plot the R/L or Haversine Distance distribution of aftershock sequences of certain mainshoks
-- background events excluded
'''
import matplotlib as mpl
#mpl.use( 'Agg') # uncomment for interactive plotting
import matplotlib.pyplot as plt
import os
import numpy as np
#------------------------------my modules--------------------------------------
import src.data_utils as data_utils
import src.seis_utils as seis_utils
from src.EqCat import *
import src.plot_hist as myplot
eqCat = EqCat() # original catalog
asCat = EqCat()
#=================================0==============================================
# dir, file, params
#================================================================================
dir_in = '%s/data' % (os.path.expanduser('.'))
file_in = 'hs_1981_2018_all.mat'
#file_b = '%s_b_Mc_D.txt'%(fileIn.split('.')[0])
dPar = {
'aMc':
np.array([2.5, 2.6, 3.0, 3.5,
4.0]), #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 constrained independently
'b': 1.0,
@author: tgoebel
'''
import matplotlib as mpl
mpl.use('Agg')
import numpy as np
import os
import matplotlib.pyplot as plt
#----------------------my modules--------------------------------------------------------
import src.data_utils as data_utils
#import src.clustering as clustering
from src.EqCat import *
eqCat = EqCat() # original catalog
eqCatMc = EqCat() # this catalog will be modified with each Mc iteration
#=================================1==============================================
# dir, file, params
#================================================================================
data_dir = 'data'
plot_dir = 'plots'
file_in = 'hs_1981_2011_all.mat'
clust_file = file_in.replace('all.mat', 'clusters.mat')
#=================================1==============================================
# dir, file, params
#================================================================================
dPar = {
@author: Litong Huang
'''
import matplotlib as mpl
mpl.use( 'Agg') # uncomment for interactive plotting
import os
import numpy as np
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import matplotlib.colors as co
import src.data_utils as data_utils
import src.seis_utils as seis_utils
from src.EqCat import *
from selector.getLanders import getLanders
eqCat = EqCat() # original catalog
eqCatMc = EqCat() # this catalog wil be modfied with each Mc iteration
catChild= EqCat()
catParent= EqCat()
#=================================1==============================================
# dir, file, params
#================================================================================
dir_in = '%s/data'%( os.path.expanduser('.'))
file_in= 'hs_1981_2018_all.mat'
map_filename = 'plots/'
fault_file = 'data/CA_faults.all.dat'
#file_b = '%s_b_Mc_D.txt'%(fileIn.split('.')[0])
dPar = { 'aMc' : np.array( [2.5,3.0,3.5,4.0]),#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,
@author: Thomas Goebel University of Memphis
'''
import matplotlib as mpl
#mpl.use( 'Agg')
import matplotlib.pyplot as plt
import numpy as np
import os
#------------------------------my modules--------------------------------------
import src.data_utils as dataIO
import src.clustering as clustering
from src.EqCat import *
eqCat = EqCat() # original catalog
eqCatMc = EqCat() # this catalog will be modified with each Mc iteration
catChild = EqCat()
catParent = 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,
@author: tgoebel
'''
#------------------------------------------------------------------------------
import matplotlib as mpl
#mpl.use( 'Agg') # turn off interactive plot
import matplotlib.pyplot as plt
import numpy as np
import scipy.io
import os
#------------------------------my modules--------------------------------------
import src.clustering as clustering
import src.data_utils as data_utils
from src.EqCat import *
eqCat = EqCat( ) # original cat
ranCat = EqCat() # randomized, Poissonian catalog
eqCatMc = EqCat() # catalog above completeness
np.random.seed( 123456)
#=================================1==============================================
# dir, file, params
#================================================================================
dir_in = 'data'
file_in= 'hs_1981_2011_all.mat'
#file_b = '%s_b_Mc_D.txt'%(fileIn.split('.')[0])
dPar = { 'aMc' : 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, # TODO: - these values should be constrained based on the data
'b' : 1.0, # use: https://github.com/tgoebel/magnitude-distribution for b-value
@author: tgoebel
'''
import matplotlib as mpl
mpl.use('Agg') # uncomment for interactive plotting
import os
import numpy as np
import matplotlib.pyplot as plt
import scipy.io
#------------------------------my modules--------------------------------------
import src.data_utils as data_utils
import src.clustering as clustering
from src.EqCat import *
eqCat = EqCat() # original catalog
eqCatMc = EqCat() # this catalog wil be modfied with each Mc iteration
catChild = EqCat()
catParent = EqCat()
#=================================1==============================================
# dir, file, params
#================================================================================
dir_in = '%s/data' % (os.path.expanduser('.'))
file_in = 'hs_1981_2018_all.mat'
#file_b = '%s_b_Mc_D.txt'%(fileIn.split('.')[0])
dPar = {
'a_Mc': np.array([2.5, 3.0, 3.5, 4.0]), #np.array([3.0, 4.0]),
# fractal dimension and b for eq. (1)
'D': 1.8, #1.6 TODO: - these values should be constrained independently
'b': 1.0,
'''
plot the R/L or Haversine Distance distribution of aftershock sequences of certain mainshoks
-- background events excluded
'''
import matplotlib as mpl
mpl.use('Agg') # uncomment for interactive plotting
import os
import numpy as np
#------------------------------my modules--------------------------------------
import src.data_utils as data_utils
import src.seis_utils as seis_utils
from src.EqCat import *
import src.plot_hist as myplot
eqCat = EqCat() # original catalog
#=================================0==============================================
# dir, file, params
#================================================================================
dir_in = '%s/data' % (os.path.expanduser('.'))
