def _percentile(axis, pctl=50, unit='um', suffix='', _dir='', **kwargs):
fname = './data2/{3}/{0}_{1}.hdf5'.format(axis, pctl, suffix, _dir)
model = kwargs.pop('model', None)
_asd = FrequencySeries.read(fname)**0.5
if model == '120QA':
amp = 10**(30.0 / 20.0)
elif model == 'compact':
amp = 10**(45.0 / 20.0)
seis = Trillium(model)
v2vel = seis.v2vel
c2v = 20.0 / 2**15
_asd = v2vel(_asd) * c2v / amp * 1e6
if unit == 'um':
asd = _asd / (2.0 * np.pi * _asd.frequencies.value)
#asd.write('./LongTerm_{0}_{1}_DISP.txt'.format(axis,pctl),format='txt')
elif unit == 'm':
asd = _asd / (2.0 * np.pi * _asd.frequencies.value) * 1e-6
asd.write('./{0}_{1}_DISP.txt'.format(axis, pctl), format='txt')
elif unit == 'um/sec':
asd = _asd
asd.write('./{0}_{1}_VELO.txt'.format(axis, pctl), format='txt')
elif unit == 'um/sec/sec':
asd = _asd * (2.0 * np.pi * _asd.frequencies.value)
#asd.write('./LongTerm_{0}_{1}_ACC.txt'.format(axis,pctl),format='txt')
elif unit == 'm/sec/sec':
asd = _asd * (2.0 * np.pi * _asd.frequencies.value) * 1e-6
else:
raise ValueError('!!1', unit)
return asd
bw = exv0.df
# Calcrate several noises
if True:
ref = exv0
_adcnoise = 2e-6 * u.V * np.ones(len(ref)) # [V/rtHz]
_adcnoise2 = np.sqrt((10 / 2**15)**2 / 12 /
(214 / 2)) * u.V * np.ones(len(ref)) # [V/rtHz]
_ampnoise = 6e-7 * u.V * np.ones(len(ref)) # [V/rtHz]
_aanoise = 7e-8 * u.V * np.ones(len(ref)) # [V/rtHz]
_adcnoise = FrequencySeries(_adcnoise, frequencies=ref.frequencies)
_adcnoise2 = FrequencySeries(_adcnoise2, frequencies=ref.frequencies)
_ampnoise = FrequencySeries(_ampnoise, frequencies=ref.frequencies)
_aanoise = FrequencySeries(_aanoise, frequencies=ref.frequencies)
amp = 10**(30 / 20.)
tr120q = Trillium('120QA')
tr240 = Trillium('240')
v2vel = tr120q.v2vel
v2vel = tr240.v2vel
adcnoise = v2vel(_adcnoise) / amp
ampnoise = v2vel(_ampnoise) / amp #
aanoise = v2vel(_aanoise) / amp
selfnoise_120q = tr120q.selfnoise()
selfnoise_240 = tr240.selfnoise()
# Plot
if True:
from gwpy.plot import Plot
plot = Plot()
ax = plot.gca(xscale='log',
xlim=(1e-3, 10),
import numpy as np
import matplotlib.pyplot as plt
from gwpy.frequencyseries import FrequencySeries
from gwpy.spectrogram import Spectrogram
from miyopy.utils.trillium import Trillium
from obspy.signal.spectral_estimation import get_nhnm, get_nlnm
from gwpy.types.array2d import Array2D
from gwpy.timeseries import TimeSeries
import astropy.units as u
amp = 10**(30.0/20.0)
c2v = 20.0/2**15
tr120 = Trillium('120QA')
v2vel = tr120.v2vel
lt, ldb = get_nlnm()
ht, hdb = get_nhnm()
lfreq, lacc = 1./lt, 10**(ldb/20)*1e6
hfreq, hacc = 1./ht, 10**(hdb/20)*1e6
lfreq, lvel = lfreq, lacc/(2.0*np.pi*lfreq)
hfreq, hvel = hfreq, hacc/(2.0*np.pi*hfreq)
lfreq, ldisp = lfreq, lvel/(2.0*np.pi*lfreq)
hfreq, hdisp = hfreq, hvel/(2.0*np.pi*hfreq)
def _plot_band_histgram(blrms,blrms2,blrms3,scale=0.9,loc=0):
blrms.override_unit('m/s')
blrms = blrms/amp*c2v/1000*1e6
def main(chnames,
start,
end,
check_timeseries=False,
check_velocity=False,
check_reduction_rate=True):
# ----------------------------------------
# TimeSeries Data from nds
# ----------------------------------------
datadict = TimeSeriesDict.fetch(chnames,
start,
end,
host='10.68.10.121',
verbose=True,
port=8088)
try:
ts_eyv = datadict['K1:PEM-SEIS_EYV_GND_EW_IN1_DQ']
ts_exv = datadict['K1:PEM-SEIS_EXV_GND_EW_IN1_DQ']
ts_ixv = datadict['K1:PEM-SEIS_IXV_GND_EW_IN1_DQ']
ts_bs = datadict['K1:PEM-SEIS_BS_GND_EW_IN1_DQ']
ts_mcf = datadict['K1:PEM-SEIS_MCF_GND_EW_IN1_DQ']
except KeyError as e:
print(
'''KeyError: Channel name not found, {0}. Please check this channel name.'''
