChild.selEventsFromID(dNND['aEqID_c'][sel], repeats=True)
catParent.selEventsFromID(dNND['aEqID_p'][sel], repeats=True)
print('after::: size of parent catalog', catParent.size(), 'size of offspring cat', catChild.size())
# ================================================================================
# calculate R/L
