def plot_r_tau_density(filename, aTau, aR, dPar, majorEQ):
a_Tbin = np.arange(dPar['Tmin'], dPar['Tmax'] + 2 * dPar['binx'],
dPar['binx'])
a_Rbin = np.arange(dPar['Rmin'], dPar['Rmax'] + 2 * dPar['biny'],
dPar['biny'])
XX, YY, ZZ = density_2D(aTau, aR, a_Tbin, a_Rbin, sigma=dPar['sigma'])
plt.figure(1, figsize=(8, 10))
ax = plt.subplot(111)
normZZ = ZZ * (dPar['binx'] * dPar['biny'] * len(aTau))
plot1 = ax.pcolormesh(XX, YY, normZZ, cmap=dPar['cmap'])
cbar = plt.colorbar(
plot1,
orientation='horizontal',
shrink=.5,
aspect=20,
)
# plot eta_0 to divide clustered and background mode
ax.plot([dPar['Tmin'], dPar['Tmax']],
-np.array([dPar['Tmin'], dPar['Tmax']]) + dPar['eta_0'],
'-',
lw=1.5,
color='w')
ax.plot([dPar['Tmin'], dPar['Tmax']],
-np.array([dPar['Tmin'], dPar['Tmax']]) + dPar['eta_0'],
'--',
lw=1.5,
color='.5')
cbar.set_label('Number of Event Pairs', labelpad=-40)
ax.set_xlabel('Rescaled Time')
ax.set_ylabel('Rescaled Distance')
ax.set_title("Year:%d Mag:%.1f/nNearest Neighbor Pairs in R-T" %
(majorEQ.data['Time'], majorEQ.data['Mag']))
plt.savefig(filename, dpi=500)
plt.clf()
'D': dPar['D'],
'Mc': f_Mc
},
correct_co_located=True)
RT_file = 'data/df1.8/%s_RT_Mc_%.1f.mat' % (file_in.split('.')[0], f_Mc)
scipy.io.savemat(RT_file, {'R': a_R, 'T': a_T}, do_compression=True)
#==================================4==============================================================
# T-R density plots
#=================================================================================================
a_Tbin = np.arange(dPar['Tmin'], dPar['Tmax'] + 2 * dPar['binx'],
dPar['binx'])
a_Rbin = np.arange(dPar['Rmin'], dPar['Rmax'] + 2 * dPar['biny'],
dPar['biny'])
XX, YY, ZZ = data_utils.density_2D(np.log10(a_T),
np.log10(a_R),
a_Tbin,
a_Rbin,
sigma=dPar['sigma'])
plt.figure(1, figsize=(8, 10))
ax = plt.subplot(111)
ax.set_title('Nearest Neighbor Pairs in R-T')
#------------------------------------------------------------------------------
normZZ = ZZ * (dPar['binx'] * dPar['biny'] * eqCatMc.size())
plot1 = ax.pcolormesh(XX, YY, normZZ, cmap=dPar['cmap'])
cbar = plt.colorbar(
plot1,
orientation='horizontal',
shrink=.5,
aspect=20,
)
#=================================================================================================
catChild = EqCat()
catParent= EqCat()
catChild.copy( ranCat)
catParent.copy( ranCat)
catChild.selEventsFromID( dNND['aEqID_c'], repeats = True)
catParent.selEventsFromID( dNND['aEqID_p'], repeats = True)
print( catChild.size(), catParent.size(), eqCatMc.size())
a_R, a_T = clustering.rescaled_t_r( catChild, catParent, dConst, correct_co_located = True)
a_Tbin = np.arange( dPar['Tmin'], dPar['Tmax']+2*dPar['binx'], dPar['binx'])
a_Rbin = np.arange( dPar['Rmin'], dPar['Rmax']+2*dPar['biny'], dPar['biny'])
a_log_T = np.log10( a_T)
a_log_R = np.log10( a_R)
XX, YY, ZZ = data_utils.density_2D( a_log_T, a_log_R, a_Tbin, a_Rbin, sigma = dPar['sigma'])
plt.figure(2, figsize= (8,10))
ax = plt.subplot(111)
ax.set_title( 'Nearest Neighbor Pairs in R-T')
#------------------------------------------------------------------------------
normZZ = ZZ*( dPar['binx']*dPar['biny']*eqCatMc.size())
plot1 = ax.pcolormesh( XX, YY, normZZ, cmap=dPar['cmap'])
cbar = plt.colorbar(plot1, orientation = 'horizontal', shrink = .5, aspect = 20,)
#ax.plot( np.log10( a_T), np.log10( a_R), 'wo', ms = 1.5, alpha = .2)
# plot eta_0 to divide clustered and background mode
ax.plot( [dPar['Tmin'], dPar['Tmax']], -np.array([dPar['Tmin'], dPar['Tmax']])+a_Eta_0[i_Bs], '-', lw = 1.5, color = 'w' )
ax.plot( [dPar['Tmin'], dPar['Tmax']], -np.array([dPar['Tmin'], dPar['Tmax']])+a_Eta_0[i_Bs],'--', lw = 1.5, color = '.5' )
#-----------------------labels and legends-------------------------------------------------------
#cbar.set_label( 'Event Pair Density [#ev./dRdT]')
cbar.set_label( 'Number of Event Pairs',labelpad=-40)