.format(e))
exit()
except Exception as e:
print('Unexpected Error!!')
print(e)
exit()
if check_timeseries:
for data in datadict.values():
plot = data.rms().plot()
plot.savefig('./{0}/img_TimeSeries_RMS_{1}.png'.format(
segname, data.name))
print('./{0}/img_TimeSeries_RMS_{1}.png'.format(
segname, data.name))
plot.close()
# ----------------------------------------
# Coherence
# ----------------------------------------
fftlength = 2**7
csd3000 = ts_exv.csd(ts_ixv, fftlength=fftlength,
overlap=fftlength / 2.0) # CSD
csd30 = ts_mcf.csd(ts_bs, fftlength=fftlength,
overlap=fftlength / 2.0) # CSD
csd60 = ts_mcf.csd(ts_ixv, fftlength=fftlength,
overlap=fftlength / 2.0) # CSD
exv = ts_exv.asd(fftlength=fftlength, overlap=fftlength / 2.0) # ASD
ixv = ts_ixv.asd(fftlength=fftlength, overlap=fftlength / 2.0) # ASD
mcf = ts_mcf.asd(fftlength=fftlength, overlap=fftlength / 2.0) # ASD
bs = ts_bs.asd(fftlength=fftlength, overlap=fftlength / 2.0) # ASD
coh3000 = np.real(csd3000) / exv / ixv # Re[coh]
coh30 = np.real(csd30) / mcf / bs # Re[coh]
coh60 = np.real(csd60) / mcf / ixv # Re[coh]
# ----------------------------------------
# SPAC model
# ----------------------------------------
freq = np.logspace(-3, 2, 1e4)
w = 2.0 * np.pi * freq
L_ixv2exv = 3000.0
L_mcf2bs = 22.0
L_mcf2ixc = 62.0
L_mcf2ixv = 62.0 # guess
L_mcf2mce = 22.0
#cp = 5500.0 # m/sec
#cr = 2910 # m/sec
L_KAGRA = 3000.0
L_Virgo = 4000.0
cr_virgo = lambda f: 150.0 + 1000.0 * np.exp(-f / 1.5
) # from M. Beker PhD thesis.
