def Sn_ET_B(freqs):
"""Return the average power spectral density noise for Einstein Telescope
(variant B) in 1/Hz.
.. note::
The data was downloaded from
https://apps.et-gw.eu/tds/?content=3&r=14323 and has range ``fmin=1``,
``fmax=10000`` (Hz).
:param freqs: Frequencies in Hz over which to evaluate the sensitivity curve.
:type freqs: 1d NumPy array
:returns: ET-B sensitivity curve in 1/Hz.
:rtype: :py:class:`~.FrequencySeries`
"""
freqs = np.asarray(freqs)
# Why is it so difficult to read files in Python packages? :(
data = pkgutil.get_data("kuibit", "data/ETB.dat").decode("utf8")
# We convert this data in a StringIO that NumPy can read, we can pass this
# to load_FrequencySeries, since its backend is np.loadtxt
#
# ET distributes Amplitude Spectral Densities
asd = load_FrequencySeries(StringIO(data))
psd = asd**2
# Resample on the requested frequencies. ETB is well-behaved, so it is fine
# to use splines.
psd.resample(freqs)
return psd
def Sn_aLIGO_plus(freqs):
"""Return the average power spectral density noise for Advanced LIGO + in
1/Hz.
.. note::
The data was downloaded from
https://dcc.ligo.org/public/0149/T1800042/005/AplusDesign.txt and has
range ``fmin=5``, ``fmax=5000`` (Hz).
The resampling to the requested frequencies is done considering the values
of the nearest neighbors.
:param freqs: Frequencies in Hz over to evaluate the sensitivity curve.
:type freqs: 1d NumPy array
:returns: aLIGO+ sensitivity curve in 1/Hz.
:rtype: :py:class:`~.FrequencySeries`
"""
freqs = np.asarray(freqs)
data = pkgutil.get_data("kuibit", "data/aLIGO_PLUS.dat").decode("utf8")
# aLIGO distributes Amplitude Spectral Densities
asd = load_FrequencySeries(StringIO(data))
psd = asd**2
# Resample on the requested frequencies. CE1 has some spikes, so it is
# better to not use splines.
psd.resample(freqs, piecewise_constant=True)
return psd
def Sn_KAGRA_D(freqs):
"""Return the average power spectral density noise for KAGRA in
1/Hz.
.. note::
The data was downloaded from
https://granite.phys.s.u-tokyo.ac.jp/svn/LCGT/trunk/sensitivity/spectrum/BW2009_VRSED.dat
and has range ``fmin=1.00231``, ``fmax=10000`` (Hz).
The resampling to the requested frequencies is done considering the values
of the nearest neighbors.
:param freqs: Frequencies in Hz over to evaluate the sensitivity curve.
:type freqs: 1d NumPy array
:returns: KAGRA sensitivity curve in 1/Hz.
:rtype: :py:class:`~.FrequencySeries`
"""
freqs = np.asarray(freqs)
data = pkgutil.get_data("kuibit", "data/KAGRA_VRSED.dat").decode("utf8")
# KAGRA distributes Amplitude Spectral Densities
asd = load_FrequencySeries(StringIO(data))
psd = asd**2
# Resample on the requested frequencies. CE1 has some spikes, so it is
# better to not use splines.
psd.resample(freqs, piecewise_constant=True)
return psd
def Sn_aLIGO(freqs):
"""Return the average power spectral density noise for advanced LIGO in
1/Hz. This is the Zero-Detuned-High-Power noise curve.
.. note::
The data was downloaded from
https://dcc.ligo.org/LIGO-T1500293-v11/public and has range ``fmin=9``,
``fmax=8192`` (Hz).
The resampling to the requested frequencies is done considering the values
of the nearest neighbors.
:param freqs: Frequencies in Hz over to evaluate the sensitivity curve.
:type freqs: 1d NumPy array
:returns: Advanced LIGO Zero-Detuned High-Power sensitivity curve in 1/Hz.
:rtype: :py:class:`~.FrequencySeries`
"""
freqs = np.asarray(freqs)
data = pkgutil.get_data("kuibit", "data/aLIGO_ZDHP.dat").decode("utf8")
# aLIGO distributes Amplitude Spectral Densities
asd = load_FrequencySeries(StringIO(data))
psd = asd**2
# Resample on the requested frequencies. CE1 has some spikes, so it is
# better to not use splines.
psd.resample(freqs, piecewise_constant=True)
return psd
def Sn_CE2(freqs):
"""Return the average power spectral density noise for Einstein Telescope in
1/Hz.
.. note::
The data was downloaded from
https://cosmicexplorer.org/data/CE2_strain.txt and has range ``fmin=3``,
``fmax=10000`` (Hz).
The resampling to the requested frequencies is done considering the values
of the nearest neighbors.
:param freqs: Frequencies in Hz over which to evaluate the sensitivity curve.
:type freqs: 1d NumPy array
:returns: CE2 sensitivity curve in 1/Hz.
:rtype: :py:class:`~.FrequencySeries`
"""
freqs = np.asarray(freqs)
data = pkgutil.get_data("kuibit", "data/CE1.dat").decode("utf8")
# CE distributes Amplitude Spectral Densities
asd = load_FrequencySeries(StringIO(data))
psd = asd**2
# Resample on the requested frequencies. CE1 has some spikes, so it is
# better to not use splines.
psd.resample(freqs, piecewise_constant=True)
return psd
def test_load_FrequencySeries(self):
path = "tests/tov/output-0000/static_tov/mp_Phi2_l2_m-1_r110.69.asc"
f, fft_real, fft_imag = np.loadtxt(path).T
ffs = fs.FrequencySeries(f, fft_real + 1j * fft_imag)
self.assertEqual(
ffs, fs.load_FrequencySeries(path, complex_on_two_columns=True))
path_ligo = "tests/tov/ligo_sens.dat"
f, fft = np.loadtxt(path_ligo).T
ffs_ligo = fs.FrequencySeries(f, fft)
self.assertEqual(ffs_ligo, fs.load_noise_curve(path_ligo))