You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
113 lines
3.1 KiB
113 lines
3.1 KiB
from pyrocko import trace
|
|
import math
|
|
import numpy as num
|
|
from pyrocko.gui.snuffling import Param, Snuffling, Switch
|
|
import scipy.signal as S
|
|
import scipy.stats as SS
|
|
|
|
|
|
def detrend_data(x_axis,data):
|
|
|
|
|
|
slope,offset,dummy,dummy2,dumy3 = SS.linregress(x_axis, data)
|
|
|
|
detrended_data = S.detrend(data)
|
|
|
|
return detrended_data, slope, offset
|
|
|
|
|
|
def retrend_data(x_axis,data,slope, offset):
|
|
|
|
retrended_data = x_axis * slope + data + offset
|
|
|
|
return retrended_data
|
|
|
|
|
|
class Notch(Snuffling):
|
|
|
|
'''Apply gaussian notch filter to data in Snuffler
|
|
|
|
The filter is defined by a central frequency and a window width. If possible,
|
|
harmonics are removed as well. Filtering carried out in frequency domain.
|
|
Data trace is detrended, FFT calculated, respective notch coefficients
|
|
calculated and multiplied onto the FFT section. Then back-transform to time
|
|
domain, adding the underlying trendline.'''
|
|
|
|
def setup(self):
|
|
'''Customization of Notch Snuffling.'''
|
|
|
|
self.set_name('Notch Filter')
|
|
|
|
self.add_parameter(Param('Center Frequency [Hz]',
|
|
'centerfreq', 50., 0.001, 1000.0, low_is_none=False))
|
|
|
|
self.add_parameter(Param('Notch width (FWHM) [Hz]',
|
|
'notchwidth', 1., 0.001, 1000.0, low_is_none=False))
|
|
|
|
self.add_parameter(Switch('Also filter harmonics',
|
|
'filter_harmonics', False))
|
|
|
|
self.set_have_post_process_hook(True)
|
|
|
|
self.FFF = {}
|
|
|
|
def call(self):
|
|
# snuffling parameters have changed
|
|
|
|
# release cached frequency responses
|
|
self.FFF = {}
|
|
|
|
# released cached processed traces in snuffler internals
|
|
viewer = self.get_viewer()
|
|
viewer.old_processed_traces = None
|
|
|
|
def post_process_hook(self, traces):
|
|
|
|
FFF = self.FFF
|
|
|
|
for tr in traces:
|
|
indi = num.arange(tr.data_len(), dtype=num.float)
|
|
detr_data, m, b = detrend_data(indi, tr.get_ydata())
|
|
|
|
tr.set_ydata(detr_data)
|
|
|
|
freqs, FT_data = tr.spectrum(pad_to_pow2=True, tfade=None)
|
|
|
|
fff_length = FT_data.size
|
|
|
|
key = (tr.deltat, fff_length)
|
|
|
|
if key not in FFF:
|
|
FFF[key] = num.ones((fff_length), dtype=num.complex)
|
|
fcenter = self.centerfreq
|
|
fwidth = self.notchwidth
|
|
|
|
iharm = 1
|
|
while iharm*fcenter - fwidth*2 < freqs[-1]:
|
|
FFF[key] *= GaussNotch(iharm*fcenter, fwidth).evaluate(freqs)
|
|
iharm += 1
|
|
if not self.filter_harmonics:
|
|
break
|
|
|
|
filtered_data = num.fft.irfft(FT_data*FFF[key])[:tr.data_len()]
|
|
|
|
retrended_data = retrend_data(indi, filtered_data, m, b)
|
|
|
|
tr.set_ydata(retrended_data)
|
|
|
|
return traces
|
|
|
|
class GaussNotch(trace.FrequencyResponse):
|
|
def __init__(self, center, fwhm):
|
|
self.center = center
|
|
self.fwhm = fwhm
|
|
|
|
def evaluate(self, freqs):
|
|
denom = self.fwhm / (2.*math.sqrt(math.log(2.)))
|
|
return 1.0 - num.exp(-((freqs-self.center)/denom)**2)
|
|
|
|
def __snufflings__():
|
|
|
|
return [ Notch()]
|
|
|