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
np.array([3.1, 3.2, 3.3, 3.4, 3.6, 3.7, 3.8,
3.9]), #np.array([3.0, 4.0]),np.array( [2.0, 2.5, 3.0, 3.5]
# fractal dimension and b for eq. (1)
'D': 1.6, #1.6 TODO: - these values should be contrained independently
'b': 1.0,
#=================plotting==============
'eta_binsize': .3,
'xmin': -13,
'xmax': 0,
}
#=================================2==============================================
# load data, select events
#================================================================================
eqCat.loadMatBin(os.path.join(dir_in, file_in))
print('total no. of events', eqCat.size())
eqCat.selectEvents(dPar['aMc'][0], None, 'Mag')
#eqCat.selector( tmin, tmax, 'Time')
print('no. of events after initial selection', eqCat.size())
#=================================1==============================================
# to cartesian coordinates
#================================================================================
# two ways to do the distance comp: 1 project into equal distance azimuthal , comp Cartersian distance in 3D
# 2 get surface distance from lon, lat (haversine), use pythagoras to include depth
eqCat.toCart_coordinates(projection='aeqd')
for dPar['Mc'] in dPar['aMc']:
print('-------------- current Mc:', dPar['Mc'], '---------------------')
# select magnitude range
eqCat.selectEvents(dPar['Mc'], None, 'Mag')
print('catalog size after MAG selection', eqCat.size())
#================================================================================
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, # run 2_eta_0.py and
# if file exists: default = load this value from ASCII file
}
#=================================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']:
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']
#=================================1==============================================
# dir, file, params
#================================================================================
dPar = {
'a_Mc': np.array([3.0, 4.0]), # , 3.0, 4.0]), #3.0,4.0]),
'alpha': 1, #exponent for test plot
#=================plotting==============
'plotFormat': 'png',
}
#=================================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())
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,
}
#=================================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.selector( tmin, tmax, 'Time')
print('no. of events after initial selection', eqCat.size())
iMc = 0
for f_Mc in dPar['a_Mc']:
# 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))
dNND['aNND'] = np.log10(dNND['aNND'])
'xmin' : 0, 'xmax' : 1.0,
'eta_0': -5.0,
#'k' :[int(300*(i+1)**(np.log10(1/6))) for i in list(range(10))],
'k':[600,400,200,50], # to calculate the r/l rate
#=================map plotting==========
'minlon':-123,'minlat':30,
'maxlon':-113, 'maxlat':40,
'minmag':2.5,
'R_Lmin': 25, # to select event-pairs in the tail
}
#=================================2==============================================
# load data, select events
#================================================================================
eqCat.loadMatBin( os.path.join( dir_in, file_in))
print( 'total no. of events', eqCat.size())
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)
None, #.2, #'silverman', # Gaussian smoothing bandwidth, default = n**(-1./(d+4)),
#=================plotting==============
'eta_0': -5.0,
#'xmin' : -13, 'xmax' : 0,
'Tmin': -8,
'Tmax': 0,
'Rmin': -5,
'Rmax': 3,
'cmap': plt.cm.RdYlGn_r,
}
#=================================2==============================================
# load data, select events
#================================================================================
eqCat.loadMatBin(os.path.join(dir_in, file_in))
print('total no. of events', eqCat.size())
eqCat.selectEvents(dPar['a_Mc'][0], None, 'Mag')
#eqCat.selector( tmin, tmax, 'Time')
print('no. of events after initial selection', eqCat.size())
# two ways to do the distance comp: 1 project into equal distance azimuthal , comp Cartersian distance in 3D
# 2 get surface distance from lon, lat (haversine), use pythagoras to include depth
eqCat.toCart_coordinates(projection='aeqd')
for i in range(dPar['a_Mc'].shape[0]):
f_Mc = dPar['a_Mc'][i]
# cut below current completeness
eqCatMc.copy(eqCat)
eqCatMc.selectEvents(f_Mc, None, 'Mag')
print('current catalog size: ', eqCatMc.size())
'cmin' : 1,
'xmin' : -13, 'xmax' : 0,
## R-T plot
'binx' : .1, 'biny' : .1,# used for density and gaussian smoothing
'sigma' : None, #if None: default = n**(-1./(d+4)), or set Gaussian bandwidth
'Tmin' : -8, 'Tmax' : 0,
'Rmin' : -5, 'Rmax' : 3,
'cmap' : plt.cm.RdYlGn_r,
'showPlot' : False,
}
#================================================================================
# 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']}
#=================================1==============================================
# dir, file, params
#================================================================================
dir_in = 'data'
file_in = 'hs_1981_2011_all.mat'
#xmin, xmax = -122, -114
#ymin, ymax = 34, 38
Mmin, Mmax = 3, None
tmin, tmax = 1990, 2018
#=================================2==============================================
# load data, select events
#================================================================================
os.chdir(dir_in)
eqCat.loadMatBin(file_in)
print('total no. of events', eqCat.size())
eqCat.selectEvents(Mmin, Mmax, 'Mag')
eqCat.selectEvents(tmin, tmax, 'Time')
print('no. of events after initial selection', eqCat.size())
#=================================3==============================================
# test plot TODO: use basemap
#================================================================================