cr_kagra = lambda f: 800.0 + 3000.0 * np.exp(-f / 1.5) # by eye
cr_kagra_x = lambda f: 2200.0 + 3000.0 * np.exp(-f / 1.5) # by eye
cp_kagra_x = lambda f: 5400.0 * np.ones(len(f))
spac = lambda f, c, L: np.real(jv(0, L * 2.0 * np.pi * f / c))
spac_kagra = lambda f, L: spac(f, cr_kagra(f), L)
spac_kagra_p = lambda f, L: spac(f, cp_kagra_x(f), L)
spac_kagra_r = lambda f, L: spac(f, cr_kagra_x(f), L)
reduction = lambda f, c, L: 1.0 - np.real(jv(0, L * 2.0 * np.pi * f / c))
# ----------------------------------------
# ASD
# ----------------------------------------
from miyopy.utils.trillium import Trillium
tr120q = Trillium('120QA')
trcpt = Trillium('compact')
v2vel_120 = tr120q.v2vel
v2vel_cpt = trcpt.v2vel
c2v = 20.0 / 2**15
print c2v
amp = 10**(30.0 / 20.0)
#ixv = v2vel_120(ixv*c2v)/amp
#exv = v2vel_120(exv*c2v)/amp
#mcf = v2vel_cpt(mcf*c2v)/amp
#bs = v2vel_cpt(bs*c2v)/amp
if True:
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
if 'UD' in ts_ixv.name:
plt.title(segname.replace('_', '\_') + ', ASD' + ', Z')
elif 'EW' in ts_ixv.name:
plt.title(segname.replace('_', '\_') + ', ASD' + ', X')
ax.loglog(ixv, label='ixv')
ax.loglog(exv, label='exv')
ax.loglog(mcf, label='mcf')
ax.loglog(bs, label='bs')
ax.set_xlim(0.01, 100)
ax.set_xlabel('Frequency [Hz]')
ax.set_ylabel('Count')
ax.legend()
if 'UD' in ts_ixv.name:
plt.savefig('./{0}/img_ASD_Z.png'.format(segname))
print('saved ./{0}/img_ASD_Z.png'.format(segname))
elif 'EW' in ts_ixv.name:
plt.savefig('./{0}/img_ASD_X.png'.format(segname))
print('saved ./{0}/img_ASD_X.png'.format(segname))
plt.close()
# ------------------------------------------
# Spatial autocorrection (SPAC)
# ------------------------------------------
if 'UD' in ts_ixv.name:
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
plt.title(segname.replace('_', '\_') + ', SPAC')
ax.axvspan(0.01, 0.1, alpha=0.2, color='gray')
ax.axvspan(2, 100, alpha=0.2, color='gray')
ax.semilogx(coh3000, 'r', label='3000 m ')
ax.semilogx(freq,
spac_kagra(freq, 3000.0),
'k--',
label='3000 m model')
ax.semilogx(coh30, 'b', label='22 m')
ax.semilogx(freq, spac_kagra(freq, 22.0), 'k--', label='3000 m model')
#ax.semilogx(coh60,'g',label='60 m ')
#ax.semilogx(freq,spac_kagra(freq,62.0),'k--',label='60 m model')
ax.set_ylim(-1.1, 1.1)
ax.set_xlim(0.01, 20)
ax.set_xlabel('Frequency [Hz]')
ax.set_ylabel('Coherence')
ax.set_yticks(np.arange(-1.0, 1.1, 0.5))
ax.text(110,
-1.1,
'START : {0}'.format(start),
rotation=90,
ha='left',
va='bottom')
ax.legend()
plt.savefig('./{0}/img_SPAC.png'.format(segname))
print('saved ./{0}/img_SPAC.png'.format(segname))
plt.close()
# ------------------------------------------
# Spatial autocorrection (SPAC) X
# ------------------------------------------
if 'EW' in ts_ixv.name:
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
plt.title(segname.replace('_', '\_') + ', X')
ax.axvspan(0.01, 0.1, alpha=0.2, color='gray')
ax.axvspan(1.5, 100, alpha=0.2, color='gray')
ax.semilogx(coh3000, 'r', label='3000 m ')
ax.semilogx(freq,
spac_kagra_r(freq, 3000.0),
'k--',
label='3000 m model')
ax.semilogx(freq,
spac_kagra_p(freq, 3000.0),
'g--',
label='3000 m model (p)')
ax.semilogx(coh30, 'b', label='22 m')
ax.semilogx(freq, spac_kagra_r(freq, 22.0), 'k--', label='22 m model')
ax.semilogx(freq,
spac_kagra_p(freq, 22.0),
'g--',
label='22 m model (p)')
#ax.semilogx(coh60,'g',label='60 m ')
#ax.semilogx(freq,spac_kagra_x(freq,62.0),'k--',label='60 m model')
ax.set_ylim(-1.1, 1.1)
ax.set_xlim(0.01, 20)
ax.set_xlabel('Frequency [Hz]')
ax.set_ylabel('Coherence')
ax.set_yticks(np.arange(-1.0, 1.1, 0.5))
ax.text(110,
-1.1,
'START : {0}'.format(start),
rotation=90,
ha='left',
va='bottom')
ax.legend()
plt.savefig('./{0}/img_SPAC_X.png'.format(segname))
print('saved ./{0}/img_SPAC_X.png'.format(segname))
plt.close()
# ------------------------------------------
# Reyleigh wave velocity in KAGRA and Virgo
# ------------------------------------------
if check_velocity:
fig, ax = plt.subplots(1, 1, figsize=(7, 6))
plt.title('Phase Velocity of Rayleigh Wave', fontsize=25)
ax.loglog(freq, cr_virgo(freq), 'k', label='Virgo')
ax.loglog(freq, cr_kagra(freq), 'r', label='KAGRA (Vertical)')
ax.loglog(freq, cr_kagra_x(freq), 'r--', label='KAGRA (Horizontal)??')
