def _make_hvsr(self,
method,
resampling,
bandwidth,
f_low=None,
f_high=None,
azimuth=None):
if method in ["squared-average", "geometric-mean"]:
ffts = self.transform()
hor = self._combine_horizontal_fd(method=method,
ew=ffts["ew"],
ns=ffts["ns"])
ver = ffts["vt"]
del ffts
elif method == "single-azimuth":
hor = self._combine_horizontal_td(method=method, azimuth=azimuth)
hor = FourierTransform.from_timeseries(hor)
ver = FourierTransform.from_timeseries(self.vt)
else:
msg = f"`method`={method} has not been implemented."
raise NotImplementedError(msg)
self.meta["method"] = method
self.meta["azimuth"] = azimuth
if resampling["res_type"] == "linear":
frq = np.linspace(resampling["minf"], resampling["maxf"],
resampling["nf"])
elif resampling["res_type"] == "log":
frq = np.geomspace(resampling["minf"], resampling["maxf"],
resampling["nf"])
else:
msg = f"`res_type`={resampling['res_type']} has not been implemented."
raise NotImplementedError(msg)
hor.smooth_konno_ohmachi_fast(frq, bandwidth)
ver.smooth_konno_ohmachi_fast(frq, bandwidth)
hor._amp /= ver._amp
hvsr = hor
del ver
if self.ns.nseries == 1:
window_length = max(self.ns.time)
else:
window_length = max(self.ns.time[0])
self.meta["Window Length"] = window_length
return Hvsr(hvsr.amplitude,
hvsr.frequency,
find_peaks=False,
f_low=f_low,
f_high=f_high,
meta=self.meta)
def _make_hvsr(self, method, resampling, bandwidth, azimuth=None):
if method in ["squared-average", "geometric-mean"]:
ffts = self.transform()
hor = self.combine_horizontals(method=method, horizontals=ffts)
ver = ffts["vt"]
del ffts
elif method == "single-azimuth":
hor = self.combine_horizontals(method=method,
horizontals={"ew": self.ew,
"ns": self.ns},
azimuth=azimuth)
if isinstance(hor, WindowedTimeSeries):
hor = FourierTransformSuite.from_timeseries(hor)
ver = FourierTransformSuite.from_timeseries(self.vt)
elif isinstance(hor, TimeSeries):
hor = FourierTransform.from_timeseries(hor)
ver = FourierTransform.from_timeseries(self.vt)
else:
raise NotImplementedError
else:
msg = f"`method`={method} has not been implemented."
raise NotImplementedError(msg)
self.meta["method"] = method
self.meta["azimuth"] = azimuth
if resampling["res_type"] == "linear":
frq = np.linspace(resampling["minf"],
resampling["maxf"],
resampling["nf"])
elif resampling["res_type"] == "log":
frq = np.geomspace(resampling["minf"],
resampling["maxf"],
resampling["nf"])
else:
raise NotImplementedError
hor.smooth_konno_ohmachi_fast(frq, bandwidth)
ver.smooth_konno_ohmachi_fast(frq, bandwidth)
hor.amp /= ver.amp
hvsr = hor
del ver
if self.ns.n_windows == 1:
window_length = max(self.ns.time)
else:
window_length = max(self.ns.time[0])
self.meta["Window Length"] = window_length
return Hvsr(hvsr.amp, hvsr.frq, find_peaks=False, meta=self.meta)
def _combine_horizontal_fd(method, ns, ew):
"""Combine horizontal components in the frequency domain.
Parameters
----------
method : {'squared-average', 'geometric-mean'}
Defines how the two horizontal components are combined.
ns, ew : FourierTransform
Frequency domain representation of each component.
Returns
-------
FourierTransform
Representing the combined horizontal components.
"""
if method == "squared-average":
horizontal = np.sqrt((ns.mag * ns.mag + ew.mag * ew.mag) / 2)
elif method == "geometric-mean":
horizontal = np.sqrt(ns.mag * ew.mag)
else:
msg = f"`method`={method} has not been implemented."
raise NotImplementedError(msg)
return FourierTransform(horizontal, ns.frequency, dtype=float)
def transform(self, **kwargs):
"""Perform Fourier transform on components.
Returns
-------
dict
With `FourierTransform`-like objects, one for for each
component, indicated by the key 'ew','ns', 'vt'.
"""
ffts = {}
for attr in ["ew", "ns", "vt"]:
tseries = getattr(self, attr)
fft = FourierTransform.from_timeseries(tseries, **kwargs)
ffts[attr] = fft
return ffts
def _combine_horizontal_fd(method, horizontals, **kwargs):
ns = horizontals["ns"]
ew = horizontals["ew"]
if method == "squared-average":
horizontal = np.sqrt((ns.mag*ns.mag + ew.mag*ew.mag)/2)
elif method == "geometric-mean":
horizontal = np.sqrt(ns.mag * ew.mag)
else:
msg = f"`method`={method} has not been implemented."
raise NotImplementedError(msg)
if isinstance(ns, FourierTransformSuite):
return FourierTransformSuite(horizontal, ns.frq)
elif isinstance(ns, FourierTransform):
return FourierTransform(horizontal, ns.frq)
else:
raise NotImplementedError
def transform(self, **kwargs):
"""Perform Fourier transform on components.
Returns
-------
dict
With `FourierTransform`-like objects, one for for each
component, indicated by the key 'ew','ns', 'vt'.
"""
ffts = {}
for attr in ["ew", "ns", "vt"]:
tseries = getattr(self, attr)
if isinstance(tseries, WindowedTimeSeries):
fft = FourierTransformSuite.from_timeseries(tseries, **kwargs)
elif isinstance(tseries, TimeSeries):
fft = FourierTransform.from_timeseries(tseries, **kwargs)
else:
raise NotImplementedError
ffts[attr] = fft
return ffts
def new_fseries(self):
self.applied_smoothing = False
self.applied_resampling = False
self.fseries = FourierTransform.from_timeseries(self.tseries)
self.update_fseries_plot()