file_in = 'hs_1981_2018_all.mat'
#file_b = '%s_b_Mc_D.txt'%(fileIn.split('.')[0])
dPar = {
'aMc':
np.array([2.5, 2.6, 3.0, 3.5,
4.0]), #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==============
@author:Thomas Goebel, University of Memphis
'''
import matplotlib as mpl
#mpl.use( 'Agg') # uncomment for interactive plotting
import matplotlib.pyplot as plt
import os
import numpy as np
#------------------------------my modules--------------------------------------
import src.data_utils as data_utils
import src.seis_utils as seis_utils
from src.EqCat import *
import src.plot_hist as myplot
eqCat = EqCat() # original catalog
asCat = EqCat()
#=================================0==============================================
# dir, file, params
#================================================================================
dir_in = '%s/data' % (os.path.expanduser('.'))
file_in = 'hs_1981_2018_all.mat'
#file_b = '%s_b_Mc_D.txt'%(fileIn.split('.')[0])
dPar = {
'aMc':
np.array([2.5, 2.6, 3.0, 3.5,
4.0]), #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 constrained independently
'b': 1.0,
- save as .mat binary for fast data I/O @author: tgoebel ''' #------------------------------------------------------------------------------ import matplotlib.pyplot as plt import numpy as np import scipy.io import os 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())
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
dPar = {'Mc':2.5,
'minMag':4.0, # minimum magnitude of mainshocks
'maxMag':8.0, # maximum magnitude of mainshocks
'k': 2,
'HD_binsize':0.05,
'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
# ================================================================================
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
'''
import matplotlib as mpl
mpl.use('Agg') # uncomment for interactive plotting
import sys
sys.path.append('/auto/home/lhuang/PycharmProjects/clustering-analysis-master')
import numpy as np
import matplotlib.pyplot as plt
from src.EqCat import *
from selector.getBigEQ import getMajor
from selector.getLanders import getLanders
from src.plot_major import *
import scipy.io
eqCat = EqCat() # original catalog
eqCatMc = EqCat() # this catalog wil be modfied with each Mc iteration
catChild = EqCat()
catParent = EqCat()
# =================================1==============================================
# dir, file, params
# ================================================================================
map_filename = 'plots/'
fault_file = 'data/CA_faults.all.dat'
# file_b = '%s_b_Mc_D.txt'%(fileIn.split('.')[0])
dPar = {
'aMc':
np.array([2.5]), # 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,
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
@author: tgoebel - Thomas Goebel University of Memphis
'''
import matplotlib as mpl
mpl.use( 'Agg') # uncomment for interactive plotting
import os
import numpy as np
import matplotlib.pyplot as plt
#------------------------------my modules--------------------------------------
import src.data_utils as dataIO
import src.clustering as clustering
from src.EqCat import *
eqCat = EqCat() # original catalog
eqCatMc = EqCat() # this catalog will be modified with each Mc iteration
catChild= EqCat()
catParent= 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, # run 2_eta_0.py and
# if file exists: default = load this value from ASCII file
TODO: - implement geo-referenced plotting with Basemap @author: tgoebel ''' #------------------------------------------------------------------------------ import matplotlib.pyplot as plt import numpy as np import scipy.io import os, sys #------------------------------my modules-------------------------------------- #import src.dataIO_utils as dataUtils from src.EqCat import * eqCat = EqCat() #print dir( dataUtils) #=================================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 #================================================================================
@author: tgoebel
'''
#------------------------------------------------------------------------------
import matplotlib as mpl
mpl.use('Agg') # turn off interactive plot
import matplotlib.pyplot as plt
import numpy as np
import scipy.io
import os
#------------------------------my modules--------------------------------------
import src.clustering as clustering
from src.EqCat import *
eqCat = EqCat()
#print dir( dataUtils)
#=================================1==============================================
# dir, file, params
#================================================================================
dir_in = '%s/data' % (os.path.expanduser('.'))
file_in = 'hs_1981_2018_all.mat'
#file_b = '%s_b_Mc_D.txt'%(fileIn.split('.')[0])
dPar = {
'aMc':
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
'''
import matplotlib as mpl
mpl.use('Agg') # uncomment for interactive plotting
import os
import numpy as np
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
#import matplotlib.colors as co
import src.data_utils as data_utils
#import src.seis_utils as seis_utils
from src.EqCat import *
#from selector.getBigEQ import getMajor
eqCat = EqCat() # original catalog
eqCatMc = EqCat() # this catalog wil be modfied with each Mc iteration
catChild = EqCat()
catParent = EqCat()
# =================================1==============================================
# dir, file, params
# ================================================================================
fault_file = 'data/CA_faults.all.dat'
# file_b = '%s_b_Mc_D.txt'%(fileIn.split('.')[0])
plottype = 'time' # TODO: change here for different plot purpose. or 'eta'
dPar = {'aMc': np.array([2.5]), # 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,
# =================map plotting==========
'minlon': -124, 'minlat': 30,