ax.loglog(freq, cp_kagra_x(freq), 'r:', label='ref. KAGRA P-wave')
ax.set_xlabel('Frequency [Hz]')
ax.set_ylabel('Velocity [m/s]')
ax.set_ylim(100, 10e3)
ax.set_xlim(1e-3, 100)
ax.legend(fontsize=15)
plt.savefig('./{0}/img_RwaveVelocity.png'.format(segname))
print('saved ./{0}/img_RwaveVelocity.png'.format(segname))
plt.close()
# ----------------------------------
# Reduction Rate of arm displacement in KAGRA and Virgo
# ----------------------------------
if check_reduction_rate:
fig, ax = plt.subplots(1, 1, figsize=(7, 6))
plt.title('Displacement Reduction of the Bedrock', fontsize=25)
ax.loglog(freq,
reduction(freq, cr_virgo(freq), L_Virgo),
'k',
label='Virgo (Z velo.)')
ax.loglog(freq,
reduction(freq, cr_kagra_x(freq), L_KAGRA),
'r',
label='KAGRA (X velo.)')
ax.set_xlim(0.01, 20)
ax.set_ylim(1e-3, 2)
ax.legend(fontsize=15)
ax.set_xlabel('Frequency [Hz]')
ax.set_ylabel('Reduction Rate')
plt.savefig('./{0}/img_ReductionRate.png'.format(segname))
print('saved ./{0}/img_ReductionRate.png'.format(segname))
plt.close()
def hoge(fname=None, unit='m', **kwargs):
#
kwargs['unit'] = unit
plot_selfnoise = kwargs.pop('plot_selfnoise', True)
peterson = kwargs.pop('peterson', True)
prefix, suffix = fname.split('_compare_')
path = prefix.split('_')
dof, suffix = suffix[:-4].split('_with_')
dof = dof.split('_vs_')
option = suffix.split('_')
dof = list(set(dof))
#print(path)
if not len(set(dof) & set(['X', 'Y', 'Z', 'H', 'V'])):
path = [path[0] + '_' + _dof for _dof in dof]
dof = list(filter(lambda x: x in ['X', 'Y', 'Z', 'H', 'V'], option))
option = list(set(option) ^ set(dof))
data = []
for _path in path:
for _dof in dof:
if 'V' in _path:
model = '120QA'
else:
model = 'compact'
data += [huge(_dof, _dir=_path, model=model, **kwargs)]
# Plot
fig, ax = plt.subplots(1, 1, figsize=(8, 7))
colors = ['blue', 'red', 'green', 'magenta']
label = ['{0}_{1}'.format(_path, _dof) for _dof in dof for _path in path]
if len(data) != len(label):
raise ValueError('plot error.')
