removed old binning code

This commit is contained in:
Anne de Jong 2020-01-17 17:03:45 +01:00
parent 1a85b60d85
commit f10e78ff41

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@ -1,86 +0,0 @@
#!/usr/bin/python
"""
Bin narrow band power in octave/third octave band data
"""
from lasp.filter.bandpass_fir import (OctaveBankDesigner,
ThirdOctaveBankDesigner)
import numpy as np
import warnings
def binPower(freq, narrow_power, band=3, start_band=-16, stop_band=13):
"""
Apply binning to narrow band frequency domain power results
Args:
freq: Array of frequency indices
narrow_power: narrow-band power values in units of [W] or [Pa^2]
band: 1, or 3
Returns:
( ['25', '31.5', '40', '50', ... ],
[float(power_25), float(power_31p5), ...]) putting NAN values where
inaccurate.
"""
if band == 3:
designer = ThirdOctaveBankDesigner()
elif band == 1:
designer = OctaveBankDesigner()
else:
raise ValueError("Parameter 'Band' should be either '1', or '3'")
freq = np.copy(freq)
narrow_power = np.copy(narrow_power)
# Exact midband, lower and upper frequency limit of each band
fm = [designer.fm(x) for x in range(start_band, stop_band+1)]
fl = [designer.fl(x) for x in range(start_band, stop_band+1)]
fu = [designer.fu(x) for x in range(start_band, stop_band+1)]
fex = [designer.nominal_txt(x) for x in range(start_band, stop_band+1)]
# print(fl)
binned_power = np.zeros(len(fm), dtype=float)
## Start: linear interpolation between bins while Parseval is conserved
# current frequency resolution
df_old = freq[1]-freq[0]
# preferred new frequency resolution
df_new = .1
# ratio of resolutions
ratio = int(df_old/df_new)
# calculate new frequency bins
freq_new = np.linspace(freq[0],freq[-1],(len(freq)-1)*ratio+1)
# calculate the new bin data
interp_power = np.interp(freq_new, freq, narrow_power)/ratio
# adapt first and last bin values so that Parseval still holds
interp_power[0] = binned_power[0]*(1.+1./ratio)/2.
interp_power[-1] = binned_power[-1]*(1.+1./ratio)/2.
# check if Parseval still holds
# print(np.sum(y, axis=0))
# print(np.sum(y_new, axis=0))
## Stop: linear interpolation between bins while Parseval is conserved
binned_power = np.zeros(len(fm), dtype=float)
for k in range(len(fm)):
# print(k)
# find the bins which are in the corresponding band
bins = (fl[k] <= freq_new) & (freq_new < fu[k])
# print(bins)
# sum the output values of these bins to obtain the band value
binned_power[k] = np.sum(interp_power[bins], axis=0)
# if no frequency bin falls in a certain band, skip previous bands
# if not any(bins):
# binned_power[0:k+1] = np.nan
# check if data is valid
if(np.isnan(binned_power).all()):
warnings.warn('Invalid frequency array, we cannot bin these values')
return fm, fex, binned_power