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authorEugeniy E. Mikhailov <evgmik@gmail.com>2024-07-13 21:04:38 -0400
committerEugeniy E. Mikhailov <evgmik@gmail.com>2024-07-13 21:04:38 -0400
commitfa086b54e06684dc8e666df6245629e896b215fd (patch)
treec197839cba4af4690a65cd00c141a7836ce0810e
parent705cc2eb112ab5e4c8329ab45fa2c89593048f2b (diff)
downloadqolab-fa086b54e06684dc8e666df6245629e896b215fd.tar.gz
qolab-fa086b54e06684dc8e666df6245629e896b215fd.zip
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-rw-r--r--qolab/math/spectral_utils.py26
1 files changed, 14 insertions, 12 deletions
diff --git a/qolab/math/spectral_utils.py b/qolab/math/spectral_utils.py
index ad5ce6e..797267e 100644
--- a/qolab/math/spectral_utils.py
+++ b/qolab/math/spectral_utils.py
@@ -1,6 +1,7 @@
import numpy as np
from scipy.fft import rfft, rfftfreq
+
def spectrum(t, y):
"""
Calculate spectrum of real value signal with stripped away zero frequency component.
@@ -8,23 +9,25 @@ def spectrum(t, y):
rate and time interval.
"""
N = t.size
- dt=np.mean(np.diff(t))
- freq=rfftfreq(N, dt)
+ dt = np.mean(np.diff(t))
+ freq = rfftfreq(N, dt)
# y= y - np.mean(y)
yf = rfft(y)
- yf *= 2/N; # produce normalized amplitudes
- return freq[1:], yf[1:]; # strip of boring freq=0
+ yf *= 2 / N # produce normalized amplitudes
+ return freq[1:], yf[1:] # strip of boring freq=0
+
-def noise_density_spectrum(t,y):
+def noise_density_spectrum(t, y):
"""
Calculate noise amplitude spectral density (ASD), the end results has unitis of y/sqrt(Hz)
i.e. it does sqrt(PSD) where PSD is powerd spectrum density.
Preserves the density independent of sampling rate and time interval.
"""
freq, yf = spectrum(t, y)
- yf = yf*np.sqrt(t[-1]-t[0]) # scales with 1/sqrt(RBW)
+ yf = yf * np.sqrt(t[-1] - t[0]) # scales with 1/sqrt(RBW)
return freq, yf
+
def noise_spectrum_smooth(fr, Ampl, Nbins=100):
"""
Smooth amplitude spectrum, especially at high frequency end.
@@ -35,16 +38,15 @@ def noise_spectrum_smooth(fr, Ampl, Nbins=100):
"""
frEdges = np.logspace(np.log10(fr[0]), np.log10(fr[-1]), Nbins)
- frCenter = np.zeros(frEdges.size-1)
- power = np.zeros(frEdges.size-1)
+ frCenter = np.zeros(frEdges.size - 1)
+ power = np.zeros(frEdges.size - 1)
for i, (frStart, frEnd) in enumerate(zip(frEdges[0:-1], frEdges[1:])):
# print (f"{i=} {frStart=} {frEnd}")
ind = (frStart <= fr) & (fr <= frEnd)
- frCenter[i] = np.mean( fr[ind] )
- power [i] = np.mean( np.power( np.abs(Ampl[ind]),2) )
+ frCenter[i] = np.mean(fr[ind])
+ power[i] = np.mean(np.power(np.abs(Ampl[ind]), 2))
ind = np.logical_not(np.isnan(frCenter))
frCenter = frCenter[ind]
power = power[ind]
# print(f'{frCenter=} {power=}')
- return frCenter, np.sqrt( power )
-
+ return frCenter, np.sqrt(power)