plt.figure()
plt.scatter(eqCat.data['Lon'],
eqCat.data['Lat'],
s=np.exp(eqCat.data['Mag']),
c=eqCat.data['Mag'],
linewidth=0)
plt.savefig(file_in.replace('mat', 'png'))
plt.show()
#------------------------------my modules--------------------------------------
from src.EqCat import *
eqCat = EqCat()
#=================================1==============================================
# dir, file, params
#================================================================================
# change to local dir where eq. catalogs are saved
# the original catalog can be found here: https://scedc.caltech.edu/research-tools/altcatalogs.html
dir_in = '%s/data/quakeData/SCSN/relocated' % (os.path.expanduser('~'))
file_in = 'hs_1981_2011_all.txt'
#=================================2==============================================
# load data
#================================================================================
os.chdir(dir_in)
eqCat.loadEqCat(file_in, 'HS_reloc', removeColumn=[24, 25, 26])
print('total no. of events: ', eqCat.size())
print(sorted(eqCat.data.keys()))
#=================================3==============================================
# test plot and save to .mat binary
#================================================================================
eqCat.saveMatBin(file_in.replace('txt', 'mat'))
newEqCat = EqCat()
newEqCat.loadMatBin(file_in.replace('txt', 'mat'))
print(newEqCat.size())
print(sorted(newEqCat.data.keys()))
from src.seis_utils import *
#import src.dataIO_utils
#------------------------------my modules--------------------------------------
from src.EqCat import *
eqCat = EqCat()
#=================================1==============================================
# dir, file, params
#================================================================================
dir_in = 'data'
file_in = 'hs_1981_2018_all.txt'
#file_in = 'test_HS.txt'
#=================================2==============================================
# load data
#================================================================================
os.chdir(dir_in)
eqCat.loadEqCat(file_in, 'HS_reloc', removeColumn=[24, 25, 26])
print(eqCat.size())
print(sorted(eqCat.data.keys()))
#=================================3==============================================
# test plot and save to .mat binary
#================================================================================
eqCat.saveMatBin(file_in.replace('txt', 'mat'))
newEqCat = EqCat()
newEqCat.loadMatBin(file_in.replace('txt', 'mat'))
print(newEqCat.size())
print(sorted(newEqCat.data.keys()))
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
'eta_0': -5.0,
# 'xmin' : -13, 'xmax' : 0,
'Tmin': -8,
'Tmax': 0,
'Rmin': -5,
'Rmax': 3,
'cmap': plt.cm.RdYlGn_r,
'R_L': 25,
'R_Lmax': 5,
}
# =================================2==============================================
# load data, select events
# ================================================================================
eqCat.loadMatBin(os.path.join(dir_in, file_in))
print('total no. of events', eqCat.size())
eqCat.selectEvents(dPar['a_Mc'][0], None, 'Mag')
# eqCat.selector( tmin, tmax, 'Time')
print('no. of events after initial selection', eqCat.size())
# two ways to do the distance comp: 1 project into equal distance azimuthal , comp Cartersian distance in 3D
# 2 get surface distance from lon, lat (haversine), use pythagoras to include depth
eqCat.toCart_coordinates(projection='aeqd')
for i in range(dPar['a_Mc'].shape[0]):
f_Mc = dPar['a_Mc'][i]
# cut below current completeness
eqCatMc.copy(eqCat)
eqCatMc.selectEvents(f_Mc, None, 'Mag')
print('current catalog size: ', eqCatMc.size())
eqCat = EqCat( ) #=================================1============================================== # dir, file, params #================================================================================ dir_in = '%s/data/quakeData/SCSN/relocated'%( os.path.expanduser( '~')) file_in= 'hs_1981_2011_all.txt' #file_in = 'test_HS.txt' #=================================2============================================== # load data #================================================================================ os.chdir( dir_in) eqCat.loadEqCat( file_in, 'HS_reloc', removeColumn=[24,25,26]) print eqCat.size() print sorted( eqCat.data.keys()) #=================================3============================================== # test plot and save to .mat binary #================================================================================ eqCat.saveMatBin( file_in.replace( 'txt', 'mat')) newEqCat = EqCat( ) newEqCat.loadMatBin( file_in.replace( 'txt', 'mat')) print newEqCat.size() print sorted( newEqCat.data.keys())
'biny': .1, # used for density and gaussian smoothing
'sigma':
None, #if None: default = n**(-1./(d+4)), or set Gaussian bandwidth
'Tmin': -8,
'Tmax': 0,
'Rmin': -5,
'Rmax': 3,
'cmap': plt.cm.RdYlGn_r,
'showPlot': False,
}
#================================================================================
# 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='aeqd')
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===================================================
plot_dir = 'plots'
file_in = 'hs_1981_2018_all.mat'
dPar = {
'a_Mc':
np.array([2.5, 3.0, 3.5,
4.0]), #np.array( [2.0, 2.5, 3.0, 3.5]),np.array([3.0, 4.0])
#separate clustered and background
'eta_0': -5.0,
}
#=================================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.selector( tmin, tmax, 'Time')
print('no. of events after initial selection', eqCat.size())
iMc = 0
for f_Mc in dPar['a_Mc']:
# 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 = 'df1.7/%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())
