def AAD_SpectralMatchin( seed1 , seed2 ) :
#seed1 = 'RSN730_SPITAK_GUK000.at2' # seeed record comp1[g]
#seed2 = 'RSN730_SPITAK_GUK090.at2' # seeed record comp2[g]
target = 'ASCE7.txt' # target spectrum (T,PSA)
dampratio = 0.05 # damping ratio for spectra
TL1 = 0; TL2 = 0 # define period range for matching
# (T1=T2=0 matches the whole spectrum)
# load target spectrum and seed record:
s1,dt,n,name1 = load_PEERNGA_record(seed1) # dt: time step, s: accelertion series
time_1 = [ dt * item for item in range( len( s1))]
s2,dt,n,name2 = load_PEERNGA_record(seed2)
time_2 = [ dt * item for item in range( len( s2))]
fs = 1/dt # sampling frequency (Hz)
tso = np.loadtxt(target)
#
tso = tso[::100]
#
To = tso[:,0] # original target spectrum periods
dso = tso[:,1] # original target spectrum psa
# Spectrum hesaplayalım
metinInfo("Orjinal kayıtların spektrumları hesaplansı")
PSA_1, PSV_1, SA_1, SV_1, SD_1 = RSFD(To , s1 , dampratio , dt )
PSA_2, PSV_2, SA_2, SV_2, SD_2 = RSFD(To , s2 , dampratio , dt )
# Ölçekleme işi yapılsın
metinInfo( "Ölçekleme işi yapılsın")
nn = 100 # percentile 100 for RotD100, 50 for RotD50, ...
(scc1,scc2,cvel1,cvel2,cdisp1,cdisp2,
PSArotnn,PSArotnnor,T,misfit,rms) = REQPYrotdnn(s1,s2,fs,dso,To,nn,
T1=TL1,T2=TL2,zi=dampratio,
nit=15,NS=100,
baseline=1,plots=1)
# HEDEF SPECTRUM OKUMAS
data_df = pd.read_csv( target, names = ["T_target" , "SA_target"], header = None, delim_whitespace= True)
#%% GORELİM
metinInfo("Görselleştirme başlasın")
plt.figure( figsize = (10,5))
plt.plot( T , PSArotnn ,"r--", lw = 2 , label="Scaled Spectrum" )
plt.plot( T , PSArotnnor , "b" , label = "Original Spectrum")
plt.plot( data_df["T_target" ] , data_df["SA_target"] , "c" , lw = 1.5 , label = "Target Spectrum")
plt.plot( To , PSA_1 , "gray" , label = "H1 Spectrum" )
plt.plot( To , PSA_2 , "gray" , label = "H2 Spectrum" )
plt.xlim( 0 , 4)
plt.xlabel( "Period (s)")
plt.legend()
plt.ylabel("Sa (g)")
plt.rcParams.update({ "font.size" : 14 })
plt.box(False)
plt.axhline( color = "k" )
plt.axvline( color = "k" )
plt.savefig( f'Output-{os.path.split(seed1)[-1].split("_")[0]}-Spectra.png')
#plt.savefig( 'Spectra.png')
#%%
fig, ax = plt.subplots( nrows = 2 , ncols = 1 , sharex = True, sharey = True, figsize = ( 10 , 8 ) , frameon = None )
for count , ( timeList , signalScale , signalOrigin , labelY ) in enumerate( zip( [time_1 , time_2] , [s1,s2] , [scc1,scc2] , [ seed1 , seed2 ]) ):
ax[ count ].plot( [dt*item for item in range(len(signalOrigin))] , signalOrigin , "r-", lw = 1 , label = "Scaled Signal")
ax[ count ].plot( timeList , signalScale , "b", lw = 2 , label = "Original Signal")
ax[ count ].set_ylabel( labelY )
ax[ count ].set_xlabel( "Time (s)")
ax[ count ].legend()
ax[ count ].axhline( color = "k" )
ax[ count ].axvline( color = "k" )
ax[ count ].set_xlim(left = 0)
ax[ count ].set(frame_on=False) # New
plt.rcParams.update({ "font.size" : 14 })
plt.savefig( f'Output-{os.path.split(seed1)[-1].split("_")[0]}-TimeSeries.png')
#plt.savefig('TimeSeries.png')
#
# SAVE SCALED THS
RSN_name = f'{os.path.split(seed1)[-1].split("_")[0]}'
headerinfo = 'accelerations in g, dt = ' + str(dt)
np.savetxt( f'./v2/v2-{RSN_name}-H1.txt' , scc1 , header = f'v2-{RSN_name}-H1' + headerinfo )
np.savetxt( f'./v2/v2-{RSN_name}-H2.txt' , scc2 , header = f'v2-{RSN_name}-H2' + headerinfo )
# OUTPUT
return( PSArotnn )
#
metinInfo( "We are done with this set")
import matplotlib.pyplot as plt
plt.close('all')
# input:
seed = 'RSN730_SPITAK_GUK000.at2' # seeed record [g]
target = 'ASCE7.txt' # target spectrum (T,PSA)
dampratio = 0.05 # damping ratio for spectra
TL1 = 0.05
TL2 = 6 # define period range for matching
# (T1=T2=0 matches the whole spectrum)
# load target spectrum and seed record:
s, dt, npts, eqname = load_PEERNGA_record(seed)
# dt: time step, s: accelertion series, npts: number of points in record
fs = 1 / dt # sampling frequency (Hz)
tso = np.loadtxt(target)
To = tso[:, 0] # original target spectrum periods
dso = tso[:, 1] # original target spectrum psa
ccs, rms, misfit, cvel, cdespl, PSAccs, PSAs, T, sf = REQPY_single(
s,
fs,
dso,
To,
T1=TL1,
T2=TL2,
zi=dampratio,
import matplotlib.pyplot as plt
plt.close('all')
# input:
seed1 = 'RSN730_SPITAK_GUK000.at2' # seeed record comp1[g]
seed2 = 'RSN730_SPITAK_GUK090.at2' # seeed record comp2[g]
target = 'ASCE7.txt' # target spectrum (T,PSA)
dampratio = 0.05 # damping ratio for spectra
TL1 = 0.05; TL2 = 6 # define period range for matching
# (T1=T2=0 matches the whole spectrum)
# load target spectrum and seed record:
s1,dt,n,name1 = load_PEERNGA_record(seed1) # dt: time step, s: accelertion series
s2,dt,n,name2 = load_PEERNGA_record(seed2)
fs = 1/dt # sampling frequency (Hz)
tso = np.loadtxt(target)
To = tso[:,0] # original target spectrum periods
dso = tso[:,1] # original target spectrum psa
nn = 100 # percentile 100 for RotD100, 50 for RotD50, ...
(scc1,scc2,cvel1,cvel2,cdisp1,cdisp2,
PSArotnn,PSArotnnor,T,misfit,rms) = REQPYrotdnn(s1,s2,fs,dso,To,nn,
T1=TL1,T2=TL2,zi=dampratio,
nit=15,NS=100,
baseline=1,plots=1)