for _data, _color, _label in zip(data, colors, label):
asd01, asd10, asd50, asd90, asd99, asdmean = _data
ax.loglog(asdmean.frequencies.value, asdmean.value, color='k')
if '_V' in _label:
_color = 'red'
if '90' in option:
ax.plot_mmm(asd50,
asd10,
asd90,
color=_color,
alpha=0.3,
label=_label)
if '99' in option:
ax.plot_mmm(asd50, asd01, asd99, color=_color, zorder=0, alpha=0.1)
if plot_selfnoise:
if 'V' in path[0]:
model = '120QA'
else:
model = 'compact'
seis = Trillium(model)
if 'um' in unit:
selfnoise = seis.selfnoise(unit=unit.replace('um', 'm')) * 1e6
else:
selfnoise = seis.selfnoise(unit=unit)
ax.loglog(selfnoise,
'g--',
label='Selfnoise of Trillium 120Q seismometer',
alpha=1.0)
if peterson:
nlnm, nhnm = peterson_noise_model(unit=unit)
ax.loglog(nlnm[0], nlnm[1], '--', color='black')
ax.loglog(nhnm[0],
nhnm[1],
'--',
label='Peterson Low and High Noise Models',
color='black')
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlabel('Frequency [Hz]', fontsize=20)
ax.set_xlim(1e-3, 100)
if unit == 'um/sec/sec':
ax.set_ylim(1e-4, 1e3)
ax.set_ylabel('Acceleration [um/sec/sec/rtHz]', fontsize=20)
elif unit == 'm/sec/sec':
ax.set_ylim(1e-10, 1e-3)
ax.set_ylabel('ASD [$\mathrm{m}/\mathrm{s}^2/ \sqrt{\mathrm{Hz}}$]',
fontsize=20)
if unit == 'um/sec':
ax.set_ylim(1e-5, 10)
ax.set_ylabel('Velocity [um/sec/rtHz]', fontsize=20)
elif unit == 'um':
ax.set_ylim(1e-6, 1e2)
ax.set_yticks(np.logspace(-6, 2, 9))
ax.set_ylabel('Displacement [um/rtHz, or um]', fontsize=20)
elif unit == 'm':
ax.set_ylim(1e-13, 1e-4)
ax.set_yticks(np.logspace(-13, -4, 10))
ax.set_ylabel('Displacement [m/rtHz', fontsize=20)
ax.legend(fontsize=14) #,loc='lower left')
ax.grid(b=True, which='major', axis='both', linestyle='-')
ax.grid(b=True, which='minor', axis='both', linestyle='--')
#plt.suptitle(fname[:-4],fontsize=20)
print('./results/' + fname)
plt.tight_layout()
plt.savefig('./results/' + fname)
plt.close()
import numpy as np
import matplotlib.pyplot as plt
from gwpy.plot import Plot
from gwpy.timeseries import TimeSeriesDict, TimeSeries
from gwpy.time import tconvert
from glue import lal
import sys
sys.path.insert(0, '../../miyopy')
from miyopy.utils.trillium import Trillium
from gwpy.frequencyseries import FrequencySeries
from astropy import units as u
from lalframe.utils import frtools
amp = 10**(30 / 20.)
_v2vel = 1 / 1202.5 * u.m / u.s / u.V
tr120q = Trillium('120QA')
v2vel = tr120q.v2vel
# --------------------
# Utils
# --------------------
def asd(ts, fftlength=2**10, percentile=50):
sg = ts.spectrogram2(fftlength=fftlength,
overlap=fftlength / 2.0)**(1 / 2.)
return sg.percentile(percentile)
def csd(ts1, ts2, fftlength=2**10):
csd = ts1.csd(ts2, fftlength=fftlength)
return csd
'X1500_TR240posEW',
format='gwf.lalframe')
strain = TimeSeries.read(gwf_fmt.format(sensor='strain'),
'CALC_STRAIN',
format='gwf.lalframe')
print('read')
# calc asd
x1500_x.name = 'x1500'
strain.name = 'strain'
median_x1500, low_x1500, high_x1500 = calc_asd(x1500_x)
median_strain, low_strain, high_strain = calc_asd(strain)
print('calc asd')
# calib
tr240 = Trillium('240')
median_x1500 = tr240.v2vel(median_x1500) / 2.0
low_x1500 = tr240.v2vel(low_x1500) / 2.0
high_x1500 = tr240.v2vel(high_x1500) / 2.0
median_strain = median_strain
low_strain = low_strain
high_strain = high_strain
# save as hdf5
hdf5_fmt = './{dataname}/Xaxis_{sensor}_{pct}.hdf5'.replace(
"{dataname}", dataname)
x1500_fmt = hdf5_fmt.replace("{sensor}", 'x1500')
save(median_x1500, fname=x1500_fmt.format(pct='50pct'))
save(low_x1500, fname=x1500_fmt.format(pct='5pct'))
save(high_x1500, fname=x1500_fmt.format(pct='95pct'))
strain_fmt = hdf5_fmt.replace("{sensor}", 'strain')