def qtransform(self, delta_t, delta_f,
frange=(0,_numpy.inf), qrange=(4,64), mismatch=0.2):
""" Return the interpolated 2d qtransform of this data
Parameters
----------
delta_t : float
The time resolution
delta_f : float
The frequency resolution
frange : {(0, inf), tuple}
frequency range
qrange : {(4, 64), tuple}
q range
mismatch : float
Mismatch between frequency tiles
Returns
-------
times : numpy.ndarray
The time that the qtransform is sampled.
freqs : numpy.ndarray
The frequencies that the qtransform is samled.
qplane : numpy.ndarray (2d)
The two dimensional interpolated qtransform of this time series.
"""
from pycbc.filter.qtransform import qtiling, qplane
q_base, q_frange = qtiling(self, qrange, frange, mismatch)
q_plane, _ = qplane(q_base, self.to_frequencyseries(), q_frange,
fres=delta_f, tres=delta_t)
times = _numpy.linspace(float(self.start_time),
float(self.end_time),
self.duration / delta_t)
freqs = _numpy.arange(int(q_frange[0]), int(q_frange[1]), delta_f)
return times, freqs, q_plane
def qtransform(self,
delta_t,
delta_f,
frange=(0, _numpy.inf),
qrange=(4, 64),
mismatch=0.2):
""" Return the interpolated 2d qtransform of this data
Parameters
----------
delta_t : float
The time resolution
delta_f : float
The frequency resolution
frange : {(0, inf), tuple}
frequency range
qrange : {(4, 64), tuple}
q range
mismatch : float
Mismatch between frequency tiles
Returns
-------
times : numpy.ndarray
The time that the qtransform is sampled.
freqs : numpy.ndarray
The frequencies that the qtransform is samled.
qplane : numpy.ndarray (2d)
The two dimensional interpolated qtransform of this time series.
"""
from pycbc.filter.qtransform import qtiling, qplane
q_base, q_frange = qtiling(self, qrange, frange, mismatch)
q_plane, _ = qplane(q_base,
self.to_frequencyseries(),
q_frange,
fres=delta_f,
tres=delta_t)
times = _numpy.linspace(float(self.start_time), float(self.end_time),
self.duration / delta_t)
freqs = _numpy.arange(int(q_frange[0]), int(q_frange[1]), delta_f)
return times, freqs, q_plane
def qtransform(self,
delta_t=None,
delta_f=None,
logfsteps=None,
frange=None,
qrange=(4, 64),
mismatch=0.2,
return_complex=False):
""" Return the interpolated 2d qtransform of this data
Parameters
----------
delta_t : {self.delta_t, float}
The time resolution to interpolate to
delta_f : float, Optional
The frequency resolution to interpolate to
logfsteps : int
Do a log interpolation (incompatible with delta_f option) and set
the number of steps to take.
frange : {(30, nyquist*0.8), tuple of ints}
frequency range
qrange : {(4, 64), tuple}
q range
mismatch : float
Mismatch between frequency tiles
return_complex: {False, bool}
return the raw complex series instead of the normalized power.
Returns
-------
times : numpy.ndarray
The time that the qtransform is sampled.
freqs : numpy.ndarray
The frequencies that the qtransform is sampled.
qplane : numpy.ndarray (2d)
The two dimensional interpolated qtransform of this time series.
"""
from pycbc.filter.qtransform import qtiling, qplane
from scipy.interpolate import interp2d
if frange is None:
frange = (30, int(self.sample_rate / 2 * 8))
q_base = qtiling(self, qrange, frange, mismatch)
_, times, freqs, q_plane = qplane(q_base,
self.to_frequencyseries(),
return_complex=return_complex)
if logfsteps and delta_f:
raise ValueError(
"Provide only one (or none) of delta_f and logfsteps")
# Interpolate if requested
if delta_f or delta_t or logfsteps:
if return_complex:
interp_amp = interp2d(times, freqs, abs(q_plane))
interp_phase = interp2d(times, freqs, _numpy.angle(q_plane))
else:
interp = interp2d(times, freqs, q_plane)
if delta_t:
times = _numpy.arange(float(self.start_time), float(self.end_time),
delta_t)
if delta_f:
freqs = _numpy.arange(int(frange[0]), int(frange[1]), delta_f)
if logfsteps:
freqs = _numpy.logspace(_numpy.log10(frange[0]),
_numpy.log10(frange[1]), logfsteps)
if delta_f or delta_t or logfsteps:
if return_complex:
q_plane = _numpy.exp(1.0j * interp_phase(times, freqs))
q_plane *= interp_amp(times, freqs)
else:
q_plane = interp(times, freqs)
return times, freqs, q_plane
def qtransform(self, delta_t=None, delta_f=None, logfsteps=None,
frange=None, qrange=(4,64), mismatch=0.2, return_complex=False):
""" Return the interpolated 2d qtransform of this data
Parameters
----------
delta_t : {self.delta_t, float}
The time resolution to interpolate to
delta_f : float, Optional
The frequency resolution to interpolate to
logfsteps : int
Do a log interpolation (incompatible with delta_f option) and set
the number of steps to take.
frange : {(30, nyquist*0.8), tuple of ints}
frequency range
qrange : {(4, 64), tuple}
q range
mismatch : float
Mismatch between frequency tiles
return_complex: {False, bool}
return the raw complex series instead of the normalized power.
Returns
-------
times : numpy.ndarray
The time that the qtransform is sampled.
freqs : numpy.ndarray
The frequencies that the qtransform is sampled.
qplane : numpy.ndarray (2d)
The two dimensional interpolated qtransform of this time series.
"""
from pycbc.filter.qtransform import qtiling, qplane
from scipy.interpolate import interp2d
if frange is None:
frange = (30, int(self.sample_rate / 2 * 8))
q_base = qtiling(self, qrange, frange, mismatch)
_, times, freqs, q_plane = qplane(q_base, self.to_frequencyseries(),
return_complex=return_complex)
if logfsteps and delta_f:
raise ValueError("Provide only one (or none) of delta_f and logfsteps")
# Interpolate if requested
if delta_f or delta_t or logfsteps:
if return_complex:
interp_amp = interp2d(times, freqs, abs(q_plane))
interp_phase = interp2d(times, freqs, _numpy.angle(q_plane))
else:
interp = interp2d(times, freqs, q_plane)
if delta_t:
times = _numpy.arange(float(self.start_time),
float(self.end_time), delta_t)
if delta_f:
freqs = _numpy.arange(int(frange[0]), int(frange[1]), delta_f)
if logfsteps:
freqs = _numpy.logspace(_numpy.log10(frange[0]),
_numpy.log10(frange[1]),
logfsteps)
if delta_f or delta_t or logfsteps:
if return_complex:
q_plane = _numpy.exp(1.0j * interp_phase(times, freqs))
q_plane *= interp_amp(times, freqs)
else:
q_plane = interp(times, freqs)
return times, freqs, q_plane
